Organize Attention Backends (#1547)
This commit is contained in:
Liangsheng Yin
49
python/sglang/srt/layers/attention/__init__.py
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49
python/sglang/srt/layers/attention/__init__.py
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@@ -0,0 +1,49 @@
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from abc import ABC, abstractmethod
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from torch import nn
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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class AttentionBackend(ABC):
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"""The base class of attention backends"""
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@abstractmethod
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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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raise NotImplementedError()
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def init_cuda_graph_state(self, max_bs: int):
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"""Init the global shared states for cuda graph."""
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raise NotImplementedError()
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def init_forward_metadata_capture_cuda_graph(
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self, bs: int, req_pool_indices, seq_lens
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):
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"""Init the metadata for a forward pass for capturing a cuda graph."""
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raise NotImplementedError()
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def init_forward_metadata_replay_cuda_graph(
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self, bs: int, req_pool_indices, seq_lens
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):
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"""Init the metadata for a forward pass for replying a cuda graph."""
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raise NotImplementedError()
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def get_cuda_graph_seq_len_fill_value(self):
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"""Get the fill value for padded seq lens. Typically, it is 0 or 1."""
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raise NotImplementedError()
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def forward(self, q, k, v, layer: nn.Module, forward_batch: ForwardBatch):
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"""Run forward on an attention layer."""
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if forward_batch.forward_mode.is_decode():
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return self.forward_decode(q, k, v, layer, forward_batch)
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else:
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return self.forward_extend(q, k, v, layer, forward_batch)
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def forward_decode(self, q, k, v, layer: nn.Module, forward_batch: ForwardBatch):
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"""Run a forward for decode."""
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raise NotImplementedError()
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def forward_extend(self, q, k, v, layer: nn.Module, forward_batch: ForwardBatch):
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"""Run a forward for extend."""
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raise NotImplementedError()
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@@ -7,15 +7,14 @@ FlashInfer is faster and Triton is easier to customize.
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Each backend supports two operators: extend (i.e. prefill with cached prefix) and decode.
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"""
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from abc import ABC, abstractmethod
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from typing import TYPE_CHECKING
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import torch
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import torch.nn as nn
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from sglang.global_config import global_config
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from sglang.srt.layers.flashinfer_utils import update_flashinfer_indices
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from sglang.srt.managers.schedule_batch import global_server_args_dict
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from sglang.srt.layers.attention import AttentionBackend
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from sglang.srt.layers.attention.flashinfer_utils import update_flashinfer_indices
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
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from sglang.srt.utils import is_hip
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@@ -33,50 +32,6 @@ if not is_hip():
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from flashinfer.decode import _grouped_size_compiled_for_decode_kernels
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class AttentionBackend(ABC):
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"""The base class of attention backends"""
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@abstractmethod
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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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raise NotImplementedError()
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def init_cuda_graph_state(self, max_bs: int):
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"""Init the global shared states for cuda graph."""
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raise NotImplementedError()
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def init_forward_metadata_capture_cuda_graph(
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self, bs: int, req_pool_indices, seq_lens
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):
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"""Init the metadata for a forward pass for capturing a cuda graph."""
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raise NotImplementedError()
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def init_forward_metadata_replay_cuda_graph(
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self, bs: int, req_pool_indices, seq_lens
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):
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"""Init the metadata for a forward pass for replying a cuda graph."""
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raise NotImplementedError()
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def get_cuda_graph_seq_len_fill_value(self):
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"""Get the fill value for padded seq lens. Typically, it is 0 or 1."""
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raise NotImplementedError()
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def forward(self, q, k, v, layer: nn.Module, forward_batch: ForwardBatch):
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"""Run forward on an attention layer."""
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if forward_batch.forward_mode.is_decode():
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return self.forward_decode(q, k, v, layer, forward_batch)
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else:
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return self.forward_extend(q, k, v, layer, forward_batch)
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def forward_decode(self, q, k, v, layer: nn.Module, forward_batch: ForwardBatch):
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"""Run a forward for decode."""
