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TensorRT-LLMs/cpp/tensorrt_llm/kernels/chunkScan/chunkcumsum.h
Kaiyu Xie 2d234357c6
Update TensorRT-LLM (#1954) 
* Update TensorRT-LLM

---------

Co-authored-by: Altair-Alpha <62340011+Altair-Alpha@users.noreply.github.com>
2024-07-16 15:30:25 +08:00

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/*
* Copyright (c) 2022-2024, NVIDIA CORPORATION. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#pragma once
#include <cuda_fp8.h>
#include <mma.h>
#include "tensorrt_llm/common/cudaBf16Fallbacks.cuh"
#include "Common.h"
#include "Poly.h"
namespace tensorrt_llm
{
namespace kernels
{
typedef void (*ChunkCumsumKernelFuncFp16)(int B_, int L_, int H_, int P_, int G_, int N_,
// const half *g_mxY_, // B*L*H*P
// const half *g_mxOs_, // B*C*H*N*P
// const half *g_mxFs_, // B *H*N*P
// const float *g_mxSt_, // B*C*H*N*P
float* g_mxdc_, // B*C*H*Q
float* g_mxdA_, // B*C*H*Q
half const* g_mxdt_, // B*L*(2*H*P+2*G*N+H) or B*L*(H*P+2*G*N+H)
float const* g_mxdb_, // H
float const* g_mxA_, // H
// const half *g_mxCB_, // B*C*G*Q*Q
// const float *g_mxD_, // H
// const half *g_mxXBC_, // B*L*(H*P+2*G*N)
half const* g_mxZ_, // B*L*(2*H*P+2*G*N+H)
bool removePadding_, int const* lastTokenIdsPtr_);
typedef void (*ChunkCumsumKernelFuncBf16)(int B_, int L_, int H_, int P_, int G_, int N_,
// const bf16 *g_mxY_, // B*L*H*P
// const bf16 *g_mxOs_, // B*C*H*N*P
// const bf16 *g_mxFs_, // B *H*N*P
// const float *g_mxSt_, // B*C*H*N*P
float* g_mxdc_, // B*C*H*Q
float* g_mxdA_, // B*C*H*Q
bf16 const* g_mxdt_, // B*L*(2*H*P+2*G*N+H) or B*L*(H*P+2*G*N+H)
float const* g_mxdb_, // H
float const* g_mxA_, // H
// const bf16 *g_mxCB_, // B*C*G*Q*Q
// const float *g_mxD_, // H
// const bf16 *g_mxXBC_, // B*L*(H*P+2*G*N)
bf16 const* g_mxZ_, // B*L*(2*H*P+2*G*N+H)
bool removePadding_, int const* lastTokenIdsPtr_);
template <int Q_, int tileH_, int warpH_, bool dtSoftplus_, class Tp_, class Wt_ = float>
__global__ std::enable_if_t<std::is_same_v<Tp_, half> || std::is_same_v<Tp_, __nv_bfloat16>> chunk_cumsum_kernel(int B_,
int L_, int H_, int P_, int G_, int N_,
// const Tp_ *g_mxY_, // B*L*H*P
// const Tp_ *g_mxOs_, // B*C*H*N*P
// const Tp_ *g_mxFs_, // B *H*N*P
// const float *g_mxSt_, // B*C*H*N*P
float* g_mxdc_, // B*C*H*Q
float* g_mxdA_, // B*C*H*Q
Tp_ const* g_mxdt_, // B*L*(2*H*P+2*G*N+H) or B*L*(H*P+2*G*N+H)
Wt_ const* g_mxdb_, // H
Wt_ const* g_mxA_, // H
// const Tp_ *g_mxCB_, // B*C*G*Q*Q
// const Wt_ *g_mxD_, // H
// const Tp_ *g_mxXBC_, // B*L*(H*P+2*G*N)
Tp_ const* g_mxZ_, // B*L*(2*H*P+2*G*N+H)
bool removePadding_, int const* lastTokenIdsPtr_)
{
using namespace tensorrt_llm::common;
auto blockIdx_x = Rn<ID>{int(blockIdx.x)};
auto blockIdx_y = Rn<ID>{int(blockIdx.y)};
auto blockIdx_z = Rn<ID>{int(blockIdx.z)};
auto threadIdx_x = Rn<ID, 32>{int(threadIdx.x)};
auto threadIdx_y = Rn<ID, warpH_>{int(threadIdx.y)};
// auto B = Rn<ID>{B_};
auto L = Rn<ID>{L_};
auto H = Rn<ID>{H_};
auto P = Rn<ID>{P_};
auto G = Rn<ID>{G_};
auto N = Rn<ID>{N_};
auto Q = cn<Q_>;
auto C = Rn<ID>{div_up(L.var, Q_)};
auto dt_dim = g_mxZ_ ? Rn<ID>{2 * H_ * P_ + 2 * G_ * N_ + H_} : Rn<ID>{H_ * P_ + 2 * G_ * N_ + H_};
