add Glm (#33823)

* Create modular_glm.py

* Update modular_glm.py

* Finalize architecture without all attentions

* Add all attentions modules

* Finalize modular

* Update given last version

* Last update

* Finalize model

* Finalize converter

* Update convert_glm_weights_to_hf.py

* style

* style

* Create __init__.py

* Aff all inits

* Update convert_glm_weights_to_hf.py

* Update convert_glm_weights_to_hf.py

* Update convert_glm_weights_to_hf.py

* Update convert_glm_weights_to_hf.py

* Update convert_glm_weights_to_hf.py

* Update convert_glm_weights_to_hf.py

* Update convert_glm_weights_to_hf.py

* Update convert_glm_weights_to_hf.py

* Update convert_glm_weights_to_hf.py

* Correct the rotary embeddings

* Remove apply_residual_connection_post_layernorm (always false)

* remove use_rms_norm (always true)

* remove past_layer_norm (always true)

* Update __init__.py

* Update config and license

* start adding tests and doc

* Add doc + style

* Update test_modeling_glm.py

* Add dummies

* Apply correct modeling

* Refactor attention to follow llama

* Update __init__.py

* Update convert_glm_weights_to_hf.py

* Correct bias

* remove linear_bias and pdrop (never used)

* apply modular

* Simplify converter

* remove dummies + style

* add model_input_names

* Add pretraining_tp to config for when eager attention is used

* Update modular to remove all pretraining_tp

* Update test_modeling_glm.py

* Update the __all__

* Update __all__

* Update __init__.py

* Update test_modeling_glm.py

* add revisions

* Add the correct repos and revisions

* style

* Update __init__.py

* update exports

* remove import of modular files

* style

* Apply Llama changes + refine converter

* Update convert_glm_weights_to_hf.py

* Update convert_glm_weights_to_hf.py

* Update convert_glm_weights_to_hf.py

* Update convert_glm_weights_to_hf.py

* Update convert_glm_weights_to_hf.py

* Update convert_glm_weights_to_hf.py

* Update convert_glm_weights_to_hf.py

* Update convert_glm_weights_to_hf.py

* style

* Use new modular converter

* add pretrainedmodel to init

* style

* Update test_modeling_glm.py

* Move config outside modular to please CI about docstrings

* Add dummies to please CI

* Update glm.md

* Update glm.md

tests/test_modeling_common.py

@@ -4938,14 +4938,17 @@ class ModelTesterMixin:
         if not hasattr(self, "_torch_compile_test_ckpt"):
             self.skipTest(f"{self.__class__.__name__} doesn't have the attribute `_torch_compile_test_ckpt`.")
         ckpt = self._torch_compile_test_ckpt
+        revision = "main" if not hasattr(self, "_torch_compile_test_revision") else self._torch_compile_test_revision
 
         os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
         batch_size = 1
         n_iter = 3
 
-        tokenizer = AutoTokenizer.from_pretrained(ckpt)
-        model = AutoModelForCausalLM.from_pretrained(ckpt, torch_dtype=torch.float16).to(torch_device)
+        tokenizer = AutoTokenizer.from_pretrained(ckpt, revision=revision)
+        model = AutoModelForCausalLM.from_pretrained(ckpt, torch_dtype=torch.float16, revision=revision).to(
+            torch_device
+        )
 
         model.generation_config.max_new_tokens = 4
 
@@ -5013,11 +5016,14 @@ class ModelTesterMixin:
         if not hasattr(self, "_torch_compile_test_ckpt"):
             self.skipTest(f"{self.__class__.__name__} doesn't have the attribute `_torch_compile_test_ckpt`.")
         ckpt = self._torch_compile_test_ckpt
+        revision = "main" if not hasattr(self, "_torch_compile_test_revision") else self._torch_compile_test_revision
 
         os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
-        tokenizer = AutoTokenizer.from_pretrained(ckpt)
-        model = AutoModelForCausalLM.from_pretrained(ckpt, torch_dtype=torch.float16).to(torch_device)
+        tokenizer = AutoTokenizer.from_pretrained(ckpt, revision=revision)
+        model = AutoModelForCausalLM.from_pretrained(ckpt, torch_dtype=torch.float16, revision=revision).to(
+            torch_device
+        )
 
         cache_implementation = "static"
         if model.config.model_type == "gemma2":