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fix: remove cublas_grouped_gemm (#5307)

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This commit is contained in:
Yineng Zhang
2025-04-11 16:22:37 -07:00
committed by GitHub
parent 034c5256cc
commit 136b8e6afb
8 changed files with 0 additions and 508 deletions
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1
sgl-kernel/CMakeLists.txt
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@@ -164,7 +164,6 @@ set(SOURCES
"csrc/elementwise/rope.cu" "csrc/elementwise/rope.cu"
"csrc/gemm/awq_kernel.cu" "csrc/gemm/awq_kernel.cu"
"csrc/gemm/bmm_fp8.cu" "csrc/gemm/bmm_fp8.cu"
"csrc/gemm/cublas_grouped_gemm.cu"
"csrc/gemm/fp8_blockwise_gemm_kernel.cu" "csrc/gemm/fp8_blockwise_gemm_kernel.cu"
"csrc/gemm/fp8_gemm_kernel.cu" "csrc/gemm/fp8_gemm_kernel.cu"
"csrc/gemm/int8_gemm_kernel.cu" "csrc/gemm/int8_gemm_kernel.cu"

262
sgl-kernel/benchmark/bench_cublas_grouped_gemm.py
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@@ -1,262 +0,0 @@
import argparse
import torch
import triton
import triton.language as tl
from sgl_kernel import cublas_grouped_gemm
WEIGHT_CONFIGS = {
"DeepSeek-V2-Lite": {
"num_routed_experts": 64,
"ffn_shapes": [
[2048, 2816],
[1408, 2048],
],
},
"DeepSeek-V2": {
"num_routed_experts": 160,
"ffn_shapes": [
[5120, 3072],
[1536, 5120],
],
},
}
# This Triton Grouped Gemm Kernel is adapted from
# https://triton-lang.org/main/getting-started/tutorials/08-grouped-gemm.html
@triton.jit
def grouped_matmul_kernel(
# device tensor of matrices pointers
group_a_ptrs,
group_b_ptrs,
group_c_ptrs,
# device tensor of gemm sizes. its shape is [group_size, 3]
# dim 0 is group_size, dim 1 is the values of <M, N, K> of each gemm
group_gemm_sizes,
# device tensor of leading dimension sizes. its shape is [group_size, 3]
# dim 0 is group_size, dim 1 is the values of <lda, ldb, ldc> of each gemm
g_lds,
# Factors for multiplication.
alphas,
betas,
# number of gemms
group_size,
# number of virtual SM
NUM_SM: tl.constexpr,
# tile sizes
BLOCK_SIZE_M: tl.constexpr,
BLOCK_SIZE_N: tl.constexpr,
BLOCK_SIZE_K: tl.constexpr,
):
tile_idx = tl.program_id(0)
last_problem_end = 0
for g in range(group_size):
# get the gemm size of the current problem
gm = tl.load(group_gemm_sizes + g * 3)
gn = tl.load(group_gemm_sizes + g * 3 + 1)
gk = tl.load(group_gemm_sizes + g * 3 + 2)
num_m_tiles = tl.cdiv(gm, BLOCK_SIZE_M)
num_n_tiles = tl.cdiv(gn, BLOCK_SIZE_N)
num_tiles = num_m_tiles * num_n_tiles
# load multiplication factors
alpha = tl.load(alphas + g)
beta = tl.load(betas + g)
# iterate through the tiles in the current gemm problem
while tile_idx >= last_problem_end and tile_idx < last_problem_end + num_tiles:
# pick up a tile from the current gemm problem
k = gk
lda = tl.load(g_lds + g * 3)
ldb = tl.load(g_lds + g * 3 + 1)
ldc = tl.load(g_lds + g * 3 + 2)
a_ptr = tl.load(group_a_ptrs + g).to(tl.pointer_type(tl.float16))
b_ptr = tl.load(group_b_ptrs + g).to(tl.pointer_type(tl.float16))
c_ptr = tl.load(group_c_ptrs + g).to(tl.pointer_type(tl.float16))
# figure out tile coordinates
tile_idx_in_gemm = tile_idx - last_problem_end
tile_m_idx = tile_idx_in_gemm // num_n_tiles
tile_n_idx = tile_idx_in_gemm % num_n_tiles
# do regular gemm here
offs_am = tile_m_idx * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bn = tile_n_idx * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + offs_am[:, None] * lda + offs_k[None, :]
b_ptrs = b_ptr + offs_k[:, None] * ldb + offs_bn[None, :]
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for kk in range(0, tl.cdiv(k, BLOCK_SIZE_K)):
a = tl.load(
a_ptrs,
mask=(offs_am[:, None] < gm)
and (offs_k[None, :] < gk - kk * BLOCK_SIZE_K),
other=0.0,
)
b = tl.load(
b_ptrs,
mask=(offs_k[:, None] < gk - kk * BLOCK_SIZE_K)
and (offs_bn[None, :] < gn),
other=0.0,
)
accumulator += tl.dot(a, b)
a_ptrs += BLOCK_SIZE_K
b_ptrs += BLOCK_SIZE_K * ldb
accumulator *= alpha
c = accumulator.to(tl.float16)
