LogP Example: "TypeError: embedding(): argument 'indices' (position 2) must be Tensor, not list" #38
Comments
|
Gustavo, I guess your data is a list, but the model expects a tensor.
…On Mon, Oct 19, 2020 at 4:48 PM Gustavo Seabra ***@***.***> wrote:
Hi,
I'm re-running the LogP example using current version of PyTorch, and the
execution stops in the reinforcement loop due to a TypeError, as below. Are
you aware of any changes in PyTorch that could be responsible for this? Is
there a solution for it?
Thanks!
for i in range(n_iterations):
for j in trange(n_policy, desc='Policy gradient...'):
cur_reward, cur_loss = RL_logp.policy_gradient(gen_data)
rewards.append(simple_moving_average(rewards, cur_reward))
rl_losses.append(simple_moving_average(rl_losses, cur_loss))
plt.plot(rewards)
plt.xlabel('Training iteration')
plt.ylabel('Average reward')
plt.show()
plt.plot(rl_losses)
plt.xlabel('Training iteration')
plt.ylabel('Loss')
plt.show()
smiles_cur, prediction_cur = estimate_and_update(RL_logp.generator,
my_predictor,
n_to_generate)
print('Sample trajectories:')
for sm in smiles_cur[:5]:
print(sm)
with the error below:
Policy gradient...: 0%| | 0/15 [00:00<?, ?it/s]./release/data.py:98: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
return torch.tensor(tensor).cuda()
Policy gradient...: 0%| | 0/15 [00:00<?, ?it/s]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-37-7a3a9698cf0c> in <module>
1 for i in range(n_iterations):
2 for j in trange(n_policy, desc='Policy gradient...'):
----> 3 cur_reward, cur_loss = RL_logp.policy_gradient(gen_data)
4 rewards.append(simple_moving_average(rewards, cur_reward))
5 rl_losses.append(simple_moving_average(rl_losses, cur_loss))
~/work/li/leadopt/generator/ReLeaSE/release/reinforcement.py in policy_gradient(self, data, n_batch, gamma, std_smiles, grad_clipping, **kwargs)
117 reward = self.get_reward(trajectory[1:-1],
118 self.predictor,
--> 119 **kwargs)
120
121 # Converting string of characters into tensor
<ipython-input-33-a8c049e9e937> in get_reward_logp(smiles, predictor, invalid_reward)
1 def get_reward_logp(smiles, predictor, invalid_reward=0.0):
----> 2 mol, prop, nan_smiles = predictor.predict([smiles])
3 if len(nan_smiles) == 1:
4 return invalid_reward
5 if (prop[0] >= 1.0) and (prop[0] <= 4.0):
~/work/li/leadopt/generator/ReLeaSE/release/rnn_predictor.py in predict(self, smiles, use_tqdm)
62 self.model[i]([torch.LongTensor(smiles_tensor).cuda(),
63 torch.LongTensor(length).cuda()],
---> 64 eval=True).detach().cpu().numpy())
65 prediction = np.array(prediction).reshape(len(self.model), -1)
66 prediction = np.min(prediction, axis=0)
/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
~/work/source/repos/OpenChem/openchem/models/Smiles2Label.py in forward(self, inp, eval)
41 else:
42 self.train()
---> 43 embedded = self.Embedding(inp)
44 output, _ = self.Encoder(embedded)
45 output = self.MLP(output)
/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
~/work/source/repos/OpenChem/openchem/modules/embeddings/basic_embedding.py in forward(self, inp)
7
8 def forward(self, inp):
----> 9 embedded = self.embedding(inp)
10 return embedded
/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/sparse.py in forward(self, input)
124 return F.embedding(
125 input, self.weight, self.padding_idx, self.max_norm,
--> 126 self.norm_type, self.scale_grad_by_freq, self.sparse)
127
128 def extra_repr(self) -> str:
/opt/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
1812 # remove once script supports set_grad_enabled
1813 _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 1814 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
1815
1816
TypeError: embedding(): argument 'indices' (position 2) must be Tensor, not list
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Well... I'm no wiz, but that much I had figured out.
The point is that there's no such thing as "my data": I'm just running the LogP Jupyter notebook from the git repo.
I assume it did work fine when it was created, with PyTorch 0.4. But maybe there was some change in PyTorch internals?
--
Gustavo Seabra
________________________________
From: Olexandr Isayev <notifications@github.com>
Sent: Monday, October 19, 2020 6:51:58 PM
To: isayev/ReLeaSE <ReLeaSE@noreply.github.com>
Cc: de Miranda Seabra, Gustavo <seabra@cop.ufl.edu>; Author <author@noreply.github.com>
Subject: Re: [isayev/ReLeaSE] LogP Example: "TypeError: embedding(): argument 'indices' (position 2) must be Tensor, not list" (#38)
[External Email]
Gustavo, I guess your data is a list, but the model expects a tensor.
On Mon, Oct 19, 2020 at 4:48 PM Gustavo Seabra ***@***.***> wrote:
Hi,
I'm re-running the LogP example using current version of PyTorch, and the
execution stops in the reinforcement loop due to a TypeError, as below. Are
you aware of any changes in PyTorch that could be responsible for this? Is
there a solution for it?
Thanks!
for i in range(n_iterations):
for j in trange(n_policy, desc='Policy gradient...'):
cur_reward, cur_loss = RL_logp.policy_gradient(gen_data)
rewards.append(simple_moving_average(rewards, cur_reward))
rl_losses.append(simple_moving_average(rl_losses, cur_loss))
plt.plot(rewards)
plt.xlabel('Training iteration')
plt.ylabel('Average reward')
plt.show()
plt.plot(rl_losses)
plt.xlabel('Training iteration')
plt.ylabel('Loss')
plt.show()
smiles_cur, prediction_cur = estimate_and_update(RL_logp.generator,
my_predictor,
n_to_generate)
print('Sample trajectories:')
for sm in smiles_cur[:5]:
print(sm)
with the error below:
Policy gradient...: 0%| | 0/15 [00:00<?, ?it/s]./release/data.py:98: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
return torch.tensor(tensor).cuda()
Policy gradient...: 0%| | 0/15 [00:00<?, ?it/s]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-37-7a3a9698cf0c> in <module>
1 for i in range(n_iterations):
2 for j in trange(n_policy, desc='Policy gradient...'):
----> 3 cur_reward, cur_loss = RL_logp.policy_gradient(gen_data)
4 rewards.append(simple_moving_average(rewards, cur_reward))
5 rl_losses.append(simple_moving_average(rl_losses, cur_loss))
~/work/li/leadopt/generator/ReLeaSE/release/reinforcement.py in policy_gradient(self, data, n_batch, gamma, std_smiles, grad_clipping, **kwargs)
117 reward = self.get_reward(trajectory[1:-1],
118 self.predictor,
--> 119 **kwargs)
120
121 # Converting string of characters into tensor
<ipython-input-33-a8c049e9e937> in get_reward_logp(smiles, predictor, invalid_reward)
1 def get_reward_logp(smiles, predictor, invalid_reward=0.0):
----> 2 mol, prop, nan_smiles = predictor.predict([smiles])
3 if len(nan_smiles) == 1:
4 return invalid_reward
5 if (prop[0] >= 1.0) and (prop[0] <= 4.0):
~/work/li/leadopt/generator/ReLeaSE/release/rnn_predictor.py in predict(self, smiles, use_tqdm)
62 self.model[i]([torch.LongTensor(smiles_tensor).cuda(),
63 torch.LongTensor(length).cuda()],
---> 64 eval=True).detach().cpu().numpy())
65 prediction = np.array(prediction).reshape(len(self.model), -1)
66 prediction = np.min(prediction, axis=0)
/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
~/work/source/repos/OpenChem/openchem/models/Smiles2Label.py in forward(self, inp, eval)
41 else:
42 self.train()
---> 43 embedded = self.Embedding(inp)
44 output, _ = self.Encoder(embedded)
45 output = self.MLP(output)
/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
~/work/source/repos/OpenChem/openchem/modules/embeddings/basic_embedding.py in forward(self, inp)
7
8 def forward(self, inp):
----> 9 embedded = self.embedding(inp)
10 return embedded
/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
/opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/sparse.py in forward(self, input)
124 return F.embedding(
125 input, self.weight, self.padding_idx, self.max_norm,
--> 126 self.norm_type, self.scale_grad_by_freq, self.sparse)
127
128 def extra_repr(self) -> str:
/opt/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
1812 # remove once script supports set_grad_enabled
1813 _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
-> 1814 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
1815
1816
TypeError: embedding(): argument 'indices' (position 2) must be Tensor, not list
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Oh makes sense. Yeah, it does not work with the latest pytorch, you still
have to run it with the old one.
On Mon, Oct 19, 2020 at 8:10 PM Gustavo Seabra <notifications@github.com>
wrote:
… Well... I'm no wiz, but that much I had figured out.
The point is that there's no such thing as "my data": I'm just running the
LogP Jupyter notebook from the git repo.
I assume it did work fine when it was created, with PyTorch 0.4. But maybe
there was some change in PyTorch internals?
--
Gustavo Seabra
________________________________
From: Olexandr Isayev ***@***.***>
Sent: Monday, October 19, 2020 6:51:58 PM
To: isayev/ReLeaSE ***@***.***>
Cc: de Miranda Seabra, Gustavo ***@***.***>; Author <
***@***.***>
Subject: Re: [isayev/ReLeaSE] LogP Example: "TypeError: embedding():
argument 'indices' (position 2) must be Tensor, not list" (#38)
[External Email]
Gustavo, I guess your data is a list, but the model expects a tensor.
On Mon, Oct 19, 2020 at 4:48 PM Gustavo Seabra ***@***.***>
wrote:
> Hi,
>
> I'm re-running the LogP example using current version of PyTorch, and the
> execution stops in the reinforcement loop due to a TypeError, as below.
Are
> you aware of any changes in PyTorch that could be responsible for this?
Is
> there a solution for it?
>
> Thanks!
>
> for i in range(n_iterations):
> for j in trange(n_policy, desc='Policy gradient...'):
> cur_reward, cur_loss = RL_logp.policy_gradient(gen_data)
> rewards.append(simple_moving_average(rewards, cur_reward))
> rl_losses.append(simple_moving_average(rl_losses, cur_loss))
>
> plt.plot(rewards)
> plt.xlabel('Training iteration')
> plt.ylabel('Average reward')
> plt.show()
> plt.plot(rl_losses)
> plt.xlabel('Training iteration')
> plt.ylabel('Loss')
> plt.show()
>
> smiles_cur, prediction_cur = estimate_and_update(RL_logp.generator,
> my_predictor,
> n_to_generate)
> print('Sample trajectories:')
> for sm in smiles_cur[:5]:
> print(sm)
>
> with the error below:
>
> Policy gradient...: 0%| | 0/15 [00:00<?, ?it/s]./release/data.py:98:
UserWarning: To copy construct from a tensor, it is recommended to use
sourceTensor.clone().detach() or
sourceTensor.clone().detach().requires_grad_(True), rather than
torch.tensor(sourceTensor).
