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Commit 4acf0e01 authored by aiss's avatar aiss
Browse files

delete hip file

parent 7dd68788
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csrc/adagrad/cpu_adagrad_hip.cpp deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#include "cpu_adagrad_hip.h"
#include <hip/hip_runtime_api.h>
#include <math.h>
#include <omp.h>
#include <torch/extension.h>
#include <iostream>
#include <memory>
#include <type_traits>
#include <unordered_map>
#include "rocblas.h"
#include "hip/hip_runtime.h"
#include "hiprand/hiprand.h"
#include "custom_hip_layers.h"
static std::unordered_map<int, std::shared_ptr<void>> s_optimizers;
// C++ interface
void Adagrad_Optimizer::Step_1(float* _params,
float* grads,
float* _exp_avg_sq,
size_t _param_size,
__half* dev_params,
bool half_precision)
{
size_t rounded_size = 0;
#if defined(__AVX512__) or defined(__AVX256__)
Step_AVX<1>(
&rounded_size, _params, grads, _exp_avg_sq, _param_size, dev_params, half_precision);
#endif
if (_param_size > rounded_size) {
float step_size = -1 * _alpha;
__half* grads_cast_h;
__half* params_cast_h;
if (half_precision) {
grads_cast_h = reinterpret_cast<__half*>(grads);
params_cast_h = reinterpret_cast<__half*>(_params);
}
for (size_t t = rounded_size; t < _param_size; t += TILE) {
size_t copy_size = TILE;
if ((t + TILE) > _param_size) copy_size = _param_size - t;
size_t offset = copy_size + t;
if ((t / TILE) >= 2) { hipStreamSynchronize(_streams[_buf_index]); }
#pragma omp parallel for
for (size_t k = t; k < offset; k++) {
float grad = half_precision ? (float)grads_cast_h[k] : grads[k];
float param = half_precision ? (float)params_cast_h[k] : _params[k];
float momentum = grads[k];
float variance = _exp_avg_sq[k];
if (_weight_decay > 0) { grad = param * _weight_decay + grad; }
variance += grad * grad;
grad = sqrt(variance);
grad += _eps;
grad = momentum / grad;
param = grad * step_size + param;
if (dev_params) _doubled_buffer[_buf_index][k - t] = param;
if (half_precision)
params_cast_h[k] = (__half)param;
else
_params[k] = param;
// STORE UPDATE TERM TO GRAD'S MEMORY
grads[k] = grad * step_size;
_exp_avg_sq[k] = variance;
}
if (dev_params) {
launch_param_update(
_doubled_buffer[_buf_index], dev_params + t, (copy_size), _streams[_buf_index]);
_buf_index = !_buf_index;
}
}
}
}
void Adagrad_Optimizer::Step_4(float* _params,
float* grads,
float* _exp_avg_sq,
size_t _param_size,
__half* dev_params,
bool half_precision)
{
size_t rounded_size = 0;
#if defined(__AVX512__) or defined(__AVX256__)
Step_AVX<4>(
&rounded_size, _params, grads, _exp_avg_sq, _param_size, dev_params, half_precision);
#endif
if (_param_size > rounded_size)
Step_1((_params + rounded_size),
(grads + rounded_size),
(_exp_avg_sq + rounded_size),
(_param_size - rounded_size),
(dev_params != nullptr ? (dev_params + rounded_size) : dev_params),
half_precision);
}
int create_adagrad_optimizer(int optimizer_id,
float alpha = 1e-2,
float eps = 1e-8,
float weight_decay = 0,
bool should_log = false)
{
auto opt = std::make_shared<Adagrad_Optimizer>(alpha, eps, weight_decay);
s_optimizers[optimizer_id] = opt;
if (should_log) {
std::string avx_type = "";
#if defined(__AVX512__)
avx_type = "AVX512";
#else
#if defined(__AVX256__)
avx_type = "AVX2";
#else
avx_type = "scalar";
#endif
#endif
printf("Adagrad Optimizer #%d is created with %s arithmetic capability.\n",
optimizer_id,
avx_type.c_str());
printf("Config: alpha=%f, weight_decay=%f\n", alpha, weight_decay);
}
return 0;
}
void Adagrad_Optimizer::Step_8(float* _params,
float* grads,
float* _exp_avg_sq,
size_t _param_size,
__half* dev_params,
bool half_precision)
{
size_t rounded_size = 0;
#if defined(__AVX512__) or defined(__AVX256__)
Step_AVX<8>(
&rounded_size, _params, grads, _exp_avg_sq, _param_size, dev_params, half_precision);
#endif
if (_param_size > rounded_size)
Step_4((_params + rounded_size),
(grads + rounded_size),
(_exp_avg_sq + rounded_size),
(_param_size - rounded_size),
(dev_params != nullptr ? (dev_params + rounded_size) : dev_params),
half_precision);
}
int ds_adagrad_step(int optimizer_id,
size_t step,
float lr,
float epsilon,
float weight_decay,
torch::Tensor& params,
torch::Tensor& grads,
torch::Tensor& exp_avg_sq)
{
auto params_c = params.contiguous();
auto grads_c = grads.contiguous();
auto exp_avg_sq_c = exp_avg_sq.contiguous();
float* params_ptr = (float*)params_c.data_ptr();
float* grads_ptr = (float*)grads_c.data_ptr();
float* exp_avg_sq_ptr = (float*)exp_avg_sq_c.data_ptr();
std::shared_ptr<Adagrad_Optimizer> opt =
std::static_pointer_cast<Adagrad_Optimizer>(s_optimizers[optimizer_id]);
opt->IncrementStep(step);
opt->update_state(lr, epsilon, weight_decay);
opt->Step_8(params_ptr, grads_ptr, exp_avg_sq_ptr, params_c.size(0));
opt->SynchronizeStreams();
return 0;
}
int ds_adagrad_step_plus_copy(int optimizer_id,
size_t step,
float lr,
float epsilon,
float weight_decay,
torch::Tensor& params,
torch::Tensor& grads,
torch::Tensor& exp_avg_sq,
torch::Tensor& gpu_params)
{
auto params_c = params.contiguous();
auto gpu_params_c = gpu_params.contiguous();
auto exp_avg_sq_c = exp_avg_sq.contiguous();
auto grads_c = grads.contiguous();
float* params_ptr = (float*)params_c.data_ptr();
float* grads_ptr = (float*)grads_c.data_ptr();
__half* gpu_params_ptr = (__half*)gpu_params_c.data_ptr();
float* exp_avg_sq_ptr = (float*)exp_avg_sq_c.data_ptr();
std::shared_ptr<Adagrad_Optimizer> opt =
std::static_pointer_cast<Adagrad_Optimizer>(s_optimizers[optimizer_id]);
opt->IncrementStep(step);
opt->update_state(lr, epsilon, weight_decay);
opt->Step_8(params_ptr,
grads_ptr,
exp_avg_sq_ptr,
params_c.size(0),
gpu_params_ptr,
(params.options().dtype() == at::kHalf));
opt->SynchronizeStreams();
return 0;
}
int destroy_adagrad_optimizer(int optimizer_id)
{
s_optimizers.erase(optimizer_id);
return 0;
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
{
m.def("adagrad_update", &ds_adagrad_step, "DeepSpeed CPU Adagrad update (C++)");
m.def("adagrad_update_copy",
&ds_adagrad_step_plus_copy,
"DeepSpeed CPU Adagrad update and param copy (C++)");
m.def("create_adagrad", &create_adagrad_optimizer, "DeepSpeed CPU Adagrad (C++)");
m.def("destroy_adagrad", &destroy_adagrad_optimizer, "DeepSpeed CPU Adagrad destroy (C++)");
}
csrc/adam/cpu_adam_hip.cpp deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#include "cpu_adam_hip.h"
#include <hip/hip_runtime_api.h>
#include <math.h>
#include <omp.h>
#include <torch/extension.h>
#include <iostream>
#include <memory>
#include <type_traits>
#include <unordered_map>
#include "rocblas.h"
#include "hip/hip_runtime.h"
#include "hiprand/hiprand.h"
#include "custom_hip_layers.h"
static std::unordered_map<int, std::shared_ptr<void>> s_optimizers;
// C++ interface
void Adam_Optimizer::Step_1(float* _params,
float* grads,
float* _exp_avg,
float* _exp_avg_sq,
size_t _param_size,
__half* dev_params,
bool half_precision)
{
size_t rounded_size = 0;
#if defined(__AVX512__) or defined(__AVX256__)
Step_AVX<1>(&rounded_size,
_params,
grads,
_exp_avg,
_exp_avg_sq,
_param_size,
dev_params,
half_precision);
#endif
if (_param_size > rounded_size) {
float betta1_minus1 = 1 - _betta1;
float betta2_minus1 = 1 - _betta2;
float step_size = -1 * _alpha / _bias_correction1;
float w_decay = -1 * _alpha * _weight_decay;
__half* grads_cast_h;
__half* params_cast_h;
if (half_precision) {
grads_cast_h = reinterpret_cast<__half*>(grads);
params_cast_h = reinterpret_cast<__half*>(_params);
}
for (size_t t = rounded_size; t < _param_size; t += TILE) {
size_t copy_size = TILE;
if ((t + TILE) > _param_size) copy_size = _param_size - t;
size_t offset = copy_size + t;
if ((t / TILE) >= 2) { hipStreamSynchronize(_streams[_buf_index]); }
#pragma omp parallel for
for (size_t k = t; k < offset; k++) {
float grad = half_precision ? (float)grads_cast_h[k] : grads[k];
float param = half_precision ? (float)params_cast_h[k] : _params[k];
float momentum = _exp_avg[k];
float variance = _exp_avg_sq[k];
if (_weight_decay > 0 && !_adamw_mode) { grad = param * _weight_decay + grad; }
momentum = momentum * _betta1;
momentum = grad * betta1_minus1 + momentum;
variance = variance * _betta2;
grad = grad * grad;
variance = grad * betta2_minus1 + variance;
grad = sqrt(variance);
grad = grad * _bias_correction2 + _eps;
grad = momentum / grad;
if (_weight_decay > 0 && _adamw_mode) { param += w_decay * param; }
param = grad * step_size + param;
if (dev_params) _doubled_buffer[_buf_index][k - t] = param;
if (half_precision)
params_cast_h[k] = (__half)param;
else
_params[k] = param;
_exp_avg[k] = momentum;
_exp_avg_sq[k] = variance;
}
if (dev_params) {
launch_param_update(
_doubled_buffer[_buf_index], dev_params + t, (copy_size), _streams[_buf_index]);
_buf_index = !_buf_index;
}
}
}
}
void Adam_Optimizer::Step_4(float* _params,
float* grads,
float* _exp_avg,
float* _exp_avg_sq,
size_t _param_size,
__half* dev_params,
bool half_precision)
{
size_t rounded_size = 0;
#if defined(__AVX512__) or defined(__AVX256__)
Step_AVX<4>(&rounded_size,
_params,
grads,
_exp_avg,
_exp_avg_sq,
_param_size,
dev_params,
half_precision);
#endif
if (_param_size > rounded_size)
Step_1((_params + rounded_size),
(grads + rounded_size),
(_exp_avg + rounded_size),
