Refactor conv to share code between reference and optimized kernels
This commit is contained in:
Nat Jeffries
parent
dce9ee5e26
commit
3aa4d9952c
@ -107,6 +107,7 @@ cc_library(
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"//tensorflow/lite/core/api",
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"//tensorflow/lite/kernels:op_macros",
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"//tensorflow/lite/kernels/internal:compatibility",
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"//tensorflow/lite/micro/kernels:conv",
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"//tensorflow/lite/micro/kernels:ethosu",
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"//tensorflow/lite/micro/kernels:fully_connected",
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"//tensorflow/lite/micro/kernels:micro_ops",
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@ -136,6 +136,43 @@ cc_library(
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}),
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)
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cc_library(
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name = "conv",
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srcs = [
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"conv_common.cc",
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] + select({
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"//conditions:default": [
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"conv.cc",
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],
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":xtensa_hifimini": [
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"xtensa/conv.cc",
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],
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}),
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hdrs = ["conv.h"],
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copts = micro_copts(),
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visibility = [
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# Kernel variants need to be visible to the examples and benchmarks.
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":micro",
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],
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deps = [
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":fixedpoint_utils",
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":kernel_util",
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":xtensa",
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"//tensorflow/lite/c:common",
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"//tensorflow/lite/kernels/internal:common",
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"//tensorflow/lite/kernels/internal:quantization_util",
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"//tensorflow/lite/kernels/internal:reference_base",
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"//tensorflow/lite/kernels/internal:tensor",
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"//tensorflow/lite/kernels:kernel_util",
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"//tensorflow/lite/kernels:padding",
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] + select({
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"//conditions:default": [],
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":xtensa_hifimini": [
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#"//third_party/xtensa/cstub64s:hifi_mini",
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],
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}),
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)
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cc_library(
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name = "kernel_runner",
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srcs = [
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@ -211,14 +248,12 @@ cc_library(
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"zeros_like.cc",
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] + select({
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"//conditions:default": [
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"conv.cc",
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"depthwise_conv.cc",
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"quantize.cc",
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"softmax.cc",
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"svdf.cc",
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],
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":xtensa_hifimini": [
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"xtensa/conv.cc",
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"xtensa/depthwise_conv.cc",
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"xtensa/quantize.cc",
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"xtensa/softmax.cc",
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@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/lite/kernels/internal/reference/conv.h"
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#include "tensorflow/lite/micro/kernels/conv.h"
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#include "CMSIS/NN/Include/arm_nn_types.h"
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#include "CMSIS/NN/Include/arm_nnfunctions.h"
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@ -21,6 +21,7 @@ limitations under the License.
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/common.h"
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#include "tensorflow/lite/kernels/internal/quantization_util.h"
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#include "tensorflow/lite/kernels/internal/reference/conv.h"
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#include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h"
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#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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@ -30,93 +31,9 @@ limitations under the License.
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namespace tflite {
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namespace {
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constexpr int kInputTensor = 0;
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constexpr int kFilterTensor = 1;
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constexpr int kBiasTensor = 2;
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constexpr int kOutputTensor = 0;
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// Conv is quantized along dimension 0:
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// https://www.tensorflow.org/lite/performance/quantization_spec
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constexpr int kConvQuantizedDimension = 0;
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struct OpData {
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TfLitePaddingValues padding;
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// Cached tensor zero point values for quantized operations.
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int32_t input_zero_point;
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int32_t filter_zero_point;
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int32_t output_zero_point;
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// The scaling factor from input to output (aka the 'real multiplier') can
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// be represented as a fixed point multiplier plus a left shift.
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int32_t output_multiplier;
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int output_shift;
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// Per channel output multiplier and shift.
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int32_t* per_channel_output_multiplier;
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int32_t* per_channel_output_shift;
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// The range of the fused activation layer. For example for kNone and
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// uint8_t these would be 0 and 255.
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int32_t output_activation_min;
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int32_t output_activation_max;
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// Index to buffer for optimizations if applicable.
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int buffer_idx;
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};
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inline PaddingType RuntimePaddingType(TfLitePadding padding) {
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switch (padding) {
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case TfLitePadding::kTfLitePaddingSame:
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return PaddingType::kSame;
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case TfLitePadding::kTfLitePaddingValid:
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return PaddingType::kValid;
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case TfLitePadding::kTfLitePaddingUnknown:
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default:
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return PaddingType::kNone;
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}
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}
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TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node,
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const TfLiteConvParams* params, int width,
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int height, int filter_width, int filter_height,
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int out_width, int out_height,
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const TfLiteType data_type, OpData* data) {
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bool has_bias = node->inputs->size == 3;
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// Check number of inputs/outputs
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TF_LITE_ENSURE(context, has_bias || node->inputs->size == 2);
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TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
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// Matching GetWindowedOutputSize in TensorFlow.
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auto padding = params->padding;
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data->padding = ComputePaddingHeightWidth(
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params->stride_height, params->stride_width,
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params->dilation_height_factor, params->dilation_width_factor, height,
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width, filter_height, filter_width, padding, &out_height, &out_width);
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// Note that quantized inference requires that all tensors have their
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// parameters set. This is usually done during quantized training.
