[NPU] Support npu op depthwise_conv2d (#34853)

* add depthwise_conv2d npu

* add some tests

* Delete test_unique_op_npu.py

* delete trans input

paddle/fluid/operators/conv_op.cc

@@ -194,11 +194,14 @@ framework::OpKernelType ConvOp::GetExpectedKernelType(
             paddle::framework::DataTypeToString(input_data_type),
             paddle::framework::DataTypeToString(filter_data_type)));
   }
+#ifndef PADDLE_WITH_ASCEND_CL
   if (input_data_type == framework::proto::VarType::FP16) {
-    PADDLE_ENFORCE_EQ(library, framework::LibraryType::kCUDNN,
-                      platform::errors::InvalidArgument(
-                          "float16 can only be used when CUDNN is used"));
+    PADDLE_ENFORCE_EQ(
+        library, framework::LibraryType::kCUDNN,
+        platform::errors::InvalidArgument(
+            "float16 can only be used when CUDNN or NPU is used"));
   }
+#endif
 #if PADDLE_WITH_CUDA
   if (input_data_type == framework::proto::VarType::BF16 &&
       library == framework::LibraryType::kCUDNN) {

paddle/fluid/operators/conv_op_npu.cc

+// Copyright (c) 2021 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/operators/conv_op.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename DeviceContext, typename T>
+class DepthwiseConvNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    // input
+    const Tensor* input = context.Input<Tensor>("Input");
+    const Tensor* filter = context.Input<Tensor>("Filter");
+    // output
+    Tensor* output = context.Output<Tensor>("Output");
+    output->mutable_data<T>(context.GetPlace());
+    // attr
+    const std::vector<int> stride = context.Attr<std::vector<int>>("strides");
+    std::vector<int> padding = context.Attr<std::vector<int>>("paddings");
+    std::vector<int> dilation = context.Attr<std::vector<int>>("dilations");
+    const std::string data_format = context.Attr<std::string>("data_format");
+    const std::string padding_algorithm =
+        context.Attr<std::string>("padding_algorithm");
+
+    // npu stream
+    auto stream =
+        context.template device_context<platform::NPUDeviceContext>().stream();
+
+    // check dimension
+    const bool channel_last = data_format == "NHWC";
+    if (channel_last) {
+      // NHWC
+      PADDLE_ENFORCE_EQ(
+          output->dims()[output->dims().size() - 1],
+          input->dims()[input->dims().size() - 1],
+          platform::errors::InvalidArgument(
+              "ShapeError: The output channels must be equal to the "
+              "input channels. But receivced output channel number is %d "
+              "and input channel number is %d",
+              output->dims()[output->dims().size() - 1],
+              input->dims()[input->dims().size() - 1]));
+    } else {
+      // NCHW
+      PADDLE_ENFORCE_EQ(
+          output->dims()[1], input->dims()[1],
+          platform::errors::InvalidArgument(
+              "ShapeError: The output channels must be equal to the "
+              "input channels. But receivced output channel number is %d "
+              "and input channel number is %d",
+              output->dims()[1], input->dims()[1]));
+    }
+
+    // update padding and dilation
+    auto in_dims = input->dims();
+    auto filter_dims = filter->dims();
+    framework::DDim in_data_dims;
+    framework::DDim filter_data_dims;
+
+    if (channel_last) {
+      in_data_dims = framework::slice_ddim(in_dims, 1, in_dims.size() - 1);
+    } else {
+      in_data_dims = framework::slice_ddim(in_dims, 2, in_dims.size());
+    }
+    filter_data_dims = framework::slice_ddim(filter_dims, 2, in_dims.size());
+
+    std::vector<int> ksize = framework::vectorize<int>(filter_data_dims);
+    UpdatePaddingAndDilation(&padding, &dilation, padding_algorithm,
+                             in_data_dims, stride, ksize);
+
+    // Transform filter (n, 1, h, w) --> (1, n, h, w)
+    Tensor transformed_filter(filter->type());
+    transformed_filter.mutable_data<T>({filter->dims()[1], filter->dims()[0],
+                                        filter->dims()[2], filter->dims()[3]},
+                                       context.device_context().GetPlace());
+    std::vector<int> perm = {1, 0, 2, 3};
+    const auto& runner_trans = NpuOpRunner(
+        "TransposeD", {*filter}, {transformed_filter}, {{"perm", perm}});
+    runner_trans.Run(stream);
+
+    // construct NPU attr
+    std::vector<int> strides(4, 1);
+    std::vector<int> dilations(4, 1);
+
+    Tensor input_tensor, output_tensor;
+    input_tensor.ShareDataWith(*input);
+    output_tensor.ShareDataWith(*output);
+
+    if (channel_last) {
+      input_tensor.set_layout(DataLayout::kNHWC);
+      output_tensor.set_layout(DataLayout::kNHWC);
+      strides[1] = stride[0];
+      strides[2] = stride[1];
+      dilations[1] = dilation[0];
+      dilations[2] = dilation[1];
+    } else {
+      strides[2] = stride[0];
+      strides[3] = stride[1];
+      dilations[2] = dilation[0];
+      dilations[3] = dilation[1];
+    }
+
+    // CANN OP
+    const auto& runner =
+        NpuOpRunner("DepthwiseConv2D", {input_tensor, transformed_filter},
+                    {output_tensor}, {{"strides", strides},
+                                      {"dilations", dilations},
+                                      {"pads", padding},
+                                      {"data_format", data_format}});
+    runner.Run(stream);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_NPU_KERNEL(
+    depthwise_conv2d,
+    ops::DepthwiseConvNPUKernel<paddle::platform::NPUDeviceContext,
+                                paddle::platform::float16>);

python/paddle/fluid/tests/unittests/npu/test_conv2d_op_depthwise_conv_npu.py

+# Copyright (c) 2021 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.fluid as fluid
+import sys
+sys.path.append("..")
+from op_test import OpTest, skip_check_grad_ci
+from test_conv2d_op import conv2d_forward_naive
+
+paddle.enable_static()
+
+
+def create_test_channel_last_class(parent):
+    class TestChannelLastCase(parent):
+        def init_data_format(self):
+            self.data_format = "NHWC"
+
+        def init_test_case_2(self):
+            N, C, H, W = self.input_size
+            self.input_size = [N, H, W, C]
+
+    cls_name = "{0}_{1}".format(parent.__name__, "ChannelLast")
+    TestChannelLastCase.__name__ = cls_name
+    globals()[cls_name] = TestChannelLastCase
+
+
+def create_test_padding_SAME_class(parent):
+    class TestPaddingSMAECase(parent):
+        def init_paddings(self):
+            self.pad = [0, 0]
+            self.padding_algorithm = "SAME"
+
+    cls_name = "{0}_{1}".format(parent.__name__, "PaddingSAMEOp")
+    TestPaddingSMAECase.__name__ = cls_name
+    globals()[cls_name] = TestPaddingSMAECase
+
+
+def create_test_padding_VALID_class(parent):
+    class TestPaddingVALIDCase(parent):
+        def init_paddings(self):
+            self.pad = [1, 1]
+            self.padding_algorithm = "VALID"
+
+    cls_name = "{0}_{1}".format(parent.__name__, "PaddingVALIDOp")
+    TestPaddingVALIDCase.__name__ = cls_name
+    globals()[cls_name] = TestPaddingVALIDCase
+
+
+@skip_check_grad_ci(
+    reason='''Inference only, it doesn't need to call check_grad.''')
+class TestDepthwiseConvNPU(OpTest):
+    def setUp(self):
+        self.op_type = "depthwise_conv2d"
+        self.dtype = np.float16
+        self.set_npu()
+        self.init_data_format()
+        self.init_test_case()
+        self.init_test_case_2()
+
+        conv2d_param = {
+            'stride': self.stride,
+            'pad': self.pad,
+            'dilation': self.dilations
+        }
+
+        input = np.random.random(self.input_size).astype(self.dtype)
+        filter = np.random.uniform(-1, 1, self.filter_size).astype(self.dtype)
+
+        output, _, _, _, _ = conv2d_forward_naive(input, filter, self.groups,
+                                                  conv2d_param, "EXPLICIT",
+                                                  self.data_format)
+
+        output = output.astype(self.dtype)
+
+        self.inputs = {
+            'Input': OpTest.np_dtype_to_fluid_dtype(input),
+            'Filter': OpTest.np_dtype_to_fluid_dtype(filter)
+        }
+
+        self.attrs = {
+            'strides': self.stride,
+            'paddings': self.pad,
+            'groups': self.groups,
+            'dilations': self.dilations,
+            'data_format': self.data_format,
+        }
+        self.outputs = {'Output': output}
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+        self.place = paddle.NPUPlace(0)
+
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.dilations = [1, 1]
+        self.stride = [2, 2]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        self.groups = 3
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [3, f_c, 3, 3]
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def init_data_format(self):
+        self.data_format = "NCHW"
+
+    def init_test_case_2(self):
+        pass
+
+
+class TestDepthwiseConvNPU2(TestDepthwiseConvNPU):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.dilations = [1, 1]
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        self.groups = 3
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [3, f_c, 3, 3]
+
+
+class TestDepthwiseConvNPU3(TestDepthwiseConvNPU):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.dilations = [2, 2]
+        self.stride = [2, 2]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        self.groups = 3
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [3, f_c, 3, 3]
+
+
