mirror of
https://github.com/pytorch/pytorch.git
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Revert "[Caffe2]Remove Caffe2 proto files (#126134)"
This reverts commit a40658481ada9ecfd5716513a8537818c79cb3ef. Reverted https://github.com/pytorch/pytorch/pull/126134 on behalf of https://github.com/malfet due to Broke bazel builds, see https://github.com/pytorch/pytorch/actions/runs/9278148147/job/25528691981 ([comment](https://github.com/pytorch/pytorch/pull/126134#issuecomment-2136373096))
This commit is contained in:
PyTorch MergeBot
@ -614,6 +614,7 @@ cc_library(
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":caffe2_core_macros",
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":caffe2_for_aten_headers",
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"//caffe2/proto:caffe2_pb",
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"//caffe2/proto:cc_proto",
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],
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)
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@ -635,6 +636,7 @@ cc_library(
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":caffe2_perfkernels_avx2",
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":caffe2_perfkernels_avx512",
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"//caffe2/proto:caffe2_pb",
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"//caffe2/proto:cc_proto",
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"//third_party/miniz-2.1.0:miniz",
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"@com_google_protobuf//:protobuf",
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"@eigen",
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@ -842,6 +844,7 @@ cc_library(
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deps = [
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":caffe2",
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":torch_headers",
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"//caffe2/proto:torch_cc_proto",
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"@kineto",
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] + if_cuda([
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"@cuda//:nvToolsExt",
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@ -35,3 +35,21 @@ proto_library(
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srcs = ["torch.proto"],
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deps = [":caffe2_proto"],
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)
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cc_proto_library(
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name = "cc_proto",
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visibility = ["//:__pkg__"],
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deps = [":proto"],
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)
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proto_library(
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name = "proto",
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srcs = [
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"caffe2_legacy.proto",
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"hsm.proto",
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"metanet.proto",
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"predictor_consts.proto",
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"prof_dag.proto",
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],
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deps = [":caffe2_proto"],
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)
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50
caffe2/proto/caffe2_legacy.proto
Normal file
50
caffe2/proto/caffe2_legacy.proto
Normal file
@ -0,0 +1,50 @@
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syntax = "proto2";
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package caffe2;
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// Original Caffe1 Datum copy: this is used in image input op to allow us to
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// load caffe1 serialized datum without having to regenerate the database.
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message CaffeDatum {
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optional int32 channels = 1;
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optional int32 height = 2;
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optional int32 width = 3;
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// the actual image data, in bytes
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optional bytes data = 4;
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optional int32 label = 5;
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// Optionally, the datum could also hold float data.
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repeated float float_data = 6;
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// If true data contains an encoded image that need to be decoded
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optional bool encoded = 7 [ default = false ];
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}
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enum LegacyPadding {
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NOTSET = 0; // Do not use old-stype padding strategies.
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// VALID and SAME are two strategies adopted in Google DistBelief: it forces
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// the input shape as follows. For SAME, the output is:
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// R_out = ceil(float(R) / float(S))
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// C_out = ceil(float(C) / float(S))
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// where R and C are row and column, S is the stride, and K is the kernel.
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// The number of padded pixels is then computed as
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// Pr = ((R_out - 1) * S + K - R)
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// Pc = ((C_out - 1) * S + K - C)
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// When Pr and Pc are even numbers, both sides (left and right, or top and
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// bottom) get half each. When Pr and Pc are odd numbers, the right and the
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// bottom gets the one additional padding pixel.
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// For VALID, padding values of 0 are always used.
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VALID = 1;
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SAME = 2;
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// CAFFE_LEGACY_POOLING is a flag that notifies the code to use the old Caffe
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// padding strategy.
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// Basically, in caffe2, after padding the convolution and pooling use the
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// same computation strategy: half-windows at the right and bottom are
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// discarded. In Caffe, convolution follows this strategy but if there are
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// some pixels in the half-windows, the pooling layer will actually put one
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// additional output. If you set LegacyPadding to this, we will compute the
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// equivalent padding strategy in caffe2 so that the output size is
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// backward compatible with Caffe.
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// THIS IS NOW DEPRECATED. ANY non-conventional use has to be manually
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// converted.
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CAFFE_LEGACY_POOLING = 3;
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}
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58
caffe2/proto/caffe2_legacy_pb2.pyi
Normal file
58
caffe2/proto/caffe2_legacy_pb2.pyi
Normal file
@ -0,0 +1,58 @@
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||||
"""
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@generated by mypy-protobuf. Do not edit manually!
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isort:skip_file
|
||||
"""
|
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import builtins
|
||||
import google.protobuf.descriptor
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import google.protobuf.internal.containers
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import google.protobuf.internal.enum_type_wrapper
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import google.protobuf.message
|
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import typing
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import typing_extensions
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DESCRIPTOR: google.protobuf.descriptor.FileDescriptor = ...
