wehub-resource-sync
937 lines
29 KiB
Python
937 lines
29 KiB
Python
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from tensorrt_test_base import TensorRTBaseTest
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import paddle
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class TestMaxTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.max
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self.api_args = {
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"x": np.random.randn(2, 4).astype("float32"),
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"axis": [0, 1],
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 4]}
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self.opt_shape = {"x": [2, 4]}
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self.max_shape = {"x": [5, 4]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestDivideTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.divide
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self.api_args = {
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"x": np.random.randn(2, 3).astype("float32"),
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"y": np.random.randn(2, 3).astype("float32"),
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}
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self.program_config = {"feed_list": ["x", "y"]}
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self.min_shape = {"x": [1, 3], "y": [1, 3]}
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self.opt_shape = {"x": [2, 3], "y": [2, 3]}
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self.max_shape = {"x": [5, 3], "y": [5, 3]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestMultiplyTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.multiply
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self.api_args = {
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"x": np.random.randn(2, 3).astype("float32"),
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"y": np.random.randn(2, 3).astype("float32"),
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}
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self.program_config = {"feed_list": ["x", "y"]}
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self.min_shape = {"x": [1, 3], "y": [1, 3]}
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self.opt_shape = {"x": [2, 3], "y": [2, 3]}
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self.max_shape = {"x": [5, 3], "y": [5, 3]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestSubtractTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.subtract
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self.api_args = {
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"x": np.random.randn(2, 3).astype("float32"),
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"y": np.random.randn(2, 3).astype("float32"),
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}
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self.program_config = {"feed_list": ["x", "y"]}
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self.min_shape = {"x": [1, 3], "y": [1, 3]}
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self.opt_shape = {"x": [2, 3], "y": [2, 3]}
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self.max_shape = {"x": [5, 3], "y": [5, 3]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestAddTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.add
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self.api_args = {
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"x": np.random.randn(2, 3).astype("float32"),
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"y": np.random.randn(2, 3).astype("float32"),
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}
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self.program_config = {"feed_list": ["x", "y"]}
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self.min_shape = {"x": [1, 3], "y": [1, 3]}
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self.opt_shape = {"x": [2, 3], "y": [2, 3]}
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self.max_shape = {"x": [5, 3], "y": [5, 3]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestElementwisePowTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle._C_ops.elementwise_pow
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self.api_args = {
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"x": np.random.randn(2, 3).astype(np.float32),
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"y": np.random.randn(2, 3).astype(np.float32),
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}
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self.program_config = {"feed_list": ["x", "y"]}
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self.min_shape = {"x": [1, 3], "y": [1, 3]}
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self.opt_shape = {"x": [2, 3], "y": [2, 3]}
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self.max_shape = {"x": [5, 3], "y": [5, 3]}
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def test_trt_result_fp16(self):
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self.check_trt_result(precision_mode="fp16")
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def test_trt_result_fp32(self):
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self.check_trt_result()
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class TestPowCase1TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.pow
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self.api_args = {
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"x": np.random.randn(2, 3).astype("float32"),
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"y": 2.5,
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 3]}
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self.opt_shape = {"x": [1, 3]}
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self.max_shape = {"x": [5, 3]}
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def test_trt_result_fp32(self):
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self.check_trt_result()
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def test_trt_result_fp16(self):
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self.check_trt_result(precision_mode="fp16")
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class TestPowCase2TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.pow
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self.api_args = {
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"x": np.random.randn(2, 3).astype("float32"),
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"y": 2,
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 3]}
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self.opt_shape = {"x": [1, 3]}
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self.max_shape = {"x": [5, 3]}
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def test_trt_result_fp32(self):
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self.check_trt_result()
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def test_trt_result_fp16(self):
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self.check_trt_result(precision_mode="fp16")
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class TestRemainderFloatTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.remainder
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self.api_args = {
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"x": np.random.randn(2, 3).astype("float32"),
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"y": np.random.uniform(low=0.1, high=1, size=(2, 3)).astype(
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"float32"
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), # Ensure y is non-zero
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}
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self.dynamic_shape_data = {
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"x": lambda shape: np.random.randn(*shape).astype("float32"),
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"y": lambda shape: np.random.uniform(
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low=0.1, high=1, size=shape
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).astype("float32"),
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}
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self.program_config = {"feed_list": ["x", "y"]}
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self.min_shape = {"x": [1, 3], "y": [1, 3]}
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self.opt_shape = {"x": [2, 3], "y": [2, 3]}
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self.max_shape = {"x": [5, 3], "y": [5, 3]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestRemainderIntTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.remainder
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self.api_args = {
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"x": np.random.randint(1, 10, size=(2, 3)).astype("int64"),
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"y": np.random.randint(1, 10, size=(2, 3)).astype(
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"int64"
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), # Ensure y is non-zero
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}
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self.dynamic_shape_data = {
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"x": lambda shape: np.random.randint(1, 10, size=shape).astype(
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"int64"
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),
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"y": lambda shape: np.random.randint(1, 10, size=shape).astype(
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"int64"
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),
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}
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self.program_config = {"feed_list": ["x", "y"]}
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self.min_shape = {"x": [1, 3], "y": [1, 3]}
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self.opt_shape = {"x": [2, 3], "y": [2, 3]}
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self.max_shape = {"x": [5, 3], "y": [5, 3]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestMinTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.min
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self.api_args = {
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"x": np.random.randn(2, 4).astype("float32"),
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"axis": [0, 1],
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 4]}
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self.opt_shape = {"x": [2, 4]}
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self.max_shape = {"x": [5, 4]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestSumTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.sum
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self.api_args = {
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"x": np.random.randn(2, 4, 6).astype("int64"),
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"axis": [1, 1],
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 4, 6]}
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self.opt_shape = {"x": [2, 4, 6]}
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self.max_shape = {"x": [5, 4, 6]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestSum1TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.sum
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self.api_args = {
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"x": np.random.randn(2, 4, 6).astype("float32"),
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"axis": [1, 1],
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 4, 6]}
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self.opt_shape = {"x": [2, 4, 6]}
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self.max_shape = {"x": [5, 4, 6]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestAnyTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.any
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self.api_args = {
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"x": np.random.randn(2, 3, 2).astype("bool"),
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"axis": [1],
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"keepdim": True,
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 3, 2]}
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self.opt_shape = {"x": [2, 3, 2]}
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self.max_shape = {"x": [5, 3, 2]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestAny1TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.any
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self.api_args = {
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"x": np.random.randn(2, 3, 2).astype("bool"),
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"axis": [1],
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"keepdim": False,
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 3, 2]}
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self.opt_shape = {"x": [2, 3, 2]}
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self.max_shape = {"x": [5, 3, 2]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestAny2TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.any
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self.api_args = {
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"x": np.random.randn(2, 3, 2).astype("bool"),
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"axis": [-1],
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"keepdim": False,
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 3, 2]}
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self.opt_shape = {"x": [2, 3, 2]}
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self.max_shape = {"x": [5, 3, 2]}
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def test_trt_result(self):