(_exp_avg_sq + rounded_size),
(_param_size - rounded_size),
(dev_params != nullptr ? (dev_params + rounded_size) : dev_params),
half_precision);
}
int create_adam_optimizer(int optimizer_id,
float alpha = 1e-3,
float betta1 = 0.9,
float betta2 = 0.999,
float eps = 1e-8,
float weight_decay = 0,
bool adamw_mode = true,
bool should_log = false)
{
auto opt =
std::make_shared<Adam_Optimizer>(alpha, betta1, betta2, eps, weight_decay, adamw_mode);
s_optimizers[optimizer_id] = opt;
if (should_log) {
std::string avx_type = "";
#if defined(__AVX512__)
avx_type = "AVX512";
#else
#if defined(__AVX256__)
avx_type = "AVX2";
#else
avx_type = "scalar";
#endif
#endif
printf("Adam Optimizer #%d is created with %s arithmetic capability.\n",
optimizer_id,
avx_type.c_str());
printf("Config: alpha=%f, betas=(%f, %f), weight_decay=%f, adam_w=%d\n",
alpha,
betta1,
betta2,
weight_decay,
(int)adamw_mode);
}
return 0;
}
void Adam_Optimizer::Step_8(float* _params,
float* grads,
float* _exp_avg,
float* _exp_avg_sq,
size_t _param_size,
__half* dev_params,
bool half_precision)
{
size_t rounded_size = 0;
#if defined(__AVX512__) or defined(__AVX256__)
Step_AVX<8>(&rounded_size,
_params,
grads,
_exp_avg,
_exp_avg_sq,
_param_size,
dev_params,
half_precision);
#endif
if (_param_size > rounded_size)
Step_4((_params + rounded_size),
(grads + rounded_size),
(_exp_avg + rounded_size),
(_exp_avg_sq + rounded_size),
(_param_size - rounded_size),
(dev_params != nullptr ? (dev_params + rounded_size) : dev_params),
half_precision);
}
int ds_adam_step(int optimizer_id,
size_t step,
float lr,
float beta1,
float beta2,
float epsilon,
float weight_decay,
bool bias_correction,
torch::Tensor& params,
torch::Tensor& grads,
torch::Tensor& exp_avg,
torch::Tensor& exp_avg_sq)
{
auto params_c = params.contiguous();
auto grads_c = grads.contiguous();
auto exp_avg_c = exp_avg.contiguous();
auto exp_avg_sq_c = exp_avg_sq.contiguous();
// assert(params.options().dtype() == grads.options().dtype());
float* params_ptr = (float*)params_c.data_ptr();
float* grads_ptr = (float*)grads_c.data_ptr();
float* exp_avg_ptr = (float*)exp_avg_c.data_ptr();
float* exp_avg_sq_ptr = (float*)exp_avg_sq_c.data_ptr();
std::shared_ptr<Adam_Optimizer> opt =
std::static_pointer_cast<Adam_Optimizer>(s_optimizers[optimizer_id]);
opt->IncrementStep(step, beta1, beta2);
opt->update_state(lr, epsilon, weight_decay, bias_correction);
opt->Step_8(params_ptr,
grads_ptr,
exp_avg_ptr,
exp_avg_sq_ptr,
params_c.size(0),
nullptr,
(params.options().dtype() == at::kHalf));
opt->SynchronizeStreams();
return 0;
}
int ds_adam_step_plus_copy(int optimizer_id,
size_t step,
float lr,
float beta1,
float beta2,
float epsilon,
float weight_decay,
bool bias_correction,
torch::Tensor& params,
torch::Tensor& grads,
torch::Tensor& exp_avg,
torch::Tensor& exp_avg_sq,
torch::Tensor& gpu_params)
{
auto params_c = params.contiguous();
auto gpu_params_c = gpu_params.contiguous();
auto exp_avg_c = exp_avg.contiguous();
auto exp_avg_sq_c = exp_avg_sq.contiguous();
auto grads_c = grads.contiguous();
float* params_ptr = (float*)params_c.data_ptr();
float* grads_ptr = (float*)grads_c.data_ptr();
__half* gpu_params_ptr = (__half*)gpu_params_c.data_ptr();
float* exp_avg_ptr = (float*)exp_avg_c.data_ptr();
float* exp_avg_sq_ptr = (float*)exp_avg_sq_c.data_ptr();
std::shared_ptr<Adam_Optimizer> opt =
std::static_pointer_cast<Adam_Optimizer>(s_optimizers[optimizer_id]);
opt->IncrementStep(step, beta1, beta2);
opt->update_state(lr, epsilon, weight_decay, bias_correction);
opt->Step_8(params_ptr,
grads_ptr,
exp_avg_ptr,
exp_avg_sq_ptr,
params_c.size(0),
gpu_params_ptr,
(params.options().dtype() == at::kHalf));
opt->SynchronizeStreams();
return 0;
}
int destroy_adam_optimizer(int optimizer_id)
{
s_optimizers.erase(optimizer_id);
return 0;
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
{
m.def("adam_update", &ds_adam_step, "DeepSpeed CPU Adam update (C++)");
m.def("adam_update_copy",
&ds_adam_step_plus_copy,
"DeepSpeed CPU Adam update and param copy (C++)");
m.def("create_adam", &create_adam_optimizer, "DeepSpeed CPU Adam (C++)");
m.def("destroy_adam", &destroy_adam_optimizer, "DeepSpeed CPU Adam destroy (C++)");
}
csrc/adam/custom_hip_kernel.hip deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#include "hip/hip_runtime.h"
#include "custom_hip_layers.h"
__global__ void param_update_kernel(const float* input, __half* output, int size)
{
int id = blockIdx.x * blockDim.x + threadIdx.x;
if (id < size) { output[id] = (__half)input[id]; }
}
void launch_param_update(const float* input, __half* output, int size, hipStream_t stream)
{
int threads = 1024;
dim3 grid_dim((size - 1) / threads + 1);
dim3 block_dim(threads);
hipLaunchKernelGGL(( param_update_kernel), dim3(grid_dim), dim3(block_dim), 0, stream, input, output, size);
}
csrc/adam/multi_tensor_adam.hip deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
/* Copyright 2020 The Microsoft DeepSpeed Team
Copyright NVIDIA/apex
This file is adapted from fused adam in NVIDIA/apex, commit a109f85
*/
#include <ATen/ATen.h>
#include <ATen/AccumulateType.h>
#include <ATen/hip/HIPContext.h>
#include <ATen/hip/Exceptions.h>
// Another possibility:
// #include <torch/all.h>
#include <assert.h>
#include "multi_tensor_apply_hip.cuh"
#include "type_shim_hip.h"
#define BLOCK_SIZE 512
#define ILP 4
typedef enum {
ADAM_MODE_0 = 0, // L2 regularization mode
ADAM_MODE_1 = 1 // Decoupled weight decay mode(AdamW)
} adamMode_t;
using MATH_T = float;
template <typename T>
struct AdamFunctor {
__device__ __forceinline__ void operator()(int chunk_size,
volatile int* noop_gmem,
TensorListMetadata<4>& tl,
const float beta1,
const float beta2,
const float beta1_correction,
const float beta2_correction,
const float epsilon,
const float lr,
adamMode_t mode,
const float decay)
{
// I'd like this kernel to propagate infs/nans.
// if(*noop_gmem == 1)
// return;
int tensor_loc = tl.block_to_tensor[blockIdx.x];
// potentially use to pass in list of scalar
// int tensor_num = tl.start_tensor_this_launch + tensor_loc;
int chunk_idx = tl.block_to_chunk[blockIdx.x];
int n = tl.sizes[tensor_loc];
T* g = (T*)tl.addresses[0][tensor_loc];
g += chunk_idx * chunk_size;
T* p = (T*)tl.addresses[1][tensor_loc];
p += chunk_idx * chunk_size;
T* m = (T*)tl.addresses[2][tensor_loc];
m += chunk_idx * chunk_size;
T* v = (T*)tl.addresses[3][tensor_loc];
v += chunk_idx * chunk_size;
n -= chunk_idx * chunk_size;
// see note in multi_tensor_scale_kernel.cu
for (int i_start = 0; i_start < n && i_start < chunk_size; i_start += blockDim.x * ILP) {
MATH_T r_g[ILP];
MATH_T r_p[ILP];
MATH_T r_m[ILP];
MATH_T r_v[ILP];
#pragma unroll
for (int ii = 0; ii < ILP; ii++) {
int i = i_start + threadIdx.x + ii * blockDim.x;
if (i < n && i < chunk_size) {
r_g[ii] = g[i];
r_p[ii] = p[i];
r_m[ii] = m[i];
r_v[ii] = v[i];
} else {
r_g[ii] = MATH_T(0);
r_p[ii] = MATH_T(0);
r_m[ii] = MATH_T(0);
r_v[ii] = MATH_T(0);
}
}
#pragma unroll
for (int ii = 0; ii < ILP; ii++) {
if (mode == ADAM_MODE_0) { // L2
r_g[ii] = r_g[ii] + (decay * r_p[ii]);
r_m[ii] = beta1 * r_m[ii] + (1 - beta1) * r_g[ii];
r_v[ii] = beta2 * r_v[ii] + (1 - beta2) * r_g[ii] * r_g[ii];
MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
MATH_T update = next_m_unbiased / denom;
r_p[ii] = r_p[ii] - (lr * update);
} else { // weight decay
r_m[ii] = beta1 * r_m[ii] + (1 - beta1) * r_g[ii];
r_v[ii] = beta2 * r_v[ii] + (1 - beta2) * r_g[ii] * r_g[ii];
MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
MATH_T update = (next_m_unbiased / denom) + (decay * r_p[ii]);
r_p[ii] = r_p[ii] - (lr * update);
}
}
#pragma unroll
for (int ii = 0; ii < ILP; ii++) {
int i = i_start + threadIdx.x + ii * blockDim.x;
if (i < n && i < chunk_size) {
p[i] = r_p[ii];
m[i] = r_m[ii];
v[i] = r_v[ii];
}
}
}
}
};
void multi_tensor_adam_cuda(int chunk_size,
at::Tensor noop_flag,
std::vector<std::vector<at::Tensor>> tensor_lists,
const float lr,
const float beta1,
const float beta2,
const float epsilon,
const int step,
const int mode,
const int bias_correction,
const float weight_decay)
{
using namespace at;
// Handle bias correction mode
float bias_correction1 = 1.0f, bias_correction2 = 1.0f;
if (bias_correction == 1) {
bias_correction1 = 1 - ::pow(beta1, step);
bias_correction2 = 1 - ::pow(beta2, step);
}
// Assume single type across p,g,m1,m2 now
DISPATCH_DOUBLE_FLOAT_AND_HALF(tensor_lists[0][0].scalar_type(),
0,
"adam",
multi_tensor_apply<4>(BLOCK_SIZE,
chunk_size,
noop_flag,
tensor_lists,
AdamFunctor<scalar_t_0>(),
beta1,
beta2,
bias_correction1,
bias_correction2,
epsilon,
lr,
(adamMode_t)mode,
weight_decay);)
AT_CUDA_CHECK(hipGetLastError());
}
csrc/adam/multi_tensor_apply_hip.cuh deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#include "hip/hip_runtime.h"
/* Copyright 2020 The Microsoft DeepSpeed Team
Copyright NVIDIA/apex
This file is adapted from fused adam in NVIDIA/apex, commit a109f85
*/
#include <ATen/ATen.h>
#include <ATen/AccumulateType.h>
#include <ATen/hip/HIPContext.h>
#include <ATen/hip/Exceptions.h>
#include <ATen/hip/impl/HIPGuardImplMasqueradingAsCUDA.h>
#include "compat.h"
#include <assert.h>
// #include <iostream>
// This header is the one-stop shop for all your multi-tensor apply needs.
