wehub-resource-sync
350 lines
16 KiB
Python
350 lines
16 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# SPDX-FileCopyrightText: Copyright 2025 NAVER Cloud HyperCLOVA team
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#
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# Copyright 2025 NAVER Cloud HyperCLOVA 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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"""HyperCLOVA X model configuration."""
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from transformers import AutoConfig
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from transformers.configuration_utils import PretrainedConfig
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class HyperCLOVAXConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a
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[`HyperCLOVAXModel`]. It is used to instantiate a HyperCLOVAX model
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according to the specified arguments, defining the model architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used
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to control the model outputs. Read the documentation from
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[`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32000):
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Vocabulary size of the HyperCLOVAX model. Defines the number of
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different tokens that can be represented by the `input_ids`
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passed when calling [`HyperCLOVAXModel`]
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 11008):
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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 32):
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Number of attention heads for each attention layer in the
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Transformer decoder.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to
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implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use
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Multi Head Attention (MHA), if `num_key_value_heads=1` the model
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will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each
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group key and value head should be constructed by meanpooling all
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the original heads within that group. For more details checkout
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[this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not
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specified, will default to `num_attention_heads`.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the
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decoder.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used
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with.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for
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initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values
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attentions (not used by all models). Only relevant if
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`config.is_decoder=True`.
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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 1):
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Beginning of stream token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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End of stream token id.
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pretraining_tp (`int`, *optional*, defaults to 1):
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Experimental feature. Tensor parallelism rank used during
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pretraining. Please refer to [this document](https://huggingface.
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co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism)
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to understand more about it. This value is necessary to ensure
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exact reproducibility of the pretraining results. Please refer to
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[this issue](https://github.com/pytorch/pytorch/issues/76232).
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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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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE
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embeddings. NOTE: if you apply new rope type and you expect the
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model to work on longer `max_position_embeddings`, we recommend
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you to update this value accordingly.
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Expected contents:
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`rope_type` (`str`):
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The sub-variant of RoPE to use. Can be one of ['default',
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'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with
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'default' being the original RoPE implementation.
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`factor` (`float`, *optional*):
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Used with all rope types except 'default'. The scaling
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factor to apply to the RoPE embeddings. In most scaling
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types, a `factor` of x will enable the model to handle
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sequences of length x * original maximum pre-trained
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length.
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`original_max_position_embeddings` (`int`, *optional*):
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Used with 'dynamic', 'longrope' and 'llama3'. The
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original max position embeddings used during pretraining.
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`attention_factor` (`float`, *optional*):
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Used with 'yarn' and 'longrope'. The scaling factor to be
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applied on the attention computation. If unspecified, it
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defaults to value recommended by the implementation, using
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the `factor` field to infer the suggested value.
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`beta_fast` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for
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extrapolation (only) in the linear ramp function. If
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unspecified, it defaults to 32.
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`beta_slow` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for
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interpolation (only) in the linear ramp function. If
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unspecified, it defaults to 1.
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`short_factor` (`List[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be
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applied to short contexts (<
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`original_max_position_embeddings`). Must be a list of
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numbers with the same length as the hidden size divided
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by the number of attention heads divided by 2
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`long_factor` (`List[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be
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applied to long contexts (<
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`original_max_position_embeddings`). Must be a list of
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numbers with the same length as the hidden size divided
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by the number of attention heads divided by 2
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`low_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to low
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frequency components of the RoPE
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`high_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to high
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frequency components of the RoPE
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attention_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output
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projection layers during self-attention.
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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 `False`):
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Whether to use a bias in up_proj, down_proj and gate_proj layers
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in the MLP layers.
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head_dim (`int`, *optional*):
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The attention head dimension. If None, it will default to
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hidden_size // num_heads
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embedding_multiplier (`float`, *optional*, defaults to `None`):
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Multiplier applied to the embedding weights. If `None`, it is
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equivalent to `1.0`.
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logits_scaling (`float`, *optional*, defaults to `None`):
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Scaling factor for logits. If `None`, it is equivalent to `1.0`.
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attention_multiplier (`float`, *optional*, defaults to `None`):
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Multiplier applied to the attention weights. If `None`, it is
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equivalent to `self.head_dim ** -0.5`.
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residual_multiplier (`float`, *optional*, defaults to `None`):
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Scaling factor for residual connections. If `None`, it is
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equivalent to `1.0`.
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use_post_norm (`bool`, *optional*, defaults to `True`):
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Determines whether to apply Peri-Layer Normalization. Set to
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False to disable this feature.
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rope_parameters (`dict`, *optional*):
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Dictionary containing the RoPE parameters used by vLLM's
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`get_rope`. When provided, takes precedence over `rope_theta`
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and `rope_scaling`. If `None`, it is derived from `rope_theta`
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and `rope_scaling` automatically.
