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buttercutter/mamba.py

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Mamba: Linear-Time Sequence Modeling with Selective State Spaces
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mamba.py
# [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/abs/2312.00752)
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from torch.nn import functional as F
from einops import rearrange, repeat
from tqdm import tqdm
import math
import os
import urllib.request
from zipfile import ZipFile
from transformers import AutoTokenizer
torch.autograd.set_detect_anomaly(True)
debugging_is_on = 0
def print_tensor_info(tensor_name, tensor):
# Check if tensor is floating point, and convert if necessary
tensor_float = tensor.float() if not tensor.is_floating_point() else tensor
# Gather the information
info = {
"shape": tuple(tensor.shape),
"min/max": (tensor.min().item(), tensor.max().item()),
"mean": tensor_float.mean().item(),
"std": tensor_float.std().item()
}
# Print the default representation and the extra information
print(f"{tensor_name} = {tensor}")
for key, value in info.items():
print(f"{key}: {value}")
USE_MAMBA = 1
USE_TRANSFORMER = ~USE_MAMBA
DIFFERENT_H_STATES_RECURRENT_UPDATE_MECHANISM = 0
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# User hyperparameters
d_model = 16
state_size = 64 # Example state size
seq_len = 100 # Example sequence length
batch_size = 128 # Example batch size
class S6(nn.Module):
def __init__(self, seq_len, d_model, state_size, device):
super(S6, self).__init__()
self.fc1 = nn.Linear(d_model, d_model, device=device)
self.fc2 = nn.Linear(d_model, state_size, device=device)
self.fc3 = nn.Linear(d_model, state_size, device=device)
self.seq_len = seq_len
self.d_model = d_model
self.state_size = state_size
#self.A = nn.Parameter(torch.ones(d_model, state_size, device=device))
#self.A = nn.Parameter(F.normalize(torch.ones(d_model, state_size, device=device), p=2, dim=-1))
#nn.init.xavier_uniform_(self.A)
# S4D real initialization, MAMBA removed imaginary portions for S4D-Inv and S4D-Lin initialization schemes
# described in [On the Parameterization and Initialization of Diagonal State Space Models](https://arxiv.org/abs/2206.11893)
# https://github.com/state-spaces/mamba/blob/fb7b5310fa865dbd62aa059b1e26f2b431363e2a/mamba_ssm/modules/mamba_simple.py#L103-L108C23
A = repeat(
torch.arange(1, state_size + 1, dtype=torch.float32, device=device),
"n -> d n",
d=d_model,
).contiguous()
A_log = torch.log(A) # For numerical stability during training process
self.A_log = nn.Parameter(A_log)
self.A_log._no_weight_decay = True
self.A = torch.zeros_like(self.A_log)
self.B = torch.zeros(batch_size, self.seq_len, self.state_size, device=device)
self.C = torch.zeros(batch_size, self.seq_len, self.state_size, device=device)
#self.delta = torch.zeros(batch_size, self.seq_len, self.d_model, device=device)
# Initialize delta parameter using a uniform distribution and apply the inverse softplus
uniform_distribution = torch.distributions.Uniform(0.001, 0.1)
# Sample from the uniform distribution and then apply the inverse softplus
self.delta = self.inverse_softplus(uniform_distribution.sample((batch_size, self.seq_len, self.d_model)))
self.dA = torch.zeros(batch_size, self.seq_len, self.d_model, self.state_size, device=device)
self.dB = torch.zeros(batch_size, self.seq_len, self.d_model, self.state_size, device=device)
# h should have dimensions [batch_size, seq_len, d_model, state_size]
self.h = torch.zeros(batch_size, self.seq_len, self.d_model, self.state_size, device=device)
self.y = torch.zeros(batch_size, self.seq_len, self.d_model, device=device)
def inverse_softplus(self, y):
return torch.log(torch.exp(y) - 1)
def discretization(self):
# discretization function is defined based on the MAMBA paper's description using ZOH on page 28
# in Section C : Mechanics on Selective SSMs
# See also "Zero-order hold discretization" maths proof inside https://studywolf.wordpress.com/tag/zero-order-hold/
"""
Here is an explanation of the mathematical rationale for the formulation of Δt used in Mamba:
The key idea is that Δt controls the discretization rate of the continuous SSM dynamics. By making Δt input-dependent, it introduces selectivity into the discrete transition matrices.
Specifically, in Mamba they parameterize Δt as:
Δt = τΔ(Parameter + sΔ(xt))
Where:
- Parameter is a learned scalar parameter that controls the baseline discretization rate
- sΔ(xt) is a projection that makes Δt input-dependent by computing a value based on xt
- τΔ(x) = softplus(x) transforms the result to be positive through the softplus nonlinearity
The rationale for this formulation is:
- Parameter provides a reasonable default discretization rate
- sΔ(xt) injects input-dependence through the projection
- softplus ensures Δt is positive as required to be a valid timestep
- The projection sΔ allows the model to learn to modulate Δt based on the input xt
- This modulation creates selectivity in how rapidly or slowly the states update
So in summary, the learned input-dependent projection allows Δt, and thus the discrete dynamics, to become selective. The softplus and scalar parameter provide useful inductive biases on top of this flexibility.
The end result is discrete transition matrices that are selective on the input, enabling powerful sequence modeling capabilities.
Credit: Claude2 AI chatbot
"""
# For numerical stability during training process
self.A = -torch.exp(self.A_log.float()) # (d_model, state_size)
#print(f"self.A.shape = {self.A.shape}")
#print(f"self.B.shape = {self.B.shape}")
#print(f"self.delta.shape = {self.delta.shape}")
# inverse() only supports square matrix
#dB = torch.matmul(torch.inverse(A * delta), torch.matmul(dA - torch.eye(A.shape[0]), B))
self.dB = torch.einsum("bld,bln->bldn", self.delta, self.B)
# https://github.com/state-spaces/mamba/blob/0131c1e94a46fc9f70bcfc9d57962963bb2f0b9e/mamba_ssm/modules/mamba_simple.py#L240
#dA = torch.matrix_exp(A * delta) # matrix_exp() only supports square matrix
self.dA = torch.exp(torch.einsum("bld,dn->bldn", self.delta, self.A))
#print(f"self.dA.shape = {self.dA.shape}")
#print(f"self.dA.requires_grad = {self.dA.requires_grad}")
return self.dA, self.dB
def forward(self, x):
# Refer to Algorithm 2 in the MAMBA paper
self.B = self.fc2(x)
self.C = self.fc3(x)
# "a large ∆ resets the state `h` and focuses on the current input `x`,
# while a small ∆ persists the state and ignores the current input."
self.delta = F.softplus(self.fc1(x))
# Uses ZOH as in MAMBA, Hungry Hippo still uses bilinear transform for discretization
self.discretization()
if DIFFERENT_H_STATES_RECURRENT_UPDATE_MECHANISM: # this will trigger in-place runtime error if without using `h_new`
#print(f"self.dA = {self.dA}, self.dB = {self.dB}")
#print(f"self.dA.shape = {self.dA.shape}")
#print(f"self.dB.shape = {self.dB.shape}")
#print(f"x.shape = {x.shape}")
#print(f"self.h.shape = {self.h.shape}")
#print(f"self.C.shape = {self.C.shape}")
global current_batch_size
current_batch_size = x.shape[0]
if self.h.shape[0] != current_batch_size:
#print("Adjusting h_new for the different batch size of input data `x`")
different_batch_size = True
# Resize self.h to match the current batch size
h_new = torch.einsum('bldn,bldn->bldn', self.dA, self.h[:current_batch_size, ...]) + rearrange(x, "b l d -> b l d 1") * self.dB
else:
different_batch_size = False
h_new = torch.einsum('bldn,bldn->bldn', self.dA, self.h) + rearrange(x, "b l d -> b l d 1") * self.dB
# y needs to have a shape of [batch_size, seq_len, d_model]
self.y = torch.einsum('bln,bldn->bld', self.C, h_new)
# Update self.h with the detached state of h_new
# Only do this if retaining gradients for self.h is not necessary for backprop
# Otherwise, store h_new in a temporary list and update self.h after the loop
global temp_buffer
temp_buffer = h_new.detach().clone() if not self.h.requires_grad else h_new.clone()
#print(f"temp_buffer.shape = {temp_buffer.shape}")
#print(f"self.y = {self.y}")
#print(f"self.dA.requires_grad = {self.dA.requires_grad}")
#print(f"self.dB.requires_grad = {self.dB.requires_grad}")
#print(f"self.C.requires_grad = {self.C.requires_grad}")
#print(f"self.h.requires_grad = {self.h.requires_grad}")
#print(f"self.y.requires_grad = {self.y.requires_grad}")
return self.y
else: # this will not trigger in-place runtime error
# h should have dimensions [batch_size, seq_len, d_model, state_size]
h = torch.zeros(x.size(0), self.seq_len, self.d_model, self.state_size, device=x.device)
y = torch.zeros_like(x)
h = torch.einsum('bldn,bldn->bldn', self.dA, h) + rearrange(x, "b l d -> b l d 1") * self.dB
# y needs to have a shape of [batch_size, seq_len, d_model]
y = torch.einsum('bln,bldn->bld', self.C, h)
return y
class MambaBlock(nn.Module):
def __init__(self, seq_len, d_model, state_size, device):
super(MambaBlock, self).__init__()
self.inp_proj = nn.Linear(d_model, 2*d_model, device=device)
self.out_proj = nn.Linear(2*d_model, d_model, device=device)
# For residual skip connection
self.D = nn.Linear(d_model, 2*d_model, device=device)
# Set _no_weight_decay attribute on bias
self.out_proj.bias._no_weight_decay = True
# Initialize bias to a small constant value
nn.init.constant_(self.out_proj.bias, 1.0)
self.S6 = S6(seq_len, 2*d_model, state_size, device)
# Add 1D convolution with kernel size 3
self.conv = nn.Conv1d(seq_len, seq_len, kernel_size=3, padding=1, device=device)
# rmsnorm
self.norm = RMSNorm(d_model, device=device)
def forward(self, x, attention_mask=None):
if attention_mask is not None:
# Apply the attention mask
x = x * attention_mask.unsqueeze(-1)
"""
x_proj.shape = torch.Size([batch_size, seq_len, 2*d_model])
x_conv.shape = torch.Size([batch_size, seq_len, 2*d_model])
x_conv_act.shape = torch.Size([batch_size, seq_len, 2*d_model])
"""
# Refer to Figure 3 in the MAMBA paper
x = self.norm(x)
x_proj = self.inp_proj(x)
#print(f"x_proj.shape = {x_proj.shape}")
# Add 1D convolution with kernel size 3
x_conv = self.conv(x_proj)
# Create a triangular mask of the same shape as the input sequence
mask = torch.tril(torch.ones(seq_len, 2*d_model, device=device))
# Add batch dimension with unsqueeze(0) -> (1, seq_len, seq_len)
# Repeat batch dim to match x_conv batches with .repeat()
current_batch_size = x.shape[0]
mask = mask.repeat(current_batch_size, 1, 1)
# Apply causal mask to zero out the masked regions
x_conv = x_conv * mask
#print(f"x_conv.shape = {x_conv.shape}")
x_conv_act = F.silu(x_conv) # Swish activation can be implemented as x * sigmoid(x)
#print(f"x_conv_act.shape = {x_conv_act.shape}")
x_ssm = self.S6(x_conv_act)
#print(f"x_ssm.shape = {x_ssm.shape}")
# residual skip connection with nonlinearity introduced by multiplication
x_residual = F.silu(self.D(x))
#print(f"x_residual.shape = {x_residual.shape}")
x_combined = x_ssm * x_residual
#print(f"x_combined.shape = {x_combined.shape}")
x_out = self.out_proj(x_combined)
#print(f"x_out.shape = {x_out.shape}")
return x_out
class Mamba(nn.Module):
def __init__(self, seq_len, d_model, state_size, vocab_size, device):
super(Mamba, self).__init__()
if vocab_size is None:
vocab_size = d_model
self.mamba_block1 = MambaBlock(seq_len, d_model, state_size, device)
self.mamba_block2 = MambaBlock(seq_len, d_model, state_size, device)
self.mamba_block3 = MambaBlock(seq_len, d_model, state_size, device)
self.final_proj = nn.Linear(d_model, vocab_size, device=device)
def forward(self, x, attention_mask=None):
x = self.mamba_block1(x, attention_mask)
x = self.mamba_block2(x, attention_mask)
x = self.mamba_block3(x, attention_mask)
x = self.final_proj(x)
return x
class RMSNorm(nn.Module):
def __init__(self,
d_model: int,
eps: float = 1e-5,
device: str ='cuda'):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(d_model, device=device))
def forward(self, x):
output = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) * self.weight
return output
# Example usage:
# Create a random input tensor
if USE_MAMBA:
x = torch.rand(batch_size, seq_len, d_model, device=device)
# Create the Mamba model
mamba = Mamba(seq_len, d_model, state_size, None, device)
# rmsnorm
norm = RMSNorm(d_model)
x = norm(x)
# Forward pass
test_output = mamba(x)
print(f"test_output.shape = {test_output.shape}") # Should be [batch_size, seq_len, d_model]
class Enwiki8Dataset(Dataset):
def __init__(self, data):
self.data = data
def __len__(self):
return len(self.data['encoded_inputs'])
def __getitem__(self, idx):
item = {key: val[idx].clone().detach() for key, val in self.data.items()}
return item
# Define a function for padding
def pad_sequences_3d(sequences, max_len=None, pad_value=0):
if sequences.ndim == 3:
# Assuming sequences is a tensor of shape (batch_size, seq_len, feature_size)
batch_size, seq_len, feature_size = sequences.shape
else:
# Assuming sequences is a tensor of shape (batch_size, seq_len)
batch_size, seq_len = sequences.shape
if max_len is None:
max_len = seq_len + 1
if sequences.ndim == 3:
# Initialize padded_sequences with the pad_value
padded_sequences = torch.full((batch_size, max_len, feature_size), fill_value=pad_value, dtype=sequences.dtype, device=sequences.device)
# Pad each sequence to the max_len
padded_sequences[:, :seq_len, :] = sequences
else:
# Initialize padded_sequences with the pad_value
padded_sequences = torch.full((batch_size, max_len), fill_value=pad_value, dtype=sequences.dtype, device=sequences.device)
# Pad each sequence to the max_len
padded_sequences[:, :seq_len] = sequences
return padded_sequences
def train(model, tokenizer, data_loader, optimizer, criterion, device, max_grad_norm=1.0, DEBUGGING_IS_ON=False):
model.train()
total_loss = 0
for batch in data_loader:
optimizer.zero_grad()
original_data = batch['input_ids'].clone().to(device) # data without downsized dimension
input_data = batch['encoded_inputs'].clone().to(device) # data with downsized dimension for Mamba model
attention_mask = batch['attention_mask'].clone().to(device)
# In most sequence modeling tasks, like language modeling, the target should be the next token
# in the sequence rather than the input token itself.
# This is because the model's goal is to predict the next word given the previous words.
# Shift the input data by one position to get the target, so that each target token
# is the next token following the input token.
target = original_data[:, 1:]
input_data = input_data[:, :-1]
#print("Before padding: ")
#print(f"target.shape = {target.shape}")
#print(f"input_data.shape = {input_data.shape}")
# Pad all the sequences in the batch:
input_data = pad_sequences_3d(input_data, pad_value=tokenizer.pad_token_id)
target = pad_sequences_3d(target, max_len=original_data.size(1), pad_value=tokenizer.pad_token_id)
#print("After padding: ")
#print(f"target.shape = {target.shape}")
#print(f"input_data.shape = {input_data.shape}")
# For Mamba model, it can only accept downsized `input_data` due to RAM memory restriction
# and already have a final_proj layer to upsize the `output` dimension to be the same as `target`
output = model(input_data, attention_mask)
#print(f"Output shape: {output.shape}")
#print(f"Target shape: {target.shape}")
loss = criterion(output.view(-1, vocab_size), target.view(-1))
loss.backward(retain_graph=True)
# Clip gradients: gradients are modified in place
#torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)
for name, param in model.named_parameters():
if 'out_proj.bias' not in name:
# clip weights but not bias for out_proj
torch.nn.utils.clip_grad_norm_(param, max_norm=max_grad_norm)
if DEBUGGING_IS_ON:
print("DEBUGGING IS ON !!!")
print_tensor_info("output", output)
print_tensor_info("target", target)
for name, parameter in model.named_parameters():
if parameter.grad is not None:
print(f"{name} gradient: {parameter.grad.data.norm(2)}")
else:
print(f"{name} has no gradient")
if USE_MAMBA and DIFFERENT_H_STATES_RECURRENT_UPDATE_MECHANISM:
# update self.h from temp_buffer
#print(f"temp_buffer = {temp_buffer}")
#print(f"temp_buffer.shape = {temp_buffer.shape}")
#print(f"current_batch_size = {current_batch_size}")
model.S6.h[:current_batch_size, ...].copy_(temp_buffer)
optimizer.step()
total_loss += loss.item()
return total_loss / len(data_loader)
def evaluate(model, data_loader, criterion, device, DEBUGGING_IS_ON=False):
model.eval()
total_loss = 0
with torch.no_grad():
for batch in data_loader:
original_data = batch['input_ids'].clone().to(device) # data without downsized dimension
input_data = batch['encoded_inputs'].clone().detach().to(device) # data with downsized dimension for Mamba model
attention_mask = batch['attention_mask'].clone().detach().to(device)
# In most sequence modeling tasks, like language modeling, the target should be the next token
# in the sequence rather than the input token itself.
# This is because the model's goal is to predict the next word given the previous words.
# Shift the input data by one position to get the target, so that each target token
# is the next token following the input token.
target = original_data[:, 1:]
input_data = input_data[:, :-1]
#print("Before padding: ")
#print(f"target.shape = {target.shape}")
#print(f"input_data.shape = {input_data.shape}")
# Pad all the sequences in the batch:
input_data = pad_sequences_3d(input_data, pad_value=tokenizer.pad_token_id)
target = pad_sequences_3d(target, max_len=original_data.size(1), pad_value=tokenizer.pad_token_id)
#print("After padding: ")
#print(f"target.shape = {target.shape}")
#print(f"input_data.shape = {input_data.shape}")
# For Mamba model, it can only accept downsized `input_data` due to RAM memory restriction
# and already have a final_proj layer to upsize the `output` dimension to be the same as `target`
output = model(input_data, attention_mask)
#print(f"Output shape: {output.shape}")
#print(f"Target shape: {target.shape}")
loss = criterion(output.view(-1, vocab_size), target.view(-1))
total_loss += loss.item()
if DEBUGGING_IS_ON:
print("DEBUGGING IS ON !!!")
print_tensor_info("output", output)
print_tensor_info("target", target)
return total_loss / len(data_loader)
def calculate_perplexity(loss):
return math.exp(loss)
def load_enwiki8_dataset():
print(f"Download and extract enwiki8 data")
url = "http://mattmahoney.net/dc/enwik8.zip"
urllib.request.urlretrieve(url, "enwik8.zip")
with ZipFile("enwik8.zip") as f:
data = f.read("enwik8").decode("utf-8")
return data
# Tokenize and encode the dataset
def encode_dataset(tokenizer, text_data):
def batch_encode(tokenizer, text_data, batch_size=1000):
# Tokenize in batches
batched_input_ids = []
for i in range(0, len(text_data), batch_size):
batch = text_data[i:i+batch_size]
inputs = tokenizer(batch, add_special_tokens=True, truncation=True,
padding='max_length', max_length=seq_len,
return_tensors='pt')
batched_input_ids.append(inputs['input_ids'])
return torch.cat(batched_input_ids)
# Assuming enwiki8_data is a list of sentences
input_ids = batch_encode(tokenizer, enwiki8_data)
# vocab_size is the number of unique tokens in the tokenizer's vocabulary
global vocab_size
vocab_size = len(tokenizer.vocab) # Note that for some tokenizers, we might access the vocab directly
print(f"vocab_size = {vocab_size}")
# Create an embedding layer
# embedding_dim is the size of the embedding vectors (MAMBA model's D)
embedding_layer = nn.Embedding(num_embeddings=vocab_size, embedding_dim=d_model)
# Pass `input_ids` through the embedding layer
# This will change `input_ids` from shape [B, L] to [B, L, D]
#encoded_inputs = embedding_layer(input_ids) ## this eats memory, so use batched_embedding_calls instead
def batch_embedding_calls(input_ids, embedding_layer, batch_size=256):
# Check if input_ids is already a tensor, if not convert it
if not isinstance(input_ids, torch.Tensor):
input_ids = torch.tensor(input_ids, dtype=torch.long)
# Calculate the number of batches needed
num_batches = math.ceil(input_ids.size(0) / batch_size)
# List to hold the output embeddings
output_embeddings = []
# Process each batch
for i in range(num_batches):
# Calculate start and end indices for the current batch
start_idx = i * batch_size
end_idx = start_idx + batch_size
# Get the batch
input_id_batch = input_ids[start_idx:end_idx]
# Call the embedding layer
with torch.no_grad(): # No need gradients for this operation
batch_embeddings = embedding_layer(input_id_batch)
# Append the result to the list
output_embeddings.append(batch_embeddings)
# Concatenate the embeddings from each batch into a single tensor
all_embeddings = torch.cat(output_embeddings, dim=0)
return all_embeddings
# `input_ids` is a list or tensor of the input IDs and `embedding_layer` is model's embedding layer
if USE_MAMBA:
# Set `batch_size` to a value that works for memory constraints
# batch_embedding_calls() is very slow, not suitable to implement directly during forward pass
encoded_inputs = batch_embedding_calls(input_ids, embedding_layer, batch_size=1).float()
elif USE_TRANSFORMER:
encoded_inputs = input_ids.long() # Cast input_ids to long if necessary
attention_mask = (input_ids != tokenizer.pad_token_id).type(input_ids.dtype)
#print(f"attention_mask.shape = {attention_mask.shape}")
#print(f"encoded_inputs.shape = {encoded_inputs.shape}")
return encoded_inputs, attention_mask, input_ids
# Load a pretrained tokenizer
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
#tokenizer = AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b')
# Use an existing special token as the padding token.
#tokenizer.pad_token = tokenizer.eos_token
# Assuming encoded_inputs is a preprocessed tensor of shape [num_samples, seq_len, d_model]
if USE_MAMBA:
encoded_inputs_file = 'encoded_inputs_mamba.pt'
elif USE_TRANSFORMER:
encoded_inputs_file = 'encoded_inputs_transformer.pt'
if os.path.exists(encoded_inputs_file):
print("Loading pre-tokenized data...")
encoded_inputs = torch.load(encoded_inputs_file)
else:
print("Tokenizing raw data...")
enwiki8_data = load_enwiki8_dataset()
encoded_inputs, attention_mask, input_ids = encode_dataset(tokenizer, enwiki8_data)
torch.save(encoded_inputs, encoded_inputs_file)
print(f"finished tokenizing data")
# Combine into a single dictionary
data = {
'input_ids': input_ids,
'encoded_inputs': encoded_inputs,
'attention_mask': attention_mask
}
# Split the data into train and validation sets
total_size = len(data['encoded_inputs'])
train_size = int(total_size * 0.8)
train_data = {key: val[:train_size] for key, val in data.items()}
val_data = {key: val[train_size:] for key, val in data.items()}
train_dataset = Enwiki8Dataset(train_data)
val_dataset = Enwiki8Dataset(val_data)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
# Initialize the model
if USE_MAMBA:
model = Mamba(seq_len, d_model, state_size, vocab_size, device).to(device)
elif USE_TRANSFORMER:
from transformers import AutoModel
# Create TinyBert model instance
bert_model = AutoModel.from_pretrained("prajjwal1/bert-tiny").to(device)
print(f"bert_model.config.hidden_size = {bert_model.config.hidden_size}")
class NextTokenPredictor(nn.Module):
def __init__(self, bert_model, vocab_size):
super(NextTokenPredictor, self).__init__()
self.bert = bert_model
self.predictor = nn.Linear(bert_model.config.hidden_size, vocab_size)
def forward(self, input_ids, attention_mask):
outputs = self.bert(input_ids, attention_mask=attention_mask)
sequence_output = outputs.last_hidden_state
prediction_scores = self.predictor(sequence_output)
return prediction_scores
model = NextTokenPredictor(bert_model, vocab_size).to(device)
# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.AdamW(model.parameters(), lr=5e-6)
# Training loop
num_epochs = 25 # Number of epochs to train for
for epoch in tqdm(range(num_epochs)): # loop over the dataset multiple times
train_loss = train(model, tokenizer, train_loader, optimizer, criterion, device, max_grad_norm=10.0, DEBUGGING_IS_ON=debugging_is_on)
val_loss = evaluate(model, val_loader, criterion, device, DEBUGGING_IS_ON=debugging_is_on)
val_perplexity = calculate_perplexity(val_loss)
print(f'Epoch: {epoch+1}, Training Loss: {train_loss:.4f}, Validation Loss: {val_loss:.4f}, Validation Perplexity: {val_perplexity:.4f}')
if train_loss < 0 or val_loss < 0:
debugging_is_on = 1
Raw
negative_training_loss.log
test_output.shape = torch.Size([256, 100, 8])
/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning:
The secret `HF_TOKEN` does not exist in your Colab secrets.
To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.
You will be able to reuse this secret in all of your notebooks.
Please note that authentication is recommended but still optional to access public models or datasets.
warnings.warn(
tokenizer_config.json: 100%
28.0/28.0 [00:00<00:00, 474B/s]
config.json: 100%
570/570 [00:00<00:00, 15.4kB/s]
vocab.txt: 100%
232k/232k [00:00<00:00, 2.79MB/s]
tokenizer.json: 100%
466k/466k [00:00<00:00, 4.03MB/s]
Tokenizing raw data...
Download and extract enwiki8 data
vocab_size = 30522
finished tokenizing data
4%|▍ | 1/25 [01:19<31:51, 79.63s/it]
Streaming output truncated to the last 5000 lines.
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device='cuda:0', grad_fn=<ViewBackward0>)
shape: (256, 100, 8)
min/max: (0.963894784450531, 1.03498375415802)
mean: 1.0009433031082153
std: 0.007903832010924816
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shape: (256, 100, 8)
min/max: (-4.062912464141846, 3.9631638526916504)
mean: -0.039700813591480255
std: 0.99717116355896
mamba_block1.inp_proj.weight gradient: 6.656166988250334e-06
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mamba_block1.out_proj.weight gradient: 8.017977961571887e-05
mamba_block1.out_proj.bias gradient: 0.0019019206520169973
mamba_block1.D.weight gradient: 2.1973764887661673e-05
mamba_block1.D.bias gradient: 2.58996915363241e-05
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DEBUGGING IS ON !!!
output = tensor([[[1.0053, 1.0070, 0.9965, ..., 1.0037, 1.0034, 0.9951],
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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DEBUGGING IS ON !!!
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