class InputBatch:
def __init__(
self,
max_num_reqs: int,
max_model_len: int,
max_num_batched_tokens: int,
device: torch.device,
pin_memory: bool,
vocab_size: int,
block_sizes: list[int], # The block_size of each kv cache group
is_spec_decode: bool = False,
logits_processing_needs_token_ids: bool = False,
):
self.is_spec_decode = is_spec_decode
self.max_num_reqs = max_num_reqs
self.max_model_len = max_model_len
self.max_num_batched_tokens = max_num_batched_tokens
self.device = device
self.pin_memory = pin_memory
self.vocab_size = vocab_size
self.logits_processing_needs_token_ids = (
logits_processing_needs_token_ids)
self._req_ids: list[Optional[str]] = []
self.req_id_to_index: dict[str, int] = {}
# TODO(woosuk): This buffer could be too large if max_model_len is big.
# Find a way to reduce the CPU memory usage.
# This buffer is not directly transferred to the GPU, so it does not
# need to be pinned.
self.token_ids_cpu_tensor = torch.zeros(
(max_num_reqs, max_model_len),
device="cpu",
dtype=torch.int32,
pin_memory=False,
)
self.token_ids_cpu = self.token_ids_cpu_tensor.numpy()
self.num_tokens = np.zeros(max_num_reqs, dtype=np.int32)
self.num_tokens_no_spec = np.zeros(max_num_reqs, dtype=np.int32)
self.num_prompt_tokens = np.zeros(max_num_reqs, dtype=np.int32)
self.num_computed_tokens_cpu_tensor = torch.zeros(
(max_num_reqs, ),
device="cpu",
dtype=torch.int32,
pin_memory=pin_memory,
)
self.num_computed_tokens_cpu = \
self.num_computed_tokens_cpu_tensor.numpy()
# Block table.
self.block_table = MultiGroupBlockTable(
max_num_reqs=max_num_reqs,
max_model_len=max_model_len,
max_num_batched_tokens=max_num_batched_tokens,
pin_memory=pin_memory,
device=device,
block_sizes=block_sizes,
)
# Sampling-related.
self.temperature = torch.empty((max_num_reqs, ),
dtype=torch.float32,
device=device)
self.temperature_cpu_tensor = torch.empty((max_num_reqs, ),
dtype=torch.float32,
device="cpu",
pin_memory=pin_memory)
self.temperature_cpu = self.temperature_cpu_tensor.numpy()
self.greedy_reqs: set[str] = set()
self.random_reqs: set[str] = set()
self.top_p = torch.empty((max_num_reqs, ),
dtype=torch.float32,
device=device)
self.top_p_cpu_tensor = torch.empty((max_num_reqs, ),
dtype=torch.float32,
device="cpu",
pin_memory=pin_memory)
self.top_p_cpu = self.top_p_cpu_tensor.numpy()
self.top_p_reqs: set[str] = set()
self.top_k = torch.empty((max_num_reqs, ),
dtype=torch.int32,
device=device)
self.top_k_cpu_tensor = torch.empty((max_num_reqs, ),
dtype=torch.int32,
device="cpu",
pin_memory=pin_memory)
self.top_k_cpu = self.top_k_cpu_tensor.numpy()
self.top_k_reqs: set[str] = set()
# IDs of requests which do not support spec decoding
self.spec_decode_unsupported_reqs: set[str] = set()
# Frequency penalty related data structures
self.frequency_penalties = torch.empty((max_num_reqs, ),
dtype=torch.float,
device=device)
self.frequency_penalties_cpu_tensor = torch.empty(
(max_num_reqs, ),
dtype=torch.float,
device="cpu",
pin_memory=pin_memory)
self.frequency_penalties_cpu = \
self.frequency_penalties_cpu_tensor.numpy()
self.frequency_penalties_reqs: set[str] = set()
# Presence penalty related data structures
self.presence_penalties = torch.empty((max_num_reqs, ),
dtype=torch.float,
device=device)
self.presence_penalties_cpu_tensor = torch.empty((max_num_reqs, ),
dtype=torch.float,
device="cpu",
pin_memory=pin_memory)
self.presence_penalties_cpu = self.presence_penalties_cpu_tensor.numpy(
)
self.presence_penalties_reqs: set[str] = set()
# Repetition penalty related data structures
self.repetition_penalties = torch.empty((max_num_reqs, ),
dtype=torch.float,
device=device)
