[2025.06.17] update train code
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@@ -1,11 +1,11 @@
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import json
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import glob
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from tokenizers import Tokenizer
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from tokenizers import Tokenizer, normalizers, pre_tokenizers, processors, decoders
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from tokenizers.normalizers import NFC
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from tokenizers.models import BPE
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from tokenizers.trainers import BpeTrainer
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from tokenizers.pre_tokenizers import ByteLevel
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from transformers import PreTrainedTokenizerFast
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import pyarrow.dataset as ds
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SPECIAL_TOKENS = [
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"<|endoftext|>",
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"<|im_start|>",
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@@ -51,24 +51,57 @@ ADDITIONAL_SPECIAL_TOKENS = [
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"<|video_pad|>"
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]
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def read_parquet_shards(file_pattern, batch_size=1000):
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dataset = ds.dataset(file_pattern, format='parquet')
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scanner = dataset.scanner(batch_size=batch_size)
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for batch in scanner.to_batches():
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yield batch.to_pandas()['text'].tolist()
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tokenizer = Tokenizer(BPE(unk_token=None))
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tokenizer.pre_tokenizer = ByteLevel(add_prefix_space=False)
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tokenizer = Tokenizer(BPE(
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unk_token=None,
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continuing_subword_prefix="",
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end_of_word_suffix="",
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fuse_unk=False,
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byte_fallback=False
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))
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tokenizer.normalizer = normalizers.Sequence([NFC()])
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regex_pattern = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+"""
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tokenizer.pre_tokenizer = pre_tokenizers.Sequence([
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pre_tokenizers.Split(
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pattern=pre_tokenizers.Regex(regex_pattern),
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behavior="isolated",
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invert=False
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),
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pre_tokenizers.ByteLevel(
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add_prefix_space=False,
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trim_offsets=False,
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use_regex=False
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)
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])
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tokenizer.post_processor = processors.ByteLevel(
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add_prefix_space=False,
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trim_offsets=False,
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use_regex=False
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)
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tokenizer.decoder = decoders.ByteLevel(
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add_prefix_space=False,
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trim_offsets=False,
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use_regex=False
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)
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trainer = BpeTrainer(
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vocab_size=32000,
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min_frequency=2,
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min_frequency=1,
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special_tokens=SPECIAL_TOKENS,
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show_progress=True
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show_progress=True,
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continuing_subword_prefix="",
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end_of_word_suffix=""
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)
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corpus_files = glob.glob('corpus/parquet/train-0000*-of-00399.parquet' )
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tokenizer.train_from_iterator(
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iterator=read_parquet_shards(corpus_files),
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trainer=trainer,
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@@ -84,17 +117,14 @@ fast_tokenizer = PreTrainedTokenizerFast(
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add_prefix_space=False,
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clean_up_tokenization_spaces=False,
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errors="replace",
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model_max_length=131072,
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model_max_length=1024,
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split_special_tokens=False,
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unk_token=None,
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bos_token=None,
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)
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CHAT_TEMPLATE = """{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}"""
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fast_tokenizer.chat_template = CHAT_TEMPLATE
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save_path = "weights/weights_tokenizer/hf_tokenizer_BBPE"
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fast_tokenizer.save_pretrained(save_path)
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print(f"Tokenizer saved to {save_path}")
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