diff --git a/onnxruntime/core/framework/session_state.cc b/onnxruntime/core/framework/session_state.cc
index 191526e455549..13f5ddf4f7d8c 100644
--- a/onnxruntime/core/framework/session_state.cc
+++ b/onnxruntime/core/framework/session_state.cc
@@ -389,6 +389,7 @@ void TryCalculateSizeFromResolvedShape(int ml_value_idx, std::unordered_map<int,
 
 }  // namespace
 
+// If this function fails NO memory planning will take place, hence lets ONLY FAIL and stop training where warranted, example SIZE overflow.
 Status SessionState::GeneratePatternGroupCache(const std::vector<std::reference_wrapper<const TensorShape>>& input_shape,
                                                const std::vector<int>& feed_mlvalue_idxs,
                                                MemoryPatternGroup* output,
@@ -425,12 +426,17 @@ Status SessionState::GeneratePatternGroupCache(const std::vector<std::reference_
       auto* arg = node->OutputDefs()[i];
       size_t is_resolved = 0;
       std::vector<int64_t> resolved_shape;
-      ORT_RETURN_IF_ERROR(TryResolveShape(arg, map, is_resolved, resolved_shape));
 
-      // Store all valid resolved shapes. They will be queried in, for example,
-      // Recv operator to bypass the dependency of output shapes on inputs.
-      if (is_resolved != 0) {
-        resolved_shapes[ml_value_idx] = resolved_shape;
+      // Tensors whose shape cannot be resolved statically will be allocated at runtime.
+      if (TryResolveShape(arg, map, is_resolved, resolved_shape).IsOK()) {
+        // Store all valid resolved shapes. They will be queried in, for example,
+        // Recv operator to bypass the dependency of output shapes on inputs.
+        if (is_resolved != 0) {
+          resolved_shapes[ml_value_idx] = resolved_shape;
+        }
+      } else {
+        LOGS(logger_, INFO) << "[Static memory planning] Could not resolve shape for tensor with ML index "
+                            << ml_value_idx << ", will allocate dynamically.";
       }
     }
   }