chore: import upstream snapshot with attribution

paddle/phi/kernels/stride/squeeze_kernel.cc

@@ -0,0 +1,147 @@
+// Copyright (c) 2023 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/kernels/squeeze_kernel.h"
+
+#include <set>
+
+#include "glog/logging.h"
+#include "paddle/common/flags.h"
+#include "paddle/phi/backends/all_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+COMMON_DECLARE_bool(use_stride_kernel);
+
+namespace phi {
+
+template <typename Context>
+void SqueezeStridedKernel(const Context& dev_ctx,
+                          const DenseTensor& input,
+                          const IntArray& axes_arr,
+                          DenseTensor* out) {
+  if (!FLAGS_use_stride_kernel) {
+    PADDLE_THROW(common::errors::Fatal(
+        "FLAGS_use_stride_kernel is closed. Strided kernel "
+        "be called, something wrong has happened!"));
+  }
+  std::vector<int64_t> axes = axes_arr.GetData();
+  std::vector<int64_t> output_dims;
+  std::vector<int64_t> output_stride;
+
+  std::set<int64_t> axes_set;
+
+  auto input_dims = input.dims();
+  auto input_stride = input.strides();
+
+  if (input.Holder() == out->Holder() && input.meta() == out->meta()) {
+    output_dims = vectorize<int64_t>(out->dims());
+    if (axes.empty()) {
+      for (int i = input_stride.size() - 1; i > 0; --i) {
+        if (input_stride[i] != input_stride[i - 1]) {
+          output_stride.insert(output_stride.begin(), input_stride[i]);
+        }
+      }
+      if (output_dims.size() > output_stride.size()) {
+        output_stride.insert(output_stride.begin(), input_stride[0]);
+      }
+    } else {
+      for (auto& item : axes) {
+        item = item < 0 ? item + input_stride.size() : item;
+        if (item != 0 && input_stride[static_cast<int>(item)] ==
+                             input_stride[static_cast<int>(item) - 1]) {
+          axes_set.insert(item);
+        }
+      }
+      for (int i = 0; i < input_stride.size(); i++) {
+        if (axes_set.count(i) == 0) {
+          output_stride.push_back(input_stride[i]);
+        }
+      }
+      if (output_dims.size() < output_stride.size()) {
+        output_stride.erase(output_stride.begin());
+      }
+    }
+
+    auto meta = out->meta();
+    meta.offset = input.offset();
+    meta.strides =
+        DDim(output_stride.data(), static_cast<int>(output_stride.size()));
+    out->set_meta(meta);
+    return;
+  }
+
+  if (axes.empty()) {
+    for (int i = 0; i < input_dims.size(); i++) {
+      if (input_dims[i] != 1) {
+        output_dims.push_back(input_dims[i]);
+        output_stride.push_back(input_stride[i]);
+      }
+    }
+  } else {
+    for (auto item : axes) {
+      auto axis = item < 0 ? item + input_dims.size() : item;
+      if (input_dims[static_cast<int>(axis)] == 1) {
+        axes_set.insert(axis);
+      }
+    }
+
+    for (int i = 0; i < input_dims.size(); i++) {
+      if (axes_set.count(i) == 0) {
+        output_dims.push_back(input_dims[i]);
+        output_stride.push_back(input_stride[i]);
+      }
+    }
+  }
+
+  auto meta = out->meta();
+  auto tmp_dim = DDim(output_dims.data(), static_cast<int>(output_dims.size()));
+  // if (product(meta.dims) > 0 && meta.dims != tmp_dim) {
+  //   PADDLE_THROW(
+  //       common::errors::Fatal("Unsqueeze kernel stride compute diff, infer
+  //       shape"
+  //                          "is %s, but compute is %s.",
+  //                          meta.dims,
+  //                          tmp_dim));
+  // }
+  meta.dims = tmp_dim;
+  meta.strides =
+      DDim(output_stride.data(), static_cast<int>(output_stride.size()));
+  meta.offset = input.offset();
+  out->set_meta(meta);
+  out->ResetHolder(input.Holder());
+  out->ShareInplaceVersionCounterWith(input);
+}
+
+template <typename Context>
+void SqueezeWithXShapeStridedKernel(const Context& dev_ctx,
+                                    const DenseTensor& x,
+                                    const IntArray& axes,
+                                    DenseTensor* out,
+                                    DenseTensor* xshape UNUSED) {
+  if (!FLAGS_use_stride_kernel) {
+    PADDLE_THROW(common::errors::Fatal(
+        "FLAGS_use_stride_kernel is closed. Strided kernel "
+        "be called, something wrong has happened!"));
+  }
+  SqueezeStridedKernel<Context>(dev_ctx, x, axes, out);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL_FOR_ALL_BACKEND_DTYPE(squeeze,
+                                         STRIDED,
+                                         phi::SqueezeStridedKernel) {}
+
+PD_REGISTER_KERNEL_FOR_ALL_BACKEND_DTYPE(squeeze_with_xshape,
+                                         STRIDED,
+                                         phi::SqueezeWithXShapeStridedKernel) {}