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raise NotImplementedError()
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def forward_extend(self, q, k, v, layer: nn.Module, forward_batch: ForwardBatch):
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"""Run a forward for extend."""
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raise NotImplementedError()
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class FlashInferAttnBackend(AttentionBackend):
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"""Flashinfer attention kernels."""
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@@ -329,151 +284,3 @@ class FlashInferAttnBackend(AttentionBackend):
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)
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return o.view(-1, layer.tp_q_head_num * layer.head_dim)
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class TritonAttnBackend(AttentionBackend):
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def __init__(self, model_runner: ModelRunner):
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# Lazy import to avoid the initialization of cuda context
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from sglang.srt.layers.triton_attention.decode_attention import (
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decode_attention_fwd,
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)
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from sglang.srt.layers.triton_attention.extend_attention import (
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extend_attention_fwd,
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)
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super().__init__()
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self.decode_attention_fwd = decode_attention_fwd
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self.extend_attention_fwd = extend_attention_fwd
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self.num_head = (
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model_runner.model_config.num_attention_heads // model_runner.tp_size
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)
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if global_server_args_dict.get("triton_attention_reduce_in_fp32", False):
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self.reduce_dtype = torch.float32
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else:
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self.reduce_dtype = torch.float16
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self.forward_metadata = None
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self.cuda_graph_max_seq_len = model_runner.model_config.context_len
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def init_forward_metadata(self, forward_batch: ForwardBatch):
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"""Init auxiliary variables for triton attention backend."""
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if forward_batch.forward_mode.is_decode():
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start_loc = torch.zeros_like(forward_batch.seq_lens, dtype=torch.int32)
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start_loc[1:] = torch.cumsum(forward_batch.seq_lens[:-1], dim=0)
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total_num_tokens = torch.sum(forward_batch.seq_lens).item()
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attn_logits = torch.empty(
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(self.num_head, total_num_tokens),
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dtype=self.reduce_dtype,
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device="cuda",
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)
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max_seq_len = torch.max(forward_batch.seq_lens).item()
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max_extend_len = None
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else:
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start_loc = attn_logits = max_seq_len = None
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prefix_lens = forward_batch.extend_prefix_lens
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max_extend_len = torch.max(forward_batch.seq_lens - prefix_lens).item()
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self.forward_metadata = start_loc, attn_logits, max_seq_len, max_extend_len
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def init_cuda_graph_state(self, max_bs: int):
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self.cuda_graph_max_total_num_tokens = max_bs * self.cuda_graph_max_seq_len
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self.cuda_graph_start_loc = torch.zeros(
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(max_bs,), dtype=torch.int32, device="cuda"
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)
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self.cuda_graph_attn_logits = torch.empty(
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(
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self.num_head,
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self.cuda_graph_max_total_num_tokens,
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),
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dtype=self.reduce_dtype,
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device="cuda",
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)
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def init_forward_metadata_capture_cuda_graph(
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self, bs: int, req_pool_indices, seq_lens
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):
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self.forward_metadata = (
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self.cuda_graph_start_loc,
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self.cuda_graph_attn_logits,
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self.cuda_graph_max_seq_len,
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None,
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)
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def init_forward_metadata_replay_cuda_graph(
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self, bs: int, req_pool_indices, seq_lens
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):
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self.cuda_graph_start_loc.zero_()
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self.cuda_graph_start_loc[1:bs] = torch.cumsum(seq_lens[: bs - 1], dim=0)
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def get_cuda_graph_seq_len_fill_value(self):
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return 1
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def forward_extend(self, q, k, v, layer: nn.Module, forward_batch: ForwardBatch):
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# TODO: reuse the buffer across layers
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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.layer_id, forward_batch.out_cache_loc, k, v
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)
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start_loc, attn_logits, max_seq_len, max_extend_len = self.forward_metadata
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self.extend_attention_fwd(
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q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
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k.contiguous(),
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v.contiguous(),
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o.view(-1, layer.tp_q_head_num, layer.v_head_dim),
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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_seq_lens,
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forward_batch.extend_start_loc,
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max_extend_len,
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layer.scaling,
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layer.logit_cap,
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)
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return o
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def forward_decode(self, q, k, v, layer: nn.Module, forward_batch: ForwardBatch):
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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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# TODO: reuse the buffer across layers
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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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start_loc, attn_logits, max_seq_len, max_extend_len = self.forward_metadata
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forward_batch.token_to_kv_pool.set_kv_buffer(
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layer.layer_id, forward_batch.out_cache_loc, k, v
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)
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self.decode_attention_fwd(
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q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
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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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o.view(-1, layer.tp_q_head_num, layer.v_head_dim),
