505 lines
20 KiB
Python
505 lines
20 KiB
Python
from dataclasses import dataclass
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type, Union
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import torch
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import torch.nn as nn
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from vllm.attention import get_attn_backend
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from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
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ModelConfig, MultiModalConfig, ParallelConfig,
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PromptAdapterConfig, SchedulerConfig)
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from vllm.distributed import broadcast_tensor_dict
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from vllm.inputs import INPUT_REGISTRY, InputRegistry
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from vllm.logger import init_logger
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from vllm.model_executor.model_loader import get_model
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from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs,
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MultiModalInputs, MultiModalRegistry)
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from vllm.sampling_params import SamplingParams
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from vllm.sequence import (IntermediateTensors, SamplerOutput,
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SequenceGroupMetadata)
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from vllm.utils import CudaMemoryProfiler, make_tensor_with_pad
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from vllm.worker.model_runner import AttentionMetadata, SamplingMetadata
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from vllm.worker.model_runner_base import (
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ModelRunnerBase, ModelRunnerInputBase,
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_add_attn_metadata_broadcastable_dict,
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_add_sampling_metadata_broadcastable_dict,
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_init_attn_metadata_from_tensor_dict,
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_init_sampling_metadata_from_tensor_dict)
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if TYPE_CHECKING:
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from vllm.attention.backends.abstract import AttentionBackend
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logger = init_logger(__name__)
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_PAD_SLOT_ID = -1
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_BATCH_SIZE_ALIGNMENT = 8
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_BATCH_SIZES_TO_CAPTURE = [1, 2, 4] + [
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_BATCH_SIZE_ALIGNMENT * i for i in range(1, 33)
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]
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@dataclass(frozen=True)
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class ModelInputForXPU(ModelRunnerInputBase):
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"""
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Used by the NeuronModelRunner.
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"""
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input_tokens: Optional[torch.Tensor] = None
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input_positions: Optional[torch.Tensor] = None
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attn_metadata: Optional["AttentionMetadata"] = None
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sampling_metadata: Optional["SamplingMetadata"] = None
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multi_modal_kwargs: Optional[BatchedTensorInputs] = None
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def as_broadcastable_tensor_dict(
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self) -> Dict[str, Union[int, torch.Tensor]]:
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tensor_dict = {
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"input_tokens": self.input_tokens,
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"input_positions": self.input_positions,
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}
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_add_attn_metadata_broadcastable_dict(tensor_dict, self.attn_metadata)
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_add_sampling_metadata_broadcastable_dict(tensor_dict,
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self.sampling_metadata)
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return tensor_dict
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@classmethod
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def from_broadcasted_tensor_dict(
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cls: Type["ModelInputForXPU"],
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tensor_dict: Dict[str, Any],
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attn_backend: Optional["AttentionBackend"] = None,
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) -> "ModelInputForXPU":
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tensor_dict = _init_sampling_metadata_from_tensor_dict(tensor_dict)
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if attn_backend is not None:
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tensor_dict = _init_attn_metadata_from_tensor_dict(
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attn_backend, tensor_dict)
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return cls(**tensor_dict)
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class XPUModelRunner(ModelRunnerBase[ModelInputForXPU]):
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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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cache_config: CacheConfig,
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load_config: LoadConfig,
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lora_config: Optional[LoRAConfig],
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multimodal_config: Optional[MultiModalConfig],
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kv_cache_dtype: Optional[str] = "auto",
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prompt_adapter_config: Optional[PromptAdapterConfig] = None,
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is_driver_worker: bool = False,
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input_registry: InputRegistry = INPUT_REGISTRY,
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mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
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*args,
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**kwargs,
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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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self.lora_config = lora_config
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self.load_config = load_config
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self.cache_config = cache_config
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self.prompt_adapter_config = prompt_adapter_config
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self.multimodal_config = multimodal_config
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self.is_driver_worker = is_driver_worker
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self.sliding_window = model_config.get_sliding_window()
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self.device_config = device_config
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self.device = self.device_config.device
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self.kv_cache_dtype = kv_cache_dtype
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self.block_size = cache_config.block_size
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self.attn_backend = get_attn_backend(
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self.model_config.get_num_attention_heads(self.parallel_config),
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self.model_config.get_head_size(),
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self.model_config.get_num_kv_heads(self.parallel_config),
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self.model_config.get_sliding_window(),
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self.model_config.dtype,
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self.kv_cache_dtype,
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self.block_size,
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)
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# Multi-modal data support
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self.input_registry = input_registry
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self.mm_registry = mm_registry
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self.multi_modal_input_mapper = mm_registry \
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.create_input_mapper(model_config)
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self.mm_registry.init_mm_limits_per_prompt(self.model_config)
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# Lazy initialization.
