forked from EngineX-Hygon/enginex-hygon-vllm
init src 0.9.2
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177
vllm/v1/attention/backends/mla/triton_mla.py
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177
vllm/v1/attention/backends/mla/triton_mla.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from typing import Any, Optional
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import torch
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from vllm import envs
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from vllm.attention.backends.abstract import (AttentionType,
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is_quantized_kv_cache)
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from vllm.attention.ops.triton_decode_attention import decode_attention_fwd
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from vllm.attention.ops.triton_flash_attention import triton_attention
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from vllm.logger import init_logger
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from vllm.platforms import current_platform
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from vllm.triton_utils import HAS_TRITON
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from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
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MLACommonImpl,
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MLACommonMetadata)
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logger = init_logger(__name__)
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class TritonMLABackend(MLACommonBackend):
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@staticmethod
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def get_name() -> str:
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return "TRITON_MLA_VLLM_V1"
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@staticmethod
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def get_impl_cls() -> type["TritonMLAImpl"]:
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return TritonMLAImpl
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class TritonMLAImpl(MLACommonImpl[MLACommonMetadata]):
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def __init__(
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self,
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num_heads: int,
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head_size: int,
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scale: float,
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num_kv_heads: int,
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alibi_slopes: Optional[list[float]],
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sliding_window: Optional[int],
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kv_cache_dtype: str,
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blocksparse_params: Optional[dict[str, Any]],
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logits_soft_cap: Optional[float],
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attn_type: str,
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kv_sharing_target_layer_name: Optional[str],
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# MLA Specific Arguments
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**mla_args) -> None:
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super().__init__(num_heads, head_size, scale, num_kv_heads,
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alibi_slopes, sliding_window, kv_cache_dtype,
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blocksparse_params, logits_soft_cap, attn_type,
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kv_sharing_target_layer_name, **mla_args)
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unsupported_features = [
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alibi_slopes, sliding_window, blocksparse_params, logits_soft_cap
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]
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if any(unsupported_features):
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raise NotImplementedError(
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"TritonMLAImpl does not support one of the following: "
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"alibi_slopes, sliding_window, blocksparse_params, "
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"logits_soft_cap")
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if attn_type != AttentionType.DECODER:
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raise NotImplementedError("Encoder self-attention and "
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"encoder/decoder cross-attention "
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"are not implemented for "
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"TritonMLAImpl")
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if is_quantized_kv_cache(self.kv_cache_dtype):
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raise NotImplementedError(
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"TritonMLA V1 with FP8 KV cache not yet supported")
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self.use_triton_flash_attn = envs.VLLM_USE_TRITON_FLASH_ATTN
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self.triton_fa_func = triton_attention if HAS_TRITON else None
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def _flash_attn_varlen_diff_headdims_rocm(self,
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q,
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k,
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v,
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softmax_scale=None,
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**kwargs):
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assert self.triton_fa_func is not None
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# Triton Attention requires a padded V
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padded_v = torch.nn.functional.pad(v, [0, q.shape[-1] - v.shape[-1]],
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value=0)
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# The output of triton_attention is a tuple of
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# [output_tensor, encoded_softmax] where encoded_softmax is always None
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output_tensor, _ = self.triton_fa_func(
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q,
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k,
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padded_v,
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None, # output
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kwargs["cu_seqlens_q"],
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kwargs["cu_seqlens_k"],
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kwargs["max_seqlen_q"],
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kwargs["max_seqlen_k"],
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kwargs["causal"],
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softmax_scale,
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None, # bias
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)
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return output_tensor
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def _flash_attn_varlen_diff_headdims(self,
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q,
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k,
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v,
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return_softmax_lse=False,
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softmax_scale=None,
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**kwargs):
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if current_platform.is_rocm() \
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and self.use_triton_flash_attn \
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and not return_softmax_lse:
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return self._flash_attn_varlen_diff_headdims_rocm(
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q, k, v, softmax_scale=softmax_scale, **kwargs)
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else:
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return super()._flash_attn_varlen_diff_headdims(
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q,
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k,
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v,
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return_softmax_lse=return_softmax_lse,
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softmax_scale=softmax_scale,
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**kwargs)
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def _forward_decode(
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self,
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q_nope: torch.Tensor,
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q_pe: torch.Tensor,
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kv_c_and_k_pe_cache: torch.Tensor,
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attn_metadata: MLACommonMetadata,
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) -> torch.Tensor:
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assert kv_c_and_k_pe_cache.numel() > 0
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assert attn_metadata.decode is not None
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if self.kv_cache_dtype.startswith("fp8"):
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raise NotImplementedError("FP8 Triton MLA not yet supported")
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B = q_nope.shape[0]
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q = torch.cat([q_nope, q_pe], dim=-1)
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o = torch.zeros(B,
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self.num_heads,
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self.kv_lora_rank,
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dtype=q.dtype,
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device=q.device)
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num_kv_splits = 4 # TODO: heuristic
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# TODO(lucas) Allocate ahead of time
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attn_logits = torch.empty(
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(
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B,
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self.num_heads,
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num_kv_splits,
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# NOTE(lucas) idk why the +1 is here but sglang has it so we
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# just mirror that
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self.kv_lora_rank + 1,
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),
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dtype=torch.float32,
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device=q.device,
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)
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# Add a head dim of 1
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kv_c_and_k_pe_cache = kv_c_and_k_pe_cache.unsqueeze(2)
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kv_c_cache = kv_c_and_k_pe_cache[..., :self.kv_lora_rank]
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PAGE_SIZE = kv_c_and_k_pe_cache.size(1)
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# Run MQA
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decode_attention_fwd(q, kv_c_and_k_pe_cache, kv_c_cache, o,
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attn_metadata.decode.block_table,
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attn_metadata.decode.seq_lens, attn_logits,
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num_kv_splits, self.scale, PAGE_SIZE)
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return self._v_up_proj(o)
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