Support bfloat16 data type (#54)

cacheflow/master/server.py

@@ -213,8 +213,8 @@ def add_server_arguments(parser: argparse.ArgumentParser):
     parser.add_argument('--use-np-cache', action='store_true',
                         help='save a numpy copy of model weights for faster loading')
     parser.add_argument('--use-dummy-weights', action='store_true', help='use dummy values for model weights')
-    # NOTE(woosuk): If FlashAttention is used, the float data type is not supported.
-    parser.add_argument('--dtype', type=str, default='half', choices=['half'], help='data type')
+    # NOTE(woosuk): FlashAttention does not support float32.
+    parser.add_argument('--dtype', type=str, default='half', choices=['half', 'bfloat16'], help='data type')
     # Parallel arguments
     parser.add_argument('--use-ray', action='store_true', help='use Ray for distributed serving, will be automatically set when using more than 1 GPU')
     parser.add_argument('--pipeline-parallel-size', '-pp', type=int, default=1, help='number of pipeline stages')

cacheflow/models/utils.py

@@ -17,6 +17,7 @@ _STR_DTYPE_TO_TORCH_DTYPE = {
     'float': torch.float,
     'float16': torch.float16,
     'float32': torch.float32,
+    'bfloat16': torch.bfloat16,
 }
 
 

csrc/activation_kernels.cu

@@ -34,7 +34,9 @@ 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_AND_HALF(
+  AT_DISPATCH_FLOATING_TYPES_AND2(
+    at::ScalarType::Half,
+    at::ScalarType::BFloat16,
     input.scalar_type(),
     "silu_and_mul_kernel",
     [&] {

csrc/attention/attention_dtypes.h

@@ -3,3 +3,7 @@
 #include "attention_generic.cuh"
 #include "dtype_float16.cuh"
 #include "dtype_float32.cuh"
+
+#ifdef ENABLE_BF16
+#include "dtype_bfloat16.cuh"
+#endif // ENABLE_BF16

csrc/attention/attention_kernels.cu

@@ -1,7 +1,7 @@
 #include <torch/extension.h>
 #include <ATen/cuda/CUDAContext.h>
 
-#include "attention_dtypes.cuh"
+#include "attention_dtypes.h"
 #include "attention_utils.cuh"
 
 #include <algorithm>
@@ -438,9 +438,13 @@ void single_query_cached_kv_attention(
   torch::Tensor& context_lens,    // [num_seqs]
   int block_size,
   int max_context_len) {
-  // TODO(woosuk): Support FP32 and BF16.
+  // TODO(woosuk): Support FP32.
   if (query.dtype() == at::ScalarType::Half) {
     CALL_KERNEL_LAUNCHER_BLOCK_SIZE(uint16_t);
+#ifdef ENABLE_BF16
+  } else if (query.dtype() == at::ScalarType::BFloat16) {
+    CALL_KERNEL_LAUNCHER_BLOCK_SIZE(__nv_bfloat16);
+#endif
   } else {
     TORCH_CHECK(false, "Unsupported data type: ", query.dtype());
   }

csrc/attention/attention_utils.cuh

@@ -1,6 +1,6 @@
 #pragma once
 
-#include "attention_dtypes.cuh"
+#include "attention_dtypes.h"
 
