[RELAY][MXNET][FRONTEND] add support for MXNET numpy operators (apach…

…e#6054)

* [RELAY][MXNET][FRONTEND] add supports for OPs in numpy from mxnet

* Update test_forward.py

* Update mxnet.py

* Update mxnet.py

* Update test_forward.py

* update and bugfix

* test for multiple dtypes

* Update test_forward.py

* add data type and optimize coding style

* replace pytest.skip with @pytest.mark.skipif

* Update test_forward.py

* update pytest style

* Update test_forward.py

* Update test_forward.py

* Update test_forward.py

* Update test_forward.py

Co-authored-by: Ubuntu <ubuntu@ip-172-31-39-169.ap-northeast-1.compute.internal>

python/tvm/relay/frontend/mxnet.py

@@ -2166,6 +2166,93 @@ def impl(inputs, input_types):
     return impl
 
 
+def _mx_npi_transpose(inputs, attrs):
+    axes = attrs.get_int_tuple("axes", None)
+    # translate default case
+    axes = None if len(axes) == 0 or axes[0] is None else axes
+    return _op.transpose(inputs[0], axes=axes)
+
+
+def _mx_npi_pad(inputs, attrs):
+    pad_mode = attrs.get_str('mode', None)
+    if pad_mode is None:
+        raise tvm.error.OpAttributeRequired(
+            'Attribute "mode" not found in operator pad.')
+    if pad_mode not in ['constant', 'edge', 'reflect']:
+        raise tvm.error.OpAttributeInvalid(
+            'Value ' + mode + ' in attribute "mode" is not valid')
+    pad_width = attrs.get_int_tuple('pad_width', None)
+    if pad_width is None:
+        raise tvm.error.OpAttributeRequired(
+            'Attribute "pad_width" not found in operator pad.')
+    if None in pad_width:
+        raise tvm.error.OpAttributeInvalid(
+            'Value None in attribute "pad_width" of operator Slice is not valid.')
+    constant_values = attrs.get_float('constant_values', 0.0)
+    padding = tuple(tuple((b, a)) for b, a in zip(pad_width[::2], pad_width[1::2]))
+
+    return _op.nn.pad(data=inputs[0],
+                      pad_width=padding,
+                      pad_value=constant_values,
+                      pad_mode=pad_mode)
+
+
+def _mx_npi_concatenate(inputs, attrs):
+    axis = attrs.get_str("axis", "0")
+    if axis == "None":
+        return _op.reshape(_op.concatenate(tuple(inputs), axis=0), (-1,))
+    else:
+        return _op.concatenate(tuple(inputs), axis=int(axis))
+
+
+def _mx_npx_reshape(inputs, attrs):
+    shape = attrs.get_int_tuple("newshape")
+    reverse = attrs.get_bool("reverse", False)
+    shape_list = list(shape)
+    new_shape_list = []
+    for num in shape_list:
+        if num > 0 or num == -1:
+            new_shape_list.append(num)
+        elif num == -2:
+            new_shape_list.append(0)
+        elif num == -4:
+            new_shape_list.append(-2)
+        elif num == -5:
+            new_shape_list.append(-3)
+        elif num == -6:
+            new_shape_list.append(-4)
+        else:
+            raise tvm.error.OpAttributeInvalid(
+                'Shape dimension %d is not supported' % num)
+    shape = tuple(new_shape_list)
+    if reverse:
+        return _op.reverse_reshape(inputs[0], newshape=shape)
+    return _op.reshape(inputs[0], newshape=shape)
+
+
+def _mx_split_v2(inputs, attrs):
+    axis = attrs.get_int("axis")
+    indices = list(attrs.get_int_tuple("indices", []))
+    # remove the prefix '0'
+    if len(indices) != 0 and indices[0] == 0:
+        indices.remove(0)
+    sections = attrs.get_int("sections", 0)
+    indices_or_sections = list(indices) if len(indices) != 0 else sections
+    res = _op.split(inputs[0], indices_or_sections=indices_or_sections, axis=axis)
+    if attrs.get_bool("squeeze_axis", False):
+        res = tuple([_op.squeeze(x, axis=[axis]) for x in res])
+    return res
+
+
+def _mx_npi_where_rscalar(inputs, attrs):
+    scalar = attrs.get_float("scalar")
+    dtype = _infer_type(inputs[1]).checked_type.dtype
+    scalar = _expr.const(scalar, dtype=dtype)
+    ones = _op.ones_like(inputs[1])
+    scalar = _op.multiply(ones, scalar)
+    return _op.where(inputs[0], inputs[1], scalar)
+
+
 # Note: due to attribute conversion constraint
 # ops in the identity set must be attribute free
 _identity_list = [
@@ -2322,6 +2409,7 @@ def impl(inputs, input_types):
     "slice_axis"    : _mx_slice_axis,
     "SliceChannel"  : _mx_split,
     "split"         : _mx_split,
+    "_split_v2"     : _mx_split_v2,
     "SwapAxis"      : _mx_swap_axis,
     "expand_dims"   : _mx_expand_dims,
     "Concat"        : _mx_concat,
@@ -2400,6 +2488,23 @@ def impl(inputs, input_types):
     "_contrib_quantized_pooling": _qnn_pooling,
     "_contrib_quantized_batch_norm" : _qnn_batch_norm,
     "_sg_mkldnn_fully_connected": _qnn_fully_connected,
+    # numpy
+    "_np_transpose"     : _mx_npi_transpose,
+    "_npi_transpose"    : _mx_npi_transpose,
+    "_npi_pad"          : _mx_npi_pad,
+    "_npi_concatenate"  : _mx_npi_concatenate,
+    "_npx_reshape"      : _mx_npx_reshape,
+    "_np_copy"          : _rename(_op.copy),
+    "_npi_power"              : _rename(_op.power),
+    "_npi_power_scalar"       : _binop_scalar(_op.power),
+    "_npi_multiply"           : _rename(_op.multiply),
+    "_npi_multiply_scalar"    : _binop_scalar(_op.multiply),
+    "_npi_add"                : _rename(_op.add),
+    "_npi_add_scalar"         : _binop_scalar(_op.add),
+    "_npi_where_rscalar"      : _mx_npi_where_rscalar,
+    "_npi_less"               : _rename(_op.less),
+    "_npi_tanh"               : _rename(_op.tanh),
+    "_npi_true_divide_scalar" : _binop_scalar(_op.divide),
 }
 
 # set identity list