Initial support of SYCL CUTLASS for XPU backend through Inductor (#2)

Summary : This patch enables an initial execution of `torch.mm` through an inductor generated SYCL CUTLASS kernel for intel PVC. 
Following the reference CUDA implementation, this implements the following key functionalities : 
- For Template Generation & rendering : 
  - `SYCLTemplate`, `CUTLASSTemplate`, `CUTLASSGemmTemplate`, `CUTLASS3xGemmTemplate` : Handles generating the full c++ code from the call to `GeneratePVC` to get the `GemmOperations` (exposed by cutlass_library), filtering the operations, constructing the Manifest & extracting the gemm instance from the emitter (exposed by cutlass_library) until the full wrapping of the c++ template code using runtime arguments & final kernel launch.
  - `cutlass_utils.py` : utility file containing relevant functions used across the codegen process.
  - `SYCLKernel`, `SYCLTemplateKernel` : Handles higher level kernel template and kernel calling from host side. Used within the previous Template classes.

- For Autotuning : 
  - `SYCLBenchmarkRequest` : Currently added as an almost dummy, not really benchmarking since we're selecting a single generated configuration for this PoC.

- For wrapping & triggering the above : 
  - `SYCLTemplateCaller` : Wrapper holding a ready to compile, execute, benchmark SYCL Template Kernel. This is the higher level construct that's added to the list of "choices" in the autotuning process for selecting the best configuration. 

- For scheduling/Execution : 
  - `SYCLCPPScheduling` & `SYCLCombinedScheduling` : Orchestrator of kernel calls across eventually nodes with different lowerings (Triton & CUTLASS SYCL for instance). Few changes have been made to this compared to original CUDA implementation.

Current state was fine-tuned to support the only type configuration exposed by cutlass on PVC so far, a.k.a `bfloat16` input and `fp32` accumulation, forcing some workarounds on pytorch side, namely related to D(layout/output node) & C(source/input_node[2]) dtypes.

Unsupported features &/or partially implemented ones are highlighted as comments `TODO (SYCL)`.

---------

Co-authored-by: Lukas Sommer <lukas.sommer@codeplay.com>

Author: OuadiElfarouki

torch/_inductor/autotune_process.py

@@ -27,6 +27,7 @@
     DLLWrapper,
     get_hash,
     PyCodeCache,
+    SYCLCodeCache,
 )
 from torch._inductor.utils import get_gpu_type, is_gpu
 from torch._logging import getArtifactLogger
@@ -935,6 +936,95 @@ def __str__(self) -> str:
         return f"{self.kernel_name=}"
 
