[Relay] Remove overwriting of matmul shapes when they are static (apa…

…che#13615)

In the Relay Matmul shape relation, we are a little over enthusiastic about unifying dynamic shapes. If one of the shapes is static, it does not need to be unified. This change only rewrites dynamic shapes to required static constraints.

* Remove overwriting of matmul shapes when they are static

* Simplify nesting

* Add shape check to dense tests.

src/relay/op/nn/nn.h

@@ -113,23 +113,32 @@ bool MatmulRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
         std::vector<PrimExpr> B_shape(tensor_b->shape.begin(), tensor_b->shape.end());
         auto sa = A_shape.size();
         auto sb = B_shape.size();
+        size_t index_swap_A;
+        size_t index_swap_B;
         if (transpose_a && transpose_b) {
-          auto tmp = A_shape[sa - 2];
-          A_shape[sa - 2] = B_shape[sb - 1];
-          B_shape[sb - 1] = tmp;
+          index_swap_A = sa - 2;
+          index_swap_B = sb - 1;
         } else if (transpose_a) {
-          auto tmp = A_shape[sa - 2];
-          A_shape[sa - 2] = B_shape[sb - 2];
-          B_shape[sb - 2] = tmp;
+          index_swap_A = sa - 2;
+          index_swap_B = sb - 2;
         } else if (transpose_b) {
-          auto tmp = A_shape[sa - 1];
-          A_shape[sa - 1] = B_shape[sb - 1];
-          B_shape[sb - 1] = tmp;
+          index_swap_A = sa - 1;
+          index_swap_B = sb - 1;
         } else {
-          auto tmp = A_shape[sa - 1];
-          A_shape[sa - 1] = B_shape[sb - 2];
-          B_shape[sb - 2] = tmp;
+          index_swap_A = sa - 1;
+          index_swap_B = sb - 2;
         }
+
+        // Rewrite dynamic axes to static where constraints allow.
+        auto tmp = A_shape[index_swap_A];
+        if (A_shape[index_swap_A].as<tir::AnyNode>()) {
+          A_shape[index_swap_A] = B_shape[index_swap_B];
+        }
+        if (B_shape[index_swap_B].as<tir::AnyNode>()) {
+          B_shape[index_swap_B] = tmp;
+        }
+
+        // Update input types with new constrained shapes.
         reporter->Assign(types[0], TensorType(A_shape, tensor_a->dtype));
         reporter->Assign(types[1], TensorType(B_shape, tensor_b_dtype));
       }

tests/python/relay/test_op_level1.py

@@ -25,6 +25,7 @@
 import tvm.topi.testing
 from tvm.contrib.nvcc import have_fp16
 import tvm.testing
+from tvm.topi.utils import get_const_tuple
 
 executor_kind = tvm.testing.parameter("graph", "vm")
 
@@ -695,6 +696,8 @@ def test_dense(executor_kind):
         w = relay.var("w", relay.TensorType((k, n), dtype))
         y = relay.nn.dense(x, w)
         yy = run_infer_type(y)
+        # Confirm that input shape has not been rewritten to become dynamic.
+        assert get_const_tuple(yy.type_args[0].shape) == (4, 2)
 
         n, c, h, w = te.size_var("n"), te.size_var("c"), te.size_var("h"), 2
         x = relay.var("x", relay.TensorType((n, c, h, w), dtype))