Dynamic Scale Factor Calculations for Key/Value Scales With FP8 KV Caching#317
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Dynamic Scale Factor Calculations for Key/Value Scales With FP8 KV Caching#317
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…cales flag in arg_utils.py
gshtras
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Dec 10, 2024
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Overall looks good, aside from a few minor questions and comments.
Also pending conflict resolution
vllm/envs.py
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| VLLM_USE_ROCM_SKINNY_GEMM: bool = True | ||
| VLLM_USE_ROCM_CUSTOM_PAGED_ATTN: bool = True | ||
| VLLM_USE_ROCM_CUSTOM_PAGED_ATTN_FP8_OUT: bool = True | ||
| VLLM_USE_ROCM_CUSTOM_PAGED_ATTN_FP8_OUT: bool = False |
| VLLM_MOE_PADDING: bool = False | ||
| VLLM_FP8_PADDING: bool = True | ||
| VLLM_ENABLE_V1_MULTIPROCESSING: bool = False | ||
| K_SCALE_CONSTANT: int = 200 |
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Do we want different values?
| for field in dataclasses.fields(attn_backend.get_metadata_cls()): | ||
| if field.name in tensor_dict: | ||
| if field.name in tensor_dict and field.name != \ | ||
| 'enable_kv_scales_calculation': |
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Not sure, why do we filter it out here?
benchmarks/P3L.py
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| engine_args = EngineArgs.from_cli_args(args) | ||
| llm = LLM(**dataclasses.asdict(engine_args)) | ||
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| llm = LLM( |
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This is not needed now with the **dataclasses.asdict(engine_args)
| self._k_scale = 1.0 | ||
| self._v_scale = 1.0 | ||
| self.calculate_kv_scales = calculate_kv_scales | ||
| self._k_scale = torch.tensor(1.0, dtype=torch.float32) |
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Possibly torch.ones is better
shajrawi
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Dec 17, 2024
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Jan 7, 2025
…ching (#317) * Changed _k_scale and _v_scale to tensors * fixed rocm paged attention with tensor kv scales * Added on the fly scale factor calculation * trying to fix attn metadata * fixed AttentionMetadata issue, updated description for calculate-kv-scales flag in arg_utils.py * Changed K and V scale constants * Removed unneeded comment * Changes to pass format.sh, also fixed lingering k_scale/v_scale : float * Fix for TP > 1 * Ran format.sh * Removed legacy kv_scale loading from the json file * Removed the outdated kv cache docs * Revert some unwanted changes --------- Co-authored-by: Gregory Shtrasberg <Gregory.Shtrasberg@amd.com> Signed-off-by: Gregory Shtrasberg <Gregory.Shtrasberg@amd.com>
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This PR implements a simple method for calculating k_scale and v_scale in the attention layer. This is especially useful in the absence of scale factors in the model checkpoints, where the previous solution was to default the scale factors to 1.0.
This feature necessitated changing k_scale and v_scale to tensors rather than floats, which should be useful for exploring different types of key & value scaling in the future (e.g. per-channel).
Here are a few PPL measurements taken using Llama 3.1 70B, demonstrating superior accuracy compared to using a scale factor of 1.0 for both k_scale and v_scale.