1173 lines
49 KiB
Python
1173 lines
49 KiB
Python
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import contextlib
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import time
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from enum import IntEnum
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from typing import Dict, List, NamedTuple, Optional, Set, Tuple
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import numpy as np
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import torch
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import torch_musa
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import torch.nn as nn
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from vllm.attention import (AttentionMetadata, AttentionMetadataPerStage,
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get_attn_backend)
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from vllm.attention.backends.flashinfer import FlashInferBackend
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from vllm.config import (DeviceConfig, LoadConfig, LoRAConfig, ModelConfig,
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ParallelConfig, SchedulerConfig, VisionLanguageConfig)
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from vllm.distributed import broadcast_tensor_dict, with_pynccl_for_all_reduce
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from vllm.distributed.device_communicators import (custom_all_reduce,
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pymccl_utils)
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from vllm.logger import init_logger
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from vllm.lora.layers import LoRAMapping
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from vllm.lora.request import LoRARequest
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from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager
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from vllm.model_executor import SamplingMetadata
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from vllm.model_executor.model_loader import get_model
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from vllm.sampling_params import SamplingParams
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from vllm.sequence import (MultiModalData, SamplerOutput, SequenceData,
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SequenceGroupMetadata)
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from vllm.utils import (CudaMemoryProfiler, get_kv_cache_torch_dtype, is_hip,
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is_pin_memory_available, make_tensor_with_pad)
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logger = init_logger(__name__)
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_PAD_SLOT_ID = -1
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LORA_WARMUP_RANK = 8
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_BATCH_SIZE_ALIGNMENT = 8
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# Capture graphs for token size 1, 2, 4, 8, 16, 24, 32, 40, ..., 256.
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# NOTE: _get_graph_batch_size needs to be updated if this list is changed.
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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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class PreparePromptMetadata(NamedTuple):
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input_tokens: List[int]
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input_positions: List[int]
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attn_metadata: Optional[AttentionMetadataPerStage]
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seq_lens: List[int]
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query_lens: List[int]
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lora_index_mapping: List[int]
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lora_prompt_mapping: List[int]
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lora_requests: Set[LoRARequest]
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multi_modal_input: Optional[torch.Tensor]
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slot_mapping: List[int]
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@classmethod
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def empty(cls):
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return PreparePromptMetadata(
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input_tokens=[],
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input_positions=[],
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attn_metadata=None,
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seq_lens=[],
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query_lens=[],
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lora_index_mapping=[],
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lora_prompt_mapping=[],
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lora_requests=set(),
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multi_modal_input=None,
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slot_mapping=[],
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)
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class PrepareDecodeMetadata(NamedTuple):
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input_tokens: List[int]
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input_positions: List[int]
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attn_metadata: Optional[AttentionMetadata]
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lora_index_mapping: List[int]
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lora_prompt_mapping: List[int]
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lora_requests: Set[LoRARequest]
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slot_mapping: List[int]
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@classmethod
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def empty(cls):
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return PrepareDecodeMetadata(
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input_tokens=[],
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input_positions=[],
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attn_metadata=None,
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lora_index_mapping=[],
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lora_prompt_mapping=[],
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lora_requests=set(),
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slot_mapping=[],
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)
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# How batches are constructed.
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class BatchType(IntEnum):
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# Every batch is prefill.
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PREFILL = 0
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# Every batch is decode.
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DECODE = 1
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# Batch is a mixture of prefill and decode.
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MIXED = 2
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class ModelRunner:
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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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load_config: LoadConfig,
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lora_config: Optional[LoRAConfig],
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kv_cache_dtype: Optional[str] = "auto",
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is_driver_worker: bool = False,
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vision_language_config: Optional[VisionLanguageConfig] = None,
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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.is_driver_worker = is_driver_worker
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# model_config can be None in tests/samplers/test_sampler.py.
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# FIXME(woosuk): This is a hack to make the tests work. Refactor this.
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self.sliding_window = (model_config.get_sliding_window()
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if model_config is not None else None)
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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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# Set after load_model.
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self.lora_manager: LRUCacheWorkerLoRAManager = None
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self.graph_runners: Dict[int, CUDAGraphRunner] = {}
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self.graph_memory_pool: Optional[Tuple[
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int, int]] = None # Set during graph capture.
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self.max_seq_len_to_capture = (self.model_config.max_seq_len_to_capture
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if self.model_config is not None else 0)
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self.pin_memory = is_pin_memory_available()
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self.kv_cache_dtype = kv_cache_dtype
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self.vision_language_config = vision_language_config
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self.attn_backend = get_attn_backend(
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self.model_config.dtype if model_config is not None else None)
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# Lazy initialization
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self.model: torch.nn.Module # Set after load_model
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self.block_size: int # Set after initial profiling.
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# When using CUDA graph, the input block tables must be padded to
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# max_seq_len_to_capture. However, creating the block table in
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# Python can be expensive. To optimize this, we cache the block table
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# in numpy and only copy the actual input content at every iteration.
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# The shape of the cached block table will be
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# (max batch size to capture, max context len to capture / block size).
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self.graph_block_tables: torch.Tensor # Set after initial profiling.
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# Set if the backend is flashinfer.
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self.flashinfer_workspace_buffer: torch.Tensor
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def load_model(self) -> None:
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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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vision_language_config=self.vision_language_config,
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parallel_config=self.parallel_config,
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scheduler_config=self.scheduler_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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if self.lora_config:
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assert hasattr(self.model, "supported_lora_modules"
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) and self.model.supported_lora_modules, (
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"Model does not support LoRA")
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assert hasattr(
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self.model,
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"embedding_modules"), "Model does not have embedding_modules"
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assert hasattr(self.model, "embedding_padding_modules"
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), "Model does not have embedding_padding_modules"
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self.lora_manager = LRUCacheWorkerLoRAManager(
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self.scheduler_config.max_num_seqs,
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self.scheduler_config.max_num_batched_tokens, self.vocab_size,
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self.lora_config, self.device, self.model.embedding_modules,
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self.model.embedding_padding_modules)
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self.model = self.lora_manager.create_lora_manager(self.model)
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if self.kv_cache_dtype == "fp8" and is_hip():
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# Currently scaled KV cache is only enabled on ROCm
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if self.model_config.quantization_param_path is not None:
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if callable(getattr(self.model, "load_kv_cache_scales", None)):
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self.model.load_kv_cache_scales(
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self.model_config.quantization_param_path)
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else:
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raise RuntimeError(
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"Using FP8 KV cache and scaling factors provided but "
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"model %s does not support loading scaling factors.",
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self.model.__class__)
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else:
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logger.warning(
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"Using FP8 KV cache but no scaling factors "
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"provided. Defaulting to scaling factors of 1.0. "
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"This may lead to less accurate results!")
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elif self.model_config.quantization_param_path is not None:
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logger.warning("KV cache scaling factors provided, "
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"but the KV cache data type is not FP8. "
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"KV cache scaling factors will not be used.")
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def set_block_size(self, block_size: int) -> None:
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self.block_size = block_size
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self.graph_block_tables = np.zeros(
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(max(_BATCH_SIZES_TO_CAPTURE), self.get_max_block_per_batch()),
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dtype=np.int32)
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def get_max_block_per_batch(self) -> int:
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block_size = self.block_size
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return (self.max_seq_len_to_capture + block_size - 1) // block_size
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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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) -> PreparePromptMetadata:
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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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lora_index_mapping: List[int] = []
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lora_prompt_mapping: List[int] = []
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lora_requests: Set[LoRARequest] = set()
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seq_lens: List[int] = []
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context_lens: List[int] = []
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query_lens: List[int] = []
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prefix_block_tables: List[List[int]] = []
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multi_modal_input_list: List[torch.Tensor] = []
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if len(seq_group_metadata_list) == 0:
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return PreparePromptMetadata.empty()
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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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computed_block_nums = seq_group_metadata.computed_block_nums
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if (self.scheduler_config is not None
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and self.scheduler_config.chunked_prefill_enabled
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and not (computed_block_nums is None
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or computed_block_nums == [])):
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raise RuntimeError(
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"chunked prefill cannot be used with prefix caching "
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"now.")
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token_chunk_size = seq_group_metadata.token_chunk_size
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seq_data = seq_group_metadata.seq_data[seq_id]
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context_len = seq_data.get_num_computed_tokens()
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# We should use get_len here because in case of preemption
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# it contains output tokens.
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seq_len = min(seq_data.get_len(), context_len + token_chunk_size)
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prompt_tokens = seq_data.get_token_ids()[context_len:seq_len]
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seq_lens.append(seq_len)
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# NOTE: This only works for oooooooxxx style attention.
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if computed_block_nums is not None and len(
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computed_block_nums) > 0 and self.sliding_window is None:
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# Prefix is not supported with sliding_window
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context_len = len(computed_block_nums) * self.block_size
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prompt_tokens = prompt_tokens[context_len:]
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prefix_block_tables.append(computed_block_nums)
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elif self.scheduler_config.chunked_prefill_enabled:
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if seq_group_metadata.block_tables is not None:
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# Prefill has chunked before.
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block_table = seq_group_metadata.block_tables[seq_id]
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prefix_block_tables.append(block_table)
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else:
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# The first prefill.
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prefix_block_tables.append([])
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else:
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prefix_block_tables.append([])
|
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# Right now, prefill start is always 0. However, this
|
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# assumption can be changed once chunked prefill is introduced.
|
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assert context_len == 0
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# actual prompt lens
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context_lens.append(context_len)
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query_lens.append(seq_len - context_len)
|
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|
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input_tokens.extend(prompt_tokens)
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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(context_len, seq_len)))
|
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lora_id = seq_group_metadata.lora_int_id
|
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|
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|
|||
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if lora_id > 0:
|
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lora_requests.add(seq_group_metadata.lora_request)
|
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lora_index_mapping += [lora_id] * (seq_len - context_len)
|
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lora_prompt_mapping.extend(
|
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[lora_id] *
|
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|
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(seq_len - context_len
|
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|
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if seq_group_metadata.sampling_params.prompt_logprobs else 1))
|
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|
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|
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if seq_group_metadata.multi_modal_data:
|
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multi_modal_input_list.append(
|
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seq_group_metadata.multi_modal_data.data)
|
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|
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|
|||
|
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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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|
|||
|
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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,
|
|||
|
|
# where start_idx is max(0, seq_len - sliding_window).
|
|||
|
|
# For example, if the prompt len is 10, sliding window is 8, and
|
|||
|
|
# block size is 4, the first two tokens are masked and the slot
|
|||
|
|
# mapping will be [-1, -1, 2, 3, 4, 5, 6, 7, 0, 1].
|
|||
|
|
start_idx = 0
|
|||
|
|
if self.sliding_window is not None:
|
|||
|
|
assert context_len == 0, (
|
|||
|
|
"Prefix caching is currently not supported with "
|
|||
|
|
"sliding window attention")
|
|||
|
|
start_idx = max(0, seq_len - self.sliding_window)
|
|||
|
|
|
|||
|
|
for i in range(context_len, seq_len):
|
|||
|
|
if i < start_idx:
|
|||
|
|
slot_mapping.append(_PAD_SLOT_ID)
|
|||
|
|
continue
|
|||
|
|
|
|||
|
|
block_number = block_table[i // self.block_size]
|
|||
|
|
block_offset = i % self.block_size
|
|||
|
|
slot = block_number * self.block_size + block_offset
|
|||
|
|
slot_mapping.append(slot)
|
|||
|
|
|
|||
|
|
max_query_len = max(query_lens)
|
|||
|
|
max_seq_len = max(seq_lens)
|
|||
|
|
assert max_query_len > 0
|
|||
|
|
|
|||
|
|
context_lens_tensor = torch.tensor(context_lens,
|
|||
|
|
dtype=torch.int,
|
|||
|
|
device=self.device)
|
|||
|
|
|
|||
|
|
if multi_modal_input_list:
|
|||
|
|
assert self.vision_language_config, (
|
|||
|
|
"Multi-modal inputs are only supported by "
|
|||
|
|
"vision language models.")
|
|||
|
|
multi_modal_input = torch.cat(multi_modal_input_list,
|
|||
|
|
dim=0).to(self.device)
|
|||
|
|
else:
|
|||
|
|
multi_modal_input = None
|
|||
|
|
|
|||
|
|
# Prepare prefix block tables
|
|||
|
|
max_prompt_block_table_len = max(len(t) for t in prefix_block_tables)
|
|||
|
|
block_tables = make_tensor_with_pad(
|
|||
|
|
prefix_block_tables,
|
|||
|
|
max_len=max_prompt_block_table_len,
|
|||
|
|
pad=0,
|
|||
|
|
dtype=torch.int,
|
|||
|
|
device=self.device,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# Query length can be shorter than key (i.e., prompt) when prefill
