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“86fd748808dee2448bf368f3b1389f91ec6e9d29”上不存在“develop/doc/api/v2/fluid/param_attr.html”
未验证 提交 ec398f1a 编写于 作者: C Chen Weihang 提交者： GitHub
浏览文件
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[Phi] Migrate batch norm xpu kernel into phi (#45592) 

* add batch norm xpu kernel

* add batch_orm_grad xpu kernel, test=kunlun

* fix lost changing error, test=kunlun

* remove batch norm op xpu, test=kunlun

* fix dtype error, test=kunlun

* fix const deduce error, test=kunlun
上级 cbabbe2e
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Showing with 416 addition and 430 deletion
paddle/fluid/operators/batch_norm_op_xpu.cc 已删除 100644 → 0
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. */
#ifdef PADDLE_WITH_XPU
#include <iterator>
#include <vector>
#include "paddle/fluid/operators/batch_norm_op.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using DDim = framework::DDim;
template <typename DeviceContext, typename T>
class BatchNormXPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
const auto epsilon = ctx.Attr<float>("epsilon");
float momentum = ctx.Attr<float>("momentum");
const auto is_test = ctx.Attr<bool>("is_test");
const auto use_global_stats = ctx.Attr<bool>("use_global_stats");
const auto trainable_stats = ctx.Attr<bool>("trainable_statistics");
bool test_mode = is_test && (!trainable_stats);
bool global_stats = test_mode || use_global_stats;
const auto &data_layout_str = ctx.Attr<std::string>("data_layout");
const auto data_layout = framework::StringToDataLayout(data_layout_str);
PADDLE_ENFORCE_EQ(data_layout_str == "NCHW" || data_layout_str == "NHWC",
true,
platform::errors::InvalidArgument(
"The 'data_layout' attribute must be NCHW or NHWC. "
"But recevived 'data_layout' is [%s].",
data_layout_str));
const auto *x = ctx.Input<Tensor>("X");
const auto &x_dims = x->dims();
PADDLE_ENFORCE_EQ(
x_dims.size() >= 2 && x_dims.size() <= 5,
true,
platform::errors::InvalidArgument(
"The size of input's dimensions should be between 2 and 5"
"But received: the size of input's dimensions is [%d]",
x_dims.size()));
int N = -1, C = -1, H = -1, W = -1, D = -1;
ExtractNCWHD(x_dims, data_layout, &N, &C, &H, &W, &D);
N = (N == 0) ? 1 : N;
C = (C == 0) ? 1 : C;
H = (H == 0) ? 1 : H;
W = (W == 0) ? 1 : W;
const auto *scale = ctx.Input<Tensor>("Scale");
const auto *bias = ctx.Input<Tensor>("Bias");
const auto *x_data = x->data<T>();
const auto *scale_data = scale->data<float>();
const auto *bias_data = bias->data<float>();
auto *y = ctx.Output<Tensor>("Y");
auto *mean_out = ctx.Output<Tensor>("MeanOut");
auto *variance_out = ctx.Output<Tensor>("VarianceOut");
auto *saved_mean = ctx.Output<Tensor>("SavedMean");
auto *saved_variance = ctx.Output<Tensor>("SavedVariance");
// alloc memory
auto *y_data = y->mutable_data<T>(ctx.GetPlace());
mean_out->mutable_data<float>(ctx.GetPlace());
variance_out->mutable_data<float>(ctx.GetPlace());
saved_mean->mutable_data<float>(ctx.GetPlace());
saved_variance->mutable_data<float>(ctx.GetPlace());
auto &dev_ctx = ctx.template device_context<DeviceContext>();
bool is_nchw = data_layout_str == "NCHW";
if (!global_stats) {
auto *mean_out_data = mean_out->data<float>();
auto *variance_out_data = variance_out->data<float>();
auto *saved_mean_data = saved_mean->data<float>();
auto *saved_variance_data = saved_variance->data<float>();
// if MomentumTensor is set, use MomentumTensor value, momentum
// is only used in this training branch
if (ctx.HasInput("MomentumTensor")) {
const auto *mom_tensor = ctx.Input<Tensor>("MomentumTensor");
Tensor mom_cpu;
