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eager mode enable sparse

This commit is contained in:
panyifeng 2020-07-21 19:03:26 +08:00
parent 304ae51a25
commit 032c5e0fdc
2 changed files with 13 additions and 8 deletions
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3
mindspore/ccsrc/pipeline/jit/static_analysis/prim.cc
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@ -150,7 +150,8 @@ PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() {
using mindspore::parse::PyObjectWrapper;
EvalResultPtr StandardPrimEvaluator::EvalPrim(const AnalysisEnginePtr &engine, const AbstractBasePtrList &args) {
if (prim_ != prim::kPrimMakeTuple && prim_ != prim::kPrimSwitch) {
if (prim_ != prim::kPrimMakeTuple && prim_ != prim::kPrimSwitch && prim_ != prim::kPrimEnvSetItem &&
prim_ != prim::kPrimEnvGetItem) {
auto ret_abstract = AbstractEval(args);
if (ret_abstract != nullptr) {
MS_LOG(DEBUG) << "StandardPrimEvaluator eval Undetermined";

18
mindspore/common/tensor.py
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@ -213,18 +213,21 @@ class IndexedSlices:
A sparse representation of a set of tensor slices at given indices.
An IndexedSlices is typically used to represent a subset of a larger
tensor dense of shape [LARGE0, D1, .. , DN] where LARGE0 >> D0.
tensor dense of shape [L0, D1, .. , DN] where L0 >> D0.
The values in indices are the indices in the first dimension of the slices
that have been extracted from the larger tensor.
The dense tensor dense represented by an IndexedSlices slices has
`dense[slices.indices[i], :, :, :, ...] = slices.values[i, :, :, :, ...]`.
IndexedSlices can only be used in `Cell`'s contruct method.
Args:
indices (Tensor): A 1-D integer Tensor of shape [D0].
values (Tensor): A Tensor of any dtype of shape [D0, D1, ..., Dn].
dense_shape: (tuple): A integer tuple containing the shape
of the corresponding dense tensor.
dense_shape (tuple): A integer tuple containing the shape
of the corresponding dense tensor.
Outputs:
IndexedSlices, composed of `indices`, `values`, `dense_shape`.
@ -250,17 +253,18 @@ class SparseTensor:
A sparse representation of a set of nonzero elememts from a tensor at given indices.
SparseTensor can only be used in `Cell`'s contruct method.
For a tensor dense, its SparseTensor(indices, values, dense_shape) has
`dense[indices[i]] = values[i]`
`dense[indices[i]] = values[i]`.
Args:
indices (Tensor): A 2-D integer Tensor of shape `[N, ndims]`,
where N and ndims are the number of values and number of dimensions in
the SparseTensor, respectively.
values (Tensor): A 1-D tensor of any type and shape `[N]`, which
supplies the values for each element in indices.
dense_shape: (tuple): A integer tuple of size `ndims`,
which specifies the dense_shape of the sparse tensor.
supplies the values for each element in indices.
dense_shape (tuple): A integer tuple of size `ndims`,
which specifies the dense_shape of the sparse tensor.
Outputs:
SparseTensor, composed of `indices`, `values`, `dense_shape`.