mirror of https://github.com/hpcaitech/ColossalAI
282 lines
12 KiB
Python
282 lines
12 KiB
Python
import torch
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from torch.fx.graph_module import GraphModule
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from typing import Callable, List, Dict, Any, Optional
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from torch.fx._compatibility import compatibility
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from packaging import version
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import inspect
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@compatibility(is_backward_compatible=True)
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class Partition:
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"""
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Adapted from https://github.com/pytorch/pytorch/blob/master/torch/fx/passes/split_module.py
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"""
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def __init__(self, name: str):
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self.name: str = name
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self.node_names: List[str] = []
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self.inputs: Dict[str, None] = {}
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self.outputs: Dict[str, None] = {}
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self.partitions_dependent_on: Dict[str, None] = {}
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self.partition_dependents: Dict[str, None] = {}
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self.graph: torch.fx.graph.Graph = torch.fx.graph.Graph()
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self.environment: Dict[torch.fx.node.Node, torch.fx.node.Node] = {}
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self.targets: Dict[str, Any] = {}
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def __repr__(self) -> str:
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return f"name: {self.name},\n" \
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f" nodes: {self.node_names},\n" \
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f" inputs: {self.inputs},\n" \
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f" outputs: {self.outputs},\n" \
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f" partitions depenent on: {self.partitions_dependent_on},\n" \
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f" parition dependents: {self.partition_dependents}"
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# Creates subgraphs out of main graph
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@compatibility(is_backward_compatible=True)
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def split_module(
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m: GraphModule,
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root_m: torch.nn.Module,
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split_callback: Callable[[torch.fx.node.Node], int],
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):
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"""
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Adapted from https://github.com/pytorch/pytorch/blob/master/torch/fx/passes/split_module.py
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Creates subgraphs out of main graph
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Args:
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m (GraphModule): Graph module to split
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root_m (torch.nn.Module): root nn module. Not currently used. Included
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because the root nn module is usually transformed via
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torch.fx._symbolic_trace.symbolic_trace (see example below)
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split_callback (Callable[[torch.fx.node.Node], int]): Callable function
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that maps a given Node instance to a numeric partition identifier.
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split_module will use this function as the policy for which operations
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appear in which partitions in the output Module.
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Returns:
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GraphModule: the module after split.
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Example:
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This is a sample setup:
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import torch
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from torch.fx.symbolic_trace import symbolic_trace
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from torch.fx.graph_module import GraphModule
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from torch.fx.node import Node
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from colossalai.fx.passes.split_module import split_module
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class MyModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.param = torch.nn.Parameter(torch.rand(3, 4))
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self.linear = torch.nn.Linear(4, 5)
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def forward(self, x, y):
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z = self.linear(x + self.param).clamp(min=0.0, max=1.0)
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w = self.linear(y).clamp(min=0.0, max=1.0)
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return z + w
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# symbolically trace model
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my_module = MyModule()
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my_module_traced = symbolic_trace(my_module)
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# random mod partitioning
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partition_counter = 0
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NPARTITIONS = 3
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def mod_partition(node: Node):
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global partition_counter
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partition = partition_counter % NPARTITIONS
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partition_counter = (partition_counter + 1) % NPARTITIONS
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return partition
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# split module in module with submodules
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module_with_submodules = split_module(
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my_module_traced, my_module, mod_partition
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)
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Output looks like this. Original graph is broken into partitions
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> print(module_with_submodules)
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GraphModule(
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(submod_0): GraphModule(
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(linear): Linear(in_features=4, out_features=5, bias=True)
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)
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(submod_1): GraphModule(
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(linear): Linear(in_features=4, out_features=5, bias=True)
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)
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(submod_2): GraphModule()
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)
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def forward(self, x, y):
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param = self.param
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submod_0 = self.submod_0(x, param, y); x = param = y = None
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getitem = submod_0[0]
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getitem_1 = submod_0[1]; submod_0 = None
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submod_1 = self.submod_1(getitem, getitem_1); getitem = getitem_1 = None
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getitem_2 = submod_1[0]
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getitem_3 = submod_1[1]; submod_1 = None
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submod_2 = self.submod_2(getitem_2, getitem_3); getitem_2 = getitem_3 = None
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return submod_2
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Output of split module is the same as output of input traced module.
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This is an example within a test setting:
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> orig_out = my_module_traced(x, y)
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> submodules_out = module_with_submodules(x, y)
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> self.assertEqual(orig_out, submodules_out)
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True
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"""
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partitions: Dict[str, Partition] = {}
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orig_nodes: Dict[str, torch.fx.node.Node] = {}
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def record_cross_partition_use(def_node: torch.fx.node.Node,
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use_node: Optional[torch.fx.node.Node]): # noqa: B950
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def_partition_name = getattr(def_node, '_fx_partition', None)
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use_partition_name = getattr(use_node, '_fx_partition', None)
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if def_partition_name != use_partition_name:
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if def_partition_name is not None:
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def_partition = partitions[def_partition_name]
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def_partition.outputs.setdefault(def_node.name)
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if use_partition_name is not None:
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def_partition.partition_dependents.setdefault(use_partition_name)
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if use_partition_name is not None:
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use_partition = partitions[use_partition_name]
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use_partition.inputs.setdefault(def_node.name)
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if def_partition_name is not None:
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use_partition.partitions_dependent_on.setdefault(def_partition_name)
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# split nodes into parititons
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for node in m.graph.nodes:
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orig_nodes[node.name] = node
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if node.op in ["placeholder"]:
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continue
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if node.op == 'output':
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torch.fx.graph.map_arg(node.args[0], lambda n: record_cross_partition_use(n, None))
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continue
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partition_name = str(split_callback(node))
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# add node to partitions
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partition = partitions.get(partition_name)
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if partition is None:
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partitions[partition_name] = partition = Partition(partition_name)
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partition.node_names.append(node.name)
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node._fx_partition = partition_name
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torch.fx.graph.map_arg(node.args, lambda def_node: record_cross_partition_use(def_node, node))
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torch.fx.graph.map_arg(node.kwargs, lambda def_node: record_cross_partition_use(def_node, node)) # noqa: B950
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# find partitions with no dependencies
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root_partitions: List[str] = []
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for partition_name, partition in partitions.items():
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if not len(partition.partitions_dependent_on):
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root_partitions.append(partition_name)
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# check partitions for circular dependencies and create topological partition ordering
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sorted_partitions: List[str] = []
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while root_partitions:
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root_partition = root_partitions.pop()
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sorted_partitions.append(root_partition)
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for dependent in partitions[root_partition].partition_dependents:
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partitions[dependent].partitions_dependent_on.pop(root_partition)
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if not partitions[dependent].partitions_dependent_on:
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root_partitions.append(dependent)
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if len(sorted_partitions) != len(partitions):
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raise RuntimeError("cycle exists between partitions!")
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# add placeholders to parititons
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for partition_name in sorted_partitions:
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partition = partitions[partition_name]
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for input in partition.inputs:
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placeholder = partition.graph.placeholder(input)
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placeholder.meta = orig_nodes[input].meta.copy()
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partition.environment[orig_nodes[input]] = placeholder
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# Transform nodes and collect targets for partition's submodule
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for node in m.graph.nodes:
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if hasattr(node, '_fx_partition'):
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partition = partitions[node._fx_partition]
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# swap out old graph nodes in kw/args with references to new nodes in this submodule
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environment = partition.environment
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gathered_args = torch.fx.graph.map_arg(node.args, lambda n: environment[n])
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gathered_kwargs = torch.fx.graph.map_arg(node.kwargs, lambda n: environment[n])
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if node.op not in ['call_module', 'get_attr']:
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target = node.target
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else:
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target_atoms = node.target.split('.')
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target_attr = m
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for atom in target_atoms:
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if not hasattr(target_attr, atom):
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raise RuntimeError(f'Operator target {node.target} not found!')
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target_attr = getattr(target_attr, atom)
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# target = target_atoms[-1]
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target = '_'.join(target_atoms)
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partition.targets[target] = target_attr
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assert isinstance(gathered_args, tuple)
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assert isinstance(gathered_kwargs, dict)
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new_node = partition.graph.create_node(op=node.op,
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target=target,
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args=gathered_args,
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kwargs=gathered_kwargs)
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new_node.meta = node.meta.copy()
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partition.environment[node] = new_node
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# Set up values to construct base module
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base_mod_env: Dict[str, torch.fx.node.Node] = {}
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base_mod_graph: torch.fx.graph.Graph = torch.fx.graph.Graph()
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base_mod_attrs: Dict[str, torch.fx.graph_module.GraphModule] = {}
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for node in m.graph.nodes:
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if node.op == 'placeholder':
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if version.parse(torch.__version__) < version.parse('1.11.0'):
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base_mod_env[node.name] = base_mod_graph.placeholder(node.name, type_expr=node.type)
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else:
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default_value = node.args[0] if len(node.args) > 0 else inspect.Signature.empty
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base_mod_env[node.name] = base_mod_graph.placeholder(node.name,
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type_expr=node.type,
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default_value=default_value)
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base_mod_env[node.name].meta = node.meta.copy()
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# Do some things iterating over the partitions in topological order again:
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# 1) Finish off submodule Graphs by setting corresponding outputs
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# 2) Construct GraphModules for each submodule
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# 3) Construct the base graph by emitting calls to those submodules in
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# topological order
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for partition_name in sorted_partitions:
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partition = partitions[partition_name]
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# Set correct output values
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output_vals = tuple(partition.environment[orig_nodes[name]] for name in partition.outputs)
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output_vals = output_vals[0] if len(output_vals) == 1 else output_vals # type: ignore[assignment]
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partition.graph.output(output_vals)
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# Construct GraphModule for this partition
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submod_name = f'submod_{partition_name}'
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base_mod_attrs[submod_name] = torch.fx.graph_module.GraphModule(partition.targets,
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partition.graph) # noqa: B950
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# Emit call in base graph to this submodule
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output_val = base_mod_graph.call_module(submod_name, tuple(base_mod_env[name] for name in partition.inputs))
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if len(partition.outputs) > 1:
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# Unpack multiple return values from submodule
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output_val_proxy = torch.fx.proxy.Proxy(output_val)
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for i, output_name in enumerate(partition.outputs):
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base_mod_env[output_name] = output_val_proxy[i].node # type: ignore[index]
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else:
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if not partition.outputs:
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continue
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base_mod_env[list(partition.outputs)[0]] = output_val
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for node in m.graph.nodes:
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if node.op == 'output':
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base_mod_graph.output(torch.fx.graph.map_arg(node.args[0], lambda n: base_mod_env[n.name])) # noqa: B950
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return torch.fx.graph_module.GraphModule(base_mod_attrs, base_mod_graph)
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