456 lines
19 KiB
Python
456 lines
19 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
import os
|
|
from dataclasses import dataclass
|
|
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, Tuple, Union
|
|
|
|
import torch
|
|
from torch import nn
|
|
|
|
from vllm.config import DeviceConfig, VllmConfig
|
|
from vllm.logger import init_logger
|
|
from vllm.lora.layers import LoRAMapping
|
|
from vllm.lora.request import LoRARequest
|
|
from vllm.model_executor import SamplingMetadata
|
|
from vllm.model_executor.layers.sampler import SamplerOutput
|
|
from vllm.model_executor.model_loader.neuron import get_neuron_model
|
|
from vllm.multimodal import BatchedTensorInputs, MultiModalKwargs
|
|
from vllm.platforms import current_platform
|
|
from vllm.sampling_params import SamplingParams
|
|
from vllm.sequence import IntermediateTensors, SequenceGroupMetadata
|
|
from vllm.utils import is_pin_memory_available, make_tensor_with_pad
|
|
from vllm.worker.model_runner_base import ModelRunnerBase, ModelRunnerInputBase
|
|
|
|
if TYPE_CHECKING:
|
|
from vllm.attention.backends.abstract import AttentionBackend
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
|
|
@dataclass(frozen=True)
|
|
class ModelInputForNeuron(ModelRunnerInputBase):
|
|
"""
|
|
Used by the NeuronModelRunner.
|
|
"""
|
|
input_tokens: Optional[torch.Tensor] = None
|
|
input_positions: Optional[torch.Tensor] = None
|
|
input_block_ids: Optional[torch.Tensor] = None
|
|
sampling_metadata: SamplingMetadata = None
|
|
multi_modal_kwargs: BatchedTensorInputs = None
|
|
adapter_ids: Optional[str] = None
|
|
|
|
def as_broadcastable_tensor_dict(
|
|
self) -> Dict[str, Union[int, torch.Tensor]]:
|
|
return {
|
|
"input_tokens": self.input_tokens,
|
|
"input_positions": self.input_positions,
|
|
"input_block_ids": self.input_block_ids,
|
|
"sampling_metadata": self.sampling_metadata,
|
|
"multi_modal_kwargs": self.multi_modal_kwargs,
|
|
}
|
|
|
|
@classmethod
|
|
def from_broadcasted_tensor_dict(
|
|
cls,
|
|
tensor_dict: Dict[str, Any],
|
|
attn_backend: Optional["AttentionBackend"] = None,
|
|
) -> "ModelInputForNeuron":
|
|
return ModelInputForNeuron(
|
|
input_tokens=tensor_dict["input_tokens"],
|
|
input_positions=tensor_dict["input_positions"],
|
|
input_block_ids=tensor_dict["input_block_ids"],
|
|
sampling_metadata=tensor_dict["sampling_metadata"],
|
|
multi_modal_kwargs=tensor_dict["multi_modal_kwargs"],
|
|
)
|
|
|
|
|
|
class NeuronModelRunner(ModelRunnerBase[ModelInputForNeuron]):
|
|
"""A model runner for AWS Neuron hardware"""
|
|
|
|
# NEURON has an upper limit on the top_k
|
|
_MAX_NEURON_SAMPLING_TOP_K = 256
|
|
|
|
def __init__(
|
|
self,
|
|
vllm_config: VllmConfig,
|
|
):
|
|
ModelRunnerBase.__init__(self, vllm_config)
|
|
|
|
if (self.model_config is not None
|
|
and self.model_config.get_sliding_window()):
|
|
logger.warning("Sliding window is not supported on Neuron. "
|
|
"The model will run without sliding window.")
|
|
self.device_config = (self.device_config if self.device_config
|
|
is not None else DeviceConfig())
|
|
self.lora_config = vllm_config.lora_config
|
|
self.device = self.device_config.device
|
|
self.pin_memory = is_pin_memory_available()
|
|
|
|
# Lazy initialization.
|
|
self.model: nn.Module # initialize after load_model.
|
|
|
|
# Once NEURON_ON_DEVICE_SAMPLING_DISABLED is set to a non-zero value,
|
|
# turn off on-device sampling.
|
|
self._on_device_sampling_disabled = int(
|
|
os.getenv("NEURON_ON_DEVICE_SAMPLING_DISABLED", "0"))
|
|
|
|
# NEURON needs to update sampling parameters when request IDs change
|
|
# across batches. This variable stores the previous batch's request IDs
|
|
# to determine if an update is needed.
|
|
self._previous_batch_request_ids: List[str] = []
|
|
|
|
if not self._on_device_sampling_disabled:
|
|
self._init_neuron_sampling()
|
|
|
|
def _init_neuron_sampling(self) -> None:
|
|
if current_platform.use_transformers_neuronx():
|
|
from transformers_neuronx.config import GenerationConfig
|
|
else:
|
|
from transformers import GenerationConfig
|
|
logger.warning(
|
|
"On-device sampling is turned on in Neuron by default, only "
|
|
"top_k, top_p, and temperature are current supported sampling "
|
|
"parameters. To turn off the on-device sampling, please set "
|
|
"the environment variable NEURON_ON_DEVICE_SAMPLING_DISABLED=1.")
|
|
self.model_config.neuron_sampling_params = GenerationConfig(
|
|
max_length=self.scheduler_config.max_model_len,
|
|
do_sample=True,
|
|
per_batch_line=True,
|
|
top_k=[self._MAX_NEURON_SAMPLING_TOP_K] \
|
|
* self.scheduler_config.max_num_seqs,
|
|
top_p=[1.0] * self.scheduler_config.max_num_seqs,
|
|
temperature=[1.0] * self.scheduler_config.max_num_seqs,
|
|
dynamic=True,
|
|
global_top_k=self._MAX_NEURON_SAMPLING_TOP_K)
|
|
|
|
def load_model(self) -> None:
|
|
self.model = get_neuron_model(self.model_config,
|
|
parallel_config=self.parallel_config,
|
|
scheduler_config=self.scheduler_config)
|
|
|
|
def get_model(self) -> nn.Module:
|
|
return self.model
|
|
|
|
def _prepare_prompt(
|
|
self,
|
|
seq_group_metadata_list: List[SequenceGroupMetadata],
|
|
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, List[int],
|
|
BatchedTensorInputs]:
|
|
assert len(seq_group_metadata_list) > 0
|
|
input_tokens: List[List[int]] = []
|
|
input_positions: List[List[int]] = []
|
|
input_block_ids: List[int] = []
|
|
|
|
seq_lens: List[int] = []
|
|
multi_modal_kwargs_list: List[MultiModalKwargs] = []
|
|
for seq_group_metadata in seq_group_metadata_list:
|
|
assert seq_group_metadata.is_prompt
|
|
seq_ids = list(seq_group_metadata.seq_data.keys())
|
|
assert len(seq_ids) == 1
|
|
seq_id = seq_ids[0]
|
|
|
|
seq_data = seq_group_metadata.seq_data[seq_id]
|
|
prompt_tokens = seq_data.get_token_ids()
|
|
seq_len = len(prompt_tokens)
|
|
seq_lens.append(seq_len)
|
|
|
|
input_tokens.append(prompt_tokens)
|
|
input_positions.append(list(range(seq_len)))
|
|
|
|
assert seq_group_metadata.block_tables is not None
|
|
block_table = seq_group_metadata.block_tables[seq_id]
|
|
assert len(block_table) == 1
|
|
input_block_ids.append(block_table[0])
|
|
|
|
mm_kwargs = seq_group_metadata.multi_modal_data
|
|
if mm_kwargs:
|
|
mm_kwargs = self.process_multi_modal_data_neuron(mm_kwargs)
|
|
multi_modal_kwargs_list.append(mm_kwargs)
|
|
|
|
max_seq_len = max(seq_lens)
|
|
assert max_seq_len > 0
|
|
input_tokens = make_tensor_with_pad(input_tokens,
|
|
pad=0,
|
|
max_len=max_seq_len,
|
|
dtype=torch.long,
|
|
device=self.device)
|
|
input_positions = make_tensor_with_pad(input_positions,
|
|
pad=0,
|
|
max_len=max_seq_len,
|
|
dtype=torch.long,
|
|
device=self.device)
|
|
input_block_ids = torch.tensor(input_block_ids,
|
|
dtype=torch.long,
|
|
device=self.device)
|
|
|
|
multi_modal_kwargs = MultiModalKwargs.batch(multi_modal_kwargs_list)
|
|
|
|
return (input_tokens, input_positions, input_block_ids, seq_lens,
|
|
multi_modal_kwargs)
|
|
|
|
def _prepare_decode(
|
|
self,
|
|
seq_group_metadata_list: List[SequenceGroupMetadata],
|
|
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
|
assert len(seq_group_metadata_list) > 0
|
|
input_tokens: List[List[int]] = []
|
|
input_positions: List[List[int]] = []
|
|
input_block_ids: List[int] = []
|
|
context_lens: List[int] = []
|
|
|
|
for seq_group_metadata in seq_group_metadata_list:
|
|
assert not seq_group_metadata.is_prompt
|
|
|
|
seq_ids = list(seq_group_metadata.seq_data.keys())
|
|
|
|
for seq_id in seq_ids:
|
|
seq_data = seq_group_metadata.seq_data[seq_id]
|
|
generation_token = seq_data.get_last_token_id()
|
|
input_tokens.append([generation_token])
|
|
|
|
seq_len = seq_data.get_len()
|
|
position = seq_len - 1
|
|
input_positions.append([position])
|
|
context_lens.append(seq_len)
|
|
|
|
assert seq_group_metadata.block_tables is not None
|
|
block_table = seq_group_metadata.block_tables[seq_id]
|
|
assert len(block_table) == 1
|
|
input_block_ids.append(block_table[0])
|
|
|
|
input_tokens = make_tensor_with_pad(input_tokens,
|
|
pad=0,
|
|
max_len=1,
|
|
dtype=torch.long,
|
|
device=self.device)
|
|
input_positions = make_tensor_with_pad(input_positions,
|
|
pad=0,
|
|
max_len=1,
|
|
dtype=torch.long,
|
|
device=self.device)
|
|
context_lens = torch.tensor(context_lens,
|
|
dtype=torch.int,
|
|
device=self.device)
|
|
input_block_ids = torch.tensor(input_block_ids,
|
|
dtype=torch.long,
|
|
device=self.device)
|
|
|
|
return input_tokens, input_positions, input_block_ids
|
|
|
|
def make_model_input_from_broadcasted_tensor_dict(
|
|
self, tensor_dict: Dict[str, Any]) -> ModelInputForNeuron:
|
|
return ModelInputForNeuron.from_broadcasted_tensor_dict(tensor_dict)
|
|
|
|
def prepare_model_input(
|
|
self,
|
|
seq_group_metadata_list: List[SequenceGroupMetadata],
|
|
virtual_engine: int = 0,
|
|
finished_requests_ids: Optional[List[str]] = None
|
|
) -> ModelInputForNeuron:
|
|
multi_modal_kwargs = None
|
|
# NOTE: We assume that all sequences in the group are all prompts or
|
|
# all decodes.
|
|
is_prompt = seq_group_metadata_list[0].is_prompt
|
|
# Prepare input tensors.
|
|
if is_prompt:
|
|
(input_tokens, input_positions, input_block_ids, seq_lens,
|
|
multi_modal_kwargs
|
|
) = self._prepare_prompt(seq_group_metadata_list)
|
|
else:
|
|
(input_tokens, input_positions,
|
|
input_block_ids) = self._prepare_decode(seq_group_metadata_list)
|
|
seq_lens = None
|
|
|
|
if not self._on_device_sampling_disabled:
|
|
for seq_group_metadata in seq_group_metadata_list:
|
|
sampling_params = seq_group_metadata.sampling_params
|
|
top_k, top_p, temperature = (
|
|
self._convert_to_neuron_sampling_params(sampling_params))
|
|
sampling_params.top_k = top_k
|
|
sampling_params.top_p = top_p
|
|
sampling_params.temperature = temperature
|
|
|
|
# we need multi_modal_data for later tokens as well
|
|
multi_modal_kwargs_list: List[MultiModalKwargs] = []
|
|
for seq_group_metadata in seq_group_metadata_list:
|
|
mm_data = seq_group_metadata.multi_modal_data
|
|
if mm_data:
|
|
multi_modal_kwargs_list.append(mm_data)
|
|
multi_modal_kwargs = MultiModalKwargs.batch(multi_modal_kwargs_list)
|
|
|
|
sampling_metadata = SamplingMetadata.prepare(
|
|
seq_group_metadata_list,
|
|
seq_lens,
|
|
# query_lens is not needed if chunked prefill is not
|
|
# supported. Since neuron worker doesn't support chunked prefill
|
|
# just use seq_lens instead.
|
|
seq_lens,
|
|
self.device,
|
|
self.pin_memory,
|
|
generators=self.get_generators(finished_requests_ids))
|
|
|
|
if current_platform.use_transformers_neuronx(
|
|
) and not self._on_device_sampling_disabled:
|
|
# Once the request IDs are changed in current iteration, we will
|
|
# update the on-device sampling parameters.
|
|
current_batch_request_ids = [
|
|
seq_group_meta_data.request_id
|
|
for seq_group_meta_data in seq_group_metadata_list
|
|
]
|
|
if current_batch_request_ids != self._previous_batch_request_ids:
|
|
self._update_neuron_sampling_params(seq_group_metadata_list)
|
|
self._previous_batch_request_ids = current_batch_request_ids
|
|
|
|
return ModelInputForNeuron(input_tokens=input_tokens,
|
|
input_positions=input_positions,
|
|
input_block_ids=input_block_ids,
|
|
sampling_metadata=sampling_metadata,
|
|
multi_modal_kwargs=multi_modal_kwargs)
|
|
|
|
def _update_neuron_sampling_params(
|
|
self, seq_group_metadata_list: List[SequenceGroupMetadata]):
|
|
# Update Neuron sampling parameters (GenerationConfig in Neuron)
|
|
current_sampling_params = self.model_config.neuron_sampling_params
|
|
assert current_sampling_params is not None, (
|
|
f"Failed to update sampling_params, "
|
|
f"current sampling params is {current_sampling_params}")
|
|
|
|
is_update_needed = False
|
|
|
|
top_k = current_sampling_params.top_k
|
|
top_p = current_sampling_params.top_p
|
|
temperature = current_sampling_params.temperature
|
|
|
|
# The index of a sequence's sampling parameters in neuron is equal to
|
|
# its index in `input_block_ids`.
|
|
for seq_group_metadata in seq_group_metadata_list:
|
|
seq_ids = list(seq_group_metadata.seq_data.keys())
|
|
sampling_params = seq_group_metadata.sampling_params
|
|
|
|
seq_group_top_k = sampling_params.top_k
|
|
seq_group_top_p = sampling_params.top_p
|
|
seq_group_temperature = sampling_params.temperature
|
|
|
|
for seq_id in seq_ids:
|
|
index = seq_group_metadata.block_tables[seq_id][0]
|
|
if (top_k[index] != seq_group_top_k
|
|
or top_p[index] != seq_group_top_p
|
|
or temperature[index] != seq_group_temperature):
|
|
is_update_needed = True
|
|
|
|
top_k[index] = seq_group_top_k
|
|
top_p[index] = seq_group_top_p
|
|
temperature[index] = seq_group_temperature
|
|
|
|
# update_generation_config is only available in transformers-neuronx
|
|
if is_update_needed and current_platform.use_transformers_neuronx():
|
|
self.model.model.update_generation_config(current_sampling_params)
|
|
|
|
def _convert_to_neuron_sampling_params(
|
|
self, sampling_params: SamplingParams) -> Tuple[int, float, float]:
|
|
# Returns the top_k, top_p and temperature parameters for neuron.
|
|
top_k = sampling_params.top_k
|
|
top_p = sampling_params.top_p
|
|
temperature = sampling_params.temperature
|
|
|
|
if temperature == 0.0:
|
|
# Enable greedy sampling on zero temperature
|
|
return (1, 1.0, 1.0)
|
|
if top_k < 1 or top_k > self._MAX_NEURON_SAMPLING_TOP_K:
|
|
top_k = self._MAX_NEURON_SAMPLING_TOP_K
|
|
|
|
return (top_k, top_p, temperature)
|
|
|
|
@torch.inference_mode()
|
|
def execute_model(
|
|
self,
|
|
model_input: ModelInputForNeuron,
|
|
kv_caches: Optional[List[torch.Tensor]] = None,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
num_steps: int = 1,
|
|
) -> Optional[List[SamplerOutput]]:
|
|
if num_steps > 1:
|
|
raise ValueError(
|
|
"NeuronModelRunner does not support multi-step execution.")
|
|
|
|
# extract top_k, top_p and temperature from model_input for neuron
|
|
# forward call
|
|
sampling_params = (torch.tensor([[
|
|
seq_group.sampling_params.top_k, seq_group.sampling_params.top_p,
|
|
seq_group.sampling_params.temperature
|
|
] for seq_group in model_input.sampling_metadata.seq_groups]))
|
|
|
|
if current_platform.use_neuronx_distributed():
|
|
hidden_states = self.model(
|
|
input_ids=model_input.input_tokens,
|
|
positions=model_input.input_positions,
|
|
input_block_ids=model_input.input_block_ids,
|
|
sampling_params=sampling_params,
|
|
adapter_ids=model_input.adapter_ids,
|
|
**MultiModalKwargs.as_kwargs(
|
|
model_input.multi_modal_kwargs or {},
|
|
device=self.device,
|
|
),
|
|
)
|
|
elif current_platform.use_transformers_neuronx():
|
|
# [TODO] validate on-device sampling
|
|
# The model signature may need change for on-device sampling
|
|
hidden_states = self.model(
|
|
input_ids=model_input.input_tokens,
|
|
positions=model_input.input_positions,
|
|
input_block_ids=model_input.input_block_ids,
|
|
**MultiModalKwargs.as_kwargs(
|
|
model_input.multi_modal_kwargs or {},
|
|
device=self.device,
|
|
),
|
|
)
|
|
|
|
# Compute the logits only if the on-device sampling is turned off as
|
|
# on-device sampling outputs the token ids.
|
|
if self._on_device_sampling_disabled:
|
|
logits = self.model.compute_logits(hidden_states,
|
|
model_input.sampling_metadata)
|
|
else:
|
|
logits = hidden_states
|
|
|
|
# Sample the next token.
|
|
output = self.model.sample(
|
|
logits=logits,
|
|
sampling_metadata=model_input.sampling_metadata,
|
|
)
|
|
return [output]
|
|
|
|
@property
|
|
def vocab_size(self) -> int:
|
|
return self.model_config.get_vocab_size()
|
|
|
|
def process_multi_modal_data_neuron(self, mm_data):
|
|
# this is a no-op for NeuronModelRunner
|
|
return mm_data
|
|
|
|
def remove_all_loras(self):
|
|
raise NotImplementedError(
|
|
"LoRAs are not supported for Transformers NeuronX framework")
|
|
|
|
def set_active_loras(self, lora_requests: Set[LoRARequest],
|
|
lora_mapping: LoRAMapping) -> None:
|
|
raise NotImplementedError(
|
|
"LoRAs are not supported for Transformers NeuronX framework")
|
|
|
|
def add_lora(self, lora_request: LoRARequest):
|
|
raise NotImplementedError(
|
|
"LoRAs are not supported for Transformers NeuronX framework")
|
|
|
|
def remove_lora(self, lora_id: int) -> bool:
|
|
raise NotImplementedError(
|
|
"LoRAs are not supported for Transformers NeuronX framework")
|
|
|
|
def pin_lora(self, lora_id: int) -> bool:
|
|
raise NotImplementedError(
|
|
"LoRAs are not supported for Transformers NeuronX framework")
|
|
|
|
def list_loras(self) -> Set[int]:
|
|
raise NotImplementedError(
|
|
"LoRAs are not supported for Transformers NeuronX framework")
|