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386 lines
10 KiB
386 lines
10 KiB
# Copyright (c) 2020 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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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle.fluid.core as core
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.framework as framework
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def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
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"""Reference forward implementation using np.matmul."""
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# np.matmul does not support the transpose flags, so we manually
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# transpose X and Y appropriately.
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if transpose_X:
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if X.ndim == 1:
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X = X.reshape((X.size, ))
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elif X.ndim == 2:
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X = X.T
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else:
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dim = [i for i in range(len(X.shape))]
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dim[-1], dim[len(X.shape) - 2] = dim[len(X.shape) - 2], dim[-1]
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X = np.transpose(X, tuple(dim))
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if transpose_Y:
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if Y.ndim == 1:
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Y = Y.reshape((Y.size, ))
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else:
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dim = [i for i in range(len(Y.shape))]
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dim[-1], dim[len(Y.shape) - 2] = dim[len(Y.shape) - 2], dim[-1]
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Y = np.transpose(Y, tuple(dim))
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Out = np.matmul(X, Y)
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if not Out.shape:
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# We do not support 0-dimensional Tensors (scalars). So where
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# np.matmul outputs a scalar, we must convert to a Tensor of
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# shape (1, ) instead.
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# Everywhere else, we are compatible with np.matmul.
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Out = np.array([Out], dtype="float64")
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return Out
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class TestMatMulV2Op(OpTest):
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"""
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case 1
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"""
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def config(self):
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self.x_shape = (100, )
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self.y_shape = (100, )
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self.trans_x = False
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self.trans_y = False
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def init_kernel_type(self):
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self.dtype = "float64"
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def setUp(self):
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self.init_kernel_type()
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self.config()
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self.op_type = "matmul_v2"
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x = np.random.random(self.x_shape).astype(self.dtype)
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y = np.random.random(self.y_shape).astype(self.dtype)
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# -0.1 ~ 0.1
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x = -0.1 + 0.2 * x
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y = -0.1 + 0.2 * y
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result = reference_matmul(x, y, self.trans_x, self.trans_y)
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result = result.astype(self.dtype)
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self.inputs = {
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'X': x,
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'Y': y,
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}
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self.attrs = {'trans_x': self.trans_x, 'trans_y': self.trans_y}
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self.outputs = {'Out': result}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X', 'Y'], 'Out')
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class TestMatMuklOp2(TestMatMulV2Op):
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"""
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case 2
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"""
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def config(self):
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self.x_shape = (100, )
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self.y_shape = (1, 3, 2, 100)
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self.trans_x = False
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self.trans_y = True
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class TestMatMuklOp3(TestMatMulV2Op):
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"""
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case 3
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"""
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def config(self):
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self.x_shape = (100, )
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self.y_shape = (1, 1, 100, 2)
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self.trans_x = False
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self.trans_y = False
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class TestMatMuklOp4(TestMatMulV2Op):
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"""
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case 4
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"""
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def config(self):
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self.x_shape = (100, )
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self.y_shape = (1, 2, 100, 2)
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self.trans_x = False
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self.trans_y = False
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class TestMatMuklOp5(TestMatMulV2Op):
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"""
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case 5
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"""
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def config(self):
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self.x_shape = (1, 1, 100, 1)
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self.y_shape = (100, )
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self.trans_x = True
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self.trans_y = False
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class TestMatMuklOp6(TestMatMulV2Op):
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"""
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case 6
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"""
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def config(self):
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self.x_shape = (1, 2, 100, 1)
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self.y_shape = (100, )
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self.trans_x = True
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self.trans_y = False
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class TestMatMuklOp7(TestMatMulV2Op):
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"""
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case 7
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"""
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def config(self):
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self.x_shape = (1, 2, 1, 100)
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self.y_shape = (100, )
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self.trans_x = False
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self.trans_y = False
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class TestMatMuklOp8(TestMatMulV2Op):
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"""
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case 8
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"""
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def config(self):
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self.x_shape = (1, 1, 2, 100)
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self.y_shape = (1, 1, 100, 2)
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self.trans_x = False
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self.trans_y = False
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class TestMatMuklOp9(TestMatMulV2Op):
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"""
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case 9
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"""
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def config(self):
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self.x_shape = (1, 1, 1, 100)
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self.y_shape = (2, 1, 2, 100)
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self.trans_x = False
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self.trans_y = True
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class TestMatMuklOp10(TestMatMulV2Op):
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"""
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case 10
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"""
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def config(self):
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self.x_shape = (1, 1, 25, 4)
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self.y_shape = (1, 2, 4, 25)
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self.trans_x = False
