Support Autotune FP16 Mix-precision on torch 3.0 new API (#1793)

zehao-intel · web-flow · commit 2e1cdc5be614 · 2024-05-17T20:43:25.000+08:00
Signed-off-by: zehao-intel &lt;zehao.huang@intel.com&gt;
diff --git a/neural_compressor/common/utils/constants.py b/neural_compressor/common/utils/constants.py
@@ -36,6 +36,7 @@
 TEQ = "teq"  # pragma: no cover
 AUTOROUND = "autoround"
 FP8_QUANT = "fp8_quant"
+MIX_PRECISION = "mix_precision"
 
 # options
 import datetime
diff --git a/neural_compressor/torch/algorithms/mix_precision/__init__.py b/neural_compressor/torch/algorithms/mix_precision/__init__.py
@@ -0,0 +1,19 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# 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
+#
+#   https://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 neural_compressor.torch.algorithms.mix_precision.half_precision_convert import HalfPrecisionConverter
+from neural_compressor.torch.algorithms.mix_precision.module_wrappers import HalfPrecisionModuleWrapper
diff --git a/neural_compressor/torch/algorithms/mix_precision/half_precision_convert.py b/neural_compressor/torch/algorithms/mix_precision/half_precision_convert.py
@@ -0,0 +1,88 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# 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
+#
+#   https://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.
+"""Half-precision Convert for Torch Modules."""
+
+from typing import Dict, Tuple
+
+import torch
+
+from neural_compressor.common import logger
+from neural_compressor.torch.algorithms.mix_precision.module_wrappers import HalfPrecisionModuleWrapper
+from neural_compressor.torch.utils import get_device
+
+
+class HalfPrecisionConverter:
+    """Converter Class for FP16 and BF16."""
+
+    dtype_mapping = {
+        "fp16": torch.float16,
+        "bf16": torch.bfloat16,
+    }
+
+    def __init__(self, configs_mapping: Dict[Tuple[str], object], *args, **kwargs):
+        """Initialize the Half-precision Converter with config.
+
+        Args:
+            configs_mapping (Dict): config class for mix-precision.
+        """
+        self.configs_mapping = configs_mapping
+        self.device = get_device()
+
+    def convert(self, model: torch.nn.Module):
+        """Convert to FP16 or BF16 model.
+
+        Args:
+            model (torch.nn.Module): the input model.
+
+        Returns:
+            mix_precision_model (torch.nn.Module): model with mix-precision.
+        """
+        if len(self.configs_mapping) > 0:
+            logger.info("Convert operators to half-precision")
+
+        if next(model.parameters()).is_cuda:
+            self.device = "cuda"
+        elif next(model.parameters()).is_cpu:
+            self.device = "cpu"
+
+        mix_precision_model = self._wrap_half_precision_model(model)
+        mix_precision_model.to(self.device)
+
+        return mix_precision_model
+
+    def _wrap_half_precision_model(self, model: torch.nn.Module, prefix=""):
+        """Wrap and replace half-precision target modules.
+
+        Args:
+            model (torch.nn.Module): the input module.
+            prefix (str): the name prefix for named children.
+
+        Returns:
+            model (torch.nn.Module): the model whose target modules have been wrapped.
+        """
+        for name, child in model.named_children():
+            op_name = prefix + "." + name if prefix != "" else name
+            for op_info, config in self.configs_mapping.items():
+                if op_name == op_info[0] and config.dtype in ("fp16", "bf16"):
+                    child = HalfPrecisionModuleWrapper(
+                        module=child, device=self.device, dtype=self.dtype_mapping[config.dtype]
+                    )
+            else:
+                self._wrap_half_precision_model(child, op_name)
+                setattr(model, name, child)
+
+        return model
diff --git a/neural_compressor/torch/algorithms/mix_precision/module_wrappers.py b/neural_compressor/torch/algorithms/mix_precision/module_wrappers.py
@@ -0,0 +1,38 @@
+#
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2024 Intel Corporation
+#
+# 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
+#
+#   https://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.
+"""Half-precision Wrapper for Torch Modules."""
+
+import torch
+
+
+class HalfPrecisionModuleWrapper(torch.nn.Module):
+    """FP16 or BF16 Module Wrapper Class."""
+
+    def __init__(self, module, device="cpu", dtype=torch.float16):
+        """Init a HalfPrecisionModuleWrapper object."""
+        super(HalfPrecisionModuleWrapper, self).__init__()
+        self.add_module("module", module)
+        self.device = device
+        self.dtype = dtype
+        self.weight = self.module.weight if hasattr(self.module, "weight") else None
+        self.bias = self.module.bias if hasattr(self.module, "bias") else None
+
+    def forward(self, X):
+        """Convert dtype."""
+        with torch.autocast(device_type=self.device, dtype=self.dtype):
+            X = self.module(X)
+        return X.float()
diff --git a/neural_compressor/torch/quantization/__init__.py b/neural_compressor/torch/quantization/__init__.py
@@ -34,6 +34,9 @@
     FP8Config,
     get_default_fp8_config,
     get_default_fp8_config_set,
+    MixPrecisionConfig,
+    get_default_mix_precision_config,
+    get_default_mix_precision_config_set,
     get_woq_tuning_config,
     DynamicQuantConfig,
     get_default_dynamic_config,
diff --git a/neural_compressor/torch/quantization/algorithm_entry.py b/neural_compressor/torch/quantization/algorithm_entry.py
@@ -24,6 +24,7 @@
     FP8_QUANT,
     GPTQ,
     HQQ,
+    MIX_PRECISION,
     RTN,
     SMOOTH_QUANT,
     STATIC_QUANT,
@@ -36,6 +37,7 @@
     FP8Config,
     GPTQConfig,
     HQQConfig,
+    MixPrecisionConfig,
     RTNConfig,
     SmoothQuantConfig,
     StaticQuantConfig,
@@ -528,3 +530,17 @@ def fp8_quant_entry(
         model.qconfig = configs_mapping
         model.save = MethodType(save, model)
         return model
+
+
+###################### Mixed Precision Algo Entry ##################################
+@register_algo(MIX_PRECISION)
+def mix_precision_entry(
+    model: torch.nn.Module, configs_mapping: Dict[Tuple[str], MixPrecisionConfig], *args, **kwargs
+) -> torch.nn.Module:
+    # only support fp16 and bf16 now, more types might be added later
+    from neural_compressor.torch.algorithms.mix_precision import HalfPrecisionConverter
+
+    half_precision_converter = HalfPrecisionConverter(configs_mapping, *args, **kwargs)
+    mix_precision_model = half_precision_converter.convert(model)
+
+    return mix_precision_model
diff --git a/neural_compressor/torch/quantization/config.py b/neural_compressor/torch/quantization/config.py
@@ -36,6 +36,7 @@
     FP8_QUANT,
     GPTQ,
     HQQ,
+    MIX_PRECISION,
     OP_NAME_OR_MODULE_TYPE,
     RTN,
     SMOOTH_QUANT,
@@ -1196,6 +1197,81 @@ def get_default_fp8_config_set() -> FP8Config:
     return FP8Config.get_config_set_for_tuning()
 
 
+######################## MixPrecision Config ###############################
+@register_config(framework_name=FRAMEWORK_NAME, algo_name=MIX_PRECISION)
+class MixPrecisionConfig(BaseConfig):
+    """Config class for mix-precision."""
+
+    name = MIX_PRECISION
+    supported_configs: List[OperatorConfig] = []
+    params_list = [
+        "dtype",
+    ]
+    supported_half_precision_ops = (
+        torch.nn.Linear,
+        torch.nn.Conv1d,
+        torch.nn.Conv2d,
+        torch.nn.Conv3d,
+    )
+
+    def __init__(
+        self,
+        dtype: Union[str, List[str]] = "fp16",
+        white_list: Optional[List[OP_NAME_OR_MODULE_TYPE]] = DEFAULT_WHITE_LIST,
+    ):
+        """Init MixPrecision config.
+
+        Args:
+        """
+        super().__init__(white_list=white_list)
+        self.dtype = dtype
+        self._post_init()
+
+    @classmethod
+    def register_supported_configs(cls) -> List[OperatorConfig]:
+        supported_configs = []
+        mix_precision_config = MixPrecisionConfig(
+            dtype=["fp16", "bf16", "fp32"],
+        )
+        operators = cls.supported_half_precision_ops
+        supported_configs.append(OperatorConfig(config=mix_precision_config, operators=operators))
+        cls.supported_configs = supported_configs
+
+    @staticmethod
+    def get_model_info(model: torch.nn.Module) -> List[Tuple[str, Callable]]:
+        white_list = tuple(MixPrecisionConfig.supported_half_precision_ops)
+        filter_result = []
+        for op_name, module in model.named_modules():
+            if isinstance(module, white_list):
+                pair = (op_name, type(module).__name__)
+                filter_result.append(pair)
+        logger.debug(f"Get model info: {filter_result}")
+        return filter_result
+
+    @classmethod
+    def get_config_set_for_tuning(cls) -> Union[None, "MixPrecisionConfig", List["MixPrecisionConfig"]]:
+        # TODO fwk owner needs to update it.
+        return MixPrecisionConfig(dtype=["fp16", "bf16", "fp32"])
+
+
+def get_default_mix_precision_config() -> MixPrecisionConfig:
+    """Generate the default mix-precision config.
+
+    Returns:
+        the default mix-precision config.
+    """
+    return MixPrecisionConfig()
+
+
+def get_default_mix_precision_config_set() -> MixPrecisionConfig:
+    """Generate the default mix-precision config set.
+
+    Returns:
+        the default mix-precision config.
+    """
+    return MixPrecisionConfig.get_config_set_for_tuning()
+
+
 ##################### Algo Configs End ###################################
 
