mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
812243bdd6
Signed-off-by: Aurelien Chartier <2567591+achartier@users.noreply.github.com> Co-authored-by: Haohang Huang <31998628+symphonylyh@users.noreply.github.com>
404 lines
17 KiB
Python
404 lines
17 KiB
Python
import json
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Dict, Generic, List, Optional, TypeVar
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import torch
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import transformers
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from tensorrt_llm import logger
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from tensorrt_llm._torch.pyexecutor.config_utils import is_nemotron_hybrid
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from tensorrt_llm._utils import torch_dtype_to_binding
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from tensorrt_llm.bindings import LayerType as LayerTypeCpp
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from tensorrt_llm.functional import AllReduceStrategy
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from tensorrt_llm.logger import logger
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from tensorrt_llm.mapping import Mapping
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from tensorrt_llm.models.modeling_utils import QuantConfig
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from tensorrt_llm.quantization.mode import QuantAlgo
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TConfig = TypeVar("TConfig", bound=transformers.PretrainedConfig)
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@dataclass
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class MoeLoadBalancerConfig:
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num_slots: Optional[int] = None
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initial_global_assignments: Optional[Dict[int,
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List[int]]] = field(default=None,
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repr=False)
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layer_updates_per_iter: int = 0
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ep_rank: Optional[int] = field(default=None, init=False)
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ep_size: Optional[int] = field(default=None, init=False)
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def setup(self, ep_rank: int, ep_size: int) -> None:
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self.ep_rank = ep_rank
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self.ep_size = ep_size
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assert self.num_slots is not None
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@property
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def num_local_slots(self) -> int:
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return self.num_slots // self.ep_size
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@property
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def slot_start(self) -> int:
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return self.ep_rank * self.num_local_slots
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@property
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def slot_end(self) -> int:
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return self.slot_start + self.num_local_slots
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def get_layer_initial_global_assignments(self, layer_idx: int) -> List[int]:
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if self.initial_global_assignments is not None:
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assert layer_idx in self.initial_global_assignments
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assert len(
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self.initial_global_assignments[layer_idx]) == self.num_slots
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return self.initial_global_assignments[layer_idx]
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else:
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return None
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@dataclass(kw_only=True)
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class ModelConfig(Generic[TConfig]):
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pretrained_config: Optional[TConfig] = None
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mapping: Mapping = field(default_factory=Mapping)
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# quantization configs
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quant_config: QuantConfig = field(default_factory=QuantConfig)
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# TODO(qijun): support per linear layer quantization
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quant_config_dict: Optional[Dict[str, QuantConfig]] = None
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# Delay weights creation to DecoderModelForCausalLM.__post_init__
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# to support mixed quantization.
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skip_create_weights_in_init: bool = False
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spec_config: Optional["DecodingBaseConfig"] = None
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lora_config: Optional["LoraConfig"] = None
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is_generation: bool = True
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max_num_tokens: int = 8192
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moe_max_num_tokens: Optional[int] = None
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moe_load_balancer: Optional[MoeLoadBalancerConfig] = None
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attn_backend: str = 'TRTLLM'
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moe_backend: str = 'CUTLASS' # options can be CUTLASS, TRTLLM
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allreduce_strategy: AllReduceStrategy = AllReduceStrategy.AUTO
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# If true, enable min-latency mode. Currently only used for Llama4.
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enable_min_latency: bool = False
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# Allow models to select op according to whether CUDA Graphs are used.
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use_cuda_graph: bool = False
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force_dynamic_quantization: bool = False
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extra_attrs: Dict = field(default_factory=dict, repr=False, init=False)
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_frozen: bool = field(default=False, init=False, repr=False)
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def __setattr__(self, key, value):
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"""
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Prevent modification of frozen instance attributes.
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However, we allow modification of 'extra_attrs' attributes for torch.compile
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and 'pretrained_config' attributes for mutimodal models. All the other
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attributes are frozen.
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This can be bypassed by manually setting '_frozen' to False. The design is
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to discourage modifying the attributes unintentionally.
