From a583906c9a4ac2c799b7bf4acabec60750bb84e8 Mon Sep 17 00:00:00 2001
From: AwesomeCodingBoy <43309460+ZhangZhiPku@users.noreply.github.com>
Date: Mon, 13 Mar 2023 21:16:20 +0800
Subject: [PATCH] Fix Executing Bugs (#408)
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* Fix Executing Bugs

* 修复了 unsqueeze 算子在多于一个轴时的顺序错误问题
* 修复了 softmax 算子在 opset 11 时默认轴错误的问题
* 修复了 图拷贝 过程中可能因为空值而出现的错误
---
 ppq/IR/quantize.py               | 7 ++++---
 ppq/executor/op/torch/default.py | 6 ++++--
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/ppq/IR/quantize.py b/ppq/IR/quantize.py
index fd62fa60..d5398fda 100644
--- a/ppq/IR/quantize.py
+++ b/ppq/IR/quantize.py
@@ -237,7 +237,8 @@ def source_op_platform(self) -> TargetPlatform:
 
     def copy(self, copy_value: bool = False):
         clone = QuantableVariable(super().copy(copy_value))
-        if copy_value: clone._fp32_value = self._fp32_value.clone()
+        if copy_value and self._fp32_value is not None: 
+            clone._fp32_value = self._fp32_value.clone()
         else: clone._fp32_value = self._fp32_value
         return clone
 
@@ -314,10 +315,10 @@ def dequantize_graph(self, expire_device: str = 'cpu'):
         """一个方便懒人的函数."""
         for operation in self.graph.operations.values():
             if isinstance(operation, QuantableOperation):
-                operation.dequantize()
+                operation.dequantize(expire_device=expire_device)
 
     def restore_quantize_state(self, expire_device: str = 'cpu'):
         """一个方便懒人的函数."""
         for operation in self.graph.operations.values():
             if isinstance(operation, QuantableOperation):
-                operation.restore_quantize_state()
+                operation.restore_quantize_state(expire_device=expire_device)
diff --git a/ppq/executor/op/torch/default.py b/ppq/executor/op/torch/default.py
index d943c698..35017bc5 100644
--- a/ppq/executor/op/torch/default.py
+++ b/ppq/executor/op/torch/default.py
@@ -1088,7 +1088,7 @@ def Unsqueeze_forward(op: Operation, values: List[torch.Tensor], ctx: TorchBacke
         axes = GET_ATTRIBUTE_FROM_OPERATION(op=op, attribute='axes', compulsive=True)
 
     if isinstance(axes, list):
-        for squeezing_dim in sorted(axes, reverse=True):
+        for squeezing_dim in sorted(axes):
             unsqueezing_tensor = torch.unsqueeze(unsqueezing_tensor, squeezing_dim)
     elif isinstance(axes, int):
         unsqueezing_tensor = torch.unsqueeze(unsqueezing_tensor, axes)
@@ -2113,9 +2113,11 @@ def Softmax_forward(op: Operation, values: List[torch.Tensor], ctx: TorchBackend
     Returns:
         torch.Tensor: [description]
     """
+    if op.opset.onnx_opset_version() >= 13: default_axis = -1
+    else: default_axis = 1
     ASSERT_NUM_OF_INPUT(op=op, values=values, min_num_of_input=1, max_num_of_input=1)
     [input] = values
-    axis = GET_ATTRIBUTE_FROM_OPERATION(op=op, attribute='axis', default=-1)
+    axis = GET_ATTRIBUTE_FROM_OPERATION(op=op, attribute='axis', default=default_axis)
     output = F.softmax(input, axis)
     return output