未验证 提交 05275010 编写于 作者: 提交者: GitHub

[inference]add reduce converter test (#35145)

* add test

* add test

* add test
上级 867f4fa0
...@@ -1079,6 +1079,16 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, ...@@ -1079,6 +1079,16 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
for (auto x : dim) { for (auto x : dim) {
if (!x) return false; if (!x) return false;
} }
} else {
if (BOOST_GET_CONST(bool, desc.GetAttr("reduce_all")) &&
!BOOST_GET_CONST(bool, desc.GetAttr("keep_dim")))
return false;
}
if (desc.HasAttr("reduce_all")) {
int out_dtype = BOOST_GET_CONST(int32_t, desc.GetAttr("out_dtype"));
if (out_dtype != -1) {
return false;
}
} }
} }
#if IS_TRT_VERSION_GE(7000) #if IS_TRT_VERSION_GE(7000)
......
# 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 TrtConvertReduceMeanTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
inputs = program_config.inputs
attrs = [
program_config.ops[i].attrs
for i in range(len(program_config.ops))
]
## dim should be in (-rank, rank), and not NONE
rank = len(inputs['input_data'].shape)
for x in attrs[0]["dim"]:
if x >= rank or x <= -rank:
return False
if len(attrs[0]["dim"]) == 0:
return False
## skip not use
if attrs[0]["out_dtype"] != -1:
return False
return True
def sample_program_configs(self):
def generate_input1(attrs: List[Dict[str, Any]]):
return np.random.random([1, 3, 64, 64]).astype(np.float32)
for keep_dim in [False, True]:
for dim in [[], [1], [0], [0, 1], [1, 2, 3], [-2, 0, 3], [-3],
[-4, 1], [3, 4, 5]]:
for reduce_all in [False, True]:
for out_dtype in [-1, 0, 1]:
dics = [{
"keep_dim": keep_dim,
"dim": dim,
"reduce_all": reduce_all,
"out_dtype": out_dtype
}, {}]
ops_config = [{
"op_type": "reduce_mean",
"op_inputs": {
"X": ["input_data"]
},
"op_outputs": {
"Out": ["reduce_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=["reduce_output_data"])
if not self.is_program_valid(program_config):
continue
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, 3, 32, 32]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]}
self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]}
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:
if (not attrs[0]['keep_dim']) and attrs[0]['reduce_all']:
return 0, 3
else:
return 1, 2
else:
if 0 in attrs[0]['dim'] or attrs[0]['reduce_all']:
return 0, 3
else:
return 1, 2
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, 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, 1e-5)
pass
def add_skip_trt_case(self):
def teller1(program_config, predictor_config):
if program_config.ops[0].attrs['out_dtype'] != -1:
return True
return False
self.add_skip_case(
teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
"NOT Implemented: we will add out_dtype not equal to -1 in the future"
)
pass
def test(self):
self.add_skip_trt_case()
self.run_test()
if __name__ == "__main__":
unittest.main()
# 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 TrtConvertReduceSumTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
inputs = program_config.inputs
attrs = [
program_config.ops[i].attrs
for i in range(len(program_config.ops))
]
## dim should be in (-rank, rank), and not NONE
rank = len(inputs['input_data'].shape)
for x in attrs[0]["dim"]:
if x >= rank or x <= -rank:
return False
if len(attrs[0]["dim"]) == 0:
return False
## skip not use
if attrs[0]["out_dtype"] != -1:
return False
return True
def sample_program_configs(self):
def generate_input1(attrs: List[Dict[str, Any]]):
return np.random.random([1, 3, 64, 64]).astype(np.float32)
for keep_dim in [False, True]:
for dim in [[], [1], [0], [0, 1], [1, 2, 3], [-2, 0, 3], [-3],
[-4, 1], [3, 4, 5]]:
for reduce_all in [False, True]:
for out_dtype in [-1, 0, 1]:
dics = [{
"keep_dim": keep_dim,
"dim": dim,
"reduce_all": reduce_all,
"out_dtype": out_dtype
}, {}]
ops_config = [{
"op_type": "reduce_sum",
"op_inputs": {
"X": ["input_data"]
},
"op_outputs": {
"Out": ["reduce_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=["reduce_output_data"])
if not self.is_program_valid(program_config):
continue
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, 3, 32, 32]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]}
self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]}
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:
if (not attrs[0]['keep_dim']) and attrs[0]['reduce_all']:
return 0, 3
else:
return 1, 2
else:
if 0 in attrs[0]['dim'] or attrs[0]['reduce_all']:
return 0, 3
else:
return 1, 2
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, 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, 1e-5)
pass
def add_skip_trt_case(self):
def teller1(program_config, predictor_config):
if program_config.ops[0].attrs['out_dtype'] != -1:
return True
return False
self.add_skip_case(
teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
"NOT Implemented: we will add out_dtype not equal to -1 in the future"
)
pass
def test(self):
self.add_skip_trt_case()
self.run_test()
if __name__ == "__main__":
unittest.main()
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