fixed buged: sgl_kernel object has no attribute 'fwd'
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maxiao1
@@ -35,199 +35,7 @@ def maybe_contiguous(x):
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return x.contiguous() if x is not None and x.stride(-1) != 1 else x
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# def flash_attn_with_kvcache(
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# q,
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# k_cache,
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# v_cache,
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# k=None,
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# v=None,
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# qv=None,
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# rotary_cos=None,
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# rotary_sin=None,
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# cache_seqlens: Optional[Union[(int, torch.Tensor)]] = None,
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# cache_batch_idx: Optional[torch.Tensor] = None,
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# cache_leftpad: Optional[torch.Tensor] = None,
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# page_table: Optional[torch.Tensor] = None,
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# cu_seqlens_q: Optional[torch.Tensor] = None,
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# cu_seqlens_k_new: Optional[torch.Tensor] = None,
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# max_seqlen_q: Optional[int] = None,
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# rotary_seqlens: Optional[torch.Tensor] = None,
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# q_descale: Optional[torch.Tensor] = None,
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# k_descale: Optional[torch.Tensor] = None,
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# v_descale: Optional[torch.Tensor] = None,
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# softmax_scale=None,
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# causal=False,
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# window_size=(-1, -1), # -1 means infinite context window
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# softcap=0.0, # 0.0 means deactivated
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# rotary_interleaved=True,
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# scheduler_metadata=None,
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# num_splits=0, # Can be tuned for speed
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# pack_gqa=None, # Can be tuned for speed
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# sm_margin=0, # Can be tuned if some SMs are used for communication
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# return_softmax_lse=False,
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# sinks=None,
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# ver=3,
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# ):
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# """
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# If k and v are not None, k_cache and v_cache will be updated *inplace* with the new values from
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# k and v. This is useful for incremental decoding: you can pass in the cached keys/values from
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# the previous step, and update them with the new keys/values from the current step, and do
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# attention with the updated cache, all in 1 kernel.
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# If you pass in k / v, you must make sure that the cache is large enough to hold the new values.
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# For example, the KV cache could be pre-allocated with the max sequence length, and you can use
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# cache_seqlens to keep track of the current sequence lengths of each sequence in the batch.
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# Also apply rotary embedding if rotary_cos and rotary_sin are passed in. The key @k will be
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# rotated by rotary_cos and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc.
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# If causal or local (i.e., window_size != (-1, -1)), the query @q will be rotated by rotary_cos
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# and rotary_sin at indices cache_seqlens, cache_seqlens + 1, etc.
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# If not causal and not local, the query @q will be rotated by rotary_cos and rotary_sin at
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# indices cache_seqlens only (i.e. we consider all tokens in @q to be at position cache_seqlens).
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# See tests/test_flash_attn.py::test_flash_attn_kvcache for examples of how to use this function.
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# Supports multi-query and grouped-query attention (MQA/GQA) by passing in KV with fewer heads
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# than Q. Note that the number of heads in Q must be divisible by the number of heads in KV.
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# For example, if Q has 6 heads and K, V have 2 heads, head 0, 1, 2 of Q will attention to head
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# 0 of K, V, and head 3, 4, 5 of Q will attention to head 1 of K, V.
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# If causal=True, the causal mask is aligned to the bottom right corner of the attention matrix.
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# For example, if seqlen_q = 2 and seqlen_k = 5, the causal mask (1 = keep, 0 = masked out) is:
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# 1 1 1 1 0
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# 1 1 1 1 1
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# If seqlen_q = 5 and seqlen_k = 2, the causal mask is:
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# 0 0
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# 0 0
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# 0 0
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# 1 0
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# 1 1
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# If the row of the mask is all zero, the output will be zero.
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# If window_size != (-1, -1), implements sliding window local attention. Query at position i
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# will only attend to keys between
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# [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q + window_size[1]] inclusive.
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# Note: Does not support backward pass.
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# Arguments:
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# q: (batch_size, seqlen, nheads, headdim)
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# k_cache: (batch_size_cache, seqlen_cache, nheads_k, headdim) if there's no page_table,
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# or (num_blocks, page_block_size, nheads_k, headdim) if there's a page_table (i.e. paged KV cache)
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# page_block_size must be a multiple of 256.
