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[xpu] add weight_only_linear #72602

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Merged
merged 1 commit into from
May 9, 2025
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[xpu] add weight_only_linear #72602

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6 changes: 4 additions & 2 deletions paddle/fluid/framework/ir/xpu/weight_only_linear_xpu_pass.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -222,8 +222,10 @@ void PermuteINT8WeightOnlyPass::ApplyImpl(ir::Graph* graph) const {
PADDLE_ENFORCE_NOT_NULL(
graph, common::errors::PreconditionNotMet("graph should not be null."));
Init(name_scope_, graph);

ApplyPermuteINT8WeightOnly(graph);
auto version = phi::backends::xpu::get_xpu_version(-1);
if (version == phi::backends::xpu::XPUVersion::XPU2) {
ApplyPermuteINT8WeightOnly(graph);
}
}

} // namespace ir
Expand Down
2 changes: 2 additions & 0 deletions paddle/phi/backends/xpu/xpu3_op_list.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -1690,6 +1690,8 @@ XPUOpMap& get_kl3_ops() {
phi::DataType::BFLOAT16})},
{"warpctc_grad", XPUKernelSet({phi::DataType::FLOAT32})},
{"warpctc", XPUKernelSet({phi::DataType::FLOAT32})},
{"weight_only_linear",
XPUKernelSet({phi::DataType::FLOAT16, phi::DataType::BFLOAT16})},
{"where_index",
XPUKernelSet({phi::DataType::INT32,
phi::DataType::BOOL,
Expand Down
22 changes: 11 additions & 11 deletions paddle/phi/kernels/fusion/xpu/weight_only_linear_kernel_xpu.cc
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Expand Up @@ -17,20 +17,20 @@

namespace phi {
template <typename T, typename Context>
void WeightOnlyLinearKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& weight,
const paddle::optional<DenseTensor>& bias,
const DenseTensor& weight_scale,
const std::string& weight_dtype,
const int32_t arch,
const int32_t group_size,
DenseTensor* out) {
void WeightOnlyLinearXpuKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& weight,
const paddle::optional<DenseTensor>& bias,
const DenseTensor& weight_scale,
const std::string& weight_dtype,
const int32_t arch,
const int32_t group_size,
DenseTensor* out) {
PADDLE_ENFORCE_EQ(
weight_dtype,
"int8",
common::errors::Fatal(
"WeightOnlyLinearKernel xpu just support int8 weight only"));
"WeightOnlyLinearXpuKernel xpu just support int8 weight only"));
phi::XPUPlace place(phi::backends::xpu::GetXPUCurrentDeviceId());
auto xpu_ctx = static_cast<const phi::XPUContext*>(&dev_ctx);
dev_ctx.template Alloc<T>(out);
Expand Down Expand Up @@ -134,6 +134,6 @@ void WeightOnlyLinearKernel(const Context& dev_ctx,
PD_REGISTER_KERNEL(weight_only_linear_xpu,
XPU,
ALL_LAYOUT,
phi::WeightOnlyLinearKernel,
phi::WeightOnlyLinearXpuKernel,
phi::dtype::float16,
phi::dtype::bfloat16) {}
141 changes: 141 additions & 0 deletions paddle/phi/kernels/xpu/weight_only_linear_kernel.cc
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Original file line number Diff line number Diff line change
@@ -0,0 +1,141 @@
// Copyright (c) 2025 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 <xft/xdnn_plugin.h>
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
#ifdef PADDLE_WITH_XPU_XRE5
#include "xblas/xblas_legacy_api.h"
#endif

namespace phi {
template <typename T, typename Context>
void WeightOnlyLinearKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& weight,
const paddle::optional<DenseTensor>& bias,
const DenseTensor& weight_scale,
const std::string& weight_dtype,
const int32_t arch,
const int32_t group_size,
DenseTensor* out) {
using XPUType = typename XPUTypeTrait<T>::Type;
int64_t n = weight.dims()[0];
int64_t k = weight.dims()[1];
int64_t m = x.numel() / k;
if (weight_dtype == "int4") {
n = n * 2;
}
out->Resize({static_cast<int64_t>(m), static_cast<int64_t>(n)});
dev_ctx.template Alloc<T>(out);

DenseTensor bias_fp32;
if (bias.is_initialized() && bias.get().dtype() == phi::DataType::FLOAT16) {
bias_fp32.Resize(bias.get().dims());
dev_ctx.template Alloc<float>(&bias_fp32);
int r = baidu::xpu::api::cast<XPUType, float>(
dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(
bias.get().data<phi::dtype::float16>()),
bias_fp32.data<float>(),
n);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
}
auto input_x = reinterpret_cast<const XPUType*>(x.data<T>());
auto input_y = reinterpret_cast<XPUType*>(out->data<T>());

baidu::xpu::xblas::FcFusionTensor<const XPUType> tensor_x{
input_x, nullptr, m, k, k, false};
baidu::xpu::xblas::FcFusionTensor<const XPUType> tensor_y_const{
input_y, nullptr, m, n, n, false};
baidu::xpu::xblas::FcFusionTensor<XPUType> tensor_y{
input_y, nullptr, m, n, n, false};
baidu::xpu::xblas::FcFusionEpilogue<float, float> epilogue{
api::Activation_t::LINEAR,
bias.is_initialized() ? (bias.get().dtype() == phi::DataType::FLOAT16
? bias_fp32.data<float>()
: bias.get().data<float>())
: nullptr,
nullptr,
weight_scale.dims().size() != 0 ? weight_scale.data<float>() : nullptr,
0,
1,
nullptr};

if (weight_dtype == "int8") {
// using TGEMM=int8_wo_t;
using TGEMM = float;
baidu::xpu::xblas::FcFusionDesc<TGEMM, float, float> desc{1.0f, 0.0f};
baidu::xpu::xblas::FcFusionTensor<const int8_t> tensor_w{
reinterpret_cast<const int8_t*>(weight.data<int8_t>()),
nullptr,
n,
k,
k,
true};
int r1 = baidu::xpu::xblas::fc_fusion<XPUType,
int8_t,
XPUType,
XPUType,
TGEMM,
float,
float,
float,
float>(dev_ctx.x_context(),
tensor_x,
tensor_w,
tensor_y_const,
tensor_y,
desc,
epilogue);
PD_CHECK(r1 == 0, "xblas::fc_fusion failed");
} else if (weight_dtype == "int4") {
// baidu::xpu::xblas::FcFusionDesc<int4_wo_int15, float, XPUType>
// desc{1.0f, 0.0f};
// baidu::xpu::xblas::FcFusionTensor<const int4_t> tensor_w{
// reinterpret_cast<const int4_t*>(weight.data<int8_t>()),
// nullptr,
// n,
// k,
// k,
// true};
// int r1 = baidu::xpu::xblas::fc_fusion<XPUType,
// int4_t,
// XPUType,
// XPUType,
// int4_wo_int15, // int8_wo_t
// float,
// XPUType,
// float,
// float>(dev_ctx.x_context(),
// tensor_x,
// tensor_w,
// tensor_y_const,
// tensor_y,
// desc,
// epilogue);
// PD_CHECK(r1 == 0, "xblas::fc_fusion failed");
PD_THROW("unsupported weight_dtype=int4");
} else {
PD_THROW("unsupported weight_dtype: ", weight_dtype);
}
}
} // namespace phi

PD_REGISTER_KERNEL(weight_only_linear,
XPU,
ALL_LAYOUT,
phi::WeightOnlyLinearKernel,
phi::dtype::float16,
phi::dtype::bfloat16) {}
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