Prefix caching (#2402)

* Prefix caching WIP

* Fixing prefix attention.

* Fixing flashinfer import.

* Fixing black.

* Fixing medusa (still wrong outputs, but functional).

* Just medusa values now.

* Fixing medusa without prefix caching.

* Fixing prefix caching.

* Medusa requires reshaping.

* Removing the logs.

* Remove router.nix

* Fixup:

- Remove logs
- Disable VLMs (they do not work)
- Disable prefix caching when user wants prefill logprobs.

* Update flake.lock

---------

Co-authored-by: Daniël de Kok <me@danieldk.eu>
This commit is contained in:
Nicolas Patry 2024-08-20 11:15:30 +02:00 committed by GitHub
parent 38773453ae
commit b70ae0969f
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
26 changed files with 405 additions and 107 deletions

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@ -316,10 +316,15 @@ impl State {
+ self.speculate
- 1;
match block_allocator
.allocate(tokens, entry.request.input_ids.clone())
.await
{
// If users wants the prefill logprobs, we cannot reuse the cache.
// So no input_ids for the radix tree.
let input_ids = if entry.request.decoder_input_details {
None
} else {
entry.request.input_ids.clone()
};
match block_allocator.allocate(tokens, input_ids).await {
None => {
// Entry is over budget
// Add it back to the front

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@ -205,6 +205,7 @@ pub struct RadixTrie {
/// call that a real time lookup would require.
time: u64,
}
impl Default for RadixTrie {
fn default() -> Self {
Self::new()

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@ -900,11 +900,11 @@
]
},
"locked": {
"lastModified": 1723515680,
"narHash": "sha256-nHdKymsHCVIh0Wdm4MvSgxcTTg34FJIYHRQkQYaSuvk=",
"lastModified": 1723602049,
"narHash": "sha256-Z/noCSn9WPkv7O77dWKLcBxe4Ub4bWyNzsL5JhjaQfw=",
"owner": "oxalica",
"repo": "rust-overlay",
"rev": "4ee3d9e9569f70d7bb40f28804d6fe950c81eab3",
"rev": "ea0bf33a11a26a62c60123c49d96011da396602c",
"type": "github"
},
"original": {

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@ -84,6 +84,7 @@
grpcio-status
grpcio-tools
hf-transfer
ipdb
loguru
mamba-ssm
marlin-kernels

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@ -6,7 +6,12 @@ from .common import Seqlen
if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false":
raise ImportError("`USE_FLASH_ATTENTION` is false.")
if SYSTEM == "cuda":
from .cuda import attention, paged_attention, reshape_and_cache, SUPPORTS_WINDOWING
from .cuda import (
attention,
paged_attention,
reshape_and_cache,
SUPPORTS_WINDOWING,
)
elif SYSTEM == "rocm":
from .rocm import attention, paged_attention, reshape_and_cache, SUPPORTS_WINDOWING
elif SYSTEM == "ipex":

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@ -76,7 +76,7 @@ def paged_attention(
# sequences or heads is large, we use V1 since there is enough work
# to parallelize.
if ATTENTION == "flashinfer":
from text_generation_server.layers.attention.flash_infer import decode_state
from text_generation_server.layers.attention.flashinfer import decode_state
return decode_state.get().forward(
query.contiguous(),
@ -221,9 +221,11 @@ SUPPORTS_WINDOWING = V2
if ATTENTION == "flashinfer":
def attention(
q,
k,
v,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
cu_seqlens,
max_s,
softmax_scale,
@ -231,14 +233,15 @@ if ATTENTION == "flashinfer":
causal=True,
softcap=0.0,
):
from text_generation_server.layers.attention.flash_infer import prefill_state
assert window_size_left == -1, "Windowing is not supported with flash infer"
from text_generation_server.layers.attention.flashinfer import (
prefill_with_paged_kv_state,
)
return prefill_state.get().forward(
q,
k,
v,
return prefill_with_paged_kv_state.get().forward(
q.contiguous(),
causal=causal,
window_left=window_size_left,
paged_kv_cache=(key_cache, value_cache),
logits_soft_cap=softcap,
sm_scale=softmax_scale,
)
@ -249,6 +252,8 @@ elif V2:
q,
k,
v,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
cu_seqlens,
max_s,
softmax_scale,
@ -289,6 +294,8 @@ else:
q,
k,
v,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
cu_seqlens,
max_s,
softmax_scale,

