With the old partitioner, suppose `add` is supported, the following code
```python
def fn(a, b, c, d):
x = a + b # add
y = c + d # add_1
return (x, y)
traced = symbolic_trace(fn)
partitioner = CapabilityBasedPartitioner(traced, supported_ops, allows_single_node_partition=True)
partitions = partitioner.propose_partitions()
```
results in the partitions `[[add], [add_1]]`. However, since these two partitions do not depend on each other, they can be aggressively merged into a single partition `[[add, add_1]]` without causing any issues. This PR introduces a new feature that allows such aggressive merging by introducing an option `aggressive_merge` to the Partitioner class.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100195
Approved by: https://github.com/SherlockNoMad
Fixes#86159 and #86108
Refactored graph partition to check for cyclic dependency on each partition merge, instead of relying on a pre-baked dependency map.
The previous implementation suffers from not updating dependency on existing partition. When a fusion happens, the updated dependency map needs to be propagated to all nodes in the graph, so each node in a partition shares an identical dependency set. Previous implementation suffers from the not identifying cyclic dependency in issue #86159.
Updated implementation does a cyclic check on partitioned graph before attempting a merge of two partitions.
- [x] python repro added with cyclic dependency after partition `TestFXGraphPasses.forward12`
- [x] fix dependency map with updated implementation using cyclic check
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86511
Approved by: https://github.com/SherlockNoMad
Subgraph matcher now handles the matching of non-Node arguments.
Here are the 4 cases
- pn is Node, gn is Node: this go through the regular _match_node() function
- pn is Noed, gn is not a Node: this is a match if only pn is a placeholder op
- pn is not Node, gn is Node: this is a no match case
- pn is not a Node, gn is not a Node: this will go through the argument comparison.
With this change
```
def target(x):
return foo(x, 3)
def pattern(x, y):
return foo(x, y)
```
is a match
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85456
Approved by: https://github.com/jerryzh168
pseudo.any is a wildcard node that can be matched with any fx node with arbitrary number of inputs and outputs.
For example, to match relu followed by one fx node:
```
def pattern(a):
y = a.relu()
z = torch.ops.pseudo.any(y)
return z
```
pseudo.oneof is a special node that can be matched with a fx node whose target is in the permissible list.
`targets` must be be a list of qualified name for operators, e.g. ["operator.add", "torch.sigmoid",
"torch.ops.aten.foo", "torch.ops.prims.bar"]
For example, using following pattern with pseudo.oneof
```
def pattern(a):
y = a.relu()
z = torch.ops.pseudo.oneof(y, targets=["relu", "torch.sigmoid", "operator.add"])
return z
```
It will have 3 matches in the following function
```
def forward(y):
z = y.relu()
x = z.relu() # first match
x = x.relu()
x = torch.sigmoid(x) # second match
x = x.relu()
return x + 1 # third match
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82853
Approved by: https://github.com/ezyang
This new version of subgraph matcher further supports
- optionally match with pattern's placeholder and output nodes
- patterns with multiple outputs
- filtering out non-containing matches
- filtering out overlapping matches
TODOs:
- [x] Update replace_pattern() to use this matcher
- [x] Fix cases with identical anchor
- [x] Introduce wildcard matching, such Any, OneOf
- [ ] Improve node comparer to match args and kwargs values
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82090
Approved by: https://github.com/ezyang
This PR introduces two components.
CapabilityBasedPartitioner for FX graph: given a list of supported operators, this partitioner tries to forms the largest subgraphs that only contain the supported ops.
Fuser utility: given a list of nodes in FX graph, it lifts them as a sub-GraphModule in the original graph.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79439
Approved by: https://github.com/jjsjann123, https://github.com/davidberard98