> return torch.tensor(tensor).cuda()
> Policy gradient...: 0%| | 0/15 [00:00<?, ?it/s]
>
>
---------------------------------------------------------------------------
> TypeError Traceback (most recent call last)
> <ipython-input-37-7a3a9698cf0c> in <module>
> 1 for i in range(n_iterations):
> 2 for j in trange(n_policy, desc='Policy gradient...'):
> ----> 3 cur_reward, cur_loss = RL_logp.policy_gradient(gen_data)
> 4 rewards.append(simple_moving_average(rewards, cur_reward))
> 5 rl_losses.append(simple_moving_average(rl_losses, cur_loss))
>
> ~/work/li/leadopt/generator/ReLeaSE/release/reinforcement.py in
policy_gradient(self, data, n_batch, gamma, std_smiles, grad_clipping,
**kwargs)
> 117 reward = self.get_reward(trajectory[1:-1],
> 118 self.predictor,
> --> 119 **kwargs)
> 120
> 121 # Converting string of characters into tensor
>
> <ipython-input-33-a8c049e9e937> in get_reward_logp(smiles, predictor,
invalid_reward)
> 1 def get_reward_logp(smiles, predictor, invalid_reward=0.0):
> ----> 2 mol, prop, nan_smiles = predictor.predict([smiles])
> 3 if len(nan_smiles) == 1:
> 4 return invalid_reward
> 5 if (prop[0] >= 1.0) and (prop[0] <= 4.0):
>
> ~/work/li/leadopt/generator/ReLeaSE/release/rnn_predictor.py in
predict(self, smiles, use_tqdm)
> 62 self.model[i]([torch.LongTensor(smiles_tensor).cuda(),
> 63 torch.LongTensor(length).cuda()],
> ---> 64 eval=True).detach().cpu().numpy())
> 65 prediction = np.array(prediction).reshape(len(self.model), -1)
> 66 prediction = np.min(prediction, axis=0)
>
> /opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in
_call_impl(self, *input, **kwargs)
> 720 result = self._slow_forward(*input, **kwargs)
> 721 else:
> --> 722 result = self.forward(*input, **kwargs)
> 723 for hook in itertools.chain(
> 724 _global_forward_hooks.values(),
>
> ~/work/source/repos/OpenChem/openchem/models/Smiles2Label.py in
forward(self, inp, eval)
> 41 else:
> 42 self.train()
> ---> 43 embedded = self.Embedding(inp)
> 44 output, _ = self.Encoder(embedded)
> 45 output = self.MLP(output)
>
> /opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in
_call_impl(self, *input, **kwargs)
> 720 result = self._slow_forward(*input, **kwargs)
> 721 else:
> --> 722 result = self.forward(*input, **kwargs)
> 723 for hook in itertools.chain(
> 724 _global_forward_hooks.values(),
>
>
~/work/source/repos/OpenChem/openchem/modules/embeddings/basic_embedding.py
in forward(self, inp)
> 7
> 8 def forward(self, inp):
> ----> 9 embedded = self.embedding(inp)
> 10 return embedded
>
> /opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in
_call_impl(self, *input, **kwargs)
> 720 result = self._slow_forward(*input, **kwargs)
> 721 else:
> --> 722 result = self.forward(*input, **kwargs)
> 723 for hook in itertools.chain(
> 724 _global_forward_hooks.values(),
>
> /opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/sparse.py in
forward(self, input)
> 124 return F.embedding(
> 125 input, self.weight, self.padding_idx, self.max_norm,
> --> 126 self.norm_type, self.scale_grad_by_freq, self.sparse)
> 127
> 128 def extra_repr(self) -> str:
>
> /opt/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py in
embedding(input, weight, padding_idx, max_norm, norm_type,
scale_grad_by_freq, sparse)
> 1812 # remove once script supports set_grad_enabled
> 1813 _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
> -> 1814 return torch.embedding(weight, input, padding_idx,
scale_grad_by_freq, sparse)
> 1815
> 1816
>
> TypeError: embedding(): argument 'indices' (position 2) must be Tensor,
not list
>
>
>
> —
> You are receiving this because you are subscribed to this thread.
