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4 | 4 | # This source code is licensed under the BSD-style license found in the |
5 | 5 | # LICENSE file in the root directory of this source tree. |
6 | 6 |
|
| 7 | +import logging |
| 8 | +from typing import Callable, Dict, List, Optional, Union |
| 9 | + |
| 10 | +import torch |
| 11 | + |
7 | 12 | from executorch.backends.qnnpack.partition.support_patterns import ( |
8 | 13 | get_dynamic_quant_addmm_with_view_copy_graph, |
9 | 14 | get_dynamic_quant_addmm_without_view_copy_graph, |
|
14 | 19 | from executorch.backends.transforms.addmm_mm_to_linear import ( |
15 | 20 | apply_addmm_mm_to_linear_transform, |
16 | 21 | ) |
17 | | -from executorch.backends.xnnpack.partition.xnnpack_partitioner import ( |
18 | | - _SingleOpDelegatePartitioner, |
19 | | -) |
| 22 | +from executorch.exir.backend.partitioner import DelegationSpec, Partitioner |
| 23 | +from torch.fx.passes.utils.matcher_utils import SubgraphMatcher |
| 24 | + |
| 25 | +logging.basicConfig(level=logging.INFO) |
| 26 | +log = logging.getLogger(__name__) |
| 27 | + |
| 28 | + |
| 29 | +class _BasePartitioner(Partitioner): |
| 30 | + """ |
| 31 | + Graph based partitioner base for on XNNPACK backend. |
| 32 | + """ |
| 33 | + |
| 34 | + def __init__(self, delegate_name, patterns): |
| 35 | + self.patterns = patterns |
| 36 | + |
| 37 | + self.delegation_spec = DelegationSpec(delegate_name, []) |
| 38 | + self.partition_tags: Dict[str, DelegationSpec] = {} |
| 39 | + |
| 40 | + @staticmethod |
| 41 | + def check_partitions(partitions: Union[dict, list]) -> None: |
| 42 | + """ |
| 43 | + Warn users if there aren't any matches |
| 44 | + """ |
| 45 | + pl = len(partitions) |
| 46 | + if pl == 0: |
| 47 | + log.warning("Nothing can be partitioned!") |
| 48 | + else: |
| 49 | + log.info(f"Found {pl} subgraphs to be partitioned.") |
| 50 | + |
| 51 | + def partition(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule: |
| 52 | + raise NotImplementedError("This is not meant to be used directly.") |
| 53 | + return graph_module |
| 54 | + |
| 55 | + |
| 56 | +class _SingleOpDelegatePartitioner(_BasePartitioner): |
| 57 | + """ |
| 58 | + Graph based partitioner base for a single "op" or "node" or a pattern match for XNNPACK backend. |
| 59 | + This is tailored for DQLinear where XNNPACK (and also QNNPACK) delegates prefers to have a single DQLinear node in the graph. |
| 60 | + This is a base class given XNNPACK and QNNPACK currently share this. |
| 61 | + """ |
| 62 | + |
| 63 | + def __init__( |
| 64 | + self, |
| 65 | + delegate_name, |
| 66 | + patterns, |
| 67 | + transforms: Optional[List[Callable[[torch.fx.Graph], torch.fx.Graph]]] = None, |
| 68 | + ): |
| 69 | + """ |
| 70 | + @param transforms: Optional list of transforms that will be applied to the graph before running the partitioner. |
| 71 | + """ |
| 72 | + super().__init__(delegate_name, patterns) |
| 73 | + self.transforms = transforms |
| 74 | + |
| 75 | + # override |
| 76 | + def partition(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule: |
| 77 | + # TODO delete this since we are not allowed to do this |
| 78 | + if self.transforms is not None: |
| 79 | + for transform in self.transforms: # pyre-ignore |
| 80 | + graph_module.graph = transform(graph_module.graph) |
| 81 | + |
| 82 | + matches = [ |
| 83 | + match |
| 84 | + for matches in ( |
| 85 | + SubgraphMatcher(pattern, ignore_literals=True).match(graph_module.graph) |
| 86 | + for pattern in self.patterns |
| 87 | + ) |
| 88 | + for match in matches |
| 89 | + ] |
| 90 | + |
| 91 | + match_sets = [ |
| 92 | + { |
| 93 | + node_in_graph |
| 94 | + for (node_in_pattern, node_in_graph) in match.nodes_map.items() |
| 95 | + if ( |
| 96 | + node_in_pattern.op != "placeholder" |
| 97 | + and node_in_graph.op != "placeholder" |
| 98 | + ) |
| 99 | + } |
| 100 | + for match in matches |
| 101 | + ] |
| 102 | + |
| 103 | + # Sort match sets in descending order of length so that any match sets |
| 104 | + # which are supersets of other match sets are processed first |
| 105 | + match_sets = sorted(match_sets, key=len, reverse=True) |
| 106 | + |
| 107 | + self.check_partitions(match_sets) |
| 108 | + |
| 109 | + # Mapping from delegation tag to match set |
| 110 | + tag_mapping = {} |
| 111 | + |
| 112 | + for (partition_id, match_set) in enumerate(match_sets): |
| 113 | + delegation_tag = f"tag{partition_id}" |
| 114 | + for node in match_set: |
| 115 | + if "delegation_tag" in node.meta: |
| 116 | + # This node already has delegation tag assigned. |
| 117 | + # Check that the current match set is a subset of the one |
| 118 | + # used to assign its delegation tag, then skip this match |
| 119 | + # set. We have this check to ensure there are no pairs of |
| 120 | + # match sets where they are overlapping but neither is a |
| 121 | + # subset of the other. |
| 122 | + if not match_set.issubset(tag_mapping[node.meta["delegation_tag"]]): |
| 123 | + raise AssertionError( |
| 124 | + f"Found match sets which are overlapping but neither is a subset of the other: {match_set}, {tag_mapping[node.meta['delegation_tag']]}" |
| 125 | + ) |
| 126 | + break |
| 127 | + node.meta["delegation_tag"] = delegation_tag |
| 128 | + self.partition_tags[delegation_tag] = self.delegation_spec |
| 129 | + tag_mapping[delegation_tag] = match_set |
| 130 | + |
| 131 | + return graph_module |
20 | 132 |
|
21 | 133 |
|
22 | 134 | class QnnpackPartitioner(_SingleOpDelegatePartitioner): |
|
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