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[Accuracy diff No.78、142、143] Improve get_numpy_tensor for rpow and pow #528

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Merged
merged 3 commits into from
Aug 22, 2025
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[Accuracy diff No.78、142、143] Improve get_numpy_tensor for rpow and pow #528

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5de6c19
improve pow
zrr1999 Aug 18, 2025
d945a31
add is_base_arg and int(const) != const
zrr1999 Aug 20, 2025
823c0e1
update
zrr1999 Aug 21, 2025
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83 changes: 54 additions & 29 deletions tester/api_config/config_analyzer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1879,42 +1879,67 @@ def get_padding_offset(bsz, max_seq_len, seq_lens_this_time):
elif api_config.api_name == "paddle.poisson":
self.numpy_tensor = numpy.random.random(self.shape).astype(self.dtype)

elif api_config.api_name in {"paddle.Tensor.__pow__","paddle.Tensor.pow", "paddle.pow"}:
# paddle.Tensor.__pow__(a, b) => a ^ b, where a is self and b is other
if self.check_arg(api_config, 0, "self") or self.check_arg(api_config, 0, "x"):
self.numpy_tensor = self.get_random_numpy_tensor(self.shape, self.dtype, min=-10, max=10)
else:
# self.check_arg(api_config, 1, "other"):
self.numpy_tensor = self.get_random_numpy_tensor(self.shape, self.dtype, min=-5, max=5)

elif api_config.api_name == "paddle.Tensor.__rpow__":
elif api_config.api_name in {"paddle.Tensor.__pow__", "paddle.Tensor.pow", "paddle.pow", "paddle.Tensor.__rpow__"}:
dtype = self.dtype
def get_max(value, dtype_max, default_max = 5):
def get_base_max(value, dtype_max, default_max = 5):
value_max = default_max
if isinstance(other, (int, float, bool, complex, numpy.number)):
assert value > 0, "other should be > 0 for paddle.Tensor.__rpow__"
if value < 1:
# value**(-max) < MAX => (1/value)**max < MAX
value = 1/value
ln_value = math.log(value)
# dy/dx = y*ln(value) < MAX, y < MAX => y*max(ln(value), 1) < MAX
output_max = dtype_max/max(1, ln_value)
value_max = math.log(output_max)/ln_value
if value <= 0:
return value_max
if value < 1:
# value**(-max) < MAX => (1/value)**max < MAX
value = 1/value
ln_value = math.log(value)
# dy/dx = y*ln(value) < MAX, y < MAX => y*max(ln(value), 1) < MAX
output_max = dtype_max/max(1, ln_value)
value_max = math.log(output_max)/ln_value
if isinstance(value, int):
value_max = math.floor(value_max)
return value_max
def get_exponent_max(value, dtype_max, default_max = 5):
value_max = default_max
if isinstance(value, (int, float, bool, numpy.number)):
if value <= 2:
return value_max
value_max = math.pow(dtype_max/value, 1/value)
if isinstance(value, int):
value_max = math.floor(value_max)
return value_max

# paddle.Tensor.__rpow__(a, b) => b ^ a, where a is self and b is other
if self.check_arg(api_config, 0, "self"):
other = self.get_arg(api_config, 1, "other")
value_max = get_max(other, numpy.finfo(self.dtype).max)
self.numpy_tensor = self.get_random_numpy_tensor(self.shape, self.dtype, min=-value_max, max=value_max)
if api_config.api_name == "paddle.Tensor.__rpow__":
# paddle.Tensor.__rpow__(a, b) => b ^ a, where a is self and b is other
is_base_arg = self.check_arg(api_config, 1, "other")
if is_base_arg:
const = self.get_arg(api_config, 0, "self")
get_max = get_base_max
default_max = 10
else:
const = self.get_arg(api_config, 1, "other")
get_max = get_exponent_max
default_max = 5
else:
# self.check_arg(api_config, 1, "other"):
self = self.get_arg(api_config, 0, "self")
value_max = get_max(other, numpy.finfo(self.dtype).max, 10)
self.numpy_tensor = self.get_random_numpy_tensor(self.shape, self.dtype, min=-value_max, max=value_max)

# paddle.Tensor.__pow__(a, b) => a ^ b, where a is self and b is other
is_base_arg = self.check_arg(api_config, 0, "self") or self.check_arg(api_config, 0, "x")
if is_base_arg:
const = self.get_arg(api_config, 1, "other")
get_max = get_base_max
default_max = 10
else:
const = self.get_arg(api_config, 0, "self")
get_max = get_exponent_max
default_max = 5
if isinstance(const, (int, float, bool, numpy.number)):
value_max = get_max(const, numpy.finfo(self.dtype).max, default_max)
if is_base_arg and int(const) != const:
# Avoid situations like (-2.3) ^ 0.5
self.numpy_tensor = self.get_random_numpy_tensor(self.shape, self.dtype, min=0, max=value_max)
else:
self.numpy_tensor = self.get_random_numpy_tensor(self.shape, self.dtype, min=-value_max, max=value_max)
else:
if is_base_arg:
# Avoid situations like (-2.3) ^ 0.5
self.numpy_tensor = self.get_random_numpy_tensor(self.shape, self.dtype, min=0, max=default_max)
else:
self.numpy_tensor = self.get_random_numpy_tensor(self.shape, self.dtype, min=-default_max, max=default_max)
elif api_config.api_name == "paddle.nn.functional.sigmoid_focal_loss":
if self.check_arg(api_config, 1, "label"):
self.numpy_tensor = numpy.random.randint(low=0, high=2, size=self.shape).astype(self.dtype)
Expand Down