未验证 提交 749945b3 编写于 作者: B baoachun 提交者: GitHub

add prelu trt converter test case (#35512)

上级 922e23bf
...@@ -34,11 +34,7 @@ class PReluOpConverter : public OpConverter { ...@@ -34,11 +34,7 @@ class PReluOpConverter : public OpConverter {
auto* input = engine_->GetITensor(op_desc.Input("X")[0]); auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
// Get attrs // Get attrs
std::string mode = BOOST_GET_CONST(std::string, op_desc.GetAttr("mode")); std::string mode = BOOST_GET_CONST(std::string, op_desc.GetAttr("mode"));
//
auto* alpha_var = scope.FindVar(op_desc.Input("Alpha")[0]); auto* alpha_var = scope.FindVar(op_desc.Input("Alpha")[0]);
PADDLE_ENFORCE_NOT_NULL(
alpha_var, platform::errors::NotFound(
"Variable Alpha of prelu TRT converter is not found."));
auto* alpha_tensor = alpha_var->GetMutable<framework::LoDTensor>(); auto* alpha_tensor = alpha_var->GetMutable<framework::LoDTensor>();
platform::CPUPlace cpu_place; platform::CPUPlace cpu_place;
...@@ -50,15 +46,9 @@ class PReluOpConverter : public OpConverter { ...@@ -50,15 +46,9 @@ class PReluOpConverter : public OpConverter {
nvinfer1::ILayer* layer = nullptr; nvinfer1::ILayer* layer = nullptr;
if (engine_->with_dynamic_shape()) { if (engine_->with_dynamic_shape()) {
#if IS_TRT_VERSION_GE(6000)
plugin::PReluPluginDynamic* plugin = new plugin::PReluPluginDynamic( plugin::PReluPluginDynamic* plugin = new plugin::PReluPluginDynamic(
alpha_data, alpha_tensor_temp->numel(), mode); alpha_data, alpha_tensor_temp->numel(), mode);
layer = engine_->AddDynamicPlugin(&input, input_num, plugin); layer = engine_->AddDynamicPlugin(&input, input_num, plugin);
#else
PADDLE_THROW(platform::errors::Fatal(
"You are running the TRT Dynamic Shape mode, need to confirm that "
"your TRT version is no less than 6.0"));
#endif
} else { } else {
#if IS_TRT_VERSION_GE(7000) #if IS_TRT_VERSION_GE(7000)
float* alpha_weight_data = engine_->GetWeightCPUData( float* alpha_weight_data = engine_->GetWeightCPUData(
......
...@@ -661,6 +661,28 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, ...@@ -661,6 +661,28 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
<< desc.Output("Out").size() << "."; << desc.Output("Out").size() << ".";
return false; return false;
} }
auto* block = desc.Block();
auto* var_desc = block->FindVar(desc.Input("Alpha")[0]);
if (!var_desc) {
VLOG(3) << "Variable Alpha of prelu TRT converter not found.";
return false;
}
auto x_var_name = desc.Input("X")[0];
auto* x_var_desc = block->FindVar(x_var_name);
const auto x_shape = x_var_desc->GetShape();
if (x_shape.size() == 1) {
VLOG(3) << "prelu op does not support input's dim is 1 in tensorrt.";
return false;
}
if (!with_dynamic_shape) {
if (x_shape.size() == 2) {
VLOG(3) << "prelu op does not support input's dim is 2 in tensorrt.";
return false;
}
}
} }
if (op_type == "roi_align") { if (op_type == "roi_align") {
......
...@@ -31,7 +31,7 @@ if(WITH_GPU AND TENSORRT_FOUND) ...@@ -31,7 +31,7 @@ if(WITH_GPU AND TENSORRT_FOUND)
foreach(target ${TEST_TRT_CONVERTER}) foreach(target ${TEST_TRT_CONVERTER})
py_test_modules(${target} MODULES ${target}) py_test_modules(${target} MODULES ${target})
set_tests_properties(${target} PROPERTIES TIMEOUT 100) set_tests_properties(${target} PROPERTIES TIMEOUT 300)
endforeach() endforeach()
endif() endif()
......
