xiangw2
commited on
Commit
•
e7addf4
1
Parent(s):
be70e13
Initial commit
Browse files- configuration_telechat.py +94 -0
- generation_utils.py +162 -0
- modeling_telechat.py +939 -0
- tokenization_telechat.py +220 -0
configuration_telechat.py
ADDED
@@ -0,0 +1,94 @@
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# coding=utf-8
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# Copyright 2022 the Big Science Workshop and 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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""" Telechat configuration"""
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from packaging import version
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from collections import OrderedDict
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from transformers.utils import is_torch_available, logging
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from transformers.configuration_utils import PretrainedConfig
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from typing import TYPE_CHECKING, Any, List, Mapping, Optional
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logger = logging.get_logger(__name__)
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class TelechatConfig(PretrainedConfig):
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"""
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Args:
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vocab_size (`int`, *optional*, defaults to 160256): Vocabulary size of the Telechat model.
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+
hidden_size (`int`, *optional*, defaults to 4096): Dimensionality of the embeddings and hidden states.
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+
ffn_hidden_size (`int`, *optional*, defaults to 12288): Dimensionality of the feed-forward hidden states.
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+
n_layer (`int`, *optional*, defaults to 30): Number of hidden layers in the Transformer
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+
n_head (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer.
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+
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): The epsilon to use in the layer normalization layers.
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initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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apply_residual_connection_post_layernorm (`bool`, *optional*, defaults to `False`): If enabled, use the layer norm of the hidden states as the residual in the transformer blocks
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hidden_dropout (`float`, *optional*, defaults to 0.0): Dropout rate of the dropout function on the bias dropout.
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+
attention_dropout (`float`, *optional*, defaults to 0.0): Dropout rate applied to the attention probs
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use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions.
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training_seqlen (`int`, *optional*, defaults to 8192): Sequence length during last finetuning.
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logn (`bool`, *optional*, defaults to `True`): Whether or not to use logN during extrapolation.
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embed_layernorm (`bool`, *optional*, defaults to `True`): Whether or not to use embedding layernorm.
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"""
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model_type = "telechat"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {
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"num_hidden_layers": "n_layer",
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"num_attention_heads": "n_head",
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}
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def __init__(
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self,
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vocab_size=160256,
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hidden_size=4096,
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n_layer=30,
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n_head=32,
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layer_norm_epsilon=1e-5,
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initializer_range=0.02,
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use_cache=True,
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bos_token_id=1,
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eos_token_id=2,
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apply_residual_connection_post_layernorm=False,
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hidden_dropout=0.0,
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attention_dropout=0.0,
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ffn_hidden_size=12288,
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training_seqlen = 8192,
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logn = True,
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embed_layernorm = False,
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**kwargs,
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+
):
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self.vocab_size = vocab_size
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n_embed = kwargs.pop("n_embed", None)
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self.hidden_size = hidden_size if n_embed is None else n_embed
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self.n_layer = n_layer
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self.n_head = n_head
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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self.use_cache = use_cache
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81 |
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self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.logn = logn
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self.ffn_hidden_size = ffn_hidden_size
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self.training_seqlen = training_seqlen
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self.embed_layernorm = embed_layernorm
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self.num_key_value_heads= kwargs.pop("num_key_value_heads", None)
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super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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generation_utils.py
ADDED
@@ -0,0 +1,162 @@
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from typing import Optional
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from collections import deque
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from queue import Queue
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import copy
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class History:
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def __init__(self, tokenizer, history):
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'''
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init from a list of dict
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'''
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# use deque to meet some special situation
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self.input_history = deque()
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self.tokenizer = tokenizer
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if history:
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self._transfer_from_list(history)
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def _transfer_from_list(self, history):
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for message in history:
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content = message.get("content")
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# the token result may not be equal to the result model gen
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message.update(self.tokenizer(content))
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self.input_history.append(message)
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+
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def append(self, message):
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content = message.get("content")
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if "input_ids" not in message or "attention_mask" not in message:
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message.update(self.tokenizer(content))
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self.input_history.append(message)
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+
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def append_left(self, message):
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content = message.get("content")
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if "input_ids" not in message or "attention_mask" not in message:
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message.update(self.tokenizer(content))
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self.input_history.appendleft(message)
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+
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def pop(self):
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x = self.input_history.pop()
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return x
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def pop_left(self):
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x = self.input_history.pop_left()
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return x
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def update(self, message):
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self.input_history.pop()
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self.append(message)
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+
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50 |
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def __len__(self):
|
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return self.input_history.__len__()
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+
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def __str__(self):
|
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return self.input_history.__str__()
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+
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56 |
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def __copy__(self):
|
57 |
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new_instance = type(self)(self.tokenizer, [])
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58 |
+
new_instance.input_history = copy.copy(self.input_history)
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59 |
+
return new_instance
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+
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+
def __deepcopy__(self, memodict={}):
|
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new_instance = type(self)(self.tokenizer, [])
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new_instance.input_history = copy.deepcopy(self.input_history)
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return new_instance
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+
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+
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class TelechatIterTextStreamer:
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"""
|
69 |
+
With reference to the TextIterStreamers in transformers, we have rewritten this class
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+
"""
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+
|
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+
def __init__(
|
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self, tokenizer, history: History = None, skip_prompt: bool = False, timeout: Optional[float] = None,
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+
**decode_kwargs
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+
):
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76 |
+
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self.tokenizer = tokenizer
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+
self.history = history
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79 |
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self.skip_prompt = skip_prompt
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80 |
+
self.timeout = timeout
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81 |
+
self.decode_kwargs = decode_kwargs
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82 |
+
|
83 |
+
self.text_queue = Queue()
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self.cache_time = 0
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self.text_until = ""
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self.token_until = []
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self.stop_signal = None
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self.next_tokens_are_prompt = True
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+
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self.history.append({"role": "bot", "content": self.text_until})
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+
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def put(self, value):
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"""
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put printable text into queue
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"""
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96 |
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if len(value.shape) > 1 and value.shape[0] > 1:
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+
raise ValueError("TextStreamer only supports batch size 1")
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98 |
+
elif len(value.shape) > 1:
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value = value[0]
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100 |
+
|
101 |
+
if self.skip_prompt and self.next_tokens_are_prompt:
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self.next_tokens_are_prompt = False
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return
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104 |
+
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105 |
+
if value[-1] == self.tokenizer.eos_token_id:
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return
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107 |
+
|
108 |
+
# there may be some smart way to decode.
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109 |
+
self.token_until.extend(value.tolist())
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110 |
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text = self.tokenizer.decode(self.token_until, **self.decode_kwargs)
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111 |
+
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112 |
+
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113 |
+
if self._is_printable(text) or self.cache_time >= 6:
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output_text = text[len(self.text_until):]
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self.text_until = text
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+
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else:
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self.cache_time+=1
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return
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+
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self.on_finalized_text(output_text)
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+
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+
def end(self):
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"""Flushes any remaining cache and prints a newline to stdout."""
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# Flush the cache, if it exists
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+
text = self.tokenizer.decode(self.token_until, **self.decode_kwargs)
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output_text = text[len(self.text_until):]
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self.text_until = text
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self.on_finalized_text(output_text, stream_end=True)
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self.clear_cache()
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+
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def clear_cache(self):
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self.cache_time = 0
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self.token_until = []
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self.text_until = ""
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self.history = None
|
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self.next_tokens_are_prompt = True
|
138 |
+
|
139 |
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def on_finalized_text(self, text: str, stream_end: bool = False):
|
140 |
+
"""Put the text tuple in the queue."""
