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d8f717b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 | from __future__ import annotations
import warnings
from typing import List, Optional, Tuple, Union, Dict, Any
from pathlib import Path
import numpy as np
try:
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.gridspec import GridSpec
HAS_MATPLOTLIB = True
except ImportError:
HAS_MATPLOTLIB = False
warnings.warn(
"Matplotlib not installed. Visualization features unavailable. "
"Install with: pip install matplotlib"
)
def _check_matplotlib():
if not HAS_MATPLOTLIB:
raise RuntimeError(
"Matplotlib is required for visualization. "
"Install with: pip install matplotlib"
)
class KeiroPalette:
# Primary line colors
PRIMARY = {
"dense": "#E74C3C", # Red - Dense baseline ("Before")
"moe": "#3498DB", # Blue - Sparse MoE ("After")
"expert_avg": "#9B59B6",# Purple - Expert average
"memory": "#2ECC71", # Green - Memory bounds
}
# Generative expert spectrum for heatmap/routing
EXPERTS = [
"#3498DB", "#2980B9", "#1ABC9C", "#27AE60",
"#F39C12", "#D35400", "#E74C3C", "#8E44AD"
]
BG_LIGHT = "#FAFAFA"
BG_DARK = "#1A1A2E"
GRID_LIGHT = "#E0E0E0"
GRID_DARK = "#2D2D44"
class BasePlot:
def __init__(self, figsize=(12, 8), dpi=150, theme="dark", title=None):
_check_matplotlib()
self.figsize = figsize
self.dpi = dpi
self.theme = theme
self.fig, self.ax = plt.subplots(figsize=figsize)
self._apply_theme()
if title:
self.ax.set_title(title, fontsize=16, fontweight='bold', pad=20)
def _apply_theme(self):
bg = KeiroPalette.BG_DARK if self.theme == "dark" else KeiroPalette.BG_LIGHT
grid = KeiroPalette.GRID_DARK if self.theme == "dark" else KeiroPalette.GRID_LIGHT
fg = 'white' if self.theme == "dark" else 'black'
self.fig.patch.set_facecolor(bg)
self.ax.set_facecolor(bg)
self.ax.tick_params(colors=fg)
self.ax.xaxis.label.set_color(fg)
self.ax.yaxis.label.set_color(fg)
self.ax.title.set_color(fg)
self.ax.grid(True, alpha=0.2, color=grid)
for spine in self.ax.spines.values():
spine.set_color(grid)
def save(self, filepath: Union[str, Path]):
filepath = Path(filepath)
filepath.parent.mkdir(parents=True, exist_ok=True)
self.fig.savefig(filepath, dpi=self.dpi, bbox_inches="tight", facecolor=self.fig.get_facecolor())
def close(self):
plt.close(self.fig)
class ResourceUtilizationPlot(BasePlot):
def __init__(self, title="Resource Utilization (Before vs After)", **kwargs):
super().__init__(title=title, **kwargs)
self.ax.set_xlabel("Time (seconds)", fontsize=12)
self.ax2 = self.ax.twinx()
self.ax.set_ylabel("Memory Allocated (MB)", fontsize=12)
self.ax2.set_ylabel("GPU Utilization (%)", fontsize=12)
if self.theme == "dark":
self.ax2.tick_params(colors='white')
self.ax2.yaxis.label.set_color('white')
for spine in self.ax2.spines.values():
spine.set_color(KeiroPalette.GRID_DARK)
def add_trace(self, time_sec: List[float], values: List[float], label: str, metric: str = "memory"):
color = KeiroPalette.PRIMARY["dense"] if "Before" in label or "Dense" in label else KeiroPalette.PRIMARY["moe"]
linestyle = "-" if metric == "memory" else "--"
axis = self.ax if metric == "memory" else self.ax2
axis.plot(
time_sec, values, label=label,
color=color, linestyle=linestyle, linewidth=2.5, alpha=0.8
)
def finalize(self):
lines1, labels1 = self.ax.get_legend_handles_labels()
lines2, labels2 = self.ax2.get_legend_handles_labels()
self.ax2.legend(lines1 + lines2, labels1 + labels2, loc="best", framealpha=0.8)
plt.tight_layout()
class KeiroDashboard:
def __init__(self, figsize=(18, 12), dpi=150, theme="dark"):
_check_matplotlib()
self.dpi = dpi
self.theme = theme
self.fig, self.axes = plt.subplots(2, 2, figsize=figsize)
self._apply_theme()
def _apply_theme(self):
bg = KeiroPalette.BG_DARK if self.theme == "dark" else KeiroPalette.BG_LIGHT
grid = KeiroPalette.GRID_DARK if self.theme == "dark" else KeiroPalette.GRID_LIGHT
fg = 'white' if self.theme == "dark" else 'black'
self.fig.patch.set_facecolor(bg)
for ax in self.axes.flat:
ax.set_facecolor(bg)
ax.tick_params(colors=fg)
ax.xaxis.label.set_color(fg)
ax.yaxis.label.set_color(fg)
ax.title.set_color(fg)
ax.grid(True, alpha=0.2, color=grid)
for spine in ax.spines.values():
spine.set_color(grid)
def plot_memory_scaling(self, ax_idx=(0,0), seq_lens=None, data_dict=None):
ax = self.axes[ax_idx]
ax.set_title("Peak Memory vs Sequence Length", fontsize=14, fontweight='bold')
ax.set_xlabel("Sequence Length")
ax.set_ylabel("Memory (MB)")
if seq_lens and data_dict:
for k, v in data_dict.items():
color = KeiroPalette.PRIMARY["dense"] if "Dense" in k else KeiroPalette.PRIMARY["moe"]
ax.plot(seq_lens, v, label=k, color=color, marker='o', linewidth=2)
ax.legend()
def plot_throughput(self, ax_idx=(0,1), seq_lens=None, data_dict=None):
ax = self.axes[ax_idx]
ax.set_title("Inference Throughput (tokens/sec)", fontsize=14, fontweight='bold')
ax.set_xlabel("Sequence Length")
ax.set_ylabel("Throughput")
if seq_lens and data_dict:
for k, v in data_dict.items():
color = KeiroPalette.PRIMARY["dense"] if "Dense" in k else KeiroPalette.PRIMARY["moe"]
ax.plot(seq_lens, v, label=k, color=color, marker='s', linewidth=2)
ax.legend()
def plot_expert_load(self, ax_idx=(1,0), expert_distribution=None):
ax = self.axes[ax_idx]
ax.set_title("MoE Expert Load Balancing", fontsize=14, fontweight='bold')
ax.set_xlabel("Expert ID")
ax.set_ylabel("Tokens Assigned (%)")
if expert_distribution:
x = np.arange(len(expert_distribution))
colors = [KeiroPalette.EXPERTS[i % len(KeiroPalette.EXPERTS)] for i in x]
total = sum(expert_distribution)
pcts = [100.0 * c / total for c in expert_distribution] if total > 0 else expert_distribution
ax.bar(x, pcts, color=colors, alpha=0.8)
ax.set_xticks(x)
ax.set_xticklabels([f"E{i}" for i in x])
ax.axhline(100.0 / len(expert_distribution), color='gray', linestyle='--', label='Perfect Balance')
ax.legend()
def plot_speedup(self, ax_idx=(1,1), seq_lens=None, base_time=None, moe_time=None):
ax = self.axes[ax_idx]
ax.set_title("MoE Speedup vs Dense", fontsize=14, fontweight='bold')
ax.set_xlabel("Sequence Length")
ax.set_ylabel("Speedup (x)")
ax.axhline(1.0, color='gray', linestyle='--', alpha=0.5)
if seq_lens and base_time and moe_time:
speedups = [b/m if m > 0 else 0 for b, m in zip(base_time, moe_time)]
ax.plot(seq_lens, speedups, color=KeiroPalette.PRIMARY["expert_avg"], marker='D', linewidth=2, label="Speedup")