// TODO: Kernel arg size limit may be <4KB for some other cards (ie Jetson)
constexpr int depth_to_max_tensors[5] = {110, 64, 48, 36, 30};
constexpr int depth_to_max_blocks[5] = {320, 320, 320, 320, 320};
template <int n>
struct TensorListMetadata {
void* addresses[n][depth_to_max_tensors[n - 1]];
int sizes[depth_to_max_tensors[n - 1]];
unsigned char block_to_tensor[depth_to_max_blocks[n - 1]];
int block_to_chunk[depth_to_max_blocks[n - 1]]; // I fear this needs to be a full int.
int start_tensor_this_launch;
};
template <typename T, typename U, typename... ArgTypes>
__global__ void multi_tensor_apply_kernel(int chunk_size,
volatile int* noop_flag,
T tl,
U callable,
ArgTypes... args)
{
// Hand the chunk information to the user-supplied functor to process however it likes.
callable(chunk_size, noop_flag, tl, args...);
}
template <int depth, typename T, typename... ArgTypes>
void multi_tensor_apply(int block_size,
int chunk_size,
const at::Tensor& noop_flag,
const std::vector<std::vector<at::Tensor>>& tensor_lists,
T callable,
ArgTypes... args)
{
TORCH_CHECK(tensor_lists.size() == depth, "tensor_lists.size() != depth");
int len0 = tensor_lists[0].size();
TORCH_CHECK(len0 > 0, "tensor_lists[0].size() is not > 0");
auto ref_device = tensor_lists[0][0].device();
TORCH_CHECK(ref_device.type() == at::kCUDA, "expected input to be on cuda");
for (int l = 0; l < tensor_lists.size(); l++) // No range-based for because I need indices
{
TORCH_CHECK(tensor_lists[l].size() == len0, "Size mismatch among tensor lists");
for (int t = 0; t < tensor_lists[l].size(); t++) {
// TODO: Print which tensor fails.
bool contiguous_memory = tensor_lists[l][t].is_contiguous();
#ifdef VERSION_GE_1_5
contiguous_memory = (contiguous_memory ||
tensor_lists[l][t].is_contiguous(at::MemoryFormat::ChannelsLast));
#endif
TORCH_CHECK(contiguous_memory, "A tensor was not contiguous.");
TORCH_CHECK(tensor_lists[l][t].device() == ref_device,
"A tensor was not on the same device as the first tensor");
TORCH_CHECK(tensor_lists[l][t].numel() == tensor_lists[0][t].numel(), "Size mismatch");
}
}
int ntensors = tensor_lists[0].size();
TensorListMetadata<depth> tl;
const at::hip::OptionalHIPGuardMasqueradingAsCUDA device_guard(device_of(tensor_lists[0][0]));
auto stream = at::hip::getCurrentHIPStreamMasqueradingAsCUDA();
tl.start_tensor_this_launch = 0;
int loc_block_info = 0;
int loc_tensor_info = 0;
for (int t = 0; t < ntensors; t++) {
tl.sizes[loc_tensor_info] = tensor_lists[0][t].numel();
for (int d = 0; d < depth; d++)
tl.addresses[d][loc_tensor_info] = tensor_lists[d][t].data_ptr();
loc_tensor_info++;
int chunks_this_tensor = (tensor_lists[0][t].numel() + chunk_size - 1) / chunk_size;
for (int chunk = 0; chunk < chunks_this_tensor; chunk++) {
// std::cout << chunks_this_tensor << std::endl;
tl.block_to_tensor[loc_block_info] = loc_tensor_info - 1;
tl.block_to_chunk[loc_block_info] = chunk;
loc_block_info++;
bool tensors_full = (loc_tensor_info == depth_to_max_tensors[depth - 1] &&
chunk == chunks_this_tensor - 1);
bool blocks_full = (loc_block_info == depth_to_max_blocks[depth - 1]);
bool last_chunk = (t == ntensors - 1 && chunk == chunks_this_tensor - 1);
if (tensors_full || blocks_full || last_chunk) {
// using accscalar_t = acc_type<scalar_t, true>;
hipLaunchKernelGGL(( multi_tensor_apply_kernel), dim3(loc_block_info), dim3(block_size), 0, stream,
chunk_size, noop_flag.DATA_PTR<int>(), tl, callable, args...);
AT_CUDA_CHECK(hipGetLastError());
// Reset. The control flow possibilities here make my brain hurt.
loc_block_info = 0;
if (chunk == chunks_this_tensor - 1) {
// std::cout << "Hit case 1 " << cond1 << " " << cond2 << " " << cond3 <<
// std::endl;
loc_tensor_info = 0;
tl.start_tensor_this_launch = t + 1;
} else {
// std::cout << "Hit case 2 " << cond1 << " " << cond2 << " " << cond3 <<
// std::endl;
tl.sizes[0] = tl.sizes[loc_tensor_info - 1];
for (int d = 0; d < depth; d++)
tl.addresses[d][0] = tl.addresses[d][loc_tensor_info - 1];
loc_tensor_info = 1;
tl.start_tensor_this_launch = t;
}
}
}
}
}
csrc/common/custom_hip_kernel.hip deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#include "hip/hip_runtime.h"
#include "custom_hip_layers.h"
__global__ void param_update_kernel(const float* input, __half* output, int size)
{
int id = blockIdx.x * blockDim.x + threadIdx.x;
if (id < size) { output[id] = (__half)input[id]; }
}
void launch_param_update(const float* input, __half* output, int size, hipStream_t stream)
{
int threads = 1024;
dim3 grid_dim((size - 1) / threads + 1);
dim3 block_dim(threads);
hipLaunchKernelGGL(( param_update_kernel), dim3(grid_dim), dim3(block_dim), 0, stream, input, output, size);
}
__global__ void param_update_kernel_half(const float* input, __half* output, int size)
{
int id = blockIdx.x * blockDim.x + threadIdx.x;
__half2* output_cast = reinterpret_cast<__half2*>(output);
if (id < size) {
float input_f = input[id];
__half2* input_h = reinterpret_cast<__half2*>(&input_f);
output_cast[id] = *input_h;
}
}
void launch_param_update_half(const float* input, __half* output, int size, hipStream_t stream)
{
int threads = 1024;
size /= 2;
dim3 grid_dim((size - 1) / threads + 1);
dim3 block_dim(threads);
hipLaunchKernelGGL(( param_update_kernel_half), dim3(grid_dim), dim3(block_dim), 0, stream, input, output, size);
}
csrc/includes/Timer_hip.h deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#ifndef __TIMER_H__
#define __TIMER_H__
#include <hip/hip_runtime.h>
#include <chrono>
#include "hip/hip_runtime.h"
class GPUTimer {
hipEvent_t start, stop;
public:
GPUTimer()
{
hipEventCreate(&start);
hipEventCreate(&stop);
}
~GPUTimer()
{
hipEventDestroy(start);
hipEventDestroy(stop);
}
inline void Record() { hipEventRecord(start); }
inline void Elapsed(float& time_elapsed)
{
hipEventRecord(stop);
hipEventSynchronize(stop);
hipEventElapsedTime(&time_elapsed, start, stop);
}
};
class CPUTimer {
std::chrono::high_resolution_clock::time_point start;
public:
CPUTimer() : start(std::chrono::high_resolution_clock::now()) {}
inline void Reset() { start = std::chrono::high_resolution_clock::now(); }
inline float Elapsed()
{
auto temp = start;
start = std::chrono::high_resolution_clock::now();
return (float)(std::chrono::duration_cast<std::chrono::microseconds>(start - temp).count() /
1e3);
}
};
#endif
csrc/includes/context_hip.h deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#pragma once
#include <ATen/hip/HIPContext.h>
#include <hip/hip_runtime_api.h>
#include <cassert>
#include <iostream>
#include <vector>
#include "rocblas.h"
#include "hip/hip_runtime.h"
#include "hiprand/hiprand.h"
#include "gemm_test_hip.h"
#define WARP_SIZE 32
#define CUDA_CHECK(callstr) \
{ \
hipError_t error_code = callstr; \
if (error_code != hipSuccess) { \
std::cerr << "CUDA error " << error_code << " at " << __FILE__ << ":" << __LINE__; \
assert(0); \
} \
}
#define CUDA_1D_KERNEL_LOOP(i, n) \
for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); i += blockDim.x * gridDim.x)
#define CUDA_2D_KERNEL_LOOP(i, n, j, m) \
for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); i += blockDim.x * gridDim.x) \
for (size_t j = blockIdx.y * blockDim.y + threadIdx.y; j < (m); j += blockDim.y * gridDim.y)
#define DS_CUDA_NUM_THREADS 512
#define DS_MAXIMUM_NUM_BLOCKS 262144
inline int DS_GET_BLOCKS(const int N)
{
return (std::max)(
(std::min)((N + DS_CUDA_NUM_THREADS - 1) / DS_CUDA_NUM_THREADS, DS_MAXIMUM_NUM_BLOCKS),
// Use at least 1 block, since CUDA does not allow empty block
1);
}
class Context {
public:
Context() : _workspace(nullptr), _seed(42), _curr_offset(0)
{
hiprandCreateGenerator(&_gen, HIPRAND_RNG_PSEUDO_DEFAULT);
hiprandSetPseudoRandomGeneratorSeed(_gen, 123);
if (rocblas_create_handle(&_cublasHandle) != rocblas_status_success) {
auto message = std::string("Fail to create cublas handle.");
std::cerr << message << std::endl;
throw std::runtime_error(message);
}
}
virtual ~Context()
{
rocblas_destroy_handle(_cublasHandle);
hipFree(_workspace);
}
static Context& Instance()
{
static Context _ctx;
return _ctx;
}
void SetWorkSpace(void* workspace)
{
if (!workspace) { throw std::runtime_error("Workspace is null."); }
_workspace = workspace;
}
void* GetWorkSpace() { return _workspace; }
hiprandGenerator_t& GetRandGenerator() { return _gen; }
hipStream_t GetCurrentStream()
{
// get current pytorch stream.
