merge: upload transformers implementation (#6)
- merge: upload `transformers` implementation (efd6eabee32eabd8675eae49359287ad061fe197) - feat: use latest modeling code (163f149452b3ecef0f4c3df5ce63c69652e45b78)
This commit is contained in:
Jonathan Tow
system
parent
39d1453f64
commit
ebf5f386bd
@ -33,7 +33,6 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-1_6b", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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"stabilityai/stablelm-2-1_6b",
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trust_remote_code=True,
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torch_dtype="auto",
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)
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model.cuda()
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@ -58,7 +57,6 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-1_6b", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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"stabilityai/stablelm-2-1_6b",
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trust_remote_code=True,
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torch_dtype="auto",
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attn_implementation="flash_attention_2",
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)
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16
config.json
16
config.json
@ -1,11 +1,7 @@
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{
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"architectures": [
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"StableLMEpochForCausalLM"
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"StableLmForCausalLM"
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],
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"auto_map": {
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"AutoConfig": "configuration_stablelm_epoch.StableLMEpochConfig",
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"AutoModelForCausalLM": "modeling_stablelm_epoch.StableLMEpochForCausalLM"
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},
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"bos_token_id": 100257,
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"eos_token_id": 100257,
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"hidden_act": "silu",
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@ -13,18 +9,16 @@
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"initializer_range": 0.02,
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"intermediate_size": 5632,
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"max_position_embeddings": 4096,
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"model_type": "stablelm_epoch",
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"norm_eps": 1e-05,
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"model_type": "stablelm",
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"layer_norm_eps": 1e-05,
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"num_attention_heads": 32,
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"num_heads": 32,
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"num_hidden_layers": 24,
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"num_key_value_heads": 32,
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"rope_pct": 0.25,
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"partial_rotary_factor": 0.25,
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"rope_theta": 10000,
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"rotary_scaling_factor": 1.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.36.2",
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"transformers_version": "4.38.0",
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"use_cache": true,
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"use_qkv_bias": true,
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"vocab_size": 100352
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183
configuration_stablelm.py
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183
configuration_stablelm.py
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@ -0,0 +1,183 @@
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# coding=utf-8
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# Copyright 2024 Stability AI and The HuggingFace Inc. team. All rights reserved.
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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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""" StableLM 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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STABLELM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"stabilityai/stablelm-3b-4e1t": "https://huggingface.co/stabilityai/stablelm-3b-4e1t/resolve/main/config.json",
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# See all StableLM models at https://huggingface.co/models?filter=stablelm
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}
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class StableLmConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`~StableLmModel`].
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It is used to instantiate an StableLM model according to the specified arguments, defining the model
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architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
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the StableLM [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used
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to control the model outputs. Read the documentation from [`PretrainedConfig`]
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for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 50304):
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Vocabulary size of the StableLM model. Defines the number of different tokens that
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can be represented by the `inputs_ids` passed when calling [`StableLmModel`].
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intermediate_size (`int`, *optional*, defaults to 6912):
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Dimension of the MLP representations.
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hidden_size (`int`, *optional*, defaults to 2560):
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Number of hidden layers in the Transformer decoder.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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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*, defaults to 32):
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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).
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max_position_embeddings (`int`, *optional*, defaults to 4096):
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The maximum sequence length that this model might ever be used with.
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Typically set this to something large 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
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all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the 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 the model's input and output word embeddings should be tied.
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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. Currently supports two scaling
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strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
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`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
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these scaling strategies behave:
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https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
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is an experimental feature, subject to breaking API changes in future versions.
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use_qkv_bias (`bool`, *optional*, defaults to `False`):
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Whether or not the model should use bias for qkv layers.
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hidden_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio after applying the MLP to the hidden states.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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partial_rotary_factor (`float`, *optional*, defaults to 0.25):
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Percentage of the query and keys which will have rotary embedding.
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bos_token_id (int, *optional*, defaults to 0):
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The id of the `BOS` token in the vocabulary.
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eos_token_id (int, *optional*, defaults to 0):
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The id of the `EOS` token in the vocabulary.
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Example:
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```python
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>>> from transformers import StableLmModel, StableLmConfig
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>>> # Initializing a StableLM stablelm-3b style configuration
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>>> configuration = StableLmConfig()
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```"""
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model_type = "stablelm"
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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=50304,
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intermediate_size=6912,
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hidden_size=2560,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=32,
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hidden_act="silu",
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max_position_embeddings=4096,
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initializer_range=0.02,
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layer_norm_eps=1.0e-5,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=10_000,
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rope_scaling=None,
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use_qkv_bias=False,
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hidden_dropout=0.0,
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attention_dropout=0.0,
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partial_rotary_factor=0.25,
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bos_token_id=0,
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eos_token_id=0,
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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.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.layer_norm_eps = layer_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.use_qkv_bias = use_qkv_bias
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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self.partial_rotary_factor = partial_rotary_factor
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self._rope_scaling_validation()
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super().__init__(
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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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# Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
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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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@ -1,117 +0,0 @@
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# Copyright 2023 Stability and The HuggingFace Inc. team. All rights reserved.
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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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""" StableLM Epoch model configuration"""
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from transformers 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 StableLMEpochConfig(PretrainedConfig):
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r"""
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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 50_304):
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Vocabulary size of the StableLM model. Defines the number of different tokens that
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can be represented by the `inputs_ids` passed when calling [`StableLMEpochModel`].
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intermediate_size (`int`, *optional*, defaults to 6912):
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Dimension of the MLP representations.
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hidden_size (`int`, *optional*, defaults to 2560):
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Dimension of the decoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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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).
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rope_pct (`float`, *optional*, defaults to 1.0):
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Percentage of hidden dimensions to allocate to rotary 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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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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Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
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initializer_range (`float`, *optional*, defaults to 1e-5):
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The standard deviation of the truncated_normal_initializer for initializing
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all weight matrices.
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norm_eps (`float`, *optional*, defaults to 1e-8):
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The epsilon used by the 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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use_qkv_bias (`bool`, *optional*, defaults to `True`):
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Whether or not the model should use bias for qkv layers.
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tie_word_embeddings(`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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"""
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model_type = "stablelm_epoch"
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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=50_304,
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intermediate_size=6912,
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hidden_size=2560,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=32,
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hidden_act="silu",
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rope_pct=0.25,
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rope_theta=10_000,
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max_position_embeddings=4096,
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initializer_range=0.02,
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norm_eps=1.0e-5,
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use_cache=True,
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use_qkv_bias=True,
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bos_token_id=0,
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eos_token_id=2,
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tie_word_embeddings=False,
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attention_dropout: float = 0.0,
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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.intermediate_size = intermediate_size
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self.hidden_size = hidden_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.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.rope_pct = rope_pct
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self.rope_theta = rope_theta
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self.initializer_range = initializer_range
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self.norm_eps = norm_eps
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self.use_cache = use_cache
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self.use_qkv_bias = use_qkv_bias
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self.tie_word_embeddings = tie_word_embeddings
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self.attention_dropout = attention_dropout
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super().__init__(
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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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@ -2,5 +2,5 @@
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"_from_model_config": true,
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"bos_token_id": 100257,
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"eos_token_id": 100257,
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"transformers_version": "4.36.2"
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"transformers_version": "4.38.0"
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}
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1341
modeling_stablelm.py
Normal file
1341
modeling_stablelm.py
Normal file
File diff suppressed because it is too large
Load Diff
@ -1,919 +0,0 @@
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# coding=utf-8
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# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team. All rights reserved.
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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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#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
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#
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# This code is based off the following work:
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# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
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# https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
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""" PyTorch StableLM Epoch model. """
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from typing import Optional, Tuple, Union
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import math
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import warnings
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import torch
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import torch.nn.functional as F
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import CrossEntropyLoss
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from transformers.cache_utils import Cache
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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||||
)
|
||||
from transformers.modeling_utils import PreTrainedModel
|
||||
from transformers.utils import logging, is_flash_attn_greater_or_equal_2_10
|
||||
|
||||
from .configuration_stablelm_epoch import StableLMEpochConfig
|
||||
|
||||
try:
|
||||
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
||||
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
|
||||
except:
|
||||
flash_attn_func, flash_attn_varlen_func = None, None
|
||||
index_first_axis, pad_input, unpad_input = None, None, None
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
# Copied from transformers.models.llama.modeling_llama._get_unpad_data
|
||||
def _get_unpad_data(attention_mask):
|
||||
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
|
||||
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
|
||||
max_seqlen_in_batch = seqlens_in_batch.max().item()
|
||||
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
|
||||
return (
|
||||
indices,
|
||||
cu_seqlens,
|
||||
max_seqlen_in_batch,
|
||||
)
|
||||
|
||||
|
||||
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
|
||||
def _make_causal_mask(
|
||||
input_ids_shape: torch.Size,
|
||||
dtype: torch.dtype,
|
||||
device: torch.device,
|
||||
past_key_values_length: int = 0,
|
||||
):
|
||||
"""Make causal mask used for bi-directional self-attention."""
