wehub-resource-sync
600 lines
18 KiB
Python
600 lines
18 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 TestEluTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.elu
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self.api_args = {
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"x": np.random.randn(3).astype("float32"),
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"alpha": 1.0,
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1]}
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self.opt_shape = {"x": [1]}
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self.max_shape = {"x": [5]}
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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(rtol=1e-3, atol=1e-3, precision_mode="fp16")
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def softmax_wrapper(x, axis=-1):
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softmax = paddle.nn.Softmax(axis=axis)
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return softmax(x)
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class TestSoftmaxCase1TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = softmax_wrapper
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self.api_args = {
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"x": np.random.randn(2, 3, 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, 3, 3]}
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self.opt_shape = {"x": [2, 3, 3]}
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self.max_shape = {"x": [5, 3, 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 TestSoftmaxCase2TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = softmax_wrapper
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self.api_args = {
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"x": np.random.randn(2).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]}
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self.opt_shape = {"x": [2]}
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self.max_shape = {"x": [5]}
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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 TestSoftmaxCase3TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = softmax_wrapper
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self.api_args = {
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"x": np.random.randn(2, 3, 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, 3, 3]}
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self.opt_shape = {"x": [2, 3, 3]}
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self.max_shape = {"x": [5, 3, 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 TestHardSigmoidTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.hardsigmoid
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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], "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_trt_result()
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class TestHardSwishTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.hardswish
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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], "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_trt_result()
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class TestReluTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.relu
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self.api_args = {"x": np.random.randn(3).astype("float32")}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1]}
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self.opt_shape = {"x": [1]}
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self.max_shape = {"x": [5]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestRelu6TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.relu6
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self.api_args = {"x": np.random.randn(3).astype("float32")}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1]}
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self.opt_shape = {"x": [2]}
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self.max_shape = {"x": [5]}
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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 TestTanhTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.tanh
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self.api_args = {"x": np.random.randn(3).astype("float32")}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1]}
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self.opt_shape = {"x": [1]}
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self.max_shape = {"x": [5]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestSigmoidTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.sigmoid
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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": [1, 3]}
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self.max_shape = {"x": [5, 3]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestSoftplusTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.Softplus()
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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": [1, 3]}
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self.max_shape = {"x": [5, 3]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestGeluTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.GELU()
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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": [1, 3]}
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self.max_shape = {"x": [5, 3]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestGeluCase1TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.GELU(True)
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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": [1, 3]}
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self.max_shape = {"x": [5, 3]}
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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 TestSiluFloatTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.silu
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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": [1, 3]}
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self.max_shape = {"x": [5, 3]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestSwishFloatTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.swish
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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": [1, 3]}
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self.max_shape = {"x": [5, 3]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestTanhShrinkOpFloatTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle._C_ops.tanh_shrink
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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]}
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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 TestStanhFloatTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.stanh
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self.api_args = {
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"x": np.random.randn(2, 3).astype("float32"),
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"scale_a": 0.67,
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"scale_b": 1.7159,
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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(self):
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self.check_trt_result()
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class TestCeluTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.celu
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self.api_args = {
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"x": np.random.randn(2, 3).astype("float32"),
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"alpha": 1.0,
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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(self):
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self.check_trt_result()
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class TestThresholdedReluTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.thresholded_relu
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self.api_args = {
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"x": np.random.randn(2, 3).astype("float32"),
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"threshold": 1.0,
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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]}
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def test_trt_result(self):
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self.check_trt_result(rtol=1e-3, atol=1e-3)
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def test_trt_result_fp16(self):
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self.check_trt_result(rtol=1e-3, atol=1e-3, precision_mode="fp16")
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class TestMishCase1TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.mish
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self.api_args = {
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"x": np.random.randn(2).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]}
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self.opt_shape = {"x": [2]}
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self.max_shape = {"x": [5]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestMishCase2TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.mish
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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]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestMishCase3TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.mish
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self.api_args = {
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"x": np.random.randn(2, 3, 4).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, 4]}
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self.opt_shape = {"x": [2, 3, 4]}
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self.max_shape = {"x": [5, 3, 4]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestMishCase4TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.mish
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self.api_args = {
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"x": np.random.randn(2, 3, 4, 2).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, 4, 2]}
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self.opt_shape = {"x": [2, 3, 4, 2]}
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self.max_shape = {"x": [5, 3, 4, 2]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestLogSigmoidTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.log_sigmoid
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x = np.random.random([1, 3, 32, 32]).astype(np.float32)
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self.api_args = {
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"x": x,
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {
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"x": [1, 3, 32, 32],
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}
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self.opt_shape = {"x": [4, 3, 32, 32]}
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self.max_shape = {"x": [4, 3, 32, 32]}
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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 TestSeluTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.selu
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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]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestLeakyReluCas1TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.leaky_relu
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self.api_args = {
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"x": np.random.randn(2, 3).astype("float32"),
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"negative_slope": 0.5,
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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": [2, 3]}
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self.max_shape = {"x": [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 TestLeakyReluCase2TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.leaky_relu
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self.api_args = {
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"x": np.random.randn(2, 3).astype("float32"),
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"negative_slope": -0.5,
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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": [2, 3]}
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self.max_shape = {"x": [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 TestLeakyRelu_Cas1TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.nn.functional.leaky_relu_
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self.api_args = {
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"x": np.random.randn(2, 3).astype("float32"),
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"negative_slope": 0.5,
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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": [2, 3]}
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|
self.max_shape = {"x": [5, 3]}
|
|
|
|
def test_trt_result_fp16(self):
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|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestLeakyRelu_Case2TRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = paddle.nn.functional.leaky_relu_
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3).astype("float32"),
|
|
"negative_slope": -0.5,
|
|
}
|
|
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_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
def prelu_wrapper(x, alpha_shape, data_format='NCHW'):
|
|
alpha = paddle.create_parameter(
|
|
shape=alpha_shape, dtype='float32', name="alpha"
|
|
)
|
|
return paddle.nn.functional.prelu(x, alpha, data_format)
|
|
|
|
|
|
class TestPReluCase1TRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = prelu_wrapper
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3).astype("float32"),
|
|
"alpha_shape": [3],
|
|
"data_format": "NCHW",
|
|
}
|
|
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_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestPReluCase2TRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = prelu_wrapper
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3).astype("float32"),
|
|
"alpha_shape": [3],
|
|
"data_format": "NHWC",
|
|
}
|
|
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_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
class TestPReluCase3TRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = prelu_wrapper
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3, 3).astype("float32"),
|
|
"alpha_shape": [3],
|
|
"data_format": "NCHW",
|
|
}
|
|
self.program_config = {"feed_list": ["x"]}
|
|
self.min_shape = {"x": [1, 3, 3]}
|
|
self.opt_shape = {"x": [2, 3, 3]}
|
|
self.max_shape = {"x": [5, 3, 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 TestPReluCase4TRTPattern(TensorRTBaseTest):
|
|
def setUp(self):
|
|
self.python_api = prelu_wrapper
|
|
self.api_args = {
|
|
"x": np.random.randn(2, 3, 3).astype("float32"),
|
|
"alpha_shape": [3],
|
|
"data_format": "NHWC",
|
|
}
|
|
self.program_config = {"feed_list": ["x"]}
|
|
self.min_shape = {"x": [1, 3, 3]}
|
|
self.opt_shape = {"x": [2, 3, 3]}
|
|
self.max_shape = {"x": [5, 3, 3]}
|
|
|
|
def test_trt_result_fp16(self):
|
|
self.check_trt_result(precision_mode="fp16")
|
|
|
|
def test_trt_result_fp32(self):
|
|
self.check_trt_result()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|