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* Add model skeletion with transformers-cli add-new-model-like * Convert config to modular, add rms_norm_eps, delete clip_qkv * Convert model to modular, add RMSNorm * Add flash attention with qk norm and no qkv clipping * Add decoder layer with RMSNorm after attention/feedforward layers * Add base and causal model * Add converter improvements from OLMo repo * Update weight loading in OLMo to HF converter * Set correct default for rms_norm_eps * Set correct pipeline_model_mapping in test * Run make fixup * Fix model type * Re-run modular conversion * Manually set config docs to fix build errors * Convert olmo-1124 to olmo_1124 to fix flash attention docs errors * Start updating tests * Update tests * Copy upstream test_eager_matches_sdpa_inference_1_bfloat16 changes to olmo_1124 * Rename input_layernorm and post_attention_layernorm to reflect their ops better * Use correct tokenizer * Remove test unsupported by GPT2 tokenizer * Create GenerationConfig outside of from_pretrained call * Use simpler init file structure * Add explicit __all__ to support simplified init * Make safetensor serialization the default * Update OLMo November 2024 docs
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<!--Copyright 2024 The HuggingFace Team. All rights reserved. | ||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with | ||
the License. You may obtain a copy of the License at | ||
http://www.apache.org/licenses/LICENSE-2.0 | ||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on | ||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | ||
specific language governing permissions and limitations under the License. | ||
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be | ||
rendered properly in your Markdown viewer. | ||
--> | ||
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# OLMo November 2024 | ||
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## Overview | ||
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The OLMo November 2024 model is a successor of the OLMo model, which was proposed in | ||
[OLMo: Accelerating the Science of Language Models](https://arxiv.org/abs/2402.00838). | ||
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The architectural changes from the original OLMo model to this model are: | ||
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- RMSNorm is used instead of standard layer norm. | ||
- Norm is applied to attention queries and keys. | ||
- Norm is applied after attention/feedforward layers rather than before. | ||
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This model was contributed by [shanearora](https://huggingface.co/shanearora). | ||
The original code can be found [here](https://github.com/allenai/OLMo/tree/main/olmo). | ||
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## Olmo1124Config | ||
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[[autodoc]] Olmo1124Config | ||
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## Olmo1124Model | ||
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[[autodoc]] Olmo1124Model | ||
- forward | ||
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## Olmo1124ForCausalLM | ||
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[[autodoc]] Olmo1124ForCausalLM | ||
- forward |
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nougat, | ||
nystromformer, | ||
olmo, | ||
olmo_1124, | ||
olmoe, | ||
omdet_turbo, | ||
oneformer, | ||
|
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# Copyright 2024 EleutherAI and The HuggingFace Inc. team. All rights reserved. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
from typing import TYPE_CHECKING | ||
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from ...utils import _LazyModule | ||
from ...utils.import_utils import define_import_structure | ||
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if TYPE_CHECKING: | ||
from .configuration_olmo_1124 import * | ||
from .modeling_olmo_1124 import * | ||
else: | ||
import sys | ||
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_file = globals()["__file__"] | ||
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) |
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src/transformers/models/olmo_1124/configuration_olmo_1124.py
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 | ||
# This file was automatically generated from src/transformers/models/olmo_1124/modular_olmo_1124.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_olmo_1124.py file directly. One of our CI enforces this. | ||
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 | ||
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from ...configuration_utils import PretrainedConfig | ||
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class Olmo1124Config(PretrainedConfig): | ||
r""" | ||
This is the configuration class to store the configuration of a [`Olmo1124Model`]. It is used to instantiate an OLMo November 2024 | ||
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 [allenai/Olmo1124-7B-hf](https://huggingface.co/allenai/Olmo1124-7B-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 50304): | ||
Vocabulary size of the Olmo1124 model. Defines the number of different tokens that can be represented by the | ||
`inputs_ids` passed when calling [`Olmo1124Model`] | ||
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 checkout [this | ||
paper](https://arxiv.org/pdf/2305.13245.pdf). 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. | ||
initializer_range (`float`, *optional*, defaults to 0.02): | ||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | ||
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*, defaults to 1): | ||
Padding token id. | ||
bos_token_id (`int`, *optional*): | ||
Beginning of stream token id. | ||
eos_token_id (`int`, *optional*, defaults to 50279): | ||
End of stream token id. | ||
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. Currently supports two scaling | ||
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is | ||
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update | ||
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how | ||
these scaling strategies behave: | ||
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an | ||
experimental feature, subject to breaking API changes in future versions. | ||
attention_bias (`bool`, defaults to `False`, *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. | ||
rms_norm_eps (`float`, *optional*, defaults to 1e-05): | ||
The epsilon used by the rms normalization layers. | ||
```python | ||
>>> from transformers import Olmo1124Model, Olmo1124Config | ||
>>> # Initializing a Olmo November 2024 7B style configuration | ||
>>> configuration = Olmo1124Config() | ||
>>> # Initializing a model from the Olmo November 2024 7B style configuration | ||
>>> model = Olmo1124Model(configuration) | ||
>>> # Accessing the model configuration | ||
>>> configuration = model.config | ||
``` | ||
""" | ||
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model_type = "olmo_1124" | ||
keys_to_ignore_at_inference = ["past_key_values"] | ||
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def __init__( | ||
self, | ||
vocab_size=50304, | ||
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, | ||
use_cache=True, | ||
pad_token_id=1, | ||
bos_token_id=None, | ||
eos_token_id=50279, | ||
tie_word_embeddings=False, | ||
rope_theta=10000.0, | ||
rope_scaling=None, | ||
attention_bias=False, | ||
attention_dropout=0.0, | ||
rms_norm_eps=1e-5, | ||
**kwargs, | ||
): | ||
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.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 | ||
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# for backward compatibility | ||
if num_key_value_heads is None: | ||
num_key_value_heads = num_attention_heads | ||
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self.num_key_value_heads = num_key_value_heads | ||
self.hidden_act = hidden_act | ||
self.initializer_range = initializer_range | ||
self.use_cache = use_cache | ||
self.rope_theta = rope_theta | ||
self.rope_scaling = rope_scaling | ||
self._rope_scaling_validation() | ||
self.attention_bias = attention_bias | ||
self.attention_dropout = attention_dropout | ||
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self.rms_norm_eps = rms_norm_eps | ||
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def _rope_scaling_validation(self): | ||
""" | ||
Validate the `rope_scaling` configuration. | ||
""" | ||
if self.rope_scaling is None: | ||
return | ||
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if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: | ||
raise ValueError( | ||
"`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}" | ||
) | ||
rope_scaling_type = self.rope_scaling.get("type", None) | ||
rope_scaling_factor = self.rope_scaling.get("factor", None) | ||
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: | ||
raise ValueError( | ||
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" | ||
) | ||
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: | ||
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}") | ||
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__all__ = ["Olmo1124Config"] |
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