Qwix

src/maxdiffusion/configs/base_wan_14b.yml

@@ -310,6 +310,7 @@ compile_topology_num_slices: -1 # Number of target slices, set to a positive int
 use_qwix_quantization: False # Whether to use qwix for quantization. If set to True, the transformer of WAN will be quantized using qwix.
 # Quantization calibration method used for weights and activations. Supported methods can be found in https://github.com/google/qwix/blob/dc2a0770351c740e5ab3cce7c0efe9f7beacce9e/qwix/qconfig.py#L70-L80
 quantization_calibration_method: "absmax"
+qwix_module_path: ".*"
 
 # Eval model on per eval_every steps. -1 means don't eval.
 eval_every: -1

src/maxdiffusion/pipelines/wan/wan_pipeline.py

@@ -243,34 +243,48 @@ def create_model(rngs: nnx.Rngs, config: HyperParameters):
     return wan_vae, vae_cache
 
   @classmethod
-  def get_basic_config(cls, dtype):
+  def get_basic_config(cls, dtype, config: HyperParameters):
     rules = [
         qwix.QtRule(
-            module_path=".*",  # Apply to all modules
+            module_path=config.qwix_module_path,
             weight_qtype=dtype,
             act_qtype=dtype,
+            op_names=("dot_general","einsum", "conv_general_dilated"),
         )
     ]
     return rules
 
   @classmethod
-  def get_fp8_config(cls, quantization_calibration_method: str):
+  def get_fp8_config(cls, config: HyperParameters):
     """
     fp8 config rules with per-tensor calibration.
     FLAX API (https://flax-linen.readthedocs.io/en/v0.10.6/guides/quantization/fp8_basics.html#flax-low-level-api):
     The autodiff does not automatically use E5M2 for gradients and E4M3 for activations/weights during training, which is the recommended practice.
     """
     rules = [
         qwix.QtRule(
-            module_path=".*",  # Apply to all modules
+            module_path=config.qwix_module_path,
             weight_qtype=jnp.float8_e4m3fn,
             act_qtype=jnp.float8_e4m3fn,
+            bwd_qtype=jnp.float8_e5m2,
+            bwd_use_original_residuals=True,
+            disable_channelwise_axes=True,  # per_tensor calibration
+            weight_calibration_method=config.quantization_calibration_method,
+            act_calibration_method=config.quantization_calibration_method,
+            bwd_calibration_method=config.quantization_calibration_method,
+            op_names=("dot_general","einsum"),
+        ),
+        qwix.QtRule(
+            module_path=config.qwix_module_path,
+            weight_qtype=jnp.float8_e4m3fn, # conv_general_dilated requires the same dtypes
+            act_qtype=jnp.float8_e4m3fn,
             bwd_qtype=jnp.float8_e4m3fn,
             bwd_use_original_residuals=True,
             disable_channelwise_axes=True,  # per_tensor calibration
-            weight_calibration_method=quantization_calibration_method,
-            act_calibration_method=quantization_calibration_method,
-            bwd_calibration_method=quantization_calibration_method,
+            weight_calibration_method=config.quantization_calibration_method,
+            act_calibration_method=config.quantization_calibration_method,
+            bwd_calibration_method=config.quantization_calibration_method,
+            op_names=("conv_general_dilated"),
         )
     ]
     return rules
@@ -281,14 +295,13 @@ def get_qt_provider(cls, config: HyperParameters) -> Optional[qwix.QtProvider]:
     if not getattr(config, "use_qwix_quantization", False):
       return None
 
-    quantization_calibration_method = getattr(config, "quantization_calibration_method", "absmax")
     match config.quantization:
       case "int8":
-        return qwix.QtProvider(cls.get_basic_config(jnp.int8))
+        return qwix.QtProvider(cls.get_basic_config(jnp.int8, config))
       case "fp8":
-        return qwix.QtProvider(cls.get_basic_config(jnp.float8_e4m3fn))
+        return qwix.QtProvider(cls.get_basic_config(jnp.float8_e4m3fn, config))
       case "fp8_full":
-        return qwix.QtProvider(cls.get_fp8_config(quantization_calibration_method))
+        return qwix.QtProvider(cls.get_fp8_config(config))
     return None
 