|
|||
|
|
# is chunked or prefix cached.
|
|||
|
|
query_lens_tensor = torch.tensor(query_lens,
|
|||
|
|
dtype=torch.long,
|
|||
|
|
device=self.device)
|
|||
|
|
subquery_start_loc = torch.zeros(query_lens_tensor.shape[0] + 1,
|
|||
|
|
dtype=torch.long,
|
|||
|
|
device=self.device)
|
|||
|
|
|
|||
|
|
seq_lens_tensor = torch.tensor(seq_lens,
|
|||
|
|
dtype=torch.int,
|
|||
|
|
device=self.device)
|
|||
|
|
seq_start_loc = torch.zeros(seq_lens_tensor.shape[0] + 1,
|
|||
|
|
dtype=torch.long,
|
|||
|
|
device=self.device)
|
|||
|
|
|
|||
|
|
torch.cumsum(query_lens_tensor,
|
|||
|
|
dim=0,
|
|||
|
|
dtype=subquery_start_loc.dtype,
|
|||
|
|
out=subquery_start_loc[1:])
|
|||
|
|
|
|||
|
|
torch.cumsum(seq_lens_tensor,
|
|||
|
|
dim=0,
|
|||
|
|
dtype=seq_start_loc.dtype,
|
|||
|
|
out=seq_start_loc[1:])
|
|||
|
|
subquery_start_loc = subquery_start_loc.int()
|
|||
|
|
seq_start_loc = seq_start_loc.int()
|
|||
|
|
|
|||
|
|
if self.attn_backend is FlashInferBackend:
|
|||
|
|
attn_metadata = self.attn_backend.make_metadata(
|
|||
|
|
is_prompt=True,
|
|||
|
|
use_cuda_graph=False,
|
|||
|
|
seq_start_loc=seq_start_loc,
|
|||
|
|
max_seq_len=max_seq_len,
|
|||
|
|
block_tables=block_tables)
|
|||
|
|
else:
|
|||
|
|
attn_metadata = self.attn_backend.make_metadata(
|
|||
|
|
is_prompt=True,
|
|||
|
|
seq_lens=seq_lens,
|
|||
|
|
seq_lens_tensor=seq_lens_tensor,
|
|||
|
|
max_query_len=max_query_len,
|
|||
|
|
max_seq_len=max_seq_len,
|
|||
|
|
subquery_start_loc=subquery_start_loc,
|
|||
|
|
seq_start_loc=seq_start_loc,
|
|||
|
|
context_lens_tensor=context_lens_tensor,
|
|||
|
|
block_tables=block_tables,
|
|||
|
|
use_cuda_graph=False,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
return PreparePromptMetadata(
|
|||
|
|
input_tokens=input_tokens,
|
|||
|
|
input_positions=input_positions,
|
|||
|
|
attn_metadata=attn_metadata,
|
|||
|
|
seq_lens=seq_lens,
|
|||
|
|
query_lens=query_lens,
|
|||
|
|
lora_index_mapping=lora_index_mapping,
|
|||
|
|
lora_prompt_mapping=lora_prompt_mapping,
|
|||
|
|
lora_requests=lora_requests,
|
|||
|
|
multi_modal_input=multi_modal_input,
|
|||
|
|
slot_mapping=slot_mapping,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
def _prepare_decode(
|
|||
|
|
self,
|
|||
|
|
seq_group_metadata_list: List[SequenceGroupMetadata],
|
|||
|
|
) -> PrepareDecodeMetadata:
|
|||
|
|
input_tokens: List[int] = []
|
|||
|
|
input_positions: List[int] = []
|
|||
|
|
slot_mapping: List[int] = []
|
|||
|
|
seq_lens: List[int] = []
|
|||
|
|
block_tables: List[List[int]] = []
|
|||
|
|
lora_index_mapping: List[int] = []
|
|||
|
|
lora_prompt_mapping: List[int] = []
|
|||
|
|
lora_requests: Set[LoRARequest] = set()
|
|||
|
|
|
|||
|
|
# The following fields are only for flashinfer
|
|||
|
|
# Please follow https://docs.flashinfer.ai/tutorials/kv_layout.html#page-layout
|
|||
|
|
# for the precise definition of the following fields.
|
|||
|
|
# An example:
|
|||
|
|
# request 1, page indices [0, 5, 8]
|
|||
|
|
# request 2, page indices [1, 6, 7]
|
|||
|
|
# request 3, page indices [3, 4]
|
|||
|
|
# paged_kv_indices is a concatenation of page indices of all requests:
|
|||
|
|
# [0, 5, 8, 1, 6, 7, 3, 4]
|
|||
|
|
# paged_kv_indptr is used to index into paged_kv_indices:
|
|||
|
|
# [0, 3, 6, 8]
|
|||
|
|
paged_kv_indices: List[int] = []
|
|||
|
|
# 0 at the beginning of paged_kv_indptr indicates the start of the
|
|||
|
|
# first request’s page indices in the paged_kv_indices list.
|
|||
|
|
paged_kv_indptr: List[int] = [0]
|
|||
|
|
# paged_kv_last_page_len is the length of the last page of each request
|
|||
|
|
paged_kv_last_page_len: List[int] = []
|
|||
|
|
|
|||
|
|
if len(seq_group_metadata_list) == 0:
|
|||
|
|
return PrepareDecodeMetadata.empty()
|
|||
|
|
|
|||
|
|
for seq_group_metadata in seq_group_metadata_list:
|
|||
|
|
assert not seq_group_metadata.is_prompt
|
|||
|
|
assert seq_group_metadata.token_chunk_size == 1
|
|||
|
|
|
|||
|
|
seq_ids = list(seq_group_metadata.seq_data.keys())
|
|||
|
|
lora_id = seq_group_metadata.lora_int_id
|
|||
|
|
|
|||
|
|
if lora_id > 0:
|
|||
|
|
lora_requests.add(seq_group_metadata.lora_request)
|
|||
|
|
|
|||
|
|
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)
|
|||
|
|
|
|||
|
|
seq_len = seq_len if self.sliding_window is None else min(
|
|||
|
|
seq_len, self.sliding_window)
|
|||
|
|
seq_lens.append(seq_len)
|
|||
|
|
|
|||
|
|
block_table = seq_group_metadata.block_tables[seq_id]
|
|||
|
|
block_number = block_table[position // self.block_size]
|
|||
|
|
block_offset = position % self.block_size
|
|||
|
|
slot = block_number * self.block_size + block_offset
|
|||
|
|
slot_mapping.append(slot)
|
|||
|
|
lora_index_mapping.append(lora_id)
|
|||
|
|
lora_prompt_mapping.append(lora_id)
|
|||
|
|
|
|||
|
|
if self.sliding_window is not None:
|
|||
|
|
sliding_window_blocks = (self.sliding_window //
|
|||
|
|
self.block_size)
|
|||
|
|
block_table = block_table[-sliding_window_blocks:]
|
|||
|
|
block_tables.append(block_table)
|
|||
|
|
|
|||
|
|
paged_kv_indices.extend(block_table)
|
|||
|
|
paged_kv_indptr.append(paged_kv_indptr[-1] + len(block_table))
|
|||
|
|
last_page_len = seq_data.get_len() % self.block_size
|
|||
|
|
if last_page_len == 0:
|
|||
|
|
last_page_len = self.block_size
|
|||
|
|
paged_kv_last_page_len.append(last_page_len)
|
|||
|
|
|
|||
|
|
# vLLM uses cuda graph only for decoding requests.
|
|||
|
|
# See `capture_model` API for more details.
|
|||
|
|
# For decoding requests, batch_size == input_tokens.
|
|||
|
|
batch_size = len(input_tokens)
|
|||
|
|
max_seq_len = max(seq_lens)
|
|||
|
|
# use_captured_graph = (not self.model_config.enforce_eager
|
|||
|
|
# and batch_size <= _BATCH_SIZES_TO_CAPTURE[-1]
|
|||
|
|
# and max_seq_len <= self.max_seq_len_to_capture)
|
|||
|
|
use_captured_graph = False
|
|||
|
|
if use_captured_graph:
|
|||
|
|
graph_batch_size = _get_graph_batch_size(batch_size)
|
|||
|
|
assert graph_batch_size >= batch_size
|
|||
|
|
for _ in range(graph_batch_size - batch_size):
|
|||
|
|
input_tokens.append(0)
|
|||
|
|
input_positions.append(0)
|
|||
|
|
slot_mapping.append(_PAD_SLOT_ID)
|
|||
|
|
seq_lens.append(1)
|
|||
|
|
block_tables.append([])
|
|||
|
|
lora_index_mapping.append(0)
|
|||
|
|
batch_size = graph_batch_size
|
|||
|
|
|
|||
|
|
seq_lens_tensor = torch.tensor(seq_lens,
|
|||
|
|
dtype=torch.int,
|
|||
|
|
device=self.device)
|
|||
|
|
|
|||
|
|
if use_captured_graph:
|
|||
|
|
# When using cuda-graph all these tensors should be
|
|||
|
|
# padded.
|
|||
|
|
assert seq_lens_tensor.shape[0] == len(input_tokens)
|
|||
|
|
assert seq_lens_tensor.shape[0] == len(input_positions)
|
|||
|
|
assert seq_lens_tensor.shape[0] == len(slot_mapping)
|
|||
|
|
|
|||
|
|
# The shape of graph_block_tables is
|
|||
|
|
# [max batch size, max context len // block size].
|
|||
|
|
input_block_tables = self.graph_block_tables[:batch_size]
|
|||
|
|
for i, block_table in enumerate(block_tables):
|
|||
|
|
if block_table:
|
|||
|
|
input_block_tables[i, :len(block_table)] = block_table
|
|||
|
|
block_tables = torch.tensor(input_block_tables, device=self.device)
|
|||
|
|
else:
|
|||
|
|
max_block_table_len = max(
|
|||
|
|
len(block_table) for block_table in block_tables)
|
|||
|
|
block_tables = make_tensor_with_pad(
|
|||
|
|
block_tables,
|
|||
|
|
max_len=max_block_table_len,
|
|||
|
|
pad=0,
|
|||
|
|
dtype=torch.int,
|
|||
|
|
device=self.device,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
if self.attn_backend is FlashInferBackend:
|
|||
|
|
if not hasattr(self, "flashinfer_workspace_buffer"):
|
|||
|
|
# Allocate 16MB workspace buffer
|
|||
|
|
# Follow the example of flashinfer: https://docs.flashinfer.ai/api/python/decode.html
|
|||
|
|
self.flashinfer_workspace_buffer = torch.empty(
|
|||
|
|
16 * 1024 * 1024, dtype=torch.uint8, device=self.device)
|
|||
|
|
paged_kv_indptr = torch.tensor(paged_kv_indptr,
|
|||
|
|
dtype=torch.int,
|
|||
|
|
device=self.device)
|
|||
|
|
paged_kv_indices = torch.tensor(paged_kv_indices,
|
|||
|
|
dtype=torch.int,
|
|||
|
|
device=self.device)
|
|||
|
|
paged_kv_last_page_len = torch.tensor(paged_kv_last_page_len,
|
|||
|
|
dtype=torch.int,
|
|||
|
|
device=self.device)
|
|||
|
|
kv_cache_dtype = get_kv_cache_torch_dtype(self.kv_cache_dtype,
|
|||
|
|
self.model_config.dtype)
|
|||
|
|
|
|||
|
|
attn_metadata = self.attn_backend.make_metadata(
|
|||
|
|
is_prompt=False,
|
|||
|
|
use_cuda_graph=False,
|
|||
|
|
workspace_buffer=self.flashinfer_workspace_buffer,
|
|||
|
|
paged_kv_indptr=paged_kv_indptr,
|
|||
|
|
paged_kv_indices=paged_kv_indices,
|
|||
|
|
paged_kv_last_page_len=paged_kv_last_page_len,
|
|||
|
|
num_qo_heads=self.model_config.get_num_attention_heads(
|
|||
|
|
self.parallel_config),
|
|||
|
|
num_kv_heads=self.model_config.get_num_kv_heads(
|
|||
|
|
self.parallel_config),
|
|||
|
|
head_dim=self.model_config.get_head_size(),
|
|||
|
|
page_size=self.block_size,
|
|||
|
|
data_type=kv_cache_dtype)
|
|||
|
|
else:
|
|||
|
|
attn_metadata = self.attn_backend.make_metadata(
|
|||
|
|
is_prompt=False,
|
|||
|
|
seq_lens=None,
|
|||
|
|
seq_lens_tensor=seq_lens_tensor,
|
|||
|
|
max_seq_len=max_seq_len,
|
|||
|
|
max_query_len=None,
|
|||
|
|
subquery_start_loc=None,
|
|||
|
|
seq_start_loc=None,
|
|||
|
|
context_lens_tensor=None,
|
|||
|
|
block_tables=block_tables,
|
|||
|
|
use_cuda_graph=use_captured_graph,
|
|||
|
|
)
|
|||
|
|
return PrepareDecodeMetadata(
|
|||
|
|
input_tokens=input_tokens,
|
|||
|
|
input_positions=input_positions,
|
|||
|
|
attn_metadata=attn_metadata,
|
|||
|
|
lora_index_mapping=lora_index_mapping,
|
|||
|
|
lora_prompt_mapping=lora_prompt_mapping,
|
|||
|
|
lora_requests=lora_requests,
|
|||
|
|
slot_mapping=slot_mapping,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
def prepare_input_tensors(
|
|||
|
|
self,
|
|||
|
|
seq_group_metadata_list: List[SequenceGroupMetadata],
|
|||
|
|
) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata, SamplingMetadata,
|
|||
|
|
Set[LoRARequest], LoRAMapping, torch.Tensor]:
|
|||
|
|
if self.is_driver_worker:
|
|||
|
|
prefill_reqs = []
|
|||
|
|
decode_reqs = []
|
|||
|
|
for seq_group_meta in seq_group_metadata_list:
|
|||
|
|
if seq_group_meta.is_prompt:
|
|||
|
|
prefill_reqs.append(seq_group_meta)
|
|||
|
|
else:
|
|||
|
|
decode_reqs.append(seq_group_meta)
|
|||
|
|
|
|||
|
|
# Prepare input tensors.
|
|||
|
|
(
|
|||
|
|
input_tokens,
|
|||
|
|
input_positions,
|
|||
|
|
prefill_attn_metadata,
|
|||
|
|
seq_lens,
|
|||
|
|
query_lens,
|
|||
|
|
lora_index_mapping,
|
|||
|
|
lora_prompt_mapping,
|
|||
|
|
lora_requests,
|
|||
|
|
multi_modal_input,
|
|||
|
|
slot_mapping,
|
|||
|
|
) = self._prepare_prompt(prefill_reqs)
|
|||
|
|
(
|
|||
|
|
decode_input_tokens,
|
|||
|
|
decode_input_positions,
|
|||
|
|
decode_attn_metadata,
|
|||
|
|
decode_lora_index_mapping,
|
|||
|
|
decode_lora_prompt_mapping,
|
|||
|
|
decode_lora_requests,
|
|||
|
|
decode_slot_mapping,
|
|||
|
|
) = self._prepare_decode(decode_reqs)
|
|||
|
|
sampling_metadata = SamplingMetadata.prepare(
|
|||
|
|
seq_group_metadata_list, seq_lens, query_lens, self.device,
|
|||
|
|
self.pin_memory)
|
|||
|
|
|
|||
|
|
if not self.scheduler_config.chunked_prefill_enabled:
|
|||
|
|
assert (len(prefill_reqs) and len(decode_reqs)) == 0
|
|||
|
|
|
|||
|
|
num_prefills = len(seq_lens)
|
|||
|
|
num_prefill_tokens = len(input_tokens)
|
|||
|
|
num_decode_tokens = len(decode_input_tokens)
|
|||
|
|
|
|||
|
|
# Coalesce tensors. Note that attn_metadata is currently not
|
|||
|
|
# coalesced for simplicity.
|
|||
|
|
input_tokens.extend(decode_input_tokens)
|
|||
|
|
input_positions.extend(decode_input_positions)
|
|||
|
|
slot_mapping.extend(decode_slot_mapping)
|
|||
|
|
lora_index_mapping.extend(decode_lora_index_mapping)
|
|||
|
|
lora_prompt_mapping.extend(decode_lora_prompt_mapping)
|
|||
|
|
lora_requests.update(decode_lora_requests)
|
|||
|
|
|
|||
|
|
input_tokens = torch.tensor(input_tokens,
|
|||
|
|
dtype=torch.long,
|
|||
|
|
device=self.device)
|
|||
|
|
input_positions = torch.tensor(input_positions,
|
|||
|
|
dtype=torch.long,
|
|||
|
|
device=self.device)
|
|||
|
|
slot_mapping = torch.tensor(slot_mapping,
|
|||
|
|
dtype=torch.long,
|
|||
|
|
device=self.device)
|
|||
|
|
|
|||
|
|
if self.lora_config:
|
|||
|
|
lora_mapping = LoRAMapping(
|
|||
|
|
lora_index_mapping,
|
|||
|
|
lora_prompt_mapping,
|
|||
|
|
)
|
|||
|
|
else:
|
|||
|
|
lora_mapping = None
|
|||
|
|
|
|||
|
|
# Broadcast the metadata.
|
|||
|
|
# If batch contains both prefill and decode, it sends 2 broadcasts.
|
|||
|
|
# If it only contains 1 type, it triggers a single broadcast.
|
|||
|
|
if (prefill_attn_metadata is not None
|
|||
|
|
and decode_attn_metadata is not None):
|
|||
|
|
batch_type = BatchType.MIXED
|
|||
|
|
elif prefill_attn_metadata is not None:
|
|||
|
|
batch_type = BatchType.PREFILL
|
|||
|
|
else:
|
|||
|
|
batch_type = BatchType.DECODE
|
|||
|
|
|
|||
|
|
metadata_dict = {
|
|||
|
|
"input_tokens": input_tokens,
|
|||
|
|
"input_positions": input_positions,
|
|||
|
|
"selected_token_indices":
|
|||
|
|
sampling_metadata.selected_token_indices,
|
|||
|
|
"lora_requests": lora_requests,
|
|||
|
|
"lora_mapping": lora_mapping,
|
|||
|
|
"multi_modal_input": multi_modal_input,
|
|||
|
|
"num_prefill_tokens": num_prefill_tokens,
|
|||
|
|
"num_decode_tokens": num_decode_tokens,
|
|||
|
|
"slot_mapping": slot_mapping,
|
|||
|
|
"num_prefills": num_prefills,
|
|||
|
|
"batch_type": batch_type,
|
|||
|
|
}
|
|||
|
|
if prefill_attn_metadata is not None:
|
|||
|
|
metadata_dict.update(prefill_attn_metadata.asdict_zerocopy())
|
|||
|
|
else:
|
|||
|
|
assert decode_attn_metadata is not None
|
|||
|
|
metadata_dict.update(decode_attn_metadata.asdict_zerocopy())
|
|||
|
|
broadcast_tensor_dict(metadata_dict, src=0)
|
|||
|
|
|
|||
|
|
# Broadcast decode attn metadata for mixed batch type.
|
|||
|
|
# The additional broadcast costs 300us overhead on 4 A10 GPUs.
|
|||
|
|
# We can potentially reduce the overhead by coelescing tensors.
|
|||
|
|
if batch_type == BatchType.MIXED:
|
|||
|
|
assert decode_attn_metadata is not None
|
|||
|
|
metadata_dict = decode_attn_metadata.asdict_zerocopy()
|
|||
|
|
broadcast_tensor_dict(metadata_dict, src=0)
|
|||
|
|
else:
|
|||
|
|
metadata_dict = broadcast_tensor_dict(src=0)
|
|||
|
|
input_tokens = metadata_dict.pop("input_tokens")
|
|||
|
|
input_positions = metadata_dict.pop("input_positions")
|
|||
|
|
slot_mapping = metadata_dict.pop("slot_mapping")
|
|||
|
|
num_prefills = metadata_dict.pop("num_prefills")
|
|||
|
|
selected_token_indices = metadata_dict.pop(
|
|||
|
|
"selected_token_indices")
|
|||
|
|
lora_mapping = metadata_dict.pop("lora_mapping")
|
|||
|
|
lora_requests = metadata_dict.pop("lora_requests")
|
|||
|
|
multi_modal_input = metadata_dict.pop("multi_modal_input")
|
|||
|
|
num_prefill_tokens = metadata_dict.pop("num_prefill_tokens")
|
|||
|
|
num_decode_tokens = metadata_dict.pop("num_decode_tokens")
|
|||
|
|
batch_type = metadata_dict.pop("batch_type")
|
|||
|
|
|
|||
|
|
# Create an attention metadata.
|
|||
|
|
prefill_attn_metadata = None
|
|||
|
|
decode_attn_metadata = None
|
|||
|
|
if batch_type == BatchType.PREFILL or batch_type == BatchType.MIXED:
|
|||
|
|
prefill_attn_metadata = self.attn_backend.make_metadata(
|
|||
|
|
**metadata_dict)
|
|||
|
|
else:
|
|||
|
|
decode_attn_metadata = self.attn_backend.make_metadata(
|
|||
|
|
**metadata_dict)
|
|||
|
|
sampling_metadata = SamplingMetadata(
|
|||
|
|
seq_groups=None,
|
|||
|
|
selected_token_indices=selected_token_indices,
|
|||
|
|
categorized_sample_indices=None,
|
|||
|
|
num_prompts=0,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# if it is a mixed batch, decode attn_metadata is broadcasted
|
|||
|
|
# separately.
|
|||
|
|
if batch_type == BatchType.MIXED:
|
|||
|
|
metadata_dict = broadcast_tensor_dict(src=0)
|
|||
|
|
decode_attn_metadata = self.attn_backend.make_metadata(
|
|||
|
|
**metadata_dict)
|
|||
|
|
|
|||
|
|
attn_metadata = AttentionMetadata(
|
|||
|
|
num_prefills=num_prefills,
|
|||
|
|
slot_mapping=slot_mapping,
|
|||
|
|
num_prefill_tokens=num_prefill_tokens,
|
|||
|
|
num_decode_tokens=num_decode_tokens,
|
|||
|
|
prefill_metadata=prefill_attn_metadata,
|
|||
|
|
decode_metadata=decode_attn_metadata,
|
|||
|
|
kv_cache_dtype=self.kv_cache_dtype,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
return (input_tokens, input_positions, attn_metadata,
|
|||
|
|
sampling_metadata, lora_requests, lora_mapping,
|
|||
|
|
multi_modal_input)
|
|||
|
|
|
|||
|
|
@torch.inference_mode()
|
|||
|
|
def execute_model(
|
|||
|
|
self,
|
|||
|
|
seq_group_metadata_list: List[SequenceGroupMetadata],
|
|||
|
|
kv_caches: List[torch.Tensor],
|
|||
|
|
) -> Optional[SamplerOutput]:
|
|||
|
|
(input_tokens, input_positions, attn_metadata, sampling_metadata,
|
|||
|
|
lora_requests, lora_mapping, multi_modal_input
|
|||
|
|
) = self.prepare_input_tensors(seq_group_metadata_list)
|
|||
|
|
|
|||
|
|
if self.lora_config:
|
|||
|
|
self.set_active_loras(lora_requests, lora_mapping)
|
|||
|
|
|
|||
|
|
# Currently cuda graph is only supported by the decode phase.
|
|||
|
|
prefill_meta = attn_metadata.prefill_metadata
|
|||
|
|
decode_meta = attn_metadata.decode_metadata
|
|||
|
|
if prefill_meta is None and decode_meta.use_cuda_graph:
|
|||
|
|
graph_batch_size = input_tokens.shape[0]
|
|||
|
|
model_executable = self.graph_runners[graph_batch_size]
|
|||
|
|
else:
|
|||
|
|
model_executable = self.model
|
|||
|
|
execute_model_kwargs = {
|
|||
|
|
"input_ids": input_tokens,
|
|||
|
|
"positions": input_positions,
|
|||
|
|
"kv_caches": kv_caches,
|
|||
|
|
"attn_metadata": attn_metadata,
|
|||
|
|
}
|
|||
|
|
if self.vision_language_config:
|
|||
|
|
execute_model_kwargs.update({"image_input": multi_modal_input})
|
|||
|
|
hidden_states = model_executable(**execute_model_kwargs)
|
|||
|
|
|
|||
|
|
# Compute the logits.
|
|||
|
|
logits = self.model.compute_logits(hidden_states, sampling_metadata)
|
|||
|
|
|
|||
|
|
# Only perform sampling in the driver worker.
|
|||
|
|
if not self.is_driver_worker:
|
|||
|
|
return None
|
|||
|
|
|
|||
|
|
# Sample the next token.
|
|||
|
|
output = self.model.sample(
|
|||
|
|
logits=logits,
|
|||
|
|
sampling_metadata=sampling_metadata,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
return output
|
|||
|
|
|
|||
|
|
@torch.inference_mode()
|
|||
|
|
def profile_run(self) -> None:
|
|||
|
|
# Enable top-k sampling to reflect the accurate memory usage.
|
|||
|
|
sampling_params = SamplingParams(top_p=0.99, top_k=self.vocab_size - 1)
|
|||
|
|
max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens
|
|||
|
|
max_num_seqs = self.scheduler_config.max_num_seqs
|
|||
|
|
|
|||
|
|
# This represents the maximum number of different requests
|
|||
|
|
# that will have unique loras, an therefore the max amount of memory
|
|||
|
|
# consumption create dummy lora request copies from the lora request
|
|||
|
|
# passed in, which contains a lora from the lora warmup path.
|
|||
|
|
dummy_lora_requests = []
|
|||
|
|
dummy_lora_requests_per_seq = []
|
|||
|
|
if self.lora_config:
|
|||
|
|
for idx in range(self.lora_config.max_loras):
|
|||
|
|
lora_id = idx + 1
|
|||
|
|
dummy_lora_request = LoRARequest(
|
|||
|
|
lora_name=f"warmup_{lora_id}",
|
|||
|
|
lora_int_id=lora_id,
|
|||
|
|
lora_local_path="/not/a/real/path",
|
|||
|
|
)
|
|||
|
|
self.lora_manager.add_dummy_lora(dummy_lora_request,
|
|||
|
|
rank=LORA_WARMUP_RANK)
|
|||
|
|
dummy_lora_requests.append(dummy_lora_request)
|
|||
|
|
dummy_lora_requests_per_seq = [
|
|||
|
|
dummy_lora_requests[idx % len(dummy_lora_requests)]
|
|||
|
|
for idx in range(max_num_seqs)
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
# Profile memory usage with max_num_sequences sequences and the total
|
|||
|
|
# number of tokens equal to max_num_batched_tokens.
|
|||
|
|
seqs: List[SequenceGroupMetadata] = []
|
|||
|
|
# Additional GPU memory may be needed for vision encoding, which needs
|
|||
|
|
# to be accounted for when calculating the GPU blocks for
|
|||
|
|
# vLLM blocker manager.
|
|||
|
|
# To exercise the worst scenario for GPU memory consumption,
|
|||
|
|
# the number of seqs (batch_size) is chosen to maximize the number
|
|||
|
|
# of images processed.
|
|||
|
|
if self.vision_language_config:
|
|||
|
|
max_num_seqs = min(
|
|||
|
|
max_num_seqs,
|
|||
|
|
int(max_num_batched_tokens /
|
|||
|
|
self.vision_language_config.image_feature_size))
|
|||
|
|
for group_id in range(max_num_seqs):
|
|||
|
|
seq_len = (max_num_batched_tokens // max_num_seqs +
|
|||
|
|
(group_id < max_num_batched_tokens % max_num_seqs))
|
|||
|
|
seq_data, fake_multi_modal_input = _prepare_fake_inputs(
|
|||
|
|
seq_len, self.vision_language_config)
|
|||
|
|
seq = SequenceGroupMetadata(
|
|||
|
|
request_id=str(group_id),
|
|||
|
|
is_prompt=True,
|
|||
|
|
seq_data={group_id: seq_data},
|
|||
|
|
sampling_params=sampling_params,
|
|||
|
|
block_tables=None,
|
|||
|
|
lora_request=dummy_lora_requests_per_seq[group_id]
|
|||
|
|
if dummy_lora_requests_per_seq else None,
|
|||
|
|
multi_modal_data=fake_multi_modal_input,
|
|||
|
|
)
|
|||
|
|
seqs.append(seq)
|
|||
|
|
|
|||
|
|
# Run the model with the dummy inputs.
|
|||
|
|
num_layers = self.model_config.get_num_layers(self.parallel_config)
|
|||
|
|
kv_caches = [None] * num_layers
|
|||
|
|
self.execute_model(seqs, kv_caches)
|
|||
|
|
torch.musa.synchronize()
|
|||
|
|
return
|
|||
|
|
|
|||
|
|
def remove_all_loras(self):
|
|||
|
|
if not self.lora_manager:
|
|||
|
|
raise RuntimeError("LoRA is not enabled.")
|
|||
|
|
self.lora_manager.remove_all_loras()
|
|||
|
|
|
|||
|
|
def set_active_loras(self, lora_requests: Set[LoRARequest],
|
|||
|
|
lora_mapping: LoRAMapping) -> None:
|
|||
|
|
if not self.lora_manager:
|
|||
|
|
raise RuntimeError("LoRA is not enabled.")
|
|||
|
|
self.lora_manager.set_active_loras(lora_requests, lora_mapping)
|
|||
|
|
|
|||
|
|
def add_lora(self, lora_request: LoRARequest) -> bool:
|
|||
|
|
if not self.lora_manager:
|
|||
|
|
raise RuntimeError("LoRA is not enabled.")
|
|||
|
|
return self.lora_manager.add_lora(lora_request)
|
|||
|
|
|
|||
|
|
def remove_lora(self, lora_id: int) -> bool:
|
|||
|
|
if not self.lora_manager:
|
|||
|
|
raise RuntimeError("LoRA is not enabled.")
|
|||
|
|
return self.lora_manager.remove_lora(lora_id)
|
|||
|
|
|
|||
|
|
def list_loras(self) -> Set[int]:
|
|||
|
|
if not self.lora_manager:
|
|||
|
|
raise RuntimeError("LoRA is not enabled.")
|
|||
|
|
return self.lora_manager.list_loras()
|
|||
|
|
|
|||
|
|
@torch.inference_mode()
|
|||
|
|
def capture_model(self, kv_caches: List[torch.Tensor]) -> None:
|
|||
|
|
"""Cuda graph capture a model.
|
|||
|
|
|
|||
|
|
Note that CUDA graph's performance gain is negligible if number
|
|||
|
|
of batched tokens are larger than 200. And since CUDA graph
|
|||
|
|
requires fixed sized tensors, supporting large/variable batch
|
|||
|
|
size requires high GPU memory overhead. Thus, vLLM only captures
|
|||
|
|
decoding requests. Mixed batch (chunked prefill + decoding) or
|
|||
|
|
prefill requests are not captured.
|
|||
|
|
|
|||
|
|
Since it is used for decoding-only, it assumes there's only 1 token
|
|||
|
|
per sequence in the batch.
|
|||
|
|
"""
|
|||
|
|
# NOTE(woosuk): This is a hack to ensure that the NCCL backend is never
|
|||
|
|
# deleted before the CUDA graphs.
|
|||
|
|
self.pynccl_backend = pymccl_utils.get_nccl_backend()
|
|||
|
|
|
|||
|
|
assert not self.model_config.enforce_eager
|
|||
|
|
logger.info("Capturing the model for CUDA graphs. This may lead to "
|
|||
|
|
"unexpected consequences if the model is not static. To "
|
|||
|
|
"run the model in eager mode, set 'enforce_eager=True' or "
|
|||
|
|
"use '--enforce-eager' in the CLI.")
|
|||
|
|
logger.info("CUDA graphs can take additional 1~3 GiB memory per GPU. "
|
|||
|
|
"If you are running out of memory, consider decreasing "
|
|||
|
|
"`gpu_memory_utilization` or enforcing eager mode. "
|
|||
|
|
"You can also reduce the `max_num_seqs` as needed "
|
|||
|
|
"to decrease memory usage.")
|
|||
|
|
start_time = time.perf_counter()
|
|||
|
|
|
|||
|
|
# Prepare dummy inputs. These will be reused for all batch sizes.
|
|||
|
|
max_batch_size = max(_BATCH_SIZES_TO_CAPTURE)
|
|||
|
|
input_tokens = torch.zeros(max_batch_size, dtype=torch.long).musa()
|
|||
|
|
input_positions = torch.zeros(max_batch_size, dtype=torch.long).musa()
|
|||
|
|
slot_mapping = torch.empty(max_batch_size, dtype=torch.long).musa()
|
|||
|
|
slot_mapping.fill_(_PAD_SLOT_ID)
|
|||
|
|
seq_lens = torch.ones(max_batch_size, dtype=torch.int32).musa()
|
|||
|
|
block_tables = torch.from_numpy(self.graph_block_tables).musa()
|
|||
|
|
|
|||
|
|
graph_batch_size = _get_graph_batch_size(
|
|||
|
|
self.scheduler_config.max_num_seqs)
|
|||
|
|
batch_size_capture_list = [
|
|||
|
|
bs for bs in _BATCH_SIZES_TO_CAPTURE if bs <= graph_batch_size
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
# NOTE(woosuk): There are 3 backends for all-reduce: custom all-reduce
|
|||
|
|
# kernel, pynccl, and PyTorch NCCL. When using CUDA graph, we use
|
|||
|
|
# either custom all-reduce kernel or pynccl. When not using CUDA
|
|||
|
|
# graph, we use either custom all-reduce kernel or PyTorch NCCL.
|
|||
|
|
# We always prioritize using custom all-reduce kernel but fall back
|
|||
|
|
# to PyTorch or pynccl if it is disabled or not supported.
|
|||
|
|
with custom_all_reduce.capture():
|
|||
|
|
# NOTE: Capturing the largest batch size first may help reduce the
|
|||
|
|
# memory usage of CUDA graph.
|
|||
|
|
for batch_size in reversed(batch_size_capture_list):
|
|||
|
|
# Create dummy attn_metadata.
|
|||
|
|
decode_metadata = self.attn_backend.make_metadata(
|
|||
|
|
is_prompt=False,
|
|||
|
|
seq_lens=None,
|
|||
|
|
seq_lens_tensor=seq_lens[:batch_size],
|
|||
|
|
max_query_len=None,
|
|||
|
|
max_seq_len=self.max_seq_len_to_capture,
|
|||
|
|
subquery_start_loc=None,
|
|||
|
|
seq_start_loc=None,
|
|||
|
|
context_lens_tensor=None,
|
|||
|
|
block_tables=block_tables[:batch_size],
|
|||
|
|
use_cuda_graph=False,
|
|||
|
|
)
|
|||
|
|
attn_metadata = AttentionMetadata(
|
|||
|
|
num_prefills=0,
|
|||
|
|
num_prefill_tokens=0,
|
|||
|
|
num_decode_tokens=batch_size,
|
|||
|
|
slot_mapping=slot_mapping[:batch_size],
|
|||
|
|
prefill_metadata=None,
|
|||
|
|
decode_metadata=decode_metadata,
|
|||
|
|
kv_cache_dtype=self.kv_cache_dtype,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
if self.lora_config:
|
|||
|
|
lora_mapping = LoRAMapping(
|
|||
|
|
[0] * batch_size,
|
|||
|
|
[0] * batch_size,
|
|||
|
|
)
|
|||
|
|
self.set_active_loras(set(), lora_mapping)
|
|||
|
|
|
|||
|
|
# graph_runner = CUDAGraphRunner(self.model)
|
|||
|
|
# graph_runner.capture(
|
|||
|
|
# input_tokens[:batch_size],
|
|||
|
|
# input_positions[:batch_size],
|
|||
|
|
# kv_caches,
|
|||
|
|
# attn_metadata,
|
|||
|
|
# memory_pool=self.graph_memory_pool,
|
|||
|
|
# )
|
|||
|
|
# self.graph_memory_pool = graph_runner.graph.pool()
|
|||
|
|
# self.graph_runners[batch_size] = graph_runner
|
|||
|
|
|
|||
|
|
end_time = time.perf_counter()
|
|||
|
|
elapsed_time = end_time - start_time
|
|||
|
|
# This usually takes < 10 seconds.
|
|||
|
|
logger.info("Graph capturing finished in %.0f secs.", elapsed_time)
|
|||
|
|
|
|||
|
|
def __del__(self) -> None:
|
|||
|
|
# Delete the CUDA graphs before deleting the pynccl communicator.
|
|||
|
|
# NOTE(woosuk): This is necessary because otherwise deadlocks can
|
|||
|
|
# happen.
|
|||
|
|
# FIXME(woosuk): This is a bit hacky. Find a more robust solution.
|
|||
|
|
# TODO(youkaichao): when we get enough user feedback that pynccl is
|
|||
|
|
# more stable than cupy, we can remove this, e.g. in v0.4.1.
|
|||
|
|
self.graph_runners.clear()
|
|||
|
|
self.pynccl_backend = None
|
|||
|
|
|
|||
|
|
@property
|
|||
|
|
def vocab_size(self) -> int:
|
|||
|
|
return self.model_config.get_vocab_size()
|
|||
|
|
|
|||
|
|
|
|||
|
|
class CUDAGraphRunner:
|
|||
|
|
|
|||
|
|
def __init__(self, model: nn.Module):
|
|||
|
|
self.model = model
|
|||
|
|
self.input_buffers: Dict[str, torch.Tensor] = {}
|
|||
|
|
self.output_buffers: Dict[str, torch.Tensor] = {}
|
|||
|
|
|
|||
|
|
self._graph: Optional[torch.musa.MUSAGraph] = None
|
|||
|
|
|
|||
|
|
@property
|
|||
|
|
def graph(self):
|
|||
|
|
assert self._graph is not None
|
|||
|
|
return self._graph
|
|||
|
|
|
|||
|
|
def capture(
|
|||
|
|
self,
|
|||
|
|
input_ids: torch.Tensor,
|
|||
|
|
positions: torch.Tensor,
|
|||
|
|
kv_caches: List[torch.Tensor],
|
|||
|
|
attn_metadata: AttentionMetadata,
|
|||
|
|
memory_pool,
|
|||
|
|
**kwargs,
|
|||
|
|
) -> None:
|
|||
|
|
assert self._graph is None
|
|||
|
|
# Run the model once without capturing the graph.
|
|||
|
|
# This is to make sure that the captured graph does not include the
|
|||
|
|
# kernel launches for initial benchmarking (e.g., Triton autotune).
|
|||
|
|
with _maybe_pynccl():
|
|||
|
|
self.model(
|
|||
|
|
input_ids,
|
|||
|
|
positions,
|
|||
|
|
kv_caches,
|
|||
|
|
attn_metadata,
|
|||
|
|
**kwargs,
|
|||
|
|
)
|
|||
|
|
torch.musa.synchronize()
|
|||
|
|
|
|||
|
|
# Capture the graph.
|
|||
|
|
# NOTE(woosuk): Python 3.8 does not support multi-line with statements.
|
|||
|
|
# https://stackoverflow.com/questions/31039022/python-multi-line-with-statement
|
|||
|
|
import pdb;pdb.set_trace()
|
|||
|
|
self._graph = torch.musa.MUSAGraph()
|
|||
|
|
with torch.musa.graph(self._graph, pool=memory_pool): # noqa: SIM117
|
|||
|
|
with _maybe_pynccl():
|
|||
|
|
hidden_states = self.model(
|
|||
|
|
input_ids,
|
|||
|
|
positions,
|
|||
|
|
kv_caches,
|
|||
|
|
attn_metadata,
|
|||
|
|
**kwargs,
|
|||
|
|
)
|
|||
|
|
torch.musa.synchronize()
|
|||
|
|
|
|||
|
|
# Save the input and output buffers.
|
|||
|
|
self.input_buffers = {
|
|||
|
|
"input_ids": input_ids,
|
|||
|
|
"positions": positions,
|
|||
|
|
"kv_caches": kv_caches,
|
|||
|
|
"slot_mapping": attn_metadata.slot_mapping,
|
|||
|
|
"seq_lens_tensor": attn_metadata.decode_metadata.seq_lens_tensor,
|
|||
|
|
"block_tables": attn_metadata.decode_metadata.block_tables,
|
|||
|
|
}
|
|||
|
|
self.output_buffers = {"hidden_states": hidden_states}
|
|||
|
|
return
|
|||
|
|
|
|||
|
|
def forward(
|
|||
|
|
self,
|
|||
|
|
input_ids: torch.Tensor,
|
|||
|
|
positions: torch.Tensor,
|
|||
|
|
kv_caches: List[torch.Tensor],
|
|||
|
|
attn_metadata: AttentionMetadata,
|
|||
|
|
**kwargs,
|
|||
|
|
) -> torch.Tensor:
|
|||
|
|
# KV caches are fixed tensors, so we don't need to copy them.
|
|||
|
|
del kv_caches
|
|||
|
|
|
|||
|
|
# Copy the input tensors to the input buffers.
|
|||
|
|
self.input_buffers["input_ids"].copy_(input_ids, non_blocking=True)
|
|||
|
|
self.input_buffers["positions"].copy_(positions, non_blocking=True)
|
|||
|
|
self.input_buffers["slot_mapping"].copy_(attn_metadata.slot_mapping,
|
|||
|
|
non_blocking=True)
|
|||
|
|
self.input_buffers["seq_lens_tensor"].copy_(
|
|||
|
|
attn_metadata.decode_metadata.seq_lens_tensor, non_blocking=True)
|
|||
|
|
self.input_buffers["block_tables"].copy_(
|
|||
|
|
attn_metadata.decode_metadata.block_tables, non_blocking=True)
|
|||
|
|
# Run the graph.
|
|||
|
|
self.graph.replay()
|
|||
|
|
|
|||
|
|
# Return the output tensor.
|
|||
|
|
return self.output_buffers["hidden_states"]
|
|||
|
|
|
|||
|
|
def __call__(self, *args, **kwargs):
|
|||
|
|
return self.forward(*args, **kwargs)
|
|||
|
|
|
|||
|
|
|
|||
|
|
@contextlib.contextmanager
|
|||
|
|
def _maybe_pynccl():
|
|||
|
|
if pymccl_utils.is_initialized(
|
|||
|
|
) and not custom_all_reduce.is_initialized():
|
|||
|
|
with with_pynccl_for_all_reduce():
|
|||
|
|
yield
|
|||
|
|
else:
|
|||
|
|
yield
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _get_graph_batch_size(batch_size: int) -> int:
|
|||
|
|
"""Returns the padded batch size given actual batch size.
|
|||
|
|
|
|||
|
|
Batch sizes are 1, 2, 4, _BATCH_SIZE_ALIGNMENT,
|
|||
|
|
2*_BATCH_SIZE_ALIGNMENT, 3*_BATCH_SIZE_ALIGNMENT...
|
|||
|
|
"""
|
|||
|
|
if batch_size <= 2:
|
|||
|
|
return batch_size
|
|||
|
|
elif batch_size <= 4:
|
|||
|
|
return 4
|
|||
|
|
else:
|
|||
|
|
return ((batch_size + _BATCH_SIZE_ALIGNMENT - 1) //
|
|||
|
|
_BATCH_SIZE_ALIGNMENT * _BATCH_SIZE_ALIGNMENT)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _prepare_fake_inputs(
|
|||
|
|
seq_len: int, vision_language_config: Optional[VisionLanguageConfig]):
|
|||
|
|
"""Prepare fake inputs for profile run."""
|
|||
|
|
if vision_language_config:
|
|||
|
|
prompt_tokens = [
|
|||
|
|
vision_language_config.image_token_id
|
|||
|
|
] * vision_language_config.image_feature_size + [0] * (
|
|||
|
|
seq_len - vision_language_config.image_feature_size)
|
|||
|
|
fake_image_input = MultiModalData(
|
|||
|
|
type=MultiModalData.Type.IMAGE,
|
|||
|
|
data=torch.zeros(vision_language_config.image_input_shape,
|
|||
|
|
dtype=torch.float16))
|
|||
|
|
else:
|
|||
|
|
prompt_tokens = [0] * seq_len
|
|||
|
|
fake_image_input = None
|
|||
|
|
return SequenceData(prompt_tokens), fake_image_input
|