[GitHub] [incubator-tvm] jroesch commented on a change in pull request #5324: [Runtime][Relay][Cleanup] Clean up for memory pass to enable heterogenous execution support.

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jroesch commented on a change in pull request #5324: [Runtime][Relay][Cleanup] Clean up for memory pass to enable heterogenous execution support.
URL: https://github.com/apache/incubator-tvm/pull/5324#discussion_r407905267
 
 

 ##########
 File path: include/tvm/relay/attrs/memory.h
 ##########
 @@ -27,10 +27,37 @@
 #include <tvm/ir/attrs.h>
 #include <tvm/relay/expr.h>
 #include <string>
+#include <vector>
 
 namespace tvm {
 namespace relay {
 
+std::vector<TensorType> FlattenTupleType(const Type& type);
+std::vector<Expr> FromTupleType(const Type& type, const Expr& expr);
+Expr ToTupleType(const Type& t, const Array<Expr>& exprs);
+
+/*!
+ * \brief Options for allocating storage.
+ */
+struct AllocStorageAttrs : public tvm::AttrsNode<AllocStorageAttrs> {
+  DataType dtype;
+  int device_id;
+  int device_type;
+
+  TVM_DECLARE_ATTRS(AllocStorageAttrs, "relay.attrs.AllocStorageAttrs") {
+    TVM_ATTR_FIELD(dtype)
+      .describe(
+         "The dtype of the tensor to allocate.")
+      .set_default(DataType::Float(32, 1));
+    TVM_ATTR_FIELD(device_id)
 
 Review comment:
   @tqchen and I talked about support for mapping virtual contexts to logical contexts, i.e schedule for n devices but map from 1 to n at runtime. This allows us to do a mixture of static and dynamic scheduling. Now that I think about it we probably want to logically group the allocations based on device id either way to ensure we place them on the right device (i.e which device do I place an allocation when I have n-gpus). 
   
   I want to store the the context directly but it doesn't extend Object, i.e is not a valid attribute type. 

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