hypercubestart commented on a change in pull request #6336:
URL: https://github.com/apache/incubator-tvm/pull/6336#discussion_r476912921
##########
File path: src/relay/transforms/gradient.cc
##########
@@ -85,7 +85,7 @@ Expr DeGlobal(const Optional<IRModule>& mod, const Expr& e) {
if (mod.defined() && x) {
BaseFunc base_func = mod.value()->Lookup(GetRef<GlobalVar>(x));
if (auto* n = base_func.as<FunctionNode>()) {
Review comment:
why do we need if else here? doesn't every GlobalVar map to a Function?
##########
File path: src/relay/transforms/gradient.cc
##########
@@ -559,6 +581,39 @@ struct ReverseAD : ExprMutator {
return ad_vars->at(var_ref);
}
+ Expr VisitExpr_(const GlobalVarNode* op) final {
+ // todo: concatenating string to add attribute seems like a brittle hack.
+ // maybe get module indexed by a rose tree of string?
+ CHECK(mod.defined());
+ auto orig_gv = GetRef<GlobalVar>(op);
+ if (ad_gvars->count(orig_gv) == 0) {
+ GlobalVar gv(op->name_hint + "_grad");
+ (*ad_gvars)[orig_gv] = gv;
+ Function orig_f =
Downcast<Function>(mod.value()->Lookup(GetRef<GlobalVar>(op)));
Review comment:
mod.value()->Lookup(orig_gv)
##########
File path: src/relay/transforms/gradient.cc
##########
@@ -559,6 +581,39 @@ struct ReverseAD : ExprMutator {
return ad_vars->at(var_ref);
}
+ Expr VisitExpr_(const GlobalVarNode* op) final {
+ // todo: concatenating string to add attribute seems like a brittle hack.
+ // maybe get module indexed by a rose tree of string?
+ CHECK(mod.defined());
+ auto orig_gv = GetRef<GlobalVar>(op);
+ if (ad_gvars->count(orig_gv) == 0) {
+ GlobalVar gv(op->name_hint + "_grad");
+ (*ad_gvars)[orig_gv] = gv;
+ Function orig_f =
Downcast<Function>(mod.value()->Lookup(GetRef<GlobalVar>(op)));
Review comment:
would it be a good idea to DeDup here?
##########
File path: tests/python/relay/test_pass_gradient.py
##########
@@ -341,5 +357,19 @@ def test_no_duplication():
counts = count_ops(gr)
assert counts['nn.dense'] == 3, "We expect 3 dense (1 forward, two
backward)"
+
+def test_global_function():
+ m = tvm.IRModule()
+ t = relay.TensorType([])
+ x = relay.Var('x', t)
+ d = GlobalVar('double')
+ m[d] = relay.Function([x], x + x)
+ y = relay.Var('y', t)
+ q = GlobalVar('q')
+ m[q] = relay.Function([y], d(d(y)))
+ g = GlobalVar('grad')
+ m[g] = tvm.relay.transform.gradient(q, m)
+
Review comment:
add type and value check
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