def unfold(g, input, dimension, size, step):
const_size = sym_help._maybe_get_const(size, "i")
const_step = sym_help._maybe_get_const(step, "i")
if not sym_help._is_value(const_size) and not sym_help._is_value(const_step):
from torch.onnx.symbolic_opset9 import unfold as _unfold
return _unfold(g, input, dimension, const_size, const_step)
if sym_help._operator_export_type == torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK:
return g.op("ATen", input, operator_s="unfold", dimension_i=dimension, size_i=size, step_i=step)
sizedim = sym_help._get_tensor_dim_size(input, dimension)
if sizedim is not None:
low_start = g.op("Constant", value_t=torch.tensor(0))
low_end = g.op("Constant", value_t=torch.tensor(sizedim))
hi_end = g.op("Constant", value_t=torch.tensor(sizedim + 1))
low_indices = g.op("Range", low_start, low_end, step)
hi_indices = g.op("Range", size, hi_end, step)
low_size = sym_help._size_helper(g, low_indices, g.op("Constant", value_t=torch.tensor(0)))
hi_size = sym_help._size_helper(g, hi_indices, g.op("Constant", value_t=torch.tensor(0)))
ndim = sym_help._get_tensor_rank(input)
perm = list(range(0, ndim))
perm.append(perm.pop(dimension))
unsqueeze_list = []
loop_condition = g.op("Constant", value_t=torch.tensor(1))
loop_condition = g.op("Cast", loop_condition, to_i=9)
loop_len = g.op("Min", low_size, hi_size)
loop = g.op("Loop", loop_len, loop_condition)
loop_block = _add_block(loop.node())
block_input_iter = _add_input_to_block(loop_block)
cond = _add_input_to_block(loop_block)
starts = loop_block.op("Gather", low_indices, block_input_iter)
ends = loop_block.op("Gather", hi_indices, block_input_iter)
axes = loop_block.op("Constant", value_t=torch.tensor([2]))
starts = sym_help._unsqueeze_helper(loop_block, starts, [0])
ends = sym_help._unsqueeze_helper(loop_block, ends, [0])
stack = loop_block.op("Slice", input, starts, ends, axes)
unsqueeze = sym_help._unsqueeze_helper(loop_block, loop_block.op("Transpose", stack, perm_i=perm), [dimension])
unsqueeze_list.append(unsqueeze)
concat = loop_block.op("Concat", *unsqueeze_list, axis_i=0)
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, concat)
loop_output = loop.node().output()
perm = [0, 1, 2, 3, 4]
perm[0], perm[dimension + 1] = perm[dimension + 1], perm[0]
transpose = g.op("Transpose", loop_output, perm_i=perm)
squeeze = sym_help._squeeze_helper(g, transpose, [0])
return squeeze
else:
return _unimplemented("Unfold", "input size not accessible")
def squeeze(g, self, dim=None):
if dim is None:
return g.op("Squeeze", self)
# dim as a tensor
if not symbolic_helper._is_constant(dim):
return symbolic_helper._squeeze_helper(g, self, [dim])
dim = symbolic_helper._get_const(dim, "i", "dim")
input_rank = symbolic_helper._get_tensor_rank(self)
adjusted_dim = dim
if input_rank is not None and dim < 0:
adjusted_dim += input_rank
dim_size = symbolic_helper._get_tensor_dim_size(self, adjusted_dim)
if (dim < 0 and input_rank is None) or dim_size is None:
# If onnx shape inference is not on, export always as dynamic.
# Because we cannot tell if observed static shape is also static at runtime.
# create "cond" node (condition is shape[i]==1)
dim_constant = g.op("Constant", value_t=torch.tensor([dim]))
size = symbolic_helper._size_helper(g, self, dim_constant)
const_one = g.op("Constant", value_t=torch.ones(1, dtype=torch.int64))
cond = g.op("Equal", size, const_one)
# create the "If" node and add the "then" and "else" blocks to it.
if_node_outputs = g.op("If", cond)
if_node = if_node_outputs.node()
if_block = utils._add_block(if_node)
squeeze_ = symbolic_helper._squeeze_helper(if_block, self, [dim])
utils._add_output_to_block(if_block, squeeze_)
else_block = utils._add_block(if_node)
identity_ = else_block.op("Identity", self)
utils._add_output_to_block(else_block, identity_)
return if_node_outputs
# For static input shape
dim = adjusted_dim
if dim_size > 1:
warnings.warn(
"This model contains a squeeze operation on dimension "
+ str(dim)
+ ". The size of "
+ "this dimension in the given input is "
+ str(dim_size)
+ ". The model will "
+ "be exported without the squeeze node. If the model is intended to be used with dynamic "
+ "input shapes, please export with dynamic_axes argument."
)
return self
return symbolic_helper._squeeze_helper(g, self, [dim])
def embedding_bag(g, embedding_matrix, indices, offsets, scale_grad_by_freq,
mode, sparse, per_sample_weights, include_last_offset,
padding_idx):
if scale_grad_by_freq and sym_help._training_mode:
return sym_help._onnx_unsupported(
'embedding_bag with scale_grad_by_freq for training mode')
if padding_idx is not None and padding_idx >= 0:
raise RuntimeError('embedding_bag with padding_idx')
loop_condition = g.op("Constant", value_t=torch.tensor(1))
loop_condition = g.op("Cast", loop_condition, to_i=9)
zero = g.op("Constant", value_t=torch.tensor([0]))
indices_len = sym_help._unsqueeze_helper(
g,
sym_help._size_helper(g, indices,
g.op("Constant", value_t=torch.tensor(0))), [0])
if not include_last_offset:
offsets = [offsets, indices_len]
offsets = g.op("Concat", *offsets, axis_i=0)
# Offsets holds the starting index position of each bag. So we create a list of the indices slices (determined by
# offsets) and gather those indices in indices_row. Then we use this subset of indices to gather from embeddings.
# The embeddings output is a loop scan output, so we can avoid creating a sequence and inserting elements in.
offsets_starts = sym_help._slice_helper(g,
offsets,
axes=[0],
starts=[0],
ends=[maxsize],
steps=[1])
offsets_ends = sym_help._slice_helper(g,
offsets,
axes=[0],
starts=[1],
ends=[maxsize],
steps=[1])
loop_len = sym_help._size_helper(g, offsets_ends,
g.op("Constant", value_t=torch.tensor(0)))
loop = g.op("Loop", loop_len, loop_condition)
loop_block = _add_block(loop.node())
block_input_iter = _add_input_to_block(loop_block)
cond = _add_input_to_block(loop_block)
indices_start = loop_block.op("Gather",
offsets_starts,
block_input_iter,
axis_i=0)
indices_end = loop_block.op("Gather",
offsets_ends,
block_input_iter,
axis_i=0)
indices_start = sym_help._unsqueeze_helper(loop_block, indices_start, [0])
indices_end = sym_help._unsqueeze_helper(loop_block, indices_end, [0])
indices_row = loop_block.op("Slice", indices, indices_start, indices_end,
zero)
embeddings = loop_block.op("Gather",
embedding_matrix,
indices_row,
axis_i=0)
if not sym_help._is_none(per_sample_weights):
per_sample_weights_row = loop_block.op("Slice", per_sample_weights,
indices_start, indices_end,
zero)
per_sample_weights_row = sym_help._unsqueeze_helper(
loop_block, per_sample_weights_row, [1])
embeddings = loop_block.op("Mul", embeddings, per_sample_weights_row)
if mode == 0:
embeddings = sym_help._reducesum_helper(loop_block,
embeddings,
axes_i=[0],
keepdims_i=0)
elif mode == 1:
embeddings = loop_block.op("ReduceMean",
embeddings,
axes_i=[0],
keepdims_i=0)
else:
embeddings = loop_block.op("ReduceMax",
embeddings,
axes_i=[0],
keepdims_i=0)
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, embeddings)
# aten::embedding_bag returns a tuple of 4 elements: output, offset2bag, bag_size, max_indices.
# But the last three outputs are not used in torch.nn.EmbeddingBag or torch.nn.functional.embedding_bag.
return loop.node().output(), None, None, None
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 diagonal(g, self, offset, dim1, dim2):
dim1_size = opset9.size(
g, self, dim=g.op("Constant", value_t=torch.LongTensor([dim1]))
)
dim2_size = opset9.size(
g, self, dim=g.op("Constant", value_t=torch.LongTensor([dim2]))
)
# Create appropriate mask
mask_shape = g.op("Concat", dim1_size, dim2_size, axis_i=0)
mask = opset9.zeros(g, mask_shape, None, None, None)
mask = g.op("EyeLike", mask, k_i=offset)
# dim1 and dim2 appended as a dimension at the end of the shape
rank = symbolic_helper._get_tensor_rank(self)
if rank is not None:
axes = list(range(rank))
axes.remove(dim1)
axes.remove(dim2)
self = g.op("Transpose", self, perm_i=axes + [dim1, dim2])
else:
return symbolic_helper._unimplemented("diagonal", "unknown input rank")
# Multiply input and mask to calculate values along diagonal
# The mask consists of one values where diagonal values are to be calculated
# For example:
# [[1.1, 1.2, 1.3], * [[1, 0, 0] = [[1.1, 0, 0],
# [2.1, 2.2, 2.3], [0, 1, 0] [0, 2.2, 0],
# [3.1, 3.2, 3.3]] [0, 0, 1]] [0, 0, 3.3]]
result = g.op("Mul", self, mask)
result = symbolic_helper._reducesum_helper(g, result, axes_i=[-1], keepdims_i=0)
# Calculate gather indices based on offset and dims
# If offset is greater than zero, set offset to zero as this aids in
# calculation of selection window
offset_op = g.op("Constant", value_t=torch.LongTensor([offset]))
if offset >= 0:
diag_size = g.op(
"Max",
g.op("Min", dim1_size, g.op("Sub", dim2_size, offset_op)),
g.op("Constant", value_t=torch.LongTensor([0])),
)
offset = 0
else:
diag_size = g.op(
"Max",
g.op("Min", g.op("Add", dim1_size, offset_op), dim2_size),
g.op("Constant", value_t=torch.LongTensor([0])),
)
diag_size = g.op("Concat", diag_size, axis_i=0)
# Calculate which diagonal values to select
# For example, in cases with offsets:
# [[0, 1.1, 0]
# [0, 0, 2.2]]
# we need to select the last two columns, so we create a tensor
# with all columns that are to be selected
# So in this example, it is [1, 2]
select_window_ones_fill = opset9.ones(g, diag_size, 4, None, None)
select_window = g.op(
"CumSum",
select_window_ones_fill,
g.op("Constant", value_t=torch.LongTensor([0])),
)
select_window = g.op(
"Add",
select_window,
g.op("Constant", value_t=torch.LongTensor([abs(offset) - 1])),
)
gather_shape = [
opset9.size(g, result, dim=g.op("Constant", value_t=torch.LongTensor([axis])))
for axis in list(range(rank))[:-2]
]
gather_shape.append(diag_size)
gather_shape = g.op("Concat", *gather_shape, axis_i=0)
gather_indices = opset9.zeros(g, gather_shape, 4, None, None)
