Add an experimental unet_diffusion_tts to perform experiments on

codes/models/diffusion/unet_diffusion.py

@@ -91,7 +91,7 @@ class Upsample(nn.Module):
                  upsampling occurs in the inner-two dimensions.
     """
 
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, factor=None):
+    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
@@ -105,8 +105,6 @@ class Upsample(nn.Module):
         else:
             self.factor = factor
         if use_conv:
-            ksize = 3
-            pad = 1
             if dims == 1:
                 ksize = 5
                 pad = 2
@@ -134,18 +132,22 @@ class Downsample(nn.Module):
                  downsampling occurs in the inner-two dimensions.
     """
 
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, factor=None):
+    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
-        ksize = 3
-        pad = 1
+
+        if ksize is None:
+            ksize = 3
+            pad = 1
+            if dims == 1:
+                ksize = 5
+                pad = 2
+
         if dims == 1:
             stride = 4
-            ksize = 5
-            pad = 2
         elif dims == 2:
             stride = 2
         else:
@@ -201,7 +203,7 @@ class ResBlock(TimestepBlock):
         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
+        padding = 1 if kernel_size == 3 else (2 if kernel_size == 5 else 0)
 
         self.in_layers = nn.Sequential(
             normalization(channels),

codes/models/gpt_voice/unet_diffusion_tts.py

@@ -1,3 +1,6 @@
+import operator
+from collections import OrderedDict
+
 from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
 from models.diffusion.unet_diffusion import AttentionPool2d, AttentionBlock, ResBlock, TimestepEmbedSequential, \
     Downsample, Upsample
@@ -294,6 +297,34 @@ class DiffusionTts(nn.Module):
         h = h.type(x.dtype)
         return self.out(h)
 
+    def benchmark(self, x, timesteps, tokens, conditioning_input):
+        profile = OrderedDict()
+        hs = []
+        emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+        from torchprofile import profile_macs
+        profile['contextual_embedder'] = profile_macs(self.contextual_embedder, args=(conditioning_input,))
+        emb2 = self.contextual_embedder(conditioning_input)
+        emb = emb1 + emb2
+
+        h = x.type(self.dtype)
+        for k, module in enumerate(self.input_blocks):
+            if isinstance(module, nn.Embedding):
+                h_tok = F.interpolate(module(tokens).permute(0,2,1), size=(h.shape[-1]), mode='nearest')
+                h = h + h_tok
+            else:
+                profile[f'in_{k}'] = profile_macs(module, args=(h,emb))
+                h = module(h, emb)
+                hs.append(h)
+        profile['middle'] = profile_macs(self.middle_block, args=(h,emb))
+        h = self.middle_block(h, emb)
+        for k, module in enumerate(self.output_blocks):
+            h = torch.cat([h, hs.pop()], dim=1)
+            profile[f'out_{k}'] = profile_macs(module, args=(h,emb))
+            h = module(h, emb)
+        h = h.type(x.dtype)
+        profile['out'] = profile_macs(self.out, args=(h,))
+        return profile
+
 
 @register_model
 def register_diffusion_tts(opt_net, opt):
@@ -302,9 +333,15 @@ def register_diffusion_tts(opt_net, opt):
 
 # Test for ~4 second audio clip at 22050Hz
 if __name__ == '__main__':
-    clip = torch.randn(2, 1, 40960)
-    tok = torch.randint(0,30, (2,200))
-    cond = torch.randn(2, 1, 40960)
+    clip = torch.randn(2, 1, 86016)
+    tok = torch.randint(0,30, (2,388))
+    cond = torch.randn(2, 1, 44000)
     ts = torch.LongTensor([555, 556])
-    model = DiffusionTts(32, conditioning_inputs_provided=True, time_embed_dim_multiplier=8)
-    print(model(clip, ts, tok, cond).shape)
+    model = DiffusionTts(64, channel_mult=[1,1.5,2, 3, 4, 6, 8, 8,   8,  8 ], num_res_blocks=[2, 2, 2, 2, 2, 2, 2, 2,   1,  1 ],
+                         token_conditioning_resolutions=[1,4,16,64], attention_resolutions=[256,512], num_heads=4, kernel_size=3,
+                         scale_factor=2, conditioning_inputs_provided=True, time_embed_dim_multiplier=4)
+    p = model.benchmark(clip, ts, tok, cond)
+    p = {k: v / 1000000000 for k, v in p.items()}
+    p = sorted(p.items(), key=operator.itemgetter(1))
+    print(p)
+    print(sum([j[1] for j in p]))

codes/models/gpt_voice/unet_diffusion_tts_experimental.py

@@ -0,0 +1,386 @@
+import operator
+from collections import OrderedDict
+
+from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
+from models.diffusion.unet_diffusion import AttentionPool2d, AttentionBlock, TimestepEmbedSequential, \
+    Downsample, Upsample, TimestepBlock
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from models.gpt_voice.mini_encoder import AudioMiniEncoder, EmbeddingCombiner
+from trainer.networks import register_model
+from utils.util import get_mask_from_lengths
+from utils.util import checkpoint
+
+
+class ResBlock(TimestepBlock):
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        dims=2,
+        kernel_size=3,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        padding = 1 if kernel_size == 3 else 2
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            conv_nd(dims, channels, self.out_channels, 1, padding=0),
+        )
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            linear(
+                emb_channels,
+                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()
+        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):
+        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]
+        h = h + emb_out
+        h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class DiffusionTts(nn.Module):
+    """
+    The full UNet model with attention and timestep embedding.
+
+    Customized to be conditioned on an aligned token prior.
+
+    :param in_channels: channels in the input Tensor.
+    :param num_tokens: number of tokens (e.g. characters) which can be provided.
+    :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_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,
+            model_channels,
+            in_channels=1,
+            num_tokens=30,
+            out_channels=2,  # mean and variance
+            dropout=0,
+            # res           1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K
+            channel_mult=  (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48),
+            num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2,   2,  2,  2,  2),
+            # spec_cond:    1, 0, 0, 1, 0, 0, 1, 0,   0,  1,  0,  0)
+            # attn:         0, 0, 0, 0, 0, 0, 0, 0,   0,  1,  1,  1
+            token_conditioning_resolutions=(1,16,),
+            attention_resolutions=(512,1024,2048),
+            conv_resample=True,
+            dims=1,
+            use_fp16=False,
+            num_heads=1,
+            num_head_channels=-1,
+            num_heads_upsample=-1,
+            kernel_size=3,
+            scale_factor=2,
+            conditioning_inputs_provided=True,
+            time_embed_dim_multiplier=4,
+    ):
+        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.attention_resolutions = attention_resolutions
+        self.dropout = dropout
+        self.channel_mult = channel_mult
+        self.conv_resample = conv_resample
+        self.dtype = torch.float16 if use_fp16 else torch.float32
+        self.num_heads = num_heads
+        self.num_head_channels = num_head_channels
+        self.num_heads_upsample = num_heads_upsample
+        self.dims = dims
+
+        padding = 1 if kernel_size == 3 else 2
+
+        time_embed_dim = model_channels * time_embed_dim_multiplier
+        self.time_embed = nn.Sequential(
+            linear(model_channels, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        self.conditioning_enabled = conditioning_inputs_provided
+        if conditioning_inputs_provided:
+            self.contextual_embedder = AudioMiniEncoder(in_channels, time_embed_dim, base_channels=32, depth=6, resnet_blocks=1,
+                             attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
+
+        self.input_blocks = nn.ModuleList(
+            [
+                TimestepEmbedSequential(
+                    conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding)
+                )
+            ]
+        )
+        token_conditioning_blocks = []
+        self._feature_size = model_channels
+        input_block_chans = [model_channels]
+        ch = model_channels
+        ds = 1
+
+        for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)):
+            if ds in token_conditioning_resolutions:
+                token_conditioning_block = nn.Embedding(num_tokens, ch)
+                token_conditioning_block.weight.data.normal_(mean=0.0, std=.02)
+                self.input_blocks.append(token_conditioning_block)
+                token_conditioning_blocks.append(token_conditioning_block)
+
+            for _ in range(num_blocks):
+                layers = [
+                    ResBlock(
+                        ch,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=int(mult * model_channels),
+                        dims=dims,
+                        kernel_size=kernel_size,
+                    )
+                ]
+                ch = int(mult * model_channels)
+                if ds in attention_resolutions:
+                    layers.append(
+                        AttentionBlock(
+                            ch,
+                            num_heads=num_heads,
+                            num_head_channels=num_head_channels,
+                        )
+                    )
+                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(
+                        Downsample(
+                            ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=1, pad=0
+                        )
+                    )
+                )
+                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,
+                kernel_size=kernel_size,
+            ),
+            AttentionBlock(
+                ch,
+                num_heads=num_heads,
+                num_head_channels=num_head_channels,
+            ),
+            ResBlock(
+                ch,
+                time_embed_dim,
+                dropout,
+                dims=dims,
+                kernel_size=kernel_size,
+            ),
+        )
+        self._feature_size += ch
+
+        self.output_blocks = nn.ModuleList([])
+        for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]:
+            for i in range(num_blocks + 1):
+                ich = input_block_chans.pop()
+                layers = [
+                    ResBlock(
+                        ch + ich,
+                        time_embed_dim,
+                        dropout,
+                        out_channels=int(model_channels * mult),
+                        dims=dims,
+                        kernel_size=kernel_size,
+                    )
+                ]
+                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,
+                        )
+                    )
+                if level and i == num_blocks:
+                    out_ch = ch
+                    layers.append(
+                        Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
+                    )
+                    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, kernel_size, padding=padding)),
+        )
+
+    def forward(self, x, timesteps, tokens, conditioning_input=None):
+        """
+        Apply the model to an input batch.
+
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :param tokens: an aligned text input.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        assert x.shape[-1] % 4096 == 0  # This model operates at base//4096 at it's bottom levels, thus this requirement.
+        if self.conditioning_enabled:
+            assert conditioning_input is not None
+
+        hs = []
+        emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+        if self.conditioning_enabled:
+            emb2 = self.contextual_embedder(conditioning_input)
+            emb = emb1 + emb2
+        else:
+            emb = emb1
+
+        h = x.type(self.dtype)
+        for k, module in enumerate(self.input_blocks):
+            if isinstance(module, nn.Embedding):
+                h_tok = F.interpolate(module(tokens).permute(0,2,1), size=(h.shape[-1]), mode='nearest')
+                h = h + h_tok
+            else:
+                h = module(h, emb)
+                hs.append(h)
+        h = self.middle_block(h, emb)
+        for module in self.output_blocks:
+            h = torch.cat([h, hs.pop()], dim=1)
+            h = module(h, emb)
+        h = h.type(x.dtype)
+        return self.out(h)
+
+    def benchmark(self, x, timesteps, tokens, conditioning_input):
+        profile = OrderedDict()
+        params = OrderedDict()
+        hs = []
+        emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+        from torchprofile import profile_macs
+        profile['contextual_embedder'] = profile_macs(self.contextual_embedder, args=(conditioning_input,))
+        params['contextual_embedder'] = sum(p.numel() for p in self.contextual_embedder.parameters())
+        emb2 = self.contextual_embedder(conditioning_input)
+        emb = emb1 + emb2
+
+        h = x.type(self.dtype)
+        for k, module in enumerate(self.input_blocks):
+            if isinstance(module, nn.Embedding):
+                h_tok = F.interpolate(module(tokens).permute(0,2,1), size=(h.shape[-1]), mode='nearest')
+                h = h + h_tok
+            else:
+                profile[f'in_{k}'] = profile_macs(module, args=(h,emb))
+                params[f'in_{k}'] = sum(p.numel() for p in module.parameters())
+                h = module(h, emb)
+                hs.append(h)
+        profile['middle'] = profile_macs(self.middle_block, args=(h,emb))
+        params['middle'] = sum(p.numel() for p in self.middle_block.parameters())
+        h = self.middle_block(h, emb)
+        for k, module in enumerate(self.output_blocks):
+            h = torch.cat([h, hs.pop()], dim=1)
+            profile[f'out_{k}'] = profile_macs(module, args=(h,emb))
+            params[f'out_{k}'] = sum(p.numel() for p in module.parameters())
+            h = module(h, emb)
+        h = h.type(x.dtype)
+        profile['out'] = profile_macs(self.out, args=(h,))
+        params['out'] = sum(p.numel() for p in self.out.parameters())
+        return profile, params
+
+
+@register_model
+def register_diffusion_tts_experimental(opt_net, opt):
+    return DiffusionTts(**opt_net['kwargs'])
+
+
+# Test for ~4 second audio clip at 22050Hz
+if __name__ == '__main__':
+    clip = torch.randn(2, 1, 86016)
+    tok = torch.randint(0,30, (2,388))
+    cond = torch.randn(2, 1, 44000)
+    ts = torch.LongTensor([555, 556])
+    model = DiffusionTts(64, channel_mult=[1,1.5,2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[2, 2, 2, 2, 2, 2, 2, 4, 4, 4],
+                         token_conditioning_resolutions=[1,4,16,64], attention_resolutions=[256,512], num_heads=4, kernel_size=3,
+                         scale_factor=2, conditioning_inputs_provided=True, time_embed_dim_multiplier=4)
+    p, r = model.benchmark(clip, ts, tok, cond)
+    p = {k: v / 1000000000 for k, v in p.items()}
+    p = sorted(p.items(), key=operator.itemgetter(1))
+    print("Computational complexity:")
+    print(p)
+    print(sum([j[1] for j in p]))
+    print()
+    print("Memory complexity:")
+    r = {k: v / 1000000 for k, v in r.items()}
+    r = sorted(r.items(), key=operator.itemgetter(1))
+    print(r)
+    print(sum([j[1] for j in r]))
+