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if (data_type != kTfLiteFloat32) {
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const TfLiteTensor* input = GetInput(context, node, kInputTensor);
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const TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
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const TfLiteTensor* bias =
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GetOptionalInputTensor(context, node, kBiasTensor);
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TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
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int num_channels = filter->dims->data[kConvQuantizedDimension];
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TF_LITE_ENSURE_STATUS(tflite::PopulateConvolutionQuantizationParams(
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context, input, filter, bias, output, params->activation,
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&data->output_multiplier, &data->output_shift,
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&data->output_activation_min, &data->output_activation_max,
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data->per_channel_output_multiplier,
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reinterpret_cast<int*>(data->per_channel_output_shift), num_channels));
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}
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return kTfLiteOk;
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}
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void* Init(TfLiteContext* context, const char* buffer, size_t length) {
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TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
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return context->AllocatePersistentBuffer(context, sizeof(OpData));
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return context->AllocatePersistentBuffer(context, sizeof(OpDataConv));
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}
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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@ -125,11 +42,11 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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int32_t buf_size = 0;
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const auto params = static_cast<const TfLiteConvParams*>(node->builtin_data);
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OpData* data = static_cast<OpData*>(node->user_data);
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OpDataConv* data = static_cast<OpDataConv*>(node->user_data);
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const TfLiteTensor* input = GetInput(context, node, kInputTensor);
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const TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
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const TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
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const TfLiteTensor* input = GetInput(context, node, kConvInputTensor);
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const TfLiteTensor* filter = GetInput(context, node, kConvWeightsTensor);
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const TfLiteTensor* output = GetOutput(context, node, kConvOutputTensor);
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RuntimeShape input_shape = GetTensorShape(input);
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RuntimeShape output_shape = GetTensorShape(output);
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@ -168,7 +85,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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static_cast<int32_t*>(context->AllocatePersistentBuffer(
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context, num_channels * sizeof(int32_t)));
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TF_LITE_ENSURE_STATUS(CalculateOpData(
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TF_LITE_ENSURE_STATUS(CalculateOpDataConv(
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context, node, params, input_dims.w, input_dims.h, filter_dims.w,
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filter_dims.h, output_dims.w, output_dims.h, input->type, data));
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@ -203,49 +120,9 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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return kTfLiteOk;
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}
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TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
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TfLiteConvParams* params, const OpData& data,
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const TfLiteEvalTensor* input,
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const TfLiteEvalTensor* filter,
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const TfLiteEvalTensor* bias,
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TfLiteEvalTensor* im2col,
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TfLiteEvalTensor* hwcn_weights,
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TfLiteEvalTensor* output) {
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const int32_t input_offset = -data.input_zero_point;
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const int32_t filter_offset = -data.filter_zero_point;
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const int32_t output_offset = data.output_zero_point;
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ConvParams op_params;
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op_params.padding_type = RuntimePaddingType(params->padding);
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op_params.padding_values.width = data.padding.width;
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op_params.padding_values.height = data.padding.height;
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op_params.stride_width = params->stride_width;
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op_params.stride_height = params->stride_height;
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op_params.dilation_width_factor = params->dilation_width_factor;
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op_params.dilation_height_factor = params->dilation_height_factor;
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op_params.input_offset = input_offset;
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op_params.weights_offset = filter_offset;
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op_params.output_offset = output_offset;
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op_params.output_multiplier = data.output_multiplier;
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op_params.output_shift = -data.output_shift;
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op_params.quantized_activation_min = data.output_activation_min;
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op_params.quantized_activation_max = data.output_activation_max;
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reference_ops::Conv(op_params, tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<uint8_t>(input),
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tflite::micro::GetTensorShape(filter),
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tflite::micro::GetTensorData<uint8_t>(filter),
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tflite::micro::GetTensorShape(bias),
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tflite::micro::GetTensorData<int32_t>(bias),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<uint8_t>(output),
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tflite::micro::GetTensorShape(im2col),
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tflite::micro::GetTensorData<uint8_t>(im2col), nullptr);
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return kTfLiteOk;
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}
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TfLiteStatus EvalQuantizedPerChannel(
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TfLiteContext* context, TfLiteNode* node, TfLiteConvParams* params,
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const OpData& data, const TfLiteEvalTensor* input,
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const OpDataConv& data, const TfLiteEvalTensor* input,
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const TfLiteEvalTensor* filter, const TfLiteEvalTensor* bias,
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TfLiteEvalTensor* output, TfLiteEvalTensor* im2col) {
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cmsis_nn_conv_params conv_params;
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@ -340,21 +217,8 @@ TfLiteStatus EvalQuantizedPerChannel(
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tflite::micro::GetTensorData<int8_t>(output)),
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ARM_MATH_SUCCESS);
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} else {
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// TODO(b/154032858): Investigate removing extra copies.
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ConvParams op_params;
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op_params.input_offset = -data.input_zero_point;
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op_params.output_offset = data.output_zero_point;
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op_params.stride_height = params->stride_height;
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op_params.stride_width = params->stride_width;
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op_params.dilation_height_factor = params->dilation_height_factor;
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op_params.dilation_width_factor = params->dilation_width_factor;
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op_params.padding_values.height = data.padding.height;
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op_params.padding_values.width = data.padding.width;
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op_params.quantized_activation_min = data.output_activation_min;
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op_params.quantized_activation_max = data.output_activation_max;
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reference_integer_ops::ConvPerChannel(
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op_params, data.per_channel_output_multiplier,
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ConvParamsQuantized(params, data), data.per_channel_output_multiplier,
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data.per_channel_output_shift, tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<int8_t>(input),
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tflite::micro::GetTensorShape(filter),
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@ -367,75 +231,59 @@ TfLiteStatus EvalQuantizedPerChannel(
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return kTfLiteOk;
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}
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TfLiteStatus EvalFloat(TfLiteContext* context, TfLiteNode* node,
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TfLiteConvParams* params, const OpData& data,
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const TfLiteEvalTensor* input,
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const TfLiteEvalTensor* filter,
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const TfLiteEvalTensor* bias, TfLiteEvalTensor* im2col,
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TfLiteEvalTensor* hwcn_weights,
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TfLiteEvalTensor* output) {
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float output_activation_min, output_activation_max;
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CalculateActivationRange(params->activation, &output_activation_min,
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&output_activation_max);
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// TODO(b/154032858): Investigate removing extra copies.
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ConvParams op_params;
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op_params.padding_type = RuntimePaddingType(params->padding);
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op_params.padding_values.width = data.padding.width;
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op_params.padding_values.height = data.padding.height;
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op_params.stride_width = params->stride_width;
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op_params.stride_height = params->stride_height;
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op_params.dilation_width_factor = params->dilation_width_factor;
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op_params.dilation_height_factor = params->dilation_height_factor;
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op_params.float_activation_min = output_activation_min;
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op_params.float_activation_max = output_activation_max;
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reference_ops::Conv(op_params, tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<float>(input),
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tflite::micro::GetTensorShape(filter),
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tflite::micro::GetTensorData<float>(filter),
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tflite::micro::GetTensorShape(bias),
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tflite::micro::GetTensorData<float>(bias),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<float>(output),
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tflite::micro::GetTensorShape(im2col),
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tflite::micro::GetTensorData<float>(im2col));
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return kTfLiteOk;
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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auto* params = reinterpret_cast<TfLiteConvParams*>(node->builtin_data);
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const TfLiteEvalTensor* input =
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tflite::micro::GetEvalInput(context, node, kInputTensor);
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tflite::micro::GetEvalInput(context, node, kConvInputTensor);
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const TfLiteEvalTensor* filter =
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tflite::micro::GetEvalInput(context, node, kFilterTensor);
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tflite::micro::GetEvalInput(context, node, kConvWeightsTensor);
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const TfLiteEvalTensor* bias =
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(NumInputs(node) == 3)
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? tflite::micro::GetEvalInput(context, node, kBiasTensor)
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? tflite::micro::GetEvalInput(context, node, kConvBiasTensor)
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: nullptr;
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TfLiteEvalTensor* output =
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tflite::micro::GetEvalOutput(context, node, kOutputTensor);
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tflite::micro::GetEvalOutput(context, node, kConvOutputTensor);
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TFLITE_DCHECK(node->user_data != nullptr);
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const OpData& data = *(static_cast<const OpData*>(node->user_data));
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const OpDataConv& data = *(static_cast<const OpDataConv*>(node->user_data));
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TF_LITE_ENSURE_EQ(context, input->type, output->type);
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TF_LITE_ENSURE_MSG(context, input->type == filter->type,
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"Hybrid models are not supported on TFLite Micro.");
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switch (input->type) { // Already know in/out types are same.
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case kTfLiteFloat32:
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EvalFloat(context, node, params, data, input, filter, bias, nullptr,
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nullptr, output);
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case kTfLiteFloat32: {
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tflite::reference_ops::Conv(
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ConvParamsFloat(params, data), tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<float>(input),
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tflite::micro::GetTensorShape(filter),
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tflite::micro::GetTensorData<float>(filter),
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tflite::micro::GetTensorShape(bias),
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tflite::micro::GetTensorData<float>(bias),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<float>(output),
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tflite::micro::GetTensorShape(nullptr), nullptr);
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break;
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}
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case kTfLiteInt8:
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return EvalQuantizedPerChannel(context, node, params, data, input, filter,
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bias, output, nullptr);
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break;
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case kTfLiteUInt8:
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return EvalQuantized(context, node, params, data, input, filter, bias,
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nullptr, nullptr, output);
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case kTfLiteUInt8: {
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reference_ops::Conv(ConvParamsQuantized(params, data),
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tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<uint8_t>(input),
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tflite::micro::GetTensorShape(filter),
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tflite::micro::GetTensorData<uint8_t>(filter),
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tflite::micro::GetTensorShape(bias),
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tflite::micro::GetTensorData<int32_t>(bias),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<uint8_t>(output),
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tflite::micro::GetTensorShape(nullptr), nullptr,
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nullptr);
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break;
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}
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default:
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TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.",
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TfLiteTypeGetName(input->type), input->type);
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@ -13,12 +13,13 @@ See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/lite/kernels/internal/reference/conv.h"
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#include "tensorflow/lite/micro/kernels/conv.h"
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#include "tensorflow/lite/c/builtin_op_data.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/common.h"
|
||||
#include "tensorflow/lite/kernels/internal/quantization_util.h"
|
||||
#include "tensorflow/lite/kernels/internal/reference/conv.h"
|
||||
#include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h"
|
||||
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
|
||||
#include "tensorflow/lite/kernels/kernel_util.h"
|
||||
@ -28,110 +29,23 @@ limitations under the License.
|
||||
namespace tflite {
|
||||
namespace {
|
||||
|
||||
constexpr int kInputTensor = 0;
|
||||
constexpr int kFilterTensor = 1;
|
||||
constexpr int kBiasTensor = 2;
|
||||
constexpr int kOutputTensor = 0;
|
||||
|
||||
// Conv is quantized along dimension 0:
|
||||
// https://www.tensorflow.org/lite/performance/quantization_spec
|
||||
constexpr int kConvQuantizedDimension = 0;
|
||||
|
||||
// This file has 2 implementation of Conv.
|
||||
|
||||
struct OpData {
|
||||
TfLitePaddingValues padding;
|
||||
|
||||
// Cached tensor zero point values for quantized operations.
|
||||
int32_t input_zero_point;
|
||||
int32_t filter_zero_point;
|
||||
int32_t output_zero_point;
|
||||
|
||||
// The scaling factor from input to output (aka the 'real multiplier') can
|
||||
// be represented as a fixed point multiplier plus a left shift.
|
||||
int32_t output_multiplier;
|
||||
int output_shift;
|
||||
|
||||
// Per channel output multiplier and shift.
|
||||
int32_t* per_channel_output_multiplier;
|
||||
int32_t* per_channel_output_shift;
|
||||
|
||||
// The range of the fused activation layer. For example for kNone and
|
||||
// uint8_t these would be 0 and 255.
|
||||
int32_t output_activation_min;
|
||||
int32_t output_activation_max;
|
||||
};
|
||||
|
||||
inline PaddingType RuntimePaddingType(TfLitePadding padding) {
|
||||
switch (padding) {
|
||||
case TfLitePadding::kTfLitePaddingSame:
|
||||
return PaddingType::kSame;
|
||||
case TfLitePadding::kTfLitePaddingValid:
|
||||
return PaddingType::kValid;
|
||||
case TfLitePadding::kTfLitePaddingUnknown:
|
||||
default:
|
||||
return PaddingType::kNone;
|
||||
}
|
||||
}
|
||||
|
||||
TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node,
|
||||
const TfLiteConvParams* params, int width,
|
||||
int height, int filter_width, int filter_height,
|
||||
int out_width, int out_height,
|
||||
const TfLiteType data_type, OpData* data) {
|
||||
bool has_bias = node->inputs->size == 3;
|
||||
// Check number of inputs/outputs
|
||||
TF_LITE_ENSURE(context, has_bias || node->inputs->size == 2);
|
||||
TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
|
||||
|
||||
// Matching GetWindowedOutputSize in TensorFlow.
|
||||
auto padding = params->padding;
|
||||
data->padding = ComputePaddingHeightWidth(
|
||||
params->stride_height, params->stride_width,
|
||||
params->dilation_height_factor, params->dilation_width_factor, height,
|
||||
width, filter_height, filter_width, padding, &out_height, &out_width);
|
||||
|
||||
// Note that quantized inference requires that all tensors have their
|
||||
// parameters set. This is usually done during quantized training.
|
||||
if (data_type != kTfLiteFloat32) {
|
||||
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
|
||||
TF_LITE_ENSURE(context, input != nullptr);
|
||||
const TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
|
||||
TF_LITE_ENSURE(context, filter != nullptr);
|
||||
const TfLiteTensor* bias =
|
||||
GetOptionalInputTensor(context, node, kBiasTensor);
|
||||
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
|
||||
TF_LITE_ENSURE(context, output != nullptr);
|
||||
int output_channels = filter->dims->data[kConvQuantizedDimension];
|
||||
|
||||
TF_LITE_ENSURE_STATUS(tflite::PopulateConvolutionQuantizationParams(
|
||||
context, input, filter, bias, output, params->activation,
|
||||
&data->output_multiplier, &data->output_shift,
|
||||
&data->output_activation_min, &data->output_activation_max,
|
||||
data->per_channel_output_multiplier,
|
||||
reinterpret_cast<int*>(data->per_channel_output_shift),
|
||||
output_channels));
|
||||
}
|
||||
return kTfLiteOk;
|
||||
}
|
||||
|
||||
void* Init(TfLiteContext* context, const char* buffer, size_t length) {
|
||||
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
|
||||
return context->AllocatePersistentBuffer(context, sizeof(OpData));
|
||||
return context->AllocatePersistentBuffer(context, sizeof(OpDataConv));
|
||||
}
|
||||
|
||||
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
|
||||
TFLITE_DCHECK(node->user_data != nullptr);
|
||||
TFLITE_DCHECK(node->builtin_data != nullptr);
|
||||
|
||||
OpData* data = static_cast<OpData*>(node->user_data);
|
||||
OpDataConv* data = static_cast<OpDataConv*>(node->user_data);
|
||||
const auto params = static_cast<const TfLiteConvParams*>(node->builtin_data);
|
||||
|
||||
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
|
||||
TfLiteTensor* output = GetOutput(context, node, kConvOutputTensor);
|
||||
TF_LITE_ENSURE(context, output != nullptr);
|
||||
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
|
||||
const TfLiteTensor* input = GetInput(context, node, kConvInputTensor);
|
||||
TF_LITE_ENSURE(context, input != nullptr);
|
||||
const TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
|
||||
const TfLiteTensor* filter = GetInput(context, node, kConvWeightsTensor);
|
||||
TF_LITE_ENSURE(context, filter != nullptr);
|
||||
|
||||
int input_width = input->dims->data[2];
|
||||
@ -169,7 +83,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
|
||||
affine_quantization->zero_point->size);
|
||||
}
|
||||
|
||||
TF_LITE_ENSURE_STATUS(CalculateOpData(
|
||||
TF_LITE_ENSURE_STATUS(CalculateOpDataConv(
|
||||
context, node, params, input_width, input_height, filter_width,
|
||||
filter_height, output_width, output_height, input->type, data));
|
||||
|
||||
@ -178,144 +92,70 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
|
||||
data->output_zero_point = output->params.zero_point;
|
||||
|
||||
return kTfLiteOk;
|
||||
} // namespace conv
|
||||
|
||||
void EvalQuantized(TfLiteContext* context, TfLiteNode* node,
|
||||
TfLiteConvParams* params, const OpData& data,
|
||||
const TfLiteEvalTensor* input,
|
||||
const TfLiteEvalTensor* filter, const TfLiteEvalTensor* bias,
|
||||
TfLiteEvalTensor* im2col, TfLiteEvalTensor* hwcn_weights,
|
||||
TfLiteEvalTensor* output) {
|
||||
const int32_t input_offset = -data.input_zero_point;
|
||||
const int32_t filter_offset = -data.filter_zero_point;
|
||||
const int32_t output_offset = data.output_zero_point;
|
||||
|
||||
// TODO(b/154032858): Investigate removing extra copies.
