wehub-resource-sync
1438 lines
43 KiB
Python
1438 lines
43 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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get_numeric_gradient,
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is_custom_device,
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)
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from testsuite import create_op
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import paddle
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from paddle.base import core
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def conv3d_forward_naive(
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input,
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filter,
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group,
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conv_param,
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padding_algorithm='EXPLICIT',
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data_format="NCDHW",
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):
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if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]:
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raise ValueError(
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f"Unknown Attr(padding_algorithm): '{padding_algorithm}'. "
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"It can only be 'SAME' or 'VALID'."
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)
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if data_format not in ["NCDHW", "NDHWC"]:
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raise ValueError(
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f"Unknown Attr(data_format): '{data_format}' ."
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"It can only be 'NCDHW' or 'NDHWC'."
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)
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channel_last = data_format == "NDHWC"
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if channel_last:
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input = np.transpose(input, [0, 4, 1, 2, 3])
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in_n, in_c, in_d, in_h, in_w = input.shape
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f_n, f_c, f_d, f_h, f_w = filter.shape
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out_n = in_n
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out_c = f_n
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assert f_c * group == in_c
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assert np.mod(out_c, group) == 0
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sub_out_c = out_c // group
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sub_f_n = f_n // group
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stride, pad, dilation = (
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conv_param['stride'],
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conv_param['pad'],
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conv_param['dilation'],
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)
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# update pad and dilation
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def _get_padding_with_SAME(input_shape, pool_size, pool_stride):
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padding = []
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for input_size, filter_size, stride_size in zip(
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input_shape, pool_size, pool_stride
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):
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out_size = int((input_size + stride_size - 1) / stride_size)
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pad_sum = np.max(
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((out_size - 1) * stride_size + filter_size - input_size, 0)
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)
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pad_0 = int(pad_sum / 2)
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pad_1 = int(pad_sum - pad_0)
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padding.append(pad_0)
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padding.append(pad_1)
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return padding
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ksize = filter.shape[2:5]
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if padding_algorithm == "VALID":
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pad = [0, 0, 0, 0, 0, 0]
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elif padding_algorithm == "SAME":
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dilation = [1, 1, 1]
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input_data_shape = input.shape[2:5]
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pad = _get_padding_with_SAME(input_data_shape, ksize, stride)
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pad_d_0, pad_d_1 = pad[0], pad[0]
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pad_h_0, pad_h_1 = pad[1], pad[1]
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pad_w_0, pad_w_1 = pad[2], pad[2]
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if len(pad) == 6:
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pad_d_0, pad_d_1 = pad[0], pad[1]
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pad_h_0, pad_h_1 = pad[2], pad[3]
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pad_w_0, pad_w_1 = pad[4], pad[5]
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out_d = (
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1
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+ (in_d + pad_d_0 + pad_d_1 - (dilation[0] * (f_d - 1) + 1))
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// stride[0]
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)
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out_h = (
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1
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+ (in_h + pad_h_0 + pad_h_1 - (dilation[1] * (f_h - 1) + 1))
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// stride[1]
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)
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out_w = (
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1
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+ (in_w + pad_w_0 + pad_w_1 - (dilation[2] * (f_w - 1) + 1))
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// stride[2]
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)
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out = np.zeros((in_n, out_c, out_d, out_h, out_w))
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d_block_d = dilation[0] * (f_d - 1) + 1
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d_block_h = dilation[1] * (f_h - 1) + 1
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d_block_w = dilation[2] * (f_w - 1) + 1
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input_pad = np.pad(
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input,
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(
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(0, 0),
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(0, 0),
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(pad_d_0, pad_d_1),
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(pad_h_0, pad_h_1),
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(pad_w_0, pad_w_1),
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),
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mode='constant',
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constant_values=0,
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)
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filter_dilation = np.zeros((f_n, f_c, d_block_d, d_block_h, d_block_w))
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filter_dilation[
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:,
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:,
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0 : d_block_d : dilation[0],
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0 : d_block_h : dilation[1],
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0 : d_block_w : dilation[2],
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] = filter
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for d in range(out_d):
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for i in range(out_h):
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for j in range(out_w):
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for g in range(group):
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input_pad_masked = input_pad[
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:,
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g * f_c : (g + 1) * f_c,
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d * stride[0] : d * stride[0] + d_block_d,
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i * stride[1] : i * stride[1] + d_block_h,
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j * stride[2] : j * stride[2] + d_block_w,
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]
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f_sub = filter_dilation[
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g * sub_f_n : (g + 1) * sub_f_n, :, :, :, :
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]
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for k in range(sub_out_c):
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out[:, g * sub_out_c + k, d, i, j] = np.sum(
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input_pad_masked * f_sub[k, :, :, :, :],
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axis=(1, 2, 3, 4),
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)
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if channel_last:
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out = np.transpose(out, [0, 2, 3, 4, 1])
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return out
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def create_test_cudnn_class(parent):
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestCUDNNCase(parent):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = (
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np.float32 if core.is_compiled_with_rocm() else np.float64
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)
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cls_name = "{}_{}".format(parent.__name__, "CUDNN")
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TestCUDNNCase.__name__ = cls_name
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globals()[cls_name] = TestCUDNNCase
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def create_test_cudnn_bf16_class(parent):
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and do not support bfloat16",
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)
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class TestConv3DCUDNNBF16(parent):
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def get_numeric_grad(self, place, check_name):
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scope = core.Scope()
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self._check_grad_helper()
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op = create_op(
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scope, self.op_type, self.inputs, self.outputs, self.attrs
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)
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return get_numeric_gradient(
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place, scope, op, self.inputs_fp32, check_name, ['Output']
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)
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.uint16
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(
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place,
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check_dygraph=(not self.use_onednn),
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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_no_filter(self):
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place = get_device_place()
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numeric_grads = self.get_numeric_grad(place, 'Input')
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self.check_grad_with_place(