 #include <float.h>
 #include <type_traits>

csrc/attention/dtype_bfloat16.cuh

@@ -0,0 +1,361 @@
+#pragma once
+
+#include "attention_generic.cuh"
+#include "dtype_float32.cuh"
+
+#include <cuda_bf16.h>
+#include <cuda_fp16.h>
+#include <stdint.h>
+
+namespace cacheflow {
+
+// Define custom BF16 vector data types.
+struct bf16_4_t {
+  __nv_bfloat162 x;
+  __nv_bfloat162 y;
+};
+
+struct bf16_8_t {
+  __nv_bfloat162 x;
+  __nv_bfloat162 y;
+  __nv_bfloat162 z;
+  __nv_bfloat162 w;
+};
+
+// BF16 vector types for Q, K, V.
+template<>
+struct Vec<__nv_bfloat16, 1> {
+  using Type = __nv_bfloat16;
+};
+template<>
+struct Vec<__nv_bfloat16, 2> {
+  using Type = __nv_bfloat162;
+};
+template<>
+struct Vec<__nv_bfloat16, 4> {
+  using Type = bf16_4_t;
+};
+template<>
+struct Vec<__nv_bfloat16, 8> {
+  using Type = bf16_8_t;
+};
+
+// FP32 accumulator vector types corresponding to Vec.
+template<>
+struct FloatVec<__nv_bfloat16> {
+  using Type = float;
+};
+template<>
+struct FloatVec<__nv_bfloat162> {
+  using Type = float2;
+};
+template<>
+struct FloatVec<bf16_4_t> {
+  using Type = Float4_;
+};
+template<>
+struct FloatVec<bf16_8_t> {
+  using Type = Float8_;
+};
+
+// Utility functions for type conversions.
+inline __device__ float2 bf1622float2(const __nv_bfloat162 val) {
+  return __bfloat1622float2(val);
+}
+
+inline __device__ __nv_bfloat162 bf162bf162(const __nv_bfloat16 val) {
+  return __bfloat162bfloat162(val);
+}
+
+// Vector addition.
+inline __device__ __nv_bfloat16 add(__nv_bfloat16 a, __nv_bfloat16 b) {
+  return a + b;
+}
+
+inline __device__ __nv_bfloat162 add(__nv_bfloat162 a, __nv_bfloat162 b) {
+  return __hadd2(a, b);
+}
+
+inline __device__ bf16_4_t add(bf16_4_t a, bf16_4_t b) {
+  bf16_4_t c;
+  c.x = add(a.x, b.x);
+  c.y = add(a.y, b.y);
+  return c;
+}
+
+inline __device__ bf16_8_t add(bf16_8_t a, bf16_8_t b) {
+  bf16_8_t c;
+  c.x = add(a.x, b.x);
+  c.y = add(a.y, b.y);
+  c.z = add(a.z, b.z);
+  c.w = add(a.w, b.w);
+  return c;
+}
+
+inline __device__ float2 add(__nv_bfloat162 a, float2 fb) {
+  float2 fa = bf1622float2(a);
+  return add(fa, fb);
+}
+
+inline __device__ Float4_ add(bf16_4_t a, Float4_ fb) {
+  Float4_ fc;
+  fc.x = add(a.x, fb.x);
+  fc.y = add(a.y, fb.y);
+  return fc;
+}
+
+inline __device__ Float8_ add(bf16_8_t a, Float8_ fb) {
+  Float8_ fc;
+  fc.x = add(a.x, fb.x);
+  fc.y = add(a.y, fb.y);
+  fc.z = add(a.z, fb.z);
+  fc.w = add(a.w, fb.w);
+  return fc;
+}
+
+// Vector multiplication.
+template<>
+inline __device__ __nv_bfloat16 mul(__nv_bfloat16 a, __nv_bfloat16 b) {
+  return __hmul(a, b);
+}
+
+template<>
+inline __device__ __nv_bfloat162 mul(__nv_bfloat162 a, __nv_bfloat162 b) {
+  return __hmul2(a, b);
+}
+
+template<>
+inline __device__ __nv_bfloat162 mul(__nv_bfloat16 a, __nv_bfloat162 b) {
+  return mul<__nv_bfloat162, __nv_bfloat162, __nv_bfloat162>(bf162bf162(a), b);
+}
+
+template<>
+inline __device__ bf16_4_t mul(bf16_4_t a, bf16_4_t b) {
+  bf16_4_t c;
+  c.x = mul<__nv_bfloat162, __nv_bfloat162, __nv_bfloat162>(a.x, b.x);
+  c.y = mul<__nv_bfloat162, __nv_bfloat162, __nv_bfloat162>(a.y, b.y);
+  return c;
+}
+
+template<>
+inline __device__ bf16_4_t mul(__nv_bfloat16 a, bf16_4_t b) {
