# coding=utf-8 # Adapted from # https://github.com/huggingface/transformers/blob/v4.40.1/src/transformers/models/olmo/modeling_olmo.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 OLMo model compatible with HuggingFace weights.""" from typing import Iterable, List, Optional, Tuple import torch from torch import nn from transformers import OlmoConfig from aphrodite.attention import Attention, AttentionMetadata from aphrodite.common.config import CacheConfig from aphrodite.common.sequence import IntermediateTensors, SamplerOutput from aphrodite.common.utils import progress_bar from aphrodite.distributed import get_tensor_model_parallel_world_size from aphrodite.modeling.layers.activation import SiluAndMul from aphrodite.modeling.layers.linear import (MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear) from aphrodite.modeling.layers.logits_processor import LogitsProcessor from aphrodite.modeling.layers.rotary_embedding import get_rope from aphrodite.modeling.layers.sampler import Sampler from aphrodite.modeling.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding) from aphrodite.modeling.model_loader.weight_utils import default_weight_loader from aphrodite.modeling.sampling_metadata import SamplingMetadata from aphrodite.quantization.base_config import QuantizationConfig class OlmoAttention(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, config: OlmoConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, ): super().__init__() self.config = config self.hidden_size = config.hidden_size tensor_model_parallel_world_size = ( get_tensor_model_parallel_world_size()) self.total_num_heads = config.num_attention_heads assert self.hidden_size % self.total_num_heads == 0 assert self.total_num_heads % tensor_model_parallel_world_size == 0 self.num_heads = (self.total_num_heads // tensor_model_parallel_world_size) self.head_dim = self.hidden_size // self.total_num_heads self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta self.clip_qkv = config.clip_qkv # Attention input projection. Projects x -> (q, k, v) self.qkv_proj = QKVParallelLinear( self.hidden_size, self.head_dim, self.total_num_heads, bias=config.attention_bias, quant_config=quant_config, ) # Rotary embeddings. self.rotary_emb = get_rope( self.head_dim, rotary_dim=self.head_dim, max_position=self.max_position_embeddings, base=self.rope_theta, ) self.scaling = self.head_dim**-0.5 self.attn = Attention(self.num_heads, self.head_dim, scale=self.scaling, cache_config=cache_config, quant_config=quant_config) # Attention output projection. self.o_proj = RowParallelLinear( self.hidden_size, self.hidden_size, bias=config.attention_bias, quant_config=quant_config, ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, kv_cache: torch.Tensor, attn_metadata: AttentionMetadata, ) -> torch.Tensor: qkv, _ = self.qkv_proj(hidden_states) if self.clip_qkv is not None: qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv) q, k, v = qkv.chunk(chunks=3, dim=-1) q, k = self.rotary_emb(positions, q, k) attn_output = self.attn(q, k, v, kv_cache, attn_metadata) output, _ = self.o_proj(attn_output) return output class OlmoMLP(nn.Module): """ This is the MLP block where the output is computed as ``MLP(LN(x))`` in ``MLP(LN(x + Attention(LN(x))))`` (plus another skip connection). """ def __init__( self, config: OlmoConfig, quant_config: Optional[QuantizationConfig] = None, ): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size # Feed-forward input projection. self.gate_up_proj = MergedColumnParallelLinear( self.hidden_size, [self.intermediate_size] * 2, bias=False, quant_config=quant_config, ) # Activation function. self.act_fn = SiluAndMul() # Feed-forward output projection. self.down_proj = RowParallelLinear( self.intermediate_size, self.hidden_size, bias=False, quant_config=quant_config, ) 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 OlmoDecoderLayer(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, config: OlmoConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None): super().__init__() # Attention block. self.self_attn = OlmoAttention(config, cache_config, quant_config) # MLP block. self.mlp = OlmoMLP(config, quant_config) # LayerNorm self.input_layernorm = nn.LayerNorm(config.hidden_size, elementwise_affine=False, bias=False) self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, elementwise_affine=False, bias=False) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, kv_cache: torch.Tensor, attn_metadata: AttentionMetadata, ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor, torch.Tensor]]]: # Attention block. residual = hidden_states hidden_states = self.input_layernorm(hidden_states) hidden_states = self.self_attn(positions, hidden_states, kv_cache, attn_metadata) hidden_states = hidden_states + residual # MLP block. residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states return hidden_states class OlmoModel(nn.Module): def __init__(self, config: OlmoConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None): super().__init__() self.config = config self.embed_tokens = VocabParallelEmbedding(config.vocab_size, config.hidden_size) self.layers = nn.ModuleList([ OlmoDecoderLayer(config, cache_config, quant_config) for layer_idx in range(config.num_hidden_layers) ]) self.norm = nn.LayerNorm(config.hidden_size, elementwise_affine=False, bias=False) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, ) -> torch.Tensor: """ :param input_ids: A tensor of shape `(batch_size, seq_len)`. """ # Get embeddings of input. # shape: (batch_size, seq_len, d_model) inputs_embeds = self.embed_tokens(input_ids) # embed positions hidden_states = inputs_embeds # Apply blocks one-by-one. for layer_idx, decoder_layer in enumerate(self.layers): # shape: (batch_size, seq_len, d_model) hidden_states = decoder_layer( positions, hidden_states, kv_caches[layer_idx], attn_metadata, ) # Apply final layer norm. # shape: (batch_size, seq_len or 1, d_model) hidden_states = self.norm(hidden_states) return hidden_states class OlmoForCausalLM(nn.Module): """ Extremely barebones HF model wrapper. """ def __init__(self, config: OlmoConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None): super().__init__() self.config = config self.model = OlmoModel(config, cache_config, quant_config) if config.tie_word_embeddings: self.lm_head = self.model.embed_tokens else: self.unpadded_vocab_size = config.vocab_size self.lm_head = ParallelLMHead( self.unpadded_vocab_size, config.hidden_size, org_num_embeddings=config.vocab_size, quant_config=quant_config, ) self.logits_processor = LogitsProcessor(config.vocab_size) self.sampler = Sampler() def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, intermediate_tensors: Optional[IntermediateTensors] = None, ) -> torch.Tensor: hidden_states = self.model( input_ids=input_ids, positions=positions, kv_caches=kv_caches, attn_metadata=attn_metadata, ) return hidden_states def compute_logits( self, hidden_states: torch.Tensor, sampling_metadata: SamplingMetadata, ) -> Optional[torch.Tensor]: logits = self.logits_processor(self.lm_head, hidden_states, sampling_metadata) return logits def sample( self, logits: torch.Tensor, sampling_metadata: SamplingMetadata, ) -> Optional[SamplerOutput]: next_tokens = self.sampler(logits, sampling_metadata) return next_tokens def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): 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)) weights_list = list(weights) for name, loaded_weight in progress_bar(weights_list, desc="Loading modules..."): if "rotary_emb.inv_freq" in name: continue if ("rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name): # Models trained using ColossalAI may include these tensors in # the checkpoint. Skip them. continue # With tie_word_embeddings, we can skip lm_head.weight # The weight might appear unnecessarily in the files if the model is # processed with quantization, LoRA, fine-tuning, etc. if self.config.tie_word_embeddings and "lm_head.weight" in name: 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 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)