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update environment and fix a bunch of deps

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James Betker 2022-07-24 23:43:25 -06:00
parent 45afefabed
commit f8108cfdb2
16 changed files with 209 additions and 116 deletions
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2
.idea/.gitignore vendored
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22
codes/models/arch_util.py
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@ -483,16 +483,24 @@ class RelativeQKBias(nn.Module):
""" """
Very simple relative position bias scheme which should be directly added to QK matrix. This bias simply applies to Very simple relative position bias scheme which should be directly added to QK matrix. This bias simply applies to
the distance from the given element. the distance from the given element.
If symmetric=False, a different bias is applied to each side of the input element, otherwise the bias is symmetric.
""" """
def __init__(self, l, max_positions=4000): def __init__(self, l, max_positions=4000, symmetric=True):
super().__init__() super().__init__()
self.emb = nn.Parameter(torch.randn(l+1) * .01) if symmetric:
o = torch.arange(0,max_positions) self.emb = nn.Parameter(torch.randn(l+1) * .01)
c = o.unsqueeze(-1).repeat(1,max_positions) o = torch.arange(0,max_positions)
r = o.unsqueeze(0).repeat(max_positions,1) c = o.unsqueeze(-1).repeat(1,max_positions)
M = ((-(r-c).abs())+l).clamp(0,l) r = o.unsqueeze(0).repeat(max_positions,1)
M = ((-(r-c).abs())+l).clamp(0,l)
else:
self.emb = nn.Parameter(torch.randn(l*2+2) * .01)
a = torch.arange(0,max_positions)
c = a.unsqueeze(-1) - a
m = (c >= -l).logical_and(c <= l)
M = (l+c+1)*m
self.register_buffer('M', M, persistent=False) self.register_buffer('M', M, persistent=False)
self.initted = False
def forward(self, n): def forward(self, n):
# Ideally, I'd return this: # Ideally, I'd return this:

7
codes/models/audio/music/unet_diffusion_music_codes.py
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@ -8,7 +8,6 @@ import torch as th
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
import torchvision # For debugging, not actually used. import torchvision # For debugging, not actually used.
from x_transformers.x_transformers import RelativePositionBias
from models.audio.music.gpt_music import GptMusicLower from models.audio.music.gpt_music import GptMusicLower
from models.audio.music.music_quantizer import MusicQuantizer from models.audio.music.music_quantizer import MusicQuantizer
@ -291,10 +290,6 @@ class AttentionBlock(nn.Module):
self.attention = QKVAttentionLegacy(self.num_heads) self.attention = QKVAttentionLegacy(self.num_heads)
self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
if relative_pos_embeddings:
self.relative_pos_embeddings = RelativePositionBias(scale=(channels // self.num_heads) ** .5, causal=False, heads=num_heads, num_buckets=32, max_distance=64)
else:
self.relative_pos_embeddings = None
def forward(self, x, mask=None): def forward(self, x, mask=None):
if self.do_checkpoint: if self.do_checkpoint:
@ -306,7 +301,7 @@ class AttentionBlock(nn.Module):
b, c, *spatial = x.shape b, c, *spatial = x.shape
x = x.reshape(b, c, -1) x = x.reshape(b, c, -1)
qkv = self.qkv(self.norm(x)) qkv = self.qkv(self.norm(x))
h = self.attention(qkv, mask, self.relative_pos_embeddings) h = self.attention(qkv, mask)
h = self.proj_out(h) h = self.proj_out(h)
return (x + h).reshape(b, c, *spatial) return (x + h).reshape(b, c, *spatial)

2
codes/models/audio/tts/diffusion_encoder.py
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@ -6,7 +6,7 @@ from functools import partial
import torch import torch
import torch.nn as nn import torch.nn as nn
from x_transformers.x_transformers import groupby_prefix_and_trim, FixedPositionalEmbedding, default, RotaryEmbedding, \ from x_transformers.x_transformers import groupby_prefix_and_trim, FixedPositionalEmbedding, default, RotaryEmbedding, \
DEFAULT_DIM_HEAD, RelativePositionBias, LearnedAlibiPositionalBias, AlibiPositionalBias, ScaleNorm, RMSNorm, Rezero, \ DEFAULT_DIM_HEAD, RelativePositionBias, LearnedAlibiPositionalBias, AlibiPositionalBias, ScaleNorm, RMSNorm, \
exists, Attention, FeedForward, Scale, ShiftTokens, GRUGating, Residual, cast_tuple, equals, LayerIntermediates, \ exists, Attention, FeedForward, Scale, ShiftTokens, GRUGating, Residual, cast_tuple, equals, LayerIntermediates, \
AttentionLayers, not_equals AttentionLayers, not_equals

