Modular transformers: modularity and inheritance for new model additions (#33248)
* update exampel * update * push the converted diff files for testing and ci * correct one example * fix class attributes and docstring * nits * oups * fixed config! * update * nitd * class attributes are not matched against the other, this is missing * fixed overwriting self.xxx now onto the attributes I think * partial fix, now order with docstring * fix docstring order? * more fixes * update * fix missing docstrings! * examples don't all work yet * fixup * nit * updated * hick * update * delete * update * update * update * fix * all default * no local import * fix more diff * some fix related to "safe imports" * push fixed * add helper! * style * add a check * all by default * add the * update * FINALLY! * nit * fix config dependencies * man that is it * fix fix * update diffs * fix the last issue * re-default to all * alll the fixes * nice * fix properties vs setter * fixup * updates * update dependencies * make sure to install what needs to be installed * fixup * quick fix for now * fix! * fixup * update * update * updates * whitespaces * nit * fix * simplify everything, and make it file agnostic (should work for image processors) * style * finish fixing all import issues * fixup * empty modeling should not be written! * Add logic to find who depends on what * update * cleanup * update * update gemma to support positions * some small nits * this is the correct docstring for gemma2 * fix merging of docstrings * update * fixup * update * take doc into account * styling * update * fix hidden activation * more fixes * final fixes! * fixup * fixup instruct blip video * update * fix bugs * align gemma2 with the rest as well * updats * revert * update * more reversiom * grind * more * arf * update * order will matter * finish del stuff * update * rename to modular * fixup * nits * update makefile * fixup * update order of the checks! * fix * fix docstring that has a call inside * fiix conversion check * style * add some initial documentation * update * update doc * some fixup * updates * yups * Mostly todo gimme a minut * update * fixup * revert some stuff * Review docs for the modular transformers (#33472) Docs * good update * fixup * mmm current updates lead to this code * okay, this fixes it * cool * fixes * update * nit * updates * nits * fix doc * update * revert bad changes * update * updates * proper update * update * update? * up * update * cool * nits * nits * bon bon * fix * ? * minimise changes * update * update * update * updates? * fixed gemma2 * kind of a hack * nits * update * remove `diffs` in favor of `modular` * fix make fix copies --------- Co-authored-by: Lysandre Debut <hi@lysand.re>
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examples/modular-transformers/configuration_new_model.py
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examples/modular-transformers/configuration_new_model.py
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from <path_to_diff_file.py>.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the diff. If any change should be done, please apply the change to the
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# diff.py file directly. One of our CI enforces this
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# Example where we only want to overwrite the defaults of an init
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from transformers import PretrainedConfig
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class NewModelConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`NewModelModel`]. It is used to instantiate an NewModel
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the NewModel-7B.
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e.g. [google/new_model-7b](https://huggingface.co/google/new_model-7b)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 256000):
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Vocabulary size of the NewModel model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`NewModelModel`]
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hidden_size (`int`, *optional*, defaults to 3072):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 24576):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 28):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*, defaults to 16):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
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`num_attention_heads`.
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head_dim (`int`, *optional*, defaults to 256):
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The attention head dimension.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
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The legacy activation function. It is overwritten by the `hidden_activation`.
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hidden_activation (`str` or `function`, *optional*):
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The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"`
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if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function.
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max_position_embeddings (`int`, *optional*, defaults to 8192):
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The maximum sequence length that this model might ever be used with.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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pad_token_id (`int`, *optional*, defaults to 0):
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Padding token id.
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eos_token_id (`int`, *optional*, defaults to 1):
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End of stream token id.
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bos_token_id (`int`, *optional*, defaults to 2):
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Beginning of stream token id.
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tie_word_embeddings (`bool`, *optional*, defaults to `True`):
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Whether to tie weight embeddings
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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```python
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>>> from transformers import NewModelModel, NewModelConfig
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>>> # Initializing a NewModel new_model-7b style configuration
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>>> configuration = NewModelConfig()
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>>> # Initializing a model from the new_model-7b style configuration
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>>> model = NewModelModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "new_model"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=256030,
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hidden_size=64,
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intermediate_size=90,
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num_hidden_layers=28,
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num_attention_heads=16,
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num_key_value_heads=16,
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head_dim=256,
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hidden_act="gelu_pytorch_tanh",
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hidden_activation=None,
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max_position_embeddings=1500,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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pad_token_id=0,
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eos_token_id=1,
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bos_token_id=2,
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tie_word_embeddings=True,
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rope_theta=10000.0,
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attention_bias=False,
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attention_dropout=0.0,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.head_dim = head_dim
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.hidden_activation = hidden_activation
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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@property
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def num_heads(self):
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return self.num_attention_heads
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