TensorRT-LLMs/docs/source/torch/features/checkpoint_loading.md

Checkpoint Loading

The PyTorch backend provides a flexible and extensible infrastructure for loading model checkpoints from different sources and formats, such as HuggingFace (HF) or custom formats, by implementing required components like the checkpoint's weight loader, mapper, and configuration parser.

Table of Contents

1. Overview
2. Core Components
3. Built-in Checkpoint Formats
4. Using Checkpoint Loaders
5. Creating Custom Checkpoint Loaders

Overview

The checkpoint loading design is built around a plugin-like architecture that is separated into four distinct components:

• Checkpoint Loaders: Orchestrates the loading process for specific formats.
• Config Loaders: Handles model configuration parsing and validation.
• Weight Loaders: Manages the actual loading of model weights from storage into memory.
• Weight Mappers: Maps and transforms loaded weights to the TRTLLM model's definition.

This modular design allows for easy extension to support new checkpoint formats while maintaining backward compatibility and performance optimizations. By separating checkpoint loading into four subcomponents, users can leverage existing implementations and introduce custom, checkpoint-specific components.

To support a new checkpoint format, you must implement all four components. If the format shares components with an existing framework (such as HF), you only need to implement the components that differ.

Core Components

BaseCheckpointLoader

The BaseCheckpointLoader is the central interface for all checkpoint loading operations. It provides a unified API regardless of the underlying checkpoint format. This interface is responsible for holding and exposing all objects required for the loading and parsing process.

Key Methods:

• load_config(checkpoint_dir, **kwargs): Loads and returns a ModelConfig object
• load_weights(checkpoint_dir, **kwargs): Loads and returns a dictionary of weights
• get_initialized_weight_mapper(model, config): Returns a weight mapper initialized at runtime for the model
• cleanup(): Releases resources and cleans up internal state

BaseConfigLoader

Loads model configurations from checkpoint directories and parses them into a TRTLLM ModelConfig:

from tensorrt_llm._torch.models.checkpoints.base_config_loader import BaseConfigLoader

class CustomConfigLoader(BaseConfigLoader):
    def load(self, checkpoint_dir: str, **kwargs) -> ModelConfig:
        # Load and parse configuration from your custom format
        pretrained_config = self._get_pretrained_config(checkpoint_dir, **kwargs)

        return ModelConfig(pretrained_config=pretrained_config,
                            ...)

    def _get_pretrained_config(self, checkpoint_dir, **kwargs):
        ...

BaseWeightLoader

Handles the loading of model weights from storage:

from tensorrt_llm._torch.models.checkpoints.base_weight_loader import BaseWeightLoader

class CustomWeightLoader(BaseWeightLoader):
    def load_weights(self, checkpoint_dir: str) -> dict[str, Any]:
        # Load weights from your custom format
        # Return a dictionary mapping parameter names to tensors
        return weights_dict

BaseWeightMapper

Transforms weights between different naming conventions and applies model-specific transformations to the TRTLLM model object.

Built-in Checkpoint Formats

HuggingFace Format

Currently, the HF checkpoint loader is the primary built-in format and supports:

• Weights loading (.safetensors, .bin, .pth): Load HF-compatible weights from disk
• Configuration parser - Parse configuration information stored by HF into a TRTLLM ModelConfig object
• Weights Mapping - Convert HF weights into a TRTLLM-compatible representation

Using Checkpoint Loaders

Basic Usage

There are two main approaches for using checkpoint loading objects

The first approach is through the llm-api, as shown in the following example:

from tensorrt_llm import LLM

hf_model_dir = "llama-models-v2/llama-v2-13b-hf"

llm = LLM(model=hf_model_dir)

In this example, the HfCheckpointLoader is selected by default.

To explicitly set the checkpoint loader, specify the required checkpoint-specific loader:

from tensorrt_llm import LLM
from tensorrt_llm._torch.models.checkpoints.hf.checkpoint_loader import HfCheckpointLoader

hf_model_dir = "llama-models-v2/llama-v2-13b-hf"

llm = LLM(model=hf_model_dir,
          checkpoint_loader=HfCheckpointLoader())

Similarly, to use a basic checkpoint loader with a specific subcomponent, provide the desired subcomponent as needed:

from tensorrt_llm import LLM
from tensorrt_llm._torch.models.checkpoints.hf.checkpoint_loader import HfCheckpointLoader

hf_model_dir = "llama-models-v2/llama-v2-13b-hf"

llm = LLM(model=hf_model_dir,
          checkpoint_loader=HfCheckpointLoader(weight_loader=MyCustomWeightLoader()))

In the second approach, you can directly use the individual checkpoint loading components:

from tensorrt_llm._torch.models.checkpoints.hf.gemma3_weight_mapper import \
    Gemma3HfWeightMapper
from tensorrt_llm._torch.models.modeling_gemma3 import Gemma3ForCausalLM

gemma3 = Gemma3ForCausalLM(model_config)
weight_mapper = Gemma3HfWeightMapper()
weight_mapper.init_model_and_config(gemma3, model_config)
gemma3.load_weights(hf_gemma3.state_dict(), weight_mapper)

Creating Custom Checkpoint Loaders

To support a new checkpoint format, implement all four components. This section provides minimal templates for each.

