Commit
•
43430cc
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Parent(s):
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Upload 2 files
Browse files- configuration.py +247 -0
- modeling.py +1585 -0
configuration.py
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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# This code has been adapted from Meta and Huggingface and inherits the above lisence.
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# The original code can be found here:
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# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/configuration_llama.py
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+
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+
"""Extended Mind LLaMA model configuration"""
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+
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class ExtendedLlamaConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`ExtendedLlamaModel`].
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It is used to instantiate an Extended Mind LLaMA model according to the specified arguments,
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defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the Extended Mind LLaMA-7B.
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+
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Configuration objects inherit from [`PretrainedConfig`]
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and can be used to control the model outputs.
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Read the documentation from [`PretrainedConfig`] for more information.
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+
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Args:
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vocab_size (`int`, *optional*, defaults to 32000):
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+
Vocabulary size of the LLaMA model. Defines the number of different tokens
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that can be represented by the `inputs_ids` passed when calling [`LlamaModel`]
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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 encoder.
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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 Transformer encoder.
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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 implement
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Grouped Query Attention. If `num_key_value_heads=num_attention_heads`,
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the model will use Multi Head Attention (MHA), if `num_key_value_heads=1
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the model will use Multi Query Attention (MQA) otherwise GQA is used.
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When converting a multi-head checkpoint to a GQA checkpoint,
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each group key and value head should be constructed by meanpooling
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all the original heads within that group. For more details checkout
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[this paper](https://arxiv.org/pdf/2305.13245.pdf).
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If it is not specified, will default to
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`num_attention_heads`.
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pretraining_tp (`int`, *optional*, defaults to `1`):
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Experimental feature. Tensor parallelism rank used during pretraining.
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Please refer to [this document]
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(https://huggingface.co/docs/transformers/parallelism)
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to understand more about it. This value is
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necessary to ensure exact reproducibility of the pretraining results.
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Please refer to [this issue]
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(https://github.com/pytorch/pytorch/issues/76232).
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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 2048):
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The maximum sequence length that this model might ever be used with.
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Llama 1 supports up to 2048 tokens,
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Llama 2 up to 4096, CodeLlama up to 16384.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer
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for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-12):
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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
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(not used by all models). Only relevant if `config.is_decoder=True`.
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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 embeddings.
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Currently supports two scaling strategies: linear and dynamic.
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Their scaling factor must be an float greater than 1. The expected format
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is `{"type": strategy name, "factor": scaling factor}`.
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When using this flag, don't update `max_position_embeddings`
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to the expected new maximum. See the following thread for more information
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on how these scaling strategies behave:
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https://www.reddit.com/r/LocalLLaMA/comments/
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14mrgpr/dynamically_scaled_rope_further_increases/.
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This is an experimental feature, subject to breaking API changes in future versions.
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+
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#### Memory Configuration ####
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use_external_mind (`bool`, *optional*, defaults to `True`):
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Whether to attend to external memories.
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use_external_mind_by_layer (`List[bool]`, *optional*,
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defaults to List[`True`, ..., `True`]):
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Whether to attend to external memories, on each decoder layer.
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topk (`int`, *optional*, defaults to `10`):
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Number of external memories for each query token to retrieve and attend to.
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memory_type (`string`, *optional*, defaults to `manual`):
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Whether to store external memories manually or in a vector database.
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memory_device (`string`, *optional*, defaults to `cpu`):
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Specify device to store memory.
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mask_by_sim (`bool`, *optional*, defaults to `True`):
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Whether or not to mask retrieved memories by similarity.
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sim_threshold (`float`, *optional*, defaults to `0.25`):
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Threshold for masking retrieved memories.
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tokenizer_all_special_ids (`list`, *optional*, defaults to `[0,1,2]`):
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Ids for special tokens to remove from memories.
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remove_special_tokens (`bool`, *optional*, defaults to `True`):
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Remove memories that correspond to tokenizer special ids.
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#### Memory Configuration ####
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Example:
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```python
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>>> from transformers import LlamaModel, LlamaConfig
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>>> # Initializing a LLaMA llama-7b style configuration
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>>> configuration = LlamaConfig()
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>>> # Initializing a model from the llama-7b style configuration
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>>> model = LlamaModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "extended-llama"
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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-5,
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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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memory_config=None,
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**kwargs,
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+
):
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if memory_config is None:
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memory_config = {
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"mask_by_sim": False,
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+
"sim_threshold": 0.25,
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"topk": 10,
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"use_external_mind": True,
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"memory_type": "manual",
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"memory_device": "cpu",
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"tokenizer_all_special_ids": [0, bos_token_id, eos_token_id],
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"use_external_mind_by_layer": [
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True for _ in range(num_hidden_layers)
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],
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"remove_special_ids": True,
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}
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for key, value in memory_config.items():
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setattr(self, key, value)
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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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+
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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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+
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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._rope_scaling_validation()
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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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**kwargs,
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)
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+
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def _rope_scaling_validation(self):
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"""
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Validate the `rope_scaling` configuration.
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"""
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+
if self.rope_scaling is None:
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return
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+
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if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
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+
raise ValueError(
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"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
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f"got {self.rope_scaling}"
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)
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+
rope_scaling_type = self.rope_scaling.get("type", None)
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+
rope_scaling_factor = self.rope_scaling.get("factor", None)
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if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
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raise ValueError(
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f"""`rope_scaling`'s type field must be one of ['linear', 'dynamic'],
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+
got {rope_scaling_type}"""
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+
)
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+
if (
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rope_scaling_factor is None
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or not isinstance(rope_scaling_factor, float)
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or rope_scaling_factor <= 1.0
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):
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raise ValueError(
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f"""`rope_scaling`'s factor field must be an float > 1,
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got {rope_scaling_factor}"""
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+
)
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+
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+
# Faiss memory not compatible with Grouped Query Attention
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+
if self.memory_type=='faiss' and self.num_key_value_heads != self.num_attention_heads:
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+
raise NotImplementedError(
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'Faiss memory not compatible with Grouped Query Attention.'
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+
)
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+
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modeling.py
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1 |
+
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
2 |
+
#
|
3 |
+
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
4 |
+
# and OPT implementations in this library. It has been modified from its
|
5 |
+
# original forms to accommodate minor architectural differences compared
|
6 |
+
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
7 |
+
#
|
8 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
9 |
+
# you may not use this file except in compliance with the License.
|
10 |
+
# You may obtain a copy of the License at
|
11 |
+
#
|
12 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
13 |
+
#
|
14 |
+
# Unless required by applicable law or agreed to in writing, software
|
15 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
16 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
17 |
+
# See the License for the specific language governing permissions and
|
18 |
+
# limitations under the License.
|
19 |
+
|
20 |
+
# This code has been adapted from Meta and Huggingface and inherits the above lisence.
|
21 |
+
# The original code can be found here:
|
22 |
+
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
|
23 |
+
# We annotate the edited code below with 'EM' comments to indicate where we have made changes.
|
24 |
+
"""PyTorch Extended LLaMA model."""
|
25 |
+
import math
|
26 |
+
from typing import List, Optional, Tuple, Union
|
27 |
+
|
28 |
+
import faiss
|
29 |
+
import numpy as np
|
30 |
+
import torch
|
31 |
+
import torch.nn.functional as F
|
32 |
+
import torch.utils.checkpoint
|
33 |
+
from einops import rearrange
|
34 |
+
from torch import nn
|
35 |
+
from torch.linalg import vector_norm
|
36 |
+
from torch.nn import CrossEntropyLoss
|
37 |
+
from transformers.activations import ACT2FN
|
38 |
+
from transformers.modeling_outputs import (
|
39 |
+
BaseModelOutputWithPast,
|
40 |
+
CausalLMOutputWithPast,
|
41 |
+
)
|
42 |
+
from transformers.modeling_utils import PreTrainedModel
|
43 |
+
from transformers.utils import (
|
44 |
+
add_start_docstrings,
|
45 |
+
add_start_docstrings_to_model_forward,
|
46 |
+
logging,
|
47 |
+
replace_return_docstrings,
|
48 |
+
)
|
49 |
+
|
50 |
+
from emts_clean.src.llama.configuration import ExtendedLlamaConfig
|
51 |
+
|
52 |
+
logger = logging.get_logger(__name__)
|
53 |
+
|
54 |
+
_CONFIG_FOR_DOC = "ExtendedLlamaConfig"
|
55 |
+
|
56 |
+
|
57 |
+
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
|
58 |
+
def _make_causal_mask(
|
59 |
+
input_ids_shape: torch.Size,
|
60 |
+
dtype: torch.dtype,
|
61 |
+
device: torch.device,
|
62 |
+
past_key_values_length: int = 0,
|
63 |
+
):
|
64 |
+
"""
|
65 |
+
Make causal mask used for bi-directional self-attention.
|
66 |
+
"""
|
67 |
+
bsz, tgt_len = input_ids_shape
|
68 |
+
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
|
69 |
+
mask_cond = torch.arange(mask.size(-1), device=device)
|
70 |
+
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
|
71 |
+
mask = mask.to(dtype)
|
72 |
+
|
73 |
+
if past_key_values_length > 0:
|
74 |
+
mask = torch.cat(
|
75 |
+
[
|
76 |
+
torch.zeros(
|
77 |
+
tgt_len, past_key_values_length, dtype=dtype, device=device
|
78 |
+
),
|
79 |
+
mask,
|
80 |
+
],
|
81 |
+
dim=-1,
|
82 |
+
)
|
83 |
+
return mask[None, None, :, :].expand(
|
84 |
+
bsz, 1, tgt_len, tgt_len + past_key_values_length
|
85 |
+
)
|
86 |
+
|
87 |
+
|
88 |
+
# Copied from transformers.models.bart.modeling_bart._expand_mask
|
89 |
+
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
90 |
+
"""
|
91 |
+
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
|
92 |
+
"""
|
93 |
+
bsz, src_len = mask.size()
|
94 |
+
tgt_len = tgt_len if tgt_len is not None else src_len
|
95 |
+
|
96 |
+
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
|
97 |
+
|
98 |
+
inverted_mask = 1.0 - expanded_mask
|
99 |
+
|
100 |
+
return inverted_mask.masked_fill(
|
101 |
+
inverted_mask.to(torch.bool), torch.finfo(dtype).min
|
102 |
+
)
|
103 |
+
|
104 |
+
|
105 |
+
class LlamaRMSNorm(nn.Module):
|
106 |
+
"""LlamaRMSNorm is equivalent to T5LayerNorm"""
|
107 |
+
|
108 |
+
def __init__(self, hidden_size, eps=1e-6):
|
109 |
+
"""
|
110 |
+
LlamaRMSNorm is equivalent to T5LayerNorm
|
111 |
+
"""
|
112 |
+
super().__init__()
|
113 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
114 |
+
self.variance_epsilon = eps
|
115 |
+
|
116 |
+
def forward(self, hidden_states):
|
117 |
+
"""Apply RMS Norm"""
|
118 |
+
input_dtype = hidden_states.dtype
|
119 |
+
hidden_states = hidden_states.to(torch.float32)
|
120 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
121 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
122 |
+
return self.weight * hidden_states.to(input_dtype)
|
123 |
+
|
124 |
+
|
125 |
+
class LlamaRotaryEmbedding(torch.nn.Module):
|
126 |
+
"""Rotary Positional Embedding"""
|
127 |
+
|
128 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
129 |
+
super().__init__()
|
130 |
+
self.dim = dim
|
131 |
+
self.max_position_embeddings = max_position_embeddings
|
132 |
+
self.base = base
|
133 |
+
inv_freq = 1.0 / (
|
134 |
+
self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
|
135 |
+
)
|
136 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
137 |
+
|
138 |
+
# Build here to make `torch.jit.trace` work.
|
139 |
+
self._set_cos_sin_cache(
|
140 |
+
seq_len=max_position_embeddings,
|
141 |
+
device=self.inv_freq.device,
|
142 |
+
dtype=torch.get_default_dtype(),
|
143 |
+
)
|
144 |
+
|
145 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
146 |
+
self.max_seq_len_cached = seq_len
|
147 |
+
t = torch.arange(
|
148 |
+
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
|
149 |
+
)
|
150 |
+
|
151 |
+
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
152 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
153 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
154 |
+
self.register_buffer(
|
155 |
+
"cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False
|
156 |
+
)
|
157 |
+
self.register_buffer(
|
158 |
+
"sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False
|
159 |
+
)
|
160 |
+
|
161 |
+
def forward(self, x, seq_len=None):
|
162 |
+
# x: [bs, num_attention_heads, seq_len, head_size]
|
163 |
+
if seq_len > self.max_seq_len_cached:
|
164 |
+
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
|
165 |
+
|
166 |
+
return (
|
167 |
+
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
|
168 |
+
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
|
169 |
+
)
|
170 |
+
|
171 |
+
|
172 |
+
class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
|
173 |
+
"""LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
174 |
+
|
175 |
+
def __init__(
|
176 |
+
self,
|
177 |
+
dim,
|
178 |
+
max_position_embeddings=2048,
|
179 |
+
base=10000,
|
180 |
+
device=None,
|
181 |
+
scaling_factor=1.0,
|
182 |
+
):
|
183 |
+
self.scaling_factor = scaling_factor
|
184 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
185 |
+
|
186 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
187 |
+
self.max_seq_len_cached = seq_len
|
188 |
+
t = torch.arange(
|
189 |
+
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
|
190 |
+
)
|
191 |
+
t = t / self.scaling_factor
|
192 |
+
|
193 |
+
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
194 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
195 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
196 |
+
self.register_buffer(
|
197 |
+
"cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False
|
198 |
+
)
|
199 |
+
self.register_buffer(
|
200 |
+
"sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False
|
201 |
+
)
|
202 |
+
|
203 |
+
|
204 |
+
class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
|
205 |
+
"""LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
|
206 |
+
|
207 |
+
def __init__(
|
208 |
+
self,
|
209 |
+
dim,
|
210 |
+
max_position_embeddings=2048,
|
211 |
+
base=10000,
|
212 |
+
device=None,
|
213 |
+
scaling_factor=1.0,
|
214 |
+
):
|
215 |
+
self.scaling_factor = scaling_factor
|
216 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
217 |
+
|
218 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
219 |
+
self.max_seq_len_cached = seq_len
|
220 |
+
|
221 |
+
if seq_len > self.max_position_embeddings:
|
222 |
+
base = self.base * (
|
223 |
+
(self.scaling_factor * seq_len / self.max_position_embeddings)
|
224 |
+
- (self.scaling_factor - 1)
|
225 |
+
) ** (self.dim / (self.dim - 2))
|
226 |
+
inv_freq = 1.0 / (
|
227 |
+
base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
|
228 |
+
)
|
229 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
230 |
+
|
231 |
+
t = torch.arange(
|
232 |
+
self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
|
233 |
+
)
|
234 |
+
|
235 |
+
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
236 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
237 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
238 |
+
self.register_buffer(
|
239 |
+
"cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False
|
240 |
+
)
|
241 |
+
self.register_buffer(
|
242 |
+
"sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False
|
243 |
+
)
|
244 |
+
|
245 |
+
|
246 |
+
def rotate_half(x):
|
247 |
+
"""Rotates half the hidden dims of the input."""
|
248 |
+
x1 = x[..., : x.shape[-1] // 2]
|
249 |
+
x2 = x[..., x.shape[-1] // 2 :]
|
250 |
+
return torch.cat((-x2, x1), dim=-1)
|
251 |
+
|
252 |
+
|
253 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
|
254 |
+
"""Apply rotary positional embedding to q and k."""
|
255 |
+
# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
|
256 |
+
cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
|
257 |
+
sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
|
258 |
+
|
259 |
+
s_q = q.size(
|
260 |
+
-2
|
261 |
+
)
|
262 |
+
# EM: Since we apply rotary pos emb after reading from cache, queries may be shorter
|
263 |
+
_q_position_ids = position_ids[:, -s_q:]
|
264 |
+
_q_cos = cos[_q_position_ids].unsqueeze(1)
|
265 |
+
_q_sin = sin[_q_position_ids].unsqueeze(1)
|
266 |
+
q_embed = (q * _q_cos) + (rotate_half(q) * _q_sin)
|
267 |
+
|
268 |
+
cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
|
269 |
+
sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
|
270 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
271 |
+
return q_embed, k_embed
|
272 |
+
|
273 |
+
|
274 |
+
class LlamaMLP(nn.Module):
|
275 |
+
"""MLP Module"""
|
276 |
+
|
277 |
+
def __init__(self, config):
|
278 |
+
super().__init__()
|
279 |
+
self.config = config
|
280 |
+
self.hidden_size = config.hidden_size
|
281 |
+
self.intermediate_size = config.intermediate_size
|
282 |
+
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
283 |
+
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
284 |
+
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
285 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
286 |
+
|
287 |
+
def forward(self, x):
|
288 |
+
if self.config.pretraining_tp > 1:
|
289 |
+
slice = self.intermediate_size // self.config.pretraining_tp
|
290 |
+
gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
|
291 |
+
up_proj_slices = self.up_proj.weight.split(slice, dim=0)
|
292 |
+
down_proj_slices = self.down_proj.weight.split(slice, dim=1)
|
293 |
+
|
294 |
+
gate_proj = torch.cat(
|
295 |
+
[
|
296 |
+
F.linear(x, gate_proj_slices[i])
|
297 |
+
for i in range(self.config.pretraining_tp)
|
298 |
+
],
|
299 |
+
dim=-1,
|
300 |
+
)
|
301 |
+
up_proj = torch.cat(
|
302 |
+
[
|
303 |
+
F.linear(x, up_proj_slices[i])
|
304 |
+
for i in range(self.config.pretraining_tp)
|
305 |
+
],
|
306 |
+
dim=-1,
|
307 |
+
)
|
308 |
+
|
309 |
+
intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
|
310 |
+
down_proj = [
|
311 |
+
F.linear(intermediate_states[i], down_proj_slices[i])
|
312 |
+
for i in range(self.config.pretraining_tp)
|
313 |
+
]
|
314 |
+
down_proj = sum(down_proj)
|
315 |
+
else:
|
316 |
+
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
317 |
+
|
318 |
+
return down_proj
|
319 |
+
|
320 |
+
|
321 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
322 |
+
"""
|
323 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
324 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
325 |
+
"""
|
326 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
327 |
+
if n_rep == 1:
|
328 |
+
return hidden_states
|
329 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(
|
330 |
+
batch, num_key_value_heads, n_rep, slen, head_dim
|
331 |
+
)
|
332 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
333 |
+
|
334 |
+
|
335 |
+
class ExtendedLlamaAttention(nn.Module):
|
336 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
337 |
+
|
338 |
+
def __init__(self, config: ExtendedLlamaConfig):
|
339 |
+
super().__init__()
|
340 |
+
self.config = config
|
341 |
+
self.hidden_size = config.hidden_size
|
342 |
+
self.num_heads = config.num_attention_heads
|
343 |
+
self.head_dim = self.hidden_size // self.num_heads
|
344 |
+
self.num_key_value_heads = config.num_key_value_heads
|
345 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
346 |
+
self.max_position_embeddings = config.max_position_embeddings
|
347 |
+
self.rope_theta = config.rope_theta
|
348 |
+
|
349 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
350 |
+
raise ValueError(
|
351 |
+
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
352 |
+
f" and `num_heads`: {self.num_heads})."
