83 lines
2.0 KiB
Python
83 lines
2.0 KiB
Python
import intel_extension_for_pytorch as ipex
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import torch
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from text_generation_server.models.flash_causal_lm import BLOCK_SIZE
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from text_generation_server.layers.attention import Seqlen
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from typing import Optional
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SUPPORTS_WINDOWING = False
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PREFILL_IN_KV_CACHE = False
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def attention(
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q: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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seqlen: Seqlen,
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block_tables: torch.Tensor,
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softmax_scale,
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window_size_left=-1,
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causal=True,
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softcap: Optional[float] = None,
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):
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out = torch.empty_like(q)
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# We do not need to check window_size_left (not supported) here, so it is already checked ahead of time at model load.
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ipex.llm.functional.varlen_attention(
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q.contiguous() if q.device.type == "xpu" else q,
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key_cache.contiguous() if key_cache.device.type == "xpu" else key_cache,
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value_cache.contiguous() if value_cache.device.type == "xpu" else value_cache,
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out,
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seqlen.cu_seqlen_q,
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seqlen.cu_seqlen_q,
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seqlen.max_q,
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seqlen.max_q,
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0.0,
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softmax_scale,
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False,
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causal,
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False,
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None,
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)
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return out
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def reshape_and_cache(
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key: torch.Tensor,
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value: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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slots: torch.Tensor,
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):
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ipex.llm.modules.PagedAttention.reshape_and_cache(
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key, value, key_cache, value_cache, slots
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)
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def paged_attention(
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query: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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kv_head_mapping: torch.Tensor,
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softmax_scale: float,
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block_tables: torch.Tensor,
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seqlen: Seqlen,
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max_s: int,
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softcap: Optional[float] = None,
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):
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out = torch.empty_like(query)
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ipex.llm.modules.PagedAttention.single_query_cached_kv_attention(
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out,
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query,
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key_cache,
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value_cache,
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kv_head_mapping,
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softmax_scale,
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block_tables,
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seqlen.input_lengths,
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BLOCK_SIZE,
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max_s,
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None,
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)
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return out
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