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未验证 提交 cbabbe2e 编写于 作者: A Aurelius84 提交者： GitHub
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[XPU]Migrate Adam XPU kernel into Phi (#45572) 

* [XPU]Migrate Adam XPU kernel into Phi

* test=kunlun
上级 e3e92c9a
隐藏空白更改
内联 并排
Showing with 703 addition and 664 deletion
paddle/fluid/operators/math/selected_rows_functor.cc
浏览文件 @ cbabbe2e
......@@ -569,8 +569,8 @@ TEMPLATE_SPECIALIZED_FOR_MERGEADD_CPU(platform::complex<double>)
#ifdef PADDLE_WITH_XPU
template <typename T>
struct MergeAdd<platform::XPUDeviceContext, T> {
phi::SelectedRows operator()(const platform::XPUDeviceContext& context,
struct MergeAdd<phi::XPUContext, T> {
phi::SelectedRows operator()(const phi::XPUContext& context,
const phi::SelectedRows& input,
const bool sorted_result = false) {
phi::SelectedRows out;
......@@ -578,7 +578,7 @@ struct MergeAdd<platform::XPUDeviceContext, T> {
return out;
}
void operator()(const platform::XPUDeviceContext& context,
void operator()(const phi::XPUContext& context,
const phi::SelectedRows& input,
phi::SelectedRows* output,
const bool sorted_result = false) {
......@@ -633,7 +633,7 @@ struct MergeAdd<platform::XPUDeviceContext, T> {
PADDLE_ENFORCE_XDNN_SUCCESS(r, "merge_dup_rows");
}
void operator()(const platform::XPUDeviceContext& context,
void operator()(const phi::XPUContext& context,
const std::vector<const phi::SelectedRows*>& inputs,
phi::SelectedRows* output,
const bool sorted_result = false) {
......@@ -838,7 +838,7 @@ struct MergeAverage<phi::CPUContext, T> {
};
#ifdef PADDLE_WITH_XPU
template struct MergeAdd<platform::XPUDeviceContext, float>;
template struct MergeAdd<phi::XPUContext, float>;
#endif
template struct MergeAverage<phi::CPUContext, int>;
......
paddle/fluid/operators/optimizers/adam_op_xpu.cc 已删除 100644 → 0
浏览文件 @ e3e92c9a
/* Copyright (c) 2016 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.
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 "gflags/gflags.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/operators/optimizers/adam_op_functor.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using float16 = paddle::platform::float16;
#ifdef PADDLE_WITH_XPU
template <typename T1, typename T2>
static int ConvertDataByType(const T1* x,
T2** y,
int len,
bool allocateFlag,
const framework::ExecutionContext& ctx) {
if (nullptr == x || nullptr == y || len <= 0)
return xpu::Error_t::INVALID_PARAM;
int r = 0;
if (allocateFlag) {
r = xpu_malloc(reinterpret_cast<void**>(y), sizeof(T2) * len);
PADDLE_ENFORCE_EQ(
r,
xpu::Error_t::SUCCESS,
platform::errors::External(
"Alloc memory in xpu for result data failed with [%d]", r));
}
T1* cpu_data = reinterpret_cast<T1*>(malloc(sizeof(T1) * len));
paddle::memory::Copy(paddle::platform::CPUPlace(),
cpu_data,
ctx.GetPlace(),
x,
len * sizeof(T1));
T2* cpu_real_data = reinterpret_cast<T2*>(malloc(sizeof(T2) * len));
for (int i = 0; i < len; i++) cpu_real_data[i] = static_cast<T2>(cpu_data[i]);
paddle::memory::Copy(ctx.GetPlace(),
*y,
paddle::platform::CPUPlace(),
cpu_real_data,
len * sizeof(T2));
free(cpu_data);
free(cpu_real_data);
return xpu::Error_t::SUCCESS;
}
template <typename T>
static void getDataPointer(const phi::DenseTensor& tensorData,
T** result,
const framework::ExecutionContext& ctx) {
if (tensorData.dtype() == paddle::experimental::DataType::FLOAT16) {
const float16* real_data =
tensorData.template data<paddle::platform::float16>();
int len = tensorData.numel();
int r = ConvertDataByType<float16, T>(real_data, result, len, true, ctx);
PADDLE_ENFORCE_EQ(
r,
xpu::Error_t::SUCCESS,
platform::errors::External(
"execute function ConvertDataByType failed with [%d]", r));
}
}
template <typename T>
static void getOutDataPointer(phi::DenseTensor* tensorData,
Tensor* out,
T** result,
const framework::ExecutionContext& ctx) {
if (tensorData->dtype() == paddle::experimental::DataType::FLOAT16) {
*result = out->template mutable_data<T>(ctx.GetPlace());
} else {
*result = tensorData->template mutable_data<T>(ctx.GetPlace());
}
}
template <typename T>
static void copyOutData(const Tensor& srcTensor,
phi::DenseTensor* dstTensor,
const framework::ExecutionContext& ctx) {
if (dstTensor->dtype() == paddle::experimental::DataType::FLOAT16) {
const T* xpu_out_data = srcTensor.template data<T>();
float16* out_data =
dstTensor->template mutable_data<float16>(ctx.GetPlace());
int len = srcTensor.numel();
int r =
ConvertDataByType<T, float16>(xpu_out_data, &out_data, len, false, ctx);
PADDLE_ENFORCE_EQ(
r,
xpu::Error_t::SUCCESS,
platform::errors::External(
"execute function ConvertDataByType failed with[%d]", r));
}
}
template <typename T>
static void setBetaData(const phi::DenseTensor& beta_pow,
phi::DenseTensor* beta_pow_out,
const T& beta) {
if (beta_pow.dtype() == paddle::experimental::DataType::FLOAT16) {
const float16* beta_pow_p = beta_pow.template data<float16>();
beta_pow_out->mutable_data<float16>(platform::CPUPlace())[0] =
static_cast<float16>(beta) * beta_pow_p[0];
} else {
const T* beta_pow_p = beta_pow.template data<T>();
beta_pow_out->mutable_data<T>(platform::CPUPlace())[0] =
beta * beta_pow_p[0];
}
}
template <typename DeviceContext, typename T>
static void scale(phi::DenseTensor* beta_pow_out,
const phi::DenseTensor& beta_pow,
