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Merged
merged 3 commits into from
Jan 16, 2021
Merged

Implemented conversion of RaggedToSparse #1276

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e896493
Implemented conversion of RaggedToSparse
TomWildenhain-Microsoft Jan 15, 2021
d69584c
Merge branch 'master' into tom/RaggedToSparse
TomWildenhain-Microsoft Jan 15, 2021
430b154
Fixed bug
TomWildenhain-Microsoft Jan 15, 2021
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17 changes: 17 additions & 0 deletions tests/test_backend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3765,6 +3765,23 @@ def func(indices, dense_shape, new_shape, shape_pad):
self._run_test_case(func, [_OUTPUT, _OUTPUT1], {_INPUT: indices_val, _INPUT1: dense_shape_val,
_INPUT2: new_shape_val, _INPUT3: shape_pad_val})

@check_tf_min_version("1.14", "ragged needs tf 1.14")
@check_opset_min_version(11, "CumSum")
def test_ragged_tensor_to_sparse(self):
splits_val1 = np.array([0, 1, 1, 5], dtype=np.int32)
splits_val2 = np.array([0, 3, 3, 5, 9, 10], dtype=np.int32)
dense_vals_val = np.array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19], dtype=np.float32)
def func(splits1, splits2, rt_dense_values):
x = tf.RaggedTensor.from_nested_row_splits(rt_dense_values, [splits1, splits2], validate=True)
s = x.to_sparse()
indices, values, shape = s.indices, s.values, s.dense_shape
indices = tf.identity(indices, name=_TFOUTPUT)
values = tf.identity(values, name=_TFOUTPUT1)
shape = tf.identity(shape, name=_TFOUTPUT2)
return indices, values, shape
self._run_test_case(func, [_OUTPUT, _OUTPUT1, _OUTPUT2],
{_INPUT: splits_val1, _INPUT1: splits_val2, _INPUT2: dense_vals_val})

@check_tf_min_version("1.14", "ragged needs tf 1.14")
@check_opset_min_version(11, "Range")
def test_ragged_range_float(self):
Expand Down
82 changes: 62 additions & 20 deletions tf2onnx/onnx_opset/tensor.py
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Expand Up @@ -2036,6 +2036,65 @@ def version_11(cls, ctx, node, **kwargs):
ctx.replace_inputs(node, [expand_node.output[0], sparse_indices, sparse_vals])


def ragged_lengths_to_sparse_indices(ctx, ragged_lens):
const_zero_int64 = ctx.make_const(utils.make_name("const_zero"), np.array(0, dtype=np.int64)).output[0]
num_cols = ctx.make_node("ReduceMax", [ragged_lens], attr={'axes': [0], 'keeepdims': True}).output[0]
num_rows = ctx.make_node("Shape", [ragged_lens]).output[0]
range_len = ctx.make_node("Mul", [num_cols, num_rows]).output[0]

# ORT seems to have a shape inference bug for the Range node. Use CumSum instead.
one_tensor = helper.make_tensor("value", TensorProto.INT64, dims=[1], vals=[1])
ones_of_shape = ctx.make_node("ConstantOfShape", [range_len], attr={"value": one_tensor}).output[0]
range_node = ctx.make_node("CumSum", [ones_of_shape, const_zero_int64], attr={'exclusive': True}).output[0]
#const_one_int64 = ctx.make_const(utils.make_name("const_one"), np.array(1, dtype=np.int64)).output[0]
#range_node = ctx.make_node("Range", [const_zero_int64, range_len, const_one_int64]).output[0]

col_indices_dense = ctx.make_node("Mod", [range_node, num_cols]).output[0]
row_indices_dense = ctx.make_node("Div", [range_node, num_cols]).output[0]
row_lens_dense = ctx.make_node("Gather", [ragged_lens, row_indices_dense]).output[0]
indices_to_keep = ctx.make_node("Less", [col_indices_dense, row_lens_dense]).output[0]
col_indices = ctx.make_node("Compress", [col_indices_dense, indices_to_keep]).output[0]
row_indices = ctx.make_node("Compress", [row_indices_dense, indices_to_keep]).output[0]
return num_rows, num_cols, row_indices, col_indices


