DeepLink-org / ditorchLinks
☆23Updated 8 months ago
Alternatives and similar repositories for ditorch
Users that are interested in ditorch are comparing it to the libraries listed below
Sorting:
- ☆95Updated 6 months ago
- ☆32Updated 8 months ago
- Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.☆40Updated 7 months ago
- ☆18Updated last week
- ☆129Updated 9 months ago
- Standalone Flash Attention v2 kernel without libtorch dependency☆110Updated last year
- ☆91Updated this week
- ☆150Updated 8 months ago
- A prefill & decode disaggregated LLM serving framework with shared GPU memory and fine-grained compute isolation.☆111Updated 4 months ago
- PyTorch distributed training acceleration framework☆52Updated last month
- A standalone GEMM kernel for fp16 activation and quantized weight, extracted from FasterTransformer☆94Updated 3 weeks ago
- ☆98Updated last year
- Decoding Attention is specially optimized for MHA, MQA, GQA and MLA using CUDA core for the decoding stage of LLM inference.☆44Updated 3 months ago
- ☆140Updated last year
- ☆59Updated 10 months ago
- A Triton JIT runtime and ffi provider in C++☆25Updated 2 weeks ago
- FlagTree is a unified compiler for multiple AI chips, which is forked from triton-lang/triton.☆89Updated this week
- Compare different hardware platforms via the Roofline Model for LLM inference tasks.☆115Updated last year
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆119Updated 4 months ago
- AI Accelerator Benchmark focuses on evaluating AI Accelerators from a practical production perspective, including the ease of use and ver…☆266Updated last month
- 使用 cutlass 实现 flash-attention 精简版,具有教学意义☆49Updated last year
- 使用 cutlass 仓库在 ada 架构上实现 fp8 的 flash attention☆75Updated last year
- Multiple GEMM operators are constructed with cutlass to support LLM inference.☆19Updated 2 months ago
- 🤖FFPA: Extend FlashAttention-2 with Split-D, ~O(1) SRAM complexity for large headdim, 1.8x~3x↑🎉 vs SDPA EA.☆220Updated last month
- Fast and memory-efficient exact attention☆95Updated last week
- Triton adapter for Ascend. Mirror of https://gitee.com/ascend/triton-ascend☆76Updated last week
- Benchmark code for the "Online normalizer calculation for softmax" paper☆101Updated 7 years ago
- ☆106Updated 4 months ago
- 使用 CUDA C++ 实现的 llama 模型推理框架☆62Updated 10 months ago
- ☆17Updated last year