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fix matmul error when input's dim is 3 #36849

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Oct 29, 2021
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45 changes: 43 additions & 2 deletions paddle/fluid/inference/tensorrt/convert/matmul_op.cc
Original file line number Diff line number Diff line change
Expand Up @@ -61,6 +61,38 @@ class MatMulOpConverter : public OpConverter {
if (fabs(alpha - 1.0) < std::numeric_limits<float>::epsilon()) {
engine_->SetITensor(output_name, layer->getOutput(0));
} else {
// IScaleLayer requires the input must have at least
// three dimensions in static shape mode and at least
// four dimensions in dynamic shape mode.
auto* matmul_out = layer->getOutput(0);
nvinfer1::Dims out_shape = matmul_out->getDimensions();
const int out_dims = out_shape.nbDims;
bool need_change_dim = false;

if (engine_->with_dynamic_shape()) {
if (out_dims == 3) {
need_change_dim = true;
}
} else {
if (out_dims == 2) {
need_change_dim = true;
}
}

if (need_change_dim) {
nvinfer1::Dims reshape_dim;
reshape_dim.nbDims = out_dims + 1;
reshape_dim.d[out_dims] = 1;
for (int i = 0; i < out_dims; i++) {
reshape_dim.d[i] = out_shape.d[i];
}

auto* reshape_layer =
TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *matmul_out);
reshape_layer->setReshapeDimensions(reshape_dim);
matmul_out = reshape_layer->getOutput(0);
}

auto create_weights = [&](float data, const std::string& type) -> float* {
std::unique_ptr<framework::Tensor> tmp_tensor(new framework::Tensor());
tmp_tensor->Resize({1});
Expand All @@ -80,9 +112,18 @@ class MatMulOpConverter : public OpConverter {
TensorRTEngine::Weight nv_power{nvinfer1::DataType::kFLOAT,
static_cast<void*>(power_data), 1};
auto* scale_layer = TRT_ENGINE_ADD_LAYER(
engine_, Scale, *layer->getOutput(0), nvinfer1::ScaleMode::kUNIFORM,
engine_, Scale, *matmul_out, nvinfer1::ScaleMode::kUNIFORM,
nv_shift.get(), nv_alpha.get(), nv_power.get());
engine_->SetITensor(output_name, scale_layer->getOutput(0));
auto* scale_out = scale_layer->getOutput(0);

if (need_change_dim) {
auto* reshape_layer =
TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *scale_out);
reshape_layer->setReshapeDimensions(out_shape);
scale_out = reshape_layer->getOutput(0);
}

engine_->SetITensor(output_name, scale_out);
}
if (test_mode) { // the test framework can not determine which is the
// output, so place the declaration inside.
Expand Down
2 changes: 1 addition & 1 deletion paddle/fluid/inference/tensorrt/op_teller.cc
Original file line number Diff line number Diff line change
Expand Up @@ -1550,7 +1550,7 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
!BOOST_GET_CONST(bool, desc.GetAttr("keep_dim")))
return false;
}
if (desc.HasAttr("reduce_all")) {
if (desc.HasAttr("out_dtype")) {
int out_dtype = BOOST_GET_CONST(int32_t, desc.GetAttr("out_dtype"));
if (out_dtype != -1) {
return false;
Expand Down
Original file line number Diff line number Diff line change
@@ -0,0 +1,213 @@
# 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


class TrtConvertMatmulTest_static(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True

def sample_program_configs(self):
def generate_input(shape):
return np.random.random(shape).astype(np.float32)

for batch in [1, 4]:
for trans_x in [True, False]:
for trans_y in [True, False]:
if trans_x and trans_y:
input1_shape = [batch, 6, 11]
input2_shape = [batch, 32, 6]
if trans_x and not trans_y:
input1_shape = [batch, 6, 11]
input2_shape = [batch, 6, 32]
if not trans_x and trans_y:
input1_shape = [batch, 32, 6]
input2_shape = [batch, 11, 6]
if not trans_x and not trans_y:
input1_shape = [batch, 32, 6]
input2_shape = [batch, 6, 11]
for alpha in [0.3, 1.0]:
dics = [{
"transpose_X": trans_x,
"transpose_Y": trans_y,
"alpha": alpha,
"fused_reshape_X": [],
"fused_reshape_Y": [],
"fused_transpose_X": [],
"fused_transpose_Y": [],
"fused_reshape_Out": [],
"fused_transpose_Out": []
}]
ops_config = [{
"op_type": "matmul",
"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):
pass

def clear_dynamic_shape():
self.dynamic_shape.max_input_shape = {}
self.dynamic_shape.min_input_shape = {}
self.dynamic_shape.opt_input_shape = {}

# for static_shape
clear_dynamic_shape()
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), (1, 3), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), (1, 3), 1e-5

def test(self):
self.run_test()


class TrtConvertMatmulTest_dynamic(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True

def sample_program_configs(self):
def generate_input(shape):
return np.random.random(shape).astype(np.float32)

for trans_x in [True]:
for trans_y in [True]:
if trans_x and trans_y:
input1_shape = [4, 4, 4]
input2_shape = [4, 4, 4]
# if trans_x and not trans_y:
# input1_shape = [4, 4, 4]
# input2_shape = [4, 4, 4]
# if not trans_x and trans_y:
# input1_shape = [batch, 32, 6]
# input2_shape = [batch, 11, 6]
# if not trans_x and not trans_y:
# input1_shape = [batch, 32, 6]
# input2_shape = [batch, 6, 11]
for alpha in [0.3, 1.0]:
dics = [{
"transpose_X": trans_x,
"transpose_Y": trans_y,
"alpha": alpha,
"fused_reshape_X": [],
"fused_reshape_Y": [],
"fused_transpose_X": [],
"fused_transpose_Y": [],
"fused_reshape_Out": [],
"fused_transpose_Out": []
}]
ops_config = [{
"op_type": "matmul",
"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": [1, 4, 4],
"input2_data": [1, 4, 4]
}
self.dynamic_shape.max_input_shape = {
"input1_data": [16, 4, 4],
"input2_data": [16, 4, 128]
}
self.dynamic_shape.opt_input_shape = {
"input1_data": [8, 4, 4],
"input2_data": [8, 4, 16]
}

attrs = [
program_config.ops[i].attrs
for i in range(len(program_config.ops))
]

# for dynamic_shape
generate_dynamic_shape(attrs)
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), (1, 3), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), (1, 3), 1e-5

def add_skip_trt_case(self):
def teller1(program_config, predictor_config):
if len(
self.dynamic_shape.min_input_shape
) != 0 and self.trt_param.precision == paddle_infer.PrecisionType.Half:
return True
return False

self.add_skip_case(
teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
"Tensorrt MatrixMultiply layer will get error when dynamic shape fp16 mode."
)

def test(self):
self.add_skip_trt_case()
self.run_test()


if __name__ == "__main__":
unittest.main()