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Add suppressions to torch/_inductor (#165062)
Adds suppressions to pyrefly will typecheck clean: https://github.com/pytorch/pytorch/issues/163283 Split this directory into two PRs to keep them from being too large. Test plan: dmypy restart && python3 scripts/lintrunner.py -a pyrefly check step 1: delete lines in the pyrefly.toml file from the project-excludes field step 2: run pyrefly check step 3: add suppressions, clean up unused suppressions before: https://gist.github.com/maggiemoss/4b3bf2037014e116bc00706a16aef199 after: INFO 0 errors (6,884 ignored) Pull Request resolved: https://github.com/pytorch/pytorch/pull/165062 Approved by: https://github.com/oulgen, https://github.com/mlazos
This commit is contained in:
Maggie Moss
committed by
PyTorch MergeBot
PyTorch MergeBot
parent
e7fd296930
commit
9944cac6e6
@ -1019,6 +1019,7 @@ def maybe_estimate_runtime_benchmark(snode: BaseSchedulerNode) -> Optional[float
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if mm_fn is None:
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return None
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bench_fn = mm_fn
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# pyrefly: ignore # unbound-name
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args_kwargs_fn = lambda: snode_args_kwargs(snode) # noqa: E731
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else:
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return None
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@ -1295,6 +1296,7 @@ class SchedulerNode(BaseSchedulerNode):
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new_order = self_dep.decide_loop_order_to_match(other_dep)
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if new_order:
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# pyrefly: ignore # bad-assignment
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metrics.num_loop_reordering += 1
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loop_ordering_log.debug(
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"Reorder loops for %s with order %s", self.get_name(), new_order
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@ -1591,6 +1593,7 @@ class FusedSchedulerNode(BaseSchedulerNode):
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self.get_name(),
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)
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return False
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# pyrefly: ignore # bad-assignment
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metrics.num_loop_reordering += 1
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loop_ordering_log.debug(
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"Reorder loops for fused node %s with order %s", self.get_name(), new_order
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@ -2322,6 +2325,7 @@ class Scheduler:
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self.name_to_fused_node = {n.get_name(): n for n in self.nodes}
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self.compute_ancestors()
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# pyrefly: ignore # bad-assignment
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metrics.ir_nodes_pre_fusion += len(self.nodes)
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from torch._inductor.debug import log_ir_post_fusion, log_ir_pre_fusion
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@ -2549,6 +2553,7 @@ class Scheduler:
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]
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return DedupList(new_items, new_membership)
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# pyrefly: ignore # not-a-type
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name_to_users: defaultdict[str, DedupList[NodeUser]] = collections.defaultdict(
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DedupList
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)
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@ -2585,12 +2590,14 @@ class Scheduler:
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else:
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name_to_users[buf1_name] = name_to_users[buf2_name]
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# pyrefly: ignore # not-a-type
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def rename(n: str) -> str:
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if n in self.mutation_renames:
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return rename(self.mutation_renames[n])
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return n
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def add_user(
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# pyrefly: ignore # not-a-type
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used_by_name: str,
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user_node: Union[BaseSchedulerNode, OutputNode],
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can_inplace: bool = False,
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@ -2600,6 +2607,7 @@ class Scheduler:
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NodeUser(user_node, can_inplace, is_weak)
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)
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# pyrefly: ignore # not-a-type
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unbacked_symbol_to_origin_node: dict[sympy.Symbol, Optional[str]] = {}
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# NB: None means that the dependency is on an input. Don't actually
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@ -2658,6 +2666,7 @@ class Scheduler:
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and (dep := next(iter(node.read_writes.writes)))
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and isinstance(dep, MemoryDep)
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):
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# pyrefly: ignore # unbound-name
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node_mode = dep.mode
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else:
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node_mode = None
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@ -3429,9 +3438,12 @@ class Scheduler:
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str(e),
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)
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continue
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# pyrefly: ignore # missing-attribute
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with multi_node.swap_as_triton_caller(choice):
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ms_fused, path = self.benchmark_codegened_module(
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mod_fused, device
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mod_fused,
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# pyrefly: ignore # bad-argument-type
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device,
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)
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new_timings[choice] = ms_fused
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if ms_fused < min_ms_fused:
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@ -3443,12 +3455,15 @@ class Scheduler:
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if min_ms_fused < (ms1 + ms2) and ms_fused_choice is not None:
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if config.multi_kernel_hints:
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hint_override_best_fusion_choice[None] = ms_fused_choice
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# pyrefly: ignore # missing-attribute
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multi_node.finalize_as_triton_callers(
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hint_override_best_fusion_choice
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)
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else:
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# pyrefly: ignore # missing-attribute
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multi_node.finalize_as_triton_caller(ms_fused_choice)
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# pyrefly: ignore # missing-attribute
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multi_node._choice_timings[None] = new_timings
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return True
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else:
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@ -3478,21 +3493,27 @@ class Scheduler:
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fut.result()
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ms1, path1 = self.benchmark_codegened_module(
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future_and_mod_l1[1], device
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future_and_mod_l1[1],
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# pyrefly: ignore # bad-argument-type
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device,
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)
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if math.isinf(ms1):
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why("register spilling of the first kernel")
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return False
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ms2, path2 = self.benchmark_codegened_module(
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future_and_mod_l2[1], device
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future_and_mod_l2[1],
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# pyrefly: ignore # bad-argument-type
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device,
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)
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if math.isinf(ms2):
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why("register spilling of the second kernel")
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return False
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ms_fused, path_fused = self.benchmark_codegened_module(
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future_and_mod_l1_fused[1], device
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future_and_mod_l1_fused[1],
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# pyrefly: ignore # bad-argument-type
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device,
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)
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if math.isinf(ms_fused):
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why("register spilling of the fused kernel")
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@ -4323,6 +4344,7 @@ class Scheduler:
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if config.expand_dimension_for_pointwise_nodes and (
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expand_analysis := self.get_expand_dim_for_pointwise_nodes(node1, node2)
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):
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# pyrefly: ignore # unbound-name
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(expand_dim, smaller_node, expand_size) = expand_analysis
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smaller_node.expand_dimension_for_pointwise_node(expand_dim, expand_size)
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shared_data_score = self.score_fusion_memory(node1, node2)
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@ -4633,6 +4655,7 @@ class Scheduler:
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device.type == "cuda"
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and (device_props := torch.cuda.get_device_properties(device)).major < 7
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):
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# pyrefly: ignore # unbound-name
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raise GPUTooOldForTriton(device_props, inspect.currentframe())
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elif is_gpu(device.type) and not device.type == "mps":
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raise TritonMissing(inspect.currentframe())
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@ -4930,6 +4953,7 @@ class Scheduler:
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if isinstance(buf.node, ir.MutationOutput) and (
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real_name := self.mutation_real_name.get(buf_name, None)
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):
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# pyrefly: ignore # unbound-name
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return is_none_layout(real_name)
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return True
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@ -5028,6 +5052,7 @@ class Scheduler:
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signatures.append(partition_signature)
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unmet_output_names = partition_input_names.union(
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# pyrefly: ignore # unsupported-operation
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unmet_output_names - returned_output_names
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)
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@ -5410,6 +5435,7 @@ class Scheduler:
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self.current_device = self.default_device_context
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# pyrefly: ignore # unbound-name
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if self.default_device_context and config.triton.autotune_at_compile_time:
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V.graph.wrapper_code.write_get_raw_stream_header()
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@ -5453,6 +5479,7 @@ class Scheduler:
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prologue, template_node, epilogue = node.get_prologue_template_epilogue(
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list(node.get_nodes())
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)
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# pyrefly: ignore # unbound-name
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self.get_backend(device).codegen_template(
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template_node, epilogue, prologue
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)
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@ -5461,6 +5488,7 @@ class Scheduler:
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self.codegen_extern_call(node)
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elif node.is_foreach():
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node = typing.cast(ForeachKernelSchedulerNode, node)
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# pyrefly: ignore # unbound-name
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backend_ = self.get_backend(device)
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from .codegen.cuda_combined_scheduling import CUDACombinedScheduling
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from .codegen.simd import SIMDScheduling
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@ -5471,12 +5499,15 @@ class Scheduler:
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raise AssertionError(f"{type(self)=}")
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backend.codegen_combo_kernel(node)
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elif isinstance(node, (FusedSchedulerNode, SchedulerNode)):
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# pyrefly: ignore # unbound-name
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self.get_backend(device).codegen_node(node)
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else:
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assert isinstance(node, NopKernelSchedulerNode)
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node.mark_run()
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# pyrefly: ignore # unbound-name
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if config.triton.debug_sync_kernel:
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# pyrefly: ignore # unbound-name
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self.get_backend(device).codegen_sync()
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self.available_buffer_names.update(node.get_buffer_names())
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