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import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.modeling_utils import PreTrainedModel
from transformers.generation import GenerationMixin
from transformers.modeling_outputs import CausalLMOutput
from .configuration_dynamicmind import DynamicMindConfig
class DynamicMindRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-5):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.eps = eps
def forward(self, x):
dtype = x.dtype
x = x.float()
var = x.pow(2).mean(dim=-1, keepdim=True)
x = x * torch.rsqrt(var + self.eps)
return (self.weight * x).to(dtype)
def apply_rope(q, k, rope_theta):
# q: [B, H, T, D], k: [B, KVH, T, D]
device = q.device
dtype = q.dtype
seq_len = q.size(-2)
head_dim = q.size(-1)
inv_freq = 1.0 / (
rope_theta ** (torch.arange(0, head_dim, 2, device=device).float() / head_dim)
)
t = torch.arange(seq_len, device=device).float()
freqs = torch.outer(t, inv_freq)
cos = freqs.cos()[None, None, :, :].to(dtype)
sin = freqs.sin()[None, None, :, :].to(dtype)
def rotate(x):
x_even = x[..., 0::2]
x_odd = x[..., 1::2]
x_rot_even = x_even * cos - x_odd * sin
x_rot_odd = x_even * sin + x_odd * cos
return torch.stack((x_rot_even, x_rot_odd), dim=-1).flatten(-2)
return rotate(q), rotate(k)
class DynamicMindAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.num_kv_heads = config.num_key_value_heads
self.head_dim = config.hidden_size // config.num_attention_heads
self.rope_theta = config.rope_theta
self.attention_dropout = config.attention_dropout
assert self.hidden_size % self.num_heads == 0
assert self.num_heads % self.num_kv_heads == 0
self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
def forward(self, x):
bsz, seq_len, _ = x.shape
q = self.q_proj(x).view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
k = self.k_proj(x).view(bsz, seq_len, self.num_kv_heads, self.head_dim).transpose(1, 2)
v = self.v_proj(x).view(bsz, seq_len, self.num_kv_heads, self.head_dim).transpose(1, 2)
q, k = apply_rope(q, k, self.rope_theta)
if self.num_kv_heads != self.num_heads:
repeats = self.num_heads // self.num_kv_heads
k = k.repeat_interleave(repeats, dim=1)
v = v.repeat_interleave(repeats, dim=1)
y = F.scaled_dot_product_attention(
q,
k,
v,
attn_mask=None,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=True,
)
y = y.transpose(1, 2).contiguous().view(bsz, seq_len, self.hidden_size)
return self.o_proj(y)
class DynamicMindMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
def forward(self, x):
return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
class DynamicMindBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.input_layernorm = DynamicMindRMSNorm(config.hidden_size, config.rms_norm_eps)
self.self_attn = DynamicMindAttention(config)
self.post_attention_layernorm = DynamicMindRMSNorm(config.hidden_size, config.rms_norm_eps)
self.mlp = DynamicMindMLP(config)
def forward(self, x):
x = x + self.self_attn(self.input_layernorm(x))
x = x + self.mlp(self.post_attention_layernorm(x))
return x
class DynamicMindPreTrainedModel(PreTrainedModel):
config_class = DynamicMindConfig
base_model_prefix = "model"
supports_gradient_checkpointing = False
_no_split_modules = ["DynamicMindBlock"]
def _init_weights(self, module):
std = 0.02
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=std)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=std)
class DynamicMindForCausalLM(DynamicMindPreTrainedModel, GenerationMixin):
_tied_weights_keys = {"lm_head.weight": "embed_tokens.weight"}
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
self.layers = nn.ModuleList([DynamicMindBlock(config) for _ in range(config.num_hidden_layers)])
self.norm = DynamicMindRMSNorm(config.hidden_size, config.rms_norm_eps)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
if config.tie_word_embeddings:
self.lm_head.weight = self.embed_tokens.weight
self.post_init()
def tie_weights(self, *args, **kwargs):
if getattr(self.config, "tie_word_embeddings", True):
self.lm_head.weight = self.embed_tokens.weight
def get_input_embeddings(self):
return self.embed_tokens
def set_input_embeddings(self, value):
self.embed_tokens = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, value):
self.lm_head = value
def forward(self, input_ids=None, labels=None, **kwargs):
x = self.embed_tokens(input_ids)
for layer in self.layers:
x = layer(x)
x = self.norm(x)
logits = self.lm_head(x)
loss = None
if labels is not None:
shift_logits = logits[:, :-1, :].contiguous()
shift_labels = labels[:, 1:].contiguous()
loss = F.cross_entropy(
shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1),
)
return CausalLMOutput(loss=loss, logits=logits)
def state_dict(self, *args, **kwargs):
sd = super().state_dict(*args, **kwargs)
# lm_head.weight is tied to embed_tokens.weight. Safetensors cannot store
# duplicate shared tensors unless one key is removed.
if getattr(self.config, "tie_word_embeddings", True):
for k in list(sd.keys()):
if k == "lm_head.weight" or k.endswith(".lm_head.weight"):
del sd[k]
return sd
def prepare_inputs_for_generation(self, input_ids, **kwargs):
return {"input_ids": input_ids}
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