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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "d071d3d0-aa2f-4582-8e43-12f22e64bbee",
"metadata": {},
"outputs": [],
"source": [
"# !pip install pytorch \n",
"# !pip install intel-extension-for-pytorch\n",
"# !pip install transformers\n",
"# !pip install datasets\n",
"# !pip install onnxruntime\n",
"# !pip install neural_compressor"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2d21c5cb-8042-4d63-8534-eb686acf4bf6",
"metadata": {},
"outputs": [],
"source": [
"from transformers import T5ForConditionalGeneration, T5Tokenizer\n",
"from datasets import Dataset\n",
"from transformers import Trainer, TrainingArguments\n",
"import torch\n",
"from torch.utils.data import DataLoader\n",
"import intel_extension_for_pytorch as ipex\n",
"import json\n",
"\n",
"# Load pre-trained FLAN-T5 model and tokenizer\n",
"model_name = \"google/flan-t5-large\" # FLAN-T5 Base Model\n",
"tokenizer = T5Tokenizer.from_pretrained(model_name)\n",
"model = T5ForConditionalGeneration.from_pretrained(model_name)\n",
"optimized_model = ipex.optimize(model, dtype=torch.float32)\n",
"# Example input-output pair for fine-tuning\n",
"data = json.load(\"t5train.json\")\n",
"\n",
"# Convert the data to a Hugging Face dataset\n",
"dataset = Dataset.from_dict(data)\n",
"dataloader = DataLoader(dataset, num_workers=4, pin_memory=True)\n",
"# Tokenize the data\n",
"def preprocess_function(examples):\n",
" model_inputs = tokenizer(examples['input_text'], padding=\"max_length\", truncation=True, max_length=2048)\n",
" labels = tokenizer(examples['output_text'], padding=\"max_length\", truncation=True, max_length=2048)\n",
" model_inputs['labels'] = labels['input_ids']\n",
" return model_inputs"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2e0d06e8-f50a-4a22-93b7-44152f06e462",
"metadata": {},
"outputs": [],
"source": [
"tokenized_datasets = dataset.map(preprocess_function, batched=True)\n",
"\n",
"# Set up the training arguments\n",
"training_args = TrainingArguments(\n",
" output_dir=\"./flan_t5_results\", # Output directory for model checkpoints\n",
" eval_strategy=\"epoch\", # Evaluation strategy to use\n",
" learning_rate=2e-5, # Learning rate for fine-tuning\n",
" per_device_train_batch_size=1, # Batch size for training\n",
" num_train_epochs=1, # Number of epochs\n",
" weight_decay=0.01, # Weight decay for regularization\n",
" save_steps=10, # Save model every 10 steps\n",
" save_total_limit=1, # Limit the number of saved models\n",
" fp16=False, # Disable mixed precision\n",
" use_cpu=True # Force CPU-only training\n",
")\n",
"\n",
"# Initialize the Trainer class\n",
"trainer = Trainer(\n",
" model=optimized_model,\n",
" args=training_args,\n",
" train_dataset=tokenized_datasets,\n",
" eval_dataset=tokenized_datasets # Use the same dataset for evaluation since we only have one data point\n",
")\n",
"\n",
"# Start training (this will fine-tune the model on the given example)\n",
"trainer.train()\n",
"\n",
"# Save the fine-tuned model\n",
"#trainer.save_model(\"./flan_t5_finetuned\")\n",
"optimized_model.save_pretrained(\"./flan_t5_finetuned\")\n",
"tokenizer.save_pretrained(\"./flan_t5_finetuned\")\n",
"\n",
"# Evaluate the model on the training data (for a single example)\n",
"optimized_model.eval()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d4b97afe-f09a-4bee-9139-ed9802da712e",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"from transformers import T5ForConditionalGeneration, T5Tokenizer\n",
"from neural_compressor.quantization import fit\n",
"from neural_compressor.config import PostTrainingQuantConfig\n",
"\n",
"# Load your FP32 model\n",
"model_path = \"./flan_t5_finetuned\"\n",
"optimized_model = T5ForConditionalGeneration.from_pretrained(model_path)\n",
"tokenizer = T5Tokenizer.from_pretrained(model_path)\n",
"\n",
"# Define the quantization configuration\n",
"quant_config = PostTrainingQuantConfig(approach='dynamic') # Dynamic quantization\n",
"\n",
"# Quantize the model\n",
"q_model = fit(model=optimized_model, conf=quant_config)\n",
"\n",
"# Save the quantized model\n",
"quantized_model_path = \"./flan_t5_quantized_fp16\"\n",
"q_model.save_pretrained(quantized_model_path)\n",
"tokenizer.save_pretrained(quantized_model_path)\n",
"\n",
"print(f\"Quantized model saved at: {quantized_model_path}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a152f3d9-7042-479b-b3ba-ff5c957be518",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from transformers import T5ForConditionalGeneration, T5Tokenizer\n",
"import os\n",
"\n",
"# Load the FP16 model\n",
"model_path = \"./flan_t5_fp16\"\n",
"model = T5ForConditionalGeneration.from_pretrained(model_path)\n",
"tokenizer = T5Tokenizer.from_pretrained(model_path)\n",
"\n",
"# Set the model to evaluation mode\n",
"model.eval()\n",
"\n",
"# Example input text\n",
"input_text = \"Translate English to French: How are you?\"\n",
"inputs = tokenizer(input_text, return_tensors=\"pt\", padding=True, truncation=True)\n",
"\n",
"# Prepare decoder input: <pad> token is used as the first decoder input\n",
"decoder_start_token_id = tokenizer.pad_token_id\n",
"decoder_input_ids = torch.tensor([[decoder_start_token_id]])\n",
"\n",
"# Create output directory if it doesn't exist\n",
"onnx_output_dir = \"./flant5\"\n",
"os.makedirs(onnx_output_dir, exist_ok=True)\n",
"\n",
"# Define the path for the ONNX model\n",
"onnx_model_path = os.path.join(onnx_output_dir, \"flan_t5_fp16.onnx\")\n",
"\n",
"# Export the model to ONNX\n",
"torch.onnx.export(\n",
" model, # Model to be converted\n",
" (inputs[\"input_ids\"], inputs[\"attention_mask\"], decoder_input_ids), # Input tuple\n",
" onnx_model_path, # Path to save the ONNX model\n",
" export_params=True, # Store the trained parameters\n",
" opset_version=13, # ONNX version\n",
" do_constant_folding=True, # Optimize constants\n",
" input_names=[\"input_ids\", \"attention_mask\", \"decoder_input_ids\"], # Input tensor names\n",
" output_names=[\"output\"], # Output tensor name\n",
" dynamic_axes={ # Dynamic shapes for batching\n",
" \"input_ids\": {0: \"batch_size\", 1: \"sequence_length\"},\n",
" \"attention_mask\": {0: \"batch_size\", 1: \"sequence_length\"},\n",
" \"decoder_input_ids\": {0: \"batch_size\", 1: \"sequence_length\"},\n",
" \"output\": {0: \"batch_size\", 1: \"sequence_length\"}\n",
" }\n",
")\n",
"\n",
"print(f\"ONNX model saved at: {onnx_model_path}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "055abefb-2d0f-4819-b859-86b77270c0be",
"metadata": {},
"outputs": [],
"source": [
"import onnxruntime as ort\n",
"import numpy as np\n",
"from transformers import T5Tokenizer\n",
"\n",
"# Load the ONNX model and tokenizer\n",
"onnx_model_path = \"./flan_t5_fp16.onnx\"\n",
"tokenizer = T5Tokenizer.from_pretrained(\"./flan_t5_fp16\")\n",
"ort_session = ort.InferenceSession(onnx_model_path)\n",
"\n",
"# Input text for the model\n",
"input_text = \"Translate English to French: How are you?\"\n",
"inputs = tokenizer(input_text, return_tensors=\"np\", padding=True, truncation=True)\n",
"\n",
"# Ensure inputs are numpy arrays\n",
"input_ids = np.array(inputs[\"input_ids\"], dtype=np.int64)\n",
"attention_mask = np.array(inputs[\"attention_mask\"], dtype=np.int64)\n",
"\n",
"# Prepare the decoder input (<pad> token for initial input to the decoder)\n",
"decoder_start_token_id = tokenizer.pad_token_id\n",
"decoder_input_ids = np.array([[decoder_start_token_id]], dtype=np.int64)\n",
"\n",
"# ONNX model inputs\n",
"onnx_inputs = {\n",
" \"input_ids\": input_ids,\n",
" \"attention_mask\": attention_mask,\n",
" \"decoder_input_ids\": decoder_input_ids\n",
"}\n",
"\n",
"# Run the ONNX model\n",
"onnx_outputs = ort_session.run(None, onnx_inputs)\n",
"\n",
"# Convert logits to token IDs\n",
"logits = onnx_outputs[0] # Shape: [batch_size, sequence_length, vocab_size]\n",
"token_ids = np.argmax(logits, axis=-1) # Get token IDs with the highest scores\n",
"\n",
"# Decode the token IDs into text\n",
"decoded_output = tokenizer.decode(token_ids[0], skip_special_tokens=True)\n",
"\n",
"print(f\"ONNX Model Output: {decoded_output}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a9110235-9c49-46ef-86e1-f446b3f12d67",
"metadata": {},
"outputs": [],
"source": []
}
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