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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "25d9b213-f625-4327-98b3-d9e67db11687",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"saving model\n",
"Model Saved\n"
]
},
{
"data": {
"text/plain": [
"'\\ngenerator = pipeline(\\'text-generation\\', model=\"facebook/opt-1.3b\")\\n\\ndef generate_text_pip(prompt):\\n generated_text = generator(prompt, max_length=1000, num_return_sequences=1)[0][\\'generated_text\\']\\n return generated_text\\nprint(generator(\"I went to boston and\"))\\n\\ndef generate_text(prompt):\\n inputs = tokenizer(prompt, return_tensors=\"pt\")\\n output = model(**inputs)\\n logits = output.logits\\n predicted_ids = logits.argmax(-1)\\n generated_text = tokenizer.decode(predicted_ids[0], skip_special_tokens=True)\\n return generated_text\\n\\ndef generate_text_from_quantized(prompt):\\n inputs = tokenizer(prompt, return_tensors=\"pt\")\\n output = model_q(**inputs)\\n logits = output.logits\\n predicted_ids = logits.argmax(-1)\\n generated_text = tokenizer.decode(predicted_ids[0], skip_special_tokens=True)\\n return generated_text\\n\\n# Create a Gradio interface\\niface = gr.Interface(fn=generate_text_pip, inputs=\"text\", outputs=\"text\", live=True)\\n\\niface_2 = gr.Interface(fn=generate_text_from_quantized, inputs=\"text\", outputs=\"text\", live=True)\\n\\n\\napp = gr.TabbedInterface([iface, iface_2],[\"Normal\", \"Quantized\"])\\n\\n# Launch the Gradio app\\napp.launch()\\n'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import gradio as gr\n",
"import tqdm\n",
"import torch\n",
"from torch import nn\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
"from functools import partial\n",
"import gc\n",
"\n",
"\n",
"# core quantization method (simulated quantization)\n",
"def pseudo_quantize_tensor(w, n_bit=4, q_group_size=-1):\n",
" org_w_shape = w.shape\n",
" if q_group_size > 0:\n",
" assert org_w_shape[-1] % q_group_size == 0\n",
" w = w.reshape(-1, q_group_size)\n",
"\n",
" assert w.dim() == 2\n",
"\n",
" # Calculate the maximum (\\alpha) and minimum values (\\beta) in the tensor.\n",
" max_val = w.amax(dim=1, keepdim=True)\n",
" assert max_val.dim() == 2 and max_val.size(0) == w.size(0) and max_val.size(1) == 1\n",
" min_val = w.amin(dim=1, keepdim=True)\n",
" assert min_val.dim() == 2 and min_val.size(0) == w.size(0) and min_val.size(1) == 1\n",
"\n",
" # Calculate the scale factor and zero point. (Formula 1 & 2)\n",
" max_int = 2 ** n_bit - 1\n",
" scales = (max_val - min_val).clamp(min=1e-5) / max_int\n",
" assert scales.shape == max_val.shape\n",
" zeros = (-torch.round(min_val / scales)).clamp_(0, max_int)\n",
" assert scales.shape == min_val.shape\n",
"\n",
" assert torch.isnan(scales).sum() == 0\n",
" assert torch.isnan(w).sum() == 0\n",
"\n",
" # Quantize W: Map values in the range [\\beta, \\alpha] to lie within [0, 2^b - 1] (Formula 3)\n",
" w = torch.clamp(torch.round(w / scales) + zeros, 0, max_int)\n",
" assert w.dim() == 2 and w.size(0) == scales.size(0) and w.size(1) == q_group_size\n",
"\n",
" # Dequantize W (pseudo quantization, the inverse transformation of Formula 3)\n",
" w = (w - zeros) * scales\n",
" assert w.dim() == 2 and w.size(0) == scales.size(0) and w.size(1) == q_group_size\n",
"\n",
" assert torch.isnan(w).sum() == 0\n",
"\n",
" w = w.reshape(org_w_shape)\n",
" return w\n",
"\n",
"@torch.no_grad()\n",
"def pseudo_quantize_model_weight(\n",
" model, w_bit, q_group_size,\n",
"):\n",
" for n, m in model.named_modules():\n",
" if isinstance(m, nn.Linear):\n",
" m.weight.data = pseudo_quantize_tensor(m.weight.data, n_bit=w_bit, q_group_size=q_group_size)\n",
" \n",
" \n",
" \n",
" \n",
"# Load the tokenizer and model\n",
"model_path = \"facebook/opt-125m\"\n",
"offload_folder = \"offload\"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)\n",
"model = AutoModelForCausalLM.from_pretrained(model_path, device_map=\"auto\", offload_folder=offload_folder)\n",
"model_q = AutoModelForCausalLM.from_pretrained(model_path, device_map=\"auto\",offload_folder=offload_folder)\n",
"pseudo_quantize_model_weight(model_q, w_bit=3, q_group_size=128)\n",
"# Define a function for model inference\n",
"\n",
"\n",
"quantized_model_path = \"facebook/opt-125m_3bit\"\n",
"print(\"saving model\")\n",
"model_q.save_pretrained(quantized_model_path)\n",
"tokenizer.save_pretrained(quantized_model_path)\n",
"print(\"Model Saved\")\n",
"'''\n",
"generator = pipeline('text-generation', model=\"facebook/opt-1.3b\")\n",
"\n",
"def generate_text_pip(prompt):\n",
" generated_text = generator(prompt, max_length=1000, num_return_sequences=1)[0]['generated_text']\n",
" return generated_text\n",
"print(generator(\"I went to boston and\"))\n",
"\n",
"def generate_text(prompt):\n",
" inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
" output = model(**inputs)\n",
" logits = output.logits\n",
" predicted_ids = logits.argmax(-1)\n",
" generated_text = tokenizer.decode(predicted_ids[0], skip_special_tokens=True)\n",
" return generated_text\n",
"\n",
"def generate_text_from_quantized(prompt):\n",
" inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
" output = model_q(**inputs)\n",
" logits = output.logits\n",
" predicted_ids = logits.argmax(-1)\n",
" generated_text = tokenizer.decode(predicted_ids[0], skip_special_tokens=True)\n",
" return generated_text\n",
"\n",
"# Create a Gradio interface\n",
"iface = gr.Interface(fn=generate_text_pip, inputs=\"text\", outputs=\"text\", live=True)\n",
"\n",
"iface_2 = gr.Interface(fn=generate_text_from_quantized, inputs=\"text\", outputs=\"text\", live=True)\n",
"\n",
"\n",
"app = gr.TabbedInterface([iface, iface_2],[\"Normal\", \"Quantized\"])\n",
"\n",
"# Launch the Gradio app\n",
"app.launch()\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5b3171de-b63f-4f5e-8347-c4a5da79c397",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"quantized model [{'generated_text': 'I went to boston and was hoping for a good time for a good time for a good time'}]\n"
]
}
],
"source": [
"model_q_path = \"facebook/opt-125m_3bit\"\n",
"\n",
"\n",
"generator_q = pipeline('text-generation', model=model_q_path)\n",
"print(\"quantized model\",generator_q(\"I went to boston and\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8e969157-5261-4245-a6d0-d394e971b347",
"metadata": {},
"outputs": [],
"source": []
}
],
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