import math
from typing import Optional, Tuple, Union, List, Dict
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from transformers import PreTrainedModel, LlamaConfig
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.models.llama.modeling_llama import (
LlamaRMSNorm,
LlamaRotaryEmbedding,
LlamaLinearScalingRotaryEmbedding,
LlamaDynamicNTKScalingRotaryEmbedding,
LlamaMLP,
apply_rotary_pos_emb,
repeat_kv,
)
from transformers.cache_utils import Cache, DynamicCache, StaticCache
class DiffLLaMAConfig(LlamaConfig):
"""
Configuration class for the DiffLLaMA model.
Inherits from LlamaConfig and can be extended with additional parameters.
"""
model_type = "diff_llama"
def __init__(
self,
num_kv_heads: int = 8,
intermediate_size: int = 3072,
rope_scaling: Optional[Dict[str, Union[str, float]]] = None,
**kwargs
):
super().__init__(**kwargs)
self.num_kv_heads = num_kv_heads
self.intermediate_size = intermediate_size
self.rope_scaling = rope_scaling or {"type": "linear", "factor": 1.0}
# Add any custom configuration parameters here
def init_method(tensor):
"""Initialize tensor with Kaiming uniform initialization."""
nn.init.kaiming_uniform_(tensor, a=math.sqrt(5))
def lambda_init_fn(depth):
"""Compute lambda initialization value based on layer depth."""
return 0.8 - 0.6 * math.exp(-0.3 * depth)
class MultiheadDiffAttn(nn.Module):
def __init__(self, config: DiffLLaMAConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.num_key_value_heads = config.num_kv_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.max_position_embeddings = config.max_position_embeddings
self.rope_theta = config.rope_theta
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
self.scaling = self.head_dim ** -0.5
self.rotary_emb = self._init_rope()
self.lambda_init = lambda_init_fn(layer_idx if layer_idx is not None else 0)
self.lambda_q1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0, std=0.1))
self.lambda_k1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0, std=0.1))
self.lambda_q2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0, std=0.1))
self.lambda_k2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0, std=0.1))
self.subln = nn.LayerNorm(self.num_heads * self.head_dim, elementwise_affine=False)
self._init_rope()
def _init_rope(self):
if not hasattr(self.config, 'rope_scaling') or self.config.rope_scaling is None:
self.rotary_emb = LlamaRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
base=self.rope_theta,
)
else:
scaling_type = self.config.rope_scaling.get("type", "linear")
scaling_factor = self.config.rope_scaling.get("factor", 1.0)
if scaling_type == "linear":
self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
scaling_factor=scaling_factor,
base=self.rope_theta,
)
elif scaling_type == "dynamic":
self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
self.head_dim,
max_position_embeddings=self.max_position_embeddings,
scaling_factor=scaling_factor,
base=self.rope_theta,
)
else:
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
batch_size, seq_length, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(batch_size, seq_length, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(batch_size, seq_length, self.num_key_value_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(value_states, position_ids)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
if past_key_value is not None:
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
# Repeat k/v heads if n_kv_heads < n_heads
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
attn_weights = torch.matmul(query_states, key_states.transpose(-1, -2))
attn_weights = attn_weights * self.scaling
lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1))
lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2))
lambda_full = lambda_1 - lambda_2 + self.lambda_init
# Apply differential attention
attn_weights_diff = attn_weights[:, :, :, :-1] - lambda_full * attn_weights[:, :, :, 1:]
attn_weights = torch.cat([attn_weights_diff, attn_weights[:, :, :, -1:]], dim=-1)
if attention_mask is not None:
# Expand attention_mask
attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
attention_mask = attention_mask.expand(batch_size, self.num_heads, seq_length, attention_mask.size(-1))
attention_mask = attention_mask.to(dtype=attn_weights.dtype) # Convert to same dtype as attn_weights
# Use a large negative number instead of negative infinity
attn_weights = attn_weights + (1.0 - attention_mask) * -10000.0
attn_weights = F.softmax(attn_weights, dim=-1)
attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, seq_length, self.num_heads * self.head_dim)
attn_output = self.subln(attn_output)
attn_output = attn_output * (1 - self.lambda_init)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
class DiffLLaMALayer(nn.Module):
"""
A single layer of the DiffLLaMA model, consisting of multi-head differential attention and a feed-forward network.
Incorporates gradient checkpointing for memory efficiency.
"""
def __init__(self, config: DiffLLaMAConfig, layer_idx: int):
super().__init__()
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.self_attn = MultiheadDiffAttn(
config=config,
layer_idx=layer_idx
)
self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.mlp = LlamaMLP(config)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position: Optional[torch.LongTensor] = None,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
class DiffLLaMAModel(PreTrainedModel):
"""
DiffLLaMAModel is a variant of LLaMA with differential attention mechanisms.