|
||||
ConvParams op_params;
|
||||
op_params.padding_type = RuntimePaddingType(params->padding);
|
||||
op_params.padding_values.width = data.padding.width;
|
||||
op_params.padding_values.height = data.padding.height;
|
||||
op_params.stride_width = params->stride_width;
|
||||
op_params.stride_height = params->stride_height;
|
||||
op_params.dilation_width_factor = params->dilation_width_factor;
|
||||
op_params.dilation_height_factor = params->dilation_height_factor;
|
||||
op_params.input_offset = input_offset;
|
||||
op_params.weights_offset = filter_offset;
|
||||
op_params.output_offset = output_offset;
|
||||
op_params.output_multiplier = data.output_multiplier;
|
||||
op_params.output_shift = -data.output_shift;
|
||||
op_params.quantized_activation_min = data.output_activation_min;
|
||||
op_params.quantized_activation_max = data.output_activation_max;
|
||||
reference_ops::Conv(op_params, tflite::micro::GetTensorShape(input),
|
||||
tflite::micro::GetTensorData<uint8_t>(input),
|
||||
tflite::micro::GetTensorShape(filter),
|
||||
tflite::micro::GetTensorData<uint8_t>(filter),
|
||||
tflite::micro::GetTensorShape(bias),
|
||||
tflite::micro::GetTensorData<int32_t>(bias),
|
||||
tflite::micro::GetTensorShape(output),
|
||||
tflite::micro::GetTensorData<uint8_t>(output),
|
||||
tflite::micro::GetTensorShape(im2col),
|
||||
tflite::micro::GetTensorData<uint8_t>(im2col), nullptr);
|
||||
}
|
||||
|
||||
void EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node,
|
||||
TfLiteConvParams* params, const OpData& data,
|
||||
const TfLiteEvalTensor* input,
|
||||
const TfLiteEvalTensor* filter,
|
||||
const TfLiteEvalTensor* bias,
|
||||
TfLiteEvalTensor* output,
|
||||
TfLiteEvalTensor* im2col) {
|
||||
// TODO(b/154032858): Investigate removing extra copies.
|
||||
ConvParams op_params;
|
||||
op_params.input_offset = -data.input_zero_point;
|
||||
op_params.output_offset = data.output_zero_point;
|
||||
op_params.stride_height = params->stride_height;
|
||||
op_params.stride_width = params->stride_width;
|
||||
op_params.dilation_height_factor = params->dilation_height_factor;
|
||||
op_params.dilation_width_factor = params->dilation_width_factor;
|
||||
op_params.padding_values.height = data.padding.height;
|
||||
op_params.padding_values.width = data.padding.width;
|
||||
op_params.quantized_activation_min = data.output_activation_min;
|
||||
op_params.quantized_activation_max = data.output_activation_max;
|
||||
|
||||
reference_integer_ops::ConvPerChannel(
|
||||
op_params, data.per_channel_output_multiplier,
|
||||
data.per_channel_output_shift, tflite::micro::GetTensorShape(input),
|
||||
tflite::micro::GetTensorData<int8_t>(input),
|
||||
tflite::micro::GetTensorShape(filter),
|
||||
tflite::micro::GetTensorData<int8_t>(filter),
|
||||
tflite::micro::GetTensorShape(bias),
|
||||
tflite::micro::GetTensorData<int32_t>(bias),
|
||||
tflite::micro::GetTensorShape(output),
|
||||
tflite::micro::GetTensorData<int8_t>(output));
|
||||
}
|
||||
|
||||
void EvalFloat(TfLiteContext* context, TfLiteNode* node,
|
||||
TfLiteConvParams* params, const OpData& data,
|
||||
const TfLiteEvalTensor* input, const TfLiteEvalTensor* filter,
|
||||
const TfLiteEvalTensor* bias, TfLiteEvalTensor* im2col,
|
||||
TfLiteEvalTensor* hwcn_weights, TfLiteEvalTensor* output) {
|
||||
float output_activation_min, output_activation_max;
|
||||
CalculateActivationRange(params->activation, &output_activation_min,
|
||||
&output_activation_max);
|
||||
// TODO(b/154032858): Investigate removing extra copies.
|
||||
ConvParams op_params;
|
||||
op_params.padding_type = RuntimePaddingType(params->padding);
|
||||
op_params.padding_values.width = data.padding.width;
|
||||
op_params.padding_values.height = data.padding.height;
|
||||
op_params.stride_width = params->stride_width;
|
||||
op_params.stride_height = params->stride_height;
|
||||
op_params.dilation_width_factor = params->dilation_width_factor;
|
||||
op_params.dilation_height_factor = params->dilation_height_factor;
|
||||
op_params.float_activation_min = output_activation_min;
|
||||
op_params.float_activation_max = output_activation_max;
|
||||
|
||||
reference_ops::Conv(op_params, tflite::micro::GetTensorShape(input),
|
||||
tflite::micro::GetTensorData<float>(input),
|
||||
tflite::micro::GetTensorShape(filter),
|
||||
tflite::micro::GetTensorData<float>(filter),
|
||||
tflite::micro::GetTensorShape(bias),
|
||||
tflite::micro::GetTensorData<float>(bias),
|
||||
tflite::micro::GetTensorShape(output),
|
||||
tflite::micro::GetTensorData<float>(output),
|
||||
tflite::micro::GetTensorShape(im2col),
|
||||
tflite::micro::GetTensorData<float>(im2col));
|
||||
}
|
||||
|
||||
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
|
||||
auto* params = reinterpret_cast<TfLiteConvParams*>(node->builtin_data);
|
||||
|
||||
const TfLiteEvalTensor* input =
|
||||
tflite::micro::GetEvalInput(context, node, kInputTensor);
|
||||
tflite::micro::GetEvalInput(context, node, kConvInputTensor);
|
||||
const TfLiteEvalTensor* filter =
|
||||
tflite::micro::GetEvalInput(context, node, kFilterTensor);
|
||||
tflite::micro::GetEvalInput(context, node, kConvWeightsTensor);
|
||||
const TfLiteEvalTensor* bias =
|
||||
(NumInputs(node) == 3)
|
||||
? tflite::micro::GetEvalInput(context, node, kBiasTensor)
|
||||
? tflite::micro::GetEvalInput(context, node, kConvBiasTensor)
|
||||
: nullptr;
|
||||
TfLiteEvalTensor* output =
|
||||
tflite::micro::GetEvalOutput(context, node, kOutputTensor);
|
||||
tflite::micro::GetEvalOutput(context, node, kConvOutputTensor);
|
||||
|
||||
TFLITE_DCHECK(node->user_data != nullptr);
|
||||
const OpData& data = *(static_cast<const OpData*>(node->user_data));
|
||||
const auto& data = *(static_cast<const OpDataConv*>(node->user_data));
|
||||
|
||||
TF_LITE_ENSURE_EQ(context, input->type, output->type);
|
||||
TF_LITE_ENSURE_MSG(context, input->type == filter->type,
|
||||
"Hybrid models are not supported on TFLite Micro.");
|
||||
|
||||
switch (input->type) { // Already know in/out types are same.
|
||||
case kTfLiteFloat32:
|
||||
EvalFloat(context, node, params, data, input, filter, bias, nullptr,
|
||||
nullptr, output);
|
||||
case kTfLiteFloat32: {
|
||||
tflite::reference_ops::Conv(
|
||||
ConvParamsFloat(params, data), tflite::micro::GetTensorShape(input),
|
||||
tflite::micro::GetTensorData<float>(input),
|
||||
tflite::micro::GetTensorShape(filter),
|
||||
tflite::micro::GetTensorData<float>(filter),
|
||||
tflite::micro::GetTensorShape(bias),
|
||||
tflite::micro::GetTensorData<float>(bias),
|
||||
tflite::micro::GetTensorShape(output),
|
||||
tflite::micro::GetTensorData<float>(output),
|
||||
tflite::micro::GetTensorShape(nullptr), nullptr);
|
||||
break;
|
||||
case kTfLiteInt8:
|
||||
EvalQuantizedPerChannel(context, node, params, data, input, filter, bias,
|
||||
output, nullptr);
|
||||
}
|
||||
case kTfLiteInt8: {
|
||||
reference_integer_ops::ConvPerChannel(
|
||||
ConvParamsQuantized(params, data), data.per_channel_output_multiplier,
|
||||
data.per_channel_output_shift, tflite::micro::GetTensorShape(input),
|
||||
tflite::micro::GetTensorData<int8_t>(input),
|
||||
tflite::micro::GetTensorShape(filter),
|
||||
tflite::micro::GetTensorData<int8_t>(filter),
|
||||
tflite::micro::GetTensorShape(bias),
|
||||
tflite::micro::GetTensorData<int32_t>(bias),
|
||||
tflite::micro::GetTensorShape(output),
|
||||
tflite::micro::GetTensorData<int8_t>(output));
|
||||
break;
|
||||
case kTfLiteUInt8:
|
||||
EvalQuantized(context, node, params, data, input, filter, bias, nullptr,
|
||||
nullptr, output);
|
||||
}
|
||||
case kTfLiteUInt8: {
|
||||
reference_ops::Conv(ConvParamsQuantized(params, data),
|
||||
tflite::micro::GetTensorShape(input),
|
||||
tflite::micro::GetTensorData<uint8_t>(input),
|
||||
tflite::micro::GetTensorShape(filter),
|
||||
tflite::micro::GetTensorData<uint8_t>(filter),
|
||||
tflite::micro::GetTensorShape(bias),
|
||||
tflite::micro::GetTensorData<int32_t>(bias),
|
||||
tflite::micro::GetTensorShape(output),
|
||||
tflite::micro::GetTensorData<uint8_t>(output),
|
||||
tflite::micro::GetTensorShape(nullptr), nullptr,
|
||||
nullptr);
|
||||
break;
|
||||
}
|
||||
default:
|
||||
TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.",
|
||||
TfLiteTypeGetName(input->type), input->type);
|
||||
|
||||
77
tensorflow/lite/micro/kernels/conv.h
Normal file
77
tensorflow/lite/micro/kernels/conv.h
Normal file
@ -0,0 +1,77 @@
|
||||
/* 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.
|
||||
==============================================================================*/
|
||||
|
||||
#ifndef TENSORFLOW_LITE_MICRO_KERNELS_CONV_H_
|
||||
#define TENSORFLOW_LITE_MICRO_KERNELS_CONV_H_
|
||||
|
||||
#include <cstdint>
|
||||
|
||||
#include "tensorflow/lite/c/builtin_op_data.h"
|
||||
#include "tensorflow/lite/c/common.h"
|
||||
#include "tensorflow/lite/kernels/internal/types.h"
|
||||
|
||||
namespace tflite {
|
||||
|
||||
struct OpDataConv {
|
||||
TfLitePaddingValues padding;
|
||||
|
||||
// Cached tensor zero point values for quantized operations.
|
||||
int32_t input_zero_point;
|
||||
int32_t filter_zero_point;
|
||||
int32_t output_zero_point;
|
||||
|
||||
// The scaling factor from input to output (aka the 'real multiplier') can
|
||||
// be represented as a fixed point multiplier plus a left shift.
|
||||
int32_t output_multiplier;
|
||||
int output_shift;
|
||||
|
||||
// Per channel output multiplier and shift.
|
||||
int32_t* per_channel_output_multiplier;
|
||||
int32_t* per_channel_output_shift;
|
||||
|
||||
// The range of the fused activation layer. For example for kNone and
|
||||
// uint8_t these would be 0 and 255.
|
||||
int32_t output_activation_min;
|
||||
int32_t output_activation_max;
|
||||
|
||||
// Index to buffer for optimizations if applicable.
|
||||
int buffer_idx;
|
||||
};
|
||||
|
||||
extern const int kConvInputTensor;
|
||||
extern const int kConvWeightsTensor;
|
||||
extern const int kConvBiasTensor;
|
||||
extern const int kConvOutputTensor;
|
||||
extern const int kConvQuantizedDimension;
|
||||
|
||||
// Returns a ConvParams struct with all the parameters needed for a
|
||||
// float computation.
|
||||
ConvParams ConvParamsFloat(TfLiteConvParams* params, const OpDataConv& data);
|
||||
|
||||
// Returns a ConvParams struct with all the parameters needed for a
|
||||
// quantized computation.
|
||||
ConvParams ConvParamsQuantized(TfLiteConvParams* params,
|
||||
const OpDataConv& data);
|
||||
|
||||
TfLiteStatus CalculateOpDataConv(TfLiteContext* context, TfLiteNode* node,
|
||||
const TfLiteConvParams* params, int width,
|
||||
int height, int filter_width,
|
||||
int filter_height, int out_width,
|
||||
int out_height, const TfLiteType data_type,
|
||||
OpDataConv* data);
|
||||
|
||||
} // namespace tflite
|
||||
|
||||
#endif // TENSORFLOW_LITE_MICRO_KERNELS_CONV_H_
|
||||
184
tensorflow/lite/micro/kernels/conv_common.cc
Normal file
184
tensorflow/lite/micro/kernels/conv_common.cc
Normal file
@ -0,0 +1,184 @@
|
||||
/* 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/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/conv.h"
|
||||
#include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h"
|
||||
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
|
||||
#include "tensorflow/lite/kernels/kernel_util.h"
|
||||
#include "tensorflow/lite/kernels/padding.h"
|
||||
#include "tensorflow/lite/micro/kernels/conv.h"
|
||||
#include "tensorflow/lite/micro/kernels/kernel_util.h"
|
||||
|
||||
namespace tflite {
|
||||
|
||||
const int kConvInputTensor = 0;
|
||||
const int kConvWeightsTensor = 1;
|
||||
const int kConvBiasTensor = 2;
|
||||
const int kConvOutputTensor = 0;
|
||||
|
||||
// Conv is quantized along dimension 0:
|
||||
// https://www.tensorflow.org/lite/performance/quantization_spec
|
||||
const int kConvQuantizedDimension = 0;
|
||||
|
||||
// Returns a ConvParams struct with all the parameters needed for a
|
||||
// float computation.
