image-match/python/py/Lib/site-packages/transformers/models/cwm/modular_cwm.py

# Copyright 2025
#
# 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.


import torch
from huggingface_hub.dataclasses import strict

from ...cache_utils import Cache, DynamicCache
from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
from ...modeling_outputs import BaseModelOutputWithPast
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, logging
from ..llama.configuration_llama import LlamaConfig
from ..llama.modeling_llama import (
    LlamaDecoderLayer,
    LlamaForCausalLM,
    LlamaModel,
    LlamaPreTrainedModel,
)
from ..qwen2.modeling_qwen2 import Qwen2Attention, Qwen2RotaryEmbedding


logger = logging.get_logger(__name__)


@auto_docstring(checkpoint="facebook/cwm")
@strict
class CwmConfig(LlamaConfig):
    model_type = "cwm"
    default_theta = 1_000_000.0

    vocab_size: int = 128256
    hidden_size: int = 6144
    intermediate_size: int = 21504
    num_hidden_layers: int = 64
    num_attention_heads: int = 48
    num_key_value_heads: int = 8
    head_dim: int = 128
    hidden_act: str = "silu"
    max_position_embeddings: int = 131072
    initializer_range: float = 0.02
    rms_norm_eps: float = 1e-5
    use_cache: bool = True
    pad_token_id: int | None = None
    eos_token_id: int | list[int] | None = None
    bos_token_id: int = 128000
    tie_word_embeddings: bool = False
    attention_dropout: float | int = 0.0
    pretraining_tp: int = 1
    mlp_bias: bool = False
    rope_parameters: dict | None = None
    sliding_window: int = 8192
    layer_types: list[str] | None = None  # ["full_attention"|"sliding_attention"] per layer

    attention_bias = AttributeError()

    def __post_init__(self, **kwargs):
        if self.rope_parameters is None:
            self.rope_parameters = {
                "rope_theta": 1_000_000.0,
                "factor": 16.0,
                "high_freq_factor": 4.0,
                "low_freq_factor": 1.0,
                "original_max_position_embeddings": 8192,
                "rope_type": "llama3",
            }

        if self.layer_types is None:
            # Default pattern: every 4th layer uses full attention, others use sliding attention
            window_pattern = 4
            self.layer_types = [
                ("full_attention" if (i % window_pattern == 0) else "sliding_attention")
                for i in range(self.num_hidden_layers)
            ]

        self.sliding_window = int(self.sliding_window) if self.sliding_window else None
        self.layer_types = list(self.layer_types)
        self.eos_token_id = self.eos_token_id if self.eos_token_id is not None else [128001, 128008, 128009]
        super().__post_init__(**kwargs)


class CwmRotaryEmbedding(Qwen2RotaryEmbedding):
    pass


class CwmAttention(Qwen2Attention):
    def __init__(self, config: CwmConfig, layer_idx: int):
        super().__init__(config=config, layer_idx=layer_idx)
        self.q_proj = torch.nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
        self.k_proj = torch.nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
        self.v_proj = torch.nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)


class CwmDecoderLayer(LlamaDecoderLayer):
    def __init__(self, config: CwmConfig, layer_idx: int):
        super().__init__(config=config, layer_idx=layer_idx)
        self.self_attn = CwmAttention(config=config, layer_idx=layer_idx)


class CwmPreTrainedModel(LlamaPreTrainedModel):
    pass


class CwmModelOutputWithPast(BaseModelOutputWithPast):
    pass


class CwmModel(LlamaModel):
    config_class = CwmConfig

    def __init__(self, config: CwmConfig):
        super().__init__(config)
        self.layers = torch.nn.ModuleList(
            [CwmDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )

    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        use_cache: bool | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> CwmModelOutputWithPast:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if inputs_embeds is None:
            inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache(config=self.config)

        if position_ids is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
            position_ids = position_ids.unsqueeze(0)

        if not isinstance(causal_mask_mapping := attention_mask, dict):
            mask_kwargs = {
                "config": self.config,
                "inputs_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "past_key_values": past_key_values,
                "position_ids": position_ids,
            }
            sliding_mask_kwargs = mask_kwargs.copy()

            causal_mask_mapping = {
                "full_attention": create_causal_mask(**mask_kwargs),
                "sliding_attention": create_sliding_window_causal_mask(**sliding_mask_kwargs),
            }

        hidden_states = inputs_embeds
        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        for i, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]):
            hidden_states = decoder_layer(
                hidden_states,
                attention_mask=causal_mask_mapping[self.config.layer_types[i]],
                position_ids=position_ids,
                past_key_values=past_key_values,
                position_embeddings=position_embeddings,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)
        return CwmModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
        )


class CwmForCausalLM(LlamaForCausalLM):
    pass


__all__ = [
    "CwmConfig",
    "CwmPreTrainedModel",
    "CwmModel",
    "CwmForCausalLM",
]