[PTQ Converter] 'Linear+relu' module conversion failed about tinyneuralnetwork

[peterjc123]

        transpose_13_f = fake_dequant_inner_81_0_0.transpose(1, 2)
        backbone_encoders_0_dropout_2 = self.backbone_encoders_0_dropout(transpose_13_f)
        add_4_f = add_3_f.__add__(backbone_encoders_0_dropout_2)
        backbone_encoders_0_norm_ff = self.backbone_encoders_0_norm_ff(add_4_f)
        backbone_encoders_0_feed_forward_w_1 = self.backbone_encoders_0_feed_forward_w_1(backbone_encoders_0_norm_ff)

  (fake_dequant_inner_81_0_0): DeQuantStub()
  (backbone_encoders_0_norm_ff): LayerNorm((128,), eps=1e-05, elementwise_affine=True)
  (backbone_encoders_0_feed_forward_w_1): LinearReLU(
    (0): Linear(in_features=128, out_features=128, bias=True)
    (1): ReLU()
    (activation_post_process): HistogramObserver(min_val=0.0, max_val=1.3926540613174438)
  )

It seems that we should not fuse backbone_encoders_0_feed_forward_w_1 and ReLU here since they are not quantized.

[peterjc123]

Looking into it further, it seems that backbone_encoders_0_feed_forward_activation is reused so that it fails in the is_quantized_analysis pass.

[Juelianqvq]

is_quantized_analysis

So the easiest way is to rename ReLU op with different names?

[peterjc123]

@Juelianqvq It seems that #301 is enough to fix it, though we should probably duplicate those ops in the future.

[Juelianqvq]

@Juelianqvq It seems that #301 is enough to fix it, though we should probably duplicate those ops in the future.

@peterjc123 Thanks for fixing this :)