[MLU]add mlu kernel for conv2dtransposed op (#43233)

paddle/fluid/operators/conv_transpose_op_mlu.cc

+/* Copyright (c) 2022 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. */
+
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/conv_transpose_op.h"
+#include "paddle/fluid/operators/mlu/mlu_baseop.h"
+#include "paddle/phi/kernels/cpu/conv_util.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using DataLayout = framework::DataLayout;
+
+template <typename T>
+class Conv2DTransposeMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    const Tensor* input = ctx.Input<Tensor>("Input");
+    const Tensor* filter = ctx.Input<Tensor>("Filter");
+    Tensor* output = ctx.Output<Tensor>("Output");
+    output->mutable_data<T>(ctx.GetPlace());
+    std::vector<int> output_padding =
+        ctx.Attr<std::vector<int>>("output_padding");
+    const std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
+    std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
+    const std::string data_format = ctx.Attr<std::string>("data_format");
+    int groups = ctx.Attr<int>("groups");
+    const std::string padding_algorithm =
+        ctx.Attr<std::string>("padding_algorithm");
+
+    // check dimension
+    const bool channel_last = data_format == "NHWC";
+
+    auto in_dims = input->dims();
+    auto filter_dims = filter->dims();
+    auto in_dims_size = in_dims.size();
+    framework::DDim in_data_dims;
+    framework::DDim filter_data_dims;
+
+    if (channel_last) {
+      in_data_dims = phi::slice_ddim(in_dims, 1, in_dims.size() - 1);
+    } else {
+      in_data_dims = phi::slice_ddim(in_dims, 2, in_dims.size());
+    }
+    filter_data_dims = phi::slice_ddim(filter_dims, 2, in_dims.size());
+
+    std::vector<int> ksize = phi::vectorize<int>(filter_data_dims);
+    phi::UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
+                                  in_data_dims, strides, ksize);
+
+    Tensor input_tensor(input->type());
+    Tensor output_tensor(output->type());
+    input_tensor.set_layout(DataLayout::kNHWC);
+    output_tensor.set_layout(DataLayout::kNHWC);
+    const std::vector<int> perm_to_nhwc = {0, 2, 3, 1};
+
+    if (channel_last) {
+      input_tensor.ShareDataWith(*input);
+      output_tensor.ShareDataWith(*output);
+    } else {
+      // transpose input from NCHW to NHWC
+      TransposeFromMLUTensor<T>(ctx, perm_to_nhwc, input, &input_tensor,
+                                true /*need_reshape_or_alloc*/);
+      auto output_dims = output->dims();
+      output_tensor.mutable_data<T>(
+          {output_dims[0], output_dims[2], output_dims[3], output_dims[1]},
+          ctx.GetPlace());
+    }
+
+    // transpose filter from MCHW to MHWC
+    Tensor trans_filter(filter->type());
+    TransposeFromMLUTensor<T>(ctx, perm_to_nhwc, filter, &trans_filter,
+                              true /*need_reshape_or_alloc*/);
+
+    // construct MLU attr
+    cnnlTensorLayout_t data_layout = CNNL_LAYOUT_NHWC;
+    MLUCnnlTensorDesc input_desc(input_tensor, data_layout,
+                                 ToCnnlDataType(input_tensor.dtype()));
+    MLUCnnlTensorDesc filter_desc(trans_filter, data_layout,
+                                  ToCnnlDataType(trans_filter.type()));
+    MLUCnnlTensorDesc output_desc(output_tensor, data_layout,
+                                  ToCnnlDataType(output_tensor.dtype()));
+    MLUCnnlConvolutionDesc conv_desc(in_dims_size, paddings.data(),
+                                     strides.data(), dilations.data(), groups,
+                                     ToCnnlDataType<T>());
+
+    MLUCnnl::ConvBackpropInput(ctx, conv_desc.get(), filter_desc.get(),
+                               GetBasePtr(&trans_filter), input_desc.get(),
+                               GetBasePtr(&input_tensor), output_desc.get(),
+                               GetBasePtr(&output_tensor));
+
+    if (!channel_last) {
+      // transpose output from NHWC to NCHW
+      const std::vector<int> perm_to_nchw = {0, 3, 1, 2};
+      TransposeFromMLUTensor<T>(ctx, perm_to_nchw, &output_tensor, output,
+                                false /*need_reshape_or_alloc*/);
+    }
+  }
+};
+
+template <typename T>
+class Conv2DTransposeGradMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    const Tensor* input = ctx.Input<Tensor>("Input");
