Unified error handling in NNVM and Relay frontends (#2828)

nnvm/python/nnvm/frontend/caffe2.py

@@ -3,7 +3,7 @@
 from __future__ import absolute_import as _abs
 import tvm
 from nnvm import symbol as _sym
-from nnvm.frontend.common import get_nnvm_op, Renamer, AttrConverter as AttrCvt
+from .common import get_nnvm_op
 from .onnx_caffe2_utils import dimension_picker, dimension_constraint, infer_channels, revert_caffe2_pad
 from . import onnx
 
@@ -73,8 +73,8 @@ class Caffe2OpConverter(object):
 
         if hasattr(cls, '_impl'):
             return getattr(cls, '_impl')
-        raise NotImplementedError('{} not implemented'.format(
-            cls.__name__))
+        raise tvm.error.OpNotImplemented(
+            'Operator {} is not implemented in frontend Caffe2.'.format(cls.__name__))
 
 
 _caffe2_internal_args = {
@@ -176,8 +176,7 @@ class Concat(Caffe2OpConverter):
                 return 1
             if order == 'NHWC':
                 return 3
-            raise RuntimeError(
-                "Unsupported storage order: {} in caffe2".format(order))
+            raise tvm.error.OpAttributeInvalid('Value {} in attribute {} of operator {} is not valid.'.format(order, 'order', 'Concat'))
 
         return AttrCvt(
             op_name='concatenate',
@@ -427,8 +426,8 @@ class Caffe2NetDef(object):
             # Add a sanitizing step to convert all byte strings in args to strings
             sym = convert_map[op_type](inputs, args, self._params)
         else:
-            raise NotImplementedError(
-                "Operator {} not implemented.".format(op_type))
+            raise tvm.error.OpNotImplemented(
+                'Operator {} is not supported in frontend Caffe2.'.format(op_type))
         return sym
 
 

nnvm/python/nnvm/frontend/common.py

@@ -7,9 +7,25 @@ from .._base import string_types
 def get_nnvm_op(op_name):
     op = getattr(_sym, op_name)
     if not op:
-        raise RuntimeError("Unable to map op_name {} to nnvm.sym".format(op_name))
+        raise OpNotImplemented(
+            'Operator {} is not supported.'.format(op))
     return op
 
+def required_attr(attr, key, op_name):
+    assert isinstance(attr, dict)
+    if key not in attr:
+        raise OpAttributeRequired(
+            'Required attribute {} not found in operator {}'.format(key, op_name))
+    return attr[key]
+
+def parse_tshape(tshape):
+    """Parse tshape in string."""
+    return [int(x.strip()) for x in tshape.strip('()').split(',')]
+
+def parse_bool_str(attr, key, default='False'):
+    """Parse bool string to boolean."""
+    return attr.get(key, default).strip().lower() in ['true', '1', 't', 'y', 'yes']
+
 class Renamer(object):
     """A simply renamer for operators.
 

nnvm/python/nnvm/frontend/coreml.py

@@ -2,11 +2,10 @@
 """CoreML frontend."""
 from __future__ import absolute_import as _abs
 import numpy as np
-
 import tvm
+from .common import SymbolTable
 from .. import symbol as _sym
 from .._base import string_types
-from .common import SymbolTable
 
 __all__ = ['from_coreml']
 
@@ -83,7 +82,8 @@ def BatchnormLayerParams(op, insym, symtab):
     """Get layer of batchnorm parameter"""
     # this changes the symbol
     if op.instanceNormalization:
-        raise NotImplementedError("instance normalization not implemented")
+        msg = 'Operator "instance normalization" is not supported in frontend CoreML.'
+        raise tvm.error.OpNotImplemented(msg)
     else:
         params = {'gamma':symtab.new_const(list(op.gamma.floatValue)),
                   'beta':symtab.new_const(list(op.beta.floatValue)),
@@ -136,7 +136,8 @@ def ActivationParams(op, insym, symtab):
         betasym = symtab.new_const(beta)
         return _sym.broadcast_mul(_sym.log(_sym.broadcast_add(
             _sym.exp(insym), betasym)), alphasym)
-    raise NotImplementedError('%s not implemented' % whichActivation)
+    raise tvm.error.OpNotImplemented(
+        'Operator {} is not supported in frontend CoreML.'.format(whichActivation))
 
 def ScaleLayerParams(op, insym, symtab):
     """Scale layer params."""
@@ -158,7 +159,8 @@ def PoolingLayerParams(op, insym, symtab):
             return _sym.global_max_pool2d(insym)
         if op.type == 1:
             return _sym.global_avg_pool2d(insym)
-        raise NotImplementedError("Only max and average pooling implemented")
+        raise tvm.error.OpNotImplemented(
+            'Operator pooling (not max or average) is not supported in frontend CoreML.')
 
     else:
         params = {'pool_size':list(op.kernelSize),
@@ -178,7 +180,8 @@ def PoolingLayerParams(op, insym, symtab):
             params['padding'] = padding
             params['ceil_mode'] = True
         else:
-            raise NotImplementedError("Other convolution padding not implemented")
+            msg = 'Value {} in attribute PoolingPaddingType of operator Pooling is not valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(op.WhichOneof('PoolingPaddingType')))
 
         # consume padding layer
         if symtab.in_padding:
@@ -190,7 +193,8 @@ def PoolingLayerParams(op, insym, symtab):
             return _sym.max_pool2d(insym, **params)
         if op.type == 1:
             return _sym.avg_pool2d(insym, **params)
-        raise NotImplementedError("Only max and average pooling implemented")
+        msg = 'Operator pooling (not max or average) is not supported in frontend CoreML.'
+        raise tvm.error.OpNotImplemented(msg)
 
 def SoftmaxLayerParams(op, insym, symtab):
     return _sym.softmax(_sym.flatten(insym))
@@ -229,7 +233,8 @@ def ConcatLayerParams(op, insyms, symtab):
     if not isinstance(insyms, list):
         insyms = [insyms]
     if op.sequenceConcat:
-        raise NotImplementedError("Sequence Concat not supported")
+        raise tvm.error.OpNotImplemented(
+            'Operator Sequence Concat is not supported in frontend CoreML.')
     ret = _sym.concatenate(*insyms, axis=1)
     return ret
 
@@ -243,14 +248,16 @@ def PaddingLayerParams(op, insym, symtab):
     if op.WhichOneof('PaddingType') == 'constant':
         constant = op.constant
         if constant.value != 0:
-            raise NotImplementedError("Padding value {} not supported.".format(constant.value))
+            msg = 'Value {} in attribute "padding value" of operator Padding is not valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(constant.value))
         padding = [b.startEdgeSize for b in op.paddingAmounts.borderAmounts]
         padding2 = [b.endEdgeSize for b in op.paddingAmounts.borderAmounts]
         for i, j in zip(padding, padding2):
             assert i == j
         symtab.set_padding(padding)
     else:
-        raise NotImplementedError("Only constant padding is supported now.")
+        raise tvm.error.OpNotImplemented(
+            'Operator "non-constant padding" is not supported in frontend CoreML.')
     return insym
 
 def PermuteLayerParams(op, insym, symtab):
@@ -259,8 +266,8 @@ def PermuteLayerParams(op, insym, symtab):
 
 def UpsampleLayerParams(op, insym, symtab):
     if op.scalingFactor[0] != op.scalingFactor[1]:
-        raise NotImplementedError("Upsampling only supported with same \
-            height and width scaling factor.")
+        raise tvm.error.OpAttributeInvalid(
+            'Height and width scaling factors of Upsample operator must be equal.')
     interpolationMode = 'NEAREST_NEIGHBOR' if op.mode == 0 else 'BILINEAR'
     return _sym.upsampling(insym, scale=op.scalingFactor[0], method=interpolationMode)
 