|
353 |
+
)
|
354 |
+
self.q_proj = nn.Linear(
|
355 |
+
self.hidden_size, self.num_heads * self.head_dim, bias=False
|
356 |
+
)
|
357 |
+
self.k_proj = nn.Linear(
|
358 |
+
self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
|
359 |
+
)
|
360 |
+
self.v_proj = nn.Linear(
|
361 |
+
self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
|
362 |
+
)
|
363 |
+
self.o_proj = nn.Linear(
|
364 |
+
self.num_heads * self.head_dim, self.hidden_size, bias=False
|
365 |
+
)
|
366 |
+
self._init_rope()
|
367 |
+
|
368 |
+
def _init_rope(self):
|
369 |
+
if self.config.rope_scaling is None:
|
370 |
+
self.rotary_emb = LlamaRotaryEmbedding(
|
371 |
+
self.head_dim,
|
372 |
+
max_position_embeddings=self.max_position_embeddings,
|
373 |
+
base=self.rope_theta,
|
374 |
+
)
|
375 |
+
else:
|
376 |
+
scaling_type = self.config.rope_scaling["type"]
|
377 |
+
scaling_factor = self.config.rope_scaling["factor"]
|
378 |
+
if scaling_type == "linear":
|
379 |
+
self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
|
380 |
+
self.head_dim,
|
381 |
+
max_position_embeddings=self.max_position_embeddings,
|
382 |
+
scaling_factor=scaling_factor,
|
383 |
+
base=self.rope_theta,
|
384 |
+
)
|
385 |
+
elif scaling_type == "dynamic":
|
386 |
+
self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
|
387 |
+
self.head_dim,
|
388 |
+
max_position_embeddings=self.max_position_embeddings,
|
389 |
+
scaling_factor=scaling_factor,
|
390 |
+
base=self.rope_theta,
|
391 |
+
)
|
392 |
+
else:
|
393 |
+
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
|
394 |
+
|
395 |
+
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
396 |
+
return (
|
397 |
+
tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
|
398 |
+
.transpose(1, 2)
|
399 |
+
.contiguous()
|
400 |
+
)
|
401 |
+
|
402 |
+
def forward(
|
403 |
+
self,
|
404 |
+
hidden_states: torch.Tensor,
|
405 |
+
attention_mask: Optional[torch.Tensor] = None,
|
406 |
+
position_ids: Optional[torch.LongTensor] = None,
|
407 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
408 |
+
output_attentions: bool = False,
|
409 |
+
output_retrieved_memory_idx: bool = False,
|
410 |
+
use_cache: bool = False,
|
411 |
+
long_range_past_key_value=None,
|
412 |
+
faiss_indexes=None,
|
413 |
+
mask_by_sim=False,
|
414 |
+
sim_threshold=0.0,
|
415 |
+
topk=None,
|
416 |
+
current_layer=None,
|
417 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
418 |
+
"""forward"""
|
419 |
+
bsz, q_len, _ = hidden_states.size()
|
420 |
+
|
421 |
+
if self.config.pretraining_tp > 1:
|
422 |
+
key_value_slicing = (
|
423 |
+
self.num_key_value_heads * self.head_dim
|
424 |
+
) // self.config.pretraining_tp
|
425 |
+
query_slices = self.q_proj.weight.split(
|
426 |
+
(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
|
427 |
+
)
|
428 |
+
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
|
429 |
+
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
|
430 |
+
|
431 |
+
query_states = [
|
432 |
+
F.linear(hidden_states, query_slices[i])
|
433 |
+
for i in range(self.config.pretraining_tp)
|
434 |
+
]
|
435 |
+
query_states = torch.cat(query_states, dim=-1)
|
436 |
+
|
437 |
+
key_states = [
|
438 |
+
F.linear(hidden_states, key_slices[i])
|
439 |
+
for i in range(self.config.pretraining_tp)
|
440 |
+
]
|
441 |
+
key_states = torch.cat(key_states, dim=-1)
|
442 |
+
|
443 |
+
value_states = [
|
444 |
+
F.linear(hidden_states, value_slices[i])
|
445 |
+
for i in range(self.config.pretraining_tp)
|
446 |
+
]
|
447 |
+
value_states = torch.cat(value_states, dim=-1)
|
448 |
+
|
449 |
+
else:
|
450 |
+
query_states = self.q_proj(hidden_states)
|
451 |
+
key_states = self.k_proj(hidden_states)
|
452 |
+
value_states = self.v_proj(hidden_states)
|
453 |
+
|
454 |
+
query_states = query_states.view(
|
455 |
+
bsz, q_len, self.num_heads, self.head_dim
|
456 |
+
).transpose(1, 2)
|
457 |
+
key_states = key_states.view(
|
458 |
+
bsz, q_len, self.num_key_value_heads, self.head_dim
|
459 |
+
).transpose(1, 2)
|
460 |
+
value_states = value_states.view(
|
461 |
+
bsz, q_len, self.num_key_value_heads, self.head_dim
|
462 |
+
).transpose(1, 2)
|
463 |
+
|
464 |
+
# EM: Read from cache before position information is added
|
465 |
+
if past_key_value is not None:
|
466 |
+
# reuse k, v, self_attention
|
467 |
+
key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
468 |
+
value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
469 |
+
|
470 |
+
past_key_value = (key_states, value_states) if use_cache else None
|
471 |
+
|
472 |
+
kv_seq_len = key_states.shape[-2]
|
473 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
474 |
+
|
475 |
+
query_states, key_states = apply_rotary_pos_emb(
|
476 |
+
query_states, key_states, cos, sin, position_ids
|
477 |
+
)
|
478 |
+
|
479 |
+
# repeat k/v heads if n_kv_heads < n_heads
|
480 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
481 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
482 |
+
bsz, nh, s_q, hd = query_states.shape
|
483 |
+
|
484 |
+
attn_weights = torch.matmul(
|
485 |
+
query_states, key_states.transpose(2, 3)
|
486 |
+
) / math.sqrt(self.head_dim)
|
487 |
+
|
488 |
+
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
489 |
+
raise ValueError(
|
490 |
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
491 |
+
f" {attn_weights.size()}"
|
492 |
+
)
|
493 |
+
|
494 |
+
# EM: Retrieve memories from cache or faiss indexes
|
495 |
+
if long_range_past_key_value is not None or faiss_indexes is not None:
|
496 |
+
if long_range_past_key_value is not None: # manual memories
|
497 |
+
k_cache, v_cache = long_range_past_key_value
|
498 |
+
k_cache = repeat_kv(k_cache, self.num_key_value_groups)
|
499 |
+
v_cache = repeat_kv(v_cache, self.num_key_value_groups)
|
500 |
+
|
501 |
+
s_cache = k_cache.size(-2)
|
502 |
+
|
503 |
+
k_cache = k_cache.to(key_states.device)
|
504 |
+
v_cache = v_cache.to(key_states.device)
|
505 |
+
|
506 |
+
# Normalize query and key vectors
|
507 |
+
q_n = query_states / vector_norm(
|
508 |
+
query_states, ord=2, dim=-1, keepdim=True
|
509 |
+
)
|
510 |
+
k_n = k_cache / vector_norm(k_cache, ord=2, dim=-1, keepdim=True)
|
511 |
+
|
512 |
+
sim = q_n.matmul(k_n.transpose(2, 3))
|
513 |
+
if s_cache < topk:
|
514 |
+
topk = s_cache # number of tokens in cache < topk
|
515 |
+
val, idx = torch.topk(sim, k=topk, dim=-1) # Retrieve topk memories
|
516 |
+
|
517 |
+
reshaped_idx = idx.reshape(bsz, nh, s_q * topk)
|
518 |
+
|
519 |
+
selected_k = k_cache.gather(
|
520 |
+
dim=-2, index=reshaped_idx.unsqueeze(-1).expand(-1, -1, -1, hd)
|
521 |
+
)
|
522 |
+
selected_v = v_cache.gather(
|
523 |
+
dim=-2, index=reshaped_idx.unsqueeze(-1).expand(-1, -1, -1, hd)
|
524 |
+
)
|
525 |
+
|
526 |
+
elif faiss_indexes is not None: # FAISS indexes
|
527 |
+
kn_index, kv_index = faiss_indexes
|
528 |
+
q_n = query_states / vector_norm(
|
529 |
+
query_states, ord=2, dim=-1, keepdim=True
|
530 |
+
)
|
531 |
+
|
532 |
+
# One-hot encoding for layer, head to only retrieve memories from the same layer, head
|
533 |
+
one_hot_encodings = (
|
534 |
+
F.one_hot(
|
535 |
+
torch.arange(
|
536 |
+
0,
|
537 |
+
nh * self.config.num_hidden_layers,
|
538 |
+
device=query_states.device,
|
539 |
+
)
|
540 |
+
)
|
541 |
+
* 10
|
542 |
+
)
|
543 |
+
q_n = torch.concat(
|
544 |
+
[
|
545 |
+
rearrange(q_n, "b h s d -> b (h s) d", h=nh),
|
546 |
+
one_hot_encodings[nh * current_layer : nh * (current_layer + 1)]
|
547 |
+
.unsqueeze(0)
|
548 |
+
.repeat_interleave(repeats=query_states.size(-2), dim=-2),
|
549 |
+
],
|
550 |
+
dim=-1,
|
551 |
+
).squeeze()
|
552 |
+
|
553 |
+
if kn_index.ntotal / (nh * self.config.num_hidden_layers) < topk:
|
554 |
+
topk = kn_index.ntotal / (nh * self.config.num_hidden_layers)
