def _index_fill_reshape_helper(g, self, dim, index):
# 1. reshape index => [1, ..., 1, dim, 1, ..., 1]
# 2. expand index => [..., dim, ...], same shape as self except for dim.
# 3. expand value as well.
# 4. apply onnx::scatter.
from torch.onnx.symbolic_opset9 import expand
if _export_onnx_opset_version <= 10:
from torch.onnx.symbolic_opset9 import scatter
else:
from torch.onnx.symbolic_opset11 import scatter
if self.type().dim() is None:
return _unimplemented("index_fill", "input rank not accesible")
self_dim = self.type().dim()
dim_value = _parse_arg(dim, 'i')
unsqueezed_index = g.op(
"Unsqueeze",
index,
axes_i=[i for i in range(self_dim) if i != dim_value])
expanded_index_shape = scatter(g, g.op("Shape", self), 0,
g.op("Unsqueeze", dim, axes_i=[0]),
g.op("Shape", index))
expanded_index = expand(g, unsqueezed_index, expanded_index_shape, None)
return expanded_index_shape, expanded_index
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 chunk(g, self, chunks, dim):
# Calculate chunk size for dynamic chunk
dim_size = g.op("Gather", g.op("Shape", self), dim, axis_i=0)
chunk_size_s = g.op("Sub", chunks, g.op("Constant", value_t=torch.tensor([1], dtype=torch.long)))
chunk_size = g.op("Div", g.op("Add", dim_size, chunk_size_s), chunks)
# Create splits vector
chunk_vec = [expand(g, chunk_size, chunk_size_s, None),
g.op("Sub", dim_size, g.op("Mul", chunk_size, chunk_size_s))]
chunk_vec = g.op("Concat", *chunk_vec, axis_i=0)
return split(g, self, chunk_vec, dim)
def index_put(g, self, indices_list_value, values, accumulate=False):
indices_list = sym_help._unpack_list(indices_list_value)
if sym_help._operator_export_type == torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK:
args = [self] + indices_list + [values, accumulate]
return g.op("ATen", *args, operator_s='index_put')
from torch.onnx.symbolic_opset9 import add, expand
accumulate = sym_help._parse_arg(accumulate, 'b')
index = indices_list[0]
if len(indices_list) > 1:
for ind in indices_list[1:]:
index = add(g, index, ind)
broadcast_index_shape = g.op("Shape", index)
indices_list = [
g.op("Unsqueeze",
expand(g, ind, broadcast_index_shape, None),
axes_i=[-1]) for ind in indices_list
]
index = g.op("Concat", *indices_list, axis_i=-1)
else:
broadcast_index_shape = g.op("Shape", index)
index = g.op("Unsqueeze", index, axes_i=[-1])
sub_data_shape = sym_help._slice_helper(g,
g.op("Shape", self),
axes=[0],
starts=[len(indices_list)],
ends=[maxsize])
values_shape = g.op("Concat",
broadcast_index_shape,
sub_data_shape,
axis_i=0)
values = g.op("Reshape", values, values_shape)
if accumulate:
dtype = self.type().scalarType()
dtype = sym_help.scalar_type_to_onnx.index(
sym_help.cast_pytorch_to_onnx[dtype])
dtype = sym_help.scalar_type_to_pytorch_type[dtype]
zeros = g.op("ConstantOfShape",
g.op("Shape", self),
value_t=torch.tensor([0], dtype=dtype))
result = g.op("ScatterND", zeros, index, values)
result = add(g, self, result)
else:
result = g.op("ScatterND", self, index, values)
return result
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 index_put(g, self, indices_list_value, values, accumulate=False):
if sym_help._is_packed_list(indices_list_value):
indices_list = sym_help._unpack_list(indices_list_value)
else:
indices_list = [indices_list_value]
if sym_help._operator_export_type == torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK:
args = [self] + indices_list + [values, accumulate]
return g.op("ATen", *args, operator_s='index_put')
from torch.onnx.symbolic_opset9 import add, expand
accumulate = sym_help._parse_arg(accumulate, 'b')
if len(indices_list) == 0:
return values
index = indices_list[0]
if len(indices_list) > 1:
for ind in indices_list[1:]:
index = add(g, index, ind)
broadcast_index_shape = g.op("Shape", index)
indices_list = [
sym_help._unsqueeze_helper(
g, expand(g, ind, broadcast_index_shape, None), [-1])
for ind in indices_list
]
index = g.op("Concat", *indices_list, axis_i=-1)
else:
# Replace index_put node with masked_scatter or masked_fill
# when inputs to the index_put node contains boolean inputs
#
# index_put -> masked_fill
# * input index contains single tensor of Bool type (e.g.: %24 <- %23).
# * input value contains single element (e.g.: %18).
#
# Torch IR
# %mask : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = aten::clone(%0, %6)
# %16 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) =
# aten::to(%8, %26, %27, %11, %12, %28, %29, %15)
# %18 : Float(requires_grad=0, device=cpu) = prim::Constant[value={1}]()
# %23 : Bool(8, strides=[1], device=cpu) = aten::view(%16, %22)
# %24 : Tensor?[] = prim::ListConstruct(%23)
# %25 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) =
# aten::index_put(%mask, %24, %18, %30)
# return (%25)
#
#
# index_put -> masked_scatter
# * input index contains single tensor of Bool type (e.g.: %32 <- %31).
# * input value contains multiple elements (e.g.: %28).
#
# Torch IR
# %mask : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = aten::clone(%0, %6)
# %28 : Float(8, strides=[1], requires_grad=0, device=cpu)
# = prim::Constant[value= 1 1 1 1 1 1 1 1 [ CPUFloatType{8} ]]()
# %15 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
# = aten::ne(%mask, %some_const)
# %23 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
# = aten::to(%15, %34, %35, %18, %19, %36, %37, %22)
# %38 : Long(requires_grad=0, device=cpu) = prim::Constant[value={0}]()
# %30 : int[] = prim::Constant[value=[-1]]()
# %31 : Bool(8, strides=[1], device=cpu) = aten::view(%23, %30)
# %32 : Tensor?[] = prim::ListConstruct(%31)
# %33 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
# = aten::index_put(%mask, %32, %28, %38)
# return (%33)
bool_inp = index
if bool_inp.type() is not None and bool_inp.type().scalarType(
) == 'Bool':
rank = sym_help._get_tensor_rank(values)
if rank is not None and rank == 0:
from torch.onnx.symbolic_opset9 import masked_fill
return masked_fill(g, self, bool_inp, values)
return masked_scatter(g, self, bool_inp, values)
broadcast_index_shape = g.op("Shape", index)
index = sym_help._unsqueeze_helper(g, index, [-1])
sub_data_shape = sym_help._slice_helper(g,
g.op("Shape", self),
axes=[0],
starts=[len(indices_list)],
ends=[maxsize])
values_shape = g.op("Concat",
broadcast_index_shape,
sub_data_shape,
axis_i=0)
# Check if values is a singular value and expand accordingly
rank = sym_help._get_tensor_rank(values)
if rank is not None and rank == 0:
values = expand(g, values, values_shape, None)
values = g.op("Reshape", values, values_shape)
dtype = self.type().scalarType()
if dtype is not None and dtype != values.type().scalarType():
values = g.op("Cast",
values,
to_i=sym_help.cast_pytorch_to_onnx[dtype])
dtype = sym_help.scalar_type_to_onnx.index(
sym_help.cast_pytorch_to_onnx[dtype])
dtype = sym_help.scalar_type_to_pytorch_type[dtype]
if accumulate:
zeros = g.op("ConstantOfShape",
g.op("Shape", self),
value_t=torch.tensor([0], dtype=dtype))
result = g.op("ScatterND", zeros, index, values)
result = add(g, self, result)
else:
result = g.op("ScatterND", self, index, values)
return result
def repeat_interleave(g, self, repeats, dim=None):
from torch.onnx.symbolic_opset9 import reshape
input = self
final_dim = dim
# if dim is None flatten
# By default, use the flattened input array, and return a flat output array
if sym_help._is_none(dim):
input = reshape(g, self, g.op("Constant", value_t=torch.tensor([-1])))
dim = 0
else:
dim = sym_help._maybe_get_scalar(dim)
repeats_dim = sym_help._get_tensor_rank(repeats)
repeats_sizes = sym_help._get_tensor_sizes(repeats)
input_sizes = sym_help._get_tensor_sizes(input)
if repeats_dim is None:
raise RuntimeError(
'Unsupported: ONNX export of repeat_interleave for unknown '
'repeats rank.')
if repeats_sizes is None:
raise RuntimeError(
'Unsupported: ONNX export of repeat_interleave for unknown '
'repeats size.')
if input_sizes is None:
raise RuntimeError(
'Unsupported: ONNX export of repeat_interleave for unknown '
'input size.')
# Handle cases where dim is negative
if dim < 0:
dim += len(input_sizes)
output_sizes = input_sizes.copy()
perm_i = [0]
for idx, input_size in enumerate(input_sizes):
perm_i.append(idx + 1)
if input_size is None:
output_sizes[idx], input_sizes[idx] = 0, -1
perm_i[0], perm_i[dim] = perm_i[dim], perm_i[0]
# Cases when repeats is a single value tensor and dim has unknown input size
if (repeats_dim == 0 or
(repeats_dim == 1
and repeats_sizes[0] == 1)) and output_sizes[dim] == 0:
if not sym_help._is_tensor(repeats):
repeats = g.op("Constant", value_t=torch.LongTensor(repeats))
reps = sym_help._size_helper(g, input, dim)
reps = unsqueeze(g, reps, 0)
repeats = g.op("Expand", repeats, reps)
# There are cases when the repeats are 1-d tensor with multiple repeats, but dim
# provided along one of the dynamic axes provided. A simple example would be
# input.shape -> [1, 1, *] where * represents the dynamic axes, and dim = 2
# Now, repeat interleaving can be performed in pytorch when the value of * matches
# with the number of elements in repeat, for example if * -> 2, number of repeats
# should be 2 as well.
else:
return torch.onnx.symbolic_opset9.repeat_interleave(
g, self, repeats, final_dim)
reps_like = g.op("ConstantOfShape",
g.op("Shape", repeats),
value_t=torch.tensor([1], dtype=torch.long))
r_splits = split(g, repeats, reps_like, 0)
i_splits = split(g, input, reps_like, dim)
output_sizes[dim], input_sizes[dim] = -1, 1
# Create a loop to iterate over each value along the dimension
# and perform individual interleaving using the repeats tensor
# Loop is of the following pattern
# input (trip_count, cond)
# int trip_count = ...;
# bool cond = ...;
# for (int i=0; i < trip_count && cond; ++i) {
# cond = ...;
# }
# Loop conditions
loop_condition = g.op("Constant", value_t=torch.tensor(1))
loop_condition = g.op("Cast", loop_condition, to_i=9)
loop_len = reps
loop = g.op("Loop", loop_len, loop_condition)
# Loop inputs
loop_block = _add_block(loop.node())
block_input_iter = _add_input_to_block(loop_block)
cond = _add_input_to_block(loop_block)
r_split = loop_block.op("SequenceAt", r_splits, block_input_iter)
i_split = loop_block.op("SequenceAt", i_splits, block_input_iter)
i_split = unsqueeze(loop_block, i_split, dim + 1)
r_concat = [
loop_block.op("Constant",
value_t=torch.LongTensor(input_sizes[:dim + 1])),
r_split,
loop_block.op("Constant",
value_t=torch.LongTensor(input_sizes[dim + 1:]))
]
r_concat = loop_block.op("Concat", *r_concat, axis_i=0)
i_split = expand(loop_block, i_split, r_concat, None)
i_split = reshape(loop_block, i_split,
g.op("Constant", value_t=torch.LongTensor(output_sizes)))
# Loop outputs
cond_out = loop_block.op("Cast", loop_condition, to_i=9)
_add_output_to_block(loop_block, cond_out)
_add_output_to_block(loop_block, i_split)
loop_out = loop.node().output()
# In this loop, the outputs are scan outputs and are concatenated along
# the zero'th dimension (by default). In order to avoid this and concatenate
# along the dimension provided, some post-processing is required
loop_out = g.op("Transpose", loop_out, perm_i=perm_i)
return reshape(g, loop_out,
g.op("Constant", value_t=torch.LongTensor(output_sizes)))
def repeat_interleave(g, self, repeats, dim=None, output_size=None):
input = self
final_dim = dim
# if dim is None flatten
# By default, use the flattened input array, and return a flat output array
if sym_help._is_none(dim):
input = sym_help._reshape_helper(
g, self, g.op("Constant", value_t=torch.tensor([-1])))
dim = 0
else:
dim = sym_help._maybe_get_scalar(dim)
repeats_dim = sym_help._get_tensor_rank(repeats)
repeats_sizes = sym_help._get_tensor_sizes(repeats)
input_sizes = sym_help._get_tensor_sizes(input)
if repeats_dim is None:
raise RuntimeError(
"Unsupported: ONNX export of repeat_interleave for unknown "
"repeats rank.")
if repeats_sizes is None:
raise RuntimeError(
"Unsupported: ONNX export of repeat_interleave for unknown "
"repeats size.")
if input_sizes is None:
raise RuntimeError(
"Unsupported: ONNX export of repeat_interleave for unknown "
"input size.")
# Handle cases where dim is negative
if dim < 0:
dim += len(input_sizes)
output_sizes = input_sizes.copy()
for idx, input_size in enumerate(input_sizes):
if input_size is None:
output_sizes[idx], input_sizes[idx] = 0, -1
print(output_sizes, input_sizes)
cond_dynamic_repeats = (repeats_dim == 1 and repeats_sizes[0] is None)
# If input size is dynamic or repeats vector is dynamic
if output_sizes[dim] == 0 or cond_dynamic_repeats:
reps = sym_help._size_helper(g, input, dim)
reps = unsqueeze(g, reps, 0)
# Check if repeats vector is a single integer value
# or a single dimension tensor with non-dynamic values
if repeats_dim == 0 or (repeats_dim == 1 and repeats_sizes[0] == 1):
if not sym_help._is_tensor(repeats):
repeats = g.op("Constant", value_t=torch.LongTensor(repeats))
repeats = g.op("Expand", repeats, reps)
# Check if repeats is dynamic
# As repeats is dynamic, we use a where node as a substitute for the if statement
# If repests_dim = 1, expand repeats otherwise use original tensor
elif cond_dynamic_repeats:
repeat_dim = sym_help._size_helper(
g, repeats, g.op("Constant", value_t=torch.LongTensor([0])))
repeat_cond = g.op("Equal", repeat_dim,
g.op("Constant", value_t=torch.LongTensor([1])))
repeats = where(g, repeat_cond, g.op("Expand", repeats, reps),
repeats)
# There are cases when the repeats are 1-d tensor with multiple repeats, but dim
# provided along one of the dynamic axes provided. A simple example would be
# input.shape -> [1, 1, *] where * represents the dynamic axes, and dim = 2
# Now, repeat interleaving can be performed in pytorch when the value of * matches
# with the number of elements in repeat, for example if * -> 2, number of repeats
# should be 2 as well.
else:
return torch.onnx.symbolic_opset9.repeat_interleave(
g, self, repeats, final_dim)
reps_like = g.op("ConstantOfShape",
g.op("Shape", repeats),
value_t=torch.tensor([1], dtype=torch.long))
r_splits = split(g, repeats, reps_like, 0)
i_splits = split(g, input, reps_like, dim)
output_sizes[dim], input_sizes[dim] = -1, 1
# Create a loop to iterate over each value along the dimension
# and perform individual interleaving using the repeats tensor
# Loop is of the following pattern
# input (trip_count, cond)
# int trip_count = ...;
# bool cond = ...;
# for (int i=0; i < trip_count && cond; ++i) {
# cond = ...;
# }
# Loop conditions
loop_condition = g.op("Constant", value_t=torch.tensor(1))
loop_condition = g.op("Cast", loop_condition, to_i=9)
loop_len = reps
# Create an empty sequence to store final expansions
final_splits = g.op("SequenceEmpty")
loop = g.op("Loop", loop_len, loop_condition, final_splits)
# Loop inputs
loop_block = _add_block(loop.node())
block_input_iter = _add_input_to_block(loop_block)
cond = _add_input_to_block(loop_block)
final_splits = _add_input_to_block(loop_block)
r_split = loop_block.op("SequenceAt", r_splits, block_input_iter)
i_split = loop_block.op("SequenceAt", i_splits, block_input_iter)
i_split = unsqueeze(loop_block, i_split, dim + 1)
r_concat = [
loop_block.op("Constant",
value_t=torch.LongTensor(input_sizes[:dim + 1])),
r_split,
loop_block.op("Constant",
value_t=torch.LongTensor(input_sizes[dim + 1:]))
]
r_concat = loop_block.op("Concat", *r_concat, axis_i=0)
i_split = expand(loop_block, i_split, r_concat, None)
i_split = sym_help._reshape_helper(
loop_block, i_split,
g.op("Constant", value_t=torch.LongTensor(output_sizes)))
final_splits = loop_block.op("SequenceInsert", final_splits, i_split)
# Loop outputs
cond_out = loop_block.op("Cast", loop_condition, to_i=9)
_add_output_to_block(loop_block, cond_out)
_add_output_to_block(loop_block, final_splits)
loop_out = loop.node().output()
loop_out = g.op("ConcatFromSequence", loop_out, axis_i=dim)
return loop_out
def index_put(g, self, indices_list_value, values, accumulate=False):
indices_list = sym_help._unpack_list(indices_list_value)
if sym_help._operator_export_type == torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK:
args = [self] + indices_list + [values, accumulate]
return g.op("ATen", *args, operator_s='index_put')
from torch.onnx.symbolic_opset9 import add, expand
accumulate = sym_help._parse_arg(accumulate, 'b')
index = indices_list[0]
if len(indices_list) > 1:
for ind in indices_list[1:]:
index = add(g, index, ind)
broadcast_index_shape = g.op("Shape", index)
indices_list = [
sym_help._unsqueeze_helper(
g, expand(g, ind, broadcast_index_shape, None), [-1])
for ind in indices_list
]
index = g.op("Concat", *indices_list, axis_i=-1)
else:
# Replace index_put node with masked_scatter or masked_fill
# when inputs to the index_put node contains boolean inputs
#
# index_put -> masked_fill
#
# before graph(%0 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=1, device=cpu),
# %some_const : Float(requires_grad=0, device=cpu)):
# %6 : None = prim::Constant()
# %mask : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = aten::clone(%0, %6)
# %8 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = aten::ne(%mask, %some_const)
# %26 : Long(requires_grad=0, device=cpu) = prim::Constant[value={11}]()
# %27 : Long(requires_grad=0, device=cpu) = prim::Constant[value={0}]()
# %11 : Device = prim::Constant[value="cpu"]()
# %12 : None = prim::Constant()
# %28 : Long(requires_grad=0, device=cpu) = prim::Constant[value={0}]()
# %29 : Long(requires_grad=0, device=cpu) = prim::Constant[value={0}]()
# %15 : None = prim::Constant()
# %16 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) =
# aten::to(%8, %26, %27, %11, %12, %28, %29, %15)
# %18 : Float(requires_grad=0, device=cpu) = prim::Constant[value={1}]()
# %30 : Long(requires_grad=0, device=cpu) = prim::Constant[value={0}]()
# %22 : int[] = prim::Constant[value=[-1]]()
# %23 : Tensor = aten::view(%16, %22)
# %24 : Tensor?[] = prim::ListConstruct(%23)
# %25 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) =
# aten::index_put(%mask, %24, %18, %30)
# return (%25)
#
# after graph(%0 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu),
# %some_const : Float(requires_grad=0, device=cpu)):
# %3 : Tensor = onnx::Equal(%0, %some_const)
# %4 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = onnx::Not(%3)
# %12 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = onnx::Cast[to=9](%4)
# %19 : Tensor = onnx::Cast[to=9](%12)
# %20 : Tensor = onnx::Constant[value={1}]()
# %21 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
# = onnx::Where(%19, %20, %0)
# return (%21)
#
# index_put -> masked_scatter
#
# before graph(%0 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=1, device=cpu),
# %some_const : Float(requires_grad=0, device=cpu)):
# %6 : None = prim::Constant()
# %mask : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = aten::clone(%0, %6)
# %28 : Float(8, strides=[1], requires_grad=0, device=cpu)
# = prim::Constant[value= 1 1 1 1 1 1 1 1 [ CPUFloatType{8} ]]()
# %15 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
# = aten::ne(%mask, %some_const)
# %34 : Long(requires_grad=0, device=cpu) = prim::Constant[value={11}]()
# %35 : Long(requires_grad=0, device=cpu) = prim::Constant[value={0}]()
# %18 : Device = prim::Constant[value="cpu"]()
# %19 : None = prim::Constant()
# %36 : Long(requires_grad=0, device=cpu) = prim::Constant[value={0}]()
# %37 : Long(requires_grad=0, device=cpu) = prim::Constant[value={0}]()
# %22 : None = prim::Constant()
# %23 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
# = aten::to(%15, %34, %35, %18, %19, %36, %37, %22)
# %38 : Long(requires_grad=0, device=cpu) = prim::Constant[value={0}]()
# %30 : int[] = prim::Constant[value=[-1]]()
# %31 : Tensor = aten::view(%23, %30)
# %32 : Tensor?[] = prim::ListConstruct(%31)
# %33 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
# = aten::index_put(%mask, %32, %28, %38)
# return (%33)
#
# after graph(%0 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu),
# %some_const : Float(requires_grad=0, device=cpu)):
# %3 : Float(8, strides=[1], requires_grad=0, device=cpu)
# = onnx::Constant[value= 1 1 1 1 1 1 1 1 [ CPUFloatType{8} ]]()
# %4 : Tensor = onnx::Equal(%0, %some_const)
# %5 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = onnx::Not(%4)
# %13 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = onnx::Cast[to=9](%5)
# %19 : Tensor = onnx::Shape(%0)
# %20 : Tensor = onnx::Expand(%13, %19)
# %21 : Tensor = onnx::NonZero(%20)
# %22 : Tensor = onnx::Transpose[perm=[1, 0]](%21)
# %23 : Tensor = onnx::Constant[value={-1}]()
# %24 : Tensor = onnx::Reshape(%3, %23)
# %25 : Tensor = onnx::Shape(%22)
# %27 : Tensor = onnx::Constant[value={0}]()
# %28 : Tensor = onnx::Gather[axis=0](%25, %27)
# %29 : Tensor = onnx::Constant[value={0}]()
# %30 : Tensor = onnx::Unsqueeze[axes=[0]](%29)
# %31 : Tensor = onnx::Unsqueeze[axes=[0]](%28)
# %32 : Tensor = onnx::Constant[value={0}]()
# %33 : Tensor = onnx::Unsqueeze[axes=[0]](%32)
# %34 : Tensor = onnx::Slice(%24, %30, %31, %33)
# %35 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
# = onnx::ScatterND(%0, %22, %34)
# return (%35)
bool_inp = list(index.node().inputs())[0]
if bool_inp.type() is not None and bool_inp.type().scalarType(
) == 'Bool':
rank = sym_help._get_tensor_rank(values)
if rank is not None and rank == 0:
from torch.onnx.symbolic_opset9 import masked_fill
return masked_fill(g, self, bool_inp, values)
return masked_scatter(g, self, bool_inp, values)
broadcast_index_shape = g.op("Shape", index)
index = sym_help._unsqueeze_helper(g, index, [-1])
sub_data_shape = sym_help._slice_helper(g,
g.op("Shape", self),
axes=[0],
starts=[len(indices_list)],
ends=[maxsize])
values_shape = g.op("Concat",
broadcast_index_shape,
sub_data_shape,
axis_i=0)
values = g.op("Reshape", values, values_shape)
if accumulate:
dtype = self.type().scalarType()
dtype = sym_help.scalar_type_to_onnx.index(
sym_help.cast_pytorch_to_onnx[dtype])
dtype = sym_help.scalar_type_to_pytorch_type[dtype]
zeros = g.op("ConstantOfShape",
g.op("Shape", self),
value_t=torch.tensor([0], dtype=dtype))
result = g.op("ScatterND", zeros, index, values)
result = add(g, self, result)
else:
result = g.op("ScatterND", self, index, values)
return result
def index_put(g, self, indices_list_value, values, accumulate=False):
if symbolic_helper._is_packed_list(indices_list_value):
indices_list = symbolic_helper._unpack_list(indices_list_value)
else:
indices_list = [indices_list_value]
if symbolic_helper.is_caffe2_aten_fallback():
args = [self] + indices_list + [values, accumulate]
return g.at("index_put", *args)
accumulate = symbolic_helper._parse_arg(accumulate, "b")
if len(indices_list) == 0:
return values
if len(indices_list) > 1:
for idx_ in range(len(indices_list)):
if indices_list[idx_].type().scalarType() == "Bool": # type: ignore[attr-defined]
# TODO(justinchuby): Remove type ignore after #81112 is checked in.
indices_list[idx_] = g.op("NonZero", indices_list[idx_])
index = indices_list[0]
for ind in indices_list[1:]:
index = opset9.add(g, index, ind)
broadcast_index_shape = g.op("Shape", index)
indices_list = [
symbolic_helper._unsqueeze_helper(
g, opset9.expand(g, ind, broadcast_index_shape, None), [-1]
)
for ind in indices_list
]
index = g.op("Concat", *indices_list, axis_i=-1)
else:
# Replace index_put node with masked_scatter or masked_fill
# when inputs to the index_put node contains a single boolean input.
#
# index_put -> masked_fill
# * input index contains single tensor of Bool type (e.g.: %24 <- %23).
# * input value contains single element (e.g.: %18).
#
# Torch IR
# %mask : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = aten::clone(%0, %6)
# %16 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) =
# aten::to(%8, %26, %27, %11, %12, %28, %29, %15)
# %18 : Float(requires_grad=0, device=cpu) = prim::Constant[value={1}]()
# %23 : Bool(8, strides=[1], device=cpu) = aten::view(%16, %22)
# %24 : Tensor?[] = prim::ListConstruct(%23)
# %25 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) =
# aten::index_put(%mask, %24, %18, %30)
# return (%25)
#
#
# index_put -> masked_scatter
# * input index contains single tensor of Bool type (e.g.: %32 <- %31).
# * input value contains multiple elements (e.g.: %28).
#
# Torch IR
# %mask : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu) = aten::clone(%0, %6)
# %28 : Float(8, strides=[1], requires_grad=0, device=cpu)
# = prim::Constant[value= 1 1 1 1 1 1 1 1 [ CPUFloatType{8} ]]()
# %15 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
# = aten::ne(%mask, %some_const)
# %23 : Bool(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
# = aten::to(%15, %34, %35, %18, %19, %36, %37, %22)
# %38 : Long(requires_grad=0, device=cpu) = prim::Constant[value={0}]()
# %30 : int[] = prim::Constant[value=[-1]]()
# %31 : Bool(8, strides=[1], device=cpu) = aten::view(%23, %30)
# %32 : Tensor?[] = prim::ListConstruct(%31)
# %33 : Float(2, 2, 2, strides=[4, 2, 1], requires_grad=0, device=cpu)
# = aten::index_put(%mask, %32, %28, %38)
# return (%33)
index = indices_list[0]
bool_inp = index
if bool_inp.type() is not None and bool_inp.type().scalarType() == "Bool": # type: ignore[attr-defined]
# TODO(justinchuby): Remove type ignore after #81112 is checked in.
rank = symbolic_helper._get_tensor_rank(values)
if rank is not None and rank == 0:
return opset9.masked_fill(g, self, bool_inp, values)
return masked_scatter(g, self, bool_inp, values)
broadcast_index_shape = g.op("Shape", index)
index = symbolic_helper._unsqueeze_helper(g, index, [-1])
sub_data_shape = symbolic_helper._slice_helper(
g, g.op("Shape", self), axes=[0], starts=[len(indices_list)], ends=[sys.maxsize]
)
values_shape = g.op("Concat", broadcast_index_shape, sub_data_shape, axis_i=0)
# Check if values is a singular value and expand accordingly
rank = symbolic_helper._get_tensor_rank(values)
if rank is not None and rank == 0:
values = opset9.expand(g, values, values_shape, None)
values = symbolic_helper._reshape_helper(g, values, values_shape)
dtype = self.type().scalarType()
if dtype is not None and dtype != values.type().scalarType():
values = g.op("Cast", values, to_i=symbolic_helper.cast_pytorch_to_onnx[dtype])
dtype = symbolic_helper.scalar_type_to_onnx.index(
symbolic_helper.cast_pytorch_to_onnx[dtype]
)
dtype = symbolic_helper.scalar_type_to_pytorch_type[dtype]
if accumulate:
zeros = g.op(
"ConstantOfShape",
g.op("Shape", self),
value_t=torch.tensor([0], dtype=dtype),
)
result = g.op("ScatterND", zeros, index, values)
result = add(g, self, result)
else:
result = g.op("ScatterND", self, index, values)
return result