@@ -341,7 +348,8 @@ def coreml_op_to_nnvm(op, inname, outname, symtab):
     """
     classname = type(op).__name__
     if classname not in _convert_map:
-        raise NotImplementedError("%s is not supported" % (classname))
+        raise tvm.error.OpNotImplemented(
+            'Operator {} is not supported in frontend CoreML.'.format(classname))
     if isinstance(inname, string_types):
         insym = symtab.get_var(inname)
     else:

nnvm/python/nnvm/frontend/keras.py

@@ -74,7 +74,8 @@ def _convert_activation(insym, keras_layer, _):
     if act_type == 'hard_sigmoid':
         transformX = (0.2 * insym) + 0.5
         return _sym.clip(transformX, a_min=0, a_max=1)
-    raise TypeError("Unsupported activation type : {}".format(act_type))
+    raise tvm.error.OpNotImplemented(
+        'Operator {} is not supported in frontend Keras.'.format(act_type))
 
 
 def _convert_advanced_activation(insym, keras_layer, symtab):
@@ -100,7 +101,8 @@ def _convert_advanced_activation(insym, keras_layer, symtab):
         theta = keras_layer.theta if hasattr(keras_layer, "theta") else 1.0
         theta_tensor = _sym.full_like(insym[0], fill_value=float(theta))
         return _sym.elemwise_mul(insym[0], _sym.greater(insym[0], theta_tensor, out_type="float32"))
-    raise TypeError("Unsupported advanced activation type : {}".format(act_type))
+    raise tvm.error.OpNotImplemented(
+        'Operator {} is not supported in frontend Keras.'.format(act_type))
 
 
 def _convert_merge(insym, keras_layer, _):
@@ -113,12 +115,9 @@ def _convert_merge(insym, keras_layer, _):
             ret = _sym.elemwise_sub(ret, insym[i])
         elif merge_type == 'Multiply':
             ret = _sym.elemwise_mul(ret, insym[i])
-        elif merge_type == 'Average':
-            raise NotImplementedError('Average merge not implemented')
-        elif merge_type == 'Maximum':
-            raise NotImplementedError('Maximum merge not implemented')
         else:
-            raise TypeError("Unsupported merge type : {}".format(merge_type))
+            raise tvm.error.OpNotImplemented(
+                'Operator {} Merge is not supported in frontend Keras.'.format(merge_type))
     return ret
 
 
@@ -135,7 +134,8 @@ def _convert_dense(insym, keras_layer, symtab):
     if input_dim > 2:
         input_shape = tuple(dim if dim else 1 for dim in _as_list(input_shape)[0])
         if input_dim != 3 or input_shape[0] != 1 or input_shape[1] != 1:
-            raise ValueError("Cannot flatten the inputs with shape.", input_shape, " for dense.")
+            msg = 'Value {} in attribute "input_shape" of operator Dense is not valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(input_shape))
         insym = _sym.squeeze(insym, axis=0)
     out = _sym.dense(data=insym, **params)
     # defuse activation
@@ -199,7 +199,8 @@ def _convert_convolution(insym, keras_layer, symtab):
         else:
             insym = _sym.pad(data=insym, pad_width=((0, 0), (0, 0), (pad_t, pad_b), (pad_l, pad_r)))
     else:
-        raise TypeError("Unsupported padding type : {}".format(keras_layer.padding))
+        msg = 'Value {} in attribute "padding" of operator Convolution is not valid.'
+        raise tvm.error.OpAttributeInvalid(msg.format(keras_layer.padding))
     if is_deconv:
         out = _sym.conv2d_transpose(data=insym, **params)
     else:
@@ -240,7 +241,8 @@ def _convert_separable_convolution(insym, keras_layer, symtab):
         insym = _sym.pad(data=insym, pad_width=(
             (0, 0), (0, 0), (pad_t, pad_b), (pad_l, pad_r)))
     else:
-        raise TypeError("Unsupported padding type : {}".format(keras_layer.padding))
+        msg = 'Value {} in attribute "padding" of operator Separable Convolution is not valid.'
+        raise tvm.error.OpAttributeInvalid(msg.format(keras_layer.padding))
     depthconv = _sym.conv2d(data=insym, **params0)
     # pointwise conv
     weight1 = weightList[1].transpose([3, 2, 0, 1])
@@ -294,13 +296,15 @@ def _convert_pooling(insym, keras_layer, symtab):
         pad_l, pad_r = _get_pad_pair(in_w, pool_w, stride_w)
         params['padding'] = [pad_t, pad_l, pad_b, pad_r]
     else:
-        raise TypeError("Unsupported padding type : {}".format(keras_layer.padding))
+        msg = 'Value {} in attribute "padding" of operator Pooling is not valid.'
+        raise tvm.error.OpAttributeInvalid(msg.format(keras_layer.padding))
     if pool_type == 'MaxPooling2D':
         return _sym.max_pool2d(insym, **params)
     if pool_type == 'AveragePooling2D':
         # TODO: in keras, padded zeros are not calculated
         return _sym.avg_pool2d(insym, **params)
-    raise TypeError("Unsupported pooling type : {}".format(keras_layer))
+    msg = 'Value {} in attribute "padding" of operator Pooling is not valid.'
+    raise tvm.error.OpAttributeInvalid(msg.format(keras_layer.padding))
 
 
 def _convert_upsample(insym, keras_layer, _):
@@ -312,30 +316,30 @@ def _convert_upsample(insym, keras_layer, _):
     elif upsample_type == "UpSampling2D":
         h, w = keras_layer.size
         if h != w:
-            raise TypeError("Unsupported upsampling type with different axes size : {}"
-                            .format(keras_layer.size))
+            raise tvm.error.OpAttributeInvalid(
+                'Upsample height ({}) must equal width ({})'.format(h, w))
         params = {'scale': h}
     elif upsample_type == "UpSampling3D":
         h, w, d = keras_layer.size
         if h != w or w != d:
-            raise TypeError("Unsupported upsampling type with different axes size : {}"
-                            .format(keras_layer.size))
+            raise tvm.error.OpAttributeInvalid(
+                'Upsample height ({}), width ({}), and depth ({}) must be equal.'.format(h, w, d))
         params = {'scale': h}
     else:
-        raise TypeError("Unsupported upsampling type : {}".format(upsample_type))
+        msg = 'Operator {} is not supported in frontend Keras.'
+        raise tvm.error.OpNotImplemented(msg.format(upsample_type))
     return _sym.upsampling(insym, **params)
 
 
 def _convert_cropping(insym, keras_layer, _):
     _check_data_format(keras_layer)
     crop_type = type(keras_layer).__name__
-    if crop_type == "Cropping1D":
-        raise NotImplementedError("Cropping1D not implemented")
-    elif crop_type == "Cropping2D":
+    if crop_type == "Cropping2D":
         (_, in_h, in_w, _) = keras_layer.input_shape
         ((crop_t, crop_b), (crop_l, crop_r)) = keras_layer.cropping
     else:
-        raise TypeError("Unrecognized cropping type : {}".format(crop_type))
+        raise tvm.error.OpNotImplemented(
+            'Operator {} is not supported in frontend Keras.'.format(crop_type))
     int32_max = np.iinfo(np.int32).max
     return _sym.strided_slice(insym, begin=[0, 0, crop_t, crop_l],
                               end=[int32_max, int32_max, in_h-crop_b, in_w-crop_r])
@@ -379,13 +383,13 @@ def _convert_padding(insym, keras_layer, _):
                 top, bottom = padding[0]
                 left, right = padding[1]
             else:
-                raise ValueError("Unrecognized padding option: {}".format(str(padding)))
+                msg = 'Value {} in attribute "padding" of operator {} is not valid.'
+                raise tvm.error.OpAttributeInvalid(msg.format(str(padding), padding_type))
         else:
-            raise ValueError("Unrecognized padding option: {}".format(str(padding)))
-    elif padding_type == 'ZeroPadding1D':
-        raise NotImplementedError("ZeroPadding1D not implemented")
+            msg = 'Value {} in attribute "padding" of operator {} is not valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(str(padding), padding_type))
     else:
-        raise ValueError("Unrecognized padding type: {}".format(padding_type))
+        raise tvm.error.OpNotImplemented('Operator {} is not supported in frontend Keras.')
     return _sym.pad(data=insym, pad_width=((0, 0), (0, 0), (top, bottom), (left, right)))
 
