wehub-resource-sync
545 lines
17 KiB
Python
545 lines
17 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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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 subprocess
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import sys
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import unittest
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import numpy as np
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from op_test import (
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convert_float_to_uint16,
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get_device_place,
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get_places,
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is_custom_device,
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)
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import paddle
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from paddle import base
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from paddle.base import core
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def np_masked_scatter(x, mask, value):
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x, mask = np.broadcast_arrays(x, mask)
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mask_prefix_sum = np.clip(mask.cumsum() - 1, a_min=0, a_max=None)
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value = value.flatten()[mask_prefix_sum].reshape(x.shape)
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return np.where(mask, value, x)
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paddle.enable_static()
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class TestMaskedScatterError(unittest.TestCase):
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def setUp(self):
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self.init()
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self.x_np = np.random.random(self.x_shape).astype(self.dtype)
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self.mask_np = np.array(
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np.random.randint(2, size=self.mask_shape), dtype='bool'
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)
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self.value_np = np.random.randn(*self.value_shape).astype(self.dtype)
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def init(self):
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self.x_shape = (50, 3)
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self.mask_shape = self.x_shape
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self.dtype = "float32"
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self.value_shape = (300, 300)
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def test_mask_error(self):
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x = paddle.to_tensor(self.x_np, dtype=self.dtype)
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mask = paddle.to_tensor(self.mask_np).astype('int32')
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value = paddle.to_tensor(self.value_np, dtype=self.dtype)
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with np.testing.assert_raises(AssertionError):
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paddle.masked_scatter(x, mask, value)
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def test_dtype_error(self):
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x = paddle.to_tensor(self.x_np, dtype=self.dtype)
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mask = paddle.to_tensor(self.mask_np).astype('bool')
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value = paddle.to_tensor(self.value_np, dtype='float64')
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with np.testing.assert_raises(AssertionError):
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paddle.masked_scatter(x, mask, value)
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@unittest.skipIf(
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core.is_compiled_with_cuda(),
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"core is compiled with CUDA",
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)
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def test_numel_error(self):
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paddle.disable_static()
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self.value_np = np.random.randn(5, 5).astype(self.dtype)
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x = paddle.to_tensor(self.x_np, dtype=self.dtype)
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mask = paddle.to_tensor(self.mask_np).astype('bool')
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value = paddle.to_tensor(self.value_np, dtype=self.dtype)
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with np.testing.assert_raises(AssertionError):
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paddle.masked_scatter(x, mask, value)
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@unittest.skipIf(
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not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA",
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)
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def test_numel_error_cuda(self):
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# The size check kernel uses asm("trap;") which fatally corrupts the
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# CUDA context. Run in a subprocess so the parent stays healthy.
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code = """
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import numpy as np
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import paddle
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paddle.disable_static()
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x_np = np.random.random((50, 3)).astype("float32")
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mask_np = np.ones((50, 3), dtype="bool")
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value_np = np.random.randn(5, 5).astype("float32")
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x = paddle.to_tensor(x_np)
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mask = paddle.to_tensor(mask_np)
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value = paddle.to_tensor(value_np)
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out = paddle.masked_scatter(x, mask, value)
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# Force synchronization so the device-side trap error surfaces.
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paddle.device.cuda.synchronize()
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"""
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proc = subprocess.run(
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[sys.executable, "-c", code],
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capture_output=True,
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text=True,
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)
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self.assertNotEqual(proc.returncode, 0)
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# Device-side printf may go to stdout; the OSError traceback is
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# on stderr. Check both for the kernel error message.
