Fix and re-run modular converter on examples

examples/modular-transformers/configuration_duplicated_method.py

@@ -5,79 +5,18 @@
 #                          modular_duplicated_method.py file directly. One of our CI enforces this.
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 
+from huggingface_hub.dataclasses import strict
+
 from ...configuration_utils import PreTrainedConfig
 from ...modeling_rope_utils import RopeParameters
+from ...utils import auto_docstring
+from ...utils.type_validators import interval
 
 
+@auto_docstring(checkpoint="meta-duplicated_method/DuplicatedMethod-2-7b-hf")
+@strict(accept_kwargs=True)
 class DuplicatedMethodConfig(PreTrainedConfig):
     r"""
-    This is the configuration class to store the configuration of a [`DuplicatedMethodModel`]. It is used to instantiate an DuplicatedMethod
-    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 DuplicatedMethod-7B.
-    e.g. [meta-duplicated_method/DuplicatedMethod-2-7b-hf](https://huggingface.co/meta-duplicated_method/DuplicatedMethod-2-7b-hf)
-
-    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 DuplicatedMethod model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`DuplicatedMethodModel`]
-        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, check out [this
-            paper](https://huggingface.co/papers/2305.13245). 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. DuplicatedMethod 1 supports up to 2048 tokens,
-            DuplicatedMethod 2 up to 4096, CodeLlama 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_parameters (`RopeParameters`, *optional*):
-            Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
-            a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
-            with longer `max_position_embeddings`.
-        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_attention_heads
-
     ```python
     >>> from transformers import DuplicatedMethodModel, DuplicatedMethodConfig
 
@@ -109,64 +48,46 @@ class DuplicatedMethodConfig(PreTrainedConfig):
         "norm": (["hidden_states"], ["hidden_states"]),
     }
 
-    def __init__(
-        self,
-        vocab_size: int | None = 32000,
-        hidden_size: int | None = 4096,
-        intermediate_size: int | None = 11008,
-        num_hidden_layers: int | None = 32,
-        num_attention_heads: int | None = 32,
-        num_key_value_heads: int | None = None,
-        hidden_act: str | None = "silu",
-        max_position_embeddings: int | None = 2048,
-        initializer_range: float | None = 0.02,
-        rms_norm_eps: int | None = 1e-6,
-        use_cache: bool | None = True,
-        pad_token_id: int | None = None,
-        bos_token_id: int | None = 1,
-        eos_token_id: int | None = 2,
-        pretraining_tp: int | None = 1,
-        tie_word_embeddings: bool | None = False,
-        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
-        attention_bias: bool | None = False,
-        attention_dropout: float | None = 0.0,
-        mlp_bias: bool | None = False,
-        head_dim: int | None = None,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-
-        # for backward compatibility
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
-        self.num_key_value_heads = num_key_value_heads
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.pretraining_tp = pretraining_tp
-        self.use_cache = use_cache
-        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
-        self.rope_parameters = rope_parameters
-
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
+    vocab_size: int = 32000
+    hidden_size: int = 4096
+    intermediate_size: int = 11008
+    num_hidden_layers: int = 32
+    num_attention_heads: int = 32
+    num_key_value_heads: int | None = None
+    hidden_act: str = "silu"
+    max_position_embeddings: int = 2048
+    initializer_range: float = interval(min=0.0, max=1.0)(default=0.02)
+    rms_norm_eps: float = 1e-6
+    use_cache: bool = True
+    pad_token_id: int | None = None
+    bos_token_id: int | None = 1
+    eos_token_id: int | list[int] | None = 2
+    pretraining_tp: int | None = 1
+    tie_word_embeddings: bool = False
+    rope_parameters: RopeParameters | dict | None = None
+    attention_bias: bool = False
+    attention_dropout: int | float | None = 0.0
+    mlp_bias: bool = False
+    head_dim: int | None = None
+
+    def __post_init__(self, **kwargs):
+        if self.head_dim is None:
+            self.head_dim = self.hidden_size // self.num_attention_heads
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+
+        super().__post_init__(**kwargs)
+
+    def validate_architecture(self):
+        """Part of `@strict`-powered validation. Validates the architecture of the config."""
+        if self.hidden_size % self.num_attention_heads != 0:
+            raise ValueError(
+                f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({self.num_attention_heads})."
+            )
 
     @property
-    def vocab_size(self):
+    def vocab_size(self):  # noqa: F811 -> we need this at we cannot delete the original for now since config dataclass refactor
         return 45
 
     @vocab_size.setter

examples/modular-transformers/configuration_my_new_model.py

@@ -5,10 +5,16 @@
 #                          modular_my_new_model.py file directly. One of our CI enforces this.
 #                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 
+from huggingface_hub.dataclasses import strict
+
 from ...configuration_utils import PreTrainedConfig
 from ...modeling_rope_utils import RopeParameters
+from ...utils import auto_docstring
+from ...utils.type_validators import interval
 
 
+@auto_docstring(checkpoint="meta-my_new_model/MyNewModel-2-7b-hf")
+@strict(accept_kwargs=True)
 class MyNewModelConfig(PreTrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`MyNewModelModel`]. It is used to instantiate an MyNewModel
@@ -145,60 +151,42 @@ class MyNewModelConfig(PreTrainedConfig):
         "norm": (["hidden_states"], ["hidden_states"]),
     }
 
-    def __init__(
-        self,
-        vocab_size: int | None = 32000,
-        hidden_size: int | None = 4096,
-        intermediate_size: int | None = 11008,
-        num_hidden_layers: int | None = 32,
-        num_attention_heads: int | None = 32,
-        num_key_value_heads: int | None = None,
-        hidden_act: str | None = "silu",
-        max_position_embeddings: int | None = 2048,
-        initializer_range: float | None = 0.02,
-        rms_norm_eps: int | None = 1e-6,
-        use_cache: bool | None = True,
-        pad_token_id: int | None = None,
-        bos_token_id: int | None = 1,
-        eos_token_id: int | None = 2,
-        pretraining_tp: int | None = 1,
-        tie_word_embeddings: bool | None = False,
-        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
-        attention_bias: bool | None = False,
-        attention_dropout: float | None = 0.0,
-        mlp_bias=True,
-        head_dim: int | None = None,
-        new_param=0,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-
-        # for backward compatibility
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
-        self.num_key_value_heads = num_key_value_heads
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.pretraining_tp = pretraining_tp
-        self.use_cache = use_cache
-        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
-        self.rope_parameters = rope_parameters
-
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )
-        self.new_param = new_param
+    vocab_size: int = 32000
+    hidden_size: int = 4096
+    intermediate_size: int = 11008
+    num_hidden_layers: int = 32
+    num_attention_heads: int = 32
+    num_key_value_heads: int | None = None
+    hidden_act: str = "silu"
+    max_position_embeddings: int = 2048
+    initializer_range: float = interval(min=0.0, max=1.0)(default=0.02)
+    rms_norm_eps: float = 1e-6
+    use_cache: bool = True
+    pad_token_id: int | None = None
+    bos_token_id: int | None = 1
+    eos_token_id: int | list[int] | None = 2
+    pretraining_tp: int | None = 1
+    tie_word_embeddings: bool = False
+    rope_parameters: RopeParameters | dict | None = None
+    attention_bias: bool = False
+    attention_dropout: int | float | None = 0.0
+
+    mlp_bias: bool = True
+    head_dim: int | None = None
+    new_param: int = 0
+
+    def __post_init__(self, **kwargs):
+        if self.head_dim is None:
+            self.head_dim = self.hidden_size // self.num_attention_heads
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+
+        super().__post_init__(**kwargs)
+
+    def validate_architecture(self):
+        """Part of `@strict`-powered validation. Validates the architecture of the config."""
+        if self.hidden_size % self.num_attention_heads != 0:
+            raise ValueError(
+                f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({self.num_attention_heads})."
+            )