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+
self.history.update({"role": "bot", "content": self.text_until, "input_ids": self.token_until,
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+
"attention_mask": [1] * len(self.token_until)})
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+
self.text_queue.put((text, self.history), timeout=self.timeout)
|
144 |
+
if stream_end:
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+
self.text_queue.put((self.stop_signal, self.history), timeout=self.timeout)
|
146 |
+
|
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+
@staticmethod
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+
def _is_printable(cp):
|
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+
"""Checks whether tokens can be decoded or not"""
|
150 |
+
if "�" in cp:
|
151 |
+
return False
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152 |
+
return True
|
153 |
+
|
154 |
+
def __iter__(self):
|
155 |
+
return self
|
156 |
+
|
157 |
+
def __next__(self):
|
158 |
+
value_now, history_until = self.text_queue.get(timeout=self.timeout)
|
159 |
+
if value_now == self.stop_signal:
|
160 |
+
raise StopIteration()
|
161 |
+
else:
|
162 |
+
return value_now, history_until
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modeling_telechat.py
ADDED
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|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2022 HuggingFace Inc. team and BigScience workshop.
|
3 |
+
#
|
4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
+
# you may not use this file except in compliance with the License.
|
6 |
+
# You may obtain a copy of the License at
|
7 |
+
#
|
8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
+
#
|
10 |
+
# Unless required by applicable law or agreed to in writing, software
|
11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
+
# See the License for the specific language governing permissions and
|
14 |
+
# limitations under the License.
|
15 |
+
|
16 |
+
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
|
17 |
+
|
18 |
+
# Copyright (c) 2021 EleutherAI
|
19 |
+
# This file is based on code by the authors denoted below and has been modified from its original version.
|
20 |
+
#
|
21 |
+
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
|
22 |
+
#
|
23 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
24 |
+
# you may not use this file except in compliance with the License.
|
25 |
+
# You may obtain a copy of the License at
|
26 |
+
#
|
27 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
28 |
+
#
|
29 |
+
# Unless required by applicable law or agreed to in writing, software
|
30 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
31 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
32 |
+
# See the License for the specific language governing permissions and
|
33 |
+
# limitations under the License.
|
34 |
+
|
35 |
+
|
36 |
+
"""PyTorch TELECHAT model."""
|
37 |
+
|
38 |
+
import warnings
|
39 |
+
from typing import Optional, Tuple, Union, List, Dict
|
40 |
+
from threading import Thread
|
41 |
+
|
42 |
+
import torch
|
43 |
+
import math
|
44 |
+
import copy
|
45 |
+
from torch import nn
|
46 |
+
import torch.utils.checkpoint
|
47 |
+
from torch.nn import functional as F
|
48 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
|
49 |
+
from transformers.modeling_outputs import (
|
50 |
+
BaseModelOutputWithPastAndCrossAttentions,
|
51 |
+
CausalLMOutputWithCrossAttentions
|
52 |
+
)
|
53 |
+
from transformers.modeling_utils import PreTrainedModel
|
54 |
+
from transformers.utils import logging
|
55 |
+
from transformers import GenerationConfig
|
56 |
+
|
57 |
+
from .configuration_telechat import TelechatConfig
|
58 |
+
from .generation_utils import History, TelechatIterTextStreamer
|
59 |
+
|
60 |
+
logger = logging.get_logger(__name__)
|
61 |
+
|
62 |
+
_CHECKPOINT_FOR_DOC = "telechat"
|
63 |
+
_CONFIG_FOR_DOC = "TelechatConfig"
|
64 |
+
|
65 |
+
TELECHAT_PRETRAINED_MODEL_ARCHIVE_LIST = []
|
66 |
+
|
67 |
+
try:
|
68 |
+
from einops import rearrange
|
69 |
+
except ImportError:
|
70 |
+
rearrange = None
|
71 |
+
|
72 |
+
use_flash_attn = True
|
73 |
+
try:
|
74 |
+
from flash_attn.flash_attn_interface import flash_attn_unpadded_func
|
75 |
+
except ImportError:
|
76 |
+
try:
|
77 |
+
from flash_attn.flash_attn_interface import flash_attn_varlen_func as flash_attn_unpadded_func
|
78 |
+
except ImportError:
|
79 |
+
flash_attn_unpadded_func = None
|
80 |
+
|
81 |
+
|
82 |
+
class RotaryEmbedding(torch.nn.Module):
|
83 |
+
# Extracted from: https://github.com/EleutherAI/gpt-neox
|
84 |
+
def __init__(self, dim, config, base=10000, precision=torch.half):
|
85 |
+
super().__init__()
|
86 |
+
self.config = config
|
87 |
+
self.dim = dim
|
88 |
+
self.base = base
|
89 |
+
self.inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float().half() / dim)).cuda()
|
90 |
+
self.max_seq_len_cached = None
|
91 |
+
self.cos_cached = None
|
92 |
+
self.sin_cached = None
|
93 |
+
self.precision = precision
|
94 |
+
|
95 |
+
def get_mscale(self, scale=1):
|
96 |
+
if scale <= 1:
|
97 |
+
return 1.0
|
98 |
+
return 0.1 * math.log(scale) + 1.0
|
99 |
+
|
100 |
+
def get_ntk_alpha(self, true_seq_len):
|
101 |
+
context_value = math.log(true_seq_len / self.config.base_seqlen, 2) + 1
|
102 |
+
# ntk_alpha = 2 ** context_value - 1
|
103 |
+
ntk_alpha = 2 ** math.ceil(context_value) - 1
|
104 |
+
ntk_alpha = max(ntk_alpha, 1)
|
105 |
+
return ntk_alpha
|
106 |
+
|
107 |
+
def forward(self, x, seq_dim=0, seq_len=None):
|
108 |
+
if seq_len is None:
|
109 |
+
seq_len = x.shape[seq_dim]
|
110 |
+
seq_len = max(seq_len, self.config.training_seqlen)
|
111 |
+
ntk_alpha = self.get_ntk_alpha(seq_len)
|
112 |
+
self.mscale = float(self.get_mscale(seq_len / self.config.training_seqlen))
|
113 |
+
if True:
|
114 |
+
base = self.base * ntk_alpha ** (self.dim / (self.dim - 2))
|
115 |
+
self.inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, device=x.device).float() / self.dim))
|
116 |
+
self.max_seq_len_cached = seq_len
|
117 |
+
t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
|
118 |
+
freqs = torch.einsum('i,j->ij', t, self.inv_freq)
|
119 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
120 |
+
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
|
121 |
+
if self.precision == torch.bfloat16:
|
122 |
+
emb = emb.float()
|
123 |
+
# [sx, 1 (b * np), hn]
|
124 |
+
self.cos_cached = self.mscale * emb.cos()[:, None, :].half()
|
125 |
+
self.sin_cached = self.mscale * emb.sin()[:, None, :].half()
|
126 |
+
if self.precision == torch.bfloat16:
|
127 |
+
self.cos_cached = self.cos_cached.bfloat16()
|
128 |
+
self.sin_cached = self.sin_cached.bfloat16()
|
129 |
+
return self.cos_cached[:seq_len, ...], self.sin_cached[:seq_len, ...]
|
130 |
+
|
131 |
+
|
132 |
+
# rotary pos emb helpers:
|
133 |
+
def rotate_half(x):
|
134 |
+
x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
|
135 |
+
return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in earlier torch versions
|
136 |
+
|
137 |
+
|
138 |
+
def apply_rotary_pos_emb_torch(q, k, cos, sin, offset: int = 0): # jitting fails with bf16
|
139 |
+
cos, sin = cos[offset:q.shape[0] + offset, ...], sin[offset:q.shape[0] + offset, ...]
|
140 |
+
return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
|
141 |
+
|
142 |
+
|
143 |
+
class MixedFusedRMSNorm(nn.Module):
|
144 |
+
# Extracted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
|
145 |
+
def __init__(self, hidden_size, eps=1e-6):
|
146 |
+
super().__init__()
|
147 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
148 |
+
self.variance_epsilon = eps
|
149 |
+
|
150 |
+
def forward(self, hidden_states):
|
151 |
+
input_dtype = hidden_states.dtype
|
152 |
+
hidden_states = hidden_states.to(torch.float32)
|
153 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
154 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
155 |
+
return self.weight * hidden_states.to(input_dtype)
|
156 |
+
|
157 |
+
|
158 |
+
class FlashSelfAttention(torch.nn.Module):
|
159 |
+
# Extracted from https://github.com/microsoft/Megatron-DeepSpeed/blob/main/megatron/model/transformer.py
|
160 |
+
"""Implement the scaled dot product attention with softmax.
|
161 |
+
Arguments
|
162 |
+
---------
|
163 |
+
softmax_scale: The temperature to use for the softmax attention.