> Reply to this email directly, view it on GitHub
> <#38><
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>
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Right. What I wonder is that PyTorch is getting the data from OpenChem. Does that mean that OpenChem is in need of an update? Or would that be a localized thing with this notebook only?
From: Olexandr Isayev <notifications@github.com>
Sent: Monday, October 19, 2020 8:15 PM
To: isayev/ReLeaSE <ReLeaSE@noreply.github.com>
Cc: de Miranda Seabra, Gustavo <seabra@cop.ufl.edu>; Author <author@noreply.github.com>
Subject: Re: [isayev/ReLeaSE] LogP Example: "TypeError: embedding(): argument 'indices' (position 2) must be Tensor, not list" (#38)
[External Email]
Oh makes sense. Yeah, it does not work with the latest pytorch, you still
have to run it with the old one.
On Mon, Oct 19, 2020 at 8:10 PM Gustavo Seabra <notifications@github.com<mailto:notifications@github.com>>
wrote:
Well... I'm no wiz, but that much I had figured out.
The point is that there's no such thing as "my data": I'm just running the
LogP Jupyter notebook from the git repo.
I assume it did work fine when it was created, with PyTorch 0.4. But maybe
there was some change in PyTorch internals?
--
Gustavo Seabra
________________________________
From: Olexandr Isayev ***@***.******@***.***>>
Sent: Monday, October 19, 2020 6:51:58 PM
To: isayev/ReLeaSE ***@***.******@***.***>>
Cc: de Miranda Seabra, Gustavo ***@***.******@***.***>>; Author <
***@***.******@***.***>>
Subject: Re: [isayev/ReLeaSE] LogP Example: "TypeError: embedding():
argument 'indices' (position 2) must be Tensor, not list" (#38)
[External Email]
Gustavo, I guess your data is a list, but the model expects a tensor.
On Mon, Oct 19, 2020 at 4:48 PM Gustavo Seabra ***@***.******@***.***>>
wrote:
> Hi,
>
> I'm re-running the LogP example using current version of PyTorch, and the
> execution stops in the reinforcement loop due to a TypeError, as below.
Are
> you aware of any changes in PyTorch that could be responsible for this?
Is
> there a solution for it?
>
> Thanks!
>
> for i in range(n_iterations):
> for j in trange(n_policy, desc='Policy gradient...'):
> cur_reward, cur_loss = RL_logp.policy_gradient(gen_data)
> rewards.append(simple_moving_average(rewards, cur_reward))
> rl_losses.append(simple_moving_average(rl_losses, cur_loss))
>
> plt.plot(rewards)
> plt.xlabel('Training iteration')
> plt.ylabel('Average reward')
> plt.show()
> plt.plot(rl_losses)
> plt.xlabel('Training iteration')
> plt.ylabel('Loss')
> plt.show()
>
> smiles_cur, prediction_cur = estimate_and_update(RL_logp.generator,
> my_predictor,
> n_to_generate)
> print('Sample trajectories:')
> for sm in smiles_cur[:5]:
> print(sm)
>
> with the error below:
>
> Policy gradient...: 0%| | 0/15 [00:00<?, ?it/s]./release/data.py:98:
UserWarning: To copy construct from a tensor, it is recommended to use
sourceTensor.clone().detach() or
sourceTensor.clone().detach().requires_grad_(True), rather than
torch.tensor(sourceTensor).