auto aStart = blockIdx_z * L;
auto cStart = blockIdx_z * C;
if (removePadding_)
{
aStart = Rn<ID>{int(blockIdx.z ? lastTokenIdsPtr_[blockIdx.z - 1] : 0)};
cStart = Rn<ID>{int(blockIdx.z ? div_up(aStart.var, Q_) + blockIdx.z - 1 : 0)};
L = Rn<ID>{lastTokenIdsPtr_[blockIdx.z] - aStart.var};
C = Rn<ID>{div_up(L.var, Q_)};
}
else
{
L = Rn<ID>{lastTokenIdsPtr_[blockIdx.z]};
C = Rn<ID>{div_up(L.var, Q_)};
}
if (blockIdx_y * Q >= L)
return;
auto thread = [=](auto iStep) { return iStep * cn<warpH_ * 32> + threadIdx_y * cn<32> + threadIdx_x; };
#pragma unroll
for (Rn<UNROLL, div_up(tileH_, warpH_ * 32)> iStep; iStep.var < iStep.size; iStep.var++)
{
float r_A = 0.f, r_db = 0.f, sum = 0.f;
if (thread(iStep) < cn<tileH_>)
r_A = g_mxA_[get(blockIdx_x * cn<tileH_> + thread(iStep))];
if (thread(iStep) < cn<tileH_> && g_mxdb_)
r_db = g_mxdb_[get(blockIdx_x * cn<tileH_> + thread(iStep))];
#pragma unroll
for (Rn<UNROLL, Q_> iQ; iQ.var < iQ.size; iQ.var++)
{
float r_dt = 0.f;
if (thread(iStep) < cn<tileH_> && blockIdx_y * Q + iQ < L)
{
r_dt = float(g_mxdt_[get((aStart + blockIdx_y * Q + iQ) * dt_dim + dt_dim - H + blockIdx_x * cn<tileH_>
+ thread(iStep))])
+ r_db;
if (dtSoftplus_)
r_dt = r_dt > 32.f ? r_dt : log1p(expf(r_dt));
sum += r_dt;
}
if (thread(iStep) < cn<tileH_>)
{
g_mxdc_[get((cStart + blockIdx_y) * H * Q + (blockIdx_x * cn<tileH_> + thread(iStep)) * Q + iQ)] = r_dt;
g_mxdA_[get((cStart + blockIdx_y) * H * Q + (blockIdx_x * cn<tileH_> + thread(iStep)) * Q + iQ)]
= sum * r_A;
}
}
}
}
ChunkCumsumKernelFuncFp16 getChunkCumsumKernelFp16(
int B_, int L_, int H_, int Q_, bool dtSoftPlus_, dim3* blockDims_, dim3* threadDims_, int* sharedMem_)
{
int B = B_;
int L = L_;
int H = H_;
// int P = P_;
// int G = G_;
// int N = N_;
int Q = Q_;
int C = div_up(L, Q);
int tileH = 1;
int warpH = 1;
auto sharedMem = 0;
*blockDims_ = dim3(H / tileH, C, B);
*threadDims_ = dim3(32, warpH);
*sharedMem_ = sharedMem;
if (dtSoftPlus_)
{
if (Q_ == 128)
return chunk_cumsum_kernel<128, 1, 1, true, half>;
else if (Q_ == 256)
return chunk_cumsum_kernel<256, 1, 1, true, half>;
else
return nullptr;
}
else
{
if (Q_ == 128)
return chunk_cumsum_kernel<128, 1, 1, false, half>;
else if (Q_ == 256)
return chunk_cumsum_kernel<256, 1, 1, false, half>;
else
return nullptr;
}
}
ChunkCumsumKernelFuncBf16 getChunkCumsumKernelBf16(
int B_, int L_, int H_, int Q_, bool dtSoftPlus_, dim3* blockDims_, dim3* threadDims_, int* sharedMem_)
{
int B = B_;
int L = L_;
int H = H_;
// int P = P_;
// int G = G_;
// int N = N_;
int Q = Q_;
int C = div_up(L, Q);
int tileH = 1;
int warpH = 1;
auto sharedMem = 0;
*blockDims_ = dim3(H / tileH, C, B);
*threadDims_ = dim3(32, warpH);
*sharedMem_ = sharedMem;
if (dtSoftPlus_)
{
if (Q_ == 128)
return chunk_cumsum_kernel<128, 1, 1, true, bf16>;
else if (Q_ == 256)
return chunk_cumsum_kernel<256, 1, 1, true, bf16>;
else
return nullptr;
}
else
{
if (Q_ == 128)
return chunk_cumsum_kernel<128, 1, 1, false, bf16>;
else if (Q_ == 256)
return chunk_cumsum_kernel<256, 1, 1, false, bf16>;
else
return nullptr;
}
}
} // namespace kernels
} // namespace tensorrt_llm
// vim: ts=2 sw=2 sts=2 et sta
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