# 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. """PyTorch OLMoE model.""" from collections.abc import Callable import torch from torch import nn from ... import initialization as init from ...cache_utils import Cache, DynamicCache from ...generation import GenerationMixin from ...masking_utils import create_causal_mask from ...modeling_outputs import MoeModelOutputWithPast from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from ...processing_utils import Unpack from ...utils import TransformersKwargs, auto_docstring, logging from ...utils.output_capturing import OutputRecorder from ..gemma.modeling_gemma import GemmaMLP from ..llama.modeling_llama import ( LlamaAttention, LlamaDecoderLayer, LlamaRMSNorm, LlamaRotaryEmbedding, apply_rotary_pos_emb, eager_attention_forward, ) from ..mixtral.modeling_mixtral import MixtralExperts, MixtralForCausalLM, MixtralModel from ..qwen2_moe.modeling_qwen2_moe import Qwen2MoeTopKRouter from .configuration_olmoe import OlmoeConfig logger = logging.get_logger(__name__) class OlmoeRMSNorm(LlamaRMSNorm): def __init__(self, hidden_size, eps=1e-5): super().__init__(hidden_size, eps) class OlmoeRotaryEmbedding(LlamaRotaryEmbedding): pass class OlmoeMLP(GemmaMLP): pass class OlmoeAttention(LlamaAttention): def __init__(self, config: OlmoeConfig, layer_idx: int | None = None): super().__init__(config, layer_idx) self.q_norm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.k_norm = OlmoeRMSNorm( (config.hidden_size // config.num_attention_heads) * config.num_key_value_heads, eps=config.rms_norm_eps ) 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, cache_position: torch.LongTensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[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) if self.config.clip_qkv is not None: # Diff with llama query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv) key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv) value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv) 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: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs) 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=getattr(self.config, "sliding_window", None), # main diff with Llama **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class OlmoeExperts(MixtralExperts): pass class OlmoeTopKRouter(Qwen2MoeTopKRouter): pass class OlmoeSparseMoeBlock(nn.Module): def __init__(self, config): super().__init__() self.gate = OlmoeTopKRouter(config) self.experts = OlmoeExperts(config) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: batch_size, sequence_length, hidden_dim = hidden_states.shape hidden_states = hidden_states.view(-1, hidden_dim) _, top_k_weights, top_k_index = self.gate(hidden_states) final_hidden_states = self.experts(hidden_states, top_k_index, top_k_weights).reshape( batch_size, sequence_length, hidden_dim ) return final_hidden_states class OlmoeDecoderLayer(LlamaDecoderLayer): def __init__(self, config: OlmoeConfig, layer_idx: int): super().__init__(config, layer_idx) self.hidden_size = config.hidden_size self.self_attn = OlmoeAttention(config=config, layer_idx=layer_idx) self.mlp = OlmoeSparseMoeBlock(config) self.input_layernorm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) @auto_docstring class OlmoePreTrainedModel(PreTrainedModel): config: OlmoeConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["OlmoeDecoderLayer"] _skip_keys_device_placement = ["past_key_values"] _supports_flash_attn = True _supports_sdpa = True _can_record_outputs = { "router_logits": OutputRecorder(OlmoeTopKRouter, index=0), "hidden_states": OlmoeDecoderLayer, "attentions": OlmoeAttention, } _supports_attention_backend = True @torch.no_grad() def _init_weights(self, module): PreTrainedModel._init_weights(self, module) if isinstance(module, OlmoeExperts): init.normal_(module.gate_up_proj, mean=0.0, std=self.config.initializer_range) init.normal_(module.down_proj, mean=0.0, std=self.config.initializer_range) elif isinstance(module, OlmoeTopKRouter): init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) @auto_docstring class OlmoeModel(MixtralModel): def __init__(self, config: OlmoeConfig): super().__init__(config) self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) self.layers = nn.ModuleList( [OlmoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.norm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = OlmoeRotaryEmbedding(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, cache_position: torch.LongTensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> MoeModelOutputWithPast: if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if use_cache and past_key_values is None: past_key_values = DynamicCache(config=self.config) if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if cache_position is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 cache_position = torch.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = create_causal_mask( # diff with mixtral: no sliding config=self.config, inputs_embeds=inputs_embeds, attention_mask=attention_mask, cache_position=cache_position, past_key_values=past_key_values, position_ids=position_ids, ) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) for decoder_layer in self.layers[: self.config.num_hidden_layers]: hidden_states = decoder_layer( hidden_states, position_embeddings=position_embeddings, attention_mask=causal_mask, position_ids=position_ids, past_key_values=past_key_values, use_cache=use_cache, cache_position=cache_position, **kwargs, ) hidden_states = self.norm(hidden_states) return MoeModelOutputWithPast( # only diff with Mistral is the output type, we need MoE last_hidden_state=hidden_states, past_key_values=past_key_values, ) class OlmoeForCausalLM(MixtralForCausalLM, GenerationMixin): _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} def __init__(self, config): super().__init__(config) self.model = OlmoeModel(config) self.num_experts = config.num_experts def forward(self, **super_kwargs): r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Example: ```python >>> from transformers import AutoTokenizer, OlmoeForCausalLM >>> model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924") >>> tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924") >>> prompt = "Hey, are you conscious? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] 'Hey, are you conscious? Can you talk to me?\nI’m not sure if you’re conscious of this, but I’m' ``` """ return super().forward(**super_kwargs) __all__ = ["OlmoeForCausalLM", "OlmoeModel", "OlmoePreTrainedModel"]