T* beta_pow_ptr,
const T& beta,
const framework::ExecutionContext& ctx) {
float16* beta_pow_out_p2 =
beta_pow_out->mutable_data<float16>(ctx.GetPlace());
Tensor xpu_beta_pow_out;
const phi::DenseTensorMeta meta_beta_pow_out(
paddle::experimental::DataType::FLOAT32, beta_pow_out->dims());
xpu_beta_pow_out.set_meta(meta_beta_pow_out);
T* beta_pow_out_ptr =
xpu_beta_pow_out.template mutable_data<T>(ctx.GetPlace());
auto& dev_ctx = ctx.template device_context<DeviceContext>();
int r = xpu::scale(dev_ctx.x_context(),
beta_pow_ptr,
beta_pow_out_ptr,
beta_pow.numel(),
false,
beta,
0.0f);
PADDLE_ENFORCE_EQ(r,
xpu::SUCCESS,
platform::errors::External(
"XPU kernel scale occur error in adam error code ",
r,
XPUAPIErrorMsg[r]));
const float* xpu_beta_pow_out_data = xpu_beta_pow_out.template data<T>();
int len = xpu_beta_pow_out.numel();
r = ConvertDataByType<T, float16>(
xpu_beta_pow_out_data, &beta_pow_out_p2, len, false, ctx);
PADDLE_ENFORCE_EQ(
r,
xpu::Error_t::SUCCESS,
platform::errors::External(
"execute function ConvertDataByType failed with [%d]", r));
}
template <typename T>
static void freeData(const phi::DenseTensor& tensorData, T* dataPtr) {
if (tensorData.dtype() == paddle::experimental::DataType::FLOAT16)
xpu_free(dataPtr);
}
template <typename DeviceContext, typename T>
class AdamOpXPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto* param_var = ctx.InputVar("Param");
PADDLE_ENFORCE_EQ(param_var->IsType<framework::LoDTensor>(),
true,
platform::errors::InvalidArgument(
"Tensor holds the wrong type，Expected Var(%s)'s "
"type is LoDTensor, "
"but the received is %s",
ctx.InputNames("Param").front(),
framework::ToTypeName(param_var->Type())));
using paddle::framework::LoDTensor;
auto& param = GET_DATA_SAFELY(
ctx.Input<LoDTensor>("Param"), "Input", "Param", "Adam");
float* param_ptr = nullptr;
getDataPointer<float>(param, &param_ptr, ctx);
auto* grad_var = ctx.InputVar("Grad");
float* grad_c = nullptr;
auto& mom1 = GET_DATA_SAFELY(
ctx.Input<LoDTensor>("Moment1"), "Input", "Moment1", "Adam");
float* mom1_ptr = nullptr;
getDataPointer<float>(mom1, &mom1_ptr, ctx);
auto& mom2 = GET_DATA_SAFELY(
ctx.Input<LoDTensor>("Moment2"), "Input", "Moment2", "Adam");
float* mom2_ptr = nullptr;
getDataPointer<float>(mom2, &mom2_ptr, ctx);
auto& lr = GET_DATA_SAFELY(
ctx.Input<LoDTensor>("LearningRate"), "Input", "LearningRate", "Adam");
float* lr_ptr = nullptr;
getDataPointer<float>(lr, &lr_ptr, ctx);
auto& beta1_pow = GET_DATA_SAFELY(
ctx.Input<LoDTensor>("Beta1Pow"), "Input", "Beta1Pow", "Adam");
auto& dev_ctx = ctx.template device_context<DeviceContext>();
float* beta1_pow_ptr = nullptr;
const float* beta1_const_pow_ptr = nullptr;
if (beta1_pow.place() == platform::CPUPlace()) {
Tensor xpu_beta1_pow;
paddle::framework::TensorCopy(
beta1_pow, ctx.GetPlace(), dev_ctx, &xpu_beta1_pow);
if (xpu_beta1_pow.dtype() == paddle::experimental::DataType::FLOAT16)
getDataPointer<float>(xpu_beta1_pow, &beta1_pow_ptr, ctx);
else
beta1_const_pow_ptr = xpu_beta1_pow.template data<float>();
} else {
if (beta1_pow.dtype() == paddle::experimental::DataType::FLOAT16)
getDataPointer<float>(beta1_pow, &beta1_pow_ptr, ctx);
else
beta1_const_pow_ptr = beta1_pow.template data<float>();
}
auto& beta2_pow = GET_DATA_SAFELY(
ctx.Input<LoDTensor>("Beta2Pow"), "Input", "Beta2Pow", "Adam");
float* beta2_pow_ptr = nullptr;
const float* beta2_const_pow_ptr = nullptr;
if (beta2_pow.place() == platform::CPUPlace()) {
Tensor xpu_beta2_pow;
paddle::framework::TensorCopy(
beta2_pow, ctx.GetPlace(), dev_ctx, &xpu_beta2_pow);
if (xpu_beta2_pow.dtype() == paddle::experimental::DataType::FLOAT16)
getDataPointer<float>(xpu_beta2_pow, &beta2_pow_ptr, ctx);
else
beta2_const_pow_ptr = xpu_beta2_pow.template data<float>();
} else {
if (beta2_pow.dtype() == paddle::experimental::DataType::FLOAT16)
getDataPointer<float>(beta2_pow, &beta2_pow_ptr, ctx);
else
beta2_const_pow_ptr = beta2_pow.template data<float>();
}
auto& param_out = GET_DATA_SAFELY(
ctx.Output<LoDTensor>("ParamOut"), "Output", "ParamOut", "Adam");
Tensor xpu_param_out;
float* param_out_ptr = nullptr;
const phi::DenseTensorMeta meta_param(
paddle::experimental::DataType::FLOAT32, param_out.dims());
xpu_param_out.set_meta(meta_param);
getOutDataPointer(&param_out, &xpu_param_out, &param_out_ptr, ctx);
auto& mom1_out = GET_DATA_SAFELY(
ctx.Output<LoDTensor>("Moment1Out"), "Output", "Moment1Out", "Adam");
Tensor xpu_mom1_out;
float* mom1_out_ptr = nullptr;
const phi::DenseTensorMeta meta_mom1(
paddle::experimental::DataType::FLOAT32, mom1_out.dims());
xpu_mom1_out.set_meta(meta_mom1);
getOutDataPointer(&mom1_out, &xpu_mom1_out, &mom1_out_ptr, ctx);
auto& mom2_out = GET_DATA_SAFELY(
ctx.Output<LoDTensor>("Moment2Out"), "Output", "Moment2Out", "Adam");
Tensor xpu_mom2_out;
float* mom2_out_ptr = nullptr;
const phi::DenseTensorMeta meta_mom2(
paddle::experimental::DataType::FLOAT32, mom2_out.dims());
xpu_mom2_out.set_meta(meta_mom2);
getOutDataPointer(&mom2_out, &xpu_mom2_out, &mom2_out_ptr, ctx);
auto* beta1_pow_out = ctx.Output<LoDTensor>("Beta1PowOut");
auto* beta2_pow_out = ctx.Output<LoDTensor>("Beta2PowOut");
bool skip_update = false;
if (ctx.HasInput("SkipUpdate")) {
auto* skip_update_tensor = ctx.Input<framework::Tensor>("SkipUpdate");
