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未验证 提交 3512bf11 编写于 作者: Z zlsh80826 提交者： GitHub
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Refine TRT unit test (#45102) 

* Reduce pool2d test configuration

* Reduce depthwise_conv2d test configuration

* Reduce trt_convert_conv2d_fusion test configuration

* Reduce trt_convert_conv2d test configuration

* Reduce trt_convert_conv2d_transpose test configuration

* Reduce trt_convert_hard_swish test configuration

* Enhance trt auto scan test error message and mechanism

* Increase FP16 trt ut tolerance
上级 c75b091b
隐藏空白更改
内联 并排
Showing with 415 addition and 398 deletion
paddle/fluid/inference/tests/api/trt_dynamic_shape_ernie_test.cc
浏览文件 @ 3512bf11
......@@ -14,8 +14,8 @@ limitations under the License. */
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "gflags/gflags.h"
#include "paddle/fluid/inference/tensorrt/helper.h"
#include "paddle/fluid/inference/tests/api/trt_test_helper.h"
......@@ -423,7 +423,7 @@ TEST(AnalysisPredictor, ernie_varlen) {
run(predictor.get(), &out_data);
std::vector<float> ref_data{
0.59814, 0.219882, 0.181978, 0.359796, 0.577414, 0.0627908};
float near_tolerance = 1e-3;
float near_tolerance = 4e-3;
for (size_t i = 0; i < out_data.size(); i++) {
EXPECT_NEAR(ref_data[i], out_data[i], near_tolerance);
}
......
python/paddle/fluid/tests/unittests/ir/inference/auto_scan_test.py
浏览文件 @ 3512bf11
......@@ -538,8 +538,9 @@ class TrtLayerAutoScanTest(AutoScanTest):
self.dynamic_shape = self.DynamicShapeParam({}, {}, {}, False)
self.num_percent_cases = float(
os.getenv('TEST_NUM_PERCENT_CASES', default='1.0'))
# Choose different tests by week
np.random.seed(int(time.strftime("%W")))
# Use a seperate random generator for skipping tests
self.skip_rng = np.random.default_rng(int(time.strftime("%W")))
def create_inference_config(self, use_trt=True) -> paddle_infer.Config:
config = paddle_infer.Config()
......@@ -555,11 +556,10 @@ class TrtLayerAutoScanTest(AutoScanTest):
precision_mode=self.trt_param.precision,
use_static=self.trt_param.use_static,
use_calib_mode=self.trt_param.use_calib_mode)
if len(self.dynamic_shape.min_input_shape
) != 0 and self.dynamic_shape.min_input_shape.keys(
) == self.dynamic_shape.max_input_shape.keys(
) and self.dynamic_shape.min_input_shape.keys(
) == self.dynamic_shape.opt_input_shape.keys():
if self.dynamic_shape.min_input_shape and (
self.dynamic_shape.min_input_shape.keys() ==
self.dynamic_shape.max_input_shape.keys() ==
self.dynamic_shape.opt_input_shape.keys()):
config.set_trt_dynamic_shape_info(
self.dynamic_shape.min_input_shape,
self.dynamic_shape.max_input_shape,
......@@ -567,6 +567,16 @@ class TrtLayerAutoScanTest(AutoScanTest):
self.dynamic_shape.disable_trt_plugin_fp16)
return config
def assert_tensors_near(self, atol: float, rtol: float,
tensor: Dict[str, np.array],
baseline: Dict[str, np.array]):
for key, arr in tensor.items():
self.assertEqual(
baseline[key].shape, arr.shape,
'The output shapes are not equal, the baseline shape is ' +
str(baseline[key].shape) + ', but got ' + str(arr.shape))
np.testing.assert_allclose(baseline[key], arr, rtol=rtol, atol=atol)
def assert_op_size(self, trt_engine_num, paddle_op_num):
last_passed_program = os.path.join(
self.cache_dir, 'transpose_flatten_concat_fuse_pass.pdmodel')
......@@ -579,14 +589,14 @@ class TrtLayerAutoScanTest(AutoScanTest):
]
trt_engine_size = sum(op_types)
paddle_op_size = op_size - trt_engine_size
self.assertTrue(
trt_engine_size == trt_engine_num,
'trt_engine_num is {}, but got {}!'.format(trt_engine_size,
trt_engine_num))
self.assertTrue(
paddle_op_size == paddle_op_num,
'paddle_op_num is {}, but got {}!'.format(paddle_op_size,
paddle_op_num))
self.assertEqual(
trt_engine_num, trt_engine_size,
'Expected trt_engine_num is {}, but got {}!'.format(
trt_engine_num, trt_engine_size))
self.assertEqual(
paddle_op_num, paddle_op_size,
'Expected paddle_op_num is {}, but got {}!'.format(
paddle_op_num, paddle_op_size))
def inference_config_str(self, config: paddle_infer.Config) -> str:
dic = {}
......@@ -602,18 +612,16 @@ class TrtLayerAutoScanTest(AutoScanTest):
return str(dic)
def run_test(self, quant=False, skip_baseline=False, *args, **kwargs):
status = True
run_flags = []
all_passes = True
def random_to_skip():
if self.skip_rng.random() < self.num_percent_cases:
return False
return True
for prog_config in self.sample_program_configs(*args, **kwargs):
# In CI, only run 10% cases
if np.random.rand() < self.num_percent_cases:
run_flags.append(True)
else:
run_flags.append(False)
for prog_config, run_flags in zip(
self.sample_program_configs(*args, **kwargs), run_flags):
if not run_flags:
if random_to_skip():
