ZwwWayne
commited on
Commit
•
21e46b7
1
Parent(s):
1248261
update model weights
Browse files- .gitattributes +1 -0
- config.json +34 -0
- configuration_internlm.py +159 -0
- generation_config.json +7 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +178 -0
- modeling_internlm2.py +1271 -0
- special_tokens_map.json +6 -0
- tokenization_internlm.py +240 -0
- tokenizer.model +3 -0
- tokenizer_config.json +41 -0
.gitattributes
CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.model filter=lfs diff=lfs merge=lfs -text
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config.json
ADDED
@@ -0,0 +1,34 @@
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{
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"architectures": [
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"InternLM2ForCausalLM"
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],
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"auto_map": {
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"AutoConfig": "configuration_internlm.InternLMConfig",
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"AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM",
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"AutoModel": "modeling_internlm2.InternLM2ForCausalLM"
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},
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"bias": false,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 32768,
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"model_type": "internlm",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"num_key_value_heads": 8,
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"pad_token_id": 2,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 1.0,
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"type": "dynamic"
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},
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"rope_theta": 1000000,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.37.2",
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"use_cache": true,
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"vocab_size": 92544
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}
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configuration_internlm.py
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# coding=utf-8
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# Copyright (c) InternLM. 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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""" InternLM model configuration"""
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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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INTERNLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
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class InternLMConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`InternLMModel`]. It is used to instantiate
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an InternLM model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the InternLM-7B.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32000):
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Vocabulary size of the InternLM model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`InternLMModel`]
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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 Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
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`num_attention_heads`.
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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. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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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-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 (not used by all models). Only
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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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Example:
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```python
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>>> from transformers import InternLMModel, InternLMConfig
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>>> # Initializing a InternLM internlm-7b style configuration
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>>> configuration = InternLMConfig()
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>>> # Initializing a model from the internlm-7b style configuration
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>>> model = InternLMModel(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 = "internlm"
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_auto_class = "AutoConfig"
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def __init__( # pylint: disable=W0102
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self,
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vocab_size=103168,
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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-6,
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use_cache=True,
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pad_token_id=0,
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bos_token_id=1,
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eos_token_id=2,
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tie_word_embeddings=False,
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bias=True,
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rope_theta=10000,
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rope_scaling=None,
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**kwargs,
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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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self.bias = bias
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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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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.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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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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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'], got {rope_scaling_type}"
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)
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if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor < 1.0:
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raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {rope_scaling_factor}")
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 2,
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"transformers_version": "4.37.2"
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}
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model-00001-of-00002.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:1fd2f90779b1457e6df4e1f3d994e6cbb5fca2bb91161b3e69a3d8ca14ddfa08
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size 1981392544
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model-00002-of-00002.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:7c3781fac6755224db8945b9c1638dc47d6bccede3eb837565d796c3f939675d
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size 1796846480
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model.safetensors.index.json
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{
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"metadata": {
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"total_size": 3778220032
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},
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"weight_map": {
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"model.layers.0.attention.wo.weight": "model-00001-of-00002.safetensors",
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"model.layers.0.attention.wqkv.weight": "model-00001-of-00002.safetensors",
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"model.layers.0.attention_norm.weight": "model-00001-of-00002.safetensors",
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"model.layers.0.feed_forward.w1.weight": "model-00001-of-00002.safetensors",
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"model.layers.0.feed_forward.w2.weight": "model-00001-of-00002.safetensors",
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"model.layers.0.feed_forward.w3.weight": "model-00001-of-00002.safetensors",
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"model.layers.0.ffn_norm.weight": "model-00001-of-00002.safetensors",
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"model.layers.1.attention.wo.weight": "model-00001-of-00002.safetensors",
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"model.layers.1.attention.wqkv.weight": "model-00001-of-00002.safetensors",
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"model.layers.1.attention_norm.weight": "model-00001-of-00002.safetensors",
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"model.layers.1.feed_forward.w1.weight": "model-00001-of-00002.safetensors",
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"model.layers.1.feed_forward.w2.weight": "model-00001-of-00002.safetensors",
