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Unverified Commit 89cdaa83 authored by Szymon Ożóg's avatar Szymon Ożóg Committed by GitHub
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[Kernel] Add more dtype support for GGUF kernels (#14043) 


Signed-off-by: default avatarSzymonOzog <szymon.ozog@aleph-alpha.com>
Signed-off-by: default avatarSzymonOzog <szymon.ozog@gmail.com>
parent b0746fae
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Showing with 319 additions and 267 deletions
csrc/quantization/gguf/gguf_kernel.cu
View file @ 89cdaa83
......@@ -5,6 +5,7 @@
#include <c10/cuda/CUDAGuard.h>
#include "cuda_compat.h"
#include "dispatch_utils.h"
#include "ggml-common.h"
#include "vecdotq.cuh"
......@@ -13,7 +14,8 @@
#include "mmq.cuh"
// Q8 gemv
static __global__ void quantize_q8_1(const half* __restrict__ x,
template <typename scalar_t>
static __global__ void quantize_q8_1(const scalar_t* __restrict__ x,
void* __restrict__ vy, const int kx,
const int kx_padded) {
const int ix = blockDim.x * blockIdx.x + threadIdx.x;
......@@ -28,7 +30,7 @@ static __global__ void quantize_q8_1(const half* __restrict__ x,
const int ib = i_padded / QK8_1; // block index
const int iqs = i_padded % QK8_1; // quant index
const float xi = ix < kx ? __half2float(x[iy * kx + ix]) : 0.0f;
const float xi = ix < kx ? static_cast<float>(x[iy * kx + ix]) : 0.0f;
float amax = fabsf(xi);
float sum = xi;
......@@ -51,14 +53,16 @@ static __global__ void quantize_q8_1(const half* __restrict__ x,
y[ib].ds.y = __float2half(sum);
}
static void quantize_row_q8_1_cuda(const half* x, void* vy, const int kx,
template <typename scalar_t>
static void quantize_row_q8_1_cuda(const scalar_t* x, void* vy, const int kx,
const int ky, cudaStream_t stream) {
const int64_t kx_padded = (kx + 512 - 1) / 512 * 512;
const int block_num_x =
(kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
const dim3 num_blocks(block_num_x, ky, 1);
const dim3 block_size(CUDA_DEQUANTIZE_BLOCK_SIZE, 1, 1);
quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, kx, kx_padded);
quantize_q8_1<scalar_t>
<<<num_blocks, block_size, 0, stream>>>(x, vy, kx, kx_padded);
}
torch::Tensor ggml_dequantize(torch::Tensor W, // quant weight
......@@ -79,101 +83,112 @@ torch::Tensor ggml_mul_mat_vec_a8(torch::Tensor W, // quant weight
int col = X.sizes()[1];
const int padded = (col + 512 - 1) / 512 * 512;
const at::cuda::OptionalCUDAGuard device_guard(device_of(X));
auto options =
torch::TensorOptions().dtype(torch::kFloat16).device(W.device());
auto options = torch::TensorOptions().dtype(X.dtype()).device(W.device());
at::Tensor Y = torch::empty({1, row}, options);
cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
options = torch::TensorOptions().dtype(torch::kInt32).device(W.device());
at::Tensor quant_X = torch::empty({1, padded / 32 * 9}, options);
quantize_row_q8_1_cuda((half*)X.data_ptr(), (void*)quant_X.data_ptr(), col, 1,
stream);
VLLM_DISPATCH_FLOATING_TYPES(X.scalar_type(), "ggml_mul_mat_vec_a8", [&] {
quantize_row_q8_1_cuda<scalar_t>((scalar_t*)X.data_ptr(),
(void*)quant_X.data_ptr(), col, 1, stream);
switch (type) {
case 2:
mul_mat_vec_q4_0_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_q4_0_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 3:
mul_mat_vec_q4_1_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_q4_1_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 6:
mul_mat_vec_q5_0_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_q5_0_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 7:
mul_mat_vec_q5_1_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_q5_1_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 8:
mul_mat_vec_q8_0_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_q8_0_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 10:
mul_mat_vec_q2_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_q2_K_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 11:
mul_mat_vec_q3_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_q3_K_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 12:
mul_mat_vec_q4_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_q4_K_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 13:
mul_mat_vec_q5_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_q5_K_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 14:
mul_mat_vec_q6_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_q6_K_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 16:
mul_mat_vec_iq2_xxs_q8_1_cuda((void*)W.data_ptr(),
(void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_iq2_xxs_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 17:
mul_mat_vec_iq2_xs_q8_1_cuda((void*)W.data_ptr(),
(void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_iq2_xs_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 18:
mul_mat_vec_iq3_xxs_q8_1_cuda((void*)W.data_ptr(),
(void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_iq3_xxs_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 19:
mul_mat_vec_iq1_s_q8_1_cuda((void*)W.data_ptr(),
(void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_iq1_s_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 20:
mul_mat_vec_iq4_nl_q8_1_cuda((void*)W.data_ptr(),
(void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_iq4_nl_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 21:
mul_mat_vec_iq3_s_q8_1_cuda((void*)W.data_ptr(),
