142 lines
5.2 KiB
Python
142 lines
5.2 KiB
Python
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# SPDX-License-Identifier: Apache-2.0
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from typing import Any, Dict, List, Optional, Type
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import torch
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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.backends.mla.common import (MLACommonBackend,
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MLACommonImpl,
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MLACommonMetadata)
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from vllm.attention.ops.triton_decode_attention import decode_attention_fwd
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import ixformer.inference.functions as ixf_ops
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import vllm.envs as envs
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from vllm import _custom_ops as ops
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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"
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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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# 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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**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 with FP8 KV cache not yet supported")
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self._k_scale = torch.tensor(1.0, dtype=torch.float32)
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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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kv_c_and_k_pe_cache_scale: torch.Tensor,
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attn_metadata: MLACommonMetadata,
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k_c_normed: torch.Tensor=None,
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k_pe: torch.Tensor=None,
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) -> torch.Tensor:
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assert kv_c_and_k_pe_cache.numel() > 0
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decode_meta = attn_metadata.decode_metadata
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assert decode_meta is not None
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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.empty(B,
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self.num_heads,
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self.kv_lora_rank,
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dtype=q_nope.dtype,
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device=q_nope.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(1)
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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(2)
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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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# decode_meta.block_tables,
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# decode_meta.seq_lens_tensor,
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# num_kv_splits, self.scale, PAGE_SIZE)
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if envs.VLLM_USE_INT8_MLA:
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q_int8, q_scale,_ = ops.scaled_int8_quant(q)
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ixf_ops.vllm_paged_attention_mla_int8(
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o, q_int8, q_scale[...,0].view(-1,q_int8.shape[-2]), kv_c_and_k_pe_cache,kv_c_and_k_pe_cache_scale, self.scale, decode_meta.block_tables, decode_meta.seq_lens_tensor, decode_meta.max_decode_seq_len,decode_meta.use_cuda_graph
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)
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else:
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# fused q concat & cache write
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ixf_ops.vllm_paged_attention_mla_fused(
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output=o,
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q_nope=q_nope,
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q_pe=q_pe.contiguous(),
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kv_cache=kv_c_and_k_pe_cache,
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scale=self.scale,
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block_tables=decode_meta.block_tables,
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context_lens=decode_meta.seq_lens_tensor,
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max_context_len=decode_meta.max_decode_seq_len,
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k_c_normed=k_c_normed,
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k_pe=k_pe,
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use_cuda_graph=decode_meta.use_cuda_graph
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)
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return self._v_up_proj_and_o_proj(o)
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