from typing import Optional, Union, ClassVar from dataclasses import dataclass import os from packaging.version import Version from loguru import logger import torch from transformers import PretrainedConfig from aphrodite.transformers_utils.config import get_config from aphrodite.common.utils import (get_cpu_memory, is_hip, get_nvcc_cuda_version) _GB = 1 << 30 class ModelConfig: """Configuration for the model. Args: model: Name or path of the huggingface model to use. tokenizer: Name or path of the huggingface tokenizer to use. tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if available, and "slow" will always use the slow tokenizer. trust_remote_code: Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer. download_dir: Directory to download and load the weights, default to the default cache directory of huggingface. load_format: The format of the model weights to load: "auto" will try to load the weights in the safetensors format and fall back to the pytorch bin format if safetensors format is not available. "pt" will load the weights in the pytorch bin format. "safetensors" will load the weights in the safetensors format. "npcache" will load the weights in pytorch format and store a numpy cache to speed up the loading. "dummy" will initialize the weights with random values, which is mainly for profiling. dtype: Data type for model weights and activations. The "auto" option will use FP16 precision for FP32 and FP16 models, and BF16 precision for BF16 models. seed: Random seed for reproducibility. revision: The specific model version to use. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version. tokenizer_revision: The specific tokenizer version to use. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version. max_model_len: Maximum length of a sequence (including prompt and output). If None, will be derived from the model. quantization: Quantization method that was used to quantize the model weights. If None, we assume the model weights are not quantized. load_in_4bit: Whether to load the FP16 model in bitsandbytes 4bit format. Works with AWQ models as well as FP16. load_in_8bit: Whether to load the FP16 model in 8bit format. Slower than load_in_smooth in terms of throughput. load_in_smooth: Whether to load the FP16 model in smoothquant format. enforce_eager: Whether to enforce eager execution. If True, we will disable CUDA graph and always execute the model in eager mode. If False, we will use CUDA graph and eager execution in hybrid. max_context_len_to_capture: Maximum context len covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode. """ def __init__( self, model: str, tokenizer: str, tokenizer_mode: str, trust_remote_code: bool, download_dir: Optional[str], load_format: str, dtype: str, seed: int, revision: Optional[str] = None, tokenizer_revision: Optional[str] = None, max_model_len: Optional[int] = None, quantization: Optional[str] = None, load_in_4bit: bool = False, load_in_8bit: bool = False, load_in_smooth: bool = False, enforce_eager: bool = False, max_context_len_to_capture: Optional[int] = None, max_log_probs: int = 10, ) -> None: self.model = model self.tokenizer = tokenizer self.tokenizer_mode = tokenizer_mode self.trust_remote_code = trust_remote_code self.download_dir = download_dir self.load_format = load_format self.seed = seed self.revision = revision self.tokenizer_revision = tokenizer_revision self.quantization = quantization self.load_in_4bit = load_in_4bit self.load_in_8bit = load_in_8bit self.load_in_smooth = load_in_smooth self.enforce_eager = enforce_eager self.max_context_len_to_capture = max_context_len_to_capture self.max_log_probs = max_log_probs if os.environ.get("APHRODITE_USE_MODELSCOPE", "False").lower() == "true": # download model from ModelScope hub, # lazy import so that modelscope is not required for normal use. from modelscope.hub.snapshot_download import