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When running in a Docker environment, the following log is output and there is no result. #108

Open callor opened 2 months ago

callor commented 2 months ago

When running in a Docker environment, the following log is output and there is no result.

2024-09-03 12:36:59 Using GPUs: []
2024-09-03 12:36:59 Using OpenAI API with model gpt-4o-2024-05-13.
2024-09-03 12:36:59 
2024-09-03 12:36:59 Generating idea 1/1
2024-09-03 12:36:59 Iteration 1/3
2024-09-03 12:36:59 {'Name': 'conditional_generation', 'Title': 'Controllable Generation with Conditional Embeddings in Diffusion Models', 'Experiment': "In this experiment, we introduce conditional embeddings to the denoiser network to enable controllable generation. We will add a new embedding layer for the conditional vector and modify the forward pass of the MLPDenoiser to include this conditional embedding. We will then train the model on the existing 2D datasets, with the conditional vector specifying different modes of the data distribution. The results will be evaluated based on the model's ability to generate samples biased towards the specified modes.", 'Interestingness': 8, 'Feasibility': 7, 'Novelty': 7}
2024-09-03 12:36:59 Iteration 2/3

2024-09-03 12:41:28                                                                                 
2024-09-03 12:41:28  <<<<<<< SEARCH                                                                 
2024-09-03 12:41:28          scheduler = CosineAnnealingLR(optimizer, T_max=config.num_train_steps) 
2024-09-03 12:41:28  =======                                                                        
2024-09-03 12:41:28          scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1000, 
2024-09-03 12:41:28  gamma=0.1)                                                                     
2024-09-03 12:41:28  >>>>>>> REPLACE                                                                
2024-09-03 12:41:28                                                                                 
2024-09-03 12:41:28 
2024-09-03 12:41:28 Please run the command python experiment.py --out_dir=run_1 and let me know the 
2024-09-03 12:41:28 results.                                                                        
2024-09-03 12:41:28 

2024-09-03 12:45:29 1. **Run 1**: StepLR with step_size=1000 and gamma=0.1
2024-09-03 12:45:29 2. **Run 2**: ExponentialLR with gamma=0.99
2024-09-03 12:45:29 3. **Run 3**: ReduceLROnPlateau with patience=10 and factor=0.1
2024-09-03 12:45:29 4. **Run 4**: CyclicLR with base_lr=1e-4 and max_lr=1e-3
2024-09-03 12:45:29 
2024-09-03 12:45:29 ### Implementation
2024-09-03 12:45:29 
2024-09-03 12:45:29 Let's start with **Run 1**: StepLR with step_size=1000 and gamma=0.1.
2024-09-03 12:45:29 
2024-09-03 12:45:29 #### Step 1: Modify `experiment.py` to use StepLR
2024-09-03 12:45:29 
2024-09-03 12:45:29 experiment.py
2024-09-03 12:45:29 ```python
2024-09-03 12:45:29 <<<<<<< SEARCH
2024-09-03 12:45:29         scheduler = CosineAnnealingLR(optimizer, T_max=config.num_train_steps)
2024-09-03 12:45:29 =======
2024-09-03 12:45:29         scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1000, gamma=0.1)
2024-09-03 12:45:29 >>>>>>> REPLACE
2024-09-03 12:45:29 ```
2024-09-03 12:45:29 
2024-09-03 12:45:29 Please run the command `python experiment.py --out_dir=run_1` and let me know the results.
2024-09-03 12:45:29                         Experiment Description for Run 1                        
2024-09-03 12:45:29 