(void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_iq3_s_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 22:
mul_mat_vec_iq2_s_q8_1_cuda((void*)W.data_ptr(),
(void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_iq2_s_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 23:
mul_mat_vec_iq4_xs_q8_1_cuda((void*)W.data_ptr(),
(void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_iq4_xs_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
case 29:
mul_mat_vec_iq1_m_q8_1_cuda((void*)W.data_ptr(),
(void*)quant_X.data_ptr(),
(half*)Y.data_ptr(), col, row, stream);
mul_mat_vec_iq1_m_q8_1_cuda<scalar_t>(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, stream);
break;
}
});
return Y;
}
......@@ -184,66 +199,67 @@ torch::Tensor ggml_mul_mat_a8(torch::Tensor W, // quant weight
int padded = (col + 512 - 1) / 512 * 512;
int batch = X.sizes()[0];
const at::cuda::OptionalCUDAGuard device_guard(device_of(X));
auto options =
torch::TensorOptions().dtype(torch::kFloat16).device(W.device());
auto options = torch::TensorOptions().dtype(X.dtype()).device(W.device());
at::Tensor Y = torch::empty({batch, row}, options);
cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
options = torch::TensorOptions().dtype(torch::kInt32).device(W.device());
at::Tensor quant_X = torch::empty({batch, padded / 32 * 9}, options);
quantize_row_q8_1_cuda((half*)X.data_ptr(), (void*)quant_X.data_ptr(), col,
batch, stream);
VLLM_DISPATCH_FLOATING_TYPES(X.scalar_type(), "ggml_mul_mat_a8", [&] {
quantize_row_q8_1_cuda((scalar_t*)X.data_ptr(), (void*)quant_X.data_ptr(),
col, batch, stream);
switch (type) {
case 2:
ggml_mul_mat_q4_0_q8_1_cuda(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
col, row, batch, padded, row, stream);
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
break;
case 3:
ggml_mul_mat_q4_1_q8_1_cuda(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
col, row, batch, padded, row, stream);
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
break;
case 6:
ggml_mul_mat_q5_0_q8_1_cuda(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
col, row, batch, padded, row, stream);
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
break;
case 7:
ggml_mul_mat_q5_1_q8_1_cuda(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
col, row, batch, padded, row, stream);
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
break;
case 8:
ggml_mul_mat_q8_0_q8_1_cuda(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
col, row, batch, padded, row, stream);
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
break;
case 10:
ggml_mul_mat_q2_K_q8_1_cuda(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
col, row, batch, padded, row, stream);
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
break;
case 11:
ggml_mul_mat_q3_K_q8_1_cuda(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
col, row, batch, padded, row, stream);
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
break;
case 12:
ggml_mul_mat_q4_K_q8_1_cuda(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
col, row, batch, padded, row, stream);
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
break;
case 13:
ggml_mul_mat_q5_K_q8_1_cuda(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
col, row, batch, padded, row, stream);
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
break;
case 14:
ggml_mul_mat_q6_K_q8_1_cuda(
(void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
col, row, batch, padded, row, stream);
(void*)W.data_ptr(), (void*)quant_X.data_ptr(),
(scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
break;
}
});
return Y;
}
csrc/quantization/gguf/mmq.cuh
View file @ 89cdaa83
This diff is collapsed.
csrc/quantization/gguf/mmvq.cuh
View file @ 89cdaa83
// copied and adapted from https://github.com/ggerganov/llama.cpp/blob/b2899/ggml-cuda/mmvq.cu
template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst, const int ncols, const int nrows) {
template <typename scalar_t, int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, scalar_t * __restrict__ dst, const int ncols, const int nrows) {
const int row = blockIdx.x*blockDim.y + threadIdx.y;
if (row >= nrows) {
......@@ -33,158 +33,177 @@ static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void *
}
if (threadIdx.x == 0) {
dst[row] = __float2half(tmp);
dst[row] = tmp;
}
}
static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
mul_mat_vec_q<scalar_t, QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
mul_mat_vec_q<scalar_t, QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
mul_mat_vec_q<scalar_t, QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
mul_mat_vec_q<scalar_t, QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
mul_mat_vec_q<scalar_t, QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
mul_mat_vec_q<scalar_t, QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
mul_mat_vec_q<scalar_t, QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
mul_mat_vec_q<scalar_t, QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
mul_mat_vec_q<scalar_t, QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
mul_mat_vec_q<scalar_t, QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq2_xxs_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_iq2_xxs_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