 
@@ -592,8 +596,10 @@ _convert_map = {
 
 def _check_unsupported_layers(model):
     for layer in model.layers:
-        if type(layer).__name__ not in _convert_map:
-            raise ValueError("Keras layer {} not supported.".format(type(layer).__name__))
+        op_name = type(layer).__name__
+        if op_name not in _convert_map:
+            raise tvm.error.OpNotImplemented(
+                'Operator {} is not supported in frontend Keras.'.format(op_name))
 
 def _as_list(arr):
     """Force being a list, ignore if already is."""
@@ -618,9 +624,11 @@ def keras_op_to_nnvm(insym, keras_layer, outname, symtab):
     symtab : nnvm.frontend.common.SymbolTable
         The global symbol table to be updated
     """
-    if type(keras_layer).__name__ not in _convert_map:
-        raise NotImplementedError("{} is not supported".format((type(keras_layer).__name__)))
-    outs = _convert_map[type(keras_layer).__name__](insym, keras_layer, symtab)
+    op_name = type(keras_layer).__name__
+    if op_name not in _convert_map:
+        raise tvm.error.OpNotImplemented(
+            'Operator {} is not supported in frontend Keras.'.format(op_name))
+    outs = _convert_map[op_name](insym, keras_layer, symtab)
     outs = _as_list(outs)
 
     for t_idx, out in enumerate(outs):

nnvm/python/nnvm/frontend/onnx.py

@@ -397,7 +397,8 @@ class Upsample(OnnxOpConverter):
         elif mode == b'linear':
             method = "BILINEAR"
         else:
-            raise ValueError("Invalid ONNX upsample mode: {}".format(mode))
+            raise tvm.error.OpAttributeInvalid(
+                'Value {} in attribute "mode" of operator Upsample is not valid.'.format(mode))
         return _sym.upsampling(inputs[0], scale=int(scales[-1]), method=method, layout='NCHW')
 
 
@@ -922,8 +923,8 @@ class GraphProto(object):
         elif op_name in convert_map:
             sym = convert_map[op_name](inputs, attrs, self._params)
         else:
-            raise NotImplementedError(
-                "Operator {} not implemented.".format(op_name))
+            raise tvm.error.OpNotImplemented(
+                'Operator {} is not supported in frontend ONNX.')
         return sym
 
     def _fix_outputs(self, op_name, outputs):

nnvm/python/nnvm/frontend/tensorflow.py

@@ -68,7 +68,8 @@ def _dimension_picker(prefix, surfix=''):
         kernel = attr['kernel_shape']
         if len(kernel) == 2:
             return prefix + '2d' + surfix
-        raise NotImplementedError("Only 2d kernel supported.")
+        raise tvm.error.OpAttributeUnimplemented(
+            'Non-2D kernels are not supported for operator {}.'.format(prefix))
     return _impl
 
 def _dimension_constraint():
@@ -129,7 +130,8 @@ def _pooling(name):
             attr['kernel_shape'] = (attr['ksize'][2], attr['ksize'][3])
             attr['strides'] = (attr['strides'][2], attr['strides'][3])
         else:
-            raise TypeError("Unsupported data_format type : {}".format(attr['data_format']))
+            msg = 'Value {} in attribute "data_format" of operator Pooling is not valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(attr['data_format']))
 
         if attr['_target_layout'] == "NCHW" and attr['data_format'] == "NHWC":
             tmp_shape = attr['_input_shapes'][inputs[0]]
@@ -158,7 +160,8 @@ def _pooling(name):
 
             attr['padding'] = [pad_v[0], pad_h[0], pad_v[1], pad_h[1]]
         else:
-            raise TypeError("Unsupported padding type : {}".format(attr['padding']))
+            msg = 'Value {} in attribute "padding" of operator Pooling is not valid.'
+            raise tvm.error.OpAttributeUnimplemented(msg.format(attr['padding']))
 
         if name == "avg_pool":
             attr['count_include_pad'] = False
@@ -232,7 +235,8 @@ def _conv(opname):
                 attr['dilations'] = (attr['dilations'][2], attr['dilations'][3])
             attr['strides'] = (attr['strides'][2], attr['strides'][3])
         else:
-            raise TypeError("Unsupported data format type : {}".format(attr['data_format']))
+            msg = 'Value {} in attribute "data_format" of operator Conv is not valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(attr['data_format']))
 
 
         if opname == 'depthwise':
@@ -276,7 +280,8 @@ def _conv(opname):
             attr['padding'] = [0, 0]
 
         else:
-            raise TypeError("Unsupported padding type : {}".format(attr['padding']))
+            msg = 'Value {} in attribute "padding" of operator Conv is not valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(attr['padding']))
 
         if 'kernel_layout' not in attr:
             if opname == 'conv':
@@ -432,7 +437,8 @@ def _reshape():
                     op_name="reshape",
                     extras={'shape':tuple(params_new[0].asnumpy().flatten())},
                     ignores=['Tshape'])(inputs, attr)
-            raise RuntimeError("Reshape with dynamic shape input not supported yet.")
+            raise tvm.error.OpAttributeUnimplemented(
+                'Attribute "dynamic shape" of operator Reshape is not supported.')
     return _impl
 
 def _bias_add():
@@ -736,7 +742,8 @@ def _pad(name):
         if padlist_key in params:
             padlist = params.pop(padlist_key).asnumpy()
         else:
-            raise RuntimeError("Required parameter {} not fount.".format(padlist_key))
+            raise tvm.error.OpAttributeRequired(
+                'Required attribute "{}" not found in operator Pad.'.format(padlist_key))
         paddings = tuple([tuple(l) for l in padlist])
         attr['pad_width'] = paddings
         attr['pad_value'] = 0
@@ -1188,8 +1195,9 @@ class GraphProto(object):
         missing_operators = self._parse_import_prerequisites(graph)
 
         if missing_operators:
-            raise NotImplementedError( \
-                "The following operators are not implemented: {}".format(missing_operators))
+            msg = 'The following operators are not supported in frontend TensorFlow: {}'
+            ops = str(list(missing_operators)).strip('[,]')
+            raise tvm.error.OpNotImplemented(msg.format(ops))
 
         for node in graph.node:
             if node.op == 'Placeholder':
@@ -1529,7 +1537,8 @@ class GraphProto(object):
                                              self._params, graph,
                                              convert_map_rnn)
         else:
-            raise NotImplementedError("Operator {} not implemented.".format(op_name))
+            raise tvm.error.OpNotImplemented(
+                'Operator {} is not supported in frontend TensorFlow.'.format(op_name))
         return sym
 
     def _fix_extranodes(self, op_name, attr, inputs):

python/tvm/relay/frontend/caffe2.py

 # pylint: disable=import-self, invalid-name, line-too-long, unused-argument
 """Caffe2 frontend"""
 from __future__ import absolute_import as _abs
+import tvm
 from .. import ir_pass
 from .. import expr as _expr
 from .. import op as _op
@@ -15,7 +16,8 @@ def dimension_picker(prefix, surfix=''):
         kernel = attr['kernel_shape']
         if len(kernel) == 2:
             return prefix + '2d' + surfix
-        raise NotImplementedError("Only 2d kernel supported.")
+        raise tvm.error.OpAttributeUnimplemented(
+            'Non-2D kernels are not supported for operator {}2d'.format(prefix))
 
     return _impl
 
@@ -27,7 +29,8 @@ def revert_caffe2_pad(pads):
     elif len(pads) == 2:
         pass
     else:
-        raise ValueError("Invalid caffe2 type padding: {}".format(pads))
+        raise tvm.error.OpAttributeInvalid(
+            'Number of pads must equal 2 or 4.')
     return pads
 
