[reland][ROCm] preshuffled weight mm (pytorch#2044)

* [ROCm][experimental] pre-shuffle weights

* add custom gemm op

* pass through swizzled

* copy paste bug causing extra matmul to execute

* correct transpose and permute logic

* swizzle.cpp is rocm-only, remove #ifndef USE_ROCM

* transpose is shallow, don't unswizzle/swizzle

* add fp8 swizzle

* remove print statement

* setup.py missing check for vec ext

* remove merge mistake

* conditionalize building sparse marlin for hip

* ruff format

* ruff check --fix

* protect swizzle.cpp inside USE_ROCM

* patch from @mxz297

Author: jeffdaily

setup.py

@@ -79,8 +79,6 @@ def use_debug_mode():
     _get_cuda_arch_flags,
 )
 
-IS_ROCM = (torch.version.hip is not None) and (ROCM_HOME is not None)
-
 
 class BuildOptions:
     def __init__(self):
@@ -255,28 +253,37 @@ def get_extensions():
         print(
             "PyTorch GPU support 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"
-        )
+    if CUDA_HOME is None and torch.cuda.is_available() and torch.version.cuda:
+        print("CUDA toolkit 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"
         )
+    if ROCM_HOME is None and torch.cuda.is_available() and torch.version.hip:
+        print("ROCm is not available. Skipping compilation of ROCm extensions")
+        print("If you'd like to compile ROCm extensions locally please install ROCm")
 
-    use_cuda = torch.cuda.is_available() and (
-        CUDA_HOME is not None or ROCM_HOME is not None
+    use_cuda = (
+        torch.cuda.is_available() and torch.version.cuda and CUDA_HOME is not None
     )
-    extension = CUDAExtension if use_cuda else CppExtension
+    use_hip = torch.cuda.is_available() and torch.version.hip and ROCM_HOME is not None
+    extension = CUDAExtension if (use_cuda or use_hip) else CppExtension
+
+    nvcc_args = [
+        "-DNDEBUG" if not debug_mode else "-DDEBUG",
+        "-O3" if not debug_mode else "-O0",
+        "-t=0",
+        "-std=c++17",
+    ]
+    hip_args = [
+        "-DNDEBUG" if not debug_mode else "-DDEBUG",
+        "-O3" if not debug_mode else "-O0",
+        "-std=c++17",
+    ]
 
     extra_link_args = []
     extra_compile_args = {
         "cxx": [f"-DPy_LIMITED_API={PY3_9_HEXCODE}"],
-        "nvcc": [
-            "-DNDEBUG" if not debug_mode else "-DDEBUG",
-            "-O3" if not debug_mode else "-O0",
-            "-t=0",
-            "-std=c++17",
-        ],
+        "nvcc": nvcc_args if use_cuda else hip_args,
     }
 
     if not IS_WINDOWS:
@@ -299,48 +306,89 @@ def get_extensions():
             extra_compile_args["nvcc"].append("-g")
             extra_link_args.append("/DEBUG")
 
+    hip_sparse_marlin_supported = True
+    if use_hip:
+        # naive search for hipblalst.h, if any found contain HIPBLASLT_ORDER_COL16 and VEC_EXT
+        found_col16 = False
+        found_vec_ext = False
+        print("ROCM_HOME", ROCM_HOME)
+        hipblaslt_headers = list(
+            glob.glob(os.path.join(ROCM_HOME, "include", "hipblaslt", "hipblaslt.h"))
+        )
+        print("hipblaslt_headers", hipblaslt_headers)
+        for header in hipblaslt_headers:
+            with open(header) as f:
+                text = f.read()
+                if "HIPBLASLT_ORDER_COL16" in text:
+                    found_col16 = True
+                if "HIPBLASLT_MATMUL_DESC_A_SCALE_POINTER_VEC_EXT" in text:
+                    found_vec_ext = True
+        if found_col16:
+            extra_compile_args["cxx"].append("-DHIPBLASLT_HAS_ORDER_COL16")
+            print("hipblaslt found extended col order enums")
+        else:
+            print("hipblaslt does not have extended col order enums")
+        if found_vec_ext:
+            extra_compile_args["cxx"].append("-DHIPBLASLT_VEC_EXT")
+            print("hipblaslt found vec ext")
+        else:
+            print("hipblaslt does not have vec ext")
+
+        # sparse_marlin depends on features in ROCm 6.4, __builtin_amdgcn_global_load_lds
+        ROCM_VERSION = tuple(int(v) for v in torch.version.hip.split(".")[:2])
+        hip_sparse_marlin_supported = ROCM_VERSION >= (6, 4)
+
     # Get base directory and source paths
     curdir = os.path.dirname(os.path.curdir)
     extensions_dir = os.path.join(curdir, "torchao", "csrc")
 
     # Collect C++ source files
     sources = list(glob.glob(os.path.join(extensions_dir, "**/*.cpp"), recursive=True))
 
+    # Collect CUDA source files
     extensions_cuda_dir = os.path.join(extensions_dir, "cuda")
     cuda_sources = list(
         glob.glob(os.path.join(extensions_cuda_dir, "**/*.cu"), recursive=True)
     )
 
+    # Collect HIP source files
     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)
     )
-    extensions_hip_dir = os.path.join(extensions_dir, "cuda", "sparse_marlin")
+    if hip_sparse_marlin_supported:
+        extensions_hip_dir = os.path.join(extensions_dir, "cuda", "sparse_marlin")
+        hip_sources += list(
+            glob.glob(os.path.join(extensions_hip_dir, "*.cu"), recursive=True)
+        )
+    extensions_hip_dir = os.path.join(extensions_dir, "rocm")
     hip_sources += list(
-        glob.glob(os.path.join(extensions_hip_dir, "*.cu"), recursive=True)
+        glob.glob(os.path.join(extensions_hip_dir, "**/*.hip"), recursive=True)
+    )
+    hip_sources += list(
+        glob.glob(os.path.join(extensions_hip_dir, "**/*.cpp"), recursive=True)
     )
 
-    # Collect CUDA source files if needed
-    if not IS_ROCM and use_cuda:
+    # Add CUDA source files if needed
+    if use_cuda:
         sources += cuda_sources
 
-    # TOOD: Remove this and use what CUDA has once we fix all the builds.
-    if IS_ROCM and use_cuda:
+    # TODO: Remove this and use what CUDA has once we fix all the builds.
+    # Add HIP source files if needed
+    if use_hip:
         # 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"
-            )
-        else:
-            sources += hip_sources
+            print("Currently only gfx942 is supported. Compiling only for gfx942.")
+        extra_compile_args["nvcc"].append("--offload-arch=gfx942")
+        sources += hip_sources
 
     use_cutlass = False
     cutlass_90a_sources = None
-    if use_cuda and not IS_ROCM and not IS_WINDOWS:
+    if use_cuda and not IS_WINDOWS:
         use_cutlass = True
         cutlass_dir = os.path.join(third_party_path, "cutlass")
         cutlass_include_dir = os.path.join(cutlass_dir, "include")

test/test_ops.py

@@ -25,8 +25,8 @@
 from torchao.sparsity.marlin import inject_24, marlin_24_workspace, pack_to_marlin_24
 from torchao.utils import TORCH_VERSION_AT_LEAST_2_5, compute_max_diff
 
-if torch.version.hip is not None:
-    pytest.skip("Skipping the test in ROCm", allow_module_level=True)
+IS_CUDA = torch.cuda.is_available() and torch.version.cuda
+IS_ROCM = torch.cuda.is_available() and torch.version.hip
 
 try:
     import torchao.ops
@@ -52,7 +52,7 @@ def _create_floatx_inputs(
         fp16_act = torch.rand(BS, IC).to(dtype) + 0.5
         return floatx_weight.to(device), scale.to(device), fp16_act.to(device)
 
-    @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+    @pytest.mark.skipif(not IS_CUDA, reason="CUDA not available")
     @parametrize("ebits,mbits", [(3, 2), (2, 2)])
     @parametrize("dtype", [torch.half, torch.bfloat16])
     def test_quant_llm_linear(self, ebits, mbits, dtype):
@@ -82,7 +82,7 @@ def test_quant_llm_linear(self, ebits, mbits, dtype):
             test_utils=test_utils,
         )
 
-    @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+    @pytest.mark.skipif(not IS_CUDA, reason="CUDA not available")
     @parametrize("BS,OC,IC,splitK", [(1, 2048, 4096, 5), (2, 8192, 8192, 6)])
     @parametrize("ebits,mbits", [(3, 2), (2, 2)])
     @parametrize("dtype", [torch.half, torch.bfloat16])
@@ -139,7 +139,7 @@ def make_test_id(param):
         return f"tiles_{param}"
 
