# mypy: allow-untyped-defs import argparse import copy from collections import defaultdict from dataclasses import dataclass from typing import NamedTuple, Sequence, Iterable, Any, List, Dict, Optional, Tuple import logging import torch from torch.fx.passes.graph_manipulation import get_size_of_node from torch.fx.node import map_arg from torch.fx._compatibility import compatibility from .operator_support import ( get_node_target, OperatorSupportBase, ) from .graph_drawer import FxGraphDrawer from .shape_prop import ShapeProp from .split_utils import split_by_tags from .tools_common import ( FxNetAccFusionsFinder, CALLABLE_NODE_OPS, Tensors, NodeList, NodeSet, is_node_output_tensor, ) __all__ = ['FxNetAccNodesFinder', 'FxNetSplitterInternalError', 'Subgraph', 'SplitResult', 'generate_inputs_for_submodules'] _LOGGER = logging.getLogger(__name__) DEFAULT_MIN_ACC_MODULE_SIZE = 1 DEFAULT_SKIP_FUSION = False DEFAULT_ALLOW_NON_TENSOR = False class _SplitterSettingBase: def __init__( self, min_acc_module_size=DEFAULT_MIN_ACC_MODULE_SIZE, skip_fusion=DEFAULT_SKIP_FUSION, allow_non_tensor=DEFAULT_ALLOW_NON_TENSOR, max_acc_splits: int = -1, ): parser = argparse.ArgumentParser() parser.add_argument( "--min-acc-module-size", "--min_acc_module_size", required=False, type=int, help="Minimum size limit of an accelerator subgraph.", ) parser.add_argument( "--max-acc-splits", "--max_acc_splits", required=False, type=int, help="Enforce a maximum number of split subgraphs.", ) parser.add_argument( "--skip-fusion", "--skip_fusion", default=False, action="store_true", help="If true then no fusion groups. Fusion group is used to " "enforce no non-tensor data flow between submodules. If we don't " "have this constrain, setting this to false is recommended as it " "can reduce overhead.", ) parser.add_argument( "--allow-non-tensor", "--allow_non_tensor", default=False, action="store_true", help="For some backends non-tensor data flow between cpu and them " "are not allowed. Therefore, if a node supported by accelerator but " "it has non-tensor inputs or outputs to a cpu node we would want to " "consider it as a cpu node during splitting. However, for some backends " "we might not care about non-tensor data flow and we can set this option " "to true to disable the functionality that prevent non-tensor data flow.", ) args, unknown = parser.parse_known_args() self.min_acc_module_size: int = args.min_acc_module_size if args.min_acc_module_size else min_acc_module_size self.skip_fusion: bool = args.skip_fusion if args.skip_fusion else skip_fusion self.allow_non_tensor: bool = args.allow_non_tensor if args.allow_non_tensor else allow_non_tensor self.max_acc_splits: int = max_acc_splits @compatibility(is_backward_compatible=False) class FxNetAccNodesFinder: """ Finds a set of nodes that can be supported on ACC, excluding nodes that have non-tensor input/output to cpu nodes to prevent non-tensor data flow between backends and cpu. I.e. if we have a chain: ACC_NODE_1 -> ACC_NODE_2 -> ACC_NODE_3 -> CPU_NODE_1 where every ACC node produces non-tensor output, then they all should be treated as CPU nodes. This behavior can be turned off by passing allow_non_tensor=True. """ def __init__( self, module: torch.fx.GraphModule, operator_support: OperatorSupportBase, allow_non_tensor: bool, ): self.module = module self.operator_support = operator_support self.allow_non_tensor = allow_non_tensor self.acc_nodes: NodeSet = set() def reduce_acc_nodes_non_tensor_input_helper( self, cpu_worklist: NodeList ): """ Transitively excludes nodes from ACC supported set. For every node in the worklist: - removes its downstream ACC nodes from ACC supported set, - if any downstream ACC node produces non-tensor output, then it gets added into the worklist. """ while cpu_worklist: node = cpu_worklist.pop(0) for user in node.users: if user in self.acc_nodes: self.acc_nodes.remove(user) if not is_node_output_tensor(user): cpu_worklist.append(user) def reduce_acc_nodes_non_tensor_input(self): """ Excludes nodes from ACC supported set that have direct upstream CPU nodes that produce non-tensor outputs. """ non_tensor_cpu_nodes: NodeList = [] for node in self.module.graph.nodes: if node.op not in CALLABLE_NODE_OPS: continue if node in self.acc_nodes: continue if is_node_output_tensor(node): continue non_tensor_cpu_nodes.append(node) self.reduce_acc_nodes_non_tensor_input_helper(non_tensor_cpu_nodes) def reduce_acc_nodes_non_tensor_output(self): """ Excludes nodes from ACC supported set that produce non-tensor outputs and have downstream CPU nodes. """ while True: new_cpu_nodes: NodeList = [] for acc_node in self.acc_nodes: if is_node_output_tensor(acc_node): continue for user in acc_node.users: if user not in self.acc_nodes: new_cpu_nodes.append(acc_node) break if not new_cpu_nodes: break for new_cpu_node in new_cpu_nodes: self.acc_nodes.remove(new_cpu_node) self.reduce_acc_nodes_non_tensor_input_helper(new_cpu_nodes) def __call__(self) -> NodeSet: submodules = dict(self.module.named_modules()) self.acc_nodes = { n for n in self.module.graph.nodes if n.op in CALLABLE_NODE_OPS and self.operator_support.is_node_supported(submodules, n) } if not self.allow_non_tensor: self.reduce_acc_nodes_non_tensor_input() self.reduce_acc_nodes_non_tensor_output() return self.acc_nodes @compatibility(is_backward_compatible=False) class FxNetSplitterInternalError(Exception): pass @compatibility(is_backward_compatible=False) @dataclass class Subgraph: is_acc: bool nodes: NodeList device_ordinal: Optional[int] = None @compatibility(is_backward_compatible=False) class SplitResult(NamedTuple): """ Stores the results of the splitter. Attributes: split_module: root module after splitting. submodule_inputs: a dict that maps submodule name to its inputs. non_acc_submodule_prefix: the prefix for non acc submodules. For acc submodule the prefix is alwasy "_run_on_acc_". """ split_module: torch.fx.GraphModule submodule_inputs: Dict[str, Any] non_acc_submodule_prefix: str @compatibility(is_backward_compatible=False) def generate_inputs_for_submodules( model: torch.nn.Module, inputs: Sequence[Any], target_submodules: Iterable[str], deepcopy: bool = False, ) -> Dict[str, Any]: """ Generate inputs for targeting submdoules in the given model. Note that if two submodules refer to the same obj, this function doesn't work. Args: model: root model. inputs: inputs to the root model. target_submodules: submodules that we want to generate inputs for. Returns: A dict that maps from submodule name to its inputs. """ handles = [] results = {} submodule_to_names = {mod: name for name, mod in model.named_modules()} def pre_forward(module, module_inputs): results[submodule_to_names[module]] = copy.deepcopy(module_inputs) if deepcopy else module_inputs for name, mod in model.named_modules(): if name in target_submodules: handles.append(mod.register_forward_pre_hook(pre_forward)) def clean_up_handles(): for h in handles: h.remove() try: with torch.no_grad(): model(*inputs) except Exception as e: clean_up_handles() raise e clean_up_handles() return results class _SplitterBase: """ Splits a GraphModule into sub-GraphModules for execution on CPU or the accelerator. Output is a GraphModule with supported and unsupported operators grouped into as few sub-GraphModules as possible. Assumes