|
164 |
+
(default: 1/sqrt(d_keys) where d_keys is computed at
|
165 |
+
runtime)
|
166 |
+
attention_dropout: The dropout rate to apply to the attention
|
167 |
+
(default: 0.0)
|
168 |
+
"""
|
169 |
+
|
170 |
+
def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0,
|
171 |
+
device=None, dtype=None):
|
172 |
+
super().__init__()
|
173 |
+
assert flash_attn_unpadded_func is not None, ('Please install FlashAttention first, '
|
174 |
+
'e.g., with pip install flash-attn')
|
175 |
+
assert rearrange is not None, 'Please install einops first, e.g., with pip install einops'
|
176 |
+
self.causal = causal
|
177 |
+
self.softmax_scale = softmax_scale
|
178 |
+
self.dropout_p = attention_dropout
|
179 |
+
|
180 |
+
def forward(self, q, k, v):
|
181 |
+
"""Implements the multihead softmax attention.
|
182 |
+
Arguments
|
183 |
+
---------
|
184 |
+
q, k, v: The tensor containing the query, key, and value. (B, S, H, D)
|
185 |
+
"""
|
186 |
+
assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q, k, v)))
|
187 |
+
assert all((i.is_cuda for i in (q, k, v)))
|
188 |
+
|
189 |
+
batch_size, seqlen_q = q.shape[0], q.shape[1]
|
190 |
+
seqlen_k = k.shape[1]
|
191 |
+
|
192 |
+
q, k, v = [rearrange(x, 'b s ... -> (b s) ...') for x in [q, k, v]]
|
193 |
+
cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32,
|
194 |
+
device=q.device)
|
195 |
+
# self.training = False
|
196 |
+
if self.training:
|
197 |
+
# during training q,k,v always have same seqlen
|
198 |
+
assert seqlen_k == seqlen_q
|
199 |
+
|
200 |
+
is_causal = self.causal
|
201 |
+
cu_seqlens_k = cu_seqlens_q
|
202 |
+
dropout_p = self.dropout_p
|
203 |
+
else:
|
204 |
+
# turn off FA causal mask after first inference autoregressive iteration
|
205 |
+
# only on first autoregressive step q,k,v have same seqlen
|
206 |
+
is_causal = seqlen_q == seqlen_k
|
207 |
+
cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
|
208 |
+
device=q.device)
|
209 |
+
dropout_p = 0
|
210 |
+
|
211 |
+
output = flash_attn_unpadded_func(
|
212 |
+
q, k, v, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
|
213 |
+
dropout_p=dropout_p,
|
214 |
+
softmax_scale=self.softmax_scale, causal=is_causal
|
215 |
+
)
|
216 |
+
|
217 |
+
output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
|
218 |
+
return output
|
219 |
+
|
220 |
+
|
221 |
+
def _make_causal_mask(
|
222 |
+
input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int
|
223 |
+
) -> torch.BoolTensor:
|
224 |
+
"""
|
225 |
+
Make causal mask used for self-attention.
|
226 |
+
"""
|
227 |
+
batch_size, target_length = input_ids_shape
|
228 |
+
mask = torch.empty((target_length, target_length + past_key_values_length), dtype=torch.bool, device=device)
|
229 |
+
# ONNX doesn't support `torch.Tensor.triu` properly, thus we use this workaround
|
230 |
+
seq_ids = torch.arange(target_length, device=device)
|
231 |
+
mask[:, past_key_values_length:] = seq_ids[:, None] < seq_ids[None, :]
|
232 |
+
|
233 |
+
if past_key_values_length > 0:
|
234 |
+
mask[:, :past_key_values_length] = False
|
235 |
+
|
236 |
+
expanded_mask = mask[None, None, :, :].expand(batch_size, 1, target_length, target_length + past_key_values_length)
|
237 |
+
return expanded_mask
|
238 |
+
|
239 |
+
|
240 |
+
def _expand_mask(mask: torch.Tensor, tgt_length: int) -> torch.BoolTensor:
|
241 |
+
"""
|
242 |
+
Expands attention_mask from `[batch_size, src_length]` to `[batch_size, 1, tgt_length, src_length]`.
|
243 |
+
"""
|
244 |
+
batch_size, src_length = mask.shape
|
245 |
+
tgt_length = tgt_length if tgt_length is not None else src_length
|
246 |
+
|
247 |
+
expanded_mask = ~(mask[:, None, None, :].to(torch.bool))
|
248 |
+
return expanded_mask.expand(batch_size, 1, tgt_length, src_length)
|
249 |
+
|
250 |
+
|
251 |
+
def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor:
|
252 |
+
"""
|
253 |
+
Dropout add function
|
254 |
+
|
255 |
+
Args:
|
256 |
+
x (`torch.tensor`, *required*):
|
257 |
+
input tensor
|
258 |
+
residual (`torch.tensor`, *required*):
|
259 |
+
residual tensor
|
260 |
+
prob (`float`, *required*):
|
261 |
+
dropout probability
|
262 |
+
training (`bool`, *required*):
|
263 |
+
training mode
|
264 |
+
"""
|
265 |
+
out = F.dropout(x, p=prob, training=training)
|
266 |
+
out = residual + out
|
267 |
+
return out
|
268 |
+
|
269 |
+
|
270 |
+
def telechat_gelu_forward(x: torch.Tensor) -> torch.Tensor:
|
271 |
+
"""
|
272 |
+
Custom bias GELU function. Adapted from Megatron-DeepSpeed code. Here we use a simple implementation (inference) to
|
273 |
+
make the model jitable.
|
274 |
+
|
275 |
+
Args:
|
276 |
+
x (`torch.tensor`, *required*):
|
277 |
+
input hidden states
|
278 |
+
"""
|
279 |
+
return x * 0.5 * (1.0 + torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x)))
|
280 |
+
|
281 |
+
|
282 |
+
def telechat_gelu_back(g: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
|
283 |
+
"""
|
284 |
+
gradient of tanh approximation of gelu gradient of actual gelu is: 0.5 * (1. + torch.erf(x * 0.70710678)) +
|
285 |
+
0.3989423 * x * torch.exp(-0.5 * x * x)
|
286 |
+
|
287 |
+
Args:
|
288 |
+
g (`torch.tensor`, *required*):
|
289 |
+
gradient output tensor
|
290 |
+
x (`torch.tensor`, *required*):
|
291 |
+
input tensor
|
292 |
+
"""
|
293 |
+
x = x[0] # x is a tuple of 1 element, needs to unpack it first
|
294 |
+
tanh_out = torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))
|
295 |
+
# sqrt(2/pi) * 3 * 0.044715 -> 0.1070322243
|
296 |
+
ff = 0.5 * x * ((1 - tanh_out * tanh_out) * (0.79788456 + 0.1070322243 * x * x)) + 0.5 * (1 + tanh_out)
|
297 |
+
return ff * g
|
298 |
+
|
299 |
+
|
300 |
+
class GeLUFunction(torch.autograd.Function):
|
301 |
+
@staticmethod
|
302 |
+
def forward(ctx, input: torch.Tensor) -> torch.Tensor:
|
303 |
+
ctx.save_for_backward(input)
|
304 |
+
return telechat_gelu_forward(input)
|
305 |
+
|
306 |
+
@staticmethod
|
307 |
+
def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor:
|
308 |
+
input = ctx.saved_tensors
|
309 |
+
tmp = telechat_gelu_back(grad_output, input)
|
310 |
+
return tmp
|
311 |
+
|
312 |
+
|
313 |
+
class TelechatGelu(nn.Module):
|
314 |
+
"""
|
315 |
+
TelechatBiasGelu wrapper function that make use of the simple function on inference mode to make the model
|
316 |
+
torchscriptable and use the autograd function in training mode to get the accurate results of the gradients Partly
|
317 |
+
copied from Megatron-DeepSpeed code and adapted for our needs
|
318 |
+
|
319 |
+
See here why autograd functions are not torchscriptable: https://github.com/pytorch/pytorch/issues/22329
|
320 |
+
"""
|
321 |
+
|
322 |
+
def __init__(self):
|
323 |
+
super().__init__()
|
324 |
+
|
325 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
326 |
+
if self.training:
|
327 |
+
return GeLUFunction.apply(x)
|
328 |
+
else:
|
329 |
+
return telechat_gelu_forward(x)
|
330 |
+
|
331 |
+
|
332 |
+
class TelechatAttention(nn.Module):
|
333 |
+
def __init__(self, config: TelechatConfig, layer_idx):
|
334 |
+
super().__init__()
|
335 |
+
self.kv_cache = None
|
336 |
+
self.layer_idx = layer_idx
|
337 |
+
|
338 |
+
self.hidden_size = config.hidden_size
|
339 |
+
self.num_heads = config.n_head
|
340 |
+
self.head_dim = self.hidden_size // self.num_heads
|
341 |
+
self.split_size = self.hidden_size
|
342 |
+
self.hidden_dropout = config.hidden_dropout
|
343 |
+
self.config = config
|
344 |
+
|
345 |
+
if self.head_dim * self.num_heads != self.hidden_size:
|
346 |
+
raise ValueError(
|
347 |
+
f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
|
348 |
+
f" {self.num_heads})."
