Reintroduces the declarative config-conversion shape that pre-dated PR #362,
applied within the post-#362 modular per-section structure. Replaces the
imperative import_config/export_config bodies with a small set of named
primitives and a recursive walker driven by per-section declarations.

Primitives in fast_llm.engine.checkpoint.external:
- RenameConfigConverter — 1:1 path rename
- ConstantExportConfigConverter — write constant on export, assert on import
- ConstantImportConfigConverter — assert on export, inject on import
- DefaultConfigConverter — rename with HF-side fallback
- OptionalConfigConverter — emit/import only when non-sentinel
- IgnoredConfigConverter — declare a field as intentionally not converted
- CustomConfigConverter — escape hatch for cross-field transforms
- NestedConfigConverter — recurse into a fixed-typed sub-config; flat-merges
  HF output into the parent (transformer side is assumed flat)
- DispatchConfigConverter — runtime type dispatch for polymorphic sub-configs

ConfigSectionConverter is the per-Fast-LLM-class converter base. Subclasses
declare their conversion via _create_config_converters() and inherit
import_config/export_config concretely. The architecture-coverage check fires
only when type(config) exactly matches the converter's declared
fast_llm_config_class — strict subclass types defer to a more specific
converter, allowing yet-to-be-migrated subclasses (e.g., Mixtral on Llama)
to call super().export_config() without tripping the parent's check on
fields the parent doesn't know about.

The walker is implicit: NestedConfigConverter / DispatchConfigConverter
call the public import_config/export_config on the sub-converter class so
subclass overrides participate, rather than a private path that bypasses
them.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Pilot of the new ConfigSectionConverter framework. Each Llama section
converter (Normalization/MLP/Attention/Block/Embeddings/Head/BaseModel)
now declares its conversion via _create_config_converters() instead of
imperative import_config/export_config bodies. Weight side is unchanged.

Notable shape decisions:
- LlamaDecoderConverter stays as a regular (imperative) class because
  Fixed/Pattern block-sequence dispatch doesn't lend itself to the
  declarative shape. LlamaBaseModelConverter wires it in via a small
  CustomConfigConverter; subclasses (Mistral, Qwen2, MTP-Llama, ...)
  continue to plug in different block converters via block_converter_class.
- _check_config is retained as an overridable classmethod and called from
  the linear_layers CustomConfigConverter, so Qwen2 can keep its
  asymmetric Q/K/V bias rule without re-implementing the export.
- IgnoredConfigConverter is used for ParameterConfig sub-fields with no
  architecture-significant content (weight, output_weight, word_embeddings),
  and for prediction_heads (which Llama HF doesn't expose; subclass
  MTP-Llama adds it imperatively).
- peft uses CustomConfigConverter to assert NoPeftConfig on export. Llama
  HF format cannot represent PEFT, so a configured LoRA now fails loudly
  rather than being silently dropped.
- Rotary remains in CustomConfigConverter — the v4/v5 transformers split
  (rope_theta/rope_scaling vs. rope_parameters) and three rope_type
  variants don't fit pure rename primitives.

Verified with live round-trips of Llama-3, Qwen2, Mistral, Mixtral, and
MTP-Llama HF configs, plus tests/models/test_checkpoint.py for all GPT
formats (139 passed, 0 failed).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

The dict of named per-block configs is unambiguously architecture
metadata; without an explicit hint it defaulted to `unknown`, hiding
it from the architecture-coverage check used by declarative checkpoint
converters.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Two additions, both required by Apriel2's nested HF schema:

- `NestedConfigConverter` gains an optional `hf_path` kwarg. When set,
  the sub-converter's output is placed under that nested key instead
  of being flat-merged. Existing flat-merge behavior is unchanged when
  `hf_path` is omitted.
- New `TypedDictContainerConfigConverter` for `dict[str, Config]`
  fields where each entry is round-tripped through a per-class
  section converter. Polymorphic dispatch via the entry's runtime
  type on export and the HF discriminator on import. A homogeneous
  mode (single registered class with `hf_type_name = None`) skips
  the discriminator entirely.

Both `DispatchConfigConverter` and `TypedDictContainerConfigConverter`
now also inject the Fast-LLM `dynamic_type_name` discriminator into
the imported sub-dict so the parent's `from_dict` dispatches to the
right `Config` subclass without a separate ConstantImport.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Stress-tests the framework's polymorphic dispatch and typed-dict
support: Apriel2's HF schema is nested (`decoder.block.mixer.{...}`,
`head.normalization`, `mixers.{name}`) and the mixer field is
heterogeneously polymorphic (Attention/Mamba/StochasticMixer/GDN/KDA).

Migrated converters: per-mixer (Attention/Mamba/GDN/KDA), the
StochasticMixer container (driven by TypedDictContainer over a
leaf-mixer registry), per-normalization (RMS/LayerNorm/NoNorm), MLP,
Block, Fixed/Pattern decoder variants (selected by Dispatch on
runtime BlockSequenceConfig type), Head, and BaseModel.

