[RMSNorm] remove invvar from rms_norm Python API return value#78438
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DanielSun11 merged 3 commits intoPaddlePaddle:developfrom Apr 3, 2026
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[RMSNorm] remove invvar from rms_norm Python API return value#78438DanielSun11 merged 3 commits intoPaddlePaddle:developfrom
DanielSun11 merged 3 commits intoPaddlePaddle:developfrom
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…ate default eps - rms_norm now returns only `out` (Tensor) instead of `(out, invvar)` tuple - Updated return type annotation from `tuple[Tensor, Tensor]` to `Tensor` - Changed default eps from 1e-5 to 0.0 in both norm.py and ops.yaml - Updated unit tests: rms_norm_wrapper now calls _C_ops directly to preserve op-level invvar output for OpTest framework; removed invvar assertion in TestRMSNormAPI Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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…Paddle#78438) * [RMSNorm] remove invvar from rms_norm Python API return value and update default eps - rms_norm now returns only `out` (Tensor) instead of `(out, invvar)` tuple - Updated return type annotation from `tuple[Tensor, Tensor]` to `Tensor` - Changed default eps from 1e-5 to 0.0 in both norm.py and ops.yaml - Updated unit tests: rms_norm_wrapper now calls _C_ops directly to preserve op-level invvar output for OpTest framework; removed invvar assertion in TestRMSNormAPI Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * default eps is none * fix ut --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
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✅ Cherry-pick successful! Created PR: #78578 |
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…Paddle#78438) * [RMSNorm] remove invvar from rms_norm Python API return value and update default eps - rms_norm now returns only `out` (Tensor) instead of `(out, invvar)` tuple - Updated return type annotation from `tuple[Tensor, Tensor]` to `Tensor` - Changed default eps from 1e-5 to 0.0 in both norm.py and ops.yaml - Updated unit tests: rms_norm_wrapper now calls _C_ops directly to preserve op-level invvar output for OpTest framework; removed invvar assertion in TestRMSNormAPI Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * default eps is none * fix ut --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
DanielSun11
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Apr 8, 2026
…Paddle#78438) * [RMSNorm] remove invvar from rms_norm Python API return value and update default eps - rms_norm now returns only `out` (Tensor) instead of `(out, invvar)` tuple - Updated return type annotation from `tuple[Tensor, Tensor]` to `Tensor` - Changed default eps from 1e-5 to 0.0 in both norm.py and ops.yaml - Updated unit tests: rms_norm_wrapper now calls _C_ops directly to preserve op-level invvar output for OpTest framework; removed invvar assertion in TestRMSNormAPI Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * default eps is none * fix ut --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
sneaxiy
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Apr 9, 2026
…value (#78608) * [RMSNorm] remove invvar from rms_norm Python API return value (#78438) * [RMSNorm] remove invvar from rms_norm Python API return value and update default eps - rms_norm now returns only `out` (Tensor) instead of `(out, invvar)` tuple - Updated return type annotation from `tuple[Tensor, Tensor]` to `Tensor` - Changed default eps from 1e-5 to 0.0 in both norm.py and ops.yaml - Updated unit tests: rms_norm_wrapper now calls _C_ops directly to preserve op-level invvar output for OpTest framework; removed invvar assertion in TestRMSNormAPI Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * default eps is none * fix ut --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> * fix doc --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
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Description
paddle.nn.functional.rms_norm原来返回(out, invvar)两个 Tensor，其中invvar（每行的逆标准差）是内部中间量，对外部用户没有实际意义。为了和torch对齐，本 PR 在 Python API 层面将其隐藏，使函数只返回归一化结果out。主要改动：
python/paddle/nn/functional/norm.py：返回类型由tuple[Tensor, Tensor]改为Tensor，动态图/PIR 模式下取_C_ops.rms_norm(...)[0]，静态图模式下只返回out；同时将eps默认值由1e-5改为实际计算时的数据类型的最小精度epspaddle/phi/ops/yaml/ops.yaml：同步将rms_normop 的epsilon默认值改为fp32的最小精度test/legacy_test/test_rms_norm_op.py：OpTest的python_apiwrapper 改为直接调_C_ops.rms_norm以保留 op 层双输出供框架校验；TestRMSNormAPI中移除对invvar的解包和数值断言是否引起精度变化
否