code/model/modules/action_model/configuration_actionmodel.py

# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""Qwen3 model configuration"""

from transformers.configuration_utils import PretrainedConfig, layer_type_validation
from transformers.modeling_rope_utils import rope_config_validation
from transformers.utils import logging


logger = logging.get_logger(__name__)


class ActionModelConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Qwen3Model`]. It is used to instantiate a
    Qwen3 model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of
    Qwen3-8B [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).

    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 151936):
            Vocabulary size of the Qwen3 model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`Qwen3Model`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 22016):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_key_value_heads (`int`, *optional*, defaults to 32):
            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, check out [this
            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
        head_dim (`int`, *optional*, defaults to 128):
            The attention head dimension.
        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 32768):
            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`.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether the model's input and output word embeddings should be tied.
        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. NOTE: if you apply new rope type
            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
            accordingly.
            Expected contents:
                `rope_type` (`str`):
                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
                    'llama3'], with 'default' being the original RoPE implementation.
                `factor` (`float`, *optional*):
                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
                    original maximum pre-trained length.
                `original_max_position_embeddings` (`int`, *optional*):
                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
                    pretraining.
                `attention_factor` (`float`, *optional*):
                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
                    computation. If unspecified, it defaults to value recommended by the implementation, using the
                    `factor` field to infer the suggested value.
                `beta_fast` (`float`, *optional*):
                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
                    ramp function. If unspecified, it defaults to 32.
                `beta_slow` (`float`, *optional*):
                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
                    ramp function. If unspecified, it defaults to 1.
                `short_factor` (`list[float]`, *optional*):
                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                    size divided by the number of attention heads divided by 2
                `long_factor` (`list[float]`, *optional*):
                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                    size divided by the number of attention heads divided by 2
                `low_freq_factor` (`float`, *optional*):
                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
                `high_freq_factor` (`float`, *optional*):
                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        use_sliding_window (`bool`, *optional*, defaults to `False`):
            Whether to use sliding window attention.
        sliding_window (`int`, *optional*, defaults to 4096):
            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
        max_window_layers (`int`, *optional*, defaults to 28):
            The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any
            additional layer afterwards will use SWA (Sliding Window Attention).
        layer_types (`list`, *optional*):
            Attention pattern for each layer.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.

    ```python
    >>> from transformers import Qwen3Model, Qwen3Config

    >>> # Initializing a Qwen3 style configuration
    >>> configuration = Qwen3Config()

    >>> # Initializing a model from the Qwen3-8B style configuration
    >>> model = Qwen3Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "qwen3"
    keys_to_ignore_at_inference = ["past_key_values"]

    # Default tensor parallel plan for base model `Qwen3`
    base_model_tp_plan = {
        "action_encoder.*.self_attn.q_proj": "colwise",
        "action_encoder.*.self_attn.k_proj": "colwise",
        "action_encoder.*.self_attn.v_proj": "colwise",
        "action_encoder.*.self_attn.o_proj": "rowwise",
        "action_encoder.*.mlp.gate_proj": "colwise",
        "action_encoder.*.mlp.up_proj": "colwise",
        "action_encoder.*.mlp.down_proj": "rowwise",

        "action_decoder.*.self_attn.q_proj": "colwise",
        "action_decoder.*.self_attn.k_proj": "colwise",
        "action_decoder.*.self_attn.v_proj": "colwise",
        "action_decoder.*.self_attn.o_proj": "rowwise",
        "action_decoder.*.mlp.gate_proj": "colwise",
        "action_decoder.*.mlp.up_proj": "colwise",
        "action_decoder.*.mlp.down_proj": "rowwise",
    }
    base_model_pp_plan = {
        "action_encoder": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "action_decoder": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }

    def __init__(
        self,
        action_size=64,
        state_size=96,
        hidden_size=1024,
        intermediate_size=3072,
        dataset_vocab_size=256,  # 假设有100个数据集
        num_data_tokens=8,
        mask_ratio=0.25,  # Action Mask 比例
        # Mask ratio sampling for DAE masking:
        # - "fixed": use `mask_ratio` for all trajectories
        # - "uniform_per_traj": sample per trajectory in [mask_ratio_min, mask_ratio_max]
        mask_ratio_mode="uniform_per_traj",
        mask_ratio_min=0.25,
        mask_ratio_max=0.75,
        # Loss mode: whether to add reconstruction loss for masked-action view (two-view training).
        # - False: only current action reconstruction (single view).
        # - True: current action recon + masked action recon (two views, two recon losses).
        use_masked_action_recon=False,
        # Optional contrastive loss on action embedding (InfoNCE). When True, adds contrastive
        # between clean and masked embeddings; typically used together with use_masked_action_recon.
        use_contrastive_loss=False,
        # Optional domain-adversarial loss (GRL + MLP): predict dataset_id from embedding, then reverse
        # gradient so encoder learns domain-invariant features. Use with multi-domain data.
        use_domain_adversarial=False,
        domain_adversarial_weight=0.1,
        domain_adversarial_lambda=1.0,
        domain_adversarial_mlp_hidden=None,
        contrastive_temperature=0.07,
        contrastive_weight=0.1,
        contrastive_use_proj=False,
        contrastive_proj_dim=256,
        contrastive_use_distributed=True,
        state_drop_prob=0.5,  # State Dropout 比例
        min_action_len=5,  # 最小切片长度
        num_encoder_layers=28,
        num_decoder_layers=28,
        num_attention_heads=16,
        num_key_value_heads=8,
        head_dim=128,
        hidden_act="silu",
        max_position_embeddings=2048,
        max_action_chunk_size=256,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        tie_word_embeddings=False,
        rope_theta=10000.0,
        rope_scaling=None,
        attention_bias=False,
        use_sliding_window=False,
        sliding_window=4096,
        max_window_layers=28,
        # layer_types=None,
        attention_dropout=0.0,
        use_vae_reparameterization=False,
        # ---- Qwen3 pretrained init (optional) ----
        # Can be a HuggingFace model id (e.g. "Qwen/Qwen3-0.6B") or a local checkpoint folder.
        qwen3_pretrained_name_or_path="Qwen/Qwen3-0.6B",
        # Copy transformer block weights into action_encoder / action_decoder.
        qwen3_init_action_encoder=True,
        qwen3_init_action_decoder=True,
        # Copy final RMSNorm weights if shape matches.
        qwen3_init_norm=True,
        # Which source layer index maps to target layer 0.
        qwen3_encoder_layer_offset=0,
        qwen3_decoder_layer_offset=0,
        **kwargs,
    ):
        self.action_size = action_size
        self.state_size = state_size
        self.max_position_embeddings = max_position_embeddings
        self.max_action_chunk_size = max_action_chunk_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        
        self.dataset_vocab_size = dataset_vocab_size
        self.num_data_tokens = num_data_tokens
        self.mask_ratio = mask_ratio
        self.mask_ratio_mode = mask_ratio_mode
        self.mask_ratio_min = mask_ratio_min
        self.mask_ratio_max = mask_ratio_max
        self.use_masked_action_recon = use_masked_action_recon
        self.use_contrastive_loss = use_contrastive_loss
        self.use_domain_adversarial = use_domain_adversarial
        self.domain_adversarial_weight = domain_adversarial_weight
        self.domain_adversarial_lambda = domain_adversarial_lambda
        self.domain_adversarial_mlp_hidden = (
            domain_adversarial_mlp_hidden if domain_adversarial_mlp_hidden is not None else hidden_size
        )
        self.contrastive_temperature = contrastive_temperature
        self.contrastive_weight = contrastive_weight
        self.contrastive_use_proj = contrastive_use_proj
        self.contrastive_proj_dim = contrastive_proj_dim
        self.contrastive_use_distributed = contrastive_use_distributed
        self.state_drop_prob = state_drop_prob
        self.min_action_len = min_action_len
        
        self.num_encoder_layers = num_encoder_layers
        self.num_decoder_layers = num_decoder_layers
        self.num_attention_heads = num_attention_heads
        self.use_sliding_window = use_sliding_window
        self.sliding_window = sliding_window if self.use_sliding_window else None
        self.max_window_layers = max_window_layers
        self.use_vae_reparameterization = use_vae_reparameterization
        print(f"use_vae_reparameterization? {use_vae_reparameterization}")

        # Qwen3 pretrained init (optional)
        self.qwen3_pretrained_name_or_path = qwen3_pretrained_name_or_path
        self.qwen3_init_action_encoder = qwen3_init_action_encoder
        self.qwen3_init_action_decoder = qwen3_init_action_decoder
        self.qwen3_init_norm = qwen3_init_norm
        self.qwen3_encoder_layer_offset = qwen3_encoder_layer_offset
        self.qwen3_decoder_layer_offset = qwen3_decoder_layer_offset

        # for backward compatibility
        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads

        self.num_key_value_heads = num_key_value_heads
        self.head_dim = head_dim
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        # Validate the correctness of rotary position embeddings parameters
        # BC: if there is a 'type' field, move it to 'rope_type'.
        if self.rope_scaling is not None and "type" in self.rope_scaling:
            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
        rope_config_validation(self)

        # self.layer_types = layer_types
        # if self.layer_types is None:
        #     self.layer_types = [
        #         "sliding_attention"
        #         if self.sliding_window is not None and i >= self.max_window_layers
        #         else "full_attention"
        #         for i in range(self.num_hidden_layers)
        #     ]
        # layer_type_validation(self.layer_types, self.num_hidden_layers)

        super().__init__(
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )


__all__ = ["ActionModelConfig"]