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Unverified Commit ca8d02ab authored by Stefan He's avatar Stefan He Committed by GitHub
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FA3 Spec Decoding to support top k = 1 and add cuda graph support (#5050) 


Co-authored-by: default avatarQingquan Song <ustcsqq@gmail.com>
Co-authored-by: default avatarChunan Zeng <zcnrex@gmail.com>
parent 3f287b85
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Showing with 270 additions and 119 deletions
python/sglang/srt/layers/attention/flashattention_backend.py
View file @ ca8d02ab
......@@ -27,19 +27,42 @@ from sgl_kernel.flash_attn import flash_attn_with_kvcache
@dataclass
class FlashAttentionMetadata:
"""Metadata for decode operations to avoid redundant computations."""
"""Metadata to be init once in the model forward pass,
each layer's forward pass can reuse the metadata."""
# Cumulative sequence lengths for query
cu_seqlens_q: torch.Tensor = None
# Cumulative sequence lengths for key
cu_seqlens_k: torch.Tensor = None
# Maximum sequence length for query
max_seq_len_q: int = 0
# Maximum sequence length for key
max_seq_len_k: int = 0
# Window size (typically used by Gemma)
window_size: tuple = (-1, -1)
# Page table, the index of KV Cache Tables/Blocks
page_table: torch.Tensor = None
# Sequence lengths for the forward batch
cache_seqlens_int32: torch.Tensor = None
class FlashAttentionBackend(AttentionBackend):
"""FlashAttention backend implementation."""
"""FlashAttention backend implementation.
Note about the init:
- If no spec decoding
- FlashAttentionBackend will be init once when the server starts.
- If spec decoding
- FlashAttentionBackend will be init once for the target worker
- FlashAttentionMultiStepBackend will be once for the draft worker
- It will spawn num_steps FlashAttentionBackend for the draft worker
Note about CUDA Graph:
- We only support CUDA Graph for Decode (Normal Decode and Draft Decode) and Target Verify.
- We don't support CUDA Graph for Extend and Draft Extend.
- When server init, init_cuda_graph_state will be called first and then init_cuda_graph_capture will be called.
- For each forward batch, init_replay_cuda_graph will be called first and then replay the graph.
"""
def __init__(
self,
......@@ -56,41 +79,42 @@ class FlashAttentionBackend(AttentionBackend):
and model_runner.model_config.is_encoder_decoder
), "Sliding window and cross attention are not supported together"
# Initialize metadata
self.forward_metadata: FlashAttentionMetadata = None
self.max_context_len = model_runner.model_config.context_len
self.device = model_runner.device
self.decode_cuda_graph_metadata = {}
self.target_verify_metadata = {}
self.req_to_token = model_runner.req_to_token_pool.req_to_token
self.page_size = model_runner.page_size
self.use_mla = (
model_runner.model_config.attention_arch == AttentionArch.MLA
) and (not global_server_args_dict["disable_mla"])
self.skip_prefill = skip_prefill
self.topk = topk
self.speculative_num_steps = speculative_num_steps
# TODO: Support Topk > 1 for FlashAttentionBackend Spec Decoding
assert (
topk <= 1
), "topk must be 1 (if spec decoding) or 0 (if no spec decoding) for FlashAttentionBackend"
self.topk = 1
self.step_id = step_id
self.speculative_num_steps = speculative_num_steps
def init_forward_metadata(self, forward_batch: ForwardBatch):
"""Initialize forward metadata to cache repetitive calculations."""
# Create metadata based on forward mode
metadata = FlashAttentionMetadata()
# Get sequence information
seqlens_in_batch = forward_batch.seq_lens
# Precompute int32 version of sequence lengths
batch_size = len(seqlens_in_batch)
device = seqlens_in_batch.device
if forward_batch.forward_mode == ForwardMode.DECODE:
if self.skip_prefill:
if forward_batch.forward_mode.is_decode():
# Skip Prefill or Draft Decode
# Note: Draft Decode will be ran on the Draft Worker
if forward_batch.spec_info is not None:
metadata.cu_seqlens_q = torch.arange(
0, batch_size * self.topk + 1, dtype=torch.int32, device=device
0, batch_size + 1, dtype=torch.int32, device=device
)
seq_lens_with_decode = seqlens_in_batch + (self.step_id + 1)
metadata.cache_seqlens_int32 = (
(seq_lens_with_decode).repeat_interleave(self.topk).to(torch.int32)
)
metadata.cache_seqlens_int32 = seq_lens_with_decode.to(torch.int32)
metadata.cu_seqlens_k = torch.nn.functional.pad(
torch.cumsum(
metadata.cache_seqlens_int32, dim=0, dtype=torch.int32
......@@ -103,86 +127,58 @@ class FlashAttentionBackend(AttentionBackend):
metadata.page_table = forward_batch.req_to_token_pool.req_to_token[
forward_batch.req_pool_indices, : metadata.max_seq_len_k
]
metadata.page_table = metadata.page_table.repeat_interleave(
self.topk, dim=0
)
