<commit_before>#   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.

from __future__ import print_function

import unittest
import numpy as np
import paddle

import paddle
import paddle.fluid.core as core
import paddle.fluid as fluid
from paddle.fluid.tests.unittests.op_test import (
    OpTest, convert_float_to_uint16, get_numeric_gradient)
from paddle.fluid.tests.unittests.testsuite import create_op
from paddle.fluid import Program, program_guard


def conv2d_forward_naive(input,
                         filter,
                         group,
                         conv_param,
                         padding_algorithm='EXPLICIT',
                         data_format='NCHW'):
    if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]:
        raise ValueError("Unknown Attr(padding_algorithm): '%s'. "
                         "It can only be 'SAME' or 'VALID'." %
                         str(padding_algorithm))

    if data_format not in ["NCHW", "NHWC"]:
        raise ValueError("Unknown Attr(data_format): '%s' ."
                         "It can only be 'NCHW' or 'NHWC'." % str(data_format))

    channel_last = (data_format == "NHWC")
    if channel_last:
        input = np.transpose(input, [0, 3, 1, 2])

    in_n, in_c, in_h, in_w = input.shape
    f_n, f_c, f_h, f_w = filter.shape
    out_n = in_n
    out_c = f_n
    assert f_c * group == in_c
    assert np.mod(out_c, group) == 0
    sub_out_c = out_c // group
    sub_f_n = f_n // group

    stride, pad, dilation = conv_param['stride'], conv_param['pad'], conv_param[
        'dilation']

    # update pad and dilation
    def _get_padding_with_SAME(input_shape, pool_size, pool_stride):
        padding = []
        for input_size, filter_size, stride_size in zip(input_shape, pool_size,
                                                        pool_stride):
            out_size = int((input_size + stride_size - 1) / stride_size)
            pad_sum = np.max((
                (out_size - 1) * stride_size + filter_size - input_size, 0))
            pad_0 = int(pad_sum / 2)
            pad_1 = int(pad_sum - pad_0)
            padding.append(pad_0)
            padding.append(pad_1)
        return padding

    ksize = filter.shape[2:4]
    if padding_algorithm == "VALID":
        pad = [0, 0, 0, 0]
    elif padding_algorithm == "SAME":
        dilation = [1, 1]
        input_data_shape = input.shape[2:4]
        pad = _get_padding_with_SAME(input_data_shape, ksize, stride)

    pad_h_0, pad_h_1 = pad[0], pad[0]
    pad_w_0, pad_w_1 = pad[1], pad[1]
    if len(pad) == 4:
        pad_h_0, pad_h_1 = pad[0], pad[1]
        pad_w_0, pad_w_1 = pad[2], pad[3]
    out_h = 1 + (in_h + pad_h_0 + pad_h_1 - (dilation[0] *
                                             (f_h - 1) + 1)) // stride[0]
    out_w = 1 + (in_w + pad_w_0 + pad_w_1 - (dilation[1] *
                                             (f_w - 1) + 1)) // stride[1]
    out = np.zeros((out_n, out_c, out_h, out_w))

    d_bolck_h = (dilation[0] * (f_h - 1) + 1)
    d_bolck_w = (dilation[1] * (f_w - 1) + 1)

    input_pad = np.pad(input, ((0, 0), (0, 0), (pad_h_0, pad_h_1),
                               (pad_w_0, pad_w_1)),
                       mode='constant',
                       constant_values=0)

    filter_dilation = np.zeros((f_n, f_c, d_bolck_h, d_bolck_w))
    filter_dilation[:, :, 0:d_bolck_h:dilation[0], 0:d_bolck_w:dilation[
        1]] = filter

    for i in range(out_h):
        for j in range(out_w):
            for g in range(group):
                input_pad_masked = \
                    input_pad[:, g * f_c:(g + 1) * f_c,
                    i * stride[0]:i * stride[0] + d_bolck_h,
                    j * stride[1]:j * stride[1] + d_bolck_w]

                f_sub = filter_dilation[g * sub_f_n:(g + 1) * sub_f_n, :, :, :]
                # sub_f_n == sub_out_c
                for k in range(sub_out_c):
                    # Multiplication of Corresponding Elements, then sum all
                    out[:, g * sub_out_c + k, i, j] = \
                        np.sum(input_pad_masked * f_sub[k, :, :, :],
                               axis=(1, 2, 3))

    if channel_last:
        out = np.transpose(out, [0, 2, 3, 1])

    return out, in_n, out_h, out_w, out_c


def create_test_cudnn_class(parent):
    @unittest.skipIf(not core.is_compiled_with_cuda(),
                     "core is not compiled with CUDA")
    class TestCUDNNCase(parent):
        def init_kernel_type(self):
            self.use_cudnn = True
            self.dtype = np.float32 if core.is_compiled_with_rocm(
            ) else np.float64

    cls_name = "{0}_{1}".format(parent.__name__, "CUDNN")
    TestCUDNNCase.__name__ = cls_name
    globals()[cls_name] = TestCUDNNCase


def create_test_cudnn_fp16_class(parent, grad_check=True):
    @unittest.skipIf(not core.is_compiled_with_cuda(),
                     "core is not compiled with CUDA")
    class TestConv2DCUDNNFp16(parent):
        def init_kernel_type(self):
            self.use_cudnn = True
            self.dtype = np.float16

        def test_check_output(self):
            if core.is_compiled_with_cuda():
                place = core.CUDAPlace(0)
                if core.is_float16_supported(place):
                    self.check_output_with_place(place, atol=2e-2)

        def test_check_grad_no_filter(self):
            place = core.CUDAPlace(0)
            if core.is_float16_supported(place) and grad_check:
                self.check_grad_with_place(
                    place, ['Input'], 'Output', no_grad_set=set(['Filter']))

        def test_check_grad_no_input(self):
            place = core.CUDAPlace(0)
            if core.is_float16_supported(place) and grad_check:
                self.check_grad_with_place(
                    place, ['Filter'], 'Output', no_grad_set=set(['Input']))

    cls_name = "{0}_{1}".format(parent.__name__, "CUDNNFp16")
    TestConv2DCUDNNFp16.__name__ = cls_name
    globals()[cls_name] = TestConv2DCUDNNFp16


def create_test_cudnn_bf16_class(parent):
    @unittest.skipIf(
        not core.is_compiled_with_cuda() or core.cudnn_version() < 8100,
        "core is not compiled with CUDA and cudnn version need larger than 8.1.0"
    )<commit_msg>fix unittest of conv2d due to V100 do not support bfloat16 (#42483)<commit_after>#   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.

from __future__ import print_function

import unittest
import numpy as np
import paddle

import paddle
import paddle.fluid.core as core
import paddle.fluid as fluid
from paddle.fluid.tests.unittests.op_test import (
    OpTest, convert_float_to_uint16, get_numeric_gradient)
from paddle.fluid.tests.unittests.testsuite import create_op
from paddle.fluid import Program, program_guard


def conv2d_forward_naive(input,
                         filter,
                         group,
                         conv_param,
                         padding_algorithm='EXPLICIT',
                         data_format='NCHW'):
    if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]:
        raise ValueError("Unknown Attr(padding_algorithm): '%s'. "
                         "It can only be 'SAME' or 'VALID'." %
                         str(padding_algorithm))

    if data_format not in ["NCHW", "NHWC"]:
        raise ValueError("Unknown Attr(data_format): '%s' ."
                         "It can only be 'NCHW' or 'NHWC'." % str(data_format))

    channel_last = (data_format == "NHWC")
    if channel_last:
        input = np.transpose(input, [0, 3, 1, 2])

    in_n, in_c, in_h, in_w = input.shape
    f_n, f_c, f_h, f_w = filter.shape
    out_n = in_n
    out_c = f_n
    assert f_c * group == in_c
    assert np.mod(out_c, group) == 0
    sub_out_c = out_c // group
    sub_f_n = f_n // group

    stride, pad, dilation = conv_param['stride'], conv_param['pad'], conv_param[
        'dilation']

    # update pad and dilation
    def _get_padding_with_SAME(input_shape, pool_size, pool_stride):
        padding = []
        for input_size, filter_size, stride_size in zip(input_shape, pool_size,
                                                        pool_stride):
            out_size = int((input_size + stride_size - 1) / stride_size)
            pad_sum = np.max((
                (out_size - 1) * stride_size + filter_size - input_size, 0))
            pad_0 = int(pad_sum / 2)
            pad_1 = int(pad_sum - pad_0)
            padding.append(pad_0)
            padding.append(pad_1)
        return padding

    ksize = filter.shape[2:4]
    if padding_algorithm == "VALID":
        pad = [0, 0, 0, 0]
    elif padding_algorithm == "SAME":
        dilation = [1, 1]
        input_data_shape = input.shape[2:4]
        pad = _get_padding_with_SAME(input_data_shape, ksize, stride)

    pad_h_0, pad_h_1 = pad[0], pad[0]
    pad_w_0, pad_w_1 = pad[1], pad[1]
    if len(pad) == 4:
        pad_h_0, pad_h_1 = pad[0], pad[1]
        pad_w_0, pad_w_1 = pad[2], pad[3]
    out_h = 1 + (in_h + pad_h_0 + pad_h_1 - (dilation[0] *
                                             (f_h - 1) + 1)) // stride[0]
    out_w = 1 + (in_w + pad_w_0 + pad_w_1 - (dilation[1] *
                                             (f_w - 1) + 1)) // stride[1]
    out = np.zeros((out_n, out_c, out_h, out_w))

    d_bolck_h = (dilation[0] * (f_h - 1) + 1)
    d_bolck_w = (dilation[1] * (f_w - 1) + 1)

    input_pad = np.pad(input, ((0, 0), (0, 0), (pad_h_0, pad_h_1),
                               (pad_w_0, pad_w_1)),
                       mode='constant',
                       constant_values=0)

    filter_dilation = np.zeros((f_n, f_c, d_bolck_h, d_bolck_w))
    filter_dilation[:, :, 0:d_bolck_h:dilation[0], 0:d_bolck_w:dilation[
        1]] = filter

    for i in range(out_h):
        for j in range(out_w):
            for g in range(group):
                input_pad_masked = \
                    input_pad[:, g * f_c:(g + 1) * f_c,
                    i * stride[0]:i * stride[0] + d_bolck_h,
                    j * stride[1]:j * stride[1] + d_bolck_w]

                f_sub = filter_dilation[g * sub_f_n:(g + 1) * sub_f_n, :, :, :]
                # sub_f_n == sub_out_c
                for k in range(sub_out_c):
                    # Multiplication of Corresponding Elements, then sum all
                    out[:, g * sub_out_c + k, i, j] = \
                        np.sum(input_pad_masked * f_sub[k, :, :, :],
                               axis=(1, 2, 3))

    if channel_last:
        out = np.transpose(out, [0, 2, 3, 1])

    return out, in_n, out_h, out_w, out_c


def create_test_cudnn_class(parent):
    @unittest.skipIf(not core.is_compiled_with_cuda(),
                     "core is not compiled with CUDA")
    class TestCUDNNCase(parent):
        def init_kernel_type(self):
            self.use_cudnn = True
            self.dtype = np.float32 if core.is_compiled_with_rocm(
            ) else np.float64

    cls_name = "{0}_{1}".format(parent.__name__, "CUDNN")
    TestCUDNNCase.__name__ = cls_name
    globals()[cls_name] = TestCUDNNCase


def create_test_cudnn_fp16_class(parent, grad_check=True):
    @unittest.skipIf(not core.is_compiled_with_cuda(),
                     "core is not compiled with CUDA")
    class TestConv2DCUDNNFp16(parent):
        def init_kernel_type(self):
            self.use_cudnn = True
            self.dtype = np.float16

        def test_check_output(self):
            if core.is_compiled_with_cuda():
                place = core.CUDAPlace(0)
                if core.is_float16_supported(place):
                    self.check_output_with_place(place, atol=2e-2)

        def test_check_grad_no_filter(self):
            place = core.CUDAPlace(0)
            if core.is_float16_supported(place) and grad_check:
                self.check_grad_with_place(
                    place, ['Input'], 'Output', no_grad_set=set(['Filter']))

        def test_check_grad_no_input(self):
            place = core.CUDAPlace(0)
            if core.is_float16_supported(place) and grad_check:
                self.check_grad_with_place(
                    place, ['Filter'], 'Output', no_grad_set=set(['Input']))

    cls_name = "{0}_{1}".format(parent.__name__, "CUDNNFp16")
    TestConv2DCUDNNFp16.__name__ = cls_name
    globals()[cls_name] = TestConv2DCUDNNFp16


def create_test_cudnn_bf16_class(parent):
    @unittest.skipIf(
        not core.is_compiled_with_cuda() or
        not core.is_bfloat16_supported(core.CUDAPlace(0)),
        "core is not compiled with CUDA and do not support bfloat16")
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