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未验证 提交 e512aa9a 编写于 作者: Q QingshuChen 提交者： GitHub
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support different precision in kunlun (#36836) 

* support different precision in kunlun

* minor

* minor

* minor
上级 5c4c55f9
隐藏空白更改
内联 并排
Showing with 126 addition and 16 deletion
cmake/external/xpu.cmake
浏览文件 @ e512aa9a
......@@ -35,7 +35,8 @@ ELSE ()
ENDIF()
SET(XPU_BASE_URL_WITHOUT_DATE "https://baidu-kunlun-product.cdn.bcebos.com/KL-SDK/klsdk-dev")
SET(XPU_BASE_URL "${XPU_BASE_URL_WITHOUT_DATE}/20211020")
SET(XPU_BASE_URL "${XPU_BASE_URL_WITHOUT_DATE}/20211029")
#SET(XPU_BASE_URL "${XPU_BASE_URL_WITHOUT_DATE}/20211020")
SET(XPU_XRE_URL "${XPU_BASE_URL}/${XPU_XRE_DIR_NAME}.tar.gz" CACHE STRING "" FORCE)
SET(XPU_XDNN_URL "${XPU_BASE_URL}/${XPU_XDNN_DIR_NAME}.tar.gz" CACHE STRING "" FORCE)
SET(XPU_XCCL_URL "${XPU_BASE_URL_WITHOUT_DATE}/20210623/${XPU_XCCL_DIR_NAME}.tar.gz" CACHE STRING "" FORCE)
......
paddle/fluid/operators/matmul_op_xpu.cc
浏览文件 @ e512aa9a
......@@ -20,6 +20,7 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/xpu_api_wrapper.h"
namespace paddle {
namespace operators {
......@@ -151,28 +152,26 @@ static void MatMulXPUFunction(const Tensor *x, const Tensor *y, Tensor *out,
x_dims.to_str().c_str(), y_dims.to_str().c_str()));
float alpha = static_cast<T>(ctx.Attr<float>("alpha"));
T *data_c = out->data<T>();
int m = mat_dim_a.height_;
int n = mat_dim_b.width_;
int k = mat_dim_a.width_;
int batch_size = mat_dim_a.batch_size_;
int ldx = mat_dim_a.trans_ ? m : k;
int ldy = mat_dim_b.trans_ ? k : n;
int ldout = n;
if (batch_size <= 1) {
int r = 0;
r = xpu::fc_fusion<XPUType, XPUType, XPUType, FCT>(
r = xpu_fc_wrapper<XPUType, FCT>(
dev_ctx.x_context(), reinterpret_cast<const XPUType *>(x->data<T>()),
reinterpret_cast<const XPUType *>(y->data<T>()),
reinterpret_cast<XPUType *>(data_c), m, n, k, mat_dim_a.trans_,
mat_dim_b.trans_, nullptr, nullptr, nullptr, ldx, ldy, ldout, alpha, 0,
nullptr, xpu::Activation_t::LINEAR);
PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
platform::errors::External(
"XPU fc_fusion kernel return wrong value[%d %s]", r,
XPUAPIErrorMsg[r]));
PADDLE_ENFORCE_EQ(
r, XPU_SUCCESS,
platform::errors::External("XPU fc kernel return wrong value[%d %s]", r,
XPUAPIErrorMsg[r]));
} else {
// batch matmul
int r = xpu::fc_batched<XPUType, XPUType, XPUType, FCT>(
......@@ -216,8 +215,10 @@ class MatMulXPUKernel : public framework::OpKernel<T> {
if (std::is_same<paddle::platform::float16, T>::value) {
MatMulXPUFunction<T, int16_t>(x, y, out, trans_x, trans_y, context);
} else {
if (std::getenv("XPU_PADDLE_MAT_MUL_FCINT32") != nullptr) {
if (std::getenv("XPU_PADDLE_FC_INT32") != nullptr) {
MatMulXPUFunction<T, int32_t>(x, y, out, trans_x, trans_y, context);
} else if (std::getenv("XPU_PADDLE_FC_LOCAL_INT16") != nullptr) {
MatMulXPUFunction<T, float>(x, y, out, trans_x, trans_y, context);
} else {
MatMulXPUFunction<T, int16_t>(x, y, out, trans_x, trans_y, context);
}
......@@ -292,8 +293,10 @@ class MatMulGradXPUKernel : public framework::OpKernel<T> {
if (std::is_same<paddle::platform::float16, T>::value) {
MatMulXPUFunction<T, int16_t>(&a, &b, out, trans_a, trans_b, context);
} else {
if (std::getenv("XPU_PADDLE_MAT_MUL_GRAD_FCINT32") != nullptr) {
if (std::getenv("XPU_PADDLE_FC_INT32") != nullptr) {
MatMulXPUFunction<T, int32_t>(&a, &b, out, trans_a, trans_b, context);
} else if (std::getenv("XPU_PADDLE_FC_LOCAL_INT16") != nullptr) {
MatMulXPUFunction<T, float>(&a, &b, out, trans_a, trans_b, context);
} else {
MatMulXPUFunction<T, int16_t>(&a, &b, out, trans_a, trans_b, context);
}
......