+    const Tensor* filter = ctx.Input<Tensor>("Filter");
+    const Tensor* output_grad =
+        ctx.Input<Tensor>(framework::GradVarName("Output"));
+    Tensor* input_grad = ctx.Output<Tensor>(framework::GradVarName("Input"));
+    Tensor* filter_grad = ctx.Output<Tensor>(framework::GradVarName("Filter"));
+
+    if ((!input_grad) && (!filter_grad)) return;
+
+    std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
+    std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
+    const int groups = ctx.Attr<int>("groups");
+    std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
+    const std::string data_format = ctx.Attr<std::string>("data_format");
+    const framework::DataLayout data_layout =
+        framework::StringToDataLayout(data_format);
+
+    auto in_dims = input->dims();
+    auto filter_dims = filter->dims();
+    auto in_dims_size = in_dims.size();
+
+    const bool channel_last = (data_layout == framework::DataLayout::kNHWC);
+
+    framework::DDim in_data_dims;
+    if (channel_last) {
+      in_data_dims = phi::slice_ddim(in_dims, 1, in_dims.size() - 1);
+    } else {
+      in_data_dims = phi::slice_ddim(in_dims, 2, in_dims.size());
+    }
+    framework::DDim filter_data_dims =
+        phi::slice_ddim(filter_dims, 2, filter_dims.size());
+    std::vector<int> ksize = phi::vectorize<int>(filter_data_dims);
+    phi::UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
+                                  in_data_dims, strides, ksize);
+
+    Tensor input_tensor(input->type());
+    Tensor output_grad_tensor(output_grad->type());
+    output_grad_tensor.set_layout(DataLayout::kNHWC);
+
+    const std::vector<int> perm_to_nhwc = {0, 2, 3, 1};
+    if (channel_last) {
+      input_tensor.ShareDataWith(*input);
+      output_grad_tensor.ShareDataWith(*output_grad);
+    } else {
+      // transpose input from NCHW to NHWC
+      TransposeFromMLUTensor<T>(ctx, perm_to_nhwc, input, &input_tensor,
+                                true /*need_reshape_or_alloc*/);
+      TransposeFromMLUTensor<T>(ctx, perm_to_nhwc, output_grad,
+                                &output_grad_tensor,
+                                true /*need_reshape_or_alloc*/);
+    }
+
+    // transpose filter from MCHW to MHWC
+    Tensor trans_filter(filter->type());
+    TransposeFromMLUTensor<T>(ctx, perm_to_nhwc, filter, &trans_filter,
+                              true /*need_reshape_or_alloc*/);
+
+    // MLU descs
+    cnnlTensorLayout_t data_layout_mlu = CNNL_LAYOUT_NHWC;
+    MLUCnnlTensorDesc input_desc(input_tensor, data_layout_mlu,
+                                 ToCnnlDataType(input_tensor.dtype()));
+    MLUCnnlTensorDesc trans_filter_desc(trans_filter, data_layout_mlu,
+                                        ToCnnlDataType(trans_filter.type()));
+    MLUCnnlTensorDesc output_grad_desc(
+        output_grad_tensor, data_layout_mlu,
+        ToCnnlDataType(output_grad_tensor.dtype()));
+    MLUCnnlConvolutionDesc conv_desc(in_dims_size, paddings.data(),
+                                     strides.data(), dilations.data(), groups,
+                                     ToCnnlDataType<T>());
+
+    if (filter_grad) {
+      filter_grad->mutable_data<T>(ctx.GetPlace());
+      Tensor filter_grad_tensor(filter_grad->type());
+      // filter_grad always MCHW
+      // filter_grad_tensor always MHWC
+      auto filter_grad_dims = filter_grad->dims();
+      filter_grad_tensor.mutable_data<T>(
+          {filter_grad_dims[0], filter_grad_dims[2], filter_grad_dims[3],
+           filter_grad_dims[1]},
+          ctx.GetPlace());
+      //}
+      filter_grad_tensor.set_layout(DataLayout::kNHWC);
+
+      MLUCnnlTensorDesc filter_grad_desc(
+          filter_grad_tensor, data_layout_mlu,
+          ToCnnlDataType(filter_grad_tensor.dtype()));
+
+      MLUCnnl::ConvBackpropFilter(
+          ctx, conv_desc.get(), output_grad_desc.get(), GetBasePtr(output_grad),
+          input_desc.get(), GetBasePtr(&input_tensor), filter_grad_desc.get(),
+          GetBasePtr(&filter_grad_tensor));
+      // transpose output from MHWC to MCHW
+      const std::vector<int> perm_to_mchw = {0, 3, 1, 2};
+      TransposeFromMLUTensor<T>(ctx, perm_to_mchw, &filter_grad_tensor,
+                                filter_grad, false /*need_reshape_or_alloc*/);
