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Support MLA for DeepSeek-V2 with Triton - step 1 (#905)

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This commit is contained in:
Ke Bao
2024-08-05 01:40:33 +08:00
committed by GitHub
parent f4d9953d9d
commit e1eae1fd15
10 changed files with 439 additions and 78 deletions
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31
python/sglang/srt/layers/radix_attention.py
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@@ -38,16 +38,22 @@ class RadixAttention(nn.Module):
num_kv_heads: int,
layer_id: int,
logit_cap: int = -1,
v_head_dim: int = -1,
):
super().__init__()
self.tp_q_head_num = num_heads
self.tp_k_head_num = num_kv_heads
self.tp_v_head_num = num_kv_heads
self.head_dim = head_dim
self.qk_head_dim = head_dim
self.v_head_dim = v_head_dim if v_head_dim != -1 else head_dim
self.scaling = scaling
self.layer_id = layer_id
if not global_server_args_dict.get("disable_flashinfer", False):
if (
not global_server_args_dict.get("disable_flashinfer", False)
and self.qk_head_dim == self.v_head_dim
):
self.extend_forward = self.extend_forward_flashinfer
self.decode_forward = self.decode_forward_flashinfer
else:
@@ -57,13 +63,17 @@ class RadixAttention(nn.Module):
self.logit_cap = logit_cap if logit_cap is not None and logit_cap > 0 else 0
def extend_forward_triton(self, q, k, v, input_metadata: InputMetadata):
o = torch.empty_like(q)
if self.qk_head_dim != self.v_head_dim:
o = q.new_empty((q.shape[0], self.tp_q_head_num * self.v_head_dim))
else:
o = torch.empty_like(q)
self.store_kv_cache(k, v, input_metadata)
extend_attention_fwd(
q.view(-1, self.tp_q_head_num, self.head_dim),
q.view(-1, self.tp_q_head_num, self.qk_head_dim),
k.contiguous(),
v.contiguous(),
o.view(-1, self.tp_q_head_num, self.head_dim),
o.view(-1, self.tp_q_head_num, self.v_head_dim),
input_metadata.token_to_kv_pool.get_key_buffer(self.layer_id),
input_metadata.token_to_kv_pool.get_value_buffer(self.layer_id),
input_metadata.req_to_token_pool.req_to_token,
@@ -82,14 +92,17 @@ class RadixAttention(nn.Module):
return o
def decode_forward_triton(self, q, k, v, input_metadata: InputMetadata):
o = torch.empty_like(q)
if self.qk_head_dim != self.v_head_dim:
o = q.new_empty((q.shape[0], self.tp_q_head_num * self.v_head_dim))
else:
o = torch.empty_like(q)
self.store_kv_cache(k, v, input_metadata)
token_attention_fwd(
q.view(-1, self.tp_q_head_num, self.head_dim),
q.view(-1, self.tp_q_head_num, self.qk_head_dim),
input_metadata.token_to_kv_pool.get_key_buffer(self.layer_id),
input_metadata.token_to_kv_pool.get_value_buffer(self.layer_id),
o.view(-1, self.tp_q_head_num, self.head_dim),
o.view(-1, self.tp_q_head_num, self.v_head_dim),
input_metadata.req_to_token_pool.req_to_token,
input_metadata.req_pool_indices,
input_metadata.triton_start_loc,
@@ -160,8 +173,8 @@ class RadixAttention(nn.Module):
return o.view(-1, self.tp_q_head_num * self.head_dim)
def forward(self, q, k, v, input_metadata: InputMetadata):
k = k.view(-1, self.tp_k_head_num, self.head_dim)
v = v.view(-1, self.tp_v_head_num, self.head_dim)
k = k.view(-1, self.tp_k_head_num, self.qk_head_dim)
v = v.view(-1, self.tp_v_head_num, self.v_head_dim)
if input_metadata.forward_mode == ForwardMode.EXTEND:
return self.extend_forward(q, k, v, input_metadata)