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combined = (proc.stdout + proc.stderr).lower()
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self.assertTrue(
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"number of true elements in mask" in combined
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or "cuda error" in combined
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or "hip error" in combined
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or "device-side assert" in combined
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or "abort" in combined,
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f"Expected masked_scatter size-check error, got:\n{combined}",
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)
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class TestMaskedScatterAPI(unittest.TestCase):
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def setUp(self):
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self.init()
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self.x_np = np.random.random(self.x_shape).astype(self.dtype)
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self.mask_np = np.array(
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np.random.randint(2, size=self.mask_shape), dtype="bool"
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)
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self.value_np = np.random.randn(*self.value_shape).astype(self.dtype)
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self.out_np = np_masked_scatter(self.x_np, self.mask_np, self.value_np)
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def init(self):
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self.x_shape = (50, 3)
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self.mask_shape = self.x_shape
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self.dtype = "float32"
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self.value_shape = (300, 300)
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def test_static_graph(self):
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paddle.enable_static()
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startup_program = base.Program()
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train_program = base.Program()
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with base.program_guard(startup_program, train_program):
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x = paddle.static.data(
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name='x', dtype=self.dtype, shape=self.x_shape
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)
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mask = paddle.static.data(
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name='mask', dtype='bool', shape=self.mask_shape
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)
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value = paddle.static.data(
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name='value', dtype=self.dtype, shape=self.value_np.shape
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)
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out = paddle.masked_scatter(x, mask, value)
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place = get_device_place()
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exe = base.Executor(place)
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res = exe.run(
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base.default_main_program(),
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feed={
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'x': self.x_np,
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'mask': self.mask_np,
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'value': self.value_np,
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},
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fetch_list=[out],
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)
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np.testing.assert_allclose(
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res[0], self.out_np, atol=1e-5, rtol=1e-5
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)
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paddle.disable_static()
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def test_dygraph(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.x_np, dtype=self.dtype)
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mask = paddle.to_tensor(self.mask_np).astype('bool')
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value = paddle.to_tensor(self.value_np, dtype=self.dtype)
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result = paddle.masked_scatter(x, mask, value)
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np.testing.assert_allclose(self.out_np, result.numpy(), rtol=1e-05)
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paddle.enable_static()
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class TestMaskedScatterAPI1(TestMaskedScatterAPI):
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def init(self):
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self.x_shape = (6, 8, 9, 18)
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self.mask_shape = self.x_shape
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self.dtype = "float32"
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self.value_shape = (300, 300)
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class TestMaskedScatterAPI2(TestMaskedScatterAPI):
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def init(self):
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self.x_shape = (168,)
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self.mask_shape = self.x_shape
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self.dtype = "float32"
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self.value_shape = (300, 300)
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class TestMaskedScatterAPI3(TestMaskedScatterAPI):
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def init(self):
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self.x_shape = (6, 8, 9, 18)
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self.mask_shape = self.x_shape
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self.dtype = "float32"
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self.value_shape = (300, 300)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestMaskedScatterFP16API1(TestMaskedScatterAPI):
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def init(self):
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self.x_shape = (6, 8, 9, 18)
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self.mask_shape = self.x_shape
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self.dtype = "float16"
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self.value_shape = (300, 300)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestMaskedScatterFP16API2(TestMaskedScatterAPI):
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def init(self):
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self.x_shape = (168,)
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self.mask_shape = self.x_shape
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self.dtype = "float16"
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self.value_shape = (300, 300)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestMaskedScatterFP16API3(TestMaskedScatterAPI):
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def init(self):
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self.x_shape = (168,)
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self.mask_shape = self.x_shape
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self.dtype = "float16"
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self.value_shape = (300, 300)
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class TestMaskedScatterAPIBroadcast(TestMaskedScatterAPI):
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def init(self):
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self.x_shape = (3, 40)
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self.mask_shape = (3, 1)
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self.dtype = "float32"
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self.value_shape = (300, 300)
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class TestMaskedScatterAPIBroadcast2(TestMaskedScatterAPI):
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def init(self):
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self.x_shape = (3, 3)
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self.mask_shape = (1, 3)
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self.dtype = "float32"