paddle::framework::TensorCopySync(
*mom_tensor, platform::CPUPlace(), &mom_cpu);
momentum = mom_tensor->data<float>()[0];
}
int r = xpu::batch_norm<T>(dev_ctx.x_context(),
x_data,
y_data,
N,
C,
H,
W,
epsilon,
momentum,
scale_data,
bias_data,
saved_mean_data,
saved_variance_data,
mean_out_data,
variance_out_data,
is_nchw);
PADDLE_ENFORCE_EQ(r,
xpu::Error_t::SUCCESS,
platform::errors::External(
"The batch_norm XPU API return wrong value[%d %s]",
r,
XPUAPIErrorMsg[r]));
} else {
const auto *mean = ctx.Input<Tensor>("Mean");
const auto *variance = ctx.Input<Tensor>("Variance");
const auto *mean_data = mean->data<float>();
const auto *variance_data = variance->data<float>();
int r = xpu::batch_norm_infer(dev_ctx.x_context(),
x_data,
y_data,
N,
C,
H,
W,
epsilon,
scale_data,
bias_data,
mean_data,
variance_data,
is_nchw);
PADDLE_ENFORCE_EQ(
r,
xpu::Error_t::SUCCESS,
platform::errors::External(
"The batch_norm_infer XPU API return wrong value[%d %s]",
r,
XPUAPIErrorMsg[r]));
}
}
};
template <typename T>
static int calculate_inv_BN_Y(xpu::Context *ctx,
T *x,
const T *scale,
const T *bias,
const T *mean,
const T *variance,
const int N,
const int C,
const int M,
const T *y) {
PADDLE_ENFORCE_EQ(x,
y,
platform::errors::InvalidArgument(
"X and Y should be inplaced in inplace mode"));
std::vector<int> tensor_shape_vec({N, C, M});
std::vector<int> array_shape_vec({1, C, 1});
// y - bias
int r1 =
xpu::broadcast_sub<T>(ctx, bias, y, x, array_shape_vec, tensor_shape_vec);
// (y - bias) / scale
int r2 = xpu::broadcast_div<T>(
ctx, scale, x, x, array_shape_vec, tensor_shape_vec);
// (y - bias) / scale / variance
int r3 = xpu::broadcast_div<T>(
ctx, variance, x, x, array_shape_vec, tensor_shape_vec);
// (y - bias) / scale / variance + mean
int r4 =
xpu::broadcast_add<T>(ctx, mean, x, x, array_shape_vec, tensor_shape_vec);
return r1 + r2 + r3 + r4;
}
template <typename T>
static int calculate_inv_var(xpu::Context *ctx,
const T *var,
const T epsilon,
const int C,
T *epsilon_data,
T *inv_var) {
int r1 = constant(ctx, epsilon_data, 1, epsilon);
std::vector<int> tensor_shape_vec({C});
std::vector<int> array_shape_vec({1});
int r2 = xpu::broadcast_add<T>(
ctx, epsilon_data, var, inv_var, array_shape_vec, tensor_shape_vec);
int r3 = xpu::rsqrt<T>(ctx, inv_var, inv_var, C);
return r1 + r2 + r3;
}
template <typename DeviceContext, typename T>
class BatchNormGradXPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
const auto *d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
const auto *scale = ctx.Input<Tensor>("Scale");
const auto *bias = ctx.Input<Tensor>("Bias");
const auto &data_layout_str = ctx.Attr<std::string>("data_layout");
bool use_global_stats = ctx.Attr<bool>("use_global_stats");
const bool is_test = ctx.Attr<bool>("is_test");
const float epsilon = ctx.Attr<float>("epsilon");
const auto data_layout = framework::StringToDataLayout(data_layout_str);
PADDLE_ENFORCE_EQ(data_layout_str == "NCHW" || data_layout_str == "NHWC",
true,
platform::errors::InvalidArgument(
"The 'data_layout' attribute must be NCHW or NHWC. "
"But recevived 'data_layout' is [%s].",
data_layout_str));
auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
use_global_stats = is_test || use_global_stats;
// batch_norm with inplace as false will take X as grad input, which
// is same as cuDNN batch_norm backward calculation, batch_norm
// with inplace as true only take Y as input and X should be calculate
// by inverse operation of batch_norm on Y
const Tensor *x;
bool is_inplace;
if (ctx.HasInput("Y")) {
x = ctx.Input<Tensor>("Y");
is_inplace = true;
// if the input of batch norm is stop_gradient, d_x is null.