paddle/fluid/operators/matmul_v2_op_xpu.cc
浏览文件 @ e512aa9a
......@@ -18,6 +18,8 @@
#include <string>
#include <vector>
#include "paddle/fluid/operators/xpu_api_wrapper.h"
namespace paddle {
namespace operators {
......@@ -74,17 +76,21 @@ static void MatMulXPUFunction(const Tensor* x, const Tensor* y, Tensor* out,
int n = mat_dim_b.width_;
int k = mat_dim_a.width_;
int batch_size = mat_dim_a.batch_size_;
int ldx = mat_dim_a.trans_ ? m : k;
int ldy = mat_dim_b.trans_ ? k : n;
int ldout = n;
if (batch_size <= 1) {
int r = 0;
r = xpu::fc<XPUType, XPUType, XPUType, FCT>(
r = xpu_fc_wrapper<XPUType, FCT>(
dev_ctx.x_context(), reinterpret_cast<const XPUType*>(x->data<T>()),
reinterpret_cast<const XPUType*>(y->data<T>()),
reinterpret_cast<XPUType*>(data_c), m, n, k, mat_dim_a.trans_,
mat_dim_b.trans_, nullptr, nullptr, nullptr);
mat_dim_b.trans_, nullptr, nullptr, nullptr, ldx, ldy, ldout, 1.0, 0,
nullptr, xpu::Activation_t::LINEAR);
PADDLE_ENFORCE_EQ(
r, XPU_SUCCESS,
platform::errors::External(
"XPU fc_fusion kernel return wrong value[%d %s] , m = %d, n = "
"XPU fc kernel return wrong value[%d %s] , m = %d, n = "
"%d, "
"k = %d, a_tr = %d, b_tr = %d",
r, XPUAPIErrorMsg[r], m, n, k, mat_dim_a.trans_, mat_dim_b.trans_));
......@@ -129,8 +135,10 @@ class MatMulV2XPUKernel : public framework::OpKernel<T> {
if (std::is_same<paddle::platform::float16, T>::value) {
MatMulXPUFunction<T, int16_t>(x, y, out, trans_x, trans_y, ctx);
} else {
if (std::getenv("XPU_PADDLE_MAT_MUL_V2_FCINT32") != nullptr) {
if (std::getenv("XPU_PADDLE_FC_INT32") != nullptr) {
MatMulXPUFunction<T, int32_t>(x, y, out, trans_x, trans_y, ctx);
} else if (std::getenv("XPU_PADDLE_FC_LOCAL_INT16") != nullptr) {
MatMulXPUFunction<T, float>(x, y, out, trans_x, trans_y, ctx);
} else {
MatMulXPUFunction<T, int16_t>(x, y, out, trans_x, trans_y, ctx);
}
......@@ -178,8 +186,10 @@ class MatMulV2XPUGradKernel : public framework::OpKernel<T> {
if (std::is_same<paddle::platform::float16, T>::value) {
MatMulXPUFunction<T, int16_t>(&a, &b, out, trans_a, trans_b, ctx);
} else {
if (std::getenv("XPU_PADDLE_MAT_MUL_GRAD_V2_FCINT32") != nullptr) {
if (std::getenv("XPU_PADDLE_FC_INT32") != nullptr) {
MatMulXPUFunction<T, int32_t>(&a, &b, out, trans_a, trans_b, ctx);
} else if (std::getenv("XPU_PADDLE_FC_LOCAL_INT16") != nullptr) {
MatMulXPUFunction<T, float>(&a, &b, out, trans_a, trans_b, ctx);
} else {
MatMulXPUFunction<T, int16_t>(&a, &b, out, trans_a, trans_b, ctx);
}
......
