Add a simple torch native attention backend (#2241)
This commit is contained in:
Qun Yang
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from __future__ import annotations
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from typing import TYPE_CHECKING, Optional
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import torch
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from torch.nn.functional import scaled_dot_product_attention
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from sglang.srt.layers.attention import AttentionBackend
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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if TYPE_CHECKING:
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.model_executor.model_runner import ModelRunner
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class TorchNativeAttnBackend(AttentionBackend):
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def __init__(self, model_runner: ModelRunner):
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super().__init__()
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self.forward_metadata = None
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self.device = model_runner.device
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def init_forward_metadata(self, forward_batch: ForwardBatch):
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"""Init the metadata for a forward pass."""
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pass
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def init_cuda_graph_state(self, max_bs: int):
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# TODO: Support CUDA graph
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raise ValueError(
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"Torch native attention does not support CUDA graph for now. Please --disable-cuda-graph"
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)
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def init_forward_metadata_capture_cuda_graph(
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self,
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bs: int,
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req_pool_indices: torch.Tensor,
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seq_lens: torch.Tensor,
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encoder_lens: Optional[torch.Tensor] = None,
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):
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# TODO: Support CUDA graph
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raise ValueError(
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"Torch native attention does not support CUDA graph for now. Please --disable-cuda-graph"
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)
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def init_forward_metadata_replay_cuda_graph(
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self,
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bs: int,
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req_pool_indices: torch.Tensor,
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seq_lens: torch.Tensor,
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seq_lens_sum: int,
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encoder_lens: Optional[torch.Tensor] = None,
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):
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# TODO: Support CUDA graph
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raise ValueError(
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"Torch native attention does not support CUDA graph for now. Please --disable-cuda-graph"
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)
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def get_cuda_graph_seq_len_fill_value(self):
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# TODO: Support CUDA graph
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raise ValueError(
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"Torch native attention does not support CUDA graph for now. Please --disable-cuda-graph"
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)
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def _run_sdpa_forward_extend(
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self,
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query: torch.Tensor,
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output: torch.Tensor,
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k_cache: torch.Tensor,
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v_cache: torch.Tensor,
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req_to_token: torch.Tensor,
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req_pool_indices: torch.Tensor,
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seq_lens: torch.Tensor,
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extend_prefix_lens: torch.Tensor,
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extend_seq_lens: torch.Tensor,
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scaling=None,
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enable_gqa=False,
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causal=False,
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):
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"""Run the extend forward by using torch native sdpa op.
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Args:
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query: [num_tokens, num_heads, head_size]
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output: [num_tokens, num_heads, head_size]
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k_cache: [max_total_num_tokens, num_heads, head_size]
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v_cache: [max_total_num_tokens, num_heads, head_size]
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req_to_token: [max_num_reqs, max_context_len]
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req_pool_indices: [num_seqs]
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seq_lens: [num_seqs]
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extend_prefix_lens: [num_seqs]
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extend_seq_lens: [num_seqs]
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scaling: float or None
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enable_gqa: bool
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causal: bool
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Returns:
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output: [num_tokens, num_heads, head_size]
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"""
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assert seq_lens.shape[0] == extend_prefix_lens.shape[0]
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assert seq_lens.shape[0] == extend_seq_lens.shape[0]
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# [num_tokens, num_heads, head_size] -> [num_heads, num_tokens, head_size]
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query = query.movedim(0, query.dim() - 2)
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start_q, start_kv = 0, 0
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for seq_idx in range(seq_lens.shape[0]):
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# TODO: this loop process a sequence per iter, this is inefficient.
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# Need optimize the performance later.
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extend_seq_len_q = extend_seq_lens[seq_idx]
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prefill_seq_len_q = extend_prefix_lens[seq_idx]
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seq_len_kv = seq_lens[seq_idx]
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end_q = start_q + extend_seq_len_q
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end_kv = start_kv + seq_len_kv
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per_req_query = query[:, start_q:end_q, :]
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per_req_query_redudant = torch.empty(
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(per_req_query.shape[0], seq_len_kv, per_req_query.shape[2]),
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dtype=per_req_query.dtype,
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device=per_req_query.device,
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)
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per_req_query_redudant[:, prefill_seq_len_q:, :] = per_req_query
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# get key and value from cache. per_req_tokens contains the kv cache
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# index for each token in the sequence.
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req_pool_idx = req_pool_indices[seq_idx]
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per_req_tokens = req_to_token[req_pool_idx, :seq_len_kv]
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per_req_key = k_cache[per_req_tokens].movedim(0, query.dim() - 2)
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per_req_value = v_cache[per_req_tokens].movedim(0, query.dim() - 2)
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per_req_out_redudant = (
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scaled_dot_product_attention(
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per_req_query_redudant.unsqueeze(0),
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per_req_key.unsqueeze(0),
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per_req_value.unsqueeze(0),
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enable_gqa=enable_gqa,
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scale=scaling,
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is_causal=causal,
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)
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.squeeze(0)
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.movedim(query.dim() - 2, 0)
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)
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output[start_q:end_q, :, :] = per_req_out_redudant[prefill_seq_len_q:, :, :]
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start_q, start_kv = end_q, end_kv
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return output
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def _run_sdpa_forward_decode(
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self,
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query: torch.Tensor,
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output: torch.Tensor,
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k_cache: torch.Tensor,
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v_cache: torch.Tensor,
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req_to_token: torch.Tensor,
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req_pool_indices: torch.Tensor,
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seq_lens: torch.Tensor,
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scaling=None,
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enable_gqa=False,
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causal=False,
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):
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"""Run the decode forward by using torch native sdpa op.
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Args:
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query: [num_tokens, num_heads, head_size]
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output: [num_tokens, num_heads, head_size]
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k_cache: [max_total_num_tokens, num_heads, head_size]
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v_cache: [max_total_num_tokens, num_heads, head_size]
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req_to_token: [max_num_reqs, max_context_len]
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req_pool_indices: [num_seqs]
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seq_lens: [num_seqs]
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scaling: float or None
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enable_gqa: bool
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causal: bool
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Returns:
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output: [num_tokens, num_heads, head_size]
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"""
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# [num_tokens, num_heads, head_size] -> [num_heads, num_tokens, head_size]
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query = query.movedim(0, query.dim() - 2)
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start_q, start_kv = 0, 0
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for seq_idx in range(seq_lens.shape[0]):
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# TODO: this loop process a sequence per iter, this is inefficient.
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# Need optimize the performance later.
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seq_len_q = 1
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seq_len_kv = seq_lens[seq_idx]
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end_q = start_q + seq_len_q
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end_kv = start_kv + seq_len_kv
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per_req_query = query[:, start_q:end_q, :]
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# get key and value from cache. per_req_tokens contains the kv cache
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# index for each token in the sequence.
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req_pool_idx = req_pool_indices[seq_idx]
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per_req_tokens = req_to_token[req_pool_idx, :seq_len_kv]
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per_req_key = k_cache[per_req_tokens].movedim(0, query.dim() - 2)
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per_req_value = v_cache[per_req_tokens].movedim(0, query.dim() - 2)
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per_req_out = (
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scaled_dot_product_attention(
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per_req_query.unsqueeze(0),
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per_req_key.unsqueeze(0),
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per_req_value.unsqueeze(0),
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enable_gqa=enable_gqa,
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scale=scaling,
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is_causal=causal,
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)
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.squeeze(0)
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.movedim(query.dim() - 2, 0)
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)
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output[start_q:end_q, :, :] = per_req_out
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start_q, start_kv = end_q, end_kv
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return output
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def forward_extend(
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self, q, k, v, layer: RadixAttention, forward_batch: ForwardBatch
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):
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if layer.qk_head_dim != layer.v_head_dim:
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o = q.new_empty((q.shape[0], layer.tp_q_head_num * layer.v_head_dim))
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else:
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o = torch.empty_like(q)
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forward_batch.token_to_kv_pool.set_kv_buffer(
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layer, forward_batch.out_cache_loc, k, v
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)
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use_gqa = layer.tp_q_head_num != layer.tp_k_head_num
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q_ = q.view(-1, layer.tp_q_head_num, layer.qk_head_dim)
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o_ = o.view(-1, layer.tp_q_head_num, layer.v_head_dim)
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self._run_sdpa_forward_extend(
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q_,
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o_,
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forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id),
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forward_batch.token_to_kv_pool.get_value_buffer(layer.layer_id),
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forward_batch.req_to_token_pool.req_to_token,
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forward_batch.req_pool_indices,
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forward_batch.seq_lens,
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forward_batch.extend_prefix_lens,
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forward_batch.extend_seq_lens,
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scaling=layer.scaling,
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enable_gqa=use_gqa,
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causal=not layer.is_cross_attention,
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)
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return o
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def forward_decode(
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self, q, k, v, layer: RadixAttention, forward_batch: ForwardBatch
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):
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# During torch.compile, there is a bug in rotary_emb that causes the
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# output value to have a 3D tensor shape. This reshapes the output correctly.
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q = q.reshape(-1, layer.tp_q_head_num * layer.qk_head_dim)
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if layer.qk_head_dim != layer.v_head_dim:
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o = q.new_empty((q.shape[0], layer.tp_q_head_num * layer.v_head_dim))
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else:
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o = torch.empty_like(q)
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forward_batch.token_to_kv_pool.set_kv_buffer(
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layer, forward_batch.out_cache_loc, k, v
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)
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use_gqa = layer.tp_q_head_num != layer.tp_k_head_num
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q_ = q.view(-1, layer.tp_q_head_num, layer.qk_head_dim)
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o_ = o.view(-1, layer.tp_q_head_num, layer.v_head_dim)
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self._run_sdpa_forward_decode(
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q_,
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o_,
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forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id),
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forward_batch.token_to_kv_pool.get_value_buffer(layer.layer_id),
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forward_batch.req_to_token_pool.req_to_token,
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forward_batch.req_pool_indices,
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forward_batch.seq_lens,
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scaling=layer.scaling,
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enable_gqa=use_gqa,
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causal=False,
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)
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return o
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