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self.check_trt_result()
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def test_trt_result_fp16(self):
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self.check_trt_result(precision_mode="fp16")
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class TestAllTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.all
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self.api_args = {
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"x": np.random.randn(2, 3, 2).astype("bool"),
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"axis": [1, 1],
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"keepdim": True,
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 3, 2]}
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self.opt_shape = {"x": [2, 3, 2]}
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self.max_shape = {"x": [5, 3, 2]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestAll1TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.all
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self.api_args = {
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"x": np.random.randn(2, 3, 2).astype("bool"),
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"axis": [1, 1],
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"keepdim": False,
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 3, 2]}
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self.opt_shape = {"x": [2, 3, 2]}
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self.max_shape = {"x": [5, 3, 2]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestCumsumCase1TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.cumsum
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self.api_args = {
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"x": np.random.randn(2, 2, 3).astype("float32"),
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"axis": -1,
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 2, 3]}
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self.opt_shape = {"x": [2, 2, 3]}
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self.max_shape = {"x": [5, 2, 3]}
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def test_trt_result_fp16(self):
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self.check_trt_result(precision_mode="fp16")
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def test_trt_result_fp32(self):
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self.check_trt_result()
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class TestCumsumCase2TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.cumsum
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self.api_args = {
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"x": np.random.randn(2, 2, 3).astype("float32"),
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"axis": 1,
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 2, 3]}
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self.opt_shape = {"x": [2, 2, 3]}
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self.max_shape = {"x": [5, 2, 3]}
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def test_trt_result_fp16(self):
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self.check_trt_result(precision_mode="fp16")
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def test_trt_result_fp32(self):
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self.check_trt_result()
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class TestCumsumCase3TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.cumsum
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self.api_args = {
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"x": np.random.randn(2, 2, 3).astype("float32"),
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"axis": 0,
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 2, 3]}
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self.opt_shape = {"x": [2, 2, 3]}
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self.max_shape = {"x": [5, 2, 3]}
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def test_trt_result_fp16(self):
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self.check_trt_result(precision_mode="fp16")
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def test_trt_result_fp32(self):
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self.check_trt_result()
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class TestCumsumCase4TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.cumsum
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self.api_args = {
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"x": np.random.randn(2, 2, 3).astype("int64"),
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"axis": 0,
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 2, 3]}
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self.opt_shape = {"x": [2, 2, 3]}
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self.max_shape = {"x": [5, 2, 3]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestFloorDivideFloatTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.floor_divide
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self.api_args = {
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"x": np.random.randn(2, 3).astype("float32"),
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"y": np.random.randn(2, 3).astype("float32"),
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}
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self.program_config = {"feed_list": ["x", "y"]}
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self.min_shape = {"x": [1, 3], "y": [1, 3]}
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self.opt_shape = {"x": [2, 3], "y": [2, 3]}
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self.max_shape = {"x": [5, 3], "y": [5, 3]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestFloorDivideIntTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.floor_divide
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self.api_args = {
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"x": np.random.randint(low=1, high=100, size=(2, 3), dtype="int64"),
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"y": np.random.randint(low=1, high=100, size=(2, 3), dtype="int64"),
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}
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self.dynamic_shape_data = {
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"x": lambda shape: np.random.randint(
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1, 100, size=shape, dtype="int64"
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),
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"y": lambda shape: np.random.randint(
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1, 100, size=shape, dtype="int64"
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),
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}
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self.program_config = {"feed_list": ["x", "y"]}
