[modular] fixes! (#33820)

* fix converter for function definitions

* small changes

* no prints

* style

examples/modular-transformers/configuration_my_new_model.py

@@ -1,8 +1,8 @@
 #           🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-#               This file was automatically generated from <path_to_diff_file.py>.
+#               This file was automatically generated from <path_to_modular_file.py>.
 #         Do NOT edit this file manually as any edits will be overwritten by the generation of
-#         the file from the diff. If any change should be done, please apply the change to the
-#                           diff.py file directly. One of our CI enforces this
+#         the file from the modular. If any change should be done, please apply the change to the
+#                           modular_xxx.py file directly. One of our CI enforces this
 #           🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 
 from ...configuration_utils import PretrainedConfig
@@ -111,8 +111,6 @@ class MyNewModelConfig(PretrainedConfig):
             Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
         head_dim (`int`, *optional*):
             The attention head dimension. If None, it will default to hidden_size // num_heads
-        new_param (`int`, *optional*, defaults to `False`):
-            A fun new parameter
 
     ```python
     >>> from transformers import MyNewModelModel, MyNewModelConfig
@@ -125,7 +123,10 @@ class MyNewModelConfig(PretrainedConfig):
 
     >>> # Accessing the model configuration
     >>> configuration = model.config
-    ```"""
+    ```
+        new_param (`int`, *optional*, defaults to `False`):
+            A fun new parameter
+    """
 
     model_type = "my_new_model"
     keys_to_ignore_at_inference = ["past_key_values"]
@@ -178,12 +179,14 @@ def __init__(
         self.rope_scaling = rope_scaling
         self.attention_bias = attention_bias
         self.attention_dropout = attention_dropout
+        self.mlp_bias = mlp_bias
         self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
         # Validate the correctness of rotary position embeddings parameters
         # BC: if there is a 'type' field, copy it it to 'rope_type'.
         if self.rope_scaling is not None and "type" in self.rope_scaling:
             self.rope_scaling["rope_type"] = self.rope_scaling["type"]
         rope_config_validation(self)
+        self.new_param = new_param
 
         super().__init__(
             pad_token_id=pad_token_id,
@@ -192,5 +195,3 @@ def __init__(
             tie_word_embeddings=tie_word_embeddings,
             **kwargs,
         )
-        self.mlp_bias = mlp_bias
-        self.new_param = new_param

examples/modular-transformers/configuration_my_new_model2.py

@@ -1,15 +1,116 @@
 #           🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-#               This file was automatically generated from <path_to_diff_file.py>.
+#               This file was automatically generated from <path_to_modular_file.py>.
 #         Do NOT edit this file manually as any edits will be overwritten by the generation of
-#         the file from the diff. If any change should be done, please apply the change to the
-#                           diff.py file directly. One of our CI enforces this
+#         the file from the modular. If any change should be done, please apply the change to the
+#                           modular_xxx.py file directly. One of our CI enforces this
 #           🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+
 from ...configuration_utils import PretrainedConfig
 from ...modeling_rope_utils import rope_config_validation
 
 
 class MyNewModel2Config(PretrainedConfig):
     r"""
+    This is the configuration class to store the configuration of a [`MyNewModel2Model`]. It is used to instantiate an MyNewModel2
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the MyNewModel2-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the MyNewModel2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MyNewModel2Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. MyNewModel2 1 supports up to 2048 tokens,
+            MyNewModel2 2 up to 4096, CodeMyNewModel2 up to 16384.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
+            understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
+            results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'my_new_model23'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'my_new_model23'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'my_new_model23'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'my_new_model23'. Scaling factor applied to high frequency components of the RoPE
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
+        head_dim (`int`, *optional*):
+            The attention head dimension. If None, it will default to hidden_size // num_heads
     This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma
     model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
     defaults will yield a similar configuration to that of the Gemma-7B.
@@ -20,6 +121,7 @@ class MyNewModel2Config(PretrainedConfig):
         vocab_size (`int`, *optional*, defaults to 256000):
             Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the
             `inputs_ids` passed when calling [`GemmaModel`]
+
     ```python
     >>> from transformers import GemmaModel, GemmaConfig
     >>> # Initializing a Gemma gemma-7b style configuration
@@ -83,7 +185,7 @@ def __init__(
         self.mlp_bias = mlp_bias
         self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
         # Validate the correctness of rotary position embeddings parameters
-        # BC: if there is a 'type' field, move it to 'rope_type'.
+        # BC: if there is a 'type' field, copy it it to 'rope_type'.
         if self.rope_scaling is not None and "type" in self.rope_scaling:
             self.rope_scaling["rope_type"] = self.rope_scaling["type"]
         rope_config_validation(self)

examples/modular-transformers/configuration_new_model.py

@@ -1,12 +1,12 @@
 #           🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
-#               This file was automatically generated from <path_to_diff_file.py>.
+#               This file was automatically generated from <path_to_modular_file.py>.
 #         Do NOT edit this file manually as any edits will be overwritten by the generation of
-#         the file from the diff. If any change should be done, please apply the change to the
-#                           diff.py file directly. One of our CI enforces this
+#         the file from the modular. If any change should be done, please apply the change to the
+#                           modular_xxx.py file directly. One of our CI enforces this
 #           🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 # Example where we only want to overwrite the defaults of an init
 
-from transformers import PretrainedConfig
+from ...configuration_utils import PretrainedConfig
 
 
 class NewModelConfig(PretrainedConfig):

examples/modular-transformers/convert_examples.sh

@@ -5,6 +5,6 @@ for file in examples/modular-transformers/modular_*; do
     # Check if it's a regular file
     if [ -f "$file" ]; then
         # Call the Python script with the file name as an argument
-        python utils/diff_model_converter.py --files_to_parse "$file"
+        python utils/modular_model_converter.py --files_to_parse "$file"
     fi
 done