+  __nv_bfloat162 s = bf162bf162(a);
+  bf16_4_t c;
+  c.x = mul<__nv_bfloat162, __nv_bfloat162, __nv_bfloat162>(s, b.x);
+  c.y = mul<__nv_bfloat162, __nv_bfloat162, __nv_bfloat162>(s, b.y);
+  return c;
+}
+
+template<>
+inline __device__ bf16_8_t mul(bf16_8_t a, bf16_8_t b) {
+  bf16_8_t c;
+  c.x = mul<__nv_bfloat162, __nv_bfloat162, __nv_bfloat162>(a.x, b.x);
+  c.y = mul<__nv_bfloat162, __nv_bfloat162, __nv_bfloat162>(a.y, b.y);
+  c.z = mul<__nv_bfloat162, __nv_bfloat162, __nv_bfloat162>(a.z, b.z);
+  c.w = mul<__nv_bfloat162, __nv_bfloat162, __nv_bfloat162>(a.w, b.w);
+  return c;
+}
+
+template<>
+inline __device__ bf16_8_t mul(__nv_bfloat16 a, bf16_8_t b) {
+  __nv_bfloat162 s = bf162bf162(a);
+  bf16_8_t c;
+  c.x = mul<__nv_bfloat162, __nv_bfloat162, __nv_bfloat162>(s, b.x);
+  c.y = mul<__nv_bfloat162, __nv_bfloat162, __nv_bfloat162>(s, b.y);
+  c.z = mul<__nv_bfloat162, __nv_bfloat162, __nv_bfloat162>(s, b.z);
+  c.w = mul<__nv_bfloat162, __nv_bfloat162, __nv_bfloat162>(s, b.w);
+  return c;
+}
+
+template<>
+inline __device__ float mul(__nv_bfloat16 a, __nv_bfloat16 b) {
+  float fa = __bfloat162float(a);
+  float fb = __bfloat162float(b);
+  return fa * fb;
+}
+
+template<>
+inline __device__ float2 mul(__nv_bfloat162 a, __nv_bfloat162 b) {
+  float2 fa = bf1622float2(a);
+  float2 fb = bf1622float2(b);
+  return mul<float2, float2, float2>(fa, fb);
+}
+
+template<>
+inline __device__ float2 mul(__nv_bfloat16 a, __nv_bfloat162 b) {
+  return mul<float2, __nv_bfloat162, __nv_bfloat162>(bf162bf162(a), b);
+}
+
+template<>
+inline __device__ Float4_ mul(bf16_4_t a, bf16_4_t b) {
+  Float4_ fc;
+  fc.x = mul<float2, __nv_bfloat162, __nv_bfloat162>(a.x, b.x);
+  fc.y = mul<float2, __nv_bfloat162, __nv_bfloat162>(a.y, b.y);
+  return fc;
+}
+
+template<>
+inline __device__ Float4_ mul(__nv_bfloat16 a, bf16_4_t b) {
+  __nv_bfloat162 s = bf162bf162(a);
+  Float4_ fc;
+  fc.x = mul<float2, __nv_bfloat162, __nv_bfloat162>(s, b.x);
+  fc.y = mul<float2, __nv_bfloat162, __nv_bfloat162>(s, b.y);
+  return fc;
+}
+
+template<>
+inline __device__ Float8_ mul(bf16_8_t a, bf16_8_t b) {
+  Float8_ fc;
+  fc.x = mul<float2, __nv_bfloat162, __nv_bfloat162>(a.x, b.x);
+  fc.y = mul<float2, __nv_bfloat162, __nv_bfloat162>(a.y, b.y);
+  fc.z = mul<float2, __nv_bfloat162, __nv_bfloat162>(a.z, b.z);
+  fc.w = mul<float2, __nv_bfloat162, __nv_bfloat162>(a.w, b.w);
+  return fc;
+}
+
+template<>
+inline __device__ Float8_ mul(__nv_bfloat16 a, bf16_8_t b) {
+  __nv_bfloat162 s = bf162bf162(a);
+  Float8_ fc;
+  fc.x = mul<float2, __nv_bfloat162, __nv_bfloat162>(s, b.x);
+  fc.y = mul<float2, __nv_bfloat162, __nv_bfloat162>(s, b.y);
+  fc.z = mul<float2, __nv_bfloat162, __nv_bfloat162>(s, b.z);
+  fc.w = mul<float2, __nv_bfloat162, __nv_bfloat162>(s, b.w);
+  return fc;
+}
+
+// Vector fused multiply-add.
+inline __device__ __nv_bfloat162 fma(__nv_bfloat162 a, __nv_bfloat162 b, __nv_bfloat162 c) {
+  return __hfma2(a, b, c);
+}
+
+inline __device__ __nv_bfloat162 fma(__nv_bfloat16 a, __nv_bfloat162 b, __nv_bfloat162 c) {
+  return __hfma2(bf162bf162(a), b, c);
+}
+
+inline __device__ bf16_4_t fma(bf16_4_t a, bf16_4_t b, bf16_4_t c) {
