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Conforming to linter.
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@shawntan
shawntan committed Sep 6, 2024
1 parent 353fbdf commit 2e6819d
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4 changes: 2 additions & 2 deletions vllm/model_executor/models/granitemoe.py
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import torch
from torch import nn
from vllm.transformers_utils.configs.granitemoe import GraniteMoeConfig

from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig, LoRAConfig
Expand All @@ -39,11 +38,12 @@
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
from vllm.model_executor.layers.vocab_parallel_embedding import (
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
from vllm.transformers_utils.configs.granitemoe import GraniteMoeConfig

from . import mixtral
from .interfaces import SupportsLoRA
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98 changes: 6 additions & 92 deletions vllm/transformers_utils/configs/granitemoe.py
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class GraniteMoeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GraniteMoeModel`]. It is used to instantiate an GraniteMoe
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 GraniteMoe-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 GraniteMoe model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`GraniteMoeModel`]
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.
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
num_local_experts (`int`, *optional*, defaults to 8): total number of experts
num_experts_per_tok (`int`, *optional*, defaults to 2): number of experts per token
output_router_logits (`bool`, *optional*, defaults to `False`):
Whether or not the router logits should be returned by the model. Enabling this will also
allow the model to output the auxiliary loss.
router_aux_loss_coef (`float`, *optional*, defaults to 0.001): router auxiliary loss coefficient
```python
>>> from transformers import GraniteMoeModel, GraniteMoeConfig
>>> # Initializing a GraniteMoe granitemoe-3b style configuration
>>> configuration = GraniteMoeConfig()
>>> # Initializing a model from the granitemoe-7b style configuration
>>> model = GraniteMoeModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""

model_type = "granitemoe"
keys_to_ignore_at_inference = ["past_key_values"]
Expand Down Expand Up @@ -196,18 +111,17 @@ def _rope_scaling_validation(self):
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` must be a dictionary with two fields, `type`"
"and `factor`, 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}"
)
raise ValueError(f"`rope_scaling`'s type field must be one of "
f"['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}"
)
f"`rope_scaling`'s factor field must be a float > 1, "
f"got {rope_scaling_factor}")

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