Checkpoint Loading#

The PyTorch backend provides a flexible and extensible infrastructure for loading model checkpoints from different formats, such as HuggingFace (HF). This system allows you to load models from various sources (e.g., HuggingFace or custom formats) by implementing the required components, such as the checkpoint’s weight loader, mapper, and configuration parser.

Table of Contents#

  1. Overview

  2. Core Components

  3. Built-in Checkpoint Formats

  4. Checkpoint I/O Policies

  5. Using Checkpoint Loaders

  6. 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: Orchestrate the loading process for specific formats

  • Config Loaders: Handle model configuration parsing and validation

  • Weight Loaders: Manage the actual loading of model weights from storage into memory

  • Weight Mappers: Map and transform loaded weights to TensorRT LLM model’s definition

This modular design allows for easy extension to support new checkpoint formats while maintaining backward compatibility and performance optimizations. By separating the checkpoint loading components into four different subcomponents, any user can employ any relevant previous work while also introducing their own custom checkpoint-specific components.

If one wishes to support a new checkpoint format, they must implement all four components. Likewise, if the format shares some components with an already supported framework (e.g., HF), only the custom-specific components need to be implemented.

Core Components#

BaseCheckpointLoader#

The BaseCheckpointLoader is the central base interface for all checkpoint loading required operators. 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, mapping, **kwargs): Loads and returns a dictionary of weights

  • get_initialized_weight_mapper(model, config): Returns a runtime initialized weight mapper for the model

  • cleanup(): Releases resources and cleans up internal state

BaseConfigLoader#

Responsible for loading model configurations from checkpoint directories and parsing them into 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, mapping: Mapping) -> 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 into TRTLLM model’s object.

Built-in Checkpoint Formats#

HuggingFace Format#

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

  • Weights loading (.safetensors/.bin/.pth) - Loading HF compatible weights from disk

  • Configuration parser - Parsing HF stored configuration information to TRTLLM ModelConfig object

  • Weights Mapping - Converting HF weights into TRTLLM compatible representation

ModelExpress (MX) Loading Path#

The PyTorch backend can use ModelExpress (MX) for peer-to-peer weight transfer from a running TensorRT-LLM source instance before falling back to Hugging Face checkpoint loading. Selecting MX does not require an MX-specific on-disk checkpoint or conversion of the Hugging Face checkpoint. For installation, MX service deployment, and configuration details, see ModelExpress (MX) Checkpoint Loading.

Checkpoint I/O Policies#

Checkpoint format describes how weights are represented and mapped. The experimental checkpoint_io_policy independently controls how file-backed checkpoint bytes reach host memory:

  • auto (default) selects rank-striped read-ahead for compatible built-in PyTorch/HF loads and selects native I/O for other configurations.

  • native always preserves the existing checkpoint loader and its synchronous SafeTensors prefetch when the load group is eligible.

  • rank_striped_read_ahead divides SafeTensors files into fixed extents. Node-local ranks issue disjoint background pread requests into the Linux page cache while the existing mapping, transformation, and H2D path runs. After every rank finishes materialization, speculative read-ahead work that is still queued or active can no longer improve first-token latency and is stopped. A worker already blocked in a synchronous pread must finish its current 8 MiB read before shutdown, so degraded storage can delay the join.

checkpoint_format: HF
load_format: auto
checkpoint_io_policy: rank_striped_read_ahead

The optimized path currently requires identical policy configuration across all ranks, the automatically constructed built-in HF loader (checkpoint_loader must be unset), load_format: auto, SafeTensors, and an active MPI model-load communicator for distributed jobs. Static incompatibility such as MX, AutoDeploy, a custom or explicitly provided loader, a non-automatic load format, or known partial-model loading selects native I/O before any rank-striped communicator or reader setup. An explicit incompatible rank_striped_read_ahead request emits a warning instead of failing startup; auto records the native selection at info level.

Checkpoint-dependent eligibility remains a coordinated preflight. Lazy Kimi loading, raw-weight caching, layer overrides, insufficient host-memory headroom, and .bin/.pth select native materialization before readers start or the model is mutated. These pre-activation runtime fallbacks log at info level for auto and warn for an explicit rank_striped_read_ahead request. With a valid node communicator, fallback preserves native prefetch collectives on that load group; if communicator setup failed or its size did not match the model-load mapping, native loading safely skips prefetch. Memory admission reserves cgroup-aware startup headroom. Reader setup failures also clean up and fall back before mapping; mapping, transformation, or H2D failure after activation never retries a partially mutated model. A later advisory read failure keeps successfully materialized weights.

Read-ahead is intentionally not TP/PP/CP/EP-selective: each active load group may warm one logical checkpoint copy per node, although the work stops when materialization completes. The 64-reader budget is per active load group per node, not a host-wide arbitration mechanism across colocated independent TRT-LLM instances. Policy logs distinguish requested, selected, activated, and effective policy, so auto selection and native fallback remain observable; activation logs also report the local reader assignment.

This policy remains separate from future ModelStreamer, MX, GMS, or snapshot integration. Those systems may change the source or bypass raw loading without requiring a new combined checkpoint format.

Using Checkpoint Loaders#

Basic Usage#

There are two main approaches to trigger the use of checkpoint loading objects.

The first approach, through 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, HfCheckpointLoader will be selected by default.

To explicitly set the checkpoint loader, you need to call 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, if one wants to use a basic implemented checkpoint loader, but with a specific subcomponent, they can provide any specific subcomponent upon need

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, one can directly use the components of the checkpoint loading.

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, you need to implement all four components. This section provides minimal templates for each component.

When to Create Custom Components#

  • Complete New Format: Implement all four components when supporting a completely new checkpoint format

  • Custom Weight Storage: Only implement a custom weight loader if you have a unique weight storage format (e.g., custom binary format, database storage, etc.)

  • Custom Configuration: Only implement a custom config loader if your configuration format cannot be parsed by existing parsers.

  • Custom Weight Mapping: Only implement 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"

    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, mapping: Mapping, **kwargs) -> dict[str, Any]:
        """
        Load weights from your custom format.
        Args:
            checkpoint_dir: Directory containing checkpoint files
            mapping: A mapping object containing the distributed configuration.
            **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
        # 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."""
        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'],
            # 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 either define 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.