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import RunAll
from ..helpers |
import make_test, Tensor, Dtype, FixedImpl, to_fp |
class Reduce_log_sum_exp(RunAll):
@staticmethod
def reduce_log_sum_exp_fp32x32():
def reduce_log_sum_exp_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1), shap... |
egative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_log_sum_exp(0, true)", name)
reduce_log_sum_exp_export_do_not_keepdims()
reduce_log_sum_exp_export_keepdims()
reduce_log_sum_exp_axis_0() |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl |
class Reduce_mean(RunAll):
@staticmethod
def reduce_mean_u32():
def reduce_mean_1D():
x = np.array([0, 1, 2,]).astype(np.uint32)
y = np.mean(x, keepdims=True).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.f... |
axis_1()
reduce_mean_1D()
reduce_mean_2D()
@staticmethod
def reduce_mean_i32():
def reduce_mean_1D():
x = np.array([0, 1, 2,]).astype(np.int32)
y = np.mean(x, keepdims=True).astype(np.int32)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y =... |
)
keepdims()
axis_1()
reduce_mean_1D()
reduce_mean_2D()
@staticmethod
def reduce_mean_i8():
def reduce_mean_1D():
x = np.array([0, 1, 2,]).astype(np.int8)
y = np.mean(x, keepdims=True).astype(np.int8)
x = Tensor(Dtype.FP8x23, ... |
n::None(()))", name)
default()
keepdims()
axis_1()
reduce_mean_1D()
reduce_mean_2D()
@staticmethod
def reduce_mean_fp8x23():
def reduce_mean_1D():
x = np.array([0, 1, 2,]).astype(np.int64)
y = np.mean(x, keepdims=True)
... |
.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
name = "reduce_mean_fp8x23_2D_axis_1"
make_test(
[x], y, "input_0.reduce_mean(Option::Some(... |
name = "reduce_mean_fp16x16_2D_keepdims"
make_test(
[x], y, "input_0.reduce_mean(Option::None(()), Option::Some(false), Option::None(()))", name)
def axis_1():
x = np.array([0, 1, 2, 3]).astype(np.int64).reshape(2, 2)
y = np.mean(x, axis=(... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl |
class Reduce_min(RunAll):
@staticmethod
def reduce_min_u32():
def reduce_min_1D():
x = np.array([0, 1, 2,]).astype(np.uint32)
y = np.minimum.reduce(x, axis=None, keepdims=True).astype(np.uint32)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype... |
", name)
default()
keepdims()
axis_1()
reduce_min_1D()
reduce_min_2D()
@staticmethod
def reduce_min_i32():
def reduce_min_1D():
x = np.array([0, 1, 2,]).astype(np.int32)
y = np.minimum.reduce(x, axis=None, keepdims=True).astyp... |
y, "input_0.reduce_min(Option::Some(array![1].span()), Option::None(()), Option::None(()))", name)
default()
keepdims()
axis_1()
reduce_min_1D()
reduce_min_2D()
@staticmethod
def reduce_min_i8():
def reduce_min_1D():
x = np.array([0, 1, 2... |
name = "reduce_min_i8_2D_axis_1"
make_test(
[x], y, "input_0.reduce_min(Option::Some(array![1].span()), Option::None(()), Option::None(()))", name)
default()
keepdims()
axis_1()
reduce_min_1D()
reduce_min_2D()
@staticmethod
... |
alse), Option::None(()))", name)
def axis_1():
x = np.array([0, 1, 2, 3]).astype(np.int64).reshape(2, 2)
y = np.minimum.reduce(x, axis=(1), keepdims=True)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
... |
y = np.minimum.reduce(x, axis=None, keepdims=False)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "reduce_min_fp16x16_2... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl |
class Reduce_sum(RunAll):
@staticmethod
def reduce_sum_no_keep_dims():
axes = np.array([1], dtype=np.uint32)
keepdims = 0
x = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [
[9, 10], [11, 12]]]).astype(np.uint32)
y = np.sum(x, axis=tuple(axes.tolist()), kee... |
ims=keepdims == 1)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_sum_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_sum(Option::Some(array![-2].span()), Option::Some(true), Option::None)", name)
@stat... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl |
import numpy as np |
class Reduce_sum_square(RunAll):
@staticmethod
def reduce_sum_square_fp8x23():
def reduce_sum_square_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.... |
