Updated the diffusion config issue and more test cases

examples/diffusers/quantization/config.py

@@ -33,7 +33,7 @@
 INT8_DEFAULT_CONFIG = {
     "quant_cfg": {
         "*weight_quantizer": {"num_bits": 8, "axis": 0},
-        "*input_quantizer": {"num_bits": 8, "axis": 0},
+        "*input_quantizer": {"num_bits": 8, "axis": None},
         "*output_quantizer": {"enable": False},
         "default": {"enable": False},
     },
@@ -112,8 +112,10 @@ def set_quant_config_attr(quant_config, trt_high_precision_dtype, quant_algo, **
 
 
 def reset_set_int8_config(quant_config, percentile, n_steps, collect_method, backbone):
-    """
-    Configure INT8 quantization with different settings for Conv2d and Linear layers.
+    """Add PercentileCalibrator to Conv2d input quantizers.
+
+    Linear layers are left unchanged — their axis settings come from the base
+    quant_config (e.g. INT8_SMOOTHQUANT_CFG or INT8_DEFAULT_CONFIG).
 
     Args:
         quant_config: The quantization configuration dictionary
@@ -122,31 +124,9 @@ def reset_set_int8_config(quant_config, percentile, n_steps, collect_method, bac
         collect_method: Method for collecting calibration statistics
         backbone: The model backbone to analyze layer types
     """
-
-    # Build a mapping of layer names to their types
-    layer_type_map = {}
     for name, module in backbone.named_modules():
-        if isinstance(module, (nn.Linear, nn.Conv2d)):
-            layer_type_map[name] = type(module)
-
-    quant_config["quant_cfg"] = {}
-    for layer_name, layer_type in layer_type_map.items():
-        wq_name = f"*{layer_name}*weight_quantizer*"
-        aq_name = f"*{layer_name}*input_quantizer*"
-        if layer_type is nn.Linear:
-            quant_config["quant_cfg"][wq_name] = {
-                "num_bits": 8,
-                "axis": 0,
-            }
-            quant_config["quant_cfg"][aq_name] = {
-                "num_bits": 8,
-                "axis": -1,
-            }
-        else:
-            quant_config["quant_cfg"][wq_name] = {
-                "num_bits": 8,
-                "axis": 0,
-            }
+        if isinstance(module, nn.Conv2d):
+            aq_name = f"*{name}*input_quantizer*"
             quant_config["quant_cfg"][aq_name] = {
                 "num_bits": 8,
                 "axis": None,

tests/examples/diffusers/test_export_diffusers_hf_ckpt.py

@@ -0,0 +1,130 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from pathlib import Path
+from typing import NamedTuple
+
+import pytest
+from _test_utils.examples.models import FLUX_SCHNELL_PATH, SDXL_1_0_PATH
+from _test_utils.examples.run_command import run_example_command
+from _test_utils.torch.misc import minimum_sm
+
+
+class DiffuserHfExportModel(NamedTuple):
+    name: str
+    path: str
+    dtype: str
+    format_type: str
+    quant_algo: str
+    collect_method: str
+    model_dtype: str = "Half"
+
+    def quantize_and_export_hf(self, tmp_path: Path) -> Path:
+        hf_ckpt_dir = tmp_path / f"{self.name}_{self.format_type}_hf_ckpt"
+        cmd_args = [
+            "python",
+            "quantize.py",
+            "--model",
+            self.name,
+            "--override-model-path",
+            self.path,
+            "--calib-size",
+            "8",
+            "--batch-size",
+            "2",
+            "--n-steps",
+            "20",
+            "--percentile",
+            "1.0",
+            "--alpha",
+            "0.8",
+            "--format",
+            self.format_type,
+            "--quant-algo",
+            self.quant_algo,
+            "--collect-method",
+            self.collect_method,
+            "--model-dtype",
+            self.model_dtype,
+            "--trt-high-precision-dtype",
+            self.dtype,
+            "--hf-ckpt-dir",
+            str(hf_ckpt_dir),
+        ]
+        run_example_command(cmd_args, "diffusers/quantization")
+        return hf_ckpt_dir
+
+
+@pytest.mark.parametrize(
+    "model",
+    [
+        DiffuserHfExportModel(
+            name="sdxl-1.0",
+            path=SDXL_1_0_PATH,
+            dtype="Half",
+            format_type="int8",
+            quant_algo="smoothquant",
+            collect_method="min-mean",
+        ),
+        DiffuserHfExportModel(
+            name="flux-schnell",
+            path=FLUX_SCHNELL_PATH,
+            dtype="BFloat16",
+            format_type="int8",
+            quant_algo="smoothquant",
+            collect_method="min-mean",
+            model_dtype="BFloat16",
+        ),
+        pytest.param(
+            DiffuserHfExportModel(
+                name="sdxl-1.0",
+                path=SDXL_1_0_PATH,
+                dtype="Half",
+                format_type="fp8",
+                quant_algo="max",
+                collect_method="default",
+            ),
+            marks=minimum_sm(89),
+        ),
+        pytest.param(
+            DiffuserHfExportModel(
+                name="flux-schnell",
+                path=FLUX_SCHNELL_PATH,
+                dtype="BFloat16",
+                format_type="fp4",
+                quant_algo="max",
+                collect_method="default",
+                model_dtype="BFloat16",
+            ),
+            marks=minimum_sm(89),
+        ),
+    ],
+    ids=[
+        "sdxl_1.0_int8_smoothquant_min_mean",
+        "flux_schnell_int8_smoothquant_min_mean",
+        "sdxl_1.0_fp8_max_default",
+        "flux_schnell_fp4_max_default",
+    ],
+)
+def test_diffusers_hf_ckpt_export(model: DiffuserHfExportModel, tmp_path: Path) -> None:
+    hf_ckpt_dir = model.quantize_and_export_hf(tmp_path)
+
+    assert hf_ckpt_dir.exists(), f"HF checkpoint directory was not created: {hf_ckpt_dir}"
+
+    config_files = list(hf_ckpt_dir.rglob("config.json"))
+    assert len(config_files) > 0, f"No config.json found in {hf_ckpt_dir}"
+
+    weight_files = list(hf_ckpt_dir.rglob("*.safetensors")) + list(hf_ckpt_dir.rglob("*.bin"))
+    assert len(weight_files) > 0, f"No weight files (.safetensors or .bin) found in {hf_ckpt_dir}"