+class TestDepthwiseConvNPU4(TestDepthwiseConvNPU):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.dilations = [2, 2]
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        self.groups = 3
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [3, f_c, 3, 3]
+
+
+@skip_check_grad_ci(
+    reason='''Inference only, it doesn't need to call check_grad.''')
+class TestDepthwiseConvNPU_Padding(OpTest):
+    def setUp(self):
+        self.op_type = "depthwise_conv2d"
+        self.dtype = np.float16
+        self.set_npu()
+        self.init_data_format()
+        self.init_paddings()
+        self.init_test_case()
+        self.init_test_case_2()
+
+        conv2d_param = {
+            'stride': self.stride,
+            'pad': self.pad,
+            'dilation': self.dilations
+        }
+
+        input = np.random.random(self.input_size).astype(self.dtype)
+        filter = np.random.uniform(-1, 1, self.filter_size).astype(self.dtype)
+
+        output, _, _, _, _ = conv2d_forward_naive(
+            input, filter, self.groups, conv2d_param, self.padding_algorithm,
+            self.data_format)
+        output = output.astype(self.dtype)
+
+        self.inputs = {
+            'Input': OpTest.np_dtype_to_fluid_dtype(input),
+            'Filter': OpTest.np_dtype_to_fluid_dtype(filter)
+        }
+
+        self.attrs = {
+            'strides': self.stride,
+            'paddings': self.pad,
+            'padding_algorithm': self.padding_algorithm,
+            'groups': self.groups,
+            'dilations': self.dilations,
+            'data_format': self.data_format
+        }
+        self.outputs = {'Output': output}
+
+    def set_npu(self):
+        self.__class__.use_npu = True
+        self.place = paddle.NPUPlace(0)
+
+    def init_test_case(self):
+        self.pad = [1, 1, 0, 1]
+        self.dilations = [1, 1]
+        self.stride = [2, 2]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        self.groups = 3
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [3, f_c, 3, 3]
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def init_data_format(self):
+        self.data_format = "NCHW"
+
+    def init_paddings(self):
+        self.pad = [1, 1, 0, 1]
+        self.padding_algorithm = "EXPLICIT"
+
+    def init_test_case_2(self):
+        pass
+
+
+class TestDepthwiseConvNPU2_Padding(TestDepthwiseConvNPU_Padding):
+    def init_test_case(self):
+        self.pad = [1, 1, 0, 1]
+        self.dilations = [1, 1]
+        self.stride = [1, 1]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        self.groups = 3
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [3, f_c, 3, 3]
+
+    def init_paddings(self):
+        self.pad = [0, 1, 0, 2]
+        self.padding_algorithm = "EXPLICIT"
+
+
+class TestDepthwiseConvNPU3_Padding(TestDepthwiseConvNPU_Padding):
+    def init_test_case(self):
+        self.pad = [1, 1, 0, 1]
+        self.dilations = [1, 1]
+        self.stride = [2, 2]
+        self.input_size = [2, 3, 5, 5]  # NCHW
+        self.groups = 3
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [3, f_c, 3, 3]
+
+    def init_paddings(self):
+        self.pad = [2, 1, 2, 3]
+        self.padding_algorithm = "EXPLICIT"
+
+
+# test channel last
+create_test_channel_last_class(TestDepthwiseConvNPU)
+create_test_channel_last_class(TestDepthwiseConvNPU2)
+create_test_channel_last_class(TestDepthwiseConvNPU_Padding)
+create_test_channel_last_class(TestDepthwiseConvNPU2_Padding)
+
+# test padding SAME
+create_test_padding_SAME_class(TestDepthwiseConvNPU_Padding)
+create_test_padding_SAME_class(TestDepthwiseConvNPU2_Padding)
+create_test_padding_SAME_class(TestDepthwiseConvNPU3_Padding)
+
+# test padding VALID
+create_test_padding_VALID_class(TestDepthwiseConvNPU_Padding)
+create_test_padding_VALID_class(TestDepthwiseConvNPU2_Padding)
+create_test_padding_VALID_class(TestDepthwiseConvNPU3_Padding)
+
+if __name__ == '__main__':
+    unittest.main()