asitstands closed pull request #9991: Random shuffle implementation
URL: https://github.com/apache/incubator-mxnet/pull/9991
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diff --git a/docs/api/python/ndarray/random.md
b/docs/api/python/ndarray/random.md
index ae9e69f758f..4341a3ce2cd 100644
--- a/docs/api/python/ndarray/random.md
+++ b/docs/api/python/ndarray/random.md
@@ -35,6 +35,8 @@ In the rest of this document, we list routines provided by
the `ndarray.random`
normal
poisson
uniform
+ multinomial
+ shuffle
mxnet.random.seed
```
diff --git a/docs/api/python/symbol/random.md b/docs/api/python/symbol/random.md
index a3492f6f840..22c686ff2fd 100644
--- a/docs/api/python/symbol/random.md
+++ b/docs/api/python/symbol/random.md
@@ -35,6 +35,8 @@ In the rest of this document, we list routines provided by
the `symbol.random` p
normal
poisson
uniform
+ multinomial
+ shuffle
mxnet.random.seed
```
diff --git a/python/mxnet/ndarray/random.py b/python/mxnet/ndarray/random.py
index af125753e5e..49e32d6fd42 100644
--- a/python/mxnet/ndarray/random.py
+++ b/python/mxnet/ndarray/random.py
@@ -24,7 +24,7 @@
__all__ = ['uniform', 'normal', 'poisson', 'exponential', 'gamma',
'multinomial',
- 'negative_binomial', 'generalized_negative_binomial']
+ 'negative_binomial', 'generalized_negative_binomial', 'shuffle']
def _random_helper(random, sampler, params, shape, dtype, ctx, out, kwargs):
@@ -431,3 +431,32 @@ def multinomial(data, shape=_Null, get_prob=False,
out=None, **kwargs):
<NDArray 2 @cpu(0)>
"""
return _internal._sample_multinomial(data, shape, get_prob, out=out,
**kwargs)
+
+
+def shuffle(data, out=None, **kwargs):
+ """Shuffle the elements randomly.
+
+ This shuffles the elements along the last axis, i.e., for each element,
+ all indices except the last one are preserved but the last one changes
randomly.
+
+ Parameters
+ ----------
+ data : NDArray
+ Input data array.
+ out : NDArray
+ Array to store the result.
+ For in-place shuffle, set this to the same array assigned to `data`.
+
+ Examples
+ --------
+ >>> data = mx.nd.array([[0, 1, 2], [3, 4, 5]])
+ >>> mx.nd.random.shuffle(data)
+ [[ 0. 2. 1.]
+ [ 5. 4. 3.]]
+ <NDArray 2x3 @cpu(0)>
+ >>> mx.nd.random.shuffle(data)
+ [[ 1. 2. 0.]
+ [ 3. 5. 4.]]
+ <NDArray 2x3 @cpu(0)>
+ """
+ return _internal._shuffle(data, out, **kwargs)
diff --git a/python/mxnet/symbol/random.py b/python/mxnet/symbol/random.py
index f0d05ad0561..76b28900b60 100644
--- a/python/mxnet/symbol/random.py
+++ b/python/mxnet/symbol/random.py
@@ -247,3 +247,30 @@ def multinomial(data, shape=_Null, get_prob=True,
**kwargs):
reward as head gradient w.r.t. this array to estimate gradient.
"""
return _internal._sample_multinomial(data, shape, get_prob, **kwargs)
+
+def shuffle(data, **kwargs):
+ """Shuffle the elements randomly.
+
+ This shuffles the elements along the last axis, i.e., for each element,
+ all indices except the last one are preserved but the last one changes
randomly.
+
+ Parameters
+ ----------
+ data : NDArray
+ Input data array.
+
+ Examples
+ --------
+ >>> data = mx.nd.array([[0, 1, 2], [3, 4, 5]])
+ >>> a = mx.sym.Variable('a')
+ >>> b = mx.sym.random.shuffle(a)
+ >>> b.eval(a=data)
+ [[ 0. 2. 1.]
+ [ 5. 4. 3.]]
+ <NDArray 2x3 @cpu(0)>
+ >>> b.eval(a=data)
+ [[ 1. 2. 0.]
+ [ 3. 5. 4.]]
+ <NDArray 2x3 @cpu(0)>
+ """
+ return _internal._shuffle(data, **kwargs)
diff --git a/src/operator/random/shuffle_op.cc
b/src/operator/random/shuffle_op.cc
new file mode 100644
index 00000000000..073797a88b9
--- /dev/null
+++ b/src/operator/random/shuffle_op.cc
@@ -0,0 +1,90 @@
+/*
+ * 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.
+ */
+
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file shuffle_op.cc
+ * \brief Operator to shuffle elements of an NDArray
+ */
+#if (__GNUC__ > 4 && !defined(__clang__major__)) || (__clang_major__ > 4 &&
__linux__)
+ #define USE_GNU_PARALLEL_SHUFFLE
+#endif
+
+#include <random>
+#include <algorithm>
+#ifdef USE_GNU_PARALLEL_SHUFFLE
+ #include <parallel/algorithm>
+#endif
+#include "./shuffle_op.h"
+
+namespace mxnet {
+namespace op {
+
+struct ShuffleCPUImpl {
+ template<typename DType>
+ static void shuffle(const OpContext& ctx,
+ mshadow::Tensor<cpu, 1, DType> out,
+ size_t n_batches,
+ size_t n_elements) {
+ using namespace mxnet_op;
+ auto& prnd = ctx.requested[0].get_random<cpu,
size_t>(ctx.get_stream<cpu>())->GetRndEngine();
+ for (size_t i = 0; i < n_batches; ++i) {
+ #ifdef USE_GNU_PARALLEL_SHUFFLE
+ __gnu_parallel::random_shuffle(out.dptr_ + i * n_elements,
+ out.dptr_ + (i + 1) * n_elements,
+ [&prnd](size_t n) {
+ std::uniform_int_distribution<size_t>
dist(0, n - 1);
+ return dist(prnd);
+ });
+ #else
+ std::shuffle(out.dptr_ + i * n_elements,
+ out.dptr_ + (i + 1) * n_elements,
+ prnd);
+ #endif
+ }
+ }
+};
+
+// No parameter is declared.
+// No backward computation is registered. Shuffling is not differentiable.
+
+NNVM_REGISTER_OP(_shuffle)
+.add_alias("shuffle")
+.describe(R"code(Randomly shuffle the elements.
+
+This shuffles the elements along the last axis, i.e., for each element,
+all indices except the last one are preserved but the last one changes
randomly.
+)code")
+.set_num_inputs(1)
+.set_num_outputs(1)
+.set_attr<nnvm::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
+.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<FResourceRequest>("FResourceRequest",
+ [](const nnvm::NodeAttrs& attrs) {
+ return std::vector<ResourceRequest>{ResourceRequest::kRandom,
ResourceRequest::kTempSpace};
+ })
+.set_attr<nnvm::FInplaceOption>("FInplaceOption",
+ [](const NodeAttrs& attrs) {
+ return std::vector<std::pair<int, int>>{{0, 0}};
+ })
+.set_attr<FCompute>("FCompute<cpu>", ShuffleForward<cpu, ShuffleCPUImpl>)
+.add_argument("data", "NDArray-or-Symbol", "Data to be shuffled.");
+
+} // namespace op
+} // namespace mxnet
diff --git a/src/operator/random/shuffle_op.cu
b/src/operator/random/shuffle_op.cu
new file mode 100644
index 00000000000..f284083dd20
--- /dev/null
+++ b/src/operator/random/shuffle_op.cu
@@ -0,0 +1,66 @@
+/*
+ * 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.
+ */
+
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file shuffle_op.cu
+ * \brief Operator to shuffle elements of an NDArray
+ */
+#include "./shuffle_op.h"
+
+namespace mxnet {
+namespace op {
+
+// Note that the outcomes may not be distributed uniformly if both of the the
batch size
+// and the length of the last axis are very large. It is because the
probability that
+// there are duplicated keys is not negligible in that case.
+struct ShuffleGPUImpl {
+ using KeyType = double; // `float` does not provide enough precision
+
+ struct AddBatchIndexKernel {
+ template<typename DType>
+ MSHADOW_XINLINE static void Map(int i, DType* keys, size_t n_elements) {
+ keys[i] += i / n_elements;
+ }
+ };
+
+ template<typename DType>
+ static void shuffle(const OpContext& ctx,
+ mshadow::Tensor<gpu, 1, DType> out,
+ size_t n_batches,
+ size_t n_elements) {
+ using namespace mxnet_op;
+ Stream<gpu> *s = ctx.get_stream<gpu>();
+ size_t size = n_batches * n_elements;
+ Random<gpu, KeyType> *prnd = ctx.requested[0].get_random<gpu, KeyType>(s);
+ Tensor<gpu, 1, KeyType> keys =
+ ctx.requested[1].get_space_typed<gpu, 1, KeyType>(Shape1(size), s);
+ prnd->SampleUniform(&keys, 0, 1);
+ if (n_batches > 1) {
+ Kernel<AddBatchIndexKernel, gpu>::Launch(s, size, keys.dptr_,
n_elements);
+ }
+ SortByKey(keys, out, true);
+ }
+};
+
+NNVM_REGISTER_OP(_shuffle)
+.set_attr<FCompute>("FCompute<gpu>", ShuffleForward<gpu, ShuffleGPUImpl>);
+
+} // namespace op
+} // namespace mxnet
diff --git a/src/operator/random/shuffle_op.h b/src/operator/random/shuffle_op.h
new file mode 100644
index 00000000000..4223203f6b6
--- /dev/null
+++ b/src/operator/random/shuffle_op.h
@@ -0,0 +1,64 @@
+/*
+ * 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.
