refactor: Update handling of assigned attributes.

examples/flux/t5.py

@@ -180,9 +180,9 @@ def load_t5_encoder(
     repo_id: str = "black-forest-labs/FLUX.1-schnell",
     max_len: int = 256,
 ) -> ml.models.PhysicalModel:
-    config = hf_hub_download(repo_id, "text_encoder_2/config.json")
+    config_path = hf_hub_download(repo_id, "text_encoder_2/config.json")
 
-    with open(config) as f:
+    with open(config_path) as f:
         config = json.load(f)
 
     t5 = t5_encode(config, name="encoder")

examples/flux/util.py

@@ -159,11 +159,11 @@ def load_flow_model(name: str, backend: ml.Backend, hf_download: bool = True):
     ckpt_path = configs[name].ckpt_path
     if (
         ckpt_path is None
-        and configs[name].repo_id is not None
-        and configs[name].repo_flow is not None
+        and (r_id := configs[name].repo_id) is not None
+        and (r_flow := configs[name].repo_flow) is not None
         and hf_download
     ):
-        ckpt_path = hf_hub_download(configs[name].repo_id, configs[name].repo_flow)
+        ckpt_path = hf_hub_download(r_id, r_flow)
 
     flux_lm = flux(configs[name].params)
     flux_pm = ml.compile(
@@ -187,11 +187,11 @@ def load_decoder(
     ckpt_path = configs[name].ae_path
     if (
         ckpt_path is None
-        and configs[name].repo_id is not None
-        and configs[name].repo_ae is not None
+        and (r_id := configs[name].repo_id) is not None
+        and (r_ae := configs[name].repo_ae) is not None
         and hf_download
     ):
-        ckpt_path = hf_hub_download(configs[name].repo_id, configs[name].repo_ae)
+        ckpt_path = hf_hub_download(r_id, r_ae)
 
     # Loading the autoencoder
     print("Init AE")

mithril/cores/python/numpy/ops.py

@@ -667,7 +667,7 @@ def scaled_dot_product_attention(
         )
 
     L, S = query.shape[-2], key.shape[-2]
-    scale_factor = 1 / np.sqrt(query.shape[-1]) if scale is None else scale
+    scale_factor = 1 / math.sqrt(query.shape[-1]) if scale is None else scale
     write_into_cache(cache, "scale_factor", scale_factor)
     attn_bias = np.zeros((L, S), dtype=query.dtype)
     if is_causal:

mithril/framework/common.py

@@ -1043,7 +1043,7 @@ def __init__(
         value: TensorValueType | ToBeDetermined = TBD,
         type: _TensorTypes = int | float | bool,
         shape: ShapeNode | None = None,
-        differentiable: bool = False,
+        differentiable: bool | None = None,
     ):
         if shape is None:
             # If shape is not provided, create a new shape with a Variadic root.
@@ -1119,8 +1119,16 @@ def match(self, other: Tensor[int | float | bool]) -> Updates:
                 updates |= non_valued.set_value(valued.value)
                 self.differentiable = False
                 other.differentiable = False
-            else:
-                self.differentiable |= other.differentiable
+            elif self.differentiable is None:
+                self.differentiable = other.differentiable
+                # Differentiable tensors can only be float type.
+                if self.differentiable:
+                    updates |= self.set_type(float)
+            elif (
+                other.differentiable is not None
+                and self.differentiable != other.differentiable
+            ):
+                raise ValueError("Differentiability mismatch!")
             # Match shapes.
             updates |= self.match_shapes(other.shape)
             updates.shape_updates.discard(other)
@@ -1180,8 +1188,16 @@ def _temp_shape(self) -> ShapeRepr | None:
         return None
 
     @property
-    def differentiable(self) -> bool:
-        return isinstance(self._value, Tensor) and self._value.differentiable
+    def differentiable(self) -> bool | None:
+        if isinstance(self._value, Tensor):
+            return self._value.differentiable
+        elif self.is_scalar:
+            # Scalars are always non-differentiable.
+            return False
+        # Differentiability of polymorphic edges are defined
+        # as None. Depending on its instant type updates, it can
+        # become True or False (e.g Tensor or int type).
+        return None
 
     @property
     def tensors(self) -> set[Tensor[int | float | bool]]:
@@ -1405,7 +1421,7 @@ def set_value(
                 updates.value_updates.add(self)
             # Update new type without automatic tensor value creation.
             updates |= self.set_type(find_type(self._value), create_tensor=False)
-            if self.is_valued:
+            if self.is_tensor and self.is_valued:
                 self.set_differentiability(False)
         return updates
 
@@ -1431,13 +1447,19 @@ def match(self, other: IOHyperEdge) -> Updates:
 
         return updates
 
-    def set_differentiability(self, differentiable: bool) -> None:
-        if self.is_tensor:
-            assert isinstance(self._value, Tensor)
-            self._value.differentiable = differentiable
-        elif differentiable:
+    def set_differentiability(self, differentiable: bool) -> Updates:
+        if self.is_scalar and differentiable:
             raise ValueError("Non-tensor edges cannot be differentiable.")
 
+        updates = Updates()
+        if differentiable:
+            # Differentiable edges can only be Tensor[float] type.
+            updates |= self.set_type(Tensor[float])
+        # Set differentiability of the _value if it is a Tensor.
+        if isinstance(self._value, Tensor):
+            self._value.differentiable = differentiable
+        return updates
+
     def add_constraint(self, constraint: Constraint) -> None:
         for type in constraint.types:
             self.constraints[type].add(constraint)