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"""
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if self._frozen:
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if key not in ('_frozen', 'extra_attrs', 'pretrained_config'):
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raise AttributeError(
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f"Cannot modify ModelConfig.'{key}' - instance is frozen")
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super().__setattr__(key, value)
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def __post_init__(self):
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if self.pretrained_config and hasattr(self.pretrained_config,
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"architectures"):
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self.is_generation = self.is_generation_model(
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self.pretrained_config.architectures)
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def get_all_reduce_strategy(strategy: str = "AUTO"):
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maps = {
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"AUTO": AllReduceStrategy.AUTO,
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"NCCL": AllReduceStrategy.NCCL,
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"UB": AllReduceStrategy.UB,
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"MINLATENCY": AllReduceStrategy.MIN_LATENCY,
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"ONESHOT": AllReduceStrategy.ONESHOT,
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"TWOSHOT": AllReduceStrategy.TWOSHOT,
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"LOWPRECISION": AllReduceStrategy.LOWPRECISION,
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"MNNVL": AllReduceStrategy.MNNVL
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}
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key = strategy.upper()
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return maps[key] if key in maps else AllReduceStrategy.AUTO
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if isinstance(self.allreduce_strategy, str):
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self.allreduce_strategy = get_all_reduce_strategy(
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self.allreduce_strategy)
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@property
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def fuse_pos_embd(self):
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if self.attn_backend == 'TRTLLM':
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return True
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elif self.attn_backend == 'FLASHINFER':
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return False
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return False
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@property
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def enable_flash_mla(self):
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if self.attn_backend == 'TRTLLM':
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if hasattr(self.pretrained_config, "kv_lora_rank") and hasattr(
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self.pretrained_config, "qk_rope_head_dim"):
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head_dim = self.pretrained_config.kv_lora_rank + self.pretrained_config.qk_rope_head_dim
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if head_dim == 576 and torch.cuda.get_device_capability() == (
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9, 0):
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return True
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return False
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def get_quant_config(self, name: Optional[str] = None) -> QuantConfig:
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if name is None or self.per_layer_quant_configs is None:
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return self.quant_config
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if name in self.per_layer_quant_configs:
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return self.per_layer_quant_configs[name]
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raise ValueError(f'quant config of {name} is not found')
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@staticmethod
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def is_generation_model(model_architectures: Optional[List[str]]) -> bool:
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if model_architectures is None:
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logger.warning(
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"Model architectures is None, default to is_generation_model=True"
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)
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return True
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return model_architectures[0] not in [
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"BertForSequenceClassification", "Qwen2ForProcessRewardModel",
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"Qwen2ForRewardModel", "LlamaForTextEmbedding"
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]
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# TODO: should be 'not model_type == ModelType.ENCODER_ONLY'
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# once ModelType is used in pytorch flow.
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@classmethod
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def from_pretrained(cls,
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checkpoint_dir: str,
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trust_remote_code=False,
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**kwargs):
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pretrained_config = transformers.AutoConfig.from_pretrained(
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checkpoint_dir,
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trust_remote_code=trust_remote_code,
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)
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# Find the cache path by looking for the config.json file which should be in all
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# huggingface models
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model_dir = Path(
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transformers.utils.hub.cached_file(checkpoint_dir,
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'config.json')).parent
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quant_config = QuantConfig()
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layer_quant_config = None
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# quantized ckpt in modelopt format
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quant_config_file = model_dir / 'hf_quant_config.json'
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if quant_config_file.exists():
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with open(quant_config_file) as f:
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quant_config_dict = json.load(f)
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json_quant_configs = quant_config_dict['quantization']
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quant_config.quant_algo = json_quant_configs.get('quant_algo', None)
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# fp8_pb_wo from modelopt is the same as FP8_BLOCK_SCALES
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if quant_config.quant_algo == "fp8_pb_wo":
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quant_config.quant_algo = 'FP8_BLOCK_SCALES'
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quant_config.kv_cache_quant_algo = json_quant_configs.get(
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'kv_cache_quant_algo', None)
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quant_config.group_size = json_quant_configs.get('group_size', None)
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quant_config.exclude_modules = json_quant_configs.get(
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'exclude_modules', None)
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if quant_config.quant_algo == QuantAlgo.MIXED_PRECISION:
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mixed_quant_config_file = model_dir / 'quant_cfg.json'
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with open(mixed_quant_config_file) as fm:
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mixed_quant_configs = json.load(fm)
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# kv_cache_quant_algo is global regardless of MIXED_PRECISION
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kv_cache_quant_algo = mixed_quant_configs[
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'kv_cache_quant_algo']
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mixed_quant_configs = mixed_quant_configs[
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'quantized_layers']
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if kv_cache_quant_algo is not None and quant_config.kv_cache_quant_algo is not None:
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if kv_cache_quant_algo != quant_config.kv_cache_quant_algo:
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raise RuntimeError(
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f"The kvcache config in 'quant_cfg.json', {kv_cache_quant_algo},"
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f"is different from 'hf_quant_config.json', {quant_config.kv_cache_quant_algo}!"
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)
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kv_cache_quant_algo = kv_cache_quant_algo or quant_config.kv_cache_quant_algo
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for layer in mixed_quant_configs:
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config = QuantConfig()
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config.kv_cache_quant_algo = kv_cache_quant_algo
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config.quant_algo = mixed_quant_configs[layer][
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'quant_algo']
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config.group_size = mixed_quant_configs[layer].get(
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'group_size', None)
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mixed_quant_configs[layer] = config
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layer_quant_config = mixed_quant_configs
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elif quant_config.quant_algo == QuantAlgo.FP8_BLOCK_SCALES:
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if quant_config.group_size is None:
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quant_config.group_size = 128
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if kwargs.get(
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'moe_backend'
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) == 'TRTLLM' and quant_config.quant_algo == "FP8_BLOCK_SCALES" and quant_config.exclude_modules is None:
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quant_config.exclude_modules = [
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"*kv_b_proj*", "*k_b_proj*", "*eh_proj"
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]
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# quantized ckpt in other formats
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elif hasattr(pretrained_config, "quantization_config"):
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hf_quant_config = pretrained_config.quantization_config
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# DeepSeek V3 FP8 ckpt
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if hf_quant_config.get(
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"quant_method") == "fp8" and hf_quant_config.get(
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"weight_block_size", []):
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quant_config.quant_algo = QuantAlgo.FP8_BLOCK_SCALES
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if kwargs.get('moe_backend') == 'TRTLLM':
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# TODO: This is a hack. Remove after fp8 bmm is integrated.
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quant_config.exclude_modules = [
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"*kv_b_proj*", "*k_b_proj*", "*eh_proj"
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]
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else:
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quant_config.exclude_modules = ["*eh_proj"]
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block_size = hf_quant_config.get("weight_block_size", [])
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assert tuple(block_size) == (
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128,
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128), "FP8_BLOCK_SCALES only supports block_size=(128,128)"
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quant_config.group_size = block_size[0]
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model_config = cls(pretrained_config=pretrained_config,
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quant_config=quant_config,
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quant_config_dict=layer_quant_config,
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**kwargs)
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model_config._frozen = True
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return model_config
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def get_bindings_model_config(self,
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tokens_per_block: Optional[int] = None
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) -> "ModelConfigCpp":
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"""
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This method is used to construct the bindings config for the model.
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Currently it adheres to gptJsonConfig.cpp::createModelConfig, which assumes
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that an engine has been created.
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Args:
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tokens_per_block: The number of tokens per block. Please note that in PyTorch flow tokens_per_block is not available in the model config, instead it is defined in the executor config.
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Returns:
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The bindings model config.
