wehub-resource-sync
296 lines
11 KiB
Python
296 lines
11 KiB
Python
# Copyright (c) 2025 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 op_test import get_device_place, is_custom_device
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import paddle
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from paddle import base, core
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class TestRandLikeAPI(unittest.TestCase):
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"""
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Test python API for rand_like function.
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"""
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def setUp(self):
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self.x_float16 = np.zeros((10, 12)).astype("float16")
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self.x_float32 = np.zeros((10, 12)).astype("float32")
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self.x_float64 = np.zeros((10, 12)).astype("float64")
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self.dtype = ["float16", "float32", "float64"]
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def test_static_api_basic(self):
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"""Test basic static API functionality"""
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paddle.enable_static()
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try:
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x_float32 = paddle.static.data(
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name="x_float32", shape=[10, 12], dtype="float32"
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)
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# Test with default parameters
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out1 = paddle.rand_like(x_float32)
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# Test with specified name
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out2 = paddle.rand_like(x_float32, name="test_rand_like")
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place = base.CPUPlace()
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if core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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exe = paddle.static.Executor(place)
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outs = exe.run(
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feed={'x_float32': self.x_float32}, fetch_list=[out1, out2]
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)
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for out in outs:
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self.assertEqual(out.shape, (10, 12))
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self.assertEqual(out.dtype, np.float32)
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self.assertTrue(((out >= 0.0) & (out <= 1.0)).all())
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finally:
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paddle.disable_static()
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def test_static_api_with_dtype(self):
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"""Test static API with different dtype specifications"""
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paddle.enable_static()
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try:
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x_float32 = paddle.static.data(
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name="x_float32", shape=[10, 12], dtype="float32"
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)
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place = base.CPUPlace()
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if core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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exe = paddle.static.Executor(place)
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# Test with different dtypes
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for dtype in self.dtype:
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if dtype == "float16" and not (
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core.is_compiled_with_cuda() or is_custom_device()
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):
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continue
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out = paddle.rand_like(x_float32, dtype=dtype)
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result = exe.run(
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feed={'x_float32': self.x_float32}, fetch_list=[out]
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)[0]
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self.assertEqual(result.shape, (10, 12))
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self.assertEqual(result.dtype, np.dtype(dtype))
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self.assertTrue(((result >= 0.0) & (result <= 1.0)).all())
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finally:
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paddle.disable_static()
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def test_static_api_with_device(self):
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"""Test static API with device specification"""
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paddle.enable_static()
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try:
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x_float32 = paddle.static.data(
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name="x_float32", shape=[10, 12], dtype="float32"
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)
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# Test with CPU device
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out1 = paddle.rand_like(x_float32, device=base.CPUPlace())
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place = base.CPUPlace()
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exe = paddle.static.Executor(place)
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result = exe.run(
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feed={'x_float32': self.x_float32}, fetch_list=[out1]
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)[0]
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self.assertEqual(result.shape, (10, 12))
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self.assertTrue(((result >= 0.0) & (result <= 1.0)).all())
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# Test with CUDA device if available
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if core.is_compiled_with_cuda() or is_custom_device():
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out2 = paddle.rand_like(
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x_float32, device=get_device_place()
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)
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place_cuda = get_device_place()
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exe_cuda = paddle.static.Executor(place_cuda)
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result_cuda = exe_cuda.run(
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feed={'x_float32': self.x_float32}, fetch_list=[out2]
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)[0]
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self.assertEqual(result_cuda.shape, (10, 12))
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self.assertTrue(
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((result_cuda >= 0.0) & (result_cuda <= 1.0)).all()
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)
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finally:
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paddle.disable_static()
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def test_dygraph_api_basic(self):
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"""Test basic dygraph API functionality"""
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for x_np in [self.x_float32, self.x_float64]:
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x = paddle.to_tensor(x_np)
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# Test with default parameters
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out1 = paddle.rand_like(x)
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self.assertEqual(out1.shape, x.shape)
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self.assertEqual(out1.dtype, x.dtype)
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self.assertTrue(
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((out1.numpy() >= 0.0) & (out1.numpy() <= 1.0)).all()
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)
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# Test with name parameter
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out2 = paddle.rand_like(x, name="test_rand_like")
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self.assertEqual(out2.shape, x.shape)
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self.assertEqual(out2.dtype, x.dtype)
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self.assertTrue(
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((out2.numpy() >= 0.0) & (out2.numpy() <= 1.0)).all()
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)
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# Test with float16 if CUDA is available
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if core.is_compiled_with_cuda() or is_custom_device():
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x = paddle.to_tensor(self.x_float16)
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out = paddle.rand_like(x)
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self.assertEqual(out.shape, x.shape)
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self.assertEqual(out.dtype, x.dtype)
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self.assertTrue(((out.numpy() >= 0.0) & (out.numpy() <= 1.0)).all())
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def test_dygraph_api_with_dtype(self):
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"""Test dygraph API with different dtype specifications"""
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x = paddle.to_tensor(self.x_float32)
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for dtype in self.dtype:
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if dtype == "float16" and not (
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core.is_compiled_with_cuda() or is_custom_device()
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):
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continue
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out = paddle.rand_like(x, dtype=dtype)
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self.assertEqual(out.shape, x.shape)
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self.assertEqual(out.dtype, getattr(paddle, dtype))
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self.assertTrue(((out.numpy() >= 0.0) & (out.numpy() <= 1.0)).all())
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def test_dygraph_api_with_requires_grad(self):
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"""Test dygraph API with requires_grad parameter"""
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x = paddle.to_tensor(self.x_float32)
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# Test requires_grad=True
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out1 = paddle.rand_like(x, requires_grad=True)
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self.assertEqual(out1.shape, x.shape)
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self.assertFalse(out1.stop_gradient)
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self.assertTrue(((out1.numpy() >= 0.0) & (out1.numpy() <= 1.0)).all())
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# Test requires_grad=False
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out2 = paddle.rand_like(x, requires_grad=False)
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self.assertEqual(out2.shape, x.shape)
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self.assertTrue(out2.stop_gradient)
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self.assertTrue(((out2.numpy() >= 0.0) & (out2.numpy() <= 1.0)).all())
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def test_dygraph_api_with_device(self):
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"""Test dygraph API with device specification"""
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x = paddle.to_tensor(self.x_float32)
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# Test with CPU device
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out1 = paddle.rand_like(x, device=paddle.CPUPlace())
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self.assertEqual(out1.shape, x.shape)
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self.assertEqual(out1.dtype, x.dtype)
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self.assertTrue(out1.place.is_cpu_place())
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self.assertTrue(((out1.numpy() >= 0.0) & (out1.numpy() <= 1.0)).all())
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# Test with CUDA device if available
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if core.is_compiled_with_cuda() or is_custom_device():
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out2 = paddle.rand_like(x, device=get_device_place())
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self.assertEqual(out2.shape, x.shape)
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self.assertEqual(out2.dtype, x.dtype)
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self.assertTrue(out2.place.is_gpu_place())
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self.assertTrue(
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((out2.numpy() >= 0.0) & (out2.numpy() <= 1.0)).all()
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)
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def test_dygraph_api_combined_params(self):
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"""Test dygraph API with combined parameters"""
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x = paddle.to_tensor(self.x_float32)
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# Test dtype + requires_grad
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out1 = paddle.rand_like(x, dtype="float64", requires_grad=True)
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self.assertEqual(out1.shape, x.shape)
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self.assertEqual(out1.dtype, paddle.float64)
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self.assertFalse(out1.stop_gradient)
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self.assertTrue(((out1.numpy() >= 0.0) & (out1.numpy() <= 1.0)).all())
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# Test all parameters together
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out2 = paddle.rand_like(
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x, name="combined_test", dtype="float64", requires_grad=False
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)
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self.assertEqual(out2.shape, x.shape)
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self.assertEqual(out2.dtype, paddle.float64)
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self.assertTrue(out2.stop_gradient)
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self.assertTrue(((out2.numpy() >= 0.0) & (out2.numpy() <= 1.0)).all())
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def test_different_shapes(self):
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"""Test with different input shapes"""
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shapes = [
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[
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1,
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],
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[5, 3],
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[2, 4, 6],
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[1, 2, 3, 4],
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]
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for shape in shapes:
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x = paddle.zeros(shape, dtype='float32')
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out = paddle.rand_like(x)
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self.assertEqual(out.shape, shape)
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self.assertTrue(((out.numpy() >= 0.0) & (out.numpy() <= 1.0)).all())
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def test_default_dtype_behavior(self):
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"""Test default dtype behavior"""
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# Test that output dtype matches input dtype when dtype=None
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dtypes_to_test = ['float32', 'float64']
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if core.is_compiled_with_cuda() or is_custom_device():
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dtypes_to_test.append('float16')
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for dtype_str in dtypes_to_test:
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x = paddle.zeros((3, 4), dtype=dtype_str)
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out = paddle.rand_like(x) # dtype=None (default)
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self.assertEqual(out.dtype, x.dtype)
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self.assertTrue(((out.numpy() >= 0.0) & (out.numpy() <= 1.0)).all())
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def test_device_consistency_default_behavior(self):
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"""Test that output tensor is on the same device as input tensor by default"""
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# Test CPU case
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x_cpu = paddle.to_tensor(self.x_float32, place=paddle.CPUPlace())
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out_cpu = paddle.rand_like(x_cpu) # No device specified
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self.assertTrue(x_cpu.place.is_cpu_place())
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self.assertTrue(out_cpu.place.is_cpu_place())
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self.assertEqual(str(x_cpu.place), str(out_cpu.place))
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# Test CUDA case if available
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if core.is_compiled_with_cuda():
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x_cuda = paddle.to_tensor(self.x_float32, place=get_device_place())
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out_cuda = paddle.rand_like(x_cuda) # No device specified
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self.assertTrue(x_cuda.place.is_gpu_place())
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self.assertTrue(out_cuda.place.is_gpu_place())
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self.assertEqual(str(x_cuda.place), str(out_cuda.place))
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if __name__ == "__main__":
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unittest.main()
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