from typing import Callable, Optional import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint from ...cache_utils import Cache from ...modeling_utils import ALL_ATTENTION_FUNCTIONS from ...utils import logging from ..llama.modeling_llama import ( LlamaAttention, LlamaDecoderLayer, LlamaForCausalLM, LlamaMLP, LlamaModel, LlamaRotaryEmbedding, eager_attention_forward, rotate_half, ) from .configuration_olmo import OlmoConfig logger = logging.get_logger(__name__) class OlmoLayerNorm(nn.Module): """LayerNorm but with no learnable weight or bias.""" def __init__(self, hidden_size: int) -> None: super().__init__() self.normalized_shape = (hidden_size,) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: orig_dtype = hidden_states.dtype return F.layer_norm(hidden_states.to(dtype=torch.float32), self.normalized_shape, None, None, eps=1e-5).to( orig_dtype ) class OlmoMLP(LlamaMLP): def __init__(self, config): super().__init__(config) self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. Args: q (`torch.Tensor`): The query tensor. k (`torch.Tensor`): The key tensor. cos (`torch.Tensor`): The cosine part of the rotary embedding. sin (`torch.Tensor`): The sine part of the rotary embedding. position_ids (`torch.Tensor`, *optional*): Deprecated and unused. unsqueeze_dim (`int`, *optional*, defaults to 1): The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. """ q_type, k_type = q.dtype, k.dtype cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed.to(q_type), k_embed.to(k_type) class OlmoAttention(LlamaAttention): def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs, ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) if self.config.clip_qkv is not None: 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_value 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_value.update(key_states, value_states, self.layer_idx, cache_kwargs) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] 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, **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class OlmoDecoderLayer(LlamaDecoderLayer): def __init__(self, config: OlmoConfig, layer_idx: int): super().__init__(config, layer_idx) self.input_layernorm = OlmoLayerNorm(config.hidden_size) self.post_attention_layernorm = OlmoLayerNorm(config.hidden_size) self.self_attn = OlmoAttention(config=config, layer_idx=layer_idx) # This is identical to LlamaRotaryEmbedding except the output cos and sin are returned # as float32 rather than the input type. class OlmoRotaryEmbedding(LlamaRotaryEmbedding): def forward(self, x, position_ids): inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) position_ids_expanded = position_ids[:, None, :].float() device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" with torch.autocast(device_type=device_type, enabled=False): # Force float32 freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() * self.attention_scaling sin = emb.sin() * self.attention_scaling return cos, sin class OlmoModel(LlamaModel): def __init__(self, config: OlmoConfig): super().__init__(config) self.layers = nn.ModuleList( [OlmoDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.norm = OlmoLayerNorm(config.hidden_size) class OlmoForCausalLM(LlamaForCausalLM): pass __all__ = [ "OlmoForCausalLM", "OlmoModel", "OlmoPreTrainedModel", # noqa: F822 ]