bff4fbfca5
### What this PR does / why we need it?
1. upgrade to 0.18.0
2. ensure kernel_block_sizes is int for Eagle drafter
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.17.0
- vLLM main:
8b6325758c
---------
Signed-off-by: Meihan-chen <jcccx.cmh@gmail.com>
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
Co-authored-by: hfadzxy <starmoon_zhang@163.com>
477 lines
23 KiB
Python
477 lines
23 KiB
Python
#
|
|
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# This file is a part of the vllm-ascend project.
|
|
#
|
|
|
|
from __future__ import annotations
|
|
|
|
import math
|
|
from contextlib import contextmanager, nullcontext
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch_npu
|
|
from vllm.config import CUDAGraphMode
|
|
from vllm.logger import logger
|
|
from vllm.utils.torch_utils import get_dtype_size
|
|
from vllm.v1.core.sched.output import SchedulerOutput
|
|
from vllm.v1.kv_cache_interface import (
|
|
AttentionSpec,
|
|
EncoderOnlyAttentionSpec,
|
|
KVCacheConfig,
|
|
KVCacheSpec,
|
|
MambaSpec,
|
|
UniformTypeKVCacheSpecs,
|
|
)
|
|
|
|
from vllm_ascend.attention.attention_v1 import AscendAttentionState
|
|
from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ
|
|
from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
|
|
from vllm_ascend.worker.npu_input_batch import NPUInputBatch
|
|
|
|
_NGRAM_GRAPH_UNIFORM_DECODE_QUERY_LEN = 1
|
|
_ATTENTION_BLOCK_SIZE_LIMIT = 128 * 128
|
|
|
|
|
|
class NPUModelRunner310(NPUModelRunner):
|
|
# Inherited from parent runner; annotated here to satisfy strict type checks.
|
|
uniform_decode_query_len: int
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
self._acl_format = ACL_FORMAT_FRACTAL_NZ
|
|
if self.speculative_config is not None and self.speculative_config.method == "ngram":
|
|
# 310P ngram requires decode-only graph shapes to be built with q_len=1.
|
|
# Keep dispatcher's internal query_len in sync to avoid key-init assert.
|
|
self.cudagraph_dispatcher.uniform_decode_query_len = _NGRAM_GRAPH_UNIFORM_DECODE_QUERY_LEN
|
|
|
|
@contextmanager
|
|
def temporary_modify_uniform_decode_query_len(self):
|
|
# This is only needed for the 310P ngram path where dispatcher uses q_len=1
|
|
# while runner's default uniform_decode_query_len remains 1 + num_spec_tokens.
|
|
# TODO: remove this temporary override after upstream supports independent
|
|
# decode capture query_len for backend-specific paths.
|
|
if self.speculative_config is None or self.speculative_config.method != "ngram":
|
|
yield
|
|
return
|
|
|
|
original_uniform_decode_query_len = self.uniform_decode_query_len
|
|
self.uniform_decode_query_len = _NGRAM_GRAPH_UNIFORM_DECODE_QUERY_LEN
|
|
try:
|
|
yield
|
|
finally:
|
|
self.uniform_decode_query_len = original_uniform_decode_query_len
|
|
|
|
def _determine_batch_execution_and_padding(
|
|
self,
|
|
num_tokens: int,
|
|
num_reqs: int,
|
|
num_scheduled_tokens_np: np.ndarray,
|
|
max_num_scheduled_tokens: int,
|
|
use_cascade_attn: bool,
|
|
allow_microbatching: bool = False,
|
|
force_eager: bool = False,
|
|
force_uniform_decode: bool | None = None,
|
|
force_has_lora: bool | None = None,
|
|
force_num_active_loras: int | None = None,
|
|
num_encoder_reqs: int = 0,
|
|
):
|
|
if self.attn_state in (AscendAttentionState.ChunkedPrefill, AscendAttentionState.PrefillCacheHit):
|
|
force_eager = True
|
|
|
|
if force_uniform_decode is None and self.attn_state == AscendAttentionState.DecodeOnly:
|
|
decode_query_len = _NGRAM_GRAPH_UNIFORM_DECODE_QUERY_LEN
|
|
if (
|
|
max_num_scheduled_tokens == decode_query_len
|
|
and num_tokens == max_num_scheduled_tokens * num_reqs
|
|
and np.all(self.input_batch.num_computed_tokens_cpu[:num_reqs] > 0)
|
|
):
|
|
# Respect explicit caller override: only force when unset.
|
|
force_uniform_decode = True
|
|
|
|
return super()._determine_batch_execution_and_padding(
|
|
num_tokens=num_tokens,
|
|
num_reqs=num_reqs,
|
|
num_scheduled_tokens_np=num_scheduled_tokens_np,
|
|
max_num_scheduled_tokens=max_num_scheduled_tokens,
|
|
use_cascade_attn=use_cascade_attn,
|
|
allow_microbatching=allow_microbatching,
|
|
force_eager=force_eager,
|
|
force_uniform_decode=force_uniform_decode,
|
|
force_has_lora=force_has_lora,
|
|
force_num_active_loras=force_num_active_loras,
|
|
num_encoder_reqs=num_encoder_reqs,
|
|
)
|
|
|
|
def _pad_query_start_loc_for_fia(
|
|
self,
|
|
num_tokens_padded: int,
|
|
num_reqs_padded: int,
|
|
num_reqs: int,
|
|
cudagraph_runtime_mode: CUDAGraphMode | None = None,
|
|
batch_desc_num_reqs: int | None = None,
|
|
) -> int:
|
|
# Keep this aligned with the dispatcher because batch_desc.num_reqs is
|
|
# generated by dispatcher._create_padded_batch_descriptor().
|
|
# For 310P ngram we intentionally set dispatcher q_len=1, while runner's
|
|
# default uniform_decode_query_len may remain 1 + num_spec_tokens.
|
|
uniform_decode_query_len = self.cudagraph_dispatcher.uniform_decode_query_len
|
|
|
|
if num_tokens_padded == num_reqs_padded * uniform_decode_query_len:
|
|
# Uniform-batch case: num_reqs must be no greater than num_reqs_padded
|
|
assert num_reqs <= num_reqs_padded
|
|
|
|
last_loc = self.query_start_loc.np[num_reqs]
|
|
self.query_start_loc.np[num_reqs + 1 : num_reqs_padded + 1] = (
|
|
self.arange_np[1 : num_reqs_padded + 1 - num_reqs] * uniform_decode_query_len + last_loc
|
|
)
|
|
else:
|
|
# Mixed-batch case: num_reqs must equal num_reqs_padded
|
|
assert num_reqs == num_reqs_padded
|
|
|
|
# Insert a dummy request instead of setting query_start_loc[num_reqs] = num_tokens_padded directly
|
|
self.query_start_loc.np[num_reqs_padded + 1] = num_tokens_padded
|
|
num_reqs_padded = num_reqs_padded + 1
|
|
|
|
self.query_start_loc.copy_to_gpu()
|
|
return num_reqs_padded
|
|
|
|
@torch.inference_mode()
|
|
def _dummy_run(
|
|
self,
|
|
num_tokens: int,
|
|
with_prefill: bool = False,
|
|
cudagraph_runtime_mode=None,
|
|
force_attention: bool = False,
|
|
uniform_decode: bool = False,
|
|
is_profile: bool = False,
|
|
create_mixed_batch: bool = False,
|
|
allow_microbatching: bool = True,
|
|
skip_eplb: bool = False,
|
|
remove_lora: bool = True,
|
|
is_graph_capturing: bool = False,
|
|
num_active_loras: int = 0,
|
|
profile_seq_lens: int | None = None,
|
|
):
|
|
temporary_context = self.temporary_modify_uniform_decode_query_len() if uniform_decode else nullcontext()
|
|
with temporary_context:
|
|
return super()._dummy_run(
|
|
num_tokens=num_tokens,
|
|
with_prefill=with_prefill,
|
|
cudagraph_runtime_mode=cudagraph_runtime_mode,
|
|
force_attention=force_attention,
|
|
uniform_decode=uniform_decode,
|
|
is_profile=is_profile,
|
|
create_mixed_batch=create_mixed_batch,
|
|
allow_microbatching=allow_microbatching,
|
|
skip_eplb=skip_eplb,
|
|
remove_lora=remove_lora,
|
|
is_graph_capturing=is_graph_capturing,
|
|
num_active_loras=num_active_loras,
|
|
profile_seq_lens=profile_seq_lens,
|
|
)
|
|
|
|
def _check_and_update_cudagraph_mode(
|
|
self,
|
|
attention_backends,
|
|
kv_cache_groups,
|
|
) -> None:
|
|
# 910B does not need this branch because runner/dispatcher query_len are
|
|
# naturally consistent there. 310P ngram needs temporary alignment.
|
|
with self.temporary_modify_uniform_decode_query_len():
|
|
super()._check_and_update_cudagraph_mode(attention_backends, kv_cache_groups)
|
|
|
|
def initialize_kv_cache_tensors(self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]:
|
|
"""
|
|
Override the base class method.
|
|
Initialize the memory buffer for KV cache.
|
|
|
|
Args:
|
|
kv_cache_config: The KV cache config
|
|
Returns:
|
|
Dict[str, torch.Tensor]: A map between layer names to their
|
|
corresponding memory buffer for KV cache.
|
|
"""
|
|
# 310P limitation: KV transfer is not supported
|
|
if self.vllm_config.kv_transfer_config is not None:
|
|
raise ValueError("KV cache transfer is not supported for 310P.")
|
|
if self.use_sparse:
|
|
raise ValueError("Deepseek Sparse Attention is not supported for 310P.")
|
|
if self.model_config.use_mla:
|
|
raise ValueError("MLAAttention is not supported for 310P.")
|
|
# Initialize the memory buffer for KV cache
|
|
kv_caches = self._allocate_kv_cache_tensors(kv_cache_config)
|
|
# Set up cross-layer KV cache sharing
|
|
for layer_name, target_layer_name in self.shared_kv_cache_layers.items():
|
|
logger.debug("%s reuses KV cache of %s", layer_name, target_layer_name)
|
|
kv_caches[layer_name] = kv_caches[target_layer_name]
|
|
|
|
from vllm.v1.worker.utils import bind_kv_cache
|
|
|
|
bind_kv_cache(kv_caches, self.compilation_config.static_forward_context, self.kv_caches)
|
|
return kv_caches
|
|
|
|
def _allocate_kv_cache_tensors(self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]:
|
|
"""
|
|
Initializes the KV cache size. The buffer needs to be reshaped to the desired shape before being used by
|
|
the models.
|
|
|
|
Args:
|
|
kv_cache_config: The KV cache config
|
|
Returns:
|
|
dict[str, torch.Tensor]: A map between layer names to their
|
|
corresponding memory buffer.
