未验证 提交 6e90ba1b 编写于 作者: Z zhupengyang 提交者: GitHub

add exp,log trt converter (#42655)

上级 5914b18a
...@@ -1744,6 +1744,8 @@ USE_TRT_CONVERTER(flatten_contiguous_range); ...@@ -1744,6 +1744,8 @@ USE_TRT_CONVERTER(flatten_contiguous_range);
USE_TRT_CONVERTER(matmul); USE_TRT_CONVERTER(matmul);
USE_TRT_CONVERTER(conv2d); USE_TRT_CONVERTER(conv2d);
USE_TRT_CONVERTER(relu); USE_TRT_CONVERTER(relu);
USE_TRT_CONVERTER(exp);
USE_TRT_CONVERTER(log);
USE_TRT_CONVERTER(sigmoid); USE_TRT_CONVERTER(sigmoid);
USE_TRT_CONVERTER(tanh); USE_TRT_CONVERTER(tanh);
USE_TRT_CONVERTER(fc); USE_TRT_CONVERTER(fc);
......
# Add TRT tests # Add TRT tests
nv_library(tensorrt_converter nv_library(tensorrt_converter
SRCS matmul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc elementwise_op.cc SRCS matmul_op.cc
batch_norm_op.cc activation_op.cc softmax_op.cc concat_op.cc dropout_op.cc group_norm_op.cc conv2d_op.cc
pad_op.cc split_op.cc prelu_op.cc leaky_relu_op.cc gelu_op.cc layer_norm_op.cc multihead_matmul_op.cc fc_op.cc
shuffle_channel_op.cc swish_op.cc instance_norm_op.cc stack_op.cc transpose_op.cc flatten_op.cc flatten_contiguous_range_op.cc pool2d_op.cc
emb_eltwise_layernorm.cc skip_layernorm.cc scale_op.cc slice_op.cc hard_sigmoid_op.cc hard_swish_op.cc clip_op.cc elementwise_op.cc
batch_norm_op.cc
activation_op.cc
unary_op.cc
softmax_op.cc
concat_op.cc
dropout_op.cc
group_norm_op.cc
pad_op.cc
split_op.cc
prelu_op.cc
leaky_relu_op.cc
gelu_op.cc
layer_norm_op.cc
multihead_matmul_op.cc
shuffle_channel_op.cc
swish_op.cc
instance_norm_op.cc
stack_op.cc
transpose_op.cc
flatten_op.cc
flatten_contiguous_range_op.cc
emb_eltwise_layernorm.cc
skip_layernorm.cc
scale_op.cc
slice_op.cc
hard_sigmoid_op.cc
hard_swish_op.cc
clip_op.cc
gather_op.cc gather_op.cc
anchor_generator_op.cc anchor_generator_op.cc
yolo_box_op.cc yolo_box_op.cc
......
/* Copyright (c) 2022 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 <NvInfer.h>
#include <string>
#include "glog/logging.h"
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/engine.h"
#include "paddle/fluid/inference/tensorrt/helper.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
class Scope;
namespace proto {
class OpDesc;
} // namespace proto
} // namespace framework
} // namespace paddle
namespace paddle {
namespace inference {
namespace tensorrt {
class UnaryOpConverter : public OpConverter {
public:
UnaryOpConverter() {}
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
// Here the two nullptr looks strange, that's because the
// framework::OpDesc's constructor is strange.
