未验证 提交 d150c3a6 编写于 作者: Y Yuanle Liu 提交者: GitHub

test=infer-coverage fix passes test bug on A10 (#46480)

上级 091ae705
...@@ -53,6 +53,7 @@ model_test ...@@ -53,6 +53,7 @@ model_test
Testing Testing
tools/__pycache__ tools/__pycache__
tools/nvcc_lazy
# This file is automatically generated. # This file is automatically generated.
# TODO(zhiqiang) Move this file to build directory. # TODO(zhiqiang) Move this file to build directory.
......
...@@ -12,18 +12,15 @@ ...@@ -12,18 +12,15 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
from auto_scan_test import PassAutoScanTest, SkipReasons from auto_scan_test import PassAutoScanTest
from program_config import TensorConfig, ProgramConfig, OpConfig from program_config import TensorConfig, ProgramConfig, OpConfig
import numpy as np 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 import unittest
import os
import hypothesis
from hypothesis import given, settings, seed, example, assume, reproduce_failure
import hypothesis.strategies as st import hypothesis.strategies as st
os.environ['NVIDIA_TF32_OVERRIDE'] = '0'
class TestConvElementwiseAdd2ActPass(PassAutoScanTest): class TestConvElementwiseAdd2ActPass(PassAutoScanTest):
""" """
...@@ -62,11 +59,11 @@ class TestConvElementwiseAdd2ActPass(PassAutoScanTest): ...@@ -62,11 +59,11 @@ class TestConvElementwiseAdd2ActPass(PassAutoScanTest):
return False return False
if padding_algorithm == "VALID": if padding_algorithm == "VALID":
if int(((input_shape[2] - (dilations[0] * (filter_shape[2] - 1) + 1)) / strides[0] + 1)) <= 0 or \ if int(((input_shape[2] - (dilations[0] * (filter_shape[2] - 1) + 1)) / strides[0] + 1)) <= 0 or \
int(((input_shape[3] - (dilations[1] * (filter_shape[3] - 1) + 1)) / strides[1] + 1)) <= 0: int(((input_shape[3] - (dilations[1] * (filter_shape[3] - 1) + 1)) / strides[1] + 1)) <= 0:
return False return False
if padding_algorithm == "EXPLICIT": if padding_algorithm == "EXPLICIT":
if int(((input_shape[2] + paddings[0] + paddings[1] - (dilations[0] * (filter_shape[2] - 1) + 1)) / strides[0] + 1)) <= 0 or \ if int(((input_shape[2] + paddings[0] + paddings[1] - (dilations[0] * (filter_shape[2] - 1) + 1)) / strides[0] + 1)) <= 0 or \
int(((input_shape[3] + paddings[2] + paddings[3] - (dilations[1] * (filter_shape[3] - 1) + 1)) / strides[1] + 1)) <= 0: int(((input_shape[3] + paddings[2] + paddings[3] - (dilations[1] * (filter_shape[3] - 1) + 1)) / strides[1] + 1)) <= 0:
return False return False
if padding_algorithm == "SAME": if padding_algorithm == "SAME":
if int((input_shape[2] + strides[0] - 1) / strides[0]) <= 0 or int( if int((input_shape[2] + strides[0] - 1) / strides[0]) <= 0 or int(
...@@ -137,17 +134,27 @@ class TestConvElementwiseAdd2ActPass(PassAutoScanTest): ...@@ -137,17 +134,27 @@ class TestConvElementwiseAdd2ActPass(PassAutoScanTest):
# 9. Generate legal elemntwise_add: X of conv2d # 9. Generate legal elemntwise_add: X of conv2d
bias_2_dict = dict() bias_2_dict = dict()
bias_2_dict[1] = [x_shape[0], f_shape[0], \ bias_2_dict[1] = [
int(((x_shape[2] + padding[0] + padding[1] - (dilations[0] * (f_shape[2] - 1) + 1)) / strides[0] + 1)), \ x_shape[0], f_shape[0],
int(((x_shape[3] + padding[2] + padding[3] - (dilations[1] * (f_shape[3] - 1) + 1)) / strides[1] + 1))] int(((x_shape[2] + padding[0] + padding[1] -
(dilations[0] * (f_shape[2] - 1) + 1)) / strides[0] + 1)),
bias_2_dict[2] = [x_shape[0], f_shape[0], \ int(((x_shape[3] + padding[2] + padding[3] -
int((x_shape[2] + strides[0] - 1) / strides[0]), \ (dilations[1] * (f_shape[3] - 1) + 1)) / strides[1] + 1))
int((x_shape[3] + strides[1] - 1) / strides[1])] ]
bias_2_dict[3] = [x_shape[0], f_shape[0], \ bias_2_dict[2] = [
int(((x_shape[2] - (dilations[0] * (f_shape[2] - 1) + 1)) / strides[0] + 1)), \ x_shape[0], f_shape[0],
int(((x_shape[3] - (dilations[1] * (f_shape[3] - 1) + 1)) / strides[1] + 1))] int((x_shape[2] + strides[0] - 1) / strides[0]),
int((x_shape[3] + strides[1] - 1) / strides[1])
]
bias_2_dict[3] = [
x_shape[0], f_shape[0],
int(((x_shape[2] - (dilations[0] *
(f_shape[2] - 1) + 1)) / strides[0] + 1)),
int(((x_shape[3] - (dilations[1] *
(f_shape[3] - 1) + 1)) / strides[1] + 1))
]
bias_index = 1 bias_index = 1
if padding_algorithm == "SAME": if padding_algorithm == "SAME":
bias_index = 2 bias_index = 2
......