tests/gpu/torch/export/test_export_diffusers.py

@@ -0,0 +1,82 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+
+import pytest
+from _test_utils.torch.diffusers_models import get_tiny_dit, get_tiny_flux, get_tiny_unet
+
+pytest.importorskip("diffusers")
+
+import modelopt.torch.quantization as mtq
+from modelopt.torch.export.diffusers_utils import generate_diffusion_dummy_inputs
+from modelopt.torch.export.unified_export_hf import export_hf_checkpoint
+
+
+def _load_config(config_path):
+    with open(config_path) as file:
+        return json.load(file)
+
+
+@pytest.mark.parametrize("model_factory", [get_tiny_unet, get_tiny_dit, get_tiny_flux])
+@pytest.mark.parametrize(
+    ("config_id", "quant_cfg"),
+    [
+        ("int8", mtq.INT8_DEFAULT_CFG),
+        ("int8_smoothquant", mtq.INT8_SMOOTHQUANT_CFG),
+        ("fp8", mtq.FP8_DEFAULT_CFG),
+    ],
+)
+def test_export_diffusers_real_quantized(tmp_path, model_factory, config_id, quant_cfg):
+    model = model_factory()
+    export_dir = tmp_path / f"export_{type(model).__name__}_{config_id}_real_quant"
+
+    def _calib_fn(m):
+        param = next(m.parameters())
+        dummy_inputs = generate_diffusion_dummy_inputs(m, param.device, param.dtype)
+        assert dummy_inputs is not None
+        m(**dummy_inputs)
+
+    mtq.quantize(model, quant_cfg, forward_loop=_calib_fn)
+
+    export_hf_checkpoint(model, export_dir=export_dir)
+
+    config_path = export_dir / "config.json"
+    assert config_path.exists()
+
+    config_data = _load_config(config_path)
+    assert "quantization_config" in config_data
+
+
+def test_export_diffusers_real_quantized_fp4(tmp_path):
+    """FP4 export test using get_tiny_dit (the only tiny model with FP4-compatible weight shapes)."""
+    model = get_tiny_dit()
+    export_dir = tmp_path / "export_DiTTransformer2DModel_fp4_real_quant"
+
+    def _calib_fn(m):
+        param = next(m.parameters())
+        dummy_inputs = generate_diffusion_dummy_inputs(m, param.device, param.dtype)
+        assert dummy_inputs is not None
+        m(**dummy_inputs)
+
+    mtq.quantize(model, mtq.NVFP4_DEFAULT_CFG, forward_loop=_calib_fn)
+
+    export_hf_checkpoint(model, export_dir=export_dir)
+
+    config_path = export_dir / "config.json"
+    assert config_path.exists()
+
+    config_data = _load_config(config_path)
+    assert "quantization_config" in config_data

tests/unit/torch/export/test_export_diffusers.py

@@ -20,9 +20,7 @@
 
 pytest.importorskip("diffusers")
 
-import modelopt.torch.quantization as mtq
 from modelopt.torch.export.convert_hf_config import convert_hf_quant_config_format
-from modelopt.torch.export.diffusers_utils import generate_diffusion_dummy_inputs
 from modelopt.torch.export.unified_export_hf import export_hf_checkpoint
 
 
@@ -84,25 +82,3 @@ def _process_stub(*_args, **_kwargs):
     config_data = _load_config(config_path)
     assert "quantization_config" in config_data
     assert config_data["quantization_config"] == convert_hf_quant_config_format(dummy_quant_config)
-
-
-@pytest.mark.parametrize("model_factory", [get_tiny_unet, get_tiny_dit, get_tiny_flux])
-def test_export_diffusers_real_quantized(tmp_path, model_factory):
-    model = model_factory()
-    export_dir = tmp_path / f"export_{type(model).__name__}_real_quant"
-
-    def _calib_fn(m):
-        param = next(m.parameters())
-        dummy_inputs = generate_diffusion_dummy_inputs(m, param.device, param.dtype)
-        assert dummy_inputs is not None
-        m(**dummy_inputs)
-
-    mtq.quantize(model, mtq.FP8_DEFAULT_CFG, forward_loop=_calib_fn)
-
-    export_hf_checkpoint(model, export_dir=export_dir)
-
-    config_path = export_dir / "config.json"
-    assert config_path.exists()
-
-    config_data = _load_config(config_path)
-    assert "quantization_config" in config_data