offs_cm = tile_m_idx * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_cn = tile_n_idx * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
c_ptrs = c_ptr + ldc * offs_cm[:, None] + offs_cn[None, :]
output_mask = (offs_am[:, None] < gm) and (offs_bn[None, :] < gn)
c += beta * tl.load(c_ptrs, mask=output_mask)
tl.store(c_ptrs, c, mask=output_mask)
# go to the next tile by advancing NUM_SM
tile_idx += NUM_SM
# get ready to go to the next gemm problem
last_problem_end = last_problem_end + num_tiles
def triton_perf_fn(group_A, group_B, group_C, dtype):
# We put the process of matrix lengths and pointers here out of fairness,
# since cublas_grouped_gemm kernel also does these work.
group_size = len(group_A)
A_addrs = []
B_addrs = []
C_addrs = []
g_sizes = []
g_lds = []
alphas = [1.0] * group_size
betas = [0.0] * group_size
for i in range(group_size):
M, N, K = group_A[i].shape[0], group_B[i].shape[1], group_A[i].shape[1]
g_sizes += [M, N, K]
g_lds += [K, N, N]
A_addrs.append(group_A[i].data_ptr())
B_addrs.append(group_B[i].data_ptr())
C_addrs.append(group_C[i].data_ptr())
d_a_ptrs = torch.tensor(A_addrs, device="cuda")
d_b_ptrs = torch.tensor(B_addrs, device="cuda")
d_c_ptrs = torch.tensor(C_addrs, device="cuda")
d_g_sizes = torch.tensor(g_sizes, dtype=torch.int32, device="cuda")
d_g_lds = torch.tensor(g_lds, dtype=torch.int32, device="cuda")
d_alphas = torch.tensor(alphas, dtype=torch.float32, device="cuda")
d_betas = torch.tensor(betas, dtype=torch.float32, device="cuda")
NUM_SM = 128
grid = (NUM_SM,)
grouped_matmul_kernel[grid](
d_a_ptrs,
d_b_ptrs,
d_c_ptrs,
d_g_sizes,
d_g_lds,
d_alphas,
d_betas,
group_size,
NUM_SM=NUM_SM,
BLOCK_SIZE_M=128,
BLOCK_SIZE_N=128,
BLOCK_SIZE_K=32,
)
def cublas_perf_fn(group_A, group_B, group_C, dtype):
cublas_grouped_gemm(group_A, group_B, group_C, dtype)
@triton.testing.perf_report(
triton.testing.Benchmark(
x_names=["M"],
x_vals=[1, 16, 32, 64, 128, 256, 512, 1024, 2048],
x_log=False,
line_arg="provider",
line_vals=[
"triton",
"cublas",
],
line_names=[
"triton",
"cublas",
],
styles=[("green", "-"), ("blue", "-")],
ylabel="gbps",
plot_name="grouped gemm",
args={},
)
)
def benchmark(M, provider, N, K):
group_size = 20 # Number of used experts per gpu is usually around 20
group_A = []
group_B_row_major = []
group_B_col_major = []
group_C = []
dtype = torch.float16
for i in range(group_size):
A = torch.rand((M, K), device="cuda", dtype=dtype)
B_row_major = torch.rand((K, N), device="cuda", dtype=dtype)
B_col_major = torch.rand((N, K), device="cuda", dtype=dtype)
C = torch.empty((M, N), device="cuda", dtype=dtype)
group_A.append(A)
group_B_row_major.append(B_row_major)
group_B_col_major.append(B_col_major)
group_C.append(C)
quantiles = [0.5, 0.2, 0.8]
if "triton" in provider:
ms, min_ms, max_ms = triton.testing.do_bench(
lambda: triton_perf_fn(group_A, group_B_row_major, group_C, dtype),
quantiles=quantiles,
)
elif "cublas" in provider:
ms, min_ms, max_ms = triton.testing.do_bench(
lambda: cublas_perf_fn(group_A, group_B_col_major, group_C, dtype),
quantiles=quantiles,
)
gbps = (
lambda ms: group_size
* (2 * M * N * K + 2 * M * N)
* group_A[0].element_size()
* 1e-9
/ (ms * 1e-3)
)
return gbps(ms), gbps(max_ms), gbps(min_ms)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--models",
nargs="+",
type=str,
default=["DeepSeek-V2"],
help="List of models to benchmark",
)
parser.add_argument(
"--tp-size",
type=int,
default=8,
help="Tensor parallel size",
)
args = parser.parse_args()
for model in args.models:
assert model in WEIGHT_CONFIGS
num_experts_per_device = (
WEIGHT_CONFIGS[model]["num_routed_experts"] // args.tp_size
)
for K, N in WEIGHT_CONFIGS[model]["ffn_shapes"]:
print(
f"{model} N={N} K={K} tp_size={args.tp_size} "
f"group_size=num_experts_per_device={num_experts_per_device}: "
)
benchmark.run(
print_data=True,
show_plots=True,
save_path="bench_grouped_gemm_res",
N=N,
K=K,
)
print("Benchmark finished!")