+ */
+
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file shuffle_op.h
+ * \brief Operator to shuffle elements of an NDArray
+ */
+#ifndef MXNET_OPERATOR_RANDOM_SHUFFLE_OP_H_
+#define MXNET_OPERATOR_RANDOM_SHUFFLE_OP_H_
+
+#include <mxnet/operator_util.h>
+#include <vector>
+#include "../elemwise_op_common.h"
+
+namespace mxnet {
+namespace op {
+
+template<typename xpu, typename ShuffleImpl>
+void ShuffleForward(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs) {
+ using namespace mxnet_op;
+ if (req[0] == kNullOp) {
+ return;
+ }
+ CHECK_NE(req[0], kAddTo) << "Shuffle does not support AddTo";
+ Stream<xpu> *s = ctx.get_stream<xpu>();
+ const TShape input_shape = inputs[0].shape_;
+ const size_t n_elements = input_shape[input_shape.ndim() - 1];
+ const size_t n_batches = inputs[0].Size() / n_elements;
+ const size_t size = inputs[0].Size();
+ MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+ Tensor<xpu, 1, DType> in = inputs[0].get_with_shape<xpu, 1,
DType>(Shape1(size), s);
+ Tensor<xpu, 1, DType> out = outputs[0].get_with_shape<xpu, 1,
DType>(Shape1(size), s);
+ if (req[0] != kWriteInplace) {
+ Copy(out, in, s);
+ }
+ ShuffleImpl::shuffle(ctx, out, n_batches, n_elements);
+ });
+}
+
+} // namespace op
+} // namespace mxnet
+
+#endif // MXNET_OPERATOR_RANDOM_SHUFFLE_OP_H_
diff --git a/tests/python/unittest/test_random.py
b/tests/python/unittest/test_random.py
index f042f57c4e9..55c32a50219 100644
--- a/tests/python/unittest/test_random.py
+++ b/tests/python/unittest/test_random.py
@@ -16,6 +16,8 @@
# under the License.
import os
+import itertools
+import math
import mxnet as mx
from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf
import numpy as np
@@ -552,6 +554,37 @@ def compute_expected_prob():
mx.test_utils.assert_almost_equal(exp_cnt_sampled.asnumpy(),
exp_cnt[sampled_classes].asnumpy(), rtol=1e-1, atol=1e-2)
mx.test_utils.assert_almost_equal(exp_cnt_true.asnumpy(),
exp_cnt[true_classes].asnumpy(), rtol=1e-1, atol=1e-2)
+@with_seed()
+def test_shuffle():
+ def hash(arr):
+ ret = 0
+ for i, n in enumerate(arr):
+ ret += int(n.asscalar()) * (10 ** i)
+ return ret
+
+ data = mx.nd.arange(0, 9).reshape((3, 3))
+ repeat = 100000
+ lastDim = data.shape[len(data.shape) - 1]
+ count = {}
+ # Count the number of each outcome in `repeat` iterations
+ for i in range(repeat):
+ ret = mx.nd.random.shuffle(data)
+ for j in range(lastDim):
+ c = count.get(hash(ret[j]), 0)
+ count[hash(ret[j])] = c + 1
+ # Check the number of different outcomes
+ assert len(count) == math.factorial(lastDim) * (data.size / lastDim)
+ # The outcomes must be uniformly distributed
+ for i in range(lastDim):
+ for p in itertools.permutations(data[i].asnumpy()):
+ assert abs(count[hash(mx.nd.array(p))] / repeat - 1 /
math.factorial(lastDim)) < 0.01
+ # Check symbol interface
+ a = mx.sym.Variable('a')
+ b = mx.sym.random.shuffle(a)
+ c = mx.sym.random.shuffle(data=b, name='c')
+ d = mx.sym.sort(c)
+ assert (d.eval(a=data, ctx=mx.current_context())[0] == data).prod() == 1
+
if __name__ == '__main__':
import nose
nose.runmodule()
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