未验证 提交 a21a2b5b 编写于 作者: X xiaoxiaohehe001 提交者: GitHub

[Paddle Inference] Add_expand_v2_trt_layer (#47002)

上级 ad4c773b
......@@ -2263,6 +2263,7 @@ USE_TRT_CONVERTER(layernorm_shift_partition)
USE_TRT_CONVERTER(generic_plugin_creater)
USE_TRT_CONVERTER(custom_plugin_creater)
USE_TRT_CONVERTER(lookup_table)
USE_TRT_CONVERTER(expand_v2)
#if PADDLE_WITH_CUSPARSELT && IS_TRT_VERSION_GE(8000)
USE_TRT_CONVERTER(sparse_fc)
USE_TRT_CONVERTER(sparse_multihead_matmul)
......
......@@ -78,7 +78,8 @@ list(
fused_token_prune_op.cc
layernorm_shift_partition_op.cc
generic_and_custom_plugin_creater.cc
fused_lookup_tables_op.cc)
fused_lookup_tables_op.cc
expand_v2_op.cc)
if(${TENSORRT_MAJOR_VERSION} GREATER_EQUAL 7 AND NOT WIN32)
list(APPEND CONVERT_FILES emb_eltwise_layernorm.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 "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 {
class ExpandV2OpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope,
bool test_mode) override {
framework::OpDesc op_desc(op, nullptr);
auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
auto input_dims = input->getDimensions();
auto output_name = op_desc.Output("Out")[0];
auto rank = input_dims.nbDims;
std::vector<int32_t> shape =
PADDLE_GET_CONST(std::vector<int32_t>, op_desc.GetAttr("shape"));
int32_t nbDims_num = shape.size();
auto* shape_tensor =
Add1DConstantLayer(shape, output_name + "_shape_tensor_");
nvinfer1::ITensor* input_shape_tensor;
if (rank < nbDims_num) {
auto* one_rank_tensor =
Add1DConstantLayer(std::vector<int32_t>(nbDims_num - rank, 1),
output_name + "_one_rank_tensor_");
auto in_shape_tensor = Shape(input);
std::vector<nvinfer1::ITensor*> itensors;
itensors.push_back(one_rank_tensor);
itensors.push_back(in_shape_tensor);
input_shape_tensor = Concat(itensors);
} else {
input_shape_tensor = Shape(input);
}
auto* shuffle = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input);
shuffle->setInput(1, *input_shape_tensor);
std::vector<int32_t> start_vec(nbDims_num, 0);
nvinfer1::Dims start;
start.nbDims = nbDims_num;
for (int32_t i = 0; i < nbDims_num; ++i) {
start.d[i] = start_vec[i];
}
nvinfer1::Dims size;
size.nbDims = nbDims_num;
nvinfer1::Dims stride;
stride.nbDims = nbDims_num;
auto starts_tensor =
Add1DConstantLayer(start_vec, output_name + "_start_tensor_");
auto one_tensor = Add1DConstantLayer(1, output_name + "_one_tensor_");
auto sizes_tensor = Max(input_shape_tensor, shape_tensor);
auto input_sub_tensor = Sub(input_shape_tensor, one_tensor);
auto strides_tensor = Min(one_tensor, input_sub_tensor);
auto layer = TRT_ENGINE_ADD_LAYER(
engine_, Slice, *shuffle->getOutput(0), start, size, stride);
layer->setInput(1, *starts_tensor);
layer->setInput(2, *sizes_tensor);
layer->setInput(3, *strides_tensor);
RreplenishLayerAndOutput(layer, "expand_v2", {output_name}, test_mode);
}
};
} // namespace tensorrt
} // namespace inference
} // namespace paddle
REGISTER_TRT_OP_CONVERTER(expand_v2, ExpandV2OpConverter);
......@@ -2109,6 +2109,27 @@ struct SimpleOpTypeSetTeller : public Teller {
}
}
if (op_type == "expand_v2") {
if (!with_dynamic_shape) {
return false;
}
if (!desc.HasAttr("shape")) {
return false;
}
auto expand_v2_inputs = desc.Inputs();
if (expand_v2_inputs.find("Shape") != expand_v2_inputs.end()) {
if (desc.Input("Shape").size() >= 1) {
return false;
}
}
if (expand_v2_inputs.find("expand_shapes_tensor") !=
expand_v2_inputs.end()) {
if (desc.Input("expand_shapes_tensor").size() >= 1) {
return false;
}
}
}
if (use_no_calib_int8) {
return int8_teller_set.count(op_type);
} else {
......@@ -2232,7 +2253,8 @@ struct SimpleOpTypeSetTeller : public Teller {
"unsqueeze2",
"layernorm_shift_partition",
"lookup_table",
"lookup_table_v2"};
"lookup_table_v2",
"expand_v2"};
std::unordered_set<std::string> teller_set{
"mul",
"matmul",
......@@ -2348,7 +2370,8 @@ struct SimpleOpTypeSetTeller : public Teller {
"fused_token_prune",
"layernorm_shift_partition",
"lookup_table",
"lookup_table_v2"};
"lookup_table_v2",
"expand_v2"};
};
struct GenericPluginTeller : public Teller {
......
