From 0e7c600cf7a2effd0119964b6250f5a31ed8d48b Mon Sep 17 00:00:00 2001 From: jomjol <30766535+jomjol@users.noreply.github.com> Date: Sun, 24 Jul 2022 18:53:25 +0200 Subject: [PATCH] Rolling v10.6.1 --- README.md | 7 +- code/components/esp-nn/CMakeLists.txt | 4 +- code/components/esp-nn/Kconfig.projbuild | 8 +- code/components/esp-nn/README.md | 7 +- code/components/esp-nn/include/esp_nn.h | 10 +- .../components/esp-nn/include/esp_nn_ansi_c.h | 1 - .../esp-nn/include/esp_nn_ansi_headers.h | 136 +- code/components/esp-nn/include/esp_nn_defs.h | 83 - .../{esp_nn_generic_opt.h => esp_nn_esp32.h} | 19 +- .../esp-nn/include/esp_nn_esp32s3.h | 76 +- .../esp-nn/src/common/common_functions.h | 67 +- .../esp-nn/src/convolution/esp_nn_conv_ansi.c | 56 +- .../src/convolution/esp_nn_conv_esp32s3.c | 185 +- .../esp-nn/src/convolution/esp_nn_conv_opt.c | 179 - .../convolution/esp_nn_depthwise_conv_ansi.c | 57 +- .../convolution/esp_nn_depthwise_conv_opt.c | 291 -- .../esp_nn_depthwise_conv_s8_esp32s3.c | 134 +- .../test_app/sdkconfig.defaults.esp32s3 | 8 - 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code/version.cpp | 6 +- firmware/bootloader.bin | Bin 26864 -> 26864 bytes firmware/dhy0540s3DropOut-10erq.tflite | Bin 0 -> 315504 bytes firmware/firmware.bin | Bin 1788992 -> 1777776 bytes firmware/html.zip | Bin 179819 -> 179824 bytes sd-card/html/edit_config_param.html | 4 +- sd-card/html/edit_reference.html | 4 +- sd-card/html/version.txt | 2 +- 209 files changed, 1791 insertions(+), 12917 deletions(-) delete mode 100644 code/components/esp-nn/include/esp_nn_defs.h rename code/components/esp-nn/include/{esp_nn_generic_opt.h => esp_nn_esp32.h} (77%) delete mode 100644 code/components/esp-nn/src/convolution/esp_nn_conv_opt.c delete mode 100644 code/components/esp-nn/src/convolution/esp_nn_depthwise_conv_opt.c delete mode 100644 code/components/esp-nn/test_app/sdkconfig.defaults.esp32s3 create mode 100644 code/components/esp-nn_20220724.zip delete mode 100644 code/components/esp32-camera-master/sensors/bf20a6.c delete mode 100644 code/components/esp32-camera-master/sensors/private_include/bf20a6.h delete mode 100644 code/components/esp32-camera-master/sensors/private_include/bf20a6_regs.h delete mode 100644 code/components/esp32-camera-master/sensors/private_include/bf20a6_settings.h delete mode 100644 code/components/esp32-camera-master/sensors/private_include/sc030iot.h delete mode 100644 code/components/esp32-camera-master/sensors/private_include/sc030iot_settings.h delete mode 100644 code/components/esp32-camera-master/sensors/private_include/sc101iot.h delete mode 100644 code/components/esp32-camera-master/sensors/private_include/sc101iot_settings.h delete mode 100644 code/components/esp32-camera-master/sensors/sc030iot.c delete mode 100644 code/components/esp32-camera-master/sensors/sc101iot.c create mode 100644 code/components/esp32-camera-master_20220724.zip delete mode 100644 code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.cc delete mode 100644 code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.h delete mode 100644 code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.cc delete mode 100644 code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.h rename code/components/tflite-lib/tensorflow/lite/micro/{arena_allocator => }/ibuffer_allocator.h (95%) delete mode 100644 code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/softmax.cc delete mode 100644 code/components/tflite-lib/tensorflow/lite/micro/kernels/lstm_eval.cc delete mode 100644 code/components/tflite-lib/tensorflow/lite/micro/kernels/lstm_eval.h delete mode 100644 code/components/tflite-lib/tensorflow/lite/micro/kernels/lstm_shared.h delete mode 100644 code/components/tflite-lib/tensorflow/lite/micro/kernels/micro_tensor_utils.cc delete mode 100644 code/components/tflite-lib/tensorflow/lite/micro/kernels/micro_tensor_utils.h delete mode 100644 code/components/tflite-lib/tensorflow/lite/micro/kernels/reduce.h delete mode 100644 code/components/tflite-lib/tensorflow/lite/micro/kernels/reduce_common.cc delete mode 100644 code/components/tflite-lib/tensorflow/lite/micro/kernels/squared_difference.cc delete mode 100644 code/components/tflite-lib/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm.cc delete mode 100644 code/components/tflite-lib/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm_test_config.h rename code/components/tflite-lib/tensorflow/lite/micro/{arena_allocator => }/recording_simple_memory_allocator.cc (97%) rename code/components/tflite-lib/tensorflow/lite/micro/{arena_allocator => }/recording_simple_memory_allocator.h (87%) rename code/components/tflite-lib/tensorflow/lite/micro/{arena_allocator => }/simple_memory_allocator.cc (99%) rename code/components/tflite-lib/tensorflow/lite/micro/{arena_allocator => }/simple_memory_allocator.h (95%) delete mode 100644 code/components/tflite-lib_20220417.zip create mode 100644 code/components/tflite-lib_20220724.zip create mode 100644 firmware/dhy0540s3DropOut-10erq.tflite diff --git a/README.md b/README.md index 61343250..678b320d 100644 --- a/README.md +++ b/README.md @@ -52,7 +52,12 @@ In other cases you can contact the developer via email: + /************************** Basic math functions ****************************/ /** @@ -80,15 +81,28 @@ void esp_nn_mul_elementwise_s8_ansi(const int8_t *input1_data, * optimization notes: Though input_offset is int32 type, * offset values are contained in 8 bits [-128, 127] */ -void esp_nn_depthwise_conv_s8_ansi(const data_dims_t *input_dims, - const int8_t *input_data, - const data_dims_t *filter_dims, +void esp_nn_depthwise_conv_s8_ansi(const int8_t *input_data, + const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t channels, + const int32_t input_offset, + const uint16_t pad_wd, + const uint16_t pad_ht, + const uint16_t stride_wd, + const uint16_t stride_ht, + const uint16_t ch_mult, const int8_t *filter_data, + const uint16_t filter_wd, + const uint16_t filter_ht, const int32_t *bias, - const data_dims_t *output_dims, int8_t *out_data, - const dw_conv_params_t *conv_params, - const quant_data_t *quant_data); + const uint16_t out_wd, + const uint16_t out_ht, + const int32_t out_offset, + const int32_t *out_shift, + const int32_t *out_mult, + const int32_t activation_min, + const int32_t activation_max); /** * @brief 2d-convolution channelwise @@ -98,26 +112,43 @@ void esp_nn_depthwise_conv_s8_ansi(const data_dims_t *input_dims, * inputs type: int8_t, output: int8_t * input offsets: although int32_t, they are contained in 8 bits [-128, 127] */ -void esp_nn_conv_s8_ansi(const data_dims_t *input_dims, - const int8_t *input_data, - const data_dims_t *filter_dims, +void esp_nn_conv_s8_ansi(const int8_t *input_data, + const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t in_channels, + const int32_t input_offset, + const uint16_t pad_wd, + const uint16_t pad_ht, + const uint16_t stride_wd, + const uint16_t stride_ht, const int8_t *filter_data, + const uint16_t filter_wd, + const uint16_t filter_ht, const int32_t *bias, - const data_dims_t *output_dims, int8_t *out_data, - const conv_params_t *conv_params, - const quant_data_t *quant_data); + const uint16_t out_wd, + const uint16_t out_ht, + const uint16_t out_channels, + const int32_t out_offset, + const int32_t *out_shift, + const int32_t *out_mult, + const int32_t activation_min, + const int32_t activation_max); -int esp_nn_get_conv_scratch_size_ansi(const data_dims_t *input_dims, - const data_dims_t *filter_dims, - const data_dims_t *output_dims, - const conv_params_t *conv_params); +int esp_nn_get_conv_scratch_size_ansi(const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t in_ch, + const uint16_t out_ch, + const uint16_t filter_wd, + const uint16_t filter_ht); void esp_nn_set_conv_scratch_buf_ansi(const void *buf); -int esp_nn_get_depthwise_conv_scratch_size_ansi(const data_dims_t *input_dims, - const data_dims_t *filter_dims, - const data_dims_t *output_dims, - const dw_conv_params_t *conv_params); +int esp_nn_get_depthwise_conv_scratch_size_ansi(const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t channels, + const uint16_t ch_mult, + const uint16_t filter_wd, + const uint16_t filter_ht); void esp_nn_set_depthwise_conv_scratch_buf_ansi(const void *buf); /************************** Activation functions *****************************/ @@ -221,6 +252,9 @@ int32_t esp_nn_get_softmax_scratch_size_opt(const int32_t width, const int32_t h */ void esp_nn_set_softmax_scratch_buf_ansi(void *buffer); +/* ANSI C function to be hooked up when optimised version needed */ +void esp_nn_set_softmax_scratch_buf_opt(void *buffer); + /** * @brief reference softmax function * @@ -234,66 +268,6 @@ void esp_nn_softmax_s8_ansi(const int8_t *input_data, const int32_t diff_min, int8_t *output_data); - -//////////////////////////// Generic optimisations ///////////////////////////// - -/************************** Convolution functions *****************************/ - -/** - * @brief 2d-convolution channelwise optimized version - * - * @note operation: result += (input + offset) * filter - * - * inputs type: int8_t, output: int8_t - * input offsets: although int32_t, they are contained in 8 bits [-128, 127] - */ -void esp_nn_conv_s8_opt(const data_dims_t *input_dims, - const int8_t *input_data, - const data_dims_t *filter_dims, - const int8_t *filter_data, - const int32_t *bias, - const data_dims_t *output_dims, - int8_t *out_data, - const conv_params_t *conv_params, - const quant_data_t *quant_data); - -/** - * @brief depthwise convolution per channel optimized version - * - * @note inputs type: int8_t, output: int8_t - * Version used in tflite is per channel. - * This version follows the same footsprints. - * Meaning, it has per out_channel shift and multiplier for - * requantization - * - * optimization notes: Though input_offset is int32 type, - * offset values are contained in 8 bits [-128, 127] - */ -void esp_nn_depthwise_conv_s8_opt(const data_dims_t *input_dims, - const int8_t *input_data, - const data_dims_t *filter_dims, - const int8_t *filter_data, - const int32_t *bias, - const data_dims_t *output_dims, - int8_t *out_data, - const dw_conv_params_t *conv_params, - const quant_data_t *quant_data); - -int esp_nn_get_conv_scratch_size_opt(const data_dims_t *input_dims, - const data_dims_t *filter_dims, - const data_dims_t *output_dims, - const conv_params_t *conv_params); -void esp_nn_set_conv_scratch_buf_opt(const void *buf); - -int esp_nn_get_depthwise_conv_scratch_size_opt(const data_dims_t *input_dims, - const data_dims_t *filter_dims, - const data_dims_t *output_dims, - const dw_conv_params_t *conv_params); -void esp_nn_set_depthwise_conv_scratch_buf_opt(const void *buf); - -/* ANSI C function to be hooked up when optimised version needed */ -void esp_nn_set_softmax_scratch_buf_opt(void *buffer); - /** * @brief optimised version of softmax function * diff --git a/code/components/esp-nn/include/esp_nn_defs.h b/code/components/esp-nn/include/esp_nn_defs.h deleted file mode 100644 index 756d8e6f..00000000 --- a/code/components/esp-nn/include/esp_nn_defs.h +++ /dev/null @@ -1,83 +0,0 @@ -// Copyright 2022 Espressif Systems (Shanghai) PTE LTD -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#pragma once - -#include - -/** - * @brief structure to club data dims - * this structure can be used for input, output and filter - */ -typedef struct data_dims { - int32_t width; - int32_t height; - int32_t channels; - - int32_t extra; // can be used as batch or any other param -} data_dims_t; - -/** - * @brief 2d data structure (width, height) - * - */ -typedef struct data_2d { - int32_t width; - int32_t height; -} data_2d_t; - -/** - * @brief min/max activation - */ -typedef struct act_params { - int32_t min; - int32_t max; -} act_params_t; - -/** - * @brief per channel quant data - * - * @note number of shift and mult elements are equal to output channels - */ -typedef struct quant_data { - int32_t *shift; - int32_t *mult; -} quant_data_t; - -/** - * @brief params specific to convolution 2d - * - */ -typedef struct conv_params { - int32_t in_offset; - int32_t out_offset; - data_2d_t stride; - data_2d_t padding; - data_2d_t dilation; - act_params_t activation; -} conv_params_t; - -/** - * @brief params specific to depthwise convolution 2d - * - */ -typedef struct dw_conv_params { - int32_t in_offset; - int32_t out_offset; - int32_t ch_mult; // channel multiplier. (in_ch * ch_mult = out_ch) - data_2d_t stride; - data_2d_t padding; - data_2d_t dilation; - act_params_t activation; -} dw_conv_params_t; diff --git a/code/components/esp-nn/include/esp_nn_generic_opt.h b/code/components/esp-nn/include/esp_nn_esp32.h similarity index 77% rename from code/components/esp-nn/include/esp_nn_generic_opt.h rename to code/components/esp-nn/include/esp_nn_esp32.h index 136cba5d..03fd8216 100644 --- a/code/components/esp-nn/include/esp_nn_generic_opt.h +++ b/code/components/esp-nn/include/esp_nn_esp32.h @@ -13,27 +13,28 @@ // limitations under the License. /** - * @file Header definitions to include for esp_nn generic optimisations - * For functions which not having optimisations, _ansi versions are picked. + * @file Header definitions to include for esp_nn optimized functions for + * the ESP32 platform. + * We are hooking up just the C versions for now. + * The file hence is exactly same as `esp_nn_ansi_c.h` */ #pragma once -#include "esp_nn_defs.h" #include "esp_nn_ansi_headers.h" #define esp_nn_add_elementwise_s8 esp_nn_add_elementwise_s8_ansi #define esp_nn_mul_elementwise_s8 esp_nn_mul_elementwise_s8_ansi -#define esp_nn_depthwise_conv_s8 esp_nn_depthwise_conv_s8_opt +#define esp_nn_depthwise_conv_s8 esp_nn_depthwise_conv_s8_ansi -#define esp_nn_conv_s8 esp_nn_conv_s8_opt +#define esp_nn_conv_s8 esp_nn_conv_s8_ansi -#define esp_nn_get_conv_scratch_size esp_nn_get_conv_scratch_size_opt -#define esp_nn_set_conv_scratch_buf esp_nn_set_conv_scratch_buf_opt +#define esp_nn_get_conv_scratch_size esp_nn_get_conv_scratch_size_ansi +#define esp_nn_set_conv_scratch_buf esp_nn_set_conv_scratch_buf_ansi -#define esp_nn_get_depthwise_conv_scratch_size esp_nn_get_depthwise_conv_scratch_size_opt -#define esp_nn_set_depthwise_conv_scratch_buf esp_nn_set_depthwise_conv_scratch_buf_opt +#define esp_nn_get_depthwise_conv_scratch_size esp_nn_get_depthwise_conv_scratch_size_ansi +#define esp_nn_set_depthwise_conv_scratch_buf esp_nn_set_depthwise_conv_scratch_buf_ansi #define esp_nn_relu6_s8 esp_nn_relu6_s8_ansi diff --git a/code/components/esp-nn/include/esp_nn_esp32s3.h b/code/components/esp-nn/include/esp_nn_esp32s3.h index 0f52c943..58b544e4 100644 --- a/code/components/esp-nn/include/esp_nn_esp32s3.h +++ b/code/components/esp-nn/include/esp_nn_esp32s3.h @@ -19,7 +19,7 @@ #pragma once -#include "esp_nn_defs.h" +#include #include "esp_nn_ansi_headers.h" /************************** Basic math functions *****************************/ @@ -85,15 +85,28 @@ void esp_nn_mul_elementwise_s8_esp32s3(const int8_t *input1_data, * optimization notes: Though input_offset is int32 type, * offset values are contained in 8 bits [-128, 127] */ -void esp_nn_depthwise_conv_s8_esp32s3(const data_dims_t *input_dims, - const int8_t *input_data, - const data_dims_t *filter_dims, +void esp_nn_depthwise_conv_s8_esp32s3(const int8_t *input_data, + const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t channels, + const int32_t input_offset, + const uint16_t pad_wd, + const uint16_t pad_ht, + const uint16_t stride_wd, + const uint16_t stride_ht, + const uint16_t ch_mult, const int8_t *filter_data, + const uint16_t filter_wd, + const uint16_t filter_ht, const int32_t *bias, - const data_dims_t *output_dims, - int8_t *output_data, - const dw_conv_params_t *conv_params, - const quant_data_t *quant_data); + int8_t *out_data, + const uint16_t out_wd, + const uint16_t out_ht, + const int32_t out_offset, + const int32_t *out_shift, + const int32_t *out_mult, + const int32_t activation_min, + const int32_t activation_max); /** * @brief 2d - convolution channelwise @@ -103,26 +116,43 @@ void esp_nn_depthwise_conv_s8_esp32s3(const data_dims_t *input_dims, * inputs type: int8_t, output: int8_t * input offsets: although int32_t, they are contained in 8 bits [-128, 127] */ -void esp_nn_conv_s8_esp32s3(const data_dims_t *input_dims, - const int8_t *input_data, - const data_dims_t *filter_dims, +void esp_nn_conv_s8_esp32s3(const int8_t *input_data, + const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t in_channels, + const int32_t input_offset, + const uint16_t pad_wd, + const uint16_t pad_ht, + const uint16_t stride_wd, + const uint16_t stride_ht, const int8_t *filter_data, + const uint16_t filter_wd, + const uint16_t filter_ht, const int32_t *bias, - const data_dims_t *output_dims, - int8_t *output_data, - const conv_params_t *conv_params, - const quant_data_t *quant_data); + int8_t *out_data, + const uint16_t out_wd, + const uint16_t out_ht, + const uint16_t out_channels, + const int32_t out_offset, + const int32_t *out_shift, + const int32_t *out_mult, + const int32_t activation_min, + const int32_t activation_max); -int esp_nn_get_conv_scratch_size_esp32s3(const data_dims_t *input_dims, - const data_dims_t *filter_dims, - const data_dims_t *output_dims, - const conv_params_t *conv_params); +int esp_nn_get_conv_scratch_size_esp32s3(const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t in_ch, + const uint16_t out_ch, + const uint16_t filter_wd, + const uint16_t filter_ht); void esp_nn_set_conv_scratch_buf_esp32s3(const void *buf); -int esp_nn_get_depthwise_conv_scratch_size_esp32s3(const data_dims_t *input_dims, - const data_dims_t *filter_dims, - const data_dims_t *output_dims, - const dw_conv_params_t *conv_params); +int esp_nn_get_depthwise_conv_scratch_size_esp32s3(const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t channels, + const uint16_t ch_mult, + const uint16_t filter_wd, + const uint16_t filter_ht); void esp_nn_set_depthwise_conv_scratch_buf_esp32s3(const void *buf); /************************** Pooling functions *****************************/ diff --git a/code/components/esp-nn/src/common/common_functions.h b/code/components/esp-nn/src/common/common_functions.h index 0a74eca4..9a5f0dcc 100644 --- a/code/components/esp-nn/src/common/common_functions.h +++ b/code/components/esp-nn/src/common/common_functions.h @@ -41,52 +41,8 @@ __NN_FORCE_INLINE__ int32_t esp_nn_clz32(uint32_t in) { -#if CONFIG_IDF_TARGET_ARCH_XTENSA __asm__ volatile("nsau %0, %0" : "+r" (in)); return in; -#elif defined(__GNUC__) - return __builtin_clz(in); -#else - int32_t count = 32; - uint32_t x = in, y = in >> 16; - if (y != 0) { - count -= 16; - x = y; - } - y = x >> 8; - if (y != 0) { - count -= 8; - x = y; - } - y = x >> 4; - if (y != 0) { - count -= 4; - x = y; - } - y = x >> 2; - if (y != 0) { - count -= 2; - x = y; - } - y = x >> 1; - if (y != 0) { - return count - 2; - } - return count - x; -#endif -} - -/** - * Signed saturate a 32 bit value to 8 bits keeping output in 32 bit variable. - */ -__NN_FORCE_INLINE__ int32_t esp_nn_saturate8(int32_t in) -{ -#if CONFIG_IDF_TARGET_ARCH_XTENSA - __asm__ volatile("clamps %0, %0, 7" : "+a"(in)); - return in; -#else - return max(INT8_MIN, min(in, INT8_MAX)); -#endif } __NN_FORCE_INLINE__ int32_t esp_nn_pick_sat_high32_of64(int64_t val64) @@ -96,6 +52,15 @@ __NN_FORCE_INLINE__ int32_t esp_nn_pick_sat_high32_of64(int64_t val64) return (int32_t) ((int64_t) (val64 + to_add) >> 31); } +/** + * Signed saturate a 32 bit value to 8 bits keeping output in 32 bit variable. + */ +__NN_FORCE_INLINE__ int32_t esp_nn_saturate8(int32_t in) +{ + __asm__ volatile("clamps %0, %0, 7" : "+a"(in)); + return in; +} + __NN_FORCE_INLINE__ int32_t esp_nn_sat_round_doubling_high_mul(int32_t in0, int32_t in1) { int32_t result; @@ -179,7 +144,7 @@ static void esp_nn_aligned_s8_pad_with_value(const int8_t *src, int8_t *dst, const uint16_t pad_ht) { /* memset with pad_val */ - memset(dst, pad_val, ((input_wd + 2 * pad_wd) * (input_ht + 2 * pad_ht)) * channels); + memset(dst, pad_val, ((input_wd + 2 * pad_wd) * (input_ht + 2 * pad_ht)) * channels * 2); dst += (pad_wd + input_wd + pad_wd) * channels; for (int i = 0; i < input_ht; i++) { @@ -191,6 +156,7 @@ static void esp_nn_aligned_s8_pad_with_value(const int8_t *src, int8_t *dst, } } +#if 0 static void esp_nn_aligned_s8_pad_end_with_value(const int8_t *src, int8_t *dst, const uint16_t input_wd, const uint16_t input_ht, @@ -203,16 +169,13 @@ static void esp_nn_aligned_s8_pad_end_with_value(const int8_t *src, int8_t *dst, for (int j = 0; j < input_wd * channels; j++) { *dst++ = *src++; } - if (pad_wd) { - memset(dst, pad_val, pad_wd * channels); - dst += pad_wd * channels; - } + memset(dst, pad_val, pad_wd * channels); + dst += pad_wd * channels; } /* pad end `pad_ht` lines at end */ - if (pad_ht) { - memset(dst, pad_val, (input_wd + pad_wd) * pad_ht * channels); - } + memset(dst, pad_val, (input_wd + pad_wd) * pad_ht * channels); } +#endif /** * @brief convert 8 bit input data to 16 bit diff --git a/code/components/esp-nn/src/convolution/esp_nn_conv_ansi.c b/code/components/esp-nn/src/convolution/esp_nn_conv_ansi.c index 677c0ad8..d04f78e1 100644 --- a/code/components/esp-nn/src/convolution/esp_nn_conv_ansi.c +++ b/code/components/esp-nn/src/convolution/esp_nn_conv_ansi.c @@ -12,14 +12,16 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include +#include #include -int esp_nn_get_conv_scratch_size_ansi(const data_dims_t *input_dims, - const data_dims_t *filter_dims, - const data_dims_t *output_dims, - const conv_params_t *conv_params) +int esp_nn_get_conv_scratch_size_ansi(const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t in_ch, + const uint16_t out_ch, + const uint16_t filter_wd, + const uint16_t filter_ht) { return 0; } @@ -106,35 +108,29 @@ void esp_nn_conv_u8_ansi(const uint8_t *input_data, * Assumption 2: Pointers are valid * Assumption 3: dialation width = 1 */ -void esp_nn_conv_s8_ansi(const data_dims_t *input_dims, - const int8_t *input_data, - const data_dims_t *filter_dims, +void esp_nn_conv_s8_ansi(const int8_t *input_data, + const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t in_channels, + const int32_t input_offset, + const uint16_t pad_wd, + const uint16_t pad_ht, + const uint16_t stride_wd, + const uint16_t stride_ht, const int8_t *filter_data, + const uint16_t filter_wd, + const uint16_t filter_ht, const int32_t *bias, - const data_dims_t *output_dims, int8_t *out_data, - const conv_params_t *conv_params, - const quant_data_t *quant_data) + const uint16_t out_wd, + const uint16_t out_ht, + const uint16_t out_channels, + const int32_t out_offset, + const int32_t *out_shift, + const int32_t *out_mult, + const int32_t activation_min, + const int32_t activation_max) { - const uint16_t input_wd = input_dims->width; - const uint16_t input_ht = input_dims->height; - const uint16_t in_channels = input_dims->channels; - const int32_t input_offset = conv_params->in_offset; - const int32_t out_offset = conv_params->out_offset; - const uint16_t pad_wd = conv_params->padding.width; - const uint16_t pad_ht = conv_params->padding.height; - const uint16_t stride_wd = conv_params->stride.width; - const uint16_t stride_ht = conv_params->stride.height; - const uint16_t filter_wd = filter_dims->width; - const uint16_t filter_ht = filter_dims->height; - const uint16_t out_wd = output_dims->width; - const uint16_t out_ht = output_dims->height; - const uint16_t out_channels = output_dims->channels; - const int32_t *out_shift = quant_data->shift; - const int32_t *out_mult = quant_data->mult; - const int32_t activation_min = conv_params->activation.min; - const int32_t activation_max = conv_params->activation.max; - int32_t out_ch_idx, out_y, out_x, in_ch_idx, filter_y_idx, filter_x_idx; for (out_y = 0; out_y < out_ht; out_y++) { diff --git a/code/components/esp-nn/src/convolution/esp_nn_conv_esp32s3.c b/code/components/esp-nn/src/convolution/esp_nn_conv_esp32s3.c index e13129b2..ea8fdfa5 100644 --- a/code/components/esp-nn/src/convolution/esp_nn_conv_esp32s3.c +++ b/code/components/esp-nn/src/convolution/esp_nn_conv_esp32s3.c @@ -12,30 +12,30 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include #include -#include #include static int16_t *scratch_buffer = NULL; -extern void esp_nn_conv_s8_mult8_1x1_esp32s3(const int8_t *input_data, - const uint16_t input_wd, - const uint16_t input_ht, - const uint16_t in_channels, - const int32_t input_offset, - const int8_t *filter_aligned, - const int32_t *bias, - int8_t *out_data, - const uint16_t out_wd, - const uint16_t out_ht, - const uint16_t out_channels, - const int32_t out_offset, - const int32_t *out_shift, - const int32_t *out_mult, - const int32_t activation_min, - const int32_t activation_max, - void *buffer /* scratch buffer */); +extern void esp_nn_conv_s16_mult8_1x1_esp32s3(const int8_t *input_data, + const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t in_channels, + const int32_t input_offset, + const int16_t *filter_data, + const int32_t *bias, + int8_t *out_data, + const uint16_t out_wd, + const uint16_t out_ht, + const uint16_t out_channels, + const int32_t out_offset, + const int32_t *out_shift, + const int32_t *out_mult, + const int32_t activation_min, + const int32_t activation_max, + void *buffer /* scratch buffer */); extern void esp_nn_conv_s16_mult4_1x1_esp32s3(const int16_t *input_data, const uint16_t input_wd, @@ -81,40 +81,34 @@ extern void esp_nn_aligned_s8_to_s16_with_offset_esp32s3(const int8_t *src, int1 extern void esp_nn_s8_to_s16_esp32s3(const int8_t *src, int16_t *dst, const int size); -static void esp_nn_conv_s8_unrolled(const data_dims_t *input_dims, - const int8_t *input_data, - const data_dims_t *filter_dims, +static void esp_nn_conv_s8_unrolled(const int8_t *input_data, + const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t in_channels, + const int32_t input_offset, + const uint16_t pad_wd, + const uint16_t pad_ht, + const uint16_t stride_wd, + const uint16_t stride_ht, const int8_t *filter_data, + const uint16_t filter_wd, + const uint16_t filter_ht, const int32_t *bias, - const data_dims_t *output_dims, int8_t *out_data, - const conv_params_t *conv_params, - const quant_data_t *quant_data) + const uint16_t out_wd, + const uint16_t out_ht, + const uint16_t out_channels, + const int32_t out_offset, + const int32_t *out_shift, + const int32_t *out_mult, + const int32_t activation_min, + const int32_t activation_max) { - const uint16_t input_wd = input_dims->width; - const uint16_t input_ht = input_dims->height; - const uint16_t in_ch = input_dims->channels; - const int32_t input_offset = conv_params->in_offset; - const int32_t out_offset = conv_params->out_offset; - const uint16_t pad_wd = conv_params->padding.width; - const uint16_t pad_ht = conv_params->padding.height; - const uint16_t stride_wd = conv_params->stride.width; - const uint16_t stride_ht = conv_params->stride.height; - const uint16_t filter_wd = filter_dims->width; - const uint16_t filter_ht = filter_dims->height; - const uint16_t out_wd = output_dims->width; - const uint16_t out_ht = output_dims->height; - const uint16_t out_ch = output_dims->channels; - const int32_t *out_shift = quant_data->shift; - const int32_t *out_mult = quant_data->mult; - const int32_t activation_min = conv_params->activation.min; - const int32_t activation_max = conv_params->activation.max; - int32_t out_ch_idx, out_y, out_x, in_ch_idx, filter_y_idx, filter_x_idx; for (out_y = 0; out_y < out_ht; out_y++) { for (out_x = 0; out_x < out_wd; out_x++) { - for (out_ch_idx = 0; out_ch_idx < out_ch; out_ch_idx++) { + for (out_ch_idx = 0; out_ch_idx < out_channels; out_ch_idx++) { int32_t conv_out = 0; const int32_t base_y = stride_ht * out_y - pad_ht; @@ -130,10 +124,10 @@ static void esp_nn_conv_s8_unrolled(const data_dims_t *input_dims, for (filter_x_idx = filter_x_start; filter_x_idx < filter_x_end; filter_x_idx++) { const int32_t in_row = base_y + filter_y_idx; const int32_t in_col = base_x + filter_x_idx; - int32_t input_base_offset = (in_row * input_wd + in_col) * in_ch; - int32_t filter_base_offset = out_ch_idx * in_ch * filter_ht * filter_wd + - (filter_y_idx * filter_wd + filter_x_idx) * in_ch; - for (in_ch_idx = 0; in_ch_idx < in_ch; in_ch_idx++) { + int32_t input_base_offset = (in_row * input_wd + in_col) * in_channels; + int32_t filter_base_offset = out_ch_idx * in_channels * filter_ht * filter_wd + + (filter_y_idx * filter_wd + filter_x_idx) * in_channels; + for (in_ch_idx = 0; in_ch_idx < in_channels; in_ch_idx++) { conv_out += (input_data[input_base_offset + in_ch_idx] + input_offset) * filter_data[filter_base_offset + in_ch_idx]; @@ -338,35 +332,18 @@ static void esp_nn_conv_s8_pad_valid_ch3_3x3(const int8_t *input_data, } } -int esp_nn_get_conv_scratch_size_esp32s3(const data_dims_t *input_dims, - const data_dims_t *filter_dims, - const data_dims_t *output_dims, - const conv_params_t *conv_params) +int esp_nn_get_conv_scratch_size_esp32s3(const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t in_ch, + const uint16_t out_ch, + const uint16_t filter_wd, + const uint16_t filter_ht) { - const uint16_t input_wd = input_dims->width; - const uint16_t input_ht = input_dims->height; - const uint16_t in_ch = input_dims->channels; - const uint16_t filter_wd = filter_dims->width; - const uint16_t filter_ht = filter_dims->height; - const uint16_t out_ch = output_dims->channels; - const uint16_t pad_wd = conv_params->padding.width; - const uint16_t pad_ht = conv_params->padding.height; - const uint16_t stride_wd = conv_params->stride.width; - const uint16_t stride_ht = conv_params->stride.height; - int filter_size = filter_wd * filter_ht * in_ch * out_ch; int input_size = input_wd * input_ht * in_ch; - - int transpose_buf_size = 2 * (8 * in_ch); /* to store intermediate data */ - if (input_wd * input_ht < 8) { - transpose_buf_size = 0; // not using this for leftover - } + int transpose_buf_size = 8 * in_ch; /* to store intermediate data */ int align_buf_size = 32; /* extra buffer for alignment */ - if (in_ch % 8 == 0 && filter_wd == 1 && filter_ht == 1 && - pad_wd == 0 && pad_ht == 0 && stride_wd == 1 && stride_ht == 1) { - return filter_size + transpose_buf_size + align_buf_size; - } - return 2 * (filter_size + input_size) + transpose_buf_size + align_buf_size; + return 2 * (filter_size + input_size + transpose_buf_size) + align_buf_size; } void esp_nn_set_conv_scratch_buf_esp32s3(void *buf) @@ -374,35 +351,29 @@ void esp_nn_set_conv_scratch_buf_esp32s3(void *buf) scratch_buffer = (int16_t *) buf; } -void esp_nn_conv_s8_esp32s3(const data_dims_t *input_dims, - const int8_t *input, - const data_dims_t *filter_dims, +void esp_nn_conv_s8_esp32s3(const int8_t *input, + const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t channels, + const int32_t input_offset, + const uint16_t pad_wd, + const uint16_t pad_ht, + const uint16_t stride_wd, + const uint16_t stride_ht, const int8_t *filter_data, + const uint16_t filter_wd, + const uint16_t filter_ht, const int32_t *bias, - const data_dims_t *output_dims, int8_t *out_data, - const conv_params_t *conv_params, - const quant_data_t *quant_data) + const uint16_t out_wd, + const uint16_t out_ht, + const uint16_t out_channels, + const int32_t out_offset, + const int32_t *out_shift, + const int32_t *out_mult, + const int32_t activation_min, + const int32_t activation_max) { - const uint16_t input_wd = input_dims->width; - const uint16_t input_ht = input_dims->height; - const uint16_t channels = input_dims->channels; - const int32_t input_offset = conv_params->in_offset; - const int32_t out_offset = conv_params->out_offset; - const uint16_t pad_wd = conv_params->padding.width; - const uint16_t pad_ht = conv_params->padding.height; - const uint16_t stride_wd = conv_params->stride.width; - const uint16_t stride_ht = conv_params->stride.height; - const uint16_t filter_wd = filter_dims->width; - const uint16_t filter_ht = filter_dims->height; - const uint16_t out_wd = output_dims->width; - const uint16_t out_ht = output_dims->height; - const uint16_t out_channels = output_dims->channels; - const int32_t *out_shift = quant_data->shift; - const int32_t *out_mult = quant_data->mult; - const int32_t activation_min = conv_params->activation.min; - const int32_t activation_max = conv_params->activation.max; - int filter_size = filter_wd * filter_ht * channels * out_channels; int input_size = input_wd * input_ht * channels; int align_len = 16 - (filter_size & 15); @@ -416,16 +387,15 @@ void esp_nn_conv_s8_esp32s3(const data_dims_t *input_dims, if (channels % 8 == 0 && filter_wd == 1 && filter_ht == 1 && pad_wd == 0 && pad_ht == 0 && stride_wd == 1 && stride_ht == 1) { - int8_t *filter_aligned = (int8_t *) scratch_buffer; - int scratch_offset = (int) (filter_aligned + filter_size); + int scratch_offset = (int) (filter_data16 + filter_size); void *scratch_buf = (void *) (scratch_offset + 16 - (scratch_offset & 15)); - memcpy(filter_aligned, filter_data, filter_size); // copy to aligned address - esp_nn_conv_s8_mult8_1x1_esp32s3( - input, input_wd, input_ht, channels, input_offset, filter_aligned, + esp_nn_s8_to_s16_esp32s3(filter_data, filter_data16, filter_size); + esp_nn_conv_s16_mult8_1x1_esp32s3( + input, input_wd, input_ht, channels, input_offset, filter_data16, bias, out_data, out_wd, out_ht, out_channels, out_offset, out_shift, out_mult, activation_min, activation_max, scratch_buf); } else if (channels % 4 == 0 && filter_wd == 1 && filter_ht == 1 && - (input_wd * input_ht) % 4 == 0 && /* TODO: remove this check */ + (input_wd * input_ht) % 16 == 0 && /* TODO: remove this check */ pad_wd == 0 && pad_ht == 0 && stride_wd == 1 && stride_ht == 1) { int scratch_offset = (int) (input_data16 + input_size); void *scratch_buf = (void *) (scratch_offset + 16 - (scratch_offset & 15)); @@ -457,7 +427,10 @@ void esp_nn_conv_s8_esp32s3(const data_dims_t *input_dims, } } else { /* Basic unrolled version */ - esp_nn_conv_s8_unrolled(input_dims, input, filter_dims, filter_data, - bias, output_dims, out_data, conv_params, quant_data); + esp_nn_conv_s8_unrolled(input, input_wd, input_ht, channels, input_offset, + pad_wd, pad_ht, stride_wd, stride_ht, + filter_data, filter_wd, filter_ht, bias, + out_data, out_wd, out_ht, out_channels, out_offset, out_shift, + out_mult, activation_min, activation_max); } } diff --git a/code/components/esp-nn/src/convolution/esp_nn_conv_opt.c b/code/components/esp-nn/src/convolution/esp_nn_conv_opt.c deleted file mode 100644 index be96430e..00000000 --- a/code/components/esp-nn/src/convolution/esp_nn_conv_opt.c +++ /dev/null @@ -1,179 +0,0 @@ -// Copyright 2020-2021 Espressif Systems (Shanghai) PTE LTD -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include - -#include - -int esp_nn_get_conv_scratch_size_opt(const data_dims_t *input_dims, - const data_dims_t *filter_dims, - const data_dims_t *output_dims, - const conv_params_t *conv_params) -{ - return 0; -} - -void esp_nn_set_conv_scratch_buf_opt(const void *buf) -{ - -} - -__attribute__ ((noinline)) -static void esp_nn_conv_s8_1x1(const data_dims_t *input_dims, - const int8_t *input_data, - const int8_t *filter_data, - const int32_t *bias, - const data_dims_t *output_dims, - int8_t *out_data, - const conv_params_t *conv_params, - const quant_data_t *quant_data) -{ - const uint16_t input_wd = input_dims->width; - const uint16_t in_channels = input_dims->channels; - const int32_t input_offset = conv_params->in_offset; - const int32_t out_offset = conv_params->out_offset; - const uint16_t stride_wd = conv_params->stride.width; - const uint16_t stride_ht = conv_params->stride.height; - const uint16_t out_wd = output_dims->width; - const uint16_t out_ht = output_dims->height; - const uint16_t out_channels = output_dims->channels; - const int32_t activation_min = conv_params->activation.min; - const int32_t activation_max = conv_params->activation.max; - - for (int32_t in_row = 0; in_row < out_ht * stride_ht; in_row += stride_ht) { - for (int32_t in_col = 0; in_col < out_wd * stride_wd; in_col += stride_wd) { - const int32_t *out_mult = quant_data->mult; - const int32_t *out_shift = quant_data->shift; - const int8_t *filter_ptr = filter_data; - const int8_t *input_base_ptr = input_data + (in_row * input_wd + in_col) * in_channels; - int32_t out_ch_idx = 0; - for (; out_ch_idx < out_channels; out_ch_idx++) { - int32_t conv_out = 0; - - const int8_t *input_ptr = input_base_ptr; - - int32_t in_ch_idx = 0; - for (; in_ch_idx < in_channels - 3; in_ch_idx += 4) { - conv_out += (*input_ptr++ + input_offset) * *filter_ptr++; - conv_out += (*input_ptr++ + input_offset) * *filter_ptr++; - conv_out += (*input_ptr++ + input_offset) * *filter_ptr++; - conv_out += (*input_ptr++ + input_offset) * *filter_ptr++; - } - for (; in_ch_idx < in_channels; in_ch_idx ++) { - conv_out += (*input_ptr++ + input_offset) * *filter_ptr++; - } - if (bias) { - conv_out += bias[out_ch_idx]; - } - conv_out = esp_nn_multiply_by_quantized_mult_fast(conv_out, *out_mult++, *out_shift++); - conv_out += out_offset; - conv_out = max(conv_out, activation_min); - conv_out = min(conv_out, activation_max); - *out_data++ = (int8_t) conv_out; - } - } - } -} - -/** - * Assumption 1: i/p channels == o/p channels - * Assumption 2: Pointers are valid - * Assumption 3: dialation width = 1 - */ -void esp_nn_conv_s8_opt(const data_dims_t *input_dims, - const int8_t *input_data, - const data_dims_t *filter_dims, - const int8_t *filter_data, - const int32_t *bias, - const data_dims_t *output_dims, - int8_t *out_data, - const conv_params_t *conv_params, - const quant_data_t *quant_data) -{ - const uint16_t filter_wd = filter_dims->width; - const uint16_t filter_ht = filter_dims->height; - - if (filter_wd == 1 && filter_ht == 1) { - esp_nn_conv_s8_1x1(input_dims, input_data, filter_data, bias, - output_dims, out_data, conv_params, quant_data); - return; - } - - const uint16_t input_wd = input_dims->width; - const uint16_t input_ht = input_dims->height; - const uint16_t in_channels = input_dims->channels; - const int32_t input_offset = conv_params->in_offset; - const int32_t out_offset = conv_params->out_offset; - const uint16_t pad_wd = conv_params->padding.width; - const uint16_t pad_ht = conv_params->padding.height; - const uint16_t stride_wd = conv_params->stride.width; - const uint16_t stride_ht = conv_params->stride.height; - const uint16_t out_wd = output_dims->width; - const uint16_t out_ht = output_dims->height; - const uint16_t out_channels = output_dims->channels; - const int32_t activation_min = conv_params->activation.min; - const int32_t activation_max = conv_params->activation.max; - - int32_t out_ch_idx, out_y, out_x, filter_y_idx, filter_x_idx; - - for (out_y = 0; out_y < out_ht; out_y++) { - for (out_x = 0; out_x < out_wd; out_x++) { - const int32_t *out_shift = quant_data->shift; - const int32_t *out_mult = quant_data->mult; - for (out_ch_idx = 0; out_ch_idx < out_channels; out_ch_idx++) { - int32_t conv_out = 0; - - const int32_t base_y = stride_ht * out_y - pad_ht; - const int32_t base_x = stride_wd * out_x - pad_wd; - - const int32_t filter_y_start = max(0, -base_y); - const int32_t filter_x_start = max(0, -base_x); - - const int32_t filter_y_end = min(filter_ht, input_ht - base_y); - const int32_t filter_x_end = min(filter_wd, input_wd - base_x); - - for (filter_y_idx = filter_y_start; filter_y_idx < filter_y_end; filter_y_idx++) { - for (filter_x_idx = filter_x_start; filter_x_idx < filter_x_end; filter_x_idx++) { - const int32_t in_row = base_y + filter_y_idx; - const int32_t in_col = base_x + filter_x_idx; - - const int8_t *input_ptr = input_data + - (in_row * input_wd + in_col) * in_channels; - const int8_t *filter_ptr = filter_data + - out_ch_idx * in_channels * filter_ht * filter_wd + - (filter_y_idx * filter_wd + filter_x_idx) * in_channels; - int32_t in_ch_idx = 0; - for (; in_ch_idx < in_channels - 3; in_ch_idx += 4) { - conv_out += (*input_ptr++ + input_offset) * *filter_ptr++; - conv_out += (*input_ptr++ + input_offset) * *filter_ptr++; - conv_out += (*input_ptr++ + input_offset) * *filter_ptr++; - conv_out += (*input_ptr++ + input_offset) * *filter_ptr++; - } - for (; in_ch_idx < in_channels; in_ch_idx ++) { - conv_out += (*input_ptr++ + input_offset) * *filter_ptr++; - } - } - } - if (bias) { - conv_out += bias[out_ch_idx]; - } - conv_out = esp_nn_multiply_by_quantized_mult_fast(conv_out, *out_mult++, *out_shift++); - conv_out += out_offset; - conv_out = max(conv_out, activation_min); - conv_out = min(conv_out, activation_max); - *out_data++ = (int8_t) conv_out; - } - } - } -} diff --git a/code/components/esp-nn/src/convolution/esp_nn_depthwise_conv_ansi.c b/code/components/esp-nn/src/convolution/esp_nn_depthwise_conv_ansi.c index 1cd02e0f..9cac6cef 100644 --- a/code/components/esp-nn/src/convolution/esp_nn_depthwise_conv_ansi.c +++ b/code/components/esp-nn/src/convolution/esp_nn_depthwise_conv_ansi.c @@ -12,13 +12,16 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include +#include + #include -int esp_nn_get_depthwise_conv_scratch_size_ansi(const data_dims_t *input_dims, - const data_dims_t *filter_dims, - const data_dims_t *output_dims, - const dw_conv_params_t *conv_params) +int esp_nn_get_depthwise_conv_scratch_size_ansi(const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t channels, + const uint16_t ch_mult, + const uint16_t filter_wd, + const uint16_t filter_ht) { return 0; } @@ -28,35 +31,29 @@ void esp_nn_set_depthwise_conv_scratch_buf_ansi(const void *buf) } -void esp_nn_depthwise_conv_s8_ansi(const data_dims_t *input_dims, - const int8_t *input_data, - const data_dims_t *filter_dims, +void esp_nn_depthwise_conv_s8_ansi(const int8_t *input_data, + const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t channels, + const int32_t input_offset, + const uint16_t pad_wd, + const uint16_t pad_ht, + const uint16_t stride_wd, + const uint16_t stride_ht, + const uint16_t ch_mult, const int8_t *filter_data, + const uint16_t filter_wd, + const uint16_t filter_ht, const int32_t *bias, - const data_dims_t *output_dims, int8_t *out_data, - const dw_conv_params_t *conv_params, - const quant_data_t *quant_data) + const uint16_t out_wd, + const uint16_t out_ht, + const int32_t out_offset, + const int32_t *out_shift, + const int32_t *out_mult, + const int32_t activation_min, + const int32_t activation_max) { - const uint16_t input_wd = input_dims->width; - const uint16_t input_ht = input_dims->height; - const uint16_t channels = input_dims->channels; - const int32_t input_offset = conv_params->in_offset; - const int32_t out_offset = conv_params->out_offset; - const uint16_t pad_wd = conv_params->padding.width; - const uint16_t pad_ht = conv_params->padding.height; - const uint16_t stride_wd = conv_params->stride.width; - const uint16_t stride_ht = conv_params->stride.height; - const uint16_t filter_wd = filter_dims->width; - const uint16_t filter_ht = filter_dims->height; - const uint16_t out_wd = output_dims->width; - const uint16_t out_ht = output_dims->height; - const int32_t *out_shift = quant_data->shift; - const int32_t *out_mult = quant_data->mult; - const int32_t activation_min = conv_params->activation.min; - const int32_t activation_max = conv_params->activation.max; - const uint16_t ch_mult = conv_params->ch_mult; - int out_idx = 0; for (int out_y = 0; out_y < out_ht; out_y++) { //height loop const int16_t base_y = (out_y * stride_ht) - pad_ht; diff --git a/code/components/esp-nn/src/convolution/esp_nn_depthwise_conv_opt.c b/code/components/esp-nn/src/convolution/esp_nn_depthwise_conv_opt.c deleted file mode 100644 index 4afea3f3..00000000 --- a/code/components/esp-nn/src/convolution/esp_nn_depthwise_conv_opt.c +++ /dev/null @@ -1,291 +0,0 @@ -// Copyright 2020-2021 Espressif Systems (Shanghai) PTE LTD -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include -#include - -int esp_nn_get_depthwise_conv_scratch_size_opt(const data_dims_t *input_dims, - const data_dims_t *filter_dims, - const data_dims_t *output_dims, - const dw_conv_params_t *conv_params) -{ - return 0; -} - -void esp_nn_set_depthwise_conv_scratch_buf_opt(const void *buf) -{ - -} - -/* common channel multiplier == 1 case */ -__attribute__ ((noinline)) -static void esp_nn_depthwise_conv_s8_ch_mult_1(const data_dims_t *input_dims, - const int8_t *input_data, - const data_dims_t *filter_dims, - const int8_t *filter_data, - const int32_t *bias, - const data_dims_t *output_dims, - int8_t *out_data, - const dw_conv_params_t *conv_params, - const quant_data_t *quant_data) -{ - const uint16_t input_wd = input_dims->width; - const uint16_t input_ht = input_dims->height; - const uint16_t channels = input_dims->channels; - const int32_t input_offset = conv_params->in_offset; - const int32_t out_offset = conv_params->out_offset; - const uint16_t pad_wd = conv_params->padding.width; - const uint16_t pad_ht = conv_params->padding.height; - const uint16_t stride_wd = conv_params->stride.width; - const uint16_t stride_ht = conv_params->stride.height; - const uint16_t filter_wd = filter_dims->width; - const uint16_t filter_ht = filter_dims->height; - const uint16_t out_wd = output_dims->width; - const uint16_t out_ht = output_dims->height; - const int32_t activation_min = conv_params->activation.min; - const int32_t activation_max = conv_params->activation.max; - - int out_idx = 0; - for (int out_y = 0; out_y < out_ht; out_y++) { //height loop - const int16_t base_y = (out_y * stride_ht) - pad_ht; - for (int out_x = 0; out_x < out_wd; out_x++) { //width_loop - const int16_t base_x = (out_x * stride_wd) - pad_wd; - - const int32_t *out_shift = quant_data->shift; - const int32_t *out_mult = quant_data->mult; - - /* Select filter so as the point doesn't lie outside block */ - int filter_y_start = max(0, -base_y); - int filter_x_start = max(0, -base_x); - int filter_y_end = min(filter_ht, input_ht - base_y); - int filter_x_end = min(filter_wd, input_wd - base_x); - - int ch_idx = 0; - for (; ch_idx < channels - 3; ch_idx += 4) {//channel_loop - int32_t result0 = 0; - int32_t result1 = 0; - int32_t result2 = 0; - int32_t result3 = 0; - - for (int filter_y_idx = filter_y_start; filter_y_idx < filter_y_end; filter_y_idx++) { - const int32_t idx_y = base_y + filter_y_idx; - for (int filter_x_idx = filter_x_start; filter_x_idx < filter_x_end; filter_x_idx++) { - const int32_t idx_x = base_x + filter_x_idx; - int32_t input_index = (idx_y * input_wd + idx_x) * channels + ch_idx; - int32_t filter_index = (filter_y_idx * filter_wd + filter_x_idx) * (channels) + ch_idx; - int32_t input_val0 = input_data[input_index + 0] + input_offset; - int32_t input_val1 = input_data[input_index + 1] + input_offset; - int32_t input_val2 = input_data[input_index + 2] + input_offset; - int32_t input_val3 = input_data[input_index + 3] + input_offset; - int32_t filter_val0 = filter_data[filter_index + 0]; - int32_t filter_val1 = filter_data[filter_index + 1]; - int32_t filter_val2 = filter_data[filter_index + 2]; - int32_t filter_val3 = filter_data[filter_index + 3]; - result0 += input_val0 * filter_val0; - result1 += input_val1 * filter_val1; - result2 += input_val2 * filter_val2; - result3 += input_val3 * filter_val3; - } - } - if (bias) { - result0 += bias[ch_idx + 0]; - result1 += bias[ch_idx + 1]; - result2 += bias[ch_idx + 2]; - result3 += bias[ch_idx + 3]; - } - result0 = esp_nn_multiply_by_quantized_mult_fast(result0, *out_mult++, *out_shift++); - result1 = esp_nn_multiply_by_quantized_mult_fast(result1, *out_mult++, *out_shift++); - result2 = esp_nn_multiply_by_quantized_mult_fast(result2, *out_mult++, *out_shift++); - result3 = esp_nn_multiply_by_quantized_mult_fast(result3, *out_mult++, *out_shift++); - - result0 += out_offset; - result1 += out_offset; - result2 += out_offset; - result3 += out_offset; - - result0 = max(result0, activation_min); - result1 = max(result1, activation_min); - result2 = max(result2, activation_min); - result3 = max(result3, activation_min); - - result0 = min(result0, activation_max); - result1 = min(result1, activation_max); - result2 = min(result2, activation_max); - result3 = min(result3, activation_max); - - out_data[out_idx++] = result0; - out_data[out_idx++] = result1; - out_data[out_idx++] = result2; - out_data[out_idx++] = result3; - } - for (; ch_idx < channels; ch_idx++) {//channel_loop - int32_t result = 0; - - for (int filter_y_idx = filter_y_start; filter_y_idx < filter_y_end; filter_y_idx++) { - const int32_t idx_y = base_y + filter_y_idx; - for (int filter_x_idx = filter_x_start; filter_x_idx < filter_x_end; filter_x_idx++) { - const int32_t idx_x = base_x + filter_x_idx; - int32_t input_index = (idx_y * input_wd + idx_x) * channels + ch_idx; - int32_t filter_index = (filter_y_idx * filter_wd + filter_x_idx) * (channels) + ch_idx; - int32_t input_val = input_data[input_index] + input_offset; - int32_t filter_val = filter_data[filter_index]; - result += input_val * filter_val; - } - } - if (bias) { - result += bias[ch_idx]; - } - result = esp_nn_multiply_by_quantized_mult_fast(result, *out_mult++, *out_shift++); - result += out_offset; - result = max(result, activation_min); - result = min(result, activation_max); - - out_data[out_idx++] = result; - } - } - } -} - -void esp_nn_depthwise_conv_s8_opt(const data_dims_t *input_dims, - const int8_t *input_data, - const data_dims_t *filter_dims, - const int8_t *filter_data, - const int32_t *bias, - const data_dims_t *output_dims, - int8_t *out_data, - const dw_conv_params_t *conv_params, - const quant_data_t *quant_data) -{ - const uint16_t ch_mult = conv_params->ch_mult; - if (ch_mult == 1) { - esp_nn_depthwise_conv_s8_ch_mult_1(input_dims, input_data, filter_dims, filter_data, - bias, output_dims, out_data, conv_params, quant_data); - return; - } - const uint16_t input_wd = input_dims->width; - const uint16_t input_ht = input_dims->height; - const uint16_t channels = input_dims->channels; - const int32_t input_offset = conv_params->in_offset; - const int32_t out_offset = conv_params->out_offset; - const uint16_t pad_wd = conv_params->padding.width; - const uint16_t pad_ht = conv_params->padding.height; - const uint16_t stride_wd = conv_params->stride.width; - const uint16_t stride_ht = conv_params->stride.height; - const uint16_t filter_wd = filter_dims->width; - const uint16_t filter_ht = filter_dims->height; - const uint16_t out_wd = output_dims->width; - const uint16_t out_ht = output_dims->height; - const int32_t activation_min = conv_params->activation.min; - const int32_t activation_max = conv_params->activation.max; - - int out_idx = 0; - for (int out_y = 0; out_y < out_ht; out_y++) { //height loop - const int16_t base_y = (out_y * stride_ht) - pad_ht; - for (int out_x = 0; out_x < out_wd; out_x++) { //width_loop - const int16_t base_x = (out_x * stride_wd) - pad_wd; - - const int32_t *out_shift = quant_data->shift; - const int32_t *out_mult = quant_data->mult; - - /* Select filter so as the point doesn't lie outside block */ - int filter_y_start = max(0, -base_y); - int filter_x_start = max(0, -base_x); - int filter_y_end = min(filter_ht, input_ht - base_y); - int filter_x_end = min(filter_wd, input_wd - base_x); - - for (int ch_idx = 0; ch_idx < channels; ch_idx++) {//channel_loop - int ch_mult_idx = 0; - for (; ch_mult_idx < ch_mult - 3; ch_mult_idx += 4) { - int32_t result0 = 0; - int32_t result1 = 0; - int32_t result2 = 0; - int32_t result3 = 0; - const int out_ch_idx = ch_idx * ch_mult + ch_mult_idx; - - for (int filter_y_idx = filter_y_start; filter_y_idx < filter_y_end; filter_y_idx++) { - const int32_t idx_y = base_y + filter_y_idx; - for (int filter_x_idx = filter_x_start; filter_x_idx < filter_x_end; filter_x_idx++) { - const int32_t idx_x = base_x + filter_x_idx; - int32_t input_index = (idx_y * input_wd + idx_x) * channels + ch_idx; - int32_t filter_index = (filter_y_idx * filter_wd + filter_x_idx) * (channels * ch_mult) + out_ch_idx; - int32_t input_val = input_data[input_index] + input_offset; - int32_t filter_val0 = filter_data[filter_index + 0]; - int32_t filter_val1 = filter_data[filter_index + 1]; - int32_t filter_val2 = filter_data[filter_index + 2]; - int32_t filter_val3 = filter_data[filter_index + 3]; - result0 += input_val * filter_val0; - result1 += input_val * filter_val1; - result2 += input_val * filter_val2; - result3 += input_val * filter_val3; - } - } - if (bias) { - result0 += bias[out_ch_idx + 0]; - result1 += bias[out_ch_idx + 1]; - result2 += bias[out_ch_idx + 2]; - result3 += bias[out_ch_idx + 3]; - } - result0 = esp_nn_multiply_by_quantized_mult_fast(result0, *out_mult++, *out_shift++); - result1 = esp_nn_multiply_by_quantized_mult_fast(result1, *out_mult++, *out_shift++); - result2 = esp_nn_multiply_by_quantized_mult_fast(result2, *out_mult++, *out_shift++); - result3 = esp_nn_multiply_by_quantized_mult_fast(result3, *out_mult++, *out_shift++); - - result0 += out_offset; - result1 += out_offset; - result2 += out_offset; - result3 += out_offset; - - result0 = max(result0, activation_min); - result1 = max(result1, activation_min); - result2 = max(result2, activation_min); - result3 = max(result3, activation_min); - result0 = min(result0, activation_max); - result1 = min(result1, activation_max); - result2 = min(result2, activation_max); - result3 = min(result3, activation_max); - - out_data[out_idx++] = result0; - out_data[out_idx++] = result1; - out_data[out_idx++] = result2; - out_data[out_idx++] = result3; - } - for (; ch_mult_idx < ch_mult; ch_mult_idx++) { - int32_t result = 0; - const int out_ch_idx = ch_idx * ch_mult + ch_mult_idx; - - for (int filter_y_idx = filter_y_start; filter_y_idx < filter_y_end; filter_y_idx++) { - const int32_t idx_y = base_y + filter_y_idx; - for (int filter_x_idx = filter_x_start; filter_x_idx < filter_x_end; filter_x_idx++) { - const int32_t idx_x = base_x + filter_x_idx; - int32_t input_index = (idx_y * input_wd + idx_x) * channels + ch_idx; - int32_t filter_index = (filter_y_idx * filter_wd + filter_x_idx) * (channels * ch_mult) + out_ch_idx; - int32_t input_val = input_data[input_index] + input_offset; - int32_t filter_val = filter_data[filter_index]; - result += input_val * filter_val; - } - } - if (bias) { - result += bias[out_ch_idx]; - } - result = esp_nn_multiply_by_quantized_mult_fast(result, *out_mult++, *out_shift++); - result += out_offset; - result = max(result, activation_min); - result = min(result, activation_max); - - out_data[out_idx++] = result; - } - } - } - } -} diff --git a/code/components/esp-nn/src/convolution/esp_nn_depthwise_conv_s8_esp32s3.c b/code/components/esp-nn/src/convolution/esp_nn_depthwise_conv_s8_esp32s3.c index 9167a43f..c588c48f 100644 --- a/code/components/esp-nn/src/convolution/esp_nn_depthwise_conv_s8_esp32s3.c +++ b/code/components/esp-nn/src/convolution/esp_nn_depthwise_conv_s8_esp32s3.c @@ -12,8 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include #include -#include #include @@ -353,59 +353,17 @@ void esp_nn_depthwise_conv_s8_ch_mult1(const int8_t *input_data, } } -int esp_nn_get_depthwise_conv_scratch_size_esp32s3(const data_dims_t *input_dims, - const data_dims_t *filter_dims, - const data_dims_t *output_dims, - const dw_conv_params_t *conv_params) +int esp_nn_get_depthwise_conv_scratch_size_esp32s3(const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t channels, + const uint16_t ch_mult, + const uint16_t filter_wd, + const uint16_t filter_ht) { - const uint16_t input_wd = input_dims->width; - const uint16_t input_ht = input_dims->height; - const uint16_t channels = input_dims->channels; - const uint16_t filter_wd = filter_dims->width; - const uint16_t filter_ht = filter_dims->height; - const uint16_t ch_mult = conv_params->ch_mult; - const uint16_t out_wd = output_dims->width; - const uint16_t out_ht = output_dims->height; - const uint16_t pad_wd = conv_params->padding.width; - const uint16_t pad_ht = conv_params->padding.height; - const uint16_t stride_wd = conv_params->stride.width; - const uint16_t stride_ht = conv_params->stride.height; - int filter_size = filter_wd * filter_ht * channels * ch_mult; - int pad_width = 0, pad_height = 0; - - if ((ch_mult == 1) && (channels % 8 == 0) && (filter_wd == 3) && (filter_ht == 3)) { - if (channels % 16 == 0) { - if (pad_wd || pad_ht) { - pad_width = pad_wd * 2; - pad_height = pad_ht * 2; - } else { - // check if we need to pad additionally - pad_width = (out_wd * stride_wd + filter_wd - 1) - input_wd; - pad_height = (out_ht * stride_ht + filter_ht - 1) - input_ht; - // printf("in(%d %d %d), out(%d %d), filter (%d %d) stride (%d %d), pad (%d %d)", - // input_wd, input_ht, channels, out_wd, out_ht, filter_wd, filter_ht, - // stride_wd, stride_ht, pad_wd, pad_ht); - } - if (pad_width || pad_height) { - int input_size = (input_wd + pad_width) * (input_ht + pad_height) * channels; - // printf("ask1 %d\n", filter_size + input_size + 16); - return filter_size + input_size + 16; // 16 for alignment - } else { - // printf("ask2 %d\n", filter_size + 16); - return filter_size + 16; // 16 for alignment - } - } else { - int input_size = input_wd * input_ht * channels; - // printf("ask3 %d\n", 2 * (filter_size + input_size) + 16); - return 2 * (filter_size + input_size) + 16; // 16 for alignment - } - } else if (ch_mult % 4 == 0) { - int input_size = input_wd * input_ht * channels; - // printf("ask4 %d\n", 2 * (filter_size + input_size) + 16); - return 2 * (filter_size + input_size) + 16; // 16 for alignment - } - return 32; // just few bytes + int padding_used = ((filter_wd == 3) && (filter_ht == 3)) * 2; + int input_size = (input_wd + padding_used) * (input_ht + padding_used) * channels; + return 2 * (filter_size + input_size) + 16; //16 for alignment } void esp_nn_set_depthwise_conv_scratch_buf_esp32s3(void *buf) @@ -418,38 +376,29 @@ void esp_nn_set_depthwise_conv_scratch_buf_esp32s3(void *buf) * Assumption 2: Pointers are valid * Assumption 3: dialation width = 1 */ - - - -void esp_nn_depthwise_conv_s8_esp32s3(const data_dims_t *input_dims, - const int8_t *input_data, - const data_dims_t *filter_dims, +void esp_nn_depthwise_conv_s8_esp32s3(const int8_t *input_data, + const uint16_t input_wd, + const uint16_t input_ht, + const uint16_t channels, + const int32_t input_offset, + const uint16_t pad_wd, + const uint16_t pad_ht, + const uint16_t stride_wd, + const uint16_t stride_ht, + const uint16_t ch_mult, const int8_t *filter_data, + const uint16_t filter_wd, + const uint16_t filter_ht, const int32_t *bias, - const data_dims_t *output_dims, int8_t *out_data, - const dw_conv_params_t *conv_params, - const quant_data_t *quant_data) + const uint16_t out_wd, + const uint16_t out_ht, + const int32_t out_offset, + const int32_t *out_shift, + const int32_t *out_mult, + const int32_t activation_min, + const int32_t activation_max) { - const uint16_t input_wd = input_dims->width; - const uint16_t input_ht = input_dims->height; - const uint16_t channels = input_dims->channels; - const int32_t input_offset = conv_params->in_offset; - const int32_t out_offset = conv_params->out_offset; - const uint16_t pad_wd = conv_params->padding.width; - const uint16_t pad_ht = conv_params->padding.height; - const uint16_t stride_wd = conv_params->stride.width; - const uint16_t stride_ht = conv_params->stride.height; - const uint16_t filter_wd = filter_dims->width; - const uint16_t filter_ht = filter_dims->height; - const uint16_t out_wd = output_dims->width; - const uint16_t out_ht = output_dims->height; - const int32_t *out_shift = quant_data->shift; - const int32_t *out_mult = quant_data->mult; - const int32_t activation_min = conv_params->activation.min; - const int32_t activation_max = conv_params->activation.max; - const uint16_t ch_mult = conv_params->ch_mult; - int filter_size = filter_wd * filter_ht * channels * ch_mult; int align_len = 16 - (filter_size & 15); int input_size = input_wd * input_ht * channels; @@ -474,27 +423,18 @@ void esp_nn_depthwise_conv_s8_esp32s3(const data_dims_t *input_dims, stride_wd, stride_ht, filter_aligned, bias, out_data, out_wd, out_ht, out_offset, out_shift, out_mult, activation_min, activation_max); - } else if ((channels % 16 == 0) && (pad_wd == 0) && (pad_ht == 0)) { + } else if ((pad_wd == 0) && (pad_ht == 0) && + // because this does not handle padding offset cases yet, run just for stride (1, 1). + // end padding of input with `-input_offset` should solve this + (stride_wd == 1) && (stride_ht == 1)) { /* process in 8 bits */ int8_t *filter_aligned = (int8_t *) scratch_buffer; - int8_t *input_padded = (int8_t *) scratch_buffer + filter_size + align_len; - - // check if we need to pad additionally - int pad_right = (out_wd * stride_wd + filter_wd - 1) - input_wd; - int pad_bottom = (out_ht * stride_ht + filter_ht - 1) - input_ht; - if (pad_right || pad_bottom) { // pad right and bottom - esp_nn_aligned_s8_pad_end_with_value(input_data, input_padded, input_wd, input_ht, - channels, -input_offset, pad_right, pad_bottom); - } else { - input_padded = (int8_t *) input_data; - } memcpy(filter_aligned, filter_data, filter_size); - esp_nn_depthwise_conv_s8_mult1_3x3_padded_esp32s3(input_padded, input_wd + pad_right, - input_ht + pad_bottom, channels, input_offset, - stride_wd, stride_ht, filter_aligned, bias, - out_data, out_wd, out_ht, out_offset, out_shift, + esp_nn_depthwise_conv_s8_mult1_3x3_padded_esp32s3(input_data, input_wd, input_ht, channels, input_offset, + stride_wd, stride_ht, filter_aligned, + bias, out_data, out_wd, out_ht, out_offset, out_shift, out_mult, activation_min, activation_max); - } else { /* (channels % 8) == 0 */ + } else { /* (channels % 8) == 0 && pad_wd == 1 && pad_ht == 1 */ esp_nn_s8_to_s16_esp32s3(filter_data, filter_data16, filter_size); esp_nn_aligned_s8_to_s16_with_offset_esp32s3(input_data, input_data16, input_size, input_offset); esp_nn_depthwise_conv_s16_mult1_3x3_esp32s3(input_data16, input_wd, input_ht, channels, diff --git a/code/components/esp-nn/test_app/sdkconfig.defaults.esp32s3 b/code/components/esp-nn/test_app/sdkconfig.defaults.esp32s3 deleted file mode 100644 index 1adc4b01..00000000 --- a/code/components/esp-nn/test_app/sdkconfig.defaults.esp32s3 +++ /dev/null @@ -1,8 +0,0 @@ -# Default configurations for ESP32-S3 - -CONFIG_ESP32S3_DEFAULT_CPU_FREQ_240=y -CONFIG_ESP32S3_SPIRAM_SUPPORT=y - -CONFIG_ESP32S3_DATA_CACHE_64KB=y -CONFIG_ESP32S3_DATA_CACHE_8WAYS=y -CONFIG_ESP32S3_DATA_CACHE_LINE_64B=y diff --git a/code/components/esp-nn/tests/src/basic_math_test.c b/code/components/esp-nn/tests/src/basic_math_test.c index 715d7c78..5b96b990 100644 --- a/code/components/esp-nn/tests/src/basic_math_test.c +++ b/code/components/esp-nn/tests/src/basic_math_test.c @@ -23,9 +23,7 @@ #include "test_utils.h" #if CONFIG_IDF_CMAKE -#if (CONFIG_SPIRAM_SUPPORT && (CONFIG_SPIRAM_USE_CAPS_ALLOC || CONFIG_SPIRAM_USE_MALLOC)) #define IDF_HEAP_CAPS 1 -#endif #if IDF_HEAP_CAPS #include "esp_heap_caps.h" @@ -140,11 +138,6 @@ void esp_nn_add_elementwise_s8_test() out_c_orig = out_data_c; out_opt_orig = out_data_opt; #endif - if (input1_orig == NULL || input2_orig == NULL || out_c_orig == NULL || - out_opt_orig == NULL) { - printf(ANSI_COLOR_RED"%s error allocating buffers\n"ANSI_COLOR_RESET, __FUNCTION__); - goto elementwise_add_test_cleanup; - } for (int i = 0; i < size; ++i) { input1[i] = rand() % 256 - 128; @@ -201,10 +194,10 @@ elementwise_add_test_cleanup: if (input2_orig) { free(input2_orig); } - if (out_c_orig) { + if (out_data_c) { free(out_c_orig); } - if (out_opt_orig) { + if (out_data_opt) { free(out_opt_orig); } } @@ -289,11 +282,6 @@ void esp_nn_mul_elementwise_s8_test() out_c_orig = out_data_c; out_opt_orig = out_data_opt; #endif - if (input1_orig == NULL || input2_orig == NULL || out_c_orig == NULL || - out_opt_orig == NULL) { - printf(ANSI_COLOR_RED"%s error allocating buffers\n"ANSI_COLOR_RESET, __FUNCTION__); - goto elementwise_mult_test_cleanup; - } for (int i = 0; i < size; ++i) { input1[i] = rand() % 256 - 128; @@ -345,10 +333,10 @@ elementwise_mult_test_cleanup: if (input2_orig) { free(input2_orig); } - if (out_c_orig) { + if (out_data_c) { free(out_c_orig); } - if (out_opt_orig) { + if (out_data_opt) { free(out_opt_orig); } } diff --git a/code/components/esp-nn/tests/src/convolution_test.c b/code/components/esp-nn/tests/src/convolution_test.c index c86bdbab..f3802257 100644 --- a/code/components/esp-nn/tests/src/convolution_test.c +++ b/code/components/esp-nn/tests/src/convolution_test.c @@ -22,9 +22,8 @@ #include "test_utils.h" #if CONFIG_IDF_CMAKE -#if (CONFIG_SPIRAM_SUPPORT && (CONFIG_SPIRAM_USE_CAPS_ALLOC || CONFIG_SPIRAM_USE_MALLOC)) #define IDF_HEAP_CAPS 1 -#endif + #if IDF_HEAP_CAPS #include "esp_heap_caps.h" #endif @@ -45,8 +44,8 @@ void esp_nn_depthwise_conv_s8_test() uint16_t filter_ht, filter_wd, ch_mult; uint16_t pad_wd, pad_ht, stride_wd, stride_ht; - // run for 15 iterations - for (int itr = 0; itr < 15; itr++) { + // run for 10 iterations + for (int itr = 0; itr < 10; itr++) { /* prepare data */ switch (itr) { case 0: // (ch_mult 1, (channels % 16) = 0), filter (3,3), pad (0,0) @@ -145,52 +144,22 @@ void esp_nn_depthwise_conv_s8_test() stride_wd = 2; stride_ht = 2; break; - case 8: // same as case 7, with large parameters - input_wd = 58; - input_ht = 58; - filter_ht = 3; - filter_wd = 3; - ch_mult = 1; - channels = 128; - pad_wd = 0; - pad_ht = 0; - stride_wd = 2; - stride_ht = 2; - break; - case 9: // (ch_mult 1, (channels % 16) = 0), filter (3,3), pad (0,0) stride (2,2) - input_wd = 6; - input_ht = 6; - filter_ht = 3; - filter_wd = 3; - ch_mult = 1; - channels = 16; - pad_wd = 0; - pad_ht = 0; - stride_wd = 2; - stride_ht = 2; - break; default: - input_wd = 6; - input_ht = 6; + input_wd = 4; + input_ht = 4; filter_ht = 3; filter_wd = 3; - ch_mult = 1; - channels = 16; - stride_wd = rand() % 2 + 1; - stride_ht = stride_wd; - pad_wd = stride_wd == 1 ? 0 : rand() % 2; - pad_ht = pad_wd; - printf("stride(%d), pad (%d)\t", stride_wd, pad_wd); + ch_mult = 4; + channels = 4; + pad_wd = 1; + pad_ht = 1; + stride_wd = 1; + stride_ht = 1; break; } uint16_t out_wd = (input_wd - filter_wd + 1) / stride_wd; uint16_t out_ht = (input_ht - filter_ht + 1) / stride_ht; - if (itr == 9) { - // expect the function to handle this gracefully - out_wd += 1; - out_ht += 1; - } int in_size = input_wd * input_ht * channels; int out_size = out_wd * out_ht * channels * ch_mult; int filter_size = filter_wd * filter_ht * channels * ch_mult + 4; @@ -241,16 +210,9 @@ void esp_nn_depthwise_conv_s8_test() out_mult[i] = 0x7eb0e200 + rand() % 50; } - data_dims_t input_dims = {.width = input_wd, .height = input_ht, .channels = channels, 1}; - data_dims_t output_dims = {.width = out_wd, .height = out_ht, .channels = channels * ch_mult, 1}; - data_dims_t filter_dims = {.width = filter_wd, .height = filter_ht, 0, 0}; - dw_conv_params_t conv_params = {.in_offset = input_offset, .out_offset = out_offset, .ch_mult = ch_mult, - .stride = {stride_wd, stride_ht}, .padding = {pad_wd, pad_ht}, - .dilation = {0, 0}, .activation = {activation_min, activation_max}}; - quant_data_t quant_data = {.shift = out_shift, .mult = out_mult}; - - int scratch_buf_size = esp_nn_get_depthwise_conv_scratch_size(&input_dims, &filter_dims, - &output_dims, &conv_params); + int scratch_buf_size = esp_nn_get_depthwise_conv_scratch_size(input_wd, input_ht, + channels, ch_mult, + filter_wd, filter_ht); if (scratch_buf_size > 0) { #if IDF_HEAP_CAPS scratch_buf = heap_caps_malloc(scratch_buf_size + 32, MALLOC_CAP_SPIRAM | MALLOC_CAP_8BIT); @@ -272,8 +234,11 @@ void esp_nn_depthwise_conv_s8_test() } /* C function */ - esp_nn_depthwise_conv_s8_ansi(&input_dims, input, &filter_dims, filter_data + 4, - bias + 1, &output_dims, out_data_c, &conv_params, &quant_data); + esp_nn_depthwise_conv_s8_ansi(input, input_wd, input_ht, channels, input_offset, + pad_wd, pad_ht, stride_wd, stride_ht, ch_mult, + filter_data + 4, filter_wd, filter_ht, + bias + 1, out_data_c, out_wd, out_ht, out_offset, out_shift, + out_mult, activation_min, activation_max); if (itr == 0) { profile_c_end(); @@ -281,8 +246,11 @@ void esp_nn_depthwise_conv_s8_test() } /* Optimized function */ - esp_nn_depthwise_conv_s8(&input_dims, input, &filter_dims, filter_data + 4, - bias + 1, &output_dims, out_data_opt, &conv_params, &quant_data); + esp_nn_depthwise_conv_s8(input, input_wd, input_ht, channels, input_offset, + pad_wd, pad_ht, stride_wd, stride_ht, ch_mult, + filter_data + 4, filter_wd, filter_ht, + bias + 1, out_data_opt, out_wd, out_ht, out_offset, out_shift, + out_mult, activation_min, activation_max); if (itr == 0) { /* disable profiler */ @@ -511,16 +479,8 @@ void esp_nn_conv_s8_test() out_mult[i] = 0x7f67f4f8 + rand() % 50; } - data_dims_t input_dims = {.width = in_wd, .height = in_ht, .channels = in_channels, 1}; - data_dims_t output_dims = {.width = out_wd, .height = out_ht, .channels = out_channels, 1}; - data_dims_t filter_dims = {.width = filter_wd, .height = filter_ht, 0, 0}; - conv_params_t conv_params = {.in_offset = input_offset, .out_offset = out_offset, - .stride = {stride_wd, stride_ht}, .padding = {pad_wd, pad_ht}, - .dilation = {0, 0}, .activation = {activation_min, activation_max}}; - quant_data_t quant_data = {.shift = out_shift, .mult = out_mult}; - - int scratch_buf_size = esp_nn_get_conv_scratch_size(&input_dims, &filter_dims, - &output_dims, &conv_params); + int scratch_buf_size = esp_nn_get_conv_scratch_size(in_wd, in_ht, in_channels, + out_channels, filter_wd, filter_ht); if (scratch_buf_size > 0) { #if IDF_HEAP_CAPS void *scratch_buf = heap_caps_malloc(scratch_buf_size + 32, MALLOC_CAP_SPIRAM | MALLOC_CAP_8BIT); @@ -542,8 +502,11 @@ void esp_nn_conv_s8_test() } /* C function */ - esp_nn_conv_s8_ansi(&input_dims, input, &filter_dims, filter_data + 2, - bias, &output_dims, out_data_c, &conv_params, &quant_data); + esp_nn_conv_s8_ansi(input, in_wd, in_ht, in_channels, input_offset, + pad_wd, pad_ht, stride_wd, stride_ht, + filter_data + 2, filter_wd, filter_ht, bias, + out_data_c, out_wd, out_ht, out_channels, out_offset, out_shift, + out_mult, activation_min, activation_max); if (itr == 0) { profile_c_end(); @@ -551,8 +514,11 @@ void esp_nn_conv_s8_test() } /* Optimized function */ - esp_nn_conv_s8(&input_dims, input, &filter_dims, filter_data + 2, - bias, &output_dims, out_data_opt, &conv_params, &quant_data); + esp_nn_conv_s8(input, in_wd, in_ht, in_channels, input_offset, + pad_wd, pad_ht, stride_wd, stride_ht, + filter_data + 2, filter_wd, filter_ht, bias, + out_data_opt, out_wd, out_ht, out_channels, out_offset, out_shift, + out_mult, activation_min, activation_max); if (itr == 0) { /* disable 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a/code/components/esp32-camera-master/.github/workflows/build.yml +++ b/code/components/esp32-camera-master/.github/workflows/build.yml @@ -8,37 +8,26 @@ on: jobs: build-master: runs-on: ubuntu-latest - strategy: - matrix: - idf_target: ["esp32", "esp32s2", "esp32s3"] steps: - name: Checkout repo uses: actions/checkout@v2 with: submodules: 'recursive' - name: esp-idf build - uses: espressif/esp-idf-ci-action@main + uses: espressif/esp-idf-ci-action@latest with: - target: ${{ matrix.idf_target }} path: 'examples' - build-release-v4_4: - name: Build for ${{ matrix.idf_target }} on ${{ matrix.idf_ver }} + build-release-v4_0: runs-on: ubuntu-latest - strategy: - matrix: - idf_ver: ["v4.4"] - idf_target: ["esp32", "esp32s2", "esp32s3"] steps: - name: Checkout repo uses: actions/checkout@v2 with: submodules: 'recursive' - name: esp-idf build - uses: espressif/esp-idf-ci-action@main + uses: espressif/esp-idf-ci-action@release-v4.0 with: - esp_idf_version: ${{ matrix.idf_ver }} - target: ${{ matrix.idf_target }} path: 'examples' build-release-v4_1: @@ -76,3 +65,15 @@ jobs: uses: espressif/esp-idf-ci-action@release-v4.3 with: path: 'examples' + + build-release-v3_3: + runs-on: ubuntu-latest + steps: + - name: Checkout repo + uses: actions/checkout@v2 + with: + submodules: 'recursive' + - name: esp-idf build + uses: espressif/esp-idf-ci-action@release-v3.3 + with: + path: 'examples' diff --git a/code/components/esp32-camera-master/.github/workflows/upload_component.yml b/code/components/esp32-camera-master/.github/workflows/upload_component.yml index f550e696..0fb12cc3 100644 --- a/code/components/esp32-camera-master/.github/workflows/upload_component.yml +++ b/code/components/esp32-camera-master/.github/workflows/upload_component.yml @@ -10,10 +10,12 @@ jobs: - uses: actions/checkout@master with: submodules: "recursive" + - name: Upload component to the component registry uses: espressif/github-actions/upload_components@master with: name: "esp32-camera" + version: "git" namespace: "espressif" - version: ${{ github.ref_name }} + service_url: ${{ secrets.IDF_COMPONENT_API_URL }} api_token: ${{ secrets.IDF_COMPONENT_API_TOKEN }} diff --git a/code/components/esp32-camera-master/CMakeLists.txt b/code/components/esp32-camera-master/CMakeLists.txt index 7c07b0bf..8090f326 100644 --- a/code/components/esp32-camera-master/CMakeLists.txt +++ b/code/components/esp32-camera-master/CMakeLists.txt @@ -1,29 +1,5 @@ -# get IDF version for comparison -set(idf_version "${IDF_VERSION_MAJOR}.${IDF_VERSION_MINOR}") - -# set conversion sources -set(COMPONENT_SRCS - conversions/yuv.c - conversions/to_jpg.cpp - conversions/to_bmp.c - conversions/jpge.cpp - conversions/esp_jpg_decode.c - ) - -set(COMPONENT_PRIV_INCLUDEDIRS - conversions/private_include - ) - -set(COMPONENT_ADD_INCLUDEDIRS - driver/include - conversions/include - ) - -set(COMPONENT_REQUIRES driver) - -# set driver sources only for supported platforms if(IDF_TARGET STREQUAL "esp32" OR IDF_TARGET STREQUAL "esp32s2" OR IDF_TARGET STREQUAL "esp32s3") - list(APPEND COMPONENT_SRCS + set(COMPONENT_SRCS driver/esp_camera.c driver/cam_hal.c driver/sccb.c @@ -38,14 +14,22 @@ if(IDF_TARGET STREQUAL "esp32" OR IDF_TARGET STREQUAL "esp32s2" OR IDF_TARGET ST sensors/gc2145.c sensors/gc032a.c sensors/bf3005.c - sensors/bf20a6.c - sensors/sc101iot.c - sensors/sc030iot.c + conversions/yuv.c + conversions/to_jpg.cpp + conversions/to_bmp.c + conversions/jpge.cpp + conversions/esp_jpg_decode.c ) - list(APPEND COMPONENT_PRIV_INCLUDEDIRS + set(COMPONENT_ADD_INCLUDEDIRS + driver/include + conversions/include + ) + + set(COMPONENT_PRIV_INCLUDEDIRS driver/private_include sensors/private_include + conversions/private_include target/private_include ) @@ -74,13 +58,8 @@ if(IDF_TARGET STREQUAL "esp32" OR IDF_TARGET STREQUAL "esp32s2" OR IDF_TARGET ST ) endif() + set(COMPONENT_REQUIRES driver) set(COMPONENT_PRIV_REQUIRES freertos nvs_flash) - set(min_version_for_esp_timer "4.2") - if (idf_version VERSION_GREATER_EQUAL min_version_for_esp_timer) - list(APPEND COMPONENT_PRIV_REQUIRES esp_timer) - endif() - + register_component() endif() - -register_component() diff --git a/code/components/esp32-camera-master/Kconfig b/code/components/esp32-camera-master/Kconfig index 66253d0a..dbf67089 100644 --- a/code/components/esp32-camera-master/Kconfig +++ b/code/components/esp32-camera-master/Kconfig @@ -69,45 +69,6 @@ menu "Camera configuration" help Enable this option if you want to use the BF3005. Disable this option to save memory. - - config BF20A6_SUPPORT - bool "Support BF20A6(BYD20A6) VGA" - default y - help - Enable this option if you want to use the BF20A6. - Disable this option to save memory. - - config SC101IOT_SUPPORT - bool "Support SC101IOT HD" - default n - help - Enable this option if you want to use the SC101IOT. - Disable this option to save memory. - - choice SC101_REGS_SELECT - prompt "SC101iot default regs" - default SC101IOT_720P_15FPS_ENABLED - depends on SC101IOT_SUPPORT - help - Currently SC010iot has several register sets available. - Select the one that matches your needs. - - config SC101IOT_720P_15FPS_ENABLED - bool "xclk20M_720p_15fps" - help - Select this option means that when xclk is 20M, the frame rate is 15fps at 720p resolution. - config SC101IOT_VGA_25FPS_ENABLED - bool "xclk20M_VGA_25fps" - help - Select this option means that when xclk is 20M, the frame rate is 25fps at VGA resolution. - endchoice - - config SC030IOT_SUPPORT - bool "Support SC030IOT VGA" - default y - help - Enable this option if you want to use the SC030IOT. - Disable this option to save memory. choice SCCB_HARDWARE_I2C_PORT bool "I2C peripheral to use for SCCB" @@ -164,24 +125,5 @@ menu "Camera configuration" help Maximum value of DMA buffer Larger values may fail to allocate due to insufficient contiguous memory blocks, and smaller value may cause DMA interrupt to be too frequent - - config CAMERA_CONVERTER_ENABLED - bool "Enable camera RGB/YUV converter" - depends on IDF_TARGET_ESP32S3 - default n - help - Enable this option if you want to use RGB565/YUV422/YUV420/YUV411 format conversion. - choice CAMERA_CONV_PROTOCOL - bool "Camera converter protocol" - depends on CAMERA_CONVERTER_ENABLED - default LCD_CAM_CONV_BT601_ENABLED - help - Supports format conversion under both BT601 and BT709 standards. - - config LCD_CAM_CONV_BT601_ENABLED - bool "BT601" - config LCD_CAM_CONV_BT709_ENABLED - bool "BT709" - endchoice endmenu diff --git a/code/components/esp32-camera-master/README.md b/code/components/esp32-camera-master/README.md index 9b5282e4..de61a5bb 100644 --- a/code/components/esp32-camera-master/README.md +++ b/code/components/esp32-camera-master/README.md @@ -25,9 +25,6 @@ This repository hosts ESP32 series Soc compatible driver for image sensors. Addi | GC0308 | 640 x 480 | color | YUV/YCbCr422
RAW Bayer
RGB565 | 1/6.5" | | GC2145 | 1600 x 1200 | color | YUV/YCbCr422
RAW Bayer
RGB565 | 1/5" | | BF3005 | 640 x 480 | color | YUV/YCbCr422
RAW Bayer
RGB565 | 1/4" | -| BF20A6 | 640 x 480 | color | YUV/YCbCr422
RAW Bayer | 1/10" | -| SC101IOT| 1280 x 720 | color | YUV/YCbCr422
Raw RGB | 1/4.2" | -| SC030IOT| 640 x 480 | color | YUV/YCbCr422