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self.trans_y = False
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class TestMatMuklOp11(TestMatMulV2Op):
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"""
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case 11
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"""
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def config(self):
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self.x_shape = (2, 1, 2, 100)
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self.y_shape = (1, 1, 100, 2)
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self.trans_x = False
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self.trans_y = False
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class TestMatMuklOp12(TestMatMulV2Op):
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"""
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case 12
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"""
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def config(self):
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self.x_shape = (2, 1, 4, 25)
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self.y_shape = (1, 1, 4, 25)
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self.trans_x = True
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self.trans_y = False
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class TestMatMuklOp13(TestMatMulV2Op):
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"""
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case 13
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"""
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def config(self):
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self.x_shape = (2, 2, 2, 50)
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self.y_shape = (2, 2, 2, 50)
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self.trans_x = True
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self.trans_y = False
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class TestMatMuklOp14(TestMatMulV2Op):
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"""
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case 14_1
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"""
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def config(self):
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self.x_shape = (3, 1, 1, 100, 2)
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self.y_shape = (1, 2, 2, 100, 2)
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self.trans_x = True
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self.trans_y = False
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class TestMatMuklOp15(TestMatMulV2Op):
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"""
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case 14_2
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"""
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def config(self):
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self.x_shape = (3, 1, 1, 2, 100)
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self.y_shape = (1, 2, 2, 100, 1)
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self.trans_x = False
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self.trans_y = False
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class TestMatMuklOp16(TestMatMulV2Op):
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"""
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case 16 : to check the gradient for special case
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"""
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def config(self):
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self.x_shape = (100)
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self.y_shape = (1, 2, 2, 100, 2)
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self.trans_x = False
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self.trans_y = False
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class TestMatMuklOp17(TestMatMulV2Op):
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"""
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case 17 : to check the gradient for special case
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"""
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def config(self):
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self.x_shape = (2, 1, 100)
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self.y_shape = (100)
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self.trans_x = False
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self.trans_y = False
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#--------------------test matmul fp16--------------------
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def create_test_fp16_class(parent, atol=0.001, max_relative_error=1.0):
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestMatMulOpFp16Case(parent):
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def init_kernel_type(self):
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=atol)
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def test_check_grad(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_grad_with_place(
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place, ['X', 'Y'],
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'Out',
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max_relative_error=max_relative_error)
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cls_name = "{0}_{1}".format(parent.__name__, "Fp16")
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TestMatMulOpFp16Case.__name__ = cls_name
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globals()[cls_name] = TestMatMulOpFp16Case
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create_test_fp16_class(TestMatMulV2Op)
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create_test_fp16_class(TestMatMuklOp2)
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create_test_fp16_class(TestMatMuklOp3)
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create_test_fp16_class(TestMatMuklOp4)
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create_test_fp16_class(TestMatMuklOp5)
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create_test_fp16_class(TestMatMuklOp6)
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create_test_fp16_class(TestMatMuklOp7)
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create_test_fp16_class(TestMatMuklOp8)
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create_test_fp16_class(TestMatMuklOp9)
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create_test_fp16_class(TestMatMuklOp10)
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create_test_fp16_class(TestMatMuklOp11)
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create_test_fp16_class(TestMatMuklOp12)
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create_test_fp16_class(TestMatMuklOp13)
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create_test_fp16_class(TestMatMuklOp14)
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create_test_fp16_class(TestMatMuklOp15)
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create_test_fp16_class(TestMatMuklOp16)
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create_test_fp16_class(TestMatMuklOp17)
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class TestMatMulV2API(unittest.TestCase):
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def setUp(self):
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self.places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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self.places.append(fluid.CUDAPlace(0))
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def check_static_result(self, place):
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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input_x = fluid.data(name="input_x", shape=[4, 3], dtype="float32")
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input_y = fluid.data(name="input_y", shape=[3, 4], dtype="float32")
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result = paddle.matmul(input_x, input_y)
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x_np = np.random.random([4, 3]).astype("float32")
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y_np = np.random.random([3, 4]).astype("float32")
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exe = fluid.Executor(place)
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fetches = exe.run(fluid.default_main_program(),
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feed={"input_x": x_np,
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"input_y": y_np},
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fetch_list=[result])
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def test_static(self):
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for place in self.places:
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self.check_static_result(place=place)
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def test_dygraph(self):
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for place in self.places:
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with fluid.dygraph.guard(place):
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input_x = np.random.random([4, 3]).astype("float64")
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input_y = np.random.random([3, 4]).astype("float64")
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x = paddle.to_tensor(input_x)
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y = paddle.to_tensor(input_y)
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result = paddle.matmul(x, y)
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def test_dygraph_fp16(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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with fluid.dygraph.guard(place):
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input_x = np.random.random([4, 3]).astype("float16")
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input_y = np.random.random([3, 4]).astype("float16")
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x = paddle.to_tensor(input_x)
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y = paddle.to_tensor(input_y)
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result = paddle.matmul(x, y)
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if __name__ == "__main__":
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unittest.main()
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