|
||||
batch_size, tgt_len = input_ids_shape
|
||||
mask = torch.full((tgt_len, tgt_len), torch.finfo(torch.float16).min, device=device)
|
||||
mask_cond = torch.arange(mask.size(-1), device=device)
|
||||
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
|
||||
mask = mask.to(dtype)
|
||||
if past_key_values_length > 0:
|
||||
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
|
||||
return mask[None, None, :, :].expand(batch_size, 1, tgt_len, tgt_len + past_key_values_length)
|
||||
|
||||
|
||||
# Copied from transformers.models.bart.modeling_bart._expand_mask
|
||||
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
||||
"""Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, tgt_seq_len, src_seq_len]`."""
|
||||
batch_size, src_len = mask.size()
|
||||
tgt_len = tgt_len if tgt_len is not None else src_len
|
||||
|
||||
expanded_mask = mask[:, None, None, :].expand(batch_size, 1, tgt_len, src_len).to(dtype)
|
||||
inverted_mask = 1.0 - expanded_mask
|
||||
|
||||
return inverted_mask.masked_fill(
|
||||
inverted_mask.to(torch.bool), torch.finfo(dtype).min
|
||||
)
|
||||
|
||||
|
||||
class RotaryEmbedding(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
max_position_embeddings: int,
|
||||
base: int = 10_000,
|
||||
device: Optional[torch.device] = None,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.dim = dim
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.base = base
|
||||
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
|
||||
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
||||
|
||||
# Build here to make `torch.jit.trace` work.
|
||||
self._set_cos_sin_cache(
|
||||
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype(),
|
||||
)
|
||||
|
||||
def _set_cos_sin_cache(self, seq_len: int, device: torch.device, dtype: torch.dtype):
|
||||
self.max_seq_len_cached = seq_len
|
||||
t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
|
||||
|
||||
# Don't do einsum, it converts fp32 to fp16 under AMP
|
||||
# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
||||
freqs = torch.outer(t, self.inv_freq)
|
||||
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
||||
emb = torch.cat((freqs, freqs), dim=-1)
|
||||
self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
|
||||
self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
|
||||
|
||||
def forward(self, x: torch.Tensor, seq_len: Optional[int] = None):
|
||||
# x: [batch_size, num_heads, seq_len, head_size]
|
||||
if seq_len > self.max_seq_len_cached:
|
||||
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.get_default_dtype())
|
||||
return (
|
||||
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
|
||||
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
|
||||
)
|
||||
|
||||
|
||||
def rotate_half(x: torch.Tensor):
|
||||
"""Rotates half the hidden dims of the input."""
|
||||
x1, x2 = torch.chunk(x, 2, dim=-1)
|
||||
return torch.cat((-x2, x1), dim=-1)
|
||||
|
||||
|
||||
def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
|
||||
# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
|
||||
cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
|
||||
sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
|
||||
cos = cos[position_ids].unsqueeze(1) # [batch_size, 1, seq_len, dim]
|
||||
sin = sin[position_ids].unsqueeze(1) # [batch_size, 1, seq_len, dim]
|
||||
q_embed = (q * cos) + (rotate_half(q) * sin)
|
||||
k_embed = (k * cos) + (rotate_half(k) * sin)
|
||||
return q_embed, k_embed
|
||||
|
||||
|
||||
class MLP(nn.Module):
|
||||
def __init__(self, config: StableLMEpochConfig):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.hidden_size = config.hidden_size
|
||||
self.intermediate_size = config.intermediate_size
|
||||
self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
|
||||
self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
|
||||
self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
|
||||
self.act_fn = nn.SiLU()
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
||||
|
||||
|
||||
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
||||
"""
|
||||
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
||||
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
||||
"""
|
||||
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
||||
if n_rep == 1:
|
||||
return hidden_states
|
||||
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
||||
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, config: StableLMEpochConfig):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.hidden_size = config.hidden_size
|
||||
self.num_heads = config.num_attention_heads
|
||||
self.head_dim = self.hidden_size // self.num_heads
|
||||
self.num_key_value_heads = config.num_key_value_heads
|
||||
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
||||
self.max_position_embeddings = config.max_position_embeddings
|
||||
self.is_causal = True
|
||||
self.attention_dropout = config.attention_dropout
|
||||
|
||||
if (self.head_dim * self.num_heads) != self.hidden_size:
|
||||
raise ValueError(
|
||||
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
||||
f" and `num_heads`: {self.num_heads})."