RAW Bayer | 1/6.5" | ## Important to Remember diff --git a/code/components/esp32-camera-master/conversions/esp_jpg_decode.c b/code/components/esp32-camera-master/conversions/esp_jpg_decode.c index 52833a73..a9615e36 100644 --- a/code/components/esp32-camera-master/conversions/esp_jpg_decode.c +++ b/code/components/esp32-camera-master/conversions/esp_jpg_decode.c @@ -21,10 +21,6 @@ #include "tjpgd.h" #elif CONFIG_IDF_TARGET_ESP32S3 #include "esp32s3/rom/tjpgd.h" -#elif CONFIG_IDF_TARGET_ESP32C3 -#include "esp32c3/rom/tjpgd.h" -#elif CONFIG_IDF_TARGET_ESP32H2 -#include "esp32h2/rom/tjpgd.h" #else #error Target CONFIG_IDF_TARGET is not supported #endif @@ -61,7 +57,7 @@ static const char * jd_errors[] = { "Not supported JPEG standard" }; -static unsigned int _jpg_write(JDEC *decoder, void *bitmap, JRECT *rect) +static uint32_t _jpg_write(JDEC *decoder, void *bitmap, JRECT *rect) { uint16_t x = rect->left; uint16_t y = rect->top; @@ -77,7 +73,7 @@ static unsigned int _jpg_write(JDEC *decoder, void *bitmap, JRECT *rect) return 0; } -static unsigned int _jpg_read(JDEC *decoder, uint8_t *buf, unsigned int len) +static uint32_t _jpg_read(JDEC *decoder, uint8_t *buf, uint32_t len) { esp_jpg_decoder_t * jpeg = (esp_jpg_decoder_t *)decoder->device; if (jpeg->len && len > (jpeg->len - jpeg->index)) { diff --git a/code/components/esp32-camera-master/conversions/jpge.cpp b/code/components/esp32-camera-master/conversions/jpge.cpp index dd6790e6..a8ab93e0 100644 --- a/code/components/esp32-camera-master/conversions/jpge.cpp +++ b/code/components/esp32-camera-master/conversions/jpge.cpp @@ -29,12 +29,7 @@ namespace jpge { if(b){ return b; } - // check if SPIRAM is enabled and allocate on SPIRAM if allocatable -#if (CONFIG_SPIRAM_SUPPORT && (CONFIG_SPIRAM_USE_CAPS_ALLOC || CONFIG_SPIRAM_USE_MALLOC)) return heap_caps_malloc(nSize, MALLOC_CAP_SPIRAM | MALLOC_CAP_8BIT); -#else - return NULL; -#endif } static inline void jpge_free(void *p) { free(p); } diff --git a/code/components/esp32-camera-master/conversions/to_bmp.c b/code/components/esp32-camera-master/conversions/to_bmp.c index e267c78f..5a54bdba 100644 --- a/code/components/esp32-camera-master/conversions/to_bmp.c +++ b/code/components/esp32-camera-master/conversions/to_bmp.c @@ -21,6 +21,19 @@ #include "esp_jpg_decode.h" #include "esp_system.h" +#if ESP_IDF_VERSION_MAJOR >= 4 // IDF 4+ +#if CONFIG_IDF_TARGET_ESP32 // ESP32/PICO-D4 +#include "esp32/spiram.h" +#elif CONFIG_IDF_TARGET_ESP32S2 +#include "esp32s2/spiram.h" +#elif CONFIG_IDF_TARGET_ESP32S3 +#include "esp32s3/spiram.h" +#else +#error Target CONFIG_IDF_TARGET is not supported +#endif +#else // ESP32 Before IDF 4.0 +#include "esp_spiram.h" +#endif #if defined(ARDUINO_ARCH_ESP32) && defined(CONFIG_ARDUHAL_ESP_LOG) #include "esp32-hal-log.h" @@ -59,12 +72,7 @@ typedef struct { static void *_malloc(size_t size) { - // check if SPIRAM is enabled and allocate on SPIRAM if allocatable -#if (CONFIG_SPIRAM_SUPPORT && (CONFIG_SPIRAM_USE_CAPS_ALLOC || CONFIG_SPIRAM_USE_MALLOC)) return heap_caps_malloc(size, MALLOC_CAP_SPIRAM | MALLOC_CAP_8BIT); -#endif - // try allocating in internal memory - return malloc(size); } //output buffer and image width @@ -160,7 +168,7 @@ static bool _rgb565_write(void * arg, uint16_t x, uint16_t y, uint16_t w, uint16 } //input buffer -static unsigned int _jpg_read(void * arg, size_t index, uint8_t *buf, size_t len) +static uint32_t _jpg_read(void * arg, size_t index, uint8_t *buf, size_t len) { rgb_jpg_decoder * jpeg = (rgb_jpg_decoder *)arg; if(buf) { diff --git a/code/components/esp32-camera-master/conversions/to_jpg.cpp b/code/components/esp32-camera-master/conversions/to_jpg.cpp index 24cc2989..9b8905a7 100644 --- a/code/components/esp32-camera-master/conversions/to_jpg.cpp +++ b/code/components/esp32-camera-master/conversions/to_jpg.cpp @@ -21,6 +21,21 @@ #include "jpge.h" #include "yuv.h" +#include "esp_system.h" +#if ESP_IDF_VERSION_MAJOR >= 4 // IDF 4+ +#if CONFIG_IDF_TARGET_ESP32 // ESP32/PICO-D4 +#include "esp32/spiram.h" +#elif CONFIG_IDF_TARGET_ESP32S2 +#include "esp32s2/spiram.h" +#elif CONFIG_IDF_TARGET_ESP32S3 +#include "esp32s3/spiram.h" +#else +#error Target CONFIG_IDF_TARGET is not supported +#endif +#else // ESP32 Before IDF 4.0 +#include "esp_spiram.h" +#endif + #if defined(ARDUINO_ARCH_ESP32) && defined(CONFIG_ARDUHAL_ESP_LOG) #include "esp32-hal-log.h" #define TAG "" @@ -35,12 +50,7 @@ static void *_malloc(size_t size) if(res) { return res; } - - // check if SPIRAM is enabled and is allocatable -#if (CONFIG_SPIRAM_SUPPORT && (CONFIG_SPIRAM_USE_CAPS_ALLOC || CONFIG_SPIRAM_USE_MALLOC)) return heap_caps_malloc(size, MALLOC_CAP_SPIRAM | MALLOC_CAP_8BIT); -#endif - return NULL; } static IRAM_ATTR void convert_line_format(uint8_t * src, pixformat_t format, uint8_t * dst, size_t width, size_t in_channels, size_t line) diff --git a/code/components/esp32-camera-master/driver/cam_hal.c b/code/components/esp32-camera-master/driver/cam_hal.c index 1604f8ac..9b7e12b5 100644 --- a/code/components/esp32-camera-master/driver/cam_hal.c +++ b/code/components/esp32-camera-master/driver/cam_hal.c @@ -18,21 +18,8 @@ #include "ll_cam.h" #include "cam_hal.h" -#if (ESP_IDF_VERSION_MAJOR == 3) && (ESP_IDF_VERSION_MINOR == 3) -#include "rom/ets_sys.h" -#else -#include "esp_timer.h" -#if CONFIG_IDF_TARGET_ESP32 -#include "esp32/rom/ets_sys.h" // will be removed in idf v5.0 -#elif CONFIG_IDF_TARGET_ESP32S2 -#include "esp32s2/rom/ets_sys.h" -#elif CONFIG_IDF_TARGET_ESP32S3 -#include "esp32s3/rom/ets_sys.h" -#endif -#endif // ESP_IDF_VERSION_MAJOR -#define ESP_CAMERA_ETS_PRINTF ets_printf - static const char *TAG = "cam_hal"; + static cam_obj_t *cam_obj = NULL; static const uint32_t JPEG_SOI_MARKER = 0xFFD8FF; // written in little-endian for esp32 @@ -106,7 +93,7 @@ void IRAM_ATTR ll_cam_send_event(cam_obj_t *cam, cam_event_t cam_event, BaseType if (xQueueSendFromISR(cam->event_queue, (void *)&cam_event, HPTaskAwoken) != pdTRUE) { ll_cam_stop(cam); cam->state = CAM_STATE_IDLE; - ESP_CAMERA_ETS_PRINTF(DRAM_STR("cam_hal: EV-%s-OVF\r\n"), cam_event==CAM_IN_SUC_EOF_EVENT ? DRAM_STR("EOF") : DRAM_STR("VSYNC")); + ESP_EARLY_LOGE(TAG, "EV-%s-OVF", cam_event==CAM_IN_SUC_EOF_EVENT ? "EOF" : "VSYNC"); } } diff --git a/code/components/esp32-camera-master/driver/esp_camera.c b/code/components/esp32-camera-master/driver/esp_camera.c index 8327445c..5b671c0e 100644 --- a/code/components/esp32-camera-master/driver/esp_camera.c +++ b/code/components/esp32-camera-master/driver/esp_camera.c @@ -57,15 +57,6 @@ #if CONFIG_BF3005_SUPPORT #include "bf3005.h" #endif -#if CONFIG_BF20A6_SUPPORT -#include "bf20a6.h" -#endif -#if CONFIG_SC101IOT_SUPPORT -#include "sc101iot.h" -#endif -#if CONFIG_SC030IOT_SUPPORT -#include "sc030iot.h" -#endif #if defined(ARDUINO_ARCH_ESP32) && defined(CONFIG_ARDUHAL_ESP_LOG) #include "esp32-hal-log.h" @@ -128,15 +119,6 @@ static const sensor_func_t g_sensors[] = { #if CONFIG_BF3005_SUPPORT {bf3005_detect, bf3005_init}, #endif -#if CONFIG_BF20A6_SUPPORT - {bf20a6_detect, bf20a6_init}, -#endif -#if CONFIG_SC101IOT_SUPPORT - {sc101iot_detect, sc101iot_init}, -#endif -#if CONFIG_SC030IOT_SUPPORT - {sc030iot_detect, sc030iot_init}, -#endif }; static esp_err_t camera_probe(const camera_config_t *config, camera_model_t *out_camera_model) @@ -236,23 +218,6 @@ static esp_err_t camera_probe(const camera_config_t *config, camera_model_t *out return ESP_OK; } -#if CONFIG_CAMERA_CONVERTER_ENABLED -static pixformat_t get_output_data_format(camera_conv_mode_t conv_mode) -{ - pixformat_t format = PIXFORMAT_RGB565; - switch (conv_mode) { - case YUV422_TO_YUV420: - format = PIXFORMAT_YUV420; - break; - case YUV422_TO_RGB565: // default format is RGB565 - default: - break; - } - ESP_LOGD(TAG, "Convert to %d format enabled", format); - return format; -} -#endif - esp_err_t esp_camera_init(const camera_config_t *config) { esp_err_t err; @@ -291,7 +256,6 @@ esp_err_t esp_camera_init(const camera_config_t *config) s_state->sensor.status.framesize = frame_size; s_state->sensor.pixformat = pix_format; - ESP_LOGD(TAG, "Setting frame size to %dx%d", resolution[frame_size].width, resolution[frame_size].height); if (s_state->sensor.set_framesize(&s_state->sensor, frame_size) != 0) { ESP_LOGE(TAG, "Failed to set frame size"); @@ -299,11 +263,6 @@ esp_err_t esp_camera_init(const camera_config_t *config) goto fail; } s_state->sensor.set_pixformat(&s_state->sensor, pix_format); -#if CONFIG_CAMERA_CONVERTER_ENABLED - if(config->conv_mode) { - s_state->sensor.pixformat = get_output_data_format(config->conv_mode); // If conversion enabled, change the out data format by conversion mode - } -#endif if (s_state->sensor.id.PID == OV2640_PID) { s_state->sensor.set_gainceiling(&s_state->sensor, GAINCEILING_2X); diff --git a/code/components/esp32-camera-master/driver/include/esp_camera.h b/code/components/esp32-camera-master/driver/include/esp_camera.h index 2025bb40..b6047d31 100644 --- a/code/components/esp32-camera-master/driver/include/esp_camera.h +++ b/code/components/esp32-camera-master/driver/include/esp_camera.h @@ -70,7 +70,6 @@ #include "driver/ledc.h" #include "sensor.h" #include "sys/time.h" -#include "sdkconfig.h" #ifdef __cplusplus extern "C" { @@ -92,19 +91,6 @@ typedef enum { CAMERA_FB_IN_DRAM /*!< Frame buffer is placed in internal DRAM */ } camera_fb_location_t; -#if CONFIG_CAMERA_CONVERTER_ENABLED -/** - * @brief Camera RGB\YUV conversion mode - */ -typedef enum { - CONV_DISABLE, - RGB565_TO_YUV422, - - YUV422_TO_RGB565, - YUV422_TO_YUV420 -} camera_conv_mode_t; -#endif - /** * @brief Configuration structure for camera initialization */ @@ -138,9 +124,6 @@ typedef struct { size_t fb_count; /*!< Number of frame buffers to be allocated. If more than one, then each frame will be acquired (double speed) */ camera_fb_location_t fb_location; /*!< The location where the frame buffer will be allocated */ camera_grab_mode_t grab_mode; /*!< When buffers should be filled */ -#if CONFIG_CAMERA_CONVERTER_ENABLED - camera_conv_mode_t conv_mode; /*!< RGB<->YUV Conversion mode */ -#endif } camera_config_t; /** diff --git a/code/components/esp32-camera-master/driver/include/sensor.h b/code/components/esp32-camera-master/driver/include/sensor.h index d5ec7463..b2bf55f1 100644 --- a/code/components/esp32-camera-master/driver/include/sensor.h +++ b/code/components/esp32-camera-master/driver/include/sensor.h @@ -27,9 +27,6 @@ typedef enum { GC032A_PID = 0x232a, GC0308_PID = 0x9b, BF3005_PID = 0x30, - BF20A6_PID = 0x20a6, - SC101IOT_PID = 0xda4a, - SC030IOT_PID = 0x9a46, } camera_pid_t; typedef enum { @@ -43,9 +40,6 @@ typedef enum { CAMERA_GC032A, CAMERA_GC0308, CAMERA_BF3005, - CAMERA_BF20A6, - CAMERA_SC101IOT, - CAMERA_SC030IOT, CAMERA_MODEL_MAX, CAMERA_NONE, } camera_model_t; @@ -61,15 +55,11 @@ typedef enum { GC032A_SCCB_ADDR = 0x21,// 0x42 >> 1 GC0308_SCCB_ADDR = 0x21,// 0x42 >> 1 BF3005_SCCB_ADDR = 0x6E, - BF20A6_SCCB_ADDR = 0x6E, - SC101IOT_SCCB_ADDR = 0x68,// 0xd0 >> 1 - SC030IOT_SCCB_ADDR = 0x68,// 0xd0 >> 1 } camera_sccb_addr_t; typedef enum { PIXFORMAT_RGB565, // 2BPP/RGB565 PIXFORMAT_YUV422, // 2BPP/YUV422 - PIXFORMAT_YUV420, // 1.5BPP/YUV420 PIXFORMAT_GRAYSCALE, // 1BPP/GRAYSCALE PIXFORMAT_JPEG, // JPEG/COMPRESSED PIXFORMAT_RGB888, // 3BPP/RGB888 diff --git a/code/components/esp32-camera-master/driver/sccb.c b/code/components/esp32-camera-master/driver/sccb.c index edc417f8..314dd982 100644 --- a/code/components/esp32-camera-master/driver/sccb.c +++ b/code/components/esp32-camera-master/driver/sccb.c @@ -25,11 +25,6 @@ static const char* TAG = "sccb"; #include "driver/i2c.h" -// support IDF 5.x -#ifndef portTICK_RATE_MS -#define portTICK_RATE_MS portTICK_PERIOD_MS -#endif - #define SCCB_FREQ CONFIG_SCCB_CLK_FREQ /*!< I2C master frequency*/ #define WRITE_BIT I2C_MASTER_WRITE /*!< I2C master write */ #define READ_BIT I2C_MASTER_READ /*!< I2C master read */ diff --git a/code/components/esp32-camera-master/driver/sensor.c b/code/components/esp32-camera-master/driver/sensor.c index 2f4c9711..bf6d313f 100644 --- a/code/components/esp32-camera-master/driver/sensor.c +++ b/code/components/esp32-camera-master/driver/sensor.c @@ -13,9 +13,6 @@ const camera_sensor_info_t camera_sensor[CAMERA_MODEL_MAX] = { {CAMERA_GC032A, "GC032A", GC032A_SCCB_ADDR, GC032A_PID, FRAMESIZE_VGA, false}, {CAMERA_GC0308, "GC0308", GC0308_SCCB_ADDR, GC0308_PID, FRAMESIZE_VGA, false}, {CAMERA_BF3005, "BF3005", BF3005_SCCB_ADDR, BF3005_PID, FRAMESIZE_VGA, false}, - {CAMERA_BF20A6, "BF20A6", BF20A6_SCCB_ADDR, BF20A6_PID, FRAMESIZE_VGA, false}, - {CAMERA_SC101IOT, "SC101IOT", SC101IOT_SCCB_ADDR, SC101IOT_PID, FRAMESIZE_HD, false}, - {CAMERA_SC030IOT, "SC030IOT", SC030IOT_SCCB_ADDR, SC030IOT_PID, FRAMESIZE_VGA, false}, }; const resolution_info_t resolution[FRAMESIZE_INVALID] = { diff --git a/code/components/esp32-camera-master/examples/main/take_picture.c b/code/components/esp32-camera-master/examples/main/take_picture.c index 1fb1039d..1cbad908 100644 --- a/code/components/esp32-camera-master/examples/main/take_picture.c +++ b/code/components/esp32-camera-master/examples/main/take_picture.c @@ -38,11 +38,6 @@ #include "freertos/FreeRTOS.h" #include "freertos/task.h" -// support IDF 5.x -#ifndef portTICK_RATE_MS -#define portTICK_RATE_MS portTICK_PERIOD_MS -#endif - #include "esp_camera.h" #define BOARD_WROVER_KIT 1 diff --git a/code/components/esp32-camera-master/idf_component.yml b/code/components/esp32-camera-master/idf_component.yml index 2b98f8d0..848e1cd8 100644 --- a/code/components/esp32-camera-master/idf_component.yml +++ b/code/components/esp32-camera-master/idf_component.yml @@ -1,2 +1,5 @@ description: ESP32 compatible driver for OV2640, OV3660, OV5640, OV7670 and OV7725 image sensors. -url: https://github.com/espressif/esp32-camera +targets: + - esp32 + - esp32s2 + - esp32s3 diff --git a/code/components/esp32-camera-master/sensors/bf20a6.c b/code/components/esp32-camera-master/sensors/bf20a6.c deleted file mode 100644 index b1179c30..00000000 --- a/code/components/esp32-camera-master/sensors/bf20a6.c +++ /dev/null @@ -1,404 +0,0 @@ -// Copyright 2015-2021 Espressif Systems (Shanghai) PTE LTD -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at - -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include -#include -#include -#include -#include "freertos/FreeRTOS.h" -#include "freertos/task.h" -#include "sccb.h" -#include "bf20a6.h" -#include "bf20a6_regs.h" -#include "bf20a6_settings.h" - -#if defined(ARDUINO_ARCH_ESP32) && defined(CONFIG_ARDUHAL_ESP_LOG) -#include "esp32-hal-log.h" -#else -#include "esp_log.h" -static const char *TAG = "bf20a6"; -#endif - -#define H8(v) ((v)>>8) -#define L8(v) ((v)&0xff) - -//#define REG_DEBUG_ON - -static int read_reg(uint8_t slv_addr, const uint16_t reg) -{ - int ret = SCCB_Read(slv_addr, reg); - // ESP_LOGI(TAG, "READ Register 0x%02x VALUE: 0x%02x", reg, ret); -#ifdef REG_DEBUG_ON - if (ret < 0) { - ESP_LOGE(TAG, "READ REG 0x%04x FAILED: %d", reg, ret); - } -#endif - return ret; -} - -static int write_reg(uint8_t slv_addr, const uint16_t reg, uint8_t value) -{ - int ret = SCCB_Write(slv_addr, reg, value); -#ifdef REG_DEBUG_ON - if (ret < 0) { - ESP_LOGE(TAG, "WRITE REG 0x%04x FAILED: %d", reg, ret); - } -#endif - return ret; -} - -#ifdef DEBUG_PRINT_REG -static int check_reg_mask(uint8_t slv_addr, uint16_t reg, uint8_t mask) -{ - return (read_reg(slv_addr, reg) & mask) == mask; -} - -static void print_regs(uint8_t slv_addr) -{ - vTaskDelay(pdMS_TO_TICKS(100)); - ESP_LOGI(TAG, "REG list look ======================"); - for (size_t i = 0xf0; i <= 0xfe; i++) { - ESP_LOGI(TAG, "reg[0x%02x] = 0x%02x", i, read_reg(slv_addr, i)); - } - ESP_LOGI(TAG, "\npage 0 ==="); - write_reg(slv_addr, 0xfe, 0x00); // page 0 - for (size_t i = 0x03; i <= 0x24; i++) { - ESP_LOGI(TAG, "p0 reg[0x%02x] = 0x%02x", i, read_reg(slv_addr, i)); - } - for (size_t i = 0x40; i <= 0x95; i++) { - ESP_LOGI(TAG, "p0 reg[0x%02x] = 0x%02x", i, read_reg(slv_addr, i)); - } - ESP_LOGI(TAG, "\npage 3 ==="); - write_reg(slv_addr, 0xfe, 0x03); // page 3 - for (size_t i = 0x01; i <= 0x43; i++) { - ESP_LOGI(TAG, "p3 reg[0x%02x] = 0x%02x", i, read_reg(slv_addr, i)); - } -} - -static int read_regs(uint8_t slv_addr, const uint16_t(*regs)[2]) -{ - int i = 0, ret = 0; - while (regs[i][0] != REGLIST_TAIL) { - if (regs[i][0] == REG_DLY) { - vTaskDelay(regs[i][1] / portTICK_PERIOD_MS); - } else { - ret = read_reg(slv_addr, regs[i][0]); - } - i++; - } - return ret; -} -#endif - -static int set_reg_bits(sensor_t *sensor, uint8_t reg, uint8_t offset, uint8_t length, uint8_t value) -{ - int ret = 0; - - ret = SCCB_Read(sensor->slv_addr, reg); - if (ret < 0) { - return ret; - } - uint8_t mask = ((1 << length) - 1) << offset; - value = (ret & ~mask) | ((value << offset) & mask); - ret = SCCB_Write(sensor->slv_addr, reg & 0xFF, value); - return ret; -} - -static int write_regs(uint8_t slv_addr, const uint16_t(*regs)[2]) -{ - int i = 0, ret = 0; - while (!ret && regs[i][0] != REGLIST_TAIL) { - if (regs[i][0] == REG_DLY) { - vTaskDelay(regs[i][1] / portTICK_PERIOD_MS); - } else { - ret = write_reg(slv_addr, regs[i][0], regs[i][1]); - } - i++; - } - return ret; -} - -static int reset(sensor_t *sensor) -{ - int ret; - // Software Reset: clear all registers and reset them to their default values - ret = write_reg(sensor->slv_addr, RESET_RELATED, 0x01); - if (ret) { - ESP_LOGE(TAG, "Software Reset FAILED!"); - return ret; - } - vTaskDelay(100 / portTICK_PERIOD_MS); - - ret = write_regs(sensor->slv_addr, bf20a6_default_init_regs); - if (ret == 0) { - ESP_LOGD(TAG, "Camera defaults loaded"); - vTaskDelay(100 / portTICK_PERIOD_MS); - } - - // int test_value = read_regs(sensor->slv_addr, bf20a6_default_init_regs); - - return ret; -} - -static int set_pixformat(sensor_t *sensor, pixformat_t pixformat) -{ - int ret = 0; - switch (pixformat) { - case PIXFORMAT_YUV422: - set_reg_bits(sensor, 0x12, 0, 1, 0); - break; - case PIXFORMAT_RAW: - set_reg_bits(sensor, 0x12, 0, 1, 0x1); - break; - default: - ESP_LOGW(TAG, "set_pix unsupport format"); - ret = -1; - break; - } - if (ret == 0) { - sensor->pixformat = pixformat; - ESP_LOGD(TAG, "Set pixformat to: %u", pixformat); - } - - return ret; -} - -static int set_framesize(sensor_t *sensor, framesize_t framesize) -{ - int ret = 0; - if (framesize > FRAMESIZE_VGA) { - return -1; - } - uint16_t w = resolution[framesize].width; - uint16_t h = resolution[framesize].height; - - sensor->status.framesize = framesize; - - // Write MSBs - ret |= SCCB_Write(sensor->slv_addr, 0x17, 0); - ret |= SCCB_Write(sensor->slv_addr, 0x18, w >> 2); - - ret |= SCCB_Write(sensor->slv_addr, 0x19, 0); - ret |= SCCB_Write(sensor->slv_addr, 0x1a, h >> 2); - - // Write LSBs - ret |= SCCB_Write(sensor->slv_addr, 0x1b, 0); - - if ((w <= 320) && (h <= 240)) { - ret |= SCCB_Write(sensor->slv_addr, 0x17, (80 - w / 4)); - ret |= SCCB_Write(sensor->slv_addr, 0x18, (80 + w / 4)); - - ret |= SCCB_Write(sensor->slv_addr, 0x19, (60 - h / 4)); - - ret |= SCCB_Write(sensor->slv_addr, 0x1a, (60 + h / 4)); - - } else if ((w <= 640) && (h <= 480)) { - ret |= SCCB_Write(sensor->slv_addr, 0x17, (80 - w / 8)); - ret |= SCCB_Write(sensor->slv_addr, 0x18, (80 + w / 8)); - - ret |= SCCB_Write(sensor->slv_addr, 0x19, (60 - h / 8)); - - ret |= SCCB_Write(sensor->slv_addr, 0x1a, (60 + h / 8)); - } - - // Delay - vTaskDelay(30 / portTICK_PERIOD_MS); - - return ret; -} - -static int set_hmirror(sensor_t *sensor, int enable) -{ - int ret = 0; - sensor->status.hmirror = enable; - //ret = write_reg(sensor->slv_addr, 0xfe, 0x00); - ret |= set_reg_bits(sensor, 0x4a, 3, 0x01, enable); - if (ret == 0) { - ESP_LOGD(TAG, "Set h-mirror to: %d", enable); - } - return ret; -} - -static int set_vflip(sensor_t *sensor, int enable) -{ - int ret = 0; - sensor->status.vflip = enable; - //ret = write_reg(sensor->slv_addr, 0xfe, 0x00); - ret |= set_reg_bits(sensor, 0x4a, 2, 0x01, enable); - if (ret == 0) { - ESP_LOGD(TAG, "Set v-flip to: %d", enable); - } - return ret; -} - -static int set_colorbar(sensor_t *sensor, int value) -{ - int ret = 0; - ret = write_reg(sensor->slv_addr, 0xb6, value); - if (ret == 0) { - sensor->status.colorbar = value; - ESP_LOGD(TAG, "Set colorbar to: %d", value); - } - return ret; -} - -static int set_sharpness(sensor_t *sensor, int level) -{ - int ret = 0; - ret = SCCB_Write(sensor->slv_addr, 0x70, level); - if (ret == 0) { - ESP_LOGD(TAG, "Set sharpness to: %d", level); - sensor->status.sharpness = level; - } - return ret; -} - -static int get_reg(sensor_t *sensor, int reg, int mask) -{ - int ret = 0; - if (mask > 0xFF) { - ESP_LOGE(TAG, "mask should not more than 0xff"); - } else { - ret = read_reg(sensor->slv_addr, reg); - } - if (ret > 0) { - ret &= mask; - } - return ret; -} - -static int set_reg(sensor_t *sensor, int reg, int mask, int value) -{ - int ret = 0; - if (mask > 0xFF) { - ESP_LOGE(TAG, "mask should not more than 0xff"); - } else { - ret = read_reg(sensor->slv_addr, reg); - } - if (ret < 0) { - return ret; - } - value = (ret & ~mask) | (value & mask); - - if (mask > 0xFF) { - - } else { - ret = write_reg(sensor->slv_addr, reg, value); - } - return ret; -} - -static int init_status(sensor_t *sensor) -{ - // write_reg(sensor->slv_addr, 0xfe, 0x00); - sensor->status.brightness = SCCB_Read(sensor->slv_addr, 0x6f); - sensor->status.contrast = SCCB_Read(sensor->slv_addr, 0xd6); - sensor->status.saturation = 0; - sensor->status.sharpness = SCCB_Read(sensor->slv_addr, 0x70); - sensor->status.denoise = 0; - sensor->status.ae_level = 0; - sensor->status.gainceiling = SCCB_Read(sensor->slv_addr, 0x13); - sensor->status.awb = 0; - sensor->status.dcw = 0; - sensor->status.agc = 0; - sensor->status.aec = 0; - sensor->status.hmirror = 0;// check_reg_mask(sensor->slv_addr, P0_CISCTL_MODE1, 0x01); - sensor->status.vflip = 0;// check_reg_mask(sensor->slv_addr, P0_CISCTL_MODE1, 0x02); - sensor->status.colorbar = 0; - sensor->status.bpc = 0; - sensor->status.wpc = 0; - sensor->status.raw_gma = 0; - sensor->status.lenc = 0; - sensor->status.quality = 0; - sensor->status.special_effect = 0; - sensor->status.wb_mode = 0; - sensor->status.awb_gain = 0; - sensor->status.agc_gain = 0; - sensor->status.aec_value = 0; - sensor->status.aec2 = 0; - return 0; -} - -static int set_dummy(sensor_t *sensor, int val) -{ - ESP_LOGW(TAG, "dummy Unsupported"); - return -1; -} -static int set_gainceiling_dummy(sensor_t *sensor, gainceiling_t val) -{ - ESP_LOGW(TAG, "gainceiling Unsupported"); - return -1; -} - -int bf20a6_detect(int slv_addr, sensor_id_t *id) -{ - if (BF20A6_SCCB_ADDR == slv_addr) { - uint8_t MIDL = SCCB_Read(slv_addr, SENSOR_ID_LOW); - uint8_t MIDH = SCCB_Read(slv_addr, SENSOR_ID_HIGH); - uint16_t PID = MIDH << 8 | MIDL; - if (BF20A6_PID == PID) { - id->PID = PID; - return PID; - } else { - ESP_LOGI(TAG, "Mismatch PID=0x%x", PID); - } - } - return 0; -} - -int bf20a6_init(sensor_t *sensor) -{ - sensor->init_status = init_status; - sensor->reset = reset; - sensor->set_pixformat = set_pixformat; - sensor->set_framesize = set_framesize; - sensor->set_contrast = set_dummy; - sensor->set_brightness = set_dummy; - sensor->set_saturation = set_dummy; - sensor->set_sharpness = set_sharpness; - sensor->set_denoise = set_dummy; - sensor->set_gainceiling = set_gainceiling_dummy; - sensor->set_quality = set_dummy; - sensor->set_colorbar = set_colorbar; - sensor->set_whitebal = set_dummy; - sensor->set_gain_ctrl = set_dummy; - sensor->set_exposure_ctrl = set_dummy; - sensor->set_hmirror = set_hmirror; // set_hmirror; - sensor->set_vflip = set_vflip; // set_vflip; - - sensor->set_aec2 = set_dummy; - sensor->set_awb_gain = set_dummy; - sensor->set_agc_gain = set_dummy; - sensor->set_aec_value = set_dummy; - - sensor->set_special_effect = set_dummy; - sensor->set_wb_mode = set_dummy; - sensor->set_ae_level = set_dummy; - - sensor->set_dcw = set_dummy; - sensor->set_bpc = set_dummy; - sensor->set_wpc = set_dummy; - - sensor->set_raw_gma = set_dummy; - sensor->set_lenc = set_dummy; - - sensor->get_reg = get_reg; - sensor->set_reg = set_reg; - sensor->set_res_raw = NULL; - sensor->set_pll = NULL; - sensor->set_xclk = NULL; - - ESP_LOGD(TAG, "BF20A6 Attached"); - return 0; -} diff --git a/code/components/esp32-camera-master/sensors/gc0308.c b/code/components/esp32-camera-master/sensors/gc0308.c index f19025eb..8b106a3a 100644 --- a/code/components/esp32-camera-master/sensors/gc0308.c +++ b/code/components/esp32-camera-master/sensors/gc0308.c @@ -88,10 +88,10 @@ static int set_reg_bits(uint8_t slv_addr, uint16_t reg, uint8_t offset, uint8_t return ret; } -static int write_regs(uint8_t slv_addr, const uint8_t (*regs)[2], size_t regs_size) +static int write_regs(uint8_t slv_addr, const uint16_t (*regs)[2]) { int i = 0, ret = 0; - while (!ret && (i < regs_size)) { + while (!ret && regs[i][0] != REGLIST_TAIL) { if (regs[i][0] == REG_DLY) { vTaskDelay(regs[i][1] / portTICK_PERIOD_MS); } else { @@ -132,12 +132,11 @@ static int reset(sensor_t *sensor) ESP_LOGE(TAG, "Software Reset FAILED!"); return ret; } - - vTaskDelay(80 / portTICK_PERIOD_MS); - ret = write_regs(sensor->slv_addr, gc0308_sensor_default_regs, sizeof(gc0308_sensor_default_regs)/(sizeof(uint8_t) * 2)); + vTaskDelay(100 / portTICK_PERIOD_MS); + ret = write_regs(sensor->slv_addr, gc0308_sensor_default_regs); if (ret == 0) { ESP_LOGD(TAG, "Camera defaults loaded"); - vTaskDelay(80 / portTICK_PERIOD_MS); + vTaskDelay(100 / portTICK_PERIOD_MS); write_reg(sensor->slv_addr, 0xfe, 0x00); #ifdef CONFIG_IDF_TARGET_ESP32 set_reg_bits(sensor->slv_addr, 0x28, 4, 0x07, 1); //frequency division for esp32, ensure pclk <= 15MHz diff --git a/code/components/esp32-camera-master/sensors/private_include/bf20a6.h b/code/components/esp32-camera-master/sensors/private_include/bf20a6.h deleted file mode 100644 index 8c925eb5..00000000 --- a/code/components/esp32-camera-master/sensors/private_include/bf20a6.h +++ /dev/null @@ -1,27 +0,0 @@ - -#ifndef __BF20A6_H__ -#define __BF20A6_H__ - -#include "sensor.h" - -/** - * @brief Detect sensor pid - * - * @param slv_addr SCCB address - * @param id Detection result - * @return - * 0: Can't detect this sensor - * Nonzero: This sensor has been detected - */ -int bf20a6_detect(int slv_addr, sensor_id_t *id); - -/** - * @brief initialize sensor function pointers - * - * @param sensor pointer of sensor - * @return - * Always 0 - */ -int bf20a6_init(sensor_t *sensor); - -#endif // __BF20A6_H__ diff --git a/code/components/esp32-camera-master/sensors/private_include/bf20a6_regs.h b/code/components/esp32-camera-master/sensors/private_include/bf20a6_regs.h deleted file mode 100644 index ab1ff69e..00000000 --- a/code/components/esp32-camera-master/sensors/private_include/bf20a6_regs.h +++ /dev/null @@ -1,12 +0,0 @@ -/* - * BF20A6 register definitions. - */ -#ifndef __BF20A6_REG_REGS_H__ -#define __BF20A6_REG_REGS_H__ - -#define SENSOR_ID_HIGH 0XFC -#define SENSOR_ID_LOW 0XFD -#define RESET_RELATED 0XF2 - - -#endif //__BF20A6_REG_REGS_H__ diff --git a/code/components/esp32-camera-master/sensors/private_include/bf20a6_settings.h b/code/components/esp32-camera-master/sensors/private_include/bf20a6_settings.h deleted file mode 100644 index 0414bbac..00000000 --- a/code/components/esp32-camera-master/sensors/private_include/bf20a6_settings.h +++ /dev/null @@ -1,158 +0,0 @@ - -#include - -#define REG_DLY 0xffff -#define REGLIST_TAIL 0xffff /* Array end token */ - -static const uint16_t bf20a6_default_init_regs[][2] = { - {0xf2,0x01}, - {0x12,0x20}, - {0x3a,0x00}, - {0xe1,0x92}, - {0xe3,0x12},// PLL Control, important for framerate(choice: 0x02\0x12\0x22\0x32\0x82) - {0xe0,0x00}, - {0x2a,0x98}, - {0xcd,0x17}, - {0xc0,0x10}, - {0xc6,0x1d}, - {0x10,0x35}, - {0xe2,0x09}, - {0xe4,0x72}, - {0xe5,0x22}, - {0xe6,0x24}, - {0xe7,0x64}, - {0xe8,0xa2}, // DVP:a2}, SPI:f2 VDDIO=1.8V,E8[2]=1},VDDIO=2.8V,E8[2]=0}, - {0x4a,0x00}, - {0x00,0x03}, - {0x1f,0x02}, - {0x22,0x02}, - {0x0c,0x31}, - - {0x00,0x00}, - {0x60,0x81}, - {0x61,0x81}, - - {0xa0,0x08}, - {0x01,0x1a}, - // {0x01,0x1a}, - // {0x01,0x1a}, - // {0x02,0x15}, - // {0x02,0x15}, - {0x02,0x15}, - {0x13,0x08}, - {0x8a,0x96}, - {0x8b,0x06}, - {0x87,0x18}, - - - {0x34,0x48}, // lens - {0x35,0x40}, - {0x36,0x40}, - - {0x71,0x44}, - {0x72,0x48}, - {0x74,0xa2}, - {0x75,0xa9}, - {0x78,0x12}, - {0x79,0xa0}, - {0x7a,0x94}, - {0x7c,0x97}, - {0x40,0x30}, - {0x41,0x30}, - {0x42,0x28}, - {0x43,0x1f}, - {0x44,0x1c}, - {0x45,0x16}, - {0x46,0x13}, - {0x47,0x10}, - {0x48,0x0D}, - {0x49,0x0C}, - {0x4B,0x0A}, - {0x4C,0x0B}, - {0x4E,0x09}, - {0x4F,0x08}, - {0x50,0x08}, - - - {0x5f,0x29}, - {0x23,0x33}, - {0xa1,0x10}, // AWB - {0xa2,0x0d}, - {0xa3,0x30}, - {0xa4,0x06}, - {0xa5,0x22}, - {0xa6,0x56}, - {0xa7,0x18}, - {0xa8,0x1a}, - {0xa9,0x12}, - {0xaa,0x12}, - {0xab,0x16}, - {0xac,0xb1}, - {0xba,0x12}, - {0xbb,0x12}, - {0xad,0x12}, - {0xae,0x56}, - {0xaf,0x0a}, - {0x3b,0x30}, - {0x3c,0x12}, - {0x3d,0x22}, - {0x3e,0x3f}, - {0x3f,0x28}, - {0xb8,0xc3}, - {0xb9,0xa3}, - {0x39,0x47}, // pure color threshold - {0x26,0x13}, - {0x27,0x16}, - {0x28,0x14}, - {0x29,0x18}, - {0xee,0x0d}, - - - {0x13,0x05}, - {0x24,0x3C}, - {0x81,0x20}, - {0x82,0x40}, - {0x83,0x30}, - {0x84,0x58}, - {0x85,0x30}, - {0x92,0x08}, - {0x86,0x80}, - {0x8a,0x96}, - {0x91,0xff}, - {0x94,0x62}, - {0x9a,0x18}, // outdoor threshold - {0xf0,0x45}, // integral time control, important for framerate(choice: 0x46\0x45\0x44..) - {0x51,0x17}, // color normal - {0x52,0x03}, - {0x53,0x5F}, - {0x54,0x47}, - {0x55,0x66}, - {0x56,0x0F}, - {0x7e,0x14}, - {0x57,0x36}, // color - {0x58,0x2A}, - {0x59,0xAA}, - {0x5a,0xA8}, - {0x5b,0x43}, - {0x5c,0x10}, - {0x5d,0x00}, - {0x7d,0x36}, - {0x5e,0x10}, - - {0xd6,0x88}, // contrast - {0xd5,0x20}, // bright - {0xb0,0x84}, // low light ctrl in gray section - {0xb5,0x08}, // the threshold of GLB_GAIN - {0xb1,0xc8}, // saturation - {0xb2,0xc0}, - {0xb3,0xd0}, - {0xb4,0xB0}, - - {0x32,0x10}, - // {0x8a,0x00}, - // {0x8b,0x10}, - {0xa0,0x09}, - {0x00,0x03}, - {0x0b,0x02}, - {REGLIST_TAIL, 0x00}, -}; diff --git a/code/components/esp32-camera-master/sensors/private_include/gc0308_settings.h b/code/components/esp32-camera-master/sensors/private_include/gc0308_settings.h index adf5f28d..32ef3816 100644 --- a/code/components/esp32-camera-master/sensors/private_include/gc0308_settings.h +++ b/code/components/esp32-camera-master/sensors/private_include/gc0308_settings.h @@ -3,9 +3,10 @@ #include -#define REG_DLY 0xff +#define REG_DLY 0xffff +#define REGLIST_TAIL 0x0000 /* Array end token */ -static const uint8_t gc0308_sensor_default_regs[][2] = { +static const uint16_t gc0308_sensor_default_regs[][2] = { {0xfe, 0x00}, {0xec, 0x20}, {0x05, 0x00}, @@ -238,21 +239,7 @@ static const uint8_t gc0308_sensor_default_regs[][2] = { {0x65, 0xd3}, {0x66, 0x60}, {0xfe, 0x00}, - - {0x01, 0x32}, //frame setting - {0x02, 0x0c}, - {0x0f, 0x01}, - {0xe2, 0x00}, - {0xe3, 0x78}, - {0xe4, 0x00}, - {0xe5, 0xfe}, - {0xe6, 0x01}, - {0xe7, 0xe0}, - {0xe8, 0x01}, - {0xe9, 0xe0}, - {0xea, 0x01}, - {0xeb, 0xe0}, - {0xfe, 0x00}, + {REGLIST_TAIL, 0x00}, }; #endif diff --git a/code/components/esp32-camera-master/sensors/private_include/ov5640_settings.h b/code/components/esp32-camera-master/sensors/private_include/ov5640_settings.h index f52572fa..fec7d679 100644 --- a/code/components/esp32-camera-master/sensors/private_include/ov5640_settings.h +++ b/code/components/esp32-camera-master/sensors/private_include/ov5640_settings.h @@ -42,8 +42,7 @@ static const DRAM_ATTR uint16_t sensor_default_regs[][2] = { {ISP_CONTROL_01, 0x83}, // turn color matrix, awb and SDE //sys reset - {0x3000, 0x20}, // reset MCU - {REG_DLY, 10}, // delay 10ms + {0x3000, 0x00}, {0x3002, 0x1c}, //clock enable diff --git a/code/components/esp32-camera-master/sensors/private_include/sc030iot.h b/code/components/esp32-camera-master/sensors/private_include/sc030iot.h deleted file mode 100644 index 19298b76..00000000 --- a/code/components/esp32-camera-master/sensors/private_include/sc030iot.h +++ /dev/null @@ -1,31 +0,0 @@ -/* - * - * SC030IOT DVP driver. - * - */ -#ifndef __SC030IOT_H__ -#define __SC030IOT_H__ - -#include "sensor.h" - -/** - * @brief Detect sensor pid - * - * @param slv_addr SCCB address - * @param id Detection result - * @return - * 0: Can't detect this sensor - * Nonzero: This sensor has been detected - */ -int sc030iot_detect(int slv_addr, sensor_id_t *id); - -/** - * @brief initialize sensor function pointers - * - * @param sensor pointer of sensor - * @return - * Always 0 - */ -int sc030iot_init(sensor_t *sensor); - -#endif // __SC030IOT_H__ diff --git a/code/components/esp32-camera-master/sensors/private_include/sc030iot_settings.h b/code/components/esp32-camera-master/sensors/private_include/sc030iot_settings.h deleted file mode 100644 index 56f5654c..00000000 --- a/code/components/esp32-camera-master/sensors/private_include/sc030iot_settings.h +++ /dev/null @@ -1,491 +0,0 @@ -//version: V01P00_20220303 -//Preview Type:0:DVP Raw 10 bit// 1:Raw 8 bit// 2:YUV422// 3:RAW16 -//Preview Type:4:RGB565// 5:Pixart SPI// 6:MIPI 10bit// 7:MIPI 12bit// 8: MTK SPI -//port 0:MIPI// 1:Parallel// 2:MTK// 3:SPI// 4:TEST// 5: HISPI// 6 : Z2P/Z4P -//I2C Mode :0:Normal 8Addr,8Data// 1:Samsung 8 Addr,8Data// 2:Micron 8 Addr,16Data -//I2C Mode :3:Stmicro 16Addr,8Data//4:Micron2 16 Addr,16Data -//Out Format :0:YCbYCr/RG_GB// 1:YCrYCb/GR_BG// 2:CbYCrY/GB_RG// 3:CrYCbY/BG_GR -//MCLK Speed :0:6M//1:8M//2:10M//3:11.4M//4:12M//5:12.5M//6:13.5M//7:15M//8:18M//9:24M -//pin :BIT0 pwdn// BIT1:reset -//avdd 0:3.3V// 1:2.5V// 2:1.8V -//dovdd 0:2.8V// 1:2.5V// 2:1.8V -//dvdd 0:1.8V// 1:1.5V// 2:1.2V - -/* -[DataBase] -DBName=Dothinkey - -[Vendor] -VendorName=SmartSens - -[Sensor] -SensorName=SC031IOT -width=640 -height=480 -port=1 -type=2 -pin=3 -SlaveID=0xd0 -mode=0 -FlagReg=0xf7 -FlagMask=0xff -FlagData=0xfa -FlagReg1=0xf8 -FlagMask1=0xff -FlagData1=0x46 -outformat=0 -mclk=20 -avdd=2.80000 -dovdd=2.800000 -dvdd=1.5 - -Ext0=0 -Ext1=0 -Ext2=0 -AFVCC=0.0000 -VPP=0.000000 -*/ -#include - -static const uint8_t sc030iot_default_init_regs[][2] = { - {0xf0, 0x30}, - {0x01, 0xff}, - {0x02, 0xff}, - {0x22, 0x07}, - {0x19, 0xff}, - {0x3f, 0x82}, - {0x30, 0x02}, - {0xf0, 0x01}, - {0x70, 0x00}, - {0x71, 0x80}, - {0x72, 0x20}, - {0x73, 0x00}, - {0x74, 0xe0}, - {0x75, 0x10}, - {0x76, 0x81}, - {0x77, 0x88}, - {0x78, 0xe1}, - {0x79, 0x01}, - {0xf5, 0x01}, - {0xf4, 0x0a}, - {0xf0, 0x36}, - {0x37, 0x79}, - {0x31, 0x82}, - {0x3e, 0x60}, - {0x30, 0xf0}, - {0x33, 0x33}, - {0xf0, 0x32}, - {0x48, 0x02}, - {0xf0, 0x33}, - {0x02, 0x12}, - {0x7c, 0x02}, - {0x7d, 0x0e}, - {0xa2, 0x04}, - {0x5e, 0x06}, - {0x5f, 0x0a}, - {0x0b, 0x58}, - {0x06, 0x38}, - {0xf0, 0x32}, - {0x48, 0x02}, - {0xf0, 0x39}, - {0x02, 0x70}, - {0xf0, 0x45}, - {0x09, 0x1c}, - {0xf0, 0x37}, - {0x22, 0x0d}, - {0xf0, 0x33}, - {0x33, 0x10}, - {0xb1, 0x80}, - {0x34, 0x40}, - {0x0b, 0x54}, - {0xb2, 0x78}, - {0xf0, 0x36}, - {0x11, 0x80}, - {0xf0, 0x30}, - {0x38, 0x44}, - {0xf0, 0x33}, - {0xb3, 0x51}, - {0x01, 0x10}, - {0x0b, 0x6c}, - {0x06, 0x24}, - {0xf0, 0x36}, - {0x31, 0x82}, - {0x3e, 0x60}, - {0x30, 0xf0}, - {0x33, 0x33}, - {0xf0, 0x34}, - {0x9f, 0x02}, - {0xa6, 0x40}, - {0xa7, 0x47}, - {0xe8, 0x5f}, - {0xa8, 0x51}, - {0xa9, 0x44}, - {0xe9, 0x36}, - {0xf0, 0x33}, - {0xb3, 0x51}, - {0x64, 0x17}, - {0x90, 0x01}, - {0x91, 0x03}, - {0x92, 0x07}, - {0x01, 0x10}, - {0x93, 0x10}, - {0x94, 0x10}, - {0x95, 0x10}, - {0x96, 0x01}, - {0x97, 0x07}, - {0x98, 0x1f}, - {0x99, 0x10}, - {0x9a, 0x20}, - {0x9b, 0x28}, - {0x9c, 0x28}, - {0xf0, 0x36}, - {0x70, 0x54}, - {0xb6, 0x40}, - {0xb7, 0x41}, - {0xb8, 0x43}, - {0xb9, 0x47}, - {0xba, 0x4f}, - {0xb0, 0x8b}, - {0xb1, 0x8b}, - {0xb2, 0x8b}, - {0xb3, 0x9b}, - {0xb4, 0xb8}, - {0xb5, 0xf0}, - {0x7e, 0x41}, - {0x7f, 0x47}, - {0x77, 0x80}, - {0x78, 0x84}, - {0x79, 0x8a}, - {0xa0, 0x47}, - {0xa1, 0x5f}, - {0x96, 0x43}, - {0x97, 0x44}, - {0x98, 0x54}, - {0xf0, 0x00}, - {0xf0, 0x01}, - {0x73, 0x00}, - {0x74, 0xe0}, - {0x70, 0x00}, - {0x71, 0x80}, - {0xf0, 0x36}, - {0x37, 0x74}, - {0xf0, 0x3f}, - {0x03, 0xa1}, - {0xf0, 0x36},//cvbs_off - {0x11, 0x80}, - {0xf0, 0x01}, - {0x79, 0xc1}, - {0xf0, 0x37}, - {0x24, 0x21}, - {0xf0, 0x36}, - {0x41, 0x00}, - {0xea, 0x09}, - {0xeb, 0x03}, - {0xec, 0x19}, - {0xed, 0x38}, - {0xe9, 0x30}, - {0xf0, 0x33}, - {0x33, 0x00}, - {0x34, 0x00}, - {0xb1, 0x00}, - {0xf0, 0x00}, - {0xe0, 0x04}, - {0xf0, 0x01}, - {0x73, 0x00}, - {0x74, 0xe0}, - {0x70, 0x00}, - {0x71, 0x80}, - {0xf0, 0x36}, - {0x32, 0x44}, - {0xf0, 0x36}, - {0x3e, 0xe0}, - {0x70, 0x56}, - {0x7c, 0x43}, - {0x7d, 0x47}, - {0x74, 0x00}, - {0x75, 0x00}, - {0x76, 0x00}, - {0xa0, 0x47}, - {0xa1, 0x5f}, - {0x96, 0x22}, - {0x97, 0x22}, - {0x98, 0x22}, - {0xf0, 0x00}, - {0x72, 0x38}, - {0x7a, 0x80}, - {0x85, 0x18}, - {0x9b, 0x35}, - {0x9e, 0x20}, - {0xd0, 0x66}, - {0xd1, 0x34}, - {0Xd3, 0x44}, - {0xd6, 0x44}, - {0xb0, 0x41}, - {0xb2, 0x48}, - {0xb3, 0xf4}, - {0xb4, 0x0b}, - {0xb5, 0x78}, - {0xba, 0xff}, - {0xbb, 0xc0}, - {0xbc, 0x90}, - {0xbd, 0x3a}, - {0xc1, 0x67}, - {0xf0, 0x01}, - {0x20, 0x11}, - {0x23, 0x90}, - {0x24, 0x15}, - {0x25, 0x87}, - {0xbc, 0x9f}, - {0xbd, 0x3a}, - {0x48, 0xe6}, - {0x49, 0xc0}, - {0x4a, 0xd0}, - {0x4b, 0x48}, - - // [cvbs_on] - {0xf0, 0x36}, - {0x11, 0x00}, - {0xf0, 0x01}, - {0x79, 0xf1}, - - // [cvbs_off] - {0xf0, 0x36}, - {0x11, 0x80}, - {0xf0, 0x01}, - {0x79, 0xc1}, -}; - -/* -[Sensor] -SensorName=SC031IOT -width=640 -height=480 -port=1 -type=2 -pin=3 -SlaveID=0xd0 -mode=0 -FlagReg=0xf7 -FlagMask=0xff -FlagData=0xfa -FlagReg1=0xf8 -FlagMask1=0xff -FlagData1=0x46 -outformat=0 -mclk=27 -avdd=2.80000 -dovdd=2.800000 -dvdd=1.5 - -Ext0=0 -Ext1=0 -Ext2=0 -AFVCC=0.0000 -VPP=0.000000 -*/ -/* 27M MCLK, 30fps -static const uint8_t sc030iot_default_init_regs[][2] = { - {0xf0, 0x30}, - {0x01, 0xff}, - {0x02, 0xff}, - {0x22, 0x07}, - {0x19, 0xff}, - {0x3f, 0x82}, - {0x30, 0x02}, - {0xf0, 0x01}, - {0x70, 0x00}, - {0x71, 0x80}, - {0x72, 0x20}, - {0x73, 0x00}, - {0x74, 0xe0}, - {0x75, 0x10}, - {0x76, 0x81}, - {0x77, 0x88}, - {0x78, 0xe1}, - {0x79, 0x01}, - {0xf5, 0x01}, - {0xf4, 0x0a}, - {0xf0, 0x36}, - {0x37, 0x79}, - {0x31, 0x82}, - {0x3e, 0x60}, - {0x30, 0xf0}, - {0x33, 0x33}, - {0xf0, 0x32}, - {0x48, 0x02}, - {0xf0, 0x33}, - {0x02, 0x12}, - {0x7c, 0x02}, - {0x7d, 0x0e}, - {0xa2, 0x04}, - {0x5e, 0x06}, - {0x5f, 0x0a}, - {0x0b, 0x58}, - {0x06, 0x38}, - {0xf0, 0x32}, - {0x48, 0x02}, - {0xf0, 0x39}, - {0x02, 0x70}, - {0xf0, 0x45}, - {0x09, 0x1c}, - {0xf0, 0x37}, - {0x22, 0x0d}, - {0xf0, 0x33}, - {0x33, 0x10}, - {0xb1, 0x80}, - {0x34, 0x40}, - {0x0b, 0x54}, - {0xb2, 0x78}, - {0xf0, 0x36}, - {0x11, 0x80}, - {0xf0, 0x30}, - {0x38, 0x44}, - {0xf0, 0x33}, - {0xb3, 0x51}, - {0x01, 0x10}, - {0x0b, 0x6c}, - {0x06, 0x24}, - {0xf0, 0x36}, - {0x31, 0x82}, - {0x3e, 0x60}, - {0x30, 0xf0}, - {0x33, 0x33}, - {0xf0, 0x34}, - {0x9f, 0x02}, - {0xa6, 0x40}, - {0xa7, 0x47}, - {0xe8, 0x5f}, - {0xa8, 0x51}, - {0xa9, 0x44}, - {0xe9, 0x36}, - {0xf0, 0x33}, - {0xb3, 0x51}, - {0x64, 0x17}, - {0x90, 0x01}, - {0x91, 0x03}, - {0x92, 0x07}, - {0x01, 0x10}, - {0x93, 0x10}, - {0x94, 0x10}, - {0x95, 0x10}, - {0x96, 0x01}, - {0x97, 0x07}, - {0x98, 0x1f}, - {0x99, 0x10}, - {0x9a, 0x20}, - {0x9b, 0x28}, - {0x9c, 0x28}, - {0xf0, 0x36}, - {0x70, 0x54}, - {0xb6, 0x40}, - {0xb7, 0x41}, - {0xb8, 0x43}, - {0xb9, 0x47}, - {0xba, 0x4f}, - {0xb0, 0x8b}, - {0xb1, 0x8b}, - {0xb2, 0x8b}, - {0xb3, 0x9b}, - {0xb4, 0xb8}, - {0xb5, 0xf0}, - {0x7e, 0x41}, - {0x7f, 0x47}, - {0x77, 0x80}, - {0x78, 0x84}, - {0x79, 0x8a}, - {0xa0, 0x47}, - {0xa1, 0x5f}, - {0x96, 0x43}, - {0x97, 0x44}, - {0x98, 0x54}, - {0xf0, 0x00}, - {0xf0, 0x01}, - {0x73, 0x00}, - {0x74, 0xe0}, - {0x70, 0x00}, - {0x71, 0x80}, - {0xf0, 0x36}, - {0x37, 0x74}, - {0xf0, 0x3f}, - {0x03, 0x93}, - {0xf0, 0x36},//cvbs_off - {0x11, 0x80}, - {0xf0, 0x01}, - {0x79, 0xc1}, - {0xf0, 0x37}, - {0x24, 0x21}, - {0xf0, 0x36}, - {0x41, 0x00}, - {0xe9, 0x2c}, - {0xf0, 0x33}, - {0x33, 0x00}, - {0x34, 0x00}, - {0xb1, 0x00}, - {0xf0, 0x00}, - {0xe0, 0x04}, - {0xf0, 0x01}, - {0x73, 0x00}, - {0x74, 0xe0}, - {0x70, 0x00}, - {0x71, 0x80}, - {0xf0, 0x36}, - {0x32, 0x44}, - {0xf0, 0x36}, - {0x3e, 0xe0}, - {0x70, 0x56}, - {0x7c, 0x43}, - {0x7d, 0x47}, - {0x74, 0x00}, - {0x75, 0x00}, - {0x76, 0x00}, - {0xa0, 0x47}, - {0xa1, 0x5f}, - {0x96, 0x22}, - {0x97, 0x22}, - {0x98, 0x22}, - {0xf0, 0x00}, - {0x72, 0x38}, - {0x7a, 0x80}, - {0x85, 0x18}, - {0x9b, 0x35}, - {0x9e, 0x20}, - {0xd0, 0x66}, - {0xd1, 0x34}, - {0Xd3, 0x44}, - {0xd6, 0x44}, - {0xb0, 0x41}, - {0xb2, 0x48}, - {0xb3, 0xf4}, - {0xb4, 0x0b}, - {0xb5, 0x78}, - {0xba, 0xff}, - {0xbb, 0xc0}, - {0xbc, 0x90}, - {0xbd, 0x3a}, - {0xc1, 0x67}, - {0xf0, 0x01}, - {0x20, 0x11}, - {0x23, 0x90}, - {0x24, 0x15}, - {0x25, 0x87}, - {0xbc, 0x9f}, - {0xbd, 0x3a}, - {0x48, 0xe6}, - {0x49, 0xc0}, - {0x4a, 0xd0}, - {0x4b, 0x48}, - - // [cvbs_on] - {0xf0, 0x36}, - {0x11, 0x00}, - {0xf0, 0x01}, - {0x79, 0xf1}, - - // [cvbs_off] - {0xf0, 0x36}, - {0x11, 0x80}, - {0xf0, 0x01}, - {0x79, 0xc1}, -}; - -*/ \ No newline at end of file diff --git a/code/components/esp32-camera-master/sensors/private_include/sc101iot.h b/code/components/esp32-camera-master/sensors/private_include/sc101iot.h deleted file mode 100644 index 85858498..00000000 --- a/code/components/esp32-camera-master/sensors/private_include/sc101iot.h +++ /dev/null @@ -1,31 +0,0 @@ -/* - * - * SC101IOT DVP driver. - * - */ -#ifndef __SC101IOT_H__ -#define __SC101IOT_H__ - -#include "sensor.h" - -/** - * @brief Detect sensor pid - * - * @param slv_addr SCCB address - * @param id Detection result - * @return - * 0: Can't detect this sensor - * Nonzero: This sensor has been detected - */ -int sc101iot_detect(int slv_addr, sensor_id_t *id); - -/** - * @brief initialize sensor function pointers - * - * @param sensor pointer of sensor - * @return - * Always 0 - */ -int sc101iot_init(sensor_t *sensor); - -#endif // __SC101IOT_H__ diff --git a/code/components/esp32-camera-master/sensors/private_include/sc101iot_settings.h b/code/components/esp32-camera-master/sensors/private_include/sc101iot_settings.h deleted file mode 100644 index 2eb14398..00000000 --- a/code/components/esp32-camera-master/sensors/private_include/sc101iot_settings.h +++ /dev/null @@ -1,257 +0,0 @@ -//Preview Type:0:DVP Raw 10 bit// 1:Raw 8 bit// 2:YUV422// 3:RAW16 -//Preview Type:4:RGB565// 5:Pixart SPI// 6:MIPI 10bit// 7:MIPI 12bit// 8: MTK SPI -//port 0:MIPI// 1:Parallel// 2:MTK// 3:SPI// 4:TEST// 5: HISPI// 6 : Z2P/Z4P -//I2C Mode :0:Normal 8Addr,8Data// 1:Samsung 8 Addr,8Data// 2:Micron 8 Addr,16Data -//I2C Mode :3:Stmicro 16Addr,8Data//4:Micron2 16 Addr,16Data -//Out Format :0:YCbYCr/RG_GB// 1:YCrYCb/GR_BG// 2:CbYCrY/GB_RG// 3:CrYCbY/BG_GR -//MCLK Speed :0:6M//1:8M//2:10M//3:11.4M//4:12M//5:12.5M//6:13.5M//7:15M//8:18M//9:24M -//pin :BIT0 pwdn// BIT1:reset -//avdd 0:2.8V// 1:2.5V// 2:1.8V -//dovdd 0:2.8V// 1:2.5V// 2:1.8V -//dvdd 0:1.8V// 1:1.5V// 2:1.2V -/* -[DataBase] -DBName=DemoSens - -[Vendor] -VendorName=SmartSens -I2C_CRC=0 - -[Sensor] -SensorName=SC101AP_raw -width=1280 -height=720 -port=1 -type=2 -pin=3 -SlaveID=0xd0 -mode=0 -FlagReg=0xf7 -FlagMask=0xff -FlagData=0xda -FlagReg1=0xf8 -FlagMask1=0xff -FlagData1=0x4a -outformat=0 -mclk=20 -avdd=2.800000 -dovdd=2.800000 -dvdd=1.200000 - -Ext0=0 -Ext1=0 -Ext2=0 -AFVCC=0.00 -VPP=0.000000 -*/ -#include - -static const uint8_t sc101iot_default_init_regs[][2] = { -#if CONFIG_SC101IOT_720P_15FPS_ENABLED // 720P+YUV422+15FPS sensor default regs -/* Here are some test results: -# size xclk fps pic pclk -# ------- ------- ------ --------- ------- --- --- --- --- --- -# 720p 4 3 err -# 720p 8 5 normal 15 -# 720p 10 7.8 normal 19 -# 720p 20 15 warning 37.5 -# VGA 8 6 normal -# VGA 20 16 normal - -*/ - {0xf0, 0x30}, - {0x01, 0xff}, - {0x02, 0xe0}, - {0x30, 0x10}, - {0x3f, 0x81}, - {0xf0, 0x00}, - {0x70, 0x6b}, - {0x72, 0x30}, - {0x84, 0xb4}, - {0x8b, 0x00}, - {0x8c, 0x20}, - {0x8d, 0x02}, - {0x8e, 0xec}, - {0x9e, 0x10}, - {0xb0, 0xc1}, - {0xc8, 0x10}, - {0xc9, 0x10}, - {0xc6, 0x00}, - {0xe0, 0x0f}, - {0xb5, 0xf0}, - {0xde, 0x80}, - {0xb5, 0xf0}, - {0xde, 0x80}, - {0xb2, 0x50}, - {0xb3, 0xfc}, - {0xb4, 0x40}, - {0xb5, 0xc0}, - {0xb6, 0x50}, - {0xb7, 0xfc}, - {0xb8, 0x40}, - {0xb9, 0xc0}, - {0xba, 0xff}, - {0xbb, 0xcc}, - {0xbc, 0xa9}, - {0xbd, 0x7d}, - {0xc1, 0x77}, - {0xf0, 0x01}, - {0x70, 0x02}, - {0x71, 0x02}, - {0x72, 0x50}, - {0x73, 0x02}, - {0x74, 0xd2}, - {0x75, 0x20}, - {0x76, 0x81}, - {0x77, 0x8c}, - {0x78, 0x81}, - {0xf4, 0x01}, - {0xf5, 0x00}, - {0xf6, 0x00}, - {0xf0, 0x36}, - {0x40, 0x03}, - {0x41, 0x01}, - {0xf0, 0x39}, - {0x02, 0x70}, - {0xf0, 0x32}, - {0x41, 0x00}, - {0x43, 0x01}, - {0x48, 0x02}, - {0xf0, 0x45}, - {0x09, 0x20}, - {0xf0, 0x33}, - {0x33, 0x10}, - {0xf0, 0x30}, - {0x38, 0x44}, - {0xf0, 0x39}, - {0x07, 0x00}, - {0x08, 0x19}, - {0x47, 0x00}, - {0x48, 0x00}, - {0xf0, 0x37}, - {0x24, 0x31}, - {0xf0, 0x34}, - {0x9f, 0x02}, - {0xa6, 0x51}, - {0xa7, 0x57}, - {0xe8, 0x5f}, - {0xa8, 0x50}, - {0xa9, 0x50}, - {0xe9, 0x50}, - {0xf0, 0x33}, - {0xb3, 0x58}, - {0xb2, 0x78}, - {0xf0, 0x34}, - {0x9f, 0x03}, - {0xa6, 0x51}, - {0xa7, 0x57}, - {0xaa, 0x01}, - {0xab, 0x28}, - {0xac, 0x01}, - {0xad, 0x38}, - {0xf0, 0x33}, - {0x0a, 0x01}, - {0x0b, 0x28}, - {0xf0, 0x33}, - {0x64, 0x0f}, - {0xec, 0x51}, - {0xed, 0x57}, - {0x06, 0x58}, - {0xe9, 0x58}, - {0xeb, 0x68}, - {0xf0, 0x33}, - {0x64, 0x0f}, - {0xf0, 0x36}, - {0x70, 0xdf}, - {0xb6, 0x40}, - {0xb7, 0x51}, - {0xb8, 0x53}, - {0xb9, 0x57}, - {0xba, 0x5f}, - {0xb0, 0x84}, - {0xb1, 0x82}, - {0xb2, 0x84}, - {0xb3, 0x88}, - {0xb4, 0x90}, - {0xb5, 0x90}, - {0xf0, 0x36}, - {0x7e, 0x50}, - {0x7f, 0x51}, - {0x77, 0x81}, - {0x78, 0x86}, - {0x79, 0x89}, - {0xf0, 0x36}, - {0x70, 0xdf}, - {0x9c, 0x51}, - {0x9d, 0x57}, - {0x90, 0x54}, - {0x91, 0x54}, - {0x92, 0x56}, - {0xf0, 0x36}, - {0xa0, 0x51}, - {0xa1, 0x57}, - {0x96, 0x33}, - {0x97, 0x43}, - {0x98, 0x43}, - {0xf0, 0x36}, - {0x70, 0xdf}, - {0x7c, 0x40}, - {0x7d, 0x53}, - {0x74, 0xd0}, - {0x75, 0xf0}, - {0x76, 0xf0}, - {0xf0, 0x37}, - {0x0f, 0xd5}, - {0x7a, 0x40}, - {0x7b, 0x57}, - {0x71, 0x09}, - {0x72, 0x09}, - {0x73, 0x05}, - {0xf0, 0x33}, - {0x01, 0x44}, - {0xf0, 0x36}, - {0x37, 0xfb}, - {0xf0, 0x36}, - {0x3c, 0x0d}, - {0xf0, 0x33}, - {0x14, 0x95}, - {0xf0, 0x33}, - {0x8f, 0x80}, - {0xf0, 0x37}, - {0x27, 0x14}, - {0x28, 0x03}, - {0xf0, 0x36}, - {0x37, 0xf4}, - {0xf0, 0x33}, - {0x01, 0x44}, - {0xf0, 0x36}, - {0x79, 0x89}, - {0xf0, 0x34}, - {0xac, 0x01}, - {0xad, 0x40}, - {0xf0, 0x33}, - {0xeb, 0x70}, - {0xf0, 0x34}, - {0xa8, 0x50}, - {0xa9, 0x50}, - {0xf0, 0x33}, - {0xb3, 0x58}, - {0xf0, 0x36}, - {0x11, 0x80}, - {0xf0, 0x36}, - {0x41, 0x51}, - {0xf0, 0x3f}, - {0x03, 0x09}, - {0xf0, 0x32}, - {0x0c, 0x06}, - {0x0d, 0x82}, - {0x0e, 0x02}, - {0x0f, 0xee}, - {0xf0, 0x36}, - {0xea, 0x09}, - {0xeb, 0xf5}, - {0xec, 0x11}, - {0xed, 0x27}, - {0xe9, 0x20}, -#endif -}; diff --git a/code/components/esp32-camera-master/sensors/sc030iot.c b/code/components/esp32-camera-master/sensors/sc030iot.c deleted file mode 100644 index 86f525f3..00000000 --- a/code/components/esp32-camera-master/sensors/sc030iot.c +++ /dev/null @@ -1,335 +0,0 @@ -/* - * SC030IOT driver. - * - * Copyright 2020-2022 Espressif Systems (Shanghai) PTE LTD - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * - */ -#include -#include -#include -#include -#include "sccb.h" -#include "xclk.h" -#include "freertos/FreeRTOS.h" -#include "freertos/task.h" - -#include "sc030iot.h" -#include "sc030iot_settings.h" - -#if defined(ARDUINO_ARCH_ESP32) && defined(CONFIG_ARDUHAL_ESP_LOG) -#include "esp32-hal-log.h" -#else -#include "esp_log.h" -static const char* TAG = "sc030"; -#endif - -#define SC030_SENSOR_ID_HIGH_REG 0XF7 -#define SC030_SENSOR_ID_LOW_REG 0XF8 -#define SC030_MAX_FRAME_WIDTH (640) -#define SC030_MAX_FRAME_HIGH (480) - -// sc030 use "i2c paging mode", so the high byte of the register needs to be written to the 0xf0 reg. -// For more information please refer to the Technical Reference Manual. -static int get_reg(sensor_t *sensor, int reg, int reg_value_mask) -{ - int ret = 0; - uint8_t reg_high = (reg>>8) & 0xFF; - uint8_t reg_low = reg & 0xFF; - - if(SCCB_Write(sensor->slv_addr, 0xf0, reg_high)) { - return -1; - } - - ret = SCCB_Read(sensor->slv_addr, reg_low); - if(ret > 0){ - ret &= reg_value_mask; - } - return ret; -} - -// sc030 use "i2c paging mode", so the high byte of the register needs to be written to the 0xf0 reg. -// For more information please refer to the Technical Reference Manual. -static int set_reg(sensor_t *sensor, int reg, int mask, int value) -{ - int ret = 0; - uint8_t reg_high = (reg>>8) & 0xFF; - uint8_t reg_low = reg & 0xFF; - - if(SCCB_Write(sensor->slv_addr, 0xf0, reg_high)) { - return -1; - } - - ret = SCCB_Write(sensor->slv_addr, reg_low, value & 0xFF); - return ret; -} - -static int set_regs(sensor_t *sensor, const uint8_t (*regs)[2], uint32_t regs_entry_len) -{ - int i=0, res = 0; - while (islv_addr, regs[i][0], regs[i][1]); - if (res) { - return res; - } - i++; - } - return res; -} - -static int set_reg_bits(sensor_t *sensor, int reg, uint8_t offset, uint8_t length, uint8_t value) -{ - int ret = 0; - ret = get_reg(sensor, reg, 0xff); - if(ret < 0){ - return ret; - } - uint8_t mask = ((1 << length) - 1) << offset; - value = (ret & ~mask) | ((value << offset) & mask); - ret = set_reg(sensor, reg & 0xFFFF, 0xFFFF, value); - return ret; -} - -#define WRITE_REGS_OR_RETURN(regs, regs_entry_len) ret = set_regs(sensor, regs, regs_entry_len); if(ret){return ret;} -#define WRITE_REG_OR_RETURN(reg, val) ret = set_reg(sensor, reg, 0xFF, val); if(ret){return ret;} -#define SET_REG_BITS_OR_RETURN(reg, offset, length, val) ret = set_reg_bits(sensor, reg, offset, length, val); if(ret){return ret;} - -static int set_hmirror(sensor_t *sensor, int enable) -{ - int ret = 0; - if(enable) { - SET_REG_BITS_OR_RETURN(0x3221, 1, 2, 0x3); // mirror on - } else { - SET_REG_BITS_OR_RETURN(0x3221, 1, 2, 0x0); // mirror off - } - - return ret; -} - -static int set_vflip(sensor_t *sensor, int enable) -{ - int ret = 0; - if(enable) { - SET_REG_BITS_OR_RETURN(0x3221, 5, 2, 0x3); // flip on - } else { - SET_REG_BITS_OR_RETURN(0x3221, 5, 2, 0x0); // flip off - } - - return ret; -} - -static int set_colorbar(sensor_t *sensor, int enable) -{ - int ret = 0; - SET_REG_BITS_OR_RETURN(0x0100, 7, 1, enable & 0xff); // enable test pattern mode - - return ret; -} - -static int set_sharpness(sensor_t *sensor, int level) -{ - int ret = 0; - SET_REG_BITS_OR_RETURN(0x00e0, 1, 1, 1); // enable edge enhancement - WRITE_REG_OR_RETURN(0x00d0, level & 0xFF); // base value - WRITE_REG_OR_RETURN(0x00d2, (level >> 8) & 0xFF); // limit - - return ret; -} - -static int set_agc_gain(sensor_t *sensor, int gain) -{ - int ret = 0; - SET_REG_BITS_OR_RETURN(0x0070, 1, 1, 1); // enable auto agc control - WRITE_REG_OR_RETURN(0x0068, gain & 0xFF); // Window weight setting1 - WRITE_REG_OR_RETURN(0x0069, (gain >> 8) & 0xFF); // Window weight setting2 - WRITE_REG_OR_RETURN(0x006a, (gain >> 16) & 0xFF); // Window weight setting3 - WRITE_REG_OR_RETURN(0x006b, (gain >> 24) & 0xFF); // Window weight setting4 - - return ret; -} - -static int set_aec_value(sensor_t *sensor, int value) -{ - int ret = 0; - SET_REG_BITS_OR_RETURN(0x0070, 0, 1, 1); // enable auto aec control - WRITE_REG_OR_RETURN(0x0072, value & 0xFF); // AE target - - return ret; -} - -static int set_awb_gain(sensor_t *sensor, int value) -{ - int ret = 0; - SET_REG_BITS_OR_RETURN(0x00b0, 0, 1, 1); // enable awb control - WRITE_REG_OR_RETURN(0x00c8, value & 0xFF); // blue gain - WRITE_REG_OR_RETURN(0x00c9, (value>>8) & 0XFF); // red gain - return ret; -} - -static int set_saturation(sensor_t *sensor, int level) -{ - int ret = 0; - SET_REG_BITS_OR_RETURN(0x00f5, 5, 1, 0); // enable saturation control - WRITE_REG_OR_RETURN(0x0149, level & 0xFF); // blue saturation gain (/128) - WRITE_REG_OR_RETURN(0x014a, (level>>8) & 0XFF); // red saturation gain (/128) - return ret; -} - -static int set_contrast(sensor_t *sensor, int level) -{ - int ret = 0; - SET_REG_BITS_OR_RETURN(0x00f5, 6, 1, 0); // enable contrast control - WRITE_REG_OR_RETURN(0x014b, level); // contrast coefficient(/64) - return ret; -} - -static int reset(sensor_t *sensor) -{ - int ret = set_regs(sensor, sc030iot_default_init_regs, sizeof(sc030iot_default_init_regs)/(sizeof(uint8_t) * 2)); - - // Delay - vTaskDelay(50 / portTICK_PERIOD_MS); - - // ESP_LOGI(TAG, "set_reg=%0x", set_reg(sensor, 0x0100, 0xffff, 0x00)); // write 0x80 to enter test mode if you want to test the sensor - // ESP_LOGI(TAG, "0x0100=%0x", get_reg(sensor, 0x0100, 0xffff)); - if (ret) { - ESP_LOGE(TAG, "reset fail"); - } - return ret; -} - -static int set_window(sensor_t *sensor, int offset_x, int offset_y, int w, int h) -{ - int ret = 0; - //sc:H_start={0x0172[1:0],0x0170},H_end={0x0172[5:4],0x0171}, - WRITE_REG_OR_RETURN(0x0170, offset_x & 0xff); - WRITE_REG_OR_RETURN(0x0171, (offset_x+w) & 0xff); - WRITE_REG_OR_RETURN(0x0172, ((offset_x>>8) & 0x03) | (((offset_x+w)>>4)&0x30)); - - //sc:V_start={0x0175[1:0],0x0173},H_end={0x0175[5:4],0x0174}, - WRITE_REG_OR_RETURN(0x0173, offset_y & 0xff); - WRITE_REG_OR_RETURN(0x0174, (offset_y+h) & 0xff); - WRITE_REG_OR_RETURN(0x0175, ((offset_y>>8) & 0x03) | (((offset_y+h)>>4)&0x30)); - - vTaskDelay(10 / portTICK_PERIOD_MS); - - return ret; -} - -static int set_framesize(sensor_t *sensor, framesize_t framesize) -{ - uint16_t w = resolution[framesize].width; - uint16_t h = resolution[framesize].height; - if(w>SC030_MAX_FRAME_WIDTH || h > SC030_MAX_FRAME_HIGH) { - goto err; - } - - uint16_t offset_x = (640-w) /2; - uint16_t offset_y = (480-h) /2; - - if(set_window(sensor, offset_x, offset_y, w, h)) { - goto err; - } - - sensor->status.framesize = framesize; - return 0; -err: - ESP_LOGE(TAG, "frame size err"); - return -1; -} - -static int set_pixformat(sensor_t *sensor, pixformat_t pixformat) -{ - int ret=0; - sensor->pixformat = pixformat; - - switch (pixformat) { - case PIXFORMAT_RGB565: - case PIXFORMAT_RAW: - case PIXFORMAT_GRAYSCALE: - ESP_LOGE(TAG, "Not support"); - break; - case PIXFORMAT_YUV422: // For now, sc030/sc031 sensor only support YUV422. - break; - default: - return -1; - } - - return ret; -} - -static int init_status(sensor_t *sensor) -{ - return 0; -} - -static int set_dummy(sensor_t *sensor, int val){ return -1; } - -static int set_xclk(sensor_t *sensor, int timer, int xclk) -{ - int ret = 0; - sensor->xclk_freq_hz = xclk * 1000000U; - ret = xclk_timer_conf(timer, sensor->xclk_freq_hz); - return ret; -} - -int sc030iot_detect(int slv_addr, sensor_id_t *id) -{ - if (SC030IOT_SCCB_ADDR == slv_addr) { - uint8_t MIDL = SCCB_Read(slv_addr, SC030_SENSOR_ID_LOW_REG); - uint8_t MIDH = SCCB_Read(slv_addr, SC030_SENSOR_ID_HIGH_REG); - uint16_t PID = MIDH << 8 | MIDL; - if (SC030IOT_PID == PID) { - id->PID = PID; - return PID; - } else { - ESP_LOGI(TAG, "Mismatch PID=0x%x", PID); - } - } - return 0; -} - -int sc030iot_init(sensor_t *sensor) -{ - // Set function pointers - sensor->reset = reset; - sensor->init_status = init_status; - sensor->set_pixformat = set_pixformat; - sensor->set_framesize = set_framesize; - - sensor->set_saturation= set_saturation; - sensor->set_colorbar = set_colorbar; - sensor->set_hmirror = set_hmirror; - sensor->set_vflip = set_vflip; - sensor->set_sharpness = set_sharpness; - sensor->set_agc_gain = set_agc_gain; - sensor->set_aec_value = set_aec_value; - sensor->set_awb_gain = set_awb_gain; - sensor->set_contrast = set_contrast; - //not supported - sensor->set_denoise = set_dummy; - sensor->set_quality = set_dummy; - sensor->set_special_effect = set_dummy; - sensor->set_wb_mode = set_dummy; - sensor->set_ae_level = set_dummy; - - - sensor->get_reg = get_reg; - sensor->set_reg = set_reg; - sensor->set_xclk = set_xclk; - - ESP_LOGD(TAG, "sc030iot Attached"); - - return 0; -} \ No newline at end of file diff --git a/code/components/esp32-camera-master/sensors/sc101iot.c b/code/components/esp32-camera-master/sensors/sc101iot.c deleted file mode 100644 index 310a0476..00000000 --- a/code/components/esp32-camera-master/sensors/sc101iot.c +++ /dev/null @@ -1,342 +0,0 @@ -/* - * SC101IOT driver. - * - * Copyright 2020-2022 Espressif Systems (Shanghai) PTE LTD - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - * - */ -#include -#include -#include -#include -#include "sccb.h" -#include "xclk.h" -#include "freertos/FreeRTOS.h" -#include "freertos/task.h" - -#include "sc101iot.h" -#include "sc101iot_settings.h" - -#if defined(ARDUINO_ARCH_ESP32) && defined(CONFIG_ARDUHAL_ESP_LOG) -#include "esp32-hal-log.h" -#else -#include "esp_log.h" -static const char* TAG = "sc101"; -#endif - -#define SC101_SENSOR_ID_HIGH_REG 0XF7 -#define SC101_SENSOR_ID_LOW_REG 0XF8 -#define SC101_MAX_FRAME_WIDTH (1280) -#define SC101_MAX_FRAME_HIGH (720) - -// sc101 use "i2c paging mode", so the high byte of the register needs to be written to the 0xf0 reg. -// For more information please refer to the Technical Reference Manual. -static int get_reg(sensor_t *sensor, int reg, int mask) -{ - int ret = 0; - uint8_t reg_high = (reg>>8) & 0xFF; - uint8_t reg_low = reg & 0xFF; - - if(SCCB_Write(sensor->slv_addr, 0xf0, reg_high)) { - return -1; - } - - ret = SCCB_Read(sensor->slv_addr, reg_low); - if(ret > 0){ - ret &= mask; - } - return ret; -} - -// sc101 use "i2c paging mode", so the high byte of the register needs to be written to the 0xf0 reg. -// For more information please refer to the Technical Reference Manual. -static int set_reg(sensor_t *sensor, int reg, int mask, int value) -{ - int ret = 0; - uint8_t reg_high = (reg>>8) & 0xFF; - uint8_t reg_low = reg & 0xFF; - - if(SCCB_Write(sensor->slv_addr, 0xf0, reg_high)) { - return -1; - } - - ret = SCCB_Write(sensor->slv_addr, reg_low, value & 0xFF); - return ret; -} - -static int set_regs(sensor_t *sensor, const uint8_t (*regs)[2], uint32_t regs_entry_len) -{ - int i=0, res = 0; - while (islv_addr, regs[i][0], regs[i][1]); - if (res) { - return res; - } - i++; - } - return res; -} - -static int set_reg_bits(sensor_t *sensor, int reg, uint8_t offset, uint8_t length, uint8_t value) -{ - int ret = 0; - ret = get_reg(sensor, reg, 0xff); - if(ret < 0){ - return ret; - } - uint8_t mask = ((1 << length) - 1) << offset; - value = (ret & ~mask) | ((value << offset) & mask); - ret = set_reg(sensor, reg & 0xFFFF, 0xFFFF, value); - return ret; -} - -#define WRITE_REGS_OR_RETURN(regs, regs_entry_len) ret = set_regs(sensor, regs, regs_entry_len); if(ret){return ret;} -#define WRITE_REG_OR_RETURN(reg, val) ret = set_reg(sensor, reg, 0xFF, val); if(ret){return ret;} -#define SET_REG_BITS_OR_RETURN(reg, offset, length, val) ret = set_reg_bits(sensor, reg, offset, length, val); if(ret){return ret;} - -static int set_hmirror(sensor_t *sensor, int enable) -{ - int ret = 0; - if(enable) { - SET_REG_BITS_OR_RETURN(0x3221, 1, 2, 0x3); // enable mirror - } else { - SET_REG_BITS_OR_RETURN(0x3221, 1, 2, 0x0); // disable mirror - } - - return ret; -} - -static int set_vflip(sensor_t *sensor, int enable) -{ - int ret = 0; - if(enable) { - SET_REG_BITS_OR_RETURN(0x3221, 5, 2, 0x3); // flip on - } else { - SET_REG_BITS_OR_RETURN(0x3221, 5, 2, 0x0); // flip off - } - - return ret; -} - -static int set_colorbar(sensor_t *sensor, int enable) -{ - int ret = 0; - SET_REG_BITS_OR_RETURN(0x0100, 7, 1, enable & 0xff); // enable colorbar mode - return ret; -} - -static int set_raw_gma(sensor_t *sensor, int enable) -{ - int ret = 0; - SET_REG_BITS_OR_RETURN(0x00f5, 1, 1, enable & 0xff); // enable gamma compensation - - return ret; -} - -static int set_sharpness(sensor_t *sensor, int level) -{ - int ret = 0; - SET_REG_BITS_OR_RETURN(0x00e0, 1, 1, 1); // enable edge enhancement - WRITE_REG_OR_RETURN(0x00d0, level & 0xFF); // base value - WRITE_REG_OR_RETURN(0x00d2, (level >> 8) & 0xFF); // limit - - return ret; -} - -static int set_agc_gain(sensor_t *sensor, int gain) -{ - int ret = 0; - SET_REG_BITS_OR_RETURN(0x0070, 1, 1, 1); // enable auto agc control - WRITE_REG_OR_RETURN(0x0068, gain & 0xFF); // Window weight setting1 - WRITE_REG_OR_RETURN(0x0069, (gain >> 8) & 0xFF); // Window weight setting2 - WRITE_REG_OR_RETURN(0x006a, (gain >> 16) & 0xFF); // Window weight setting3 - WRITE_REG_OR_RETURN(0x006b, (gain >> 24) & 0xFF); // Window weight setting4 - - return ret; -} - -static int set_aec_value(sensor_t *sensor, int value) -{ - int ret = 0; - SET_REG_BITS_OR_RETURN(0x0070, 0, 1, 1); // enable auto aec control - WRITE_REG_OR_RETURN(0x0072, value & 0xFF); // AE target - - return ret; -} - -static int set_awb_gain(sensor_t *sensor, int value) -{ - int ret = 0; - SET_REG_BITS_OR_RETURN(0x00b0, 0, 1, 1); // enable awb control - WRITE_REG_OR_RETURN(0x00c8, value & 0xFF); // blue gain - WRITE_REG_OR_RETURN(0x00c9, (value>>8) & 0XFF); // red gain - return ret; -} - -static int set_saturation(sensor_t *sensor, int level) -{ - int ret = 0; - SET_REG_BITS_OR_RETURN(0x00f5, 5, 1, 0); // enable saturation control - WRITE_REG_OR_RETURN(0x0149, level & 0xFF); // blue saturation gain (/128) - WRITE_REG_OR_RETURN(0x014a, (level>>8) & 0XFF); // red saturation gain (/128) - return ret; -} - -static int set_contrast(sensor_t *sensor, int level) -{ - int ret = 0; - SET_REG_BITS_OR_RETURN(0x00f5, 6, 1, 0); // enable contrast control - WRITE_REG_OR_RETURN(0x014b, level); // contrast coefficient(/64) - return ret; -} - -static int reset(sensor_t *sensor) -{ - int ret = set_regs(sensor, sc101iot_default_init_regs, sizeof(sc101iot_default_init_regs)/(sizeof(uint8_t) * 2)); - - // Delay - vTaskDelay(50 / portTICK_PERIOD_MS); - - // ESP_LOGI(TAG, "set_reg=%0x", set_reg(sensor, 0x0100, 0xffff, 0x00)); // write 0x80 to enter test mode if you want to test the sensor - // ESP_LOGI(TAG, "0x0100=%0x", get_reg(sensor, 0x0100, 0xffff)); - if (ret) { - ESP_LOGE(TAG, "reset fail"); - } - return ret; -} - -static int set_window(sensor_t *sensor, int offset_x, int offset_y, int w, int h) -{ - int ret = 0; - //sc:H_start={0x0172[3:0],0x0170},H_end={0x0172[7:4],0x0171}, - WRITE_REG_OR_RETURN(0x0170, offset_x & 0xff); - WRITE_REG_OR_RETURN(0x0171, (offset_x+w) & 0xff); - WRITE_REG_OR_RETURN(0x0172, ((offset_x>>8) & 0x0f) | (((offset_x+w)>>4)&0xf0)); - - //sc:V_start={0x0175[3:0],0x0173},H_end={0x0175[7:4],0x0174}, - WRITE_REG_OR_RETURN(0x0173, offset_y & 0xff); - WRITE_REG_OR_RETURN(0x0174, (offset_y+h) & 0xff); - WRITE_REG_OR_RETURN(0x0175, ((offset_y>>8) & 0x0f) | (((offset_y+h)>>4)&0xf0)); - - vTaskDelay(10 / portTICK_PERIOD_MS); - - return ret; -} - -static int set_framesize(sensor_t *sensor, framesize_t framesize) -{ - uint16_t w = resolution[framesize].width; - uint16_t h = resolution[framesize].height; - if(w>SC101_MAX_FRAME_WIDTH || h > SC101_MAX_FRAME_HIGH) { - goto err; - } - - uint16_t offset_x = (SC101_MAX_FRAME_WIDTH-w) /2; - uint16_t offset_y = (SC101_MAX_FRAME_HIGH-h) /2; - - if(set_window(sensor, offset_x, offset_y, w, h)) { - goto err; - } - - sensor->status.framesize = framesize; - return 0; -err: - ESP_LOGE(TAG, "frame size err"); - return -1; -} - -static int set_pixformat(sensor_t *sensor, pixformat_t pixformat) -{ - int ret=0; - sensor->pixformat = pixformat; - - switch (pixformat) { - case PIXFORMAT_RGB565: - case PIXFORMAT_RAW: - case PIXFORMAT_GRAYSCALE: - ESP_LOGE(TAG, "Not support"); - break; - case PIXFORMAT_YUV422: // For now, sc101 sensor only support YUV422. - break; - default: - ret = -1; - } - - return ret; -} - -static int init_status(sensor_t *sensor) -{ - return 0; -} - -static int set_dummy(sensor_t *sensor, int val){ return -1; } - -static int set_xclk(sensor_t *sensor, int timer, int xclk) -{ - int ret = 0; - sensor->xclk_freq_hz = xclk * 1000000U; - ret = xclk_timer_conf(timer, sensor->xclk_freq_hz); - return ret; -} - -int sc101iot_detect(int slv_addr, sensor_id_t *id) -{ - if (SC101IOT_SCCB_ADDR == slv_addr) { - uint8_t MIDL = SCCB_Read(slv_addr, SC101_SENSOR_ID_LOW_REG); - uint8_t MIDH = SCCB_Read(slv_addr, SC101_SENSOR_ID_HIGH_REG); - uint16_t PID = MIDH << 8 | MIDL; - if (SC101IOT_PID == PID) { - id->PID = PID; - return PID; - } else { - ESP_LOGI(TAG, "Mismatch PID=0x%x", PID); - } - } - return 0; -} - -int sc101iot_init(sensor_t *sensor) -{ - // Set function pointers - sensor->reset = reset; - sensor->init_status = init_status; - sensor->set_pixformat = set_pixformat; - sensor->set_framesize = set_framesize; - sensor->set_hmirror = set_hmirror; - sensor->set_vflip = set_vflip; - sensor->set_colorbar = set_colorbar; - sensor->set_raw_gma = set_raw_gma; - sensor->set_sharpness = set_sharpness; - sensor->set_agc_gain = set_agc_gain; - sensor->set_aec_value = set_aec_value; - sensor->set_awb_gain = set_awb_gain; - sensor->set_saturation= set_saturation; - sensor->set_contrast = set_contrast; - - sensor->set_denoise = set_dummy; - sensor->set_quality = set_dummy; - sensor->set_special_effect = set_dummy; - sensor->set_wb_mode = set_dummy; - sensor->set_ae_level = set_dummy; - - - sensor->get_reg = get_reg; - sensor->set_reg = set_reg; - sensor->set_xclk = set_xclk; - - ESP_LOGD(TAG, "sc101iot Attached"); - - return 0; -} \ No newline at end of file diff --git a/code/components/esp32-camera-master/target/esp32/ll_cam.c b/code/components/esp32-camera-master/target/esp32/ll_cam.c index 1e3def87..d0f0c862 100644 --- a/code/components/esp32-camera-master/target/esp32/ll_cam.c +++ b/code/components/esp32-camera-master/target/esp32/ll_cam.c @@ -34,14 +34,10 @@ static inline int gpio_ll_get_level(gpio_dev_t *hw, int gpio_num) #include "xclk.h" #include "cam_hal.h" -#if (ESP_IDF_VERSION_MAJOR >= 4) && (ESP_IDF_VERSION_MINOR >= 3) -#include "esp_rom_gpio.h" -#endif - #if (ESP_IDF_VERSION_MAJOR >= 5) #define GPIO_PIN_INTR_POSEDGE GPIO_INTR_POSEDGE #define GPIO_PIN_INTR_NEGEDGE GPIO_INTR_NEGEDGE -#define gpio_matrix_in(a,b,c) esp_rom_gpio_connect_in_signal(a,b,c) +#define gpio_matrix_in(a,b,c) gpio_iomux_in(a,b) #endif static const char *TAG = "esp32 ll_cam"; @@ -237,7 +233,7 @@ static void IRAM_ATTR ll_cam_dma_isr(void *arg) //DBG_PIN_SET(0); } -bool IRAM_ATTR ll_cam_stop(cam_obj_t *cam) +bool ll_cam_stop(cam_obj_t *cam) { I2S0.conf.rx_start = 0; I2S_ISR_DISABLE(in_suc_eof); diff --git a/code/components/esp32-camera-master/target/esp32s2/ll_cam.c b/code/components/esp32-camera-master/target/esp32s2/ll_cam.c index 54764329..e54b81f0 100644 --- a/code/components/esp32-camera-master/target/esp32s2/ll_cam.c +++ b/code/components/esp32-camera-master/target/esp32s2/ll_cam.c @@ -21,15 +21,10 @@ #include "xclk.h" #include "cam_hal.h" -#if (ESP_IDF_VERSION_MAJOR >= 4) && (ESP_IDF_VERSION_MINOR >= 3) -#include "esp_rom_gpio.h" -#endif - #if (ESP_IDF_VERSION_MAJOR >= 5) #define GPIO_PIN_INTR_POSEDGE GPIO_INTR_POSEDGE #define GPIO_PIN_INTR_NEGEDGE GPIO_INTR_NEGEDGE -#define gpio_matrix_in(a,b,c) esp_rom_gpio_connect_in_signal(a,b,c) -#define ets_delay_us(a) esp_rom_delay_us(a) +#define gpio_matrix_in(a,b,c) gpio_iomux_in(a,b) #endif static const char *TAG = "s2 ll_cam"; @@ -75,7 +70,7 @@ static void IRAM_ATTR ll_cam_dma_isr(void *arg) } } -bool IRAM_ATTR ll_cam_stop(cam_obj_t *cam) +bool ll_cam_stop(cam_obj_t *cam) { I2S0.conf.rx_start = 0; diff --git a/code/components/esp32-camera-master/target/esp32s3/ll_cam.c b/code/components/esp32-camera-master/target/esp32s3/ll_cam.c index 2211a0ed..ce405d16 100644 --- a/code/components/esp32-camera-master/target/esp32s3/ll_cam.c +++ b/code/components/esp32-camera-master/target/esp32s3/ll_cam.c @@ -22,15 +22,10 @@ #include "soc/gdma_reg.h" #include "ll_cam.h" #include "cam_hal.h" -#include "esp_rom_gpio.h" #if (ESP_IDF_VERSION_MAJOR >= 5) -#include "soc/gpio_sig_map.h" -#include "soc/gpio_periph.h" -#include "soc/io_mux_reg.h" -#define gpio_matrix_in(a,b,c) esp_rom_gpio_connect_in_signal(a,b,c) -#define gpio_matrix_out(a,b,c,d) esp_rom_gpio_connect_out_signal(a,b,c,d) -#define ets_delay_us(a) esp_rom_delay_us(a) +#define gpio_matrix_in(a,b,c) gpio_iomux_in(a,b) +#define gpio_matrix_out(a,b,c,d) gpio_iomux_out(a,b,c) #endif static const char *TAG = "s3 ll_cam"; @@ -79,7 +74,7 @@ static void IRAM_ATTR ll_cam_dma_isr(void *arg) } } -bool IRAM_ATTR ll_cam_stop(cam_obj_t *cam) +bool ll_cam_stop(cam_obj_t *cam) { if (cam->jpeg_mode || !cam->psram_mode) { GDMA.channel[cam->dma_num].in.int_ena.in_suc_eof = 0; @@ -175,7 +170,6 @@ static esp_err_t ll_cam_dma_init(cam_obj_t *cam) } GDMA.channel[cam->dma_num].in.conf1.in_check_owner = 0; - // GDMA.channel[cam->dma_num].in.conf1.in_ext_mem_bk_size = 2; GDMA.channel[cam->dma_num].in.peri_sel.sel = 5; //GDMA.channel[cam->dma_num].in.pri.rx_pri = 1;//rx prio 0-15 @@ -184,52 +178,8 @@ static esp_err_t ll_cam_dma_init(cam_obj_t *cam) return ESP_OK; } -#if CONFIG_CAMERA_CONVERTER_ENABLED -static esp_err_t ll_cam_converter_config(cam_obj_t *cam, const camera_config_t *config) -{ - esp_err_t ret = ESP_OK; - - switch (config->conv_mode) { - case YUV422_TO_YUV420: - if (config->pixel_format != PIXFORMAT_YUV422) { - ret = ESP_FAIL; - } else { - ESP_LOGI(TAG, "YUV422 to YUV420 mode"); - LCD_CAM.cam_rgb_yuv.cam_conv_yuv2yuv_mode = 1; - LCD_CAM.cam_rgb_yuv.cam_conv_yuv_mode = 0; - LCD_CAM.cam_rgb_yuv.cam_conv_trans_mode = 1; - } - break; - case YUV422_TO_RGB565: - if (config->pixel_format != PIXFORMAT_YUV422) { - ret = ESP_FAIL; - } else { - ESP_LOGI(TAG, "YUV422 to RGB565 mode"); - LCD_CAM.cam_rgb_yuv.cam_conv_yuv2yuv_mode = 3; - LCD_CAM.cam_rgb_yuv.cam_conv_yuv_mode = 0; - LCD_CAM.cam_rgb_yuv.cam_conv_trans_mode = 0; - } - break; - default: - break; - } -#if CONFIG_LCD_CAM_CONV_BT709_ENABLED - LCD_CAM.cam_rgb_yuv.cam_conv_protocol_mode = 1; -#else - LCD_CAM.cam_rgb_yuv.cam_conv_protocol_mode = 0; -#endif - LCD_CAM.cam_rgb_yuv.cam_conv_data_out_mode = 0; - LCD_CAM.cam_rgb_yuv.cam_conv_data_in_mode = 0; - LCD_CAM.cam_rgb_yuv.cam_conv_mode_8bits_on = 1; - LCD_CAM.cam_rgb_yuv.cam_conv_bypass = 1; - cam->conv_mode = config->conv_mode; - return ret; -} -#endif - esp_err_t ll_cam_config(cam_obj_t *cam, const camera_config_t *config) { - esp_err_t ret = ESP_OK; if (REG_GET_BIT(SYSTEM_PERIP_CLK_EN1_REG, SYSTEM_LCD_CAM_CLK_EN) == 0) { REG_CLR_BIT(SYSTEM_PERIP_CLK_EN1_REG, SYSTEM_LCD_CAM_CLK_EN); REG_SET_BIT(SYSTEM_PERIP_CLK_EN1_REG, SYSTEM_LCD_CAM_CLK_EN); @@ -265,21 +215,15 @@ esp_err_t ll_cam_config(cam_obj_t *cam, const camera_config_t *config) LCD_CAM.cam_rgb_yuv.val = 0; -#if CONFIG_CAMERA_CONVERTER_ENABLED - if (config->conv_mode) { - ret = ll_cam_converter_config(cam, config); - if(ret != ESP_OK) { - return ret; - } - } -#endif - LCD_CAM.cam_ctrl.cam_update = 1; LCD_CAM.cam_ctrl1.cam_start = 1; - ret = ll_cam_dma_init(cam); + esp_err_t err = ll_cam_dma_init(cam); + if(err != ESP_OK) { + return err; + } - return ret; + return ESP_OK; } void ll_cam_vsync_intr_enable(cam_obj_t *cam, bool en) @@ -473,7 +417,6 @@ size_t IRAM_ATTR ll_cam_memcpy(cam_obj_t *cam, uint8_t *out, const uint8_t *in, } return len / 2; } - // just memcpy memcpy(out, in, len); @@ -490,22 +433,8 @@ esp_err_t ll_cam_set_sample_mode(cam_obj_t *cam, pixformat_t pix_format, uint32_ } cam->fb_bytes_per_pixel = 1; // frame buffer stores Y8 } else if (pix_format == PIXFORMAT_YUV422 || pix_format == PIXFORMAT_RGB565) { -#if CONFIG_CAMERA_CONVERTER_ENABLED - switch (cam->conv_mode) { - case YUV422_TO_YUV420: - cam->in_bytes_per_pixel = 1.5; // for DMA receive - cam->fb_bytes_per_pixel = 1.5; // frame buffer stores YUV420 - break; - case YUV422_TO_RGB565: - default: - cam->in_bytes_per_pixel = 2; // for DMA receive + cam->in_bytes_per_pixel = 2; // camera sends YU/YV cam->fb_bytes_per_pixel = 2; // frame buffer stores YU/YV/RGB565 - break; - } -#else - cam->in_bytes_per_pixel = 2; // for DMA receive - cam->fb_bytes_per_pixel = 2; // frame buffer stores YU/YV/RGB565 -#endif } else if (pix_format == PIXFORMAT_JPEG) { cam->in_bytes_per_pixel = 1; cam->fb_bytes_per_pixel = 1; diff --git a/code/components/esp32-camera-master/target/private_include/ll_cam.h b/code/components/esp32-camera-master/target/private_include/ll_cam.h index c27db0c4..7d30c370 100644 --- a/code/components/esp32-camera-master/target/private_include/ll_cam.h +++ b/code/components/esp32-camera-master/target/private_include/ll_cam.h @@ -116,14 +116,8 @@ typedef struct { //for RGB/YUV modes uint16_t width; uint16_t height; -#if CONFIG_CAMERA_CONVERTER_ENABLED - float in_bytes_per_pixel; - float fb_bytes_per_pixel; - camera_conv_mode_t conv_mode; -#else uint8_t in_bytes_per_pixel; uint8_t fb_bytes_per_pixel; -#endif uint32_t fb_size; cam_state_t state; diff --git a/code/components/esp32-camera-master_20220724.zip b/code/components/esp32-camera-master_20220724.zip new file mode 100644 index 0000000000000000000000000000000000000000..64f2f896b59b1c4d1075de98fba133eb502e5fac GIT binary patch literal 308908 zcmbTd1C%bomL^)ZZQHi(s#82=+qO^Hwr$(CZQFL$oO}Cq_e{^sd$(Wg^=IyXt&E+y z;)|__9SYLGASeL;Xc4(r%Kv2kp9{|4yRoAkGZU?$zO}J~KCQLBqm!`%J)Nn!lbN#t zy~2O=M3%VwUwV@LfAn;eR0f2ms&%4gf&^FUv4+Hn%dO zbGNoSQtGo9V1VgDe}~V9aY)l#KU`E=<1>uhEwnhGtz({;=92pHmNR7EA|S5S7ITcB 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newline at end of file +const char* GIT_BRANCH="rolling"; +const char* BUILD_TIME="2022-07-24 18:32"; \ No newline at end of file diff --git a/code/main/version.h b/code/main/version.h index 79ec6223..ac0d49de 100644 --- a/code/main/version.h +++ b/code/main/version.h @@ -13,7 +13,7 @@ extern "C" #include "Helper.h" #include -const char* GIT_BASE_BRANCH = "master - v10.6.0 - 2022-07-17"; +const char* GIT_BASE_BRANCH = "master - v10.6.1 - 2022-07-24"; const char* git_base_branch(void) diff --git a/code/platformio.ini b/code/platformio.ini index c26b91de..abf5731f 100644 --- a/code/platformio.ini +++ b/code/platformio.ini @@ -14,7 +14,7 @@ src_dir = main [env:esp32cam] -platform = espressif32@4.4 +platform = espressif32@4.4.0 ;platform = espressif32 board = esp32cam framework = espidf diff --git a/code/sdkconfig.esp32cam b/code/sdkconfig.esp32cam index 28ab25b6..5437b6a8 100644 --- a/code/sdkconfig.esp32cam +++ b/code/sdkconfig.esp32cam @@ -1233,9 +1233,6 @@ CONFIG_OV2640_SUPPORT=y # CONFIG_GC032A_SUPPORT is not set # CONFIG_GC0308_SUPPORT is not set # CONFIG_BF3005_SUPPORT is not set -CONFIG_BF20A6_SUPPORT=y -# CONFIG_SC101IOT_SUPPORT is not set -CONFIG_SC030IOT_SUPPORT=y # CONFIG_SCCB_HARDWARE_I2C_PORT0 is not set CONFIG_SCCB_HARDWARE_I2C_PORT1=y CONFIG_SCCB_CLK_FREQ=100000 diff --git a/code/version.cpp b/code/version.cpp index 591dd9da..8d45958b 100644 --- a/code/version.cpp +++ b/code/version.cpp @@ -1,4 +1,4 @@ -const char* GIT_REV="dc27911"; +const char* GIT_REV="189093d"; const char* GIT_TAG=""; -const char* GIT_BRANCH="master"; -const char* BUILD_TIME="2022-07-17 09:03"; \ No newline at end of file +const char* GIT_BRANCH="rolling"; +const char* BUILD_TIME="2022-07-24 18:32"; \ No newline at end of file diff --git a/firmware/bootloader.bin b/firmware/bootloader.bin index c5435579d2dd44915c4aa5468f6fd2a583e72a00..2de9e348ef4efd2fdc6adf3db70ef7d49bc821ca 100644 GIT binary patch delta 57 zcmV-90LK6D(E;$$0kH8I2r)Q1Gc`IgGqd>_)=nV~DObp+MaFSk4rL^-tkl?cAfwHa 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      "Fast" (First time RGB, then only check if image is shifted) + "Default" = use only R-Channel, "HighAccuracy" = use all Channels (RGB, 3x slower),
      "Fast" (First time RGB, then only check if image is shifted) diff --git a/sd-card/html/edit_reference.html b/sd-card/html/edit_reference.html index fa65a39d..4eae6bac 100644 --- a/sd-card/html/edit_reference.html +++ b/sd-card/html/edit_reference.html @@ -38,7 +38,7 @@ table { - + @@ -252,7 +252,7 @@ table { SaveConfigToServer(basepath); SaveCanvasToImage(canvas, "/config/reference.jpg", true, basepath); - showReference(); + showReference(param); UpdatePage(); alert("Reference is updated!"); } diff --git a/sd-card/html/version.txt b/sd-card/html/version.txt index 83ecc0b2..3b85ad66 100644 --- a/sd-card/html/version.txt +++ b/sd-card/html/version.txt @@ -1 +1 @@ -14.2.1 \ No newline at end of file +14.2.2 \ No newline at end of file

