Move ConvT residual test to qdq_rules

Signed-off-by: gcunhase <[email protected]>

Author: gcunhase

tests/unit/onnx/test_qdq_rules_int8.py

@@ -21,6 +21,7 @@
 from _test_utils.onnx.quantization.lib_test_models import (
     build_conv_act_pool_model,
     build_conv_batchnorm_sig_mul_model,
+    build_convtranspose_conv_residual_model,
     build_r1a_model,
     build_resnet_block,
     build_resnet_block_with_downsample,
@@ -124,6 +125,35 @@ def test_resnet_residual_connection_with_downsample(tmp_path):
     _check_resnet_residual_connection(onnx_path)
 
 
+def test_convtranspose_conv_residual_int8(tmp_path):
+    onnx_model = build_convtranspose_conv_residual_model()
+    onnx_path = os.path.join(tmp_path, "convtranspose_conv_residual_model.onnx")
+    save_onnx(onnx_model, onnx_path)
+
+    quantize(onnx_path, quantize_mode="int8", high_precision_dtype="fp16")
+
+    # Output model should be produced in the same tmp_path
+    output_onnx_path = onnx_path.replace(".onnx", ".quant.onnx")
+
+    # Check that quantized explicit model is generated
+    assert os.path.isfile(output_onnx_path)
+
+    # Load the output model and check QDQ node placements
+    graph = gs.import_onnx(onnx.load(output_onnx_path))
+
+    # Check that Conv and ConvTransposed are quantized
+    conv_nodes = [n for n in graph.nodes if "Conv" in n.op]
+    assert _assert_nodes_are_quantized(conv_nodes)
+
+    # Check that only 1 input of Add is quantized
+    add_nodes = [n for n in graph.nodes if n.op == "Add"]
+    for node in add_nodes:
+        quantized_inputs = [inp for inp in node.inputs if inp.inputs[0].op == "DequantizeLinear"]
+        assert len(quantized_inputs) == 1, (
+            f"More than one input of {node.name} is being quantized, but only one should be quantized!"
+        )
+
+
 def test_conv_batchnorm_sig_mul_int8(tmp_path):
     onnx_model = build_conv_batchnorm_sig_mul_model()
     onnx_path = os.path.join(tmp_path, "conv_batchnorm_sig_mul_model.onnx")

tests/unit/onnx/test_quantize_int8.py

@@ -19,14 +19,9 @@
 import onnx_graphsurgeon as gs
 import pytest
 import torch
-from _test_utils.onnx.quantization.lib_test_models import (
-    SimpleMLP,
-    build_convtranspose_conv_residual_model,
-    export_as_onnx,
-)
+from _test_utils.onnx.quantization.lib_test_models import SimpleMLP, export_as_onnx
 
 import modelopt.onnx.quantization as moq
-from modelopt.onnx.utils import save_onnx
 
 
 def assert_nodes_are_quantized(nodes):
@@ -60,32 +55,3 @@ def test_int8(tmp_path, high_precision_dtype):
     # Check that all MatMul nodes are quantized
     mm_nodes = [n for n in graph.nodes if n.op == "MatMul"]
     assert assert_nodes_are_quantized(mm_nodes)
-
-
-def test_convtranspose_conv_residual_int8(tmp_path):
-    onnx_model = build_convtranspose_conv_residual_model()
-    onnx_path = os.path.join(tmp_path, "convtranspose_conv_residual_model.onnx")
-    save_onnx(onnx_model, onnx_path)
-
-    moq.quantize(onnx_path, quantize_mode="int8", high_precision_dtype="fp16")
-
-    # Output model should be produced in the same tmp_path
-    output_onnx_path = onnx_path.replace(".onnx", ".quant.onnx")
-
-    # Check that quantized explicit model is generated
-    assert os.path.isfile(output_onnx_path)
-
-    # Load the output model and check QDQ node placements
-    graph = gs.import_onnx(onnx.load(output_onnx_path))
-
-    # Check that Conv and ConvTransposed are quantized
-    conv_nodes = [n for n in graph.nodes if "Conv" in n.op]
-    assert assert_nodes_are_quantized(conv_nodes)
-
-    # Check that only 1 input of Add is quantized
-    add_nodes = [n for n in graph.nodes if n.op == "Add"]
-    for node in add_nodes:
-        quantized_inputs = [inp for inp in node.inputs if inp.inputs[0].op == "DequantizeLinear"]
-        assert len(quantized_inputs) == 1, (
-            f"More than one input of {node.name} is being quantized, but only one should be quantized!"
-        )