continue
# if program is invalid, we should skip that cases.
......@@ -657,30 +665,32 @@ class TrtLayerAutoScanTest(AutoScanTest):
else:
raise NotImplementedError
if quant and pred_config.tensorrt_precision_mode(
) != paddle_infer.PrecisionType.Int8:
if pred_config.tensorrt_precision_mode(
) != paddle_infer.PrecisionType.Int8 and quant:
continue
if pred_config.tensorrt_precision_mode(
) == paddle_infer.PrecisionType.Int8 and not quant:
continue
ignore_flag = False
for ignore_info in self.ignore_cases:
if ignore_info[0](prog_config, pred_config):
for teller, reason, note in self.ignore_cases:
if teller(prog_config, pred_config):
ignore_flag = True
if ignore_info[1] == IgnoreReasons.TRT_NOT_IMPLEMENTED:
if reason == IgnoreReasons.TRT_NOT_IMPLEMENTED:
self.ignore_log(
"[TRT_NOT_IMPLEMENTED] " + ignore_info[2] +
' ' + ' vs ' +
self.inference_config_str(pred_config))
elif ignore_info[1] == IgnoreReasons.TRT_NOT_SUPPORT:
self.ignore_log(
"[TRT_NOT_SUPPORT] " + ignore_info[2] + ' ' +
' vs ' + self.inference_config_str(pred_config))
'[TRT_NOT_IMPLEMENTED] {} vs {}'.format(
note,
self.inference_config_str(pred_config)))
elif reason == IgnoreReasons.TRT_NOT_SUPPORT:
self.ignore_log('[TRT_NOT_SUPPORT] {} vs {}'.format(
note, self.inference_config_str(pred_config)))
else:
raise NotImplementedError
break
if ignore_flag:
continue
try:
pred_config_deserialize = paddle_infer.Config(pred_config)
results.append(
......@@ -688,24 +698,23 @@ class TrtLayerAutoScanTest(AutoScanTest):
pred_config, feed_data))
self.assert_tensors_near(atol, rtol, results[-1],
results[0])
if not ignore_flag:
self.assert_op_size(nodes_num[0], nodes_num[1])
trt_engine_num, paddle_op_num = nodes_num
self.assert_op_size(trt_engine_num, paddle_op_num)
# deserialize test
if nodes_num[0] > 0:
if trt_engine_num > 0:
self.run_test_config(model, params, prog_config,
pred_config_deserialize, feed_data)
self.success_log('RUN predictor_config {} done'.format(
self.inference_config_str(pred_config)))
except Exception as e:
self.fail_log(
self.inference_config_str(pred_config) +
'\033[1;31m \nERROR INFO: {}\033[0m'.format(str(e)))
if not ignore_flag:
status = False
continue
self.success_log('RUN predictor_config ' +
self.inference_config_str(pred_config) +
' done')
all_passes = False
self.assertTrue(status)
self.assertTrue(all_passes)
# TODO(wilber): just for backward compatible
def add_skip_case(self, teller: [
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_affine_channel.py
浏览文件 @ 3512bf11
......@@ -135,7 +135,7 @@ class TrtConvertAffineChannelTest(TrtLayerAutoScanTest):
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
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -144,7 +144,7 @@ class TrtConvertAffineChannelTest(TrtLayerAutoScanTest):
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
attrs, True), (1e-3, 1e-3)
def test(self):
self.run_test()
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_batch_norm.py
浏览文件 @ 3512bf11
......@@ -205,7 +205,7 @@ class TrtConvertBatchNormTest(TrtLayerAutoScanTest):
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
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -214,7 +214,7 @@ class TrtConvertBatchNormTest(TrtLayerAutoScanTest):
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
attrs, True), (1e-3, 1e-3)
def add_skip_trt_case(self):
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_conv2d.py
浏览文件 @ 3512bf11
......@@ -12,13 +12,14 @@
# 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 unittest
import paddle.inference as paddle_infer
import itertools
from functools import partial
from typing import Optional, List, Callable, Dict, Any, Set
import numpy as np
import paddle.inference as paddle_infer
from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons
from program_config import TensorConfig, ProgramConfig
class TrtConvertConv2dTest(TrtLayerAutoScanTest):
......@@ -52,62 +53,74 @@ class TrtConvertConv2dTest(TrtLayerAutoScanTest):
def generate_weight1(attrs: List[Dict[str, Any]]):
return np.random.random([24, 3, 3, 3]).astype(np.float32)
for batch in [1, 4]:
for strides in [[1, 1], [2, 2], [1, 2]]:
for paddings in [[0, 3], [1, 2, 3, 4]]:
for groups in [1, 3]:
for padding_algorithm in ['EXPLICIT', 'SAME', 'VALID']:
for dilations in [[1, 1], [2, 2], [1, 2]]:
for data_format in ['NCHW']:
dics = [{
"data_fromat": data_format,
"dilations": dilations,
"padding_algorithm": padding_algorithm,
"groups": groups,
"paddings": paddings,
"strides": strides,
"data_format": data_format
}, {}]
ops_config = [{
"op_type": "conv2d",
"op_inputs": {
"Input": ["input_data"],
"Filter": ["conv2d_weight"]
},
"op_outputs": {
"Output": ["conv_output_data"]
},
"op_attrs": dics[0]
}, {
"op_type": "relu",
"op_inputs": {
"X": ["conv_output_data"]
},
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": dics[1]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"conv2d_weight":
TensorConfig(data_gen=partial(
generate_weight1, dics))
},
inputs={
"input_data":
TensorConfig(data_gen=partial(
generate_input1, batch, dics))
},
outputs=["output_data"])
yield program_config
batch_options = [1, 2]
strides_options = [[2, 2], [1, 2]]
paddings_options = [[0, 3], [1, 2, 3, 4]]
groups_options = [1, 3]
padding_altorithm_options = ['EXPLICIT', 'SAME', 'VALID']
dilations_options = [[1, 2]]
data_format_options = ['NCHW']
configurations = [
batch_options,
strides_options,
paddings_options,
groups_options,
padding_altorithm_options,
dilations_options,
data_format_options,
]
for batch, strides, paddings, groups, padding_algorithm, dilations, data_format in itertools.product(
*configurations):
attrs = [{
"data_fromat": data_format,
"dilations": dilations,
"padding_algorithm": padding_algorithm,
"groups": groups,
"paddings": paddings,
"strides": strides,
"data_format": data_format,
}, {}]
ops_config = [{
"op_type": "conv2d",
"op_inputs": {
"Input": ["input_data"],
"Filter": ["conv2d_weight"]
},
"op_outputs": {
"Output": ["conv_output_data"]
},
"op_attrs": attrs[0]
}, {
"op_type": "relu",
"op_inputs": {
"X": ["conv_output_data"]
},
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": attrs[1]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"conv2d_weight":
TensorConfig(data_gen=partial(generate_weight1, attrs))
},
inputs={
"input_data":
TensorConfig(
data_gen=partial(generate_input1, batch, attrs))
},
outputs=["output_data"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
......@@ -146,10 +159,10 @@ class TrtConvertConv2dTest(TrtLayerAutoScanTest):
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)
attrs, False), (1e-3, 1e-3)
self.trt_param.precision = paddle_infer.PrecisionType.Int8
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False), (1e-5, 1e-5)
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -158,10 +171,10 @@ class TrtConvertConv2dTest(TrtLayerAutoScanTest):
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)
attrs, True), (1e-3, 1e-3)
self.trt_param.precision = paddle_infer.PrecisionType.Int8
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True), (1e-5, 1e-5)
attrs, True), (1e-3, 1e-3)
def test(self):
self.run_test()
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_conv2d_fusion.py
浏览文件 @ 3512bf11
......@@ -12,13 +12,14 @@
# 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
import unittest
from itertools import product
from functools import partial
from typing import Optional, List, Callable, Dict, Any, Set
import unittest
import numpy as np
import paddle.inference as paddle_infer
from program_config import TensorConfig, ProgramConfig
from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons
class TrtConvertConv2dFusionTest(TrtLayerAutoScanTest):
......@@ -58,68 +59,78 @@ class TrtConvertConv2dFusionTest(TrtLayerAutoScanTest):
def generate_weight2(attrs: List[Dict[str, Any]]):
return np.random.random([24, 1, 1]).astype(np.float32)
for batch in [1, 4]:
for strides in [[1, 1], [2, 2], [1, 2]]:
for paddings in [[0, 3], [1, 2, 3, 4]]:
for groups in [2, 3]:
for padding_algorithm in ['EXPLICIT', 'SAME', 'VALID']:
for dilations in [[1, 1], [2, 2], [1, 2]]:
for data_format in ['NCHW']:
dics = [{
"data_fromat": data_format,
"dilations": dilations,
"padding_algorithm": padding_algorithm,
"groups": groups,
"paddings": paddings,
"strides": strides,
"data_format": data_format
}, {
"axis": 1
}]
ops_config = [{