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fastalg_SAD_criteria = -1; - int alignment_algo = 0; // 0 = "Default" (nur R-Kanal), 1 = "HighAccurity" (RGB-Kanal), 2 = "Fast" (1.x RGB, dann isSimilar) + int alignment_algo = 0; // 0 = "Default" (nur R-Kanal), 1 = "HighAccuracy" (RGB-Kanal), 2 = "Fast" (1.x RGB, dann isSimilar) }; diff --git a/code/components/tflite-lib/CMakeLists.txt b/code/components/tflite-lib/CMakeLists.txt index ab666ce0..eed31a57 100644 --- a/code/components/tflite-lib/CMakeLists.txt +++ b/code/components/tflite-lib/CMakeLists.txt @@ -25,8 +25,7 @@ list(REMOVE_ITEM srcs_kernels "${tfmicro_kernels_dir}/depthwise_conv.cc" "${tfmicro_kernels_dir}/fully_connected.cc" "${tfmicro_kernels_dir}/mul.cc" - "${tfmicro_kernels_dir}/pooling.cc" - "${tfmicro_kernels_dir}/softmax.cc") + "${tfmicro_kernels_dir}/pooling.cc") FILE(GLOB esp_nn_kernels "${tfmicro_kernels_dir}/esp_nn/*.cc") @@ -39,8 +38,6 @@ set(lib_srcs "${tflite_dir}/kernels/kernel_util.cc" "${tflite_dir}/micro/memory_planner/greedy_memory_planner.cc" "${tflite_dir}/micro/memory_planner/linear_memory_planner.cc" - "${tflite_dir}/micro/arena_allocator/recording_simple_memory_allocator.cc" - "${tflite_dir}/micro/arena_allocator/simple_memory_allocator.cc" "${tflite_dir}/c/common.cc" "${tflite_dir}/core/api/error_reporter.cc" "${tflite_dir}/core/api/flatbuffer_conversions.cc" diff --git a/code/components/tflite-lib/tensorflow/lite/builtin_ops.h b/code/components/tflite-lib/tensorflow/lite/builtin_ops.h index 67014928..19ce3e2c 100644 --- a/code/components/tflite-lib/tensorflow/lite/builtin_ops.h +++ b/code/components/tflite-lib/tensorflow/lite/builtin_ops.h @@ -179,8 +179,6 @@ typedef enum { kTfLiteBuiltinMultinomial = 149, kTfLiteBuiltinGelu = 150, kTfLiteBuiltinDynamicUpdateSlice = 151, - kTfLiteBuiltinRelu0To1 = 152, - kTfLiteBuiltinUnsortedSegmentProd = 153, } TfLiteBuiltinOperator; #ifdef __cplusplus diff --git a/code/components/tflite-lib/tensorflow/lite/c/builtin_op_data.h b/code/components/tflite-lib/tensorflow/lite/c/builtin_op_data.h index b8fdb7d1..7f160972 100644 --- a/code/components/tflite-lib/tensorflow/lite/c/builtin_op_data.h +++ b/code/components/tflite-lib/tensorflow/lite/c/builtin_op_data.h @@ -518,9 +518,6 @@ typedef struct { bool approximate; } TfLiteGeluParams; -typedef struct { - int num_segments; -} TfLiteUnsortedSegmentProdParams; #ifdef __cplusplus } // extern "C" #endif // __cplusplus diff --git a/code/components/tflite-lib/tensorflow/lite/c/c_api_types.h b/code/components/tflite-lib/tensorflow/lite/c/c_api_types.h index d947213b..d2524969 100644 --- a/code/components/tflite-lib/tensorflow/lite/c/c_api_types.h +++ b/code/components/tflite-lib/tensorflow/lite/c/c_api_types.h @@ -113,13 +113,7 @@ typedef struct TfLiteQuantizationParams { } TfLiteQuantizationParams; // -------------------------------------------------------------------------- -// Opaque types used by c_api.h, c_api_opaque.h and common.h. - -// TfLiteOpaqueContext is an opaque version of TfLiteContext; -typedef struct TfLiteOpaqueContext TfLiteOpaqueContext; - -// TfLiteOpaqueNode is an opaque version of TfLiteNode; -typedef struct TfLiteOpaqueNode TfLiteOpaqueNode; +// Opaque types used by c_api_opaque.h. // TfLiteOpaqueTensor is an opaque version of TfLiteTensor; typedef struct TfLiteOpaqueTensor TfLiteOpaqueTensor; diff --git a/code/components/tflite-lib/tensorflow/lite/c/common.cc b/code/components/tflite-lib/tensorflow/lite/c/common.cc index 8548424d..956e9d69 100644 --- a/code/components/tflite-lib/tensorflow/lite/c/common.cc +++ b/code/components/tflite-lib/tensorflow/lite/c/common.cc @@ -14,33 +14,13 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/lite/c/common.h" - #include "tensorflow/lite/c/c_api_types.h" -#ifdef TF_LITE_TENSORFLOW_PROFILER -#include - -#include "tensorflow/lite/core/macros.h" -#include "tensorflow/lite/tensorflow_profiler_logger.h" -#endif #ifndef TF_LITE_STATIC_MEMORY #include #include #endif // TF_LITE_STATIC_MEMORY -#ifdef TF_LITE_TENSORFLOW_PROFILER -namespace tflite { -// Use weak symbols here (even though they are guarded by macros) to avoid -// build breakage when building a benchmark requires TFLite runs. The main -// benchmark library should have tensor_profiler_logger dependency. -TFLITE_ATTRIBUTE_WEAK void OnTfLiteTensorAlloc(TfLiteTensor* tensor, - size_t num_bytes); - -TFLITE_ATTRIBUTE_WEAK void OnTfLiteTensorDealloc(TfLiteTensor* tensor); -} // namespace tflite - -#endif // TF_LITE_TENSORFLOW_PROFILER - extern "C" { size_t TfLiteIntArrayGetSizeInBytes(int size) { @@ -119,12 +99,7 @@ void TfLiteFloatArrayFree(TfLiteFloatArray* a) { free(a); } void TfLiteTensorDataFree(TfLiteTensor* t) { if (t->allocation_type == kTfLiteDynamic || t->allocation_type == kTfLitePersistentRo) { - if (t->data.raw) { -#ifdef TF_LITE_TENSORFLOW_PROFILER - tflite::OnTfLiteTensorDealloc(t); -#endif - free(t->data.raw); - } + free(t->data.raw); } t->data.raw = nullptr; } @@ -186,7 +161,7 @@ void TfLiteTensorFree(TfLiteTensor* t) { t->dims = nullptr; if (t->dims_signature) { - TfLiteIntArrayFree((TfLiteIntArray*)t->dims_signature); + TfLiteIntArrayFree((TfLiteIntArray *) t->dims_signature); } t->dims_signature = nullptr; @@ -216,12 +191,16 @@ void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims, } TfLiteStatus TfLiteTensorCopy(const TfLiteTensor* src, TfLiteTensor* dst) { - if (!src || !dst) return kTfLiteOk; - if (src->bytes != dst->bytes) return kTfLiteError; - if (src == dst) return kTfLiteOk; + if (!src || !dst) + return kTfLiteOk; + if (src->bytes != dst->bytes) + return kTfLiteError; + if (src == dst) + return kTfLiteOk; dst->type = src->type; - if (dst->dims) TfLiteIntArrayFree(dst->dims); + if (dst->dims) + TfLiteIntArrayFree(dst->dims); dst->dims = TfLiteIntArrayCopy(src->dims); memcpy(dst->data.raw, src->data.raw, src->bytes); dst->buffer_handle = src->buffer_handle; @@ -239,17 +218,8 @@ void TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor) { // TODO(b/145340303): Tensor data should be aligned. if (!tensor->data.raw) { tensor->data.raw = (char*)malloc(num_bytes); -#ifdef TF_LITE_TENSORFLOW_PROFILER - tflite::OnTfLiteTensorAlloc(tensor, num_bytes); -#endif } else if (num_bytes > tensor->bytes) { -#ifdef TF_LITE_TENSORFLOW_PROFILER - tflite::OnTfLiteTensorDealloc(tensor); -#endif tensor->data.raw = (char*)realloc(tensor->data.raw, num_bytes); -#ifdef TF_LITE_TENSORFLOW_PROFILER - tflite::OnTfLiteTensorAlloc(tensor, num_bytes); -#endif } tensor->bytes = num_bytes; } diff --git a/code/components/tflite-lib/tensorflow/lite/c/common.h b/code/components/tflite-lib/tensorflow/lite/c/common.h index 8b8ffbe8..6a109e1e 100644 --- a/code/components/tflite-lib/tensorflow/lite/c/common.h +++ b/code/components/tflite-lib/tensorflow/lite/c/common.h @@ -173,9 +173,9 @@ void TfLiteFloatArrayFree(TfLiteFloatArray* a); } \ } while (false) #else // TF_LITE_STRIP_ERROR_STRINGS -#define ARGS_UNUSED(...) (void)sizeof(#__VA_ARGS__) -#define TF_LITE_KERNEL_LOG(context, ...) ARGS_UNUSED(__VA_ARGS__) -#define TF_LITE_MAYBE_KERNEL_LOG(context, ...) ARGS_UNUSED(__VA_ARGS__) +#define UNUSED(...) (void)sizeof(#__VA_ARGS__) +#define TF_LITE_KERNEL_LOG(context, ...) UNUSED(__VA_ARGS__) +#define TF_LITE_MAYBE_KERNEL_LOG(context, ...) UNUSED(__VA_ARGS__) #endif // TF_LITE_STRIP_ERROR_STRINGS // Check whether value is true, and if not return kTfLiteError from @@ -842,32 +842,6 @@ typedef struct TfLiteContext { size_t* bytes); } TfLiteContext; -// `TfLiteRegistrationExternal` is an external version of `TfLiteRegistration` -// for C API which doesn't use internal types (such as `TfLiteContext`) but only -// uses stable API types (such as `TfLiteOpaqueContext`). The purpose of each -// field is the exactly the same as with `TfLiteRegistration`. -typedef struct TfLiteRegistrationExternal { - // Custom op name. - const char* custom_name; - - // The version of the op. The verion should be higher than 0. - const int version; - - // Initializes the op from serialized data. - void* (*init)(TfLiteOpaqueContext* context, const char* buffer, - size_t length); - - // The pointer `buffer` is the data previously returned by an init invocation. - void (*free)(TfLiteOpaqueContext* context, void* buffer); - - // Called when the inputs that this node depends on have been resized. - TfLiteStatus (*prepare)(TfLiteOpaqueContext* context, TfLiteOpaqueNode* node); - - // Called when the node is executed. (should read node->inputs and output to - // node->outputs). - TfLiteStatus (*invoke)(TfLiteOpaqueContext* context, TfLiteOpaqueNode* node); -} TfLiteRegistrationExternal; - typedef struct TfLiteRegistration { // Initializes the op from serialized data. // Called only *once* for the lifetime of the op, so any one-time allocations @@ -929,31 +903,8 @@ typedef struct TfLiteRegistration { // Note: It is the responsibility of the registration binder to set this // properly. int version; - - // The external version of `TfLiteRegistration`. Since we can't use internal - // types (such as `TfLiteContext`) for C API to maintain ABI stability. - // C API user will provide `TfLiteRegistrationExternal` to implement custom - // ops. We keep it inside of `TfLiteRegistration` and use it to route - // callbacks properly. - TfLiteRegistrationExternal* registration_external; } TfLiteRegistration; -// Old version of `TfLiteRegistration` to maintain binary backward -// compatibility. -// WARNING: This structure is deprecated / not an official part of the API. -// It should be only used for binary backward compatibility. -typedef struct TfLiteRegistration_V1 { - void* (*init)(TfLiteContext* context, const char* buffer, size_t length); - void (*free)(TfLiteContext* context, void* buffer); - TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node); - TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node); - const char* (*profiling_string)(const TfLiteContext* context, - const TfLiteNode* node); - int32_t builtin_code; - const char* custom_name; - int version; -} TfLiteRegistration_V1; - // The flags used in `TfLiteDelegate`. Note that this is a bitmask, so the // values should be 1, 2, 4, 8, ...etc. typedef enum TfLiteDelegateFlags { diff --git a/code/components/tflite-lib/tensorflow/lite/core/api/flatbuffer_conversions.cc b/code/components/tflite-lib/tensorflow/lite/core/api/flatbuffer_conversions.cc index 5175d903..e92d754f 100644 --- a/code/components/tflite-lib/tensorflow/lite/core/api/flatbuffer_conversions.cc +++ b/code/components/tflite-lib/tensorflow/lite/core/api/flatbuffer_conversions.cc @@ -836,16 +836,6 @@ TfLiteStatus ParseOpDataTfLite(const Operator* op, BuiltinOperator op_type, *builtin_data = params.release(); return kTfLiteOk; } - case BuiltinOperator_UNSORTED_SEGMENT_PROD: { - auto params = safe_allocator.Allocate(); - TF_LITE_ENSURE(error_reporter, params != nullptr); - if (const auto* unsorted_segment_prod_params = - op->builtin_options_as_UnsortedSegmentProdOptions()) { - params->num_segments = unsorted_segment_prod_params->num_segments(); - } - *builtin_data = params.release(); - return kTfLiteOk; - } // Below are the ops with no builtin_data structure. // TODO(aselle): Implement call in BuiltinOptions, but nullptrs are // ok for now, since there is no call implementation either. @@ -858,7 +848,6 @@ TfLiteStatus ParseOpDataTfLite(const Operator* op, BuiltinOperator op_type, case BuiltinOperator_MATRIX_DIAG: case BuiltinOperator_MATRIX_SET_DIAG: case BuiltinOperator_RELU_N1_TO_1: - case BuiltinOperator_RELU_0_TO_1: case BuiltinOperator_SELECT: case BuiltinOperator_SELECT_V2: case BuiltinOperator_SLICE: diff --git a/code/components/tflite-lib/tensorflow/lite/core/api/op_resolver.h b/code/components/tflite-lib/tensorflow/lite/core/api/op_resolver.h index cec1f2dd..49ac778e 100644 --- a/code/components/tflite-lib/tensorflow/lite/core/api/op_resolver.h +++ b/code/components/tflite-lib/tensorflow/lite/core/api/op_resolver.h @@ -23,16 +23,6 @@ limitations under the License. #include "tensorflow/lite/core/api/error_reporter.h" #include "tensorflow/lite/schema/schema_generated.h" -// Opaque type similar to TfLiteDelegate / TfLiteOpaqueDelegate. -// This is used for cases (e.g. when using "TF Lite with Google Play Services") -// where the TF Lite runtime might be built using a newer (or older) -// version of the TF Lite sources than the app, and hence might have a -// different definition of the TfLiteDelegate type. TF Lite APIs use -// TfLiteOpaqueDelegate rather than TfLiteDelegate when they want to -// refer to a delegate defined with that potentially different version -// of the TfLiteDelegate type. -struct TfLiteOpaqueDelegateStruct; - namespace tflite { /// Abstract interface that returns TfLiteRegistrations given op codes or custom @@ -47,10 +37,8 @@ class OpResolver { virtual const TfLiteRegistration* FindOp(const char* op, int version) const = 0; - // Represents a sequence of delegates. using TfLiteDelegatePtrVector = std::vector>; - // Returns optional delegates for resolving and handling ops in the flatbuffer // model. This may be used in addition to the standard TfLiteRegistration // lookup for graph resolution. @@ -59,55 +47,16 @@ class OpResolver { return {}; } - // Represents a function that creates a TfLite delegate instance. + // Represent a function that creates a TfLite delegate instance. using TfLiteDelegateCreator = std::function( int /*num_threads*/)>; - - // Represents a sequence of delegate creator functions. using TfLiteDelegateCreators = std::vector; - // Returns a vector of delegate creators to create optional delegates for // resolving and handling ops in the flatbuffer model. This may be used in // addition to the standard TfLiteRegistration lookup for graph resolution. - // - // Note that this method is not used (will not be called) if you are using - // TF Lite in Google Play Services; the GetOpaqueDelegateCreators method - // (see below) is used for that case. virtual TfLiteDelegateCreators GetDelegateCreators() const { return {}; } - // TODO(b/202712825): it would be nice if we could avoid the need for separate - // "opaque" types & methods for use only with TF Lite in Google Play Services. - - // Represents an opaque delegate instance. - // WARNING: Experimental interface, subject to change. - using TfLiteOpaqueDelegatePtr = - std::unique_ptr; - - // Represents a function that creates an opaque delegate instance. - // WARNING: Experimental interface, subject to change. - using TfLiteOpaqueDelegateCreator = - std::function; - - // Represents a sequence of opaque delegate creator functions. - // WARNING: Experimental interface, subject to change. - using TfLiteOpaqueDelegateCreators = std::vector; - - // Returns a vector of opaque delegate creators to create optional opaque - // delegates for resolving and handling ops in the flatbuffer model. This may - // be used in addition to the standard TfLiteRegistration lookup for graph - // resolution. - // - // Note that this method will be called only if you are using TF Lite in - // Google Play Services; if you are using regular TF Lite, GetDelegateCreators - // (see above) is used instead. - // - // WARNING: Experimental interface, subject to change. - virtual TfLiteOpaqueDelegateCreators GetOpaqueDelegateCreators() const { - return {}; - } - virtual ~OpResolver() {} private: diff --git a/code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/hard_swish.h b/code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/hard_swish.h index b1204cc5..cda1b5cf 100644 --- a/code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/hard_swish.h +++ b/code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/hard_swish.h @@ -23,9 +23,9 @@ namespace tflite { namespace reference_ops { inline int16_t SaturatingLeftShift(int16_t value, int amount) { - int64_t result = static_cast(value) * (1 << amount); - result = std::min(result, std::numeric_limits::max()); - result = std::max(result, std::numeric_limits::min()); + int32_t result = static_cast(value) * (1 << amount); + result = std::min(result, std::numeric_limits::max()); + result = std::max(result, std::numeric_limits::min()); return result; } diff --git a/code/components/tflite-lib/tensorflow/lite/kernels/internal/runtime_shape.h b/code/components/tflite-lib/tensorflow/lite/kernels/internal/runtime_shape.h index c2678b57..13693643 100644 --- a/code/components/tflite-lib/tensorflow/lite/kernels/internal/runtime_shape.h +++ b/code/components/tflite-lib/tensorflow/lite/kernels/internal/runtime_shape.h @@ -27,11 +27,6 @@ class RuntimeShape { public: RuntimeShape& operator=(RuntimeShape const&) = delete; - // RuntimeShape in TFLM supports up to 5 dimensions. - // The name kMaxSmallSize comes from the same file of the upstream - // tensorflow lite repo and need to be kept the same for max reuse. - static constexpr int kMaxSmallSize = 5; - RuntimeShape() : size_(0) {} explicit RuntimeShape(int dimensions_count) : size_(dimensions_count) {} @@ -109,9 +104,11 @@ class RuntimeShape { sizeof(int32_t) * shape.DimensionsCount()); } + // A maximum of 4 dimensions are supported on TFLM. + static constexpr int kMaxSize = 5; int32_t size_; union { - int32_t dims_[kMaxSmallSize]; + int32_t dims_[kMaxSize]; }; }; diff --git a/code/components/tflite-lib/tensorflow/lite/kernels/internal/types.h b/code/components/tflite-lib/tensorflow/lite/kernels/internal/types.h index c44ba48e..77644bc0 100644 --- a/code/components/tflite-lib/tensorflow/lite/kernels/internal/types.h +++ b/code/components/tflite-lib/tensorflow/lite/kernels/internal/types.h @@ -974,11 +974,11 @@ struct StridedSliceParams { int8_t strides_count; int32_t strides[5]; - uint16_t begin_mask; - uint16_t ellipsis_mask; - uint16_t end_mask; - uint16_t new_axis_mask; - uint16_t shrink_axis_mask; + int16_t begin_mask; + int16_t ellipsis_mask; + int16_t end_mask; + int16_t new_axis_mask; + int16_t shrink_axis_mask; }; struct TanhParams { diff --git a/code/components/tflite-lib/tensorflow/lite/kernels/kernel_util.h b/code/components/tflite-lib/tensorflow/lite/kernels/kernel_util.h index ed3a566f..22689436 100644 --- a/code/components/tflite-lib/tensorflow/lite/kernels/kernel_util.h +++ b/code/components/tflite-lib/tensorflow/lite/kernels/kernel_util.h @@ -308,7 +308,7 @@ TfLiteStatus CalculateShapeForBroadcast(TfLiteContext* context, const TfLiteTensor* input3, TfLiteIntArray** output_shape); -// Return the size of given type in bytes. Return 0 in case of string. +// Return the size of given type in bytes. Return 0 in in case of string. int TfLiteTypeGetSize(TfLiteType type); // Whether the current platform is mobile (Android or iOS). diff --git a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.cc b/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.cc deleted file mode 100644 index 0f75d286..00000000 --- a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.cc +++ /dev/null @@ -1,165 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.h" - -#include "tensorflow/lite/micro/memory_helpers.h" -#include "tensorflow/lite/micro/micro_error_reporter.h" - -namespace tflite { - -NonPersistentArenaBufferAllocator::NonPersistentArenaBufferAllocator( - uint8_t* buffer, size_t buffer_size) - : buffer_head_(buffer), - buffer_tail_(buffer + buffer_size), - head_temp_(buffer), - next_temp_(buffer) {} - -NonPersistentArenaBufferAllocator::~NonPersistentArenaBufferAllocator() {} - -// Allocates a temporary buffer. This buffer is not resizable. -uint8_t* NonPersistentArenaBufferAllocator::AllocateTemp(size_t size, - size_t alignment) { - uint8_t* const aligned_result = AlignPointerUp(next_temp_, alignment); - const size_t available_memory = buffer_tail_ - aligned_result; - if (available_memory < size) { - MicroPrintf( - "Failed to allocate temp memory. Requested: %u, " - "available %u, missing: %u", - size, available_memory, size - available_memory); - return nullptr; - } - next_temp_ = aligned_result + size; - temp_buffer_ptr_check_sum_ ^= reinterpret_cast(aligned_result); - temp_buffer_count_++; - return aligned_result; -} - -// Signals that a temporary buffer is no longer needed. -void NonPersistentArenaBufferAllocator::DeallocateTemp(uint8_t* temp_buf) { - temp_buffer_ptr_check_sum_ ^= reinterpret_cast(temp_buf); - temp_buffer_count_--; -} - -// Returns true if all temporary buffers are already deallocated. -bool NonPersistentArenaBufferAllocator::IsAllTempDeallocated() { - if (temp_buffer_count_ != 0 || temp_buffer_ptr_check_sum_ != 0) { - MicroPrintf( - "Number of allocated temp buffers: %d. Checksum passing status: %d", - temp_buffer_count_, !temp_buffer_ptr_check_sum_); - return false; - } - return true; -} - -// Signals that all temporary allocations can be reclaimed. TFLM calls this -// API when it knows that all temporary buffers that it requested has been -// deallocated. The goal of API is to facilitate implementations of -// INonPersistentBufferAllocator can reuse buffer with some reasonable -// complexity. -TfLiteStatus NonPersistentArenaBufferAllocator::ResetTempAllocations() { - if (!IsAllTempDeallocated()) { - MicroPrintf( - "All temp buffers must be freed before calling ResetTempAllocations()"); - return kTfLiteError; - } - next_temp_ = head_temp_; - return kTfLiteOk; -} - -// Returns a buffer that is resizable viable ResizeBuffer(). -uint8_t* NonPersistentArenaBufferAllocator::AllocateResizableBuffer( - size_t size, size_t alignment) { - // Only supports one resizable buffer, which starts at the buffer head. - uint8_t* expected_resizable_buf = AlignPointerUp(buffer_head_, alignment); - - if (head_temp_ != expected_resizable_buf) { - MicroPrintf( - "Cannot allocate a new resizable buffer when one is already allocated"); - return nullptr; - } - - if (ResizeBuffer(expected_resizable_buf, size, alignment) == kTfLiteOk) { - return expected_resizable_buf; - } - return nullptr; -} - -// Resizes a buffer that is previously returned by the AllocateResizableBuffer. -// Note that ResizeBuffer(old_resizable_buf, 0, 1) effectively deallocates -// a previous allocated resizable buffer. -TfLiteStatus NonPersistentArenaBufferAllocator::ResizeBuffer( - uint8_t* resizable_buf, size_t size, size_t alignment) { - // Only supports one resizable buffer, which starts at the buffer head. - uint8_t* expect_resizable_buf = AlignPointerUp(buffer_head_, alignment); - if (resizable_buf != expect_resizable_buf) { - MicroPrintf("Internal error: buffer is not resizable"); - return kTfLiteError; - } - if (head_temp_ != next_temp_) { - MicroPrintf("ResetTempAllocations() is not called before ResizeBuffer()."); - return kTfLiteError; - } - - const size_t available_memory = buffer_tail_ - expect_resizable_buf; - if (available_memory < size) { - MicroPrintf( - "Failed to resize buffer. Requested: %u, available %u, missing: %u", - size, available_memory, size - available_memory); - return kTfLiteError; - } - head_temp_ = expect_resizable_buf + size; - next_temp_ = head_temp_; - - return kTfLiteOk; -} - -// Frees up the memory occupied by the resizable buffer. -TfLiteStatus NonPersistentArenaBufferAllocator::DeallocateResizableBuffer( - uint8_t* resizable_buf) { - return ResizeBuffer(resizable_buf, 0, 1); -} - -// Returns a pointer pointing to the start of the overlay memory, which is -// used for activation tensors and scratch buffers by kernels at Invoke stage. -uint8_t* NonPersistentArenaBufferAllocator::GetOverlayMemoryAddress() const { - return buffer_head_; -} - -// Reserves the size of the overlay memory. This overlay is reserved for the -// kernels at Invoke stage. This is referred to as the overlay because before -// Invoket state, the same memory can be used for temp buffers. The layout of -// the memory is planned by the memory planner separately at Invoke stage. -TfLiteStatus -NonPersistentArenaBufferAllocator::ReserveNonPersistentOverlayMemory( - size_t size, size_t alignment) { - uint8_t* expect_resizable_buf = AlignPointerUp(buffer_head_, alignment); - return ResizeBuffer(expect_resizable_buf, size, alignment); -} - -// Returns the size of non-persistent buffer in use. -size_t NonPersistentArenaBufferAllocator::GetNonPersistentUsedBytes() const { - return (next_temp_ - buffer_head_); -} - -// Returns the number of bytes available with a given alignment. This number -// takes in account any temporary allocations. -size_t NonPersistentArenaBufferAllocator::GetAvailableMemory( - size_t alignment) const { - uint8_t* const aligned_temp = AlignPointerUp(next_temp_, alignment); - uint8_t* const aligned_tail = AlignPointerDown(buffer_tail_, alignment); - return aligned_tail - aligned_temp; -} - -} // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.h b/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.h deleted file mode 100644 index aad41d3f..00000000 --- a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.h +++ /dev/null @@ -1,104 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_NON_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ -#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_NON_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ - -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h" -#include "tensorflow/lite/micro/compatibility.h" - -namespace tflite { - -// Implement INonPersistentBufferAllocator on an arena that is dedicated for -// non-persistent buffers. -class NonPersistentArenaBufferAllocator : public INonPersistentBufferAllocator { - public: - NonPersistentArenaBufferAllocator(uint8_t* buffer, size_t buffer_size); - virtual ~NonPersistentArenaBufferAllocator(); - - // Allocates a temporary buffer. This buffer is not resizable. - uint8_t* AllocateTemp(size_t size, size_t alignment) override; - - // Signals that a temporary buffer is no longer needed. - void DeallocateTemp(uint8_t* buf) override; - - // Returns true if all temporary buffers are already deallocated. - bool IsAllTempDeallocated() override; - - // Signals that all temporary allocations can be reclaimed. TFLM calls this - // API when it knows that all temporary buffers that it requested has been - // deallocated. - TfLiteStatus ResetTempAllocations() override; - - // Returns a buffer that is resizable viable ResizeBuffer(). - uint8_t* AllocateResizableBuffer(size_t size, size_t alignment) override; - - // Resizes a buffer that is previously returned by the - // AllocateResizableBuffer. - TfLiteStatus ResizeBuffer(uint8_t* resizable_buf, size_t size, - size_t alignment) override; - - // Frees up the memory occupied by the resizable buffer. - TfLiteStatus DeallocateResizableBuffer(uint8_t* resizable_buf) override; - - // Returns a pointer pointing to the start of the overlay memory, which is - // used for activation tensors and scratch buffers by kernels at Invoke stage. - uint8_t* GetOverlayMemoryAddress() const override; - - // Reserves the size of the overlay memory. This overlay is reserved for the - // kernels at Invoke stage. This is referred to as the overlay because before - // Invoket state, the same memory can be used for temp buffers. The layout of - // the memory is planned by the memory planner separately at Invoke stage. - TfLiteStatus ReserveNonPersistentOverlayMemory(size_t size, - size_t alignment) override; - - // Returns the size of non-persistent buffer in use. - size_t GetNonPersistentUsedBytes() const override; - - // Returns the number of bytes available with a given alignment. This number - // takes in account any temporary allocations. - size_t GetAvailableMemory(size_t alignment) const override; - - TF_LITE_REMOVE_VIRTUAL_DELETE - - private: - // The memory arena that this allocator manages. - uint8_t* const buffer_head_; - uint8_t* const buffer_tail_; - - // The whole region is split into two parts: - // buffer_head_ to head_temp_ - 1 belongs to the only resizable buffer. - // head_temp_ to buffer_tail_ can be used for (non-resizable) temp buffers. - uint8_t* head_temp_; - - // next_temp_ points to the next available temp buffer allocation address and - // its range is between head_temp_ and buffer_tail_ - uint8_t* next_temp_; - - // XOR Check sum for outstanding temp buffers. - // If all temp buffers are deallocated OR no temp buffers are allocated, - // temp_buffer_ptr_check_sum_ == nullptr. - intptr_t temp_buffer_ptr_check_sum_ = 0; - // Count of outstanding temp buffers. - int temp_buffer_count_ = 0; -}; - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_NON_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ diff --git a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.cc b/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.cc deleted file mode 100644 index 0ccc8fb1..00000000 --- a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.cc +++ /dev/null @@ -1,52 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.h" - -#include "tensorflow/lite/micro/memory_helpers.h" -#include "tensorflow/lite/micro/micro_error_reporter.h" - -namespace tflite { - -PersistentArenaBufferAllocator::PersistentArenaBufferAllocator( - uint8_t* buffer, size_t buffer_size) - : buffer_head_(buffer), - buffer_tail_(buffer + buffer_size), - tail_temp_(buffer_tail_) {} - -PersistentArenaBufferAllocator::~PersistentArenaBufferAllocator() {} - -uint8_t* PersistentArenaBufferAllocator::AllocatePersistentBuffer( - size_t size, size_t alignment) { - uint8_t* const aligned_result = - AlignPointerDown(tail_temp_ - size, alignment); - if (aligned_result < buffer_head_) { -#ifndef TF_LITE_STRIP_ERROR_STRINGS - const size_t missing_memory = buffer_head_ - aligned_result; - MicroPrintf( - "Failed to allocate tail memory. Requested: %u, " - "available %u, missing: %u", - size, size - missing_memory, missing_memory); -#endif - return nullptr; - } - tail_temp_ = aligned_result; - return aligned_result; -} - -size_t PersistentArenaBufferAllocator::GetPersistentUsedBytes() const { - return buffer_tail_ - tail_temp_; -} - -} // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.h b/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.h deleted file mode 100644 index 10145d72..00000000 --- a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.h +++ /dev/null @@ -1,59 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ -#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ - -#include -#include - -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h" -#include "tensorflow/lite/micro/compatibility.h" - -namespace tflite { - -// PersistentArenaBufferAllocator is an implementatation of -// IPersistentBufferAllocator interface on an arena that is dedicated for -// persistent buffers. -class PersistentArenaBufferAllocator : public IPersistentBufferAllocator { - public: - PersistentArenaBufferAllocator(uint8_t* buffer, size_t buffer_size); - virtual ~PersistentArenaBufferAllocator(); - - // Allocates persistent memory. The persistent buffer is never freed. - // Returns nullptr if errors occured. - uint8_t* AllocatePersistentBuffer(size_t size, size_t alignment) override; - - // Returns the size of all persistent allocations in bytes. - size_t GetPersistentUsedBytes() const override; - - TF_LITE_REMOVE_VIRTUAL_DELETE - private: - // The memory arena that this allocator manages. - uint8_t* const buffer_head_; - uint8_t* const buffer_tail_; - - // The whole region is split into two parts: - // tail_temp_ to buffer_tail_ contains allocated buffers; - // buffer_head_ to tail_temp_ - 1 belongs to still available spaces. - // So in essence, the allocated region grows from the bottom and emulates - // SimpleMemoryAllocator's persistent part. - uint8_t* tail_temp_; -}; - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_PERSISTENT_ARENA_BUFFER_ALLOCATOR_H_ diff --git a/code/components/tflite-lib/tensorflow/lite/micro/fake_micro_context.cc b/code/components/tflite-lib/tensorflow/lite/micro/fake_micro_context.cc index 36dd062a..5a5ba9ab 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/fake_micro_context.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/fake_micro_context.cc @@ -16,10 +16,10 @@ limitations under the License. #include "tensorflow/lite/micro/fake_micro_context.h" #include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/micro/arena_allocator/simple_memory_allocator.h" #include "tensorflow/lite/micro/micro_allocator.h" #include "tensorflow/lite/micro/micro_arena_constants.h" #include "tensorflow/lite/micro/micro_error_reporter.h" +#include "tensorflow/lite/micro/simple_memory_allocator.h" namespace tflite { namespace { diff --git a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h b/code/components/tflite-lib/tensorflow/lite/micro/ibuffer_allocator.h similarity index 95% rename from code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h rename to code/components/tflite-lib/tensorflow/lite/micro/ibuffer_allocator.h index b92d6b2d..3767cb9f 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/ibuffer_allocator.h @@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_IBUFFER_ALLOCATOR_H_ -#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_IBUFFER_ALLOCATOR_H_ +#ifndef TENSORFLOW_LITE_MICRO_IBUFFER_ALLOCATOR_H_ +#define TENSORFLOW_LITE_MICRO_IBUFFER_ALLOCATOR_H_ #include #include @@ -97,4 +97,4 @@ class INonPersistentBufferAllocator { } // namespace tflite -#endif // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_IBUFFER_ALLOCATOR_H_ +#endif // TENSORFLOW_LITE_MICRO_IBUFFER_ALLOCATOR_H_ diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/activations.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/activations.cc index e0b79631..c556ac64 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/activations.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/activations.cc @@ -24,7 +24,6 @@ limitations under the License. #include "tensorflow/lite/kernels/kernel_util.h" #include "tensorflow/lite/kernels/op_macros.h" #include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/micro_error_reporter.h" #include "tensorflow/lite/micro/micro_utils.h" namespace tflite { @@ -61,8 +60,8 @@ TfLiteStatus ReluEval(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } default: { - MicroPrintf("Only float32 is supported currently, got %s", - TfLiteTypeGetName(input->type)); + TF_LITE_KERNEL_LOG(context, "Only float32 is supported currently, got %s", + TfLiteTypeGetName(input->type)); return kTfLiteError; } } @@ -100,8 +99,8 @@ TfLiteStatus Relu6Eval(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } default: { - MicroPrintf("Only float32 is supported currently, got %s", - TfLiteTypeGetName(input->type)); + TF_LITE_KERNEL_LOG(context, "Only float32 is supported currently, got %s", + TfLiteTypeGetName(input->type)); return kTfLiteError; } } @@ -110,11 +109,25 @@ TfLiteStatus Relu6Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_RELU() { - return tflite::micro::RegisterOp(ReluInit, ReluPrepare, ReluEval); + return {/*init=*/ReluInit, + /*free=*/nullptr, + /*prepare=*/ReluPrepare, + /*invoke=*/ReluEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_RELU6() { - return tflite::micro::RegisterOp(Relu6Init, Relu6Prepare, Relu6Eval); + return {/*init=*/Relu6Init, + /*free=*/nullptr, + /*prepare=*/Relu6Prepare, + /*invoke=*/Relu6Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/add.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/add.cc index f75db4e5..75523d14 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/add.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/add.cc @@ -159,7 +159,14 @@ TfLiteStatus AddEval(TfLiteContext* context, TfLiteNode* node) { } TfLiteRegistration Register_ADD() { - return tflite::micro::RegisterOp(AddInit, AddPrepare, AddEval); + return {/*init=*/AddInit, + /*free=*/nullptr, + /*prepare=*/AddPrepare, + /*invoke=*/AddEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/add_n.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/add_n.cc index ce064687..5d0ab724 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/add_n.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/add_n.cc @@ -208,7 +208,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_ADD_N() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/arg_min_max.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/arg_min_max.cc index a8aa5a48..8217a4a0 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/arg_min_max.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/arg_min_max.cc @@ -104,11 +104,25 @@ TfLiteStatus ArgMaxEval(TfLiteContext* context, TfLiteNode* node) { } // namespace arg_min_max TfLiteRegistration Register_ARG_MAX() { - return tflite::micro::RegisterOp(nullptr, nullptr, arg_min_max::ArgMaxEval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/nullptr, + /*invoke=*/arg_min_max::ArgMaxEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_ARG_MIN() { - return tflite::micro::RegisterOp(nullptr, nullptr, arg_min_max::ArgMinEval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/nullptr, + /*invoke=*/arg_min_max::ArgMinEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/assign_variable.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/assign_variable.cc index a770d0aa..e28ebebb 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/assign_variable.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/assign_variable.cc @@ -95,7 +95,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace. TfLiteRegistration Register_ASSIGN_VARIABLE() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/batch_to_space_nd.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/batch_to_space_nd.cc index be82d942..07b680df 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/batch_to_space_nd.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/batch_to_space_nd.cc @@ -105,7 +105,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace. TfLiteRegistration Register_BATCH_TO_SPACE_ND() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/broadcast_args.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/broadcast_args.cc index be2672ec..fa333249 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/broadcast_args.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/broadcast_args.cc @@ -84,8 +84,14 @@ TfLiteStatus BroadcastArgsEval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_BROADCAST_ARGS() { - return tflite::micro::RegisterOp(nullptr, BroadcastArgsPrepare, - BroadcastArgsEval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/BroadcastArgsPrepare, + /*invoke=*/BroadcastArgsEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } -} // namespace tflite +} // namespace tflite \ No newline at end of file diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/broadcast_to.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/broadcast_to.cc index 63a14db2..5302faf1 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/broadcast_to.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/broadcast_to.cc @@ -116,8 +116,14 @@ TfLiteStatus BroadcastToEval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_BROADCAST_TO() { - return tflite::micro::RegisterOp(nullptr, BroadcastToPrepare, - BroadcastToEval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/BroadcastToPrepare, + /*invoke=*/BroadcastToEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } -} // namespace tflite +} // namespace tflite \ No newline at end of file diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/call_once.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/call_once.cc index 200242b2..4db39f7d 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/call_once.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/call_once.cc @@ -82,7 +82,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace. TfLiteRegistration Register_CALL_ONCE() { - return tflite::micro::RegisterOp(Init, Prepare, Eval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/cast.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/cast.cc index a1f4516b..dc651a24 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/cast.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/cast.cc @@ -108,7 +108,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_CAST() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/ceil.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/ceil.cc index a390a735..d0a48f91 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/ceil.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/ceil.cc @@ -67,7 +67,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace ceil TfLiteRegistration Register_CEIL() { - return tflite::micro::RegisterOp(nullptr, ceil::Prepare, ceil::Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/ceil::Prepare, + /*invoke=*/ceil::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/circular_buffer.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/circular_buffer.cc index a66a61c5..bda3e66a 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/circular_buffer.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/circular_buffer.cc @@ -108,7 +108,14 @@ TfLiteStatus CircularBufferEval(TfLiteContext* context, TfLiteNode* node) { } TfLiteRegistration* Register_CIRCULAR_BUFFER() { - static TfLiteRegistration r = tflite::micro::RegisterOp(CircularBufferInit, CircularBufferPrepare, CircularBufferEval); + static TfLiteRegistration r = {/*init=*/CircularBufferInit, + /*free=*/nullptr, + /*prepare=*/CircularBufferPrepare, + /*invoke=*/CircularBufferEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; return &r; } diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/comparisons.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/comparisons.cc index cff15e4d..925c3fb5 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/comparisons.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/comparisons.cc @@ -583,33 +583,69 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { } // namespace comparisons TfLiteRegistration Register_EQUAL() { - return tflite::micro::RegisterOp(comparisons::Init, comparisons::Prepare, - comparisons::EqualEval); + return {/*init=*/comparisons::Init, + /*free=*/nullptr, + /*prepare=*/comparisons::Prepare, + /*invoke=*/comparisons::EqualEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_NOT_EQUAL() { - return tflite::micro::RegisterOp(comparisons::Init, comparisons::Prepare, - comparisons::NotEqualEval); + return {/*init=*/comparisons::Init, + /*free=*/nullptr, + /*prepare=*/comparisons::Prepare, + /*invoke=*/comparisons::NotEqualEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_GREATER() { - return tflite::micro::RegisterOp(comparisons::Init, comparisons::Prepare, - comparisons::GreaterEval); + return {/*init=*/comparisons::Init, + /*free=*/nullptr, + /*prepare=*/comparisons::Prepare, + /*invoke=*/comparisons::GreaterEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_GREATER_EQUAL() { - return tflite::micro::RegisterOp(comparisons::Init, comparisons::Prepare, - comparisons::GreaterEqualEval); + return {/*init=*/comparisons::Init, + /*free=*/nullptr, + /*prepare=*/comparisons::Prepare, + /*invoke=*/comparisons::GreaterEqualEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_LESS() { - return tflite::micro::RegisterOp(comparisons::Init, comparisons::Prepare, - comparisons::LessEval); + return {/*init=*/comparisons::Init, + /*free=*/nullptr, + /*prepare=*/comparisons::Prepare, + /*invoke=*/comparisons::LessEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_LESS_EQUAL() { - return tflite::micro::RegisterOp(comparisons::Init, comparisons::Prepare, - comparisons::LessEqualEval); + return {/*init=*/comparisons::Init, + /*free=*/nullptr, + /*prepare=*/comparisons::Prepare, + /*invoke=*/comparisons::LessEqualEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/concatenation.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/concatenation.cc index 34622c22..d727a0d5 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/concatenation.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/concatenation.cc @@ -148,12 +148,12 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE(context, input != nullptr); int num_dimensions = NumDimensions(input); - if (num_dimensions > RuntimeShape::kMaxSmallSize) { + if (num_dimensions > 4) { TF_LITE_KERNEL_LOG( context, - "Op Concatenation does not currently support num dimensions > %d " + "Op Concatenation does not currently support num dimensions >4 " "Tensor has %d dimensions.", - RuntimeShape::kMaxSmallSize, num_dimensions); + num_dimensions); return kTfLiteError; } micro_context->DeallocateTempTfLiteTensor(input); @@ -252,8 +252,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace concatenation TfLiteRegistration Register_CONCATENATION() { - return tflite::micro::RegisterOp(concatenation::Init, concatenation::Prepare, - concatenation::Eval); + return {/*init=*/concatenation::Init, + /*free=*/nullptr, + /*prepare=*/concatenation::Prepare, + /*invoke=*/concatenation::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/conv.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/conv.cc index 87ea92e6..0fed1223 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/conv.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/conv.cc @@ -25,7 +25,6 @@ limitations under the License. #include "tensorflow/lite/kernels/kernel_util.h" #include "tensorflow/lite/kernels/padding.h" #include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/micro_error_reporter.h" namespace tflite { namespace { @@ -68,47 +67,23 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { tflite::micro::GetTensorShape(filter), tflite::micro::GetTensorData(filter), tflite::micro::GetTensorShape(bias), - tflite::micro::GetOptionalTensorData(bias), + tflite::micro::GetTensorData(bias), tflite::micro::GetTensorShape(output), tflite::micro::GetTensorData(output), tflite::micro::GetTensorShape(nullptr), nullptr); break; } case kTfLiteInt16: { - switch (bias->type) { - case kTfLiteInt32: { - reference_integer_ops::ConvPerChannel( - ConvParamsQuantized(params, data), - data.per_channel_output_multiplier, data.per_channel_output_shift, - tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetOptionalTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - break; - } - case kTfLiteInt64: { - reference_integer_ops::ConvPerChannel( - ConvParamsQuantized(params, data), - data.per_channel_output_multiplier, data.per_channel_output_shift, - tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), - tflite::micro::GetOptionalTensorData(bias), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - break; - } - default: - MicroPrintf("Bias type %s (%d) not supported.", - TfLiteTypeGetName(bias->type), bias->type); - return kTfLiteError; - } + reference_integer_ops::ConvPerChannel( + ConvParamsQuantized(params, data), data.per_channel_output_multiplier, + data.per_channel_output_shift, tflite::micro::GetTensorShape(input), + tflite::micro::GetTensorData(input), + tflite::micro::GetTensorShape(filter), + tflite::micro::GetTensorData(filter), + tflite::micro::GetTensorShape(bias), + tflite::micro::GetTensorData(bias), + tflite::micro::GetTensorShape(output), + tflite::micro::GetTensorData(output)); break; } case kTfLiteInt8: { @@ -119,14 +94,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { tflite::micro::GetTensorShape(filter), tflite::micro::GetTensorData(filter), tflite::micro::GetTensorShape(bias), - tflite::micro::GetOptionalTensorData(bias), + tflite::micro::GetTensorData(bias), tflite::micro::GetTensorShape(output), tflite::micro::GetTensorData(output)); break; } default: - MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type), - input->type); + TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", + TfLiteTypeGetName(input->type), input->type); return kTfLiteError; } return kTfLiteOk; @@ -135,7 +110,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_CONV_2D() { - return tflite::micro::RegisterOp(Init, ConvPrepare, Eval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/ConvPrepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/conv_test.h b/code/components/tflite-lib/tensorflow/lite/micro/kernels/conv_test.h index 47ba8ac4..38b69525 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/conv_test.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/conv_test.h @@ -97,16 +97,6 @@ TfLiteStatus TestConvQuantizedPerChannel( float output_scale, int output_zero_point, TfLiteConvParams* conv_params, TfLiteRegistration registration, int16_t* output_data); -TfLiteStatus TestConvQuantizedPerChannel( - int* input_dims_data, const float* input_data, int16_t* input_quantized, - float input_scale, int input_zero_point, int* filter_dims_data, - const float* filter_data, int8_t* filter_data_quantized, - int* bias_dims_data, const float* bias_data, int32_t* bias_data_quantized, - float* bias_scales, int* bias_zero_points, int* output_dims_data, - const float* expected_output_data, int16_t* expected_output_data_quantized, - float output_scale, int output_zero_point, TfLiteConvParams* conv_params, - TfLiteRegistration registration, int16_t* output_data); - } // namespace testing } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/cumsum.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/cumsum.cc index eedc61fd..61f7af23 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/cumsum.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/cumsum.cc @@ -169,7 +169,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_CUMSUM() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/depth_to_space.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/depth_to_space.cc index ec000540..cce93c9c 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/depth_to_space.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/depth_to_space.cc @@ -136,7 +136,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_DEPTH_TO_SPACE() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/depthwise_conv.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/depthwise_conv.cc index d2468ff9..8a58433a 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/depthwise_conv.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/depthwise_conv.cc @@ -62,7 +62,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { tflite::micro::GetTensorShape(filter), tflite::micro::GetTensorData(filter), tflite::micro::GetTensorShape(bias), - tflite::micro::GetOptionalTensorData(bias), + tflite::micro::GetTensorData(bias), tflite::micro::GetTensorShape(output), tflite::micro::GetTensorData(output)); break; @@ -76,7 +76,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { tflite::micro::GetTensorShape(filter), tflite::micro::GetTensorData(filter), tflite::micro::GetTensorShape(bias), - tflite::micro::GetOptionalTensorData(bias), + tflite::micro::GetTensorData(bias), tflite::micro::GetTensorShape(output), tflite::micro::GetTensorData(output)); break; @@ -92,7 +92,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_DEPTHWISE_CONV_2D() { - return tflite::micro::RegisterOp(Init, DepthwiseConvPrepare, Eval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/DepthwiseConvPrepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/depthwise_conv.h b/code/components/tflite-lib/tensorflow/lite/micro/kernels/depthwise_conv.h index 562438d7..7a7eb0ba 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/depthwise_conv.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/depthwise_conv.h @@ -1,4 +1,4 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -49,32 +49,6 @@ TfLiteStatus CalculateOpDataDepthwiseConv( TfLiteStatus DepthwiseConvPrepare(TfLiteContext* context, TfLiteNode* node); -// This is the most generic TfLiteRegistration. The actual supported types may -// still be target dependent. The only requirement is that every implementation -// (reference or optimized) must define this function. -TfLiteRegistration Register_DEPTHWISE_CONV_2D(); - -#if defined(CMSIS_NN) -// Returns a TfLiteRegistration struct for kernel variant that only supports -// int8 activations and int8 weights and uses the latency optimized -// implementations. -TfLiteRegistration Register_DEPTHWISE_CONV_2D_INT8(); - -// Returns a TfLiteRegistration struct for kernel variant that only supports -// int16 activations and int8 weights and uses the latency optimized -// implementations. -TfLiteRegistration Register_DEPTHWISE_CONV_2D_INT16(); - -#else -inline TfLiteRegistration Register_DEPTHWISE_CONV_2D_INT8() { - return Register_DEPTHWISE_CONV_2D(); -} - -inline TfLiteRegistration Register_DEPTHWISE_CONV_2D_INT16() { - return Register_DEPTHWISE_CONV_2D(); -} -#endif - } // namespace tflite #endif // TENSORFLOW_LITE_MICRO_KERNELS_DEPTHWISE_CONV_H_ diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/dequantize.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/dequantize.cc index 1cf7f133..4438ea33 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/dequantize.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/dequantize.cc @@ -57,13 +57,6 @@ TfLiteStatus DequantizeEval(TfLiteContext* context, TfLiteNode* node) { tflite::micro::GetTensorShape(output), tflite::micro::GetTensorData(output)); break; - case kTfLiteUInt8: - reference_ops::Dequantize(data->quantization_params, - tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - break; default: MicroPrintf("Input %s, output %s not supported.", TfLiteTypeGetName(input->type), @@ -81,8 +74,14 @@ TfLiteStatus DequantizeEval(TfLiteContext* context, TfLiteNode* node) { } TfLiteRegistration Register_DEQUANTIZE() { - return tflite::micro::RegisterOp(DequantizeInit, DequantizePrepare, - DequantizeEval); + return {/*init=*/DequantizeInit, + /*free=*/nullptr, + /*prepare=*/DequantizePrepare, + /*invoke=*/DequantizeEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/dequantize_common.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/dequantize_common.cc index 438f9cda..4be5ad89 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/dequantize_common.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/dequantize_common.cc @@ -41,9 +41,8 @@ TfLiteStatus DequantizePrepare(TfLiteContext* context, TfLiteNode* node) { TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0); TF_LITE_ENSURE(context, output != nullptr); - TF_LITE_ENSURE(context, input->type == kTfLiteInt8 || - input->type == kTfLiteInt16 || - input->type == kTfLiteUInt8); + TF_LITE_ENSURE(context, + input->type == kTfLiteInt8 || input->type == kTfLiteInt16); TF_LITE_ENSURE(context, output->type == kTfLiteFloat32); if (output->type == kTfLiteInt32) { diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/detection_postprocess.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/detection_postprocess.cc index 326d87b5..efe57e2f 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/detection_postprocess.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/detection_postprocess.cc @@ -149,6 +149,8 @@ void* Init(TfLiteContext* context, const char* buffer, size_t length) { return op_data; } +void Free(TfLiteContext* context, void* buffer) {} + TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { auto* op_data = static_cast(node->user_data); @@ -800,7 +802,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration* Register_DETECTION_POSTPROCESS() { - static TfLiteRegistration r = tflite::micro::RegisterOp(Init, Prepare, Eval); + static TfLiteRegistration r = {/*init=*/Init, + /*free=*/Free, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; return &r; } diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/elementwise.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/elementwise.cc index b1cb1dcb..366dd610 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/elementwise.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/elementwise.cc @@ -1,4 +1,4 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -16,8 +16,6 @@ limitations under the License. #include #include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" #include "tensorflow/lite/kernels/internal/tensor_ctypes.h" #include "tensorflow/lite/kernels/kernel_util.h" #include "tensorflow/lite/micro/kernels/kernel_util.h" @@ -29,22 +27,6 @@ namespace micro { namespace elementwise { namespace { -constexpr int kAbsNameId = 0; -constexpr int kRsrqtNameId = 1; - -const int kElementwiseInputTensor = 0; -const int kElementwiseOutputTensor = 0; - -struct OpDataAbsRsqrt { - int32_t multiplier; - int shift; - int input_offset; - int output_offset; - bool needs_rescale; - TfLiteQuantizationType input_quantization_type; - TfLiteType input_type; -}; - bool IsNumericSupportedType(const TfLiteType type) { return type == kTfLiteFloat32; } @@ -53,57 +35,11 @@ bool IsLogicalSupportedType(const TfLiteType type) { return type == kTfLiteBool; } -bool IsAbsSupportedType(const TfLiteType type) { - return type == kTfLiteFloat32 || type == kTfLiteInt8 || type == kTfLiteInt16; -} - -bool IsRsqrtSupportedType(const TfLiteType type) { - return type == kTfLiteFloat32 || type == kTfLiteInt8; -} - -inline void SetAbsOutputMultiplier(const float input_scale, - const float output_scale, - int32_t* multiplier, int* shift) { - QuantizeMultiplier(static_cast(input_scale / output_scale), - multiplier, shift); -} - -inline void SetRsqrtOutputMultiplier(const float input_scale, - const float output_scale, - int32_t* multiplier, int* shift) { - const double scale = - 1. / static_cast((std::sqrt(input_scale) * output_scale)); - QuantizeMultiplier(scale, multiplier, shift); -} - typedef bool (*IsSupportedType)(TfLiteType); template TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteNode* node) { MicroContext* micro_context = GetMicroContext(context); - TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - TfLiteTensor* input = - micro_context->AllocateTempInputTensor(node, kElementwiseInputTensor); - TF_LITE_ENSURE(context, input != nullptr); - TfLiteTensor* output = - micro_context->AllocateTempOutputTensor(node, kElementwiseOutputTensor); - TF_LITE_ENSURE(context, output != nullptr); - TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type); - if (!IsSupportedType(input->type)) { - TF_LITE_KERNEL_LOG(context, "Input data type %s (%d) is not supported.", - TfLiteTypeGetName(input->type), input->type); - return kTfLiteError; - } - micro_context->DeallocateTempTfLiteTensor(input); - micro_context->DeallocateTempTfLiteTensor(output); - return kTfLiteOk; -} - -typedef bool (*IsSupportedType)(TfLiteType); -template -TfLiteStatus PrepareAbsRsqrt(TfLiteContext* context, TfLiteNode* node) { - MicroContext* micro_context = GetMicroContext(context); TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, 0); @@ -117,87 +53,14 @@ TfLiteStatus PrepareAbsRsqrt(TfLiteContext* context, TfLiteNode* node) { return kTfLiteError; } - auto* op_data = static_cast(node->user_data); - op_data->input_type = input->type; - - // For int16 type input, we support both quantized and non-quantized - // evaluation. - if (op_nameid == kAbsNameId) { - op_data->input_quantization_type = input->quantization.type; - } - - if (input->type == kTfLiteInt8 || - (input->type == kTfLiteInt16 && - input->quantization.type != kTfLiteNoQuantization)) { - TF_LITE_ENSURE_EQ(context, input->quantization.type, - kTfLiteAffineQuantization); - TF_LITE_ENSURE_EQ(context, output->quantization.type, - kTfLiteAffineQuantization); - const auto* input_params = - reinterpret_cast(input->quantization.params); - const auto* output_params = reinterpret_cast( - output->quantization.params); - TF_LITE_ENSURE(context, input_params != nullptr); - TF_LITE_ENSURE(context, input_params->scale != nullptr); - TF_LITE_ENSURE(context, input_params->scale->size > 0); - TF_LITE_ENSURE(context, input_params->zero_point->size > 0); - TF_LITE_ENSURE(context, output_params != nullptr); - TF_LITE_ENSURE(context, output_params->scale != nullptr); - TF_LITE_ENSURE(context, output_params->scale->size > 0); - TF_LITE_ENSURE(context, output_params->zero_point->size > 0); - op_data->input_offset = input_params->zero_point->data[0]; - op_data->output_offset = output_params->zero_point->data[0]; - if (input->type == kTfLiteInt16) { - TF_LITE_ENSURE_EQ(context, op_data->input_offset, 0); - TF_LITE_ENSURE_EQ(context, op_data->output_offset, 0); - } - const float input_scale = input_params->scale->data[0]; - const float output_scale = output_params->scale->data[0]; - op_data->needs_rescale = input_scale != output_scale; - if (op_nameid == kAbsNameId && op_data->needs_rescale) { - SetAbsOutputMultiplier(input_scale, output_scale, &op_data->multiplier, - &op_data->shift); - } else if (op_nameid == kRsrqtNameId) { - SetRsqrtOutputMultiplier(input_scale, output_scale, &op_data->multiplier, - &op_data->shift); - } - } micro_context->DeallocateTempTfLiteTensor(input); micro_context->DeallocateTempTfLiteTensor(output); return kTfLiteOk; } -template -inline TfLiteStatus EvalImplQuantized( - TfLiteContext* context, TfLiteNode* node, - T func(TfLiteContext*, TfLiteNode*, T), - TfLiteStatus validate_input_func(TfLiteContext*, TfLiteNode*, T), - TfLiteType expected_type) { - const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0); - TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0); - TF_LITE_ENSURE_TYPES_EQ(context, input->type, expected_type); - const size_t num_elements = ElementCount(*input->dims); - const T* in_data = tflite::micro::GetTensorData(input); - T* out_data = tflite::micro::GetTensorData(output); - for (size_t i = 0; i < num_elements; ++i) { - if (validate_input_func) { - TF_LITE_ENSURE_OK(context, - validate_input_func(context, node, in_data[i])); - } - out_data[i] = func(context, node, in_data[i]); - } - return kTfLiteOk; -} - -template -inline T AbsHelper(T i) { - return std::abs(i); -} - template inline TfLiteStatus EvalImpl(TfLiteContext* context, TfLiteNode* node, - T func(T), TfLiteStatus validate_input_func(T), - TfLiteType expected_type) { + T func(T), TfLiteType expected_type) { const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0); TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0); TF_LITE_ENSURE_TYPES_EQ(context, input->type, expected_type); @@ -205,9 +68,6 @@ inline TfLiteStatus EvalImpl(TfLiteContext* context, TfLiteNode* node, const T* in_data = tflite::micro::GetTensorData(input); T* out_data = tflite::micro::GetTensorData(output); for (size_t i = 0; i < num_elements; ++i) { - if (validate_input_func) { - TF_LITE_ENSURE_OK(context, validate_input_func(in_data[i])); - } out_data[i] = func(in_data[i]); } return kTfLiteOk; @@ -215,114 +75,16 @@ inline TfLiteStatus EvalImpl(TfLiteContext* context, TfLiteNode* node, inline TfLiteStatus EvalNumeric(TfLiteContext* context, TfLiteNode* node, float float_func(float)) { - return EvalImpl(context, node, float_func, - /*validate_input_func=*/nullptr, kTfLiteFloat32); + return EvalImpl(context, node, float_func, kTfLiteFloat32); } inline TfLiteStatus EvalLogical(TfLiteContext* context, TfLiteNode* node, - bool bool_func(bool)) { - return EvalImpl(context, node, bool_func, - /*validate_input_func=*/nullptr, kTfLiteBool); -} - -void* ElementWiseAbsRsqrtInit(TfLiteContext* context, const char* buffer, - size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpDataAbsRsqrt)); -} - -template -inline T AbsEvalQuantized(TfLiteContext* context, TfLiteNode* node, T i) { - const auto* op_data = static_cast(node->user_data); - const int kMin = std::numeric_limits::min(); - const int kMax = std::numeric_limits::max(); - - const int32_t value = std::abs(i - op_data->input_offset); - if (!op_data->needs_rescale) { - return static_cast( - std::min(std::max(static_cast(value + op_data->output_offset), - static_cast(kMin)), - static_cast(kMax))); - } - - const int32_t output = tflite::MultiplyByQuantizedMultiplier( - value, op_data->multiplier, op_data->shift) + - op_data->output_offset; - return static_cast(std::min( - std::max(static_cast(output), static_cast(kMin)), - static_cast(kMax))); -} - -template -inline T RsqrtEvalQuantized(TfLiteContext* context, TfLiteNode* node, T i) { - const auto* op_data = static_cast(node->user_data); - const int kMin = std::numeric_limits::min(); - const int kMax = std::numeric_limits::max(); - - const int32_t value = (i - op_data->input_offset); - const int32_t kShift = 20; // Shift to keep value integer. - if (value == 0) { - // Assume that any value close to 0 represents the max output value. - return static_cast(kMax); - } - int32_t inv_sqrt_multiplier; - int inv_sqrt_shift; - GetInvSqrtQuantizedMultiplierExp(value, kReverseShift, &inv_sqrt_multiplier, - &inv_sqrt_shift); - const int32_t data = tflite::MultiplyByQuantizedMultiplier( - static_cast(1), inv_sqrt_multiplier, inv_sqrt_shift + kShift); - const int32_t output = - tflite::MultiplyByQuantizedMultiplier(data, op_data->multiplier, - op_data->shift - kShift) + - op_data->output_offset; - return static_cast(std::min( - std::max(static_cast(output), static_cast(kMin)), - static_cast(kMax))); -} - -template -TfLiteStatus validate_input_func(TfLiteContext* context, TfLiteNode* node, - T i) { - const auto* op_data = static_cast(node->user_data); - - TF_LITE_ENSURE_MSG(context, i >= op_data->input_offset, - "Rsqrt is only defined for positive values"); - return static_cast(kTfLiteOk); + return EvalImpl(context, node, bool_func, kTfLiteBool); } TfLiteStatus AbsEval(TfLiteContext* context, TfLiteNode* node) { - OpDataAbsRsqrt* op_data = reinterpret_cast(node->user_data); - TfLiteType type = op_data->input_type; - TfLiteQuantizationType input_quantization_type = - op_data->input_quantization_type; - TfLiteStatus eval_result; - - switch (type) { - case kTfLiteFloat32: - eval_result = EvalNumeric(context, node, std::abs); - break; - case kTfLiteInt8: - eval_result = - EvalImplQuantized(context, node, AbsEvalQuantized, - /*validate_input_func=*/nullptr, type); - break; - case kTfLiteInt16: - eval_result = - input_quantization_type == kTfLiteNoQuantization - ? EvalImpl(context, node, AbsHelper, - /*validate_input_func=*/nullptr, type) - : EvalImplQuantized(context, node, AbsEvalQuantized, - /*validate_input_func=*/nullptr, - type); - break; - default: - TF_LITE_KERNEL_LOG(context, "Current data type %s is not supported.", - TfLiteTypeGetName(type)); - return kTfLiteError; - break; - } - return eval_result; + return EvalNumeric(context, node, std::abs); } TfLiteStatus SinEval(TfLiteContext* context, TfLiteNode* node) { @@ -342,23 +104,7 @@ TfLiteStatus SqrtEval(TfLiteContext* context, TfLiteNode* node) { } TfLiteStatus RsqrtEval(TfLiteContext* context, TfLiteNode* node) { - const auto* op_data = static_cast(node->user_data); - TfLiteType type = op_data->input_type; - switch (type) { - case kTfLiteFloat32: - return EvalImpl( - context, node, [](float f) { return 1.f / std::sqrt(f); }, - /*validate_input_func=*/nullptr, type); - case kTfLiteInt8: - return EvalImplQuantized(context, node, - elementwise::RsqrtEvalQuantized, - elementwise::validate_input_func, type); - - default: - TF_LITE_KERNEL_LOG(context, "Current data type %s is not supported.", - TfLiteTypeGetName(type)); - return kTfLiteError; - } + return EvalNumeric(context, node, [](float f) { return 1.f / std::sqrt(f); }); } TfLiteStatus SquareEval(TfLiteContext* context, TfLiteNode* node) { @@ -373,57 +119,101 @@ TfLiteStatus LogicalNotEval(TfLiteContext* context, TfLiteNode* node) { } // namespace elementwise TfLiteRegistration Register_ABS() { - return tflite::micro::RegisterOp( - elementwise::ElementWiseAbsRsqrtInit, - elementwise::PrepareAbsRsqrt, - elementwise::AbsEval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/ + elementwise::GenericPrepare, + /*invoke=*/elementwise::AbsEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_SIN() { - return tflite::micro::RegisterOp( - nullptr, elementwise::GenericPrepare, - elementwise::SinEval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/ + elementwise::GenericPrepare, + /*invoke=*/elementwise::SinEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_COS() { - return tflite::micro::RegisterOp( - nullptr, elementwise::GenericPrepare, - elementwise::CosEval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/ + elementwise::GenericPrepare, + /*invoke=*/elementwise::CosEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_LOG() { - return tflite::micro::RegisterOp( - nullptr, elementwise::GenericPrepare, - elementwise::LogEval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/ + elementwise::GenericPrepare, + /*invoke=*/elementwise::LogEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_SQRT() { - return tflite::micro::RegisterOp( - nullptr, elementwise::GenericPrepare, - elementwise::SqrtEval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/ + elementwise::GenericPrepare, + /*invoke=*/elementwise::SqrtEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_RSQRT() { - return tflite::micro::RegisterOp( - elementwise::ElementWiseAbsRsqrtInit, - elementwise::PrepareAbsRsqrt, - elementwise::RsqrtEval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/ + elementwise::GenericPrepare, + /*invoke=*/elementwise::RsqrtEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_SQUARE() { - return tflite::micro::RegisterOp( - nullptr, elementwise::GenericPrepare, - elementwise::SquareEval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/ + elementwise::GenericPrepare, + /*invoke=*/elementwise::SquareEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_LOGICAL_NOT() { - return tflite::micro::RegisterOp( - nullptr, elementwise::GenericPrepare, - elementwise::LogicalNotEval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/ + elementwise::GenericPrepare, + /*invoke=*/elementwise::LogicalNotEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro } // namespace ops -} // namespace tflite \ No newline at end of file +} // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/elu.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/elu.cc index 0b64e89d..b2cd19cc 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/elu.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/elu.cc @@ -146,7 +146,14 @@ TfLiteStatus EluEval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_ELU() { - return tflite::micro::RegisterOp(EluInit, EluPrepare, EluEval); + return {/*init=*/EluInit, + /*free=*/nullptr, + /*prepare=*/EluPrepare, + /*invoke=*/EluEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/add.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/add.cc index 2f1ac58d..47a17d9f 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/add.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/add.cc @@ -196,7 +196,14 @@ TfLiteStatus AddEval(TfLiteContext* context, TfLiteNode* node) { } TfLiteRegistration Register_ADD() { - return tflite::micro::RegisterOp(AddInit, AddPrepare, AddEval); + return {/*init=*/AddInit, + /*free=*/nullptr, + /*prepare=*/AddPrepare, + /*invoke=*/AddEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/conv.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/conv.cc index 919dd006..09260482 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/conv.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/conv.cc @@ -112,24 +112,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { #if ESP_NN if (input->type == kTfLiteInt8) { - data_dims_t input_dims = { - .width = input_width, .height = input_height, - .channels = input->dims->data[3], 1 - }; - data_dims_t output_dims = { - .width = output_width, .height = output_height, - .channels = output->dims->data[3], 1 - }; - data_dims_t filter_dims = {.width = filter_width, .height = filter_height, 0, 0}; - conv_params_t conv_params = { - .in_offset = 0, .out_offset = 0, - .stride = {params.stride_width, params.stride_height}, - .padding = {data->op_data.padding.width, data->op_data.padding.height}, - .dilation = {0, 0}, .activation = {-128, 127} - }; - int scratch_buf_size = esp_nn_get_conv_scratch_size( - &input_dims, &filter_dims, &output_dims, &conv_params); + input_width, input_height, input->dims->data[3], + output->dims->data[3], filter_width, filter_height); if (scratch_buf_size > 0) { TF_LITE_ENSURE_STATUS(context->RequestScratchBufferInArena( context, scratch_buf_size, &data->buffer_idx)); @@ -206,33 +191,18 @@ inline void EvalQuantizedPerChannel( const int input_size = input_width * input_height * input_depth; const int output_size = output_width * output_height * output_depth; - data_dims_t input_dims = { - .width = input_width, .height = input_height, - .channels = input_depth, 1 - }; - data_dims_t output_dims = { - .width = output_width, .height = output_height, - .channels = output_depth, 1 - }; - data_dims_t filter_dims = {.width = filter_width, .height = filter_height, 0, 0}; - conv_params_t conv_params = { - .in_offset = input_offset, .out_offset = output_offset, - .stride = {stride_width, stride_height}, - .padding = {pad_width, pad_height}, - .dilation = {0, 0}, - .activation = {activation_min, activation_max} - }; - quant_data_t quant_data = { - .shift = data.op_data.per_channel_output_shift, - .mult = data.op_data.per_channel_output_multiplier - }; - for (int i_batch = 0; i_batch < batch_size; i_batch++) { - esp_nn_conv_s8(&input_dims, input_data + i_batch * input_size, - &filter_dims, tflite::micro::GetTensorData(filter), + esp_nn_conv_s8(input_data + i_batch * input_size, + input_width, input_height, input_depth, input_offset, + pad_width, pad_height, stride_width, stride_height, + tflite::micro::GetTensorData(filter), + filter_width, filter_height, tflite::micro::GetTensorData(bias), - &output_dims, output_data + i_batch * output_size, - &conv_params, &quant_data); + output_data + i_batch * output_size, + output_width, output_height, output_depth, output_offset, + data.op_data.per_channel_output_shift, + data.op_data.per_channel_output_multiplier, + activation_min, activation_max); } } else { reference_integer_ops::ConvPerChannel( @@ -329,16 +299,21 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { TfLiteTypeGetName(input->type), input->type); return kTfLiteError; } - long long time_this_instance = esp_timer_get_time() - start_time; - conv_total_time += time_this_instance; - //printf("time this instance: %llu\n", time_this_instance / 1000); + conv_total_time += esp_timer_get_time() - start_time; return kTfLiteOk; } } // namespace TfLiteRegistration Register_CONV_2D() { - return tflite::micro::RegisterOp(Init, Prepare, Eval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/depthwise_conv.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/depthwise_conv.cc index a2460248..5f2d9d50 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/depthwise_conv.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/depthwise_conv.cc @@ -112,36 +112,21 @@ inline void EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node, if (data.buffer_idx > -1) { scratch_buf = context->GetScratchBuffer(context, data.buffer_idx); } - esp_nn_set_depthwise_conv_scratch_buf(scratch_buf); - data_dims_t input_dims = { - .width = input_width, .height = input_height, - .channels = input_depth, 1 - }; - data_dims_t output_dims = { - .width = output_width, .height = output_height, - .channels = output_depth, 1 - }; - data_dims_t filter_dims = {.width = filter_width, .height = filter_height, 0, 0}; - dw_conv_params_t conv_params = { - .in_offset = input_offset, .out_offset = output_offset, - .ch_mult = depth_multiplier, - .stride = {stride_width, stride_height}, - .padding = {pad_width, pad_height}, .dilation = {0, 0}, - .activation = {activation_min, activation_max} - }; - quant_data_t quant_data = { - .shift = data.op_data.per_channel_output_shift, - .mult = data.op_data.per_channel_output_multiplier - }; - for (int i_batch = 0; i_batch < batch_size; i_batch++) { - esp_nn_depthwise_conv_s8(&input_dims, input_data + i_batch * input_size, - &filter_dims, tflite::micro::GetTensorData(filter), + esp_nn_depthwise_conv_s8(input_data + i_batch * input_size, input_width, + input_height, input_depth, input_offset, + pad_width, pad_height, + stride_width, stride_height, depth_multiplier, + tflite::micro::GetTensorData(filter), + filter_width, filter_height, tflite::micro::GetTensorData(bias), - &output_dims, output_data + i_batch * output_size, - &conv_params, &quant_data); + output_data + i_batch * output_size, + output_width, output_height, output_offset, + data.op_data.per_channel_output_shift, + data.op_data.per_channel_output_multiplier, + activation_min, activation_max); } } else { reference_integer_ops::DepthwiseConvPerChannel( @@ -224,25 +209,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { #if ESP_NN if (input->type == kTfLiteInt8) { - data_dims_t input_dims = { - .width = input_width, .height = input_height, - .channels = input->dims->data[3], 1 - }; - data_dims_t output_dims = { - .width = output_width, .height = output_height, - .channels = output->dims->data[3], 1 - }; - data_dims_t filter_dims = {.width = filter_width, .height = filter_height, 0, 0}; - dw_conv_params_t conv_params = { - .in_offset = 0, .out_offset = 0, - .ch_mult = params.depth_multiplier, - .stride = {params.stride_width, params.stride_height}, - .padding = {data->op_data.padding.width, data->op_data.padding.height}, - .dilation = {0, 0}, .activation = {-128, 127} - }; - int scratch_buf_size = esp_nn_get_depthwise_conv_scratch_size( - &input_dims, &filter_dims, &output_dims, &conv_params); + input_width, input_height, input->dims->data[3], + params.depth_multiplier, filter_width, filter_height); if (scratch_buf_size > 0) { TF_LITE_ENSURE_STATUS(context->RequestScratchBufferInArena( context, scratch_buf_size, &data->buffer_idx)); @@ -330,17 +299,21 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { TfLiteTypeGetName(input->type), input->type); return kTfLiteError; } - long long time_this_instance = esp_timer_get_time() - start_time; - dc_total_time += time_this_instance; - // printf("time this instance: %llu\n", time_this_instance / 1000); - + dc_total_time += esp_timer_get_time() - start_time; return kTfLiteOk; } } // namespace TfLiteRegistration Register_DEPTHWISE_CONV_2D() { - return tflite::micro::RegisterOp(Init, Prepare, Eval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/fully_connected.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/fully_connected.cc index 484cffb6..5e1705da 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/fully_connected.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/fully_connected.cc @@ -185,7 +185,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_FULLY_CONNECTED() { - return tflite::micro::RegisterOp(Init, Prepare, Eval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/mul.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/mul.cc index 02413f5c..0e8a82f4 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/mul.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/mul.cc @@ -118,7 +118,14 @@ TfLiteStatus MulEval(TfLiteContext* context, TfLiteNode* node) { } TfLiteRegistration Register_MUL() { - return tflite::micro::RegisterOp(MulInit, MulPrepare, MulEval); + return {/*init=*/MulInit, + /*free=*/nullptr, + /*prepare=*/MulPrepare, + /*invoke=*/MulEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/pooling.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/pooling.cc index b450929e..d55bab82 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/pooling.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/pooling.cc @@ -221,11 +221,25 @@ void* Init(TfLiteContext* context, const char* buffer, size_t length) { } // namespace TfLiteRegistration Register_AVERAGE_POOL_2D() { - return tflite::micro::RegisterOp(Init, PoolingPrepare, AverageEval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/PoolingPrepare, + /*invoke=*/AverageEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_MAX_POOL_2D() { - return tflite::micro::RegisterOp(Init, PoolingPrepare, MaxEval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/PoolingPrepare, + /*invoke=*/MaxEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/softmax.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/softmax.cc deleted file mode 100644 index 9a967839..00000000 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/softmax.cc +++ /dev/null @@ -1,208 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/micro/kernels/softmax.h" - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/softmax.