[OP] Add rms_norm into TOPI (apache#15326)

This PR introduces the operator root mean square, `rms_norm`, into TOPI.

Author: cyx-6

include/tvm/topi/nn/rms_norm.h

@@ -0,0 +1,96 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \brief root mean square normalization op constructions
+ * \file nn/rms_norm.h
+ */
+#ifndef TVM_TOPI_NN_RMS_NORM_H_
+#define TVM_TOPI_NN_RMS_NORM_H_
+
+#include <tvm/te/operation.h>
+#include <tvm/topi/reduction.h>
+#include <tvm/topi/tags.h>
+
+#include <string>
+
+namespace tvm {
+namespace topi {
+namespace nn {
+
+using namespace tvm::te;
+
+/*!
+ * \brief Root mean square normalization.
+ * \param data N-D tensor with shape [d_0, d_1, ..., d_{N-1}]
+ * \param weight K-D tensor with shape [r_0, r_1, ..., r_{K-1}] where K == len(axis) and
+ *               d_{axis_k} == r_k
+ * \param bias Optional, K-D tensor with shape [r_0, r_1, ..., r_{K-1}] where
+ *             d_{axis_k} == r_k
+ * \param axis The axis to normalize over.
+ * \param epsilon The epsilon value to avoid division by zero.
+ * \param name The name of the operation.
+ * \param tag The tag to mark the operation.
+ * \return The normalized tensor, with the same shape as data.
+ */
+inline Tensor rms_norm(const Tensor& data, const Tensor& weight, const Tensor& bias,
+                       const Array<Integer>& axis, double epsilon, std::string name = "T_rms_norm",
+                       std::string tag = kInjective) {
+  const auto& data_type = data->dtype;
+  const auto& weight_type = weight.defined() ? weight->dtype : data_type;
+  ICHECK(data_type == weight_type) << "rms_norm: data and weight must have the same type";
+  const auto& bias_type = bias.defined() ? bias->dtype : data_type;
+  ICHECK(data_type == bias_type) << "rms_norm: data and bias must have the same type";
+
+  auto square = multiply(data, data);
+  auto square_sum = sum(square, axis, /*keepdims=*/false, /*atleast1d=*/true);
+
+  auto ndim = data->shape.size();
+  ICHECK_NE(ndim, 0) << "Cannot reduce a 0 dim Tensor";
+  auto real_axis = GetRealAxis(static_cast<int>(ndim), axis);
+  auto reduce_extent = make_const(data->dtype, 1);
+  for (int i : real_axis) {
+    reduce_extent *= data->shape[i];
+  }
+  auto rms_norm_func = [&](const Array<Var>& indices) {
+    Array<Var> reduce_indices, non_reduce_indices;
+    for (int i = 0, n = static_cast<int>(indices.size()); i < n; ++i) {
+      if (std::find(real_axis.begin(), real_axis.end(), i) != real_axis.end()) {
+        reduce_indices.push_back(indices[i]);
+      } else {
+        non_reduce_indices.push_back(indices[i]);
+      }
+    }
+    auto output =
+        data(indices) * weight(reduce_indices) *
+        tvm::rsqrt(square_sum(non_reduce_indices) / reduce_extent + make_const(data_type, epsilon));
+    if (bias.defined()) {
+      output += bias(reduce_indices);
+    }
+    return output;
+  };
+  auto rms_norm = tvm::te::compute(data->shape, rms_norm_func, name, tag);
+  return rms_norm;
+}
+
+}  // namespace nn
+}  // namespace topi
+}  // namespace tvm
+
+#endif  // TVM_TOPI_NN_RMS_NORM_H_

python/tvm/topi/nn/__init__.py

@@ -41,6 +41,7 @@
 from .instance_norm import instance_norm
 from .layer_norm import layer_norm
 from .group_norm import group_norm
+from .rms_norm import rms_norm
 from .local_response_norm import *
 from .bitserial_conv2d import *
 from .bitserial_dense import *

python/tvm/topi/nn/rms_norm.py

@@ -0,0 +1,46 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Root mean square normalization operator."""
+from .. import cpp
+
+
+def rms_norm(data, weight, bias, axis, epsilon=1e-5):
+    """Root mean square normalization operator. The output will have the same data type as input.
+
+    Parameters
+    ----------
+    data : tvm.te.Tensor
+        N-D with shape (d_0, d_1, ..., d_{N-1})
+
+    weight: tvm.te.Tensor
+        K-D with shape (r_0, r_1, ..., r_{K-1}) where K == len(axis) and d_{axis_k} == r_k
+
+    bias: tvm.te.Tensor
+        Optional, K-D with shape (r_0, r_1, ..., r_{K-1}) where K == len(axis) and d_{axis_k} == r_k
+
+    axis : list of int
+        Axis over the normalization applied
+
+    epsilon : float
+        The epsilon value to avoid division by zero.
+
+    Returns
+    -------
+    result : tvm.te.Tensor
+        N-D with shape (d_0, d_1, ..., d_{N-1})
+    """
+    return cpp.nn.rms_norm(data, weight, bias, axis, epsilon)

python/tvm/topi/testing/__init__.py

@@ -46,6 +46,7 @@
 from .instance_norm_python import instance_norm_python
 from .layer_norm_python import layer_norm_python
 from .group_norm_python import group_norm_python
+from .rms_norm_python import rms_norm_python
 from .lrn_python import lrn_python
 from .l2_normalize_python import l2_normalize_python
 from .gather_python import gather_python

python/tvm/topi/testing/rms_norm_python.py

@@ -0,0 +1,51 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name, line-too-long, unused-variable, too-many-locals
+"""Root mean square normalization in python"""
+import numpy as np
+
+
+def rms_norm_python(data, weight, bias, axis, epsilon=1e-5):
+    """Root mean square normalization operator in Python.
+
+    Parameters
+    ----------
+    data : numpy.ndarray
+        N-D with shape (d_0, d_1, ..., d_{N-1})
+
+    weight: numpy.ndarray
+        K-D with shape (r_0, r_1, ..., r_{K-1}) where K == len(axis) and d_{axis_k} == r_k
+
+    bias: numpy.ndarray
+        Optional, K-D with shape (r_0, r_1, ..., r_{K-1}) where K == len(axis) and d_{axis_k} == r_k
+
+    axis : int or tuple of ints
+        Axis over the normalization applied
+
+    epsilon : float
+        The epsilon value to avoid division by zero.
+
+    Returns
+    -------
+    result : np.ndarray
+        N-D with shape (d_0, d_1, ..., d_{N-1})
+    """
+    square_mean = np.mean(np.square(data), axis, keepdims=True)
+    result = data * weight / np.sqrt(square_mean + epsilon)
+    if bias is not None:
+        result += bias
+    return result

src/topi/nn.cc

@@ -35,6 +35,7 @@
 #include <tvm/topi/nn/local_response_norm.h>
 #include <tvm/topi/nn/mapping.h>
 #include <tvm/topi/nn/pooling.h>
+#include <tvm/topi/nn/rms_norm.h>
 #include <tvm/topi/nn/softmax.h>
 
 namespace tvm {
@@ -176,5 +177,10 @@ TVM_REGISTER_GLOBAL("topi.nn.instance_norm").set_body([](TVMArgs args, TVMRetVal
   *rv = nn::instance_norm(args[0], args[1], args[2], args[3], static_cast<double>(args[4]));
 });
 
+/* Ops from nn/rms_norm.h */
+TVM_REGISTER_GLOBAL("topi.nn.rms_norm").set_body([](TVMArgs args, TVMRetValue* rv) {
+  *rv = nn::rms_norm(args[0], args[1], args[2], args[3], static_cast<double>(args[4]));
+});
+
 }  // namespace topi
 }  // namespace tvm

tests/python/topi/python/test_topi_rms_norm.py

@@ -0,0 +1,68 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Test code for rms_norm."""
+import numpy as np
+import pytest
+import tvm
+from tvm import te
+from tvm import topi
+from tvm.topi.utils import get_const_tuple
+import tvm.topi.testing
+
+import tvm.testing
+
+
+_rms_norm_schedule = {
+    "generic": topi.generic.schedule_injective,
+}
+
+
+# only test on llvm because schedule is missing
+@tvm.testing.parametrize_targets("llvm")
+@pytest.mark.parametrize(
+    "shape,axis", [([4, 16], (1,)), ([4, 16, 16], (1, 2)), ([("a", 4), ("b", 16)], (1,))]
+)
+@pytest.mark.parametrize("dtype", ["float32", "float16"])
+def test_rms_norm(target, dev, shape, axis, dtype, episilon=1e-5, rtol=5e-3, atol=1e-4):
+    shape_te = [te.var(v[0]) if isinstance(v, tuple) else v for v in shape]
+    scale_shape_te = [shape_te[dim] for dim in axis]
+    data = te.placeholder(shape_te, dtype=dtype, name="data")
+    weight = te.placeholder(scale_shape_te, dtype=dtype, name="weight")
+    bias = te.placeholder(scale_shape_te, dtype=dtype, name="weight")
+    B = topi.nn.rms_norm(data, weight, bias, axis, episilon)
+
+    shape_np = [v[1] if isinstance(v, tuple) else v for v in shape]
+    scale_shape_np = [shape_np[dim] for dim in axis]
+    data_np = np.random.uniform(size=shape_np).astype(dtype)
+    weight_np = np.random.uniform(size=scale_shape_np).astype(dtype)
+    bias_np = np.random.uniform(size=scale_shape_np).astype(dtype)
+    b_np = tvm.topi.testing.rms_norm_python(data_np, weight_np, bias_np, axis, episilon)
+
+    with tvm.target.Target(target):
+        s_func = tvm.topi.testing.dispatch(target, _rms_norm_schedule)
+        s = s_func([B])
+    data_tvm = tvm.nd.array(data_np, dev)
+    weight_tvm = tvm.nd.array(weight_np, dev)
+    bias_tvm = tvm.nd.array(bias_np, dev)
+    b_tvm = tvm.nd.array(np.zeros(shape_np, dtype=dtype), dev)
+    f = tvm.build(s, [data, weight, bias, B], target)
+    f(data_tvm, weight_tvm, bias_tvm, b_tvm)
+    tvm.testing.assert_allclose(b_tvm.numpy(), b_np, rtol=rtol, atol=atol)
+
+
+if __name__ == "__main__":
+    tvm.testing.main()