未验证 提交 5928856e 编写于 作者: X xiaoxiaohehe001 提交者: GitHub

[Paddle Inference]Add Concat op TRT converter unittest (#35545)

* add_concat_teller

* add_concat_teller

* add_concat_teller

* add_concat_teller

* add_concat_teller
上级 5d03c3eb
......@@ -305,6 +305,12 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
} else {
if (axis <= 0) return false;
}
auto concat_inputs = desc.Inputs();
if (concat_inputs.find("AxisTensor") != concat_inputs.end()) {
if (desc.Input("AxisTensor").size() >= 1) {
return false;
}
}
}
}
if (op_type == "transpose2" || op_type == "transpose") {
......
# 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 TrtConvertConcatTest(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 input dimension should be less than or equal to the set axis.
if len(inputs['concat_input1'].shape) <= 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)
elif self.dims == 1:
return np.ones([24]).astype(np.float32)
def generate_input2(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)
elif self.dims == 1:
return np.ones([24]).astype(np.float32)
def generate_input3(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)
elif self.dims == 1:
return np.ones([24]).astype(np.float32)
def generate_weight1(attrs: List[Dict[str, Any]]):
return np.zeros([1]).astype(np.int32)
for dims in [1, 2, 3, 4]:
for num_input in [0, 1]:
for batch in [1, 2, 4]:
for axis in [-1, 0, 1, 2, 3]:
self.num_input = num_input
self.dims = dims
dics = [{"axis": axis}, {}]
dics_intput = [{
"X": [
"concat_input1", "concat_input2",
"concat_input3"
],
"AxisTensor": ["AxisTensor"],
}, {
"X": [
"concat_input1", "concat_input2",
"concat_input3"
]
}]
dics_inputs = [{
"concat_input1": TensorConfig(data_gen=partial(
generate_input1, dics, batch)),
"concat_input2": TensorConfig(data_gen=partial(
generate_input2, dics, batch)),
"concat_input3": TensorConfig(data_gen=partial(
generate_input3, dics, batch)),
"AxisTensor": TensorConfig(data_gen=partial(
generate_weight1, dics))
}, {
"concat_input1": TensorConfig(data_gen=partial(
generate_input1, dics, batch)),
"concat_input2": TensorConfig(data_gen=partial(
generate_input2, dics, batch)),
"concat_input3": TensorConfig(data_gen=partial(
generate_input3, dics, batch))
}]
ops_config = [{
"op_type": "concat",
"op_inputs": dics_intput[num_input],
"op_outputs": {
"Out": ["concat_output"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs=dics_inputs[num_input],
outputs=["concat_output"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
if self.num_input == 0:
if self.dims == 4:
self.dynamic_shape.min_input_shape = {
"concat_input1": [1, 3, 24, 24],
"concat_input2": [1, 3, 24, 24],
"concat_input3": [1, 3, 24, 24],
"AxisTensor": [1]
}
self.dynamic_shape.max_input_shape = {
"concat_input1": [4, 3, 48, 48],
"concat_input2": [4, 3, 48, 48],
"concat_input3": [4, 3, 48, 48],
"AxisTensor": [1]
}
self.dynamic_shape.opt_input_shape = {
"concat_input1": [1, 3, 24, 24],
"concat_input2": [1, 3, 24, 24],
"concat_input3": [1, 3, 24, 24],
"AxisTensor": [1]
}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {
"concat_input1": [1, 3, 24],
"concat_input2": [1, 3, 24],
"concat_input3": [1, 3, 24],
"AxisTensor": [1]
}
self.dynamic_shape.max_input_shape = {
"concat_input1": [4, 12, 48],
"concat_input2": [4, 12, 48],
"concat_input3": [4, 12, 48],
"AxisTensor": [1]
}
self.dynamic_shape.opt_input_shape = {
"concat_input1": [1, 3, 24],
"concat_input2": [1, 3, 24],
"concat_input3": [1, 3, 24],
"AxisTensor": [1]
}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {
"concat_input1": [1, 24],
"concat_input2": [1, 24],
"concat_input3": [1, 24],
"AxisTensor": [1]
}
self.dynamic_shape.max_input_shape = {
"concat_input1": [4, 48],
"concat_input2": [4, 48],
"concat_input3": [4, 48],
"AxisTensor": [1]
}
self.dynamic_shape.opt_input_shape = {
"concat_input1": [1, 24],
"concat_input2": [1, 24],
"concat_input3": [1, 24],
"AxisTensor": [1]
}
elif self.dims == 1:
self.dynamic_shape.min_input_shape = {
"concat_input1": [24],
"concat_input2": [24],
"concat_input3": [24],
"AxisTensor": [0]
}
self.dynamic_shape.max_input_shape = {
"concat_input1": [48],
"concat_input2": [48],
"concat_input3": [48],
"AxisTensor": [0]
}
self.dynamic_shape.opt_input_shape = {
"concat_input1": [24],
"concat_input2": [24],
"concat_input3": [24],
"AxisTensor": [0]
}
elif self.num_input == 1:
if self.dims == 4:
self.dynamic_shape.min_input_shape = {
"concat_input1": [1, 3, 24, 24],
"concat_input2": [1, 3, 24, 24],
"concat_input3": [1, 3, 24, 24],
}
self.dynamic_shape.max_input_shape = {
"concat_input1": [4, 3, 48, 48],
"concat_input2": [4, 3, 48, 48],
"concat_input3": [4, 3, 48, 48]
}
self.dynamic_shape.opt_input_shape = {
"concat_input1": [1, 3, 24, 24],
"concat_input2": [1, 3, 24, 24],
"concat_input3": [1, 3, 24, 24]
}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {
"concat_input1": [1, 3, 24],
"concat_input2": [1, 3, 24],
"concat_input3": [1, 3, 24]
}
self.dynamic_shape.max_input_shape = {
"concat_input1": [4, 12, 48],
"concat_input2": [4, 12, 48],
"concat_input3": [4, 12, 48]
}
self.dynamic_shape.opt_input_shape = {
"concat_input1": [1, 3, 24],
"concat_input2": [1, 3, 24],
"concat_input3": [1, 3, 24]
}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {
"concat_input1": [1, 24],
"concat_input2": [1, 24],
"concat_input3": [1, 24]
}
self.dynamic_shape.max_input_shape = {
"concat_input1": [4, 48],
"concat_input2": [4, 48],
"concat_input3": [4, 48]
}
self.dynamic_shape.opt_input_shape = {
"concat_input1": [1, 24],
"concat_input2": [1, 24],
"concat_input3": [1, 24]
}
elif self.dims == 1:
self.dynamic_shape.min_input_shape = {
"concat_input1": [24],
"concat_input2": [24],
"concat_input3": [24]
}
self.dynamic_shape.max_input_shape = {
"concat_input1": [48],
"concat_input2": [48],
"concat_input3": [48]
}
self.dynamic_shape.opt_input_shape = {
"concat_input1": [24],
"concat_input2": [24],
"concat_input3": [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:
if attrs[0]['axis'] >= 0:
return 1, 4
else:
return 0, 5
else:
if attrs[0]['axis'] > 0:
return 1, 4
else:
return 0, 5
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 len(program_config.inputs) == 4:
return True
return False
self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT,
"INPUT AxisTensor NOT SUPPORT")
def test(self):
self.add_skip_trt_case()
self.run_test()
if __name__ == "__main__":
unittest.main()
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