[PT][WC] Add WC tests for functional models (#2446)

### Changes

Fixed bug with `channel_idx` for Torch Conv metatypes
Fixed bug in `get_module_by_name()` if base model has weights as a
`torch.nn.Parameter`

### Reason for changes

<!--- Why should the change be applied -->

### Related tickets

124822

### Tests

Added `test_compress_weights_functional_model`

Author: l-bat

nncf/quantization/algorithms/weight_compression/torch_backend.py

@@ -40,12 +40,16 @@
 
 def split_weight_name(weight_name: str) -> Tuple[str, str]:
     index = weight_name.rfind(".")
+    if index == -1:
+        return str(), weight_name
     module_name = weight_name[:index]
     weight_attr_name = weight_name[index + 1 :]
     return module_name, weight_attr_name
 
 
 def get_module_by_name(module_name: str, model: torch.nn.Module) -> torch.nn.Module:
+    if not module_name:
+        return model
     curr_module = model
     for name in module_name.split("."):
         for child_name, child_module in curr_module.named_children():
@@ -161,8 +165,12 @@ def get_channel_agnostic_reduction_axes(
             elif weight_port_id == 2:
                 reduction_axes = [max(0, ndims - 2)]
         elif node_with_weight.metatype in PTWeightCompressionAlgoBackend.CONVOLUTION_METATYPES:
-            layer_attributes = node_with_weight.layer_attributes
-            channel_idx = layer_attributes.get_target_dim_for_compression()
+            channel_idx = (
+                1
+                if node_with_weight.metatype
+                in [om.PTConvTranspose1dMetatype, om.PTConvTranspose2dMetatype, om.PTConvTranspose3dMetatype]
+                else 0
+            )
             reduction_axes = [i for i in range(ndims) if i != channel_idx]
         return tuple(reduction_axes)
 

tests/torch/ptq/test_weights_compression.py

@@ -17,6 +17,7 @@
 from nncf import SensitivityMetric
 from nncf.quantization import compress_weights
 from nncf.torch import wrap_model
+from nncf.torch.quantization.layers import WeightsDecompressor
 
 DATA_BASED_SENSITIVITY_METRICS = (
     SensitivityMetric.HESSIAN_INPUT_ACTIVATION,
@@ -52,6 +53,32 @@ def forward(self, input_ids):
         return res
 
 
+class NestedMatMul(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.w = torch.nn.Parameter(torch.ones(size=(300, 300), dtype=torch.float32))
+
+    def forward(self, input):
+        return input @ self.w
+
+
+class FunctionalModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv_w = torch.nn.Parameter(torch.ones(size=(5, 3, 3, 3), dtype=torch.float32))
+        self.matmul_w = torch.nn.Parameter(torch.ones(size=(1, 3, 300, 300), dtype=torch.float32))
+        self.conv_tr_w = torch.nn.Parameter(torch.rand(size=(5, 4, 3, 3)))
+        self.nested_matmul = NestedMatMul()
+
+    def forward(self, input_):
+        x = input_.to(torch.float32)
+        x = x @ self.matmul_w
+        x = self.nested_matmul(x)
+        x = F.conv2d(x, self.conv_w)
+        x = F.conv_transpose2d(x, self.conv_tr_w)
+        return x
+
+
 class ConvolutionModel(torch.nn.Module):
     def __init__(self):
         super().__init__()
@@ -98,6 +125,20 @@ def test_compress_weights():
     assert n_compressed_weights == n_target_modules
 
 
+def test_compress_weights_functional_model():
+    model = FunctionalModel()
+
+    input_ids = torch.randint(0, 10, [1, 3, 300, 300])
+    wrapped_model = wrap_model(model, example_input=input_ids, trace_parameters=True)
+    compressed_model = compress_weights(wrapped_model)
+
+    n_compressed_weights = 0
+    for layer in compressed_model.nncf.external_op.values():
+        if isinstance(layer, WeightsDecompressor):
+            n_compressed_weights += 1
+    assert n_compressed_weights == 4
+
+
 def test_compress_weights_conv():
     model = ConvolutionModel()