forked from EngineX-Cambricon/enginex-mlu370-vllm
119 lines
4.5 KiB
Python
119 lines
4.5 KiB
Python
"""Attention layer."""
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from typing import Any, Dict, List, Optional
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import torch
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import torch.nn as nn
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from vllm.attention import AttentionMetadata, AttentionType
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from vllm.attention.layer import Attention
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from vllm_mlu.attention.selector import vllm__attention__selector__get_attn_backend as get_attn_backend
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from vllm.config import CacheConfig
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
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from vllm_mlu._mlu_utils import USE_PAGED
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from vllm_mlu.mlu_hijack_utils import MluHijackObject
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'''
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=============================
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Modify by vllm_mlu
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=============================
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@brief: add a arg use_mla for function get_attn_backend, _cached_get_attn_backend,
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which_attn_to_use
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'''
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'''
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==================
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End of MLU Hijack
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==================
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'''
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def vllm__attention__layer__Attention__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: Optional[int] = None,
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alibi_slopes: Optional[List[float]] = None,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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blocksparse_params: Optional[Dict[str, Any]] = None,
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logits_soft_cap: Optional[float] = None,
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use_mla: bool = False,
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prefix: str = "",
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) -> None:
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super(Attention, self).__init__()
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self.use_mla = use_mla
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if cache_config is not None:
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kv_cache_dtype = cache_config.cache_dtype
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block_size = cache_config.block_size
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sliding_window = cache_config.sliding_window
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is_attention_free = cache_config.is_attention_free
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else:
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kv_cache_dtype = "auto"
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block_size = 16
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sliding_window = None
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is_attention_free = False
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if num_kv_heads is None:
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num_kv_heads = num_heads
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# The default k/v_scale is set to 1.0. This is ignored
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# when kv-cache is not fp8, and should be used with
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# kv-cache in fp8_e5m2. For kv-cache in fp8_e4m3, we
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# expect the pre-quantized k/v_scale to be loaded along
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# with the model weights.
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self.kv_cache_dtype = kv_cache_dtype
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self._k_scale = 1.0
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self._v_scale = 1.0
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quant_method = quant_config.get_quant_method(
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self, prefix=prefix) if quant_config else None
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if quant_method is not None:
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assert isinstance(quant_method, BaseKVCacheMethod)
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# TODO (mgoin): kv cache dtype should be specified in the FP8
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# checkpoint config and become the "auto" behavior
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if self.kv_cache_dtype == "fp8_e5m2":
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raise ValueError("fp8_e5m2 kv-cache is not supported with "
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"fp8 checkpoints.")
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# If quantization is enabled, we make "k_scale" and "v_scale"
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# parameters so that it can be loaded from the model checkpoint.
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# The k/v_scale will then be converted back to native float32
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# values after weight loading.
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self.quant_method = quant_method
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self.quant_method.create_weights(self)
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# During model initialization, the default dtype is set as the model
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# weight and activation dtype.
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dtype = torch.get_default_dtype()
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attn_backend = get_attn_backend(head_size, dtype, kv_cache_dtype,
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block_size, is_attention_free,
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blocksparse_params is not None,
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use_mla=use_mla)
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impl_cls = attn_backend.get_impl_cls()
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self.impl = impl_cls(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)
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def vllm__attention__layer__Attention__forward(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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attn_type: AttentionType = AttentionType.DECODER,
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) -> torch.Tensor:
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return self.impl.forward(query,
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key,
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value,
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kv_cache,
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attn_metadata,
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self._k_scale,
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self._v_scale,
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attn_type=attn_type,
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use_mla=self.use_mla)
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MluHijackObject.apply_hijack(Attention,
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Attention.__init__,
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vllm__attention__layer__Attention__init__)
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MluHijackObject.apply_hijack(Attention,
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Attention.forward,
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vllm__attention__layer__Attention__forward)
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