5
sgl-kernel/csrc/common_extension.cc
View File

@@ -112,11 +112,6 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
m.def("sgl_per_token_quant_fp8(Tensor input, Tensor output_q, Tensor output_s) -> ()"); m.def("sgl_per_token_quant_fp8(Tensor input, Tensor output_q, Tensor output_s) -> ()");
m.impl("sgl_per_token_quant_fp8", torch::kCUDA, &sgl_per_token_quant_fp8); m.impl("sgl_per_token_quant_fp8", torch::kCUDA, &sgl_per_token_quant_fp8);
m.def(
"cublas_grouped_gemm(Tensor[] inputs, Tensor[] weights, Tensor[] outputs,"
" ScalarType out_dtype, int cublas_handle, int cuda_stream) -> ()");
m.impl("cublas_grouped_gemm", torch::kCUDA, &cublas_grouped_gemm);
m.def( m.def(
"cutlass_scaled_fp4_mm(Tensor! out, Tensor a, Tensor b," "cutlass_scaled_fp4_mm(Tensor! out, Tensor a, Tensor b,"
" Tensor block_scale_a, Tensor block_scale_b," " Tensor block_scale_a, Tensor block_scale_b,"

172
sgl-kernel/csrc/gemm/cublas_grouped_gemm.cu
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@@ -1,172 +0,0 @@
// References:
// https://docs.nvidia.com/cuda/cublas/index.html#cublasgemmgroupedbatchedex
// https://github.com/NVIDIA/CUDALibrarySamples/blob/master/cuBLAS/Extensions/GemmGroupedBatchedEx/cublas_GemmGroupedBatchedEx_example.cu
// https://github.com/zhihu/ZhiLight/blob/main/src/nn/linear/gemm_grouped.cpp
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/util/Exception.h>
#include <cublas_v2.h>
#include <cudaTypedefs.h>
#include <cuda_fp16.h>
#include <cuda_runtime.h>
#include <torch/all.h>
#include <cstdio>
#include <cstdlib>
#include <string>
#include <vector>
#include "utils.h"
static void check_group_count(
const std::vector<torch::Tensor>& inputs,
const std::vector<torch::Tensor>& weights,
const std::vector<torch::Tensor>& outputs) {
TORCH_CHECK(
((inputs.size() == weights.size()) && (inputs.size() == outputs.size())),
"The group count of inputs, weights and outputs should be the same.");
}
static void check_device_dtype(const torch::Dtype& dtype, const std::vector<torch::Tensor>& tensors) {
for (const auto& t : tensors) {
TORCH_CHECK(dtype == t.dtype(), "dtype of all the tensors should be the same");
TORCH_CHECK(t.is_cuda(), "All tensors should be in Cuda memory");
}
}
static std::vector<int> get_dims(const std::vector<torch::Tensor>& tensors, int dim) {
std::vector<int> results;
for (const auto& t : tensors) {
TORCH_CHECK(t.dim() == 2, "Should pass in 2D matrices");
results.push_back(t.size(dim));
}
return std::move(results);
}
static std::vector<int> get_strides(const std::vector<torch::Tensor>& tensors, int dim) {
std::vector<int> results;
for (const auto& t : tensors) {
results.push_back(t.stride(dim));
}
return std::move(results);
}
static void check_equal(const std::vector<int>& a, const std::vector<int>& b, const std::string& err_msg) {
for (int i = 0; i < a.size(); ++i) {
TORCH_CHECK(a[i] == b[i], err_msg);
}
}
static std::vector<void*> get_tensor_ptrs(const std::vector<torch::Tensor>& tensors) {
std::vector<void*> ptrs;
for (auto& t : tensors) {
ptrs.push_back(t.data_ptr());
}
return std::move(ptrs);
}
static torch::Tensor create_ptr_pointer(const std::vector<void*>& ptrs, cudaStream_t stream) {
auto options = torch::TensorOptions().dtype(torch::kDouble).device(torch::kCUDA);
torch::Tensor gpu_ptrs = torch::empty({static_cast<int>(ptrs.size())}, options);
TORCH_CHECK(
cudaMemcpyAsync(gpu_ptrs.data_ptr(), ptrs.data(), sizeof(void*) * ptrs.size(), cudaMemcpyHostToDevice, stream) ==
CUBLAS_STATUS_SUCCESS);
return gpu_ptrs;
}
// We want compute input @ weight^T in row major
// This is equivalent to computing weight @ input^T in col major
// Cublas only accepts matrix in column major, so this arrangement is needed
void cublas_grouped_gemm(
const std::vector<torch::Tensor>& inputs, // b: (m, k) row major = (k, m) col major
const std::vector<torch::Tensor>& weights, // a: (n, k) row major = (n, k)^T col major
const std::vector<torch::Tensor>& outputs, // c: (m, n) row major = (n, m) col major
const torch::Dtype& out_dtype,
int64_t cublas_handle,
int64_t cuda_stream) {
TORCH_CHECK(
out_dtype == torch::kHalf || out_dtype == torch::kBFloat16,
"cublas grouped_gemm can"
"only be applied to float16 and bfloat16 dtype");
int group_count = inputs.size();
check_group_count(inputs, weights, outputs);
std::vector<int> group_size(group_count, 1);
// Make sure all tensors are on cuda and use the same dtype
check_device_dtype(out_dtype, inputs);
check_device_dtype(out_dtype, weights);
check_device_dtype(out_dtype, outputs);
// Weights should be transposed to (n, k) of column major
std::vector<cublasOperation_t> transa_array(group_count, CUBLAS_OP_T);
std::vector<cublasOperation_t> transb_array(group_count, CUBLAS_OP_N);
// Get dim arrays
std::vector<int> m_array = get_dims(weights, 0);
std::vector<int> n_array = get_dims(inputs, 0);
std::vector<int> k_array = get_dims(inputs, 1);
// Make sure the dimensions in each group match
std::vector<int> m_array1 = get_dims(outputs, 1);
std::vector<int> n_array1 = get_dims(outputs, 0);
std::vector<int> k_array1 = get_dims(weights, 1);
check_equal(m_array, m_array1, "sizes don't match on m dimension");
check_equal(n_array, n_array1, "sizes don't match on n dimension");
check_equal(k_array, k_array1, "sizes don't match on k dimension");
// Get leading dimensions
std::vector<int> lda_array = get_strides(weights, 0);
std::vector<int> ldb_array = get_strides(inputs, 0);
std::vector<int> ldc_array = get_strides(outputs, 0);
// Use default scaling factors
std::vector<float> alpha_array(group_count, 1);
std::vector<float> beta_array(group_count, 0);
std::vector<void*> a_array = get_tensor_ptrs(weights);
std::vector<void*> b_array = get_tensor_ptrs(inputs);
std::vector<void*> c_array = get_tensor_ptrs(outputs);
auto stream = reinterpret_cast<cudaStream_t>(cuda_stream);
// Should allocate tensors for storage of pointers
torch::Tensor d_a = create_ptr_pointer(a_array, stream);
torch::Tensor d_b = create_ptr_pointer(b_array, stream);
torch::Tensor d_c = create_ptr_pointer(c_array, stream);
#if defined CUDA_VERSION && CUDA_VERSION >= 12050
auto handle = reinterpret_cast<cublasHandle_t>(cublas_handle);
cudaDataType_t cuda_data_type = (out_dtype == torch::kHalf ? CUDA_R_16F : CUDA_R_16BF);
auto status = cublasGemmGroupedBatchedEx(
handle,
transa_array.data(),
transb_array.data(),
m_array.data(),
n_array.data(),
k_array.data(),
alpha_array.data(),
(void**)d_a.data_ptr(),
cuda_data_type,
lda_array.data(),
(void**)d_b.data_ptr(),
cuda_data_type,
ldb_array.data(),
beta_array.data(),
(void**)d_c.data_ptr(),
cuda_data_type,
ldc_array.data(),
group_count,
group_size.data(),
CUBLAS_COMPUTE_32F);
TORCH_CHECK(status == CUBLAS_STATUS_SUCCESS, "cublas grouped gemm failed: ", cublasGetStatusString(status));
TORCH_CHECK(cudaStreamSynchronize(stream) == cudaSuccess, "Failed when stream synchronization");
return;
#endif
TORCH_CHECK_NOT_IMPLEMENTED(
false, "Cublas GroupGemm is not implemented with current compute capability: ", getSMVersion());
}

