text stringlengths 1 2.05k |
|---|
output = y, func_sig = "input_0.scatter(updates:input_1, indices:input_2, axis:Option::Some(1), reduction:Option::Some('none'))",
name= name)
def axis1_max():
x1 = np.zeros((3, 3)).astype(np.int8)
x2 = np.arange(1, 10).re... |
ter(updates:input_1, indices:input_2, axis:Option::Some(0), reduction:Option::Some('none'))",
name= name)
def axis1():
x1 = np.zeros((3, 3)).astype(np.int32)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.int32)
x3 = np.a... |
scatter_3D()
@staticmethod
def scatter_u32():
def scatter_3D():
def default():
x1 = np.zeros((3, 3)).astype(np.uint32)
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.uint32)
x3 = np.array(
[[0,1,2],
... |
x2 = np.arange(1, 10).reshape((3, 3)).astype(np.uint32)
x3 = np.array(
[[0,1,2],
[2,0,1],
[1,0,1]],
)
y = scatter_elements(x1, x3, x2, 0, 'add')
x1 = Tensor(Dtype.U32, x1.sha... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl
def scatter_nd_impl(data, indices, updates, reduction="none"):
assert indices.shape[-1] <= len(data.shape)
assert updates.shape == indices.shape[:-1] + data.shape[indices.shape[-1] :]
output = np.copy(data)
for i in np.ndindex(indices.shape... |
class Scatter_nd(RunAll):
@staticmethod
def scatter_nd_fp16x16():
def scatter_nd_3D():
def default():
x1 = data.astype(np.int64)
x2 = indices.astype(np.int64)
x3 = updates.astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduc... |
eduction='mul')
x1 = Tensor(Dtype.FP16x16, x1.shape, to_fp(x1.flatten(), FixedImpl.FP16x16))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.FP16x16, x3.shape, to_fp(x3.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to... |
to_fp(
y.flatten(), FixedImpl.FP16x16))
name = "scatter_nd_fp16x16_3d_min"
make_test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::Some('min'))",
name= name... |
test(
inputs = [x1, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::Some('add'))",
name= name)
def mul():
x1 = data.astype(np.int64)
x2 = indices.astype(np.int64)... |
x3 = updates.astype(np.uint32)
y = scatter_nd_impl(x1, x2, x3, reduction='min')
x1 = Tensor(Dtype.FP8x23, x1.shape, to_fp(x1.flatten(), FixedImpl.FP8x23))
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.FP8x23, x3.shape, to_fp(x3.flatten... |
, x3, reduction='add')
x1 = Tensor(Dtype.U32, x1.shape, x1.flatten())
x2 = Tensor(Dtype.U32, x2.shape, x2.flatten())
x3 = Tensor(Dtype.U32, x3.shape, x3.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "scatter_nd_u32_ad... |
, x3, x2], output = y, func_sig = "input_0.scatter_nd(updates:input_1, indices:input_2, reduction:Option::Some('max'))",
name= name)
def min():
x1 = np.arange(0,12).reshape((4,3)).astype(np.int32)
x2 = np.array([[0],[1]]).astype(np.uint3... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
scalar = lambda x: Tensor(Dtype.I32, (), np.array([x]).astype(np.int32).flatten()) |
class Sequence_at(RunAll):
@staticmethod
def sequence_at_u32():
def positive_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(0, 6, shape).astype(np.uint32)
tensor = Tensor(Dty... |
es.shape, values.flatten())
sequence.append(tensor)
position = scalar(-2)
name = "sequence_at_i32_negative"
make_test([sequence, position], sequence[-2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
positive_position()
nega... |
ence[2], "SequenceTrait::sequence_at(input_0, input_1)", name, Trait.SEQUENCE)
def negative_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = T... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait |
class Sequence_construct(RunAll):
@staticmethod
def sequence_construct_u32():
sequence = []
tensor_cnt = np.random.randint(1, 10)
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