|
349 |
+
)
|
350 |
+
|
351 |
+
# Layer-wise attention scaling
|
352 |
+
self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
|
353 |
+
self.beta = 1.0
|
354 |
+
|
355 |
+
self.num_key_value_heads = config.num_key_value_heads if config.num_key_value_heads else self.num_heads
|
356 |
+
self.kv_projection_size = self.head_dim * self.num_key_value_heads
|
357 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
358 |
+
self.query = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
|
359 |
+
self.key_value = nn.Linear(self.hidden_size, self.kv_projection_size * 2, bias=False)
|
360 |
+
self.dense = nn.Linear(self.hidden_size, self.hidden_size)
|
361 |
+
self.attention_dropout = nn.Dropout(config.attention_dropout)
|
362 |
+
self.rotary_emb = RotaryEmbedding(self.head_dim, config=config)
|
363 |
+
|
364 |
+
self.core_attention_flash = FlashSelfAttention(
|
365 |
+
causal=True, attention_dropout=config.attention_dropout
|
366 |
+
)
|
367 |
+
|
368 |
+
self.last_key_layer = None
|
369 |
+
# logn_list = [math.log(i, 4096) if i > 4096 else 1 for i in range(1, 32768)]
|
370 |
+
# self.logn_tensor = torch.tensor(logn_list)[None, :, None, None].half().cuda()
|
371 |
+
|
372 |
+
def repeat_kv(self, hidden_states, n_rep):
|
373 |
+
slen, batch, num_key_value_heads_per_partition, head_dim = hidden_states.shape
|
374 |
+
if n_rep == 1:
|
375 |
+
return hidden_states
|
376 |
+
hidden_states = hidden_states[:, :, :, None, :].expand(slen, batch, num_key_value_heads_per_partition, n_rep,
|
377 |
+
head_dim)
|
378 |
+
return hidden_states.reshape(slen, batch, num_key_value_heads_per_partition * n_rep, head_dim)
|
379 |
+
|
380 |
+
def split_tensor_along_last_dim(self,
|
381 |
+
tensor: torch.Tensor,
|
382 |
+
num_partitions: int,
|
383 |
+
contiguous_split_chunks: bool = False,
|
384 |
+
):
|
385 |
+
|
386 |
+
# Get the size and dimension.
|
387 |
+
last_dim = tensor.dim() - 1
|
388 |
+
last_dim_size = tensor.size()[last_dim] // num_partitions
|
389 |
+
# Split.
|
390 |
+
tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
|
391 |
+
# Note: torch.split does not create contiguous tensors by default.
|
392 |
+
if contiguous_split_chunks:
|
393 |
+
return tuple(chunk.contiguous() for chunk in tensor_list)
|
394 |
+
|
395 |
+
return tensor_list
|
396 |
+
|
397 |
+
def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
|
398 |
+
batch_size_and_num_heads, seq_length, _ = x.shape
|
399 |
+
batch_size = batch_size_and_num_heads // self.num_heads
|
400 |
+
x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)
|
401 |
+
x = x.permute(0, 2, 1, 3)
|
402 |
+
return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)
|
403 |
+
|
404 |
+
def forward(
|
405 |
+
self,
|
406 |
+
hidden_states: torch.Tensor,
|
407 |
+
residual: torch.Tensor,
|
408 |
+
attention_mask: torch.Tensor,
|
409 |
+
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
410 |
+
use_cache: bool = False,
|
411 |
+
output_attentions: bool = False,
|
412 |
+
):
|
413 |
+
hidden_states = hidden_states.transpose(1, 0)
|
414 |
+
query_layer = self.query(hidden_states)
|
415 |
+
new_tensor_shape = query_layer.size()[:-1] + \
|
416 |
+
(self.num_heads,
|
417 |
+
self.head_dim)
|
418 |
+
query_layer = query_layer.view(*new_tensor_shape)
|
419 |
+
|
420 |
+
mixed_kv_layer = self.key_value(hidden_states)
|
421 |
+
new_tensor_shape = mixed_kv_layer.size()[:-1] + \
|
422 |
+
(self.num_key_value_heads,
|
423 |
+
2 * self.head_dim)
|
424 |
+
mixed_kv_layer = mixed_kv_layer.view(*new_tensor_shape)
|
425 |
+
(key_layer, value_layer) = self.split_tensor_along_last_dim(mixed_kv_layer, 2)
|
426 |
+
|
427 |
+
output_size = (query_layer.size(1),
|
428 |
+
query_layer.size(2),
|
429 |
+
query_layer.size(0),
|
430 |
+
key_layer.size(0),
|
431 |
+
key_layer.size(2)
|
432 |
+
)
|
433 |
+
|
434 |
+
query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1)
|
435 |
+
key_layer = key_layer.view(output_size[3], output_size[0] * output_size[4], -1)
|
436 |
+
|
437 |
+
apply_rotary_fn = apply_rotary_pos_emb_torch
|
438 |
+
|
439 |
+
seq_len = key_layer.shape[0]
|
440 |
+
offset = 0
|
441 |
+
|
442 |
+
if use_cache and layer_past != None:
|
443 |
+
past_key, past_value = layer_past
|
444 |
+
offset = past_key.shape[0]
|
445 |
+
seq_len += offset
|
446 |
+
|
447 |
+
cos, sin = self.rotary_emb(value_layer, seq_len=seq_len)
|
448 |
+
|
449 |
+
query_layer, key_layer = apply_rotary_fn(query_layer, key_layer, cos, sin, offset=offset)
|
450 |
+
if use_cache:
|
451 |
+
if layer_past != None:
|
452 |
+
past_key, past_value = layer_past
|
453 |
+
key_layer = torch.cat((past_key, key_layer[-1, ...].unsqueeze(0)), dim=0)
|
454 |
+
value_layer = torch.cat((past_value, value_layer[-1, ...].unsqueeze(0)), dim=0)
|
455 |
+
layer_past = key_layer, value_layer
|
456 |
+
|
457 |
+
s_value, bz, kv_head, dim = value_layer.shape
|
458 |
+
s_key = key_layer.shape[0]
|
459 |
+
s_query = query_layer.shape[0]
|
460 |
+
q_head = output_size[1]
|
461 |
+
|
462 |
+
query_layer = query_layer.reshape((s_query, bz, q_head, dim))
|
463 |
+
key_layer = key_layer.reshape((s_key, bz, kv_head, dim))
|
464 |
+
|
465 |
+
key_layer = self.repeat_kv(key_layer, self.num_key_value_groups)
|
466 |
+
value_layer = self.repeat_kv(value_layer, self.num_key_value_groups)
|
467 |
+
|
468 |
+
if self.config.flash_attn:
|
469 |
+