hipStream_t stream = at::hip::getCurrentHIPStreamMasqueradingAsCUDA();
return stream;
}
hipStream_t GetNewStream() { return at::hip::getStreamFromPoolMasqueradingAsCUDA(); }
rocblas_handle GetCublasHandle() { return _cublasHandle; }
std::pair<uint64_t, uint64_t> IncrementOffset(uint64_t offset_inc)
{
uint64_t offset = _curr_offset;
_curr_offset += offset_inc;
return std::pair<uint64_t, uint64_t>(_seed, offset);
}
void SetSeed(uint64_t new_seed) { _seed = new_seed; }
void TestGemmFP16(bool test_gemm, int batch_size, int seq_len, int head_num, int size_per_head)
{
// avoid rerun.
if (_gemm_algos.size() > 0) return;
if (test_gemm) {
rocblas_handle handle = GetCublasHandle();
std::unique_ptr<GemmTest<__half>> test_qkv_fw(
new GemmTest<__half>(batch_size * seq_len, // M
head_num * size_per_head, // N
head_num * size_per_head, // K
rocblas_operation_transpose,
rocblas_operation_none,
handle));
std::unique_ptr<GemmTest<__half>> test_inter(
new GemmTest<__half>(batch_size * seq_len, // M
4 * head_num * size_per_head, // N
head_num * size_per_head, // K
rocblas_operation_transpose,
rocblas_operation_none,
handle));
std::unique_ptr<GemmTest<__half>> test_output(
new GemmTest<__half>(batch_size * seq_len, // M
head_num * size_per_head, // N
4 * head_num * size_per_head, // K
rocblas_operation_transpose,
rocblas_operation_none,
handle));
std::unique_ptr<StridedGemmTest<__half>> test_attn_scores(
new StridedGemmTest<__half>(batch_size * head_num, // batch
seq_len, // M
seq_len, // N
size_per_head, // K
rocblas_operation_transpose,
rocblas_operation_none,
handle));
std::unique_ptr<StridedGemmTest<__half>> test_attn_context(
new StridedGemmTest<__half>(batch_size * head_num, // batch
size_per_head, // M
seq_len, // N
seq_len, // K
rocblas_operation_none,
rocblas_operation_none,
handle));
_gemm_algos.push_back(test_qkv_fw->TestAlgo(100));
_gemm_algos.push_back(test_inter->TestAlgo(100));
_gemm_algos.push_back(test_output->TestAlgo(100));
_gemm_algos.push_back(test_attn_scores->TestAlgo(100));
_gemm_algos.push_back(test_attn_context->TestAlgo(100));
} else {
// Use default algo.
_gemm_algos.push_back(std::array<int, 3>({99, 99, 99}));
_gemm_algos.push_back(std::array<int, 3>({99, 99, 99}));
_gemm_algos.push_back(std::array<int, 3>({99, 99, 99}));
_gemm_algos.push_back(std::array<int, 3>({99, 99, 99}));
_gemm_algos.push_back(std::array<int, 3>({99, 99, 99}));
}
}
const std::vector<std::array<int, 3>>& GetGemmAlgos() const { return _gemm_algos; }
private:
hiprandGenerator_t _gen;
rocblas_handle _cublasHandle;
void* _workspace;
uint64_t _seed;
uint64_t _curr_offset;
std::vector<std::array<int, 3>> _gemm_algos;
};
csrc/includes/cpu_adagrad_hip.h deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#pragma once
#define NOMINMAX // Windows idiosyncrasy
// https://stackoverflow.com/questions/4913922/possible-problems-with-nominmax-on-visual-c
#include <hip/hip_fp16.h>
#include <hip/hip_runtime_api.h>
#include <stdio.h>
#include <cassert>
#include "hip/hip_runtime.h"
#include "custom_hip_layers.h"
#include "simd.h"
#define STEP(SPAN) \
void Step_##SPAN(float* _params, \
float* grads, \
float* _exp_avg_sq, \
size_t _param_size, \
__half* dev_param = nullptr, \
bool half_precision = false);
class Adagrad_Optimizer {
public:
Adagrad_Optimizer(float alpha = 1e-2, float eps = 1e-8, float weight_decay = 0)
: _alpha(alpha), _eps(eps), _weight_decay(weight_decay), _buf_index(false)
{
hipHostMalloc((void**)_doubled_buffer, TILE * sizeof(float));
hipHostMalloc((void**)(_doubled_buffer + 1), TILE * sizeof(float));
_streams[0] = Context::Instance().GetCurrentStream();
_streams[1] = Context::Instance().GetNewStream();
}
~Adagrad_Optimizer()
{
hipHostFree(_doubled_buffer[0]);
hipHostFree(_doubled_buffer[1]);
}
#if defined(__AVX512__) or defined(__AVX256__)
template <int span>
void Step_AVX(size_t* rounded_size,
float* _params,
float* grads,
float* _exp_avg_sq,
size_t param_size,
__half* dev_param = nullptr,
bool half_precision = false);
#endif
STEP(1)
STEP(4)
STEP(8)
inline void SynchronizeStreams()
{
for (int i = 0; i < 2; i++) hipStreamSynchronize(_streams[i]);
}
inline void IncrementStep(size_t step)
{
_step++;
if (_step != step) { _step = step; }
}
inline void update_state(float lr, float epsilon, float weight_decay)
{
_alpha = lr;
_eps = epsilon;
_weight_decay = weight_decay;
}
private:
float _alpha;
float _eps;
float _weight_decay;
float _betta1_t;
float _betta2_t;
size_t _step;
float* _doubled_buffer[2];
bool _buf_index;
hipStream_t _streams[2];
};
#if defined(__AVX512__) or defined(__AVX256__)
template <int span>
void Adagrad_Optimizer::Step_AVX(size_t* rounded_size,
float* _params,
float* grads,
float* _exp_avg_sq,
size_t _param_size,
__half* dev_params,
bool half_precision)
{
size_t new_rounded_size = 0;
AVX_Data eps_4;
eps_4.data = SIMD_SET(_eps);
float step_size = -1 * _alpha;
AVX_Data step_size_4;
step_size_4.data = SIMD_SET(step_size);
AVX_Data weight_decay4;
if (_weight_decay > 0) weight_decay4.data = SIMD_SET(_weight_decay);
new_rounded_size = ROUND_DOWN(_param_size, SIMD_WIDTH * span);
for (size_t t = 0; t < new_rounded_size; t += TILE) {
size_t copy_size = TILE;
if ((t + TILE) > new_rounded_size) copy_size = new_rounded_size - t;
size_t offset = copy_size + t;
if ((t / TILE) >= 2) { hipStreamSynchronize(_streams[_buf_index]); }
#pragma omp parallel for
for (size_t i = t; i < offset; i += SIMD_WIDTH * span) {
AVX_Data grad_4[span];
simd_load<span>(grad_4, grads + i, half_precision);
AVX_Data momentum_4[span];
simd_load<span>(momentum_4, grads + i, false);
AVX_Data variance_4[span];
simd_load<span>(variance_4, _exp_avg_sq + i, false);
AVX_Data param_4[span];
simd_load<span>(param_4, _params + i, half_precision);
if (_weight_decay > 0) { simd_fma<span>(grad_4, param_4, weight_decay4, grad_4); }
simd_fma<span>(variance_4, grad_4, grad_4, variance_4);
simd_sqrt<span>(grad_4, variance_4);
simd_add<span>(grad_4, grad_4, eps_4);
simd_div<span>(grad_4, momentum_4, grad_4);
simd_fma<span>(param_4, grad_4, step_size_4, param_4);
simd_store<span>(_params + i, param_4, half_precision);
if (dev_params) {
simd_store<span>(_doubled_buffer[_buf_index] + (i - t), param_4, half_precision);
}
simd_store<span>(_exp_avg_sq + i, variance_4, false);
}
if (dev_params) {
if (half_precision)
launch_param_update_half(
_doubled_buffer[_buf_index], dev_params + t, copy_size, _streams[_buf_index]);
else
launch_param_update(
_doubled_buffer[_buf_index], dev_params + t, copy_size, _streams[_buf_index]);
_buf_index = !_buf_index;
}
}
*rounded_size = new_rounded_size;
}
#endif
csrc/includes/cpu_adam_hip.h deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#pragma once
#define NOMINMAX // Windows idiosyncrasy
// https://stackoverflow.com/questions/4913922/possible-problems-with-nominmax-on-visual-c
#include <hip/hip_fp16.h>
#include <hip/hip_runtime_api.h>
#include <stdio.h>
#include <cassert>
#include "hip/hip_runtime.h"
#include "custom_hip_layers.h"
#include "simd.h"
#define STEP(SPAN) \
void Step_##SPAN(float* _params, \
float* grads, \
float* _exp_avg, \
float* _exp_avg_sq, \
size_t _param_size, \
__half* dev_param = nullptr, \
bool half_precision = false);
class Adam_Optimizer {
public:
Adam_Optimizer(float alpha = 1e-3,
float betta1 = 0.9,
float betta2 = 0.999,
float eps = 1e-8,
float weight_decay = 0,
bool adamw_mode = true)
: _alpha(alpha),
_betta1(betta1),
_betta2(betta2),
_eps(eps),
_weight_decay(weight_decay),
_betta1_t(1.0),
_betta2_t(1.0),
_step(0),
_buf_index(false),
_adamw_mode(adamw_mode)
{
hipHostMalloc((void**)_doubled_buffer, TILE * sizeof(float));
hipHostMalloc((void**)(_doubled_buffer + 1), TILE * sizeof(float));
_streams[0] = Context::Instance().GetCurrentStream();
_streams[1] = Context::Instance().GetNewStream();
}
~Adam_Optimizer()
{
hipHostFree(_doubled_buffer[0]);
hipHostFree(_doubled_buffer[1]);
}
#if defined(__AVX512__) or defined(__AVX256__)
template <int span>
void Step_AVX(size_t* rounded_size,
float* _params,
float* grads,
float* _exp_avg,
float* _exp_avg_sq,
size_t param_size,
__half* dev_param = nullptr,
bool half_precision = false);
#endif
STEP(1)
STEP(4)
STEP(8)
inline void SynchronizeStreams()
{
for (int i = 0; i < 2; i++) hipStreamSynchronize(_streams[i]);
}
inline void IncrementStep(size_t step, float beta1, float beta2)
{
if (beta1 != _betta1 || beta2 != _betta2) {
_step = step;
_betta1 = beta1;
_betta2 = beta2;
_betta1_t = std::pow(_betta1, step);
_betta2_t = std::pow(_betta2, step);
} else {
_step++;
if (_step != step) {
_betta1_t = std::pow(_betta1, step);
_betta2_t = std::pow(_betta2, step);
_step = step;
} else {
_betta1_t *= _betta1;
_betta2_t *= _betta2;
}
}
}
inline void update_state(float lr, float epsilon, float weight_decay, bool bias_correction)
{
_alpha = lr;
_eps = epsilon;
_weight_decay = weight_decay;
_bias_correction1 = 1.0f;
_bias_correction2 = 1.0f;
if (bias_correction == 1) {
_bias_correction1 = 1 - _betta1_t;
_bias_correction2 = 1 / sqrt(1 - _betta2_t);
}
}
private:
float _alpha;
float _betta1;
float _betta2;
float _eps;
float _weight_decay;
float _betta1_t;
float _betta2_t;
size_t _step;
float _bias_correction1;
float _bias_correction2;
float* _doubled_buffer[2];
bool _buf_index;
bool _adamw_mode;
hipStream_t _streams[2];
};
#if defined(__AVX512__) or defined(__AVX256__)
template <int span>
void Adam_Optimizer::Step_AVX(size_t* rounded_size,
float* _params,
float* grads,
float* _exp_avg,
float* _exp_avg_sq,
size_t _param_size,
__half* dev_params,
bool half_precision)
{
size_t new_rounded_size = 0;
AVX_Data betta1_4;
betta1_4.data = SIMD_SET(_betta1);
AVX_Data betta2_4;
betta2_4.data = SIMD_SET(_betta2);
float betta1_minus1 = 1 - _betta1;
float betta2_minus1 = 1 - _betta2;
AVX_Data betta1_minus1_4;
betta1_minus1_4.data = SIMD_SET(betta1_minus1);
AVX_Data betta2_minus1_4;
betta2_minus1_4.data = SIMD_SET(betta2_minus1);
AVX_Data bias2_sqrt;
bias2_sqrt.data = SIMD_SET(_bias_correction2);
AVX_Data eps_4;
eps_4.data = SIMD_SET(_eps);
float step_size = -1 * _alpha / _bias_correction1;
AVX_Data step_size_4;
step_size_4.data = SIMD_SET(step_size);
float w_decay = -1 * _alpha * _weight_decay;
AVX_Data weight_decay4;
if (_weight_decay > 0)
weight_decay4.data = (_adamw_mode ? SIMD_SET(w_decay) : SIMD_SET(_weight_decay));
new_rounded_size = ROUND_DOWN(_param_size, SIMD_WIDTH * span);
for (size_t t = 0; t < new_rounded_size; t += TILE) {
size_t copy_size = TILE;
if ((t + TILE) > new_rounded_size) copy_size = new_rounded_size - t;
size_t offset = copy_size + t;
if ((t / TILE) >= 2) { hipStreamSynchronize(_streams[_buf_index]); }
#pragma omp parallel for
for (size_t i = t; i < offset; i += SIMD_WIDTH * span) {
AVX_Data grad_4[span];
simd_load<span>(grad_4, grads + i, half_precision);
AVX_Data momentum_4[span];
simd_load<span>(momentum_4, _exp_avg + i, false);
AVX_Data variance_4[span];
simd_load<span>(variance_4, _exp_avg_sq + i, false);
AVX_Data param_4[span];
simd_load<span>(param_4, _params + i, half_precision);
if (_weight_decay > 0 && !_adamw_mode) {
simd_fma<span>(grad_4, param_4, weight_decay4, grad_4);
}
simd_mul<span>(momentum_4, momentum_4, betta1_4);
simd_fma<span>(momentum_4, grad_4, betta1_minus1_4, momentum_4);
simd_mul<span>(variance_4, variance_4, betta2_4);
simd_mul<span>(grad_4, grad_4, grad_4);
simd_fma<span>(variance_4, grad_4, betta2_minus1_4, variance_4);
simd_sqrt<span>(grad_4, variance_4);
simd_fma<span>(grad_4, grad_4, bias2_sqrt, eps_4);
simd_div<span>(grad_4, momentum_4, grad_4);
if (_weight_decay > 0 && _adamw_mode) {
simd_fma<span>(param_4, param_4, weight_decay4, param_4);
}
simd_fma<span>(param_4, grad_4, step_size_4, param_4);
simd_store<span>(_params + i, param_4, half_precision);
if (dev_params) {
simd_store<span>(_doubled_buffer[_buf_index] + (i - t), param_4, half_precision);
}
simd_store<span>(_exp_avg + i, momentum_4, false);
simd_store<span>(_exp_avg_sq + i, variance_4, false);
}
if (dev_params) {
if (half_precision)
launch_param_update_half(
_doubled_buffer[_buf_index], dev_params + t, copy_size, _streams[_buf_index]);
else
launch_param_update(
_doubled_buffer[_buf_index], dev_params + t, copy_size, _streams[_buf_index]);
_buf_index = !_buf_index;
}
}
*rounded_size = new_rounded_size;
}
#endif
csrc/includes/cublas_wrappers_hip.h deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#pragma once
#include <assert.h>
#include <rocblas.h>
#include <hip/hip_runtime.h>
#include <hip/hip_fp16.h>
#include <hip/hip_runtime.h>
#ifndef __HIP_PLATFORM_HCC__
#include <mma.h>
#endif
#include <stdio.h>
int cublas_gemm_ex(rocblas_handle handle,
rocblas_operation transa,
rocblas_operation transb,
int m,
int n,
int k,
const float* alpha,
const float* beta,
const float* A,
const float* B,
float* C,
#ifdef __HIP_PLATFORM_HCC__
rocblas_gemm_algo algo = rocblas_gemm_algo_standard);
#else
cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT);
#endif
int cublas_gemm_ex(rocblas_handle handle,
rocblas_operation transa,
rocblas_operation transb,
int m,
int n,
int k,
const float* alpha,
const float* beta,
const __half* A,
const __half* B,
__half* C,
#ifdef __HIP_PLATFORM_HCC__
rocblas_gemm_algo algo = rocblas_gemm_algo_standard);
#else
cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT_TENSOR_OP);
#endif
int cublas_strided_batched_gemm(rocblas_handle handle,
int m,
int n,
int k,
const float* alpha,
const float* beta,
const float* A,
const float* B,
float* C,
rocblas_operation op_A,
rocblas_operation op_B,
int stride_A,
int stride_B,
int stride_C,
int batch,
#ifdef __HIP_PLATFORM_HCC__
rocblas_gemm_algo algo = rocblas_gemm_algo_standard);
#else
cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT);
#endif
int cublas_strided_batched_gemm(rocblas_handle handle,
int m,
int n,
int k,
const float* alpha,
const float* beta,
const __half* A,
const __half* B,
__half* C,
rocblas_operation op_A,
rocblas_operation op_B,
int stride_A,
int stride_B,
int stride_C,
int batch,
#ifdef __HIP_PLATFORM_HCC__
rocblas_gemm_algo algo = rocblas_gemm_algo_standard);
#else
cublasGemmAlgo_t algo = CUBLAS_GEMM_DEFAULT_TENSOR_OP);
#endif
csrc/includes/custom_hip_layers.h deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#pragma once
#include <hip/hip_runtime.h>
#include <hip/hip_fp16.h>
#include <stdio.h>
#include <stdlib.h>
#ifdef __HIP_PLATFORM_HCC__
#define HALF_PRECISION_AVAILABLE = 1
#include <hip/hip_cooperative_groups.h>
#else
#if __CUDA_ARCH__ >= 700
#define HALF_PRECISION_AVAILABLE = 1
#endif
#include <cooperative_groups.h>
#endif
#include <hiprand/hiprand_kernel.h>
#include "context_hip.h"
#include "cublas_wrappers_hip.h"
#define CUDA_CHECK(callstr) \
{ \
hipError_t error_code = callstr; \
if (error_code != hipSuccess) { \
std::cerr << "CUDA error " << error_code << " at " << __FILE__ << ":" << __LINE__; \
assert(0); \
} \
}
#define MAX_THREADS 1024
#define THREADS 256
#define MAX_THREAD_STRIDE 32
#define TILE_DIM 32
// Maximum sequence-length support based on the number of threads (2048) allowed in each block and
// this MAX is 8K For higher sequence length we need to use higher Max, like for 64K : 32
#define MAX_THREAD_ITERATIONS 8 // Maximum 8K
#define MAX_WARP_NUM 32
#define MAX_REGISTERS 256
#define MAX_REG 256
#define WARP_SIZE_BITS 5
template <typename T>
void launch_quantize_kernel(T* vals,
int total_count,
int group_num,
int num_bits,
hipStream_t stream);
template <typename T>
void launch_sr_quantize_kernel(T* vals,
int total_count,
int group_num,
int num_bits,
hipStream_t stream);
template <typename T>
void launch_quantize_kernel_asym(T* vals,
int total_count,
int group_num,
int num_bits,
hipStream_t stream);
template <typename T>
void launch_sr_quantize_kernel_asym(T* vals,
int total_count,
int group_num,
int num_bits,
hipStream_t stream);
// Fused bias add with gelu activation
template <typename T>
void launch_bias_gelu(const T* input,
const T* bias,
T* output,
int intermediate_size,
int batch_size,
hipStream_t stream);
template <typename T>
void launch_gelu(const T* input,
T* output,
int intermediate_size,
int batch_size,
hipStream_t stream);
template <typename T>
void launch_d_gelu(T* d_output,
const T* input,
const T* bias,
int intermediate_size,
int batch_size,
hipStream_t stream);
// Custom fused bias add with layer normalization
template <typename T>
void launch_bias_residual_layer_norm(T* vals,
const T* residual,
const T* gamma,
const T* beta,
float epsilon,
int batch_size,
int hidden_dim,
hipStream_t stream,
bool preLayerNorm,
bool training,
T* vars,
T* means);
template <typename T>
void launch_bias_residual_layer_norm(T* vals,
const T* residual,
const T* gamma,
const T* beta,
float epsilon,
int batch_size,
int hidden_dim,
hipStream_t stream,
bool preLayerNorm,
bool training,
T* vars);
template <typename T>
void launch_layerNorm_backward_fused_add(const T* out_grad1,
const T* out_grad2,
const T* X_data,
const T* vars,
const T* means,
const T* gamma,
T* gamma_grad,
T* betta_grad,
T* inp_grad,
int batch_size,
int hidden_dim,
hipStream_t stream[2]);
template <typename T>
void launch_layerNorm_backward_fused_add(const T* out_grad1,
const T* out_grad2,
const T* vals_hat,
const T* vars,
const T* gamma,
T* gamma_grad,
T* betta_grad,
T* inp_grad,
int batch_size,
int hidden_dim,
hipStream_t stream[2],
bool invertible = false,
const T* betta = nullptr);
template <typename T>
void launch_layerNorm_backward(const T* out_grad,
const T* X_data,
const T* vars,
const T* means,
const T* gamma,
T* gamma_grad,
T* betta_grad,
T* inp_grad,
int batch_size,
int hidden_dim,
hipStream_t stream[2]);
template <typename T>
void launch_layerNorm_backward(const T* out_grad,
const T* vals_hat,
const T* vars,
const T* gamma,
T* gamma_grad,
T* betta_grad,
T* inp_grad,
int batch_size,
int hidden_dim,
hipStream_t stream[2],
bool invertible = false,
const T* betta = nullptr);
template <typename T>
void launch_layerNorm_backward_nreversible(const T* out_grad,
const T* vals,
const T* out_grad_trans,
const T* vals_trans,
const T* means,
const T* vars,
const T* gamma,
T* gamma_grad,
T* betta_grad,
T* inp_grad,
int batch_size,
int hidden_dim,
hipStream_t stream[2]);
template <typename T>
void Transpose(const T* inp_mat, T* out_mat, int rows, int cols, hipStream_t stream);
template <typename T>
void launch_attn_softmax_backward(T* out_grad,
const T* soft_inp,
int batch_size,
int heads,
int seq_length,
hipStream_t stream);
template <typename T>
void launch_attn_softmax_backward_v2(T* out_grad,
const T* soft_inp,
int batch_size,
int heads,
int seq_length,
hipStream_t stream);
// Custom softmax with scaling and attention mask addition
template <typename T>
void launch_attn_softmax(T* vals,
const T* attn_mask,
int batch_size,
int heads,
int sequence_length,
hipStream_t stream);
template <typename T>
void launch_transform_0213(T* output,
const T* vals,
int batch_size,
int seq_length,
int hidden_dim,
int heads,
hipStream_t stream);
// Custom bias add
template <typename T>
void launch_bias_add_transform_0213(T* outputs,
const T* vals,
const T* bias,
int batch_size,
int seq_length,
int hidden_dim,
int heads,
hipStream_t stream,
int trans_count);
// 4D transform [0, 1, 2, 3] -> [0, 2, 1, 3]
template <typename T>
void launch_transform4d_0213(T* out,
const T* in,
int batch_size,
int heads,
int seq_length,
int hidden_dim,
hipStream_t stream,
int trans_count);
template <typename T>
void launch_dropout(T* vals,
const T* bias,
uint8_t* mask,
int batch,
int dim,
float ratio,
hipStream_t stream);
template <typename T>
void launch_dropout(T* vals_out,
const T* vals,
uint8_t* mask,
int total_count,
int dim,
float ratio,
hipStream_t stream,
bool bwd = false);
template <typename T>
void launch_dropout(T* out,
const T* vals,
const T* residual,
const T* bias,
uint8_t* mask,
int batch,
int dim,
float ratio,
hipStream_t stream);
template <typename T>
void launch_dropout_grad(T* vals, uint8_t* mask, int total_count, float ratio, hipStream_t stream);
template <typename T>
void launch_dropout_grad(T* vals_out,
const T* vals,
uint8_t* mask,
int total_count,
float ratio,
hipStream_t stream);
template <typename T>
void launch_fuse_transpose_bias_kernel(const T* inp,
T* out,
int rows,
int cols,
hipStream_t stream);
void launch_param_update(const float* input, __half* output, int size, hipStream_t stream);
void launch_param_update_half(const float* input, __half* output, int size, hipStream_t stream);
csrc/includes/dropout_hip.h deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#pragma once
#include <hip/hip_runtime.h>
#include <hip/hip_fp16.h>
#include <stdio.h>
template <typename T>
class Dropout {
public:
struct Config {
float ratio;
uint32_t dim;
bool training;
Config(float r, uint32_t d) : ratio(r), dim(d), training(true) {}
float RATIO() const { return training ? ratio : 0.0; }
inline void SetDim(uint32_t d) { dim = d; }
};
Dropout(const Config& config) : _config(config), _mask(nullptr) {}
virtual ~Dropout() {}
void Forward(int bsz, T* out, const T* vals, hipStream_t stream, bool bwd = false)
{
launch_dropout<T>(
out, vals, _mask, bsz * _config.dim, _config.dim, _config.RATIO(), stream, bwd);
}
void ForwardWithBias(int bsz, T* vals, const T* bias, hipStream_t stream)
{
launch_dropout<T>(vals, bias, _mask, bsz, _config.dim, _config.RATIO(), stream);
}
void ForwardWithBias(int bsz,
T* out,
const T* vals,
const T* residual,
const T* bias,
hipStream_t stream)
{
launch_dropout<T>(
out, vals, residual, bias, _mask, bsz, _config.dim, _config.RATIO(), stream);
}
void Backward(int bsz, T* d_vals, hipStream_t stream)
{
launch_dropout_grad<T>(d_vals, _mask, bsz * _config.dim, _config.RATIO(), stream);
}
void Backward(int bsz, T* d_vals_out, const T* d_vals, hipStream_t stream)
{
launch_dropout_grad<T>(
d_vals_out, d_vals, _mask, bsz * _config.dim, _config.RATIO(), stream);
}
bool HasDropout() const { return _config.RATIO() > 0.0; }
void SetTrainingMode(bool training) { _config.training = training; }
void SetMask(uint8_t* mask)
{
if (!mask) { throw std::runtime_error("Dropout mask is null."); }
_mask = mask;
}
Config GetConfig() const { return _config; }
inline void SetDimension(uint32_t dim) { _config.SetDim(dim); }
private:
uint8_t* _mask;
Config _config;
};
csrc/includes/ds_transformer_hip.h deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#pragma once
#include <hip/hip_runtime_api.h>
#include <hiprand/hiprand.h>
#include <memory>
#include <vector>
#include "rocblas.h"
#include "hip/hip_runtime.h"
#include "dropout_hip.h"
#include "feed_forward_hip.h"
#include "gelu_hip.h"
#include "general_kernels_hip.h"
#include "normalize_layer_hip.h"
#include "softmax_hip.h"
#include "strided_batch_gemm_hip.h"
struct BertGemmAlgos {
int m_gemm_qkv_algo;
int m_gemm_inter_algo;
int m_gemm_output_algo;
int m_gemm_batch1_algo;
int m_gemm_batch2_algo;
BertGemmAlgos()
: m_gemm_qkv_algo(-1),
m_gemm_inter_algo(-1),
m_gemm_output_algo(-1),
m_gemm_batch1_algo(-1),
m_gemm_batch2_algo(-1)
{
}
};
template <typename T>
class BertTransformerLayer {
public:
BertTransformerLayer(unsigned layer_id,
unsigned batch_size,
unsigned hidden_size,
unsigned num_heads,
unsigned intermediate_size,
unsigned seq_length,
float attn_dropout_ratio,
float hidden_output_dropout_ratio,
float layer_norm_eps,
bool pre_or_postLayerNorm,
const std::vector<std::array<int, 3>>& gemm_algos,
bool attn_dropout_checkpoint,
bool normalize_invertible,
bool gelu_checkpoint,
bool stochastic_mode);
virtual ~BertTransformerLayer();
void Forward(unsigned bsz,
const T* input_ptr,
const T* input_mask_ptr,
const T* attn_qkvw_ptr,
const T* attn_qkvb_ptr,
const T* attn_ow_ptr,
const T* attn_ob_ptr,
const T* attn_nw_ptr,
const T* attn_nb_ptr,
const T* inter_w_ptr,
const T* inter_b_ptr,
const T* output_w_ptr,
const T* output_b_ptr,
const T* norm_w_ptr,
const T* norm_b_ptr,
T* out_ptr,
T* inp_norm_ptr,
T* q_tf_ptr,
T* k_tf_ptr,
T* v_tf_ptr,
T* softmax_output_ptr,
T* ctx_bufB_ptr,
T* attn_o_inp_ptr,
T* add_res_ptr,
T* ff1_inp_ptr,
T* gelu_inp_ptr,
T* ff2_inp_ptr);
void Backward(unsigned bsz,
const T* grad_output_ptr,
const T* input_ptr,
const T* output_ptr,
const T* inp_norm_ptr,
const T* q_tf_ptr,
const T* k_tf_ptr,
const T* v_tf_ptr,
const T* softmax_output_ptr,
const T* ctx_bufB_ptr,
const T* attn_o_inp_ptr,
const T* add_res_ptr,
const T* ff1_inp_ptr,
const T* gelu_inp_ptr,
const T* ff2_inp_ptr,
const T* input_mask_ptr,
const T* attn_qkvw_ptr,
const T* attn_ow_ptr,
const T* attn_nw_ptr,
const T* attn_nb_ptr,
const T* inter_w_ptr,
const T* inter_b_ptr,
const T* output_w_ptr,
const T* norm_w_ptr,
const T* norm_b_ptr,
T* grad_input_ptr,
T* grad_attn_qkvw_ptr,
T* grad_attn_qkvb_ptr,
T* grad_attn_ow_ptr,
T* grad_attn_ob_ptr,
T* grad_attn_nw_ptr,
T* grad_attn_nb_ptr,
T* grad_inter_w_ptr,
T* grad_inter_b_ptr,
T* grad_output_w_ptr,
T* grad_output_b_ptr,
T* grad_norm_w_ptr,
T* grad_norm_b_ptr);
void SetIntermediateBuffers(uint8_t* attn_prob_dropout_mask_ptr,
uint8_t* attn_output_dropout_mask_ptr,
uint8_t* layer_output_dropout_mask_ptr,
T* layer_norm_var,
T* layer_norm_mean,
T* attn_layer_norm_var,
T* attn_layer_norm_mean);
inline unsigned GetBatchSize() const { return _batch_size; }
inline unsigned GetNumHeads() const { return _heads; }
inline unsigned GetSeqLength() const { return _seq_length; }
inline unsigned GetIntermediateSize() const { return _intermediate_size; }
void SetSeqLength(unsigned seq_len);
inline unsigned GetHiddenSize() const { return _hidden_size; }
void SetTrainingMode(bool training);
inline bool IsTrainingMode() const { return _training; }
inline bool GeluCheckpoint() const { return _gelu_checkpoint; }
private:
void Initialize();
size_t getWorkspaceSize(int maxBatchSize) const;
// Params
unsigned _layer_id;
unsigned _batch_size;
unsigned _hidden_size;
unsigned _heads;
unsigned _size_per_head;
unsigned _intermediate_size;
unsigned _seq_length;
bool _pre_or_postLayerNorm;
rocblas_handle _cublasHandle;
hipStream_t _stream;
// layers
FeedForward<T> _qkv_linear;
FeedForward<T> _attn_out_linear;
Normalize_Layer<T> _attn_layer_norm;
Normalize_Layer<T> _layer_norm;
Normalize_Layer<T>* _last_normalize;
FeedForward<T> _ff1, _ff2;
Softmax<T> _softmax;
Gelu<T> _gelu;
Dropout<T> _attn_prob_dropout;
Dropout<T> _attn_output_dropout;
Dropout<T> _layer_output_dropout;
StridedBatchGemm<T> _attn_scores;
StridedBatchGemm<T> _attn_context;
bool _training;
// Memory saving flags
bool _attn_dropout_checkpoint;
bool _normalize_invertible;
bool _gelu_checkpoint;
// High Performance flags
bool _stochastic_mode;
};
csrc/includes/feed_forward_hip.h deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#ifndef __FEEDFORWARD_H__
#define __FEEDFORWARD_H__
#include <hip/hip_runtime.h>
#include <hip/hip_fp16.h>
#include <stdio.h>
#include "custom_hip_layers.h"
template <typename T>
class FeedForward {
public:
struct Config {
int batchSize, outputSize;
int inputSize;
std::array<int, 3> gemm_algos;
Config(int batch, int outputs, int inputs, const std::array<int, 3>& algos)
: batchSize(batch), outputSize(outputs), inputSize(inputs), gemm_algos(algos)
{
}
};
FeedForward(Config config) : config_(config) {}
~FeedForward() {}
void Forward(int bsz,
const T* input_ptr,
const T* weights,
T* out,
rocblas_handle& _cublasHandle)
{
float alpha = T(1.);
float beta = T(0.);
cublas_gemm_ex(_cublasHandle,
rocblas_operation_transpose,
rocblas_operation_none,
config_.outputSize,
bsz,
config_.inputSize,
&alpha,
&beta,
weights,
input_ptr,
out,
#ifdef __HIP_PLATFORM_HCC__
rocblas_gemm_algo(config_.gemm_algos[0]));
#else
cublasGemmAlgo_t(config_.gemm_algos[0]));
#endif
}
void Backward(int bsz,
const T* out_grad,
const T* input_ptr,
const T* weights,
T* weights_grad,
T* bias_grad,
rocblas_handle& _cublasHandle,
hipStream_t& stream,
T* inp_grad_out = nullptr,
T* out_grad_trans_out = nullptr)
{
float alpha = (T)1.0, beta = (T)0.0;
cublas_gemm_ex(_cublasHandle,
rocblas_operation_none,
rocblas_operation_transpose,
config_.inputSize,
config_.outputSize,
bsz,
&alpha,
&beta,
input_ptr,
out_grad,
weights_grad,
#ifdef __HIP_PLATFORM_HCC__
rocblas_gemm_algo(config_.gemm_algos[1]));
#else
cublasGemmAlgo_t(config_.gemm_algos[1]));
#endif
cublas_gemm_ex(_cublasHandle,
rocblas_operation_none,
rocblas_operation_none,
config_.inputSize,
bsz,
config_.outputSize,
&alpha,
&beta,
weights,
out_grad,
inp_grad_out,
#ifdef __HIP_PLATFORM_HCC__
rocblas_gemm_algo(config_.gemm_algos[2]));
#else
cublasGemmAlgo_t(config_.gemm_algos[2]));
#endif
launch_fuse_transpose_bias_kernel<T>(out_grad, bias_grad, bsz, config_.outputSize, stream);
}
private:
Config config_;
};
#endif
csrc/includes/gelu_hip.h deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#pragma once
#include <hip/hip_runtime.h>
#include <hip/hip_fp16.h>
#include <stdio.h>
#include "custom_hip_layers.h"
template <typename T>
class Gelu {
public:
struct Config {
uint32_t intermediate_size;
Config(uint32_t inter_size) : intermediate_size(inter_size) {}
};
Gelu(const Config& config) : _config(config) {}
virtual ~Gelu() {}
void ForwardWithBiasAdd(int bsz,
const T* input_buf,
const T* bias,
T* output,
hipStream_t stream)
{
launch_bias_gelu<T>(input_buf, bias, output, _config.intermediate_size, bsz, stream);
}
void Backward(int bsz, T* d_output, const T* input_buf, const T* bias, hipStream_t stream)
{
launch_d_gelu<T>(d_output, input_buf, bias, _config.intermediate_size, bsz, stream);
}
private:
Config _config;
};
csrc/includes/gemm_test_hip.h deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#pragma once
#include <hip/hip_fp16.h>
#ifndef __HIP_PLATFORM_HCC__
#include <cuda_profiler_api.h>
#endif
#include <array>
#include <cstdio>
#include <cstdlib>
#include <ctime>
#include <limits>
#include <memory>
#include "StopWatch.h"
#include "cublas_wrappers_hip.h"
template <typename T>
void check(T result, char const* const func, const char* const file, int const line)
{
if (result) {
std::cout << (std::string("CUDA runtime error: ") + +file + ":" + std::to_string(line) +
" \n");
}
}
#define check_cuda_error(val) check((val), #val, __FILE__, __LINE__)
template <typename T>
class GemmTest {
public:
GemmTest(int m, int n, int k, rocblas_operation ta, rocblas_operation tb, rocblas_handle h)
: M(m), N(n), K(k), transa(ta), transb(tb), handle(h)
{
check_cuda_error(hipMalloc((void**)&A, sizeof(T) * M * K));
check_cuda_error(hipMalloc((void**)&B, sizeof(T) * K * N));
check_cuda_error(hipMalloc((void**)&C, sizeof(T) * M * N));
}
~GemmTest()
{
check_cuda_error(hipFree(A));
check_cuda_error(hipFree(B));
check_cuda_error(hipFree(C));
}
std::array<int, 3> TestAlgo(int loops)
{
float alpha = (T)1.0f;
float beta = (T)0.0f;
int algo_fw = Run(loops, [=](int algo) {
cublas_gemm_ex(handle,
rocblas_operation_transpose,
rocblas_operation_none,
N,
M,
K,
&alpha,
&beta,
B,
A,
C,
#ifdef __HIP_PLATFORM_HCC__
static_cast<rocblas_gemm_algo>(algo));
#else
static_cast<cublasGemmAlgo_t>(algo));
#endif
});
int algo_bw1 = Run(loops, [=](int algo) {
cublas_gemm_ex(handle,
rocblas_operation_none,
rocblas_operation_transpose,
K,
N,
M,
&alpha,
&beta,
A,
C,
B,
#ifdef __HIP_PLATFORM_HCC__
static_cast<rocblas_gemm_algo>(algo));
#else
static_cast<cublasGemmAlgo_t>(algo));
#endif
});
int algo_bw2 = Run(loops, [=](int algo) {
cublas_gemm_ex(handle,
rocblas_operation_none,
rocblas_operation_none,
K,
M,
N,
&alpha,
&beta,
B,
C,
A,
#ifdef __HIP_PLATFORM_HCC__
static_cast<rocblas_gemm_algo>(algo));
#else
static_cast<cublasGemmAlgo_t>(algo));
#endif
});
return std::array<int, 3>({algo_fw, algo_bw1, algo_bw2});
}
template <typename Func>
int Run(int loops, Func f)
{
float fast_latency = (std::numeric_limits<float>::max)();
int fast_algo = 0;
#ifdef __HIP_PLATFORM_HCC__
for (int algo = (int)rocblas_gemm_algo_standard; algo <= (int)rocblas_gemm_algo_standard;
#else
for (int algo = (int)CUBLAS_GEMM_DEFAULT_TENSOR_OP;
algo <= (int)CUBLAS_GEMM_ALGO15_TENSOR_OP;
#endif
algo++) {
int warm_up = 5;
for (int i = 0; i < warm_up; ++i) f(algo);
hipDeviceSynchronize();
Stopwatch timer;
timer.Restart();
for (int i = 0; i < loops; ++i) f(algo);
hipDeviceSynchronize();
timer.Stop();
float avg_latency = (float)timer.GetTimeInSeconds() * 1000 / loops;
printf("algo-%d: %.3fms\n", algo, avg_latency);
if (avg_latency < fast_latency) {
fast_latency = avg_latency;
fast_algo = algo;
}
}
printf("fast_algo %d: %.3f ms\n", fast_algo, fast_latency);
return fast_algo;
}
private:
int M, N, K;
rocblas_handle handle;
rocblas_operation transa, transb;
T *A, *B, *C;
};
template <typename T>
class StridedGemmTest {
public:
StridedGemmTest(int b,
int m,
int n,
int k,
rocblas_operation ta,
rocblas_operation tb,
rocblas_handle h)
: bsz(b), M(m), N(n), K(k), transa(ta), transb(tb), handle(h)
{
check_cuda_error(hipMalloc((void**)&A, sizeof(T) * M * K * bsz));
check_cuda_error(hipMalloc((void**)&B, sizeof(T) * K * N * bsz));
check_cuda_error(hipMalloc((void**)&C, sizeof(T) * M * N * bsz));
}
~StridedGemmTest()
{
check_cuda_error(hipFree(A));
check_cuda_error(hipFree(B));
check_cuda_error(hipFree(C));
}
std::array<int, 3> TestAlgo(int loops)
{
float alpha = (T)1.0f;
float beta = (T)0.0f;
int algo_fw = Run(loops, [=](int algo) {
int stride_a = M * K;
int stride_b = N * K;
int stride_c = M * N;
cublas_strided_batched_gemm(handle,
M,
N,
K,
&alpha,
&beta,
A,
B,
C,
transa,
transb,
stride_a,
stride_b,
stride_c,
bsz,
#ifdef __HIP_PLATFORM_HCC__
static_cast<rocblas_gemm_algo>(algo));
#else
static_cast<cublasGemmAlgo_t>(algo));
#endif
});
int algo_bw1 = Run(loops, [=](int algo) {
int mb = (transa == rocblas_operation_transpose ? K : M);
int kb = (transa == rocblas_operation_transpose ? M : K);
int stride_a = mb * N;
int stride_b = N * kb;
int stride_c = M * K;
// B need to transpose.