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global___LegacyPadding = LegacyPadding
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class _LegacyPadding(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[LegacyPadding], type):
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DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor = ...
|
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NOTSET = LegacyPadding.V(0)
|
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VALID = LegacyPadding.V(1)
|
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SAME = LegacyPadding.V(2)
|
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CAFFE_LEGACY_POOLING = LegacyPadding.V(3)
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class LegacyPadding(metaclass=_LegacyPadding):
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V = typing.NewType('V', int)
|
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NOTSET = LegacyPadding.V(0)
|
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VALID = LegacyPadding.V(1)
|
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SAME = LegacyPadding.V(2)
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CAFFE_LEGACY_POOLING = LegacyPadding.V(3)
|
||||
|
||||
class CaffeDatum(google.protobuf.message.Message):
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||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
CHANNELS_FIELD_NUMBER: int
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||||
HEIGHT_FIELD_NUMBER: int
|
||||
WIDTH_FIELD_NUMBER: int
|
||||
DATA_FIELD_NUMBER: int
|
||||
LABEL_FIELD_NUMBER: int
|
||||
FLOAT_DATA_FIELD_NUMBER: int
|
||||
ENCODED_FIELD_NUMBER: int
|
||||
channels: int = ...
|
||||
height: int = ...
|
||||
width: int = ...
|
||||
data: bytes = ...
|
||||
label: int = ...
|
||||
float_data: google.protobuf.internal.containers.RepeatedScalarFieldContainer[float] = ...
|
||||
encoded: bool = ...
|
||||
|
||||
def __init__(self,
|
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*,
|
||||
channels : typing.Optional[int] = ...,
|
||||
height : typing.Optional[int] = ...,
|
||||
width : typing.Optional[int] = ...,
|
||||
data : typing.Optional[bytes] = ...,
|
||||
label : typing.Optional[int] = ...,
|
||||
float_data : typing.Optional[typing.Iterable[float]] = ...,
|
||||
encoded : typing.Optional[bool] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"channels",b"channels",u"data",b"data",u"encoded",b"encoded",u"height",b"height",u"label",b"label",u"width",b"width"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"channels",b"channels",u"data",b"data",u"encoded",b"encoded",u"float_data",b"float_data",u"height",b"height",u"label",b"label",u"width",b"width"]) -> None: ...
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global___CaffeDatum = CaffeDatum
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62
caffe2/proto/hsm.proto
Normal file
62
caffe2/proto/hsm.proto
Normal file
@ -0,0 +1,62 @@
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syntax = "proto2";
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package caffe2;
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// Hierarchical Softmax protobuffer convention:
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// The HSM operator requires a hierarchy of vocabulary words in the form of a
|
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// tree from the user. This tree is expressed using the proto format.
|
||||
// TreeProto points to the root NodeProto which can recursively contain children
|
||||
// NodeProtos (internal nodes) or word_ids (leaf nodes).
|
||||
|
||||
// The aforementioned TreeProto is internally translated into a list of word_ids
|
||||
// tagged with a list of NodeProtos that lie in the path from the root to that
|
||||
// word_id using hsm_util.create_hierarchy(tree_proto).
|
||||
// Specifically, HierarchyProto contains a list of PathProtos. Each PathProto
|
||||
// belongs to a word_id and contains a list of PathNodeProtos. Each
|
||||
// PathNodeProto contains information about the number of children the node has
|
||||
// (length), the index of the child node that lies in the path from root to
|
||||
// word_id (target) and a cumulative sum of children nodes (index; this acts as
|
||||
// the weight parameter matrix offset).
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|
||||
// Each node in the hierarchy contains links to either leaf nodes or more
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// non-terminal nodes
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||||
message NodeProto {
|
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// Links to non-terminal children nodes
|
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repeated NodeProto children = 1;
|
||||
// Links to terminal (leaf) nodes
|
||||
repeated int32 word_ids = 2;
|
||||
optional int32 offset = 3;
|
||||
optional string name = 4;
|
||||
repeated float scores = 5;
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}
|
||||
|
||||
// Protobuf format to accept hierarchy for hierarchical softmax operator.
|
||||
// TreeProto points to the root node.
|
||||
message TreeProto {
|
||||
optional NodeProto root_node = 1;
|
||||
}
|
||||
|
||||
// Internal Protobuf format which represents the path in the tree hierarchy for
|
||||
// each word in the vocabulary.
|
||||
message HierarchyProto {
|
||||
optional int32 size = 1;
|
||||
repeated PathProto paths = 2;
|
||||
}
|
||||
|
||||
// Each PathProto belongs to a word and is an array of nodes in the
|
||||
// path from the root to the leaf (which is the word itself) in the tree.
|
||||
message PathProto {
|
||||
optional int32 word_id = 1;
|
||||
repeated PathNodeProto path_nodes = 2;
|
||||
}
|
||||
|
||||
// Represents a node in the path from the root node all the way down to the
|
||||
// word (leaf).
|
||||
message PathNodeProto {
|
||||
// Parameter matrix offset for this node
|
||||
optional int32 index = 1;
|
||||
// Number of children
|
||||
optional int32 length = 2;
|
||||
// Index of the next node in the path
|
||||
optional int32 target = 3;
|
||||
}
|
||||
109
caffe2/proto/hsm_pb2.pyi
Normal file
109
caffe2/proto/hsm_pb2.pyi
Normal file
@ -0,0 +1,109 @@
|
||||
"""
|
||||
@generated by mypy-protobuf. Do not edit manually!
|
||||
isort:skip_file
|
||||
"""
|
||||
import builtins
|
||||
import google.protobuf.descriptor
|
||||
import google.protobuf.internal.containers
|
||||
import google.protobuf.message
|
||||
import typing
|
||||
import typing_extensions
|
||||
|
||||
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor = ...
