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dea4864ce0
Summary: These changes in D76442012 got reverted after the PR landed due to aps_models/ads/launchers/pearl/tests/ne/e2e_deterministic_tests:pearl_e2e_ne_tests failing with `Config not loaded due to no timely response from configerator. Likely configerator_proxy or falcon_proxy are not healthy`, but that test failing is definitely transient and unrelated to my changes, so re-creating the diff Test Plan: ensure tests pass Rollback Plan: Differential Revision: D77871099 Pull Request resolved: https://github.com/pytorch/pytorch/pull/157715 Approved by: https://github.com/meetv18
107 lines
2.8 KiB
Python
107 lines
2.8 KiB
Python
import io
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import json
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import struct
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from dataclasses import dataclass
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from typing import Any, Optional
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import torch
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_metadata_fn: str = "model.safetensors.index.json"
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FILE_NAME = "model-{cpt_idx}-of-{num_files}"
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SHARDED_FILE_NAME = "shard-{shard_idx}-model-{cpt_idx}-of-{num_files}"
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SUFFIX = ".safetensors"
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# metadata keys
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CUSTOM_METADATA_KEY = "DCP_SHARDING_INFO"
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DEFAULT_EXTRA_METADATA_KEY = "__metadata__"
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SAVED_OFFSETS_KEY = "saved_offsets"
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SHAPE_KEY = "shape"
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DATA_KEY = "data"
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DTYPE_KEY = "dtype"
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DATA_OFFSETS_KEY = "data_offsets"
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DTYPE_MAP = {
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"F16": torch.float16,
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"F32": torch.float32,
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"F64": torch.float64,
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"I8": torch.int8,
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"U8": torch.uint8,
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"I16": torch.int16,
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"I32": torch.int32,
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"I64": torch.int64,
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"BF16": torch.bfloat16,
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}
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HF_DCP_VERSION: float = 1.0
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DCP_VERSION_KEY = "DCP_VERSION"
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DCP_SHARDING_INFO_KEY = "DCP_SHARDING_INFO"
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FORMAT_KEY = "format"
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FORMAT_VALUE = "pt"
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NUM_BYTES_FOR_HEADER_LEN = 8
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@dataclass
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class _HFStorageInfo:
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"""This is the per entry storage info."""
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relative_path: str
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offset: int
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length: int
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shape: torch.Size
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dtype: torch.dtype
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def _gen_file_name(
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index: int, largest_index: int, shard_index: Optional[int] = None
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) -> str:
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if shard_index is not None:
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return (
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SHARDED_FILE_NAME.format(
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shard_idx=f"{shard_index}".zfill(5),
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cpt_idx=f"{index}".zfill(5),
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num_files=f"{largest_index}".zfill(5),
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)
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+ SUFFIX
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)
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else:
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return (
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FILE_NAME.format(
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cpt_idx=f"{index}".zfill(5), num_files=f"{largest_index}".zfill(5)
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)
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+ SUFFIX
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)
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def _get_safetensors_file_metadata(file_bytes: io.IOBase) -> tuple[Any, int]:
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# this uses the same logic that's done in HF code base
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# https://github.com/2404589803/huggingface_hub/blob/main/src/huggingface_hub/hf_api.py#L5308
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# and follows their documentation on how their files are serialized
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# https://huggingface.co/docs/safetensors/index#format
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header_len_bytes = file_bytes.read(NUM_BYTES_FOR_HEADER_LEN)
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header_len = struct.unpack("<Q", header_len_bytes)[0]
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header_json = file_bytes.read(header_len)
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metadata = json.loads(header_json)
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return (metadata, header_len + NUM_BYTES_FOR_HEADER_LEN)
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def _get_dtype(dtype_str: str) -> torch.dtype:
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try:
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dtype = DTYPE_MAP[dtype_str]
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except KeyError:
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dtype = torch.get_default_dtype()
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return dtype
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def _get_dcp_custom_metadata(metadata: Any) -> Optional[Any]:
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if DEFAULT_EXTRA_METADATA_KEY in metadata:
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custom_metadata = metadata[DEFAULT_EXTRA_METADATA_KEY]
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if CUSTOM_METADATA_KEY in custom_metadata:
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return json.loads(custom_metadata[CUSTOM_METADATA_KEY])
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return None
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