altanh commented on a change in pull request #8597:
URL: https://github.com/apache/tvm/pull/8597#discussion_r683865198
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File path: tests/python/relay/test_backend_interpreter.py
##########
@@ -222,28 +218,72 @@ def test_tuple_passing():
dev = tvm.cpu()
target = tvm.target.Target("llvm")
- exec = relay.create_executor(mod=mod, device=dev, target=target)
- f = exec.evaluate(gv)
+ f = relay.create_executor(mod=mod, device=dev, target=target).evaluate(gv)
# First use a Python tuple.
out = f((10, 8))
- tvm.testing.assert_allclose(out.numpy(), np.array(10))
+ testing.assert_allclose(out.numpy(), np.array(10))
# Second use a tuple value.
value_tuple = container.tuple_object([nd.array(np.array(11)),
nd.array(np.array(12))])
out = f(value_tuple)
- tvm.testing.assert_allclose(out.numpy(), np.array(11))
+ testing.assert_allclose(out.numpy(), np.array(11))
+
+
+def test_dynamic():
+ n = 3
+ m = 2
+ x = relay.Var("x", relay.TensorType([relay.Any(), m], "float32"))
+ y = relay.Var("y", relay.TensorType([relay.Any(), m], "float32"))
+ xx = x - relay.expr.const(3.0)
+ yy = y * relay.expr.const(5.0)
+ z = relay.op.concatenate([xx, yy], axis=0)
+ mod = tvm.IRModule()
+ mod["main"] = relay.Function([x, y], z)
+ x_np = np.random.uniform(size=(n, m)).astype("float32")
+ y_np = np.random.uniform(size=(n, m)).astype("float32")
+ expected = np.concatenate([x_np - 3.0, y_np * 5.0], axis=0)
+ check_eval(None, [x_np, y_np], expected, mod)
+
+
+def test_ref_global_from_expr():
+ n = 3
+ x = relay.Var("x", relay.TensorType([n], "float32"))
+ y = relay.Var("y", relay.TensorType([n], "float32"))
+ mod = tvm.IRModule()
+ mod["add"] = relay.Function([x, y], relay.add(x, y))
+ x_np = np.random.uniform(size=(n)).astype("float32")
+ y_np = np.random.uniform(size=(n)).astype("float32")
Review comment:
```suggestion
y_np = np.random.uniform(size=(n,)).astype("float32")
```
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