Support double sparsity (#1459)
This commit is contained in:
Shuo Yang
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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 DoubleSparseAttnBackend(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.double_sparsity_attention import (
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flash_decode_attention_fwd,
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flash_decode_sparse_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 = flash_decode_attention_fwd
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self.decode_sparse_attention_fwd = flash_decode_sparse_attention_fwd
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self.extend_attention_fwd = extend_attention_fwd
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self.num_head = model_runner.model_config.num_attention_heads
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self.head_dim = model_runner.model_config.hidden_size // self.num_head
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self.heavy_token_num = model_runner.server_args.ds_heavy_token_num
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self.sorted_channels = model_runner.sorted_channels
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self.sparse_decode_thresold = (
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model_runner.server_args.ds_sparse_decode_threshold
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)
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self.att_out_approx: torch.Tensor = None
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self.mid_out: torch.Tensor = None
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self.mid_o_logexpsum: torch.Tensor = None
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# TODO: Change the hard-coded block_seq_num
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self.BLOCK_SEQ = 128
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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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min_seq_len = torch.min(forward_batch.seq_lens).item()
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max_extend_len = None
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# NOTE: Align sequence order with req_to_token order
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ds_req_to_token = forward_batch.req_to_token_pool.req_to_token[
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forward_batch.req_pool_indices
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]
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bsz = forward_batch.seq_lens.shape[0]
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att_out_approx = torch.empty(
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[self.num_head, bsz, max_seq_len],
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dtype=self.reduce_dtype,
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device="cuda",
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)
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block_seq_num = (
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self.heavy_token_num + self.BLOCK_SEQ - 1
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) // self.BLOCK_SEQ
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mid_out = torch.empty(
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[bsz, self.num_head, block_seq_num, self.head_dim],
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dtype=torch.float32,
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device="cuda",
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)
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mid_o_logexpsum = torch.empty(
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[bsz, self.num_head, block_seq_num], dtype=torch.float32, device="cuda"
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)
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self.att_out_approx = att_out_approx
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self.mid_out = mid_out
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self.mid_o_logexpsum = mid_o_logexpsum
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else:
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start_loc = attn_logits = max_seq_len = min_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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ds_req_to_token = None
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self.forward_metadata = (
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start_loc,
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attn_logits,
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max_seq_len,
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min_seq_len,
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max_extend_len,
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ds_req_to_token,
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)
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def init_cuda_graph_state(self, max_bs: int):
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# TODO(Andy): Support CUDA graph for double sparse attention
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raise ValueError(
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"Double sparse attention does not support CUDA graph for now. Please --disable-cuda-graph"
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)
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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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k_label = torch.gather(
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k,
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2,
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self.sorted_channels[layer.layer_id]
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.unsqueeze(0)
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.expand(k.shape[0], -1, -1),
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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, k_label
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)
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(
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start_loc,
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attn_logits,
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max_seq_len,
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min_seq_len,
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max_extend_len,
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ds_req_to_token,
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) = 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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# TODO: Add min seqlen
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(
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start_loc,
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attn_logits,
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max_seq_len,
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min_seq_len,
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max_extend_len,
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ds_req_to_token,
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) = self.forward_metadata
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k_label = torch.gather(
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k,
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2,
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self.sorted_channels[layer.layer_id]
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.unsqueeze(0)
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.expand(k.shape[0], -1, -1),
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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, k_label
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)
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# NOTE(Andy) shouldn't be used when max_len_in_batch < heavy_token_num
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# and set a minimum value for sparse_decode
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if (
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min_seq_len < self.heavy_token_num
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or max_seq_len < self.sparse_decode_thresold
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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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else:
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# TODO(Andy): indexing with torch.gather or torch.index_select or customized kernel
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q_label = torch.gather(
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q.view(-1, layer.tp_q_head_num, layer.qk_head_dim),
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2,
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self.sorted_channels[layer.layer_id]
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.unsqueeze(0)
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.expand(q.shape[0], -1, -1),
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)
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self.decode_sparse_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.qk_head_dim),
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q_label,
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forward_batch.token_to_kv_pool.get_label_buffer(layer.layer_id),
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ds_req_to_token,
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forward_batch.seq_lens,
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max_seq_len,
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layer.scaling,
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layer.logit_cap,
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self.heavy_token_num,
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self.att_out_approx,
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self.mid_out,
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self.mid_o_logexpsum,
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self.BLOCK_SEQ,
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)
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return o
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