 
@@ -103,8 +106,8 @@ class Caffe2OpConverter(object):
 
         if hasattr(cls, '_impl'):
             return getattr(cls, '_impl')
-        raise NotImplementedError('{} not implemented'.format(
-            cls.__name__))
+        raise tvm.error.OpNotInplemented(
+            'Operator {} is not supported in frontend Caffe2.'.format(cls.__name__))
 
 
 _caffe2_internal_args = [
@@ -224,8 +227,8 @@ class Concat(Caffe2OpConverter):
                 return 1
             if order == 'NHWC':
                 return 3
-            raise RuntimeError(
-                "Unsupported storage order: {} in caffe2".format(order))
+            raise tvm.error.OpAttributeUnimplemented(
+                'Order {} is not supported in operator Concat.'.format(order))
 
         return AttrCvt(
             op_name='concatenate',
@@ -517,8 +520,8 @@ class Caffe2NetDef(object):
             # Add a sanitizing step to convert all byte strings in args to strings
             func = convert_map[op_type](inputs, args, self._params)
         else:
-            raise NotImplementedError(
-                "Operator {} not implemented.".format(op_type))
+            raise tvm.error.OpNotImplemented(
+                'Operator {} is not supported in frontend Caffe2.'.format(op_type))
         return func
 
 

python/tvm/relay/frontend/coreml.py

 # pylint: disable=invalid-name, import-self, unused-argument, unused-variable, inconsistent-return-statements
 """CoreML frontend."""
 from __future__ import absolute_import as _abs
+import tvm
 import numpy as np
 from .. import ir_pass
 from .. import expr as _expr
@@ -81,7 +82,8 @@ def _BatchnormLayerParams(op, inexpr, etab):
     """Get layer of batchnorm parameter"""
     # this changes the symbol
     if op.instanceNormalization:
-        raise NotImplementedError("instance normalization not implemented")
+        raise tvm.error.OpNotImplemented(
+            'Operator "instance normalization" is not supported in frontend CoreML.')
     else:
         params = {'gamma':etab.new_const(list(op.gamma.floatValue)),
                   'beta':etab.new_const(list(op.beta.floatValue)),
@@ -142,7 +144,8 @@ def _ActivationParams(op, inexpr, etab):
         alpha_expr = etab.new_const(alpha)
         beta_expr = etab.new_const(beta)
         return _op.multiply(_op.log(_op.add(_op.exp(inexpr), beta_expr)), alpha_expr)
-    raise NotImplementedError('%s not implemented' % whichActivation)
+    raise tvm.error.OpNotImplemented(
+        'Operator {} is not supported in frontend CoreML.'.format(whichActivation))
 
 
 def _ScaleLayerParams(op, inexpr, etab):
@@ -164,7 +167,8 @@ def _PoolingLayerParams(op, inexpr, etab):
             return _op.nn.global_max_pool2d(inexpr)
         if op.type == 1:
             return _op.nn.global_avg_pool2d(inexpr)
-        raise NotImplementedError("Only max and average pooling implemented")
+        raise tvm.error.OpNotImplemented(
+            'Only Max and Average Pooling are supported in frontend CoreML.')
 
     else:
         params = {'pool_size':list(op.kernelSize),
@@ -184,7 +188,9 @@ def _PoolingLayerParams(op, inexpr, etab):
             params['padding'] = padding
             params['ceil_mode'] = True
         else:
-            raise NotImplementedError("Other convolution padding not implemented")
+            msg = 'PoolingPaddingType {} is not supported in operator Pooling.'
+            op_name = op.WhichOneof('PoolingPaddingType')
+            raise tvm.error.OpAttributeUnimplemented(msg.format(op_name))
 
         # consume padding layer
         if etab.in_padding:
@@ -196,7 +202,8 @@ def _PoolingLayerParams(op, inexpr, etab):
             return _op.nn.max_pool2d(inexpr, **params)
         if op.type == 1:
             return _op.nn.avg_pool2d(inexpr, **params)
-        raise NotImplementedError("Only max and average pooling implemented")
+        raise tvm.error.OpNotImplemented(
+            'Only Max and Average Pooling are supported in CoreML.')
 
 
 def _SoftmaxLayerParams(op, inexpr, etab):
@@ -239,7 +246,8 @@ def _ConcatLayerParams(op, inexpr, etab):
     if not isinstance(inexpr, list):
         inexpr = [inexpr]
     if op.sequenceConcat:
-        raise NotImplementedError("Sequence Concat not supported")
+        raise tvm.error.OpNotImplemented(
+            'Operator Sequence Concat is not supported in frontend CoreML.')
     ret = _op.concatenate(inexpr, axis=1)
     return ret
 
@@ -255,14 +263,16 @@ def _PaddingLayerParams(op, inexpr, etab):
     if op.WhichOneof('PaddingType') == 'constant':
         constant = op.constant
         if constant.value != 0:
-            raise NotImplementedError("Padding value {} not supported.".format(constant.value))
+            raise tvm.error.OpAttributeUnimplemented(
+                '{} is not supported in operator Padding.'.format(constant.value))
         padding = [b.startEdgeSize for b in op.paddingAmounts.borderAmounts]
         padding2 = [b.endEdgeSize for b in op.paddingAmounts.borderAmounts]
         for i, j in zip(padding, padding2):
             assert i == j
         etab.set_padding(padding)
     else:
-        raise NotImplementedError("Only constant padding is supported now.")
+        raise tvm.error.OpNotImplemented(
+            'Non-constant padding is not supported in frontend CoreML.')
     return inexpr
 
 
@@ -273,8 +283,8 @@ def _PermuteLayerParams(op, inexpr, etab):
 
 def _UpsampleLayerParams(op, inexpr, etab):
     if op.scalingFactor[0] != op.scalingFactor[1]:
-        raise NotImplementedError("Upsampling only supported with same \
-            height and width scaling factor.")
+        raise tvm.error.OpAttributeUnimplemented(
+            'Upsample height and width must be equal.')
     interpolationMode = 'NEAREST_NEIGHBOR' if op.mode == 0 else 'BILINEAR'
     return _op.nn.upsampling(inexpr, scale=op.scalingFactor[0], method=interpolationMode)
 
@@ -364,7 +374,8 @@ def coreml_op_to_relay(op, inname, outname, etab):
     """
     classname = type(op).__name__
     if classname not in _convert_map:
-        raise NotImplementedError("%s is not supported" % (classname))
+        raise tvm.error.OpNotImplemented(
+            'Operator {} is not supported in frontend CoreML.'.format(classname))
     if isinstance(inname, _base.string_types):
         insym = etab.get_expr(inname)
     else:

python/tvm/relay/frontend/keras.py

@@ -2,6 +2,7 @@
 """Keras frontend."""
 from __future__ import absolute_import as _abs
 import sys
+import tvm
 import numpy as np
 from .. import ir_pass
 from .. import expr as _expr
@@ -91,7 +92,8 @@ def _convert_activation(inexpr, keras_layer, _):
         x = (_expr.const(0.2, dtype='float32') * inexpr) + _expr.const(0.5, dtype='float32')
         return _op.clip(x, a_min=0., a_max=1.)
 
