szha closed pull request #11573: Add stable nrm2 Reducer
URL: https://github.com/apache/incubator-mxnet/pull/11573
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diff --git a/3rdparty/mshadow b/3rdparty/mshadow
index a8c650ce8a7..463c0dffe3e 160000
--- a/3rdparty/mshadow
+++ b/3rdparty/mshadow
@@ -1 +1 @@
-Subproject commit a8c650ce8a708608a282c4d1e251c57873a8db25
+Subproject commit 463c0dffe3eae8c39caf7989c85b7244823df27e
diff --git a/src/operator/mshadow_op.h b/src/operator/mshadow_op.h
index 5953568c7fa..81a55c4a013 100644
--- a/src/operator/mshadow_op.h
+++ b/src/operator/mshadow_op.h
@@ -697,6 +697,22 @@ struct product {
MSHADOW_XINLINE static void Reduce(volatile DType& dst, volatile DType src,
volatile DType& none) { // NOLINT(*)
Reduce(dst, src);
}
+ /*! \brief combine the results of two reducers */
+ template<typename DType>
+ MSHADOW_XINLINE static void Merge(volatile DType& dst_val, volatile DType&
src_val) { // NOLINT(*)
+ Reduce(dst_val, src_val);
+ }
+ /*! \brief combine the results of two reducers */
+ template<typename DType>
+ MSHADOW_XINLINE static void Merge(volatile DType& dst_val, volatile DType&
dst_residual, volatile DType& src_val, volatile DType& src_residual) { //
NOLINT(*)
+ Reduce(dst_val, src_val);
+ }
+ /*! \brief finalize reduction */
+ template<typename DType>
+ MSHADOW_XINLINE static void Finalize(volatile DType& dst) {} // NOLINT(*)
+ /*! \brief finalize reduction */
+ template<typename DType>
+ MSHADOW_XINLINE static void Finalize(volatile DType& dst, volatile DType&
none) {} // NOLINT(*)
/*!
*\brief calculate gradient of redres with respect to redsrc,
* redres: reduced result, redsrc: one of reduction element
@@ -762,6 +778,26 @@ struct nansum {
residual = (t - dst) - y;
dst = t;
}
+ /*! \brief combine the results of two reducers */
+ template<typename DType>
+ MSHADOW_XINLINE static void Merge(volatile DType& dst_val, volatile DType&
src_val) { // NOLINT(*)
+ Reduce(dst_val, src_val);
+ }
+ /*! \brief combine the results of two reducers */
+ template<typename DType>
+ MSHADOW_XINLINE static void Merge(volatile DType& dst_val, volatile DType&
dst_residual, volatile DType& src_val, volatile DType& src_residual) { //
NOLINT(*)
+ DType t1 = dst_val + src_val;
+ DType e = t1 - src_val;
+ DType t2 = ((src_val - e) + (dst_val - (t1 - e))) + dst_residual +
src_residual;
+ dst_val = t1 + t2;
+ dst_residual = t2 - (dst_val - t1);
+ }
+ /*! \brief finalize reduction */
+ template<typename DType>
+ MSHADOW_XINLINE static void Finalize(volatile DType& dst) {} // NOLINT(*)
+ /*! \brief finalize reduction */
+ template<typename DType>
+ MSHADOW_XINLINE static void Finalize(volatile DType& dst, volatile DType&
residual) {} // NOLINT(*)
/*!
*\brief set the initial value during reduction
*/
@@ -799,6 +835,22 @@ struct nanprod {
MSHADOW_XINLINE static void Reduce(volatile DType& dst, volatile DType src,
volatile DType& none) { // NOLINT(*)
Reduce(dst, src);
}
+ /*! \brief combine the results of two reducers */
+ template<typename DType>
+ MSHADOW_XINLINE static void Merge(volatile DType& dst_val, volatile DType&
src_val) { // NOLINT(*)
+ Reduce(dst_val, src_val);
+ }
+ /*! \brief combine the results of two reducers */
+ template<typename DType>
+ MSHADOW_XINLINE static void Merge(volatile DType& dst_val, volatile DType&
dst_residual, volatile DType& src_val, volatile DType& src_residual) { //
NOLINT(*)
+ Reduce(dst_val, src_val);
+ }
+ /*! \brief finalize reduction */
+ template<typename DType>
+ MSHADOW_XINLINE static void Finalize(volatile DType& dst) {} // NOLINT(*)
+ /*! \brief finalize reduction */
+ template<typename DType>
+ MSHADOW_XINLINE static void Finalize(volatile DType& dst, volatile DType&
none) {} // NOLINT(*)
/*!
*\brief set the initial value during reduction
*/
@@ -806,6 +858,7 @@ struct nanprod {
MSHADOW_XINLINE static void SetInitValue(DType & initv) { // NOLINT(*)
initv = 1;
}
+
/*!
*\brief set the initial value during reduction
*/
@@ -815,6 +868,76 @@ struct nanprod {
}
};
+/*! \brief compute l2 norm */
+struct nrm2 {
+ /*! \brief do reduction into dst */
+ template<typename DType>
+ MSHADOW_XINLINE static void Reduce(volatile DType& sum_of_squares, volatile
DType src) { // NOLINT(*)
+ sum_of_squares += src * src;
+ }
+ /*! \brief do stable reduction into dst */
+ template<typename DType>
+ MSHADOW_XINLINE static void Reduce(volatile DType& sum_of_squares, volatile
DType src, volatile DType& scale) { // NOLINT(*)
+ if (src != 0) {
+ DType abs = mshadow_op::abs::Map(src);
+ if (scale < abs) {
+ sum_of_squares = 1 + sum_of_squares * (scale / abs) * (scale / abs);
+ scale = abs;
+ } else {
+ sum_of_squares = sum_of_squares + (abs / scale) * (abs / scale);
+ }
+ }
+ }
+ /*! \brief combine the results of two reducers */
+ template<typename DType>
+ MSHADOW_XINLINE static void Merge(volatile DType& dst_val, volatile DType&
src_val) { // NOLINT(*)
+ dst_val += src_val;
+ }
+ /*! \brief combine the results of two reducers */
+ template<typename DType>
+ MSHADOW_XINLINE static void Merge(volatile DType& dst_ssq, volatile DType&
dst_scale, volatile DType& src_ssq, volatile DType& src_scale) { // NOLINT(*)
+ if (dst_scale != 0 && dst_scale >= src_scale) {
+ dst_ssq = dst_ssq + src_ssq * (src_scale / dst_scale) * (src_scale /
dst_scale);
+ } else if (src_scale != 0 && dst_scale < src_scale) {
+ dst_ssq = src_ssq + dst_ssq * (dst_scale / src_scale) * (dst_scale /
src_scale);
+ dst_scale = src_scale;
+ }
+ }
+ /*! \brief finalize reduction result */
+ template<typename DType>
+ MSHADOW_XINLINE static void Finalize(volatile DType& sum_of_squares) { //
NOLINT(*)
+ sum_of_squares = math::sqrt(sum_of_squares);
+ }
+ /*! \brief finalize reduction result */
+ template<typename DType>
+ MSHADOW_XINLINE static void Finalize(volatile DType& sum_of_squares,
volatile DType& scale) { // NOLINT(*)
+ sum_of_squares = scale * math::sqrt(sum_of_squares);
+ }
+ /*!
+ *\brief calculate gradient of redres with respect to redsrc,
+ * redres: reduced result, redsrc: one of reduction element
+ */
+ template<typename DType>
+ MSHADOW_XINLINE static DType PartialGrad(DType redres, DType redsrc) {
+ return redsrc / redres;
+ }
+ /*!
+ *\brief set the initial value during reduction
+ */
+ template<typename DType>
+ MSHADOW_XINLINE static void SetInitValue(DType &sum_of_squares) { //
NOLINT(*)
+ sum_of_squares = 0;
+ }
+ /*!
+ *\brief set the initial value during reduction
+ */
+ template<typename DType>
+ MSHADOW_XINLINE static void SetInitValue(DType &sum_of_squares, DType
&scale) { // NOLINT(*)
+ SetInitValue(sum_of_squares);
+ scale = 0;
+ }
+};
+
struct nanprod_grad : public mxnet_op::tunable {
template<typename DType>
MSHADOW_XINLINE static DType Map(DType a, DType b) {
diff --git a/src/operator/tensor/broadcast_reduce-inl.cuh
b/src/operator/tensor/broadcast_reduce-inl.cuh
index b6bb39a1984..5c9b45f547f 100644
--- a/src/operator/tensor/broadcast_reduce-inl.cuh
+++ b/src/operator/tensor/broadcast_reduce-inl.cuh
@@ -123,27 +123,32 @@ __global__ void reduce_kernel(const int N, const int M,
const bool addto,
// Fix bx to avoid bank conflicts. Assumes warpSize number of banks
const int fbx = (do_transpose && ((bx & (warpSize - 1)) == 0)) ? (bx +
1) : bx;
const int it0 = tidx + tidy*fbx;
- shTile[it0] = val;
+ shTile[it0 * 2] = val;
+ shTile[it0 * 2 + 1] = residual;
__syncthreads();
for (int t=1;t < by;t <<= 1) {
- DType tmp, residual;
- Reducer::SetInitValue(tmp, residual);
- if (tidy + t < by) tmp = shTile[it0 + t*fbx];
+ DType tmp, tmp_residual;
+ Reducer::SetInitValue(tmp, tmp_residual);
+ if (tidy + t < by) {
+ tmp = shTile[(it0 + t*fbx) * 2];
+ tmp_residual = shTile[(it0 + t*fbx) * 2 + 1];
+ }
__syncthreads();
- Reducer::Reduce(shTile[it0], tmp, residual);
+ Reducer::Merge(shTile[it0 * 2], shTile[it0 * 2 + 1], tmp,
tmp_residual);
__syncthreads();
}
if (idx < N && tidy == 0) {
- assign(&small[idx + m0*N], addto, shTile[tidx]);
+ Reducer::Finalize(shTile[tidx * 2], shTile[tidx * 2 + 1]);
+ assign(&small[idx + m0*N], addto, shTile[tidx * 2]);
}
} else {
if (idx < N) {
+ Reducer::Finalize(val, residual);
assign(&small[idx + m0*N], addto, val);
}
}
}
}
-
}
template<typename Reducer, int ndim, typename DType, typename OP1, typename
OP2, int unroll>
@@ -207,27 +212,32 @@ __global__ void reduce_kernel(const int N, const int M,
const bool addto,
// Fix bx to avoid bank conflicts. Assumes warpSize number of banks
const int fbx = (do_transpose && ((bx & (warpSize - 1)) == 0)) ? (bx +
1) : bx;
const int it0 = tidx + tidy*fbx;
- shTile[it0] = val;
+ shTile[it0 * 2] = val;
+ shTile[it0 * 2 + 1] = residual;
__syncthreads();
for (int t=1;t < by;t <<= 1) {
- DType tmp, residual;
- Reducer::SetInitValue(tmp, residual);
- if (tidy + t < by) tmp = shTile[it0 + t*fbx];
+ DType tmp, tmp_residual;
+ Reducer::SetInitValue(tmp, tmp_residual);
+ if (tidy + t < by) {
+ tmp = shTile[(it0 + t*fbx) * 2];
+ tmp_residual = shTile[(it0 + t*fbx) * 2 + 1];
+ }
__syncthreads();
- Reducer::Reduce(shTile[it0], tmp, residual);
+ Reducer::Merge(shTile[it0 * 2], shTile[it0 * 2 + 1], tmp,
tmp_residual);
__syncthreads();
}
if (idx < N && tidy == 0) {
- assign(&small[idx + m0*N], addto, shTile[tidx]);
+ Reducer::Finalize(shTile[tidx * 2], shTile[tidx * 2 + 1]);
+ assign(&small[idx + m0*N], addto, shTile[tidx * 2]);
}
} else {
if (idx < N) {
+ Reducer::Finalize(val, residual);
assign(&small[idx + m0*N], addto, val);
}
}
}
}
-
}
// Simple reduction of lines when M is small
@@ -244,6 +254,7 @@ __global__ void reduce_lines_kernel(const int N, const int
M, const bool addto,
}
if (idx < N) {
+ Reducer::Finalize(val, residual);
assign(&small_out[idx], addto, val);
}
@@ -453,7 +464,7 @@ ReduceImplConfig<ndim> ConfigureReduceImpl(const TShape&
small, const TShape& bi
by++;
}
config.kernel_1.shMemSize = (config.kernel_1.blockDim.x > 1) ?
- config.kernel_1.blockDim.x*by*sizeof(DType) : 0;
+ config.kernel_1.blockDim.x*by*sizeof(DType) * 2 : 0;
// Maximum number of times we want TB to loop in M
// Max size of M-block each TB can handle
int maxMblock = config.kernel_1.blockDim.x*config.maxLoopPerTB;
@@ -464,7 +475,7 @@ ReduceImplConfig<ndim> ConfigureReduceImpl(const TShape&
small, const TShape& bi
ceil_idiv<unsigned int>(config.N, config.kernel_1.blockDim.x));
config.kernel_1.gridDim.y = std::min(kBaseGridNum, config.Mnext);
config.kernel_1.shMemSize = (config.kernel_1.blockDim.y > 1) ?
