mirror of https://github.com/InternLM/InternLM
47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import torch
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from internlm.core.context import global_context as gpc
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DATASET_TYPE_IDS_MAP = {"en": 0, "cn": 1}
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def get_dataset_type_id(path):
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import re
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match_idxes = []
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for key, idx in DATASET_TYPE_IDS_MAP.items():
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if re.search(rf"/[z_]*{key}/", path):
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match_idxes.append(idx)
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assert len(match_idxes) == 1, f"{path}, match_idxes should be 1, but got {match_idxes} from {DATASET_TYPE_IDS_MAP}"
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return match_idxes[0]
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def unpack_data(input_ids, cu_seqlens):
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"""
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input_ids: (n, packed_length)
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Return:
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output: (batch_size, max_length)
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"""
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bsz = input_ids.shape[0]
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num_sequence = gpc.config.data["micro_bsz"]
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outputs = torch.zeros(bsz, num_sequence, gpc.config.data.seq_len, device=input_ids.device, dtype=input_ids.dtype)
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for i in range(bsz):
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output = torch.zeros(num_sequence, gpc.config.data.seq_len, device=input_ids.device, dtype=input_ids.dtype)
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cu_seqlens_slice = cu_seqlens[i]
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for j in range(num_sequence):
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seq_length = cu_seqlens_slice[j + 1] - cu_seqlens_slice[j]
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output[j, 0:seq_length] = input_ids[0, cu_seqlens_slice[j] : cu_seqlens_slice[j + 1]]
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outputs[i] = output
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if bsz == 1:
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outputs = outputs.squeeze(0)
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return outputs
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