wehub-resource-sync
494 lines
19 KiB
Python
494 lines
19 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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from utils import dygraph_guard, static_guard
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import paddle
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from paddle import base, core
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# Test python API
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class TestRandnLikeAPI(unittest.TestCase):
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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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self.place = get_device_place()
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def test_static_api_basic(self):
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"""Test basic static API functionality"""
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with (
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static_guard(),
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paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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),
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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.randn_like(x_float32)
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# Test with specified name
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out2 = paddle.randn_like(x_float32, name="test_randn_like")
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exe = paddle.static.Executor(self.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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# Test normal distribution range (approximately 99.7% within 3 std)
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self.assertTrue(((out >= -25) & (out <= 25)).all())
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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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with (
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static_guard(),
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paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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),
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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.randn_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 >= -25) & (result <= 25)).all())
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# Test with CUDA device if available
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if core.is_compiled_with_cuda():
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out2 = paddle.randn_like(x_float32, device=base.CUDAPlace(0))
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place_cuda = base.CUDAPlace(0)
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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 >= -25) & (result_cuda <= 25)).all()
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)
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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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with (
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static_guard(),
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paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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),
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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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exe = paddle.static.Executor(self.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 core.is_compiled_with_cuda():
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continue
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out = paddle.randn_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 >= -25) & (result <= 25)).all())
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def test_static_api_with_fp16(self):
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with static_guard():
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if paddle.is_compiled_with_cuda() or is_custom_device():
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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_float16 = paddle.static.data(
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name="x_float16", shape=[10, 12], dtype="float16"
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)
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exe = paddle.static.Executor(self.place)
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outlist1 = [
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paddle.randn_like(x_float16, dtype=dtype)
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for dtype in self.dtype
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]
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outs1 = exe.run(
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feed={'x_float16': self.x_float16}, fetch_list=outlist1
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)
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for out, dtype in zip(outs1, self.dtype):
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self.assertEqual(out.dtype, np.dtype(dtype))
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self.assertTrue(
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((out >= -25) & (out <= 25)).all(), True
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)
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def test_static_api_with_fp32(self):
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with (
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static_guard(),
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paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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),
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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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exe = paddle.static.Executor(self.place)
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outlist2 = [
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paddle.randn_like(x_float32, dtype=dtype)
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for dtype in self.dtype
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]
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outs2 = exe.run(
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feed={'x_float32': self.x_float32}, fetch_list=outlist2
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)
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for out, dtype in zip(outs2, self.dtype):
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self.assertEqual(out.dtype, np.dtype(dtype))
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self.assertTrue(((out >= -25) & (out <= 25)).all(), True)
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def test_static_api_with_fp64(self):
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with (
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static_guard(),
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paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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),
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):
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x_float64 = paddle.static.data(
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name="x_float64", shape=[10, 12], dtype="float64"
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)
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exe = paddle.static.Executor(self.place)
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outlist3 = [
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paddle.randn_like(x_float64, dtype=dtype)
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for dtype in self.dtype
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]
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outs3 = exe.run(
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feed={'x_float64': self.x_float64}, fetch_list=outlist3
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)
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for out, dtype in zip(outs3, self.dtype):
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self.assertEqual(out.dtype, np.dtype(dtype))
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self.assertTrue(((out >= -25) & (out <= 25)).all(), True)
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def test_dygraph_api_basic(self):
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"""Test basic dygraph API functionality"""
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with dygraph_guard():
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for x_np in [self.x_float32, self.x_float64]:
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x = paddle.to_tensor(x_np, place=self.place)
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# Test with default parameters
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out1 = paddle.randn_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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# Check device consistency
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self.assertEqual(str(x.place), str(out1.place))
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self.assertTrue(
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((out1.numpy() >= -25) & (out1.numpy() <= 25)).all()
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)
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# Test with name parameter
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out2 = paddle.randn_like(x, name="test_randn_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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# Check device consistency
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self.assertEqual(str(x.place), str(out2.place))
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self.assertTrue(
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((out2.numpy() >= -25) & (out2.numpy() <= 25)).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():
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x = paddle.to_tensor(self.x_float16, place=self.place)
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out = paddle.randn_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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# Check device consistency
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self.assertEqual(str(x.place), str(out.place))
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self.assertTrue(
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((out.numpy() >= -25) & (out.numpy() <= 25)).all()
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)
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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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with dygraph_guard():
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x = paddle.to_tensor(self.x_float32, place=self.place)
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for dtype in self.dtype:
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if dtype == "float16" and not core.is_compiled_with_cuda():
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continue
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out = paddle.randn_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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# Check device consistency with input
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self.assertEqual(str(x.place), str(out.place))
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self.assertTrue(
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((out.numpy() >= -25) & (out.numpy() <= 25)).all()
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)
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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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with dygraph_guard():
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x = paddle.to_tensor(self.x_float32, place=self.place)
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# Test requires_grad=True
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out1 = paddle.randn_like(x, requires_grad=True)
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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.assertFalse(out1.stop_gradient)
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# Check device consistency
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self.assertEqual(str(x.place), str(out1.place))
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self.assertTrue(
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((out1.numpy() >= -25) & (out1.numpy() <= 25)).all()
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)
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# Test requires_grad=False
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out2 = paddle.randn_like(x, requires_grad=False)
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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.stop_gradient)
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# Check device consistency
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self.assertEqual(str(x.place), str(out2.place))
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self.assertTrue(
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((out2.numpy() >= -25) & (out2.numpy() <= 25)).all()
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)
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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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with dygraph_guard():
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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.randn_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(
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((out1.numpy() >= -25) & (out1.numpy() <= 25)).all()
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)
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# Test with CUDA device if available
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if core.is_compiled_with_cuda():
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out2 = paddle.randn_like(x, device=paddle.CUDAPlace(0))
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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() >= -25) & (out2.numpy() <= 25)).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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with dygraph_guard():
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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.randn_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(
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((out1.numpy() >= -25) & (out1.numpy() <= 25)).all()
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)
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# Test all parameters together
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out2 = paddle.randn_like(
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x,
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name="combined_test",
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dtype="float64",
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device=paddle.CPUPlace(),
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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.place.is_cpu_place())
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self.assertTrue(
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((out2.numpy() >= -25) & (out2.numpy() <= 25)).all()
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)
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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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with dygraph_guard():
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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.randn_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(
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self.x_float32, place=paddle.CUDAPlace(0)
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)
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out_cuda = paddle.randn_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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def test_device_override_behavior(self):
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"""Test that explicitly specified device overrides input tensor device"""
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with dygraph_guard():
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if not core.is_compiled_with_cuda():
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return
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# Create tensor on GPU
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x_gpu = paddle.to_tensor(self.x_float32, place=paddle.CUDAPlace(0))
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# Force output to CPU using device parameter
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out_cpu = paddle.randn_like(x_gpu, device=paddle.CPUPlace())
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self.assertTrue(x_gpu.place.is_gpu_place())
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self.assertTrue(out_cpu.place.is_cpu_place())
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self.assertNotEqual(str(x_gpu.place), str(out_cpu.place))
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# Create tensor on CPU
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x_cpu = paddle.to_tensor(self.x_float32, place=paddle.CPUPlace())
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# Force output to GPU using device parameter
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out_gpu = paddle.randn_like(x_cpu, device=paddle.CUDAPlace(0))
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self.assertTrue(x_cpu.place.is_cpu_place())
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self.assertTrue(out_gpu.place.is_gpu_place())
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self.assertNotEqual(str(x_cpu.place), str(out_gpu.place))
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def test_different_shapes(self):
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"""Test with different input shapes"""
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with dygraph_guard():
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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.randn_like(x)
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self.assertEqual(out.shape, shape)
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self.assertEqual(str(x.place), str(out.place))
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self.assertTrue(
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((out.numpy() >= -25) & (out.numpy() <= 25)).all()
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)
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def test_default_dtype_behavior(self):
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"""Test default dtype behavior"""
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with dygraph_guard():
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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():
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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.randn_like(x) # dtype=None (default)
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self.assertEqual(out.dtype, x.dtype)
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self.assertEqual(str(x.place), str(out.place))
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self.assertTrue(
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((out.numpy() >= -25) & (out.numpy() <= 25)).all()
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)
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def test_dygraph_api(self):
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"""Legacy test method - kept for backward compatibility"""
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with dygraph_guard():
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for x in [
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self.x_float32,
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self.x_float64,
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]:
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x_inputs = paddle.to_tensor(x, place=self.place)
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for dtype in self.dtype:
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out = paddle.randn_like(x_inputs, dtype=dtype)
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self.assertEqual(out.numpy().dtype, np.dtype(dtype))
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self.assertTrue(
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((out.numpy() >= -25) & (out.numpy() <= 25)).all(), True
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)
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x_inputs = paddle.to_tensor(self.x_float32)
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out = paddle.randn_like(x_inputs)
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self.assertEqual(out.numpy().dtype, np.dtype("float32"))
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self.assertTrue(
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((out.numpy() >= -25) & (out.numpy() <= 25)).all(), True
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)
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if paddle.is_compiled_with_cuda() or is_custom_device():
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x_inputs = paddle.to_tensor(self.x_float16)
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for dtype in self.dtype:
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out = paddle.randn_like(x_inputs, dtype=dtype)
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self.assertEqual(out.numpy().dtype, np.dtype(dtype))
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self.assertTrue(
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((out.numpy() >= -25) & (out.numpy() <= 25)).all(), True
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)
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class TestRandnLikeOpForDygraph(unittest.TestCase):
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"""
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Test randn_like operation in dygraph mode with different scenarios.
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"""
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def run_net(self, use_cuda=False):
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place = base.CUDAPlace(0) if use_cuda else base.CPUPlace()
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with base.dygraph.guard(place):
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# Test basic functionality
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x1 = paddle.zeros([3, 4], dtype='float32')
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out1 = paddle.randn_like(x1)
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# Test with different dtype
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x2 = paddle.zeros([3, 4], dtype='float32')
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out2 = paddle.randn_like(x2, dtype='float64')
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# Test with requires_grad
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x3 = paddle.zeros([2, 5], dtype='float32')
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out3 = paddle.randn_like(x3, requires_grad=True)
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# Test with device specification
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x4 = paddle.zeros([4, 3], dtype='float32')
|
|
out4 = paddle.randn_like(x4, device=place)
|
|
|
|
# Test with all parameters including device
|
|
x5 = paddle.zeros([2, 3], dtype='float32')
|
|
out5 = paddle.randn_like(
|
|
x5,
|
|
name="test_all_params",
|
|
dtype='float64',
|
|
device=place,
|
|
requires_grad=False,
|
|
)
|
|
|
|
def test_run(self):
|
|
self.run_net(False)
|
|
if core.is_compiled_with_cuda():
|
|
self.run_net(True)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|