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[Model] Add Granite model (#7436)

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Co-authored-by: Nick Hill <nickhill@us.ibm.com>
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Shawn Tan
2024-09-01 21:37:18 -04:00
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# coding=utf-8
# Copyright 2024 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.
"""Granite model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
from transformers.utils import logging
logger = logging.get_logger(__name__)
class GraniteConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of
a [`GraniteModel`]. It is used to instantiate an Granite
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 Granite-3B.
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 Granite model. Defines the number of
different tokens that can be represented by the `inputs_ids`
passed when calling [`GraniteModel`]
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.
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.
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`, *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.
embedding_multiplier (`float`, *optional*, defaults to 1.0):
embedding multiplier
logits_scaling (`float`, *optional*, defaults to 1.0):
divisor for output logits
residual_multiplier (`float`, *optional*, defaults to 1.0):
residual multiplier
attention_multiplier (`float`, *optional*, defaults to 1.0):
attention multiplier
```python
>>> from transformers import GraniteModel, GraniteConfig
>>> # Initializing a Granite granite-3b style configuration
>>> configuration = GraniteConfig()
>>> # Initializing a model from the granite-7b style configuration
>>> model = GraniteModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "granite"
keys_to_ignore_at_inference = ["past_key_values"]
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,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
mlp_bias=False,
embedding_multiplier=1.0,
logits_scaling=1.0,
residual_multiplier=1.0,
attention_multiplier=1.0,
**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.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.embedding_multiplier = embedding_multiplier
self.logits_scaling = logits_scaling
self.residual_multiplier = residual_multiplier
self.attention_multiplier = attention_multiplier
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,
)
rope_config_validation(self)