[Common]Fix Test for SDPA and Concat Unified Scales Test (#3207)

### Changes

Changes the model from having the same input tensor to the QKV to having
different input for each

### Reason for changes

The former model was causing an error with openvino

Author: anzr299

tests/openvino/native/test_unified_scales.py

@@ -17,12 +17,16 @@
 
 class TestUnifiedScales(TemplateTestUnifiedScales):
     def get_backend_specific_model(self, model: torch.nn.Module) -> ov.Model:
-        input_shape = model.INPUT_SHAPE
+        q_input_shape = model.Q_INPUT_SHAPE
+        kv_input_shape = model.KV_INPUT_SHAPE
+
         backend_model = ov.convert_model(
             model,
             example_input=(
-                torch.randn(input_shape),
-                torch.randn(input_shape),
+                torch.ones(q_input_shape),
+                torch.ones(q_input_shape),
+                torch.ones(kv_input_shape),
+                torch.ones(kv_input_shape),
             ),
         )
 

tests/torch/fx/test_unified_scales.py

@@ -11,19 +11,21 @@
 
 import torch
 
-from nncf.torch.nncf_network import NNCFNetwork
 from tests.cross_fw.test_templates.test_unified_scales import TemplateTestUnifiedScales
 from tests.torch.fx.helpers import get_torch_fx_model_q_transformed
 
 
 class TestUnifiedScales(TemplateTestUnifiedScales):
-    def get_backend_specific_model(self, model: torch.nn.Module) -> NNCFNetwork:
-        input_shape = model.INPUT_SHAPE
+    def get_backend_specific_model(self, model: torch.nn.Module) -> torch.fx.GraphModule:
+        q_input_shape = model.Q_INPUT_SHAPE
+        kv_input_shape = model.KV_INPUT_SHAPE
         backend_model = get_torch_fx_model_q_transformed(
             model,
             (
-                torch.randn(input_shape),
-                torch.randn(input_shape),
+                torch.ones(q_input_shape),
+                torch.ones(q_input_shape),
+                torch.ones(kv_input_shape),
+                torch.ones(kv_input_shape),
             ),
         )
 

tests/torch/quantization/test_unified_scales.py

@@ -718,12 +718,15 @@ def test_unified_scales_with_shared_nodes():
 
 class TestUnifiedScales(TemplateTestUnifiedScales):
     def get_backend_specific_model(self, model: torch.nn.Module) -> NNCFNetwork:
-        input_shape = model.INPUT_SHAPE
+        q_input_shape = model.Q_INPUT_SHAPE
+        kv_input_shape = model.KV_INPUT_SHAPE
         backend_model = wrap_model(
             model,
             (
-                torch.randn(input_shape),
-                torch.randn(input_shape),
+                torch.ones(q_input_shape),
+                torch.ones(q_input_shape),
+                torch.ones(kv_input_shape),
+                torch.ones(kv_input_shape),
             ),
             trace_parameters=True,
         )

tests/torch/test_models/synthetic.py

@@ -665,16 +665,17 @@ def forward(self, x):
 
 
 class ConcatSDPABlock(torch.nn.Module):
-    INPUT_SHAPE = (2, 10, 6)
+    Q_INPUT_SHAPE = [2, 10, 6]
+    KV_INPUT_SHAPE = [2, 10, 12]
 
     def __init__(self):
         super().__init__()
 
-    def forward(self, x, y):
+    def forward(self, x, y, z, w):
         concatenated_input = torch.cat((x, y), dim=-1)
         query = concatenated_input
-        key = concatenated_input
-        value = concatenated_input
-        attn_output = torch.nn.functional.scaled_dot_product_attention(query, key, value, dropout_p=0.2)
+        key = z
+        value = w
+        attn_output = torch.nn.functional.scaled_dot_product_attention(query, key, value)
 
         return attn_output