Add nv_enable_options and nv_disable_options

thunder/executors/nvfuserex_impl.py

@@ -438,6 +438,8 @@ class FusionDefinitionWrapper:
     last_used: None | FusionDefinition = None
     last_inputs: None | Sequence[tuple] = None
     store_inputs: bool = False
+    enable_options: None | list[str] = None
+    disable_options: None | list[str] = None
 
     def __call__(self, *args):
         fd = self.get_fd(self.to_descriptors(args))
@@ -446,8 +448,21 @@ def __call__(self, *args):
         if self.store_inputs:
             self.last_inputs = args
 
+        kwargs = {}
         # Set device if set in one of the "factory" methods like full, iota, or uniform
-        kwargs = {"device": fd._selected_device} if hasattr(fd, "_selected_device") else {}
+        if hasattr(fd, "_selected_device"):
+            kwargs["device"] = fd._selected_device
+
+        if nvfuser_version() >= LooseVersion("0.2.23"):
+            # nvFuser expects empty list instead of None values.
+            kwargs["_enable_options"] = self.enable_options if self.enable_options is not None else []
+            kwargs["_disable_options"] = self.disable_options if self.disable_options is not None else []
+
+        elif self.enable_options or self.disable_options:
+            warnings.warn(
+                f"nv_enable_options/nv_disable_options require nvFuser version 0.2.23 and above, found version {nvfuser_version()}. These options will be ignored."
+            )
+
         with annotate_for_profile(self.name):
             return fd.execute(args, **kwargs)
 
@@ -540,6 +555,10 @@ def create_fusion_definition_wrapper(
     store_inputs: None | bool = get_compile_option(
         "nv_store_fusion_inputs", "Allow nvFuser to store fusion inputs for repro."
     )
+    enable_options: None | list[str] = get_compile_option("nv_enable_options", "List of NVFUSER_ENABLE options to set.")
+    disable_options: None | list[str] = get_compile_option(
+        "nv_disable_options", "List of NVFUSER_DISABLE options to set."
+    )
 
     tensor_indices = []
     for idx, x in enumerate(sorted_unique_inputs):
@@ -561,6 +580,8 @@ def get_fd(input_descriptors) -> FusionDefinition:
         get_fd.cache_info,
         get_fd.cache_clear,
         store_inputs=store_inputs,
+        enable_options=enable_options,
+        disable_options=disable_options,
     )
     return fdw
 

thunder/tests/test_nvfuser.py

@@ -1077,3 +1077,44 @@ def sdpa_fn(q, k, v, dropout_p, is_causal, scale):
     ref_outputs = (ref_attn_out, *(inp.grad for inp in ref_tensor_inputs))
     for nv_out, ref_out in zip(nv_outputs, ref_outputs):
         torch.testing.assert_close(nv_out, ref_out)
+
+
+@instantiate(
+    dtypes=(thunder.float32,),
+    devicetypes=(devices.DeviceType.CUDA,),
+    executors=(nvFuserExecutor,),
+    decorators=(
+        pytest.mark.skipif(
+            nvfuser_version() is None or nvfuser_version() < LooseVersion("0.2.23"),
+            reason="Requires nvFuser version 0.2.23 or later",
+        ),
+    ),
+)
+def test_enable_disable_options(executor, device: str, thunder_dtype: dtypes.dtype):
+
+    def fn(a, b):
+        return torch.matmul(a, b)
+
+    m, n, k = 24, 16, 16
+
+    dtype = ltorch.to_torch_dtype(thunder_dtype)
+    inps = [
+        torch.randn(m, k, device="cuda", dtype=dtype),
+        torch.randn(k, n, device="cuda", dtype=dtype),
+    ]
+
+    compiled_func = thunder.jit(
+        fn,
+        executors_list=executor.executors_list(),
+        nv_enable_matmul=True,
+        nv_enable_options=["fuse_matmul"],
+        nv_disable_options=["matmul_expr_eval", "kernel_reuse"],
+    )
+    # The above combination of options enables matmul codegen and disables expr evaluation for matmul.
+    # Since matmul scheduler does not support float32 inputs, the execution should raise an error.
+    # By default, without using these options, the given fusion will run through expr eval scheduler correctly.
+    # NOTE: This test relies on `float32` being unsupported by nvFuser matmul scheduler.
+    # If this support is added, the test will need to be updated since it will no longer
+    # verify the functionality of the above flags.
+    with pytest.raises(RuntimeError, match="Can not find a scheduler to schedule fusion segment"):
+        out = compiled_func(*inps)