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858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe a671953 8a58ffe 858e8b2 a671953 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe 858e8b2 8a58ffe | 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 | """Training dynamics analyzer."""
import math
from pathlib import Path
from typing import Any, Dict, List, Optional
try:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
HAS_MATPLOTLIB = True
except ImportError:
HAS_MATPLOTLIB = False
class TrainingDynamicsAnalyzer:
"""Analyzes and visualizes training metrics.
Analysis items:
- Loss curve: Convergence patterns, spike detection
- LR schedule: Warmup + Cosine decay verification
- Gradient Norm: Training stability, explosion/vanishing detection
- Throughput: tokens/sec stability, bottleneck detection
"""
def __init__(self, save_dir: str = "./eval_results"):
self.save_dir = Path(save_dir)
self.save_dir.mkdir(parents=True, exist_ok=True)
def analyze_metrics(self, metrics_history: Dict[str, list]) -> Dict[str, Any]:
"""Analyzes training metrics.
Args:
metrics_history: Trainer.metrics.history dictionary
Returns:
Analysis results
"""
print("\n" + "=" * 70)
print("🔬 Training Dynamics Analysis")
print("=" * 70)
analysis = {}
# ── Loss analysis ──
if metrics_history.get("train_loss"):
losses = metrics_history["train_loss"]
analysis["loss"] = {
"initial": round(losses[0], 4),
"final": round(losses[-1], 4),
"minimum": round(min(losses), 4),
"total_reduction": round(losses[0] - losses[-1], 4),
}
# Spike detection (sudden increase of 50% or more compared to previous value)
spikes = []
for i in range(1, len(losses)):
if losses[i] > losses[i-1] * 1.5:
step = metrics_history["step"][i] if "step" in metrics_history else i
spikes.append({"step": step, "loss": round(losses[i], 4)})
analysis["loss"]["spikes"] = spikes
print(f"\n 📉 Loss Analysis:")
print(f" Initial: {analysis['loss']['initial']:.4f}")
print(f" Final: {analysis['loss']['final']:.4f}")
print(f" Minimum: {analysis['loss']['minimum']:.4f}")
print(f" Reduction: {analysis['loss']['total_reduction']:.4f}")
print(f" Spikes: {len(spikes)}")
if spikes:
for s in spikes[:5]:
print(f" Step {s['step']}: Loss = {s['loss']}")
# ── Gradient Norm analysis ──
if metrics_history.get("grad_norm"):
gnorms = metrics_history["grad_norm"]
analysis["grad_norm"] = {
"mean": round(sum(gnorms) / len(gnorms), 4),
"max": round(max(gnorms), 4),
"min": round(min(gnorms), 4),
"clipped_pct": round(sum(1 for g in gnorms if g >= 1.0) / len(gnorms) * 100, 1),
}
print(f"\n 📐 Gradient Norm Analysis:")
print(f" Mean: {analysis['grad_norm']['mean']:.4f}")
print(f" Max: {analysis['grad_norm']['max']:.4f}")
print(f" Clipping rate: {analysis['grad_norm']['clipped_pct']:.1f}%")
if analysis["grad_norm"]["clipped_pct"] > 50:
print(f" ⚠️ Clipping is frequent → consider lowering LR or extending warmup")
# ── Throughput analysis ──
if metrics_history.get("tokens_per_sec"):
tps = metrics_history["tokens_per_sec"]
tps_valid = [t for t in tps if t > 0]
if tps_valid:
analysis["throughput"] = {
"mean": round(sum(tps_valid) / len(tps_valid)),
"std": round((sum((t - sum(tps_valid)/len(tps_valid))**2 for t in tps_valid) / len(tps_valid))**0.5),
"min": round(min(tps_valid)),
"max": round(max(tps_valid)),
}
print(f"\n ⚡ Throughput Analysis:")
print(f" Mean: {analysis['throughput']['mean']:,} tokens/sec")
print(f" StdDev: {analysis['throughput']['std']:,}")
print(f" Range: [{analysis['throughput']['min']:,}, {analysis['throughput']['max']:,}]")
return analysis
def plot_training_curves(
self,
metrics_history: Dict[str, list],
save_path: Optional[str] = None,
):
"""Visualizes training curves as a 4-panel chart."""
