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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
from typing import Dict
import torch
from enum import Enum, IntEnum
class NormTypeEnum(Enum):
LayerNorm: str = "layer_norm"
RMSNorm: str = "rms_norm"
class DtypeEnum(Enum):
# The torch dtype must always be the first value (so we return torch.dtype)
fp16 = torch.float16, "torch.float16", "fp16", "float16", "half"
fp32 = torch.float32, "torch.float32", "fp32", "float32", "float"
bf16 = torch.bfloat16, "torch.bfloat16", "bf16", "bfloat16", "bfloat"
int8 = torch.int8, "torch.int8", "int8"
# Copied from https://stackoverflow.com/a/43210118
# Allows us to use multiple values for each Enum index and returns first
# listed value when Enum is called
def __new__(cls, *values):
obj = object.__new__(cls)
# first value is canonical value
obj._value_ = values[0]
for other_value in values[1:]:
cls._value2member_map_[other_value] = obj
obj._all_values = values
return obj
def __repr__(self):
return "<%s.%s: %s>" % (
self.__class__.__name__,
self._name_,
", ".join([repr(v) for v in self._all_values]),
)
ELEM_SIZES: Dict[torch.dtype, int] = {
torch.float16: 2,
torch.bfloat16: 2,
torch.float32: 4,
torch.float64: 8,
torch.int8: 1,
torch.uint8: 1,
torch.int16: 2,
torch.int32: 4,
torch.int64: 8,
torch.bool: 1,
}
class ActivationType(IntEnum):
"""
Types of activations supported by DS-Inference
"""
GELU = 0
RELU = 1
SILU = 2
GEGLU = 3
ReGLU = 4
SiGLU = 5
IDENTITY = 6
InvalidType = -1
def is_gated(act_fn: ActivationType) -> bool:
"""
Return True if the given activation function is gated.
"""
if not isinstance(act_fn, ActivationType):
act_fn = ActivationType(act_fn)
return act_fn in [ActivationType.GEGLU, ActivationType.ReGLU, ActivationType.SiGLU]
def elem_size(dtype: torch.dtype) -> int:
"""
Return size in bytes of the given dtype.
"""
try:
return ELEM_SIZES[dtype]
except KeyError:
raise ValueError("Unknown dtype size for {}".format(dtype))
def ceil_div(a: int, b: int) -> int:
"""
Return ceil(a / b).
"""
return -(-a // b)
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