# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from paddle import enable_static from paddle.fluid import core from paddle.fluid.tests.unittests.eager_op_test import ( OpTest, convert_float_to_uint16, ) @unittest.skipIf( not core.supports_bfloat16(), "place does not support BF16 evaluation" ) class TestMatmulBf16MklDNNOp(OpTest): def generate_data(self): self.x_fp32 = np.random.random((25, 2, 2)).astype(np.float32) self.y_fp32 = np.random.random((25, 2, 2)).astype(np.float32) self.out = self.alpha * np.matmul(self.x_fp32, self.y_fp32) def set_attributes(self): self.attrs = { 'alpha': self.alpha, "use_mkldnn": self.use_mkldnn, "mkldnn_data_type": self.mkldnn_data_type, "force_fp32_output": self.force_fp32_output, 'transpose_X': False, 'transpose_Y': False, } def setUp(self): self.op_type = "matmul" self.alpha = 1.0 self.use_mkldnn = True self.dtype = np.uint16 self.mkldnn_data_type = "bfloat16" self.force_fp32_output = False self.generate_data() self.set_attributes() if not self.force_fp32_output: self.out = convert_float_to_uint16(self.out) self.outputs = {'Out': self.out} self.x_bf16 = convert_float_to_uint16(self.x_fp32) self.y_bf16 = convert_float_to_uint16(self.y_fp32) self.inputs = {'X': self.x_bf16, 'Y': self.y_bf16} def test_check_output(self): self.check_output_with_place(core.CPUPlace()) def test_check_grad(self): self.calculate_grads() self.check_grad_with_place( core.CPUPlace(), ["X", "Y"], "Out", check_dygraph=False, user_defined_grads=[self.dx, self.dy], user_defined_grad_outputs=[convert_float_to_uint16(self.dout)], ) def matmul_grad(self, x, transpose_x, y, transpose_y): x_transpose_axes = [1, 0] if x.ndim == 2 else [0, 2, 1] y_transpose_axes = [1, 0] if y.ndim == 2 else [0, 2, 1] x = np.transpose(x, x_transpose_axes) if transpose_x else x y = np.transpose(y, y_transpose_axes) if transpose_y else y return self.alpha * np.matmul(x, y) def calculate_grads(self): x_transpose_axes = [1, 0] if self.x_fp32.ndim == 2 else [0, 2, 1] y_transpose_axes = [1, 0] if self.y_fp32.ndim == 2 else [0, 2, 1] x = ( np.transpose(self.x_fp32, x_transpose_axes) if self.attrs['transpose_X'] is True else self.x_fp32 ) y = ( np.transpose(self.y_fp32, y_transpose_axes) if self.attrs['transpose_Y'] is True else self.y_fp32 ) dout = self.alpha * np.matmul(x, y) if ( self.attrs['transpose_X'] is True and self.attrs['transpose_Y'] is True ): self.dx = self.matmul_grad(self.y_fp32, True, dout, True) self.dy = self.matmul_grad(dout, True, self.x_fp32, True) elif ( self.attrs['transpose_X'] is True and self.attrs['transpose_Y'] is False ): self.dx = self.matmul_grad(self.y_fp32, False, dout, True) self.dy = self.matmul_grad(self.x_fp32, False, dout, False) elif ( self.attrs['transpose_X'] is False and self.attrs['transpose_Y'] is True ): self.dx = self.matmul_grad(dout, False, self.y_fp32, False) self.dy = self.matmul_grad(dout, True, self.x_fp32, False) else: self.dx = self.matmul_grad(dout, False, self.y_fp32, True) self.dy = self.matmul_grad(self.x_fp32, True, dout, False) self.dout = dout class TestDnnlMatMulOpAlpha(TestMatmulBf16MklDNNOp): def generate_data(self): self.x_fp32 = np.random.random((17, 2, 3)).astype(np.float32) self.y_fp32 = np.random.random((17, 3, 2)).astype(np.float32) self.alpha = 2.0 self.out = self.alpha * np.matmul(self.x_fp32, self.y_fp32) class TestDnnlMatMulOp2D(TestMatmulBf16MklDNNOp): def generate_data(self): self.x_fp32 = np.random.random((12, 9)).astype(np.float32) self.y_fp32 = np.random.random((9, 12)).astype(np.float32) self.out = np.matmul(self.x_fp32, self.y_fp32) class TestDnnlMatMulOpTransposeX(TestMatmulBf16MklDNNOp): def generate_data(self): self.x_fp32 = np.random.random((12, 9)).astype(np.float32) self.y_fp32 = np.random.random((12, 9)).astype(np.float32) self.out = np.matmul(np.transpose(self.x_fp32), self.y_fp32) def set_attributes(self): self.attrs = { "use_mkldnn": self.use_mkldnn, "mkldnn_data_type": self.mkldnn_data_type, 'transpose_X': True, 'transpose_Y': False, } class TestDnnlMatMulOpTransposeY(TestMatmulBf16MklDNNOp): def generate_data(self): self.x_fp32 = np.random.random((12, 9)).astype(np.float32) self.y_fp32 = np.random.random((12, 9)).astype(np.float32) self.out = np.matmul(self.x_fp32, np.transpose(self.y_fp32)) def set_attributes(self): self.attrs = { "use_mkldnn": self.use_mkldnn, "mkldnn_data_type": self.mkldnn_data_type, 'transpose_Y': True, 'transpose_X': False, } class TestMatmulBf16MklDNNForceFp32Output(TestMatmulBf16MklDNNOp): def generate_data(self): self.x_fp32 = np.random.random((12, 9)).astype(np.float32) self.y_fp32 = np.random.random((9, 12)).astype(np.float32) self.force_fp32_output = True self.alpha = 0.5 self.out = self.alpha * np.matmul(self.x_fp32, self.y_fp32) if __name__ == "__main__": enable_static() unittest.main()