197 lines
7.7 KiB
Python
197 lines
7.7 KiB
Python
from typing import List, Optional, Tuple
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import torch
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from torch import nn
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from vllm.config import (DeviceConfig, ModelConfig, ParallelConfig,
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SchedulerConfig)
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from vllm.logger import init_logger
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from vllm.model_executor import SamplingMetadata
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from vllm.model_executor.model_loader.neuron import get_neuron_model
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from vllm.sequence import SamplerOutput, SequenceGroupMetadata
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from vllm.utils import is_pin_memory_available, make_tensor_with_pad
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logger = init_logger(__name__)
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class NeuronModelRunner:
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def __init__(
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self,
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model_config: ModelConfig,
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parallel_config: ParallelConfig,
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scheduler_config: SchedulerConfig,
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device_config: DeviceConfig,
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):
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self.model_config = model_config
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self.parallel_config = parallel_config
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self.scheduler_config = scheduler_config
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if model_config is not None and model_config.get_sliding_window():
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logger.warning("Sliding window is not supported on Neuron. "
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"The model will run without sliding window.")
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self.device_config = (device_config
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if device_config is not None else DeviceConfig())
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self.device = self.device_config.device
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self.pin_memory = is_pin_memory_available()
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# Lazy initialization.
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self.model: nn.Module # initialize after load_model.
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def load_model(self) -> None:
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self.model = get_neuron_model(self.model_config,
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parallel_config=self.parallel_config,
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scheduler_config=self.scheduler_config)
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def _prepare_prompt(
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self,
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seq_group_metadata_list: List[SequenceGroupMetadata],
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, List[int]]:
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assert len(seq_group_metadata_list) > 0
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input_tokens: List[List[int]] = []
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input_positions: List[List[int]] = []
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input_block_ids: List[int] = []
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seq_lens: List[int] = []
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for seq_group_metadata in seq_group_metadata_list:
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assert seq_group_metadata.is_prompt
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seq_ids = list(seq_group_metadata.seq_data.keys())
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assert len(seq_ids) == 1
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seq_id = seq_ids[0]
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seq_data = seq_group_metadata.seq_data[seq_id]
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prompt_tokens = seq_data.get_token_ids()
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seq_len = len(prompt_tokens)
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seq_lens.append(seq_len)
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input_tokens.append(prompt_tokens)
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input_positions.append(list(range(seq_len)))
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assert seq_group_metadata.block_tables is not None
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block_table = seq_group_metadata.block_tables[seq_id]
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assert len(block_table) == 1
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input_block_ids.append(block_table[0])
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max_seq_len = max(seq_lens)
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assert max_seq_len > 0
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input_tokens = make_tensor_with_pad(input_tokens,
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max_seq_len,
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pad=0,
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dtype=torch.long,
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device=self.device)
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input_positions = make_tensor_with_pad(input_positions,
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max_seq_len,
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pad=0,
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dtype=torch.long,
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device=self.device)
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input_block_ids = torch.tensor(input_block_ids,
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dtype=torch.long,
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device=self.device)
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return input_tokens, input_positions, input_block_ids, seq_lens
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def _prepare_decode(
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self,
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seq_group_metadata_list: List[SequenceGroupMetadata],
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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assert len(seq_group_metadata_list) > 0
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input_tokens: List[List[int]] = []
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input_positions: List[List[int]] = []
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input_block_ids: List[int] = []
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context_lens: List[int] = []
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for seq_group_metadata in seq_group_metadata_list:
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assert not seq_group_metadata.is_prompt
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seq_ids = list(seq_group_metadata.seq_data.keys())
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for seq_id in seq_ids:
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seq_data = seq_group_metadata.seq_data[seq_id]
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generation_token = seq_data.get_last_token_id()
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input_tokens.append([generation_token])
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seq_len = seq_data.get_len()
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position = seq_len - 1
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input_positions.append([position])
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context_lens.append(seq_len)
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assert seq_group_metadata.block_tables is not None
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block_table = seq_group_metadata.block_tables[seq_id]
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assert len(block_table) == 1
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input_block_ids.append(block_table[0])
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input_tokens = make_tensor_with_pad(input_tokens,
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max_len=1,
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pad=0,
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dtype=torch.long,
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device=self.device)
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input_positions = make_tensor_with_pad(input_positions,
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max_len=1,
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pad=0,
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dtype=torch.long,
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device=self.device)
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context_lens = torch.tensor(context_lens,
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dtype=torch.int,
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device=self.device)
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input_block_ids = torch.tensor(input_block_ids,
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dtype=torch.long,
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device=self.device)
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return input_tokens, input_positions, input_block_ids
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def prepare_input_tensors(
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self,
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seq_group_metadata_list: List[SequenceGroupMetadata],
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, SamplingMetadata]:
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# NOTE: We assume that all sequences in the group are all prompts or
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# all decodes.
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is_prompt = seq_group_metadata_list[0].is_prompt
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# Prepare input tensors.
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if is_prompt:
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(input_tokens, input_positions, input_block_ids,
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seq_lens) = self._prepare_prompt(seq_group_metadata_list)
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else:
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(input_tokens, input_positions,
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input_block_ids) = self._prepare_decode(seq_group_metadata_list)
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seq_lens = []
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sampling_metadata = SamplingMetadata.prepare(
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seq_group_metadata_list,
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seq_lens,
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# query_lens is not needed if chunked prefill is not
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# supported. Since neuron worker doesn't support chunked prefill
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# just use seq_lens instead.
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seq_lens,
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self.device,
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self.pin_memory)
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return (input_tokens, input_positions, input_block_ids,
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sampling_metadata)
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@torch.inference_mode()
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def execute_model(
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self,
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seq_group_metadata_list: List[SequenceGroupMetadata],
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) -> Optional[SamplerOutput]:
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(input_tokens, input_positions, input_block_ids, sampling_metadata
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) = self.prepare_input_tensors(seq_group_metadata_list)
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hidden_states = self.model(
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input_ids=input_tokens,
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positions=input_positions,
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input_block_ids=input_block_ids,
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)
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# Compute the logits.
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logits = self.model.compute_logits(hidden_states, sampling_metadata)
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# Sample the next token.
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output = self.model.sample(
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logits=logits,
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sampling_metadata=sampling_metadata,
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)
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return output
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@property
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def vocab_size(self) -> int:
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return self.model_config.get_vocab_size()
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