PADDLE_ENFORCE_EQ(skip_update_tensor->numel(),
1,
platform::errors::InvalidArgument(
"Input(SkipUpdate) size must be 1, but get %d",
skip_update_tensor->numel()));
std::vector<bool> skip_update_vec;
paddle::framework::TensorToVector(
*skip_update_tensor, ctx.device_context(), &skip_update_vec);
skip_update = skip_update_vec[0];
}
// skip_update=true, just copy input to output, and TensorCopy will call
// mutable_data
if (skip_update) {
VLOG(4) << "Adam skip update";
framework::TensorCopy(
param,
ctx.GetPlace(),
ctx.template device_context<platform::DeviceContext>(),
&param_out);
framework::TensorCopy(
mom1,
ctx.GetPlace(),
ctx.template device_context<platform::DeviceContext>(),
&mom1_out);
framework::TensorCopy(
mom2,
ctx.GetPlace(),
ctx.template device_context<platform::DeviceContext>(),
&mom2_out);
framework::TensorCopy(
beta1_pow,
beta1_pow.place(),
ctx.template device_context<platform::DeviceContext>(),
beta1_pow_out);
framework::TensorCopy(
beta2_pow,
beta2_pow.place(),
ctx.template device_context<platform::DeviceContext>(),
beta2_pow_out);
return;
}
PADDLE_ENFORCE_EQ(beta1_pow_out->numel(),
1,
platform::errors::InvalidArgument(
"Tensor holds the wrong size, Expected beta1 pow "
"output size is 1, but received "
"value is:%d.",
beta1_pow_out->numel()));
PADDLE_ENFORCE_EQ(beta2_pow_out->numel(),
1,
platform::errors::InvalidArgument(
"Tensor holds the wrong size, Expected beta2 pow "
"output size is 1, but received "
"value is:%d.",
beta2_pow_out->numel()));
bool use_global_beta_pow = ctx.Attr<bool>("use_global_beta_pow");
VLOG(4) << "use_global_beta_pow:" << use_global_beta_pow;
float beta1 = static_cast<float>(ctx.Attr<float>("beta1"));
if (ctx.HasInput("Beta1Tensor")) {
auto* beta1_tensor = ctx.Input<framework::Tensor>("Beta1Tensor");
beta1 = static_cast<float>(GetAttrFromTensor(beta1_tensor));
}
float beta2 = static_cast<float>(ctx.Attr<float>("beta2"));
if (ctx.HasInput("Beta2Tensor")) {
auto* beta2_tensor = ctx.Input<framework::Tensor>("Beta2Tensor");
beta2 = static_cast<float>(GetAttrFromTensor(beta2_tensor));
}
float epsilon = static_cast<float>(ctx.Attr<float>("epsilon"));
if (ctx.HasInput("EpsilonTensor")) {
auto* epsilon_tensor = ctx.Input<framework::Tensor>("EpsilonTensor");
epsilon = static_cast<float>(GetAttrFromTensor(epsilon_tensor));
}
if (grad_var->IsType<framework::LoDTensor>()) {
auto& grad = GET_DATA_SAFELY(
ctx.Input<LoDTensor>("Grad"), "Input", "Grad", "Adam");
getDataPointer<float>(grad, &grad_c, ctx);
int r = xpu::adam(
dev_ctx.x_context(),
grad_c != nullptr ? grad_c : grad.template data<float>(),
mom1_ptr != nullptr ? mom1_ptr : mom1.template data<float>(),
mom2_ptr != nullptr ? mom2_ptr : mom2.template data<float>(),
param_ptr != nullptr ? param_ptr : param.template data<float>(),
beta1_pow_ptr != nullptr ? beta1_pow_ptr : beta1_const_pow_ptr,
beta2_pow_ptr != nullptr ? beta2_pow_ptr : beta2_const_pow_ptr,
lr_ptr != nullptr ? lr_ptr : lr.template data<float>(),
mom1_out_ptr,
mom2_out_ptr,
param_out_ptr,
beta1,
beta2,
epsilon,
param.numel());
xpu_wait(dev_ctx.x_context()->xpu_stream);
PADDLE_ENFORCE_EQ(
r == xpu::Error_t::SUCCESS,
true,
platform::errors::External("XPU API return wrong value[%d],", r));
freeData<float>(grad, grad_c);
copyOutData<float>(xpu_mom1_out, &mom1_out, ctx);
copyOutData<float>(xpu_mom2_out, &mom2_out, ctx);
copyOutData<float>(xpu_param_out, &param_out, ctx);
if (!use_global_beta_pow) {
// update in cpu and then copy to xpu
if (beta1_pow.place() == platform::CPUPlace() &&
beta2_pow.place() == platform::CPUPlace()) {
setBetaData(beta1_pow, beta1_pow_out, beta1);
setBetaData(beta2_pow, beta2_pow_out, beta2);
} else {
float* beta1_pow_out_p1 = nullptr;
if (beta1_pow_out->dtype() ==
paddle::experimental::DataType::FLOAT16) {
scale<DeviceContext, float>(
beta1_pow_out, beta1_pow, beta1_pow_ptr, beta1, ctx);
} else {
const float* beta1_pow_data = beta1_pow.template data<float>();
beta1_pow_out_p1 =
beta1_pow_out->mutable_data<float>(ctx.GetPlace());
r = xpu::scale(dev_ctx.x_context(),
beta1_pow_data,
beta1_pow_out_p1,
beta1_pow.numel(),
false,
beta1,
0.0f);
xpu_wait(dev_ctx.x_context()->xpu_stream);
PADDLE_ENFORCE_EQ(
r,
xpu::SUCCESS,
platform::errors::External(
"XPU kernel scale occur error in adam error code ",
r,
XPUAPIErrorMsg[r]));
}
float* beta2_pow_out_p1 = nullptr;
if (beta2_pow_out->dtype() ==
paddle::experimental::DataType::FLOAT16) {
scale<DeviceContext, float>(
beta2_pow_out, beta2_pow, beta2_pow_ptr, beta2, ctx);
} else {
const float* beta2_pow_data = beta2_pow.template data<float>();
beta2_pow_out_p1 =
beta2_pow_out->mutable_data<float>(ctx.GetPlace());
r = xpu::scale(dev_ctx.x_context(),
beta2_pow_data,
beta2_pow_out_p1,
beta2_pow.numel(),
false,
beta2,
0.0f);
xpu_wait(dev_ctx.x_context()->xpu_stream);
PADDLE_ENFORCE_EQ(
r,
xpu::SUCCESS,
platform::errors::External(
"XPU kernel scale occur error in adam error code ",
r,
XPUAPIErrorMsg[r]));
}
}
}
} else if (grad_var->IsType<phi::SelectedRows>()) {
auto* grad = ctx.Input<phi::SelectedRows>("Grad");
if (grad->rows().size() == 0) {
VLOG(3) << "grad row size is 0!!";
return;
}
std::vector<int64_t> cpu_rows(grad->rows().begin(), grad->rows().end());
bool is_strict_sorted = true;
for (size_t i = 1; i < cpu_rows.size(); ++i) {
if (cpu_rows[i - 1] >= cpu_rows[i]) {