+  bf16_4_t d;
+  d.x = fma(a.x, b.x, c.x);
+  d.y = fma(a.y, b.y, c.y);
+  return d;
+}
+
+inline __device__ bf16_4_t fma(__nv_bfloat16 a, bf16_4_t b, bf16_4_t c) {
+  __nv_bfloat162 s = bf162bf162(a);
+  bf16_4_t d;
+  d.x = fma(s, b.x, c.x);
+  d.y = fma(s, b.y, c.y);
+  return d;
+}
+
+inline __device__ bf16_8_t fma(bf16_8_t a, bf16_8_t b, bf16_8_t c) {
+  bf16_8_t d;
+  d.x = fma(a.x, b.x, c.x);
+  d.y = fma(a.y, b.y, c.y);
+  d.z = fma(a.z, b.z, c.z);
+  d.w = fma(a.w, b.w, c.w);
+  return d;
+}
+
+inline __device__ bf16_8_t fma(__nv_bfloat16 a, bf16_8_t b, bf16_8_t c) {
+  __nv_bfloat162 s = bf162bf162(a);
+  bf16_8_t d;
+  d.x = fma(s, b.x, c.x);
+  d.y = fma(s, b.y, c.y);
+  d.z = fma(s, b.z, c.z);
+  d.w = fma(s, b.w, c.w);
+  return d;
+}
+
+inline __device__ float fma(__nv_bfloat16 a, __nv_bfloat16 b, float fc) {
+  return __bfloat162float(a) * __bfloat162float(b) + fc;
+}
+
+inline __device__ float2 fma(__nv_bfloat162 a, __nv_bfloat162 b, float2 fc) {
+  float2 fa = bf1622float2(a);
+  float2 fb = bf1622float2(b);
+  return fma(fa, fb, fc);
+}
+
+inline __device__ float2 fma(__nv_bfloat16 a, __nv_bfloat162 b, float2 fc) {
+  return fma(bf162bf162(a), b, fc);
+}
+
+inline __device__ Float4_ fma(bf16_4_t a, bf16_4_t b, Float4_ fc) {
+  Float4_ fd;
+  fd.x = fma(a.x, b.x, fc.x);
+  fd.y = fma(a.y, b.y, fc.y);
+  return fd;
+}
+
+inline __device__ Float4_ fma(__nv_bfloat16 a, bf16_4_t b, Float4_ fc) {
+  __nv_bfloat162 s = bf162bf162(a);
+  Float4_ fd;
+  fd.x = fma(s, b.x, fc.x);
+  fd.y = fma(s, b.y, fc.y);
+  return fd;
+}
+
+inline __device__ Float8_ fma(bf16_8_t a, bf16_8_t b, Float8_ fc) {
+  Float8_ fd;
+  fd.x = fma(a.x, b.x, fc.x);
+  fd.y = fma(a.y, b.y, fc.y);
+  fd.z = fma(a.z, b.z, fc.z);
+  fd.w = fma(a.w, b.w, fc.w);
+  return fd;
+}
+
+inline __device__ Float8_ fma(__nv_bfloat16 a, bf16_8_t b, Float8_ fc) {
+  __nv_bfloat162 s = bf162bf162(a);
+  Float8_ fd;
+  fd.x = fma(s, b.x, fc.x);
+  fd.y = fma(s, b.y, fc.y);
+  fd.z = fma(s, b.z, fc.z);
+  fd.w = fma(s, b.w, fc.w);
+  return fd;
+}
+
+// Vector sum.
+template<>
+inline __device__ float sum(__nv_bfloat16 v) {
+  return __bfloat162float(v);
+}
+
+template<>
+inline __device__ float sum(__nv_bfloat162 v) {
+  float2 vf = bf1622float2(v);
+  return vf.x + vf.y;
+}
+
+template<>
+inline __device__ float sum(bf16_4_t v) {
+  return sum(v.x) + sum(v.y);
+}
+
+template<>
+inline __device__ float sum(bf16_8_t v) {
+  return sum(v.x) + sum(v.y) + sum(v.z) + sum(v.w);
+}
+
+// From float32 to bfloat16.
+inline __device__ void from_float(__nv_bfloat16& dst, float src) {
+  dst = __float2bfloat16(src);
+}
+
+inline __device__ void from_float(__nv_bfloat162& dst, float2 src) {
+  dst = __float22bfloat162_rn(src);
+}
+
+inline __device__ void from_float(bf16_4_t& dst, Float4_ src) {
+  dst.x = __float22bfloat162_rn(src.x);
+  dst.y = __float22bfloat162_rn(src.y);
+}
+
+inline __device__ void from_float(bf16_8_t& dst, Float8_ src) {
+  dst.x = __float22bfloat162_rn(src.x);
+  dst.y = __float22bfloat162_rn(src.y);
+  dst.z = __float22bfloat162_rn(src.z);
+  dst.w = __float22bfloat162_rn(src.w);
+}
+
+} // namespace cacheflow

