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import numpy as np
import torch as t
import torch.nn as nn
import torch.nn.functional as F
from jukebox.transformer.ops import filter_logits
from jukebox.transformer.transformer import Transformer
from jukebox.utils.logger import get_range
from jukebox.utils.torch_utils import empty_cache
def get_normal(*shape, std=0.01):
w = t.empty(shape)
nn.init.normal_(w, std=std)
return w
def roll(x, n):
return t.cat((x[:, -n:], x[:, :-n]), dim=1)
def split_chunks(length, chunk_size):
n_passes = (length + chunk_size - 1) // chunk_size
chunk_sizes = [*[chunk_size] * (n_passes - 1), (length - 1) % chunk_size + 1]
assert sum(chunk_sizes) == length
return chunk_sizes
class PositionEmbedding(nn.Module):
def __init__(self, input_shape, width, init_scale=1.0, pos_init=False):
super().__init__()
self.input_shape = input_shape
self.input_dims = input_dims = np.prod(input_shape)
self.pos_init = pos_init
if pos_init:
self.register_buffer('pos', t.tensor(get_pos_idx(input_shape)).long())
self._pos_embs = nn.ModuleList()
for i in range(len(input_shape)):
emb = nn.Embedding(input_shape[i], width)
nn.init.normal_(emb.weight, std=0.02)
self._pos_embs.append(emb)
else:
self.pos_emb = nn.Parameter(get_normal(input_dims, width, std=0.01 * init_scale))
def forward(self):
if self.pos_init:
pos_emb = sum([self._pos_embs[i](self.pos[:,i]) for i in range(len(self.input_shape))])
else:
pos_emb = self.pos_emb
return pos_emb
class ConditionalAutoregressive2D(nn.Module):
def __init__(self, input_shape, bins,
width=128, depth=2, heads=1,
attn_dropout=0.0, resid_dropout=0.0, emb_dropout=0.0, mask=True,
zero_out=False, init_scale=1.0, res_scale=False, pos_init=False,
m_attn=0.25, m_mlp=1,
checkpoint_res=0, checkpoint_attn=0, checkpoint_mlp=0,
attn_order=0, blocks=None, spread=None, x_cond=False, y_cond=False,
encoder_dims=0, only_encode=False, merged_decoder=False, prime_len=None):
super().__init__()
self.input_shape = input_shape
self.input_dims = input_dims = np.prod(input_shape)
self.encoder_dims = encoder_dims
self.bins = bins
self.width = width
self.depth = depth
self.x_emb = nn.Embedding(bins, width)
nn.init.normal_(self.x_emb.weight, std=0.02 * init_scale)
self.x_emb_dropout = nn.Dropout(emb_dropout)
self.y_cond = y_cond
self.x_cond = x_cond
if not y_cond:
self.start_token = nn.Parameter(get_normal(1, width, std=0.01 * init_scale))
self.pos_emb = PositionEmbedding(input_shape=input_shape, width=width, init_scale=init_scale, pos_init=pos_init)
self.pos_emb_dropout = nn.Dropout(emb_dropout)
self.transformer = Transformer(n_in=width, n_ctx=input_dims, n_head=heads, n_depth=depth,
attn_dropout=attn_dropout, resid_dropout=resid_dropout,
afn='quick_gelu', scale=True, mask=mask,
zero_out=zero_out, init_scale=init_scale, res_scale=res_scale,
m_attn=m_attn, m_mlp=m_mlp,
checkpoint_attn=checkpoint_attn, checkpoint_mlp=checkpoint_mlp, checkpoint_res=checkpoint_res,
attn_order=attn_order, blocks=blocks, spread=spread,
encoder_dims=encoder_dims, prime_len=prime_len)
self.only_encode = only_encode
self.prime_len = prime_len
if merged_decoder:
# Merged piped model uses this setup
self.add_cond_after_transformer = False
self.share_x_emb_x_out = False
else:
self.add_cond_after_transformer = True
self.share_x_emb_x_out = True
if not only_encode:
self.x_out = nn.Linear(width, bins, bias=False)
if self.share_x_emb_x_out:
self.x_out.weight = self.x_emb.weight
self.loss = t.nn.CrossEntropyLoss()
def preprocess(self, x):