is_strict_sorted = false;
break;
}
}
phi::SelectedRows tmp_grad_merge;
const phi::SelectedRows* grad_merge_ptr;
if (is_strict_sorted) {
grad_merge_ptr = grad;
} else {
scatter::MergeAdd<platform::XPUDeviceContext, float> merge_func;
merge_func(ctx.template device_context<platform::XPUDeviceContext>(),
*grad,
&tmp_grad_merge,
true);
xpu_wait(dev_ctx.x_context()->xpu_stream);
grad_merge_ptr = &tmp_grad_merge;
}
auto& grad_merge = *grad_merge_ptr;
auto& grad_tensor = grad_merge.value();
getDataPointer<float>(grad_tensor, &grad_c, ctx);
int row_count = grad_merge.rows().size();
std::vector<int> rows(row_count);
xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
int* xpu_rows = RAII_GUARD.alloc_l3_or_gm<int>(row_count);
std::vector<int64_t> merge_rows(grad_merge.rows().begin(),
grad_merge.rows().end());
for (size_t i = 0; i < grad_merge.rows().size(); ++i) {
rows[i] = static_cast<int>(merge_rows[i]);
}
xpu_wait(dev_ctx.x_context()->xpu_stream);
memory::Copy(ctx.GetPlace(),
xpu_rows,
platform::CPUPlace(),
rows.data(),
row_count * sizeof(int));
auto row_numel = grad_tensor.numel() / grad_merge.rows().size();
auto ori_rows = param.numel() / row_numel;
int lazy_mode = static_cast<int>(ctx.Attr<bool>("lazy_mode"));
int r = xpu::sparse_adam(
dev_ctx.x_context(),
grad_c != nullptr ? grad_c : grad_tensor.template data<float>(),
mom1_ptr != nullptr ? mom1_ptr : mom1.template data<float>(),
mom2_ptr != nullptr ? mom2_ptr : mom2.template data<float>(),
param_ptr != nullptr ? param_ptr : param.template data<float>(),
beta1_pow_ptr != nullptr ? beta1_pow_ptr : beta1_const_pow_ptr,
beta2_pow_ptr != nullptr ? beta2_pow_ptr : beta2_const_pow_ptr,
lr_ptr != nullptr ? lr_ptr : lr.template data<float>(),
mom1_out_ptr,
mom2_out_ptr,
param_out_ptr,
beta1,
beta2,
epsilon,
ori_rows,
xpu_rows,
row_numel,
grad_merge.rows().size(),
lazy_mode);
PADDLE_ENFORCE_EQ(
r == xpu::Error_t::SUCCESS,
true,
platform::errors::External("XPU API return wrong value[%d],", r));
freeData<float>(grad_tensor, grad_c);
copyOutData<float>(xpu_mom1_out, &mom1_out, ctx);
copyOutData<float>(xpu_mom2_out, &mom2_out, ctx);
copyOutData<float>(xpu_param_out, &param_out, ctx);
if (!use_global_beta_pow) {
// update in cpu and then copy to xpu
if (beta1_pow.place() == platform::CPUPlace() &&
beta2_pow.place() == platform::CPUPlace()) {
setBetaData(beta1_pow, beta1_pow_out, beta1);
setBetaData(beta2_pow, beta2_pow_out, beta2);
} else {
float* beta1_pow_out_p1 = nullptr;
if (beta1_pow_out->dtype() ==
paddle::experimental::DataType::FLOAT16) {
scale<DeviceContext, float>(
beta1_pow_out, beta1_pow, beta1_pow_ptr, beta1, ctx);
} else {
const float* beta1_pow_data = beta1_pow.template data<float>();
beta1_pow_out_p1 =
beta1_pow_out->mutable_data<float>(ctx.GetPlace());
r = xpu::scale(dev_ctx.x_context(),
beta1_pow_data,
beta1_pow_out_p1,
beta1_pow.numel(),
false,
beta1,
0.0f);
xpu_wait(dev_ctx.x_context()->xpu_stream);
PADDLE_ENFORCE_EQ(
r,
xpu::SUCCESS,
platform::errors::External(
"XPU kernel scale occur error in adam error code ",
r,
XPUAPIErrorMsg[r]));
}
float* beta2_pow_out_p1 = nullptr;
if (beta2_pow_out->dtype() ==
paddle::experimental::DataType::FLOAT16) {
scale<DeviceContext, float>(
beta2_pow_out, beta2_pow, beta2_pow_ptr, beta2, ctx);
} else {
const float* beta2_pow_data = beta2_pow.template data<float>();
beta2_pow_out_p1 =
beta2_pow_out->mutable_data<float>(ctx.GetPlace());
r = xpu::scale(dev_ctx.x_context(),
beta2_pow_data,
beta2_pow_out_p1,
beta2_pow.numel(),
false,
beta2,
0.0f);
xpu_wait(dev_ctx.x_context()->xpu_stream);
PADDLE_ENFORCE_EQ(
r,
xpu::SUCCESS,
platform::errors::External(
"XPU kernel scale occur error in adam error code ",
r,
XPUAPIErrorMsg[r]));
}
}
}
} else {
PADDLE_ENFORCE_EQ(1,
2,
platform::errors::InvalidArgument(
"Variable type not supported by adam_op"));
}
freeData<float>(param, param_ptr);
freeData<float>(mom1, mom1_ptr);
freeData<float>(mom2, mom2_ptr);
freeData<float>(lr, lr_ptr);
}
};
#endif
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
#ifdef PADDLE_WITH_XPU
REGISTER_OP_XPU_KERNEL(
adam,
ops::AdamOpXPUKernel<paddle::platform::XPUDeviceContext, float>,
ops::AdamOpXPUKernel<paddle::platform::XPUDeviceContext,
paddle::platform::float16>);
#endif
paddle/phi/kernels/CMakeLists.txt
浏览文件 @ cbabbe2e
......@@ -22,6 +22,7 @@ set_property(GLOBAL PROPERTY PHI_KERNELS "")
# [ 1. Common kernel compilation dependencies ]
set(COMMON_KERNEL_DEPS
dense_tensor
string_tensor
sparse_coo_tensor
sparse_csr_tensor
kernel_context
......@@ -30,6 +31,7 @@ set(COMMON_KERNEL_DEPS
convert_utils
lod_utils
custom_kernel
string_infermeta
phi_tensor_utils)
set(COMMON_KERNEL_DEPS
${COMMON_KERNEL_DEPS}
......@@ -67,21 +69,7 @@ set(COMMON_KERNEL_DEPS
sequence_padding
sequence_scale
fft
phi_data_layout_transform)
set(COMMON_KERNEL_DEPS
${COMMON_KERNEL_DEPS}
dense_tensor
string_tensor
sparse_coo_tensor
sparse_csr_tensor
kernel_context
kernel_factory
arg_map_context
convert_utils
lod_utils
custom_kernel
string_infermeta
phi_data_layout_transform
gpc
utf8proc
device_memory_aligment)
......@@ -136,7 +124,7 @@ else()
"strings/cpu/*.cc")
endif()
file(GLOB kernel_xpu "xpu/*.cc")
file(GLOB kernel_xpu "xpu/*.cc" "selected_rows/xpu/*.cc")
add_library(phi_cpu ${kernel_cc})
kernel_declare("${kernel_cc}")
......
