torch custom_op support: norm #552
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yzh119
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Follow up of #552. This PR adds torch library annotation to all FlashInfer kernels so that torch.compile can recognize the kernels. Most changes are tedious. I manually ran subsets of pytest test cases when I made these changes, but since there are too many of them and also some of them didn't pass even before I made the change, I cannot guarantee it's all working. To run tests with torch.compile, pass `FLASHINFER_TEST_TORCH_COMPILE=1` env. ```bash # With torch.compile FLASHINFER_TEST_TORCH_COMPILE=1 pytest -svx tests/test_norm.py # Without torch.compile pytest -svx tests/test_norm.py ``` Notable changes: * For the prefill and decode pybind, it used to return `Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]` depending on `return_lse`. This causes trouble for `torch.compile`. I changed the pybind interface to accept a `maybe_lse: Optional[torch.Tensor]` and only return one tensor. The allocation of the lse tensor is moved to Python side. The Python API does not change. * `chain_speculative_sampling` pybind: Move the allocation of `accepted` and `emitted` from C++ to Python. This is because `torch.compile` doesn't like returning input tensor as output tensor. The Python API does not change. Piggyback changes: * `BatchPrefillWithRaggedKVCacheWrapper.plan`: Bugfix qo_indptr not on CPU * `merge_state`: Fix typo in docs * Change `run_return_lse(...)` to `run(..., return_lse=True)` because torch.compile does not recognize `functools.partial`. * In tests, change `flashinfer.xxx()` to `flashinfer.<module>.xxx()` so that the monkeypatch works. Unsupported for torch.compile: * `flashinfer.quantization.segment_packbits`: Because it's data dependent. Untouched: * `sparse.py`: Tests didn't pass beforehand, so I skiped this. Also, it doesn't seem like need custom_op annotations, as it does not have CUDA kernels. Failed test cases: * batch_decode non contiguous kv: `test_batch_decode_with_paged_kv_cache[False-kv_dtype0-q_dtype0-True-0.0-NONE-NHD-128-4-4-1-54-12]`
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Here's the reason why docs fail to build after #552: As specified in `conf.py`, Sphinx mocks `torch`. The mock makes the following predicate behave badly: `TorchVersion(torch_version) < TorchVersion("2.4")`. The fix is to explicitly pass in an env var indicating docs building. Also changing the way that `prefill.py` imports compiled `_kernels` so that it's consistent with other files.
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🤖 I have created a release *beep* *boop* --- ## [0.2.0](v0.1.6...v0.2.0) (2024-12-17) [Release Blog](https://flashinfer.ai/2024/12/16/flashinfer-v02-release.html). ### Features * add `rotary_dim` argument to rope APIs for partial apply rope ([#599](#599)) ([eb9bc71](eb9bc71)) * add a `use_softmax` field in variant class ([#533](#533)) ([d81af97](d81af97)) * add an option `non_blocking` to plan function ([#622](#622)) ([560af6f](560af6f)) * add gemma_rmsnorm and gemma_fused_add_rmsnorm ([#477](#477)) ([1a6b17e](1a6b17e)) * add group size 3 to GQA decode dispatch ([#558](#558)) ([6227562](6227562)) * add JIT compilation support for FA3 templates ([#672](#672)) ([d4e8d79](d4e8d79)) * allow the cascade kernels to be executed using varying sequence lenghts ([#627](#627)) ([92ac440](92ac440)) * CUDAGraph compatibility of multi-level cascade inference APIs ([#586](#586)) ([2332e8a](2332e8a)) * fix the maximal grid dimension in prefill planning with CUDA graphs ([#639](#639)) ([86ca89a](86ca89a)) * improve the precision of the FusedAddRMSNormKernel function ([#587](#587)) ([c7dc921](c7dc921)) * JIT compilation ([#507](#507)) ([3613a5b](3613a5b)) * modify group-gemm stage number ([#497](#497)) ([52dab1d](52dab1d)) * non-contiguous query with paged kv cache ([#553](#553)) ([89f2c4a](89f2c4a)) * pass a dynamic token count to the cascade kernels ([#635](#635)) ([5fe9f7d](5fe9f7d)) * simplify prefill JIT compilation ([#605](#605)) ([fe4f898](fe4f898)) * specify gemm backend ([#648](#648)) ([0cc1a51](0cc1a51)) * support cached cos/sin in rope APIs ([#585](#585)) ([83e541d](83e541d)) * support huggingface transformer style rope interface ([#568](#568)) ([4f40420](4f40420)) * support sm90 cutlass group gemm ([#509](#509)) ([794bdda](794bdda)) * torch custom_op fix for rope ([#569](#569)) ([3e104bc](3e104bc)) * torch custom_op support: norm ([#552](#552)) ([f6e0010](f6e0010)) * torch.compile and custom_op support ([#554](#554)) ([9bf916f](9bf916f)) * warmup for jit kernel tests ([#629](#629)) ([8f5f349](8f5f349)) ### Bug Fixes * AOT compiler flags on non-sm90 ([#522](#522)) ([0aa4726](0aa4726)) * batch decode kernel redundant store output to gmem ([#505](#505)) ([90e42a7](90e42a7)) * compatible with torch 2.2 ([#478](#478)) ([ac41d1b](ac41d1b)) * #452 ([b53a46f](b53a46f)) * remove redundant load ([#495](#495)) ([2de16b0](2de16b0)) * update bmm fp8 test ([#487](#487)) ([45eac04](45eac04)) ### Performance Improvements * accelerate JIT compilation speed ([#618](#618)) ([eaf73fd](eaf73fd)) * Dense and sparse customizable flashattention-3 template ([#667](#667)) ([51236c9](51236c9)) * fix prefill kernel performance degradation (step 1) ([#602](#602)) ([595cf60](595cf60)) * fix the performance issue of `append_paged_kv_cache` ([#588](#588)) ([e15f7c9](e15f7c9)) * improve parallelism in RoPE with pos_ids ([#609](#609)) ([ff05155](ff05155)) * improve plan performance by using non-blocking memcpy ([#547](#547)) ([41ebe6d](41ebe6d)) * reduce the read and write of shared memory in the FusedAddRMSNormKernel ([#592](#592)) ([2043ca2](2043ca2)) * reduce total_num_tiles_q by one ([#644](#644)) ([553ace5](553ace5)) * remove unnecessary contiguous operation in block sparse attention ([#561](#561)) ([7a7ad46](7a7ad46)) * speedup jit compilation of prefill attention kernels ([#632](#632)) ([a059586](a059586)) * use cuda-core implemention for io-bound block-sparse attention ([#560](#560)) ([3fbf028](3fbf028)) --- This PR was generated with [Release Please](https://github.com/googleapis/release-please). See [documentation](https://github.com/googleapis/release-please#release-please). --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Zihao Ye <expye@outlook.com>
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Add torch custom_op (aka, torch library, torch.compile) support for
norm.py. It should be a no-op for PyTorch < 2.4.Testing is done by
torch.compile-- as we expect the custom_op marks can isolate out our kernels during torch.compile. To avoid changes to tests, I introduced some magic that replaces the kernels with atorch.compile-ed version. For example, to run with/without torch.compile:If this PR looks good, I'll add it to more kernels.