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[TorchFX] YoloV11 support (#3105)
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### Changes

* Torch SDPA pattern is updated
* As the concat node has his input nodes in format `args=([inp_1, ...,
inp_n], dim)`, thus it should be treated differently. Retrieving concat
inputs by input port id was supported in each TorchFX transformation

### Reason for changes

* To support quantization of ultralytics/yolo11n in TorchFX backend

### Related tickets

#2766 
157032

### Tests

* `tests/torch/fx/test_model_transformer.py` and
`tests/torch/fx/test_compress_weights.py` are updated to check all cases
with the concat node. All .`dot` / `.json` were checked manually.
* `tests/torch/fx/test_models.py` is updated with `YOLO11N_SDPABlock`
synthetic model to check the correctness of SDPA pattern matching
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@daniil-lyakhov
daniil-lyakhov authored Nov 25, 2024
1 parent 0c22b38 commit 7ea17f2
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40 changes: 25 additions & 15 deletions nncf/experimental/torch/fx/transformations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -129,7 +129,7 @@ def module_insertion_transformation(model: torch.fx.GraphModule):
user.replace_input_with(target_node, new_node)

else:
prev_node = target_node.args[target_point.input_port_id]
prev_node = _get_node_by_input_port_id(target_node, target_point.input_port_id)
_set_new_node_meta(new_node, [prev_node], module_to_insert, model)
target_node.replace_input_with(prev_node, new_node)

Expand Down Expand Up @@ -222,27 +222,25 @@ def constant_update_fn(
:param updated_node_name: Name of the constant node after updating. Default is `nodename` + `_updated_constant`.
"""
graph = model.graph
node_name = updated_node_name if updated_node_name else node.name + "_updated_constant"
args = list(node.args)
# A bias node suppose to have constant on the second input port.
if args[input_port_id].op != "get_attr":
old_const = _get_node_by_input_port_id(node, input_port_id)

if old_const.op != "get_attr":
raise nncf.InternalError(
f"Constant on input port {input_port_id} for {node} is expected,"
f" but node {args[input_port_id]} is present."
f"Constant on input port {input_port_id} for {node} is expected," f" but node {old_const} is present."
)

node_name = updated_node_name if updated_node_name else old_const.name + "_updated_constant"
# Update metadata of the new constant node.
previous_const = args[input_port_id]
consumer_nodes = list(previous_const.users)
consumer_nodes = list(old_const.users)
# This list of consumer nodes is topologically sorted
# To ensure the updated node has the right order,
# we insert constant node before the node placed at the highest order in topological order.
sorted_consumer_nodes = [node for node in graph.nodes if node in consumer_nodes]

with graph.inserting_before(sorted_consumer_nodes[0]):
new_constant = create_getattr_from_value(model, graph, node_name, value)
new_const = create_getattr_from_value(model, graph, node_name, value)

previous_const.replace_all_uses_with(new_constant, propagate_meta=True)
old_const.replace_all_uses_with(new_const, propagate_meta=True)
graph.eliminate_dead_code()


Expand Down Expand Up @@ -428,9 +426,7 @@ def insert_one_qdq(model: torch.fx.GraphModule, target_point: PTTargetPoint, qua
dq_node = graph.call_function(dequantize_op, tuple(dq_inputs), {})
dq_node.meta["val"] = copy(meta_val)

args = list(target_node.args)
args[target_point.input_port_id] = dq_node
target_node.args = tuple(args)
target_node.replace_input_with(input_node, dq_node)
else:
raise nncf.InternalError(f"Unexpected target type: {target_point.target_type}")

Expand Down Expand Up @@ -471,7 +467,21 @@ def get_input_node(target_point: PTTargetPoint, target_node: torch.fx.Node) -> t
raise nncf.InternalError(f"Unexpected target type: {target_type}")
if target_type == TargetType.OPERATOR_POST_HOOK:
return target_node
return target_node.args[target_point.input_port_id]

return _get_node_by_input_port_id(target_node, target_point.input_port_id)


def _get_node_by_input_port_id(node: torch.fx.Node, input_port_id: int) -> torch.fx.Node:
"""
Retrieves an input node from the given node and the input port id.
:param node: Given input node.
:param input_port_id: Given input port id.
:return: An input node from the given node and the input port id.
"""
if node.target == torch.ops.aten.cat.default:
return node.args[0][input_port_id]
return node.args[input_port_id]


def get_ctx_manager(graph: torch.fx.Graph, target_point: PTTargetPoint) -> Callable:
Expand Down
7 changes: 5 additions & 2 deletions nncf/torch/quantization/ignored_patterns.py
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Original file line number Diff line number Diff line change
Expand Up @@ -55,7 +55,7 @@ def _add_softmax_reshape_matmul(
# \
# \
# \
# RESHAPE RESHAPE||TRANSPOSE||GATHER||SQUEEZE||CONCAT
# RESHAPE || TRANSPOSE RESHAPE||TRANSPOSE||GATHER||SQUEEZE||CONCAT
# \ /
# \ /
# \ /
Expand All @@ -66,7 +66,10 @@ def _add_softmax_reshape_matmul(
branch_matmul_nodes = reshape_squeeze_metatypes + gather_metatypes + transpose_metatypes + concat_metatypes
softmax = pattern.add_node(**{GraphPattern.LABEL_ATTR: "SOFTMAX", GraphPattern.METATYPE_ATTR: om.PTSoftmaxMetatype})
reshape = pattern.add_node(
**{GraphPattern.LABEL_ATTR: "RESHAPE", GraphPattern.METATYPE_ATTR: reshape_squeeze_metatypes}
**{
GraphPattern.LABEL_ATTR: "RESHAPE",
GraphPattern.METATYPE_ATTR: reshape_squeeze_metatypes + transpose_metatypes,
}
)
matmul = pattern.add_node(**{GraphPattern.LABEL_ATTR: "MATMUL", GraphPattern.METATYPE_ATTR: matmul_metatypes})
matmul_branch_nodes = pattern.add_node(
Expand Down
63 changes: 63 additions & 0 deletions tests/torch/data/reference_graphs/fx/post_quantization_compressed/yolo11n_sdpa_block.dot
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@@ -0,0 +1,63 @@
strict digraph {
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"28 matmul_1" -> "29 output" [label="(1, 2, 4)", style=solid];
}
65 changes: 65 additions & 0 deletions tests/torch/data/reference_graphs/fx/quantized/yolo11n_sdpa_block.dot
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@@ -0,0 +1,65 @@
strict digraph {
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"27 transpose_1" -> "28 matmul_1" [label="(1, 2, 2)", style=solid];
"28 matmul_1" -> "29 output" [label="(1, 2, 4)", style=solid];
}
21 changes: 21 additions & 0 deletions tests/torch/data/reference_graphs/fx/reference_metatypes/yolo11n_sdpa_block.json
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@@ -0,0 +1,21 @@
{
"kqv_weight": "PTConstNoopMetatype",
"x": "PTInputNoopMetatype",
"linear": "PTLinearMetatype",
"slice_1": "PTGatherMetatype",
"slice_2": "PTGatherMetatype",
"slice_3": "PTGatherMetatype",
"slice_4": "PTGatherMetatype",
"slice_5": "PTGatherMetatype",
"slice_6": "PTGatherMetatype",
"slice_7": "PTGatherMetatype",
"slice_8": "PTGatherMetatype",
"slice_9": "PTGatherMetatype",
"transpose": "PTTransposeMetatype",
"matmul": "PTMatMulMetatype",
"div_": "PTDivMetatype",
"softmax": "PTSoftmaxMetatype",
"transpose_1": "PTTransposeMetatype",
"matmul_1": "PTMatMulMetatype",
"output_1": "PTOutputNoopMetatype"
}
38 changes: 38 additions & 0 deletions tests/torch/data/reference_graphs/fx/transformed/cat_constant_update.dot
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@@ -0,0 +1,38 @@
strict digraph {
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70 changes: 36 additions & 34 deletions tests/torch/data/reference_graphs/fx/transformed/constant_update.dot
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@@ -1,36 +1,38 @@
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"3 conv_b_weight" -> "9 conv2d_1" [label="(3, 3, 1, 1)", style=solid];
"4 conv_b_bias" -> "9 conv2d_1" [label="(3,)", style=solid];
"5 conv_c_weight" -> "14 conv2d_2" [label="(3, 9, 1, 1)", style=solid];
"6 conv_c_bias" -> "14 conv2d_2" [label="(3,)", style=solid];
"7 x" -> "8 conv2d" [label="(1, 3, 3, 3)", style=solid];
"8 conv2d" -> "9 conv2d_1" [label="(1, 3, 3, 3)", style=solid];
"8 conv2d" -> "11 add_" [label="(1, 3, 3, 3)", style=solid];
"9 conv2d_1" -> "12 add__1" [label="(1, 3, 3, 3)", style=solid];
"10 bias_updated_constant0" -> "11 add_" [label="(1,)", style=solid];
"10 bias_updated_constant0" -> "12 add__1" [label="(1,)", style=solid];
"10 bias_updated_constant0" -> "15 add" [label="(1,)", style=solid];
"11 add_" -> "13 cat" [label="(1, 3, 3, 3)", style=solid];
"12 add__1" -> "13 cat" [label="(1, 3, 3, 3)", style=solid];
"13 cat" -> "14 conv2d_2" [label="(1, 9, 3, 3)", style=solid];
"14 conv2d_2" -> "15 add" [label="(1, 3, 3, 3)", style=solid];
"15 add" -> "16 output_1" [label="(1, 3, 3, 3)", style=solid];
}
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