fix the attribute types section in IR.md #2590
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TMVector
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Feb 6, 2020
gramalingam
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Feb 11, 2020
wschin
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Feb 11, 2020
* fix the attribute types section in IR.md * update per comments.
wschin
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Feb 17, 2020
* ONNX Training proposal.
Major changes:
1. Add a protobuf message, `TrainingInfoProto` originally designed in
#2013, to store training information.
2. In `TrainingInfoProto`, the user can store training algorithm in
`algorithm` field as a `GraphProto`.
3. The user can also store initialization algorithm for resetting the
model in `TrainingInfoProto.initialization` (proposed by @tbennun in
#2517 and agreed by Training WG).
4. `ModelProto.graph` is callable inside `TrainingInfoProto.algorithm`.
`ModelProto.graph.initializer` are visible to nodes in
`TrainingInfoProto.algorithm.node`.
5. This PR also introduces a `Gradient` operator to differentiate a
function represented by a (sub-)graph. This idea is from #2168.
Contribution list:
Baihan Huang: spec design.
Tal Ben-Nun: model initialization design.
Wei-Sheng Chin: spec design, Gradient operator design.
Jonny Shipton and active WG members and participants: many valuable comments and reviews.
Co-authored-by: Sherlock <baihan.huang@gmail.com>
Co-authored-by: Tal Ben-Nun <tbennun@users.noreply.github.com>
Co-authored-by: Jonny Shipton <tmvector@gmail.com>
* Address comments
* Address a comment
* Move Gradient to ai.onnx.training
Update Gradient test models
* Address comments
1. Create initialization_binding instead of
using update_binding for initialization.
2. Swap key and velue in update_binding.
3. Refine documents accordingly.
* Clarify sementics of algorithm and initialization
* Fix typos
* Address comment and explain the two computation modes of ModelProto.training_info
* Fix typo and explain default behavior
* Update onnx/checker.cc
Co-Authored-By: Jonny Shipton <tmvector@gmail.com>
* Address comments
* Make normalization_binding a repeated field
* Add GraphCall operator
* Polish GraphCall
* GraphCall now uses position to map inputs and outputs
* Address comments:
1. Clarify GraphCall's semantic.
2. Implicitly force trainable tensors to be inference graph's inputs.
3. Training operators cannot be called in the inference graph.
* Add accidently removed changes back
* Use protobuf lite
* Polish the helper script
* Fix windows build and polish helper script
* Fix linux and mac builds
* One more line
* fix the attribute types section in IR.md (#2590)
* fix the attribute types section in IR.md
* update per comments.
* Some changes around the behavior of optional inference inputs.
1. Use pass-by-value to optional inference inputs.
2. Due to the semantic of GraphCall, we implicitly force trainable
inputs to be added into inference graph's input list.
Revise docs
* Update spec per WG discussion
* update_binding is optional now because user might only want to store initialization
* Polish doc
* Address comments. Polish words.
* Use an alternative field to declar global variables.
In yesterday's Operator SIG meeting, we agree to still
put global variables in the inference graph and add a
model-level field to indicate global variables. This way
we can have smaller impact to the inference engines, because
they don't need to move trainable tensors to a new field.
* polish docs
* Allow training initializers to be promoted to global & mutable variables
* Merge the functions of global_mutable_initializer_names into update_binding
* Polish docs
* Remove restriction on using ai.onnx.training in the inference graph
* Split training register from ai.onnx register file
Co-authored-by: Sherlock <baihan.huang@gmail.com>
Co-authored-by: Tal Ben-Nun <tbennun@users.noreply.github.com>
Co-authored-by: Jonny Shipton <tmvector@gmail.com>
Co-authored-by: Ke Zhang <kezhan@microsoft.com>
jcwchen
pushed a commit
to jcwchen/onnx
that referenced
this pull request
Sep 23, 2020
* fix the attribute types section in IR.md * update per comments.
jcwchen
pushed a commit
to jcwchen/onnx
that referenced
this pull request
Sep 23, 2020
* ONNX Training proposal.
Major changes:
1. Add a protobuf message, `TrainingInfoProto` originally designed in
onnx#2013, to store training information.
2. In `TrainingInfoProto`, the user can store training algorithm in
`algorithm` field as a `GraphProto`.
3. The user can also store initialization algorithm for resetting the
model in `TrainingInfoProto.initialization` (proposed by @tbennun in
onnx#2517 and agreed by Training WG).
4. `ModelProto.graph` is callable inside `TrainingInfoProto.algorithm`.
`ModelProto.graph.initializer` are visible to nodes in
`TrainingInfoProto.algorithm.node`.
5. This PR also introduces a `Gradient` operator to differentiate a
function represented by a (sub-)graph. This idea is from onnx#2168.
Contribution list:
Baihan Huang: spec design.
Tal Ben-Nun: model initialization design.
Wei-Sheng Chin: spec design, Gradient operator design.
Jonny Shipton and active WG members and participants: many valuable comments and reviews.
Co-authored-by: Sherlock <baihan.huang@gmail.com>
Co-authored-by: Tal Ben-Nun <tbennun@users.noreply.github.com>
Co-authored-by: Jonny Shipton <tmvector@gmail.com>
* Address comments
* Address a comment
* Move Gradient to ai.onnx.training
Update Gradient test models
* Address comments
1. Create initialization_binding instead of
using update_binding for initialization.
2. Swap key and velue in update_binding.
3. Refine documents accordingly.
* Clarify sementics of algorithm and initialization
* Fix typos
* Address comment and explain the two computation modes of ModelProto.training_info
* Fix typo and explain default behavior
* Update onnx/checker.cc
Co-Authored-By: Jonny Shipton <tmvector@gmail.com>
* Address comments
* Make normalization_binding a repeated field
* Add GraphCall operator
* Polish GraphCall
* GraphCall now uses position to map inputs and outputs
* Address comments:
1. Clarify GraphCall's semantic.
2. Implicitly force trainable tensors to be inference graph's inputs.
3. Training operators cannot be called in the inference graph.
* Add accidently removed changes back
* Use protobuf lite
* Polish the helper script
* Fix windows build and polish helper script
* Fix linux and mac builds
* One more line
* fix the attribute types section in IR.md (onnx#2590)
* fix the attribute types section in IR.md
* update per comments.
* Some changes around the behavior of optional inference inputs.
1. Use pass-by-value to optional inference inputs.
2. Due to the semantic of GraphCall, we implicitly force trainable
inputs to be added into inference graph's input list.
Revise docs
* Update spec per WG discussion
* update_binding is optional now because user might only want to store initialization
* Polish doc
* Address comments. Polish words.
* Use an alternative field to declar global variables.
In yesterday's Operator SIG meeting, we agree to still
put global variables in the inference graph and add a
model-level field to indicate global variables. This way
we can have smaller impact to the inference engines, because
they don't need to move trainable tensors to a new field.
* polish docs
* Allow training initializers to be promoted to global & mutable variables
* Merge the functions of global_mutable_initializer_names into update_binding
* Polish docs
* Remove restriction on using ai.onnx.training in the inference graph
* Split training register from ai.onnx register file
Co-authored-by: Sherlock <baihan.huang@gmail.com>
Co-authored-by: Tal Ben-Nun <tbennun@users.noreply.github.com>
Co-authored-by: Jonny Shipton <tmvector@gmail.com>
Co-authored-by: Ke Zhang <kezhan@microsoft.com>
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