def ancestral_sample(self, n_samples, z_conds=None, y=None):
z = t.zeros((n_samples, self.n_ctx), dtype=t.long, device='cuda') + \
t.arange(0, self.n_ctx, dtype=t.long, device='cuda').view(1, self.n_ctx)
if z_conds is not None:
z_cond = z_conds[0]
assert_shape(z_cond, (n_samples, self.n_ctx // 4))
assert (z // 4 == repeat(z_cond, 4, 1)).all(), f'z: {z}, z_cond: {z_cond}, diff: {(z // 4) - repeat(z_cond, 4, 1)}'
return z
def primed_sample(self, n_samples, z, z_conds=None, y=None):
prime = z.shape[1]
assert_shape(z, (n_samples, prime))
start = z[:,-1:] + 1
z_rest = (t.arange(0, self.n_ctx - prime, dtype=t.long, device='cuda').view(1, self.n_ctx - prime) + start).view(n_samples, self.n_ctx - prime)
z = t.cat([z, z_rest], dim=1)
if z_conds is not None:
z_cond = z_conds[0]
assert_shape(z_cond, (n_samples, self.n_ctx // 4))
assert (z // 4 == repeat(z_cond, 4, 1)).all(), f'z: {z}, z_cond: {z_cond}, diff: {(z // 4) - repeat(z_cond, 4, 1)}'
return z
def forward(self, x):
N, T = x.shape[0], x.shape[-1]
emb = self.input_emb_width
assert_shape(x, (N, emb, T))
xs = []
# 64, 32, ...
iterator = zip(list(range(self.levels)), self.downs_t, self.strides_t)
for level, down_t, stride_t in iterator:
level_block = self.level_blocks[level]
x = level_block(x)
emb, T = self.output_emb_width, T // (stride_t ** down_t)
assert_shape(x, (N, emb, T))
xs.append(x)
return xs
def get_encoder_kv(self, prime, fp16=False, sample=False):
if self.n_tokens != 0 and self.use_tokens:
if sample:
self.prime_prior.cuda()
N = prime.shape[0]
prime_acts = self.prime_prior(prime, None, None, None, fp16=fp16)
assert_shape(prime_acts, (N, self.prime_loss_dims, self.prime_acts_width))
assert prime_acts.dtype == t.float, f'Expected t.float, got {prime_acts.dtype}'
encoder_kv = self.prime_state_ln(self.prime_state_proj(prime_acts))
assert encoder_kv.dtype == t.float, f'Expected t.float, got {encoder_kv.dtype}'
if sample:
self.prime_prior.cpu()
if fp16:
encoder_kv = encoder_kv.half()
else:
encoder_kv = None
return encoder_kv
def prior_preprocess(self, xs, conds):
N = xs[0].shape[0]
for i in range(len(xs)):
x, shape, dims = xs[i], self.prior_shapes[i], self.prior_dims[i]
bins, bins_shift = int(self.prior_bins[i]), int(self.prior_bins_shift[i])
assert isinstance(x, t.cuda.LongTensor), x
assert (0 <= x).all() and (x < bins).all()
#assert_shape(x, (N, *shape))
xs[i] = (xs[i] + bins_shift).view(N, -1)
for i in range(len(conds)):
cond, shape, dims = conds[i], self.prior_shapes[i], self.prior_dims[i]
if cond is not None:
assert_shape(cond, (N, dims, self.prior_width))
else:
conds[i] = t.zeros((N, dims, self.prior_width), dtype=t.float, device='cuda')
return t.cat(xs, dim=1), t.cat(conds, dim=1)
def sample(self, n_samples, z=None, memory=None, z_conds=None, y=None, fp16=False, temp=1.0, top_k=0, top_p=0.0,
chunk_size=None, sample_tokens=None, train=False):
N = n_samples
if z is not None: assert z.shape[0] == N, f"Expected shape ({N},**), got shape {z.shape}"
if y is not None: assert y.shape[0] == N, f"Expected shape ({N},**), got shape {y.shape}"
if z_conds is not None:
for z_cond in z_conds:
assert z_cond.shape[0] == N, f"Expected shape ({N},**), got shape {z_cond.shape}"
no_past_context = (z is None or z.shape[1] == 0)
if dist.get_rank() == 0:
name = {True: 'Ancestral', False: 'Primed'}[no_past_context]
print(f"{name} sampling {n_samples} samples with temp={temp}, top_k={top_k}, top_p={top_p}")
with t.no_grad():
# Currently x_cond only uses immediately above layer
x_cond, y_cond, prime = self.get_cond(z_conds, y)
if self.single_enc_dec:
# assert chunk_size % self.prime_loss_dims == 0. TODO: Check if needed
if no_past_context:
z, x_cond = self.prior_preprocess([prime], [None, x_cond])
else:
z, x_cond = self.prior_preprocess([prime, z], [None, x_cond])
if sample_tokens is not None:
sample_tokens += self.n_tokens
z = self.prior.primed_sample(n_samples, z, x_cond, y_cond, fp16=fp16, temp=temp,
top_k=top_k, top_p=top_p, chunk_size=chunk_size, sample_tokens=sample_tokens)
z = self.prior_postprocess(z)
else:
encoder_kv = self.get_encoder_kv(prime, fp16=fp16, sample=True)
if no_past_context:
if train:
z = self.prior.sample(n_samples, x_cond, y_cond, encoder_kv, fp16=fp16, temp=temp, top_k=top_k,
top_p=top_p, sample_tokens=sample_tokens)
else:
z, memory = self.prior.sample_recurrent(n_samples, x_cond, y_cond, encoder_kv, fp16=fp16, temp=temp, top_k=top_k,
top_p=top_p, sample_tokens=sample_tokens)
else:
z, memory = self.prior.primed_sample(n_samples, z, memory, x_cond, y_cond, encoder_kv, fp16=fp16, temp=temp,
top_k=top_k, top_p=top_p, chunk_size=chunk_size, sample_tokens=sample_tokens)
if sample_tokens is None:
assert_shape(z, (N, *self.z_shape))
return z, memory
def forward(self, xs, all_levels=True):
if all_levels:
assert len(xs) == self.levels
else:
assert len(xs) == 1
x = xs[-1]
N, T = x.shape[0], x.shape[-1]
emb = self.output_emb_width
assert_shape(x, (N, emb, T))
# 32, 64 ...
iterator = reversed(list(zip(list(range(self.levels)), self.downs_t, self.strides_t)))
for level, down_t, stride_t in iterator:
level_block = self.level_blocks[level]
x = level_block(x)
emb, T = self.output_emb_width, T * (stride_t ** down_t)
assert_shape(x, (N, emb, T))
if level != 0 and all_levels:
x = x + xs[level - 1]
x = self.out(x)
return x
def forward(self, y):
assert len(y.shape) == 2, f"Expected shape with 2 dims, got {y.shape}"
assert y.shape[
-1] == 4 + self.max_bow_genre_size, f"Expected shape (N,{4 + self.max_bow_genre_size}), got {y.shape}"
assert isinstance(
y, t.cuda.LongTensor
), f"Expected dtype {t.cuda.LongTensor}, got {y.dtype}"
N = y.shape[0]
total_length, offset, length, artist, genre = y[:, 0:
1], y[:, 1:
2], y[:, 2:
3], y[:, 3:
4], y[:,
4:]
# Start embedding of length 1
artist_emb = self.artist_emb(artist)
# Empty genre slots are denoted by -1. We mask these out.
mask = (genre >= 0).float().unsqueeze(2)
genre_emb = (self.bow_genre_emb(genre.clamp(0)) * mask).sum(
dim=1, keepdim=True)
start_emb = genre_emb + artist_emb
assert_shape(start_emb, (N, 1, self.out_width))
# Pos embedding of length n_ctx
if self.include_time_signal:
start, end = offset, offset + length
total_length, start, end = total_length.float(), start.float(
), end.float()
pos_emb = self.total_length_emb(
total_length) + self.absolute_pos_emb(
start, end) + self.relative_pos_emb(
start / total_length, end / total_length)
assert_shape(pos_emb, (N, self.n_time, self.out_width))
else:
pos_emb = None
return start_emb, pos_emb
def forward(self, x, x_cond=None):
N = x.shape[0]
assert_shape(x, (N, *self.x_shape))
if x_cond is not None:
assert_shape(x_cond, (N, *self.x_shape, self.width))
else:
x_cond = 0.0
# Embed x
x = x.long()
x = self.x_emb(x)
assert_shape(x, (N, *self.x_shape, self.width))
x = x + x_cond
# Run conditioner
x = self.preprocess(x)
x = self.cond(x)
x = self.postprocess(x)
x = self.ln(x)
return x
def get_alignment(x, zs, labels, prior, fp16, hps):
level = hps.levels - 1 # Top level used
n_ctx, n_tokens = prior.n_ctx, prior.n_tokens
z = zs[level]
bs, total_length = z.shape[0], z.shape[1]
if total_length < n_ctx:
padding_length = n_ctx - total_length
z = t.cat([
z,
t.zeros(bs, n_ctx - total_length, dtype=z.dtype, device=z.device)
],
dim=1)
total_length = z.shape[1]
else:
padding_length = 0
hop_length = int(hps.hop_fraction[level] * prior.n_ctx)
n_head = prior.prior.transformer.n_head
alignment_head, alignment_layer = prior.alignment_head, prior.alignment_layer
attn_layers = set([alignment_layer])
alignment_hops = {}
indices_hops = {}
prior.cuda()
empty_cache()
for start in get_starts(total_length, n_ctx, hop_length):
end = start + n_ctx
# set y offset, sample_length and lyrics tokens
y, indices_hop = prior.get_y(labels, start, get_indices=True)
assert len(indices_hop) == bs
for indices in indices_hop:
assert len(indices) == n_tokens
z_bs = t.chunk(z, bs, dim=0)
y_bs = t.chunk(y, bs, dim=0)
w_hops = []
for z_i, y_i in zip(z_bs, y_bs):
w_hop = prior.z_forward(z_i[:, start:end], [],
y_i,
fp16=fp16,
get_attn_weights=attn_layers)
assert len(w_hop) == 1
w_hops.append(w_hop[0][:, alignment_head])
del w_hop
w = t.cat(w_hops, dim=0)
del w_hops
assert_shape(w, (bs, n_ctx, n_tokens))
alignment_hop = w.float().cpu().numpy()
assert_shape(alignment_hop, (bs, n_ctx, n_tokens))
del w
# alignment_hop has shape (bs, n_ctx, n_tokens)
# indices_hop is a list of len=bs, each entry of len hps.n_tokens
indices_hops[start] = indices_hop
alignment_hops[start] = alignment_hop
prior.cpu()
empty_cache()
# Combine attn for each hop into attn for full range
# Use indices to place them into correct place for corresponding source tokens
alignments = []
for item in range(bs):
# Note each item has different length lyrics
full_tokens = labels['info'][item]['full_tokens']
alignment = np.zeros((total_length, len(full_tokens) + 1))
for start in reversed(get_starts(total_length, n_ctx, hop_length)):
end = start + n_ctx
alignment_hop = alignment_hops[start][item]
indices = indices_hops[start][item]
assert len(indices) == n_tokens
assert alignment_hop.shape == (n_ctx, n_tokens)
alignment[start:end, indices] = alignment_hop
alignment = alignment[:total_length - padding_length, :
-1] # remove token padding, and last lyric index
alignments.append(alignment)
return alignments
