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A10G
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import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from .. import models
from ..models import register
@register("bottleneck")
class Bottleneck(nn.Module):
def __init__(
self,
bottleneck_dim: int,
input_dim: int,
output_dim: int,
token_nums: int,
regularizer=None,
**kwargs
):
super().__init__()
self.token_nums = token_nums
self.input_dim = input_dim
self.output_dim = output_dim
if bottleneck_dim > 0:
self.bottleneck_dim = bottleneck_dim
else:
assert self.input_dim == self.output_dim, "input_dim and output_dim must be the same when bottleneck_dim is not specified"
self.bottleneck_dim = self.input_dim
self.project_dim = self.bottleneck_dim
if self.bottleneck_dim > 0:
self.in_linear = nn.Linear(self.input_dim, self.project_dim)
self.out_linear = nn.Linear(self.bottleneck_dim, self.output_dim)
else:
self.in_linear = self.out_linear = lambda x: x
regularizer['args']['dim'] = self.bottleneck_dim
regularizer['args']['token_nums'] = self.token_nums
self.regularizer = models.make(regularizer)
def project_in(self, x):
assert len(x.shape) == 3, "Input shape must be (batch, n_tokens, e_dim)"
z = self.in_linear(x)
return z
def project_out(self, z_cat):
z = self.out_linear(z_cat)
return z
def decode(self, bottleneck_rep):
regularized_z = self.regularizer.decode(bottleneck_rep)
return self.project_out(regularized_z)
def forward(self, x):
z = self.project_in(x)
projected_z = z
regularized_output = self.regularizer(z)
x_hat = self.project_out(regularized_output['regularized_z'])
bottleneck_rep = regularized_output.pop('bottleneck_rep')
return {
'output': x_hat,
'bottleneck_rep': bottleneck_rep,
'projected_z': projected_z,
**regularized_output,
}
@register("simvq")
class SimVectorQuantizer(nn.Module):
def __init__(
self,
dim,
codebook_size,
l2_normalized=False,
same_index_shape=True,
stochastic=False,
stochastic_temperature=1.0,
**kwargs,
):
super().__init__()
self.codebook_size = codebook_size
self.dim = dim
assert isinstance(l2_normalized, bool)
self.l2_normalized = l2_normalized
self.stochastic = stochastic
self.eval_deterministic = False
self.default_stochastic_temperature = stochastic_temperature
if self.stochastic:
if stochastic_temperature > 0: # fixed temperature
self.stochastic_temperature_inv = 1 / stochastic_temperature
else: # set stochastic_temperature < 0 to use learnable temperature
self.stochastic_temperature_inv = nn.Parameter(torch.tensor(10.0))
# for clear inference code, we remove the codebook init from LLM's embedding
self.embedding = nn.Embedding(self.codebook_size, self.dim)
self.embedding_proj = nn.Linear(self.dim, self.dim)
self.same_index_shape = same_index_shape
def set_eval_deterministic(self, deterministic=True):
self.eval_deterministic = deterministic
def set_stochastic_temperature(self, temperature):
self.stochastic_temperature_inv = 1 / temperature
@torch.autocast(device_type='cuda', enabled=False)
def get_emb(self):
emb = self.embedding_proj(self.embedding.weight)
if self.l2_normalized:
emb = F.normalize(emb, p=2, dim=-1)
# assert emb.dtype == torch.float32, f"Embedding weight dtype is {emb.dtype}, expected float32"
return emb
@torch.autocast(device_type='cuda', enabled=False)
def forward(self, z):
emb = self.get_emb()
z = z.to(emb)
# z = z.float()
assert len(z.shape) == 3, "Input shape must be (batch, n_tokens, e_dim)"
if self.l2_normalized:
z = F.normalize(z, p=2, dim=-1)
z_flattened = rearrange(z, 'b n d -> (b n) d')
if self.stochastic:
# sample the softmaxed cosine similarity
assert self.l2_normalized, "Stochastic sampling requires l2 normalization"
cos_sim = torch.einsum("bd,nd->bn", z_flattened, emb)
probs = F.softmax(cos_sim * self.stochastic_temperature_inv, dim=-1)
if self.eval_deterministic and not self.training:
q_indices = torch.argmax(probs, dim=-1)
else:
q_indices = torch.multinomial(probs, 1).squeeze(-1)
else:
d = (
torch.sum(z_flattened**2, dim=1, keepdim=True)
+ torch.sum(emb**2, dim=1)
- 2
* torch.einsum(
"bd,dn->bn", z_flattened, rearrange(emb, "n d -> d n")
)
)
q_indices = torch.argmin(d, dim=1)
quantized = F.embedding(q_indices, emb, self.embedding.padding_idx, self.embedding.max_norm,
self.embedding.norm_type, self.embedding.scale_grad_by_freq, self.embedding.sparse).view(z.shape) # (b, n, d)
# preserve gradients
quantized = z + (quantized - z).detach()
if self.same_index_shape:
q_indices = q_indices.reshape(quantized.shape[0], quantized.shape[1])
return_dict = {
'unregularized_z': z, # but l2 normalized if l2_normalized=True
'emb': emb, # but l2 normalized if l2_normalized=True
'regularized_z': quantized,
'bottleneck_rep': q_indices
}
return return_dict
def get_codebook_entry(self, indices, shape=None):
# shape specifying (batch, height, width, channel)
indices_shape = indices.shape
indices_flatten = rearrange(indices, '... -> (...)')
# get quantized latent vectors
emb = self.get_emb()
z_q = F.embedding(indices_flatten, emb)
# z_q = self.embedding(indices_flatten)
if self.l2_normalized:
z_q = F.normalize(z_q, p=2, dim=-1)
if shape is not None:
z_q = z_q.reshape(shape)
else:
z_q = z_q.reshape([*indices_shape, self.dim])
return z_q
def decode(self, indices):
return self.get_codebook_entry(indices) |