# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # Adapted from # https://github.com/huggingface/transformers/blob/main/src/transformers/models/olmo2/modeling_olmo2.py # Copyright 2024 The vLLM team. # Copyright 2024 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # 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. """Inference-only OLMo2 model compatible with HuggingFace weights.""" from collections.abc import Iterable from functools import partial from itertools import islice import torch from torch import nn from transformers import Olmo2Config from vllm.attention.layer import Attention from vllm.compilation.decorators import support_torch_compile from vllm.config import VllmConfig from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size from vllm.distributed.communication_op import tensor_model_parallel_all_gather from vllm.distributed.parallel_state import get_tensor_model_parallel_rank from vllm.distributed.utils import split_tensor_along_last_dim from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import ( MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear, ) from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding, ) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.models.interfaces import SupportsLoRA, SupportsPP from vllm.model_executor.models.utils import ( AutoWeightsLoader, extract_layer_index, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix, ) from vllm.sequence import IntermediateTensors from vllm.transformers_utils.configs import Olmo3Config class Olmo2Attention(nn.Module): """ This is the attention block where the output is computed as `Attention(LN(x))` in `MLP(LN(x + Attention(LN(x))))` (plus another skip connection). """ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() self.config = vllm_config.model_config.hf_config assert isinstance(self.config, (Olmo2Config, Olmo3Config)) hidden_size = self.config.hidden_size self.tp_size = get_tensor_model_parallel_world_size() self.total_num_heads = self.config.num_attention_heads assert hidden_size % self.total_num_heads == 0 assert self.total_num_heads % self.tp_size == 0 self.num_heads = self.total_num_heads // self.tp_size self.total_num_kv_heads = ( self.config.num_key_value_heads or self.total_num_heads ) if self.total_num_kv_heads >= self.tp_size: assert self.total_num_kv_heads % self.tp_size == 0 else: assert self.tp_size % self.total_num_kv_heads == 0 self.num_kv_heads = max(1, self.total_num_kv_heads // self.tp_size) self.head_dim = hidden_size // self.total_num_heads self.q_size = self.num_heads * self.head_dim self.kv_size = self.num_kv_heads * self.head_dim self.max_position_embeddings = self.config.max_position_embeddings # Attention input projection. Projects x -> (q, k, v) self.qkv_proj = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=False, quant_config=vllm_config.quant_config, prefix=f"{prefix}.qkv_proj", ) self.tp_rank = get_tensor_model_parallel_rank() self.k_norm = RMSNorm( self.total_num_kv_heads * self.head_dim, eps=self.config.rms_norm_eps, ) self.q_norm = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps) self.scaling = self.head_dim**-0.5 layer_idx = extract_layer_index(prefix) sliding_window = None if ( layer_types := getattr(self.config, "layer_types", None) ) is not None and layer_types[layer_idx] == "sliding_attention": sliding_window = self.config.sliding_window self.attn = Attention( self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads, cache_config=vllm_config.cache_config, quant_config=vllm_config.quant_config, per_layer_sliding_window=sliding_window, prefix=f"{prefix}.attn", ) # Rotary embeddings. Rope scaling is only applied on full attention layers. if sliding_window is None: rope_parameters = self.config.rope_parameters else: rope_theta = self.config.rope_parameters["rope_theta"] rope_parameters = {"rope_type": "default", "rope_theta": rope_theta} self.rotary_emb = get_rope( self.head_dim, rotary_dim=self.head_dim, max_position=self.max_position_embeddings, rope_parameters=rope_parameters, ) # Attention output projection. self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=False, quant_config=vllm_config.quant_config, prefix=f"{prefix}.o_proj", ) def _apply_qk_norm( self, q: torch.Tensor, k: torch.Tensor ) -> tuple[torch.Tensor, torch.Tensor]: if self.tp_size > 1: q = tensor_model_parallel_all_gather(q.contiguous()) k = tensor_model_parallel_all_gather(k.contiguous()) q = self.q_norm(q) k = self.k_norm(k) if self.tp_size > 1: splitter = partial(split_tensor_along_last_dim, num_partitions=self.tp_size) q = splitter(q)[self.tp_rank] k = splitter(k)[self.tp_rank] return q, k def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, ) -> torch.Tensor: qkv, _ = self.qkv_proj(hidden_states) q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) q, k = self._apply_qk_norm(q, k) q, k = self.rotary_emb(positions, q, k) attn_output = self.attn(q, k, v) output, _ = self.o_proj(attn_output) return output class Olmo2MLP(nn.Module): """ This is the MLP block where the output is computed as `MLP(x)` in `LN(MLP(x + LN(Attention(x))))` (plus another skip connection). """ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config assert isinstance(config, (Olmo2Config, Olmo3Config)) hidden_size = config.hidden_size intermediate_size = config.intermediate_size # Feed-forward input projection. self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=False, quant_config=vllm_config.quant_config, prefix=f"{prefix}.gate_up_proj", ) # Activation function. self.act_fn = SiluAndMul() # Feed-forward output projection. self.down_proj = RowParallelLinear( intermediate_size, hidden_size, bias=False, quant_config=vllm_config.quant_config, prefix=f"{prefix}.down_proj", ) def forward( self, x: torch.Tensor, ) -> torch.Tensor: gate_up, _ = self.gate_up_proj(x) x = self.act_fn(gate_up) x, _ = self.down_proj(x) return x class Olmo2DecoderLayer(nn.Module): """ This is a typical transformer block where the output is computed as `MLP(LN(x + Attention(LN(x))))` (plus another skip connection). """ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config assert isinstance(config, (Olmo2Config, Olmo3Config)) # Attention block. self.self_attn = Olmo2Attention( vllm_config=vllm_config, prefix=f"{prefix}.self_attn" ) # MLP block. self.mlp = Olmo2MLP(vllm_config=vllm_config, prefix=f"{prefix}.mlp") # LayerNorm self.post_attention_layernorm = RMSNorm( config.hidden_size, eps=config.rms_norm_eps ) self.post_feedforward_layernorm = RMSNorm( config.hidden_size, eps=config.rms_norm_eps ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, ) -> torch.Tensor: # Attention block. residual = hidden_states hidden_states = self.self_attn(positions, hidden_states) hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = hidden_states + residual # MLP block. residual = hidden_states hidden_states = self.mlp(hidden_states) hidden_states = self.post_feedforward_layernorm(hidden_states) hidden_states = residual + hidden_states return hidden_states @support_torch_compile class Olmo2Model(nn.Module): def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() self.config = vllm_config.model_config.hf_config assert isinstance(self.config, (Olmo2Config, Olmo3Config)) self.embed_tokens = VocabParallelEmbedding( self.config.vocab_size, self.config.hidden_size, prefix=f"{prefix}.embed_tokens", ) self.start_layer, self.end_layer, self.layers = make_layers( self.config.num_hidden_layers, lambda prefix: Olmo2DecoderLayer(vllm_config=vllm_config, prefix=prefix), prefix=f"{prefix}.layers", ) self.norm = RMSNorm( self.config.hidden_size, eps=self.config.rms_norm_eps, ) self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory( ["hidden_states"], self.config.hidden_size ) def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, intermediate_tensors: IntermediateTensors | None, inputs_embeds: torch.Tensor | None = None, ) -> torch.Tensor | IntermediateTensors: """ :param input_ids: A tensor of shape `(batch_size, seq_len)`. """ if get_pp_group().is_first_rank: if inputs_embeds is not None: hidden_states = inputs_embeds # Get embeddings of input. # shape: (batch_size, seq_len, d_model) else: hidden_states = self.embed_tokens(input_ids) else: assert intermediate_tensors is not None hidden_states = intermediate_tensors["hidden_states"] assert isinstance(hidden_states, torch.Tensor) # Apply blocks one-by-one. for layer in islice(self.layers, self.start_layer, self.end_layer): # shape: (batch_size, seq_len, d_model) hidden_states = layer(positions, hidden_states) if not get_pp_group().is_last_rank: return IntermediateTensors({"hidden_states": hidden_states}) # Apply final layer norm. # shape: (batch_size, seq_len or 1, d_model) hidden_states = self.norm(hidden_states) return hidden_states def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] params_dict = dict(self.named_parameters(remove_duplicate=False)) loaded_params: set[str] = set() for name, loaded_weight in weights: if is_pp_missing_parameter(name, self): continue for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue param = params_dict[name] weight_loader = param.weight_loader # type: ignore weight_loader(param, loaded_weight, shard_id) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) loaded_params.add(name) return loaded_params class Olmo2ForCausalLM(nn.Module, SupportsPP, SupportsLoRA): """ Extremely barebones HF model wrapper. """ packed_modules_mapping = { "qkv_proj": [ "q_proj", "k_proj", "v_proj", ], "gate_up_proj": [ "gate_proj", "up_proj", ], } def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config assert isinstance(config, (Olmo2Config, Olmo3Config)) self.config = config self.model = Olmo2Model( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model") ) if config.tie_word_embeddings: self.lm_head = self.model.embed_tokens else: self.lm_head = ParallelLMHead( config.vocab_size, config.hidden_size, quant_config=vllm_config.quant_config, prefix=maybe_prefix(prefix, "lm_head"), ) self.logits_processor = LogitsProcessor(config.vocab_size) self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors ) def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.model.embed_input_ids(input_ids) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, intermediate_tensors: IntermediateTensors | None = None, inputs_embeds: torch.Tensor | None = None, ) -> torch.Tensor | IntermediateTensors: hidden_states = self.model( input_ids=input_ids, positions=positions, intermediate_tensors=intermediate_tensors, inputs_embeds=inputs_embeds, ) return hidden_states def compute_logits( self, hidden_states: torch.Tensor, ) -> torch.Tensor | None: logits = self.logits_processor(self.lm_head, hidden_states) return logits def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]): loader = AutoWeightsLoader( self, skip_prefixes=( ["lm_head.weight"] if self.config.tie_word_embeddings else None ), ) return loader.load_weights(weights)