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" - -#include "freertos/FreeRTOS.h" -#include - -#if ESP_NN -#include -#endif - -long long softmax_total_time = 0; - -namespace tflite { -namespace { -// Softmax parameter data that persists in user_data -const int kInt16LUTArraySize = 513; - -struct NodeData { - SoftmaxParams op_data; -#if ESP_NN - int buffer_idx; -#endif -}; - -static void* Init(TfLiteContext* context, const char* buffer, size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(NodeData)); -} - -void SoftmaxQuantized(TfLiteContext* context, const TfLiteEvalTensor* input, - TfLiteEvalTensor* output, const NodeData* data) { - if (input->type == kTfLiteInt8) { - if (output->type == kTfLiteInt16) { - tflite::reference_ops::Softmax( - data->op_data, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { -#if ESP_NN - const int32_t input_beta_multiplier = data->op_data.input_multiplier; - const int32_t input_beta_left_shift = data->op_data.input_left_shift; - const int diff_min = data->op_data.diff_min; - const RuntimeShape input_shape = tflite::micro::GetTensorShape(input); - const RuntimeShape output_shape = tflite::micro::GetTensorShape(output); - const int trailing_dim = input_shape.DimensionsCount() - 1; - const int outer_size = - MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape); - const int depth = - MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim); - const int8_t *in_ptr = tflite::micro::GetTensorData(input); - int8_t *out_ptr = tflite::micro::GetTensorData(output); - void *scratch_buf = NULL; - if (data->buffer_idx > -1) { - scratch_buf = context->GetScratchBuffer(context, data->buffer_idx); - } - esp_nn_set_softmax_scratch_buf(scratch_buf); - esp_nn_softmax_s8(in_ptr, outer_size, depth, input_beta_multiplier, - input_beta_left_shift, diff_min, out_ptr); -#else - tflite::reference_ops::Softmax( - data->op_data, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); -#endif - } - } else { - tflite::reference_ops::SoftmaxInt16( - data->op_data, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } -} - -static TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { - const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0); - TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0); - - TFLITE_DCHECK(node->user_data != nullptr); - NodeData data = *static_cast(node->user_data); - - long long start_time = esp_timer_get_time(); - switch (input->type) { - case kTfLiteFloat32: { - tflite::reference_ops::Softmax( - data.op_data, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } - break; - case kTfLiteInt8: - case kTfLiteInt16: { - SoftmaxQuantized(context, input, output, &data); - } - break; - default: - TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", - TfLiteTypeGetName(input->type), input->type); - return kTfLiteError; - } - softmax_total_time += esp_timer_get_time() - start_time; - return kTfLiteOk; -} - -static TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - MicroContext* micro_context = GetMicroContext(context); - - TF_LITE_ENSURE_EQ(context, NumInputs(node), 1); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, 0); - TF_LITE_ENSURE(context, input != nullptr); - TF_LITE_ENSURE(context, NumDimensions(input) >= 1); - TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0); - TF_LITE_ENSURE(context, output != nullptr); - - TF_LITE_ENSURE(context, node->user_data != nullptr); - NodeData* data = static_cast(node->user_data); - // Only allocate LUTs for KTfLiteInt16 data type - if (input->type == kTfLiteInt16) { - void* raw_exp_lut = context->AllocatePersistentBuffer( - context, sizeof(int16_t) * kInt16LUTArraySize); - TF_LITE_ENSURE(context, raw_exp_lut != nullptr); - data->op_data.exp_lut = reinterpret_cast(raw_exp_lut); - void* one_over_one_plus_x_lut = context->AllocatePersistentBuffer( - context, sizeof(int16_t) * kInt16LUTArraySize); - TF_LITE_ENSURE(context, one_over_one_plus_x_lut != nullptr); - data->op_data.one_over_one_plus_x_lut = - reinterpret_cast(one_over_one_plus_x_lut); - } - - if (output->type == kTfLiteInt16) { - TF_LITE_ENSURE(context, - input->type == kTfLiteInt8 || input->type == kTfLiteInt16); - } else { - TF_LITE_ENSURE_EQ(context, input->type, output->type); - } - - // Populate LUT if required - if (input->type == kTfLiteInt16) { - TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0); - // exp LUT only used on negative values - // we consider exp(-10.0) is insignificant to accumulation - gen_lut( - [](float value) { return std::exp(value); }, -10.0f, 0.0f, -1.0f, 1.0f, - data->op_data.exp_lut); - gen_lut( - [](float value) { return 1.0f / (1.0f + value); }, 0.0f, 1.0f, -1.0f, - 1.0f, data->op_data.one_over_one_plus_x_lut); - data->op_data.zero_point = output->params.zero_point; - data->op_data.scale = output->params.scale; - } - - auto* params = static_cast(node->builtin_data); - auto ret_val = - CalculateSoftmaxParams(context, input, output, params, &data->op_data); - -#if ESP_NN - if (output->type == kTfLiteInt8 && input->type == kTfLiteInt8) { - const int32_t input_width = input->dims->data[1]; - const int32_t input_height = input->dims->data[2]; - int scratch_buf_size = esp_nn_get_softmax_scratch_size(input_width, - input_height); - if (scratch_buf_size > 0) { - TF_LITE_ENSURE_STATUS(context->RequestScratchBufferInArena( - context, scratch_buf_size, &data->buffer_idx)); - } - } -#endif - - micro_context->DeallocateTempTfLiteTensor(input); - micro_context->DeallocateTempTfLiteTensor(output); - return ret_val; -} - -} // namespace - -TfLiteRegistration Register_SOFTMAX() { - return tflite::micro::RegisterOp(Init, Prepare, Eval); -} - -} // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/exp.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/exp.cc index ae26f636..d1b0f6cb 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/exp.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/exp.cc @@ -72,7 +72,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_EXP() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/expand_dims.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/expand_dims.cc index 4b105bf6..6dcba4d5 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/expand_dims.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/expand_dims.cc @@ -146,7 +146,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_EXPAND_DIMS() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/fill.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/fill.cc index 9f438b89..d8a2b09d 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/fill.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/fill.cc @@ -135,7 +135,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_FILL() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/floor.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/floor.cc index 6b2a4cc2..b8be1cf0 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/floor.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/floor.cc @@ -42,7 +42,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace floor TfLiteRegistration Register_FLOOR() { - return tflite::micro::RegisterOp(nullptr, nullptr, floor::Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/nullptr, + /*invoke=*/floor::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/floor_div.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/floor_div.cc index 333a1eba..d11e4969 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/floor_div.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/floor_div.cc @@ -123,7 +123,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_FLOOR_DIV() { - return tflite::micro::RegisterOp(Init, Prepare, Eval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/floor_mod.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/floor_mod.cc index 9bb49497..083bd5cb 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/floor_mod.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/floor_mod.cc @@ -121,7 +121,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_FLOOR_MOD() { - return tflite::micro::RegisterOp(Init, Prepare, Eval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/fully_connected.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/fully_connected.cc index a083edd7..c0be3814 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/fully_connected.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/fully_connected.cc @@ -1,4 +1,4 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -55,7 +55,10 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { TfLiteTensor* output = micro_context->AllocateTempOutputTensor( node, kFullyConnectedOutputTensor); TF_LITE_ENSURE(context, output != nullptr); + TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type); + TF_LITE_ENSURE_MSG(context, input->type == filter->type, + "Hybrid models are not supported on TFLite Micro."); TF_LITE_ENSURE_OK(context, CalculateOpDataFullyConnected( context, params->activation, input->type, @@ -123,23 +126,6 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { break; } - case kTfLiteInt16: { - const int64_t* bias_data = - nullptr != bias ? tflite::micro::GetTensorData(bias) - : nullptr; - - tflite::reference_integer_ops::FullyConnected( - FullyConnectedParamsQuantized(data), - tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(filter), - tflite::micro::GetTensorData(filter), - tflite::micro::GetTensorShape(bias), bias_data, - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - break; - } - default: { TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.", TfLiteTypeGetName(input->type), input->type); @@ -152,7 +138,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_FULLY_CONNECTED() { - return tflite::micro::RegisterOp(Init, Prepare, Eval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/fully_connected.h b/code/components/tflite-lib/tensorflow/lite/micro/kernels/fully_connected.h index 93026cd5..e1215da6 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/fully_connected.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/fully_connected.h @@ -1,4 +1,4 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -81,24 +81,6 @@ inline TfLiteRegistration Register_FULLY_CONNECTED_INT8() { } #endif - -#if defined(CMSIS_NN) -// Returns a TfLiteRegistration struct for kernel variant that only supports -// int16. -TfLiteRegistration Register_FULLY_CONNECTED_INT16(); - -#else -// Note that while this block gets used for both reference and optimized kernels -// that do not have any specialized implementations, the only goal here is to -// define fallback implementation that allow reference kernels to still be used -// from applications that call a more specific kernel variant. - -inline TfLiteRegistration Register_FULLY_CONNECTED_INT16() { - return Register_FULLY_CONNECTED(); -} - -#endif - } // namespace tflite #endif // TENSORFLOW_LITE_MICRO_KERNELS_FULLY_CONNECTED_H_ diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/gather.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/gather.cc index 6035efa7..0b7c23f9 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/gather.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/gather.cc @@ -218,7 +218,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_GATHER() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/gather_nd.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/gather_nd.cc index eaa1abca..c604ae15 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/gather_nd.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/gather_nd.cc @@ -195,7 +195,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_GATHER_ND() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/hard_swish.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/hard_swish.cc index 055e12e6..060dfc14 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/hard_swish.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/hard_swish.cc @@ -68,8 +68,14 @@ TfLiteStatus HardSwishEval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_HARD_SWISH() { - return tflite::micro::RegisterOp(HardSwishInit, tflite::HardSwishPrepare, - HardSwishEval); + return {/*init=*/HardSwishInit, + /*free=*/nullptr, + /*prepare=*/tflite::HardSwishPrepare, + /*invoke=*/HardSwishEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/if.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/if.cc index 39eca8b4..050aeac4 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/if.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/if.cc @@ -115,7 +115,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace. TfLiteRegistration Register_IF() { - return tflite::micro::RegisterOp(Init, Prepare, Eval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_runner.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_runner.cc index 341eec77..fd84dec1 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_runner.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_runner.cc @@ -15,9 +15,9 @@ limitations under the License. #include "tensorflow/lite/micro/kernels/kernel_runner.h" -#include "tensorflow/lite/micro/arena_allocator/simple_memory_allocator.h" #include "tensorflow/lite/micro/micro_arena_constants.h" #include "tensorflow/lite/micro/micro_error_reporter.h" +#include "tensorflow/lite/micro/simple_memory_allocator.h" #include "tensorflow/lite/micro/test_helpers.h" namespace tflite { @@ -30,7 +30,7 @@ uint8_t KernelRunner::kKernelRunnerBuffer_[]; KernelRunner::KernelRunner(const TfLiteRegistration& registration, TfLiteTensor* tensors, int tensors_size, TfLiteIntArray* inputs, TfLiteIntArray* outputs, - void* builtin_data, TfLiteIntArray* intermediates) + void* builtin_data) : registration_(registration), allocator_(SimpleMemoryAllocator::Create(GetMicroErrorReporter(), kKernelRunnerBuffer_, @@ -54,7 +54,6 @@ KernelRunner::KernelRunner(const TfLiteRegistration& registration, node_.inputs = inputs; node_.outputs = outputs; node_.builtin_data = builtin_data; - node_.intermediates = intermediates; } bool KernelRunner::ValidateTempBufferDeallocated() { diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_runner.h b/code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_runner.h index 68722edb..9dddde50 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_runner.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_runner.h @@ -18,9 +18,9 @@ limitations under the License. #include "tensorflow/lite/c/common.h" #include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/micro/arena_allocator/simple_memory_allocator.h" #include "tensorflow/lite/micro/fake_micro_context.h" #include "tensorflow/lite/micro/mock_micro_graph.h" +#include "tensorflow/lite/micro/simple_memory_allocator.h" namespace tflite { namespace micro { @@ -35,8 +35,7 @@ class KernelRunner { public: KernelRunner(const TfLiteRegistration& registration, TfLiteTensor* tensors, int tensors_size, TfLiteIntArray* inputs, - TfLiteIntArray* outputs, void* builtin_data, - TfLiteIntArray* intermediates = nullptr); + TfLiteIntArray* outputs, void* builtin_data); // Calls init and prepare on the kernel (i.e. TfLiteRegistration) struct. Any // exceptions will be DebugLog'd and returned as a status code. diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_util.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_util.cc index 91c0bc91..14664b91 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_util.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_util.cc @@ -36,21 +36,6 @@ int ValidateTensorIndexing(const TfLiteContext* context, int index, } // namespace -TfLiteRegistration RegisterOp( - void* (*init)(TfLiteContext* context, const char* buffer, size_t length), - TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node), - TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node)) { - return {/*init=*/init, - /*free=*/nullptr, - /*prepare=*/prepare, - /*invoke=*/invoke, - /*profiling_string=*/nullptr, - /*builtin_code=*/0, - /*custom_name=*/nullptr, - /*version=*/0, - /*registration_external=*/nullptr}; -} - // Returns a mutable tensor for a given input index. is_variable must be checked // during prepare when the full TfLiteTensor is available. TfLiteEvalTensor* GetMutableEvalInput(const TfLiteContext* context, diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_util.h b/code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_util.h index d6f20c72..6c5d7f18 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_util.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_util.h @@ -27,11 +27,6 @@ limitations under the License. namespace tflite { namespace micro { -TfLiteRegistration RegisterOp( - void* (*init)(TfLiteContext* context, const char* buffer, size_t length), - TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node), - TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node)); - // Returns a mutable tensor for a given input index. is_variable must be checked // during prepare when the full TfLiteTensor is available. TfLiteEvalTensor* GetMutableEvalInput(const TfLiteContext* context, @@ -45,33 +40,19 @@ const TfLiteEvalTensor* GetEvalInput(const TfLiteContext* context, TfLiteEvalTensor* GetEvalOutput(const TfLiteContext* context, const TfLiteNode* node, int index); -// Returns data for a TfLiteEvalTensor struct that are expected to exist. +// Returns data for a TfLiteEvalTensor struct. template T* GetTensorData(TfLiteEvalTensor* tensor) { - TFLITE_DCHECK(tensor != nullptr); - return reinterpret_cast(tensor->data.raw); + return tensor != nullptr ? reinterpret_cast(tensor->data.raw) : nullptr; } -// Returns const data for a TfLiteEvalTensor struct that are expected to exist. +// Returns const data for a TfLiteEvalTensor struct. template const T* GetTensorData(const TfLiteEvalTensor* tensor) { TFLITE_DCHECK(tensor != nullptr); return reinterpret_cast(tensor->data.raw); } -// Returns data for a TfLiteEvalTensor struct that could be null. -template -T* GetOptionalTensorData(TfLiteEvalTensor* tensor) { - return tensor == nullptr ? nullptr : reinterpret_cast(tensor->data.raw); -} - -// Returns const data for a TfLiteEvalTensor struct that could be null. -template -const T* GetOptionalTensorData(const TfLiteEvalTensor* tensor) { - return tensor == nullptr ? nullptr - : reinterpret_cast(tensor->data.raw); -} - // Returns the shape of a TfLiteEvalTensor struct. const RuntimeShape GetTensorShape(const TfLiteEvalTensor* tensor); diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/l2_pool_2d.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/l2_pool_2d.cc index 2b2a27bf..250cd3be 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/l2_pool_2d.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/l2_pool_2d.cc @@ -136,7 +136,14 @@ TfLiteStatus L2Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_L2_POOL_2D() { - return tflite::micro::RegisterOp(nullptr, L2Prepare, L2Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/L2Prepare, + /*invoke=*/L2Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/l2norm.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/l2norm.cc index 45858e78..289e4de5 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/l2norm.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/l2norm.cc @@ -137,7 +137,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace l2norm TfLiteRegistration Register_L2NORM_REF() { - return tflite::micro::RegisterOp(l2norm::Init, l2norm::Prepare, l2norm::Eval); + return {/*init=*/l2norm::Init, + /*free=*/nullptr, + /*prepare=*/l2norm::Prepare, + /*invoke=*/l2norm::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_L2_NORMALIZATION() { return Register_L2NORM_REF(); } diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/leaky_relu.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/leaky_relu.cc index 96c1b1b1..70ee3856 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/leaky_relu.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/leaky_relu.cc @@ -88,8 +88,14 @@ TfLiteStatus LeakyReluEval(TfLiteContext* context, TfLiteNode* node) { } TfLiteRegistration Register_LEAKY_RELU() { - return tflite::micro::RegisterOp(LeakyReluInit, LeakyReluPrepare, - LeakyReluEval); + return {/*init=*/LeakyReluInit, + /*free=*/nullptr, + /*prepare=*/LeakyReluPrepare, + /*invoke=*/LeakyReluEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/log_softmax.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/log_softmax.cc index 5fd87612..0af74def 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/log_softmax.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/log_softmax.cc @@ -142,7 +142,14 @@ TfLiteStatus LogSoftmaxEval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_LOG_SOFTMAX() { - return tflite::micro::RegisterOp(nullptr, LogSoftmaxPrepare, LogSoftmaxEval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/LogSoftmaxPrepare, + /*invoke=*/LogSoftmaxEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/logical.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/logical.cc index c85e0c5b..e2d2b5f8 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/logical.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/logical.cc @@ -34,11 +34,29 @@ TfLiteStatus LogicalAndEval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_LOGICAL_OR() { - return tflite::micro::RegisterOp(nullptr, nullptr, LogicalOrEval); + // Init, Free, Prepare, Eval are satisfying the Interface required by + // TfLiteRegistration. + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/nullptr, + /*invoke=*/LogicalOrEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_LOGICAL_AND() { - return tflite::micro::RegisterOp(nullptr, nullptr, LogicalAndEval); + // Init, Free, Prepare, Eval are satisfying the Interface required by + // TfLiteRegistration. + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/nullptr, + /*invoke=*/LogicalAndEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/logistic.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/logistic.cc index f8ac1c23..77f94ec0 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/logistic.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/logistic.cc @@ -106,6 +106,13 @@ TfLiteStatus LogisticEval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_LOGISTIC() { - return tflite::micro::RegisterOp(LogisticInit, LogisticPrepare, LogisticEval); + return {/*init=*/LogisticInit, + /*free=*/nullptr, + /*prepare=*/LogisticPrepare, + /*invoke=*/LogisticEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/lstm_eval.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/lstm_eval.cc deleted file mode 100644 index f157a8d0..00000000 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/lstm_eval.cc +++ /dev/null @@ -1,2955 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/micro/kernels/lstm_eval.h" - -#include -#include -#include -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/op_macros.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/micro_tensor_utils.h" -namespace tflite { -namespace { - -void ComputeRowSums( - int32_t* input_to_input_row_sums, int32_t* input_to_forget_row_sums, - int32_t* input_to_cell_row_sums, int32_t* input_to_output_row_sums, - int32_t* aux_input_to_input_row_sums, int32_t* aux_input_to_forget_row_sums, - int32_t* aux_input_to_cell_row_sums, int32_t* aux_input_to_output_row_sums, - int32_t* recurrent_to_input_row_sums, int32_t* recurrent_to_forget_row_sums, - int32_t* recurrent_to_cell_row_sums, int32_t* recurrent_to_output_row_sums, - int32_t* projection_weights_row_sums, int32_t* row_sums, int n_cell, - int n_input, int n_aux_input, int n_output, - const int8_t* input_to_input_weights_ptr, - const int8_t* input_to_forget_weights_ptr, - const int8_t* input_to_cell_weights_ptr, - const int8_t* input_to_output_weights_ptr, - const int8_t* aux_input_to_input_weights_ptr, - const int8_t* aux_input_to_forget_weights_ptr, - const int8_t* aux_input_to_cell_weights_ptr, - const int8_t* aux_input_to_output_weights_ptr, - const int8_t* recurrent_to_input_weights_ptr, - const int8_t* recurrent_to_forget_weights_ptr, - const int8_t* recurrent_to_cell_weights_ptr, - const int8_t* recurrent_to_output_weights_ptr, - const int8_t* projection_weights_ptr, bool use_cifg, - const float* aux_input_ptr) { - // Compute the row sums for dequantization - if (!use_cifg) { - micro_tensor_utils::ReductionSumVector( - input_to_input_weights_ptr, input_to_input_row_sums, n_cell, n_input); - } - micro_tensor_utils::ReductionSumVector( - input_to_forget_weights_ptr, input_to_forget_row_sums, n_cell, n_input); - micro_tensor_utils::ReductionSumVector( - input_to_cell_weights_ptr, input_to_cell_row_sums, n_cell, n_input); - micro_tensor_utils::ReductionSumVector( - input_to_output_weights_ptr, input_to_output_row_sums, n_cell, n_input); - - if (aux_input_ptr) { - if (!use_cifg) { - micro_tensor_utils::ReductionSumVector(aux_input_to_input_weights_ptr, - aux_input_to_input_row_sums, - n_cell, n_aux_input); - } - micro_tensor_utils::ReductionSumVector(aux_input_to_forget_weights_ptr, - aux_input_to_forget_row_sums, n_cell, - n_aux_input); - micro_tensor_utils::ReductionSumVector(aux_input_to_cell_weights_ptr, - aux_input_to_cell_row_sums, n_cell, - n_aux_input); - micro_tensor_utils::ReductionSumVector(aux_input_to_output_weights_ptr, - aux_input_to_output_row_sums, n_cell, - n_aux_input); - } - if (!use_cifg) { - micro_tensor_utils::ReductionSumVector(recurrent_to_input_weights_ptr, - recurrent_to_input_row_sums, n_cell, - n_output); - } - micro_tensor_utils::ReductionSumVector(recurrent_to_forget_weights_ptr, - recurrent_to_forget_row_sums, n_cell, - n_output); - micro_tensor_utils::ReductionSumVector(recurrent_to_cell_weights_ptr, - recurrent_to_cell_row_sums, n_cell, - n_output); - micro_tensor_utils::ReductionSumVector(recurrent_to_output_weights_ptr, - recurrent_to_output_row_sums, n_cell, - n_output); - - if (projection_weights_ptr != nullptr) { - micro_tensor_utils::ReductionSumVector( - projection_weights_ptr, projection_weights_row_sums, n_output, n_cell); - } -} - -// Calculates a single LSTM gate. -// -// Implements the following formula: (* is matrix multiply) -// gate = activate(W_input * input + W_aux * aux_input + -// W_peephole * cell + W_recurrent * prev_output + bias) -// with layer norm: -// gate = activate(W_norm * normalize(...) + bias) // not adding bias inside -// -// Activation is sigmoid except for the "cell" gate (configurable, usually tanh) -// -// Parameters: -// Input vectors (to LSTM): | Size: | Optional? -// input | n_input | -// aux_input | n_aux_input | y (bidir LSTM) -// Input vectors (persistent states): -// output_state | n_output | -// cell_state | n_cell | -// 'Constant' inputs: -// input_to_gate_weights | n_cell * n_input | -// aux_input_to_gate_weights | n_cell * n_aux_input | y (bidir LSTM) -// recurrent_to_gate_weights | n_cell * n_output | -// cell_to_gate_weights | n_cell | y (peephole) -// gate_bias | n_cell | -// layer_norm_coefficients | n_cell | y (layer norm) -// Output vector: -// gate | n_cell | -// Scalar parameters: -// n_batch - batch size / number of vectors -// n_input, n_aux_input, n_output, n_cell - size of vectors. -// activation - activation to use. -// is_input_all_zeros, is_aux_input_all_zeros - if input vectors are all zero. -// use_layer_norm - if doing layer norm LSTM. -inline void CalculateLstmGateFloat( - const float* input, const float* input_to_gate_weights, - const float* aux_input, const float* aux_input_to_gate_weights, - const float* output_state, const float* recurrent_to_gate_weights, - const float* cell_state, const float* cell_to_gate_weights, - const float* layer_norm_coefficients, const float* gate_bias, - const int n_batch, const int n_input, const int n_aux_input, - const int n_output, const int n_cell, - const TfLiteFusedActivation activation, float* gate, - const bool is_input_all_zeros, const bool is_aux_input_all_zeros) { - const bool use_peephole = (cell_to_gate_weights != nullptr); - const bool use_layer_norm = (layer_norm_coefficients != nullptr); - - // Initialize scratch buffers with bias for regular lstm or initialize with - // zero for layer norm lstm. - if (use_layer_norm) { - memset(gate, 0, n_cell * n_batch * sizeof(float)); - } else { - micro_tensor_utils::VectorBatchVectorAssign(gate_bias, n_cell, n_batch, - gate); - } - // For each batch and cell: compute input_weight * input. - // Skip if input is all zeros. - if (!is_input_all_zeros) { - micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_gate_weights, n_cell, n_input, input, n_batch, gate); - } - // For each batch and cell: compute aux_input_weight * aux_input. - // Skip if auxiliary input is not available or all zeros. - if (!is_aux_input_all_zeros) { - micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate( - aux_input_to_gate_weights, n_cell, n_aux_input, aux_input, n_batch, - gate); - } - // For each batch and cell: compute recurrent_weight * output_state. - micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_gate_weights, n_cell, n_output, output_state, n_batch, gate); - // For each batch and cell: compute cell_weight .* cell_state (peephole LSTM) - if (use_peephole) { - micro_tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_gate_weights, n_cell, cell_state, n_batch, gate); - } - // Do layer normalization (if layer norm LSTM) - if (use_layer_norm) { - micro_tensor_utils::MeanStddevNormalization(gate, gate, n_cell, n_batch); - micro_tensor_utils::VectorBatchVectorCwiseProduct( - layer_norm_coefficients, n_cell, gate, n_batch, gate); - micro_tensor_utils::VectorBatchVectorAdd(gate_bias, n_cell, n_batch, gate); - } - // Apply activation - micro_tensor_utils::ApplyActivationToVector(gate, n_batch * n_cell, - activation, gate); -} - -// Updates the LSTM cell state, used by both float and hybrid LSTM versions. -// -// Implements the following formula: -// cell_state_new = clip(forget_gate * cell_state + input_gate * cell_gate) -// -// With CIFG LSTM, input gate is replaced by (1-forget_gate). -// -// Parameters: -// - n_batch, n_cell: sizes of vectors -// - cell_state: input/output vector, size n_batch*n_cell -// - input_gate: input vector, size n_batch*n_cell. -// - forget_gate: input/scratch vector, size n_batch*n_cell, modified with CIFG -// - cell_gate: input vector, size n_batch*n_cell. -// - use_cifg: use 1-forget_gate instead of input_gate. -// - clip: if > 0, clip the resulting cell state to [-clip, +clip]. -void UpdateLstmCellFloat(int n_batch, int n_cell, float* cell_state, - const float* input_gate, float* forget_gate, - const float* cell_gate, bool use_cifg, float clip) { - micro_tensor_utils::VectorVectorCwiseProduct(forget_gate, cell_state, - n_batch * n_cell, cell_state); - - if (use_cifg) { - // With CIFG, input_gate = 1-forget_gate. Use the forget_gate array as - // scratch, as input_gate array is not allocated in this case. (Be careful - // not to write to the scratch before reading the forget gate data.) - float* scratch = forget_gate; - micro_tensor_utils::Sub1Vector(forget_gate, n_batch * n_cell, scratch); - micro_tensor_utils::VectorVectorCwiseProductAccumulate( - cell_gate, scratch, n_batch * n_cell, cell_state); - } else { - micro_tensor_utils::VectorVectorCwiseProductAccumulate( - cell_gate, input_gate, n_batch * n_cell, cell_state); - } - if (clip > 0.0f) { - micro_tensor_utils::CwiseClipping(cell_state, n_batch * n_cell, clip); - } -} - -// Calculates the output state tensor of an LSTM step. -// -// Implements the following formula: -// output_no_projection = output_gate .* activate(cell_state) -// (elementwise vector product) -// If no projection is used: -// output = output_state = output_no_projection -// With projection: -// output = output_state = clip(W*output_no_projection + bias) -// -// Output might not have a different 'stride' than n_batch, so we need to copy. -// -// Parameters: -// - n_batch: batches: the number of distinct vectors in each array. -// - n_cell, n_output: sizes of vectors. -// - cell_state, output_gate: input vectors, size n_batch*n_cell. -// - projection_weights, projection_weights_scale, projection_bias: -// constant inputs, describing projection matrix and bias. -// - proj_clip: if > 0, clip the output of the projection. -// - output_state: output vector, size n_batch*n_output. Must be contigous. -// - scratch: scratch area, size n_batch*n_cell. -void CalculateLstmOutputFloat(int n_batch, int n_cell, int n_output, - const float* cell_state, const float* output_gate, - TfLiteFusedActivation activation, - const float* projection_weights, - const float* projection_bias, - const float proj_clip, float* output_state, - float* scratch) { - micro_tensor_utils::ApplyActivationToVector(cell_state, n_batch * n_cell, - activation, scratch); - micro_tensor_utils::VectorVectorCwiseProduct(output_gate, scratch, - n_batch * n_cell, scratch); - - const bool use_projection = (projection_weights != nullptr); - const bool use_projection_bias = (projection_bias != nullptr); - - if (use_projection) { - if (use_projection_bias) { - micro_tensor_utils::VectorBatchVectorAssign(projection_bias, n_output, - n_batch, output_state); - } else { - memset(output_state, 0, n_batch * n_output * sizeof(float)); - } - micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate( - projection_weights, n_output, n_cell, scratch, n_batch, output_state); - if (proj_clip > 0.0f) { - micro_tensor_utils::CwiseClipping(output_state, n_batch * n_output, - proj_clip); - } - } else { - std::memcpy(output_state, scratch, n_batch * n_output * sizeof(float)); - } -} - -// Calculates a single LSTM gate, hybrid version. -// Implements the same functionality as CalculateLstmGateFloat. -void CalculateLstmGateHybrid( - // Input and weights - const int8_t* input, const float* input_sf, const int32_t* input_zp, - const int8_t* input_to_gate_weights, - const uint8_t* input_to_gate_weights_ledger, - const float input_to_gate_weights_scale, int32_t* input_to_gate_row_sums, - // Aux input and weights - const int8_t* aux_input, const float* aux_input_sf, - const int32_t* aux_input_zp, const int8_t* aux_input_to_gate_weights, - const float aux_input_to_gate_weights_scale, - int32_t* aux_input_to_gate_row_sums, - // Output state and weights - const int8_t* output_state, const float* output_state_sf, - const int32_t* output_state_zp, const int8_t* recurrent_to_gate_weights, - const uint8_t* recurrent_to_gate_weights_ledger, - const float recurrent_to_gate_weights_scale, - int32_t* recurrent_to_gate_row_sums, - // Cell state and weights (peephole LSTM) - const float* cell_state, const int8_t* cell_to_gate_weights, - const float cell_to_gate_weights_scale, - // Layer normalization coefficients (layer norm LSTM) + gate bias - const float* layer_norm_coefficients, const float* gate_bias, - // Array sizes - const int n_batch, const int n_input, const int n_aux_input, - const int n_output, const int n_cell, - const TfLiteFusedActivation activation, - // Output - float* gate, - // Parameters for performance optimizations - const bool is_input_all_zeros, const bool is_aux_input_all_zeros, - const bool is_output_state_all_zeros, bool* compute_row_sums, - // Scratch arrays - float* scratch0, // size: n_batch - float* scratch1, // size: n_cell, only used if peephole LSTM - float* scales, // size: n_batch - int32_t* accum_scratch // For MatrixBatchVectorMultiplyAccumulate -) { - const bool use_peephole = (cell_to_gate_weights != nullptr); - const bool use_layer_norm = (layer_norm_coefficients != nullptr); - - // Initialize scratch buffers with bias for regular lstm or initialize with - // zero for layer norm lstm. - if (use_layer_norm) { - memset(gate, 0, n_cell * n_batch * sizeof(float)); - } else { - micro_tensor_utils::VectorBatchVectorAssign(gate_bias, n_cell, n_batch, - gate); - } - // For each batch and cell: compute input_weight * input. - // Skip if input is all zeros. - if (!is_input_all_zeros) { - if (input_to_gate_weights_ledger != nullptr) { - for (int i = 0; i < n_batch; i++) { - scales[i] = input_to_gate_weights_scale * input_sf[i]; - } - micro_tensor_utils::SparseMatrixBatchVectorMultiplyAccumulate( - input_to_gate_weights, input_to_gate_weights_ledger, n_cell, n_input, - input, scales, n_batch, gate); - - } else { - micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_gate_weights, n_cell, n_input, input, - input_to_gate_weights_scale, input_sf, n_batch, gate, - /*per_channel_scale=*/nullptr, input_zp, accum_scratch, - input_to_gate_row_sums, compute_row_sums, scratch0, nullptr); - } - } - // For each batch and cell: compute aux_input_weight * aux_input. - // Skip if auxiliary input is not available or all zeros. - if (!is_aux_input_all_zeros) { - micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate( - aux_input_to_gate_weights, n_cell, n_aux_input, aux_input, - aux_input_to_gate_weights_scale, aux_input_sf, n_batch, gate, - /*per_channel_scale=*/nullptr, aux_input_zp, accum_scratch, - aux_input_to_gate_row_sums, compute_row_sums, scratch0, nullptr); - } - // For each batch and cell: compute recurrent_weight * output_state. - // Skip if output state is all zeros. - if (!is_output_state_all_zeros) { - if (recurrent_to_gate_weights_ledger != nullptr) { - for (int i = 0; i < n_batch; i++) { - scales[i] = recurrent_to_gate_weights_scale * input_sf[i]; - } - micro_tensor_utils::SparseMatrixBatchVectorMultiplyAccumulate( - recurrent_to_gate_weights, recurrent_to_gate_weights_ledger, n_cell, - n_output, output_state, scales, n_batch, gate); - } else { - micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_gate_weights, n_cell, n_output, output_state, - recurrent_to_gate_weights_scale, output_state_sf, n_batch, gate, - /*per_channel_scale=*/nullptr, output_state_zp, accum_scratch, - recurrent_to_gate_row_sums, compute_row_sums, scratch0, nullptr); - } - } - // For each batch and cell: compute cell_weight .* cell_state (peephole LSTM) - if (use_peephole) { - float* recovered_cell_weights = scratch1; - micro_tensor_utils::VectorScalarMultiply(cell_to_gate_weights, n_cell, - cell_to_gate_weights_scale, - recovered_cell_weights); - micro_tensor_utils::VectorBatchVectorCwiseProductAccumulate( - recovered_cell_weights, n_cell, cell_state, n_batch, gate); - } - // Do layer normalization (if layer norm LSTM) - if (use_layer_norm) { - micro_tensor_utils::MeanStddevNormalization(gate, gate, n_cell, n_batch); - micro_tensor_utils::VectorBatchVectorCwiseProduct( - layer_norm_coefficients, n_cell, gate, n_batch, gate); - micro_tensor_utils::VectorBatchVectorAdd(gate_bias, n_cell, n_batch, gate); - } - // Apply activation - micro_tensor_utils::ApplyActivationToVector(gate, n_cell * n_batch, - activation, gate); -} - -// Calculates the output state tensor of an LSTM step. See Float version too. -// -// Parameters: -// - n_batch: batches: the number of distinct vectors in each array. -// - n_cell, n_output: sizes of vectors. -// - cell_state, output_gate: input vectors, size n_batch*n_cell. -// - projection_weights, projection_weights_scale, projection_bias: -// constant inputs, describing projection matrix and bias. -// - proj_clip: if > 0, clip the output of the projection. -// - output_state: output vector, size n_batch*n_output. Must be contigous. -// - asymmetric_quantize_inputs: parameter to control quantization. -// - projection_weights_row_sums, compute_row_sums: Data for optimized -// MatrixBatchVectorMultiplyAccumulate. -// - scratch0: scratch area of size n_batch*n_cell -// - scratch1: scratch area of size n_batch*n_cell -// - scratch2: scratch area of size n_batch -// - scratch3: scratch area of size n_batch -// - scratch4: scratch area used by MatrixBatchVectorMultiplyAccumulate -// - scales: scratch area of size n_batch -void CalculateLstmOutputHybrid( - int n_batch, int n_cell, int n_output, const float* cell_state, - const float* output_gate, TfLiteFusedActivation activation, - const int8_t* projection_weights, const uint8_t* projection_weights_ledger, - float projection_weights_scale, const float* projection_bias, - const float proj_clip, float* output_state, bool asymmetric_quantize_inputs, - int32_t* projection_weights_row_sums, bool* compute_row_sums, - float* scratch0, int8_t* scratch1, float* scratch2, int32_t* scratch3, - int32_t* scratch4, float* scales) { - micro_tensor_utils::ApplyActivationToVector(cell_state, n_batch * n_cell, - activation, scratch0); - micro_tensor_utils::VectorVectorCwiseProduct(output_gate, scratch0, - n_batch * n_cell, scratch0); - - const bool use_projection = (projection_weights != nullptr); - const bool use_projection_bias = (projection_bias != nullptr); - - if (use_projection) { - if (use_projection_bias) { - micro_tensor_utils::VectorBatchVectorAssign(projection_bias, n_output, - n_batch, output_state); - } else { - memset(output_state, 0, n_batch * n_output * sizeof(float)); - } - if (!micro_tensor_utils::IsZeroVector(scratch0, n_batch * n_cell)) { - // Save quantization and matmul computation for all zero output. - micro_tensor_utils::BatchQuantizeFloats(scratch0, n_batch, n_cell, - scratch1, scratch2, scratch3, - asymmetric_quantize_inputs); - if (projection_weights_ledger != nullptr) { - for (int i = 0; i < n_batch; i++) { - scales[i] = projection_weights_scale * scratch2[i]; - } - micro_tensor_utils::SparseMatrixBatchVectorMultiplyAccumulate( - projection_weights, projection_weights_ledger, n_output, n_cell, - scratch1, scales, n_batch, output_state); - } else { - micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate( - projection_weights, n_output, n_cell, scratch1, - projection_weights_scale, scratch2, n_batch, output_state, - /*per_channel_scale=*/nullptr, scratch3, scratch4, - projection_weights_row_sums, compute_row_sums, scratch2, nullptr); - } - } - if (proj_clip > 0.0f) { - micro_tensor_utils::CwiseClipping(output_state, n_batch * n_output, - proj_clip); - } - } else { - std::memcpy(output_state, scratch0, n_batch * n_output * sizeof(float)); - } -} - -// Calculates a single LSTM gate, int8x8_16 version. -// Implements the same functionality as CalculateLstmGateFloat. -void CalculateLstmGateInteger8x8_16( - // Input and weights - const int8_t* input, const int8_t* input_to_gate_weights, - const int32_t* input_to_gate_bias, const int32_t input_to_gate_scale_a, - const int32_t input_to_gate_scale_b, - // Output state and weights - const int8_t* output_state, const int8_t* recurrent_to_gate_weights, - const int32_t* recurrent_to_gate_bias, - const int32_t recurrent_to_gate_scale_a, - const int32_t recurrent_to_gate_scale_b, - // Cell state and weights - const int16_t* cell_state, const int16_t* cell_to_gate_weights, - const int32_t cell_to_gate_scale_a, const int32_t cell_to_gate_scale_b, - // Layer normalization parameters (layer norm LSTM) - const int16_t* layer_norm_coefficients, const int32_t* layer_norm_bias, - const int32_t layer_norm_input_scale_a, - const int32_t layer_norm_input_scale_b, - const int32_t layer_norm_variance_guard, - // Array sizes - const int n_batch, const int n_input, const int n_output, const int n_cell, - const TfLiteFusedActivation activation, - // Output - int16_t* gate, - // Parameters for performance optimizations - // Scratch arrays - int32_t* scratch5) { - const bool use_peephole = (cell_to_gate_weights != nullptr); - const bool use_layer_norm = (layer_norm_coefficients != nullptr); - - // Initialize scratch buffers with zeros. Note that unlike float and hybrid - // versions, bias is only used in layer normalization. - memset(gate, 0, n_batch * n_cell * sizeof(int16_t)); - // For each batch and cell: compute input_weight * input. - micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input, input_to_gate_bias, input_to_gate_weights, input_to_gate_scale_a, - input_to_gate_scale_b, n_batch, n_input, n_cell, 0, scratch5, gate, - nullptr); - // Note: no aux_input. - - // For each batch and cell: compute recurrent_weight * output_state. - micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate( - output_state, recurrent_to_gate_bias, recurrent_to_gate_weights, - recurrent_to_gate_scale_a, recurrent_to_gate_scale_b, n_batch, n_output, - n_cell, 0, scratch5, gate, nullptr); - // For each batch and cell: compute cell_weight * cell_state (peephole LSTM) - if (use_peephole) { - micro_tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_gate_weights, n_output, cell_state, n_batch, - cell_to_gate_scale_a, cell_to_gate_scale_b, gate); - } - // Do layer normalization (if layer norm LSTM) - if (use_layer_norm) { - micro_tensor_utils::ApplyLayerNorm( - gate, layer_norm_coefficients, layer_norm_bias, - layer_norm_input_scale_a, layer_norm_input_scale_b, - layer_norm_variance_guard, n_batch, n_cell, gate); - } - // Apply activation - switch (activation) { - case kTfLiteActSigmoid: - micro_tensor_utils::ApplySigmoid(gate, n_batch, n_cell, gate); - break; - case kTfLiteActTanh: - micro_tensor_utils::ApplyTanh(3, gate, n_batch, n_cell, gate); - break; - default: - // Only Sigmoid or Tanh is used. - TFLITE_ASSERT_FALSE; - } -} - -// Updates the LSTM cell state, used by both integer LSTM versions. -// Also see UpdateLstmCellFloat. -// -// Parameters: -// - n_batch, n_cell: sizes of vectors -// - cell_state: input/output vector, size n_batch*n_cell -// - cell_state_scale: scaling factor of cell state. -// - input_gate: input vector, size n_batch*n_cell. -// - forget_gate: input/scratch vector, size n_batch*n_cell, always modified. -// - cell_gate: input vector, size n_batch*n_cell. -// - use_cifg: use 1-forget_gate instead of input_gate. -// - clip: if > 0, clip the resulting cell state to [-clip, +clip]. -void UpdateLstmCellInteger(int n_batch, int n_cell, int16_t* cell_state, - int32_t cell_state_scale, const int16_t* input_gate, - int16_t* forget_gate, const int16_t* cell_gate, - bool use_cifg, int16_t clip) { - // Use the forget_gate array as scratch, as input_gate array is not allocated - // in CIFG case. (Be careful not to write to the scratch before reading the - // forget gate data.) - int16_t* scratch = forget_gate; - - micro_tensor_utils::CwiseMul(forget_gate, cell_state, n_batch, n_cell, 15, - cell_state); - if (use_cifg) { - micro_tensor_utils::Sub1Vector(forget_gate, n_batch * n_cell, scratch); - micro_tensor_utils::CwiseMul(scratch, cell_gate, n_batch, n_cell, - 30 + cell_state_scale, scratch); - } else { - micro_tensor_utils::CwiseMul(input_gate, cell_gate, n_batch, n_cell, - 30 + cell_state_scale, scratch); - } - micro_tensor_utils::CwiseAdd(cell_state, scratch, n_batch, n_cell, - cell_state); - - if (clip > 0) { - micro_tensor_utils::CwiseClipping(cell_state, n_batch * n_cell, clip); - } -} - -// Calculates the output state tensor of an LSTM step. See Float and hybrid -// versions as well. -// -// Parameters: -// - n_batch: batches: the number of distinct vectors in each array. -// - n_cell, n_output: sizes of vectors. -// - cell_state, output_gate: input vectors, size n_batch*n_cell. -// - cell_state_scale: scaling of cell_state. -// - hidden_scale_[a|b]: effective scale of cell_state.*output_gate -// - hidden_zp: zero_point for cell_state.*output_gate -// - projection_weights, proj_scale_[a|b], projection_bias: -// constant inputs, describing projection matrix and bias. -// - output_state_zp: zero point of output_state. (Input, calibrated value.) -// - quantized_proj_clip: if > 0, clip the output of the projection. -// - output_state: output vector, size n_batch*n_output. Must be contigous. -// - scratch0: scratch area of size n_batch*n_cell -// - scratch1: scratch area of size n_batch*n_cell -// - scratch2: scratch area used by MatrixBatchVectorMultiplyAccumulate -void CalculateLstmOutputInteger8x8_16( - int n_batch, int n_cell, int n_output, const int16_t* cell_state, - int32_t cell_state_scale, const int16_t* output_gate, - int32_t hidden_scale_a, int32_t hidden_scale_b, int32_t hidden_zp, - const int8_t* projection_weights, int32_t proj_scale_a, - int32_t proj_scale_b, const int32_t* projection_bias, - int32_t output_state_zp, int8_t quantized_proj_clip, int8_t* output_state, - int16_t* scratch0, int8_t* scratch1, int32_t* scratch2) { - // Note: unlike float/hybrid, the activation is always Tanh. - micro_tensor_utils::ApplyTanh(15 + cell_state_scale, cell_state, n_batch, - n_cell, scratch0); - micro_tensor_utils::CwiseMul(output_gate, scratch0, hidden_scale_a, - hidden_scale_b, n_batch, n_cell, hidden_zp, - scratch1); - - const bool use_projection = (projection_weights != nullptr); - - if (use_projection) { - // Note: no bias like in float/hybrid - memset(output_state, 0, n_batch * n_output * sizeof(int8_t)); - micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate( - scratch1, projection_bias, projection_weights, proj_scale_a, - proj_scale_b, n_batch, n_cell, n_output, output_state_zp, scratch2, - output_state, nullptr); - if (quantized_proj_clip > 0) { - micro_tensor_utils::CwiseClipping(output_state, n_batch * n_output, - quantized_proj_clip); - } - } else { - std::memcpy(output_state, scratch1, n_batch * n_output * sizeof(int8_t)); - } -} - -// Calculates a single LSTM gate, int8x8_8 version. -// Implements the same functionality as CalculateLstmGateFloat. -void CalculateLstmGateInteger8x8_8( - // Inputs and weights - const int8_t* input, int32_t input_zp, const int8_t* input_to_gate_weight, - const int32_t input_to_gate_scale_a, const int32_t input_to_gate_scale_b, - const int32_t input_times_weights_scale_a, - const int32_t input_times_weights_scale_b, - const int32_t input_times_weights_zp, - // Output state and weights - const int8_t* output_state, const int32_t output_state_zp, - const int8_t* recurrent_to_gate_weight, - const int32_t recurrent_to_gate_scale_a, - const int32_t recurrent_to_gate_scale_b, - const int32_t output_state_times_weights_scale_a, - const int32_t output_state_times_weights_scale_b, - const int32_t output_state_times_weights_zp, - // Layer normalization parameters (layer norm LSTM) - const int16_t* layer_norm_gate_weight, - const int32_t layer_norm_gate_scale_a, - const int32_t layer_norm_gate_scale_b, const int32_t* gate_bias, - // Array sizes - const int n_batch, const int n_input, const int n_output, const int n_cell, - const TfLiteFusedActivation activation, - // Output - int16_t* gate, - // Scratch arrays, both sized n_batch*n_cell - int8_t* scratch0, int8_t* scratch1) { - // Multiply input * input_weights => scratch0 - micro_tensor_utils::MatrixBatchVectorMultiply( - input, input_zp, input_to_gate_weight, input_to_gate_scale_a, - input_to_gate_scale_b, n_batch, n_input, n_cell, scratch0, - input_times_weights_zp); - // Multiply output_state * recurrent_weights => scratch1 - micro_tensor_utils::MatrixBatchVectorMultiply( - output_state, output_state_zp, recurrent_to_gate_weight, - recurrent_to_gate_scale_a, recurrent_to_gate_scale_b, n_batch, n_output, - n_cell, scratch1, output_state_times_weights_zp); - // Add scratch0 + scratch1 => gate - micro_tensor_utils::TwoGateSaturatingAdd( - scratch0, input_times_weights_zp, scratch1, output_state_times_weights_zp, - input_times_weights_scale_a, input_times_weights_scale_b, - output_state_times_weights_scale_a, output_state_times_weights_scale_b, - n_batch, n_cell, gate); - // Apply layer normalization. - micro_tensor_utils::ApplyLayerNormFloat( - gate, layer_norm_gate_weight, layer_norm_gate_scale_a, - layer_norm_gate_scale_b, gate_bias, n_batch, n_cell, gate); - // Apply activation. - switch (activation) { - case kTfLiteActSigmoid: - micro_tensor_utils::ApplySigmoidFloat(gate, n_batch, n_cell, gate); - break; - case kTfLiteActTanh: - micro_tensor_utils::ApplyTanhFloat(gate, n_batch, n_cell, -12, gate); - break; - default: - // Only Sigmoid or Tanh is used. - TFLITE_ASSERT_FALSE; - } -} - -// Calculates the output state tensor of an LSTM step. See Float and hybrid -// versions as well. -// -// Parameters: -// - n_batch: batches: the number of distinct vectors in each array. -// - n_cell, n_output: sizes of vectors. -// - cell_state, output_gate: input vectors, size n_batch*n_cell. -// - projection_weights, proj_scale_[a|b], projection_bias: -// constant inputs, describing projection matrix and bias. -// - output_state_zp: zero point of the output state. -// - quantized_proj_clip: if > 0, clip the output of the projection. -// - output_state: output vector, size n_batch*n_output. Must be contigous. -// - scratch: scratch area of size n_batch*n_cell -void CalculateLstmOutputInteger8x8_8( - int n_batch, int n_cell, int n_output, const int16_t* cell_state, - const int16_t* output_gate, const int8_t* projection_weights, - int32_t proj_scale_a, int32_t proj_scale_b, const int32_t* projection_bias, - int32_t output_state_zp, int32_t quantized_proj_clip, int8_t* output_state, - int16_t* scratch) { - // Note: unlike float/hybrid, the activation is always Tanh. - micro_tensor_utils::ApplyTanhFloat(cell_state, n_batch, n_cell, -15, scratch); - micro_tensor_utils::CwiseMul(output_gate, scratch, n_batch, n_cell, - 15 + 15 - 15, scratch); - // Note: no bias like in float/hybrid - micro_tensor_utils::MatrixBatchVectorMultiply( - scratch, projection_weights, proj_scale_a, proj_scale_b, projection_bias, - n_batch, n_cell, n_output, output_state_zp, output_state); - if (quantized_proj_clip > 0) { - micro_tensor_utils::CwiseClipping(output_state, n_batch * n_output, - quantized_proj_clip); - } -} - -// Performs an LSTM batch inference step for input specified by input_ptr. -// The LSTM cell is specified by the pointers to its weights (*_weights_ptr) and -// biases (*_bias_ptr), and buffers (*_scratch), along with additional -// parameters: -// - params: various LSTM params including activation, clipping, etc., -// - n_batch: size of batch, -// - n_cell: number of cells (or units), -// - n_input: the input size, -// - n_aux_input: the auxiliary input size. -// - n_output: the output size. -// - output_batch_leading_dim: the leading dimension of the output buffer. -// -// Input of size 'n_batch * n_input': -// input_ptr -// Input of size 'n_batch * n_aux_input': -// aux_input_ptr - optional (can be nullptr) -// -// LSTM weights: -// Input weights of size 'n_cell * n_input': -// input_to_input_weights - optional -// input_to_forget_weights -// input_to_cell_weights -// input_to_output_weights -// Auxiliary input weights of size 'n_cell * n_aux_input': -// aux_input_to_input_weights - optional -// aux_input_to_forget_weights - optional -// aux_input_to_cell_weights - optional -// aux_input_to_output_weights - optional -// Recurrent weights of size 'n_cell * n_output': -// recurrent_to_input_weights - optional -// recurrent_to_forget_weights -// recurrent_to_cell_weights -// recurrent_to_input_weights -// Peephole weights of size 'n_cell', representing diagonal matrices. -// cell_to_input_weights - optional -// cell_to_cell_weights - optional -// cell_to_output_weights - optional -// Projection weights of size 'n_output * n_cell' -// projection_weights_ptr - optional -// Gate biases of size 'n_cell': -// input_gate_bias_ptr - optional -// forget_gate_bias_ptr -// cell_gate_bias_ptr -// output_gate_bias_ptr -// -// Layer norm coefficients of size 'n_cell', representing diagonal matrices. -// input_layer_norm_coefficients_ptr - optional -// forget_layer_norm_coefficients_ptr - optional -// cell_layer_norm_coefficients_ptr - optional -// output_layer_norm_coefficients_ptr - optional -// -// The pointers to the cell and output state and the output are updated. -// -// The pointers input_ptr, aux_input_ptr, and output_ptr point to data aligned -// in batch_major order, and each step processes batch_size many inputs from -// input_ptr, and updates batch_size many cell and output states. -// -// The output_batch_dim is output.shape[-1], i.e. the outermost dimension of the -// output tensor, and in most cases will be equal to n_output. It is usually not -// when we want to store the LSTM output into a slice of the output tensor, e.g. -// for bidirectional LSTMs with merge_outputs. In this case, the batched -// operations cannot be used since they assume that the batched outputs are -// contiguous, and we manually loop over the batched outputs. -inline void LstmStepFloat( - const float* input_ptr, const float* input_to_input_weights_ptr, - const float* input_to_forget_weights_ptr, - const float* input_to_cell_weights_ptr, - const float* input_to_output_weights_ptr, const float* aux_input_ptr, - const float* aux_input_to_input_weights_ptr, - const float* aux_input_to_forget_weights_ptr, - const float* aux_input_to_cell_weights_ptr, - const float* aux_input_to_output_weights_ptr, - const float* recurrent_to_input_weights_ptr, - const float* recurrent_to_forget_weights_ptr, - const float* recurrent_to_cell_weights_ptr, - const float* recurrent_to_output_weights_ptr, - const float* cell_to_input_weights_ptr, - const float* cell_to_forget_weights_ptr, - const float* cell_to_output_weights_ptr, - const float* input_layer_norm_coefficients_ptr, - const float* forget_layer_norm_coefficients_ptr, - const float* cell_layer_norm_coefficients_ptr, - const float* output_layer_norm_coefficients_ptr, - const float* input_gate_bias_ptr, const float* forget_gate_bias_ptr, - const float* cell_gate_bias_ptr, const float* output_gate_bias_ptr, - const float* projection_weights_ptr, const float* projection_bias_ptr, - const TfLiteLSTMParams* params, int n_batch, int n_cell, int n_input, - int n_aux_input, int n_output, int output_batch_leading_dim, - float* output_state_ptr, float* cell_state_ptr, float* scratch0, - float* scratch1, float* scratch2, float* scratch3, float* output_ptr) { - // Since we have already checked that weights are all there or none, we can - // check the existence of only one to the get the condition. - const bool use_cifg = (input_to_input_weights_ptr == nullptr); - - // Make named scratch buffers. - float* input_gate_scratch = scratch0; - float* forget_gate_scratch = scratch1; - float* cell_gate_scratch = scratch2; - float* output_gate_scratch = scratch3; - - // Check if inputs are all zeros so we can skip some computations. - const bool is_input_all_zeros = - micro_tensor_utils::IsZeroVector(input_ptr, n_batch * n_input); - const bool is_aux_input_all_zeros = - (aux_input_ptr == nullptr || - micro_tensor_utils::IsZeroVector(aux_input_ptr, n_batch * n_aux_input)); - if (!use_cifg) { - // Calculate the input gate. (If not CIFG.) - CalculateLstmGateFloat( - input_ptr, input_to_input_weights_ptr, aux_input_ptr, - aux_input_to_input_weights_ptr, output_state_ptr, - recurrent_to_input_weights_ptr, cell_state_ptr, - cell_to_input_weights_ptr, input_layer_norm_coefficients_ptr, - input_gate_bias_ptr, n_batch, n_input, n_aux_input, n_output, n_cell, - /*activation=*/kTfLiteActSigmoid, input_gate_scratch, - is_input_all_zeros, is_aux_input_all_zeros); - } - // Calculate the forget gate. - CalculateLstmGateFloat( - input_ptr, input_to_forget_weights_ptr, aux_input_ptr, - aux_input_to_forget_weights_ptr, output_state_ptr, - recurrent_to_forget_weights_ptr, cell_state_ptr, - cell_to_forget_weights_ptr, forget_layer_norm_coefficients_ptr, - forget_gate_bias_ptr, n_batch, n_input, n_aux_input, n_output, n_cell, - /*activation=*/kTfLiteActSigmoid, forget_gate_scratch, is_input_all_zeros, - is_aux_input_all_zeros); - // Calculate the cell update gate. - CalculateLstmGateFloat(input_ptr, input_to_cell_weights_ptr, aux_input_ptr, - aux_input_to_cell_weights_ptr, output_state_ptr, - recurrent_to_cell_weights_ptr, /*cell_state=*/nullptr, - /*cell_to_gate_weights=*/nullptr, - cell_layer_norm_coefficients_ptr, cell_gate_bias_ptr, - n_batch, n_input, n_aux_input, n_output, n_cell, - params->activation, cell_gate_scratch, - is_input_all_zeros, is_aux_input_all_zeros); - // Update the cell state. - UpdateLstmCellFloat(n_batch, n_cell, cell_state_ptr, input_gate_scratch, - forget_gate_scratch, cell_gate_scratch, use_cifg, - params->cell_clip); - // Calculate output gate. - CalculateLstmGateFloat( - input_ptr, input_to_output_weights_ptr, aux_input_ptr, - aux_input_to_output_weights_ptr, output_state_ptr, - recurrent_to_output_weights_ptr, cell_state_ptr, - cell_to_output_weights_ptr, output_layer_norm_coefficients_ptr, - output_gate_bias_ptr, n_batch, n_input, n_aux_input, n_output, n_cell, - /*activation=*/kTfLiteActSigmoid, output_gate_scratch, is_input_all_zeros, - is_aux_input_all_zeros); - // Update the output state. - CalculateLstmOutputFloat(n_batch, n_cell, n_output, cell_state_ptr, - output_gate_scratch, params->activation, - projection_weights_ptr, projection_bias_ptr, - params->proj_clip, output_state_ptr, scratch2); - // Copy output state to the output. Note that the output's rows may not be - // contiguous (output_batch_leading_dim != n_output). - for (int b = 0; b < n_batch; b++) { - std::memcpy(output_ptr + b * output_batch_leading_dim, - output_state_ptr + b * n_output, n_output * sizeof(float)); - } -} - -// Same as above but with quantized weight matrices. In detail: -// Input of size 'n_batch * n_input': -// input_ptr -// Input of size 'n_batch * n_aux_input': -// aux_input_ptr - optional (can be nullptr) -// -// LSTM weights: -// Quantized input weights of size 'n_cell * n_input': -// input_to_input_weights - optional -// input_to_forget_weights -// input_to_cell_weights -// input_to_input_weights -// Quantized auxiliary input weights of size 'n_cell * n_aux_input': -// aux_input_to_input_weights - optional -// aux_input_to_forget_weights - optional -// aux_input_to_cell_weights - optional -// aux_input_to_output_weights - optional -// Quantized recurrent weights of size 'n_cell * n_output': -// recurrent_to_input_weights - optional -// recurrent_to_forget_weights -// recurrent_to_cell_weights -// recurrent_to_input_weights -// Quantized peephole weights of size 'n_cell', representing diagonal matrices. -// cell_to_input_weights - optional -// cell_to_cell_weights - optional -// cell_to_output_weights - optional -// Quantized projection weights of size 'n_output * n_cell' -// projection_weights_ptr - optional -// Weight scales (scalars) for each of the weights above. -// input_to_input_weights_scale - optional -// input_to_forget_weights_scale -// input_to_cell_weights_scale -// input_to_output_weights_scale -// aux_input_to_input_weights_scale - optional -// aux_input_to_forget_weights_scale - optional -// aux_input_to_cell_weights_scale - optional -// aux_input_to_output_weights_scale - optional -// recurrent_to_input_weights_scale - optional -// recurrent_to_forget_weights_scale -// recurrent_to_cell_weights_scale -// recurrent_to_output_weights_scale -// cell_to_input_weights_scale, -// cell_to_forget_weights_scale, -// cell_to_output_weights_scale, -// projection_weights_scale - optional -// Gate biases of size 'n_cell': -// input_gate_bias_ptr - optional -// forget_gate_bias_ptr -// cell_gate_bias_ptr -// output_gate_bias_ptr -// -// Layer norm coefficients of size 'n_cell', representing diagonal matrices. -// input_layer_norm_coefficients_ptr - optional -// forget_layer_norm_coefficients_ptr - optional -// cell_layer_norm_coefficients_ptr - optional -// output_layer_norm_coefficients_ptr - optional -// -// Temporary pre-allocated storage for quantized values: -// quantized_input_ptr (same size as input_ptr) -// quantized_output_state_ptr (same size as output_state_ptr) -// quantized_output_scratch (same size as cell_state_ptr) -// Temporary pre-allocated storage for recovered values: -// recovered_cell_weights (same size as cell_to_*_weights) -// -// Outputs: -// output_state_ptr - size 'n_batch * n_output' -// cell_state_ptr - size 'n_batch * n_cell' -// output_ptr - size 'n_batch * output_batch_leading_dim' -inline void LstmStepHybrid( - const float* input_ptr, const int8_t* input_to_input_weights_ptr, - const uint8_t* input_to_input_weights_ledger_ptr, - float input_to_input_weights_scale, - const int8_t* input_to_forget_weights_ptr, - const uint8_t* input_to_forget_weights_ledger_ptr, - float input_to_forget_weights_scale, - const int8_t* input_to_cell_weights_ptr, - const uint8_t* input_to_cell_weights_ledger_ptr, - float input_to_cell_weights_scale, - const int8_t* input_to_output_weights_ptr, - const uint8_t* input_to_output_weights_ledger_ptr, - float input_to_output_weights_scale, const float* aux_input_ptr, - const int8_t* aux_input_to_input_weights_ptr, - float aux_input_to_input_weights_scale, - const int8_t* aux_input_to_forget_weights_ptr, - float aux_input_to_forget_weights_scale, - const int8_t* aux_input_to_cell_weights_ptr, - float aux_input_to_cell_weights_scale, - const int8_t* aux_input_to_output_weights_ptr, - float aux_input_to_output_weights_scale, - const int8_t* recurrent_to_input_weights_ptr, - const uint8_t* recurrent_to_input_weights_ledger_ptr, - float recurrent_to_input_weights_scale, - const int8_t* recurrent_to_forget_weights_ptr, - const uint8_t* recurrent_to_forget_weights_ledger_ptr, - float recurrent_to_forget_weights_scale, - const int8_t* recurrent_to_cell_weights_ptr, - const uint8_t* recurrent_to_cell_weights_ledger_ptr, - float recurrent_to_cell_weights_scale, - const int8_t* recurrent_to_output_weights_ptr, - const uint8_t* recurrent_to_output_weights_ledger_ptr, - float recurrent_to_output_weights_scale, - const int8_t* cell_to_input_weights_ptr, float cell_to_input_weights_scale, - const int8_t* cell_to_forget_weights_ptr, - float cell_to_forget_weights_scale, - const int8_t* cell_to_output_weights_ptr, - float cell_to_output_weights_scale, - const float* input_layer_norm_coefficients_ptr, - const float* forget_layer_norm_coefficients_ptr, - const float* cell_layer_norm_coefficients_ptr, - const float* output_layer_norm_coefficients_ptr, - const float* input_gate_bias_ptr, const float* forget_gate_bias_ptr, - const float* cell_gate_bias_ptr, const float* output_gate_bias_ptr, - const int8_t* projection_weights_ptr, - const uint8_t* projection_weights_ledger_ptr, - float projection_weights_scale, const float* projection_bias_ptr, - const TfLiteLSTMParams* params, int n_batch, int n_cell, int n_input, - int n_aux_input, int n_output, int output_batch_leading_dim, - float* scratch0, float* scratch1, float* scratch2, float* scratch3, - float* scales, float* input_sf, float* aux_input_sf, float* output_state_sf, - float* scaling_factors_scratch, float* recovered_cell_weights, - int8_t* quantized_input_ptr, int8_t* quantized_aux_input_ptr, - int8_t* quantized_output_state_ptr, int8_t* quantized_output_scratch, - float* output_state_ptr, float* cell_state_ptr, int32_t* accum_scratch_ptr, - float* output_ptr, int32_t* input_zp, int32_t* aux_input_zp, - int32_t* output_state_zp, int32_t* row_sums, int row_sums_size, - bool* compute_row_sums, bool asymmetric_quantize_inputs) { - // Since we have already checked that weights are all there or none, we - // can check the existence of only one to the get the condition. - const bool use_cifg = (input_to_input_weights_ptr == nullptr); - // Make named scratch buffers for the different gates. - float* input_gate_scratch = scratch0; - float* forget_gate_scratch = scratch1; - float* cell_gate_scratch = scratch2; - float* output_gate_scratch = scratch3; - - int32_t* input_to_input_row_sums = nullptr; - int32_t* input_to_forget_row_sums = nullptr; - int32_t* input_to_cell_row_sums = nullptr; - int32_t* input_to_output_row_sums = nullptr; - int32_t* aux_input_to_input_row_sums = nullptr; - int32_t* aux_input_to_forget_row_sums = nullptr; - int32_t* aux_input_to_cell_row_sums = nullptr; - int32_t* aux_input_to_output_row_sums = nullptr; - int32_t* recurrent_to_input_row_sums = nullptr; - int32_t* recurrent_to_forget_row_sums = nullptr; - int32_t* recurrent_to_cell_row_sums = nullptr; - int32_t* recurrent_to_output_row_sums = nullptr; - int32_t* projection_weights_row_sums = nullptr; - - if (asymmetric_quantize_inputs) { - int num_row_sums = use_cifg ? 6 : 8; - if (aux_input_ptr != nullptr) { - num_row_sums += use_cifg ? 