77 lines
1.6 KiB
Python
77 lines
1.6 KiB
Python
import intel_extension_for_pytorch as ipex
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import torch
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SUPPORTS_WINDOWING = False
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def attention(
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q,
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k,
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v,
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out,
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cu_seqlens,
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max_s,
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softmax_scale,
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window_size_left=-1,
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):
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if window_size_left != -1:
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raise ValueError(
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f"XPU version of Flash Attention does not support window attention (window_size_left != -1, got window_size_left={window_size_left})."
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)
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return ipex.llm.functional.varlen_attention(
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q,
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k,
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v,
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out,
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cu_seqlens,
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cu_seqlens,
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max_s,
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max_s,
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0.0,
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softmax_scale,
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False,
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True,
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False,
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None,
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)
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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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out: torch.Tensor,
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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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input_lengths: torch.Tensor,
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max_s: int,
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):
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query = query.contiguous()
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block_size = value_cache.shape[3]
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return 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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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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