wehub-resource-sync
864 lines
28 KiB
Python
864 lines
28 KiB
Python
# Copyright (c) 2018 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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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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skip_check_grad_ci,
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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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class ElementwiseMulOp(OpTest):
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def init_kernel_type(self):
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self.use_onednn = False
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def setUp(self):
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self.op_type = "elementwise_mul"
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self.prim_op_type = "prim"
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self.python_api = paddle.multiply
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self.public_python_api = paddle.multiply
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self.dtype = np.float64
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self.axis = -1
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self.init_dtype()
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self.init_input_output()
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self.init_kernel_type()
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self.init_axis()
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self.if_enable_cinn()
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y),
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}
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self.outputs = {'Out': self.out}
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self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn}
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def test_check_output(self):
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# TODO(wangzhongpu): support onednn op in dygraph mode
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self.check_output(
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check_dygraph=(not self.use_onednn),
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check_pir=(not self.use_onednn),
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check_pir_onednn=self.check_pir_onednn,
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)
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def test_check_grad_normal(self):
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# TODO(wangzhongpu): support onednn op in dygraph mode
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self.check_grad(
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['X', 'Y'],
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'Out',
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check_dygraph=(not self.use_onednn),
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check_prim=False,
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check_prim_pir=(not self.use_onednn),
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check_pir=(not self.use_onednn),
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check_pir_onednn=self.check_pir_onednn,
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)
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def test_check_grad_ignore_x(self):
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# TODO(wangzhongpu): support onednn op in dygraph mode
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self.check_grad(
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['Y'],
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'Out',
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no_grad_set=set("X"),
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check_dygraph=(not self.use_onednn),
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check_prim=False,
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check_prim_pir=(not self.use_onednn),
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check_pir=(not self.use_onednn),
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check_pir_onednn=self.check_pir_onednn,
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)
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def test_check_grad_ignore_y(self):
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# TODO(wangzhongpu): support onednn op in dygraph mode
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self.check_grad(
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['X'],
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'Out',
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no_grad_set=set('Y'),
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check_dygraph=(not self.use_onednn),
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check_prim=False,
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check_prim_pir=(not self.use_onednn),
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check_pir=(not self.use_onednn),
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check_pir_onednn=self.check_pir_onednn,
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)
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.out = np.multiply(self.x, self.y)
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def init_dtype(self):
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pass
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def init_axis(self):
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pass
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def if_enable_cinn(self):
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pass
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class TestComplexElementwiseMulOpWithCheckGrad(ElementwiseMulOp):
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def setUp(self):
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self.op_type = "elementwise_mul"
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self.prim_op_type = "prim"
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self.python_api = paddle.multiply
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self.public_python_api = paddle.multiply
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self.dtype = np.complex128
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self.axis = -1
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self.init_dtype()
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self.init_input_output()
