File size: 7,115 Bytes
388fd6e | 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 | """Benchmark SSMGamma and GammaSingleBlock performance.
Compares CUDA-optimized vs PyTorch implementations.
Measures throughput, latency, and memory usage.
"""
import torch
import torch.nn as nn
import time
import argparse
from pathlib import Path
from typing import Dict, Tuple
from gamma_space_model import SSMGamma, GammaSingleBlock, HAS_CUDA_OPS
def benchmark_forward_pass(
model: nn.Module,
batch_size: int,
seq_len: int,
d_model: int,
num_iterations: int = 100,
warmup_iterations: int = 20,
device: str = "cpu",
dtype: torch.dtype = torch.float32,
) -> Dict[str, float]:
"""
Benchmark forward pass latency.
Args:
model: Model to benchmark
batch_size: Batch size
seq_len: Sequence length
d_model: Model dimension (state_dim for SSMGamma)
num_iterations: Number of iterations to benchmark
warmup_iterations: Number of warmup iterations
device: Device to run on
dtype: Data type
Returns:
Dictionary with latency statistics (ms)
"""
model.eval()
# Create dummy input
x = torch.randn(batch_size, seq_len, d_model, dtype=dtype, device=device)
print(f" Warming up for {warmup_iterations} iterations...")
with torch.no_grad():
for _ in range(warmup_iterations):
_ = model(x)
if device == "cuda":
torch.cuda.synchronize()
print(f" Benchmarking {num_iterations} iterations...")
# Forward timing
times = []
with torch.no_grad():
for _ in range(num_iterations):
torch.cuda.synchronize() if device == "cuda" else None
start = time.perf_counter()
_ = model(x)
torch.cuda.synchronize() if device == "cuda" else None
end = time.perf_counter()
times.append((end - start) * 1000) # Convert to ms
times = torch.tensor(times)
return {
"mean_latency_ms": times.mean().item(),
"median_latency_ms": times.median().item(),
"min_latency_ms": times.min().item(),
"max_latency_ms": times.max().item(),
"std_latency_ms": times.std().item(),
"throughput_samples_per_sec": 1000.0 / times.mean().item() * batch_size,
}
def benchmark_memory(
model: nn.Module,
batch_size: int,
seq_len: int,
d_model: int,
device: str = "cuda",
) -> Dict[str, float]:
"""
Benchmark memory usage.
Args:
model: Model to benchmark
batch_size: Batch size
seq_len: Sequence length
d_model: Model dimension
device: Device (cuda for GPU memory)
Returns:
Dictionary with memory statistics
"""
if device != "cuda":
return {"gpu_memory_allocated_mb": 0.0, "gpu_memory_reserved_mb": 0.0}
model.eval()
# Clear cache
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats()
# Create input
x = torch.randn(batch_size, seq_len, d_model, device="cuda")
# Forward pass
with torch.no_grad():
_ = model(x)
torch.cuda.synchronize()
allocated = torch.cuda.memory_allocated() / (1024 ** 2) # MB
reserved = torch.cuda.memory_reserved() / (1024 ** 2) # MB
max_allocated = torch.cuda.max_memory_allocated() / (1024 ** 2) # MB
return {
"gpu_memory_allocated_mb": allocated,
"gpu_memory_reserved_mb": reserved,
"gpu_memory_max_allocated_mb": max_allocated,
}
def run_benchmarks(args):
"""Run comprehensive benchmarks."""
print("\n" + "=" * 80)
print("SSM GAMMA BENCHMARK SUITE")
print("=" * 80)
device = "cuda" if torch.cuda.is_available() and args.device == "cuda" else "cpu"
print(f"\nDevice: {device}")
print(f"CUDA optimizations available: {HAS_CUDA_OPS}")
# Test configurations
configs = [
(4, 128, 64, "Small (seq=128)"),
(8, 512, 128, "Medium (seq=512)"),
(16, 2048, 256, "Large (seq=2048)"),
]
results = {}
for batch_size, seq_len, d_model, config_name in configs:
print(f"\n{'-' * 80}")
print(f"Configuration: {config_name}")
print(f" Batch size: {batch_size}")
print(f" Sequence length: {seq_len}")
print(f" Model dimension: {d_model}")
print(f" Total tokens: {batch_size * seq_len:,}")
# SSMGamma benchmark
print(f"\n SSMGamma benchmark:")
ssm = SSMGamma(state_dim=d_model, hidden_dim=d_model * 2).to(device)
ssm_results = benchmark_forward_pass(
ssm, batch_size, seq_len, d_model,
num_iterations=args.iterations,
device=device,
)
print(f" Latency: {ssm_results['mean_latency_ms']:.3f} ± {ssm_results['std_latency_ms']:.3f} ms")
print(f" Throughput: {ssm_results['throughput_samples_per_sec']:.0f} tokens/sec")
if device == "cuda":
mem = benchmark_memory(ssm, batch_size, seq_len, d_model)
print(f" GPU Memory: {mem['gpu_memory_max_allocated_mb']:.1f} MB")
# GammaSingleBlock benchmark
print(f"\n GammaSingleBlock benchmark:")
block = GammaSingleBlock(d_model=d_model, hidden_dim=d_model * 2).to(device)
block_results = benchmark_forward_pass(
block, batch_size, seq_len, d_model,
num_iterations=args.iterations,
device=device,
)
print(f" Latency: {block_results['mean_latency_ms']:.3f} ± {block_results['std_latency_ms']:.3f} ms")
print(f" Throughput: {block_results['throughput_samples_per_sec']:.0f} tokens/sec")
if device == "cuda":
mem = benchmark_memory(block, batch_size, seq_len, d_model)
print(f" GPU Memory: {mem['gpu_memory_max_allocated_mb']:.1f} MB")
# Store results
results[config_name] = {
"ssm_gamma": ssm_results,
"gamma_block": block_results,
}
print(f"\n{'=' * 80}")
print("Benchmark complete!")
print(f"{'=' * 80}\n")
return results
def main():
parser = argparse.ArgumentParser(description="Benchmark SSM Gamma blocks")
parser.add_argument(
"--device",
type=str,
choices=["cpu", "cuda"],
default="cuda" if torch.cuda.is_available() else "cpu",
help="Device to benchmark on"
)
parser.add_argument(
"--iterations",
type=int,
default=100,
help="Number of benchmark iterations"
)
parser.add_argument(
"--warmup",
type=int,
default=20,
help="Number of warmup iterations"
)
args = parser.parse_args()
run_benchmarks(args)
if __name__ == "__main__":
main()
|