Add some fused elementwise kernels for grok-1 (#4398)

Co-authored-by: dhou-xai <dhou@x.ai>
Co-authored-by: Hanming Lu <69857889+hanming-lu@users.noreply.github.com>

python/sglang/srt/layers/elementwise.py

@@ -0,0 +1,411 @@
+from typing import Tuple
+
+import torch
+import triton
+import triton.language as tl
+
+fused_softcap_autotune = triton.autotune(
+    configs=[
+        triton.Config(kwargs={"BLOCK_SIZE": 128}, num_warps=4),
+        triton.Config(kwargs={"BLOCK_SIZE": 128}, num_warps=8),
+        triton.Config(kwargs={"BLOCK_SIZE": 128}, num_warps=16),
+        triton.Config(kwargs={"BLOCK_SIZE": 256}, num_warps=4),
+        triton.Config(kwargs={"BLOCK_SIZE": 256}, num_warps=8),
+        triton.Config(kwargs={"BLOCK_SIZE": 512}, num_warps=4),
+        triton.Config(kwargs={"BLOCK_SIZE": 512}, num_warps=8),
+        triton.Config(kwargs={"BLOCK_SIZE": 512}, num_warps=16),
+        triton.Config(kwargs={"BLOCK_SIZE": 1024}, num_warps=4),
+        triton.Config(kwargs={"BLOCK_SIZE": 1024}, num_warps=8),
+        triton.Config(kwargs={"BLOCK_SIZE": 1024}, num_warps=16),
+        triton.Config(kwargs={"BLOCK_SIZE": 1024}, num_warps=32),
+        triton.Config(kwargs={"BLOCK_SIZE": 2048}, num_warps=32),
+        triton.Config(kwargs={"BLOCK_SIZE": 4096}, num_warps=32),
+        triton.Config(kwargs={"BLOCK_SIZE": 8192}, num_warps=32),
+        triton.Config(kwargs={"BLOCK_SIZE": 16384}, num_warps=32),
+        triton.Config(kwargs={"BLOCK_SIZE": 32768}, num_warps=32),
+    ],
+    key=["n_ele"],
+)
+
+
+@triton.jit
+def fused_softcap_kernel(
+    output_ptr,
+    input_ptr,
+    n_ele,
+    softcap_const: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    pid = tl.program_id(axis=0)
+    block_start = pid * BLOCK_SIZE
+    offsets = block_start + tl.arange(0, BLOCK_SIZE)
+    mask = offsets < n_ele
+    x = tl.load(input_ptr + offsets, mask=mask)
+    fx = x.to(tl.float32)
+    fxs = fx / softcap_const
+    exped = tl.exp(2 * fxs)
+    top = exped - 1
+    bottom = exped + 1
+    output = top / bottom * softcap_const
+    tl.store(output_ptr + offsets, output, mask=mask)
+
+
+fused_softcap_kernel_autotuned = fused_softcap_autotune(fused_softcap_kernel)
+
+
+def fused_softcap(x, softcap_const, autotune=False):
+    output = torch.empty_like(x, dtype=torch.float32)
+    n_elements = output.numel()
+    if autotune:
+        grid = lambda meta: (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
+        fused_softcap_kernel_autotuned[grid](output, x, n_elements, softcap_const)
+    else:
+        fused_softcap_kernel[(triton.cdiv(n_elements, 128),)](
+            output, x, n_elements, softcap_const, BLOCK_SIZE=128, num_warps=8
+        )
+    return output
+
+
+# cast to float + softcap
+class Softcap:
+    def __init__(self, softcap_const: float):
+        self.softcap_const = softcap_const
+
+    def __call__(self, *args, **kwargs):
+        return self.forward(*args, **kwargs)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if x.is_cuda:
+            return self.forward_cuda(x)
+        else:
+            return self.forward_native(x)
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.tanh(x.float() / self.softcap_const) * self.softcap_const
+
+    def forward_cuda(self, x: torch.Tensor, autotune=False) -> torch.Tensor:
+        return fused_softcap(x, self.softcap_const, autotune=autotune)
+
+
+rmsnorm_autotune = triton.autotune(
+    configs=[
+        triton.Config(kwargs={"BLOCK_SIZE": 1024}, num_warps=4, num_stages=1),
+        triton.Config(kwargs={"BLOCK_SIZE": 1024}, num_warps=8, num_stages=1),
