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Support numpy.prod operation #30212 #21873

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5bce71d
Update numpy.py
Hmm-1224 Mar 13, 2025
6fb3be2
Update excluded_concrete_tests.txt
Hmm-1224 Mar 13, 2025
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18 changes: 3 additions & 15 deletions keras/src/backend/openvino/excluded_concrete_tests.txt
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -9,9 +9,7 @@ NumpyDtypeTest::test_any
NumpyDtypeTest::test_argmax
NumpyDtypeTest::test_argmin
NumpyDtypeTest::test_argpartition
NumpyDtypeTest::test_argsort
NumpyDtypeTest::test_array
NumpyDtypeTest::test_bincount
NumpyDtypeTest::test_bitwise
NumpyDtypeTest::test_ceil
NumpyDtypeTest::test_concatenate
Expand All @@ -20,19 +18,13 @@ NumpyDtypeTest::test_cross
NumpyDtypeTest::test_cumprod
NumpyDtypeTest::test_cumsum_bool
NumpyDtypeTest::test_diag
NumpyDtypeTest::test_diff
NumpyDtypeTest::test_digitize
NumpyDtypeTest::test_dot
NumpyDtypeTest::test_einsum
NumpyDtypeTest::test_empty
NumpyDtypeTest::test_exp2
NumpyDtypeTest::test_exp
NumpyDtypeTest::test_expm1
NumpyDtypeTest::test_eye
NumpyDtypeTest::test_flip
NumpyDtypeTest::test_floor
NumpyDtypeTest::test_full_like
NumpyDtypeTest::test_hstack
NumpyDtypeTest::test_identity
NumpyDtypeTest::test_inner
NumpyDtypeTest::test_isclose
Expand Down Expand Up @@ -91,22 +83,19 @@ NumpyOneInputOpsCorrectnessTest::test_any
NumpyOneInputOpsCorrectnessTest::test_argmax
NumpyOneInputOpsCorrectnessTest::test_argmin
NumpyOneInputOpsCorrectnessTest::test_argpartition
NumpyOneInputOpsCorrectnessTest::test_argsort
NumpyOneInputOpsCorrectnessTest::test_array
NumpyOneInputOpsCorrectnessTest::test_bincount
NumpyOneInputOpsCorrectnessTest::test_bitwise_invert
NumpyOneInputOpsCorrectnessTest::test_conj
NumpyOneInputOpsCorrectnessTest::test_correlate
NumpyOneInputOpsCorrectnessTest::test_cumprod
NumpyOneInputOpsCorrectnessTest::test_diag
NumpyOneInputOpsCorrectnessTest::test_diagonal
NumpyOneInputOpsCorrectnessTest::test_diff
NumpyOneInputOpsCorrectnessTest::test_dot
NumpyOneInputOpsCorrectnessTest::test_exp
NumpyOneInputOpsCorrectnessTest::test_expand_dims
NumpyOneInputOpsCorrectnessTest::test_exp2
NumpyOneInputOpsCorrectnessTest::test_expm1
NumpyOneInputOpsCorrectnessTest::test_flip
NumpyOneInputOpsCorrectnessTest::test_floor_divide
NumpyOneInputOpsCorrectnessTest::test_hstack
NumpyOneInputOpsCorrectnessTest::test_imag
NumpyOneInputOpsCorrectnessTest::test_isfinite
NumpyOneInputOpsCorrectnessTest::test_isinf
Expand Down Expand Up @@ -167,7 +156,6 @@ NumpyTwoInputOpsCorrectnessTest::test_cross
NumpyTwoInputOpsCorrectnessTest::test_digitize
NumpyTwoInputOpsCorrectnessTest::test_divide_no_nan
NumpyTwoInputOpsCorrectnessTest::test_einsum
NumpyTwoInputOpsCorrectnessTest::test_full_like
NumpyTwoInputOpsCorrectnessTest::test_inner
NumpyTwoInputOpsCorrectnessTest::test_isclose
NumpyTwoInputOpsCorrectnessTest::test_linspace
Expand All @@ -177,4 +165,4 @@ NumpyTwoInputOpsCorrectnessTest::test_quantile
NumpyTwoInputOpsCorrectnessTest::test_take_along_axis
NumpyTwoInputOpsCorrectnessTest::test_tensordot
NumpyTwoInputOpsCorrectnessTest::test_vdot
NumpyTwoInputOpsCorrectnessTest::test_where
NumpyTwoInputOpsCorrectnessTest::test_where
210 changes: 193 additions & 17 deletions keras/src/backend/openvino/numpy.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -336,9 +336,36 @@ def argmin(x, axis=None, keepdims=False):


