未验证 提交 aee4f8ab 编写于 作者: Z zhoutianzi666 提交者: GitHub

[Paddle-TRT] matmul_v2 support (#44918)

* Support matmul_v2 in PaddleTensorRT
上级 da10fb3b
......@@ -2107,6 +2107,7 @@ USE_TRT_CONVERTER(transpose2);
USE_TRT_CONVERTER(flatten);
USE_TRT_CONVERTER(flatten_contiguous_range);
USE_TRT_CONVERTER(matmul);
USE_TRT_CONVERTER(matmul_v2);
USE_TRT_CONVERTER(conv2d);
USE_TRT_CONVERTER(relu);
USE_TRT_CONVERTER(exp);
......
......@@ -3,6 +3,7 @@ list(
APPEND
CONVERT_FILES
matmul_op.cc
matmul_v2_op.cc
conv2d_op.cc
fc_op.cc
pool2d_op.cc
......
/* Copyright (c) 2021 PaddlePaddle 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 "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/plugin/matmul_op_int8_plugin.h"
namespace paddle {
namespace framework {
class Scope;
namespace proto {
class OpDesc;
} // namespace proto
} // namespace framework
} // namespace paddle
namespace paddle {
namespace inference {
namespace tensorrt {
/*
* MatMulV2Op, IMatrixMultiplyLayer in TRT. This Layer doesn't has weights.
*/
class MatMulV2OpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope,
bool test_mode) override {
VLOG(3) << "convert a fluid matmul_v2 op to tensorrt matmul layer ";
framework::OpDesc op_desc(op, nullptr);
nvinfer1::ILayer* layer = nullptr;
// Declare inputs
auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
auto* input2 = engine_->GetITensor(op_desc.Input("Y")[0]);
nvinfer1::Dims dims_x = input1->getDimensions();
nvinfer1::Dims dims_y = input2->getDimensions();
bool transpose_X = PADDLE_GET_CONST(bool, op_desc.GetAttr("trans_x"));
bool transpose_Y = PADDLE_GET_CONST(bool, op_desc.GetAttr("trans_y"));
auto output_name = op_desc.Output("Out")[0];
nvinfer1::MatrixOperation matrix_operation_X =
transpose_X ? nvinfer1::MatrixOperation::kTRANSPOSE
: nvinfer1::MatrixOperation::kNONE;
nvinfer1::MatrixOperation matrix_operation_Y =
transpose_Y ? nvinfer1::MatrixOperation::kTRANSPOSE
: nvinfer1::MatrixOperation::kNONE;
int one_num = 0;
nvinfer1::ITensor* new_shape_tensor = nullptr;
if (dims_x.nbDims < dims_y.nbDims) {
one_num = dims_y.nbDims - dims_x.nbDims;
new_shape_tensor = Shape(input1);
std::vector<int32_t> one_vec(one_num, 1);
auto* one_tensor = Add1DConstantLayer(one_vec);
new_shape_tensor =
Concat(std::vector<nvinfer1::ITensor*>{one_tensor, new_shape_tensor});
auto* reshape_layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input1);
reshape_layer->setInput(1, *new_shape_tensor);
layer = TRT_ENGINE_ADD_LAYER(engine_,
MatrixMultiply,
*reshape_layer->getOutput(0),
matrix_operation_X,
*input2,
matrix_operation_Y);
} else if (dims_x.nbDims > dims_y.nbDims) {
one_num = dims_x.nbDims - dims_y.nbDims;
new_shape_tensor = Shape(input2);
std::vector<int32_t> one_vec(one_num, 1);
auto* one_tensor = Add1DConstantLayer(one_vec);
new_shape_tensor =
Concat(std::vector<nvinfer1::ITensor*>{one_tensor, new_shape_tensor});
auto* reshape_layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input2);
reshape_layer->setInput(1, *new_shape_tensor);
layer = TRT_ENGINE_ADD_LAYER(engine_,
MatrixMultiply,
*input1,
matrix_operation_X,
*reshape_layer->getOutput(0),
matrix_operation_Y);
} else {
layer = TRT_ENGINE_ADD_LAYER(engine_,
MatrixMultiply,
*input1,
matrix_operation_X,
*input2,
matrix_operation_Y);
}
VLOG(3) << "Convert a fluid matmul_v2_op_float to TensorRT ";
RreplenishLayerAndOutput(layer, "matmul_v2_op", {output_name}, test_mode);
}
};
} // namespace tensorrt
} // namespace inference
} // namespace paddle
REGISTER_TRT_OP_CONVERTER(matmul_v2, MatMulV2OpConverter);
......@@ -326,6 +326,20 @@ struct SimpleOpTypeSetTeller : public Teller {
}
}
if (op_type == "matmul_v2") {
if (!with_dynamic_shape) {
return false;
}
auto* block = desc.Block();
if (block == nullptr) {
VLOG(3) << "The block desc is nullptr, we can't continue to analyze. "
"Developers need to check whether block_desc is passed in "
"the pass.";
return false;
}
return true;
}
if (op_type == "matmul") {
auto* block = desc.Block();
if (block == nullptr) {
......@@ -2081,6 +2095,7 @@ struct SimpleOpTypeSetTeller : public Teller {
std::unordered_set<std::string> int8_teller_set{
"mul",
"matmul",
"matmul_v2",
"conv2d",
"conv2d_fusion",
"pool2d",
......@@ -2190,6 +2205,7 @@ struct SimpleOpTypeSetTeller : public Teller {
std::unordered_set<std::string> teller_set{
"mul",
"matmul",
"matmul_v2",
"conv2d",
"conv2d_fusion",
"pool2d",
......
