MiniGPT-4/test1.txt

from torchviz import make_dot
dot = make_dot(query_output.last_hidden_state, params=dict(self.Qformer.bert.named_parameters()))
log_dir = '/mnt/pfs-guan-ssai/nlu/wanghanzi/multimodal/PromptMoE/'
dot.render(filename="Pre_PromptMoE_RawProb_backward_graph", directory=log_dir, format="pdf")


# Pre-Prompt-MoE
model.Qformer.bert.encoder.layer[6].experts.gate.weight.grad
model.Qformer.bert.encoder.layer[8].experts.gate.weight.grad
model.Qformer.bert.encoder.layer[10].experts.gate.weight.grad
model.Qformer.bert.encoder.layer[6].experts.experts[0].dense1.weight.grad
model.Qformer.bert.encoder.layer[10].experts.experts[0].dense1.weight.grad
model.Qformer.bert.encoder.layer[8].experts.experts[0].dense1.weight.grad
model.Qformer.bert.encoder.layer[8].experts.experts[1].dense1.weight.grad
model.Qformer.bert.encoder.layer[8].experts.experts[2].dense1.weight.grad


model.Qformer.bert.encoder.layer[8].experts.gate.weight.grad
model.Qformer.bert.encoder.layer[9].intermediate_query.dense.weight
model.Qformer.bert.encoder.layer[9].intermediate_query.dense.weight.grad
model.Qformer.bert.encoder.layer[10].intermediate.dense.weight.grad
model.Qformer.bert.encoder.layer[11].intermediate.dense.weight.grad

model.Qformer.bert.encoder.layer[10].intermediate_query.dense.weight
model.Qformer.bert.encoder.layer[10].experts.experts[2].dense1.weight
model.Qformer.bert.encoder.layer[10].experts.experts[1].dense1.weight
model.Qformer.bert.encoder.layer[10].experts.experts[0].dense1.weight
model.Qformer.bert.encoder.layer[10].intermediate_query.dense.weight == model.Qformer.bert.encoder.layer[10].experts.experts[0].dense1.weight

# Pre-MoE gate-sentence
# model.Qformer.bert.encoder.layer[8].experts.gate.weight.grad 不更新

# Pre-MoE gate-token
# 正常更新

# Post-MoE gate-sentence
model.Qformer.bert.encoder.layer[8].experts.gate.weight.grad
# model.Qformer.bert.encoder.layer[8].experts.gate.weight.grad 正常更新
# model.Qformer.bert.encoder.layer[6].experts.gate.weight.grad 全是0/-0
# model.Qformer.bert.encoder.layer[10].experts.gate.weight.grad 全是0/-0

# Route-MoE 
# Pre-MoE 算的beam_scores有问题

# Post-Route 会更新多个expert的参数；会更新gate的参数
# Layer 6 更新了两个expert的参数 (layer 6 layer 8)
# model.Qformer.bert.encoder.layer[11].intermediate.dense.weight.grad 是0？都是0
# model.Qformer.bert.encoder.layer[11].output.dense.weight.grad

model.Qformer.bert.encoder.layer[6].experts.gate.weight.grad
model.Qformer.bert.encoder.layer[6].experts.experts[0].intermediate_query.dense.weight.grad
model.Qformer.bert.encoder.layer[6].experts.experts[1].intermediate_query.dense.weight.grad

model.Qformer.bert.encoder.layer[7].experts.gate.weight.grad
model.Qformer.bert.encoder.layer[7].experts.experts[0].intermediate_query.dense.weight.grad
model.Qformer.bert.encoder.layer[7].experts.experts[1].intermediate_query.dense.weight.grad

model.Qformer.bert.encoder.layer[8].experts.gate.weight.grad
model.Qformer.bert.encoder.layer[8].experts.experts[0].intermediate_query.dense.weight.grad
model.Qformer.bert.encoder.layer[8].experts.experts[1].intermediate_query.dense.weight.grad

