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未验证 提交 1238115e 编写于 作者: J JingZhuangzhuang 提交者： GitHub
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fix conv2d convert test (#35627) 

* support nnadapter and ascend310

* modify code

* add anchor_generator convert test

* add gelu convert test

* add conv2d convert test

* modify anchor_operator convert test

* modify conv2d test

* modify con2d convert test

* modify conv2d convert test

* modify conv2d convert test

* modify conv2d test

* fix WITH_PYTHON compile error

* modify test file

* modify test file

* modify test file

* modify test file

* modify test file

* modify test file

* modify test file

* modify test file
Co-authored-by: Nxiaoxiaohehe001 <hiteezsf@163.com>
Co-authored-by: Njiweibo <jiweibo@baidu.com>
上级 be4d0026
隐藏空白更改
内联 并排
Showing with 342 addition and 108 deletion
paddle/fluid/inference/tensorrt/convert/conv2d_op.cc
浏览文件 @ 1238115e
......@@ -86,7 +86,20 @@ void ConvertConv2d(TensorRTEngine* engine, const framework::proto::OpDesc& op,
nvinfer1::DimsHW nv_ksize(filter_h, filter_w);
nvinfer1::DimsHW nv_dilations(dilations[0], dilations[1]);
nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
nvinfer1::DimsHW nv_paddings;
nvinfer1::Dims nv_pre_paddings;
nvinfer1::Dims nv_post_paddings;
if (paddings.size() == 2) {
nv_paddings.d[0] = paddings[0];
nv_paddings.d[1] = paddings[1];
} else {
nv_pre_paddings.nbDims = 2;
nv_post_paddings.nbDims = 2;
nv_pre_paddings.d[0] = paddings[0];
nv_pre_paddings.d[1] = paddings[2];
nv_post_paddings.d[0] = paddings[1];
nv_post_paddings.d[1] = paddings[3];
}
TensorRTEngine::Weight weight{nvinfer1::DataType::kFLOAT,
static_cast<void*>(weight_data),
......@@ -116,7 +129,13 @@ void ConvertConv2d(TensorRTEngine* engine, const framework::proto::OpDesc& op,
layer, platform::errors::Fatal("TensorRT create conv2d/conv2d_transpose"
" layer failed."));
layer->setStride(nv_strides);
layer->setPadding(nv_paddings);
if (paddings.size() == 2) {
layer->setPadding(nv_paddings);
} else {
layer->setPrePadding(nv_pre_paddings);
layer->setPostPadding(nv_post_paddings);
}
layer->setNbGroups(groups);
if (padding_algorithm == "SAME") {
layer->setPaddingMode(nvinfer1::PaddingMode::kSAME_UPPER);
......
paddle/fluid/inference/tensorrt/op_teller.cc
浏览文件 @ 1238115e
......@@ -149,13 +149,6 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
return false;
for (auto& teller : tellers_) {
if (op_type == "depthwise_conv2d") {
std::vector<int> paddings =
BOOST_GET_CONST(std::vector<int>, desc.GetAttr("paddings"));
if (paddings.size() > 2) return false;
}
if (op_type == "relu" || op_type == "relu6" || op_type == "tanh" ||
op_type == "sigmoid") {
auto* block = desc.Block();
......@@ -208,9 +201,6 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
std::vector<int> paddings =
BOOST_GET_CONST(std::vector<int>, desc.GetAttr("paddings"));
// conv2d and conv2d_transpose need padding check
if (paddings.size() > 2 && op_type != "conv2d_fusion") return false;
if (desc.Input("Input").size() != 1) {
VLOG(3) << "TRT Conv2d expect 1 input, but got "
<< desc.Input("Input").size() << " input.";
......@@ -223,6 +213,14 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
return false;
}
if (desc.HasAttr("padding_algorithm")) {
auto padding_algorithm =
BOOST_GET_CONST(std::string, desc.GetAttr("padding_algorithm"));
if (padding_algorithm == "SAME" || padding_algorithm == "VALID") {
return false;
}
}
if (desc.HasAttr("enable_int8")) {
if (op_type == "conv2d" || op_type == "conv2d_fusion") {
if (!desc.HasAttr("Input_scale")) {
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_conv2d.py
浏览文件 @ 1238115e
......@@ -22,7 +22,6 @@ from typing import Optional, List, Callable, Dict, Any, Set
class TrtConvertConv2dTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
# TODO: This is just the example to remove the wrong attrs.
inputs = program_config.inputs
weights = program_config.weights
attrs = [
......@@ -30,113 +29,136 @@ class TrtConvertConv2dTest(TrtLayerAutoScanTest):
for i in range(len(program_config.ops))
]
# groups restriction.
if inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[
1] * attrs[0]['groups']:
return False
# others restriction, todo.
return True
def sample_program_configs(self):
def generate_input1(attrs: List[Dict[str, Any]]):
# TODO: This is just the example to illustrate the releation between axis and input.
# for each attr, can generate different datas
self.trt_param.workspace_size = 1073741824
def generate_input1(batch, attrs: List[Dict[str, Any]]):
if attrs[0]['groups'] == 1:
return np.ones([2, 3, 64, 64]).astype(np.float32)
return np.ones([batch, 3, 64, 64]).astype(np.float32)
elif attrs[0]['groups'] == 2:
return np.ones([batch, 6, 64, 64]).astype(np.float32)
else:
return np.ones([1, 3, 64, 64]).astype(np.float32)
return np.ones([batch, 9, 64, 64]).astype(np.float32)
def generate_weight1(attrs: List[Dict[str, Any]]):
return np.random.random([24, 3, 3, 3]).astype(np.float32)
# for strides in [[1, 1], [2, 2], [1, 2], [2, 3]]:
# for paddings in [[0, 3], [3, 1], [1, 1, 1, 1]]:
# for groups in [1, 2]:
# for padding_algotithm in ['EXPLICIT', 'SAME', 'VALID']:
# for dilations in [[1, 1], [1, 2]]:
# for data_format in ['NCHW']:
for strides in [[1, 1], [2, 2]]:
for paddings in [[0, 3], [3, 1]]:
for groups in [1]:
for padding_algotithm in ['EXPLICIT']:
for dilations in [[1, 1]]:
for data_format in ['NCHW']:
dics = [{
"data_fromat": data_format,
"dilations": dilations,
"padding_algorithm": padding_algotithm,
"groups": groups,
"paddings": paddings,
"strides": strides,
"data_format": data_format
}, {}]
ops_config = [{
"op_type": "conv2d",
"op_inputs": {
"Input": ["input_data"],
"Filter": ["conv2d_weight"]
},
"op_outputs": {
"Output": ["conv_output_data"]
},
"op_attrs": dics[0]
}, {
"op_type": "relu",
"op_inputs": {
"X": ["conv_output_data"]
},
"op_outputs": {
"Out": ["relu_output_data"]
},
"op_attrs": dics[1]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"conv2d_weight": TensorConfig(
data_gen=partial(generate_weight1,
dics))
},
inputs={
"input_data": TensorConfig(
data_gen=partial(generate_input1,
dics))
},
outputs=["relu_output_data"])
yield program_config
for batch in [1, 2, 4]:
for strides in [[1, 1], [2, 2], [1, 2]]:
for paddings in [[0, 3], [1, 2, 3, 4]]:
for groups in [1, 2, 3]:
for padding_algorithm in ['EXPLICIT', 'SAME', 'VALID']:
for dilations in [[1, 1], [2, 2], [1, 2]]:
for data_format in ['NCHW']:
dics = [{
"data_fromat": data_format,
"dilations": dilations,
"padding_algorithm": padding_algorithm,
"groups": groups,
"paddings": paddings,
"strides": strides,
"data_format": data_format
}, {}]
if padding_algorithm == 'EXPLICIT':
ops_config = [{
"op_type": "conv2d",
"op_inputs": {
"Input": ["input_data"],
"Filter": ["conv2d_weight"]
},
"op_outputs": {
"Output": ["conv_output_data"]
},
"op_attrs": dics[0]
}, {
"op_type": "relu",
"op_inputs": {
"X": ["conv_output_data"]
},
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": dics[1]
}]
else:
ops_config = [{
"op_type": "conv2d",
"op_inputs": {
"Input": ["input_data"],
"Filter": ["conv2d_weight"]
},
"op_outputs": {
"Output": ["output_data"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"conv2d_weight":
TensorConfig(data_gen=partial(
generate_weight1, dics))
},
inputs={
"input_data":
TensorConfig(data_gen=partial(
generate_input1, batch, dics))
},
outputs=["output_data"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
if len(attrs[0]['paddings']) == 4:
if attrs[0]['groups'] == 1:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 3, 32, 32],
'': []
"output_data": [1, 24, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 3, 64, 64],
'': []
"output_data": [4, 24, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 3, 64, 64],
'': []
"output_data": [1, 24, 64, 64]
}
elif attrs[0]['groups'] == 2:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 6, 32, 32],
"output_data": [1, 24, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 6, 64, 64],
"output_data": [4, 24, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 6, 64, 64],
"output_data": [1, 24, 64, 64]
}
else:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 3, 32, 32]
"input_data": [1, 9, 32, 32],
"output_data": [1, 24, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 3, 64, 64]
"input_data": [4, 9, 64, 64],
"output_data": [4, 24, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 3, 64, 64]
"input_data": [1, 9, 64, 64],
"output_data": [1, 24, 64, 64]
}
def clear_dynamic_shape():
......@@ -145,11 +167,7 @@ class TrtConvertConv2dTest(TrtLayerAutoScanTest):
self.dynamic_shape.opt_input_shape = {}
def generate_trt_nodes_num(attrs, dynamic_shape):
# TODO: This is just the example, need to be fixed.
if len(attrs[0]['paddings']) == 4:
return 1, 2
else:
return 1, 2
return 1, 2
attrs = [
program_config.ops[i].attrs
......@@ -169,6 +187,7 @@ class TrtConvertConv2dTest(TrtLayerAutoScanTest):
attrs, False), (1e-5, 1e-5)
# for dynamic_shape
generate_dynamic_shape(attrs)
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(attrs,
......@@ -181,29 +200,18 @@ class TrtConvertConv2dTest(TrtLayerAutoScanTest):
attrs, True), (1e-5, 1e-5)
def add_skip_trt_case(self):
# TODO(wilber): This is just the example to illustrate the skip usage.
def teller1(program_config, predictor_config):
if len(program_config.ops[0].attrs['paddings']) == 4:
if program_config.ops[0].attrs[
'padding_algorithm'] == "SAME" or program_config.ops[
0].attrs['padding_algorithm'] == "VALID":
return True
return False
self.add_skip_case(
teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
"NOT Implemented: we need to add support in the future ....TODO, just for the example"
"When padding_algorithm is 'SAME' or 'VALID', Trt dose not support. In this case, trt build error is caused by scale op."
)
def teller2(program_config, predictor_config):
if (
program_config.ops[0].attrs['dilations'][0] == 1 and
program_config.ops[0].attrs['dilations'][0] == 2
) or program_config.ops[0].attrs['padding_algorithm'] != 'EXPLICIT':
return True
return False
self.add_skip_case(teller2, SkipReasons.TRT_NOT_SUPPORT,
"TODO, just for the example")
pass
def test(self):
self.add_skip_trt_case()
self.run_test()
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_conv2d_fusion.py 0 → 100644
浏览文件 @ 1238115e
# 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
class TrtConvertConv2dFusionTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
inputs = program_config.inputs
weights = program_config.weights
attrs = [
program_config.ops[i].attrs
for i in range(len(program_config.ops))
]
if inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[
1] * attrs[0]['groups']:
return False
if attrs[0]['groups'] <= 1:
return False
return True
def sample_program_configs(self):
self.trt_param.workspace_size = 1073741824
def generate_input1(batch, attrs: List[Dict[str, Any]]):
if attrs[0]['groups'] == 2:
return np.ones([batch, 6, 64, 64]).astype(np.float32)
else:
return np.ones([batch, 9, 64, 64]).astype(np.float32)
def generate_weight1(attrs: List[Dict[str, Any]]):
return np.random.random([24, 3, 3, 3]).astype(np.float32)
def generate_weight2(attrs: List[Dict[str, Any]]):
return np.random.random([24, 1, 1]).astype(np.float32)
for batch in [1, 2, 4]:
for strides in [[1, 1], [2, 2], [1, 2]]:
for paddings in [[0, 3], [1, 2, 3, 4]]:
for groups in [2, 3]:
for padding_algorithm in ['EXPLICIT', 'SAME', 'VALID']:
for dilations in [[1, 1], [2, 2], [1, 2]]:
for data_format in ['NCHW']:
dics = [{
"data_fromat": data_format,
"dilations": dilations,
"padding_algorithm": padding_algorithm,
"groups": groups,
"paddings": paddings,
"strides": strides,
"data_format": data_format
}, {
"axis": 1
}]
ops_config = [{
"op_type": "conv2d",
"op_inputs": {
"Input": ["input_data"],
"Filter": ["conv2d_weight"]
},
"op_outputs": {
"Output": ["conv_output_data"]
},
"op_attrs": dics[0]
}, {
"op_type": "elementwise_add",
"op_inputs": {
"X": ["conv_output_data"],
"Y": ["elementwise_weight"]
},
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": dics[1]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"conv2d_weight":
TensorConfig(data_gen=partial(
generate_weight1, dics)),
"elementwise_weight": TensorConfig(
data_gen=partial(
generate_weight2, dics))
},
inputs={
"input_data":
TensorConfig(data_gen=partial(
generate_input1, batch, dics))
},
outputs=["output_data"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
if attrs[0]['groups'] == 2:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 6, 32, 32],
"output_data": [1, 24, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 6, 64, 64],
"output_data": [4, 24, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 6, 64, 64],
"output_data": [1, 24, 64, 64]
}
else:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 9, 32, 32],
"output_data": [1, 24, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 9, 64, 64],
"output_data": [4, 24, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 9, 64, 64],
"output_data": [1, 24, 64, 64]
}
def clear_dynamic_shape():
self.dynamic_shape.min_input_shape = {}
self.dynamic_shape.max_input_shape = {}
self.dynamic_shape.opt_input_shape = {}
def generate_trt_nodes_num(attrs, dynamic_shape):
return 1, 2
attrs = [
program_config.ops[i].attrs
for i in range(len(program_config.ops))
]
# for static_shape
clear_dynamic_shape()
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False), (1e-5, 1e-5)
self.trt_param.precision = paddle_infer.PrecisionType.Int8
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False), (1e-5, 1e-5)
# for dynamic_shape
generate_dynamic_shape(attrs)
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(attrs,
True), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True), (1e-5, 1e-5)
self.trt_param.precision = paddle_infer.PrecisionType.Int8
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True), (1e-5, 1e-5)
def add_skip_trt_case(self):
def teller1(program_config, predictor_config):
if program_config.ops[0].attrs[
'padding_algorithm'] == "SAME" or program_config.ops[
0].attrs['padding_algorithm'] == "VALID":
return True
return False
self.add_skip_case(
teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
"When padding_algorithm is 'SAME' or 'VALID', Trt dose not support. In this case, trt build error is caused by scale op."
)
def test(self):
self.add_skip_trt_case()
self.run_test()
def test_quant(self):
self.add_skip_trt_case()
self.run_test(quant=True)
if __name__ == "__main__":
unittest.main()
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