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self.init_kernel_type()
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self.init_axis()
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self.if_enable_cinn()
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y),
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}
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self.outputs = {'Out': self.out}
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self.attrs = {'axis': self.axis}
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def init_input_output(self):
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self.x = np.array([3 + 4j, 1 + 2j]).astype(self.dtype)
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self.y = np.array([3 + 4j, 5 + 6j]).astype(self.dtype)
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self.out = np.multiply(self.x, self.y)
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def if_enable_cinn(self):
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self.enable_cinn = False
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def test_check_grad_normal(self):
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self.check_grad(
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['X', 'Y'],
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'Out',
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check_pir=True,
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check_pir_onednn=self.check_pir_onednn,
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)
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def test_check_grad_ignore_x(self):
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self.check_grad(
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['Y'],
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'Out',
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no_grad_set=set("X"),
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check_pir=True,
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check_pir_onednn=self.check_pir_onednn,
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)
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def test_check_grad_ignore_y(self):
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self.check_grad(
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['X'],
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'Out',
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no_grad_set=set('Y'),
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check_pir=True,
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check_pir_onednn=self.check_pir_onednn,
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)
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class TestElementwiseMulOp_ZeroDim1(ElementwiseMulOp):
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, []).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, []).astype(self.dtype)
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self.out = np.multiply(self.x, self.y)
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class TestElementwiseMulOp_ZeroDim2(ElementwiseMulOp):
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, []).astype(self.dtype)
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self.out = np.multiply(self.x, self.y)
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class TestElementwiseMulOp_ZeroDim3(ElementwiseMulOp):
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, []).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.out = np.multiply(self.x, self.y)
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class TestElementwiseMulOp_ZeroSize1(ElementwiseMulOp):
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, [3]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [0, 3]).astype(self.dtype)
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self.out = np.multiply(self.x, self.y)
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def test_check_grad_normal(self):
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pass
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def test_check_grad_ignore_x(self):
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pass
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def test_check_grad_ignore_y(self):
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pass
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class TestElementwiseMulOp_ZeroSize2(TestElementwiseMulOp_ZeroSize1):
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, [1, 3, 4]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [0, 3, 4]).astype(self.dtype)
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self.out = np.multiply(self.x, self.y)
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class TestElementwiseMulOp_ZeroSize3(TestElementwiseMulOp_ZeroSize1):
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, [1, 0, 2]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [3, 0, 1]).astype(self.dtype)
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self.out = np.multiply(self.x, self.y)
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@unittest.skipIf(
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not (paddle.is_compiled_with_cuda() or is_custom_device())
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or paddle.is_compiled_with_rocm(),
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"BFP16 test runs only on CUDA",
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)
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class TestBF16ElementwiseMulOp(OpTest):
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def setUp(self):
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self.op_type = "elementwise_mul"
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self.prim_op_type = "prim"
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self.python_api = paddle.multiply
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self.public_python_api = paddle.multiply
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self.dtype = np.uint16
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self.x = np.random.uniform(0.1, 1, [13, 17]).astype(np.float32)
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self.y = np.random.uniform(0.1, 1, [13, 17]).astype(np.float32)
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self.out = np.multiply(self.x, self.y)
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self.axis = -1
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(convert_float_to_uint16(self.x)),
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'Y': OpTest.np_dtype_to_base_dtype(convert_float_to_uint16(self.y)),
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}
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self.outputs = {'Out': convert_float_to_uint16(self.out)}
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self.attrs = {'axis': self.axis, 'use_onednn': False}
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self.if_enable_cinn()
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def test_check_output(self):
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self.check_output(
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check_pir=True, check_pir_onednn=self.check_pir_onednn
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)
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def test_check_grad_normal(self):
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self.check_grad(
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['X', 'Y'],
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'Out',
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check_prim=False,
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check_prim_pir=True,
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check_pir=True,
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check_pir_onednn=self.check_pir_onednn,
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)
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def test_check_grad_ignore_x(self):
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self.check_grad(
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['Y'],
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'Out',
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no_grad_set=set("X"),
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check_prim=False,
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check_prim_pir=True,
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check_pir=True,
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check_pir_onednn=self.check_pir_onednn,
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)
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def test_check_grad_ignore_y(self):
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self.check_grad(
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['X'],
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'Out',
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no_grad_set=set('Y'),
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check_prim=False,
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check_prim_pir=True,
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check_pir=True,
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check_pir_onednn=self.check_pir_onednn,
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)
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def if_enable_cinn(self):
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self.enable_cinn = False
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@skip_check_grad_ci(
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reason="[skip shape check] Use y_shape(1) to test broadcast."
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)
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class TestElementwiseMulOp_scalar(ElementwiseMulOp):
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def setUp(self):
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self.op_type = "elementwise_mul"
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self.prim_op_type = "prim"
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self.python_api = paddle.multiply
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self.public_python_api = paddle.multiply
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self.inputs = {
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'X': np.random.rand(10, 3, 4).astype(np.float64),
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'Y': np.random.rand(1).astype(np.float64),
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}
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self.outputs = {'Out': self.inputs['X'] * self.inputs['Y']}
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self.init_kernel_type()
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class TestElementwiseMulOp_Vector(ElementwiseMulOp):
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def setUp(self):
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self.op_type = "elementwise_mul"
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self.prim_op_type = "prim"
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self.python_api = paddle.multiply
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self.public_python_api = paddle.multiply
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self.inputs = {
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'X': np.random.random((100,)).astype("float64"),
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'Y': np.random.random((100,)).astype("float64"),
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}
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self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])}
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self.init_kernel_type()
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class ElementwiseMulOp_broadcast(OpTest):
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def init_kernel_type(self):
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self.use_onednn = False
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def setUp(self):
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self.op_type = "elementwise_mul"
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self.prim_op_type = "prim"
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self.python_api = paddle.multiply
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self.public_python_api = paddle.multiply
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self.init_dtype()
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self.init_kernel_type()
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self.init_axis()
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self.init_input_attr_output()
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self.if_check_prim()
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self.if_check_dygraph()
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def test_check_output(self):
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self.check_output(
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check_dygraph=self.check_dygraph,