|
||||
|
||||
class NodeProto(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
CHILDREN_FIELD_NUMBER: int
|
||||
WORD_IDS_FIELD_NUMBER: int
|
||||
OFFSET_FIELD_NUMBER: int
|
||||
NAME_FIELD_NUMBER: int
|
||||
SCORES_FIELD_NUMBER: int
|
||||
word_ids: google.protobuf.internal.containers.RepeatedScalarFieldContainer[int] = ...
|
||||
offset: int = ...
|
||||
name: typing.Text = ...
|
||||
scores: google.protobuf.internal.containers.RepeatedScalarFieldContainer[float] = ...
|
||||
|
||||
@property
|
||||
def children(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___NodeProto]: ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
children : typing.Optional[typing.Iterable[global___NodeProto]] = ...,
|
||||
word_ids : typing.Optional[typing.Iterable[int]] = ...,
|
||||
offset : typing.Optional[int] = ...,
|
||||
name : typing.Optional[typing.Text] = ...,
|
||||
scores : typing.Optional[typing.Iterable[float]] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"name",b"name",u"offset",b"offset"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"children",b"children",u"name",b"name",u"offset",b"offset",u"scores",b"scores",u"word_ids",b"word_ids"]) -> None: ...
|
||||
global___NodeProto = NodeProto
|
||||
|
||||
class TreeProto(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
ROOT_NODE_FIELD_NUMBER: int
|
||||
|
||||
@property
|
||||
def root_node(self) -> global___NodeProto: ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
root_node : typing.Optional[global___NodeProto] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"root_node",b"root_node"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"root_node",b"root_node"]) -> None: ...
|
||||
global___TreeProto = TreeProto
|
||||
|
||||
class HierarchyProto(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
SIZE_FIELD_NUMBER: int
|
||||
PATHS_FIELD_NUMBER: int
|
||||
size: int = ...
|
||||
|
||||
@property
|
||||
def paths(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___PathProto]: ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
size : typing.Optional[int] = ...,
|
||||
paths : typing.Optional[typing.Iterable[global___PathProto]] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"size",b"size"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"paths",b"paths",u"size",b"size"]) -> None: ...
|
||||
global___HierarchyProto = HierarchyProto
|
||||
|
||||
class PathProto(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
WORD_ID_FIELD_NUMBER: int
|
||||
PATH_NODES_FIELD_NUMBER: int
|
||||
word_id: int = ...
|
||||
|
||||
@property
|
||||
def path_nodes(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___PathNodeProto]: ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
word_id : typing.Optional[int] = ...,
|
||||
path_nodes : typing.Optional[typing.Iterable[global___PathNodeProto]] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"word_id",b"word_id"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"path_nodes",b"path_nodes",u"word_id",b"word_id"]) -> None: ...
|
||||
global___PathProto = PathProto
|
||||
|
||||
class PathNodeProto(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
INDEX_FIELD_NUMBER: int
|
||||
LENGTH_FIELD_NUMBER: int
|
||||
TARGET_FIELD_NUMBER: int
|
||||
index: int = ...
|
||||
length: int = ...
|
||||
target: int = ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
index : typing.Optional[int] = ...,
|
||||
length : typing.Optional[int] = ...,
|
||||
target : typing.Optional[int] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"index",b"index",u"length",b"length",u"target",b"target"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"index",b"index",u"length",b"length",u"target",b"target"]) -> None: ...
|
||||
global___PathNodeProto = PathNodeProto
|
||||
50
caffe2/proto/metanet.proto
Normal file
50
caffe2/proto/metanet.proto
Normal file
@ -0,0 +1,50 @@
|
||||
syntax = "proto2";
|
||||
|
||||
import "caffe2/proto/caffe2.proto";
|
||||
|
||||
package caffe2;
|
||||
|
||||
message ModelInfo {
|
||||
optional string project = 1;
|
||||
optional string modelClass = 2;
|
||||
optional string version = 3;
|
||||
optional string predictorType = 4 [ default = "SINGLE_PREDICTOR" ];
|
||||
optional string modelId = 5;
|
||||
}
|
||||
|
||||
message BlobsMap {
|
||||
required string key = 1;
|
||||
repeated string value = 2;
|
||||
}
|
||||
|
||||
message NetsMap {
|
||||
required string key = 1;
|
||||
required NetDef value = 2;
|
||||
}
|
||||
|
||||
message PlansMap {
|
||||
required string key = 1;
|
||||
required PlanDef value = 2;
|
||||
}
|
||||
|
||||
message StringMap {
|
||||
required string key = 1;
|
||||
required string value = 2;
|
||||
}
|
||||
|
||||
message MetaNetDef {
|
||||
repeated BlobsMap blobs = 1;
|
||||
// Text-format serialized NetDefs.
|
||||
repeated NetsMap nets = 2;
|
||||
// Info about where the model comes from. Possible use cases:
|
||||
// 1) sanity check or diagnose
|
||||
// 2) provide info for evaluation.