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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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start_loc,
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forward_batch.seq_lens,
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attn_logits,
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max_seq_len,
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layer.scaling,
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layer.logit_cap,
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)
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return o
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161
python/sglang/srt/layers/attention/triton_backend.py
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161
python/sglang/srt/layers/attention/triton_backend.py
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@@ -0,0 +1,161 @@
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from __future__ import annotations
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from typing import TYPE_CHECKING
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import torch
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import torch.nn as nn
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from sglang.srt.layers.attention import AttentionBackend
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from sglang.srt.managers.schedule_batch import global_server_args_dict
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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.model_executor.model_runner import ModelRunner
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class TritonAttnBackend(AttentionBackend):
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def __init__(self, model_runner: ModelRunner):
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# Lazy import to avoid the initialization of cuda context
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from sglang.srt.layers.attention.triton_ops.decode_attention import (
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decode_attention_fwd,
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)
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from sglang.srt.layers.attention.triton_ops.extend_attention import (
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extend_attention_fwd,
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)
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super().__init__()
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self.decode_attention_fwd = decode_attention_fwd
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self.extend_attention_fwd = extend_attention_fwd
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self.num_head = (
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model_runner.model_config.num_attention_heads // model_runner.tp_size
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)
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if global_server_args_dict.get("triton_attention_reduce_in_fp32", False):
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self.reduce_dtype = torch.float32
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else:
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self.reduce_dtype = torch.float16
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self.forward_metadata = None
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self.cuda_graph_max_seq_len = model_runner.model_config.context_len
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def init_forward_metadata(self, forward_batch: ForwardBatch):
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"""Init auxiliary variables for triton attention backend."""
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if forward_batch.forward_mode.is_decode():
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start_loc = torch.zeros_like(forward_batch.seq_lens, dtype=torch.int32)
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start_loc[1:] = torch.cumsum(forward_batch.seq_lens[:-1], dim=0)
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total_num_tokens = torch.sum(forward_batch.seq_lens).item()
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attn_logits = torch.empty(
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(self.num_head, total_num_tokens),
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dtype=self.reduce_dtype,
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device="cuda",
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)
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max_seq_len = torch.max(forward_batch.seq_lens).item()
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max_extend_len = None
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else:
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start_loc = attn_logits = max_seq_len = None
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prefix_lens = forward_batch.extend_prefix_lens
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max_extend_len = torch.max(forward_batch.seq_lens - prefix_lens).item()
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self.forward_metadata = start_loc, attn_logits, max_seq_len, max_extend_len
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def init_cuda_graph_state(self, max_bs: int):
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self.cuda_graph_max_total_num_tokens = max_bs * self.cuda_graph_max_seq_len
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self.cuda_graph_start_loc = torch.zeros(
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(max_bs,), dtype=torch.int32, device="cuda"
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)
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self.cuda_graph_attn_logits = torch.empty(
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(
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self.num_head,
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self.cuda_graph_max_total_num_tokens,
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),
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dtype=self.reduce_dtype,
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device="cuda",
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)
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def init_forward_metadata_capture_cuda_graph(
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self, bs: int, req_pool_indices, seq_lens
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):
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self.forward_metadata = (
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self.cuda_graph_start_loc,
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self.cuda_graph_attn_logits,
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self.cuda_graph_max_seq_len,
|
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None,
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)
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def init_forward_metadata_replay_cuda_graph(
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self, bs: int, req_pool_indices, seq_lens
|
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):
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self.cuda_graph_start_loc.zero_()
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self.cuda_graph_start_loc[1:bs] = torch.cumsum(seq_lens[: bs - 1], dim=0)
|
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def get_cuda_graph_seq_len_fill_value(self):
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return 1
|
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|
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def forward_extend(self, q, k, v, layer: nn.Module, forward_batch: ForwardBatch):
|
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# TODO: reuse the buffer across layers
|
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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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|
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forward_batch.token_to_kv_pool.set_kv_buffer(
|
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layer.layer_id, forward_batch.out_cache_loc, k, v
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)
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start_loc, attn_logits, max_seq_len, max_extend_len = self.forward_metadata
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self.extend_attention_fwd(
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q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
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k.contiguous(),
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v.contiguous(),
|
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o.view(-1, layer.tp_q_head_num, layer.v_head_dim),
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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_seq_lens,
|
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forward_batch.extend_start_loc,
|
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max_extend_len,
|
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layer.scaling,
|
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layer.logit_cap,
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)
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return o
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|
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def forward_decode(self, q, k, v, layer: nn.Module, forward_batch: ForwardBatch):
|
||||
# During torch.compile, there is a bug in rotary_emb that causes the
|
||||
# output value to have a 3D tensor shape. This reshapes the output correctly.
|
||||
q = q.reshape(-1, layer.tp_q_head_num * layer.qk_head_dim)
|
||||
|
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# TODO: reuse the buffer across layers
|
||||
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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|
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start_loc, attn_logits, max_seq_len, max_extend_len = self.forward_metadata
|
||||
|
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forward_batch.token_to_kv_pool.set_kv_buffer(
|
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layer.layer_id, forward_batch.out_cache_loc, k, v
|
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)
|
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|
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self.decode_attention_fwd(
|
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q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
|
||||
forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id),
|
||||
forward_batch.token_to_kv_pool.get_value_buffer(layer.layer_id),
|
||||
o.view(-1, layer.tp_q_head_num, layer.v_head_dim),
|
||||
forward_batch.req_to_token_pool.req_to_token,
|
||||
forward_batch.req_pool_indices,
|
||||
start_loc,
|
||||
forward_batch.seq_lens,
|
||||
attn_logits,
|
||||
max_seq_len,
|
||||
layer.scaling,
|
||||
layer.logit_cap,
|
||||
)
|
||||
return o
|
||||
@@ -22,7 +22,9 @@ import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
from sglang.srt.layers.triton_attention.prefill_attention import context_attention_fwd
|
||||
from sglang.srt.layers.attention.triton_ops.prefill_attention import (
|
||||
context_attention_fwd,
|
||||
)
|
||||
|
||||
CUDA_CAPABILITY = torch.cuda.get_device_capability()
|
||||
|
||||
@@ -37,7 +37,7 @@ import numpy as np
|
||||
import torch
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from sglang.srt.layers.attention_backend import AttentionBackend
|
||||
from sglang.srt.layers.attention import AttentionBackend
|
||||
from sglang.srt.managers.schedule_batch import ImageInputs, ModelWorkerBatch
|
||||
from sglang.srt.mem_cache.memory_pool import BaseTokenToKVPool, ReqToTokenPool
|
||||
from sglang.srt.model_executor.model_runner import ModelRunner
|
||||
|
||||
@@ -39,7 +39,8 @@ from vllm.model_executor.models import ModelRegistry
|
||||
|
||||
from sglang.srt.configs.model_config import AttentionArch, ModelConfig
|
||||
from sglang.srt.constrained import disable_cache
|
||||
from sglang.srt.layers.attention_backend import FlashInferAttnBackend, TritonAttnBackend
|
||||
from sglang.srt.layers.attention.flashinfer_backend import FlashInferAttnBackend
|
||||
from sglang.srt.layers.attention.triton_backend import TritonAttnBackend
|
||||
from sglang.srt.layers.logits_processor import LogitsProcessorOutput
|
||||
from sglang.srt.layers.sampler import Sampler
|
||||
from sglang.srt.lora.lora_manager import LoRAManager
|
||||
|
||||
Reference in New Issue
Block a user