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Merged
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Nov 24, 2020
Merged

Added conversion for SparseReshape #1204

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e80343b
Added conversion for SparseReshape
TomWildenhain-Microsoft Nov 24, 2020
94ec42d
Removed empty line
TomWildenhain-Microsoft Nov 24, 2020
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37 changes: 37 additions & 0 deletions tests/test_backend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3503,6 +3503,43 @@ def func(indices, values, dense_shape, default):
return dense_, indicator_
self._run_test_case(func, [_OUTPUT, _OUTPUT1], {_INPUT: i_val, _INPUT1: v_val, _INPUT2: ds_val, _INPUT3: d_val})

@check_opset_min_version(11, "CumSum")
def test_sparse_reshape(self):
indices_val = np.array([[1, 0, 0], [1, 0, 2], [1, 1, 3], [3, 2, 2], [3, 2, 3]], dtype=np.int64)
values_val = np.array([1.5, 1.6, 1.7, 1.8, 1.9], dtype=np.int64)
dense_shape_val = np.array([5, 3, 4], dtype=np.int64)
new_shape_val = np.array([2, -1, 1, 3], dtype=np.int64)
def func(indices, values, dense_shape, new_shape):
st = tf.SparseTensor(indices, values, dense_shape)
st_ = tf.sparse.reshape(st, new_shape)
indices_ = st_.indices
dense_shape_ = st_.dense_shape
indices_ = tf.identity(indices_, name=_TFOUTPUT)
dense_shape_ = tf.identity(dense_shape_, name=_TFOUTPUT1)
return indices_, dense_shape_
self._run_test_case(func, [_OUTPUT, _OUTPUT1], {_INPUT: indices_val, _INPUT1: values_val,
_INPUT2: dense_shape_val, _INPUT3: new_shape_val})

@check_opset_min_version(11, "CumSum")
def test_sparse_reshape_unknown_rank(self):
indices_val = np.array([[1, 0, 0], [1, 0, 2], [1, 1, 3], [3, 2, 2], [3, 2, 3]], dtype=np.int64)
values_val = np.array([1.5, 1.6, 1.7, 1.8, 1.9], dtype=np.int64)
dense_shape_val = np.array([5, 3, 4], dtype=np.int64)
new_shape_val = np.array([2, 10, 1, 3], dtype=np.int64)
shape_pad_val = np.zeros((1, 2), dtype=np.int64)
def func(indices, dense_shape, new_shape, shape_pad):
st = tf.SparseTensor(indices, values_val, dense_shape)
# Some hackery to make the rank unknown
new_shape_ = tf.pad(new_shape, shape_pad, constant_values=0)
st_ = tf.sparse.reshape(st, new_shape_)
indices_ = st_.indices
dense_shape_ = st_.dense_shape
indices_ = tf.identity(indices_, name=_TFOUTPUT)
dense_shape_ = tf.identity(dense_shape_, name=_TFOUTPUT1)
return indices_, dense_shape_
self._run_test_case(func, [_OUTPUT, _OUTPUT1], {_INPUT: indices_val, _INPUT1: dense_shape_val,
_INPUT2: new_shape_val, _INPUT3: shape_pad_val})

@check_opset_min_version(9, "Compress")
def test_dynamic_partition_both_vector(self):
data_val = np.array([1, 2, 3, 4, 5, 6, 7, 8], dtype=np.float32)
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51 changes: 51 additions & 0 deletions tf2onnx/onnx_opset/tensor.py
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Expand Up @@ -1857,6 +1857,57 @@ def version_11(cls, ctx, node, **kwargs):
ctx.replace_inputs(node, [expand_node.output[0], sparse_indices, sparse_vals])


@tf_op("SparseReshape")
class SparseReshape:
@classmethod
def version_11(cls, ctx, node, **kwargs):
indices_inp, shape_inp, new_shape_inp = node.input

product_curr_dims = ctx.make_node("ReduceProd", [shape_inp], attr={'axes': [0], 'keepdims': 1}).output[0]
product_new_dims = ctx.make_node("ReduceProd", [new_shape_inp], attr={'axes': [0], 'keepdims': 1}).output[0]
neg_missing_dims = ctx.make_node("Div", [product_curr_dims, product_new_dims]).output[0]
pos_missing_dims = ctx.make_node("Neg", [neg_missing_dims]).output[0]
zero_const = ctx.make_const(utils.make_name("cosnt_zero"), np.array(0, dtype=np.int64)).output[0]
one_const = ctx.make_const(utils.make_name("cosnt_one"), np.array(1, dtype=np.int64)).output[0]
unknown_dim_loc = ctx.make_node("Less", [new_shape_inp, zero_const]).output[0]

new_shape = ctx.make_node("Where", [unknown_dim_loc, pos_missing_dims, new_shape_inp]).output[0]

zero_tensor = helper.make_tensor("value", TensorProto.INT64, dims=[1], vals=[0])
def cum_prod_of_vector(vector):
shape = ctx.get_shape(vector)
rank = shape[0] if shape is not None else -1
if rank != -1:
lower_tri = np.tri(rank, rank, dtype=np.bool)
lower_triangular_bool = ctx.make_const(utils.make_name("lower_tri_const"), lower_tri).output[0]
else:
rank = ctx.make_node("Shape", [vector]).output[0]
rank_sq = ctx.make_node("Concat", [rank, rank], attr={'axis': 0}).output[0]
square_of_rank = ctx.make_node("ConstantOfShape", [rank_sq], attr={'value': zero_tensor}).output[0]
identity_matrix = ctx.make_node("EyeLike", [square_of_rank]).output[0]
lower_triangular = ctx.make_node("CumSum", [identity_matrix, zero_const]).output[0]
lower_triangular_bool = ctx.make_node("Cast", [lower_triangular],
attr={'to': TensorProto.BOOL}).output[0]
terms = ctx.make_node("Where", [lower_triangular_bool, one_const, vector]).output[0]
return ctx.make_node("ReduceProd", [terms], attr={'axes': [1], 'keepdims': 0}).output[0]

cum_prod_curr_shape = cum_prod_of_vector(shape_inp)
cum_prod_new_shape = cum_prod_of_vector(new_shape)
cum_prod_new_concat = ctx.make_node("Concat", [product_curr_dims, cum_prod_new_shape],
attr={'axis': 0}).output[0]
pads = ctx.make_const(utils.make_name("pad_const"), np.array([0, -1], dtype=np.int64)).output[0]
cum_prod_new_inc = ctx.make_node("Pad", [cum_prod_new_concat, pads]).output[0]

flat_indices = ctx.make_node("MatMul", [indices_inp, cum_prod_curr_shape]).output[0]
indices_unsqueeze = ctx.make_node("Unsqueeze", [flat_indices], attr={'axes': [1]}).output[0]
mod_indices = ctx.make_node("Mod", [indices_unsqueeze, cum_prod_new_inc], op_name_scope=node.name).output[0]
new_indices = ctx.make_node("Div", [mod_indices, cum_prod_new_shape], op_name_scope=node.name).output[0]

ctx.replace_all_inputs(node.output[0], new_indices)
ctx.replace_all_inputs(node.output[1], new_shape)
ctx.remove_node(node.name)

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3 blank lines and pylint doesn't complain :)

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oops


@tf_op("SparseFillEmptyRows")
class SparseFillEmptyRows:
@classmethod
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