chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/full_kernel.cc

@@ -0,0 +1,197 @@
+/* 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/phi/kernels/full_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
+#include "paddle/phi/kernels/impl/full_with_tensor_kernel_impl.h"
+
+namespace phi {
+
+template <typename T, typename Context, typename VType>
+void FullValue(const Context& dev_ctx, DenseTensor* tensor, VType val) {
+  dev_ctx.template Alloc<T>(tensor);
+  if (tensor->numel() == 0) {
+    return;
+  }
+  auto t = EigenVector<T>::Flatten(*tensor);
+  t.device(*dev_ctx.eigen_device()) = t.constant(static_cast<T>(val));
+}
+
+template <typename T, typename Context>
+void FullKernel(const Context& dev_ctx,
+                const IntArray& shape,
+                const Scalar& val,
+                DataType dtype UNUSED,
+                DenseTensor* out) {
+  out->Resize(shape.GetData());
+  if (out->numel() == 0) {
+    dev_ctx.template Alloc<T>(out);
+    return;
+  }
+  FullValue<T>(dev_ctx, out, val.to<T>());
+}
+
+template <typename T, typename Context>
+void FullLikeKernel(const Context& dev_ctx,
+                    const DenseTensor& x UNUSED,
+                    const Scalar& val,
+                    DataType dtype UNUSED,
+                    DenseTensor* out) {
+  if (out->numel() == 0) {
+    dev_ctx.template Alloc<T>(out);
+    out->Resize(x.dims());
+    return;
+  }
+  if (!std::is_same<T, complex64>::value &&
+      !std::is_same<T, complex128>::value && !std::is_same<T, int64_t>::value) {
+    auto value = val.to<double>();
+    using CommonType = typename std::common_type<
+        float,
+        typename std::conditional<std::is_same<T, float16>::value, float, T>::
+            type>::type;
+
+    auto common_type_value = static_cast<CommonType>(value);
+
+    // Check whether the filled value is valid
+    bool is_out_range = true;
+    if (std::isinf(value) || std::isnan(value)) {
+      is_out_range = false;
+    }
+
+    if ((common_type_value >=
+         static_cast<CommonType>(std::numeric_limits<T>::lowest())) &&
+        (common_type_value <=
+         static_cast<CommonType>(std::numeric_limits<T>::max()))) {
+      is_out_range = false;
+    }
+
+    PADDLE_ENFORCE_EQ(
+        is_out_range,
+        false,
+        common::errors::InvalidArgument(
+            "The filled value is out of range for target type, "
+            "current kernel type is %s, the range should between %f "
+            "and %f, but now value is %f.",
+            typeid(T).name(),
+            static_cast<CommonType>(std::numeric_limits<T>::lowest()),
+            static_cast<CommonType>(std::numeric_limits<T>::max()),
+            static_cast<float>(value)));
+    FullValue<T>(dev_ctx, out, value);
+  } else {
+    FullValue<T>(dev_ctx, out, val.to<T>());
+  }
+}
+
+template <typename T, typename Context>
+void FullIntArrayKernel(const Context& dev_ctx,
+                        const std::vector<int64_t>& shape,
+                        DataType dtype UNUSED,
+                        DenseTensor* out) {
+  out->Resize({static_cast<int64_t>(shape.size())});
+  T* out_data = dev_ctx.template Alloc<T>(out);
+  if (out->numel() == 0) {
+    return;
+  }
+  for (size_t i = 0; i < shape.size(); ++i) {
+    int64_t val = shape[i];
+    out_data[i] = static_cast<T>(val);
+  }
+}
+#ifdef _WIN32
+template PADDLE_API void FullKernel<int, CPUContext>(const CPUContext&,
+                                                     const IntArray&,
+                                                     const Scalar&,
+                                                     DataType dtype UNUSED,
+                                                     DenseTensor*);
+template PADDLE_API void FullKernel<int64_t, CPUContext>(const CPUContext&,
+                                                         const IntArray&,
+                                                         const Scalar&,
+                                                         DataType dtype UNUSED,
+                                                         DenseTensor*);
+template PADDLE_API void FullKernel<float, CPUContext>(const CPUContext&,
+                                                       const IntArray&,
+                                                       const Scalar&,
+                                                       DataType dtype UNUSED,
+                                                       DenseTensor*);
+template PADDLE_API void FullKernel<double, CPUContext>(const CPUContext&,
+                                                        const IntArray&,
+                                                        const Scalar&,
+                                                        DataType dtype UNUSED,
+                                                        DenseTensor*);
+#endif
+}  // namespace phi
+
+PD_REGISTER_KERNEL(full,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::FullKernel,
+                   float,
+                   double,
+                   int8_t,
+                   uint8_t,
+                   int16_t,
+                   int,
+                   int64_t,
+                   bool,
+                   phi::float8_e4m3fn,
+                   phi::float8_e5m2,
+                   phi::float16,
+                   phi::bfloat16,
+                   phi::complex64,
+                   phi::complex128) {}
+
+PD_REGISTER_KERNEL(full_like,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::FullLikeKernel,
+                   float,
+                   double,
+                   uint8_t,
+                   int8_t,
+                   int16_t,
+                   int,
+                   int64_t,
+                   bool,
+                   phi::float16,
+                   phi::bfloat16,
+                   phi::complex64,
+                   phi::complex128) {
+  kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND);
+}
+
+PD_REGISTER_KERNEL(
+    full_int_array, CPU, ALL_LAYOUT, phi::FullIntArrayKernel, int, int64_t) {}
+
+PD_REGISTER_KERNEL(full_with_tensor,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::FullWithTensorKernel,
+                   float,
+                   double,
+                   int8_t,
+                   uint8_t,
+                   int16_t,
+                   int,
+                   int64_t,
+                   bool,
+                   phi::float16,
+                   phi::bfloat16,
+                   phi::complex64,
+                   phi::complex128) {
+  kernel->InputAt(0).SetBackend(phi::Backend::CPU);
+}