test_matmul_op.py

#   Copyright (c) 2018 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 eager_op_test import OpTest, paddle_static_guard

import paddle
from paddle import fluid


def generate_compatible_shapes(dim_X, dim_Y, transpose_X, transpose_Y):
    BATCH_SIZE = 2
    M = 3
    N = 4
    K = 5
    if (dim_X == 1 and transpose_X) or (dim_Y == 1 and transpose_Y):
        K = 1
    if dim_X == 1:
        if transpose_X:
            shape_X = [M]
        else:
            shape_X = [K]
    if dim_Y == 1:
        if transpose_Y:
            shape_Y = [N]
        else:
            shape_Y = [K]
    if dim_X >= 2:
        if transpose_X:
            shape_X = [K, M]
        else:
            shape_X = [M, K]
    if dim_X == 3:
        shape_X = [BATCH_SIZE] + shape_X
    if dim_Y >= 2:
        if transpose_Y:
            shape_Y = [N, K]
        else:
            shape_Y = [K, N]
    if dim_Y == 3:
        shape_Y = [BATCH_SIZE] + shape_Y
    return shape_X, shape_Y


def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
    """Reference forward implementation using np.matmul."""
    # np.matmul does not support the transpose flags, so we manually
    # transpose X and Y appropriately.
    if transpose_X:
        if X.ndim == 1:
            X = X.reshape((X.size, 1))
        elif X.ndim == 2:
            X = X.T
        else:
            dim = list(range(len(X.shape)))
            dim[-1], dim[len(X.shape) - 2] = dim[len(X.shape) - 2], dim[-1]
            X = np.transpose(X, tuple(dim))
    if transpose_Y:
        if Y.ndim == 1:
            Y = Y.reshape((1, Y.size))
        else:
            dim = list(range(len(Y.shape)))
            dim[-1], dim[len(Y.shape) - 2] = dim[len(Y.shape) - 2], dim[-1]
            Y = np.transpose(Y, tuple(dim))

    Out = np.matmul(X, Y)
    if not Out.shape:
        # We do not support 0-dimensional Tensors (scalars). So where
        # np.matmul outputs a scalar, we must convert to a Tensor of
        # shape (1, ) instead.
        # Everywhere else, we are compatible with np.matmul.
        Out = np.array([Out], dtype="float32")
    return Out


class Generator:
    def setUp(self):
        self.op_type = "matmul"
        X = np.random.random(self.shape_X).astype("float32")
        Y = np.random.random(self.shape_Y).astype("float32")
        Out = reference_matmul(X, Y, self.transpose_X, self.transpose_Y)
        self.inputs = {'X': X, 'Y': Y}
        self.attrs = {
            'transpose_X': self.transpose_X,
            'transpose_Y': self.transpose_Y,
        }
        self.outputs = {'Out': Out}

    def test_check_output(self):
        self.check_output(check_cinn=True)

    def test_check_grad_normal(self):
        self.check_grad(
            ['X', 'Y'], 'Out', max_relative_error=1e-3, check_cinn=True
        )

    def test_check_grad_ignore_x(self):
        self.check_grad(
            ['Y'],
            'Out',
            max_relative_error=1e-3,
            no_grad_set=set("X"),
            check_cinn=True,
        )

    def test_check_grad_ignore_y(self):
        self.check_grad(
            ['X'],
            'Out',
            max_relative_error=1e-3,
            no_grad_set=set('Y'),
            check_cinn=True,
        )


# Test case n-dim
def generate_compatible_shapes_ndim(dim, transpose_X, transpose_Y):
    M = 2
    N = 4
    K = 3
    shape_X = [2 for _ in range(dim - 2)]
    shape_Y = [2 for _ in range(dim - 2)]

    if transpose_X:
        shape_X += [K, M]
    else:
        shape_X += [M, K]

    if transpose_Y:
        shape_Y += [N, K]
    else:
        shape_Y += [K, N]

    return shape_X, shape_Y


# # Test case n-dim
for dim in [4]:
    for transpose_X in [False, True]:
        for transpose_Y in [False, True]:
            test_name = (
                'TestMatMulOp_dimX_{}_dim_Y_{}_transX_{}_transY_{}'.format(
                    dim, dim, transpose_X, transpose_Y
                )
            )
            shape_X, shape_Y = generate_compatible_shapes_ndim(
                dim, transpose_X, transpose_Y
            )
            globals()[test_name] = type(
                test_name,
                (Generator, OpTest),
                {
                    'shape_X': shape_X,
                    'shape_Y': shape_Y,
                    'transpose_X': transpose_X,
                    'transpose_Y': transpose_Y,
                },
            )


class API_TestMm(unittest.TestCase):
    def test_out(self):
        with paddle_static_guard():
            with fluid.program_guard(fluid.Program()):
                x = paddle.static.data(name="x", shape=[2], dtype="float64")
                y = paddle.static.data(name='y', shape=[2], dtype='float64')
                res = paddle.static.data(
                    name="output", shape=[1], dtype="float64"
                )
                result = paddle.mm(x, y)
                exe = fluid.Executor(fluid.CPUPlace())
                data1 = np.random.rand(2)
                data2 = np.random.rand(2)
                np_res = exe.run(
                    feed={'x': data1, 'y': data2}, fetch_list=[result]
                )
                expected_result = np.matmul(
                    data1.reshape(1, 2), data2.reshape(2, 1)
                )

            np.testing.assert_allclose(
                np_res,
                expected_result,
                rtol=1e-05,
                atol=1e-05,
                err_msg='two value is            {}\n{}, check diff!'.format(
                    np_res, expected_result
                ),
            )

    def test_dygraph_without_out(self):
        device = fluid.CPUPlace()
        with fluid.dygraph.guard(device):
            input_array1 = np.random.rand(3, 4).astype("float64")
            input_array2 = np.random.rand(4, 3).astype("float64")
            data1 = fluid.dygraph.to_variable(input_array1)
            data2 = fluid.dygraph.to_variable(input_array2)
            out = paddle.mm(data1, data2)
            expected_result = np.matmul(input_array1, input_array2)
        np.testing.assert_allclose(expected_result, out.numpy(), rtol=1e-05)


class Test_API_Matmul(unittest.TestCase):
    def test_dygraph_without_out(self):
        device = fluid.CPUPlace()
        with fluid.dygraph.guard(device):
            input_array1 = np.random.rand(3, 4).astype("float64")
            input_array2 = np.random.rand(4, 3).astype("float64")
            data1 = fluid.dygraph.to_variable(input_array1)
            data2 = fluid.dygraph.to_variable(input_array2)
            out = paddle.matmul(data1, data2)
            expected_result = np.matmul(input_array1, input_array2)
        np.testing.assert_allclose(expected_result, out.numpy(), rtol=1e-05)


class API_TestMmError(unittest.TestCase):
    def test_errors(self):
        with paddle_static_guard():

            def test_error1():
                with fluid.program_guard(fluid.Program(), fluid.Program()):
                    data1 = paddle.static.data(
                        name="data1", shape=[10, 2], dtype="float32"
                    )
                    data2 = paddle.static.data(
                        name="data2", shape=[3, 10], dtype="float32"
                    )
                    paddle.mm(data1, data2)

            self.assertRaises(ValueError, test_error1)

            def test_error2():
                with fluid.program_guard(fluid.Program(), fluid.Program()):
                    data1 = paddle.static.data(
                        name="data1", shape=[-1, 10, 2], dtype="float32"
                    )
                    data2 = paddle.static.data(
                        name="data2", shape=[-1, 2, 10], dtype="float32"
                    )
                    paddle.mm(data1, data2)

            test_error2()

            def test_error3():
                with fluid.program_guard(fluid.Program(), fluid.Program()):
                    data1 = paddle.static.data(
                        name="data1", shape=[10, 10, 2], dtype="float32"
                    )
                    data2 = paddle.static.data(
                        name="data2", shape=[3, 2, 10], dtype="float32"
                    )
                    paddle.mm(data1, data2)

            self.assertRaises(ValueError, test_error3)


if __name__ == "__main__":
    unittest.main()