|
||||
optional ModelInfo modelInfo = 3;
|
||||
repeated PlansMap plans = 4;
|
||||
repeated StringMap applicationSpecificInfo = 5;
|
||||
repeated string blobsOrder = 6;
|
||||
repeated string preLoadBlobs = 7;
|
||||
optional TensorBoundShapes tensorBoundShapes = 8;
|
||||
repeated string requestOnlyEmbeddings = 9;
|
||||
optional AOTConfig aotConfig = 10;
|
||||
}
|
||||
160
caffe2/proto/metanet_pb2.pyi
Normal file
160
caffe2/proto/metanet_pb2.pyi
Normal file
@ -0,0 +1,160 @@
|
||||
"""
|
||||
@generated by mypy-protobuf. Do not edit manually!
|
||||
isort:skip_file
|
||||
"""
|
||||
import builtins
|
||||
import caffe2.proto.caffe2_pb2
|
||||
import google.protobuf.descriptor
|
||||
import google.protobuf.internal.containers
|
||||
import google.protobuf.message
|
||||
import typing
|
||||
import typing_extensions
|
||||
|
||||
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor = ...
|
||||
|
||||
class ModelInfo(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
PROJECT_FIELD_NUMBER: int
|
||||
MODELCLASS_FIELD_NUMBER: int
|
||||
VERSION_FIELD_NUMBER: int
|
||||
PREDICTORTYPE_FIELD_NUMBER: int
|
||||
MODELID_FIELD_NUMBER: int
|
||||
project: typing.Text = ...
|
||||
modelClass: typing.Text = ...
|
||||
version: typing.Text = ...
|
||||
predictorType: typing.Text = ...
|
||||
modelId: typing.Text = ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
project : typing.Optional[typing.Text] = ...,
|
||||
modelClass : typing.Optional[typing.Text] = ...,
|
||||
version : typing.Optional[typing.Text] = ...,
|
||||
predictorType : typing.Optional[typing.Text] = ...,
|
||||
modelId : typing.Optional[typing.Text] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"modelClass",b"modelClass",u"modelId",b"modelId",u"predictorType",b"predictorType",u"project",b"project",u"version",b"version"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"modelClass",b"modelClass",u"modelId",b"modelId",u"predictorType",b"predictorType",u"project",b"project",u"version",b"version"]) -> None: ...
|
||||
global___ModelInfo = ModelInfo
|
||||
|
||||
class BlobsMap(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
KEY_FIELD_NUMBER: int
|
||||
VALUE_FIELD_NUMBER: int
|
||||
key: typing.Text = ...
|
||||
value: google.protobuf.internal.containers.RepeatedScalarFieldContainer[typing.Text] = ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
key : typing.Optional[typing.Text] = ...,
|
||||
value : typing.Optional[typing.Iterable[typing.Text]] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"key",b"key"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"key",b"key",u"value",b"value"]) -> None: ...
|
||||
global___BlobsMap = BlobsMap
|
||||
|
||||
class NetsMap(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
KEY_FIELD_NUMBER: int
|
||||
VALUE_FIELD_NUMBER: int
|
||||
key: typing.Text = ...
|
||||
|
||||
@property
|
||||
def value(self) -> caffe2.proto.caffe2_pb2.NetDef: ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
key : typing.Optional[typing.Text] = ...,
|
||||
value : typing.Optional[caffe2.proto.caffe2_pb2.NetDef] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"key",b"key",u"value",b"value"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"key",b"key",u"value",b"value"]) -> None: ...
|
||||
global___NetsMap = NetsMap
|
||||
|
||||
class PlansMap(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
KEY_FIELD_NUMBER: int
|
||||
VALUE_FIELD_NUMBER: int
|
||||
key: typing.Text = ...
|
||||
|
||||
@property
|
||||
def value(self) -> caffe2.proto.caffe2_pb2.PlanDef: ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
key : typing.Optional[typing.Text] = ...,
|
||||
value : typing.Optional[caffe2.proto.caffe2_pb2.PlanDef] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"key",b"key",u"value",b"value"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"key",b"key",u"value",b"value"]) -> None: ...
|
||||
global___PlansMap = PlansMap
|
||||
|
||||
class StringMap(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
KEY_FIELD_NUMBER: int
|
||||
VALUE_FIELD_NUMBER: int
|
||||
key: typing.Text = ...
|
||||
value: typing.Text = ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
key : typing.Optional[typing.Text] = ...,
|
||||
value : typing.Optional[typing.Text] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"key",b"key",u"value",b"value"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"key",b"key",u"value",b"value"]) -> None: ...
|
||||
global___StringMap = StringMap
|
||||
|
||||
class MetaNetDef(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
BLOBS_FIELD_NUMBER: int
|
||||
NETS_FIELD_NUMBER: int
|
||||
MODELINFO_FIELD_NUMBER: int
|
||||
PLANS_FIELD_NUMBER: int
|
||||
APPLICATIONSPECIFICINFO_FIELD_NUMBER: int
|
||||
BLOBSORDER_FIELD_NUMBER: int
|
||||
PRELOADBLOBS_FIELD_NUMBER: int
|
||||
TENSORBOUNDSHAPES_FIELD_NUMBER: int
|
||||
REQUESTONLYEMBEDDINGS_FIELD_NUMBER: int
|
||||
AOTCONFIG_FIELD_NUMBER: int
|
||||
blobsOrder: google.protobuf.internal.containers.RepeatedScalarFieldContainer[typing.Text] = ...
