onnx 模型导出与算法更新 (#231)

* 添加样例文件

* onnx模型导出与算法更新

* 为dump_torch_to_onnx函数添加了默认参数
* EXPORT_OVERLAPPED_CONFIG 现在是过时参数，你将使用TQC上的QuantizationVisiblity属性来进行导出控制。该属性有三个可选项：强制导出、TQC激活时导出、不导出。
* 修改了 exporter 逻辑以适配新的QuantizationVisiblity属性
* 修改了onnx qdq的导出逻辑，现在将尽可能消除对称量化中的激活函数。
* 修改了 graphwise analyser 的逻辑，现在允许分析多输出算子的误差
* 修改了 layerwise equalization 的逻辑，现在允许 include act，支持conv1d, conv2d conv3d, convtranpose1d, convtranspose2d, convtranspose3d, gemm, matmul
* 修复了 passive parameter pass 中的 pad 量化错误
* 修复了 quant alignment pass 中 pooling 算子的对齐错误
* 修复了 核心量化函数在启动 cuda kernel 的情况下无法处理 cpu tensor 的问题
* 修改 openvino 量化策略，负数部分现在可以取到-128(曾经是-127)
* 给 dsp quantizer 添加了一个新的量化类型

* 添加测试样例

* 修复ci错误

ppq/IR/base/graph.py

@@ -234,7 +234,9 @@ def copy(self, copy_value: bool = False):
                         'however its value is not an instance of torch.Tensor, '
                         'ppq will automaticall convert it to torch.Tensor now.')
             self.value = convert_any_to_torch_tensor(self.value)
-        return Variable(name=self.name, value=self.value.clone(), is_parameter=self.is_parameter)
+        if isinstance(self.value, torch.Tensor):
+            value = self.value.clone()
+        return Variable(name=self.name, value=value, is_parameter=self.is_parameter)
 
 
 class Operation(OperationBase, Serializable):

ppq/api/interface.py

@@ -157,7 +157,7 @@ def dump_torch_to_onnx(
     model: torch.nn.Module,
     onnx_export_file: str,
     input_shape: List[int],
-    input_dtype: torch.dtype,
+    input_dtype: torch.dtype = torch.float,
     inputs: List[Any] = None,
     device: str = 'cuda'):
     """

ppq/core/common.py

@@ -2,7 +2,6 @@
 # PPQ System configuration
 # You can modify following codes for your own purpose.
 
-
 # Observer 中，最小 scale 限制，所有小于该值的 scale 将被该值覆盖
 OBSERVER_MIN_SCALE = 1e-8
 # Observer 中，最小 scale 的手动覆盖属性
@@ -64,9 +63,6 @@
 DEFAULT_OPSET_VERSION = 11
 STRICT_OPSET_CHECKING = False
 
-# 导出 qdq 节点时是否需要导出状态已经是 overlap 的节点
-EXPORT_OVERLAPPED_CONFIG = False
-
 # LSTM 算子的权重缓存属性
 LSTM_FLATTEN_WEIGHT_ATTRIB = 'LSTM_FLATTEN_WEIGHT_ATTRIB'
 # GRU 算子的权重缓存属性
@@ -90,4 +86,7 @@
 CHECKPOINT_TOLERANCE = 1
 
 # 要做 Bias Correction 的算子种类
-BIAS_CORRECTION_INTERST_TYPE = {'Conv', 'Gemm', 'ConvTranspose'}
+BIAS_CORRECTION_INTERST_TYPE = {'Conv', 'Gemm', 'ConvTranspose'}
+
+# 导出 qdq 节点时是否需要导出状态已经是 overlap 的节点
+EXPORT_OVERLAPPED_CONFIG = False

ppq/core/quant.py

@@ -4,15 +4,20 @@
 """
 
 import time  # for hash generation
-from abc import abstractmethod
 from enum import Enum
 from typing import Any, Iterable, List
 
 import torch
 
+from .common import EXPORT_OVERLAPPED_CONFIG
 from .storage import Serializable
 
 
+class QuantizationVisiblity(Enum):
+    FORCE_EXPORT       = 1
+    EXPOET_WHEN_ACTIVE = 2
+    INTERNAL           = 3
+
 class NetworkFramework(Enum):
     PPL     = 1
     ONNX    = 2
@@ -365,7 +370,7 @@ def __init__(
         offset: Any               = None,
         observer_algorithm: str   = None,
         detail: Any               = None,
-        require_export: bool      = None,
+        visiblity: QuantizationVisiblity      = QuantizationVisiblity.EXPOET_WHEN_ACTIVE,
         state: QuantizationStates = QuantizationStates.INITIAL
     ):
         """Create a PPQ Tensor Quantization Configuration Instance.
@@ -395,7 +400,13 @@ def __init__(
             detail (Any, optional): Only used by PPQ internal logic, detail is used to store some internal data,
                 you are not supposed to use it.
 