+    }
+
+    if (input_grad) {
+      input_grad->mutable_data<T>(ctx.GetPlace());
+      Tensor input_grad_tensor(input_grad->type());
+      input_tensor.set_layout(DataLayout::kNHWC);
+
+      if (channel_last) {
+        input_grad_tensor.ShareDataWith(*input_grad);
+      } else {
+        auto input_grad_dims = input_grad->dims();
+        input_grad_tensor.mutable_data<T>(
+            {input_grad_dims[0], input_grad_dims[2], input_grad_dims[3],
+             input_grad_dims[1]},
+            ctx.GetPlace());
+      }
+
+      MLUCnnlTensorDesc input_grad_desc(
+          input_grad_tensor, data_layout_mlu,
+          ToCnnlDataType(input_grad_tensor.dtype()));
+
+      cnnlDataType_t tensor_dtype = ToCnnlDataType<T>();
+      cnnlDataType_t dt_onchip = ToCnnlDataType<T>();
+      MLUCnnl::Conv2D(ctx, conv_desc.get(), tensor_dtype, dt_onchip,
+                      nullptr /* input_position */, nullptr /* input_scale */,
+                      nullptr /* input_offset */, nullptr /* filter_position */,
+                      nullptr /* filter_scale */, nullptr /* filter_offset */,
+                      output_grad_desc.get(), GetBasePtr(&output_grad_tensor),
+                      trans_filter_desc.get(), GetBasePtr(&trans_filter),
+                      nullptr /* bias_desc*/, nullptr /* bias */,
+                      input_grad_desc.get(), GetBasePtr(&input_grad_tensor));
+      if (!channel_last) {
+        // transpose output from NHWC to NCHW
+        const std::vector<int> perm_to_nchw = {0, 3, 1, 2};
+        TransposeFromMLUTensor<T>(ctx, perm_to_nchw, &input_grad_tensor,
+                                  input_grad, false /*need_reshape_or_alloc*/);
+      }
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_MLU_KERNEL(conv2d_transpose, ops::Conv2DTransposeMLUKernel<float>,
+                       ops::Conv2DTransposeMLUKernel<plat::float16>);
+
+REGISTER_OP_MLU_KERNEL(conv2d_transpose_grad,
+                       ops::Conv2DTransposeGradMLUKernel<float>,
+                       ops::Conv2DTransposeGradMLUKernel<plat::float16>);

paddle/fluid/operators/mlu/mlu_baseop.h

@@ -1159,7 +1159,7 @@ class MLUCnnl {
 
   static void ConvBackpropInput(
       const ExecutionContext& ctx, const cnnlConvolutionDescriptor_t conv_desc,
-      const cnnlTensorDescriptor_t input_desc, const void* filter,
+      const cnnlTensorDescriptor_t filter_desc, const void* filter,
       const cnnlTensorDescriptor_t out_backprop_desc, const void* out_backprop,
       const cnnlTensorDescriptor_t in_backprop_desc, void* in_backprop);
 

python/paddle/fluid/tests/unittests/mlu/test_conv2d_transposed_op_mlu.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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+
+import paddle
+import paddle.nn as nn
+
+paddle.enable_static()
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from paddle.fluid.tests.unittests.op_test import OpTest
+
+
+def conv2dtranspose_forward_naive(input_, filter_, attrs):
+    padding_algorithm = attrs['padding_algorithm']
+    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 attrs['data_format'] == 'NHWC':
+        input_ = np.transpose(input_, [0, 3, 1, 2])
+    in_n, in_c, in_h, in_w = input_.shape
+    f_c, f_out_c, f_h, f_w = filter_.shape
+    groups = attrs['groups']
+    assert in_c == f_c
+    out_c = f_out_c * groups
+    sub_in_c = in_c // groups
+
+    stride, pad, dilations = attrs['strides'], attrs['paddings'], attrs[
+        'dilations']
+
+    # update pad and dilation
+    def _get_padding_with_SAME(input_shape, kernel_size, kernel_stride):
+        padding = []
+        for input_size, filter_size, stride_size in zip(input_shape,
+                                                        kernel_size,
+                                                        kernel_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":
+        dilations = [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]
+
+    d_bolck_h = dilations[0] * (f_h - 1) + 1
+    d_bolck_w = dilations[1] * (f_w - 1) + 1
+    out_h = (in_h - 1) * stride[0] + d_bolck_h
+    out_w = (in_w - 1) * stride[1] + d_bolck_w
+    if 'output_size' in attrs:
+        output_size = attrs['output_size']
+        out_h = output_size[0] + pad_h_0 + pad_h_1
+        out_w = output_size[1] + pad_w_0 + pad_w_1