paddle/phi/kernels/funcs/adam_functors.h
浏览文件 @ cbabbe2e
......@@ -19,8 +19,142 @@
#include "paddle/phi/kernels/funcs/algorithm.h"
#ifdef PADDLE_WITH_XPU
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/backends/xpu/xpu_header.h"
// See Note [ Why still include the fluid headers? ]
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/memory/memcpy.h"
#endif
namespace phi {
namespace funcs {
using float16 = dtype::float16;
#ifdef PADDLE_WITH_XPU
template <typename Context, typename T1, typename T2>
static int ConvertDataByType(
const T1* x, T2** y, int len, bool allocateFlag, const Context& dev_ctx) {
if (nullptr == x || nullptr == y || len <= 0)
return xpu::Error_t::INVALID_PARAM;
int r = 0;
if (allocateFlag) {
r = xpu_malloc(reinterpret_cast<void**>(y), sizeof(T2) * len);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
}
T1* cpu_data = reinterpret_cast<T1*>(malloc(sizeof(T1) * len));
paddle::memory::Copy(
CPUPlace(), cpu_data, dev_ctx.GetPlace(), x, len * sizeof(T1));
T2* cpu_real_data = reinterpret_cast<T2*>(malloc(sizeof(T2) * len));
for (int i = 0; i < len; i++) cpu_real_data[i] = static_cast<T2>(cpu_data[i]);
paddle::memory::Copy(
dev_ctx.GetPlace(), *y, CPUPlace(), cpu_real_data, len * sizeof(T2));
free(cpu_data);
free(cpu_real_data);
return xpu::Error_t::SUCCESS;
}
template <typename Context, typename T>
static void GetDataPointer(const phi::DenseTensor& tensorData,
T** result,
const Context& dev_ctx) {
if (tensorData.dtype() == DataType::FLOAT16) {
const float16* real_data = tensorData.template data<float16>();
int len = tensorData.numel();
int r = ConvertDataByType<Context, float16, T>(
real_data, result, len, true, dev_ctx);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
}
}
template <typename Context, typename T>
static void GetOutDataPointer(DenseTensor* tensorData,
DenseTensor* out,
T** result,
const Context& dev_ctx) {
if (tensorData->dtype() == DataType::FLOAT16) {
*result = dev_ctx.template Alloc<T>(out);
} else {
*result = dev_ctx.template Alloc<T>(tensorData);
}
}
template <typename Context, typename T>
static void CopyOutData(const DenseTensor& srcTensor,
phi::DenseTensor* dstTensor,
const Context& dev_ctx) {
if (dstTensor->dtype() == DataType::FLOAT16) {
const T* xpu_out_data = srcTensor.template data<T>();
float16* out_data = dev_ctx.template Alloc<float16>(dstTensor);
int len = srcTensor.numel();
int r = ConvertDataByType<Context, T, float16>(
xpu_out_data, &out_data, len, false, dev_ctx);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
}
}
template <typename T>
static void FreeData(const phi::DenseTensor& tensorData, T* dataPtr) {
if (tensorData.dtype() == DataType::FLOAT16) xpu_free(dataPtr);
}
template <typename Context, typename T>
static void SetBetaData(const phi::DenseTensor& beta_pow,
phi::DenseTensor* beta_pow_out,
const T& beta,
const Context& dev_ctx) {
if (beta_pow.dtype() == DataType::FLOAT16) {
const float16* beta_pow_p = beta_pow.template data<float16>();
dev_ctx.template HostAlloc<float16>(beta_pow_out)[0] =
static_cast<float16>(beta) * beta_pow_p[0];
} else {
const T* beta_pow_p = beta_pow.template data<T>();
dev_ctx.template HostAlloc<T>(beta_pow_out)[0] = beta * beta_pow_p[0];
}
}
template <typename Context, typename T>
static void Scale(phi::DenseTensor* beta_pow_out,
const phi::DenseTensor& beta_pow,
T* beta_pow_ptr,
const T& beta,
const Context& dev_ctx) {
float16* beta_pow_out_p2 = dev_ctx.template Alloc<float16>(beta_pow_out);
DenseTensor xpu_beta_pow_out;
const phi::DenseTensorMeta meta_beta_pow_out(DataType::FLOAT32,
beta_pow_out->dims());
xpu_beta_pow_out.set_meta(meta_beta_pow_out);
T* beta_pow_out_ptr = dev_ctx.template Alloc<T>(&xpu_beta_pow_out);
int r = xpu::scale(dev_ctx.x_context(),
beta_pow_ptr,
beta_pow_out_ptr,
beta_pow.numel(),
false,
beta,
0.0f);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
const float* xpu_beta_pow_out_data =
dev_ctx.template Alloc<T>(&xpu_beta_pow_out);
int len = xpu_beta_pow_out.numel();
r = ConvertDataByType<Context, T, float16>(
xpu_beta_pow_out_data, &beta_pow_out_p2, len, false, dev_ctx);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
}
#endif
struct GPUAdam;
struct CPUAdam;
......