When to Create Custom Components

• Complete New Format: Implement all four components to support a new checkpoint format
• Custom Weight Storage: Implement only a custom weight loader if you have a unique weight storage format (such as a custom binary format or database storage)
• Custom Configuration: Implement only a custom config loader if your configuration format cannot be parsed by existing loaders
• Custom Weight Mapping: Implement only a custom weight mapper if your model has unique weight naming or transformation requirements that are checkpoint-specific

Step 1: Create the Checkpoint Loader

from typing import Optional
from tensorrt_llm._torch.models.checkpoints.base_checkpoint_loader import BaseCheckpointLoader
from tensorrt_llm._torch.models.checkpoints.base_config_loader import BaseConfigLoader
from tensorrt_llm._torch.models.checkpoints.base_weight_loader import BaseWeightLoader
from tensorrt_llm._torch.models.checkpoints.base_weight_mapper import BaseWeightMapper
from tensorrt_llm._torch.models.modeling_utils import register_checkpoint_loader

@register_checkpoint_loader("CUSTOM_FORMAT")
class CustomCheckpointLoader(BaseCheckpointLoader):
    def __init__(self,
                 *,
                 weight_loader: Optional[BaseWeightLoader] = None,
                 weight_mapper: Optional[BaseWeightMapper] = None,
                 config_loader: Optional[BaseConfigLoader] = None):
        self._weight_loader = weight_loader or self.get_default_weight_loader()
        self._config_loader = config_loader or self.get_default_config_loader()
        self._weight_mapper = weight_mapper
        self._checkpoint_format = "CUSTOM_FORMAT" # Set the checkpoint format name

    def get_default_weight_loader(self) -> BaseWeightLoader:
        return CustomWeightLoader()

    def get_default_config_loader(self) -> BaseConfigLoader:
        return CustomConfigLoader()

Step 2: Create the Checkpoint Weight Loader

from typing import Any
from tensorrt_llm._torch.models.checkpoints.base_weight_loader import BaseWeightLoader
from tensorrt_llm._torch.models.modeling_utils import register_checkpoint_weight_loader

@register_checkpoint_weight_loader("CUSTOM_FORMAT")
class CustomWeightLoader(BaseWeightLoader):
    def load_weights(self, checkpoint_dir: str, **kwargs) -> dict[str, Any]:
        """
        Load weights from your custom format.

        Args:
            checkpoint_dir: Directory containing checkpoint files
            **kwargs: Additional loading parameters

        Returns:
            Dictionary mapping parameter names to tensors
        """
        weights = {} # Implement your custom weight loading logic here

        # Examples:
        # - Load from custom binary files
        # - Load from databases
        # - Load from compressed archives
        # - Apply custom preprocessing

        return weights

Step 3: Create the Checkpoint Config Loader

from tensorrt_llm._torch.model_config import ModelConfig
from tensorrt_llm._torch.models.checkpoints.base_config_loader import BaseConfigLoader
from tensorrt_llm._torch.models.modeling_utils import register_config_loader

@register_config_loader("CUSTOM_FORMAT")
class CustomConfigLoader(BaseConfigLoader):
    def load(self, checkpoint_dir: str, **kwargs) -> ModelConfig:
        """
        Load and parse configuration from your custom format.

        Args:
            checkpoint_dir: Directory containing configuration files
            **kwargs: Additional loading parameters

        Returns:
            ModelConfig object containing parsed configuration
        """
        # Load your custom configuration format here
        # Examples:
        # - Parse YAML/TOML files
        # - Convert from proprietary formats

        pretrained_config = self._load_pretrained_config(checkpoint_dir, **kwargs)

        return ModelConfig(
            pretrained_config=pretrained_config,
            # Add other ModelConfig parameters as needed
        )

    def _load_pretrained_config(self, checkpoint_dir: str, **kwargs):
        """Load the raw configuration from your custom format."""
        # Implement as needed
        pass

Step 4: Create the Checkpoint Weight Mapper

from torch import nn
from tensorrt_llm._torch.models.checkpoints.base_weight_mapper import BaseWeightMapper
from tensorrt_llm._torch.models.modeling_utils import register_mapper

@register_mapper("CUSTOM_FORMAT")
class CustomWeightMapper(BaseWeightMapper):
    def __init__(self):
        super().__init__()
        # Define any weight transformation callbacks
        self._callbacks = [
            # Add your custom weight transformation functions
            # self._custom_transform_function,
        ]

    def map_weights(self) -> None:
        """
        Define mappings between source and target weight names.
        """
        self.mapping.update({
            # Map source names to target names
            # 'target_module_name': ['source_param1', 'source_param2'],
            # For example: 'qkv_proj': ['q_proj', 'k_proj', 'v_proj']
        })

    def apply_callbacks(self, module: nn.Module, module_name: str,
                        module_names_breakdown: list[str],
                        weights: dict) -> list[dict]:
        """
        Apply weight transformations for modules that require special handling.

        Args:
            module: The target module
            module_name: The specific module name being processed
            module_names_breakdown: Module path components
            weights: Source weights dictionary

        Returns:
            List of transformed weight dictionaries
        """
        module_weights = []

        for new_name in self._mapping[module_name]:
            # Filter weights for this specific parameter
            fw = self.filter_weights(
                '.'.join(module_names_breakdown + [new_name]), weights)

            # Apply transformation callbacks
            for callback in self._callbacks:
                fw = callback(module, new_name, fw)

            module_weights.append(fw)

        return module_weights

    def should_skip_module(self, module_name: str) -> bool:
        """
        Define which modules should be skipped during loading.
        """
        # Add logic to skip specific modules based on your requirements
        # Examples:
        # - Skip LoRA-specific modules
        # - Skip temporary/auxiliary modules

        return super().should_skip_module(module_name)

Note: When creating a custom mapper, you can define either a checkpoint-format-specific mapper. For example:

@register_mapper("CUSTOM_FORMAT")
class CustomWeightMapper(BaseWeightMapper)

Alternatively, you can define a checkpoint-model-specific mapper. For example:

@register_mapper("CUSTOM_FORMAT", "Gemma3ForCausalLM")
class CustomWeightMapper(BaseWeightMapper)

By setting the model name, the registered mapper will be associated with the specific model.