self.repetition_penalties_cpu_tensor = torch.empty(
(max_num_reqs, ),
dtype=torch.float,
device="cpu",
pin_memory=pin_memory)
self.repetition_penalties_cpu = \
self.repetition_penalties_cpu_tensor.numpy()
self.repetition_penalties_reqs: set[str] = set()
# lora related
self.request_lora_mapping = np.zeros((self.max_num_reqs, ),
dtype=np.int32)
self.lora_id_to_request_ids: dict[int, set[str]] = {}
self.lora_id_to_lora_request: dict[int, LoRARequest] = {}
# req_index -> generator
# NOTE(woosuk): The indices of the requests that do not have their own
# generator should not be included in the dictionary.
self.generators: dict[int, torch.Generator] = {}
self.num_logprobs: dict[str, int] = {}
# NOTE(rob): num_prompt_logprobs only includes reqs
# that are currently in the prefill phase.
self.num_prompt_logprobs: dict[str, int] = {}
# To accumulate prompt logprobs tensor chunks across prefill steps.
self.in_progress_prompt_logprobs_cpu: dict[str, LogprobsTensors] = {}
# Internal representation of per-step batch state changes, used for
# reordering persistent batch and generating logitsprocs batch state
# updates. Should reset each step.
self.batch_update_builder = BatchUpdateBuilder()
# Define logits processors.
# TODO(andy): logits processor list should be extensible via engine
# constructor argument; for now the list is fixed.
self.logitsprocs = init_builtin_logitsprocs(
pin_memory_available=pin_memory,
max_num_reqs=max_num_reqs + 1,
device=device)
# TODO convert this to LogitsProcessor
self.has_allowed_token_ids: set[str] = set()
# NOTE(lufang): In the mask tensor, if the corresponding token allowed,
# the value is False. Since we use masked_fill_ to set -inf.
self.allowed_token_ids_mask: Optional[torch.Tensor] = None
self.allowed_token_ids_mask_cpu_tensor: Optional[torch.Tensor] = None
# req_index -> bad_words_token_ids
self.bad_words_token_ids: dict[int, list[list[int]]] = {}
self.req_output_token_ids: list[Optional[list[int]]] = []
# This is updated each time the batch constituents change.
self.sampling_metadata = self._make_sampling_metadata()
self.pooling_params: dict[str, PoolingParams] = {}
@property
def req_ids(self) -> list[str]:
# None elements should only be present transiently
# while performing state updates to the batch.
return cast(list[str], self._req_ids)
def _get_next_add_index(self) -> int:
if (req_index := self.batch_update_builder.pop_removed()) is not None:
# Fill the empty index.
return req_index
# Append to end
return self.num_reqs
def _register_add_request(self, request: "CachedRequestState") -> int:
"""Track add-request operations"""
req_index = self._get_next_add_index()
assert req_index < self.max_num_reqs
params = (request.sampling_params
if request.sampling_params else request.pooling_params)
self.batch_update_builder.added.append(
(req_index, params, request.output_token_ids))
return req_index
def add_request(
self,
request: "CachedRequestState",
) -> int:
req_index = self._register_add_request(request)
req_id = request.req_id
if req_index == len(self._req_ids):
self._req_ids.append(req_id)
self.req_output_token_ids.append(request.output_token_ids)
else:
self._req_ids[req_index] = req_id
self.req_output_token_ids[req_index] = request.output_token_ids
self.req_id_to_index[req_id] = req_index
# Copy the prompt token ids and output token ids.
num_prompt_tokens = len(request.prompt_token_ids)
self.num_prompt_tokens[req_index] = num_prompt_tokens
self.token_ids_cpu[
req_index, :num_prompt_tokens] = request.prompt_token_ids
start_idx = num_prompt_tokens
end_idx = start_idx + len(request.output_token_ids)
