xai-org/x-algorithm · error · ValueError

Unknown named dimension: {named_dim} for namespace: {namespa

Error message

Unknown named dimension: {named_dim} for namespace: {namespace}

What it means

When building a ShardingSpec, _sharding_rule looks up the requested named dimension inside the per-namespace config dict. If the dimension name (e.g. 'in_dim') is absent from that namespace's config, it raises this ValueError naming both the dimension and the namespace.

Source

Thrown at phoenix/xrex/models/sharding_context.py:132

            ]
        flattened_physical_axes, _ = jax.tree.flatten(physical_axes)
        return axis_group_size(flattened_physical_axes, self.mesh)


PerNamespaceShardingConfig = dict[str, ShardingSpec]
ShardingConfig = dict[str, PerNamespaceShardingConfig]


def make_sharding_context_from_config(
    name: str, mesh: Mesh, config: ShardingConfig
) -> ShardingContext:
    ctx = ShardingContext(name, mesh)

    def _sharding_rule(
        named_dim: str, config: PerNamespaceShardingConfig = None, namespace: str = None
    ) -> ShardingSpec:
        if named_dim not in config:
            raise ValueError(f"Unknown named dimension: {named_dim} for namespace: {namespace}")
        return config[named_dim]

    for namespace, c in config.items():
        ctx.register_sharding_rule(namespace)(
            partial(_sharding_rule, config=c, namespace=namespace)
        )
    return ctx


default_sharding_config = {
    "default": {
        "batch": ("expert", "replica", "data"),
        "batch_attn": ("expert", "replica", "data"),
        "dense_activation_model": "model",
        "dense_activation_seq": "seq",
        "embed": None,
        "head": ("seq", "model"),
        "hidden": None,

View on GitHub (pinned to 24c60942c5)

Solutions

1. Add the missing named dimension with its PartitionSpec to the namespace's config in the sharding config
2. Verify spelling/casing of the dimension against what the model code requests
3. If the dim should not be sharded, add an explicit entry mapping it to replicated (None)

Example fix

# before
sharding:
  attention:
    in_dim: ["data"]
# after
sharding:
  attention:
    in_dim: ["data"]
    out_dim: ["model"]

for ns, dims in cfg['sharding'].items():
    assert required_dims[ns] <= set(dims), f'{ns} missing {required_dims[ns] - set(dims)}'

try:
    spec = rule(named_dim, config, namespace)
except ValueError as e:
    if 'Unknown named dimension' in str(e):
        return None  # replicate
    raise

When it happens

Trigger: A layer asks for a named dim like 'out_dim' under namespace 'attention' but the sharding config for 'attention' only defines 'in_dim'; renaming a dimension in model code without updating the sharding config.

Common situations: Model refactors that rename tensor dimensions; configs written for a different model variant missing entries for some namespaces; typos in dimension names.

Related errors

• {namespace} is already exist in ShardingContext {self.name},
• async_emb axis {axis!r} is not a mesh axis of {mesh}
• async_emb requires token shards to vary across the communica
• async_emb requires exactly one token shard per communicator
• async_emb tokens_per_batch={tokens_per_batch} does not shard

AI-assisted analysis of xai-org/x-algorithm@24c60942c5 (2026-08-28). Data as JSON: /api/errors/0b8a8e7e56d67bdc. Report an issue: GitHub.