Skip to content

Commit

Permalink
Passes formatting.
Browse files Browse the repository at this point in the history
  • Loading branch information
@shawntan
shawntan committed Aug 13, 2024
1 parent 13460ac commit b79bb1d
Show file tree
Hide file tree
Showing 2 changed files with 55 additions and 35 deletions.
4 changes: 2 additions & 2 deletions vllm/model_executor/models/granite.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -25,8 +25,6 @@

import torch
from torch import nn
# from transformers import GraniteConfig
from vllm.transformers_utils.configs.granite import GraniteConfig

from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig, LoRAConfig
Expand All @@ -50,6 +48,8 @@
default_weight_loader, kv_cache_scales_loader, maybe_remap_kv_scale_name)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors, SamplerOutput
# from transformers import GraniteConfig
from vllm.transformers_utils.configs.granite import GraniteConfig
from vllm.utils import is_hip

from .interfaces import SupportsLoRA
Expand Down
86 changes: 53 additions & 33 deletions vllm/transformers_utils/configs/granite.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -23,51 +23,61 @@
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.
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.
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`]
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.
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`.
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.
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.
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`.
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):
Expand All @@ -79,23 +89,33 @@ class GraniteConfig(PretrainedConfig):
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.
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.
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
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
Expand Down

0 comments on commit b79bb1d

Please sign in to comment.