better merging strategy
This commit is contained in:
Arthur Zucker
parent
f1e1decc92
commit
7ea9bcd3dc
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@ -77,74 +77,6 @@ from transformers import PretrainedConfig
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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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e.g. [google/gemma-7b](https://huggingface.co/google/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 256000):
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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 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 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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@ -168,7 +100,6 @@ class GemmaConfig(PretrainedConfig):
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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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@ -176,16 +107,23 @@ class GemmaConfig(PretrainedConfig):
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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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# 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.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.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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@ -194,3 +132,23 @@ class GemmaConfig(PretrainedConfig):
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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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@ -21,7 +21,6 @@ from typing import List, Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from transformers import PretrainedConfig
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from transformers.models.llama.modeling_llama import (
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@ -32,7 +31,7 @@ from transformers.models.llama.modeling_llama import (
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apply_rotary_pos_emb,
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repeat_kv,
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)
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from transformers.models.llama.configuration_llama import LlamaConfig
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from ...activations import ACT2FN
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from ...cache_utils import Cache
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from ...modeling_outputs import CausalLMOutputWithPast
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@ -162,6 +161,32 @@ class GemmaConfig(PretrainedConfig):
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**kwargs,
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)
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# Example where we only want to overwrite the defaults of an init?
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class GemmaConfig(LlamaConfig):
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def __init__(
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self,
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vocab_size=256000,
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hidden_size=3072,
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intermediate_size=24576,
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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=8192,
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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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):
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super().__init__(self)
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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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@ -382,7 +407,7 @@ class GemmaModel(LlamaModel):
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cache_position,
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)
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# Example where we ony modify the docstring and call super
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class GemmaForCausalLM(LlamaForCausalLM):
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def forward(
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self,
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@ -423,52 +448,18 @@ class GemmaForCausalLM(LlamaForCausalLM):
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>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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"What is your favorite condiment?"
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```"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
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outputs = self.model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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cache_position=cache_position,
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)
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hidden_states = outputs[0]
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logits = self.lm_head(hidden_states)
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logits = logits.float()
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loss = None
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if labels is not None:
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# Shift so that tokens < n predict n
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shift_logits = logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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# Flatten the tokens
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loss_fct = CrossEntropyLoss()
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shift_logits = shift_logits.view(-1, self.config.vocab_size)
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shift_labels = shift_labels.view(-1)
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# Enable model parallelism
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shift_labels = shift_labels.to(shift_logits.device)
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loss = loss_fct(shift_logits, shift_labels)
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if not return_dict:
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output = (logits,) + outputs[1:]
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return (loss,) + output if loss is not None else output
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return CausalLMOutputWithPast(
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loss=loss,
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logits=logits,
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past_key_values=outputs.past_key_values,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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return super().forward(
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input_ids,
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attention_mask,
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position_ids,
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past_key_values,
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inputs_embeds,
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labels,
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use_cache,
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output_attentions,
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output_hidden_states,
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return_dict,
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cache_position,
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)
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@ -137,7 +137,6 @@ def _get_unpad_data(attention_mask):
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max_seqlen_in_batch,
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)
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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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@ -1180,8 +1179,14 @@ class GemmaForCausalLM(GemmaPreTrainedModel):
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)
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hidden_states = outputs[0]
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logits = self.lm_head(hidden_states)
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if self.config.pretraining_tp > 1:
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lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
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logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
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logits = torch.cat(logits, dim=-1)
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else:
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logits = self.lm_head(hidden_states)
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logits = logits.float()
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loss = None
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if labels is not None:
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# Shift so that tokens < n predict n
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@ -236,6 +236,7 @@ def find_classes_in_file(module, old_id="llama", new_id="gemma"):
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wrapper.visit(class_finder)
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return class_finder
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DOCSTRING_NODE = m.SimpleStatementLine(body=[m.Expr(value=m.SimpleString(value=m.MatchIfTrue(lambda value: re.search(r'\"\"\"[\s\S]*\"\"\"',value) is not None)))])
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class SuperTransformer(cst.CSTTransformer):
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METADATA_DEPENDENCIES = (ParentNodeProvider,)
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@ -255,6 +256,9 @@ class SuperTransformer(cst.CSTTransformer):
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}
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for stmt in existing_body:
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if self.python_module.code_for_node(stmt).strip() not in existing_nodes:
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if m.matches(stmt, DOCSTRING_NODE) and self.has_docstring:
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print("Oh docstring")
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continue
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de_duplicated_new_body.append(stmt)
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existing_nodes.add(stmt)
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else:
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@ -263,7 +267,11 @@ class SuperTransformer(cst.CSTTransformer):
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def replace_super_calls(self, node: cst.IndentedBlock, func_name: str) -> cst.CSTNode:
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new_body = []
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self.has_docstring = False
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for expr in node.body:
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if m.matches(node.body[0], DOCSTRING_NODE):
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self.has_docstring = True
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if m.matches(
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expr,
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m.SimpleStatementLine(
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@ -295,7 +303,8 @@ class SuperTransformer(cst.CSTTransformer):
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if updated_node.name.value in self.updated_methods:
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name = updated_node.name.value
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new_body = self.replace_super_calls(updated_node.body, name)
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return updated_node.with_changes(body=new_body)
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# dont't change the current func's default params
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return updated_node.with_changes(body=new_body, params=updated_node.params)
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return updated_node
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def leave_Return(self, original_node: cst.Return, updated_node: cst.Return) -> cst.CSTNode:
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@ -335,6 +344,9 @@ def replace_call_to_super(class_finder: ClassFinder, updated_node: cst.ClassDef,
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| ```
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"""
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original_node = class_finder.classes[class_name]
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# TODO here is where we merge stuff from super. We can choose to merge the docstring as well!
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# We could also check the docstring here
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original_methods = {f.name.value: f for f in original_node.body.body if m.matches(f, m.FunctionDef())}
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# Copy methods from original node to replacement node, preserving decorators
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@ -343,7 +355,7 @@ def replace_call_to_super(class_finder: ClassFinder, updated_node: cst.ClassDef,
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for name, func in original_methods.items():
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if name in updated_methods:
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# Replace the method in the replacement class, preserving decorators
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func = func.with_changes(body=updated_methods[name].body)
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func = func.with_changes(body=updated_methods[name].body, params = updated_methods[name].params )
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end_meth.append(func)
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result_node = original_node.with_changes(body=cst.IndentedBlock(body=end_meth))
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