paddle/fluid/operators/xpu_api_wrapper.h 0 → 100644
浏览文件 @ e512aa9a
/* Copyright (c) 2020 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
#ifdef PADDLE_WITH_XPU
#include <vector>
namespace paddle {
namespace operators {
template <typename XPUType, typename FCT>
int xpu_fc_wrapper(xpu::Context* ctx, const XPUType* x, const XPUType* w,
XPUType* y, int m, int n, int k, bool x_trans, bool w_trans,
const float* x_maxptr, const float* w_maxptr,
float* y_maxptr, int ldx, int ldw, int ldy, float alpha,
float beta, const float* bias,
const xpu::Activation_t& act) {
int r = 0;
if (x_trans && std::getenv("XPU_PADDLE_FC_TRANS_A") != nullptr &&
std::is_same<float, XPUType>::value) {
XPUType* l3_addr = nullptr;
xpu::ctx_guard RAII_GUARD(ctx);
l3_addr = RAII_GUARD.alloc_l3_or_gm<XPUType>(m * k);
if (l3_addr == nullptr) return XPUERR_NOMEM;
std::vector<int> shape = {k, m};
std::vector<int> axis = {1, 0};
r = xpu::transpose<XPUType>(ctx, x, l3_addr, shape, axis);
if (r != XPU_SUCCESS) return r;
r = xpu::fc_fusion<XPUType, XPUType, XPUType, FCT>(
ctx, l3_addr, w, y, m, n, k, false, w_trans, x_maxptr, w_maxptr,
y_maxptr, k, ldw, ldy, alpha, beta, bias, act);
if (r != XPU_SUCCESS) return r;
} else {
r = xpu::fc_fusion<XPUType, XPUType, XPUType, FCT>(
ctx, x, w, y, m, n, k, x_trans, w_trans, x_maxptr, w_maxptr, y_maxptr,
ldx, ldw, ldy, alpha, beta, bias, act);
}
return r;
}
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/platform/device_context.cc
浏览文件 @ e512aa9a
......@@ -222,9 +222,13 @@ XPUDeviceContext::XPUDeviceContext(XPUPlace place) : place_(place) {
context_ = xpu::create_context();
const int MAX_XPU_NUM = 16;
const int l3_size = 13.5 * 1024 * 1024;
static void* l3ptrs[MAX_XPU_NUM] = {nullptr};
int l3_size = 13.5 * 1024 * 1024;
if (std::getenv("XPU_PADDLE_L3_SIZE") != nullptr) {
l3_size = atoi(std::getenv("XPU_PADDLE_L3_SIZE"));
}
auto selected_xpus = GetXPUSelectedDevices();
for (unsigned int i = 0; i < selected_xpus.size(); i++) {
if (place.device == selected_xpus[i]) {
......
paddle/fluid/platform/xpu/xpu2_op_list.h
浏览文件 @ e512aa9a
......@@ -90,6 +90,12 @@ XPUOpMap& get_kl2_ops() {
pOpKernelType(vartype::FP16, XPUPlace())})},
{"adam", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"adamw", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"reduce_sum", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"reduce_sum_grad",
XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"softmax", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"softmax_grad",
XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"softmax_with_cross_entropy",
XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"softmax_with_cross_entropy_grad",
......@@ -171,6 +177,39 @@ XPUOpMap& get_kl2_ops() {
pOpKernelType(vartype::INT32, XPUPlace()),
pOpKernelType(vartype::INT8, XPUPlace()),
pOpKernelType(vartype::FP32, XPUPlace())})},
{"matmul_v2", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"matmul_v2_grad",
XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"matmul", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"matmul_grad", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"relu", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"relu_grad", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"assign_value",
XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"dropout", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"dropout_grad",
XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"elementwise_div",
XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"elementwise_div_grad",
XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"range", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
pOpKernelType(vartype::INT64, XPUPlace())})},
{"reshape2", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"reshape2_grad",
XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"shape", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
pOpKernelType(vartype::INT64, XPUPlace())})},
{"one_hot_v2", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
pOpKernelType(vartype::INT64, XPUPlace())})},
{"layer_norm", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"layer_norm_grad",
XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"lookup_table_v2",
XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"lookup_table_v2_grad",
XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"scale", XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace())})},
{"flatten_contiguous_range",
XPUKernelSet({pOpKernelType(vartype::INT64, XPUPlace()),
......
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