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"""
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# TODO smor- this isn't robust, and currently tested for LlamaConfig only
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# TODO smor- currently assuming no rnn layers, no MOE
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from tensorrt_llm.bindings import ModelConfig as ModelConfigCpp
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num_heads = self.pretrained_config.num_attention_heads // (
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self.mapping.tp_size * self.mapping.cp_size)
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hidden_size = self.pretrained_config.hidden_size // self.mapping.tp_size
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model_config_cpp = ModelConfigCpp(
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vocab_size=self.pretrained_config.vocab_size,
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num_layers=self.pretrained_config.num_hidden_layers,
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num_attention_layers=self.get_num_attention_layers(),
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num_rnn_layers=0,
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num_heads=num_heads,
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hidden_size=hidden_size,
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data_type=torch_dtype_to_binding(
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self.pretrained_config.torch_dtype))
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# For kv cache size calculation: set tokens_per_block
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if tokens_per_block is None:
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logger.warning(
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f"tokens_per_block is not set, using default value {model_config_cpp.tokens_per_block}"
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)
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else:
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model_config_cpp.tokens_per_block = tokens_per_block
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# For kv cache size calculation: set num_kv_heads
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num_kv_heads = getattr(
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self.pretrained_config, "num_key_value_heads",
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num_heads) // (self.mapping.tp_size * self.mapping.cp_size)
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model_config_cpp.set_num_kv_heads(num_kv_heads)
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mlp_hidden_size = None
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if self.pretrained_config.intermediate_size is not None:
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mlp_hidden_size = self.pretrained_config.intermediate_size // self.mapping.tp_size
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else:
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# TODO: once tensorrt_llm._torch.AutoConfig is implemented, the following logic
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# should be moved to tensorrt_llm._torch.AutoConfig of the relevant modeling_xxx file
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if hasattr(self.pretrained_config, "architectures"
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) and self.pretrained_config.architectures is not None:
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architectures = self.pretrained_config.architectures
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if len(architectures
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) == 1 and architectures[0] == "DeciLMForCausalLM":
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mlp_hidden_size = self._infer_nemotron_ffn_mult()
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else:
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raise ValueError(
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f"Inferring mlp hidden size for model architecture: {architectures} isn't supported yet"
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)
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if mlp_hidden_size is None:
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raise ValueError(
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f"Failed to infer mlp hidden size for model: {self.pretrained_config.model_type}"
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)
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# For kv cache size calculation: set size_per_head
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head_dim_names = ["head_size", "head_dim"]
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for head_dim_name in head_dim_names:
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if head_dim_name in self.pretrained_config:
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head_size = getattr(self.pretrained_config, head_dim_name)
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break
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else:
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logger.warning(
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f"head_size/head_dim is not set, using default value {hidden_size // num_heads}"
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)
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head_size = hidden_size // num_heads
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model_config_cpp.mlp_hidden_size = mlp_hidden_size
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model_config_cpp.size_per_head = head_size
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# NOTE: this method is not robust, for Gemma3ForCausalLM only
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layer_types = self.get_layer_types()
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if layer_types is not None:
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model_config_cpp.layer_types = layer_types
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return model_config_cpp
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def _infer_nemotron_ffn_mult(self):
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# TODO smor: this is a hack to support Nemotron-Super-49B-v1 with LoRA, tracked by TRTLLM-5045 ticket
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# Nemotron-NAS has variable ffn_mult for each layer, we need to find the maximum
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# so that we don't set a too small mlp_hidden_size. This solution leads to a memory
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# consumption that is higher than required.
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biggest_ffn_mult = max(
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[x.ffn.ffn_mult for x in self.pretrained_config.block_configs])
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from tensorrt_llm._torch.models.modeling_nemotron_nas import \
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_ffn_mult_to_intermediate_size
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mlp_hidden_size = _ffn_mult_to_intermediate_size(
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biggest_ffn_mult, self.pretrained_config.hidden_size)
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return mlp_hidden_size
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def get_layer_types(self) -> Optional[List[LayerTypeCpp]]:
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"""
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This method is a hack to support the effort to switch to KvCacheManagerCpp.
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Currently, it is only tested for Gemma3ForCausalLM. For other models, it will return None.
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"""
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if self.pretrained_config.architectures[0] in ["Gemma3ForCausalLM"]:
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logger.debug(
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f"Setting layer types for {self.pretrained_config.architectures}"
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)
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return [
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LayerTypeCpp.ATTENTION,
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] * self.pretrained_config.num_hidden_layers
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else:
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return None
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def get_num_attention_layers(self):
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if is_nemotron_hybrid(self.pretrained_config):
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return self.pretrained_config.hybrid_override_pattern.count("*")
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else:
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return self.pretrained_config.num_hidden_layers
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