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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
121 lines
3.5 KiB
Python
121 lines
3.5 KiB
Python
"""
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AttnRes Benchmark: Liger (Triton) vs PyTorch
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Kimi Attention Residuals: softmax attention over depth blocks.
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"""
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import os
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import sys
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import torch
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))
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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.ops import LigerAttnResFunction
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from liger_kernel.utils import infer_device
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device = infer_device()
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def setup_attn_res(input: SingleBenchmarkRunInput):
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"""Create input tensors for AttnRes from benchmark config."""
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cfg = input.extra_benchmark_config
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if isinstance(input.x, str):
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model_cfg = MODEL_REGISTRY[input.x]
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seq_len = cfg["seq_len"]
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hidden_size = model_cfg.hidden_size
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dtype = model_cfg.dtype
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else:
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seq_len = input.x
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hidden_size = cfg["hidden_size"]
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dtype = cfg["dtype"]
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# V: [N, B, T, D]
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V = torch.randn(
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cfg["N"],
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cfg["bsz"],
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seq_len,
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hidden_size,
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device=device,
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dtype=dtype,
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requires_grad=True,
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)
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w_query = torch.randn(hidden_size, device=device, dtype=dtype) * 0.02
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w_norm = torch.ones(hidden_size, device=device, dtype=dtype)
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eps = cfg.get("eps", 1e-6)
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if input.kernel_provider == "liger":
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fn = lambda: LigerAttnResFunction.apply(V, w_query, w_norm, eps)
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elif input.kernel_provider == "pytorch":
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from test.transformers.test_attn_res import pytorch_attn_res
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fn = lambda: pytorch_attn_res(V, w_query, w_norm, eps)
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else:
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raise ValueError(f"Invalid provider: {input.kernel_provider}")
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return V, lambda _: fn()
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if __name__ == "__main__":
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args = parse_benchmark_script_args()
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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="attn_res",
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setup_fn=setup_attn_res,
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model_keys=["hidden_size", "dtype"],
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extra_configs={
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"N": 8,
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"bsz": 1,
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"eps": 1e-6,
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},
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probe_provider="pytorch",
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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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probe_seq_len = 1024
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common_configs = build_token_length_sweep(
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kernel_name="attn_res",
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probe_x=probe_seq_len,
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model=model,
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setup_fn=setup_attn_res,
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model_keys=["hidden_size", "dtype"],
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extra_configs={
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"N": 8,
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"bsz": 1,
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"eps": 1e-6,
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},
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probe_provider="pytorch",
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overwrite=args.overwrite,
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)
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common_configs["kernel_providers"] = ["pytorch", "liger"]
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run_benchmarks(
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bench_test_fn=build_speed_bench_fn(setup_attn_res),
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kernel_operation_modes=["forward", "backward", "full"],
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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_attn_res),
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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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