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# v_cache: (batch_size_cache, seqlen_cache, nheads_k, headdim_v) if there's no page_table,
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# or (num_blocks, page_block_size, nheads_k, headdim_v) if there's a page_table (i.e. paged KV cache)
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# k [optional]: (batch_size, seqlen_new, nheads_k, headdim). If not None, we concatenate
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# k with k_cache, starting at the indices specified by cache_seqlens.
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# v [optional]: (batch_size, seqlen_new, nheads_k, headdim_v). Similar to k.
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# qv [optional]: (batch_size, seqlen, nheads, headdim_v)
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# rotary_cos [optional]: (seqlen_ro, rotary_dim / 2). If not None, we apply rotary embedding
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# to k and q. Only applicable if k and v are passed in. rotary_dim must be divisible by 16.
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# rotary_sin [optional]: (seqlen_ro, rotary_dim / 2). Similar to rotary_cos.
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# cache_seqlens: int, or (batch_size,), dtype torch.int32. The sequence lengths of the
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# KV cache.
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# cache_batch_idx: (batch_size,), dtype torch.int32. The indices used to index into the KV cache.
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# If None, we assume that the batch indices are [0, 1, 2, ..., batch_size - 1].
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# If the indices are not distinct, and k and v are provided, the values updated in the cache
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# might come from any of the duplicate indices.
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# cache_leftpad: (batch_size,), dtype torch.int32. The index that the KV cache starts. If None, assume 0.
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# page_table [optional]: (batch_size, max_num_blocks_per_seq), dtype torch.int32.
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# softmax_scale: float. The scaling of QK^T before applying softmax.
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# Default to 1 / sqrt(headdim).
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# causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
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# window_size: (left, right). If not (-1, -1), implements sliding window local attention.
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# softcap: float. Anything > 0 activates softcapping attention.
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# rotary_interleaved: bool. Only applicable if rotary_cos and rotary_sin are passed in.
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# If True, rotary embedding will combine dimensions 0 & 1, 2 & 3, etc. If False,
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# rotary embedding will combine dimensions 0 & rotary_dim / 2, 1 & rotary_dim / 2 + 1
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# (i.e. GPT-NeoX style).
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# num_splits: int. If > 1, split the key/value into this many chunks along the sequence.
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# If num_splits == 1, we don't split the key/value. If num_splits == 0, we use a heuristic
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# to automatically determine the number of splits.
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# Don't change this unless you know what you are doing.
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# return_softmax_lse: bool. Whether to return the logsumexp of the attention scores.
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# Return:
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# out: (batch_size, seqlen, nheads, headdim).
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# softmax_lse [optional, if return_softmax_lse=True]: (batch_size, nheads, seqlen). The
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# logsumexp of each row of the matrix QK^T * scaling (e.g., log of the softmax
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# normalization factor).
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# """
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# if ver == 4:
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# raise NotImplementedError("haven't implemented flash_attn_with_kvcache for fa4")
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# assert k_cache.stride(-1) == 1, "k_cache must have contiguous last dimension"
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# assert v_cache.stride(-1) == 1, "v_cache must have contiguous last dimension"
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# if softmax_scale is None:
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# softmax_scale = (q.shape[-1] + (qv.shape[-1] if qv is not None else 0)) ** (
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# -0.5
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# )
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# if cache_seqlens is not None and isinstance(cache_seqlens, int):
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# cache_seqlens = torch.full(
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# (k_cache.shape[0],), cache_seqlens, dtype=torch.int32, device=k_cache.device
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# )
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# cache_seqlens = maybe_contiguous(cache_seqlens)
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# q, k_cache, k, v = [maybe_contiguous(x) for x in (q, k_cache, k, v)]
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# v_cache = (
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# v_cache.contiguous()
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# if v_cache.stride(-1) != 1 and v_cache.stride(-3) != 1
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# else v_cache
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# )
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# cu_seqlens_q, cu_seqlens_k_new = [
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# maybe_contiguous(x) for x in (cu_seqlens_q, cu_seqlens_k_new)
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# ]
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# page_table, cache_batch_idx, cache_leftpad = [
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# maybe_contiguous(x) for x in (page_table, cache_batch_idx, cache_leftpad)
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# ]
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# rotary_cos, rotary_sin = [maybe_contiguous(x) for x in (rotary_cos, rotary_sin)]
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# rotary_seqlens = maybe_contiguous(rotary_seqlens)
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# if hasattr(torch.version, 'hip') and torch.version.hip is not None:
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# # HIP环境回退
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# from flash_attn import flash_attn_with_kvcache as fa_with_kv
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# out, softmax_lse, *rest = fa_with_kv(
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# q, k, v, k_cache, v_cache, cache_seqlens, cache_batch_idx,
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# block_tables, softmax_scale, causal, alibi_slopes, out
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# )
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# else:
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# out, softmax_lse, *rest = torch.ops.sgl_kernel.fwd.default(
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# q,
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# k_cache,
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# v_cache,
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# k,
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# v,
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# qv,
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# None, # out
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# cu_seqlens_q,
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# None, # cu_seqlens_k
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# cu_seqlens_k_new,
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# None, # seqused_q
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# cache_seqlens,
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# max_seqlen_q,
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# None, # max_seqlen_k
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# page_table,
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# cache_batch_idx,
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# cache_leftpad,
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# rotary_cos,
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# rotary_sin,
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# rotary_seqlens,
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# q_descale,
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# k_descale,
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# v_descale,
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# softmax_scale,
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# causal,
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# window_size[0],
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# window_size[1],
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# softcap,
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# rotary_interleaved,
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# scheduler_metadata,
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# num_splits,
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# pack_gqa,
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# sm_margin,
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# sinks,
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# )
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# return (out, softmax_lse) if return_softmax_lse else out
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def flash_attn_with_kvcache(
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q,
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k_cache,
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@@ -265,27 +73,27 @@ def flash_attn_with_kvcache(
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raise NotImplementedError("haven't implemented flash_attn_with_kvcache for fa4")
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# HIP环境检测和回退
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# if hasattr(torch.version, 'hip') and torch.version.hip is not None:
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# # 简单PyTorch回退，处理实际的张量形状
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# # q: [1, 4, 256], k_cache: [411528, 1, 1, 256], v_cache: [411528, 1, 1, 256]
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if hasattr(torch.version, 'hip') and torch.version.hip is not None:
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# 简单PyTorch回退，处理实际的张量形状
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# q: [1, 4, 256], k_cache: [411528, 1, 1, 256], v_cache: [411528, 1, 1, 256]
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# if softmax_scale is None:
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# softmax_scale = (q.shape[-1]) ** (-0.5)
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if softmax_scale is None:
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softmax_scale = (q.shape[-1]) ** (-0.5)
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# # 重塑以匹配attention计算
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# q_reshaped = q.unsqueeze(1) # [1, 1, 4, 256]
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# k_reshaped = k_cache.squeeze(1).squeeze(1) # [411528, 256]
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# v_reshaped = v_cache.squeeze(1).squeeze(1) # [411528, 256]
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# 重塑以匹配attention计算
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q_reshaped = q.unsqueeze(1) # [1, 1, 4, 256]
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k_reshaped = k_cache.squeeze(1).squeeze(1) # [411528, 256]
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v_reshaped = v_cache.squeeze(1).squeeze(1) # [411528, 256]
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# # 简单的点积attention
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# scores = torch.matmul(q, k_reshaped.T) * softmax_scale # [1, 4, 411528]
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# attn_weights = torch.softmax(scores, dim=-1)
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# out = torch.matmul(attn_weights, v_reshaped) # [1, 4, 256]
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# 简单的点积attention
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scores = torch.matmul(q, k_reshaped.T) * softmax_scale # [1, 4, 411528]
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attn_weights = torch.softmax(scores, dim=-1)
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out = torch.matmul(attn_weights, v_reshaped) # [1, 4, 256]
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# if return_softmax_lse:
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# softmax_lse = torch.zeros(1, 4, 1, device=q.device)
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# return out, softmax_lse
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# return out
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if return_softmax_lse:
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softmax_lse = torch.zeros(1, 4, 1, device=q.device)
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return out, softmax_lse
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return out
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# 原始sgl_kernel实现
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assert k_cache.stride(-1) == 1, "k_cache must have contiguous last dimension"
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