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self.model: nn.Module # Set after init_Model
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def load_model(self) -> None:
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with CudaMemoryProfiler() as m:
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self.model = get_model(
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model_config=self.model_config,
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device_config=self.device_config,
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load_config=self.load_config,
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lora_config=self.lora_config,
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parallel_config=self.parallel_config,
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scheduler_config=self.scheduler_config,
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cache_config=self.cache_config,
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)
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self.model_memory_usage = m.consumed_memory
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logger.info("Loading model weights took %.4f GB",
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self.model_memory_usage / float(2**30))
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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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@torch.inference_mode()
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def profile_run(self) -> None:
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# Enable top-k sampling to reflect the accurate memory usage.
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sampling_params = SamplingParams(top_p=0.99, top_k=self.vocab_size - 1)
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max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens
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max_num_seqs = self.scheduler_config.max_num_seqs
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# Profile memory usage with max_num_sequences sequences and the total
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# number of tokens equal to max_num_batched_tokens.
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seqs: List[SequenceGroupMetadata] = []
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# Additional GPU memory may be needed for multi-modal encoding, which
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# needs to be accounted for when calculating the GPU blocks for
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# vLLM blocker manager.
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# To exercise the worst scenario for GPU memory consumption,
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# the number of seqs (batch_size) is chosen to maximize the number
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# of images processed.
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max_mm_tokens = self.mm_registry.get_max_multimodal_tokens(
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self.model_config)
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if max_mm_tokens > 0:
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max_num_seqs_orig = max_num_seqs
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max_num_seqs = min(max_num_seqs,
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max_num_batched_tokens // max_mm_tokens)
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if max_num_seqs < 1:
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expr = (f"min({max_num_seqs_orig}, "
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f"{max_num_batched_tokens} // {max_mm_tokens})")
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logger.warning(
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"Computed max_num_seqs (%s) to be less than 1. "
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"Setting it to the minimum value of 1.", expr)
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max_num_seqs = 1
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for group_id in range(max_num_seqs):
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seq_len = (max_num_batched_tokens // max_num_seqs +
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(group_id < max_num_batched_tokens % max_num_seqs))
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seq_data, dummy_multi_modal_data = self.input_registry \
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.dummy_data_for_profiling(self.model_config,
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seq_len,
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self.mm_registry)
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seq = SequenceGroupMetadata(
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request_id=str(group_id),
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is_prompt=True,
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seq_data={group_id: seq_data},
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sampling_params=sampling_params,
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block_tables=None,
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lora_request=None,
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multi_modal_data=dummy_multi_modal_data,
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)
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seqs.append(seq)
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# Run the model with the dummy inputs.
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num_layers = self.model_config.get_num_layers(self.parallel_config)
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kv_caches = [None] * num_layers
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model_input = self.prepare_model_input(seqs)
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self.execute_model(model_input, kv_caches)
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torch.xpu.synchronize()
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return
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def make_model_input_from_broadcasted_tensor_dict(
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self, tensor_dict: Dict[str, Any]) -> ModelInputForXPU:
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return (ModelInputForXPU.from_broadcasted_tensor_dict(
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tensor_dict,
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attn_backend=self.attn_backend,
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))
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def prepare_model_input(
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self,
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seq_group_metadata_list: List[SequenceGroupMetadata],
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virtual_engine: int = 0,
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finished_requests_ids: Optional[List[str]] = None
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) -> ModelInputForXPU:
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multi_modal_kwargs = None
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if self.is_driver_worker:
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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, attn_metadata, seq_lens,
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multi_modal_kwargs
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) = self._prepare_prompt(seq_group_metadata_list)
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else:
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(input_tokens, input_positions,
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attn_metadata) = 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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# subquery_lens is not needed if chunked prefill is not
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# supported. Since CPU 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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pin_memory=False,
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generators=self.get_generators(finished_requests_ids))
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# Broadcast the metadata.
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metadata_dict = {
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"input_tokens": input_tokens,
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"input_positions": input_positions,
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"selected_token_indices":
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sampling_metadata.selected_token_indices,
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"multi_modal_kwargs": multi_modal_kwargs,
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}
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metadata_dict.update(attn_metadata.asdict_zerocopy())
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broadcast_tensor_dict(metadata_dict, src=0)
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else:
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metadata_dict = broadcast_tensor_dict(src=0)
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input_tokens = metadata_dict.pop("input_tokens")
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input_positions = metadata_dict.pop("input_positions")
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selected_token_indices = metadata_dict.pop(
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"selected_token_indices")
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multi_modal_kwargs = metadata_dict.pop("multi_modal_kwargs")
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attn_metadata = self.attn_backend.make_metadata(**metadata_dict)
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sampling_metadata = SamplingMetadata(
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seq_groups=None,
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selected_token_indices=selected_token_indices,
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categorized_sample_indices=None,
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num_prompts=0,
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)
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return ModelInputForXPU(input_tokens=input_tokens,
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input_positions=input_positions,
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attn_metadata=attn_metadata,
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sampling_metadata=sampling_metadata,
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multi_modal_kwargs=multi_modal_kwargs)
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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, AttentionMetadata]:
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assert len(seq_group_metadata_list) > 0
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input_tokens: List[int] = []
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input_positions: List[int] = []
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slot_mapping: List[int] = []
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seq_lens: List[int] = []
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block_tables: List[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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assert seq_group_metadata.token_chunk_size == 1
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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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seq_len = seq_len if self.sliding_window is None else min(
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seq_len, self.sliding_window)
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seq_lens.append(seq_len)
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block_table = seq_group_metadata.block_tables[seq_id]
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block_number = block_table[position // self.block_size]
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block_offset = position % self.block_size
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slot = block_number * self.block_size + block_offset
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slot_mapping.append(slot)
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if self.sliding_window is not None:
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sliding_window_blocks = (self.sliding_window //
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self.block_size)
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block_table = block_table[-sliding_window_blocks:]
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block_tables.append(block_table)
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max_decode_seq_len = max(seq_lens)
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input_tokens = torch.tensor(input_tokens,
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dtype=torch.long,
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device=self.device)
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input_positions = torch.tensor(input_positions,
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dtype=torch.long,
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device=self.device)
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slot_mapping = torch.tensor(slot_mapping,
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dtype=torch.long,
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device=self.device)
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seq_lens_tensor = torch.tensor(seq_lens,
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dtype=torch.int,
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device=self.device)
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block_tables = make_tensor_with_pad(
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block_tables,
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pad=0,
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dtype=torch.int,
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device=self.device,
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)
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attn_metadata = self.attn_backend.make_metadata(
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is_prompt=False,
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slot_mapping=slot_mapping,
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seq_lens=seq_lens,
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seqlen_q=None,
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max_seqlen=None,
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seq_lens_tensor=seq_lens_tensor,
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max_decode_seq_len=max_decode_seq_len,
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num_prefill_tokens=0,
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num_decode_tokens=len(input_tokens),
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num_prefills=0,
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block_tables=block_tables,
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)
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return (
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input_tokens,
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input_positions,
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attn_metadata,
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)
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@torch.inference_mode()
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def execute_model(
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self,
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model_input: ModelInputForXPU,
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kv_caches: List[torch.Tensor],
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intermediate_tensors: Optional[IntermediateTensors] = None,
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num_steps: int = 1,
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) -> Optional[List[SamplerOutput]]:
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if num_steps > 1:
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raise ValueError(
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"XPUModelRunner does not support multi-step execution.")
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model_executable = self.model
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execute_model_kwargs = {
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"input_ids":
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model_input.input_tokens,
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"positions":
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model_input.input_positions,
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"kv_caches":
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kv_caches,
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"attn_metadata":
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model_input.attn_metadata,
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**MultiModalInputs.as_kwargs(model_input.multi_modal_kwargs or {},
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device=self.device),
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}
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hidden_states = model_executable(**execute_model_kwargs)
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# Compute the logits.
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logits = self.model.compute_logits(hidden_states,
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model_input.sampling_metadata)
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# Only perform sampling in the driver worker.
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if not self.is_driver_worker:
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return []
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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=model_input.sampling_metadata,
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)
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return [output]
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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, AttentionMetadata, List[int],
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BatchedTensorInputs]:
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assert len(seq_group_metadata_list) > 0
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input_tokens: List[int] = []
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input_positions: List[int] = []
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slot_mapping: List[int] = []
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seq_lens: List[int] = []
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multi_modal_inputs_list: List[MultiModalInputs] = []
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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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computed_len = seq_data.get_num_computed_tokens()
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seq_len = len(prompt_tokens)
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seq_lens.append(seq_len) # Prompt token num
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input_tokens.extend(prompt_tokens) # Token ids
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# Token position ids
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# NOTE(woosuk): Here we assume that the first token in the prompt
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# is always the first token in the sequence.
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input_positions.extend(list(range(computed_len, seq_len)))
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mm_data = seq_group_metadata.multi_modal_data
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if mm_data:
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mm_kwargs = self.multi_modal_input_mapper(mm_data)
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multi_modal_inputs_list.append(mm_kwargs)
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if seq_group_metadata.block_tables is None:
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# During memory profiling, the block tables are not initialized
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# yet. In this case, we just use a dummy slot mapping.
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slot_mapping.extend([_PAD_SLOT_ID] * seq_len)
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continue
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# Compute the slot mapping.
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block_table = seq_group_metadata.block_tables[seq_id]
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# Mask the [0, start_idx) tokens of the prompt with _PAD_SLOT_ID,
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# where start_idx is max(0, seq_len - sliding_window).
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# For example, if the prompt len is 10, sliding window is 8, and
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# block size is 4, the first two tokens are masked and the slot
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# mapping will be [-1, -1, 2, 3, 4, 5, 6, 7, 0, 1].
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start_idx = 0
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if self.sliding_window is not None:
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start_idx = max(0, seq_len - self.sliding_window)
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for i in range(computed_len, seq_len):
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if i < start_idx:
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slot_mapping.append(_PAD_SLOT_ID)
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continue
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block_number = block_table[i //
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self.block_size] # type: ignore
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block_offset = i % self.block_size # type: ignore
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slot = block_number * self.block_size + block_offset
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slot_mapping.append(slot)
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num_prompt_tokens = len(input_tokens)
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input_tokens = torch.tensor(input_tokens,
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dtype=torch.long,
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device=self.device) # type: ignore
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input_positions = torch.tensor(input_positions,
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dtype=torch.long,
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device=self.device) # type: ignore
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slot_mapping = torch.tensor(slot_mapping,
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dtype=torch.long,
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device=self.device) # type: ignore
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|
|
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max_seqlen = max(seq_lens)
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tmp = [0]
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|
tmp.extend(seq_lens)
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seqlen = torch.tensor(tmp)
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|
seqlen_q = torch.cumsum(seqlen, dim=0).to(device=self.device)
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|
|
|
attn_metadata = self.attn_backend.make_metadata(
|
|
is_prompt=True,
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|
slot_mapping=slot_mapping,
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|
seq_lens=seq_lens,
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|
seqlen_q=seqlen_q,
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|
max_seqlen=max_seqlen,
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|
seq_lens_tensor=None,
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|
max_decode_seq_len=None,
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|
num_prefills=len(seq_lens),
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|
num_prefill_tokens=num_prompt_tokens,
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|
num_decode_tokens=0,
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|
block_tables=torch.tensor([], device=self.device, dtype=torch.int),
|
|
)
|
|
|
|
multi_modal_kwargs = MultiModalInputs.batch(multi_modal_inputs_list)
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|
|
|
return (input_tokens, input_positions, attn_metadata, seq_lens,
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|
multi_modal_kwargs)
|