Copyfromto #30
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Copyfromto #30
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@reminisce I changed the csr's python api from (row_idx, indptr) to (indptr, row_idx) so that it's consistent with backend order to avoid confusion. |
sparse embedding unit test pass Conflicts: src/operator/tensor/elemwise_unary_op.h tests/cpp/ndarray_test.cc tests/python/unittest/test_sparse_ndarray.py tests/python/unittest/test_sparse_operator.py
reminisce
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May 13, 2017
include/mxnet/ndarray.h
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| auto stype = storage_type(); | ||
| CHECK_NE(stype, kDefaultStorage); | ||
| if (stype == kRowSparseStorage || stype == kCSRStorage) { | ||
| return aux_shape(0).Size() == 0; |
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For a csr, is it more correct to use indptr.Size() = num_rows+1 and indptr[num_rows]=0 to determine whether it's a zero csr? aux_shape(0).Size()=0 for csr are more like this csr has not been initialized, instead of this csr is a zero matrix.
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Yes that's why I commented that it's just a hint for whether the ndarray is initialized thus containing zeros. I assume this function will be called quite often implementing various sparse operators. indptr[num_rows] is more accurate but that involves reading memory from GPU, which is more expensive.
Usually csr is some user input which is non-zero. So this check works for most of the cases. Maybe I should change the change to storage_is_initialized to avoid confusion?
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Agree that changing storage_is_initialized makes more sense.
include/mxnet/ndarray.h
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| NDArrayStorageType storage_type_) | ||
| // Constructor for a non-default storage chunk | ||
| Chunk(NDArrayStorageType storage_type_, const TShape &storage_shape_, Context ctx_, | ||
| bool delay_alloc_, int dtype, std::vector<int> aux_types_, |
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std::vector aux_types_ --> const std::vector& aux_types_
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* Test input a graph. * Update foreach to execute the subgraph. * print inputs/outputs in foreach. * Remove print. * add test code for foreach. * exec foreach outside the engine. * Implements forward of foreach. * Add support for variable numbers of inputs and outputs. * Add a python wrapper for foreach. * Fix the order of inputs. * add test with lstm. * hide C version of foreach. * fix a bug temporarily. * Test free variables. * change for the new interface of InputGraph attribute. * Add attribute to the subgraph. * Handle free variables. * Get all input symbols of a subgraph. * Fix shape, dtype and storage inference. * reorganize the output of foreach. * Add a gluon RNN unroll with symbol foreach. * print unnecessary print. * have imperative and symbolic foreach. * Fix an error after moving foreach. * Fix imperative foreach * Fix a minor problem. * Use CachedOp to execute subgraph. * update TODO. * make foreach op use FStatefulComputeEx. TODO we need to change stateful executor to handle subgraph. * Add backward. * Fix bugs. * enable backward test in lstm. * Fix a bug in foreach backward for free variables. * change for the new CachedOp. * Detect the backward computation. * Fix bugs in foreach. * fix tests. * update tests. * check state shape. * enable nested foreach. * remove print. * fix a bug in test. * handle infer storage type for backward. * address comments. * address comments. * move some common functions out. * address comments. * fix lint. * Fix lint. * add doc. * undo modification in imperative.h * add doc and remove example code. * fix lint. * fix lint. * Fix lint. * make nd.foreach and sym.foreach consistent. * fix compile error. * address comments. * update. * check for loop only works for dense arrays. * move control flow op out of nn/ * fix include. * add a test in gluon. * work for GPU. * small fix. * remove subgraph_name * create loop state for reuse in the future. * move code. * Revert "remove subgraph_name" This reverts commit 977f562. * cut graph. * rename new var nodes. * Fix tests. * Fix bugs caused by ctypes (#29) * Add save/load json in testcases for foreach (#30) * support subgraph in stateful executor. * Fix compilation. * fix a bug when a subgraph has variable nodes. * Fix a bug of getting symbols. * copy var nodes. * Fix getting op states. * fix lint error. * address comments. * fix lint error. * simplify the execution of subgraph in the main thread. * fix lint error. * avoid waiting for computation in each iteration. * reuse cached op for inference. * share memory across mini-batches. * reuse memory. reuse memory between iterations in inference. reuse memory between mini-batches in training. * add tests for multiple batches. * remove entry. * add benchmark for foreach. * benchmark large batch size. * Fix the benchmark for GPU. * address comments. * update shape/dtype/storage inference. * update contrib API docs. * support nested foreach. * use a single CachedOp for all iterations. * use large dim. * update benchmark. * update benchmark. * update benchmark. * update benchmark. * return symbol arrays correctly in MXSymbolCutSubgraph. * return symbol arrays in MXSymbolGetInputSymbols. * fix lint error. * use cachedop to infer storage in backward. * fix scala API. * update comments. * fix scala. * fix test. * fix attribute name. * move benchmark. * fix the mapping of operator inputs/outputs and subgraph inputs/outputs. * add tests for dtype/shape inference. * reorganize tests. * fix a bug of cutting NodeEntry. When two node entries refer to the same output of a node, we should create only one var node for these two node entries. * fix lint error. * handle the case that outputs are inputs. * handle the case that inputs aren't used. * handle the case without output data. * fix a bug in foreach backward. * fix a bug when there isn't output data. * Fix lint error. * test diff Gluon RNN cells. * test all symbol RNN cells. * adjust the test precision. * Fix a bug in getting a list of variable names. We can't get a list of variable names from a hashtable. The order can't be guaranteed. Python2 and Python3 output different orders. * fix lint error. * Test 1D array. * fix a bug when subgraph inputs and outputs share NDArray. * fix. * fix * add comments.
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Cast storage during copy from to if the storage type doesn't match. Now we have a temporary way to test dot_backward.
Also added FResource for dot_backward. @reminisce