fix roformer models run on cuda (#84)

* fix roformer models run on cuda

* fix roformer models run on cpu/cuda/mps

---------

Co-authored-by: 周志洋 <[email protected]>

Author: young01ai

audio_separator/separator/uvr_lib_v5/attend.py

@@ -70,8 +70,11 @@ def flash_attn(self, q, k, v):
 
         config = self.cuda_config if is_cuda else self.cpu_config
 
-        # pytorch 2.0 flash attn: q, k, v, mask, dropout, softmax_scale
+        # sdpa_flash kernel only supports float16 on sm80+ architecture gpu
+        if is_cuda and q.dtype != torch.float16:
+            config = FlashAttentionConfig(False, True, True)
 
+        # pytorch 2.0 flash attn: q, k, v, mask, dropout, softmax_scale
         with torch.backends.cuda.sdp_kernel(**config._asdict()):
             out = F.scaled_dot_product_attention(q, k, v, dropout_p=self.dropout if self.training else 0.0)
 

audio_separator/separator/uvr_lib_v5/bs_roformer.py

@@ -457,11 +457,10 @@ def forward(
         """
 
         original_device = raw_audio.device
-        
         x_is_mps = True if original_device.type == 'mps' else False
 
-        if x_is_mps:
-            raw_audio = raw_audio.cpu()
+        # if x_is_mps:
+        #     raw_audio = raw_audio.cpu()
 
         device = raw_audio.device
 
@@ -517,13 +516,11 @@ def forward(
 
         x = self.final_norm(x)
 
-        num_stems = len(self.mask_estimators)
-
         mask = torch.stack([fn(x) for fn in self.mask_estimators], dim=1)
         mask = rearrange(mask, 'b n t (f c) -> b n f t c', c=2)
 
-        if x_is_mps:
-            mask = mask.to('cpu')
+        # if x_is_mps:
+        #     mask = mask.to('cpu')
 
         # modulate frequency representation
 
@@ -540,11 +537,14 @@ def forward(
 
         stft_repr = rearrange(stft_repr, 'b n (f s) t -> (b n s) f t', s=self.audio_channels)
 
-        recon_audio = torch.istft(stft_repr, **self.stft_kwargs, window=stft_window, return_complex=False)
+        recon_audio = torch.istft(stft_repr.cpu() if x_is_mps else stft_repr,
+                                  **self.stft_kwargs,
+                                  window=stft_window.cpu() if x_is_mps else stft_window,
+                                  return_complex=False).to(device)
 
-        recon_audio = rearrange(recon_audio, '(b n s) t -> b n s t', s=self.audio_channels, n=num_stems)
+        recon_audio = rearrange(recon_audio, '(b n s) t -> b n s t', s=self.audio_channels, n=self.num_stems)
 
-        if num_stems == 1:
+        if self.num_stems == 1:
             recon_audio = rearrange(recon_audio, 'b 1 s t -> b s t')
 
         # if a target is passed in, calculate loss for learning
@@ -585,15 +585,15 @@ def forward(
 
         if not return_loss_breakdown:
             # Move the result back to the original device if it was moved to CPU for MPS compatibility
-            if x_is_mps:
-                total_loss = total_loss.to(original_device)
+            # if x_is_mps:
+            #     total_loss = total_loss.to(original_device)
             return total_loss
 
         # For detailed loss breakdown, ensure all components are moved back to the original device for MPS
-        if x_is_mps:
-            loss = loss.to(original_device)
-            multi_stft_resolution_loss = multi_stft_resolution_loss.to(original_device)
-            weighted_multi_resolution_loss = weighted_multi_resolution_loss.to(original_device)
+        # if x_is_mps:
+        #     loss = loss.to(original_device)
+        #     multi_stft_resolution_loss = multi_stft_resolution_loss.to(original_device)
+        #     weighted_multi_resolution_loss = weighted_multi_resolution_loss.to(original_device)
 
         return total_loss, (loss, multi_stft_resolution_loss)
 

audio_separator/separator/uvr_lib_v5/mel_band_roformer.py

@@ -390,8 +390,8 @@ def forward(
         original_device = raw_audio.device
         x_is_mps = True if original_device.type == 'mps' else False
 
-        if x_is_mps:
-            raw_audio = raw_audio.cpu()
+        # if x_is_mps:
+        #     raw_audio = raw_audio.cpu()
 
         device = raw_audio.device
 
@@ -418,7 +418,8 @@ def forward(
 
         batch_arange = torch.arange(batch, device=device)[..., None]
 
-        x = stft_repr[batch_arange, self.freq_indices.cpu()] if x_is_mps else stft_repr[batch_arange, self.freq_indices]
+        # x = stft_repr[batch_arange, self.freq_indices.cpu()] if x_is_mps else stft_repr[batch_arange, self.freq_indices]
+        x = stft_repr[batch_arange, self.freq_indices]
 
         x = rearrange(x, 'b f t c -> b t (f c)')
 