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check_pir=self.check_dygraph,
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check_pir_onednn=self.check_pir_onednn,
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)
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def test_check_grad_normal(self):
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self.check_grad(
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['X', 'Y'],
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'Out',
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check_dygraph=self.check_dygraph,
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check_prim=self.check_prim,
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check_pir=self.check_dygraph,
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check_pir_onednn=self.check_pir_onednn,
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)
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def test_check_grad_ignore_x(self):
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self.check_grad(
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['Y'],
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'Out',
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no_grad_set=set("X"),
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check_dygraph=self.check_dygraph,
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check_prim=self.check_prim,
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check_pir=self.check_dygraph,
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check_pir_onednn=self.check_pir_onednn,
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)
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def test_check_grad_ignore_y(self):
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self.check_grad(
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['X'],
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'Out',
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no_grad_set=set('Y'),
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check_dygraph=self.check_dygraph,
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check_prim=self.check_prim,
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check_pir=self.check_dygraph,
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check_pir_onednn=self.check_pir_onednn,
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)
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def init_input_attr_output(self):
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self.x = np.random.uniform(0.1, 1, [13, 17, 1]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [17, 17]).astype(self.dtype)
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self.out = np.multiply(self.x, self.y)
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y),
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}
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self.outputs = {'Out': self.out}
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self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn}
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def init_dtype(self):
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self.dtype = np.float64
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def init_axis(self):
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self.axis = -1
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def if_check_prim(self):
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self.check_prim = False
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def if_check_dygraph(self):
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self.check_dygraph = (not self.use_onednn) and (self.axis == -1)
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class TestElementwiseMulOp_broadcast_0(ElementwiseMulOp_broadcast):
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def init_input_attr_output(self):
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self.x = np.random.rand(100, 2, 3).astype(self.dtype)
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self.y = np.random.rand(100).astype(self.dtype)
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self.out = self.x * self.y.reshape(100, 1, 1)
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y),
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}
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self.outputs = {'Out': self.out}
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self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn}
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def init_axis(self):
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self.axis = 0
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class TestElementwiseMulOp_broadcast_1(ElementwiseMulOp_broadcast):
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def init_input_attr_output(self):
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self.inputs = {
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'X': np.random.rand(2, 100, 3).astype(np.float64),
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'Y': np.random.rand(100).astype(np.float64),
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}
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self.attrs = {'axis': self.axis}
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self.outputs = {
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'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 100, 1)
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}
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def init_axis(self):
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self.axis = 1
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class TestElementwiseMulOp_broadcast_2(ElementwiseMulOp_broadcast):
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def init_input_attr_output(self):
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self.inputs = {
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'X': np.random.rand(2, 3, 100).astype(np.float64),
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'Y': np.random.rand(100).astype(np.float64),
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}
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self.attrs = {'axis': self.axis}
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self.outputs = {
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'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 1, 100)
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}
