[NPU] Support npu kernel for pad3d op (#34815)

* [NPU] Support npu kernel for pad3d op

* fix for comment of zhouwei25

* fix some bugs according to qili93's comments

* add support and test for paddings in input

* delete VLOG used for debug

paddle/fluid/operators/pad3d_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 Licnse. */
+
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+static inline std::vector<int> GetPaddings(
+    const framework::ExecutionContext& context) {
+  std::vector<int> paddings(6);
+  auto* paddings_t = context.Input<Tensor>("Paddings");
+  if (paddings_t) {
+    TensorToVector(*paddings_t, context.device_context(), &paddings);
+  } else {
+    auto pads = context.Attr<std::vector<int>>("paddings");
+    std::copy(pads.begin(), pads.end(), paddings.data());
+  }
+  return paddings;
+}
+
+template <typename T>
+class Pad3dNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* x = context.Input<Tensor>("X");
+    auto in_dims = x->dims();
+
+    std::vector<int> pads = GetPaddings(context);
+    auto mode = context.Attr<std::string>("mode");
+    float value = context.Attr<float>("value");
+    auto data_format = context.Attr<std::string>("data_format");
+
+    auto* out = context.Output<Tensor>("Out");
+
+    PADDLE_ENFORCE_LT(abs(value), 1e-5,
+                      platform::errors::Unimplemented(
+                          "Ascend npu only support constant_values=0 right now,"
+                          "but received constant_value is %f .",
+                          value));
+
+    PADDLE_ENFORCE_EQ(mode, "constant",
+                      platform::errors::Unimplemented(
+                          "Ascend npu only support mode=constant right now,"
+                          "but received mode is %s .",
+                          mode));
+
+    std::vector<int> paddings(
+        {0, 0, 0, 0, pads[4], pads[5], pads[2], pads[3], pads[0], pads[1]});
+    if (data_format == "NCDHW") {
+      out->Resize({in_dims[0], in_dims[1], in_dims[2] + pads[4] + pads[5],
+                   in_dims[3] + pads[2] + pads[3],
+                   in_dims[4] + pads[0] + pads[1]});
+    } else {
+      out->Resize({in_dims[0], in_dims[1] + pads[4] + pads[5],
+                   in_dims[2] + pads[2] + pads[3],
+                   in_dims[3] + pads[0] + pads[1], in_dims[4]});
+      paddings = {0,       0,       pads[4], pads[5], pads[2],
+                  pads[3], pads[0], pads[1], 0,       0};
+    }
+    out->mutable_data<T>(context.GetPlace());
+
+    NpuOpRunner runner;
+    runner.SetType("PadV3")
+        .AddInput(*x)
+        .AddInput(std::move(paddings))
+        .AddInput(
+            std::vector<int>({0}))  // npu only support constant_value=0 now
+        .AddOutput(*out)
+        .AddAttr("mode", mode);
+
+    auto stream =
+        context.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+    runner.Run(stream);
+  }
+};
+
+template <typename T>
+class Pad3dGradNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    std::vector<int> pads = GetPaddings(context);
+    auto mode = context.Attr<std::string>("mode");
+    auto data_format = context.Attr<std::string>("data_format");
+
+    auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
+    auto* d_in = context.Output<Tensor>(framework::GradVarName("X"));
+    auto d_in_dims = d_in->dims();
+    d_in->mutable_data<T>(context.GetPlace());
+
+    const int pad_left = pads[0];
+    const int pad_top = pads[2];
+    const int pad_front = pads[4];
+
+    auto stream =
+        context.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    std::vector<int64_t> size(
+        {d_in_dims[0], d_in_dims[1], d_in_dims[2], d_in_dims[3], d_in_dims[4]});
+    if (mode == "constant") {  // this method can be only used for constant mode
+      std::vector<int> offsets({0, 0, pad_front, pad_top, pad_left});
+      if (data_format == "NDHWC") {
+        offsets = {0, pad_front, pad_top, pad_left, 0};
+      }
+      const auto& runner = NpuOpRunner("SliceD", {*d_out}, {*d_in},
+                                       {{"offsets", offsets}, {"size", size}});
+      runner.Run(stream);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_NPU_KERNEL(pad3d, ops::Pad3dNPUKernel<plat::float16>,
+                       ops::Pad3dNPUKernel<float>, ops::Pad3dNPUKernel<int>);
+
+REGISTER_OP_NPU_KERNEL(pad3d_grad, ops::Pad3dNPUKernel<plat::float16>,
+                       ops::Pad3dGradNPUKernel<float>);

python/paddle/fluid/tests/unittests/npu/test_pad3d_op_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.
+
+import unittest
+import numpy as np
+import sys
+sys.path.append("..")
+import op_test
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+import paddle.fluid.core as core
+from paddle.fluid import Program, program_guard, Executor, default_main_program
+import paddle.fluid as fluid
+
+
+class TestPad3dNPUOp(op_test.OpTest):
+    def setUp(self):
+        paddle.enable_static()
+        self.__class__.use_npu = True
+        self.op_type = "pad3d"
+        self.place = paddle.NPUPlace(0)
+
+        self.x_type = "float32"
+        self.mode = "constant"
+        self.variable_paddings = False
+        self.initTestCase()
+
+        self.value = 0  #Asend npu only support constant_values = 0 right now.
+        self.inputs = {'X': np.random.random(self.shape).astype(self.x_type)}
+        self.attrs = {}
+        if self.variable_paddings:
+            self.attrs['paddings'] = []
+            self.inputs['Paddings'] = np.array(self.paddings).flatten().astype(
+                "int32")
+        else:
+            self.attrs['paddings'] = np.array(self.paddings).flatten().astype(
+                "int32")
+        self.attrs['value'] = self.value
+        self.attrs['mode'] = self.mode
+        self.attrs['data_format'] = self.data_format
+        if self.data_format == "NCDHW":
+            paddings = [
+                (0, 0),
+                (0, 0),
+                (self.paddings[4], self.paddings[5]),
+                (self.paddings[2], self.paddings[3]),
+                (self.paddings[0], self.paddings[1]),
+            ]
+        else:
+            paddings = [
+                (0, 0),
+                (self.paddings[4], self.paddings[5]),
+                (self.paddings[2], self.paddings[3]),
+                (self.paddings[0], self.paddings[1]),
+                (0, 0),
+            ]
+
+        out = np.pad(self.inputs['X'],
+                     paddings,
+                     mode=self.mode,
+                     constant_values=self.value)
+
+        self.outputs = {'Out': out}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad(self):
+        self.check_grad_with_place(self.place, ['X'], 'Out')
+
+    def initTestCase(self):
+        self.shape = (2, 3, 4, 5, 6)
+        self.paddings = [0, 0, 0, 0, 0, 0]
+        self.data_format = "NCDHW"
+
+
+class TestCase1(TestPad3dNPUOp):
+    def initTestCase(self):
+        self.shape = (3, 4, 5, 6, 7)
+        self.paddings = [0, 1, 2, 3, 4, 5]
+        self.data_format = "NCDHW"
+        self.x_type = "float16"
+
+    def test_check_grad(self):
+        self.__class__.no_need_check_grad = True
+        pass
+
+
+class TestCase2(TestPad3dNPUOp):
+    def initTestCase(self):
+        self.shape = (4, 5, 6, 7, 8)
+        self.paddings = [1, 1, 1, 1, 1, 1]
+        self.data_format = "NDHWC"
+        self.variable_paddings = True
+
+
+class TestPadAPI(unittest.TestCase):
+    def _get_numpy_out(self,
+                       input_data,
+                       pad,
+                       mode,
+                       value=0,
+                       data_format="NCDHW"):
+        if mode == "constant" and len(pad) == len(input_data.shape) * 2:
+            pad = np.reshape(pad, (-1, 2)).tolist()
+        elif data_format == "NCDHW":
+            pad = [
+                (0, 0),
+                (0, 0),
+                (pad[4], pad[5]),
+                (pad[2], pad[3]),
+                (pad[0], pad[1]),
+            ]
+        elif data_format == "NDHWC":
+            pad = [
+                (0, 0),
+                (pad[4], pad[5]),
+                (pad[2], pad[3]),
+                (pad[0], pad[1]),
+                (0, 0),
+            ]
+        elif data_format == "NCHW":
+            pad = [
+                (0, 0),
+                (0, 0),
+                (pad[2], pad[3]),
+                (pad[0], pad[1]),
+            ]
+        elif data_format == "NHWC":
+            pad = [
+                (0, 0),
+                (pad[2], pad[3]),
+                (pad[0], pad[1]),
+                (0, 0),
+            ]
+        elif data_format == "NCL":
+            pad = [
+                (0, 0),
+                (0, 0),
+                (pad[0], pad[1]),
+            ]
+        elif data_format == "NLC":
+            pad = [
+                (0, 0),
+                (pad[0], pad[1]),
+                (0, 0),
+            ]
+