3 : 4; - } - if (projection_weights_ptr != nullptr) { - num_row_sums += ceil(static_cast(n_output) / n_cell); - } - TFLITE_DCHECK(row_sums_size == num_row_sums); - input_to_input_row_sums = row_sums; - input_to_forget_row_sums = - use_cifg ? input_to_input_row_sums : input_to_input_row_sums + n_cell; - input_to_cell_row_sums = input_to_forget_row_sums + n_cell; - input_to_output_row_sums = input_to_cell_row_sums + n_cell; - if (aux_input_ptr != nullptr) { - aux_input_to_input_row_sums = input_to_output_row_sums + n_cell; - aux_input_to_forget_row_sums = use_cifg - ? aux_input_to_input_row_sums - : aux_input_to_input_row_sums + n_cell; - aux_input_to_cell_row_sums = aux_input_to_forget_row_sums + n_cell; - aux_input_to_output_row_sums = aux_input_to_cell_row_sums + n_cell; - } - recurrent_to_input_row_sums = aux_input_ptr - ? aux_input_to_output_row_sums + n_cell - : input_to_output_row_sums + n_cell; - recurrent_to_forget_row_sums = use_cifg - ? recurrent_to_input_row_sums - : recurrent_to_input_row_sums + n_cell; - recurrent_to_cell_row_sums = recurrent_to_forget_row_sums + n_cell; - recurrent_to_output_row_sums = recurrent_to_cell_row_sums + n_cell; - if (projection_weights_ptr != nullptr) { - projection_weights_row_sums = recurrent_to_output_row_sums + n_cell; - } - if (*compute_row_sums) { - ComputeRowSums( - input_to_input_row_sums, input_to_forget_row_sums, - input_to_cell_row_sums, input_to_output_row_sums, - aux_input_to_input_row_sums, aux_input_to_forget_row_sums, - aux_input_to_cell_row_sums, aux_input_to_output_row_sums, - recurrent_to_input_row_sums, recurrent_to_forget_row_sums, - recurrent_to_cell_row_sums, recurrent_to_output_row_sums, - projection_weights_row_sums, row_sums, n_cell, n_input, n_aux_input, - n_output, input_to_input_weights_ptr, input_to_forget_weights_ptr, - input_to_cell_weights_ptr, input_to_output_weights_ptr, - aux_input_to_input_weights_ptr, aux_input_to_forget_weights_ptr, - aux_input_to_cell_weights_ptr, aux_input_to_output_weights_ptr, - recurrent_to_input_weights_ptr, recurrent_to_forget_weights_ptr, - recurrent_to_cell_weights_ptr, recurrent_to_output_weights_ptr, - projection_weights_ptr, use_cifg, aux_input_ptr); - *compute_row_sums = false; - } - } - - // Check if inputs are all zeros so we can skip some computations. - const bool is_input_all_zeros = - micro_tensor_utils::IsZeroVector(input_ptr, n_batch * n_input); - const bool is_aux_input_all_zeros = - (aux_input_ptr == nullptr || - micro_tensor_utils::IsZeroVector(aux_input_ptr, n_batch * n_aux_input)); - const bool is_output_state_all_zeros = - micro_tensor_utils::IsZeroVector(output_state_ptr, n_batch * n_output); - // Quantize inputs. - if (!is_input_all_zeros) { - micro_tensor_utils::BatchQuantizeFloats( - input_ptr, n_batch, n_input, quantized_input_ptr, input_sf, input_zp, - asymmetric_quantize_inputs); - } - if (!is_aux_input_all_zeros) { - micro_tensor_utils::BatchQuantizeFloats( - aux_input_ptr, n_batch, n_aux_input, quantized_aux_input_ptr, - aux_input_sf, aux_input_zp, asymmetric_quantize_inputs); - } - if (!is_output_state_all_zeros) { - micro_tensor_utils::BatchQuantizeFloats( - output_state_ptr, n_batch, n_output, quantized_output_state_ptr, - output_state_sf, output_state_zp, asymmetric_quantize_inputs); - } - if (!use_cifg) { - // Calculate the input gate. (If not CIFG.) - CalculateLstmGateHybrid( - quantized_input_ptr, input_sf, input_zp, input_to_input_weights_ptr, - input_to_input_weights_ledger_ptr, input_to_input_weights_scale, - input_to_input_row_sums, quantized_aux_input_ptr, aux_input_sf, - aux_input_zp, aux_input_to_input_weights_ptr, - aux_input_to_input_weights_scale, aux_input_to_input_row_sums, - quantized_output_state_ptr, output_state_sf, output_state_zp, - recurrent_to_input_weights_ptr, recurrent_to_input_weights_ledger_ptr, - recurrent_to_input_weights_scale, recurrent_to_input_row_sums, - cell_state_ptr, cell_to_input_weights_ptr, cell_to_input_weights_scale, - input_layer_norm_coefficients_ptr, input_gate_bias_ptr, n_batch, - n_input, n_aux_input, n_output, n_cell, kTfLiteActSigmoid, - input_gate_scratch, is_input_all_zeros, is_aux_input_all_zeros, - is_output_state_all_zeros, compute_row_sums, scaling_factors_scratch, - recovered_cell_weights, scales, accum_scratch_ptr); - } - // Calculate the forget gate. - CalculateLstmGateHybrid( - quantized_input_ptr, input_sf, input_zp, input_to_forget_weights_ptr, - input_to_forget_weights_ledger_ptr, input_to_forget_weights_scale, - input_to_forget_row_sums, quantized_aux_input_ptr, aux_input_sf, - aux_input_zp, aux_input_to_forget_weights_ptr, - aux_input_to_forget_weights_scale, aux_input_to_forget_row_sums, - quantized_output_state_ptr, output_state_sf, output_state_zp, - recurrent_to_forget_weights_ptr, recurrent_to_forget_weights_ledger_ptr, - recurrent_to_forget_weights_scale, recurrent_to_forget_row_sums, - cell_state_ptr, cell_to_forget_weights_ptr, cell_to_forget_weights_scale, - forget_layer_norm_coefficients_ptr, forget_gate_bias_ptr, n_batch, - n_input, n_aux_input, n_output, n_cell, kTfLiteActSigmoid, - forget_gate_scratch, is_input_all_zeros, is_aux_input_all_zeros, - is_output_state_all_zeros, compute_row_sums, scaling_factors_scratch, - recovered_cell_weights, scales, accum_scratch_ptr); - // Calculate the cell update gate. - CalculateLstmGateHybrid( - quantized_input_ptr, input_sf, input_zp, input_to_cell_weights_ptr, - input_to_cell_weights_ledger_ptr, input_to_cell_weights_scale, - input_to_cell_row_sums, quantized_aux_input_ptr, aux_input_sf, - aux_input_zp, aux_input_to_cell_weights_ptr, - aux_input_to_cell_weights_scale, aux_input_to_cell_row_sums, - quantized_output_state_ptr, output_state_sf, output_state_zp, - recurrent_to_cell_weights_ptr, recurrent_to_cell_weights_ledger_ptr, - recurrent_to_cell_weights_scale, recurrent_to_cell_row_sums, - /*cell_state=*/nullptr, /*cell_to_gate_weights=*/nullptr, - /*cell_to_gate_weights_scale=*/0.0f, cell_layer_norm_coefficients_ptr, - cell_gate_bias_ptr, n_batch, n_input, n_aux_input, n_output, n_cell, - params->activation, cell_gate_scratch, is_input_all_zeros, - is_aux_input_all_zeros, is_output_state_all_zeros, compute_row_sums, - scaling_factors_scratch, recovered_cell_weights, scales, - accum_scratch_ptr); - // Update the cell state. - UpdateLstmCellFloat(n_batch, n_cell, cell_state_ptr, input_gate_scratch, - forget_gate_scratch, cell_gate_scratch, use_cifg, - params->cell_clip); - // Calculate the output gate. - CalculateLstmGateHybrid( - quantized_input_ptr, input_sf, input_zp, input_to_output_weights_ptr, - input_to_output_weights_ledger_ptr, input_to_output_weights_scale, - input_to_output_row_sums, quantized_aux_input_ptr, aux_input_sf, - aux_input_zp, aux_input_to_output_weights_ptr, - aux_input_to_output_weights_scale, aux_input_to_output_row_sums, - quantized_output_state_ptr, output_state_sf, output_state_zp, - recurrent_to_output_weights_ptr, recurrent_to_output_weights_ledger_ptr, - recurrent_to_output_weights_scale, recurrent_to_output_row_sums, - cell_state_ptr, cell_to_output_weights_ptr, cell_to_output_weights_scale, - output_layer_norm_coefficients_ptr, output_gate_bias_ptr, n_batch, - n_input, n_aux_input, n_output, n_cell, kTfLiteActSigmoid, - output_gate_scratch, is_input_all_zeros, is_aux_input_all_zeros, - is_output_state_all_zeros, compute_row_sums, scaling_factors_scratch, - recovered_cell_weights, scales, accum_scratch_ptr); - // Update the output state. - CalculateLstmOutputHybrid( - n_batch, n_cell, n_output, cell_state_ptr, output_gate_scratch, - params->activation, projection_weights_ptr, projection_weights_ledger_ptr, - projection_weights_scale, projection_bias_ptr, params->proj_clip, - output_state_ptr, asymmetric_quantize_inputs, projection_weights_row_sums, - compute_row_sums, scratch2, quantized_output_scratch, input_sf, input_zp, - accum_scratch_ptr, scales); - // Copy output state to the output. Note that the output's rows may not be - // contiguous (output_batch_leading_dim != n_output). - for (int b = 0; b < n_batch; b++) { - std::memcpy(output_ptr + b * output_batch_leading_dim, - output_state_ptr + b * n_output, n_output * sizeof(float)); - } -} - -// Fully quantized lstm kernel for 16 bit gate matmul output. -// -// Input tensor of size n_batch * n_input: -// input_ptr -// -// LSTM weights: -// Quantized input weights of size 'n_cell * n_input': -// input_to_input_weight_ptr - optional -// input_to_forget_weight_ptr - optional -// input_to_cell_weight_ptr - optional -// input_to_output_weight_ptr - optional -// -// Quantized recurrent weights of size 'n_cell * n_output': -// recurrent_to_input_weight_ptr - optional -// recurrent_to_forget_weights_ptr -// recurrent_to_cell_weights_ptr -// recurrent_to_input_weights_ptr -// -// Quantized peephole weights of size 'n_cell', representing diagonal matrices. -// cell_to_input_weights - optional -// cell_to_cell_weights - optional -// cell_to_output_weights - optional -// -// Quantized projection weights of size 'n_output * n_cell' -// projection_weight_ptr - optional -// -// Weight scales (scalars) for each of the weights above. -// effective_input_to_input_scale_a - optional -// effective_input_to_input_scale_b - optional -// effective_input_to_forget_scale_a -// effective_input_to_forget_scale_b -// effective_input_to_cell_scale_a -// effective_input_to_cell_scale_b -// effective_input_to_output_scale_a -// effective_input_to_output_scale_b -// effective_recurrent_to_input_scale_a - optional -// effective_recurrent_to_input_scale_b - optional -// effective_recurrent_to_forget_scale_a -// effective_recurrent_to_forget_scale_b -// effective_recurrent_to_cell_scale_a -// effective_recurrent_to_cell_scale_b -// effective_recurrent_to_output_scale_a -// effective_recurrent_to_output_scale_b -// effective_proj_scale_a - optional -// effective_proj_scale_b - optional -// -// Gate biases of size 'n_cell': -// input_gate_bias_ptr - optional -// forget_gate_bias_ptr -// cell_gate_bias_ptr -// output_gate_bias_ptr -// -// Layer norm coefficients of size 'n_cell', representing diagonal matrices. -// layer_norm_input_weight_ptr - optional -// layer_norm_forget_weight_ptr - optional -// layer_norm_cell_weight_ptr - optional -// layer_norm_output_weight_ptr - optional -// -// Layer norm scales of size 'n_cell'. -// layer_norm_input_scale_a - optional -// layer_norm_input_scale_b - optional -// layer_norm_forget_scale_a - optional -// layer_norm_forget_scale_b - optional -// layer_norm_cell_scale_a - optional -// layer_norm_cell_scale_b - optional -// layer_norm_output_scale_a - optional -// layer_norm_output_scale_b - optional -// -// Scalar values: -// quantized_cell_clip: quantized clip value for cell. -// quantized_proj_clip: quantized clip value for projection. -// cell_state_scale: the power of two scale for cell state. -// -// Zero points: -// output_state_zp: zero point of output state -// hidden_zp: zero point for hidden state. -// -// Temporary pre-allocated storage for the calculation. Each is of size n_cell * -// n_batch. -// scratch0 -// scratch1 -// scratch2 -// scratch3 -// scratch4 -// scratch5: this scratch buffer is created purely for optimizing the -// MatrixBatchVectorMultiplyAccumulate. -// -// Outputs: -// output_state_ptr - size 'n_batch * n_output' -// cell_state_ptr - size 'n_batch * n_cell' -// output_ptr - size 'n_batch * n_output' -// TODO(b/159947023): scratch0 is not used if (!cifg). Don't allocate then. -inline void LstmStepInteger8x8_16( - const int8_t* input_ptr, const int8_t* input_to_input_weight_ptr, - int32_t effective_input_to_input_scale_a, - int32_t effective_input_to_input_scale_b, - const int8_t* input_to_forget_weight_ptr, - int32_t effective_input_to_forget_scale_a, - int32_t effective_input_to_forget_scale_b, - const int8_t* input_to_cell_weight_ptr, - int32_t effective_input_to_cell_scale_a, - int32_t effective_input_to_cell_scale_b, - const int8_t* input_to_output_weight_ptr, - int32_t effective_input_to_output_scale_a, - int32_t effective_input_to_output_scale_b, - const int8_t* recurrent_to_input_weight_ptr, - int32_t effective_recurrent_to_input_scale_a, - int32_t effective_recurrent_to_input_scale_b, - const int8_t* recurrent_to_forget_weight_ptr, - int32_t effective_recurrent_to_forget_scale_a, - int32_t effective_recurrent_to_forget_scale_b, - const int8_t* recurrent_to_cell_weight_ptr, - int32_t effective_recurrent_to_cell_scale_a, - int32_t effective_recurrent_to_cell_scale_b, - const int8_t* recurrent_to_output_weight_ptr, - int32_t effective_recurrent_to_output_scale_a, - int32_t effective_recurrent_to_output_scale_b, - const int16_t* cell_to_input_weight_ptr, - int32_t effective_cell_to_input_scale_a, - int32_t effective_cell_to_input_scale_b, - const int16_t* cell_to_forget_weight_ptr, - int32_t effective_cell_to_forget_scale_a, - int32_t effective_cell_to_forget_scale_b, - const int16_t* cell_to_output_weight_ptr, - int32_t effective_cell_to_output_scale_a, - int32_t effective_cell_to_output_scale_b, - const int8_t* projection_weight_ptr, int32_t effective_proj_scale_a, - int32_t effective_proj_scale_b, int32_t hidden_zp, - int32_t effective_hidden_scale_a, int32_t effective_hidden_scale_b, - const int16_t* layer_norm_input_weight_ptr, - int32_t layer_norm_input_scale_a, int32_t layer_norm_input_scale_b, - const int16_t* layer_norm_forget_weight_ptr, - int32_t layer_norm_forget_scale_a, int32_t layer_norm_forget_scale_b, - const int16_t* layer_norm_cell_weight_ptr, int32_t layer_norm_cell_scale_a, - int32_t layer_norm_cell_scale_b, - const int16_t* layer_norm_output_weight_ptr, - int32_t layer_norm_output_scale_a, int32_t layer_norm_output_scale_b, - const int32_t* input_gate_bias_ptr, const int32_t* forget_gate_bias_ptr, - const int32_t* cell_gate_bias_ptr, const int32_t* output_gate_bias_ptr, - int16_t quantized_cell_clip, int8_t quantized_proj_clip, - int32_t cell_state_scale, int32_t input_variance_guard, - int32_t forget_variance_guard, int32_t cell_variance_guard, - int32_t output_variance_guard, - const int32_t* input_to_forget_effective_bias, - const int32_t* recurrent_to_forget_effective_bias, - const int32_t* input_to_cell_effective_bias, - const int32_t* recurrent_to_cell_effective_bias, - const int32_t* input_to_output_effective_bias, - const int32_t* recurrent_to_output_effective_bias, - const int32_t* input_to_input_effective_bias, - const int32_t* recurrent_to_input_effective_bias, - const int32_t* projection_effective_bias, int n_batch, int n_cell, - int n_input, int n_output, int8_t* output_state_ptr, - int32_t output_state_zp, int16_t* cell_state_ptr, int8_t* output_ptr, - int16_t* scratch0, int16_t* scratch1, int16_t* scratch2, int16_t* scratch3, - int8_t* scratch4, int32_t* scratch5) { - // Make named scratch buffers for the different gates. - int16_t* input_gate_scratch = scratch0; - int16_t* forget_gate_scratch = scratch1; - int16_t* cell_gate_scratch = scratch2; - int16_t* output_gate_scratch = scratch3; - - // Since we have already checked that weights are all there or none, we - // can check the existence of only one to the get the condition. - const bool use_cifg = (input_to_input_weight_ptr == nullptr); - - // Check for nullptrs. - TFLITE_DCHECK(input_to_forget_effective_bias); - TFLITE_DCHECK(recurrent_to_forget_effective_bias); - TFLITE_DCHECK(input_to_cell_effective_bias); - TFLITE_DCHECK(recurrent_to_cell_effective_bias); - TFLITE_DCHECK(input_to_output_effective_bias); - TFLITE_DCHECK(recurrent_to_output_effective_bias); - if (!use_cifg) { - TFLITE_DCHECK(input_to_input_effective_bias); - TFLITE_DCHECK(recurrent_to_input_effective_bias); - } - const bool use_projection = (projection_weight_ptr != nullptr); - if (use_projection) { - TFLITE_DCHECK(projection_effective_bias); - } - if (!use_cifg) { - // Calculate the input gate. (If not CIFG.) - CalculateLstmGateInteger8x8_16( - input_ptr, input_to_input_weight_ptr, input_to_input_effective_bias, - effective_input_to_input_scale_a, effective_input_to_input_scale_b, - output_state_ptr, recurrent_to_input_weight_ptr, - recurrent_to_input_effective_bias, effective_recurrent_to_input_scale_a, - effective_recurrent_to_input_scale_b, cell_state_ptr, - cell_to_input_weight_ptr, effective_cell_to_input_scale_a, - effective_cell_to_input_scale_b, layer_norm_input_weight_ptr, - input_gate_bias_ptr, layer_norm_input_scale_a, layer_norm_input_scale_b, - input_variance_guard, n_batch, n_input, n_output, n_cell, - kTfLiteActSigmoid, input_gate_scratch, scratch5); - } - // Calculate the forget gate. - CalculateLstmGateInteger8x8_16( - input_ptr, input_to_forget_weight_ptr, input_to_forget_effective_bias, - effective_input_to_forget_scale_a, effective_input_to_forget_scale_b, - output_state_ptr, recurrent_to_forget_weight_ptr, - recurrent_to_forget_effective_bias, effective_recurrent_to_forget_scale_a, - effective_recurrent_to_forget_scale_b, cell_state_ptr, - cell_to_forget_weight_ptr, effective_cell_to_forget_scale_a, - effective_cell_to_forget_scale_b, layer_norm_forget_weight_ptr, - forget_gate_bias_ptr, layer_norm_forget_scale_a, - layer_norm_forget_scale_b, forget_variance_guard, n_batch, n_input, - n_output, n_cell, kTfLiteActSigmoid, forget_gate_scratch, scratch5); - // Calculate the cell update gate. - CalculateLstmGateInteger8x8_16( - input_ptr, input_to_cell_weight_ptr, input_to_cell_effective_bias, - effective_input_to_cell_scale_a, effective_input_to_cell_scale_b, - output_state_ptr, recurrent_to_cell_weight_ptr, - recurrent_to_cell_effective_bias, effective_recurrent_to_cell_scale_a, - effective_recurrent_to_cell_scale_b, cell_state_ptr, - /*cell_to_gate_weights=*/nullptr, /*cell_to_gate_scale_a=*/0, - /*cell_to_gate_scale_b=*/0, layer_norm_cell_weight_ptr, - cell_gate_bias_ptr, layer_norm_cell_scale_a, layer_norm_cell_scale_b, - cell_variance_guard, n_batch, n_input, n_output, n_cell, kTfLiteActTanh, - cell_gate_scratch, scratch5); - // Update the cell state. - UpdateLstmCellInteger(n_batch, n_cell, cell_state_ptr, cell_state_scale, - input_gate_scratch, forget_gate_scratch, - cell_gate_scratch, use_cifg, quantized_cell_clip); - // Calculate the output gate. - CalculateLstmGateInteger8x8_16( - input_ptr, input_to_output_weight_ptr, input_to_output_effective_bias, - effective_input_to_output_scale_a, effective_input_to_output_scale_b, - output_state_ptr, recurrent_to_output_weight_ptr, - recurrent_to_output_effective_bias, effective_recurrent_to_output_scale_a, - effective_recurrent_to_output_scale_b, cell_state_ptr, - cell_to_output_weight_ptr, effective_cell_to_output_scale_a, - effective_cell_to_output_scale_b, layer_norm_output_weight_ptr, - output_gate_bias_ptr, layer_norm_output_scale_a, - layer_norm_output_scale_b, output_variance_guard, n_batch, n_input, - n_output, n_cell, kTfLiteActSigmoid, output_gate_scratch, scratch5); - // Update the output state. - CalculateLstmOutputInteger8x8_16( - n_batch, n_cell, n_output, cell_state_ptr, cell_state_scale, - output_gate_scratch, effective_hidden_scale_a, effective_hidden_scale_b, - hidden_zp, projection_weight_ptr, effective_proj_scale_a, - effective_proj_scale_b, projection_effective_bias, output_state_zp, - quantized_proj_clip, output_state_ptr, scratch0, scratch4, scratch5); - // Copy output state to the output. Note that unlike float or hybrid, output - // is always contiguous. - std::memcpy(output_ptr, output_state_ptr, - n_batch * n_output * sizeof(int8_t)); -} - -// Fully quantized lstm kernel for 8 bit gate matmul output. -// -// Input tensor of size n_batch * n_input: -// input_ptr -// -// LSTM weights: -// Quantized input weights of size 'n_cell * n_input': -// input_to_input_weight_ptr - optional -// input_to_forget_weight_ptr - optional -// input_to_cell_weight_ptr - optional -// input_to_output_weight_ptr - optional -// -// Quantized recurrent weights of size 'n_cell * n_output': -// recurrent_to_input_weight_ptr - optional -// recurrent_to_forget_weights_ptr -// recurrent_to_cell_weights_ptr -// recurrent_to_input_weights_ptr -// -// Quantized peephole weights of size 'n_cell', representing diagonal matrices. -// cell_to_input_weights - optional -// cell_to_cell_weights - optional -// cell_to_output_weights - optional -// -// Quantized projection weights of size 'n_output * n_cell' -// projection_weight_ptr - optional -// -// Weight scales (scalars) for each of the weights above. -// effective_input_to_input_scale_a - optional -// effective_input_to_input_scale_b - optional -// effective_input_to_forget_scale_a -// effective_input_to_forget_scale_b -// effective_input_to_cell_scale_a -// effective_input_to_cell_scale_b -// effective_input_to_output_scale_a -// effective_input_to_output_scale_b -// effective_recurrent_to_input_scale_a - optional -// effective_recurrent_to_input_scale_b - optional -// effective_recurrent_to_forget_scale_a -// effective_recurrent_to_forget_scale_b -// effective_recurrent_to_cell_scale_a -// effective_recurrent_to_cell_scale_b -// effective_recurrent_to_output_scale_a -// effective_recurrent_to_output_scale_b -// effective_proj_scale_a - optional -// effective_proj_scale_b - optional -// -// Gate biases of size 'n_cell': -// input_gate_bias_ptr - optional -// forget_gate_bias_ptr -// cell_gate_bias_ptr -// output_gate_bias_ptr -// -// Layer norm coefficients of size 'n_cell', representing diagonal matrices. -// layer_norm_input_weight_ptr - optional -// layer_norm_forget_weight_ptr - optional -// layer_norm_cell_weight_ptr - optional -// layer_norm_output_weight_ptr - optional -// -// Layer norm scales of size 'n_cell'. -// layer_norm_input_scale_a - optional -// layer_norm_input_scale_b - optional -// layer_norm_forget_scale_a - optional -// layer_norm_forget_scale_b - optional -// layer_norm_cell_scale_a - optional -// layer_norm_cell_scale_b - optional -// layer_norm_output_scale_a - optional -// layer_norm_output_scale_b - optional -// -// Scalar values: -// quantized_cell_clip: quantized clip value for cell. -// quantized_proj_clip: quantized clip value for projection. -// cell_state_scale: the power of two scale for cell state. -// -// Zero points: -// input_zp: zero point for input tensor. -// output_state_zp: zero point of output state. -// hidden_zp: zero point for hidden state. -// -// Temporary pre-allocated storage for the calculation. Each is of size n_cell * -// n_batch. -// scratch0 -// scratch1 -// scratch2 -// scratch3 -// scratch4 -// scratch5 -// scratch6 -// scratch7 -// -// Outputs: -// output_state_ptr - size 'n_batch * n_output' -// cell_state_ptr - size 'n_batch * n_cell' -// output_ptr - size 'n_batch * n_output' -// -// Can move zero point calculation into Prepare() for better perfomance. -// TODO(b/159947023): scratch5 is unused, remove. -inline void LstmStepInteger8x8_8( - const int8_t* input_ptr, int32_t input_zp, - const int8_t* input_to_input_weight_ptr, - int32_t effective_input_to_input_scale_a, - int32_t effective_input_to_input_scale_b, - const int8_t* input_to_forget_weight_ptr, - int32_t effective_input_to_forget_scale_a, - int32_t effective_input_to_forget_scale_b, - const int8_t* input_to_cell_weight_ptr, - int32_t effective_input_to_cell_scale_a, - int32_t effective_input_to_cell_scale_b, - const int8_t* input_to_output_weight_ptr, - int32_t effective_input_to_output_scale_a, - int32_t effective_input_to_output_scale_b, - const int8_t* recurrent_to_input_weight_ptr, - int32_t effective_recurrent_to_input_scale_a, - int32_t effective_recurrent_to_input_scale_b, - const int8_t* recurrent_to_forget_weight_ptr, - int32_t effective_recurrent_to_forget_scale_a, - int32_t effective_recurrent_to_forget_scale_b, - const int8_t* recurrent_to_cell_weight_ptr, - int32_t effective_recurrent_to_cell_scale_a, - int32_t effective_recurrent_to_cell_scale_b, - const int8_t* recurrent_to_output_weight_ptr, - int32_t effective_recurrent_to_output_scale_a, - int32_t effective_recurrent_to_output_scale_b, - const int8_t* cell_to_input_weight_ptr, - int32_t effective_cell_to_input_scale_a, - int32_t effective_cell_to_input_scale_b, - const int8_t* cell_to_forget_weight_ptr, - int32_t effective_cell_to_forget_scale_a, - int32_t effective_cell_to_forget_scale_b, - const int8_t* cell_to_output_weight_ptr, - int32_t effective_cell_to_output_scale_a, - int32_t effective_cell_to_output_scale_b, - const int8_t* projection_weight_ptr, int32_t effective_proj_scale_a, - int32_t effective_proj_scale_b, const int16_t* layer_norm_input_weight_ptr, - int32_t layer_norm_input_scale_a, int32_t layer_norm_input_scale_b, - const int16_t* layer_norm_forget_weight_ptr, - int32_t layer_norm_forget_scale_a, int32_t layer_norm_forget_scale_b, - const int16_t* layer_norm_cell_weight_ptr, int32_t layer_norm_cell_scale_a, - int32_t layer_norm_cell_scale_b, - const int16_t* layer_norm_output_weight_ptr, - int32_t layer_norm_output_scale_a, int32_t layer_norm_output_scale_b, - const int32_t* input_gate_bias_ptr, const int32_t* forget_gate_bias_ptr, - const int32_t* cell_gate_bias_ptr, const int32_t* output_gate_bias_ptr, - const int32_t* projection_bias_ptr, const TfLiteLSTMParams* params, - const int32_t* intermediate_scale_a, const int32_t* intermediate_scale_b, - const int32_t* intermediate_zp, int16_t quantized_cell_clip, - int8_t quantized_proj_clip, int n_batch, int n_cell, int n_input, - int n_output, int output_batch_leading_dim, int8_t* output_state_ptr, - int32_t output_state_zp, int16_t* cell_state_ptr, int8_t* output_ptr, - int8_t* scratch0, int8_t* scratch1, int16_t* scratch2, int16_t* scratch3, - int16_t* scratch4, int16_t* scratch5, int16_t* scratch6, - int16_t* scratch7) { - // TODO(b/159066113): scratch5 is unused, remove. - - // Make named scratch buffers for the different gates. - int16_t* forget_gate_scratch = scratch2; - int16_t* cell_gate_scratch = scratch3; - int16_t* output_gate_scratch = scratch4; - // no-CIFG is not supported here - - // Calculate the forget gate. - CalculateLstmGateInteger8x8_8( - input_ptr, input_zp, input_to_forget_weight_ptr, - effective_input_to_forget_scale_a, effective_input_to_forget_scale_b, - intermediate_scale_a[2], intermediate_scale_b[2], intermediate_zp[4], - output_state_ptr, output_state_zp, recurrent_to_forget_weight_ptr, - effective_recurrent_to_forget_scale_a, - effective_recurrent_to_forget_scale_b, intermediate_scale_a[3], - intermediate_scale_b[3], intermediate_zp[5], layer_norm_forget_weight_ptr, - layer_norm_forget_scale_a, layer_norm_forget_scale_b, - forget_gate_bias_ptr, n_batch, n_input, n_output, n_cell, - kTfLiteActSigmoid, forget_gate_scratch, scratch0, scratch1); - // Calculate the cell update gate. - CalculateLstmGateInteger8x8_8( - input_ptr, input_zp, input_to_cell_weight_ptr, - effective_input_to_cell_scale_a, effective_input_to_cell_scale_b, - intermediate_scale_a[4], intermediate_scale_b[4], intermediate_zp[7], - output_state_ptr, output_state_zp, recurrent_to_cell_weight_ptr, - effective_recurrent_to_cell_scale_a, effective_recurrent_to_cell_scale_b, - intermediate_scale_a[5], intermediate_scale_b[5], intermediate_zp[8], - layer_norm_cell_weight_ptr, layer_norm_cell_scale_a, - layer_norm_cell_scale_b, cell_gate_bias_ptr, n_batch, n_input, n_output, - n_cell, kTfLiteActTanh, cell_gate_scratch, scratch0, scratch1); - // Update the cell state. - UpdateLstmCellInteger(n_batch, n_cell, cell_state_ptr, - /*cell_state_scale=*/-15, /*input_gate=*/nullptr, - forget_gate_scratch, cell_gate_scratch, - /*use_cifg=*/true, quantized_cell_clip); - // Calculate the output gate. - CalculateLstmGateInteger8x8_8( - input_ptr, input_zp, input_to_output_weight_ptr, - effective_input_to_output_scale_a, effective_input_to_output_scale_b, - intermediate_scale_a[6], intermediate_scale_b[6], intermediate_zp[10], - output_state_ptr, output_state_zp, recurrent_to_output_weight_ptr, - effective_recurrent_to_output_scale_a, - effective_recurrent_to_output_scale_b, intermediate_scale_a[11], - intermediate_scale_b[7], intermediate_zp[7], layer_norm_output_weight_ptr, - layer_norm_output_scale_a, layer_norm_output_scale_b, - output_gate_bias_ptr, n_batch, n_input, n_output, n_cell, - kTfLiteActSigmoid, output_gate_scratch, scratch0, scratch1); - // Update the output state. - CalculateLstmOutputInteger8x8_8( - n_batch, n_cell, n_output, cell_state_ptr, output_gate_scratch, - projection_weight_ptr, effective_proj_scale_a, effective_proj_scale_b, - projection_bias_ptr, output_state_zp, quantized_proj_clip, - output_state_ptr, scratch2); - // Copy output state to the output. Note that unlike float or hybrid, output - // is always contigous. - std::memcpy(output_ptr, output_state_ptr, - n_batch * n_output * sizeof(int8_t)); -} - -} // namespace - -TfLiteStatus EvalFloatLstm( - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* input_to_input_weights, - const TfLiteEvalTensor* input_to_forget_weights, - const TfLiteEvalTensor* input_to_cell_weights, - const TfLiteEvalTensor* input_to_output_weights, - const TfLiteEvalTensor* recurrent_to_input_weights, - const TfLiteEvalTensor* recurrent_to_forget_weights, - const TfLiteEvalTensor* recurrent_to_cell_weights, - const TfLiteEvalTensor* recurrent_to_output_weights, - const TfLiteEvalTensor* cell_to_input_weights, - const TfLiteEvalTensor* cell_to_forget_weights, - const TfLiteEvalTensor* cell_to_output_weights, - const TfLiteEvalTensor* input_layer_norm_coefficients, - const TfLiteEvalTensor* forget_layer_norm_coefficients, - const TfLiteEvalTensor* cell_layer_norm_coefficients, - const TfLiteEvalTensor* output_layer_norm_coefficients, - const TfLiteEvalTensor* aux_input, - const TfLiteEvalTensor* aux_input_to_input_weights, - const TfLiteEvalTensor* aux_input_to_forget_weights, - const TfLiteEvalTensor* aux_input_to_cell_weights, - const TfLiteEvalTensor* aux_input_to_output_weights, - const TfLiteEvalTensor* input_gate_bias, - const TfLiteEvalTensor* forget_gate_bias, - const TfLiteEvalTensor* cell_gate_bias, - const TfLiteEvalTensor* output_gate_bias, - const TfLiteEvalTensor* projection_weights, - const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params, - bool forward_sequence, bool time_major, int output_offset, - float* scratch_buffer, TfLiteEvalTensor* output_state, - TfLiteEvalTensor* cell_state, TfLiteEvalTensor* output) { - TFLITE_DCHECK(input->dims->size >= 2 && input->dims->size <= 3); - int max_time, n_batch; - if (input->dims->size == 3) { - max_time = (time_major) ? input->dims->data[0] : input->dims->data[1]; - n_batch = (time_major) ? input->dims->data[1] : input->dims->data[0]; - } else { - max_time = 1; - n_batch = input->dims->data[0]; - } - const int n_input = input->dims->data[input->dims->size - 1]; - const int aux_input_size = - (aux_input) ? aux_input->dims->data[aux_input->dims->size - 1] : 0; - - // n_cell and n_output will be the same size when there is no projection. - const int n_cell = input_to_output_weights->dims->data[0]; - const int n_output = recurrent_to_output_weights->dims->data[1]; - - // Since we have already checked that weights are all there or none, we can - // check the existence of only one to the get the condition. - const bool use_cifg = (input_to_input_weights == nullptr); - - // Index the scratch buffers pointers to the global scratch buffer. - float* input_gate_scratch = nullptr; - float* cell_gate_scratch = nullptr; - float* forget_gate_scratch = nullptr; - float* output_gate_scratch = nullptr; - if (use_cifg) { - cell_gate_scratch = scratch_buffer; - forget_gate_scratch = scratch_buffer + n_cell * n_batch; - output_gate_scratch = scratch_buffer + 2 * n_cell * n_batch; - } else { - input_gate_scratch = scratch_buffer; - cell_gate_scratch = scratch_buffer + n_cell * n_batch; - forget_gate_scratch = scratch_buffer + 2 * n_cell * n_batch; - output_gate_scratch = scratch_buffer + 3 * n_cell * n_batch; - } - - const int output_batch_leading_dim = - output->dims->data[output->dims->size - 1]; - if (time_major) { - // Loop through the sequence. - const int input_step = n_batch * n_input; - const int output_step = n_batch * output_batch_leading_dim; - for (int t = 0; t < max_time; t++) { - // If this is the forward_sequence, step forward, otherwise step - // backwards. - const int t_rel = forward_sequence ? t : max_time - t - 1; - const float* input_ptr = - tflite::micro::GetTensorData(input) + t_rel * input_step; - const float* aux_input_ptr = nullptr; - if (aux_input) { - aux_input_ptr = - tflite::micro::GetTensorData(aux_input) + t_rel * input_step; - } - float* output_ptr = tflite::micro::GetTensorData(output) + - t_rel * output_step + output_offset; - - LstmStepFloat( - input_ptr, - input_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_input_weights), - input_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_forget_weights), - input_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_cell_weights), - input_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_output_weights), - aux_input_ptr, - aux_input_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(aux_input_to_input_weights), - aux_input_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - aux_input_to_forget_weights), - aux_input_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(aux_input_to_cell_weights), - aux_input_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - aux_input_to_output_weights), - recurrent_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(recurrent_to_input_weights), - recurrent_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_forget_weights), - recurrent_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(recurrent_to_cell_weights), - recurrent_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_output_weights), - cell_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_input_weights), - cell_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_forget_weights), - cell_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_output_weights), - input_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - input_layer_norm_coefficients), - forget_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - forget_layer_norm_coefficients), - cell_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - cell_layer_norm_coefficients), - output_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - output_layer_norm_coefficients), - input_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_gate_bias), - forget_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(forget_gate_bias), - cell_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_gate_bias), - output_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(output_gate_bias), - projection_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(projection_weights), - projection_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(projection_bias), - params, n_batch, n_cell, n_input, aux_input_size, n_output, - output_batch_leading_dim, - tflite::micro::GetTensorData(output_state), - tflite::micro::GetTensorData(cell_state), input_gate_scratch, - forget_gate_scratch, cell_gate_scratch, output_gate_scratch, - output_ptr); - } - } else { - for (int b = 0; b < n_batch; b++) { - const int input_step = n_input; - const int output_step = output_batch_leading_dim; - for (int t = 0; t < max_time; t++) { - // If this is the forward_sequence, step forward, otherwise step - // backwards. - const int t_rel = forward_sequence ? t : max_time - t - 1; - const int time_offset = b * max_time + t_rel; - const float* input_ptr = tflite::micro::GetTensorData(input) + - time_offset * input_step; - const float* aux_input_ptr = nullptr; - if (aux_input) { - aux_input_ptr = tflite::micro::GetTensorData(aux_input) + - time_offset * input_step; - } - float* output_ptr = tflite::micro::GetTensorData(output) + - time_offset * output_step + output_offset; - - // Offset the {output,cell}_state pointers to the right batch. - float* output_state_ptr = - tflite::micro::GetTensorData(output_state) + - b * output_batch_leading_dim; - float* cell_state_ptr = - tflite::micro::GetTensorData(cell_state) + b * n_cell; - // Offset the scratch pointers to the right batch. - float* input_gate_scratch_ptr = - input_gate_scratch ? input_gate_scratch + b * n_cell : nullptr; - float* forget_gate_scratch_ptr = forget_gate_scratch + b * n_cell; - float* cell_gate_scratch_ptr = cell_gate_scratch + b * n_cell; - float* output_gate_scratch_ptr = output_gate_scratch + b * n_cell; - - LstmStepFloat( - input_ptr, - input_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_input_weights), - input_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_forget_weights), - input_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_cell_weights), - input_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_output_weights), - aux_input_ptr, - aux_input_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - aux_input_to_input_weights), - aux_input_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - aux_input_to_forget_weights), - aux_input_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - aux_input_to_cell_weights), - aux_input_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - aux_input_to_output_weights), - recurrent_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_input_weights), - recurrent_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_forget_weights), - recurrent_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_cell_weights), - recurrent_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_output_weights), - cell_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_input_weights), - cell_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_forget_weights), - cell_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_output_weights), - input_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - input_layer_norm_coefficients), - forget_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - forget_layer_norm_coefficients), - cell_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - cell_layer_norm_coefficients), - output_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - output_layer_norm_coefficients), - input_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_gate_bias), - forget_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(forget_gate_bias), - cell_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_gate_bias), - output_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(output_gate_bias), - projection_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(projection_weights), - projection_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(projection_bias), - params, - /*n_batch=*/1, n_cell, n_input, aux_input_size, n_output, - output_batch_leading_dim, output_state_ptr, cell_state_ptr, - input_gate_scratch_ptr, forget_gate_scratch_ptr, - cell_gate_scratch_ptr, output_gate_scratch_ptr, output_ptr); - } - } - } - return kTfLiteOk; -} - -TfLiteStatus EvalHybridLstm( - const HybridLstmScales* hybrid_lstm_scales, const TfLiteEvalTensor* input, - const TfLiteEvalTensor* input_to_input_weights, - const TfLiteEvalTensor* input_to_input_weights_ledger, - const TfLiteEvalTensor* input_to_forget_weights, - const TfLiteEvalTensor* input_to_forget_weights_ledger, - const TfLiteEvalTensor* input_to_cell_weights, - const TfLiteEvalTensor* input_to_cell_weights_ledger, - const TfLiteEvalTensor* input_to_output_weights, - const TfLiteEvalTensor* input_to_output_weights_ledger, - const TfLiteEvalTensor* recurrent_to_input_weights, - const TfLiteEvalTensor* recurrent_to_input_weights_ledger, - const TfLiteEvalTensor* recurrent_to_forget_weights, - const TfLiteEvalTensor* recurrent_to_forget_weights_ledger, - const TfLiteEvalTensor* recurrent_to_cell_weights, - const TfLiteEvalTensor* recurrent_to_cell_weights_ledger, - const TfLiteEvalTensor* recurrent_to_output_weights, - const TfLiteEvalTensor* recurrent_to_output_weights_ledger, - const TfLiteEvalTensor* cell_to_input_weights, - const TfLiteEvalTensor* cell_to_forget_weights, - const TfLiteEvalTensor* cell_to_output_weights, - const TfLiteEvalTensor* input_layer_norm_coefficients, - const TfLiteEvalTensor* forget_layer_norm_coefficients, - const TfLiteEvalTensor* cell_layer_norm_coefficients, - const TfLiteEvalTensor* output_layer_norm_coefficients, - const TfLiteEvalTensor* aux_input, - const TfLiteEvalTensor* aux_input_to_input_weights, - const TfLiteEvalTensor* aux_input_to_forget_weights, - const TfLiteEvalTensor* aux_input_to_cell_weights, - const TfLiteEvalTensor* aux_input_to_output_weights, - const TfLiteEvalTensor* input_gate_bias, - const TfLiteEvalTensor* forget_gate_bias, - const TfLiteEvalTensor* cell_gate_bias, - const TfLiteEvalTensor* output_gate_bias, - const TfLiteEvalTensor* projection_weights, - const TfLiteEvalTensor* projection_weights_ledger, - const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params, - bool forward_sequence, bool time_major, int output_offset, - float* scratch_buffer, float* input_sf, float* aux_input_sf, - float* output_state_sf, float* prod_scaling_factors, - float* recovered_cell_weights, int8_t* input_quantized, - int8_t* aux_input_quantized, int8_t* output_state_quantized, - int8_t* cell_state_quantized, float* scales, TfLiteEvalTensor* output_state, - TfLiteEvalTensor* cell_state, int32_t* output_scratch_buffer, - TfLiteEvalTensor* output, int32_t* input_zp, int32_t* aux_input_zp, - int32_t* output_state_zp, int32_t* row_sums, int row_sums_size, - bool* compute_row_sums) { - TFLITE_DCHECK(input->dims->size >= 2 && input->dims->size <= 3); - const int n_input = input->dims->data[input->dims->size - 1]; - int max_time, n_batch; - if (input->dims->size == 2) { - max_time = 1; - n_batch = input->dims->data[0]; - } else { - max_time = (time_major) ? input->dims->data[0] : input->dims->data[1]; - n_batch = (time_major) ? input->dims->data[1] : input->dims->data[0]; - } - const int aux_input_size = - (aux_input) ? aux_input->dims->data[aux_input->dims->size - 1] : 0; - // n_cell and n_output will be the same size when there is no projection. - const int n_cell = input_to_output_weights->dims->data[0]; - const int n_output = recurrent_to_output_weights->dims->data[1]; - - // Since we have already checked that weights are all there or none, we can - // check the existence of only one to get the condition. - const bool use_cifg = (input_to_input_weights == nullptr); - - float* input_gate_scratch = nullptr; - float* cell_gate_scratch = nullptr; - float* forget_gate_scratch = nullptr; - float* output_gate_scratch = nullptr; - if (use_cifg) { - cell_gate_scratch = scratch_buffer; - forget_gate_scratch = scratch_buffer + n_cell * n_batch; - output_gate_scratch = scratch_buffer + 2 * n_cell * n_batch; - } else { - input_gate_scratch = scratch_buffer; - cell_gate_scratch = scratch_buffer + n_cell * n_batch; - forget_gate_scratch = scratch_buffer + 2 * n_cell * n_batch; - output_gate_scratch = scratch_buffer + 3 * n_cell * n_batch; - } - - const int output_batch_leading_dim = - output->dims->data[output->dims->size - 1]; - - int32_t* input_zp_ptr = nullptr; - int32_t* aux_input_zp_ptr = nullptr; - int32_t* output_state_zp_ptr = nullptr; - int32_t* row_sums_ptr = nullptr; - if (params->asymmetric_quantize_inputs) { - input_zp_ptr = input_zp; - aux_input_zp_ptr = aux_input_zp; - output_state_zp_ptr = output_state_zp; - row_sums_ptr = row_sums; - } - - if (time_major) { - // Feed the sequence into the LSTM step-by-step. - const int input_step = n_batch * n_input; - const int output_step = n_batch * output_batch_leading_dim; - for (int t = 0; t < max_time; t++) { - // If this is the forward_sequence, step forward, otherwise step - // backwards. - const int t_rel = forward_sequence ? t : max_time - t - 1; - const float* input_ptr = - tflite::micro::GetTensorData(input) + t_rel * input_step; - const float* aux_input_ptr = nullptr; - if (aux_input) { - aux_input_ptr = - tflite::micro::GetTensorData(aux_input) + t_rel * input_step; - } - float* output_ptr = tflite::micro::GetTensorData(output) + - t_rel * output_step + output_offset; - LstmStepHybrid( - input_ptr, - input_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_input_weights), - input_to_input_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - input_to_input_weights_ledger), - hybrid_lstm_scales->input_to_input_weights_scale, - input_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_forget_weights), - input_to_forget_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - input_to_forget_weights_ledger), - hybrid_lstm_scales->input_to_forget_weights_scale, - input_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_cell_weights), - input_to_cell_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - input_to_cell_weights_ledger), - hybrid_lstm_scales->input_to_cell_weights_scale, - input_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_output_weights), - input_to_output_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - input_to_output_weights_ledger), - hybrid_lstm_scales->input_to_output_weights_scale, aux_input_ptr, - aux_input_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - aux_input_to_input_weights), - hybrid_lstm_scales->aux_input_to_input_weights_scale, - aux_input_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - aux_input_to_forget_weights), - hybrid_lstm_scales->aux_input_to_forget_weights_scale, - aux_input_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(aux_input_to_cell_weights), - hybrid_lstm_scales->aux_input_to_cell_weights_scale, - aux_input_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - aux_input_to_output_weights), - hybrid_lstm_scales->aux_input_to_output_weights_scale, - recurrent_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_input_weights), - recurrent_to_input_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_input_weights_ledger), - hybrid_lstm_scales->recurrent_to_input_weights_scale, - recurrent_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_forget_weights), - recurrent_to_forget_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_forget_weights_ledger), - hybrid_lstm_scales->recurrent_to_forget_weights_scale, - recurrent_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(recurrent_to_cell_weights), - recurrent_to_cell_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_cell_weights_ledger), - hybrid_lstm_scales->recurrent_to_cell_weights_scale, - recurrent_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_output_weights), - recurrent_to_output_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_output_weights_ledger), - hybrid_lstm_scales->recurrent_to_output_weights_scale, - cell_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_input_weights), - hybrid_lstm_scales->cell_to_input_weights_scale, - cell_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_forget_weights), - hybrid_lstm_scales->cell_to_forget_weights_scale, - cell_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_output_weights), - hybrid_lstm_scales->cell_to_output_weights_scale, - input_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - input_layer_norm_coefficients), - forget_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - forget_layer_norm_coefficients), - cell_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - cell_layer_norm_coefficients), - output_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - output_layer_norm_coefficients), - input_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_gate_bias), - forget_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(forget_gate_bias), - cell_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_gate_bias), - output_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(output_gate_bias), - projection_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(projection_weights), - projection_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - projection_weights_ledger), - hybrid_lstm_scales->projection_weights_scale, - projection_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(projection_bias), - params, n_batch, n_cell, n_input, aux_input_size, n_output, - output_batch_leading_dim, input_gate_scratch, forget_gate_scratch, - cell_gate_scratch, output_gate_scratch, scales, input_sf, - aux_input_sf, output_state_sf, prod_scaling_factors, - recovered_cell_weights, input_quantized, aux_input_quantized, - output_state_quantized, cell_state_quantized, - tflite::micro::GetTensorData(output_state), - tflite::micro::GetTensorData(cell_state), - output_scratch_buffer, output_ptr, input_zp_ptr, aux_input_zp_ptr, - output_state_zp_ptr, row_sums_ptr, row_sums_size, compute_row_sums, - params->asymmetric_quantize_inputs); - } - } else { - for (int b = 0; b < n_batch; b++) { - const int input_step = n_input; - const int output_step = output_batch_leading_dim; - for (int t = 0; t < max_time; t++) { - // If this is the forward_sequence, step forward, otherwise step - // backwards. - const int t_rel = forward_sequence ? t : max_time - t - 1; - const int time_offset = b * max_time + t_rel; - const float* input_ptr = tflite::micro::GetTensorData(input) + - time_offset * input_step; - const float* aux_input_ptr = nullptr; - if (aux_input) { - aux_input_ptr = tflite::micro::GetTensorData(aux_input) + - time_offset * input_step; - } - float* output_ptr = tflite::micro::GetTensorData(output) + - time_offset * output_step + output_offset; - - // Offset the {output,cell}_state pointers to the right batch. - float* output_state_ptr = - tflite::micro::GetTensorData(output_state) + - b * output_batch_leading_dim; - float* cell_state_ptr = - tflite::micro::GetTensorData(cell_state) + b * n_cell; - // Offset the scratch pointers to the right batch. - float* input_gate_scratch_ptr = - input_gate_scratch ? input_gate_scratch + b * n_cell : nullptr; - float* forget_gate_scratch_ptr = forget_gate_scratch + b * n_cell; - float* cell_gate_scratch_ptr = cell_gate_scratch + b * n_cell; - float* output_gate_scratch_ptr = output_gate_scratch + b * n_cell; - - LstmStepHybrid( - input_ptr, - input_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_input_weights), - input_to_input_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - input_to_input_weights_ledger), - hybrid_lstm_scales->input_to_input_weights_scale, - input_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_forget_weights), - input_to_forget_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - input_to_forget_weights_ledger), - hybrid_lstm_scales->input_to_forget_weights_scale, - input_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_cell_weights), - input_to_cell_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - input_to_cell_weights_ledger), - hybrid_lstm_scales->input_to_cell_weights_scale, - input_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_output_weights), - input_to_output_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - input_to_output_weights_ledger), - hybrid_lstm_scales->input_to_output_weights_scale, aux_input_ptr, - aux_input_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - aux_input_to_input_weights), - hybrid_lstm_scales->aux_input_to_input_weights_scale, - aux_input_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - aux_input_to_forget_weights), - hybrid_lstm_scales->aux_input_to_forget_weights_scale, - aux_input_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - aux_input_to_cell_weights), - hybrid_lstm_scales->aux_input_to_cell_weights_scale, - aux_input_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - aux_input_to_output_weights), - hybrid_lstm_scales->aux_input_to_output_weights_scale, - recurrent_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_input_weights), - recurrent_to_input_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_input_weights_ledger), - hybrid_lstm_scales->recurrent_to_input_weights_scale, - recurrent_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_forget_weights), - recurrent_to_forget_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_forget_weights_ledger), - hybrid_lstm_scales->recurrent_to_forget_weights_scale, - recurrent_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_cell_weights), - recurrent_to_cell_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_cell_weights_ledger), - hybrid_lstm_scales->recurrent_to_cell_weights_scale, - recurrent_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_output_weights), - recurrent_to_output_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_output_weights_ledger), - hybrid_lstm_scales->recurrent_to_output_weights_scale, - cell_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_input_weights), - hybrid_lstm_scales->cell_to_input_weights_scale, - cell_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_forget_weights), - hybrid_lstm_scales->cell_to_forget_weights_scale, - cell_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_output_weights), - hybrid_lstm_scales->cell_to_output_weights_scale, - input_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - input_layer_norm_coefficients), - forget_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - forget_layer_norm_coefficients), - cell_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - cell_layer_norm_coefficients), - output_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - output_layer_norm_coefficients), - input_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_gate_bias), - forget_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(forget_gate_bias), - cell_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_gate_bias), - output_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(output_gate_bias), - projection_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(projection_weights), - projection_weights_ledger == nullptr - ? nullptr - : tflite::micro::GetTensorData( - projection_weights_ledger), - hybrid_lstm_scales->projection_weights_scale, - projection_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(projection_bias), - params, - /*n_batch=*/1, n_cell, n_input, aux_input_size, n_output, - output_batch_leading_dim, input_gate_scratch_ptr, - forget_gate_scratch_ptr, cell_gate_scratch_ptr, - output_gate_scratch_ptr, scales, input_sf, aux_input_sf, - output_state_sf, prod_scaling_factors, recovered_cell_weights, - input_quantized, aux_input_quantized, output_state_quantized, - cell_state_quantized, output_state_ptr, cell_state_ptr, - output_scratch_buffer, output_ptr, input_zp_ptr, aux_input_zp_ptr, - output_state_zp_ptr, row_sums_ptr, row_sums_size, compute_row_sums, - params->asymmetric_quantize_inputs); - } - } - } - - return kTfLiteOk; -} - -TfLiteStatus EvalInteger8x8_16Lstm( - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* input_to_input_weights, - const TfLiteEvalTensor* input_to_forget_weights, - const TfLiteEvalTensor* input_to_cell_weights, - const TfLiteEvalTensor* input_to_output_weights, - const TfLiteEvalTensor* recurrent_to_input_weights, - const TfLiteEvalTensor* recurrent_to_forget_weights, - const TfLiteEvalTensor* recurrent_to_cell_weights, - const TfLiteEvalTensor* recurrent_to_output_weights, - const TfLiteEvalTensor* cell_to_input_weights, - const TfLiteEvalTensor* cell_to_forget_weights, - const TfLiteEvalTensor* cell_to_output_weights, - const TfLiteEvalTensor* input_layer_norm_coefficients, - const TfLiteEvalTensor* forget_layer_norm_coefficients, - const TfLiteEvalTensor* cell_layer_norm_coefficients, - const TfLiteEvalTensor* output_layer_norm_coefficients, - const TfLiteEvalTensor* input_gate_bias, - const TfLiteEvalTensor* forget_gate_bias, - const TfLiteEvalTensor* cell_gate_bias, - const TfLiteEvalTensor* output_gate_bias, - const TfLiteEvalTensor* projection_weights, - const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params, - bool forward_sequence, bool time_major, - const IntegerLstmParameter* integer_lstm_param, int32_t output_state_zp, - TfLiteEvalTensor* output_state, TfLiteEvalTensor* cell_state, - TfLiteEvalTensor* output, int16_t* scratch0, int16_t* scratch1, - int16_t* scratch2, int16_t* scratch3, int8_t* scratch4, int32_t* scratch5) { - TFLITE_DCHECK(input->dims->size >= 2 && input->dims->size <= 3); - const int n_input = input->dims->data[input->dims->size - 1]; - int max_time, n_batch; - if (input->dims->size == 2) { - max_time = 1; - n_batch = input->dims->data[0]; - } else { - max_time = (time_major) ? input->dims->data[0] : input->dims->data[1]; - n_batch = (time_major) ? input->dims->data[1] : input->dims->data[0]; - } - - // n_cell and n_output will be the same size when there is no projection. - const int n_cell = input_to_output_weights->dims->data[0]; - const int n_output = recurrent_to_output_weights->dims->data[1]; - - // Get params for time/batch/sequence. - const int output_batch_leading_dim = - output->dims->data[output->dims->size - 1]; - - if (time_major) { - const int input_step = n_batch * n_input; - const int output_step = n_batch * output_batch_leading_dim; - for (int t = 0; t < max_time; t++) { - const int t_rel = t; - int8_t* output_ptr = - tflite::micro::GetTensorData(output) + t_rel * output_step; - const int8_t* input_ptr = - tflite::micro::GetTensorData(input) + t_rel * input_step; - LstmStepInteger8x8_16( - input_ptr, - input_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_input_weights), - integer_lstm_param->effective_input_to_input_scale_a, - integer_lstm_param->effective_input_to_input_scale_b, - input_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_forget_weights), - integer_lstm_param->effective_input_to_forget_scale_a, - integer_lstm_param->effective_input_to_forget_scale_b, - input_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_cell_weights), - integer_lstm_param->effective_input_to_cell_scale_a, - integer_lstm_param->effective_input_to_cell_scale_b, - input_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_output_weights), - integer_lstm_param->effective_input_to_output_scale_a, - integer_lstm_param->effective_input_to_output_scale_b, - recurrent_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_input_weights), - integer_lstm_param->effective_recurrent_to_input_scale_a, - integer_lstm_param->effective_recurrent_to_input_scale_b, - recurrent_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_forget_weights), - integer_lstm_param->effective_recurrent_to_forget_scale_a, - integer_lstm_param->effective_recurrent_to_forget_scale_b, - recurrent_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(recurrent_to_cell_weights), - integer_lstm_param->effective_recurrent_to_cell_scale_a, - integer_lstm_param->effective_recurrent_to_cell_scale_b, - recurrent_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_output_weights), - integer_lstm_param->effective_recurrent_to_output_scale_a, - integer_lstm_param->effective_recurrent_to_output_scale_b, - cell_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_input_weights), - integer_lstm_param->effective_cell_to_input_scale_a, - integer_lstm_param->effective_cell_to_input_scale_b, - cell_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_forget_weights), - integer_lstm_param->effective_cell_to_forget_scale_a, - integer_lstm_param->effective_cell_to_forget_scale_b, - cell_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_output_weights), - integer_lstm_param->effective_cell_to_output_scale_a, - integer_lstm_param->effective_cell_to_output_scale_b, - projection_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(projection_weights), - integer_lstm_param->effective_proj_scale_a, - integer_lstm_param->effective_proj_scale_b, - integer_lstm_param->hidden_zp, - integer_lstm_param->effective_hidden_scale_a, - integer_lstm_param->effective_hidden_scale_b, - input_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - input_layer_norm_coefficients), - integer_lstm_param->layer_norm_input_scale_a, - integer_lstm_param->layer_norm_input_scale_b, - forget_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - forget_layer_norm_coefficients), - integer_lstm_param->layer_norm_forget_scale_a, - integer_lstm_param->layer_norm_forget_scale_b, - cell_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - cell_layer_norm_coefficients), - integer_lstm_param->layer_norm_cell_scale_a, - integer_lstm_param->layer_norm_cell_scale_b, - output_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - output_layer_norm_coefficients), - integer_lstm_param->layer_norm_output_scale_a, - integer_lstm_param->layer_norm_output_scale_b, - input_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_gate_bias), - forget_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(forget_gate_bias), - cell_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_gate_bias), - output_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(output_gate_bias), - integer_lstm_param->quantized_cell_clip, - integer_lstm_param->quantized_proj_clip, - integer_lstm_param->cell_scale, - integer_lstm_param->input_variance_guard, - integer_lstm_param->forget_variance_guard, - integer_lstm_param->cell_variance_guard, - integer_lstm_param->output_variance_guard, - integer_lstm_param->input_to_forget_effective_bias, - integer_lstm_param->recurrent_to_forget_effective_bias, - integer_lstm_param->input_to_cell_effective_bias, - integer_lstm_param->recurrent_to_cell_effective_bias, - integer_lstm_param->input_to_output_effective_bias, - integer_lstm_param->recurrent_to_output_effective_bias, - integer_lstm_param->input_to_input_effective_bias, - integer_lstm_param->recurrent_to_input_effective_bias, - integer_lstm_param->projection_effective_bias, n_batch, n_cell, - n_input, n_output, tflite::micro::GetTensorData(output_state), - output_state_zp, tflite::micro::GetTensorData(cell_state), - output_ptr, scratch0, scratch1, scratch2, scratch3, scratch4, - scratch5); - } - } else { - for (int b = 0; b < n_batch; b++) { - const int input_step = n_input; - const int output_step = output_batch_leading_dim; - for (int t = 0; t < max_time; t++) { - // If this is the forward_sequence, step forward, otherwise step - // backwards. - const int t_rel = forward_sequence ? t : max_time - t - 1; - const int time_offset = b * max_time + t_rel; - const int8_t* input_ptr = tflite::micro::GetTensorData(input) + - time_offset * input_step; - int8_t* output_ptr = tflite::micro::GetTensorData(output) + - time_offset * output_step; - - // Offset the {output,cell}_state pointers to the right batch. - int8_t* output_state_ptr = - tflite::micro::GetTensorData(output_state) + - b * output_batch_leading_dim; - int16_t* cell_state_ptr = - tflite::micro::GetTensorData(cell_state) + b * n_cell; - - LstmStepInteger8x8_16( - input_ptr, - input_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_input_weights), - integer_lstm_param->effective_input_to_input_scale_a, - integer_lstm_param->effective_input_to_input_scale_b, - input_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_forget_weights), - integer_lstm_param->effective_input_to_forget_scale_a, - integer_lstm_param->effective_input_to_forget_scale_b, - input_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_cell_weights), - integer_lstm_param->effective_input_to_cell_scale_a, - integer_lstm_param->effective_input_to_cell_scale_b, - input_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_output_weights), - integer_lstm_param->effective_input_to_output_scale_a, - integer_lstm_param->effective_input_to_output_scale_b, - recurrent_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_input_weights), - integer_lstm_param->effective_recurrent_to_input_scale_a, - integer_lstm_param->effective_recurrent_to_input_scale_b, - recurrent_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_forget_weights), - integer_lstm_param->effective_recurrent_to_forget_scale_a, - integer_lstm_param->effective_recurrent_to_forget_scale_b, - recurrent_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_cell_weights), - integer_lstm_param->effective_recurrent_to_cell_scale_a, - integer_lstm_param->effective_recurrent_to_cell_scale_b, - recurrent_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData( - recurrent_to_output_weights), - integer_lstm_param->effective_recurrent_to_output_scale_a, - integer_lstm_param->effective_recurrent_to_output_scale_b, - cell_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_input_weights), - integer_lstm_param->effective_cell_to_input_scale_a, - integer_lstm_param->effective_cell_to_input_scale_b, - cell_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_forget_weights), - integer_lstm_param->effective_cell_to_forget_scale_a, - integer_lstm_param->effective_cell_to_forget_scale_b, - cell_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_output_weights), - integer_lstm_param->effective_cell_to_output_scale_a, - integer_lstm_param->effective_cell_to_output_scale_b, - projection_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(projection_weights), - integer_lstm_param->effective_proj_scale_a, - integer_lstm_param->effective_proj_scale_b, - integer_lstm_param->hidden_zp, - integer_lstm_param->effective_hidden_scale_a, - integer_lstm_param->effective_hidden_scale_b, - input_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - input_layer_norm_coefficients), - integer_lstm_param->layer_norm_input_scale_a, - integer_lstm_param->layer_norm_input_scale_b, - forget_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - forget_layer_norm_coefficients), - integer_lstm_param->layer_norm_forget_scale_a, - integer_lstm_param->layer_norm_forget_scale_b, - cell_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - cell_layer_norm_coefficients), - integer_lstm_param->layer_norm_cell_scale_a, - integer_lstm_param->layer_norm_cell_scale_b, - output_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - output_layer_norm_coefficients), - integer_lstm_param->layer_norm_output_scale_a, - integer_lstm_param->layer_norm_output_scale_b, - input_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_gate_bias), - forget_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(forget_gate_bias), - cell_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_gate_bias), - output_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(output_gate_bias), - integer_lstm_param->quantized_cell_clip, - integer_lstm_param->quantized_proj_clip, - integer_lstm_param->cell_scale, - integer_lstm_param->input_variance_guard, - integer_lstm_param->forget_variance_guard, - integer_lstm_param->cell_variance_guard, - integer_lstm_param->output_variance_guard, - integer_lstm_param->input_to_forget_effective_bias, - integer_lstm_param->recurrent_to_forget_effective_bias, - integer_lstm_param->input_to_cell_effective_bias, - integer_lstm_param->recurrent_to_cell_effective_bias, - integer_lstm_param->input_to_output_effective_bias, - integer_lstm_param->recurrent_to_output_effective_bias, - integer_lstm_param->input_to_input_effective_bias, - integer_lstm_param->recurrent_to_input_effective_bias, - integer_lstm_param->projection_effective_bias, /*n_batch=*/1, - n_cell, n_input, n_output, output_state_ptr, output_state_zp, - cell_state_ptr, output_ptr, scratch0, scratch1, scratch2, scratch3, - scratch4, scratch5); - } - } - } - - return kTfLiteOk; -} - -TfLiteStatus EvalInteger8x8_8Lstm( - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* input_to_input_weights, - const TfLiteEvalTensor* input_to_forget_weights, - const TfLiteEvalTensor* input_to_cell_weights, - const TfLiteEvalTensor* input_to_output_weights, - const TfLiteEvalTensor* recurrent_to_input_weights, - const TfLiteEvalTensor* recurrent_to_forget_weights, - const TfLiteEvalTensor* recurrent_to_cell_weights, - const TfLiteEvalTensor* recurrent_to_output_weights, - const TfLiteEvalTensor* cell_to_input_weights, - const TfLiteEvalTensor* cell_to_forget_weights, - const TfLiteEvalTensor* cell_to_output_weights, - const TfLiteEvalTensor* input_layer_norm_coefficients, - const TfLiteEvalTensor* forget_layer_norm_coefficients, - const TfLiteEvalTensor* cell_layer_norm_coefficients, - const TfLiteEvalTensor* output_layer_norm_coefficients, - const TfLiteEvalTensor* input_gate_bias, - const TfLiteEvalTensor* forget_gate_bias, - const TfLiteEvalTensor* cell_gate_bias, - const TfLiteEvalTensor* output_gate_bias, - const TfLiteEvalTensor* projection_weights, - const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params, - TfLiteEvalTensor* output_state, TfLiteEvalTensor* cell_state, - TfLiteEvalTensor* output, const IntegerLstmParameter* integer_lstm_param, - int32_t input_zp, int32_t output_state_zp, int8_t* scratch0, - int8_t* scratch1, int16_t* scratch2, int16_t* scratch3, int16_t* scratch4, - int16_t* scratch5, int16_t* scratch6, int16_t* scratch7) { - TFLITE_DCHECK(input->dims->size >= 2 && input->dims->size <= 3); - const int n_input = input->dims->data[input->dims->size - 1]; - int max_time, n_batch; - if (input->dims->size == 2) { - max_time = 1; - n_batch = input->dims->data[0]; - } else { - max_time = input->dims->data[0]; - n_batch = input->dims->data[1]; - } - - // n_cell and n_output will be the same size when there is no projection. - const int n_cell = input_to_output_weights->dims->data[0]; - const int n_output = recurrent_to_output_weights->dims->data[1]; - - // Get params for time/batch/sequence. - const int output_batch_leading_dim = - output->dims->data[output->dims->size - 1]; - const int input_step = n_batch * n_input; - const int output_step = n_batch * output_batch_leading_dim; - - for (int t = 0; t < max_time; t++) { - const int t_rel = t; - int8_t* output_ptr = - tflite::micro::GetTensorData(output) + t_rel * output_step; - // Input can be int8 asymmetric or int16 symmetric. - const int8_t* input_ptr = - tflite::micro::GetTensorData(input) + t_rel * input_step; - LstmStepInteger8x8_8( - input_ptr, input_zp, - - input_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_input_weights), - integer_lstm_param->effective_input_to_input_scale_a, - integer_lstm_param->effective_input_to_input_scale_b, - - input_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_forget_weights), - integer_lstm_param->effective_input_to_forget_scale_a, - integer_lstm_param->effective_input_to_forget_scale_b, - - input_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_cell_weights), - integer_lstm_param->effective_input_to_cell_scale_a, - integer_lstm_param->effective_input_to_cell_scale_b, - - input_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_to_output_weights), - integer_lstm_param->effective_input_to_output_scale_a, - integer_lstm_param->effective_input_to_output_scale_b, - - recurrent_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(recurrent_to_input_weights), - integer_lstm_param->effective_recurrent_to_input_scale_a, - integer_lstm_param->effective_recurrent_to_input_scale_b, - - recurrent_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(recurrent_to_forget_weights), - integer_lstm_param->effective_recurrent_to_forget_scale_a, - integer_lstm_param->effective_recurrent_to_forget_scale_b, - - recurrent_to_cell_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(recurrent_to_cell_weights), - integer_lstm_param->effective_recurrent_to_cell_scale_a, - integer_lstm_param->effective_recurrent_to_cell_scale_b, - - recurrent_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(recurrent_to_output_weights), - integer_lstm_param->effective_recurrent_to_output_scale_a, - integer_lstm_param->effective_recurrent_to_output_scale_b, - - cell_to_input_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_input_weights), - integer_lstm_param->effective_cell_to_input_scale_a, - integer_lstm_param->effective_cell_to_input_scale_b, - - cell_to_forget_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_forget_weights), - integer_lstm_param->effective_cell_to_forget_scale_a, - integer_lstm_param->effective_cell_to_forget_scale_b, - - cell_to_output_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_to_output_weights), - integer_lstm_param->effective_cell_to_output_scale_a, - integer_lstm_param->effective_cell_to_output_scale_b, - - projection_weights == nullptr - ? nullptr - : tflite::micro::GetTensorData(projection_weights), - integer_lstm_param->effective_proj_scale_a, - integer_lstm_param->effective_proj_scale_b, - - input_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - input_layer_norm_coefficients), - integer_lstm_param->layer_norm_input_scale_a, - integer_lstm_param->layer_norm_input_scale_b, - - forget_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - forget_layer_norm_coefficients), - integer_lstm_param->layer_norm_forget_scale_a, - integer_lstm_param->layer_norm_forget_scale_b, - - cell_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - cell_layer_norm_coefficients), - integer_lstm_param->layer_norm_cell_scale_a, - integer_lstm_param->layer_norm_cell_scale_b, - - output_layer_norm_coefficients == nullptr - ? nullptr - : tflite::micro::GetTensorData( - output_layer_norm_coefficients), - integer_lstm_param->layer_norm_output_scale_a, - integer_lstm_param->layer_norm_output_scale_b, - - input_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(input_gate_bias), - forget_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(forget_gate_bias), - cell_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(cell_gate_bias), - output_gate_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(output_gate_bias), - projection_bias == nullptr - ? nullptr - : tflite::micro::GetTensorData(projection_bias), - - params, integer_lstm_param->intermediate_scale_a, - integer_lstm_param->intermediate_scale_b, - integer_lstm_param->intermediate_zp, - integer_lstm_param->quantized_cell_clip, - integer_lstm_param->quantized_proj_clip, n_batch, n_cell, n_input, - n_output, output_batch_leading_dim, - tflite::micro::GetTensorData(output_state), output_state_zp, - tflite::micro::GetTensorData(cell_state), output_ptr, scratch0, - scratch1, scratch2, scratch3, scratch4, scratch5, scratch6, scratch7); - } - - return kTfLiteOk; -} - -} // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/lstm_eval.h b/code/components/tflite-lib/tensorflow/lite/micro/kernels/lstm_eval.h deleted file mode 100644 index 218b4938..00000000 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/lstm_eval.h +++ /dev/null @@ -1,250 +0,0 @@ -/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_H_ - -#include -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" - -namespace tflite { - -// Pamameters for integer LSTM. -// Consider split this into two Integer Parameters if more fields are added. -struct IntegerLstmParameter { - int32_t effective_input_to_input_scale_a; - int32_t effective_input_to_input_scale_b; - int32_t effective_recurrent_to_input_scale_a; - int32_t effective_recurrent_to_input_scale_b; - int32_t effective_cell_to_input_scale_a; - int32_t effective_cell_to_input_scale_b; - int32_t effective_input_to_forget_scale_a; - int32_t effective_input_to_forget_scale_b; - int32_t effective_recurrent_to_forget_scale_a; - int32_t effective_recurrent_to_forget_scale_b; - int32_t effective_cell_to_forget_scale_a; - int32_t effective_cell_to_forget_scale_b; - int32_t effective_input_to_cell_scale_a; - int32_t effective_input_to_cell_scale_b; - int32_t effective_recurrent_to_cell_scale_a; - int32_t effective_recurrent_to_cell_scale_b; - int32_t effective_input_to_output_scale_a; - int32_t effective_input_to_output_scale_b; - int32_t effective_recurrent_to_output_scale_a; - int32_t effective_recurrent_to_output_scale_b; - int32_t effective_cell_to_output_scale_a; - int32_t effective_cell_to_output_scale_b; - int32_t effective_proj_scale_a; - int32_t effective_proj_scale_b; - int32_t effective_hidden_scale_a; - int32_t effective_hidden_scale_b; - int32_t layer_norm_input_scale_a; - int32_t layer_norm_input_scale_b; - int32_t layer_norm_forget_scale_a; - int32_t layer_norm_forget_scale_b; - int32_t layer_norm_cell_scale_a; - int32_t layer_norm_cell_scale_b; - int32_t layer_norm_output_scale_a; - int32_t layer_norm_output_scale_b; - // Quantized clip value for cell and projection. Zero value means no clipping. - int16_t quantized_cell_clip; - int8_t quantized_proj_clip; - int32_t hidden_zp; - int32_t cell_scale; - - int32_t input_variance_guard; - int32_t forget_variance_guard; - int32_t cell_variance_guard; - int32_t output_variance_guard; - - // Pre-calculate bias + zero_point * weight. - int32_t* input_to_forget_effective_bias; - int32_t* recurrent_to_forget_effective_bias; - int32_t* input_to_cell_effective_bias; - int32_t* recurrent_to_cell_effective_bias; - int32_t* input_to_output_effective_bias; - int32_t* recurrent_to_output_effective_bias; - int32_t* input_to_input_effective_bias; - int32_t* recurrent_to_input_effective_bias; - int32_t* projection_effective_bias; - - // Scale and zero point for intermediate tensors. - // Used only in the 8x8_8 case. - int32_t intermediate_scale_a[8]; - int32_t intermediate_scale_b[8]; - int32_t intermediate_zp[12]; -}; - -// Scales for hybrid op with integer inputs and float weights -struct HybridLstmScales { - float input_to_input_weights_scale; - float input_to_forget_weights_scale; - float input_to_cell_weights_scale; - float input_to_output_weights_scale; - float aux_input_to_input_weights_scale; - float aux_input_to_forget_weights_scale; - float aux_input_to_cell_weights_scale; - float aux_input_to_output_weights_scale; - float recurrent_to_input_weights_scale; - float recurrent_to_forget_weights_scale; - float recurrent_to_cell_weights_scale; - float recurrent_to_output_weights_scale; - float cell_to_input_weights_scale; - float cell_to_forget_weights_scale; - float cell_to_output_weights_scale; - float projection_weights_scale; -}; - -TfLiteStatus EvalFloatLstm( - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* input_to_input_weights, - const TfLiteEvalTensor* input_to_forget_weights, - const TfLiteEvalTensor* input_to_cell_weights, - const TfLiteEvalTensor* input_to_output_weights, - const TfLiteEvalTensor* recurrent_to_input_weights, - const TfLiteEvalTensor* recurrent_to_forget_weights, - const TfLiteEvalTensor* recurrent_to_cell_weights, - const TfLiteEvalTensor* recurrent_to_output_weights, - const TfLiteEvalTensor* cell_to_input_weights, - const TfLiteEvalTensor* cell_to_forget_weights, - const TfLiteEvalTensor* cell_to_output_weights, - const TfLiteEvalTensor* input_layer_norm_coefficients, - const TfLiteEvalTensor* forget_layer_norm_coefficients, - const TfLiteEvalTensor* cell_layer_norm_coefficients, - const TfLiteEvalTensor* output_layer_norm_coefficients, - const TfLiteEvalTensor* aux_input, - const TfLiteEvalTensor* aux_input_to_input_weights, - const TfLiteEvalTensor* aux_input_to_forget_weights, - const TfLiteEvalTensor* aux_input_to_cell_weights, - const TfLiteEvalTensor* aux_input_to_output_weights, - const TfLiteEvalTensor* input_gate_bias, - const TfLiteEvalTensor* forget_gate_bias, - const TfLiteEvalTensor* cell_gate_bias, - const TfLiteEvalTensor* output_gate_bias, - const TfLiteEvalTensor* projection_weights, - const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params, - bool forward_sequence, bool time_major, int output_offset, - float* scratch_buffer, TfLiteEvalTensor* output_state, - TfLiteEvalTensor* cell_state, TfLiteEvalTensor* output); - -TfLiteStatus EvalHybridLstm( - const HybridLstmScales* hybrid_lstm_scales, const TfLiteEvalTensor* input, - const TfLiteEvalTensor* input_to_input_weights, - const TfLiteEvalTensor* input_to_input_weights_ledger, - const TfLiteEvalTensor* input_to_forget_weights, - const TfLiteEvalTensor* input_to_forget_weights_ledger, - const TfLiteEvalTensor* input_to_cell_weights, - const TfLiteEvalTensor* input_to_cell_weights_ledger, - const TfLiteEvalTensor* input_to_output_weights, - const TfLiteEvalTensor* input_to_output_weights_ledger, - const TfLiteEvalTensor* recurrent_to_input_weights, - const TfLiteEvalTensor* recurrent_to_input_weights_ledger, - const TfLiteEvalTensor* recurrent_to_forget_weights, - const TfLiteEvalTensor* recurrent_to_forget_weights_ledger, - const TfLiteEvalTensor* recurrent_to_cell_weights, - const TfLiteEvalTensor* recurrent_to_cell_weights_ledger, - const TfLiteEvalTensor* recurrent_to_output_weights, - const TfLiteEvalTensor* recurrent_to_output_weights_ledger, - const TfLiteEvalTensor* cell_to_input_weights, - const TfLiteEvalTensor* cell_to_forget_weights, - const TfLiteEvalTensor* cell_to_output_weights, - const TfLiteEvalTensor* input_layer_norm_coefficients, - const TfLiteEvalTensor* forget_layer_norm_coefficients, - const TfLiteEvalTensor* cell_layer_norm_coefficients, - const TfLiteEvalTensor* output_layer_norm_coefficients, - const TfLiteEvalTensor* aux_input, - const TfLiteEvalTensor* aux_input_to_input_weights, - const TfLiteEvalTensor* aux_input_to_forget_weights, - const TfLiteEvalTensor* aux_input_to_cell_weights, - const TfLiteEvalTensor* aux_input_to_output_weights, - const TfLiteEvalTensor* input_gate_bias, - const TfLiteEvalTensor* forget_gate_bias, - const TfLiteEvalTensor* cell_gate_bias, - const TfLiteEvalTensor* output_gate_bias, - const TfLiteEvalTensor* projection_weights, - const TfLiteEvalTensor* projection_weights_ledger, - const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params, - bool forward_sequence, bool time_major, int output_offset, - float* scratch_buffer, float* input_sf, float* aux_input_sf, - float* output_state_sf, float* prod_scaling_factors, - float* recovered_cell_weights, int8_t* input_quantized, - int8_t* aux_input_quantized, int8_t* output_state_quantized, - int8_t* cell_state_quantized, float* scales, TfLiteEvalTensor* output_state, - TfLiteEvalTensor* cell_state, int32_t* output_scratch_buffer, - TfLiteEvalTensor* output, int32_t* input_zp, int32_t* aux_input_zp, - int32_t* output_state_zp, int32_t* row_sums, int row_sums_size, - bool* compute_row_sums); - -TfLiteStatus EvalInteger8x8_16Lstm( - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* input_to_input_weights, - const TfLiteEvalTensor* input_to_forget_weights, - const TfLiteEvalTensor* input_to_cell_weights, - const TfLiteEvalTensor* input_to_output_weights, - const TfLiteEvalTensor* recurrent_to_input_weights, - const TfLiteEvalTensor* recurrent_to_forget_weights, - const TfLiteEvalTensor* recurrent_to_cell_weights, - const TfLiteEvalTensor* recurrent_to_output_weights, - const TfLiteEvalTensor* cell_to_input_weights, - const TfLiteEvalTensor* cell_to_forget_weights, - const TfLiteEvalTensor* cell_to_output_weights, - const TfLiteEvalTensor* input_layer_norm_coefficients, - const TfLiteEvalTensor* forget_layer_norm_coefficients, - const TfLiteEvalTensor* cell_layer_norm_coefficients, - const TfLiteEvalTensor* output_layer_norm_coefficients, - const TfLiteEvalTensor* input_gate_bias, - const TfLiteEvalTensor* forget_gate_bias, - const TfLiteEvalTensor* cell_gate_bias, - const TfLiteEvalTensor* output_gate_bias, - const TfLiteEvalTensor* projection_weights, - const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params, - bool forward_sequence, bool time_major, - const IntegerLstmParameter* integer_lstm_param, int32_t output_state_zp, - TfLiteEvalTensor* output_state, TfLiteEvalTensor* cell_state, - TfLiteEvalTensor* output, int16_t* scratch0, int16_t* scratch1, - int16_t* scratch2, int16_t* scratch3, int8_t* scratch4, int32_t* scratch5); - -TfLiteStatus EvalInteger8x8_8Lstm( - const TfLiteEvalTensor* input, - const TfLiteEvalTensor* input_to_input_weights, - const TfLiteEvalTensor* input_to_forget_weights, - const TfLiteEvalTensor* input_to_cell_weights, - const TfLiteEvalTensor* input_to_output_weights, - const TfLiteEvalTensor* recurrent_to_input_weights, - const TfLiteEvalTensor* recurrent_to_forget_weights, - const TfLiteEvalTensor* recurrent_to_cell_weights, - const TfLiteEvalTensor* recurrent_to_output_weights, - const TfLiteEvalTensor* cell_to_input_weights, - const TfLiteEvalTensor* cell_to_forget_weights, - const TfLiteEvalTensor* cell_to_output_weights, - const TfLiteEvalTensor* input_layer_norm_coefficients, - const TfLiteEvalTensor* forget_layer_norm_coefficients, - const TfLiteEvalTensor* cell_layer_norm_coefficients, - const TfLiteEvalTensor* output_layer_norm_coefficients, - const TfLiteEvalTensor* input_gate_bias, - const TfLiteEvalTensor* forget_gate_bias, - const TfLiteEvalTensor* cell_gate_bias, - const TfLiteEvalTensor* output_gate_bias, - const TfLiteEvalTensor* projection_weights, - const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params, - TfLiteEvalTensor* output_state, TfLiteEvalTensor* cell_state, - TfLiteEvalTensor* output, const IntegerLstmParameter* integer_lstm_param, - int8_t* scratch0, int8_t* scratch1, int16_t* scratch2, int16_t* scratch3, - int16_t* scratch4, int16_t* scratch5, int16_t* scratch6, int16_t* scratch7); - -} // namespace tflite -#endif // TENSORFLOW_LITE_MICRO_KERNELS_LSTM_EVAL_H_ diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/lstm_shared.h b/code/components/tflite-lib/tensorflow/lite/micro/kernels/lstm_shared.h deleted file mode 100644 index ee34b848..00000000 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/lstm_shared.h +++ /dev/null @@ -1,67 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_LSTM_SHARED_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_LSTM_SHARED_H_ - -namespace tflite { - -// Input Tensors of size {n_batch, n_input} -constexpr int kLstmInputTensor = 0; - -// Input weight tensors of size: {n_cell, n_input} -constexpr int kLstmInputToInputWeightsTensor = 1; // Optional -constexpr int kLstmInputToForgetWeightsTensor = 2; -constexpr int kLstmInputToCellWeightsTensor = 3; -constexpr int kLstmInputToOutputWeightsTensor = 4; - -// Recurrent weight tensors of size {n_cell, n_output} -constexpr int kLstmRecurrentToInputWeightsTensor = 5; // Optional -constexpr int kLstmRecurrentToForgetWeightsTensor = 6; -constexpr int kLstmRecurrentToCellWeightsTensor = 7; -constexpr int kLstmRecurrentToOutputWeightsTensor = 8; - -// Peephole weights tensors of size {n_cell}, representing a diagonal matrix. -constexpr int kLstmCellToInputWeightsTensor = 9; // Optional -constexpr int kLstmCellToForgetWeightsTensor = 10; // Optional -constexpr int kLstmCellToOutputWeightsTensor = 11; // Optional - -// Gates bias tensors of size {n_cell} -constexpr int kLstmInputGateBiasTensor = 12; // Optional -constexpr int kLstmForgetGateBiasTensor = 13; -constexpr int kLstmCellGateBiasTensor = 14; -constexpr int kLstmOutputGateBiasTensor = 15; - -// Projection weight tensor of size {n_output, n_cell} -constexpr int kLstmProjectionWeightsTensor = 16; // Optional -// Projection bias tensor of size {n_output} -constexpr int kLstmProjectionBiasTensor = 17; // Optional - -// These state tensors are defined as variable tensors, and will be modified by -// this op. -constexpr int kLstmOutputStateTensor = 18; -constexpr int kLstmCellStateTensor = 19; - -// Layer norm coefficient tensors of size {n_cell}, representing a diagonal -// matrix. -constexpr int kLstmInputLayerNormCoefficientsTensor = 20; // Optional -constexpr int kLstmForgetLayerNormCoefficientsTensor = 21; // Optional -constexpr int kLstmCellLayerNormCoefficientsTensor = 22; // Optional -constexpr int kLstmOutputLayerNormCoefficientsTensor = 23; // Optional - -// Output tensors. -constexpr int kLstmOutputTensor = 0; - -} // namespace tflite -#endif // TENSORFLOW_LITE_MICRO_KERNELS_LSTM_SHARED_H_ diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/maximum_minimum.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/maximum_minimum.cc index 7964f1e6..a6d358fb 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/maximum_minimum.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/maximum_minimum.cc @@ -115,17 +115,29 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace maximum_minimum TfLiteRegistration Register_MAXIMUM() { - return tflite::micro::RegisterOp( - nullptr, nullptr, - maximum_minimum::Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/nullptr, + /*invoke=*/ + maximum_minimum::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_MINIMUM() { - return tflite::micro::RegisterOp( - nullptr, nullptr, - maximum_minimum::Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/nullptr, + /*invoke=*/ + maximum_minimum::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/micro_ops.h b/code/components/tflite-lib/tensorflow/lite/micro/kernels/micro_ops.h index c4dec92d..17064fee 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/micro_ops.