configuration_hrm_text.py

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#           This file was automatically generated from src/transformers/models/hrm_text/modular_hrm_text.py.
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# Copyright 2026 The Sapient AI Authors and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from huggingface_hub.dataclasses import strict

from transformers.configuration_utils import PreTrainedConfig
from transformers.modeling_rope_utils import RopeParameters
from transformers.utils import auto_docstring
from transformers.utils.generic import is_flash_attention_requested, split_attention_implementation
from transformers.utils.type_validators import interval


@auto_docstring(checkpoint="sapientinc/HRM-Text-1B")
@strict
class HrmTextConfig(PreTrainedConfig):
    r"""
    H_cycles (`int`, *optional*, defaults to 2):
        Number of high-level cycles.
    L_cycles (`int`, *optional*, defaults to 3):
        Number of low-level cycles per H-cycle.
    L_bp_cycles (`list[int]`, *optional*, defaults to `[2]`):
        Training-time gradient-routing list; left-padded with `1`s up to `L_cycles` inside the model.
        Inference-time no-op.
    embedding_scale (`float`, *optional*):
        Token-embedding multiplier. If `None`, defaults to `1 / initializer_range`.
    prefix_lm (`bool`, *optional*, defaults to `True`):
        Instruction tokens attend bidirectionally, response tokens attend causally.
    num_layers_per_stack (`int`, *optional*):
        Real number of transformer blocks inside each
        of the H / L stacks. Set automatically on first construction: the value passed as
        `num_hidden_layers` is remembered here and `num_hidden_layers` is then rewritten to
        `num_layers_per_stack * H_cycles * (L_cycles + 1)` so that
        `DynamicCache(config=...)` pre-allocates one slot per unique attention invocation
        under the recurrent forward. Do not set this directly on first construction — pass
        the real per-stack count as `num_hidden_layers` and let `__post_init__` split it.
    """

    model_type = "hrm_text"
    keys_to_ignore_at_inference = ["past_key_values"]

    base_model_tp_plan = {
        **{f"{stack}.layers.*.self_attn.q_proj": "colwise" for stack in ("L_module", "H_module")},
        **{f"{stack}.layers.*.self_attn.k_proj": "colwise" for stack in ("L_module", "H_module")},
        **{f"{stack}.layers.*.self_attn.v_proj": "colwise" for stack in ("L_module", "H_module")},
        **{f"{stack}.layers.*.self_attn.gate_proj": "colwise" for stack in ("L_module", "H_module")},
        **{f"{stack}.layers.*.self_attn.o_proj": "rowwise" for stack in ("L_module", "H_module")},
        **{f"{stack}.layers.*.mlp.gate_proj": "colwise" for stack in ("L_module", "H_module")},
        **{f"{stack}.layers.*.mlp.up_proj": "colwise" for stack in ("L_module", "H_module")},
        **{f"{stack}.layers.*.mlp.down_proj": "rowwise" for stack in ("L_module", "H_module")},
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }

    vocab_size: int = 151808
    hidden_size: int = 1536
    intermediate_size: int = 4096
    num_hidden_layers: int = 16
    num_attention_heads: int = 12
    hidden_act: str = "silu"
    max_position_embeddings: int = 2048
    initializer_range: float = interval(min=0.0, max=1.0)(default=0.02)
    rms_norm_eps: float = 1e-6
    use_cache: bool = True
    pad_token_id: int | None = None
    bos_token_id: int | None = None
    eos_token_id: int | list[int] | None = None
    tie_word_embeddings: bool = False
    rope_parameters: RopeParameters | dict | None = None
    attention_bias: bool = False
    attention_dropout: int | float | None = 0.0
    mlp_bias: bool = False
    head_dim: int = 128

    H_cycles: int = 2
    L_cycles: int = 3
    L_bp_cycles: list[int] | None = None
    embedding_scale: float | None = None
    prefix_lm: bool = True
    num_layers_per_stack: int | None = None  # Usually inferred in post init

    def __post_init__(self, **kwargs):
        if self.L_bp_cycles is None:
            # Default `[2]` = backprop only the last 2 L-iterations per H-cycle (training-time
            # gradient-routing knob). Left-padding to length `L_cycles` is performed inside
            # [`HrmTextModel`] since it depends on `L_cycles`.
            self.L_bp_cycles = [2]

        if self.embedding_scale is None:
            self.embedding_scale = 1.0 / self.initializer_range

        if self.num_layers_per_stack is None:
            # Initial construction, or legacy checkpoint where `num_hidden_layers` carries the
            # real per-stack count: remember that value and rewrite `num_hidden_layers` to the
            # inflated total, so standard HF cache allocation gives us one slot per unique
            # attention invocation. Serialised configs round-trip as (inflated, real) pairs.
            self.num_layers_per_stack = self.num_hidden_layers
            self.num_hidden_layers = self.num_layers_per_stack * self.H_cycles * (self.L_cycles + 1)

        super().__post_init__(**kwargs)

    def validate_architecture(self):
        """Part of `@strict`-powered validation. Validates the architecture of the config."""
        if self.hidden_size % self.num_attention_heads != 0:
            raise ValueError(
                f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention "
                f"heads ({self.num_attention_heads})."
            )

    @property
    def _attn_implementation(self):
        return self._attn_implementation_internal

    @_attn_implementation.setter
    def _attn_implementation(self, value: str | dict | None):
        if value is not None and self.prefix_lm:
            _, base_implementation = split_attention_implementation(value)
            if is_flash_attention_requested(requested_attention_implementation=base_implementation):
                raise ValueError(
                    f"`attn_implementation={value!r}` is not supported when "
                    "`config.prefix_lm=True`: FlashAttention cannot represent the PrefixLM 4-D mask "
                    "overlay. Use `'sdpa'` (default) or `'flex_attention'`, or set `config.prefix_lm=False`."
                )
        PreTrainedConfig._attn_implementation.__set__(self, value)


__all__ = ["HrmTextConfig"]