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3270dae | 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 | """Benchmarking suite for evaluating trained models."""
import time
from pathlib import Path
from typing import Optional, Dict
import torch
from torch.utils.data import DataLoader
from taoTrain.core import BaseModel
from taoTrain.config import TrainingConfig
from taoTrain.data.loaders import get_dataloader
from taoTrain.inference import Inferencer
class BenchmarkRunner:
"""Run benchmarks on a trained model."""
def __init__(
self,
model: BaseModel,
device: torch.device,
dtype: torch.dtype = torch.float32,
):
"""
Initialize benchmark runner.
Args:
model: Trained model
device: Device for inference
dtype: Data type
"""
self.model = model.to(device)
self.model.eval()
self.device = device
self.dtype = dtype
@staticmethod
def load_from_checkpoint(
checkpoint_path: str | Path,
device: Optional[torch.device] = None,
) -> "BenchmarkRunner":
"""Load model from checkpoint."""
if device is None:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
checkpoint = torch.load(checkpoint_path, map_location=device)
# Reconstruct model config
from taoTrain.config import ModelConfig
from taoTrain.models import get_model
model_config = ModelConfig(**checkpoint.get("config", {}).get("model", {}))
model = get_model(model_config, device=device)
model.load_state_dict(checkpoint["model_state_dict"])
return BenchmarkRunner(model, device)
def benchmark_perplexity(
self,
dataset: "DataLoader",
num_batches: Optional[int] = None,
) -> float:
"""
Compute perplexity on a dataset.
Args:
dataset: DataLoader for evaluation
num_batches: Limit evaluation to N batches
Returns:
Perplexity (exp of average loss)
"""
total_loss = 0.0
total_tokens = 0
with torch.no_grad():
for batch_idx, batch in enumerate(dataset):
if num_batches and batch_idx >= num_batches:
break
# Move to device
input_ids = batch["input_ids"].to(self.device)
attention_mask = batch.get("attention_mask")
if attention_mask is not None:
attention_mask = attention_mask.to(self.device)
labels = batch.get("labels")
if labels is not None:
labels = labels.to(self.device)
# Forward pass
with torch.autocast(
device_type="cuda" if self.device.type == "cuda" else "cpu",
dtype=torch.bfloat16 if self.dtype == torch.bfloat16 else torch.float32,
):
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
labels=labels,
)
loss = outputs.get("loss")
if loss is not None:
total_loss += loss.item() * input_ids.shape[0]
total_tokens += input_ids.shape[0]
avg_loss = total_loss / total_tokens if total_tokens > 0 else float('inf')
perplexity = torch.exp(torch.tensor(avg_loss)).item()
return perplexity
def benchmark_throughput(
self,
batch_size: int = 32,
seq_length: int = 1024,
num_iters: int = 10,
) -> Dict[str, float]:
"""
Benchmark forward pass throughput.
Args:
batch_size: Batch size
seq_length: Sequence length
num_iters: Number of iterations
Returns:
Dict with throughput metrics
"""
# Create dummy batch
dummy_input = torch.randint(
0, self.model.config.vocab_size,
(batch_size, seq_length)
).to(self.device)
# Warmup
with torch.no_grad():
for _ in range(2):
_ = self.model(dummy_input)
torch.cuda.synchronize() if torch.cuda.is_available() else None
# Benchmark forward pass
start = time.time()
with torch.no_grad():
for _ in range(num_iters):
_ = self.model(dummy_input)
torch.cuda.synchronize() if torch.cuda.is_available() else None
elapsed = time.time() - start
total_tokens = batch_size * seq_length * num_iters
tokens_per_sec = total_tokens / elapsed
return {
"throughput_tokens_per_sec": tokens_per_sec,
"throughput_samples_per_sec": (batch_size * num_iters) / elapsed,
"avg_time_per_iter_ms": (elapsed / num_iters) * 1000,
}
def benchmark_memory(self) -> Dict[str, float]:
"""
Benchmark peak GPU memory usage.
Returns:
Dict with memory stats
"""
if not torch.cuda.is_available():
return {"peak_memory_gb": 0.0}
torch.cuda.reset_peak_memory_stats()
torch.cuda.synchronize()
# Create dummy batch
dummy_input = torch.randint(
0, self.model.config.vocab_size,
(16, 1024)
).to(self.device)
with torch.no_grad():
_ = self.model(dummy_input)
torch.cuda.synchronize()
peak_memory = torch.cuda.max_memory_allocated() / (1024 ** 3) # GB
return {"peak_memory_gb": peak_memory}
def run_all_benchmarks(
self,
dataset: Optional["DataLoader"] = None,
batch_size: int = 32,
seq_length: int = 1024,
) -> Dict[str, float]:
"""
Run all benchmarks.
Args:
dataset: DataLoader for perplexity benchmark
batch_size: Batch size for throughput benchmark
seq_length: Sequence length for throughput benchmark
Returns:
Dict with all benchmark results
"""
results = {}
if dataset is not None:
print("Running perplexity benchmark...")
ppl = self.benchmark_perplexity(dataset, num_batches=10)
results["perplexity"] = ppl
print("Running throughput benchmark...")
throughput = self.benchmark_throughput(batch_size, seq_length)
results.update(throughput)
print("Running memory benchmark...")
memory = self.benchmark_memory()
results.update(memory)
return results
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