Support Dynamic Tanh (DyT) (#618)

Author: Tcc0403

benchmark/benchmarks_visualizer.py

@@ -8,8 +8,8 @@
 import pandas as pd
 import seaborn as sns
 
-DATA_PATH = "data/all_benchmark_data.csv"
-VISUALIZATIONS_PATH = "visualizations/"
+DATA_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "data/all_benchmark_data.csv"))
+VISUALIZATIONS_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "visualizations/"))
 
 
 @dataclass

benchmark/scripts/benchmark_dyt.py

@@ -0,0 +1,139 @@
+import os
+import sys
+
+import torch
+import triton
+
+from utils import QUANTILES
+from utils import SingleBenchmarkRunInput
+from utils import SingleBenchmarkRunOutput
+from utils import _test_memory
+from utils import parse_benchmark_script_args
+from utils import run_benchmarks
+
+from liger_kernel.utils import infer_device
+
+device = infer_device()
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))
+
+
+def bench_speed_dyt(input: SingleBenchmarkRunInput) -> SingleBenchmarkRunOutput:
+    from test.transformers.test_dyt import LigerDyT
+    from test.transformers.test_dyt import TorchDyT
+
+    BT = input.x
+    provider = input.kernel_provider
+    mode = input.kernel_operation_mode
+    extra_benchmark_config = input.extra_benchmark_config
+    hidden_size = extra_benchmark_config["hidden_size"]
+    dtype = extra_benchmark_config["dtype"]
+
+    x_shape = (BT, hidden_size)
+    torch_dyt = TorchDyT(hidden_size=hidden_size).to(device)
+    torch_compile_dyt = torch.compile(TorchDyT(hidden_size=hidden_size).to(device))
+    triton_dyt = LigerDyT(hidden_size=hidden_size).to(device)
+
+    x = torch.randn(x_shape, dtype=dtype, device=device)
+    dy = torch.randn_like(x)
+    x.requires_grad_(True)
+
+    def fwd():
+        if provider == "liger":
+            return triton_dyt(x)
+        elif provider == "torch":
+            return torch_dyt(x)
+        elif provider == "torch_compile":
+            return torch_compile_dyt(x)
+
+    if mode == "forward":
+        ms_50, ms_20, ms_80 = triton.testing.do_bench(fwd, quantiles=QUANTILES, grad_to_none=[x], rep=500)
+    elif mode == "backward":
+        y = fwd()
+        ms_50, ms_20, ms_80 = triton.testing.do_bench(
+            lambda: y.backward(dy, retain_graph=True),
+            quantiles=QUANTILES,
+            grad_to_none=[x],
+            rep=500,
+        )
+    elif mode == "full":
+
+        def full():
+            y = fwd()
+            y.backward(dy)
+
+        ms_50, ms_20, ms_80 = triton.testing.do_bench(full, quantiles=QUANTILES, grad_to_none=[x], rep=500)
+
+    return SingleBenchmarkRunOutput(
+        y_20=ms_20,
+        y_50=ms_50,
+        y_80=ms_80,
+    )
+
+
+def bench_memory_dyt(input: SingleBenchmarkRunInput) -> SingleBenchmarkRunOutput:
+    from test.transformers.test_dyt import LigerDyT
+    from test.transformers.test_dyt import TorchDyT
+
+    BT = input.x
+    provider = input.kernel_provider
+    extra_benchmark_config = input.extra_benchmark_config
+    hidden_size = extra_benchmark_config["hidden_size"]
+    dtype = extra_benchmark_config["dtype"]
+
+    x_shape = (BT, hidden_size)
+    torch_dyt = TorchDyT(hidden_size=hidden_size).to(device)
+    torch_compile_dyt = torch.compile(TorchDyT(hidden_size=hidden_size).to(device))
+    triton_dyt = LigerDyT(hidden_size=hidden_size).to(device)
+
+    x = torch.randn(x_shape, dtype=dtype, device=device)
+    dy = torch.randn_like(x)
+    x.requires_grad_(True)
+
+    def fwd():
+        if provider == "liger":
+            return triton_dyt(x)
+        elif provider == "torch":
+            return torch_dyt(x)
+        elif provider == "torch_compile":
+            return torch_compile_dyt(x)
+
+    def full():
+        y = fwd()
+        y.backward(dy, retain_graph=True)
+
+    mem_50, mem_20, mem_80 = _test_memory(full, quantiles=QUANTILES)
+    return SingleBenchmarkRunOutput(
+        y_20=mem_20,
+        y_50=mem_50,
+        y_80=mem_80,
+    )
+
+
+if __name__ == "__main__":
+    args = parse_benchmark_script_args()
+
+    common_configs = {
+        "kernel_name": "dyt",
+        "x_name": "BT",
+        "x_label": "batch_size * seq_len",
+        "x_values": [2**i for i in range(10, 15)],
+        "kernel_providers": ["liger", "torch", "torch_compile"],
+        "extra_benchmark_configs": [{"hidden_size": 4096, "dtype": torch.float32}],
+        "overwrite": args.overwrite,
+    }
+
+    run_benchmarks(
+        bench_test_fn=bench_speed_dyt,
+        kernel_operation_modes=["forward", "backward", "full"],
+        metric_name="speed",
+        metric_unit="ms",
+        **common_configs,
+    )
+    run_benchmarks(
+        bench_test_fn=bench_memory_dyt,
+        kernel_operation_modes=["full"],
+        metric_name="memory",
+        metric_unit="MB",
+        **common_configs,
+    )

src/liger_kernel/ops/dyt.py

@@ -0,0 +1,225 @@
+import operator
+
+import torch
+import triton
+import triton.language as tl
+
+from liger_kernel.ops.utils import calculate_settings
+from liger_kernel.ops.utils import compare_version
+from liger_kernel.ops.utils import ensure_contiguous
+from liger_kernel.ops.utils import infer_device
+
+if compare_version("triton", operator.ge, "3.0.0"):
+    try:
+        # typical import path with dispatch available
+        from triton.language.extra.libdevice import tanh
+    except ModuleNotFoundError:
+        # for working with NGC containers
+        from triton.language.extra.cuda.libdevice import tanh
+else:
+    from triton.language.math import tanh
+
+
+@triton.jit
+def _dyt_fwd_kernel(
+    x_ptr,
+    x_row_stride,
+    alpha_ptr,
+    gamma_ptr,
+    beta_ptr,
+    y_ptr,
+    y_row_stride,
+    n_cols,
+    BLOCK_SIZE: tl.constexpr,
+):
+    """
+    Reference:
+    https://arxiv.org/abs/2503.10622
+
+    Shapes:
+        - x: (BT, C)
+        - alpha: (1)
+        - gamma: (C)
+        - beta: (C)
+    """
+    row_idx = tl.program_id(0)
+    offsets = tl.arange(0, BLOCK_SIZE)
+    mask = offsets < n_cols
+
+    x_ptr += row_idx * x_row_stride
+    y_ptr += row_idx * y_row_stride
+
+    alpha = tl.load(alpha_ptr)
+    gamma = tl.load(gamma_ptr + offsets, mask=mask)
+    beta = tl.load(beta_ptr + offsets, mask=mask)
+    x = tl.load(x_ptr + offsets, mask=mask)
+    y = gamma * tanh((alpha * x).cast(tl.float32)) + beta
+    tl.store(y_ptr + offsets, y, mask=mask)
+
+
+@triton.jit
+def _dyt_bwd_kernel(
+    x_ptr,
+    x_row_stride,
+    dy_ptr,
+    dy_row_stride,
+    dx_ptr,
+    dx_row_stride,
+    alpha_ptr,
+    dalpha_ptr,
+    gamma_ptr,
+    dgamma_ptr,
+    dgamma_row_stride,
+    n_cols,
+    n_rows,
+    ROWS_PER_PROGRAM: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    """
+    Reference:
+    https://arxiv.org/abs/2503.10622
+
+    Shapes:
+        - x: (BT, C)
+        - alpha: (1)
+        - gamma: (C)
+        - dx: (BT, C)
+        - dy: (BT, C)
+        - dgamma: (sm_count, C)
+        - dalpha: (sm_count,)
+    """
+    # d(gamma * tanh(alpha * x) + beta) / dx
+    # = gamma * (1 - tanh^2(alpha * x)) * alpha
+    # d(gamma * tanh(alpha * x) + beta) / dalpha
+    # = gamma * (1 - tanh^2(alpha * x)) * x
+    # d(gamma * tanh(alpha * x) + beta) / dgamma
+    # = tanh(alpha * x)
+    # d(gamma * tanh(alpha * x)) / dbeta = 1
+    pid = tl.program_id(0)
+
+    row_start = pid * ROWS_PER_PROGRAM
+    row_end = min((pid + 1) * ROWS_PER_PROGRAM, n_rows)
+    offsets = tl.arange(0, BLOCK_SIZE)
+    mask = offsets < n_cols
+
+    dalpha = 0.0
+    dgamma = tl.zeros((BLOCK_SIZE,), dtype=tl.float32)
+
+    x_ptr += row_start * x_row_stride
+    dx_ptr += row_start * dx_row_stride
+    dy_ptr += row_start * dy_row_stride
+    alpha = tl.load(alpha_ptr)
+    gamma = tl.load(gamma_ptr + offsets, mask=mask, other=0.0)
+
+    for _ in tl.range(row_start, row_end):
+        dy = tl.load(dy_ptr + offsets, mask=mask, other=0.0)
+        x = tl.load(x_ptr + offsets, mask=mask, other=0.0)
+        tanh_ax = tanh((alpha * x).cast(tl.float32))
+        sech2_ax = 1 - tanh_ax * tanh_ax
+
+        dx = dy * gamma * sech2_ax * alpha
+        dalpha += tl.sum(dy * gamma * sech2_ax * x)
+        dgamma += dy * tanh_ax
+        tl.store(dx_ptr + offsets, dx, mask=mask)
+
+        dy_ptr += dy_row_stride
+        x_ptr += x_row_stride
+        dx_ptr += dx_row_stride
+