self.token_ids_cpu[req_index,
start_idx:end_idx] = request.output_token_ids
# Number of token ids in token_ids_cpu.
# NOTE(woosuk): This may include spec decode tokens.
self.num_tokens[req_index] = request.num_tokens
# Number of tokens without spec decode tokens.
self.num_tokens_no_spec[req_index] = request.num_tokens
self.num_computed_tokens_cpu[req_index] = request.num_computed_tokens
self.block_table.add_row(request.block_ids, req_index)
if sampling_params := request.sampling_params:
if (self.is_spec_decode
and is_spec_decode_unsupported(sampling_params)):
self.spec_decode_unsupported_reqs.add(req_id)
if sampling_params.sampling_type == SamplingType.GREEDY:
# Avoid later division by zero.
self.temperature_cpu[req_index] = -1.0
self.greedy_reqs.add(req_id)
else:
self.temperature_cpu[req_index] = sampling_params.temperature
self.random_reqs.add(req_id)
self.top_p_cpu[req_index] = sampling_params.top_p
if sampling_params.top_p < 1:
self.top_p_reqs.add(req_id)
top_k = sampling_params.top_k
if 0 < top_k < self.vocab_size:
self.top_k_reqs.add(req_id)
else:
top_k = self.vocab_size
self.top_k_cpu[req_index] = top_k
self.frequency_penalties_cpu[
req_index] = sampling_params.frequency_penalty
if sampling_params.frequency_penalty != 0.0:
self.frequency_penalties_reqs.add(req_id)
self.presence_penalties_cpu[
req_index] = sampling_params.presence_penalty
if sampling_params.presence_penalty != 0.0:
self.presence_penalties_reqs.add(req_id)
self.repetition_penalties_cpu[
req_index] = sampling_params.repetition_penalty
if sampling_params.repetition_penalty != 1.0:
self.repetition_penalties_reqs.add(req_id)
# NOTE(woosuk): self.generators should not include the requests that
# do not have their own generator.
if request.generator is not None:
self.generators[req_index] = request.generator
if sampling_params.logprobs is not None:
self.num_logprobs[req_id] = sampling_params.logprobs
if sampling_params.prompt_logprobs is not None:
self.num_prompt_logprobs[
req_id] = sampling_params.prompt_logprobs
if sampling_params.allowed_token_ids:
self.has_allowed_token_ids.add(req_id)
if self.allowed_token_ids_mask_cpu_tensor is None:
# Lazy allocation for this tensor, which can be large.
# False means we don't fill with -inf.
self.allowed_token_ids_mask = torch.zeros(
self.max_num_reqs,
self.vocab_size,
dtype=torch.bool,
device=self.device)
self.allowed_token_ids_mask_cpu_tensor = torch.zeros(
self.max_num_reqs,
self.vocab_size,
dtype=torch.bool,
device="cpu")
self.allowed_token_ids_mask_cpu_tensor[req_index] = True
# False means we don't fill with -inf.
self.allowed_token_ids_mask_cpu_tensor[req_index][
sampling_params.allowed_token_ids] = False
if sampling_params.bad_words_token_ids:
self.bad_words_token_ids[
req_index] = sampling_params.bad_words_token_ids
else:
assert request.pooling_params is not None
self.pooling_params[req_id] = request.pooling_params
# Add request lora ID
if request.lora_request:
lora_id = request.lora_request.lora_int_id
if lora_id not in self.lora_id_to_request_ids:
self.lora_id_to_request_ids[lora_id] = set()
self.request_lora_mapping[req_index] = lora_id
self.lora_id_to_request_ids[lora_id].add(request.req_id)
self.lora_id_to_lora_request[lora_id] = request.lora_request
else:
# No LoRA
self.request_lora_mapping[req_index] = 0
return req_index
def remove_request(self, req_id: str) -> Optional[int]:
"""This method must always be followed by a call to condense().
Args:
req_id: request to remove
Returns:
Removed request index, or `None` if `req_id` not recognized
"""
req_index = self.req_id_to_index.pop(req_id, None)
if req_index is None:
return None
self.batch_update_builder.removed_append(req_index)
self._req_ids[req_index] = None
self.req_output_token_ids[req_index] = None
self.greedy_reqs.discard(req_id)
self.random_reqs.discard(req_id)
self.top_p_reqs.discard(req_id)
self.top_k_reqs.discard(req_id)
self.spec_decode_unsupported_reqs.discard(req_id)
self.frequency_penalties_reqs.discard(req_id)
self.presence_penalties_reqs.discard(req_id)
self.repetition_penalties_reqs.discard(req_id)
self.generators.pop(req_index, None)
self.num_logprobs.pop(req_id, None)
self.num_prompt_logprobs.pop(req_id, None)
self.in_progress_prompt_logprobs_cpu.pop(req_id, None)
# LoRA
lora_id = self.request_lora_mapping[req_index]
if lora_id != 0:
self.lora_id_to_request_ids[lora_id].discard(req_id)
if len(self.lora_id_to_request_ids[lora_id]) == 0:
self.lora_id_to_request_ids.pop(lora_id)
self.lora_id_to_lora_request.pop(lora_id)
self.request_lora_mapping[req_index] = 0
self.has_allowed_token_ids.discard(req_id)
if self.allowed_token_ids_mask_cpu_tensor is not None:
# False means we don't fill with -inf.
self.allowed_token_ids_mask_cpu_tensor[req_index].fill_(False)
self.bad_words_token_ids.pop(req_index, None)
self.pooling_params.pop(req_id, None)
return req_index
def swap_states(self, i1: int, i2: int) -> None:
self.batch_update_builder.moved.append(
(i1, i2, MoveDirectionality.SWAP))
old_id_i1 = self._req_ids[i1]
old_id_i2 = self._req_ids[i2]
self._req_ids[i1], self._req_ids[i2] =\
self._req_ids[i2], self._req_ids[i1] # noqa
self.req_output_token_ids[i1], self.req_output_token_ids[i2] =\
self.req_output_token_ids[i2], self.req_output_token_ids[i1]
assert old_id_i1 is not None and old_id_i2 is not None
self.req_id_to_index[old_id_i1], self.req_id_to_index[old_id_i2] =\
self.req_id_to_index[old_id_i2], self.req_id_to_index[old_id_i1]
self.num_tokens[i1], self.num_tokens[i2] =\
self.num_tokens[i2], self.num_tokens[i1]
self.num_tokens_no_spec[i1], self.num_tokens_no_spec[i2] =\
self.num_tokens_no_spec[i2], self.num_tokens_no_spec[i1]
self.num_prompt_tokens[i1], self.num_prompt_tokens[i2] =\
self.num_prompt_tokens[i2], self.num_prompt_tokens[i1]
self.num_computed_tokens_cpu[i1], self.num_computed_tokens_cpu[i2] =\
self.num_computed_tokens_cpu[i2], self.num_computed_tokens_cpu[i1]
self.temperature_cpu[i1], self.temperature_cpu[i2] =\
self.temperature_cpu[i2], self.temperature_cpu[i1]
self.top_p_cpu[i1], self.top_p_cpu[i2] =\
self.top_p_cpu[i2], self.top_p_cpu[i1]
self.top_k_cpu[i1], self.top_k_cpu[i2] =\
self.top_k_cpu[i2], self.top_k_cpu[i1]
self.frequency_penalties_cpu[i1], self.frequency_penalties_cpu[i2] =\
self.frequency_penalties_cpu[i2], self.frequency_penalties_cpu[i1]
self.presence_penalties_cpu[i1], self.presence_penalties_cpu[i2] =\
self.presence_penalties_cpu[i2], self.presence_penalties_cpu[i1]
self.repetition_penalties_cpu[i1], self.repetition_penalties_cpu[i2] =\
self.repetition_penalties_cpu[i2], self.repetition_penalties_cpu[i1]