"op_type": "conv2d",
"op_inputs": {
"Input": ["input_data"],
"Filter": ["conv2d_weight"]
},
"op_outputs": {
"Output": ["conv_output_data"]
},
"op_attrs": dics[0]
}, {
"op_type": "elementwise_add",
"op_inputs": {
"X": ["conv_output_data"],
"Y": ["elementwise_weight"]
},
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": dics[1]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"conv2d_weight":
TensorConfig(data_gen=partial(
generate_weight1, dics)),
"elementwise_weight":
TensorConfig(data_gen=partial(
generate_weight2, dics))
},
inputs={
"input_data":
TensorConfig(data_gen=partial(
generate_input1, batch, dics))
},
outputs=["output_data"])
yield program_config
batch_options = [1, 2]
strides_options = [[1, 2], [2, 2]]
paddings_options = [[0, 3], [1, 2, 3, 4]]
groups_options = [2, 3]
padding_algorithm_options = ['EXPLICIT', 'SAME', 'VALID']
dilations_options = [[1, 2]]
data_format_options = ['NCHW']
configurations = [
batch_options,
strides_options,
paddings_options,
groups_options,
padding_algorithm_options,
dilations_options,
data_format_options,
]
for (batch, strides, paddings, groups, padding_algorithm, dilations,
data_format) in product(*configurations):
attrs = [{
"strides": strides,
"paddings": paddings,
"groups": groups,
"padding_algorithm": padding_algorithm,
"dilations": dilations,
"data_format": data_format,
}, {
"axis": 1
}]
ops_config = [{
"op_type": "conv2d",
"op_inputs": {
"Input": ["input_data"],
"Filter": ["conv2d_weight"]
},
"op_outputs": {
"Output": ["conv_output_data"]
},
"op_attrs": attrs[0]
}, {
"op_type": "elementwise_add",
"op_inputs": {
"X": ["conv_output_data"],
"Y": ["elementwise_weight"]
},
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": attrs[1]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"conv2d_weight":
TensorConfig(data_gen=partial(generate_weight1, attrs)),
"elementwise_weight":
TensorConfig(data_gen=partial(generate_weight2, attrs))
},
inputs={
"input_data":
TensorConfig(
data_gen=partial(generate_input1, batch, attrs))
},
outputs=["output_data"])
yield program_config
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
......@@ -131,8 +142,8 @@ class TrtConvertConv2dFusionTest(TrtLayerAutoScanTest):
"output_data": [1, 24, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, input_groups, 64, 64],
"output_data": [4, 24, 64, 64]
"input_data": [2, input_groups, 64, 64],
"output_data": [2, 24, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, input_groups, 64, 64],
......@@ -158,10 +169,10 @@ class TrtConvertConv2dFusionTest(TrtLayerAutoScanTest):
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)
attrs, False), (1e-3, 1e-3)
self.trt_param.precision = paddle_infer.PrecisionType.Int8
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False), (1e-5, 1e-5)
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -170,10 +181,10 @@ class TrtConvertConv2dFusionTest(TrtLayerAutoScanTest):
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)
attrs, True), (1e-3, 1e-3)
self.trt_param.precision = paddle_infer.PrecisionType.Int8
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True), (1e-5, 1e-5)
attrs, True), (1e-3, 1e-3)
def test(self):
self.run_test()
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_conv2d_transpose.py
浏览文件 @ 3512bf11
......@@ -62,14 +62,14 @@ class TrtConvertConv2dTransposeTest(TrtLayerAutoScanTest):
3]).astype(np.float32)
for num_channels in [2, 4, 6]:
for batch in [1, 2, 4]:
for strides in [[1, 1], [2, 2], [1, 2]]:
for batch in [1, 4]:
for strides in [[2, 2], [1, 2]]:
for paddings in [[0, 3], [1, 2, 3, 4]]:
for groups in [2]:
for padding_algorithm in [
'EXPLICIT', 'SAME', 'VALID'
]:
for dilations in [[1, 1], [2, 2], [1, 2]]:
for dilations in [[2, 2], [1, 2]]:
for data_format in ['NCHW']:
self.num_channels = num_channels
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_depthwise_conv2d.py
浏览文件 @ 3512bf11
......@@ -12,13 +12,14 @@
# 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 unittest
import itertools
from functools import partial
from typing import List, Dict, Any
import numpy as np
from program_config import TensorConfig, ProgramConfig
from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons
import paddle.inference as paddle_infer
from functools import partial
from typing import Optional, List, Callable, Dict, Any, Set
import unittest
class TrtConvertDepthwiseConv2dTest(TrtLayerAutoScanTest):
......@@ -40,113 +41,93 @@ class TrtConvertDepthwiseConv2dTest(TrtLayerAutoScanTest):
self.trt_param.workspace_size = 1073741824
def generate_input1(batch, attrs: List[Dict[str, Any]]):
if attrs[0]['groups'] == 1:
return np.ones([batch, 1, 64, 64]).astype(np.float32)
elif attrs[0]['groups'] == 2:
return np.ones([batch, 2, 64, 64]).astype(np.float32)
else:
return np.ones([batch, 3, 64, 64]).astype(np.float32)
groups = attrs[0]['groups']
return np.ones([batch, groups, 64, 64]).astype(np.float32)
def generate_weight1(attrs: List[Dict[str, Any]]):
return np.random.random([24, 1, 3, 3]).astype(np.float32)
for batch in [1, 2, 4]:
for strides in [[1, 1], [2, 2], [1, 2]]:
for paddings in [[0, 3], [1, 2, 3, 4]]:
for groups in [1, 2, 3]:
for padding_algorithm in ['EXPLICIT', 'SAME', 'VALID']:
for dilations in [[1, 1], [2, 2], [1, 2]]:
for data_format in ['NCHW']:
dics = [{
"data_fromat": data_format,
"dilations": dilations,
"padding_algorithm": padding_algorithm,
"groups": groups,
"paddings": paddings,
"strides": strides,
"data_format": data_format
}]
ops_config = [{
"op_type": "depthwise_conv2d",
"op_inputs": {
"Input": ["input_data"],
"Filter": ["conv2d_weight"]
},
"op_outputs": {
"Output": ["output_data"]
},
"op_attrs": dics[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"conv2d_weight":
TensorConfig(data_gen=partial(
generate_weight1, dics))
},
inputs={
"input_data":
TensorConfig(data_gen=partial(
generate_input1, batch, dics))
},
outputs=["output_data"])
yield program_config
batch_options = [1, 4]
strides_options = [[1, 2]]
paddings_options = [[0, 3], [1, 2, 3, 4]]
groups_options = [1, 3]
padding_algorithm_options = ['EXPLICIT', 'SAME', 'VAILD']
dilations_options = [[1, 1], [1, 2]]
data_format_options = ['NCHW']
configurations = [
batch_options,
strides_options,
paddings_options,
groups_options,
padding_algorithm_options,
dilations_options,
data_format_options,
]
for (batch, strides, paddings, groups, padding_algorithm, dilations,
data_format) in itertools.product(*configurations):
attrs = [{
"strides": strides,
"paddings": paddings,
"groups": groups,
"padding_algorithm": padding_algorithm,
"dilations": dilations,
"data_fromat": data_format,
}]
ops_config = [{
"op_type": "depthwise_conv2d",
"op_inputs": {
"Input": ["input_data"],
"Filter": ["conv2d_weight"]
},
"op_outputs": {
"Output": ["output_data"]
},
"op_attrs": attrs[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={
"conv2d_weight":
TensorConfig(data_gen=partial(generate_weight1, attrs))
},
inputs={
"input_data":
TensorConfig(
data_gen=partial(generate_input1, batch, attrs))
},
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 attrs[0]['groups'] == 1:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 1, 32, 32],
"output_data": [1, 24, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 1, 64, 64],
"output_data": [4, 24, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 1, 64, 64],
"output_data": [1, 24, 64, 64]
}
elif attrs[0]['groups'] == 2:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 2, 32, 32],
"output_data": [1, 24, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 2, 64, 64],
"output_data": [4, 24, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 2, 64, 64],
"output_data": [1, 24, 64, 64]
}
else:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 3, 32, 32],
"output_data": [1, 24, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 3, 64, 64],
"output_data": [4, 24, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 3, 64, 64],
"output_data": [1, 24, 64, 64]
}
groups = attrs[0]['groups']
self.dynamic_shape.min_input_shape = {
"input_data": [1, groups, 32, 32],
"output_data": [1, 24, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, groups, 64, 64],
"output_data": [4, 24, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, groups, 64, 64],
"output_data": [1, 24, 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):
def generate_trt_nodes_num():