rocblas_operation op_b = (transb == rocblas_operation_transpose ? rocblas_operation_none : rocblas_operation_transpose);
// Calculate d_A.
cublas_strided_batched_gemm(handle,
mb,
kb,
N,
&alpha,
&beta,
(transa == rocblas_operation_transpose ? B : C),
(transa == rocblas_operation_transpose ? C : B),
A,
rocblas_operation_none,
op_b,
stride_a,
stride_b,
stride_c,
bsz,
#ifdef __HIP_PLATFORM_HCC__
static_cast<rocblas_gemm_algo>(algo));
#else
static_cast<cublasGemmAlgo_t>(algo));
#endif
});
int algo_bw2 = Run(loops, [=](int algo) {
// A need to transpose.
rocblas_operation op_a = (transa == rocblas_operation_transpose ? rocblas_operation_none : rocblas_operation_transpose);
int stride_a = M * K;
int stride_b = M * N;
int stride_c = N * K;
// Calculate d_B.
cublas_strided_batched_gemm(handle,
K,
N,
M,
&alpha,
&beta,
A,
C,
B,
op_a,
rocblas_operation_none,
stride_a,
stride_b,
stride_c,
bsz,
#ifdef __HIP_PLATFORM_HCC__
static_cast<rocblas_gemm_algo>(algo));
#else
static_cast<cublasGemmAlgo_t>(algo));
#endif
});
return std::array<int, 3>({algo_fw, algo_bw1, algo_bw2});
}
template <typename Func>
int Run(int loops, Func f)
{
float fast_latency = (std::numeric_limits<float>::max)();
int fast_algo = 0;
#ifdef __HIP_PLATFORM_HCC__
for (int algo = (int)rocblas_gemm_algo_standard; algo <= (int)rocblas_gemm_algo_standard;
#else
for (int algo = (int)CUBLAS_GEMM_DEFAULT_TENSOR_OP;
algo <= (int)CUBLAS_GEMM_ALGO15_TENSOR_OP;
#endif
algo++) {
int warm_up = 5;
for (int i = 0; i < warm_up; ++i) f(algo);
hipDeviceSynchronize();
Stopwatch timer;
timer.Restart();
for (int i = 0; i < loops; ++i) f(algo);
hipDeviceSynchronize();
timer.Stop();
float avg_latency = (float)timer.GetTimeInSeconds() * 1000 / loops;
printf("algo-%d: %.3fms\n", algo, avg_latency);
if (avg_latency < fast_latency) {
fast_latency = avg_latency;
fast_algo = algo;
}
}
printf("fast_algo %d: %.3f ms\n", fast_algo, fast_latency);
return fast_algo;
}
private:
int bsz, M, N, K;
rocblas_handle handle;
rocblas_operation transa, transb;
T *A, *B, *C;
};
csrc/includes/general_kernels_hip.h deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#include <hip/hip_runtime.h>
#include <hip/hip_fp16.h>
#include <stdio.h>
#include <stdlib.h>
#ifdef __HIP_PLATFORM_HCC__
#include <hip/hip_cooperative_groups.h>
#else
#include <cooperative_groups.h>
#endif
#include <hiprand/hiprand_kernel.h>
#include "context_hip.h"
#include "cublas_wrappers_hip.h"
#define THREADS 256
#define TILE_DIM 32
#define minus_infinity -1 * std::numeric_limits<float>::infinity()
#define FINAL_MASK 0xffffffff
template <typename T>
void launch_fused_add2(T* out,
const T* inp1,
const T* inp2,
int batch_size,
int seq_length,
int hidden_size,
hipStream_t& stream);
template <typename T>
void launch_fused_add4(T* out,
const T* inp1,
const T* inp2,
const T* inp3,
const T* inp4,
int batch_size,
int seq_length,
int hidden_size,
hipStream_t& stream);
template <typename T>
void launch_fused_add3(T* out,
const T* inp1,
const T* inp2,
const T* inp3,
int batch_size,
int seq_length,
int hidden_size,
hipStream_t& stream);
csrc/includes/normalize_layer_hip.h deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#pragma once
#include <hip/hip_runtime.h>
#include <hip/hip_fp16.h>
#include <stdio.h>
#include <fstream>
#include "custom_hip_layers.h"
using namespace std;
template <typename T>
class Normalize_Layer {
public:
struct Config {
uint32_t batchSize;
uint32_t seqLength;
uint32_t hiddenDim;
float epsilon;
bool training;
bool useMean;
Config(uint32_t batch,
uint32_t seq,
uint32_t h,
float epsilon = 1e-12,
bool training = true,
bool useMean = true)
: batchSize(batch),
seqLength(seq),
hiddenDim(h),
epsilon(epsilon),
training(training),
useMean(useMean)
{
}
};
Normalize_Layer(Config config)
: config_(config), vars(nullptr), means(nullptr), vals_hat(nullptr)
{
}
~Normalize_Layer() {}
void ForwardCheckpoint(int bsz, // batch * seq
T* vals,
const T* residual,
const T* gamma,
const T* betta,
hipStream_t& stream,
bool preLayerNorm = false)
{
launch_bias_residual_layer_norm(vals,
residual,
gamma,
betta,
config_.epsilon,
bsz,
config_.hiddenDim,
stream,
preLayerNorm,
config_.training,
vars,
means);
}
void Forward(int bsz,
T* vals,
const T* residual,
const T* gamma,
const T* betta,
hipStream_t& stream,
bool preLayerNorm = false)
{
launch_bias_residual_layer_norm(vals,
residual,
gamma,
betta,
config_.epsilon,
bsz,
config_.hiddenDim,
stream,
preLayerNorm,
config_.training,
vars);
}
void Backward(int bsz,
const T* out_grad,
const T* gamma,
T* gamma_grad,
T* betta_grad,
hipStream_t stream[2],
T* inp_grad_out,
const T* norm_in = nullptr)
{
launch_layerNorm_backward(out_grad,
norm_in,
vars,
means,
gamma,
gamma_grad,
betta_grad,
inp_grad_out,
bsz,
config_.hiddenDim,
stream);
}
void Backward(int bsz,
const T* out_grad,
const T* gamma,
const T* betta,
T* gamma_grad,
T* betta_grad,
hipStream_t stream[2],
T* inp_grad_out,
const T* norm_out)
{
launch_layerNorm_backward(out_grad,
norm_out,
vars,
gamma,
gamma_grad,
betta_grad,
inp_grad_out,
bsz,
config_.hiddenDim,
stream,
!config_.useMean,
betta);
}
void BackwardFusedAdd(int bsz,
const T* out_grad1,
const T* out_grad2,
const T* gamma,
T* gamma_grad,
T* betta_grad,
hipStream_t stream[2],
T* inp_grad_out,
const T* norm_in = nullptr)
{
launch_layerNorm_backward_fused_add(out_grad1,
out_grad2,
norm_in,
vars,
means,
gamma,
gamma_grad,
betta_grad,
inp_grad_out,
bsz,
config_.hiddenDim,
stream);
}
void BackwardFusedAdd(int bsz,
const T* out_grad1,
const T* out_grad2,
const T* gamma,
const T* betta,
T* gamma_grad,
T* betta_grad,
hipStream_t stream[2],
T* inp_grad_out,
const T* norm_out)
{
launch_layerNorm_backward_fused_add(out_grad1,
out_grad2,
norm_out,
vars,
gamma,
gamma_grad,
betta_grad,
inp_grad_out,
bsz,
config_.hiddenDim,
stream,
!config_.useMean,
betta);
}
inline bool UseMean() const { return config_.useMean; }
inline void SetVar(T* variance)
{
if (!variance) { throw std::runtime_error("Normalize variance is null."); }
vars = variance;
}
inline void SetMean(T* mean)
{
if (!mean) { throw std::runtime_error("Normalize mean is null."); }
means = mean;
}
private:
Config config_;
T* vars;
T* means;
T* vals_hat;
};
csrc/includes/quantizer_hip.h deleted 100644 → 0
View file @ 7dd68788
// !!! This is a file automatically generated by hipify!!!
#pragma once
#include <cooperative_groups.h>
#include <hip/hip_runtime.h>
#include <hip/hip_fp16.h>
#include <stdio.h>
#include <stdlib.h>
#include <cassert>
#include <iostream>
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