values = np.random.randint(0, 6, shape).astype(np.uint32)
ten... |
quence_construct_fp8x23"
make_test([sequence], sequence, "SequenceTrait::sequence_construct(input_0)", name, Trait.SEQUENCE)
@staticmethod
def sequence_construct_fp16x16():
sequence = []
tensor_cnt = np.random.randint(1, 10)
shape = np.random.randint(1, 4, 2)
for _ in ... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, Dtype, Tensor, Trait |
class Sequence_empty(RunAll):
@staticmethod
def sequence_empty_u32():
def default():
shape=(0,)
x = np.zeros(shape, dtype=np.uint32)
t = Tensor(Dtype.U32, shape, x.flatten())
make_test(
inputs=[],
output=[t],
... |
make_test(
inputs=[],
output=[t],
func_sig="SequenceTrait::sequence_empty()",
name="sequence_empty_fp16x16",
trait=Trait.SEQUENCE
)
default() |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
scalar = lambda x: Tensor(Dtype.I32, (), np.array([x]).astype(np.int32).flatten()) |
class Sequence_erase(RunAll):
@staticmethod
def sequence_erase_u32():
def positive_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(0, 6, shape).astype(np.uint32)
tensor = Tens... |
y_position()
@staticmethod
def sequence_erase_i32():
def positive_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int32)
tensor = Tensor(Dtype.I32, va... |
sequence_erase_i8():
def positive_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.int8)
tensor = Tensor(Dtype.I8, values.shape, values.flatten())
... |
ition():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP8x23, values.shape, to_fp(values.flatten(), FixedImpl.FP8x23))
sequence.a... |
thod
def sequence_erase_fp16x16():
def positive_position():
sequence = []
shape = np.random.randint(1, 4, 2)
for _ in range(5):
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP16x16, values.shape, to_fp(... |
n()
negative_position()
empty_position() |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
scalar = lambda x: Tensor(Dtype.I32, (), np.array([x]).astype(np.int32).flatten()) |
class Sequence_insert(RunAll):
@staticmethod
def sequence_insert_u32():
def default():
sequence = []
tensor_cnt = 3
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
val = np.random.randint(0, 6, shape).astype(np.uint32)
... |
np.random.randint(0, 6, shape).astype(np.int8)
t = Tensor(Dtype.I8, val.shape, val.flatten())
sequence.append(t)
val = np.random.randint(0, 6, shape).astype(np.int8)
tensor = Tensor(Dtype.I8, val.shape, val.flatten())
position = np.random.randint(-2... |
sequence.append(t)
val = np.random.randint(0, 6, shape).astype(np.float64)
tensor = Tensor(Dtype.FP16x16, val.shape, to_fp(
val.flatten(), FixedImpl.FP16x16))
position = np.random.randint(-2, 2)
expected_sequence = sequence.copy()
expected_s... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
scalar = lambda x: Tensor(Dtype.U32, (), np.array([x]).astype(np.uint32).flatten()) |
class Sequence_length(RunAll):
@staticmethod
def sequence_length_u32():
def default():
sequence = []
tensor_cnt = np.random.randint(1, 10)
shape = np.random.randint(1, 4, 2)
for _ in range(tensor_cnt):
values = np.random.randint(0, 6, shap... |
tensor = Tensor(Dtype.I32, values.shape, values.flatten())
sequence.append(tensor)
name = "sequence_length_i32_broadcast"
make_test([sequence], scalar(len(sequence)), "input_0.sequence_length()", name, Trait.SEQUENCE)
default()
broadcast()
@staticmethod
... |
me, Trait.SEQUENCE)
def broadcast():
sequence = []
tensor_cnt = np.random.randint(1, 10)
for _ in range(tensor_cnt):
shape = np.random.randint(1, 4, 2)
values = np.random.randint(-6, 6, shape).astype(np.float64)
tensor = Tenso... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl
def shrink(input_array: np.ndarray, bias: float, lambd: float) -> np.ndarray:
