Fix Clip initializing issue in r2.0.0 (#10585)

* update post process

Signed-off-by: yaoyu-33 <yaoyu.094@gmail.com>

* Fix wild card matching in nemo2

Signed-off-by: yaoyu-33 <yaoyu.094@gmail.com>

* Apply isort and black reformatting

Signed-off-by: yaoyu-33 <yaoyu-33@users.noreply.github.com>

---------

Signed-off-by: yaoyu-33 <yaoyu.094@gmail.com>
Signed-off-by: yaoyu-33 <yaoyu-33@users.noreply.github.com>
Co-authored-by: yaoyu-33 <yaoyu-33@users.noreply.github.com>

nemo/collections/multimodal/models/vision_language_foundation/clip/megatron_clip_models.py

@@ -424,26 +424,24 @@ def __init__(self, *args, **kwargs):
         # TODO (yuya): need to handle post_process correctly in order to enable PP
         self.output_dim = kwargs.pop('output_dim')
         super().__init__(*args, **kwargs)
-        if self.post_process:
-            self.final_layernorm = TENorm(
-                config=self.config,
-                hidden_size=self.config.hidden_size,
-                eps=self.config.layernorm_epsilon,
-            )
-            self.head = torch.nn.Linear(
-                self.config.hidden_size,
-                self.output_dim,
-                bias=False,
-            )
+        self.final_layernorm = TENorm(
+            config=self.config,
+            hidden_size=self.config.hidden_size,
+            eps=self.config.layernorm_epsilon,
+        )
+        self.head = torch.nn.Linear(
+            self.config.hidden_size,
+            self.output_dim,
+            bias=False,
+        )
 
     def forward(self, x):
         x = super().forward(
             x,
         )
-        if self.post_process:
-            x = self.final_layernorm(x)
-            x = x[:, 0]
-            x = self.head(x)
+        x = self.final_layernorm(x)
+        x = x[:, 0]
+        x = self.head(x)
         return x
 
 

nemo/collections/vlm/neva/model/llava.py

@@ -111,7 +111,7 @@ def convert_state(self, source, target):
             "language_model.model.layers.*.post_attention_layernorm.weight": "language_model.decoder.layers.*.mlp.linear_fc1.layer_norm_weight",
             "language_model.model.norm.weight": "language_model.decoder.final_layernorm.weight",
             "language_model.lm_head.weight": "language_model.output_layer.weight",
-            "vision_tower.vision_model.*": "vision_model.vision_model.*",
+            "vision_tower.vision_model.**": "vision_model.vision_model.**",
         }
         if "vision_projection.encoder.linear_fc1.weight" in target.module.state_dict().keys():
             mapping.update(

nemo/lightning/io/state.py

@@ -326,8 +326,28 @@ def call_transform(self, ctx: TransformCTX, *args, **kwargs):
 
 
 def _match_keys(keys: List[str], pattern: str) -> np.ndarray:
-    regex_pattern = re.compile("^" + pattern.replace("*", r"([^.]+)") + "$")
-    wildcard_matches = [[] for _ in range(pattern.count("*"))]
+    escaped_pattern = ''
+    i = 0
+    wildcard_positions = []
+    while i < len(pattern):
+        if pattern[i : i + 2] == '**':
+            escaped_pattern += r'(.+)'  # Match any characters including dots
+            wildcard_positions.append('**')
+            i += 2
+        elif pattern[i] == '*':
+            escaped_pattern += r'([^.]+)'  # Match any characters except dots
+            wildcard_positions.append('*')
+            i += 1
+        else:
+            if pattern[i] == '.':
+                escaped_pattern += r'\.'  # Escape the dot
+            else:
+                escaped_pattern += pattern[i]
+            i += 1
+
+    regex_pattern = re.compile("^" + escaped_pattern + "$")
+    num_wildcards = len(wildcard_positions)
+    wildcard_matches = [[] for _ in range(num_wildcards)]
 
     for key in filter(lambda x: x is not None, keys):
         match = regex_pattern.match(key)