wehub-resource-sync
274 lines
8.1 KiB
Python
274 lines
8.1 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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from paddle import _C_ops
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class TestFlattenTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.full
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self.api_args = {"shape": [3, 2], "fill_value": 1.0}
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self.program_config = {"feed_list": []}
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self.min_shape = {}
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self.opt_shape = {}
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self.max_shape = {}
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def test_trt_result(self):
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self.check_trt_result()
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class TestAssignTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.assign
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self.api_args = {
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"x": np.random.random([2, 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, 2]}
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self.opt_shape = {"x": [2, 2]}
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self.max_shape = {"x": [3, 2]}
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def test_trt_result(self):
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self.check_trt_result()
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def assign_value_api(input, dtype, values):
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output = paddle.zeros_like(input)
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return _C_ops.assign_value_(
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output,
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list(input.shape),
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dtype,
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values,
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paddle.framework._current_expected_place(),
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)
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def assign_value_api_case2(input, dtype, values):
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return _C_ops.assign_value(
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list(input.shape),
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dtype,
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values,
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paddle.framework._current_expected_place(),
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)
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class TestAssignValueInTRTPattern(TensorRTBaseTest):
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def test_trt_result(self):
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test_cases = [
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# Test case 1
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(
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assign_value_api,
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{
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"x": np.random.random([2, 2]).astype("int32"),
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"dtype": paddle.int32,
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"values": [1, 1, 1, 1],
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},
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),
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# Test case 2
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(
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assign_value_api_case2,
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{
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"x": np.random.random([2, 2]).astype("float32"),
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"dtype": paddle.float32,
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"values": [1.0, 1.0, 1.0, 1.0],
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},
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),
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]
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for python_api, api_args in test_cases:
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with self.subTest(python_api=python_api, api_args=api_args):
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self.python_api = python_api
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self.api_args = api_args
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {}
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self.opt_shape = {}
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self.max_shape = {}
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self.check_trt_result()
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class TestArangeTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.arange
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self.api_args = {
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"start": np.array([0]).astype("int64"),
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"end": np.array([6]).astype("int64"),
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"step": np.array([1]).astype("int64"),
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}
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self.program_config = {"feed_list": []}
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self.min_shape = {}
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self.opt_shape = {}
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self.max_shape = {}
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def test_trt_result(self):
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self.check_trt_result()
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class TestArangeTRTPatternCase1(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.arange
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self.api_args = {
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"start": np.array([0]).astype("float32"),
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"end": np.array([6]).astype("float32"),
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"step": np.array([1]).astype("float32"),
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}
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self.program_config = {"feed_list": []}
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self.min_shape = {}
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self.opt_shape = {}
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self.max_shape = {}
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def test_trt_result(self):
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self.check_trt_result()
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class TestAssignOutTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.assign
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self.api_args = {
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"x": np.random.random([2, 2]).astype("float32"),
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"output": np.zeros((2, 2), dtype="float32"),
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}
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self.program_config = {"feed_list": ["x", "output"]}
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self.min_shape = {"x": [1, 2], "output": [1, 2]}
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self.opt_shape = {"x": [2, 2], "output": [2, 2]}
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self.max_shape = {"x": [3, 2], "output": [3, 2]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestFullLikeBoolTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.full_like
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self.api_args = {
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"input": np.random.randn(3, 2).astype("bool"),
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"fill_value": True,
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}
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self.program_config = {"feed_list": ["input"]}
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self.min_shape = {"input": [1, 2]}
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self.opt_shape = {"input": [3, 2]}
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self.max_shape = {"input": [5, 2]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestFullLikeFloatTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.full_like
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self.api_args = {
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"input": np.random.randn(3, 2).astype("float32"),
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"fill_value": 5.0,
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}
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self.program_config = {"feed_list": ["input"]}
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self.min_shape = {"input": [1, 2]}
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self.opt_shape = {"input": [3, 2]}
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self.max_shape = {"input": [5, 2]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestFullLikeIntTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.full_like
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self.api_args = {
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"input": np.random.randn(3, 2).astype("int64"),
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"fill_value": 5,
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}
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self.program_config = {"feed_list": ["input"]}
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self.min_shape = {"input": [1, 2]}
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self.opt_shape = {"input": [3, 2]}
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self.max_shape = {"input": [5, 2]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestFullLikeDynamicTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.full_like
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self.api_args = {
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"input": np.random.randn(3, 2).astype("float32"),
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"fill_value": np.array([5.0]).astype("float32"),
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}
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self.program_config = {"feed_list": ["input", "fill_value"]}
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self.min_shape = {"input": [1, 2]}
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self.opt_shape = {"input": [3, 2]}
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self.max_shape = {"input": [5, 2]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestFullWithTensorTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.tensor.fill_constant
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self.api_args = {
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"shape": np.array([1]).astype("int64"),
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"dtype": "float32",
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"value": np.array([0.0]).astype("float32"),
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}
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self.program_config = {"feed_list": ["value", "shape"]}
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self.min_shape = {}
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self.opt_shape = {}
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self.max_shape = {}
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def test_trt_result(self):
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self.check_trt_result()
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class TestFullWithTensorCase1TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.tensor.fill_constant
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self.api_args = {
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"shape": [1, 1],
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"dtype": np.float32,
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"value": np.array([1.0]).astype("float32"),
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}
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self.program_config = {"feed_list": ["value"]}
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self.min_shape = {}
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self.opt_shape = {}
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self.max_shape = {}
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def test_trt_result(self):
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self.check_trt_result()
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class TestMeshgridTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = paddle.meshgrid
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self.api_args = {
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"x": [
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np.random.random([20]).astype("float32"),
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np.random.random([30]).astype("float32"),
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],
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [[10], [20]]}
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self.opt_shape = {"x": [[20], [30]]}
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self.max_shape = {"x": [[30], [40]]}
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def test_trt_result(self):
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self.check_trt_result()
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if __name__ == "__main__":
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unittest.main()
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