|
555 |
+
|
556 |
+
val, idx = kn_index.search(q_n.to("cpu").detach().numpy(), k=topk)
|
557 |
+
val = torch.tensor(val - 100).reshape(bsz, nh, s_q, topk) #Similarity includes scale factor from one-hot encoding
|
558 |
+
reshaped_idx = torch.tensor(
|
559 |
+
idx % (kn_index.ntotal / (nh * self.config.num_hidden_layers))
|
560 |
+
).reshape(bsz, nh, s_q * topk)
|
561 |
+
|
562 |
+
selected_k = rearrange(
|
563 |
+
torch.tensor(kv_index.reconstruct_batch(idx.flatten()))[:, :hd],
|
564 |
+
"(h s) d -> 1 h s d",
|
565 |
+
h=nh,
|
566 |
+
).to(query_states.device)
|
567 |
+
|
568 |
+
selected_v = rearrange(
|
569 |
+
torch.tensor(kv_index.reconstruct_batch(idx.flatten()))[:, hd:],
|
570 |
+
"(h s) d -> 1 h s d",
|
571 |
+
h=nh,
|
572 |
+
).to(query_states.device)
|
573 |
+
|
574 |
+
attn_weight_cache = torch.matmul(
|
575 |
+
query_states, selected_k.transpose(2, 3)
|
576 |
+
) / math.sqrt(self.head_dim)
|
577 |
+
# EM: Mask by similarity
|
578 |
+
if mask_by_sim:
|
579 |
+
sim_mask = (
|
580 |
+
rearrange(~(val > sim_threshold).bool(), "b h s i -> b h (s i)")
|
581 |
+
.unsqueeze(-2)
|
582 |
+
.expand(-1, -1, s_q, -1)
|
583 |
+
).to(query_states.device)
|
584 |
+
attn_weight_cache = attn_weight_cache.masked_fill(
|
585 |
+
sim_mask, torch.finfo(query_states.dtype).min
|
586 |
+
)
|
587 |
+
# EM: Concatenate cache and current attention weights, values
|
588 |
+
attn_weights = torch.cat([attn_weight_cache, attn_weights], dim=-1)
|
589 |
+
value_states = torch.cat([selected_v, value_states], dim=-2)
|
590 |
+
|
591 |
+
min_val = torch.finfo(attn_weights.dtype).min
|
592 |
+
|
593 |
+
# EM: Create mask for external memories, queries only attend to their own memories
|
594 |
+
def _create_external_memories_mask(k, s_q, device, min_val=min_val):
|
595 |
+
mask = torch.ones(s_q, s_q * k, device=device, dtype=torch.float32)
|
596 |
+
for i in range(s_q):
|
597 |
+
mask[i, i * k : (i + 1) * k] = 0
|
598 |
+
|
599 |
+
filled = mask.masked_fill(mask.bool(), min_val)
|
600 |
+
return filled
|
601 |
+
|
602 |
+
if attention_mask is not None:
|
603 |
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
604 |
+
raise ValueError(
|
605 |
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
606 |
+
)
|
607 |
+
# EM: Concatenate attention mask with external memories mask
|
608 |
+
if long_range_past_key_value is not None or faiss_indexes is not None:
|
609 |
+
memory_mask = _create_external_memories_mask(
|
610 |
+
k=topk, s_q=s_q, device=attn_weights.device
|
611 |
+
)
|
612 |
+
attention_mask = (
|
613 |
+
torch.cat(
|
614 |
+
[
|
615 |
+
memory_mask,
|
616 |
+
attention_mask.squeeze(dim=[0, 1]),
|
617 |
+
],
|
618 |
+
dim=1,
|
619 |
+
)
|
620 |
+
.unsqueeze(dim=0)
|
621 |
+
.unsqueeze(dim=1)
|
622 |
+
)
|
623 |
+
attn_weights = attn_weights + attention_mask
|
624 |
+
|
625 |
+
# upcast attention to fp32
|
626 |
+
attn_weights = nn.functional.softmax(
|
627 |
+
attn_weights, dim=-1, dtype=torch.float32
|
628 |
+
).to(query_states.dtype)
|
629 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
630 |
+
|
631 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
632 |
+
raise ValueError(
|
633 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
634 |
+
f" {attn_output.size()}"
|
635 |
+
)
|
636 |
+
|
637 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
638 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
639 |
+
|
640 |
+
if self.config.pretraining_tp > 1:
|
641 |
+
attn_output = attn_output.split(
|
642 |
+
self.hidden_size // self.config.pretraining_tp, dim=2
|
643 |
+
)
|
644 |
+
o_proj_slices = self.o_proj.weight.split(
|
645 |
+
self.hidden_size // self.config.pretraining_tp, dim=1
|
646 |
+
)
|
647 |
+
attn_output = sum(
|
648 |
+
F.linear(attn_output[i], o_proj_slices[i])
|
649 |
+
for i in range(self.config.pretraining_tp)
|
650 |
+
)
|
651 |
+
else:
|
652 |
+
attn_output = self.o_proj(attn_output)
|
653 |
+
|
654 |
+
if not output_attentions:
|
655 |
+
attn_weights = None
|
656 |
+
|
657 |
+
if not output_retrieved_memory_idx:
|
658 |
+
reshaped_idx = None
|
659 |
+
return attn_output, attn_weights, past_key_value, reshaped_idx
|
660 |
+
|
661 |
+
|
662 |
+
class ExtendedLlamaDecoderLayer(nn.Module):
|
663 |
+
"""Decoder Layer for LLaMA"""
|
664 |
+
|
665 |
+
def __init__(self, config: ExtendedLlamaConfig):
|
666 |
+
super().__init__()
|
667 |
+
self.hidden_size = config.hidden_size
|
668 |
+
self.self_attn = ExtendedLlamaAttention(config=config)
|
669 |
+
self.mlp = LlamaMLP(config)
|
670 |
+
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
671 |
+
self.post_attention_layernorm = LlamaRMSNorm(
|
672 |
+
config.hidden_size, eps=config.rms_norm_eps
|
673 |
+
)
|
674 |
+
|
675 |
+
def forward(
|
676 |
+
self,
|
677 |
+
hidden_states: torch.Tensor,
|
678 |
+
attention_mask: Optional[torch.Tensor] = None,
|
679 |
+
position_ids: Optional[torch.LongTensor] = None,
|
680 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
681 |
+
output_attentions: Optional[bool] = False,
|
682 |
+
output_retrieved_memory_idx: Optional[bool] = False,
|
683 |
+
use_cache: Optional[bool] = False,
|
684 |
+
long_range_past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
685 |
+
faiss_indexes: Tuple = None,
|
686 |
+
mask_by_sim: bool = False,
|
687 |
+
sim_threshold: float = None,
|
688 |
+
topk: int = None,
|
689 |
+
current_layer=None,
|
690 |
+
) -> Tuple[
|
691 |
+
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
|
692 |
+
]:
|
693 |
+
"""
|
694 |
+
Args:
|
695 |
+
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
696 |
+
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
697 |
+
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
698 |
+
output_attentions (`bool`, *optional*):
|
699 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
700 |
+
returned tensors for more detail.
|
701 |
+
use_cache (`bool`, *optional*):
|
702 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
703 |
+
(see `past_key_values`).
|
704 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
705 |
+
"""
|
706 |
+
|
707 |
+
residual = hidden_states
|
708 |
+
|
709 |
+
hidden_states = self.input_layernorm(hidden_states)
|
710 |
+
|
711 |
+
# Self Attention
|
712 |
+
(
|
713 |
+
hidden_states,
|
714 |
+
self_attn_weights,
|
715 |
+
present_key_value,
|
716 |
+
selected_idx,
|
717 |
+
) = self.self_attn(
|
718 |
+
hidden_states=hidden_states,
|
719 |
+
attention_mask=attention_mask,
|
720 |
+
position_ids=position_ids,
|
721 |
+
past_key_value=past_key_value,
|
722 |
+
output_attentions=output_attentions,
|
723 |
+
output_retrieved_memory_idx=output_retrieved_memory_idx,
|
724 |
+
use_cache=use_cache,
|
725 |
+
long_range_past_key_value=long_range_past_key_value,
|
726 |
+
faiss_indexes=faiss_indexes,
|
727 |
+
mask_by_sim=mask_by_sim,
|
728 |
+
sim_threshold=sim_threshold,
|
729 |
+
topk=topk,
|
730 |
+
current_layer=current_layer,
|
731 |
+
)
|
732 |
+
hidden_states = residual + hidden_states
|
733 |
+
|
734 |
+
# Fully Connected
|
735 |
+
residual = hidden_states
|
736 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
737 |
+
hidden_states = self.mlp(hidden_states)
|
738 |
+
hidden_states = residual + hidden_states
|
739 |
+
|
740 |
+
outputs = (hidden_states,)
|
741 |
+
|
742 |
+
if output_attentions:
|
743 |
+
outputs += (self_attn_weights,)
|
744 |
+
|
745 |
+
if use_cache:
|
746 |
+
outputs += (present_key_value,)
|
747 |
+
|
748 |
+
if output_retrieved_memory_idx:
|
749 |
+
outputs += (selected_idx,)
|
750 |
+
|
751 |
+
return outputs
|
752 |
+
|
753 |
+
|
754 |
+
LLAMA_START_DOCSTRING = r"""
|
755 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
756 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
757 |
+
etc.)