# NOTE: the following is unsafe
# self.token_ids_cpu[i1, ...], self.token_ids_cpu[i2, ...], =\
# self.token_ids_cpu[i2, ...], self.token_ids_cpu[i1, ...]
# instead, we need to temporiarily copy the data for one of the indices
# TODO(lucas): optimize this by only copying valid indices
tmp = self.token_ids_cpu[i1, ...].copy()
self.token_ids_cpu[i1, ...] = self.token_ids_cpu[i2, ...]
self.token_ids_cpu[i2, ...] = tmp
swap_dict_values(self.generators, i1, i2)
swap_dict_values(self.bad_words_token_ids, i1, i2)
self.request_lora_mapping[i1], self.request_lora_mapping[i2] =\
self.request_lora_mapping[i2], self.request_lora_mapping[i1]
if self.allowed_token_ids_mask_cpu_tensor is not None:
self.allowed_token_ids_mask_cpu_tensor[i1], \
self.allowed_token_ids_mask_cpu_tensor[i2] =\
self.allowed_token_ids_mask_cpu_tensor[i2], \
self.allowed_token_ids_mask_cpu_tensor[i1]
self.block_table.swap_row(i1, i2)
def condense(self) -> None:
"""Slide non-empty requests down into lower, empty indices.
Any consecutive empty indices at the very end of the list are not
filled.
Args:
empty_req_indices: empty indices which may be filled.
Returns:
swaps: list of (from,to) swap tuples for moved requests
empty_req_indices: indices not filled by condensation
"""
if not (empty_req_indices := self.batch_update_builder.removed):
# All removed requests were replaced by added requests, or else no
# requests were removed at all. No condense() needed
return
num_reqs = self.num_reqs
if num_reqs == 0:
# The batched states are empty.
self._req_ids.clear()
self.req_output_token_ids.clear()
return
# NOTE(woosuk): This function assumes that the empty_req_indices
# is sorted in descending order.
last_req_index = num_reqs + len(empty_req_indices) - 1
while empty_req_indices:
# Find the largest non-empty index.
while last_req_index in empty_req_indices:
last_req_index -= 1
# Find the smallest empty index.
empty_index = self.batch_update_builder.peek_removed()
assert empty_index is not None
if empty_index >= last_req_index:
break
# Move active request down into empty request
# index.
self.batch_update_builder.pop_removed()
self.batch_update_builder.moved.append(
(last_req_index, empty_index,
MoveDirectionality.UNIDIRECTIONAL))
req_id = self._req_ids[last_req_index]
output_token_ids = self.req_output_token_ids[last_req_index]
assert req_id is not None
self._req_ids[empty_index] = req_id
self._req_ids[last_req_index] = None
self.req_output_token_ids[empty_index] = output_token_ids
self.req_output_token_ids[last_req_index] = None
self.req_id_to_index[req_id] = empty_index
num_tokens = self.num_tokens[last_req_index]
self.token_ids_cpu[empty_index, :num_tokens] = self.token_ids_cpu[
last_req_index, :num_tokens]
self.num_tokens[empty_index] = num_tokens
self.num_tokens_no_spec[empty_index] = self.num_tokens_no_spec[
last_req_index]
