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195 lines
7.6 KiB
195 lines
7.6 KiB
# Copyright 2025 the HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch.nn as nn
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from ...modeling_rope_utils import RopeParameters
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from ...utils import auto_docstring, can_return_tuple
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from ..llama.configuration_llama import LlamaConfig
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from ..llama.modeling_llama import (
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LlamaDecoderLayer,
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LlamaForCausalLM,
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LlamaModel,
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LlamaPreTrainedModel,
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)
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from ..nemotron.modeling_nemotron import NemotronMLP
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class Jais2Config(LlamaConfig):
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r"""
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This is the configuration class to store the configuration of a [`Jais2Model`]. It is used to instantiate a Jais2
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model according to the specified arguments, defining the model architecture.
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[inceptionai/Jais-2-8B-Chat](https://huggingface.co/inceptionai/Jais-2-8B-Chat).
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 150272):
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Vocabulary size of the Jais2 model.
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hidden_size (`int`, *optional*, defaults to 3328):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 26624):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 26):
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Number of attention heads for each attention layer.
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num_key_value_heads (`int`, *optional*):
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Number of key_value heads for Grouped Query Attention.
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hidden_act (`str`, *optional*, defaults to `"relu2"`):
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The non-linear activation function in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 8192):
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The maximum sequence length.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer.
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layer_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether to return last key/values attentions.
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pad_token_id (`int`, *optional*):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 0):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 150024):
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End of stream token id.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings.
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attention_bias (`bool`, *optional*, defaults to `True`):
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Whether to use a bias in the query, key, value and output projection layers.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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mlp_bias (`bool`, *optional*, defaults to `True`):
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Whether to use a bias in up_proj, down_proj and gate_proj layers.
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head_dim (`int`, *optional*):
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The attention head dimension.
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rope_parameters (`dict`, *optional*):
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The RoPE parameters.
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"""
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model_type = "jais2"
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base_model_tp_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.mlp.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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}
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def __init__(
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self,
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vocab_size: int | None = 150272,
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hidden_size: int | None = 3328,
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intermediate_size: int | None = 26624,
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num_hidden_layers: int | None = 32,
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num_attention_heads: int | None = 26,
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num_key_value_heads: int | None = None,
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hidden_act: str | None = "relu2",
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max_position_embeddings: int | None = 8192,
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initializer_range: float | None = 0.02,
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layer_norm_eps: float | None = 1e-5,
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use_cache: bool | None = True,
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pad_token_id: int | None = None,
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bos_token_id: int | None = 0,
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eos_token_id: int | None = 150024,
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tie_word_embeddings: bool | None = False,
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attention_bias: bool | None = True,
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attention_dropout: float | None = 0.0,
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mlp_bias: bool | None = True,
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head_dim: int | None = None,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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**kwargs,
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):
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super().__init__(
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vocab_size=vocab_size,
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hidden_size=hidden_size,
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intermediate_size=intermediate_size,
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num_hidden_layers=num_hidden_layers,
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num_attention_heads=num_attention_heads,
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num_key_value_heads=num_key_value_heads,
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hidden_act=hidden_act,
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max_position_embeddings=max_position_embeddings,
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initializer_range=initializer_range,
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use_cache=use_cache,
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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attention_bias=attention_bias,
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attention_dropout=attention_dropout,
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mlp_bias=mlp_bias,
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head_dim=head_dim,
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rope_parameters=rope_parameters,
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**kwargs,
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)
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self.layer_norm_eps = layer_norm_eps
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del self.rms_norm_eps
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del self.pretraining_tp
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class Jais2MLP(NemotronMLP):
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pass
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class Jais2DecoderLayer(LlamaDecoderLayer):
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def __init__(self, config: Jais2Config, layer_idx: int):
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super().__init__(config, layer_idx)
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self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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class Jais2PreTrainedModel(LlamaPreTrainedModel):
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pass
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class Jais2Model(LlamaModel):
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def __init__(self, config: Jais2Config):
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super().__init__(config)
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self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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class Jais2ForCausalLM(LlamaForCausalLM):
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@can_return_tuple
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@auto_docstring
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def forward(self, **super_kwargs):
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r"""
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Example:
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```python
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>>> from transformers import AutoTokenizer, Jais2ForCausalLM
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>>> model = Jais2ForCausalLM.from_pretrained("inceptionai/Jais-2-8B-Chat")
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>>> tokenizer = AutoTokenizer.from_pretrained("inceptionai/Jais-2-8B-Chat")
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>>> prompt = "Hey, are you conscious? Can you talk to me?"
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>>> inputs = tokenizer(prompt, return_tensors="pt")
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>>> # Generate
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>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
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>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
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```"""
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return super().forward(**super_kwargs)
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__all__ = [
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"Jais2Config",
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"Jais2Model",
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"Jais2ForCausalLM",
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"Jais2PreTrainedModel",
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]
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