# 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
from program_config import TensorConfig, ProgramConfig
import numpy as np
import paddle.inference as paddle_infer
from functools import partial
from typing import List, Dict, Any
import unittest
class TrtConvertExpandV2Test(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
attrs = [
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
if len(attrs[0]['shape']) < self.dims:
return False
if self.dims == 1:
if len(attrs[0]['shape']) == 4:
return False
return True
def sample_program_configs(self):
def generate_input1(attrs: List[Dict[str, Any]]):
if self.dims == 4:
self.input_shape = [1, 1, 4, 6]
return np.random.random([1, 1, 4, 6]).astype(np.float32)
elif self.dims == 3:
self.input_shape = [1, 8, 6]
return np.random.random([1, 8, 6]).astype(np.float32)
elif self.dims == 2:
self.input_shape = [1, 48]
return np.random.random([1, 48]).astype(np.float32)
elif self.dims == 1:
self.input_shape = [48]
return np.random.random([48]).astype(np.float32)
def generate_weight1(attrs: List[Dict[str, Any]]):
return np.array([1, 48]).astype(np.int32)
def generate_shapeT1_data(attrs: List[Dict[str, Any]]):
return np.array([2]).astype(np.int32)
def generate_shapeT2_data(attrs: List[Dict[str, Any]]):
return np.array([24]).astype(np.int32)
for dims in [4, 3, 2, 1]:
for shape in [[10, 12, -1, -1], [8, 64, -1, -1], [6, 8, -1]]:
dics = [
{
"shape": shape,
},
]
self.dims = dims
dics_intput = [{"X": ["expand_v2_input"]}]
ops_config = [{
"op_type": "expand_v2",
"op_inputs": dics_intput[0],
"op_outputs": {
"Out": ["expand_v2_out"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"expand_v2_input":
TensorConfig(data_gen=partial(generate_input1, dics))
},
outputs=["expand_v2_out"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
if self.dims == 4:
self.dynamic_shape.min_input_shape = {
"expand_v2_input": [1, 1, 4, 6]
}
self.dynamic_shape.max_input_shape = {
"expand_v2_input": [10, 1, 4, 6]
}
self.dynamic_shape.opt_input_shape = {
"expand_v2_input": [1, 1, 4, 6]
}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {
"expand_v2_input": [1, 8, 6]
}
self.dynamic_shape.max_input_shape = {
"expand_v2_input": [4, 8, 6]
}
self.dynamic_shape.opt_input_shape = {
"expand_v2_input": [1, 8, 6]
}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {
"expand_v2_input": [1, 48]
}
self.dynamic_shape.max_input_shape = {
"expand_v2_input": [4, 48]
}
self.dynamic_shape.opt_input_shape = {
"expand_v2_input": [1, 48]
}
elif self.dims == 1:
self.dynamic_shape.min_input_shape = {"expand_v2_input": [48]}
self.dynamic_shape.max_input_shape = {"expand_v2_input": [48]}
self.dynamic_shape.opt_input_shape = {"expand_v2_input": [48]}
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 dynamic_shape:
return 1, 2
else:
return 0, 3
attrs = [
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
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 add_skip_trt_case(self):
pass
def test(self):
self.add_skip_trt_case()
self.run_test()
class TrtConvertExpandV2Test2(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
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]]):
if self.dims == 1:
self.input_shape = [1]
return np.random.random([1]).astype(np.float32)
for dims in [1]:
for shape in [[10, 12, -1, -1], [8, 64, -1, -1]]:
dics = [
{
"shape": shape,
},
]
self.dims = dims
dics_intput = [
{
"X": ["expand_v2_input"],
"Shape": ["shapeT1_data"]
},
]
ops_config = [
{
"op_type": "fill_constant",
"op_inputs": {},
"op_outputs": {
"Out": ["shapeT1_data"]
},