snapshot_download # pylint: disable=C model_path = snapshot_download(model_id=model, cache_dir=download_dir, revision=revision) self.model = model_path self.download_dir = model_path self.tokenizer = model_path self.hf_config = get_config(self.model, trust_remote_code, revision) self.dtype = _get_and_verify_dtype(self.hf_config, dtype) self.max_model_len = _get_and_verify_max_len(self.hf_config, max_model_len) self._verify_load_format() self._verify_tokenizer_mode() self._verify_quantization() self._verify_cuda_graph() def _verify_load_format(self) -> None: load_format = self.load_format.lower() supported_load_format = [ "auto", "pt", "safetensors", "npcache", "dummy" ] rocm_not_supported_load_format = [] if load_format not in supported_load_format: raise ValueError( f"Unknown load format: {self.load_format}. Must be one of " "'auto', 'pt', 'safetensors', 'npcache', or 'dummy'.") if is_hip() and load_format in rocm_not_supported_load_format: rocm_supported_load_format = [ f for f in supported_load_format if (f not in rocm_not_supported_load_format) ] raise ValueError( f"load format \'{load_format}\' is not supported in ROCm. " f"Supported load format are " f"{rocm_supported_load_format}") # TODO: Remove this check once HF updates the pt weights of Mixtral. architectures = getattr(self.hf_config, "architectures", []) if "MixtralForCausalLM" in architectures and load_format == "pt": raise ValueError( "Currently, the 'pt' format is not supported for Mixtral. " "Please use the 'safetensors' format instead. ") self.load_format = load_format def _verify_tokenizer_mode(self) -> None: tokenizer_mode = self.tokenizer_mode.lower() if tokenizer_mode not in ["auto", "slow"]: raise ValueError( f"Unknown tokenizer mode: {self.tokenizer_mode}. Must be " "either 'auto' or 'slow'.") self.tokenizer_mode = tokenizer_mode def _verify_quantization(self) -> None: supported_quantization = [ "aqlm", "awq", "bnb", "exl2", "gguf", "gptq", "quip", "squeezellm", "marlin" ] rocm_not_supported_quantization = ["aqlm", "awq", "bnb", "quip"] if self.quantization is not None: self.quantization = self.quantization.lower() if self.model.endswith("gguf"): if self.quantization is None: self.quantization = "gguf" elif self.quantization != "gguf": raise ValueError( f"GGUF file cannot be used in ({self.quantization}).") # Parse quantization method from the HF model config, if available. hf_quant_config = getattr(self.hf_config, "quantization_config", None) if hf_quant_config is not None: hf_quant_method = str(hf_quant_config["quant_method"]).lower() # If the GPTQ model is serialized in marlin format, use marlin. if (hf_quant_method == "gptq" and "is_marlin_format" in hf_quant_config and hf_quant_config["is_marlin_format"]): hf_quant_method = "marlin" if self.quantization is None: self.quantization = hf_quant_method elif self.quantization != hf_quant_method: raise ValueError( "Quantization method specified in the model config " f"({hf_quant_method}) does not match the quantization " f"method specified in the `quantization` argument " f"({self.quantization}).") if self.load_in_4bit: # the kernels seem to not work with 4bit weight_only if torch.cuda.get_device_capability(0)[0] < 8: raise ValueError( "load_in_4bit quantization is not supported on GPUs with " "compute capability less than 8.0.") if self.quantization is None: self.quantization = "bnb" self.hf_config.quantization_config = { "bits": 4, "quant_mode": "weight_only", "quant_method": "bnb", "group_size": 128, "zero_point": True, "from_float": True } elif self.quantization == "awq": logger.warning("AWQ model is being loaded in 4bit bnb format.") self.quantization = "bnb" self.hf_config.quantization_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "gemm" } elif self.quantization != "bnb": raise ValueError("4bit quantization is not supported in " f"{self.quantization}.") if self.load_in_8bit: if