mul_mat_vec_q<scalar_t, QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq2_xs_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_iq2_xs_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
mul_mat_vec_q<scalar_t, QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq2_s_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_iq2_s_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI2_S, block_iq2_s, 1, vec_dot_iq2_s_q8_1>
mul_mat_vec_q<scalar_t, QK_K, QI2_S, block_iq2_s, 1, vec_dot_iq2_s_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq3_xxs_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_iq3_xxs_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
mul_mat_vec_q<scalar_t, QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq1_s_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_iq1_s_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI1_S, block_iq1_s, 1, vec_dot_iq1_s_q8_1>
mul_mat_vec_q<scalar_t, QK_K, QI1_S, block_iq1_s, 1, vec_dot_iq1_s_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq1_m_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_iq1_m_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI1_M, block_iq1_m, 1, vec_dot_iq1_m_q8_1>
mul_mat_vec_q<scalar_t, QK_K, QI1_M, block_iq1_m, 1, vec_dot_iq1_m_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq4_nl_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_iq4_nl_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK4_NL, QI4_NL, block_iq4_nl, VDR_Q4_0_Q8_1_MMVQ, vec_dot_iq4_nl_q8_1>
mul_mat_vec_q<scalar_t, QK4_NL, QI4_NL, block_iq4_nl, VDR_Q4_0_Q8_1_MMVQ, vec_dot_iq4_nl_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq4_xs_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_iq4_xs_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI4_XS, block_iq4_xs, 1, vec_dot_iq4_xs_q8_1>
mul_mat_vec_q<scalar_t, QK_K, QI4_XS, block_iq4_xs, 1, vec_dot_iq4_xs_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
static void mul_mat_vec_iq3_s_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
template<typename scalar_t>
static void mul_mat_vec_iq3_s_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(block_num_y, 1, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI3_XS, block_iq3_s, 1, vec_dot_iq3_s_q8_1>
mul_mat_vec_q<scalar_t, QK_K, QI3_XS, block_iq3_s, 1, vec_dot_iq3_s_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
tests/kernels/test_gguf.py
View file @ 89cdaa83
......@@ -22,7 +22,7 @@ def get_gguf_sample_tensors(
return GGUFReader(sample_file).tensors
DTYPES = [torch.half]
DTYPES = [torch.half, torch.bfloat16, torch.float32]
# Hidden_size for testing, must match the sample file in HF repo,
# we have `hidden_size = 256, 1024` for test in HF repo currently.
HIDDEN_SIZES = [256, 1024]
......@@ -52,7 +52,7 @@ QUANT_TYPES = [
@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("dtype", [torch.half])
@pytest.mark.parametrize("quant_type", QUANT_TYPES)
@torch.inference_mode()
def test_dequantize(hidden_size: int, dtype: torch.dtype,
......@@ -122,7 +122,13 @@ def test_mmq(num_tokens: int, hidden_size: int, dtype: torch.dtype,
ref_output = x @ weight.T
qweight = torch.tensor(tensor.data, device="cuda")
output = ops.ggml_mul_mat_a8(qweight, x, quant_type,
qweight.shape[0]).to(dtype)
torch.testing.assert_close(output, ref_output, atol=1, rtol=1e-1)
output = ops.ggml_mul_mat_a8(qweight, x, quant_type, qweight.shape[0])
atols = {torch.half: 1, torch.bfloat16: 1.5, torch.float: 1.2}
# test matrix has inputs centered around 0 and lower precision from
# bfloat16 tends to accumulate and can greatly inflate rtol
# since outputs are also very close to 0
rtols = {torch.half: 1e-1, torch.bfloat16: 1e4, torch.float: 2e1}
torch.testing.assert_close(output,
ref_output,
atol=atols[dtype],
rtol=rtols[dtype])
vllm/_custom_ops.py
View file @ 89cdaa83
......@@ -436,7 +436,7 @@ if hasattr(torch.ops._C, "ggml_dequantize"):
quant_type: int,
row: torch.SymInt,
) -> torch.Tensor:
return torch.empty((1, row), dtype=torch.float16, device=W.device)
return torch.empty((1, row), dtype=X.dtype, device=W.device)
@register_fake("_C::ggml_mul_mat_a8")
def _ggml_mul_mat_a8_fake(
......@@ -446,7 +446,7 @@ if hasattr(torch.ops._C, "ggml_dequantize"):
row: torch.SymInt,
) -> torch.Tensor:
batch = X.size(0)
return torch.empty((batch, row), dtype=torch.float16, device=W.device)
return torch.empty((batch, row), dtype=X.dtype, device=W.device)
# cutlass
......
vllm/model_executor/layers/quantization/gguf.py
View file @ 89cdaa83
......@@ -32,7 +32,7 @@ class GGUFConfig(QuantizationConfig):
return "gguf"
def get_supported_act_dtypes(self) -> List[torch.dtype]:
return [torch.half]
return [torch.half, torch.bfloat16, torch.float32]
@classmethod
def get_min_capability(cls) -> int:
......@@ -134,6 +134,7 @@ class GGUFLinearMethod(LinearMethodBase):
output_partition_sizes: List[int], input_size: int,
output_size: int, params_dtype: torch.dtype,
**extra_weight_attrs):
self.params_dtype = params_dtype
output_size_per_partition = sum(output_partition_sizes)
tensor_shape = (output_size_per_partition, input_size_per_partition)
......@@ -326,7 +327,7 @@ class GGUFEmbeddingMethod(GGUFLinearMethod):
x_flat = x.flatten()
quant = torch.index_select(qweight, dim=0, index=x_flat)
dequant = ops.ggml_dequantize(quant, qweight_type, hidden_size,
x_flat.shape[0])
x_flat.shape[0]).to(self.params_dtype)
return dequant.view(*x.shape, hidden_size)
......
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