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class TestElementwiseMulOp_broadcast_3(ElementwiseMulOp_broadcast):
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def init_input_attr_output(self):
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self.inputs = {
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'X': np.random.rand(2, 10, 12, 3).astype(np.float64),
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'Y': np.random.rand(10, 12).astype(np.float64),
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}
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self.attrs = {'axis': self.axis}
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self.outputs = {
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'Out': self.inputs['X'] * self.inputs['Y'].reshape(1, 10, 12, 1)
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}
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def init_axis(self):
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self.axis = 1
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class TestElementwiseMulOp_broadcast_4(ElementwiseMulOp_broadcast):
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def init_input_attr_output(self):
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self.inputs = {
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'X': np.random.rand(10, 2, 11).astype(np.float64),
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'Y': np.random.rand(10, 1, 11).astype(np.float64),
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}
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self.attrs = {'axis': self.axis}
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self.outputs = {'Out': self.inputs['X'] * self.inputs['Y']}
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class TestElementwiseMulOp_broadcast_5(ElementwiseMulOp_broadcast):
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def init_input_attr_output(self):
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self.inputs = {
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'X': np.random.rand(10, 4, 2, 3).astype(np.float64),
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'Y': np.random.rand(10, 4, 1, 3).astype(np.float64),
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}
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self.attrs = {'axis': self.axis}
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self.outputs = {'Out': self.inputs['X'] * self.inputs['Y']}
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestElementwiseMulOpFp16(ElementwiseMulOp):
|
|
def init_dtype(self):
|
|
self.dtype = np.float16
|
|
|
|
def if_enable_cinn(self):
|
|
pass
|
|
|
|
def test_check_output(self):
|
|
# TODO(wangzhongpu): support onednn op in dygraph mode
|
|
self.check_output(
|
|
check_dygraph=(not self.use_onednn),
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def test_check_grad_normal(self):
|
|
# TODO(wangzhongpu): support onednn op in dygraph mode
|
|
self.check_grad(
|
|
['X', 'Y'],
|
|
'Out',
|
|
check_dygraph=(not self.use_onednn),
|
|
check_prim=False,
|
|
check_prim_pir=(not self.use_onednn),
|
|
check_pir=(not self.use_onednn),
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def test_check_grad_ignore_x(self):
|
|
# TODO(wangzhongpu): support onednn op in dygraph mode
|
|
self.check_grad(
|
|
['Y'],
|
|
'Out',
|
|
no_grad_set=set("X"),
|
|
check_dygraph=(not self.use_onednn),
|
|
check_prim=False,
|
|
check_prim_pir=(not self.use_onednn),
|
|
check_pir=(not self.use_onednn),
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def test_check_grad_ignore_y(self):
|
|
# TODO(wangzhongpu): support onednn op in dygraph mode
|
|
self.check_grad(
|
|
['X'],
|
|
'Out',
|
|
no_grad_set=set('Y'),
|
|
check_dygraph=(not self.use_onednn),
|
|
check_prim=False,
|
|
check_prim_pir=(not self.use_onednn),
|
|
check_pir=(not self.use_onednn),
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
|
|
class TestElementwiseMulOp_commonuse_1(ElementwiseMulOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_mul"
|
|
self.prim_op_type = "prim"
|
|
self.python_api = paddle.multiply
|
|
self.public_python_api = paddle.multiply
|
|
self.inputs = {
|
|
'X': np.random.rand(2, 3, 100).astype(np.float64),
|
|
'Y': np.random.rand(1, 1, 100).astype(np.float64),
|
|
}
|
|
self.outputs = {'Out': self.inputs['X'] * self.inputs['Y']}
|
|
self.init_kernel_type()
|
|
|
|
|
|
class TestElementwiseMulOp_commonuse_2(ElementwiseMulOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_mul"
|
|
self.prim_op_type = "prim"
|
|
self.python_api = paddle.multiply
|
|
self.public_python_api = paddle.multiply
|
|
self.inputs = {
|
|
'X': np.random.rand(30, 3, 1, 5).astype(np.float64),
|
|
'Y': np.random.rand(30, 1, 4, 1).astype(np.float64),
|
|
}
|
|
self.outputs = {'Out': self.inputs['X'] * self.inputs['Y']}
|
|
self.init_kernel_type()
|
|
|
|
|
|
class TestElementwiseMulOp_xsize_lessthan_ysize(ElementwiseMulOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_mul"
|
|
self.prim_op_type = "prim"
|
|
self.python_api = paddle.multiply
|
|
self.public_python_api = paddle.multiply
|
|
self.inputs = {
|
|
'X': np.random.rand(10, 10).astype(np.float64),
|
|
'Y': np.random.rand(2, 2, 10, 10).astype(np.float64),
|
|
}
|
|
|
|
self.attrs = {'axis': 2}
|
|
|
|
self.outputs = {
|
|
'Out': self.inputs['X'].reshape(1, 1, 10, 10) * self.inputs['Y']
|
|
}
|
|
self.init_kernel_type()
|
|
|
|
|
|
class TestComplexElementwiseMulOp(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_mul"
|
|
self.prim_op_type = "prim"
|
|
self.python_api = paddle.multiply
|
|
self.init_base_dtype()
|
|
self.init_input_output()
|
|
|
|
self.inputs = {
|
|
'X': OpTest.np_dtype_to_base_dtype(self.x),
|
|
'Y': OpTest.np_dtype_to_base_dtype(self.y),
|
|
}
|
|
self.attrs = {'axis': -1, 'use_onednn': False}
|
|
self.outputs = {'Out': self.out}
|
|
|
|
def init_base_dtype(self):
|
|
self.dtype = np.complex128
|
|
|
|
def init_input_output(self):
|
|
self.x = np.random.random((2, 3, 4, 5)).astype(
|
|
self.dtype
|
|
) + 1j * np.random.random((2, 3, 4, 5)).astype(self.dtype)
|
|
self.y = np.random.random((2, 3, 4, 5)).astype(
|
|
self.dtype
|
|
) + 1j * np.random.random((2, 3, 4, 5)).astype(self.dtype)
|
|
self.out = self.x * self.y
|
|
|
|
def test_check_output(self):
|
|
self.check_output(
|
|
check_pir=True, check_pir_onednn=self.check_pir_onednn
|
|
)
|
|
|
|
def test_check_grad_normal(self):
|
|
self.check_grad(
|
|
['X', 'Y'],
|
|
'Out',
|
|
check_pir=True,
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def test_check_grad_ignore_x(self):
|
|
self.check_grad(
|
|
['Y'],
|
|
