current state
This commit is contained in:
Arthur Zucker
parent
e62a5bb05b
commit
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@ -25,7 +25,6 @@ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache, StaticCache
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from ...configuration_utils import PretrainedConfig
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from ...modeling_attn_mask_utils import AttentionMaskConverter
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from ...modeling_outputs import (
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BaseModelOutputWithPast,
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@ -62,172 +61,6 @@ import torch.utils.checkpoint
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logger = logging.get_logger(__name__)
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class GemmaConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma
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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 Gemma-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 32000):
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Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`GemmaModel`]
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 11008):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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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*):
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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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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with. Gemma 1 supports up to 2048 tokens,
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Gemma 2 up to 4096, CodeGemma up to 16384.
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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*):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 1):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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End of stream token id.
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pretraining_tp (`int`, *optional*, defaults to 1):
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Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
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document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
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necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
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issue](https://github.com/pytorch/pytorch/issues/76232).
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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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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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
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strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
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`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
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these scaling strategies behave:
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https://www.reddit.com/r/LocalGemma/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
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experimental feature, subject to breaking API changes in future versions.
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attention_bias (`bool`, *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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mlp_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
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```python
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>>> from transformers import GemmaModel, GemmaConfig
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>>> # Initializing a Gemma gemma-7b style configuration
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>>> configuration = GemmaConfig()
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>>> # Initializing a model from the gemma-7b style configuration
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>>> model = GemmaModel(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 = "gemma"
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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=32000,
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hidden_size=4096,
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intermediate_size=11008,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=None,
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hidden_act="silu",
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max_position_embeddings=2048,
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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=None,
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bos_token_id=1,
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eos_token_id=2,
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pretraining_tp=1,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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attention_bias=False,
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attention_dropout=0.0,
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mlp_bias=False,
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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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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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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.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.pretraining_tp = pretraining_tp
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self._rope_scaling_validation()
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.mlp_bias = mlp_bias
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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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def _rope_scaling_validation(self):
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"""
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Validate the `rope_scaling` configuration.
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"""
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if self.rope_scaling is None:
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return
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if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
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raise ValueError(
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"`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}"
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)
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rope_scaling_type = self.rope_scaling.get("type", None)
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rope_scaling_factor = self.rope_scaling.get("factor", None)
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if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
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raise ValueError(
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f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
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)
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if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
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raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
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class GemmaRMSNorm(nn.Module):
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def __init__(self, dim: int, eps: float = 1e-6):
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super().__init__()
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@ -225,11 +225,11 @@ class DiffConverterTransformer(CSTTransformer):
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self.inserted_functions = set()
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self.new_body = []
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def visit_functionDef(self, node):
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parent_node = self.get_metadata(cst.metadata.ParentNodeProvider, node)
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def leave_FunctionDef(self, original_node, node):
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parent_node = self.get_metadata(cst.metadata.ParentNodeProvider, original_node)
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if m.matches(parent_node, m.Module()):
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self.new_body.append(node)
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return node
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def visit_ImportFrom(self, node: cst.ImportFrom) -> None:
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if m.matches(node.module, m.Attribute()):
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@ -284,23 +284,23 @@ class DiffConverterTransformer(CSTTransformer):
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else:
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class_finder = self.visited_module[super_file_name]
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# extract nested dependencies from the base class
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if self.visited_module[super_file_name]:
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class_finder = self.visited_module[super_file_name]
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list_dependencies = {dep:class_finder.class_start_line.get(dep,1000)for dep in class_finder.class_dependency_mapping[class_name]}
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for dependency, _ in sorted(list_dependencies.items(), key=lambda x:x[1]):
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node = class_finder.global_nodes.get(dependency, None)
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# make sure the class is not re-defined by the diff file
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if node is not None and node not in self.new_body:
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if dependency not in self.class_mapping:
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self.new_body.append(node)
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self.class_mapping[dependency] = node
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list_dependencies = {dep:class_finder.class_start_line.get(dep,1000)for dep in class_finder.class_dependency_mapping[class_name]}
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for dependency, _ in sorted(list_dependencies.items(), key=lambda x:x[1]):
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node = class_finder.global_nodes.get(dependency, None)
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# make sure the class is not re-defined by the diff file
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if node is not None and node not in self.new_body:
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if dependency not in self.class_mapping:
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self.new_body.append(node)
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self.class_mapping[dependency] = node
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updated_node = class_finder.classes[class_name]
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updated_node = replace_call_to_super(class_finder, updated_node, class_name)
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self.class_mapping[class_name] = updated_node
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self.new_body.append(updated_node)
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if "Config" in class_name:
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self.config_body = [updated_node]
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else:
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self.new_body.append(updated_node)
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return updated_node
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def leave_If(self, original_node, node):
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@ -319,6 +319,7 @@ class DiffConverterTransformer(CSTTransformer):
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new_body = []
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for visiter in self.visited_module.values():
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new_body += list(visiter.imports.values())
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self.config_body = list(visiter.imports.values()) + self.config_body
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return node.with_changes(body=[*new_body, *self.new_body])
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@ -395,7 +396,6 @@ class SuperTransformer(cst.CSTTransformer):
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def convert_file(diff_file):
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# Parse the Python file
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with open(diff_file, "r") as file:
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@ -408,8 +408,12 @@ def convert_file(diff_file):
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with open(diff_file.replace("diff_", "modeling_"), "w") as f:
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f.write(ruffed_code)
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# with open(diff_file.replace("diff_", "modeling_draft_"), "w") as f:
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# f.write(ruffed_code)
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if hasattr(transformers, "config_bod"):
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config_module = cst.Module(body = [*transformers.config_body])
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with open(diff_file.replace("diff_", "config_"), "w") as f:
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ruffed_code = fix_ruff(config_module.code)
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f.write(ruffed_code)
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if __name__ == "__main__":
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