|
||||
ConvParams ConvParamsFloat(TfLiteConvParams* params, const OpDataConv& data) {
|
||||
ConvParams op_params;
|
||||
CalculateActivationRange(params->activation, &op_params.float_activation_min,
|
||||
&op_params.float_activation_max);
|
||||
op_params.padding_type = tflite::micro::RuntimePaddingType(params->padding);
|
||||
op_params.padding_values.width = data.padding.width;
|
||||
op_params.padding_values.height = data.padding.height;
|
||||
op_params.stride_width = params->stride_width;
|
||||
op_params.stride_height = params->stride_height;
|
||||
op_params.dilation_width_factor = params->dilation_width_factor;
|
||||
op_params.dilation_height_factor = params->dilation_height_factor;
|
||||
return op_params;
|
||||
}
|
||||
|
||||
// Returns a ConvParams struct with all the parameters needed for a
|
||||
// quantized computation.
|
||||
ConvParams ConvParamsQuantized(TfLiteConvParams* params,
|
||||
const OpDataConv& data) {
|
||||
ConvParams op_params;
|
||||
op_params.input_offset = -data.input_zero_point;
|
||||
op_params.weights_offset = -data.filter_zero_point;
|
||||
op_params.output_offset = data.output_zero_point;
|
||||
op_params.output_multiplier = data.output_multiplier;
|
||||
op_params.output_shift = -data.output_shift;
|
||||
op_params.padding_type = tflite::micro::RuntimePaddingType(params->padding);
|
||||
op_params.padding_values.height = data.padding.height;
|
||||
op_params.padding_values.width = data.padding.width;
|
||||
op_params.stride_height = params->stride_height;
|
||||
op_params.stride_width = params->stride_width;
|
||||
op_params.dilation_height_factor = params->dilation_height_factor;
|
||||
op_params.dilation_width_factor = params->dilation_width_factor;
|
||||
op_params.quantized_activation_min = data.output_activation_min;
|
||||
op_params.quantized_activation_max = data.output_activation_max;
|
||||
return op_params;
|
||||
}
|
||||
|
||||
TfLiteStatus CalculateOpDataConv(TfLiteContext* context, TfLiteNode* node,
|
||||
const TfLiteConvParams* params, int width,
|
||||
int height, int filter_width,
|
||||
int filter_height, int out_width,
|
||||
int out_height, const TfLiteType data_type,
|
||||
OpDataConv* data) {
|
||||
bool has_bias = node->inputs->size == 3;
|
||||
// Check number of inputs/outputs
|
||||
TF_LITE_ENSURE(context, has_bias || node->inputs->size == 2);
|
||||
TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
|
||||
|
||||
// Matching GetWindowedOutputSize in TensorFlow.
|
||||
auto padding = params->padding;
|
||||
data->padding = ComputePaddingHeightWidth(
|
||||
params->stride_height, params->stride_width,
|
||||
params->dilation_height_factor, params->dilation_width_factor, height,
|
||||
width, filter_height, filter_width, padding, &out_height, &out_width);
|
||||
|
||||
// Note that quantized inference requires that all tensors have their
|
||||
// parameters set. This is usually done during quantized training.
|
||||
if (data_type != kTfLiteFloat32) {
|
||||
const TfLiteTensor* input = GetInput(context, node, kConvInputTensor);
|
||||
TF_LITE_ENSURE(context, input != nullptr);
|
||||
const TfLiteTensor* filter = GetInput(context, node, kConvWeightsTensor);
|
||||
TF_LITE_ENSURE(context, filter != nullptr);
|
||||
const TfLiteTensor* bias =
|
||||
GetOptionalInputTensor(context, node, kConvBiasTensor);
|
||||
TfLiteTensor* output = GetOutput(context, node, kConvOutputTensor);
|
||||
TF_LITE_ENSURE(context, output != nullptr);
|
||||
int output_channels = filter->dims->data[kConvQuantizedDimension];
|
||||
|
||||
TF_LITE_ENSURE_STATUS(tflite::PopulateConvolutionQuantizationParams(
|
||||
context, input, filter, bias, output, params->activation,
|
||||
&data->output_multiplier, &data->output_shift,
|
||||
&data->output_activation_min, &data->output_activation_max,
|
||||
data->per_channel_output_multiplier,
|
||||
reinterpret_cast<int*>(data->per_channel_output_shift),
|
||||
output_channels));
|
||||
}
|
||||
return kTfLiteOk;
|
||||
}
|
||||
|
||||
void* InitConv(TfLiteContext* context, const char* buffer, size_t length) {
|
||||
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
|
||||
return context->AllocatePersistentBuffer(context, sizeof(OpDataConv));
|
||||
}
|
||||
|
||||
TfLiteStatus PrepareConv(TfLiteContext* context, TfLiteNode* node) {
|
||||
TFLITE_DCHECK(node->user_data != nullptr);
|
||||
TFLITE_DCHECK(node->builtin_data != nullptr);
|
||||
|
||||
OpDataConv* data = static_cast<OpDataConv*>(node->user_data);
|
||||
const auto params = static_cast<const TfLiteConvParams*>(node->builtin_data);
|
||||
|
||||
TfLiteTensor* output = GetOutput(context, node, kConvOutputTensor);
|
||||
TF_LITE_ENSURE(context, output != nullptr);
|
||||
const TfLiteTensor* input = GetInput(context, node, kConvInputTensor);
|
||||
TF_LITE_ENSURE(context, input != nullptr);
|
||||
const TfLiteTensor* filter = GetInput(context, node, kConvWeightsTensor);
|
||||
TF_LITE_ENSURE(context, filter != nullptr);
|
||||
|
||||
int input_width = input->dims->data[2];
|
||||
int input_height = input->dims->data[1];
|
||||
int filter_width = filter->dims->data[2];
|
||||
int filter_height = filter->dims->data[1];
|
||||
int output_width = output->dims->data[2];
|
||||
int output_height = output->dims->data[1];
|
||||
|
||||
// Dynamically allocate per-channel quantization parameters.
|
||||
const int num_channels = filter->dims->data[kConvQuantizedDimension];
|
||||
data->per_channel_output_multiplier =
|
||||
static_cast<int32_t*>(context->AllocatePersistentBuffer(
|
||||
context, num_channels * sizeof(int32_t)));
|
||||
data->per_channel_output_shift =
|
||||
static_cast<int32_t*>(context->AllocatePersistentBuffer(
|
||||
context, num_channels * sizeof(int32_t)));
|
||||
|
||||
// All per-channel quantized tensors need valid zero point and scale arrays.
|
||||
if (input->type == kTfLiteInt8) {
|
||||
TF_LITE_ENSURE_EQ(context, filter->quantization.type,
|
||||
kTfLiteAffineQuantization);
|
||||
|
||||
const auto* affine_quantization =
|
||||
static_cast<TfLiteAffineQuantization*>(filter->quantization.params);
|
||||
TF_LITE_ENSURE(context, affine_quantization);
|
||||
TF_LITE_ENSURE(context, affine_quantization->scale);
|
||||
TF_LITE_ENSURE(context, affine_quantization->zero_point);
|
||||
|
||||
TF_LITE_ENSURE(context,
|
||||
affine_quantization->scale->size == 1 ||
|
||||
affine_quantization->scale->size ==
|
||||
filter->dims->data[kConvQuantizedDimension]);
|
||||
TF_LITE_ENSURE_EQ(context, affine_quantization->scale->size,
|
||||
affine_quantization->zero_point->size);
|
||||
}
|
||||
|
||||
TF_LITE_ENSURE_STATUS(CalculateOpDataConv(
|
||||
context, node, params, input_width, input_height, filter_width,
|
||||
filter_height, output_width, output_height, input->type, data));
|
||||
|
||||
data->input_zero_point = input->params.zero_point;
|
||||
data->filter_zero_point = filter->params.zero_point;
|
||||
data->output_zero_point = output->params.zero_point;
|
||||
|
||||
return kTfLiteOk;
|
||||
}
|
||||
|
||||
} // namespace tflite
|
||||
@ -18,6 +18,7 @@ limitations under the License.
|
||||
|
||||
#include <cstdint>
|
||||
|
||||
#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/types.h"
|
||||
@ -69,6 +70,18 @@ const RuntimeShape GetTensorShape(const TfLiteEvalTensor* tensor);
|
||||
bool HaveSameShapes(const TfLiteEvalTensor* input1,
|
||||
const TfLiteEvalTensor* input2);
|
||||
|
||||
inline PaddingType RuntimePaddingType(TfLitePadding padding) {
|
||||
switch (padding) {
|
||||
case TfLitePadding::kTfLitePaddingSame:
|
||||
return PaddingType::kSame;
|
||||
case TfLitePadding::kTfLitePaddingValid:
|
||||
return PaddingType::kValid;
|
||||
case TfLitePadding::kTfLitePaddingUnknown:
|
||||
default:
|
||||
return PaddingType::kNone;
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace micro
|
||||
} // namespace tflite
|
||||
|
||||
|
||||
@ -13,12 +13,13 @@ See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
==============================================================================*/
|
||||
|
||||
#include "tensorflow/lite/kernels/internal/reference/conv.h"
|
||||
#include "tensorflow/lite/micro/kernels/conv.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/conv.h"
|
||||
#include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h"
|
||||
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
|
||||
#include "tensorflow/lite/kernels/kernel_util.h"
|
||||
@ -30,36 +31,6 @@ limitations under the License.
|
||||
namespace tflite {
|
||||
namespace {
|
||||
|
||||
constexpr int kInputTensor = 0;
|
||||
constexpr int kFilterTensor = 1;
|
||||
constexpr int kBiasTensor = 2;
|
||||
constexpr int kOutputTensor = 0;
|
||||
|
||||
// Conv is quantized along dimension 0:
|
||||
// https://www.tensorflow.org/lite/performance/quantization_spec
|
||||
constexpr int kConvQuantizedDimension = 0;
|
||||
|
||||
struct OpData {
|
||||
TfLitePaddingValues padding;
|
||||
// The scaling factor from input to output (aka the 'real multiplier') can
|
||||
// be represented as a fixed point multiplier plus a left shift.
|
||||
int32_t output_multiplier;
|
||||
int output_shift;
|
||||
|
||||
// Cached tensor zero point values for quantized operations.
|
||||
int32_t input_zero_point;
|
||||
int32_t output_zero_point;
|
||||
|
||||
// Per channel output multiplier and shift.
|
||||
int32_t* per_channel_output_multiplier;
|
||||
int32_t* per_channel_output_shift;
|
||||
|
||||
// The range of the fused activation layer. For example for kNone and
|
||||
// uint8_t these would be 0 and 255.
|
||||
int32_t output_activation_min;
|
||||
int32_t output_activation_max;
|
||||
};
|
||||
|
||||
#if defined(HIFIMINI)
|
||||
void ConvPerChannel(const ConvParams& params, const int32_t* output_multiplier,
|
||||
const int32_t* output_shift,
|
||||
@ -263,47 +234,9 @@ inline void Conv1x32Input32x32Filter(
|
||||
}
|
||||
#endif
|
||||
|
||||
TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node,
|
||||
TfLiteConvParams* params, int width, int height,
|
||||
int filter_width, int filter_height, int out_width,
|
||||
int out_height, const TfLiteType data_type,
|
||||
OpData* data) {
|
||||
bool has_bias = node->inputs->size == 3;
|
||||
// Check number of inputs/outputs
|
||||
TF_LITE_ENSURE(context, has_bias || node->inputs->size == 2);
|
||||
TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
|
||||
|
||||
// Matching GetWindowedOutputSize in TensorFlow.
|
||||
auto padding = params->padding;
|
||||
data->padding = ComputePaddingHeightWidth(
|
||||
params->stride_height, params->stride_width,
|
||||
params->dilation_height_factor, params->dilation_width_factor, height,
|
||||
width, filter_height, filter_width, padding, &out_height, &out_width);
|
||||
|
||||
// Note that quantized inference requires that all tensors have their
|
||||
// parameters set. This is usually done during quantized training.
|
||||
if (data_type != kTfLiteFloat32) {
|
||||
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
|
||||
const TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
|
||||
const TfLiteTensor* bias =
|
||||
GetOptionalInputTensor(context, node, kBiasTensor);
|
||||
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
|
||||
int output_channels = filter->dims->data[kConvQuantizedDimension];
|
||||
|
||||
return tflite::PopulateConvolutionQuantizationParams(
|
||||
context, input, filter, bias, output, params->activation,
|
||||
&data->output_multiplier, &data->output_shift,
|
||||
&data->output_activation_min, &data->output_activation_max,
|
||||
data->per_channel_output_multiplier,
|
||||
reinterpret_cast<int*>(data->per_channel_output_shift),
|
||||
output_channels);
|
||||
}
|
||||
return kTfLiteOk;
|
||||
}
|
||||
|
||||
void* Init(TfLiteContext* context, const char* buffer, size_t length) {
|
||||
TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
|
||||
return context->AllocatePersistentBuffer(context, sizeof(OpData));
|
||||
return context->AllocatePersistentBuffer(context, sizeof(OpDataConv));
|
||||
}
|
||||
|
||||
TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
|
||||
@ -311,11 +244,11 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
|
||||
TFLITE_DCHECK(node->builtin_data != nullptr);
|
||||
auto* params = reinterpret_cast<TfLiteConvParams*>(node->builtin_data);
|
||||
|
||||
TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
|
||||
const TfLiteTensor* input = GetInput(context, node, kInputTensor);
|
||||
const TfLiteTensor* filter = GetInput(context, node, kFilterTensor);
|
||||
TfLiteTensor* output = GetOutput(context, node, kConvOutputTensor);
|
||||
const TfLiteTensor* input = GetInput(context, node, kConvInputTensor);
|
||||
const TfLiteTensor* filter = GetInput(context, node, kConvWeightsTensor);
|
||||
|
||||
auto* op_data = reinterpret_cast<OpData*>(node->user_data);
|
||||
auto* op_data = reinterpret_cast<OpDataConv*>(node->user_data);
|
||||
|
||||
int input_width = input->dims->data[2];
|
||||
int input_height = input->dims->data[1];
|
||||
@ -356,71 +289,26 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
|
||||
affine_quantization->zero_point->size);
|
||||
}
|
||||
|
||||
return CalculateOpData(context, node, params, input_width, input_height,
|
||||
filter_width, filter_height, output_width,
|
||||
output_height, input->type, op_data);
|
||||
}
|
||||
|
||||
void EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node,
|
||||
TfLiteConvParams* params, OpData* data,
|
||||
const TfLiteEvalTensor* input,
|
||||
const TfLiteEvalTensor* filter,
|
||||
const TfLiteEvalTensor* bias,
|
||||
TfLiteEvalTensor* output,
|
||||
TfLiteEvalTensor* im2col) {
|
||||
// TODO(b/154032858): Investigate removing extra copies.