cache_loc = forward_batch.out_cache_loc.view(
self.speculative_num_steps, -1
).T
# Calculate page table indices and cache location indices to update the page table.
batch_indices = torch.arange(
batch_size, device=device
).repeat_interleave(self.topk * (self.step_id + 1))
topk_indices = torch.arange(self.topk, device=device).repeat(
batch_size * (self.step_id + 1)
)
row_indices = batch_indices * self.topk + topk_indices
page_table_col_base_indices = seqlens_in_batch.unsqueeze(
1
) + torch.arange(self.step_id + 1, device=device)
page_table_col_indices = page_table_col_base_indices.view(-1).repeat(
self.topk
)
cache_loc_col_indices = torch.arange(
self.step_id + 1, device=device, dtype=torch.int32
).repeat(batch_size * self.topk)
metadata.page_table[row_indices, page_table_col_indices] = cache_loc[
row_indices, cache_loc_col_indices
].to(torch.int32)
else:
for idx, single_seq_len in enumerate(seq_lens_with_decode):
real_bsz_start_idx = idx
real_bsz_end_idx = idx + 1
metadata.page_table[
real_bsz_start_idx:real_bsz_end_idx,
(single_seq_len - (self.step_id + 1)) : single_seq_len,
] = cache_loc[
real_bsz_start_idx:real_bsz_end_idx, : (self.step_id + 1)
]
else: # Normal Decode without Spec Decoding
metadata.cache_seqlens_int32 = seqlens_in_batch.to(torch.int32)
metadata.cu_seqlens_k = torch.nn.functional.pad(
torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)
)
# Precompute maximum sequence length
metadata.max_seq_len_k = forward_batch.seq_lens_cpu.max().item()
# Precompute page table
metadata.page_table = forward_batch.req_to_token_pool.req_to_token[
forward_batch.req_pool_indices, : metadata.max_seq_len_k
]
metadata.cu_seqlens_q = torch.arange(
0, batch_size + 1, dtype=torch.int32, device=device
)
elif forward_batch.forward_mode == ForwardMode.TARGET_VERIFY:
elif forward_batch.forward_mode.is_target_verify():
# Note: Target Verify will be ran on the Target Worker
draft_token_num = forward_batch.spec_info.draft_token_num
metadata.cu_seqlens_q = torch.arange(
0, batch_size * draft_token_num + 1, dtype=torch.int32, device=device
metadata.cache_seqlens_int32 = (
forward_batch.seq_lens + draft_token_num
).to(torch.int32)
metadata.max_seq_len_q = draft_token_num
metadata.max_seq_len_k = (
forward_batch.seq_lens_cpu.max().item() + draft_token_num
)
aug_seq_lens = (forward_batch.seq_lens + draft_token_num).to(torch.int32)
metadata.cache_seqlens_int32 = aug_seq_lens.repeat_interleave(
forward_batch.spec_info.draft_token_num
metadata.cu_seqlens_q = torch.arange(
0,
batch_size * draft_token_num + 1,
draft_token_num,
dtype=torch.int32,
device=device,
)
metadata.cu_seqlens_k = torch.nn.functional.pad(
torch.cumsum(metadata.cache_seqlens_int32, dim=0, dtype=torch.int32),
(1, 0),
)
metadata.max_seq_len_k = (
forward_batch.seq_lens_cpu.max().item() + draft_token_num
)
metadata.page_table = forward_batch.req_to_token_pool.req_to_token[
forward_batch.req_pool_indices, : metadata.max_seq_len_k
].repeat_interleave(draft_token_num, dim=0)
aug_cum_len = torch.nn.functional.pad(
torch.cumsum(aug_seq_lens, dim=0, dtype=torch.int32), (1, 0)
)
for idx, single_seq_len in enumerate(aug_seq_lens):
metadata.page_table[
idx * draft_token_num : (idx + 1) * draft_token_num, :single_seq_len
] *= forward_batch.spec_info.custom_mask[
aug_cum_len[idx]
* draft_token_num : aug_cum_len[idx + 1]
* draft_token_num
].view(
draft_token_num, -1
)
]
metadata.max_seq_len_q = 1
else:
elif forward_batch.forward_mode.is_extend_or_draft_extend():
# Normal or Draft Extend (Both of them will be ran on the Target Worker)
metadata.cache_seqlens_int32 = seqlens_in_batch.to(torch.int32)
metadata.cu_seqlens_k = torch.nn.functional.pad(
torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)
......@@ -208,7 +204,6 @@ class FlashAttentionBackend(AttentionBackend):