8
codes/models/diffusion/unet_diffusion.py
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@ -8,7 +8,6 @@ import torch as th
import torch.nn as nn import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
import torchvision # For debugging, not actually used. import torchvision # For debugging, not actually used.
from x_transformers.x_transformers import RelativePositionBias
from models.diffusion.fp16_util import convert_module_to_f16, convert_module_to_f32 from models.diffusion.fp16_util import convert_module_to_f16, convert_module_to_f32
from models.diffusion.nn import ( from models.diffusion.nn import (
@ -298,7 +297,6 @@ class AttentionBlock(nn.Module):
num_head_channels=-1, num_head_channels=-1,
use_new_attention_order=False, use_new_attention_order=False,
do_checkpoint=True, do_checkpoint=True,
relative_pos_embeddings=False,
): ):
super().__init__() super().__init__()
self.channels = channels self.channels = channels
@ -320,10 +318,6 @@ class AttentionBlock(nn.Module):
self.attention = QKVAttentionLegacy(self.num_heads) self.attention = QKVAttentionLegacy(self.num_heads)
self.proj_out = zero_module(conv_nd(1, channels, channels, 1)) self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
if relative_pos_embeddings:
self.relative_pos_embeddings = RelativePositionBias(scale=(channels // self.num_heads) ** .5, causal=False, heads=num_heads, num_buckets=32, max_distance=64)
else:
self.relative_pos_embeddings = None
def forward(self, x, mask=None): def forward(self, x, mask=None):
if self.do_checkpoint: if self.do_checkpoint:
@ -335,7 +329,7 @@ class AttentionBlock(nn.Module):
b, c, *spatial = x.shape b, c, *spatial = x.shape
x = x.reshape(b, c, -1) x = x.reshape(b, c, -1)
qkv = self.qkv(self.norm(x)) qkv = self.qkv(self.norm(x))
h = self.attention(qkv, mask, self.relative_pos_embeddings) h = self.attention(qkv, mask)
h = self.proj_out(h) h = self.proj_out(h)
return (x + h).reshape(b, c, *spatial) return (x + h).reshape(b, c, *spatial)

51
codes/models/lucidrains/x_transformers.py
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@ -10,11 +10,8 @@ from collections import namedtuple
from einops import rearrange, repeat, reduce from einops import rearrange, repeat, reduce
from einops.layers.torch import Rearrange from einops.layers.torch import Rearrange
from entmax import entmax15
from torch.utils.checkpoint import checkpoint from torch.utils.checkpoint import checkpoint
from x_transformers.autoregressive_wrapper import AutoregressiveWrapper
DEFAULT_DIM_HEAD = 64 DEFAULT_DIM_HEAD = 64
Intermediates = namedtuple('Intermediates', [ Intermediates = namedtuple('Intermediates', [
@ -1274,51 +1271,3 @@ class ContinuousTransformerWrapper(nn.Module):
if len(res) > 1: if len(res) > 1:
return tuple(res) return tuple(res)
return res[0] return res[0]
class XTransformer(nn.Module):
def __init__(
self,
*,
dim,
tie_token_emb=False,
**kwargs
):
super().__init__()
enc_kwargs, kwargs = groupby_prefix_and_trim('enc_', kwargs)
dec_kwargs, kwargs = groupby_prefix_and_trim('dec_', kwargs)
assert 'dim' not in enc_kwargs and 'dim' not in dec_kwargs, 'dimension of either encoder or decoder must be set with `dim` keyword'
enc_transformer_kwargs = pick_and_pop(['num_tokens', 'max_seq_len'], enc_kwargs)
enc_transformer_kwargs['emb_dropout'] = enc_kwargs.pop('emb_dropout', 0)
enc_transformer_kwargs['num_memory_tokens'] = enc_kwargs.pop('num_memory_tokens', None)
enc_transformer_kwargs['use_pos_emb'] = enc_kwargs.pop('use_pos_emb', True)
dec_transformer_kwargs = pick_and_pop(['num_tokens', 'max_seq_len'], dec_kwargs)
dec_transformer_kwargs['emb_dropout'] = dec_kwargs.pop('emb_dropout', 0)
dec_transformer_kwargs['use_pos_emb'] = dec_kwargs.pop('use_pos_emb', True)
self.encoder = TransformerWrapper(
**enc_transformer_kwargs,
attn_layers=Encoder(dim=dim, **enc_kwargs)
)
self.decoder = TransformerWrapper(
**dec_transformer_kwargs,
attn_layers=Decoder(dim=dim, cross_attend=True, **dec_kwargs)
)
if tie_token_emb:
self.decoder.token_emb = self.encoder.token_emb
self.decoder = AutoregressiveWrapper(self.decoder)
@torch.no_grad()
def generate(self, seq_in, seq_out_start, seq_len, src_mask=None, src_attn_mask=None, **kwargs):
encodings = self.encoder(seq_in, mask=src_mask, attn_mask=src_attn_mask, return_embeddings=True)
return self.decoder.generate(seq_out_start, seq_len, context=encodings, context_mask=src_mask, **kwargs)
def forward(self, src, tgt, src_mask=None, tgt_mask=None, src_attn_mask=None):
enc = self.encoder(src, mask=src_mask, attn_mask=src_attn_mask, return_embeddings=True)
out = self.decoder(tgt, context=enc, mask=tgt_mask, context_mask=src_mask)
return out

