def index_fill(g, self, dim, index, value):
dim_value = sym_help._parse_arg(dim, 'i')
if sym_help._operator_export_type == torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK:
return g.op("ATen", self, index, value, dim_i=dim_value, operator_s="index_fill")
expanded_index_shape, expanded_index = sym_help._index_fill_reshape_helper(g, self, dim, index)
value = sym_help._maybe_get_scalar(value)
value = sym_help._if_scalar_type_as(g, value, self)
expanded_value = expand(g, value, expanded_index_shape, None)
return scatter(g, self, dim, expanded_index, expanded_value)
def index_fill(g, self, dim, index, value):
dim_value = sym_help._parse_arg(dim, "i")
if sym_help.is_caffe2_aten_fallback():
return g.at("index_fill",
self,
index,
value,
overload_name="int_Scalar",
dim_i=dim_value)
expanded_index_shape, expanded_index = sym_help._index_fill_reshape_helper(
g, self, dim, index)
value = sym_help._maybe_get_scalar(value)
value = sym_help._if_scalar_type_as(g, value, self)
expanded_value = expand(g, value, expanded_index_shape, None)
return scatter(g, self, dim, expanded_index, expanded_value)
def addcmul_symbolic(g, self, tensor1, tensor2, value=1, out=None):
from torch.onnx.symbolic_opset9 import add, mul
if out is not None:
sym_help._unimplemented("addcmul",
"Out parameter is not supported for addcmul")
x = mul(g, tensor1, tensor2)
value = sym_help._maybe_get_scalar(value)
if sym_help._scalar(value) != 1:
value = sym_help._if_scalar_type_as(g, value, x)
if not sym_help._is_value(value):
value = g.op("Constant",
value_t=torch.tensor(value, dtype=torch.float32))
x = mul(g, x, value)
return add(g, self, x)
def constant_pad_nd(g, input, padding, value=None):
mode = "constant"
value = sym_help._maybe_get_scalar(value)
value = sym_help._if_scalar_type_as(g, value, input)
pad = _prepare_onnx_paddings(g, sym_help._get_tensor_rank(input), padding)
return g.op("Pad", input, pad, value, mode_s=mode)
def _comparison_operator(g, input, other, op_name):
other = sym_help._maybe_get_scalar(other)
other = sym_help._if_scalar_type_as(g, other, input)
_, input, other = _try_cast_integer_to_float(g, input, other)
return g.op(op_name, input, other)
