Mitchins/codet5-small-terminal-describer-ONNX

CodeT5-small Terminal Describer ONNX

This repository contains the ONNX (FP32 and INT8 quantized) versions of the fine-tuned CodeT5-small model for terminal command description. The base PyTorch model was trained on a combined dataset derived from NL2Bash, TLDR Pages, and NL2SH-ALFA.

For details on the training process, evaluation results, and performance metrics of the PyTorch model, please refer to the main model repository: Mitchins/codet5-small-terminal-describer

Model Structure

This repository is structured to provide both FP32 and INT8 quantized ONNX models, along with all necessary tokenizer and configuration files in the root for easy loading.

• Root Directory: Contains config.json, tokenizer files (vocab.json, merges.txt, tokenizer_config.json, special_tokens_map.json, added_tokens.json, spiece.model, generation_config.json), and this README.md.
• fp32/ directory: Contains the FP32 ONNX models (encoder_model.onnx, decoder_model.onnx, decoder_with_past_model.onnx).
• int8/ directory: Contains the INT8 quantized ONNX models (encoder_model.onnx, decoder_model.onnx).

Usage

Python Inference Example (ONNX Runtime)

To perform inference using the ONNX models with onnxruntime, you can use the following Python code snippet. This example demonstrates how to load the encoder and decoder models and perform a generation step.

from transformers import AutoTokenizer
import onnxruntime
import numpy as np
import os

# --- Configuration ---
# Path to the directory containing the ONNX models and tokenizer files
# Make sure to download the model files from this repository first.
# Example:
# huggingface-cli download Mitchins/codet5-small-terminal-describer-ONNX --local-dir ./codet5-small-terminal-describer-ONNX
model_dir = "." # Current directory if downloaded locally

# --- Load Tokenizer and Config ---
tokenizer = AutoTokenizer.from_pretrained(model_dir)

# --- Load ONNX Sessions (FP32 example) ---
encoder_session = onnxruntime.InferenceSession(os.path.join(model_dir, 'fp32/encoder_model.onnx'))
decoder_session = onnxruntime.InferenceSession(os.path.join(model_dir, 'fp32/decoder_model.onnx'))
decoder_with_past_session = onnxruntime.InferenceSession(os.path.join(model_dir, 'fp32/decoder_with_past_model.onnx'))

# For INT8 models:
# encoder_session_int8 = onnxruntime.InferenceSession(os.path.join(model_dir, 'int8/encoder_model.onnx'))
# decoder_session_int8 = onnxruntime.InferenceSession(os.path.join(model_dir, 'int8/decoder_model.onnx'))

# --- Inference Function ---
def generate_description_onnx(command, max_length=50, current_encoder_session=encoder_session, current_decoder_session=decoder_session, current_decoder_with_past_session=decoder_with_past_session):
    input_text = f'describe: {command}'
    input_ids = tokenizer(input_text, return_tensors='np').input_ids
    attention_mask = np.ones(input_ids.shape, dtype=np.int64)

    # 1. Encode input
    encoder_outputs = current_encoder_session.run(None, {
        "input_ids": input_ids,
        "attention_mask": attention_mask
    })
    encoder_hidden_states = encoder_outputs[0]

    # 2. Initialize decoder input
    decoder_input_ids = np.array([[tokenizer.pad_token_id]], dtype=np.int64) # Start with pad_token_id

    generated_tokens = []
    past_decoder_key_values = None
    past_encoder_key_values = None

    for _ in range(max_length):
        if past_decoder_key_values is None:
            # First step: use decoder_session
            decoder_outputs = current_decoder_session.run(None, {
                "input_ids": decoder_input_ids,
                "encoder_hidden_states": encoder_hidden_states,
                "encoder_attention_mask": attention_mask
            })
            logits = decoder_outputs[0]
            
            # Collect all present key-value pairs from the first decoder output
            past_decoder_key_values = []
            past_encoder_key_values = []
            # Assuming 6 layers for CodeT5-small, each with 2 key/value pairs for decoder and 2 for encoder
            for i in range(1, len(decoder_outputs), 4):
                past_decoder_key_values.append(decoder_outputs[i])   # present.X.decoder.key
                past_decoder_key_values.append(decoder_outputs[i+1]) # present.X.decoder.value
                past_encoder_key_values.append(decoder_outputs[i+2]) # present.X.encoder.key
                past_encoder_key_values.append(decoder_outputs[i+3]) # present.X.encoder.value

        else:
            # Subsequent steps: use decoder_with_past_session
            decoder_inputs = {
                "input_ids": decoder_input_ids[:, -1:], # Only pass the last generated token
                "encoder_attention_mask": attention_mask # Encoder attention mask is constant
            }
            
            # Add past_key_values to decoder_inputs
            # Assuming 6 layers for CodeT5-small
            for i in range(6):
                decoder_inputs[f"past_key_values.{i}.decoder.key"] = past_decoder_key_values[i*2]
                decoder_inputs[f"past_key_values.{i}.decoder.value"] = past_decoder_key_values[i*2+1]
                decoder_inputs[f"past_key_values.{i}.encoder.key"] = past_encoder_key_values[i*2]
                decoder_inputs[f"past_key_values.{i}.encoder.value"] = past_encoder_key_values[i*2+1]

            decoder_outputs = current_decoder_with_past_session.run(None, decoder_inputs)
            logits = decoder_outputs[0]
            
            # Update only the decoder key-value pairs from the output of decoder_with_past_session
            new_past_decoder_key_values = []
            for i in range(1, len(decoder_outputs), 2): # Iterate in groups of 2 for decoder key/value
                new_past_decoder_key_values.append(decoder_outputs[i])   # present.X.decoder.key
                new_past_decoder_key_values.append(decoder_outputs[i+1]) # present.X.decoder.value
            past_decoder_key_values = new_past_decoder_key_values

        next_token_logits = logits[:, -1, :]
        next_token = np.argmax(next_token_logits, axis=-1)

        if next_token.item() == tokenizer.eos_token_id:
            break

        generated_tokens.append(next_token.item())
        decoder_input_ids = np.concatenate([decoder_input_ids, next_token.reshape(1, 1)], axis=-1)

    description = tokenizer.decode(generated_tokens, skip_special_tokens=True)
    return description

# --- Example Usage ---
command_input = "ls -l"
description = generate_description_onnx(command_input)
print(f"Command: {command_input}")
print(f"Description: {description}")

# Example with INT8 models (uncomment to use)
# encoder_session_int8 = onnxruntime.InferenceSession(os.path.join(model_dir, 'int8/encoder_model.onnx'))
# decoder_session_int8 = onnxruntime.InferenceSession(os.path.join(model_dir, 'int8/decoder_model.onnx'))
# description_int8 = generate_description_onnx(command_input, current_encoder_session=encoder_session_int8, current_decoder_session=decoder_session_int8)
# print(f"Description (INT8): {description_int8}")