Specialize ArgumentSpecs on tuple elements too#11863
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apaszke wants to merge 2 commits intopytorch:masterfrom
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Specialize ArgumentSpecs on tuple elements too#11863apaszke wants to merge 2 commits intopytorch:masterfrom
apaszke wants to merge 2 commits intopytorch:masterfrom
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zdevito
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Sep 20, 2018
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This looks good. A few nits.
| size_t hashCode() const { | ||
| return hash_code; | ||
| } | ||
| std::vector<TypePtr> getTypes(Graph& graph) const { |
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torch/csrc/jit/type.h
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| struct UndefinedTensorType; | ||
| using UndefinedTensorTypePtr = std::shared_ptr<UndefinedTensorType>; | ||
| // This node represents a single Tensor value with a specific size |
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torch/csrc/jit/type.h
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| return rhs.kind() == kind(); | ||
| } | ||
| bool isSubtypeOf(const TypePtr rhs) const override { | ||
| if (rhs->kind() == TypeKind::DynamicType) |
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This is pretty important because a common situation of passing LSTM hidden states as a tuple completely trashes performance of a network.
Cleans up all our propagation/undef specialization passes, at a cost of increased complexity of
ArgumentSpecandGraphExecutor. An alternative would be to simply flatten all tuple inputs to a graph ahead of time, but that might just end up being confusing in the future (you never know if you're working with a graph that can have tuple or not).