chore: import upstream snapshot with attribution

2026-07-13 12:40:42 +08:00
commit e25996e7db
15472 changed files with 3536181 additions and 0 deletions
@@ -0,0 +1,86 @@
+// 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.
+
+#pragma once
+
+#include "paddle/phi/kernels/fake_dequantize_kernel.h"
+#include "paddle/phi/kernels/funcs/fake_dequantize_functor.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void FakeDequantizeMaxAbsKernel(const Context& dev_ctx,
+                                const DenseTensor& x,
+                                const DenseTensor& scale,
+                                float max_range,
+                                DenseTensor* out) {
+  dev_ctx.template Alloc<T>(out);
+  funcs::DequantizeFunctor<Context, T>()(
+      dev_ctx, &x, &scale, static_cast<T>(max_range), out);
+}
+
+template <typename T, typename Context>
+void FakeChannelWiseDequantizeMaxAbsKernel(
+    const Context& dev_ctx,
+    const DenseTensor& x,
+    const std::vector<const DenseTensor*>& scales,
+    const std::vector<int>& quant_bits,
+    int quant_axis,
+    int x_num_col_dims,
+    DenseTensor* out) {
+  int max_range = 1;
+  dev_ctx.template Alloc<T>(out);
+  int scale_num = scales.size();
+  if (scale_num == 1) {
+    PADDLE_ENFORCE_EQ(
+        scales[0]->numel(),
+        x.dims()[quant_axis],
+        common::errors::PreconditionNotMet(
+            "The number of first scale values must be the same with "
+            "quant_axis dimension value of Input(X) when the `Scales` has "
+            "only one element, but %ld != %ld here.",
+            scales[0]->numel(),
+            x.dims()[quant_axis]));
+    max_range *= (std::pow(2, quant_bits[0] - 1) - 1);
+  } else if (scale_num == 2) {
+    PADDLE_ENFORCE_EQ(
+        scales[0]->numel(),
+        x.dims()[x_num_col_dims],
+        common::errors::PreconditionNotMet(
+            "The number of first scale values must be the same with "
+            "corresponding dimension value of Input(X) when the `Scales` "
+            "has two elements, but %ld != %ld here.",
+            scales[0]->numel(),
+            x.dims()[1]));
+    PADDLE_ENFORCE_EQ(scales[1]->numel(),
+                      1,
+                      common::errors::PreconditionNotMet(
+                          "The second scale tensor should only have one "
+                          "value at now, but it has %ld values here.",
+                          scales[1]->numel()));
+    max_range *= (std::pow(2, quant_bits[0] - 1) - 1) *
+                 (std::pow(2, quant_bits[1] - 1) - 1);
+  }
+  funcs::ChannelDequantizeFunctor<Context, T>()(
+      dev_ctx,
+      &x,
+      (const_cast<std::vector<const DenseTensor*>*>(&scales))->data(),
+      scale_num,
+      static_cast<T>(max_range),
+      quant_axis,
+      x_num_col_dims,
+      out);
+}
+
+}  // namespace phi