mirror of https://github.com/hpcaitech/ColossalAI
refactor code
parent
01d289d8e5
commit
5eb5ff1464
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@ -9,7 +9,15 @@
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#include <ATen/ATen.h>
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#include "compat.h"
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#ifndef TORCH_CHECK
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#define TORCH_CHECK AT_CHECK
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#endif
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#ifdef VERSION_GE_1_3
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#define DATA_PTR data_ptr
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#else
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#define DATA_PTR data
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#endif
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#define DISPATCH_HALF_AND_BFLOAT(TYPE, NAME, ...) \
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switch (TYPE) { \
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@ -214,90 +222,3 @@
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AT_ERROR(#NAME, "not implemented for '", toString(GTYPE), toString(PTYPE), \
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"'"); \
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}
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template <typename T>
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__device__ __forceinline__ T reduce_block_into_lanes(
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T *x, T val, int lanes = 1,
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bool share_result = false) // lanes is intended to be <= 32.
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{
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int tid = threadIdx.x + threadIdx.y * blockDim.x;
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int blockSize =
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blockDim.x * blockDim.y; // blockSize is intended to be a multiple of 32.
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if (blockSize >= 64) {
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x[tid] = val;
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__syncthreads();
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}
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#pragma unroll
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for (int i = (blockSize >> 1); i >= 64; i >>= 1) {
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if (tid < i) x[tid] = x[tid] + x[tid + i];
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__syncthreads();
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}
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T final;
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if (tid < 32) {
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if (blockSize >= 64)
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final = x[tid] + x[tid + 32];
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else
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final = val;
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// __SYNCWARP();
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#pragma unroll
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for (int i = 16; i >= lanes; i >>= 1)
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final = final + __shfl_down_sync(0xffffffff, final, i);
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}
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if (share_result) {
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if (tid < lanes) x[tid] = final; // EpilogueOp
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// Make sure the smem result is visible to all warps.
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__syncthreads();
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}
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return final;
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}
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template <typename T>
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__device__ __forceinline__ T reduce_block_into_lanes_max_op(
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T *x, T val, int lanes = 1,
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bool share_result = false) // lanes is intended to be <= 32.
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{
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int tid = threadIdx.x + threadIdx.y * blockDim.x;
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int blockSize =
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blockDim.x * blockDim.y; // blockSize is intended to be a multiple of 32.
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if (blockSize >= 64) {
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x[tid] = val;
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__syncthreads();
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}
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#pragma unroll
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for (int i = (blockSize >> 1); i >= 64; i >>= 1) {
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if (tid < i) x[tid] = fmaxf(fabsf(x[tid]), fabsf(x[tid + i]));
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__syncthreads();
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}
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T final;
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if (tid < 32) {
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if (blockSize >= 64)
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final = fmaxf(fabsf(x[tid]), fabsf(x[tid + 32]));
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else
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final = val;
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// __SYNCWARP();
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#pragma unroll
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for (int i = 16; i >= lanes; i >>= 1)
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final =
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fmaxf(fabsf(final), fabsf(__shfl_down_sync(0xffffffff, final, i)));
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}
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if (share_result) {
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if (tid < lanes) x[tid] = final; // EpilogueOp
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// Make sure the smem result is visible to all warps.
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__syncthreads();
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}
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return final;
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}
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@ -2,10 +2,10 @@
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#include <ATen/ATen.h>
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#include "../type_shim.h"
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#include "micros.h"
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namespace infer {
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namespace dtype {
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namespace colossalAI {
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namespace common {
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template <typename T>
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class MPTypeTrait {
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@ -31,5 +31,5 @@ class MPTypeTrait<at::BFloat16> {
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using Type = float;
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};
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} // namespace dtype
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} // namespace infer
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} // namespace common
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} // namespace colossalAI
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@ -2,13 +2,13 @@
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#include <torch/extension.h>
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#include <stdio.h>
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#include "type_shim.h"
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#include "include/mp_type_traits.h"
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#include "../common/micros.h"
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#include "../common/mp_type_traits.h"
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template<typename T>
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__device__ __forceinline__ T silu_kernel(const T& x) {
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// x * sigmoid(x)
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using MT = typename infer::dtype::MPTypeTrait<T>::Type;
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using MT = typename colossalAI::common::MPTypeTrait<T>::Type;
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return static_cast<T>((static_cast<MT>(x)) / (static_cast<MT>(1.0f) + expf(static_cast<MT>(-x))));
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}
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@ -17,7 +17,7 @@ __global__ void act_and_mul_kernel(
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const scalar_t* __restrict__ ins_data,
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scalar_t* __restrict__ outs_data,
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const int64_t numel) {
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using MT = typename infer::dtype::MPTypeTrait<scalar_t>::Type;
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using MT = typename colossalAI::common::MPTypeTrait<scalar_t>::Type;
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int64_t idx = static_cast<int64_t>(threadIdx.x) + static_cast<int64_t>(blockIdx.x) * static_cast<int64_t>(blockDim.x);
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const int64_t grid_size = blockDim.x * gridDim.x;
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@ -1,10 +0,0 @@
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// modified from https://github.com/NVIDIA/apex/blob/master/csrc/compat.h
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#ifndef TORCH_CHECK
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#define TORCH_CHECK AT_CHECK
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#endif
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#ifdef VERSION_GE_1_3
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#define DATA_PTR data_ptr
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#else
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#define DATA_PTR data
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#endif
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@ -2,7 +2,7 @@
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#include <torch/extension.h>
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#include <stdio.h>
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#include "type_shim.h"
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#include "../common/micros.h"
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template<typename scalar_t>
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__global__ void decode_kv_cache_memcpy_kernel(
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@ -310,3 +310,90 @@ __inline__ __device__ void blockReduce<ReduceType::kMax, 4>(float *pval) {
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}
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warpReduce<ReduceType::kMax, num>(pval);
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}
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template <typename T>
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__device__ __forceinline__ T reduce_block_into_lanes(
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T *x, T val, int lanes = 1,
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bool share_result = false) // lanes is intended to be <= 32.
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{
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int tid = threadIdx.x + threadIdx.y * blockDim.x;
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int blockSize =
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blockDim.x * blockDim.y; // blockSize is intended to be a multiple of 32.
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if (blockSize >= 64) {
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x[tid] = val;
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__syncthreads();
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}
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#pragma unroll
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for (int i = (blockSize >> 1); i >= 64; i >>= 1) {
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if (tid < i) x[tid] = x[tid] + x[tid + i];
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__syncthreads();
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}
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T final;
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if (tid < 32) {
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if (blockSize >= 64)
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final = x[tid] + x[tid + 32];
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else
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final = val;
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// __SYNCWARP();
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#pragma unroll
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for (int i = 16; i >= lanes; i >>= 1)
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final = final + __shfl_down_sync(0xffffffff, final, i);
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}
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if (share_result) {
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if (tid < lanes) x[tid] = final; // EpilogueOp
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// Make sure the smem result is visible to all warps.
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__syncthreads();
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}
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return final;
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}
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template <typename T>
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__device__ __forceinline__ T reduce_block_into_lanes_max_op(
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T *x, T val, int lanes = 1,
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bool share_result = false) // lanes is intended to be <= 32.
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{
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int tid = threadIdx.x + threadIdx.y * blockDim.x;
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int blockSize =
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blockDim.x * blockDim.y; // blockSize is intended to be a multiple of 32.
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if (blockSize >= 64) {
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x[tid] = val;
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__syncthreads();
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}
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#pragma unroll
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for (int i = (blockSize >> 1); i >= 64; i >>= 1) {
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if (tid < i) x[tid] = fmaxf(fabsf(x[tid]), fabsf(x[tid + i]));
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__syncthreads();
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}
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T final;
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if (tid < 32) {
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if (blockSize >= 64)
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final = fmaxf(fabsf(x[tid]), fabsf(x[tid + 32]));
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else
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final = val;
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// __SYNCWARP();
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#pragma unroll
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for (int i = 16; i >= lanes; i >>= 1)
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final =
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fmaxf(fabsf(final), fabsf(__shfl_down_sync(0xffffffff, final, i)));
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}
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if (share_result) {
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if (tid < lanes) x[tid] = final; // EpilogueOp
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// Make sure the smem result is visible to all warps.
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__syncthreads();
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}
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return final;
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}
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@ -7,7 +7,7 @@
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#include <cassert>
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#include <vector>
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#include "compat.h"
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#include "../common/micros.h"
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namespace {
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@ -9,7 +9,7 @@
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#include "ATen/AccumulateType.h"
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#include "ATen/cuda/CUDAContext.h"
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#include "ATen/cuda/DeviceUtils.cuh"
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#include "type_shim.h"
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#include "../common/micros.h"
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template <typename U>
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__device__ void cuWelfordOnlineSum(const U curr, U& mu, U& sigma2, U& count) {
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@ -15,7 +15,7 @@
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#include <assert.h>
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#include "multi_tensor_apply.cuh"
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#include "type_shim.h"
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#include "../common/micros.h"
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#define BLOCK_SIZE 512
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#define ILP 4
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@ -12,7 +12,7 @@
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#include <assert.h>
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#include <c10/cuda/CUDAGuard.h>
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#include "compat.h"
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#include "../common/micros.h"
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// #include <iostream>
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@ -11,7 +11,8 @@
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#include <assert.h>
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#include "multi_tensor_apply.cuh"
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#include "type_shim.h"
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#include "../common/micros.h"
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#include "include/block_reduce.h"
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#define BLOCK_SIZE 512
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#define ILP 4
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#include <assert.h>
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#include "multi_tensor_apply.cuh"
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#include "type_shim.h"
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#include "../common/micros.h"
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#define BLOCK_SIZE 512
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#define ILP 4
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@ -10,7 +10,7 @@
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#include <sstream>
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#include "multi_tensor_apply.cuh"
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#include "type_shim.h"
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#include "../common/micros.h"
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#define BLOCK_SIZE 512
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#define ILP 4
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@ -7,7 +7,7 @@
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#include <assert.h>
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#include <cuda_runtime.h>
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#include "compat.h"
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#include "../common/micros.h"
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#include "multi_tensor_apply.cuh"
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#define BLOCK_SIZE 512
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#include <torch/extension.h>
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#include "scaled_masked_softmax.h"
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#include "type_shim.h"
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#include "../common/micros.h"
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namespace multihead_attn {
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namespace fused_softmax {
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@ -10,7 +10,7 @@
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#include <torch/extension.h>
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#include "scaled_upper_triang_masked_softmax.h"
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#include "type_shim.h"
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#include "../common/micros.h"
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namespace multihead_attn {
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namespace fused_softmax {
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