+        triton.Config(kwargs={"BLOCK_SIZE": 1024}, num_warps=16, num_stages=1),
+        triton.Config(kwargs={"BLOCK_SIZE": 1024}, num_warps=4),
+        triton.Config(kwargs={"BLOCK_SIZE": 1024}, num_warps=8),
+        triton.Config(kwargs={"BLOCK_SIZE": 1024}, num_warps=16),
+        triton.Config(kwargs={"BLOCK_SIZE": 1024}, num_warps=4, num_stages=4),
+        triton.Config(kwargs={"BLOCK_SIZE": 1024}, num_warps=8, num_stages=4),
+        triton.Config(kwargs={"BLOCK_SIZE": 1024}, num_warps=16, num_stages=4),
+        triton.Config(kwargs={"BLOCK_SIZE": 1024}, num_warps=8, num_stages=8),
+        triton.Config(kwargs={"BLOCK_SIZE": 1024}, num_warps=16, num_stages=8),
+        triton.Config(kwargs={"BLOCK_SIZE": 2048}, num_warps=8),
+        triton.Config(kwargs={"BLOCK_SIZE": 2048}, num_warps=16),
+        triton.Config(kwargs={"BLOCK_SIZE": 2048}, num_warps=8, num_stages=4),
+        triton.Config(kwargs={"BLOCK_SIZE": 2048}, num_warps=16, num_stages=4),
+        triton.Config(kwargs={"BLOCK_SIZE": 4096}, num_warps=8),
+        triton.Config(kwargs={"BLOCK_SIZE": 4096}, num_warps=16),
+        triton.Config(kwargs={"BLOCK_SIZE": 8192}, num_warps=8),
+        triton.Config(kwargs={"BLOCK_SIZE": 8192}, num_warps=16),
+        triton.Config(kwargs={"BLOCK_SIZE": 8192}, num_warps=32),
+        triton.Config(kwargs={"BLOCK_SIZE": 8192}, num_warps=8, num_stages=1),
+        triton.Config(kwargs={"BLOCK_SIZE": 8192}, num_warps=16, num_stages=1),
+        triton.Config(kwargs={"BLOCK_SIZE": 8192}, num_warps=32, num_stages=1),
+        triton.Config(kwargs={"BLOCK_SIZE": 8192}, num_warps=8, num_stages=4),
+        triton.Config(kwargs={"BLOCK_SIZE": 8192}, num_warps=16, num_stages=4),
+        triton.Config(kwargs={"BLOCK_SIZE": 8192}, num_warps=32, num_stages=4),
+        triton.Config(kwargs={"BLOCK_SIZE": 16384}, num_warps=8),
+        triton.Config(kwargs={"BLOCK_SIZE": 16384}, num_warps=16),
+        triton.Config(kwargs={"BLOCK_SIZE": 16384}, num_warps=32),
+        triton.Config(kwargs={"BLOCK_SIZE": 16384}, num_warps=8, num_stages=1),
+        triton.Config(kwargs={"BLOCK_SIZE": 16384}, num_warps=16, num_stages=1),
+        triton.Config(kwargs={"BLOCK_SIZE": 16384}, num_warps=32, num_stages=1),
+        triton.Config(kwargs={"BLOCK_SIZE": 16384}, num_warps=8, num_stages=4),
+        triton.Config(kwargs={"BLOCK_SIZE": 16384}, num_warps=16, num_stages=4),
+        triton.Config(kwargs={"BLOCK_SIZE": 16384}, num_warps=32, num_stages=4),
+    ],
+    key=["hidden_dim"],
+)
+
+
+@triton.jit
+def fused_dual_residual_rmsnorm_kernel(
+    output_ptr,
+    mid_ptr,
+    activ_ptr,
+    residual_ptr,
+    weight1_ptr,
+    weight2_ptr,
+    eps: tl.constexpr,
+    hidden_dim: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    pid = tl.program_id(axis=0)
+    input_start = pid * hidden_dim
+
+    offsets = tl.arange(0, BLOCK_SIZE)
+    mask = offsets < hidden_dim
+
+    a_ = tl.load(activ_ptr + input_start + offsets, mask=mask, other=0.0)
+    a = a_.to(tl.float32)
+    rms = tl.sqrt(tl.sum(a * a, axis=0) / hidden_dim + eps)
+
+    r = tl.load(residual_ptr + input_start + offsets, mask=mask, other=0.0)
+    w1_ = tl.load(weight1_ptr + offsets, mask=mask, other=0.0)
+    w1 = w1_.to(tl.float32)
+
+    a2r = r + (a / rms * w1).to(r.dtype)
+    tl.store(
+        mid_ptr + input_start + offsets,
+        a2r,
+        mask=mask,
+    )
+
+    a2r = a2r.to(tl.float32)
+    rms2 = tl.sqrt(tl.sum(a2r * a2r, axis=0) / hidden_dim + eps)
+
+    w2_ = tl.load(weight2_ptr + offsets, mask=mask, other=0.0)
+    w2 = w2_.to(tl.float32)
+
+    tl.store(
+        output_ptr + input_start + offsets,
+        a2r / rms2 * w2,  # implicitly casts to output dtype here
+        mask=mask,
+    )
+
+
+fused_dual_residual_rmsnorm_kernel_autotune = rmsnorm_autotune(
+    fused_dual_residual_rmsnorm_kernel
+)
+
+
+def fused_dual_residual_rmsnorm(x, residual, weight1, weight2, eps, autotune=False):
+    assert len(x.shape) == 2
+    assert x.shape == residual.shape and x.dtype == residual.dtype
+    output, mid = torch.empty_like(x), torch.empty_like(x)
+    bs, hidden_dim = x.shape
+    if autotune:
+        fused_dual_residual_rmsnorm_kernel_autotune[(bs,)](
+            output, mid, x, residual, weight1, weight2, eps=eps, hidden_dim=hidden_dim
+        )
+    else:
+        config = {
+            "BLOCK_SIZE": triton.next_power_of_2(hidden_dim),
+            "num_warps": max(
+                min(triton.next_power_of_2(triton.cdiv(hidden_dim, 256)), 32), 4
+            ),
+        }
+
+        fused_dual_residual_rmsnorm_kernel[(bs,)](
+            output,
+            mid,
+            x,
+            residual,
+            weight1,
+            weight2,
+            eps=eps,
+            hidden_dim=hidden_dim,
+            **config,
+        )
+
+    return output, mid
+
+
+@triton.jit
+def fused_rmsnorm_kernel(
+    output_ptr,
+    activ_ptr,
+    weight_ptr,
+    eps: tl.constexpr,
+    hidden_dim: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    pid = tl.program_id(axis=0)
+    input_start = pid * hidden_dim
+
+    offsets = tl.arange(0, BLOCK_SIZE)
+    mask = offsets < hidden_dim
+
+    a_ = tl.load(activ_ptr + input_start + offsets, mask=mask, other=0.0)
+    a = a_.to(tl.float32)
+    rms = tl.sqrt(tl.sum(a * a, axis=0) / hidden_dim + eps)
+
+    w1_ = tl.load(weight_ptr + offsets, mask=mask, other=0.0)
+    w1 = w1_.to(tl.float32)
+
+    a_rms = a / rms * w1
+
+    tl.store(
+        output_ptr + input_start + offsets,
+        a_rms,  # implicitly casts to output dtype here
+        mask=mask,
+    )
+
+
+def fused_rmsnorm(x, weight, eps, autotune=False, inplace=False):
+    assert len(x.shape) == 2
+    if inplace:
+        output = x
+    else:
+        output = torch.empty_like(x)
+    bs, hidden_dim = x.shape
+    config = {
+        "BLOCK_SIZE": triton.next_power_of_2(hidden_dim),
+        "num_warps": max(
+            min(triton.next_power_of_2(triton.cdiv(hidden_dim, 256)), 32), 4
+        ),
+    }
+
+    fused_rmsnorm_kernel[(bs,)](
+        output, x, weight, eps=eps, hidden_dim=hidden_dim, **config
+    )
+    return output
+
+
+class FusedDualResidualRMSNorm:
+    """
+    Fused implementation of
+    y = RMSNorm2(RMSNorm1(x) + residual))
+    """
+
+    def __init__(self, rmsnorm1, rmsnorm2) -> None:  # the one after rmsnorm1
+        self.rmsnorm1 = rmsnorm1
+        self.rmsnorm2 = rmsnorm2
+        self.variance_epsilon = self.rmsnorm1.variance_epsilon
+        assert self.rmsnorm1.variance_epsilon == self.rmsnorm2.variance_epsilon
+        assert self.rmsnorm1.weight.shape == self.rmsnorm2.weight.shape
+
+    def __call__(self, *args, **kwargs):
+        return self.forward(*args, **kwargs)
+
+    def forward(
+        self, x: torch.Tensor, residual: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        if x.is_cuda:
+            return self.forward_cuda(x, residual)
+        else:
+            return self.forward_flashinfer(x, residual)
+
+    def forward_cuda(
+        self, x: torch.Tensor, residual: torch.Tensor, autotune=False
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        return fused_dual_residual_rmsnorm(
+            x,
+            residual,
+            self.rmsnorm1.weight,
+            self.rmsnorm2.weight,
+            self.variance_epsilon,
+            autotune=autotune,
+        )
+
+    def forward_flashinfer(
+        self,
+        x: torch.Tensor,
+        residual: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        normed1 = self.rmsnorm1(x)
+        residual = normed1 + residual
+        return self.rmsnorm2(residual), residual
+
+    def forward_native(
+        self,
+        x: torch.Tensor,
+        residual: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        normed1 = self.rmsnorm1.forward_native(x)
+        residual = normed1 + residual
+        return self.rmsnorm2.forward_native(residual), residual
+
+
+# gelu on first half of vector
+@triton.jit
+def gelu_and_mul_kernel(
+    out_hidden_states_ptr,  # (bs, hidden_dim)
+    out_scales_ptr,  # (bs,)
+    hidden_states_ptr,  # (bs, hidden_dim * 2)
+    quant_max: tl.constexpr,
+    static_scale: tl.constexpr,
+    hidden_dim: tl.constexpr,  # the output hidden_dim
+    BLOCK_SIZE: tl.constexpr,
+):
+    pid = tl.program_id(axis=0)
+
+    input_start = pid * hidden_dim * 2
+    output_start = pid * hidden_dim
+
+    input1_offs = tl.arange(0, BLOCK_SIZE)
+    mask = tl.arange(0, BLOCK_SIZE) < hidden_dim  # shared for input1, input3, output
+    input3_offs = hidden_dim + tl.arange(0, BLOCK_SIZE)
+    output_offs = tl.arange(0, BLOCK_SIZE)
+
+    x1 = tl.load(
+        hidden_states_ptr + input_start + input1_offs, mask=mask, other=0.0
+    ).to(tl.float32)
+    x3 = tl.load(
+        hidden_states_ptr + input_start + input3_offs, mask=mask, other=0.0
+    ).to(tl.float32)
+
+    # gelu
+    # cast down before mul to better match training?
+    gelu_x1 = 0.5 * (1.0 + tl.erf(x1 * 0.7071067811865475)) * x1
+    out = x3 * gelu_x1.to(hidden_states_ptr.dtype.element_ty)
+
+    if quant_max is not None:
+        raise NotImplementedError()
+
+    tl.store(out_hidden_states_ptr + output_start + output_offs, out, mask=mask)
+
+
+def gelu_and_mul_triton(
+    hidden_states,
+    scales=None,
+    quantize=None,  # dtype to quantize to
+    out=None,
+):
+    bs, in_hidden_dim = hidden_states.shape
+    hidden_dim = in_hidden_dim // 2
+
+    if out is None:
+        out_hidden_states = torch.empty(
+            (bs, hidden_dim),
+            dtype=quantize or hidden_states.dtype,
+            device=hidden_states.device,
+        )
+    else:
+        assert out.shape == (bs, hidden_dim)
+        assert out.dtype == (quantize or hidden_states.dtype)
+        out_hidden_states = out
+    out_scales = None
+    static_scale = False
+    if quantize is not None:
+        if scales is None:
+            out_scales = torch.empty(
+                (bs,), dtype=torch.float32, device=hidden_states.device
+            )
+        else:
+            out_scales = scales
+            static_scale = True
+
+    config = {
+        # 8 ele per thread (not tuned)
+        "num_warps": max(
+            min(triton.next_power_of_2(triton.cdiv(hidden_dim, 8 * 32)), 32), 4
+        ),
+    }
+
+    gelu_and_mul_kernel[(bs,)](
+        out_hidden_states,
+        out_scales,
+        hidden_states,
+        quant_max=torch.finfo(quantize).max if quantize is not None else None,
+        static_scale=static_scale,
+        hidden_dim=hidden_dim,
+        BLOCK_SIZE=triton.next_power_of_2(hidden_dim),
+        **config,
+    )
+
+    if quantize is not None:
+        return out_hidden_states, out_scales
+    else:
+        return out_hidden_states, None