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/micro_ops.h @@ -76,14 +76,11 @@ TfLiteRegistration Register_SHAPE(); TfLiteRegistration Register_SLICE(); TfLiteRegistration Register_SPACE_TO_BATCH_ND(); TfLiteRegistration Register_SPACE_TO_DEPTH(); -TfLiteRegistration Register_SQUARED_DIFFERENCE(); TfLiteRegistration Register_SQUEEZE(); TfLiteRegistration Register_SUB(); TfLiteRegistration Register_SVDF(); TfLiteRegistration Register_TRANSPOSE(); TfLiteRegistration Register_TRANSPOSE_CONV(); -// TODO(b/230666079): resolve conflict with xtensa implementation -TfLiteRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM(); TfLiteRegistration Register_VAR_HANDLE(); TfLiteRegistration Register_WHILE(); TfLiteRegistration Register_ZEROS_LIKE(); @@ -106,12 +103,14 @@ TfLiteRegistration Register_LESS_EQUAL(); TfLiteRegistration Register_LOG(); TfLiteRegistration Register_LOGICAL_NOT(); TfLiteRegistration Register_MAXIMUM(); +TfLiteRegistration Register_MEAN(); TfLiteRegistration Register_MINIMUM(); TfLiteRegistration Register_NEG(); TfLiteRegistration Register_NOT_EQUAL(); TfLiteRegistration Register_PACK(); TfLiteRegistration Register_PAD(); TfLiteRegistration Register_PADV2(); +TfLiteRegistration Register_REDUCE_MAX(); TfLiteRegistration Register_RESHAPE(); TfLiteRegistration Register_RESIZE_NEAREST_NEIGHBOR(); TfLiteRegistration Register_ROUND(); @@ -122,6 +121,7 @@ TfLiteRegistration Register_SPLIT_V(); TfLiteRegistration Register_SQRT(); TfLiteRegistration Register_SQUARE(); TfLiteRegistration Register_STRIDED_SLICE(); +TfLiteRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM(); TfLiteRegistration Register_UNPACK(); TfLiteRegistration Register_L2_NORMALIZATION(); TfLiteRegistration Register_TANH(); diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/micro_tensor_utils.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/micro_tensor_utils.cc deleted file mode 100644 index 88b097c7..00000000 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/micro_tensor_utils.cc +++ /dev/null @@ -1,809 +0,0 @@ -/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/micro/kernels/micro_tensor_utils.h" - -#include -#include -#include -#include -#include -#include - -#include "fixedpoint/fixedpoint.h" // from @gemmlowp -#include "tensorflow/lite/kernels/internal/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/cppmath.h" -#include "tensorflow/lite/kernels/op_macros.h" - -namespace tflite { -namespace micro_tensor_utils { - -namespace { -const int32_t kInt16Max = std::numeric_limits::max(); -const int32_t kInt16Min = std::numeric_limits::min(); -} // namespace - -void PortableSymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float* min_value, - float* max_value, float* scaling_factor) { - auto minmax = std::minmax_element(values, values + size); - *min_value = *minmax.first; - *max_value = *minmax.second; - - PortableSymmetricQuantizeFloats(values, size, quantized_values, *min_value, - *max_value, scaling_factor); -} - -void PortableSymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float min_value, - float max_value, float* scaling_factor) { - const int32_t kScale = 127; - const float range = std::max(std::abs(min_value), std::abs(max_value)); - if (range == 0) { - memset(quantized_values, 0, size * sizeof(int8_t)); - *scaling_factor = 1; - return; - } - *scaling_factor = range / kScale; - const float scaling_factor_inv = kScale / range; - for (int i = 0; i < size; ++i) { - const int32_t quantized_value = - static_cast(TfLiteRound(values[i] * scaling_factor_inv)); - // Clamp: just in case some odd numeric offset. - quantized_values[i] = static_cast( - std::min(kScale, std::max(-kScale, quantized_value))); - } -} - -void PortableAsymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, - float* scaling_factor, int32_t* offset) { - const int32_t kMinScale = -128; - const int32_t kMaxScale = 127; - const double qmin_double = kMinScale; - const double qmax_double = kMaxScale; - const auto minmax = std::minmax_element(values, values + size); - const double rmin = static_cast(std::min(0.0f, *minmax.first)); - const double rmax = static_cast(std::max(0.0f, *minmax.second)); - if (rmin == rmax) { - memset(quantized_values, 0, size * sizeof(int8_t)); - *scaling_factor = 1; - *offset = 0; - return; - } else { - double scale = (rmax - rmin) / (qmax_double - qmin_double); - const double zero_point_from_min = qmin_double - rmin / scale; - const double zero_point_from_max = qmax_double - rmax / scale; - const double zero_point_from_min_error = - std::abs(qmin_double) + std::abs(rmin / scale); - const double zero_point_from_max_error = - std::abs(qmax_double) + std::abs(rmax / scale); - const double zero_point_double = - zero_point_from_min_error < zero_point_from_max_error - ? zero_point_from_min - : zero_point_from_max; - int8_t nudged_zero_point = 0; - if (zero_point_double <= qmin_double) { - nudged_zero_point = kMinScale; - } else if (zero_point_double >= qmax_double) { - nudged_zero_point = kMaxScale; - } else { - nudged_zero_point = static_cast(round(zero_point_double)); - } - *scaling_factor = scale; - *offset = nudged_zero_point; - } - const float scaling_factor_inv = 1.0f / *scaling_factor; - for (int i = 0; i < size; ++i) { - const int32_t quantized_value = static_cast( - TfLiteRound(*offset + values[i] * scaling_factor_inv)); - quantized_values[i] = - std::min(kMaxScale, std::max(kMinScale, quantized_value)); - } -} - -void PortableMatrixBatchVectorMultiplyAccumulate(const float* matrix, - int m_rows, int m_cols, - const float* vector, - int n_batch, float* result) { - float* result_in_batch = result; - for (int b = 0; b < n_batch; b++) { - const float* matrix_ptr = matrix; - for (int r = 0; r < m_rows; r++) { - float dot_prod = 0.0f; - const float* vector_in_batch = vector + b * m_cols; - for (int c = 0; c < m_cols; c++) { - dot_prod += *matrix_ptr++ * *vector_in_batch++; - } - *result_in_batch += dot_prod; - ++result_in_batch; - } - } -} - -void PortableMatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vectors, const float* scaling_factors, - int n_batch, float* __restrict__ result) { - for (int batch = 0; batch < n_batch; ++batch, vectors += m_cols) { - const float batch_scaling_factor = scaling_factors[batch]; - // Get the address of the first row. - const int8_t* row_ptr = matrix; - for (int row = 0; row < m_rows; ++row) { - // Initialize the dot product sum for the row to 0. - int32_t dotprod = 0; - // TODO(b/230666277): remove this -#if defined(__GNUC__) - // Prefetch the row to cache. - __builtin_prefetch(row_ptr, 0 /* prefetch for read */, - 3 /* temporal locality */); -#endif - for (int col = 0; col < m_cols; ++col, ++row_ptr) { - dotprod += (*row_ptr) * (vectors[col]); - } // for col - *result += dotprod * batch_scaling_factor; - ++result; - } // for row - } // for batch -} - -void PortableMatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vectors, const float* scaling_factors, - int n_batch, float* __restrict__ result, const float* per_channel_scale, - const int32_t* input_offset, int32_t* scratch, int32_t* row_sums, - bool* compute_row_sums, CpuBackendContext* context) { - if (input_offset == nullptr) { - PortableMatrixBatchVectorMultiplyAccumulate( - matrix, m_rows, m_cols, vectors, scaling_factors, n_batch, result); - return; - } - if (!compute_row_sums || *compute_row_sums) { - PortableReductionSumVector(matrix, row_sums, m_rows, m_cols); - if (compute_row_sums) { - *compute_row_sums = false; - } - } - - for (int batch = 0; batch < n_batch; ++batch, vectors += m_cols) { - const float batch_scaling_factor = scaling_factors[batch]; - const int32_t batch_offset = input_offset[batch]; - const int8_t* row_ptr = matrix; - for (int row = 0; row < m_rows; ++row) { - int32_t dotprod = 0; - float scale = batch_scaling_factor; - if (per_channel_scale) { - scale *= per_channel_scale[row]; - } -#if defined(__GNUC__) - // Prefetch the row to cache. - __builtin_prefetch(row_ptr, 0 /* prefetch for read */, - 3 /* temporal locality */); -#endif - for (int col = 0; col < m_cols; ++col, ++row_ptr) { - dotprod += (*row_ptr) * vectors[col]; - } // for col - dotprod -= row_sums[row] * batch_offset; - *result += dotprod * scale; - ++result; - } // for row - } // for batch -} - -void PortableSparseMatrixBatchVectorMultiplyAccumulate1x4( - const float* __restrict__ matrix, const int32_t* __restrict__ segments, - const int32_t* __restrict__ indices, int m_rows, int m_cols, - const float* __restrict__ vector, int n_batch, float* __restrict__ result) { - const int kBlockSize = 4; - TFLITE_DCHECK_EQ(m_cols % kBlockSize, 0); - for (int batch = 0; batch < n_batch; batch++) { - const float* matrix_ptr = matrix; - for (int row = 0; row < m_rows; row++) { - float dot_prod = 0.0f; - const float* vector_in_batch = vector + batch * m_cols; - for (int i = segments[row]; i < segments[row + 1]; i++) { - const int block_start_index = indices[i] * kBlockSize; - const float* vector_block_in_batch_ptr = - vector_in_batch + block_start_index; - for (int c = 0; c < kBlockSize; c++) { - dot_prod += *matrix_ptr++ * *vector_block_in_batch_ptr++; - } - } - result[batch * m_rows + row] += dot_prod; - } - } -} - -void PortableSparseMatrixBatchVectorMultiplyAccumulate1x16( - const int8_t* __restrict__ matrix, const int32_t* __restrict__ segments, - const int32_t* __restrict__ indices, int m_rows, int m_cols, - const int8_t* __restrict__ vector, const int32_t* __restrict__ bias_vector, - int n_batch, const int32_t input_offset, const int32_t output_multiplier, - const int32_t output_shift, const int32_t output_offset, - const int32_t output_activation_min, const int32_t output_activation_max, - int8_t* __restrict__ result) { - const int kBlockSize = 16; - TFLITE_DCHECK_EQ(m_cols % kBlockSize, 0); - for (int batch = 0; batch < n_batch; ++batch) { - const int8_t* matrix_ptr = matrix; - for (int row = 0; row < m_rows; ++row) { - int32_t dot_prod = 0; - const int8_t* vector_in_batch = vector + batch * m_cols; - for (int i = segments[row]; i < segments[row + 1]; ++i) { - const int block_start_index = indices[i] * kBlockSize; - const int8_t* vector_block_in_batch_ptr = - vector_in_batch + block_start_index; - for (int c = 0; c < kBlockSize; c++) { - dot_prod += *matrix_ptr * *vector_block_in_batch_ptr++; - dot_prod += *matrix_ptr++ * input_offset; - } - } - const int32_t bias_value = bias_vector != nullptr ? bias_vector[row] : 0; - dot_prod = MultiplyByQuantizedMultiplier(dot_prod + bias_value, - output_multiplier, output_shift); - dot_prod += output_offset; - result[batch * m_rows + row] = - static_cast(ActivationFunctionWithMinMax( - dot_prod, output_activation_min, output_activation_max)); - } - } -} - -void PortableSparseMatrixBatchVectorMultiplyAccumulate( - const float* __restrict__ matrix, const uint8_t* __restrict__ ledger, - int m_rows, int m_cols, const float* __restrict__ vector, int n_batch, - float* __restrict__ result) { - const int kBlockSize = 16; - TFLITE_DCHECK_EQ( // NOLINT - m_cols % kBlockSize, 0); - for (int batch = 0; batch < n_batch; batch++) { - const float* matrix_ptr = matrix; - const uint8_t* ledger_ptr = ledger; - for (int row = 0; row < m_rows; row++) { - float dot_prod = 0.0f; - int num_nonzero_blocks = *ledger_ptr++; - if (num_nonzero_blocks > 0) { - const float* vector_in_batch = vector + batch * m_cols; - for (int i = 0; i < num_nonzero_blocks; i++) { - const int block_start_index = *ledger_ptr++ * kBlockSize; - const float* vector_block_in_batch_ptr = - vector_in_batch + block_start_index; - for (int c = 0; c < kBlockSize; c++) { - dot_prod += *matrix_ptr++ * *vector_block_in_batch_ptr++; - } - } - } - result[batch * m_rows + row] += dot_prod; - } - } -} - -void PortableSparseMatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const uint8_t* ledger, const int m_rows, - const int m_cols, const int8_t* __restrict__ vectors, - const float* scaling_factors, int n_batch, float* __restrict__ result) { - static const int kBlockSize = 16; - TFLITE_DCHECK_EQ( // NOLINT - m_cols % kBlockSize, 0); - for (int batch = 0; batch < n_batch; ++batch, vectors += m_cols) { - const float batch_scaling_factor = scaling_factors[batch]; - const uint8_t* ledger_ptr = ledger; - // Get the address of the first row. - const int8_t* row_ptr = matrix; - for (int row = 0; row < m_rows; ++row) { - // Initialize the dot product sum for the row to 0. - int32_t dotprod = 0; -#if defined(__GNUC__) - // Prefetch the row to cache. - __builtin_prefetch(row_ptr, 0 /* prefetch for read */, - 3 /* temporal locality */); -#endif - int num_nonzero_blocks = *ledger_ptr++; - for (int i = 0; i < num_nonzero_blocks; i++) { - const int block_start_index = *ledger_ptr++ * kBlockSize; - const int8_t* vector_block_ptr = vectors + block_start_index; - for (int c = 0; c < kBlockSize; c++) { - dotprod += (*row_ptr++) * (*vector_block_ptr++); - } // for block - } // for num_nonzero_blocks - result[batch * m_rows + row] += dotprod * batch_scaling_factor; - } // for row - } // for batch -} - -template -void PortableMatrixBatchVectorMultiplyAccumulateImpl( - const int8_t* input, const int32_t* bias, - const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, - int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, - T* output) { - const int16_t output_max = std::numeric_limits::max(); - const int16_t output_min = std::numeric_limits::min(); - for (int batch = 0; batch < n_batch; ++batch) { - for (int row = 0; row < n_output; ++row) { - int32_t acc = bias[row]; - for (int col = 0; col < n_input; ++col) { - int8_t input_val = input[batch * n_input + col]; - int8_t weights_val = input_to_gate_weights[row * n_input + col]; - acc += input_val * weights_val; - } - acc = MultiplyByQuantizedMultiplier(acc, multiplier, shift); - acc += output_zp; - acc += output[batch * n_output + row]; - if (acc > output_max) { - acc = output_max; - } - if (acc < output_min) { - acc = output_min; - } - output[batch * n_output + row] = static_cast(acc); - } - } -} - -void PortableMatrixBatchVectorMultiplyAccumulate( - const int8_t* input, const int32_t* bias, - const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, - int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, - int32_t* scratch, int16_t* output, CpuBackendContext* context) { - PortableMatrixBatchVectorMultiplyAccumulateImpl( - input, bias, input_to_gate_weights, multiplier, shift, n_batch, n_input, - n_output, output_zp, output); -} - -void PortableMatrixBatchVectorMultiplyAccumulate( - const int8_t* input, const int32_t* bias, - const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, - int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, - int32_t* scratch, int8_t* output, CpuBackendContext* context) { - PortableMatrixBatchVectorMultiplyAccumulateImpl( - input, bias, input_to_gate_weights, multiplier, shift, n_batch, n_input, - n_output, output_zp, output); -} - -void PortableMatrixBatchVectorMultiply(const int8_t* input, - int32_t input_zeropoint, - const int8_t* input_to_gate_weights, - int32_t input_to_gate_effective_scale_a, - int32_t input_to_gate_effective_scale_b, - int32_t n_batch, int32_t n_input, - int32_t n_cell, int8_t* gate_output, - int8_t gate_output_zp) { - const int32_t int8_max = std::numeric_limits::max(); - const int32_t int8_min = std::numeric_limits::min(); - for (int batch = 0; batch < n_batch; ++batch) { - for (int row = 0; row < n_cell; ++row) { - int32_t acc = 0; - for (int col = 0; col < n_input; ++col) { - int32_t input_val = input[batch * n_input + col]; - int8_t weights_val = input_to_gate_weights[row * n_input + col]; - acc += (input_val - input_zeropoint) * weights_val; - } - acc = MultiplyByQuantizedMultiplier(acc, input_to_gate_effective_scale_a, - input_to_gate_effective_scale_b); - acc += gate_output_zp; - if (acc > int8_max) { - acc = int8_max; - } - if (acc < int8_min) { - acc = int8_min; - } - gate_output[batch * n_cell + row] = static_cast(acc); - } - } -} - -void PortableMatrixBatchVectorMultiply( - const int16_t* hidden, const int8_t* hidden_to_output_weights, - int32_t proj_effective_scale_a, int32_t proj_effective_scale_b, - const int32_t* gate_bias, int32_t n_batch, int32_t n_hidden, - int32_t n_output, int32_t output_zp, int8_t* proj_output) { - const int16_t int8_max = std::numeric_limits::max(); - const int16_t int8_min = std::numeric_limits::min(); - for (int batch = 0; batch < n_batch; ++batch) { - for (int row = 0; row < n_output; ++row) { - int64_t acc = gate_bias[row]; - for (int col = 0; col < n_hidden; ++col) { - int16_t input_val = hidden[batch * n_hidden + col]; - int8_t weights_val = hidden_to_output_weights[row * n_hidden + col]; - int64_t curr = acc; - acc += input_val * weights_val; - if (input_val * weights_val > 0 && acc < curr) { - acc = std::numeric_limits::max(); - } - if (input_val * weights_val < 0 && acc > curr) { - acc = std::numeric_limits::min(); - } - } - acc = MultiplyByQuantizedMultiplier(acc, proj_effective_scale_a, - proj_effective_scale_b); - acc += output_zp; - if (acc > int8_max) { - acc = int8_max; - } - if (acc < int8_min) { - acc = int8_min; - } - proj_output[batch * n_output + row] = acc; - } - } -} - -void PortableApplyLayerNorm(const int16_t* input, - const int16_t* layer_norm_weights, - const int32_t* bias, int32_t layer_norm_scale_a, - int32_t layer_norm_scale_b, int32_t variance_limit, - int n_batch, int n_input, int16_t* output) { - // The square of std::pow(2, 10), which is the extra factor that makes sure - // normalized values has enough resolution. - static const int kTwoToPower20 = 1 << 20; - for (int i = 0; i < n_batch; ++i) { - int64_t sum = 0; - int64_t sum_sq = 0; - for (int j = 0; j < n_input; ++j) { - const int32_t index = i * n_input + j; - int32_t val = static_cast(input[index]); - sum += val; - sum_sq += val * val; - } - int32_t mean = - static_cast(static_cast(sum) * 1024 / n_input); - // TODO(b/173994730): Avoids overflow but only works for POT n_input. - int32_t temp = kTwoToPower20 / n_input; - int64_t variance = - sum_sq * temp - static_cast(mean) * static_cast(mean); - int32_t variance2 = static_cast(variance / kTwoToPower20); - if (variance2 < 1) { - variance2 = variance_limit; - } - int32_t stddev_inverse_a; - int stddev_inverse_b; - GetInvSqrtQuantizedMultiplierExp(variance2, /*reverse_shift*/ -1, - &stddev_inverse_a, &stddev_inverse_b); - - for (int j = 0; j < n_input; ++j) { - const int32_t index = i * n_input + j; - int32_t val = static_cast(input[index]); - int32_t shifted = 1024 * val - mean; - int32_t rescaled = MultiplyByQuantizedMultiplier( - shifted, stddev_inverse_a, stddev_inverse_b); - int64_t val3 = rescaled * layer_norm_weights[j] + bias[j]; - int32_t val4 = - static_cast((val3 > 0 ? val3 + 512 : val3 - 512) / 1024); - int32_t val5 = MultiplyByQuantizedMultiplier(val4, layer_norm_scale_a, - layer_norm_scale_b + 12); - val5 = std::min(std::max(kInt16Min, val5), kInt16Max); - output[index] = static_cast(val5); - } - } -} - -void PortableApplyLayerNormFloat(const int16_t* input, - const int16_t* layer_norm_weights, - int32_t layer_norm_scale_a, - int32_t layer_norm_scale_b, - const int32_t* bias, int n_batch, int n_input, - int16_t* output) { - const int32_t int16_max = std::numeric_limits::max(); - const int32_t int16_min = std::numeric_limits::min(); - const float layer_norm_scale = - layer_norm_scale_a * - std::pow(2.0, static_cast(layer_norm_scale_b - 31)); - const float bias_scale = - static_cast(std::pow(2.0, -10)) * layer_norm_scale; - - for (int batch = 0; batch < n_batch; ++batch) { - float sum = 0.0f; - float sum_sq = 0.0f; - for (int i = 0; i < n_input; ++i) { - const int index = batch * n_input + i; - const float value = static_cast(input[index]); - sum += value; - sum_sq += value * value; - } - const float mean = sum / n_input; - float stddev_inv = 0.0f; - const float variance = sum_sq / n_input - mean * mean; - if (variance == 0) { - stddev_inv = 1.0f / std::sqrt(1e-8f); - } else { - stddev_inv = 1.0f / std::sqrt(variance); - } - for (int i = 0; i < n_input; ++i) { - const int index = batch * n_input + i; - const float normalized_value = - (static_cast(input[index]) - mean) * stddev_inv; - const float weighted_normalized_value = - normalized_value * layer_norm_weights[i] * layer_norm_scale + - bias[i] * bias_scale; - const int32_t quant_output = static_cast(round( - weighted_normalized_value * static_cast(std::pow(2, 12)))); - output[index] = std::min(int16_max, std::max(int16_min, quant_output)); - } - } -} - -void PortableMatrixScalarMultiplyAccumulate(const int8_t* matrix, - int32_t scalar, int32_t n_row, - int32_t n_col, int32_t* output) { - for (int i = 0; i < n_row; ++i) { - int32_t row_sum = 0; - for (int j = 0; j < n_col; ++j) { - row_sum += *matrix++; - } - output[i] += row_sum * scalar; - } -} - -void PortableApplySigmoid(const int16_t* input, int32_t n_batch, - int32_t n_input, int16_t* output) { - for (int batch = 0; batch < n_batch; ++batch) { - for (int c = 0; c < n_input; c++) { - using F3 = gemmlowp::FixedPoint; - using F0 = gemmlowp::FixedPoint; - const int index = batch * n_input + c; - F3 sigmoid_input = F3::FromRaw(input[index]); - F0 sigmoid_output = gemmlowp::logistic(sigmoid_input); - output[index] = sigmoid_output.raw(); - } - } -} - -void PortableApplySigmoidFloat(const int16_t* input, int32_t n_batch, - int32_t n_input, int16_t* output) { - const int32_t int16_max = std::numeric_limits::max(); - const int32_t int16_min = std::numeric_limits::min(); - for (int batch = 0; batch < n_batch; ++batch) { - for (int i = 0; i < n_input; ++i) { - const int index = batch * n_input + i; - const float float_input = - input[index] * static_cast(std::pow(2, -12)); - const float float_output = 1.0f / (1.0f + std::exp(-float_input)); - const int32_t quant_output = static_cast( - float_output * static_cast(std::pow(2, 15))); - const int32_t quant_output_clamped = - std::min(int16_max, std::max(int16_min, quant_output)); - output[index] = static_cast(quant_output_clamped); - } - } -} - -template -void PortableApplyTanhImpl(const int16_t* input, int32_t n_batch, - int32_t n_input, int16_t* output) { - using FX = gemmlowp::FixedPoint; - using F0 = gemmlowp::FixedPoint; - for (int batch = 0; batch < n_batch; ++batch) { - for (int i = 0; i < n_input; ++i) { - const int index = batch * n_input + i; - FX tanh_input = FX::FromRaw(input[index]); - F0 tanh_output = gemmlowp::tanh(tanh_input); - output[index] = tanh_output.raw(); - } - } -} - -void PortableApplyTanh(int32_t integer_bits, const int16_t* input, - int32_t n_batch, int32_t n_input, int16_t* output) { - if (integer_bits > 6) { - TFLITE_ASSERT_FALSE; - } -#define DISPATCH_TANH(i) \ - case i: \ - PortableApplyTanhImpl(input, n_batch, n_input, output); \ - break; - switch (integer_bits) { - DISPATCH_TANH(0); - DISPATCH_TANH(1); - DISPATCH_TANH(2); - DISPATCH_TANH(3); - DISPATCH_TANH(4); - DISPATCH_TANH(5); - DISPATCH_TANH(6); - default: - return; - } -#undef DISPATCH_TANH -} - -void PortableApplyTanhFloat(const int16_t* input, int32_t n_batch, - int32_t n_input, int32_t integer_bits, - int16_t* output) { - const int32_t int16_max = std::numeric_limits::max(); - const int32_t int16_min = std::numeric_limits::min(); - const double two = 2.0; - for (int batch = 0; batch < n_batch; ++batch) { - for (int i = 0; i < n_input; ++i) { - const int index = batch * n_input + i; - const float float_input = - input[index] * std::pow(two, static_cast(integer_bits)); - const float float_output = std::tanh(float_input); - const int32_t quant_output = static_cast( - float_output * static_cast(std::pow(2, 15))); - const int32_t quant_output_clamped = - std::min(int16_max, std::max(int16_min, quant_output)); - output[index] = static_cast(quant_output_clamped); - } - } -} - -void PortableCwiseMul(const int16_t* input_1, const int16_t* input_2, - int n_batch, int n_input, int shift, int16_t* output) { - for (int batch = 0; batch < n_batch; ++batch) { - for (int i = 0; i < n_input; ++i) { - const int index = batch * n_input + i; - const int16_t a = input_1[index]; - const int16_t b = input_2[index]; - const int32_t value = static_cast(a) * static_cast(b); - output[index] = - static_cast(gemmlowp::RoundingDivideByPOT(value, shift)); - } - } -} - -void PortableCwiseMul(const int16_t* input_1, const int16_t* input_2, - int32_t multiplier, int32_t shift, int32_t n_batch, - int32_t n_input, int32_t output_zp, int8_t* output) { - for (int batch = 0; batch < n_batch; ++batch) { - for (int i = 0; i < n_input; ++i) { - const int index = batch * n_input + i; - const int16_t a = input_1[index]; - const int16_t b = input_2[index]; - int32_t value = static_cast(a) * static_cast(b); - value = MultiplyByQuantizedMultiplier(value, multiplier, shift); - value -= output_zp; - value = std::min(std::max(static_cast(-128), value), - static_cast(127)); - - output[index] = static_cast(value); - } - } -} - -void PortableCwiseAdd(const int16_t* input_1, const int16_t* input_2, - int n_batch, int n_input, int16_t* output) { - for (int batch = 0; batch < n_batch; ++batch) { - for (int i = 0; i < n_input; ++i) { - const int index = batch * n_input + i; - int32_t sum = input_1[index] + input_2[index]; - const int32_t sum_clamped = std::min(kInt16Max, std::max(kInt16Min, sum)); - output[index] = static_cast(sum_clamped); - } - } -} - -float PortableVectorVectorDotProduct(const float* vector1, const float* vector2, - int v_size) { - float result = 0.0; - for (int v = 0; v < v_size; v++) { - result += *vector1++ * *vector2++; - } - return result; -} - -namespace { -inline int32_t VectorVectorDotProduct(const int16_t* vector1, - const int16_t* vector2, int v_size) { - int32_t result = 0; - for (int v = 0; v < v_size; v++) { - result += *vector1++ * *vector2++; - } - return result; -} -} // namespace - -void PortableBatchVectorBatchVectorDotProduct(const int16_t* vector1, - const int16_t* vector2, - int v_size, int n_batch, - int32_t* result) { - for (int b = 0; b < n_batch; b++) { - result[b] = VectorVectorDotProduct(vector1, vector2, v_size); - vector1 += v_size; - vector2 += v_size; - } -} - -void PortableVectorBatchVectorCwiseProductAccumulate( - const int16_t* vector, int v_size, const int16_t* batch_vector, int n_batch, - int32_t multiplier, int shift, int16_t* result) { - for (int b = 0; b < n_batch; b++) { - for (int v = 0; v < v_size; v++) { - int32_t prod = vector[v] * *batch_vector++; - prod = MultiplyByQuantizedMultiplier(prod, multiplier, shift); - int32_t output = prod + *result; - output = std::max(std::min(static_cast(32767), output), - static_cast(-32768)); - *result++ = output; - } - } -} - -void PortableSub1Vector(const float* vector, int v_size, float* result) { - for (int v = 0; v < v_size; v++) { - *result++ = 1.0f - *vector++; - } -} - -void PortableSub1Vector(const int16_t* vector, int v_size, int16_t* result) { - static const int16_t kOne = 32767; - for (int v = 0; v < v_size; v++) { - *result++ = kOne - *vector++; - } -} - -void PortableVectorScalarMultiply(const int8_t* vector, const int v_size, - const float scale, float* result) { - for (int v = 0; v < v_size; ++v) { - *result++ = scale * *vector++; - } -} - -void PortableMeanStddevNormalization(const float* __restrict__ input_vector, - float* __restrict__ output_vector, - int v_size, int n_batch) { - for (int batch = 0; batch < n_batch; ++batch) { - float sum = 0.0f; - for (int i = 0; i < v_size; ++i) { - sum += input_vector[i]; - } - const float mean = sum / v_size; - float sum_diff_sq = 0.0f; - for (int i = 0; i < v_size; ++i) { - const float diff = input_vector[i] - mean; - sum_diff_sq += diff * diff; - } - const float variance = sum_diff_sq / v_size; - constexpr float kNormalizationConstant = 1e-8f; - const float stddev_inv = - 1.0f / std::sqrt(variance + kNormalizationConstant); - for (int i = 0; i < v_size; ++i) { - output_vector[i] = (input_vector[i] - mean) * stddev_inv; - } - input_vector += v_size; - output_vector += v_size; - } -} - -void PortableTwoGateSaturatingAdd(const int8_t* input, int8_t input_zp, - const int8_t* recurrent, int8_t recurrent_zp, - int32_t input_effective_scale_a, - int32_t input_effective_scale_b, - int32_t recurrent_effective_scale_a, - int32_t recurrent_effective_scale_b, - int32_t n_batch, int32_t n_cell, - int16_t* output) { - const int32_t int16_max = std::numeric_limits::max(); - const int32_t int16_min = std::numeric_limits::min(); - for (int i = 0; i < n_batch * n_cell; ++i) { - int32_t x = static_cast(input[i]) - static_cast(input_zp); - int32_t h = - static_cast(recurrent[i]) - static_cast(recurrent_zp); - int32_t x_scaled = MultiplyByQuantizedMultiplier(x, input_effective_scale_a, - input_effective_scale_b); - int32_t h_scaled = MultiplyByQuantizedMultiplier( - h, recurrent_effective_scale_a, recurrent_effective_scale_b); - int32_t y = h_scaled + x_scaled; - if (y > int16_max) { - y = int16_max; - } - if (y < int16_min) { - y = int16_min; - } - output[i] = static_cast(y); - } -} - -} // namespace micro_tensor_utils -} // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/micro_tensor_utils.h b/code/components/tflite-lib/tensorflow/lite/micro/kernels/micro_tensor_utils.h deleted file mode 100644 index 673ba6a3..00000000 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/micro_tensor_utils.h +++ /dev/null @@ -1,874 +0,0 @@ -/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -// This file and the associated .cc file is branched from -// tensorflow/lite/kernels/internal/reference/portable_tensor_utils* -// TFLM needs to create its own because the original files are coupled with -// the tensor_utils module, which we cannot reuse due to its use of the -// Eigen library. - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_MICRO_TENSOR_UTILS_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_MICRO_TENSOR_UTILS_H_ - -#include -#include -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" - -#if defined(_MSC_VER) -#define __restrict__ __restrict -#endif - -namespace tflite { - -// Not all backends support CpuBackendContext usage, so forward declare to avoid -// pulling in its implementation. -// TODO(b/230666277): consider removing this since micro does not utilize it -class CpuBackendContext; - -namespace micro_tensor_utils { - -template -inline bool PortableIsZeroVector(const T* vector, int v_size) { - for (int i = 0; i < v_size; ++i) { - if (vector[i] != 0) { - return false; - } - } - return true; -} - -void PortableSymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float* min_value, - float* max_value, float* scaling_factor); - -void PortableSymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float min_value, - float max_value, float* scaling_factor); - -void PortableAsymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, - float* scaling_factor, int32_t* offset); - -// Multiply a matrix by a batch vector, and store results in a batch-size -// vector. -void PortableMatrixBatchVectorMultiplyAccumulate(const float* matrix, - int m_rows, int m_cols, - const float* vector, - int n_batch, float* result); - -void PortableMatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vectors, const float* scaling_factors, - int n_batch, float* __restrict__ result); - -void PortableMatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vectors, const float* scaling_factors, - int n_batch, float* __restrict__ result, const float* per_channel_scale, - const int32_t* input_offset, int32_t* scratch, int32_t* row_sums, - bool* compute_row_sums, CpuBackendContext* context); - -void PortableMatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vector, const float* scaling_factors, - int n_batch, int32_t* scratch, float* __restrict__ result, - CpuBackendContext* context); - -void PortableSparseMatrixBatchVectorMultiplyAccumulate1x4( - const float* __restrict__ matrix, const int32_t* __restrict__ segments, - const int32_t* __restrict__ indices, int m_rows, int m_cols, - const float* __restrict__ vector, int n_batch, float* __restrict__ result); - -void PortableSparseMatrixBatchVectorMultiplyAccumulate( - const float* __restrict__ matrix, const uint8_t* __restrict__ ledger, - int m_rows, int m_cols, const float* __restrict__ vector, int n_batch, - float* __restrict__ result); - -void PortableSparseMatrixBatchVectorMultiplyAccumulate1x16( - const int8_t* __restrict__ matrix, const int32_t* __restrict__ segments, - const int32_t* __restrict__ indices, int m_rows, int m_cols, - const int8_t* __restrict__ vector, const int32_t* __restrict__ bias_vector, - int n_batch, const int32_t input_offset, const int32_t output_multiplier, - const int32_t output_shift, const int32_t output_offset, - const int32_t output_activation_min, const int32_t output_activation_max, - int8_t* __restrict__ result); - -void PortableSparseMatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const uint8_t* ledger, const int m_rows, - const int m_cols, const int8_t* __restrict__ vectors, - const float* scaling_factors, int n_batch, float* __restrict__ result); - -// Dot product of two vectors. -float PortableVectorVectorDotProduct(const float* vector1, const float* vector2, - int v_size); - -void PortableBatchVectorBatchVectorDotProduct(const int16_t* vector1, - const int16_t* vector2, - int v_size, int n_batch, - int32_t* result); - -void PortableVectorBatchVectorCwiseProductAccumulate( - const int16_t* vector, int v_size, const int16_t* batch_vector, int n_batch, - int32_t multiplier, int shift, int16_t* result); - -void PortableMatrixBatchVectorMultiplyAccumulate( - const int8_t* input, const int32_t* bias, - const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, - int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, - int32_t* scratch, int16_t* output, CpuBackendContext* context); - -void PortableMatrixBatchVectorMultiplyAccumulate( - const int8_t* input, const int32_t* bias, - const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, - int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, - int32_t* scratch, int8_t* output, CpuBackendContext* context); - -void PortableMatrixBatchVectorMultiply(const int8_t* input, - int32_t input_zeropoint, - const int8_t* input_to_gate_weights, - int32_t input_to_gate_effective_scale_a, - int32_t input_to_gate_effective_scale_b, - int32_t n_batch, int32_t n_input, - int32_t n_cell, int8_t* gate_output, - int8_t gate_output_zp); - -void PortableMatrixBatchVectorMultiply( - const int16_t* hidden, const int8_t* hidden_to_output_weights, - int32_t proj_effective_scale_a, int32_t proj_effective_scale_b, - const int32_t* gate_bias, int32_t n_batch, int32_t n_hidden, - int32_t n_output, int32_t output_zp, int8_t* proj_output); - -void PortableMatrixScalarMultiplyAccumulate(const int8_t* matrix, - int32_t scalar, int32_t n_row, - int32_t n_col, int32_t* output); - -void PortableApplyLayerNorm(const int16_t* input, - const int16_t* layer_norm_weights, - const int32_t* bias, int32_t layer_norm_scale_a, - int32_t layer_norm_scale_b, int32_t variance_limit, - int n_batch, int n_input, int16_t* output); - -void PortableApplyLayerNormFloat(const int16_t* input, - const int16_t* layer_norm_weights, - int32_t layer_norm_scale_a, - int32_t layer_norm_scale_b, - const int32_t* bias, int n_batch, int n_input, - int16_t* output); - -void PortableApplySigmoid(const int16_t* input, int32_t n_batch, - int32_t n_input, int16_t* output); - -void PortableApplySigmoidFloat(const int16_t* input, int32_t n_batch, - int32_t n_input, int16_t* output); - -void PortableApplyTanh(int32_t integer_bits, const int16_t* input, - int32_t n_batch, int32_t n_input, int16_t* output); - -void PortableApplyTanhFloat(const int16_t* input, int32_t n_batch, - int32_t n_input, int32_t integer_bits, - int16_t* output); - -void PortableCwiseMul(const int16_t* input_1, const int16_t* input_2, - int n_batch, int n_input, int shift, int16_t* output); - -void PortableCwiseMul(const int16_t* input_1, const int16_t* input_2, - int32_t multiplier, int32_t shift, int32_t n_batch, - int32_t n_input, int32_t output_zp, int8_t* output); - -void PortableCwiseAdd(const int16_t* input_1, const int16_t* input_2, - int n_batch, int n_input, int16_t* output); - -template -inline void PortableCwiseClipping(T* vector, const int v_size, - const T& clipping_value) { - for (int i = 0; i < v_size; i++) { - vector[i] = std::max(std::min(clipping_value, vector[i]), - static_cast(-clipping_value)); - } -} - -// Batch vector initialization with another vector. -void PortableVectorBatchVectorAssign(const float* vector, int v_size, - int n_batch, float* batch_vector); - -// Compute "1.0f - elements of vector" (used in CIFG). -void PortableSub1Vector(const float* vector, int v_size, float* result); - -void PortableSub1Vector(const int16_t* vector, int v_size, int16_t* result); - -// Multiply all elements of vector with a scalar. -void PortableVectorScalarMultiply(const int8_t* vector, int v_size, float scale, - float* result); - -// Reduce-sum on a vector: -// input_vector: pointer to input vector. -// output_vector: pointer to vector. -// output_size: output vector size. -// reduction_size: number of consecutive elements from input vector which are -// added to get one element of output. -template -inline void PortableReductionSumVector(const INPUT* input_vector, - OUTPUT* output_vector, int output_size, - int reduction_size) { - for (int o = 0; o < output_size; o++) { - OUTPUT result = 0; - for (int r = 0; r < reduction_size; r++) { - result += input_vector[r]; - } - output_vector[o] = result; - input_vector += reduction_size; - } -} - -// Layer norm for each batch. -void PortableMeanStddevNormalization(const float* __restrict__ input_vector, - float* __restrict__ output_vector, - int v_size, int n_batch); - -// Saturate Add. -void PortableTwoGateSaturatingAdd(const int8_t* input, int8_t input_zp, - const int8_t* recurrent, int8_t recurrent_zp, - int32_t input_effective_scale_a, - int32_t input_effective_scale_b, - int32_t recurrent_effective_scale_a, - int32_t recurrent_effective_scale_b, - int32_t n_batch, int32_t n_cell, - int16_t* output); - -// Add another vector for each batch in the batch vector. -template -inline void VectorBatchVectorAdd(const T* vector, int v_size, int n_batch, - T* batch_vector) { - for (int b = 0; b < n_batch; b++) { - for (int i = 0; i < v_size; ++i) { - batch_vector[i] += vector[i]; - } - batch_vector += v_size; - } -} - -// Cwise product of two vectors. -template -inline void VectorVectorCwiseProduct(const T* vector1, const T* vector2, - int v_size, T* result) { - for (int v = 0; v < v_size; v++) { - *result++ = *vector1++ * *vector2++; - } -} - -// Cwise product of a vector and a batch-vector. -template -inline void VectorBatchVectorCwiseProduct(const T* vector, int v_size, - const T* batch_vector, int n_batch, - T* result) { - for (int b = 0; b < n_batch; b++) { - VectorVectorCwiseProduct(vector, batch_vector, v_size, result); - // Update the pointers. - result += v_size; - batch_vector += v_size; - } -} - -// Reduce-sum on a float input vector: -// input_vector: float pointer to input vector. -// output_vector: float pointer to vector. -// output_size: output vector size. -// reduction_size: number of consecutive elements from input vector which are -// added to get one element of output. -inline void ReductionSumVector(const float* input_vector, float* output_vector, - int output_size, int reduction_size) { - PortableReductionSumVector(input_vector, output_vector, output_size, - reduction_size); -} - -// Same as above but input/output is 32 bit integer. -inline void ReductionSumVector(const int32_t* input_vector, - int32_t* output_vector, int output_size, - int reduction_size) { - PortableReductionSumVector(input_vector, output_vector, output_size, - reduction_size); -} - -// Same as above but input is 8 bit integer. -inline void ReductionSumVector(const int8_t* input_vector, - int32_t* output_vector, int output_size, - int reduction_size) { - PortableReductionSumVector(input_vector, output_vector, output_size, - reduction_size); -} - -// Cwise product and accumulate of two vectors. Since it's a MAC operation, the -// assumption here is that result array is initialized to valid values. -template -inline void VectorVectorCwiseProductAccumulate(const T* __restrict__ vector1, - const T* __restrict__ vector2, - int v_size, - T* __restrict__ result) { - for (int v = 0; v < v_size; v++) { - *result++ += *vector1++ * *vector2++; - } -} - -// Batch vector initialization with another vector. -template -inline void VectorBatchVectorAssign(const T* vector, int v_size, int n_batch, - T* batch_vector) { - for (int b = 0; b < n_batch; b++) { - std::copy_n(vector, v_size, batch_vector + b * v_size); - } -} - -inline void SymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float* min, - float* max, float* scaling_factor) { - PortableSymmetricQuantizeFloats(values, size, quantized_values, min, max, - scaling_factor); -} - -inline void SymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float min_value, - float max_value, float* scaling_factor) { - PortableSymmetricQuantizeFloats(values, size, quantized_values, min_value, - max_value, scaling_factor); -} - -inline void AsymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, - float* scaling_factor, int32_t* offset) { - PortableAsymmetricQuantizeFloats(values, size, quantized_values, - scaling_factor, offset); -} - -// Helper function to quantize floats. -// float_data_ptr input float vectors -// n_batch number of input vectors -// n_data size of a single input vector -// quantized_data_ptr (out) vector with quantized data -// scaling_factors (out) scaling factors (one per vector) -// zero_points (out) zero points (one per vector) -// do_asymmetric controls if the quantization should be asymmetric. -inline void BatchQuantizeFloats(const float* float_data_ptr, int n_batch, - int n_data, int8_t* quantized_data_ptr, - float* scaling_factors, int32_t* zero_points, - bool do_asymmetric) { - for (int b = 0; b < n_batch; ++b) { - const int offset = b * n_data; - if (do_asymmetric) { - AsymmetricQuantizeFloats(float_data_ptr + offset, n_data, - quantized_data_ptr + offset, &scaling_factors[b], - &zero_points[b]); - } else { - float unused_min, unused_max; - SymmetricQuantizeFloats(float_data_ptr + offset, n_data, - quantized_data_ptr + offset, &unused_min, - &unused_max, &scaling_factors[b]); - } - } -} - -// Check if all entries of a vector are zero for float. -inline bool IsZeroVector(const float* vector, int v_size) { - return PortableIsZeroVector(vector, v_size); -} - -// Check if all entries of a vector are zero for int8_t. -inline bool IsZeroVector(const int8_t* vector, int v_size) { - return PortableIsZeroVector(vector, v_size); -} - -// Apply Layer Normalization (https://arxiv.org/abs/1607.06450) to a Quantized -// vector. -// Parameters: -// - input: batch vector of size n_batch * n_input; 16 bit. -// - layer_norm_weights: the quantized layer normalization weights. -// - bias: the bias for the layer normalization. -// - layer_norm_scale_a: multiplier for scale factor. -// - layer_norm_scale_b: shift for scale factor. -// - variance_limit: the guard to make sure the inverse does not overflow. -// - n_batch: the number of batches. -// - n_input: the size for input and output. -// - output: the 16 bit output -inline void ApplyLayerNorm(const int16_t* input, - const int16_t* layer_norm_weights, - const int32_t* bias, int32_t layer_norm_scale_a, - int32_t layer_norm_scale_b, int32_t variance_limit, - int n_batch, int n_input, int16_t* output) { - PortableApplyLayerNorm(input, layer_norm_weights, bias, layer_norm_scale_a, - layer_norm_scale_b, variance_limit, n_batch, n_input, - output); -} - -// Same as above but the internal calculation is done in float. -inline void ApplyLayerNormFloat(const int16_t* input, - const int16_t* layer_norm_weights, - int32_t layer_norm_scale_a, - int32_t layer_norm_scale_b, const int32_t* bias, - int n_batch, int n_input, int16_t* output) { - PortableApplyLayerNormFloat(input, layer_norm_weights, layer_norm_scale_a, - layer_norm_scale_b, bias, n_batch, n_input, - output); -} - -// Apply Sigmoid to a quantized vector. -// Parameters: -// - input: batch vector of size n_batch * n_input; 16 bit. -// - n_batch: the number of batches. -// - n_input: the size for input and output. -// - output: the 16 bit output -// The input is in Q3.12 format and the output is in Q0.15 format. -inline void ApplySigmoid(const int16_t* input, int32_t n_batch, int32_t n_input, - int16_t* output) { - PortableApplySigmoid(input, n_batch, n_input, output); -} - -// Same as above but the internal calcualtion is float. -inline void ApplySigmoidFloat(const int16_t* input, int32_t n_batch, - int32_t n_input, int16_t* output) { - PortableApplySigmoidFloat(input, n_batch, n_input, output); -} - -// Apply Tanh to a quantized vector. -// Parameters: -// - integer_bits: the integer bits of the input. -// Currently supports 0, 1, 2, 3, 4, 5, 6. -// - input: batch vector of size n_batch * n_input; 16 bit. -// - n_batch: the number of batches. -// - n_input: the size for input and output. -// - output: the 16 bit output -// The input is in Qm.15-m format and the output is in Q0.15 format. -inline void ApplyTanh(int32_t integer_bits, const int16_t* input, - int32_t n_batch, int32_t n_input, int16_t* output) { - PortableApplyTanh(integer_bits, input, n_batch, n_input, output); -} - -// Apply Tanh to a quantized vector. Tbe internal calculation is in float. -// - Input has 2^(integer_bits) as scale. -// - Output has Q0.15 as scale. -inline void ApplyTanhFloat(const int16_t* input, int32_t n_batch, - int32_t n_input, int32_t integer_bits, - int16_t* output) { - PortableApplyTanhFloat(input, n_batch, n_input, integer_bits, output); -} - -// Element-wise multiplication of two quantized vectors. -// Parameters: -// - input_1: batch vector of size n_batch * n_input; 16 bit. -// - input_2: batch vector of size n_batch * n_input; 16 bit. -// - n_batch: the number of batches. -// - n_input: the size for input and output. -// - shift: the shift needed to produce the output. -// - output: the 16 bit output of size n_batch * n_input. -// Output does not need to be initialized. -inline void CwiseMul(const int16_t* input_1, const int16_t* input_2, - int n_batch, int n_input, int shift, int16_t* output) { - PortableCwiseMul(input_1, input_2, n_batch, n_input, shift, output); -} - -// Element-wise multiplication of two quantized vectors with rescaling. -// Parameters: -// - input_1: batch vector of size n_batch * n_input; 16 bit. -// - input_2: batch vector of size n_batch * n_input; 16 bit. -// - multiplier: the multiplier part of scale. -// - shift: the shift part of scale. -// - n_batch: the number of batches. -// - n_input: the size for input and output. -// - output: the 8 bit output of size n_batch * n_input. -// - output_zp: the zero point of output. -// Output does not need to be initialized. -// Multiplier ("m") and shift ("s") are connected to scale ("s") with s = m * -// 2^(s - 31). -inline void CwiseMul(const int16_t* input_1, const int16_t* input_2, - int32_t multiplier, int32_t shift, int32_t n_batch, - int32_t n_input, int32_t output_zp, int8_t* output) { - PortableCwiseMul(input_1, input_2, multiplier, shift, n_batch, n_input, - output_zp, output); -} - -// Element-wise in-place clipping of a vector. Overloaded for float, int16_t, -// int8_t. Parameters: -// - vector: vector of size v_size. -// - v_size: the size of the vector. -// - clipping_value: the value used for clipping. -inline void CwiseClipping(float* vector, const int v_size, - const float clipping_value) { - PortableCwiseClipping(vector, v_size, clipping_value); -} - -inline void CwiseClipping(int16_t* vector, const int v_size, - const int16_t clipping_value) { - PortableCwiseClipping(vector, v_size, clipping_value); -} - -inline void CwiseClipping(int8_t* vector, const int v_size, - const int8_t clipping_value) { - PortableCwiseClipping(vector, v_size, clipping_value); -} - -// Element-wise saturating addition of two quantized vectors without rescaling. -// Parameters: -// - input_1: batch vector of size n_batch * n_input; 16 bit. -// - input_2: batch vector of size n_batch * n_input; 16 bit. -// - n_batch: the number of batches. -// - n_input: the size for input and output. -// - output: the 8 bit output of size n_batch * n_input. -// Output does not need to be initialized. -inline void CwiseAdd(const int16_t* input_1, const int16_t* input_2, - int n_batch, int n_input, int16_t* output) { - PortableCwiseAdd(input_1, input_2, n_batch, n_input, output); -} - -inline void MeanStddevNormalization(const float* input_vector, - float* output_vector, int v_size, - int n_batch) { - PortableMeanStddevNormalization(input_vector, output_vector, v_size, n_batch); -} - -inline void Sub1Vector(const float* vector, int v_size, float* result) { - PortableSub1Vector(vector, v_size, result); -} - -inline void Sub1Vector(const int16_t* vector, int v_size, int16_t* result) { - PortableSub1Vector(vector, v_size, result); -} - -// Multiply all elements of vector with a scalar. -inline void VectorScalarMultiply(const int8_t* vector, int v_size, float scale, - float* result) { - PortableVectorScalarMultiply(vector, v_size, scale, result); -} - -// Saturate Add with rescale on both inputs. -inline void TwoGateSaturatingAdd(const int8_t* input, int8_t input_zp, - const int8_t* recurrent, int8_t recurrent_zp, - int32_t input_effective_scale_a, - int32_t input_effective_scale_b, - int32_t recurrent_effective_scale_a, - int32_t recurrent_effective_scale_b, - int32_t n_batch, int32_t n_cell, - int16_t* output) { - PortableTwoGateSaturatingAdd( - input, input_zp, recurrent, recurrent_zp, input_effective_scale_a, - input_effective_scale_b, recurrent_effective_scale_a, - recurrent_effective_scale_b, n_batch, n_cell, output); -} - -// Multiplies a matrix by a "batched" vector (i.e. a matrix with a batch -// dimension composed by input vectors independent from each other). The result -// of the multiplication is accumulated to the passed result buffer. -// More specifically, for a matrix M of shape [n, i] and a batched-vector -// of shape [i, batch] it will first compute the product of shape [n, batch]. -// This product will be accumulated to the result buffer. -inline void MatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows, - int m_cols, const float* vector, - int n_batch, float* result) { - PortableMatrixBatchVectorMultiplyAccumulate(matrix, m_rows, m_cols, vector, - n_batch, result); -} - -// Same as the function above, but the matrix is a sparse tensor with block -// pattern 1x4. -// This function assumes that m_cols is a multiple of the block size (4 in this -// case) so that there's no incomplete block. -inline void MatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vector, const float* scaling_factors, - int n_batch, float* __restrict__ result) { - PortableMatrixBatchVectorMultiplyAccumulate(matrix, m_rows, m_cols, vector, - scaling_factors, n_batch, result); -} - -inline void MatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vectors, const float* scaling_factors, - int n_batch, float* __restrict__ result, const float* per_channel_scale, - const int32_t* input_offset, int32_t* scratch, int32_t* row_sums, - bool* compute_row_sums, CpuBackendContext* context) { - PortableMatrixBatchVectorMultiplyAccumulate( - matrix, m_rows, m_cols, vectors, scaling_factors, n_batch, result, - per_channel_scale, input_offset, scratch, row_sums, compute_row_sums, - context); -} - -inline void MatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vector, const float* scaling_factors, - int n_batch, int32_t* scratch, float* __restrict__ result, - CpuBackendContext* context) { - PortableMatrixBatchVectorMultiplyAccumulate(matrix, m_rows, m_cols, vector, - scaling_factors, n_batch, result); -} - -// Same as the function above, but the matrix is a sparse tensor with block -// pattern 1x4. -// This function assumes that m_cols is a multiple of the block size (4 in this -// case) so that there's no incomplete block. -inline void SparseMatrixBatchVectorMultiplyAccumulate1x4( - const float* __restrict__ matrix, const int32_t* __restrict__ segments, - const int32_t* __restrict__ indices, int m_rows, int m_cols, - const float* __restrict__ vector, int n_batch, float* __restrict__ result) { - PortableSparseMatrixBatchVectorMultiplyAccumulate1x4( - matrix, segments, indices, m_rows, m_cols, vector, n_batch, result); -} - -// Same as the function above, but the matrix is stored in block compressed -// sparse row format with block pattern 1x16 which consists of two arrays: -// 1. A matrix array stores non-zero blocks of the matrix in row major. -// 2. A ledger array stores nrows groups, one group per row. Each group starts -// with an integer representing the number of non-zero blocks for the -// corresponding row and follows with column indexes of the first element -// of each non-zero block. -// This function assumes that -// 1. m_cols is a multiple of 16 so that all blocks are full blocks. -// 2. m_cols < 254 * 16 so that block index can be represented by uint8. -inline void SparseMatrixBatchVectorMultiplyAccumulate( - const float* __restrict__ matrix, const uint8_t* __restrict__ ledger, - int m_rows, int m_cols, const float* __restrict__ vector, int n_batch, - float* __restrict__ result) { - PortableSparseMatrixBatchVectorMultiplyAccumulate( - matrix, ledger, m_rows, m_cols, vector, n_batch, result); -} - -// Same as the function above, but the matrix is a sparse tensor with block -// pattern 1x16. -// This function assumes that m_cols is a multiple of the block size (16 in this -// case) so that there's no incomplete block. Also, it assumes all offsets of -// input, output and filter are zero. -inline void SparseMatrixBatchVectorMultiplyAccumulate1x16( - const int8_t* __restrict__ matrix, const int32_t* __restrict__ segments, - const int32_t* __restrict__ indices, int m_rows, int m_cols, - const int8_t* __restrict__ vector, const int32_t* __restrict__ bias_vector, - int n_batch, const int32_t input_offset, const int32_t output_multiplier, - const int32_t output_shift, const int32_t output_offset, - const int32_t output_activation_min, const int32_t output_activation_max, - int8_t* __restrict__ result) { - PortableSparseMatrixBatchVectorMultiplyAccumulate1x16( - matrix, segments, indices, m_rows, m_cols, vector, bias_vector, n_batch, - input_offset, output_multiplier, output_shift, output_offset, - output_activation_min, output_activation_max, result); -} - -// Same as the function above, but the matrix is stored in block compressed -// sparse row format with block pattern 1x16 which consists of two arrays: -// 1. A matrix array stores non-zero blocks of the matrix in row major. -// 2. A ledger array stores nrows groups, one group per row. Each group starts -// with an integer representing the number of non-zero blocks for the -// corresponding row followed by column index of the first element of -// each non-zero block. -// This function assumes that -// 1. m_cols is a multiple of 16 so that all blocks are full blocks. -// 2. m_cols < 254 * 16 so that block index can be represented by uint8. -inline void SparseMatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const uint8_t* ledger, const int m_rows, - const int m_cols, const int8_t* __restrict__ vectors, - const float* scaling_factors, int n_batch, float* __restrict__ result) { - PortableSparseMatrixBatchVectorMultiplyAccumulate( - matrix, ledger, m_rows, m_cols, vectors, scaling_factors, n_batch, - result); -} - -// Same as the above 8, 8, 8 integer matmul except for the presence of zero -// point and non-accumulative. -// TODO(b/148688698): remove this function by folding zero point calculation in -// prepare() function. -inline void MatrixBatchVectorMultiplyAccumulate( - const int8_t* input, const int32_t* bias, - const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, - int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, - int32_t* scratch, int16_t* output, CpuBackendContext* context) { - PortableMatrixBatchVectorMultiplyAccumulate( - input, bias, input_to_gate_weights, multiplier, shift, n_batch, n_input, - n_output, output_zp, scratch, output, context); -} - -// Same as above but has 16 bit and 8 bit input and 8 bit output. -// Used in projection when hidden is 16bit. -inline void MatrixBatchVectorMultiplyAccumulate( - const int8_t* input, const int32_t* bias, - const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift, - int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp, - int32_t* scratch, int8_t* output, CpuBackendContext* context) { - PortableMatrixBatchVectorMultiplyAccumulate( - input, bias, input_to_gate_weights, multiplier, shift, n_batch, n_input, - n_output, output_zp, scratch, output, context); -} - -// Same as the function above, but provides separate scaling factor for the -// matrix and the vectors. The scaling factors are multiplied in the -// scaling_factor_scratch buffer. -inline void MatrixBatchVectorMultiplyAccumulate( - const int8_t* __restrict__ matrix, const int m_rows, const int m_cols, - const int8_t* __restrict__ vectors, const float matrix_scaling_factor, - const float* vector_scaling_factors, int n_batch, - float* __restrict__ result, const float* per_channel_scale, - const int32_t* input_offset, int32_t* scratch, int32_t* row_sums, - bool* compute_row_sums, float* scaling_factor_scratch, - CpuBackendContext* context) { - for (int b = 0; b < n_batch; ++b) { - scaling_factor_scratch[b] = - vector_scaling_factors[b] * matrix_scaling_factor; - } - MatrixBatchVectorMultiplyAccumulate(matrix, m_rows, m_cols, vectors, - scaling_factor_scratch, n_batch, result, - per_channel_scale, input_offset, scratch, - row_sums, compute_row_sums, context); -} - -// Multiplies a matrix with a scalar and reduce the result on each row to a -// scalar. -// Parameters: -// - matrix: matrix of size n_row * n_col -// - scalar: the scalar that is multiplied to each element in the matrix -// - n_row: the row count of the matrix -// - n_col: the column count of the matrix -// - output: the 32bit output -// Note: We do not need saturation because the int8 * int8 is safe from overflow -// in (2^31-1) / (2^14) = 131072, which is bigger than the n_row. Non-zero -// initial output value is not exceptionally large. -inline void MatrixScalarMultiplyAccumulate(const int8_t* matrix, int32_t scalar, - int32_t n_row, int32_t n_col, - int32_t* output) { - PortableMatrixScalarMultiplyAccumulate(matrix, scalar, n_row, n_col, output); -} - -// Same as the above 8, 8, 8 integer matmul except for the presence of zero -// point and non-accumulative. -// TODO(b/148688698): remove this function by folding zero point calculation in -// prepare() function. -inline void MatrixBatchVectorMultiply(const int8_t* input, - int32_t input_zeropoint, - const int8_t* input_to_gate_weights, - int32_t input_to_gate_effective_scale_a, - int32_t input_to_gate_effective_scale_b, - int32_t n_batch, int32_t n_input, - int32_t n_cell, int8_t* gate_output, - int8_t gate_output_zp) { - PortableMatrixBatchVectorMultiply( - input, input_zeropoint, input_to_gate_weights, - input_to_gate_effective_scale_a, input_to_gate_effective_scale_b, n_batch, - n_input, n_cell, gate_output, gate_output_zp); -} - -// Same as above but has 16 bit and 8 bit input and 8 bit output. -// Used in projection when hidden is 16bit. -inline void MatrixBatchVectorMultiply(const int16_t* hidden, - const int8_t* hidden_to_output_weights, - int32_t proj_effective_scale_a, - int32_t proj_effective_scale_b, - const int32_t* gate_bias, int32_t n_batch, - int32_t n_hidden, int32_t n_output, - int32_t output_zp, int8_t* proj_output) { - PortableMatrixBatchVectorMultiply(hidden, hidden_to_output_weights, - proj_effective_scale_a, - proj_effective_scale_b, gate_bias, n_batch, - n_hidden, n_output, output_zp, proj_output); -} - -// Cwise product and accumulate of a vector and a batch-vector. Since it's a MAC -// operation, the assumption here is that result array is initialized to valid -// values. -template -inline void VectorBatchVectorCwiseProductAccumulate(const T* vector, int v_size, - const T* batch_vector, - int n_batch, T* result) { - for (int b = 0; b < n_batch; b++) { - VectorVectorCwiseProductAccumulate(vector, batch_vector, v_size, result); - // Update the pointers. - result += v_size; - batch_vector += v_size; - } -} - -// Same as above, but inputs are 16bit integer and output is 16bit integer. -inline void VectorBatchVectorCwiseProductAccumulate( - const int16_t* vector, int v_size, const int16_t* batch_vector, int n_batch, - int32_t multiplier, int shift, int16_t* result) { - PortableVectorBatchVectorCwiseProductAccumulate( - vector, v_size, batch_vector, n_batch, multiplier, shift, result); -} - -// Apply Rectified Linear to elements of a vector. -inline void ApplyReluToVector(const float* vector, int v_size, float* result) { - for (int v = 0; v < v_size; v++) { - result[v] = std::max(0.0f, vector[v]); - } -} - -// Apply Rectified Linear 1 (cap to [-1;1]) to elements of a vector -inline void ApplyRelu1ToVector(const float* vector, int v_size, float* result) { - for (int v = 0; v < v_size; v++) { - result[v] = std::max(-1.0f, std::min(vector[v], 1.0f)); - } -} - -// Apply Rectified Linear 6 (cap to [0;6]) to elements of a vector -inline void ApplyRelu6ToVector(const float* vector, int v_size, float* result) { - for (int v = 0; v < v_size; v++) { - result[v] = std::max(0.0f, std::min(vector[v], 6.0f)); - } -} - -// Apply tanh to elements of a vector -inline void ApplyTanhToVector(const float* vector, int v_size, float* result) { - for (int v = 0; v < v_size; v++) { - result[v] = std::tanh(vector[v]); - } -} - -// Apply signbit to elements of a vector -inline void ApplySignbitToVector(const float* vector, int v_size, - float* result) { - for (int v = 0; v < v_size; v++) { - result[v] = std::signbit(vector[v]); - } -} - -// Apply sigmoid to elements of a vector. -inline void ApplySigmoidToVector(const float* vector, int v_size, - float* result) { - for (int v = 0; v < v_size; v++) { - result[v] = 1.0f / (1.0f + std::exp(-vector[v])); - } -} - -// Apply appropriate activation function to elements of a vector. -inline void ApplyActivationToVector(const float* vector, int v_size, - TfLiteFusedActivation activation, - float* result) { - switch (activation) { - case kTfLiteActNone: - return; - case kTfLiteActRelu: - return ApplyReluToVector(vector, v_size, result); - case kTfLiteActReluN1To1: - return ApplyRelu1ToVector(vector, v_size, result); - case kTfLiteActRelu6: - return ApplyRelu6ToVector(vector, v_size, result); - case kTfLiteActTanh: - return ApplyTanhToVector(vector, v_size, result); - case kTfLiteActSignBit: - return ApplySignbitToVector(vector, v_size, result); - case kTfLiteActSigmoid: - return ApplySigmoidToVector(vector, v_size, result); - } -} - -} // namespace micro_tensor_utils - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_MICRO_TENSOR_UTILS_H_ diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/mirror_pad.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/mirror_pad.cc index 90d3bd9e..a19561f6 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/mirror_pad.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/mirror_pad.cc @@ -209,7 +209,14 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_MIRROR_PAD() { - return tflite::micro::RegisterOp(Init, Prepare, Eval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/mul.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/mul.cc index 59f006b0..e8295197 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/mul.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/mul.cc @@ -61,7 +61,14 @@ TfLiteStatus MulEval(TfLiteContext* context, TfLiteNode* node) { } TfLiteRegistration Register_MUL() { - return tflite::micro::RegisterOp(MulInit, MulPrepare, MulEval); + return {/*init=*/MulInit, + /*free=*/nullptr, + /*prepare=*/MulPrepare, + /*invoke=*/MulEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/neg.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/neg.cc index 59dd8cb8..74a95ca3 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/neg.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/neg.cc @@ -51,7 +51,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace neg TfLiteRegistration Register_NEG() { - return tflite::micro::RegisterOp(nullptr, nullptr, neg::Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/nullptr, + /*invoke=*/neg::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/pack.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/pack.cc index 56f3b96e..098a0482 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/pack.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/pack.cc @@ -108,7 +108,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace pack TfLiteRegistration Register_PACK() { - return tflite::micro::RegisterOp(nullptr, nullptr, pack::Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/nullptr, + /*invoke=*/pack::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/pad.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/pad.cc index b645f983..1428b16e 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/pad.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/pad.cc @@ -223,12 +223,26 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace pad TfLiteRegistration Register_PAD() { - return tflite::micro::RegisterOp(pad::Init, pad::Prepare, pad::Eval); + return {/*init=*/pad::Init, + /*free=*/nullptr, + /*prepare=*/pad::Prepare, + /*invoke=*/pad::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } // Also register Pad as PadV2. TfLiteRegistration Register_PADV2() { - return tflite::micro::RegisterOp(pad::Init, pad::Prepare, pad::Eval); + return {/*init=*/pad::Init, + /*free=*/nullptr, + /*prepare=*/pad::Prepare, + /*invoke=*/pad::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/pooling.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/pooling.cc index a2ef8b62..b3781636 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/pooling.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/pooling.cc @@ -88,11 +88,25 @@ void* Init(TfLiteContext* context, const char* buffer, size_t length) { } // namespace TfLiteRegistration Register_AVERAGE_POOL_2D() { - return tflite::micro::RegisterOp(Init, PoolingPrepare, AverageEval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/PoolingPrepare, + /*invoke=*/AverageEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_MAX_POOL_2D() { - return tflite::micro::RegisterOp(Init, PoolingPrepare, MaxEval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/PoolingPrepare, + /*invoke=*/MaxEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/prelu.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/prelu.cc index 54cc0e02..dc0c32c0 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/prelu.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/prelu.cc @@ -69,7 +69,14 @@ TfLiteStatus PreluEval(TfLiteContext* context, TfLiteNode* node) { } TfLiteRegistration Register_PRELU() { - return tflite::micro::RegisterOp(PreluInit, PreluPrepare, PreluEval); + return {/*init=*/PreluInit, + /*free=*/nullptr, + /*prepare=*/PreluPrepare, + /*invoke=*/PreluEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/quantize.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/quantize.cc index b5eb9c3c..97f5a004 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/quantize.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/quantize.cc @@ -34,8 +34,14 @@ void* Init(TfLiteContext* context, const char* buffer, size_t length) { } // namespace TfLiteRegistration Register_QUANTIZE() { - return tflite::micro::RegisterOp(Init, PrepareQuantizeReference, - EvalQuantizeReference); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/PrepareQuantizeReference, + /*invoke=*/EvalQuantizeReference, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/quantize_common.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/quantize_common.cc index 94220529..cca3489d 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/quantize_common.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/quantize_common.cc @@ -53,19 +53,15 @@ TfLiteStatus PrepareQuantizeReference(TfLiteContext* context, TF_LITE_ENSURE(context, affine_quantization->scale); TF_LITE_ENSURE(context, affine_quantization->scale->size == 1); - TF_LITE_ENSURE( - context, input->type == kTfLiteFloat32 || input->type == kTfLiteInt32 || - input->type == kTfLiteInt16 || input->type == kTfLiteInt8 || - input->type == kTfLiteUInt8); + TF_LITE_ENSURE(context, + input->type == kTfLiteFloat32 || input->type == kTfLiteInt32 || + input->type == kTfLiteInt16 || input->type == kTfLiteInt8); TF_LITE_ENSURE(context, output->type == kTfLiteInt8 || output->type == kTfLiteInt16 || - output->type == kTfLiteInt32 || - output->type == kTfLiteUInt8); + output->type == kTfLiteInt32); if ((input->type == kTfLiteInt16 && output->type == kTfLiteInt8) || (input->type == kTfLiteInt8 && output->type == kTfLiteInt8) || - (input->type == kTfLiteInt8 && output->type == kTfLiteUInt8) || - (input->type == kTfLiteUInt8 && output->type == kTfLiteInt8) || (input->type == kTfLiteInt8 && output->type == kTfLiteInt16) || (input->type == kTfLiteInt8 && output->type == kTfLiteInt32) || (input->type == kTfLiteInt16 && output->type == kTfLiteInt16) || @@ -113,9 +109,9 @@ TfLiteStatus EvalQuantizeReference(TfLiteContext* context, TfLiteNode* node) { tflite::micro::GetTensorData(output)); return kTfLiteOk; default: - MicroPrintf("Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); + TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", + TfLiteTypeGetName(input->type), + TfLiteTypeGetName(output->type)); return kTfLiteError; } } else if (input->type == kTfLiteInt32) { @@ -136,9 +132,9 @@ TfLiteStatus EvalQuantizeReference(TfLiteContext* context, TfLiteNode* node) { tflite::micro::GetTensorData(output)); break; default: - MicroPrintf("Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); + TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", + TfLiteTypeGetName(input->type), + TfLiteTypeGetName(output->type)); return kTfLiteError; } } else if (input->type == kTfLiteInt16) { @@ -166,9 +162,9 @@ TfLiteStatus EvalQuantizeReference(TfLiteContext* context, TfLiteNode* node) { tflite::micro::GetTensorData(output)); return kTfLiteOk; default: - MicroPrintf("Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); + TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", + TfLiteTypeGetName(input->type), + TfLiteTypeGetName(output->type)); return kTfLiteError; } } else if (input->type == kTfLiteInt8) { @@ -183,13 +179,6 @@ TfLiteStatus EvalQuantizeReference(TfLiteContext* context, TfLiteNode* node) { data->input_zero_point, data->quantization_params.zero_point, tflite::micro::GetTensorData(output)); break; - case kTfLiteUInt8: - reference_ops::Requantize( - tflite::micro::GetTensorData(input), size, - data->requantize_output_multiplier, data->requantize_output_shift, - data->input_zero_point, data->quantization_params.zero_point, - tflite::micro::GetTensorData(output)); - break; case kTfLiteInt16: reference_ops::Requantize( tflite::micro::GetTensorData(input), size, @@ -205,31 +194,15 @@ TfLiteStatus EvalQuantizeReference(TfLiteContext* context, TfLiteNode* node) { tflite::micro::GetTensorData(output)); break; default: - MicroPrintf("Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); - return kTfLiteError; - } - } else if (input->type == kTfLiteUInt8) { - size_t size = ElementCount(*input->dims); - switch (output->type) { - case kTfLiteInt8: - reference_ops::Requantize( - tflite::micro::GetTensorData(input), size, - data->requantize_output_multiplier, data->requantize_output_shift, - data->input_zero_point, data->quantization_params.zero_point, - tflite::micro::GetTensorData(output)); - break; - default: - MicroPrintf("Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); + TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", + TfLiteTypeGetName(input->type), + TfLiteTypeGetName(output->type)); return kTfLiteError; } } else { - MicroPrintf("Input %s, output %s not supported.", - TfLiteTypeGetName(input->type), - TfLiteTypeGetName(output->type)); + TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.", + TfLiteTypeGetName(input->type), + TfLiteTypeGetName(output->type)); return kTfLiteError; } diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/read_variable.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/read_variable.cc index 422c0384..f9124f04 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/read_variable.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/read_variable.cc @@ -81,7 +81,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace. TfLiteRegistration Register_READ_VARIABLE() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/reduce.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/reduce.cc index 7e862ba1..40aed2d5 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/reduce.