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[Accuracy diff No.89] Fix accuracy diff for paddle.incubate.nn.functional.fused_bias_act API #389

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Merged
merged 5 commits into from
Jul 17, 2025
+30 −24
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[Accuracy diff No.89] Fix accuracy diff for paddle.incubate.nn.functional.fused_bias_act API #389

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5c60069
fix(rules.py): 精度修复，fused_bias_act 转换的实现错误
hushenwei2000 Jul 17, 2025
85f355d
fix(rules.py): 精度修复，fused_bias_act 转换的实现错误
hushenwei2000 Jul 17, 2025
fd801c6
fix(rules.py): 精度修复，fused_bias_act 转换的实现错误
hushenwei2000 Jul 17, 2025
d5be285
fix(rules.py): 精度修复，fused_bias_act 转换的实现错误
hushenwei2000 Jul 17, 2025
d34e660
fix(rules.py): 精度修复，fused_bias_act 转换的实现错误
hushenwei2000 Jul 17, 2025
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20 changes: 0 additions & 20 deletions tester/api_config/5_accuracy/accuracy_gpu_error.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -2280,26 +2280,6 @@ paddle.einsum("mixy,bmxy,kmxy->bixy", Tensor([32, 32, 1, 1],"float32"), Tensor([
paddle.gammaln(Tensor([10, 20, 1],"float32"), )
paddle.gammaln(Tensor([2, 3, 4, 5],"float32"), )
paddle.gammaln(Tensor([2, 3, 4, 5],"float64"), )
paddle.incubate.nn.functional.fused_bias_act(Tensor([2, 22016],"int32"), None, act_method="swiglu", compute_dtype="fp16", dequant_scales=Tensor([22016],"float32"), shift=None, smooth=None, quant_scale=0.0009313154732808471, quant_round_type=0, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(Tensor([2, 22016],"int32"), None, act_method="swiglu", compute_dtype="fp16", dequant_scales=Tensor([22016],"float32"), shift=None, smooth=None, quant_scale=0.0009654839523136616, quant_round_type=0, quant_max_bound=127.0, quant_min_bound=-127.0, )
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@wanghuancoder wanghuancoder Jul 17, 2025

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这些case不需要删除，还在这个文件里放着就可以。这里放着：

  1. 可以用于未来小范围的回归测试
  2. 如果所有问题都修复后，可以改变文件名。但这些case都是宝贵的测试用例还是需要保留下来的。

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@hushenwei2000 hushenwei2000 Jul 17, 2025

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好的，已更新第二个 commit 恢复了这些文件

paddle.incubate.nn.functional.fused_bias_act(Tensor([2, 22016],"int32"), None, act_method="swiglu", compute_dtype="fp16", dequant_scales=Tensor([22016],"float32"), shift=None, smooth=None, quant_scale=0.0009928022045642138, quant_round_type=0, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(Tensor([2, 22016],"int32"), None, act_method="swiglu", compute_dtype="fp16", dequant_scales=Tensor([22016],"float32"), shift=None, smooth=None, quant_scale=0.0010831302497535944, quant_round_type=0, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(Tensor([2, 22016],"int32"), None, act_method="swiglu", compute_dtype="fp16", dequant_scales=Tensor([22016],"float32"), shift=None, smooth=None, quant_scale=0.0010981468949466944, quant_round_type=0, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(Tensor([2, 22016],"int32"), None, act_method="swiglu", compute_dtype="fp16", dequant_scales=Tensor([22016],"float32"), shift=None, smooth=None, quant_scale=0.0014022786635905504, quant_round_type=0, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(Tensor([2, 22016],"int32"), None, act_method="swiglu", compute_dtype="fp16", dequant_scales=Tensor([22016],"float32"), shift=None, smooth=None, quant_scale=0.001479289960116148, quant_round_type=0, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(Tensor([2, 22016],"int32"), None, act_method="swiglu", compute_dtype="fp16", dequant_scales=Tensor([22016],"float32"), shift=None, smooth=None, quant_scale=0.0016999575309455395, quant_round_type=0, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(Tensor([464, 22016],"int32"), None, act_method="swiglu", compute_dtype="fp16", dequant_scales=Tensor([22016],"float32"), shift=None, smooth=None, quant_scale=0.0009313154732808471, quant_round_type=0, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(Tensor([464, 22016],"int32"), None, act_method="swiglu", compute_dtype="fp16", dequant_scales=Tensor([22016],"float32"), shift=None, smooth=None, quant_scale=0.0009654839523136616, quant_round_type=0, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(Tensor([464, 22016],"int32"), None, act_method="swiglu", compute_dtype="fp16", dequant_scales=Tensor([22016],"float32"), shift=None, smooth=None, quant_scale=0.0009928022045642138, quant_round_type=0, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(Tensor([464, 22016],"int32"), None, act_method="swiglu", compute_dtype="fp16", dequant_scales=Tensor([22016],"float32"), shift=None, smooth=None, quant_scale=0.0010831302497535944, quant_round_type=0, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(Tensor([464, 22016],"int32"), None, act_method="swiglu", compute_dtype="fp16", dequant_scales=Tensor([22016],"float32"), shift=None, smooth=None, quant_scale=0.0010981468949466944, quant_round_type=0, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(Tensor([464, 22016],"int32"), None, act_method="swiglu", compute_dtype="fp16", dequant_scales=Tensor([22016],"float32"), shift=None, smooth=None, quant_scale=0.0014022786635905504, quant_round_type=0, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(Tensor([464, 22016],"int32"), None, act_method="swiglu", compute_dtype="fp16", dequant_scales=Tensor([22016],"float32"), shift=None, smooth=None, quant_scale=0.001479289960116148, quant_round_type=0, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(Tensor([464, 22016],"int32"), None, act_method="swiglu", compute_dtype="fp16", dequant_scales=Tensor([22016],"float32"), shift=None, smooth=None, quant_scale=0.0016999575309455395, quant_round_type=0, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(x=Tensor([2, 20, 10],"int32"), bias=Tensor([10],"float16"), dequant_scales=Tensor([10],"float32"), act_method="gelu", compute_dtype="fp16", )
paddle.incubate.nn.functional.fused_bias_act(x=Tensor([2, 20, 512],"int32"), bias=Tensor([512],"float16"), dequant_scales=Tensor([512],"float32"), shift=Tensor([256],"float16"), smooth=Tensor([256],"float16"), act_method="geglu", compute_dtype="fp16", quant_scale=0.5, quant_round_type=1, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(x=Tensor([2, 20, 512],"int32"), bias=Tensor([512],"float16"), dequant_scales=Tensor([512],"float32"), shift=Tensor([512],"float16"), smooth=Tensor([512],"float16"), act_method="gelu", compute_dtype="fp16", quant_scale=0.5, quant_round_type=1, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_bias_act(x=Tensor([2, 20, 512],"int32"), bias=Tensor([512],"float32"), dequant_scales=Tensor([512],"float32"), shift=Tensor([512],"float32"), smooth=Tensor([512],"float32"), act_method="gelu", compute_dtype="fp32", quant_scale=0.5, quant_round_type=1, quant_max_bound=127.0, quant_min_bound=-127.0, )
paddle.incubate.nn.functional.fused_layer_norm(Tensor([1, 64],"float16"), norm_weight=None, norm_bias=None, epsilon=1e-06, begin_norm_axis=1, bias=None, residual=Tensor([1, 64],"float16"), )
paddle.incubate.nn.functional.fused_layer_norm(Tensor([100, 512],"float16"), norm_weight=None, norm_bias=None, epsilon=1e-05, begin_norm_axis=1, bias=None, residual=Tensor([100, 512],"float16"), )
paddle.incubate.nn.functional.fused_layer_norm(Tensor([101, 64],"float16"), norm_weight=None, norm_bias=None, epsilon=1e-05, begin_norm_axis=1, bias=Tensor([64],"float16"), residual=Tensor([101, 64],"float16"), )
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1 change: 1 addition & 0 deletions tester/base.py
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Expand Up @@ -594,6 +594,7 @@ def get_arg(api_config, arg_pos, arg_name, default=None):
# some accuracy error can be considered tolerable
special_accuracy_atol_rtol = {
# "API": (atol, rtol),
"paddle.incubate.nn.functional.fused_bias_act": (127, 1e-2)
}

torch_error_skip = frozenset(
Expand Down
66 changes: 39 additions & 27 deletions tester/paddle_to_torch/rules.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1866,6 +1866,26 @@ def fused_bias_act(
) -> torch.Tensor:
import torch.nn.functional as F

def quant_helper_func(input, scale, round_type, max_bound, min_bound):
quant_value = max_bound * scale * input

if round_type == 0:
quant_value = torch.round(quant_value)
else:
quant_value = torch.where(quant_value >= 0, torch.ceil(quant_value - 0.5), torch.floor(quant_value + 0.5))

quant_value = torch.clamp(quant_value, min=min_bound, max=max_bound)

return quant_value

def swiglu(x):
x, gate = x.chunk(2, dim=-1)
return x * torch.sigmoid(x) * gate

def geglu(x):
x, gate = x.chunk(2, dim=-1)
return F.gelu(x) * gate

if compute_dtype != 'default':
if compute_dtype == 'fp16':
compute_dtype = 'float16'
Expand All @@ -1877,30 +1897,14 @@ def fused_bias_act(
x = x.to(getattr(torch, compute_dtype))
else:
x = x.float() if not x.is_floating_point() else x

if dequant_scales is not None:
dequant_scales = dequant_scales.to(x.dtype)
x = x * dequant_scales

if bias is not None:
bias = bias.to(x.dtype)
x = x + bias
if shift is not None:
repeat_factor = x.shape[-1] // shift.shape[-1]
shift = shift.repeat(repeat_factor)
shift = shift.to(x.dtype)
x = x + shift
if smooth is not None:
repeat_factor = x.shape[-1] // smooth.shape[-1]
smooth = smooth.repeat(repeat_factor)
smooth = smooth.to(x.dtype)
x = x * smooth

def swiglu(x):
x, gate = x.chunk(2, dim=-1)
return x * torch.sigmoid(x) * gate

def geglu(x):
x, gate = x.chunk(2, dim=-1)
return F.gelu(x) * gate

act_method = act_method.lower()
if act_method == 'gelu':
Expand All @@ -1917,17 +1921,25 @@ def geglu(x):
x = geglu(x)
else:
raise ValueError(f"Unsupported activation method: {act_method}")

if shift is not None:
repeat_factor = x.shape[-1] // shift.shape[-1]
shift = shift.repeat(repeat_factor)
shift = shift.to(x.dtype)
x = x + shift

if smooth is not None:
repeat_factor = x.shape[-1] // smooth.shape[-1]
smooth = smooth.repeat(repeat_factor)
smooth = smooth.to(x.dtype)
x = x * smooth

if quant_scale > 0:
x = x / quant_scale
if quant_round_type == 0:
x = torch.round(x) # Round to nearest, ties to even
elif quant_round_type == 1:
x = torch.where(x >= 0, torch.ceil(x - 0.5), torch.floor(x + 0.5))
else:
raise ValueError(f"Unsupported quant_round_type: {quant_round_type}")
x = x * quant_scale
x = torch.clamp(x, min=quant_min_bound, max=quant_max_bound)
x = quant_helper_func(x, quant_scale, quant_round_type, quant_max_bound, quant_min_bound)
print("after quant", x)

x = x.to(getattr(torch, "int8"))

return x
"""
core = "result = fused_bias_act(**kwargs)"
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