self.quantization is None: self.quantization = "bnb" elif self.quantization != "bnb": raise ValueError("8bit quantization is not supported in " f"{self.quantization}.") self.hf_config.quantization_config = { "bits": 8, "quant_mode": "llm_int8", "quant_method": "bnb", "group_size": 128, "zero_point": True, "from_float": True } self.enforce_eager = True if self.load_in_smooth: if self.quantization is None: self.quantization = "bnb" elif self.quantization != "bnb": raise ValueError("Smooth quantization is not supported in " f"{self.quantization}.") self.hf_config.quantization_config = { "bits": 8, "quant_mode": "smoothquant", "quant_method": "bnb", "group_size": 128, "zero_point": True, "from_float": True } self.enforce_eager = True if self.quantization is not None: if self.quantization not in supported_quantization: raise ValueError( f"Unknown quantization method: {self.quantization}. Must " f"be one of {supported_quantization}.") if is_hip( ) and self.quantization in rocm_not_supported_quantization: raise ValueError( f"{self.quantization} quantization is currently not " "supported in ROCm.") if self.quantization != "marlin": logger.warning( f"{self.quantization} quantization is not fully " "optimized yet. The speed can be slower than " "non-quantized models.") def _verify_cuda_graph(self) -> None: if self.max_context_len_to_capture is None: self.max_context_len_to_capture = self.max_model_len self.max_context_len_to_capture = min(self.max_context_len_to_capture, self.max_model_len) def verify_with_parallel_config( self, parallel_config: "ParallelConfig", ) -> None: total_num_attention_heads = self.hf_config.num_attention_heads tensor_parallel_size = parallel_config.tensor_parallel_size if total_num_attention_heads % tensor_parallel_size != 0: raise ValueError( f"Total number of attention heads ({total_num_attention_heads})" " must be divisible by tensor parallel size " f"({tensor_parallel_size}).") total_num_hidden_layers = self.hf_config.num_hidden_layers pipeline_parallel_size = parallel_config.pipeline_parallel_size if total_num_hidden_layers % pipeline_parallel_size != 0: raise ValueError( f"Total number of hidden layers ({total_num_hidden_layers}) " "must be divisible by pipeline parallel size " f"({pipeline_parallel_size}).") def get_sliding_window(self) -> Optional[int]: return getattr(self.hf_config, "sliding_window", None) def get_vocab_size(self) -> int: return self.hf_config.vocab_size def get_hidden_size(self) -> int: return self.hf_config.hidden_size def get_head_size(self) -> int: if hasattr(self.hf_config, "head_dim"): return self.hf_config.head_dim # FIXME: This may not be true for all models. return self.hf_config.hidden_size // self.hf_config.num_attention_heads def get_total_num_kv_heads(self) -> int: """Returns the total number of KV heads.""" # For GPTBigCode & Falcon: # NOTE: for falcon, when new_decoder_architecture is True, the # multi_query flag is ignored and we use n_head_kv for the number of # KV heads. falcon_model_types = ["falcon", "RefinedWeb", "RefinedWebModel"] new_decoder_arch_falcon = ( self.hf_config.model_type in falcon_model_types and getattr(self.hf_config, "new_decoder_architecture", False)) if not new_decoder_arch_falcon and getattr(self.hf_config, "multi_query", False): # Multi-query attention, only one KV head. # Currently, tensor parallelism is not supported in this case. return 1 attributes = [ # For Falcon: "n_head_kv", "num_kv_heads", # For LLaMA-2: "num_key_value_heads", # For ChatGLM: "multi_query_group_num", ] for attr in attributes: num_kv_heads = getattr(self.hf_config, attr, None) if num_kv_heads is not None: return num_kv_heads # For non-grouped-query attention models, the number of KV heads is # equal to the number of attention heads. return self.hf_config.num_attention_heads def get_num_kv_heads(self, parallel_config: "ParallelConfig") -> int: """Returns the