@@ -438,12 +439,10 @@ def forward(
 
             x, = unpack(x, ps, '* f d')
 
-        num_stems = len(self.mask_estimators)
-
         masks = torch.stack([fn(x) for fn in self.mask_estimators], dim=1)
         masks = rearrange(masks, 'b n t (f c) -> b n f t c', c=2)
-        if x_is_mps:
-            masks = masks.cpu()
+        # if x_is_mps:
+        #     masks = masks.cpu()
 
         stft_repr = rearrange(stft_repr, 'b f t c -> b 1 f t c')
 
@@ -452,29 +451,35 @@ def forward(
 
         masks = masks.type(stft_repr.dtype)
 
-        if x_is_mps:
-            scatter_indices = repeat(self.freq_indices.cpu(), 'f -> b n f t', b=batch, n=num_stems, t=stft_repr.shape[-1])
-        else:
-            scatter_indices = repeat(self.freq_indices, 'f -> b n f t', b=batch, n=num_stems, t=stft_repr.shape[-1])
-        stft_repr_expanded_stems = repeat(stft_repr, 'b 1 ... -> b n ...', n=num_stems)
-        masks_summed = torch.zeros_like(stft_repr_expanded_stems).scatter_add_(2, scatter_indices, masks)
+        # if x_is_mps:
+        #     scatter_indices = repeat(self.freq_indices.cpu(), 'f -> b n f t', b=batch, n=num_stems, t=stft_repr.shape[-1])
+        # else:
+        #     scatter_indices = repeat(self.freq_indices, 'f -> b n f t', b=batch, n=num_stems, t=stft_repr.shape[-1])
+        scatter_indices = repeat(self.freq_indices, 'f -> b n f t', b=batch, n=self.num_stems, t=stft_repr.shape[-1])
+        stft_repr_expanded_stems = repeat(stft_repr, 'b 1 ... -> b n ...', n=self.num_stems)
+        masks_summed = torch.zeros_like(stft_repr_expanded_stems.cpu() if x_is_mps else stft_repr_expanded_stems
+                                       ).scatter_add_(2, scatter_indices.cpu() if x_is_mps else scatter_indices,
+                                                      masks.cpu() if x_is_mps else masks).to(device)
 
         denom = repeat(self.num_bands_per_freq, 'f -> (f r) 1', r=channels)
-        if x_is_mps:
-            denom = denom.cpu()
+        # if x_is_mps:
+        #     denom = denom.cpu()
 
         masks_averaged = masks_summed / denom.clamp(min=1e-8)
 
         stft_repr = stft_repr * masks_averaged
 
         stft_repr = rearrange(stft_repr, 'b n (f s) t -> (b n s) f t', s=self.audio_channels)
 
-        recon_audio = torch.istft(stft_repr, **self.stft_kwargs, window=stft_window, return_complex=False,
+        recon_audio = torch.istft(stft_repr.cpu() if x_is_mps else stft_repr,
+                                  **self.stft_kwargs,
+                                  window=stft_window.cpu() if x_is_mps else stft_window,
+                                  return_complex=False,
                                   length=istft_length)
 
-        recon_audio = rearrange(recon_audio, '(b n s) t -> b n s t', b=batch, s=self.audio_channels, n=num_stems)
+        recon_audio = rearrange(recon_audio, '(b n s) t -> b n s t', b=batch, s=self.audio_channels, n=self.num_stems)
 
-        if num_stems == 1:
+        if self.num_stems == 1:
             recon_audio = rearrange(recon_audio, 'b 1 s t -> b s t')
 
         if not exists(target):
@@ -512,17 +517,17 @@ def forward(
 
 
         # Move the total loss back to the original device if necessary
-        if x_is_mps:
-            total_loss = total_loss.to(original_device)
+        # if x_is_mps:
+        #     total_loss = total_loss.to(original_device)
 
-        if not return_loss_breakdown:
-            return total_loss
+        # if not return_loss_breakdown:
+        #     return total_loss
 
         # If detailed loss breakdown is requested, ensure all components are on the original device
-        return total_loss, (loss.to(original_device) if x_is_mps else loss, 
-                            multi_stft_resolution_loss.to(original_device) if x_is_mps else multi_stft_resolution_loss)
+        # return total_loss, (loss.to(original_device) if x_is_mps else loss, 
+        #                     multi_stft_resolution_loss.to(original_device) if x_is_mps else multi_stft_resolution_loss)
 
-        # if not return_loss_breakdown:
-        #     return total_loss
+        if not return_loss_breakdown:
+            return total_loss
 
-        # return total_loss, (loss, multi_stft_resolution_loss)
+        return total_loss, (loss, multi_stft_resolution_loss)

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "audio-separator"
-version = "0.17.4"
+version = "0.17.5"
 description = "Easy to use audio stem separation, using various models from UVR trained primarily by @Anjok07"
 authors = ["Andrew Beveridge <[email protected]>"]
 license = "MIT"