5
codes/requirements.txt
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@ -30,11 +30,13 @@ Unidecode==1.0.22
tgt == 1.4.4 tgt == 1.4.4
pyworld == 0.2.10 pyworld == 0.2.10
audio2numpy audio2numpy
SoundFile
# For text stuff # For text stuff
transformers transformers
tokenizers tokenizers
jiwer # calculating WER jiwer # calculating WER
omegaconf
# lucidrains stuff # lucidrains stuff
vector_quantize_pytorch vector_quantize_pytorch
@ -42,4 +44,5 @@ linear_attention_transformer
rotary-embedding-torch rotary-embedding-torch
axial_positional_embedding axial_positional_embedding
g-mlp-pytorch g-mlp-pytorch
x-clip x-clip
x_transformers

14
codes/trainer/eval/music_diffusion_fid.py
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@ -315,14 +315,15 @@ class MusicDiffusionFid(evaluator.Evaluator):
if __name__ == '__main__': if __name__ == '__main__':
# For multilevel SR: # For multilevel SR:
"""
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_diffusion_multilevel_sr.yml', 'generator', diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_diffusion_multilevel_sr.yml', 'generator',
also_load_savepoint=False, strict_load=False, also_load_savepoint=False, strict_load=False,
load_path='X:\\dlas\\experiments\\train_music_diffusion_multilevel_sr\\models\\18000_generator.pth' load_path='X:\\dlas\\experiments\\train_music_diffusion_multilevel_sr_archived_prev2\\models\\18000_generator.pth'
).cuda() ).cuda()
opt_eval = {'path': 'Y:\\split\\yt-music-eval', # eval music, mostly electronica. :) opt_eval = {'path': 'Y:\\split\\yt-music-eval', # eval music, mostly electronica. :)
#'path': 'E:\\music_eval', # this is music from the training dataset, including a lot more variety. #'path': 'E:\\music_eval', # this is music from the training dataset, including a lot more variety.
'diffusion_steps': 256, # basis: 192 'diffusion_steps': 64, # basis: 192
'conditioning_free': True, 'conditioning_free_k': 1, 'use_ddim': False, 'clip_audio': True, 'conditioning_free': False, 'conditioning_free_k': 1, 'use_ddim': True, 'clip_audio': True,
'diffusion_schedule': 'cosine', 'diffusion_type': 'chained_sr', 'diffusion_schedule': 'cosine', 'diffusion_type': 'chained_sr',
} }
@ -334,13 +335,12 @@ if __name__ == '__main__':
).cuda() ).cuda()
opt_eval = {'path': 'Y:\\split\\yt-music-eval', # eval music, mostly electronica. :) opt_eval = {'path': 'Y:\\split\\yt-music-eval', # eval music, mostly electronica. :)
#'path': 'E:\\music_eval', # this is music from the training dataset, including a lot more variety. #'path': 'E:\\music_eval', # this is music from the training dataset, including a lot more variety.
'diffusion_steps': 256, # basis: 192 'diffusion_steps': 64, # basis: 192
'conditioning_free': True, 'conditioning_free_k': 1, 'use_ddim': False, 'clip_audio': True, 'conditioning_free': False, 'conditioning_free_k': 1, 'use_ddim': True, 'clip_audio': True,
'diffusion_schedule': 'cosine', 'diffusion_type': 'from_codes_quant', 'diffusion_schedule': 'cosine', 'diffusion_type': 'from_codes_quant',
} }
"""
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 11, 'device': 'cuda', 'opt': {}} env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 12, 'device': 'cuda', 'opt': {}}
eval = MusicDiffusionFid(diffusion, opt_eval, env) eval = MusicDiffusionFid(diffusion, opt_eval, env)
fds = [] fds = []
for i in range(2): for i in range(2):