if (d_x) {
PADDLE_ENFORCE_EQ(d_x,
d_y,
platform::errors::InvalidArgument(
"X@GRAD and Y@GRAD not inplace in inplace mode"));
}
} else {
x = ctx.Input<Tensor>("X");
is_inplace = false;
if (d_x) {
PADDLE_ENFORCE_NE(
d_x,
d_y,
platform::errors::InvalidArgument(
"X@GRAD and Y@GRAD inplaced in non-inplace mode"));
}
}
const auto &x_dims = x->dims();
PADDLE_ENFORCE_EQ(
x_dims.size() >= 2 && x_dims.size() <= 5,
true,
platform::errors::InvalidArgument(
"The size of input's dimensions should be between 2 and 5"
"But received: the size of input's dimensions is [%d]",
x_dims.size()));
int N = -1, C = -1, H = -1, W = -1, D = -1;
ExtractNCWHD(x_dims, data_layout, &N, &C, &H, &W, &D);
N = (N == 0) ? 1 : N;
C = (C == 0) ? 1 : C;
H = (H == 0) ? 1 : H;
W = (W == 0) ? 1 : W;
const auto *x_data = x->data<T>();
const auto *d_y_data = d_y->data<T>();
const auto *scale_data = scale->data<float>();
// init output
T *d_x_data = nullptr;
T *d_bias_data = nullptr;
T *d_scale_data = nullptr;
if (d_x) {
d_x_data = d_x->mutable_data<T>(ctx.GetPlace());
}
if (d_scale && d_bias) {
d_scale_data = d_scale->mutable_data<float>(ctx.GetPlace());
d_bias_data = d_bias->mutable_data<float>(ctx.GetPlace());
}
PADDLE_ENFORCE_EQ(
scale->dims().size(),
1UL,
platform::errors::InvalidArgument(
"The size of scale's dimensions must equal to 1. But received: "
"the size of scale's dimensions is [%d], the dimensions of scale "
"is [%s].",
scale->dims().size(),
scale->dims()));
PADDLE_ENFORCE_EQ(
scale->dims()[0],
C,
platform::errors::InvalidArgument(
"The first dimension of scale must equal to Channels[%d]. But "
"received: the first dimension of scale is [%d]",
C,
scale->dims()[0]));
auto &dev_ctx = ctx.template device_context<DeviceContext>();
xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
const auto *batch_mean = ctx.Input<Tensor>("SavedMean");
const auto *batch_inv_std = ctx.Input<Tensor>("SavedVariance");
const auto *global_mean = ctx.Input<Tensor>("Mean");
const auto *global_var = ctx.Input<Tensor>("Variance");
// TODO(guozibin): hadle the situation case of N * H * W = 1
if (is_inplace) {
float *global_inv_std_data = nullptr;
if (use_global_stats) {
global_inv_std_data =
RAII_GUARD.alloc_l3_or_gm<float>(global_var->numel());
float *epsilon_data = RAII_GUARD.alloc_l3_or_gm<float>(1);
int r1 = calculate_inv_var(dev_ctx.x_context(),
global_var->data<float>(),
epsilon,
C,
epsilon_data,
global_inv_std_data);
PADDLE_ENFORCE_EQ(
r1,
XPU_SUCCESS,
platform::errors::External("XPU API(batch_norm_grad "
"calculate_inv_var function) "
"return wrong value[%d %s]",
r1,
XPUAPIErrorMsg[r1]));
}
auto px = *x;
auto *inv_std_data =
use_global_stats ? global_inv_std_data : batch_inv_std->data<float>();
auto mean_data = use_global_stats ? global_mean->data<float>()
: batch_mean->data<float>();
int r2 = calculate_inv_BN_Y(dev_ctx.x_context(),
px.mutable_data<T>(ctx.GetPlace()),
scale->data<float>(),
bias->data<float>(),
mean_data,
inv_std_data,
N,
C,
H * W,
x->data<T>());
PADDLE_ENFORCE_EQ(
r2,
XPU_SUCCESS,
platform::errors::External("XPU API(batch_norm_grad "
"calculate_inv_BN_Y function) "
"return wrong value[%d %s]",
r2,
XPUAPIErrorMsg[r2]));
}
int r3;
bool is_nchw = data_layout_str == "NCHW";
if (use_global_stats) {
r3 = xpu::batch_norm_grad<T>(dev_ctx.x_context(),
x_data,
d_y_data,
d_x_data,
N,
C,
H,
W,
scale_data,
nullptr,
nullptr,
d_scale_data,
d_bias_data,
is_nchw,
global_mean->data<float>(),
global_var->data<float>(),
epsilon);
} else {
if (!d_x) {
d_x_data = RAII_GUARD.alloc_l3_or_gm<T>(x->numel());