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"""
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model_type = "hyperclovax"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=32000,
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hidden_size=4096,
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intermediate_size=11008,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=None,
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hidden_act="silu",
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max_position_embeddings=2048,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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pad_token_id=None,
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bos_token_id=1,
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eos_token_id=2,
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pretraining_tp=1,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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attention_bias=False,
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attention_dropout=0.0,
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mlp_bias=False,
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head_dim=None,
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embedding_multiplier=None, # mup
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logits_scaling=None, # mup
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attention_multiplier=None, # mup
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residual_multiplier=None, # mup
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use_post_norm=True, # post-norm(peri-LN)
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rope_parameters=None,
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auto_map=None,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.pretraining_tp = pretraining_tp
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.mlp_bias = mlp_bias
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self.head_dim = (
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head_dim
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if head_dim is not None
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else self.hidden_size // self.num_attention_heads
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)
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# Derive rope_parameters for vLLM's get_rope() from rope_theta /
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# rope_scaling, unless the caller already provided rope_parameters.
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if rope_parameters is None:
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if rope_scaling is not None:
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# Shallow-copy to avoid mutating the caller's dict.
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rope_parameters = dict(rope_scaling)
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# BC: 'type' field -> 'rope_type', remove stale key.
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if "type" in rope_parameters:
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rope_parameters.setdefault("rope_type", rope_parameters.pop("type"))
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else:
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rope_parameters = {"rope_type": "default"}
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if "rope_theta" not in rope_parameters:
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rope_parameters["rope_theta"] = rope_theta
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self.rope_parameters = rope_parameters
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# BC: keep self.rope_scaling consistent for HF serialization.
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if self.rope_scaling is not None and "type" in self.rope_scaling:
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self.rope_scaling["rope_type"] = self.rope_scaling["type"]
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# mup
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self.embedding_multiplier = (
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embedding_multiplier if embedding_multiplier is not None else 1.0
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)
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self.logits_scaling = logits_scaling if logits_scaling is not None else 1.0
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self.attention_multiplier = (
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attention_multiplier
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if attention_multiplier is not None
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else self.head_dim**-0.5
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)
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self.residual_multiplier = (
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residual_multiplier if residual_multiplier is not None else 1.0
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)
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# post-norm (Peri-LN)
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self.use_post_norm = use_post_norm
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super().__init__(
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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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auto_map=auto_map,
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**kwargs,
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)
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class HCXVisionConfig(PretrainedConfig):
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"""Vendored HyperCLOVAX Vision config with transformers v5 fix.
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The original remote code config does not handle empty initialization
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(text_config=None), which breaks transformers v5's @strict validation.
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TODO: Remove this class once HyperCLOVAX is upstreamed to transformers.
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Tracking PR: https://github.com/huggingface/transformers/pull/44956
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"""
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model_type = "hyperclovax_vlm"
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keys_to_ignore_at_inference = ["past_key_values"]
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text_config_attribute_map = {
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"n_embd": "hidden_size",
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"n_positions": "max_position_embeddings",
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"n_head": "num_attention_heads",
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"n_layer": "num_hidden_layers",
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}
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def __init__(
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self,
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text_config=None,
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vision_config=None,
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use_nth_layer=-2,
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img_start_id=100009,
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decoder_max_length=4096,
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anyres=False,
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unpad=False,
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max_num_grids=-1,
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num_queries_vis_abstractor=-1,
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ignore_index=-100,
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proj_pos_emb=True,
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proj_prenorm=False,
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use_1x1_grid=False,
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**kwargs,
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):
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for key, val in self.text_config_attribute_map.items():
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if text_config is not None and key in text_config:
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text_config[val] = text_config.pop(key)
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self.text_config = None
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if text_config is not None:
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_text_config = AutoConfig.for_model(text_config["model_type"])
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self.text_config = _text_config.from_dict(text_config)
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self.hidden_size = self.text_config.hidden_size
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self.vision_config = None
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if vision_config is not None:
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_vision_config = AutoConfig.for_model(vision_config["model_type"])
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self.vision_config = _vision_config.from_dict(vision_config)
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self.use_nth_layer = use_nth_layer
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self.decoder_max_length = decoder_max_length
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self.anyres = anyres
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self.unpad = unpad
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self.max_num_grids = max_num_grids
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self.num_queries_vis_abstractor = num_queries_vis_abstractor
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self.img_start_id = img_start_id
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self.ignore_index = ignore_index
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self.proj_pos_emb = proj_pos_emb
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self.proj_prenorm = proj_prenorm
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self.use_1x1_grid = use_1x1_grid
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super().__init__(**kwargs)
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def get_text_config(self, decoder=False):
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if self.text_config is not None:
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return self.text_config
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return self
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