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import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, GenerationMixin
from transformers.modeling_outputs import CausalLMOutputWithPast
from .configuration_trm_hrm import TRMHRMConfig
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-8):
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
self.eps = eps
def forward(self, x):
return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps) * self.weight
class RoPE(nn.Module):
def __init__(self, dim, max_seq_len=4096, base=10000):
super().__init__()
assert dim % 2 == 0
half = dim // 2
inv_freq = 1.0 / (base ** (torch.arange(half).float() / half))
pos = torch.arange(max_seq_len).float()
angles = torch.einsum("i,j->ij", pos, inv_freq)
self.register_buffer("sin", angles.sin()[None, :, :], persistent=False)
self.register_buffer("cos", angles.cos()[None, :, :], persistent=False)
def forward(self, x):
seq_len = x.size(1)
sin = self.sin[:, :seq_len, :].to(device=x.device, dtype=x.dtype)
cos = self.cos[:, :seq_len, :].to(device=x.device, dtype=x.dtype)
half = x.size(-1) // 2
x1 = x[..., :half]
x2 = x[..., half:]
return torch.cat([x1 * cos - x2 * sin, x1 * sin + x2 * cos], dim=-1)
class CausalDepthwiseConv1d(nn.Module):
def __init__(self, dim, kernel_size, dilation):
super().__init__()
self.left_padding = dilation * (kernel_size - 1)
self.conv = nn.Conv1d(
dim,
dim,
kernel_size=kernel_size,
dilation=dilation,
groups=dim,
bias=True,
)
def forward(self, x):
h = x.transpose(1, 2)
h = F.pad(h, (self.left_padding, 0))
h = self.conv(h)
return h.transpose(1, 2)
class CausalDilatedDepthwiseMixer(nn.Module):
def __init__(self, dim, kernel_size, dilations):
super().__init__()
self.convs = nn.ModuleList([
CausalDepthwiseConv1d(dim, kernel_size, d)
for d in dilations
])
self.proj = nn.Linear(dim * len(dilations), dim)
def forward(self, x):
return self.proj(torch.cat([conv(x) for conv in self.convs], dim=-1))
class TinyRecursiveBlock(nn.Module):
def __init__(self, config, dilations, rope):
super().__init__()
self.norm = RMSNorm(config.dim)
self.rope = rope
self.mixer = CausalDilatedDepthwiseMixer(
config.dim,
config.kernel_size,
dilations,
)
self.up = nn.Linear(config.dim, config.hidden_dim * 2)
self.down = nn.Linear(config.hidden_dim, config.dim)
self.dropout = nn.Dropout(config.dropout)
self.res_scale = nn.Parameter(torch.tensor(0.1))
def forward(self, x):
h = self.norm(x)
h = self.rope(h)
h = self.mixer(h)
a, gate = self.up(h).chunk(2, dim=-1)
h = a * F.silu(gate)
h = self.down(h)
h = self.dropout(h)
return x + self.res_scale * h
class TRMUnit(nn.Module):
def __init__(self, config, n_steps, dilations, rope):
super().__init__()
self.block = TinyRecursiveBlock(config, dilations, rope)
self.n_steps = n_steps
def forward(self, x):
for _ in range(self.n_steps):
x = self.block(x)
return x
class TRMHRMForCausalLM(PreTrainedModel, GenerationMixin):
config_class = TRMHRMConfig
base_model_prefix = "trm_hrm"
supports_gradient_checkpointing = False
_tied_weights_keys = {}
all_tied_weights_keys = {}
def __init__(self, config):
super().__init__(config)
self.token_embedding = nn.Embedding(
config.vocab_size,
config.dim,
padding_idx=config.pad_token_id,
)
self.rope = RoPE(config.dim, config.max_seq_len)
self.low = TRMUnit(config, config.low_steps, config.low_dilations, self.rope)
self.mid = TRMUnit(config, config.mid_steps, config.mid_dilations, self.rope)
self.high = TRMUnit(config, config.high_steps, config.high_dilations, self.rope)
self.low_to_mid = nn.Linear(config.dim, config.dim)
self.mid_to_high = nn.Linear(config.dim, config.dim)
self.high_to_mid = nn.Linear(config.dim, config.dim)
self.mid_to_low = nn.Linear(config.dim, config.dim)
self.final_norm = RMSNorm(config.dim)
self.lm_head = nn.Linear(config.dim, config.vocab_size, bias=False)
self.post_init()
@classmethod
def _can_set_experts_implementation(cls):
return False
def _init_weights(self, module):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
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=0.02)
def get_input_embeddings(self):
return self.token_embedding
def set_input_embeddings(self, value):
self.token_embedding = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, value):
self.lm_head = value
def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **kwargs):
if input_ids.size(1) > self.config.max_seq_len:
input_ids = input_ids[:, -self.config.max_seq_len:]
if attention_mask is not None:
attention_mask = attention_mask[:, -self.config.max_seq_len:]
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
}
def forward(
self,
input_ids=None,
attention_mask=None,
labels=None,
return_dict=True,
**kwargs,
):
low = self.token_embedding(input_ids)
if attention_mask is not None:
low = low * attention_mask.unsqueeze(-1).to(low.dtype)
mid = torch.zeros_like(low)
high = torch.zeros_like(low)
for _ in range(self.config.cycles):
low = self.low(low)
mid = mid + 0.25 * self.low_to_mid(low)
mid = self.mid(mid)
high = high + 0.25 * self.mid_to_high(mid)
high = self.high(high)
mid = mid + 0.25 * self.high_to_mid(high)
low = low + 0.25 * self.mid_to_low(mid)
logits = self.lm_head(self.final_norm(low))
loss = None
if labels is not None:
loss = F.cross_entropy(
logits[:, :-1, :].contiguous().view(-1, self.config.vocab_size),
labels[:, 1:].contiguous().view(-1),
ignore_index=-100,
)
if not return_dict:
return (loss, logits) if loss is not None else (logits,)
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=None,
hidden_states=None,
attentions=None,
)
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