[PHI] Migrate reshape kernel

paddle/fluid/operators/mkldnn/reshape_mkldnn_op.cc

@@ -100,9 +100,6 @@ class ReshapeMKLDNNKernel : public framework::OpKernel<T> {
       case ReshapeKernelOpName::reshape:
         InferShapeReshapeOp(ctx, x_dims, out_dims);
         break;
-      case ReshapeKernelOpName::reshape2:
-        InferShapeReshape2Op(ctx, x_dims, out_dims);
-        break;
       case ReshapeKernelOpName::squeeze:
         InferShapeSqueezeOp(ctx, x_dims, out_dims);
         break;
@@ -131,17 +128,6 @@ class ReshapeMKLDNNKernel : public framework::OpKernel<T> {
     ChangeReshapeOutDimsIfNeeded(ctx, x_dims, out_dims);
   }
 
-  void InferShapeReshape2Op(const framework::ExecutionContext& ctx,
-                            framework::DDim& x_dims,            // NOLINT
-                            framework::DDim& out_dims) const {  // NOLINT
-    auto* out = ctx.Output<phi::DenseTensor>("Out");
-    auto* xshape = ctx.Output<phi::DenseTensor>("XShape");
-    auto xshape_dims = xshape->dims();
-    x_dims = phi::slice_ddim(xshape_dims, 1, xshape_dims.size());
-    out_dims = out->dims();
-    ChangeReshapeOutDimsIfNeeded(ctx, x_dims, out_dims);
-  }
-
   // in reshape1/2 ops  "ShapeTensor" has highest priority and "Shape" has
   // second highest priority
   void ChangeReshapeOutDimsIfNeeded(
@@ -433,14 +419,6 @@ REGISTER_OP_KERNEL(
     ops::ReshapeGradMKLDNNKernel<paddle::platform::bfloat16,
                                  ReshapeKernelOpName::reshape>);
 
-REGISTER_OP_KERNEL(
-    reshape2,
-    MKLDNN,
-    paddle::platform::CPUPlace,
-    ops::ReshapeMKLDNNKernel<float, ReshapeKernelOpName::reshape2>,
-    ops::ReshapeMKLDNNKernel<paddle::platform::bfloat16,
-                             ReshapeKernelOpName::reshape2>);
-
 REGISTER_OP_KERNEL(
     reshape2_grad,
     MKLDNN,

paddle/phi/kernels/onednn/reshape_kernel.cc

@@ -0,0 +1,179 @@
+/* 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/phi/backends/onednn/onednn_reuse.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+static DDim ValidateShape(const std::vector<int64_t>& shape,
+                          const DDim& in_dims) {
+  const int64_t in_size = product(in_dims);
+  auto in_dims_vec = vectorize(in_dims);
+  bool all_positive = std::all_of(in_dims_vec.cbegin(),
+                                  in_dims_vec.cend(),
+                                  [](int64_t i) { return i > 0; });
+  // only one dimension can be set to -1, whose size will be automatically
+  // infered
+  const int64_t unk_dim_val = -1;
+  const int64_t copy_dim_val = 0;
+
+  std::vector<int64_t> output_shape(shape.size(), 0);
+  int64_t capacity = 1;
+  int unk_dim_idx = -1;
+  for (size_t i = 0; i < shape.size(); ++i) {
+    if (shape[i] == unk_dim_val) {
+      PADDLE_ENFORCE_EQ(
+          unk_dim_idx,
+          -1,
+          errors::InvalidArgument(
+              "Only one dimension value of 'shape' in ReshapeOp can "
+              "be -1. But received shape = [%s], shape[%d] is also -1.",
+              make_ddim(shape),
+              i));
+      unk_dim_idx = i;
+    } else if (shape[i] == copy_dim_val) {
+      PADDLE_ENFORCE_LT(
+          static_cast<int>(i),
+          in_dims.size(),
+          errors::InvalidArgument(
+              "The index of 0 in `shape` must be less than "
+              "the input tensor X's dimensions. "
+              "But received shape = [%s], shape[%d] = 0, X's shape = [%s], "
+              "X's dimensions = %d.",
+              make_ddim(shape),
+              i,
+              in_dims,
+              in_dims.size()));
+    } else {
+      PADDLE_ENFORCE_GT(
+          shape[i],
+          0,
+          errors::InvalidArgument(
+              "Each dimension value of 'shape' in ReshapeOp must not "
+              "be negative except one unknown dimension. "
+              "But received  shape = [%s], shape[%d] = %d.",
+              make_ddim(shape),
+              i,
+              shape[i]));
+    }
+
+    capacity *= (shape[i] ? shape[i] : in_dims[i]);
+    output_shape[i] = (shape[i] ? static_cast<int64_t>(shape[i]) : in_dims[i]);
+  }
+
+  if (unk_dim_idx != -1) {
+    if (all_positive) {
+      // in_size < 0 and is un-determinate in compile time, skip the check,
+      // for example, in_dims = [-1, 8, 1, 1], shape = [-1, 3, 8],
+      // capacity = -24, in_size = -8, output_shape[0] = 0
+      // the following check will fail.
+      output_shape[unk_dim_idx] = -in_size / capacity;
+      PADDLE_ENFORCE_EQ(
+          output_shape[unk_dim_idx] * capacity,
+          -in_size,
+          errors::InvalidArgument(
+              "The 'shape' attribute in ReshapeOp is invalid. "
+              "The input tensor X'size must be divisible by known "
+              "capacity of 'shape'. "
+              "But received X's shape = [%s], X's size = %d, "
+              "'shape' is [%s], known capacity of 'shape' is %d.",
+              in_dims,
+              in_size,
+              make_ddim(shape),
+              capacity));
+    } else {
+      output_shape[unk_dim_idx] = -1;
+    }
+  } else {
+    if (all_positive) {
+      PADDLE_ENFORCE_EQ(
+          capacity,
+          in_size,
+          errors::InvalidArgument(
+              "The 'shape' in ReshapeOp is invalid. "
+              "The input tensor X'size must be equal to the capacity of "
+              "'shape'. "
+              "But received X's shape = [%s], X's size = %d, 'shape' is "
+              "[%s], the capacity of 'shape' is %d.",
+              in_dims,
+              in_size,
+              make_ddim(shape),
+              capacity));
+    }
+  }
+  return make_ddim(output_shape);
+}
+
+template <typename T, typename Context>
+void ExecuteReshape(const Context& dev_ctx,
+                    const DenseTensor& x,
+                    const IntArray& shape,
+                    const DDim& x_dims,
+                    DenseTensor* out) {
+  auto out_dims = ValidateShape(shape.GetData(), x_dims);
+  auto x_vec_dims = vectorize(x_dims);
+
+  funcs::ReorderOneDNNHandler reorder_handler(
+      x_vec_dims,
+      x.dtype(),
+      funcs::ToOneDNNDataType(x.dtype()),
+      dev_ctx.GetEngine());
+
+  auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory(
+      x.mem_desc(), funcs::to_void_cast(x.data<T>()));
+  out->Resize(x_dims);  // to match x numel, format is changed later
+  // reorder is done into a plain tag to allow usage with blocked formats
+  auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory(
+      out, funcs::GetPlainOneDNNFormat(x_dims.size()), dev_ctx.GetPlace());
+  auto reorder_p = reorder_handler.AcquireReorder(reorder_dst_memory_p,
+                                                  reorder_src_memory_p);
+
+  auto& astream = OneDNNContext::tls().get_stream();
+  reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p);
+
+  astream.wait();
+
+  out->Resize(out_dims);
+  out->set_mem_desc(
+      reorder_dst_memory_p->get_desc().reshape(vectorize(out_dims)));
+}
+
+template <typename T, typename Context>
+void ReshapeKernel(const Context& dev_ctx,
+                   const DenseTensor& x,
+                   const IntArray& shape,
+                   DenseTensor* out) {
+  auto x_dims = x.dims();
+  ExecuteReshape<T, Context>(dev_ctx, x, shape, x_dims, out);
+}
+
+template <typename T, typename Context>
+void ReshapeWithXShape(const Context& dev_ctx,
+                       const DenseTensor& x,
+                       const IntArray& shape,
+                       DenseTensor* out,
+                       DenseTensor* xshape) {
+  auto x_dims = slice_ddim(xshape->dims(), 1, xshape->dims().size());
+  ExecuteReshape<T, Context>(dev_ctx, x, shape, x_dims, out);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(
+    reshape, OneDNN, ONEDNN, phi::ReshapeKernel, float, phi::dtype::bfloat16) {}
+
+PD_REGISTER_KERNEL(reshape_with_xshape,
+                   OneDNN,
+                   ONEDNN,
+                   phi::ReshapeWithXShape,
+                   float,
+                   phi::dtype::bfloat16) {}