[ET-VK][ez] Allow logit linear layer to be lowered to Vulkan

backends/vulkan/_passes/int4_weight_only_quantizer.py

@@ -118,9 +118,6 @@ def _vk_replace_linear_int4(
     # Use custom vulkan linear layer as default
     linear_class: Type[torch.nn.Module] = VkWeightOnlyInt4Linear,
     copy_weights: bool = False,
-    # Serves the same purpose as `tensor_dim_limit` in
-    # executorch.backends.vulkan.partitioner.VulkanSupportedOperators
-    feature_limit: int = 16384,
 ):
     for name, child in module.named_children():
         if isinstance(child, torch.nn.Linear) and (
@@ -131,8 +128,6 @@ def _vk_replace_linear_int4(
             if (
                 _check_linear_int4_k(child.in_features, groupsize, inner_k_tiles)
                 or padding_allowed
-            ) and (
-                child.out_features < feature_limit and child.in_features < feature_limit
             ):
                 new_linear = linear_class(
                     child.in_features,
@@ -175,7 +170,6 @@ def __init__(
         inner_k_tiles: Optional[int] = 8,
         device: torch.device = torch.device("cpu"),  # noqa
         precision: torch.dtype = torch.float32,
-        feature_limit: int = 16384,
     ) -> None:
         super().__init__()
         assert inner_k_tiles in [2, 4, 8]
@@ -186,9 +180,6 @@ def __init__(
         self.padding_allowed: bool = padding_allowed
         self.device: torch.device = device
         self.precision: torch.dtype = precision
-        # Serves the same purpose as `tensor_dim_limit` in
-        # executorch.backends.vulkan.partitioner.VulkanSupportedOperators
-        self.feature_limit = feature_limit
 
     @torch.no_grad()
     def _create_quantized_state_dict(
@@ -197,10 +188,7 @@ def _create_quantized_state_dict(
         cur_state_dict = model.state_dict()
         for fqn, mod in model.named_modules():
             # Add additional check to make sure features do not exceed feature limit
-            if isinstance(mod, torch.nn.Linear) and (
-                mod.out_features < self.feature_limit
-                and mod.in_features < self.feature_limit
-            ):
+            if isinstance(mod, torch.nn.Linear):
                 out_features = mod.out_features
                 in_features = mod.in_features
                 logging.info(f"linear: {fqn}, in={in_features}, out={out_features}")

backends/vulkan/op_registry.py

@@ -392,6 +392,7 @@ def register_int8_mm_op(features: OpFeatures):
 
 @update_features(exir_ops.edge.et_vk.linear_weight_int4.default)
 def register_int4_mm_op(features: OpFeatures):
+    features.buffer_impl = True
     features.texture_impl = TextureImplFeatures(
         uses_axis_map=False,
         valid_packed_dims={PackedDim.WIDTH},
@@ -400,6 +401,7 @@ def register_int4_mm_op(features: OpFeatures):
     features.optimal_storage = VkStorageType.TEXTURE_3D
     features.optimal_layout = VkMemoryLayout.TENSOR_WIDTH_PACKED
     features.handles_own_prepacking = True
+    features.skip_limits_check = {1}
     return features
 
 

examples/models/llama/export_llama_lib.py

@@ -793,10 +793,6 @@ def _to_edge_and_lower_llama(  # noqa: C901
                 args.enable_dynamic_shape,
             )
         )
-        # Apply XNNPACK after Vulkan so that undelegated ops can be accelerated by XNNPACK
-        partitioners.append(
-            get_xnnpack_partitioner(dynamic_quant_only_partitioner=False)
-        )
         modelname = f"vulkan_{modelname}"
 
         # Need to remove asserts from the graph to prevent graph breaks

examples/models/llama/source_transformation/quantize.py

@@ -206,17 +206,6 @@ def quantize(  # noqa C901
         q_group_size = 256 if group_size is None else group_size
         model = VkInt4WeightOnlyQuantizer(groupsize=q_group_size).quantize(model)
 
-        # Apply additional quantizer for linear layers that aren't lowered to Vulkan
-        # at the moment
-        from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer
-
-        # 1. Quantize in checkpoint dtype.
-        model = Int8DynActInt4WeightQuantizer(
-            precision=checkpoint_torch_dtype, groupsize=q_group_size
-        ).quantize(model)
-        # 2. Set the computation dtype (what weights/acts dequantize to).
-        model = set_8da4w_computation_dtype(model, computation_torch_dtype)
-
         return model
     else:
         raise Exception(f"Unrecognized quantize mode: {qmode}")