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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2017/12/05 18:22:03 UTC
[GitHub] piiswrong closed pull request #8891: rsp[:] = const_value, used in constant initializer for rsp weight
piiswrong closed pull request #8891: rsp[:] = const_value, used in constant initializer for rsp weight
URL: https://github.com/apache/incubator-mxnet/pull/8891
This is a PR merged from a forked repository.
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As this is a foreign pull request (from a fork), the diff is supplied
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diff --git a/python/mxnet/ndarray/sparse.py b/python/mxnet/ndarray/sparse.py
index 229044e289..700dee0b07 100644
--- a/python/mxnet/ndarray/sparse.py
+++ b/python/mxnet/ndarray/sparse.py
@@ -658,8 +658,7 @@ def __setitem__(self, key, value):
if value.handle is not self.handle:
value.copyto(self)
elif isinstance(value, numeric_types):
- raise ValueError("Assigning numeric types to RowSparseNDArray " \
- "is not implemented yet.")
+ _internal._set_value(float(value), out=self)
elif isinstance(value, (np.ndarray, np.generic)):
warnings.warn('Assigning non-NDArray object to RowSparseNDArray is not efficient',
RuntimeWarning)
diff --git a/src/kvstore/kvstore_dist_server.h b/src/kvstore/kvstore_dist_server.h
index 49aa001910..f1637c4e57 100644
--- a/src/kvstore/kvstore_dist_server.h
+++ b/src/kvstore/kvstore_dist_server.h
@@ -247,7 +247,12 @@ class KVStoreDistServer {
NDArray rsp = stored;
stored.CheckAndAlloc({mshadow::Shape1(recved.shape()[0])});
mshadow::Stream<cpu> *s = ctx.get_stream<cpu>();
- op::PopulateFullIdxRspImpl(s, &rsp);
+ using namespace mxnet::op;
+ nnvm::dim_t nnr = rsp.shape()[0];
+ MSHADOW_IDX_TYPE_SWITCH(rsp.aux_type(rowsparse::kIdx), IType, {
+ IType* idx = rsp.aux_data(rowsparse::kIdx).dptr<IType>();
+ mxnet_op::Kernel<PopulateFullIdxRspKernel, cpu>::Launch(s, nnr, idx);
+ });
mshadow::Copy(rsp.data().FlatTo1D<cpu, float>(),
recved.data().FlatTo1D<cpu, float>(), s);
on_complete();
diff --git a/src/ndarray/ndarray.cc b/src/ndarray/ndarray.cc
index 4a1963ae21..f09f168977 100644
--- a/src/ndarray/ndarray.cc
+++ b/src/ndarray/ndarray.cc
@@ -309,30 +309,34 @@ void SetValueOp(const real_t &rhs, NDArray *out) {
CHECK_NE(out->is_none(), true) << "Set value target must not be empty";
// important: callback must always capture by value
NDArray ret = *out;
- switch (ret.ctx().dev_mask()) {
- case cpu::kDevMask: {
- Engine::Get()->PushSync([rhs, ret](RunContext ctx) {
- CHECK(ret.storage_type() == kDefaultStorage);
- TBlob tmp = ret.data();
- ndarray::Eval<cpu>(rhs, &tmp, ctx);
- }, ret.ctx(), {}, {ret.var()},
- FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
- break;
- }
+ const NDArrayStorageType stype = ret.storage_type();
+ Engine::Get()->PushSync([rhs, ret, stype](RunContext ctx) {
+ TBlob tmp = ret.data();
+ switch (ret.ctx().dev_mask()) {
+ case cpu::kDevMask: {
+ if (stype == kDefaultStorage) {
+ ndarray::Eval<cpu>(rhs, &tmp, ctx);
+ } else {
+ ndarray::Eval(ctx.get_stream<cpu>(), rhs, ret);
+ }
+ break;
+ }
#if MXNET_USE_CUDA
- case gpu::kDevMask: {
- Engine::Get()->PushSync([rhs, ret](RunContext ctx) {
- TBlob tmp = ret.data();
- ndarray::Eval<gpu>(rhs, &tmp, ctx);
+ case gpu::kDevMask: {
+ if (stype == kDefaultStorage) {
+ ndarray::Eval<gpu>(rhs, &tmp, ctx);
+ } else {
+ ndarray::Eval(ctx.get_stream<gpu>(), rhs, ret);
+ }
// Wait GPU kernel to complete
ctx.get_stream<gpu>()->Wait();
- }, ret.ctx(), {}, {ret.var()},
- FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
- break;
- }
+ break;
+ }
#endif
- default: LOG(FATAL) << MXNET_GPU_NOT_ENABLED_ERROR;
- }
+ default: LOG(FATAL) << MXNET_GPU_NOT_ENABLED_ERROR;
+ }
+ }, ret.ctx(), {}, {ret.var()},
+ FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
}
/*!
diff --git a/src/ndarray/ndarray_function.cc b/src/ndarray/ndarray_function.cc
index ef0adbe5f2..552555adf8 100644
--- a/src/ndarray/ndarray_function.cc
+++ b/src/ndarray/ndarray_function.cc
@@ -183,5 +183,18 @@ void ElementwiseSum<cpu>(mshadow::Stream<cpu>* s,
}
}
+
+template<>
+void Eval<cpu>(mshadow::Stream<cpu> *s,
+ const real_t val, const NDArray& dst) {
+ NDArray temp = dst;
+ const NDArrayStorageType stype = temp.storage_type();
+ if (stype == kRowSparseStorage) {
+ SetValueRspImpl(s, val, &temp);
+ } else {
+ LOG(FATAL) << "Not implemented for storage type" << stype;
+ }
+}
+
} // namespace ndarray
} // namespace mxnet
diff --git a/src/ndarray/ndarray_function.cu b/src/ndarray/ndarray_function.cu
index 445f8459ae..06b5ad46a0 100644
--- a/src/ndarray/ndarray_function.cu
+++ b/src/ndarray/ndarray_function.cu
@@ -203,5 +203,17 @@ void ElementwiseSum<gpu>(mshadow::Stream<gpu>* s,
}
}
+template<>
+void Eval<gpu>(mshadow::Stream<gpu> *s,
+ const real_t val, const NDArray& dst) {
+ NDArray temp = dst;
+ const NDArrayStorageType stype = temp.storage_type();
+ if (stype == kRowSparseStorage) {
+ SetValueRspImpl(s, val, &temp);
+ } else {
+ LOG(FATAL) << "Not implemented for storage type" << stype;
+ }
+}
+
} // namespace ndarray
} // namespace mxnet
diff --git a/src/ndarray/ndarray_function.h b/src/ndarray/ndarray_function.h
index 6e6df3954c..518bb77317 100644
--- a/src/ndarray/ndarray_function.h
+++ b/src/ndarray/ndarray_function.h
@@ -32,6 +32,7 @@
#include <mxnet/ndarray.h>
#include <vector>
#include "../operator/mshadow_op.h"
+#include "../operator/tensor/init_op.h"
namespace mxnet {
/*! \brief namespace to support all possible Ndarray operator */
@@ -179,6 +180,30 @@ void ElementwiseSum(mshadow::Stream<xpu>* s,
const std::vector<NDArray>& nds,
NDArray* out);
+/*!
+ * \brief Set a row_sparse NDArray with val
+ * \param s - The device stream
+ * \param val - The value to be set
+ * \param dst - NDArray which is to be set to val
+ */
+template<typename xpu>
+void SetValueRspImpl(mshadow::Stream<xpu> *s,
+ const real_t val, NDArray *dst) {
+ CHECK_EQ(dst->storage_type(), kRowSparseStorage);
+ using namespace mxnet::op;
+ nnvm::dim_t nnr = dst->shape()[0];
+ dst->CheckAndAlloc({mshadow::Shape1(nnr)});
+ MSHADOW_IDX_TYPE_SWITCH(dst->aux_type(rowsparse::kIdx), IType, {
+ IType* idx = dst->aux_data(rowsparse::kIdx).dptr<IType>();
+ mxnet_op::Kernel<PopulateFullIdxRspKernel, xpu>::Launch(s, nnr, idx);
+ });
+ Fill<false>(s, dst->data(), kWriteTo, val);
+}
+
+template<typename xpu>
+void Eval(mshadow::Stream<xpu> *s,
+ const real_t val, const NDArray& dst);
+
// broadcasting
template <typename Device>
void EvalBroadcast(TBlob const& src, TBlob* ret, int size, RunContext ctx);
diff --git a/src/operator/random/sample_op.h b/src/operator/random/sample_op.h
index 240825bfff..84bb85145b 100644
--- a/src/operator/random/sample_op.h
+++ b/src/operator/random/sample_op.h
@@ -428,13 +428,17 @@ void SampleComputeEx_(const nnvm::NodeAttrs& attrs,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& outputs,
SampleMaster<xpu, Sampler> sample_master) {
+ using namespace mxnet::op;
NDArray output = outputs[0];
mshadow::Stream<xpu> *s = ctx.get_stream<xpu>();
if (output.storage_type() == kRowSparseStorage) {
// indices
nnvm::dim_t nnr = output.shape()[0];
output.CheckAndAlloc({mshadow::Shape1(nnr)});
- PopulateFullIdxRspImpl(s, &output);
+ MSHADOW_IDX_TYPE_SWITCH(output.aux_type(rowsparse::kIdx), IType, {
+ IType* idx = output.aux_data(rowsparse::kIdx).dptr<IType>();
+ mxnet_op::Kernel<PopulateFullIdxRspKernel, xpu>::Launch(s, nnr, idx);
+ });
// data
TBlob out_blob = output.data();
sample_master.op(attrs, ctx, req[0], &out_blob);
diff --git a/src/operator/tensor/init_op.cu b/src/operator/tensor/init_op.cu
index aeea2895b0..37660e1d36 100644
--- a/src/operator/tensor/init_op.cu
+++ b/src/operator/tensor/init_op.cu
@@ -43,6 +43,7 @@ void FillZerosCsrImpl(mshadow::Stream<mshadow::gpu> *s, const NDArray& dst) {
});
}
+
NNVM_REGISTER_OP(_zeros)
.set_attr<FCompute>("FCompute<gpu>", FillCompute<gpu, 0>)
.set_attr<FComputeEx>("FComputeEx<gpu>", FillComputeZerosEx<gpu>);
diff --git a/src/operator/tensor/init_op.h b/src/operator/tensor/init_op.h
index 4d899704a1..3f5014d8ca 100644
--- a/src/operator/tensor/init_op.h
+++ b/src/operator/tensor/init_op.h
@@ -291,19 +291,6 @@ inline void FillDnsZerosRspImpl(mshadow::Stream<xpu> *s, NDArray *dst) {
});
}
-// Fill full indices NDArray with zeros by updating the aux shape.
-template<typename xpu>
-void PopulateFullIdxRspImpl(mshadow::Stream<xpu> *s, NDArray *dst) {
- using namespace rowsparse;
- CHECK_EQ(dst->storage_type(), kRowSparseStorage);
- nnvm::dim_t nnr = dst->shape()[0];
- dst->CheckAndAllocAuxData(kIdx, mshadow::Shape1(nnr));
- MSHADOW_IDX_TYPE_SWITCH(dst->aux_type(kIdx), IType, {
- IType* idx = dst->aux_data(kIdx).dptr<IType>();
- mxnet_op::Kernel<PopulateFullIdxRspKernel, xpu>::Launch(s, nnr, idx);
- });
-}
-
/*!
* \brief Fill a rsp NDArray with zeros by updating the aux shape.
* \tparam xpu - cpu or gpu
diff --git a/tests/python/gpu/test_operator_gpu.py b/tests/python/gpu/test_operator_gpu.py
index cecda21a07..ad187443f5 100644
--- a/tests/python/gpu/test_operator_gpu.py
+++ b/tests/python/gpu/test_operator_gpu.py
@@ -38,6 +38,7 @@
from test_sparse_ndarray import test_create_sparse_nd_empty, test_create_sparse_nd_from_sparse
from test_sparse_ndarray import test_create_sparse_nd_from_dense, test_create_sparse_nd_infer_shape
from test_sparse_ndarray import test_sparse_nd_check_format, test_sparse_nd_copy
+from test_sparse_ndarray import test_sparse_nd_setitem
from test_sparse_operator import *
from test_ndarray import *
diff --git a/tests/python/unittest/test_init.py b/tests/python/unittest/test_init.py
index e642e65ec3..efd6ef3674 100644
--- a/tests/python/unittest/test_init.py
+++ b/tests/python/unittest/test_init.py
@@ -44,8 +44,24 @@ def test_aux_init():
assert (mod.get_params()[1]['bn_moving_var'].asnumpy() == 1).all()
assert (mod.get_params()[1]['bn_moving_mean'].asnumpy() == 0).all()
+def test_rsp_const_init():
+ def check_rsp_const_init(init, val):
+ shape = (10, 10)
+ x = mx.symbol.Variable("data", stype='csr')
+ weight = mx.symbol.Variable("weight", shape=(shape[1], 2),
+ init=init, stype='row_sparse')
+ dot = mx.symbol.sparse.dot(x, weight)
+ mod = mx.mod.Module(dot, label_names=None)
+ mod.bind(data_shapes=[('data', shape)])
+ mod.init_params()
+ assert (list(mod.get_params()[0].values())[0].asnumpy() == val).all()
+
+ check_rsp_const_init(mx.initializer.Constant(value=2.), 2.)
+ check_rsp_const_init(mx.initializer.Zero(), 0.)
+ check_rsp_const_init(mx.initializer.One(), 1.)
if __name__ == '__main__':
test_variable_init()
test_default_init()
test_aux_init()
+ test_rsp_const_init()
diff --git a/tests/python/unittest/test_sparse_ndarray.py b/tests/python/unittest/test_sparse_ndarray.py
index e59e476601..e404997e49 100644
--- a/tests/python/unittest/test_sparse_ndarray.py
+++ b/tests/python/unittest/test_sparse_ndarray.py
@@ -93,7 +93,7 @@ def check_sparse_nd_setitem(stype, shape, dst):
x = mx.nd.zeros(shape=shape, stype=stype)
x[:] = dst
dst_nd = mx.nd.array(dst) if isinstance(dst, (np.ndarray, np.generic)) else dst
- assert same(x.asnumpy(), dst_nd.asnumpy())
+ assert np.all(x.asnumpy() == dst_nd.asnumpy() if isinstance(dst_nd, NDArray) else dst)
shape = rand_shape_2d()
for stype in ['row_sparse', 'csr']:
@@ -102,7 +102,8 @@ def check_sparse_nd_setitem(stype, shape, dst):
check_sparse_nd_setitem(stype, shape, rand_ndarray(shape, stype))
# numpy assignment
check_sparse_nd_setitem(stype, shape, np.ones(shape))
-
+ # scalar assigned to row_sparse NDArray
+ check_sparse_nd_setitem('row_sparse', shape, 2)
def test_sparse_nd_slice():
shape = (rnd.randint(2, 10), rnd.randint(2, 10))
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