7
sgl-kernel/include/sgl_kernel_ops.h
View File

@@ -160,13 +160,6 @@ void sgl_per_token_group_quant_int8(
double int8_max); double int8_max);
void sgl_per_tensor_quant_fp8(at::Tensor input, at::Tensor output_q, at::Tensor output_s, bool is_static); void sgl_per_tensor_quant_fp8(at::Tensor input, at::Tensor output_q, at::Tensor output_s, bool is_static);
void sgl_per_token_quant_fp8(at::Tensor input, at::Tensor output_q, at::Tensor output_s); void sgl_per_token_quant_fp8(at::Tensor input, at::Tensor output_q, at::Tensor output_s);
void cublas_grouped_gemm(
const std::vector<torch::Tensor>& inputs,
const std::vector<torch::Tensor>& weights,
const std::vector<torch::Tensor>& outputs,
const torch::Dtype& out_dtype,
int64_t cublas_handle,
int64_t cuda_stream);
void bmm_fp8( void bmm_fp8(
at::Tensor A, at::Tensor A,
at::Tensor B, at::Tensor B,

1
sgl-kernel/python/sgl_kernel/__init__.py
View File

@@ -25,7 +25,6 @@ from sgl_kernel.elementwise import (
from sgl_kernel.gemm import ( from sgl_kernel.gemm import (
awq_dequantize, awq_dequantize,
bmm_fp8, bmm_fp8,
cublas_grouped_gemm,
cutlass_scaled_fp4_mm, cutlass_scaled_fp4_mm,
fp8_blockwise_scaled_mm, fp8_blockwise_scaled_mm,
fp8_scaled_mm, fp8_scaled_mm,

20
sgl-kernel/python/sgl_kernel/gemm.py
View File

@@ -121,26 +121,6 @@ def sgl_per_tensor_quant_fp8(
) )
def cublas_grouped_gemm(
inputs: List[torch.Tensor],
weights: List[torch.Tensor],
outputs: List[torch.Tensor],
out_dtype: torch.dtype,
) -> None:
assert (
len(inputs) > 0 and len(weights) > 0 and len(outputs) > 0
), "Inputs/weights/outputs should not be empty!"
cublas_handle = torch.cuda.current_blas_handle()
torch.ops.sgl_kernel.cublas_grouped_gemm.default(
inputs,
weights,
outputs,
out_dtype,
cublas_handle,
get_cuda_stream(),
)
def sgl_per_token_quant_fp8( def sgl_per_token_quant_fp8(
input: torch.Tensor, input: torch.Tensor,
output_q: torch.Tensor, output_q: torch.Tensor,

40
sgl-kernel/tests/test_cublas_grouped_gemm.py
View File

@@ -1,40 +0,0 @@
import pytest
import torch
from sgl_kernel import cublas_grouped_gemm
def torch_grouped_gemm(a_array, b_array, out_dtype):
return [torch.matmul(a, b.t()).to(out_dtype) for a, b in zip(a_array, b_array)]
skip_condition = not torch.cuda.is_available() or (
torch.version.cuda is None
or tuple(map(int, torch.version.cuda.split("."))) < (12, 5)
)
@pytest.mark.skipif(
skip_condition, reason="CUDA not available or CUDA version lower than 12.5"
)
@pytest.mark.parametrize("out_dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("M", [1, 16, 32, 256, 1024])
@pytest.mark.parametrize("N", [2, 16, 128, 256, 4096])
@pytest.mark.parametrize("K", [3, 16, 32, 512, 8192])
def test_grouped_gemm_accuracy(out_dtype, M, N, K):
a = torch.randn((M, K), device="cuda", dtype=out_dtype) * 5
b = torch.randn((N, K), device="cuda", dtype=out_dtype) * 5
expected = torch.matmul(a, b.t()).to(out_dtype)
a_array = [a]
b_array = [b]
c_array = [torch.empty((M, N), device="cuda", dtype=out_dtype)]
result_torch = torch_grouped_gemm(a_array, b_array, out_dtype)[0]
cublas_grouped_gemm(a_array, b_array, c_array, out_dtype)
torch.testing.assert_close(result_torch, expected)
torch.testing.assert_close(c_array[0], expected)
if __name__ == "__main__":
pytest.main([__file__])