[ET-VK][int4] patch 4-bit source transformation quantizer to support linear modules with biases

Pull Request resolved: #8224

While LLaMa does not have biases, there are some models which will have biases in their linear modules.

Add support in the source transform quantizer for biases.
ghstack-source-id: 264952608
@exported-using-ghexport

Differential Revision: [D69072087](https://our.internmc.facebook.com/intern/diff/D69072087/)

Co-authored-by: Nathanael See <[email protected]>

Author: pytorchbot

backends/vulkan/_passes/int4_weight_only_quantizer.py

@@ -39,11 +39,12 @@ def __init__(
             from torchao.utils import find_multiple
 
             self.origin_in_features = in_features
-            in_features = find_multiple(in_features, (1024,))
+            # pyre-ignore[6]: Incompatible parameter type
+            in_features = find_multiple(in_features, 1024)
 
+        self.use_bias = bias
         self.in_features = in_features
         self.out_features = out_features
-        assert not bias, "require bias=False"
         self.device = device
         self.groupsize = groupsize
         self.inner_k_tiles = inner_k_tiles
@@ -80,20 +81,28 @@ def __init__(
                 device=device,
             ),
         )
+        if bias:
+            self.register_buffer(
+                "bias",
+                torch.empty((out_features,), dtype=torch.float32, device=device),
+            )
 
     def forward(self, input: torch.Tensor) -> torch.Tensor:
         if self.padding:
             input = F.pad(input, pad=(0, self.in_features - self.origin_in_features))
         # The forward method is replaced. In the original implementation, the forward
         # method is torchao.quantization.GPTQ.linear_forward_int4; here a Vulkan custom
         # operator is called instead.
-        return torch.ops.et_vk.linear_weight_int4(
+        r = torch.ops.et_vk.linear_weight_int4(
             input,
             self.weight,
             self.groupsize,
             self.scales_and_zeros,
             self.inner_k_tiles,
         )
+        if self.use_bias:
+            return r + self.bias
+        return r
 
 
 # This function is coped from torchao.quantization.GPTQ._replace_linear_int4
@@ -128,7 +137,7 @@ def _vk_replace_linear_int4(
                 new_linear = linear_class(
                     child.in_features,
                     child.out_features,
-                    bias=False,
+                    bias=child.bias is not None,
                     device=child.weight.device,
                     groupsize=groupsize,
                     inner_k_tiles=inner_k_tiles,
@@ -138,6 +147,9 @@ def _vk_replace_linear_int4(
                 if copy_weights and child.weight.device != torch.device("meta"):
                     # pyre-fixme[16]: `Module` has no attribute `weight`.
                     new_linear.weight = child.weight
+                    if child.bias is not None:
+                        # pyre-fixme[16]: `Module` has no attribute `bias`.
+                        new_linear.bias = child.bias
                 setattr(module, name, new_linear)
         else:
             _vk_replace_linear_int4(
@@ -189,7 +201,6 @@ def _create_quantized_state_dict(
                 mod.out_features < self.feature_limit
                 and mod.in_features < self.feature_limit
             ):
-                assert not mod.bias
                 out_features = mod.out_features
                 in_features = mod.in_features
                 logging.info(f"linear: {fqn}, in={in_features}, out={out_features}")
@@ -210,7 +221,8 @@ def _create_quantized_state_dict(
                         logging.warn(
                             f"warning: {fqn} is padded to satisfy in_features % 1024 == 0"
                         )
-                        padded_in_features = find_multiple(in_features, (1024,))
+                        # pyre-ignore[6]: Incompatible parameter type
+                        padded_in_features = find_multiple(in_features, 1024)
                         weight = F.pad(
                             weight, pad=(0, padded_in_features - in_features)
                         )