Fix docstring args names (#735)

torchao/float8/float8_utils.py

@@ -209,7 +209,7 @@ def pad_tensor_for_matmul(
 
     Args:
         tensor: The tensor to pad.
-        both: Whether to pad both dimensions or just the second dimension.
+        dims: Dimensions to pad.
 
     Returns:
         torch.Tensor: The padded tensor.

torchao/float8/inference.py

@@ -151,7 +151,7 @@ def from_float(
         Create an nn.Linear with fp8 compute from another nn.Linear
 
         Args:
-            mod (torch.nn.Linear): nn.Linear to convert
+            module (torch.nn.Linear): nn.Linear to convert
             quant_config (QuantConfig): Configuration for the weight and activation casting
         """
         forward_config = ScaledMMConfig(

torchao/ops.py

@@ -115,7 +115,7 @@ def dequantize_tensor_core_tiled_layout(packed_w: Tensor, scales_and_zeros: Tens
     Args:
         packed_w: torch.tensor: 4D tensor with shape `(N / 8) x (K / (inner_k_tiles * 16)) x 32 x inner_k_tiles / 2`, dtype is torch.int32
         scales_and_zeros: torch.tensor: 3D tensor with shape `numQGroups x N x 2`, dtype is torch.bfloat16 where numQGroups is K / qGroupSize
-        qGroupSize: int
+        group_size: int
         inner_k_tiles: int
 
     Returns:
@@ -158,4 +158,4 @@ def _(packed_w: Tensor, scales_and_zeros: Tensor, group_size: int, inner_k_tiles
     torch._check(scales_and_zeros.size(1) == N, lambda: "scales_and_zeros must have N at dim 1")
     torch._check(scales_and_zeros.size(2) == 2, lambda: "scales_and_zeros must have 2 at dim 2")
 
-    return torch.empty((N, K), dtype=torch.bfloat16, device=packed_w.device)
+    return torch.empty((N, K), dtype=torch.bfloat16, device=packed_w.device)

torchao/quantization/prototype/mixed_precision/scripts/naive_intNwo.py

@@ -12,7 +12,7 @@ def intN_weight_only(group_size=32, n=8, symmetric=False):
     '''
         Apply int N-bit weight only quantization to a linear layer.
         Args:
-            `groupsize`: parameter for quantization, controls the granularity of quantization, smaller size is more fine grained, choices are [512, 256, 128, 64, 32]
+            `group_size`: parameter for quantization, controls the granularity of quantization, smaller size is more fine grained, choices are [512, 256, 128, 64, 32]
             `n`: number of bits to quantize to, choices are [8, 6, 5, 4, 3, 2]
         Usage:
             from torchao.quantization import quantize_

torchao/quantization/quant_primitives.py

@@ -33,7 +33,7 @@
 class MappingType(Enum):
     """How floating point number is mapped to integer number
 
-    symmetric mapping means floating point range is symetrically mapped to integer range
+    symmetric mapping means floating point range is symmetrically mapped to integer range
     let's say we have floating point range (-3.5, 10.2) and integer range (-8, 7) (int4)
     we'll use (-10.2, 10.2) as the range for floating point and map that to (-8, 7)
     e.g. scale = (10.2 - (-10.2)) / (7 - (-8))
@@ -167,7 +167,7 @@ def quantize_affine(
       output_dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor
       quant_min (Optional[int]): minimum quantized value for output Tensor, if not specified, it will be derived from dtype
       quant_max (Optional[int]): maximum quantized value for output Tensor, if not specified, it will be derived from dtype
-      zero_point_domain (ZeroPointDomain): the domain that zero_point is in, should be eitehr integer or float
+      zero_point_domain (ZeroPointDomain): the domain that zero_point is in, should be either integer or float
         if zero_point is in integer domain, zero point is added to the quantized integer value during
         quantization
         if zero_point is in floating point domain, zero point is subtracted from the floating point (unquantized)
@@ -287,11 +287,11 @@ def dequantize_affine(
                                e.g. when size is the same as the input tensor dimension, we are using per tensor quantization
       scale (Tensor): quantization parameter for affine quantization
       zero_point (Tensor): quantization parameter for affine quantization
-      dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor
+      input_dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor
       quant_min (Optional[int]): minimum quantized value for input Tensor
       quant_max (Optional[int]): maximum quantized value for input Tensor
       output_dtype (torch.dtype): dtype for output Tensor, default is fp32
-      zero_point_domain (ZeroPointDomain): the domain that zero_point is in, should be eitehr integer or float
+      zero_point_domain (ZeroPointDomain): the domain that zero_point is in, should be either integer or float
         if zero_point is in integer domain, zero point is added to the quantized integer value during
         quantization
         if zero_point is in floating point domain, zero point is subtracted from the floating point (unquantized)
@@ -413,7 +413,7 @@ def fake_quantize_affine(
       quant_dtype (torch.dtype): desired quantized dtype for determining and validating quant_min and quant_max values.
       quant_min (Optional[int]): minimum quantized value for output Tensor, if not specified, it will be derived from dtype
       quant_max (Optional[int]): maximum quantized value for output Tensor, if not specified, it will be derived from dtype
-      zero_point_domain (ZeroPointDomain): the domain that zero_point is in, should be eitehr integer or float
+      zero_point_domain (ZeroPointDomain): the domain that zero_point is in, should be either integer or float
         if zero_point is in integer domain, zero point is added to the quantized integer value during
         quantization
         if zero_point is in floating point domain, zero point is subtracted from the floating point (unquantized)
@@ -549,7 +549,7 @@ def choose_qparams_affine(
 
           If we don't need zero to be exactly representable, we won't do rounding and clamping for zero_point
 
-        zero_point_domain (ZeroPointDomain): the domain that zero_point is in, should be eitehr integer or float
+        zero_point_domain (ZeroPointDomain): the domain that zero_point is in, should be either integer or float
             if zero_point is in integer domain, zero point is added to the quantized integer value during
             quantization
             if zero_point is in floating point domain, zero point is subtracted from the floating point (unquantized)