text stringlengths 1 2.05k |
|---|
me)
default()
broadcast()
@staticmethod
def less_equal_i8():
def default():
x = np.random.randint(-3, 3, (2, 2)).astype(np.int8)
y = np.random.randint(-3, 3, (2, 2)).astype(np.int8)
z = np.less_equal(x, y)
x = Tensor(Dtype.I8, x.shape, x... |
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.I32, z.shape, z.flatten())
name = "less_equal_fp8x23_broadcast"
make_test([x, y], z, "input_0.less_equal(@input_1)", name)
default()
broadcast()
@staticmethod
def less_equal_fp16x16():
def default():
... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
from typing |
import Optional
def linear(
i: np.ndarray,
w: np.ndarray,
b: Optional[np.ndarray] = None,
) -> np.ndarray:
return np.dot(i, w.T) + b |
class Linear(RunAll):
@staticmethod
def linear_i32():
i = np.random.randint(-5, 9, (3)).astype(np.int32)
w = np.random.randint(-5, 9, (2, 3)).astype(np.int32)
b = np.random.randint(-5, 9, (2)).astype(np.int32)
y = linear(i, w, b)
i = Tensor(Dtype.I32, i.shape, i.flatten... |
y = linear(i, w, b)
i = Tensor(Dtype.FP8x23, i.shape, to_fp(
i.flatten(), FixedImpl.FP8x23))
w = Tensor(Dtype.FP8x23, w.shape, to_fp(
w.flatten(), FixedImpl.FP8x23))
b = Tensor(Dtype.FP8x23, b.shape, to_fp(
b.flatten(), FixedImpl.FP8x23))
y = Tensor(D... |
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Log(RunAll):
@staticmethod
def log_fp8x23():
x = np.random.uniform(1, 127, (2, 2)).astype(np.float64)
y = np.log(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, Trait
def logsoftmax(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 (x - x_max) - np.log(s) |
class Logsoftmax(RunAll):
def logsoftmax_fp8x23():
def axis_0():
x = np.random.uniform(-3, 3, (2, 2)).astype(np.float64)
y = logsoftmax(x, 0)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.... |
axis_0()
axis_1() |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl |
class Matmul(RunAll):
@staticmethod
def matmul_u32():
def matmul_1D():
a = np.random.randint(0, 255, (3)).astype(np.uint32)
b = np.random.randint(0, 255, (3)).astype(np.uint32)
y = np.matmul(a, b).reshape((1))
a = Tensor(Dtype.U32, a.shape, a.flatten())
... |
matmul_2x1()
matmul_1x2()
@staticmethod
def matmul_i32():
def matmul_1D():
a = np.random.randint(-127, 127, (3)).astype(np.int32)
b = np.random.randint(-127, 127, (3)).astype(np.int32)
y = np.matmul(a, b).reshape((1))
a = Tensor(Dtype.I32, a.shap... |
matmul_1D()
matmul_2x2()
matmul_2x1()
matmul_1x2()
@staticmethod
def matmul_i8():
def matmul_1D():
a = np.random.randint(-4, 5, (3)).astype(np.int8)
b = np.random.randint(-4, 5, (3)).astype(np.int8)
y = np.matmul(a, b).reshape((1))
... |
matmul_2x2()
matmul_2x1()
matmul_1x2()
@staticmethod
def matmul_fp8x23():
def matmul_1D():
a = np.random.randint(-3, 4, (3)).astype(np.int64)
b = np.random.randint(-3, 4, (3)).astype(np.int64)
y = np.matmul(a, b).reshape((1))
a = Tensor(D... |
np.int64)
b = np.random.randint(-3, 4, (2, 1)).astype(np.int64)
y = np.matmul(a, b)
a = Tensor(Dtype.FP8x23, a.shape, to_fp(
a.flatten(), FixedImpl.FP8x23))
b = Tensor(Dtype.FP8x23, b.shape, to_fp(
b.flatten(), FixedImpl.FP8x23))
... |
-3, 4, (1, 2)).astype(np.int64)
y = np.matmul(a, b)
a = Tensor(Dtype.FP16x16, a.shape, to_fp(
a.flatten(), FixedImpl.FP16x16))
b = Tensor(Dtype.FP16x16, b.shape, to_fp(
b.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait |
class Max(RunAll):
@staticmethod
def max_u32_two_tensors():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
z = np.maximum(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
... |
st_two_tensors"
make_test([x, y], z, "TensorTrait::max(array![input_0, input_1].span())", name)
default()
broadcast()
@staticmethod
def max_i8_two_tensors():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.int8)
y = np.random.randint(... |
(1, 2)).astype(np.float64)
z = np.maximum(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
... |
om.randint(0, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
z = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
m = np.maximum(np.maximum(x, y), z)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U3... |
om.randint(0, 6, (2, 2)).astype(np.int32)
y = np.random.randint(0, 6, (1, 2)).astype(np.int32)
z = np.random.randint(0, 6, (1, 1)).astype(np.int32)
m = np.maximum(np.maximum(x, y), z)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, ... |
