[tab] new.txt (plaintext)
actor_val:  46%|████▌     | 46/100 [00:41<00:44,  1.20it/s][2025-12-24 14:31:53] train_sft.py:377 - SFT rollout sample: tokens_len=4159, response_length=3814, sample=[{'content': 'You are a helpful assistant that can interact multiple times with a computer shell to solve programming tasks.
Your response must contain exactly ONE bash code block with ONE command (or commands connected with && or ||).

Format your response as shown in <format_example>.

<format_example>
```bash
your_command_here
```
</format_example>

Failure to follow these rules will cause your response to be rejected.', 'role': 'system'}, {'content': '<stdout>
   276	        options=checkpoint_options,
   277	        handler_registry=handler_registry,
   278	    )
   279	
   280	    # --- Create DataLoaderIterator from dataloader ---
   281	    array_record_files = [
   282	        os.path.join(args.data_dir, x)
   283	        for x in os.listdir(args.data_dir)
   284	        if x.endswith(".array_record")
   285	    ]
   286	    grain_dataloader = get_dataloader(
   287	        array_record_files,
   288	        args.seq_len,
   289	        # NOTE: We deliberately pass the global batch size
   290	        # The dataloader shards the dataset across all processes
   291	        args.batch_size,
   292	        *image_shape,
   293	        num_workers=8,
   294	        prefetch_buffer_size=1,
   295	        seed=args.seed,
   296	    )
</stdout>', 'role': 'user'}, {'content': '```bash
cat -n utils/dataloader.py
```
', 'role': 'assistant'}, {'content': '<stdout>
     1	import jax
     2	import numpy as np
     3	import grain
     4	from typing import Any
     5	import pickle
     6	
     7	
     8	class EpisodeLengthFilter(grain.transforms.Filter):
     9	    """
    10	    A Grain Filter that keeps only episodes with sufficient length.
    11	    """
    12	    
    13	    def __init__(self, seq_len: int, image_h: int, image_w: int, image_c: int):
    14	        """Initializes the filter with sequence length requirements."""
    15	        self.seq_len = seq_len
    16	        self.image_h = image_h
    17	        self.image_w = image_w
    18	        self.image_c = image_c
    19	    
    20	    def filter(self, element: Any) -> bool:
    21	        """
    22	        Filters episodes based on length.
    23	        
    24	        Args:
    25	            element: A dictionary representing one record from the DataSource.
    26	                     Expected to contain \'raw_video\' (bytes) and \'sequence_length\' (int)
    27	        
    28	        Returns:
    29	            True if the episode has sufficient length, False otherwise.
    30	        """
    31	        assert isinstance(element, bytes)
    32	        element = pickle.loads(element)
    33	        
    34	        current_episode_len = element["sequence_length"]
    35	        if current_episode_len < self.seq_len:
    36	            print(f"Filtering out episode with length {current_episode_len}, which is "
    37	                f"shorter than the requested sequence length {self.seq_len}.")
    38	            return False
    39	        
    40	        return True
    41	
    42	
    43	class ProcessEpisodeAndSlice(grain.transforms.RandomMap):
    44	    """
    45	    A Grain Transformation that combines parsing, slicing, and normalizing.
    46	    """
    47	
    48	    def __init__(self, seq_len: int, image_h: int, image_w: int, image_c: int):
    49	        """Initializes the transformation with processing parameters."""
    50	        self.seq_len = seq_len
    51	        self.image_h = image_h
    52	        self.image_w = image_w
    53	        self.image_c = image_c
    54	
    55	    def random_map(self, element: dict, rng: np.random.Generator) -> Any:
    56	        """
    57	        Processes a single raw episode from the data source.
    58	
    59	        Args:
    60	            element: A dictionary representing one record from the DataSource.
    61	                     Expected to contain \'raw_video\' (bytes) and \'sequence_length\' (int)
    62	            rng: A per-record random number generator provided by the Grain sampler.
    63	
    64	        Returns:
    65	            A processed video sequence as a NumPy array with shape
    66	            (seq_len, height, width, channels) and dtype float32.
    67	        """
    68	        assert isinstance(element, bytes)
    69	        element = pickle.loads(element)
    70	        
    71	        video_shape = (
    72	            element["sequence_length"],
    73	            self.image_h,
    74	            self.image_w,
    75	            self.image_c,
    76	        )
    77	        episode_tensor = np.frombuffer(element["raw_video"], dtype=np.uint8)
    78	        episode_tensor = episode_tensor.reshape(video_shape)
    79	
    80	        current_episode_len = episode_tensor.shape[0]
    81	        if current_episode_len < self.seq_len:
    82	            raise ValueError(f"Episode length {current_episode_len} is shorter than "
    83	                           f"requested sequence length {self.seq_len}. This should "
    84	                           f"have been filtered out.")