# Input: x is NHWC and uint8. Converted to NL and long
# Can include stuff like bitpacking, reordering here.
N = x.shape[0]
return x.view(N, -1).long()
def postprocess(self, x, sample_tokens=None):
# Convert back from NL and long to NHWC
N = x.shape[0]
assert (0 <= x).all() and (x < self.bins).all()
if sample_tokens is None or sample_tokens==self.input_dims:
return x.view(N, *self.input_shape)
else:
return x.view(N, -1)
def forward(self, x, x_cond=None, y_cond=None, encoder_kv=None, fp16=False, loss_full=False,
encode=False, get_preds=False, get_acts=False, get_sep_loss=False):
# Preprocess.
with t.no_grad():
x = self.preprocess(x)
N, D = x.shape
assert isinstance(x, t.cuda.LongTensor)
assert (0 <= x).all() and (x < self.bins).all()
if self.y_cond:
assert y_cond is not None
assert y_cond.shape == (N, 1, self.width)
else:
assert y_cond is None
if self.x_cond:
assert x_cond is not None
assert x_cond.shape == (N, D, self.width) or x_cond.shape == (N, 1, self.width), f"{x_cond.shape} != {(N, D, self.width)} nor {(N, 1, self.width)}. Did you pass the correct --sample_length?"
else:
assert x_cond is None
x_cond = t.zeros((N, 1, self.width), device=x.device, dtype=t.float)
x_t = x # Target
x = self.x_emb(x) # X emb
x = roll(x, 1) # Shift by 1, and fill in start token
if self.y_cond:
x[:,0] = y_cond.view(N, self.width)
else:
x[:,0] = self.start_token
x = self.x_emb_dropout(x) + self.pos_emb_dropout(self.pos_emb()) + x_cond # Pos emb and dropout
x = self.transformer(x, encoder_kv=encoder_kv, fp16=fp16) # Transformer
if self.add_cond_after_transformer: # Piped doesnt add x_cond
x = x + x_cond
acts = x
if self.only_encode:
return x
x = self.x_out(x) # Predictions
if get_sep_loss:
assert self.prime_len is not None
x_prime = x[:, :self.prime_len].reshape(-1, self.bins)
x_gen = x[:, self.prime_len:].reshape(-1, self.bins)
prime_loss = F.cross_entropy(x_prime, x_t[:, :self.prime_len].reshape(-1)) / np.log(2.)
gen_loss = F.cross_entropy(x_gen, x_t[:, self.prime_len:].reshape(-1)) / np.log(2.)
loss = (prime_loss, gen_loss) # Note order! Prime is first
else:
loss = F.cross_entropy(x.view(-1, self.bins), x_t.view(-1)) / np.log(2.) # Loss
if get_preds:
return loss, x
elif get_acts:
return loss, acts
else:
return loss, None
def get_emb(self, sample_t, n_samples, x, x_cond, y_cond):
N, D = n_samples, self.input_dims
if sample_t == 0:
# Fill in start token
x = t.empty(n_samples, 1, self.width).cuda()
if self.y_cond:
x[:, 0] = y_cond.view(N, self.width)
else:
x[:, 0] = self.start_token
else:
assert isinstance(x, t.cuda.LongTensor)
assert (0 <= x).all() and (x < self.bins).all()
x = self.x_emb(x)
assert x.shape == (n_samples, 1, self.width)
if x_cond.shape == (N, D, self.width):
cond = x_cond[:, sample_t:sample_t + 1, :]
else:
cond = x_cond
x = x + self.pos_emb()[sample_t:sample_t + 1] + cond # Pos emb, dropout is identity at eval time
assert x.shape == (n_samples, 1, self.width)
return x, cond
def sample(self, n_samples, x_cond=None, y_cond=None, encoder_kv=None, fp16=False, temp=1.0, top_k=0, top_p=0.0,
get_preds=False, sample_tokens=None):
assert self.training == False
if sample_tokens is None: sample_tokens=self.input_dims
N, D = n_samples, self.input_dims
if self.y_cond:
assert y_cond is not None
assert y_cond.shape == (N, 1, self.width)
else:
assert y_cond is None
if self.x_cond:
assert x_cond is not None
assert x_cond.shape == (N, D, self.width) or x_cond.shape == (N, 1, self.width), f"Got {x_cond.shape}, expected ({N}, {D}/{1}, {self.width})"
else:
assert x_cond is None
x_cond = t.zeros((N, 1, self.width), dtype=t.float).cuda()
with t.no_grad():
xs, x = [], None
if get_preds:
preds = []
for sample_t in get_range(range(0, sample_tokens)):
x, cond = self.get_emb(sample_t, n_samples, x, x_cond, y_cond)
self.transformer.check_cache(n_samples, sample_t, fp16)
x = self.transformer(x, encoder_kv=encoder_kv, sample=True, fp16=fp16) # Transformer
if self.add_cond_after_transformer:
x = x + cond
assert x.shape == (n_samples, 1, self.width)
x = self.x_out(x) # Predictions
if get_preds:
preds.append(x.clone())
# Adjust logits
x = x / temp
x = filter_logits(x, top_k=top_k, top_p=top_p)
x = t.distributions.Categorical(logits=x).sample() # Sample and replace x
assert x.shape == (n_samples, 1)
xs.append(x.clone())
del x
self.transformer.del_cache()
x = t.cat(xs, dim=1)
if get_preds:
preds = t.cat(preds, dim=1)
x = self.postprocess(x, sample_tokens)
if get_preds:
return x, preds
else:
return x
def primed_sample(self, n_samples, x, x_cond=None, y_cond=None, encoder_kv=None, fp16=False, temp=1.0, top_k=0,
top_p=0.0, get_preds=False, chunk_size=None, sample_tokens=None):
assert self.training == False
if sample_tokens is None: sample_tokens=self.input_dims
# Preprocess.
with t.no_grad():
x = self.preprocess(x)
assert isinstance(x, t.cuda.LongTensor)
assert (0 <= x).all() and (x < self.bins).all()
assert x.shape[0] == n_samples
xs = t.split(x, 1, dim=1)
xs = list(xs)
assert len(xs) < sample_tokens
N, D = n_samples, self.input_dims
if self.y_cond:
assert y_cond is not None
assert y_cond.shape == (N, 1, self.width)
else:
assert y_cond is None
if self.x_cond:
assert x_cond is not None
assert x_cond.shape == (N, D, self.width) or x_cond.shape == (N, 1, self.width), f"Got {x_cond.shape}, expected ({N}, {D}/{1}, {self.width})"
else:
assert x_cond is None
x_cond = t.zeros((N, 1, self.width), dtype=t.float).cuda()
with t.no_grad():
if get_preds:
preds = []
# Fill up key/value cache for past context by runing forward pass.
# We do so in chunks instead of doing the whole past in one forward pass to reduce max memory usage.
if chunk_size is None:
chunk_size = len(xs)
#assert len(xs) % chunk_size == 0, f'expected {len(xs)} to be divisible by {chunk_size}'
chunk_sizes = split_chunks(len(xs), chunk_size)
x_primes = []
start = 0
x = None
for current_chunk_size in get_range(chunk_sizes):
xs_prime, conds_prime = [], []
for sample_t in range(start, start + current_chunk_size):
x_prime, cond_prime = self.get_emb(sample_t, n_samples, x, x_cond, y_cond)
x = xs[sample_t]
xs_prime.append(x_prime)
conds_prime.append(cond_prime)
start = start + current_chunk_size
x_prime, cond_prime = t.cat(xs_prime, dim=1), t.cat(conds_prime, dim=1)
assert x_prime.shape == (n_samples, current_chunk_size, self.width)
assert cond_prime.shape == (n_samples, current_chunk_size, self.width)
del xs_prime
del conds_prime
if not get_preds:
del cond_prime
x_prime = self.transformer(x_prime, encoder_kv=encoder_kv, sample=True, fp16=fp16)
if get_preds:
if self.add_cond_after_transformer:
x_prime = x_prime + cond_prime
assert x_prime.shape == (n_samples, current_chunk_size, self.width)
del cond_prime
x_primes.append(x_prime)
else:
del x_prime
if get_preds:
x_prime = t.cat(x_primes, dim=1)
assert x_prime.shape == (n_samples, len(xs), self.width)
x_prime = self.x_out(x_prime) # Predictions
preds.append(x_prime)
empty_cache()
self.transformer.check_cache(n_samples, len(xs), fp16)
x = xs[-1]
assert x.shape == (n_samples, 1)
empty_cache()
for sample_t in get_range(range(len(xs), sample_tokens)):
x, cond = self.get_emb(sample_t, n_samples, x, x_cond, y_cond)
self.transformer.check_cache(n_samples, sample_t, fp16)
x = self.transformer(x, encoder_kv=encoder_kv, sample=True, fp16=fp16) # Transformer
if self.add_cond_after_transformer:
x = x + cond
assert x.shape == (n_samples, 1, self.width)
x = self.x_out(x) # Predictions
if get_preds:
preds.append(x)
# Adjust logits
x = x / temp
x = filter_logits(x, top_k=top_k, top_p=top_p)
x = t.distributions.Categorical(logits=x).sample() # Sample and replace x
assert x.shape == (n_samples, 1)
xs.append(x.clone())
del x
self.transformer.del_cache()
x = t.cat(xs, dim=1)
if get_preds:
preds = t.cat(preds, dim=1)
x = self.postprocess(x, sample_tokens)
if get_preds:
return x, preds
else:
return x
def check_sample(self, chunk_size):
bs, l, d = (4, self.input_dims, self.width)
prime = int(self.input_dims//8*7)
enc_l = self.encoder_dims
with t.no_grad():
y_cond = t.randn(bs, 1, d).cuda() if self.y_cond else None
x_cond = t.randn(bs, l, d).cuda() if self.x_cond else None
encoder_kv = t.randn(bs, enc_l, d).cuda()
x, preds_sample = self.sample(bs, x_cond, y_cond, encoder_kv, get_preds=True)
loss, preds_forw = self.forward(x, x_cond, y_cond, encoder_kv, get_preds=True)
max_err = t.max(t.abs(preds_sample - preds_forw))
assert max_err <= 1e-6, f"Max err is {max_err} {[i for i in range(l) if t.max(t.abs(preds_sample - preds_forw)[:, i, :]) > 1e-6]}"
x_prime = x.view(bs, -1)[:,:prime]
# unchunked
x, preds_sample = self.primed_sample(bs, x_prime.clone(), x_cond, y_cond, encoder_kv, get_preds=True)
assert (x.view(bs, -1)[:,:prime] == x_prime).all(), "Priming samples don't match"
loss, preds_forw = self.forward(x, x_cond, y_cond, encoder_kv, get_preds=True)
max_err = t.max(t.abs(preds_sample - preds_forw))
assert max_err <= 1e-6, f"Max err is {max_err} {[i for i in range(l) if t.max(t.abs(preds_sample - preds_forw)[:, i, :]) > 1e-6]}"
# chunked
x, preds_sample = self.primed_sample(bs, x_prime.clone(), x_cond, y_cond, encoder_kv, get_preds=True, chunk_size=chunk_size)
assert (x.view(bs, -1)[:,:prime] == x_prime).all(), "Priming samples don't match"
loss, preds_forw = self.forward(x, x_cond, y_cond, encoder_kv, get_preds=True)
max_err = t.max(t.abs(preds_sample - preds_forw))
assert max_err <= 1e-6, f"Max err is {max_err} {[i for i in range(l) if t.max(t.abs(preds_sample - preds_forw)[:, i, :]) > 1e-6]}"
def test_prior(input_shape, encoder_dims, blocks, heads, chunk_size):
bins = 512
width = 32
depth = 2
prime_len = encoder_dims
for x_cond in [True, False]:
for y_cond in [True, False]:
for attn_order in [0,2,6,12]:
prior = ConditionalAutoregressive2D(input_shape, bins,
width=width, depth=depth, heads=heads,
attn_order=attn_order, blocks=blocks,
x_cond=x_cond, y_cond=y_cond,
encoder_dims=encoder_dims, prime_len=prime_len).cuda()
prior.training = False
prior.check_sample(chunk_size)
print(f"Checked x_cond: {x_cond}, y_cond: {y_cond}, attn_order: {attn_order}")
# prior.apply(_convert_mlp_traced)
# prior.check_sample()
# print(f"Checked traced x_cond: {x_cond}, y_cond: {y_cond}")
if __name__ == '__main__':
from jukebox.utils.dist_utils import setup_dist_from_mpi
setup_dist_from_mpi(port=29600)
test_cases = [
((6144,), 384, 64, 2, 23),
((6144,), 384, 64, 2, 8),
((8192,), 512, 128, 2, 16),
]
for test_case in test_cases:
test_prior(*test_case)
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