that only "call_module", "call_function" and "call_method" from FX IR can potentially be executed on the accelerator. Given the following graph: ==> b ==> // \\ a d \\ // ==> c ==> class SimpleModule(torch.nn.Module): def forward(self, a): b = torch.sin(a) c = torch.cos(a) d = b + c return d and providing "operator_support" that indicates that 'b' and 'c' can be executed on the accelerator, we will get the following split result: main: def forward(self, a): run_on_acc_0_0 = self._run_on_acc_0_0(a) getitem = run_on_acc_0_0[0] getitem_1 = run_on_acc_0_0[1] run_on_cpu_1_1 = self._run_on_cpu_1_1(getitem, getitem_1) return run_on_cpu_1_1 _run_on_acc_0_0: def forward(self, a): sin_1 = torch.sin(a) cos_1 = torch.cos(a) return (sin_1, cos_1) _run_on_cpu_1_1: def forward(self, sin_1, cos_1): add_1 = sin_1 + cos_1 return add_1 """ # PCIe bandwidth for the backend, default to 100 GB/s PCIe_BW = 100 * 2 ** 30 def __init__( self, module: torch.fx.GraphModule, sample_input: Sequence[Any], operator_support: OperatorSupportBase, settings: _SplitterSettingBase, non_acc_submodule_name: str = "_run_on_cpu_", return_tuple: bool = False, ): """ Preprocesses graph before splitting: - finds nodes supported by ACC, - finds fusion groups for ACC nodes having non-tensor IO, - builds a graph of direct dependencies, - builds a map of fused nodes to their fusions. As a result we get self.acc_nodes, self.deps and self.fusions. """ assert isinstance(module, torch.fx.GraphModule) self.module = module ShapeProp(self.module).propagate(*sample_input) self.settings = settings self.operator_support = operator_support self.sample_input = sample_input self.acc_nodes = FxNetAccNodesFinder(self.module, self.operator_support, self.settings.allow_non_tensor)() if self.settings.skip_fusion: self.fusions = {} else: self.fusions = FxNetAccFusionsFinder(module, self.acc_nodes)() # Modify deps to add more deps for fused nodes self.deps = self.find_deps() self.update_deps_for_fusions() self.non_acc_submodule_name = non_acc_submodule_name self._node_submodule_map: Dict[str, str] = {} self._return_tuple = return_tuple self.tags: List[str] = [] # =============================================================== # Helpers for ctor and initial state # =============================================================== def get_node_submodule_map(self) -> Dict[str, str]: """ Returns a map from node name to submodule name, e.g. node: main_module_impl_impl_over_arch_unary_multiple_embedding _pooling_embedding_pooling_sparse_entity_equivalence_key _proxy_embedding_bag maps to submodule name of: _run_on_acc_1 """ return self._node_submodule_map def find_deps(self) -> Dict[torch.fx.Node, NodeSet]: """ Builds a graph of node dependencies. Leaf nodes don't have any dependencies and the "output" node doesn't have nodes depending on it. Resulting graph has only direct dependencies, i.e. there are no transitive dependencies. """ deps: Dict[torch.fx.Node, NodeSet] = defaultdict(set) for node in self.module.graph.nodes: if node.op not in CALLABLE_NODE_OPS: continue for user in node.users: if user.op != "output": deps[user].add(node) return deps def update_deps_for_fusions(self): """ Updates graph of dependencies so that: - nodes from the same fusion depend on the same set of outer nodes, - outer nodes depending on a fusion depend on all nodes in that fusion. """ for node in self.fusions: fusion = self.fusions[node] for fused_neighbor in fusion: self.deps[node].update(self.deps[fused_neighbor] - fusion) for user in fused_neighbor.users: if user not in fusion: self.deps[user].add(node) # =============================================================== # Helpers for preview # =============================================================== def _lower_model_to_backend( self, mod: torch.fx.GraphModule, inputs: Tensors ) -> torch.nn.Module: """ Lower the model to a backend. """ return mod def _find_culprit( self, mod: torch.fx.GraphModule, inputs: Tensors ) -> str: """ When an error occurs during lowering or running the lowered mod, we use this function to find culprits in the `mod` that causes the error. """ return "Unable to find a culprit because _find_culprit() function is not implemented." def _draw_graph_based_on_node_support( self, mod: torch.fx.GraphModule, supported_nodes: NodeList ): color_map = { "default": "AliceBlue", "supported": "chartreuse1", "unsupported": "crimson", } class CustomDrawer(FxGraphDrawer): def _get_node_style(self, node): template = super()._get_node_style(node) if node in supported_nodes: template["fillcolor"] = color_map["supported"] elif node.op in CALLABLE_NODE_OPS: template["fillcolor"] = color_map["unsupported"] else: template["fillcolor"] = color_map["default"] return template drawer = CustomDrawer(mod, "node_support", ignore_getattr=True) dot_graph = drawer.get_main_dot_graph() # pyre-fixme[16]: `pydot.Dot` has no attribute `write_raw`. dot_graph.write_raw("node_support.dot") def node_support_preview(self, dump_graph: bool = False): submodules = dict(self.module.named_modules()) supported_nodes: NodeList = [] supported_node_types = defaultdict(set) unsupported_node_types = defaultdict(set) def get_dtype(arg): tensor_meta = arg.meta.get("tensor_meta") return getattr(tensor_meta, "dtype", None) for node in self.module.graph.nodes: if node.op not in CALLABLE_NODE_OPS: continue target = get_node_target(submodules, node) # Store dtype of arg in node.args. If arg doesn't have dtype, i.e. not a tensor, we'll store None. arg_dtypes = [ get_dtype(arg) if isinstance(arg, torch.fx.Node) else None for arg in node.args ] # Find last non-None element. If all elements are None, return max_len. last_index = len(arg_dtypes) - next( ( i for i, dtype in enumerate(reversed(arg_dtypes)) if dtype is not None ), len(arg_dtypes), ) # Strip None elements at the end. arg_dtypes_tuple = tuple(arg_dtypes[:last_index]) kwarg_dtypes_tuple = tuple( (k, get_dtype(arg)) for k, arg in node.kwargs.items() if isinstance(arg, torch.fx.Node) ) if self.operator_support.is_node_supported(submodules, node): supported_nodes.append(node) supported_node_types[target].add((arg_dtypes_tuple, kwarg_dtypes_tuple)) else: unsupported_node_types[target].add((arg_dtypes_tuple, kwarg_dtypes_tuple)) if dump_graph: self._draw_graph_based_on_node_support(self.module, supported_nodes) reports = "\nSupported node types in the model:\n" for t, dtypes in supported_node_types.items(): for arg_dtypes_tuple, kwarg_dtypes_tuple in dtypes: reports += f"{t}: ({arg_dtypes_tuple}, {dict(kwarg_dtypes_tuple)})\n" reports += "\nUnsupported node types in the model:\n" for t, dtypes in unsupported_node_types.items(): for arg_dtypes_tuple, kwarg_dtypes_tuple in dtypes: reports += f"{t}: ({arg_dtypes_tuple}, {dict(kwarg_dtypes_tuple)})\n" print(reports) # Return reports for testing purpose return reports def split_preview(self, dump_graph: bool = False): reports = "" subgraphs = self.put_nodes_into_subgraphs() acc_subgraphs_num = len([g for g in subgraphs if g.is_acc]) cpu_subgraphs_num = len(subgraphs) - acc_subgraphs_num reports += f"Before removing small acc subgraphs, total {len(subgraphs)} subgraphs are created:" reports += f" {acc_subgraphs_num} acc subgraphs and {cpu_subgraphs_num} cpu subgraphs.