 
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.skipif(not IS_CUDA, reason="CUDA not available")
 # @pytest.mark.skipif(TORCH_VERSION_AT_LEAST_2_5, reason="weight packing is updated in 2.5+")
 @pytest.mark.parametrize("shape, inner_k_tiles", TEST_CONFIGS_UNPACK, ids=make_test_id)
 def test_unpack_tensor_core_tiled_layout_correctness(shape, inner_k_tiles):
@@ -157,7 +157,7 @@ def test_unpack_tensor_core_tiled_layout_correctness(shape, inner_k_tiles):
 
 
 # TODO: Fix "test_aot_dispatch_dynamic" test failure
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.skipif(not IS_CUDA, reason="CUDA not available")
 # @pytest.mark.skipif(TORCH_VERSION_AT_LEAST_2_5, reason="weight packing is updated in 2.5+")
 @pytest.mark.parametrize("shape, inner_k_tiles", TEST_CONFIGS_UNPACK, ids=make_test_id)
 def test_unpack_tensor_core_tiled_layout_op(shape, inner_k_tiles):
@@ -203,7 +203,7 @@ def dequant_ref(q, scales, zeros, group_size, nbits=4, dtype=torch.bfloat16):
     return dq.reshape(n, k)
 
 
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.skipif(not IS_CUDA, reason="CUDA not available")
 # @pytest.mark.skipif(TORCH_VERSION_AT_LEAST_2_5, reason="weight packing is updated in 2.5+")
 @pytest.mark.parametrize(
     "shape, inner_k_tiles, group_size", TEST_CONFIGS_DEQUANT, ids=str
@@ -271,7 +271,7 @@ def test_dequantize_tensor_core_tiled_layout_correctness_quant_dequant(
 
 
 # This test differs from one above in that it uses `unpack_tensor_core_tiled_layout` to unpack then dequantize
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.skipif(not IS_CUDA, reason="CUDA not available")
 # @pytest.mark.skipif(TORCH_VERSION_AT_LEAST_2_5, reason="weight packing is updated in 2.5+")
 @pytest.mark.parametrize(
     "shape, inner_k_tiles, group_size", TEST_CONFIGS_DEQUANT, ids=str
@@ -337,7 +337,7 @@ def test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant(
     assert diff_op_ao < 1e-1
 
 
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.skipif(not IS_CUDA, reason="CUDA not available")
 # @pytest.mark.skipif(TORCH_VERSION_AT_LEAST_2_5, reason="weight packing is updated in 2.5+")
 @pytest.mark.parametrize(
     "shape, inner_k_tiles, group_size", TEST_CONFIGS_DEQUANT, ids=str
@@ -448,7 +448,7 @@ def reshape_w(w):
     )
 
 
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.skipif(not IS_CUDA, reason="CUDA not available")
 @pytest.mark.parametrize(
     "batch_size, k_chunk, n_chunk, num_bits, group_size, mnk_factors",
     MARLIN_TEST_PARAMS,
@@ -538,7 +538,7 @@ def test_marlin_24(batch_size, k_chunk, n_chunk, num_bits, group_size, mnk_facto
 )
 
 
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")
+@pytest.mark.skipif(not IS_CUDA, reason="CUDA not available")
 @pytest.mark.parametrize(
     "batch_size, k_chunk, n_chunk, num_bits, group_size, mnk_factors",
     MARLIN_TEST_PARAMS,
@@ -617,5 +617,27 @@ def test_marlin_qqq(batch_size, k_chunk, n_chunk, num_bits, group_size, mnk_fact
     )
 
 
+@pytest.mark.skipif(not IS_ROCM, reason="ROCm not available")
+def test_swizzle_mm():
+    test_utils = [
+        "test_schema",
+        "test_autograd_registration",
+        "test_faketensor",
+    ]
+
+    # TODO: Figure out why test fails unless torch >= 2.5
+    if TORCH_VERSION_AT_LEAST_2_5:
+        test_utils.append("test_aot_dispatch_dynamic")
+
+    mat1 = torch.randint(0, 16, dtype=torch.float, size=(16, 32), device="cuda")
+    mat2 = torch.randint(0, 16, dtype=torch.float, size=(32, 16), device="cuda")
+
+    opcheck(
+        torch.ops.torchao.swizzle_mm,
+        (mat1, mat2, False, False),
+        test_utils=test_utils,
+    )
+
+
 if __name__ == "__main__":
     pytest.main(sys.argv)

torchao/__init__.py

@@ -43,13 +43,14 @@
     quantize_,
 )
 
-from . import dtypes, optim, testing
+from . import dtypes, optim, swizzle, testing
 
 __all__ = [
     "dtypes",
     "autoquant",
     "optim",
     "quantize_",
+    "swizzle",
     "testing",
     "ops",
 ]