framework::OpDesc op_desc(op, nullptr);
VLOG(3) << "convert a fluid unary op to tensorrt unary layer whose "
"type is "
<< op_type_;
nvinfer1::ITensor* input_tensor =
engine_->GetITensor(op_desc.Input("X")[0]);
auto op_pair = ops.find(op_type_);
nvinfer1::IUnaryLayer* layer =
TRT_ENGINE_ADD_LAYER(engine_, Unary, *input_tensor, op_pair->second);
auto output_name = op_desc.Output("Out")[0];
RreplenishLayerAndOutput(layer, op_type_, {output_name}, test_mode);
}
protected:
std::string op_type_;
static const std::unordered_map<std::string, nvinfer1::UnaryOperation> ops;
};
const std::unordered_map<std::string, nvinfer1::UnaryOperation>
UnaryOpConverter::ops = {
{"exp", nvinfer1::UnaryOperation::kEXP},
{"log", nvinfer1::UnaryOperation::kLOG},
};
class ExpOpConverter : public UnaryOpConverter {
public:
ExpOpConverter() { op_type_ = "exp"; }
};
class LogOpConverter : public UnaryOpConverter {
public:
LogOpConverter() { op_type_ = "log"; }
};
} // namespace tensorrt
} // namespace inference
} // namespace paddle
REGISTER_TRT_OP_CONVERTER(exp, ExpOpConverter);
REGISTER_TRT_OP_CONVERTER(log, LogOpConverter);
...@@ -65,6 +65,8 @@ struct SimpleOpTypeSetTeller : public Teller { ...@@ -65,6 +65,8 @@ struct SimpleOpTypeSetTeller : public Teller {
"conv2d_fusion", "conv2d_fusion",
"pool2d", "pool2d",
"relu", "relu",
"exp",
"log",
"softmax", "softmax",
"sigmoid", "sigmoid",
"hard_swish", "hard_swish",
...@@ -128,6 +130,8 @@ struct SimpleOpTypeSetTeller : public Teller { ...@@ -128,6 +130,8 @@ struct SimpleOpTypeSetTeller : public Teller {
"conv2d_fusion", "conv2d_fusion",
"pool2d", "pool2d",
"relu", "relu",
"exp",
"log",
"softmax", "softmax",
"sigmoid", "sigmoid",
"hard_swish", "hard_swish",
...@@ -200,7 +204,7 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, ...@@ -200,7 +204,7 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
for (auto& teller : tellers_) { for (auto& teller : tellers_) {
if (op_type == "relu" || op_type == "relu6" || op_type == "tanh" || if (op_type == "relu" || op_type == "relu6" || op_type == "tanh" ||
op_type == "sigmoid") { op_type == "sigmoid" || op_type == "exp" || op_type == "log") {
auto* block = desc.Block(); auto* block = desc.Block();
if (block == nullptr) { if (block == nullptr) {
VLOG(3) << "The block desc is nullptr, we can't continue to analyze. " VLOG(3) << "The block desc is nullptr, we can't continue to analyze. "
......
# Copyright (c) 2022 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 unittest
import numpy as np
import paddle.inference as paddle_infer
from functools import partial
from typing import Optional, List, Callable, Dict, Any, Set
class TrtConvertActivationTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True
def sample_program_configs(self):
def generate_input1(dims, batch, attrs: List[Dict[str, Any]]):
if dims == 1:
return np.ones([32]).astype(np.float32)
elif dims == 2:
return np.ones([3, 32]).astype(np.float32)
elif dims == 3:
return np.ones([3, 32, 32]).astype(np.float32)
else:
return np.ones([batch, 3, 32, 32]).astype(np.float32)
for dims in [1, 2, 3, 4]:
for batch in [1, 4]:
for op_type in ["exp", "log"]:
self.dims = dims
dics = [{}]
ops_config = [{
"op_type": op_type,
"op_inputs": {
"X": ["input_data"]
},
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data": TensorConfig(data_gen=partial(
generate_input1, dims, 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 self.dims == 1:
self.dynamic_shape.min_input_shape = {"input_data": [1]}
self.dynamic_shape.max_input_shape = {"input_data": [64]}
self.dynamic_shape.opt_input_shape = {"input_data": [32]}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {"input_data": [1, 16]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 32]}
self.dynamic_shape.opt_input_shape = {"input_data": [3, 32]}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {"input_data": [1, 16, 16]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 32]}
self.dynamic_shape.opt_input_shape = {"input_data": [3, 32, 32]}
else:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 3, 16, 16]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 3, 32, 32]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 3, 32, 32]
}
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):
if self.dims == 1:
return 0, 3
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
# 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
def test(self):
self.run_test()
if __name__ == "__main__":
unittest.main()
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册