[Core] Set linear_weights directly on the layer

csrc/quantization/gptq/q_gemm.cu

@@ -2067,7 +2067,7 @@ void gptq_shuffle
     const at::cuda::OptionalCUDAGuard device_guard(device_of(q_weight));
     vllm::gptq::shuffle_exllama_weight(
         (uint32_t*) q_weight.data_ptr(),
-        q_perm.device().is_meta() ? NULL : (int*) q_perm.data_ptr(),
+        q_perm.device().is_meta() || q_perm.numel() == 0 ? NULL : (int*) q_perm.data_ptr(),
         q_weight.size(0) * 32 / bit,
         q_weight.size(1),
         bit

tests/kernels/test_moe.py

@@ -73,7 +73,7 @@ def test_mixtral_moe(dtype: torch.dtype):
     ).cuda()
 
     # Load the weights
-    vllm_moe.gate.linear_weights["weight"][:] = hf_moe.gate.weight.data
+    vllm_moe.gate.weight.data[:] = hf_moe.gate.weight.data
     for i in range(config.num_local_experts):
         weights = (hf_moe.experts[i].w1.weight.data,
                    hf_moe.experts[i].w3.weight.data)

vllm/lora/layers.py

@@ -368,7 +368,7 @@ def set_mapping(
     def apply_weights(self, x: torch.Tensor,
                       bias: Optional[torch.Tensor]) -> torch.Tensor:
         output = self.base_layer.linear_method.apply_weights(
-            self.base_layer.linear_weights, x, bias)
+            self.base_layer, x, bias)
         _apply_lora(
             x,
             self.lora_a_stacked,
@@ -402,10 +402,6 @@ def forward(self, input_):
                        if self.base_layer.skip_bias_add else None)
         return output, output_bias
 
-    @property
-    def linear_weights(self):
-        return self.base_layer.linear_weights
-
     @classmethod
     def can_replace_layer(cls, source_layer: nn.Module,
                           lora_config: LoRAConfig, packed_modules_list: List,
@@ -505,7 +501,7 @@ def set_lora(
     def apply_weights(self, x: torch.Tensor,
                       bias: Optional[torch.Tensor]) -> torch.Tensor:
         output = self.base_layer.linear_method.apply_weights(
-            self.base_layer.linear_weights, x, bias)
+            self.base_layer, x, bias)
         _apply_lora_packed_nslice(
             x,
             self.lora_a_stacked,
@@ -746,7 +742,7 @@ def set_lora(
     def apply_weights(self, x: torch.Tensor,
                       bias: Optional[torch.Tensor]) -> torch.Tensor:
         output = self.base_layer.linear_method.apply_weights(
-            self.base_layer.linear_weights, x, bias)
+            self.base_layer, x, bias)
         _apply_lora_packed_nslice(
             x,
             self.lora_a_stacked,
@@ -838,7 +834,7 @@ def set_mapping(
 
     def apply_weights(self, x: torch.Tensor) -> torch.Tensor:
         output = self.base_layer.linear_method.apply_weights(
-            self.base_layer.linear_weights, x)
+            self.base_layer, x)
         _apply_lora(
             x,
             self.lora_a_stacked,

vllm/model_executor/layers/linear.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional
+from typing import List, Optional
 
 import torch
 import torch.nn.functional as F
@@ -29,19 +29,24 @@ class LinearMethodBase(ABC):
     """Base class for different (maybe quantized) linear methods."""
 
     @abstractmethod
-    def create_weights(self, input_size_per_partition: int,
+    def create_weights(self, layer: torch.nn.Module,
+                       input_size_per_partition: int,
                        output_size_per_partition: int, input_size: int,
-                       output_size: int,
-                       params_dtype: torch.dtype) -> Dict[str, Any]:
-        """Create weights for a linear layer."""
+                       output_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs):
+        """Create weights for a linear layer.
+
+        The weights will be set as attributes of the layer."""
         raise NotImplementedError
 
     @abstractmethod
     def apply_weights(self,
-                      weights: Dict[str, torch.Tensor],
+                      layer: torch.nn.Module,
                       x: torch.Tensor,
                       bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-        """Apply the weights to the input tensor."""
+        """Apply the weights in layer to the input tensor.
+
+        Expects create_weights to have been called before on the layer."""
         raise NotImplementedError
 
 
@@ -56,22 +61,24 @@ class UnquantizedLinearMethod(LinearMethodBase):
     def __init__(self, separate_bias_add: bool = False):
         self.separate_bias_add = separate_bias_add
 
-    def create_weights(self, input_size_per_partition: int,
+    def create_weights(self, layer: torch.nn.Module,
+                       input_size_per_partition: int,
                        output_size_per_partition: int, input_size: int,
-                       output_size: int,
-                       params_dtype: torch.dtype) -> Dict[str, Any]:
+                       output_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs):
         weight = Parameter(torch.empty(output_size_per_partition,
                                        input_size_per_partition,
                                        dtype=params_dtype),
                            requires_grad=False)
         set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
-        return {"weight": weight}
+        layer.register_parameter("weight", weight)
+        set_weight_attrs(weight, extra_weight_attrs)
 
     def apply_weights(self,
-                      weights: Dict[str, torch.Tensor],
+                      layer: torch.nn.Module,
                       x: torch.Tensor,
                       bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-        weight = weights["weight"]
+        weight = layer.weight
         if self.separate_bias_add:
             if bias is not None:
                 return F.linear(x, weight) + bias
@@ -112,12 +119,9 @@ def __init__(
         if linear_method is None:
             linear_method = UnquantizedLinearMethod()
         self.linear_method = linear_method
-        self.linear_weights = self.linear_method.create_weights(
-            self.input_size, self.output_size, self.input_size,
-            self.output_size, self.params_dtype)
-        for name, weight in self.linear_weights.items():
-            if isinstance(weight, torch.Tensor):
-                self.register_parameter(name, weight)
+        self.linear_method.create_weights(self, self.input_size,
+                                          self.output_size, self.input_size,
+                                          self.output_size, self.params_dtype)
         if bias:
             self.bias = Parameter(
                 torch.empty(self.output_size, dtype=self.params_dtype))
@@ -127,7 +131,7 @@ def __init__(
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         bias = self.bias if not self.skip_bias_add else None
-        output = self.linear_method.apply_weights(self.linear_weights, x, bias)
+        output = self.linear_method.apply_weights(self, x, bias)
         output_bias = self.bias if self.skip_bias_add else None
         return output, output_bias
 
@@ -178,13 +182,13 @@ def __init__(
         if linear_method is None:
             linear_method = UnquantizedLinearMethod()
         self.linear_method = linear_method
-        self.linear_weights = self.linear_method.create_weights(
-            self.input_size, self.output_size_per_partition, self.input_size,
-            self.output_size, self.params_dtype)
-        for name, weight in self.linear_weights.items():
-            if isinstance(weight, torch.Tensor):
-                self.register_parameter(name, weight)
-                set_weight_attrs(weight, {"weight_loader": self.weight_loader})
+        self.linear_method.create_weights(self,
+                                          self.input_size,
+                                          self.output_size_per_partition,
+                                          self.input_size,
+                                          self.output_size,
+                                          self.params_dtype,
+                                          weight_loader=self.weight_loader)
         if bias:
             self.bias = Parameter(
                 torch.empty(self.output_size_per_partition,
@@ -212,8 +216,7 @@ def forward(self, input_):
         bias = self.bias if not self.skip_bias_add else None
 
         # Matrix multiply.
-        output_parallel = self.linear_method.apply_weights(
-            self.linear_weights, input_, bias)
+        output_parallel = self.linear_method.apply_weights(self, input_, bias)
         if self.gather_output:
             # All-gather across the partitions.
             output = tensor_model_parallel_all_gather(output_parallel)
@@ -524,13 +527,13 @@ def __init__(
         if linear_method is None:
             linear_method = UnquantizedLinearMethod()
         self.linear_method = linear_method
-        self.linear_weights = self.linear_method.create_weights(
-            self.input_size_per_partition, self.output_size, self.input_size,
-            self.output_size, self.params_dtype)
-        for name, weight in self.linear_weights.items():
-            if isinstance(weight, torch.Tensor):
-                self.register_parameter(name, weight)
-                set_weight_attrs(weight, {"weight_loader": self.weight_loader})
+        self.linear_method.create_weights(self,
+                                          self.input_size_per_partition,
+                                          self.output_size,
+                                          self.input_size,
+                                          self.output_size,
+                                          self.params_dtype,
+                                          weight_loader=self.weight_loader)
 
         if not reduce_results and (bias and not skip_bias_add):
             raise ValueError("When not reduce the results, adding bias to the "
@@ -570,7 +573,7 @@ def forward(self, input_):
 
         # Matrix multiply.
         output_parallel = self.linear_method.apply_weights(
-            self.linear_weights, input_parallel)
+            self, input_parallel)
         if self.reduce_results and self.tp_size > 1:
             output_ = tensor_model_parallel_all_reduce(output_parallel)
         else:

vllm/model_executor/layers/quantization/awq.py

@@ -79,10 +79,11 @@ class AWQLinearMethod(LinearMethodBase):
     def __init__(self, quant_config: AWQConfig):
         self.quant_config = quant_config
 
-    def create_weights(self, input_size_per_partition: int,
+    def create_weights(self, layer: torch.nn.Module,
+                       input_size_per_partition: int,
                        output_size_per_partition: int, input_size: int,
-                       output_size: int,
-                       params_dtype: torch.dtype) -> Dict[str, Any]:
+                       output_size: int, params_dtype: torch.dtype,
+                       **extra_weight_attrs):
         if input_size_per_partition % self.quant_config.group_size != 0:
             raise ValueError(
                 "The input size is not aligned with the quantized "
@@ -136,19 +137,21 @@ def create_weights(self, input_size_per_partition: int,
             "input_dim": 0,
             "output_dim": 1,
         })
-        return {
-            "qweight": qweight,
-            "qzeros": qzeros,
-            "scales": scales,
-        }
+
+        layer.register_parameter("qweight", qweight)
+        set_weight_attrs(qweight, extra_weight_attrs)
+        layer.register_parameter("qzeros", qzeros)
+        set_weight_attrs(qzeros, extra_weight_attrs)
+        layer.register_parameter("scales", scales)
+        set_weight_attrs(scales, extra_weight_attrs)
 
     def apply_weights(self,
-                      weights: Dict[str, Any],
+                      layer: torch.nn.Module,
                       x: torch.Tensor,
                       bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-        qweight = weights["qweight"]
-        scales = weights["scales"]
-        qzeros = weights["qzeros"]
+        qweight = layer.qweight
+        scales = layer.scales
+        qzeros = layer.qzeros
         pack_factor = self.quant_config.pack_factor
         out_shape = (x.shape[:-1] + (qweight.shape[-1] * pack_factor, ))
         reshaped_x = x.reshape(-1, x.shape[-1])
@@ -163,5 +166,5 @@ def apply_weights(self,
             out = ops.awq_gemm(reshaped_x, qweight, scales, qzeros,
                                pack_factor)
         if bias is not None:
-            out = out + bias
+            out.add_(bias)
         return out.reshape(out_shape)

vllm/model_executor/layers/quantization/gptq.py

@@ -89,12 +89,14 @@ def __init__(self, quant_config: GPTQConfig):
 