\n" subgraphs = self.remove_small_acc_subgraphs(subgraphs) acc_subgraphs_num = len([g for g in subgraphs if g.is_acc]) cpu_subgraphs_num = len(subgraphs) - acc_subgraphs_num reports += f"After removing small acc subgraphs, total {len(subgraphs)} subgraphs are created:" reports += f" {acc_subgraphs_num} acc subgraphs and {cpu_subgraphs_num} cpu subgraphs.\n" for i, subgraph in enumerate(subgraphs): reports += f"_run_on_acc_{i}: " if subgraph.is_acc else f"{self.non_acc_submodule_name}{i}: " reports += f"{len(subgraph.nodes)} node(s)\n" self.tag(subgraphs) split_mod = self.split(remove_tag=True) split_mod.eval() if dump_graph: drawer = FxGraphDrawer( split_mod, "preview", ignore_getattr=True ) dot_graphs = drawer.get_all_dot_graphs() for name, dot_graph in dot_graphs.items(): # pyre-fixme[16]: `pydot.Dot` has no attribute `write_raw`. dot_graph.write_raw(f"{name}.dot") max_qps: float = self.PCIe_BW bottleneck_module = "" for node in split_mod.graph.nodes: if node.op == "call_module" and "acc" in node.target: reports += f"\nProcessing acc submodule {node.target}\n" submod = getattr(split_mod, node.target) def get_submod_inputs(main_mod, submod, example_inputs): sub_inputs = None def get_inputs(self, inputs): nonlocal sub_inputs sub_inputs = inputs handle = submod.register_forward_pre_hook(get_inputs) main_mod(*example_inputs) handle.remove() return sub_inputs submod_inputs = get_submod_inputs( split_mod, submod, self.sample_input ) ShapeProp(submod).propagate(*submod_inputs) total_input_bytes = 0 total_output_bytes = 0 reports += "Checking inputs...\n" for n in submod.graph.nodes: if n.op == "placeholder": if not is_node_output_tensor(n): reports += f"Input {n.name} is not a tensor, this might cause problems during lowering!\n" else: total_input_bytes += get_size_of_node(submod, n)[0] if n.op == "output": output_node = n reports += "Checking outputs...\n" def get_bytes(node: torch.fx.Node): nonlocal total_output_bytes nonlocal reports if not is_node_output_tensor(node): reports += f"Output {node.name} is not a tensor, this might cause problems during lowering!\n" else: total_output_bytes += get_size_of_node(submod, node)[0] map_arg(output_node.args, get_bytes) # type: ignore[possibly-undefined] qps = self.PCIe_BW / max(total_input_bytes, total_output_bytes) reports += f"Total input size in bytes is {total_input_bytes}, total output size in bytes is {total_output_bytes}," reports += f" theoretical max qps (bounds by PCIe bandwidth) for this submodule is {qps}.\n" if qps < max_qps: max_qps = qps bottleneck_module = node.target try: lowered_submod = self._lower_model_to_backend(submod, submod_inputs) except RuntimeError: reports += "Run into an error during lowering!\n" reports += self._find_culprit(submod, submod_inputs) continue try: lowered_submod(*submod_inputs) except RuntimeError: reports += "Run into an error during inference!\n" reports += self._find_culprit(submod, submod_inputs) else: reports += "Lowering and running succeed!\n" reports += f"\nTheoretical max qps (bounds by PCIe bandwidth) for this model is {max_qps}," reports += f" bottleneck is submodule {bottleneck_module}." print(reports) # return the reports for testing purposes return reports # =============================================================== # Helpers for extend_acc_subgraph() method # =============================================================== def find_reverse_deps( self, tag_id: Optional[int] = None ) -> Dict[torch.fx.Node, NodeSet]: """ Builds reversed topological node dependencies, if tag_id is specified, we ignore nodes that are in later subgraph i.e. nodes have greater tag_id. """ result: Dict[torch.fx.Node, NodeSet] = defaultdict(set) for node in self.module.graph.nodes: if node.op not in CALLABLE_NODE_OPS: continue for user in node.users: if user.op not in CALLABLE_NODE_OPS: continue if tag_id is None or (int(user.tag.split("_")[-1]) < tag_id): result[node].add(user) return result def update_reverse_deps_for_fusions( self, deps: Dict[torch.fx.Node, NodeSet] ): processed_node = set() for node, fusion in self.fusions.items(): if node in processed_node: continue new_dep = set() # Create a new dependency set which include all the # dependencies of the nodes in the fusion group for n in fusion: new_dep.update(deps[n]) # Exclude nodes in the fusion new_dep.difference_update(fusion) # Update dependency for n in fusion: deps[n] = new_dep for arg in n.all_input_nodes: if arg not in fusion: deps[arg].update(fusion) processed_node.add(n) def find_parent_nodes_of_subgraph(self, tag: str) -> NodeSet: """ Finds parent nodes of the `tag` subgraph. Traverse the inputs of nodes in the subgraph, if input doesn't belong to the subgraph and is not a placeholder, we consider it as the parent node of the subgraph. """ parent_nodes = set() for node in self.module.graph.nodes: if node.op in CALLABLE_NODE_OPS and node.tag == tag: for arg in node.all_input_nodes: if arg.op in CALLABLE_NODE_OPS and arg.tag != tag: parent_nodes.add(arg) return parent_nodes def extend_acc_subgraph(self, tag: str): """ Extend the acc subgraph with `tag` going the reversed topological direction. """ # Dict that maps node to its users and ignore users that # are in the subgraph that has greater tag deps = self.find_reverse_deps(tag_id=int(tag.split("_")[-1])) self.update_reverse_deps_for_fusions(deps) # Parent nodes of the subgraph parent_nodes = self.find_parent_nodes_of_subgraph(tag) visited_nodes: NodeSet = set() while parent_nodes: node = None # Find a acc node that depends on visited nodes only for n in parent_nodes: if deps[n] <= visited_nodes and n in self.acc_nodes: node = n break if node is None: break # Put the node into `tag` subgraph node.tag = tag # type: ignore[attr-defined] parent_nodes.remove(node) visited_nodes.add(node) # If node is in a fusion group, add all fusion buddies to parent nodes if node in self.fusions: for fusion_node in self.fusions[node]: if fusion_node not in visited_nodes: parent_nodes.add(fusion_node) # Add inputs of the node to parent nodes for arg in node.all_input_nodes: if arg.op in CALLABLE_NODE_OPS and arg not in visited_nodes: parent_nodes.add(arg) # =============================================================== # Helpers for split() method # =============================================================== def starter_nodes(self) -> Tuple[NodeSet, NodeSet]: """ Finds nodes that consume module inputs or get_attr nodes. """ starter_cpu_nodes: NodeSet = set() starter_acc_nodes: NodeSet = set() for node in self.module.graph.nodes: if node.op not in {"placeholder", "get_attr"}: continue for user in node.users: if user in self.acc_nodes: starter_acc_nodes.add(user) else: starter_cpu_nodes.add(user) return starter_cpu_nodes, starter_acc_nodes def put_nodes_into_subgraphs(self) -> List[Subgraph]: # We start graph traversal from leaf nodes current_cpu_nodes, current_acc_nodes = self.starter_nodes() visited_nodes: NodeSet = set() # Determine which subgraph to start from based on which subgraph has # 0-dep node acc_subgraph: bool = not any(len(self.deps[n]) == 0 for n in current_cpu_nodes) current_subgraph_nodes: NodeList = [] # Result accumulator subgraphs: List[Subgraph] = [] while current_cpu_nodes or current_acc_nodes: # Find the first node that should belong to the current subgraph and has all dependencies resolved current_nodes = current_acc_nodes if acc_subgraph else current_cpu_nodes node = next( (n for n in current_nodes if self.deps[n] <= visited_nodes), None, ) # If nothing was found, then it's time to flip the mode and start a new subgraph if node is None: if not current_subgraph_nodes: raise FxNetSplitterInternalError("Subgraph can't be empty") subgraphs.append( Subgraph(is_acc=acc_subgraph, nodes=current_subgraph_nodes) ) acc_subgraph = not acc_subgraph current_subgraph_nodes = [] continue current_nodes.remove(node) visited_nodes.add(node) current_subgraph_nodes.append(node) # Add fusion buddies if node in self.fusions: if node in self.acc_nodes: current_acc_nodes.update(self.fusions[node] - visited_nodes) else: current_cpu_nodes.update(self.fusions[node] - visited_nodes) # Put depending nodes into the queue for user in node.users: if user.op not in CALLABLE_NODE_OPS: continue # Add downstream nodes if user in self.acc_nodes: current_acc_nodes.add(user) else: current_cpu_nodes.add(user) # Check if the last subgraph was not created if current_subgraph_nodes: subgraphs.append( Subgraph(is_acc=acc_subgraph, nodes=current_subgraph_nodes) ) if not subgraphs: raise FxNetSplitterInternalError("Couldn't create subgraphs") return subgraphs def remove_small_acc_subgraphs(self, subgraphs: List[Subgraph]) -> List[Subgraph]: """ This pass finds ACC submodules with less than specified size and merges them with adjacent CPU submodules. """ result: List[Subgraph] = [] for subgraph in subgraphs: if subgraph.is_acc: if len(subgraph.nodes) >= self.settings.min_acc_module_size: result.append(subgraph) else: print( "Eliminating acc subgraph because it's smaller than the threshold: " f"{len(subgraph.nodes)} < {self.settings.min_acc_module_size}" ) if result: result[-1].nodes.extend(subgraph.nodes) else: subgraph.is_acc = False result.append(subgraph) else: if result and not result[-1].is_acc: result[-1].nodes.extend(subgraph.nodes) else: result.append(subgraph) return result def tag(self, subgraphs: List[Subgraph]): self.tags = [] for subgraph in subgraphs: tag = f"_run_on_acc_{len(self.tags)}" if subgraph.is_acc else f"{self.non_acc_submodule_name}{len(self.tags)}" self.tags.append(tag) for node in subgraph.nodes: if hasattr(node, "tag"): raise FxNetSplitterInternalError(f"Node {node} was already tagged") node.tag = tag # type: ignore[attr-defined] self._node_submodule_map[node.name] = tag def split(self, remove_tag: bool = False) -> torch.fx.GraphModule: split_module = split_by_tags(self.module, self.tags, return_tuple=self._return_tuple) if remove_tag: for node in self.module.graph.nodes: if hasattr(node, "tag"): del node.tag return split_module # type: ignore[return-value] def __call__(self) -> torch.fx.GraphModule: subgraphs = self.put_nodes_into_subgraphs() subgraphs = self.remove_small_acc_subgraphs(subgraphs) acc_subgraphs_count = len([s for s in subgraphs if s.is_acc]) non_acc_subgraphs_count = len(subgraphs) - acc_subgraphs_count print(f"Got {acc_subgraphs_count} acc subgraphs and {non_acc_subgraphs_count} non-acc subgraphs") self.tag(subgraphs) return self.split() def generate_split_results(self) -> SplitResult: split_module = self() submodule_names = [] for name, mod in split_module.named_children(): submodule_names.append(name) if ( self.settings.max_acc_splits > 0 and len(submodule_names) > self.settings.max_acc_splits ): raise ValueError( "Cannot fulfill max_acc_splits limit. " "This may cause split fragmentation and " "result in performance issues." ) submodule_inputs = generate_inputs_for_submodules(split_module, self.sample_input, submodule_names) return SplitResult(split_module, submodule_inputs, self.non_acc_submodule_name)