output_array = np.where(input_array > lambd, input_array - bias,
np.where(input_array < -lambd, input_array + bias, 0))
return output_array |
class Shrink(RunAll):
@staticmethod
def shrink_fp8x23():
def shrink_hard():
x = np.random.uniform(-3, 3, (3, 3, 3)).astype(np.float64)
bias = np.float64(0)
lambd = np.float64(1)
y = shrink(x, bias, lambd)
x = Tensor(Dtype.FP8x23, x.shape, to... |
as = np.float64(1)
lambd = np.float64(1)
y = shrink(x, bias, lambd)
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 = "... |
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
import tensorflow as tf
class Sigmoid(RunAll):
@staticmethod
def fp8x23():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float32)
y = tf.keras.activations.sigmoid(x).num... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl |
class Sign(RunAll):
@staticmethod
def sign_i8():
def sign():
x = np.array(range(-5, 6)).astype(np.int8)
y = np.array([-1, -1, -1, -1, -1, 0, 1, 1, 1, 1, 1]).astype(np.int8)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, ... |
1, 1, 1, 1, 1]).astype(np.int64), FixedImpl.FP8x23)
x = Tensor(Dtype.FP8x23, x.shape, x.flatten())
y = Tensor(Dtype.FP8x23, y.shape, y.flatten())
name = "sign_fP8x23"
make_test(
[x], y, "input_0.sign()", name)
sign() |
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Sin(RunAll):
@staticmethod
def sin_fp8x23():
x = np.random.uniform(-3, 7, (2, 2)).astype(np.float64)
y = np.sin(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
... |
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Sinh(RunAll):
@staticmethod
def sinh_fp8x23():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
y = np.sinh(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl |
class Slice(RunAll):
@staticmethod
def slice_u32():
def slice_2D():
x = np.random.randint(0, 255, (2, 4)).astype(np.uint32)
y = x[0:2, 2:4]
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "slice_... |
def slice_2D():
x = np.random.randint(-127, 127, (2, 4)).astype(np.int8)
y = x[0:2, 2:4]
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
name = "slice_i8_2d"
make_test(
[x], y, "input_0.slice(... |
, 3].span()))", name)
slice_2D()
slice_3D()
@staticmethod
def slice_fp16x16():
def slice_2D():
x = to_fp(np.random.randint(-127, 127, (2, 4)
).astype(np.int64), FixedImpl.FP16x16)
y = x[0:2, 2:4]
x = Tensor(Dt... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def softmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
x_max = np.max(x, axis=axis, keepdims=True)
tmp = np.exp(x - x_max)
s = np.sum(tmp, axis=axis, keepdims=True)
return tmp / s |
class Softmax(RunAll):
@staticmethod
def axis_0():
x = np.abs(np.random.randn(3, 4, 5).astype(np.float32))
y = softmax(x, axis=0)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
... |
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def softmax_zero(x: np.ndarray, axis: int = -1) -> np.ndarray:
x_max = np.max(x, axis=axis, keepdims=True)
tmp = np.exp(x - x_max)
tmp = np.where(x == 0.0, 0.0, tmp)
s = np.sum(t... |
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def softplus(x: np.ndarray) -> np.ndarray:
return np.log(np.exp(x) + 1)
class Softplus(RunAll):
@staticmethod
def softplus_fp():
def fp8x23():
x = np.random.uni... |
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def softsign(x: np.ndarray) -> np.ndarray:
return x / (1 + np.abs(x))
class Softsign(RunAll):
@staticmethod
def softsign_fp():
def fp8x23():
x = np.random.unifo... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def space_to_depth(data: np.ndarray, blocksize: int = 2) -> np.ndarray:
if len(data.shape) != 4:
raise RuntimeError(f"Unexpected shape {data.shape!r}.")
b, C, H, W = data.shape
tmpshape = (
b,
C,
H
blocksize,
... |
class Space_to_depth(RunAll):
@staticmethod
def fp8x23():
x = np.random.uniform(-3, 3, (1, 2, 2, 4)).astype(np.float64)
y = space_to_depth(x)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
... |
e_to_depth_u32"
make_test([x], y, "NNTrait::space_to_depth(@input_0, 2)",
name, Trait.NN) |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl |
class Split(RunAll):
@staticmethod
def split_u32():
def split_1D():
x = np.random.randint(0, 255, 6).astype(np.uint32)
y = [
np.array(x[0:2]).astype(np.uint32),
np.array(x[2:4]).astype(np.uint32),
np.array(x[4:6]).astype(np.uint32)... |
]
_y = [
Tensor(Dtype.U32, y[0].shape, y[0].flatten()),
Tensor(Dtype.U32, y[1].shape, y[1].flatten()),
]
name = "split_u32_2d_variable_parts"
make_test(
[_x], _y, "input_0.split(1, Option::None(()), Option::Some(TensorTrait:... |
None(()))", name)
def split_2d_uneven():
x = np.random.randint(0, 255, (2, 8)).astype(np.uint32)
y = [
np.array(x[0:2, 0:3]).astype(np.uint32),
np.array(x[0:2, 3:6]).astype(np.uint32),
np.array(x[0:2, 6:8]).astype(np.uint32)... |
name = "split_fp16x16_1d_variable_parts"
make_test(
[_x], _y, "input_0.split(0, Option::None(()), Option::Some(TensorTrait::<u32>::new(shape: array![2].span(), data: array![2, 4].span(),)))", name)
def split_2D():
x = to_fp(np.random.randint(-127, 127, (2, 6)
... |
Tensor(Dtype.FP16x16, y[2].shape, y[2].flatten()),
]
name = "split_fp16x16_zero_size"
make_test(
[_x], _y, "input_0.split(0, Option::None(()), Option::Some(TensorTrait::<u32>::new(shape: array![3].span(), data: array![0, 0, 0].span(),)))", name)
... |
1, Option::Some(3), Option::None(()))", name)
split_1D()
split_2D()
split_zero_size()
split_1d_uneven()
split_2d_uneven() |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl |
class Split_to_sequence(RunAll):
@staticmethod
def split_to_sequence_u32():
def split_to_sequence_1D():
x = np.random.randint(0, 255, 6).astype(np.uint32)
y = [
np.array(x[0:2]).astype(np.uint32),
np.array(x[2:4]).astype(np.uint32),
... |
TensorTrait::<u32>::new(shape: array![1].span(), data: array![2].span(),)))", name)
y = [
np.array(x[0:2, 0:2]).astype(np.uint32),
np.array(x[0:2, 2:6]).astype(np.uint32)
]
_y = [
Tensor(Dtype.U32, y[0].shape, y[0].flatten()),
... |
Tensor(Dtype.U32, y[2].shape, y[2].flatten()),
Tensor(Dtype.U32, y[3].shape, y[3].flatten()),
]
name = "split_to_sequence_u32_1d_uneven"
make_test(
[_x], _y, "input_0.split_to_sequence(0, 1, Option::Some(TensorTrait::<u32>::new(shape: array![1].span()... |
, y[2].shape, y[2].flatten()),
Tensor(Dtype.U32, y[3].shape, y[3].flatten()),
Tensor(Dtype.U32, y[4].shape, y[4].flatten()),
Tensor(Dtype.U32, y[5].shape, y[5].flatten()),
Tensor(Dtype.U32, y[6].shape, y[6].flatten()),
Tensor(Dtype.U32, y[7... |
np.array(x[0:1]).astype(np.uint32),
np.array(x[1:2]).astype(np.uint32),
np.array(x[2:3]).astype(np.uint32),
np.array(x[3:4]).astype(np.uint32),
np.array(x[4:5]).astype(np.uint32),
np.array(x[5:6]).astype(np.uint32),
np... |
Tensor(Dtype.FP16x16, y[1].shape, y[1].flatten()),
Tensor(Dtype.FP16x16, y[2].shape, y[2].flatten()),
]
name = "split_to_sequence_fp16x16_1d_equal_parts"
make_test(
[_x], _y, "input_0.split_to_sequence(0, 1, Option::Some(TensorTrait::<u32>::new(shape:... |
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