ROCm Support : Tile_Layout kernel

setup.py

@@ -71,11 +71,13 @@ def use_debug_mode():
 from torch.utils.cpp_extension import (
     CUDA_HOME,
     IS_WINDOWS,
+    ROCM_HOME,
     BuildExtension,
     CppExtension,
     CUDAExtension,
 )
 
+IS_ROCM = (torch.version.hip is not None) and (ROCM_HOME is not None)
 
 class BuildOptions:
     def __init__(self):
@@ -250,13 +252,18 @@ def get_extensions():
         print(
             "PyTorch GPU support is not available. Skipping compilation of CUDA extensions"
         )
-    if CUDA_HOME is None and torch.cuda.is_available():
-        print("CUDA toolkit is not available. Skipping compilation of CUDA extensions")
+
+    if (CUDA_HOME is None and ROCM_HOME is None) and torch.cuda.is_available():
+        print(
+            "CUDA toolkit or ROCm is not available. Skipping compilation of CUDA extensions"
+        )
         print(
             "If you'd like to compile CUDA extensions locally please install the cudatoolkit from https://anaconda.org/nvidia/cuda-toolkit"
         )
 
-    use_cuda = torch.cuda.is_available() and CUDA_HOME is not None
+    use_cuda = torch.cuda.is_available() and (
+        CUDA_HOME is not None or ROCM_HOME is not None
+    )
     extension = CUDAExtension if use_cuda else CppExtension
 
     extra_link_args = []
@@ -272,7 +279,8 @@ def get_extensions():
 
         if debug_mode:
             extra_compile_args["cxx"].append("-g")
-            extra_compile_args["nvcc"].append("-g")
+            if "nvcc" in extra_compile_args:
+                extra_compile_args["nvcc"].append("-g")
             extra_link_args.extend(["-O0", "-g"])
     else:
         extra_compile_args["cxx"].extend(
@@ -284,8 +292,8 @@ def get_extensions():
             extra_compile_args["nvcc"].append("-g")
             extra_link_args.append("/DEBUG")
 
-    this_dir = os.path.dirname(os.path.curdir)
-    extensions_dir = os.path.join(this_dir, "torchao", "csrc")
+    curdir = os.path.dirname(os.path.curdir)
+    extensions_dir = os.path.join(curdir, "torchao", "csrc")
     sources = list(glob.glob(os.path.join(extensions_dir, "**/*.cpp"), recursive=True))
 
     extensions_cuda_dir = os.path.join(extensions_dir, "cuda")
@@ -314,6 +322,25 @@ def get_extensions():
                 "-I" + cutlass_extensions_include_dir,
             ]
         )
+
+    curdir = os.path.dirname(os.path.curdir)
+    extensions_dir = os.path.join(curdir, "torchao", "csrc")
+    sources = list(glob.glob(os.path.join(extensions_dir, "**/*.cpp"), recursive=True))
+
+    extensions_cuda_dir = os.path.join(extensions_dir, "cuda")
+    cuda_sources = list(
+        glob.glob(os.path.join(extensions_cuda_dir, "**/*.cu"), recursive=True)
+    )
+
+    extensions_hip_dir = os.path.join(
+        extensions_dir, "cuda", "tensor_core_tiled_layout"
+    )
+    hip_sources = list(
+        glob.glob(os.path.join(extensions_hip_dir, "*.cu"), recursive=True)
+    )
+
+    if not IS_ROCM and use_cuda:
+        sources += cuda_sources
     else:
         # Remove CUTLASS-based kernels from the cuda_sources list.  An
         # assumption is that these files will have "cutlass" in its
@@ -325,6 +352,21 @@ def get_extensions():
         )
         sources = [s for s in sources if s not in cutlass_sources]
 
+    # TOOD: Remove this and use what CUDA has once we fix all the builds.
+    if IS_ROCM and use_cuda:
+        # Add ROCm GPU architecture check
+        gpu_arch = torch.cuda.get_device_properties(0).name
+        if gpu_arch != "gfx942":
+            print(f"Warning: Unsupported ROCm GPU architecture: {gpu_arch}")
+            print(
+                "Currently only gfx942 is supported. Skipping compilation of ROCm extensions"
+            )
+            return None
+        sources += hip_sources
+
+    if len(sources) == 0:
+        return None
+
     ext_modules = []
     if len(sources) > 0:
         ext_modules.append(

torchao/csrc/cuda/tensor_core_tiled_layout/tensor_core_tiled_layout.cu

@@ -1,4 +1,4 @@
-#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 800  // at least Ampere
+#if (defined(USE_ROCM) && ROCM_VERSION >= 60200) || !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 800
 
 #include <ATen/ATen.h>
 #include <ATen/core/Tensor.h>
@@ -7,13 +7,24 @@
 #include <c10/cuda/CUDAGuard.h>
 #include <torch/library.h>
 
+#if defined(USE_ROCM)
+#include <hip/hip_bf16.h>
+#include <hip/hip_fp16.h>
+#include <hip/hip_runtime.h>
+#endif
+
 template <typename U, typename V>
 constexpr __host__ __device__ auto divUp(U a, V b) -> decltype(a + b) {
   static_assert(std::is_integral<U>::value && std::is_integral<V>::value, "");
   const uint64_t blocks = a / b + (a % b != 0);
   return blocks;
 }
+
+#if defined(USE_ROCM)
+constexpr int32_t kWarpSize = 64;
+#else
 constexpr int32_t kWarpSize = 32;
+#endif
 
 //Simple data structure to represent 4 pairs of bfloat16s, used for vectorized dequantization
 //https://github.com/pytorch/pytorch/blob/b6689e0fb83a1578959ab0d9c6d2d9e11f7df21a/aten/src/ATen/native/cuda/int4mm.cu#L178-L180
@@ -30,38 +41,71 @@ inline __device__ bf16x2x4 convert_i4x8_to_bf16x2x4(uint32_t source) {
   uint32_t const source_i4s = source;
 
   // First, we extract the i4s and construct an intermediate fp16 number.
+#if !defined(USE_ROCM)
   static constexpr uint32_t immLut = (0xf0 & 0xcc) | 0xaa;
+#endif
   static constexpr uint32_t MASK = 0x000f000f;
   static constexpr uint32_t I4s_TO_BF16s_MAGIC_NUM = 0x43004300;
 
   // We don't have enough mantissa to remove as much shift overhead as FP16, so
   // we must loop. No shift needed for first item.
   uint32_t i4s = source_i4s;
+// AMD MI300X ISA that performs two bitwise operations in a single instruction:
+// v_and_or_b32 performs H[0] = (i4s & MASK) | I4s_TO_BF16s_MAGIC_NUM
+//   - First ANDs `i4s` with `MASK` (0x000f000f) to extract 4-bit values
+//   - Then ORs the result with `I4s_TO_BF16s_MAGIC_NUM` (0x43004300) to convert them to bfloat16
+#if defined(USE_ROCM)
+  asm volatile("v_and_or_b32 %0, %1, %2, %3"
+               : "=v"(h[0])
+               : "v"(i4s), "v"(MASK), "v"(I4s_TO_BF16s_MAGIC_NUM));
+#else
   asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
                : "=r"(h[0])
                : "r"(i4s), "n"(MASK), "n"(I4s_TO_BF16s_MAGIC_NUM), "n"(immLut));
+#endif
+
 #pragma unroll
   for (int ii = 1; ii < kElements / 2; ++ii) {
     i4s >>= 4; // or is it 8?
     // (i4s & 0x000f000f) | 0x43004300
+#if defined(USE_ROCM)
+    asm volatile("v_and_or_b32 %0, %1, %2, %3"
+        : "=v"(h[ii])
+        : "v"(i4s), "v"(MASK), "v"(I4s_TO_BF16s_MAGIC_NUM));
+#else
     asm volatile(
         "lop3.b32 %0, %1, %2, %3, %4;\n"
         : "=r"(h[ii])
         : "r"(i4s), "n"(MASK), "n"(I4s_TO_BF16s_MAGIC_NUM), "n"(immLut));
+#endif
   }
 
   // This is the BF16 {-136, -136} represented as an integer.
-  static constexpr uint32_t BF16_BIAS = 0xC308C308;
-  static constexpr uint32_t BF16_ONE = 0x3F803F80;
+#if defined(USE_ROCM)
+#if ROCM_VERSION >= 60200
+  auto BF16_SCALE_FACTOR = __bfloat162bfloat162(__hip_bfloat16(__hip_bfloat16_raw{0xC308}));
+  auto BF16_UNIT_VALUE = __bfloat162bfloat162(__hip_bfloat16(__hip_bfloat16_raw{0x3F80}));
+#else
+  auto BF16_SCALE_FACTOR = __bfloat162bfloat162(__hip_bfloat16{0xC308});
+  auto BF16_UNIT_VALUE = __bfloat162bfloat162(__hip_bfloat16{0x3F80});
+#endif
+#else
+  static constexpr uint32_t BF16_SCALE_FACTOR = 0xC308C308;
+  static constexpr uint32_t BF16_UNIT_VALUE = 0x3F803F80;
+#endif
 
 // Finally, we construct the output numbers.
 #pragma unroll
   for (int ii = 0; ii < kElements / 2; ++ii) {
     // Since this section is for Ampere+, we use bf16 fma to do the bias
     // subtraction
+#if defined(USE_ROCM)
+    result.vals[ii] = __hfma2(result.vals[ii], BF16_UNIT_VALUE, BF16_SCALE_FACTOR);
+#else
     asm("fma.rn.bf16x2 %0, %1, %2, %3;\n"
         : "=r"(h[ii])
-        : "r"(h[ii]), "r"(BF16_ONE), "r"(BF16_BIAS));
+        : "r"(h[ii]), "r"(BF16_UNIT_VALUE), "r"(BF16_SCALE_FACTOR));
+#endif
   }
 
   return result;
@@ -123,11 +167,22 @@ __global__ void _dequantize_int4_kernel(
       // All b values within a 16x16 tile should fall within the same q group
       // Hence we load 1 scale and zero per loop
       int qgroup = ks[0] /  groupSize;
+#if defined(USE_ROCM)
+      __nv_bfloat162 scale2 = __bfloat162bfloat162(__hip_bfloat16(1.0f));
+      __nv_bfloat162 zero2 = __bfloat162bfloat162(__hip_bfloat16(1.0f));
+
+      if (scales_and_zeros) {
+        const auto& sz = *scales_and_zeros;
+        const __nv_bfloat16* pSZ = reinterpret_cast<const __nv_bfloat16*>(&sz[qgroup][n0][0]);
+
+        scale2 = __bfloat162bfloat162(pSZ[0]);
+        zero2 = __bfloat162bfloat162(pSZ[1]);
+      }
+#else
       const __nv_bfloat16 *pSZ = reinterpret_cast<const __nv_bfloat16*>(&scales_and_zeros.value()[qgroup][n0][0]);
-
-      // Vectorize scales and zeros
       __nv_bfloat162 scale2 = __bfloat162bfloat162(pSZ[0]);
       __nv_bfloat162 zero2 = __bfloat162bfloat162(pSZ[1]);
+#endif
 
   #pragma unroll
       for (int i = 0; i < 4; i++) {