[contrib] Support for xentropy extension. (#34)

* enable deprecated fused adam optimizer

* enable deprecated fused lamb

* enable xentropy extension

* add warpsize 32 for nv and 64 for amd

* update compiler arguments

* update the syncwarp conditions

* update syncwarp condition

apex/contrib/csrc/xentropy/xentropy_kernel.cu

@@ -85,6 +85,14 @@
 
 #define ALIGN_BYTES 16
 
+#ifdef __HIP_PLATFORM_HCC__
+#define WARP_SIZE 64
+#define SYNCWARP(mask)
+#else
+#define WARP_SIZE 32
+#define SYNCWARP(mask) __syncwarp(mask)
+#endif
+
 using Tensor = at::Tensor;
 using TensorList = at::TensorList;
 using ScalarType = at::ScalarType;
@@ -126,7 +134,7 @@ inline dim3 SoftMax_getBlockSize(int ILP, uint64_t dim_size) {
   uint64_t max_block_size = std::min(dim_size / ILP, static_cast<uint64_t>(max_threads));
   while (block_size < (max_block_size/2)) block_size *= 2;
   // Launch at least a single warp - the kernel assumes that.
-  block_size = std::max(block_size, static_cast<uint64_t>(32));
+  block_size = std::max(block_size, static_cast<uint64_t>(WARP_SIZE));
   return dim3(block_size);
 }
 
@@ -195,15 +203,15 @@ blockReduce(AccumT* smem, AccumT val,
   AccumT warpVal = defaultVal;
 
   // First warp will perform per-warp reductions for the remaining warps
-  uint32_t mask = (((uint64_t)1) << (blockDim.x / 32)) - 1;
-  if (threadIdx.x < 32) {
-    int lane = threadIdx.x % 32;
-    if (lane < blockDim.x / 32) {
+  uint32_t mask = (((uint64_t)1) << (blockDim.x / WARP_SIZE)) - 1;
+  if (threadIdx.x < WARP_SIZE) {
+    int lane = threadIdx.x % WARP_SIZE;
+    if (lane < blockDim.x / WARP_SIZE) {
 #pragma unroll
-      for (int i = 0; i < 32; ++i) {
-        warpVal = r(warpVal, smem[lane * 32 + i]);
+      for (int i = 0; i < WARP_SIZE; ++i) {
+        warpVal = r(warpVal, smem[lane * WARP_SIZE + i]);
       }
-      __syncwarp(mask);
+      SYNCWARP(mask);
       smem[lane] = warpVal;
     }
   }
@@ -214,7 +222,7 @@ blockReduce(AccumT* smem, AccumT val,
   AccumT blockVal = defaultVal;
 
   if (threadIdx.x == 0) {
-    for (int i = 0; i < blockDim.x / 32; ++i) {
+    for (int i = 0; i < blockDim.x / WARP_SIZE; ++i) {
       blockVal = r(blockVal, smem[i]);
     }
     smem[0] = blockVal;
@@ -249,16 +257,16 @@ blockReduce(AccumT* smem,
   AccumT warpVal2 = defaultVal2;
 
   // First warp will perform per-warp reductions for the remaining warps
-  uint32_t mask = (((uint64_t)1) << (blockDim.x / 32)) - 1;
-  if (threadIdx.x < 32) {
-    int lane = threadIdx.x % 32;
-    if (lane < blockDim.x / 32) {
+  uint32_t mask = (((uint64_t)1) << (blockDim.x / WARP_SIZE)) - 1;
+  if (threadIdx.x < WARP_SIZE) {
+    int lane = threadIdx.x % WARP_SIZE;
+    if (lane < blockDim.x / WARP_SIZE) {
 #pragma unroll
-      for (int i = 0; i < 32; ++i) {
-        warpVal1 = r1(warpVal1, smem[lane * 32 + i]);
-        warpVal2 = r2(warpVal2, smem[lane * 32 + i + blockDim.x]);
+      for (int i = 0; i < WARP_SIZE; ++i) {
+        warpVal1 = r1(warpVal1, smem[lane * WARP_SIZE + i]);
+        warpVal2 = r2(warpVal2, smem[lane * WARP_SIZE + i + blockDim.x]);
       }
-      __syncwarp(mask);
+      SYNCWARP(mask);
       smem[lane] = warpVal1;
       smem[lane + blockDim.x] = warpVal2;
     }
@@ -271,7 +279,7 @@ blockReduce(AccumT* smem,
   AccumT blockVal2 = defaultVal2;
 
   if (threadIdx.x == 0) {
-    for (int i = 0; i < blockDim.x / 32; ++i) {
+    for (int i = 0; i < blockDim.x / WARP_SIZE; ++i) {
       blockVal1 = r1(blockVal1, smem[i]);
       blockVal2 = r2(blockVal2, smem[i + blockDim.x]);
     }

setup.py

@@ -269,9 +269,12 @@ if "--xentropy" in sys.argv:
     from torch.utils.cpp_extension import BuildExtension
     cmdclass['build_ext'] = BuildExtension
 
-    if torch.utils.cpp_extension.CUDA_HOME is None:
+    is_rocm_pytorch = check_if_rocm_pytorch()
+
+    if torch.utils.cpp_extension.CUDA_HOME is None and (not is_rocm_pytorch):
         raise RuntimeError("--xentropy was requested, but nvcc was not found.  Are you sure your environment has nvcc available?  If you're installing within a container from https://hub.docker.com/r/pytorch/pytorch, only images whose names contain 'devel' will provide nvcc.")
     else:
+        if not is_rocm_pytorch:
             ext_modules.append(
                 CUDAExtension(name='xentropy_cuda',
                               sources=['apex/contrib/csrc/xentropy/interface.cpp',
@@ -279,6 +282,14 @@ if "--xentropy" in sys.argv:
                               include_dirs=[os.path.join(this_dir, 'csrc')],
                               extra_compile_args={'cxx': ['-O3'] + version_dependent_macros,
                                                   'nvcc':['-O3'] + version_dependent_macros}))
+        else:
+             ext_modules.append(
+                CUDAExtension(name='xentropy_cuda',
+                              sources=['apex/contrib/csrc/xentropy/interface.cpp',
+                                       'apex/contrib/csrc/xentropy/hip/xentropy_kernel.hip'],
+                              include_dirs=[os.path.join(this_dir, 'csrc/hip')],
+                              extra_compile_args=['-O3'] + version_dependent_macros))
+   
 
 if "--deprecated_fused_adam" in sys.argv:
     from torch.utils.cpp_extension import CUDAExtension