- config.kernel_1.blockDim.x*config.kernel_1.blockDim.y*sizeof(DType) :
0;
+ config.kernel_1.blockDim.x*config.kernel_1.blockDim.y*sizeof(DType) *
2 : 0;
// Maximum number of times we want TB to loop in M
// Max size of M-block each TB can handle
int maxMblock = config.kernel_1.blockDim.y*config.maxLoopPerTB;
diff --git a/src/operator/tensor/broadcast_reduce-inl.h
b/src/operator/tensor/broadcast_reduce-inl.h
index 76ec92a9e72..713e3f1ac60 100644
--- a/src/operator/tensor/broadcast_reduce-inl.h
+++ b/src/operator/tensor/broadcast_reduce-inl.h
@@ -165,6 +165,7 @@ MSHADOW_XINLINE void seq_reduce_assign(const int idx, const
int M, const bool ad
coord = unravel(k, rshape);
Reducer::Reduce(val, OP::Map(big[j + dot(coord, rstride)]), residual);
}
+ Reducer::Finalize(val, residual);
assign(&small[idx], addto, val);
}
@@ -256,6 +257,7 @@ MSHADOW_XINLINE void seq_reduce_assign(const int idx, const
int M, const bool ad
Reducer::Reduce(val, OP1::Map(big[idx_big], OP2::Map(lhs[idx_lhs],
rhs[idx_rhs])), residual);
}
+ Reducer::Finalize(val, residual);
assign(&small[idx], addto, val);
}
diff --git a/src/operator/tensor/broadcast_reduce_op.h
b/src/operator/tensor/broadcast_reduce_op.h
index ac7199a9482..d9a749e0db8 100644
--- a/src/operator/tensor/broadcast_reduce_op.h
+++ b/src/operator/tensor/broadcast_reduce_op.h
@@ -1005,9 +1005,8 @@ void LpNormCompute(const nnvm::NodeAttrs& attrs,
ReduceAxesComputeImpl<xpu, mshadow::red::sum, false, mshadow_op::abs>(
ctx, inputs, req, outputs, small);
} else if (param.ord == 2) {
- ReduceAxesComputeImpl<xpu, mshadow::red::sum, false, mshadow_op::square>(
+ ReduceAxesComputeImpl<xpu, mshadow_op::nrm2, false, mshadow_op::identity>(
ctx, inputs, req, outputs, small);
- SqRootForL2<xpu>(ctx, req[0], outputs[0]);
}
}
diff --git a/tests/python/unittest/test_ndarray.py
b/tests/python/unittest/test_ndarray.py
index cf5906ae454..b57e71d73b2 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -17,6 +17,7 @@
import mxnet as mx
import numpy as np
+from distutils.version import LooseVersion
import os
import pickle as pkl
import unittest
@@ -1276,10 +1277,19 @@ def test_ndarray_astype():
@with_seed()
def test_norm(ctx=default_context()):
+ try:
+ import scipy
+ assert LooseVersion(scipy.__version__) >= LooseVersion('0.1')
+ from scipy.linalg import norm as sp_norm
+ except (AssertionError, ImportError):
+ print("Could not import scipy.linalg.norm or scipy is too old. "
+ "Falling back to numpy.linalg.norm which is not numerically
stable.")
+ from numpy.linalg import norm as sp_norm
+
def l1norm(input_data, axis=0, keepdims=False):
return np.sum(abs(input_data), axis=axis, keepdims=keepdims)
def l2norm(input_data, axis=0, keepdims=False):
- return np.linalg.norm(input_data, axis=axis, keepdims=keepdims)
+ return sp_norm(input_data, axis=axis, keepdims=keepdims)
in_data_dim = random_sample([4,5,6], 1)[0]
in_data_shape = rand_shape_nd(in_data_dim)
diff --git a/tests/python/unittest/test_operator.py
b/tests/python/unittest/test_operator.py
index ae5cba21711..5a2067eab4a 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -23,6 +23,7 @@
import math
import random
import itertools
+from distutils.version import LooseVersion
from numpy.testing import assert_allclose, assert_array_equal
from mxnet.test_utils import *
from mxnet.base import py_str, MXNetError, _as_list
@@ -3031,13 +3032,22 @@ def npy_layer_norm(data, gamma, beta, axis=1, eps=1E-5):
grad_nodes={'data': req, 'gamma': req, 'beta':
req},
numeric_eps=1e-2, rtol=1e-2, atol=1e-2)
-@unittest.skip("Flaky test:
https://github.com/apache/incubator-mxnet/issues/11509";)
@with_seed()
def test_norm():
+ try:
+ import scipy
+ assert LooseVersion(scipy.__version__) >= LooseVersion('0.1')
+ from scipy.linalg import norm as sp_norm
+ except (AssertionError, ImportError):
+ print("Could not import scipy.linalg.norm or scipy is too old. "
+ "Falling back to numpy.linalg.norm which is not numerically
stable.")
+ from numpy.linalg import norm as sp_norm
+
def l1norm(input_data, axis=0, keepdims=True):
return np.sum(abs(input_data), axis=axis, keepdims=keepdims)
- def l2norm(input_data, axis=0, keepdims=True):
- return np.linalg.norm(input_data, axis=axis, keepdims=keepdims)
+
+ def l2norm(input_data, axis=0, keepdims=True):
+ return sp_norm(input_data, axis=axis, keepdims=keepdims)
ctx = default_context()
data = mx.symbol.Variable('data')
@@ -3051,7 +3061,7 @@ def l2norm(input_data, axis=0, keepdims=True):
for i in range(in_data_dim):
norm_sym = mx.symbol.norm(data=data, ord=order, axis=i,
keepdims=True)
npy_out = l1norm(in_data, i) if order is 1 else
l2norm(in_data, i)
- npy_out_backward = np.sign(in_data) if order is 1 else
in_data/npy_out
+ npy_out_backward = np.sign(in_data) if order is 1 else
in_data/npy_out
check_symbolic_forward(norm_sym, [in_data], [npy_out],
rtol=1e-2 if dtype is np.float16 else
1e-5,
atol=1e-2 if dtype is np.float16 else
1e-5, ctx=ctx)
@@ -3059,22 +3069,23 @@ def l2norm(input_data, axis=0, keepdims=True):
[npy_out_backward],
rtol=1e-2 if dtype is np.float16 else
1e-5,
atol=1e-2 if dtype is np.float16 else
1e-5, ctx=ctx)
- # check gradient
- check_numeric_gradient(norm_sym, [in_data],
numeric_eps=epsilon, rtol=1e-2, atol=1e-3)
- if i < in_data_dim-1:
- norm_sym = mx.symbol.norm(data=data, ord=order, axis=(i,
i+1), keepdims=True)
- npy_out = l1norm(in_data, (i, i+1)) if order is 1 else
l2norm(in_data, (i, i+1))
- npy_out_backward = np.sign(in_data) if order is 1 else
in_data/npy_out
- check_symbolic_forward(norm_sym, [in_data], [npy_out],
- rtol=1e-2 if dtype is np.float16
else 1e-5,
- atol=1e-2 if dtype is np.float16
else 1e-5, ctx=ctx)
- check_symbolic_backward(norm_sym, [in_data],
[np.ones(npy_out.shape)],
- [npy_out_backward],
- rtol=1e-2 if dtype is np.float16
else 1e-5,
- atol=1e-2 if dtype is np.float16
else 1e-5, ctx=ctx)
- # check gradient
- check_numeric_gradient(norm_sym, [in_data],
numeric_eps=epsilon, rtol=1e-2, atol=1e-3)
-
+ # Disable numeric gradient
https://github.com/apache/incubator-mxnet/issues/11509
+ # # check gradient
+ # check_numeric_gradient(norm_sym, [in_data],
numeric_eps=epsilon, rtol=1e-2, atol=1e-3)
+ # if i < in_data_dim-1:
+ # norm_sym = mx.symbol.norm(data=data, ord=order, axis=(i,
i+1), keepdims=True)
+ # npy_out = l1norm(in_data, (i, i+1)) if order is 1 else
l2norm(in_data, (i, i+1))
+ # npy_out_backward = np.sign(in_data) if order is 1 else
in_data/npy_out
+ # check_symbolic_forward(norm_sym, [in_data], [npy_out],
+ # rtol=1e-2 if dtype is np.float16
else 1e-5,
+ # atol=1e-2 if dtype is np.float16
else 1e-5, ctx=ctx)
+ # check_symbolic_backward(norm_sym, [in_data],
[np.ones(npy_out.shape)],
+ # [npy_out_backward],
+ # rtol=1e-2 if dtype is np.float16
else 1e-5,
+ # atol=1e-2 if dtype is np.float16
else 1e-5, ctx=ctx)
+ # # check gradient
+ # check_numeric_gradient(norm_sym, [in_data],
numeric_eps=epsilon, rtol=1e-2, atol=1e-3)
+
def test_layer_norm():
for dtype, forward_check_eps in zip([np.float16, np.float32, np.float64],
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