jacobrenn commited on
Commit
c3f71b8
1 Parent(s): e4cb1d1

Update instruct_pipeline.py

Browse files
Files changed (1) hide show
  1. instruct_pipeline.py +50 -102
instruct_pipeline.py CHANGED
@@ -1,16 +1,8 @@
1
- import logging
2
  import re
3
- from typing import List
4
 
5
  import numpy as np
6
  from transformers import Pipeline, PreTrainedTokenizer
7
 
8
- from transformers.utils import is_tf_available
9
-
10
- if is_tf_available():
11
- import tensorflow as tf
12
-
13
- logger = logging.getLogger(__name__)
14
 
15
  INSTRUCTION_KEY = "### Instruction:"
16
  RESPONSE_KEY = "### Response:"
@@ -61,22 +53,9 @@ class InstructionTextGenerationPipeline(Pipeline):
61
  def __init__(
62
  self, *args, do_sample: bool = True, max_new_tokens: int = 256, top_p: float = 0.92, top_k: int = 0, **kwargs
63
  ):
64
- """Initialize the pipeline
65
-
66
- Args:
67
- do_sample (bool, optional): Whether or not to use sampling. Defaults to True.
68
- max_new_tokens (int, optional): Max new tokens after the prompt to generate. Defaults to 128.
69
- top_p (float, optional): If set to float < 1, only the smallest set of most probable tokens with
70
- probabilities that add up to top_p or higher are kept for generation. Defaults to 0.92.
71
- top_k (int, optional): The number of highest probability vocabulary tokens to keep for top-k-filtering.
72
- Defaults to 0.
73
- """
74
- super().__init__(*args, do_sample=do_sample, max_new_tokens=max_new_tokens, top_p=top_p, top_k=top_k,
75
- **kwargs)
76
-
77
- def _sanitize_parameters(self,
78
- return_full_text: bool = None,
79
- **generate_kwargs):
80
  preprocess_params = {}
81
 
82
  # newer versions of the tokenizer configure the response key as a special token. newer versions still may
@@ -100,12 +79,10 @@ class InstructionTextGenerationPipeline(Pipeline):
100
  forward_params = generate_kwargs
101
  postprocess_params = {
102
  "response_key_token_id": response_key_token_id,
103
- "end_key_token_id": end_key_token_id
 
104
  }
105
 
106
- if return_full_text is not None:
107
- postprocess_params["return_full_text"] = return_full_text
108
-
109
  return preprocess_params, forward_params, postprocess_params
110
 
111
  def preprocess(self, instruction_text, **generate_kwargs):
@@ -121,92 +98,63 @@ class InstructionTextGenerationPipeline(Pipeline):
121
  def _forward(self, model_inputs, **generate_kwargs):
122
  input_ids = model_inputs["input_ids"]
123
  attention_mask = model_inputs.get("attention_mask", None)
124
-
125
- if input_ids.shape[1] == 0:
126
- input_ids = None
127
- attention_mask = None
128
- in_b = 1
129
- else:
130
- in_b = input_ids.shape[0]
131
-
132
  generated_sequence = self.model.generate(
133
  input_ids=input_ids.to(self.model.device),
134
- attention_mask=attention_mask.to(self.model.device) if attention_mask is not None else None,
135
  pad_token_id=self.tokenizer.pad_token_id,
136
  **generate_kwargs,
137
- )
138
-
139
- out_b = generated_sequence.shape[0]
140
- if self.framework == "pt":
141
- generated_sequence = generated_sequence.reshape(in_b, out_b // in_b, *generated_sequence.shape[1:])
142
- elif self.framework == "tf":
143
- generated_sequence = tf.reshape(generated_sequence, (in_b, out_b // in_b, *generated_sequence.shape[1:]))
144
-
145
  instruction_text = model_inputs.pop("instruction_text")
146
  return {"generated_sequence": generated_sequence, "input_ids": input_ids, "instruction_text": instruction_text}
147
 
148
- def postprocess(self, model_outputs, response_key_token_id, end_key_token_id, return_full_text: bool = False):
149
-
150
- generated_sequence = model_outputs["generated_sequence"][0]
151
  instruction_text = model_outputs["instruction_text"]
152
 
153
- generated_sequence: List[List[int]] = generated_sequence.numpy().tolist()
154
- records = []
155
- for sequence in generated_sequence:
156
-
157
- # The response will be set to this variable if we can identify it.
158
- decoded = None
159
-
160
- # If we have token IDs for the response and end, then we can find the tokens and only decode between them.
161
- if response_key_token_id and end_key_token_id:
162
- # Find where "### Response:" is first found in the generated tokens. Considering this is part of the
163
- # prompt, we should definitely find it. We will return the tokens found after this token.
164
- try:
165
- response_pos = sequence.index(response_key_token_id)
166
- except ValueError:
167
- logger.warn(f"Could not find response key {response_key_token_id} in: {sequence}")
168
- response_pos = None
169
-
170
- if response_pos:
171
- # Next find where "### End" is located. The model has been trained to end its responses with this
172
- # sequence (or actually, the token ID it maps to, since it is a special token). We may not find
173
- # this token, as the response could be truncated. If we don't find it then just return everything
174
- # to the end. Note that even though we set eos_token_id, we still see the this token at the end.
175
- try:
176
- end_pos = sequence.index(end_key_token_id)
177
- except ValueError:
178
- end_pos = None
179
-
180
- decoded = self.tokenizer.decode(sequence[response_pos + 1 : end_pos]).strip()
181
 
182
- if not decoded:
183
- # Otherwise we'll decode everything and use a regex to find the response and end.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
184
 
185
- fully_decoded = self.tokenizer.decode(sequence)
186
 
187
- # The response appears after "### Response:". The model has been trained to append "### End" at the
188
- # end.
189
- m = re.search(r"#+\s*Response:\s*(.+?)#+\s*End", fully_decoded, flags=re.DOTALL)
190
 
 
 
 
 
 
 
191
  if m:
192
  decoded = m.group(1).strip()
193
- else:
194
- # The model might not generate the "### End" sequence before reaching the max tokens. In this case,
195
- # return everything after "### Response:".
196
- m = re.search(r"#+\s*Response:\s*(.+)", fully_decoded, flags=re.DOTALL)
197
- if m:
198
- decoded = m.group(1).strip()
199
- else:
200
- logger.warn(f"Failed to find response in:\n{fully_decoded}")
201
-
202
- # If the full text is requested, then append the decoded text to the original instruction.
203
- # This technically isn't the full text, as we format the instruction in the prompt the model has been
204
- # trained on, but to the client it will appear to be the full text.
205
- if return_full_text:
206
- decoded = f"{instruction_text}\n{decoded}"
207
-
208
- rec = {"generated_text": decoded}
209
-
210
- records.append(rec)
211
-
212
- return records
 
 
1
  import re
 
2
 
3
  import numpy as np
4
  from transformers import Pipeline, PreTrainedTokenizer
5
 
 
 
 
 
 
 
6
 
7
  INSTRUCTION_KEY = "### Instruction:"
8
  RESPONSE_KEY = "### Response:"
 
53
  def __init__(
54
  self, *args, do_sample: bool = True, max_new_tokens: int = 256, top_p: float = 0.92, top_k: int = 0, **kwargs
55
  ):
56
+ super().__init__(*args, do_sample=do_sample, max_new_tokens=max_new_tokens, top_p=top_p, top_k=top_k, **kwargs)
57
+
58
+ def _sanitize_parameters(self, return_instruction_text=False, **generate_kwargs):
 
 
 
 
 
 
 
 
 
 
 
 
 
59
  preprocess_params = {}
60
 
61
  # newer versions of the tokenizer configure the response key as a special token. newer versions still may
 
79
  forward_params = generate_kwargs
80
  postprocess_params = {
81
  "response_key_token_id": response_key_token_id,
82
+ "end_key_token_id": end_key_token_id,
83
+ "return_instruction_text": return_instruction_text,
84
  }
85
 
 
 
 
86
  return preprocess_params, forward_params, postprocess_params
87
 
88
  def preprocess(self, instruction_text, **generate_kwargs):
 
98
  def _forward(self, model_inputs, **generate_kwargs):
99
  input_ids = model_inputs["input_ids"]
100
  attention_mask = model_inputs.get("attention_mask", None)
 
 
 
 
 
 
 
 
101
  generated_sequence = self.model.generate(
102
  input_ids=input_ids.to(self.model.device),
103
+ attention_mask=attention_mask,
104
  pad_token_id=self.tokenizer.pad_token_id,
105
  **generate_kwargs,
106
+ )[0].cpu()
 
 
 
 
 
 
 
107
  instruction_text = model_inputs.pop("instruction_text")
108
  return {"generated_sequence": generated_sequence, "input_ids": input_ids, "instruction_text": instruction_text}
109
 
110
+ def postprocess(self, model_outputs, response_key_token_id, end_key_token_id, return_instruction_text):
111
+ sequence = model_outputs["generated_sequence"]
 
112
  instruction_text = model_outputs["instruction_text"]
113
 
114
+ # The response will be set to this variable if we can identify it.
115
+ decoded = None
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
116
 
117
+ # If we have token IDs for the response and end, then we can find the tokens and only decode between them.
118
+ if response_key_token_id and end_key_token_id:
119
+ # Find where "### Response:" is first found in the generated tokens. Considering this is part of the
120
+ # prompt, we should definitely find it. We will return the tokens found after this token.
121
+ response_pos = None
122
+ response_positions = np.where(sequence == response_key_token_id)[0]
123
+ if len(response_positions) == 0:
124
+ pass
125
+ else:
126
+ response_pos = response_positions[0]
127
+
128
+ if response_pos:
129
+ # Next find where "### End" is located. The model has been trained to end its responses with this
130
+ # sequence (or actually, the token ID it maps to, since it is a special token). We may not find
131
+ # this token, as the response could be truncated. If we don't find it then just return everything
132
+ # to the end. Note that even though we set eos_token_id, we still see the this token at the end.
133
+ end_pos = None
134
+ end_positions = np.where(sequence == end_key_token_id)[0]
135
+ if len(end_positions) > 0:
136
+ end_pos = end_positions[0]
137
+
138
+ decoded = self.tokenizer.decode(sequence[response_pos + 1 : end_pos]).strip()
139
+ else:
140
+ # Otherwise we'll decode everything and use a regex to find the response and end.
141
 
142
+ fully_decoded = self.tokenizer.decode(sequence)
143
 
144
+ # The response appears after "### Response:". The model has been trained to append "### End" at the
145
+ # end.
146
+ m = re.search(r"#+\s*Response:\s*(.+?)#+\s*End", fully_decoded, flags=re.DOTALL)
147
 
148
+ if m:
149
+ decoded = m.group(1).strip()
150
+ else:
151
+ # The model might not generate the "### End" sequence before reaching the max tokens. In this case,
152
+ # return everything after "### Response:".
153
+ m = re.search(r"#+\s*Response:\s*(.+)", fully_decoded, flags=re.DOTALL)
154
  if m:
155
  decoded = m.group(1).strip()
156
+
157
+ if return_instruction_text:
158
+ return {"instruction_text": instruction_text, "generated_text": decoded}
159
+
160
+ return decoded