|
758 |
+
|
759 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
760 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
761 |
+
and behavior.
|
762 |
+
|
763 |
+
Parameters:
|
764 |
+
config ([`ExtendedLlamaConfig`]):
|
765 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
766 |
+
load the weights associated with the model, only the configuration. Check out the
|
767 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
768 |
+
"""
|
769 |
+
|
770 |
+
|
771 |
+
@add_start_docstrings(
|
772 |
+
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
773 |
+
LLAMA_START_DOCSTRING,
|
774 |
+
)
|
775 |
+
class LlamaPreTrainedModel(PreTrainedModel):
|
776 |
+
"""Wrapper class"""
|
777 |
+
|
778 |
+
config_class = ExtendedLlamaConfig
|
779 |
+
base_model_prefix = "model"
|
780 |
+
supports_gradient_checkpointing = True
|
781 |
+
_no_split_modules = ["LlamaDecoderLayer"]
|
782 |
+
_skip_keys_device_placement = "past_key_values"
|
783 |
+
|
784 |
+
def _init_weights(self, module):
|
785 |
+
std = self.config.initializer_range
|
786 |
+
if isinstance(module, nn.Linear):
|
787 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
788 |
+
if module.bias is not None:
|
789 |
+
module.bias.data.zero_()
|
790 |
+
elif isinstance(module, nn.Embedding):
|
791 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
792 |
+
if module.padding_idx is not None:
|
793 |
+
module.weight.data[module.padding_idx].zero_()
|
794 |
+
|
795 |
+
def _set_gradient_checkpointing(self, module, value=False):
|
796 |
+
if isinstance(module, ExtendedLlamaModel):
|
797 |
+
module.gradient_checkpointing = value
|
798 |
+
|
799 |
+
|
800 |
+
LLAMA_INPUTS_DOCSTRING = r"""
|
801 |
+
Args:
|
802 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
803 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
804 |
+
it.
|
805 |
+
|
806 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
807 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
808 |
+
|
809 |
+
[What are input IDs?](../glossary#input-ids)
|
810 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
811 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
812 |
+
|
813 |
+
- 1 for tokens that are **not masked**,
|
814 |
+
- 0 for tokens that are **masked**.
|
815 |
+
|
816 |
+
[What are attention masks?](../glossary#attention-mask)
|
817 |
+
|
818 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
819 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
820 |
+
|
821 |
+
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
|
822 |
+
`past_key_values`).
|
823 |
+
|
824 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
825 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
826 |
+
information on the default strategy.
|
827 |
+
|
828 |
+
- 1 indicates the head is **not masked**,
|
829 |
+
- 0 indicates the head is **masked**.
|
830 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
831 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
832 |
+
config.n_positions - 1]`.
|
833 |
+
|
834 |
+
[What are position IDs?](../glossary#position-ids)
|
835 |
+
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
836 |
+
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
837 |
+
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
|
838 |
+
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
|
839 |
+
|
840 |
+
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
841 |
+
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
842 |
+
|
843 |
+
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
844 |
+
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
845 |
+
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
846 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
847 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
848 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
849 |
+
model's internal embedding lookup matrix.
|
850 |
+
use_cache (`bool`, *optional*):
|
851 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
852 |
+
`past_key_values`).
|
853 |
+
output_attentions (`bool`, *optional*):
|
854 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
855 |
+
tensors for more detail.
|
856 |
+
output_hidden_states (`bool`, *optional*):
|
857 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
858 |
+
more detail.
|
859 |
+
return_dict (`bool`, *optional*):
|
860 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
861 |
+
"""
|
862 |
+
|
863 |
+
|
864 |
+
@add_start_docstrings(
|
865 |
+
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
866 |
+
LLAMA_START_DOCSTRING,
|
867 |
+
)
|
868 |
+
class ExtendedLlamaModel(LlamaPreTrainedModel):
|
869 |
+
"""
|
870 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
|
871 |
+
|
872 |
+
Args:
|
873 |
+
config: LlamaConfig
|
874 |
+
"""
|
875 |
+
|
876 |
+
def __init__(self, config: ExtendedLlamaConfig):
|
877 |
+
super().__init__(config)
|
878 |
+
self.padding_idx = config.pad_token_id
|
879 |
+
self.vocab_size = config.vocab_size
|
880 |
+
|
881 |
+
self.embed_tokens = nn.Embedding(
|
882 |
+
config.vocab_size, config.hidden_size, self.padding_idx
|
883 |
+
)
|
884 |
+
self.layers = nn.ModuleList(
|
885 |
+
[ExtendedLlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)]
|
886 |
+
)
|
887 |
+
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
888 |
+
|
889 |
+
self.gradient_checkpointing = False
|
890 |
+
# Initialize weights and apply final processing
|
891 |
+
self.mask_by_sim = config.mask_by_sim
|
892 |
+
self.sim_threshold = config.sim_threshold
|
893 |
+
self.topk = config.topk
|
894 |
+
self.use_external_mind = config.use_external_mind
|
895 |
+
self.use_external_mind_by_layer = config.use_external_mind_by_layer
|
896 |
+
self.post_init()
|
897 |
+
|
898 |
+
def get_input_embeddings(self):
|
899 |
+
return self.embed_tokens
|
900 |
+
|
901 |
+
def set_input_embeddings(self, value):
|
902 |
+
self.embed_tokens = value
|
903 |
+
|
904 |
+
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
|
905 |
+
def _prepare_decoder_attention_mask(
|
906 |
+
self, attention_mask, input_shape, inputs_embeds, past_key_values_length
|
907 |
+
):
|
908 |
+
# create causal mask
|
909 |
+
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
910 |
+
combined_attention_mask = None
|
911 |
+
if input_shape[-1] > 1:
|
912 |
+
combined_attention_mask = _make_causal_mask(
|
913 |
+
input_shape,
|
914 |
+
inputs_embeds.dtype,
|
915 |
+
device=inputs_embeds.device,
|
916 |
+
past_key_values_length=past_key_values_length,
|
917 |
+
)
|
918 |
+
|
919 |
+
if attention_mask is not None:
|
920 |
+
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
921 |
+
expanded_attn_mask = _expand_mask(
|
922 |
+
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
|
923 |
+
).to(inputs_embeds.device)
|
924 |
+
combined_attention_mask = (
|
925 |
+
expanded_attn_mask
|
926 |
+
if combined_attention_mask is None
|
927 |
+
else expanded_attn_mask + combined_attention_mask
|
928 |
+
)
|
929 |
+
|
930 |
+
return combined_attention_mask
|
931 |
+
|
932 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
933 |
+
def forward(
|
934 |
+
self,
|
935 |
+
input_ids: torch.LongTensor = None,
|
936 |
+
attention_mask: Optional[torch.Tensor] = None,
|
937 |
+
position_ids: Optional[torch.LongTensor] = None,
|
938 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
939 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
940 |
+
use_cache: Optional[bool] = None,
|
941 |
+
output_attentions: Optional[bool] = None,
|
942 |
+
output_retrieved_memory_idx: Optional[bool] = None,
|
943 |
+
output_hidden_states: Optional[bool] = None,
|
944 |
+
return_dict: Optional[bool] = None,
|
945 |
+
use_external_mind: Optional[bool] = None,
|
946 |
+
long_range_past_key_values: Optional[List[Tuple[torch.FloatTensor]]] = None,
|
947 |
+
faiss_indexes: Tuple = None,
|
948 |
+
topk: int = None,
|
949 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
950 |
+
"""forward"""
|
951 |
+
output_attentions = (
|
952 |
+
output_attentions
|
953 |
+
if output_attentions is not None
|
954 |
+
else self.config.output_attentions
|
955 |
+
)
|
956 |
+
output_retrieved_memory_idx = (
|
957 |
+
output_retrieved_memory_idx
|
958 |
+