+    tl.store(dgamma_ptr + pid * dgamma_row_stride + offsets, dgamma, mask=mask)
+    tl.store(dalpha_ptr + pid, dalpha)
+
+    pass
+
+
+def liger_dyt_fwd(x, alpha, gamma, beta):
+    shape = x.shape
+    dim = shape[-1]
+    x = x.view(-1, dim)
+    n_rows, n_cols = x.shape
+    y = torch.empty_like(x)
+    BLOCK_SIZE, num_warps = calculate_settings(n_cols)
+    _dyt_fwd_kernel[(n_rows,)](
+        x_ptr=x,
+        alpha_ptr=alpha,
+        gamma_ptr=gamma,
+        beta_ptr=beta,
+        y_ptr=y,
+        x_row_stride=x.stride(0),
+        y_row_stride=y.stride(0),
+        n_cols=n_cols,
+        BLOCK_SIZE=BLOCK_SIZE,
+        num_warps=num_warps,
+    )
+    return y.view(*shape)
+
+
+def liger_dyt_bwd(dy, x, alpha, gamma):
+    shape = dy.shape
+    dtype = x.dtype
+    dim = shape[-1]
+    dy = dy.view(-1, dim)
+    x = x.view(-1, dim)
+    n_rows, n_cols = dy.shape
+    BLOCK_SIZE, num_warps = calculate_settings(n_cols)
+    sm_count = 1
+    device = infer_device()
+    if device == "cuda":
+        sm_count = torch.cuda.get_device_properties(x.device).multi_processor_count
+    elif device == "xpu":
+        sm_count = torch.xpu.get_device_properties(x.device).gpu_subslice_count
+    if n_cols > BLOCK_SIZE:
+        raise RuntimeError(
+            f"Feature dimension {dim} exceeds maximum supported size of {BLOCK_SIZE}. Consider using a smaller feature dimension."
+        )
+
+    dx = torch.empty_like(x, dtype=torch.float32)
+    _dalpha = torch.empty((sm_count,), dtype=torch.float32, device=x.device)
+    _dgamma = torch.empty((sm_count, n_cols), dtype=torch.float32, device=x.device)
+
+    grid = (sm_count,)
+    rows_per_program = triton.cdiv(n_rows, sm_count)
+    _dyt_bwd_kernel[grid](
+        x_ptr=x,
+        x_row_stride=x.stride(0),
+        dy_ptr=dy,
+        dy_row_stride=dy.stride(0),
+        dx_ptr=dx,
+        dx_row_stride=dx.stride(0),
+        alpha_ptr=alpha,
+        dalpha_ptr=_dalpha,
+        gamma_ptr=gamma,
+        dgamma_ptr=_dgamma,
+        dgamma_row_stride=_dgamma.stride(0),
+        n_cols=n_cols,
+        n_rows=n_rows,
+        ROWS_PER_PROGRAM=rows_per_program,
+        BLOCK_SIZE=BLOCK_SIZE,
+        num_warps=num_warps,
+    )
+    dalpha = _dalpha.sum(dim=0, keepdim=True).to(dtype)
+    dgamma = _dgamma.sum(dim=0).to(dtype)
+    dbeta = dy.sum(dim=0).to(dtype)
+    return dx.view(*shape), dalpha, dgamma, dbeta
+
+
+class LigerDyTFunction(torch.autograd.Function):
+    @staticmethod
+    @ensure_contiguous
+    def forward(ctx, x, alpha, gamma, beta):
+        y = liger_dyt_fwd(x, alpha, gamma, beta)
+        ctx.save_for_backward(x, alpha, gamma)
+        return y
+
+    @staticmethod
+    @ensure_contiguous
+    def backward(ctx, grad_output):
+        x, alpha, gamma = ctx.saved_tensors
+        dx, dalpha, dgamma, dbeta = liger_dyt_bwd(
+            grad_output,
+            x,
+            alpha,
+            gamma,
+        )
+
+        return (dx, dalpha, dgamma, dbeta)

src/liger_kernel/transformers/__init__.py

@@ -1,5 +1,6 @@
 from liger_kernel.transformers.auto_model import AutoLigerKernelForCausalLM  # noqa: F401
 from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss  # noqa: F401
+from liger_kernel.transformers.dyt import LigerDyT  # noqa: F401
 from liger_kernel.transformers.fused_linear_cross_entropy import LigerFusedLinearCrossEntropyLoss  # noqa: F401
 from liger_kernel.transformers.fused_linear_jsd import LigerFusedLinearJSD  # noqa: F401
 from liger_kernel.transformers.geglu import LigerGEGLUMLP  # noqa: F401