q, k, v = [rearrange(x, 's b ... -> b s ...').contiguous() for x in
|
470 |
+
(query_layer, key_layer, value_layer)]
|
471 |
+
context_layer = self.core_attention_flash(q, k, v)
|
472 |
+
context_layer = rearrange(context_layer, 'b s h d -> b s (h d)').contiguous()
|
473 |
+
else:
|
474 |
+
##[sq, b, np, hn] -> [sq, b * np, hn]
|
475 |
+
query_layer = query_layer.reshape(s_query, bz * self.num_heads, dim)
|
476 |
+
# [sk, b, np, hn] -> [sk, b * np, hn]
|
477 |
+
key_layer = key_layer.reshape(s_key, bz * self.num_heads, dim)
|
478 |
+
matmul_result = self.inv_norm_factor * torch.einsum('bik,bkj->bij', query_layer.transpose(0, 1),
|
479 |
+
key_layer.transpose(0, 1).transpose(1, 2))
|
480 |
+
|
481 |
+
attention_scores = matmul_result.view(bz, self.num_heads, s_query, s_key)
|
482 |
+
|
483 |
+
input_dtype = attention_scores.dtype
|
484 |
+
if input_dtype == torch.float16:
|
485 |
+
attention_scores = attention_scores.to(torch.float)
|
486 |
+
attn_weights = torch.masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
|
487 |
+
attention_probs = F.softmax(attn_weights, dim=-1).to(input_dtype) ##dtype = torch.float32
|
488 |
+
attention_probs = self.attention_dropout(attention_probs)
|
489 |
+
attention_probs_reshaped = attention_probs.view(bz * self.num_heads, s_query, s_key)
|
490 |
+
|
491 |
+
value_layer = value_layer.reshape(s_key, bz * self.num_heads, dim)
|
492 |
+
context_layer = torch.bmm(attention_probs_reshaped, value_layer.transpose(0, 1))
|
493 |
+
context_layer = self._merge_heads(context_layer)
|
494 |
+
output_tensor = self.dense(context_layer)
|
495 |
+
|
496 |
+
output_tensor = dropout_add(output_tensor, residual, self.hidden_dropout, self.training)
|
497 |
+
present = None
|
498 |
+
outputs = (output_tensor, present)
|
499 |
+
if output_attentions:
|
500 |
+
outputs += (attention_probs,)
|
501 |
+
|
502 |
+
return output_tensor, layer_past
|
503 |
+
|
504 |
+
|
505 |
+
class TelechatMLP(nn.Module):
|
506 |
+
def __init__(self, config: TelechatConfig):
|
507 |
+
super().__init__()
|
508 |
+
hidden_size = config.hidden_size
|
509 |
+
self.gate_proj = nn.Linear(hidden_size, config.ffn_hidden_size, bias=False)
|
510 |
+
self.up_proj = nn.Linear(hidden_size, config.ffn_hidden_size, bias=False)
|
511 |
+
self.down_proj = nn.Linear(config.ffn_hidden_size, hidden_size, bias=True)
|
512 |
+
self.hidden_dropout = config.hidden_dropout
|
513 |
+
|
514 |
+
def forward(self, hidden_states: torch.Tensor, residual: torch.Tensor) -> torch.Tensor:
|
515 |
+
intermediate_output = self.down_proj(F.silu(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))
|
516 |
+
output = dropout_add(intermediate_output, residual, self.hidden_dropout, self.training)
|
517 |
+
return output
|
518 |
+
|
519 |
+
|
520 |
+
class TelechatBlock(nn.Module):
|
521 |
+
def __init__(self, config: TelechatConfig, layer_idx):
|
522 |
+
super().__init__()
|
523 |
+
hidden_size = config.hidden_size
|
524 |
+
|
525 |
+
self.input_layernorm = MixedFusedRMSNorm(hidden_size, eps=config.layer_norm_epsilon)
|
526 |
+
self.num_heads = config.n_head
|
527 |
+
self.layer_idx = layer_idx
|
528 |
+
self.self_attention = TelechatAttention(config, layer_idx)
|
529 |
+
self.post_attention_layernorm = MixedFusedRMSNorm(hidden_size, eps=config.layer_norm_epsilon)
|
530 |
+
|
531 |
+
self.mlp = TelechatMLP(config)
|
532 |
+
|
533 |
+
self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
|
534 |
+
self.hidden_dropout = config.hidden_dropout
|
535 |
+
|
536 |
+
def forward(
|
537 |
+
self,
|
538 |
+
hidden_states: torch.Tensor,
|
539 |
+
attention_mask: torch.Tensor,
|
540 |
+
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
541 |
+
use_cache: bool = False,
|
542 |
+
output_attentions: bool = False,
|
543 |
+
):
|
544 |
+
layernorm_output = self.input_layernorm(hidden_states)
|
545 |
+
if self.apply_residual_connection_post_layernorm:
|
546 |
+
residual = layernorm_output
|
547 |
+
else:
|
548 |
+
residual = hidden_states
|
549 |
+
|
550 |
+
attn_outputs = self.self_attention(
|
551 |
+
layernorm_output,
|
552 |
+
residual,
|
553 |
+
layer_past=layer_past,
|
554 |
+
attention_mask=attention_mask,
|
555 |
+
use_cache=use_cache,
|
556 |
+
output_attentions=output_attentions,
|
557 |
+
)
|
558 |
+
|
559 |
+
attention_output = attn_outputs[0]
|
560 |
+
outputs = attn_outputs[1:]
|
561 |
+
layernorm_output = self.post_attention_layernorm(attention_output)
|
562 |
+
|
563 |
+
if self.apply_residual_connection_post_layernorm:
|
564 |
+
residual = layernorm_output
|
565 |
+
else:
|
566 |
+
residual = attention_output
|
567 |
+
output = self.mlp(layernorm_output, residual)
|
568 |
+
|
569 |
+
if use_cache:
|
570 |
+
outputs = (output,) + outputs
|
571 |
+
else:
|
572 |
+
outputs = (output,) + outputs[1:]
|
573 |
+
|
574 |
+
return outputs
|
575 |
+
|
576 |
+
|
577 |
+
class TelechatPreTrainedModel(PreTrainedModel):
|
578 |
+
config_class = TelechatConfig
|
579 |
+
base_model_prefix = "transformer"
|
580 |
+
supports_gradient_checkpointing = True
|
581 |
+
_no_split_modules = ["TelechatBlock"]
|
582 |
+
_skip_keys_device_placement = "past_key_values"
|
583 |
+
|
584 |
+
def __init__(self, *inputs, **kwargs):
|
585 |
+
super().__init__(*inputs, **kwargs)
|
586 |
+
|
587 |
+
def _init_weights(self, module: nn.Module):
|
588 |
+
"""Initialize the weights."""
