Remove unused kernels (#1880)

Author: matthewdouglas

agents/api_surface.md

@@ -390,8 +390,7 @@ bitsandbytes.optim.optimizer.Optimizer8bit(params, defaults, optim_bits=32, is_p
 bitsandbytes.optim.optimizer.Optimizer2State(
     optimizer_name, params, lr=1e-3, betas=(0.9, 0.999),
     eps=1e-8, weight_decay=0.0, optim_bits=32, args=None,
-    min_8bit_size=4096, percentile_clipping=100,
-    block_wise=True, max_unorm=0.0, skip_zeros=False,
+    min_8bit_size=4096, max_unorm=0.0, skip_zeros=False,
     is_paged=False, alpha=0.0, t_alpha=None, t_beta3=None,
 )
 ```
@@ -405,8 +404,7 @@ bitsandbytes.optim.optimizer.Optimizer2State(
 bitsandbytes.optim.optimizer.Optimizer1State(
     optimizer_name, params, lr=1e-3, betas=(0.9, 0.0),
     eps=1e-8, weight_decay=0.0, optim_bits=32, args=None,
-    min_8bit_size=4096, percentile_clipping=100,
-    block_wise=True, max_unorm=0.0, skip_zeros=False,
+    min_8bit_size=4096, max_unorm=0.0, skip_zeros=False,
     is_paged=False,
 )
 ```
@@ -532,8 +530,6 @@ All bnb optimizers share these parameters beyond the standard PyTorch ones:
 |-----------|------|---------|-------------|
 | `optim_bits` | `int` | 32 | 32 for full precision state, 8 for quantized state |
 | `min_8bit_size` | `int` | 4096 | Parameters smaller than this use 32-bit state even in 8-bit mode |
-| `percentile_clipping` | `int` | 100 | Gradient clipping at a percentile. 100 = disabled |
-| `block_wise` | `bool` | `True` | Block-wise quantization of optimizer states (vs global) |
 | `max_unorm` | `float` | 0.0 | Maximum update norm relative to weight norm. 0 = disabled |
 | `skip_zeros` | `bool` | `False` | Skip zero gradients in sparse models |
 | `is_paged` | `bool` | `False` | Use CUDA managed memory for state offloading |
@@ -1313,7 +1309,6 @@ removed in a future release.
 | `quantize_no_absmax` | `functional` | `quantize_blockwise` |
 | `dequantize_no_absmax` | `functional` | `dequantize_blockwise` |
 | `optimizer_update_8bit` | `functional` | `optimizer_update_8bit_blockwise` |
-| `percentile_clipping` | `functional` | N/A (still used internally by non-blockwise path) |
 
 ---
 

agents/architecture_guide.md

@@ -231,10 +231,6 @@ All ops are defined with the namespace `bitsandbytes::`:
 **Optimizer ops:**
 - `optimizer_update_32bit` — 32-bit optimizer step (Adam, Lion, SGD, etc.)
 - `optimizer_update_8bit_blockwise` — 8-bit blockwise optimizer step
-- `optimizer_update_8bit` — 8-bit non-blockwise optimizer step (legacy)
-
-**Utility ops:**
-- `percentile_clipping` — adaptive gradient clipping by percentile
 
 ---
 
@@ -745,10 +741,8 @@ The base class `Optimizer2State.update_step()` then dispatches based on state dt
 def update_step(self, group, p, gindex, pindex):
     if state["state1"].dtype == torch.float:
         F.optimizer_update_32bit(self.optimizer_name, grad, p, state1, ...)
-    elif state["state1"].dtype == torch.uint8 and config["block_wise"]:
+    elif state["state1"].dtype == torch.uint8:
         F.optimizer_update_8bit_blockwise(self.optimizer_name, grad, p, state1, ...)
-    elif state["state1"].dtype == torch.uint8 and not config["block_wise"]:
-        F.optimizer_update_8bit(self.optimizer_name, grad, p, state1, ...)
 ```
 
 ### Optimizer state initialization
@@ -1080,7 +1074,7 @@ Optimizer8bit.step():
       ├── p.data = p.data.contiguous()
       ├── config = self.get_config(gindex, pindex, group)
       │
-      ├── state["state1"].dtype == uint8 and block_wise:
+      ├── state["state1"].dtype == uint8:
       │   F.optimizer_update_8bit_blockwise("adam", grad, p, state1, state2,
       │       beta1, beta2, ..., qmap1, qmap2, absmax1, absmax2, ...)
       │     ↓

agents/security_guide.md

@@ -445,7 +445,6 @@ bitsandbytes/autograd/_functions.py:
 ```
 bitsandbytes/functional.py:
   - optimizer_update_8bit_blockwise() — 8-bit optimizer step
-  - percentile_clipping() — gradient clipping for optimizer stability
 
 csrc/ops.cu / kernels.cu:
   - Optimizer kernel implementations