self.num_prompt_tokens[empty_index] = self.num_prompt_tokens[
last_req_index]
self.num_computed_tokens_cpu[
empty_index] = self.num_computed_tokens_cpu[last_req_index]
self.block_table.move_row(last_req_index, empty_index)
self.temperature_cpu[empty_index] = self.temperature_cpu[
last_req_index]
self.top_p_cpu[empty_index] = self.top_p_cpu[last_req_index]
self.top_k_cpu[empty_index] = self.top_k_cpu[last_req_index]
self.frequency_penalties_cpu[
empty_index] = self.frequency_penalties_cpu[last_req_index]
self.presence_penalties_cpu[
empty_index] = self.presence_penalties_cpu[last_req_index]
self.repetition_penalties_cpu[
empty_index] = self.repetition_penalties_cpu[last_req_index]
generator = self.generators.pop(last_req_index, None)
if generator is not None:
self.generators[empty_index] = generator
self.request_lora_mapping[empty_index] = self.request_lora_mapping[
last_req_index]
# TODO convert these to LogitsProcessors
if self.allowed_token_ids_mask_cpu_tensor is not None:
self.allowed_token_ids_mask_cpu_tensor[
empty_index] = self.allowed_token_ids_mask_cpu_tensor[
last_req_index]
bad_words_token_ids = self.bad_words_token_ids.pop(
last_req_index, None)
if bad_words_token_ids is not None:
self.bad_words_token_ids[empty_index] = bad_words_token_ids
# Decrement last_req_index since it is now empty.
last_req_index -= 1
# Trim lists to the batch size.
del self._req_ids[self.num_reqs:]
del self.req_output_token_ids[self.num_reqs:]
def refresh_metadata(self):
"""Apply batch updates, reset input batch at end of step
* Apply batch add/remove/permute to logits procs' states
* If batch state is modified, update sampling metadata
"""
batch_update = self.batch_update_builder.get_and_reset(self.num_reqs)
for logit_proc in self.logitsprocs.all:
logit_proc.update_state(batch_update)
if batch_update:
self.sampling_metadata = self._make_sampling_metadata()
def _make_sampling_metadata(self) -> SamplingMetadata:
num_reqs = self.num_reqs
if not self.all_greedy:
temperature = copy_slice(self.temperature_cpu_tensor,
self.temperature, num_reqs)
else:
temperature = None
if not self.no_top_p:
copy_slice(self.top_p_cpu_tensor, self.top_p, num_reqs)
if not self.no_top_k:
copy_slice(self.top_k_cpu_tensor, self.top_k, num_reqs)
if not self.no_penalties:
# Since syncing these tensors is expensive only copy them
# if necessary i.e. if there are requests which require
# penalties to be applied during sampling.
copy_slice(self.frequency_penalties_cpu_tensor,
self.frequency_penalties, num_reqs)
copy_slice(self.presence_penalties_cpu_tensor,
self.presence_penalties, num_reqs)
copy_slice(self.repetition_penalties_cpu_tensor,
self.repetition_penalties, num_reqs)
needs_prompt_token_ids = (not self.no_penalties or
(self.num_reqs > 0
and self.logits_processing_needs_token_ids))
if needs_prompt_token_ids:
# The prompt tokens are used only for applying penalties or
# step pooling during the sampling/pooling process.
# Hence copy these tensors only when there are requests which
# need penalties/step_pooler to be applied.
prompt_token_ids = self._make_prompt_token_ids_tensor()
else:
prompt_token_ids = None
allowed_token_ids_mask: Optional[torch.Tensor] = None
if not self.no_allowed_token_ids:
assert self.allowed_token_ids_mask is not None
copy_slice(self.allowed_token_ids_mask_cpu_tensor,
self.allowed_token_ids_mask, num_reqs)
allowed_token_ids_mask = self.allowed_token_ids_mask[:num_reqs]
return SamplingMetadata(
temperature=temperature,
all_greedy=self.all_greedy,
all_random=self.all_random,
top_p=None if self.no_top_p else self.top_p[:num_reqs],
top_k=None if self.no_top_k else self.top_k[:num_reqs],
generators=self.generators,
max_num_logprobs=self.max_num_logprobs,
prompt_token_ids=prompt_token_ids,
frequency_penalties=self.frequency_penalties[:num_reqs],
presence_penalties=self.presence_penalties[:num_reqs],
repetition_penalties=self.repetition_penalties[:num_reqs],
output_token_ids=cast(list[list[int]], self.req_output_token_ids),
no_penalties=self.no_penalties,
allowed_token_ids_mask=allowed_token_ids_mask,
bad_words_token_ids=self.bad_words_token_ids,
logitsprocs=self.logitsprocs,
)
@property
def pooling_metadata(self) -> PoolingMetadata:
if len(self.pooling_params) == 0:
pooling_params = []
else:
# Note, for now this assumes that all request in the batch
# are either sampling or pooling requests
assert len(self.req_ids) == len(self.pooling_params)
pooling_params = [
self.pooling_params[req_id] for req_id in self.req_ids
]
return PoolingMetadata(
prompt_lens=torch.from_numpy(
self.num_prompt_tokens[:self.num_reqs]).to(self.device),
prompt_token_ids=self.sampling_metadata.prompt_token_ids,
pooling_params=pooling_params,
)
def _make_prompt_token_ids_tensor(self) -> torch.Tensor:
max_prompt_len = self.num_prompt_tokens[:self.num_reqs].max()
prompt_token_ids_cpu_tensor = torch.empty(
(self.num_reqs, max_prompt_len),
device="cpu",
dtype=torch.int64,
pin_memory=self.pin_memory,
)
prompt_token_ids = prompt_token_ids_cpu_tensor.numpy()
prompt_token_ids[:] = self.token_ids_cpu[:self.
num_reqs, :max_prompt_len]
# Use the value of vocab_size as a pad since we don't have a
# token_id of this value.
for i in range(self.num_reqs):
prompt_token_ids[i, self.num_prompt_tokens[i]:] = self.vocab_size
return prompt_token_ids_cpu_tensor.to(device=self.device,
non_blocking=True)
def make_lora_inputs(
self, num_scheduled_tokens: np.ndarray
) -> tuple[tuple[int, ...], tuple[int, ...], set[LoRARequest]]:
"""
Given the num_scheduled_tokens for each request in the batch, return
datastructures used to activate the current LoRAs.
Returns:
1. prompt_lora_mapping: A tuple of size self.num_reqs where,
prompt_lora_mapping[i] is the LoRA id to use for the ith prompt.
2. token_lora_mapping: A tuple of size np.sum(num_scheduled_tokens)
where, token_lora_mapping[i] is the LoRA id to use for ith token.
3. lora_requests: Set of relevant LoRA requests.
"""
req_lora_mapping = self.request_lora_mapping[:self.num_reqs]
prompt_lora_mapping = tuple(req_lora_mapping)
token_lora_mapping = tuple(
req_lora_mapping.repeat(num_scheduled_tokens))
active_lora_requests: set[LoRARequest] = set(
self.lora_id_to_lora_request.values())
return prompt_lora_mapping, token_lora_mapping, active_lora_requests
@property
def num_reqs(self) -> int:
return len(self.req_id_to_index)
@property
def all_greedy(self) -> bool:
return len(self.random_reqs) == 0
@property
def all_random(self) -> bool:
return len(self.greedy_reqs) == 0
@property
def no_top_p(self) -> bool:
return len(self.top_p_reqs) == 0
@property
def no_top_k(self) -> bool:
return len(self.top_k_reqs) == 0
@property
def no_penalties(self) -> bool:
return (len(self.presence_penalties_reqs) == 0
and len(self.frequency_penalties_reqs) == 0
and len(self.repetition_penalties_reqs) == 0)
@property
def max_num_logprobs(self) -> Optional[int]:
return max(self.num_logprobs.values()) if self.num_logprobs else None
@property
def no_prompt_logprob(self) -> bool:
return not self.num_prompt_logprobs
@property
def no_allowed_token_ids(self) -> bool:
return len(self.has_allowed_token_ids) == 0