+        out = np.pad(input_data, pad, mode=mode, constant_values=value)
+        return out
+
+    def test_static(self):
+        paddle.enable_static()
+        self.place = fluid.NPUPlace(0) if fluid.core.is_compiled_with_npu(
+        ) else fluid.CPUPlace()
+        with program_guard(Program(), Program()):
+            input_shape = (1, 2, 3, 4, 5)
+            pad = [1, 2, 1, 1, 3, 4]
+            mode = "constant"
+            value = 0
+            input_data = np.random.rand(*input_shape).astype(np.float32)
+            x = paddle.fluid.data(name="x", shape=input_shape)
+            result1 = F.pad(x=x,
+                            pad=pad,
+                            value=value,
+                            mode=mode,
+                            data_format="NCDHW")
+            result2 = F.pad(x=x,
+                            pad=pad,
+                            value=value,
+                            mode=mode,
+                            data_format="NDHWC")
+            exe = Executor(self.place)
+            fetches = exe.run(default_main_program(),
+                              feed={"x": input_data},
+                              fetch_list=[result1, result2])
+
+            np_out1 = self._get_numpy_out(
+                input_data, pad, mode, value, data_format="NCDHW")
+            np_out2 = self._get_numpy_out(
+                input_data, pad, mode, value, data_format="NDHWC")
+            self.assertTrue(np.allclose(fetches[0], np_out1))
+            self.assertTrue(np.allclose(fetches[1], np_out2))
+
+    def test_dygraph_1(self):
+        paddle.disable_static()
+        paddle.device.set_device("npu")
+        input_shape = (1, 2, 3, 4, 5)
+        pad = [1, 2, 1, 1, 3, 4]
+
+        mode = "constant"
+        value = 0
+        input_data = np.random.rand(*input_shape).astype(np.float32)
+        tensor_data = paddle.to_tensor(input_data)
+
+        np_out1 = self._get_numpy_out(
+            input_data, pad, mode, value, data_format="NCDHW")
+        np_out2 = self._get_numpy_out(
+            input_data, pad, mode, value, data_format="NDHWC")
+
+        y1 = F.pad(tensor_data,
+                   pad=pad,
+                   mode=mode,
+                   value=value,
+                   data_format="NCDHW")
+        y2 = F.pad(tensor_data,
+                   pad=pad,
+                   mode=mode,
+                   value=value,
+                   data_format="NDHWC")
+
+        self.assertTrue(np.allclose(y1.numpy(), np_out1))
+        self.assertTrue(np.allclose(y2.numpy(), np_out2))
+
+    def test_dygraph_2(self):
+        paddle.disable_static()
+        paddle.device.set_device("npu")
+        input_shape = (2, 3, 4, 5)
+        pad = [1, 1, 3, 4]
+
+        mode = "constant"
+        value = 0
+        input_data = np.random.rand(*input_shape).astype(np.float32)
+        tensor_data = paddle.to_tensor(input_data)
+
+        np_out1 = self._get_numpy_out(
+            input_data, pad, mode, value, data_format="NCHW")
+        np_out2 = self._get_numpy_out(
+            input_data, pad, mode, value, data_format="NHWC")
+
+        y1 = F.pad(tensor_data,
+                   pad=pad,
+                   mode=mode,
+                   value=value,
+                   data_format="NCHW")
+        y2 = F.pad(tensor_data,
+                   pad=pad,
+                   mode=mode,
+                   value=value,
+                   data_format="NHWC")
+
+        self.assertTrue(np.allclose(y1.numpy(), np_out1))
+        self.assertTrue(np.allclose(y2.numpy(), np_out2))
+
+    def test_dygraph_3(self):
+        paddle.disable_static()
+        paddle.device.set_device("npu")
+        input_shape = (3, 4, 5)
+        pad = [3, 4]
+
+        mode = "constant"
+        value = 0
+        input_data = np.random.rand(*input_shape).astype(np.float32)
+        tensor_data = paddle.to_tensor(input_data)
+
+        np_out1 = self._get_numpy_out(
+            input_data, pad, mode, value, data_format="NCL")
+        np_out2 = self._get_numpy_out(
+            input_data, pad, mode, value, data_format="NLC")
+
+        y1 = F.pad(tensor_data,
+                   pad=pad,
+                   mode=mode,
+                   value=value,
+                   data_format="NCL")
+        y2 = F.pad(tensor_data,
+                   pad=pad,
+                   mode=mode,