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@ -9,6 +9,10 @@ prefill_state: ContextVar[flashinfer.BatchPrefillWithRaggedKVCacheWrapper] = Con
"prefill_state"
)
prefill_with_paged_kv_state: ContextVar[
flashinfer.BatchPrefillWithPagedKVCacheWrapper
] = ContextVar("prefill_with_paged_kv_state")
decode_state: ContextVar[flashinfer.BatchDecodeWithPagedKVCacheWrapper] = ContextVar(
"decode_state"
)
@ -24,6 +28,78 @@ def get_workspace(device):
return workspace
def create_prefill_with_paged_kv_state(
*,
device: torch.device,
):
"""Create a prefill state that uses the KV cache."""
workspace_buffer = get_workspace(device)
return flashinfer.BatchPrefillWithPagedKVCacheWrapper(
workspace_buffer, kv_layout="NHD", use_cuda_graph=False
)
@contextmanager
def use_prefill_with_paged_kv_state(
*,
state: flashinfer.BatchPrefillWithPagedKVCacheWrapper,
block_tables: torch.Tensor,
cu_seqlens: torch.Tensor,
input_lengths: torch.Tensor,
num_heads: int,
num_kv_heads: int,
head_size: int,
page_size: int,
query_dtype: str = "float16",
):
"""
Context manager to set the active flashinfer prefill state to the given
`state` and parameters. This state will be used by all calls to the
`attention` function while the context manager is active.
"""
indptr = torch.zeros(
input_lengths.shape[0] + 1, device=input_lengths.device, dtype=torch.int32
)
# Round up to page size and then calculate the cumulative sum to get
# the indices into the block table.
torch.add(input_lengths, page_size - 1, out=indptr[1:])
indptr[1:].div_(page_size, rounding_mode="floor")
indptr[1:].cumsum_(-1)
# Get the lengths of the last page in a block.
if page_size == 1:
last_page_len = torch.ones(
input_lengths.shape[0], dtype=torch.int32, device=input_lengths.device
)
else:
last_page_len = torch.empty(
input_lengths.shape[0], dtype=torch.int32, device=input_lengths.device
)
torch.sub(input_lengths, 1, out=last_page_len)
last_page_len.remainder_(page_size)
last_page_len += 1
token = prefill_with_paged_kv_state.set(state)
try:
state.begin_forward(
qo_indptr=cu_seqlens,
paged_kv_indptr=indptr,
paged_kv_indices=block_tables,
paged_kv_last_page_len=last_page_len,
num_qo_heads=num_heads,
num_kv_heads=num_kv_heads,
head_dim=head_size,
q_data_type=query_dtype,
page_size=page_size,
)
yield
finally:
state.end_forward()
if token is not None:
prefill_with_paged_kv_state.reset(token)
def create_prefill_state(
*,
device: torch.device,

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@ -32,6 +32,8 @@ class MedusaModel(torch.nn.Module):
)
def forward(self, x):
if not self.heads:
return None
speculative_logits = torch.stack([head(x) for head in self.heads], dim=1)
return speculative_logits

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@ -298,6 +298,8 @@ class FlashCohereAttention(torch.nn.Module):
query,
key,
value,
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill,
max_s,
self.softmax_scale,

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@ -337,6 +337,8 @@ class DbrxAttention(torch.nn.Module):
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill,
max_s,
self.softmax_scale,

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@ -365,6 +365,8 @@ class DeepseekV2Attention(torch.nn.Module):
query,
key,
value,
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill,
max_s,
self.softmax_scale,

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@ -238,6 +238,8 @@ class FlashGemma2Attention(torch.nn.Module):
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill,
max_s,
self.softmax_scale,

View File

@ -232,6 +232,8 @@ class FlashGemmaAttention(torch.nn.Module):
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill,
max_s,
self.softmax_scale,

View File

@ -232,6 +232,8 @@ class FlashGPT2Attention(torch.nn.Module):
query,
key,
value,
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill,
max_s,
self.softmax_scale,

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@ -220,6 +220,8 @@ class FlashLlamaAttention(torch.nn.Module):
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill,
max_s,
self.softmax_scale,

View File

@ -219,6 +219,8 @@ class MistralAttention(torch.nn.Module):
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill,
max_s,
self.softmax_scale,

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@ -276,6 +276,8 @@ class MixtralAttention(torch.nn.Module):
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill,
max_s,
self.softmax_scale,

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@ -173,6 +173,8 @@ class FlashNeoxAttention(torch.nn.Module):
qkv[:, 0],
qkv[:, 1],
qkv[:, 2],
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill,
max_s,
self.softmax_scale,

View File

@ -194,6 +194,8 @@ class FlashPhiAttention(torch.nn.Module):
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill,
max_s,
self.softmax_scale,

View File

@ -137,6 +137,8 @@ class Qwen2Attention(torch.nn.Module):
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill,
max_s,
self.softmax_scale,