}
if (!d_scale) {
d_scale_data = RAII_GUARD.alloc_l3_or_gm<float>(C);
}
if (!d_bias_data) {
d_bias_data = RAII_GUARD.alloc_l3_or_gm<float>(C);
}
r3 = xpu::batch_norm_grad<T>(dev_ctx.x_context(),
x_data,
d_y_data,
d_x_data,
N,
C,
H,
W,
scale_data,
batch_mean->data<float>(),
batch_inv_std->data<float>(),
d_scale_data,
d_bias_data,
is_nchw);
}
PADDLE_ENFORCE_EQ(
r3,
XPU_SUCCESS,
platform::errors::External("XPU API(batch_norm_grad) return "
"wrong value[%d %s]",
r3,
XPUAPIErrorMsg[r3]));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_XPU_KERNEL(
batch_norm,
ops::BatchNormXPUKernel<paddle::platform::XPUDeviceContext, float>);
REGISTER_OP_XPU_KERNEL(
batch_norm_grad,
ops::BatchNormGradXPUKernel<paddle::platform::XPUDeviceContext, float>);
#endif // PADDLE_WITH_XPU
paddle/phi/kernels/xpu/batch_norm_grad_kernel.cc 0 → 100644
浏览文件 @ ec398f1a
// 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/batch_norm_grad_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/norm_utils.h"
namespace phi {
template <typename T>
static int CalculateInvBNY(xpu::Context *ctx,
T *x,
const T *scale,
const T *bias,
const T *mean,
const T *variance,
const int N,
const int C,
const int M,
const T *y) {
PADDLE_ENFORCE_EQ(x,
y,
phi::errors::InvalidArgument(
"X and Y should be inplaced in inplace mode"));
std::vector<int> tensor_shape_vec({N, C, M});
std::vector<int> array_shape_vec({1, C, 1});
// y - bias
int r1 =
xpu::broadcast_sub<T>(ctx, bias, y, x, array_shape_vec, tensor_shape_vec);
// (y - bias) / scale
int r2 = xpu::broadcast_div<T>(
ctx, scale, x, x, array_shape_vec, tensor_shape_vec);
// (y - bias) / scale / variance
int r3 = xpu::broadcast_div<T>(
ctx, variance, x, x, array_shape_vec, tensor_shape_vec);
// (y - bias) / scale / variance + mean
int r4 =
xpu::broadcast_add<T>(ctx, mean, x, x, array_shape_vec, tensor_shape_vec);
return r1 + r2 + r3 + r4;
}
template <typename T>
static int CalculateInvVar(xpu::Context *ctx,
const T *var,
const T epsilon,
const int C,
T *epsilon_data,
T *inv_var) {
int r1 = constant(ctx, epsilon_data, 1, epsilon);
std::vector<int> tensor_shape_vec({C});
std::vector<int> array_shape_vec({1});
int r2 = xpu::broadcast_add<T>(
ctx, epsilon_data, var, inv_var, array_shape_vec, tensor_shape_vec);
int r3 = xpu::rsqrt<T>(ctx, inv_var, inv_var, C);
return r1 + r2 + r3;
}
template <typename T, typename Context>
void BatchNormGradKernel(const Context &dev_ctx,
const DenseTensor &x,
const DenseTensor &scale,
const DenseTensor &bias,
const paddle::optional<DenseTensor> &mean,
const paddle::optional<DenseTensor> &variance,
const DenseTensor &saved_mean,
const DenseTensor &saved_variance,
const paddle::optional<DenseTensor> &reserve_space,
const DenseTensor &y_grad,
float momentum,
float epsilon,
const std::string &data_layout,
bool is_test,
bool use_global_stats,
bool trainable_statistics,
bool fuse_with_relu,
DenseTensor *x_grad,
DenseTensor *scale_grad,
DenseTensor *bias_grad) {
const auto *d_y = &y_grad;
PADDLE_ENFORCE_EQ(data_layout == "NCHW" || data_layout == "NHWC",
true,
phi::errors::InvalidArgument(
"The 'data_layout' attribute must be NCHW or NHWC. "
"But recevived 'data_layout' is [%s].",
data_layout));
const auto data_layout_val =
paddle::framework::StringToDataLayout(data_layout);
use_global_stats = is_test || use_global_stats;
// batch_norm with inplace as false will take X as grad input, which
// is same as cuDNN batch_norm backward calculation, batch_norm
// with inplace as true only take Y as input and X should be calculate