@tf_op("RaggedTensorToSparse")
class RaggedTensorToSparse:
@classmethod
def version_11(cls, ctx, node, **kwargs):
# https://www.tensorflow.org/guide/ragged_tensor#multiple_ragged_dimensions
dense_values = node.inputs[-1]
nested_splits = node.inputs[:-1]
sparse_indices = None
dense_shape_dims = []
for split in nested_splits:
if ctx.get_dtype(split.output[0]) != TensorProto.INT64:
split = ctx.make_node("Cast", [split.output[0]], attr={'to': TensorProto.INT64})
max_int64 = int(utils.get_max_value(np.int64))
slice1 = GraphBuilder(ctx).make_slice(
{"data": split.output[0], "ends": [max_int64], "starts": [1], "axes": [0]})
slice2 = GraphBuilder(ctx).make_slice(
{"data": split.output[0], "ends": [-1], "starts": [0], "axes": [0]})
ragged_lens = ctx.make_node("Sub", [slice1, slice2]).output[0]
num_rows, num_cols, row_indices, col_indices = ragged_lengths_to_sparse_indices(ctx, ragged_lens)
if not dense_shape_dims:
dense_shape_dims.append(num_rows)
dense_shape_dims.append(num_cols)
if sparse_indices is None:
row_indices = GraphBuilder(ctx).make_unsqueeze({"data": row_indices, "axes": [1]})
else:
row_indices = ctx.make_node("Gather", [sparse_indices, row_indices]).output[0]
col_indices = GraphBuilder(ctx).make_unsqueeze({"data": col_indices, "axes": [1]})
sparse_indices = ctx.make_node("Concat", [row_indices, col_indices], attr={'axis': 1},
op_name_scope=node.name).output[0]
dense_shape = ctx.make_node("Concat", dense_shape_dims, attr={'axis': 0}, op_name_scope=node.name).output[0]

ctx.replace_all_inputs(node.output[0], sparse_indices)
ctx.replace_all_inputs(node.output[1], dense_values.output[0])
ctx.replace_all_inputs(node.output[2], dense_shape)
ctx.remove_node(node.name)


@tf_op("RaggedRange")
class RaggedRange:
@classmethod
Expand Down Expand Up @@ -2076,34 +2135,17 @@ def version_11(cls, ctx, node, **kwargs):

const_zero_list = ctx.make_const(utils.make_name("const_zero_list"), np.array([0], dtype=np.int64)).output[0]

max_row_len = ctx.make_node("ReduceMax", [row_lens], attr={'axes': [0], 'keeepdims': False}).output[0]
inp_shape = ctx.make_node("Shape", [row_lens]).output[0]
range_len = ctx.make_node("Mul", [max_row_len, inp_shape]).output[0]

# ORT seems to have a shape inference bug for the Range node. Use CumSum instead.
one_tensor = helper.make_tensor("value", TensorProto.INT64, dims=[1], vals=[1])
ones_of_shape = ctx.make_node("ConstantOfShape", [range_len], attr={"value": one_tensor}).output[0]
range_node = ctx.make_node("CumSum", [ones_of_shape, const_zero_int64], attr={'exclusive': True}).output[0]
#const_one_int64 = ctx.make_const(utils.make_name("const_one"), np.array(1, dtype=np.int64)).output[0]
#range_node = ctx.make_node("Range", [const_zero_int64, range_len, const_one_int64]).output[0]

col_indices_dense = ctx.make_node("Mod", [range_node, max_row_len]).output[0]
row_indices_dense = ctx.make_node("Div", [range_node, max_row_len]).output[0]
row_lens_dense = ctx.make_node("Gather", [row_lens, row_indices_dense]).output[0]
indices_to_keep = ctx.make_node("Less", [col_indices_dense, row_lens_dense]).output[0]
col_indices = ctx.make_node("Compress", [col_indices_dense, indices_to_keep]).output[0]
row_indices = ctx.make_node("Compress", [row_indices_dense, indices_to_keep]).output[0]

num_rows, _, row_indices, col_indices = ragged_lengths_to_sparse_indices(ctx, row_lens)

split_ends = ctx.make_node("CumSum", [row_lens, const_zero_int64]).output[0]
splits_out = ctx.make_node("Concat", [const_zero_list, split_ends], attr={'axis': 0}).output[0]
col_indices_cast = ctx.make_node("Cast", [col_indices], attr={'to': data_dtype}).output[0]

if ctx.get_rank(starts) != 1:
starts = ctx.make_node("Expand", [starts, inp_shape]).output[0]
starts = ctx.make_node("Expand", [starts, num_rows]).output[0]

if ctx.get_rank(deltas) != 1:
deltas = ctx.make_node("Expand", [deltas, inp_shape]).output[0]
deltas = ctx.make_node("Expand", [deltas, num_rows]).output[0]

gather_starts = ctx.make_node("Gather", [starts, row_indices]).output[0]
gather_deltas = ctx.make_node("Gather", [deltas, row_indices]).output[0]
Expand Down