class GPUModelRunner(LoRAModelRunnerMixin):
def __init__(
self,
vllm_config: VllmConfig,
device: torch.device,
):
self.vllm_config = vllm_config
self.model_config = vllm_config.model_config
self.cache_config = vllm_config.cache_config
self.lora_config = vllm_config.lora_config
self.load_config = vllm_config.load_config
self.parallel_config = vllm_config.parallel_config
self.scheduler_config = vllm_config.scheduler_config
self.speculative_config = vllm_config.speculative_config
self.prompt_adapter_config = vllm_config.prompt_adapter_config
self.observability_config = vllm_config.observability_config
from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
set_cpu_offload_max_bytes(
int(self.cache_config.cpu_offload_gb * 1024**3))
model_config = self.model_config
cache_config = self.cache_config
scheduler_config = self.scheduler_config
parallel_config = self.parallel_config
self.device = device
self.pin_memory = is_pin_memory_available()
self.dtype = self.model_config.dtype
if cache_config.cache_dtype == "auto":
self.kv_cache_dtype = self.dtype
else:
self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[
cache_config.cache_dtype]
self.is_multimodal_model = model_config.is_multimodal_model
self.max_model_len = model_config.max_model_len
self.max_num_tokens = scheduler_config.max_num_batched_tokens
self.max_num_reqs = scheduler_config.max_num_seqs
# Model-related.
self.num_query_heads = model_config.get_num_attention_heads(
parallel_config)
self.hidden_size = model_config.get_hidden_size()
self.attention_chunk_size = model_config.attention_chunk_size
self.cascade_attn_enabled = not self.model_config.disable_cascade_attn
# Multi-modal data support
self.mm_registry = MULTIMODAL_REGISTRY
self.uses_mrope = model_config.uses_mrope
encoder_compute_budget, encoder_cache_size = compute_encoder_budget(
model_config=model_config,
scheduler_config=scheduler_config,
mm_registry=self.mm_registry,
)
self.max_num_encoder_input_tokens = encoder_compute_budget
self.encoder_cache_size = encoder_cache_size
# Sampler
self.sampler = Sampler()
# Lazy initializations
# self.model: nn.Module # Set after load_model
# Initialize in initialize_kv_cache
self.kv_caches: list[torch.Tensor] = []
self.attn_metadata_builders: list[AttentionMetadataBuilder] = []
self.attn_backends: list[type[AttentionBackend]] = []
# self.kv_cache_config: KVCacheConfig
# self.input_batch: InputBatch # Persistent batch.
# req_id -> (input_id -> encoder_output)
self.encoder_cache: dict[str, dict[int, torch.Tensor]] = {}
# Set up speculative decoding.
self.use_spec_decode = False
self.use_aux_hidden_state_outputs = False
if self.speculative_config:
self.use_spec_decode = True
# NOTE(Jiayi): currently we put the entire draft model on
# the last PP rank. This is not ideal if there are many
# layers in the draft model.
if get_pp_group().is_last_rank:
if self.speculative_config.method == "ngram":
self.drafter = NgramProposer(self.vllm_config)
elif self.speculative_config.use_eagle():
self.drafter = EagleProposer(self.vllm_config, self.device,
self) # type: ignore
if self.speculative_config.method == "eagle3":
self.use_aux_hidden_state_outputs = True
elif self.speculative_config.method == "medusa":
self.drafter = MedusaProposer(
vllm_config=self.vllm_config,
device=self.device) # type: ignore
else:
raise ValueError("Unknown speculative decoding method: "
f"{self.speculative_config.method}")
self.rejection_sampler = RejectionSampler()
# Request states.
self.requests: dict[str, CachedRequestState] = {}
self.input_batch = InputBatch(
max_num_reqs=self.max_num_reqs,
max_model_len=self.max_model_len,
max_num_batched_tokens=self.max_num_tokens,
device=self.device,
pin_memory=self.pin_memory,
vocab_size=self.model_config.get_vocab_size(),
block_size=self.cache_config.block_size,
)
self.use_cuda_graph = (self.vllm_config.compilation_config.level
== CompilationLevel.PIECEWISE
and not self.model_config.enforce_eager)
# TODO(woosuk): Provide an option to tune the max cudagraph batch size.
# The convention is different.
# self.cudagraph_batch_sizes sorts in ascending order.
# The batch sizes in the config are in descending order.
self.cudagraph_batch_sizes = list(
reversed(
self.vllm_config.compilation_config.cudagraph_capture_sizes))
# Cache the device properties.
self.device_properties = torch.cuda.get_device_properties(self.device)
self.num_sms = self.device_properties.multi_processor_count
# Persistent buffers for CUDA graphs.
self.input_ids = torch.zeros(self.max_num_tokens,
dtype=torch.int32,
device=self.device)
self.positions = torch.zeros(self.max_num_tokens,
dtype=torch.int64,
device=self.device)
self.query_start_loc = torch.zeros(self.max_num_reqs + 1,
dtype=torch.int32,
device=self.device)
self.seq_lens = torch.zeros(self.max_num_reqs,
dtype=torch.int32,
device=self.device)
self.slot_mapping = torch.zeros(self.max_num_tokens,
dtype=torch.int64,
device=self.device)
# None in the first PP rank. The rest are set after load_model.
self.intermediate_tensors: Optional[IntermediateTensors] = None
# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
if self.uses_mrope:
# NOTE: `mrope_positions` is implemented with one additional dummy
# position on purpose to make it non-contiguous so that it can work
# with torch compile.
# See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1926431923
# NOTE: When M-RoPE is enabled, position ids are 3D regardless of
# the modality of inputs. For text-only inputs, each dimension has
# identical position IDs, making M-RoPE functionally equivalent to
# 1D-RoPE.
# See page 5 of https://arxiv.org/abs/2409.12191
self.mrope_positions = torch.zeros((3, self.max_num_tokens + 1),
dtype=torch.int64,
device=self.device)
self.mrope_positions_cpu = torch.zeros(
(3, self.max_num_tokens + 1),
dtype=torch.int64,
device="cpu",
pin_memory=self.pin_memory)
# Only relevant for models using ALiBi (e.g, MPT)
self.use_alibi = check_use_alibi(model_config)
self.inputs_embeds = torch.zeros(
(self.max_num_tokens, self.hidden_size),
dtype=self.dtype,
device=self.device)
# OPTIMIZATION: Cache the tensors rather than creating them every step.
# Keep in int64 to avoid overflow with long context
self.arange_np = np.arange(max(self.max_num_reqs + 1,
self.max_model_len,
self.max_num_tokens),
dtype=np.int64)
# NOTE(woosuk): These tensors are "stateless", i.e., they are literally
# a faster version of creating a new tensor every time. Thus, we should
# not make any assumptions about the values in these tensors.
self.input_ids_cpu = torch.zeros(self.max_num_tokens,
dtype=torch.int32,
device="cpu",
pin_memory=self.pin_memory)
self.positions_cpu = torch.zeros(self.max_num_tokens,
dtype=torch.int64,
device="cpu",
pin_memory=self.pin_memory)
self.positions_np = self.positions_cpu.numpy()
self.query_start_loc_cpu = torch.zeros(self.max_num_reqs + 1,
dtype=torch.int32,
device="cpu",
pin_memory=self.pin_memory)
self.query_start_loc_np = self.query_start_loc_cpu.numpy()
self.seq_lens_cpu = torch.zeros(self.max_num_reqs,
dtype=torch.int32,
device="cpu",
pin_memory=self.pin_memory)
self.seq_lens_np = self.seq_lens_cpu.numpy()
def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> bool:
"""
Update the order of requests in the batch based on the attention
backend's needs. For example, some attention backends (namely MLA) may
want to separate requests based on if the attention computation will be
compute-bound or memory-bound.
Args:
scheduler_output: The scheduler output.
Returns:
True if the batch was reordered, False otherwise.
"""
batch_reordered = self.attn_metadata_builders[0].reorder_batch(
self.input_batch, scheduler_output)
# For models with multiple KV cache groups, the groups should agree on
# the same order of requests. We ensure this by only allowing the first
# group to reorder the batch and asserting that all other groups do not
# reorder the batch.
for i in range(1, len(self.kv_cache_config.kv_cache_groups)):
assert not self.attn_metadata_builders[i].reorder_batch(
self.input_batch, scheduler_output)
return batch_reordered
def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
"""Update the cached states and the persistent batch with the scheduler
output.
The updated states are used by the `_prepare_inputs` function to create
the input GPU tensors for the model.
The SamplingMetadata is updated and copied to the GPU if there is a
new/resumed/paused/finished request in the batch.
"""
# Remove finished requests from the cached states.
for req_id in scheduler_output.finished_req_ids:
self.requests.pop(req_id, None)
self.encoder_cache.pop(req_id, None)
# Remove the finished requests from the persistent batch.
# NOTE(woosuk): There could be an edge case where finished_req_ids and
# scheduled_req_ids overlap. This happens when a request is aborted and
# then resubmitted with the same ID. In this case, we treat them as two
# distinct requests - clearing the cached states for the first request
# and handling the second as a new request.
removed_req_indices: list[int] = []
for req_id in scheduler_output.finished_req_ids:
req_index = self.input_batch.remove_request(req_id)
if req_index is not None:
removed_req_indices.append(req_index)
# Free the cached encoder outputs.
for req_id, input_id in scheduler_output.free_encoder_input_ids:
encoder_outputs = self.encoder_cache.get(req_id)
if encoder_outputs is not None:
encoder_outputs.pop(input_id, None)
if not encoder_outputs:
self.encoder_cache.pop(req_id, None)
# Remove the unscheduled requests from the persistent batch.
# NOTE(woosuk): The unscheduled requests are either preempted requests
# or running requests that are not scheduled in this step. We remove
# them from the persistent batch but keep their cached states since
# they will be scheduled again sometime in the future.
scheduled_req_ids = scheduler_output.num_scheduled_tokens.keys()
cached_req_ids = self.input_batch.req_id_to_index.keys()
unscheduled_req_ids = cached_req_ids - scheduled_req_ids
# NOTE(woosuk): The persistent batch optimization assumes that
# consecutive batches contain mostly the same requests. If batches
# have low request overlap (e.g., alternating between two distinct
# sets of requests), this optimization becomes very inefficient.
for req_id in unscheduled_req_ids:
req_index = self.input_batch.remove_request(req_id)
assert req_index is not None
removed_req_indices.append(req_index)
req_ids_to_add: list[str] = []
# Add new requests to the cached states.
for new_req_data in scheduler_output.scheduled_new_reqs:
req_id = new_req_data.req_id
sampling_params = new_req_data.sampling_params
if sampling_params.sampling_type == SamplingType.RANDOM_SEED:
generator = torch.Generator(device=self.device)
generator.manual_seed(sampling_params.seed)
else:
generator = None
self.requests[req_id] = CachedRequestState(
req_id=req_id,
prompt_token_ids=new_req_data.prompt_token_ids,
mm_inputs=new_req_data.mm_inputs,
mm_positions=new_req_data.mm_positions,
sampling_params=sampling_params,
generator=generator,
block_ids=new_req_data.block_ids,
num_computed_tokens=new_req_data.num_computed_tokens,
output_token_ids=[],
lora_request=new_req_data.lora_request,
)
# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
if self.uses_mrope:
image_grid_thw = []
video_grid_thw = []
second_per_grid_ts = []
audio_feature_lengths = []
use_audio_in_video = False
for mm_input in self.requests[req_id].mm_inputs:
if mm_input.get("image_grid_thw") is not None:
image_grid_thw.extend(
mm_input["image_grid_thw"].tolist())
if mm_input.get("video_grid_thw") is not None:
video_grid_thw.extend(
mm_input["video_grid_thw"].tolist())
if mm_input.get("second_per_grid_ts") is not None:
second_per_grid_ts.extend(
mm_input["second_per_grid_ts"])
if mm_input.get("audio_feature_lengths") is not None:
audio_feature_lengths.extend(
mm_input["audio_feature_lengths"])
if mm_input.get("use_audio_in_video") is True:
use_audio_in_video = True
hf_config = self.model_config.hf_config
self.requests[req_id].mrope_positions, \
self.requests[req_id].mrope_position_delta = \
MRotaryEmbedding.get_input_positions_tensor(
self.requests[req_id].prompt_token_ids,
hf_config=hf_config,
image_grid_thw=image_grid_thw,
video_grid_thw=video_grid_thw,
second_per_grid_ts=second_per_grid_ts,
audio_feature_lengths=audio_feature_lengths,
use_audio_in_video=use_audio_in_video,
)
req_ids_to_add.append(req_id)
# Update the states of the running/resumed requests.
for req_data in scheduler_output.scheduled_cached_reqs:
req_id = req_data.req_id
req_state = self.requests[req_id]
# Update the cached states.
num_computed_tokens = req_data.num_computed_tokens
req_state.num_computed_tokens = num_computed_tokens
# Add the sampled token(s) from the previous step (if any).
# This doesn't include "unverified" tokens like spec decode tokens.
num_new_tokens = (num_computed_tokens +
len(req_data.new_token_ids) -
req_state.num_tokens)
if num_new_tokens == 1:
# Avoid slicing list in most common case.
req_state.output_token_ids.append(req_data.new_token_ids[-1])
elif num_new_tokens > 0:
req_state.output_token_ids.extend(
req_data.new_token_ids[-num_new_tokens:])
# Update the block IDs.
if not req_data.resumed_from_preemption:
# Append the new blocks to the existing block IDs.
for i in range(len(self.kv_cache_config.kv_cache_groups)):
req_state.block_ids[i].extend(req_data.new_block_ids[i])
else:
# The request is resumed from preemption.
# Replace the existing block IDs with the new ones.
req_state.block_ids = req_data.new_block_ids
req_index = self.input_batch.req_id_to_index.get(req_id)
if req_index is None:
# The request is not in the persistent batch.
# The request was either preempted and resumed later, or was not
# scheduled in the previous step and needs to be added again.
req_ids_to_add.append(req_id)
continue
# Update the persistent batch.
self.input_batch.num_computed_tokens_cpu[req_index] = (
num_computed_tokens)
self.input_batch.block_table.append_row(req_data.new_block_ids,
req_index)
# Add new_token_ids to token_ids_cpu.
start_token_index = num_computed_tokens
end_token_index = num_computed_tokens + len(req_data.new_token_ids)
self.input_batch.token_ids_cpu[
req_index,
start_token_index:end_token_index] = req_data.new_token_ids
self.input_batch.num_tokens_no_spec[req_index] = end_token_index
# Add spec_token_ids to token_ids_cpu.
spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(
req_id, ())
if spec_token_ids:
start_index = end_token_index
end_token_index += len(spec_token_ids)
self.input_batch.token_ids_cpu[
req_index, start_index:end_token_index] = spec_token_ids
# NOTE(woosuk): `num_tokens` here may include spec decode tokens.
self.input_batch.num_tokens[req_index] = end_token_index
# Check if the batch has changed. If not, we can skip copying the
# sampling metadata from CPU to GPU.
batch_changed = len(removed_req_indices) > 0 or len(req_ids_to_add) > 0
# Add the new or resumed requests to the persistent batch.
# The smaller empty indices are filled first.
removed_req_indices.sort(reverse=True)
for req_id in req_ids_to_add:
req_state = self.requests[req_id]
if removed_req_indices:
# Fill the empty index.
req_index = removed_req_indices.pop()
else:
# Append to the end.
req_index = None
self.input_batch.add_request(req_state, req_index)
# Condense the batched states if there are empty indices.
if removed_req_indices:
self.input_batch.condense(removed_req_indices)
batch_reordered = self._may_reorder_batch(scheduler_output)
if batch_changed or batch_reordered:
self.input_batch.refresh_sampling_metadata()
def _prepare_inputs(
self,
scheduler_output: "SchedulerOutput",
) -> tuple[dict[str, FlashAttentionMetadata], torch.Tensor,
Optional[SpecDecodeMetadata]]:
total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
assert total_num_scheduled_tokens > 0
num_reqs = self.input_batch.num_reqs
assert num_reqs > 0
# OPTIMIZATION: Start copying the block table first.
# This way, we can overlap the copy with the following CPU operations.
self.input_batch.block_table.commit(num_reqs)
# Get the number of scheduled tokens for each request.
req_ids = self.input_batch.req_ids
tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
num_scheduled_tokens = np.array(tokens, dtype=np.int32)
max_num_scheduled_tokens = max(tokens)
# Get request indices.
# E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
req_indices = np.repeat(self.arange_np[:num_reqs],
num_scheduled_tokens)
# Get batched arange.
# E.g., [2, 5, 3] -> [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
# Equivalent to but faster than:
# np.concatenate([np.arange(n) for n in num_scheduled_tokens])
# Step 1. [2, 5, 3] -> [2, 7, 10]
cu_num_tokens = np.cumsum(num_scheduled_tokens)
# Step 2. [2, 7, 10] -> [0, 0, 2, 2, 2, 2, 2, 7, 7, 7]
cumsums_offsets = np.repeat(cu_num_tokens - num_scheduled_tokens,
num_scheduled_tokens)
# Step 3. [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
arange = self.arange_np[:total_num_scheduled_tokens] - cumsums_offsets
# Get positions.
positions_np = self.positions_np[:total_num_scheduled_tokens]
np.add(self.input_batch.num_computed_tokens_cpu[req_indices],
arange,
out=positions_np)
# Calculate M-RoPE positions.
# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
if self.uses_mrope:
self._calc_mrope_positions(scheduler_output)
# Get token indices.
# E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
# -> [0, 1, M, M + 1, M + 2, M + 3, M + 4, 2 * M, 2 * M + 1, 2 * M + 2]
# where M is the max_model_len.
token_indices = (positions_np +
req_indices * self.input_batch.token_ids_cpu.shape[1])
# NOTE(woosuk): We use torch.index_select instead of np.take here
# because torch.index_select is much faster than np.take for large
# tensors.
torch.index_select(self.input_batch.token_ids_cpu_tensor.flatten(),
0,
torch.from_numpy(token_indices),
out=self.input_ids_cpu[:total_num_scheduled_tokens])
# Calculate the slot mapping for each KV cache group.
for kv_cache_group_id, kv_cache_group_spec in enumerate(
self.kv_cache_config.kv_cache_groups):
block_size = kv_cache_group_spec.kv_cache_spec.block_size
block_table: BlockTable = self.input_batch.block_table[
kv_cache_group_id]
# E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
# -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1]
# where K is the max_num_blocks_per_req and the block size is 2.
# NOTE(woosuk): We can't simply use `token_indices // block_size`
# here because M (max_model_len) is not necessarily divisible by
# block_size.
block_table_indices = (
req_indices * block_table.max_num_blocks_per_req +
positions_np // block_size)
block_table_cpu = block_table.get_cpu_tensor()
block_numbers = block_table_cpu.flatten(
)[block_table_indices].numpy()
block_offsets = positions_np % block_size
np.add(
block_numbers * block_size,
block_offsets,
out=block_table.slot_mapping_np[:total_num_scheduled_tokens])
# Prepare the attention metadata.
self.query_start_loc_np[0] = 0
self.query_start_loc_np[1:num_reqs + 1] = cu_num_tokens
self.seq_lens_np[:num_reqs] = (
self.input_batch.num_computed_tokens_cpu[:num_reqs] +
num_scheduled_tokens)
# Copy the tensors to the GPU.
self.input_ids[:total_num_scheduled_tokens].copy_(
self.input_ids_cpu[:total_num_scheduled_tokens], non_blocking=True)
if self.uses_mrope:
# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
self.mrope_positions[:, :total_num_scheduled_tokens].copy_(
self.mrope_positions_cpu[:, :total_num_scheduled_tokens],
non_blocking=True)
else:
# Common case (1D positions)
self.positions[:total_num_scheduled_tokens].copy_(
self.positions_cpu[:total_num_scheduled_tokens],
non_blocking=True)
self.query_start_loc[:num_reqs + 1].copy_(
self.query_start_loc_cpu[:num_reqs + 1], non_blocking=True)
self.seq_lens[:num_reqs].copy_(self.seq_lens_cpu[:num_reqs],
non_blocking=True)
# Fill unused with -1. Needed for reshape_and_cache
self.seq_lens[num_reqs:].fill_(0)
self.query_start_loc[num_reqs + 1:].fill_(-1)
query_start_loc = self.query_start_loc[:num_reqs + 1]
seq_lens = self.seq_lens[:num_reqs]
common_attn_metadata = CommonAttentionMetadata(
query_start_loc=query_start_loc, seq_lens=seq_lens)
attn_metadata: dict[str, FlashAttentionMetadata] = {}
# Prepare the attention metadata for each KV cache group and make layers
# in the same group share the same metadata.
for kv_cache_group_id, kv_cache_group_spec in enumerate(
self.kv_cache_config.kv_cache_groups):
# Prepare for cascade attention if enabled & beneficial.
common_prefix_len = 0
if self.cascade_attn_enabled:
common_prefix_len = self._compute_cascade_attn_prefix_len(
num_scheduled_tokens,
scheduler_output.
num_common_prefix_blocks[kv_cache_group_id],
kv_cache_group_spec.kv_cache_spec,
self.attn_metadata_builders[kv_cache_group_id],
)
attn_metadata_i = (
self.attn_metadata_builders[kv_cache_group_id].build(
num_reqs=num_reqs,
num_actual_tokens=total_num_scheduled_tokens,
max_query_len=max_num_scheduled_tokens,
common_prefix_len=common_prefix_len,
common_attn_metadata=common_attn_metadata))
for layer_name in kv_cache_group_spec.layer_names:
attn_metadata[layer_name] = attn_metadata_i
use_spec_decode = len(
scheduler_output.scheduled_spec_decode_tokens) > 0
if not use_spec_decode:
# NOTE(woosuk): Due to chunked prefills, the batch may contain
# partial requests. While we should not sample any token
# from these partial requests, we do so for simplicity.
# We will ignore the sampled tokens from the partial requests.
# TODO: Support prompt logprobs.
logits_indices = query_start_loc[1:] - 1
spec_decode_metadata = None
else:
# Get the number of draft tokens for each request.
# Iterate over the dictionary rather than all requests since not all
# requests have draft tokens.
num_draft_tokens = np.zeros(num_reqs, dtype=np.int32)
for req_id, draft_token_ids in (
scheduler_output.scheduled_spec_decode_tokens.items()):
req_idx = self.input_batch.req_id_to_index[req_id]
num_draft_tokens[req_idx] = len(draft_token_ids)
spec_decode_metadata = self._calc_spec_decode_metadata(
num_draft_tokens, cu_num_tokens)
logits_indices = spec_decode_metadata.logits_indices
# Hot-Swap lora model
if self.lora_config:
self.set_active_loras(self.input_batch, num_scheduled_tokens)
return attn_metadata, logits_indices, spec_decode_metadata
def _compute_cascade_attn_prefix_len(
self,
num_scheduled_tokens: np.ndarray,
num_common_prefix_blocks: int,
kv_cache_spec: KVCacheSpec,
attn_metadata_builder: AttentionMetadataBuilder,
) -> int:
"""Compute the length of the common prefix for cascade attention.
NOTE(woosuk): The common prefix length returned by this function
represents the length used specifically for cascade attention, not the
actual number of tokens shared between requests. When cascade attention
is disabled (use_cascade=False), this function returns 0 even if
requests share common tokens. Additionally, the common prefix length is
truncated to a multiple of the block size and may be further truncated
due to implementation details explained below.
Args:
num_scheduled_tokens: Number of tokens scheduled per request.
num_common_prefix_blocks: Number of shared KV cache blocks.
Returns:
int: Length of common prefix in tokens.
"""
common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size
if common_prefix_len == 0:
# Common case.
return 0
# NOTE(woosuk): Cascade attention uses two attention kernels: one
# for the common prefix and the other for the rest. For the first
# kernel, we concatenate all the query tokens (possibly from
# different requests) and treat them as if they are from the same
# request. Then, we use bi-directional attention to process the
# common prefix in the KV cache. Importantly, this means that the
# first kernel does not do any masking.
# Consider the following example:
# Request 1's input query: [D, E, X]
# Request 1's kv cache: [A, B, C, D, E, X]
# Request 1's num_computed_tokens: 3 (i.e., [A, B, C])
# Request 2's input query: [E, Y]
# Request 2's kv cache: [A, B, C, D, E, Y]
# Request 2's num_computed_tokens: 4 (i.e., [A, B, C, D])
# If we use [A, B, C, D, E] as the common prefix, then the
# first kernel will compute the bi-directional attention between
# input query [D, E, X, E, Y] and common prefix [A, B, C, D, E].
# However, this is wrong because D in Request 1 should not attend to
# E in the common prefix (i.e., we need masking).
# To avoid this, [A, B, C, D] should be the common prefix.
# That is, the common prefix should be capped by the minimum
# num_computed_tokens among the requests, and plus one to include
# the first token of the query.
# In practice, we use [A, B, C] as the common prefix, instead of
# [A, B, C, D] (i.e., the common prefix is capped by the minimum
# num_computed_tokens, without plus one).
# This is because of an implementation detail: We want to always
# use two kernels for cascade attention. Let's imagine:
# Request 3's input query: [D]
# Request 3's kv cache: [A, B, C, D]
# Request 3's num_computed_tokens: 3 (i.e., [A, B, C])
# If we use [A, B, C, D] as the common prefix for Request 1-3,
# then Request 3 will be processed only by the first kernel,
# and the second kernel will get an empty input. While this is not
# a fundamental problem, our current implementation does not support
# this case.
num_reqs = len(num_scheduled_tokens)
common_prefix_len = min(
common_prefix_len,
self.input_batch.num_computed_tokens_cpu[:num_reqs].min())
# common_prefix_len should be a multiple of the block size.
common_prefix_len = (common_prefix_len // kv_cache_spec.block_size *
kv_cache_spec.block_size)
use_sliding_window = (isinstance(kv_cache_spec, SlidingWindowSpec) or
(isinstance(kv_cache_spec, FullAttentionSpec)
and kv_cache_spec.sliding_window is not None))
assert isinstance(kv_cache_spec, AttentionSpec)
use_cascade = attn_metadata_builder.use_cascade_attention(
common_prefix_len=common_prefix_len,
query_lens=num_scheduled_tokens,
num_query_heads=self.num_query_heads,
num_kv_heads=kv_cache_spec.num_kv_heads,
use_alibi=self.use_alibi,
use_sliding_window=use_sliding_window,
num_sms=self.num_sms,
)
return common_prefix_len if use_cascade else 0
def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
mrope_pos_ptr = 0
for index, req_id in enumerate(self.input_batch.req_ids):
req = self.requests[req_id]
assert req.mrope_positions is not None
num_computed_tokens = \
self.input_batch.num_computed_tokens_cpu[index]
num_scheduled_tokens = \
scheduler_output.num_scheduled_tokens[req_id]
num_prompt_tokens = len(req.prompt_token_ids)
if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens:
prompt_part_len = max(0,
num_prompt_tokens - num_computed_tokens)
completion_part_len = max(
0, num_scheduled_tokens - prompt_part_len)
else:
prompt_part_len = num_scheduled_tokens
completion_part_len = 0
assert num_scheduled_tokens == prompt_part_len + completion_part_len
if prompt_part_len > 0:
# prompt's mrope_positions are pre-computed
dst_start = mrope_pos_ptr
dst_end = mrope_pos_ptr + prompt_part_len
src_start = num_computed_tokens
src_end = num_computed_tokens + prompt_part_len
self.mrope_positions_cpu[:, dst_start:dst_end] = \
req.mrope_positions[:,src_start:src_end]
mrope_pos_ptr += prompt_part_len
if completion_part_len > 0:
# compute completion's mrope_positions on-the-fly
dst_start = mrope_pos_ptr
dst_end = mrope_pos_ptr + completion_part_len
self.mrope_positions_cpu[:, dst_start:dst_end] = \
MRotaryEmbedding.get_next_input_positions_tensor(
req.mrope_position_delta,
context_len=num_computed_tokens +
prompt_part_len,
seq_len=num_computed_tokens +
prompt_part_len +
completion_part_len,
)
mrope_pos_ptr += completion_part_len
def _calc_spec_decode_metadata(
self,
num_draft_tokens: np.ndarray,
cu_num_scheduled_tokens: np.ndarray,
) -> SpecDecodeMetadata:
# Inputs:
# cu_num_scheduled_tokens: [ 4, 104, 107, 207, 209]
# num_draft_tokens: [ 3, 0, 2, 0, 1]
# Outputs:
# cu_num_draft_tokens: [ 3, 3, 5, 5, 6]
# logits_indices: [ 0, 1, 2, 3, 103, 104, 105, 106,
# 206, 207, 208]
# target_logits_indices: [ 0, 1, 2, 5, 6, 9]
# bonus_logits_indices: [ 3, 4, 7, 8, 10]
# Compute the logits indices.
# [4, 1, 3, 1, 2]
num_sampled_tokens = num_draft_tokens + 1
# Step 1. [4, 5, 8, 9, 11]
cu_num_sampled_tokens = np.cumsum(num_sampled_tokens, dtype=np.int32)
total_num_sampled_tokens = cu_num_sampled_tokens[-1]
# Step 2. [0, 0, 0, 0, 4, 5, 5, 5, 8, 9, 9]
cumsums_offsets = np.repeat(cu_num_sampled_tokens - num_sampled_tokens,
num_sampled_tokens)
# Step 3. [0, 1, 2, 3, 0, 0, 1, 2, 0, 0, 1]
arange = self.arange_np[:total_num_sampled_tokens] - cumsums_offsets
# Step 4. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207]
logits_indices = np.repeat(
cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens)
# Step 5. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208]
logits_indices += arange
# Compute the bonus logits indices.
bonus_logits_indices = cu_num_sampled_tokens - 1
# Compute the draft logits indices.
# [3, 3, 5, 5, 6]
cu_num_draft_tokens = np.cumsum(num_draft_tokens, dtype=np.int32)
total_num_draft_tokens = cu_num_draft_tokens[-1]
# [0, 0, 0, 3, 3, 5]
cumsums_offsets = np.repeat(cu_num_draft_tokens - num_draft_tokens,
num_draft_tokens)
# [0, 1, 2, 0, 1, 0]
arange = self.arange_np[:total_num_draft_tokens] - cumsums_offsets
# [0, 0, 0, 5, 5, 9]
target_logits_indices = np.repeat(
cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens)
# [0, 1, 2, 5, 6, 9]
target_logits_indices += arange
# TODO: Optimize the CPU -> GPU copy.
cu_num_draft_tokens = torch.from_numpy(cu_num_draft_tokens).to(
self.device, non_blocking=True)
logits_indices = torch.from_numpy(logits_indices).to(self.device,
non_blocking=True)
target_logits_indices = torch.from_numpy(target_logits_indices).to(
self.device, non_blocking=True)
bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to(
self.device, non_blocking=True)
# Compute the draft token ids.
# draft_token_indices: [ 1, 2, 3, 105, 106, 208]
draft_token_ids = self.input_ids[logits_indices]
draft_token_ids = draft_token_ids[target_logits_indices + 1]
metadata = SpecDecodeMetadata(
draft_token_ids=draft_token_ids,
num_draft_tokens=num_draft_tokens.tolist(),
cu_num_draft_tokens=cu_num_draft_tokens,
target_logits_indices=target_logits_indices,
bonus_logits_indices=bonus_logits_indices,
logits_indices=logits_indices,
)
return metadata
def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"):
scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs
if not scheduled_encoder_inputs:
return
# Batch the multi-modal inputs.
mm_inputs = list[MultiModalKwargs]()
req_ids_pos = list[tuple[str, int, PlaceholderRange]]()
for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
req_state = self.requests[req_id]
for mm_input_id in encoder_input_ids:
mm_inputs.append(req_state.mm_inputs[mm_input_id])
req_ids_pos.append(
(req_id, mm_input_id, req_state.mm_positions[mm_input_id]))
# Batch mm inputs as much as we can: if a request in the batch has
# multiple modalities or a different modality than the previous one,
# we process it separately to preserve item order.
# FIXME(ywang96): This is a hacky way to deal with multiple modalities
# in the same batch while still being able to benefit from batching
# multimodal inputs. The proper solution should be reordering the
# encoder outputs.
grouped_mm_inputs_list = group_mm_inputs_by_modality(mm_inputs)
encoder_outputs = []
for grouped_mm_inputs in grouped_mm_inputs_list:
batched_mm_inputs = MultiModalKwargs.batch(grouped_mm_inputs)
batched_mm_inputs = MultiModalKwargs.as_kwargs(
batched_mm_inputs,
dtype=self.model_config.dtype,
device=self.device,
)
# Run the encoder.
# `curr_group_outputs` is either of the following:
# 1. A tensor of shape (num_items, feature_size, hidden_size)
# in case feature_size is fixed across all multimodal items.
# 2. A list or tuple (length: num_items) of tensors, each of shape
# (feature_size, hidden_size) in case the feature size is dynamic
# depending on the input multimodal items.
curr_group_outputs = self.model.get_multimodal_embeddings(
**batched_mm_inputs)
sanity_check_mm_encoder_outputs(
curr_group_outputs,
expected_num_items=len(grouped_mm_inputs),
)
for output in curr_group_outputs:
encoder_outputs.append(output)
# Cache the encoder outputs.
for (req_id, input_id, pos_info), output in zip(
req_ids_pos,
encoder_outputs,
):
if req_id not in self.encoder_cache:
self.encoder_cache[req_id] = {}
self.encoder_cache[req_id][input_id] = scatter_mm_placeholders(
output,
is_embed=pos_info.is_embed,
)
def _gather_mm_embeddings(
self,
scheduler_output: "SchedulerOutput",
) -> list[torch.Tensor]:
mm_embeds: list[torch.Tensor] = []
for req_id in self.input_batch.req_ids:
num_scheduled_tokens = scheduler_output.num_scheduled_tokens[
req_id]
req_state = self.requests[req_id]
num_computed_tokens = req_state.num_computed_tokens
mm_positions = req_state.mm_positions
for i, pos_info in enumerate(mm_positions):
start_pos = pos_info.offset
num_encoder_tokens = pos_info.length
# The encoder output is needed if the two ranges overlap:
# [num_computed_tokens,
# num_computed_tokens + num_scheduled_tokens) and
# [start_pos, start_pos + num_encoder_tokens)
if start_pos >= num_computed_tokens + num_scheduled_tokens:
# The encoder output is not needed in this step.
break
if start_pos + num_encoder_tokens <= num_computed_tokens:
# The encoder output is already processed and stored
# in the decoder's KV cache.
continue
start_idx = max(num_computed_tokens - start_pos, 0)
end_idx = min(
num_computed_tokens - start_pos + num_scheduled_tokens,
num_encoder_tokens)
assert start_idx < end_idx
assert req_id in self.encoder_cache
assert i in self.encoder_cache[req_id]
encoder_output = self.encoder_cache[req_id][i]
if (is_embed := pos_info.is_embed) is not None:
is_embed = is_embed[start_idx:end_idx]
mm_embeds_item = gather_mm_placeholders(
encoder_output[start_idx:end_idx],
is_embed=is_embed,
)
mm_embeds.append(mm_embeds_item)
return mm_embeds
def get_model(self) -> nn.Module:
return self.model
def apply_grammar_bitmask(
self,
scheduler_output: "SchedulerOutput",
logits: torch.Tensor,
):
grammar_bitmask = scheduler_output.grammar_bitmask
if grammar_bitmask is None:
return
# We receive the structured output bitmask from the scheduler,
# compacted to contain bitmasks only for structured output requests.
# The order of the requests in the bitmask is not guaranteed to be the
# same as the order of the requests in the gpu runner's batch. We need
# to sort the bitmask to match the order of the requests used here.
# Get the batch indices of the structured output requests.
# Keep track of the number of speculative tokens scheduled for every
# request in the batch, as the logit indices are offset by this amount.
struct_out_req_batch_indices: dict[str, int] = {}
cumulative_offset = 0
seq = sorted(self.input_batch.req_id_to_index.items(),
key=lambda x: x[1])
for req_id, batch_index in seq:
logit_index = batch_index + cumulative_offset
cumulative_offset += len(
scheduler_output.scheduled_spec_decode_tokens.get(req_id, []))
if req_id in scheduler_output.structured_output_request_ids:
struct_out_req_batch_indices[req_id] = logit_index
out_indices = []
# Reorder the bitmask to match the order of the requests in the batch.
sorted_bitmask = np.zeros_like(grammar_bitmask,
shape=(logits.shape[0],
grammar_bitmask.shape[1]))
cumulative_index = 0
seq = sorted(scheduler_output.structured_output_request_ids.items(),
key=lambda x: x[1])
for req_id, _ in seq:
logit_index = struct_out_req_batch_indices[req_id]
num_spec_tokens = len(
scheduler_output.scheduled_spec_decode_tokens.get(req_id, []))
for i in range(1 + num_spec_tokens):
sorted_bitmask[logit_index + i] = \
grammar_bitmask[cumulative_index + i]
out_indices.append(logit_index + i)
cumulative_index += 1 + num_spec_tokens
grammar_bitmask = sorted_bitmask
# Serialization of np.ndarray is much more efficient than a tensor,
# so we receive it in that format.
grammar_bitmask = torch.from_numpy(grammar_bitmask)
xgr.apply_token_bitmask_inplace(
logits,
grammar_bitmask.to(self.device, non_blocking=True),
indices=out_indices,
)
def sync_and_slice_intermediate_tensors(
self, num_tokens: int, intermediate_tensors: IntermediateTensors,
sync_self: bool) -> IntermediateTensors:
assert self.intermediate_tensors is not None
tp = self.vllm_config.parallel_config.tensor_parallel_size
enabled_sp = self.vllm_config.compilation_config.pass_config. \
enable_sequence_parallelism
if enabled_sp:
# When sequence parallelism is enabled, we always pad num_tokens
# to be a multiple of tensor_parallel_size (tp) earlier
assert num_tokens % tp == 0
is_residual_scattered = tp > 1 and enabled_sp \
and num_tokens % tp == 0
# When sequence parallelism is enabled, the "residual" tensor is sharded
# across tensor parallel ranks, so each rank only needs its own slice.
if sync_self:
assert intermediate_tensors is not None
for k, v in intermediate_tensors.items():
is_scattered = "residual" and is_residual_scattered
copy_len = num_tokens // tp if is_scattered else \
num_tokens
self.intermediate_tensors[k][:copy_len].copy_(
v[:copy_len], non_blocking=True)
return IntermediateTensors({
k:
v[:num_tokens // tp]
if k == "residual" and is_residual_scattered else v[:num_tokens]
for k, v in self.intermediate_tensors.items()
})
@torch.inference_mode()
def execute_model(
self,
scheduler_output: "SchedulerOutput",
intermediate_tensors: Optional[IntermediateTensors] = None,
) -> Union[ModelRunnerOutput, IntermediateTensors]:
self._update_states(scheduler_output)
if not scheduler_output.total_num_scheduled_tokens:
if not has_kv_transfer_group():
# Return empty ModelRunnerOutput if there's no work to do.
return EMPTY_MODEL_RUNNER_OUTPUT
return self.kv_connector_no_forward(scheduler_output)
# Prepare the decoder inputs.
attn_metadata, logits_indices, spec_decode_metadata = (
self._prepare_inputs(scheduler_output))
num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
if (self.use_cuda_graph
and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]):
# Use piecewise CUDA graphs.
# Add padding to the batch size.
num_input_tokens = self.vllm_config.pad_for_cudagraph(
num_scheduled_tokens)
else:
# Eager mode.
# Pad tokens to multiple of tensor_parallel_size when
# enabled collective fusion for SP
tp_size = self.vllm_config.parallel_config.tensor_parallel_size
if self.vllm_config.compilation_config.pass_config. \
enable_sequence_parallelism and tp_size > 1:
from vllm.utils import round_up
num_input_tokens = round_up(num_scheduled_tokens, tp_size)
else:
num_input_tokens = num_scheduled_tokens
# _prepare_inputs may reorder the batch, so we must gather multi
# modal outputs after that to ensure the correct order
if self.is_multimodal_model:
# Run the multimodal encoder if any.
self._execute_mm_encoder(scheduler_output)
mm_embeds = self._gather_mm_embeddings(scheduler_output)
else:
mm_embeds = []
if self.is_multimodal_model and get_pp_group().is_first_rank:
# NOTE(woosuk): To unify token ids and soft tokens (vision
# embeddings), we always use embeddings (rather than token ids)
# as input to the multimodal model, even when the input is text.
input_ids = self.input_ids[:num_scheduled_tokens]
if mm_embeds:
inputs_embeds = self.model.get_input_embeddings(
input_ids, mm_embeds)
else:
inputs_embeds = self.model.get_input_embeddings(input_ids)
# TODO(woosuk): Avoid the copy. Optimize.
self.inputs_embeds[:num_scheduled_tokens].copy_(inputs_embeds)
inputs_embeds = self.inputs_embeds[:num_input_tokens]
input_ids = None
else:
# For text-only models, we use token ids as input.
# While it is possible to use embeddings as input just like the
# multimodal models, it is not desirable for performance since
# then the embedding layer is not included in the CUDA graph.
input_ids = self.input_ids[:num_input_tokens]
inputs_embeds = None
if self.uses_mrope:
positions = self.mrope_positions[:, :num_input_tokens]
else:
positions = self.positions[:num_input_tokens]
if get_pp_group().is_first_rank:
intermediate_tensors = None
else:
intermediate_tensors = self.sync_and_slice_intermediate_tensors(
num_input_tokens, intermediate_tensors, True)
# Run the decoder.
# Use persistent buffers for CUDA graphs.
with set_forward_context(attn_metadata,
self.vllm_config,
num_tokens=num_input_tokens):
self.maybe_setup_kv_connector(scheduler_output)
model_output = self.model(
input_ids=input_ids,
positions=positions,
intermediate_tensors=intermediate_tensors,
inputs_embeds=inputs_embeds,
)
self.maybe_wait_for_kv_save()
finished_sending, finished_recving = (
self.get_finished_kv_transfers(scheduler_output))
if self.use_aux_hidden_state_outputs:
hidden_states, aux_hidden_states = model_output
else:
hidden_states = model_output
# Broadcast PP output for external_launcher (torchrun)
# to make sure we are synced across pp ranks
# TODO: Support overlapping mirco-batches
# https://github.com/vllm-project/vllm/issues/18019
broadcast_pp_output = \
self.parallel_config.distributed_executor_backend \
== "external_launcher" and len(get_pp_group().ranks) > 0
if not get_pp_group().is_last_rank:
# For mid-pipeline stages, return the hidden states.
if not broadcast_pp_output:
return hidden_states
assert isinstance(hidden_states, IntermediateTensors)
get_pp_group().send_tensor_dict(hidden_states.tensors,
all_gather_group=get_tp_group())
logits = None
else:
sample_hidden_states = hidden_states[logits_indices]
logits = self.model.compute_logits(sample_hidden_states, None)
if broadcast_pp_output:
model_output_broadcast_data = {
"logits": logits.contiguous(),
} if logits is not None else {}
model_output_broadcast_data = get_pp_group().broadcast_tensor_dict(
model_output_broadcast_data, src=len(get_pp_group().ranks) - 1)
assert model_output_broadcast_data is not None
logits = model_output_broadcast_data["logits"]
# Apply structured output bitmasks if present
if scheduler_output.grammar_bitmask is not None:
self.apply_grammar_bitmask(scheduler_output, logits)
# Sample the next token and get logprobs if needed.
sampling_metadata = self.input_batch.sampling_metadata
if spec_decode_metadata is None:
sampler_output = self.sampler(
logits=logits,
sampling_metadata=sampling_metadata,
)
else:
# When indexing with a tensor (bonus_logits_indices), PyTorch
# creates a new tensor with separate storage from the original
# logits tensor. This means any in-place operations on bonus_logits
# won't affect the original logits tensor.
assert logits is not None
bonus_logits = logits[spec_decode_metadata.bonus_logits_indices]
sampler_output = self.sampler(
logits=bonus_logits,
sampling_metadata=sampling_metadata,
)
bonus_token_ids = sampler_output.sampled_token_ids
# Just like `bonus_logits`, `target_logits` is a new tensor with
# separate storage from the original `logits` tensor. Therefore,
# it is safe to update `target_logits` in place.
target_logits = logits[spec_decode_metadata.target_logits_indices]
output_token_ids = self.rejection_sampler(
spec_decode_metadata,
None, # draft_probs
target_logits,
bonus_token_ids,
sampling_metadata,
)
sampler_output.sampled_token_ids = output_token_ids
# TODO(woosuk): The following loop can be slow since it iterates over
# the requests one by one. Optimize.
discard_sampled_tokens_req_indices = []
for i, req_id in enumerate(self.input_batch.req_ids):
req_state = self.requests[req_id]
seq_len = (req_state.num_computed_tokens +
scheduler_output.num_scheduled_tokens[req_id])
if seq_len < req_state.num_tokens:
# Ignore the sampled token for partial prefills.
# Rewind the generator state as if the token was not sampled.
# This relies on cuda-specific torch-internal impl details
generator = self.input_batch.generators.get(i)
if generator is not None:
generator.set_offset(generator.get_offset() - 4)
# Record the index of the request that should not be sampled,
# so that we could clear the sampled tokens before returning.
discard_sampled_tokens_req_indices.append(i)
# NOTE: GPU -> CPU Sync happens here.
# Move as many CPU operations as possible before this sync point.
logprobs_tensors = sampler_output.logprobs_tensors
logprobs_lists = logprobs_tensors.tolists() \
if logprobs_tensors is not None else None
# Compute prompt logprobs if needed.
prompt_logprobs_dict = self._get_prompt_logprobs_dict(
hidden_states[:num_scheduled_tokens],
scheduler_output,
)
# Get the valid generated tokens.
sampled_token_ids = sampler_output.sampled_token_ids
max_gen_len = sampled_token_ids.shape[-1]
if max_gen_len == 1:
# No spec decode tokens.
valid_sampled_token_ids = sampled_token_ids.tolist()
else:
# Includes spec decode tokens.
valid_sampled_token_ids = self.rejection_sampler.parse_output(
sampled_token_ids,
self.input_batch.vocab_size,
)
# Mask out the sampled tokens that should not be sampled.
for i in discard_sampled_tokens_req_indices:
valid_sampled_token_ids[i].clear()
if not self.use_spec_decode:
# Speculative decoding is not enabled.
spec_token_ids = None
elif self.speculative_config.method == "ngram":
assert isinstance(self.drafter, NgramProposer)
spec_token_ids = self.generate_draft_token_ids(
valid_sampled_token_ids, sampling_metadata)
elif self.speculative_config.method == "medusa":
assert isinstance(self.drafter, MedusaProposer)
if max_gen_len == 1:
hidden_states = sample_hidden_states
else:
indices = []
offset = 0
for num_draft, tokens in zip(
spec_decode_metadata.num_draft_tokens,
valid_sampled_token_ids):
indices.append(offset + len(tokens) - 1)
offset += num_draft + 1
indices = torch.tensor(indices,
device=sample_hidden_states.device)
hidden_states = sample_hidden_states[indices]
spec_token_ids = self.drafter.propose(
target_hidden_states=hidden_states,
sampling_metadata=sampling_metadata,
)
elif self.speculative_config.use_eagle():
assert isinstance(self.drafter, EagleProposer)
# TODO(woosuk): Refactor the loop.
next_token_ids: list[int] = []
for i, token_ids in enumerate(valid_sampled_token_ids):
if token_ids:
# Common case.
next_token_id = token_ids[-1]
else:
# Partial prefill (rare case).
# Get the next token id from the request state.
req_id = self.input_batch.req_ids[i]
req_state = self.requests[req_id]
seq_len = (req_state.num_computed_tokens +
scheduler_output.num_scheduled_tokens[req_id])
next_token_id = req_state.get_token_id(seq_len)
next_token_ids.append(next_token_id)
next_token_ids = torch.tensor(next_token_ids,
dtype=torch.int32,
device=self.device)
# At this moment, we assume all eagle layers belong to the same KV
# cache group, thus using the same attention metadata.
eagle_attn_metadata = attn_metadata[
self.drafter.attn_layer_names[0]]
# NOTE: deepseek_mtp uses MLA which does not have `block_table`
if hasattr(eagle_attn_metadata, "block_table"):
block_table = eagle_attn_metadata.block_table
else:
block_table = None
if spec_decode_metadata is None:
# input_ids can be None for multimodal models.
target_token_ids = self.input_ids[:num_scheduled_tokens]
target_positions = positions[:num_scheduled_tokens]
if self.use_aux_hidden_state_outputs:
target_hidden_states = torch.cat(
[h[:num_scheduled_tokens] for h in aux_hidden_states],
dim=-1)
else:
target_hidden_states = hidden_states[:num_scheduled_tokens]
target_slot_mapping = eagle_attn_metadata.slot_mapping
cu_num_tokens = eagle_attn_metadata.query_start_loc
else:
# TODO(woosuk): Refactor this.
num_draft_tokens = spec_decode_metadata.num_draft_tokens
num_rejected_tokens = [
n + 1 - len(valid_sampled_token_ids[i]) if n > 0 else 0
for i, n in enumerate(num_draft_tokens)
]
num_rejected_tokens_tensor = async_tensor_h2d(
num_rejected_tokens,
dtype=torch.int32,
target_device=self.device,
pin_memory=True)
num_tokens = num_scheduled_tokens - sum(num_rejected_tokens)
cu_num_tokens, token_indices = self.drafter.prepare_inputs(
eagle_attn_metadata.query_start_loc,
num_rejected_tokens_tensor,
num_tokens,
)
target_token_ids = self.input_ids[token_indices]
target_positions = positions[token_indices]
if self.use_aux_hidden_state_outputs:
target_hidden_states = torch.cat(
[h[token_indices] for h in aux_hidden_states], dim=-1)
else:
target_hidden_states = hidden_states[token_indices]
target_slot_mapping = eagle_attn_metadata.slot_mapping[
token_indices]
draft_token_ids = self.drafter.propose(
target_token_ids=target_token_ids,
target_positions=target_positions,
target_hidden_states=target_hidden_states,
target_slot_mapping=target_slot_mapping,
next_token_ids=next_token_ids,
cu_num_tokens=cu_num_tokens,
block_table=block_table,
sampling_metadata=sampling_metadata,
)
spec_token_ids = draft_token_ids.tolist()
# Clear KVConnector state after all KVs are generated.
if has_kv_transfer_group():
get_kv_transfer_group().clear_connector_metadata()
return ModelRunnerOutput(
req_ids=self.input_batch.req_ids,
req_id_to_index=self.input_batch.req_id_to_index,
sampled_token_ids=valid_sampled_token_ids,
spec_token_ids=spec_token_ids,
logprobs=logprobs_lists,
prompt_logprobs_dict=prompt_logprobs_dict,
finished_sending=finished_sending,
finished_recving=finished_recving,
)
def kv_connector_no_forward(
self, scheduler_output: "SchedulerOutput") -> ModelRunnerOutput:
# KV send/recv even if no work to do.
with set_forward_context(None, self.vllm_config):
self.maybe_setup_kv_connector(scheduler_output)
finished_sending, finished_recving = (
self.get_finished_kv_transfers(scheduler_output))
if not finished_sending and not finished_recving:
return EMPTY_MODEL_RUNNER_OUTPUT
output = copy.copy(EMPTY_MODEL_RUNNER_OUTPUT)
output.finished_sending = finished_sending
output.finished_recving = finished_recving
return output
@staticmethod
def maybe_setup_kv_connector(scheduler_output: "SchedulerOutput"):
# Update KVConnector with the KVConnector metadata forward().
if has_kv_transfer_group():
kv_connector = get_kv_transfer_group()
assert isinstance(kv_connector, KVConnectorBase_V1)
assert scheduler_output.kv_connector_metadata is not None
kv_connector.bind_connector_metadata(
scheduler_output.kv_connector_metadata)
# Background KV cache transfers happen here.
# These transfers are designed to be async and the requests
# involved may be disjoint from the running requests.
# Do this here to save a collective_rpc.
kv_connector.start_load_kv(get_forward_context())
@staticmethod
def maybe_wait_for_kv_save() -> None:
if has_kv_transfer_group():
get_kv_transfer_group().wait_for_save()
@staticmethod
def get_finished_kv_transfers(
scheduler_output: "SchedulerOutput",
) -> tuple[Optional[set[str]], Optional[set[str]]]:
if has_kv_transfer_group():
return get_kv_transfer_group().get_finished(
scheduler_output.finished_req_ids)
return None, None
def generate_draft_token_ids(
self,
sampled_token_ids: list[list[int]],
sampling_metadata: SamplingMetadata,
) -> list[list[int]]:
# TODO(woosuk): Optimize.
draft_token_ids: list[list[int]] = []
for i, sampled_ids in enumerate(sampled_token_ids):
num_sampled_ids = len(sampled_ids)
if not num_sampled_ids:
# Skip speculative decoding.
draft_token_ids.append([])
continue
# Skip requests that require sampling parameters that are not
# supported with speculative decoding.
req_id = self.input_batch.req_ids[i]
if not is_spec_decode_supported(req_id, self.input_batch):
draft_token_ids.append([])
continue
# Add sampled_token_ids to token_ids_cpu.
start_idx = self.input_batch.num_tokens_no_spec[i]
end_idx = start_idx + num_sampled_ids
if end_idx >= self.max_model_len:
# Skip requests that have already reached the max model length.
draft_token_ids.append([])
continue
self.input_batch.token_ids_cpu[i, start_idx:end_idx] = sampled_ids
drafter_output = self.drafter.propose(
self.input_batch.token_ids_cpu[i, :end_idx])
if drafter_output is None or len(drafter_output) == 0:
draft_token_ids.append([])
else:
draft_token_ids.append(drafter_output.tolist())
return draft_token_ids
def load_model(self) -> None:
logger.info("Starting to load model %s...", self.model_config.model)
with DeviceMemoryProfiler() as m: # noqa: SIM117
time_before_load = time.perf_counter()
self.model = get_model(vllm_config=self.vllm_config)
if self.lora_config:
self.model = self.load_lora_model(self.model,
self.model_config,
self.scheduler_config,
self.lora_config,
self.device)
if hasattr(self, "drafter"):
logger.info("Loading drafter model...")
self.drafter.load_model(self.model)
if self.use_aux_hidden_state_outputs:
self.model.set_aux_hidden_state_layers(
self.model.get_eagle3_aux_hidden_state_layers())
time_after_load = time.perf_counter()
self.model_memory_usage = m.consumed_memory
logger.info("Model loading took %.4f GiB and %.6f seconds",
self.model_memory_usage / GiB_bytes,
time_after_load - time_before_load)
prepare_communication_buffer_for_model(self.model)
def save_tensorized_model(
self,
tensorizer_config: "TensorizerConfig",
) -> None:
TensorizerLoader.save_model(
self.model,
tensorizer_config=tensorizer_config,
)
def _get_prompt_logprobs_dict(
self,
hidden_states: torch.Tensor,
scheduler_output: "SchedulerOutput",
) -> dict[str, Optional[LogprobsTensors]]:
num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs
if not num_prompt_logprobs_dict:
return {}
in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu
prompt_logprobs_dict: dict[str, Optional[LogprobsTensors]] = {}
# Since prompt logprobs are a rare feature, prioritize simple,
# maintainable loop over optimal performance.
completed_prefill_reqs = []
for req_id, num_prompt_logprobs in num_prompt_logprobs_dict.items():
num_tokens = scheduler_output.num_scheduled_tokens[req_id]
# Get metadata for this request.
request = self.requests[req_id]
num_prompt_tokens = len(request.prompt_token_ids)
prompt_token_ids = torch.tensor(request.prompt_token_ids).to(
self.device, non_blocking=True)
# Set up target LogprobsTensors object.
logprobs_tensors = in_progress_dict.get(req_id)
if not logprobs_tensors:
# Create empty logprobs CPU tensors for the entire prompt.
# If chunked, we'll copy in slice by slice.
logprobs_tensors = LogprobsTensors.empty_cpu(
num_prompt_tokens - 1, num_prompt_logprobs + 1)
in_progress_dict[req_id] = logprobs_tensors
# Determine number of logits to retrieve.
start_idx = request.num_computed_tokens
start_tok = start_idx + 1
num_remaining_tokens = num_prompt_tokens - start_tok
if num_tokens <= num_remaining_tokens:
# This is a chunk, more tokens remain.
# In the == case, there are no more prompt logprobs to produce
# but we want to defer returning them to the next step where we
# have new generated tokens to return.
num_logits = num_tokens
else:
# This is the last chunk of prompt tokens to return.
num_logits = num_remaining_tokens
completed_prefill_reqs.append(req_id)
prompt_logprobs_dict[req_id] = logprobs_tensors
if num_logits <= 0:
# This can happen for the final chunk if we prefilled exactly
# (num_prompt_tokens - 1) tokens for this request in the prior
# step. There are no more prompt logprobs to produce.
continue
# Get the logits corresponding to this req's prompt tokens.
# If this is a partial request (i.e. chunked prefill),
# then there is prompt logprob generated for each index.
req_idx = self.input_batch.req_id_to_index[req_id]
offset = self.query_start_loc_np[req_idx].item()
prompt_hidden_states = hidden_states[offset:offset + num_logits]
logits = self.model.compute_logits(prompt_hidden_states, None)
# Get the "target" tokens for each index. For prompt at index i,
# the token at prompt index i+1 is the "sampled" token we want
# to gather the logprob for.
tgt_token_ids = prompt_token_ids[start_tok:start_tok + num_logits]
# Compute prompt logprobs.
logprobs = self.sampler.compute_logprobs(logits)
token_ids, logprobs, ranks = self.sampler.gather_logprobs(
logprobs, num_prompt_logprobs, tgt_token_ids)
# Transfer GPU->CPU async.
chunk_slice = slice(start_idx, start_idx + num_logits)
logprobs_tensors.logprob_token_ids[chunk_slice].copy_(
token_ids, non_blocking=True)
logprobs_tensors.logprobs[chunk_slice].copy_(logprobs,
non_blocking=True)
logprobs_tensors.selected_token_ranks[chunk_slice].copy_(
ranks, non_blocking=True)
# Remove requests that have completed prefill from the batch
# num_prompt_logprobs_dict.
for req_id in completed_prefill_reqs:
del num_prompt_logprobs_dict[req_id]
del in_progress_dict[req_id]
# Must synchronize the non-blocking GPU->CPU transfers.
if prompt_logprobs_dict:
torch.cuda.synchronize()
return prompt_logprobs_dict
@torch.inference_mode()
def _dummy_run(
self,
num_tokens: int,
skip_attn: bool = True,
) -> torch.Tensor:
# Set num_scheduled_tokens based on num_tokens and max_num_seqs
# for dummy run with LoRA so that the num_reqs collectively
# has num_tokens in total.
assert num_tokens <= self.scheduler_config.max_num_batched_tokens
max_num_reqs = self.scheduler_config.max_num_seqs
num_reqs = max_num_reqs if num_tokens >= max_num_reqs else num_tokens
min_tokens_per_req = num_tokens // num_reqs
num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs
num_scheduled_tokens_list[-1] += num_tokens % num_reqs
assert sum(num_scheduled_tokens_list) == num_tokens
assert len(num_scheduled_tokens_list) == num_reqs
num_scheduled_tokens = np.array(num_scheduled_tokens_list,
dtype=np.int32)
if skip_attn:
attn_metadata: Optional[dict[str, FlashAttentionMetadata]] = None
else:
query_start_loc = self.query_start_loc[:num_reqs + 1]
seq_lens = self.seq_lens[:num_reqs]
common_attn_metadata = CommonAttentionMetadata(
query_start_loc=query_start_loc, seq_lens=seq_lens)
attn_metadata = {}
for kv_cache_group_id, kv_cache_group_spec in enumerate(
self.kv_cache_config.kv_cache_groups):
attn_metadata_i = (
self.attn_metadata_builders[kv_cache_group_id].build(
num_reqs=num_tokens,
num_actual_tokens=num_tokens,
max_query_len=num_tokens,
common_prefix_len=0,
common_attn_metadata=common_attn_metadata,
))
for layer_name in kv_cache_group_spec.layer_names:
attn_metadata[layer_name] = attn_metadata_i
with self.maybe_dummy_run_with_lora(self.lora_config,
num_scheduled_tokens):
model = self.model
if self.is_multimodal_model:
input_ids = None
inputs_embeds = self.inputs_embeds[:num_tokens]
else:
input_ids = self.input_ids[:num_tokens]
inputs_embeds = None
if self.uses_mrope:
positions = self.mrope_positions[:, :num_tokens]
else:
positions = self.positions[:num_tokens]
if get_pp_group().is_first_rank:
intermediate_tensors = None
else:
if self.intermediate_tensors is None:
self.intermediate_tensors = (
self.model.make_empty_intermediate_tensors(
batch_size=self.max_num_tokens,
dtype=self.model_config.dtype,
device=self.device))
intermediate_tensors = self.sync_and_slice_intermediate_tensors(
num_tokens, None, False)
with set_forward_context(attn_metadata,
self.vllm_config,
num_tokens=num_tokens):
outputs = model(
input_ids=input_ids,
positions=positions,
intermediate_tensors=intermediate_tensors,
inputs_embeds=inputs_embeds,
)
if self.use_aux_hidden_state_outputs:
hidden_states, _ = outputs
else:
hidden_states = outputs
if self.use_spec_decode and self.speculative_config.use_eagle():
assert isinstance(self.drafter, EagleProposer)
self.drafter.dummy_run(num_tokens)
logit_indices = np.cumsum(num_scheduled_tokens) - 1
return hidden_states[logit_indices]
@torch.inference_mode()
def _dummy_sampler_run(
self,
hidden_states: torch.Tensor,
) -> torch.Tensor:
# The dummy hidden states may contain special values,
# like `inf` or `nan`.
# To avoid breaking the sampler, we use a random tensor here instead.
hidden_states = torch.rand_like(hidden_states)
logits = self.model.compute_logits(hidden_states, None)
num_reqs = logits.size(0)
dummy_tensors = lambda v: torch.full(
(num_reqs, ), v, device=self.device)
dummy_metadata = SamplingMetadata(
temperature=dummy_tensors(0.5),
all_greedy=False,
all_random=False,
top_p=dummy_tensors(0.9),
top_k=dummy_tensors(logits.size(1) - 1),
min_p=None,
generators={},
max_num_logprobs=None,
no_penalties=True,
prompt_token_ids=None,
frequency_penalties=dummy_tensors(0.1),
presence_penalties=dummy_tensors(0.1),
repetition_penalties=dummy_tensors(0.1),
output_token_ids=[[] for _ in range(num_reqs)],
min_tokens={},
logit_bias=[None for _ in range(num_reqs)],
allowed_token_ids_mask=None,
bad_words_token_ids={},
)
try:
sampler_output = self.sampler(logits=logits,
sampling_metadata=dummy_metadata)
except RuntimeError as e:
if 'out of memory' in str(e):
raise RuntimeError(
"CUDA out of memory occurred when warming up sampler with "
f"{num_reqs} dummy requests. Please try lowering "
"`max_num_seqs` or `gpu_memory_utilization` when "
"initializing the engine.") from e
else:
raise e
if self.use_spec_decode:
draft_token_ids = [[0] for _ in range(num_reqs)]
dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
draft_token_ids, self.device)
num_tokens = sum(len(ids) for ids in draft_token_ids)
# draft_probs = torch.randn(
# num_tokens, logits.shape[-1], device=self.device,
# dtype=logits.dtype)
draft_probs = None
target_logits = torch.randn(num_tokens,
logits.shape[-1],
device=self.device,
dtype=logits.dtype)
# NOTE(woosuk): Here, we should use int32 because the sampler uses
# int32 for bonus_token_ids. If the dtype mismatches, re-compilation
# will occur at runtime.
bonus_token_ids = torch.zeros(num_reqs,
device=self.device,
dtype=torch.int32)
self.rejection_sampler(
dummy_spec_decode_metadata,
draft_probs,
target_logits,
bonus_token_ids,
dummy_metadata,
)
return sampler_output
def profile_run(self) -> None:
# Profile with multimodal encoder & encoder cache.
# TODO: handle encoder-decoder models once we support them.
if (self.is_multimodal_model and self.max_num_encoder_input_tokens > 0
and self.encoder_cache_size > 0):
# NOTE: Currently model is profiled with a single non-text
# modality with the max possible input tokens even when
# it supports multiple.
max_tokens_by_modality_dict = self.mm_registry \
.get_max_tokens_per_item_by_nonzero_modality(self.model_config)
dummy_data_modality, max_tokens_per_mm_item = max(
max_tokens_by_modality_dict.items(), key=lambda item: item[1])
# Check how many items of this modality can be supported by
# the encoder budget.
encoder_budget = min(self.max_num_encoder_input_tokens,
self.encoder_cache_size)
max_num_mm_items_encoder_budget = cdiv(encoder_budget,
max_tokens_per_mm_item)
# Check how many items of this modality can be supported by
# the decoder budget.
max_mm_items_per_req = self.mm_registry.get_mm_limits_per_prompt(
self.model_config)[dummy_data_modality]
# NOTE: We do not consider max_num_batched_tokens on purpose
# because the multimodal embeddings can be generated in advance
# and chunked prefilled.
max_num_mm_items_decoder_budget = self.max_num_reqs * \
max_mm_items_per_req
max_num_mm_items = min(max_num_mm_items_encoder_budget,
max_num_mm_items_decoder_budget)
logger.info(
"Encoder cache will be initialized with a budget of %s tokens,"
" and profiled with %s %s items of the maximum feature size.",
encoder_budget, max_num_mm_items, dummy_data_modality)
# Create dummy batch of multimodal inputs.
dummy_mm_kwargs = self.mm_registry.get_decoder_dummy_data(
model_config=self.model_config,
seq_len=self.max_num_tokens,
mm_counts={
dummy_data_modality: 1
},
).multi_modal_data
batched_dummy_mm_inputs = MultiModalKwargs.batch(
[dummy_mm_kwargs] * max_num_mm_items)
batched_dummy_mm_inputs = MultiModalKwargs.as_kwargs(
batched_dummy_mm_inputs,
dtype=self.model_config.dtype,
device=self.device,
)
# Run multimodal encoder.
dummy_encoder_outputs = self.model.get_multimodal_embeddings(
**batched_dummy_mm_inputs)
sanity_check_mm_encoder_outputs(
dummy_encoder_outputs,
expected_num_items=max_num_mm_items,
)
# Cache the dummy encoder outputs.
self.encoder_cache["tmp"] = dict(enumerate(dummy_encoder_outputs))
hidden_states = self._dummy_run(self.max_num_tokens)
if get_pp_group().is_last_rank:
sampler_output = self._dummy_sampler_run(hidden_states)
else:
sampler_output = None
torch.cuda.synchronize()
del hidden_states, sampler_output
self.encoder_cache.clear()
gc.collect()
def capture_model(self) -> None:
if not self.use_cuda_graph:
logger.warning(
"Skipping CUDA graph capture. Please add "
"-O %s to use CUDA graphs.", CompilationLevel.PIECEWISE)
return
start_time = time.perf_counter()
start_free_gpu_memory = torch.cuda.mem_get_info()[0]
# Trigger CUDA graph capture for specific shapes.
# Capture the large shapes first so that the smaller shapes
# can reuse the memory pool allocated for the large shapes.
with graph_capture(device=self.device):
skip_attn = not self.vllm_config.compilation_config.full_cuda_graph
for num_tokens in reversed(self.cudagraph_batch_sizes):
for _ in range(self.vllm_config.compilation_config.
cudagraph_num_of_warmups):
self._dummy_run(num_tokens, skip_attn=skip_attn)
self._dummy_run(num_tokens, skip_attn=skip_attn)
end_time = time.perf_counter()
end_free_gpu_memory = torch.cuda.mem_get_info()[0]
elapsed_time = end_time - start_time
cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory
# This usually takes 5~20 seconds.
logger.info("Graph capturing finished in %.0f secs, took %.2f GiB",
elapsed_time, cuda_graph_size / (1 << 30))
def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None:
"""
Initialize the attention backends and attention metadata builders.
"""
assert len(self.attn_backends) == 0 and len(
self.attn_metadata_builders
) == 0, "Attention backends are already initialized"
for i, kv_cache_group_spec in enumerate(
kv_cache_config.kv_cache_groups):
kv_cache_spec = kv_cache_group_spec.kv_cache_spec
if not isinstance(kv_cache_spec, AttentionSpec):
raise NotImplementedError(
"Only AttentionSpec is supported for now.")
attn_backend_i = get_attn_backend(
kv_cache_spec.head_size,
self.dtype,
kv_cache_spec.dtype,
kv_cache_spec.block_size,
self.model_config.is_attention_free,
use_mla=kv_cache_spec.use_mla,
)
if attn_backend_i is None:
error_msg = (
f"Error with get_attn_backend: {kv_cache_spec.head_size=}, "
f"{self.dtype=}, {kv_cache_spec.dtype=}, "
f"{kv_cache_spec.block_size=}, "
f"{self.model_config.is_attention_free=}, "
f"{kv_cache_spec.use_mla=}")
logger.error(error_msg)
raise NotImplementedError(
"Non-Attention backend is not supported by V1 "
"GPUModelRunner.")
if self.vllm_config.compilation_config.full_cuda_graph:
attn_backend_name = attn_backend_i.__name__
flash_attn_version = get_flash_attn_version()
if attn_backend_name != "FlashAttentionBackend" or \
flash_attn_version != 3:
raise ValueError(
f"full_cuda_graph is only supported with "
f"FA3. Current attention backend is "
f"{attn_backend_name}, FlashAttention version is "
f"{flash_attn_version}.")
block_table_i = self.input_batch.block_table[i]
attn_metadata_builder_i = attn_backend_i.get_builder_cls()(
weakref.proxy(self), kv_cache_spec, block_table_i)
self.attn_backends.append(attn_backend_i)
self.attn_metadata_builders.append(attn_metadata_builder_i)
def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
"""
Initialize KV cache based on `kv_cache_config`.
Args:
kv_cache_config: Configuration for the KV cache, including the KV
cache size of each layer
"""
if len(kv_cache_config.kv_cache_groups) > 1:
raise NotImplementedError(
"Hybrid models with more than one KV cache type are not "
"supported yet.")
self.kv_cache_config = kv_cache_config
self.initialize_attn_backend(kv_cache_config)
kv_caches: dict[str, torch.Tensor] = {}
for i, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
kv_cache_spec = kv_cache_group.kv_cache_spec
for layer_name in kv_cache_group.layer_names:
tensor_config = kv_cache_config.tensors[layer_name]
assert tensor_config.size % kv_cache_spec.page_size_bytes == 0
num_blocks = tensor_config.size // kv_cache_spec.page_size_bytes
# `num_blocks` is the number of blocks the model runner can use.
# `kv_cache_config.num_blocks` is the number of blocks that
# KVCacheManager may allocate.
# Since different GPUs may have different number of layers and
# different memory capacities, `num_blocks` can be different on
# different GPUs, and `kv_cache_config.num_blocks` is set to
# the min of all `num_blocks`. Verify it here.
assert num_blocks >= kv_cache_config.num_blocks
if isinstance(kv_cache_spec, AttentionSpec):
kv_cache_shape = self.attn_backends[i].get_kv_cache_shape(
num_blocks, kv_cache_spec.block_size,
kv_cache_spec.num_kv_heads, kv_cache_spec.head_size)
dtype = kv_cache_spec.dtype
kv_caches[layer_name] = torch.zeros(kv_cache_shape,
dtype=dtype,
device=self.device)
else:
# TODO: add new branches when introducing more types of
# KV cache specs.
raise ValueError("Unknown KV cache spec type.")
if self.speculative_config and self.speculative_config.use_eagle():
assert isinstance(self.drafter, EagleProposer)
# validate all draft model layers belong to the same kv cache
# group
self.drafter.validate_same_kv_cache_group(kv_cache_config)
bind_kv_cache(
kv_caches,
self.vllm_config.compilation_config.static_forward_context,
self.kv_caches)
if has_kv_transfer_group():
get_kv_transfer_group().register_kv_caches(kv_caches)
def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
"""
Generates the KVCacheSpec by parsing the kv cache format from each
Attention module in the static forward context.
Returns:
KVCacheSpec: A dictionary mapping layer names to their KV cache
format. Layers that do not need KV cache are not included.
"""
layers = get_layers_from_vllm_config(self.vllm_config, Attention)
block_size = self.vllm_config.cache_config.block_size
use_mla = self.vllm_config.model_config.use_mla
kv_cache_spec: dict[str, KVCacheSpec] = {}
for layer_name, attn_module in layers.items():
# TODO: Support other attention modules, e.g., cross-attention
if attn_module.attn_type == AttentionType.DECODER:
if attn_module.sliding_window is not None:
kv_cache_spec[layer_name] = SlidingWindowSpec(
block_size=block_size,
num_kv_heads=attn_module.num_kv_heads,
head_size=attn_module.head_size,
dtype=self.kv_cache_dtype,
sliding_window=attn_module.sliding_window,
use_mla=use_mla)
else:
kv_cache_spec[layer_name] = FullAttentionSpec(
block_size=block_size,
num_kv_heads=attn_module.num_kv_heads,
head_size=attn_module.head_size,
dtype=self.kv_cache_dtype,
use_mla=use_mla)
elif attn_module.attn_type in (AttentionType.ENCODER,
AttentionType.ENCODER_ONLY):
# encoder-only attention does not need KV cache.
continue
elif attn_module.attn_type == AttentionType.ENCODER_DECODER:
raise NotImplementedError
else:
raise ValueError(
f"Unknown attention type: {attn_module.attn_type}")
return kv_cache_spec