if output_retrieved_memory_idx is not None
|
959 |
+
else False
|
960 |
+
)
|
961 |
+
output_hidden_states = (
|
962 |
+
output_hidden_states
|
963 |
+
if output_hidden_states is not None
|
964 |
+
else self.config.output_hidden_states
|
965 |
+
)
|
966 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
967 |
+
|
968 |
+
return_dict = (
|
969 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
970 |
+
)
|
971 |
+
use_external_mind = (
|
972 |
+
use_external_mind
|
973 |
+
if use_external_mind is not None
|
974 |
+
else self.use_external_mind
|
975 |
+
)
|
976 |
+
topk = topk if topk is not None else self.topk
|
977 |
+
|
978 |
+
# retrieve input_ids and inputs_embeds
|
979 |
+
if input_ids is not None and inputs_embeds is not None:
|
980 |
+
raise ValueError(
|
981 |
+
"You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
|
982 |
+
)
|
983 |
+
elif input_ids is not None:
|
984 |
+
batch_size, seq_length = input_ids.shape
|
985 |
+
elif inputs_embeds is not None:
|
986 |
+
batch_size, seq_length, _ = inputs_embeds.shape
|
987 |
+
else:
|
988 |
+
raise ValueError(
|
989 |
+
"You have to specify either decoder_input_ids or decoder_inputs_embeds"
|
990 |
+
)
|
991 |
+
|
992 |
+
seq_length_with_past = seq_length
|
993 |
+
past_key_values_length = 0
|
994 |
+
|
995 |
+
if past_key_values is not None:
|
996 |
+
past_key_values_length = past_key_values[0][0].shape[2]
|
997 |
+
seq_length_with_past = seq_length_with_past + past_key_values_length
|
998 |
+
|
999 |
+
# EM: Range of position ids is total seq length since we apply rotary pos emb after reading from cache
|
1000 |
+
if position_ids is None:
|
1001 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
1002 |
+
position_ids = torch.arange(
|
1003 |
+
seq_length_with_past,
|
1004 |
+
dtype=torch.long,
|
1005 |
+
device=device,
|
1006 |
+
)
|
1007 |
+
position_ids = position_ids.unsqueeze(0).view(-1, seq_length_with_past)
|
1008 |
+
else:
|
1009 |
+
position_ids = position_ids.view(-1, seq_length_with_past).long()
|
1010 |
+
|
1011 |
+
if inputs_embeds is None:
|
1012 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
1013 |
+
# embed positions
|
1014 |
+
if attention_mask is None:
|
1015 |
+
attention_mask = torch.ones(
|
1016 |
+
(batch_size, seq_length_with_past),
|
1017 |
+
dtype=torch.bool,
|
1018 |
+
device=inputs_embeds.device,
|
1019 |
+
)
|
1020 |
+
attention_mask = self._prepare_decoder_attention_mask(
|
1021 |
+
attention_mask,
|
1022 |
+
(batch_size, seq_length),
|
1023 |
+
inputs_embeds,
|
1024 |
+
past_key_values_length,
|
1025 |
+
)
|
1026 |
+
|
1027 |
+
hidden_states = inputs_embeds
|
1028 |
+
|
1029 |
+
if self.gradient_checkpointing and self.training:
|
1030 |
+
if use_cache:
|
1031 |
+
logger.warning_once(
|
1032 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
1033 |
+
)
|
1034 |
+
use_cache = False
|
1035 |
+
|
1036 |
+
# decoder layers
|
1037 |
+
all_hidden_states = () if output_hidden_states else None
|
1038 |
+
all_self_attns = () if output_attentions else None
|
1039 |
+
next_decoder_cache = () if use_cache else None
|
1040 |
+
all_idx = () if output_retrieved_memory_idx else None
|
1041 |
+
|
1042 |
+
for idx, decoder_layer in enumerate(self.layers):
|
1043 |
+
if output_hidden_states:
|
1044 |
+
all_hidden_states += (hidden_states,)
|
1045 |
+
|
1046 |
+
past_key_value = (
|
1047 |
+
past_key_values[idx] if past_key_values is not None else None
|
1048 |
+
)
|
1049 |
+
|
1050 |
+
long_range_past_key_value = (
|
1051 |
+
long_range_past_key_values[idx]
|
1052 |
+
if (
|
1053 |
+
long_range_past_key_values is not None
|
1054 |
+
and self.use_external_mind_by_layer[idx]
|
1055 |
+
and use_external_mind is True
|
1056 |
+
)
|
1057 |
+
else None
|
1058 |
+
)
|
1059 |
+
|
1060 |
+
if long_range_past_key_value is not None and faiss_indexes is not None:
|
1061 |
+
raise NotImplementedError(
|
1062 |
+
"""Using faiss and passing key value pairs
|
1063 |
+
manually are mutually exclusive right now."""
|
1064 |
+
)
|
1065 |
+
|
1066 |
+
if self.gradient_checkpointing and self.training:
|
1067 |
+
|
1068 |
+
def create_custom_forward(module):
|
1069 |
+
def custom_forward(*inputs):
|
1070 |
+
# None for past_key_value
|
1071 |
+
return module(*inputs, past_key_value, output_attentions)
|
1072 |
+
|
1073 |
+
return custom_forward
|
1074 |
+
|
1075 |
+
layer_outputs = torch.utils.checkpoint.checkpoint(
|
1076 |
+
create_custom_forward(decoder_layer),
|
1077 |
+
hidden_states,
|
1078 |
+
attention_mask,
|
1079 |
+
position_ids,
|
1080 |
+
)
|
1081 |
+
else:
|
1082 |
+
layer_outputs = decoder_layer(
|
1083 |
+
hidden_states,
|
1084 |
+
attention_mask=attention_mask,
|
1085 |
+
position_ids=position_ids,
|
1086 |
+
past_key_value=past_key_value,
|
1087 |
+
output_attentions=output_attentions,
|
1088 |
+
output_retrieved_memory_idx=output_retrieved_memory_idx,
|
1089 |
+
use_cache=use_cache,
|
1090 |
+
topk=topk,
|
1091 |
+
long_range_past_key_value=long_range_past_key_value,
|
1092 |
+
faiss_indexes=faiss_indexes,
|
1093 |
+
mask_by_sim=self.mask_by_sim,
|
1094 |
+
sim_threshold=self.sim_threshold,
|
1095 |
+
current_layer=idx,
|
1096 |
+
)
|
1097 |
+
|
1098 |
+
hidden_states = layer_outputs[0]
|
1099 |
+
|
1100 |
+
if use_cache:
|
1101 |
+
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
1102 |
+
|
1103 |
+
if output_attentions:
|
1104 |
+
all_self_attns += (layer_outputs[1],)
|
1105 |
+
|
1106 |
+
if output_retrieved_memory_idx:
|
1107 |
+
idx = (
|
1108 |
+
3
|
1109 |
+
if (use_cache & output_attentions)
|
1110 |
+
else 2
|
1111 |
+
if (use_cache or output_attentions)
|
1112 |
+
else 1
|
1113 |
+
)
|
1114 |
+
all_idx += (layer_outputs[idx],) # Record which memories were retrieved
|
1115 |
+
hidden_states = self.norm(hidden_states)
|
1116 |
+
|
1117 |
+
# add hidden states from the last decoder layer
|
1118 |
+
if output_hidden_states:
|
1119 |
+
all_hidden_states += (hidden_states,)
|
1120 |
+
|
1121 |
+
next_cache = next_decoder_cache if use_cache else None
|
1122 |
+
if not return_dict:
|
1123 |
+
return tuple(
|
1124 |
+
v
|
1125 |
+
for v in [
|
1126 |
+
hidden_states,
|
1127 |
+
next_cache,
|
1128 |
+
all_hidden_states,
|
1129 |
+
all_self_attns,
|
1130 |
+
all_idx,
|
1131 |
+
]
|
1132 |
+
if v is not None
|
1133 |
+
)
|
1134 |
+
return BaseModelOutputWithPast(
|
1135 |
+
last_hidden_state=hidden_states,
|
1136 |
+
past_key_values=next_cache,
|
1137 |
+
hidden_states=all_hidden_states,
|
1138 |
+
attentions=(all_self_attns, all_idx), # EM: Return idx of retrieved memories
|
1139 |
+
)
|
1140 |
+
|
1141 |
+
|
1142 |
+
class ExtendedLlamaForCausalLM(LlamaPreTrainedModel):
|
1143 |
+
"""LlamaForCausalLM"""
|
1144 |
+
|
1145 |
+
_tied_weights_keys = ["lm_head.weight"]
|
1146 |
+
|
1147 |
+
def __init__(self, config, external_memories=None):
|
1148 |
+
super().__init__(config)
|
1149 |
+
self.model = ExtendedLlamaModel(config)
|
1150 |
+
self.vocab_size = config.vocab_size
|
1151 |
+
self.tokenizer_all_special_ids = config.tokenizer_all_special_ids
|
1152 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
1153 |
+
|
1154 |
+
self.use_external_mind = config.use_external_mind
|
1155 |
+
self.memory_type = config.memory_type
|
1156 |
+
self.memory_device = config.memory_device
|
1157 |
+
self.remove_special_ids = config.remove_special_ids
|
1158 |
+
self.memory_ids = None
|
1159 |
+
self.memories = None
|
1160 |
+
|
1161 |
+
# EM: Memory token ids
|
1162 |
+
if external_memories is not None:
|
1163 |
+
self.memory_ids = external_memories
|
1164 |
+
|
1165 |
+
# Initialize weights and apply final processing
|
1166 |
+
self.post_init()
|
1167 |
+
|
1168 |
+
# EM: Clear memory cache
|
1169 |
+
def clear_memory(self):
|
1170 |
+
"""Clear memory cache."""
|
1171 |
+
self.memory_ids = None
|
1172 |
+
self.memories = None
|
1173 |
+
|
1174 |
+
def get_input_embeddings(self):
|
1175 |
+
return self.model.embed_tokens
|
1176 |
+
|
1177 |
+
def set_input_embeddings(self, value):
|
1178 |
+
self.model.embed_tokens = value
|
1179 |
+
|
1180 |
+
def get_output_embeddings(self):
|
1181 |
+
return self.lm_head
|
1182 |
+
|
1183 |
+
def set_output_embeddings(self, new_embeddings):
|
1184 |
+
"""Set output embeddings."""
