import argparse import dataclasses import io import os import re import time from dataclasses import dataclass from functools import partial from typing import BinaryIO, Generator, Optional, Tuple, Type, Union import torch from torch import nn from transformers import PretrainedConfig import vllm.envs as envs from vllm.config import ModelConfig, ParallelConfig from vllm.engine.arg_utils import EngineArgs from vllm.engine.llm_engine import LLMEngine from vllm.logger import init_logger from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig) from vllm.model_executor.layers.vocab_parallel_embedding import ( VocabParallelEmbedding) from vllm.utils import FlexibleArgumentParser tensorizer_error_msg = None try: from tensorizer import (DecryptionParams, EncryptionParams, TensorDeserializer, TensorSerializer) from tensorizer.stream_io import open_stream from tensorizer.utils import (convert_bytes, get_mem_usage, no_init_or_tensor) _read_stream, _write_stream = (partial( open_stream, mode=mode, ) for mode in ("rb", "wb+")) except ImportError as e: tensorizer_error_msg = str(e) __all__ = [ 'EncryptionParams', 'DecryptionParams', 'TensorDeserializer', 'TensorSerializer', 'open_stream', 'convert_bytes', 'get_mem_usage', 'no_init_or_tensor', 'TensorizerConfig' ] logger = init_logger(__name__) @dataclass class TensorizerConfig: tensorizer_uri: str vllm_tensorized: Optional[bool] = False verify_hash: Optional[bool] = False num_readers: Optional[int] = None encryption_keyfile: Optional[str] = None s3_access_key_id: Optional[str] = None s3_secret_access_key: Optional[str] = None s3_endpoint: Optional[str] = None model_class: Optional[Type[torch.nn.Module]] = None hf_config: Optional[PretrainedConfig] = None dtype: Optional[Union[str, torch.dtype]] = None _is_sharded: bool = False def __post_init__(self): # check if the configuration is for a sharded vLLM model self._is_sharded = isinstance(self.tensorizer_uri, str) \ and re.search(r'%0\dd', self.tensorizer_uri) is not None def _construct_tensorizer_args(self) -> "TensorizerArgs": tensorizer_args = { "tensorizer_uri": self.tensorizer_uri, "vllm_tensorized": self.vllm_tensorized, "verify_hash": self.verify_hash, "num_readers": self.num_readers, "encryption_keyfile": self.encryption_keyfile, "s3_access_key_id": self.s3_access_key_id, "s3_secret_access_key": self.s3_secret_access_key, "s3_endpoint": self.s3_endpoint, } return TensorizerArgs(**tensorizer_args) # type: ignore def verify_with_parallel_config( self, parallel_config: "ParallelConfig", ) -> None: if parallel_config.tensor_parallel_size > 1 \ and not self._is_sharded: raise ValueError( "For a sharded model, tensorizer_uri should include a" " string format template like '%04d' to be formatted" " with the rank of the shard") def verify_with_model_config(self, model_config: "ModelConfig") -> None: if (model_config.quantization is not None and self.tensorizer_uri is not None): logger.warning( "Loading a model using Tensorizer with quantization on vLLM" " is unstable and may lead to errors.") def open_stream(self, tensorizer_args: Optional["TensorizerArgs"] = None): if tensorizer_args is None: tensorizer_args = self._construct_tensorizer_args() return open_stream(self.tensorizer_uri, **tensorizer_args.stream_params) def load_with_tensorizer(tensorizer_config: TensorizerConfig, **extra_kwargs) -> nn.Module: tensorizer = TensorizerAgent(tensorizer_config, **extra_kwargs) return tensorizer.deserialize() @dataclass class TensorizerArgs: tensorizer_uri: Union[io.BufferedIOBase, io.RawIOBase, BinaryIO, str, bytes, os.PathLike, int] vllm_tensorized: Optional[bool] = False verify_hash: Optional[bool] = False num_readers: Optional[int] = None encryption_keyfile: Optional[str] = None s3_access_key_id: Optional[str] = None s3_secret_access_key: Optional[str] = None s3_endpoint: Optional[str] = None """ Args for the TensorizerAgent class. These are used to configure the behavior of the TensorDeserializer when loading tensors from a serialized model. Args: tensorizer_uri: Path to serialized model tensors. Can be a local file path or a S3 URI. vllm_tensorized: If True, indicates that the serialized model is a vLLM model. This is used to determine the behavior of the TensorDeserializer when loading tensors from a serialized model. It is far faster to deserialize a vLLM model as it utilizes tensorizer's optimized GPU loading. Note that this is now deprecated, as serialized vLLM models are now automatically inferred as vLLM models. verify_hash: If True, the hashes of each tensor will be verified against the hashes stored in the metadata. A `HashMismatchError` will be raised if any of the hashes do not match. num_readers: Controls how many threads are allowed to read concurrently from the source file. Default is `None`, which will dynamically set the number of readers based on the number of available resources and model size. This greatly increases performance. encryption_keyfile: File path to a binary file containing a binary key to use for decryption. `None` (the default) means no decryption. See the example script in examples/tensorize_vllm_model.py. s3_access_key_id: The access key for the S3 bucket. Can also be set via the S3_ACCESS_KEY_ID environment variable. s3_secret_access_key: The secret access key for the S3 bucket. Can also be set via the S3_SECRET_ACCESS_KEY environment variable. s3_endpoint: The endpoint for the S3 bucket. Can also be set via the S3_ENDPOINT_URL environment variable. """ def __post_init__(self): self.file_obj = self.tensorizer_uri self.s3_access_key_id = self.s3_access_key_id or envs.S3_ACCESS_KEY_ID self.s3_secret_access_key = (self.s3_secret_access_key or envs.S3_SECRET_ACCESS_KEY) self.s3_endpoint = self.s3_endpoint or envs.S3_ENDPOINT_URL self.stream_params = { "s3_access_key_id": self.s3_access_key_id, "s3_secret_access_key": self.s3_secret_access_key, "s3_endpoint": self.s3_endpoint, } self.deserializer_params = { "verify_hash": self.verify_hash, "encryption": self.encryption_keyfile, "num_readers": self.num_readers } if self.encryption_keyfile: with open_stream( self.encryption_keyfile, **self.stream_params, ) as stream: key = stream.read() decryption_params = DecryptionParams.from_key(key) self.deserializer_params['encryption'] = decryption_params @staticmethod def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: """Tensorizer CLI arguments""" # Tensorizer options arg group group = parser.add_argument_group( 'tensorizer options', description=('Options for configuring the behavior of the' ' tensorizer deserializer when ' 'load_format=tensorizer is specified when ' 'initializing an LLMEngine, either via the CLI ' 'when running the vLLM OpenAI inference server ' 'with a JSON string passed to ' '--model-loader-extra-config or as arguments given ' 'to TensorizerConfig when passed to ' 'model_loader_extra_config in the constructor ' 'for LLMEngine.')) group.add_argument( "--tensorizer-uri", help="Path to serialized model tensors. Can be a local file path," " or an HTTP(S) or S3 URI.", ) group.add_argument( "--verify-hash", action="store_true", help="If enabled, the hashes of each tensor will be verified" " against the hashes stored in the file metadata. An exception" " will be raised if any of the hashes do not match.", ) group.add_argument( "--encryption-keyfile", default=None, help="The file path to a binary file containing a binary key to " "use for decryption. Can be a file path or S3 network URI.") group.add_argument( "--num-readers", default=None, type=int, help="Controls how many threads are allowed to read concurrently " "from the source file. Default is `None`, which will dynamically " "set the number of readers based on the available resources " "and model size. This greatly increases performance.") group.add_argument( "--s3-access-key-id", default=None, help="The access key for the S3 bucket. Can also be set via the " "S3_ACCESS_KEY_ID environment variable.", ) group.add_argument( "--s3-secret-access-key", default=None, help="The secret access key for the S3 bucket. Can also be set via " "the S3_SECRET_ACCESS_KEY environment variable.", ) group.add_argument( "--s3-endpoint", default=None, help="The endpoint for the S3 bucket. Can also be set via the " "S3_ENDPOINT_URL environment variable.", ) return parser @classmethod def from_cli_args(cls, args: argparse.Namespace) -> "TensorizerArgs": attrs = [attr.name for attr in dataclasses.fields(cls)] tensorizer_args = cls(**{ attr: getattr(args, attr) for attr in attrs if hasattr(args, attr) }) return tensorizer_args class TensorizerAgent: """ A class for performing tensorizer deserializations specifically for vLLM models using plaid_mode. Uses TensorizerArgs to configure the behavior of the TensorDeserializer when loading tensors from a serialized model. For deserializations of HuggingFace models, TensorDeserializer is instead used as an iterator directly in the func hf_model_weights_iterator in vllm/model_executor/model_loader/weight_utils.py """ def __init__(self, tensorizer_config: TensorizerConfig, quant_config: QuantizationConfig, **extra_kwargs): if tensorizer_error_msg is not None: raise ImportError( "Tensorizer is not installed. Please install tensorizer " "to use this feature with `pip install vllm[tensorizer]`. " "Error message: {}".format(tensorizer_error_msg)) self.tensorizer_config = tensorizer_config self.tensorizer_args = ( self.tensorizer_config._construct_tensorizer_args()) self.extra_kwargs = extra_kwargs if extra_kwargs.get("quant_config", None) is not None: self.quant_config = extra_kwargs["quant_config"] else: self.quant_config = quant_config self.model = self._init_model() def _init_model(self): assert self.tensorizer_config.hf_config is not None model_args = self.tensorizer_config.hf_config model_args.torch_dtype = self.tensorizer_config.dtype assert self.tensorizer_config.model_class is not None with no_init_or_tensor(): return self.tensorizer_config.model_class( config=model_args, quant_config=self.quant_config, **self.extra_kwargs) def _resize_lora_embeddings(self): """Modify LoRA embedding layers to use bigger tensors to allow for adapter added tokens.""" for child in self.model.modules(): if (isinstance(child, VocabParallelEmbedding) and child.weight.shape[0] < child.num_embeddings_per_partition): new_weight = torch.empty(child.num_embeddings_per_partition, child.embedding_dim, dtype=child.weight.dtype, device=child.weight.device) new_weight[:child.weight.shape[0]].copy_(child.weight.data) new_weight[child.weight.shape[0]:].fill_(0) child.weight.data = new_weight def _check_tensors_on_meta_device(self): for tensor in self.model.state_dict().values(): if tensor.device.type == 'meta': raise ValueError( "The serialized model contains tensors on the meta device," " indicating that some tensors were not loaded properly." " Please check that the parameters of the model being" " specified match that of the serialized model, such as" " its quantization.") def deserialize(self): """ Deserialize the model using the TensorDeserializer. This method is specifically for vLLM models using tensorizer's plaid_mode. The deserializer makes use of tensorizer_args.stream_params to configure the behavior of the stream when loading tensors from a serialized model. The deserializer_params are used to configure the behavior of the TensorDeserializer when loading tensors themselves. Documentation on these params can be found in TensorizerArgs Returns: nn.Module: The deserialized model. """ before_mem = get_mem_usage() start = time.perf_counter() with _read_stream( self.tensorizer_config.tensorizer_uri, **self.tensorizer_args.stream_params ) as stream, TensorDeserializer( stream, dtype=self.tensorizer_config.dtype, device=f'cuda:{torch.cuda.current_device()}', **self.tensorizer_args.deserializer_params) as deserializer: deserializer.load_into_module(self.model) end = time.perf_counter() total_bytes_str = convert_bytes(deserializer.total_tensor_bytes) duration = end - start per_second = convert_bytes(deserializer.total_tensor_bytes / duration) after_mem = get_mem_usage() deserializer.close() logger.info("Deserialized %s in %0.2fs, %s/s", total_bytes_str, end - start, per_second) logger.info("Memory usage before: %s", before_mem) logger.info("Memory usage after: %s", after_mem) self._check_tensors_on_meta_device() self._resize_lora_embeddings() del self.model.vllm_tensorized_marker return self.model.eval() def tensorizer_weights_iterator( tensorizer_args: "TensorizerArgs" ) -> Generator[Tuple[str, torch.Tensor], None, None]: logger.warning( "Deserializing HuggingFace models is not optimized for " "loading on vLLM, as tensorizer is forced to load to CPU. " "Consider deserializing a vLLM model instead for faster " "load times. See the examples/tensorize_vllm_model.py example " "script for serializing vLLM models.") deserializer_args = tensorizer_args.deserializer_params stream_params = tensorizer_args.stream_params stream = open_stream(tensorizer_args.tensorizer_uri, **stream_params) with TensorDeserializer(stream, **deserializer_args, device="cpu") as state: for name, param in state.items(): yield name, param del state def is_vllm_tensorized(tensorizer_config: "TensorizerConfig") -> bool: """ Infer if the model is a vLLM model by checking the weights for a vLLM tensorized marker. Args: tensorizer_config: The TensorizerConfig object containing the tensorizer_uri to the serialized model. Returns: bool: True if the model is a vLLM model, False otherwise. """ tensorizer_args = tensorizer_config._construct_tensorizer_args() deserializer = TensorDeserializer(open_stream( tensorizer_args.tensorizer_uri, **tensorizer_args.stream_params), **tensorizer_args.deserializer_params, lazy_load=True) if tensorizer_config.vllm_tensorized: logger.warning( "Please note that newly serialized vLLM models are automatically " "inferred as vLLM models, so setting vllm_tensorized=True is " "only necessary for models serialized prior to this change.") return True if (".vllm_tensorized_marker" in deserializer): return True return False def serialize_vllm_model( model: nn.Module, tensorizer_config: TensorizerConfig, ) -> nn.Module: model.register_parameter( "vllm_tensorized_marker", nn.Parameter(torch.tensor((1, ), device="meta"), requires_grad=False)) tensorizer_args = tensorizer_config._construct_tensorizer_args() encryption_params = None if (keyfile := tensorizer_config.encryption_keyfile) is not None: with open(keyfile, "rb") as f: key = f.read() encryption_params = EncryptionParams(key=key) output_file = tensorizer_args.tensorizer_uri if tensorizer_config._is_sharded: from vllm.distributed import get_tensor_model_parallel_rank output_file = output_file % get_tensor_model_parallel_rank() with _write_stream(output_file, **tensorizer_args.stream_params) as stream: serializer = TensorSerializer(stream, encryption=encryption_params) serializer.write_module(model) serializer.close() logger.info("Successfully serialized model to %s", str(output_file)) return model def tensorize_vllm_model(engine_args: EngineArgs, tensorizer_config: TensorizerConfig, generate_keyfile: bool = True): """Utility to load a model and then serialize it with Tensorizer Intended to be used separately from running a vLLM server since it creates its own Engine instance. """ engine_config = engine_args.create_engine_config() tensorizer_config.verify_with_model_config(engine_config.model_config) tensorizer_config.verify_with_parallel_config( engine_config.parallel_config) # generate the encryption key before creating the engine to support sharding if generate_keyfile and (keyfile := tensorizer_config.encryption_keyfile) is not None: encryption_params = EncryptionParams.random() with _write_stream( keyfile, s3_access_key_id=tensorizer_config.s3_access_key_id, s3_secret_access_key=tensorizer_config.s3_secret_access_key, s3_endpoint=tensorizer_config.s3_endpoint, ) as stream: stream.write(encryption_params.key) engine = LLMEngine.from_engine_args(engine_args) if tensorizer_config._is_sharded: # if the engine is a distributed engine (for tensor parallel) then each # worker shard needs to serialize its part of the model. engine.model_executor._run_workers( "save_tensorized_model", tensorizer_config=tensorizer_config, ) else: # with a single worker, we can get to the underlying model directly serialize_vllm_model( engine.model_executor.driver_worker.model_runner.model, tensorizer_config, )