def argsort(x, axis=-1):
raise NotImplementedError(
"`argsort` is not supported with openvino backend"
)
x = get_ov_output(x)
x_shape = x.get_partial_shape()
rank = x_shape.rank.get_length()
if rank == 0:
return OpenVINOKerasTensor(ov_opset.constant([0], Type.i32).output(0))
if axis is None:
flatten_shape = ov_opset.constant([-1], Type.i32).output(0)
x = ov_opset.reshape(x, flatten_shape, False).output(0)
x_shape_tensor = ov_opset.shape_of(x, Type.i32).output(0)
k = ov_opset.reduce_prod(
x_shape_tensor, ov_opset.constant([0], Type.i32), keep_dims=False
)
axis = 0
else:
if axis < 0:
axis = rank + axis
x_shape_tensor = ov_opset.shape_of(x, Type.i32).output(0)
k = ov_opset.gather(
x_shape_tensor,
ov_opset.constant(axis, Type.i32).output(0),
ov_opset.constant(0, Type.i32).output(0),
).output(0)
sorted_indices = ov_opset.topk(
x,
k=k,
axis=axis,
mode="min",
sort="value",
).output(1)
return OpenVINOKerasTensor(sorted_indices)


def array(x, dtype=None):
Expand Down Expand Up @@ -380,9 +407,48 @@ def average(x, axis=None, weights=None):


def bincount(x, weights=None, minlength=0, sparse=False):
raise NotImplementedError(
"`bincount` is not supported with openvino backend"
)
if x is None:
raise ValueError("input x is None")
if sparse:
raise ValueError("Unsupported value `sparse=True`")
x = get_ov_output(x)
x_type = x.get_element_type()
shape_x = ov_opset.shape_of(x, "i64").output(0)
rank_x = ov_opset.shape_of(shape_x, "i64").output(0)
rank_x = ov_opset.convert(rank_x, x_type).output(0)
scalar_shape = ov_opset.constant([], x_type).output(0)
rank_x = ov_opset.reshape(rank_x, scalar_shape, False).output(0)
Comment on lines +416 to +420
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@gemini-code-assist gemini-code-assist bot Nov 26, 2025

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medium

The calculation of rank_x is a bit complex. You can simplify this by using ov_opset.rank(x) which directly returns the rank of the tensor as a scalar tensor. This would make the code more readable and concise.

Suggested change
shape_x = ov_opset.shape_of(x, "i64").output(0)
rank_x = ov_opset.shape_of(shape_x, "i64").output(0)
rank_x = ov_opset.convert(rank_x, x_type).output(0)
scalar_shape = ov_opset.constant([], x_type).output(0)
rank_x = ov_opset.reshape(rank_x, scalar_shape, False).output(0)
rank_x = ov_opset.rank(x).output(0)
rank_x = ov_opset.convert(rank_x, x_type).output(0)

const_minus_one = ov_opset.constant(-1, x_type).output(0)
rank_minus_one = ov_opset.add(rank_x, const_minus_one).output(0)
minlength = get_ov_output(minlength)
minlength = ov_opset.convert(minlength, x_type).output(0)
const_one = ov_opset.constant(1, x_type).output(0)
const_zero = ov_opset.constant(0, x_type).output(0)
max_element = ov_opset.reduce_max(x, const_zero, keep_dims=False).output(0)
depth = ov_opset.add(max_element, const_one).output(0)
depth = ov_opset.maximum(depth, minlength).output(0)
depth_scalar = ov_opset.reduce_max(
depth, const_zero, keep_dims=False
).output(0)
one_hot = ov_opset.one_hot(
x, depth_scalar, const_one, const_zero, axis=-1
).output(0)
if weights is not None:
weights = get_ov_output(weights)
weights_type = weights.get_element_type()
weights_new = ov_opset.reshape(weights, [-1, 1], False).output(0)
one_hot = ov_opset.convert(one_hot, weights_type).output(0)
final_one_hot = ov_opset.multiply(one_hot, weights_new).output(0)
final_output = ov_opset.reduce_sum(
final_one_hot, rank_minus_one, keep_dims=False
).output(0)
return OpenVINOKerasTensor(final_output)
else:
final_output = ov_opset.reduce_sum(
one_hot, rank_minus_one, keep_dims=False
).output(0)
final_output = ov_opset.convert(final_output, Type.i32).output(0)
return OpenVINOKerasTensor(final_output)


def broadcast_to(x, shape):
Expand Down Expand Up @@ -502,7 +568,76 @@ def diagonal(x, offset=0, axis1=0, axis2=1):


def diff(a, n=1, axis=-1):
raise NotImplementedError("`diff` is not supported with openvino backend")
if n == 0:
return OpenVINOKerasTensor(get_ov_output(a))
if n < 0:
raise ValueError("order must be non-negative but got " + repr(n))
a = get_ov_output(a)
a_type = a.get_element_type()
if isinstance(a, np.ndarray):
rank = a.ndim
else:
rank = a.get_partial_shape().rank.get_length()
if axis < 0:
axis = axis + rank
result = a
for _ in range(n):
rank = result.get_partial_shape().rank.get_length()
strides = ov_opset.constant(
np.array([1] * rank, dtype=np.int64), Type.i64
).output(0)

begin_upper_list = [0] * rank
begin_upper_list[axis] = 1
begin_upper = ov_opset.constant(
np.array(begin_upper_list, dtype=np.int64), Type.i64
).output(0)
end_upper = ov_opset.constant(
np.array([0] * rank, dtype=np.int64), Type.i64
).output(0)
begin_mask_upper = [1] * rank
begin_mask_upper[axis] = 0
end_mask_upper = [1] * rank
upper = ov_opset.strided_slice(
data=result,
begin=begin_upper,
end=end_upper,
strides=strides,
begin_mask=begin_mask_upper,
end_mask=end_mask_upper,
new_axis_mask=[],
shrink_axis_mask=[],
ellipsis_mask=[],
).output(0)

begin_lower = ov_opset.constant(
np.array([0] * rank, dtype=np.int64), Type.i64
).output(0)
end_lower_list = [0] * rank
end_lower_list[axis] = -1
end_lower = ov_opset.constant(
np.array(end_lower_list, dtype=np.int64), Type.i64
).output(0)
begin_mask_lower = [1] * rank
end_mask_lower = [1] * rank
end_mask_lower[axis] = 0
lower = ov_opset.strided_slice(
data=result,
begin=begin_lower,
end=end_lower,
strides=strides,
begin_mask=begin_mask_lower,
end_mask=end_mask_lower,
new_axis_mask=[],
shrink_axis_mask=[],
ellipsis_mask=[],
).output(0)

if a_type == Type.boolean:
result = ov_opset.not_equal(upper, lower).output(0)
else:
result = ov_opset.subtract(upper, lower).output(0)
return OpenVINOKerasTensor(result)


def digitize(x, bins):
Expand All @@ -512,11 +647,30 @@ def digitize(x, bins):


def dot(x, y):
raise NotImplementedError("`dot` is not supported with openvino backend")
element_type = None
if isinstance(x, OpenVINOKerasTensor):
element_type = x.output.get_element_type()
if isinstance(y, OpenVINOKerasTensor):
element_type = y.output.get_element_type()
x = get_ov_output(x, element_type)
y = get_ov_output(y, element_type)
x, y = _align_operand_types(x, y, "dot()")
if x.get_partial_shape().rank == 0 or y.get_partial_shape().rank == 0:
return OpenVINOKerasTensor(ov_opset.multiply(x, y).output(0))
return OpenVINOKerasTensor(ov_opset.matmul(x, y, False, False).output(0))
Comment on lines 649 to +660
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@gemini-code-assist gemini-code-assist bot Nov 26, 2025

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high

The implementation of dot using matmul is not entirely correct for all cases. While matmul and dot are equivalent for 1D and 2D tensors, their behavior differs for higher-dimensional arrays. numpy.dot(a, b) is equivalent to numpy.tensordot(a, b, axes=([-1], [-2])) for N-D arrays, which is different from numpy.matmul's broadcasting behavior. This implementation might lead to incorrect results for arrays with more than 2 dimensions. Since tensordot is not yet implemented for this backend, this might be a temporary solution, but it's worth noting this potential discrepancy.



def empty(shape, dtype=None):
raise NotImplementedError("`empty` is not supported with openvino backend")
dtype = standardize_dtype(dtype) or config.floatx()
ov_type = OPENVINO_DTYPES[dtype]
if isinstance(shape, tuple):
shape = list(shape)
elif isinstance(shape, int):
shape = [shape]
shape_node = ov_opset.constant(shape, Type.i32).output(0)
const_zero = ov_opset.constant(0, dtype=ov_type).output(0)
empty_tensor = ov_opset.broadcast(const_zero, shape_node).output(0)
return OpenVINOKerasTensor(empty_tensor)
Comment on lines 663 to +673
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medium

The implementation of empty is almost identical to the zeros function. To reduce code duplication and improve maintainability, you could implement empty by calling zeros. Note that numpy.empty creates an uninitialized array, but creating an array of zeros is a safer and common alternative when uninitialized arrays are not supported.