+                   value=value,
+                   data_format="NLC")
+
+        self.assertTrue(np.allclose(y1.numpy(), np_out1))
+        self.assertTrue(np.allclose(y2.numpy(), np_out2))
+
+
+class TestPad1dAPI(unittest.TestCase):
+    def _get_numpy_out(self,
+                       input_data,
+                       pad,
+                       mode,
+                       value=0.0,
+                       data_format="NCL"):
+        if data_format == "NCL":
+            pad = [
+                (0, 0),
+                (0, 0),
+                (pad[0], pad[1]),
+            ]
+        else:
+            pad = [
+                (0, 0),
+                (pad[0], pad[1]),
+                (0, 0),
+            ]
+
+        out = np.pad(input_data, pad, mode=mode, constant_values=value)
+        return out
+
+    def test_class(self):
+        paddle.disable_static()
+        paddle.device.set_device("npu")
+        input_shape = (3, 4, 5)
+        pad = [1, 2]
+        pad_int = 1
+        value = 0
+        input_data = np.random.rand(*input_shape).astype(np.float32)
+
+        pad_constant = nn.Pad1D(padding=pad, mode="constant", value=value)
+        pad_constant_int = nn.Pad1D(
+            padding=pad_int, mode="constant", value=value)
+
+        data = paddle.to_tensor(input_data)
+
+        output = pad_constant(data)
+        np_out = self._get_numpy_out(
+            input_data, pad, "constant", value=value, data_format="NCL")
+        self.assertTrue(np.allclose(output.numpy(), np_out))
+
+        output = pad_constant_int(data)
+        np_out = self._get_numpy_out(
+            input_data, [pad_int] * 2,
+            "constant",
+            value=value,
+            data_format="NCL")
+        self.assertTrue(np.allclose(output.numpy(), np_out))
+
+
+class TestPad2dAPI(unittest.TestCase):
+    def _get_numpy_out(self,
+                       input_data,
+                       pad,
+                       mode,
+                       value=0.0,
+                       data_format="NCHW"):
+        if data_format == "NCHW":
+            pad = [
+                (0, 0),
+                (0, 0),
+                (pad[2], pad[3]),
+                (pad[0], pad[1]),
+            ]
+        else:
+            pad = [
+                (0, 0),
+                (pad[2], pad[3]),
+                (pad[0], pad[1]),
+                (0, 0),
+            ]
+
+        out = np.pad(input_data, pad, mode=mode, constant_values=value)
+        return out
+
+    def test_class(self):
+        paddle.disable_static()
+        paddle.device.set_device("npu")
+        input_shape = (3, 4, 5, 6)
+        pad = [1, 2, 2, 1]
+        pad_int = 1
+        value = 0
+        input_data = np.random.rand(*input_shape).astype(np.float32)
+
+        pad_constant = nn.Pad2D(padding=pad, mode="constant", value=value)
+        pad_constant_int = nn.Pad2D(
+            padding=pad_int, mode="constant", value=value)
+
+        data = paddle.to_tensor(input_data)
+
+        output = pad_constant(data)
+        np_out = self._get_numpy_out(
+            input_data, pad, "constant", value=value, data_format="NCHW")
+        self.assertTrue(np.allclose(output.numpy(), np_out))
+
+        output = pad_constant_int(data)
+        np_out = self._get_numpy_out(
+            input_data, [pad_int] * 4,
+            "constant",
+            value=value,
+            data_format="NCHW")
+        self.assertTrue(np.allclose(output.numpy(), np_out))
+
+
+class TestPad3dAPI(unittest.TestCase):
+    def _get_numpy_out(self,
+                       input_data,
+                       pad,
+                       mode,
+                       value=0.0,
+                       data_format="NCDHW"):
+        if data_format == "NCDHW":
+            pad = [
+                (0, 0),
+                (0, 0),
+                (pad[4], pad[5]),
+                (pad[2], pad[3]),
+                (pad[0], pad[1]),
+            ]
+        else:
+            pad = [
+                (0, 0),
+                (pad[4], pad[5]),
+                (pad[2], pad[3]),
+                (pad[0], pad[1]),
+                (0, 0),
+            ]
+
+        out = np.pad(input_data, pad, mode=mode, constant_values=value)
+        return out
+
+    def test_class(self):
+        paddle.disable_static()
+        paddle.device.set_device("npu")
+        input_shape = (3, 4, 5, 6, 7)
