未验证 提交 09f920a2 编写于 作者: X xiaoxiaohehe001 提交者: GitHub

[Paddle Inference]Add Transpose op TRT converter unittest (#35138)

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller
上级 c563609a
......@@ -9,7 +9,6 @@ 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 <bitset>
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
namespace paddle {
......@@ -43,32 +42,11 @@ class TransposeOpConverter : public OpConverter {
axis[i]--;
}
}
nvinfer1::Permutation perm;
for (int i = 0; i < dims; i++) {
int j = engine_->with_dynamic_shape() ? i : i + 1;
perm.order[i] = axis[j];
}
// Permutation is valid if it has nbDims unique values from range [0,
// nbDims-1]
auto is_valid_permutation = [&](int dims,
const nvinfer1::Permutation& permutation) {
std::bitset<nvinfer1::Dims::MAX_DIMS> found;
for (int i = 0; i < dims; ++i) {
const int x = permutation.order[i];
if ((x < 0) || (x >= dims) || found[x])
return false; // Out of bounds or duplicate
found.set(x);
}
return true;
};
PADDLE_ENFORCE_EQ(is_valid_permutation(dims, perm), true,
platform::errors::InvalidArgument(
"Invalid permutation dimensions for trt transpose op "
"converter: duplicate or out of bound."));
auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input);
layer->setFirstTranspose(perm);
......
......@@ -13,7 +13,7 @@
// limitations under the License.
#include "paddle/fluid/inference/tensorrt/op_teller.h"
#include <bitset>
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/data_layout.h"
......@@ -316,11 +316,36 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
if (op_type == "transpose2" || op_type == "transpose") {
if (!desc.HasAttr("axis")) {
return false;
} else {
}
std::vector<int> axis =
BOOST_GET_CONST(std::vector<int>, desc.GetAttr("axis"));
if (!with_dynamic_shape && axis[0] != 0) return false;
if (axis.size() >= nvinfer1::Dims::MAX_DIMS) return false;
if (axis[0] == 0 && axis.size() == 2) return false;
auto* block = desc.Block();
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();
int dims = x_shape.size();
std::vector<int> perm(nvinfer1::Dims::MAX_DIMS);
for (int i = 0; i < dims; i++) {
perm[i] = axis[i];
}
auto is_valid_permutation = [&](int dims,
const std::vector<int>& permutation) {
std::bitset<nvinfer1::Dims::MAX_DIMS> found;
for (int i = 0; i < dims; ++i) {
const int x = permutation[i];
if ((x < 0) || (x >= dims) || found[x])
return false; // Out of bounds or duplicate
found.set(x);
}
return true;
};
if (!is_valid_permutation(dims, perm)) {
VLOG(3) << "Invalid permutation dimensions for trt transpose op "
"converter: duplicate or out of bound.";
}
}
if (op_type == "flatten2" || op_type == "flatten") {
......
# 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 TrtConvertTransposeTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
inputs = program_config.inputs
weights = program_config.weights
outputs = program_config.outputs
attrs = [
program_config.ops[i].attrs
for i in range(len(program_config.ops))
]
#The shape of input and axis should be equal.
if len(inputs['transpose_input'].shape) != len(attrs[0]['axis']):
return False
return True
def sample_program_configs(self):
def generate_input1(attrs: List[Dict[str, Any]], batch):
if self.dims == 4:
return np.ones([batch, 3, 24, 24]).astype(np.float32)
elif self.dims == 3:
return np.ones([batch, 3, 24]).astype(np.float32)
elif self.dims == 2:
return np.ones([batch, 24]).astype(np.float32)
for dims in [2, 3, 4]:
for batch in [1, 2, 4]:
for axis in [[0, 1, 3, 2], [0, 3, 2, 1], [3, 2, 0, 1],
[0, 1, 2, 3], [0, 1, 2], [2, 0, 1], [1, 0],
[0, 1]]:
self.dims = dims
dics = [{"axis": axis}, {}]
ops_config = [{
"op_type": "transpose",
"op_inputs": {
"X": ["transpose_input"]
},
"op_outputs": {
"Out": ["transpose_out"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"transpose_input": TensorConfig(data_gen=partial(
generate_input1, dics, batch))
},
outputs=["transpose_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 = {
"transpose_input": [1, 3, 24, 24]
}
self.dynamic_shape.max_input_shape = {
"transpose_input": [9, 6, 48, 48]
}
self.dynamic_shape.opt_input_shape = {
"transpose_input": [1, 3, 48, 24]
}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {
"transpose_input": [1, 3, 24]
}
self.dynamic_shape.max_input_shape = {
"transpose_input": [9, 6, 48]
}
self.dynamic_shape.opt_input_shape = {
"transpose_input": [1, 3, 24]
}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {
"transpose_input": [1, 24]
}
self.dynamic_shape.max_input_shape = {
"transpose_input": [9, 48]
}
self.dynamic_shape.opt_input_shape = {
"transpose_input": [1, 24]
}
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 == True:
return 1, 2
else:
if attrs[0]['axis'][0] == 0:
return 1, 2
else:
return 0, 3
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 add_skip_trt_case(self):
def teller1(program_config, predictor_config):
if program_config.ops[0].attrs['axis'] == [0, 1]:
return True
return False
self.add_skip_case(
teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
"INPUT AXIS [0, 1] NOT SUPPORT: we need to add support in the future"
)
def test(self):
self.add_skip_trt_case()
self.run_test()
if __name__ == "__main__":
unittest.main()
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