Fix chat templates (#917)
* Update pyproject.toml * Update tokenizer_utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * Update _utils.py * Update _utils.py * Update tokenizer_utils.py * Update tokenizer_utils.py * fix_tokenizer * Update tokenizer_utils.py * Update tokenizer_utils.py * Update save.py * Update save.py * Update save.py * Update save.py * Update save.py * Update loader.py * Update pyproject.toml * Update _utils.py * Update gemma2.py * Update gemma2.py * Update _utils.py * gemma 2 mask * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update _utils.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update _utils.py * Update llama.py * Update llama.py * Update llama.py * Update _utils.py * Update _utils.py * Update _utils.py * Update _utils.py * Update _utils.py * Update _utils.py * Torch 2.4 Xformers 0.0.27post2 * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Gemma 2 fixes * Update gemma2.py * Update llama.py * Update llama.py * Update save.py * Update save.py * Update llama.py * Update cross_entropy_loss.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Update dpo.py * Providing more flexibility for users to customize their llama when using LoRA (#910) * Update llama.py * Update llama.py * Update llama.py * Update llama.py * Update chat_templates.py * return model * Update tokenizer_utils.py * Update chat_templates.py * Update tokenizer_utils.py * Train on completions --------- Co-authored-by: Po-Lung Wang <Brownwang0426@gmail.com>
This commit is contained in:
Daniel Han
GitHub
parent
7b98a2c133
commit
95591be42e
1 changed files with 138 additions and 27 deletions
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@ -1458,9 +1458,10 @@ extra_eos_tokens = None,
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ollama_eos = '\n'.join(f'PARAMETER stop "{eos}"' for eos in ollama_eos)
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# Ollama modelfile
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part = '"""'
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modelfile = 'FROM {__FILE_LOCATION__}\n\n'\
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'TEMPLATE """' + system_modelfile + input_modelfile + output_modelfile + \
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'"""\n\n' + ollama_eos
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'TEMPLATE ' + part + system_modelfile + input_modelfile + output_modelfile + \
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part + '\n\n' + ollama_eos
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# HF Jinja Chat template
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def process(part, which, content = "message['content']"):
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@ -1659,6 +1660,70 @@ extra_eos_tokens = None,
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pass
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# From https://www.geeksforgeeks.org/longest-common-substring-array-strings/
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# Longest Common Substring in an Array of Strings
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def _longest_common_substring(arr):
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n = len(arr)
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s = arr[0]
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l = len(s)
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res = ""
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for i in range(l):
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for j in range(i + 1, l + 1):
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stem = s[i:j]
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k = 1
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for k in range(1, n):
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if stem not in arr[k]:
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break
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if (k + 1 == n and len(res) < len(stem)):
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res = stem
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return res
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pass
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def _find_common_token_ids(component, tokenizer):
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"""
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\n### User:\n\n
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\n\n### User:\n\n
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etc
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we need to find the middle most repeatted part.
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Tokenizers can tokenize newlines or spaces as 1 token!
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"""
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right_text = ""
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if component.endswith (" "): right_text = " "
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elif component.endswith("\n"): right_text = "\n"
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left_text = ""
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if component.startswith (" "): left_text = " "
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elif component.startswith("\n"): left_text = "\n"
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stripped = component.strip()
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# Add current pieces and also newlines
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all_input_ids = []
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for left in range(3):
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for right in range(3):
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x = left*left_text + stripped + right*right_text
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x = tokenizer(x, add_special_tokens = False).input_ids
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all_input_ids.append(x)
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x = left*"\n" + stripped + right*"\n"
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x = tokenizer(x, add_special_tokens = False).input_ids
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all_input_ids.append(x)
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pass
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pass
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substring = _longest_common_substring([str(x + [0]) for x in all_input_ids])
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substring = substring.split(", ")[:-1]
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substring = [int(x) for x in substring]
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# Also get rest of tokenized string
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original = tokenizer(component, add_special_tokens = False).input_ids
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# Get optional left and right
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for j in range(len(original)):
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if original[j : j + len(substring)] == substring: break
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optional_left = original[:j]
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optional_right = original[j+len(substring):]
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return substring, optional_left, optional_right
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pass
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def train_on_responses_only(
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trainer,
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instruction_part = None,
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@ -1685,41 +1750,87 @@ def train_on_responses_only(
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response_part = tokenizer._unsloth_output_part
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pass
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instruction_ids = tokenizer(instruction_part, add_special_tokens = False).input_ids
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response_ids = tokenizer(response_part, add_special_tokens = False).input_ids
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# Get most common tokens since tokenizers can tokenize stuff differently!