Suggested change
def empty(shape, dtype=None):
raise NotImplementedError("`empty` is not supported with openvino backend")
dtype = standardize_dtype(dtype) or config.floatx()
ov_type = OPENVINO_DTYPES[dtype]
if isinstance(shape, tuple):
shape = list(shape)
elif isinstance(shape, int):
shape = [shape]
shape_node = ov_opset.constant(shape, Type.i32).output(0)
const_zero = ov_opset.constant(0, dtype=ov_type).output(0)
empty_tensor = ov_opset.broadcast(const_zero, shape_node).output(0)
return OpenVINOKerasTensor(empty_tensor)
def empty(shape, dtype=None):
return zeros(shape, dtype=dtype)



def equal(x1, x2):
Expand All @@ -533,14 +687,17 @@ def equal(x1, x2):

def exp(x):
x = get_ov_output(x)
x_type = x.get_element_type()
if x_type.is_integral():
ov_type = OPENVINO_DTYPES[config.floatx()]
x = ov_opset.convert(x, ov_type)
return OpenVINOKerasTensor(ov_opset.exp(x).output(0))


def expand_dims(x, axis):
if isinstance(x, OpenVINOKerasTensor):
x = x.output
else:
assert False
x = get_ov_output(x)
if isinstance(axis, tuple):
axis = list(axis)
axis = ov_opset.constant(axis, Type.i32).output(0)
return OpenVINOKerasTensor(ov_opset.unsqueeze(x, axis).output(0))

Expand Down Expand Up @@ -571,9 +728,15 @@ def full(shape, fill_value, dtype=None):


def full_like(x, fill_value, dtype=None):
raise NotImplementedError(
"`full_like` is not supported with openvino backend"
)
x = get_ov_output(x)
shape_x = ov_opset.shape_of(x)
if dtype is not None:
ov_type = OPENVINO_DTYPES[standardize_dtype(dtype)]
else:
ov_type = x.get_element_type()
const_value = ov_opset.constant(fill_value, ov_type).output(0)
res = ov_opset.broadcast(const_value, shape_x).output(0)
return OpenVINOKerasTensor(res)


def greater(x1, x2):
Expand Down Expand Up @@ -601,7 +764,20 @@ def greater_equal(x1, x2):


def hstack(xs):
raise NotImplementedError("`hstack` is not supported with openvino backend")
if not isinstance(xs, (list, tuple)):
raise TypeError("Input to `hstack` must be a list or tuple of tensors.")
if len(xs) == 0:
raise ValueError("Input list to `hstack` cannot be empty.")
element_type = None
for x in xs:
if isinstance(x, OpenVINOKerasTensor):
element_type = x.output.get_element_type()
break
xs = [get_ov_output(x, element_type) for x in xs]
xs = _align_operand_types(xs[0], xs[1], "hstack()")
Comment on lines +771 to +777
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critical

The type alignment for the input tensors in hstack is incorrect. The line xs = _align_operand_types(xs[0], xs[1], "hstack()") only aligns the first two tensors and then overwrites the xs list with a tuple containing only these two aligned tensors. If xs contains more than two tensors, the rest are discarded, leading to incorrect results. All tensors in xs should be aligned to a common data type before concatenation.

Suggested change
element_type = None
for x in xs:
if isinstance(x, OpenVINOKerasTensor):
element_type = x.output.get_element_type()
break
xs = [get_ov_output(x, element_type) for x in xs]
xs = _align_operand_types(xs[0], xs[1], "hstack()")
xs = [get_ov_output(x) for x in xs]
if len(xs) > 1:
result_keras_type = dtypes.result_type(
*[ov_to_keras_type(x.get_element_type()) for x in xs]
)
result_ov_type = OPENVINO_DTYPES[result_keras_type]
aligned_xs = []
for x in xs:
if x.get_element_type() != result_ov_type:
aligned_xs.append(ov_opset.convert(x, result_ov_type).output(0))
else:
aligned_xs.append(x)
xs = aligned_xs

rank = len(xs[0].get_partial_shape())
axis = 1 if rank > 1 else 0
return OpenVINOKerasTensor(ov_opset.concat(xs, axis=axis).output(0))


def identity(n, dtype=None):
Expand Down
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