paddle/phi/kernels/selected_rows/xpu/adam_kernel.cc 0 → 100644
浏览文件 @ cbabbe2e
// Copyright (c) 2022 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/selected_rows/adam_kernel.h"
#include "paddle/phi/backends/xpu/xpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/funcs/adam_functors.h"
// See Note [ Why still include the fluid headers? ]
#include "paddle/fluid/operators/math/selected_rows_functor.h"
namespace phi {
namespace sr {
using float16 = dtype::float16;
template <typename T, typename Context>
void AdamDenseParamSparseGradKernel(
const Context& dev_ctx,
const DenseTensor& param,
const SelectedRows& grad,
const DenseTensor& learning_rate,
const DenseTensor& moment1,
const DenseTensor& moment2,
const DenseTensor& beta1_pow,
const DenseTensor& beta2_pow,
const paddle::optional<DenseTensor>& master_param,
const paddle::optional<DenseTensor>& skip_update,
const Scalar& beta1,
const Scalar& beta2,
const Scalar& epsilon,
bool lazy_mode,
int64_t min_row_size_to_use_multithread,
bool multi_precision,
bool use_global_beta_pow,
DenseTensor* param_out,
DenseTensor* moment1_out,
DenseTensor* moment2_out,
DenseTensor* beta1_pow_out,
DenseTensor* beta2_pow_out,
DenseTensor* master_param_outs) {
float* param_ptr = nullptr;
funcs::GetDataPointer<Context, float>(param, &param_ptr, dev_ctx);
float* mom1_ptr = nullptr;
funcs::GetDataPointer<Context, float>(moment1, &mom1_ptr, dev_ctx);
float* mom2_ptr = nullptr;
funcs::GetDataPointer<Context, float>(moment2, &mom2_ptr, dev_ctx);
float* lr_ptr = nullptr;
funcs::GetDataPointer<Context, float>(learning_rate, &lr_ptr, dev_ctx);
float* beta1_pow_ptr = nullptr;
const float* beta1_const_pow_ptr = nullptr;
if (beta1_pow.place() == CPUPlace()) {
DenseTensor xpu_beta1_pow;
phi::Copy(dev_ctx, beta1_pow, beta1_pow.place(), false, &xpu_beta1_pow);
if (xpu_beta1_pow.dtype() == DataType::FLOAT16)
funcs::GetDataPointer<Context, float>(
xpu_beta1_pow, &beta1_pow_ptr, dev_ctx);
else
beta1_const_pow_ptr = xpu_beta1_pow.template data<float>();
} else {
if (beta1_pow.dtype() == DataType::FLOAT16)
funcs::GetDataPointer<Context, float>(beta1_pow, &beta1_pow_ptr, dev_ctx);
else
beta1_const_pow_ptr = beta1_pow.template data<float>();
}
float* beta2_pow_ptr = nullptr;
const float* beta2_const_pow_ptr = nullptr;
if (beta2_pow.place() == CPUPlace()) {
DenseTensor xpu_beta2_pow;
phi::Copy(dev_ctx, beta2_pow, beta2_pow.place(), false, &xpu_beta2_pow);
if (xpu_beta2_pow.dtype() == DataType::FLOAT16)
funcs::GetDataPointer<Context, float>(
xpu_beta2_pow, &beta2_pow_ptr, dev_ctx);
else
beta2_const_pow_ptr = xpu_beta2_pow.template data<float>();
} else {
if (beta2_pow.dtype() == DataType::FLOAT16)
funcs::GetDataPointer<Context, float>(beta2_pow, &beta2_pow_ptr, dev_ctx);
else
beta2_const_pow_ptr = beta2_pow.template data<float>();
}
DenseTensor xpu_param_out;
float* param_out_ptr = nullptr;
const phi::DenseTensorMeta meta_param(DataType::FLOAT32, param_out->dims());
xpu_param_out.set_meta(meta_param);
funcs::GetOutDataPointer<Context, float>(
param_out, &xpu_param_out, &param_out_ptr, dev_ctx);
DenseTensor xpu_mom1_out;
float* mom1_out_ptr = nullptr;
const phi::DenseTensorMeta meta_mom1(DataType::FLOAT32, moment1_out->dims());
xpu_mom1_out.set_meta(meta_mom1);
funcs::GetOutDataPointer<Context, float>(
moment1_out, &xpu_mom1_out, &mom1_out_ptr, dev_ctx);
DenseTensor xpu_mom2_out;
float* mom2_out_ptr = nullptr;
const phi::DenseTensorMeta meta_mom2(DataType::FLOAT32, moment2_out->dims());
xpu_mom2_out.set_meta(meta_mom2);
funcs::GetOutDataPointer<Context, float>(
moment2_out, &xpu_mom2_out, &mom2_out_ptr, dev_ctx);
bool skip_update_ = false;
if (skip_update.is_initialized()) {
PADDLE_ENFORCE_EQ(
skip_update->numel(),
1,
errors::InvalidArgument("Input(SkipUpdate) size must be 1, but get %d",
skip_update->numel()));
std::vector<bool> skip_update_vec;
paddle::framework::TensorToVector(*skip_update, dev_ctx, &skip_update_vec);
skip_update_ = skip_update_vec[0];
}
if (skip_update_) {
VLOG(4) << "Adam skip update";
phi::Copy(dev_ctx, param, dev_ctx.GetPlace(), false, param_out);
phi::Copy(dev_ctx, moment1, dev_ctx.GetPlace(), false, moment1_out);
phi::Copy(dev_ctx, moment2, dev_ctx.GetPlace(), false, moment2_out);
phi::Copy(dev_ctx, beta1_pow, beta1_pow.place(), false, beta1_pow_out);
phi::Copy(dev_ctx, beta2_pow, beta2_pow.place(), false, beta2_pow_out);
return;
}
PADDLE_ENFORCE_EQ(
beta1_pow_out->numel(),
1,
errors::InvalidArgument("Tensor holds the wrong size, Expected beta1 pow "
"output size is 1, but received "
"value is:%d.",
beta1_pow_out->numel()));
PADDLE_ENFORCE_EQ(
beta2_pow_out->numel(),
1,
errors::InvalidArgument("Tensor holds the wrong size, Expected beta2 pow "
"output size is 1, but received "
"value is:%d.",
beta2_pow_out->numel()));
VLOG(4) << "use_global_beta_pow:" << use_global_beta_pow;
auto beta1_ = beta1.to<float>();
auto beta2_ = beta2.to<float>();
auto epsilon_ = epsilon.to<float>();
float* grad_c = nullptr;
if (grad.rows().size() == 0) {
VLOG(3) << "grad row size is 0!!";
return;
}
std::vector<int64_t> cpu_rows(grad.rows().begin(), grad.rows().end());
bool is_strict_sorted = true;
for (size_t i = 1; i < cpu_rows.size(); ++i) {
if (cpu_rows[i - 1] >= cpu_rows[i]) {
is_strict_sorted = false;
break;
}
}
SelectedRows tmp_grad_merge;
const SelectedRows* grad_merge_ptr;
if (is_strict_sorted) {
grad_merge_ptr = &grad;
} else {
paddle::operators::math::scatter::MergeAdd<Context, float> merge_func;
merge_func(dev_ctx, grad, &tmp_grad_merge, true);
xpu_wait(dev_ctx.x_context()->xpu_stream);
grad_merge_ptr = &tmp_grad_merge;
}
auto& grad_merge = *grad_merge_ptr;
auto& grad_tensor = grad_merge.value();
funcs::GetDataPointer<Context, float>(grad_tensor, &grad_c, dev_ctx);
