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""" JetMoE model configuration"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from transformers import PreTrainedTokenizer, TensorType, is_torch_available
from transformers.configuration_utils import PretrainedConfig
from transformers.onnx import OnnxConfigWithPast, PatchingSpec
from transformers.utils import logging
import torch.nn.init as init
import json
logger = logging.get_logger(__name__)
class JetMoEConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`JetMoEModel`]. It is used to instantiate a
JetMoE 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 JetMoE
[jetmoe-small](https://huggingface.co/jetmoe-small) architecture. 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 50400):
Vocabulary size of the JetMoE model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`JetMoEModel`].
n_positions (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
n_embd (`int`, *optional*, defaults to 4096):
Dimensionality of the embeddings and hidden states.
n_layer (`int`, *optional*, defaults to 28):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
rotary_dim (`int`, *optional*, defaults to 64):
Number of dimensions in the embedding that Rotary Position Embedding is applied to.
n_inner (`int`, *optional*, defaults to None):
Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
activation_function (`str`, *optional*, defaults to `"gelu_new"`):
Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`int`, *optional*, defaults to 0.1):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon to use in the layer normalization layers.
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).
Example:
```python
>>> from transformers import JetMoEConfig, JetMoEModel
>>> # Initializing a JetMoE 6B configuration
>>> configuration = JetMoEConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = JetMoEModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "jetmoe"
attribute_map = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "num_layers",
}
def __init__(
self,
vocab_size=50295,
hidden_size=1024,
num_layers=24,
num_attention_heads=16,
kv_channels = 128,
ffn_hidden_size=2048,
max_position_embeddings=4096,
rotary_percent=1.0,
activation_function="silu",
glu=True,
moe_num_experts=8,
moe_top_k=2,
use_cache=True,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=True,
bias=True,
rope_theta=10000.0,
rms_norm_eps=1e-6,
initializer_range=0.01,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.num_attention_heads = num_attention_heads
self.kv_channels = kv_channels
self.ffn_hidden_size = ffn_hidden_size
self.max_position_embeddings = max_position_embeddings
self.rotary_percent = rotary_percent
self.activation_function = activation_function
self.glu = glu
self.moe_num_experts = moe_num_experts
self.moe_top_k = moe_top_k
self.use_cache = use_cache
self.initializer_range = initializer_range
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.init_method = init.xavier_uniform_
self.output_layer_init_method = init.xavier_uniform_
self.bias = bias
self.rope_theta = rope_theta
self.rms_norm_eps = rms_norm_eps
super().__init__(
bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
)
def to_dict(self):
"""Returns a dictionary representation of the config, excluding non-serializable attributes."""
return {k: v for k, v in self.__dict__.items() if k not in ['init_method', 'output_layer_init_method', 'torch_dtype', '_pre_quantization_dtype', 'quantization_config']}
def to_json_string(self, use_diff=False):
"""Serializes this instance to a JSON string, excluding non-serializable attributes.
Args:
use_diff (bool): Whether to use differences with the default config. This argument is
accepted for compatibility with the transformers library but is not
used in this custom implementation.
"""
config_dict = self.to_dict() # Assuming you have a to_dict method as shown earlier
return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
class JetMoEOnnxConfig(OnnxConfigWithPast):
def __init__(
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: List[PatchingSpec] = None,
use_past: bool = False,
):
"""
Initialize the JetMoEOnnxConfig.
Args:
config (PretrainedConfig): Pretrained model configuration.
task (str): Task description.
patching_specs (List[PatchingSpec]): List of patching specifications.
use_past (bool): Whether to use past tokens in the configuration.
"""
super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)
if not getattr(self._config, "pad_token_id", None):
# TODO: how to do that better?
self._config.pad_token_id = 0
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
"""
Define the input mappings.
Returns:
Mapping[str, Mapping[int, str]]: Input mappings.
"""
common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
if self.use_past:
self.fill_with_past_key_values_(common_inputs, direction="inputs")
common_inputs["attention_mask"] = {0: "batch", 1: "past_sequence + sequence"}
else:
common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}
return common_inputs
@property
def num_layers(self) -> int:
"""
Get the number of layers.
Returns:
int: Number of layers.
"""
return self._config.n_layer
@property
def num_attention_heads(self) -> int:
"""
Get the number of attention heads.
Returns:
int: Number of attention heads.
"""
return self._config.n_head
def generate_dummy_inputs(
self,
tokenizer: PreTrainedTokenizer,
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
"""
Generate dummy inputs for testing.
Args:
tokenizer (PreTrainedTokenizer): Pretrained tokenizer.
batch_size (int): Batch size.
seq_length (int): Sequence length.
is_pair (bool): Whether the input is a pair.
framework (Optional[TensorType]): Tensor framework.
Returns:
Mapping[str, Any]: Dummy inputs.
"""
common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
)
# We need to order the input in the way they appears in the forward()
ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]})
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
batch, seqlen = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
past_key_values_length = seqlen + 2
past_shape = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
ordered_inputs["past_key_values"] = [
(torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(self.num_layers)
]
ordered_inputs["attention_mask"] = common_inputs["attention_mask"]
if self.use_past:
mask_dtype = ordered_inputs["attention_mask"].dtype
ordered_inputs["attention_mask"] = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
)
return ordered_inputs
@property
def default_onnx_opset(self) -> int:
"""
Get the default ONNX opset version.
Returns:
int: Default ONNX opset version.
"""
return 13
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