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

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* 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
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70
example/19_binary_elementwise/elementwise_add_4d.cpp
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@@ -6,7 +6,7 @@
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/device_binary_elementwise.hpp"
#include "ck/tensor_operation/gpu/device/device_elementwise.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
@@ -16,29 +16,21 @@
using F16 = ck::half_t;
using F32 = float;
using ABDataType = F16;
using CDataType = F16;
using EltwiseComputeDataType = F32;
using ABDataType = F16;
using CDataType = F16;
using Add = ck::tensor_operation::element_wise::Add;
using DeviceElementwiseAddInstance =
ck::tensor_operation::device::DeviceBinaryElementwise<ABDataType,
ABDataType,
CDataType,
EltwiseComputeDataType,
Add,
4,
8,
8,
8,
8>;
ck::tensor_operation::device::DeviceElementwise<ck::Tuple<ABDataType, ABDataType>,
ck::Tuple<CDataType>,
Add,
4,
8,
ck::Sequence<8, 8>,
ck::Sequence<8>>;
template <typename HostTensorA,
typename HostTensorB,
typename HostTensorC,
typename ComputeDataType,
typename Functor>
template <typename HostTensorA, typename HostTensorB, typename HostTensorC, typename Functor>
void host_elementwise4D(HostTensorC& C,
const HostTensorA& A,
const HostTensorB& B,
@@ -52,11 +44,11 @@ void host_elementwise4D(HostTensorC& C,
for(std::size_t h = 0; h < shape[2]; ++h)
for(std::size_t w = 0; w < shape[3]; ++w)
{
ComputeDataType a_val = ck::type_convert<ComputeDataType>(A(n, c, h, w));
ComputeDataType b_val = ck::type_convert<ComputeDataType>(B(n, c, h, w));
ComputeDataType c_val = 0;
auto a_val = A(n, c, h, w);
auto b_val = B(n, c, h, w);
ctype c_val = 0;
functor(c_val, a_val, b_val);
C(n, c, h, w) = ck::type_convert<ctype>(c_val);
C(n, c, h, w) = c_val;
}
}
@@ -85,23 +77,24 @@ int main()
b_device_buf.GetDeviceBuffer()};
std::array<void*, 1> output = {c_device_buf.GetDeviceBuffer()};
std::vector<ck::index_t> a_strides{a.mDesc.GetStrides().begin(), a.mDesc.GetStrides().end()};
std::vector<ck::index_t> b_strides{b.mDesc.GetStrides().begin(), b.mDesc.GetStrides().end()};
std::vector<ck::index_t> c_strides{c.mDesc.GetStrides().begin(), c.mDesc.GetStrides().end()};
std::array<ck::index_t, 4> abc_lengths;
std::array<ck::index_t, 4> a_strides;
std::array<ck::index_t, 4> b_strides;
std::array<ck::index_t, 4> c_strides;
std::copy(nchw.begin(), nchw.end(), abc_lengths.begin());
std::copy(a.mDesc.GetStrides().begin(), a.mDesc.GetStrides().end(), a_strides.begin());
std::copy(b.mDesc.GetStrides().begin(), b.mDesc.GetStrides().end(), b_strides.begin());
std::copy(c.mDesc.GetStrides().begin(), c.mDesc.GetStrides().end(), c_strides.begin());
auto broadcastAdd = DeviceElementwiseAddInstance{};
auto argument =
broadcastAdd.MakeArgumentPointer(input,
output,
std::vector<ck::index_t>{nchw.begin(), nchw.end()},
{{a_strides}, b_strides},
{c_strides},
Add{});
auto argument = broadcastAdd.MakeArgumentPointer(
abc_lengths, {a_strides, b_strides}, {c_strides}, input, output, Add{});
if(!broadcastAdd.IsSupportedArgument(argument.get()))
{
throw std::runtime_error("The runtime parameters seems not supported by the "
"DeviceBinaryElementwise instance, exiting!");
throw std::runtime_error(
"The runtime parameters seems not supported by the device instance, exiting!");
};
auto broadcastAdd_invoker_ptr = broadcastAdd.MakeInvokerPointer();
@@ -116,11 +109,8 @@ int main()
c_device_buf.FromDevice(c.mData.data());
Tensor<CDataType> host_c(nchw);
host_elementwise4D<Tensor<ABDataType>,
Tensor<ABDataType>,
Tensor<CDataType>,
EltwiseComputeDataType,
Add>(host_c, a, b, nchw, Add{});
host_elementwise4D<Tensor<ABDataType>, Tensor<ABDataType>, Tensor<CDataType>, Add>(
host_c, a, b, nchw, Add{});
pass &=
ck::utils::check_err(c.mData, host_c.mData, "Error: Incorrect results c", 1e-3, 1e-3);