-    raise TypeError("Unsupported activation type : {}".format(act_type))
+    raise tvm.error.OpNotImplemented(
+        'Operator {} is not supported in frontend Keras.'.format(act_type))
 
 
 def _convert_advanced_activation(inexpr, keras_layer, etab):
@@ -118,7 +120,8 @@ def _convert_advanced_activation(inexpr, keras_layer, etab):
         return _op.multiply(inexpr, _op.greater(inexpr, \
             _expr.const(theta, dtype='float32')).astype('float32'))
 
-    raise TypeError("Unsupported advanced activation type : {}".format(act_type))
+    raise tvm.error.OpNotImplemented(
+        'Operator {} is not supported in frontend Keras.'.format(act_type))
 
 
 def _convert_merge(inexpr, keras_layer, _):
@@ -136,7 +139,8 @@ def _convert_merge(inexpr, keras_layer, _):
             ret = _op.add(ret, inexpr[i])
         ret = ret / _expr.const(len(inexpr), dtype='float32')
     else:
-        raise TypeError("Unsupported merge type : {}".format(merge_type))
+        raise tvm.error.OpNotImplemented(
+            'Operator {} is not supported in frontend Keras.'.format(merge_type))
     return ret
 
 
@@ -150,7 +154,8 @@ def _convert_dense(inexpr, keras_layer, etab):
     if input_dim > 2:
         input_shape = tuple(dim if dim else 1 for dim in _as_list(input_shape)[0])
         if input_dim != 3 or input_shape[0] != 1 or input_shape[1] != 1:
-            raise ValueError("Cannot flatten the inputs with shape.", input_shape, " for dense.")
+            raise tvm.error.OpAttributeInvalid(
+                'Input shape {} is not valid for operator Dense.'.format(input_shape))
         inexpr = _op.squeeze(inexpr, axis=0)
     out = _op.nn.dense(data=inexpr, **params)
     if keras_layer.use_bias:
@@ -214,7 +219,9 @@ def _convert_convolution(inexpr, keras_layer, etab):
             inexpr = _op.nn.pad(data=inexpr, pad_width=(
                 (0, 0), (0, 0), (pad_t, pad_b), (pad_l, pad_r)))
     else:
-        raise TypeError("Unsupported padding type : {}".format(keras_layer.padding))
+        msg = 'Padding with {} is not supported for operator Convolution ' \
+              'in frontend Keras.'
+        raise tvm.error.OpAttributeUnimplemented(msg.format(keras_layer.padding))
     if is_deconv:
         out = _op.nn.conv2d_transpose(data=inexpr, **params)
     else:
@@ -260,7 +267,10 @@ def _convert_separable_convolution(inexpr, keras_layer, etab):
             inexpr = _op.nn.pad(data=inexpr, pad_width=(
                 (0, 0), (0, 0), (pad_t, pad_b), (pad_l, pad_r)))
     else:
-        raise TypeError("Unsupported padding type : {}".format(keras_layer.padding))
+        msg = 'Padding with {} is not supported for operator Separable ' \
+              'Convolution in frontend Keras.'
+        raise tvm.error.OpAttributeUnimplemented(msg.format(keras_layer.padding))
+
     depthconv = _op.nn.conv2d(data=inexpr, **params0)
     # pointwise conv
     weight1 = weightList[1].transpose([3, 2, 0, 1])
@@ -313,13 +323,15 @@ def _convert_pooling(inexpr, keras_layer, etab):
         pad_l, pad_r = _get_pad_pair(in_w, pool_w, stride_w)
         params['padding'] = [pad_t, pad_l, pad_b, pad_r]
     else:
-        raise TypeError("Unsupported padding type : {}".format(keras_layer.padding))
+        raise tvm.error.OpAttributeUnimplemented(
+            'Padding with {} is not supported in operator Pooling.'.format(keras_layer.padding))
     if pool_type == 'MaxPooling2D':
         return _op.nn.max_pool2d(inexpr, **params)
     if pool_type == 'AveragePooling2D':
         params['count_include_pad'] = False
         return _op.nn.avg_pool2d(inexpr, **params)
-    raise TypeError("Unsupported pooling type : {}".format(keras_layer))
+    raise tvm.error.OpNotImplemented(
+        'Operator {} is not supported for frontend Keras.'.format(keras_layer))
 
 
 def _convert_upsample(inexpr, keras_layer, _):
@@ -331,8 +343,8 @@ def _convert_upsample(inexpr, keras_layer, _):
     elif upsample_type == 'UpSampling2D':
         h, w = keras_layer.size
         if h != w:
-            raise TypeError("Unsupported upsampling type with different axes size : {}"
-                            .format(keras_layer.size))
+            raise tvm.error.OpAttributeInvalid(
+                'Height must equal width for operator Upsample.')
         params = {'scale': h}
 
         if hasattr(keras_layer, 'interpolation'):
@@ -345,24 +357,24 @@ def _convert_upsample(inexpr, keras_layer, _):
     elif upsample_type == 'UpSampling3D':
         h, w, d = keras_layer.size
         if h != w or w != d:
-            raise TypeError("Unsupported upsampling type with different axes size : {}"
-                            .format(keras_layer.size))
+            raise tvm.error.OpAttributeInvalid(
+                'Height, width, and depth must all be equal for operator Upsample.')
         params = {'scale': h}
     else:
-        raise TypeError("Unsupported upsampling type : {}".format(upsample_type))
+        raise tvm.error.OpNotImplemented(
+            'Operator {} is not supported for frontend Keras.'.format(upsample_type))
     return _op.nn.upsampling(inexpr, **params)
 
 
 def _convert_cropping(inexpr, keras_layer, _):
     _check_data_format(keras_layer)
     crop_type = type(keras_layer).__name__
-    if crop_type == 'Cropping1D':
-        raise NotImplementedError("Cropping1D not implemented")
-    elif crop_type == 'Cropping2D':
+    if crop_type == 'Cropping2D':
         (_, in_h, in_w, _) = keras_layer.input_shape
         ((crop_t, crop_b), (crop_l, crop_r)) = keras_layer.cropping
     else:
-        raise TypeError("Unrecognized cropping type : {}".format(crop_type))
+        raise tvm.error.OpNotImplemented(
+            'Operator {} is not supported for frontend Keras.'.format(crop_type))
     int32_max = np.iinfo(np.int32).max
     return _op.strided_slice(inexpr, begin=[0, 0, crop_t, crop_l], \
         end=[int32_max, int32_max, in_h-crop_b, in_w-crop_r])
@@ -407,14 +419,18 @@ def _convert_padding(inexpr, keras_layer, _):
                 top, bottom = padding[0]
                 left, right = padding[1]
             else:
-                raise ValueError("Unrecognized padding option: {}".format(str(padding)))
+                msg = 'Value {} in attribute "padding" of operator Padding ' \
+                      'is not valid.'
+                raise tvm.error.OpAttributeInvalid(msg.format(str(padding)))
         else:
-            raise ValueError("Unrecognized padding option: {}".format(str(padding)))
-    elif padding_type == 'ZeroPadding1D':
-        raise NotImplementedError("ZeroPadding1D not implemented")
+            msg = 'Value {} in attribute "padding" of operator Padding is ' \
+                  'not valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(str(padding)))
     else:
-        raise ValueError("Unrecognized padding type: {}".format(padding_type))
-    return _op.nn.pad(data=inexpr, pad_width=((0, 0), (0, 0), (top, bottom), (left, right)))
+        msg = 'Operator {} is not supported in frontend Keras.'
+        raise tvm.error.OpNotImplemented(msg.format(padding_type))
+    return _op.nn.pad(data=inexpr,
+                      pad_width=((0, 0), (0, 0), (top, bottom), (left, right)))
 