|
||||
)
|
||||
|
||||
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.use_qkv_bias)
|
||||
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.use_qkv_bias)
|
||||
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.use_qkv_bias)
|
||||
self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
|
||||
|
||||
self._init_rope()
|
||||
|
||||
def _init_rope(self):
|
||||
self.rotary_ndims = int(self.head_dim * self.config.rope_pct)
|
||||
self.rotary_emb = RotaryEmbedding(
|
||||
self.rotary_ndims,
|
||||
max_position_embeddings=self.config.max_position_embeddings,
|
||||
base=self.config.rope_theta,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.FloatTensor,
|
||||
attention_mask: torch.FloatTensor,
|
||||
position_ids: torch.LongTensor,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||||
output_attentions: Optional[bool] = False,
|
||||
use_cache: Optional[bool] = False,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
|
||||
query_states = self.q_proj(hidden_states)
|
||||
key_states = self.k_proj(hidden_states)
|
||||
value_states = self.v_proj(hidden_states)
|
||||
|
||||
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||||
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||||
|
||||
query_rot = query_states[..., : self.rotary_ndims]
|
||||
query_pass = query_states[..., self.rotary_ndims :]
|
||||
key_rot = key_states[..., : self.rotary_ndims]
|
||||
key_pass = key_states[..., self.rotary_ndims :]
|
||||
|
||||
kv_seq_len = key_states.shape[-2]
|
||||
if past_key_value is not None:
|
||||
kv_seq_len += past_key_value[0].shape[-2]
|
||||
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
||||
query_states, key_states = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
|
||||
|
||||
# [batch_size, num_heads, seq_len, head_dim]
|
||||
query_states = torch.cat((query_states, query_pass), dim=-1)
|
||||
key_states = torch.cat((key_states, key_pass), dim=-1)
|
||||
|
||||
if past_key_value is not None:
|
||||
# Reuse k, v, self_attention
|
||||
key_states = torch.cat((past_key_value[0], key_states), dim=2)
|
||||
value_states = torch.cat((past_key_value[1], value_states), dim=2)
|
||||
|
||||
past_key_value = (key_states, value_states) if use_cache else None
|
||||
|
||||
# Repeat k/v heads if n_kv_heads < n_heads
|
||||
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
||||
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||||
|
||||
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
||||
|
||||
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
||||
raise ValueError(
|
||||
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
||||
f" {attn_weights.size()}"
|
||||
)
|
||||
|
||||
if attention_mask is not None:
|
||||
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
||||
raise ValueError(
|
||||
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
||||
)
|
||||
attn_weights = attn_weights + attention_mask
|
||||
|
||||
# Upcast attention to fp32
|
||||
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
||||
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
|
||||
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
||||
raise ValueError(
|
||||
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
||||
f" {attn_output.size()}"
|
||||
)
|
||||
|
||||
# Merge heads
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
||||
|
||||
# Final linear projection
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
if not output_attentions:
|
||||
attn_weights = None
|
||||
|
||||
return attn_output, attn_weights, past_key_value
|
||||
|
||||
|
||||
class FlashAttention2(Attention):
|
||||
"""
|
||||
Reference: https://github.com/huggingface/transformers/blob/5d36025ca13d05151b7a0c761e90d429c4644a30/src/transformers/models/llama/modeling_llama.py#L456
|
||||
"""
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
||||
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
|
||||
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
|
||||
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.LongTensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Cache] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
**kwargs,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
# FlashAttention2 attention does not support output_attentions
|
||||
if "padding_mask" in kwargs:
|
||||
warnings.warn(
|
||||
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
|
||||
)
|
||||
|
||||
# overwrite attention_mask with padding_mask
|
||||
attention_mask = kwargs.pop("padding_mask")
|
||||
|
||||
output_attentions = False
|
||||
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
|
||||
query_states = self.q_proj(hidden_states)
|
||||
key_states = self.k_proj(hidden_states)
|
||||
value_states = self.v_proj(hidden_states)
|
||||
|
||||
# Flash attention requires the input to have the shape
|
||||
# batch_size x seq_length x head_dim x hidden_dim
|
||||
# therefore we just need to keep the original shape
|
||||
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||||
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
||||
|
||||
query_rot = query_states[..., : self.rotary_ndims]
|
||||
query_pass = query_states[..., self.rotary_ndims :]
|
||||
key_rot = key_states[..., : self.rotary_ndims]
|
||||
key_pass = key_states[..., self.rotary_ndims :]
|
||||
|
||||
kv_seq_len = key_states.shape[-2]
|
||||
if past_key_value is not None:
|
||||
kv_seq_len += past_key_value[0].shape[-2]
|
||||
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
||||
query_states, key_states = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
|
||||
|
||||
# [batch_size, num_heads, seq_len, head_dim]
|
||||
query_states = torch.cat((query_states, query_pass), dim=-1)
|
||||
key_states = torch.cat((key_states, key_pass), dim=-1)
|
||||
|
||||
if past_key_value is not None:
|
||||
# Reuse k, v, self_attention
|
||||
key_states = torch.cat((past_key_value[0], key_states), dim=2)
|
||||
value_states = torch.cat((past_key_value[1], value_states), dim=2)
|
||||
|
||||
past_key_value = (key_states, value_states) if use_cache else None
|
||||
|
||||
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
|
||||
# to be able to avoid many of these transpose/reshape/view.
|
||||
query_states = query_states.transpose(1, 2)
|
||||
key_states = key_states.transpose(1, 2)
|
||||
value_states = value_states.transpose(1, 2)
|
||||
|
||||
dropout_rate = self.attention_dropout if self.training else 0.0
|
||||
|
||||
attn_output = self._flash_attention_forward(
|
||||
query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
|
||||
)
|
||||
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
if not output_attentions:
|
||||
attn_weights = None
|
||||
|
||||
return attn_output, attn_weights, past_key_value
|
||||
|
||||
def _flash_attention_forward(
|
||||
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
|
||||
):
|
||||
"""
|
||||
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
||||
first unpad the input, then computes the attention scores and pad the final attention scores.
|
||||
|
||||
Args:
|
||||
query_states (`torch.Tensor`):
|
||||
Input query states to be passed to Flash Attention API
|
||||
key_states (`torch.Tensor`):
|
||||
Input key states to be passed to Flash Attention API
|
||||
value_states (`torch.Tensor`):
|
||||
Input value states to be passed to Flash Attention API
|
||||
attention_mask (`torch.Tensor`):
|
||||
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
||||
position of padding tokens and 1 for the position of non-padding tokens.
|
||||
dropout (`int`, *optional*):
|
||||
Attention dropout
|
||||
softmax_scale (`float`, *optional*):
|
||||
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
||||
"""
|
||||
if not self._flash_attn_uses_top_left_mask:
|
||||
causal = self.is_causal
|
||||
else:
|
||||
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in FlashAttention2 __init__.
|
||||
causal = self.is_causal and query_length != 1
|
||||
|
||||
# Contains at least one padding token in the sequence
|
||||
if attention_mask is not None:
|
||||
batch_size = query_states.shape[0]
|
||||
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
||||
query_states, key_states, value_states, attention_mask, query_length
|
||||
)
|
||||
|
||||
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
||||
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
||||
|
||||
attn_output_unpad = flash_attn_varlen_func(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
cu_seqlens_q=cu_seqlens_q,
|
||||
cu_seqlens_k=cu_seqlens_k,
|
||||
max_seqlen_q=max_seqlen_in_batch_q,
|
||||
max_seqlen_k=max_seqlen_in_batch_k,
|
||||
dropout_p=dropout,
|
||||
softmax_scale=softmax_scale,
|
||||
causal=causal,
|
||||
)
|
||||
|
||||
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
||||
else:
|
||||
attn_output = flash_attn_func(
|
||||
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
|
||||
)
|
||||
|
||||
return attn_output
|
||||
|
||||
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
||||
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
||||
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
||||
|
||||
key_layer = index_first_axis(
|
||||
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
||||
)
|
||||
value_layer = index_first_axis(
|
||||
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
||||
)
|
||||
if query_length == kv_seq_len:
|
||||
query_layer = index_first_axis(
|
||||
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
||||
)
|
||||
cu_seqlens_q = cu_seqlens_k
|
||||
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
||||
indices_q = indices_k
|
||||
elif query_length == 1:
|
||||
max_seqlen_in_batch_q = 1
|
||||
cu_seqlens_q = torch.arange(
|
||||
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
||||
) # There is a memcpy here, that is very bad.