// by inverse operation of batch_norm on Y
bool is_inplace = false;
if (x_grad) {
PADDLE_ENFORCE_NE(x_grad,
d_y,
phi::errors::InvalidArgument(
"X@GRAD and Y@GRAD inplaced in non-inplace mode"));
}
const auto &x_dims = x.dims();
PADDLE_ENFORCE_EQ(
x_dims.size() >= 2 && x_dims.size() <= 5,
true,
phi::errors::InvalidArgument(
"The size of input's dimensions should be between 2 and 5"
"But received: the size of input's dimensions is [%d]",
x_dims.size()));
int N = -1, C = -1, H = -1, W = -1, D = -1;
funcs::ExtractNCWHD(x_dims, data_layout_val, &N, &C, &H, &W, &D);
N = (N == 0) ? 1 : N;
C = (C == 0) ? 1 : C;
H = (H == 0) ? 1 : H;
W = (W == 0) ? 1 : W;
const auto *x_data = x.data<T>();
const auto *d_y_data = y_grad.data<T>();
const auto *scale_data = scale.data<float>();
// init output
T *x_grad_data = nullptr;
T *bias_grad_data = nullptr;
T *scale_grad_data = nullptr;
if (x_grad) {
x_grad_data = dev_ctx.template Alloc<T>(x_grad);
}
if (scale_grad && bias_grad) {
scale_grad_data = dev_ctx.template Alloc<T>(scale_grad);
bias_grad_data = dev_ctx.template Alloc<T>(bias_grad);
}
PADDLE_ENFORCE_EQ(
scale.dims().size(),
1UL,
phi::errors::InvalidArgument(
"The size of scale's dimensions must equal to 1. But received: "
"the size of scale's dimensions is [%d], the dimensions of scale "
"is [%s].",
scale.dims().size(),
scale.dims()));
PADDLE_ENFORCE_EQ(
scale.dims()[0],
C,
phi::errors::InvalidArgument(
"The first dimension of scale must equal to Channels[%d]. But "
"received: the first dimension of scale is [%d]",
C,
scale.dims()[0]));
xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
const auto *global_mean = mean.get_ptr();
const auto *global_var = variance.get_ptr();
// TODO(guozibin): hadle the situation case of N * H * W = 1
if (is_inplace) {
float *global_inv_std_data = nullptr;
if (use_global_stats) {
global_inv_std_data =
RAII_GUARD.alloc_l3_or_gm<float>(global_var->numel());
float *epsilon_data = RAII_GUARD.alloc_l3_or_gm<float>(1);
int r1 = CalculateInvVar(dev_ctx.x_context(),
global_var->data<float>(),
epsilon,
C,
epsilon_data,
global_inv_std_data);
PADDLE_ENFORCE_EQ(r1,
XPU_SUCCESS,
phi::errors::External("XPU API(batch_norm_grad "
"CalculateInvVar function) "
"return wrong value[%d %s]",
r1,
XPUAPIErrorMsg[r1]));
}
// Here is a trick, x is a const input,
// but trans to a non-const var, is it risky?
auto px = x;
auto *inv_std_data =
use_global_stats ? global_inv_std_data : saved_variance.data<float>();
auto *mean_data = use_global_stats ? global_mean->data<float>()
: saved_mean.data<float>();
int r2 = CalculateInvBNY(dev_ctx.x_context(),
px.data<T>(),
scale.data<float>(),
bias.data<float>(),
mean_data,
inv_std_data,
N,
C,
H * W,
x.data<T>());
PADDLE_ENFORCE_EQ(r2,
XPU_SUCCESS,
phi::errors::External("XPU API(batch_norm_grad "
"CalculateInvBNY function) "
"return wrong value[%d %s]",
r2,
XPUAPIErrorMsg[r2]));
}
int r3;
bool is_nchw = data_layout == "NCHW";
if (use_global_stats) {
r3 = xpu::batch_norm_grad<T>(dev_ctx.x_context(),
x_data,
d_y_data,
x_grad_data,
N,
C,
H,
W,
scale_data,
nullptr,
nullptr,
scale_grad_data,
bias_grad_data,
is_nchw,
global_mean->data<float>(),
global_var->data<float>(),
epsilon);
} else {
if (!x_grad) {
x_grad_data = RAII_GUARD.alloc_l3_or_gm<T>(x.numel());
}
if (!scale_grad) {
scale_grad_data = RAII_GUARD.alloc_l3_or_gm<float>(C);
}
if (!bias_grad_data) {
bias_grad_data = RAII_GUARD.alloc_l3_or_gm<float>(C);
}
r3 = xpu::batch_norm_grad<T>(dev_ctx.x_context(),
x_data,
d_y_data,
x_grad_data,
