Refactor converter to isolate translate_function_signature logic | feat(converter) #684

justinchuby · 2023-04-27T02:21:01Z

Stack from ghstack (oldest at bottom):

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 [email protected]

Move version_utils to `_internal` so that it can be used my onnxscript [ghstack-poisoned]

[ghstack-poisoned]

This change adds the capability to auto generate `OpSchema`. ### Changes - Implement the `opschema` property in `OnnxFunction` [ghstack-poisoned]

This change adds the capability to auto generate `OpSchema`. ### Changes - Implement the `opschema` property in `OnnxFunction` ### TODO Test on all torch_lib 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 [ghstack-poisoned]

…t(op_schema)" 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 [ghstack-poisoned]

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 [ghstack-poisoned]

…t(op_schema)" 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 [ghstack-poisoned]

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 [ghstack-poisoned]

…t(op_schema)" 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 [ghstack-poisoned]

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 [ghstack-poisoned]

…t(op_schema)" 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 [ghstack-poisoned]

…nction_signature logic | feat(converter)" 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 <justinchumicrosoft.com> [ghstack-poisoned]

… logic | feat(converter)" 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 <justinchumicrosoft.com> [ghstack-poisoned]

…nction_signature logic | feat(converter)" 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 <justinchumicrosoft.com> [ghstack-poisoned]

… logic | feat(converter)" 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 <justinchumicrosoft.com> [ghstack-poisoned]

…at(converter) Signed-off-by: Justin Chu <justinchumicrosoft.com> ghstack-source-id: 82ec025 Pull Request resolved: #684

titaiwangms

~~The purpose of extracting the code out from an existing function should be~~

onnxscript/converter.py

…nction_signature logic | feat(converter)" 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 <justinchumicrosoft.com> [ghstack-poisoned]

… logic | feat(converter)" 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 <justinchumicrosoft.com> [ghstack-poisoned]

Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom): * #692 * #674 * #684 * #626 * #631 * __->__ #688

…nction_signature logic | feat(converter)" 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 <justinchumicrosoft.com> [ghstack-poisoned]

… logic | feat(converter)" 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 <justinchumicrosoft.com> [ghstack-poisoned]

…nction_signature logic | feat(converter)" 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 <justinchumicrosoft.com> [ghstack-poisoned]

… logic | feat(converter)" 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 <justinchumicrosoft.com> [ghstack-poisoned]

…nction_signature logic | feat(converter)" 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 <justinchumicrosoft.com> [ghstack-poisoned]

… logic | feat(converter)" 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 <justinchumicrosoft.com> [ghstack-poisoned]

Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom): * #692 * #674 * #626 * #684 * __->__ #631 * #688

…nction_signature logic | feat(converter)" 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 <justinchumicrosoft.com> [ghstack-poisoned]

… logic | feat(converter)" 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 <justinchumicrosoft.com> [ghstack-poisoned]

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

justinchuby added 30 commits April 12, 2023 17:30

Move version_utils to _internal | chore

c6505cf

Move version_utils to `_internal` so that it can be used my onnxscript [ghstack-poisoned]

Merge attrs and attr_protos in IRFunction | chore(irbuilder)

268ad8f

[ghstack-poisoned]

Auto generate OpSchema for functions | feat

327a7d6

This change adds the capability to auto generate `OpSchema`. ### Changes - Implement the `opschema` property in `OnnxFunction` [ghstack-poisoned]

justinchuby added the change base before merge Remember to change the merge base to main when the PR is ready to merge label Apr 27, 2023

justinchuby added 2 commits April 27, 2023 03:58

justinchuby mentioned this pull request Apr 27, 2023

Implement split_function_and_wrangler | test(torchlib) #686

Closed

justinchuby added 2 commits April 27, 2023 04:08

This was referenced Apr 27, 2023

Implement split_function_and_wrangler | test(torchlib) #688

Merged

Create the OpLike protocol and refactor Op | feat(values) #692

Closed

justinchuby added the topic: api label Apr 27, 2023

justinchuby added a commit that referenced this pull request Apr 27, 2023

Refactor converter to isolate translate_function_signature logic | fe…

fda5c3d

…at(converter) Signed-off-by: Justin Chu <justinchumicrosoft.com> ghstack-source-id: 82ec025 Pull Request resolved: #684

titaiwangms reviewed Apr 27, 2023

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gramalingam approved these changes Apr 27, 2023

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justinchuby added 2 commits April 27, 2023 22:28

justinchuby added a commit that referenced this pull request Apr 27, 2023

Implement split_function_and_wrangler | test(torchlib) (#688)

a05ab66

Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom): * #692 * #674 * #684 * #626 * #631 * __->__ #688

justinchuby added 6 commits April 28, 2023 02:47

justinchuby added a commit that referenced this pull request Apr 28, 2023

Analyze type annotation for input types | feat(op_schema) (#631)

0b633ea

Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at bottom): * #692 * #674 * #626 * #684 * __->__ #631 * #688

justinchuby changed the base branch from gh/justinchuby/19/base to main April 28, 2023 03:46

justinchuby added 2 commits April 28, 2023 04:06

justinchuby merged commit d3ce597 into main Apr 28, 2023

justinchuby deleted the gh/justinchuby/19/head branch April 28, 2023 04:25

justinchuby restored the gh/justinchuby/19/head branch April 28, 2023 04:52

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Refactor converter to isolate translate_function_signature logic | feat(converter) #684

Refactor converter to isolate translate_function_signature logic | feat(converter) #684

justinchuby commented Apr 27, 2023 •

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Refactor converter to isolate translate_function_signature logic | feat(converter) #684

Refactor converter to isolate translate_function_signature logic | feat(converter) #684

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