from abc import abstractmethod
import math
import numpy as np
import torch
import torch as th
import torch.nn as nn
import torch.nn.functional as F
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.music_quantizer import MusicQuantizer
from models.diffusion.fp16_util import convert_module_to_f16, convert_module_to_f32
from models.diffusion.nn import (
conv_nd,
linear,
avg_pool_nd,
zero_module,
normalization,
timestep_embedding,
)
from models.lucidrains.x_transformers import Encoder
from trainer.networks import register_model
from utils.util import checkpoint, print_network, ceil_multiple
class TimestepBlock(nn.Module):
"""
Any module where forward() takes timestep embeddings as a second argument.
"""
@abstractmethod
def forward(self, x, emb):
"""
Apply the module to `x` given `emb` timestep embeddings.
"""
class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
"""
A sequential module that passes timestep embeddings to the children that
support it as an extra input.
"""
def forward(self, x, emb):
for layer in self:
if isinstance(layer, TimestepBlock):
x = layer(x, emb)
else:
x = layer(x)
return x
class Upsample(nn.Module):
"""
An upsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
upsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None, factor=None, ksize=3, pad=1):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
if factor is None:
if dims == 1:
self.factor = 4
else:
self.factor = 2
else:
self.factor = factor
if use_conv:
if dims == 1:
ksize = 5
pad = 2
self.conv = conv_nd(dims, self.channels, self.out_channels, ksize, padding=pad)
def forward(self, x):
assert x.shape[1] == self.channels
if self.dims == 3:
x = F.interpolate(
x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
)
x = F.interpolate(x, scale_factor=self.factor, mode="nearest")
if self.use_conv:
x = self.conv(x)
return x
class Downsample(nn.Module):
"""
A downsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
downsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None, factor=None, ksize=None, pad=None):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
if ksize is None:
ksize = 3
pad = 1
if dims == 1:
ksize = 5
pad = 2
if dims == 1:
stride = 4
elif dims == 2:
stride = 2
else:
stride = (1,2,2)
if factor is not None:
stride = factor
if use_conv:
self.op = conv_nd(
dims, self.channels, self.out_channels, ksize, stride=stride, padding=pad
)
else:
assert self.channels == self.out_channels
self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
def forward(self, x):
assert x.shape[1] == self.channels
return self.op(x)
class ResBlock(TimestepBlock):
"""
A residual block that can optionally change the number of channels.
:param channels: the number of input channels.
:param emb_channels: the number of timestep embedding channels.
:param dropout: the rate of dropout.
:param out_channels: if specified, the number of out channels.
:param use_conv: if True and out_channels is specified, use a spatial
convolution instead of a smaller 1x1 convolution to change the
channels in the skip connection.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param up: if True, use this block for upsampling.
:param down: if True, use this block for downsampling.
"""
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
use_conv=False,
use_scale_shift_norm=False,
dims=2,
up=False,
down=False,
kernel_size=3,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.use_scale_shift_norm = use_scale_shift_norm
padding = 1 if kernel_size == 3 else (2 if kernel_size == 5 else 0)
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding),
)
self.updown = up or down
if up:
self.h_upd = Upsample(channels, False, dims)
self.x_upd = Upsample(channels, False, dims)
elif down:
self.h_upd = Downsample(channels, False, dims)
self.x_upd = Downsample(channels, False, dims)
else:
self.h_upd = self.x_upd = nn.Identity()
self.emb_layers = nn.Sequential(
nn.SiLU(),
linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding)
),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
elif use_conv:
self.skip_connection = conv_nd(
dims, channels, self.out_channels, kernel_size, padding=padding
)
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
def forward(self, x, emb):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
:param x: an [N x C x ...] Tensor of features.
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
:return: an [N x C x ...] Tensor of outputs.