|
||||
preLoadBlobs: google.protobuf.internal.containers.RepeatedScalarFieldContainer[typing.Text] = ...
|
||||
requestOnlyEmbeddings: google.protobuf.internal.containers.RepeatedScalarFieldContainer[typing.Text] = ...
|
||||
|
||||
@property
|
||||
def blobs(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___BlobsMap]: ...
|
||||
|
||||
@property
|
||||
def nets(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___NetsMap]: ...
|
||||
|
||||
@property
|
||||
def modelInfo(self) -> global___ModelInfo: ...
|
||||
|
||||
@property
|
||||
def plans(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___PlansMap]: ...
|
||||
|
||||
@property
|
||||
def applicationSpecificInfo(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___StringMap]: ...
|
||||
|
||||
@property
|
||||
def tensorBoundShapes(self) -> caffe2.proto.caffe2_pb2.TensorBoundShapes: ...
|
||||
|
||||
@property
|
||||
def aotConfig(self) -> caffe2.proto.caffe2_pb2.AOTConfig: ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
blobs : typing.Optional[typing.Iterable[global___BlobsMap]] = ...,
|
||||
nets : typing.Optional[typing.Iterable[global___NetsMap]] = ...,
|
||||
modelInfo : typing.Optional[global___ModelInfo] = ...,
|
||||
plans : typing.Optional[typing.Iterable[global___PlansMap]] = ...,
|
||||
applicationSpecificInfo : typing.Optional[typing.Iterable[global___StringMap]] = ...,
|
||||
blobsOrder : typing.Optional[typing.Iterable[typing.Text]] = ...,
|
||||
preLoadBlobs : typing.Optional[typing.Iterable[typing.Text]] = ...,
|
||||
tensorBoundShapes : typing.Optional[caffe2.proto.caffe2_pb2.TensorBoundShapes] = ...,
|
||||
requestOnlyEmbeddings : typing.Optional[typing.Iterable[typing.Text]] = ...,
|
||||
aotConfig : typing.Optional[caffe2.proto.caffe2_pb2.AOTConfig] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"aotConfig",b"aotConfig",u"modelInfo",b"modelInfo",u"tensorBoundShapes",b"tensorBoundShapes"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"aotConfig",b"aotConfig",u"applicationSpecificInfo",b"applicationSpecificInfo",u"blobs",b"blobs",u"blobsOrder",b"blobsOrder",u"modelInfo",b"modelInfo",u"nets",b"nets",u"plans",b"plans",u"preLoadBlobs",b"preLoadBlobs",u"requestOnlyEmbeddings",b"requestOnlyEmbeddings",u"tensorBoundShapes",b"tensorBoundShapes"]) -> None: ...
|
||||
global___MetaNetDef = MetaNetDef
|
||||
36
caffe2/proto/predictor_consts.proto
Normal file
36
caffe2/proto/predictor_consts.proto
Normal file
@ -0,0 +1,36 @@
|
||||
syntax = "proto2";
|
||||
|
||||
package caffe2;
|
||||
|
||||
message PredictorConsts {
|
||||
// Important - to ensure ordered traversal of the DB, these must be
|
||||
// set in the given (lexicographic) order in the input DBReader.
|
||||
optional string META_NET_DEF = 1 [ default = "!!META_NET_DEF" ];
|
||||
|
||||
// The key the Predictor sets in the global workspace for DBReader
|
||||
// consumed by the LoadOp in GLOBAL_INIT_NET.
|
||||
|
||||
optional string PREDICTOR_DBREADER = 2 [ default = "!!PREDICTOR_DBREADER" ];
|
||||
|
||||
// Blob types used in MetaNetDef blobs
|
||||
optional string PARAMETERS_BLOB_TYPE = 3 [ default = "PARAMETERS_BLOB_TYPE" ];
|
||||
optional string INPUTS_BLOB_TYPE = 4 [ default = "INPUTS_BLOB_TYPE" ];
|
||||
optional string OUTPUTS_BLOB_TYPE = 5 [ default = "OUTPUTS_BLOB_TYPE" ];
|
||||
|
||||
// Net types used in MetaNetDef nets
|
||||
optional string GLOBAL_INIT_NET_TYPE = 6 [ default = "GLOBAL_INIT_NET_TYPE" ];
|
||||
optional string PREDICT_INIT_NET_TYPE = 7
|
||||
[ default = "PREDICT_INIT_NET_TYPE" ];
|
||||
optional string PREDICT_NET_TYPE = 8 [ default = "PREDICT_NET_TYPE" ];
|
||||
optional string SINGLE_PREDICTOR = 9 [ default = "SINGLE_PREDICTOR" ];
|
||||
optional string MULTI_PREDICTOR = 10 [ default = "MULTI_PREDICTOR" ];
|
||||
optional string TRAIN_INIT_PLAN_TYPE = 11
|
||||
[ default = "TRAIN_INIT_PLAN_TYPE" ];
|
||||
optional string TRAIN_PLAN_TYPE = 12 [ default = "TRAIN_PLAN_TYPE" ];
|
||||
|
||||
// Shape info blob name
|
||||
optional string SHAPE_INFO_BLOB = 13 [ default = "SHAPE_INFO_BLOB" ];
|
||||
// Sequential blob reader name
|
||||
optional string DEFERRED_BLOB_READER = 14
|
||||
[ default = "__DEFERRED_BLOB_READER__" ];
|
||||
}
|
||||
63
caffe2/proto/predictor_consts_pb2.pyi
Normal file
63
caffe2/proto/predictor_consts_pb2.pyi
Normal file
@ -0,0 +1,63 @@
|
||||
"""
|
||||
@generated by mypy-protobuf. Do not edit manually!