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self.value_shape = (300, 300)
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class TestMaskedScatterAPIBroadcast3(TestMaskedScatterAPI):
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def init(self):
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self.x_shape = (120,)
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self.mask_shape = (300, 120)
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self.dtype = "float32"
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self.value_shape = (300, 300)
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class TestMaskedScatterAPIBroadcast4(TestMaskedScatterAPI):
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def init(self):
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self.x_shape = (300, 40)
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self.mask_shape = (40,)
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self.dtype = "float32"
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self.value_shape = (300, 300)
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class TestMaskedScatterAPIBroadcast5(TestMaskedScatterAPI):
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def init(self):
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self.x_shape = (300, 40)
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self.mask_shape = (40,)
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self.dtype = "float32"
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self.value_shape = (300, 300)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestMaskedScatterFP16APIBroadcast(TestMaskedScatterAPI):
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def init(self):
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self.x_shape = (3, 40)
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self.mask_shape = (3, 1)
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self.dtype = "float16"
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self.value_shape = (300, 300)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestMaskedScatterFP16APIBroadcast2(TestMaskedScatterAPI):
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def init(self):
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self.x_shape = (300, 1)
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self.mask_shape = (300, 40)
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self.dtype = "float16"
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self.value_shape = (300, 300)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestMaskedScatterFP16APIBroadcast3(TestMaskedScatterAPI):
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def init(self):
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self.x_shape = (300, 1)
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self.mask_shape = (300, 40)
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self.dtype = "float16"
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self.value_shape = (300, 300)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support bfloat16",
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)
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class TestMaskedScatterBF16(TestMaskedScatterAPI):
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def init(self):
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self.x_shape = (300, 1)
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self.mask_shape = (300, 1)
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self.dtype = "uint16"
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self.value_shape = (300, 300)
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def setUp(self):
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self.init()
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self.x_np = convert_float_to_uint16(
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np.random.random(self.x_shape).astype("float32")
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)
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self.mask_np = np.array(
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np.random.randint(2, size=self.mask_shape), dtype="bool"
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)
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self.value_np = convert_float_to_uint16(
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np.random.randn(*self.value_shape).astype("float32")
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)
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self.out_np = np_masked_scatter(self.x_np, self.mask_np, self.value_np)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support bfloat16",
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)
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class TestMaskedScatterBF16APIBroadcast2(TestMaskedScatterBF16):
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def init(self):
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self.x_shape = (300, 1)
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self.mask_shape = (300, 3)
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self.dtype = "uint16"
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self.value_shape = (300, 300)
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class TestMaskedScatterCPU(TestMaskedScatterAPI):
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"""Explicitly run masked_scatter tests on CPUPlace to guarantee CPU
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coverage regardless of whether the build includes CUDA."""
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def test_static_graph(self):
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paddle.enable_static()
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startup_program = base.Program()
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train_program = base.Program()
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with base.program_guard(startup_program, train_program):
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x = paddle.static.data(
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name='x', dtype=self.dtype, shape=self.x_shape
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)
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mask = paddle.static.data(
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name='mask', dtype='bool', shape=self.mask_shape
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)
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value = paddle.static.data(
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name='value', dtype=self.dtype, shape=self.value_np.shape
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)
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out = paddle.masked_scatter(x, mask, value)
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place = core.CPUPlace()
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exe = base.Executor(place)
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res = exe.run(
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base.default_main_program(),
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feed={
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'x': self.x_np,
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'mask': self.mask_np,
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'value': self.value_np,
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},
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fetch_list=[out],
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)
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np.testing.assert_allclose(
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res[0], self.out_np, atol=1e-5, rtol=1e-5
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)
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paddle.disable_static()
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def test_dygraph(self):
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paddle.disable_static(paddle.CPUPlace())
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x = paddle.to_tensor(self.x_np, dtype=self.dtype)
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mask = paddle.to_tensor(self.mask_np).astype('bool')
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value = paddle.to_tensor(self.value_np, dtype=self.dtype)
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result = paddle.masked_scatter(x, mask, value)
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np.testing.assert_allclose(self.out_np, result.numpy(), rtol=1e-05)
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paddle.enable_static()
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def test_dygraph_grad(self):