+    out_pad_h = 0
+    out_pad_w = 0
+    if 'output_padding' in attrs:
+        out_pad_h = attrs['output_padding'][0]
+        out_pad_w = attrs['output_padding'][1]
+    out = np.zeros((in_n, out_c, out_h + out_pad_h, out_w + out_pad_w),
+                   dtype=input_.dtype)
+
+    for n in range(in_n):
+        for i in range(in_h):
+            for j in range(in_w):
+                for g in range(groups):
+                    input_masked = input_[n, g * sub_in_c:(g + 1) * sub_in_c, i,
+                                          j]  # (c)
+                    input_masked = np.reshape(input_masked, (sub_in_c, 1, 1))
+                    input_masked = np.tile(input_masked, (1, f_h, f_w))
+
+                    for k in range(f_out_c):
+                        tmp_out = np.sum(
+                            input_masked *
+                            filter_[g * sub_in_c:(g + 1) * sub_in_c, k, :, :],
+                            axis=0)
+                        i1, i2 = i * stride[0], i * stride[0] + d_bolck_h
+                        j1, j2 = j * stride[1], j * stride[1] + d_bolck_w
+                        out[n, g * f_out_c + k, i1:i2:dilations[0],
+                            j1:j2:dilations[1]] += tmp_out
+
+    out = out[:, :, pad_h_0:out_h - pad_h_1 + out_pad_h,
+              pad_w_0:out_w - pad_w_1 + out_pad_w]
+    if attrs['data_format'] == 'NHWC':
+        out = np.transpose(out, [0, 2, 3, 1])
+    return out
+
+
+class TestConv2DTransposeOp(OpTest):
+
+    def setUp(self):
+        # init as conv transpose
+        self.dtype = np.float32
+        self.set_mlu()
+        self.need_check_grad = True
+        self.is_test = False
+        self.use_cudnn = False
+        self.use_mkldnn = False
+        self.output_size = None
+        self.output_padding = []
+        self.data_format = "NCHW"
+        self.pad = [0, 0]
+        self.padding_algorithm = "EXPLICIT"
+        self.init_op_type()
+        self.init_test_case()
+
+        input_ = np.random.random(self.input_size).astype(self.dtype)
+        filter_ = np.random.random(self.filter_size).astype(self.dtype)
+
+        self.inputs = {'Input': input_, 'Filter': filter_}
+        self.attrs = {
+            'strides': self.stride,
+            'paddings': self.pad,
+            'padding_algorithm': self.padding_algorithm,
+            'groups': self.groups,
+            'dilations': self.dilations,
+            'use_cudnn': self.use_cudnn,
+            'is_test': self.is_test,
+            'use_mkldnn': self.use_mkldnn,
+            'data_format': self.data_format
+        }
+        if self.output_size is not None:
+            self.attrs['output_size'] = self.output_size
+
+        if len(self.output_padding) > 0:
+            self.attrs['output_padding'] = self.output_padding
+
+        output = conv2dtranspose_forward_naive(input_, filter_,
+                                               self.attrs).astype(self.dtype)
+
+        self.outputs = {'Output': output}
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.device.MLUPlace(0)
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad_no_input(self):
+        if self.need_check_grad:
+            self.check_grad_with_place(self.place, ['Filter'],
+                                       'Output',
+                                       max_relative_error=0.02,
+                                       no_grad_set=set(['Input']))
+
+    def test_check_grad_no_filter(self):
+        if self.need_check_grad:
+            self.check_grad_with_place(self.place, ['Input'],
+                                       'Output',
+                                       no_grad_set=set(['Filter']))
+
+    def test_check_grad(self):
+        if self.need_check_grad:
+            self.check_grad_with_place(self.place,
+                                       set(['Input', 'Filter']),
+                                       'Output',
+                                       max_relative_error=0.02)
+
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+    def init_op_type(self):
+        self.op_type = "conv2d_transpose"
+
+
+class TestWithSymmetricPad(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+
+class TestWithAsymmetricPad(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.pad = [1, 0, 1, 2]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+
