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Fix base OPT-125M and finetuned OPT models in Colab TPU instances

This commit is contained in:
vfbd 2022-07-05 15:28:58 -04:00
parent c94f875608
commit 2a78b66932
1 changed files with 3 additions and 2 deletions
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5
tpu_mtj_backend.py
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@ -1225,13 +1225,14 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
if utils.num_shards is not None: if utils.num_shards is not None:
utils.current_shard += 1 utils.current_shard += 1
for key in sorted(model_dict.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)): for key in sorted(model_dict.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)):
model_spec_key = max((k for k in model_spec.keys() if key.endswith(k)), key=len, default=None)
# Some model weights are used by transformers but not by MTJ. # Some model weights are used by transformers but not by MTJ.
# We have to materialize these weights anyways because # We have to materialize these weights anyways because
# transformers will throw a tantrum otherwise. To attain # transformers will throw a tantrum otherwise. To attain
# the least possible memory usage, we create them as meta # the least possible memory usage, we create them as meta
# tensors, which don't take up any actual CPU or TPU memory. # tensors, which don't take up any actual CPU or TPU memory.
if key not in model_spec: if model_spec_key is None:
model_dict[key] = torch.empty(model_dict[key].shape, dtype=model_dict[key].dtype, device="meta") model_dict[key] = torch.empty(model_dict[key].shape, dtype=model_dict[key].dtype, device="meta")
utils.bar.update(1) utils.bar.update(1)
continue continue
@ -1246,7 +1247,7 @@ def load_model(path: str, driver_version="tpu_driver0.1_dev20210607", hf_checkpo
if current_offset != model_dict[key].seek_offset: if current_offset != model_dict[key].seek_offset:
f.read(model_dict[key].seek_offset - current_offset) f.read(model_dict[key].seek_offset - current_offset)
current_offset = model_dict[key].seek_offset current_offset = model_dict[key].seek_offset
spec = model_spec[key] spec = model_spec[model_spec_key]
transforms = set(spec.get("transforms", ())) transforms = set(spec.get("transforms", ()))
if not isinstance(model_dict[key], torch_lazy_loader.LazyTensor): if not isinstance(model_dict[key], torch_lazy_loader.LazyTensor):
error = f"Duplicate key {repr(key)}" error = f"Duplicate key {repr(key)}"