[Relax][Frontend][TFLite] Fix DENSIFY tests for CI compatibility

  This commit fixes two issues in the DENSIFY test suite that caused
  failures in TVM's CI environment:

  1. Remove dependency on tfl.Int32VectorStartValuesVector
     - This flatbuffers helper is absent in older tflite package versions
       used in CI Docker images.
     - _tflite_int32_table now builds the int32 vector manually using
       builder.StartVector(4, len(values), 4).

  2. Fix flatbuffers IsNestedError in _build_buffer
     - The original implementation called _tflite_byte_vector (which
       invokes builder.StartVector) after tfl.BufferStart, violating
       flatbuffers' no-nesting rule.
     - Fixed by creating the data vector before entering the Buffer table.

  Also simplifies convert_fully_connected to use get_tensor_expr
  directly, as suggested by gemini-code-assist, since the prefetched
  node handling is already encapsulated there.

Author: Aharrypotter

python/tvm/relax/frontend/tflite/tflite_frontend.py

@@ -337,7 +337,6 @@ def convert_op_to_relax(self):
                 continue
 
             ret = self.bb.normalize(ret)
-            # print("Op Code:", op_code_str, " Shape:", ret.struct_info)
 
             if len(output_tensors) == 1:
                 tensor_idx = output_tensors[0].tensor_idx
@@ -1899,15 +1898,8 @@ def convert_fully_connected(self, op):
             TensorType.UINT8,
             TensorType.FLOAT32,
         )
-        weight_tensor_type_str = self.get_tensor_type_str(weight_tensor_type)
 
-        if self.has_expr(weight_tensor.tensor_idx):
-            weight_expr = self.get_expr(weight_tensor.tensor_idx)
-        else:
-            weight_value = self.get_tensor_value_or_prefetched(weight_tensor)
-            weight_expr = self.exp_tab.new_const(
-                weight_value, dtype=weight_tensor_type_str, source_name=weight_tensor.tensor.Name()
-            )
+        weight_expr = self.get_tensor_expr(weight_tensor)
         weight_shape = weight_expr.struct_info.shape
         weight_expr = relax.op.permute_dims(weight_expr, [1, 0])
 

tests/python/relax/test_frontend_tflite.py

@@ -2461,6 +2461,30 @@ def main(
 # Since TensorFlow does not provide an API to create sparse TFLite models,
 # we manually build them using the flatbuffers API.
 
+# Compatibility shim: schema-generated tflite packages (as used in CI) do not
+# re-export builder helpers at the package top-level. Bind them from submodules
+# so that the rest of the test file can use tfl.XXXStart / tfl.XXXEnd uniformly.
+if not hasattr(tfl, "Int32VectorStart"):
+    _tflite_helper_modules = [
+        "AddOptions",
+        "Buffer",
+        "Conv2DOptions",
+        "DimensionMetadata",
+        "FullyConnectedOptions",
+        "Int32Vector",
+        "Model",
+        "Operator",
+        "OperatorCode",
+        "SparsityParameters",
+        "SubGraph",
+        "Tensor",
+    ]
+    for _mod_name in _tflite_helper_modules:
+        _mod = __import__(f"tflite.{_mod_name}", fromlist=[_mod_name])
+        for _attr_name in dir(_mod):
+            if not _attr_name.startswith("_"):
+                setattr(tfl, _attr_name, getattr(_mod, _attr_name))
+
 _DENSIFY_TEST_VALUES = np.array([1.0, 2.0], dtype=np.float32)
 _DENSIFY_TEST_DENSE = np.array([[1.0, 0.0], [0.0, 2.0]], dtype=np.float32)
 _DENSIFY_ROW_PTRS = [0, 1, 2]
@@ -2494,7 +2518,13 @@ def _tflite_byte_vector(builder, data):
 
 
 def _tflite_int32_table(builder, values):
-    values_vec = _tflite_int32_vector(builder, tfl.Int32VectorStartValuesVector, values)
+    # Build the values vector directly without relying on version-specific
+    # helper tfl.Int32VectorStartValuesVector, which is absent in older
+    # tflite package versions used in CI.
+    builder.StartVector(4, len(values), 4)
+    for value in reversed(values):
+        builder.PrependInt32(value)
+    values_vec = builder.EndVector()
     tfl.Int32VectorStart(builder)
     tfl.Int32VectorAddValues(builder, values_vec)
     return tfl.Int32VectorEnd(builder)
@@ -2519,9 +2549,14 @@ def _build_tensor(builder, buffer_idx, shape, sparsity=None):
 
 
 def _build_buffer(builder, data=None):
-    tfl.BufferStart(builder)
+    # Build the data vector before starting the Buffer table to avoid
+    # flatbuffers IsNestedError (vectors cannot be created inside tables).
+    data_offset = None
     if data is not None:
-        tfl.BufferAddData(builder, _tflite_byte_vector(builder, data))
+        data_offset = _tflite_byte_vector(builder, data)
+    tfl.BufferStart(builder)
+    if data_offset is not None:
+        tfl.BufferAddData(builder, data_offset)
     return tfl.BufferEnd(builder)