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place,
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['Input'],
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'Output',
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no_grad_set={'Filter'},
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check_dygraph=(not self.use_onednn),
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user_defined_grads=[numeric_grads],
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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_no_input(self):
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place = get_device_place()
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numeric_grads = self.get_numeric_grad(place, 'Filter')
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self.check_grad_with_place(
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place,
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['Filter'],
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'Output',
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no_grad_set={'Input'},
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check_dygraph=(not self.use_onednn),
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user_defined_grads=[numeric_grads],
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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(self):
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place = get_device_place()
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numeric_input_grads = self.get_numeric_grad(place, 'Input')
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numeric_filter_grads = self.get_numeric_grad(place, 'Filter')
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self.check_grad_with_place(
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place,
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['Input', 'Filter'],
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'Output',
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user_defined_grads=[numeric_input_grads, numeric_filter_grads],
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check_dygraph=(not self.use_onednn),
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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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cls_name = "{}_{}".format(parent.__name__, "CUDNNBF16OP")
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TestConv3DCUDNNBF16.__name__ = cls_name
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globals()[cls_name] = TestConv3DCUDNNBF16
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def create_test_padding_SAME_class(parent):
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class TestPaddingSAMECase(parent):
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def init_paddings(self):
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self.pad = [0, 0, 0]
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self.padding_algorithm = "SAME"
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cls_name = "{}_{}".format(parent.__name__, "PaddingSAMEOp")
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TestPaddingSAMECase.__name__ = cls_name
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globals()[cls_name] = TestPaddingSAMECase
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def create_test_padding_VALID_class(parent):
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class TestPaddingVALIDCase(parent):
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def init_paddings(self):
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self.pad = [1, 1, 1]
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self.padding_algorithm = "VALID"
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cls_name = "{}_{}".format(parent.__name__, "PaddingVALIDOp")
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TestPaddingVALIDCase.__name__ = cls_name
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globals()[cls_name] = TestPaddingVALIDCase
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def create_test_cudnn_padding_SAME_class(parent):
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestCUDNNPaddingSAMECase(parent):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = (
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np.float32 if core.is_compiled_with_rocm() else np.float64
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)
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def init_paddings(self):
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self.pad = [1, 1, 1]
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self.padding_algorithm = "SAME"
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cls_name = "{}_{}".format(parent.__name__, "CudnnPaddingSAMEOp")
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TestCUDNNPaddingSAMECase.__name__ = cls_name
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globals()[cls_name] = TestCUDNNPaddingSAMECase
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def create_test_cudnn_padding_VALID_class(parent):
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestCUDNNPaddingVALIDCase(parent):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = (
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np.float32 if core.is_compiled_with_rocm() else np.float64
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)
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def init_paddings(self):
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self.pad = [1, 1, 1]
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self.padding_algorithm = "VALID"
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cls_name = "{}_{}".format(parent.__name__, "CudnnPaddingVALIDOp")
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TestCUDNNPaddingVALIDCase.__name__ = cls_name
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globals()[cls_name] = TestCUDNNPaddingVALIDCase
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def create_test_channel_last_class(parent):
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class TestChannelLastCase(parent):
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def init_data_format(self):
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self.data_format = "NDHWC"
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def init_test_case_2(self):
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N, C, D, H, W = self.input_size
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self.input_size = [N, D, H, W, C]
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cls_name = "{}_{}".format(parent.__name__, "ChannelLast")
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TestChannelLastCase.__name__ = cls_name
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globals()[cls_name] = TestChannelLastCase
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def create_test_cudnn_channel_last_class(parent):
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestCudnnChannelLastCase(parent):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = (
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np.float32 if core.is_compiled_with_rocm() else np.float64
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)
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def init_data_format(self):
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self.data_format = "NDHWC"
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def init_test_case_2(self):
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N, C, D, H, W = self.input_size
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self.input_size = [N, D, H, W, C]
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cls_name = "{}_{}".format(parent.__name__, "CudnnChannelLast")
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TestCudnnChannelLastCase.__name__ = cls_name
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globals()[cls_name] = TestCudnnChannelLastCase
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def conv3d_wrapper(
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x,
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weight,
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stride=1,
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padding=0,
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padding_algorithm="EXPLICIT",
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groups=1,
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dilation=1,
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data_format="NCDHW",
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):
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if data_format == "AnyLayout":
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data_format = "NCDHW"
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if padding_algorithm is None:
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padding_algorithm = "EXPLICIT"
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return paddle._C_ops.conv3d(
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x,
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weight,
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stride,
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padding,
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padding_algorithm,
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groups,
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dilation,
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data_format,
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)
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class TestConv3DOp(OpTest):
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def setUp(self):
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self.op_type = "conv3d"
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self.python_api = conv3d_wrapper
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self.use_cudnn = False
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self.use_onednn = False
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self.data_format = "AnyLayout"
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self.dtype = np.float64
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self.init_kernel_type()
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self.init_group()
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self.init_dilation()
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self.init_test_case()
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conv3d_param = {
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'stride': self.stride,
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'pad': self.pad,
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'dilation': self.dilation,
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}
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if self.is_bfloat16_op():
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input = np.random.random(self.input_size).astype(np.float32)
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filter = np.random.random(self.filter_size).astype(np.float32)
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else:
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input = np.random.random(self.input_size).astype(self.dtype)
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filter = np.random.random(self.filter_size).astype(self.dtype)
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output = conv3d_forward_naive(
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input,
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filter,
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self.groups,
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conv3d_param,
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)
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if self.is_bfloat16_op():
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output = convert_float_to_uint16(output)
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self.inputs = {
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'Input': convert_float_to_uint16(input),
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'Filter': convert_float_to_uint16(filter),
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}
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self.inputs_fp32 = {
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'Input': OpTest.np_dtype_to_base_dtype(input),