metadata.max_seq_len_q = metadata.max_seq_len_k
# Precompute strided indices
# [0, page_size, 2 * page_size, ...]
if self.page_size > 1:
self.strided_indices = torch.arange(
0, metadata.page_table.shape[1], self.page_size, device=self.device
......@@ -227,7 +222,6 @@ class FlashAttentionBackend(AttentionBackend):
forward_batch: ForwardBatch,
save_kv_cache=True,
):
if k is not None:
assert v is not None
if save_kv_cache:
......@@ -262,7 +256,7 @@ class FlashAttentionBackend(AttentionBackend):
page_table = metadata.page_table
# # Use Flash Attention for prefill
# Use Flash Attention for prefill
if not self.use_mla:
# Do multi-head attention
kv_cache = forward_batch.token_to_kv_pool.get_kv_buffer(layer.layer_id)
......@@ -368,7 +362,6 @@ class FlashAttentionBackend(AttentionBackend):
if layer.sliding_window_size is not None
else (-1, -1)
)
page_table = metadata.page_table
if not self.use_mla:
......@@ -437,7 +430,6 @@ class FlashAttentionBackend(AttentionBackend):
k_descale=layer.k_scale,
v_descale=layer.v_scale,
)
return o.view(-1, layer.tp_q_head_num * layer.v_head_dim)
def init_cuda_graph_state(self, max_bs: int):
......@@ -449,11 +441,6 @@ class FlashAttentionBackend(AttentionBackend):
This creates fixed-size tensors that will be reused during CUDA graph replay
to avoid memory allocations.
"""
if self.speculative_num_steps > 0:
raise NotImplementedError(
"FlashAttentionBackend Spec Decoding does not support CUDA graph yet, stay tuned!"
)
self.decode_cuda_graph_metadata = {
# Page table for token mapping (batch_size, max_context_len)
"page_table": torch.zeros(
......@@ -462,6 +449,39 @@ class FlashAttentionBackend(AttentionBackend):
dtype=torch.int32,
device=self.device,
),
"page_table_draft_decode": torch.zeros(
max_bs,
(self.max_context_len + self.page_size - 1) // self.page_size,
dtype=torch.int32,
device=self.device,
),
"strided_indices": torch.arange(
0, self.max_context_len, self.page_size, device=self.device
),
"cache_seqlens": torch.zeros(max_bs, dtype=torch.int32, device=self.device),
"cu_seqlens_q": torch.arange(
0, max_bs + 128, dtype=torch.int32, device=self.device
),
"cu_seqlens_k": torch.zeros(
max_bs + 128, dtype=torch.int32, device=self.device
),
}
self.target_verify_metadata = {
"page_table": torch.zeros(
max_bs,
(self.max_context_len + self.page_size - 1) // self.page_size,
dtype=torch.int32,
device=self.device,
),
"cache_seqlens": torch.zeros(max_bs, dtype=torch.int32, device=self.device),
"cu_seqlens_q": torch.zeros(
max_bs + 128, dtype=torch.int32, device=self.device
),
"cu_seqlens_k": torch.zeros(
max_bs + 128, dtype=torch.int32, device=self.device
),
"max_seqlen_q": 0,
"strided_indices": torch.arange(
0, self.max_context_len, self.page_size, device=self.device
),
......@@ -479,27 +499,89 @@ class FlashAttentionBackend(AttentionBackend):
):
"""Initialize forward metadata for capturing CUDA graph."""
metadata = FlashAttentionMetadata()
# Get sequence information
metadata.cache_seqlens_int32 = seq_lens.to(torch.int32)
batch_size = len(seq_lens)
device = seq_lens.device
metadata.cu_seqlens_k = torch.nn.functional.pad(
torch.cumsum(seq_lens, dim=0, dtype=torch.int32), (1, 0)
)
# Precompute maximum sequence length
metadata.max_seq_len_k = seq_lens.max().item()
# Precompute page table
metadata.page_table = self.decode_cuda_graph_metadata["page_table"][
req_pool_indices, :
]
if forward_mode.is_cuda_graph():
# Precompute cumulative sequence lengths
metadata.cu_seqlens_q = torch.arange(
0, batch_size + 1, dtype=torch.int32, device=device
if forward_mode.is_decode():
if spec_info is not None:
# Draft Decode
metadata.cu_seqlens_q = torch.arange(
0, bs + 1, dtype=torch.int32, device=device
)
metadata.cache_seqlens_int32 = self.decode_cuda_graph_metadata[
"cache_seqlens"
][:bs]
metadata.cu_seqlens_q = self.decode_cuda_graph_metadata["cu_seqlens_q"][
: bs + 1
]
metadata.cu_seqlens_k = torch.nn.functional.pad(
torch.cumsum(
metadata.cache_seqlens_int32, dim=0, dtype=torch.int32
),
(1, 0),
)
metadata.max_seq_len_k = seq_lens.max().item() + (self.step_id + 1)
metadata.page_table = self.decode_cuda_graph_metadata[
"page_table_draft_decode"
][req_pool_indices, :]
else:
# Normal Decode
# Get sequence information
metadata.cache_seqlens_int32 = seq_lens.to(torch.int32)
batch_size = len(seq_lens)
device = seq_lens.device
metadata.cu_seqlens_k = torch.nn.functional.pad(
torch.cumsum(seq_lens, dim=0, dtype=torch.int32), (1, 0)
)
# Precompute maximum sequence length
metadata.max_seq_len_k = seq_lens.max().item()
# Precompute page table
metadata.page_table = self.decode_cuda_graph_metadata["page_table"][
req_pool_indices, :
]
# Precompute cumulative sequence lengths
metadata.cu_seqlens_q = torch.arange(
0, batch_size + 1, dtype=torch.int32, device=device
)
self.decode_cuda_graph_metadata[bs] = metadata
elif forward_mode.is_target_verify():
draft_token_num = spec_info.draft_token_num
metadata.cache_seqlens_int32 = self.target_verify_metadata["cache_seqlens"][
:bs
]
metadata.cache_seqlens_int32.copy_(
(seq_lens + draft_token_num).to(torch.int32)
)
else:
raise ValueError("Do not support Prefill Mode cuda graph")
self.decode_cuda_graph_metadata[bs] = metadata
metadata.max_seq_len_q = draft_token_num
metadata.max_seq_len_k = seq_lens.max().item() + draft_token_num
metadata.cu_seqlens_q = self.target_verify_metadata["cu_seqlens_q"][
torch.arange(
0,
bs * draft_token_num + 1,
draft_token_num,
dtype=torch.int32,
device=device,
)
]
cu_k = self.target_verify_metadata["cu_seqlens_k"][: (bs + 1)]
cu_k.copy_(
torch.nn.functional.pad(
torch.cumsum(
metadata.cache_seqlens_int32, dim=0, dtype=torch.int32
),
(1, 0),
)
)
metadata.cu_seqlens_k = cu_k
metadata.page_table = self.target_verify_metadata["page_table"][
req_pool_indices, :
]
self.target_verify_metadata[bs] = metadata
self.forward_metadata = metadata
def init_forward_metadata_replay_cuda_graph(
......@@ -512,28 +594,91 @@ class FlashAttentionBackend(AttentionBackend):
forward_mode: ForwardMode,
spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
seq_lens_cpu: Optional[torch.Tensor],
out_cache_loc: torch.Tensor = None,
):
