Create the OpLike protocol and refactor Op | feat(values) #692
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- Removes `is_single_op` because it is unused. Signed-off-by: Justin Chu <justinchu@microsoft.com> [ghstack-poisoned]
This was referenced Apr 27, 2023
justinchuby
added
module: torchlib
## Changes - Creates the `OpLike` protocol that defines common attributes and methods for `Op`, `OnnxFunction` and `TraceOnlyFunction` so we can assume a common interface. - Implement `param_schemas` for `TraceOnlyFunction` - Refactor `param_schemas` to extract common logic. - Removes `is_single_op` from `Op` because it is unused. Signed-off-by: Justin Chu <justinchumicrosoft.com> [ghstack-poisoned]
gramalingam
reviewed
Apr 27, 2023
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| class TraceOnlyFunction: | ||
| class TraceOnlyFunction(onnxscript.values.OpLike): |
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What are the differences between a TraceOnlyFunction and an OnnxFunction? Eg., would it make sense for an OnnxFunction to be an extension (or derived class) of a TraceOnlyFunction?
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A TraceOnlyFunction is simply a python function with added attributes to make it more like an onnx function. To me it is a semantically different thing that happens to implement the same protocol for convenience. But I could be wrong.
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Let me see if a TraceOnlyFunction can be derived from Op
titaiwangms
reviewed
Apr 27, 2023
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I suggest we move TraceOnlyFunction to values.py. It would make more sense to the existence of OpLike. And if we decide to go with the current setting, we should change the file name tracing to something more specific. Tracing seems general... Codes are all good!
## Changes - Creates the `OpLike` protocol that defines common attributes and methods for `Op`, `OnnxFunction` and `TraceOnlyFunction` so we can assume a common interface. - Implement `param_schemas` for `TraceOnlyFunction` - Refactor `param_schemas` to extract common logic. - Removes `is_single_op` from `Op` because it is unused. Signed-off-by: Justin Chu <justinchumicrosoft.com> [ghstack-poisoned]
justinchuby
added a commit
that referenced
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Apr 27, 2023
## Changes - Creates the `OpLike` protocol that defines common attributes and methods for `Op`, `OnnxFunction` and `TraceOnlyFunction` so we can assume a common interface. - Implement `param_schemas` for `TraceOnlyFunction` - Refactor `param_schemas` to extract common logic. - Removes `is_single_op` from `Op` because it is unused. Signed-off-by: Justin Chu <justinchumicrosoft.com> [ghstack-poisoned]
## Changes - Creates the `OpLike` protocol that defines common attributes and methods for `Op`, `OnnxFunction` and `TraceOnlyFunction` so we can assume a common interface. - Implement `param_schemas` for `TraceOnlyFunction` - Refactor `param_schemas` to extract common logic. - Removes `is_single_op` from `Op` because it is unused. Signed-off-by: Justin Chu <justinchumicrosoft.com> [ghstack-poisoned]
## Changes - Creates the `OpLike` protocol that defines common attributes and methods for `Op`, `OnnxFunction` and `TraceOnlyFunction` so we can assume a common interface. - Implement `param_schemas` for `TraceOnlyFunction` - Refactor `param_schemas` to extract common logic. - Removes `is_single_op` from `Op` because it is unused. Signed-off-by: Justin Chu <justinchumicrosoft.com> [ghstack-poisoned]
justinchuby
added a commit
that referenced
this pull request
Apr 28, 2023
## Changes - Creates the `OpLike` protocol that defines common attributes and methods for `Op`, `OnnxFunction` and `TraceOnlyFunction` so we can assume a common interface. - Implement `param_schemas` for `TraceOnlyFunction` - Refactor `param_schemas` to extract common logic. - Removes `is_single_op` from `Op` because it is unused. Signed-off-by: Justin Chu <justinchumicrosoft.com> [ghstack-poisoned]
justinchuby
added a commit
that referenced
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Apr 28, 2023
…at(converter) (#684) Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom): * #692 * #674 * #626 * __->__ #684 This change refactors the `translate_function_def` method in `Converter` to isolate the signature handling logic to `translate_function_signature`. `translate_function_signature` is used in #674 to handle function signatures so we do not need to translate the function body for general python functions incompatible with ONNX. Signed-off-by: Justin Chu <justinchu@microsoft.com>
## Changes - Creates the `OpLike` protocol that defines common attributes and methods for `Op`, `OnnxFunction` and `TraceOnlyFunction` so we can assume a common interface. - Implement `param_schemas` for `TraceOnlyFunction` - Refactor `param_schemas` to extract common logic. - Removes `is_single_op` from `Op` because it is unused. Signed-off-by: Justin Chu <justinchumicrosoft.com> [ghstack-poisoned]
## Changes - Creates the `OpLike` protocol that defines common attributes and methods for `Op`, `OnnxFunction` and `TraceOnlyFunction` so we can assume a common interface. - Implement `param_schemas` for `TraceOnlyFunction` - Refactor `param_schemas` to extract common logic. - Removes `is_single_op` from `Op` because it is unused. Signed-off-by: Justin Chu <justinchumicrosoft.com> [ghstack-poisoned]
justinchuby
added a commit
that referenced
this pull request
Apr 28, 2023
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom): * #692 * #674 * __->__ #626 * #684 This change adds the capability to auto generate `OpSchema`. ### Changes - Implement the `opschema` property in `OnnxFunction` - Test on all torch_lib functions ### Next PR Support trace_only functions ## Example ```python from onnxscript.function_libs.torch_aten.ops import core, nn print("core.aten_abs.opschema: ", core.aten_abs.opschema) print("nn.aten_cross_entropy_loss.opschema: ", nn.aten_cross_entropy_loss.opschema) ``` Results ``` core.aten_abs.opschema: OpSchema( name='aten_abs', domain='onnxscript.atenlib', since_version=1, doc='abs(Tensor self) -> Tensor', type_constraints=[OpSchema.TypeConstraintParam(type_param_str='TReal', allowed_type_strs=['tensor(float)', 'tensor(int8)', 'tensor(int16)', 'tensor(int32)', 'tensor(int64)', 'tensor(float16)', 'tensor(double)', 'tensor(bfloat16)'], description='')], inputs=[OpSchema.FormalParameter(name='self', type_str='TReal', description='', param_option=<FormalParameterOption.Single: 0>, is_homogeneous=True, min_arity=1, differentiation_category=<DifferentiationCategory.Unknown: 0>)], outputs=[OpSchema.FormalParameter(name='return_val', type_str='TReal', description='', param_option=<FormalParameterOption.Single: 0>, is_homogeneous=True, min_arity=1, differentiation_category=<DifferentiationCategory.Unknown: 0>)], attributes={} ) nn.aten_cross_entropy_loss.opschema: OpSchema( name='aten_cross_entropy_loss', domain='onnxscript.atenlib', since_version=1, doc='cross_entropy_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, SymInt ignore_index=-100, float label_smoothing=0.0) -> Tensor', type_constraints=[OpSchema.TypeConstraintParam(type_param_str='TFloatOrBFloat16', allowed_type_strs=['tensor(float)', 'tensor(float16)', 'tensor(double)', 'tensor(bfloat16)'], description=''), OpSchema.TypeConstraintParam(type_param_str='T1', allowed_type_strs=['tensor(float)', 'tensor(float16)', 'tensor(double)', 'tensor(bfloat16)'], description='')], inputs=[OpSchema.FormalParameter(name='self', type_str='TFloatOrBFloat16', description='', param_option=<FormalParameterOption.Single: 0>, is_homogeneous=True, min_arity=1, differentiation_category=<DifferentiationCategory.Unknown: 0>), OpSchema.FormalParameter(name='weight', type_str='T1', description='', param_option=<FormalParameterOption.Optional: 1>, is_homogeneous=True, min_arity=1, differentiation_category=<DifferentiationCategory.Unknown: 0>)], outputs=[OpSchema.FormalParameter(name='result_10', type_str='TFloatOrBFloat16', description='', param_option=<FormalParameterOption.Single: 0>, is_homogeneous=True, min_arity=1, differentiation_category=<DifferentiationCategory.Unknown: 0>)], attributes={'ignore_index': OpSchema.Attribute(name='ignore_index', type=<AttrType.INT: 2>, description='', default_value=name: "ignore_index" i: -100 type: INT , required=False), 'label_smoothing': OpSchema.Attribute(name='label_smoothing', type=<AttrType.FLOAT: 1>, description='', default_value=name: "label_smoothing" f: 0.0 type: FLOAT , required=False), 'reduction': OpSchema.Attribute(name='reduction', type=<AttrType.INT: 2>, description='', default_value=name: "reduction" i: 1 type: INT , required=False), 'target': OpSchema.Attribute(name='target', type=<AttrType.INTS: 7>, description='', default_value=, required=True)} ) ``` Fixes #476
titaiwangms
approved these changes
Apr 28, 2023
## Changes - Creates the `OpLike` protocol that defines common attributes and methods for `Op`, `OnnxFunction` and `TraceOnlyFunction` so we can assume a common interface. - Implement `param_schemas` for `TraceOnlyFunction` - Refactor `param_schemas` to extract common logic. - Removes `is_single_op` from `Op` because it is unused. Signed-off-by: Justin Chu <justinchumicrosoft.com> [ghstack-poisoned]
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Stack from ghstack (oldest at bottom):
get_schemain Op | chore!(api) #698Changes
OpLikeprotocol that defines common attributes and methods forOp,OnnxFunctionandTraceOnlyFunctionso we can assume a common interface.param_schemasforTraceOnlyFunctionparam_schemasto extract common logic.is_single_opfromOpbecause it is unused.Signed-off-by: Justin Chu justinchu@microsoft.com