> return torch.tensor(tensor).cuda()
> Policy gradient...: 0%| | 0/15 [00:00<?, ?it/s]
>
>
---------------------------------------------------------------------------
> TypeError Traceback (most recent call last)
> <ipython-input-37-7a3a9698cf0c> in <module>
> 1 for i in range(n_iterations):
> 2 for j in trange(n_policy, desc='Policy gradient...'):
> ----> 3 cur_reward, cur_loss = RL_logp.policy_gradient(gen_data)
> 4 rewards.append(simple_moving_average(rewards, cur_reward))
> 5 rl_losses.append(simple_moving_average(rl_losses, cur_loss))
>
> ~/work/li/leadopt/generator/ReLeaSE/release/reinforcement.py in
policy_gradient(self, data, n_batch, gamma, std_smiles, grad_clipping,
**kwargs)
> 117 reward = self.get_reward(trajectory[1:-1],
> 118 self.predictor,
> --> 119 **kwargs)
> 120
> 121 # Converting string of characters into tensor
>
> <ipython-input-33-a8c049e9e937> in get_reward_logp(smiles, predictor,
invalid_reward)
> 1 def get_reward_logp(smiles, predictor, invalid_reward=0.0):
> ----> 2 mol, prop, nan_smiles = predictor.predict([smiles])
> 3 if len(nan_smiles) == 1:
> 4 return invalid_reward
> 5 if (prop[0] >= 1.0) and (prop[0] <= 4.0):
>
> ~/work/li/leadopt/generator/ReLeaSE/release/rnn_predictor.py in
predict(self, smiles, use_tqdm)
> 62 self.model[i]([torch.LongTensor(smiles_tensor).cuda(),
> 63 torch.LongTensor(length).cuda()],
> ---> 64 eval=True).detach().cpu().numpy())
> 65 prediction = np.array(prediction).reshape(len(self.model), -1)
> 66 prediction = np.min(prediction, axis=0)
>
> /opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in
_call_impl(self, *input, **kwargs)
> 720 result = self._slow_forward(*input, **kwargs)
> 721 else:
> --> 722 result = self.forward(*input, **kwargs)
> 723 for hook in itertools.chain(
> 724 _global_forward_hooks.values(),
>
> ~/work/source/repos/OpenChem/openchem/models/Smiles2Label.py in
forward(self, inp, eval)
> 41 else:
> 42 self.train()
> ---> 43 embedded = self.Embedding(inp)
> 44 output, _ = self.Encoder(embedded)
> 45 output = self.MLP(output)
>
> /opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in
_call_impl(self, *input, **kwargs)
> 720 result = self._slow_forward(*input, **kwargs)
> 721 else:
> --> 722 result = self.forward(*input, **kwargs)
> 723 for hook in itertools.chain(
> 724 _global_forward_hooks.values(),
>
>
~/work/source/repos/OpenChem/openchem/modules/embeddings/basic_embedding.py
in forward(self, inp)
> 7
> 8 def forward(self, inp):
> ----> 9 embedded = self.embedding(inp)
> 10 return embedded
>
> /opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in
_call_impl(self, *input, **kwargs)
> 720 result = self._slow_forward(*input, **kwargs)
> 721 else:
> --> 722 result = self.forward(*input, **kwargs)
> 723 for hook in itertools.chain(
> 724 _global_forward_hooks.values(),
>
> /opt/anaconda3/lib/python3.7/site-packages/torch/nn/modules/sparse.py in
forward(self, input)
> 124 return F.embedding(
> 125 input, self.weight, self.padding_idx, self.max_norm,
> --> 126 self.norm_type, self.scale_grad_by_freq, self.sparse)
> 127
> 128 def extra_repr(self) -> str:
>
> /opt/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py in
embedding(input, weight, padding_idx, max_norm, norm_type,
scale_grad_by_freq, sparse)
> 1812 # remove once script supports set_grad_enabled
> 1813 _no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
> -> 1814 return torch.embedding(weight, input, padding_idx,
scale_grad_by_freq, sparse)
> 1815
> 1816
>
> TypeError: embedding(): argument 'indices' (position 2) must be Tensor,
not list
>
>
>
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Hi,
I'm re-running the LogP example using current version of PyTorch, and the execution stops in the reinforcement loop due to a TypeError, as below. Are you aware of any changes in PyTorch that could be responsible for this? Is there a solution for it?
Thanks!
with the error below:
The text was updated successfully, but these errors were encountered: