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Cover beta in all Conv paths. (#14008)

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### Motivation and Context
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This commit is contained in:
JiCheng 2022-12-22 01:02:48 +08:00 committed by GitHub
parent ccc4487553
commit 1a177a1713
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3 changed files with 81 additions and 18 deletions
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7
onnxruntime/core/mlas/lib/convolve.cpp
View file

@ -676,6 +676,7 @@ Return Value:
const size_t GroupCount = Parameters->GroupCount;
const size_t BatchGroupCount = Parameters->BatchCount * GroupCount;
const float Beta = Parameters->Beta;
size_t BatchGroupStart;
size_t BatchGroupRemaining;
@ -709,9 +710,9 @@ Return Value:
// Invoke the non-threaded GEMM directly with the input tensor.
//
MlasSgemmOperation(CblasNoTrans, Parameters->u.GemmDirect.TransB, FilterCount,
OutputSize, K, 1.0f, filter, K, input, Parameters->u.GemmDirect.ldb, 0.0f,
output, OutputSize);
MlasSgemmOperation(CblasNoTrans, Parameters->u.GemmDirect.TransB, FilterCount, OutputSize,
K, 1.0f, filter, K, input, Parameters->u.GemmDirect.ldb, Beta, output,
OutputSize);
//
// Apply the activation with optional bias.

29
onnxruntime/core/mlas/lib/scalar/SconvDepthwiseKernelScalar.cpp
View file

@ -47,6 +47,7 @@ Arguments:
--*/
{
const size_t W = Parameters->InputShape[1];
const float beta = Parameters->Beta;
if (W > 1) {
@ -79,9 +80,8 @@ Arguments:
auto out_col = Parameters->OutputShape[1];
if (pad_left == 1) {
float dotsum = w01 * row0[1] + w02 * row0[2] +
w11 * row1[1] + w12 * row1[2] +
w21 * row2[1] + w22 * row2[2];
float dotsum = w01 * row0[1] + w02 * row0[2] + w11 * row1[1] + w12 * row1[2] +
w21 * row2[1] + w22 * row2[2] + (beta == 0.f ? 0.f : *Output * beta);
*Output++ = dotsum;
out_col--;
row0 += stride_w;
@ -90,10 +90,9 @@ Arguments:
}
for (; out_col > pad_right; out_col--) {
float dotsum =
w00 * row0[0] + w01 * row0[1] + w02 * row0[2] +
w10 * row1[0] + w11 * row1[1] + w12 * row1[2] +
w20 * row2[0] + w21 * row2[1] + w22 * row2[2];
float dotsum = w00 * row0[0] + w01 * row0[1] + w02 * row0[2] + w10 * row1[0] +
w11 * row1[1] + w12 * row1[2] + w20 * row2[0] + w21 * row2[1] +
w22 * row2[2] + (beta == 0.f ? 0.f : *Output * beta);
*Output++ = dotsum;
row0 += stride_w;
row1 += stride_w;
@ -101,10 +100,8 @@ Arguments:
}
if (out_col == 1) { // pad_right == 1
float dotsum =
w00 * row0[0] + w01 * row0[1] +
w10 * row1[0] + w11 * row1[1] +
w20 * row2[0] + w21 * row2[1];
float dotsum = w00 * row0[0] + w01 * row0[1] + w10 * row1[0] + w11 * row1[1] +
w20 * row2[0] + w21 * row2[1] + (beta == 0.f ? 0.f : *Output * beta);
*Output++ = dotsum;
}
@ -129,15 +126,17 @@ Arguments:
const float w0 = Filter[pad_left ? 1 : 0];
const float w1 = Filter[pad_left ? 4 : 3];
const float w2 = Filter[pad_left ? 7 : 6];
auto init_v = (beta == 0.f ? 0.f : *Output * beta);
if (pad_top == 1) {
*Output++ = w1 * Input[0] + w2 * ((H + pad_top <= 2) ? 0.0f : Input[1]);
*Output++ = w1 * Input[0] + w2 * ((H + pad_top <= 2) ? 0.0f : Input[1]) + init_v;
out_row--;
}
for (const float* row = Input + pad_top * stride_h - pad_top; out_row > pad_bottom; --out_row) {
// All pixels are in the input col
*Output++ = w0 * row[0] + w1 * row[1] + w2 * row[2];
auto init = (beta == 0.f ? 0.f : *Output * beta);
*Output++ = w0 * row[0] + w1 * row[1] + w2 * row[2] + init;
row += stride_h;
}
@ -146,9 +145,9 @@ Arguments:
// out_row == 1 when arrive here
if (pad_bottom == 1) {
const float* row = Input + H - 2;
*Output++ = w0 * row[0] + w1 * row[1];
*Output++ = w0 * row[0] + w1 * row[1] + init_v;
} else { // pad_bottom == 2 and H == 1 and padding_top == 0
*Output++ = w0 * Input[0];
*Output++ = w0 * Input[0] + init_v;
}
}
}

63
onnxruntime/test/optimizer/conv_add_act_test.cc Normal file
View file

@ -0,0 +1,63 @@
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
#include <vector>
#include "gtest/gtest.h"
#include "graph_transform_test_builder.h"
#include "core/graph/graph.h"
namespace onnxruntime {
namespace test {
#ifndef DISABLE_CONTRIB_OPS
void TestConvPath(const std::vector<int64_t>& input_shape, const std::vector<int64_t>& weights_shape,
const std::vector<int64_t>& output_shape, int64_t group) {
auto build_test_case = [&](ModelTestBuilder& builder) {
auto* input_arg = builder.MakeInput<float>(input_shape, -31, 31);
auto* output_arg = builder.MakeOutput();
auto* bias_arg = builder.MakeInitializer<float>({weights_shape[0]}, -20.f, 20.f);
auto* add_arg = builder.MakeInput<float>(output_shape, -20.f, 20.f);
auto* weight_arg = builder.MakeInitializer<float>(weights_shape, -2.f, 2.f);
auto* conv_out_arg = builder.MakeIntermediate();
auto& conv_node = builder.AddNode("Conv", {input_arg, weight_arg, bias_arg}, {conv_out_arg});
conv_node.AddAttribute("group", group);
builder.AddNode("Add", {conv_out_arg, add_arg}, {output_arg});
};
auto check_graph = [&](InferenceSessionWrapper& session) {
auto op_to_count = CountOpsInGraph(session.GetGraph());
EXPECT_EQ(op_to_count["com.microsoft.FusedConv"], 1);
};
InlinedHashSet<std::string> disabled_optimizers = {"NchwcTransformer"};
TransformerTester(build_test_case,
check_graph,
TransformerLevel::Default,
TransformerLevel::Level3, 12, 0.0001, 0.000001,
0, {}, disabled_optimizers);
}
TEST(ConvAddActivationFusionTests, ConvExpandThenGemm) {
// hit MlasConvAlgorithmExpandThenGemm
TestConvPath({1, 16, 5, 5}, {16, 16, 3, 3}, {1, 16, 3, 3}, 1);
}
TEST(ConvAddActivationFusionTests, ConvDepthwise) {
// MlasConvAlgorithmDepthwise or MlasConvAlgorithmExpandThenGemmSegmented
TestConvPath({1, 16, 5, 5}, {16, 1, 3, 3}, {1, 16, 3, 3}, 16);
}
TEST(ConvAddActivationFusionTests, ConvGemmDirect) {
// MlasConvAlgorithmGemmDirect
TestConvPath({1, 16, 5, 5}, {16, 16, 1, 1}, {1, 16, 5, 5}, 1);
// MlasConvAlgorithmGemmDirect
TestConvPath({1, 16, 5, 5}, {16, 1, 5, 5}, {1, 16, 1, 1}, 16);
}
#endif // DISABLE_CONTRIB_OPS
} // namespace test
} // namespace onnxruntime