未验证 提交 9f88d327 编写于 作者: B baoachun 提交者: GitHub

add hard_sigmoid trt converter test cases (#35876)

上级 e7617512
...@@ -1265,6 +1265,24 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, ...@@ -1265,6 +1265,24 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
} }
} }
if (op_type == "hard_sigmoid") {
if (!with_dynamic_shape) {
auto* block = desc.Block();
if (block == nullptr) {
VLOG(3) << "The block is null.";
return false;
}
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();
if (x_shape.size() <= 2) {
VLOG(3) << "hard_sigmoid op does not support input's dim less than 3 "
"in tensorrt.";
return false;
}
}
}
if ((*teller)(op_type, desc, use_no_calib_int8)) return true; if ((*teller)(op_type, desc, use_no_calib_int8)) return true;
} }
......
# 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 TrtConvertHardSigmoidTest_dim_2(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True
def sample_program_configs(self):
def generate_input(shape):
return np.random.random(shape).astype(np.float32)
for batch in [1, 2, 4]:
for shape in [[batch, 64], [batch, 32, 64], [batch, 64, 32, 128]]:
self.input_dim = len(shape)
for slope in [0.1, 0.5]:
for offset in [0.2, 0.7]:
dics = [{"slope": slope, "offset": offset}]
ops_config = [{
"op_type": "hard_sigmoid",
"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_input, shape))
},
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.input_dim == 2:
self.dynamic_shape.min_input_shape = {"input_data": [1, 8]}
self.dynamic_shape.max_input_shape = {"input_data": [64, 128]}
self.dynamic_shape.opt_input_shape = {"input_data": [2, 16]}
elif self.input_dim == 3:
self.dynamic_shape.min_input_shape = {"input_data": [1, 8, 8]}
self.dynamic_shape.max_input_shape = {
"input_data": [64, 128, 256]
}
self.dynamic_shape.opt_input_shape = {"input_data": [2, 16, 64]}
elif self.input_dim == 4:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 8, 8, 4]
}
self.dynamic_shape.max_input_shape = {
"input_data": [64, 128, 256, 512]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [2, 16, 64, 128]
}
def clear_dynamic_shape():
self.dynamic_shape.max_input_shape = {}
self.dynamic_shape.min_input_shape = {}
self.dynamic_shape.opt_input_shape = {}
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(), (1, 2), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), (1, 2), 1e-5
# for dynamic_shape
generate_dynamic_shape(attrs)
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), (1, 2), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), (1, 2), 1e-5
def add_skip_trt_case(self):
def teller(program_config, predictor_config):
if len(self.dynamic_shape.
min_input_shape) == 0 and self.input_dim == 2:
return True
return False
self.add_skip_case(
teller, SkipReasons.TRT_NOT_SUPPORT,
"Need to repair the case: the output of trt and GPU has diff when inputs' dims is 2 in static shape mode."
)
def test(self):
self.add_skip_trt_case()
self.run_test()
if __name__ == "__main__":
unittest.main()
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