csrc/attention/dtype_float32.cuh

@@ -6,7 +6,7 @@
 
 namespace cacheflow {
 
-// Define FP32 vector data types.
+// Define custom FP32 vector data types.
 struct Float4_ {
   float2 x;
   float2 y;

csrc/cache_kernels.cu

@@ -14,14 +14,16 @@ void swap_blocks(
   torch::Device dst_device = dst.device();
   cudaMemcpyKind memcpy_type;
   if (src_device.is_cuda() && dst_device.is_cuda()) {
-    assert(src_device.index() == dst_device.index());
+    TORCH_CHECK(
+      src_device.index() == dst_device.index(),
+      "src and dst must be on the same GPU");
     memcpy_type = cudaMemcpyDeviceToDevice;
   } else if (src_device.is_cuda() && dst_device.is_cpu()) {
     memcpy_type = cudaMemcpyDeviceToHost;
   } else if (src_device.is_cpu() && dst_device.is_cuda()) {
     memcpy_type = cudaMemcpyHostToDevice;
   } else {
-    assert(false);
+    TORCH_CHECK(false, "Invalid device combination");
   }
 
   void *src_ptr = src.data_ptr();
@@ -29,6 +31,7 @@ void swap_blocks(
 
   const int64_t block_size_in_bytes = src.element_size() * src[0].numel();
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+  // NOTE(woosuk): This can be slow if the number of blocks is large.
   for (const auto& pair : block_mapping) {
     int64_t src_block_number = pair.first;
     int64_t dst_block_number = pair.second;
@@ -122,7 +125,9 @@ 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_AND_HALF(
+  AT_DISPATCH_FLOATING_TYPES_AND2(
+    at::ScalarType::Half,
+    at::ScalarType::BFloat16,
     key_caches[0].scalar_type(), "copy_blocks_kernel", ([&] {
       cacheflow::copy_blocks_kernel<scalar_t><<<grid, block, 0, stream>>>(
         key_cache_ptrs_tensor.data_ptr<int64_t>(),
@@ -176,6 +181,50 @@ __global__ void reshape_and_cache_kernel(
   }
 }
 
+} // namespace cacheflow
+
+void reshape_and_cache(
+  torch::Tensor& key,           // [num_tokens, num_heads, head_size]
+  torch::Tensor& value,         // [num_tokens, num_heads, head_size]
+  torch::Tensor& key_cache,     // [num_blocks, num_heads, head_size/x, block_size, x]
+  torch::Tensor& value_cache,   // [num_blocks, num_heads, head_size, block_size]
+  torch::Tensor& slot_mapping)  // [num_tokens]
+{
+  int num_tokens = key.size(0);
+  int num_heads = key.size(1);
+  int head_size = key.size(2);
+  int block_size = key_cache.size(3);
+  int x = key_cache.size(4);
+
+  int key_stride = key.stride(0);
+  int value_stride = value.stride(0);
+
+  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,
+    key.scalar_type(),
+    "reshape_and_cache_kernel",
+    [&] {
+      cacheflow::reshape_and_cache_kernel<scalar_t><<<grid, block, 0, stream>>>(
+        key.data_ptr<scalar_t>(),
+        value.data_ptr<scalar_t>(),
+        key_cache.data_ptr<scalar_t>(),
+        value_cache.data_ptr<scalar_t>(),
+        slot_mapping.data_ptr<int>(),
+        key_stride,
+        value_stride,
+        num_heads,
+        head_size,
+        block_size,
+        x);
+    });
+}
+
+namespace cacheflow {
+
 // Grid: (num_blocks, block_size).
 template<typename scalar_t>
 __global__ void gather_cached_kv_kernel(
@@ -296,45 +345,6 @@ __global__ void gather_cached_kv_kernel_optimized(
 