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modeling_internlm2.py
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|
1 |
+
# coding=utf-8
|
2 |
+
# # Copyright (c) InternLM. All rights reserved.
|
3 |
+
#
|
4 |
+
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
5 |
+
# and OPT implementations in this library. It has been modified from its
|
6 |
+
# original forms to accommodate minor architectural differences compared
|
7 |
+
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
8 |
+
#
|
9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
10 |
+
# you may not use this file except in compliance with the License.
|
11 |
+
# You may obtain a copy of the License at
|
12 |
+
#
|
13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
14 |
+
#
|
15 |
+
# Unless required by applicable law or agreed to in writing, software
|
16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
18 |
+
# See the License for the specific language governing permissions and
|
19 |
+
# limitations under the License.
|
20 |
+
""" PyTorch InternLM2 model."""
|
21 |
+
import math
|
22 |
+
import queue
|
23 |
+
import threading
|
24 |
+
import warnings
|
25 |
+
from typing import List, Optional, Tuple, Union
|
26 |
+
|
27 |
+
import torch
|
28 |
+
import torch.utils.checkpoint
|
29 |
+
from einops import rearrange
|
30 |
+
from torch import nn
|
31 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
32 |
+
from transformers.activations import ACT2FN
|
33 |
+
from transformers.modeling_outputs import (
|
34 |
+
BaseModelOutputWithPast,
|
35 |
+
CausalLMOutputWithPast,
|
36 |
+
SequenceClassifierOutputWithPast,
|
37 |
+
)
|
38 |
+
from transformers.modeling_utils import PreTrainedModel
|
39 |
+
from transformers.utils import (
|
40 |
+
add_start_docstrings,
|
41 |
+
add_start_docstrings_to_model_forward,
|
42 |
+
logging,
|
43 |
+
replace_return_docstrings,
|
44 |
+
)
|
45 |
+
|
46 |
+
try:
|
47 |
+
from transformers.generation.streamers import BaseStreamer
|
48 |
+
except: # noqa # pylint: disable=bare-except
|
49 |
+
BaseStreamer = None
|
50 |
+
|
51 |
+
from .configuration_internlm import InternLMConfig as InternLM2Config
|
52 |
+
|
53 |
+
logger = logging.get_logger(__name__)
|
54 |
+
|
55 |
+
_CONFIG_FOR_DOC = "InternLM2Config"
|
56 |
+
|
57 |
+
|
58 |
+
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
|
59 |
+
def _make_causal_mask(
|
60 |
+
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
|
61 |
+
):
|
62 |
+
"""
|
63 |
+
Make causal mask used for bi-directional self-attention.
|
64 |
+
"""
|
65 |
+
bsz, tgt_len = input_ids_shape
|
66 |
+
mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
|
67 |
+
mask_cond = torch.arange(mask.size(-1), device=device)
|
68 |
+
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
|
69 |
+
mask = mask.to(dtype)
|
70 |
+
|
71 |
+
if past_key_values_length > 0:
|
72 |
+
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
|
73 |
+
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
|
74 |
+
|
75 |
+
|
76 |
+
# Copied from transformers.models.bart.modeling_bart._expand_mask
|
77 |
+
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
78 |
+
"""
|
79 |
+
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
|
80 |
+
"""
|
81 |
+
bsz, src_len = mask.size()
|
82 |
+
tgt_len = tgt_len if tgt_len is not None else src_len
|
83 |
+
|
84 |
+
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
|
85 |
+
|
86 |
+
inverted_mask = 1.0 - expanded_mask
|
87 |
+
|
88 |
+
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
|
89 |
+
|
90 |
+
|
91 |
+
class InternLM2RMSNorm(nn.Module):
|
92 |
+
def __init__(self, hidden_size, eps=1e-6):
|
93 |
+
"""
|
94 |
+
InternLM2RMSNorm is equivalent to T5LayerNorm
|
95 |
+
"""
|
96 |
+
super().__init__()
|
97 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
98 |
+
self.variance_epsilon = eps
|
99 |
+
|
100 |
+
def forward(self, hidden_states):
|
101 |
+
input_dtype = hidden_states.dtype
|
102 |
+
hidden_states = hidden_states.to(torch.float32)
|
103 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
104 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
105 |
+
return self.weight * hidden_states.to(input_dtype)
|
106 |
+
|
107 |
+
|
108 |
+
class InternLM2RotaryEmbedding(nn.Module):
|
109 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
110 |
+
super().__init__()