|
||||
ConvParams op_params;
|
||||
op_params.input_offset = -data->input_zero_point;
|
||||
op_params.output_offset = data->output_zero_point;
|
||||
op_params.stride_height = params->stride_height;
|
||||
op_params.stride_width = params->stride_width;
|
||||
op_params.dilation_height_factor = params->dilation_height_factor;
|
||||
op_params.dilation_width_factor = params->dilation_width_factor;
|
||||
op_params.padding_values.height = data->padding.height;
|
||||
op_params.padding_values.width = data->padding.width;
|
||||
op_params.quantized_activation_min = data->output_activation_min;
|
||||
op_params.quantized_activation_max = data->output_activation_max;
|
||||
|
||||
#if defined(HIFIMINI)
|
||||
ConvPerChannel(op_params, data->per_channel_output_multiplier,
|
||||
data->per_channel_output_shift,
|
||||
tflite::micro::GetTensorShape(input),
|
||||
tflite::micro::GetTensorData<int8_t>(input),
|
||||
tflite::micro::GetTensorShape(filter),
|
||||
tflite::micro::GetTensorData<int8_t>(filter),
|
||||
tflite::micro::GetTensorShape(bias),
|
||||
tflite::micro::GetTensorData<int32_t>(bias),
|
||||
tflite::micro::GetTensorShape(output),
|
||||
tflite::micro::GetTensorData<int8_t>(output));
|
||||
#else
|
||||
reference_integer_ops::ConvPerChannel(
|
||||
op_params, data->per_channel_output_multiplier,
|
||||
data->per_channel_output_shift, tflite::micro::GetTensorShape(input),
|
||||
tflite::micro::GetTensorData<int8_t>(input),
|
||||
tflite::micro::GetTensorShape(filter),
|
||||
tflite::micro::GetTensorData<int8_t>(filter),
|
||||
tflite::micro::GetTensorShape(bias),
|
||||
tflite::micro::GetTensorData<int32_t>(bias),
|
||||
tflite::micro::GetTensorShape(output),
|
||||
tflite::micro::GetTensorData<int8_t>(output));
|
||||
#endif
|
||||
return CalculateOpDataConv(context, node, params, input_width, input_height,
|
||||
filter_width, filter_height, output_width,
|
||||
output_height, input->type, op_data);
|
||||
}
|
||||
|
||||
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
|
||||
TFLITE_DCHECK(node->user_data != nullptr);
|
||||
TFLITE_DCHECK(node->builtin_data != nullptr);
|
||||
auto* params = reinterpret_cast<TfLiteConvParams*>(node->builtin_data);
|
||||
auto* op_data = reinterpret_cast<OpData*>(node->user_data);
|
||||
auto* op_data = reinterpret_cast<OpDataConv*>(node->user_data);
|
||||
|
||||
TfLiteEvalTensor* output =
|
||||
tflite::micro::GetEvalOutput(context, node, kOutputTensor);
|
||||
tflite::micro::GetEvalOutput(context, node, kConvOutputTensor);
|
||||
const TfLiteEvalTensor* input =
|
||||
tflite::micro::GetEvalInput(context, node, kInputTensor);
|
||||
tflite::micro::GetEvalInput(context, node, kConvInputTensor);
|
||||
const TfLiteEvalTensor* filter =
|
||||
tflite::micro::GetEvalInput(context, node, kFilterTensor);
|
||||
tflite::micro::GetEvalInput(context, node, kConvWeightsTensor);
|
||||
const TfLiteEvalTensor* bias =
|
||||
(NumInputs(node) == 3)
|
||||
? tflite::micro::GetEvalInput(context, node, kBiasTensor)
|
||||
? tflite::micro::GetEvalInput(context, node, kConvBiasTensor)
|
||||
: nullptr;
|
||||
|
||||
#if defined(HIFIMINI)
|
||||
@ -446,10 +334,34 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
|
||||
#endif
|
||||
|
||||
switch (input->type) {
|
||||
case kTfLiteInt8:
|
||||
EvalQuantizedPerChannel(context, node, params, op_data, input, filter,
|
||||
bias, output, nullptr);
|
||||
case kTfLiteInt8: {
|
||||
#if defined(HIFIMINI)
|
||||
ConvPerChannel(ConvParamsQuantized(params, *op_data),
|
||||
op_data->per_channel_output_multiplier,
|
||||
op_data->per_channel_output_shift,
|
||||
tflite::micro::GetTensorShape(input),
|
||||
tflite::micro::GetTensorData<int8_t>(input),
|
||||
tflite::micro::GetTensorShape(filter),
|
||||
tflite::micro::GetTensorData<int8_t>(filter),
|
||||
tflite::micro::GetTensorShape(bias),
|
||||
tflite::micro::GetTensorData<int32_t>(bias),
|
||||
tflite::micro::GetTensorShape(output),
|
||||
tflite::micro::GetTensorData<int8_t>(output));
|
||||
#else
|
||||
reference_integer_ops::ConvPerChannel(
|
||||
ConvParamsQuantized(params, op_data),
|
||||
data->per_channel_output_multiplier, data->per_channel_output_shift,
|
||||
tflite::micro::GetTensorShape(input),
|
||||
tflite::micro::GetTensorData<int8_t>(input),
|
||||
tflite::micro::GetTensorShape(filter),
|
||||
tflite::micro::GetTensorData<int8_t>(filter),
|
||||
tflite::micro::GetTensorShape(bias),
|
||||
tflite::micro::GetTensorData<int32_t>(bias),
|
||||
tflite::micro::GetTensorShape(output),
|
||||
tflite::micro::GetTensorData<int8_t>(output));
|
||||
#endif
|
||||
break;
|
||||
}
|
||||
default:
|
||||
TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.",
|
||||
TfLiteTypeGetName(input->type), input->type);
|
||||
|
||||
@ -316,6 +316,7 @@ tensorflow/lite/micro/kernels/circular_buffer.cc \
|
||||
tensorflow/lite/micro/kernels/comparisons.cc \
|
||||
tensorflow/lite/micro/kernels/concatenation.cc \
|
||||
tensorflow/lite/micro/kernels/conv.cc \
|
||||
tensorflow/lite/micro/kernels/conv_common.cc \
|
||||
tensorflow/lite/micro/kernels/conv_test_common.cc \
|
||||
tensorflow/lite/micro/kernels/depthwise_conv.cc \
|
||||
tensorflow/lite/micro/kernels/dequantize.cc \
|
||||
|
||||
Loading…
Reference in New Issue
Block a user