mirror of
https://github.com/huggingface/text-generation-inference.git
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59ea38cbca
* Simplify the `attention` function - Use one definition rather than multiple. - Add `key`/`value` arguments, so that we don't need the `PREFILL_IN_KVCACHE` constant. - Make it kwargs-only (to avoid mixing up the various `Tensor` args). * Fixup flashinfer support
99 lines
2.4 KiB
Python
99 lines
2.4 KiB
Python
import intel_extension_for_pytorch as ipex
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import torch
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from text_generation_server.layers.attention.kv_cache import KVCache
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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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def attention(
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*,
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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: KVCache,
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seqlen: Seqlen,
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block_tables: torch.Tensor,
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softmax_scale: float,
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window_size_left: int = -1,
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causal: bool = True,
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softcap: Optional[float] = None,
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):
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if softcap is not None:
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raise NotImplementedError("softcap is not available in IPEX")
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out = torch.empty_like(query)
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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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query.contiguous() if query.device.type == "xpu" else query,
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key.contiguous() if key.device.type == "xpu" else key,
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value.contiguous() if value.device.type == "xpu" else value,
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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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kv_cache: KVCache,
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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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if softcap is not None:
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raise NotImplementedError("softcap is not available in IPEX")
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out = torch.empty_like(query)
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input_lengths = seqlen.input_lengths + seqlen.cache_lengths
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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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kv_cache.key,
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kv_cache.value,
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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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return out
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__all__ = [
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"SUPPORTS_WINDOWING",
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"attention",
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"paged_attention",
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"reshape_and_cache",
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]
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