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'Filter': OpTest.np_dtype_to_base_dtype(filter),
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}
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else:
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output = output.astype(self.dtype)
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self.inputs = {
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'Input': OpTest.np_dtype_to_base_dtype(input),
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'Filter': OpTest.np_dtype_to_base_dtype(filter),
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}
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self.attrs = {
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'strides': self.stride,
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'paddings': self.pad,
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'groups': self.groups,
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'dilation': self.dilation,
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'use_cudnn': self.use_cudnn,
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'use_onednn': self.use_onednn,
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'data_format': self.data_format,
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}
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self.outputs = {'Output': output}
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def has_cudnn(self):
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return (
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core.is_compiled_with_cuda() or is_custom_device()
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) and self.use_cudnn
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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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place = get_device_place() if self.has_cudnn() else core.CPUPlace()
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self.check_output_with_place(
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place,
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atol=1e-5,
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check_dygraph=(not self.use_onednn),
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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(self):
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place = get_device_place() if self.has_cudnn() else core.CPUPlace()
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# TODO(wangzhongpu): support onednn op in dygraph mode
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self.check_grad_with_place(
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place,
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{'Input', 'Filter'},
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'Output',
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max_relative_error=0.03,
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check_dygraph=(not self.use_onednn),
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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_no_filter(self):
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place = get_device_place() if self.has_cudnn() else core.CPUPlace()
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# TODO(wangzhongpu): support onednn op in dygraph mode
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self.check_grad_with_place(
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place,
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['Input'],
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'Output',
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max_relative_error=0.03,
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no_grad_set={'Filter'},
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check_dygraph=(not self.use_onednn),
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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_no_input(self):
|
|
place = get_device_place() if self.has_cudnn() else core.CPUPlace()
|
|
# TODO(wangzhongpu): support onednn op in dygraph mode
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Filter'],
|
|
'Output',
|
|
max_relative_error=0.03,
|
|
no_grad_set={'Input'},
|
|
check_dygraph=(not self.use_onednn),
|
|
check_pir=True,
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def init_test_case(self):
|
|
self.pad = [0, 0, 0]
|
|
self.stride = [1, 1, 1]
|
|
self.input_size = [2, 3, 4, 4, 4] # NCDHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [6, f_c, 3, 3, 3]
|
|
|
|
def init_test_case_2(self):
|
|
pass
|
|
|
|
def init_dilation(self):
|
|
self.dilation = [1, 1, 1]
|
|
|
|
def init_group(self):
|
|
self.groups = 1
|
|
|
|
def init_kernel_type(self):
|
|
pass
|
|
|
|
|
|
class TestCase1(TestConv3DOp):
|
|
def init_test_case(self):
|
|
self.pad = [1, 1, 1]
|
|
self.stride = [1, 1, 1]
|
|
self.input_size = [2, 3, 4, 4, 4] # NCDHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [6, f_c, 3, 3, 3]
|
|
|
|
|
|
class TestWithGroup1(TestConv3DOp):
|
|
def init_group(self):
|
|
self.groups = 3
|
|
|
|
|
|
class TestWithGroup2(TestCase1):
|
|
def init_group(self):
|
|
self.groups = 3
|
|
|
|
|
|
class TestWith1x1(TestConv3DOp):
|
|
def init_test_case(self):
|
|
self.pad = [0, 0, 0]
|
|
self.stride = [1, 1, 1]
|
|
self.input_size = [2, 3, 4, 4, 4]
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [120, f_c, 1, 1, 1]
|
|
|
|
def init_dilation(self):
|
|
self.dilation = [1, 1, 1]
|
|
|
|
def init_group(self):
|
|
self.groups = 3
|
|
|
|
|
|
class TestWithInput1x1Filter1x1(TestConv3DOp):
|
|
def init_test_case(self):
|
|
self.pad = [0, 0, 0]
|
|
self.stride = [1, 1, 1]
|
|
self.input_size = [40, 3, 1, 1, 1]
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [120, f_c, 1, 1, 1]
|
|
|
|
def init_dilation(self):
|
|
self.dilation = [1, 1, 1]
|
|
|
|
def init_group(self):
|
|
self.groups = 3
|
|
|
|
|
|
class TestWithDilation(TestConv3DOp):
|
|
def init_test_case(self):
|
|
self.pad = [0, 0, 0]
|
|
self.stride = [1, 1, 1]
|
|
self.input_size = [2, 3, 6, 6, 6]
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [24, f_c, 2, 2, 2]
|
|
|
|
def init_dilation(self):
|
|
self.dilation = [2, 2, 2]
|
|
|
|
def init_group(self):
|
|
self.groups = 3
|
|
|
|
|
|
# ---------------- Conv3DCUDNN ----------------
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestCUDNN(TestConv3DOp):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestFP16CUDNN(TestConv3DOp):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.dtype = np.float16
|
|
|
|
def test_check_output(self):
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
if core.is_float16_supported(place):
|
|
self.check_output_with_place(
|
|
place,
|
|
atol=2e-2,
|
|
check_pir=True,
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestWithGroup1CUDNN(TestWithGroup1):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestFP16WithGroup1CUDNN(TestWithGroup1):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.dtype = np.float16
|
|
|
|
def test_check_output(self):
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
if core.is_float16_supported(place):
|
|
self.check_output_with_place(
|
|
place,
|
|
atol=2e-2,
|
|
check_pir=True,
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestWithGroup2CUDNN(TestWithGroup2):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestFP16WithGroup2CUDNN(TestWithGroup2):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.dtype = np.float16
|
|
|
|
def test_check_output(self):
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
if core.is_float16_supported(place):
|
|
self.check_output_with_place(
|
|
place,
|
|
atol=2e-2,
|
|
check_pir=True,
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestWith1x1CUDNN(TestWith1x1):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestFP16With1x1CUDNN(TestWith1x1):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.dtype = np.float16
|
|
|
|
def test_check_output(self):
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
if core.is_float16_supported(place):
|
|
self.check_output_with_place(
|
|
place,
|
|
atol=2e-2,
|
|
check_pir=True,
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestWithInput1x1Filter1x1CUDNN(TestWithInput1x1Filter1x1):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestFP16WithInput1x1Filter1x1CUDNN(TestWithInput1x1Filter1x1):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.dtype = np.float16
|
|
|
|
def test_check_output(self):
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
if core.is_float16_supported(place):
|
|
self.check_output_with_place(
|
|
place,
|
|
atol=2e-2,
|
|
check_pir=True,
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
|
|
class TestCUDNNExhaustiveSearch(TestCUDNN):
|
|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.exhaustive_search = True
|
|
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
|
|
|
|
|
|
# ----------------Conv3DCUDNN bf16----------------
|
|
create_test_cudnn_bf16_class(TestConv3DOp)
|
|
create_test_cudnn_bf16_class(TestWithGroup1)
|
|
create_test_cudnn_bf16_class(TestWithGroup2)
|
|
create_test_cudnn_bf16_class(TestWith1x1)
|
|
create_test_cudnn_bf16_class(TestWithInput1x1Filter1x1)
|
|
|
|
|
|
# ---- test asymmetric padding ----
|
|
|
|
|
|
class TestConv3DOp_2(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "conv3d"
|
|
self.python_api = conv3d_wrapper
|
|
self.use_cudnn = False
|
|
self.use_onednn = False
|
|
self.data_format = "NCDHW"
|
|
self.dtype = np.float64
|
|
self.init_kernel_type()
|
|
self.init_group()
|
|
self.init_dilation()
|
|
self.init_data_format()
|
|
self.init_test_case()
|
|
self.init_paddings()
|
|
|
|
self.init_test_case_2()
|
|
|
|
conv3d_param = {
|
|
'stride': self.stride,
|
|
'pad': self.pad,
|
|
'dilation': self.dilation,
|
|
}
|
|
|
|
input = np.random.random(self.input_size).astype(self.dtype)
|
|
filter = np.random.random(self.filter_size).astype(self.dtype)
|
|
output = conv3d_forward_naive(
|
|
input,
|
|
filter,
|
|
self.groups,
|
|
conv3d_param,
|
|
self.padding_algorithm,
|
|
self.data_format,
|
|
).astype(self.dtype)
|
|
|
|
self.inputs = {
|
|
'Input': OpTest.np_dtype_to_base_dtype(input),
|
|
'Filter': OpTest.np_dtype_to_base_dtype(filter),
|
|
}
|
|
self.attrs = {
|
|
'strides': self.stride,
|
|
'paddings': self.pad,
|
|
'padding_algorithm': self.padding_algorithm,
|
|
'groups': self.groups,
|
|
'dilation': self.dilation,
|
|
'use_cudnn': self.use_cudnn,
|
|
'use_onednn': self.use_onednn,
|
|
'data_format': self.data_format,
|
|
}
|
|
self.outputs = {'Output': output}
|
|
|
|
def has_cudnn(self):
|
|
return (
|
|
core.is_compiled_with_cuda() or is_custom_device()
|
|
) and self.use_cudnn
|
|
|
|
def test_check_output(self):
|
|
place = get_device_place() if self.has_cudnn() else core.CPUPlace()
|
|
self.check_output_with_place(
|
|
place,
|
|
atol=1e-5,
|
|
check_pir=True,
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def test_check_grad(self):
|
|
if self.dtype == np.float16:
|
|
return
|
|
place = get_device_place() if self.has_cudnn() else core.CPUPlace()
|
|
self.check_grad_with_place(
|
|
place,
|
|
{'Input', 'Filter'},
|
|
'Output',
|
|
max_relative_error=0.03,
|
|
check_pir=True,
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def test_check_grad_no_filter(self):
|
|
if self.dtype == np.float16:
|
|
return
|
|
place = get_device_place() if self.has_cudnn() else core.CPUPlace()
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Input'],
|
|
'Output',
|
|
max_relative_error=0.03,
|
|
no_grad_set={'Filter'},
|
|
check_pir=True,
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def test_check_grad_no_input(self):
|
|
if self.dtype == np.float16:
|
|
return
|
|
place = get_device_place() if self.has_cudnn() else core.CPUPlace()
|
|
self.check_grad_with_place(
|
|
place,
|
|
['Filter'],
|
|
'Output',
|
|
max_relative_error=0.03,
|
|
no_grad_set={'Input'},
|
|
check_pir=True,
|
|
check_pir_onednn=self.check_pir_onednn,
|
|
)
|
|
|
|
def init_test_case(self):
|
|
self.stride = [1, 1, 1]
|
|
self.input_size = [2, 3, 4, 4, 4] # NCDHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [6, f_c, 3, 3, 3]
|
|
|
|
def init_test_case_2(self):
|
|
pass
|
|
|
|
def init_dilation(self):
|
|
self.dilation = [1, 1, 1]
|
|
|
|
def init_group(self):
|
|
self.groups = 1
|
|
|
|
def init_kernel_type(self):
|
|
pass
|
|
|
|
def init_paddings(self):
|
|
self.pad = [0, 0, 0]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
def init_data_format(self):
|
|
self.data_format = "NCDHW"
|
|
|
|
|
|
class TestConv3DOp_AsyPadding(TestConv3DOp_2):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1, 2]
|
|
self.input_size = [2, 3, 4, 4, 4] # NCDHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [6, f_c, 3, 3, 3]
|
|
|
|
def init_paddings(self):
|
|
self.pad = [1, 0, 1, 0, 0, 2]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestConv3DOp_DiffDataInDiffDim(TestConv3DOp_2):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1, 2]
|
|
self.input_size = [2, 3, 4, 5, 5] # NCDHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [6, f_c, 3, 4, 3]
|
|
|
|
def init_paddings(self):
|
|
self.pad = [1, 0, 1, 0, 0, 2]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
create_test_padding_SAME_class(TestConv3DOp_DiffDataInDiffDim)
|
|
create_test_padding_VALID_class(TestConv3DOp_DiffDataInDiffDim)
|
|
create_test_channel_last_class(TestConv3DOp_DiffDataInDiffDim)
|
|
|
|
|
|
class TestCase1_AsyPadding(TestConv3DOp_2):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1, 1]
|
|
self.input_size = [2, 3, 4, 4, 4] # NCDHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [6, f_c, 3, 3, 3]
|
|
|
|
def init_paddings(self):
|
|
self.pad = [0, 0, 1, 0, 0, 2]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestWithGroup1_AsyPadding(TestConv3DOp_2):
|
|
def init_group(self):
|
|
self.groups = 3
|
|
|
|
def init_paddings(self):
|
|
self.pad = [1, 1, 1, 0, 0, 2]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestWithGroup2_AsyPadding(TestConv3DOp_2):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1, 1]
|
|
self.input_size = [2, 3, 4, 4, 4] # NCDHW
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [6, f_c, 3, 3, 3]
|
|
|
|
def init_group(self):
|
|
self.groups = 3
|
|
|
|
def init_paddings(self):
|
|
self.pad = [1, 1, 0, 1, 0, 2]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestWith1x1_AsyPadding(TestConv3DOp_2):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1, 1]
|
|
self.input_size = [2, 3, 4, 4, 4]
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [120, f_c, 1, 1, 1]
|
|
|
|
def init_dilation(self):
|
|
self.dilation = [1, 1, 1]
|
|
|
|
def init_group(self):
|
|
self.groups = 3
|
|
|
|
def init_paddings(self):
|
|
self.pad = [0, 0, 1, 0, 0, 2]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
class TestWithDilation_AsyPadding(TestConv3DOp_2):
|
|
def init_test_case(self):
|
|
self.stride = [1, 1, 1]
|
|
self.input_size = [2, 3, 6, 6, 6]
|
|
assert np.mod(self.input_size[1], self.groups) == 0
|
|
f_c = self.input_size[1] // self.groups
|
|
self.filter_size = [24, f_c, 2, 2, 2]
|
|
|
|
def init_dilation(self):
|
|
self.dilation = [2, 2, 2]
|
|
|
|
def init_group(self):
|
|
self.groups = 3
|
|
|
|
def init_paddings(self):
|
|
self.pad = [0, 0, 1, 0, 1, 0]
|
|
self.padding_algorithm = "EXPLICIT"
|
|
|
|
|
|
create_test_cudnn_class(TestConv3DOp_AsyPadding)
|
|
create_test_cudnn_class(TestWithGroup1_AsyPadding)
|
|
create_test_cudnn_class(TestWithGroup2_AsyPadding)
|
|
create_test_cudnn_class(TestWith1x1_AsyPadding)
|
|
create_test_cudnn_class(TestWithDilation_AsyPadding)
|
|
|
|
create_test_padding_SAME_class(TestConv3DOp_AsyPadding)
|
|
create_test_padding_SAME_class(TestWithGroup1_AsyPadding)
|
|
create_test_padding_SAME_class(TestWith1x1_AsyPadding)
|
|
|
|
create_test_padding_VALID_class(TestConv3DOp_AsyPadding)
|
|
create_test_padding_VALID_class(TestWithGroup1_AsyPadding)
|
|
create_test_padding_VALID_class(TestWith1x1_AsyPadding)
|
|
|
|
create_test_cudnn_padding_SAME_class(TestConv3DOp_AsyPadding)
|
|
create_test_cudnn_padding_SAME_class(TestWithGroup1_AsyPadding)
|
|
create_test_cudnn_padding_SAME_class(TestWith1x1_AsyPadding)
|
|
|
|
create_test_cudnn_padding_VALID_class(TestConv3DOp_AsyPadding)
|
|
create_test_cudnn_padding_VALID_class(TestWithGroup1_AsyPadding)
|
|
create_test_cudnn_padding_VALID_class(TestWith1x1_AsyPadding)
|
|
|
|
create_test_channel_last_class(TestConv3DOp_AsyPadding)
|
|
create_test_channel_last_class(TestWithGroup1_AsyPadding)
|
|
create_test_channel_last_class(TestWith1x1_AsyPadding)
|
|
|
|
create_test_channel_last_class(TestConv3DOp_AsyPadding)
|
|
create_test_channel_last_class(TestWithGroup1_AsyPadding)
|
|
create_test_channel_last_class(TestWith1x1_AsyPadding)
|
|
|
|
create_test_cudnn_channel_last_class(TestConv3DOp_AsyPadding)
|
|
create_test_cudnn_channel_last_class(TestWithGroup1_AsyPadding)
|
|
create_test_cudnn_channel_last_class(TestWith1x1_AsyPadding)
|
|
|
|
create_test_cudnn_channel_last_class(TestConv3DOp_AsyPadding)
|
|
create_test_cudnn_channel_last_class(TestWithGroup1_AsyPadding)
|
|
create_test_cudnn_channel_last_class(TestWith1x1_AsyPadding)
|
|
|
|
# FIXME(typhoonzero): find a way to determine if
|
|
# using cudnn > 6 in python
|
|
# class TestWithDilationCUDNN(TestWithDilation):
|
|
# def init_op_type(self):
|
|
# self.op_type = "conv3d"
|
|
|
|
|
|
# --------- test python API ---------------
|
|
class TestConv3DAPI(unittest.TestCase):
|
|
def api_run(self):
|
|
input_NDHWC = paddle.static.data(
|
|
name="input_NDHWC",
|
|
shape=[2, 5, 5, 5, 3],
|
|
dtype="float32",
|
|
)
|
|
input_NDHWC_in_channel = 5
|
|
|
|
input_NCDHW = paddle.static.data(
|
|
name="input_NCDHW",
|
|
shape=[2, 3, 5, 5, 3],
|
|
dtype="float32",
|
|
)
|
|
input_NCDHW_in_channel = 3
|
|
|
|
paddle.nn.Conv3D(
|
|
in_channels=input_NCDHW_in_channel,
|
|
out_channels=3,
|
|
kernel_size=[3, 3, 3],
|
|
stride=[1, 1, 1],
|
|
padding=0,
|
|
dilation=[1, 1, 1],
|
|
groups=1,
|
|
data_format="NCDHW",
|
|
)(input_NCDHW)
|
|
|
|
paddle.nn.Conv3D(
|
|
in_channels=input_NCDHW_in_channel,
|
|
out_channels=3,
|
|
kernel_size=[3, 3, 3],
|
|
stride=[1, 1, 1],
|
|
padding=[1, 2, 1, 0, 1, 0],
|
|
dilation=[1, 1, 1],
|
|
groups=1,
|
|
data_format="NCDHW",
|
|
)(input_NCDHW)
|
|
|
|
paddle.nn.Conv3D(
|
|
in_channels=input_NCDHW_in_channel,
|
|
out_channels=3,
|
|
kernel_size=[3, 3, 3],
|
|
stride=[1, 1, 1],
|
|
padding=[[0, 0], [0, 0], [1, 1], [1, 1], [1, 1]],
|
|
dilation=[1, 1, 1],
|
|
groups=1,
|
|
data_format="NCDHW",
|
|
)(input_NCDHW)
|
|
|
|
paddle.nn.Conv3D(
|
|
in_channels=input_NDHWC_in_channel,
|
|
out_channels=3,
|
|
kernel_size=[3, 3, 3],
|
|
stride=[1, 1, 1],
|
|
padding=[[0, 0], [1, 1], [1, 1], [1, 1], [0, 0]],
|
|
dilation=[1, 1, 1],
|
|
groups=1,
|
|
data_format="NDHWC",
|
|
)(input_NDHWC)
|
|
|
|
