feat: add LigerMLP module

benchmark/scripts/benchmark_mlp.py

@@ -0,0 +1,110 @@
+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 transformers.models.llama.configuration_llama import LlamaConfig
+from transformers.models.llama.modeling_llama import LlamaMLP
+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.mlp import LigerMLP
+from liger_kernel.utils import infer_device
+
+device = infer_device()
+
+
+def setup_mlp(input: SingleBenchmarkRunInput):
+    """Create input tensor and SwiGLU MLP layer from benchmark config."""
+    cfg = input.extra_benchmark_config
+    if isinstance(input.x, str):
+        model_cfg = MODEL_REGISTRY[input.x]
+        seq_len = cfg["seq_len"]
+        hidden_size = model_cfg.hidden_size
+        intermediate_size = model_cfg.intermediate_size
+        dtype = model_cfg.dtype
+    else:
+        seq_len = input.x
+        hidden_size = cfg["hidden_size"]
+        intermediate_size = cfg["intermediate_size"]
+        dtype = cfg["dtype"]
+
+    llama_config = LlamaConfig(
+        hidden_size=hidden_size,
+        intermediate_size=intermediate_size,
+        hidden_act=cfg["hidden_act"],
+    )
+    x = torch.randn(
+        cfg["bsz"],
+        seq_len,
+        hidden_size,
+        device=device,
+        dtype=dtype,
+        requires_grad=True,
+    )
+    if input.kernel_provider == "liger":
+        layer = LigerMLP(config=llama_config).to(device).to(dtype)
+    elif input.kernel_provider == "huggingface":
+        layer = LlamaMLP(config=llama_config).to(device).to(dtype)
+    else:
+        raise ValueError(f"Invalid provider: {input.kernel_provider} for MLP")
+    return x, layer
+
+
+if __name__ == "__main__":
+    args = parse_benchmark_script_args()
+
+    if args.sweep_mode == "model_config":
+        common_configs = build_model_config_sweep(
+            kernel_name="mlp",
+            setup_fn=setup_mlp,
+            model_keys=["hidden_size", "intermediate_size", "dtype"],
+            probe_provider="huggingface",
+            extra_configs={
+                "bsz": 1,
+                "hidden_act": "silu",
+            },
+            probe_dim="T",
+            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="mlp",
+            probe_x=probe_seq_len,
+            model=model,
+            setup_fn=setup_mlp,
+            model_keys=["hidden_size", "intermediate_size", "dtype"],
+            extra_configs={
+                "bsz": 1,
+                "hidden_act": "silu",
+            },
+            scale_dim="T",
+            x_label="total tokens",
+            probe_provider="huggingface",
+            overwrite=args.overwrite,
+        )
+
+    common_configs["kernel_providers"] = ["huggingface", "liger"]
+
+    run_benchmarks(
+        bench_test_fn=build_speed_bench_fn(setup_mlp),
+        kernel_operation_modes=["forward", "backward", "full"],
+        metric_name="speed",
+        metric_unit="ms",
+        **common_configs,
+    )
+    run_benchmarks(
+        bench_test_fn=build_memory_bench_fn(setup_mlp),
+        kernel_operation_modes=["full", "forward", "backward"],
+        metric_name="memory",
+        metric_unit="MB",
+        **common_configs,
+    )

src/liger_kernel/ops/__init__.py

@@ -70,6 +70,7 @@
 from liger_kernel.ops.mhc import LigerMHCCoeffsFunction  # noqa: F401
 from liger_kernel.ops.mhc import LigerMHCPostResFunction  # noqa: F401
 from liger_kernel.ops.mhc import LigerMHCPreFunction  # noqa: F401
+from liger_kernel.ops.mlp import LigerMLPFunction  # noqa: F401
 from liger_kernel.ops.modulated_rms_norm import LigerModulatedRMSNormFunction  # noqa: F401
 from liger_kernel.ops.modulated_rms_norm import modulated_rms_norm_backward  # noqa: F401
 from liger_kernel.ops.modulated_rms_norm import modulated_rms_norm_forward  # noqa: F401