+class TestWithSAMEPad(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.stride = [2, 1]
+        self.dilations = [1, 2]
+        self.groups = 1
+        self.input_size = [2, 3, 6, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 4, 3]
+        self.padding_algorithm = 'SAME'
+
+
+class TestWithVALIDPad(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.padding_algorithm = 'VALID'
+
+
+class TestWithGroups(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 2
+        self.input_size = [2, 4, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 3, 3, 3]
+
+
+class TestWithStride(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [2, 2]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+
+class TestWithDilation(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.groups = 1
+        self.dilations = [2, 2]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+
+class TestWithEvenUpsample(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.pad = [2, 2]
+        self.stride = [2, 2]
+        self.groups = 1
+        self.dilations = [1, 1]
+        self.output_size = [14, 14]
+        self.input_size = [2, 3, 7, 7]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 5, 5]
+
+
+class TestWithEvenUpsampleOutputPadding(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.pad = [2, 2]
+        self.stride = [2, 2]
+        self.groups = 1
+        self.dilations = [1, 1]
+        self.output_padding = [1, 1]
+        self.input_size = [2, 3, 7, 7]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 5, 5]
+
+
+class Test_NHWC(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 5, 5, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.data_format = 'NHWC'
+
+
+class TestWithSymmetricPad_NHWC(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 5, 5, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.data_format = 'NHWC'
+
+
+class TestWithAsymmetricPad_NHWC(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.pad = [1, 0, 1, 2]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 5, 5, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.data_format = 'NHWC'
+
+
+class TestWithGroups_NHWC(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 2
+        self.input_size = [2, 5, 5, 4]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 3, 3, 3]
+        self.data_format = 'NHWC'
+
+
+class TestWithStride_NHWC(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [2, 2]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 5, 5, 3]  # NCHW
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.data_format = 'NHWC'
+
+
+class TestWithDilation_NHWC(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.groups = 1
+        self.dilations = [2, 2]
+        self.input_size = [2, 5, 5, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.data_format = 'NHWC'
+
+
+class TestWithEvenUpsample_NHWC(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.pad = [2, 2]
+        self.stride = [2, 2]
+        self.groups = 1
+        self.dilations = [1, 1]
+        self.output_size = [14, 14]
+        self.input_size = [2, 7, 7, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 5, 5]
+        self.data_format = 'NHWC'
+
+
+class TestWithEvenUpsample_NHWC_output_padding(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.pad = [2, 2]
+        self.stride = [2, 2]
+        self.groups = 1
+        self.dilations = [1, 1]
+        self.output_padding = [1, 1]
+        self.input_size = [2, 7, 7, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 5, 5]
+        self.data_format = 'NHWC'
+
+
+class TestMLU_FP16(TestConv2DTransposeOp):
+
+    def init_test_case(self):
+        self.dtype = np.float16
+        self.set_mlu()
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.groups = 1
+        self.dilations = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        f_c = self.input_size[1]
+        self.filter_size = [f_c, 6, 3, 3]
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.device.MLUPlace(0)
+
+    def init_op_type(self):
+        self.need_check_grad = False
+        self.op_type = "conv2d_transpose"
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place, atol=1e-2)
+
+
+class TestMLU_NHWC_FP16(TestMLU_FP16):
+
+    def init_test_case(self):
+        self.dtype = np.float16
