File size: 7,187 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 | """Profile the DPLR convolutional frequency path.
This is a small remote-friendly profiler for choosing TileLang/Triton kernel
targets. It focuses on S4TernaryDPLRSSM rather than the older Gamma fallback
because this is the SSM core used by the TaoNet comparison work.
"""
from __future__ import annotations
import argparse
import json
import sys
import time
from contextlib import nullcontext
from pathlib import Path
from typing import Any
import torch
REPO_ROOT = Path(__file__).resolve().parents[1]
if str(REPO_ROOT) not in sys.path:
sys.path.insert(0, str(REPO_ROOT))
from gamma_space_model import S4TernaryDPLRSSM
DTYPES = {
"fp32": torch.float32,
"float32": torch.float32,
"bf16": torch.bfloat16,
"bfloat16": torch.bfloat16,
"fp16": torch.float16,
"float16": torch.float16,
}
def synchronize(device: torch.device) -> None:
if device.type == "cuda":
torch.cuda.synchronize(device)
def memory_stats(device: torch.device) -> dict[str, float | None]:
if device.type != "cuda":
return {"peak_allocated_mb": None, "peak_reserved_mb": None}
return {
"peak_allocated_mb": torch.cuda.max_memory_allocated(device) / (1024**2),
"peak_reserved_mb": torch.cuda.max_memory_reserved(device) / (1024**2),
}
def run_timed(fn, *, device: torch.device, warmup: int, repeats: int) -> dict[str, float]:
for _ in range(warmup):
fn()
synchronize(device)
latencies = []
for _ in range(repeats):
if device.type == "cuda":
torch.cuda.reset_peak_memory_stats(device)
synchronize(device)
start = time.perf_counter()
fn()
synchronize(device)
latencies.append(time.perf_counter() - start)
return {
"mean_ms": sum(latencies) / len(latencies) * 1000.0,
"min_ms": min(latencies) * 1000.0,
}
def profiler_table(prof: torch.profiler.profile, row_limit: int) -> list[dict[str, Any]]:
rows = []
for event in prof.key_averages().table(
sort_by="cuda_time_total",
row_limit=row_limit,
).splitlines():
rows.append({"row": event})
return rows
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
parser.add_argument("--dtype", choices=sorted(DTYPES), default="bf16")
parser.add_argument("--batch-size", type=int, default=4)
parser.add_argument("--seq-len", type=int, default=512)
parser.add_argument("--d-model", type=int, default=64)
parser.add_argument("--hidden-dim", type=int, default=256)
parser.add_argument("--rank", type=int, default=1)
parser.add_argument("--warmup", type=int, default=2)
parser.add_argument("--repeats", type=int, default=5)
parser.add_argument("--profile", action="store_true")
parser.add_argument("--row-limit", type=int, default=20)
parser.add_argument("--method", choices=["forward", "direct", "transfer"], default="forward")
parser.add_argument("--output", type=Path, default=None)
args = parser.parse_args()
device = torch.device(args.device)
dtype = DTYPES[args.dtype]
model = S4TernaryDPLRSSM(
state_dim=args.d_model,
hidden_dim=args.hidden_dim,
rank=args.rank,
kernel_mode="conv",
kernel_threshold=1,
).to(device=device)
model.train()
x = torch.randn(args.batch_size, args.seq_len, args.d_model, device=device, dtype=dtype)
autocast_enabled = device.type == "cuda" and dtype in {torch.float16, torch.bfloat16}
def autocast_context():
if not autocast_enabled:
return nullcontext()
return torch.autocast(device_type=device.type, dtype=dtype, enabled=True)
def apply_model() -> torch.Tensor:
if args.method == "forward":
y, _ = model(x, return_state=False)
return y
fft_dtype = torch.float32 if x.dtype in {torch.float16, torch.bfloat16} else x.dtype
fft_len = 1 << max(1, (2 * args.seq_len - 1).bit_length())
with torch.autocast(device_type=device.type, enabled=False):
u_channels = x.transpose(1, 2).to(dtype=fft_dtype)
u_f = torch.fft.rfft(u_channels, n=fft_len)
if args.method == "direct":
y_f = model._apply_frequency_response(
u_f=u_f,
seq_len=args.seq_len,
fft_len=fft_len,
dtype=fft_dtype,
device=device,
)
else:
transfer = model._compute_frequency_response(
seq_len=args.seq_len,
fft_len=fft_len,
dtype=fft_dtype,
device=device,
use_cache=False,
)
y_f = torch.einsum("foi,bif->bof", transfer, u_f)
y = torch.fft.irfft(y_f, n=fft_len)[..., : args.seq_len]
return y.transpose(1, 2).to(dtype=x.dtype)
def forward_only() -> None:
with torch.no_grad():
with autocast_context():
y = apply_model()
y.sum().item()
def forward_backward() -> None:
model.zero_grad(set_to_none=True)
with autocast_context():
y = apply_model()
loss = y.square().mean()
loss.backward()
forward_stats = run_timed(
forward_only,
device=device,
warmup=args.warmup,
repeats=args.repeats,
)
forward_backward_stats = run_timed(
forward_backward,
device=device,
warmup=args.warmup,
repeats=args.repeats,
)
tokens = args.batch_size * args.seq_len
report: dict[str, Any] = {
"config": vars(args) | {"device": str(device), "dtype": str(dtype).replace("torch.", "")},
"forward": {
**forward_stats,
"tokens_per_s": tokens / max(forward_stats["mean_ms"] / 1000.0, 1e-12),
},
"forward_backward": {
**forward_backward_stats,
"tokens_per_s": tokens / max(forward_backward_stats["mean_ms"] / 1000.0, 1e-12),
**memory_stats(device),
},
"frequency_grid_cache_entries": len(model._frequency_grid_cache),
}
if args.profile:
activities = [torch.profiler.ProfilerActivity.CPU]
if device.type == "cuda":
activities.append(torch.profiler.ProfilerActivity.CUDA)
with torch.profiler.profile(activities=activities, record_shapes=True) as prof:
forward_backward()
report["profiler_table"] = profiler_table(prof, args.row_limit)
text = json.dumps(report, indent=2, sort_keys=True, default=str)
print(text)
if args.output is not None:
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(text, encoding="utf-8")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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