|
|
"""
|
|
# init kv cache tensors
|
|
kv_cache: dict[str, list[torch.Tensor] | tuple[torch.Tensor, torch.Tensor]] = {}
|
|
# get kv cache spec for each layer
|
|
layer_kv_cache_spec: dict[str, KVCacheSpec] = {}
|
|
for group_kv_cache_spec in kv_cache_config.kv_cache_groups:
|
|
for layer_name in group_kv_cache_spec.layer_names:
|
|
layer_kv_cache_spec[layer_name] = group_kv_cache_spec.kv_cache_spec
|
|
# Allocate kv cache buffers according to the kv_cache_config and kv_cache_spec
|
|
for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
|
|
for idx in range(len(kv_cache_tensor.shared_by)):
|
|
layer_name = kv_cache_tensor.shared_by[idx]
|
|
if layer_name in self.runner_only_attn_layers:
|
|
continue
|
|
if "linear_attn" in layer_name and layer_name not in kv_cache:
|
|
cache_spec = layer_kv_cache_spec[layer_name]
|
|
assert isinstance(cache_spec, MambaSpec)
|
|
assert kv_cache_tensor.size % cache_spec.page_size_bytes == 0
|
|
num_blocks = kv_cache_tensor.size // cache_spec.page_size_bytes
|
|
assert num_blocks >= kv_cache_config.num_blocks
|
|
raw_tensor = torch.zeros(kv_cache_tensor.size, dtype=torch.int8, device=self.device)
|
|
state_tensors = []
|
|
target_idx = 0
|
|
start_idx = 0
|
|
for shape, dtype in zip(cache_spec.shapes, cache_spec.dtypes):
|
|
target_shape = (num_blocks, *shape)
|
|
target_idx += math.prod(target_shape) * get_dtype_size(dtype)
|
|
tensor = raw_tensor[start_idx:target_idx].view(dtype).view(target_shape)
|
|
start_idx = target_idx
|
|
state_tensors.append(tensor)
|
|
for layer_name_inner in kv_cache_tensor.shared_by:
|
|
if "linear_attn" in layer_name_inner:
|
|
kv_cache[layer_name_inner] = state_tensors
|
|
elif "attn" in layer_name and layer_name not in kv_cache:
|
|
kv_cache_spec = layer_kv_cache_spec[layer_name]
|
|
assert isinstance(kv_cache_spec, AttentionSpec)
|
|
assert kv_cache_tensor.size % kv_cache_spec.page_size_bytes == 0
|
|
num_blocks = kv_cache_tensor.size // kv_cache_spec.page_size_bytes
|
|
assert num_blocks >= kv_cache_config.num_blocks
|
|
# Page attention operation on 310P limits block_size * head_size <= 128 * 128
|
|
supported_sizes = [
|
|
support_size
|
|
for support_size in self.attn_backend.get_supported_kernel_block_sizes()
|
|
if support_size * kv_cache_spec.head_size <= _ATTENTION_BLOCK_SIZE_LIMIT
|
|
]
|
|
if supported_sizes:
|
|
block_size = supported_sizes[0]
|
|
block_size_chunk = kv_cache_spec.block_size // block_size
|
|
kv_cache_shape = self.attn_backend.get_kv_cache_shape(
|
|
num_blocks * block_size_chunk,
|
|
block_size,
|
|
kv_cache_spec.num_kv_heads,
|
|
kv_cache_spec.head_size,
|
|
)
|
|
else:
|
|
kv_cache_shape = self.attn_backend.get_kv_cache_shape(
|
|
num_blocks, kv_cache_spec.block_size, kv_cache_spec.num_kv_heads, kv_cache_spec.head_size
|
|
)
|
|
k_shape = kv_cache_shape[1:]
|
|
v_shape = k_shape
|
|
dtype = kv_cache_spec.dtype
|
|
k_cache = torch_npu.empty_with_format(
|
|
size=k_shape, dtype=dtype, device=self.device, acl_format=self._acl_format
|
|
)
|
|
v_cache = torch_npu.empty_with_format(
|
|
size=v_shape, dtype=dtype, device=self.device, acl_format=self._acl_format
|
|
)
|
|
for layer_name_inner in kv_cache_tensor.shared_by:
|
|
# shared the kvcache between the self_attn specs in the same group
|
|
if "attn" in layer_name_inner and "linear_attn" not in layer_name_inner:
|
|
kv_cache[layer_name_inner] = (k_cache, v_cache)
|
|
layer_names = set()
|
|
for group in kv_cache_config.kv_cache_groups:
|
|
for layer_name in group.layer_names:
|
|
if layer_name in self.runner_only_attn_layers:
|
|
continue
|
|
layer_names.add(layer_name)
|
|
assert layer_names == set(kv_cache.keys()), "Some layers are not correctly initialized"
|
|
return kv_cache
|
|
|
|
# Override this function because of tensor.copy_(other) accuracy issue.
|
|
# TODO: This override will be removed after tensor.copy_(other) accuracy issue is resolved.
|
|
def _prepare_input_ids(
|
|
self,
|
|
scheduler_output: SchedulerOutput,
|
|
total_num_scheduled_tokens: int,
|
|
cu_num_tokens: np.ndarray,
|
|
) -> None:
|
|
"""Prepare the input IDs for the current batch.
|
|
|
|
Carefully handles the `prev_sampled_token_ids` which can be cached
|
|
from the previous engine iteration, in which case those tokens on the
|
|
GPU need to be copied into the corresponding slots into input_ids."""
|
|
|
|
if self.input_batch.prev_sampled_token_ids is None:
|
|
# Normal scheduling case
|
|
self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
|
|
if self.enable_prompt_embeds:
|
|
self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
|
|
self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
|
|
return
|
|
|
|
# Async scheduling case, where some decode requests from the previous
|
|
# iteration won't have entries in input_ids_cpu and need to be copied
|
|
# on the NPU from prev_sampled_token_ids.
|
|
prev_req_id_to_index = self.input_batch.prev_req_id_to_index
|
|
assert prev_req_id_to_index is not None
|
|
sample_flattened_indices: list[int] = []
|
|
spec_flattened_indices: list[int] = []
|
|
prev_common_req_indices: list[int] = []
|
|
prev_draft_token_indices: list[int] = []
|
|
indices_match = True
|
|
max_flattened_index = -1
|
|
total_num_spec_tokens = 0
|
|
scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens
|
|
|
|
for req_id, cur_index in self.input_batch.req_id_to_index.items():
|
|
if (prev_index := prev_req_id_to_index.get(req_id)) is not None:
|
|
prev_common_req_indices.append(prev_index)
|
|
draft_len = len(scheduled_spec_tokens.get(req_id, ()))
|
|
total_num_spec_tokens += draft_len
|
|
flattened_index = cu_num_tokens[cur_index].item() - 1
|
|
sample_flattened_indices.append(flattened_index - draft_len)
|
|
spec_flattened_indices.extend(range(flattened_index - draft_len + 1, flattened_index + 1))
|
|
start = prev_index * self.num_spec_tokens
|
|
prev_draft_token_indices.extend(range(start, start + draft_len))
|
|
indices_match &= prev_index == flattened_index
|
|
max_flattened_index = max(max_flattened_index, flattened_index)
|
|
num_common_tokens = len(sample_flattened_indices)
|
|
total_without_spec = total_num_scheduled_tokens - total_num_spec_tokens
|
|
if num_common_tokens < total_without_spec:
|
|
self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
|
|
if self.enable_prompt_embeds:
|
|
self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
|
|
self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
|
|
if num_common_tokens == 0:
|
|
return
|
|
if indices_match and max_flattened_index == (num_common_tokens - 1):
|
|
# NOTE: Override the copy_ function here
|
|
indices = torch.arange(num_common_tokens, device=self.input_ids.gpu.device)
|
|
source = self.input_batch.prev_sampled_token_ids[:num_common_tokens, 0]
|
|
self.input_ids.gpu.index_copy_(0, indices, source)
|
|
if self.enable_prompt_embeds:
|
|
self.is_token_ids.gpu[:num_common_tokens] = True
|
|
return
|
|
# Upload the index tensors asynchronously so the scatter can be non-blocking.
|
|
sampled_tokens_index_tensor = torch.tensor(
|
|
sample_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
|
|
).to(self.device, non_blocking=True)
|
|
prev_common_req_indices_tensor = torch.tensor(
|
|
prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
|
|
).to(self.device, non_blocking=True)
|
|
self.input_ids.gpu.scatter_(
|
|
dim=0,
|
|
index=sampled_tokens_index_tensor,
|
|
src=self.input_batch.prev_sampled_token_ids[prev_common_req_indices_tensor, 0],
|
|
)
|
|
# Scatter the draft tokens after the sampled tokens are scattered.
|
|
if self._draft_token_ids is None or not spec_flattened_indices:
|
|
return
|
|
assert isinstance(self._draft_token_ids, torch.Tensor)
|
|
draft_tokens_index_tensor = torch.tensor(
|
|
spec_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
|
|
).to(self.device, non_blocking=True)
|
|
prev_draft_token_indices_tensor = torch.tensor(
|
|
prev_draft_token_indices, dtype=torch.int64, pin_memory=self.pin_memory
|
|
).to(self.device, non_blocking=True)
|
|
draft_token_ids = self._draft_token_ids.to(dtype=torch.int32)
|
|
self.input_ids.gpu.scatter_(
|
|
dim=0,
|
|
index=draft_tokens_index_tensor,
|
|
src=draft_token_ids.flatten()[prev_draft_token_indices_tensor],
|
|
)
|
|
|
|
def may_reinitialize_input_batch(self, kv_cache_config: KVCacheConfig) -> None:
|
|
"""
|
|
Re-initialize the input batch if the block sizes are different from
|
|
`[self.cache_config.block_size]`. This usually happens when there
|
|
are multiple KV cache groups.
|
|
|
|
Args:
|
|
kv_cache_config: The KV cache configuration.
|
|
"""
|
|
block_sizes = [
|
|
kv_cache_group.kv_cache_spec.block_size
|
|
for kv_cache_group in kv_cache_config.kv_cache_groups
|
|
if not isinstance(kv_cache_group.kv_cache_spec, EncoderOnlyAttentionSpec)
|
|
]
|
|
|
|
# Generate kernel_block_sizes that matches each block_size
|
|
# For attention backends that support virtual block splitting,
|
|
# use the supported block sizes from the backend
|
|
# For other backends (like Mamba), use [0] (no splitting)
|
|
self.kernel_block_sizes = []
|
|
for kv_cache_group_id, kv_cache_group in enumerate(kv_cache_config.kv_cache_groups):
|
|
kv_cache_spec = kv_cache_group.kv_cache_spec
|
|
if isinstance(kv_cache_spec, UniformTypeKVCacheSpecs):
|
|
kv_cache_spec = next(iter(kv_cache_spec.kv_cache_specs.values()))
|
|
if isinstance(kv_cache_spec, EncoderOnlyAttentionSpec):
|
|
continue
|
|
elif isinstance(kv_cache_spec, AttentionSpec):
|
|
try:
|
|
attn_groups = self.attn_groups[kv_cache_group_id]
|
|
backend = attn_groups[0].backend
|
|
# Page attention operation on 310P limits block_size * head_size <= 128 * 128
|
|
supported_sizes = [
|
|
support_size
|
|
for support_size in backend.get_supported_kernel_block_sizes()
|
|
if support_size * kv_cache_spec.head_size <= _ATTENTION_BLOCK_SIZE_LIMIT
|
|
]
|
|
kernel_block_size_list = supported_sizes if supported_sizes else [self.cache_config.block_size]
|
|
except IndexError:
|
|
kernel_block_size_list = [self.cache_config.block_size]
|
|
self.kernel_block_sizes.append(kernel_block_size_list)
|
|
else:
|
|
self.kernel_block_sizes.append([0])
|
|
|
|
if block_sizes != [self.cache_config.block_size] or self.kernel_block_sizes != [[self.cache_config.block_size]]:
|
|
assert self.offload_config.uva.cpu_offload_gb == 0, (
|
|
"Cannot re-initialize the input batch when CPU weight "
|
|
"offloading is enabled. See https://github.com/vllm-project/vllm/pull/18298 " # noqa: E501
|
|
"for more details."
|
|
)
|
|
self.input_batch = NPUInputBatch(
|
|
max_num_reqs=self.max_num_reqs,
|
|
max_model_len=max(self.model_config.max_model_len, self.max_encoder_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_sizes=block_sizes,
|
|
is_spec_decode=bool(self.vllm_config.speculative_config),
|
|
logitsprocs=self.input_batch.logitsprocs,
|
|
is_pooling_model=self.is_pooling_model,
|
|
num_speculative_tokens=(
|
|
self.vllm_config.speculative_config.num_speculative_tokens
|
|
if self.vllm_config.speculative_config
|
|
else 0
|
|
),
|
|
kernel_block_sizes=self.kernel_block_sizes,
|
|
)
|