[NPU] add pool2 op and tests

paddle/fluid/operators/pool_op_npu.cc

@@ -0,0 +1,300 @@
+/* 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/pool_op.h"
+#include "paddle/fluid/framework/fleet/ascend_wrapper.h"
+#include "paddle/fluid/framework/generator.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/operators/fill_constant_op.h"
+
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class NPUPoolOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto &dev_ctx = ctx.template device_context<platform::NPUDeviceContext>();
+    const Tensor *in_x = ctx.Input<Tensor>("X");
+    Tensor *out = ctx.Output<Tensor>("Out");
+    out->mutable_data<T>(ctx.GetPlace());
+
+    std::string pooling_type = ctx.Attr<std::string>("pooling_type");
+    std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
+    std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
+    std::string data_format = ctx.Attr<std::string>("data_format");
+
+    bool global_pooling = ctx.Attr<bool>("global_pooling");
+    bool ceil_mode = ctx.Attr<bool>("ceil_mode");
+    bool exclusive = ctx.Attr<bool>("exclusive");
+    bool adaptive = ctx.Attr<bool>("adaptive");
+    std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
+
+    const bool channel_last = data_format == "NHWC";
+
+    auto in_x_dims = in_x->dims();
+    auto out_dims = out->dims();
+    framework::DDim data_dims;
+    framework::DDim out_data_dims;
+
+    Tensor in_x_tensor, out_tensor;
+    in_x_tensor.ShareDataWith(*in_x);
+    out_tensor.ShareDataWith(*out);
+    std::vector<int> ksize_vec(4, 1);
+    std::vector<int> strides_vec(4, 1);
+
+    if (channel_last) {
+      data_dims = framework::slice_ddim(in_x_dims, 1, in_x_dims.size() - 1);
+      out_data_dims = framework::slice_ddim(out_dims, 1, out_dims.size() - 1);
+      ksize_vec[1] = ksize[0];
+      ksize_vec[2] = ksize[1];
+      strides_vec[1] = strides[0];
+      strides_vec[2] = strides[1];
+      in_x_tensor.set_layout(DataLayout::kNHWC);
+      out_tensor.set_layout(DataLayout::kNHWC);
+    } else {
+      data_dims = framework::slice_ddim(in_x_dims, 2, in_x_dims.size());
+      out_data_dims = framework::slice_ddim(out_dims, 2, out_dims.size());
+      ksize_vec[2] = ksize[0];
+      ksize_vec[3] = ksize[1];
+      strides_vec[2] = strides[0];
+      strides_vec[3] = strides[1];
+    }
+    UpdatePadding(&paddings, global_pooling, adaptive, padding_algorithm,
+                  data_dims, strides, ksize);
+    PADDLE_ENFORCE_LT(
+        std::max(paddings[0], paddings[1]), ksize[0],
+        platform::errors::InvalidArgument(
+            "Paddings should be less than %d, but max(pads[0], pads[1]) is %d.",
+            ksize[0], std::max(paddings[0], paddings[1])));
+    PADDLE_ENFORCE_LT(
+        std::max(paddings[2], paddings[3]), ksize[1],
+        platform::errors::InvalidArgument(
+            "Paddings should be less than %d, but max(pads[2], pads[3]) is %d.",
+            ksize[1], std::max(paddings[2], paddings[3])));
+
+    if (adaptive) {
+      std::string pooling_mode = "AdaptiveAvgPool2d";
+      if (pooling_type == "max") {
+        pooling_mode = "AdaptiveMaxPool2d";
+      }
+
+      // AdaptiveAvgPool2d only support NCHW
+      Tensor transformed_input, transformed_output;
+      if (pooling_type == "avg" && channel_last) {
+        transformed_input.mutable_data<T>(
+            framework::make_dim(in_x_dims[0], in_x_dims[3], in_x_dims[1],
+                                in_x_dims[2]),
+            ctx.GetPlace());
+        transformed_output.mutable_data<T>(
+            framework::make_dim(out_dims[0], out_dims[3], out_dims[1],
+                                out_dims[2]),
+            ctx.GetPlace());
+
+        const auto &trans_runner =
+            NpuOpRunner("TransData", {in_x_tensor}, {transformed_input},
+                        {{"src_format", std::string("NHWC")},
+                         {"dst_format", std::string("NCHW")}});
+        trans_runner.Run(dev_ctx.stream());
+      } else {
+        transformed_input.ShareDataWith(in_x_tensor);
+        transformed_output.ShareDataWith(out_tensor);
+      }
+
+      const auto &runner = NpuOpRunner(
+          pooling_mode, {transformed_input}, {transformed_output},
+          {{"output_size", framework::vectorize<int>(out_data_dims)}});
+      runner.Run(dev_ctx.stream());
+
+      if (pooling_type == "avg" && channel_last) {
+        const auto &trans_runner =
+            NpuOpRunner("TransData", {transformed_output}, {out_tensor},
+                        {{"src_format", std::string("NCHW")},
+                         {"dst_format", std::string("NHWC")}});
+        trans_runner.Run(dev_ctx.stream());
+      }
+    } else {
+      std::string pooling_mode = "AvgPoolV2";
+      if (pooling_type == "max") {
+        PADDLE_ENFORCE_EQ(
+            exclusive, true,
+            platform::errors::InvalidArgument(
+                "MaxPool only support exclusive=false, but got true"));
+        pooling_mode = "MaxPoolV3";
+      }
+
+      const auto &runner =
+          NpuOpRunner(pooling_mode, {in_x_tensor}, {out_tensor},
+                      {{"ksize", ksize_vec},
+                       {"strides", strides_vec},
+                       {"padding_mode", std::string("CALCULATED")},
+                       {"pads", paddings},
+                       {"data_format", data_format},
+                       {"global_pooling", global_pooling},
+                       {"ceil_mode", ceil_mode},
+                       {"exclusive", exclusive}});
+      runner.Run(dev_ctx.stream());
+    }
+  }
+};
+
+template <typename T>
+class NPUPoolGradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto &dev_ctx = ctx.template device_context<platform::NPUDeviceContext>();
+    const Tensor *in_x = ctx.Input<Tensor>("X");
+    const Tensor *out = ctx.Input<Tensor>("Out");
+    const Tensor *out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    Tensor *in_x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    in_x_grad->mutable_data<T>(ctx.GetPlace());
+
+    std::string pooling_type = ctx.Attr<std::string>("pooling_type");
+    std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
+    std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
+    std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
+    bool ceil_mode = ctx.Attr<bool>("ceil_mode");
+    bool exclusive = ctx.Attr<bool>("exclusive");
+    bool adaptive = ctx.Attr<bool>("adaptive");
+    std::string data_format = ctx.Attr<std::string>("data_format");
+    bool global_pooling = ctx.Attr<bool>("global_pooling");
+    std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
+
+    const bool channel_last = data_format == "NHWC";
+
+    // update paddings
+    auto in_x_dims = in_x->dims();
+    auto out_dims = out->dims();
+    framework::DDim data_dims;
+    framework::DDim out_data_dims;
+    std::vector<int> ksize_vec(4, 1);
+    std::vector<int> strides_vec(4, 1);
+
+    Tensor in_x_tensor, out_tensor, out_grad_tensor, in_x_grad_tensor;
+    in_x_tensor.ShareDataWith(*in_x);
+    out_tensor.ShareDataWith(*out);
+    out_grad_tensor.ShareDataWith(*out_grad);
+    in_x_grad_tensor.ShareDataWith(*in_x_grad);
+    if (channel_last) {
+      data_dims = framework::slice_ddim(in_x_dims, 1, in_x_dims.size() - 1);
+      out_data_dims = framework::slice_ddim(out_dims, 1, out_dims.size() - 1);
+      ksize_vec[1] = ksize[0];
+      ksize_vec[2] = ksize[1];
+      strides_vec[1] = strides[0];
+      strides_vec[2] = strides[1];
+      in_x_tensor.set_layout(DataLayout::kNHWC);
+      out_tensor.set_layout(DataLayout::kNHWC);
+      out_grad_tensor.set_layout(DataLayout::kNHWC);
+      in_x_grad_tensor.set_layout(DataLayout::kNHWC);
+    } else {
+      data_dims = framework::slice_ddim(in_x_dims, 2, in_x_dims.size());
+      out_data_dims = framework::slice_ddim(out_dims, 2, out_dims.size());
+      ksize_vec[2] = ksize[0];
+      ksize_vec[3] = ksize[1];
+      strides_vec[2] = strides[0];
