Avoid compiling kernels for double data type (#933)

csrc/activation_kernels.cu

 #include <torch/extension.h>
 #include <ATen/cuda/CUDAContext.h>
 
+#include "dispatch_utils.h"
+
 namespace vllm {
 
 template<typename T>
@@ -34,9 +36,7 @@ void silu_and_mul(
   dim3 grid(num_tokens);
   dim3 block(std::min(d, 1024));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  AT_DISPATCH_FLOATING_TYPES_AND2(
-    at::ScalarType::Half,
-    at::ScalarType::BFloat16,
+  VLLM_DISPATCH_FLOATING_TYPES(
     input.scalar_type(),
     "silu_and_mul_kernel",
     [&] {
@@ -71,9 +71,7 @@ __global__ void activation_kernel(
   dim3 grid(num_tokens);                                                                  \
   dim3 block(std::min(d, 1024));                                                          \
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();                           \
-  AT_DISPATCH_FLOATING_TYPES_AND2(                                                        \
-    at::ScalarType::Half,                                                                 \
-    at::ScalarType::BFloat16,                                                             \
+  VLLM_DISPATCH_FLOATING_TYPES(                                                           \
     input.scalar_type(),                                                                  \
     "activation_kernel",                                                                  \
     [&] {                                                                                 \

csrc/cache_kernels.cu

 #include <torch/extension.h>
 #include <ATen/cuda/CUDAContext.h>
 
+#include "dispatch_utils.h"
+
 #include <algorithm>
 #include <cassert>
 #include <map>
@@ -125,9 +127,7 @@ void copy_blocks(
   dim3 grid(num_layers, num_pairs);
   dim3 block(std::min(1024, numel_per_block));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  AT_DISPATCH_FLOATING_TYPES_AND2(
-    at::ScalarType::Half,
-    at::ScalarType::BFloat16,
+  VLLM_DISPATCH_FLOATING_TYPES(
     key_caches[0].scalar_type(), "copy_blocks_kernel", ([&] {
       vllm::copy_blocks_kernel<scalar_t><<<grid, block, 0, stream>>>(
         key_cache_ptrs_tensor.data_ptr<int64_t>(),
@@ -202,9 +202,7 @@ void reshape_and_cache(
   dim3 grid(num_tokens);
   dim3 block(std::min(num_heads * head_size, 512));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  AT_DISPATCH_FLOATING_TYPES_AND2(
-    at::ScalarType::Half,
-    at::ScalarType::BFloat16,
+  VLLM_DISPATCH_FLOATING_TYPES(
     key.scalar_type(),
     "reshape_and_cache_kernel",
     [&] {
@@ -364,9 +362,7 @@ void gather_cached_kv(
   dim3 grid(num_tokens);
   dim3 block(std::min(num_heads * head_size, 512));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  AT_DISPATCH_FLOATING_TYPES_AND2(
-    at::ScalarType::Half,
-    at::ScalarType::BFloat16,
+  VLLM_DISPATCH_FLOATING_TYPES(
     key.scalar_type(),
     "gather_cached_kv_kernel_optimized",
     [&] {

csrc/dispatch_utils.h

+/*
+ * Adapted from
+ * https://github.com/pytorch/pytorch/blob/v2.0.1/aten/src/ATen/Dispatch.h
+ */
+#include <torch/extension.h>
+
+#define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)              \
+  AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__)      \
+  AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)       \
+  AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__)
+
+#define VLLM_DISPATCH_FLOATING_TYPES(TYPE, NAME, ...)             \
+  AT_DISPATCH_SWITCH(                                             \
+    TYPE, NAME, VLLM_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))

csrc/layernorm_kernels.cu

 #include <torch/extension.h>
 #include <ATen/cuda/CUDAContext.h>
 
+#include "dispatch_utils.h"
 #include "reduction_utils.cuh"
 
 namespace vllm {
@@ -46,9 +47,7 @@ void rms_norm(
   dim3 grid(num_tokens);
   dim3 block(std::min(hidden_size, 1024));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  AT_DISPATCH_FLOATING_TYPES_AND2(
-    at::ScalarType::Half,
-    at::ScalarType::BFloat16,
+  VLLM_DISPATCH_FLOATING_TYPES(
     input.scalar_type(),
     "rms_norm_kernel",
     [&] {

csrc/pos_encoding_kernels.cu

 #include <torch/extension.h>
 #include <ATen/cuda/CUDAContext.h>
 
+#include "dispatch_utils.h"
+
 namespace vllm {
 
 template<typename scalar_t>
@@ -83,9 +85,7 @@ void rotary_embedding_neox(
   dim3 grid(num_tokens);
   dim3 block(std::min(num_heads * rot_dim / 2, 512));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  AT_DISPATCH_FLOATING_TYPES_AND2(
-    at::ScalarType::Half,
-    at::ScalarType::BFloat16,
+  VLLM_DISPATCH_FLOATING_TYPES(
     query.scalar_type(),
     "rotary_embedding_neox",
     [&] {