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 +1,98 @@
+// 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.
+
+#pragma once
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/kernels/funcs/fc_functor.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void QuantLinearKernel(const Context& dev_ctx,
+                       const DenseTensor& x,
+                       const DenseTensor& w,
+                       const optional<DenseTensor>& bias,
+                       int in_num_col_dims,
+                       const std::string& activation_type,
+                       bool padding_weights,
+                       float scale_in,
+                       const std::vector<float>& scale_weights,
+                       int quant_round_type,
+                       float quant_max_bound,
+                       float quant_min_bound,
+                       DenseTensor* y) {
+  bool with_relu = activation_type == "relu" ? true : false;
+  auto w_dims = w.dims();
+
+  auto input_dims = x.dims();
+  std::vector<int64_t> output_dims;
+  auto in_mat_dims = common::flatten_to_2d(input_dims, in_num_col_dims);
+  auto w_dims0 = padding_weights ? w_dims[0] - 4 : w_dims[0];
+  auto w_dims1 = padding_weights ? w_dims[1] - 4 : w_dims[1];
+  PADDLE_ENFORCE_EQ(
+      in_mat_dims[1],
+      w_dims0,
+      common::errors::InvalidArgument(
+          "The input's second dimension and weight's first dimension is "
+          "expected to be the same. But received input's second dimension is"
+          "%d, input's shape is %s; weight's first dimension is %d, weight's"
+          " shape is %s.",
+          in_mat_dims[1],
+          in_mat_dims,
+          w_dims0,
+          make_ddim({w_dims0, w_dims1})));
+
+  output_dims.reserve(static_cast<size_t>(in_num_col_dims + 1));
+  for (int i = 0; i < in_num_col_dims; ++i) {
+    output_dims.push_back(input_dims[i]);
+  }
+  output_dims.push_back(w_dims1);
+
+  y->Resize(output_dims);
+  y->set_lod(x.lod());
+
+  auto out_dims = y->dims();
+  int M = common::product(out_dims) / w_dims1;
+
+  const T* input_data = x.data<T>();
+  auto* output_data = dev_ctx.template Alloc<T>(y, y->numel() * sizeof(T));
+  auto bias_data = bias ? bias.get_ptr()->data<T>() : NULL;
+
+  PADDLE_ENFORCE_EQ(
+      w.dtype(),
+      DataType::INT8,
+      common::errors::InvalidArgument(
+          "The weight's datatype is expected to be int8 when use quant. But "
+          "received weight's datatype is %d",
+          static_cast<int>(w.dtype())));
+  funcs::FCInt8Functor<Context, T> fc;
+  fc(dev_ctx,
+     M,
+     w_dims1,
+     w_dims0,
+     input_data,
+     &w,
+     output_data,
+     scale_in,
+     scale_weights,
+     quant_round_type,
+     quant_max_bound,
+     quant_min_bound,
+     bias_data,
+     with_relu,
+     padding_weights);
+  return;
+}
+
+}  // namespace phi