+      strides_vec[3] = strides[1];
+    }
+    UpdatePadding(&paddings, global_pooling, adaptive, padding_algorithm,
+                  data_dims, strides, ksize);
+
+    PADDLE_ENFORCE_LT(
+        std::max(paddings[0], paddings[1]), ksize[0],
+        platform::errors::InvalidArgument(
+            "Paddings should be less than %d, but max(pads[0], pads[1]) is %d.",
+            ksize[0], std::max(paddings[0], paddings[1])));
+    PADDLE_ENFORCE_LT(
+        std::max(paddings[2], paddings[3]), ksize[1],
+        platform::errors::InvalidArgument(
+            "Paddings should be less than %d, but max(pads[2], pads[3]) is %d.",
+            ksize[1], std::max(paddings[2], paddings[3])));
+
+    if (adaptive || (global_pooling && pooling_type == "max")) {
+      PADDLE_ENFORCE_EQ(data_dims[0] % out_data_dims[0], 0,
+                        platform::errors::InvalidArgument(
+                            "When adaptive = True, H and W must be divisible, "
+                            "but input dims is %s, output dims is %s",
+                            data_dims, out_data_dims));
+      PADDLE_ENFORCE_EQ(data_dims[1] % out_data_dims[1], 0,
+                        platform::errors::InvalidArgument(
+                            "When adaptive = True, H and W must be divisible, "
+                            "but input dims is %s, output dims is %s",
+                            data_dims, out_data_dims));
+      if (channel_last) {
+        strides_vec[1] = data_dims[0] / out_data_dims[0];
+        strides_vec[2] = data_dims[1] / out_data_dims[1];
+        ksize_vec[1] = strides_vec[1];
+        ksize_vec[2] = strides_vec[2];
+      } else {
+        strides_vec[2] = data_dims[0] / out_data_dims[0];
+        strides_vec[3] = data_dims[1] / out_data_dims[1];
+        ksize_vec[2] = strides_vec[2];
+        ksize_vec[3] = strides_vec[3];
+      }
+    }
+
+    NPUAttributeMap attrs = {{"ksize", ksize_vec},
+                             {"strides", strides_vec},
+                             {"padding_mode", std::string("CALCULATED")},
+                             {"pads", paddings},
+                             {"data_format", data_format},
+                             {"global_pooling", global_pooling},
+                             {"ceil_mode", ceil_mode},
+                             {"exclusive", exclusive}};
+
+    if (pooling_type == "max") {
+      if (global_pooling) {
+        for (auto &s : strides_vec) {
+          s = 1;
+        }
+        PADDLE_ENFORCE_LT(std::max(data_dims[0], data_dims[1]), 255,
+                          platform::errors::InvalidArgument(
+                              "MaxPoolGrad H, W must be less than 255 when "
+                              "global_pooling = True, but got %s",
+                              data_dims));
+        attrs["global_pooling"] = false;
+      }
+
+      const auto &runner = NpuOpRunner(
+          "MaxPoolV3Grad", {in_x_tensor, out_tensor, out_grad_tensor},
+          {in_x_grad_tensor}, attrs);  // 0: floor, 1: ceil
+      runner.Run(dev_ctx.stream());
+    } else if (pooling_type == "avg") {
+      PADDLE_ENFORCE(strides[0] == strides[1],
+                     platform::errors::InvalidArgument(
+                         "AvgPoolGrad dose not support Asymmetric strides. but "
+                         "strides = (%d, %d)",
+                         strides[0], strides[1]));
+
+      NpuOpRunner runner;
+      runner.SetType("AvgPoolV2Grad");
+      runner.AddInput(framework::vectorize<int>(in_x->dims()));
+      runner.AddInput(out_grad_tensor);
+      runner.AddOutput(in_x_grad_tensor);
+      runner.AddAttrs(attrs);
+      runner.Run(dev_ctx.stream());
+    }
+  }
+};
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+REGISTER_OP_NPU_KERNEL(pool2d, ops::NPUPoolOpKernel<float>,
+                       ops::NPUPoolOpKernel<plat::float16>);
+REGISTER_OP_NPU_KERNEL(pool2d_grad, ops::NPUPoolGradOpKernel<float>,
+                       ops::NPUPoolGradOpKernel<plat::float16>);