"""
return checkpoint(
self._forward, x, emb
)
def _forward(self, x, emb):
if self.updown:
in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
h = in_rest(x)
h = self.h_upd(h)
x = self.x_upd(x)
h = in_conv(h)
else:
h = self.in_layers(x)
emb_out = self.emb_layers(emb).type(h.dtype)
while len(emb_out.shape) < len(h.shape):
emb_out = emb_out[..., None]
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = th.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
h = h + emb_out
h = self.out_layers(h)
return self.skip_connection(x) + h
class AttentionBlock(nn.Module):
"""
An attention block that allows spatial positions to attend to each other.
Originally ported from here, but adapted to the N-d case.
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
"""
def __init__(
self,
channels,
num_heads=1,
num_head_channels=-1,
use_new_attention_order=False,
do_checkpoint=True,
relative_pos_embeddings=False,
):
super().__init__()
self.channels = channels
self.do_checkpoint = do_checkpoint
if num_head_channels == -1:
self.num_heads = num_heads
else:
assert (
channels % num_head_channels == 0
), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
self.num_heads = channels // num_head_channels
self.norm = normalization(channels)
self.qkv = conv_nd(1, channels, channels * 3, 1)
if use_new_attention_order:
# split qkv before split heads
self.attention = QKVAttention(self.num_heads)
else:
# split heads before split qkv
self.attention = QKVAttentionLegacy(self.num_heads)
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):
if self.do_checkpoint:
return checkpoint(self._forward, x, mask)
else:
return self._forward(x, mask)
def _forward(self, x, mask):
b, c, *spatial = x.shape
x = x.reshape(b, c, -1)
qkv = self.qkv(self.norm(x))
h = self.attention(qkv, mask, self.relative_pos_embeddings)
h = self.proj_out(h)
return (x + h).reshape(b, c, *spatial)
def count_flops_attn(model, _x, y):
"""
A counter for the `thop` package to count the operations in an
attention operation.
Meant to be used like:
macs, params = thop.profile(
model,
inputs=(inputs, timestamps),
custom_ops={QKVAttention: QKVAttention.count_flops},
)
"""
b, c, *spatial = y[0].shape
num_spatial = int(np.prod(spatial))
# We perform two matmuls with the same number of ops.
# The first computes the weight matrix, the second computes
# the combination of the value vectors.
matmul_ops = 2 * b * (num_spatial ** 2) * c
model.total_ops += th.DoubleTensor([matmul_ops])
class QKVAttentionLegacy(nn.Module):
"""
A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
"""
def __init__(self, n_heads):
super().__init__()
self.n_heads = n_heads
def forward(self, qkv, mask=None, rel_pos=None):
"""
Apply QKV attention.
:param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
:return: an [N x (H * C) x T] tensor after attention.
"""
bs, width, length = qkv.shape
assert width % (3 * self.n_heads) == 0
ch = width // (3 * self.n_heads)
q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
scale = 1 / math.sqrt(math.sqrt(ch))
weight = th.einsum(
"bct,bcs->bts", q * scale, k * scale
) # More stable with f16 than dividing afterwards
if rel_pos is not None:
weight = rel_pos(weight.reshape(bs, self.n_heads, weight.shape[-2], weight.shape[-1])).reshape(bs * self.n_heads, weight.shape[-2], weight.shape[-1])
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
if mask is not None:
# The proper way to do this is to mask before the softmax using -inf, but that doesn't work properly on CPUs.
mask = mask.repeat(self.n_heads, 1).unsqueeze(1)
weight = weight * mask
a = th.einsum("bts,bcs->bct", weight, v)
return a.reshape(bs, -1, length)
@staticmethod
def count_flops(model, _x, y):
return count_flops_attn(model, _x, y)
class QKVAttention(nn.Module):
"""
A module which performs QKV attention and splits in a different order.
"""
def __init__(self, n_heads):
super().__init__()
self.n_heads = n_heads
def forward(self, qkv, mask=None, rel_pos=None):
"""
Apply QKV attention.
:param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
:return: an [N x (H * C) x T] tensor after attention.