+        pad = [1, 2, 2, 1, 1, 0]
+        pad_int = 1
+        value = 0
+        input_data = np.random.rand(*input_shape).astype(np.float32)
+
+        pad_constant = nn.Pad3D(padding=pad, mode="constant", value=value)
+        pad_constant_int = nn.Pad3D(
+            padding=pad_int, mode="constant", value=value)
+
+        data = paddle.to_tensor(input_data)
+
+        output = pad_constant(data)
+        np_out = self._get_numpy_out(
+            input_data, pad, "constant", value=value, data_format="NCDHW")
+        self.assertTrue(np.allclose(output.numpy(), np_out))
+
+        output = pad_constant_int(data)
+        np_out = self._get_numpy_out(
+            input_data, [pad_int] * 6,
+            "constant",
+            value=value,
+            data_format="NCDHW")
+        self.assertTrue(np.allclose(output.numpy(), np_out))
+
+
+class TestPad3dOpNpuError(unittest.TestCase):
+    def test_errors(self):
+        def test_value():
+            input_shape = (1, 2, 3, 4, 5)
+            data = np.random.rand(*input_shape).astype(np.float32)
+            x = paddle.fluid.data(name="x", shape=input_shape)
+            y = F.pad(x, pad=[1, 1, 1, 1, 1, 1], value=1, mode='constant')
+            place = paddle.NPUPlace()
+            exe = Executor(place)
+            outputs = exe.run(feed={'x': data}, fetch_list=[y.name])
+
+        def test_mode_1():
+            input_shape = (1, 2, 3, 4, 5)
+            data = np.random.rand(*input_shape).astype(np.float32)
+            x = paddle.fluid.data(name="x", shape=input_shape)
+            y = F.pad(x, pad=[1, 1, 1, 1, 1, 1], mode='reflect')
+            place = paddle.NPUPlace()
+            exe = Executor(place)
+            outputs = exe.run(feed={'x': data}, fetch_list=[y.name])
+
+        def test_mode_2():
+            input_shape = (1, 2, 3, 4, 5)
+            data = np.random.rand(*input_shape).astype(np.float32)
+            x = paddle.fluid.data(name="x", shape=input_shape)
+            y = F.pad(x, pad=[1, 1, 1, 1, 1, 1], mode='replicate')
+            place = paddle.NPUPlace()
+            exe = Executor(place)
+            outputs = exe.run(feed={'x': data}, fetch_list=[y.name])
+
+        def test_mode_3():
+            input_shape = (1, 2, 3, 4, 5)
+            data = np.random.rand(*input_shape).astype(np.float32)
+            x = paddle.fluid.data(name="x", shape=input_shape)
+            y = F.pad(x, pad=[1, 1, 1, 1, 1, 1], mode='circular')
+            place = paddle.CPUPlace()
+            exe = Executor(place)
+            outputs = exe.run(feed={'x': data}, fetch_list=[y.name])
+
+        self.assertRaises(Exception, test_value)
+
+        self.assertRaises(Exception, test_mode_1)
+
+        self.assertRaises(Exception, test_mode_2)
+
+        self.assertRaises(Exception, test_mode_3)
+
+
+class TestPadDataformatError(unittest.TestCase):
+    def test_errors(self):
+        def test_ncl():
+            input_shape = (1, 2, 3, 4)
+            pad = paddle.to_tensor(np.array([2, 1, 2, 1]).astype('int32'))
+            data = np.arange(
+                np.prod(input_shape), dtype=np.float64).reshape(input_shape) + 1
+            my_pad = nn.Pad1D(padding=pad, mode="replicate", data_format="NCL")
+            data = paddle.to_tensor(data)
+            result = my_pad(data)
+
+        def test_nchw():
+            input_shape = (1, 2, 4)
+            pad = paddle.to_tensor(np.array([2, 1, 2, 1]).astype('int32'))
+            data = np.arange(
+                np.prod(input_shape), dtype=np.float64).reshape(input_shape) + 1
+            my_pad = nn.Pad1D(padding=pad, mode="replicate", data_format="NCHW")
+            data = paddle.to_tensor(data)
+            result = my_pad(data)
+
+        def test_ncdhw():
+            input_shape = (1, 2, 3, 4)
+            pad = paddle.to_tensor(np.array([2, 1, 2, 1]).astype('int32'))
+            data = np.arange(
+                np.prod(input_shape), dtype=np.float64).reshape(input_shape) + 1
+            my_pad = nn.Pad1D(
+                padding=pad, mode="replicate", data_format="NCDHW")
+            data = paddle.to_tensor(data)
+            result = my_pad(data)
+
+        self.assertRaises(AssertionError, test_ncl)
+
+        self.assertRaises(AssertionError, test_nchw)
+
+        self.assertRaises(AssertionError, test_ncdhw)
+
+
+if __name__ == '__main__':
+    unittest.main()