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self.min_shape = {"x": [1, 3], "y": [1, 3]}
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self.opt_shape = {"x": [2, 3], "y": [2, 3]}
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self.max_shape = {"x": [5, 3], "y": [5, 3]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestLogFloatTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.log
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self.api_args = {
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"x": np.random.randn(2, 3).astype("float32"),
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 3]}
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self.opt_shape = {"x": [2, 3]}
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self.max_shape = {"x": [5, 3]}
|
|
|
|
def test_trt_result(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestLogIntTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle.log
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3).astype("int32"),
|
|
}
|
|
self.program_config = {"feed_list": ["x"]}
|
|
self.min_shape = {"x": [1, 3]}
|
|
self.opt_shape = {"x": [2, 3]}
|
|
self.max_shape = {"x": [5, 3]}
|
|
|
|
def test_trt_result(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestClipTRTPatternCase1(TensorRTBaseTest):
|
|
'''min/max is attr, and x/min/max is float'''
|
|
|
|
def setUp(self):
|
|
self.python_api = paddle.clip
|
|
self.api_args = {
|
|
"x": np.array([[2, 0.3, 0.5, 0.9], [0.1, 0.2, 6, 7]]).astype(
|
|
"float32"
|
|
),
|
|
"min": 2.2,
|
|
"max": 5.5,
|
|
}
|
|
self.program_config = {"feed_list": ["x"]}
|
|
self.min_shape = {"x": [1, 4]}
|
|
self.opt_shape = {"x": [2, 4]}
|
|
self.max_shape = {"x": [5, 4]}
|
|
|
|
def test_trt_result(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestClipTRTPatternCase2(TensorRTBaseTest):
|
|
def setUp(self):
|
|
'''min/max is attr, and x is int, min/max is float'''
|
|
self.python_api = paddle.clip
|
|
self.api_args = {
|
|
"x": np.array([[2, 3, 5, 9], [1, 2, 6, 7]]).astype("int64"),
|
|
"min": 2.2,
|
|
"max": 5.5,
|
|
}
|
|
self.program_config = {"feed_list": ["x"]}
|
|
self.min_shape = {"x": [1, 4]}
|
|
self.opt_shape = {"x": [2, 4]}
|
|
self.max_shape = {"x": [5, 4]}
|
|
|
|
def test_trt_result(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestClipTRTPatternCase3(TensorRTBaseTest):
|
|
'''min/max is input, and x/min/max is float'''
|
|
|
|
def setUp(self):
|
|
self.python_api = paddle.clip
|
|
self.api_args = {
|
|
"x": np.array([[2, 0.3, 0.5, 0.9], [0.1, 0.2, 6, 7]]).astype(
|
|
"float32"
|
|
),
|
|
"min": np.array([2.2]).astype("float32"),
|
|
"max": np.array([5.2]).astype("float32"),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "min", "max"]}
|
|
self.min_shape = {"x": [1, 4]}
|
|
self.opt_shape = {"x": [2, 4]}
|
|
self.max_shape = {"x": [5, 4]}
|
|
|
|
def test_trt_result(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestClipTRTPatternCase4(TensorRTBaseTest):
|
|
'''min/max is input, and x is int, min/max is float'''
|
|
|
|
def setUp(self):
|
|
self.python_api = paddle.clip
|
|
self.api_args = {
|
|
"x": np.array([[2, 3, 5, 9], [1, 2, 6, 7]]).astype("int64"),
|
|
"min": np.array([2]).astype("float32"),
|
|
"max": np.array([5]).astype("float32"),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "min", "max"]}
|
|
self.min_shape = {"x": [1, 4]}
|
|
self.opt_shape = {"x": [2, 4]}
|
|
self.max_shape = {"x": [5, 4]}
|
|
|
|
def test_trt_result(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestIsnanFP32TRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle.isnan
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3).astype("float32"),
|
|
}
|
|
self.program_config = {"feed_list": ["x"]}
|
|
self.min_shape = {"x": [1, 3]}
|
|
self.opt_shape = {"x": [2, 3]}
|
|
self.max_shape = {"x": [5, 3]}
|
|
|
|
def test_trt_result(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestIsnanFP16TRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle.isnan
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3).astype("float32"),
|
|
}
|
|
self.program_config = {"feed_list": ["x"]}
|
|
self.min_shape = {"x": [1, 3]}
|
|
self.opt_shape = {"x": [2, 3]}
|
|
self.max_shape = {"x": [5, 3]}
|
|
|
|
def test_trt_result(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
|
|
class TestIsnanIntTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle.isnan
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3).astype("int64"),
|
|
}
|
|
self.program_config = {"feed_list": ["x"]}
|
|
self.min_shape = {"x": [1, 3]}
|
|
self.opt_shape = {"x": [2, 3]}
|
|
self.max_shape = {"x": [5, 3]}
|
|
|
|
def test_trt_result(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestMaximumTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle.maximum
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3, 4).astype("float32"),
|
|
"y": np.random.randn(2, 3, 4).astype("float32"),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "y"]}
|
|
self.min_shape = {"x": [1, 3, 4], "y": [1, 3, 4]}
|
|
self.opt_shape = {"x": [2, 3, 4], "y": [2, 3, 4]}
|
|
self.max_shape = {"x": [5, 3, 4], "y": [5, 3, 4]}
|
|
|
|
def test_trt_result_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestMaximumBroadcastTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle.maximum
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3, 4).astype("float32"),
|
|
"y": np.random.randn(4).astype("float32"),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "y"]}
|
|
self.min_shape = {"x": [1, 3, 4], "y": [4]}
|
|
self.opt_shape = {"x": [2, 3, 4], "y": [4]}
|
|
self.max_shape = {"x": [5, 3, 4], "y": [4]}
|
|
|
|
def test_trt_result_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestMaximumIntTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle.maximum
|
|
self.api_args = {
|
|
"x": np.random.randint(
|
|
low=1, high=100, size=(2, 3, 4), dtype="int64"
|
|
),
|
|
"y": np.random.randint(
|
|
low=1, high=100, size=(2, 3, 4), dtype="int64"
|
|
),
|
|
}
|
|
self.dynamic_shape_data = {
|
|
"x": lambda shape: np.random.randint(
|
|
1, 100, size=shape, dtype="int64"
|
|
),
|
|
"y": lambda shape: np.random.randint(
|
|
1, 100, size=shape, dtype="int64"
|
|
),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "y"]}
|
|
self.min_shape = {"x": [1, 3, 4], "y": [1, 3, 4]}
|
|
self.opt_shape = {"x": [2, 3, 4], "y": [2, 3, 4]}
|
|
self.max_shape = {"x": [5, 3, 4], "y": [5, 3, 4]}
|
|
|
|
def test_trt_result_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestMinimumTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle.minimum
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3, 4).astype("float32"),
|
|
"y": np.random.randn(2, 3, 4).astype("float32"),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "y"]}
|
|
self.min_shape = {"x": [1, 3, 4], "y": [1, 3, 4]}
|
|
self.opt_shape = {"x": [2, 3, 4], "y": [2, 3, 4]}
|
|
self.max_shape = {"x": [5, 3, 4], "y": [5, 3, 4]}
|
|
|
|
def test_trt_result_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestMinimumBroadcastTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle.minimum
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3, 4).astype("float32"),
|
|
"y": np.random.randn(4).astype("float32"),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "y"]}
|
|
self.min_shape = {"x": [1, 3, 4], "y": [4]}
|
|
self.opt_shape = {"x": [2, 3, 4], "y": [4]}
|
|
self.max_shape = {"x": [5, 3, 4], "y": [4]}
|
|
|
|
def test_trt_result_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestMinimumIntTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle.minimum
|
|
self.api_args = {
|
|
"x": np.random.randint(
|
|
low=1, high=100, size=(2, 3, 4), dtype="int64"
|
|
),
|
|
"y": np.random.randint(
|
|
low=1, high=100, size=(2, 3, 4), dtype="int64"
|
|
),
|
|
}
|
|
self.dynamic_shape_data = {
|
|
"x": lambda shape: np.random.randint(
|
|
1, 100, size=shape, dtype="int64"
|
|
),
|
|
"y": lambda shape: np.random.randint(
|
|
1, 100, size=shape, dtype="int64"
|
|
),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "y"]}
|
|
self.min_shape = {"x": [1, 3, 4], "y": [1, 3, 4]}
|
|
self.opt_shape = {"x": [2, 3, 4], "y": [2, 3, 4]}
|
|
self.max_shape = {"x": [5, 3, 4], "y": [5, 3, 4]}
|
|
|
|
def test_trt_result_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestGreaterEqualTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle._C_ops.greater_equal
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3).astype("float32"),
|
|
"y": np.random.randn(2, 3).astype("float32"),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "y"]}
|
|
self.min_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.opt_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.max_shape = {"x": [5, 3], "y": [5, 3]}
|
|
|
|
def test_trt_result_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestGreaterEqual_TRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle._C_ops.greater_equal_
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3).astype("float32"),
|
|
"y": np.random.randn(2, 3).astype("float32"),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "y"]}
|
|
self.min_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.opt_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.max_shape = {"x": [5, 3], "y": [5, 3]}
|
|
|
|
def test_trt_result_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestGreaterEqualINTTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle._C_ops.greater_equal
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3).astype("int64"),
|
|
"y": np.random.randn(2, 3).astype("int64"),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "y"]}
|
|
self.min_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.opt_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.max_shape = {"x": [5, 3], "y": [5, 3]}
|
|
|
|
def test_trt_result_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestGreaterEqual_INTTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle._C_ops.greater_equal_
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3).astype("int64"),
|
|
"y": np.random.randn(2, 3).astype("int64"),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "y"]}
|
|
self.min_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.opt_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.max_shape = {"x": [5, 3], "y": [5, 3]}
|
|
|
|
def test_trt_result_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestGreaterEqualErrorTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle._C_ops.greater_equal
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3).astype("bool"),
|
|
"y": np.random.randn(2, 3).astype("bool"),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "y"]}
|
|
self.min_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.opt_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.max_shape = {"x": [5, 3], "y": [5, 3]}
|
|
|
|
def test_trt_result(self):
|
|
self.check_marker(expected_result=False)
|
|
|
|
|
|
class TestLessEqualTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle._C_ops.less_equal
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3).astype("float32"),
|
|
"y": np.random.randn(2, 3).astype("float32"),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "y"]}
|
|
self.min_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.opt_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.max_shape = {"x": [5, 3], "y": [5, 3]}
|
|
|
|
def test_trt_result_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestLessEqual_TRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle._C_ops.less_equal_
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3).astype("float32"),
|
|
"y": np.random.randn(2, 3).astype("float32"),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "y"]}
|
|
self.min_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.opt_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.max_shape = {"x": [5, 3], "y": [5, 3]}
|
|
|
|
def test_trt_result_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestLessEqualINTTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle._C_ops.less_equal
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3).astype("int64"),
|
|
"y": np.random.randn(2, 3).astype("int64"),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "y"]}
|
|
self.min_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.opt_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.max_shape = {"x": [5, 3], "y": [5, 3]}
|
|
|
|
def test_trt_result_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestLessEqual_INTTRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle._C_ops.less_equal_
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3).astype("int64"),
|
|
"y": np.random.randn(2, 3).astype("int64"),
|
|
}
|
|
self.program_config = {"feed_list": ["x", "y"]}
|
|
self.min_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.opt_shape = {"x": [1, 3], "y": [1, 3]}
|
|
self.max_shape = {"x": [5, 3], "y": [5, 3]}
|
|
|
|
def test_trt_result_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
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self.check_trt_result()
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class TestLessEqualErrorTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle._C_ops.less_equal
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self.api_args = {
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"x": np.random.randn(2, 3).astype("bool"),
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"y": np.random.randn(2, 3).astype("bool"),
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}
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self.program_config = {"feed_list": ["x", "y"]}
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self.min_shape = {"x": [1, 3], "y": [1, 3]}
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self.opt_shape = {"x": [1, 3], "y": [1, 3]}
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self.max_shape = {"x": [5, 3], "y": [5, 3]}
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def test_trt_result(self):
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self.check_marker(expected_result=False)
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if __name__ == '__main__':
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unittest.main()
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