File size: 6,653 Bytes
29f25be | 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 | """Representative CUDA profile and optional norm/backbone compilation comparison."""
import argparse
import copy
import json
import sys
import time
from pathlib import Path
ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, str(ROOT / "src"))
import torch
from vimeml.training.data import write_json
from vimeml.training.model_factory import model_from_checkpoint
from vimeml.training.runtime_v2 import fuse_rmsnorm, compile_backbone
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--checkpoint", type=Path, required=True)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--variant", choices=("norm", "backbone"), default="norm")
args = parser.parse_args()
if args.output.exists() and any(args.output.iterdir()):
parser.error("Use a fresh output; preserve previous measurements.")
args.output.mkdir(parents=True, exist_ok=True)
optimize = fuse_rmsnorm if args.variant == "norm" else compile_backbone
torch.set_num_threads(4)
torch.manual_seed(42)
saved = torch.load(args.checkpoint, map_location="cpu", weights_only=True)
base = model_from_checkpoint(saved).cuda()
del saved
batch, width = 256, 24
inputs = torch.randint(4, base.config.vocab_size, (batch, width), device="cuda")
lengths = torch.randint(14, width + 1, (batch,), device="cuda")
labels = torch.roll(inputs, -1, 1)
labels[torch.arange(width, device="cuda")[None, :] >= lengths[:, None]] = -100
tokens = int((labels != -100).sum())
weights = copy.deepcopy(base.state_dict())
def step(model, optimizer):
optimizer.zero_grad(set_to_none=True)
with torch.autocast("cuda", dtype=torch.bfloat16):
loss = model(inputs, labels)["loss_sum"] / tokens
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1, error_if_nonfinite=True)
optimizer.step()
torch.cuda.synchronize()
return float(loss)
results = {}
optimized_name = "fused_rmsnorm" if args.variant == "norm" else "compiled_backbone"
for name in ("eager", optimized_name):
model = model_from_checkpoint(
{
"format": "vimeml_tiny_gpt_v2",
"architecture": "tiny_gpt_v2",
"model_config": base.configuration(),
"model": weights,
}
).cuda()
if name != "eager":
results["fusion"] = optimize(model)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4, fused=True)
start = time.perf_counter()
for _ in range(5):
step(model, optimizer)
initialization_seconds = time.perf_counter() - start
start = time.perf_counter()
for _ in range(60):
value = step(model, optimizer)
duration = time.perf_counter() - start
results[name] = {
"steps": 60,
"seconds": duration,
"milliseconds_per_update": duration / 60 * 1000,
"effective_tokens_per_second": tokens * 60 / duration,
"initialization_seconds": initialization_seconds,
"final_fixture_loss": value,
}
if name == "eager":
with torch.profiler.profile(
activities=[
torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA,
]
) as profile:
step(model, optimizer)
events = profile.key_averages()
(args.output / "profile.txt").write_text(
events.table(sort_by="self_cpu_time_total", row_limit=25)
+ "\n"
+ events.table(sort_by="self_cuda_time_total", row_limit=25),
encoding="utf-8",
)
del optimizer, model
# Check loss and every parameter gradient on the exact same batch/weights.
reference = model_from_checkpoint(
{"format": "vimeml_tiny_gpt_v2", "model_config": base.configuration(), "model": weights}
).cuda()
candidate = model_from_checkpoint(
{"format": "vimeml_tiny_gpt_v2", "model_config": base.configuration(), "model": weights}
).cuda()
optimize(candidate)
losses = []
for model in (reference, candidate):
model.zero_grad(set_to_none=True)
with torch.autocast("cuda", dtype=torch.bfloat16):
loss = model(inputs, labels)["loss_sum"] / tokens
loss.backward()
losses.append(float(loss))
torch.testing.assert_close(
torch.tensor(losses[0]), torch.tensor(losses[1]), rtol=5e-4, atol=5e-4
)
maximum_gradient_difference = 0.0
relative_gradient_l2 = {}
for (name, left), (other, right) in zip(
reference.named_parameters(), candidate.named_parameters()
):
assert name == other
maximum_gradient_difference = max(
maximum_gradient_difference, float((left.grad - right.grad).abs().max())
)
relative_gradient_l2[name] = float(
torch.linalg.vector_norm(left.grad - right.grad)
/ torch.linalg.vector_norm(left.grad).clamp_min(1e-12)
)
# BF16 fusion changes rounding; compare whole-gradient error rather than
# relative errors of individual near-zero elements, then prove FP32 parity.
assert max(relative_gradient_l2.values()) < 0.02, relative_gradient_l2
for model in (reference, candidate):
model.zero_grad(set_to_none=True)
(model(inputs, labels)["loss_sum"] / tokens).backward()
for (name, left), (other, right) in zip(
reference.named_parameters(), candidate.named_parameters()
):
torch.testing.assert_close(
left.grad, right.grad, rtol=2e-4, atol=2e-5, msg=lambda m: name + " " + m
)
results.update(
status="passed",
batch_size=batch,
padded_width=width,
valid_tokens=tokens,
padding_fraction=1 - tokens / (batch * width),
gradient_max_absolute_difference=maximum_gradient_difference,
bf16_max_parameter_gradient_relative_l2=max(relative_gradient_l2.values()),
fp32_all_parameter_gradient_parity=True,
fusion_speedup=results["eager"]["milliseconds_per_update"]
/ results[optimized_name]["milliseconds_per_update"],
note="Fixed representative batch; no data loader. Speed excludes compilation and is not a full-epoch estimate.",
formal_training_started=False,
)
write_json(args.output / "report.json", results)
print(json.dumps(results, indent=2), flush=True)
if __name__ == "__main__":
main()
|