int row_count = grad_merge.rows().size();
std::vector<int> rows(row_count);
xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
int* xpu_rows = RAII_GUARD.alloc_l3_or_gm<int>(row_count);
std::vector<int64_t> merge_rows(grad_merge.rows().begin(),
grad_merge.rows().end());
for (size_t i = 0; i < grad_merge.rows().size(); ++i) {
rows[i] = static_cast<int>(merge_rows[i]);
}
xpu_wait(dev_ctx.x_context()->xpu_stream);
paddle::memory::Copy(dev_ctx.GetPlace(),
xpu_rows,
CPUPlace(),
rows.data(),
row_count * sizeof(int));
auto row_numel = grad_tensor.numel() / grad_merge.rows().size();
auto ori_rows = param.numel() / row_numel;
int r = xpu::sparse_adam(
dev_ctx.x_context(),
grad_c != nullptr ? grad_c : grad_tensor.template data<float>(),
mom1_ptr != nullptr ? mom1_ptr : moment1.template data<float>(),
mom2_ptr != nullptr ? mom2_ptr : moment2.template data<float>(),
param_ptr != nullptr ? param_ptr : param.template data<float>(),
beta1_pow_ptr != nullptr ? beta1_pow_ptr : beta1_const_pow_ptr,
beta2_pow_ptr != nullptr ? beta2_pow_ptr : beta2_const_pow_ptr,
lr_ptr != nullptr ? lr_ptr : learning_rate.template data<float>(),
mom1_out_ptr,
mom2_out_ptr,
param_out_ptr,
beta1_,
beta2_,
epsilon_,
ori_rows,
xpu_rows,
row_numel,
grad_merge.rows().size(),
lazy_mode);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
funcs::FreeData<float>(grad_tensor, grad_c);
funcs::CopyOutData<Context, float>(xpu_mom1_out, moment1_out, dev_ctx);
funcs::CopyOutData<Context, float>(xpu_mom2_out, moment1_out, dev_ctx);
funcs::CopyOutData<Context, float>(xpu_param_out, moment1_out, dev_ctx);
if (!use_global_beta_pow) {
// update in cpu and then copy to xpu
if (beta1_pow.place() == CPUPlace() && beta2_pow.place() == CPUPlace()) {
funcs::SetBetaData<Context, float>(
beta1_pow, beta1_pow_out, beta1_, dev_ctx);
funcs::SetBetaData<Context, float>(
beta2_pow, beta2_pow_out, beta2_, dev_ctx);
} else {
float* beta1_pow_out_p1 = nullptr;
if (beta1_pow_out->dtype() == DataType::FLOAT16) {
funcs::Scale<Context, float>(
beta1_pow_out, beta1_pow, beta1_pow_ptr, beta1_, dev_ctx);
} else {
const float* beta1_pow_data = beta1_pow.template data<float>();
beta1_pow_out_p1 = dev_ctx.template Alloc<float>(beta1_pow_out);
r = xpu::scale(dev_ctx.x_context(),
beta1_pow_data,
beta1_pow_out_p1,
beta1_pow.numel(),
false,
beta1_,
0.0f);
xpu_wait(dev_ctx.x_context()->xpu_stream);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
}
float* beta2_pow_out_p1 = nullptr;
if (beta2_pow_out->dtype() == DataType::FLOAT16) {
funcs::Scale<Context, float>(
beta2_pow_out, beta2_pow, beta2_pow_ptr, beta2_, dev_ctx);
} else {
const float* beta2_pow_data = beta2_pow.template data<float>();
beta2_pow_out_p1 = dev_ctx.template Alloc<float>(beta2_pow_out);
r = xpu::scale(dev_ctx.x_context(),
beta2_pow_data,
beta2_pow_out_p1,
beta2_pow.numel(),
false,
beta2_,
0.0f);
xpu_wait(dev_ctx.x_context()->xpu_stream);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
}
}
}
funcs::FreeData<float>(param, param_ptr);
funcs::FreeData<float>(moment1, mom1_ptr);
funcs::FreeData<float>(moment2, mom2_ptr);
funcs::FreeData<float>(learning_rate, lr_ptr);
}
} // namespace sr
} // namespace phi
PD_REGISTER_KERNEL(adam_dense_param_sparse_grad,
XPU,
ALL_LAYOUT,
phi::sr::AdamDenseParamSparseGradKernel,
float,
phi::dtype::float16) {
// Skip beta1_pow, beta2_pow, skip_update data transform
kernel->InputAt(5).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(6).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(8).SetBackend(phi::Backend::ALL_BACKEND);
}
paddle/phi/kernels/xpu/adam_kernel.cc 0 → 100644
浏览文件 @ cbabbe2e
// Copyright (c) 2022 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/adam_kernel.h"
#include "paddle/phi/backends/xpu/xpu_context.h"
#include "paddle/phi/common/float16.h"
#include "paddle/phi/core/enforce.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/tensor_utils.h"
#include "paddle/phi/kernels/funcs/adam_functors.h"
namespace phi {
using float16 = dtype::float16;
template <typename T, typename Context>
void AdamDenseKernel(const Context& dev_ctx,
const DenseTensor& param,
const DenseTensor& grad,
const DenseTensor& learning_rate,
const DenseTensor& moment1,
const DenseTensor& moment2,
const DenseTensor& beta1_pow,
const DenseTensor& beta2_pow,
const paddle::optional<DenseTensor>& master_param,
const paddle::optional<DenseTensor>& skip_update,
const Scalar& beta1,
const Scalar& beta2,
const Scalar& epsilon,
bool lazy_mode,
int64_t min_row_size_to_use_multithread,
bool multi_precision,
bool use_global_beta_pow,
DenseTensor* param_out,
DenseTensor* moment1_out,
DenseTensor* moment2_out,
DenseTensor* beta1_pow_out,
DenseTensor* beta2_pow_out,
DenseTensor* master_param_outs) {
float* param_ptr = nullptr;
funcs::GetDataPointer<Context, float>(param, &param_ptr, dev_ctx);
float* mom1_ptr = nullptr;
funcs::GetDataPointer<Context, float>(moment1, &mom1_ptr, dev_ctx);
float* mom2_ptr = nullptr;
funcs::GetDataPointer<Context, float>(moment2, &mom2_ptr, dev_ctx);
float* lr_ptr = nullptr;
funcs::GetDataPointer<Context, float>(learning_rate, &lr_ptr, dev_ctx);
float* beta1_pow_ptr = nullptr;
const float* beta1_const_pow_ptr = nullptr;
if (beta1_pow.place() == CPUPlace()) {
DenseTensor xpu_beta1_pow;
phi::Copy(dev_ctx, beta1_pow, beta1_pow.place(), false, &xpu_beta1_pow);
if (xpu_beta1_pow.dtype() == DataType::FLOAT16)
funcs::GetDataPointer<Context, float>(
xpu_beta1_pow, &beta1_pow_ptr, dev_ctx);
else
beta1_const_pow_ptr = xpu_beta1_pow.template data<float>();
} else {
if (beta1_pow.dtype() == DataType::FLOAT16)
funcs::GetDataPointer<Context, float>(beta1_pow, &beta1_pow_ptr, dev_ctx);
else
beta1_const_pow_ptr = beta1_pow.template data<float>();
