chore: import upstream snapshot with attribution

paddle/phi/kernels/set_kernel.cc

@@ -0,0 +1,150 @@
+// Copyright (c) 2024 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/set_kernel.h"
+#include <cstring>
+#include "paddle/phi/common/memory_utils.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/full_kernel.h"
+namespace phi {
+
+// Compute the minimum number of elements required in storage to hold
+// a strided view described by dims, stride and offset.
+static int64_t ComputeRequiredStorageSize(const std::vector<int64_t>& dims,
+                                          const std::vector<int64_t>& stride,
+                                          int64_t offset) {
+  int64_t required = offset;
+  for (size_t i = 0; i < dims.size(); ++i) {
+    if (dims[i] > 0) {
+      required += (dims[i] - 1) * stride[i];
+    }
+  }
+  return required + 1;  // +1 for the last element itself
+}
+
+template <typename T, typename Context>
+void SetKernel(const Context& dev_ctx,
+               const DenseTensor& x,
+               const DenseTensor& source,
+               const std::vector<int64_t>& dims,
+               const std::vector<int64_t>& stride,
+               int64_t offset,
+               DenseTensor* out) {
+  auto meta = out->meta();
+  meta.dims = DDim(dims.data(), static_cast<int>(dims.size()));
+  meta.strides = DDim(stride.data(), static_cast<int>(stride.size()));
+  meta.offset = offset;
+  if (x.numel() == 0 || source.numel() == 0) {
+    int64_t out_numel = 1;
+    for (auto d : dims) {
+      out_numel *= d;
+    }
+    if (source.numel() == 0 && x.numel() != 0) {
+      // Source is empty but x has storage. Reuse x's storage and apply
+      // the user-specified meta, matching PyTorch behavior.
+      if (out_numel == 0) {
+        // Output has 0 elements — no storage needed, just set meta.
+        out->set_meta(meta);
+        out->ShareInplaceVersionCounterWith(x);
+        return;
+      }
+      // If the strided view requires more storage than x provides,
+      // allocate a larger zero-filled buffer and copy x's data into it
+      // to avoid out-of-bounds reads on elements beyond x's allocation.
+      int64_t required_size = ComputeRequiredStorageSize(dims, stride, offset);
+      if (required_size > x.numel()) {
+        DenseTensor tmp;
+        std::vector<int64_t> alloc_shape = {required_size};
+        Full<T, Context>(dev_ctx, alloc_shape, 0, &tmp);
+        if (dev_ctx.GetPlace().GetType() == phi::AllocationType::CPU) {
+          std::memcpy(tmp.data<T>(), x.data<T>(), x.numel() * sizeof(T));
+        } else {
+          memory_utils::Copy(dev_ctx.GetPlace(),
+                             tmp.data<T>(),
+                             dev_ctx.GetPlace(),
+                             x.data<T>(),
+                             x.numel() * sizeof(T),
+                             nullptr);
+        }
+        out->clear();
+        *out = DenseTensor{tmp.Holder(), meta};
+      } else {
+        out->set_meta(meta);
+      }
+    } else if (source.numel() == 0 && x.numel() == 0 && out_numel != 0) {
+      // Both x and source are 0-size but user wants non-zero shape.
+      // Allocate zero-filled storage to avoid null pointer access.
+      int64_t required_size = ComputeRequiredStorageSize(dims, stride, offset);
+      DenseTensor tmp;
+      std::vector<int64_t> alloc_shape = {required_size};
+      Full<T, Context>(dev_ctx, alloc_shape, 0, &tmp);
+      out->clear();
+      *out = DenseTensor{tmp.Holder(), meta};
+    } else if (source.numel() != 0) {
+      out->clear();
+      *out = DenseTensor{source.Holder(), meta};
+    } else {
+      // Both 0-size, output also 0-size
+      out->clear();
+      *out = DenseTensor{source.Holder(), meta};
+    }
+    out->ShareInplaceVersionCounterWith(x);
+    return;
+  }
+  if (x.IsSharedWith(source)) {
+    out->set_meta(meta);
+  } else {
+    // reset holder to nullptr
+    out->clear();
+    *out = DenseTensor{source.Holder(), meta};
+  }
+  out->ShareInplaceVersionCounterWith(x);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(set,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::SetKernel,
+                   bool,
+                   uint8_t,
+                   int8_t,
+                   int16_t,
+                   int,
+                   int64_t,
+                   float,
+                   double,
+                   phi::float16,
+                   phi::bfloat16,
+                   phi::complex64,
+                   phi::complex128) {}
+
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+PD_REGISTER_KERNEL(set,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::SetKernel,
+                   bool,
+                   uint8_t,
+                   int8_t,
+                   int16_t,
+                   int,
+                   int64_t,
+                   float,
+                   double,
+                   phi::float16,
+                   phi::bfloat16,
+                   phi::complex64,
+                   phi::complex128) {}
+#endif