[Tentative] Adding 192 head dim (step_size = 12) #454
Conversation
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@Narsil May you write unit test for it? And you can ref https://github.com/zhyncs/dl/blob/master/flashinfer_build.sh to compile from source. |
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Which tests would you like me to add, batch_prefill_kernels ? others ? Wdym ref to build from source ? I am building already. |
https://github.com/flashinfer-ai/flashinfer/blob/main/python/tests/test_batch_prefill_kernels.py
ok |
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Are the tests ran anywhere ? |
There is currently no CI configured, you can use pytest in the local development environment to run. |
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Hi @Narsil , thanks for your contribution! Line 67 in 0d61871 if compilation successes, you can run unittests such as https://github.com/flashinfer-ai/flashinfer/blob/0d618712faff20a84bbd513d02ac01e16be19306/python/tests/test_batch_prefill_kernels.py and see how does it work. |
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Hi @Narsil Any update? |
This PR implements the JIT compilation (#170 ) of flashinfer, after this PR, flashinfer will compile kernels just-in-time for different input data types and shapes, and cached the kernels at the disk, instead of pre-compile a set of kernels in the wheel. # Motivation The pip wheel size is exploding as we add support to more data types, more head dimensions, more attention variants and more kernel implementation. Pre-compile everything is not sustainable, and impedes development speed. This PR refactors the codebase to use torch's [JIT Compiling Extensions](https://pytorch.org/tutorials/advanced/cpp_extension.html#jit-compiling-extensions) feature instead of pre-compile kernels in the wheel. ## Attention Variants We learned from [FlexAttention](https://pytorch.org/blog/flexattention/) and describes every attention variant as a template class, each instance of the struct can carry some closure variable defined in local memory or shared memory, below are two examples (logits soft cap and alibi attention, the programming interface is tentative and will be updated as we improve the programmability of the JIT template): ```cuda template <typename ParamsT> struct LogitsSoftCap { using DTypeQ = typename ParamsT::DTypeQ; using DTypeKV = typename ParamsT::DTypeKV; using DTypeO = typename ParamsT::DTypeO; uint32_t qo_len, kv_len; uint32_t window_left; __device__ __host__ LogitsSoftCap(const ParamsT& params, uint32_t batch_idx, uint8_t* smem_ptr) { qo_len = params.get_qo_len(batch_idx); kv_len = params.get_kv_len(batch_idx); window_left = kv_len; } template <typename T> __device__ __forceinline__ T QueryTransform(const ParamsT& params, T q) { return float(q) * params.sm_scale * math::ptx_rcp(params.logits_soft_cap); } template <typename T> __device__ __forceinline__ T LogitsTransform(const ParamsT& params, T logits, uint32_t batch_idx, uint32_t qo_idx, uint32_t kv_idx, uint32_t qo_head_idx, uint32_t kv_head_idx) { return params.logits_soft_cap * math::log2e * float(math::tanh(logits)); } __device__ __forceinline__ bool LogitsMask(const ParamsT& params, uint32_t batch_idx, uint32_t qo_idx, uint32_t kv_idx, uint32_t qo_head_idx, uint32_t kv_head_idx) { return true; } }; template <typename ParamsT> struct ALIBIAttention { using DTypeQ = typename ParamsT::DTypeQ; using DTypeKV = typename ParamsT::DTypeKV; using DTypeO = typename ParamsT::DTypeO; using IdType = typename ParamsT::IdType; uint32_t qo_len, kv_len; uint32_t window_left; __device__ __host__ ALIBIAttention(const ParamsT& params, uint32_t batch_idx, uint8_t* smem_ptr) { qo_len = params.get_qo_len(batch_idx); kv_len = params.get_kv_len(batch_idx); window_left = kv_len; } template <typename T> __device__ __forceinline__ T QueryTransform(const ParamsT& params, T q) { return float(q) * params.sm_scale * math::log2e; } template <typename T> __device__ __forceinline__ T LogitsTransform(const ParamsT& params, T logits, uint32_t batch_idx, uint32_t qo_idx, uint32_t kv_idx, uint32_t qo_head_idx, uint32_t kv_head_idx) { return logits + params.alibi_slopes[qo_head_idx] * float(int(kv_idx) - int(qo_idx)); } __device__ __forceinline__ bool LogitsMask(const ParamsT& params, uint32_t batch_idx, uint32_t qo_idx, uint32_t kv_idx, uint32_t qo_head_idx, uint32_t kv_head_idx) { return true; } }; ``` User can customize their own `ParamsT` class and variants class to define their own attention variants, we hope such refactor will make the codebase more concise and extensive. # Roadmap After this PR, we will add support for: 1. PyPI wheels #153 2. fp8 tensor cores attention: #502 3. different head dimensions: #142 #454 #455 4. flashattention3 #369 5. multi-head latency attention #237 6. Generate ParamsT and Attention variants description from python dsl The development of this features have been blocked by the limitation of wheel size (binary size >= 2GB will trigger some linking issues), I hope this PR will make development easier in the future.
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cc @yzh119 |
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Hi @zhyncs, I'll create a PR to support any While I appreciate the effort of this PR, I think the implementation is not correct because the |
Not sure if this PR actually works correctly (I'm going to check it).
Deepseek models use head_dim=192, and this cannot be compiled because of this static assert.
This modification works by jumping like
step_size=4+ jumping by 8 every iteration.Let me know if this is interesting in here.