|
1185 |
+
self.lm_head = new_embeddings
|
1186 |
+
|
1187 |
+
def set_decoder(self, decoder):
|
1188 |
+
"""Set decoder."""
|
1189 |
+
self.model = decoder
|
1190 |
+
|
1191 |
+
def get_decoder(self):
|
1192 |
+
"""Get decoder."""
|
1193 |
+
return self.model
|
1194 |
+
|
1195 |
+
@add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
|
1196 |
+
@replace_return_docstrings(
|
1197 |
+
output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
|
1198 |
+
)
|
1199 |
+
def forward(
|
1200 |
+
self,
|
1201 |
+
input_ids: torch.LongTensor = None,
|
1202 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1203 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1204 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
1205 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1206 |
+
labels: Optional[torch.LongTensor] = None,
|
1207 |
+
use_cache: Optional[bool] = None,
|
1208 |
+
output_attentions: Optional[bool] = None,
|
1209 |
+
output_hidden_states: Optional[bool] = None,
|
1210 |
+
output_retrieved_memory_idx: Optional[bool] = None,
|
1211 |
+
return_dict: Optional[bool] = None,
|
1212 |
+
use_external_mind: Optional[bool] = None,
|
1213 |
+
topk: int = None,
|
1214 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
1215 |
+
r"""
|
1216 |
+
Args:
|
1217 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
1218 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
1219 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
1220 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
1221 |
+
|
1222 |
+
Returns:
|
1223 |
+
|
1224 |
+
Example:
|
1225 |
+
|
1226 |
+
```python
|
1227 |
+
>>> from transformers import AutoTokenizer, LlamaForCausalLM
|
1228 |
+
|
1229 |
+
>>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
1230 |
+
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
1231 |
+
|
1232 |
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
1233 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
1234 |
+
|
1235 |
+
>>> # Generate
|
1236 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
1237 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
1238 |
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
1239 |
+
```"""
|
1240 |
+
|
1241 |
+
# EM: Generate key value cache once on first call
|
1242 |
+
if (
|
1243 |
+
self.memory_ids is not None and self.memories is None
|
1244 |
+
):
|
1245 |
+
self.memories = self.generate_cache(
|
1246 |
+
torch.tensor(self.memory_ids, device=self.device),
|
1247 |
+
cache_type=self.memory_type,
|
1248 |
+
)
|
1249 |
+
# EM: Remove special tokens from memory cache
|
1250 |
+
if self.remove_special_ids:
|
1251 |
+
idx_to_remove = [
|
1252 |
+
token_idx
|
1253 |
+
for token_idx, token in enumerate(self.memory_ids[0])
|
1254 |
+
if token in self.tokenizer_all_special_ids
|
1255 |
+
]
|
1256 |
+
if self.memory_type == "manual":
|
1257 |
+
mask = torch.ones(self.memories[0][0].size(), dtype=torch.bool)
|
1258 |
+
mask[:, :, idx_to_remove, :] = False
|
1259 |
+
|
1260 |
+
new_size = (
|
1261 |
+
self.memories[0][0].size(0),
|
1262 |
+
self.memories[0][0].size(1),
|
1263 |
+
-1,
|
1264 |
+
self.memories[0][0].size(3),
|
1265 |
+
)
|
1266 |
+
self.memories = [
|
1267 |
+
(ks[mask].view(new_size), vs[mask].view(new_size))
|
1268 |
+
for ks, vs in self.memories
|
1269 |
+
]
|
1270 |
+
else:
|
1271 |
+
kn_index, kv_index = self.memories
|
1272 |
+
all_idx_to_remove = [
|
1273 |
+
[
|
1274 |
+
i
|
1275 |
+
for i in range(0, kn_index.ntotal)
|
1276 |
+
if (
|
1277 |
+
i
|
1278 |
+
% (
|
1279 |
+
kn_index.ntotal
|
1280 |
+
/ (
|
1281 |
+
self.config.num_attention_heads
|
1282 |
+
* self.config.num_hidden_layers
|
1283 |
+
)
|
1284 |
+
)
|
1285 |
+
)
|
1286 |
+
== j
|
1287 |
+
]
|
1288 |
+
for j in idx_to_remove
|
1289 |
+
]
|
1290 |
+
kn_index.remove_ids(
|
1291 |
+
np.array(all_idx_to_remove).flatten().astype("int64")
|
1292 |
+
)
|
1293 |
+
kv_index.remove_ids(
|
1294 |
+
np.array(all_idx_to_remove).flatten().astype("int64")
|
1295 |
+
)
|
1296 |
+
|
1297 |
+
output_attentions = (
|
1298 |
+
output_attentions
|
1299 |
+
if output_attentions is not None
|
1300 |
+
else self.config.output_attentions
|
1301 |
+
)
|
1302 |
+
output_retrieved_memory_idx = (
|
1303 |
+
output_retrieved_memory_idx
|
1304 |
+
if output_retrieved_memory_idx is not None
|
1305 |
+
else False
|
1306 |
+
)
|
1307 |
+
output_hidden_states = (
|
1308 |
+
output_hidden_states
|
1309 |
+
if output_hidden_states is not None
|
1310 |
+
else self.config.output_hidden_states
|
1311 |
+
)
|
1312 |
+
return_dict = (
|
1313 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
1314 |
+
)
|
1315 |
+
|
1316 |
+
use_external_mind = (
|
1317 |
+
use_external_mind
|
1318 |
+
if use_external_mind is not None
|
1319 |
+
else self.use_external_mind
|
1320 |
+
)
|
1321 |
+
topk = topk if topk is not None else None
|
1322 |
+
|
1323 |
+
long_range_past_key_values = None
|
1324 |
+
faiss_indexes = None
|
1325 |
+
if hasattr(self, "memories") and isinstance(self.memories, list):
|
1326 |
+
long_range_past_key_values = self.memories
|
1327 |
+
elif hasattr(self, "memories"):
|
1328 |
+
faiss_indexes = self.memories
|
1329 |
+
|
1330 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
1331 |
+
outputs = self.model(
|
1332 |
+
input_ids=input_ids,
|
1333 |
+
attention_mask=attention_mask,
|
1334 |
+
position_ids=position_ids,
|
1335 |
+
past_key_values=past_key_values,
|
1336 |
+
inputs_embeds=inputs_embeds,
|
1337 |
+
use_cache=use_cache,
|
1338 |
+
output_attentions=output_attentions,
|
1339 |
+
output_retrieved_memory_idx=output_retrieved_memory_idx,
|
1340 |
+
output_hidden_states=output_hidden_states,
|
1341 |
+
return_dict=return_dict,
|
1342 |
+
long_range_past_key_values=long_range_past_key_values,
|
1343 |
+
faiss_indexes=faiss_indexes,
|
1344 |
+
use_external_mind=use_external_mind,
|
1345 |
+
topk=topk,
|
1346 |
+
)
|
1347 |
+
|
1348 |
+
hidden_states = outputs[0]
|
1349 |
+
if self.config.pretraining_tp > 1:
|
1350 |
+
lm_head_slices = self.lm_head.weight.split(
|
1351 |
+
self.vocab_size // self.config.pretraining_tp, dim=0
|
1352 |
+
)
|
1353 |
+
logits = [
|
1354 |
+
F.linear(hidden_states, lm_head_slices[i])
|
1355 |
+
for i in range(self.config.pretraining_tp)
|
1356 |
+
]
|
1357 |
+
logits = torch.cat(logits, dim=-1)
|
1358 |
+
else:
|
1359 |
+
logits = self.lm_head(hidden_states)
|
1360 |
+
logits = logits.float()
|
1361 |
+
|
1362 |
+
loss = None
|
1363 |
+
if labels is not None:
|
1364 |
+
# Shift so that tokens < n predict n
|
1365 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
1366 |
+
shift_labels = labels[..., 1:].contiguous()
|
1367 |
+
# Flatten the tokens
|
1368 |
+
loss_fct = CrossEntropyLoss()
|
1369 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
1370 |
+
shift_labels = shift_labels.view(-1)
|
1371 |
+
# Enable model parallelism
|
1372 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
1373 |
+
loss = loss_fct(shift_logits, shift_labels)
|
1374 |
+
|
1375 |
+
if not return_dict:
|
1376 |
+
output = (logits,) + outputs[1:]
|
1377 |
+
return (loss,) + output if loss is not None else output
|
1378 |
+
|
1379 |
+
return CausalLMOutputWithPast(
|
1380 |
+
loss=loss,
|
1381 |
+
logits=logits,
|
1382 |
+
past_key_values=outputs.past_key_values,
|
1383 |
+
hidden_states=outputs.hidden_states,
|
1384 |
+
attentions=outputs.attentions,
|
1385 |
+
)
|
1386 |
+
|
1387 |
+
# EM: Add method to generate key-value cache
|
1388 |
+
def generate_cache(
|
1389 |
+
self,
|
1390 |
+
input_ids: torch.LongTensor,
|
1391 |
+
stride: int = 512,
|
1392 |
+
max_len: int = 3072,
|
1393 |
+
cache_type: str = "manual",
|
1394 |
+
):
|
1395 |
+
"""Stride over memory inputs to get kv pairs"""
|
1396 |
+
if cache_type not in ["manual", "faiss"]:
|
1397 |
+
raise NotImplementedError(f"Cache type {cache_type} not implemented.")