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paddle.disable_static(paddle.CPUPlace())
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x = paddle.to_tensor(self.x_np, dtype=self.dtype)
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x.stop_gradient = False
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mask = paddle.to_tensor(self.mask_np).astype('bool')
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value = paddle.to_tensor(self.value_np, dtype=self.dtype)
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value.stop_gradient = False
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result = paddle.masked_scatter(x, mask, value)
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loss = paddle.sum(result)
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loss.backward()
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self.assertEqual(list(x.grad.shape), list(self.x_np.shape))
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self.assertEqual(list(value.grad.shape), list(self.value_np.shape))
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paddle.enable_static()
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class TestMaskedScatterCPU1(TestMaskedScatterCPU):
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def init(self):
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self.x_shape = (6, 8, 9, 18)
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self.mask_shape = self.x_shape
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self.dtype = "float32"
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self.value_shape = (300, 300)
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class TestMaskedScatterCPU2(TestMaskedScatterCPU):
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def init(self):
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self.x_shape = (168,)
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self.mask_shape = self.x_shape
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self.dtype = "float32"
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self.value_shape = (300, 300)
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class TestMaskedScatterCPUFloat64(TestMaskedScatterCPU):
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def init(self):
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self.x_shape = (50, 3)
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self.mask_shape = self.x_shape
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self.dtype = "float64"
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self.value_shape = (300, 300)
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class TestMaskedScatterCPUBroadcast(TestMaskedScatterCPU):
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def init(self):
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self.x_shape = (3, 40)
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self.mask_shape = (3, 1)
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self.dtype = "float32"
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self.value_shape = (300, 300)
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class TestMaskedScatterCPUBroadcast2(TestMaskedScatterCPU):
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def init(self):
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self.x_shape = (3, 3)
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self.mask_shape = (1, 3)
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self.dtype = "float32"
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self.value_shape = (300, 300)
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class TestMaskedScatterCPUBroadcast3(TestMaskedScatterCPU):
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def init(self):
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self.x_shape = (120,)
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self.mask_shape = (300, 120)
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self.dtype = "float32"
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self.value_shape = (300, 300)
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class TestMaskedScatterCPUBroadcast4(TestMaskedScatterCPU):
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def init(self):
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self.x_shape = (300, 40)
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self.mask_shape = (40,)
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self.dtype = "float32"
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self.value_shape = (300, 300)
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class TestMaskedScatterCPUZeroSize(TestMaskedScatterCPU):
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def init(self):
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self.x_shape = (3, 0)
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self.mask_shape = self.x_shape
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self.dtype = "float32"
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self.value_shape = (300, 300)
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class TestMaskedScatterCPUZeroSize2(TestMaskedScatterCPU):
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def init(self):
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self.x_shape = (0,)
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self.mask_shape = self.x_shape
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self.dtype = "float32"
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self.value_shape = (300, 300)
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class TestMaskedScatterCPUZeroSize3(TestMaskedScatterCPU):
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def init(self):
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self.x_shape = (0, 5, 3)
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self.mask_shape = self.x_shape
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self.dtype = "float64"
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self.value_shape = (300, 300)
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class TestMaskedScatterAPI_ZeroSize(unittest.TestCase):
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def setUp(self):
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self.init()
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self.x_np = np.random.random(self.x_shape).astype(self.dtype)
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self.mask_np = np.array(
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np.random.randint(2, size=self.mask_shape), dtype="bool"
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)
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self.value_np = np.random.randn(*self.value_shape).astype(self.dtype)
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self.out_np = np_masked_scatter(self.x_np, self.mask_np, self.value_np)
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self.places = get_places()
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def init(self):
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self.x_shape = (3, 0)
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self.mask_shape = self.x_shape
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self.dtype = "float32"
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self.value_shape = (300, 300)
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def _test_dygraph(self, place):
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paddle.disable_static(place)
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x = paddle.to_tensor(self.x_np, dtype=self.dtype)
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x.stop_gradient = False
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mask = paddle.to_tensor(self.mask_np).astype('bool')
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value = paddle.to_tensor(self.value_np, dtype=self.dtype)
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result = paddle.masked_scatter(x, mask, value)
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np.testing.assert_allclose(self.out_np, result.numpy(), rtol=1e-05)
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paddle.sum(result).backward()
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np.testing.assert_allclose(x.grad.shape, x.shape)
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paddle.enable_static()
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def test_dygraph(self):
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for place in self.places:
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self._test_dygraph(place)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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