[low-bit optim] Change 8-bit and FP8 optim block size from 2048 to 25…

…6 to match new bnb v0.44 (pytorch#927)

test/prototype/test_low_bit_optim.py

@@ -3,6 +3,7 @@
 
 import pytest
 import torch
+from packaging.version import Version
 from torch import nn
 from torch.testing._internal.common_utils import (
     TestCase,
@@ -105,8 +106,11 @@ def test_optim_8bit_correctness(self, optim_name):
         model1 = nn.Sequential(nn.Linear(32, 1024), nn.ReLU(), nn.Linear(1024, 128)).to(device)
         model2 = copy.deepcopy(model1)
 
+        # https://github.com/bitsandbytes-foundation/bitsandbytes/releases/tag/v0.44.0
+        block_size = 256 if Version(bnb.__version__) >= Version("0.44.0") else 2048
+
         optim1 = getattr(bnb.optim, optim_name)(model1.parameters())
-        optim2 = getattr(low_bit_optim, optim_name)(model2.parameters())
+        optim2 = getattr(low_bit_optim, optim_name)(model2.parameters(), block_size=block_size)
 
         for _ in range(2):
             x = torch.randn(4, 32, device=device)

torchao/prototype/low_bit_optim/README.md

@@ -19,7 +19,7 @@ model = ...
 optim = Adam8bit(model.parameters())
 ```
 
-To use 4-bit Adam, replace the above with `Adam4bit`. Similarly for `AdamFp8`. You can also change quantization block size by passing `block_size=value` to the optimizer. By default, block size is 2048 for 8-bit and FP8 optimizers, and 128 for 4-bit optimizers.
+To use 4-bit Adam, replace the above with `Adam4bit`. Similarly for `AdamFp8`. You can also change quantization block size by passing `block_size=value` to the optimizer. By default, block size is 256 for 8-bit and FP8 optimizers, and 128 for 4-bit optimizers.
 
 **Other optimizers**: AdamW is also available as `AdamW8bit`, `AdamW4bit`, and `AdamWFp8`. Other optimizers can be added based on demand.
 

torchao/prototype/low_bit_optim/adam.py

@@ -161,7 +161,7 @@ def __init__(
         weight_decay=0,
         amsgrad=False,
         *,
-        block_size=2048,
+        block_size=256,
     ) -> None:
         super().__init__(params, lr, betas, eps, weight_decay, amsgrad, block_size=block_size, is_adamw=False)
 
@@ -199,7 +199,7 @@ def __init__(
         weight_decay=0,
         amsgrad=False,
         *,
-        block_size=2048,
+        block_size=256,
     ) -> None:
         super().__init__(params, lr, betas, eps, weight_decay, amsgrad, block_size=block_size, is_adamw=False)
 
@@ -218,7 +218,7 @@ def __init__(
         weight_decay=1e-2,
         amsgrad=False,
         *,
-        block_size=2048,
+        block_size=256,
     ) -> None:
         super().__init__(params, lr, betas, eps, weight_decay, amsgrad, block_size=block_size, is_adamw=True)
 
@@ -256,7 +256,7 @@ def __init__(
         weight_decay=1e-2,
         amsgrad=False,
         *,
-        block_size=2048,
+        block_size=256,
     ) -> None:
         super().__init__(params, lr, betas, eps, weight_decay, amsgrad, block_size=block_size, is_adamw=True)
 

torchao/prototype/low_bit_optim/subclass_8bit.py

@@ -53,7 +53,7 @@ def dequantize(self, output_dtype=None):
         return dequant_with_qmap(self.codes, self.qmap, self.scale).to(dtype)
 
     @classmethod
-    def zeros(cls, shape, signed: bool = True, block_size: int = 2048, device=None):
+    def zeros(cls, shape, signed: bool = True, block_size: int = 256, device=None):
         codes = torch.zeros(shape, dtype=torch.uint8, device=device)
         scale = torch.zeros(codes.numel() // block_size, device=device)
         qmap = torch.tensor(QMAP_SIGNED if signed else QMAP_UNSIGNED, device=device)

torchao/prototype/low_bit_optim/subclass_fp8.py

@@ -60,7 +60,7 @@ def dequantize(self, output_dtype=None):
         return float_data.view(self.codes.shape).to(dtype)
 
     @classmethod
-    def zeros(cls, shape, block_size: int = 2048, device=None):
+    def zeros(cls, shape, block_size: int = 256, device=None):
         codes = torch.zeros(shape, dtype=DTYPE, device=device)
         scale = torch.zeros(codes.numel() // block_size, device=device)
         return cls(codes, scale)