# """Initialize forward metadata for replaying CUDA graph."""
metadata = self.decode_cuda_graph_metadata[bs]
device = seq_lens.device
seq_lens = seq_lens[:bs]
req_pool_indices = req_pool_indices[:bs]
seq_lens_cpu = seq_lens_cpu[:bs]
if forward_mode.is_decode():
metadata = self.decode_cuda_graph_metadata[bs]
if spec_info is not None:
# Draft Decode
max_len = seq_lens_cpu.max().item()
metadata.max_seq_len_k = max_len + (self.step_id + 1)
metadata.cache_seqlens_int32.copy_(
(seq_lens + (self.step_id + 1)).to(torch.int32)
)
# For CPU operations
max_len = seq_lens_cpu[:bs].max().item()
metadata.max_seq_len_k = max_len
metadata.max_seq_len_k = seq_lens_cpu.max().item() + (self.step_id + 1)
# For GPU operations
seq_lens_in_batch = seq_lens[:bs]
metadata.cache_seqlens_int32 = seq_lens_in_batch.to(torch.int32)
metadata.cu_seqlens_k = torch.nn.functional.pad(
torch.cumsum(seq_lens_in_batch, dim=0, dtype=torch.int32), (1, 0)
)
metadata.cu_seqlens_k.copy_(
torch.nn.functional.pad(
torch.cumsum(
metadata.cache_seqlens_int32, dim=0, dtype=torch.int32
),
(1, 0),
)
)
page_table = self.req_to_token[
req_pool_indices, : metadata.max_seq_len_k
]
metadata.page_table[:, : metadata.max_seq_len_k].copy_(page_table)
else:
# Normal Decode
max_len = seq_lens_cpu.max().item()
metadata.max_seq_len_k = max_len
metadata.cache_seqlens_int32 = seq_lens.to(torch.int32)
metadata.cu_seqlens_k = torch.nn.functional.pad(
torch.cumsum(seq_lens, dim=0, dtype=torch.int32), (1, 0)
)
max_seq_pages = (
metadata.max_seq_len_k + self.page_size - 1
) // self.page_size
page_indices = self.req_to_token[
:,
self.decode_cuda_graph_metadata["strided_indices"][:max_seq_pages],
]
page_indices = page_indices[req_pool_indices] // self.page_size
metadata.page_table[:, :max_seq_pages].copy_(page_indices)
metadata.page_table[:, max_seq_pages:].fill_(0)
elif forward_mode.is_target_verify():
metadata = self.target_verify_metadata[bs]
draft_token_num = spec_info.draft_token_num
metadata.cu_seqlens_q.copy_(
torch.arange(
0,
bs * draft_token_num + 1,
draft_token_num,
dtype=torch.int32,
device=device,
)
)
metadata.cache_seqlens_int32.copy_(
(seq_lens + draft_token_num).to(torch.int32)
)
metadata.max_seq_len_k = seq_lens_cpu.max().item() + draft_token_num
metadata.cu_seqlens_k.copy_(
torch.nn.functional.pad(
torch.cumsum(
metadata.cache_seqlens_int32, dim=0, dtype=torch.int32
),
(1, 0),
)
)
page_table = self.req_to_token[req_pool_indices, : metadata.max_seq_len_k]
metadata.page_table[:, : metadata.max_seq_len_k].copy_(page_table)
max_seq_pages = (metadata.max_seq_len_k + self.page_size - 1) // self.page_size
page_indices = self.req_to_token[
:, self.decode_cuda_graph_metadata["strided_indices"][:max_seq_pages]
]
page_indices = page_indices[req_pool_indices[:bs]] // self.page_size
metadata.page_table[:, :max_seq_pages].copy_(page_indices)
metadata.page_table[:, max_seq_pages:].fill_(0)
self.forward_metadata = metadata
def get_cuda_graph_seq_len_fill_value(self):
......@@ -555,7 +700,6 @@ class FlashAttentionMultiStepBackend:
self.attn_backends.append(
FlashAttentionBackend(
model_runner,
skip_prefill=True,
topk=self.topk,
speculative_num_steps=self.speculative_num_steps,
step_id=i,
......@@ -570,7 +714,10 @@ class FlashAttentionMultiStepBackend:
for i in range(self.speculative_num_steps):
self.attn_backends[i].init_cuda_graph_state(max_bs)
def init_forward_metadata_capture_cuda_graph(self, forward_batch: ForwardBatch):
def init_forward_metadata_capture_cuda_graph(
self,
forward_batch: ForwardBatch,
):
assert forward_batch.spec_info is not None
assert isinstance(forward_batch.spec_info, EagleDraftInput)
......@@ -601,4 +748,5 @@ class FlashAttentionMultiStepBackend:
forward_mode=ForwardMode.DECODE,
spec_info=forward_batch.spec_info,
seq_lens_cpu=forward_batch.seq_lens_cpu,
out_cache_loc=forward_batch.out_cache_loc,
)
python/sglang/srt/model_executor/forward_batch_info.py
View file @ ca8d02ab
......@@ -104,6 +104,9 @@ class ForwardMode(IntEnum):
or self == ForwardMode.IDLE
)
def is_extend_or_draft_extend(self):
return self == ForwardMode.EXTEND or self == ForwardMode.DRAFT_EXTEND
def is_dummy_first(self):
return self == ForwardMode.DUMMY_FIRST
......
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