# Copyright (c) 2021 PaddlePaddle 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.
from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons
from program_config import TensorConfig, ProgramConfig
import numpy as np
import paddle.inference as paddle_infer
from functools import partial
from typing import Optional, List, Callable, Dict, Any, Set
import unittest
import os
class TrtConvertMatmulTest_dynamic(TrtLayerAutoScanTest):
def sample_program_configs(self):
def generate_input(shape):
return np.random.random(shape).astype(np.float32)
for batch in [10, 11, 12, 13, 14, 15]:
for trans_x in [False]:
for trans_y in [False]:
input1_shape = [batch, 64, 350, 75]
input2_shape = [75, 25]
dics = [{
"trans_x": trans_x,
"trans_y": trans_y,
}]
ops_config = [{
"op_type": "matmul_v2",
"op_inputs": {
"X": ["input1_data"],
"Y": ["input2_data"]
},
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input1_data":
TensorConfig(
data_gen=partial(generate_input, input1_shape)),
"input2_data":
TensorConfig(
data_gen=partial(generate_input, input2_shape))
},
outputs=["output_data"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
self.dynamic_shape.min_input_shape = {
"input1_data": [10, 64, 350, 75],
"input2_data": [75, 25]
}
self.dynamic_shape.max_input_shape = {
"input1_data": [100, 64, 350, 75],
"input2_data": [75, 25]
}
self.dynamic_shape.opt_input_shape = {
"input1_data": [15, 64, 350, 75],
"input2_data": [75, 25]
}
attrs = [
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
# The output has little diff between gpu and trt in CI-Windows-Inference
tol_fp32 = 1e-5
tol_half = 1e-5
if (os.name == 'nt'):
tol_fp32 = 1e-3
tol_half = 1e-3
# for dynamic_shape
generate_dynamic_shape(attrs)
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), (1, 3), tol_fp32
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), (1, 3), tol_half
def add_skip_trt_case(self):
pass
def test(self):
self.add_skip_trt_case()
self.run_test()
class TrtConvertMatmulTest_dynamic2(TrtLayerAutoScanTest):
def sample_program_configs(self):
def generate_input(shape):
return np.random.random(shape).astype(np.float32)
for batch in [10, 11, 12, 13, 14, 15]:
for trans_x in [False]:
for trans_y in [False]:
input1_shape = [60, 40]
input2_shape = [batch, 40, 90]
dics = [{
"trans_x": trans_x,
"trans_y": trans_y,
}]
ops_config = [{
"op_type": "matmul_v2",
"op_inputs": {
"X": ["input1_data"],
"Y": ["input2_data"]
},
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input1_data":
TensorConfig(
data_gen=partial(generate_input, input1_shape)),
"input2_data":
TensorConfig(
data_gen=partial(generate_input, input2_shape))
},
outputs=["output_data"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
self.dynamic_shape.min_input_shape = {
"input1_data": [60, 40],
"input2_data": [10, 40, 90]
}
self.dynamic_shape.max_input_shape = {
"input1_data": [60, 40],
"input2_data": [20, 40, 90]
}
self.dynamic_shape.opt_input_shape = {
"input1_data": [60, 40],
"input2_data": [15, 40, 90]
}
attrs = [
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
# The output has little diff between gpu and trt in CI-Windows-Inference
tol_fp32 = 1e-5
tol_half = 1e-5
if (os.name == 'nt'):
tol_fp32 = 1e-3
tol_half = 1e-3
# for dynamic_shape
generate_dynamic_shape(attrs)
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), (1, 3), tol_fp32
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), (1, 3), tol_half
def add_skip_trt_case(self):
pass
def test(self):
self.add_skip_trt_case()
self.run_test()
if __name__ == "__main__":
unittest.main()
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