|
111 |
+
|
112 |
+
self.dim = dim
|
113 |
+
self.max_position_embeddings = max_position_embeddings
|
114 |
+
self.base = base
|
115 |
+
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
116 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
117 |
+
|
118 |
+
# Build here to make `torch.jit.trace` work.
|
119 |
+
self._set_cos_sin_cache(
|
120 |
+
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
|
121 |
+
)
|
122 |
+
|
123 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
124 |
+
self.max_seq_len_cached = seq_len
|
125 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
126 |
+
|
127 |
+
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
128 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
129 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
130 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
131 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
132 |
+
|
133 |
+
def forward(self, x, seq_len=None):
|
134 |
+
# x: [bs, num_attention_heads, seq_len, head_size]
|
135 |
+
if seq_len > self.max_seq_len_cached:
|
136 |
+
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.float32)
|
137 |
+
|
138 |
+
return (
|
139 |
+
self.cos_cached[:seq_len].to(dtype=x.dtype),
|
140 |
+
self.sin_cached[:seq_len].to(dtype=x.dtype),
|
141 |
+
)
|
142 |
+
|
143 |
+
|
144 |
+
class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
|
145 |
+
"""InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
146 |
+
|
147 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
148 |
+
self.scaling_factor = scaling_factor
|
149 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
150 |
+
|
151 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
152 |
+
self.max_seq_len_cached = seq_len
|
153 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
154 |
+
t = t / self.scaling_factor
|
155 |
+
|
156 |
+
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
157 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
158 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
159 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
160 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
161 |
+
|
162 |
+
|
163 |
+
class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
|
164 |
+
"""InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
|
165 |
+
Credits to the Reddit users /u/bloc97 and /u/emozilla.
|
166 |
+
"""
|
167 |
+
|
168 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
169 |
+
self.scaling_factor = scaling_factor
|
170 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
171 |
+
|
172 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
173 |
+
self.max_seq_len_cached = seq_len
|
174 |
+
|
175 |
+
if seq_len > self.max_position_embeddings:
|
176 |
+
base = self.base * (
|
177 |
+
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
|
178 |
+
) ** (self.dim / (self.dim - 2))
|
179 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
180 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
181 |
+
|
182 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
183 |
+
|
184 |
+
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
185 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
186 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
187 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
188 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
189 |
+
|
190 |
+
|
191 |
+
def rotate_half(x):
|
192 |
+
"""Rotates half the hidden dims of the input."""
