[MetaSchedule] Fix for RewriteLayout + AllocateConst when the rank of…

… the rewritten weight doesn't change (apache#13851)

[MetaSchedule] Fix for RewriteLayout + AllocateConst when the rank of
the rewritten weight doesn't change

src/relay/backend/te_compiler_cache.cc

@@ -585,7 +585,26 @@ class ScheduleBuilder : public ExprVisitor {
                       << "Only one layout-free constant is supported by RewriteLayout for now";
                   auto constant = const_collector.constants[0];
 
-                  if (constant.Shape().size() == index_map->initial_indices.size()) {
+                  auto is_constant_transformed = [index_map](runtime::NDArray c) {
+                    if (c.Shape().size() != index_map->initial_indices.size()) {
+                      return true;
+                    }
+                    size_t src_size_1d = 1;
+                    Array<PrimExpr> orig_shape;
+                    for (size_t i = 0; i < c.Shape().size(); ++i) {
+                      src_size_1d *= c->shape[i];
+                      orig_shape.push_back(PrimExpr(static_cast<int>((c->shape[i]))));
+                    }
+                    auto dst_shape = index_map->MapShape(orig_shape);
+                    std::vector<int64_t> dst_shape_int;
+                    size_t dst_size_1d = 1;
+                    for (size_t i = 0; i < dst_shape.size(); ++i) {
+                      dst_size_1d *= dst_shape[i].as<IntImmNode>()->value;
+                    }
+                    return src_size_1d != dst_size_1d;
+                  };
+
+                  if (!is_constant_transformed(constant)) {
                     // This is the first case, reached during the MetaScheduleLayoutRewrite pass.
                     //
                     // A layout-free constant having the same rank as an input to the index map

tests/python/unittest/test_meta_schedule_relay_integration.py

@@ -871,5 +871,79 @@ def test_disabled_pass_param():
     pytest.fail("'disabled_pass' argument does not work")
 
 
+def test_rewrite_layout_link_params_1x1_conv2d():
+    I, O, H, W = 32, 16, 256, 256
+    kH = kW = 1
+
+    strides = (1, 1)
+    padding = (0, 0)
+
+    data_shape = (1, H, W, I)
+    w_shape = (kH, kW, I, O)
+
+    data = relay.var("data", shape=data_shape, dtype="float32")
+    weight = relay.var("weight", shape=w_shape, dtype="float32")
+
+    conv = relay.nn.conv2d(
+        data=data,
+        weight=weight,
+        kernel_size=(kH, kW),
+        channels=O,
+        padding=padding,
+        strides=strides,
+        data_layout="NHWC",
+        kernel_layout="HWIO",
+        out_dtype="float32",
+    )
+
+    mod = tvm.IRModule.from_expr(conv)
+
+    weight_np = np.random.randn(*w_shape).astype("float32")
+
+    params = {"weight": weight_np}
+
+    data_np = np.random.randn(*data_shape).astype("float32")
+
+    ref = (
+        relay.create_executor("graph", mod=mod, device=tvm.cpu(0), target="llvm")
+        .evaluate()(*[data_np, weight_np])
+        .numpy()
+    )
+
+    link_params = True
+
+    target = "llvm --num-cores=4"
+
+    executor = relay.backend.Executor("graph", {"link-params": link_params})
+    mod = mod.with_attr("executor", executor)
+
+    with tempfile.TemporaryDirectory() as work_dir:
+        database = ms.relay_integration.tune_relay(
+            mod=mod,
+            target=target,
+            params=params,
+            work_dir=work_dir,
+            max_trials_global=8,
+            strategy="replay-trace",
+        )
+
+        lib = ms.relay_integration.compile_relay(
+            database=database,
+            mod=mod,
+            target=target,
+            params=params,
+        )
+
+    dev = tvm.device(target, 0)
+    runtime = tvm.contrib.graph_executor.GraphModule(lib["default"](dev))
+
+    runtime.set_input("data", data_np)
+    runtime.run()
+
+    out = runtime.get_output(0).numpy()
+
+    np.testing.assert_allclose(ref, out, rtol=1e-4, atol=1e-4)
+
+
 if __name__ == "__main__":
     tvm.testing.main()