 
 def _convert_concat(inexpr, keras_layer, _):
@@ -601,8 +617,10 @@ _convert_map = {
 
 def _check_unsupported_layers(model):
     for layer in model.layers:
-        if type(layer).__name__ not in _convert_map:
-            raise ValueError("Keras layer {} not supported.".format(type(layer).__name__))
+        op_name = type(layer).__name__
+        if op_name not in _convert_map:
+            raise tvm.error.OpNotImplemented(
+                'Operator {} is not supported in frontend Keras.'.format(op_name))
 
 
 def keras_op_to_relay(inexpr, keras_layer, outname, etab):
@@ -622,9 +640,11 @@ def keras_op_to_relay(inexpr, keras_layer, outname, etab):
     etab : relay.frontend.common.ExprTable
         The global expression table to be updated.
     """
-    if type(keras_layer).__name__ not in _convert_map:
-        raise NotImplementedError("{} is not supported".format((type(keras_layer).__name__)))
-    outs = _convert_map[type(keras_layer).__name__](inexpr, keras_layer, etab)
+    op_name = type(keras_layer).__name__
+    if op_name not in _convert_map:
+        raise tvm.error.OpNotImplemented(
+            'Operator {} is not supported for frontend Keras.'.format(op_name))
+    outs = _convert_map[op_name](inexpr, keras_layer, etab)
     outs = _as_list(outs)
     for t_idx, out in enumerate(outs):
         name = outname + ":" + str(t_idx)

python/tvm/relay/frontend/mxnet.py

@@ -3,10 +3,12 @@
 from __future__ import absolute_import as _abs
 
 import json
+import tvm
 from .. import ir_pass
 from .. import expr as _expr
 from .. import op as _op
 from ... import nd as _nd
+
 from .common import StrAttrsDict
 from .nnvm_common import _rename, _binop_scalar, _rbinop_scalar, _reduce
 from .nnvm_common import _arg_reduce, _init_op, _softmax_op, _cast
@@ -41,7 +43,8 @@ def _get_channel_axis(layout, op_name):
         return 1
     if layout == "NHWC":
         return 3
-    raise RuntimeError("layout: {} is not supported in {}".format(layout, op_name))
+    raise tvm.error.OpAttributeInvalid(
+        'Value {} in attribute "layout" of operator {} is not valid.'.format(layout, op_name))
 
 
 def _mx_activations(inputs, attrs):
@@ -61,7 +64,8 @@ def _mx_activations(inputs, attrs):
             return _op.add(_op.log(_op.add(one, exp_neg_abs_x)),
                            _op.nn.relu(x))
         return _stable_softrelu(inputs[0])
-    raise RuntimeError("Do not support act_type: {}".format(act_type))
+    raise tvm.error.OpNotImplemented(
+        'Operator {} is not supported for frontend MXNet.'.format(act_type))
 
 
 def _mx_compare(new_op, wrapper):
@@ -74,7 +78,8 @@ def _mx_compare(new_op, wrapper):
 def _mx_conv2d(inputs, attrs):
     kernel_size = attrs.get_int_tuple("kernel")
     if len(kernel_size) != 2:
-        raise RuntimeError("non-2d kernel is not supported in conv2d")
+        raise tvm.error.OpAttributeInvalid(
+            'Non-2D kernels are not supported for operator Conv2D.')
     data_layout = attrs.get_str("layout", "NCHW")
     channel_axis = _get_channel_axis(data_layout, "conv2d")
 
@@ -102,10 +107,12 @@ def _mx_conv2d(inputs, attrs):
 
 def _mx_conv2d_transpose(inputs, attrs):
     if "target_shape" in attrs.attrs:
-        raise RuntimeError("target_shape is not supported in conv2d_transpose")
+        raise tvm.error.OpAttributeUnimplemented(
+            'Attribute "target_shape" is not supported for operator Conv2D-transpose.')
     kernel_size = attrs.get_int_tuple("kernel")
     if len(kernel_size) != 2:
-        raise RuntimeError("non-2d kernel is not supported in conv2d")
+        raise tvm.error.OpAttributeInvalid(
+            'Non-2D kernels are not supported for operator Conv2D-transpose.')
     data_layout = attrs.get_str("layout", "NCHW")
     channel_axis = _get_channel_axis(data_layout, "conv2d_transpose")
 
@@ -140,7 +147,8 @@ def _mx_pooling(inputs, attrs):
     def _pool2d(new_op, is_avg):
         kernel_size = attrs.get_int_tuple("kernel")
         if len(kernel_size) != 2:
-            raise RuntimeError("non-2d kernel is not supported in pool2d")
+            raise tvm.error.OpAttributeInvalid(
+                'Only 2D kernels are supported for operator Pool2D.')
         new_attrs = {}
         new_attrs["pool_size"] = kernel_size
         new_attrs["strides"] = attrs.get_int_tuple("stride", (1, 1))
@@ -158,7 +166,8 @@ def _mx_pooling(inputs, attrs):
         if global_pool:
             return _op.nn.global_avg_pool2d(inputs[0])
         return _pool2d(_op.nn.avg_pool2d, True)
-    raise RuntimeError("Do not support pool_type:{}".format(pool_type))
+    raise tvm.error.OpNotImplemented(
+        'Operator {} Pooling is not supported for frontend MXNet.'.format(pool_type.capitalize()))
 
 
 def _mx_dropout(inputs, attrs):
@@ -172,7 +181,8 @@ def _mx_BlockGrad(inputs, attrs): #pylint: disable=unused-argument
 
 def _mx_batch_norm(inputs, attrs):
     if attrs.get_bool("output_mean_var", False):
-        raise RuntimeError("batch_norm do not support output_mean_var")
+        raise tvm.error.OpAttributeUnimplemented(
+            'Attribute "output_mean_var" is not supported for operator Batch Norm.')
     if attrs.get_bool("use_global_stats", False):
         _warn_not_used("use_global_stats", "batch_norm")
     new_attrs = {}
@@ -188,10 +198,18 @@ def _mx_slice(inputs, attrs):
     begin = attrs.get_int_tuple('begin', None)
     end = attrs.get_int_tuple('end', None)
     stride = attrs.get_int_tuple('step', None)
-    if begin is None or end is None:
-        raise RuntimeError("begin and end are required parameters.")
-    if None in begin or None in end:
-        raise RuntimeError("None in begin or end is not supported yet.")
+    if begin is None:
+        raise tvm.error.OpAttributeRequired(
+            'Attribute "begin" not found in operator Slice.')
+    if end is None:
+        raise tvm.error.OpAttributeRequired(
+            'Attribute "end" not found in operator Slice.')
+    if None in begin:
+        raise tvm.error.OpAttributeInvalid(
+            'Value None in attribute "begin" of operator Slice is not valid.')
+    if None in end:
+        raise tvm.error.OpAttributeInvalid(
+            'Value None in attribute "end" of operator Slice is not valid.')
     new_attrs = {'begin': begin, 'end': end}
     if stride is not None:
         new_attrs['strides'] = stride
@@ -295,7 +313,8 @@ def _mx_leaky_relu(inputs, attrs):
         upper_bound = attrs.get_float("upper_bound")
         alpha = (lower_bound + upper_bound) / 2.0
         return _op.nn.leaky_relu(inputs[0], alpha=alpha)
-    raise RuntimeError("act_type: {} is not supported".format(act_type))
+    raise tvm.error.OpNotImplemented(
+        'Operator {} is not supported for frontend MXNet.'.format(act_type))
 
 
 def _mx_make_power(power):
@@ -389,7 +408,9 @@ def _mx_batch_dot(inputs, attrs):
     transpose_a = attrs.get_bool("transpose_a", False)
     transpose_b = attrs.get_bool("transpose_b", False)
     if transpose_a is True:
-        raise RuntimeError("batch_dot: only support transpose_a=False")
+        msg = 'Value {} in attribute "transpose_a" of operator batch_dot ' \
+              'is not valid.'
+        raise tvm.error.OpAttributeInvalid(msg.format(transpose_a))
     if transpose_b is False:
         b = _op.transpose(b, axes=[0, 2, 1])
     return _op.batch_matmul(a, b)
@@ -398,7 +419,8 @@ def _mx_batch_dot(inputs, attrs):
 def _mx_arange(inputs, attrs):
     assert len(inputs) == 0
     if attrs.get_int("repeat", 1) != 1:
-        raise RuntimeError("arange doesn't support repeat")
+        raise tvm.error.OpAttributeUnimplemented(
+            'Attribute "repeat" is not supported in operator arange.')
     new_attrs = {}
     new_attrs["start"] = attrs.get_float("start", 0)
     new_attrs["stop"] = attrs.get_float("stop")
@@ -482,15 +504,20 @@ def _mx_box_nms(inputs, attrs):
     in_format = attrs.get_str('in_format', 'corner')
     out_format = attrs.get_str('out_format', 'corner')
     if coord_start != 2:
-        raise RuntimeError('coord_start %s is not supported.' % coord_start)
+        raise tvm.error.OpAttributeInvalid(
+            'Value of attribute "coord_start" must equal 2 for operator box_nms.')
     if score_index != 1:
-        raise RuntimeError('score_index %s is not supported.' % score_index)
+        raise tvm.error.OpAttributeInvalid(
+            'Value of attribute "score_index" must equal 1 for operator box_nms.')
     if id_index != -1 and int(id_index) != 0:
-        raise RuntimeError('id_index %s is not supported.' % id_index)
+        raise tvm.error.OpAttributeInvalid(
+            'Value of attribute "id_index" must equal either -1 or 0 for operator box_nms.')
     if in_format != 'corner':
-        raise RuntimeError('in_format %s is not supported.' % in_format)
+        raise tvm.error.OpAttributeInvalid(
+            'Value of attribute "in_format" must equal "corner" for operator box_nms.')
     if out_format != 'corner':
-        raise RuntimeError('out_format %s is not supported.' % out_format)
+        raise tvm.error.OpAttributeInvalid(
+            'Value of attribute "out_format" must equal "corner" for operator box_nms.')
 
     ret = _op.vision.get_valid_counts(inputs[0], score_threshold=valid_thresh)
     nms_out = _op.vision.non_max_suppression(ret[1],
@@ -508,7 +535,8 @@ def _mx_l2_normalize(inputs, attrs):
     new_attrs = {}
     mode = attrs.get_str('mode', 'instance')
     if mode != 'channel':
-        raise RuntimeError('mode %s is not supported.' % mode)
+        raise tvm.error.OpAttributeInvalid(
+            'Value of attribute "mode" must equal "channel" for operator l2_normalize.')
     new_attrs['eps'] = attrs.get_float('eps', 1e-10)
     new_attrs['axis'] = [1]
     return _op.nn.l2_normalize(inputs[0], **new_attrs)
@@ -771,7 +799,8 @@ def _from_mxnet_impl(symbol, shape_dict, dtype_info):
                 raise RuntimeError("unexpected type %s" % type(res))
             node_map[nid] = res
         else:
-            raise RuntimeError("{} is not supported in relay frontend".format(op_name))
+            raise tvm.error.OpNotImplemented(
+                'Operator {} is not supported in frontend MXNet.'.format(op_name))
 
     outputs = [node_map[e[0]][e[1]] for e in jgraph["heads"]]
     outputs = outputs[0] if len(outputs) == 1 else _expr.Tuple(outputs)

python/tvm/relay/frontend/onnx.py

@@ -3,6 +3,7 @@
 from __future__ import absolute_import as _abs
 
 import logging
+import tvm
 import numpy as np
 from ... import nd as _nd
 from .. import ir_pass
@@ -18,7 +19,9 @@ def dimension_picker(prefix, surfix=''):
         kernel = attr['kernel_shape']
         if len(kernel) == 2:
             return prefix + '2d' + surfix
-        raise NotImplementedError("Only 2d kernel supported.")
+        msg = 'Only 2D kernels are supported for operator {}.'
+        op_name = prefix + '2d'
+        raise tvm.error.OpAttributeInvalid(msg.format(op_name))
 
     return _impl
 
@@ -29,7 +32,8 @@ def revert_caffe2_pad(pads):
     elif len(pads) == 2:
         pass
     else:
-        raise ValueError("Invalid caffe2 type padding: {}".format(pads))
+        raise tvm.error.OpAttributeInvalid(
+            'Number of pads must be either 2 or 4.')
     return pads
 
 def dimension_constraint():
@@ -461,7 +465,8 @@ class Upsample(OnnxOpConverter):
         elif mode == b'linear':
             method = "BILINEAR"
         else:
-            raise ValueError("Invalid ONNX upsample mode: {}".format(mode))
+            raise tvm.error.OpAttributeInvalid(
+                'Value {} in attribute "mode" of operator Upsample is not valid.'.format(mode))
         attr = {'scale':int(scales[-1]), 'method':method, 'layout':'NCHW'}
         return AttrCvt('upsampling')(inputs, attr)
 
@@ -718,8 +723,9 @@ class ConstantFill(OnnxOpConverter):
                 shape = params[get_name(inputs[0])].asnumpy()
             else:
                 if 'extra_shape' in attr:
-                    raise ImportError(
-                        "Extra Shape not supported with fill_like")
+                    raise tvm.error.OpAttributeInvalid('Attribute "extra_shape" not '
+                                                       'supported with "fill_like" for '
+                                                       'operator ConstantFill.')
                 return _op.full_like(inputs[0], inputs[1])
 
         if 'extra_shape' in attr:

python/tvm/relay/frontend/tensorflow.py

@@ -27,7 +27,8 @@ def _get_relay_op(op_name):
             op = getattr(_op.image, op_name)
 
     if not op:
-        raise RuntimeError("Unable to map op_name {} to relay".format(op_name))
+        raise tvm.error.OpNotImplemented(
+            'Operator {} is not supported for frontend TensorFlow.'.format(op_name))
     return op
 
 class AttrCvt(object):
@@ -99,7 +100,8 @@ class AttrCvt(object):
         new_attrs = {}
         for k in attrs.keys():
             if k in self._excludes:
-                raise NotImplementedError("Attribute {} not supported yet.".format(k))
+                raise tvm.error.OpAttributeUnimplemented(
+                    'Attribute {} in operator {} is not supported.'.format(k, op_name))
             elif k in self._disables:
                 logging.warning("Attribute %s is disabled in relay.%s", k, op_name)
             elif k in self._ignores:
@@ -148,7 +150,8 @@ class AttrCvt(object):
         """Wrapper for getting required attributes."""
         assert isinstance(attr, dict)
         if key not in attr:
-            raise AttributeError("Required attribute {} not found.".format(key))
+            raise tvm.error.OpAttributeRequired(
+                'Attribute {} not found in operator {}'.format(key, self._op_name))
         return attr[key]
 
 def _get_pad_pair(input1d, kernel1d, stride1d):
@@ -178,7 +181,8 @@ def _dimension_picker(prefix, surfix=''):
         kernel = attr['kernel_shape']
         if len(kernel) == 2:
             return prefix + '2d' + surfix
-        raise NotImplementedError("Only 2d kernel supported.")
+        raise tvm.error.OpAttributeInvalid(
+            'Only 2D kernels are supported for operator {}'.format(prefix + '2d'))
     return _impl
 
 def _dimension_constraint():
@@ -238,7 +242,9 @@ def _pooling(name):
             attr['kernel_shape'] = (attr['ksize'][2], attr['ksize'][3])
             attr['strides'] = (attr['strides'][2], attr['strides'][3])
         else:
-            raise TypeError("Unsupported data_format type : {}".format(attr['data_format']))
+            msg = 'Value {} of attribute "data_format" of operator Pooling ' \
+                  'is not valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(attrs['data_format']))
 
         if attr['_target_layout'] == "NCHW" and attr['data_format'] == "NHWC":
             tmp_shape = attr['_input_shapes'][inputs[0]]
@@ -267,7 +273,9 @@ def _pooling(name):
 
             attr['padding'] = [pad_v[0], pad_h[0], pad_v[1], pad_h[1]]
         else:
-            raise TypeError("Unsupported padding type : {}".format(attr['padding']))
+            msg = 'Value {} in attribute "padding" of operator Pooling is ' \
+                  'not valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(attr['padding']))
 
         if name == "avg_pool":
             attr['count_include_pad'] = False
@@ -341,7 +349,9 @@ def _conv(opname):
                 attr['dilations'] = (attr['dilations'][2], attr['dilations'][3])
             attr['strides'] = (attr['strides'][2], attr['strides'][3])
         else:
-            raise TypeError("Unsupported data format type : {}".format(attr['data_format']))
+            msg = 'Value {} in attribute "data_format" of operator Conv is ' \
+                  'not valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(attr['data_format']))
 
 
         if opname == 'depthwise':
@@ -386,7 +396,9 @@ def _conv(opname):
             attr['padding'] = [0, 0]
 
         else:
-            raise TypeError("Unsupported padding type : {}".format(attr['padding']))
+            msg = 'Value {} in attribute "padding" of operator Conv is not ' \
+                  'valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(attr['padding']))
 
         if 'kernel_layout' not in attr:
             if opname == 'conv':
@@ -791,7 +803,8 @@ def _pad(name):
         if padlist_key in params:
             padlist = params.pop(padlist_key).asnumpy()
         else:
-            raise RuntimeError("Required parameter {} not fount.".format(padlist_key))
+            raise tvm.error.OpAttributeRequired(
+                'Attribute {} not found in operator Pad.'.format(padlist_key))
         paddings = tuple([tuple(l) for l in padlist])
         attr['pad_width'] = paddings
         attr['pad_value'] = 0

python/tvm/relay/frontend/tflite.py

@@ -3,6 +3,7 @@
 from __future__ import absolute_import as _abs
 import math
 import numpy as np
+import tvm
 from .. import ir_pass
 from .. import expr as _expr
 from .. import op as _op
@@ -59,8 +60,10 @@ class OperatorConverter(object):
                 unsupported_ops_set.add(op_code_str)
 
         if unsupported_ops_set:
-            raise NotImplementedError("Unsupported Ops: %s" % (
-                ','.join(unsupported_ops_set)))
+            msg = 'The following operators are not supported in frontend ' \
+                  'TFLite: {}'
+            ops = str(list(unsupported_ops_set)).strip('[,]')
+            raise tvm.error.OpNotImplemented(msg.format(ops))
 
     def convert_op_to_relay(self):
         """Convert TFLite ops to relay ops"""
@@ -205,8 +208,8 @@ class OperatorConverter(object):
             # finally convert back if necessary
             in_expr = _op.transpose(in_expr, axes=(0, 2, 3, 1))
         else:
-            raise NotImplementedError("Not support input shape length {} of reshape : "
-                                      .format(str(input_shape_length)))
+            msg = 'Input shape length {} for operator Reshape is not valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(input_shape_length))
 
         out = _op.reshape(in_expr, newshape=tuple(target_shape))
 
@@ -223,8 +226,8 @@ class OperatorConverter(object):
         elif len(target_shape) == 4:
             out = _op.transpose(out, axes=(0, 3, 1, 2))
         else:
-            raise NotImplementedError("Not support to reshape to shape length {}: "
-                                      .format(str(len(target_shape))))
+            raise tvm.error.OpAttributeInvalid(
+                'Length of target shape must be between 1 and 5 for operator Reshape.')
 
         return out
 
@@ -330,8 +333,8 @@ class OperatorConverter(object):
             # finally convert back if necessary
             in_expr = _op.transpose(in_expr, axes=(0, 2, 3, 1))
         else:
-            raise NotImplementedError("Not support input shape length {} of squeeze : "
-                                      .format(str(input_shape_length)))
+            msg = 'Input shape length {} for operator Squeeze is not valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(input_shape_length))
 
         out = _op.squeeze(in_expr, axis=tuple(squeeze_axis))
 
@@ -348,8 +351,8 @@ class OperatorConverter(object):
         elif output_shape_length == 4:
             out = _op.transpose(out, axes=(0, 3, 1, 2))
         else:
-            raise NotImplementedError("Not support to squeeze to length {} : "
-                                      .format(str(output_shape_length)))
+            msg = 'Output shape length {} for operator Squeeze is not valid.'
+            raise tvm.error.OpAttributeInvalid(msg.format(output_shape_length))
 
         return out
 
@@ -369,8 +372,8 @@ class OperatorConverter(object):
         if fused_activation_fn == ActivationFunctionType.TANH:
             return _op.tanh(in_expr)
         fused_activation_fn_str = self.activation_fn_type[fused_activation_fn]
-        raise NotImplementedError("Unsupported fused activation fn {}"
-                                  .format(fused_activation_fn_str))
+        raise tvm.error.OpNotImplemented(
+            'Operator {} is not supported for frontend TFLite.'.format(fused_activation_fn_str))
 
     def convert_conv(self, op, conv_type):
         """convolution implementation."""
@@ -409,7 +412,8 @@ class OperatorConverter(object):
             assert depth_multiplier == 1, "TF frontend have transformed it be 1 " \
                                           "no matter original value be set by 0.25, 0.5 or any else"
         else:
-            raise ValueError("Not support conv type: {}".format(conv_type))
+            raise tvm.error.OpNotImplemented(
+                'Operator {} is not supported for frontend TFLite.'.format(conv_type))
 
         stride_h = conv_options.StrideH()
         stride_w = conv_options.StrideW()
@@ -466,7 +470,8 @@ class OperatorConverter(object):
                                                           (pad_top, pad_bottom),
                                                           (pad_left, pad_right)))
         else:
-            raise NotImplementedError("Not support padding format: {}".format(padding))
+            raise tvm.error.OpAttributeUnimplemented(
+                'Padding format {} is not supported for operator Conv.'.format(padding))
 
         out = _op.nn.conv2d(data=in_expr, weight=weight_expr, **params)
 
@@ -529,14 +534,16 @@ class OperatorConverter(object):
             pad_left, pad_right = get_pad_value(input_w, filter_w, stride_w)
             params['padding'] = [pad_top, pad_left, pad_bottom, pad_right]
         else:
-            raise NotImplementedError("Not support padding format: {}".format(padding))
+            raise tvm.error.OpAttributeUnimplemented(
+                'Padding format {} for operator Pool2D is not supported.'.format(padding))
 
         if pool_type == "average":
             out = _op.nn.avg_pool2d(in_expr, **params)
         elif pool_type == "max":
             out = _op.nn.max_pool2d(in_expr, **params)
         else:
-            raise ValueError("Not support pool type: {}".format(pool_type))
+            raise tvm.error.OpNotImplemented(
+                'Operator {} is not supported for frontend TFLite.'.format(pool_type + ' pool'))
 
         # If we have fused activations
         if fused_activation_fn != ActivationFunctionType.NONE: