From 6d55f6a4251e957bbe21bf0e0fd11b9e7ffb6fe6 Mon Sep 17 00:00:00 2001
From: shubhambhokare1 <shubhambhokare@gmail.com>
Date: Thu, 24 Oct 2024 16:55:42 +0000
Subject: [PATCH] avoid double reshape logic

---
 docs/Changelog.md                             |  11 ++--
 docs/Operators.md                             |  11 ++--
 .../test/case/node/rmsnormalization.py        |  22 +++-----
 .../model.onnx                                | Bin 163 -> 163 bytes
 .../test_data_set_0/output_0.pb               |   2 +-
 .../model.onnx                                | Bin 4357 -> 2931 bytes
 .../test_data_set_0/output_0.pb               |   2 +-
 .../model.onnx                                | Bin 159 -> 159 bytes
 .../test_data_set_0/output_0.pb               | Bin 107 -> 59 bytes
 .../model.onnx                                | Bin 4353 -> 2927 bytes
 .../test_data_set_0/output_0.pb               | Bin 107 -> 59 bytes
 .../model.onnx                                | Bin 178 -> 178 bytes
 .../test_data_set_0/output_0.pb               | Bin 107 -> 59 bytes
 .../model.onnx                                | Bin 4788 -> 3354 bytes
 .../test_data_set_0/output_0.pb               | Bin 107 -> 59 bytes
 .../model.onnx                                | Bin 182 -> 182 bytes
 .../test_data_set_0/output_0.pb               |   4 +-
 .../model.onnx                                | Bin 4792 -> 3358 bytes
 .../test_data_set_0/output_0.pb               |   4 +-
 .../model.onnx                                | Bin 202 -> 202 bytes
 .../test_data_set_0/output_0.pb               | Bin 254 -> 133 bytes
 .../model.onnx                                | Bin 4769 -> 3215 bytes
 .../test_data_set_0/output_0.pb               | Bin 254 -> 133 bytes
 .../model.onnx                                | Bin 198 -> 198 bytes
 .../test_data_set_0/output_0.pb               | Bin 254 -> 133 bytes
 .../model.onnx                                | Bin 4765 -> 3211 bytes
 .../test_data_set_0/output_0.pb               | Bin 254 -> 133 bytes
 .../model.onnx                                | Bin 194 -> 194 bytes
 .../test_data_set_0/output_0.pb               | Bin 254 -> 133 bytes
 .../model.onnx                                | Bin 4761 -> 3207 bytes
 .../test_data_set_0/output_0.pb               | Bin 254 -> 133 bytes
 .../model.onnx                                | Bin 213 -> 213 bytes
 .../test_data_set_0/output_0.pb               | Bin 254 -> 133 bytes
 .../model.onnx                                | Bin 5190 -> 3644 bytes
 .../test_data_set_0/output_0.pb               | Bin 254 -> 133 bytes
 .../model.onnx                                | Bin 217 -> 217 bytes
 .../test_data_set_0/output_0.pb               |   3 +-
 .../model.onnx                                | Bin 5194 -> 3648 bytes
 .../test_data_set_0/output_0.pb               |   3 +-
 .../model.onnx                                | Bin 221 -> 221 bytes
 .../test_data_set_0/output_0.pb               | Bin 254 -> 133 bytes
 .../model.onnx                                | Bin 5198 -> 3652 bytes
 .../test_data_set_0/output_0.pb               | Bin 254 -> 133 bytes
 .../model.onnx                                | Bin 187 -> 187 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 4381 -> 2955 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 183 -> 183 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 4377 -> 2951 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 179 -> 179 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 4373 -> 2947 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 175 -> 175 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 4369 -> 2943 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 194 -> 194 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 4804 -> 3370 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 198 -> 198 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 4808 -> 3374 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 202 -> 202 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 4812 -> 3378 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 206 -> 206 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 4816 -> 3382 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 166 -> 166 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 .../model.onnx                                | Bin 4510 -> 3160 bytes
 .../test_data_set_0/output_0.pb               | Bin 976 -> 496 bytes
 onnx/defs/nn/defs.cc                          |  51 +++++-------------
 80 files changed, 36 insertions(+), 77 deletions(-)

diff --git a/docs/Changelog.md b/docs/Changelog.md
index f9a036f694e..ad21a5fb093 100644
--- a/docs/Changelog.md
+++ b/docs/Changelog.md
@@ -28979,8 +28979,7 @@ This version of the operator has been available since version 23 of the default
 ### <a name="RMSNormalization-23"></a>**RMSNormalization-23**</a>
 
   This is RMS normalization defined in ONNX as function as described in the paper https://arxiv.org/pdf/1910.07467.
-        The overall computation can be split into two stages. The first stage is standardization, which makes the
-        normalized elements have zero mean and unit variances. The root mean squared norm is taken over the last D dimensions,
+        The overall computation can be split into two stages. The root mean squared norm is taken over the last D dimensions,
         where D is the dimension of normalized_shape. For example, if normalized_shape is (3, 5) (a 2-dimensional shape),
         the rms norm is computed over the last 2 dimensions of the input. The computation required by standardization can be
         described by the following equations.
@@ -28993,7 +28992,7 @@ This version of the operator has been available since version 23 of the default
         Normalized = Div(X, SqrtRMS)
         ```
         where `normalized_axes` is `[axis, ..., rank of X - 1]`. The variables `RMS` stand for root mean square,
-        The second stage then scales and shifts the outcome of the first stage using:
+        The second stage then scales the outcome of the first stage using:
         ```
         Y= Mul(Normalized, Scale)
         ```
@@ -29015,8 +29014,6 @@ This version of the operator has been available since version 23 of the default
 <dd>The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).</dd>
 <dt><tt>epsilon</tt> : float (default is 1e-05)</dt>
 <dd>The epsilon value to use to avoid division by zero.</dd>
-<dt><tt>stash_type</tt> : int (default is 1)</dt>
-<dd>Type of Mean and InvStdDev. This also specifies stage one's computation precision.</dd>
 </dl>
 
 #### Inputs
@@ -29025,7 +29022,7 @@ This version of the operator has been available since version 23 of the default
 <dt><tt>X</tt> : T</dt>
 <dd>The output of the layer for which the skip connection is being created. In general, the shape is (N, C, D1, D2, ... , Dn) for n-dimensional data, where D1 to Dn are the spatial dimension sizes and N is the batch size, C is the number of channels. The root mean squared norm is taken over the last D dimensions, D is determined by the axis attribute.</dd>
 <dt><tt>scale</tt> : V</dt>
-<dd>Scale tensor.</dd>
+<dd>Scale tensor. Shape is the normalized shape ([axis, .., Dn]) or a scalar (which will be broadcasted to the normalized shape.</dd>
 </dl>
 
 #### Outputs
@@ -29040,8 +29037,6 @@ This version of the operator has been available since version 23 of the default
 <dl>
 <dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
 <dd>Constrain input X type to float tensors.</dd>
-<dt><tt>U</tt> : tensor(float), tensor(double)</dt>
-<dd>Constrain mean and inv_std_var to be float tensors.</dd>
 <dt><tt>V</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
 <dd>Constrain output Y and scale type to float tensors.</dd>
 </dl>
diff --git a/docs/Operators.md b/docs/Operators.md
index 72c406288b3..05ea166f3f2 100644
--- a/docs/Operators.md
+++ b/docs/Operators.md
@@ -21104,8 +21104,7 @@ expect(
 ### <a name="RMSNormalization"></a><a name="rmsnormalization">**RMSNormalization**</a>
 