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Q_must, Q_left, Q_right = _find_common_token_ids(instruction_part, tokenizer)
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A_must, A_left, A_right = _find_common_token_ids(response_part, tokenizer)
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instruction_length = len(instruction_ids)
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response_length = len(response_ids)
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max_length = max(instruction_length, response_length)
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# Store some temporary stuff
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A_first = A_must[0]
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len_A_must = len(A_must)
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A_left_reversed = A_left[::-1]
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A_right_forward = A_right
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Q_first = Q_must[0]
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len_Q_must = len(Q_must)
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Q_left_reversed = Q_left[::-1]
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Q_right_forward = Q_right
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def _train_on_responses_only(examples):
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input_ids_ = examples["input_ids"]
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all_labels = []
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for input_ids in input_ids_:
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labels = [-100] * len(input_ids)
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m = len(input_ids) - max_length
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first_response = response_ids[0]
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first_instruction = instruction_ids[0]
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n = len(input_ids)
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labels = [-100] * n
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n_minus_1 = n - 1
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j = 0
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while j < m:
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if input_ids[j] == first_response:
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if input_ids[j : j+response_length] == response_ids:
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j = j + response_length
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start = j
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while j < m:
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if input_ids[j] == first_instruction and input_ids[j : j+instruction_length] == instruction_ids:
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j = j + instruction_length
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labels[start : j] = input_ids[start : j]
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break
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elif j == (m-1):
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j = m
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labels[start:] = input_ids[start:]
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break
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while j < n:
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# Find <assistant>
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if (input_ids[j] == A_first) and \
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(input_ids[j : (k := j + len_A_must)] == A_must):
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# Now backtrack to get previous optional tokens
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for optional_left in A_left_reversed:
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if j < 1: break
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if optional_left == input_ids[j-1]: j -= 1
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else: break
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pass
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# And forwards look as well
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for optional_right in A_right_forward:
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if k >= n_minus_1: break
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if optional_right == input_ids[k+1]: k += 1
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else: break
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pass
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# assistant_j = j
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assistant_k = k
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j = assistant_k
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# Given <assistant>, now find next user
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while j < n:
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# Find <user>
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# Also accept last final item if assistant is the last turn
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if (j == n_minus_1) or \
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((input_ids[j] == Q_first) and \
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(input_ids[j : (k := j + len_Q_must)] == Q_must)):
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# Now backtrack to get previous optional tokens
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for optional_left in Q_left_reversed:
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if j < 1: break
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if optional_left == input_ids[j-1]: j -= 1
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else: break
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pass
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j += 1
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# And forwards look as well
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for optional_right in Q_right_forward:
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if k >= n_minus_1: break
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if optional_right == input_ids[k+1]: k += 1
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else: break
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pass
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user_j = j
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# Account for last item
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if user_j != n_minus_1:
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# user_k = k
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# j = user_k
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j = k
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else:
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user_j = n
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k = n
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pass
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# Now copy input_ids to labels
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labels[assistant_k : user_j] = input_ids[assistant_k : user_j]
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# print(assistant_j, assistant_k, user_j, user_k)
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break
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pass
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j += 1
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pass
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pass
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j += 1
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