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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
128 lines
4.3 KiB
Python
128 lines
4.3 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 transformers.models.llama4.configuration_llama4 import Llama4TextConfig
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from transformers.models.llama4.modeling_llama4 import Llama4TextRotaryEmbedding
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from transformers.models.llama4.modeling_llama4 import apply_rotary_emb
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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.llama4_rope import liger_llama4_text_rotary_pos_emb
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from liger_kernel.utils import infer_device
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from liger_kernel.utils import transformers_version_dispatch
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device = infer_device()
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def setup_llama4_rope(input: SingleBenchmarkRunInput):
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"""Create input tensors and Llama4 RoPE embedding 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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num_q_heads = model_cfg.num_attention_heads
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num_kv_heads = model_cfg.num_key_value_heads
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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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num_q_heads = cfg["num_attention_heads"]
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num_kv_heads = cfg["num_key_value_heads"]
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dtype = cfg["dtype"]
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head_dim = hidden_size // num_q_heads
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config = Llama4TextConfig(
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hidden_size=hidden_size,
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num_attention_heads=num_q_heads,
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num_key_value_heads=num_kv_heads,
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head_dim=head_dim,
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max_position_embeddings=seq_len,
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)
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rotary_emb = transformers_version_dispatch(
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"4.48.0",
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Llama4TextRotaryEmbedding,
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Llama4TextRotaryEmbedding,
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before_kwargs={"config": config, "device": device},
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after_kwargs={"config": config, "device": device},
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)
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q = torch.randn(
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(1, seq_len, num_q_heads, head_dim),
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device=device,
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requires_grad=True,
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dtype=dtype,
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)
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k = torch.randn(
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(1, seq_len, num_kv_heads, head_dim),
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device=device,
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requires_grad=True,
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dtype=dtype,
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)
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pos_ids = torch.arange(seq_len, device=device, dtype=torch.long).unsqueeze(0)
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freqs_cis = rotary_emb(q, pos_ids)
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if input.kernel_provider == "liger":
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fwd_fn = lambda: liger_llama4_text_rotary_pos_emb(q, k, freqs_cis)
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elif input.kernel_provider == "huggingface":
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fwd_fn = lambda: apply_rotary_emb(q, k, freqs_cis)
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else:
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raise ValueError(f"Invalid provider: {input.kernel_provider} for Llama4 RoPE embedding")
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return q, lambda _: fwd_fn()[0]
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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="llama4_rope",
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setup_fn=setup_llama4_rope,
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model_keys=["hidden_size", "num_attention_heads", "num_key_value_heads", "dtype"],
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probe_provider="huggingface",
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probe_dim="T",
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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 = 2048
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common_configs = build_token_length_sweep(
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kernel_name="llama4_rope",
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probe_x=probe_seq_len,
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model=model,
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setup_fn=setup_llama4_rope,
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model_keys=["hidden_size", "num_attention_heads", "num_key_value_heads", "dtype"],
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scale_dim="T",
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x_label="sequence length",
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probe_provider="huggingface",
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overwrite=args.overwrite,
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)
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common_configs["kernel_providers"] = ["huggingface", "liger"]
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run_benchmarks(
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bench_test_fn=build_speed_bench_fn(setup_llama4_rope),
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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_llama4_rope),
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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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