Use fully qualified name as tp_name for tensors and storages#1379
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colesbury merged 1 commit intopytorch:masterfrom Apr 27, 2017
Merged
Use fully qualified name as tp_name for tensors and storages#1379colesbury merged 1 commit intopytorch:masterfrom
colesbury merged 1 commit intopytorch:masterfrom
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An alternative way of achieving the same effect would be to define the concrete Tensor and Storage classes in C instead of Python, but you'd be basically doing the same thing: setting the |
apaszke
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apaszke
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* Have Kernel Inherit IrContainer (pytorch#1375) * Kernel<-Fusion Step 1 - Convert ExprSort to StmtSort (pytorch#1376) * Kernel<-Fusion Step 2 - Mutator refactor (pytorch#1377) * Kernel<-Fusion Step 3 - Debug print for expr_eval and type promotion fix (pytorch#1379) * Kernel<-Fusion Step 4 - Have kernel inherit Fusion (pytorch#1380) * Kernel<-Fusion Step 5 - Move lowering passes into their own files (pytorch#1382) * Kernel<-Fusion Step 6 - Remove kir::IrBuilder (pytorch#1383) * Kernel<-Fusion Step 7 - Remove kir functions from ComputeAtMap (pytorch#1384) * Kernel<-Fusion Step 8 - Clean up [lower/executor] utils (pytorch#1387) * Kernel<-Fusion Step 9 - Remove TensorView::fuserTv (pytorch#1388) * Kernel<-Fusion Step 10 - Remove lowerVal/lowerExpr (pytorch#1389) * Kernel<-Fusion Step 11 - Finish cleaning up kir (pytorch#1390)
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Currently, AttributeErrors on tensors and storages are ambiguous. It's unclear if they refer to the CUDA, sparse, or regular CPU classes:
Types defined in C don't have this problem because their
tp_namefield contains the fully qualified name:This changes the
tp_namefield on the Tensor and Storage classes to be their fully qualified name. The__name__attribute is still the "simple" class name, which is necessary for pickling in Python 2. This matches the behavior for types defined in C: