Add print statements

modeling_cogvlm.py

@@ -243,33 +243,33 @@ class VisionExpertAttention(nn.Module):
 
         if print_values:
 
-            torch.save(query_states, "query_states.pt")
-            torch.save(key_states, "key_states.pt")
-            torch.save(value_states, "value_states.pt")
-
-            from huggingface_hub import HfApi
-
-            api = HfApi()
-            api.upload_file(
-                path_or_fileobj="query_states.pt",
-                path_in_repo="query_states.pt",
-                repo_id="nielsr/test-cogvlm",
-                repo_type="dataset",
-            )
-            api = HfApi()
-            api.upload_file(
-                path_or_fileobj="key_states.pt",
-                path_in_repo="key_states.pt",
-                repo_id="nielsr/test-cogvlm",
-                repo_type="dataset",
-            )
-            api = HfApi()
-            api.upload_file(
-                path_or_fileobj="value_states.pt",
-                path_in_repo="value_states.pt",
-                repo_id="nielsr/test-cogvlm",
-                repo_type="dataset",
-            )
+            # torch.save(query_states, "query_states.pt")
+            # torch.save(key_states, "key_states.pt")
+            # torch.save(value_states, "value_states.pt")
+
+            # from huggingface_hub import HfApi
+
+            # api = HfApi()
+            # api.upload_file(
+            #     path_or_fileobj="query_states.pt",
+            #     path_in_repo="query_states.pt",
+            #     repo_id="nielsr/test-cogvlm",
+            #     repo_type="dataset",
+            # )
+            # api = HfApi()
+            # api.upload_file(
+            #     path_or_fileobj="key_states.pt",
+            #     path_in_repo="key_states.pt",
+            #     repo_id="nielsr/test-cogvlm",
+            #     repo_type="dataset",
+            # )
+            # api = HfApi()
+            # api.upload_file(
+            #     path_or_fileobj="value_states.pt",
+            #     path_in_repo="value_states.pt",
+            #     repo_id="nielsr/test-cogvlm",
+            #     repo_type="dataset",
+            # )
 
         kv_seq_len = key_states.shape[-2]
         if past_key_value is not None:
@@ -473,31 +473,31 @@ class CogVLMModel(CogVLMPreTrainedModel):
                 images_features = rearrange(images_features, 'b n d -> (b n) d')
                 images_features = images_features.to(dtype=inputs_embeds.dtype, device=inputs_embeds.device)
 
-                from huggingface_hub import HfApi
+                # from huggingface_hub import HfApi
 
-                torch.save(images_features, "images_features.pt")
-                torch.save(inputs_embeds, "inputs_embeds.pt")
-                torch.save(token_type_ids, "token_type_ids.pt")
+                # torch.save(images_features, "images_features.pt")
+                # torch.save(inputs_embeds, "inputs_embeds.pt")
+                # torch.save(token_type_ids, "token_type_ids.pt")
                 
-                api = HfApi()
-                api.upload_file(
-                    path_or_fileobj="images_features.pt",
-                    path_in_repo="images_features.pt",
-                    repo_id="nielsr/test-cogvlm",
-                    repo_type="dataset",
-                )
-                api.upload_file(
-                    path_or_fileobj="inputs_embeds.pt",
-                    path_in_repo="inputs_embeds.pt",
-                    repo_id="nielsr/test-cogvlm",
-                    repo_type="dataset",
-                )
-                api.upload_file(
-                    path_or_fileobj="token_type_ids.pt",
-                    path_in_repo="token_type_ids.pt",
-                    repo_id="nielsr/test-cogvlm",
-                    repo_type="dataset",
-                )
+                # api = HfApi()
+                # api.upload_file(
+                #     path_or_fileobj="images_features.pt",
+                #     path_in_repo="images_features.pt",
+                #     repo_id="nielsr/test-cogvlm",
+                #     repo_type="dataset",
+                # )
+                # api.upload_file(
+                #     path_or_fileobj="inputs_embeds.pt",
+                #     path_in_repo="inputs_embeds.pt",
+                #     repo_id="nielsr/test-cogvlm",
+                #     repo_type="dataset",
+                # )
+                # api.upload_file(
+                #     path_or_fileobj="token_type_ids.pt",
+                #     path_in_repo="token_type_ids.pt",
+                #     repo_id="nielsr/test-cogvlm",
+                #     repo_type="dataset",
+                # )
 