2024-09-03 12:45:29                                 Update notes.txt                                
2024-09-03 12:45:29 
2024-09-03 12:45:29 notes.txt                                                                       
2024-09-03 12:45:29 
2024-09-03 12:45:29                                                                                 
2024-09-03 12:45:29  <<<<<<< SEARCH                                                                 
2024-09-03 12:45:29  Description: Baseline results.                                                 
2024-09-03 12:45:29  =======                                                                        
2024-09-03 12:45:29  Description: Baseline results.                                                 
2024-09-03 12:45:29                                                                                 
2024-09-03 12:45:29  ## Run 1: StepLR with step_size=1000 and gamma=0.1                             
2024-09-03 12:45:29  Results: {'circle': {'training_time': 49.43490982055664, 'eval_loss':          
2024-09-03 12:45:29  0.45279408530201143, 'inference_time': 3.512603759765625, 'kl_divergence':     
2024-09-03 12:45:29  0.37352884410994314}, 'dino': {'training_time': 51.082834005355835, 'eval_loss 
2024-09-03 12:45:29  0.7123583286924435, 'inference_time': 3.617689609527588, 'kl_divergence':      
2024-09-03 12:45:29  4.161902163032134}, 'line': {'training_time': 53.97785544395447, 'eval_loss':  
2024-09-03 12:45:29  0.805292817485302, 'inference_time': 3.6049094200134277, 'kl_divergence':      
2024-09-03 12:45:29  0.16017912127875994}, 'moons': {'training_time': 52.51440739631653, 'eval_loss 
2024-09-03 12:45:29  0.6206451436442793, 'inference_time': 3.5346617698669434, 'kl_divergence':     
2024-09-03 12:45:29  0.12766315859985514}}                                                          

2024-09-03 12:45:29 
2024-09-03 12:45:29 experiment.py                                                                   
2024-09-03 12:45:29 
2024-09-03 12:45:29                                                                                 
2024-09-03 12:45:29  <<<<<<< SEARCH                                                                 
2024-09-03 12:45:29          scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1000, 
2024-09-03 12:45:29  gamma=0.1)                                                                     
2024-09-03 12:45:29  =======                                                                        
2024-09-03 12:45:29          scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer,          
2024-09-03 12:45:29  gamma=0.99)                                                                    
2024-09-03 12:45:29  >>>>>>> REPLACE                                                                
2024-09-03 12:45:29                                                                                 
2024-09-03 12:45:29 
2024-09-03 12:45:29 Please run the command python experiment.py --out_dir=run_2 and let me know the 
2024-09-03 12:45:29 results.                                                                        
2024-09-03 12:45:29 

2024-09-03 12:49:34 
2024-09-03 12:49:34 **Run 1**: We tested the `StepLR` learning rate scheduler with `step_size=1000` and `gamma=0.1`. This scheduler decreases the learning rate by a factor of 0.1 every 1000 steps. The results are as follows:
2024-09-03 12:49:34 

2024-09-03 13:24:39 experiment.py
2024-09-03 13:24:39 ```python
2024-09-03 13:24:39 <<<<<<< SEARCH
2024-09-03 13:24:39         self.conditional_mlp = SinusoidalEmbedding(embedding_dim, scale=25.0)
2024-09-03 13:24:39 >>>>>>> REPLACE
2024-09-03 13:24:39 
2024-09-03 13:24:39 ^^^ Expected `=======` not >>>>>>> REPLACE
2024-09-03 13:24:52 Let's correct the error by moving the self.conditional_mlp assignment after the 
2024-09-03 13:24:52 super().__init__() call in the MLPDenoiser class.                               
2024-09-03 13:24:52 
2024-09-03 13:24:52 Here is the correct SEARCH/REPLACE block:                                       
2024-09-03 13:24:52 
2024-09-03 13:24:52 experiment.py                                                                   
2024-09-03 13:24:52 
2024-09-03 13:24:52                                                                                 
2024-09-03 13:24:52  <<<<<<< SEARCH                                                                 
2024-09-03 13:24:52              conditional_dim: int = 2,                                          
2024-09-03 13:24:52      ):                                                                         
2024-09-03 13:24:52          self.conditional_mlp = SinusoidalEmbedding(embedding_dim, scale=25.0)  
2024-09-03 13:24:52  =======                                                                        
2024-09-03 13:24:52              conditional_dim: int = 2,                                          
2024-09-03 13:24:52      ):                                                                         
2024-09-03 13:24:52  >>>>>>> REPLACE                                                                
2024-09-03 13:24:52          super().__init__()                                                     
2024-09-03 13:24:52          self.conditional_mlp = SinusoidalEmbedding(embedding_dim, scale=25.0)  
2024-09-03 13:24:52  >>>>>>> REPLACE                                                                
2024-09-03 13:24:52                                                                                 
2024-09-03 13:24:52 
2024-09-03 13:24:52 Please update the file with the above change and re-run the experiment using the
2024-09-03 13:24:52 command python experiment.py --out_dir=run_1.                                   
2024-09-03 13:24:52 
2024-09-03 13:24:52 Tokens: 7,063 sent, 163 received. Cost: $0.04 message, $0.11 session.
2024-09-03 13:24:52 Applied edit to experiment.py
2024-09-03 13:24:55 Training model...
2024-09-03 13:24:55 Traceback (most recent call last):
2024-09-03 13:24:55   File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 227, in <module>
2024-09-03 13:24:55     noise_pred = model(noisy, timesteps)
2024-09-03 13:24:55                  ^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:24:55   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
2024-09-03 13:24:55     return self._call_impl(*args, **kwargs)
2024-09-03 13:24:55            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:24:55   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
2024-09-03 13:24:55     return forward_call(*args, **kwargs)
2024-09-03 13:24:55            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:24:55   File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 76, in forward
2024-09-03 13:24:55     conditional_emb = self.conditional_mlp(x[:, 2])
2024-09-03 13:24:55                       ^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:24:55   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1729, in __getattr__
2024-09-03 13:24:55     raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
2024-09-03 13:24:55 AttributeError: 'MLPDenoiser' object has no attribute 'conditional_mlp'
2024-09-03 13:24:55 
2024-09-03 13:25:08 Let's correct the error by moving the `self.conditional_mlp` assignment after the `super().__init__()` call in the `MLPDenoiser` class.
2024-09-03 13:25:08 
2024-09-03 13:25:08 Here is the correct *SEARCH/REPLACE* block:
2024-09-03 13:25:08 
2024-09-03 13:25:08 experiment.py
2024-09-03 13:25:08 ```python
2024-09-03 13:25:08 <<<<<<< SEARCH
2024-09-03 13:25:08             conditional_dim: int = 2,
2024-09-03 13:25:08     ):
2024-09-03 13:25:08         self.conditional_mlp = SinusoidalEmbedding(embedding_dim, scale=25.0)
2024-09-03 13:25:08 =======
2024-09-03 13:25:08             conditional_dim: int = 2,
2024-09-03 13:25:08     ):
2024-09-03 13:25:08 >>>>>>> REPLACE
2024-09-03 13:25:08         super().__init__()
2024-09-03 13:25:08         self.conditional_mlp = SinusoidalEmbedding(embedding_dim, scale=25.0)
2024-09-03 13:25:08 >>>>>>> REPLACE
2024-09-03 13:25:08 ```
2024-09-03 13:25:08 
2024-09-03 13:25:08 Please update the file with the above change and re-run the experiment using the command `python experiment.py --out_dir=run_1`.
2024-09-03 13:25:08 Run 1 failed with return code 1
2024-09-03 13:25:08 Run failed with the following error Traceback (most recent call last):
2024-09-03 13:25:08   File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 227, in <module>
2024-09-03 13:25:08     noise_pred = model(noisy, timesteps)
2024-09-03 13:25:08                  ^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:08   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
2024-09-03 13:25:08     return self._call_impl(*args, **kwargs)
2024-09-03 13:25:08            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:08   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
2024-09-03 13:25:08     return forward_call(*args, **kwargs)
2024-09-03 13:25:08            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:08   File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 76, in forward
2024-09-03 13:25:08     conditional_emb = self.conditional_mlp(x[:, 2])
2024-09-03 13:25:08                       ^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:08   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1729, in __getattr__
2024-09-03 13:25:08     raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
2024-09-03 13:25:08 AttributeError: 'MLPDenoiser' object has no attribute 'conditional_mlp'
2024-09-03 13:25:08 
2024-09-03 13:25:08 The error indicates that the conditional_mlp attribute is not being properly    
2024-09-03 13:25:08 initialized in the MLPDenoiser class. This is likely because we missed adding   
2024-09-03 13:25:08 the initialization of self.conditional_mlp in the constructor.                  
2024-09-03 13:25:08 
2024-09-03 13:25:08 Let's fix this by ensuring that self.conditional_mlp is properly initialized in 
2024-09-03 13:25:08 the MLPDenoiser class constructor.                                              
2024-09-03 13:25:08 
2024-09-03 13:25:08 experiment.py                                                                   
2024-09-03 13:25:08 
2024-09-03 13:25:08                                                                                 
2024-09-03 13:25:08  <<<<<<< SEARCH                                                                 
2024-09-03 13:25:08          self.network = nn.Sequential(                                          
2024-09-03 13:25:08  =======                                                                        
2024-09-03 13:25:08          self.conditional_mlp = SinusoidalEmbedding(embedding_dim, scale=25.0)  
2024-09-03 13:25:08          self.network = nn.Sequential(                                          
2024-09-03 13:25:08  >>>>>>> REPLACE                                                                
2024-09-03 13:25:08                                                                                 
2024-09-03 13:25:08 
2024-09-03 13:25:08 Please update the file with the above change and re-run the experiment using the
2024-09-03 13:25:08 command python experiment.py --out_dir=run_1.                                   
2024-09-03 13:25:08 
2024-09-03 13:25:08 Tokens: 7,524 sent, 153 received. Cost: $0.04 message, $0.15 session.
2024-09-03 13:25:08 Applied edit to experiment.py
2024-09-03 13:25:10 Training model...
2024-09-03 13:25:10 Traceback (most recent call last):
2024-09-03 13:25:10   File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 228, in <module>
2024-09-03 13:25:10     noise_pred = model(noisy, timesteps)
2024-09-03 13:25:10                  ^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:10   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
2024-09-03 13:25:10     return self._call_impl(*args, **kwargs)
2024-09-03 13:25:10            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:10   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
2024-09-03 13:25:10     return forward_call(*args, **kwargs)
2024-09-03 13:25:10            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:10   File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 79, in forward
2024-09-03 13:25:10     return self.network(emb)
2024-09-03 13:25:10            ^^^^^^^^^^^^^^^^^
2024-09-03 13:25:10   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
2024-09-03 13:25:10     return self._call_impl(*args, **kwargs)
2024-09-03 13:25:10            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:10   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
2024-09-03 13:25:10     return forward_call(*args, **kwargs)
2024-09-03 13:25:10            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:10   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/container.py", line 219, in forward
2024-09-03 13:25:10     input = module(input)
2024-09-03 13:25:10             ^^^^^^^^^^^^^
2024-09-03 13:25:10   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
2024-09-03 13:25:10     return self._call_impl(*args, **kwargs)
2024-09-03 13:25:10            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:10   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
2024-09-03 13:25:10     return forward_call(*args, **kwargs)
2024-09-03 13:25:10            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:10   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/linear.py", line 117, in forward
2024-09-03 13:25:10     return F.linear(input, self.weight, self.bias)
2024-09-03 13:25:10            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:10 RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 384x256)
