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"""
Terminal visualization for RND1 generation.
This module provides real-time visualization of the diffusion denoising process,
showing token evolution and generation progress in the terminal using rich
formatting when available.
"""
import torch
from typing import Optional
from tqdm import tqdm
try:
from rich.console import Console
from rich.live import Live
from rich.text import Text
from rich.panel import Panel
from rich.progress import Progress, BarColumn, TextColumn, TimeRemainingColumn, MofNCompleteColumn
from rich.layout import Layout
RICH_AVAILABLE = True
except ImportError:
RICH_AVAILABLE = False
class TerminalVisualizer:
"""
Rich-based visualization for diffusion process with live updates.
Provides real-time visualization of the token denoising process during
diffusion-based language generation, with colored highlighting of masked
positions and progress tracking.
"""
def __init__(self, tokenizer, show_visualization: bool = True):
"""
Initialize the terminal visualizer.
Args:
tokenizer: The tokenizer for decoding tokens to text
show_visualization: Whether to show visualization (requires rich)
"""
self.tokenizer = tokenizer
self.show_visualization = show_visualization and RICH_AVAILABLE
if not RICH_AVAILABLE and show_visualization:
print("Warning: Install 'rich' for better visualization. Falling back to simple progress bar.")
self.show_visualization = False
if self.show_visualization:
self.console = Console()
self.live = None
self.progress = None
self.layout = None
else:
self.pbar = None
self.current_tokens = None
self.mask_positions = None
self.total_steps = 0
self.current_step = 0
def start_visualization(self, initial_tokens: torch.LongTensor, mask_positions: torch.BoolTensor, total_steps: int):
"""
Start the visualization.
Args:
initial_tokens: Initial token IDs (possibly masked)
mask_positions: Boolean mask indicating which positions are masked
total_steps: Total number of diffusion steps
"""
if not self.show_visualization:
self.pbar = tqdm(total=total_steps, desc="Diffusion")
return
self.current_tokens = initial_tokens.clone()
self.mask_positions = mask_positions
self.total_steps = total_steps
self.current_step = 0
self.layout = Layout()
self.layout.split_column(
Layout(name="header", size=3),
Layout(name="text", ratio=1),
Layout(name="progress", size=3)
)
self.progress = Progress(
TextColumn("[bold blue]Diffusion"),
BarColumn(),
MofNCompleteColumn(),
TextColumn("•"),
TextColumn("[cyan]Masks: {task.fields[masks]}"),
TimeRemainingColumn(),
)
self.progress_task = self.progress.add_task(
"Generating",
total=total_steps,
masks=mask_positions.sum().item()
)
self.live = Live(self.layout, console=self.console, refresh_per_second=4)
self.live.start()
self._update_display()
def update_step(self, tokens: torch.LongTensor, maskable: Optional[torch.BoolTensor], step: int,
entropy: Optional[torch.FloatTensor] = None, confidence: Optional[torch.FloatTensor] = None):
"""
Update visualization for current step.
Args:
tokens: Current token IDs
maskable: Boolean mask of remaining masked positions
step: Current step number
entropy: Optional entropy scores for each position
confidence: Optional confidence scores for each position
"""
if not self.show_visualization:
if self.pbar:
self.pbar.update(1)
masks = maskable.sum().item() if maskable is not None else 0
self.pbar.set_postfix({'masks': masks})
return
self.current_tokens = tokens.clone()
self.mask_positions = maskable
self.current_step = step
masks_remaining = maskable.sum().item() if maskable is not None else 0
self.progress.update(
self.progress_task,
advance=1,
masks=masks_remaining
)
self._update_display()
def _update_display(self):
"""Update the live display."""
if not self.live:
return
header = Text("🎭 RND1-Base Generation", style="bold magenta", justify="center")
self.layout["header"].update(Panel(header, border_style="bright_blue"))
text_display = self._format_text_with_masks()
self.layout["text"].update(
Panel(
text_display,
title="[bold]Generated Text",
subtitle=f"[dim]Step {self.current_step}/{self.total_steps}[/dim]",
border_style="cyan"
)
)
self.layout["progress"].update(Panel(self.progress))
def _format_text_with_masks(self) -> Text:
"""
Format text with colored masks.
Returns:
Rich Text object with formatted tokens
"""
text = Text()
if self.current_tokens is None:
return text
token_ids = self.current_tokens[0] if self.current_tokens.dim() > 1 else self.current_tokens
mask_flags = self.mask_positions[0] if self.mask_positions is not None and self.mask_positions.dim() > 1 else self.mask_positions
for i, token_id in enumerate(token_ids):
if mask_flags is not None and i < len(mask_flags) and mask_flags[i]:
# Alternate colors for visual effect
text.append("[MASK]", style="bold red on yellow" if self.current_step % 2 == 0 else "bold yellow on red")
else:
try:
token_str = self.tokenizer.decode([token_id.item()], skip_special_tokens=False)
# Skip special tokens in display
if token_str not in ["<|endoftext|>", "<|im_start|>", "<|im_end|>", "<s>", "</s>"]:
# Color based on position
text.append(token_str, style="green" if i < len(token_ids) // 2 else "cyan")
except:
continue
return text
def stop_visualization(self):
"""Stop the visualization and display final result."""
if not self.show_visualization:
if self.pbar:
self.pbar.close()
print("\n✨ Generation complete!\n")
return
if self.live:
self.live.stop()
self.console.print("\n[bold green]✨ Generation complete![/bold green]\n")
# Display final text
if self.current_tokens is not None:
try:
token_ids = self.current_tokens[0] if self.current_tokens.dim() > 1 else self.current_tokens
final_text = self.tokenizer.decode(token_ids, skip_special_tokens=True)
self.console.print(Panel(
final_text,
title="[bold]Final Generated Text",
border_style="green",
padding=(1, 2)
))
except:
pass
class SimpleProgressBar:
"""
Simple progress bar fallback when rich is not available.
Provides basic progress tracking using tqdm when the rich library
is not installed.
"""
def __init__(self, total_steps: int):
"""
Initialize simple progress bar.
Args:
total_steps: Total number of steps
"""
self.pbar = tqdm(total=total_steps, desc="Diffusion")
def update(self, masks_remaining: int = 0):
"""
Update progress bar.
Args:
masks_remaining: Number of masks still remaining
"""
self.pbar.update(1)
self.pbar.set_postfix({'masks': masks_remaining})
def close(self):
"""Close the progress bar."""
self.pbar.close()
print("\n✨ Generation complete!\n") |