Refactor attention backend (#1381)
This commit is contained in:
Lianmin Zheng
@@ -15,25 +15,14 @@ limitations under the License.
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"""Radix attention."""
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from typing import Optional
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import torch
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from flashinfer.cascade import merge_state
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from torch import nn
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from sglang.global_config import global_config
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from sglang.srt.layers.triton_attention.decode_attention import decode_attention_fwd
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from sglang.srt.layers.triton_attention.extend_attention import extend_attention_fwd
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from sglang.srt.model_executor.forward_batch_info import ForwardMode, InputMetadata
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from sglang.srt.model_executor.model_runner import global_server_args_dict
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from sglang.srt.model_executor.forward_batch_info import InputMetadata
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class RadixAttention(nn.Module):
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"""
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The attention layer implementation.
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Now it has two backends: FlashInfer and Triton.
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FlashInfer is faster and Triton is easier to customize.
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It supports two operators: extend (i.e. prefill with cached prefix) and decode.
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"""
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def __init__(
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@@ -43,8 +32,8 @@ class RadixAttention(nn.Module):
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scaling: float,
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num_kv_heads: int,
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layer_id: int,
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sliding_window_size: Optional[int] = None,
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logit_cap: int = -1,
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sliding_window_size: int = -1,
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logit_cap: float = 0.0,
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v_head_dim: int = -1,
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):
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super().__init__()
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@@ -56,164 +45,14 @@ class RadixAttention(nn.Module):
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self.v_head_dim = v_head_dim if v_head_dim != -1 else head_dim
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self.scaling = scaling
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self.layer_id = layer_id
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self.logit_cap = logit_cap if logit_cap is not None and logit_cap > 0 else 0
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self.sliding_window_size = sliding_window_size if sliding_window_size else -1
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# Choose backend
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if (
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global_server_args_dict["attention_backend"] == "flashinfer"
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and self.qk_head_dim == self.v_head_dim
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):
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self.extend_forward = self.extend_forward_flashinfer
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self.decode_forward = self.decode_forward_flashinfer
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elif global_server_args_dict["attention_backend"] == "triton":
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self.extend_forward = self.extend_forward_triton
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self.decode_forward = self.decode_forward_triton
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else:
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raise ValueError(
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f"Invalid attention backend: {global_server_args_dict['attention_backend']}"
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)
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def extend_forward_triton(self, q, k, v, input_metadata: InputMetadata):
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if self.qk_head_dim != self.v_head_dim:
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o = q.new_empty((q.shape[0], self.tp_q_head_num * self.v_head_dim))
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else:
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o = torch.empty_like(q)
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self.store_kv_cache(k, v, input_metadata)
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extend_attention_fwd(
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q.view(-1, self.tp_q_head_num, self.qk_head_dim),
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k.contiguous(),
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v.contiguous(),
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o.view(-1, self.tp_q_head_num, self.v_head_dim),
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input_metadata.token_to_kv_pool.get_key_buffer(self.layer_id),
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input_metadata.token_to_kv_pool.get_value_buffer(self.layer_id),
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input_metadata.req_to_token_pool.req_to_token,
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input_metadata.req_pool_indices,
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input_metadata.triton_start_loc,
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input_metadata.seq_lens,
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input_metadata.triton_prefix_lens,
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input_metadata.extend_start_loc,
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input_metadata.extend_seq_lens,
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input_metadata.triton_max_seq_len,
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input_metadata.triton_max_extend_len,
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sm_scale=self.scaling,
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logit_cap=self.logit_cap,
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)
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return o
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def decode_forward_triton(self, q, k, v, input_metadata: InputMetadata):
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if self.qk_head_dim != self.v_head_dim:
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o = q.new_empty((q.shape[0], self.tp_q_head_num * self.v_head_dim))
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else:
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o = torch.empty_like(q)
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self.store_kv_cache(k, v, input_metadata)
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decode_attention_fwd(
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q.view(-1, self.tp_q_head_num, self.qk_head_dim),
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input_metadata.token_to_kv_pool.get_key_buffer(self.layer_id),
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input_metadata.token_to_kv_pool.get_value_buffer(self.layer_id),
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o.view(-1, self.tp_q_head_num, self.v_head_dim),
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input_metadata.req_to_token_pool.req_to_token,
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input_metadata.req_pool_indices,
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input_metadata.triton_start_loc,
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input_metadata.seq_lens,
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input_metadata.triton_max_seq_len,
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input_metadata.total_num_tokens,
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sm_scale=self.scaling,
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logit_cap=self.logit_cap,
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)
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return o
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def extend_forward_flashinfer(self, q, k, v, input_metadata: InputMetadata):
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# using two wrappers is unnecessary in the current PR, but are prepared for future PRs
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prefill_wrapper_paged = input_metadata.flashinfer_prefill_wrapper_paged
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if self.sliding_window_size != -1:
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prefill_wrapper_paged = prefill_wrapper_paged[0]
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else:
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if isinstance(prefill_wrapper_paged, list):
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prefill_wrapper_paged = prefill_wrapper_paged[1]
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if not input_metadata.flashinfer_use_ragged:
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if k is not None:
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assert v is not None
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self.store_kv_cache(k, v, input_metadata)
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o = prefill_wrapper_paged.forward(
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q.contiguous().view(-1, self.tp_q_head_num, self.head_dim),
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input_metadata.token_to_kv_pool.get_kv_buffer(self.layer_id),
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causal=True,
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sm_scale=self.scaling,
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window_left=self.sliding_window_size,
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logits_soft_cap=self.logit_cap,
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)
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else:
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o1, s1 = (
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input_metadata.flashinfer_prefill_wrapper_ragged.forward_return_lse(
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q.contiguous().view(-1, self.tp_q_head_num, self.head_dim),
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k.contiguous().view(-1, self.tp_k_head_num, self.head_dim),
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v.contiguous().view(-1, self.tp_v_head_num, self.head_dim),
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causal=True,
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sm_scale=self.scaling,
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logits_soft_cap=self.logit_cap,
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)
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)
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if input_metadata.extend_no_prefix:
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o = o1
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else:
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o2, s2 = prefill_wrapper_paged.forward_return_lse(
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q.contiguous().view(-1, self.tp_q_head_num, self.head_dim),
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input_metadata.token_to_kv_pool.get_kv_buffer(self.layer_id),
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causal=False,
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sm_scale=self.scaling,
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logits_soft_cap=self.logit_cap,
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)
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o, _ = merge_state(o1, s1, o2, s2)
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self.store_kv_cache(k, v, input_metadata)
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if input_metadata.total_num_tokens >= global_config.layer_sync_threshold:
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torch.cuda.synchronize()
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return o.view(-1, self.tp_q_head_num * self.head_dim)
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def decode_forward_flashinfer(self, q, k, v, input_metadata: InputMetadata):
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decode_wrapper = input_metadata.flashinfer_decode_wrapper
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if self.sliding_window_size != -1:
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decode_wrapper = decode_wrapper[0]
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else:
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if isinstance(decode_wrapper, list):
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decode_wrapper = decode_wrapper[1]
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if k is not None:
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assert v is not None
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self.store_kv_cache(k, v, input_metadata)
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o = decode_wrapper.forward(
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q.contiguous().view(-1, self.tp_q_head_num, self.head_dim),
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input_metadata.token_to_kv_pool.get_kv_buffer(self.layer_id),
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sm_scale=self.scaling,
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logits_soft_cap=self.logit_cap,
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)
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return o.view(-1, self.tp_q_head_num * self.head_dim)
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self.logit_cap = logit_cap
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self.sliding_window_size = sliding_window_size or -1
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def forward(self, q, k, v, input_metadata: InputMetadata):
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if k is not None:
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# For cross-layer sharing, kv can be None
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assert v is not None
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k = k.view(-1, self.tp_k_head_num, self.qk_head_dim)
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v = v.view(-1, self.tp_v_head_num, self.v_head_dim)
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if input_metadata.forward_mode.is_extend():
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return self.extend_forward(q, k, v, input_metadata)
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elif input_metadata.forward_mode.is_decode():
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return self.decode_forward(q, k, v, input_metadata)
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def store_kv_cache(self, cache_k, cache_v, input_metadata: InputMetadata):
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input_metadata.token_to_kv_pool.set_kv_buffer(
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self.layer_id, input_metadata.out_cache_loc, cache_k, cache_v
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)
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return input_metadata.attn_backend.forward(q, k, v, self, input_metadata)
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