image-match/python/py/Lib/site-packages/transformers/models/olmo3/modular_olmo3.py

# Copyright 2025 the HuggingFace 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 collections.abc import Callable

import torch
import torch.nn as nn
from huggingface_hub.dataclasses import strict

from ...cache_utils import Cache, DynamicCache
from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
from ...modeling_outputs import BaseModelOutputWithPast
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
from ...processing_utils import Unpack
from ...utils import auto_docstring
from ...utils.generic import TransformersKwargs
from ..gemma2.modeling_gemma2 import Gemma2RotaryEmbedding
from ..olmo2.configuration_olmo2 import Olmo2Config
from ..olmo2.modeling_olmo2 import (
    Olmo2Attention,
    Olmo2DecoderLayer,
    Olmo2ForCausalLM,
    Olmo2Model,
    Olmo2PreTrainedModel,
    Olmo2RMSNorm,
    apply_rotary_pos_emb,
    eager_attention_forward,
)


@auto_docstring(checkpoint="allenai/Olmo-3-7B-Instruct")
@strict
class Olmo3Config(Olmo2Config):
    r"""
    Example:

    ```python
    >>> from transformers import Olmo3Model, Olmo3Config

    >>> # Initializing a Olmo3 7B style configuration
    >>> configuration = Olmo3Config()

    >>> # Initializing a model from the Olmo3 7B style configuration
    >>> model = Olmo3Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```
    """

    model_type = "olmo3"
    keys_to_ignore_at_inference = ["past_key_values"]
    base_model_tp_plan = {
        "layers.*.self_attn.q_proj": "colwise_gather_output",  # we need to replicate here due to the added norm on q and k
        "layers.*.self_attn.k_proj": "colwise_gather_output",  # we need to replicate here due to the added norm on q and k
        "layers.*.self_attn.v_proj": "colwise_gather_output",  # we need to replicate here due to the added norm on q and k
        "layers.*.self_attn.o_proj": "rowwise_split_input",  # input is replicated due to the added norm on q and k
        "layers.*.mlp.gate_proj": "colwise",
        "layers.*.mlp.up_proj": "colwise",
        "layers.*.mlp.down_proj": "rowwise",
    }
    base_model_pp_plan = {
        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
        "norm": (["hidden_states"], ["hidden_states"]),
    }

    sliding_window: int | None = 4096
    layer_types: list[str] | None = None

    def __post_init__(self, **kwargs):
        if self.num_key_value_heads is None:
            self.num_key_value_heads = self.num_attention_heads

        if self.layer_types is None:
            self.layer_types = [
                "sliding_attention" if (i + 1) % 4 != 0 else "full_attention" for i in range(self.num_hidden_layers)
            ]

        super().__post_init__(**kwargs)


class Olmo3RMSNorm(Olmo2RMSNorm):
    pass


# Olmo3 attention is identical to OLMo 2 attention except:
# - Sliding window attention is used for 3 out of 4 layers.
class Olmo3Attention(Olmo2Attention):
    def __init__(self, config: Olmo3Config, layer_idx: int):
        super().__init__(config, layer_idx=layer_idx)
        self.attention_type = config.layer_types[layer_idx]
        self.sliding_window = config.sliding_window if self.attention_type == "sliding_attention" else None

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask: torch.Tensor | None,
        past_key_values: Cache | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states = self.q_norm(self.q_proj(hidden_states))
        key_states = self.k_norm(self.k_proj(hidden_states))
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(hidden_shape).transpose(1, 2)
        key_states = key_states.view(hidden_shape).transpose(1, 2)
        value_states = value_states.view(hidden_shape).transpose(1, 2)

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_values is not None:
            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)

        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
            self.config._attn_implementation, eager_attention_forward
        )

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            sliding_window=self.sliding_window,
            **kwargs,
        )

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights


class Olmo3DecoderLayer(Olmo2DecoderLayer):
    pass


class Olmo3RotaryEmbedding(Gemma2RotaryEmbedding):
    pass


class Olmo3PreTrainedModel(Olmo2PreTrainedModel):
    pass


# The OLMo 3 model is identical to the OLMo 2 model, except:
# - Sliding window attention is used for 3 out of 4 layers.
# - RoPE scaling is not applied to sliding window attention layers.
class Olmo3Model(Olmo2Model):
    def __init__(self, config: Olmo3Config):
        super().__init__(config)
        self.norm = Olmo3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.layers = nn.ModuleList(
            [Olmo3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.rotary_emb = Olmo3RotaryEmbedding(config=config)

    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        use_cache: bool | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPast:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if inputs_embeds is None:
            inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache(config=self.config)

        if position_ids is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
            position_ids = position_ids.unsqueeze(0)

        # It may already have been prepared by e.g. `generate`
        if not isinstance(causal_mask_mapping := attention_mask, dict):
            # Prepare mask arguments
            mask_kwargs = {
                "config": self.config,
                "inputs_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "past_key_values": past_key_values,
                "position_ids": position_ids,
            }
            # Create the masks
            causal_mask_mapping = {
                "full_attention": create_causal_mask(**mask_kwargs),
                "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
            }

        hidden_states = inputs_embeds
        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        for i, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]):
            hidden_states = decoder_layer(
                hidden_states,
                attention_mask=causal_mask_mapping[self.config.layer_types[i]],
                position_ids=position_ids,
                past_key_values=past_key_values,
                position_embeddings=position_embeddings,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
        )


class Olmo3ForCausalLM(Olmo2ForCausalLM):
    pass


__all__ = [
    "Olmo3Config",
    "Olmo3ForCausalLM",
    "Olmo3Model",
    "Olmo3PreTrainedModel",
]