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Merged
merged 11 commits into from
Jul 29, 2025
Merged

Fix All Accuracy Backtest Errors #458

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4f3b94b
fix empty add __getitem__ __setitem__ as_complex
cangtianhuang Jul 29, 2025
b1b2072
Merge remote-tracking branch 'upstream/main' into develop
cangtianhuang Jul 29, 2025
bf072de
fix fused_rotary_position_embedding
cangtianhuang Jul 29, 2025
f0b866d
fix batch_norm interpolate linear
cangtianhuang Jul 29, 2025
aa5dd69
fix max_pool3d
cangtianhuang Jul 29, 2025
72f07ca
fix
cangtianhuang Jul 29, 2025
93d7b15
fix batch_norm empty interpolate
cangtianhuang Jul 29, 2025
b60901c
refine FusedRotaryPositionEmbeddingRule LinearRule
cangtianhuang Jul 29, 2025
1044774
fix
cangtianhuang Jul 29, 2025
1c18f7e
fix setitem
cangtianhuang Jul 29, 2025
00011c0
fix FusedRotaryPositionEmbeddingRule SetitemRule
cangtianhuang Jul 29, 2025
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4 changes: 3 additions & 1 deletion tester/api_config/5_accuracy/accuracy_gpu_error_dtype_diff.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -9985,4 +9985,6 @@ paddle.linalg.lstsq(Tensor([9, 9],"float32"), Tensor([9, 5],"float32"), rcond=1e
paddle.ldexp(Tensor([10, 20, 1],"float16"), Tensor([1],"int32"), )
paddle.ldexp(Tensor([207],"float16"), Tensor([207],"int32"), )
paddle.ldexp(Tensor([247],"float16"), Tensor([247],"int32"), )
paddle.ldexp(Tensor([5, 6, 6],"float16"), Tensor([6],"int32"), )
paddle.ldexp(Tensor([5, 6, 6],"float16"), Tensor([6],"int32"), )
paddle.Tensor.__add__(Tensor([10, 1024],"float32"), Tensor([10, 1024],"bfloat16"), )
paddle.Tensor.__add__(Tensor([4, 3, 2],"float32"), Tensor([4, 3, 2],"bfloat16"), )
31 changes: 23 additions & 8 deletions tester/api_config/config_analyzer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1838,14 +1838,29 @@ def get_padding_offset(bsz, max_seq_len, seq_lens_this_time):
# padding value should not be too large
self.numpy_tensor = self.get_random_numpy_tensor(self.shape, self.dtype, min=0, max=10)

elif api_config.api_name in {"paddle.Tensor.__getitem__","paddle.Tensor.__setitem__"} and (len(api_config.args) > 1 and str(api_config.args[1]) == str(self) or str(api_config.args[0]) != str(self)):
arr = self.get_arg(api_config, 0, "arr")
min_dim = min(arr.shape)
indices = (numpy.random.randint(0, min_dim, size=self.numel())).astype("int64")
if self.dtype == 'bool':
ind = numpy.random.choice(self.numel(), self.get_arg(api_config, 2, "value").shape[0], replace=False)
indices[ind] = 1
self.numpy_tensor = indices.reshape(self.shape)
elif api_config.api_name == "paddle.Tensor.__getitem__":
if self.check_arg(api_config, 1, "item"):
arr = self.get_arg(api_config, 0, "arr")
min_dim = min(arr.shape)
if self.dtype == "bool":
indices = numpy.random.choice([0, 1], size=self.numel())
else:
indices = numpy.random.randint(0, min_dim, size=self.numel())
self.numpy_tensor = indices.reshape(self.shape).astype(self.dtype)

elif api_config.api_name == "paddle.Tensor.__setitem__":
if self.check_arg(api_config, 1, "item"):
arr = self.get_arg(api_config, 0, "arr")
value = self.get_arg(api_config, 2, "value")
min_dim = min(arr.shape)
if value is not None and hasattr(value, "shape"):
indices = numpy.zeros(self.numel(), dtype="int64")
num_true = min(value.shape[0], self.numel())
true_indices = numpy.random.choice(self.numel(), size=num_true, replace=False)
indices[true_indices] = 1
else:
indices = numpy.random.choice([0, 1], size=self.numel())
self.numpy_tensor = indices.reshape(self.shape).astype(self.dtype)

elif api_config.api_name == "paddle.poisson":
self.numpy_tensor = numpy.random.random(self.shape).astype(self.dtype)
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75 changes: 75 additions & 0 deletions tester/api_config/torch_error_skip.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -1806,3 +1806,78 @@ paddle.digamma(x=Tensor([6, 6, 119304648],"float16"), )
paddle.digamma(x=Tensor([6, 6, 19884108, 6],"float16"), )
paddle.digamma(x=Tensor([6, 6, 6, 19884108],"float16"), )
paddle.linalg.multi_dot(list[Tensor([4],"float16"),Tensor([4, 1073741825],"float16"),Tensor([1073741825, 4],"float16"),Tensor([4, 5],"float16"),], )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[0.7999999999999999,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[1.2999999999999998,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[1.4999999999999998,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[1.5999999999999996,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[2.0999999999999996,], mode="linear", align_corners=True, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[2.4999999999999996,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[2.6999999999999997,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[2.8999999999999995,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[3.2999999999999994,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[3.2999999999999994,], mode="linear", align_corners=True, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[3.6999999999999993,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[3.6999999999999993,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[3.9999999999999996,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[4.099999999999999,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[4.499999999999999,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[4.599999999999999,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[4.899999999999999,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[4.999999999999998,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[5.199999999999998,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[5.599999999999999,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[5.699999999999998,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[6.199999999999998,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[6.299999999999998,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[6.699999999999998,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[6.899999999999999,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[7.199999999999998,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[7.599999999999998,], mode="linear", align_corners=True, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[7.699999999999998,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[7.999999999999998,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[8.199999999999998,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[8.499999999999998,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float32"), size=None, scale_factor=list[8.599999999999998,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[0.7999999999999999,], mode="linear", align_corners=True, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[1.1999999999999997,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[1.5999999999999996,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[1.6999999999999997,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[2.5999999999999996,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[2.5999999999999996,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[2.8999999999999995,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[3.0999999999999996,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[3.1999999999999997,], mode="linear", align_corners=True, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[3.3999999999999995,], mode="linear", align_corners=True, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[3.4999999999999996,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[3.8999999999999995,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[4.099999999999999,], mode="linear", align_corners=True, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[4.399999999999999,], mode="linear", align_corners=True, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[4.699999999999998,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[4.899999999999999,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[4.999999999999998,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[5.199999999999998,], mode="linear", align_corners=True, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[5.399999999999999,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[5.699999999999998,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[5.999999999999998,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[6.399999999999999,], mode="linear", align_corners=True, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[6.499999999999998,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[6.799999999999998,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[6.999999999999998,], mode="linear", align_corners=False, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[7.199999999999998,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[7.399999999999999,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[7.699999999999998,], mode="linear", align_corners=False, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[7.699999999999998,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[7.999999999999998,], mode="linear", align_corners=True, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[8.299999999999999,], mode="linear", align_corners=True, align_mode=0, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[8.499999999999998,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 10, 4],"float64"), size=None, scale_factor=list[8.599999999999998,], mode="linear", align_corners=True, align_mode=1, data_format="NWC", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 10],"float32"), size=None, scale_factor=list[0.6,2.9999999999999996,], mode="bilinear", align_corners=True, align_mode=0, data_format="NCHW", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 10],"float32"), size=None, scale_factor=list[0.6,4.199999999999999,], mode="bilinear", align_corners=True, align_mode=0, data_format="NCHW", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 10],"float32"), size=None, scale_factor=list[1.7999999999999998,4.199999999999999,], mode="bilinear", align_corners=False, align_mode=1, data_format="NCHW", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 10],"float32"), size=None, scale_factor=list[2.9999999999999996,1.7999999999999998,], mode="bilinear", align_corners=True, align_mode=1, data_format="NCHW", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 10],"float32"), size=None, scale_factor=list[2.9999999999999996,2.9999999999999996,], mode="bilinear", align_corners=False, align_mode=0, data_format="NCHW", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 10],"float64"), size=None, scale_factor=list[0.6,4.199999999999999,], mode="bilinear", align_corners=True, align_mode=0, data_format="NCHW", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 10],"float64"), size=None, scale_factor=list[1.7999999999999998,2.9999999999999996,], mode="bilinear", align_corners=False, align_mode=1, data_format="NCHW", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 10],"float64"), size=None, scale_factor=list[1.7999999999999998,4.199999999999999,], mode="bilinear", align_corners=False, align_mode=0, data_format="NCHW", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 10],"float64"), size=None, scale_factor=list[2.9999999999999996,1.7999999999999998,], mode="bilinear", align_corners=False, align_mode=1, data_format="NCHW", name=None, )
paddle.nn.functional.interpolate(Tensor([2, 2, 10, 10],"float64"), size=None, scale_factor=list[2.9999999999999996,1.7999999999999998,], mode="bilinear", align_corners=True, align_mode=0, data_format="NCHW", name=None, )
1 change: 1 addition & 0 deletions tester/base_config.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -39,6 +39,7 @@ special_accuracy_atol_rtol:
# All configs that report dtype diff when not in not_check_dtype list should be
# copied to tester/api_config/5_accuracy/accuracy_gpu_error_dtype_diff.txt
not_check_dtype:
- paddle.Tensor.__add__
- paddle.Tensor.cumsum
- paddle.Tensor.frexp
- paddle.add
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