reduce_sum_square_export_do_not_keepdims()
reduce_sum_square_export_keepdims()
reduce_sum_square_axis_0()
@staticmethod
def reduce_sum_square_fp16x16():
def reduce_sum_square_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
... |
make_test(
[x], y, "input_0.reduce_sum_square(0, true)", name)
reduce_sum_square_export_do_not_keepdims()
reduce_sum_square_export_keepdims()
reduce_sum_square_axis_0()
@staticmethod
def reduce_sum_square_i8():
def reduce_sum_square_export_do_not_keepdi... |
quare_i8_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(0, true)", name)
reduce_sum_square_export_do_not_keepdims()
reduce_sum_square_export_keepdims()
reduce_sum_square_axis_0()
@staticmethod
def reduce_su... |
Dtype.I32, y.shape, y.flatten())
name = "reduce_sum_square_i32_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(0, true)", name)
reduce_sum_square_export_do_not_keepdims()
reduce_sum_square_export_keepdims()
... |
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "reduce_sum_square_u32_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_sum_square(0, true)", name)
reduce_sum_square_export... |
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, Trait, FixedImpl
import tensorflow as tf
class Relu(RunAll):
@staticmethod
def relu_i32():
x = np.random.randint(-5, 9, (2, 2)).astype(np.int32)
layer = tf.keras.layers.ReLU()
y =... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, Tensor, Dtype
original_shape = [2, 3, 4]
data = np.random.random_sample(original_shape).astype(np.int32)
def reshape_reference_implementation(
data: np.ndarray, shape: np.ndarray, allowzero: int = 0
) -> np.ndarray:
new_shape = np.copy(shape)
if allowzero == 0:
zeros_i... |
class Reshape(RunAll):
@staticmethod
def reshape_reordered_all_dims():
y = reshape_reference_implementation(
data, np.array([4, 2, 3], dtype=np.int64))
x = Tensor(Dtype.I32, data.shape, data.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reshape_reor... |
data, np.array([2, -1, 2], dtype=np.int64))
x = Tensor(Dtype.I32, data.shape, data.flatten())
y = Tensor(Dtype.I32, y.shape, y.flatten())
name = "reshape_negative_dim"
make_test([x], y, "input_0.reshape(array![2, -1, 2].span(), false)", name)
@staticmethod
def reshape_negative... |
import numpy as np
from typing |
import Any, Callable
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl
def _cartesian(arrays: list[np.ndarray], out: np.ndarray | None = None) -> np.ndarray:
arrays = [np.asarray(x) for x in arrays]
dtype = arrays[0].dtype
n = np.prod([x.size for x in arrays])
if out is None:
out = np.zeros([n, len(arrays)],... |
i_start, i_end)]
coeffs = [compute_coeff(x) for x in args]
return np.array(coeffs) / sum(coeffs)
def nearest_coeffs(
ratio: float | int | np.ndarray, mode: str = "round_prefer_floor"
) -> np.ndarray:
if isinstance(ratio, int) or ratio.is_integer():
return np.array([0, 1])
if mode == "round_... |
de == "half_pixel":
x_ori = (x + 0.5) / scale_factor - 0.5
elif coordinate_transformation_mode == "half_pixel_symmetric":
adjustment = output_width_int / output_width
center = input_width / 2
offset = center * (1 - adjustment)
x_ori = offset + (x + 0.5) / scale_factor - 0.5
... |
get_coeffs,
roi=None if roi is None else [roi[0], roi[n]],
exclude_outside=exclude_outside,
**kwargs,
)
def _get_all_coords(data: np.ndarray) -> np.ndarray:
return _cartesian(
[list(range(data.shape[i])) for i in range(len(data.shape))]
)
def interpolate_nd(
data: ... |
lueError(
f"Invalid keep_aspect_ratio_policy={keep_aspect_ratio_policy!r}"
)
scale_factors = [scale if i in axes else 1.0 for i in range(r)]
def round_half_up(x: float) -> int:
return int(x + 0.5)
output_size = [
... |
class Resize(RunAll):
@staticmethod
def resize_upsample_scales_nearest() -> None:
data = np.array(
[
[
[
[1, 2],
[3, 4],
]
]
],
dtype=np.float32,
... |
ype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_scales_nearest"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales,"
fu... |
ption::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_downsample_sizes_nearest() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
]
... |
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_scales_linear"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "scales... |
::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::LINEAR),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_downsample_scales_linear() -> None:
data = np.array(
... |
lambda x, _: linear_coeffs(x, None),
scale_factors=scales,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), Fixed... |
tion::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Op... |
sig, name)
@staticmethod
def resize_downsample_scales_cubic() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
... |
e_factors=scales,
coordinate_transformation_mode="align_corners",
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP1... |
c_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::CUBIC),"... |
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
scales = np.array([1.0, 1.0, 2.0, 2.0], dtype=np.float32)
output = interp... |
les,
exclude_outside=True,
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), Fixed... |
func_sig += "Option::None,"
func_sig += "scales,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(TRANSFORMATION_MODE::ASYMMETRIC),"
func_sig += "Option::None,"
func_sig += "Option::None,"
f... |
c_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::LINEAR),"
func_sig += "Option::None,)"
make_test([x[0], x[1], x[2]], y, func_sig, name)
@staticmethod
def resize_tf_crop_and_resize_extrapolation_value() -> N... |
func_sig += "Option::None,)"
make_test([x[0], x[1], x[2]], y, func_sig, name)
@staticmethod
def resize_downsample_sizes_linear_pytorch_half_pixel() -> None:
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6... |
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 8, 8], dtype=np.int64)
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x, mode="floor"),
output_size=sizes,
coordinate_transformation_mode="align... |
x = [data, sizes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_upsample_sizes... |
c_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(TRANSFORMATION_MODE::HALF_PIXEL),"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_si... |
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
sizes = np.array([1, 1, 3, 3], dt... |
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name = "resize_downsample_scales_cubic_antialias"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig... |
ig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(MODE::CUBIC),"
func_sig += "Option::None,)"
make_test([x[0], x[1]], y, func_sig, name)
@staticmethod
def resize_upsample_scales_nearest_axes_2_3() -> None:
axes = np.array([2, 3], dtype=np.int64)... |
_: nearest_coeffs(x), scale_factors=scales, axes=axes
).astype(np.float32)
x = [data, scales, axes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.FP16x16, x[1].shape, to_fp(x[1].flatten(), FixedImpl.FP16x16))
... |
"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "axes,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::None,"
... |
xes_2_3() -> None:
axes = np.array([2, 3], dtype=np.int64)
data = np.array(
[
[
[
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
... |
[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16],
]
]
],
dtype=np.float32,
)
roi = np.array([0.6, 0.4, 0.8, 0.6], dtype=np.float32)
sizes = np.array([... |
]
],
dtype=np.float32,
)
sizes = np.array([7, 8], dtype=np.int64)
output = interpolate_nd(
data,
lambda x, _: nearest_coeffs(x),
output_size=sizes,
axes=axes,
keep_aspect_ratio_policy=keep_aspect_ratio_policy,
... |
= [data, sizes, axes]
y = output
x[0] = Tensor(Dtype.FP16x16, x[0].shape, to_fp(x[0].flatten(), FixedImpl.FP16x16))
x[1] = Tensor(Dtype.U32, x[1].shape, x[1].flatten())
x[2] = Tensor(Dtype.U32, x[2].shape, x[2].flatten())
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten()... |
name = "resize_downsample_sizes_nearest_not_larger"
func_sig = "data.resize("
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "sizes,"
func_sig += "Option::None,"
func_sig += "axes,"
func_sig += "Option::None,"
func_sig += "Option::No... |
func_sig += "Option::None,"
func_sig += "Option::None,"
func_sig += "Option::Some(KEEP_ASPECT_RATIO_POLICY::NOT_SMALLER),"
func_sig += "Option::Some(MODE::NEAREST),"
func_sig += "Option::None,)"
make_test([x[0], x[1], x[2]], y, func_sig, name)
@staticmethod
def resize... |
ymmetric",
).astype(np.float32)
x = [data, scales]
y = output
for i in range(len(x)):
x[i] = Tensor(Dtype.FP16x16, x[i].shape, to_fp(x[i].flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(y.flatten(), FixedImpl.FP16x16))
name ... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl |
class Reverse_sequence(RunAll):
@staticmethod
def Reverse_sequence_u32():
def reverse_sequence_u32_4x4_batch():
x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], dtype=np.uint32).reshape((4, 4))
y = np.array([0, 1, 2, 3, 5, 4, 6, 7, 10, 9, 8, 11, 15, 14, 13, 12... |
name
)
def reverse_sequence_u32_3x3_time():
x = np.array([0,1,2,3,4,5,6,7,8], dtype=np.uint32).reshape(3,3)
y = np.array([0,7,8,3,4,5,6,1,2], dtype=np.uint32).reshape(3,3)
_x = Tensor(Dtype.U32, x.shape, x.flatten())
_y = Tensor(Dtype.U32, y.shape,... |
(
[_x],
_y,
"input_0.reverse_sequence(TensorTrait::<usize>::new(array![4].span(), array![4,3,2,1].span()), Option::Some(1), Option::Some(0))",
name
)
reverse_sequence_i32_batch()
reverse_sequence_i32_time()
... |
np.array([0, 1, 2, 3, 5, 4, 6, 7, 10, 9, 8, 11, 15, 14, 13, 12], dtype=np.int64).reshape(4, 4), FixedImpl.FP16x16)
_x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
_y = Tensor(Dtype.FP16x16, y.shape, y.flatten())
name = "reverse_sequence_fp16x16_batch_equal_parts"
make_te... |
me(1))",
name
)
def reverse_sequence_different_dimensions_2_4():
x = np.array([1,2,3,4,5,6,7,8], dtype=np.uint32).reshape(2,4)
y = np.array([5,6,7,8,1,2,3,4], dtype=np.uint32).reshape(2,4)
_x = Tensor(Dtype.U32, x.shape, x.flatten())
... |
name
)
def reverse_sequence_different_dimensions_3x9_time():
x = np.array([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26], dtype=np.uint32).reshape(3,9)
y = np.array([18,10,20,12,22,14,24,16,8,9,1,11,3,13,5,15,7,17,0,19,2,21,4,23,6,25,26], dtype=n... |
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Round(RunAll):
@staticmethod
def round_fp8x23():
x = np.array([0.1, 0.5, 0.9, 1.2, 1.5, 1.8, 2.3, 2.5, 2.7, -1.1, -1.5, -1.9, -2.2, -2.5, -2.8]).astype(np.float64)
y = n... |
import os
import glob
# Directory path where Python files/modules are located
directory_path = 'nodegen/node/'
# Get all files in the directory
all_files = os.listdir(directory_path)
# Filter Python files using glob and '*.py' pattern
python_files = [file[:-3] for file in all_files if file.endswith('.py')]
fixed =... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def scatter_elements(data, indices, updates, axis=0, reduction="none"):
if axis < 0:
axis = data.ndim + axis
idx_xsection_shape = indices.shape[:axis] + indices.shape[axis + 1 :]
def make_slice(arr, axis, i):
slc = [slice(None)... |
tes[updates_idx[iter]]
)
return scattered |
class Scatter(RunAll):
@staticmethod
def scatter_fp16x16():
def scatter():
def default():
x1 = np.zeros((3, 3)).astype(np.int64)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.int64)
x3 = np.array(
[[0,1,2... |
ices:input_2, axis:Option::Some(1), reduction:Option::Some('none'))",
name= name)
def axis_1_add():
x1 = np.zeros((3, 3)).astype(np.int64)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.int64)
x3 = np.array(
... |
ame = "scatter_fp8x23_default"
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(0), reduction:Option::Some('none'))",
name= name)
def axis1():
... |
make_test(
inputs = [x1, x2, x3], output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(0), reduction:Option::Some('mul'))",
name= name)
default()
axis1()
axis1_mul()
scatter()
... |
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