number of KV heads per GPU.""" total_num_kv_heads = self.get_total_num_kv_heads() # If tensor parallelism is used, we divide the number of KV heads by # the tensor parallel size. We will replicate the KV heads in the # case where the number of KV heads is smaller than the tensor # parallel size so each GPU has at least one KV head. return max(1, total_num_kv_heads // parallel_config.tensor_parallel_size) def get_num_layers(self, parallel_config: "ParallelConfig") -> int: total_num_hidden_layers = self.hf_config.num_hidden_layers return total_num_hidden_layers // parallel_config.pipeline_parallel_size class CacheConfig: """Configuration for the KV cache. Args: block_size: Size of a cache block in number of tokens. gpu_memory_utilization: Fraction of GPU memory to use for the Aphrodite execution. swap_space: Size of the CPU swap space per GPU (in GiB). cache_dtype: Data Type for KV cache storage. cache_quant_params_path: Path to the scales and zero points of KV cache quantization when cache_dtype is int8. """ def __init__( self, block_size: int, gpu_memory_utilization: float, swap_space: int, cache_dtype: str, cache_quant_params_path: Optional[str] = None, sliding_window: Optional[int] = None, context_shift: bool = False, ) -> None: self.block_size = block_size self.gpu_memory_utilization = gpu_memory_utilization self.swap_space_bytes = swap_space * _GB self.cache_dtype = cache_dtype self.sliding_window = sliding_window self.cache_quant_params_path = cache_quant_params_path self.context_shift = context_shift self._verify_args() self._verify_cache_dtype() # Will be set after profiling. self.num_gpu_blocks = None self.num_cpu_blocks = None def _verify_args(self) -> None: if self.gpu_memory_utilization > 1.0: raise ValueError( "GPU memory utilization must be less than 1.0. Got " f"{self.gpu_memory_utilization}.") def _verify_cache_dtype(self) -> None: if self.cache_dtype in ["auto", "int8"]: pass elif self.cache_dtype == "fp8_e5m2": nvcc_cuda_version = get_nvcc_cuda_version() if nvcc_cuda_version < Version("11.8"): raise ValueError( "FP8 is not supported when cuda version is lower than " "11.8. If you think you have the correct cuda version, " "please make sure you've properly exported CUDA_HOME.") device_name = torch.cuda.get_device_name() if "AMD" in device_name: raise NotImplementedError( "FP8_E5M2 KV Cache on AMD GPU has not been supported yet.") logger.info( "Using fp8_e5m2 data type to store kv cache. It reduces " "the GPU memory footprint and boosts the performance. " "But it may cause slight accuracy drop. " "Currently we only support fp8 without scaling factors and " "make e5m2 as a default format.") else: raise ValueError(f"Unknown kv cache dtype: {self.cache_dtype}") def verify_with_parallel_config( self, parallel_config: "ParallelConfig", ) -> None: total_cpu_memory = get_cpu_memory() # FIXME: Here, it is assumed that the GPUs in a tensor parallel # group are in the same node. However, the GPUs may span multiple nodes. num_gpus_per_node = parallel_config.tensor_parallel_size cpu_memory_usage = self.swap_space_bytes * num_gpus_per_node msg = (f"{cpu_memory_usage / _GB:.2f} GiB out of " f"the {total_cpu_memory / _GB:.2f} GiB total CPU memory is " "allocated for the swap space.") if cpu_memory_usage > 0.7 * total_cpu_memory: raise ValueError("Too large swap space. " + msg) elif cpu_memory_usage > 0.4 * total_cpu_memory: logger.warning("Possibly too large swap space. " + msg) class ParallelConfig: """Configuration for the distributed execution. Args: pipeline_parallel_size: Number of pipeline parallel groups. tensor_parallel_size: Number of tensor parallel groups. worker_use_ray: Whether to use Ray for model workers. Will be set to True if either pipeline_parallel_size or tensor_parallel_size is greater than 1. disable_custom_all_reduce: Disable the custom all-reduce kernel and fall back to NCCL. """ def __init__( self, pipeline_parallel_size: int, tensor_parallel_size: int, worker_use_ray: bool, max_parallel_loading_workers: Optional[int] = None, disable_custom_all_reduce: bool = False, ) -> None: self.pipeline_parallel_size = pipeline_parallel_size self.tensor_parallel_size = tensor_parallel_size self.worker_use_ray = worker_use_ray self.max_parallel_loading_workers = max_parallel_loading_workers self.disable_custom_all_reduce = disable_custom_all_reduce self.world_size = pipeline_parallel_size * tensor_parallel_size if self.world_size > 1: self.worker_use_ray = True self._verify_args() def _verify_args(self) -> None: if self.pipeline_parallel_size > 1: raise NotImplementedError( "Pipeline parallelism is not supported yet.") if is_hip(): self.disable_custom_all_reduce = True logger.info( "Disabled the custom all-reduce kernel because it is not " "supported on AMD GPUs.") elif self.pipeline_parallel_size > 1: self.disable_custom_all_reduce = True logger.info( "Disabled the custom all-reduce kernel because it is not " "supported with pipeline parallelism.") class SchedulerConfig: """Scheduler configuration. Args: max_num_batched_tokens: Maximum number of tokens to be processed in a single iteration. max_num_seqs: Maximum number of sequences to be processed in a single iteration. max_model_len: Maximum length of a sequence (including prompt and generated text). max_paddings: Maximum number of paddings to be added to a batch. """ def __init__( self, max_num_batched_tokens: Optional[int], max_num_seqs: int, max_model_len: int, max_paddings: int, ) -> None: if max_num_batched_tokens is not None: self.max_num_batched_tokens = max_num_batched_tokens else: # If max_model_len is too short, use 2048 as the default value for # higher throughput. self.max_num_batched_tokens = max(max_model_len, 2048) self.max_num_seqs = max_num_seqs self.max_model_len = max_model_len self.max_paddings = max_paddings self._verify_args() def _verify_args(self) -> None: if self.max_num_batched_tokens < self.max_model_len: raise ValueError( f"max_num_batched_tokens ({self.max_num_batched_tokens}) is " f"smaller than max_model_len ({self.max_model_len}). " "This effectively limits the maximum sequence length to " "max_num_batched_tokens and makes Aphrodite reject longer " "sequences. Please increase max_num_batched_tokens or " "decrease max_model_len.") if self.max_num_batched_tokens < self.max_num_seqs: raise ValueError( f"max_num_batched_tokens ({self.max_num_batched_tokens}) must " "be greater than or equal to max_num_seqs " f"({self.max_num_seqs}).") class DeviceConfig: def __init__(self, device: str = "cuda") -> None: self.device = torch.device(device) @dataclass class LoRAConfig: max_lora_rank: int max_loras: int max_cpu_loras: Optional[int] = None lora_dtype: Optional[torch.dtype] = None lora_extra_vocab_size: int = 256 # This is a constant. lora_vocab_padding_size: ClassVar[int] = 256 def __post_init__(self): # Keep this in sync with kernels/punica/bgmv/bgmv_config.h possible_max_ranks = (8, 16, 32, 64) possible_lora_extra_vocab_size = (0, 256, 512) if self.max_lora_rank not in possible_max_ranks: raise ValueError( f"max_lora_rank ({self.max_lora_rank}) must be one of " f"{possible_max_ranks}.") if self.lora_extra_vocab_size not in possible_lora_extra_vocab_size: raise ValueError( f"lora_extra_vocab_size ({self.lora_extra_vocab_size}) " f"must be one of {possible_lora_extra_vocab_size}.") if self.max_loras < 1: raise ValueError(f"max_loras ({self.max_loras}) must be >= 1.") if self.max_cpu_loras is None: self.max_cpu_loras = self.max_loras elif self.max_cpu_loras < self.max_loras: raise ValueError( f"max_cpu_loras ({self.max_cpu_loras}) must be >= " f"max_num_seqs ({self.max_loras})") def verify_with_model_config(self, model_config: ModelConfig): if self.lora_dtype in (None, "auto"): self.lora_dtype = model_config.dtype elif isinstance(self.lora_dtype, str): self.lora_dtype = getattr(torch, self.lora_dtype) if (model_config.quantization is not None and model_config.quantization == "gguf"): raise ValueError("LoRA is not supported with GGUF quantization.") def verify_with_scheduler_config(self, scheduler_config: SchedulerConfig): if scheduler_config.max_num_batched_tokens > 65528: raise ValueError( "Due to limitations of the custom LoRA CUDA kernel, " "max_num_batched_tokens must be <= 65528 when " "LoRA is enabled.") _STR_DTYPE_TO_TORCH_DTYPE = { "half": torch.float16, "float16": torch.float16, "float": torch.float32, "float32": torch.float32, "bfloat16": torch.bfloat16, } _ROCM_NOT_SUPPORTED_DTYPE = ["float", "float32"] def _get_and_verify_dtype( config: PretrainedConfig, dtype: Union[str, torch.dtype], ) -> torch.dtype: # NOTE: getattr(config, "torch_dtype", torch.float32) is not correct # because config.torch_dtype can be None. config_dtype = getattr(config, "torch_dtype", None) if config_dtype is None: config_dtype = torch.float32 if isinstance(dtype, str): dtype = dtype.lower() if dtype == "auto": if config_dtype == torch.float32: # Following the common practice, we use float16 for float32 # models. torch_dtype = torch.float16 else: torch_dtype = config_dtype else: if dtype not in _STR_DTYPE_TO_TORCH_DTYPE: raise ValueError(f"Unknown dtype: {dtype}") torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype] elif isinstance(dtype, torch.dtype): torch_dtype = dtype else: raise ValueError(f"Unknown dtype: {dtype}") if is_hip() and torch_dtype == torch.float32: rocm_supported_dtypes = [ k for k, v in _STR_DTYPE_TO_TORCH_DTYPE.items() if (k not in _ROCM_NOT_SUPPORTED_DTYPE) ] raise ValueError(f"dtype \'{dtype}\' is not supported in ROCm. " f"Supported dtypes are {rocm_supported_dtypes}") # Verify the dtype. if torch_dtype != config_dtype: if torch_dtype == torch.float32: # Upcasting to float32 is allowed. pass elif config_dtype == torch.float32: # Downcasting from float32 to float16 or bfloat16 is allowed. pass else: # Casting between float16 and bfloat16 is allowed with a warning. logger.warning(f"Casting {config_dtype} to {torch_dtype}.") return torch_dtype def _get_and_verify_max_len( hf_config: PretrainedConfig, max_model_len: Optional[int], ) -> int: """Get and verify the model's maximum length.""" derived_max_model_len = float("inf") possible_keys = [ # OPT "max_position_embeddings", # GPT-2 "n_positions", # MPT "max_seq_len", # ChatGLM2 "seq_length", # Others "max_sequence_length", "max_seq_length", "seq_len", ] for key in possible_keys: max_len_key = getattr(hf_config, key, None) if max_len_key is not None: derived_max_model_len = min(derived_max_model_len, max_len_key) if derived_max_model_len == float("inf"): if max_model_len is not None: # If max_model_len is specified, we use it. return max_model_len default_max_len = 2048 logger.warning( "The model's config.json does not contain any of the following " "keys to determine the original maximum length of the model: " f"{possible_keys}. Assuming the model's maximum length is " f"{default_max_len}.") derived_max_model_len = default_max_len rope_scaling = getattr(hf_config, "rope_scaling", None) if rope_scaling is not None: assert "factor" in rope_scaling scaling_factor = rope_scaling["factor"] if rope_scaling["type"] == "yarn": derived_max_model_len = rope_scaling[ "original_max_position_embeddings"] derived_max_model_len *= scaling_factor if max_model_len is None: max_model_len = derived_max_model_len elif max_model_len > derived_max_model_len: # hope this works scaling_factor = max_model_len / derived_max_model_len hf_config.rope_scaling = {"factor": scaling_factor, "type": "dynamic"} logger.warning( f"User-specified max_model_len {max_model_len} is higher than " f"the original {derived_max_model_len}. " "Attempting to use RoPE scaling.") derived_max_model_len = max_model_len return int(max_model_len)