Incorporates mixed precision training and gradient checkpointing for optimized performance.
"""
config_class = DiffLLaMAConfig
def __init__(self, config: DiffLLaMAConfig):
super().__init__(config)
self.config = config
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([
DiffLLaMALayer(config, layer_idx=i) for i in range(config.num_hidden_layers)
])
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.rotary_emb = LlamaRotaryEmbedding(
dim=config.hidden_size // config.num_attention_heads,
max_position_embeddings=config.max_position_embeddings,
base=config.rope_theta,
)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[Tuple[torch.FloatTensor, torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
"""
Forward pass for the DiffLLaMAModel with performance optimizations.
Args:
input_ids: Input token IDs.
attention_mask: Attention mask.
position_ids: Position IDs.
past_key_values: Past key and value tensors for caching.
inputs_embeds: Input embeddings.
use_cache: Whether to return present key and value for caching.
output_attentions: Whether to output attention weights.
output_hidden_states: Whether to output hidden states.
return_dict: Whether to return a dict.
cache_position: Position IDs for caching.
Returns:
Model output, either as a tuple or a BaseModelOutputWithPast.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(seq_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).expand(batch_size, -1)
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# Position embeddings are handled within each layer; remove pre-computation
# Removed the following lines:
# cos, sin = self.rotary_emb(position_ids, seq_len=seq_length)
# position_embeddings = (cos, sin)
hidden_states = inputs_embeds
# Attention mask
if attention_mask is None:
attention_mask = torch.ones((batch_size, seq_length), device=hidden_states.device)
# Initialize lists to store outputs
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_cache = () if use_cache else None
for idx, layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
layer_outputs = layer(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_values[idx] if past_key_values is not None else None,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
)
# Correctly unpack layer_outputs based on the configuration
hidden_states = layer_outputs[0]
if use_cache:
present_key_value = layer_outputs[-1]
next_cache += (present_key_value,)
if output_attentions:
self_attn_weights = layer_outputs[1]
all_self_attns += (self_attn_weights,)
hidden_states = self.norm(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = None
if use_cache:
next_cache = (
next_cache.to_legacy_cache() if isinstance(next_cache, Cache) else next_cache
)
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
class DiffLLaMAForCausalLM(PreTrainedModel):
"""
DiffLLaMA model with a causal language modeling head.
Incorporates mixed precision training for optimized performance.
"""
config_class = DiffLLaMAConfig
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config: DiffLLaMAConfig):
super().__init__(config)
self.model = DiffLLaMAModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
"""Return input embeddings."""
return self.model.get_input_embeddings()
def set_input_embeddings(self, value):
"""Set input embeddings."""
self.model.set_input_embeddings(value)
def get_output_embeddings(self):
"""Return output embeddings (language modeling head)."""
return self.lm_head
def set_output_embeddings(self, new_embeddings):
"""Set output embeddings (language modeling head)."""
self.lm_head = new_embeddings
def set_decoder(self, decoder):
"""Set the decoder model."""
self.model = decoder
def get_decoder(self):
"""Get the decoder model."""
return self.model
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[Tuple[torch.FloatTensor, torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
"""
Forward pass for DiffLLaMAForCausalLM with performance optimizations.
Args:
input_ids: Input token IDs.
attention_mask: Attention mask.
position_ids: Position IDs.
past_key_values: Past key and value tensors for caching.
inputs_embeds: Input embeddings.
labels: Labels for computing the loss.
use_cache: Whether to return past key and value tensors.
output_attentions: Whether to output attention weights.
output_hidden_states: Whether to output hidden states.
return_dict: Whether to return a dict.
cache_position: Position IDs for caching.
Returns:
CausalLMOutputWithPast or tuple containing loss and logits.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# Get outputs from the model
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
)
hidden_states = outputs.last_hidden_state if return_dict else outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = nn.CrossEntropyLoss()
shift_logits = shift_logits.view(-1, self.config.vocab_size)
shift_labels = shift_labels.view(-1)
# Compute loss using mixed precision if enabled
if shift_logits.dtype == torch.float16:
with torch.cuda.amp.autocast(enabled=False):
loss = loss_fct(shift_logits, shift_labels)
else:
loss = loss_fct(shift_logits, shift_labels)
if not return_dict:
if use_cache:
return ((loss, logits) + outputs[1:]) if loss is not None else (logits,) + outputs[1:]
else:
return (loss, logits) if loss is not None else (logits,)
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
):
if past_key_values:
input_ids = input_ids[:, -1:]
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update(
{
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
"cache_position": kwargs.get("cache_position"),
}
)
return model_inputs