|
193 |
+
x1 = x[..., : x.shape[-1] // 2]
|
194 |
+
x2 = x[..., x.shape[-1] // 2 :]
|
195 |
+
return torch.cat((-x2, x1), dim=-1)
|
196 |
+
|
197 |
+
|
198 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
|
199 |
+
cos = cos[position_ids].unsqueeze(1)
|
200 |
+
sin = sin[position_ids].unsqueeze(1)
|
201 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
202 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
203 |
+
|
204 |
+
return q_embed, k_embed
|
205 |
+
|
206 |
+
|
207 |
+
class InternLM2MLP(nn.Module):
|
208 |
+
def __init__(self, config):
|
209 |
+
super().__init__()
|
210 |
+
self.config = config
|
211 |
+
self.hidden_size = config.hidden_size
|
212 |
+
self.intermediate_size = config.intermediate_size
|
213 |
+
self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
214 |
+
self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
215 |
+
self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
216 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
217 |
+
|
218 |
+
def forward(self, x):
|
219 |
+
down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))
|
220 |
+
|
221 |
+
return down_proj
|
222 |
+
|
223 |
+
|
224 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
225 |
+
"""
|
226 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
227 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
228 |
+
"""
|
229 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
230 |
+
if n_rep == 1:
|
231 |
+
return hidden_states
|
232 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
233 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
234 |
+
|
235 |
+
|
236 |
+
class InternLM2Attention(nn.Module):
|
237 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
238 |
+
|
239 |
+
def __init__(self, config: InternLM2Config):
|
240 |
+
super().__init__()
|
241 |
+
self.config = config
|
242 |
+
self.hidden_size = config.hidden_size
|
243 |
+
self.num_heads = config.num_attention_heads
|
244 |
+
self.head_dim = self.hidden_size // self.num_heads
|
245 |
+
self.num_key_value_heads = config.num_key_value_heads
|
246 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
247 |
+
self.max_position_embeddings = config.max_position_embeddings
|
248 |
+
self.is_causal = True
|
249 |
+
|
250 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
251 |
+
raise ValueError(
|
252 |
+
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
253 |
+
f" and `num_heads`: {self.num_heads})."
|
254 |
+
)
|
255 |
+
|
256 |
+
self.wqkv = nn.Linear(
|
257 |
+
self.hidden_size,
|
258 |
+
(self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
|
259 |
+
bias=config.bias,
|
260 |
+
)
|
261 |
+
|
262 |
+
self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
|
263 |
+
self._init_rope()
|
264 |
+
|
265 |
+
def _init_rope(self):
|
266 |
+
if self.config.rope_scaling is None:
|
267 |
+
self.rotary_emb = InternLM2RotaryEmbedding(
|
268 |
+
self.head_dim,
|
269 |
+
max_position_embeddings=self.max_position_embeddings,
|
270 |
+
base=self.config.rope_theta,
|
271 |
+
)
|
272 |
+
else:
|
273 |
+
scaling_type = self.config.rope_scaling["type"]
|
274 |
+
scaling_factor = self.config.rope_scaling["factor"]
|
275 |
+
if scaling_type == "dynamic":
|
276 |
+
self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
|
277 |
+
self.head_dim,
|
278 |
+
max_position_embeddings=self.max_position_embeddings,
|
279 |
+
base=self.config.rope_theta,
|
280 |
+
scaling_factor=scaling_factor
|
281 |
+
)
|
282 |
+
elif scaling_type == "linear":
|
283 |
+
self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
|
284 |
+
self.head_dim,
|
285 |
+
max_position_embeddings=self.max_position_embeddings,
|
286 |
+
base=self.config.rope_theta,
|
287 |
+
scaling_factor=scaling_factor
|
288 |
+
)
|
289 |
+
else:
|
290 |
+
raise ValueError("Currently we only support rotary embedding's type being 'dynamic' or 'linear'.")
|
291 |
+
return self.rotary_emb
|
292 |
+
|
293 |
+
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
294 |
+
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
295 |
+
|
296 |
+
def forward(
|
297 |
+
self,
|
298 |
+
hidden_states: torch.Tensor,
|
299 |
+
attention_mask: Optional[torch.Tensor] = None,
|
300 |
+
position_ids: Optional[torch.LongTensor] = None,
|
301 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
302 |
+
output_attentions: bool = False,
|
303 |
+
use_cache: bool = False,
|
304 |
+
**kwargs,
|
305 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
306 |
+
if "padding_mask" in kwargs:
|
307 |
+
warnings.warn(
|
308 |
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. "
|
309 |
+
"Please make sure use `attention_mask` instead.`"
|
310 |
+
)
|
311 |
+
|
312 |
+
bsz, q_len, _ = hidden_states.size()
|
313 |
+
|
314 |
+
qkv_states = self.wqkv(hidden_states)
|
315 |
+
|
316 |
+
qkv_states = rearrange(
|
317 |
+
qkv_states,
|
318 |
+
"b q (h gs d) -> b q h gs d",
|
319 |
+
gs=2 + self.num_key_value_groups,
|
320 |
+
d=self.head_dim,
|
321 |
+
)
|
322 |
+
|
323 |
+
query_states = qkv_states[..., : self.num_key_value_groups, :]
|
324 |
+
query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
|
325 |
+
key_states = qkv_states[..., -2, :]
|
326 |
+
value_states = qkv_states[..., -1, :]
|
327 |
+
|
328 |
+
query_states = query_states.transpose(1, 2)
|
329 |
+
key_states = key_states.transpose(1, 2)
|
330 |
+
value_states = value_states.transpose(1, 2)
|
331 |
+
|
332 |
+
kv_seq_len = key_states.shape[-2]
|
333 |
+
if past_key_value is not None:
|
334 |
+
kv_seq_len += past_key_value[0].shape[-2]
|
335 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
336 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
337 |
+
|
338 |
+
if past_key_value is not None:
|
339 |
+
# reuse k, v, self_attention
|
340 |
+
key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
341 |
+
value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
342 |
+
|
343 |
+
past_key_value = (key_states, value_states) if use_cache else None
|
344 |
+
|
345 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
346 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
347 |
+
|
348 |
+
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
349 |
+
|
350 |
+
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
351 |
+
raise ValueError(
|
352 |
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
353 |
+
f" {attn_weights.size()}"
|
354 |
+
)
|
355 |
+
|
356 |
+
if attention_mask is not None:
|
357 |
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
358 |
+
raise ValueError(
|
359 |
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
360 |
+
)
|
361 |
+
attn_weights = attn_weights + attention_mask
|
362 |
+
|
363 |
+
# upcast attention to fp32
|
364 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
365 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
366 |
+
|
367 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
368 |
+
raise ValueError(
|
369 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
370 |
+
f" {attn_output.size()}"
|
371 |
+
)
|
372 |
+
|
373 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
374 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
375 |
+
|
376 |
+
attn_output = self.wo(attn_output)
|
377 |
+
|
378 |
+
if not output_attentions:
|
379 |
+
attn_weights = None
|
380 |
+
|
381 |
+
return attn_output, attn_weights, past_key_value
|
382 |
+
|
383 |
+
|
384 |
+
class InternLM2FlashAttention2(InternLM2Attention):
|
385 |
+
"""
|
386 |
+
InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
|
387 |
+
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
388 |
+
flash attention and deal with padding tokens in case the input contains any of them.
|
389 |
+
"""
|
390 |
+
|
391 |
+
def forward(
|
392 |
+
self,
|
393 |
+
hidden_states: torch.Tensor,
|
394 |
+
attention_mask: Optional[torch.LongTensor] = None,
|
395 |
+
position_ids: Optional[torch.LongTensor] = None,
|
396 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
397 |
+
output_attentions: bool = False,
|
398 |
+
use_cache: bool = False,
|
399 |
+
**kwargs,
|
400 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
401 |
+
# InternLM2FlashAttention2 attention does not support output_attentions
|
402 |
+
if "padding_mask" in kwargs:
|
403 |
+
warnings.warn(
|
404 |
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. "
|
405 |
+
"Please make sure use `attention_mask` instead.`"
|
406 |
+
)
|
407 |
+
|
408 |
+
# overwrite attention_mask with padding_mask
|
409 |
+
attention_mask = kwargs.pop("padding_mask")
|
410 |
+
|
411 |
+
output_attentions = False
|
412 |
+
|
413 |
+
bsz, q_len, _ = hidden_states.size()
|
414 |
+
|
415 |
+
qkv_states = self.wqkv(hidden_states)
|
416 |
+
|
417 |
+
qkv_states = rearrange(
|
418 |
+
qkv_states,
|
419 |
+
"b q (h gs d) -> b q h gs d",
|
420 |
+
gs=self.num_heads + 2 * self.num_key_value_heads,
|
421 |
+
d=self.head_dim,
|
422 |
+
q=q_len,
|
423 |
+
)
|
424 |
+
|
425 |
+
query_states = qkv_states[..., : self.num_key_value_groups, :]
|
426 |
+
query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
|
427 |
+
key_states = qkv_states[..., -2, :]
|
428 |
+
value_states = qkv_states[..., -1, :]
|
429 |
+
|
430 |
+
kv_seq_len = key_states.shape[-2]
|
431 |
+
if past_key_value is not None:
|
432 |
+
kv_seq_len += past_key_value[0].shape[-2]
|
433 |
+
|
434 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
435 |
+
|
436 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
437 |
+
|
438 |
+
if past_key_value is not None:
|
439 |
+
# reuse k, v, self_attention
|
440 |
+
key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
441 |
+
value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
442 |
+
|
443 |
+
past_key_value = (key_states, value_states) if use_cache else None
|
444 |
+
|
445 |
+
query_states = query_states.transpose(1, 2)
|
446 |
+
key_states = key_states.transpose(1, 2)
|
447 |
+
value_states = value_states.transpose(1, 2)
|
448 |
+
|
449 |
+
dropout_rate = 0.0 if not self.training else self.attention_dropout
|
450 |
+
|
451 |
+
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
452 |
+
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
453 |
+
# cast them back in the correct dtype just to be sure everything works as expected.
|
454 |
+
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
455 |
+
# in fp32. (InternLM2RMSNorm handles it correctly)
|
456 |
+
|
457 |
+
input_dtype = query_states.dtype
|
458 |
+
if input_dtype == torch.float32:
|
459 |
+
# Handle the case where the model is quantized
|
460 |
+
if hasattr(self.config, "_pre_quantization_dtype"):
|
461 |
+
target_dtype = self.config._pre_quantization_dtype
|
462 |
+
else:
|
463 |
+
target_dtype = self.q_proj.weight.dtype
|
464 |
+
|
465 |
+
logger.warning_once(
|
466 |
+
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
467 |
+
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back "
|
468 |
+
f"the input in {target_dtype}."
|
469 |
+
)
|
470 |
+
|
471 |
+
query_states = query_states.to(target_dtype)
|
472 |
+
key_states = key_states.to(target_dtype)
|
473 |
+
value_states = value_states.to(target_dtype)
|
474 |
+
|
475 |
+
attn_output = self._flash_attention_forward(
|
476 |
+
query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
|
477 |
+
)
|
478 |
+
|
479 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
480 |
+
attn_output = self.wo(attn_output)
|
481 |
+
|
482 |
+
if not output_attentions:
|
483 |
+
attn_weights = None
|
484 |
+
|
485 |
+
return attn_output, attn_weights, past_key_value
|
486 |
+
|
487 |
+
|
488 |
+
class InternLM2DecoderLayer(nn.Module):
|
489 |
+
def __init__(self, config: InternLM2Config):
|
490 |
+
super().__init__()
|
491 |
+
self.hidden_size = config.hidden_size
|
492 |
+
self.attention = (
|
493 |
+
InternLM2Attention(config=config)
|
494 |
+
if not getattr(config, "_flash_attn_2_enabled", False)
|
495 |
+
else InternLM2FlashAttention2(config=config)
|
496 |
+
)
|
497 |
+
self.feed_forward = InternLM2MLP(config)
|
498 |
+
self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
499 |
+
self.ffn_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
500 |
+
|
501 |
+
def forward(
|
502 |
+
self,
|
503 |
+
hidden_states: torch.Tensor,
|
504 |
+
attention_mask: Optional[torch.Tensor] = None,
|
505 |
+
position_ids: Optional[torch.LongTensor] = None,
|
506 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
507 |
+
output_attentions: Optional[bool] = False,
|
508 |
+
use_cache: Optional[bool] = False,
|
509 |
+
**kwargs,
|
510 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
511 |
+
"""
|
512 |
+
Args:
|
513 |
+
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
514 |
+
attention_mask (`torch.FloatTensor`, *optional*):
|
515 |
+
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
|
516 |
+
query_sequence_length, key_sequence_length)` if default attention is used.
|
517 |
+
output_attentions (`bool`, *optional*):
|
518 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
519 |
+
returned tensors for more detail.
|
520 |
+
use_cache (`bool`, *optional*):
|
521 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
522 |
+
(see `past_key_values`).
|
523 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
524 |
+
"""
|
525 |
+
if "padding_mask" in kwargs:
|
526 |
+
warnings.warn(
|
527 |
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. "
|
528 |
+
"Please make sure use `attention_mask` instead.`"
|
529 |
+
)
|
530 |
+
|
531 |
+
residual = hidden_states
|
532 |
+
|
533 |
+
hidden_states = self.attention_norm(hidden_states)
|
534 |
+
|
535 |
+
# Self Attention
|
536 |
+
hidden_states, self_attn_weights, present_key_value = self.attention(
|
537 |
+
hidden_states=hidden_states,
|
538 |
+
attention_mask=attention_mask,
|
539 |
+
position_ids=position_ids,
|
540 |
+
past_key_value=past_key_value,
|
541 |
+
output_attentions=output_attentions,
|
542 |
+
use_cache=use_cache,
|
543 |
+
**kwargs,
|
544 |
+
)
|
545 |
+
hidden_states = residual + hidden_states
|
546 |
+
|
547 |
+
# Fully Connected
|
548 |
+
residual = hidden_states
|
549 |
+
hidden_states = self.ffn_norm(hidden_states)
|
550 |
+
hidden_states = self.feed_forward(hidden_states)
|
551 |
+
hidden_states = residual + hidden_states
|
552 |
+
|
553 |
+
outputs = (hidden_states,)
|
554 |
+
|
555 |
+
if output_attentions:
|
556 |
+
outputs += (self_attn_weights,)
|
557 |
+
|
558 |
+
if use_cache:
|
559 |
+
outputs += (present_key_value,)
|
560 |
+
|
561 |
+
return outputs
|
562 |
+
|
563 |
+
|
564 |
+
InternLM2_START_DOCSTRING = r"""
|
565 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
566 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
567 |
+
etc.)
|
568 |
+
|
569 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
570 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
571 |
+
and behavior.
|
572 |
+
|
573 |
+
Parameters:
|
574 |
+
config ([`InternLM2Config`]):
|
575 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
576 |
+
load the weights associated with the model, only the configuration. Check out the
|
577 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
578 |
+
"""
|
579 |
+
|
580 |
+
|
581 |
+
@add_start_docstrings(
|
582 |
+
"The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
|
583 |
+
InternLM2_START_DOCSTRING,
|
584 |
+
)
|
585 |
+
class InternLM2PreTrainedModel(PreTrainedModel):
|
586 |
+
config_class = InternLM2Config
|
587 |
+
base_model_prefix = "model"
|
588 |
+
supports_gradient_checkpointing = True
|
589 |
+
_no_split_modules = ["InternLM2DecoderLayer"]
|
590 |
+
_skip_keys_device_placement = "past_key_values"
|
591 |
+
_supports_flash_attn_2 = True
|
592 |
+
|
593 |
+
def _init_weights(self, module):
|
594 |
+
std = self.config.initializer_range
|
595 |
+
if isinstance(module, nn.Linear):
|
596 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
597 |
+
if module.bias is not None:
|
598 |
+
module.bias.data.zero_()
|
599 |
+
elif isinstance(module, nn.Embedding):
|
600 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
601 |
+
if module.padding_idx is not None:
|
602 |
+
module.weight.data[module.padding_idx].zero_()
|
603 |
+
|
604 |
+
|
605 |
+
InternLM2_INPUTS_DOCSTRING = r"""
|
606 |
+
Args:
|
607 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
608 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
609 |
+
it.
|
610 |
+
|
611 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
612 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
613 |
+
|
614 |
+
|