155 lines
6.1 KiB
Python
155 lines
6.1 KiB
Python
"""Radix attention."""
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import numpy as np
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import torch
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from torch import nn
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from sglang.srt.layers.context_flashattention_nopad import context_attention_fwd
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from sglang.srt.layers.extend_attention import extend_attention_fwd
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from sglang.srt.layers.token_attention import token_attention_fwd
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from sglang.srt.managers.controller.model_runner import ForwardMode, InputMetadata
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class RadixAttention(nn.Module):
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def __init__(
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self, num_heads: int, head_dim: int, scaling: float, num_kv_heads: int,
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layer_id: int, logit_cap: int = -1
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):
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super().__init__()
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self.tp_q_head_num = num_heads
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self.tp_k_head_num = num_kv_heads
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self.tp_v_head_num = num_kv_heads
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self.head_dim = head_dim
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self.layer_id = layer_id
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assert np.allclose(scaling, 1.0 / (head_dim**0.5))
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from sglang.srt.managers.controller.model_runner import global_server_args_dict
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if global_server_args_dict.get("enable_flashinfer", False):
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self.prefill_forward = self.prefill_forward_flashinfer
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self.extend_forward = self.prefill_forward_flashinfer
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self.decode_forward = self.decode_forward_flashinfer
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# flashinfer only accepts a boolean logit_cap argument
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if logit_cap > 0:
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assert logit_cap == 30
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self.logit_cap = True
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else:
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self.logit_cap = False
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else:
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self.prefill_forward = self.prefill_forward_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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self.logit_cap = logit_cap
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def prefill_forward_triton(self, q, k, v, input_metadata: InputMetadata):
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o = torch.empty_like(q)
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context_attention_fwd(
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q.view(-1, self.tp_q_head_num, self.head_dim),
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k,
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v,
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o.view(-1, self.tp_q_head_num, self.head_dim),
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input_metadata.start_loc,
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input_metadata.seq_lens,
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input_metadata.max_seq_len,
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self.logit_cap,
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)
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self.store_kv_cache(k, v, input_metadata)
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return o
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def extend_forward_triton(self, q, k, v, input_metadata: InputMetadata):
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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.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.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.start_loc,
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input_metadata.seq_lens,
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input_metadata.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.max_seq_len,
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input_metadata.max_extend_len,
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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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o = torch.empty_like(q)
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self.store_kv_cache(k, v, input_metadata)
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token_attention_fwd(
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q.view(-1, self.tp_q_head_num, self.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.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.start_loc,
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input_metadata.seq_lens,
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input_metadata.max_seq_len,
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input_metadata.other_kv_index,
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input_metadata.total_num_tokens,
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self.logit_cap,
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)
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return o
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def prefill_forward_flashinfer(self, q, k, v, input_metadata: InputMetadata):
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self.store_kv_cache(k, v, input_metadata)
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o = input_metadata.flashinfer_prefill_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.kv_data[self.layer_id],
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logits_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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def decode_forward_flashinfer(self, q, k, v, input_metadata: InputMetadata):
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self.store_kv_cache(k, v, input_metadata)
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o = input_metadata.flashinfer_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.kv_data[self.layer_id],
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logits_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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def forward(self, q, k, v, input_metadata: InputMetadata):
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k = k.view(-1, self.tp_k_head_num, self.head_dim)
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v = v.view(-1, self.tp_v_head_num, self.head_dim)
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if input_metadata.forward_mode == ForwardMode.PREFILL:
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return self.prefill_forward(q, k, v, input_metadata)
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elif input_metadata.forward_mode == ForwardMode.EXTEND:
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return self.extend_forward(q, k, v, input_metadata)
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elif input_metadata.forward_mode == ForwardMode.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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key_buffer = input_metadata.token_to_kv_pool.get_key_buffer(self.layer_id)
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value_buffer = input_metadata.token_to_kv_pool.get_value_buffer(self.layer_id)
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if input_metadata.out_cache_loc is not None:
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key_buffer[input_metadata.out_cache_loc] = cache_k
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value_buffer[input_metadata.out_cache_loc] = cache_v
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elif input_metadata.out_cache_cont_start is not None:
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key_buffer[
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input_metadata.out_cache_cont_start : input_metadata.out_cache_cont_end
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] = cache_k
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value_buffer[
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input_metadata.out_cache_cont_start : input_metadata.out_cache_cont_end
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] = cache_v
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else:
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raise RuntimeError()
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