 
+class SYCLBenchmarkRequest(GPUDeviceBenchmarkMixin, BenchmarkRequest):
+    # Important: Instances of this class have to be serializable
+    # across process boundaries. Do not put Tensors in here!
+    # TODO (SYCL) : Complete the bmrq class to enable full autotuning
+    def __init__(
+        self,
+        kernel_name: str,
+        input_tensor_meta: Union[TensorMeta, list[TensorMeta]],
+        output_tensor_meta: Union[TensorMeta, list[TensorMeta]],
+        extra_args: Iterable[Any],
+        source_code: str,
+    ) -> None:
+        super().__init__(kernel_name, input_tensor_meta, output_tensor_meta, extra_args)
+        self.source_code = source_code
+        self.workspace_size: int = 0  # TODO (SYCL): workspace size remains 0
+        self.workspace: Optional[torch.Tensor] = None
+        self.DLL: Optional[DLLWrapper] = None
+        self._workspace_size_updated = False
+        self.hash_key: str = ""
+        self.source_file: str = ""
+        self.hash_key, self.source_file = SYCLCodeCache.write(self.source_code, "so")
+
+    def precompile(self):
+        # Prepopulate SYCLCodeCache
+        autotuning_log.debug("Precompiling %s", self)
+        SYCLCodeCache.compile(self.source_code, "so")
+        autotuning_log.debug("Done precompiling %s", self)
+
+    def make_run_fn(
+        self, *input_tensors: torch.Tensor, output_tensor: torch.Tensor
+    ) -> Callable[[], None]:
+        self.ensure_dll_loaded()
+        self.update_workspace_size()  # TODO (SYCL): No effect on workspace_size being unused (remains = 0)
+        args = [
+            c_void_p(tensor.data_ptr())
+            for tensor in list(input_tensors) + [output_tensor]
+        ]
+        autotuning_log.debug(
+            "make_run_fn: self.kernel_name=%s, self.source_file=%s, self.hash_key=%s, self.DLL=%s, args=%s, self.extra_args=%s",
+            self.kernel_name,
+            self.source_file,
+            self.hash_key,
+            self.DLL,
+            args,
+            self.extra_args,
+        )
+        queue_ptr = c_void_p(torch.xpu.current_stream().sycl_queue)
+        run_method = getattr(self.DLL, self.kernel_name)
+        workspace_ptr = c_void_p(0)
+        if self.workspace_size > 0:
+            self.workspace = torch.zeros(
+                (self.workspace_size + 7) // 8,
+                dtype=torch.float64,
+                device=output_tensor.device,
+            )
+            workspace_ptr = c_void_p(self.workspace.data_ptr())
+
+        # Generate partial function.
+        return functools.partial(
+            run_method,
+            *args,
+            *self.extra_args,
+            None,  # null workspace size ptr
+            workspace_ptr,  # set workspace ptr,
+            queue_ptr,
+        )
+
+    def update_workspace_size(self) -> None:
+        if self._workspace_size_updated:
+            return
+        # TODO (SYCL): Harcoded to zero since no SLM is used on PVC at the moment
+        self.workspace_size = 0
+        self._workspace_size_updated = True
+
+    def ensure_dll_loaded(self):
+        if self.DLL is None:
+            self.DLL, self.hash_key, self.source_file = SYCLCodeCache.load(
+                self.source_code, "so"
+            )
+
+    def cleanup_run_fn(self) -> None:
+        if self.DLL is not None:
+            self.DLL.close()
+        self.workspace = None
+
+    def __str__(self) -> str:
+        return f"{self.kernel_name=}, {self.source_file=}, {self.hash_key=}"
+
+
 def benchmark_in_sub_process(
     choices: list[TritonTemplateCaller],
 ) -> dict[TritonTemplateCaller, float]:

torch/_inductor/codegen/common.py

@@ -394,6 +394,7 @@ def init_backend_registration() -> None:
     from .mps import MetalScheduling
     from .triton import TritonScheduling
     from .wrapper import PythonWrapperCodegen
+    from .xpu_combined_scheduling import SYCLCombinedScheduling
 
     if get_scheduling_for_device("cpu") is None:
         cpu_backends = {
@@ -424,9 +425,10 @@ def init_backend_registration() -> None:
         )
 
     if get_scheduling_for_device("xpu") is None:
+        # SYCLCombinedScheduling combines Triton and SYCL C++ scheduling for XPU devices via delegation
         register_backend_for_device(
             "xpu",
-            TritonScheduling,
+            SYCLCombinedScheduling,
             PythonWrapperCodegen,
             CppWrapperGpu,
         )

torch/_inductor/codegen/xpu/cutlass_utils.py

@@ -0,0 +1,263 @@
+# mypy: allow-untyped-defs
+import functools
+import logging
+import os
+import sys
+from dataclasses import dataclass
+from typing import Any, Optional
+
+import sympy
+
+import torch
+from torch._inductor.utils import clear_on_fresh_inductor_cache
+
+from ... import config
+from ...ir import Layout
+from ...runtime.runtime_utils import cache_dir
+from ...virtualized import V
+
+
+log = logging.getLogger(__name__)
+
+
+@functools.lru_cache(None)
+def try_import_cutlass() -> bool:
+    """
+    Currently only supporting user specified cutlass_dir or falling to the
+    default ../third_party/cutlass/ (build from source setups).
+    """
+    # Copy CUTLASS python scripts to a temp dir and add the temp dir to Python search path.
+
+    cutlass_py_full_path = os.path.abspath(
+        os.path.join(config.cutlass_dir, "python/cutlass_library")
+    )
+    tmp_cutlass_py_full_path = os.path.abspath(
+        os.path.join(cache_dir(), "torch_cutlass_library")
+    )
+    dst_link = os.path.join(tmp_cutlass_py_full_path, "cutlass_library")
+
+    if os.path.isdir(cutlass_py_full_path):
+        if tmp_cutlass_py_full_path not in sys.path:
+            if os.path.exists(dst_link):
+                assert os.path.islink(dst_link), (
+                    f"{dst_link} is not a symlink. Try to remove {dst_link} manually and try again."
+                )
+                assert os.path.realpath(os.readlink(dst_link)) == os.path.realpath(
+                    cutlass_py_full_path
+                ), f"Symlink at {dst_link} does not point to {cutlass_py_full_path}"
+            else:
+                os.makedirs(tmp_cutlass_py_full_path, exist_ok=True)
+                os.symlink(cutlass_py_full_path, dst_link)
+            sys.path.append(tmp_cutlass_py_full_path)
+        try:
+            import cutlass_library.generator  # noqa: F401
+            import cutlass_library.library  # noqa: F401
+            import cutlass_library.manifest  # noqa: F401
+
+            return True
+        except ImportError as e:
+            log.debug(
+                "Failed to import CUTLASS packages: %s, ignoring the CUTLASS backend.",
+                str(e),
+            )
+    else:
+        log.debug(
+            "Failed to import CUTLASS packages: CUTLASS repo does not exist: %s",
+            cutlass_py_full_path,
+        )
+    return False
+
+
+@functools.lru_cache(8)
+def _normalize_sycl_arch(arch: str) -> str:
+    if int(arch) == 11:
+        return "11"
+    else:
+        raise NotImplementedError(f"Unsupported sycl arch: {arch}")
+
+
+@dataclass
+class CUTLASSArgs:
+    """
+    CUTLASS args used to initialize a CUTLASS Manifest.
+    """
+
+    architectures: Optional[str] = None
+    cuda_version: Optional[str] = None  # Unused in generator.py for PVC
+    instantiation_level: Optional[str] = None  # Unused YET in generator.py for PVC
+
+    operations = "all"
+    build_dir = ""
+    curr_build_dir = ""
+    generator_target = ""
+    kernels = "all"
+    ignore_kernels = ""
+    exclude_kernels = ""
+    # UNUSED at the moment, part of Manifest class in cutlass_library
+    kernel_filter_file: None = None
+    selected_kernel_list: None = None
+    interface_dir: None = None
+    filter_by_cc = False
+    disable_full_archs_compilation = False
+
+    def __post_init__(self):
+        if self.architectures is None:
+            raise RuntimeError(f"{self.architectures=} is None!")
+        self.architectures = _normalize_sycl_arch(self.architectures)
+
+
+@clear_on_fresh_inductor_cache
+@functools.lru_cache(None)
+def _gen_ops_cached(arch) -> list[Any]:
+    # Import cutlass python scripts.
+    assert try_import_cutlass()
+    import cutlass_library.generator as cutlass_generator
+    import cutlass_library.manifest as cutlass_manifest
+
+    if arch is None:
+        log.error(
+            "Cannot detect XPU arch %s."
+            "Will discard all cutlass ops. "
+            "Please consider setting _inductor.xpu.arch",
+            arch,
+        )
+        return []
+    arch = _normalize_sycl_arch(arch)
+
+    sycl_version = "2025.0.1"  # Placeholder, Unused in GeneratePVC
+
+    args = CUTLASSArgs(
+        architectures=arch,
+        instantiation_level="0",  # TODO (SYCL) : Make it config param once enabled in cutlass_library/generator.py
+        cuda_version=sycl_version,
+    )
+    manifest = cutlass_manifest.Manifest(args)
+
+    if arch == "11":
+        cutlass_generator.GeneratePVC(manifest, sycl_version)
+    else:
+        log.error("Invalid XPU arch")
+        return []
+    return manifest.operations
+
+
+def gen_ops() -> list[Any]:
+    """
+    Generates all supported CUTLASS operations.
+    """
+    # Currently limited to PVC (arch 1100), harcoding arch
+    # TODO :(SYCL) get_xpu_arch()
+    arch = "11"
+    return _gen_ops_cached(arch)
+
+
+def torch_dtype_to_cutlass_type(
+    torch_dtype: torch.dtype,
+) -> "cutlass_library.library.DataType":  # type: ignore[name-defined] # noqa: F821
+    # Import cutlass python scripts.
+    assert try_import_cutlass()
+    import cutlass_library  # type: ignore[import]
+
+    if torch_dtype == torch.float:
+        return cutlass_library.library.DataType.f32
+    elif torch_dtype == torch.half:
+        return cutlass_library.library.DataType.f16
+    elif torch_dtype == torch.bfloat16:
+        return cutlass_library.library.DataType.bf16
+    else:
+        raise NotImplementedError(f"Unsupported data type: {torch_dtype=}")
+
+
+def dtype_match(
+    torch_dtype: Optional[torch.dtype],
+    cutlass_dtype: "cutlass_library.library.DataType",  # type: ignore[name-defined]  # noqa: F821
+) -> bool:
+    # Import cutlass python scripts.
+    assert try_import_cutlass()
+    import cutlass_library
+
+    if torch_dtype == torch.float:
+        return cutlass_dtype == cutlass_library.library.DataType.f32
+    elif torch_dtype == torch.half:
+        return cutlass_dtype == cutlass_library.library.DataType.f16
+    elif torch_dtype == torch.bfloat16:
+        return cutlass_dtype == cutlass_library.library.DataType.bf16
+    elif torch_dtype == torch.int8:
+        return cutlass_dtype == cutlass_library.library.DataType.s8
+    elif torch_dtype == torch.uint8:
+        return cutlass_dtype == cutlass_library.library.DataType.u8
+    elif torch_dtype == torch.int32:
+        return cutlass_dtype == cutlass_library.library.DataType.s32
+    else:
+        return False
+
+
+def get_accumulator_dtype(
+    input_torch_dtypes: list[torch.dtype],
+) -> Optional[torch.dtype]:
+    """
+    Given a pair of input torch dtypes, returns the inferred accumulator torch dtype.
+    """
+    # TODO (SYCL) : Extend this once other accumulator & input types are supported
+    if len(input_torch_dtypes) != 2:
+        return None
+
+    if all(dtype == torch.bfloat16 for dtype in input_torch_dtypes):
+        return torch.float
+    else:
+        raise NotImplementedError(f"Unsupported data types: {input_torch_dtypes}")
+
+
+def get_alignments(torch_dtype: torch.dtype) -> list[int]:
+    """
+    Returns all possible valid CUTLASS alignments in terms of the number of elements for a given dtype.
+    """
+    # TODO (SYCL): Extend for other types & double-check alignments
+    if torch_dtype == torch.bfloat16:
+        return [8, 4, 2, 1]
+    elif torch_dtype == torch.float:
+        return [4, 2, 1]
+    else:
+        raise NotImplementedError(f"unsupported {torch_dtype=} for alignments")
+
+
+def get_max_alignment(inductor_layout: Layout) -> int:
+    """
+    Returns the max alignment (in terms of number of elements) for a given Inductor Layout.
+    """
+
+    dtype = inductor_layout.dtype
+    size = inductor_layout.size
+    offset = inductor_layout.offset
+
+    def is_static_int(number):
+        return isinstance(number, (int, sympy.Integer))
+
+    def a_factor_of(x, alignment):
+        if is_static_int(x) and is_static_int(alignment):
+            return x % alignment == 0
+        rem = sympy.Mod(x, alignment)
+        return V.graph.sizevars.evaluate_expr(sympy.Eq(rem, 0))
+
+    try:
+        contiguous_dim = inductor_layout.stride.index(1)
+    except ValueError:
+        # No dim with stride 1 found, return 1
+        return 1
+    alignments = get_alignments(dtype)
+    for alignment in alignments:
+        if not a_factor_of(size[contiguous_dim], alignment) or not a_factor_of(
+            offset, alignment
+        ):
+            continue
+        if all(
+            (dim == contiguous_dim)
+            or a_factor_of(inductor_layout.stride[dim], alignment)
+            for dim in range(len(size))
+        ):
+            return alignment
+    return 1
+
+
+# TODO (SYCL) : Add helpers for CUTLASS kernels testing & benchmarking once standalone
+# runner is enabled.