mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-09-09 13:00:52 +00:00
[misc] update pre-commit and run all files (#4752)
* [misc] update pre-commit * [misc] run pre-commit * [misc] remove useless configuration files * [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
GitHub
@@ -28,7 +28,7 @@ def data_gen_for_lm():
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# LM data gen
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# the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
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data = data_gen()
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data['labels'] = data['input_ids'].clone()
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data["labels"] = data["input_ids"].clone()
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return data
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@@ -36,7 +36,7 @@ def data_gen_for_pretraining():
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# pretraining data gen
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# `next_sentence_label` is the label for next sentence prediction, 0 or 1
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data = data_gen_for_lm()
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data['next_sentence_label'] = torch.tensor([1], dtype=torch.int64)
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data["next_sentence_label"] = torch.tensor([1], dtype=torch.int64)
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return data
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@@ -44,7 +44,7 @@ def data_gen_for_sequence_classification():
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# sequence classification data gen
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# `labels` is the label for sequence classification, 0 or 1
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data = data_gen()
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data['labels'] = torch.tensor([1], dtype=torch.int64)
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data["labels"] = torch.tensor([1], dtype=torch.int64)
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return data
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@@ -52,7 +52,7 @@ def data_gen_for_token_classification():
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# token classification data gen
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# `labels` is the type not the token id for token classification, 0 or 1
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data = data_gen()
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data['labels'] = torch.tensor([[1, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.int64)
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data["labels"] = torch.tensor([[1, 0, 0, 0, 0, 0, 0, 0]], dtype=torch.int64)
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return data
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@@ -67,32 +67,276 @@ def data_gen_for_mcq():
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# data = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
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# data = {k: v.unsqueeze(0) for k, v in encoding.items()}
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# data['labels'] = torch.tensor([0], dtype=torch.int64)
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input_ids = torch.tensor([[[
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101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037, 4825, 1010, 2003, 3591,
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4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2007, 1037, 9292, 1998, 1037, 5442, 1012, 102, 102, 5442,
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1012, 102, 102
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],
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[
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101, 1999, 3304, 1010, 10733, 2366, 1999, 5337, 10906, 1010, 2107, 2004, 2012, 1037,
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4825, 1010, 2003, 3591, 4895, 14540, 6610, 2094, 1012, 102, 2009, 2003, 8828, 2096,
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2218, 1999, 1996, 2192, 1012, 102, 0, 0, 1012, 102, 0, 0
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]]])
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token_type_ids = torch.tensor([[[
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1
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],
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[
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0
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]]])
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attention_mask = torch.tensor([[[
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1
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],
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[
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0
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]]])
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input_ids = torch.tensor(
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[
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[
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[
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101,
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1999,
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3304,
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1010,
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10733,
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2366,
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1999,
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5337,
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10906,
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1010,
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2107,
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2004,
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2012,
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1037,
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4825,
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1010,
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2003,
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3591,
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4895,
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14540,
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6610,
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2094,
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1012,
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102,
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2009,
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2003,
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8828,
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2007,
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1037,
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9292,
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1998,
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1037,
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5442,
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1012,
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102,
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102,
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5442,
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1012,
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102,
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102,
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],
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[
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101,
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1999,
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3304,
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1010,
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10733,
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2366,
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1999,
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5337,
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10906,
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1010,
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2107,
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2004,
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2012,
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1037,
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4825,
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1010,
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2003,
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3591,
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4895,
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14540,
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6610,
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2094,
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1012,
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102,
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2009,
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2003,
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8828,
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2096,
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2218,
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1999,
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1996,
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2192,
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1012,
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102,
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0,
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0,
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1012,
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102,
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0,
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0,
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],
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]
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]
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)
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token_type_ids = torch.tensor(
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[
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[
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[
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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],
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[
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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0,
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0,
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1,
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1,
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0,
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0,
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],
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]
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]
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)
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attention_mask = torch.tensor(
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[
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[
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[
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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],
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[
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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1,
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0,
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0,
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1,
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1,
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0,
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0,
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],
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]
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]
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)
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labels = torch.tensor([0], dtype=torch.int64)
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return dict(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask, labels=labels)
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@@ -103,9 +347,9 @@ def data_gen_for_qa():
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# no need for labels and use start and end position instead
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data = data_gen()
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start_positions = torch.tensor([0], dtype=torch.int64)
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data['start_positions'] = start_positions
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data["start_positions"] = start_positions
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end_positions = torch.tensor([1], dtype=torch.int64)
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data['end_positions'] = end_positions
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data["end_positions"] = end_positions
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return data
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@@ -114,69 +358,90 @@ output_transform_fn = lambda x: x
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# define loss funciton
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loss_fn_for_bert_model = lambda x: torch.nn.functional.mse_loss(x.last_hidden_state, torch.ones_like(x.last_hidden_state
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))
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loss_fn_for_bert_model = lambda x: torch.nn.functional.mse_loss(
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x.last_hidden_state, torch.ones_like(x.last_hidden_state)
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)
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loss_fn = lambda x: x.loss
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config = transformers.BertConfig(hidden_size=128,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=256,
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hidden_dropout_prob=0,
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attention_probs_dropout_prob=0)
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config = transformers.BertConfig(
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hidden_size=128,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=256,
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hidden_dropout_prob=0,
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attention_probs_dropout_prob=0,
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)
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# register the BERT variants
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model_zoo.register(name='transformers_bert',
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model_fn=lambda: transformers.BertModel(config, add_pooling_layer=False),
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data_gen_fn=data_gen,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn_for_bert_model,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_bert_for_pretraining',
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model_fn=lambda: transformers.BertForPreTraining(config),
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data_gen_fn=data_gen_for_pretraining,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_bert_lm_head_model',
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model_fn=lambda: transformers.BertLMHeadModel(config),
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data_gen_fn=data_gen_for_lm,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_bert_for_masked_lm',
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model_fn=lambda: transformers.BertForMaskedLM(config),
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data_gen_fn=data_gen_for_lm,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_bert_for_sequence_classification',
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model_fn=lambda: transformers.BertForSequenceClassification(config),
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data_gen_fn=data_gen_for_sequence_classification,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_bert_for_token_classification',
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model_fn=lambda: transformers.BertForTokenClassification(config),
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data_gen_fn=data_gen_for_token_classification,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_bert_for_next_sentence',
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model_fn=lambda: transformers.BertForNextSentencePrediction(config),
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data_gen_fn=data_gen_for_sequence_classification,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_bert_for_mcq',
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model_fn=lambda: transformers.BertForMultipleChoice(config),
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data_gen_fn=data_gen_for_mcq,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_bert_for_question_answering',
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model_fn=lambda: transformers.BertForQuestionAnswering(config),
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data_gen_fn=data_gen_for_qa,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(
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name="transformers_bert",
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model_fn=lambda: transformers.BertModel(config, add_pooling_layer=False),
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data_gen_fn=data_gen,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn_for_bert_model,
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model_attribute=ModelAttribute(has_control_flow=True),
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)
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model_zoo.register(
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name="transformers_bert_for_pretraining",
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model_fn=lambda: transformers.BertForPreTraining(config),
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data_gen_fn=data_gen_for_pretraining,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True),
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)
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model_zoo.register(
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name="transformers_bert_lm_head_model",
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model_fn=lambda: transformers.BertLMHeadModel(config),
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data_gen_fn=data_gen_for_lm,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True),
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)
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model_zoo.register(
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name="transformers_bert_for_masked_lm",
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model_fn=lambda: transformers.BertForMaskedLM(config),
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data_gen_fn=data_gen_for_lm,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True),
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)
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model_zoo.register(
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name="transformers_bert_for_sequence_classification",
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model_fn=lambda: transformers.BertForSequenceClassification(config),
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data_gen_fn=data_gen_for_sequence_classification,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True),
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)
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model_zoo.register(
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name="transformers_bert_for_token_classification",
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model_fn=lambda: transformers.BertForTokenClassification(config),
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data_gen_fn=data_gen_for_token_classification,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True),
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)
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model_zoo.register(
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name="transformers_bert_for_next_sentence",
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model_fn=lambda: transformers.BertForNextSentencePrediction(config),
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data_gen_fn=data_gen_for_sequence_classification,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True),
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)
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model_zoo.register(
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name="transformers_bert_for_mcq",
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model_fn=lambda: transformers.BertForMultipleChoice(config),
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data_gen_fn=data_gen_for_mcq,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True),
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)
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model_zoo.register(
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name="transformers_bert_for_question_answering",
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model_fn=lambda: transformers.BertForQuestionAnswering(config),
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data_gen_fn=data_gen_for_qa,
|
||||
output_transform_fn=output_transform_fn,
|
||||
loss_fn=loss_fn,
|
||||
model_attribute=ModelAttribute(has_control_flow=True),
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user