2024-09-03 13:25:10 
2024-09-03 13:25:26 The error indicates that the `conditional_mlp` attribute is not being properly initialized in the `MLPDenoiser` class. This is likely because we missed adding the initialization of `self.conditional_mlp` in the constructor.
2024-09-03 13:25:26 
2024-09-03 13:25:26 Let's fix this by ensuring that `self.conditional_mlp` is properly initialized in the `MLPDenoiser` class constructor.
2024-09-03 13:25:26 
2024-09-03 13:25:26 experiment.py
2024-09-03 13:25:26 ```python
2024-09-03 13:25:26 <<<<<<< SEARCH
2024-09-03 13:25:26         self.network = nn.Sequential(
2024-09-03 13:25:26 =======
2024-09-03 13:25:26         self.conditional_mlp = SinusoidalEmbedding(embedding_dim, scale=25.0)
2024-09-03 13:25:26         self.network = nn.Sequential(
2024-09-03 13:25:26 >>>>>>> REPLACE
2024-09-03 13:25:26 ```
2024-09-03 13:25:26 
2024-09-03 13:25:26 Please update the file with the above change and re-run the experiment using the command `python experiment.py --out_dir=run_1`.
2024-09-03 13:25:26 Run 1 failed with return code 1
2024-09-03 13:25:26 Run failed with the following error Traceback (most recent call last):
2024-09-03 13:25:26   File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 228, in <module>
2024-09-03 13:25:26     noise_pred = model(noisy, timesteps)
2024-09-03 13:25:26                  ^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:26   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
2024-09-03 13:25:26     return self._call_impl(*args, **kwargs)
2024-09-03 13:25:26            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:26   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
2024-09-03 13:25:26     return forward_call(*args, **kwargs)
2024-09-03 13:25:26            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:26   File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 79, in forward
2024-09-03 13:25:26     return self.network(emb)
2024-09-03 13:25:26            ^^^^^^^^^^^^^^^^^
2024-09-03 13:25:26   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
2024-09-03 13:25:26     return self._call_impl(*args, **kwargs)
2024-09-03 13:25:26            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:26   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
2024-09-03 13:25:26     return forward_call(*args, **kwargs)
2024-09-03 13:25:26            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:26   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/container.py", line 219, in forward
2024-09-03 13:25:26     input = module(input)
2024-09-03 13:25:26             ^^^^^^^^^^^^^
2024-09-03 13:25:26   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
2024-09-03 13:25:26     return self._call_impl(*args, **kwargs)
2024-09-03 13:25:26            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:26   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
2024-09-03 13:25:26     return forward_call(*args, **kwargs)
2024-09-03 13:25:26            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:26   File "/usr/local/lib/python3.11/site-packages/torch/nn/modules/linear.py", line 117, in forward
2024-09-03 13:25:26     return F.linear(input, self.weight, self.bias)
2024-09-03 13:25:26            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:26 RuntimeError: mat1 and mat2 shapes cannot be multiplied (256x512 and 384x256)
2024-09-03 13:25:26 
2024-09-03 13:25:26 The error indicates that the shapes of the matrices being multiplied do not     
2024-09-03 13:25:26 match. This is likely because the concatenated embedding vector has a different 
2024-09-03 13:25:26 size than expected by the first linear layer in the self.network.               
2024-09-03 13:25:26 
2024-09-03 13:25:26 Since we are concatenating four embeddings (x1_emb, x2_emb, t_emb, and          
2024-09-03 13:25:26 conditional_emb), each of size embedding_dim, the total size of the concatenated
2024-09-03 13:25:26 embedding vector should be embedding_dim * 4.                                   
2024-09-03 13:25:26 
2024-09-03 13:25:26 Let's fix this by updating the input size of the first linear layer in the      
2024-09-03 13:25:26 self.network to embedding_dim * 4.                                              
2024-09-03 13:25:26 
2024-09-03 13:25:26 Here is the SEARCH/REPLACE block:                                               
2024-09-03 13:25:26 
2024-09-03 13:25:26 experiment.py                                                                   
2024-09-03 13:25:26 
2024-09-03 13:25:26                                                                                 
2024-09-03 13:25:26  <<<<<<< SEARCH                                                                 
2024-09-03 13:25:26              nn.Linear(embedding_dim * 3, hidden_dim),                          
2024-09-03 13:25:26  =======                                                                        
2024-09-03 13:25:26              nn.Linear(embedding_dim * 4, hidden_dim),                          
2024-09-03 13:25:26  >>>>>>> REPLACE                                                                
