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296 lines
12 KiB
296 lines
12 KiB
# Copyright 2025
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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
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from ...cache_utils import Cache, DynamicCache
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from ...configuration_utils import layer_type_validation
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from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
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from ...modeling_outputs import BaseModelOutputWithPast
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, logging
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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 ..qwen2.modeling_qwen2 import Qwen2Attention, Qwen2RotaryEmbedding
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logger = logging.get_logger(__name__)
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class CwmConfig(LlamaConfig):
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"""
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Configuration for Code World Model (CWM).
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This is an inherited Llama3-compatible configuration with layer-interleaved
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sliding-window attention. Configures a `CwmModel`. Designed to yield a configuration mirroring the model in the
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[facebook/cwm](https://huggingface.co/facebook/cwm) architecture by default. Other models include:
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- [facebook/cwm-sft](https://huggingface.co/facebook/cwm-sft)
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- [facebook/cwm-pretrain](https://huggingface.co/facebook/cwm-pretrain)
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Args:
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vocab_size (`int`, *optional*, defaults to 128256):
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Vocabulary size of the CWM model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`CwmModel`]
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hidden_size (`int`, *optional*, defaults to 6144):
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Dimension of the hidden representations
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intermediate_size (`int`, *optional*, defaults to 21504):
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Dimension of the MLP representations
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num_hidden_layers (`int`, *optional*, defaults to 64):
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Number of hidden layers in the Transformer decoder
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num_attention_heads (`int`, *optional*, defaults to 48):
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Number of attention heads for each attention layer in the Transformer decoder
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num_key_value_heads (`int`, *optional*, defaults to 8):
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This is the number of key_value heads that should be used to implement Grouped Query Attention (GQA).
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If it is not specified, will default to `num_attention_heads`.
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head_dim (`int`, *optional*, defaults to 128):
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The attention head dimension.
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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 decoder.
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max_position_embeddings (`int`, *optional*, defaults to 131072):
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The maximum sequence length that this model might ever be used with. CWM's attention allows sequence
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lengths up to 131072 tokens.
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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 initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-05):
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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 attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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pad_token_id (`int`, *optional*):
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Padding token id.
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eos_token_id (`int` or `list[int]`, *optional*, defaults to `[128001, 128008, 128009]`):
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The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
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bos_token_id (`int`, *optional*, defaults to 128000):
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The id of the *beginning-of-sequence* token.
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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_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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pretraining_tp (`int`, *optional*, defaults to 1):
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Tensor parallelism degree used during pretraining. See [this
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document](https://huggingface.co/docs/transformers/parallelism) and [this
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issue](https://github.com/pytorch/pytorch/issues/76232).
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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 in the MLP layers.
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rope_parameters (`RopeParameters`, *optional*):
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Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
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a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
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with longer `max_position_embeddings`.
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sliding_window (`int`, *optional*, defaults to 8192):
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Sliding window attention window size.
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layer_types (`List[str]`, *optional*):
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List of layer types for each layer. Each element should be either "full_attention" or "sliding_attention".
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If not specified, will default to alternating pattern based on the provided window pattern.
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"""
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model_type = "cwm"
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default_theta = 1_000_000.0
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def __init__(
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self,
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vocab_size: int = 128256,
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hidden_size: int = 6144,
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intermediate_size: int = 21504,
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num_hidden_layers: int = 64,
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num_attention_heads: int = 48,
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num_key_value_heads: int = 8,
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head_dim: int = 128,
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hidden_act: str = "silu",
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max_position_embeddings: int = 131072,
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initializer_range: float = 0.02,
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rms_norm_eps: float = 1e-5,
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use_cache: bool = True,
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pad_token_id: int | None = None,
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eos_token_id=[128001, 128008, 128009],
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bos_token_id: int = 128000,
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tie_word_embeddings: bool = False,
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attention_dropout: float = 0.0,
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pretraining_tp: int = 1,
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mlp_bias: bool = False,
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rope_parameters: dict | None = None,
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# CWM interleaved sliding window fields
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sliding_window: int = 8192,
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layer_types: list[str] | None = None, # ["full_attention"|"sliding_attention"] per layer
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**kwargs,
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):
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if rope_parameters is None:
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rope_parameters = {
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"rope_theta": 1_000_000.0,
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"factor": 16.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3",
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}
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if layer_types is None:
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# Default pattern: every 4th layer uses full attention, others use sliding attention
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window_pattern = 4
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layer_types = [
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("full_attention" if (i % window_pattern == 0) else "sliding_attention")
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for i in range(num_hidden_layers)
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]
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else:
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layer_type_validation(layer_types, num_hidden_layers)
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self.sliding_window = int(sliding_window) if sliding_window else None
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self.layer_types = list(layer_types)
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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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head_dim=head_dim,
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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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rms_norm_eps=rms_norm_eps,
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use_cache=use_cache,
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pad_token_id=pad_token_id,
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eos_token_id=list(eos_token_id),
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bos_token_id=bos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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attention_bias=False,
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attention_dropout=attention_dropout,
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rope_parameters=rope_parameters,
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pretraining_tp=pretraining_tp,
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mlp_bias=mlp_bias,
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**kwargs,
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)
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# CWM models don't use attention bias, remove it from config
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del self.attention_bias
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class CwmRotaryEmbedding(Qwen2RotaryEmbedding):
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pass
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class CwmAttention(Qwen2Attention):
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def __init__(self, config: CwmConfig, layer_idx: int):
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super().__init__(config=config, layer_idx=layer_idx)
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self.q_proj = torch.nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
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self.k_proj = torch.nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = torch.nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
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class CwmDecoderLayer(LlamaDecoderLayer):
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def __init__(self, config: CwmConfig, layer_idx: int):
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super().__init__(config=config, layer_idx=layer_idx)
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self.attention_type = config.layer_types[layer_idx]
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self.self_attn = CwmAttention(config=config, layer_idx=layer_idx)
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class CwmPreTrainedModel(LlamaPreTrainedModel):
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pass
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class CwmModelOutputWithPast(BaseModelOutputWithPast):
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pass
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class CwmModel(LlamaModel):
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config_class = CwmConfig
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def __init__(self, config: CwmConfig):
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super().__init__(config)
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self.layers = torch.nn.ModuleList(
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[CwmDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
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)
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def forward(
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self,
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input_ids: torch.LongTensor | None = None,
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attention_mask: torch.Tensor | None = None,
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position_ids: torch.LongTensor | None = None,
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past_key_values: Cache | None = None,
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inputs_embeds: torch.FloatTensor | None = None,
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cache_position: torch.LongTensor | None = None,
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use_cache: bool | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> CwmModelOutputWithPast:
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if (input_ids is None) ^ (inputs_embeds is not None):
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raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
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if inputs_embeds is None:
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inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)
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if use_cache and past_key_values is None:
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past_key_values = DynamicCache(config=self.config)
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if cache_position is None:
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past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
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cache_position: torch.Tensor = (
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torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
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)
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if position_ids is None:
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position_ids = cache_position.unsqueeze(0)
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if not isinstance(causal_mask_mapping := attention_mask, dict):
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mask_kwargs = {
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"config": self.config,
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"input_embeds": inputs_embeds,
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"attention_mask": attention_mask,
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"cache_position": cache_position,
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"past_key_values": past_key_values,
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"position_ids": position_ids,
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}
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sliding_mask_kwargs = mask_kwargs.copy()
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causal_mask_mapping = {
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"full_attention": create_causal_mask(**mask_kwargs),
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"sliding_attention": create_sliding_window_causal_mask(**sliding_mask_kwargs),
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}
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hidden_states = inputs_embeds
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position_embeddings = self.rotary_emb(hidden_states, position_ids)
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for decoder_layer in self.layers[: self.config.num_hidden_layers]:
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hidden_states = decoder_layer(
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hidden_states,
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attention_mask=causal_mask_mapping[decoder_layer.attention_type],
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position_ids=position_ids,
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past_key_values=past_key_values,
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cache_position=cache_position,
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position_embeddings=position_embeddings,
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**kwargs,
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)
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hidden_states = self.norm(hidden_states)
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return CwmModelOutputWithPast(
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last_hidden_state=hidden_states,
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past_key_values=past_key_values,
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)
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class CwmForCausalLM(LlamaForCausalLM):
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pass
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__all__ = [
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"CwmConfig",
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"CwmPreTrainedModel",
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"CwmModel",
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"CwmForCausalLM",
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]
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