View File

@ -208,6 +208,8 @@ class FlashRWAttention(torch.nn.Module):
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill,
max_s,
self.softmax_scale,
@ -326,6 +328,8 @@ class FlashRWLargeAttention(torch.nn.Module):
query,
torch.select(kv, dim=2, index=0),
torch.select(kv, dim=2, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill,
max_s,
self.softmax_scale,

View File

@ -293,6 +293,8 @@ class FlashMQAttention(torch.nn.Module):
query,
torch.select(key_value, dim=1, index=0),
torch.select(key_value, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill,
max_s,
self.softmax_scale,

View File

@ -242,6 +242,8 @@ class Starcoder2Attention(torch.nn.Module):
query,
torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill,
max_s,
self.softmax_scale,

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@ -43,6 +43,7 @@ from text_generation_server.models.globals import (
ATTENTION,
BLOCK_SIZE,
CUDA_GRAPHS,
PREFIX_CACHING,
get_adapter_to_index,
)
from text_generation_server.layers.attention import Seqlen
@ -138,6 +139,9 @@ class FlashCausalLMBatch(Batch):
block_tables_tensor: torch.Tensor
# tensor of length \sum_{i=0}^{b} max_s_i holding the paged attention slots for all sequences
slots: torch.Tensor
# size [b], containing the number of blocks that can be retrieved from the cache
prefix_lens: List[int]
prefix_lens_tensor: torch.Tensor
max_seqlen: int
@ -146,6 +150,9 @@ class FlashCausalLMBatch(Batch):
prefill_next_token_indices: Optional[torch.tensor]
prefill_cu_outlens: Optional[List[int]]
# Prefixes
prefix_ids: List[List[int]]
# All tokens
all_input_ids: List[List[int]]
all_input_ids_tensor: torch.Tensor
@ -213,6 +220,7 @@ class FlashCausalLMBatch(Batch):
prefix_offsets = []
read_offsets = []
all_input_ids = []
prefix_ids = []
requests_idx_mapping = {}
all_prefill_logprobs = True
@ -230,7 +238,7 @@ class FlashCausalLMBatch(Batch):
# Cumulative length
cumulative_length = 0
cumulative_max_length = 0
cumulative_slot_tokens = 0
prefill_out_cumulative_length = 0
num_blocks = 0
@ -240,6 +248,7 @@ class FlashCausalLMBatch(Batch):
block_tables = []
slots = []
prefix_lens = []
# Parse batch
for i, (r, tokenized_input) in enumerate(
@ -255,6 +264,19 @@ class FlashCausalLMBatch(Batch):
):
tokenized_input = tokenized_input[1:]
orig_input_length = len(tokenized_input)
if PREFIX_CACHING:
prefix_len = r.prefix_len
if prefix_len == orig_input_length:
assert prefix_len > 0
prefix_len -= 1
else:
prefix_len = 0
prefix_ids.append(tokenized_input[:prefix_len])
tokenized_input = tokenized_input[prefix_len:]
input_length = len(tokenized_input)
input_lengths.append(input_length)
@ -264,7 +286,9 @@ class FlashCausalLMBatch(Batch):
all_input_ids.append(tokenized_input)
# Position ids
request_position_ids = torch.arange(0, input_length, dtype=torch.int32)
request_position_ids = torch.arange(
prefix_len, orig_input_length, dtype=torch.int32
)
position_ids.append(request_position_ids)
# Add cumulative lengths of all previous inputs
@ -288,11 +312,17 @@ class FlashCausalLMBatch(Batch):
# Remove one as the first token des not have a past
speculative_length = get_speculate()
speculative_length = 0 if speculative_length is None else speculative_length
total_tokens = input_length + max_new_tokens - 1 + speculative_length
# Tokens that need to be mapped to blocks.
block_tokens = orig_input_length + max_new_tokens - 1 + speculative_length
# Tokens that need to be mapped to slots. We don't need slots for the
# cached prefix (if present).
slot_tokens = input_length + max_new_tokens - 1 + speculative_length
# blocks and slots can be empty (for example in warmup)
if not r.blocks:
needed_blocks = math.ceil(total_tokens / BLOCK_SIZE)
needed_blocks = math.ceil(block_tokens / BLOCK_SIZE)
request_blocks = [
b for b in range(num_blocks, num_blocks + needed_blocks)
]
@ -303,16 +333,20 @@ class FlashCausalLMBatch(Batch):
]
else:
request_blocks = r.blocks
request_slots = r.slots
request_slots = r.slots[
prefix_len: #: orig_input_length + max_new_tokens + speculative_length
]
block_tables.append(request_blocks)
slots.extend(request_slots[:total_tokens])
slots.extend(request_slots)
prefix_lens.append(prefix_len)
num_blocks += len(request_blocks)
start_slots.append(cumulative_max_length)
start_slots.append(cumulative_slot_tokens)
request_slot_indices = torch.arange(
cumulative_max_length,
cumulative_max_length + input_length,
cumulative_slot_tokens,
cumulative_slot_tokens + input_length,
dtype=torch.int64,
)
slot_indices.append(request_slot_indices)
@ -348,7 +382,7 @@ class FlashCausalLMBatch(Batch):
# Update
cumulative_length += input_length
cumulative_max_length += total_tokens
cumulative_slot_tokens += slot_tokens
max_seqlen = max(max_seqlen, input_length)
max_blocks = max(max_blocks, len(request_blocks))
max_length = max(
@ -425,12 +459,14 @@ class FlashCausalLMBatch(Batch):
)
slots = torch.tensor(slots, dtype=torch.int64, device=device)
block_tables_tensor = torch.zeros(
(len(block_tables), max_blocks), dtype=torch.int32, device="cpu"
)
for i, request_blocks in enumerate(block_tables):
block_tables_tensor[i, : len(request_blocks)] = torch.tensor(request_blocks)
block_tables_tensor = block_tables_tensor.to(device)
prefix_lens_tensor = torch.tensor(prefix_lens, dtype=torch.int32, device=device)
return cls(
batch_id=pb.id,
@ -445,6 +481,8 @@ class FlashCausalLMBatch(Batch):
block_tables=block_tables,
block_tables_tensor=block_tables_tensor,
slots=slots,
prefix_lens=prefix_lens,
prefix_lens_tensor=prefix_lens_tensor,
max_seqlen=max_seqlen,
prefill_head_indices=prefill_head_indices,
prefill_next_token_indices=prefill_next_token_indices,
@ -455,6 +493,7 @@ class FlashCausalLMBatch(Batch):
read_offsets=read_offsets,
all_input_ids=all_input_ids,
all_input_ids_tensor=all_input_ids_tensor,
prefix_ids=prefix_ids,
next_token_chooser=next_token_chooser,
stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens,
@ -510,8 +549,10 @@ class FlashCausalLMBatch(Batch):
start_slots = []
block_tables = []
all_input_ids = []
prefix_ids = []
input_lengths = []
prefix_lens = []
prefix_offsets = []
read_offsets = []
@ -533,11 +574,14 @@ class FlashCausalLMBatch(Batch):
# Get length
request_input_length = self.input_lengths[idx]
prefix_len = self.prefix_lens[idx]
max_seqlen = max(max_seqlen, request_input_length)
all_input_ids.append(self.all_input_ids[idx])
prefix_ids.append(self.prefix_ids[idx])
input_lengths.append(request_input_length)
prefix_lens.append(prefix_len)
prefix_offsets.append(self.prefix_offsets[idx])
read_offsets.append(self.read_offsets[idx])
@ -582,6 +626,7 @@ class FlashCausalLMBatch(Batch):
block_tables_tensor = self.block_tables_tensor[indices]
input_lengths_tensor = self.input_lengths_tensor[indices]
slots = self.slots[slot_filtering_indices]
prefix_lens_tensor = self.prefix_lens_tensor[indices]
next_token_chooser = self.next_token_chooser.filter(indices)
top_n_tokens_tensor = self.top_n_tokens_tensor[indices]
speculative_ids = (
@ -617,10 +662,13 @@ class FlashCausalLMBatch(Batch):
prefill_cu_outlens=None,
input_lengths=input_lengths,
input_lengths_tensor=input_lengths_tensor,
prefix_lens=prefix_lens,
prefix_lens_tensor=prefix_lens_tensor,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
all_input_ids=all_input_ids,
all_input_ids_tensor=all_input_ids_tensor,
prefix_ids=prefix_ids,
next_token_chooser=next_token_chooser,
stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens,
@ -681,6 +729,7 @@ class FlashCausalLMBatch(Batch):
block_tables_tensor = batches[0].block_tables_tensor.new_zeros(
(total_batch_size, max_blocks)
)
prefix_lens_tensor = batches[0].prefix_lens_tensor.new_empty(total_batch_size)
all_input_ids_tensor = batches[0].all_input_ids_tensor.new_zeros(
(total_batch_size, max_length)
)
@ -698,7 +747,9 @@ class FlashCausalLMBatch(Batch):
start_slots = []
block_tables = []
prefix_lens = []
all_input_ids = []
prefix_ids = []
input_lengths = []
prefix_offsets = []
@ -760,10 +811,14 @@ class FlashCausalLMBatch(Batch):
start_index:end_index, : batch.block_tables_tensor.shape[1]
] = batch.block_tables_tensor[:, :max_blocks]
prefix_lens_tensor[start_index:end_index] = batch.prefix_lens_tensor
start_slots.append(batch.start_slots + cumulative_slots)
block_tables.extend(batch.block_tables)
prefix_lens.extend(batch.prefix_lens)
all_input_ids.extend(batch.all_input_ids)
prefix_ids.extend(batch.prefix_ids)
input_lengths.extend(batch.input_lengths)
prefix_offsets.extend(batch.prefix_offsets)
@ -809,6 +864,8 @@ class FlashCausalLMBatch(Batch):
slot_indices=slot_indices,
block_tables=block_tables,
block_tables_tensor=block_tables_tensor,
prefix_lens=prefix_lens,
prefix_lens_tensor=prefix_lens_tensor,
slots=slots,
max_seqlen=max_seqlen,
prefill_head_indices=None,
@ -820,6 +877,7 @@ class FlashCausalLMBatch(Batch):
read_offsets=read_offsets,
all_input_ids=all_input_ids,
all_input_ids_tensor=all_input_ids_tensor,
prefix_ids=prefix_ids,
next_token_chooser=next_token_chooser,
stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens,
@ -970,19 +1028,22 @@ class FlashCausalLM(Model):
self.kv_cache = []
if ATTENTION == "flashinfer":
from text_generation_server.layers.attention.flash_infer import (
from text_generation_server.layers.attention.flashinfer import (
create_prefill_state,
create_decode_state,
create_prefill_with_paged_kv_state,
)
self.prefill_state = create_prefill_state(device=device)
self.prefill_with_paged_kv_state = create_prefill_with_paged_kv_state(
device=device
)
if not CUDA_GRAPHS:
self.decode_state = create_decode_state(
device=device,
num_heads=self.num_heads,
num_kv_heads=self.num_kv_heads,
)
self.decode_state = create_decode_state(
device=device,
num_heads=self.num_heads,
num_kv_heads=self.num_kv_heads,
)
super().__init__(
model_id=model_id,
@ -1074,12 +1135,23 @@ class FlashCausalLM(Model):
input_ids = torch.zeros(bs, dtype=torch.int64, device=self.device)
position_ids = torch.zeros(bs, dtype=torch.int32, device=self.device)
slots = torch.arange(bs, dtype=torch.int64, device=self.device)
input_lengths = torch.ones(bs, dtype=torch.int32, device=self.device) * max_s
block_tables = (
torch.arange(max_bt, dtype=torch.int32, device=self.device)
.repeat(bs)
.reshape((bs, max_bt))
input_lengths = [max_s] * bs
prefix_lengths = [0] * bs
input_lengths_tensor = (
torch.ones(bs, dtype=torch.int32, device=self.device) * max_s
)
prefix_lengths_tensor = torch.zeros(bs, dtype=torch.int32, device=self.device)
block_tables = torch.arange(
max_bt, dtype=torch.int32, device=self.device
).repeat(bs)
block_tables = block_tables.reshape((bs, max_bt))
if ATTENTION == "flashinfer":
block_tables = block_tables_to_ragged(
block_tables=block_tables,
input_lengths=input_lengths,
prefix_lens=prefix_lengths,
)
self.cuda_graphs[bs] = {
"input_ids": input_ids,
@ -1087,14 +1159,14 @@ class FlashCausalLM(Model):
"kv_cache": self.kv_cache,
"block_tables": block_tables,
"slots": slots,
"input_lengths": input_lengths,
"input_lengths": input_lengths_tensor,
}
input_lengths_ = Seqlen(input_lengths=input_lengths)
input_lengths_ = Seqlen(input_lengths=input_lengths_tensor)
graph = torch.cuda.CUDAGraph()
self.cuda_graphs[bs]["graph"] = graph
if ATTENTION == "flashinfer":
from text_generation_server.layers.attention.flash_infer import (
from text_generation_server.layers.attention.flashinfer import (
create_decode_state_cuda_graphs,
)
@ -1104,7 +1176,7 @@ class FlashCausalLM(Model):
last_page_len = torch.ones(bs, dtype=torch.int32, device=self.device)
state = create_decode_state_cuda_graphs(
device=input_ids.device,
block_tables=block_tables.view(-1),
block_tables=block_tables,
block_tables_ptr=block_tables_ptr,
last_page_len=last_page_len,
num_heads=self.num_heads,
@ -1120,7 +1192,10 @@ class FlashCausalLM(Model):
block_tables=block_tables,
cu_seqlen_prefill=None,
input_lengths=input_lengths,
input_lengths_tensor=input_lengths_tensor,
state=state,
prefix_lens=prefix_lengths,
prefix_lens_tensor=prefix_lengths_tensor,
):
self.model.forward(
input_ids=input_ids,
@ -1138,7 +1213,7 @@ class FlashCausalLM(Model):
torch.cuda.synchronize()
with torch.cuda.graph(graph, pool=MEM_POOL):
input_lengths = Seqlen(input_lengths=input_lengths)
input_lengths_tensor = Seqlen(input_lengths=input_lengths_tensor)
logits, speculative_logits = self.model.forward(
input_ids=input_ids,
position_ids=position_ids,
@ -1146,7 +1221,7 @@ class FlashCausalLM(Model):
kv_cache=self.kv_cache,
block_tables=block_tables,
slots=slots,
input_lengths=input_lengths,
input_lengths=input_lengths_tensor,
max_s=max_s,
prefill_cache_indices=None,
lm_head_indices=None,
@ -1334,6 +1409,9 @@ class FlashCausalLM(Model):
input_lengths = (
input_lengths.unsqueeze(-1).expand(B, new_length) + arange_int
).view(-1)
prefix_lens_tensor = (
batch.prefix_lens_tensor.unsqueeze(-1).expand(B, new_length)
).reshape(-1)
# Add Copy the block tables for all members
block_tables = (
@ -1354,6 +1432,7 @@ class FlashCausalLM(Model):
block_tables = batch.block_tables_tensor
slots = batch.slots[batch.slot_indices]
input_lengths = batch.input_lengths_tensor
prefix_lens_tensor = batch.prefix_lens_tensor
max_s = batch.max_seqlen
lm_head_indices = batch.prefill_head_indices
@ -1372,10 +1451,20 @@ class FlashCausalLM(Model):
cuda_graph = None
if cu_seqlen_prefill is not None or cuda_graph is None:
input_lengths = input_lengths + prefix_lens_tensor
if PREFIX_CACHING:
block_tables = block_tables_to_ragged(
block_tables=block_tables,
input_lengths=batch.input_lengths,
prefix_lens=batch.prefix_lens,
)
with self._forward_context(
block_tables=block_tables,
cu_seqlen_prefill=cu_seqlen_prefill,
input_lengths=input_lengths,
input_lengths=batch.input_lengths,
input_lengths_tensor=input_lengths,
prefix_lens=batch.prefix_lens,
prefix_lens_tensor=prefix_lens_tensor,
):
input_lengths = Seqlen(input_lengths=input_lengths)
logits, speculative_logits = self.model.forward(
@ -1399,20 +1488,32 @@ class FlashCausalLM(Model):
# Static inputs are potentially padded
cuda_graph["input_ids"][: input_ids.shape[0]] = input_ids
cuda_graph["position_ids"][: position_ids.shape[0]] = position_ids
cuda_graph["block_tables"][
: block_tables.shape[0], : block_tables.shape[1]
] = block_tables
if ATTENTION == "flashinfer":
block_tables = block_tables_to_ragged(
block_tables=block_tables,
input_lengths=batch.input_lengths,
prefix_lens=batch.prefix_lens,
)
cuda_graph["block_tables"][: block_tables.shape[0]] = block_tables
else:
cuda_graph["block_tables"][
: block_tables.shape[0], : block_tables.shape[1]
] = block_tables
cuda_graph["slots"].fill_(-1)
cuda_graph["slots"][: slots.shape[0]] = slots
cuda_graph["input_lengths"].zero_()
cuda_graph["input_lengths"][: input_lengths.shape[0]] = input_lengths
cuda_graph["input_lengths"][: input_lengths.shape[0]] = (
input_lengths + prefix_lens_tensor
)
state = cuda_graph.get("state")
with self._forward_context(
block_tables=block_tables,
block_tables=cuda_graph["block_tables"],
cu_seqlen_prefill=None,
input_lengths=input_lengths,
state=state,
input_lengths=batch.input_lengths,
input_lengths_tensor=cuda_graph["input_lengths"],
prefix_lens=batch.prefix_lens,
prefix_lens_tensor=prefix_lens_tensor,
state=cuda_graph.get("state"),
):
# Replay the graph
cuda_graph["graph"].replay()
@ -1610,6 +1711,7 @@ class FlashCausalLM(Model):
batch.read_offsets,
batch.stopping_criterias,
batch.all_input_ids,
batch.prefix_ids,
batch.next_token_chooser.do_sample,
batch.next_token_chooser.seeds,
batch.top_n_tokens,
@ -1627,6 +1729,7 @@ class FlashCausalLM(Model):
read_offset,
stopping_criteria,
all_input_ids,
prefix_ids,
do_sample,
seed,
top_n_tokens,
@ -1701,18 +1804,18 @@ class FlashCausalLM(Model):
out_end_index = batch.prefill_cu_outlens[i + 1]
# Remove generated token to only have prefill and add nan for first prompt token
request_prefill_logprobs = [float("nan")] + prefill_logprobs[
out_start_index : out_end_index - 1
]
request_prefill_logprobs = (
[float("nan")] * (len(prefix_ids) + 1)
) + prefill_logprobs[out_start_index : out_end_index - 1]
prefill_token_ids = all_input_ids[:-1]
prefill_texts = self.tokenizer.batch_decode(
prefill_token_ids,
prefix_ids + prefill_token_ids,
clean_up_tokenization_spaces=False,
skip_special_tokens=False,
)
prefill_tokens = Tokens(
prefill_token_ids,
prefix_ids + prefill_token_ids,
request_prefill_logprobs,
prefill_texts,
is_special=[],
@ -1794,33 +1897,68 @@ class FlashCausalLM(Model):
*,
block_tables: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor],
input_lengths: torch.Tensor,
input_lengths: List[int],
input_lengths_tensor: torch.Tensor,
prefix_lens: List[int],
prefix_lens_tensor: torch.Tensor,
state: Optional[Any] = None,
) -> ContextManager:
if ATTENTION != "flashinfer":
return nullcontext()
from text_generation_server.layers.attention.flash_infer import (
from text_generation_server.layers.attention.flashinfer import (
use_decode_state,
use_prefill_state,
use_prefill_with_paged_kv_state,
)
# has_prefix_lens = any(prefix_len > 0 for prefix_len in prefix_lens)
if cu_seqlen_prefill is not None:
return use_prefill_state(
state=state if state is not None else self.prefill_state,
return use_prefill_with_paged_kv_state(
state=(
state if state is not None else self.prefill_with_paged_kv_state
),
# block_tables=block_tables_to_ragged(
# block_tables=block_tables,
# input_lengths=input_lengths,
# prefix_lens=prefix_lens,
# ),
block_tables=block_tables,
cu_seqlens=cu_seqlen_prefill,
num_heads=self.num_heads,
num_kv_heads=self.num_kv_heads,
head_size=self.head_size,
)
else:
assert input_lengths is not None
return use_decode_state(
state=state if state is not None else self.decode_state,
input_lengths=input_lengths,
block_tables=block_tables.view(-1),
input_lengths=input_lengths_tensor,
num_heads=self.num_heads,
num_kv_heads=self.num_kv_heads,
head_size=self.head_size,
page_size=BLOCK_SIZE,
)
else:
assert input_lengths_tensor is not None
return use_decode_state(
state=state if state is not None else self.decode_state,
input_lengths=input_lengths_tensor,
block_tables=block_tables,
num_heads=self.num_heads,
num_kv_heads=self.num_kv_heads,
head_size=self.head_size,
page_size=BLOCK_SIZE,
)
def block_tables_to_ragged(
*, block_tables: torch.Tensor, input_lengths: List[int], prefix_lens: List[int]
) -> torch.Tensor:
"""Convert block table to ragged format compatible with FlashInfer."""
assert len(input_lengths) == len(prefix_lens)
total_len = sum(input_lengths) + sum(prefix_lens)
block_tables_ragged = torch.empty(
total_len, dtype=torch.int32, device=block_tables.device
)
offset = 0
for i, (input_length, prefix_len) in enumerate(zip(input_lengths, prefix_lens)):
seq_len = prefix_len + input_length
block_tables_ragged[offset : offset + seq_len] = block_tables[i][:seq_len]
offset += seq_len
return block_tables_ragged

