[FIX] Verify that tensor reshape is valid. (apache#6215)

src/relay/op/tensor/transform.cc

@@ -25,6 +25,7 @@
 
 #include <tvm/ir/error.h>
 #include <tvm/relay/attrs/transform.h>
+#include <tvm/relay/expr.h>
 #include <tvm/relay/op.h>
 #include <tvm/runtime/packed_func.h>
 #include <tvm/tir/data_layout.h>
@@ -581,6 +582,36 @@ bool ReshapeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
     oshape.Set(infer_idx, infer_dim);
   }
 
+  // Verify that the sum of dimensions in the output shape is the sum of
+  // dimensions in the input shape
+  bool found_dynamic = false;
+  int64_t oshape_sum = 1;
+  for (auto& x : oshape) {
+    // Check if we have a dynamic shape. If we do, we can't verify if the
+    // reshape is valid. Dynamic shapes are marker by using Any, but can also
+    // occur from SizeVar's. In the case of SizeVar, the shape expression can
+    // be an AST. We can't easily check if we have an AST because of a ShapeVar
+    // or some other reason, so our check for dynamic shape is just if we can
+    // convert the shape to in integer or not.
+    if (!x->IsInstance<tvm::Integer::ContainerType>()) {
+      found_dynamic = true;
+      break;
+    }
+    oshape_sum *= Downcast<tvm::Integer>(x)->value;
+  }
+  int64_t data_shape_sum = 1;
+  for (auto& x : data_shape) {
+    if (!x->IsInstance<tvm::Integer::ContainerType>()) {
+      found_dynamic = true;
+      break;
+    }
+    data_shape_sum *= Downcast<tvm::Integer>(x)->value;
+  }
+  if (!found_dynamic) {
+    CHECK_EQ(oshape_sum, data_shape_sum)
+        << "Input tensor shape and reshaped shape are not compatible";
+  }
+
   if (param->reverse) {
     reporter->Assign(types[1],
                      TensorType(Array<IndexExpr>(oshape.rbegin(), oshape.rend()), data->dtype));

tests/python/relay/test_op_level3.py

@@ -21,6 +21,7 @@
 import tvm
 from tvm import te
 from tvm import relay
+from tvm.error import TVMError
 from tvm.relay import create_executor, transform
 from tvm.relay.testing import ctx_list, check_grad, run_infer_type
 
@@ -282,6 +283,13 @@ def verify_reshape(shape, newshape, oshape):
     verify_reshape((2, 3, 4), (2, -4, -1, 3, -2), (2, 1, 3, 4))
 
 
+def test_reshape_fail():
+    with pytest.raises(TVMError) as reshape_err:
+        x = relay.var("x", relay.TensorType([2,3], "float32"))
+        z = relay.reshape(x, [7])
+        zz = run_infer_type(z)
+
+
 def test_reshape_like_infer_type():
     # concrete shape
     x = relay.var("x", relay.TensorType((1, 2, 3), "float32"))
@@ -1070,6 +1078,7 @@ def verify_sparse_to_dense(sparse_indices, sparse_values, default_value, output_
     test_transpose()
     test_reshape_infer_type()
     test_reshape()
+    test_reshape_fail()
     test_reshape_like_infer_type()
     test_reshape_like()
     test_take_infer_type()

tests/python/relay/test_pass_combine_parallel_dense.py

@@ -187,7 +187,7 @@ def check(i, j, k, scale1, scale2, newshape):
         tvm.ir.assert_structural_equal(y, y_expected, map_free_vars=True)
 
     check(3, 5, 4, 0.5, 0.25, (1, 1, 15))
-    check(100, 200, 300, 0.5, 0.25, (1, 1, 200))
+    check(100, 200, 300, 0.5, 0.25, (1, 1, 20000))
 
 
 def test_combine_parallel_dense_flat():
@@ -369,7 +369,7 @@ def check(i, j, k, scale1, scale2, newshape1, newshape2):
         tvm.ir.assert_structural_equal(y, y_expected, map_free_vars=True)
 
     check(3, 5, 4, 0.5, 0.25, (1, 1, 15), (1, 1, 30))
-    check(100, 200, 300, 0.5, 0.25, (1, 1, 200), (1, 1, 400))
+    check(100, 200, 300, 0.5, 0.25, (1, 1, 20000), (1, 1, 40000))
 
 
 if __name__ == "__main__":