File size: 8,109 Bytes
58ae689 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 |
"""
Gradio app for SmolLM2-135M inference with streaming output.
Uses Lightning checkpoint saved from training.
"""
import sys
from pathlib import Path
from typing import List, Optional
import gradio as gr
import torch
from transformers import AutoConfig, AutoTokenizer
from model import SmolConfig, SmolLM2
from train import SmolLM2Module
# Device setup
DEVICE = "cpu"
if torch.cuda.is_available():
DEVICE = "cuda"
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
DEVICE = "mps"
# Globals
model: Optional[SmolLM2] = None
tokenizer = None
# Allow SmolConfig to be deserialized from Lightning checkpoints when torch.load
try:
torch.serialization.add_safe_globals([SmolConfig]) # type: ignore[attr-defined]
except Exception:
pass
def load_model_checkpoint(checkpoint_path: str = "checkpoints/smollm2-final-step-05000.ckpt"):
"""Load Lightning checkpoint and return status string."""
global model, tokenizer
ckpt = Path(checkpoint_path)
if not ckpt.exists():
return f"❌ Checkpoint not found: {ckpt}"
try:
hf_cfg = AutoConfig.from_pretrained("HuggingFaceTB/SmolLM2-135M")
config = SmolConfig.from_hf(hf_cfg)
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M")
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
module = SmolLM2Module.load_from_checkpoint(
str(ckpt),
config=config,
tokenizer=tokenizer,
map_location=DEVICE,
strict=False,
)
module.eval()
model = module.model.to(DEVICE).eval()
return f"✅ Model loaded from {ckpt} on {DEVICE}"
except Exception as e: # pragma: no cover - interactive
model = None
return f"❌ Error loading model: {e}"
def stream_generate(
prompt: str,
max_new_tokens: int,
temperature: float,
top_k: int,
top_p: float,
):
"""Generator that yields only the generated text (without prompt)."""
global model, tokenizer
if model is None or tokenizer is None:
yield "⚠️ Load the model first (click Reload Model)."
return
if not prompt or not prompt.strip():
yield "⚠️ Please enter a prompt."
return
# Tokenize prompt
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
input_ids = inputs["input_ids"].to(DEVICE)
# Guard against context overflow
if input_ids.shape[1] >= model.config.max_position_embeddings:
yield f"⚠️ Prompt too long ({input_ids.shape[1]} tokens). Max is {model.config.max_position_embeddings}."
return
generated = input_ids
past_key_values: Optional[List] = None
prompt_length = input_ids.shape[1]
with torch.no_grad():
for _ in range(max_new_tokens):
if past_key_values is None:
current_input = generated
else:
current_input = generated[:, -1:]
logits, past_key_values = model(
current_input,
past_key_values=past_key_values,
use_cache=True,
)
next_token_logits = logits[:, -1, :] / max(temperature, 1e-6)
# top-k
if top_k > 0:
values, _ = torch.topk(next_token_logits, top_k)
min_keep = values[:, -1].unsqueeze(-1)
next_token_logits = torch.where(
next_token_logits < min_keep,
torch.full_like(next_token_logits, float("-inf")),
next_token_logits,
)
# top-p
if top_p < 1.0:
sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
probs = torch.softmax(sorted_logits, dim=-1)
cumulative = torch.cumsum(probs, dim=-1)
sorted_mask = cumulative > top_p
sorted_mask[..., 1:] = sorted_mask[..., :-1].clone()
sorted_mask[..., 0] = 0
mask = sorted_mask.scatter(1, sorted_indices, sorted_mask)
next_token_logits = torch.where(mask, torch.full_like(next_token_logits, float("-inf")), next_token_logits)
probs = torch.softmax(next_token_logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
generated = torch.cat([generated, next_token], dim=1)
# Decode only the generated part (skip the prompt)
generated_text = tokenizer.decode(generated[0][prompt_length:], skip_special_tokens=True)
yield generated_text
# Initial load
INITIAL_STATUS = load_model_checkpoint()
def chat_stream(message, history, max_tokens, temperature, top_k, top_p):
"""Gradio wrapper for streaming chat."""
if history is None:
history = []
# Convert history from tuple format to dict format if needed
if history and isinstance(history[0], (list, tuple)):
# Convert from tuple format [(user, assistant), ...] to dict format
new_history = []
for h in history:
if isinstance(h, (list, tuple)) and len(h) >= 2:
if h[0]: # User message
new_history.append({"role": "user", "content": str(h[0])})
if h[1]: # Assistant message
new_history.append({"role": "assistant", "content": str(h[1])})
history = new_history
# Append user message
user_msg = (message or "").strip()
if not user_msg:
yield history
return
history.append({"role": "user", "content": user_msg})
history.append({"role": "assistant", "content": ""})
stream = stream_generate(user_msg, max_tokens, temperature, top_k, top_p)
for partial in stream:
# Update the last assistant message with generated text
if partial:
history[-1] = {"role": "assistant", "content": str(partial)}
yield history
def clear_chat():
return "", []
with gr.Blocks(title="SmolLM2-135M Text Generator") as demo:
gr.Markdown(
"""
# 🤖 SmolLM2-135M Text Generator
Generate text with your trained SmolLM2-135M checkpoint (streaming output).
"""
)
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Model Status")
status_text = gr.Textbox(value=INITIAL_STATUS, label="Status", interactive=False, lines=2)
load_btn = gr.Button("🔄 Reload Model", variant="secondary")
ckpt_input = gr.Textbox(
value="checkpoints/smollm2-step=05000-train_loss=0.0918.ckpt",
label="Checkpoint path",
interactive=True,
)
load_btn.click(fn=lambda p: load_model_checkpoint(p), inputs=ckpt_input, outputs=status_text)
gr.Markdown("### Generation Parameters")
max_tokens = gr.Slider(10, 500, value=100, step=10, label="Max Tokens")
temperature = gr.Slider(0.1, 2.0, value=0.8, step=0.1, label="Temperature")
top_k = gr.Slider(0, 100, value=50, step=5, label="Top-K")
top_p = gr.Slider(0.1, 1.0, value=1.0, step=0.05, label="Top-P")
with gr.Column(scale=2):
gr.Markdown("### 💬 Chat Interface")
chatbot = gr.Chatbot(label="Conversation", height=500)
with gr.Row():
msg = gr.Textbox(label="Your Message", placeholder="Type your prompt here...", scale=4, lines=2)
submit_btn = gr.Button("Send ➤", variant="primary", scale=1)
clear_btn = gr.Button("🗑️ Clear Chat", variant="stop")
msg.submit(fn=chat_stream, inputs=[msg, chatbot, max_tokens, temperature, top_k, top_p], outputs=chatbot)
submit_btn.click(fn=chat_stream, inputs=[msg, chatbot, max_tokens, temperature, top_k, top_p], outputs=chatbot).then(fn=lambda: "", outputs=msg)
clear_btn.click(fn=clear_chat, outputs=[msg, chatbot])
if __name__ == "__main__":
demo.queue().launch(share=False, server_name="0.0.0.0", server_port=7860)
|