return 1, 2
attrs = [
......@@ -156,26 +137,24 @@ class TrtConvertDepthwiseConv2dTest(TrtLayerAutoScanTest):
# 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
yield self.create_inference_config(), generate_trt_nodes_num(), 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)
yield self.create_inference_config(), generate_trt_nodes_num(), (1e-3,
1e-3)
self.trt_param.precision = paddle_infer.PrecisionType.Int8
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False), (1e-5, 1e-5)
yield self.create_inference_config(), generate_trt_nodes_num(), (1e-3,
1e-3)
# 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
yield self.create_inference_config(), generate_trt_nodes_num(), 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)
yield self.create_inference_config(), generate_trt_nodes_num(), (1e-3,
1e-3)
self.trt_param.precision = paddle_infer.PrecisionType.Int8
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True), (1e-5, 1e-5)
yield self.create_inference_config(), generate_trt_nodes_num(), (1e-3,
1e-3)
def add_skip_trt_case(self):
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_depthwise_conv2d_transpose.py
浏览文件 @ 3512bf11
......@@ -145,7 +145,7 @@ class TrtConvertDepthwiseConv2dTransposeTest(TrtLayerAutoScanTest):
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-3)
attrs, False), (1e-3, 1e-3)
# self.trt_param.precision = paddle_infer.PrecisionType.Int8
# yield self.create_inference_config(), generate_trt_nodes_num(
# attrs, False), (1e-5, 1e-5)
......@@ -157,7 +157,7 @@ class TrtConvertDepthwiseConv2dTransposeTest(TrtLayerAutoScanTest):
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)
attrs, True), (1e-3, 1e-3)
# self.trt_param.precision = paddle_infer.PrecisionType.Int8
# yield self.create_inference_config(), generate_trt_nodes_num(
# attrs, True), (1e-5, 1e-5)
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py
浏览文件 @ 3512bf11
......@@ -105,7 +105,7 @@ class TrtConvertElementwiseTest_one_input_special_case0(TrtLayerAutoScanTest):
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
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -114,7 +114,7 @@ class TrtConvertElementwiseTest_one_input_special_case0(TrtLayerAutoScanTest):
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
attrs, True), (1e-3, 1e-3)
def add_skip_trt_case(self):
pass
......@@ -200,7 +200,7 @@ class TrtConvertElementwiseTest_one_input_special_case1(TrtLayerAutoScanTest):
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
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -209,7 +209,7 @@ class TrtConvertElementwiseTest_one_input_special_case1(TrtLayerAutoScanTest):
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
attrs, True), (1e-3, 1e-3)
def add_skip_trt_case(self):
pass
......@@ -316,7 +316,7 @@ class TrtConvertElementwiseTest_one_input(TrtLayerAutoScanTest):
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
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -325,7 +325,7 @@ class TrtConvertElementwiseTest_one_input(TrtLayerAutoScanTest):
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
attrs, True), (1e-3, 1e-3)
def add_skip_trt_case(self):
pass
......@@ -460,14 +460,14 @@ class TrtConvertElementwiseTest_two_input_without_broadcast(
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
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
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
yield self.create_inference_config(), (1, 3), (1e-3, 1e-3)
def add_skip_trt_case(self):
pass
......@@ -590,14 +590,14 @@ class TrtConvertElementwiseTest_two_input_with_broadcast(TrtLayerAutoScanTest):
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
yield self.create_inference_config(), (1, 3), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
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
yield self.create_inference_config(), (1, 3), (1e-3, 1e-3)
def add_skip_trt_case(self):
pass
......@@ -706,14 +706,14 @@ class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest):
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), (0, 3), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), (0, 3), 1e-5