'Out',
|
|
no_grad_set=set("X"),
|
|
check_pir=True,
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def test_check_grad_ignore_y(self):
|
|
self.check_grad(
|
|
['X'],
|
|
'Out',
|
|
no_grad_set=set('Y'),
|
|
check_pir=True,
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
|
|
class TestRealComplexElementwiseMulOp(TestComplexElementwiseMulOp):
|
|
def init_base_dtype(self):
|
|
self.dtype = np.complex128
|
|
|
|
def init_input_output(self):
|
|
self.x = np.random.random((2, 3, 4, 5)).astype(self.dtype)
|
|
self.y = np.random.random((2, 3, 4, 5)).astype(
|
|
self.dtype
|
|
) + 1j * np.random.random((2, 3, 4, 5)).astype(self.dtype)
|
|
self.out = self.x * self.y
|
|
|
|
|
|
class TestElementwiseMulop(unittest.TestCase):
|
|
def test_dygraph_mul(self):
|
|
paddle.disable_static()
|
|
|
|
np_a = np.random.random((2, 3, 4)).astype(np.float32)
|
|
np_b = np.random.random((2, 3, 4)).astype(np.float32)
|
|
|
|
tensor_a = paddle.to_tensor(np_a, dtype="float32")
|
|
tensor_b = paddle.to_tensor(np_b, dtype="float32")
|
|
|
|
# normal case: nparray * tenor
|
|
expect_out = np_a * np_b
|
|
actual_out = np_a * tensor_b
|
|
np.testing.assert_allclose(actual_out, expect_out)
|
|
|
|
# normal case: tensor * nparray
|
|
actual_out = tensor_a * np_b
|
|
np.testing.assert_allclose(actual_out, expect_out)
|
|
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestMulApiZeroSize(unittest.TestCase):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(1, 3, 4).astype('float32')
|
|
self.y_numpy = np.random.rand(0, 3, 4).astype('float32')
|
|
|
|
def _executed_api(self, x, y, name=None):
|
|
return paddle.multiply(x, y, name)
|
|
|
|
def test_declarative(self):
|
|
self.init_data()
|
|
with base.program_guard(base.Program()):
|
|
x = paddle.static.data(
|
|
name="x", shape=self.x_numpy.shape, dtype=self.x_numpy.dtype
|
|
)
|
|
y = paddle.static.data(
|
|
name="y", shape=self.y_numpy.shape, dtype=self.y_numpy.dtype
|
|
)
|
|
z = self._executed_api(x, y)
|
|
|
|
place = base.CPUPlace()
|
|
exe = base.Executor(place)
|
|
z_value = exe.run(
|
|
feed={"x": self.x_numpy, "y": self.y_numpy}, fetch_list=[z]
|
|
)
|
|
np_z = np.multiply(self.x_numpy, self.y_numpy)
|
|
np.testing.assert_allclose(z_value[0], np_z, rtol=1e-05, atol=1e-05)
|
|
|
|
def test_dygraph(self):
|
|
self.init_data()
|
|
places = (
|
|
[paddle.CPUPlace(), get_device_place()]
|
|
if core.is_compiled_with_cuda()
|
|
else [paddle.CPUPlace()]
|
|
)
|
|
for place in places:
|
|
with base.dygraph.guard(place):
|
|
x = paddle.to_tensor(self.x_numpy)
|
|
y = paddle.to_tensor(self.y_numpy)
|
|
z = self._executed_api(x, y)
|
|
np_z = np.multiply(self.x_numpy, self.y_numpy)
|
|
np.testing.assert_allclose(z, np_z, rtol=1e-05, atol=1e-05)
|
|
|
|
|
|
class TestMulApiZeroSize2(TestMulApiZeroSize):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(3).astype('float32')
|
|
self.y_numpy = np.random.rand(0, 3).astype('float32')
|
|
|
|
|
|
class TestMulApiZeroSize3(TestMulApiZeroSize):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(2, 0).astype('float32')
|
|
self.y_numpy = np.random.rand(1, 0).astype('float32')
|
|
|
|
|
|
class TestMulApiZeroSize4(TestMulApiZeroSize):
|
|
def init_data(self):
|
|
self.x_numpy = np.random.rand(1, 0, 2).astype('float32')
|
|
self.y_numpy = np.random.rand(3, 0, 1).astype('float32')
|
|
|
|
|
|
@unittest.skipIf(
|
|
not core.is_compiled_with_cuda(), "core is not compiled with CUDA"
|
|
)
|
|
class TestElementwiseMulop_Stride(ElementwiseMulOp):
|
|
def setUp(self):
|
|
self.op_type = "elementwise_mul"
|
|
self.python_api = paddle.multiply
|
|
self.public_python_api = paddle.multiply
|
|
self.transpose_api = paddle.transpose
|
|
self.as_stride_api = paddle.as_strided
|
|
self.init_dtype()
|
|
self.init_input_output()
|
|
|
|
self.inputs_stride = {
|
|
'X': OpTest.np_dtype_to_base_dtype(self.x),
|
|
'Y': OpTest.np_dtype_to_base_dtype(self.y_trans),
|
|
}
|
|
|
|
self.inputs = {
|
|
'X': OpTest.np_dtype_to_base_dtype(self.x),
|
|
'Y': OpTest.np_dtype_to_base_dtype(self.y),
|
|
}
|
|
|
|
self.outputs = {'Out': self.out}
|
|
|
|
def test_check_output(self):
|
|
place = get_device_place()
|
|
self.check_strided_forward = True
|
|
self.check_output(
|
|
place,
|
|
)
|
|
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
|
|
self.out = np.multiply(self.x, self.y)
|
|
self.perm = [1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
def test_check_grad_normal(self):
|
|
pass
|
|
|
|
def test_check_grad_ignore_x(self):
|
|
pass
|
|
|
|
def test_check_grad_ignore_y(self):
|
|
pass
|
|
|
|
|
|
class TestElementwiseMulop_Stride1(TestElementwiseMulop_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
|
self.out = np.multiply(self.x, self.y)
|
|
self.perm = [0, 1, 3, 2]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseMulop_Stride2(TestElementwiseMulop_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
|
self.out = np.multiply(self.x, self.y)
|
|
self.perm = [0, 2, 1, 3]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseMulop_Stride3(TestElementwiseMulop_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
|
|
self.out = np.multiply(self.x, self.y)
|
|
self.perm = [0, 1, 3, 2]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseMulop_Stride4(TestElementwiseMulop_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.uniform(0.1, 1, [1, 2, 13, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
|
|
self.out = np.multiply(self.x, self.y)
|
|
self.perm = [1, 0, 2, 3]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseMulop_Stride5(TestElementwiseMulop_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "as_stride"
|
|
self.x = np.random.uniform(0.1, 1, [23, 10, 1, 17]).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [23, 2, 13, 20]).astype(self.dtype)
|
|
self.y_trans = self.y
|
|
self.y = self.y[:, 0:1, :, 0:1]
|
|
self.out = np.multiply(self.x, self.y)
|
|
self.shape_param = [23, 1, 13, 1]
|
|
self.stride_param = [520, 260, 20, 1]
|
|
|
|
|
|
class TestElementwiseMulop_Stride_ZeroDim1(TestElementwiseMulop_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.uniform(0.1, 1, []).astype(self.dtype)
|
|
self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
|
|
self.out = np.multiply(self.x, self.y)
|
|
self.perm = [1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseMulop_Stride_ZeroSize1(TestElementwiseMulop_Stride):
|
|
def init_data(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.rand(1, 0, 2).astype('float32')
|
|
self.y = np.random.rand(3, 0, 1).astype('float32')
|
|
self.out = np.multiply(self.x, self.y)
|
|
self.perm = [2, 1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|