fix a warning

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "vector-quantize-pytorch"
-version = "1.16.3"
+version = "1.17.1"
 description = "Vector Quantization - Pytorch"
 authors = [
     { name = "Phil Wang", email = "[email protected]" }

vector_quantize_pytorch/finite_scalar_quantization.py

@@ -12,7 +12,7 @@
 import torch.nn as nn
 from torch.nn import Module
 from torch import Tensor, int32
-from torch.cuda.amp import autocast
+from torch.amp import autocast
 
 from einops import rearrange, pack, unpack
 
@@ -159,7 +159,7 @@ def indices_to_codes(self, indices):
 
         return codes
 
-    @autocast(enabled = False)
+    @autocast('cuda', enabled = False)
     def forward(self, z):
         """
         einstein notation
@@ -187,7 +187,7 @@ def forward(self, z):
         # whether to force quantization step to be full precision or not
 
         force_f32 = self.force_quantization_f32
-        quantization_context = partial(autocast, enabled = False) if force_f32 else nullcontext
+        quantization_context = partial(autocast, 'cuda', enabled = False) if force_f32 else nullcontext
 
         with quantization_context():
             orig_dtype = z.dtype

vector_quantize_pytorch/lookup_free_quantization.py

@@ -16,7 +16,7 @@
 from torch import nn, einsum
 import torch.nn.functional as F
 from torch.nn import Module
-from torch.cuda.amp import autocast
+from torch.amp import autocast
 
 from einops import rearrange, reduce, pack, unpack
 
@@ -293,7 +293,7 @@ def forward(
 
         force_f32 = self.force_quantization_f32
 
-        quantization_context = partial(autocast, enabled = False) if force_f32 else nullcontext
+        quantization_context = partial(autocast, 'cuda', enabled = False) if force_f32 else nullcontext
 
         with quantization_context():
 

vector_quantize_pytorch/residual_fsq.py

@@ -8,7 +8,7 @@
 from torch import nn
 from torch.nn import Module, ModuleList
 import torch.nn.functional as F
-from torch.cuda.amp import autocast
+from torch.amp import autocast
 
 from vector_quantize_pytorch.finite_scalar_quantization import FSQ
 
@@ -167,7 +167,7 @@ def forward(
 
         # go through the layers
 
-        with autocast(enabled = False):
+        with autocast('cuda', enabled = False):
             for quantizer_index, (layer, scale) in enumerate(zip(self.layers, self.scales)):
 
                 if should_quantize_dropout and quantizer_index > rand_quantize_dropout_index:

vector_quantize_pytorch/residual_lfq.py

@@ -6,7 +6,7 @@
 from torch import nn
 from torch.nn import Module, ModuleList
 import torch.nn.functional as F
-from torch.cuda.amp import autocast
+from torch.amp import autocast
 
 from vector_quantize_pytorch.lookup_free_quantization import LFQ
 
@@ -156,7 +156,7 @@ def forward(
 
         # go through the layers
 
-        with autocast(enabled = False):
+        with autocast('cuda', enabled = False):
             for quantizer_index, layer in enumerate(self.layers):
 
                 if should_quantize_dropout and quantizer_index > rand_quantize_dropout_index:

vector_quantize_pytorch/vector_quantize_pytorch.py

@@ -9,7 +9,7 @@
 import torch.nn.functional as F
 import torch.distributed as distributed
 from torch.optim import Optimizer
-from torch.cuda.amp import autocast
+from torch.amp import autocast
 
 import einx
 from einops import rearrange, repeat, reduce, pack, unpack
@@ -458,7 +458,7 @@ def expire_codes_(self, batch_samples):
         batch_samples = rearrange(batch_samples, 'h ... d -> h (...) d')
         self.replace(batch_samples, batch_mask = expired_codes)
 
-    @autocast(enabled = False)
+    @autocast('cuda', enabled = False)
     def forward(
         self,
         x,
@@ -671,7 +671,7 @@ def expire_codes_(self, batch_samples):
         batch_samples = rearrange(batch_samples, 'h ... d -> h (...) d')
         self.replace(batch_samples, batch_mask = expired_codes)
 
-    @autocast(enabled = False)
+    @autocast('cuda', enabled = False)
     def forward(
         self,
         x,