yield self.create_inference_config(), (0, 3), (1e-3, 1e-3)
# 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
yield self.create_inference_config(), (1, 2), (1e-3, 1e-3)
def add_skip_trt_case(self):
pass
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_flatten.py
浏览文件 @ 3512bf11
......@@ -103,7 +103,7 @@ class TrtConvertFlattenTest_dim_2(TrtLayerAutoScanTest):
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
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -112,7 +112,7 @@ class TrtConvertFlattenTest_dim_2(TrtLayerAutoScanTest):
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
attrs, True), (1e-3, 1e-3)
def test(self):
self.run_test()
......@@ -196,11 +196,12 @@ class TrtConvertFlattenTest_dim_3(TrtLayerAutoScanTest):
# 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
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -209,7 +210,7 @@ class TrtConvertFlattenTest_dim_3(TrtLayerAutoScanTest):
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
attrs, True), (1e-3, 1e-3)
def test(self):
self.run_test()
......@@ -297,7 +298,7 @@ class TrtConvertFlattenTest_dim_4(TrtLayerAutoScanTest):
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
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -306,7 +307,7 @@ class TrtConvertFlattenTest_dim_4(TrtLayerAutoScanTest):
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
attrs, True), (1e-3, 1e-3)
def test(self):
self.run_test()
......@@ -394,7 +395,7 @@ class TrtConvertFlattenTest_dim_5(TrtLayerAutoScanTest):
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
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -403,7 +404,7 @@ class TrtConvertFlattenTest_dim_5(TrtLayerAutoScanTest):
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
attrs, True), (1e-3, 1e-3)
def test(self):
self.run_test()
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_flatten_contiguous_range.py
浏览文件 @ 3512bf11
......@@ -95,11 +95,12 @@ class TrtConvertFlattenContiguousRangeTest(TrtLayerAutoScanTest):
# 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
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -108,7 +109,7 @@ class TrtConvertFlattenContiguousRangeTest(TrtLayerAutoScanTest):
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
attrs, True), (1e-3, 1e-3)
def test(self):
self.run_test()
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_hard_swish.py
浏览文件 @ 3512bf11
......@@ -41,8 +41,8 @@ class TrtConvertHardSwishTest(TrtLayerAutoScanTest):
return np.ones([1, 3, 32, 32]).astype(np.float32)
for threshold in [6.0, 7.0, 100.0, 0.0, -1.0]:
for scale in [5.0, 6.0, 7.0, -1.0, 0.0, 100.0]:
for offset in [3.0, 4.0, 5.0, -1.0, 0.0, 100.0]:
for scale in [5.0, 7.0, -1.0, 0.0, 100.0]:
for offset in [3.0, 5.0, -1.0, 0.0, 100.0]:
dics = [{
"threshold": threshold,
"scale": scale,
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_mish.py
浏览文件 @ 3512bf11
......@@ -143,7 +143,7 @@ class TrtConvertMishTest(TrtLayerAutoScanTest):
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
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -152,7 +152,7 @@ class TrtConvertMishTest(TrtLayerAutoScanTest):
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
attrs, True), (1e-3, 1e-3)
def add_skip_trt_case(self):
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_pool2d.py
浏览文件 @ 3512bf11
......@@ -19,6 +19,7 @@ import paddle.inference as paddle_infer
from functools import partial
from typing import Optional, List, Callable, Dict, Any, Set
import unittest
import itertools
class TrtConvertPool2dTest(TrtLayerAutoScanTest):
......@@ -52,69 +53,71 @@ class TrtConvertPool2dTest(TrtLayerAutoScanTest):
def generate_weight1(attrs: List[Dict[str, Any]]):
return np.random.random([24, 3, 3, 3]).astype(np.float32)
for strides in [[1, 1], [1, 2], [2, 2]]:
for paddings in [[0, 2], [0, 3]]:
for pooling_type in ['max', 'avg']:
for padding_algotithm in ['EXPLICIT', 'SAME', 'VAILD']:
for ksize in [[2, 3], [3, 3]]:
for data_format in ['NCHW']:
for global_pooling in [True, False]:
for exclusive in [False, True]:
for adaptive in [True, False]:
for ceil_mode in [False, True]:
dics = [{
"pooling_type":
pooling_type,
"ksize": ksize,
"data_fromat": data_format,
"padding_algorithm":
padding_algotithm,
"paddings": paddings,
"strides": strides,
"data_format": data_format,
"global_pooling":
global_pooling,
"exclusive": exclusive,
"adaptive": adaptive,
"ceil_mode": ceil_mode
}]
ops_config = [{
"op_type":
"pool2d",
"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_input1,
dics))
},
outputs=["output_data"])
yield program_config
strides_options = [[1, 2]]
paddings_options = [[0, 2]]
pooling_type_options = ['max', 'avg']
padding_algorithm_options = ['EXPLICIT', 'SAME', 'VAILD']
ksize_options = [[2, 3], [3, 3]]
data_format_options = ['NCHW']
global_pooling_options = [True, False]
exclusive_options = [True, False]
adaptive_option = [True, False]
ceil_mode_options = [True, False]
configurations = [
strides_options, paddings_options, pooling_type_options,
padding_algorithm_options, ksize_options, data_format_options,
global_pooling_options, exclusive_options, adaptive_option,
ceil_mode_options
]
for (strides, paddings, pooling_type, padding_algorithm, ksize,
data_format, global_pooling, exclusive, adaptive,
ceil_mode) in itertools.product(*configurations):
attrs = [{
"strides": strides,
"paddings": paddings,
"pooling_type": pooling_type,
"padding_algorithm": padding_algorithm,
"ksize": ksize,
"data_format": data_format,
"global_pooling": global_pooling,
"exclusive": exclusive,
"adaptive": adaptive,
"ceil_mode": ceil_mode,
}]
ops_config = [{
"op_type": "pool2d",
"op_inputs": {
"X": ["input_data"]
},
"op_outputs": {
"Out": ["output_data"]
},
"op_attrs": attrs[0]
}]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data":
TensorConfig(data_gen=partial(generate_input1, attrs))
},
outputs=["output_data"])
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.max_input_shape = {"input_data": [1, 3, 64, 64]}
self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]}
def clear_dynamic_shape():
......@@ -136,7 +139,7 @@ class TrtConvertPool2dTest(TrtLayerAutoScanTest):
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
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -145,7 +148,7 @@ class TrtConvertPool2dTest(TrtLayerAutoScanTest):
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
attrs, True), (1e-3, 1e-3)
def add_skip_trt_case(self):
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_prelu.py
浏览文件 @ 3512bf11
......@@ -192,7 +192,7 @@ class TrtConvertPreluTest(TrtLayerAutoScanTest):
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
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -201,7 +201,7 @@ class TrtConvertPreluTest(TrtLayerAutoScanTest):
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
attrs, True), (1e-3, 1e-3)
def add_skip_trt_case(self):
ver = paddle_infer.get_trt_compile_version()
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_scale.py
浏览文件 @ 3512bf11
......@@ -137,7 +137,7 @@ class TrtConvertScaleTest(TrtLayerAutoScanTest):
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
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -146,7 +146,7 @@ class TrtConvertScaleTest(TrtLayerAutoScanTest):
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
attrs, True), (1e-3, 1e-3)
def add_skip_trt_case(self):
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_skip_layernorm.py
浏览文件 @ 3512bf11
......@@ -198,7 +198,7 @@ class TrtConvertSkipLayernormTest(TrtLayerAutoScanTest):
# 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
# attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -207,7 +207,7 @@ class TrtConvertSkipLayernormTest(TrtLayerAutoScanTest):
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
attrs, True), (1e-3, 1e-3)
def add_skip_trt_case(self):
pass
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_swish.py
浏览文件 @ 3512bf11
......@@ -118,7 +118,7 @@ class TrtConvertSwishTest(TrtLayerAutoScanTest):
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
attrs, False), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
......@@ -127,7 +127,7 @@ class TrtConvertSwishTest(TrtLayerAutoScanTest):
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
attrs, True), (1e-3, 1e-3)
def test(self):
self.run_test()
......
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