    85	        
    86	        max_start_idx = current_episode_len - self.seq_len
    87	        
    88	        start_idx = rng.integers(0, max_start_idx + 1)
    89	
    90	        seq = episode_tensor[start_idx : start_idx + self.seq_len]
    91	
    92	        return seq
    93	
    94	
    95	def get_dataloader(
    96	    array_record_paths: list[str],
    97	    seq_len: int,
    98	    global_batch_size: int,
    99	    image_h: int,
   100	    image_w: int,
   101	    image_c: int,
   102	    num_workers: int = 1,
   103	    prefetch_buffer_size: int = 1,
   104	    seed: int = 42,
   105	):
   106	    """
   107	    Creates a data loading pipeline using Grain.
   108	    """
   109	    if not array_record_paths:
   110	        raise ValueError("array_record_paths list cannot be empty.")
   111	
   112	    num_processes = jax.process_count()
   113	
   114	    if global_batch_size % num_processes != 0:
   115	        raise ValueError(
   116	            f"Global batch size {global_batch_size} must be divisible by "
   117	            f"the number of JAX processes {num_processes} for proper sharding."
   118	        )
   119	    per_process_batch_size = global_batch_size // num_processes
   120	
   121	    source = grain.sources.ArrayRecordDataSource(array_record_paths)
   122	    
   123	    sampler = grain.samplers.IndexSampler(
   124	        num_records=len(source),
   125	        shard_options=grain.sharding.ShardByJaxProcess(drop_remainder=True),
   126	        shuffle=True,
   127	        num_epochs=None,
   128	        seed=seed,
   129	    )
   130	
   131	    operations = [
   132	        EpisodeLengthFilter(
   133	            seq_len=seq_len, image_h=image_h, image_w=image_w, image_c=image_c
   134	        ),
   135	        ProcessEpisodeAndSlice(
   136	            seq_len=seq_len, image_h=image_h, image_w=image_w, image_c=image_c
   137	        ),
   138	        grain.transforms.Batch(batch_size=per_process_batch_size, drop_remainder=True),
   139	    ]
   140	
   141	    read_options = grain.ReadOptions(
   142	        prefetch_buffer_size=prefetch_buffer_size,
   143	        num_threads=1,
   144	    )
   145	    dataloader = grain.DataLoader(
   146	        data_source=source,
   147	        sampler=sampler,
   148	        operations=operations,
   149	        worker_count=num_workers,
   150	        worker_buffer_size=1,
   151	        read_options=read_options,
   152	    )
   153	
   154	    return dataloader
   155	
</stdout>', 'role': 'user'}, {'content': "```bash
cat -n utils/dataloader.py | sed -n '10,30p'
```
", 'role': 'assistant'}, {'content': '<stdout>
    10	    A Grain Filter that keeps only episodes with sufficient length.
    11	    """
    12	    
    13	    def __init__(self, seq_len: int, image_h: int, image_w: int, image_c: int):
    14	        """Initializes the filter with sequence length requirements."""
    15	        self.seq_len = seq_len
    16	        self.image_h = image_h
    17	        self.image_w = image_w
    18	        self.image_c = image_c
    19	    
    20	    def filter(self, element: Any) -> bool:
    21	        """
    22	        Filters episodes based on length.
    23	        
    24	        Args:
    25	            element: A dictionary representing one record from the DataSource.
    26	                     Expected to contain \'raw_video\' (bytes) and \'sequence_length\' (int)
    27	        
    28	        Returns:
    29	            True if the episode has sufficient length, False otherwise.
    30	        """
</stdout>', 'role': 'user'}, {'content': "```bash
cat -n utils/dataloader.py | sed -n '56,76p'
```
", 'role': 'assistant'}, {'content': '<stdout>
    56	        """
    57	        Processes a single raw episode from the data source.
    58	
    59	        Args:
    60	            element: A dictionary representing one record from the DataSource.
    61	                     Expected to contain \'raw_video\' (bytes) and \'sequence_length\' (int)
    62	            rng: A per-record random number generator provided by the Grain sampler.
    63	
    64	        Returns:
    65	            A processed video sequence as a NumPy array with shape
    66	            (seq_len, height, width, channels) and dtype float32.
    67	        """
    68	        assert isinstance(element, bytes)
    69	        element = pickle.loads(element)
    70	        
    71	        video_shape = (
    72	            element["sequence_length"],
    73	            self.image_h,
    74	            self.image_w,
    75	            self.image_c,
    76	        )
</stdout>', 'role': 'user'}, {'content': "```bash
cat -n utils/dataloader.py | sed -n '67,87p'
```
", 'role': 'assistant'}, {'content': '<stdout>
    67	        """
    68	        assert isinstance(element, bytes)
    69	        element = pickle.loads(element)
    70	        
    71	        video_shape = (
    72	            element["sequence_length"],
    73	            self.image_h,
    74	            self.image_w,
    75	            self.image_c,
    76	        )
    77	        episode_tensor = np.frombuffer(element["raw_video"], dtype=np.uint8)
    78	        episode_tensor = episode_tensor.reshape(video_shape)
    79	
    80	        current_episode_len = episode_tensor.shape[0]
    81	        if current_episode_len < self.seq_len:
    82	            raise ValueError(f"Episode length {current_episode_len} is shorter than "
    83	                           f"requested sequence length {self.seq_len}. This should "
    84	                           f"have been filtered out.")
    85	        
    86	        max_start_idx = current_episode_len - self.seq_len
    87	        
</stdout>', 'role': 'user'}, {'content': "```bash
cat -n utils/dataloader.py | sed -n '46,66p'
```
", 'role': 'assistant'}, {'content': '<stdout>
    46	    """
    47	
    48	    def __init__(self, seq_len: int, image_h: int, image_w: int, image_c: int):
    49	        """Initializes the transformation with processing parameters."""
    50	        self.seq_len = seq_len
    51	        self.image_h = image_h
    52	        self.image_w = image_w
    53	        self.image_c = image_c
    54	
    55	    def random_map(self, element: dict, rng: np.random.Generator) -> Any:
    56	        """
    57	        Processes a single raw episode from the data source.
    58	
    59	        Args:
    60	            element: A dictionary representing one record from the DataSource.
    61	                     Expected to contain \'raw_video\' (bytes) and \'sequence_length\' (int)
    62	            rng: A per-record random number generator provided by the Grain sampler.
    63	
    64	        Returns:
    65	            A processed video sequence as a NumPy array with shape
    66	            (seq_len, height, width, channels) and dtype float32.
</stdout>', 'role': 'user'}, {'content': "```bash
cat -n utils/dataloader.py | sed -n '80,100p'
```
", 'role': 'assistant'}, {'content': '<stdout>
    80	        current_episode_len = episode_tensor.shape[0]
    81	        if current_episode_len < self.seq_len:
    82	            raise ValueError(f"Episode length {current_episode_len} is shorter than "
    83	                           f"requested sequence length {self.seq_len}. This should "
    84	                           f"have been filtered out.")
    85	        
    86	        max_start_idx = current_episode_len - self.seq_len
    87	        
    88	        start_idx = rng.integers(0, max_start_idx + 1)
    89	
    90	        seq = episode_tensor[start_idx : start_idx + self.seq_len]
    91	
    92	        return seq
    93	
    94	
    95	def get_dataloader(
    96	    array_record_paths: list[str],
    97	    seq_len: int,
    98	    global_batch_size: int,
    99	    image_h: int,
   100	    image_w: int,
</stdout>', 'role': 'user'}, {'content': "```bash
cat -n utils/dataloader.py | sed -n '67,87p'
```
", 'role': 'assistant'}, {'content': '<stdout>
    67	        """
    68	        assert isinstance(element, bytes)
    69	        element = pickle.loads(element)
    70	        
    71	        video_shape = (
    72	            element["sequence_length"],
    73	            self.image_h,
    74	            self.image_w,
    75	            self.image_c,
    76	        )
    77	        episode_tensor = np.frombuffer(element["raw_video"], dtype=np.uint8)
    78	        episode_tensor = episode_tensor.reshape(video_shape)
    79	
    80	        current_episode_len = episode_tensor.shape[0]
    81	        if current_episode_len < self.seq_len:
    82	            raise ValueError(f"Episode length {current_episode_len} is shorter than "
    83	                           f"requested sequence length {self.seq_len}. This should "
    84	                           f"have been filtered out.")
    85	        
    86	        max_start_idx = current_episode_len - self.seq_len
    87	        
</stdout>', 'role': 'user'}, {'content': "```bash
cat -n utils/dataloader.py | sed -n '45,65p'
```
", 'role': 'assistant'}, {'content': '<stdout>
    45	    A Grain Transformation that combines parsing, slicing, and normalizing.
    46	    """
    47	
    48	    def __init__(self, seq_len: int, image_h: int, image_w: int, image_c: int):
    49	        """Initializes the transformation with processing parameters."""
    50	        self.seq_len = seq_len
    51	        self.image_h = image_h
    52	        self.image_w = image_w
    53	        self.image_c = image_c
    54	
    55	    def random_map(self, element: dict, rng: np.random.Generator) -> Any:
    56	        """
    57	        Processes a single raw episode from the data source.
    58	
    59	        Args:
    60	            element: A dictionary representing one record from the DataSource.
    61	                     Expected to contain \'raw_video\' (bytes) and \'sequence_length\' (int)
    62	            rng: A per-record random number generator provided by the Grain sampler.
    63	
    64	        Returns:
    65	            A processed video sequence as a NumPy array with shape
</stdout>', 'role': 'user'}, {'content': "```bash
cat -n utils/dataloader.py | sed -n '33,53p'
```
", 'role': 'assistant'}]