|
||||
indices_q = cu_seqlens_q[:-1]
|
||||
query_layer = query_layer.squeeze(1)
|
||||
else:
|
||||
# The -q_len: slice assumes left padding.
|
||||
attention_mask = attention_mask[:, -query_length:]
|
||||
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
|
||||
|
||||
return (
|
||||
query_layer,
|
||||
key_layer,
|
||||
value_layer,
|
||||
indices_q,
|
||||
(cu_seqlens_q, cu_seqlens_k),
|
||||
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
||||
)
|
||||
|
||||
|
||||
ATTENTION_CLASSES = {
|
||||
"eager": Attention,
|
||||
"flash_attention_2": FlashAttention2,
|
||||
}
|
||||
|
||||
|
||||
class DecoderLayer(nn.Module):
|
||||
def __init__(self, config: StableLMEpochConfig):
|
||||
super().__init__()
|
||||
self.self_attn = ATTENTION_CLASSES[config._attn_implementation](config=config)
|
||||
self.mlp = MLP(config)
|
||||
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
|
||||
self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: Optional[torch.FloatTensor],
|
||||
attention_mask: Optional[torch.FloatTensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
||||
output_attentions: Optional[bool] = False,
|
||||
use_cache: Optional[bool] = False,
|
||||
) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
|
||||
residual = hidden_states
|
||||
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
|
||||
# Self Attention
|
||||
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
||||
hidden_states=hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
# Fully Connected
|
||||
residual = hidden_states
|
||||
hidden_states = self.post_attention_layernorm(hidden_states)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
outputs = (hidden_states,)
|
||||
|
||||
if output_attentions:
|
||||
outputs += (self_attn_weights,)
|
||||
|
||||
if use_cache:
|
||||
outputs += (present_key_value,)
|
||||
|
||||
return outputs
|
||||
|
||||
|
||||
class StableLMEpochPreTrainedModel(PreTrainedModel):
|
||||
"""An abstract class to handle weights initialization and a simple interface
|
||||
for downloading and loading pretrained models.
|
||||
"""
|
||||
|
||||
config_class = StableLMEpochConfig
|
||||
base_model_prefix = "model"
|
||||
supports_gradient_checkpointing = True
|
||||
_no_split_modules = ["DecoderLayer"]
|
||||
_skip_keys_device_placement = "past_key_values"
|
||||
_supports_flash_attn_2 = True
|
||||
|
||||
def _init_weights(self, module: nn.Module):
|
||||
"""Initialize the weights"""
|
||||
if isinstance(module, nn.Linear):
|
||||
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
||||
if module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.Embedding):
|
||||
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
||||
if module.padding_idx is not None:
|
||||
module.weight.data[module.padding_idx].zero_()
|
||||
elif isinstance(module, nn.LayerNorm):
|
||||
module.bias.data.zero_()
|
||||
module.weight.data.fill_(1.0)
|
||||
|
||||
def _set_gradient_checkpointing(self, module: nn.Module, value=False):
|
||||
if isinstance(module, StableLMEpochModel):
|
||||
module.gradient_checkpointing = value
|
||||
|
||||
|
||||
class StableLMEpochModel(StableLMEpochPreTrainedModel):
|
||||
def __init__(self, config: StableLMEpochConfig):
|
||||
super().__init__(config)
|
||||
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id)
|
||||
self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
||||
self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_eps)
|
||||
|
||||
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
||||
self.gradient_checkpointing = False
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embed_tokens
|
||||
|
||||
def set_input_embeddings(self, value: nn.Module):
|
||||
self.embed_tokens = value
|
||||
|
||||
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
|
||||
def _prepare_decoder_attention_mask(
|
||||
self,
|
||||
attention_mask: torch.Tensor,
|
||||
input_shape: torch.Size,
|
||||
inputs_embeds: torch.Tensor,
|
||||
past_key_values_length: int,
|
||||
):
|
||||
# Create causal mask
|
||||
# [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
|
||||
combined_attention_mask = None
|
||||
if input_shape[-1] > 1:
|
||||
combined_attention_mask = _make_causal_mask(
|
||||
input_shape,
|
||||
inputs_embeds.dtype,
|
||||
device=inputs_embeds.device,
|
||||
past_key_values_length=past_key_values_length,
|
||||