 
diff --git a/test/3x/torch/test_autotune.py b/test/3x/torch/test_autotune.py
@@ -7,7 +7,13 @@
 import transformers
 
 from neural_compressor.common import logger
-from neural_compressor.torch.quantization import RTNConfig, TuningConfig, autotune, get_all_config_set
+from neural_compressor.torch.quantization import (
+    MixPrecisionConfig,
+    RTNConfig,
+    TuningConfig,
+    autotune,
+    get_all_config_set,
+)
 from neural_compressor.torch.utils import constants
 
 FAKE_DOUBLE_QUANT_CONFIGS = {
@@ -332,6 +338,74 @@ def eval_acc_fn(model):
         )
         self.assertIsNone(best_model)
 
+    @reset_tuning_target
+    def test_autotune_mix_precision_default(self):
+        from neural_compressor.torch.algorithms.mix_precision import HalfPrecisionModuleWrapper
+
+        baseline = [1]
+        acc_res_lst = baseline + [0.9, 0.99, 1]
+
+        def eval_acc_fn(model):
+            res = acc_res_lst.pop(0)
+            return res
+
+        custom_tune_config = TuningConfig(config_set=[MixPrecisionConfig(dtype=["fp16", "bf16", "fp32"])], max_trials=3)
+        best_model = autotune(model=build_simple_torch_model(), tune_config=custom_tune_config, eval_fn=eval_acc_fn)
+
+        self.assertIsNotNone(best_model)
+        self.assertTrue(isinstance(best_model.fc1, HalfPrecisionModuleWrapper))
+        self.assertTrue(isinstance(best_model.fc2, HalfPrecisionModuleWrapper))
+        self.assertTrue(isinstance(best_model.fc3, HalfPrecisionModuleWrapper))
+
+    @reset_tuning_target
+    def test_autotune_mix_precision_set_op_name(self):
+        from neural_compressor.common.base_config import ComposableConfig, config_registry
+        from neural_compressor.torch.algorithms.mix_precision import HalfPrecisionModuleWrapper
+
+        baseline = [1]
+        acc_res_lst = baseline + [0.9, 1.1]
+
+        def eval_acc_fn(model):
+            res = acc_res_lst.pop(0)
+            return res
+
+        config1 = {
+            "mix_precision": {
+                "global": {
+                    "dtype": "bf16",
+                },
+                "local": {
+                    "fc2": {
+                        "dtype": "fp32",
+                    }
+                },
+            }
+        }
+        config2 = {
+            "mix_precision": {
+                "global": {
+                    "dtype": "fp16",
+                },
+                "local": {
+                    "fc1": {
+                        "dtype": "fp32",
+                    }
+                },
+            }
+        }
+
+        registered_configs = config_registry.get_cls_configs()
+        config1 = ComposableConfig.from_dict(config1, config_registry=registered_configs["torch"])
+        config2 = ComposableConfig.from_dict(config2, config_registry=registered_configs["torch"])
+
+        custom_tune_config = TuningConfig(config_set=[config1, config2], max_trials=2)
+        best_model = autotune(model=build_simple_torch_model(), tune_config=custom_tune_config, eval_fn=eval_acc_fn)
+
+        self.assertIsNotNone(best_model)
+        self.assertTrue(isinstance(best_model.fc1, torch.nn.Linear))
+        self.assertTrue(isinstance(best_model.fc2, HalfPrecisionModuleWrapper))
+        self.assertTrue(isinstance(best_model.fc3, HalfPrecisionModuleWrapper))
+
 
 if __name__ == "__main__":
     unittest.main()