|
589 |
+
if isinstance(module, nn.Linear):
|
590 |
+
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
591 |
+
if module.bias is not None:
|
592 |
+
module.bias.data.zero_()
|
593 |
+
|
594 |
+
elif isinstance(module, nn.Embedding):
|
595 |
+
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
596 |
+
if module.padding_idx is not None:
|
597 |
+
module.weight.data[module.padding_idx].zero_()
|
598 |
+
|
599 |
+
elif isinstance(module, LayerNorm):
|
600 |
+
module.bias.data.zero_()
|
601 |
+
module.weight.data.fill_(1.0)
|
602 |
+
|
603 |
+
def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False):
|
604 |
+
if isinstance(module, TelechatModel):
|
605 |
+
module.gradient_checkpointing = value
|
606 |
+
|
607 |
+
|
608 |
+
class TelechatModel(TelechatPreTrainedModel):
|
609 |
+
def __init__(self, config: TelechatConfig):
|
610 |
+
super().__init__(config)
|
611 |
+
|
612 |
+
self.embed_dim = config.hidden_size
|
613 |
+
self.num_heads = config.n_head
|
614 |
+
self.config = config
|
615 |
+
self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
|
616 |
+
if self.config.embed_layernorm:
|
617 |
+
self.word_embeddings_layernorm = MixedFusedRMSNorm(self.embed_dim, eps=config.layer_norm_epsilon)
|
618 |
+
|
619 |
+
self.h = nn.ModuleList([TelechatBlock(config, _) for _ in range(config.num_hidden_layers)])
|
620 |
+
self.ln_f = MixedFusedRMSNorm(self.embed_dim, eps=config.layer_norm_epsilon)
|
621 |
+
self.gradient_checkpointing = False
|
622 |
+
self.post_init()
|
623 |
+
|
624 |
+
def get_input_embeddings(self):
|
625 |
+
return self.word_embeddings
|
626 |
+
|
627 |
+
def _prepare_attn_mask(
|
628 |
+
self, attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int
|
629 |
+
) -> torch.BoolTensor:
|
630 |
+
combined_attention_mask = None
|
631 |
+
device = attention_mask.device
|
632 |
+
_, src_length = input_shape
|
633 |
+
|
634 |
+
if src_length > 1:
|
635 |
+
combined_attention_mask = _make_causal_mask(
|
636 |
+
input_shape, device=device, past_key_values_length=past_key_values_length
|
637 |
+
)
|
638 |
+
expanded_attn_mask = _expand_mask(attention_mask, tgt_length=src_length)
|
639 |
+
combined_attention_mask = (
|
640 |
+
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
|
641 |
+
)
|
642 |
+
|
643 |
+
return combined_attention_mask
|
644 |
+
|
645 |
+
def set_input_embeddings(self, new_embeddings: torch.Tensor):
|
646 |
+
self.word_embeddings = new_embeddings
|
647 |
+
|
648 |
+
def forward(
|
649 |
+
self,
|
650 |
+
input_ids: Optional[torch.LongTensor] = None,
|
651 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
652 |
+
attention_mask: Optional[torch.Tensor] = None,
|
653 |
+
inputs_embeds: Optional[torch.LongTensor] = None,
|
654 |
+
use_cache: Optional[bool] = None,
|
655 |
+
output_attentions: Optional[bool] = None,
|
656 |
+
output_hidden_states: Optional[bool] = None,
|
657 |
+
return_dict: Optional[bool] = None,
|
658 |
+
**deprecated_arguments,
|
659 |
+
) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
|
660 |
+
|
661 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
662 |
+
output_hidden_states = (
|
663 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
664 |
+
)
|
665 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
666 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
667 |
+
|
668 |
+
if input_ids is not None:
|
669 |
+
batch_size, seq_length = input_ids.shape
|
670 |
+
elif inputs_embeds is not None:
|
671 |
+
batch_size, seq_length, _ = inputs_embeds.shape
|
672 |
+
|
673 |
+
if past_key_values is None:
|
674 |
+
past_key_values = tuple([None] * len(self.h))
|
675 |
+
# input_ids = torch.load("Megatron-LM-0624-3B/tensors/input_ids.pt").to(input_ids.device)
|
676 |
+
if inputs_embeds is None:
|
677 |
+
inputs_embeds = self.word_embeddings(input_ids)
|
678 |
+
hidden_states = inputs_embeds
|
679 |
+
# print(f"[INFO_Telechat]: inputs_embeds={inputs_embeds}")
|
680 |
+
if self.config.embed_layernorm:
|
681 |
+
hidden_states = self.word_embeddings_layernorm(inputs_embeds)
|
682 |
+
|
683 |
+
presents = () if use_cache else None
|
684 |
+
all_self_attentions = () if output_attentions else None
|
685 |
+
all_hidden_states = () if output_hidden_states else None
|
686 |
+
|
687 |
+
if self.gradient_checkpointing and self.training:
|
688 |
+
if use_cache:
|
689 |
+
use_cache = False
|
690 |
+
|
691 |
+
seq_length_with_past = seq_length
|
692 |
+
past_key_values_length = 0
|
693 |
+
if past_key_values[0] is not None:
|
694 |
+
past_key_values_length = past_key_values[0][0].shape[2]
|
695 |
+
seq_length_with_past = seq_length_with_past + past_key_values_length
|
696 |
+
if attention_mask is None:
|
697 |
+
attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
|
698 |
+
else:
|
699 |
+
attention_mask = attention_mask.to(hidden_states.device)
|
700 |
+
causal_mask = self._prepare_attn_mask(
|
701 |
+
attention_mask,
|
702 |
+
input_shape=(batch_size, seq_length),
|
703 |
+
past_key_values_length=past_key_values_length,
|
704 |
+
)
|
705 |
+
|
706 |
+
# print(f"[INFO_Telechat]: word_embeddings_layernorm={hidden_states}")
|
707 |
+
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
|
708 |
+
if output_hidden_states:
|
709 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
710 |
+
|
711 |
+
if self.gradient_checkpointing and self.training:
|
712 |
+
|
713 |
+
def create_custom_forward(module):
|
714 |
+
def custom_forward(*inputs):
|
715 |
+
# None for past_key_value
|
716 |
+
return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
|
717 |
+
|
718 |
+
return custom_forward
|
719 |
+
|
720 |
+
outputs = torch.utils.checkpoint.checkpoint(
|
721 |
+
create_custom_forward(block),
|
722 |
+
hidden_states,
|
723 |
+
causal_mask,
|
724 |
+
layer_past,
|
725 |
+
)
|
726 |
+
else:
|
727 |
+
outputs = block(
|
728 |
+
hidden_states,
|
729 |
+
layer_past=layer_past,
|
730 |
+
attention_mask=causal_mask,
|
731 |
+
use_cache=use_cache,
|
732 |
+
output_attentions=output_attentions,
|
733 |
+
)
|
734 |
+
|
735 |
+
# print(f"[INFO_Telechat]: outputs{i}={outputs}")
|
736 |
+
hidden_states = outputs[0]
|
737 |
+
if use_cache is True:
|
738 |
+
presents = presents + (outputs[1],)
|
739 |
+
|
740 |
+
if output_attentions:
|
741 |
+
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
|
742 |
+
hidden_states = self.ln_f(hidden_states)
|
743 |
+
# print(f"[INFO_Telechat]: hidden_states={hidden_states}")
|
744 |
+
# ref = torch.load("Megatron-LM-0624-3B/tensors/final_layernorm.pt")
|
745 |
+
# print(hidden_states.squeeze()[2048:])
|
746 |
+
# print(ref.squeeze())
|
747 |
+
# print(torch.max(hidden_states.squeeze()[2048:] - ref.squeeze().to(hidden_states.device)))
|
748 |
+
# exit()
|
749 |
+
# print(ref.shape,hidden_states.shape)
|
750 |
+
# print(hidden_states)
|
751 |
+
# exit()
|
752 |
+
if output_hidden_states:
|
753 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
754 |
+
if not return_dict:
|
755 |
+
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
|
756 |
+
return BaseModelOutputWithPastAndCrossAttentions(
|
757 |
+
last_hidden_state=hidden_states,
|
758 |
+
past_key_values=presents,
|
759 |
+
hidden_states=all_hidden_states,
|
760 |
+
attentions=all_self_attentions,
|
761 |
+
)
|
762 |
+
|
763 |
+
|
764 |
+
class TelechatForCausalLM(TelechatPreTrainedModel):
|
765 |
+
# _tied_weights_keys = ["lm_head.weight"]
|
766 |
+
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
|
767 |
+
|
768 |
+
def __init__(self, config: TelechatConfig):
|
769 |
+
super().__init__(config)
|
770 |
+
self.transformer = TelechatModel(config)
|
771 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
772 |
+
self.post_init()
|
773 |
+
|
774 |
+
def get_output_embeddings(self):
|
775 |
+
return self.lm_head
|
776 |
+
|
777 |
+
def set_output_embeddings(self, new_embeddings: torch.Tensor):
|
778 |
+
self.lm_head = new_embeddings
|
779 |
+
|
780 |
+
def prepare_inputs_for_generation(
|
781 |
+
self,
|
782 |
+
input_ids: torch.LongTensor,
|
783 |
+
past_key_values: Optional[torch.Tensor] = None,
|
784 |
+
attention_mask: Optional[torch.Tensor] = None,
|
785 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
786 |
+
**kwargs,
|
787 |
+
) -> dict:
|
788 |
+
if past_key_values:
|
789 |
+
input_ids = input_ids[:, -1].unsqueeze(-1)
|
790 |
+
if inputs_embeds is not None and past_key_values is None:
|
791 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
792 |
+
else:
|
793 |
+
model_inputs = {"input_ids": input_ids}
|
794 |
+
|
795 |
+
model_inputs.update(
|
796 |
+
{
|
797 |
+
"past_key_values": past_key_values,
|
798 |
+
"use_cache": kwargs.get("use_cache"),
|
799 |
+
"attention_mask": attention_mask,
|
800 |
+
}
|
801 |
+
)
|
802 |
+
return model_inputs
|
803 |
+
|
804 |
+
def forward(
|
805 |
+
self,
|
806 |
+
input_ids: Optional[torch.LongTensor] = None,
|
807 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
808 |
+
attention_mask: Optional[torch.Tensor] = None,
|
809 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
810 |
+
labels: Optional[torch.Tensor] = None,
|
811 |
+
use_cache: Optional[bool] = None,
|
812 |
+
output_attentions: Optional[bool] = None,
|
813 |
+
output_hidden_states: Optional[bool] = None,
|
814 |
+
return_dict: Optional[bool] = None,
|
815 |
+
**deprecated_arguments,
|
816 |
+
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
|
817 |
+
|
818 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
819 |
+
|
820 |
+
transformer_outputs = self.transformer(
|
821 |
+
input_ids,
|
822 |
+
past_key_values=past_key_values,
|
823 |
+
attention_mask=attention_mask,
|
824 |
+
inputs_embeds=inputs_embeds,
|
825 |
+
use_cache=use_cache,
|
826 |
+
output_attentions=output_attentions,
|
827 |
+
output_hidden_states=output_hidden_states,
|
828 |
+
return_dict=return_dict,
|
829 |
+
)
|
830 |
+
hidden_states = transformer_outputs[0]
|
831 |
+
lm_logits = self.lm_head(hidden_states)
|
832 |
+
|
833 |
+
loss = None
|
834 |
+
if labels is not None:
|
835 |
+
labels = labels.to(lm_logits.device)
|
836 |
+
shift_logits = lm_logits[..., :-1, :].contiguous()
|
837 |
+
shift_labels = labels[..., 1:].contiguous()
|
838 |
+
batch_size, seq_length, vocab_size = shift_logits.shape
|
839 |
+
loss_fct = CrossEntropyLoss()
|
840 |
+
loss = loss_fct(
|
841 |
+
shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)
|
842 |
+
)
|
843 |
+
|
844 |
+
if not return_dict:
|
845 |
+
output = (lm_logits,) + transformer_outputs[1:]
|
846 |
+
return ((loss,) + output) if loss is not None else output
|
847 |
+
|
848 |
+
return CausalLMOutputWithCrossAttentions(
|
849 |
+
loss=loss,
|
850 |
+
logits=lm_logits,
|
851 |
+
past_key_values=transformer_outputs.past_key_values,
|
852 |
+
hidden_states=transformer_outputs.hidden_states,
|
853 |
+
attentions=transformer_outputs.attentions,
|
854 |
+
)
|
855 |
+
|
856 |
+
def chat(self, tokenizer, question: str = '', history: Union[List[Dict], History] = None, stream: bool = False,
|
857 |
+
generation_config: Optional[GenerationConfig] = None, **kwargs):
|
858 |
+
"""
|
859 |
+
Args:
|
860 |
+
tokenizer: the tokenizer of telechat
|
861 |
+
question: question which the model reply in this turn
|
862 |
+
history: history which will format the input for telechat
|
863 |
+
stream: if return the full text at last or yield the text in token
|
864 |
+
generation_config: configuration for generation
|
865 |
+
**kwargs: args which will update the generation config or pass to model forward
|
866 |
+
"""
|
867 |
+
generation_config = generation_config or self.generation_config
|
868 |
+
if not generation_config:
|
869 |
+
logger.error("generation_config is None")
|
870 |
+
raise ValueError("generation_config must not be None")
|
871 |
+
if not question:
|
872 |
+
logger.error("question is empty")
|
873 |
+
raise ValueError("question must not be empty")
|
874 |
+
if history is None:
|
875 |
+
history = []
|
876 |
+
|
877 |
+
# we update and check generate_config here for building inputs.
|
878 |
+
|
879 |
+
generation_config = copy.deepcopy(generation_config)
|
880 |
+
user_id = generation_config.user_token_id
|
881 |
+
bot_id = generation_config.bot_token_id
|
882 |
+
model_kwargs = generation_config.update(**kwargs)
|
883 |
+
generation_config.validate()
|
884 |
+
|
885 |
+
# transfer to History
|
886 |
+
if not isinstance(history, History):
|
887 |
+
history = History(tokenizer, history)
|
888 |
+
|
889 |
+
inputs = self.build_inputs_for_chat(tokenizer, question, history, generation_config, user_id, bot_id)
|
890 |
+
history.append({"role": "user", "content": question})
|
891 |
+
if stream:
|
892 |
+
streamer = TelechatIterTextStreamer(tokenizer, history, skip_prompt=True)
|
893 |
+
Thread(target=self.generate, kwargs=dict(
|
894 |
+
inputs=inputs.to(self.device), streamer=streamer,
|
895 |
+
generation_config=generation_config, **model_kwargs
|
896 |
+
)).start()
|
897 |
+
return streamer
|
898 |
+
else:
|
899 |
+
outputs = self.generate(inputs.to(self.device), generation_config=generation_config, **model_kwargs)
|
900 |
+
response = tokenizer.decode(outputs[0][len(inputs[0]):-1])
|
901 |
+
history.append({"role": "bot", "content": response})
|
902 |
+
return response, history
|
903 |
+
|
904 |
+
def build_inputs_for_chat(self, tokenizer, question, history, generation_config, usr_id, bot_id):
|
905 |
+
"""
|
906 |
+
check history and build inputs here
|
907 |
+
"""
|
908 |
+
# first tokenize question
|
909 |
+
q_token = tokenizer(question)
|
910 |
+
qa_history = copy.deepcopy(history)
|
911 |
+
|
912 |
+
# get the max length we should build our inputs in
|
913 |
+
model_max_length = self.config.seq_length
|
914 |
+
build_max_length = max(0, model_max_length - generation_config.max_new_tokens - 1) \
|
915 |
+
if generation_config.max_new_tokens else max(0, generation_config.max_length)
|
916 |
+
if build_max_length < 3:
|
917 |
+
logger.warning("the model can not meet the requirements of input length,Please check config")
|
918 |
+
raise ValueError("")
|
919 |
+
|
920 |
+
# trunc left
|
921 |
+
input_tokens = [usr_id] + q_token["input_ids"][-build_max_length + 1:] + [bot_id]
|
922 |
+
length = len(input_tokens)
|
923 |
+
|
924 |
+
while len(qa_history) != 0:
|
925 |
+
message = qa_history.pop()
|
926 |
+
if message["role"] == "user":
|
927 |
+
tokens = [usr_id] + message["input_ids"]
|
928 |
+
elif message["role"] == "bot":
|
929 |
+
tokens = [bot_id] + message["input_ids"] + [generation_config.eos_token_id]
|
930 |
+
else:
|
931 |
+
tokens = []
|
932 |
+
if len(tokens) + length >= build_max_length:
|
933 |
+
break
|
934 |
+
else:
|
935 |
+
input_tokens = tokens + input_tokens
|
936 |
+
|
937 |
+
input_tokens = [generation_config.bos_token_id] + input_tokens
|
938 |
+
|
939 |
+
return torch.tensor([input_tokens], dtype=torch.int64)
|
tokenization_telechat.py
ADDED
@@ -0,0 +1,220 @@
|
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|
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|
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|
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|
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|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
from shutil import copyfile
|
3 |
+
from typing import Any, Dict, List, Optional, Tuple
|
4 |
+
import sentencepiece as spm
|
5 |
+
from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
|
6 |
+
from transformers.utils import logging
|
7 |
+
|
8 |
+
logger = logging.get_logger(__name__)
|
9 |
+
|
10 |
+
VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
