Update

test/quantization/test_quant_primitives.py

@@ -526,8 +526,9 @@ def test_groupwise_affine_dequantize_tensor_from_qparams(self):
         groupsize = 128
 
         if TORCH_VERSION_AT_LEAST_2_5:
-            input_uint8 = (input[::, ::2] << 4 | input[::, 1::2]).to(torch.uint8)
-            w_bf16 = groupwise_affine_dequantize_tensor_from_qparams(input_uint8, scales, zeros, n_bit, groupsize)
+            if not is_device(input.device.type, "cpu"):
+                input = (input[::, ::2] << 4 | input[::, 1::2]).to(torch.uint8)
+            w_bf16 = groupwise_affine_dequantize_tensor_from_qparams(input, scales, zeros, n_bit, groupsize)
         else:
             w_bf16 = groupwise_affine_dequantize_tensor_from_qparams(input, scales, zeros, n_bit, groupsize)
         w_bf16_ref = _groupwise_affine_dequantize_tensor_from_qparams(input, scales, zeros, n_bit, groupsize)

torchao/prototype/hqq/hqq_tinygemm_linear.py

@@ -13,6 +13,7 @@
 
 import torch.nn.functional as F
 from torchao.utils import TORCH_VERSION_AT_LEAST_2_5
+from torchao.dtypes.utils import is_device
 
 
 class HQQLinearTorchWeightOnlyInt4(torch.nn.Module):
@@ -162,9 +163,14 @@ def process_hqq_quants(self, W_q, meta):
         W_q_torch, scales_torch, zeros_torch = self.hqq_quants_to_torch_quants(
             W_q=W_q, scales=scales, zeros=zeros, shape=shape, nbits=self.nbits
         )
-        self.weight_int4pack = torch.ops.aten._convert_weight_to_int4pack(
-            W_q_torch, self.inner_k_tiles
-        )
+        if is_device(W_q.device.type, "cpu"):
+            self.weight_int4pack = torch.ops.aten._convert_weight_to_int4pack_for_cpu(
+                W_q_torch, self.inner_k_tiles
+            )
+        else:
+            self.weight_int4pack = torch.ops.aten._convert_weight_to_int4pack(
+                W_q_torch, self.inner_k_tiles
+            )
         self.scales_and_zeros = self.pack_scales_and_zeros(scales_torch, zeros_torch)
 
         del W_q_torch, scales_torch, zeros_torch
@@ -200,7 +206,8 @@ def hqq_quants_to_torch_quants(
             .contiguous()
         )
         if TORCH_VERSION_AT_LEAST_2_5:
-            W_q = (W_q[::, ::2] << 4 | W_q[::, 1::2]).to(torch.uint8)
+            if not is_device(W_q.device.type, "cpu"):
+                W_q = (W_q[::, ::2] << 4 | W_q[::, 1::2]).to(torch.uint8)
 
         # group_dequantize_tensor_from_qparams
         # W_r = W_q*scales + min_val

torchao/quantization/GPTQ.py

@@ -36,6 +36,7 @@
     pack_tinygemm_scales_and_zeros,
     per_token_dynamic_quant,
 )
+from torchao.dtypes.utils import is_device
 
 aten = torch.ops.aten
 
@@ -765,9 +766,14 @@ def _create_quantized_state_dict(
                     self.precision,  # dtype for scales_and_zeros
                 )
                 # TODO: just get the device from mod.weight.device?
-                weight_int4pack = torch.ops.aten._convert_weight_to_int4pack(
-                    w_int4x8.to(self.device), self.inner_k_tiles
-                )
+                if is_device(w_int4x8.device.type, "cpu"):
+                    weight_int4pack = torch.ops.aten._convert_weight_to_int4pack_for_cpu(
+                        w_int4x8.to(self.device), self.inner_k_tiles
+                    )
+                else:
+                    weight_int4pack = torch.ops.aten._convert_weight_to_int4pack(
+                        w_int4x8.to(self.device), self.inner_k_tiles
+                    )
                 cur_state_dict[f"{fqn}.weight"] = weight_int4pack.to(self.device)
                 cur_state_dict[f"{fqn}.scales_and_zeros"] = scales_and_zeros.to(
                     self.device
@@ -851,9 +857,14 @@ def make_names_and_values_dict_func(q, qparams):
             # how much we need to pad the weight
             delta_k = int((new_k - k) / 2)
             q = q.to(self.device)
-            final_q = torch.ops.aten._convert_weight_to_int4pack(
-                F.pad(q, pad=(0, delta_k)), inner_k_tiles
-            )
+            if is_device(self.device.type, "cpu"):
+                final_q = torch.ops.aten._convert_weight_to_int4pack_for_cpu(
+                    F.pad(q, pad=(0, delta_k)), inner_k_tiles
+                )
+            else:
+                final_q = torch.ops.aten._convert_weight_to_int4pack(
+                    F.pad(q, pad=(0, delta_k)), inner_k_tiles
+                )
             scales = qparams[0].to(torch.bfloat16).to(self.device)
             zeros = qparams[1].to(torch.bfloat16).to(self.device)
             scales_and_zeros = pack_tinygemm_scales_and_zeros(scales, zeros)

torchao/quantization/qat/linear.py

@@ -29,6 +29,7 @@
 from .utils import (
     _get_qmin_qmax,
 )
+from torchao.dtypes.utils import is_device
 
 
 class FakeQuantizedLinear(torch.nn.Linear):
@@ -373,10 +374,16 @@ def _convert_qat_linear_4w(self, module: torch.nn.Module):
                     n_bit,
                     config.group_size,
                 )
-                q_weight = torch.ops.aten._convert_weight_to_int4pack(
-                    q_weight.to(child.weight.device),
-                    child.inner_k_tiles,
-                )
+                if is_device(q_weight.device.type, "cpu"):
+                    q_weight = torch.ops.aten._convert_weight_to_int4pack_for_cpu(
+                        q_weight.to(child.weight.device),
+                        child.inner_k_tiles,
+                    )
+                else:
+                    q_weight = torch.ops.aten._convert_weight_to_int4pack(
+                        q_weight.to(child.weight.device),
+                        child.inner_k_tiles,
+                    )
                 quantized_linear.weight = q_weight
                 quantized_linear.scales_and_zeros = scales_and_zeros
             else:

torchao/quantization/utils.py

@@ -418,7 +418,7 @@ def groupwise_affine_dequantize_tensor_from_qparams(
     # need to handle single column case so check for dtype/size from groupwise_affine_quantize_tensor_from_qparams path
     if TORCH_VERSION_AT_LEAST_2_5 and (
         w_int4x8.dtype == torch.uint8 or w_int4x8.shape[-1] > 1
-    ):
+    ) and not is_device(w_int4x8.device.type, "cpu"):
         data = w_int4x8.to(torch.int32)
         high_bits = data >> 4
         low_bits = data & 0x0F