ax.legend()
def save(self, filepath: Union[str, Path]):
filepath = Path(filepath)
filepath.parent.mkdir(parents=True, exist_ok=True)
plt.tight_layout()
self.fig.savefig(filepath, dpi=self.dpi, bbox_inches="tight", facecolor=self.fig.get_facecolor())
def close(self):
plt.close(self.fig)
class ColorPalette(KeiroPalette):
pass
class DomainScorePlot(BasePlot):
def __init__(self, figsize=(10, 6), **kwargs):
super().__init__(figsize=figsize, title="Per-Domain Perplexity", **kwargs)
def plot_comparison(self, rows: List[Dict], include_dense: bool = False):
if not rows: return
domains = [r["domain"] for r in rows]
before = [r["ppl_before"] for r in rows]
after = [r["ppl_after"] for r in rows]
x = np.arange(len(domains))
width = 0.35 if not include_dense else 0.25
self.ax.bar(x - width/2, before, width, label='Before (Dense)', color=KeiroPalette.PRIMARY["dense"])
self.ax.bar(x + width/2, after, width, label='After (MoE)', color=KeiroPalette.PRIMARY["moe"])
if include_dense:
dense = [r.get("ppl_dense", 0) for r in rows]
self.ax.bar(x + 1.5*width, dense, width, label='Dense Baseline', color=KeiroPalette.PRIMARY["expert_avg"])
self.ax.set_xticks(x)
self.ax.set_xticklabels(domains, rotation=45, ha='right')
self.ax.set_ylabel("Perplexity (Lower is better)")
self.ax.legend()
self.fig.tight_layout()
class TrainingConvergencePlot(BasePlot):
def __init__(self, figsize=(10, 6), **kwargs):
super().__init__(figsize=figsize, title="Training Convergence", **kwargs)
def plot_history(self, history: Dict):
train_loss = history.get("train_loss", [])
val_loss = history.get("val_loss", [])
if train_loss:
self.ax.plot(train_loss, label="Train Loss", color=KeiroPalette.PRIMARY["dense"])
if val_loss:
if len(val_loss) < len(train_loss):
x_val = np.linspace(0, len(train_loss)-1, len(val_loss))
self.ax.plot(x_val, val_loss, label="Val Loss", marker='o', color=KeiroPalette.PRIMARY["moe"])
else:
self.ax.plot(val_loss, label="Val Loss", color=KeiroPalette.PRIMARY["moe"])
self.ax.set_xlabel("Steps (or Epochs)")
self.ax.set_ylabel("Cross Entropy Loss")
self.ax.legend()
class ExpertRoutingHeatmap(BasePlot):
def __init__(self, figsize=(12, 8), **kwargs):
super().__init__(figsize=figsize, title="Expert Routing by Domain", **kwargs)
def plot_routing(self, spec_dict: Dict):
affinity = spec_dict.get("affinity")
domains = spec_dict.get("domains")
labels = spec_dict.get("expert_labels")
if affinity is None or domains is None:
return
# Convert torch tensor to numpy
if hasattr(affinity, "cpu"):
matrix = affinity.cpu().numpy()
else:
matrix = np.array(affinity)
# Plotting
im = self.ax.imshow(matrix, aspect="auto", cmap="viridis")
self.ax.set_xticks(range(len(domains)))
self.ax.set_xticklabels(domains, rotation=45, ha='right')
# Only show individual expert labels if there aren't too many
if labels and len(labels) <= 64:
self.ax.set_yticks(range(len(labels)))
self.ax.set_yticklabels(labels, fontsize=6)
else:
self.ax.set_ylabel(f"{len(labels)} Layer-Experts")
self.ax.set_yticks([]) # Hide Y-axis labels for readability if too dense
self.fig.colorbar(im, ax=self.ax, fraction=0.046, pad=0.04)
self.fig.tight_layout()
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