Merge pull request #436 from akx/quanitze

Fix typo "quanitze"

benchmarking/switchback/speed_benchmark.py

@@ -8,7 +8,7 @@ from bitsandbytes.triton.quantize_rowwise import quantize_rowwise
 from bitsandbytes.triton.quantize_columnwise_and_transpose import quantize_columnwise_and_transpose
 from bitsandbytes.triton.int8_matmul_rowwise_dequantize import int8_matmul_rowwise_dequantize
 from bitsandbytes.triton.quantize_global import quantize_global, quantize_global_transpose
-from bitsandbytes.triton.int8_matmul_mixed_dequanitze import int8_matmul_mixed_dequanitze
+from bitsandbytes.triton.int8_matmul_mixed_dequantize import int8_matmul_mixed_dequantize
 
 # KNOW ISSUE: need to optimize "w_quantize_colwise_transpose" when embeddim is too large.
 
@@ -72,8 +72,8 @@ if __name__ == '__main__':
                 get_time('standard_gx', lambda : g.matmul(w), info)
                 get_time('rowwise_fwd', lambda : int8_matmul_rowwise_dequantize(x_int8, w_int8.t(), state_x_rowwise, state_w_columnwise, None), info)
                 get_time('rowwise_bwd', lambda : int8_matmul_rowwise_dequantize(g_int8, wt_int8.t(), state_x_rowwise, state_w_rowwise, None), info)
-                get_time('global_fwd', lambda : int8_matmul_mixed_dequanitze(x_int8, w_int8.t(), state_x_rowwise, state_w_global, None), info)
-                get_time('global_bwd', lambda : int8_matmul_mixed_dequanitze(g_int8, wt_int8.t(), state_x_rowwise, state_w_global, None), info)
+                get_time('global_fwd', lambda : int8_matmul_mixed_dequantize(x_int8, w_int8.t(), state_x_rowwise, state_w_global, None), info)
+                get_time('global_bwd', lambda : int8_matmul_mixed_dequantize(g_int8, wt_int8.t(), state_x_rowwise, state_w_global, None), info)
                 get_time('x_quantize_rowwise', lambda : quantize_rowwise(x), info)
                 get_time('g_quantize_rowwise', lambda : quantize_rowwise(g), info)
                 get_time('w_quantize_rowwise', lambda : quantize_rowwise(w), info)

bitsandbytes/nn/triton_based_modules.py

@@ -10,7 +10,7 @@ from bitsandbytes.triton.quantize_rowwise import quantize_rowwise
 from bitsandbytes.triton.quantize_columnwise_and_transpose import quantize_columnwise_and_transpose
 from bitsandbytes.triton.int8_matmul_rowwise_dequantize import int8_matmul_rowwise_dequantize
 from bitsandbytes.triton.quantize_global import quantize_global, quantize_global_transpose
-from bitsandbytes.triton.int8_matmul_mixed_dequanitze import int8_matmul_mixed_dequanitze
+from bitsandbytes.triton.int8_matmul_mixed_dequantize import int8_matmul_mixed_dequantize
 
 
 class _switchback_global(torch.autograd.Function):
@@ -29,7 +29,7 @@ class _switchback_global(torch.autograd.Function):
 
         # matmult, fused dequant and add bias
         # call "mixed" because we are mixing rowwise quantized and global quantized
-        return int8_matmul_mixed_dequanitze(
+        return int8_matmul_mixed_dequantize(
             X_int8, W_int8.t(), state_X, state_W, bias
         ).view(*X_3D.size()[:-1], -1)
 
@@ -47,7 +47,7 @@ class _switchback_global(torch.autograd.Function):
             # so we transpose once then call .t() in the matmul
             G_int8, state_G = quantize_rowwise(G)
             W_int8, state_W = quantize_global_transpose(W)
-            grad_X = int8_matmul_mixed_dequanitze(G_int8, W_int8.t(), state_G, state_W, None).view(
+            grad_X = int8_matmul_mixed_dequantize(G_int8, W_int8.t(), state_G, state_W, None).view(
                 *G_3D.size()[:-1], -1
             )
         if ctx.needs_input_grad[1]:
@@ -119,7 +119,7 @@ class _switchback_global_mem_efficient(torch.autograd.Function):
 
         # matmult, fused dequant and add bias
         # call "mixed" because we are mixing rowwise quantized and global quantized
-        return int8_matmul_mixed_dequanitze(
+        return int8_matmul_mixed_dequantize(
             X_int8, W_int8.t(), state_X, state_W, bias
         ).view(*X_3D_sz[:-1], -1)
 
@@ -143,7 +143,7 @@ class _switchback_global_mem_efficient(torch.autograd.Function):
             G_int8, state_G = quantize_rowwise(G)
             del G
             W_int8 = W_int8.t().contiguous()
-            grad_X = int8_matmul_mixed_dequanitze(G_int8, W_int8.t(), state_G, state_W, None).view(
+            grad_X = int8_matmul_mixed_dequantize(G_int8, W_int8.t(), state_G, state_W, None).view(
                 *G_3D_sz[:-1], -1
             )
 
@@ -215,7 +215,7 @@ class SwitchBackLinear(nn.Linear):
                     X_int8, self.W_int8.t(), state_X, self.state_W, self.bias
                 ).view(*x.size()[:-1], -1)
             else:
-                return int8_matmul_mixed_dequanitze(
+                return int8_matmul_mixed_dequantize(
                     X_int8, self.W_int8.t(), state_X, self.state_W, self.bias
                 ).view(*x.size()[:-1], -1)
 

bitsandbytes/triton/int8_matmul_mixed_dequanitze.py → bitsandbytes/triton/int8_matmul_mixed_dequantize.py

@@ -2,7 +2,7 @@ import torch
 from bitsandbytes.triton.triton_utils import is_triton_available
 
 if not is_triton_available():
-    def int8_matmul_mixed_dequanitze(a, b, state_x, state_w, bias): return None
+    def int8_matmul_mixed_dequantize(a, b, state_x, state_w, bias): return None
 else:
 
     import triton
@@ -136,7 +136,7 @@ else:
             tl.atomic_add(C, acc, mask=mask)
 
 
-    def int8_matmul_mixed_dequanitze(a, b, state_x, state_w, bias):
+    def int8_matmul_mixed_dequantize(a, b, state_x, state_w, bias):
         device = a.device
         divfactor = 1. / (127. * 127.)
         has_bias = 0 if bias is None else 1