paddle.nn.Conv3D(
|
|
in_channels=input_NCDHW_in_channel,
|
|
out_channels=3,
|
|
kernel_size=[3, 3, 3],
|
|
stride=[1, 1, 1],
|
|
padding="SAME",
|
|
dilation=[1, 1, 1],
|
|
groups=1,
|
|
data_format="NCDHW",
|
|
)(input_NCDHW)
|
|
|
|
paddle.nn.Conv3D(
|
|
in_channels=input_NCDHW_in_channel,
|
|
out_channels=3,
|
|
kernel_size=[3, 3, 3],
|
|
stride=[1, 1, 1],
|
|
padding="VALID",
|
|
dilation=[1, 1, 1],
|
|
groups=1,
|
|
data_format="NCDHW",
|
|
)(input_NCDHW)
|
|
|
|
def test_api(self):
|
|
with paddle.pir_utils.OldIrGuard():
|
|
self.api_run()
|
|
with paddle.pir_utils.IrGuard():
|
|
self.api_run()
|
|
|
|
|
|
class TestConv3DAPI_Error(unittest.TestCase):
|
|
def test_api(self):
|
|
with paddle.pir_utils.OldIrGuard():
|
|
input = paddle.static.data(
|
|
name="input",
|
|
shape=[2, 5, 5, 5, 4],
|
|
dtype="float32",
|
|
)
|
|
|
|
# ValueError: cudnn
|
|
def run_1():
|
|
paddle.static.nn.conv3d(
|
|
input=input,
|
|
num_filters=3,
|
|
filter_size=3,
|
|
stride=1,
|
|
padding=0,
|
|
dilation=1,
|
|
groups=1,
|
|
use_cudnn=[0],
|
|
data_format="NCDHW",
|
|
)
|
|
|
|
self.assertRaises(ValueError, run_1)
|
|
|
|
# ValueError: data_format
|
|
def run_2():
|
|
paddle.static.nn.conv3d(
|
|
input=input,
|
|
num_filters=3,
|
|
filter_size=[3, 3, 3],
|
|
stride=[1, 1, 1],
|
|
padding=0,
|
|
dilation=[1, 1, 1],
|
|
groups=1,
|
|
use_cudnn=False,
|
|
data_format="NCHWC",
|
|
)
|
|
|
|
self.assertRaises(ValueError, run_2)
|
|
|
|
# ValueError: padding
|
|
def run_3():
|
|
paddle.static.nn.conv3d(
|
|
input=input,
|
|
num_filters=3,
|
|
filter_size=3,
|
|
stride=1,
|
|
padding="SAMEE",
|
|
dilation=1,
|
|
groups=1,
|
|
use_cudnn=False,
|
|
data_format="NCDHW",
|
|
)
|
|
|
|
self.assertRaises(ValueError, run_3)
|
|
|
|
def run_4():
|
|
paddle.static.nn.conv3d(
|
|
input=input,
|
|
num_filters=3,
|
|
filter_size=3,
|
|
stride=1,
|
|
padding=[[0, 1], [0, 0], [0, 1], [0, 1], [0, 1]],
|
|
dilation=1,
|
|
groups=1,
|
|
use_cudnn=False,
|
|
data_format="NCDHW",
|
|
)
|
|
|
|
self.assertRaises(ValueError, run_4)
|
|
|
|
def run_5():
|
|
paddle.static.nn.conv3d(
|
|
input=input,
|
|
num_filters=3,
|
|
filter_size=0,
|
|
stride=0,
|
|
padding=[[0, 1], [0, 1], [0, 1], [0, 1], [0, 1]],
|
|
dilation=1,
|
|
groups=1,
|
|
use_cudnn=False,
|
|
data_format="NDHWC",
|
|
)
|
|
|
|
self.assertRaises(ValueError, run_5)
|
|
|
|
# ValueError: channel dimension
|
|
x = paddle.static.data(
|
|
name="x",
|
|
shape=[2, 5, 5, 5, -1],
|
|
dtype="float32",
|
|
)
|
|
|
|
def run_6():
|
|
paddle.static.nn.conv3d(
|
|
input=x,
|
|
num_filters=3,
|
|
filter_size=3,
|
|
stride=1,
|
|
padding=0,
|
|
dilation=1,
|
|
groups=1,
|
|
use_cudnn=False,
|
|
data_format="NDHWC",
|
|
)
|
|
|
|
self.assertRaises(ValueError, run_6)
|
|
|
|
# ValueError: groups
|
|
def run_7():
|
|
paddle.static.nn.conv3d(
|
|
input=input,
|
|
num_filters=3,
|
|
filter_size=3,
|
|
stride=1,
|
|
padding=0,
|
|
dilation=1,
|
|
groups=3,
|
|
use_cudnn=False,
|
|
data_format="NDHWC",
|
|
)
|
|
|
|
self.assertRaises(ValueError, run_7)
|
|
|
|
# ValueError: filter num
|
|
def run_8():
|
|
paddle.static.nn.conv3d(
|
|
input=input,
|
|
num_filters=0,
|
|
filter_size=0,
|
|
stride=0,
|
|
padding=0,
|
|
dilation=0,
|
|
groups=1,
|
|
use_cudnn=False,
|
|
data_format="NDHWC",
|
|
)
|
|
|
|
self.assertRaises(ValueError, run_8)
|
|
|
|
|
|
class TestPIRConv3DAPI_Error(unittest.TestCase):
|
|
def test_api(self):
|
|
with paddle.pir_utils.IrGuard():
|
|
input = paddle.static.data(
|
|
name="input",
|
|
shape=[2, 5, 5, 5, 4],
|
|
dtype="float32",
|
|
)
|
|
input_NCDHW_in_channel = 5
|
|
input_NDHWC_in_channel = 4
|
|
|
|
# ValueError: cudnn
|
|
# def run_1():
|
|
# model = paddle.nn.Conv3D(
|
|
# in_channels=input_NCDHW_in_channel,
|
|
# out_channels=3,
|
|
# kernel_size=3,
|
|
# stride=1,
|
|
# padding=0,
|
|
# dilation=1,
|
|
# groups=1,
|
|
# data_format="NCDHW",
|
|
# )
|
|
# model._use_cudnn = [0]
|
|
# model(input)
|
|
#
|
|
# self.assertRaises(ValueError, run_1)
|
|
|
|
# ValueError: data_format
|
|
def run_2():
|
|
paddle.nn.Conv3D(
|
|
in_channels=input_NCDHW_in_channel,
|
|
out_channels=3,
|
|
kernel_size=[3, 3, 3],
|
|
stride=[1, 1, 1],
|
|
padding=0,
|
|
dilation=[1, 1, 1],
|
|
groups=1,
|
|
data_format="NCHWC",
|
|
)(input)
|
|
|
|
self.assertRaises(ValueError, run_2)
|
|
|
|
# ValueError: padding
|
|
def run_3():
|
|
paddle.nn.Conv3D(
|
|
in_channels=input_NCDHW_in_channel,
|
|
out_channels=3,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding="SAMEE",
|
|
dilation=1,
|
|
groups=1,
|
|
data_format="NCDHW",
|
|
)(input)
|
|
|
|
self.assertRaises(ValueError, run_3)
|
|
|
|
def run_4():
|
|
paddle.nn.Conv3D(
|
|
in_channels=input_NCDHW_in_channel,
|
|
out_channels=3,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=[[0, 1], [0, 0], [0, 1], [0, 1], [0, 1]],
|
|
dilation=1,
|
|
groups=1,
|
|
data_format="NCDHW",
|
|
)(input)
|
|
|
|
self.assertRaises(ValueError, run_4)
|
|
|
|
def run_5():
|
|
paddle.nn.Conv3D(
|
|
in_channels=input_NDHWC_in_channel,
|
|
out_channels=3,
|
|
kernel_size=0,
|
|
stride=0,
|
|
padding=[[0, 1], [0, 1], [0, 1], [0, 1], [0, 1]],
|
|
dilation=1,
|
|
groups=1,
|
|
data_format="NDHWC",
|
|
)(input)
|
|
|
|
self.assertRaises(ValueError, run_5)
|
|
|
|
# ValueError: channel dimension
|
|
x = paddle.static.data(
|
|
name="x",
|
|
shape=[2, 5, 5, 5, -1],
|
|
dtype="float32",
|
|
)
|
|
x_NCDHW_in_channel = 5
|
|
x_NDHWC_in_channel = -1
|
|
|
|
def run_6():
|
|
paddle.nn.Conv3D(
|
|
in_channels=x_NDHWC_in_channel,
|
|
out_channels=3,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=0,
|
|
dilation=1,
|
|
groups=1,
|
|
data_format="NDHWC",
|
|
)(x)
|
|
|
|
self.assertRaises(AssertionError, run_6)
|
|
|
|
# ValueError: groups
|
|
def run_7():
|
|
paddle.nn.Conv3D(
|
|
in_channels=x_NDHWC_in_channel,
|
|
out_channels=3,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=0,
|
|
dilation=1,
|
|
groups=3,
|
|
data_format="NDHWC",
|
|
)(x)
|
|
|
|
self.assertRaises(ValueError, run_7)
|
|
|
|
# ValueError: filter num
|
|
def run_8():
|
|
paddle.nn.Conv3D(
|
|
in_channels=x_NDHWC_in_channel,
|
|
out_channels=0,
|
|
kernel_size=0,
|
|
stride=0,
|
|
padding=0,
|
|
dilation=0,
|
|
groups=1,
|
|
data_format="NDHWC",
|
|
)(x)
|
|
|
|
self.assertRaises(AssertionError, run_8)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|