if not HAS_MATPLOTLIB:
print("⚠️ matplotlib required: pip install matplotlib")
return
fig, axes = plt.subplots(2, 2, figsize=(16, 10))
fig.suptitle("Training Dynamics", fontsize=16, fontweight="bold")
steps = metrics_history.get("step", list(range(len(metrics_history.get("train_loss", [])))))
# ── (1) Loss ──
ax = axes[0, 0]
if metrics_history.get("train_loss"):
ax.plot(steps[:len(metrics_history["train_loss"])],
metrics_history["train_loss"],
color="#2563eb", alpha=0.6, linewidth=0.8, label="Train Loss")
# Moving average (smoothing)
if len(metrics_history["train_loss"]) > 20:
window = min(50, len(metrics_history["train_loss"]) // 5)
smoothed = self._moving_average(metrics_history["train_loss"], window)
ax.plot(steps[window-1:len(smoothed)+window-1],
smoothed, color="#1d4ed8", linewidth=2, label=f"Smoothed (window={window})")
if metrics_history.get("val_loss"):
val_steps = [steps[i] for i in range(0, len(steps),
max(1, len(steps)//len(metrics_history["val_loss"])))][:len(metrics_history["val_loss"])]
ax.plot(val_steps, metrics_history["val_loss"],
"o-", color="#dc2626", linewidth=2, markersize=5, label="Val Loss")
ax.set_xlabel("Step")
ax.set_ylabel("Loss")
ax.set_title("Training & Validation Loss")
ax.legend()
ax.grid(True, alpha=0.3)
# ── (2) Learning Rate ──
ax = axes[0, 1]
if metrics_history.get("learning_rate"):
ax.plot(steps[:len(metrics_history["learning_rate"])],
metrics_history["learning_rate"],
color="#059669", linewidth=2)
ax.set_xlabel("Step")
ax.set_ylabel("Learning Rate")
ax.set_title("Learning Rate Schedule")
ax.ticklabel_format(style="scientific", axis="y", scilimits=(0,0))
ax.grid(True, alpha=0.3)
# ── (3) Gradient Norm ──
ax = axes[1, 0]
if metrics_history.get("grad_norm"):
ax.plot(steps[:len(metrics_history["grad_norm"])],
metrics_history["grad_norm"],
color="#d97706", alpha=0.6, linewidth=0.8)
ax.axhline(y=1.0, color="red", linestyle="--", alpha=0.5, label="Clip threshold")
ax.legend()
ax.set_xlabel("Step")
ax.set_ylabel("Gradient Norm")
ax.set_title("Gradient Norm (clipped at 1.0)")
ax.grid(True, alpha=0.3)
# ── (4) Throughput ──
ax = axes[1, 1]
if metrics_history.get("tokens_per_sec"):
tps = metrics_history["tokens_per_sec"]
ax.plot(steps[:len(tps)], tps, color="#7c3aed", alpha=0.6, linewidth=0.8)
if tps:
avg_tps = sum(tps) / len(tps)
ax.axhline(y=avg_tps, color="#7c3aed", linestyle="--", alpha=0.5,
label=f"Avg: {avg_tps:,.0f}")
ax.legend()
ax.set_xlabel("Step")
ax.set_ylabel("Tokens/sec")
ax.set_title("Training Throughput")
ax.grid(True, alpha=0.3)
plt.tight_layout()
save_path = save_path or str(self.save_dir / "training_curves.png")
fig.savefig(save_path, dpi=150, bbox_inches="tight")
print(f"\n 📊 Training curves saved: {save_path}")
plt.close(fig)
def plot_position_loss(
self,
position_losses: List[float],
save_path: Optional[str] = None,
):
"""Visualizes loss distribution by position."""
if not HAS_MATPLOTLIB:
return
fig, ax = plt.subplots(figsize=(12, 5))
positions = list(range(len(position_losses)))
ax.plot(positions, position_losses, color="#2563eb", linewidth=1.5)
ax.fill_between(positions, position_losses, alpha=0.1, color="#2563eb")
ax.set_xlabel("Position in Sequence", fontsize=12)
ax.set_ylabel("Cross-Entropy Loss", fontsize=12)
ax.set_title("Loss by Position (earlier positions have less context)", fontsize=13, fontweight="bold")
ax.grid(True, alpha=0.3)
# Mark key regions
if len(position_losses) > 100:
early_avg = sum(position_losses[:50]) / 50
late_avg = sum(position_losses[-200:]) / 200
ax.axhline(y=early_avg, color="red", linestyle="--", alpha=0.4,
label=f"Early avg (0-50): {early_avg:.2f}")
ax.axhline(y=late_avg, color="green", linestyle="--", alpha=0.4,
label=f"Late avg (-200): {late_avg:.2f}")
ax.legend()
plt.tight_layout()
save_path = save_path or str(self.save_dir / "position_loss.png")
fig.savefig(save_path, dpi=150, bbox_inches="tight")
print(f" 📊 Position loss saved: {save_path}")
plt.close(fig)
@staticmethod
def _moving_average(data: list, window: int) -> list:
"""Compute moving average."""
result = []
for i in range(window - 1, len(data)):
avg = sum(data[i - window + 1 : i + 1]) / window
result.append(avg)
return result
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