|
11 |
+
|
12 |
+
# TODO: when we get download url from huggingface, refresh the map
|
13 |
+
PRETRAINED_VOCAB_FILES_MAP = {
|
14 |
+
"vocab_file": {},
|
15 |
+
"tokenizer_file": {},
|
16 |
+
}
|
17 |
+
|
18 |
+
|
19 |
+
class TelechatTokenizer(PreTrainedTokenizer):
|
20 |
+
|
21 |
+
vocab_files_names = VOCAB_FILES_NAMES
|
22 |
+
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
23 |
+
model_input_names = ["input_ids", "attention_mask"]
|
24 |
+
|
25 |
+
def __init__(
|
26 |
+
self,
|
27 |
+
vocab_file,
|
28 |
+
unk_token="<unk>",
|
29 |
+
bos_token="<_start>",
|
30 |
+
eos_token="<_end>",
|
31 |
+
pad_token="<_pad>",
|
32 |
+
sp_model_kwargs: Optional[Dict[str, Any]] = None,
|
33 |
+
add_bos_token=True,
|
34 |
+
add_eos_token=False,
|
35 |
+
clean_up_tokenization_spaces=False,
|
36 |
+
**kwargs,
|
37 |
+
):
|
38 |
+
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
|
39 |
+
bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
|
40 |
+
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
|
41 |
+
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
|
42 |
+
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
|
43 |
+
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
44 |
+
self.sp_model.Load(vocab_file)
|
45 |
+
super().__init__(
|
46 |
+
bos_token=bos_token,
|
47 |
+
eos_token=eos_token,
|
48 |
+
unk_token=unk_token,
|
49 |
+
pad_token=pad_token,
|
50 |
+
add_bos_token=add_bos_token,
|
51 |
+
add_eos_token=add_eos_token,
|
52 |
+
sp_model_kwargs=self.sp_model_kwargs,
|
53 |
+
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
54 |
+
**kwargs,
|
55 |
+
)
|
56 |
+
self.vocab_file = vocab_file
|
57 |
+
self.add_bos_token = add_bos_token
|
58 |
+
self.add_eos_token = add_eos_token
|
59 |
+
|
60 |
+
|
61 |
+
def __getstate__(self):
|
62 |
+
state = self.__dict__.copy()
|
63 |
+
state["sp_model"] = None
|
64 |
+
return state
|
65 |
+
|
66 |
+
def __setstate__(self, d):
|
67 |
+
self.__dict__ = d
|
68 |
+
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
69 |
+
self.sp_model.Load(self.vocab_file)
|
70 |
+
|
71 |
+
@property
|
72 |
+
def vocab_size(self):
|
73 |
+
"""Returns vocab size"""
|
74 |
+
return self.sp_model.get_piece_size()
|
75 |
+
|
76 |
+
def get_vocab(self):
|
77 |
+
"""Returns vocab as a dict"""
|
78 |
+
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
79 |
+
vocab.update(self.added_tokens_encoder)
|
80 |
+
return vocab
|
81 |
+
|
82 |
+
def _tokenize(self, text):
|
83 |
+
"""Returns a tokenized string."""
|
84 |
+
return self.sp_model.encode(text, out_type=str)
|
85 |
+
|
86 |
+
def _convert_token_to_id(self, token):
|
87 |
+
"""Converts a token (str) in an id using the vocab."""
|
88 |
+
return self.sp_model.piece_to_id(token)
|
89 |
+
|
90 |
+
def _convert_id_to_token(self, index):
|
91 |
+
"""Converts an index (integer) in a token (str) using the vocab."""
|
92 |
+
token = self.sp_model.IdToPiece(index)
|
93 |
+
return token
|
94 |
+
|
95 |
+
def convert_tokens_to_string(self, tokens):
|
96 |
+
"""Converts a sequence of tokens (string) in a single string."""
|
97 |
+
current_sub_tokens = []
|
98 |
+
out_string = ""
|
99 |
+
prev_is_special = False
|
100 |
+
for i, token in enumerate(tokens):
|
101 |
+
# make sure that special tokens are not decoded using sentencepiece model
|
102 |
+
if token in self.all_special_tokens:
|
103 |
+
if not prev_is_special and i != 0:
|
104 |
+
out_string += " "
|
105 |
+
out_string += self.sp_model.decode(current_sub_tokens) + token
|
106 |
+
prev_is_special = True
|
107 |
+
current_sub_tokens = []
|
108 |
+
else:
|
109 |
+
current_sub_tokens.append(token)
|
110 |
+
prev_is_special = False
|
111 |
+
out_string += self.sp_model.decode(current_sub_tokens)
|
112 |
+
return out_string
|
113 |
+
|
114 |
+
def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
115 |
+
"""
|
116 |
+
Save the vocabulary and special tokens file to a directory.
|
117 |
+
|
118 |
+
Args:
|
119 |
+
save_directory (`str`):
|
120 |
+
The directory in which to save the vocabulary.
|
121 |
+
|
122 |
+
Returns:
|
123 |
+
`Tuple(str)`: Paths to the files saved.
|
124 |
+
"""
|
125 |
+
if not os.path.isdir(save_directory):
|
126 |
+
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
127 |
+
return
|
128 |
+
out_vocab_file = os.path.join(
|
129 |
+
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
130 |
+
)
|
131 |
+
|
132 |
+
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
133 |
+
copyfile(self.vocab_file, out_vocab_file)
|
134 |
+
elif not os.path.isfile(self.vocab_file):
|
135 |
+
with open(out_vocab_file, "wb") as fi:
|
136 |
+
content_spiece_model = self.sp_model.serialized_model_proto()
|
137 |
+
fi.write(content_spiece_model)
|
138 |
+
|
139 |
+
return (out_vocab_file,)
|
140 |
+
|
141 |
+
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
142 |
+
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
|
143 |
+
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
|
144 |
+
|
145 |
+
output = bos_token_id + token_ids_0 + eos_token_id
|
146 |
+
|
147 |
+
if token_ids_1 is not None:
|
148 |
+
output = output + bos_token_id + token_ids_1 + eos_token_id
|
149 |
+
|
150 |
+
return output
|
151 |
+
|
152 |
+
def get_special_tokens_mask(
|
153 |
+
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
154 |
+
) -> List[int]:
|
155 |
+
"""
|
156 |
+
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
157 |
+
special tokens using the tokenizer `prepare_for_model` method.
|
158 |
+
|
159 |
+
Args:
|
160 |
+
token_ids_0 (`List[int]`):
|
161 |
+
List of IDs.
|
162 |
+
token_ids_1 (`List[int]`, *optional*):
|
163 |
+
Optional second list of IDs for sequence pairs.
|
164 |
+
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
165 |
+
Whether or not the token list is already formatted with special tokens for the model.
|
166 |
+
|
167 |
+
Returns:
|
168 |
+
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
169 |
+
"""
|
170 |
+
if already_has_special_tokens:
|
171 |
+
return super().get_special_tokens_mask(
|
172 |
+
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
173 |
+
)
|
174 |
+
|
175 |
+
bos_token_id = [1] if self.add_bos_token else []
|
176 |
+
eos_token_id = [1] if self.add_eos_token else []
|
177 |
+
|
178 |
+
if token_ids_1 is None:
|
179 |
+
return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
|
180 |
+
return (
|
181 |
+
bos_token_id
|
182 |
+
+ ([0] * len(token_ids_0))
|
183 |
+
+ eos_token_id
|
184 |
+
+ bos_token_id
|
185 |
+
+ ([0] * len(token_ids_1))
|
186 |
+
+ eos_token_id
|
187 |
+
)
|
188 |
+
|
189 |
+
def create_token_type_ids_from_sequences(
|
190 |
+
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
191 |
+
) -> List[int]:
|
192 |
+
"""
|
193 |
+
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
|
194 |
+
sequence pair mask has the following format:
|
195 |
+
|
196 |
+
```
|
197 |
+
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
|
198 |
+
| first sequence | second sequence |
|
199 |
+
```
|
200 |
+
|
201 |
+
if token_ids_1 is None, only returns the first portion of the mask (0s).
|
202 |
+
|
203 |
+
Args:
|
204 |
+
token_ids_0 (`List[int]`):
|
205 |
+
List of ids.
|
206 |
+
token_ids_1 (`List[int]`, *optional*):
|
207 |
+
Optional second list of IDs for sequence pairs.
|
208 |
+
|
209 |
+
Returns:
|
210 |
+
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
|
211 |
+
"""
|
212 |
+
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
|
213 |
+
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
|
214 |
+
|
215 |
+
output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
|
216 |
+
|
217 |
+
if token_ids_1 is not None:
|
218 |
+
output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
|
219 |
+
|
220 |
+
return output
|