   This is RMS normalization defined in ONNX as function as described in the paper https://arxiv.org/pdf/1910.07467.
-        The overall computation can be split into two stages. The first stage is standardization, which makes the
-        normalized elements have zero mean and unit variances. The root mean squared norm is taken over the last D dimensions,
+        The overall computation can be split into two stages. The root mean squared norm is taken over the last D dimensions,
         where D is the dimension of normalized_shape. For example, if normalized_shape is (3, 5) (a 2-dimensional shape),
         the rms norm is computed over the last 2 dimensions of the input. The computation required by standardization can be
         described by the following equations.
@@ -21118,7 +21117,7 @@ expect(
         Normalized = Div(X, SqrtRMS)
         ```
         where `normalized_axes` is `[axis, ..., rank of X - 1]`. The variables `RMS` stand for root mean square,
-        The second stage then scales and shifts the outcome of the first stage using:
+        The second stage then scales the outcome of the first stage using:
         ```
         Y= Mul(Normalized, Scale)
         ```
@@ -21140,8 +21139,6 @@ This version of the operator has been available since version 23 of the default
 <dd>The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).</dd>
 <dt><tt>epsilon</tt> : float (default is 1e-05)</dt>
 <dd>The epsilon value to use to avoid division by zero.</dd>
-<dt><tt>stash_type</tt> : int (default is 1)</dt>
-<dd>Type of Mean and InvStdDev. This also specifies stage one's computation precision.</dd>
 </dl>
 
 #### Inputs
@@ -21150,7 +21147,7 @@ This version of the operator has been available since version 23 of the default
 <dt><tt>X</tt> : T</dt>
 <dd>The output of the layer for which the skip connection is being created. In general, the shape is (N, C, D1, D2, ... , Dn) for n-dimensional data, where D1 to Dn are the spatial dimension sizes and N is the batch size, C is the number of channels. The root mean squared norm is taken over the last D dimensions, D is determined by the axis attribute.</dd>
 <dt><tt>scale</tt> : V</dt>
-<dd>Scale tensor.</dd>
+<dd>Scale tensor. Shape is the normalized shape ([axis, .., Dn]) or a scalar (which will be broadcasted to the normalized shape.</dd>
 </dl>
 
 #### Outputs
@@ -21165,8 +21162,6 @@ This version of the operator has been available since version 23 of the default
 <dl>
 <dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
 <dd>Constrain input X type to float tensors.</dd>
-<dt><tt>U</tt> : tensor(float), tensor(double)</dt>
-<dd>Constrain mean and inv_std_var to be float tensors.</dd>
 <dt><tt>V</tt> : tensor(float16), tensor(float), tensor(double), tensor(bfloat16)</dt>
 <dd>Constrain output Y and scale type to float tensors.</dd>
 </dl>
diff --git a/onnx/backend/test/case/node/rmsnormalization.py b/onnx/backend/test/case/node/rmsnormalization.py
index 7cbce993a0d..8fcf059dc52 100644
--- a/onnx/backend/test/case/node/rmsnormalization.py
+++ b/onnx/backend/test/case/node/rmsnormalization.py
@@ -20,26 +20,18 @@ def _rms_normalization(X, W, axis=-1, epsilon=1e-5):  # type: ignore
         # which means the last axis.
         axis = axis + rank
 
-    # Parameter used to convert N-D tensor RMS normalization to equivalent 2-D matirx operations.
-    row_number = np.prod(shape[:axis]).astype(np.int64)
-    col_number = np.prod(shape[axis:]).astype(np.int64)
-
-    # After reshaping input tensor X into a matrix,
-    # RMS normalization is equivalent to conducting
-    # standardization on each column vector (s.t. each
-    # column has zero mean and unit variance).
-    x_mat = np.reshape(X, (row_number, col_number))
     # This computes RMS for every x_mat's column.
-    x_squared = np.power(x_mat, 2)
-    x_squared_mean = np.sum(x_squared, axis=1, keepdims=True) / col_number
+    x_squared = np.power(X, 2)
+    x_squared_mean = np.mean(x_squared, axis=tuple(range(axis, len(shape))), keepdims=True)
     rms = np.sqrt(x_squared_mean)
+    # epsilon adjustment to avoid divide-by-zero.
     rms_plus_epsilon = rms + epsilon
     rms_plus_epsilon_sqrt = np.sqrt(rms_plus_epsilon)
     rms_reciprocal = np.reciprocal(rms_plus_epsilon_sqrt)
-    # Standardization step. y_mat is zero-mean and unit-variance.
-    y_mat = x_mat * rms_reciprocal
-    # Apply affine transform on normalization outcome. W is linear coefficient.
-    Y = np.reshape(y_mat, shape) * W
+
+    y_mat = X * rms_reciprocal
+    # W is linear coefficient.
+    Y = y_mat * W
 
     return Y
 
diff --git a/onnx/backend/test/data/node/test_rms_normalization_2d_axis0/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_2d_axis0/model.onnx
index e68797e208b586c974de930503284b0833208309..877d8e804c238eec4d82a57d56995b68ed2a945d 100644
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delta 24
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_2d_axis0/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_2d_axis0/test_data_set_0/output_0.pb
index 94a5940759b..da934be6afa 100644
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_2d_axis0_expanded/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_2d_axis0_expanded/model.onnx
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_2d_axis0_expanded/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_2d_axis0_expanded/test_data_set_0/output_0.pb
index 94a5940759b..da934be6afa 100644
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_2d_axis1/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_2d_axis1/model.onnx
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_1/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_1/model.onnx
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_1/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_1/test_data_set_0/output_0.pb
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_1_expanded/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_1_expanded/model.onnx
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2/model.onnx
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2/test_data_set_0/output_0.pb
index e5036704d38..3b1a01d37d3 100644
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2_expanded/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_2d_axis_negative_2_expanded/test_data_set_0/output_0.pb
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_3d_axis0_epsilon/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_3d_axis0_epsilon/test_data_set_0/output_0.pb
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon/model.onnx
index 6cff0bfdc34a65a9efa4c502bd2ca561f6e67d09..a77885d2d50254566c6f8d2d196ea5e8e887349b 100644
GIT binary patch
delta 28
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delta 28
gcmX@ac!+VrT2XEx9xf&hCLm@8Vpb;>E(QT{08qOFzW@LL

diff --git a/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon/test_data_set_0/output_0.pb
index 2a8a89c458ac50db5311994b4435f53ce5a0c499..9be9a6198193c003e0d4d31a49b989bc13339917 100644
GIT binary patch
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon_expanded/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon_expanded/model.onnx
index 87cee468d9ec00b103c3a815fc2d8fd0887b3b6d..4b24c5339c44fd2849b487893744903e2fee4059 100644
GIT binary patch
delta 231
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delta 655
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon_expanded/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_3d_axis2_epsilon_expanded/test_data_set_0/output_0.pb
index 2a8a89c458ac50db5311994b4435f53ce5a0c499..9be9a6198193c003e0d4d31a49b989bc13339917 100644
GIT binary patch
literal 133
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literal 254
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsilon/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsilon/model.onnx
index c13f13b6dfe0ef4c7fa1a67adfa551305f1d0987..bc1775231a55f6508aef7fc067406407d9824926 100644
GIT binary patch
delta 12
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delta 11
Scmcc0c$IO&0Y>hL2SorJs|0TV

diff --git a/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsilon/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsilon/test_data_set_0/output_0.pb
index 12b2b5c69deee81daaafd84845ba5519cf1e4d88..8f35d4859f6a46a1b4c2e9d75aed53593feccdab 100644
GIT binary patch
literal 133
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literal 254
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsilon_expanded/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsilon_expanded/model.onnx
index 48d1c6ea1093431938062918b3299e45d8158849..3f54a4db4b4c3dee5ab3a298173e273ad3a940c3 100644
GIT binary patch
delta 258
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delta 648
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsilon_expanded/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_1_epsilon_expanded/test_data_set_0/output_0.pb
index 12b2b5c69deee81daaafd84845ba5519cf1e4d88..8f35d4859f6a46a1b4c2e9d75aed53593feccdab 100644
GIT binary patch
literal 133
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literal 254
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_2_epsilon/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_2_epsilon/model.onnx
index 242a85c258e21a18d2b5ae323d25006e36324262..86944f836d300366e65ec5f2181697c6d766ba91 100644
GIT binary patch
delta 16
Xcmcb~c$0C$k%`mz*_>Fo7zD%tHdh3^

delta 28
gcmcb~c$0C$5m9a-9xf&hCLm@8Vpb;>E(QT{09U321ONa4

diff --git a/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_2_epsilon/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_2_epsilon/test_data_set_0/output_0.pb
index 0f7b90d9f40..767156b78d5 100644
--- a/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_2_epsilon/test_data_set_0/output_0.pb
+++ b/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_2_epsilon/test_data_set_0/output_0.pb
@@ -1,2 +1 @@
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YJ�
q�A��,	@��(
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B
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\ No newline at end of file
diff --git a/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_2_epsilon_expanded/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_2_epsilon_expanded/model.onnx
index cc8579889ed3c8afdc247588c3684347e64e5b6d..aeeb4cf1fdbfa8f0c7399f4b0fa17839c3894338 100644
GIT binary patch
delta 258
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delta 648
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_2_epsilon_expanded/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_2_epsilon_expanded/test_data_set_0/output_0.pb
index 0f7b90d9f40..767156b78d5 100644
--- a/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_2_epsilon_expanded/test_data_set_0/output_0.pb
+++ b/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_2_epsilon_expanded/test_data_set_0/output_0.pb
@@ -1,2 +1 @@
-B
YJ�
q�A��,	@��(
�qÿ�O%��|忞�N��@īܥNM@���8k����p�eH�?��bҠ�?;�4*�)ǿ=��v۹�����(�?����:�?Թ-(緻���[��?ٯ:p�?5�~�1��?7W��c�p�����?Y��J���?sk-q�l��	q��z�aV�?�0}���q���������
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\ No newline at end of file
+
B
YJxgI@����+�}@uj"@Ykֿ-CJ?��=L9���νF�=�֡?8�ݽ�r�=�[�>�!?c��_>��?l���N����?�M$��m��X@��Ȁ��=���<i�O?p��?
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_3_epsilon/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_3_epsilon/model.onnx
index 8e6a739db87c47b87a557d1531d469d1d544ce71..70b8c7eb14f489d9083a59d7f89b041c8160e075 100644
GIT binary patch
delta 16
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delta 28
gcmcc1c$aa)2~ln#9xf&hCLm@8Vpb;>E(QT{09e`t5C8xG

diff --git a/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_3_epsilon/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_3_epsilon/test_data_set_0/output_0.pb
index ba1964af19fbcdde52971f95cfdd1469048c4ed1..3a7edc37efdea5c1a8c1bca715574ad473331554 100644
GIT binary patch
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literal 254
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_3_epsilon_expanded/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_3d_axis_negative_3_epsilon_expanded/model.onnx
index 627f856a0dcde393b3f076b0fd228756472d9975..814faddb87a96964e73e102efbf4f66cbec2eb6d 100644
GIT binary patch
delta 258
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delta 632
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis0/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_4d_axis0/model.onnx
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis0/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_4d_axis0/test_data_set_0/output_0.pb
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis0_expanded/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_4d_axis0_expanded/model.onnx
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis0_expanded/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_4d_axis0_expanded/test_data_set_0/output_0.pb
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis1/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_4d_axis1/test_data_set_0/output_0.pb
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis1_expanded/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_4d_axis1_expanded/model.onnx
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis3_expanded/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_4d_axis3_expanded/model.onnx
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis3_expanded/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_4d_axis3_expanded/test_data_set_0/output_0.pb
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_1/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_1/model.onnx
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GIT binary patch
delta 32
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delta 32
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_1/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_1/test_data_set_0/output_0.pb
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_1_expanded/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_1_expanded/model.onnx
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_1_expanded/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_1_expanded/test_data_set_0/output_0.pb
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_2/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_2/model.onnx
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GIT binary patch
delta 11
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delta 11
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_2/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_2/test_data_set_0/output_0.pb
index 8ff90b5b247b0e67581db7dcbbd3bd346339d4ef..dd76ed4db7240c638fe15fb16495a32b32dd2375 100644
GIT binary patch
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_2_expanded/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_2_expanded/model.onnx
index 5a3a3560ac5b07230e20376b03e2e53f8af48c11..8d7e9761e368b68aa13f3809cf23da4a2d338c27 100644
GIT binary patch
delta 249
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delta 647
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_2_expanded/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_2_expanded/test_data_set_0/output_0.pb
index 8ff90b5b247b0e67581db7dcbbd3bd346339d4ef..dd76ed4db7240c638fe15fb16495a32b32dd2375 100644
GIT binary patch
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_3/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_3/model.onnx
index 2943096d4549d2930cbb5ffd124c3298522f90de..147c149741da3315ad5f5d5c29f02493db87f8a2 100644
GIT binary patch
delta 32
jcmX@bc#3htMoC5?0WKyECLm@8Viq7~bz<RS5D*6dV9x{T

delta 32
jcmX@bc#3htMoDfV0WKyECLm@8Viq7~bz<RS5D*6dVaWsY

diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_3/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_3/test_data_set_0/output_0.pb
index 5adb2a736805c69d9f60e3bac62a161f69311e57..815f3818762ceb34505ac5509c817ac8a03cceac 100644
GIT binary patch
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_3_expanded/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_3_expanded/model.onnx
index 749d5e6c97d43035fe9e87db4b0577cc7f69046b..5a0ee2ddece3ce6e139a3de97a0a2646c2ce1c00 100644
GIT binary patch
delta 269
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_3_expanded/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_3_expanded/test_data_set_0/output_0.pb
index 5adb2a736805c69d9f60e3bac62a161f69311e57..815f3818762ceb34505ac5509c817ac8a03cceac 100644
GIT binary patch
literal 496
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_4/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_4/model.onnx
index 8a6b17c5d7f4d4cd4ad19a1055399ba2791f25c7..938d61549288961e89672c3172e904f8dfee3931 100644
GIT binary patch
delta 32
jcmX@dc#d(xR!K%70WKyECLm@8Viq7~bz<RS5D*6dVlxBv

delta 32
jcmX@dc#d(xR!MFl0WKyECLm@8Viq7~bz<RS5D*6dV=V*!

diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_4/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_4/test_data_set_0/output_0.pb
index 1e88ea81c18f9d410aeb99f7d58797fd0d9e2e9a..7853e26898cef3c8c307aac6629f5120e6b55adb 100644
GIT binary patch
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_4_expanded/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_4_expanded/model.onnx
index 9e5b78b99f3fd546d5bc604a6b97758944341cf7..0dc9a0b04f02a7aca611e6c7e0b94bc2d51308f1 100644
GIT binary patch
delta 269
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_4_expanded/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_4d_axis_negative_4_expanded/test_data_set_0/output_0.pb
index 1e88ea81c18f9d410aeb99f7d58797fd0d9e2e9a..7853e26898cef3c8c307aac6629f5120e6b55adb 100644
GIT binary patch
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_default_axis/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_default_axis/model.onnx
index c87658e4e742f3f2ee232f71a37975324802b8fd..62c9188be8d4a08b71945738609cd0182aa89a86 100644
GIT binary patch
delta 11
ScmZ3+xQuZ^FC*i`J_!I9Lj$D%

delta 11
ScmZ3+xQuZ^FC+KFJ_!I9YXh$U

diff --git a/onnx/backend/test/data/node/test_rms_normalization_default_axis/test_data_set_0/output_0.pb b/onnx/backend/test/data/node/test_rms_normalization_default_axis/test_data_set_0/output_0.pb
index b011cbed80bf606a8562bc27a3340282e2fa5c0b..97258e9e0441c611437cc3bffde2fe37fc02b02c 100644
GIT binary patch
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diff --git a/onnx/backend/test/data/node/test_rms_normalization_default_axis_expanded/model.onnx b/onnx/backend/test/data/node/test_rms_normalization_default_axis_expanded/model.onnx
index c80bc72c399718200dff66945669c8f35e212483..5523f42ceb88504cbeff9cfcc8002ad1e6348533 100644
GIT binary patch
delta 243
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delta 639
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diff --git a/onnx/defs/nn/defs.cc b/onnx/defs/nn/defs.cc
index c82cc0512c5..8e0e3c861db 100644
--- a/onnx/defs/nn/defs.cc
+++ b/onnx/defs/nn/defs.cc
@@ -2835,8 +2835,7 @@ ONNX_OPERATOR_SET_SCHEMA(
 
 static const char* RMSNormalization_ver23_doc = R"DOC(
       This is RMS normalization defined in ONNX as function as described in the paper https://arxiv.org/pdf/1910.07467.
-      The overall computation can be split into two stages. The first stage is standardization, which makes the
-      normalized elements have zero mean and unit variances. The root mean squared norm is taken over the last D dimensions,
+      The overall computation can be split into two stages. The root mean squared norm is taken over the last D dimensions,
       where D is the dimension of normalized_shape. For example, if normalized_shape is (3, 5) (a 2-dimensional shape),
       the rms norm is computed over the last 2 dimensions of the input. The computation required by standardization can be
       described by the following equations.
@@ -2849,7 +2848,7 @@ static const char* RMSNormalization_ver23_doc = R"DOC(
       Normalized = Div(X, SqrtRMS)
       ```
       where `normalized_axes` is `[axis, ..., rank of X - 1]`. The variables `RMS` stand for root mean square,
-      The second stage then scales and shifts the outcome of the first stage using:
+      The second stage then scales the outcome of the first stage using:
       ```
       Y= Mul(Normalized, Scale)
       ```
@@ -2874,11 +2873,6 @@ ONNX_OPERATOR_SET_SCHEMA(
             "epsilon",
             "The epsilon value to use to avoid division by zero.",
             AttributeProto::FLOAT, 1e-5f)
-        .Attr(
-            "stash_type",
-            "Type of Mean and InvStdDev. This also specifies stage one's computation precision.",
-            AttributeProto::INT,
-            static_cast<int64_t>(ONNX_NAMESPACE::TensorProto_DataType_FLOAT))
         .AllowUncheckedAttributes()
         .Input(0,
                "X",
@@ -2889,7 +2883,8 @@ ONNX_OPERATOR_SET_SCHEMA(
                "T")
         .Input(1,
                "scale",
-               "Scale tensor.",
+               "Scale tensor. Shape is the normalized shape ([axis, .., Dn]) or a scalar (which will be broadcasted to "
+               "the normalized shape.",
                "V")
         .Output(0,
                 "Y",
@@ -2899,10 +2894,6 @@ ONNX_OPERATOR_SET_SCHEMA(
             "T",
             {"tensor(float16)", "tensor(float)", "tensor(double)", "tensor(bfloat16)"},
             "Constrain input X type to float tensors.")
-        .TypeConstraint(
-            "U",
-            {"tensor(float)", "tensor(double)"},
-            "Constrain mean and inv_std_var to be float tensors.")
         .TypeConstraint(
             "V",
             {"tensor(float16)", "tensor(float)", "tensor(double)", "tensor(bfloat16)"},
@@ -2960,44 +2951,28 @@ ONNX_OPERATOR_SET_SCHEMA(
                 tp.add_dims(1);
                 return tp;
               };
-              // The treatment of "axis" is different in "RMSNormalization" and in Reduction operations.
-              // This complicates the function definition, requiring reshaping inputs/outputs.
-              // Input X shape: [d[0], ..., d[axis-1], d[axis], ..., d[rank-1]]
-              // This is treated as a 2D shape [d[0] * ... * d[axis-1], d[axis] * ... * d[rank-1]]
-              // Normalization is applied to the second dimension.
-              // Output Y has same shape as X
-              // Outputs InvStdDev have shape: [d[0], ..., d[axis-1], 1, ..., 1]
+
               FunctionBuilder builder(functionProto);
               builder.Const("FloatEpsilon", ToTensor<float>(epsilon))
                   .Add("Epsilon = Cast (FloatEpsilon)", "to", U)
                   .Add("XShape = Shape (X)") // shape of input tensor: 1D tensor
                   .Add("Rank = Size (XShape)") // rank of input tensor: scalar
-                  .Add("Zero1D = Constant()", "value", mktensor(0)) // [0] : 1D tensor
                   .Add("Axis1D = Constant()", "value", mktensor(axis)) // [axis] : 1D tensor
-                  .Add("PrefixShape = Slice (XShape, Zero1D, Axis1D)") // [d[0], ..., d[axis-1]]
                   .Add(
                       axis >= 0 // number of axes that are reduced =
-                          ? "NumReducedAxes = Sub (Rank, Axis1D)" // [rank - axis]: 1D tensor
-                          : "NumReducedAxes = Neg (Axis1D)") // [-axis] : 1D tensor
-                  .Add(
-                      "SuffixShape = ConstantOfShape (NumReducedAxes)",
-                      "value",
-                      mktensor(1)) // [1, ..., 1] for reduced axes
-                  .Add("ReducedShape = Concat <axis = 0> (PrefixShape, SuffixShape)") // [d[0], ..., d[axis-1], 1, ..., 1]
-                  .Add("X2D = Flatten (X)", "axis", axis)
-                  .Add("XU = Cast (X2D)", "to", U);
-              builder.Add("Axes_1 = Constant()", "value", mktensor(1))
-                    .Add("XSquared = Mul (XU, XU)")
-                    .Add("XSquaredMean = ReduceMean (XSquared, Axes_1)")
+                          ? "PosAxis1D = Identity (Axis1D)" // [axis]: 1D tensor
+                          : "PosAxis1D = Sub (Rank, Axis1D)") // [rank - axis] : 1D tensor
+                  .Add("ReduceAxes = Range(PosAxis1D, Rank)")
+                  .Add("XU = Cast (X)", "to", U);
+              builder.Add("XSquared = Mul (XU, XU)")
+                    .Add("XSquaredMean = ReduceMean (XSquared, ReduceAxes)")
                     .Add("RMS = Sqrt (XSquaredMean)")
                     .Add("RMSPlusEpsilon = Add (RMS, Epsilon)")
                     .Add("SqrtRMS = Sqrt (RMSPlusEpsilon)")
                     .Add("Normalized = Div (XU, SqrtRMS)")
-                    .Add("NormalizedT = Cast (Normalized)", "to", T)
-                    .Add("Scale2D = Flatten <axis = 0> (Scale)")
-                    .Add("Scaled = Mul (NormalizedT, Scale2D)");
+                    .Add("NormalizedT = Cast (Normalized)", "to", T);
+              builder.Add("Y = Mul (NormalizedT, Scale)");
 
-              builder.Add("Y = Reshape (Scaled, XShape)");
               schema.BuildFunction(functionProto);
               return true;
           }));