   @classmethod

src/maxdiffusion/tests/wan_transformer_test.py

@@ -19,7 +19,7 @@
 import jax.numpy as jnp
 import pytest
 import unittest
-from unittest.mock import Mock, patch
+from unittest.mock import Mock, patch, call
 from absl.testing import absltest
 from flax import nnx
 from jax.sharding import Mesh
@@ -291,28 +291,43 @@ def test_get_qt_provider(self, mock_qt_rule):
     config_int8 = Mock(spec=HyperParameters)
     config_int8.use_qwix_quantization = True
     config_int8.quantization = "int8"
+    config_int8.qwix_module_path = ".*"
     provider_int8 = WanPipeline.get_qt_provider(config_int8)
     self.assertIsNotNone(provider_int8)
-    mock_qt_rule.assert_called_once_with(module_path=".*", weight_qtype=jnp.int8, act_qtype=jnp.int8)
+    mock_qt_rule.assert_called_once_with(module_path=".*", weight_qtype=jnp.int8, act_qtype=jnp.int8, op_names=("dot_general","einsum", "conv_general_dilated"))
 
     # Case 3: Quantization enabled, type 'fp8'
     mock_qt_rule.reset_mock()
     config_fp8 = Mock(spec=HyperParameters)
     config_fp8.use_qwix_quantization = True
     config_fp8.quantization = "fp8"
+    config_int8.qwix_module_path = ".*"
     provider_fp8 = WanPipeline.get_qt_provider(config_fp8)
     self.assertIsNotNone(provider_fp8)
-    mock_qt_rule.assert_called_once_with(module_path=".*", weight_qtype=jnp.float8_e4m3fn, act_qtype=jnp.float8_e4m3fn)
+    mock_qt_rule.assert_called_once_with(module_path=".*", weight_qtype=jnp.float8_e4m3fn, act_qtype=jnp.float8_e4m3fn, op_names=("dot_general","einsum", "conv_general_dilated"))
 
     # Case 4: Quantization enabled, type 'fp8_full'
     mock_qt_rule.reset_mock()
     config_fp8_full = Mock(spec=HyperParameters)
     config_fp8_full.use_qwix_quantization = True
     config_fp8_full.quantization = "fp8_full"
     config_fp8_full.quantization_calibration_method = "absmax"
+    config_int8.qwix_module_path = ".*"
     provider_fp8_full = WanPipeline.get_qt_provider(config_fp8_full)
     self.assertIsNotNone(provider_fp8_full)
-    mock_qt_rule.assert_called_once_with(
+    expected_calls = [
+      call(module_path=".*",  # Apply to all modules
+        weight_qtype=jnp.float8_e4m3fn,
+        act_qtype=jnp.float8_e4m3fn,
+        bwd_qtype=jnp.float8_e5m2,
+        bwd_use_original_residuals=True,
+        disable_channelwise_axes=True,  # per_tensor calibration
+        weight_calibration_method=config_fp8_full.quantization_calibration_method,
+        act_calibration_method=config_fp8_full.quantization_calibration_method,
+        bwd_calibration_method=config_fp8_full.quantization_calibration_method,
+        op_names=("dot_general","einsum"),
+        ),
+      call(
         module_path=".*",  # Apply to all modules
         weight_qtype=jnp.float8_e4m3fn,
         act_qtype=jnp.float8_e4m3fn,
@@ -322,7 +337,10 @@ def test_get_qt_provider(self, mock_qt_rule):
         weight_calibration_method=config_fp8_full.quantization_calibration_method,
         act_calibration_method=config_fp8_full.quantization_calibration_method,
         bwd_calibration_method=config_fp8_full.quantization_calibration_method,
-    )
+        op_names=("conv_general_dilated"),
+      )
+    ]
+    mock_qt_rule.assert_has_calls(expected_calls, any_order=True)
 
     # Case 5: Invalid quantization type
     config_invalid = Mock(spec=HyperParameters)
@@ -341,6 +359,7 @@ def test_quantize_transformer_enabled(self, mock_get_dummy_inputs, mock_quantize
     mock_config = Mock(spec=HyperParameters)
     mock_config.use_qwix_quantization = True
     mock_config.quantization = "fp8_full"
+    mock_config.qwix_module_path = ".*"
     mock_config.per_device_batch_size = 1
 
     mock_model = Mock(spec=WanModel)