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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
125 lines
3.9 KiB
Python
125 lines
3.9 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.jsd import LigerJSD
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from liger_kernel.utils import infer_device
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device = infer_device()
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class TorchJSD(torch.nn.Module):
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def __init__(
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self,
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beta: float = 0.5,
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ignore_index: int = -100,
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dtype: torch.dtype = torch.float,
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):
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super(TorchJSD, self).__init__()
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self.kl = torch.nn.KLDivLoss(reduction="none", log_target=True)
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self.beta = beta
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self.ignore_index = ignore_index
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self.dtype = dtype
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def forward(
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self,
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log_q: torch.Tensor, # input
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log_p: torch.Tensor, # target
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label=None,
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):
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log_p, log_q = log_p.to(torch.float), log_q.to(torch.float)
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log_p, log_q = log_p.view(-1, log_p.size(-1)), log_q.view(-1, log_q.size(-1))
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m = torch.lerp(torch.exp(log_q), torch.exp(log_p), self.beta)
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loss = self.beta * self.kl(torch.log(m), log_p).sum(dim=-1) + (1 - self.beta) * self.kl(
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torch.log(m), log_q
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).sum(dim=-1)
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if label is not None:
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loss = torch.where(label != self.ignore_index, loss, 0.0)
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n_non_ignore = (label != self.ignore_index).sum().item()
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if n_non_ignore == 0:
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loss = 0.0
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else:
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loss = (loss / n_non_ignore).sum()
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else:
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loss = (loss / log_q.shape[0]).sum()
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return loss.to(self.dtype)
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def setup_jsd(input: SingleBenchmarkRunInput):
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"""Create input tensors and JSD loss 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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BT = cfg["seq_len"] * cfg["bsz"]
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V = model_cfg.vocab_size
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else:
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BT = input.x
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V = cfg["vocab_size"]
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_input = torch.randn(BT, V, requires_grad=True, device=device).log_softmax(dim=-1)
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target = torch.randn(BT, V, device=device).log_softmax(dim=-1)
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if input.kernel_provider == "liger":
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loss_fn = LigerJSD()
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elif input.kernel_provider == "torch":
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loss_fn = TorchJSD()
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else:
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raise ValueError(f"Invalid provider: {input.kernel_provider} for JSD")
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return _input, lambda _: loss_fn(_input, target)
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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="jsd",
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setup_fn=setup_jsd,
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model_keys=["vocab_size"],
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probe_provider="torch",
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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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probe_seq_len = 1024
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common_configs = build_token_length_sweep(
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kernel_name="jsd",
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probe_x=probe_seq_len,
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model=model,
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setup_fn=setup_jsd,
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model_keys=["vocab_size"],
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scale_dim="BT",
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x_label="total tokens",
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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"] = ["torch", "liger"]
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run_benchmarks(
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bench_test_fn=build_speed_bench_fn(setup_jsd),
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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_jsd),
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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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