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cuda : optimize argmax (llama/10441)

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* cuda : optimize argmax

* remove unused parameter

ggml-ci

* fixup : use full warps

ggml-ci

* Apply suggestions from code review

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* fix ub

* ggml : check ne00 <= INT32_MAX in argmax and argsort

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
This commit is contained in:
Diego Devesa 2024-11-21 18:18:50 +01:00 committed by Georgi Gerganov
parent 04662748aa
commit 2a4b5c9d7e
4 changed files with 81 additions and 67 deletions
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108
ggml/src/ggml-cuda/argmax.cu
View File

@ -1,57 +1,69 @@
#include "common.cuh"
#include "argmax.cuh"
#include "sum.cuh"
#include <algorithm>
#include <cstdint>
static __global__ void argmax_f32(
const float * x, int32_t * dst, const int64_t ncols, const int64_t nrows) {
#include "argmax.cuh"
#include "common.cuh"
#include "sum.cuh"
int argmax_thread = 0;
const int64_t row0 = (int64_t)blockIdx.x*WARP_SIZE;
static __global__ void argmax_f32(const float * __restrict__ x, int32_t * __restrict__ dst, const int64_t ncols) {
const int64_t row = blockIdx.x;
#pragma unroll
for (int64_t row1 = 0; row1 < WARP_SIZE; ++row1) {
const int64_t row = row0 + row1;
float maxval = -FLT_MAX;
int argmax = -1;
const float * rowx = x + row * ncols;
if (row >= nrows) {
break;
for (int32_t col = threadIdx.x; col < ncols; col += blockDim.x) {
const float val = rowx[col];
if (val > maxval) {
maxval = val;
argmax = col;
}
float maxval = -FLT_MAX;
int argmax = -1;
for (int32_t col = threadIdx.x; col < ncols; col += WARP_SIZE) {
const float val = x[row*ncols + col];
const int bigger = val > maxval;
const int not_bigger = bigger ^ 0x00000001;
maxval = maxval*not_bigger + val*bigger;
argmax = argmax*not_bigger + col*bigger;
}
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, mask, WARP_SIZE);
const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, mask, WARP_SIZE);
const int bigger = val > maxval;
const int not_bigger = bigger ^ 0x00000001;
maxval = maxval*not_bigger + val*bigger;
argmax = argmax*not_bigger + col*bigger;
}
const int store = row1 == threadIdx.x;
argmax_thread += store*argmax;
}
const int row = row0 + threadIdx.x;
if (row >= nrows) {
return;
#pragma unroll
for (int offset = 16; offset > 0; offset >>= 1) {
const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, offset, WARP_SIZE);
const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, offset, WARP_SIZE);
if (val > maxval) {
maxval = val;
argmax = col;
}
}
dst[row] = argmax_thread;
const int n_warps = blockDim.x / WARP_SIZE;
const int lane_id = threadIdx.x % WARP_SIZE;
const int warp_id = threadIdx.x / WARP_SIZE;
if (n_warps > 1) {
constexpr int max_warps = 1024 / WARP_SIZE;
__shared__ float shared_maxval[max_warps];
__shared__ int shared_argmax[max_warps];
if (lane_id == 0) {
shared_maxval[warp_id] = maxval;
shared_argmax[warp_id] = argmax;
}
__syncthreads();
if (warp_id == 0) {
if (lane_id < n_warps) {
maxval = shared_maxval[lane_id];
argmax = shared_argmax[lane_id];
}
#pragma unroll
for (int offset = 16; offset > 0; offset >>= 1) {
const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, offset, WARP_SIZE);
const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, offset, WARP_SIZE);
if (val > maxval) {
maxval = val;
argmax = col;
}
}
}
}
if (warp_id == 0 && lane_id == 0) {
dst[row] = argmax;
}
}
void ggml_cuda_argmax(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@ -70,10 +82,10 @@ void ggml_cuda_argmax(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
cudaStream_t stream = ctx.stream();
const int64_t num_blocks = (nrows + WARP_SIZE - 1) / WARP_SIZE;
const dim3 blocks_dim(WARP_SIZE, 1, 1);
const int64_t num_blocks = nrows;
const int64_t num_threads = std::min<int64_t>(1024, (ne00 + WARP_SIZE - 1) / WARP_SIZE * WARP_SIZE);
const dim3 blocks_dim(num_threads, 1, 1);
const dim3 blocks_num(num_blocks, 1, 1);
argmax_f32<<<blocks_num, blocks_dim, 0, stream>>>(src0_d, dst_d, ne00, nrows);
argmax_f32<<<blocks_num, blocks_dim, 0, stream>>>(src0_d, dst_d, ne00);
}

30
ggml/src/ggml-cuda/common.cuh
View File

@ -180,8 +180,8 @@ static __device__ __forceinline__ int warp_reduce_sum(int x) {
return __reduce_add_sync(0xffffffff, x);
#else
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
x += __shfl_xor_sync(0xffffffff, x, mask, 32);
for (int offset = 16; offset > 0; offset >>= 1) {
x += __shfl_xor_sync(0xffffffff, x, offset, 32);
}
return x;
#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_AMPERE
@ -189,17 +189,17 @@ static __device__ __forceinline__ int warp_reduce_sum(int x) {
static __device__ __forceinline__ float warp_reduce_sum(float x) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
x += __shfl_xor_sync(0xffffffff, x, mask, 32);
for (int offset = 16; offset > 0; offset >>= 1) {
x += __shfl_xor_sync(0xffffffff, x, offset, 32);
}
return x;
}
static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32);
a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32);
for (int offset = 16; offset > 0; offset >>= 1) {
a.x += __shfl_xor_sync(0xffffffff, a.x, offset, 32);
a.y += __shfl_xor_sync(0xffffffff, a.y, offset, 32);
}
return a;
}
@ -209,16 +209,16 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
const half2 a_other = __shfl_xor_sync(0xffffffff, a, mask, 32);
for (int offset = 16; offset > 0; offset >>= 1) {
const half2 a_other = __shfl_xor_sync(0xffffffff, a, offset, 32);
reinterpret_cast<half&>(a.x) += __low2half(a_other);
reinterpret_cast<half&>(a.y) += __high2half(a_other);
}
return a;
#else
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32));
for (int offset = 16; offset > 0; offset >>= 1) {
a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, offset, 32));
}
return a;
#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
@ -231,8 +231,8 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
static __device__ __forceinline__ float warp_reduce_max(float x) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
for (int offset = 16; offset > 0; offset >>= 1) {
x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, offset, 32));
}
return x;
}
@ -275,8 +275,8 @@ static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const hal
static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
for (int offset = 16; offset > 0; offset >>= 1) {
x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, offset, 32));
}
return x;
#else

8
ggml/src/ggml-cuda/quantize.cu
View File

@ -69,8 +69,8 @@ static __global__ void quantize_mmq_q8_1(
// Exchange max. abs. value between vals_per_scale/4 threads.
#pragma unroll
for (int mask = vals_per_scale/8; mask > 0; mask >>= 1) {
amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, mask, WARP_SIZE));
for (int offset = vals_per_scale/8; offset > 0; offset >>= 1) {
amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, offset, WARP_SIZE));
}
float sum;
@ -79,8 +79,8 @@ static __global__ void quantize_mmq_q8_1(
// Exchange calculate sum across vals_per_sum/4 threads.
#pragma unroll
for (int mask = vals_per_sum/8; mask > 0; mask >>= 1) {
sum += __shfl_xor_sync(0xFFFFFFFF, sum, mask, WARP_SIZE);
for (int offset = vals_per_sum/8; offset > 0; offset >>= 1) {
sum += __shfl_xor_sync(0xFFFFFFFF, sum, offset, WARP_SIZE);
}
}

2
ggml/src/ggml.c
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@ -2255,6 +2255,7 @@ struct ggml_tensor * ggml_argmax(
struct ggml_context * ctx,
struct ggml_tensor * a) {
GGML_ASSERT(ggml_is_matrix(a));
GGML_ASSERT(a->ne[0] <= INT32_MAX);
struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, a->ne[1]);
@ -4138,6 +4139,7 @@ struct ggml_tensor * ggml_argsort(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_sort_order order) {
GGML_ASSERT(a->ne[0] <= INT32_MAX);
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_I32, GGML_MAX_DIMS, a->ne);
ggml_set_op_params_i32(result, 0, (int32_t) order);