)
|
||||
|
||||
if attention_mask is not None:
|
||||
# [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
|
||||
expanded_attn_mask = _expand_mask(
|
||||
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
|
||||
).to(inputs_embeds.device)
|
||||
combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
|
||||
|
||||
return combined_attention_mask
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.FloatTensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
) -> Union[Tuple, BaseModelOutputWithPast]:
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||||
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
# Retrieve input_ids and inputs_embeds
|
||||
if input_ids is not None and inputs_embeds is not None:
|
||||
raise ValueError(
|
||||
"You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
|
||||
)
|
||||
elif input_ids is not None:
|
||||
batch_size, seq_length = input_ids.shape
|
||||
elif inputs_embeds is not None:
|
||||
batch_size, seq_length, _ = inputs_embeds.shape
|
||||
else:
|
||||
raise ValueError(
|
||||
"You have to specify either decoder_input_ids or decoder_inputs_embeds"
|
||||
)
|
||||
|
||||
seq_length_with_past = seq_length
|
||||
past_key_values_length = 0
|
||||
|
||||
if position_ids is None:
|
||||
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
||||
position_ids = torch.arange(
|
||||
past_key_values_length,
|
||||
seq_length + past_key_values_length,
|
||||
dtype=torch.long,
|
||||
device=device,
|
||||
)
|
||||
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
||||
else:
|
||||
position_ids = position_ids.view(-1, seq_length).long()
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.embed_tokens(input_ids)
|
||||
# Embed positions
|
||||
if self._use_flash_attention_2:
|
||||
# 2d mask is passed through the layers
|
||||
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
|
||||
else:
|
||||
if attention_mask is None:
|
||||
attention_mask = torch.ones(
|
||||
(batch_size, seq_length_with_past),
|
||||
dtype=torch.bool,
|
||||
device=inputs_embeds.device,
|
||||
)
|
||||
attention_mask = self._prepare_decoder_attention_mask(
|
||||
attention_mask,
|
||||
(batch_size, seq_length),
|
||||
inputs_embeds,
|
||||
past_key_values_length,
|
||||
)
|
||||
|
||||
hidden_states = inputs_embeds
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
if use_cache:
|
||||
logger.warning(
|
||||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
||||
)
|
||||
use_cache = False
|
||||
|
||||
# Decoder layers
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_self_attns = () if output_attentions else None
|
||||
next_decoder_cache = () if use_cache else None
|
||||
|
||||
for idx, decoder_layer in enumerate(self.layers):
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
past_key_value = (
|
||||
past_key_values[idx] if past_key_values is not None else None
|
||||
)
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
|
||||
def create_custom_forward(module):
|
||||
def custom_forward(*inputs):
|
||||
# None for past_key_value
|
||||
return module(*inputs, past_key_value, output_attentions)
|
||||
|
||||
return custom_forward
|
||||
|
||||
layer_outputs = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(decoder_layer),
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
position_ids,
|
||||
)
|
||||
else:
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
)
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
|
||||
if use_cache:
|
||||
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
||||
|
||||
if output_attentions:
|
||||
all_self_attns += (layer_outputs[1],)
|
||||
|
||||
hidden_states = self.norm(hidden_states)
|
||||
|
||||
# Add hidden states from the last decoder layer
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
next_cache = next_decoder_cache if use_cache else None
|
||||
if not return_dict:
|
||||
return tuple(
|
||||
v
|
||||
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
|
||||
if v is not None
|
||||
)
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=next_cache,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_self_attns,
|
||||
)
|
||||
|
||||
|
||||
class StableLMEpochForCausalLM(StableLMEpochPreTrainedModel):
|
||||
_tied_weights_keys = ["lm_head.weight"]
|
||||
|
||||
def __init__(self, config: StableLMEpochConfig):
|
||||
super().__init__(config)
|
||||
|
||||
self.model = StableLMEpochModel(config)
|
||||
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||||
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.model.embed_tokens
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.model.embed_tokens = value
|
||||
|
||||
def get_output_embeddings(self):
|
||||
return self.lm_head
|
||||
|
||||
def set_output_embeddings(self, new_embeddings: nn.Module):
|
||||
self.lm_head = new_embeddings
|
||||
|
||||
def get_decoder(self):
|
||||
return self.model
|
||||
|
||||
def set_decoder(self, decoder):
|
||||
self.model = decoder
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.FloatTensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
) -> Union[Tuple, CausalLMOutputWithPast]:
|
||||
output_attentions = (
|
||||
output_attentions
|
||||
if output_attentions is not None
|
||||
else self.config.output_attentions
|
||||
)
|
||||
output_hidden_states = (
|
||||
output_hidden_states
|
||||
if output_hidden_states is not None
|
||||
else self.config.output_hidden_states
|
||||
)
|
||||
return_dict = (
|
||||
return_dict if return_dict is not None else self.config.use_return_dict
|
||||
)
|
||||
|
||||
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
||||
outputs = self.model(
|
||||
input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
)
|
||||
|
||||
hidden_states = outputs[0]
|
||||
logits = self.lm_head(hidden_states).float()
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
# Shift so that tokens < n predict n
|
||||
shift_logits = logits[..., :-1, :].contiguous()
|
||||
shift_labels = labels[..., 1:].contiguous()
|
||||
# Flatten the tokens
|
||||
loss_fct = CrossEntropyLoss()
|
||||
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
||||
shift_labels = shift_labels.view(-1)
|
||||
# Enable model parallelism
|
||||
shift_labels = shift_labels.to(shift_logits.device)
|
||||
loss = loss_fct(shift_logits, shift_labels)
|
||||
|
||||
if not return_dict:
|
||||
output = (logits,) + outputs[1:]
|
||||
return (loss,) + output if loss is not None else output
|
||||
|
||||
return CausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
logits=logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
def prepare_inputs_for_generation(
|
||||
self,
|
||||
input_ids,
|
||||
past_key_values: Optional[torch.Tensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
inputs_embeds: Optional[torch.Tensor] = None,
|
||||
**kwargs,
|
||||
):
|
||||
# Trim decoder_input_ids if past is used
|
||||
if past_key_values is not None:
|
||||
past_length = past_key_values[0][0].shape[2]
|
||||
|
||||
# Some generation methods already pass only the last input ID
|
||||
if input_ids.shape[1] > past_length:
|
||||
remove_prefix_length = past_length
|
||||
else:
|
||||
# Default to old behavior: keep only final ID
|
||||
remove_prefix_length = input_ids.shape[1] - 1
|
||||
|
||||
input_ids = input_ids[:, remove_prefix_length:]
|
||||
|
||||
position_ids = kwargs.get("position_ids", None)
|
||||
if attention_mask is not None and position_ids is None:
|
||||
# Create position_ids on the fly for batch generation
|
||||
position_ids = attention_mask.long().cumsum(-1) - 1
|
||||
position_ids.masked_fill_(attention_mask == 0, 1)
|
||||
if past_key_values:
|
||||
position_ids = position_ids[:, -1].unsqueeze(-1)
|
||||
|
||||
# If `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
||||
if inputs_embeds is not None and past_key_values is None:
|
||||
model_inputs = {"inputs_embeds": inputs_embeds}
|
||||
else:
|
||||
model_inputs = {"input_ids": input_ids}
|
||||
|
||||
model_inputs.update(
|
||||
{
|
||||
"attention_mask": attention_mask,
|
||||
"past_key_values": past_key_values,
|
||||
"use_cache": kwargs.get("use_cache"),
|
||||
"position_ids": position_ids,
|
||||
}
|
||||
)
|
||||
return model_inputs
|
||||
|
||||
@staticmethod
|
||||
def _reorder_cache(past_key_values, beam_idx):
|
||||
reordered_past = ()
|
||||
for layer_past in past_key_values:
|
||||
reordered_past += (
|
||||
tuple(
|
||||
past_state.index_select(0, beam_idx.to(past_state.device))
|
||||
for past_state in layer_past
|
||||
),
|
||||
)
|
||||
return reordered_past
|
||||
|
||||
|
||||
StableLMEpochConfig.register_for_auto_class()
|
||||
StableLMEpochForCausalLM.register_for_auto_class("AutoModelForCausalLM")
|
||||
Loading…
Reference in New Issue
Block a user