The imperative weight-side `get_converters` methods are preserved
unchanged so the multimodal Apriel2 converter (which inherits from
the text-only one) keeps working without modification.

PatternDecoder's `blocks` dict uses the homogeneous mode of
TypedDictContainer (single-class registry, no discriminator). The
attention rotary-type translation (default ↔ mistral_1d) and Mamba's
auxiliary HF fields (d_conv, conv_bias, dt_proj_bias derived from
linear-config bias flags) remain on `CustomConfigConverter` since
they're shape-changing transforms.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

…primitives

Each format inherits Llama's `_create_config_converters` and replaces only the
fields that diverge:
  * Mistral: ConstantImportConfigConverter pinning `add_linear_biases=False` for
    attention and MLP (HF format has no `attention_bias`/`mlp_bias`); rename
    `window_size` <-> `sliding_window`.
  * Qwen2: ConstantImportConfigConverter for `add_linear_biases`; CustomConfigConverter
    for `head_size` (no `head_dim` HF field, derive on import); CustomConfigConverter
    for per-layer biases (always Q/K/V=True, dense=False); the head_dim relationship
    `heads * head_size == hidden_size` moves to `_validate_export` on the base-model
    converter; the use_mrope guard moves to `import_config`.
  * MTP-Llama: RenameConfigConverter for `prediction_heads` (Llama blanket-ignores it).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

`MixtralMLPConverter` switches its `fast_llm_config_class` to `MoEMLPConfig` so the
architecture-coverage check sees MoE-specific fields. The config-side overrides:
  * `add_linear_biases` -> ConstantImportConfigConverter (Mixtral has no `mlp_bias`).
  * `experts` <-> `num_local_experts` and `experts_per_token` <-> `num_experts_per_tok`
    via RenameConfigConverter.
  * `shared_experts=0` and `routing=topk` pinned via ConstantImportConfigConverter so
    they round-trip cleanly without an HF representation.
  * `router` covered by IgnoredConfigConverter (Mixtral's gate is a default `LinearConfig`).
The Fast-LLM dynamic-type discriminator (`type: "moe"`) is injected via an `import_config`
override since the MLP is wrapped via `NestedConfigConverter` rather than `DispatchConfigConverter`.

Diffusion-Dream and Diffusion-Llama need no migration: they only override `architecture`,
`get_transformers_configuration_class`, and `_export_config` (auto_map). They inherit the
declarative converters from their parents (Qwen2 and Llama).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

…itives

`AprielMambaConverter`, `GatedDeltaNetConverter`, and `KimiDeltaAttentionConverter` become
`ConfigSectionConverter` subclasses with their HF-side fields nested under the appropriate
HF subkey (`ssm_cfg` for Mamba, `linear_attn_config` for GDN/KDA).

Mamba's three sibling-default fields (`d_inner`, `d_xb`, `dt_rank`) read the HF root's
`hidden_size` directly via `DefaultConfigConverter.hf_default_fn` / `CustomConfigConverter`,
removing the need for an explicit `parent_context` plumbing through the framework. The
per-layer convolution and dt biases use `CustomConfigConverter` to pick up the mixer-wide
`add_linear_biases` fallback when unset; the existing `_check_config` per-layer assertions
move to `_validate_export`.

`AprielBlockConverter` (the per-block dispatcher) and `AprielDecoderConverter` (the
`hybrid_block_layout` driver) stay imperative because Apriel's HF format encodes the
mixer type in a parent-level list rather than a per-block discriminator, which
`DispatchConfigConverter` doesn't model. The `type: "mamba"`/`"gdn"`/`"kda"` Fast-LLM
discriminator is injected via a one-line `import_config` override on each leaf converter
(same pattern Mixtral uses).

The HF format has no test coverage in `tests/models/test_checkpoint.py` or
`tests/models/test_hf_roundtrip.py`, so verification was a synthesized live round-trip
covering each mixer leaf plus a hybrid attention+Mamba pattern decoder.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

…larative primitives

`PixtralNormalizationConverter` collapses to a single `_create_config_converters` override
that pins `epsilon=1e-5` via `ConstantImportConfigConverter` (asserts on export, injects
on import; no HF write). `PixtralEmbeddingsConverter` becomes a `ConfigSectionConverter`
with declarations for `patch_height` (rename to `patch_size`), `patch_width` (mirror
`patch_size` on import), `num_channels` (export-only constant 3), nested `normalization`,
and an `IgnoredConfigConverter` for `patch_embeddings`. The `patch_height == patch_width`
and `patch_embeddings.bias.enabled in (None, False)` checks move to `_validate_export`.

The remaining Llava and Apriel2 multimodal converters stay imperative: they're cross-section
aggregators (vision_config + text_config + top-level merge) whose shape doesn't fit a single
ConfigSectionConverter, often with parent-context dependencies (e.g., the adapter's
intermediate_size derives from the text model's hidden_size).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>