"op_attrs": {
"dtype": 2,
"str_value": "10",
"shape": [1],
},
},
{
"op_type": "expand_v2",
"op_inputs": dics_intput[0],
"op_outputs": {
"Out": ["expand_v2_out"]
},
"op_attrs": dics[0]
},
]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"expand_v2_input":
TensorConfig(data_gen=partial(generate_input1, dics))
},
outputs=["expand_v2_out"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape():
if self.dims == 1:
self.dynamic_shape.min_input_shape = {"expand_v2_input": [1]}
self.dynamic_shape.max_input_shape = {"expand_v2_input": [1]}
self.dynamic_shape.opt_input_shape = {"expand_v2_input": [1]}
def clear_dynamic_shape():
self.dynamic_shape.min_input_shape = {}
self.dynamic_shape.max_input_shape = {}
self.dynamic_shape.opt_input_shape = {}
clear_dynamic_shape()
# for dynamic_shape
generate_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 add_skip_trt_case(self):
pass
def test(self):
self.add_skip_trt_case()
self.run_test()
class TrtConvertExpandV2Test3(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
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]]):
if self.dims == 4:
self.input_shape = [1, 1, 4, 6]
return np.random.random([1, 1, 4, 6]).astype(np.float32)
elif self.dims == 3:
self.input_shape = [1, 4, 6]
return np.random.random([1, 4, 6]).astype(np.float32)
for dims in [4, 3]:
for shape in [[10, 12, -1, -1], [8, 64, -1, -1]]:
dics = [
{
"shape": shape,
},
]
self.dims = dims
dics_intput = [
{
"X": ["expand_v2_input"],
"expand_shapes_tensor": [
"shapeT1_data", "shapeT2_data", "shapeT3_data",
"shapeT4_data"
]
},
]
ops_config = [
{
"op_type": "fill_constant",
"op_inputs": {},
"op_outputs": {
"Out": ["shapeT1_data"]
},
"op_attrs": {
"dtype": 2,
"str_value": "10",
"shape": [1],
},
},
{
"op_type": "fill_constant",
"op_inputs": {},
"op_outputs": {
"Out": ["shapeT2_data"]
},
"op_attrs": {
"dtype": 2,
"str_value": "12",
"shape": [1],
},
},
{
"op_type": "fill_constant",
"op_inputs": {},
"op_outputs": {
"Out": ["shapeT3_data"]
},
"op_attrs": {
"dtype": 2,
"str_value": "4",
"shape": [1],
},
},
{
"op_type": "fill_constant",
"op_inputs": {},
"op_outputs": {
"Out": ["shapeT4_data"]
},
"op_attrs": {
"dtype": 2,
"str_value": "6",
"shape": [1],
},
},
{
"op_type": "expand_v2",
"op_inputs": dics_intput[0],
"op_outputs": {
"Out": ["expand_v2_out"]
},
"op_attrs": dics[0]
},
]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"expand_v2_input":
TensorConfig(data_gen=partial(generate_input1, dics))
},
outputs=["expand_v2_out"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape():
if self.dims == 4:
self.dynamic_shape.min_input_shape = {
"expand_v2_input": [1, 1, 4, 6]
}
self.dynamic_shape.max_input_shape = {
"expand_v2_input": [10, 1, 4, 6]
}
self.dynamic_shape.opt_input_shape = {
"expand_v2_input": [1, 1, 4, 6]
}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {
"expand_v2_input": [1, 4, 6]
}
self.dynamic_shape.max_input_shape = {
"expand_v2_input": [4, 4, 6]
}
self.dynamic_shape.opt_input_shape = {
"expand_v2_input": [1, 4, 6]
}
def clear_dynamic_shape():
self.dynamic_shape.min_input_shape = {}
self.dynamic_shape.max_input_shape = {}
self.dynamic_shape.opt_input_shape = {}
clear_dynamic_shape()
# for dynamic_shape
generate_dynamic_shape()
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), (4, 3), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), (4, 3), 1e-5
def add_skip_trt_case(self):
pass
def test(self):
self.add_skip_trt_case()
self.run_test()
if __name__ == "__main__":
unittest.main()
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