|
1398 |
+
|
1399 |
+
prev_end_loc = 0
|
1400 |
+
long_range_past_key_values = None
|
1401 |
+
faiss_indexes = None
|
1402 |
+
for b_idx in range(
|
1403 |
+
0, input_ids.size(-1), stride
|
1404 |
+
): # generate kv-pairs using stride
|
1405 |
+
end_loc = min(b_idx + max_len, input_ids.size(-1))
|
1406 |
+
trg_len = end_loc - prev_end_loc
|
1407 |
+
subseq = input_ids[:, b_idx:end_loc].to(self.model.device)
|
1408 |
+
with torch.inference_mode():
|
1409 |
+
outputs = self.model(
|
1410 |
+
subseq,
|
1411 |
+
use_cache=True,
|
1412 |
+
use_external_mind=False,
|
1413 |
+
)
|
1414 |
+
to_cache = [
|
1415 |
+
(kv[0][:, :, -trg_len:], kv[1][:, :, -trg_len:])
|
1416 |
+
for kv in outputs.past_key_values
|
1417 |
+
]
|
1418 |
+
long_range_past_key_values, faiss_indexes = self.cache(
|
1419 |
+
to_cache,
|
1420 |
+
cache_type,
|
1421 |
+
long_range_past_key_values=long_range_past_key_values,
|
1422 |
+
faiss_indexes=faiss_indexes,
|
1423 |
+
)
|
1424 |
+
|
1425 |
+
prev_end_loc = end_loc
|
1426 |
+
if end_loc == input_ids.size(-1):
|
1427 |
+
break
|
1428 |
+
if long_range_past_key_values is not None:
|
1429 |
+
return long_range_past_key_values
|
1430 |
+
else:
|
1431 |
+
return faiss_indexes
|
1432 |
+
|
1433 |
+
# EM: Add method to cache key value pairs
|
1434 |
+
def cache(
|
1435 |
+
self,
|
1436 |
+
to_cache: List,
|
1437 |
+
cache_type: str = "manual",
|
1438 |
+
long_range_past_key_values: List = None,
|
1439 |
+
faiss_indexes: faiss.IndexFlatIP = None,
|
1440 |
+
max_length_cache=100000,
|
1441 |
+
verbose=False,
|
1442 |
+
):
|
1443 |
+
"""Cache key value pairs for Extended Mind attention."""
|
1444 |
+
if (long_range_past_key_values is not None) & (faiss_indexes is not None):
|
1445 |
+
raise NotImplementedError(
|
1446 |
+
"Using faiss and passing key value pairs manually are mutually exclusive right now."
|
1447 |
+
)
|
1448 |
+
# To avoid spinning up a new index for each layer, we add one-hot encodings to the keys so that queries match with the appropriate layer, head
|
1449 |
+
if cache_type == "faiss": # add one-hot encoding to match layer, head indices
|
1450 |
+
one_hot_encodings = (
|
1451 |
+
F.one_hot(
|
1452 |
+
torch.arange(
|
1453 |
+
0,
|
1454 |
+
self.config.num_attention_heads * self.config.num_hidden_layers,
|
1455 |
+
)
|
1456 |
+
)
|
1457 |
+
* 10
|
1458 |
+
)
|
1459 |
+
# New indices, one to store normalized keys with one-hot encodings, another to retrieve kv pairs without normalization
|
1460 |
+
if faiss_indexes is None:
|
1461 |
+
faiss_indexes = (
|
1462 |
+
faiss.IndexFlatIP(
|
1463 |
+
to_cache[0][0].size(-1) + one_hot_encodings.size(-1)
|
1464 |
+
),
|
1465 |
+
faiss.IndexFlatIP(to_cache[0][0].size(-1) * 2),
|
1466 |
+
)
|
1467 |
+
kn_index, kv_index = faiss_indexes
|
1468 |
+
for l_idx, (k, v) in enumerate(to_cache):
|
1469 |
+
k_n = (k / vector_norm(k, ord=2, dim=-1, keepdim=True)).to("cpu") #Normalize keys for cosine sim
|
1470 |
+
# Indices are 2 dimensional, so flatten
|
1471 |
+
|
1472 |
+
# Add normalized keys with one-hot encodings
|
1473 |
+
k_n = torch.concat(
|
1474 |
+
[
|
1475 |
+
rearrange(
|
1476 |
+
k_n,
|
1477 |
+
"b h s d -> b (h s) d",
|
1478 |
+
h=self.config.num_attention_heads,
|
1479 |
+
),
|
1480 |
+
one_hot_encodings[
|
1481 |
+
self.config.num_attention_heads
|
1482 |
+
* l_idx : self.config.num_attention_heads
|
1483 |
+
* (l_idx + 1)
|
1484 |
+
]
|
1485 |
+
.unsqueeze(0)
|
1486 |
+
.repeat_interleave(repeats=k.size(-2), dim=-2),
|
1487 |
+
],
|
1488 |
+
dim=-1,
|
1489 |
+
)
|
1490 |
+
kn_index.add(k_n.squeeze().numpy())
|
1491 |
+
|
1492 |
+
# Add unnormalized keys and values
|
1493 |
+
k = rearrange(
|
1494 |
+
k, "b h s d -> b (h s) d", h=self.config.num_attention_heads
|
1495 |
+
)
|
1496 |
+
v = rearrange(
|
1497 |
+
v, "b h s d -> b (h s) d", h=self.config.num_attention_heads
|
1498 |
+
)
|
1499 |
+
kv_index.add(
|
1500 |
+
torch.concat([k.squeeze(), v.squeeze()], dim=1).to("cpu").numpy()
|
1501 |
+
)
|
1502 |
+
else:
|
1503 |
+
# Simply use list to store key value pairs
|
1504 |
+
if long_range_past_key_values is None:
|
1505 |
+
long_range_past_key_values = [
|
1506 |
+
(k.to(self.memory_device), v.to(self.memory_device))
|
1507 |
+
for k, v in to_cache
|
1508 |
+
]
|
1509 |
+
else:
|
1510 |
+
long_range_past_key_values = [
|
1511 |
+
(
|
1512 |
+
torch.concat(
|
1513 |
+
[kv[0], to_cache[ind][0].to(self.memory_device)], dim=2
|
1514 |
+
),
|
1515 |
+
torch.concat(
|
1516 |
+
[kv[1], to_cache[ind][1].to(self.memory_device)], dim=2
|
1517 |
+
),
|
1518 |
+
)
|
1519 |
+
for ind, kv in enumerate(long_range_past_key_values)
|
1520 |
+
]
|
1521 |
+
if (
|
1522 |
+
long_range_past_key_values is not None
|
1523 |
+
): # set a limit on manual memory length
|
1524 |
+
if long_range_past_key_values[0][0].size(-2) > max_length_cache:
|
1525 |
+
long_range_past_key_values = [
|
1526 |
+
(kv[0][:, :, -max_length_cache:], kv[1][:, :, -max_length_cache:])
|
1527 |
+
for kv in long_range_past_key_values
|
1528 |
+
]
|
1529 |
+
if verbose:
|
1530 |
+
if cache_type == "faiss":
|
1531 |
+
print(f"{kn_index.ntotal} keys in faiss index")
|
1532 |
+
else:
|
1533 |
+
print(f"{long_range_past_key_values[0][0].size(-2)} cached kvs")
|
1534 |
+
|
1535 |
+
return (
|
1536 |
+
long_range_past_key_values,
|
1537 |
+
(kn_index, kv_index) if cache_type == "faiss" else None,
|
1538 |
+
)
|
1539 |
+
|
1540 |
+
def prepare_inputs_for_generation(
|
1541 |
+
self,
|
1542 |
+
input_ids,
|
1543 |
+
past_key_values=None,
|
1544 |
+
attention_mask=None,
|
1545 |
+
inputs_embeds=None,
|
1546 |
+
**kwargs,
|
1547 |
+
):
|
1548 |
+
if past_key_values:
|
1549 |
+
input_ids = input_ids[:, -1:]
|
1550 |
+
|
1551 |
+
position_ids = kwargs.get("position_ids", None)
|
1552 |
+
if attention_mask is not None and position_ids is None:
|
1553 |
+
# create position_ids on the fly for batch generation
|
1554 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
1555 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
1556 |
+
|
1557 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
1558 |
+
if inputs_embeds is not None and past_key_values is None:
|
1559 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
1560 |
+
else:
|
1561 |
+
model_inputs = {"input_ids": input_ids}
|
1562 |
+
|
1563 |
+
model_inputs.update(
|
1564 |
+
{
|
1565 |
+
"position_ids": position_ids,
|
1566 |
+
"past_key_values": past_key_values,
|
1567 |
+
"use_cache": kwargs.get("use_cache"),
|
1568 |
+
"attention_mask": attention_mask,
|
1569 |
+
"use_external_mind": kwargs.get("use_external_mind"), # EM: Add config here
|
1570 |
+
"topk": kwargs.get("topk"),
|
1571 |
+
}
|
1572 |
+
)
|
1573 |
+
return model_inputs
|
1574 |
+
|
1575 |
+
@staticmethod
|
1576 |
+
def _reorder_cache(past_key_values, beam_idx):
|
1577 |
+
reordered_past = ()
|
1578 |
+
for layer_past in past_key_values:
|
1579 |
+
reordered_past += (
|
1580 |
+
tuple(
|
1581 |
+
past_state.index_select(0, beam_idx.to(past_state.device))
|
1582 |
+
for past_state in layer_past
|
1583 |
+
),
|
1584 |
+
)
|
1585 |
+
return reordered_past
|