CLVP v1

codes/models/clip/clvp.py

@@ -0,0 +1,147 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import einsum
+
+from models.arch_util import AttentionBlock
+from models.lucidrains.x_transformers import ContinuousTransformerWrapper, Encoder
+from trainer.networks import register_model
+from utils.util import opt_get, checkpoint
+
+
+def exists(val):
+    return val is not None
+
+
+def masked_mean(t, mask):
+    t = t.masked_fill(~mask, 0.)
+    return t.sum(dim = 1) / mask.sum(dim = 1)
+
+
+class CollapsingTransformer(nn.Module):
+    def __init__(self, model_dim, output_dims, heads, dropout, depth, mask_percentage=0, **encoder_kwargs):
+        super().__init__()
+        self.transformer = ContinuousTransformerWrapper(
+            max_seq_len=-1,
+            use_pos_emb=False,
+            attn_layers=Encoder(
+                dim=model_dim,
+                depth=depth,
+                heads=heads,
+                ff_dropout=dropout,
+                ff_mult=1,
+                attn_dropout=dropout,
+                use_rmsnorm=True,
+                ff_glu=True,
+                rotary_pos_emb=True,
+                **encoder_kwargs,
+            ))
+        self.pre_combiner = nn.Sequential(nn.Conv1d(model_dim, output_dims, 1),
+                                          AttentionBlock(output_dims, num_heads=heads, do_checkpoint=False),
+                                          nn.Conv1d(output_dims, output_dims, 1))
+        self.mask_percentage = mask_percentage
+
+    def forward(self, x, **transformer_kwargs):
+        h = self.transformer(x, **transformer_kwargs)
+        h = h.permute(0,2,1)
+        h = checkpoint(self.pre_combiner, h).permute(0,2,1)
+        if self.training:
+            mask = torch.rand_like(h.float()) > self.mask_percentage
+        else:
+            mask = torch.ones_like(h.float()).bool()
+        return masked_mean(h, mask)
+
+
+class CLVP(nn.Module):
+    """
+    Contrastic Language-Voice Pretraining model for generating embedding that can be used to associate text and
+    speech clips.
+    """
+
+    def __init__(
+            self,
+            model_dim=512,
+            transformer_heads=8,
+            dropout=.1,
+            num_text_tokens=256,
+            text_enc_depth=6,
+            text_mask_percentage=0,
+            conditioning_enc_depth=4,
+            mel_channels=80,
+            speech_enc_depth=6,
+            speech_mask_percentage=0,
+            latent_multiplier=4,
+    ):
+        super().__init__()
+        latent_dim = latent_multiplier*model_dim
+        self.temperature = nn.Parameter(torch.tensor(1.))
+
+        self.cond_emb = nn.Sequential(nn.Conv1d(mel_channels, model_dim//2, kernel_size=5, stride=2, padding=2),
+                                      nn.Conv1d(model_dim//2, model_dim, kernel_size=3, stride=2, padding=1))
+        self.conditioning_transformer = CollapsingTransformer(model_dim, model_dim*2, transformer_heads, dropout, conditioning_enc_depth, 0)
+
+        self.text_emb = nn.Embedding(num_text_tokens, model_dim)
+        self.text_transformer = CollapsingTransformer(model_dim, latent_dim, transformer_heads, dropout, text_enc_depth, text_mask_percentage, use_rms_scaleshift_norm=True)
+        self.to_text_latent = nn.Linear(latent_dim, latent_dim, bias=False)
+
+        self.speech_emb = nn.Conv1d(mel_channels, model_dim, kernel_size=5, padding=2)
+        self.speech_transformer = CollapsingTransformer(model_dim, latent_dim, transformer_heads, dropout, speech_enc_depth, speech_mask_percentage)
+        self.to_speech_latent = nn.Linear(latent_dim, latent_dim, bias=False)
+
+    def get_grad_norm_parameter_groups(self):
+        return {
+            'conditioning': list(self.conditioning_transformer.parameters()),
+            'text': list(self.text_transformer.parameters()),
+            'speech': list(self.speech_transformer.parameters()) + list(self.mel_head.parameters()),
+        }
+
+    def forward(
+            self,
+            text,
+            mel_input,
+            mel_cond,
+            return_loss=False
+    ):
+        b, device = text.shape[0], text.device
+
+        text_emb = self.text_emb(text)
+        cond_emb = self.cond_emb(mel_cond).permute(0,2,1)
+        speech_emb = self.speech_emb(mel_input).permute(0,2,1)
+
+        enc_cond = self.conditioning_transformer(cond_emb)
+        enc_text = self.text_transformer(text_emb, norm_scale_shift_inp=enc_cond)
+        enc_speech = self.speech_transformer(speech_emb)
+
+        text_latents = self.to_text_latent(enc_text)
+        speech_latents = self.to_speech_latent(enc_speech)
+
+        text_latents, speech_latents = map(lambda t: F.normalize(t, p=2, dim=-1), (text_latents, speech_latents))
+
+        temp = self.temperature.exp()
+
+        if not return_loss:
+            sim = einsum('n d, n d -> n', text_latents, speech_latents) * temp
+            return sim
+
+        sim = einsum('i d, j d -> i j', text_latents, speech_latents) * temp
+        labels = torch.arange(b, device=device)
+        loss = (F.cross_entropy(sim, labels) + F.cross_entropy(sim.t(), labels)) / 2
+        return loss
+
+
+@register_model
+def register_clvp(opt_net, opt):
+    return CLVP(**opt_get(opt_net, ['kwargs'], {}))
+
+
+if __name__ == '__main__':
+    clip = CLVP()
+    clip(torch.randint(0,256,(2,120)),
+         torch.randn(2,80,100),
+         torch.randn(2,80,95),
+         return_loss=True)
+    nonloss = clip(torch.randint(0,256,(2,120)),
+         torch.randn(2,80,100),
+         torch.randn(2,80,95),
+         return_loss=False)
+    print(nonloss.shape)

codes/models/lucidrains/x_transformers.py

@@ -319,6 +319,23 @@ class RMSNorm(nn.Module):
         norm = torch.norm(x, dim = -1, keepdim = True) * self.scale
         return x / norm.clamp(min = self.eps) * self.g
 
+class RMSScaleShiftNorm(nn.Module):
+    def __init__(self, dim, eps = 1e-8):
+        super().__init__()
+        self.scale = dim ** -0.5
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(dim))
+        self.scale_shift_process = nn.Linear(dim*2, dim*2)
+
+    def forward(self, x, norm_scale_shift_inp):
+        norm = torch.norm(x, dim = -1, keepdim = True) * self.scale
+        norm = x / norm.clamp(min = self.eps) * self.g
+
+        ss_emb = self.scale_shift_process(norm_scale_shift_inp)
+        scale, shift = torch.chunk(ss_emb, 2, dim=1)
+        h = norm * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+        return h
+
 # residual and residual gates
 
 class Residual(nn.Module):
@@ -677,6 +694,7 @@ class AttentionLayers(nn.Module):
         cross_attend = False,
         only_cross = False,
         use_scalenorm = False,
+        use_rms_scaleshift_norm = False,
         use_rmsnorm = False,
         use_rezero = False,
         alibi_pos_bias = False,
@@ -738,6 +756,7 @@ class AttentionLayers(nn.Module):
 
         norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
         norm_class = RMSNorm if use_rmsnorm else norm_class
+        norm_class = RMSScaleShiftNorm if use_rms_scaleshift_norm else norm_class
         norm_fn = partial(norm_class, dim)
 
         norm_fn = nn.Identity if use_rezero else norm_fn
@@ -846,7 +865,8 @@ class AttentionLayers(nn.Module):
         context_mask = None,
         attn_mask = None,
         mems = None,
-        return_hiddens = False
+        return_hiddens = False,
+        norm_scale_shift_inp = None,
     ):
         
         assert not (self.cross_attend ^ (exists(context) or exists(full_context))), 'context must be passed in if cross_attend is set to True'
@@ -858,6 +878,9 @@ class AttentionLayers(nn.Module):
         prev_cross_attn = None
 
         mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
+        norm_args = {}
+        if exists(norm_scale_shift_inp):
+            norm_args['norm_scale_shift_inp'] = norm_scale_shift_inp
 
         rotary_pos_emb = None
         if exists(self.rotary_pos_emb):
@@ -874,7 +897,7 @@ class AttentionLayers(nn.Module):
             pre_branch_norm, post_branch_norm, post_main_norm = norm
 
             if exists(pre_branch_norm):
-                x = pre_branch_norm(x)
+                x = pre_branch_norm(x, **norm_args)
 
             if layer_type == 'a':
                 out, inter = checkpoint(block, x, None, mask, None, attn_mask, self.pia_pos_emb, rotary_pos_emb, prev_attn, layer_mem)
@@ -887,7 +910,7 @@ class AttentionLayers(nn.Module):
                 out = checkpoint(block, x)
 
             if exists(post_branch_norm):
-                out = post_branch_norm(out)
+                out = post_branch_norm(out, **norm_args)
 
             x = residual_fn(out, residual)
 
@@ -900,7 +923,7 @@ class AttentionLayers(nn.Module):
                 prev_cross_attn = inter.pre_softmax_attn
 
             if exists(post_main_norm):
-                x = post_main_norm(x)
+                x = post_main_norm(x, **norm_args)
 
             if layer_type == 'c':
                 cross_attn_count += 1