[modular] Allow method with the same name in case of @property decorator (#39308)

* fix

* add example

* fix

* Update modular_model_converter.py

examples/modular-transformers/configuration_duplicated_method.py

@@ -0,0 +1,216 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from examples/modular-transformers/modular_duplicated_method.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_duplicated_method.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+
+from ...configuration_utils import PretrainedConfig
+from ...modeling_rope_utils import rope_config_validation
+
+
+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_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',
+                    'duplicated_method3'], 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 'duplicated_method3'. 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 'duplicated_method3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'duplicated_method3'. 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_attention_heads
+
+    ```python
+    >>> from transformers import DuplicatedMethodModel, DuplicatedMethodConfig
+
+    >>> # Initializing a DuplicatedMethod duplicated_method-7b style configuration
+    >>> configuration = DuplicatedMethodConfig()
+
+    >>> # Initializing a model from the duplicated_method-7b style configuration
+    >>> model = DuplicatedMethodModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "duplicated_method"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    # Default tensor parallel plan for base model `DuplicatedMethodModel`
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        mlp_bias=False,
+        head_dim=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.rope_theta = rope_theta
+        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)
+
+        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,
+        )
+
+    @property
+    def vocab_size(self):
+        return 45
+
+    @vocab_size.setter
+    def vocab_size(self, value):
+        self.vocab_size = value

examples/modular-transformers/modeling_multimodal2.py

@@ -498,6 +498,7 @@ class Multimodal2VisionPreTrainedModel(PreTrainedModel):
     supports_gradient_checkpointing = True
     _supports_sdpa = True
     _supports_flash_attn_2 = True
+    _supports_flash_attn_3 = True
     _supports_flex_attn = True
     _supports_attention_backend = True
 

examples/modular-transformers/modeling_my_new_model2.py

@@ -65,7 +65,7 @@ class MyNewModel2RotaryEmbedding(nn.Module):
     def __init__(self, config: MyNewModel2Config, device=None):
         super().__init__()
         # BC: "rope_type" was originally "type"
-        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
+        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
             self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
         else:
             self.rope_type = "default"
@@ -290,6 +290,7 @@ class MyNewModel2PreTrainedModel(PreTrainedModel):
     _no_split_modules = ["MyNewModel2DecoderLayer"]
     _skip_keys_device_placement = ["past_key_values"]
     _supports_flash_attn_2 = True
+    _supports_flash_attn_3 = True
     _supports_sdpa = True
     _supports_flex_attn = True
     _supports_cache_class = True

examples/modular-transformers/modeling_new_task_model.py

@@ -96,6 +96,7 @@ class NewTaskModelPreTrainedModel(PreTrainedModel):
     _supports_quantized_cache = True
     _supports_static_cache = True
     _supports_flash_attn_2 = True
+    _supports_flash_attn_3 = True
     _supports_sdpa = True
     _supports_flex_attn = True
     _supports_attention_backend = True

examples/modular-transformers/modeling_super.py

@@ -48,7 +48,7 @@ class SuperRotaryEmbedding(nn.Module):
     def __init__(self, config: SuperConfig, device=None):
         super().__init__()
         # BC: "rope_type" was originally "type"
-        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
+        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
             self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
         else:
             self.rope_type = "default"
@@ -289,6 +289,7 @@ class SuperPreTrainedModel(PreTrainedModel):
     _no_split_modules = ["SuperDecoderLayer"]
     _skip_keys_device_placement = ["past_key_values"]
     _supports_flash_attn_2 = True
+    _supports_flash_attn_3 = True
     _supports_sdpa = True
     _supports_flex_attn = True
     _supports_cache_class = True

examples/modular-transformers/modular_duplicated_method.py

@@ -0,0 +1,11 @@
+from transformers.models.llama.configuration_llama import LlamaConfig
+
+
+class DuplicatedMethodConfig(LlamaConfig):
+    @property
+    def vocab_size(self):
+        return 45
+
+    @vocab_size.setter
+    def vocab_size(self, value):
+        self.vocab_size = value

utils/modular_model_converter.py

@@ -972,12 +972,37 @@ def replace_class_node(
     # Use all original modeling attributes, and potentially override some with values in the modular
     new_class_attributes = list({**original_modeling_class_attributes, **modular_class_attributes}.values())
 
-    original_modeling_methods = {
-        node.name.value: node for node in original_modeling_node.body.body if m.matches(node, m.FunctionDef())
-    }
-    modular_methods = {
-        node.name.value: node for node in modular_class_node.body.body if m.matches(node, m.FunctionDef())
-    }
+    # Check class methods defined in the modular and associated modeling
+    original_modeling_methods = {}
+    for node in original_modeling_node.body.body:
+        if m.matches(node, m.FunctionDef()):
+            # Due to the @property and @name.setter decorators, methods can sometimes have the same name, so we need a way
+            # to separate them
+            if node.name.value in original_modeling_methods:
+                # If it's already present, and the decorator is @property, it means the node already added was the setter
+                if node.decorators[0].decorator.value == "property":
+                    original_modeling_methods[f"{node.name.value}_setter"] = original_modeling_methods[node.name.value]
+                    original_modeling_methods[node.name.value] = node
+                # In this case current node is the setter
+                else:
+                    original_modeling_methods[f"{node.name.value}_setter"] = node
+            else:
+                original_modeling_methods[node.name.value] = node
+    modular_methods = {}
+    for node in modular_class_node.body.body:
+        if m.matches(node, m.FunctionDef()):
+            # Due to the @property and @name.setter decorators, methods can sometimes have the same name, so we need a way
+            # to separate them
+            if node.name.value in modular_methods:
+                # If it's already present, and the decorator is @property, it means the node already added was the setter
+                if node.decorators[0].decorator.value == "property":
+                    modular_methods[f"{node.name.value}_setter"] = modular_methods[node.name.value]
+                    modular_methods[node.name.value] = node
+                # In this case current node is the setter
+                else:
+                    modular_methods[f"{node.name.value}_setter"] = node
+            else:
+                modular_methods[node.name.value] = node
 
     new_class_methods = []
     # Iterate over the methods of the original modeling code, and add them to the list of methods to add