N,
C,
H,
W,
scale_data,
saved_mean.data<float>(),
saved_variance.data<float>(),
scale_grad_data,
bias_grad_data,
is_nchw);
}
PADDLE_ENFORCE_EQ(r3,
XPU_SUCCESS,
phi::errors::External("XPU API(batch_norm_grad) return "
"wrong value[%d %s]",
r3,
XPUAPIErrorMsg[r3]));
}
} // namespace phi
PD_REGISTER_KERNEL(
batch_norm_grad, XPU, ALL_LAYOUT, phi::BatchNormGradKernel, float) {}
paddle/phi/kernels/xpu/batch_norm_kernel.cc 0 → 100644
浏览文件 @ ec398f1a
// 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/batch_norm_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/norm_utils.h"
namespace phi {
template <typename T, typename Context>
void BatchNormKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& scale,
const DenseTensor& bias,
const DenseTensor& mean,
const DenseTensor& variance,
float momentum,
float epsilon,
const std::string& data_layout_str,
bool is_test,
bool use_global_stats,
bool trainable_statistics,
bool fuse_with_relu,
DenseTensor* y,
DenseTensor* mean_out,
DenseTensor* variance_out,
DenseTensor* saved_mean,
DenseTensor* saved_variance,
DenseTensor* reserve_space) {
bool test_mode = is_test && (!trainable_statistics);
bool global_stats = test_mode || use_global_stats;
const auto data_layout =
paddle::framework::StringToDataLayout(data_layout_str);
PADDLE_ENFORCE_EQ(data_layout_str == "NCHW" || data_layout_str == "NHWC",
true,
phi::errors::InvalidArgument(
"The 'data_layout' attribute must be NCHW or NHWC. "
"But recevived 'data_layout' is [%s].",
data_layout_str));
const auto& x_dims = x.dims();
PADDLE_ENFORCE_EQ(
x_dims.size() >= 2 && x_dims.size() <= 5,
true,
phi::errors::InvalidArgument(
"The size of input's dimensions should be between 2 and 5"
"But received: the size of input's dimensions is [%d]",
x_dims.size()));
int N = -1, C = -1, H = -1, W = -1, D = -1;
funcs::ExtractNCWHD(x_dims, data_layout, &N, &C, &H, &W, &D);
N = (N == 0) ? 1 : N;
C = (C == 0) ? 1 : C;
H = (H == 0) ? 1 : H;
W = (W == 0) ? 1 : W;
const auto* x_data = x.data<T>();
const auto* scale_data = scale.data<float>();
const auto* bias_data = bias.data<float>();
// alloc memory
auto* y_data = dev_ctx.template Alloc<T>(y);
dev_ctx.template Alloc<float>(mean_out);
dev_ctx.template Alloc<float>(variance_out);
dev_ctx.template Alloc<float>(saved_mean);
dev_ctx.template Alloc<float>(saved_variance);
bool is_nchw = data_layout_str == "NCHW";
if (!global_stats) {
auto* mean_out_data = mean_out->data<float>();
auto* variance_out_data = variance_out->data<float>();
auto* saved_mean_data = saved_mean->data<float>();
auto* saved_variance_data = saved_variance->data<float>();
int r = xpu::batch_norm<T>(dev_ctx.x_context(),
x_data,
y_data,
N,
C,
H,
W,
epsilon,
momentum,
scale_data,
bias_data,
saved_mean_data,
saved_variance_data,
mean_out_data,
variance_out_data,
is_nchw);
PADDLE_ENFORCE_EQ(r,
xpu::Error_t::SUCCESS,
phi::errors::External(
"The batch_norm XPU API return wrong value[%d %s]",
r,
XPUAPIErrorMsg[r]));
} else {
const auto* mean_data = mean.data<float>();
const auto* variance_data = variance.data<float>();
int r = xpu::batch_norm_infer(dev_ctx.x_context(),
x_data,
y_data,
N,
C,
H,
W,
epsilon,
scale_data,
bias_data,
mean_data,
variance_data,
is_nchw);
PADDLE_ENFORCE_EQ(
r,
xpu::Error_t::SUCCESS,
phi::errors::External(
"The batch_norm_infer XPU API return wrong value[%d %s]",
r,
XPUAPIErrorMsg[r]));
}
}
} // namespace phi
PD_REGISTER_KERNEL(batch_norm, XPU, ALL_LAYOUT, phi::BatchNormKernel, float) {}
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