wehub-resource-sync
408 lines
12 KiB
Python
408 lines
12 KiB
Python
# Copyright (c) 2023 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 (
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convert_float_to_uint16,
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get_device,
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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_fill(x, mask, value):
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if not np.isscalar(value):
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value = value[0]
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x, mask = np.broadcast_arrays(x, mask)
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result = np.copy(x)
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for idx, m in np.ndenumerate(mask):
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if m:
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result[idx] = value
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return result
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paddle.enable_static()
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class TestMaskedFillAPI(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(1).astype(self.dtype)
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self.out_np = np_masked_fill(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.scalar_value = False
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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_fill(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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if self.scalar_value:
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value = self.value_np[0]
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else:
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value = paddle.to_tensor(self.value_np, dtype=self.dtype)
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result = paddle.masked_fill(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 TestMaskedFillAPI1(TestMaskedFillAPI):
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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.scalar_value = False
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class TestMaskedFillAPI2(TestMaskedFillAPI):
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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.scalar_value = False
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class TestMaskedFillAPI3(TestMaskedFillAPI):
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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.scalar_value = True
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class TestMaskedFillGrad(unittest.TestCase):
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def setUp(self):
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self.typelist = ['float32', 'float64', 'int32', 'int64']
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self.places = get_places()
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self.dtype = "float32"
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def test_backward(self):
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paddle.disable_static()
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expected_np = np.array(
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[[2, 1, 1], [2, 1, 1], [2, 1, 1], [2, 1, 1]]
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).astype('float32')
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expected_y_grad = np.array(
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[[1, 0, 0], [1, 0, 0], [1, 0, 0], [1, 0, 0]]
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).astype('float32')
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expected_v_grad = np.array(8).astype('float32')
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for idx, p in enumerate(self.places):
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if idx == 0:
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paddle.set_device('cpu')
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else:
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paddle.set_device(get_device())
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for dtype in self.typelist:
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v = paddle.to_tensor(np.array(1).astype(self.dtype))
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x = paddle.ones((4, 3), dtype=self.dtype)
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mask = paddle.to_tensor(np.array([0, 1, 1]).astype("bool"))
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x.stop_gradient = False
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v.stop_gradient = False
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y = x * 2
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y.retain_grads()
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ny = y.masked_fill(mask=mask, value=v)
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ny.retain_grads() # if ny grad is none, v_grad should be 0
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loss = ny.sum()
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loss.backward()
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self.assertEqual(
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(ny.numpy().astype('float32') == expected_np).all(), True
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)
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self.assertEqual(
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(y.grad.numpy().astype('float32') == expected_y_grad).all(),
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True,
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)
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self.assertEqual(
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(v.grad.numpy().astype('float32') == expected_v_grad).all(),
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True,
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)
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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 TestMaskedFillFP16API1(TestMaskedFillAPI):
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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.scalar_value = False
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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 TestMaskedFillFP16API2(TestMaskedFillAPI):
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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.scalar_value = False
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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 TestMaskedFillFP16API3(TestMaskedFillAPI):
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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.scalar_value = True
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class TestMaskedFillAPIBroadcast(TestMaskedFillAPI):
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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.scalar_value = False
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class TestMaskedFillAPIBroadcast2(TestMaskedFillAPI):
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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.scalar_value = False
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class TestMaskedFillAPIBroadcast3(TestMaskedFillAPI):
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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.scalar_value = False
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class TestMaskedFillAPIBroadcast4(TestMaskedFillAPI):
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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.scalar_value = False
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class TestMaskedFillAPIBroadcast5(TestMaskedFillAPI):
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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.scalar_value = True
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class TestMaskedFillAPIBroadcast6(TestMaskedFillAPI):
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def init(self):
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self.x_shape = (1, 1)
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self.mask_shape = (40, 40)
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self.dtype = "float32"
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self.scalar_value = True
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class TestMaskedFillAPIBroadcast7(TestMaskedFillAPI):
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def init(self):
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self.x_shape = (15,)
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self.mask_shape = (40, 1)
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self.dtype = "float32"
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self.scalar_value = True
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class TestMaskedFillAPIBroadcast8(TestMaskedFillAPI):
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def init(self):
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self.x_shape = (3, 1, 1)
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self.mask_shape = (
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120,
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40,
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)
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self.dtype = "float32"
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self.scalar_value = True
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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 TestMaskedFillFP16APIBroadcast(TestMaskedFillAPI):
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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.scalar_value = False
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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 TestMaskedFillFP16APIBroadcast2(TestMaskedFillAPI):
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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.scalar_value = False
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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 TestMaskedFillFP16APIBroadcast3(TestMaskedFillAPI):
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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.scalar_value = True
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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 TestMaskedFillBF16(TestMaskedFillAPI):
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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.scalar_value = False
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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(1).astype("float32")
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)
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self.out_np = np_masked_fill(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 TestMaskedFillBF16APIBroadcast2(TestMaskedFillBF16):
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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.scalar_value = False
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class TestMaskedFillAPI_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(1).astype(self.dtype)
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self.out_np = np_masked_fill(self.x_np, self.mask_np, self.value_np)
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def init(self):
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self.x_shape = (0, 3)
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self.mask_shape = self.x_shape
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self.dtype = "float32"
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self.scalar_value = False
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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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x.stop_gradient = False
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mask = paddle.to_tensor(self.mask_np).astype('bool')
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if self.scalar_value:
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value = self.value_np[0]
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else:
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value = paddle.to_tensor(self.value_np, dtype=self.dtype)
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result = paddle.masked_fill(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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np.testing.assert_allclose(x.grad.numpy(), np.zeros(x.shape))
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class TestMaskedFillAPI_ZeroSize2(TestMaskedFillAPI_ZeroSize):
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# x_grad shape [2, 3], filled with 0.
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def init(self):
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self.x_shape = (1, 3)
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self.mask_shape = (0, 3)
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self.dtype = "float32"
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self.scalar_value = False
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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