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/reduce.cc @@ -1,4 +1,4 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -23,41 +23,331 @@ limitations under the License. #include "tensorflow/lite/kernels/internal/types.h" #include "tensorflow/lite/kernels/kernel_util.h" #include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/reduce.h" #include "tensorflow/lite/micro/micro_utils.h" namespace tflite { +namespace ops { +namespace micro { +namespace reduce { + +constexpr int kMaxNumberOfAxis = 4; +constexpr int kMaxNumberOfReducedAxis = 2; + +struct OpData { + int32_t multiplier; + int shift; + int temp_buffer_idx; + int resolved_axis_idx; + int input_zp; + float input_scale; + int output_zp; + float output_scale; + int num_output_elements; +}; void* InitReduce(TfLiteContext* context, const char* buffer, size_t length) { - return context->AllocatePersistentBuffer(context, sizeof(OpDataReduce)); + return context->AllocatePersistentBuffer(context, sizeof(OpData)); +} + +TfLiteStatus PrepareSimple(TfLiteContext* context, TfLiteNode* node) { + MicroContext* micro_context = GetMicroContext(context); + + // Inputs Tensor (dtype depends on quantization): + // [0] = Input + // [1] = Axis + TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, 0); + + // Outputs Tensor (dtype depends on quantization): + // [0] = Output + + // Validate number of inputs and outputs + TF_LITE_ENSURE_EQ(context, node->inputs->size, 2); + TF_LITE_ENSURE_EQ(context, node->outputs->size, 1); + + // Validate axis type + TfLiteTensor* axis = micro_context->AllocateTempInputTensor(node, 1); + TF_LITE_ENSURE(context, axis != nullptr); + TF_LITE_ENSURE_TYPES_EQ(context, axis->type, kTfLiteInt32); + + if (input->type == kTfLiteInt8) { + OpData* data = static_cast(node->user_data); + TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0); + const double real_multiplier = static_cast(input->params.scale) / + static_cast(output->params.scale); + QuantizeMultiplier(real_multiplier, &data->multiplier, &data->shift); + micro_context->DeallocateTempTfLiteTensor(output); + } + micro_context->DeallocateTempTfLiteTensor(axis); + micro_context->DeallocateTempTfLiteTensor(input); + return kTfLiteOk; } TfLiteStatus PrepareMax(TfLiteContext* context, TfLiteNode* node) { - return PrepareMaxHelper(context, node, - static_cast(node->user_data)); + TF_LITE_ENSURE_OK(context, PrepareSimple(context, node)); + + MicroContext* micro_context = GetMicroContext(context); + OpData* op_data = static_cast(node->user_data); + TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, 0); + TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0); + TfLiteTensor* axis = micro_context->AllocateTempInputTensor(node, 1); + + op_data->input_scale = input->params.scale; + op_data->output_scale = output->params.scale; + op_data->num_output_elements = NumElements(output); + + context->RequestScratchBufferInArena(context, sizeof(int) * input->dims->size, + &op_data->temp_buffer_idx); + context->RequestScratchBufferInArena( + context, sizeof(int) * static_cast(ElementCount(*axis->dims)), + &op_data->resolved_axis_idx); + + micro_context->DeallocateTempTfLiteTensor(input); + micro_context->DeallocateTempTfLiteTensor(output); + micro_context->DeallocateTempTfLiteTensor(axis); + return kTfLiteOk; } TfLiteStatus PrepareMeanOrSum(TfLiteContext* context, TfLiteNode* node) { - return PrepareMeanOrSumHelper(context, node, - static_cast(node->user_data)); + MicroContext* micro_context = GetMicroContext(context); + TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, 0); + OpData* op_data = reinterpret_cast(node->user_data); + TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0); + if (input->type == kTfLiteInt8 || input->type == kTfLiteInt16) { + const double real_multiplier = static_cast(input->params.scale) / + static_cast(output->params.scale); + QuantizeMultiplier(real_multiplier, &op_data->multiplier, &op_data->shift); + } + + int output_size = NumElements(output); + if (input->type == kTfLiteInt8 || input->type == kTfLiteInt16) { + context->RequestScratchBufferInArena(context, output_size * sizeof(int32_t), + &op_data->temp_buffer_idx); + op_data->input_zp = input->params.zero_point; + op_data->input_scale = input->params.scale; + op_data->output_zp = output->params.zero_point; + op_data->output_scale = output->params.scale; + } + + TF_LITE_ENSURE_OK(context, PrepareSimple(context, node)); + // TODO(b/144955155): Support uint8_t(b/144955155) and int8_t(b/144955018) + micro_context->DeallocateTempTfLiteTensor(input); + micro_context->DeallocateTempTfLiteTensor(output); + return kTfLiteOk; +} + +void ResolveAxis(const int* axis_data, int axis_count, + tflite::MeanParams* op_params) { + int i = 0; + for (; i < axis_count; ++i) { + op_params->axis[i] = static_cast(axis_data[i]); + } + for (; i < 4; ++i) { + op_params->axis[i] = 1; + } + op_params->axis_count = axis_count; } TfLiteStatus EvalMean(TfLiteContext* context, TfLiteNode* node) { - return EvalMeanHelper(context, node, - static_cast(node->user_data)); + const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0); + const TfLiteEvalTensor* axis = tflite::micro::GetEvalInput(context, node, 1); + TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0); + TfLiteReducerParams* params = + reinterpret_cast(node->builtin_data); + OpData* op_data = reinterpret_cast(node->user_data); + + int num_axis = static_cast(ElementCount(*axis->dims)); + int temp_index[kMaxNumberOfAxis]; + int resolved_axis[kMaxNumberOfReducedAxis]; + + tflite::MeanParams op_params; + ResolveAxis(tflite::micro::GetTensorData(axis), num_axis, &op_params); + + // Special case mean implementation exists for 4D mean across axes 1 and 2. + bool special_case_4d_axes_1_and_2 = + input->dims->size == 4 && op_params.axis_count == 2 && + ((op_params.axis[0] == 1 && op_params.axis[1] == 2) || + (op_params.axis[0] == 2 && op_params.axis[1] == 1)); + + switch (input->type) { + case kTfLiteFloat32: { + // Defer to specialized implementation for 4D Mean across axes 1 & 2. + if (params->keep_dims && special_case_4d_axes_1_and_2) { + reference_ops::Mean(op_params, tflite::micro::GetTensorShape(input), + tflite::micro::GetTensorData(input), + tflite::micro::GetTensorShape(output), + tflite::micro::GetTensorData(output)); + } else { + TF_LITE_ENSURE( + context, + reference_ops::Mean( + tflite::micro::GetTensorData(input), input->dims->data, + input->dims->size, tflite::micro::GetTensorData(output), + output->dims->data, output->dims->size, + tflite::micro::GetTensorData(axis), num_axis, + params->keep_dims, temp_index, resolved_axis, + tflite::micro::GetTensorData(output))); + } + } break; + case kTfLiteInt8: { + // Defer to specialized implementation for 4D Mean across axes 1 & 2. + if (params->keep_dims && special_case_4d_axes_1_and_2) { + reference_integer_ops::Mean( + op_params, op_data->multiplier, op_data->shift, + tflite::micro::GetTensorShape(input), + tflite::micro::GetTensorData(input), op_data->input_zp, + tflite::micro::GetTensorShape(output), + tflite::micro::GetTensorData(output), op_data->output_zp); + } else if (op_data->input_zp == op_data->output_zp && + op_data->input_scale == op_data->output_scale) { + int32_t* temp_buffer = static_cast( + context->GetScratchBuffer(context, op_data->temp_buffer_idx)); + TF_LITE_ENSURE( + context, + reference_ops::Mean( + tflite::micro::GetTensorData(input), input->dims->data, + input->dims->size, tflite::micro::GetTensorData(output), + output->dims->data, output->dims->size, + tflite::micro::GetTensorData(axis), num_axis, + params->keep_dims, temp_index, resolved_axis, temp_buffer)); + } else { + int32_t* temp_buffer = static_cast( + context->GetScratchBuffer(context, op_data->temp_buffer_idx)); + TF_LITE_ENSURE( + context, + reference_ops::QuantizedMeanOrSum( + tflite::micro::GetTensorData(input), op_data->input_zp, + op_data->input_scale, input->dims->data, input->dims->size, + tflite::micro::GetTensorData(output), + op_data->output_zp, op_data->output_scale, output->dims->data, + output->dims->size, tflite::micro::GetTensorData(axis), + num_axis, params->keep_dims, temp_index, resolved_axis, + temp_buffer, false)); + } + } break; + case kTfLiteInt16: { + // Defer to specialized implementation for 4D Mean across axes 1 & 2. + if (params->keep_dims && special_case_4d_axes_1_and_2) { + reference_integer_ops::Mean( + op_params, op_data->multiplier, op_data->shift, + tflite::micro::GetTensorShape(input), + tflite::micro::GetTensorData(input), op_data->input_zp, + tflite::micro::GetTensorShape(output), + tflite::micro::GetTensorData(output), op_data->output_zp); + } else if (op_data->input_zp == op_data->output_zp && + op_data->input_scale == op_data->output_scale) { + int32_t* temp_buffer = static_cast( + context->GetScratchBuffer(context, op_data->temp_buffer_idx)); + TF_LITE_ENSURE( + context, + reference_ops::Mean(tflite::micro::GetTensorData(input), + input->dims->data, input->dims->size, + tflite::micro::GetTensorData(output), + output->dims->data, output->dims->size, + tflite::micro::GetTensorData(axis), + num_axis, params->keep_dims, temp_index, + resolved_axis, temp_buffer)); + } else { + int32_t* temp_buffer = static_cast( + context->GetScratchBuffer(context, op_data->temp_buffer_idx)); + TF_LITE_ENSURE( + context, + reference_ops::QuantizedMeanOrSum( + tflite::micro::GetTensorData(input), op_data->input_zp, + op_data->input_scale, input->dims->data, input->dims->size, + tflite::micro::GetTensorData(output), + op_data->output_zp, op_data->output_scale, output->dims->data, + output->dims->size, tflite::micro::GetTensorData(axis), + num_axis, params->keep_dims, temp_index, resolved_axis, + temp_buffer, false)); + } + } break; + default: + TF_LITE_ENSURE_MSG(context, false, + "Currently, only float32, int8 or uint8 input type " + "is supported."); + } + return kTfLiteOk; } TfLiteStatus EvalMax(TfLiteContext* context, TfLiteNode* node) { - OpDataReduce* op_data = static_cast(node->user_data); - return EvalMaxHelper(context, node, op_data); + const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0); + const TfLiteEvalTensor* axis = tflite::micro::GetEvalInput(context, node, 1); + TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0); + TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type); + TfLiteReducerParams* params = + static_cast(node->builtin_data); + OpData* op_data = static_cast(node->user_data); + + // Interpret an axis tensor with null dimensions as a scalar + int num_axis = static_cast(ElementCount(*axis->dims)); + int* temp_buffer = static_cast( + context->GetScratchBuffer(context, op_data->temp_buffer_idx)); + int* resolved_axis = static_cast( + context->GetScratchBuffer(context, op_data->resolved_axis_idx)); + switch (input->type) { + case kTfLiteFloat32: + TF_LITE_ENSURE( + context, + reference_ops::ReduceGeneric( + tflite::micro::GetTensorData(input), input->dims->data, + input->dims->size, tflite::micro::GetTensorData(output), + output->dims->data, output->dims->size, + tflite::micro::GetTensorData(axis), num_axis, + params->keep_dims, temp_buffer, resolved_axis, + std::numeric_limits::lowest(), + [](const float current, const float in) -> float { + return (in > current) ? in : current; + })); + break; + case kTfLiteInt8: + TF_LITE_ENSURE_EQ(context, static_cast(op_data->input_scale), + static_cast(op_data->output_scale)); + TF_LITE_ENSURE_EQ(context, op_data->input_zp, op_data->output_zp); + TF_LITE_ENSURE( + context, + reference_ops::ReduceGeneric( + tflite::micro::GetTensorData(input), input->dims->data, + input->dims->size, tflite::micro::GetTensorData(output), + output->dims->data, output->dims->size, + tflite::micro::GetTensorData(axis), num_axis, + params->keep_dims, temp_buffer, resolved_axis, + std::numeric_limits::lowest(), + [](const int8_t current, const int8_t in) -> int8_t { + return (in > current) ? in : current; + })); + break; + default: + TF_LITE_KERNEL_LOG(context, + "Only float32 and int8 types are supported.\n"); + return kTfLiteError; + } + return kTfLiteOk; } +} // namespace reduce + TfLiteRegistration Register_MEAN() { - return tflite::micro::RegisterOp(InitReduce, PrepareMeanOrSum, EvalMean); + return {/*init=*/reduce::InitReduce, + /*free=*/nullptr, + /*prepare=*/reduce::PrepareMeanOrSum, + /*invoke=*/reduce::EvalMean, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } TfLiteRegistration Register_REDUCE_MAX() { - return tflite::micro::RegisterOp(InitReduce, PrepareMax, EvalMax); + return {/*init=*/reduce::InitReduce, + /*free=*/nullptr, + /*prepare=*/reduce::PrepareMax, + /*invoke=*/reduce::EvalMax, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } +} // namespace micro +} // namespace ops } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/reduce.h b/code/components/tflite-lib/tensorflow/lite/micro/kernels/reduce.h deleted file mode 100644 index cd94b3f5..00000000 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/reduce.h +++ /dev/null @@ -1,61 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_REDUCE_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_REDUCE_H_ - -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/types.h" - -namespace tflite { - -extern const int kMaxNumberOfAxis; -extern const int kMaxNumberOfReducedAxis; - -struct OpDataReduce { - int32_t multiplier; - int shift; - int temp_buffer_idx; - int resolved_axis_idx; - int input_zp; - float input_scale; - int output_zp; - float output_scale; - int num_output_elements; -}; - -TfLiteStatus PrepareMaxHelper(TfLiteContext* context, TfLiteNode* node, - OpDataReduce* op_data); - -TfLiteStatus PrepareMeanOrSumHelper(TfLiteContext* context, TfLiteNode* node, - OpDataReduce* op_data); - -TfLiteStatus EvalMaxHelper(TfLiteContext* context, TfLiteNode* node, - OpDataReduce* op_data); -TfLiteStatus EvalMeanHelper(TfLiteContext* context, TfLiteNode* node, - OpDataReduce* op_data); - -void ReduceResolveAxis(const int* axis_data, int axis_count, - MeanParams* op_params); - -TfLiteRegistration Register_MEAN(); -TfLiteRegistration Register_REDUCE_MAX(); - -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_REDUCE_H_ diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/reduce_common.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/reduce_common.cc deleted file mode 100644 index 97452300..00000000 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/reduce_common.cc +++ /dev/null @@ -1,311 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/integer_ops/mean.h" -#include "tensorflow/lite/kernels/internal/reference/reduce.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/internal/types.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/reduce.h" -#include "tensorflow/lite/micro/micro_error_reporter.h" -#include "tensorflow/lite/micro/micro_utils.h" - -namespace tflite { - -const int kMaxNumberOfAxis = 4; -const int kMaxNumberOfReducedAxis = 2; - -TfLiteStatus PrepareSimple(TfLiteContext* context, TfLiteNode* node, - int32_t* multiplier, int* shift) { - MicroContext* micro_context = GetMicroContext(context); - - // Inputs Tensor (dtype depends on quantization): - // [0] = Input - // [1] = Axis - TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, 0); - - // Outputs Tensor (dtype depends on quantization): - // [0] = Output - - // Validate number of inputs and outputs - TF_LITE_ENSURE_EQ(context, node->inputs->size, 2); - TF_LITE_ENSURE_EQ(context, node->outputs->size, 1); - - // Validate axis type - TfLiteTensor* axis = micro_context->AllocateTempInputTensor(node, 1); - TF_LITE_ENSURE(context, axis != nullptr); - TF_LITE_ENSURE_TYPES_EQ(context, axis->type, kTfLiteInt32); - - if (input->type == kTfLiteInt8) { - TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0); - const double real_multiplier = static_cast(input->params.scale) / - static_cast(output->params.scale); - QuantizeMultiplier(real_multiplier, multiplier, shift); - micro_context->DeallocateTempTfLiteTensor(output); - } - micro_context->DeallocateTempTfLiteTensor(axis); - micro_context->DeallocateTempTfLiteTensor(input); - return kTfLiteOk; -} - -TfLiteStatus PrepareMaxHelper(TfLiteContext* context, TfLiteNode* node, - OpDataReduce* op_data) { - TF_LITE_ENSURE_OK(context, PrepareSimple(context, node, &op_data->multiplier, - &op_data->shift)); - - MicroContext* micro_context = GetMicroContext(context); - TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, 0); - TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0); - TfLiteTensor* axis = micro_context->AllocateTempInputTensor(node, 1); - - op_data->input_scale = input->params.scale; - op_data->output_scale = output->params.scale; - op_data->num_output_elements = NumElements(output); - - context->RequestScratchBufferInArena(context, sizeof(int) * input->dims->size, - &op_data->temp_buffer_idx); - context->RequestScratchBufferInArena( - context, sizeof(int) * static_cast(ElementCount(*axis->dims)), - &op_data->resolved_axis_idx); - - micro_context->DeallocateTempTfLiteTensor(input); - micro_context->DeallocateTempTfLiteTensor(output); - micro_context->DeallocateTempTfLiteTensor(axis); - return kTfLiteOk; -} - -TfLiteStatus PrepareMeanOrSumHelper(TfLiteContext* context, TfLiteNode* node, - OpDataReduce* op_data) { - MicroContext* micro_context = GetMicroContext(context); - TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, 0); - TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0); - if (input->type == kTfLiteInt8 || input->type == kTfLiteInt16) { - const double real_multiplier = static_cast(input->params.scale) / - static_cast(output->params.scale); - QuantizeMultiplier(real_multiplier, &op_data->multiplier, &op_data->shift); - } - - int output_size = NumElements(output); - if (input->type == kTfLiteInt8 || input->type == kTfLiteInt16) { - context->RequestScratchBufferInArena(context, output_size * sizeof(int32_t), - &op_data->temp_buffer_idx); - op_data->input_zp = input->params.zero_point; - op_data->input_scale = input->params.scale; - op_data->output_zp = output->params.zero_point; - op_data->output_scale = output->params.scale; - } - - TF_LITE_ENSURE_OK( - context, - PrepareSimple(context, node, &(op_data->multiplier), &(op_data->shift))); - // TODO(b/144955155): Support uint8_t(b/144955155) and int8_t(b/144955018) - micro_context->DeallocateTempTfLiteTensor(input); - micro_context->DeallocateTempTfLiteTensor(output); - return kTfLiteOk; -} - -void ResolveAxis(const int* axis_data, int axis_count, - tflite::MeanParams* op_params) { - int i = 0; - for (; i < axis_count; ++i) { - op_params->axis[i] = static_cast(axis_data[i]); - } - for (; i < 4; ++i) { - op_params->axis[i] = 1; - } - op_params->axis_count = axis_count; -} - -TfLiteStatus EvalMeanHelper(TfLiteContext* context, TfLiteNode* node, - OpDataReduce* op_data) { - const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0); - const TfLiteEvalTensor* axis = tflite::micro::GetEvalInput(context, node, 1); - TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0); - TfLiteReducerParams* params = - reinterpret_cast(node->builtin_data); - - int num_axis = static_cast(ElementCount(*axis->dims)); - int temp_index[kMaxNumberOfAxis]; - int resolved_axis[kMaxNumberOfReducedAxis]; - - tflite::MeanParams op_params; - ResolveAxis(tflite::micro::GetTensorData(axis), num_axis, &op_params); - - // Special case mean implementation exists for 4D mean across axes 1 and 2. - bool special_case_4d_axes_1_and_2 = - input->dims->size == 4 && op_params.axis_count == 2 && - ((op_params.axis[0] == 1 && op_params.axis[1] == 2) || - (op_params.axis[0] == 2 && op_params.axis[1] == 1)); - - switch (input->type) { - case kTfLiteFloat32: { - // Defer to specialized implementation for 4D Mean across axes 1 & 2. - if (params->keep_dims && special_case_4d_axes_1_and_2) { - reference_ops::Mean(op_params, tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output)); - } else { - TF_LITE_ENSURE( - context, - reference_ops::Mean( - tflite::micro::GetTensorData(input), input->dims->data, - input->dims->size, tflite::micro::GetTensorData(output), - output->dims->data, output->dims->size, - tflite::micro::GetTensorData(axis), num_axis, - params->keep_dims, temp_index, resolved_axis, - tflite::micro::GetTensorData(output))); - } - } break; - case kTfLiteInt8: { - // Defer to specialized implementation for 4D Mean across axes 1 & 2. - if (params->keep_dims && special_case_4d_axes_1_and_2) { - reference_integer_ops::Mean( - op_params, op_data->multiplier, op_data->shift, - tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), op_data->input_zp, - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output), op_data->output_zp); - } else if (op_data->input_zp == op_data->output_zp && - op_data->input_scale == op_data->output_scale) { - int32_t* temp_buffer = static_cast( - context->GetScratchBuffer(context, op_data->temp_buffer_idx)); - TF_LITE_ENSURE( - context, - reference_ops::Mean( - tflite::micro::GetTensorData(input), input->dims->data, - input->dims->size, tflite::micro::GetTensorData(output), - output->dims->data, output->dims->size, - tflite::micro::GetTensorData(axis), num_axis, - params->keep_dims, temp_index, resolved_axis, temp_buffer)); - } else { - int32_t* temp_buffer = static_cast( - context->GetScratchBuffer(context, op_data->temp_buffer_idx)); - TF_LITE_ENSURE( - context, - reference_ops::QuantizedMeanOrSum( - tflite::micro::GetTensorData(input), op_data->input_zp, - op_data->input_scale, input->dims->data, input->dims->size, - tflite::micro::GetTensorData(output), - op_data->output_zp, op_data->output_scale, output->dims->data, - output->dims->size, tflite::micro::GetTensorData(axis), - num_axis, params->keep_dims, temp_index, resolved_axis, - temp_buffer, false)); - } - } break; - case kTfLiteInt16: { - // Defer to specialized implementation for 4D Mean across axes 1 & 2. - if (params->keep_dims && special_case_4d_axes_1_and_2) { - reference_integer_ops::Mean( - op_params, op_data->multiplier, op_data->shift, - tflite::micro::GetTensorShape(input), - tflite::micro::GetTensorData(input), op_data->input_zp, - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output), op_data->output_zp); - } else if (op_data->input_zp == op_data->output_zp && - op_data->input_scale == op_data->output_scale) { - int32_t* temp_buffer = static_cast( - context->GetScratchBuffer(context, op_data->temp_buffer_idx)); - TF_LITE_ENSURE( - context, - reference_ops::Mean(tflite::micro::GetTensorData(input), - input->dims->data, input->dims->size, - tflite::micro::GetTensorData(output), - output->dims->data, output->dims->size, - tflite::micro::GetTensorData(axis), - num_axis, params->keep_dims, temp_index, - resolved_axis, temp_buffer)); - } else { - int32_t* temp_buffer = static_cast( - context->GetScratchBuffer(context, op_data->temp_buffer_idx)); - TF_LITE_ENSURE( - context, - reference_ops::QuantizedMeanOrSum( - tflite::micro::GetTensorData(input), op_data->input_zp, - op_data->input_scale, input->dims->data, input->dims->size, - tflite::micro::GetTensorData(output), - op_data->output_zp, op_data->output_scale, output->dims->data, - output->dims->size, tflite::micro::GetTensorData(axis), - num_axis, params->keep_dims, temp_index, resolved_axis, - temp_buffer, false)); - } - } break; - default: - TF_LITE_ENSURE_MSG(context, false, - "Currently, only float32, int8 or uint8 input type " - "is supported."); - } - return kTfLiteOk; -} - -TfLiteStatus EvalMaxHelper(TfLiteContext* context, TfLiteNode* node, - OpDataReduce* op_data) { - const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0); - const TfLiteEvalTensor* axis = tflite::micro::GetEvalInput(context, node, 1); - TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0); - TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type); - TfLiteReducerParams* params = - static_cast(node->builtin_data); - - // Interpret an axis tensor with null dimensions as a scalar - int num_axis = static_cast(ElementCount(*axis->dims)); - int* temp_buffer = static_cast( - context->GetScratchBuffer(context, op_data->temp_buffer_idx)); - int* resolved_axis = static_cast( - context->GetScratchBuffer(context, op_data->resolved_axis_idx)); - switch (input->type) { - case kTfLiteFloat32: - TF_LITE_ENSURE( - context, - reference_ops::ReduceGeneric( - tflite::micro::GetTensorData(input), input->dims->data, - input->dims->size, tflite::micro::GetTensorData(output), - output->dims->data, output->dims->size, - tflite::micro::GetTensorData(axis), num_axis, - params->keep_dims, temp_buffer, resolved_axis, - std::numeric_limits::lowest(), - [](const float current, const float in) -> float { - return (in > current) ? in : current; - })); - break; - case kTfLiteInt8: - TF_LITE_ENSURE_EQ(context, static_cast(op_data->input_scale), - static_cast(op_data->output_scale)); - TF_LITE_ENSURE_EQ(context, op_data->input_zp, op_data->output_zp); - TF_LITE_ENSURE( - context, - reference_ops::ReduceGeneric( - tflite::micro::GetTensorData(input), input->dims->data, - input->dims->size, tflite::micro::GetTensorData(output), - output->dims->data, output->dims->size, - tflite::micro::GetTensorData(axis), num_axis, - params->keep_dims, temp_buffer, resolved_axis, - std::numeric_limits::lowest(), - [](const int8_t current, const int8_t in) -> int8_t { - return (in > current) ? in : current; - })); - break; - default: - MicroPrintf("Only float32 and int8 types are supported."); - return kTfLiteError; - } - return kTfLiteOk; -} - -} // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/reshape.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/reshape.cc index 832ba261..d14ed82e 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/reshape.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/reshape.cc @@ -110,7 +110,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace reshape TfLiteRegistration Register_RESHAPE() { - return tflite::micro::RegisterOp(nullptr, reshape::Prepare, reshape::Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/reshape::Prepare, + /*invoke=*/reshape::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/resize_bilinear.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/resize_bilinear.cc index a90057b9..55c23846 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/resize_bilinear.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/resize_bilinear.cc @@ -111,7 +111,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_RESIZE_BILINEAR() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/resize_nearest_neighbor.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/resize_nearest_neighbor.cc index ce507445..a02159af 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/resize_nearest_neighbor.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/resize_nearest_neighbor.cc @@ -117,8 +117,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace resize_nearest_neighbor TfLiteRegistration Register_RESIZE_NEAREST_NEIGHBOR() { - return tflite::micro::RegisterOp(nullptr, resize_nearest_neighbor::Prepare, - resize_nearest_neighbor::Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/resize_nearest_neighbor::Prepare, + /*invoke=*/resize_nearest_neighbor::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/round.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/round.cc index 0bda8783..76d8e6bf 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/round.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/round.cc @@ -68,7 +68,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace round TfLiteRegistration Register_ROUND() { - return tflite::micro::RegisterOp(nullptr, round::Prepare, round::Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/round::Prepare, + /*invoke=*/round::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/shape.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/shape.cc index 02f663a8..df962f62 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/shape.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/shape.cc @@ -60,7 +60,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_SHAPE() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/slice.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/slice.cc index 212cf47f..40d9fdd7 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/slice.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/slice.cc @@ -151,7 +151,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_SLICE() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/softmax.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/softmax.cc index c2cee3c5..f6a30010 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/softmax.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/softmax.cc @@ -83,7 +83,14 @@ TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_SOFTMAX() { - return tflite::micro::RegisterOp(SoftmaxInit, SoftmaxPrepare, SoftmaxEval); + return {/*init=*/SoftmaxInit, + /*free=*/nullptr, + /*prepare=*/SoftmaxPrepare, + /*invoke=*/SoftmaxEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/softmax.h b/code/components/tflite-lib/tensorflow/lite/micro/kernels/softmax.h index 7096d202..8d605eab 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/softmax.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/softmax.h @@ -1,4 +1,4 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -23,13 +23,6 @@ namespace tflite { void* SoftmaxInit(TfLiteContext* context, const char* buffer, size_t length); -// Common helper function to SoftmaxPrepare. -TfLiteStatus CalculateSoftmaxParams(TfLiteContext* context, - const TfLiteTensor* input, - TfLiteTensor* output, - const TfLiteSoftmaxParams* params, - SoftmaxParams* op_data); - TfLiteStatus SoftmaxPrepare(TfLiteContext* context, TfLiteNode* node); // This is the most generic TfLiteRegistration. The actual supported types may @@ -37,7 +30,7 @@ TfLiteStatus SoftmaxPrepare(TfLiteContext* context, TfLiteNode* node); // (reference or optimized) must define this function. TfLiteRegistration Register_SOFTMAX(); -#if defined(XTENSA) || defined(CMSIS_NN) +#if defined(XTENSA) // Returns a TfLiteRegistration struct for kernel variant that only supports // int8 input and int16 output. TfLiteRegistration Register_SOFTMAX_INT8_INT16(); @@ -47,23 +40,6 @@ inline TfLiteRegistration Register_SOFTMAX_INT8_INT16() { } #endif -#if defined(CMSIS_NN) -// Returns a TfLiteRegistration struct for kernel variant that only supports -// int8 input/output and uses the latency optimized implementations. -TfLiteRegistration Register_SOFTMAX_INT8(); - -// Returns a TfLiteRegistration struct for kernel variant that only supports -// int16 input/output and uses the latency optimized implementations. -TfLiteRegistration Register_SOFTMAX_INT16(); - -#else -inline TfLiteRegistration Register_SOFTMAX_INT8() { return Register_SOFTMAX(); } - -inline TfLiteRegistration Register_SOFTMAX_INT16() { - return Register_SOFTMAX(); -} -#endif - } // namespace tflite #endif // TENSORFLOW_LITE_MICRO_KERNELS_SOFTMAX_H_ diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/softmax_common.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/softmax_common.cc index b5378dae..d93f5f26 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/softmax_common.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/softmax_common.cc @@ -1,4 +1,4 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -28,59 +28,11 @@ namespace { // Softmax parameter data that persists in user_data const int kInt16LUTArraySize = 513; -TfLiteStatus InitializeLutForInt16(TfLiteContext* context, - const TfLiteTensor* input, - TfLiteTensor* output, - SoftmaxParams* op_data) { - // Only allocate LUTs for KTfLiteInt16 data type - if (input->type == kTfLiteInt16) { - void* raw_exp_lut = context->AllocatePersistentBuffer( - context, sizeof(int16_t) * kInt16LUTArraySize); - TF_LITE_ENSURE(context, raw_exp_lut != nullptr); - op_data->exp_lut = reinterpret_cast(raw_exp_lut); - void* one_over_one_plus_x_lut = context->AllocatePersistentBuffer( - context, sizeof(int16_t) * kInt16LUTArraySize); - TF_LITE_ENSURE(context, one_over_one_plus_x_lut != nullptr); - op_data->one_over_one_plus_x_lut = - reinterpret_cast(one_over_one_plus_x_lut); - } - - if (output->type == kTfLiteInt16) { - TF_LITE_ENSURE(context, - input->type == kTfLiteInt8 || input->type == kTfLiteInt16); - } else { - TF_LITE_ENSURE_EQ(context, input->type, output->type); - } - - // Populate LUT if required - if (input->type == kTfLiteInt16) { - TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0); - // exp LUT only used on negative values - // we consider exp(-10.0) is insignificant to accumulation - gen_lut( - [](float value) { return std::exp(value); }, -10.0f, 0.0f, -1.0f, 1.0f, - op_data->exp_lut); - gen_lut( - [](float value) { return 1.0f / (1.0f + value); }, 0.0f, 1.0f, -1.0f, - 1.0f, op_data->one_over_one_plus_x_lut); - op_data->zero_point = output->params.zero_point; - op_data->scale = output->params.scale; - } - - return kTfLiteOk; -} - -} // namespace - TfLiteStatus CalculateSoftmaxParams(TfLiteContext* context, const TfLiteTensor* input, TfLiteTensor* output, const TfLiteSoftmaxParams* params, SoftmaxParams* op_data) { - if (InitializeLutForInt16(context, input, output, op_data) != kTfLiteOk) { - return kTfLiteError; - } - if (input->type == kTfLiteInt8 || input->type == kTfLiteInt16) { if (input->type == kTfLiteInt16) { TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0); @@ -131,6 +83,8 @@ TfLiteStatus CalculateSoftmaxParams(TfLiteContext* context, return kTfLiteOk; } +} // namespace + void* SoftmaxInit(TfLiteContext* context, const char* buffer, size_t length) { TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); return context->AllocatePersistentBuffer(context, sizeof(SoftmaxParams)); @@ -149,6 +103,40 @@ TfLiteStatus SoftmaxPrepare(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE(context, node->user_data != nullptr); SoftmaxParams* op_data = static_cast(node->user_data); + // Only allocate LUTs for KTfLiteInt16 data type + if (input->type == kTfLiteInt16) { + void* raw_exp_lut = context->AllocatePersistentBuffer( + context, sizeof(int16_t) * kInt16LUTArraySize); + TF_LITE_ENSURE(context, raw_exp_lut != nullptr); + op_data->exp_lut = reinterpret_cast(raw_exp_lut); + void* one_over_one_plus_x_lut = context->AllocatePersistentBuffer( + context, sizeof(int16_t) * kInt16LUTArraySize); + TF_LITE_ENSURE(context, one_over_one_plus_x_lut != nullptr); + op_data->one_over_one_plus_x_lut = + reinterpret_cast(one_over_one_plus_x_lut); + } + + if (output->type == kTfLiteInt16) { + TF_LITE_ENSURE(context, + input->type == kTfLiteInt8 || input->type == kTfLiteInt16); + } else { + TF_LITE_ENSURE_EQ(context, input->type, output->type); + } + + // Populate LUT if required + if (input->type == kTfLiteInt16) { + TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0); + // exp LUT only used on negative values + // we consider exp(-10.0) is insignificant to accumulation + gen_lut( + [](float value) { return std::exp(value); }, -10.0f, 0.0f, -1.0f, 1.0f, + op_data->exp_lut); + gen_lut( + [](float value) { return 1.0f / (1.0f + value); }, 0.0f, 1.0f, -1.0f, + 1.0f, op_data->one_over_one_plus_x_lut); + op_data->zero_point = output->params.zero_point; + op_data->scale = output->params.scale; + } auto* params = static_cast(node->builtin_data); auto ret_val = diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/space_to_batch_nd.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/space_to_batch_nd.cc index 21f81312..4e01becb 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/space_to_batch_nd.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/space_to_batch_nd.cc @@ -114,7 +114,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace. TfLiteRegistration Register_SPACE_TO_BATCH_ND() { - return tflite::micro::RegisterOp(Init, Prepare, Eval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/space_to_depth.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/space_to_depth.cc index 30519b27..9c0cc445 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/space_to_depth.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/space_to_depth.cc @@ -121,7 +121,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_SPACE_TO_DEPTH() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/split.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/split.cc index 06584d45..5d90d983 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/split.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/split.cc @@ -120,7 +120,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace split TfLiteRegistration Register_SPLIT() { - return tflite::micro::RegisterOp(nullptr, split::Prepare, split::Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/split::Prepare, + /*invoke=*/split::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/split_v.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/split_v.cc index 3ea35130..c1c41124 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/split_v.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/split_v.cc @@ -121,7 +121,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace split_v TfLiteRegistration Register_SPLIT_V() { - return tflite::micro::RegisterOp(nullptr, split_v::Prepare, split_v::Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/split_v::Prepare, + /*invoke=*/split_v::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/squared_difference.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/squared_difference.cc deleted file mode 100644 index ca924e26..00000000 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/squared_difference.cc +++ /dev/null @@ -1,247 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/reference/binary_function.h" -#include "tensorflow/lite/kernels/internal/reference/integer_ops/add.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/micro_context.h" -#include "tensorflow/lite/micro/micro_error_reporter.h" - -namespace tflite { -namespace { -constexpr int kInputTensor1 = 0; -constexpr int kInputTensor2 = 1; -constexpr int kOutputTensor = 0; - -struct OpData { - bool requires_broadcast; - ArithmeticParams arithmetic_params; -}; - -template -T SquaredDifference(T input1, T input2) { - const T difference = input1 - input2; - return difference * difference; -} - -void* SquaredDifferenceInit(TfLiteContext* context, const char* buffer, - size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer(context, sizeof(OpData)); -} - -TfLiteStatus SquaredDifferencePrepare(TfLiteContext* context, - TfLiteNode* node) { - TFLITE_DCHECK(node->user_data != nullptr); - OpData* data = reinterpret_cast(node->user_data); - data->requires_broadcast = false; - - TF_LITE_ENSURE_EQ(context, NumInputs(node), 2); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - - MicroContext* micro_context = GetMicroContext(context); - - TfLiteTensor* input1 = - micro_context->AllocateTempInputTensor(node, kInputTensor1); - TF_LITE_ENSURE(context, input1 != nullptr); - TfLiteTensor* input2 = - micro_context->AllocateTempInputTensor(node, kInputTensor2); - TF_LITE_ENSURE(context, input2 != nullptr); - TfLiteTensor* output = - micro_context->AllocateTempOutputTensor(node, kOutputTensor); - TF_LITE_ENSURE(context, output != nullptr); - - TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type); - output->type = input2->type; - - // Ensure the quantization parameters are equivalent. - if (input1->type == kTfLiteInt8) { - const auto& input1_quantization_params = input1->params; - const auto& input2_quantization_params = input2->params; - const auto& output_quantization_params = output->params; - const int32_t integer_type_min = std::numeric_limits::min(); - const int32_t integer_type_max = std::numeric_limits::max(); - TF_LITE_ENSURE(context, - input1_quantization_params.zero_point >= integer_type_min); - TF_LITE_ENSURE(context, - input1_quantization_params.zero_point <= integer_type_max); - TF_LITE_ENSURE(context, - input2_quantization_params.zero_point >= integer_type_min); - TF_LITE_ENSURE(context, - input2_quantization_params.zero_point <= integer_type_max); - TF_LITE_ENSURE(context, - output_quantization_params.zero_point >= integer_type_min); - TF_LITE_ENSURE(context, - output_quantization_params.zero_point <= integer_type_max); - data->arithmetic_params.input1_offset = - -input1_quantization_params.zero_point; - data->arithmetic_params.input2_offset = - -input2_quantization_params.zero_point; - data->arithmetic_params.output_offset = - output_quantization_params.zero_point; - - // shift to make integer for scales. - // 7 is selected so that maximum shifted result 255^2 * (1 << (7 * 2 )) - // does not overflow signed 32-bit integer - data->arithmetic_params.left_shift = 7; - const double twice_max_input_scale = - 2.0 * static_cast(std::max(input1_quantization_params.scale, - input2_quantization_params.scale)); - const double real_input1_multiplier = - static_cast(input1_quantization_params.scale) / - twice_max_input_scale; - double real_input2_multiplier = - static_cast(input2_quantization_params.scale) / - twice_max_input_scale; - const double real_output_multiplier = - (twice_max_input_scale * twice_max_input_scale) / - static_cast((1 << data->arithmetic_params.left_shift * 2) * - output_quantization_params.scale); - QuantizeMultiplierSmallerThanOneExp( - real_input1_multiplier, &data->arithmetic_params.input1_multiplier, - &data->arithmetic_params.input1_shift); - QuantizeMultiplierSmallerThanOneExp( - real_input2_multiplier, &data->arithmetic_params.input2_multiplier, - &data->arithmetic_params.input2_shift); - QuantizeMultiplierSmallerThanOneExp( - real_output_multiplier, &data->arithmetic_params.output_multiplier, - &data->arithmetic_params.output_shift); - data->arithmetic_params.quantized_activation_min = - std::numeric_limits::min(); - data->arithmetic_params.quantized_activation_max = - std::numeric_limits::max(); - } - - data->requires_broadcast = !HaveSameShapes(input1, input2); - - micro_context->DeallocateTempTfLiteTensor(input1); - micro_context->DeallocateTempTfLiteTensor(input2); - micro_context->DeallocateTempTfLiteTensor(output); - return kTfLiteOk; -} - -inline int8_t SquaredDifference(int8_t x, int8_t y, - const ArithmeticParams& params) { - const int32_t input1_val = params.input1_offset + x; - const int32_t input2_val = params.input2_offset + y; - const int32_t shifted_input1_val = input1_val * (1 << params.left_shift); - const int32_t shifted_input2_val = input2_val * (1 << params.left_shift); - const int32_t scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, params.input1_multiplier, params.input1_shift); - const int32_t scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, params.input2_multiplier, params.input2_shift); - const int32_t raw_diff = scaled_input1_val - scaled_input2_val; - - // Max of this is 255^2 * (1 << 14), so won't overflow 32 bits. - const int32_t squared_raw_diff = raw_diff * raw_diff; - const int32_t raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - squared_raw_diff, params.output_multiplier, params.output_shift) + - params.output_offset; - const int32_t clamped_output = - std::min(params.quantized_activation_max, - std::max(params.quantized_activation_min, raw_output)); - return static_cast(clamped_output); -} - -template -void EvalQuantizedSquaredDifference(TfLiteContext* context, TfLiteNode* node, - const OpData* data, - const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2, - TfLiteEvalTensor* output) { - const auto* op_data = static_cast(node->user_data); - if (data->requires_broadcast) { - reference_integer_ops::BroadcastBinaryFunction4DSlow( - op_data->arithmetic_params, tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output), - reference_integer_ops::CheckArithmeticParams, SquaredDifference); - } else { - const int flat_size = tflite::micro::GetTensorShape(input1).FlatSize(); - reference_integer_ops::ElementWise( - flat_size, op_data->arithmetic_params, - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorData(output), - reference_integer_ops::CheckArithmeticParams, SquaredDifference); - } -} - -template -void EvalSquaredDifference(TfLiteContext* context, TfLiteNode* node, - const OpData* data, const TfLiteEvalTensor* input1, - const TfLiteEvalTensor* input2, - TfLiteEvalTensor* output) { - if (data->requires_broadcast) { - reference_ops::BroadcastBinaryFunction4DSlow( - tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output), SquaredDifference); - } else { - reference_ops::BinaryFunction( - tflite::micro::GetTensorShape(input1), - tflite::micro::GetTensorData(input1), - tflite::micro::GetTensorShape(input2), - tflite::micro::GetTensorData(input2), - tflite::micro::GetTensorShape(output), - tflite::micro::GetTensorData(output), SquaredDifference); - } -} - -TfLiteStatus SquaredDifferenceEval(TfLiteContext* context, TfLiteNode* node) { - OpData* data = reinterpret_cast(node->user_data); - - const TfLiteEvalTensor* input1 = - tflite::micro::GetEvalInput(context, node, kInputTensor1); - const TfLiteEvalTensor* input2 = - tflite::micro::GetEvalInput(context, node, kInputTensor2); - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kOutputTensor); - - if (output->type == kTfLiteFloat32) { - EvalSquaredDifference(context, node, data, input1, input2, output); - } else if (output->type == kTfLiteInt32) { - EvalSquaredDifference(context, node, data, input1, input2, output); - } else if (output->type == kTfLiteInt8) { - EvalQuantizedSquaredDifference(context, node, data, input1, input2, - output); - } else { - MicroPrintf( - "SquaredDifference only supports FLOAT32, INT32 and INT8 now, got %d.", - output->type); - return kTfLiteError; - } - - return kTfLiteOk; -} -} // namespace - -TfLiteRegistration Register_SQUARED_DIFFERENCE() { - return tflite::micro::RegisterOp( - SquaredDifferenceInit, SquaredDifferencePrepare, SquaredDifferenceEval); -} - -} // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/squeeze.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/squeeze.cc index e81b5b56..0eb767db 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/squeeze.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/squeeze.cc @@ -111,7 +111,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_SQUEEZE() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/strided_slice.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/strided_slice.cc index 832e2ccd..d5b73b8f 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/strided_slice.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/strided_slice.cc @@ -193,8 +193,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace strided_slice TfLiteRegistration Register_STRIDED_SLICE() { - return tflite::micro::RegisterOp(strided_slice::Init, strided_slice::Prepare, - strided_slice::Eval); + return {/*init=*/strided_slice::Init, + /*free=*/nullptr, + /*prepare=*/strided_slice::Prepare, + /*invoke=*/strided_slice::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/sub.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/sub.cc index 40bddbad..de99149f 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/sub.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/sub.cc @@ -162,7 +162,14 @@ TfLiteStatus SubEval(TfLiteContext* context, TfLiteNode* node) { } TfLiteRegistration Register_SUB() { - return tflite::micro::RegisterOp(SubInit, SubPrepare, SubEval); + return {/*init=*/SubInit, + /*free=*/nullptr, + /*prepare=*/SubPrepare, + /*invoke=*/SubEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/svdf.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/svdf.cc index 5994db94..f8a2bed2 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/svdf.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/svdf.cc @@ -100,7 +100,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_SVDF() { - return tflite::micro::RegisterOp(Init, PrepareSvdf, Eval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/PrepareSvdf, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/tanh.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/tanh.cc index e97a9035..a9f01c71 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/tanh.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/tanh.cc @@ -195,8 +195,14 @@ TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) { } // namespace activations TfLiteRegistration Register_TANH() { - return tflite::micro::RegisterOp( - activations::TanhInit, activations::TanhPrepare, activations::TanhEval); + return {/*init=*/activations::TanhInit, + /*free=*/nullptr, + /*prepare=*/activations::TanhPrepare, + /*invoke=*/activations::TanhEval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro } // namespace ops diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/transpose.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/transpose.cc index 9f77e04d..ba3d6e94 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/transpose.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/transpose.cc @@ -116,6 +116,13 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_TRANSPOSE() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/transpose_conv.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/transpose_conv.cc index 0b2afd5b..dcf007c5 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/transpose_conv.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/transpose_conv.cc @@ -266,7 +266,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { tflite::micro::GetTensorShape(filter), tflite::micro::GetTensorData(filter), tflite::micro::GetTensorShape(bias), - tflite::micro::GetOptionalTensorData(bias), + tflite::micro::GetTensorData(bias), tflite::micro::GetTensorShape(output), tflite::micro::GetTensorData(output), tflite::micro::GetTensorShape(nullptr), nullptr); @@ -282,7 +282,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { tflite::micro::GetTensorShape(filter), tflite::micro::GetTensorData(filter), tflite::micro::GetTensorShape(bias), - tflite::micro::GetOptionalTensorData(bias), + tflite::micro::GetTensorData(bias), tflite::micro::GetTensorShape(output), tflite::micro::GetTensorData(output), tflite::micro::GetTensorShape(nullptr), nullptr, scratch_buffer); @@ -293,7 +293,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { context->GetScratchBuffer(context, data.scratch_buffer_index)); // TODO(b/192090531): Remove this once all 8x16 transpose conv models use // 64-bit biases. - if (bias != nullptr && bias->type == kTfLiteInt16) { + if (bias->type == kTfLiteInt16) { std::int64_t* bias_converted_buffer = static_cast(context->GetScratchBuffer( context, data.bias_converted_buffer_index)); @@ -319,7 +319,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { tflite::micro::GetTensorShape(filter), tflite::micro::GetTensorData(filter), tflite::micro::GetTensorShape(bias), - tflite::micro::GetOptionalTensorData(bias), + tflite::micro::GetTensorData(bias), tflite::micro::GetTensorShape(output), tflite::micro::GetTensorData(output), tflite::micro::GetTensorShape(nullptr), nullptr, scratch_buffer); @@ -337,7 +337,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_TRANSPOSE_CONV() { - return tflite::micro::RegisterOp(Init, Prepare, Eval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm.cc deleted file mode 100644 index 7f3c50e4..00000000 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm.cc +++ /dev/null @@ -1,1696 +0,0 @@ -/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include -#include - -#include "tensorflow/lite/c/builtin_op_data.h" -#include "tensorflow/lite/c/common.h" -#include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/internal/quantization_util.h" -#include "tensorflow/lite/kernels/internal/tensor_ctypes.h" -#include "tensorflow/lite/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/kernel_util.h" -#include "tensorflow/lite/micro/kernels/lstm_eval.h" -#include "tensorflow/lite/micro/kernels/lstm_shared.h" -#include "tensorflow/lite/micro/kernels/micro_tensor_utils.h" -#include "tensorflow/lite/micro/micro_error_reporter.h" - -namespace tflite { - -namespace { - -constexpr int scratch_index_size = 12; - -struct UnidirectionalSequenceLstmOpData { - // If the lstm is layer norm. - bool use_layer_norm; - // The scratch index. - int scratch_index[scratch_index_size]; - - int32_t row_sums_size; - int32_t* row_sums; - bool compute_row_sums = false; - - int32_t input_zero_point; - int32_t output_state_zero_point; - - IntegerLstmParameter integer_lstm_param; - HybridLstmScales hybrid_lstm_scales; -}; - -TfLiteStatus PopulateQuantizedLstmParams8x8_16( - TfLiteContext* context, TfLiteNode* node, - IntegerLstmParameter* integer_lstm_param) { - MicroContext* micro_context = GetMicroContext(context); - - // Calculate quantized clip for projection and cell. - const auto* params = - static_cast(node->builtin_data); - const float cell_clip = params->cell_clip; - const float proj_clip = params->proj_clip; - - TfLiteTensor* cell_state = - micro_context->AllocateTempInputTensor(node, kLstmCellStateTensor); - TF_LITE_ENSURE(context, cell_state != nullptr); - TF_LITE_ENSURE(context, cell_state->is_variable); - TfLiteTensor* output_tensor = - micro_context->AllocateTempOutputTensor(node, kLstmOutputTensor); - - TF_LITE_ENSURE(context, - cell_state->quantization.type != kTfLiteNoQuantization); - auto* cell_state_params = - static_cast(cell_state->quantization.params); - TF_LITE_ENSURE(context, - output_tensor->quantization.type != kTfLiteNoQuantization); - auto* proj_params = static_cast( - output_tensor->quantization.params); - if (cell_clip > 0.0f) { - integer_lstm_param->quantized_cell_clip = static_cast(std::min( - std::max(cell_clip / cell_state_params->scale->data[0], -32768.0f), - 32767.0f)); - } else { - integer_lstm_param->quantized_cell_clip = 0; - } - if (proj_clip > 0.0f) { - integer_lstm_param->quantized_proj_clip = static_cast(std::min( - std::max(proj_clip / proj_params->scale->data[0], -128.0f), 127.0f)); - } else { - integer_lstm_param->quantized_proj_clip = 0; - } - - // Calculate effective scales. - UnidirectionalSequenceLstmOpData* op_data = - static_cast(node->user_data); - const bool use_layer_norm = op_data->use_layer_norm; - - TfLiteTensor* input = - micro_context->AllocateTempInputTensor(node, kLstmInputTensor); - - TfLiteTensor* input_to_input_weights = micro_context->AllocateTempInputTensor( - node, kLstmInputToInputWeightsTensor); - TfLiteTensor* input_to_forget_weights = - micro_context->AllocateTempInputTensor(node, - kLstmInputToForgetWeightsTensor); - TfLiteTensor* input_to_cell_weights = micro_context->AllocateTempInputTensor( - node, kLstmInputToCellWeightsTensor); - TfLiteTensor* input_to_output_weights = - micro_context->AllocateTempInputTensor(node, - kLstmInputToOutputWeightsTensor); - - TfLiteTensor* recurrent_to_input_weights = - micro_context->AllocateTempInputTensor( - node, kLstmRecurrentToInputWeightsTensor); - TfLiteTensor* recurrent_to_forget_weights = - micro_context->AllocateTempInputTensor( - node, kLstmRecurrentToForgetWeightsTensor); - TfLiteTensor* recurrent_to_cell_weights = - micro_context->AllocateTempInputTensor(node, - kLstmRecurrentToCellWeightsTensor); - TfLiteTensor* recurrent_to_output_weights = - micro_context->AllocateTempInputTensor( - node, kLstmRecurrentToOutputWeightsTensor); - - TfLiteTensor* cell_to_input_weights = micro_context->AllocateTempInputTensor( - node, kLstmCellToInputWeightsTensor); - TfLiteTensor* cell_to_forget_weights = micro_context->AllocateTempInputTensor( - node, kLstmCellToForgetWeightsTensor); - TfLiteTensor* cell_to_output_weights = micro_context->AllocateTempInputTensor( - node, kLstmCellToOutputWeightsTensor); - - TfLiteTensor* input_layer_norm_coefficients = - micro_context->AllocateTempInputTensor( - node, kLstmInputLayerNormCoefficientsTensor); - TfLiteTensor* forget_layer_norm_coefficients = - micro_context->AllocateTempInputTensor( - node, kLstmForgetLayerNormCoefficientsTensor); - TfLiteTensor* cell_layer_norm_coefficients = - micro_context->AllocateTempInputTensor( - node, kLstmCellLayerNormCoefficientsTensor); - TfLiteTensor* output_layer_norm_coefficients = - micro_context->AllocateTempInputTensor( - node, kLstmOutputLayerNormCoefficientsTensor); - - TfLiteTensor* projection_weights = micro_context->AllocateTempInputTensor( - node, kLstmProjectionWeightsTensor); - - TfLiteTensor* output_state = - micro_context->AllocateTempInputTensor(node, kLstmOutputStateTensor); - TF_LITE_ENSURE(context, output_state != nullptr); - TF_LITE_ENSURE(context, output_state->is_variable); - - // Since we have already checked that weights are all there or none, we can - // check the existence of only one to get the condition. - const bool use_cifg = (input_to_input_weights == nullptr); - const bool use_peephole = (cell_to_output_weights != nullptr); - const bool use_projection = (projection_weights != nullptr); - - // Get intermediate scales and zero points. - float intermediate_scale[5]; - int32_t intermediate_zp[5]; - for (int i = 0; i < 4; ++i) { - if (use_layer_norm) { - TfLiteTensor* intermediate = - micro_context->AllocateTempIntermediateTensor(node, i); - TF_LITE_ENSURE(context, - intermediate->quantization.type != kTfLiteNoQuantization); - auto* params_intermediate = static_cast( - intermediate->quantization.params); - intermediate_scale[i] = params_intermediate->scale->data[0]; - intermediate_zp[i] = params_intermediate->zero_point->data[0]; - if (intermediate != nullptr) { - micro_context->DeallocateTempTfLiteTensor(intermediate); - } - } else { - // Q3.12 for activation functions. - intermediate_scale[i] = std::pow(2.0f, -12.0f); - intermediate_zp[i] = 0; - } - } - // In the absence of projection, hidden becomes otuput and this intermediate - // is ignored. - TfLiteTensor* hidden = micro_context->AllocateTempIntermediateTensor(node, 4); - TF_LITE_ENSURE(context, hidden->quantization.type != kTfLiteNoQuantization); - auto* hidden_params = - static_cast(hidden->quantization.params); - intermediate_scale[4] = hidden_params->scale->data[0]; - intermediate_zp[4] = hidden_params->zero_point->data[0]; - if (hidden != nullptr) { - micro_context->DeallocateTempTfLiteTensor(hidden); - } - - // Scales. - const float default_scale = 1.0; - float input_scale = default_scale; - float input_to_input_weight_scale = default_scale; - float recurrent_to_input_weight_scale = default_scale; - float cell_to_input_weight_scale = default_scale; - float input_to_forget_weight_scale = default_scale; - float recurrent_to_forget_weight_scale = default_scale; - float cell_to_forget_weight_scale = default_scale; - float input_to_cell_weight_scale = default_scale; - float recurrent_to_cell_weight_scale = default_scale; - float input_to_output_weight_scale = default_scale; - float recurrent_to_output_weight_scale = default_scale; - float cell_to_output_weight_scale = default_scale; - float projection_weight_scale = default_scale; - float layer_norm_input_scale = default_scale; - float layer_norm_forget_scale = default_scale; - float layer_norm_cell_scale = default_scale; - float layer_norm_output_scale = default_scale; - float output_state_scale = default_scale; - int cell_scale = 1; - - // Effective scales. - float effective_input_to_input_scale = default_scale; - float effective_recurrent_to_input_scale = default_scale; - float effective_cell_to_input_scale = default_scale; - float effective_input_to_forget_scale = default_scale; - float effective_recurrent_to_forget_scale = default_scale; - float effective_cell_to_forget_scale = default_scale; - float effective_input_to_cell_scale = default_scale; - float effective_recurrent_to_cell_scale = default_scale; - float effective_input_to_output_scale = default_scale; - float effective_recurrent_to_output_scale = default_scale; - float effective_cell_to_output_scale = default_scale; - float effective_proj_scale = default_scale; - float effective_hidden_scale = default_scale; - - // Populate scales. - if (!use_cifg) { - input_to_input_weight_scale = input_to_input_weights->params.scale; - recurrent_to_input_weight_scale = recurrent_to_input_weights->params.scale; - } - - if (use_peephole) { - if (!use_cifg) { - cell_to_input_weight_scale = cell_to_input_weights->params.scale; - } - cell_to_forget_weight_scale = cell_to_forget_weights->params.scale; - cell_to_output_weight_scale = cell_to_output_weights->params.scale; - } - - if (use_layer_norm) { - if (!use_cifg) { - layer_norm_input_scale = input_layer_norm_coefficients->params.scale; - } - layer_norm_forget_scale = forget_layer_norm_coefficients->params.scale; - layer_norm_cell_scale = cell_layer_norm_coefficients->params.scale; - layer_norm_output_scale = output_layer_norm_coefficients->params.scale; - } - - if (use_projection) { - projection_weight_scale = projection_weights->params.scale; - } - output_state_scale = output_state->params.scale; - - input_to_forget_weight_scale = input_to_forget_weights->params.scale; - input_to_cell_weight_scale = input_to_cell_weights->params.scale; - input_to_output_weight_scale = input_to_output_weights->params.scale; - recurrent_to_forget_weight_scale = recurrent_to_forget_weights->params.scale; - recurrent_to_cell_weight_scale = recurrent_to_cell_weights->params.scale; - recurrent_to_output_weight_scale = recurrent_to_output_weights->params.scale; - - // Check cell state (already used above) - TF_LITE_ENSURE(context, CheckedLog2(cell_state->params.scale, &cell_scale)); - // TF_LITE_ENSURE(context, cell_scale <= -9); - integer_lstm_param->cell_scale = cell_scale; - input_scale = input->params.scale; - - // Calculate effective scales. - if (!use_cifg) { - effective_input_to_input_scale = - input_to_input_weight_scale * input_scale / intermediate_scale[0]; - effective_recurrent_to_input_scale = recurrent_to_input_weight_scale * - output_state_scale / - intermediate_scale[0]; - } - effective_input_to_forget_scale = - input_to_forget_weight_scale * input_scale / intermediate_scale[1]; - effective_recurrent_to_forget_scale = recurrent_to_forget_weight_scale * - output_state_scale / - intermediate_scale[1]; - - effective_input_to_cell_scale = - input_to_cell_weight_scale * input_scale / intermediate_scale[2]; - effective_recurrent_to_cell_scale = recurrent_to_cell_weight_scale * - output_state_scale / - intermediate_scale[2]; - - effective_input_to_output_scale = - input_to_output_weight_scale * input_scale / intermediate_scale[3]; - effective_recurrent_to_output_scale = recurrent_to_output_weight_scale * - output_state_scale / - intermediate_scale[3]; - - effective_hidden_scale = - std::pow(2.0f, -15.0f) / intermediate_scale[4] * std::pow(2.0f, -15.0f); - - effective_proj_scale = - projection_weight_scale * intermediate_scale[4] / output_state_scale; - - if (use_peephole) { - if (!use_cifg) { - effective_cell_to_input_scale = - std::pow(2.0f, static_cast(cell_scale)) * - cell_to_input_weight_scale / intermediate_scale[0]; - } - effective_cell_to_forget_scale = - std::pow(2.0f, static_cast(cell_scale)) * - cell_to_forget_weight_scale / intermediate_scale[1]; - effective_cell_to_output_scale = - std::pow(2.0f, static_cast(cell_scale)) * - cell_to_output_weight_scale / intermediate_scale[3]; - } - - // Decompose scales. - int shift_output; - QuantizeMultiplier(static_cast(effective_input_to_input_scale), - &integer_lstm_param->effective_input_to_input_scale_a, - &shift_output); - integer_lstm_param->effective_input_to_input_scale_b = - static_cast(shift_output); - QuantizeMultiplier(static_cast(effective_recurrent_to_input_scale), - &integer_lstm_param->effective_recurrent_to_input_scale_a, - &shift_output); - integer_lstm_param->effective_recurrent_to_input_scale_b = - static_cast(shift_output); - QuantizeMultiplier(static_cast(effective_cell_to_input_scale), - &integer_lstm_param->effective_cell_to_input_scale_a, - &shift_output); - integer_lstm_param->effective_cell_to_input_scale_b = - static_cast(shift_output); - QuantizeMultiplier(static_cast(effective_input_to_forget_scale), - &integer_lstm_param->effective_input_to_forget_scale_a, - &shift_output); - integer_lstm_param->effective_input_to_forget_scale_b = - static_cast(shift_output); - QuantizeMultiplier(static_cast(effective_recurrent_to_forget_scale), - &integer_lstm_param->effective_recurrent_to_forget_scale_a, - &shift_output); - integer_lstm_param->effective_recurrent_to_forget_scale_b = - static_cast(shift_output); - QuantizeMultiplier(static_cast(effective_cell_to_forget_scale), - &integer_lstm_param->effective_cell_to_forget_scale_a, - &shift_output); - integer_lstm_param->effective_cell_to_forget_scale_b = - static_cast(shift_output); - QuantizeMultiplier(static_cast(effective_input_to_cell_scale), - &integer_lstm_param->effective_input_to_cell_scale_a, - &shift_output); - integer_lstm_param->effective_input_to_cell_scale_b = - static_cast(shift_output); - QuantizeMultiplier(static_cast(effective_recurrent_to_cell_scale), - &integer_lstm_param->effective_recurrent_to_cell_scale_a, - &shift_output); - integer_lstm_param->effective_recurrent_to_cell_scale_b = - static_cast(shift_output); - QuantizeMultiplier(static_cast(effective_input_to_output_scale), - &integer_lstm_param->effective_input_to_output_scale_a, - &shift_output); - integer_lstm_param->effective_input_to_output_scale_b = - static_cast(shift_output); - QuantizeMultiplier(static_cast(effective_recurrent_to_output_scale), - &integer_lstm_param->effective_recurrent_to_output_scale_a, - &shift_output); - integer_lstm_param->effective_recurrent_to_output_scale_b = - static_cast(shift_output); - QuantizeMultiplier(static_cast(effective_cell_to_output_scale), - &integer_lstm_param->effective_cell_to_output_scale_a, - &shift_output); - integer_lstm_param->effective_cell_to_output_scale_b = - static_cast(shift_output); - QuantizeMultiplier(static_cast(effective_proj_scale), - &integer_lstm_param->effective_proj_scale_a, - &shift_output); - integer_lstm_param->effective_proj_scale_b = - static_cast(shift_output); - QuantizeMultiplier(static_cast(effective_hidden_scale), - &integer_lstm_param->effective_hidden_scale_a, - &shift_output); - integer_lstm_param->effective_hidden_scale_b = - static_cast(shift_output); - QuantizeMultiplier(static_cast(layer_norm_input_scale), - &integer_lstm_param->layer_norm_input_scale_a, - &shift_output); - integer_lstm_param->layer_norm_input_scale_b = - static_cast(shift_output); - QuantizeMultiplier(static_cast(layer_norm_forget_scale), - &integer_lstm_param->layer_norm_forget_scale_a, - &shift_output); - integer_lstm_param->layer_norm_forget_scale_b = - static_cast(shift_output); - QuantizeMultiplier(static_cast(layer_norm_cell_scale), - &integer_lstm_param->layer_norm_cell_scale_a, - &shift_output); - integer_lstm_param->layer_norm_cell_scale_b = - static_cast(shift_output); - QuantizeMultiplier(static_cast(layer_norm_output_scale), - &integer_lstm_param->layer_norm_output_scale_a, - &shift_output); - integer_lstm_param->layer_norm_output_scale_b = - static_cast(shift_output); - - integer_lstm_param->hidden_zp = intermediate_zp[4]; - - // 10000 is used to make sure the kernel logic does not overflow. - if (!use_cifg) { - integer_lstm_param->input_variance_guard = - std::max(1, static_cast(10000 * layer_norm_input_scale)); - } - integer_lstm_param->forget_variance_guard = - std::max(1, static_cast(10000 * layer_norm_forget_scale)); - integer_lstm_param->cell_variance_guard = - std::max(1, static_cast(10000 * layer_norm_cell_scale)); - integer_lstm_param->output_variance_guard = - std::max(1, static_cast(10000 * layer_norm_output_scale)); - - if (cell_state != nullptr) { - micro_context->DeallocateTempTfLiteTensor(cell_state); - } - if (output_tensor != nullptr) { - micro_context->DeallocateTempTfLiteTensor(output_tensor); - } - if (input != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input); - } - if (input_to_input_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_input_weights); - } - if (input_to_forget_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_forget_weights); - } - if (input_to_cell_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_cell_weights); - } - if (input_to_output_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_output_weights); - } - if (recurrent_to_input_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(recurrent_to_input_weights); - } - if (recurrent_to_forget_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(recurrent_to_forget_weights); - } - if (recurrent_to_cell_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(recurrent_to_cell_weights); - } - if (recurrent_to_output_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(recurrent_to_output_weights); - } - if (cell_to_input_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(cell_to_input_weights); - } - if (cell_to_forget_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(cell_to_forget_weights); - } - if (cell_to_output_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(cell_to_output_weights); - } - if (input_layer_norm_coefficients != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_layer_norm_coefficients); - } - if (forget_layer_norm_coefficients != nullptr) { - micro_context->DeallocateTempTfLiteTensor(forget_layer_norm_coefficients); - } - if (cell_layer_norm_coefficients != nullptr) { - micro_context->DeallocateTempTfLiteTensor(cell_layer_norm_coefficients); - } - if (output_layer_norm_coefficients != nullptr) { - micro_context->DeallocateTempTfLiteTensor(output_layer_norm_coefficients); - } - if (projection_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(projection_weights); - } - if (output_state != nullptr) { - micro_context->DeallocateTempTfLiteTensor(output_state); - } - - return kTfLiteOk; -} - -// Temporary buffers used for hybrid mode -enum HybridTempBuffer { - kPrimaryScratchBuffer = 0, - kInputQuantized = 1, - kOutputStateQuantized = 2, - kCellStateQuantized = 3, - kInputScalingFactors = 4, - kOutputStateScalingFactors = 5, - kProductScalingFactors = 6, - kRecoveredCellWeights = 7, - kAccumScratch = 8, - kInputZeroPoints = 9, - kOutputStateZeroPoints = 10, - kScales = 11, - kNumHybridTempBuffers = 12, -}; - -void* UnidirectionalSequenceLstmInit(TfLiteContext* context, const char* buffer, - size_t length) { - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - return context->AllocatePersistentBuffer( - context, sizeof(UnidirectionalSequenceLstmOpData)); -} - -// Check that input tensor dimensions matches with each other. -TfLiteStatus SetHybridScales(TfLiteContext* context, TfLiteNode* node) { - UnidirectionalSequenceLstmOpData* op_data = - reinterpret_cast(node->user_data); - MicroContext* micro_context = GetMicroContext(context); - - TfLiteTensor* input_to_input_weights = micro_context->AllocateTempInputTensor( - node, kLstmInputToInputWeightsTensor); - op_data->hybrid_lstm_scales.input_to_input_weights_scale = - (input_to_input_weights != nullptr) ? input_to_input_weights->params.scale - : 1.0f; - - TfLiteTensor* input_to_forget_weights = - micro_context->AllocateTempInputTensor(node, - kLstmInputToForgetWeightsTensor); - op_data->hybrid_lstm_scales.input_to_forget_weights_scale = - (input_to_forget_weights != nullptr) - ? input_to_forget_weights->params.scale - : 1.0f; - - TfLiteTensor* input_to_cell_weights = micro_context->AllocateTempInputTensor( - node, kLstmInputToCellWeightsTensor); - op_data->hybrid_lstm_scales.input_to_cell_weights_scale = - (input_to_cell_weights != nullptr) ? input_to_cell_weights->params.scale - : 1.0f; - - TfLiteTensor* input_to_output_weights = - micro_context->AllocateTempInputTensor(node, - kLstmInputToOutputWeightsTensor); - op_data->hybrid_lstm_scales.input_to_output_weights_scale = - (input_to_output_weights != nullptr) - ? input_to_output_weights->params.scale - : 1.0f; - - op_data->hybrid_lstm_scales.aux_input_to_input_weights_scale = 1.0f; - op_data->hybrid_lstm_scales.aux_input_to_forget_weights_scale = 1.0f; - op_data->hybrid_lstm_scales.aux_input_to_cell_weights_scale = 1.0f; - op_data->hybrid_lstm_scales.aux_input_to_output_weights_scale = 1.0f; - - TfLiteTensor* recurrent_to_input_weights = - micro_context->AllocateTempInputTensor( - node, kLstmRecurrentToInputWeightsTensor); - op_data->hybrid_lstm_scales.recurrent_to_input_weights_scale = - (recurrent_to_input_weights != nullptr) - ? recurrent_to_input_weights->params.scale - : 1.0f; - - TfLiteTensor* recurrent_to_forget_weights = - micro_context->AllocateTempInputTensor( - node, kLstmRecurrentToForgetWeightsTensor); - op_data->hybrid_lstm_scales.recurrent_to_forget_weights_scale = - (recurrent_to_forget_weights != nullptr) - ? recurrent_to_forget_weights->params.scale - : 1.0f; - - TfLiteTensor* recurrent_to_cell_weights = - micro_context->AllocateTempInputTensor(node, - kLstmRecurrentToCellWeightsTensor); - op_data->hybrid_lstm_scales.recurrent_to_cell_weights_scale = - (recurrent_to_cell_weights != nullptr) - ? recurrent_to_cell_weights->params.scale - : 1.0f; - - TfLiteTensor* recurrent_to_output_weights = - micro_context->AllocateTempInputTensor( - node, kLstmRecurrentToOutputWeightsTensor); - op_data->hybrid_lstm_scales.recurrent_to_output_weights_scale = - (recurrent_to_output_weights != nullptr) - ? recurrent_to_output_weights->params.scale - : 1.0f; - - TfLiteTensor* cell_to_input_weights = micro_context->AllocateTempInputTensor( - node, kLstmCellToInputWeightsTensor); - op_data->hybrid_lstm_scales.cell_to_input_weights_scale = - (cell_to_input_weights != nullptr) ? cell_to_input_weights->params.scale - : 1.0f; - - TfLiteTensor* cell_to_forget_weights = micro_context->AllocateTempInputTensor( - node, kLstmCellToForgetWeightsTensor); - op_data->hybrid_lstm_scales.cell_to_forget_weights_scale = - (cell_to_forget_weights != nullptr) ? cell_to_forget_weights->params.scale - : 1.0f; - - TfLiteTensor* cell_to_output_weights = micro_context->AllocateTempInputTensor( - node, kLstmCellToOutputWeightsTensor); - op_data->hybrid_lstm_scales.cell_to_output_weights_scale = - (cell_to_output_weights != nullptr) ? cell_to_output_weights->params.scale - : 1.0f; - - TfLiteTensor* projection_weights = micro_context->AllocateTempInputTensor( - node, kLstmProjectionWeightsTensor); - op_data->hybrid_lstm_scales.projection_weights_scale = - (projection_weights != nullptr) ? projection_weights->params.scale : 1.0f; - - if (input_to_input_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_input_weights); - } - - if (input_to_forget_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_forget_weights); - } - - if (input_to_cell_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_cell_weights); - } - - if (input_to_output_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_output_weights); - } - - if (recurrent_to_input_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(recurrent_to_input_weights); - } - - if (recurrent_to_forget_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(recurrent_to_forget_weights); - } - - if (recurrent_to_cell_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(recurrent_to_cell_weights); - } - - if (recurrent_to_output_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(recurrent_to_output_weights); - } - - if (cell_to_input_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(cell_to_input_weights); - } - - if (cell_to_forget_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(cell_to_forget_weights); - } - - if (cell_to_output_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(cell_to_output_weights); - } - - if (projection_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(projection_weights); - } - - return kTfLiteOk; -} - -// Check that input tensor dimensions matches with each other. -TfLiteStatus CheckInputTensorDimensions(TfLiteContext* context, - TfLiteNode* node, int n_input, - int n_output, int n_cell, - bool use_layer_norm, bool is_integer) { - MicroContext* micro_context = GetMicroContext(context); - - const auto* params = reinterpret_cast(node->builtin_data); - - // Making sure clipping parameters have valid values. - // == 0 means no clipping - // > 0 means clipping - TF_LITE_ENSURE(context, params->cell_clip >= 0); - TF_LITE_ENSURE(context, params->proj_clip >= 0); - - TfLiteTensor* input_to_input_weights = micro_context->AllocateTempInputTensor( - node, kLstmInputToInputWeightsTensor); - if (input_to_input_weights != nullptr) { - TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->size, 2); - TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[0], n_cell); - TF_LITE_ENSURE_EQ(context, input_to_input_weights->dims->data[1], n_input); - } - - TfLiteTensor* input_to_forget_weights = - micro_context->AllocateTempInputTensor(node, - kLstmInputToForgetWeightsTensor); - TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->size, 2); - TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[0], n_cell); - TF_LITE_ENSURE_EQ(context, input_to_forget_weights->dims->data[1], n_input); - - TfLiteTensor* input_to_cell_weights = micro_context->AllocateTempInputTensor( - node, kLstmInputToCellWeightsTensor); - TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->size, 2); - TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[0], n_cell); - TF_LITE_ENSURE_EQ(context, input_to_cell_weights->dims->data[1], n_input); - - TfLiteTensor* recurrent_to_input_weights = - micro_context->AllocateTempInputTensor( - node, kLstmRecurrentToInputWeightsTensor); - if (recurrent_to_input_weights != nullptr) { - TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->size, 2); - TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->data[0], - n_cell); - TF_LITE_ENSURE_EQ(context, recurrent_to_input_weights->dims->data[1], - n_output); - } - - TfLiteTensor* recurrent_to_forget_weights = - micro_context->AllocateTempInputTensor( - node, kLstmRecurrentToForgetWeightsTensor); - TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->size, 2); - TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[0], - n_cell); - TF_LITE_ENSURE_EQ(context, recurrent_to_forget_weights->dims->data[1], - n_output); - - TfLiteTensor* recurrent_to_cell_weights = - micro_context->AllocateTempInputTensor(node, - kLstmRecurrentToCellWeightsTensor); - TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->size, 2); - TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[0], n_cell); - TF_LITE_ENSURE_EQ(context, recurrent_to_cell_weights->dims->data[1], - n_output); - - // We make sure the input-gate's parameters are either both present (regular - // LSTM) or not at all (CIFG-LSTM). - const bool cifg_weights_all_or_none = - ((input_to_input_weights != nullptr) && - (recurrent_to_input_weights != nullptr)) || - ((input_to_input_weights == nullptr) && - (recurrent_to_input_weights == nullptr)); - TF_LITE_ENSURE(context, cifg_weights_all_or_none == true); - - TfLiteTensor* cell_to_input_weights = micro_context->AllocateTempInputTensor( - node, kLstmCellToInputWeightsTensor); - if (cell_to_input_weights != nullptr) { - TF_LITE_ENSURE_EQ(context, cell_to_input_weights->dims->size, 1); - TF_LITE_ENSURE_EQ(context, cell_to_input_weights->dims->data[0], n_cell); - TF_LITE_ENSURE_TYPES_EQ( - context, cell_to_input_weights->type, - is_integer ? kTfLiteInt16 : input_to_forget_weights->type); - } - - TfLiteTensor* cell_to_forget_weights = micro_context->AllocateTempInputTensor( - node, kLstmCellToForgetWeightsTensor); - if (cell_to_forget_weights != nullptr) { - TF_LITE_ENSURE_EQ(context, cell_to_forget_weights->dims->size, 1); - TF_LITE_ENSURE_EQ(context, cell_to_forget_weights->dims->data[0], n_cell); - TF_LITE_ENSURE_TYPES_EQ( - context, cell_to_forget_weights->type, - is_integer ? kTfLiteInt16 : input_to_forget_weights->type); - } - - TfLiteTensor* cell_to_output_weights = micro_context->AllocateTempInputTensor( - node, kLstmCellToOutputWeightsTensor); - if (cell_to_output_weights != nullptr) { - TF_LITE_ENSURE_EQ(context, cell_to_output_weights->dims->size, 1); - TF_LITE_ENSURE_EQ(context, cell_to_output_weights->dims->data[0], n_cell); - TF_LITE_ENSURE_TYPES_EQ( - context, cell_to_output_weights->type, - is_integer ? kTfLiteInt16 : input_to_forget_weights->type); - } - - // Making sure the peephole weights are there all or none. - const bool use_cifg = (input_to_input_weights == nullptr); - const bool peephole_weights_all_or_none = - ((cell_to_input_weights != nullptr || use_cifg) && - (cell_to_forget_weights != nullptr) && - (cell_to_output_weights != nullptr)) || - ((cell_to_input_weights == nullptr) && - (cell_to_forget_weights == nullptr) && - (cell_to_output_weights == nullptr)); - TF_LITE_ENSURE(context, peephole_weights_all_or_none == true); - - // Make sure the input gate bias is present only when not a CIFG-LSTM. - TfLiteTensor* input_gate_bias = - micro_context->AllocateTempInputTensor(node, kLstmInputGateBiasTensor); - if (use_cifg) { - TF_LITE_ENSURE_EQ(context, input_gate_bias, nullptr); - } else { - TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->size, 1); - TF_LITE_ENSURE_EQ(context, input_gate_bias->dims->data[0], n_cell); - if (is_integer) { - TF_LITE_ENSURE_TYPES_EQ(context, input_gate_bias->type, kTfLiteInt32); - } else { - TF_LITE_ENSURE_TYPES_EQ(context, input_gate_bias->type, kTfLiteFloat32); - } - } - - TfLiteTensor* forget_gate_bias = - micro_context->AllocateTempInputTensor(node, kLstmForgetGateBiasTensor); - TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->size, 1); - TF_LITE_ENSURE_EQ(context, forget_gate_bias->dims->data[0], n_cell); - if (is_integer) { - TF_LITE_ENSURE_TYPES_EQ(context, forget_gate_bias->type, kTfLiteInt32); - } else { - TF_LITE_ENSURE_TYPES_EQ(context, forget_gate_bias->type, kTfLiteFloat32); - } - - TfLiteTensor* cell_gate_bias = - micro_context->AllocateTempInputTensor(node, kLstmCellGateBiasTensor); - TF_LITE_ENSURE_EQ(context, cell_gate_bias->dims->size, 1); - TF_LITE_ENSURE_EQ(context, cell_gate_bias->dims->data[0], n_cell); - if (is_integer) { - TF_LITE_ENSURE_TYPES_EQ(context, cell_gate_bias->type, kTfLiteInt32); - } else { - TF_LITE_ENSURE_TYPES_EQ(context, cell_gate_bias->type, kTfLiteFloat32); - } - - TfLiteTensor* output_gate_bias = - micro_context->AllocateTempInputTensor(node, kLstmOutputGateBiasTensor); - TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->size, 1); - TF_LITE_ENSURE_EQ(context, output_gate_bias->dims->data[0], n_cell); - if (is_integer) { - TF_LITE_ENSURE_TYPES_EQ(context, output_gate_bias->type, kTfLiteInt32); - } else { - TF_LITE_ENSURE_TYPES_EQ(context, output_gate_bias->type, kTfLiteFloat32); - } - - TfLiteTensor* projection_weights = micro_context->AllocateTempInputTensor( - node, kLstmProjectionWeightsTensor); - if (projection_weights != nullptr) { - TF_LITE_ENSURE_EQ(context, projection_weights->dims->size, 2); - TF_LITE_ENSURE_EQ(context, projection_weights->dims->data[0], n_output); - TF_LITE_ENSURE_EQ(context, projection_weights->dims->data[1], n_cell); - } - - TfLiteTensor* projection_bias = - micro_context->AllocateTempInputTensor(node, kLstmProjectionBiasTensor); - if (projection_bias != nullptr) { - TF_LITE_ENSURE_EQ(context, projection_bias->dims->size, 1); - TF_LITE_ENSURE_EQ(context, projection_bias->dims->data[0], n_output); - if (is_integer) { - TF_LITE_ENSURE_TYPES_EQ(context, projection_bias->type, kTfLiteInt32); - } else { - TF_LITE_ENSURE_TYPES_EQ(context, projection_bias->type, kTfLiteFloat32); - } - } - - // Making sure the projection tensors are consistent: - // 1) If projection weight is not present, then projection bias should not be - // present. - // 2) If projection weight is present, then projection bias is optional. - const bool projecton_tensors_consistent = - ((projection_weights != nullptr) || (projection_bias == nullptr)); - TF_LITE_ENSURE(context, projecton_tensors_consistent == true); - - if (use_layer_norm) { - TfLiteTensor* input_layer_norm_coefficients = - micro_context->AllocateTempInputTensor( - node, kLstmInputLayerNormCoefficientsTensor); - if (use_cifg) { - TF_LITE_ENSURE_EQ(context, input_layer_norm_coefficients, nullptr); - } else { - TF_LITE_ENSURE(context, input_layer_norm_coefficients != nullptr); - TF_LITE_ENSURE_EQ(context, input_layer_norm_coefficients->dims->size, 1); - TF_LITE_ENSURE_EQ(context, input_layer_norm_coefficients->dims->data[0], - n_cell); - if (is_integer) { - TF_LITE_ENSURE_TYPES_EQ(context, input_layer_norm_coefficients->type, - kTfLiteInt16); - } else { - TF_LITE_ENSURE_TYPES_EQ(context, input_layer_norm_coefficients->type, - kTfLiteFloat32); - } - } - - TfLiteTensor* forget_layer_norm_coefficients = - micro_context->AllocateTempInputTensor( - node, kLstmForgetLayerNormCoefficientsTensor); - TF_LITE_ENSURE_EQ(context, forget_layer_norm_coefficients->dims->size, 1); - TF_LITE_ENSURE_EQ(context, forget_layer_norm_coefficients->dims->data[0], - n_cell); - if (is_integer) { - TF_LITE_ENSURE_TYPES_EQ(context, forget_layer_norm_coefficients->type, - kTfLiteInt16); - } else { - TF_LITE_ENSURE_TYPES_EQ(context, forget_layer_norm_coefficients->type, - kTfLiteFloat32); - } - - TfLiteTensor* cell_layer_norm_coefficients = - micro_context->AllocateTempInputTensor( - node, kLstmCellLayerNormCoefficientsTensor); - TF_LITE_ENSURE_EQ(context, cell_layer_norm_coefficients->dims->size, 1); - TF_LITE_ENSURE_EQ(context, cell_layer_norm_coefficients->dims->data[0], - n_cell); - if (is_integer) { - TF_LITE_ENSURE_TYPES_EQ(context, cell_layer_norm_coefficients->type, - kTfLiteInt16); - } else { - TF_LITE_ENSURE_TYPES_EQ(context, cell_layer_norm_coefficients->type, - kTfLiteFloat32); - } - - TfLiteTensor* output_layer_norm_coefficients = - micro_context->AllocateTempInputTensor( - node, kLstmOutputLayerNormCoefficientsTensor); - TF_LITE_ENSURE_EQ(context, output_layer_norm_coefficients->dims->size, 1); - TF_LITE_ENSURE_EQ(context, output_layer_norm_coefficients->dims->data[0], - n_cell); - if (is_integer) { - TF_LITE_ENSURE_TYPES_EQ(context, output_layer_norm_coefficients->type, - kTfLiteInt16); - } else { - TF_LITE_ENSURE_TYPES_EQ(context, output_layer_norm_coefficients->type, - kTfLiteFloat32); - } - if (input_layer_norm_coefficients != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_layer_norm_coefficients); - } - if (forget_layer_norm_coefficients != nullptr) { - micro_context->DeallocateTempTfLiteTensor(forget_layer_norm_coefficients); - } - if (cell_layer_norm_coefficients != nullptr) { - micro_context->DeallocateTempTfLiteTensor(cell_layer_norm_coefficients); - } - if (output_layer_norm_coefficients != nullptr) { - micro_context->DeallocateTempTfLiteTensor(output_layer_norm_coefficients); - } - } - - if (input_to_input_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_input_weights); - } - if (input_to_forget_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_forget_weights); - } - if (input_to_cell_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_cell_weights); - } - if (recurrent_to_input_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(recurrent_to_input_weights); - } - if (recurrent_to_forget_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(recurrent_to_forget_weights); - } - micro_context->DeallocateTempTfLiteTensor(recurrent_to_cell_weights); - if (cell_to_input_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(cell_to_input_weights); - } - if (cell_to_forget_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(cell_to_forget_weights); - } - if (cell_to_output_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(cell_to_output_weights); - } - if (input_gate_bias != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_gate_bias); - } - if (forget_gate_bias != nullptr) { - micro_context->DeallocateTempTfLiteTensor(forget_gate_bias); - } - if (cell_gate_bias != nullptr) { - micro_context->DeallocateTempTfLiteTensor(cell_gate_bias); - } - if (output_gate_bias != nullptr) { - micro_context->DeallocateTempTfLiteTensor(output_gate_bias); - } - if (projection_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(projection_weights); - } - if (projection_bias != nullptr) { - micro_context->DeallocateTempTfLiteTensor(projection_bias); - } - - return kTfLiteOk; -} - -TfLiteStatus PrecomputeZeroPointTimesWeightWithBias( - TfLiteContext* context, int32_t zero_point, - const TfLiteTensor* weight_tensor, const TfLiteTensor* bias_tensor, - int32_t** output) { - if (weight_tensor == nullptr) { - return kTfLiteOk; - } - - const RuntimeShape& weight_shape = GetTensorShape(weight_tensor); - TF_LITE_ENSURE_EQ(context, weight_shape.DimensionsCount(), 2); - const int row = weight_shape.Dims(0); - const int col = weight_shape.Dims(1); - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - *output = static_cast( - context->AllocatePersistentBuffer(context, row * sizeof(int32_t))); - - if (bias_tensor == nullptr) { - memset(*output, 0, row * sizeof(int32_t)); - } else { - const int32_t* bias = GetTensorData(bias_tensor); - memcpy(*output, bias, row * sizeof(int32_t)); - } - if (zero_point != 0) { - const int8_t* weight = GetTensorData(weight_tensor); - micro_tensor_utils::MatrixScalarMultiplyAccumulate(weight, zero_point, row, - col, *output); - } - return kTfLiteOk; -} - -TfLiteStatus PopulatePrecomputedZPTimesWeightsWithBias( - TfLiteContext* context, UnidirectionalSequenceLstmOpData* op_data, - TfLiteNode* node) { - MicroContext* micro_context = GetMicroContext(context); - - TfLiteTensor* input = - micro_context->AllocateTempInputTensor(node, kLstmInputTensor); - TfLiteTensor* output_state = - micro_context->AllocateTempInputTensor(node, kLstmOutputStateTensor); - TF_LITE_ENSURE(context, output_state != nullptr); - TF_LITE_ENSURE(context, output_state->is_variable); - - const int32_t input_zero_point = -input->params.zero_point; - const int32_t output_state_zero_point = -output_state->params.zero_point; - - TfLiteTensor* input_to_input_weights = micro_context->AllocateTempInputTensor( - node, kLstmInputToInputWeightsTensor); - TfLiteTensor* input_to_forget_weights = - micro_context->AllocateTempInputTensor(node, - kLstmInputToForgetWeightsTensor); - TfLiteTensor* input_to_cell_weights = micro_context->AllocateTempInputTensor( - node, kLstmInputToCellWeightsTensor); - TfLiteTensor* input_to_output_weights = - micro_context->AllocateTempInputTensor(node, - kLstmInputToOutputWeightsTensor); - - TfLiteTensor* recurrent_to_input_weights = - micro_context->AllocateTempInputTensor( - node, kLstmRecurrentToInputWeightsTensor); - TfLiteTensor* recurrent_to_forget_weights = - micro_context->AllocateTempInputTensor( - node, kLstmRecurrentToForgetWeightsTensor); - TfLiteTensor* recurrent_to_cell_weights = - micro_context->AllocateTempInputTensor(node, - kLstmRecurrentToCellWeightsTensor); - TfLiteTensor* recurrent_to_output_weights = - micro_context->AllocateTempInputTensor( - node, kLstmRecurrentToOutputWeightsTensor); - - TfLiteTensor* projection_weights = micro_context->AllocateTempInputTensor( - node, kLstmProjectionWeightsTensor); - TfLiteTensor* projection_bias = - micro_context->AllocateTempInputTensor(node, kLstmProjectionBiasTensor); - - IntegerLstmParameter* integer_lstm_params = &op_data->integer_lstm_param; - - TfLiteTensor* intermediate = - micro_context->AllocateTempIntermediateTensor(node, 4); - TF_LITE_ENSURE(context, - intermediate->quantization.type != kTfLiteNoQuantization); - const auto* params = - static_cast(intermediate->quantization.params); - const int32_t hidden_zp = params->zero_point->data[0]; - - // Get bias and perform zero point calculation. - // When there is layer normalization, the gate bias does not apply to matmul - // directly: - // y = ln(w * x + w * r + w * c) + b. - const bool is_layer_norm = op_data->use_layer_norm; - - // Forget gate. - TfLiteTensor* forget_gate_bias = is_layer_norm - ? nullptr - : micro_context->AllocateTempInputTensor( - node, kLstmForgetGateBiasTensor); - TF_LITE_ENSURE_OK( - context, - PrecomputeZeroPointTimesWeightWithBias( - context, input_zero_point, input_to_forget_weights, forget_gate_bias, - &(integer_lstm_params->input_to_forget_effective_bias))); - - TF_LITE_ENSURE_OK( - context, - PrecomputeZeroPointTimesWeightWithBias( - context, output_state_zero_point, recurrent_to_forget_weights, - nullptr, &(integer_lstm_params->recurrent_to_forget_effective_bias))); - - // Modulation gate. - TfLiteTensor* cell_gate_bias = is_layer_norm - ? nullptr - : micro_context->AllocateTempInputTensor( - node, kLstmCellGateBiasTensor); - TF_LITE_ENSURE_OK( - context, - PrecomputeZeroPointTimesWeightWithBias( - context, input_zero_point, input_to_cell_weights, cell_gate_bias, - &(integer_lstm_params->input_to_cell_effective_bias))); - TF_LITE_ENSURE_OK( - context, - PrecomputeZeroPointTimesWeightWithBias( - context, output_state_zero_point, recurrent_to_cell_weights, nullptr, - &(integer_lstm_params->recurrent_to_cell_effective_bias))); - - // Output gate. - TfLiteTensor* output_gate_bias = is_layer_norm - ? nullptr - : micro_context->AllocateTempInputTensor( - node, kLstmOutputGateBiasTensor); - TF_LITE_ENSURE_OK( - context, - PrecomputeZeroPointTimesWeightWithBias( - context, input_zero_point, input_to_output_weights, output_gate_bias, - &(integer_lstm_params->input_to_output_effective_bias))); - - TF_LITE_ENSURE_OK( - context, - PrecomputeZeroPointTimesWeightWithBias( - context, output_state_zero_point, recurrent_to_output_weights, - nullptr, &(integer_lstm_params->recurrent_to_output_effective_bias))); - - // Input gate. The calculation is only meaningful for non-cifg case. - TfLiteTensor* input_gate_bias = is_layer_norm - ? nullptr - : micro_context->AllocateTempInputTensor( - node, kLstmInputGateBiasTensor); - TF_LITE_ENSURE_OK( - context, - PrecomputeZeroPointTimesWeightWithBias( - context, input_zero_point, input_to_input_weights, input_gate_bias, - &(integer_lstm_params->input_to_input_effective_bias))); - TF_LITE_ENSURE_OK( - context, - PrecomputeZeroPointTimesWeightWithBias( - context, output_state_zero_point, recurrent_to_input_weights, nullptr, - &(integer_lstm_params->recurrent_to_input_effective_bias))); - - // Projection bias. The calculation is only meaningful for with projection. - TF_LITE_ENSURE_OK(context, - PrecomputeZeroPointTimesWeightWithBias( - context, hidden_zp, projection_weights, projection_bias, - &(integer_lstm_params->projection_effective_bias))); - - if (input != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input); - } - if (output_state != nullptr) { - micro_context->DeallocateTempTfLiteTensor(output_state); - } - if (input_to_input_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_input_weights); - } - if (input_to_forget_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_forget_weights); - } - if (input_to_cell_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_cell_weights); - } - if (input_to_output_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_output_weights); - } - if (recurrent_to_input_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(recurrent_to_input_weights); - } - if (recurrent_to_forget_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(recurrent_to_forget_weights); - } - if (recurrent_to_cell_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(recurrent_to_cell_weights); - } - if (recurrent_to_output_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(recurrent_to_output_weights); - } - if (projection_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(projection_weights); - } - if (projection_bias != nullptr) { - micro_context->DeallocateTempTfLiteTensor(projection_bias); - } - if (forget_gate_bias != nullptr) { - micro_context->DeallocateTempTfLiteTensor(forget_gate_bias); - } - if (cell_gate_bias != nullptr) { - micro_context->DeallocateTempTfLiteTensor(cell_gate_bias); - } - if (output_gate_bias != nullptr) { - micro_context->DeallocateTempTfLiteTensor(output_gate_bias); - } - if (input_gate_bias != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_gate_bias); - } - - if (intermediate != nullptr) { - micro_context->DeallocateTempTfLiteTensor(intermediate); - } - - return kTfLiteOk; -} - -// Resize the output and state tensors based on the sizes of the input tensors. -// Allocate a temporary scratch tensor. Also check that the sizes of the input -// tensors match each other. -TfLiteStatus UnidirectionalSequenceLstmPrepare(TfLiteContext* context, - TfLiteNode* node) { - UnidirectionalSequenceLstmOpData* op_data = - reinterpret_cast(node->user_data); - - MicroContext* micro_context = GetMicroContext(context); - - // Check we have all the inputs and outputs we need. - bool use_layer_norm = false; - if (node->inputs->size == 24) { - TfLiteTensor* forget_layer_norm_coefficients = - micro_context->AllocateTempInputTensor( - node, kLstmForgetLayerNormCoefficientsTensor); - if (forget_layer_norm_coefficients == nullptr) { - use_layer_norm = false; - } else { - use_layer_norm = true; - } - if (forget_layer_norm_coefficients != nullptr) { - micro_context->DeallocateTempTfLiteTensor(forget_layer_norm_coefficients); - } - } else if (node->inputs->size == 20) { - // This is deprecated and is only kept here for backward compatibility. - use_layer_norm = false; - } else { - MicroPrintf("The LSTM Full kernel expects 20 or 24 inputs. Got %d inputs", - node->inputs->size); - return kTfLiteError; - } - TF_LITE_ENSURE_EQ(context, node->outputs->size, 1); - op_data->use_layer_norm = use_layer_norm; - - // Inferring batch size, number of outputs and sequence length and - // number of cells from the input tensors. - TfLiteTensor* input = - micro_context->AllocateTempInputTensor(node, kLstmInputTensor); - op_data->input_zero_point = input->params.zero_point; - const bool is_integer = input->type == kTfLiteInt8; - TF_LITE_ENSURE(context, input->dims->size > 1); - const auto* params = - reinterpret_cast( - node->builtin_data); - const bool time_major = params->time_major; - const int n_batch = time_major ? input->dims->data[1] : input->dims->data[0]; - const int n_input = input->dims->data[2]; - - TfLiteTensor* input_to_output_weights = - micro_context->AllocateTempInputTensor(node, - kLstmInputToOutputWeightsTensor); - const int n_cell = input_to_output_weights->dims->data[0]; - TF_LITE_ENSURE_EQ(context, input_to_output_weights->dims->size, 2); - TF_LITE_ENSURE_EQ(context, input_to_output_weights->dims->data[1], n_input); - - TfLiteTensor* recurrent_to_output_weights = - micro_context->AllocateTempInputTensor( - node, kLstmRecurrentToOutputWeightsTensor); - TF_LITE_ENSURE_EQ(context, recurrent_to_output_weights->dims->size, 2); - TF_LITE_ENSURE_EQ(context, recurrent_to_output_weights->dims->data[0], - n_cell); - const int n_output = recurrent_to_output_weights->dims->data[1]; - - // Check that input tensor dimensions matches with each other. - TF_LITE_ENSURE_OK( - context, CheckInputTensorDimensions(context, node, n_input, n_output, - n_cell, use_layer_norm, is_integer)); - - // Get the pointer to output, output_state and cell_state buffer tensors. - TfLiteTensor* output = - micro_context->AllocateTempOutputTensor(node, kLstmOutputTensor); - - TfLiteTensor* output_state = - micro_context->AllocateTempInputTensor(node, kLstmOutputStateTensor); - TF_LITE_ENSURE(context, output_state != nullptr); - TF_LITE_ENSURE(context, output_state->is_variable); - op_data->output_state_zero_point = output_state->params.zero_point; - TfLiteTensor* cell_state = - micro_context->AllocateTempInputTensor(node, kLstmCellStateTensor); - TF_LITE_ENSURE(context, cell_state != nullptr); - TF_LITE_ENSURE(context, cell_state->is_variable); - - // Check the shape of input state tensors. - // These tensor may be 1D or 2D. It's fine as long as the total size is - // correct. - TF_LITE_ENSURE_EQ(context, NumElements(output_state), n_batch * n_output); - TF_LITE_ENSURE_EQ(context, NumElements(cell_state), n_batch * n_cell); - - // Check the shape of output tensor against that of input tensor - TF_LITE_ENSURE_EQ(context, output->dims->size, 3); - TF_LITE_ENSURE_EQ(context, input->dims->data[0], output->dims->data[0]); - TF_LITE_ENSURE_EQ(context, input->dims->data[1], output->dims->data[1]); - TF_LITE_ENSURE_EQ(context, output->dims->data[2], n_output); - - if (is_integer) { - const int num_intermediate_tensors = node->intermediates->size; - TF_LITE_ENSURE(context, num_intermediate_tensors == 5); - } - - TfLiteTensor* input_to_input_weights = micro_context->AllocateTempInputTensor( - node, kLstmInputToInputWeightsTensor); - - const bool use_cifg = (input_to_input_weights == nullptr); - - // Create a primary scratch buffer for hybrid and float - // If is_integer, primary scratch buffer has a different size - if (!is_integer) { - int scratch_buffer_size[2]; - scratch_buffer_size[0] = n_batch; - - if (use_cifg) { - // Reserving space for Cell, Forget, Output gates - scratch_buffer_size[1] = n_cell * 3; - } else { - // Reserving space for Input, Cell, Forget, Output gates - scratch_buffer_size[1] = n_cell * 4; - } - - TF_LITE_ENSURE_OK(context, - context->RequestScratchBufferInArena( - context, - scratch_buffer_size[0] * scratch_buffer_size[1] * - TfLiteTypeGetSize(input->type), - &(op_data->scratch_index[kPrimaryScratchBuffer]))); - } - - if (IsHybridOp(input, input_to_output_weights)) { - TF_LITE_ENSURE(context, kNumHybridTempBuffers <= scratch_index_size); - - TF_LITE_ENSURE_OK(context, SetHybridScales(context, node)); - - op_data->compute_row_sums = true; - - // Allocate temporary tensors to store quantized values of input, - // output_state and cell_state tensors. - - TF_LITE_ENSURE_OK(context, - context->RequestScratchBufferInArena( - context, - GetTensorShape(input).FlatSize() * - TfLiteTypeGetSize(input_to_output_weights->type), - &(op_data->scratch_index[kInputQuantized]))); - - TF_LITE_ENSURE_OK(context, - context->RequestScratchBufferInArena( - context, - GetTensorShape(output_state).FlatSize() * - TfLiteTypeGetSize(input_to_output_weights->type), - &(op_data->scratch_index[kOutputStateQuantized]))); - - TF_LITE_ENSURE_OK(context, - context->RequestScratchBufferInArena( - context, - GetTensorShape(cell_state).FlatSize() * - TfLiteTypeGetSize(input_to_output_weights->type), - &(op_data->scratch_index[kCellStateQuantized]))); - - TF_LITE_ENSURE_OK(context, - context->RequestScratchBufferInArena( - context, n_batch * TfLiteTypeGetSize(kTfLiteFloat32), - &(op_data->scratch_index[kScales]))); - - // Allocate temporary buffers to store scaling factors and product scaling - // factors. The latter is a convenience storage which allows to quantize - // a vector once (which produces the scaling factors) and multiply it with - // different matrices (which requires multiplying the scaling factors with - // the scaling factor of the matrix). - - TF_LITE_ENSURE_OK(context, - context->RequestScratchBufferInArena( - context, n_batch * TfLiteTypeGetSize(kTfLiteFloat32), - &(op_data->scratch_index[kInputScalingFactors]))); - - TF_LITE_ENSURE_OK( - context, context->RequestScratchBufferInArena( - context, n_batch * TfLiteTypeGetSize(kTfLiteFloat32), - &(op_data->scratch_index[kOutputStateScalingFactors]))); - - TF_LITE_ENSURE_OK(context, - context->RequestScratchBufferInArena( - context, n_batch * TfLiteTypeGetSize(kTfLiteFloat32), - &(op_data->scratch_index[kProductScalingFactors]))); - - // Allocate a temporary buffer to store the recovered cell weights. Since - // this is used for diagonal matrices, only need to store n_cell values. - TF_LITE_ENSURE_OK(context, - context->RequestScratchBufferInArena( - context, n_cell * TfLiteTypeGetSize(kTfLiteFloat32), - &(op_data->scratch_index[kRecoveredCellWeights]))); - - // Allocate a temporary buffer to store the accumulated int32 values. - TF_LITE_ENSURE_OK( - context, - context->RequestScratchBufferInArena( - context, n_cell * n_batch * TfLiteTypeGetSize(kTfLiteInt32), - &(op_data->scratch_index[kAccumScratch]))); - - TF_LITE_ENSURE_OK(context, - context->RequestScratchBufferInArena( - context, n_batch * TfLiteTypeGetSize(kTfLiteFloat32), - &(op_data->scratch_index[kInputZeroPoints]))); - - TF_LITE_ENSURE_OK(context, - context->RequestScratchBufferInArena( - context, n_batch * TfLiteTypeGetSize(kTfLiteFloat32), - &(op_data->scratch_index[kOutputStateZeroPoints]))); - - int row_sums_rows = use_cifg ? 6 : 8; - TfLiteTensor* projection_weights = micro_context->AllocateTempInputTensor( - node, kLstmProjectionWeightsTensor); - if (projection_weights != nullptr) { - row_sums_rows += ceil(static_cast(n_output) / n_cell); - } - op_data->row_sums_size = row_sums_rows; - TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr); - op_data->row_sums = static_cast(context->AllocatePersistentBuffer( - context, row_sums_rows * n_cell * sizeof(int32_t))); - if (projection_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(projection_weights); - } - } - - if (is_integer) { - // Integer UnidirectionalSequenceLSTM prepare function for 8x8->16. - // This code path needs 5 intermediate tensors per Op. - // Populate quantization parameters. - PopulateQuantizedLstmParams8x8_16(context, node, - &op_data->integer_lstm_param); - // Allocate scratch buffer. Need 4 16-bit buffer with size n_batch * n_cell - // and 1 8-bit buffer with size n_batch * n_cell. For integer - // UnidirectionalSequenceLSTM, we do not need the extra 32-bit buffer. - for (int i = 0; i < 5; ++i) { - TfLiteType buffer_type = kTfLiteInt16; - - if (i == 4) { - buffer_type = kTfLiteInt8; - } - - TF_LITE_ENSURE_OK( - context, - context->RequestScratchBufferInArena( - context, n_batch * n_cell * TfLiteTypeGetSize(buffer_type), - &(op_data->scratch_index[i]))); - } - - // Populate precomputed zp * weight. - TF_LITE_ENSURE_OK(context, PopulatePrecomputedZPTimesWeightsWithBias( - context, op_data, node)); - } - - if (input != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input); - } - if (input_to_output_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_output_weights); - } - if (recurrent_to_output_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(recurrent_to_output_weights); - } - if (output != nullptr) { - micro_context->DeallocateTempTfLiteTensor(output); - } - if (output_state != nullptr) { - micro_context->DeallocateTempTfLiteTensor(output_state); - } - if (cell_state != nullptr) { - micro_context->DeallocateTempTfLiteTensor(cell_state); - } - - if (input_to_input_weights != nullptr) { - micro_context->DeallocateTempTfLiteTensor(input_to_input_weights); - } - return kTfLiteOk; -} - -TfLiteStatus UnidirectionalSequenceLstmEval(TfLiteContext* context, - TfLiteNode* node) { - TFLITE_DCHECK(context->GetScratchBuffer != nullptr); - - const auto* params = - reinterpret_cast( - node->builtin_data); - const UnidirectionalSequenceLstmOpData* op_data = - reinterpret_cast(node->user_data); - const bool use_layer_norm = op_data->use_layer_norm; - const bool time_major = params->time_major; - - const TfLiteEvalTensor* input = - tflite::micro::GetEvalInput(context, node, kLstmInputTensor); - - const TfLiteEvalTensor* input_to_input_weights = tflite::micro::GetEvalInput( - context, node, kLstmInputToInputWeightsTensor); - - const TfLiteEvalTensor* input_to_forget_weights = tflite::micro::GetEvalInput( - context, node, kLstmInputToForgetWeightsTensor); - - const TfLiteEvalTensor* input_to_cell_weights = - tflite::micro::GetEvalInput(context, node, kLstmInputToCellWeightsTensor); - - const TfLiteEvalTensor* input_to_output_weights = tflite::micro::GetEvalInput( - context, node, kLstmInputToOutputWeightsTensor); - - const TfLiteEvalTensor* recurrent_to_input_weights = - tflite::micro::GetEvalInput(context, node, - kLstmRecurrentToInputWeightsTensor); - - const TfLiteEvalTensor* recurrent_to_forget_weights = - tflite::micro::GetEvalInput(context, node, - kLstmRecurrentToForgetWeightsTensor); - - const TfLiteEvalTensor* recurrent_to_cell_weights = - tflite::micro::GetEvalInput(context, node, - kLstmRecurrentToCellWeightsTensor); - - const TfLiteEvalTensor* recurrent_to_output_weights = - tflite::micro::GetEvalInput(context, node, - kLstmRecurrentToOutputWeightsTensor); - - const TfLiteEvalTensor* cell_to_input_weights = - tflite::micro::GetEvalInput(context, node, kLstmCellToInputWeightsTensor); - - const TfLiteEvalTensor* cell_to_forget_weights = tflite::micro::GetEvalInput( - context, node, kLstmCellToForgetWeightsTensor); - - const TfLiteEvalTensor* cell_to_output_weights = tflite::micro::GetEvalInput( - context, node, kLstmCellToOutputWeightsTensor); - - const TfLiteEvalTensor* input_gate_bias = - tflite::micro::GetEvalInput(context, node, kLstmInputGateBiasTensor); - - const TfLiteEvalTensor* forget_gate_bias = - tflite::micro::GetEvalInput(context, node, kLstmForgetGateBiasTensor); - - const TfLiteEvalTensor* cell_gate_bias = - tflite::micro::GetEvalInput(context, node, kLstmCellGateBiasTensor); - - const TfLiteEvalTensor* output_gate_bias = - tflite::micro::GetEvalInput(context, node, kLstmOutputGateBiasTensor); - - const TfLiteEvalTensor* projection_weights = - tflite::micro::GetEvalInput(context, node, kLstmProjectionWeightsTensor); - - const TfLiteEvalTensor* projection_bias = - tflite::micro::GetEvalInput(context, node, kLstmProjectionBiasTensor); - - TfLiteEvalTensor* output_state = - tflite::micro::GetMutableEvalInput(context, node, kLstmOutputStateTensor); - - TfLiteEvalTensor* cell_state = - tflite::micro::GetMutableEvalInput(context, node, kLstmCellStateTensor); - - TFLITE_DCHECK(cell_state != nullptr); - - const TfLiteEvalTensor* input_layer_norm_coefficients = - use_layer_norm ? tflite::micro::GetEvalInput( - context, node, kLstmInputLayerNormCoefficientsTensor) - : nullptr; - const TfLiteEvalTensor* forget_layer_norm_coefficients = - use_layer_norm - ? tflite::micro::GetEvalInput(context, node, - kLstmForgetLayerNormCoefficientsTensor) - : nullptr; - const TfLiteEvalTensor* cell_layer_norm_coefficients = - use_layer_norm ? tflite::micro::GetEvalInput( - context, node, kLstmCellLayerNormCoefficientsTensor) - : nullptr; - const TfLiteEvalTensor* output_layer_norm_coefficients = - use_layer_norm - ? tflite::micro::GetEvalInput(context, node, - kLstmOutputLayerNormCoefficientsTensor) - : nullptr; - - TfLiteEvalTensor* output = - tflite::micro::GetEvalOutput(context, node, kLstmOutputTensor); - - // Copy out the LSTM specific params so they can be passed in the function. - TfLiteLSTMParams lstm_params; - lstm_params.activation = params->activation; - lstm_params.cell_clip = params->cell_clip; - lstm_params.proj_clip = params->proj_clip; - lstm_params.asymmetric_quantize_inputs = params->asymmetric_quantize_inputs; - - switch (input_to_output_weights->type) { - case kTfLiteFloat32: { - // Index the scratch buffers pointers to the global scratch buffer. - return EvalFloatLstm( - input, input_to_input_weights, input_to_forget_weights, - input_to_cell_weights, input_to_output_weights, - recurrent_to_input_weights, recurrent_to_forget_weights, - recurrent_to_cell_weights, recurrent_to_output_weights, - cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, - input_layer_norm_coefficients, forget_layer_norm_coefficients, - cell_layer_norm_coefficients, output_layer_norm_coefficients, - /*aux_input=*/nullptr, - /*aux_input_to_input_weights=*/nullptr, - /*aux_input_to_forget_weights=*/nullptr, - /*aux_input_to_cell_weights=*/nullptr, - /*aux_input_to_output_weights=*/nullptr, input_gate_bias, - forget_gate_bias, cell_gate_bias, output_gate_bias, - projection_weights, projection_bias, &lstm_params, - /*forward_sequence=*/true, time_major, - /*output_offset=*/0, - reinterpret_cast(context->GetScratchBuffer( - context, op_data->scratch_index[kPrimaryScratchBuffer])), - output_state, cell_state, output); - } break; - case kTfLiteUInt8: - case kTfLiteInt8: { - const bool is_hybrid = input->type == kTfLiteFloat32; - if (is_hybrid) { - // Index the scratch buffers pointers to the global scratch buffer. - UnidirectionalSequenceLstmOpData* op_data_rw = - reinterpret_cast( - node->user_data); - return EvalHybridLstm( - &(op_data->hybrid_lstm_scales), input, input_to_input_weights, - /*input_to_input_weights_ledger*/ nullptr, input_to_forget_weights, - /*input_to_forget_weights_ledger*/ nullptr, input_to_cell_weights, - /*input_to_cell_weights_ledger*/ nullptr, input_to_output_weights, - /*input_to_output_weights_ledger*/ nullptr, - recurrent_to_input_weights, - /*recurrent_to_input_weights_ledger*/ nullptr, - recurrent_to_forget_weights, - /*recurrent_to_forget_weights_ledger*/ nullptr, - recurrent_to_cell_weights, - /*recurrent_to_cell_weights_ledger*/ nullptr, - recurrent_to_output_weights, - /*recurrent_to_output_weights_ledger*/ nullptr, - cell_to_input_weights, cell_to_forget_weights, - cell_to_output_weights, input_layer_norm_coefficients, - forget_layer_norm_coefficients, cell_layer_norm_coefficients, - output_layer_norm_coefficients, - /*aux_input=*/nullptr, - /*aux_input_to_input_weights=*/nullptr, - /*aux_input_to_forget_weights=*/nullptr, - /*aux_input_to_cell_weights=*/nullptr, - /*aux_input_to_output_weights=*/nullptr, input_gate_bias, - forget_gate_bias, cell_gate_bias, output_gate_bias, - projection_weights, /*projection_weights_ledger*/ nullptr, - projection_bias, &lstm_params, - /*forward_sequence=*/true, time_major, - /*output_offset=*/0, - reinterpret_cast(context->GetScratchBuffer( - context, op_data->scratch_index[kPrimaryScratchBuffer])), - reinterpret_cast(context->GetScratchBuffer( - context, op_data->scratch_index[kInputScalingFactors])), - /*aux_input_sf=*/nullptr, - reinterpret_cast(context->GetScratchBuffer( - context, op_data->scratch_index[kOutputStateScalingFactors])), - reinterpret_cast(context->GetScratchBuffer( - context, op_data->scratch_index[kProductScalingFactors])), - reinterpret_cast(context->GetScratchBuffer( - context, op_data->scratch_index[kRecoveredCellWeights])), - reinterpret_cast(context->GetScratchBuffer( - context, op_data->scratch_index[kInputQuantized])), - /*aux_input_quantized=*/nullptr, - reinterpret_cast(context->GetScratchBuffer( - context, op_data->scratch_index[kOutputStateQuantized])), - reinterpret_cast(context->GetScratchBuffer( - context, op_data->scratch_index[kCellStateQuantized])), - reinterpret_cast(context->GetScratchBuffer( - context, op_data->scratch_index[kScales])), - output_state, cell_state, - reinterpret_cast(context->GetScratchBuffer( - context, op_data->scratch_index[kAccumScratch])), - output, - reinterpret_cast(context->GetScratchBuffer( - context, op_data->scratch_index[kInputZeroPoints])), - /*aux_input_zp=*/nullptr, - reinterpret_cast(context->GetScratchBuffer( - context, op_data->scratch_index[kOutputStateZeroPoints])), - op_data_rw->row_sums, op_data_rw->row_sums_size, - &op_data_rw->compute_row_sums); - } else { - return EvalInteger8x8_16Lstm( - input, input_to_input_weights, input_to_forget_weights, - input_to_cell_weights, input_to_output_weights, - recurrent_to_input_weights, recurrent_to_forget_weights, - recurrent_to_cell_weights, recurrent_to_output_weights, - cell_to_input_weights, cell_to_forget_weights, - cell_to_output_weights, input_layer_norm_coefficients, - forget_layer_norm_coefficients, cell_layer_norm_coefficients, - output_layer_norm_coefficients, input_gate_bias, forget_gate_bias, - cell_gate_bias, output_gate_bias, projection_weights, - projection_bias, &lstm_params, /*forward_sequence=*/true, - time_major, &op_data->integer_lstm_param, - op_data->output_state_zero_point, output_state, cell_state, output, - reinterpret_cast( - context->GetScratchBuffer(context, op_data->scratch_index[0])), - reinterpret_cast( - context->GetScratchBuffer(context, op_data->scratch_index[1])), - reinterpret_cast( - context->GetScratchBuffer(context, op_data->scratch_index[2])), - reinterpret_cast( - context->GetScratchBuffer(context, op_data->scratch_index[3])), - reinterpret_cast( - context->GetScratchBuffer(context, op_data->scratch_index[4])), - nullptr); - } - } break; - default: - MicroPrintf("Type %s is not currently supported.", - TfLiteTypeGetName(input_to_output_weights->type)); - return kTfLiteError; - } -} - -} // namespace - -TfLiteRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM() { - return tflite::micro::RegisterOp(UnidirectionalSequenceLstmInit, - UnidirectionalSequenceLstmPrepare, - UnidirectionalSequenceLstmEval); -} - -} // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm_test_config.h b/code/components/tflite-lib/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm_test_config.h deleted file mode 100644 index e37c0efd..00000000 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm_test_config.h +++ /dev/null @@ -1,244 +0,0 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_KERNELS_UNIDIRECTIONAL_SEQUENCE_LSTM_TEST_CONFIG_H_ -#define TENSORFLOW_LITE_MICRO_KERNELS_UNIDIRECTIONAL_SEQUENCE_LSTM_TEST_CONFIG_H_ - -#include "tensorflow/lite/c/common.h" - -namespace tflite { -namespace testing { - -// TODO(b/230666079) enable below tests for xtensa when the xtensa -// kernel is reconciled with reference kernel -#if !defined(XTENSA) - -typedef struct LstmIntegerTestConfig { - const int n_batch; - const int n_input; - const int n_cell; - const int n_output; - const int sequence_length; - const bool time_major; - const bool use_cifg; - const bool use_peephole; - const bool use_projection_weights; - const bool use_projection_bias; - const bool use_layer_norm; - const bool use_8x8_8_implementation; - float intermediate_scale[5][2]; - int intermediate_zp[5][2]; - TfLiteAffineQuantization* intermediate_qparam; - - const float* input; - int8_t* input_quant; - - const float* input_to_input_weights; - int8_t* lstm_i2i_quant; - const float* input_to_forget_weights; - int8_t* lstm_i2f_quant; - const float* input_to_cell_weights; - int8_t* lstm_i2c_quant; - const float* input_to_output_weights; - int8_t* lstm_i2o_quant; - - const float* recurrent_to_input_weights; - int8_t* lstm_r2i_quant; - const float* recurrent_to_forget_weights; - int8_t* lstm_r2f_quant; - const float* recurrent_to_cell_weights; - int8_t* lstm_r2c_quant; - const float* recurrent_to_output_weights; - int8_t* lstm_r2o_quant; - - const float* cell_to_input_weights; - int16_t* lstm_c2i_quant; - const float* cell_to_forget_weights; - int16_t* lstm_c2f_quant; - const float* cell_to_output_weights; - int16_t* lstm_c2o_quant; - - const float* input_gate_bias; - int32_t* lstm_igate_bias_quant; - const float* forget_gate_bias; - int32_t* lstm_fgate_bias_quant; - const float* cell_gate_bias; - int32_t* lstm_cgate_bias_quant; - const float* output_gate_bias; - int32_t* lstm_ogate_bias_quant; - - const float* projection_weights; - int8_t* lstm_proj_w_quant; - const float* projection_bias; - int32_t* projection_bias_quant; - - int16_t* output_state; - int16_t* cell_state; - - const float* input_layer_norm_coefficients; - int16_t* lstm_input_layer_norm_coeff_quant; - const float* forget_layer_norm_coefficients; - int16_t* lstm_forget_layer_norm_coeff_quant; - const float* cell_layer_norm_coefficients; - int16_t* lstm_cell_layer_norm_coeff_quant; - const float* output_layer_norm_coefficients; - int16_t* lstm_output_layer_norm_coeff_quant; - - int8_t* output; - const int8_t* expected_output; - - bool asymmetric_quantize_inputs; - const float ranges[25][2]; -} LstmIntegerTestConfig; - -typedef struct LstmFloatTestConfig { - const int n_batch; - const int n_input; - const int n_cell; - const int n_output; - const int sequence_length; - const bool time_major; - const bool use_cifg; - const bool use_peephole; - const bool use_projection_weights; - const bool use_projection_bias; - const bool use_layer_norm; - const float cell_clip; - const float proj_clip; - - const float* input_original; - float* input; - - const float* input_to_input_weights; - const float* input_to_forget_weights; - const float* input_to_cell_weights; - const float* input_to_output_weights; - - const float* recurrent_to_input_weights; - const float* recurrent_to_forget_weights; - const float* recurrent_to_cell_weights; - const float* recurrent_to_output_weights; - - const float* cell_to_input_weights; - const float* cell_to_forget_weights; - const float* cell_to_output_weights; - - const float* input_gate_bias; - const float* forget_gate_bias; - const float* cell_gate_bias; - const float* output_gate_bias; - - const float* projection_weights; - const float* projection_bias; - - float* output_state; - float* cell_state; - - const float* input_layer_norm_coefficients; - const float* forget_layer_norm_coefficients; - const float* cell_layer_norm_coefficients; - const float* output_layer_norm_coefficients; - - float* output; - const float* expected_output_original; - float* expected_output; -} LstmFloatTestConfig; - -typedef struct LstmWeightQuantizationBuffers { - int8_t* lstm_i2i_quant; - float* lstm_i2i_scale; - int* lstm_i2i_zp; - TfLiteAffineQuantization* lstm_i2i_qparam; - - int8_t* lstm_i2f_quant; - float* lstm_i2f_scale; - int* lstm_i2f_zp; - TfLiteAffineQuantization* lstm_i2f_qparam; - - int8_t* lstm_i2c_quant; - float* lstm_i2c_scale; - int* lstm_i2c_zp; - TfLiteAffineQuantization* lstm_i2c_qparam; - - int8_t* lstm_i2o_quant; - float* lstm_i2o_scale; - int* lstm_i2o_zp; - TfLiteAffineQuantization* lstm_i2o_qparam; - - int8_t* lstm_r2i_quant; - float* lstm_r2i_scale; - int* lstm_r2i_zp; - TfLiteAffineQuantization* lstm_r2i_qparam; - - int8_t* lstm_r2f_quant; - float* lstm_r2f_scale; - int* lstm_r2f_zp; - TfLiteAffineQuantization* lstm_r2f_qparam; - - int8_t* lstm_r2c_quant; - float* lstm_r2c_scale; - int* lstm_r2c_zp; - TfLiteAffineQuantization* lstm_r2c_qparam; - - int8_t* lstm_r2o_quant; - float* lstm_r2o_scale; - int* lstm_r2o_zp; - TfLiteAffineQuantization* lstm_r2o_qparam; - - int8_t* lstm_c2i_quant; - float* lstm_c2i_scale; - int* lstm_c2i_zp; - TfLiteAffineQuantization* lstm_c2i_qparam; - - int8_t* lstm_c2f_quant; - float* lstm_c2f_scale; - int* lstm_c2f_zp; - TfLiteAffineQuantization* lstm_c2f_qparam; - - int8_t* lstm_c2o_quant; - float* lstm_c2o_scale; - int* lstm_c2o_zp; - TfLiteAffineQuantization* lstm_c2o_qparam; - - int8_t* lstm_proj_w_quant; - float* lstm_proj_w_scale; - int* lstm_proj_w_zp; - TfLiteAffineQuantization* lstm_proj_w_qparam; -} LstmWeightQuantizationBuffers; - -extern LstmIntegerTestConfig lstm_integer_no_peephole_config; - -extern LstmIntegerTestConfig lstm_integer_peephole_config; - -extern LstmFloatTestConfig lstm_no_cifg_no_peephole_no_proj_config; - -extern LstmFloatTestConfig lstm_cifg_peephole_no_proj_config; - -extern LstmFloatTestConfig lstm_no_cifg_peephole_proj_config; - -extern LstmFloatTestConfig lstm_no_cifg_peephole_proj_bias_config; - -extern LstmWeightQuantizationBuffers lstm_no_cifg_no_peephole_no_proj_buffers; - -extern LstmWeightQuantizationBuffers lstm_cifg_peephole_no_proj_buffers; - -extern LstmWeightQuantizationBuffers lstm_no_cifg_peephole_proj_buffers; - -extern LstmFloatTestConfig cifg_peephole_no_proj_config_layer_norm; - -#endif // !defined(XTENSA) -} // namespace testing -} // namespace tflite - -#endif // TENSORFLOW_LITE_MICRO_KERNELS_UNIDIRECTIONAL_SEQUENCE_LSTM_TEST_CONFIG_H_ diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/unpack.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/unpack.cc index d199add0..13bb7dcf 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/unpack.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/unpack.cc @@ -103,7 +103,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace unpack TfLiteRegistration Register_UNPACK() { - return tflite::micro::RegisterOp(nullptr, nullptr, unpack::Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/nullptr, + /*invoke=*/unpack::Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace micro diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/var_handle.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/var_handle.cc index db044f3f..8354c918 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/var_handle.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/var_handle.cc @@ -87,7 +87,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace. TfLiteRegistration Register_VAR_HANDLE() { - return tflite::micro::RegisterOp(Init, Prepare, Eval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/while.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/while.cc index 811c9eae..576c19b0 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/while.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/while.cc @@ -127,7 +127,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace. TfLiteRegistration Register_WHILE() { - return tflite::micro::RegisterOp(Init, Prepare, Eval); + return {/*init=*/Init, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/kernels/zeros_like.cc b/code/components/tflite-lib/tensorflow/lite/micro/kernels/zeros_like.cc index fd6e6612..733564c9 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/kernels/zeros_like.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/kernels/zeros_like.cc @@ -81,7 +81,14 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { } // namespace TfLiteRegistration Register_ZEROS_LIKE() { - return tflite::micro::RegisterOp(nullptr, Prepare, Eval); + return {/*init=*/nullptr, + /*free=*/nullptr, + /*prepare=*/Prepare, + /*invoke=*/Eval, + /*profiling_string=*/nullptr, + /*builtin_code=*/0, + /*custom_name=*/nullptr, + /*version=*/0}; } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/micro_allocation_info.cc b/code/components/tflite-lib/tensorflow/lite/micro/micro_allocation_info.cc index edab2b83..ab313e66 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/micro_allocation_info.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/micro_allocation_info.cc @@ -16,10 +16,8 @@ limitations under the License. #include "tensorflow/lite/c/c_api_types.h" #include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/kernels/kernel_util.h" #include "tensorflow/lite/micro/memory_helpers.h" #include "tensorflow/lite/micro/memory_planner/greedy_memory_planner.h" -#include "tensorflow/lite/micro/micro_error_reporter.h" namespace tflite { @@ -150,30 +148,6 @@ TfLiteStatus AllocationInfoBuilder::FreeAllocationInfo() { return kTfLiteOk; } -TfLiteStatus AllocationInfoBuilder::ValidateSubgraph( - const SubGraph* subgraph, TfLiteEvalTensor* eval_tensors) { - uint32_t operators_size = NumSubgraphOperators(subgraph); - - for (uint32_t i = 0; i < operators_size; i++) { - const auto op = subgraph->operators()->Get(i); - for (size_t n = 0; - op->intermediates() != nullptr && n < op->intermediates()->size(); - n++) { - const int tensor_index = op->intermediates()->Get(n); - size_t tensor_size = -1; - TF_LITE_ENSURE_STATUS(TfLiteEvalTensorByteLength( - &eval_tensors[tensor_index], &tensor_size)); - if (tensor_size != 0) { - MicroPrintf( - "Does not support intermediate tensor with non-zero size: %d", - tensor_size); - return kTfLiteError; - } - } - } - return kTfLiteOk; -} - TfLiteStatus AllocationInfoBuilder::InitializeAllocationInfo( const int32_t* offline_offsets, SubgraphAllocations* allocations) { AllocationInfo* allocation_info = info_.allocation_info; @@ -184,10 +158,6 @@ TfLiteStatus AllocationInfoBuilder::InitializeAllocationInfo( TfLiteEvalTensor* eval_tensors = allocations[subgraph_idx].tensors; AllocationInfo* subgraph_allocation_info = &allocation_info[info_.subgraph_offsets[subgraph_idx]]; - - // Ensure constraints are met. - TF_LITE_ENSURE_STATUS(ValidateSubgraph(subgraph, eval_tensors)); - for (size_t i = 0; i < subgraph->tensors()->size(); ++i) { AllocationInfo* current = &subgraph_allocation_info[i]; current->output_ptr = &(eval_tensors[i].data.data); @@ -197,10 +167,8 @@ TfLiteStatus AllocationInfoBuilder::InitializeAllocationInfo( current->first_created = kUninitializedLifetime; current->last_used = kUninitializedLifetime; - current->needs_allocating = - (eval_tensors[i].data.data == nullptr) && - (!subgraph->tensors()->Get(i)->is_variable()) && - (current->bytes != 0); + current->needs_allocating = (eval_tensors[i].data.data == nullptr) && + (!subgraph->tensors()->Get(i)->is_variable()); if (offline_offsets) { current->offline_offset = offline_offsets[i]; } else { @@ -213,8 +181,8 @@ TfLiteStatus AllocationInfoBuilder::InitializeAllocationInfo( &allocation_info[info_.scratch_offset]; for (size_t i = 0; i < info_.scratch_buffer_count; i++) { AllocationInfo* current = &scratch_allocation_info[i]; - current->first_created = kUninitializedLifetime; - current->last_used = kUninitializedLifetime; + current->first_created = -1; + current->last_used = -1; current->needs_allocating = true; current->offline_offset = kOnlinePlannedBuffer; } diff --git a/code/components/tflite-lib/tensorflow/lite/micro/micro_allocation_info.h b/code/components/tflite-lib/tensorflow/lite/micro/micro_allocation_info.h index bc6825ef..af303307 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/micro_allocation_info.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/micro_allocation_info.h @@ -128,10 +128,6 @@ class AllocationInfoBuilder { // count monotonically increases through the lifetime marking process. void UpdateLastUsed(AllocationInfo* current, int allocation_scope_count); - // Validate if a subgraph satisfies assumptions. - TfLiteStatus ValidateSubgraph(const SubGraph* subgraph, - TfLiteEvalTensor* eval_tensors); - const tflite::Model* model_ = nullptr; INonPersistentBufferAllocator* non_persistent_allocator_ = nullptr; ErrorReporter* reporter_ = nullptr; diff --git a/code/components/tflite-lib/tensorflow/lite/micro/micro_allocator.cc b/code/components/tflite-lib/tensorflow/lite/micro/micro_allocator.cc index 7e5192cf..b71c7502 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/micro_allocator.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/micro_allocator.cc @@ -26,7 +26,6 @@ limitations under the License. #include "tensorflow/lite/core/api/op_resolver.h" #include "tensorflow/lite/core/api/tensor_utils.h" #include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/micro/arena_allocator/simple_memory_allocator.h" #include "tensorflow/lite/micro/compatibility.h" #include "tensorflow/lite/micro/flatbuffer_utils.h" #include "tensorflow/lite/micro/memory_helpers.h" @@ -35,6 +34,7 @@ limitations under the License. #include "tensorflow/lite/micro/micro_allocation_info.h" #include "tensorflow/lite/micro/micro_arena_constants.h" #include "tensorflow/lite/micro/micro_error_reporter.h" +#include "tensorflow/lite/micro/simple_memory_allocator.h" #include "tensorflow/lite/schema/schema_generated.h" #include "tensorflow/lite/schema/schema_utils.h" diff --git a/code/components/tflite-lib/tensorflow/lite/micro/micro_allocator.h b/code/components/tflite-lib/tensorflow/lite/micro/micro_allocator.h index 35b07f16..d2967c21 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/micro_allocator.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/micro_allocator.h @@ -21,10 +21,10 @@ limitations under the License. #include "tensorflow/lite/c/common.h" #include "tensorflow/lite/core/api/error_reporter.h" #include "tensorflow/lite/core/api/flatbuffer_conversions.h" -#include "tensorflow/lite/micro/arena_allocator/simple_memory_allocator.h" #include "tensorflow/lite/micro/compatibility.h" #include "tensorflow/lite/micro/flatbuffer_utils.h" #include "tensorflow/lite/micro/memory_planner/micro_memory_planner.h" +#include "tensorflow/lite/micro/simple_memory_allocator.h" #include "tensorflow/lite/schema/schema_generated.h" namespace tflite { diff --git a/code/components/tflite-lib/tensorflow/lite/micro/micro_context.cc b/code/components/tflite-lib/tensorflow/lite/micro/micro_context.cc index 9ec694b8..1526b976 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/micro_context.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/micro_context.cc @@ -80,16 +80,6 @@ TfLiteTensor* MicroContext::AllocateTempOutputTensor(const TfLiteNode* node, return AllocateTempTfLiteTensor(tensor_index); } -TfLiteTensor* MicroContext::AllocateTempIntermediateTensor( - const TfLiteNode* node, int index) { - const int tensor_index = GetTensorIndex(index, node->intermediates->size, - node->intermediates->data); - if (tensor_index < 0) { - return nullptr; - } - return AllocateTempTfLiteTensor(tensor_index); -} - void MicroContext::DeallocateTempTfLiteTensor(TfLiteTensor* tensor) { return allocator_.DeallocateTempTfLiteTensor(tensor); } diff --git a/code/components/tflite-lib/tensorflow/lite/micro/micro_context.h b/code/components/tflite-lib/tensorflow/lite/micro/micro_context.h index e7be6544..1db2575e 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/micro_context.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/micro_context.h @@ -73,13 +73,6 @@ class MicroContext { virtual TfLiteTensor* AllocateTempOutputTensor(const TfLiteNode* node, int index); - // Returns a temporary TfLiteTensor struct for the specified intermediate - // tensor of a given mode. This is the recommended API over the deprecated - // GetIntermediates/GetIntermediatesSafe to get a temp intermediate tensor. - // The returned tensor shall be freed via calling DeallocateTempTfLiteTensor. - virtual TfLiteTensor* AllocateTempIntermediateTensor(const TfLiteNode* node, - int index); - // Deallocates a temp TfLiteTensor. // Virtual so that it can be faked for kernel tests. virtual void DeallocateTempTfLiteTensor(TfLiteTensor* tensor); diff --git a/code/components/tflite-lib/tensorflow/lite/micro/micro_mutable_op_resolver.h b/code/components/tflite-lib/tensorflow/lite/micro/micro_mutable_op_resolver.h index 237bd595..8676189d 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/micro_mutable_op_resolver.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/micro_mutable_op_resolver.h @@ -1,4 +1,4 @@ -/* Copyright 2022 The TensorFlow Authors. All Rights Reserved. +/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -25,11 +25,9 @@ limitations under the License. #include "tensorflow/lite/kernels/op_macros.h" #include "tensorflow/lite/micro/compatibility.h" #include "tensorflow/lite/micro/kernels/conv.h" -#include "tensorflow/lite/micro/kernels/depthwise_conv.h" #include "tensorflow/lite/micro/kernels/ethosu.h" #include "tensorflow/lite/micro/kernels/fully_connected.h" #include "tensorflow/lite/micro/kernels/micro_ops.h" -#include "tensorflow/lite/micro/kernels/reduce.h" #include "tensorflow/lite/micro/kernels/softmax.h" #include "tensorflow/lite/micro/micro_op_resolver.h" #include "tensorflow/lite/schema/schema_generated.h" @@ -121,8 +119,8 @@ class MicroMutableOpResolver : public MicroOpResolver { ParseAbs); } - TfLiteStatus AddAdd(const TfLiteRegistration& registration = Register_ADD()) { - return AddBuiltin(BuiltinOperator_ADD, registration, ParseAdd); + TfLiteStatus AddAdd() { + return AddBuiltin(BuiltinOperator_ADD, tflite::Register_ADD(), ParseAdd); } TfLiteStatus AddAddN() { @@ -209,10 +207,9 @@ class MicroMutableOpResolver : public MicroOpResolver { tflite::Register_DEPTH_TO_SPACE(), ParseDepthToSpace); } - TfLiteStatus AddDepthwiseConv2D( - const TfLiteRegistration& registration = Register_DEPTHWISE_CONV_2D()) { - return AddBuiltin(BuiltinOperator_DEPTHWISE_CONV_2D, registration, - ParseDepthwiseConv2D); + TfLiteStatus AddDepthwiseConv2D() { + return AddBuiltin(BuiltinOperator_DEPTHWISE_CONV_2D, + Register_DEPTHWISE_CONV_2D(), ParseDepthwiseConv2D); } TfLiteStatus AddDequantize() { @@ -375,7 +372,8 @@ class MicroMutableOpResolver : public MicroOpResolver { } TfLiteStatus AddMean() { - return AddBuiltin(BuiltinOperator_MEAN, Register_MEAN(), ParseReducer); + return AddBuiltin(BuiltinOperator_MEAN, tflite::ops::micro::Register_MEAN(), + ParseReducer); } TfLiteStatus AddMinimum() { @@ -428,8 +426,8 @@ class MicroMutableOpResolver : public MicroOpResolver { } TfLiteStatus AddReduceMax() { - return AddBuiltin(BuiltinOperator_REDUCE_MAX, Register_REDUCE_MAX(), - ParseReducer); + return AddBuiltin(BuiltinOperator_REDUCE_MAX, + tflite::ops::micro::Register_REDUCE_MAX(), ParseReducer); } TfLiteStatus AddRelu() { @@ -556,9 +554,10 @@ class MicroMutableOpResolver : public MicroOpResolver { } TfLiteStatus AddUnidirectionalSequenceLSTM() { - return AddBuiltin(BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, - Register_UNIDIRECTIONAL_SEQUENCE_LSTM(), - ParseUnidirectionalSequenceLSTM); + return AddBuiltin( + BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM, + tflite::ops::micro::Register_UNIDIRECTIONAL_SEQUENCE_LSTM(), + ParseUnidirectionalSequenceLSTM); } TfLiteStatus AddVarHandle() { diff --git a/code/components/tflite-lib/tensorflow/lite/micro/micro_profiler.cc b/code/components/tflite-lib/tensorflow/lite/micro/micro_profiler.cc index 72f3d37f..d8a86c6b 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/micro_profiler.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/micro_profiler.cc @@ -14,7 +14,6 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/lite/micro/micro_profiler.h" -#include #include #include "tensorflow/lite/kernels/internal/compatibility.h" @@ -39,7 +38,7 @@ void MicroProfiler::EndEvent(uint32_t event_handle) { end_ticks_[event_handle] = GetCurrentTimeTicks(); } -uint32_t MicroProfiler::GetTotalTicks() const { +int32_t MicroProfiler::GetTotalTicks() const { int32_t ticks = 0; for (int i = 0; i < num_events_; ++i) { ticks += end_ticks_[i] - start_ticks_[i]; @@ -50,9 +49,8 @@ uint32_t MicroProfiler::GetTotalTicks() const { void MicroProfiler::Log() const { #if !defined(TF_LITE_STRIP_ERROR_STRINGS) for (int i = 0; i < num_events_; ++i) { - uint32_t ticks = end_ticks_[i] - start_ticks_[i]; - MicroPrintf("%s took %" PRIu32 " ticks (%d ms).", tags_[i], ticks, - TicksToMs(ticks)); + int32_t ticks = end_ticks_[i] - start_ticks_[i]; + MicroPrintf("%s took %d ticks (%d ms).", tags_[i], ticks, TicksToMs(ticks)); } #endif } @@ -61,8 +59,8 @@ void MicroProfiler::LogCsv() const { #if !defined(TF_LITE_STRIP_ERROR_STRINGS) MicroPrintf("\"Event\",\"Tag\",\"Ticks\""); for (int i = 0; i < num_events_; ++i) { - uint32_t ticks = end_ticks_[i] - start_ticks_[i]; - MicroPrintf("%d,%s,%" PRIu32, i, tags_[i], ticks); + int32_t ticks = end_ticks_[i] - start_ticks_[i]; + MicroPrintf("%d,%s,%d", i, tags_[i], ticks); } #endif } diff --git a/code/components/tflite-lib/tensorflow/lite/micro/micro_profiler.h b/code/components/tflite-lib/tensorflow/lite/micro/micro_profiler.h index 41f41a35..8a1ba5de 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/micro_profiler.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/micro_profiler.h @@ -51,7 +51,7 @@ class MicroProfiler { // Returns the sum of the ticks taken across all the events. This number // is only meaningful if all of the events are disjoint (the end time of // event[i] <= start time of event[i+1]). - uint32_t GetTotalTicks() const; + int32_t GetTotalTicks() const; // Prints the profiling information of each of the events in human readable // form. @@ -68,8 +68,8 @@ class MicroProfiler { static constexpr int kMaxEvents = 1024; const char* tags_[kMaxEvents]; - uint32_t start_ticks_[kMaxEvents]; - uint32_t end_ticks_[kMaxEvents]; + int32_t start_ticks_[kMaxEvents]; + int32_t end_ticks_[kMaxEvents]; int num_events_ = 0; TF_LITE_REMOVE_VIRTUAL_DELETE; diff --git a/code/components/tflite-lib/tensorflow/lite/micro/micro_time.cc b/code/components/tflite-lib/tensorflow/lite/micro/micro_time.cc index 2d74fdba..bbe3f1a8 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/micro_time.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/micro_time.cc @@ -38,21 +38,21 @@ namespace tflite { // for a platform to support Tensorflow Lite for Microcontrollers profiling. // This returns 0 by default because timing is an optional feature that builds // without errors on platforms that do not need it. -uint32_t ticks_per_second() { return 0; } +int32_t ticks_per_second() { return 0; } // Reference implementation of the GetCurrentTimeTicks() function that's // required for a platform to support Tensorflow Lite for Microcontrollers // profiling. This returns 0 by default because timing is an optional feature // that builds without errors on platforms that do not need it. -uint32_t GetCurrentTimeTicks() { return 0; } +int32_t GetCurrentTimeTicks() { return 0; } #else // defined(TF_LITE_USE_CTIME) // For platforms that support ctime, we implment the micro_time interface in // this central location. -uint32_t ticks_per_second() { return CLOCKS_PER_SEC; } +int32_t ticks_per_second() { return CLOCKS_PER_SEC; } -uint32_t GetCurrentTimeTicks() { return clock(); } +int32_t GetCurrentTimeTicks() { return clock(); } #endif } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/micro_time.h b/code/components/tflite-lib/tensorflow/lite/micro/micro_time.h index 7a8ab455..fac9069b 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/micro_time.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/micro_time.h @@ -21,14 +21,14 @@ namespace tflite { // These functions should be implemented by each target platform, and provide an // accurate tick count along with how many ticks there are per second. -uint32_t ticks_per_second(); +int32_t ticks_per_second(); // Return time in ticks. The meaning of a tick varies per platform. -uint32_t GetCurrentTimeTicks(); +int32_t GetCurrentTimeTicks(); -inline uint32_t TicksToMs(int32_t ticks) { - return static_cast(1000.0f * static_cast(ticks) / - static_cast(ticks_per_second())); +inline int32_t TicksToMs(int32_t ticks) { + return static_cast(1000.0f * static_cast(ticks) / + static_cast(ticks_per_second())); } } // namespace tflite diff --git a/code/components/tflite-lib/tensorflow/lite/micro/recording_micro_allocator.cc b/code/components/tflite-lib/tensorflow/lite/micro/recording_micro_allocator.cc index fd84370a..53b3806d 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/recording_micro_allocator.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/recording_micro_allocator.cc @@ -17,12 +17,12 @@ limitations under the License. #include "tensorflow/lite/core/api/error_reporter.h" #include "tensorflow/lite/kernels/internal/compatibility.h" -#include "tensorflow/lite/micro/arena_allocator/recording_simple_memory_allocator.h" #include "tensorflow/lite/micro/compatibility.h" #include "tensorflow/lite/micro/memory_helpers.h" #include "tensorflow/lite/micro/memory_planner/greedy_memory_planner.h" #include "tensorflow/lite/micro/micro_allocator.h" #include "tensorflow/lite/micro/micro_error_reporter.h" +#include "tensorflow/lite/micro/recording_simple_memory_allocator.h" namespace tflite { diff --git a/code/components/tflite-lib/tensorflow/lite/micro/recording_micro_allocator.h b/code/components/tflite-lib/tensorflow/lite/micro/recording_micro_allocator.h index 0667287f..6b039c03 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/recording_micro_allocator.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/recording_micro_allocator.h @@ -16,9 +16,9 @@ limitations under the License. #ifndef TENSORFLOW_LITE_MICRO_RECORDING_MICRO_ALLOCATOR_H_ #define TENSORFLOW_LITE_MICRO_RECORDING_MICRO_ALLOCATOR_H_ -#include "tensorflow/lite/micro/arena_allocator/recording_simple_memory_allocator.h" #include "tensorflow/lite/micro/compatibility.h" #include "tensorflow/lite/micro/micro_allocator.h" +#include "tensorflow/lite/micro/recording_simple_memory_allocator.h" namespace tflite { diff --git a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/recording_simple_memory_allocator.cc b/code/components/tflite-lib/tensorflow/lite/micro/recording_simple_memory_allocator.cc similarity index 97% rename from code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/recording_simple_memory_allocator.cc rename to code/components/tflite-lib/tensorflow/lite/micro/recording_simple_memory_allocator.cc index 0efb6512..6d3e72bd 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/recording_simple_memory_allocator.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/recording_simple_memory_allocator.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/lite/micro/arena_allocator/recording_simple_memory_allocator.h" +#include "tensorflow/lite/micro/recording_simple_memory_allocator.h" #include diff --git a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/recording_simple_memory_allocator.h b/code/components/tflite-lib/tensorflow/lite/micro/recording_simple_memory_allocator.h similarity index 87% rename from code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/recording_simple_memory_allocator.h rename to code/components/tflite-lib/tensorflow/lite/micro/recording_simple_memory_allocator.h index 1abe43dd..a251e940 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/recording_simple_memory_allocator.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/recording_simple_memory_allocator.h @@ -13,11 +13,11 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_RECORDING_SIMPLE_MEMORY_ALLOCATOR_H_ -#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_RECORDING_SIMPLE_MEMORY_ALLOCATOR_H_ +#ifndef TENSORFLOW_LITE_MICRO_RECORDING_SIMPLE_MEMORY_ALLOCATOR_H_ +#define TENSORFLOW_LITE_MICRO_RECORDING_SIMPLE_MEMORY_ALLOCATOR_H_ -#include "tensorflow/lite/micro/arena_allocator/simple_memory_allocator.h" #include "tensorflow/lite/micro/compatibility.h" +#include "tensorflow/lite/micro/simple_memory_allocator.h" namespace tflite { @@ -62,4 +62,4 @@ class RecordingSimpleMemoryAllocator : public SimpleMemoryAllocator { } // namespace tflite -#endif // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_RECORDING_SIMPLE_MEMORY_ALLOCATOR_H_ +#endif // TENSORFLOW_LITE_MICRO_RECORDING_SIMPLE_MEMORY_ALLOCATOR_H_ diff --git a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/simple_memory_allocator.cc b/code/components/tflite-lib/tensorflow/lite/micro/simple_memory_allocator.cc similarity index 99% rename from code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/simple_memory_allocator.cc rename to code/components/tflite-lib/tensorflow/lite/micro/simple_memory_allocator.cc index 3e3ea4bd..e5d87afb 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/simple_memory_allocator.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/simple_memory_allocator.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/lite/micro/arena_allocator/simple_memory_allocator.h" +#include "tensorflow/lite/micro/simple_memory_allocator.h" #include #include diff --git a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/simple_memory_allocator.h b/code/components/tflite-lib/tensorflow/lite/micro/simple_memory_allocator.h similarity index 95% rename from code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/simple_memory_allocator.h rename to code/components/tflite-lib/tensorflow/lite/micro/simple_memory_allocator.h index 92d0e425..d88c4a3d 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/arena_allocator/simple_memory_allocator.h +++ b/code/components/tflite-lib/tensorflow/lite/micro/simple_memory_allocator.h @@ -13,16 +13,16 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_SIMPLE_MEMORY_ALLOCATOR_H_ -#define TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_SIMPLE_MEMORY_ALLOCATOR_H_ +#ifndef TENSORFLOW_LITE_MICRO_SIMPLE_MEMORY_ALLOCATOR_H_ +#define TENSORFLOW_LITE_MICRO_SIMPLE_MEMORY_ALLOCATOR_H_ #include #include #include "tensorflow/lite/c/common.h" #include "tensorflow/lite/core/api/error_reporter.h" -#include "tensorflow/lite/micro/arena_allocator/ibuffer_allocator.h" #include "tensorflow/lite/micro/compatibility.h" +#include "tensorflow/lite/micro/ibuffer_allocator.h" namespace tflite { @@ -147,4 +147,4 @@ class SimpleMemoryAllocator : public INonPersistentBufferAllocator, } // namespace tflite -#endif // TENSORFLOW_LITE_MICRO_ARENA_ALLOCATOR_SIMPLE_MEMORY_ALLOCATOR_H_ +#endif // TENSORFLOW_LITE_MICRO_SIMPLE_MEMORY_ALLOCATOR_H_ diff --git a/code/components/tflite-lib/tensorflow/lite/micro/test_helpers.cc b/code/components/tflite-lib/tensorflow/lite/micro/test_helpers.cc index 2411bbf8..2adea777 100644 --- a/code/components/tflite-lib/tensorflow/lite/micro/test_helpers.cc +++ b/code/components/tflite-lib/tensorflow/lite/micro/test_helpers.cc @@ -110,9 +110,7 @@ class ModelBuilder { // Adds a node to the model with given input and output Tensors. Node AddNode(Operator op, std::initializer_list inputs, - std::initializer_list outputs, - std::initializer_list intermediates = - std::initializer_list{}); + std::initializer_list outputs); void AddMetadata(const char* description_string, const int32_t* metadata_buffer_data, size_t num_elements); @@ -167,17 +165,12 @@ ModelBuilder::Operator ModelBuilder::RegisterOp(BuiltinOperator op, ModelBuilder::Node ModelBuilder::AddNode( ModelBuilder::Operator op, std::initializer_list inputs, - std::initializer_list outputs, - std::initializer_list intermediates) { + std::initializer_list outputs) { TFLITE_DCHECK(next_operator_id_ <= kMaxOperators); operators_[next_operator_id_] = tflite::CreateOperator( *builder_, op, builder_->CreateVector(inputs.begin(), inputs.size()), builder_->CreateVector(outputs.begin(), outputs.size()), - BuiltinOptions_NONE, - /*builtin_options=*/0, - /*custom_options=*/0, tflite::CustomOptionsFormat_FLEXBUFFERS, - /*mutating_variable_inputs =*/0, - builder_->CreateVector(intermediates.begin(), intermediates.size())); + BuiltinOptions_NONE); next_operator_id_++; return next_operator_id_ - 1; } @@ -281,12 +274,9 @@ const Model* BuildSimpleStatefulModel() { const int median_tensor = model_builder.AddTensor(TensorType_INT8, {3}); const int invoke_count_tensor = model_builder.AddTensor(TensorType_INT32, {1}); - const int intermediate_tensor = - model_builder.AddTensor(TensorType_FLOAT32, {0}); model_builder.AddNode(op_id, {input_tensor}, - {median_tensor, invoke_count_tensor}, - {intermediate_tensor}); + {median_tensor, invoke_count_tensor}); return model_builder.BuildModel({input_tensor}, {median_tensor, invoke_count_tensor}); } diff --git a/code/components/tflite-lib/tensorflow/lite/schema/schema_generated.h b/code/components/tflite-lib/tensorflow/lite/schema/schema_generated.h index d30dbfe8..e5ce189f 100644 --- a/code/components/tflite-lib/tensorflow/lite/schema/schema_generated.h +++ b/code/components/tflite-lib/tensorflow/lite/schema/schema_generated.h @@ -397,9 +397,6 @@ struct GeluOptionsT; struct DynamicUpdateSliceOptions; struct DynamicUpdateSliceOptionsT; -struct UnsortedSegmentProdOptions; -struct UnsortedSegmentProdOptionsT; - struct OperatorCode; struct OperatorCodeT; @@ -878,13 +875,11 @@ enum BuiltinOperator { BuiltinOperator_MULTINOMIAL = 149, BuiltinOperator_GELU = 150, BuiltinOperator_DYNAMIC_UPDATE_SLICE = 151, - BuiltinOperator_RELU_0_TO_1 = 152, - BuiltinOperator_UNSORTED_SEGMENT_PROD = 153, BuiltinOperator_MIN = BuiltinOperator_ADD, - BuiltinOperator_MAX = BuiltinOperator_UNSORTED_SEGMENT_PROD + BuiltinOperator_MAX = BuiltinOperator_DYNAMIC_UPDATE_SLICE }; -inline const BuiltinOperator (&EnumValuesBuiltinOperator())[154] { +inline const BuiltinOperator (&EnumValuesBuiltinOperator())[152] { static const BuiltinOperator values[] = { BuiltinOperator_ADD, BuiltinOperator_AVERAGE_POOL_2D, @@ -1037,15 +1032,13 @@ inline const BuiltinOperator (&EnumValuesBuiltinOperator())[154] { BuiltinOperator_RANDOM_UNIFORM, BuiltinOperator_MULTINOMIAL, BuiltinOperator_GELU, - BuiltinOperator_DYNAMIC_UPDATE_SLICE, - BuiltinOperator_RELU_0_TO_1, - BuiltinOperator_UNSORTED_SEGMENT_PROD + BuiltinOperator_DYNAMIC_UPDATE_SLICE }; return values; } inline const char * const *EnumNamesBuiltinOperator() { - static const char * const names[155] = { + static const char * const names[153] = { "ADD", "AVERAGE_POOL_2D", "CONCATENATION", @@ -1198,15 +1191,13 @@ inline const char * const *EnumNamesBuiltinOperator() { "MULTINOMIAL", "GELU", "DYNAMIC_UPDATE_SLICE", - "RELU_0_TO_1", - "UNSORTED_SEGMENT_PROD", nullptr }; return names; } inline const char *EnumNameBuiltinOperator(BuiltinOperator e) { - if (flatbuffers::IsOutRange(e, BuiltinOperator_ADD, BuiltinOperator_UNSORTED_SEGMENT_PROD)) return ""; + if (flatbuffers::IsOutRange(e, BuiltinOperator_ADD, BuiltinOperator_DYNAMIC_UPDATE_SLICE)) return ""; const size_t index = static_cast(e); return EnumNamesBuiltinOperator()[index]; } @@ -1330,12 +1321,11 @@ enum BuiltinOptions { BuiltinOptions_BucketizeOptions = 115, BuiltinOptions_GeluOptions = 116, BuiltinOptions_DynamicUpdateSliceOptions = 117, - BuiltinOptions_UnsortedSegmentProdOptions = 118, BuiltinOptions_MIN = BuiltinOptions_NONE, - BuiltinOptions_MAX = BuiltinOptions_UnsortedSegmentProdOptions + BuiltinOptions_MAX = BuiltinOptions_DynamicUpdateSliceOptions }; -inline const BuiltinOptions (&EnumValuesBuiltinOptions())[119] { +inline const BuiltinOptions (&EnumValuesBuiltinOptions())[118] { static const BuiltinOptions values[] = { BuiltinOptions_NONE, BuiltinOptions_Conv2DOptions, @@ -1454,14 +1444,13 @@ inline const BuiltinOptions (&EnumValuesBuiltinOptions())[119] { BuiltinOptions_RandomOptions, BuiltinOptions_BucketizeOptions, BuiltinOptions_GeluOptions, - BuiltinOptions_DynamicUpdateSliceOptions, - BuiltinOptions_UnsortedSegmentProdOptions + BuiltinOptions_DynamicUpdateSliceOptions }; return values; } inline const char * const *EnumNamesBuiltinOptions() { - static const char * const names[120] = { + static const char * const names[119] = { "NONE", "Conv2DOptions", "DepthwiseConv2DOptions", @@ -1580,14 +1569,13 @@ inline const char * const *EnumNamesBuiltinOptions() { "BucketizeOptions", "GeluOptions", "DynamicUpdateSliceOptions", - "UnsortedSegmentProdOptions", nullptr }; return names; } inline const char *EnumNameBuiltinOptions(BuiltinOptions e) { - if (flatbuffers::IsOutRange(e, BuiltinOptions_NONE, BuiltinOptions_UnsortedSegmentProdOptions)) return ""; + if (flatbuffers::IsOutRange(e, BuiltinOptions_NONE, BuiltinOptions_DynamicUpdateSliceOptions)) return ""; const size_t index = static_cast(e); return EnumNamesBuiltinOptions()[index]; } @@ -2064,10 +2052,6 @@ template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_DynamicUpdateSliceOptions; }; -template<> struct BuiltinOptionsTraits { - static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentProdOptions; -}; - struct BuiltinOptionsUnion { BuiltinOptions type; void *value; @@ -3036,14 +3020,6 @@ struct BuiltinOptionsUnion { return type == BuiltinOptions_DynamicUpdateSliceOptions ? reinterpret_cast(value) : nullptr; } - tflite::UnsortedSegmentProdOptionsT *AsUnsortedSegmentProdOptions() { - return type == BuiltinOptions_UnsortedSegmentProdOptions ? - reinterpret_cast(value) : nullptr; - } - const tflite::UnsortedSegmentProdOptionsT *AsUnsortedSegmentProdOptions() const { - return type == BuiltinOptions_UnsortedSegmentProdOptions ? - reinterpret_cast(value) : nullptr; - } }; bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type); @@ -10683,60 +10659,6 @@ inline flatbuffers::Offset CreateDynamicUpdateSliceOp flatbuffers::Offset CreateDynamicUpdateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -struct UnsortedSegmentProdOptionsT : public flatbuffers::NativeTable { - typedef UnsortedSegmentProdOptions TableType; - int32_t num_segments; - UnsortedSegmentProdOptionsT() - : num_segments(0) { - } -}; - -struct UnsortedSegmentProdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { - typedef UnsortedSegmentProdOptionsT NativeTableType; - enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE { - VT_NUM_SEGMENTS = 4 - }; - int32_t num_segments() const { - return GetField(VT_NUM_SEGMENTS, 0); - } - bool Verify(flatbuffers::Verifier &verifier) const { - return VerifyTableStart(verifier) && - VerifyField(verifier, VT_NUM_SEGMENTS) && - verifier.EndTable(); - } - UnsortedSegmentProdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; - void UnPackTo(UnsortedSegmentProdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; - static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); -}; - -struct UnsortedSegmentProdOptionsBuilder { - flatbuffers::FlatBufferBuilder &fbb_; - flatbuffers::uoffset_t start_; - void add_num_segments(int32_t num_segments) { - fbb_.AddElement(UnsortedSegmentProdOptions::VT_NUM_SEGMENTS, num_segments, 0); - } - explicit UnsortedSegmentProdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) - : fbb_(_fbb) { - start_ = fbb_.StartTable(); - } - UnsortedSegmentProdOptionsBuilder &operator=(const UnsortedSegmentProdOptionsBuilder &); - flatbuffers::Offset Finish() { - const auto end = fbb_.EndTable(start_); - auto o = flatbuffers::Offset(end); - return o; - } -}; - -inline flatbuffers::Offset CreateUnsortedSegmentProdOptions( - flatbuffers::FlatBufferBuilder &_fbb, - int32_t num_segments = 0) { - UnsortedSegmentProdOptionsBuilder builder_(_fbb); - builder_.add_num_segments(num_segments); - return builder_.Finish(); -} - -flatbuffers::Offset CreateUnsortedSegmentProdOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); - struct OperatorCodeT : public flatbuffers::NativeTable { typedef OperatorCode TableType; int8_t deprecated_builtin_code; @@ -11238,9 +11160,6 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { const tflite::DynamicUpdateSliceOptions *builtin_options_as_DynamicUpdateSliceOptions() const { return builtin_options_type() == tflite::BuiltinOptions_DynamicUpdateSliceOptions ? static_cast(builtin_options()) : nullptr; } - const tflite::UnsortedSegmentProdOptions *builtin_options_as_UnsortedSegmentProdOptions() const { - return builtin_options_type() == tflite::BuiltinOptions_UnsortedSegmentProdOptions ? static_cast(builtin_options()) : nullptr; - } const flatbuffers::Vector *custom_options() const { return GetPointer *>(VT_CUSTOM_OPTIONS); } @@ -11745,10 +11664,6 @@ template<> inline const tflite::DynamicUpdateSliceOptions *Operator::builtin_opt return builtin_options_as_DynamicUpdateSliceOptions(); } -template<> inline const tflite::UnsortedSegmentProdOptions *Operator::builtin_options_as() const { - return builtin_options_as_UnsortedSegmentProdOptions(); -} - struct OperatorBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; @@ -15858,32 +15773,6 @@ inline flatbuffers::Offset CreateDynamicUpdateSliceOp _fbb); } -inline UnsortedSegmentProdOptionsT *UnsortedSegmentProdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { - auto _o = new UnsortedSegmentProdOptionsT(); - UnPackTo(_o, _resolver); - return _o; -} - -inline void UnsortedSegmentProdOptions::UnPackTo(UnsortedSegmentProdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { - (void)_o; - (void)_resolver; - { auto _e = num_segments(); _o->num_segments = _e; } -} - -inline flatbuffers::Offset UnsortedSegmentProdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { - return CreateUnsortedSegmentProdOptions(_fbb, _o, _rehasher); -} - -inline flatbuffers::Offset CreateUnsortedSegmentProdOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { - (void)_rehasher; - (void)_o; - struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentProdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; - auto _num_segments = _o->num_segments; - return tflite::CreateUnsortedSegmentProdOptions( - _fbb, - _num_segments); -} - inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new OperatorCodeT(); UnPackTo(_o, _resolver); @@ -16827,10 +16716,6 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob auto ptr = reinterpret_cast(obj); return verifier.VerifyTable(ptr); } - case BuiltinOptions_UnsortedSegmentProdOptions: { - auto ptr = reinterpret_cast(obj); - return verifier.VerifyTable(ptr); - } default: return true; } } @@ -17317,10 +17202,6 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c auto ptr = reinterpret_cast(obj); return ptr->UnPack(resolver); } - case BuiltinOptions_UnsortedSegmentProdOptions: { - auto ptr = reinterpret_cast(obj); - return ptr->UnPack(resolver); - } default: return nullptr; } } @@ -17795,10 +17676,6 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff auto ptr = reinterpret_cast(value); return CreateDynamicUpdateSliceOptions(_fbb, ptr, _rehasher).Union(); } - case BuiltinOptions_UnsortedSegmentProdOptions: { - auto ptr = reinterpret_cast(value); - return CreateUnsortedSegmentProdOptions(_fbb, ptr, _rehasher).Union(); - } default: return 0; } } @@ -18273,10 +18150,6 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL value = new tflite::DynamicUpdateSliceOptionsT(*reinterpret_cast(u.value)); break; } - case BuiltinOptions_UnsortedSegmentProdOptions: { - value = new tflite::UnsortedSegmentProdOptionsT(*reinterpret_cast(u.value)); - break; - } default: break; } @@ -18869,11 +18742,6 @@ inline void BuiltinOptionsUnion::Reset() { delete ptr; break; } - case BuiltinOptions_UnsortedSegmentProdOptions: { - auto ptr = reinterpret_cast(value); - delete ptr; - break; - } default: break; } value = nullptr; diff --git a/code/components/tflite-lib_20220417.zip b/code/components/tflite-lib_20220417.zip deleted file mode 100644 index fcc45d2205787dafc06934de2aa00c7ebc528f46..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 740090 zcma&MV~{XFx2-p}ZQC>N*tTukwr$(CZQHhOoB3{Ta&nTY2~W=knb%;NcQSA^P;uEpc|y%rhF!%uuI@yWu$!@8)ZS- z29#;^w+SVrhI%xtX) 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