Phi 2.0
Model.py
This file, named model.py
, defines the architecture of the PhiForCausalLM model, which is a variant of the GPT (Generative Pre-trained Transformer) model specifically designed for causal language modeling tasks.
The model is implemented using the TRT-LLM (TensorRT-based Large Language Model) framework.
Here's a detailed breakdown of the code:
The necessary modules and classes are imported from the TRT-LLM framework.
The PhiDecoderLayer
class is defined, which represents a single decoder layer in the PhiForCausalLM model. It consists of the following components:
input_layernorm
: A layer normalization module applied to the input.attention
: An attention module that performs self-attention on the input.mlp
: A multi-layer perceptron (MLP) module that applies non-linear transformations to the attended features.
The forward pass of the PhiDecoderLayer
applies the layer normalization, attention, and MLP modules sequentially, with residual connections.
The PhiModel
class represents the overall architecture of the PhiForCausalLM model. It consists of the following components:
vocab_embedding
: An embedding layer that maps input token IDs to dense vectors.layers
: A list ofPhiDecoderLayer
instances that form the decoder stack.ln_f
: A final layer normalization module applied to the output of the decoder stack.
The forward pass of the PhiModel
first applies the vocabulary embedding to the input token IDs, then passes the embedded features through the decoder layers, and finally applies the final layer normalization.
The PhiForCausalLM
class is the main model class that inherits from DecoderModelForCausalLM
and TopModelMixin
.
It combines the PhiModel
with a language modeling head (ParallelLMHead
) for causal language modeling tasks.
The check_config
method is used to set default values for certain configuration parameters if they are not provided. The convert_hf_checkpoint
class method is used to convert a Hugging Face checkpoint to a TRT-LLM checkpoint. It takes the Hugging Face model directory, data type, and output directory as input, converts the Hugging Face model configuration and weights to the TRT-LLM format, and optionally saves the converted checkpoint to the specified output directory.
Overall, this code defines a custom GPT-based model architecture called PhiForCausalLM, which is implemented using the TRT-LLM framework. The model is designed for causal language modeling tasks and can be converted from Hugging Face checkpoints to the TRT-LLM format for efficient inference using TensorRT.
Last updated