ROCm/composable_kernel
1
0
Fork 0
mirror of https://github.com/ROCm/composable_kernel.git synced 2026-05-05 06:01:23 +00:00
Code Issues Packages Projects Releases Wiki Activity

Batchnorm-forward and Batchnorm-infer Implemented using generic kernels (#320)

Browse Source 
* Implement multiple-reduction in one kernel (kernels, device ops, examples)

* Add generic elementwise kernel and device interface

* Add generator for normal-distributed data initialization

* Add host refer implementation of batchnorm-forward and batchnorm-infer

* Add examples for implementing batchnorm-forward and batchnorm-infer using generic kernels

* Remove un-needed including in batchnorm example

* Renaming generic_elementwise to elementiwise in kernel and device classes/functions

* Change in gemm_layernorm examples to use DeviceElementwise instead of Device5AryElementwise

* Change in exampe 19_binary_elementwise to use DeviceElementwise instead of DeviceBinaryElementwise

* Change in device_cgemm_4gemm_xdl_cshuffle.hpp to use kernel_elementwise instead of kernel_binary_elementwise

* Add DeviceElementwiseBase and use it in device_normalize_instance.cpp

* Removing and renaming files

* Update to synchronize gemm_layernorm client example to the generic element-wise device op API

* Update to synchronize with the latest headers directory and HostTensorDescriptor interface renaming

* Merge two static member functions in device_elementwise.hpp

* Remove unary_elementwise_1d kernel and device
This commit is contained in:
Qianfeng
2022-08-15 23:11:02 +08:00
committed by GitHub
parent 5ee304595c
commit 53ea4713af
47 changed files with 5195 additions and 1707 deletions
Download Patch File Download Diff File Expand all files Collapse all files

57
include/ck/tensor_operation/gpu/element/element_wise_operation.hpp
View File

@@ -198,17 +198,44 @@ struct Normalize
// FIXME: is double absolutely necessary?
Normalize(double epsilon = 1e-4) : epsilon_(epsilon) {}
template <typename T>
__host__ __device__ constexpr void operator()(
T& y, const T& x, const T& mean, const T& mean_square, const T& gamma, const T& beta) const;
template <typename T1, typename T2, typename T3>
__host__ __device__ constexpr void operator()(T1& y,
const T1& x,
const T2& mean,
const T2& mean_square,
const T3& gamma,
const T3& beta) const;
template <>
__host__ __device__ constexpr void operator()<float>(float& y,
const float& x,
const float& mean,
const float& mean_square,
const float& gamma,
const float& beta) const
__host__ __device__ constexpr void operator()<half_t, float, half_t>(half_t& y,
const half_t& x,
const float& mean,
const float& mean_square,
const half_t& gamma,
const half_t& beta) const
{
using ck::math::sqrt;
float variance = mean_square - (mean * mean);
float tmp_x = type_convert<float>(x);
float tmp_gamma = type_convert<float>(gamma);
float tmp_beta = type_convert<float>(beta);
float tmp_y =
((tmp_x - mean) / sqrt(variance + type_convert<float>(epsilon_))) * tmp_gamma +
tmp_beta;
y = type_convert<half_t>(tmp_y);
};
template <>
__host__ __device__ constexpr void operator()<float, float, float>(float& y,
const float& x,
const float& mean,
const float& mean_square,
const float& gamma,
const float& beta) const
{
using ck::math::sqrt;
@@ -217,12 +244,12 @@ struct Normalize
};
template <>
__host__ __device__ constexpr void operator()<double>(double& y,
const double& x,
const double& mean,
const double& mean_square,
const double& gamma,
const double& beta) const
__host__ __device__ constexpr void operator()<double, double, double>(double& y,
const double& x,
const double& mean,
const double& mean_square,
const double& gamma,
const double& beta) const
{
using ck::math::sqrt;