# 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 TrtConvertPreluTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True
def sample_program_configs(self):
def generate_input(batch, dim1, dim2, dim3):
shape = [batch]
if dim1 != 0:
shape.append(dim1)
if dim2 != 0:
shape.append(dim2)
if dim3 != 0:
shape.append(dim3)
return np.random.random(shape).astype(np.float32)
def generate_alpha(attrs: List[Dict[str, Any]], dim1, dim2, dim3):
if attrs[0]["mode"] == "all":
return np.random.random(size=(1)).astype(np.float32)
elif attrs[0]["mode"] == "channel":
shape = [1]
if dim1 != 0:
shape.append(dim1)
if dim2 != 0:
shape.append(1)
if dim3 != 0:
shape.append(1)
return np.random.random(size=shape).astype(np.float32)
elif attrs[0]["mode"] == "element":
shape = [1]
if dim1 != 0:
shape.append(dim1)
if dim2 != 0:
shape.append(dim2)
if dim3 != 0:
shape.append(dim3)
return np.random.random(size=shape).astype(np.float32)
for batch in [1, 4]:
for dim1 in [0, 3]:
for dim2 in [0, 16]:
for dim3 in [0, 32]:
self.dim1 = dim1
self.dim2 = dim2
self.dim3 = dim3
if dim1 == 0 and dim2 != 0:
continue
if dim1 == 0 and dim2 == 0 and dim3 != 0:
continue
for mode in ["all", "channel", "element"]:
if mode == "channel" and dim1 == 0:
continue
dics = [{"mode": mode}]
ops_config = [{
"op_type": "prelu",
"op_inputs": {
"X": ["input_data"],
"Alpha": ["alpha_weight"]
},
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"alpha_weight": TensorConfig(
data_gen=partial(generate_alpha, dics,
dim1, dim2, dim3))
},
inputs={
"input_data": TensorConfig(
data_gen=partial(generate_input, batch,
dim1, dim2, dim3)),
},
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 self.dim1 == 0:
self.dynamic_shape.min_input_shape = {"input_data": [1], }
self.dynamic_shape.max_input_shape = {"input_data": [4], }
self.dynamic_shape.opt_input_shape = {"input_data": [2], }
else:
if self.dim2 == 0 and self.dim3 == 0:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 1],
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 64],
}
self.dynamic_shape.opt_input_shape = {
"input_data": [2, 3],
}
elif self.dim2 != 0 and self.dim3 != 0:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 1, 1, 1],
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 64, 128, 128],
}
self.dynamic_shape.opt_input_shape = {
"input_data": [2, 3, 16, 32],
}
elif self.dim3 == 0:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 1, 1],
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 64, 256],
}
self.dynamic_shape.opt_input_shape = {
"input_data": [2, 3, 128],
}
def clear_dynamic_shape():
self.dynamic_shape.max_input_shape = {}
self.dynamic_shape.min_input_shape = {}
self.dynamic_shape.opt_input_shape = {}
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(), (1, 2), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), (1, 2), 1e-5
# for dynamic_shape
generate_dynamic_shape(attrs)
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), (1, 2), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), (1, 2), 1e-5
def add_skip_trt_case(self):
def teller1(program_config, predictor_config):
if self.dim1 == 0 and self.dim2 == 0 and self.dim3 == 0:
return True
return False
self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT,
"Trt does not support 1-dimensional input.")
def teller2(program_config, predictor_config):
if (len(self.dynamic_shape.min_input_shape) == 0):
if self.dim1 != 0 and self.dim2 == 0 and self.dim3 == 0:
return True
return False
self.add_skip_case(
teller2, SkipReasons.TRT_NOT_SUPPORT,
"Need to repair the case: the output of GPU and tensorrt has diff when the input dimension is 2 in static shape mode."
)
def test(self):
self.add_skip_trt_case()
self.run_test()
if __name__ == "__main__":
unittest.main()
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