# There might be cases where offset value is greater than number of rows/columns
# and might cause the diagonal to overrun and as a result of this, diag_size would be zero.
# For example, if
# offset = 9, dim1_size = 2 (columns), dim2_size = 4 (rows)
# diag_size = max(min(2, (4-9)), 0) = 0, based on calculation above
# Cases with diagonal overrun always result in diag_size = max(0, -ve value) = 0
# In cases without diagonal overrun, we select the appropriate rows/columns along which we
# are calculating diagonal values. In cases with diagonal overrun, we return a tensor which has
# the dimension of the row/column where overrun occurred as 0-dim, as we are essentially
# returning an empty tensor
overrun_cond = g.op(
"Not",
g.op(
"Equal",
diag_size,
g.op("Constant", value_t=torch.tensor(0, dtype=torch.int64)),
),
)
if_op = g.op("If", overrun_cond)
if_node = if_op.node()
if_block = utils._add_block(if_node)
gather_indices_if_block = if_block.op("Add", gather_indices, select_window)
gather_indices_if_block = symbolic_helper._unsqueeze_helper(
if_block, gather_indices_if_block, [rank - 1]
)
final_non_overrun_ = if_block.op(
"GatherND", result, gather_indices_if_block, batch_dims_i=rank - 2
)
utils._add_output_to_block(if_block, final_non_overrun_)
else_block = utils._add_block(if_node)
final_overrun_ = opset9.zeros(else_block, gather_shape, 6, None, None)
utils._add_output_to_block(else_block, final_overrun_)
return if_op
def tensor_split(g, self, indices_or_sections, dim, _outputs=None):
axis = g.op("Constant", value_t=torch.tensor(dim, dtype=torch.long))
axis = opset11.unsqueeze(g, axis, 0)
const_1 = g.op("Constant", value_t=torch.tensor(1, dtype=torch.long))
if symbolic_helper._is_split_static(indices_or_sections, _outputs):
split_val = symbolic_helper._node_get(indices_or_sections.node(), "value")
if split_val.dim() > 0:
start = g.op("Constant", value_t=torch.tensor([0], dtype=torch.long))
res = []
assert _outputs is not None
for i in range(_outputs - 1):
end = g.op(
"Gather",
indices_or_sections,
g.op("Constant", value_t=torch.tensor([i], dtype=torch.long)),
axis_i=0,
)
res.append(g.op("Slice", self, start, end, axis))
start = end
end = symbolic_helper._size_helper(g, self, axis)
res.append(g.op("Slice", self, start, end, axis))
return res
split_size = symbolic_helper._get_const(
indices_or_sections, "i", "indices_or_sections"
)
size = symbolic_helper._get_tensor_dim_size(self, dim)
if size is None:
if _outputs is not None:
size = split_size * _outputs
else:
raise errors.SymbolicValueError(
"Unknown dimension size not supported", self
)
min_split_size = size // split_size
num_splits_one_extra = size % split_size
splits = num_splits_one_extra * [min_split_size + 1]
leftover = (split_size - num_splits_one_extra) * [min_split_size]
splits = g.op(
"Constant", value_t=torch.tensor(splits + leftover, dtype=torch.long)
)
return g.op("Split", self, splits, axis_i=dim, outputs=_outputs)
if (
symbolic_helper._is_tensor(indices_or_sections)
and symbolic_helper._get_tensor_rank(indices_or_sections) == 1
):
loop_len = symbolic_helper._size_helper(
g, indices_or_sections, g.op("Constant", value_t=torch.tensor(0))
)
loop_len = opset11.unsqueeze(g, loop_len, 0)
loop_condition = g.op("Cast", const_1, to_i=_C_onnx.TensorProtoDataType.BOOL)
# To make the first slice in the below loop work,
# we pad a zero to the first position so that it will be the initial start of slice.
padding_0 = g.op("Constant", value_t=torch.tensor([0], dtype=torch.long))
indices_or_sections = g.op("Concat", padding_0, indices_or_sections, axis_i=0)
final_splits = g.op("SequenceEmpty")
loop = g.op("Loop", loop_len, loop_condition, final_splits)
# Loop inputs
loop_block = utils._add_block(loop.node())
block_input_iter = utils._add_input_to_block(loop_block)
cond = utils._add_input_to_block(loop_block)
final_splits = utils._add_input_to_block(loop_block)
start = loop_block.op("Gather", indices_or_sections, block_input_iter, axis_i=0)
end = loop_block.op(
"Gather",
indices_or_sections,
loop_block.op("Add", block_input_iter, const_1),
axis_i=0,
)
slice = loop_block.op("Slice", self, start, end, axis)
final_splits = loop_block.op("SequenceInsert", final_splits, slice)
# Loop outputs
cond_out = loop_block.op("Identity", loop_condition)
utils._add_output_to_block(loop_block, cond_out)
utils._add_output_to_block(loop_block, final_splits)
loop_out = loop.node().output()
start = g.op(
"Gather",
indices_or_sections,
g.op("Constant", value_t=torch.tensor(-1, dtype=torch.long)),
axis_i=0,
)
start = opset11.unsqueeze(g, start, 0)
end = symbolic_helper._size_helper(g, self, axis)
last_slice = g.op("Slice", self, start, end, axis)
return g.op("SequenceInsert", loop_out, last_slice)
else: # scalar tensor
dim_size = symbolic_helper._size_helper(g, self, axis)
min_split_size = g.op("Div", dim_size, indices_or_sections)
min_split_size_plus_1 = g.op(
"Add",
min_split_size,
const_1,
)
num_splits_one_extra = g.op("Mod", dim_size, indices_or_sections)
splits = g.op("Tile", min_split_size_plus_1, num_splits_one_extra)
leftover = g.op(
"Tile",
min_split_size,
g.op(
"Sub",
opset11.unsqueeze(g, indices_or_sections, 0),
num_splits_one_extra,
),
)
splits = g.op("Concat", splits, leftover, axis_i=0)
if _outputs is None:
return g.op("SplitToSequence", self, splits, axis_i=dim)
return g.op("Split", self, splits, axis_i=dim, outputs=_outputs)
def unfold(g, input, dimension, size, step):
const_size = symbolic_helper._maybe_get_const(size, "i")
const_step = symbolic_helper._maybe_get_const(step, "i")
if not symbolic_helper._is_value(
const_size) and not symbolic_helper._is_value(const_step):
return opset9.unfold(g, input, dimension, const_size, const_step)
if symbolic_helper.is_caffe2_aten_fallback():
return g.at("unfold",
input,
dimension_i=dimension,
size_i=size,
step_i=step)
sizedim = symbolic_helper._get_tensor_dim_size(input, dimension)
if sizedim is not None:
low_start = g.op("Constant", value_t=torch.tensor(0))
low_end = g.op("Constant", value_t=torch.tensor(sizedim))
hi_end = g.op("Constant", value_t=torch.tensor(sizedim + 1))
low_indices = g.op("Range", low_start, low_end, step)
hi_indices = g.op("Range", size, hi_end, step)
low_size = symbolic_helper._size_helper(
g, low_indices, g.op("Constant", value_t=torch.tensor(0)))
hi_size = symbolic_helper._size_helper(
g, hi_indices, g.op("Constant", value_t=torch.tensor(0)))
ndim = symbolic_helper._get_tensor_rank(input)
assert ndim is not None
perm = list(range(0, ndim))
perm.append(perm.pop(dimension))
unsqueeze_list = []
loop_condition = g.op("Constant", value_t=torch.tensor(1))
loop_condition = g.op("Cast", loop_condition, to_i=9)
loop_len = g.op("Min", low_size, hi_size)
loop = g.op("Loop", loop_len, loop_condition)
loop_block = utils._add_block(loop.node())
block_input_iter = utils._add_input_to_block(loop_block)
cond = utils._add_input_to_block(loop_block)
starts = loop_block.op("Gather", low_indices, block_input_iter)
ends = loop_block.op("Gather", hi_indices, block_input_iter)
axes = loop_block.op("Constant", value_t=torch.tensor([2]))
starts = symbolic_helper._unsqueeze_helper(loop_block, starts, [0])
ends = symbolic_helper._unsqueeze_helper(loop_block, ends, [0])
stack = loop_block.op("Slice", input, starts, ends, axes)
unsqueeze = symbolic_helper._unsqueeze_helper(
loop_block, loop_block.op("Transpose", stack, perm_i=perm),
[dimension])
unsqueeze_list.append(unsqueeze)
concat = loop_block.op("Concat", *unsqueeze_list, axis_i=0)
cond_out = loop_block.op("Cast", loop_condition, to_i=9)
utils._add_output_to_block(loop_block, cond_out)
utils._add_output_to_block(loop_block, concat)
loop_output = loop.node().output()
perm = [0, 1, 2, 3, 4]
perm[0], perm[dimension + 1] = perm[dimension + 1], perm[0]
transpose = g.op("Transpose", loop_output, perm_i=perm)
squeeze = symbolic_helper._squeeze_helper(g, transpose, [0])
return squeeze
else:
return symbolic_helper._unimplemented("Unfold",
"input size not accessible")