"""
bs, width, length = qkv.shape
assert width % (3 * self.n_heads) == 0
ch = width // (3 * self.n_heads)
q, k, v = qkv.chunk(3, dim=1)
scale = 1 / math.sqrt(math.sqrt(ch))
weight = th.einsum(
"bct,bcs->bts",
(q * scale).view(bs * self.n_heads, ch, length),
(k * scale).view(bs * self.n_heads, ch, length),
) # More stable with f16 than dividing afterwards
if rel_pos is not None:
weight = rel_pos(weight)
if mask is not None:
# The proper way to do this is to mask before the softmax using -inf, but that doesn't work properly on CPUs.
mask = mask.repeat(self.n_heads, 1).unsqueeze(1)
weight = weight * mask
weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
return a.reshape(bs, -1, length)
@staticmethod
def count_flops(model, _x, y):
return count_flops_attn(model, _x, y)
class UNetMusicModel(nn.Module):
"""
The full UNet model with attention and timestep embedding.
:param in_channels: channels in the input Tensor.
:param model_channels: base channel count for the model.
:param out_channels: channels in the output Tensor.
:param num_res_blocks: number of residual blocks per downsample.
:param attention_resolutions: a collection of downsample rates at which
attention will take place. May be a set, list, or tuple.
For example, if this contains 4, then at 4x downsampling, attention
will be used.
:param dropout: the dropout probability.
:param channel_mult: channel multiplier for each level of the UNet.
:param conv_resample: if True, use learned convolutions for upsampling and
downsampling.
:param dims: determines if the signal is 1D, 2D, or 3D.
:param num_classes: if specified (as an int), then this model will be
class-conditional with `num_classes` classes.
:param num_heads: the number of attention heads in each attention layer.
:param num_heads_channels: if specified, ignore num_heads and instead use
a fixed channel width per attention head.
:param num_heads_upsample: works with num_heads to set a different number
of heads for upsampling. Deprecated.
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
:param resblock_updown: use residual blocks for up/downsampling.
:param use_new_attention_order: use a different attention pattern for potentially
increased efficiency.
"""
def __init__(
self,
in_channels,
input_vec_dim,
model_channels,
out_channels,
num_res_blocks,
attention_resolutions,
dropout=0,
channel_mult=(1, 2, 4, 8),
ar_prior=False,
conv_resample=True,
dims=2,
num_classes=None,
use_fp16=False,
num_heads=1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
use_raw_y_as_embedding=False,
unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
):
super().__init__()
if num_heads_upsample == -1:
num_heads_upsample = num_heads
self.in_channels = in_channels
self.model_channels = model_channels
self.out_channels = out_channels
self.num_res_blocks = num_res_blocks
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.num_classes = num_classes
self.dtype = th.float16 if use_fp16 else th.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
self.unconditioned_percentage = unconditioned_percentage
self.ar_prior = ar_prior
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
if self.ar_prior:
self.ar_input = nn.Linear(input_vec_dim, model_channels)
self.ar_prior_intg = Encoder(
dim=model_channels,
depth=4,
heads=num_heads,
ff_dropout=dropout,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_pos_emb=True,
zero_init_branch_output=True,
ff_mult=1,
)
else:
self.input_converter = nn.Linear(input_vec_dim, model_channels)
self.code_converter = Encoder(
dim=model_channels,
depth=4,
heads=num_heads,
ff_dropout=dropout,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_pos_emb=True,
zero_init_branch_output=True,
ff_mult=1,
)
self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
self.x_processor = conv_nd(dims, in_channels, model_channels, 3, padding=1)
if self.num_classes is not None:
self.label_emb = nn.Embedding(num_classes, time_embed_dim)
self.use_raw_y_as_embedding = use_raw_y_as_embedding
assert not ((self.num_classes is not None) and use_raw_y_as_embedding) # These are mutually-exclusive.
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, model_channels*2, model_channels, 1, padding=0)
)
]
)
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, mult in enumerate(channel_mult):
for _ in range(num_res_blocks):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=int(mult * model_channels),
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = int(mult * model_channels)
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
num_heads=num_heads,
num_head_channels=num_head_channels,
use_new_attention_order=use_new_attention_order,
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
)
if resblock_updown
else Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch
)
)
)
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self._feature_size += ch
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
),
AttentionBlock(
ch,
num_heads=num_heads,
num_head_channels=num_head_channels,
use_new_attention_order=use_new_attention_order,
),
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
),
)
self._feature_size += ch
self.output_blocks = nn.ModuleList([])
for level, mult in list(enumerate(channel_mult))[::-1]:
for i in range(num_res_blocks + 1):
ich = input_block_chans.pop()
layers = [
ResBlock(
ch + ich,
time_embed_dim,
dropout,
out_channels=int(model_channels * mult),
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = int(model_channels * mult)
if ds in attention_resolutions:
layers.append(
AttentionBlock(
ch,
num_heads=num_heads_upsample,
num_head_channels=num_head_channels,
use_new_attention_order=use_new_attention_order,
)
)
if level and i == num_res_blocks:
out_ch = ch
layers.append(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_scale_shift_norm=use_scale_shift_norm,
up=True,
)
if resblock_updown
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
)
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
self.out = nn.Sequential(
normalization(ch),
nn.SiLU(),
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
)
def forward(self, x, timesteps, y, conditioning_free=False):
orig_x_shape = x.shape[-1]
cm = ceil_multiple(x.shape[-1], 16)
if cm != 0:
pc = (cm - x.shape[-1]) / x.shape[-1]
x = F.pad(x, (0, cm - x.shape[-1]))
y = F.pad(y.permute(0,2,1), (0, int(pc * y.shape[-1]))).permute(0,2,1)
unused_params = []
hs = []
emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
if conditioning_free:
expanded_code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1).permute(0,2,1)
if self.ar_prior:
unused_params.extend(list(self.ar_input.parameters()) + list(self.ar_prior_intg.parameters()))
else:
unused_params.extend(list(self.input_converter.parameters()) + list(self.code_converter.parameters()))
else:
code_emb = self.ar_input(y) if self.ar_prior else self.input_converter(y)
if self.training and self.unconditioned_percentage > 0:
unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
device=code_emb.device) < self.unconditioned_percentage
code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(y.shape[0], 1, 1),
code_emb)
code_emb = self.ar_prior_intg(code_emb) if self.ar_prior else self.code_converter(code_emb)
expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=x.shape[-1], mode='nearest')
h = x.type(self.dtype)
expanded_code_emb = expanded_code_emb.type(self.dtype)
h = self.x_processor(h)
h = torch.cat([h, expanded_code_emb], dim=1)
for module in self.input_blocks:
h = module(h, emb)
hs.append(h)
h = self.middle_block(h, emb)
for module in self.output_blocks:
h = th.cat([h, hs.pop()], dim=1)
h = module(h, emb)
h = h.type(x.dtype)
# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
extraneous_addition = 0
for p in unused_params:
extraneous_addition = extraneous_addition + p.mean()
h = h + extraneous_addition * 0
out = self.out(h)
return out[:, :, :orig_x_shape]
class UNetMusicModelWithQuantizer(nn.Module):
def __init__(self, freeze_quantizer_until=20000, **kwargs):
super().__init__()
self.internal_step = 0
self.freeze_quantizer_until = freeze_quantizer_until
self.diff = UNetMusicModel(**kwargs)
self.m2v = MusicQuantizer(inp_channels=256, inner_dim=[1024,1024,512], codevector_dim=1024, codebook_size=512, codebook_groups=2)
self.m2v.quantizer.temperature = self.m2v.min_gumbel_temperature
del self.m2v.up
def update_for_step(self, step, *args):
self.internal_step = step
qstep = max(0, self.internal_step - self.freeze_quantizer_until)
self.m2v.quantizer.temperature = max(
self.m2v.max_gumbel_temperature * self.m2v.gumbel_temperature_decay**qstep,
self.m2v.min_gumbel_temperature,
)
def forward(self, x, timesteps, truth_mel, disable_diversity=False, conditioning_input=None, conditioning_free=False):
quant_grad_enabled = self.internal_step > self.freeze_quantizer_until
with torch.set_grad_enabled(quant_grad_enabled):
proj, diversity_loss = self.m2v(truth_mel, return_decoder_latent=True)
proj = proj.permute(0,2,1)
