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https://github.com/hpcaitech/ColossalAI.git
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19e1a5cf16
* [feature] refactor colo attention (#5462) * [extension] update api * [feature] add colo attention * [feature] update sdpa * [feature] update npu attention * [feature] update flash-attn * [test] add flash attn test * [test] update flash attn test * [shardformer] update modeling to fit colo attention (#5465) * [misc] refactor folder structure * [shardformer] update llama flash-attn * [shardformer] fix llama policy * [devops] update tensornvme install * [test] update llama test * [shardformer] update colo attn kernel dispatch * [shardformer] update blip2 * [shardformer] update chatglm * [shardformer] update gpt2 * [shardformer] update gptj * [shardformer] update opt * [shardformer] update vit * [shardformer] update colo attention mask prep * [shardformer] update whisper * [test] fix shardformer tests (#5514) * [test] fix shardformer tests * [test] fix shardformer tests
57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
from ..base_extension import _Extension
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class FlashAttentionSdpaCudaExtension(_Extension):
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def __init__(self):
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super().__init__(name="flash_attention_sdpa_cuda", support_aot=False, support_jit=False)
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def is_available(self) -> bool:
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# cuda extension can only be built if cuda is available
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try:
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import torch
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cuda_available = torch.cuda.is_available()
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except:
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cuda_available = False
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return cuda_available
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def assert_compatible(self) -> bool:
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pass
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def build_aot(self) -> None:
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raise NotImplementedError("Flash attention SDPA does not require ahead-of-time compilation.")
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def build_jit(self) -> None:
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raise NotImplementedError("Flash attention SDPA does not require just-in-time compilation.")
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def load(self):
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from typing import Optional
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import torch
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def flash_attention(
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q: torch.Tensor,
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k: torch.Tensor,
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v: torch.Tensor,
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dropout_p: float = 0.0,
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scale: Optional[float] = None,
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attention_mask: Optional[torch.Tensor] = None,
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is_causal: bool = False,
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cu_seqlens_q: Optional[torch.Tensor] = None,
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cu_seqlens_kv: Optional[torch.Tensor] = None,
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max_seqlen_q: Optional[int] = None,
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max_seqlen_kv: Optional[int] = None,
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q_indices: Optional[torch.Tensor] = None,
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kv_indices: Optional[torch.Tensor] = None,
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):
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return torch.nn.functional.scaled_dot_product_attention(
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q,
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k,
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v,
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attn_mask=attention_mask,
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dropout_p=dropout_p,
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scale=scale,
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)
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return flash_attention
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