...@@ -12,18 +12,15 @@ ...@@ -12,18 +12,15 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
from auto_scan_test import PassAutoScanTest, IgnoreReasons from auto_scan_test import PassAutoScanTest
from program_config import TensorConfig, ProgramConfig, OpConfig from program_config import TensorConfig, ProgramConfig, OpConfig
import numpy as np 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 import unittest
import os
import hypothesis
from hypothesis import given, settings, seed, example, assume, reproduce_failure
import hypothesis.strategies as st import hypothesis.strategies as st
os.environ['NVIDIA_TF32_OVERRIDE'] = '0'
class TestConvElementwiseAddActPass(PassAutoScanTest): class TestConvElementwiseAddActPass(PassAutoScanTest):
""" """
...@@ -60,11 +57,11 @@ class TestConvElementwiseAddActPass(PassAutoScanTest): ...@@ -60,11 +57,11 @@ class TestConvElementwiseAddActPass(PassAutoScanTest):
return False return False
if padding_algorithm == "VALID": if padding_algorithm == "VALID":
if ((input_shape[2] - (dilations[0] * (filter_shape[2] - 1) + 1)) / strides[0] + 1) <= 1 or \ if ((input_shape[2] - (dilations[0] * (filter_shape[2] - 1) + 1)) / strides[0] + 1) <= 1 or \
((input_shape[3] - (dilations[1] * (filter_shape[3] - 1) + 1)) / strides[1] + 1) <= 1: ((input_shape[3] - (dilations[1] * (filter_shape[3] - 1) + 1)) / strides[1] + 1) <= 1:
return False return False
if padding_algorithm == "EXPLICIT": if padding_algorithm == "EXPLICIT":
if ((input_shape[2] + paddings[0] + paddings[1] - (dilations[0] * (filter_shape[2] - 1) + 1)) / strides[0] + 1) <= 1 or \ if ((input_shape[2] + paddings[0] + paddings[1] - (dilations[0] * (filter_shape[2] - 1) + 1)) / strides[0] + 1) <= 1 or \
((input_shape[3] + paddings[2] + paddings[3] - (dilations[1] * (filter_shape[3] - 1) + 1)) / strides[1] + 1) <= 1: ((input_shape[3] + paddings[2] + paddings[3] - (dilations[1] * (filter_shape[3] - 1) + 1)) / strides[1] + 1) <= 1:
return False return False
if data_format == "NCHW": if data_format == "NCHW":
if input_shape[1] != filter_shape[1] * groups: if input_shape[1] != filter_shape[1] * groups:
......
...@@ -12,18 +12,16 @@ ...@@ -12,18 +12,16 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
from auto_scan_test import PassAutoScanTest, SkipReasons from auto_scan_test import PassAutoScanTest
from program_config import TensorConfig, ProgramConfig, OpConfig from program_config import TensorConfig, ProgramConfig, OpConfig
import numpy as np 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 import unittest
import os
import hypothesis
from hypothesis import given, settings, seed, example, assume, reproduce_failure
import hypothesis.strategies as st import hypothesis.strategies as st
os.environ['NVIDIA_TF32_OVERRIDE'] = '0'
class TestConvEltwiseaddBnFusePass(PassAutoScanTest): class TestConvEltwiseaddBnFusePass(PassAutoScanTest):
""" """
...@@ -69,11 +67,11 @@ class TestConvEltwiseaddBnFusePass(PassAutoScanTest): ...@@ -69,11 +67,11 @@ class TestConvEltwiseaddBnFusePass(PassAutoScanTest):
return False return False
if padding_algorithm == "VALID": if padding_algorithm == "VALID":
if ((input_shape[2] - (dilations[0] * (filter_shape[2] - 1) + 1)) / strides[0] + 1) <= 1 or \ if ((input_shape[2] - (dilations[0] * (filter_shape[2] - 1) + 1)) / strides[0] + 1) <= 1 or \
((input_shape[3] - (dilations[1] * (filter_shape[3] - 1) + 1)) / strides[1] + 1) <= 1: ((input_shape[3] - (dilations[1] * (filter_shape[3] - 1) + 1)) / strides[1] + 1) <= 1:
return False return False
if padding_algorithm == "EXPLICIT": if padding_algorithm == "EXPLICIT":
if ((input_shape[2] + paddings[0] + paddings[1] - (dilations[0] * (filter_shape[2] - 1) + 1)) / strides[0] + 1) <= 1 or \ if ((input_shape[2] + paddings[0] + paddings[1] - (dilations[0] * (filter_shape[2] - 1) + 1)) / strides[0] + 1) <= 1 or \
((input_shape[3] + paddings[2] + paddings[3] - (dilations[1] * (filter_shape[3] - 1) + 1)) / strides[1] + 1) <= 1: ((input_shape[3] + paddings[2] + paddings[3] - (dilations[1] * (filter_shape[3] - 1) + 1)) / strides[1] + 1) <= 1:
return False return False
if data_format == "NCHW": if data_format == "NCHW":
......
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