# Make sure this does not cause issues in DDP by explicitly using the parameters for nothing.
if not quant_grad_enabled:
unused = 0
for p in self.m2v.parameters():
unused = unused + p.mean() * 0
proj = proj + unused
diversity_loss = diversity_loss * 0
diff = self.diff(x, timesteps, proj, conditioning_free=conditioning_free)
if disable_diversity:
return diff
return diff, diversity_loss
def get_debug_values(self, step, __):
if self.m2v.total_codes > 0:
return {'histogram_codes': self.m2v.codes[:self.m2v.total_codes]}
else:
return {}
class UNetMusicModelARPrior(nn.Module):
def __init__(self, freeze_unet=False, **kwargs):
super().__init__()
self.internal_step = 0
self.ar = GptMusicLower(dim=512, layers=12)
for p in self.ar.parameters():
p.DO_NOT_TRAIN = True
p.requires_grad = False
self.diff = UNetMusicModel(ar_prior=True, **kwargs)
if freeze_unet:
for p in self.diff.parameters():
p.DO_NOT_TRAIN = True
p.requires_grad = False
for p in list(self.diff.ar_prior_intg.parameters()) + list(self.diff.ar_input.parameters()):
del p.DO_NOT_TRAIN
p.requires_grad = True
def get_grad_norm_parameter_groups(self):
groups = {
'input_blocks': list(self.diff.input_blocks.parameters()),
'output_blocks': list(self.diff.output_blocks.parameters()),
'ar_prior_intg': list(self.diff.ar_prior_intg.parameters()),
'time_embed': list(self.diff.time_embed.parameters()),
}
return groups
def forward(self, x, timesteps, truth_mel, disable_diversity=False, conditioning_input=None, conditioning_free=False):
with torch.no_grad():
prior = self.ar(truth_mel, conditioning_input, return_latent=True)
diff = self.diff(x, timesteps, prior, conditioning_free=conditioning_free)
return diff
@register_model
def register_unet_diffusion_music_codes(opt_net, opt):
return UNetMusicModelWithQuantizer(**opt_net['args'])
@register_model
def register_unet_diffusion_music_ar_prior(opt_net, opt):
return UNetMusicModelARPrior(**opt_net['args'])
if __name__ == '__main__':
clip = torch.randn(2, 256, 300)
cond = torch.randn(2, 256, 300)
ts = torch.LongTensor([600, 600])
model = UNetMusicModelARPrior(in_channels=256, out_channels=512, model_channels=640, num_res_blocks=3, input_vec_dim=512,
attention_resolutions=(2,4), channel_mult=(1,2,3), dims=1,
use_scale_shift_norm=True, dropout=.1, num_heads=8, unconditioned_percentage=.4, freeze_unet=True)
print_network(model)
model.get_grad_norm_parameter_groups()
ar_weights = torch.load('D:\\dlas\\experiments\\train_music_gpt\\models\\44500_generator_ema.pth')
model.ar.load_state_dict(ar_weights, strict=True)
diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_unet_music\\models\\55500_generator_ema.pth')
pruned_diff_weights = {}
for k,v in diff_weights.items():
if k.startswith('diff.'):
pruned_diff_weights[k.replace('diff.', '')] = v
model.diff.load_state_dict(pruned_diff_weights, strict=False)
torch.save(model.state_dict(), 'sample.pth')
model(clip, ts, cond, cond)