     def create_weights(
         self,
+        layer: torch.nn.Module,
         input_size_per_partition: int,
         output_size_per_partition: int,
         input_size: int,
         output_size: int,
         params_dtype: torch.dtype,
-    ) -> Dict[str, Any]:
+        **extra_weight_attrs,
+    ):
         del output_size  # Unused.
         if input_size_per_partition % self.quant_config.group_size != 0:
             raise ValueError(
@@ -179,37 +181,40 @@ def create_weights(
             "input_dim": scale_and_zero_input_dim,
             "output_dim": 1,
         })
-        return {
-            "qweight": qweight,
-            "g_idx": g_idx,
-            "qzeros": qzeros,
-            "scales": scales,
-            "exllama_state": exllama_state,
-        }
+
+        layer.register_parameter("qweight", qweight)
+        set_weight_attrs(qweight, extra_weight_attrs)
+        layer.register_parameter("g_idx", g_idx)
+        set_weight_attrs(g_idx, extra_weight_attrs)
+        layer.register_parameter("qzeros", qzeros)
+        set_weight_attrs(qzeros, extra_weight_attrs)
+        layer.register_parameter("scales", scales)
+        set_weight_attrs(scales, extra_weight_attrs)
+
+        layer.exllama_state = exllama_state
 
     def apply_weights(self,
-                      weights: Dict[str, Any],
+                      layer: torch.nn.Module,
                       x: torch.Tensor,
                       bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-        qweight = weights["qweight"]
+        qweight = layer.qweight
         out_shape = x.shape[:-1] + (qweight.shape[-1], )
         reshaped_x = x.reshape(-1, x.shape[-1])
         # exllama needs to shuffle the weight after the weight is loaded
         # here we do the shuffle on first forward pass
-        if weights["exllama_state"] == ExllamaState.UNINITIALIZED:
+        if layer.exllama_state == ExllamaState.UNINITIALIZED:
             if self.quant_config.desc_act:
-                weights["g_idx"] = torch.argsort(weights["g_idx"]).to(
-                    torch.int)
+                layer.g_idx.data = torch.argsort(layer.g_idx).to(torch.int)
             else:
-                weights["g_idx"] = torch.empty((1, 1), device="meta")
-            weights["exllama_state"] = ExllamaState.READY
-            ops.gptq_shuffle(weights["qweight"], weights["g_idx"],
+                layer.g_idx.data = torch.empty((0, ),
+                                               device=layer.g_idx.device)
+            layer.exllama_state = ExllamaState.READY
+            ops.gptq_shuffle(layer.qweight, layer.g_idx,
                              self.quant_config.weight_bits)
-        output = ops.gptq_gemm(reshaped_x, weights["qweight"],
-                               weights["qzeros"], weights["scales"],
-                               weights["g_idx"],
-                               weights["exllama_state"] == ExllamaState.READY,
+        output = ops.gptq_gemm(reshaped_x, layer.qweight, layer.qzeros,
+                               layer.scales, layer.g_idx,
+                               layer.exllama_state == ExllamaState.READY,
                                self.quant_config.weight_bits)
         if bias is not None:
-            output = output + bias
+            output.add_(bias)
         return output.reshape(out_shape)

vllm/model_executor/layers/quantization/marlin.py

@@ -91,12 +91,14 @@ def __init__(self, quant_config: MarlinConfig):
 
     def create_weights(
         self,
+        layer: torch.nn.Module,
         input_size_per_partition: int,
         output_size_per_partition: int,
         input_size: int,
         output_size: int,
         params_dtype: torch.dtype,
-    ) -> Dict[str, Any]:
+        **extra_weight_attrs,
+    ):
         del output_size  # Unused.
 
         if params_dtype != torch.float16:
@@ -187,21 +189,22 @@ def create_weights(
                                           dtype=torch.int),
                               requires_grad=False)
 
-        return {
-            "B": qweight,
-            "s": scales,
-            "workspace": workspace,
-        }
+        layer.register_parameter("B", qweight)
+        set_weight_attrs(qweight, extra_weight_attrs)
+        layer.register_parameter("s", scales)
+        set_weight_attrs(scales, extra_weight_attrs)
+        layer.register_parameter("workspace", workspace)
+        set_weight_attrs(workspace, extra_weight_attrs)
 
     def apply_weights(
         self,
-        weights: Dict[str, Any],
+        layer: torch.nn.Module,
         x: torch.Tensor,
         bias: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
-        qweight = weights["B"]
-        scales = weights["s"]
-        workspace = weights["workspace"]
+        qweight = layer.B
+        scales = layer.s
+        workspace = layer.workspace
 
         x_2d = x.view(-1, x.shape[-1])