Upload configuration_rwkv_hybrid.py

configuration_rwkv_hybrid.py

@@ -0,0 +1,254 @@
+# coding=utf-8
+# Copyright 2025 RWKV team. All rights reserved.
+# 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.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""RwkvHybrid model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.utils import logging
+from typing import Optional, Union, List
+
+
+logger = logging.get_logger(__name__)
+
+
+class RwkvHybridConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`RwkvHybridModel`]. It is used to instantiate a
+    RwkvHybrid model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    RwkvHybrid-7B-beta.
+
+    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 RwkvHybrid model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`RwkvHybridModel`]
+        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 checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
+        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
+        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 that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        head_size (`int`, *optional*, defaults to 64):
+            Dimensionality of each RWKV attention head. Defines the hidden dimension size for RWKV attention mechanisms.
+        head_size_divisor (`int`, *optional*, defaults to 8):
+            Constraint for head_size initialization, typically set to the square root of head_size. Ensures divisibility
+            between hidden_size and head_size.
+        wkv_version (`int`, *optional*, defaults to 7):
+            Version of RWKV attention implementation. Currently supports:
+            - 6: Original implementation requiring `wkv_has_gate=True` and `wkv_use_vfirst=False`
+            - 7: Improved version requiring `wkv_use_vfirst=True`
+        wkv_has_gate (`bool`, *optional*, defaults to False):
+            Whether to include gating mechanism in RWKV attention. Required for version 6.
+        wkv_has_group_norm (`bool`, *optional*, defaults to True):
+            Whether to apply group normalization in RWKV attention layers.
+        wkv_use_vfirst (`bool`, *optional*, defaults to True):
+            Whether to prioritize value projection in RWKV attention computation. Required for version 7.
+        wkv_layers (`Union[str, List[int]]`, *optional*, defaults to None):
+            Specifies which layers use RWKV attention:
+            - `"full"` or `None`: All layers use RWKV
+            - List of integers: Only specified layers (e.g., `[0,1,2]`) use RWKV attention
+
+    ```python
+    >>> from transformers import RwkvHybridModel, RwkvHybridConfig
+
+    >>> # Initializing a RwkvHybrid style configuration
+    >>> configuration = RwkvHybridConfig()
+
+    >>> # Initializing a model from the RwkvHybrid-7B style configuration
+    >>> model = RwkvHybridModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "rwkv_hybrid"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    # Default tensor parallel plan for base model `RwkvHybrid`
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+
+    def __init__(
+        self,
+        vocab_size: int = 151936,
+        hidden_size: int = 4096,
+        intermediate_size: int = 22016,
+        num_hidden_layers: int = 32,
+        num_attention_heads: int = 32,
+        num_key_value_heads: int = 32,
+        head_size: int = 64,
+        head_size_divisor: int = 8,
+        hidden_act: str = "silu",
+        max_position_embeddings: int = 32768,
+        initializer_range: float = 0.02,
+        rms_norm_eps: float = 1e-6,
+        use_cache: bool = True,
+        tie_word_embeddings: bool = False,
+        rope_theta: float = 10000.0,
+        rope_scaling: Optional[dict] = None,
+        use_sliding_window: bool = False,
+        sliding_window: int = 4096,
+        max_window_layers: int = 28,
+        attention_dropout: float = 0.0,
+        wkv_version: int = 7,
+        wkv_has_gate: bool = False,
+        wkv_has_group_norm: bool = True,
+        wkv_use_vfirst: bool = True,
+        wkv_layers: Optional[Union[str, List[int]]] = None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_wkv_heads = hidden_size // head_size
+        assert hidden_size % head_size == 0, "hidden_size must be divisible by head_size"
+        self.num_attention_heads = num_attention_heads
+        self.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window if use_sliding_window else None
+        self.max_window_layers = max_window_layers
+        self.head_size = head_size
+        self.head_size_divisor = head_size_divisor
+        self.wkv_version = wkv_version
+
+        self.wkv_has_gate = wkv_has_gate
+        self.wkv_has_group_norm = wkv_has_group_norm
+        self.wkv_use_vfirst = wkv_use_vfirst
+
+        if self.wkv_version == 7:
+            assert self.wkv_use_vfirst, "wkv_use_vfirst must be True for wkv_version 7"
+        elif self.wkv_version == 6:
+            assert self.wkv_has_gate, "wkv_has_gate must be True for wkv_version 6"
+            assert not self.wkv_use_vfirst, "wkv_use_vfirst must be False for wkv_version 6"
+        else:
+            raise NotImplementedError(f"Unsupported wkv_version: {self.wkv_version}, \
+                                        wkv_version must be 6 or 7")
+
+        if wkv_layers == "full" or wkv_layers is None:
+            self.wkv_layers = list(range(num_hidden_layers))
+        elif isinstance(wkv_layers, list):
+            if all(isinstance(layer, int) for layer in wkv_layers):
+                self.wkv_layers = wkv_layers
+            else:
+                raise ValueError(
+                    "All elements in wkv_layers must be integers.")
+        else:
+            raise TypeError(
+                "wkv_layers must be either 'full', None, or a list of integers.")
+
+        # 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.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_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)
+
+        super().__init__(
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )