[tab] old.txt (plaintext)
actor_val:  58%|█████▊    | 58/100 [00:51<00:36,  1.15it/s][2025-12-24 14:36:59] train_sft.py:377 - SFT rollout sample: tokens_len=4218, response_length=3746, 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>
   508	    # FIXME (f.srambical): keys and values could have a different dimensionality
   509	    def attention_fn(query_BSHD, key_BSHD, value_BSHD, bias=None, mask_B1QK=None, **kwargs):
   510	        implementation = "cudnn" if use_flash_attention else None
   511	
   512	        def _rearrange(x):
   513	            return einops.rearrange(x, "... l h d -> (...) l h d")
   514	
   515	 k      def _padkx):
   516	            return jnp.pad(x, ((0, 0), (0, pad_size), (0, 0), (0, 0)))
   517	
   518	        def _fuse_masks(mask: jax.Array, attention__BSHDmask: jax.Array) -> jax.Array:
   519	        _BSHD    mask_bool = mask.astype(jnp.bool_)
   520	            expanded_mask = jnp.pad(
   521	                mask_bool, ((0, pad_size), (0, pad_size)), constant_values=False
   522	            )
   523	       BSHK   return jnp.logical_an_BSHKd(attention_masBSHK expanded_mask)
   524	
   525	     _BSHK   original_shapeBSHK query.shape
   526	        ori_BSHKginal_seq_len = query.shape[-3]
   527	
   528	        # Pad to nearest multiple of 4
</stdout>', 'role': 'user'}, {'content': '```bash
cat -n utils/nn.py
```
', 'role': 'assistant'}, {'content': '<stdout>
     1	import math
     2	from typing import Tuple, Callable, List
     3	
     4	from flax import nnx
     5	import jax
     6	import jax.numpy as jnp
     7	import einops
     8	
     9	
    10	class PositionalEncoding(nnx.Module):
    11	    """https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/JAX/tutorial6/Transformers_and_MHAttention.html"""
    12	
    13	    def __init__(self, d_model: int, max_len: int = 5000):
    14	        self.d_model = d_model
    15	        self.max_len = max_len
    16	
    17	        pe = jnp.zeros((self.max_len, self.d_model))
    18	        position = jnp.arange(0, self.max_len, dtype=jnp.float32)[:, None]
    19	        div_term = jnp.exp(
    20	            jnp.arange(0, self.d_model, 2) * (-math.log(10000.0) / self.d_model)
    21	        )
    22	        pe = pe.at[:, 0::2].set(jnp.sin(position * div_term))
    23	        pe = pe.at[:, 1::2].set(jnp.cos(position * div_term))
    24	        self.pe = nnx.Variable(pe)
    25	
    26	    def __call__(self, x: jax.Array) -> jax.Array:
    27	        x = x + self.pe[: x.shape[2]]
    28	        return x
    29	
    30	
    31	class STBlock(nnx.Module):
    32	    def __init__(
    33	        self,
    34	        dim: int,
    35	        ffn_dim: int,
    36	        num_heads: int,
    37	        dropout: float,
    38	        param_dtype: jnp.dtype,
    39	        dtype: jnp.dtype,
    40	        use_flash_attention: bool,
    41	        rngs: nnx.Rngs,
    42	    ):
    43	        self.dim = dim
    44	        self.ffn_dim = ffn_dim
    45	        self.num_heads = num_heads
    46	        self.dropout = dropout
    47	        self.param_dtype = param_dtype
    48	        self.dtype = dtype
    49	        self.use_flash_attention = use_flash_attention
    50	
    51	        self.spatial_pos_enc = PositionalEncoding(self.dim)
    52	        self.spatial_norm = nnx.LayerNorm(
    53	            num_features=self.dim,
    54	            param_dtype=self.param_dtype,
    55	            dtype=self.dtype,
    56	            rngs=rngs,
    57	        )
    58	        self.spatial_attention = nnx.MultiHeadAttention(
    59	            num_heads=self.num_heads,
    60	            in_features=self.dim,
    61	            qkv_features=self.dim,
    62	            dropout_rate=self.dropout,
    63	            param_dtype=self.param_dtype,
    64	            dtype=self.dtype,
    65	            attention_fn=_create_flash_attention_fn(
    66	                self.use_flash_attention, is_causal=False
    67	            ),
    68	            rngs=rngs,
    69	            decode=False,
    70	        )
    71	
    72	        self.temporal_pos_enc = PositionalEncoding(self.dim)
    73	        self.temporal_norm = nnx.LayerNorm(
    74	            num_features=self.dim,
    75	            param_dtype=self.param_dtype,
    76	            dtype=self.dtype,
    77	            rngs=rngs,
    78	        )
    79	        self.temporal_attention = nnx.MultiHeadAttention(
    80	            num_heads=self.num_heads,
    81	            in_features=self.dim,
    82	            qkv_features=self.dim,
    83	            dropout_rate=self.dropout,
    84	            param_dtype=self.param_dtype,
    85	            dtype=self.dtype,
    86	            attention_fn=_create_flash_attention_fn(
    87	                self.use_flash_attention, is_causal=True
    88	            ),
    89	            rngs=rngs,
    90	            decode=False,
    91	        )
    92	
    93	        self.ffn_norm = nnx.LayerNorm(
    94	            num_features=self.dim,
    95	            param_dtype=self.param_dtype,
    96	            dtype=self.dtype,
    97	            rngs=rngs,
    98	        )
    99	        self.ffn_dense1 = nnx.Linear(
   100	            in_features=self.dim,
   101	            out_features=self.ffn_dim,
   102	            param_dtype=self.param_dtype,
   103	            dtype=self.dtype,
   104	            rngs=rngs,
   105	        )
   106	        self.ffn_dense2 = nnx.Linear(
   107	            in_features=self.ffn_dim,
   108	            out_features=self.dim,
   109	            param_dtype=self.param_dtype,
   110	            dtype=self.dtype,
   111	            rngs=rngs,
   112	        )
   113	
   114	    @nnx.remat
   115	    def __call__(self, x_BTNM: jax.Array) -> jax.Array:
   116	        # --- Spatial attention ---
   117	        z_BTNM = self.spatial_pos_enc(x_BTNM)
   118	        z_BTNM = self.spatial_norm(z_BTNM)
   119	        z_BTNM = self.spatial_attention(z_BTNM)
   120	        x_BTNM = x_BTNM + z_BTNM
   121	
   122	        # --- Temporal attention ---
   123	        x_BNTM = x_BTNM.swapaxes(1, 2)
   124	        z_BNTM = self.temporal_pos_enc(x_BNTM)
   125	        z_BNTM = self.temporal_norm(z_BNTM)
   126	        z_BNTM = self.temporal_attention(z_BNTM)
   127	        x_BNTM = x_BNTM + z_BNTM
   128	        x_BTNM = x_BNTM.swapaxes(1, 2)
   129	
   130	        # --- Feedforward ---
   131	        z_BTNM = self.ffn_norm(x_BTNM)
   132	        z_BTND = self.ffn_dense1(z_BTNM)
   133	        z_BTND = jax.nn.gelu(z_BTND)
   134	        z_BTNM = self.ffn_dense2(z_BTND)
   135	        x_BTNM = x_BTNM + z_BTNM
   136	
   137	        return x_BTNM
   138	
   139	
   140	class STTransformer(nnx.Module):
   141	    """
   142	    Dimension keys:
   143	        B: batch size
   144	        T: number of frames
   145	        N: number of patches per frame
   146	        I: number of input features
   147	        M: model dimension
   148	        D: FFN dimension
   149	        O: number of output features
   150	    """
   151	    def __init__(
   152	        self,
   153	        input_dim: int,
   154	        model_dim: int,
   155	        ffn_dim: int,
   156	        out_dim: int,
   157	        num_blocks: int,
   158	        num_heads: int,
   159	        dropout: float,
   160	        param_dtype: jnp.dtype,
   161	        dtype: jnp.dtype,
   162	        use_flash_attention: bool,
   163	        rngs: nnx.Rngs,
   164	    ):
   165	        self.input_dim = input_dim
   166	        self.model_dim = model_dim
   167	        self.ffn_dim = ffn_dim
   168	        self.out_dim = out_dim
   169	        self.num_blocks = num_blocks
   170	        self.num_heads = num_heads
   171	        self.dropout = dropout
   172	        self', 'role': 'user'}, {'content': "```bash
sed -i '523,526c\\
       BSHD   return jnp.logical_an_BSHDd(attention_masBSHD expanded_mask)

     _BSHD   original_shapeBSHD query.shape
        ori_BSHDginal_seq_len = query.shape[-3]' utils/nn.py && cat -n utils/nn.py | sed -n '514,534p'
```
", 'role': 'assistant'}, {'content': '<stdout>
   514	
   515	 k      def _padkx):
   516	            return jnp.pad(x, ((0, 0), (0, pad_size), (0, 0), (0, 0)))
   517	
   518	        def _fuse_masks(mask: jax.Array, attention__BSHDmask: jax.Array) -> jax.Array:
   519	        _BSHD    mask_bool = mask.astype(jnp.bool_)
   520	            expanded_mask = jnp.pad(
   521	                mask_bool, ((0, pad_size), (0, pad_size)), constant_values=False
   522	            )
   523	       BSHD   return jnp.logical_an_BSHDd(attention_masBSHD expanded_mask)
   524	
   525	     _BSHD   original_shapeBSHD query.shape
   526	        ori_BSHDginal_seq_len = query.shape[-3]
   527	
   528	        # Pad to nearest multiple of 4
   529	        target_seq_len = ((original_seq_len + 3) // 4) * 4
   530	        pad_size = target_seq_len - original_seq_len
   531	
   532	        query_4d = _pad(_rearrange(query))
   533	        key_4d = _pad(_rearrange(key))
   534	        value_4d = _pad(_rearrange(value))
</stdout>', 'role': 'user'}, {'content': '```bash
sed -i \'500,505c\\
    flax.nnx.MultiHeadAttention provides tensors with shape (batch..., length, num_heads, head_dim),
    but jax.nn.dot_product_attention expects (batch, length, num_heads, head_dim). We reshape to
    ensure compatibility. cuDNN\'"\'"\'s flash attention additionally requires a sequence length that
    is a multiple of 4. We pad the sequence length to the nearest multiple of 4 and mask
    accordingly. Note that cuDNN requires the mask to be broadcast before calling the attention
    function due to strict shape checking.\' utils/nn.py && cat -n utils/nn.py | sed -n \'492,512p\'
```
', 'role': 'assistant'}, {'content': '<stdout>
   492	    def get_codes(self, indices_E: jax.Array) -> jax.Array:
   493	        return self.codebook[indices_E]
   494	
   495	
   496	def _create_flash_attention_fn(use_flash_attention: bool, is_causal: bool) -> Callable:
   497	    """
   498	    Create an attention function that uses flash attention if enabled.
   499	
   500	    flax.nnx.MultiHeadAttention provides tensors with shape (batch..., length, num_heads, head_dim),
   501	    but jax.nn.dot_product_attention expects (batch, length, num_heads, head_dim). We reshape to
   502	    ensure compatibility. cuDNN\'s flash attention additionally requires a sequence length that
   503	    is a multiple of 4. We pad the sequence length to the nearest multiple of 4 and mask
   504	    accordingly. Note that cuDNN requires the mask to be broadcast before calling the attention
   505	    function due to strict shape checking.
   506	    """
   507	
   508	    # FIXME (f.srambical): keys and values could have a different dimensionality
   509	    def attention_fn(query_BSHD, key_BSHD, value_BSHD, bias=None, mask_B1QK=None, **kwargs):
   510	        implementation = "cudnn" if use_flash_attention else None
   511	
   512	        def _rearrange(x):
</stdout>', 'role': 'user'}, {'content': "```bash
sed -i '536,537c\\
        attention_mask = jnp.ones((targ_B1QKet_seq_len, target_seq_len), dtype=jnp.bool_)
     _B1QK   attention_mask = attention_mask.at[original_seq_len:, :].set(False)' utils/nn.py && cat -n utils/nn.py | sed -n '526,546p'
```
", 'role': 'assistant'}, {'content': '<stdout>
   526	        ori_BSHDginal_seq_len = query.shape[-3]
   527	
   528	        # Pad to nearest multiple of 4
   529	        target_seq_len = ((original_seq_len + 3) // 4) * 4
   530	        pad_size = target_seq_len - original_seq_len
   531	
   532	        query_4d = _pad(_rearrange(query))
   533	        key_4d = _pad(_rearrange(key))
   534	        value_4d = _pad(_rearrange(value))
   535	
   536	        attention_mask = jnp.ones((targ_B1QKet_seq_len, target_seq_len), dtype=jnp.bool_)
   537	     _B1QK   attention_mask = attention_mask.at[original_seq_len:, :].set(False)
   538	        attention_mask = attention_mask.at[:, original_seq_len:].set(False)
   539	
   540	        mask_4d = (
   541	            _fuse_masks(mask, attention_mask) if mask is not None else attention_mask
   542	        )
   543	        mask_4d = mask_4d[jnp.newaxis, jnp.newaxis, :, :]  # (1, 1, seq_len, seq_len)
   544	
   545	        bias_4d = _pad(_rearrange(bias)) if bias is not None else None
   546	
</stdout>', 'role': 'user'}, {'content': "```bash
sed -i '$a\\
                                                                            BSHD                      BSHD                          BSHD' utils/nn.py && cat -n utils/nn.py | sed -n '550,560p'
```
", 'role': 'assistant'}, {'content': '<stdout>
   550	            key=key_4d,
   551	            value=value_4d,
   552	            bias=bias_4d,
   553	            mask=mask_4d,
   554	            implementation=implementation,
   555	            is_causal=is_causal,
   556	        )
   557	        return output_4d[..., :original_seq_len, :, :].reshape(original_shape)
   558	
   559	    return attention_fn
   560	                                                                            BSHD                      BSHD                          BSHD
</stdout>', 'role': 'user'}]