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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
from typing import Dict
import torch
CORE_PARAM = "_ds_core_param_key"
STR_TO_DTYPE = {
"torch.float32": torch.float32,
"torch.float64": torch.float64,
"torch.float16": torch.float16,
"torch.bfloat16": torch.bfloat16,
"torch.int64": torch.int64,
"torch.int32": torch.int32,
"torch.int16": torch.int16,
"torch.int8": torch.int8,
"torch.uint8": torch.uint8,
"torch.bool": torch.bool,
}
class InferenceParameter(torch.Tensor):
"""
An extension of the torch.Tensor class to support our inference focused features. One important
thing to note here is that an InferenceParam can be used a torch.Tensor, but outputs of
torch.Tensor operations will not be InferenceParams.
"""
@staticmethod
def __new__(cls, tensor, *args, **kwargs):
new_tensor = super().__new__(cls, tensor, *args, **kwargs)
if hasattr(tensor, "_aux_attrs"):
setattr(new_tensor, "_aux_attrs", tensor.aux_attrs)
return new_tensor
def to(self, *args, **kwargs):
new_tensor = super().to(*args, **kwargs)
if hasattr(self, "_aux_attrs"):
setattr(new_tensor, "_aux_attrs", self.aux_attrs)
try:
_ = torch.device(args[0])
for name, attr in new_tensor.aux_attrs.items():
new_attr = attr.to(*args, **kwargs)
setattr(new_tensor, name, new_attr)
new_tensor.aux_attrs[name] = new_attr
except:
pass
return new_tensor
@classmethod
def initialize(cls, core_param: torch.Tensor, **kwargs) -> 'InferenceParameter':
"""
Create the inference parameter.
"""
param = InferenceParameter(core_param)
setattr(param, "_aux_attrs", kwargs)
for attr_name, attr in kwargs.items():
if hasattr(param, attr_name):
raise ValueError(f"Attribute {attr_name} already exists on param.")
if not isinstance(attr, torch.Tensor):
raise ValueError(f"Attribute {attr_name} must be a tensor.")
setattr(param, attr_name, attr)
return param
@classmethod
def initialize_raw(self, **kwargs) -> 'InferenceParameter':
"""
All kwargs must be torch.Tensors and must include the core parameter.
"""
if CORE_PARAM not in kwargs:
raise ValueError(f"Must provide core parameter, with key {CORE_PARAM}.")
return InferenceParameter.initialize(kwargs[CORE_PARAM], **kwargs)
@property
def aux_attrs(self) -> Dict[str, torch.Tensor]:
"""
Dictionary of auxiliary attributes.
"""
return self._aux_attrs
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