DLBlas is dedicated to leveraging the latest technologies to achieve the ultimate performance of operators. For example, EP_MoE utilizes cutting-edge industry technologies such as DeepEP and DeepGemm to implement highly efficient MoE modules.
DLBlas is meant to be an operator library for Triton-based operators. As such, kernel developers register their kernels to the library and users ask for a operator by giving operator name and input tensors.
it improves over Triton's autotuner in the following ways:
-
operator selection: given the same operator, e.g. matmul, there may be different kernel implementations; we want to find the best one based on the input tensors.
-
customized configuration search: instead of enumerating all possible kernel configurations (BLOCK_SIZE etc.), we want to use advanced algorithm e.g. a bayesian optimizer to search for the best configurations. This needs a flexbile definition of search space and search policy. For DSA hardware, the configuration space is large.
-
caching the best operator implementation and kernel configurations are cached for the input tensors. It is shape, dtype, device specific.
- 04/24/2026 🚀: The KernelSwift fully automatic intelligent operator generation system achieves 100% correctness on NV chips and an average of over 75% correctness on domestic chips, with an average speedup ratio of 3.4x. After manual refinement of the automatically generated operators, an average of 100% correctness is attained on domestic chips, significantly boosting the efficiency of operator development and migration.
cd DLBlas
python setup.py install
There are a couple of ways to apply dlblas kernels.
- get op from dlblas
from dlblas.utils import get_op
args = parse_args()
dtype = torch.float16
device = 'cuda'
a = torch.randn(
(args.m, args.k),
dtype=dtype,
device=device,
)
b = torch.randn(
(args.k, args.n),
dtype=dtype,
device=device,
)
matmul = get_op('matmul', (a, b))
# test
out = matmul(a, b)
ref_out = a @ b
tol = {
'atol': 1.0,
}
if torch.allclose(out, ref_out, **tol):
print('✅ Triton and Torch match')
else:
print('❌ Triton and Torch differ')
- import kernel functions from the kernel file
from dlblas.kernels.rms_norm import rms_norm
rms_norm(...)
- import dlblas and use the kernels directly
import dlblas
dlblas.topk_gating(...)
| Kernel | API |
|---|---|
| silu_and_mul | from dlblas.kernels.activation import silu_and_mul |
| add_rms_norm | from dlblas.kernels.add_rms_norm import call |
| rotary_pos_emb | from dlblas.kernels.apply_rotary_pos_emb import apply_rotary_pos_emb |
| ffn | from dlblas.kernels.ffn import call |
| flash_attention_v2 | from dlblas.kernels.flash_attention_v2 import FlashAttentionV2 |
| fp8_gemm | from dlblas.kernels.fp8_gemm import fp8_gemm |
| fused_rotary_and_fa | from dlblas.kernels.fused_rotary_and_fa import FusedRotaryAndFA |
| partial_rotary_emb | from dlblas.kernels.partial_rotary_emb import PartialRotaryEmb |
| topk_gating | from dlblas.kernels.topk_gating import TopKGatingFunc |