Added helper functions to match nodes in the graph that are decomposed from their source (leaf modules, or functional ops), as a result of dynamo tracing.
`get_source_partitions(graph: torch.fx.Graph, wanted_sources: List[Any]) -> Dict[Any, SourcePartition]`
Args:
* graph: The graph we want to partition
* wanted_sources: List of sources of nodes that were decomposed from this source. This can be a function (ex. torch.nn.functional.linear) or a leaf module type (ex. torch.nn.Linear)
Returns:
* Dictionary mapping sources (ex. torch.nn.modules.linear.Linear) to a list of SourcePartitions that correspond to the list of nodes that were flattened from a module of that type.
```
@dataclass
class SourcePartition():
# Nodes in a particular partition
nodes: List[Node]
# Module type
module_type: Type
# Nodes in the graph that are needed as inputs to the partition
input_nodes: List[Node] = field(default_factory=list)
# Nodes in the partition that are being used by nodes outside of the partition
output_nodes: List[Node] = field(default_factory=list)
# Parameters that are being used
params: List[str] = field(default_factory=list)
```
Example:
Original:
```
x -> linear -> linear -> relu -> linear
```
Traced graph:
```
.graph():
%arg0 : [#users=1] = placeholder[target=arg0]
%_param_constant0 : [#users=1] = get_attr[target=_param_constant0]
%t_default : [#users=1] = call_function[target=torch.ops.aten.t.default](args = (%_param_constant0,), kwargs = {})
%_param_constant1 : [#users=1] = get_attr[target=_param_constant1]
%addmm_default : [#users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%_param_constant1, %arg0, %t_default), kwargs = {})
%_param_constant0_1 : [#users=1] = get_attr[target=_param_constant0]
%t_default_1 : [#users=1] = call_function[target=torch.ops.aten.t.default](args = (%_param_constant0_1,), kwargs = {})
%_param_constant1_1 : [#users=1] = get_attr[target=_param_constant1]
%addmm_default_1 : [#users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%_param_constant1_1, %addmm_default, %t_default_1), kwargs = {})
%relu_default : [#users=1] = call_function[target=torch.ops.aten.relu.default](args = (%addmm_default_1,), kwargs = {})
%_param_constant2 : [#users=1] = get_attr[target=_param_constant2]
%t_default_2 : [#users=1] = call_function[target=torch.ops.aten.t.default](args = (%_param_constant2,), kwargs = {})
%_param_constant3 : [#users=1] = get_attr[target=_param_constant3]
%addmm_default_2 : [#users=1] = call_function[target=torch.ops.aten.addmm.default](args = (%_param_constant3, %relu_default, %t_default_2), kwargs = {})
return [addmm_default_2]
```
Result of `get_module_partitions`:
```
{<class 'torch.nn.modules.linear.Linear'>: [
ModulePartition(nodes=[_param_constant0, t_default, _param_constant1, addmm_default], module_type=<class 'torch.nn.modules.linear.Linear'>, input_nodes=[arg0], output_nodes=[addmm_default], params=["_param_constant0", "_param_constant1"]),
ModulePartition(nodes=[_param_constant0_1, t_default_1, _param_constant1_1, addmm_default_1], module_type=<class 'torch.nn.modules.linear.Linear'>, input_nodes=[addmm_default], output_nodes=[addmm_default_1], params=["_param_constant0_1", "_param_constant1_1"]),
ModulePartition(nodes=[_param_constant2, t_default_2, _param_constant3, addmm_default_2], module_type=<class 'torch.nn.modules.linear.Linear'>, input_nodes=[relu_default], output_nodes=[addmm_default_2], params=["_param_constant2", "_param_constant3"])],
<class 'torch.nn.modules.activation.ReLU'>: [
ModulePartition(nodes=[relu_default], module_type=<class 'torch.nn.modules.activation.ReLU'>, input_nodes=[addmm_default_1], output_nodes=[relu_default], params=[])]}
```
Also added helper function to check if two module partitions are connected:
`check_subgraphs_connected(subgraph1: SourcePartition, subgraph2: SourcePartition) -> bool`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98628
Approved by: https://github.com/cccclai