2024-09-03 13:25:26                                                                                 
2024-09-03 13:25:26 
2024-09-03 13:25:26 Please update the file with the above change and re-run the experiment using the
2024-09-03 13:25:26 command python experiment.py --out_dir=run_1.                                   
2024-09-03 13:25:26 
2024-09-03 13:25:26 Tokens: 6,906 sent, 201 received. Cost: $0.04 message, $0.19 session.
2024-09-03 13:25:26 Applied edit to experiment.py
2024-09-03 13:25:29 Training model...
2024-09-03 13:25:29 /app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py:229: UserWarning: Using a target size (torch.Size([256, 3])) that is different to the input size (torch.Size([256, 2])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2024-09-03 13:25:29   loss = F.mse_loss(noise_pred, noise)
2024-09-03 13:25:29 Traceback (most recent call last):
2024-09-03 13:25:29   File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 229, in <module>
2024-09-03 13:25:29     loss = F.mse_loss(noise_pred, noise)
2024-09-03 13:25:29            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:29   File "/usr/local/lib/python3.11/site-packages/torch/nn/functional.py", line 3383, in mse_loss
2024-09-03 13:25:29     expanded_input, expanded_target = torch.broadcast_tensors(input, target)
2024-09-03 13:25:29                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:29   File "/usr/local/lib/python3.11/site-packages/torch/functional.py", line 77, in broadcast_tensors
2024-09-03 13:25:29     return _VF.broadcast_tensors(tensors)  # type: ignore[attr-defined]
2024-09-03 13:25:29            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:29 RuntimeError: The size of tensor a (2) must match the size of tensor b (3) at non-singleton dimension 1
2024-09-03 13:25:29 
2024-09-03 13:25:29 The error indicates that the shapes of the matrices being multiplied do not match. This is likely because the concatenated embedding vector has a different size than expected by the first linear layer in the `self.network`.
2024-09-03 13:25:29 
2024-09-03 13:25:29 Since we are concatenating four embeddings (`x1_emb`, `x2_emb`, `t_emb`, and `conditional_emb`), each of size `embedding_dim`, the total size of the concatenated embedding vector should be `embedding_dim * 4`.
2024-09-03 13:25:29 
2024-09-03 13:25:29 Let's fix this by updating the input size of the first linear layer in the `self.network` to `embedding_dim * 4`.
2024-09-03 13:25:29 
2024-09-03 13:25:29 Here is the *SEARCH/REPLACE* block:
2024-09-03 13:25:29 
2024-09-03 13:25:29 experiment.py
2024-09-03 13:25:29 ```python
2024-09-03 13:25:29 <<<<<<< SEARCH
2024-09-03 13:25:29             nn.Linear(embedding_dim * 3, hidden_dim),
2024-09-03 13:25:29 =======
2024-09-03 13:25:29             nn.Linear(embedding_dim * 4, hidden_dim),
2024-09-03 13:25:29 >>>>>>> REPLACE
2024-09-03 13:25:29 ```
2024-09-03 13:25:29 
2024-09-03 13:25:29 Please update the file with the above change and re-run the experiment using the command `python experiment.py --out_dir=run_1`.
2024-09-03 13:25:29 Run 1 failed with return code 1
2024-09-03 13:25:29 Run failed with the following error /app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py:229: UserWarning: Using a target size (torch.Size([256, 3])) that is different to the input size (torch.Size([256, 2])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
2024-09-03 13:25:29   loss = F.mse_loss(noise_pred, noise)
2024-09-03 13:25:29 Traceback (most recent call last):
2024-09-03 13:25:29   File "/app/AI-Scientist/results/2d_diffusion/20240903_042411_conditional_generation/experiment.py", line 229, in <module>
2024-09-03 13:25:29     loss = F.mse_loss(noise_pred, noise)
2024-09-03 13:25:29            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:29   File "/usr/local/lib/python3.11/site-packages/torch/nn/functional.py", line 3383, in mse_loss
2024-09-03 13:25:29     expanded_input, expanded_target = torch.broadcast_tensors(input, target)
2024-09-03 13:25:29                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:29   File "/usr/local/lib/python3.11/site-packages/torch/functional.py", line 77, in broadcast_tensors
2024-09-03 13:25:29     return _VF.broadcast_tensors(tensors)  # type: ignore[attr-defined]
2024-09-03 13:25:29            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-09-03 13:25:29 RuntimeError: The size of tensor a (2) must match the size of tensor b (3) at non-singleton dimension 1
2024-09-03 13:25:29 
2024-09-03 13:25:29 Max iterations reached
2024-09-03 13:25:29 Not all experiments completed.
2024-09-03 13:25:29 Experiments failed for idea 20240903_042411_conditional_generation
2024-09-03 13:25:29 FINISHED IDEA
2024-09-03 13:25:29 Completed idea: conditional_generation, Success: False
2024-09-03 13:25:29 All ideas evaluated.
conglu1997 commented 2 months ago

Experiments failed for idea 20240903_042411_conditional_generation

The AI Scientist has a model-dependent success rate. Not every idea succeeds, especially with weaker models. Please see our paper for more details.