[Kernel] Support MulAndSilu (vllm-project#11624)

Signed-off-by: Jee Jee Li <[email protected]>
Signed-off-by: hzh <[email protected]>

Author: jeejeelee

csrc/activation_kernels.cu

@@ -9,8 +9,16 @@
 
 namespace vllm {
 
+template <typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&),
+          bool act_first>
+__device__ __forceinline__ scalar_t compute(const scalar_t& x,
+                                            const scalar_t& y) {
+  return act_first ? ACT_FN(x) * y : x * ACT_FN(y);
+}
 // Activation and gating kernel template.
-template <typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&)>
+
+template <typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&),
+          bool act_first>
 __global__ void act_and_mul_kernel(
     scalar_t* __restrict__ out,          // [..., d]
     const scalar_t* __restrict__ input,  // [..., 2, d]
@@ -19,7 +27,7 @@ __global__ void act_and_mul_kernel(
   for (int64_t idx = threadIdx.x; idx < d; idx += blockDim.x) {
     const scalar_t x = VLLM_LDG(&input[token_idx * 2 * d + idx]);
     const scalar_t y = VLLM_LDG(&input[token_idx * 2 * d + d + idx]);
-    out[token_idx * d + idx] = ACT_FN(x) * y;
+    out[token_idx * d + idx] = compute<scalar_t, ACT_FN, act_first>(x, y);
   }
 }
 
@@ -55,7 +63,9 @@ __device__ __forceinline__ T gelu_tanh_kernel(const T& x) {
 }  // namespace vllm
 
 // Launch activation and gating kernel.
-#define LAUNCH_ACTIVATION_GATE_KERNEL(KERNEL)                            \
+// Use ACT_FIRST (bool) indicating whether to apply the activation function
+// first.
+#define LAUNCH_ACTIVATION_GATE_KERNEL(KERNEL, ACT_FIRST)                 \
   int d = input.size(-1) / 2;                                            \
   int64_t num_tokens = input.numel() / input.size(-1);                   \
   dim3 grid(num_tokens);                                                 \
@@ -64,27 +74,35 @@ __device__ __forceinline__ T gelu_tanh_kernel(const T& x) {
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();          \
   VLLM_DISPATCH_FLOATING_TYPES(                                          \
       input.scalar_type(), "act_and_mul_kernel", [&] {                   \
-        vllm::act_and_mul_kernel<scalar_t, KERNEL<scalar_t>>             \
+        vllm::act_and_mul_kernel<scalar_t, KERNEL<scalar_t>, ACT_FIRST>  \
             <<<grid, block, 0, stream>>>(out.data_ptr<scalar_t>(),       \
                                          input.data_ptr<scalar_t>(), d); \
       });
 
 void silu_and_mul(torch::Tensor& out,    // [..., d]
                   torch::Tensor& input)  // [..., 2 * d]
 {
-  LAUNCH_ACTIVATION_GATE_KERNEL(vllm::silu_kernel);
+  LAUNCH_ACTIVATION_GATE_KERNEL(vllm::silu_kernel, true);
+}
+
+void mul_and_silu(torch::Tensor& out,    // [..., d]
+                  torch::Tensor& input)  // [..., 2 * d]
+{
+  // The difference between mul_and_silu and silu_and_mul is that mul_and_silu
+  // applies the silu to the latter half of the input.
+  LAUNCH_ACTIVATION_GATE_KERNEL(vllm::silu_kernel, false);
 }
 
 void gelu_and_mul(torch::Tensor& out,    // [..., d]
                   torch::Tensor& input)  // [..., 2 * d]
 {
-  LAUNCH_ACTIVATION_GATE_KERNEL(vllm::gelu_kernel);
+  LAUNCH_ACTIVATION_GATE_KERNEL(vllm::gelu_kernel, true);
 }
 
 void gelu_tanh_and_mul(torch::Tensor& out,    // [..., d]
                        torch::Tensor& input)  // [..., 2 * d]
 {
-  LAUNCH_ACTIVATION_GATE_KERNEL(vllm::gelu_tanh_kernel);
+  LAUNCH_ACTIVATION_GATE_KERNEL(vllm::gelu_tanh_kernel, true);
 }
 
 namespace vllm {

csrc/ops.h

@@ -86,6 +86,8 @@ void batched_rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
 
 void silu_and_mul(torch::Tensor& out, torch::Tensor& input);
 
+void mul_and_silu(torch::Tensor& out, torch::Tensor& input);
+
 void gelu_and_mul(torch::Tensor& out, torch::Tensor& input);
 
 void gelu_tanh_and_mul(torch::Tensor& out, torch::Tensor& input);

csrc/torch_bindings.cpp

@@ -55,6 +55,9 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("silu_and_mul(Tensor! out, Tensor input) -> ()");
   ops.impl("silu_and_mul", torch::kCUDA, &silu_and_mul);
 
+  ops.def("mul_and_silu(Tensor! out, Tensor input) -> ()");
+  ops.impl("mul_and_silu", torch::kCUDA, &mul_and_silu);
+
   // Activation function used in GeGLU with `none` approximation.
   ops.def("gelu_and_mul(Tensor! out, Tensor input) -> ()");
   ops.impl("gelu_and_mul", torch::kCUDA, &gelu_and_mul);

tests/kernels/test_activation.py

@@ -6,8 +6,9 @@
 
 from tests.kernels.utils import opcheck
 from vllm.model_executor.layers.activation import (FastGELU, FatreluAndMul,
-                                                   GeluAndMul, NewGELU,
-                                                   QuickGELU, SiluAndMul)
+                                                   GeluAndMul, MulAndSilu,
+                                                   NewGELU, QuickGELU,
+                                                   SiluAndMul)
 from vllm.platforms import current_platform
 
 from .allclose_default import get_default_atol, get_default_rtol
@@ -21,8 +22,9 @@
 ]
 
 
-@pytest.mark.parametrize("activation",
-                         ["silu", "gelu", "gelu_tanh", "fatrelu"])
+@pytest.mark.parametrize(
+    "activation",
+    ["silu_and_mul", "mul_and_silu", "gelu", "gelu_tanh", "fatrelu"])
 @pytest.mark.parametrize("num_tokens", NUM_TOKENS)
 @pytest.mark.parametrize("d", D)
 @pytest.mark.parametrize("dtype", DTYPES)
@@ -40,9 +42,12 @@ def test_act_and_mul(
     current_platform.seed_everything(seed)
     torch.set_default_device(device)
     x = torch.randn(num_tokens, 2 * d, dtype=dtype)
-    if activation == "silu":
+    if activation == "silu_and_mul":
         layer = SiluAndMul()
         fn = torch.ops._C.silu_and_mul
+    if activation == "mul_and_silu":
+        layer = MulAndSilu()
+        fn = torch.ops._C.mul_and_silu
     elif activation == "gelu":
         layer = GeluAndMul(approximate="none")
         fn = torch.ops._C.gelu_and_mul
@@ -55,8 +60,9 @@ def test_act_and_mul(
         fn = torch.ops._C.fatrelu_and_mul
     out = layer(x)
     ref_out = layer.forward_native(x)
-    # The SiLU, GELU and FatReLU implementations are equivalent to the native
-    # PyTorch implementations, so we can do exact comparison.
+    # The SiluAndMul, MulAndSilu, GELU and FatReLU implementations are
+    # equivalent to the native PyTorch implementations, so we can do exact
+    # comparison.
     torch.testing.assert_close(out, ref_out, atol=0.0, rtol=0.0)
 
     d = x.shape[-1] // 2

vllm/model_executor/layers/activation.py

@@ -87,6 +87,41 @@ def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
         return out
 
 
+@CustomOp.register("mul_and_silu")
+class MulAndSilu(CustomOp):
+    """An activation function for SwiGLU.
+
+    The function computes x -> x[:d] * silu(x[d:]) where d = x.shape[-1] // 2.
+
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+
+    def __init__(self):
+        super().__init__()
+        if current_platform.is_cuda_alike() or current_platform.is_cpu():
+            self.op = torch.ops._C.mul_and_silu
+        elif current_platform.is_xpu():
+            from vllm._ipex_ops import ipex_ops
+            self.op = ipex_ops.silu_and_mul
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        d = x.shape[-1] // 2
+        return x[..., :d] * F.silu(x[..., d:])
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        output_shape = (x.shape[:-1] + (d, ))
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        self.op(out, x)
+        return out
+
+    # TODO implement forward_xpu for MulAndSilu
+    # def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+
+
 @CustomOp.register("gelu_and_mul")
 class GeluAndMul(CustomOp):
     """An activation function for GeGLU.

vllm/model_executor/models/molmo.py

@@ -23,7 +23,8 @@
 from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
                          InputContext, token_inputs)
 from vllm.model_executor import SamplingMetadata
-from vllm.model_executor.layers.activation import QuickGELU, SiluAndMul
+from vllm.model_executor.layers.activation import (MulAndSilu, QuickGELU,
+                                                   SiluAndMul)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                MergedColumnParallelLinear,
@@ -462,15 +463,6 @@ def forward(
         return output
 
 
-class SwiGLU(nn.Module):
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x, gate = x.chunk(2, dim=-1)
-        # Note that the order is reversed compared to
-        # SiluAndMul.
-        return x * F.silu(gate)
-
-
 class LanuageModelMLP(nn.Module):
     """Molmo's LLM mlp."""
 
@@ -489,7 +481,7 @@ def __init__(self,
             quant_config=quant_config,
         )
         # Activation function.
-        self.act_fn = SwiGLU()
+        self.act_fn = MulAndSilu()
         # Feed-forward output projection.
         self.down_proj = RowParallelLinear(
             self.intermediate_size,

vllm/model_executor/models/ultravox.py

@@ -16,7 +16,7 @@
 from vllm import envs
 from vllm.attention import AttentionMetadata
 from vllm.config import VllmConfig
-from vllm.model_executor.layers.activation import SiluAndMul, get_act_fn
+from vllm.model_executor.layers.activation import MulAndSilu, get_act_fn
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
 from vllm.model_executor.model_loader.loader import DefaultModelLoader
@@ -248,15 +248,6 @@ def forward(self, audio_embeds: torch.Tensor) -> torch.Tensor:
         return audio_embeds
 
 
-class FlippedSiluAndMul(SiluAndMul):
-    """Ultravox is trained with SwiGLU with flipped halves."""
-
-    def forward(self, x: torch.Tensor):
-        a, b = x.chunk(2, dim=-1)
-        flipped = torch.cat((b, a), dim=-1)
-        return super().forward(flipped)
-
-
 class UltravoxProjector(nn.Module):
 
     def __init__(self, config: UltravoxConfig):
@@ -269,7 +260,7 @@ def __init__(self, config: UltravoxConfig):
         dim = self.hidden_dim
 
         if config.projector_act == "swiglu":
-            self.act = FlippedSiluAndMul()
+            self.act = MulAndSilu()
             dim = dim // 2
         else:
             self.act = get_act_fn(config.projector_act)