                 # print("First values of text embeddings:", inputs_embeds[0, :3, :3])
                 # print("First values of images_features:", images_features[0, :3])
@@ -590,41 +590,41 @@ class CogVLMModel(CogVLMPreTrainedModel):
 
         hidden_states = inputs_embeds
 
-        torch.save(hidden_states, "initial_hidden_states.pt")
-        torch.save(attention_mask, "initial_attention_mask.pt")
-        torch.save(token_type_ids, "initial_token_type_ids.pt")
-        torch.save(position_ids, "initial_position_ids.pt")
+        # torch.save(hidden_states, "initial_hidden_states.pt")
+        # torch.save(attention_mask, "initial_attention_mask.pt")
+        # torch.save(token_type_ids, "initial_token_type_ids.pt")
+        # torch.save(position_ids, "initial_position_ids.pt")
         
-        from huggingface_hub import HfApi
-
-        api = HfApi()
-        api.upload_file(
-            path_or_fileobj="initial_hidden_states.pt",
-            path_in_repo="initial_hidden_states.pt",
-            repo_id="nielsr/test-cogvlm",
-            repo_type="dataset",
-        )
-        api = HfApi()
-        api.upload_file(
-            path_or_fileobj="initial_attention_mask.pt",
-            path_in_repo="initial_attention_mask.pt",
-            repo_id="nielsr/test-cogvlm",
-            repo_type="dataset",
-        )
-        api = HfApi()
-        api.upload_file(
-            path_or_fileobj="initial_token_type_ids.pt",
-            path_in_repo="initial_token_type_ids.pt",
-            repo_id="nielsr/test-cogvlm",
-            repo_type="dataset",
-        )
-        api = HfApi()
-        api.upload_file(
-            path_or_fileobj="initial_position_ids.pt",
-            path_in_repo="initial_position_ids.pt",
-            repo_id="nielsr/test-cogvlm",
-            repo_type="dataset",
-        )
+        # from huggingface_hub import HfApi
+
+        # api = HfApi()
+        # api.upload_file(
+        #     path_or_fileobj="initial_hidden_states.pt",
+        #     path_in_repo="initial_hidden_states.pt",
+        #     repo_id="nielsr/test-cogvlm",
+        #     repo_type="dataset",
+        # )
+        # api = HfApi()
+        # api.upload_file(
+        #     path_or_fileobj="initial_attention_mask.pt",
+        #     path_in_repo="initial_attention_mask.pt",
+        #     repo_id="nielsr/test-cogvlm",
+        #     repo_type="dataset",
+        # )
+        # api = HfApi()
+        # api.upload_file(
+        #     path_or_fileobj="initial_token_type_ids.pt",
+        #     path_in_repo="initial_token_type_ids.pt",
+        #     repo_id="nielsr/test-cogvlm",
+        #     repo_type="dataset",
+        # )
+        # api = HfApi()
+        # api.upload_file(
+        #     path_or_fileobj="initial_position_ids.pt",
+        #     path_in_repo="initial_position_ids.pt",
+        #     repo_id="nielsr/test-cogvlm",
+        #     repo_type="dataset",
+        # )
 
         # decoder layers
         all_hidden_states = () if output_hidden_states else None
@@ -648,16 +648,16 @@ class CogVLMModel(CogVLMPreTrainedModel):
             )
             hidden_states = layer_outputs[0]
 
-            if idx == 0:
-                torch.save(hidden_states, "hidden_states_after_layer_0.pt")
+            # if idx == 0:
+            #     torch.save(hidden_states, "hidden_states_after_layer_0.pt")
                 
-                api = HfApi()
-                api.upload_file(
-                    path_or_fileobj="hidden_states_after_layer_0.pt",
-                    path_in_repo="hidden_states_after_layer_0.pt",
-                    repo_id="nielsr/test-cogvlm",
-                    repo_type="dataset",
-                )
+            #     api = HfApi()
+            #     api.upload_file(
+            #         path_or_fileobj="hidden_states_after_layer_0.pt",
+            #         path_in_repo="hidden_states_after_layer_0.pt",
+            #         repo_id="nielsr/test-cogvlm",
+            #         repo_type="dataset",
+            #     )
 
             if use_cache:
                 next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)