|
||||
isort:skip_file
|
||||
"""
|
||||
import builtins
|
||||
import google.protobuf.descriptor
|
||||
import google.protobuf.message
|
||||
import typing
|
||||
import typing_extensions
|
||||
|
||||
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor = ...
|
||||
|
||||
class PredictorConsts(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
META_NET_DEF_FIELD_NUMBER: int
|
||||
PREDICTOR_DBREADER_FIELD_NUMBER: int
|
||||
PARAMETERS_BLOB_TYPE_FIELD_NUMBER: int
|
||||
INPUTS_BLOB_TYPE_FIELD_NUMBER: int
|
||||
OUTPUTS_BLOB_TYPE_FIELD_NUMBER: int
|
||||
GLOBAL_INIT_NET_TYPE_FIELD_NUMBER: int
|
||||
PREDICT_INIT_NET_TYPE_FIELD_NUMBER: int
|
||||
PREDICT_NET_TYPE_FIELD_NUMBER: int
|
||||
SINGLE_PREDICTOR_FIELD_NUMBER: int
|
||||
MULTI_PREDICTOR_FIELD_NUMBER: int
|
||||
TRAIN_INIT_PLAN_TYPE_FIELD_NUMBER: int
|
||||
TRAIN_PLAN_TYPE_FIELD_NUMBER: int
|
||||
SHAPE_INFO_BLOB_FIELD_NUMBER: int
|
||||
DEFERRED_BLOB_READER_FIELD_NUMBER: int
|
||||
META_NET_DEF: typing.Text = ...
|
||||
PREDICTOR_DBREADER: typing.Text = ...
|
||||
PARAMETERS_BLOB_TYPE: typing.Text = ...
|
||||
INPUTS_BLOB_TYPE: typing.Text = ...
|
||||
OUTPUTS_BLOB_TYPE: typing.Text = ...
|
||||
GLOBAL_INIT_NET_TYPE: typing.Text = ...
|
||||
PREDICT_INIT_NET_TYPE: typing.Text = ...
|
||||
PREDICT_NET_TYPE: typing.Text = ...
|
||||
SINGLE_PREDICTOR: typing.Text = ...
|
||||
MULTI_PREDICTOR: typing.Text = ...
|
||||
TRAIN_INIT_PLAN_TYPE: typing.Text = ...
|
||||
TRAIN_PLAN_TYPE: typing.Text = ...
|
||||
SHAPE_INFO_BLOB: typing.Text = ...
|
||||
DEFERRED_BLOB_READER: typing.Text = ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
META_NET_DEF : typing.Optional[typing.Text] = ...,
|
||||
PREDICTOR_DBREADER : typing.Optional[typing.Text] = ...,
|
||||
PARAMETERS_BLOB_TYPE : typing.Optional[typing.Text] = ...,
|
||||
INPUTS_BLOB_TYPE : typing.Optional[typing.Text] = ...,
|
||||
OUTPUTS_BLOB_TYPE : typing.Optional[typing.Text] = ...,
|
||||
GLOBAL_INIT_NET_TYPE : typing.Optional[typing.Text] = ...,
|
||||
PREDICT_INIT_NET_TYPE : typing.Optional[typing.Text] = ...,
|
||||
PREDICT_NET_TYPE : typing.Optional[typing.Text] = ...,
|
||||
SINGLE_PREDICTOR : typing.Optional[typing.Text] = ...,
|
||||
MULTI_PREDICTOR : typing.Optional[typing.Text] = ...,
|
||||
TRAIN_INIT_PLAN_TYPE : typing.Optional[typing.Text] = ...,
|
||||
TRAIN_PLAN_TYPE : typing.Optional[typing.Text] = ...,
|
||||
SHAPE_INFO_BLOB : typing.Optional[typing.Text] = ...,
|
||||
DEFERRED_BLOB_READER : typing.Optional[typing.Text] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"DEFERRED_BLOB_READER",b"DEFERRED_BLOB_READER",u"GLOBAL_INIT_NET_TYPE",b"GLOBAL_INIT_NET_TYPE",u"INPUTS_BLOB_TYPE",b"INPUTS_BLOB_TYPE",u"META_NET_DEF",b"META_NET_DEF",u"MULTI_PREDICTOR",b"MULTI_PREDICTOR",u"OUTPUTS_BLOB_TYPE",b"OUTPUTS_BLOB_TYPE",u"PARAMETERS_BLOB_TYPE",b"PARAMETERS_BLOB_TYPE",u"PREDICTOR_DBREADER",b"PREDICTOR_DBREADER",u"PREDICT_INIT_NET_TYPE",b"PREDICT_INIT_NET_TYPE",u"PREDICT_NET_TYPE",b"PREDICT_NET_TYPE",u"SHAPE_INFO_BLOB",b"SHAPE_INFO_BLOB",u"SINGLE_PREDICTOR",b"SINGLE_PREDICTOR",u"TRAIN_INIT_PLAN_TYPE",b"TRAIN_INIT_PLAN_TYPE",u"TRAIN_PLAN_TYPE",b"TRAIN_PLAN_TYPE"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"DEFERRED_BLOB_READER",b"DEFERRED_BLOB_READER",u"GLOBAL_INIT_NET_TYPE",b"GLOBAL_INIT_NET_TYPE",u"INPUTS_BLOB_TYPE",b"INPUTS_BLOB_TYPE",u"META_NET_DEF",b"META_NET_DEF",u"MULTI_PREDICTOR",b"MULTI_PREDICTOR",u"OUTPUTS_BLOB_TYPE",b"OUTPUTS_BLOB_TYPE",u"PARAMETERS_BLOB_TYPE",b"PARAMETERS_BLOB_TYPE",u"PREDICTOR_DBREADER",b"PREDICTOR_DBREADER",u"PREDICT_INIT_NET_TYPE",b"PREDICT_INIT_NET_TYPE",u"PREDICT_NET_TYPE",b"PREDICT_NET_TYPE",u"SHAPE_INFO_BLOB",b"SHAPE_INFO_BLOB",u"SINGLE_PREDICTOR",b"SINGLE_PREDICTOR",u"TRAIN_INIT_PLAN_TYPE",b"TRAIN_INIT_PLAN_TYPE",u"TRAIN_PLAN_TYPE",b"TRAIN_PLAN_TYPE"]) -> None: ...
|
||||
global___PredictorConsts = PredictorConsts
|
||||
68
caffe2/proto/prof_dag.proto
Normal file
68
caffe2/proto/prof_dag.proto
Normal file
@ -0,0 +1,68 @@
|
||||
syntax = "proto2";
|
||||
|
||||
package caffe2;
|
||||
|
||||
// A few notes about the Caffe2's protobuffer convention:
|
||||
// (1) Most objects are registered by their types, such as operators and nets.
|
||||
// For these, we have a string-type field "type" for registration purposes.
|
||||
// (2) We do not use extension because that used to create quite some conflicts
|
||||
// in Caffe's protobuf design.
|
||||
// (3) We have not used any proto3 specific features, such as Any or Map. This
|
||||
// is mainly for backward compatibility purposes but we may consider using
|
||||
// those in the future.
|
||||
|
||||
// A two number summary for a value. It also has count for restoring.
|
||||
message TwoNumberStatsProto {
|
||||
optional float mean = 1;
|
||||
optional float stddev = 2;
|
||||
optional int64 count = 3;
|
||||
}
|
||||
|
||||
// Blob profiling information. Profile for a blob is created every time
|
||||
// a node outputs to the blob.
|
||||
message BlobProfile {
|
||||
// Name of the blob (corresponds to OperatorDef.output).
|
||||
optional string name = 1; // required
|
||||
|
||||
// Profiling statistics.
|
||||
optional TwoNumberStatsProto bytes_used = 3;
|
||||
}
|
||||
|
||||
// Protobuf format to serialize profiler data.
|
||||
message ProfDAGProto {
|
||||
// The name for the operator
|
||||
required string name = 1;
|
||||
// The mean execution time
|
||||
required float mean = 2;
|
||||
// The standard deviation
|
||||
required float stddev = 3;
|
||||
|
||||
// New field to represent the numbers above, and with count.
|
||||
optional TwoNumberStatsProto execution_time = 4;
|
||||
|
||||
// Blob profiles that this node outputs.
|
||||
repeated BlobProfile output_profile = 5;
|
||||
|
||||
// The extra_info from the operator device option.
|
||||
repeated string extra_info = 7;
|
||||
}
|
||||
|
||||
// Operator profiling information.
|
||||
//
|
||||
// Note: The indices for elements of 'stats' and the indices of
|
||||
// 'output_profile' inside each 'stats' are assumed to match the
|
||||
// indices of 'op' elements of a corresponding NetDef and the 'output'
|
||||
// indices within each 'op'.
|
||||
message ProfDAGProtos {
|
||||
repeated ProfDAGProto stats = 1;
|
||||
optional string net_name = 2;
|
||||
repeated OpProfile ops_stats = 3;
|
||||
}
|
||||
|
||||
// Represents specification of an operation cost.
|
||||
message OpProfile {
|
||||
optional string idx = 1;
|
||||
optional string net_name = 2;
|
||||
optional string type = 3;
|
||||
optional float exec_time_secs = 4;
|
||||
}
|
||||
126
caffe2/proto/prof_dag_pb2.pyi
Normal file
126
caffe2/proto/prof_dag_pb2.pyi
Normal file
@ -0,0 +1,126 @@
|
||||
"""
|
||||
@generated by mypy-protobuf. Do not edit manually!
|
||||
isort:skip_file
|
||||
"""
|
||||
import builtins
|
||||
import google.protobuf.descriptor
|
||||
import google.protobuf.internal.containers
|
||||
import google.protobuf.message
|
||||
import typing
|
||||
import typing_extensions
|
||||
|
||||
DESCRIPTOR: google.protobuf.descriptor.FileDescriptor = ...
|
||||
|
||||
class TwoNumberStatsProto(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
MEAN_FIELD_NUMBER: int
|
||||
STDDEV_FIELD_NUMBER: int
|
||||
COUNT_FIELD_NUMBER: int
|
||||
mean: float = ...
|
||||
stddev: float = ...
|
||||
count: int = ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
mean : typing.Optional[float] = ...,
|
||||
stddev : typing.Optional[float] = ...,
|
||||
count : typing.Optional[int] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"count",b"count",u"mean",b"mean",u"stddev",b"stddev"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"count",b"count",u"mean",b"mean",u"stddev",b"stddev"]) -> None: ...
|
||||
global___TwoNumberStatsProto = TwoNumberStatsProto
|
||||
|
||||
class BlobProfile(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
NAME_FIELD_NUMBER: int
|
||||
BYTES_USED_FIELD_NUMBER: int
|
||||
name: typing.Text = ...
|
||||
|
||||
@property
|
||||
def bytes_used(self) -> global___TwoNumberStatsProto: ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
name : typing.Optional[typing.Text] = ...,
|
||||
bytes_used : typing.Optional[global___TwoNumberStatsProto] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"bytes_used",b"bytes_used",u"name",b"name"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"bytes_used",b"bytes_used",u"name",b"name"]) -> None: ...
|
||||
global___BlobProfile = BlobProfile
|
||||
|
||||
class ProfDAGProto(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
NAME_FIELD_NUMBER: int
|
||||
MEAN_FIELD_NUMBER: int
|
||||
STDDEV_FIELD_NUMBER: int
|
||||
EXECUTION_TIME_FIELD_NUMBER: int
|
||||
OUTPUT_PROFILE_FIELD_NUMBER: int
|
||||
EXTRA_INFO_FIELD_NUMBER: int
|
||||
name: typing.Text = ...
|
||||
mean: float = ...
|
||||
stddev: float = ...
|
||||
extra_info: google.protobuf.internal.containers.RepeatedScalarFieldContainer[typing.Text] = ...
|
||||
|
||||
@property
|
||||
def execution_time(self) -> global___TwoNumberStatsProto: ...
|
||||
|
||||
@property
|
||||
def output_profile(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___BlobProfile]: ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
name : typing.Optional[typing.Text] = ...,
|
||||
mean : typing.Optional[float] = ...,
|
||||
stddev : typing.Optional[float] = ...,
|
||||
execution_time : typing.Optional[global___TwoNumberStatsProto] = ...,
|
||||
output_profile : typing.Optional[typing.Iterable[global___BlobProfile]] = ...,
|
||||
extra_info : typing.Optional[typing.Iterable[typing.Text]] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"execution_time",b"execution_time",u"mean",b"mean",u"name",b"name",u"stddev",b"stddev"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"execution_time",b"execution_time",u"extra_info",b"extra_info",u"mean",b"mean",u"name",b"name",u"output_profile",b"output_profile",u"stddev",b"stddev"]) -> None: ...
|
||||
global___ProfDAGProto = ProfDAGProto
|
||||
|
||||
class ProfDAGProtos(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
STATS_FIELD_NUMBER: int
|
||||
NET_NAME_FIELD_NUMBER: int
|
||||
OPS_STATS_FIELD_NUMBER: int
|
||||
net_name: typing.Text = ...
|
||||
|
||||
@property
|
||||
def stats(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___ProfDAGProto]: ...
|
||||
|
||||
@property
|
||||
def ops_stats(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___OpProfile]: ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
stats : typing.Optional[typing.Iterable[global___ProfDAGProto]] = ...,
|
||||
net_name : typing.Optional[typing.Text] = ...,
|
||||
ops_stats : typing.Optional[typing.Iterable[global___OpProfile]] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"net_name",b"net_name"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"net_name",b"net_name",u"ops_stats",b"ops_stats",u"stats",b"stats"]) -> None: ...
|
||||
global___ProfDAGProtos = ProfDAGProtos
|
||||
|
||||
class OpProfile(google.protobuf.message.Message):
|
||||
DESCRIPTOR: google.protobuf.descriptor.Descriptor = ...
|
||||
IDX_FIELD_NUMBER: int
|
||||
NET_NAME_FIELD_NUMBER: int
|
||||
TYPE_FIELD_NUMBER: int
|
||||
EXEC_TIME_SECS_FIELD_NUMBER: int
|
||||
idx: typing.Text = ...
|
||||
net_name: typing.Text = ...
|
||||
type: typing.Text = ...
|
||||
exec_time_secs: float = ...
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
idx : typing.Optional[typing.Text] = ...,
|
||||
net_name : typing.Optional[typing.Text] = ...,
|
||||
type : typing.Optional[typing.Text] = ...,
|
||||
exec_time_secs : typing.Optional[float] = ...,
|
||||
) -> None: ...
|
||||
def HasField(self, field_name: typing_extensions.Literal[u"exec_time_secs",b"exec_time_secs",u"idx",b"idx",u"net_name",b"net_name",u"type",b"type"]) -> bool: ...
|
||||
def ClearField(self, field_name: typing_extensions.Literal[u"exec_time_secs",b"exec_time_secs",u"idx",b"idx",u"net_name",b"net_name",u"type",b"type"]) -> None: ...
|
||||
global___OpProfile = OpProfile
|
||||
Reference in New Issue
Block a user