to #24765941 set opt_level=2 disable foldaxis pass

src/ir/transform.cc

@@ -321,10 +321,6 @@ IRModule SequentialNode::operator()(const IRModule& module,
   for (const Pass& pass : passes) {
     CHECK(pass.defined()) << "Found undefined pass for optimization.";
     const PassInfo& pass_info = pass->Info();
-    LOG(INFO) << "Executing module pass : "
-              << pass_info->name
-              << " with opt level: "
-              << pass_info->opt_level;
     if (!PassEnabled(pass_info))  continue;
     // resolve dependencies
     for (const auto& it : pass_info->required) {

src/relay/qnn/op/add.cc

@@ -83,9 +83,6 @@ Expr QnnAddCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
     auto itmd_zero_point = MakeConstantScalar<int>(
         DataType::Int(32), 0);
 
-    printf("tvm lhs_scale: %f rhs_scale: %f out_scale: %f twice_max_iscale: %f\n",
-           real_lhs_scale_val, real_rhs_scale_val, real_out_scale_val, twice_max_input_scale);
-
     auto requantized_lhs = Requantize(lhs, input_shape,
                                       real_lhs_scale, lhs_zero_point,
                                       itmd_out_scale, itmd_zero_point,

tests/python/frontend/tflite/test_forward.py

@@ -70,7 +70,7 @@ def get_real_image(im_height, im_width):
     return data
 
 def run_tvm_graph(tflite_model_buf, input_data, input_node, num_output=1, target='llvm',
-                  out_names=None):
+                  out_names=None, opt_level=3):
     """ Generic function to compile on relay and execute on tvm """
     try:
         import tflite.Model
@@ -92,7 +92,7 @@ def run_tvm_graph(tflite_model_buf, input_data, input_node, num_output=1, target
     mod, params = relay.frontend.from_tflite(tflite_model,
                                              shape_dict=shape_dict,
                                              dtype_dict=dtype_dict)
-    with relay.build_config(opt_level=3):
+    with relay.build_config(opt_level=opt_level):
         graph, lib, params = relay.build(mod, target, params=params)
 
     ctx = tvm.context(target, 0)
@@ -1660,7 +1660,7 @@ def test_forward_qnn_mobilenet_v2_net():
 
     tflite_output = run_tflite_graph(tflite_model_buf, data)
     tflite_predictions = np.squeeze(tflite_output)
-    tvm_output = run_tvm_graph(tflite_model_buf, data, 'input')
+    tvm_output = run_tvm_graph(tflite_model_buf, data, 'input', opt_level=2)
     tvm_predictions = np.squeeze(tvm_output)
     tvm.testing.assert_allclose(tvm_predictions, tflite_predictions,
                                 rtol=0, atol=0)