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https://github.com/linkedin/Liger-Kernel.git
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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
104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
import torch
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from benchmark_model_configs import MODEL_REGISTRY
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from benchmark_model_configs import build_model_config_sweep
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from benchmark_model_configs import build_token_length_sweep
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from benchmark_model_configs import get_benchmark_model_config
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from utils import SingleBenchmarkRunInput
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from utils import build_memory_bench_fn
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from utils import build_speed_bench_fn
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from utils import parse_benchmark_script_args
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from utils import run_benchmarks
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from liger_kernel.transformers.group_norm import LigerGroupNorm
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from liger_kernel.utils import infer_device
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device = infer_device()
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def setup_group_norm(input: SingleBenchmarkRunInput):
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"""Create input tensor and GroupNorm layer from benchmark config."""
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cfg = input.extra_benchmark_config
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H = cfg["H"]
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if isinstance(input.x, str):
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model_cfg = MODEL_REGISTRY[input.x]
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num_channels = model_cfg.hidden_size
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dtype = model_cfg.dtype
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M = max(1, cfg["bsz"] * cfg["seq_len"] // H)
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else:
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# input.x is BT; derive M from BT / H
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M = max(1, input.x // H)
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num_channels = cfg["hidden_size"]
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dtype = cfg["dtype"]
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num_groups = num_channels // cfg["channels_per_group"]
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x = torch.randn(M, num_channels, H, device=device, dtype=dtype, requires_grad=True)
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if input.kernel_provider == "liger":
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layer = LigerGroupNorm(num_channels=num_channels, num_groups=num_groups, eps=cfg["eps"]).to(
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device=device, dtype=dtype
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)
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elif input.kernel_provider == "torch":
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layer = torch.nn.GroupNorm(num_groups=num_groups, num_channels=num_channels, eps=cfg["eps"]).to(
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device=device, dtype=dtype
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)
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else:
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raise ValueError(f"Invalid provider: {input.kernel_provider} for GroupNorm")
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return x, layer
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if __name__ == "__main__":
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args = parse_benchmark_script_args()
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extra_configs = {
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"channels_per_group": 4,
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"eps": 1e-6,
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"H": 512,
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}
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probe_bt = 1024
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if args.sweep_mode == "model_config":
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common_configs = build_model_config_sweep(
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kernel_name="group_norm",
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setup_fn=setup_group_norm,
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model_keys=["hidden_size", "dtype"],
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probe_provider="torch",
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extra_configs=extra_configs,
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probe_dim="BT",
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bt=args.bt,
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overwrite=args.overwrite,
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)
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else:
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model = get_benchmark_model_config(args.model)
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common_configs = build_token_length_sweep(
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kernel_name="group_norm",
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probe_x=probe_bt,
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model=model,
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setup_fn=setup_group_norm,
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model_keys=["hidden_size", "dtype"],
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extra_configs=extra_configs,
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scale_dim="BT",
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x_label="B*T",
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probe_provider="torch",
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overwrite=args.overwrite,
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)
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common_configs["kernel_providers"] = ["liger", "torch"]
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run_benchmarks(
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bench_test_fn=build_speed_bench_fn(setup_group_norm),
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kernel_operation_modes=["full", "forward", "backward"],
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metric_name="speed",
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metric_unit="ms",
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**common_configs,
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)
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run_benchmarks(
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bench_test_fn=build_memory_bench_fn(setup_group_norm),
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kernel_operation_modes=["full"],
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metric_name="memory",
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metric_unit="MB",
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**common_configs,
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)
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