未验证 提交 b861022a 编写于 作者: F feng_shuai 提交者: GitHub

strided_slice (#41573)

* strided_slice

* fix: compiler error because of size()

* fix: warning

* fix : warning

* init input_shape

* fix:forget punctuation
上级 43d5cca6
......@@ -1755,6 +1755,7 @@ USE_TRT_CONVERTER(deformable_conv);
USE_TRT_CONVERTER(pool3d)
USE_TRT_CONVERTER(fused_preln_embedding_eltwise_layernorm)
USE_TRT_CONVERTER(preln_skip_layernorm)
USE_TRT_CONVERTER(strided_slice)
#endif
namespace paddle_infer {
......
......@@ -23,6 +23,7 @@ nv_library(tensorrt_converter
pool3d_op.cc
deformable_conv_op.cc
preln_emb_eltwise_layernorm.cc
strided_slice_op.cc
preln_skip_layernorm.cc
DEPS tensorrt_engine tensorrt_plugin operator scope framework_proto op_registry)
......
/* 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 "paddle/fluid/inference/tensorrt/convert/op_converter.h"
namespace paddle {
namespace framework {
class Scope;
namespace proto {
class OpDesc;
} // namespace proto
} // namespace framework
} // namespace paddle
namespace paddle {
namespace inference {
namespace tensorrt {
/*
* Stack converter from fluid to tensorRT.
*/
class StridedSliceOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
VLOG(4) << "convert fluid StridedSlice op to tensorrt Slice layer";
framework::OpDesc op_desc(op, nullptr);
auto* input = engine_->GetITensor(op_desc.Input("Input")[0]);
nvinfer1::Dims input_dims = input->getDimensions();
std::vector<int> axes =
BOOST_GET_CONST(std::vector<int>, op_desc.GetAttr("axes"));
std::vector<int> starts =
BOOST_GET_CONST(std::vector<int>, op_desc.GetAttr("starts"));
std::vector<int> ends =
BOOST_GET_CONST(std::vector<int>, op_desc.GetAttr("ends"));
std::vector<int> strides =
BOOST_GET_CONST(std::vector<int>, op_desc.GetAttr("strides"));
nvinfer1::Dims start;
start.nbDims = input_dims.nbDims;
int axes_size = axes.size();
for (int i = 0; i < start.nbDims; i++) {
start.d[i] = 0;
}
for (int i = 0; i < axes_size; i++) {
start.d[axes[i]] = starts[i];
}
nvinfer1::Dims stride;
stride.nbDims = input_dims.nbDims;
for (int i = 0; i < stride.nbDims; i++) {
stride.d[i] = 1;
}
for (int i = 0; i < axes_size; i++) {
stride.d[axes[i]] = strides[i];
}
nvinfer1::Dims size;
size.nbDims = input_dims.nbDims;
for (int i = 0; i < size.nbDims; i++) {
size.d[i] = 1;
}
auto output_name = op_desc.Output("Out")[0];
auto create_weights = [&](const std::vector<int>& data,
const std::string& type) -> int* {
std::unique_ptr<framework::Tensor> tmp_tensor(new framework::Tensor());
int data_size = data.size();
tmp_tensor->Resize({data_size});
auto* tmp_data = tmp_tensor->mutable_data<int>(platform::CPUPlace());
for (int i = 0; i < data_size; i++) {
tmp_data[i] = data[i];
}
engine_->SetWeights(output_name + "_add_slice_op_" + type,
std::move(tmp_tensor));
return tmp_data;
};
std::vector<int> const_weight(input_dims.nbDims, 1);
for (int i = 0; i < axes_size; i++) {
const_weight[axes[i]] = strides[i];
}
int* weight_data = create_weights(const_weight, "size");
TensorRTEngine::Weight weight{nvinfer1::DataType::kINT32,
static_cast<void*>(weight_data),
static_cast<size_t>(input_dims.nbDims)};
int input_dim_size = input_dims.nbDims;
nvinfer1::Dims input_shape;
input_shape.nbDims = 1;
input_shape.d[0] = input_dim_size;
auto const_layer =
TRT_ENGINE_ADD_LAYER(engine_, Constant, input_shape, weight.get());
auto shape_layer = TRT_ENGINE_ADD_LAYER(engine_, Shape, *input);
auto size_layer = TRT_ENGINE_ADD_LAYER(
engine_, ElementWise, *shape_layer->getOutput(0),
*const_layer->getOutput(0), nvinfer1::ElementWiseOperation::kDIV);
auto* layer =
TRT_ENGINE_ADD_LAYER(engine_, Slice, *input, start, size, stride);
layer->setInput(2, *size_layer->getOutput(0));
RreplenishLayerAndOutput(layer, "strided_slice", {output_name}, test_mode);
}
};
} // namespace tensorrt
} // namespace inference
} // namespace paddle
REGISTER_TRT_OP_CONVERTER(strided_slice, StridedSliceOpConverter);
......@@ -117,6 +117,7 @@ struct SimpleOpTypeSetTeller : public Teller {
"multihead_matmul",
"skip_layernorm",
"slice",
"strided_slice",
"fused_preln_embedding_eltwise_layernorm",
"preln_skip_layernorm"};
std::unordered_set<std::string> teller_set{
......@@ -178,6 +179,7 @@ struct SimpleOpTypeSetTeller : public Teller {
"multihead_matmul",
"skip_layernorm",
"slice",
"strided_slice",
"fused_preln_embedding_eltwise_layernorm",
"preln_skip_layernorm",
"multiclass_nms3"};
......
# 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 TrtConvertStridedSliceTest(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))
]
return True
def sample_program_configs(self):
def generate_input1(attrs: List[Dict[str, Any]]):
return np.ones([1, 56, 56, 192]).astype(np.float32)
for axes in [[1, 2]]:
for starts in [[1, 1]]:
for ends in [[10000000, 10000000]]:
for decrease_axis in [[]]:
for infer_flags in [[1, 1]]:
for strides in [[2, 2]]:
dics = [{
"axes": axes,
"starts": starts,
"ends": ends,
"decrease_axis": decrease_axis,
"infer_flags": infer_flags,
"strides": strides
}]
ops_config = [{
"op_type": "strided_slice",
"op_inputs": {
"Input": ["input_data"]
},
"op_outputs": {
"Out": ["slice_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,
dics))
},
outputs=["slice_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 = {
"input_data": [1, 56, 56, 192]
}
self.dynamic_shape.max_input_shape = {
"input_data": [8, 56, 56, 192]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [4, 56, 56, 192]
}
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):
inputs = program_config.inputs
if dynamic_shape:
for i in range(len(attrs[0]["starts"])):
if attrs[0]["starts"][i] < 0 or attrs[0]["ends"][i] < 0:
return 0, 3
if not dynamic_shape:
for x in attrs[0]["axes"]:
if x == 0:
return 0, 3
return 1, 2
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(), generate_trt_nodes_num(attrs,
True), 1e-5
def test(self):
self.run_test()
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