[Inductor] Support store SPIR-V binary file output from Intel Triton. (pytorch#130849)

Pull Request resolved: pytorch#130849
Approved by: https://github.com/peterbell10, https://github.com/EikanWang

Author: etaf

torch/_inductor/codecache.py

@@ -386,7 +386,7 @@ def get_path(
 def get_hash(content: Union[str, bytes], extra: str = "", hash_type: str = "code"):
     if hash_type == "code":
         return code_hash(content, extra)
-    if hash_type in ["cubin", "hsaco"]:
+    if hash_type in ["cubin", "hsaco", "spv"]:
         return code_hash(repr(content))
     raise AssertionError(f"Unknown hash type {hash_type}")
 
@@ -1582,8 +1582,7 @@ class CudaKernelParamCache:
     cache_clear = staticmethod(cache.clear)
 
     @classmethod
-    def set(cls, key: str, params: Dict[str, str], cubin: str) -> None:
-        bin_type = "cubin" if torch.version.hip is None else "hsaco"
+    def set(cls, key: str, params: Dict[str, str], cubin: str, bin_type: str) -> None:
         _, path = write(
             cubin,
             bin_type,

torch/_inductor/codegen/wrapper.py

@@ -1491,7 +1491,7 @@ def generate_save_uncompiled_kernels(self):
                     if not kernel.cuda_kernel_saved:
                         if len(kernel.launchers) == 0:
                             kernel.precompile()
-                        kernel.save_cuda_kernel(
+                        kernel.save_gpu_kernel(
                             grid=(0, 0, 0),   # use dummy grid
                             stream="stream",  # use dummy stream
                             launcher=kernel.launchers[0],

torch/_inductor/runtime/triton_heuristics.py

@@ -723,7 +723,7 @@ def autotune_to_one_config(self, *args, **kwargs):
         if self.save_cache_hook:
             self.save_cache_hook(self.launchers[0].config, time_taken_ns)
 
-    def save_cuda_kernel(self, grid, stream, launcher):
+    def save_gpu_kernel(self, grid, stream, launcher):
         if callable(grid):
             grid_x, grid_y, grid_z = grid(launcher.config.kwargs)
         else:
@@ -753,12 +753,9 @@ def save_cuda_kernel(self, grid, stream, launcher):
         }
         from torch._inductor.codecache import CudaKernelParamCache
 
-        binary = (
-            launcher.bin.asm["cubin"]
-            if self.device_props.type != "hip"
-            else launcher.bin.asm["hsaco"]
-        )
-        CudaKernelParamCache.set(key, params, binary)
+        bin_type = {"hip": "hsaco", "xpu": "spv"}.get(self.device_props.type, "cubin")
+        binary = launcher.bin.asm[bin_type]
+        CudaKernelParamCache.set(key, params, binary, bin_type)
 
         self.cuda_kernel_saved = True
 
@@ -831,7 +828,7 @@ def run(self, *args, grid, stream, **kwargs):
 
         (launcher,) = self.launchers
         if launcher.store_cubin:
-            self.save_cuda_kernel(grid, stream, launcher)
+            self.save_gpu_kernel(grid, stream, launcher)
 
         if launcher.config.pre_hook is not None:
             launcher.config.pre_hook(