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 1
+        self.input_size = [2, 5, 5, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 3, 3]
+        self.data_format = 'NHWC'
+
+
+class TestMLUWithGroups_NHWC_FP16(TestMLU_FP16):
+
+    def init_test_case(self):
+        self.dtype = np.float16
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.groups = 2
+        self.input_size = [2, 5, 5, 4]  # NCHW
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 3, 3, 3]
+        self.data_format = 'NHWC'
+
+
+class TestMLUWithEvenUpsample_NHWC_FP16(TestMLU_FP16):
+
+    def init_test_case(self):
+        self.dtype = np.float16
+        self.pad = [2, 2]
+        self.stride = [2, 2]
+        self.groups = 1
+        self.dilations = [1, 1]
+        self.output_size = [14, 14]
+        self.input_size = [2, 7, 7, 3]  # NHWC
+        f_c = self.input_size[-1]
+        self.filter_size = [f_c, 6, 5, 5]
+        self.data_format = 'NHWC'
+
+
+class TestConv2DTransposeAPI(unittest.TestCase):
+
+    def setUp(self):
+        self.set_mlu()
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.device.MLUPlace(0)
+
+    def test_case1(self):
+        data1 = fluid.layers.data(name='data1',
+                                  shape=[3, 5, 5],
+                                  dtype='float32')
+        data2 = fluid.layers.data(name='data2',
+                                  shape=[5, 5, 3],
+                                  dtype='float32')
+        out1 = fluid.layers.conv2d_transpose(input=data1,
+                                             groups=1,
+                                             num_filters=6,
+                                             filter_size=3,
+                                             data_format='NCHW')
+        out2 = fluid.layers.conv2d_transpose(input=data2,
+                                             groups=1,
+                                             num_filters=6,
+                                             filter_size=3,
+                                             data_format='NHWC')
+        out3 = fluid.layers.conv2d_transpose(input=data1,
+                                             groups=1,
+                                             num_filters=6,
+                                             filter_size=3,
+                                             padding=[[0, 0], [1, 1], [1, 1],
+                                                      [0, 0]],
+                                             data_format='NHWC')
+        out4 = fluid.layers.conv2d_transpose(input=data1,
+                                             groups=3,
+                                             num_filters=6,
+                                             filter_size=3,
+                                             padding=[[0, 0], [0, 0], [2, 1],
+                                                      [0, 0]],
+                                             data_format='NCHW')
+        out5 = fluid.layers.conv2d_transpose(input=data2,
+                                             groups=1,
+                                             num_filters=6,
+                                             filter_size=3,
+                                             padding='SAME',
+                                             data_format='NCHW')
+        out6 = fluid.layers.conv2d_transpose(input=data1,
+                                             groups=1,
+                                             num_filters=6,
+                                             filter_size=3,
+                                             padding='VALID',
+                                             data_format='NHWC')
+        out7 = fluid.layers.conv2d_transpose(input=data1,
+                                             groups=1,
+                                             num_filters=6,
+                                             output_size=[7, 7],
+                                             padding=[0, 0],
+                                             data_format='NHWC')
+
+        data1_np = np.random.random((2, 3, 5, 5)).astype("float32")
+        data2_np = np.random.random((2, 5, 5, 3)).astype("float32")
+
+        exe = fluid.Executor(self.place)
+        exe.run(fluid.default_startup_program())
+        results = exe.run(fluid.default_main_program(),
