llama/weight.py

The weight.py file in the llama folder of the TensorRT-LLM library plays a crucial role in loading and processing the pre-trained weights of the LLAMA model for use with the TensorRT-LLM framework.

It provides functions to load weights from various sources, such as Hugging Face checkpoints, GPTQ (Generative Pre-trained Transformer Quantization) checkpoints, and Meta's LLAMA checkpoints.

Let's analyze the key functions in this file and discuss how they relate to the TensorRT-LLM processes.

load_from_hf_checkpoint function:

• This function loads weights from a Hugging Face checkpoint directory.

• It iterates over the shard files in the checkpoint directory and loads the weights using the load_state_dict function from the utils.py file.

• It processes the loaded weights, splitting them based on the tensor parallel size (tp_size) and rank (tp_rank) specified in the mapping configuration.

• It handles specific weight transformations, such as concatenating and duplicating Q, K, V weights for attention layers and handling MoE (Mixture of Experts) weights.

• The processed weights are stored in a dictionary with keys corresponding to the TensorRT-LLM model structure.

load_from_hf_llama function:

• This function loads weights from a Hugging Face LLAMA model.

• It takes a pre-loaded Hugging Face LLAMA model as input and extracts the named parameters.

• It processes the weights similar to the load_from_hf_checkpoint function, handling Q, K, V concatenation, MoE weight stacking, and splitting based on tensor parallel configuration.

• The processed weights are stored in a dictionary with keys corresponding to the TensorRT-LLM model structure.

load_from_gptq_llama function:

• This function loads weights from a GPTQ LLAMA checkpoint in the SafeTensors format.

• It uses the safe_open function from the safetensors library to open the checkpoint file.

• It iterates over the weights in the checkpoint, processing them based on their names and the corresponding TensorRT-LLM model components.

• It handles weight quantization, unpacking, and preprocessing for the GPTQ format.

• The processed weights are stored in a dictionary with keys corresponding to the TensorRT-LLM model structure.

load_from_meta_llama function:

• This function loads weights from Meta's LLAMA checkpoints.

• It handles different checkpoint configurations, such as combining or splitting checkpoints based on the tensor parallel size and rank.

• It processes the weights, permuting and concatenating them as needed for the TensorRT-LLM model structure.

• It handles specific weight transformations for attention layers, MLP layers, and normalization layers.

• The processed weights are stored in a dictionary with keys corresponding to the TensorRT-LLM model structure.

These functions in the weight.py file are essential for loading and processing pre-trained weights from various sources and adapting them to the TensorRT-LLM model structure.

They handle the complexities of different checkpoint formats, quantization schemes, and parallelism configurations.

The processed weights are then used to initialize the corresponding components of the TensorRT-LLM model defined in the model.py file.

By loading the pre-trained weights, the TensorRT-LLM model can leverage the knowledge and performance of the original LLAMA model while benefiting from the optimizations and acceleration provided by the TensorRT-LLM framework.

During the model compilation process, the processed weights are used to populate the TensorRT-LLM model's layers and parameters. The TensorRT-LLM compiler takes these initialized weights and applies further optimizations, such as layer fusion and precision calibration, to generate an optimized TensorRT engine for efficient inference.

Overall, the weight.py file plays a vital role in bridging the gap between pre-trained LLAMA models and the TensorRT-LLM framework. It enables the seamless integration of LLAMA weights into the TensorRT-LLM model, allowing users to leverage the performance benefits of TensorRT while utilizing the knowledge and capabilities of the LLAMA model.