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import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutput
from .modules import STU, Attention, MLP
from .utils import nearest_power_of_two
from .layers import STULayer, AttentionLayer
from .configuration_ministu import MiniSTUConfig
from .filters import get_spectral_filters
try:
from liger_kernel.transformers.rms_norm import LigerRMSNorm as TritonNorm
triton_norm = True
except ImportError as e:
print(
f"Unable to import Triton-based RMSNorm: {e}. Falling back to PyTorch implementation."
)
from torch.nn import RMSNorm
triton_norm = False
# Load the tokenizer
#from transformers import AutoModelForCausalLM, AutoTokenizer
#model_name = "Hazan-Lab/STU-426M"
#tokenizer = AutoTokenizer.from_pretrained(
# model_name,
# trust_remote_code=True
#)
class MiniSTU(PreTrainedModel):
config_class = MiniSTUConfig
def __init__(self, config) -> None:
super(MiniSTU, self).__init__(config)
self.n_layers = config.n_layers
self.n = nearest_power_of_two(config.seq_len * 2 - 1, round_up=True)
if isinstance(config.torch_dtype, torch.dtype):
torch_dtype = config.torch_dtype
else:
torch_dtype = getattr(torch, config.torch_dtype)
device = torch.device(config.device)
self.phi = get_spectral_filters(
config.seq_len,
config.num_eigh,
config.use_hankel_L,
device=device,
dtype=torch_dtype,
)
self.use_approx = config.use_approx
self.use_hankel_L = config.use_hankel_L
self.tok_emb = nn.Embedding(
config.vocab_size, config.n_embd, dtype=torch_dtype, device=device
)
self.dropout = nn.Dropout(config.dropout)
self.layers = nn.ModuleList()
for layer_idx in range(self.n_layers):
if layer_idx % 2 == 0:
self.layers.append(STULayer(config, self.phi, self.n))
else:
self.layers.append(
AttentionLayer(config)
if config.use_attn
else STULayer(config, self.phi, self.n)
)
self.norm = TritonNorm(config.n_embd) if triton_norm else RMSNorm(config.n_embd)
self.lm_head = nn.Linear(
config.n_embd, config.vocab_size, bias=config.bias, dtype=torch_dtype, device=device
)
self.tok_emb.weight = self.lm_head.weight
self.std = (config.n_embd) ** -0.5
self.apply(self._init_weights)
print("Model Parameter Count: %.2fM\n" % (self._get_num_params() / 1e6,))
def forward(
self,
input_ids: torch.Tensor,
labels: torch.Tensor = None,
**kwargs
) -> CausalLMOutput:
# Compute embeddings
tok_emb = self.tok_emb(input_ids)
x = self.dropout(tok_emb)
# Pass through layers
for layer in self.layers:
x = layer(x)
# Normalize and project to vocabulary
x = self.norm(x)
logits = self.lm_head(x)
loss = None
if labels is not None:
# Shift so that tokens predict the next token
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(
shift_logits.view(-1, shift_logits.size(-1)),
shift_labels.view(-1)
)
return CausalLMOutput(
loss=loss,
logits=logits,
)
def _get_num_params(self):
n_params = sum(p.numel() for p in self.parameters())
if hasattr(self, "pos_emb") and self.pos_emb is not None:
n_params -= self.pos_emb.weight.numel()
if self.tok_emb.weight is not self.lm_head.weight:
n_params -= self.tok_emb.weight.numel()
return n_params
def _init_weights(self, module):
if isinstance(module, nn.Linear):
if hasattr(module, "SCALE_INIT"):
self.std *= (2 * self.n_layers) ** -0.5
torch.nn.init.normal_(module.weight, mean=0.0, std=self.std)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=self.std)
elif isinstance(module, STU):
if self.use_approx:
torch.nn.init.xavier_normal_(module.M_inputs)
torch.nn.init.xavier_normal_(module.M_filters)
else:
torch.nn.init.xavier_normal_(module.M_phi_plus)
if not self.use_hankel_L:
torch.nn.init.xavier_normal_(module.M_phi_minus)
elif isinstance(module, Attention):
torch.nn.init.xavier_normal_(module.c_attn.weight)
torch.nn.init.xavier_normal_(module.c_proj.weight)
if module.c_attn.bias is not None:
torch.nn.init.zeros_(module.c_attn.bias)
if module.c_proj.bias is not None:
torch.nn.init.zeros_(module.c_proj.bias)
@staticmethod
def top_k_top_p_filtering(
logits: torch.Tensor,
top_k: int = 50,
top_p: float = 0.95,
filter_value: float = float("-inf"),
):
"""
Filters a distribution of logits using top-k and/or nucleus (top-p) filtering.
"""
# top_k
if top_k > 0:
top_k = min(top_k, logits.size(-1))
# Remove all logits that are not in the top k
indices_to_remove = logits < torch.topk(logits, top_k, dim=-1).values[:, -1, None]
logits[indices_to_remove] = filter_value
# top_p (nucleus)
if 0 < top_p < 1.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probs > top_p
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
sorted_indices_to_remove[:, 0] = False
indices_to_remove = sorted_indices_to_remove.scatter(
dim=1, index=sorted_indices, src=sorted_indices_to_remove
)
logits[indices_to_remove] = filter_value
return logits
def generate(
self,
input_ids: torch.LongTensor,
max_new_tokens: int = 50,
temperature: float = 0.5,
top_k: int = 50,
top_p: float = 0.95,
eos_token_id: int = None,
pad_token_id: int = 0,
**kwargs
):
"""
Naive token-by-token generation loop that uses top-k/top-p filtering and optional temperature.
Args:
input_ids (torch.LongTensor): shape (batch_size, sequence_length).
max_new_tokens (int): max number of tokens to generate (beyond input_ids length).
temperature (float): sampling temperature (>=0).
top_k (int): Top-K sampling cutoff.
top_p (float): Nucleus sampling cutoff.
eos_token_id (int): If set, stop generation when this token is produced.
pad_token_id (int): If set, can be used to pad sequences. (Not fully used here.)
kwargs: Unused arguments (like num_beams) for compatibility.
Returns:
torch.LongTensor: shape (batch_size, sequence_length + generated_tokens).
"""
device = input_ids.device
#print("1=====================")
#print(tokenizer.decode(input_ids[0], skip_special_tokens=True))
#print("1=====================")
# We'll accumulate new tokens into generated_ids
generated_ids = input_ids.clone()
for _ in range(max_new_tokens):
# Forward pass to get logits for the last token
outputs = self.forward(generated_ids)
logits = outputs.logits[:, -1, :] # shape: (batch_size, vocab_size)
# Scale logits by temperature
if temperature != 1.0:
logits = logits / temperature
# Filter logits using top-k and/or top-p
logits = self.top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
# Convert to probabilities
probabilities = F.softmax(logits, dim=-1)
# Sample from the distribution
next_token = torch.multinomial(probabilities, num_samples=1) # (batch_size, 1)
# Append next token
generated_ids = torch.cat([generated_ids, next_token], dim=1)
# If eos_token_id is set and any sample produced it, we optionally could break early
if eos_token_id is not None:
# Check if all sequences in the batch ended
# or if you want to do a more fine-grained approach
if (next_token == eos_token_id).all():
break
#print("2=====================")
#print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
#print("2=====================")
return generated_ids |