-            require_export (bool, optional): If require_export == True, PPQ exporter will export this TQC ignoring state checks.
+            visiblity (Visiblity): visiblity is the attribute that controls export logic.
+
+            Currently, there are 3 Visiblity level in PPQ:
+            if Visiblity == FORCE_EXPORT, ppq exporter will export this TQC 
+                ignoring state check(even if current TQC has been overrlapped).
+            if Visiblity == EXPORT_WHEN_ACTIVD, ppq exporter will export this TQC only when it has been actived.
+            if Visiblity == INTERNAL, This TQC will not be exported.
 
             state (QuantizationStates, optional):
                 Defaults to QuantizationStates.INITIAL, see QuantizationStates for more detail.
@@ -416,17 +427,25 @@ def __init__(
         self.detail = {} if detail is None else detail
         self._father_config = self # union-find
         self._hash = self.__create_hash()
-        self._require_export = require_export
+        self._visiblity = visiblity
         super().__init__()
 
-    @ abstractmethod
-    def export(self) -> str:
-        raise Exception('Implement this first')
+    def can_export(self) -> bool:
+        if self.visiblity == QuantizationVisiblity.INTERNAL: return False
+        type_check  = isinstance(self.scale, torch.Tensor) and isinstance(self.offset, torch.Tensor)
+        valid_states = {QuantizationStates.BAKED, QuantizationStates.PASSIVE_BAKED}
+
+        if EXPORT_OVERLAPPED_CONFIG: valid_states.add(QuantizationStates.OVERLAPPED)
+        state_check = QuantizationStates.is_activated(self.state) or self.state in valid_states
+
+        if (state_check or self.visiblity == QuantizationVisiblity.FORCE_EXPORT):
+            if type_check: return True
+        return False
 
     def __eq__(self, o: object) -> bool:
         if not isinstance(o, TensorQuantizationConfig):
-            raise TypeError('Can only compare TensorQuantizationConfig object '\
-                'with another TensorQuantizationConfig object.')
+            raise TypeError('Can only compare TensorQuantizationConfig object '
+                            'with another TensorQuantizationConfig object.')
         return self._hash == o._hash
 
     def __str__(self) -> str:
@@ -509,17 +528,13 @@ def is_revisable(self):
         })
 
     @ property
-    def exportable(self) -> bool:
-        value_check = isinstance(self.scale, torch.Tensor)
-        if self._require_export is None:
-            state_check = QuantizationStates.can_export(self.state)
-            return (value_check and state_check)
-        else: return (self._require_export and value_check)
-
-    @ exportable.setter
-    def exportable(self, export_override: bool):
-        self._require_export = export_override
-
+    def visiblity(self) -> bool:
+        return self._visiblity
+
+    @ visiblity.setter
+    def visiblity(self, visiblity: bool):
+        self._visiblity = visiblity
+
     @ property
     def scale(self) -> torch.Tensor:
         if self.dominated_by == self: return self._scale

ppq/parser/caffe_exporter.py

@@ -40,12 +40,7 @@ def export_quantization_config(self, config_path: str, graph: BaseGraph):
         for operation in graph.operations.values():
             if not isinstance(operation, QuantableOperation): continue
             for config, var in operation.config_with_variable:
-                if not QuantizationStates.can_export(config.state):
-                    raise PermissionError(
-                        'Can not export quant config cause not all quantization configurations '
-                        'have been correctly initialized(or some of them has been deactivated). '
-                        f'Operation {operation.name} has an invalid quantization config({config.state}) '
-                        f'at variable {var.name}.')
+                if not config.can_export(): continue
 
                 # PATCH 2021.11.25
                 # REMOVE BIAS FROM CONFIGURATION

ppq/parser/ncnn_exporter.py

@@ -18,6 +18,8 @@ def export_quantization_config(self, config_path: str, graph: BaseGraph):
             if op.is_computing_op and isinstance(op, QuantableOperation):
                 fd.write(f'{op.name}_param_0 ')
                 param_cfg = op.config.input_quantization_config[1]
+                if not param_cfg.can_export(): continue
+
                 assert param_cfg.state in {QuantizationStates.BAKED, QuantizationStates.ACTIVATED}\
                     and param_cfg.observer_algorithm in {'minmax', 'Minmax'} and \
                         param_cfg.policy.has_property(QuantizationProperty.PER_CHANNEL)
@@ -32,6 +34,7 @@ def export_quantization_config(self, config_path: str, graph: BaseGraph):
                 for s in scale:
                     fd.write('%f '% s)
                 fd.write('\n')
+
         for op in topo_order:
             if op.is_computing_op and isinstance(op, QuantableOperation):
                 fd.write(f'{op.name} ')

ppq/parser/nxp_exporter.py

@@ -63,9 +63,8 @@ def export(self, file_path: str, graph: BaseGraph,
                 if variable.is_parameter and not export_param: continue
                 for config in configs:
                     if config is None: continue # source_op can be None
-                    if config.state in {QuantizationStates.ACTIVATED, QuantizationStates.BAKED,
-                                        QuantizationStates.OVERLAPPED, QuantizationStates.PASSIVE_BAKED}:
-                        if config.state == QuantizationStates.OVERLAPPED: config = config.dominated_by
+                    if config.can_export():
+
                         tensor_range = config.scale * pow(2, config.num_of_bits - 1)
                         min_val, max_val = -tensor_range, tensor_range - config.scale
                         min_tensor = numpy_helper.from_array(