+                          feed={
+                              "data1": data1_np,
+                              "data2": data2_np
+                          },
+                          fetch_list=[out1, out2, out3, out4, out5, out6, out7],
+                          return_numpy=True)
+        self.assertIsNotNone(results[0])
+        self.assertIsNotNone(results[1])
+        self.assertIsNotNone(results[2])
+        self.assertIsNotNone(results[3])
+        self.assertIsNotNone(results[4])
+        self.assertIsNotNone(results[5])
+        self.assertIsNotNone(results[6])
+
+
+class TestConv2DTransposeOpException(unittest.TestCase):
+
+    def setUp(self):
+        self.set_mlu()
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.device.MLUPlace(0)
+
+    def test_exception(self):
+        data = fluid.layers.data(name='data', shape=[3, 5, 5], dtype="float32")
+
+        def attr_data_format():
+            out = fluid.layers.conv2d_transpose(input=data,
+                                                groups=1,
+                                                num_filters=6,
+                                                filter_size=3,
+                                                data_format="NCDHW")
+
+        self.assertRaises(ValueError, attr_data_format)
+
+        def attr_padding_str():
+            out = fluid.layers.conv2d_transpose(input=data,
+                                                groups=1,
+                                                num_filters=6,
+                                                filter_size=3,
+                                                padding='Vald')
+
+        self.assertRaises(ValueError, attr_padding_str)
+
+        def attr_padding_list():
+            out = fluid.layers.conv2d_transpose(input=data,
+                                                groups=1,
+                                                num_filters=6,
+                                                filter_size=3,
+                                                padding=[[1, 1], [1, 1], [0, 0],
+                                                         [0, 0]])
+
+        self.assertRaises(ValueError, attr_padding_list)
+
+        def attr_padding_with_data_format():
+            out = fluid.layers.conv2d_transpose(input=data,
+                                                groups=1,
+                                                num_filters=6,
+                                                filter_size=3,
+                                                padding=[[1, 1], [0, 0], [0, 0],
+                                                         [1, 1]],
+                                                data_format='NHWC')
+
+        self.assertRaises(ValueError, attr_padding_with_data_format)
+
+        error_input = fluid.layers.data(name='error_data',
+                                        shape=[1],
+                                        dtype="float32")
+
+        def error_input_size():
+            out = fluid.layers.conv2d_transpose(input=error_input,
+                                                groups=1,
+                                                num_filters=6,
+                                                filter_size=3)
+
+        self.assertRaises(ValueError, error_input_size)
+
+        def error_groups():
+            out = fluid.layers.conv2d_transpose(input=data,
+                                                groups=0,
+                                                num_filters=6,
+                                                filter_size=3,
+                                                data_format='NHWC')
+
+        self.assertRaises(ValueError, error_groups)
+
+
+class TestConv2DTransposeRepr(unittest.TestCase):
+
+    def setUp(self):
+        self.set_mlu()
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.device.MLUPlace(0)
+
+    def test_case(self):
+        paddle.disable_static()
+        x_var = paddle.uniform((2, 4, 8, 8), dtype='float32', min=-1., max=1.)
+        conv = nn.Conv2DTranspose(4, 6, (3, 3), output_padding=1, stride=2)
+        print(conv)
+        y_var = conv(x_var)
+        y_np = y_var.numpy()
+        self.assertIsNotNone(y_np)
+        paddle.enable_static()
+
+
+if __name__ == '__main__':
+    unittest.main()