View File

@ -5,9 +5,8 @@ from typing import Dict, Optional
from text_generation_server.utils.log import log_master
PREFIX_CACHING = os.getenv("USE_PREFIX_CACHING", False)
log_master(logger.info, f"Using Attention = {PREFIX_CACHING}")
PREFIX_CACHING = os.getenv("USE_PREFIX_CACHING", "0").lower() in {"1", "true"}
log_master(logger.info, f"Using prefix caching = {PREFIX_CACHING}")
ATTENTION = os.getenv("ATTENTION", "flashinfer" if PREFIX_CACHING else "paged")
_expected = {"paged", "flashdecoding", "flashinfer"}
assert (
@ -29,7 +28,6 @@ elif ATTENTION == "flashinfer":
else:
BLOCK_SIZE = 16
cuda_graphs = os.getenv("CUDA_GRAPHS")
if cuda_graphs is not None:
try:

View File

@ -11,7 +11,9 @@ from text_generation_server.pb import generate_pb2
from text_generation_server.models.flash_causal_lm import (
FlashCausalLMBatch,
FlashCausalLM,
block_tables_to_ragged,
)
from text_generation_server.models.globals import PREFIX_CACHING, ATTENTION
from text_generation_server.utils.log import log_master
from transformers import AutoProcessor
from text_generation_server.layers.attention import Seqlen
@ -254,6 +256,8 @@ class VlmCausalLM(FlashCausalLM):
trust_remote_code: bool,
**kwargs,
):
if PREFIX_CACHING:
raise NotImplementedError("Vlm do not work with prefix caching yet")
if processor_kwargs is None:
processor_kwargs = {}
self.processor = processor_class.from_pretrained(
@ -310,6 +314,9 @@ class VlmCausalLM(FlashCausalLM):
input_lengths = (
input_lengths.unsqueeze(-1).expand(B, new_length) + arange_int
).view(-1)
prefix_lens_tensor = (
batch.prefix_lens_tensor.unsqueeze(-1).expand(B, new_length)
).reshape(-1)
# Add Copy the block tables for all members
block_tables = (
@ -330,6 +337,7 @@ class VlmCausalLM(FlashCausalLM):
block_tables = batch.block_tables_tensor
slots = batch.slots[batch.slot_indices]
input_lengths = batch.input_lengths_tensor
prefix_lens_tensor = batch.prefix_lens_tensor
max_s = batch.max_seqlen
lm_head_indices = batch.prefill_head_indices
@ -349,43 +357,68 @@ class VlmCausalLM(FlashCausalLM):
else:
cuda_graph = None
if cu_seqlen_prefill is not None or cuda_graph is None:
input_lengths = Seqlen(input_lengths=input_lengths)
logits, speculative_logits = self.model.forward(
input_ids=input_ids,
position_ids=position_ids,
cu_seqlen_prefill=cu_seqlen_prefill,
kv_cache=kv_cache,
input_lengths = input_lengths + prefix_lens_tensor
if PREFIX_CACHING:
block_tables = block_tables_to_ragged(
block_tables=block_tables,
input_lengths=batch.input_lengths,
prefix_lens=batch.prefix_lens,
)
with self._forward_context(
block_tables=block_tables,
slots=slots,
input_lengths=input_lengths,
max_s=max_s,
prefill_cache_indices=batch.prefill_cache_indices,
lm_head_indices=lm_head_indices,
pixel_values=batch.pixel_values,
pixel_attention_mask=batch.pixel_attention_mask,
image_sizes=batch.image_sizes,
)
if batch.prefill_cache_indices is not None:
batch.prefill_cache_indices = None
if batch.pixel_values is not None:
batch.pixel_values = None
if batch.pixel_attention_mask is not None:
batch.pixel_attention_mask = None
if batch.image_sizes is not None:
batch.image_sizes = None
return logits, speculative_logits
cu_seqlen_prefill=cu_seqlen_prefill,
input_lengths=batch.input_lengths,
input_lengths_tensor=input_lengths,
prefix_lens=batch.prefix_lens,
prefix_lens_tensor=prefix_lens_tensor,
):
input_lengths = Seqlen(input_lengths=input_lengths)
logits, speculative_logits = self.model.forward(
input_ids=input_ids,
position_ids=position_ids,
cu_seqlen_prefill=cu_seqlen_prefill,
kv_cache=kv_cache,
block_tables=block_tables,
slots=slots,
input_lengths=input_lengths,
max_s=max_s,
prefill_cache_indices=batch.prefill_cache_indices,
lm_head_indices=lm_head_indices,
pixel_values=batch.pixel_values,
pixel_attention_mask=batch.pixel_attention_mask,
image_sizes=batch.image_sizes,
)
if batch.prefill_cache_indices is not None:
batch.prefill_cache_indices = None
if batch.pixel_values is not None:
batch.pixel_values = None
if batch.pixel_attention_mask is not None:
batch.pixel_attention_mask = None
if batch.image_sizes is not None:
batch.image_sizes = None
return logits, speculative_logits
# Copy inputs to the static inputs of the cuda graph
# Static inputs are potentially padded
cuda_graph["input_ids"][: input_ids.shape[0]] = input_ids
cuda_graph["position_ids"][: position_ids.shape[0]] = position_ids
cuda_graph["block_tables"][
: block_tables.shape[0], : block_tables.shape[1]
] = block_tables
if ATTENTION == "flashinfer":
block_tables = block_tables_to_ragged(
block_tables=block_tables,
input_lengths=batch.input_lengths,
prefix_lens=batch.prefix_lens,
)
cuda_graph["block_tables"][: block_tables.shape[0]] = block_tables
else:
cuda_graph["block_tables"][
: block_tables.shape[0], : block_tables.shape[1]
] = block_tables
cuda_graph["slots"].fill_(-1)
cuda_graph["slots"][: slots.shape[0]] = slots
cuda_graph["input_lengths"].zero_()
cuda_graph["input_lengths"][: input_lengths.shape[0]] = input_lengths
cuda_graph["input_lengths"][: input_lengths.shape[0]] = (
input_lengths + prefix_lens_tensor
)
# Replay the graph
cuda_graph["graph"].replay()