mirror of
https://github.com/linkedin/Liger-Kernel.git
synced 2026-10-12 02:17:47 +00:00
lowdy1
ace042ec55
## Summary This PR is a descendant of [[PR #1195](https://github.com/linkedin/Liger-Kernel/pull/1195)] and continues the benchmark refactor effort by migrating linear-space-complexity benchmark scripts to the new reusable `setup` helper infrastructure. ### Benefits * Significantly reduces benchmark script complexity * Makes new benchmark additions easier and more consistent * Centralizes probing and sweep logic * Improves maintainability of benchmark infrastructure ### Notes This PR mainly focuses on kernels with approximately linear memory complexity and migrates them to the new benchmark helper APIs introduced in the parent refactor. - [ ] run `make test` to ensure correctness - [x] run `make checkstyle` to ensure code style - [ ] run `make test-convergence` to ensure convergence
128 lines
4.5 KiB
Python
128 lines
4.5 KiB
Python
import torch
|
|
|
|
from benchmark_model_configs import MODEL_REGISTRY
|
|
from benchmark_model_configs import build_model_config_sweep
|
|
from benchmark_model_configs import build_token_length_sweep
|
|
from benchmark_model_configs import get_benchmark_model_config
|
|
from utils import SingleBenchmarkRunInput
|
|
from utils import build_memory_bench_fn
|
|
from utils import build_speed_bench_fn
|
|
from utils import parse_benchmark_script_args
|
|
from utils import run_benchmarks
|
|
|
|
from liger_kernel.transformers.fused_linear_cross_entropy import LigerFusedLinearCrossEntropyLoss
|
|
from liger_kernel.utils import infer_device
|
|
|
|
device = infer_device()
|
|
|
|
|
|
class TorchLMHeadCE(torch.nn.Module):
|
|
"""Ground truth implementation of the linear fused with torch based cross entropy loss.
|
|
|
|
:param H: hidden size
|
|
:param V: vocab size
|
|
:param ignore_index: index to ignore
|
|
:param reduction: reduction method
|
|
"""
|
|
|
|
def __init__(self, H: int, V: int, dtype: torch.dtype, ignore_index: int = -100):
|
|
super().__init__()
|
|
self.lin = torch.nn.Linear(in_features=H, out_features=V, bias=False, dtype=dtype)
|
|
self.ce_loss = torch.nn.CrossEntropyLoss(ignore_index=ignore_index, reduction="mean")
|
|
|
|
def forward(self, x, y):
|
|
logits = self.lin(x)
|
|
return self.ce_loss(logits, y)
|
|
|
|
|
|
class LigerLMHeadCE(torch.nn.Module):
|
|
def __init__(self, H: int, V: int, dtype: torch.dtype, ignore_index: int = -100, accum_dtype=None):
|
|
super().__init__()
|
|
self.lin = torch.nn.Linear(in_features=H, out_features=V, bias=False, dtype=dtype)
|
|
self.ce_loss = LigerFusedLinearCrossEntropyLoss(
|
|
ignore_index=ignore_index, reduction="mean", accum_dtype=accum_dtype
|
|
)
|
|
|
|
def forward(self, x, y):
|
|
return self.ce_loss(self.lin.weight, x, y)
|
|
|
|
|
|
def setup_fused_linear_cross_entropy(input: SingleBenchmarkRunInput):
|
|
"""Create input tensor, target, and fused linear CE from benchmark config."""
|
|
cfg = input.extra_benchmark_config
|
|
if isinstance(input.x, str):
|
|
model_cfg = MODEL_REGISTRY[input.x]
|
|
BT = cfg["seq_len"] * cfg["bsz"]
|
|
V = model_cfg.vocab_size
|
|
H = model_cfg.hidden_size
|
|
dtype = model_cfg.dtype
|
|
else:
|
|
BT = input.x
|
|
V = cfg["vocab_size"]
|
|
H = cfg["hidden_size"]
|
|
dtype = cfg["dtype"]
|
|
|
|
_input = torch.randn(BT, H, requires_grad=True, dtype=dtype, device=device)
|
|
target = torch.randint(V, (BT, 1), dtype=torch.long, device=device).squeeze(1)
|
|
|
|
if input.kernel_provider == "liger":
|
|
lm_head_ce = LigerLMHeadCE(H=H, V=V, dtype=dtype).to(device)
|
|
elif input.kernel_provider == "liger-fp32-accum":
|
|
lm_head_ce = LigerLMHeadCE(H=H, V=V, dtype=dtype, accum_dtype=torch.float32).to(device)
|
|
else:
|
|
lm_head_ce = TorchLMHeadCE(H=H, V=V, dtype=dtype).to(device)
|
|
return _input, lambda _: lm_head_ce(_input, target)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
args = parse_benchmark_script_args()
|
|
|
|
if args.sweep_mode == "model_config":
|
|
common_configs = build_model_config_sweep(
|
|
kernel_name="fused_linear_cross_entropy",
|
|
setup_fn=setup_fused_linear_cross_entropy,
|
|
model_keys=["hidden_size", "vocab_size", "dtype"],
|
|
probe_provider="torch",
|
|
extra_configs={
|
|
"eps": 1e-6,
|
|
},
|
|
probe_dim="BT",
|
|
bt=args.bt,
|
|
overwrite=args.overwrite,
|
|
)
|
|
else:
|
|
model = get_benchmark_model_config(args.model)
|
|
probe_seq_len = 1024
|
|
|
|
common_configs = build_token_length_sweep(
|
|
kernel_name="fused_linear_cross_entropy",
|
|
probe_x=probe_seq_len,
|
|
model=model,
|
|
setup_fn=setup_fused_linear_cross_entropy,
|
|
model_keys=["hidden_size", "vocab_size", "dtype"],
|
|
extra_configs={
|
|
"eps": 1e-6,
|
|
},
|
|
scale_dim="BT",
|
|
x_label="total tokens",
|
|
probe_provider="torch",
|
|
overwrite=args.overwrite,
|
|
)
|
|
|
|
common_configs["kernel_providers"] = ["torch", "liger", "liger-fp32-accum"]
|
|
|
|
run_benchmarks(
|
|
bench_test_fn=build_speed_bench_fn(setup_fused_linear_cross_entropy),
|
|
kernel_operation_modes=["forward", "backward", "full"],
|
|
metric_name="speed",
|
|
metric_unit="ms",
|
|
**common_configs,
|
|
)
|
|
run_benchmarks(
|
|
bench_test_fn=build_memory_bench_fn(setup_fused_linear_cross_entropy),
|
|
kernel_operation_modes=["full"],
|
|
metric_name="memory",
|
|
metric_unit="MB",
|
|
**common_configs,
|
|
)
|