}
float* beta2_pow_ptr = nullptr;
const float* beta2_const_pow_ptr = nullptr;
if (beta2_pow.place() == CPUPlace()) {
DenseTensor xpu_beta2_pow;
phi::Copy(dev_ctx, beta2_pow, beta2_pow.place(), false, &xpu_beta2_pow);
if (xpu_beta2_pow.dtype() == DataType::FLOAT16)
funcs::GetDataPointer<Context, float>(
xpu_beta2_pow, &beta2_pow_ptr, dev_ctx);
else
beta2_const_pow_ptr = xpu_beta2_pow.template data<float>();
} else {
if (beta2_pow.dtype() == DataType::FLOAT16)
funcs::GetDataPointer<Context, float>(beta2_pow, &beta2_pow_ptr, dev_ctx);
else
beta2_const_pow_ptr = beta2_pow.template data<float>();
}
DenseTensor xpu_param_out;
float* param_out_ptr = nullptr;
const phi::DenseTensorMeta meta_param(DataType::FLOAT32, param_out->dims());
xpu_param_out.set_meta(meta_param);
funcs::GetOutDataPointer<Context, float>(
param_out, &xpu_param_out, &param_out_ptr, dev_ctx);
DenseTensor xpu_mom1_out;
float* mom1_out_ptr = nullptr;
const phi::DenseTensorMeta meta_mom1(DataType::FLOAT32, moment1_out->dims());
xpu_mom1_out.set_meta(meta_mom1);
funcs::GetOutDataPointer<Context, float>(
moment1_out, &xpu_mom1_out, &mom1_out_ptr, dev_ctx);
DenseTensor xpu_mom2_out;
float* mom2_out_ptr = nullptr;
const phi::DenseTensorMeta meta_mom2(DataType::FLOAT32, moment2_out->dims());
xpu_mom2_out.set_meta(meta_mom2);
funcs::GetOutDataPointer<Context, float>(
moment2_out, &xpu_mom2_out, &mom2_out_ptr, dev_ctx);
bool skip_update_ = false;
if (skip_update.is_initialized()) {
PADDLE_ENFORCE_EQ(
skip_update->numel(),
1,
errors::InvalidArgument("Input(SkipUpdate) size must be 1, but get %d",
skip_update->numel()));
std::vector<bool> skip_update_vec;
paddle::framework::TensorToVector(*skip_update, dev_ctx, &skip_update_vec);
skip_update_ = skip_update_vec[0];
}
if (skip_update_) {
VLOG(4) << "Adam skip update";
phi::Copy(dev_ctx, param, dev_ctx.GetPlace(), false, param_out);
phi::Copy(dev_ctx, moment1, dev_ctx.GetPlace(), false, moment1_out);
phi::Copy(dev_ctx, moment2, dev_ctx.GetPlace(), false, moment2_out);
phi::Copy(dev_ctx, beta1_pow, beta1_pow.place(), false, beta1_pow_out);
phi::Copy(dev_ctx, beta2_pow, beta2_pow.place(), false, beta2_pow_out);
return;
}
PADDLE_ENFORCE_EQ(
beta1_pow_out->numel(),
1,
errors::InvalidArgument("Tensor holds the wrong size, Expected beta1 pow "
"output size is 1, but received "
"value is:%d.",
beta1_pow_out->numel()));
PADDLE_ENFORCE_EQ(
beta2_pow_out->numel(),
1,
errors::InvalidArgument("Tensor holds the wrong size, Expected beta2 pow "
"output size is 1, but received "
"value is:%d.",
beta2_pow_out->numel()));
VLOG(4) << "use_global_beta_pow:" << use_global_beta_pow;
auto beta1_ = beta1.to<float>();
auto beta2_ = beta2.to<float>();
auto epsilon_ = epsilon.to<float>();
float* grad_c = nullptr;
funcs::GetDataPointer<Context, float>(grad, &grad_c, dev_ctx);
int r = xpu::adam(
dev_ctx.x_context(),
grad_c != nullptr ? grad_c : grad.template data<float>(),
mom1_ptr != nullptr ? mom1_ptr : moment1.template data<float>(),
mom2_ptr != nullptr ? mom2_ptr : moment2.template data<float>(),
param_ptr != nullptr ? param_ptr : param.template data<float>(),
beta1_pow_ptr != nullptr ? beta1_pow_ptr : beta1_const_pow_ptr,
beta2_pow_ptr != nullptr ? beta2_pow_ptr : beta2_const_pow_ptr,
lr_ptr != nullptr ? lr_ptr : learning_rate.template data<float>(),
mom1_out_ptr,
mom2_out_ptr,
param_out_ptr,
beta1_,
beta2_,
epsilon_,
param.numel());
xpu_wait(dev_ctx.x_context()->xpu_stream);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
funcs::FreeData<float>(grad, grad_c);
funcs::CopyOutData<Context, float>(xpu_mom1_out, moment1_out, dev_ctx);
funcs::CopyOutData<Context, float>(xpu_mom2_out, moment2_out, dev_ctx);
funcs::CopyOutData<Context, float>(xpu_param_out, param_out, dev_ctx);
if (!use_global_beta_pow) {
// update in cpu and then copy to xpu
if (beta1_pow.place() == CPUPlace() && beta2_pow.place() == CPUPlace()) {
funcs::SetBetaData<Context, float>(
beta1_pow, beta1_pow_out, beta1_, dev_ctx);
funcs::SetBetaData<Context, float>(
beta2_pow, beta2_pow_out, beta2_, dev_ctx);
} else {
float* beta1_pow_out_p1 = nullptr;
if (beta1_pow_out->dtype() == DataType::FLOAT16) {
funcs::Scale<Context, float>(
beta1_pow_out, beta1_pow, beta1_pow_ptr, beta1_, dev_ctx);
} else {
const float* beta1_pow_data = beta1_pow.template data<float>();
beta1_pow_out_p1 = dev_ctx.template Alloc<float>(beta1_pow_out);
r = xpu::scale(dev_ctx.x_context(),
beta1_pow_data,
beta1_pow_out_p1,
beta1_pow.numel(),
false,
beta1_,
0.0f);
xpu_wait(dev_ctx.x_context()->xpu_stream);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
}
float* beta2_pow_out_p1 = nullptr;
if (beta2_pow_out->dtype() == DataType::FLOAT16) {
funcs::Scale<Context, float>(
beta2_pow_out, beta2_pow, beta2_pow_ptr, beta2_, dev_ctx);
} else {
const float* beta2_pow_data = beta2_pow.template data<float>();
beta2_pow_out_p1 = dev_ctx.template Alloc<float>(beta2_pow_out);
r = xpu::scale(dev_ctx.x_context(),
beta2_pow_data,
beta2_pow_out_p1,
beta2_pow.numel(),
false,
beta2_,
0.0f);
xpu_wait(dev_ctx.x_context()->xpu_stream);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "adam");
}
}
}
funcs::FreeData<float>(param, param_ptr);
funcs::FreeData<float>(moment1, mom1_ptr);
funcs::FreeData<float>(moment2, mom2_ptr);
funcs::FreeData<float>(learning_rate, lr_ptr);
}
} // namespace phi
PD_REGISTER_KERNEL(
adam, XPU, ALL_LAYOUT, phi::AdamDenseKernel, float, phi::dtype::float16) {
// Skip beta1_pow, beta2_pow, skip_update data transform
kernel->InputAt(5).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(6).SetBackend(phi::Backend::ALL_BACKEND);
kernel->InputAt(8).SetBackend(phi::Backend::ALL_BACKEND);
}
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