model.Qformer.bert.encoder.layer[9].experts.gate.weight.grad
model.Qformer.bert.encoder.layer[9].experts.experts[0].intermediate_query.dense.weight.grad
model.Qformer.bert.encoder.layer[9].experts.experts[1].intermediate_query.dense.weight.grad

model.Qformer.bert.encoder.layer[10].experts.gate.weight.grad
model.Qformer.bert.encoder.layer[10].experts.experts[0].intermediate_query.dense.weight.grad
model.Qformer.bert.encoder.layer[10].experts.experts[1].intermediate_query.dense.weight.grad

model.Qformer.bert.encoder.layer[11].experts.gate.weight.grad
model.Qformer.bert.encoder.layer[11].experts.experts[0].intermediate_query.dense.weight.grad
model.Qformer.bert.encoder.layer[11].experts.experts[1].intermediate_query.dense.weight.grad


(Pdb) [p for n, p in self.model.named_parameters() if n == 'Qformer.bert.encoder.layer.10.experts.experts.0.dense1.weight']
[Parameter containing:
tensor([[-0.0328,  0.0414,  0.0010,  ..., -0.0068,  0.0244,  0.0587],
        [ 0.0120,  0.0458,  0.0171,  ..., -0.0439, -0.0107, -0.0397],
        [ 0.0239,  0.0191, -0.0145,  ...,  0.0008, -0.0067,  0.0090],
        ...,
        [ 0.0174, -0.0465, -0.0106,  ..., -0.0095,  0.0153, -0.0195],
        [-0.0151, -0.0082, -0.0320,  ..., -0.0016, -0.0232, -0.0147],
        [ 0.0142, -0.0286,  0.0161,  ..., -0.0160, -0.0306, -0.0272]],
       device='cuda:0', requires_grad=True)]
(Pdb) [p for n, p in self.model.named_parameters() if n == 'Qformer.bert.encoder.layer.8.experts.experts.0.dense1.weight']
[Parameter containing:
tensor([[ 0.0024,  0.0218, -0.0186,  ..., -0.0178, -0.0067,  0.0820],
        [-0.0759, -0.0002, -0.0548,  ...,  0.0292,  0.0531,  0.0779],
        [-0.0220, -0.0037, -0.0520,  ..., -0.0426, -0.0261, -0.0357],
        ...,
        [-0.0448,  0.0471,  0.0133,  ..., -0.0062, -0.0217, -0.0203],
        [ 0.0532,  0.0197,  0.0320,  ..., -0.0010, -0.0838,  0.0682],
        [ 0.0284,  0.0038, -0.0007,  ..., -0.0305,  0.0296,  0.0056]],
       device='cuda:0', requires_grad=True)]
(Pdb) [p for n, p in self.model.named_parameters() if n == 'Qformer.bert.encoder.layer.6.experts.experts.0.dense1.weight']
[Parameter containing:
tensor([[ 6.5176e-02, -4.6473e-02, -2.7396e-02,  ...,  2.1774e-03,
          6.1457e-02,  1.9180e-03],
        [ 7.3707e-03,  6.1392e-02, -2.7108e-02,  ...,  4.0778e-02,
         -1.9791e-02, -1.1612e-02],
        [ 2.1193e-02, -3.8323e-02, -6.0238e-02,  ..., -1.4539e-02,
          9.2965e-02,  3.9153e-02],
        ...,
        [ 5.3203e-03, -1.7276e-02, -3.2191e-02,  ..., -1.6435e-02,
         -1.8553e-02, -2.8158e-02],
        [-6.9853e-02,  9.2719e-03, -1.8895e-03,  ..., -2.6425e-02,
          1.4880e-03,  3.4505e-02],
        [-1.2168e-03,  3.7038e-02,  4.8047e-02,  ..., -3.4523e-03,
         -1.3030e-05, -1.4778e-02]], device='cuda:0', requires_grad=True)]