p8x23_three_tensors():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
m = np.maximum(np.maximum(x, y), z)
... |
m = np.maximum(np.maximum(x, y), z)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.fla... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait |
class Min(RunAll):
@staticmethod
def min_u32_two_tensors():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
z = np.minimum(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
... |
st_two_tensors"
make_test([x, y], z, "TensorTrait::min(array![input_0, input_1].span())", name)
default()
broadcast()
@staticmethod
def min_i8_two_tensors():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.int8)
y = np.random.randint(... |
(1, 2)).astype(np.float64)
z = np.minimum(x, y)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.flatten(), FixedImpl.FP8x23))
y = Tensor(Dtype.FP8x23, y.shape, to_fp(
y.flatten(), FixedImpl.FP8x23))
z = Tensor(Dtype.FP8x23, z.shape, to_fp(
... |
om.randint(0, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
z = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
m = np.minimum(np.minimum(x, y), z)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U3... |
om.randint(0, 6, (2, 2)).astype(np.int32)
y = np.random.randint(0, 6, (1, 2)).astype(np.int32)
z = np.random.randint(0, 6, (1, 1)).astype(np.int32)
m = np.minimum(np.minimum(x, y), z)
x = Tensor(Dtype.I32, x.shape, x.flatten())
y = Tensor(Dtype.I32, y.shape, ... |
p8x23_three_tensors():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
m = np.minimum(np.minimum(x, y), z)
... |
m = np.minimum(np.minimum(x, y), z)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
z.fla... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl |
class Mul(RunAll):
@staticmethod
def mul_u32():
def default():
x = np.random.randint(3, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 3, (3, 3, 3)).astype(np.uint32)
z = x * y
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(D... |
dom.randint(-3, 3, (3, 3, 3)).astype(np.int8)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
z = x * y
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
z = Tensor(Dtype.I8, z.shape, z.flatten())
name ... |
make_test([x, y], z, "input_0 * input_1", name)
default()
broadcast()
@staticmethod
def mul_fp16x16():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
z = x * y
... |
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_test, to_fp, Tensor, Dtype, FixedImpl
class Neg(RunAll):
@staticmethod
def neg_i32():
x = np.random.randint(-127, 127, (2, 2)).astype(np.int32)
y = np.negative(x)
x = Tensor(Dtype.I32, x.shape, x.flatten())
... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl |
class Nonzero(RunAll):
@staticmethod
def nonzero_u32():
def nonzero_2D():
x = np.random.randint(0, 255, (2, 4)).astype(np.uint32)
y = np.array(np.nonzero(x), dtype=np.int64)
x = Tensor(Dtype.U32, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flat... |
make_test(
[x], y, "input_0.nonzero()", name)
def nonzero_3D():
x = np.random.randint(-127, 127, (20, 10, 5)).astype(np.int8)
y = np.array(np.nonzero(x), dtype=np.int64)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.... |
o_fp(np.random.randint(-127, 127, (20, 10, 5)
).astype(np.int64), FixedImpl.FP16x16)
y = np.array(np.nonzero(x), dtype=np.int64)
x = Tensor(Dtype.FP16x16, x.shape, x.flatten())
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "n... |
import numpy as np
from nodegen.node import RunAll
from ..helpers import make_node, make_test, Tensor, Dtype
class Not(RunAll):
@staticmethod
def not_bool():
x = np.random.uniform(True, False, (1, 1)).astype(bool)
y = ~(x)
x = Tensor(Dtype.Bool, x.shape, x.flatten())
y = Tensor... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl |
class Or(RunAll):
@staticmethod
def or_u32():
def default():
x = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
y = np.random.randint(0, 6, (3, 3, 3)).astype(np.uint32)
z = np.logical_or(x, y)
x = Tensor(Dtype.U32, x.shape, x.flatten())
... |
):
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.int8)
z = np.logical_or(x, y)
x = Tensor(Dtype.I8, x.shape, x.flatten())
y = Tensor(Dtype.I8, y.shape, y.flatten())
... |
t"
make_test([x, y], z, "input_0.or(@input_1)", name)
default()
broadcast()
@staticmethod
def or_fp16x16():
def default():
x = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
y = np.random.randint(-3, 3, (3, 3, 3)).astype(np.float64)
... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl |
class Pow(RunAll):
@staticmethod
def pow_fp8x23():
def default():
x = np.array([1, 2, 3]).astype(np.float64)
y = np.array([1, 2, 3]).astype(np.float64)
z = np.array(pow(x, y), dtype=np.float64)
x = Tensor(Dtype.FP8x23, x.shape, to_fp(
x.fl... |
4)
z = np.array(pow(x, y), dtype=np.float64)
x = Tensor(Dtype.FP16x16, x.shape, to_fp(
x.flatten(), FixedImpl.FP16x16))
y = Tensor(Dtype.FP16x16, y.shape, to_fp(
y.flatten(), FixedImpl.FP16x16))
z = Tensor(Dtype.FP16x16, z.shape, to_fp(
... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait
def random_uniform_like(x: np.ndarray, high: int=1,low: int=0,seed: int=25) ->np.ndarray:
dtype = np.float64
if seed is None or np.isnan(seed):
state = np.random.RandomState()
else:
state = np.random.RandomState(seed=int(seed))
... |
class Random_uniform_like(RunAll):
@staticmethod
def fp8x23():
x = np.random.uniform(1, 10, (1, 2, 2, 4)).astype(np.float64)
y = random_uniform_like(x)
args = [10, 1]
args_str = get_data_statement(to_fp(np.array(args).flatten(), FixedImpl.FP8x23), Dtype.FP8x23)
x = Tens... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl, Trait, get_data_statement |
class Range(RunAll):
@staticmethod
def fp8x23():
args = [1, 5, 0.3]
args_str = get_data_statement(to_fp(np.array(args).flatten(), FixedImpl.FP8x23), Dtype.FP8x23)
y = np.arange(*args)
print(y)
y = Tensor(Dtype.FP8x23, y.shape, to_fp(y.flatten(), FixedI... |
rgs).flatten(), Dtype.U32)
y = np.arange(*args)
print(y)
y = Tensor(Dtype.U32, y.shape, y.flatten())
name = "range_u32"
make_test(
[],
y,
f"TensorTrait::range({','.join(args_str)})",
name,
... |
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_l1(RunAll):
@staticmethod
def reduce_l1_fp8x23():
def reduce_l1_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.float32), shape).astype(... |
xis_0()
@staticmethod
def reduce_l1_fp16x16():
def reduce_l1_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.float32), shape).astype(np.int64)
... |
)
@staticmethod
def reduce_l1_i8():
def reduce_l1_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int8)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int8)
y = np... |
l1_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int32)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.int32)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=False).astype(np.... |
= [3, 2, 2]
axes = np.array([2], dtype=np.uint32)
keepdims = False
x = np.reshape(np.arange(1, np.prod(shape) + 1, dtype=np.float32), shape).astype(np.uint32)
y = np.sum(a=np.abs(x), axis=tuple(axes), keepdims=False).astype(np.uint32)
x = Tensor(Dtype.U32, x.... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_node, make_test, to_fp, Tensor, Dtype, FixedImpl |
import numpy as np |
class Reduce_l2(RunAll):
@staticmethod
def reduce_l2_fp8x23():
def reduce_l2_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.float32), shape).astype(... |
e)
reduce_l2_export_do_not_keepdims()
reduce_l2_export_keepdims()
reduce_l2_axis_0()
@staticmethod
def reduce_l2_fp16x16():
def reduce_l2_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.array([2], dtype=np.int64)
keepdims = Fal... |
ake_node([x], [y], name)
make_test(
[x], y, "input_0.reduce_l2(0, true)", name)
reduce_l2_export_do_not_keepdims()
reduce_l2_export_keepdims()
reduce_l2_axis_0()
@staticmethod
def reduce_l2_complex64():
def reduce_l2_axis_0():
... |
import numpy as np
from nodegen.node |
import RunAll
from ..helpers |
import make_test, to_fp, Tensor, Dtype, FixedImpl |
class Reduce_log_sum(RunAll):
@staticmethod
def reduce_log_sum_fp8x23():
def reduce_log_sum_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.float32),... |
e_log_sum(0, true)", name)
reduce_log_sum_export_do_not_keepdims()
reduce_log_sum_export_keepdims()
reduce_log_sum_axis_0()
@staticmethod
def reduce_log_sum_fp16x16():
def reduce_log_sum_export_do_not_keepdims():
shape = [3, 2, 2]
axes = np.arra... |
o_fp(
y.flatten(), FixedImpl.FP8x23))
name = "reduce_log_sum_fp16x16_export_negative_axes_keepdims"
make_test(
[x], y, "input_0.reduce_log_sum(0, true)", name)
reduce_log_sum_export_do_not_keepdims()
reduce_log_sum_export_keepdims()
... |
import numpy as np
from nodegen.node |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.