 } // namespace cacheflow
 
-void reshape_and_cache(
-  torch::Tensor& key,           // [num_tokens, num_heads, head_size]
-  torch::Tensor& value,         // [num_tokens, num_heads, head_size]
-  torch::Tensor& key_cache,     // [num_blocks, num_heads, head_size/x, block_size, x]
-  torch::Tensor& value_cache,   // [num_blocks, num_heads, head_size, block_size]
-  torch::Tensor& slot_mapping)  // [num_tokens]
-{
-  int num_tokens = key.size(0);
-  int num_heads = key.size(1);
-  int head_size = key.size(2);
-  int block_size = key_cache.size(3);
-  int x = key_cache.size(4);
-
-  int key_stride = key.stride(0);
-  int value_stride = value.stride(0);
-
-  dim3 grid(num_tokens);
-  dim3 block(std::min(num_heads * head_size, 512));
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
-    key.scalar_type(),
-    "reshape_and_cache_kernel",
-    [&] {
-      cacheflow::reshape_and_cache_kernel<scalar_t><<<grid, block, 0, stream>>>(
-        key.data_ptr<scalar_t>(),
-        value.data_ptr<scalar_t>(),
-        key_cache.data_ptr<scalar_t>(),
-        value_cache.data_ptr<scalar_t>(),
-        slot_mapping.data_ptr<int>(),
-        key_stride,
-        value_stride,
-        num_heads,
-        head_size,
-        block_size,
-        x);
-    });
-}
-
-
 void gather_cached_kv(
   torch::Tensor& key,           // [out] [num_tokens, num_heads, head_size]
   torch::Tensor& value,         // [out] [num_tokens, num_heads, head_size]
@@ -354,7 +364,9 @@ 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_AND_HALF(
+  AT_DISPATCH_FLOATING_TYPES_AND2(
+    at::ScalarType::Half,
+    at::ScalarType::BFloat16,
     key.scalar_type(),
     "gather_cached_kv_kernel_optimized",
     [&] {

csrc/layernorm_kernels.cu

@@ -46,7 +46,9 @@ 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_AND_HALF(
+  AT_DISPATCH_FLOATING_TYPES_AND2(
+    at::ScalarType::Half,
+    at::ScalarType::BFloat16,
     input.scalar_type(),
     "rms_norm_kernel",
     [&] {

csrc/pos_encoding_kernels.cu

@@ -64,7 +64,9 @@ 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_AND_HALF(
+  AT_DISPATCH_FLOATING_TYPES_AND2(
+    at::ScalarType::Half,
+    at::ScalarType::BFloat16,
     query.scalar_type(),
     "rotary_embedding_neox",
     [&] {

setup.py

@@ -1,9 +1,22 @@
 import setuptools
+import torch
 from torch.utils import cpp_extension
 
 CXX_FLAGS = ['-g']
 NVCC_FLAGS = ['-O2']
 
+if not torch.cuda.is_available():
+    raise RuntimeError(
+        f'Cannot find CUDA at CUDA_HOME: {cpp_extension.CUDA_HOME}. '
+        'CUDA must be available in order to build the package.')
+
+# FIXME(woosuk): Consider the case where the machine has multiple GPUs with
+# different compute capabilities.
+compute_capability = torch.cuda.get_device_capability()
+major, minor = compute_capability
+# Enable bfloat16 support if the compute capability is >= 8.0.
+if major >= 8:
+    NVCC_FLAGS.append('-DENABLE_BF16')
 
 ext_modules = []
 
@@ -23,7 +36,7 @@ attention_extension = cpp_extension.CUDAExtension(
 )
 ext_modules.append(attention_extension)
 
-# Positional encodings.
+# Positional encoding kernels.
 positional_encoding_extension = cpp_extension.CUDAExtension(
     name='cacheflow.pos_encoding_ops',
     sources=['csrc/pos_encoding.cpp', 'csrc/pos_encoding_kernels.cu'],
@@ -39,6 +52,7 @@ layernorm_extension = cpp_extension.CUDAExtension(
 )
 ext_modules.append(layernorm_extension)
 
+# Activation kernels.
 activation_extension = cpp_extension.CUDAExtension(
     name='cacheflow.activation_ops',
     sources=['csrc/activation.cpp', 'csrc/activation_kernels.cu'],