PR comments

vllm/model_executor/layers/fused_moe/__init__.py

@@ -18,8 +18,8 @@
 
     __all__ += [
         "fused_moe",
-        "fused_topk",
         "fused_experts",
+        "fused_topk",
         "get_config_file_name",
         "grouped_topk",
     ]

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -442,8 +442,8 @@ def fused_experts(hidden_states: torch.Tensor,
     assert hidden_states.shape[1] == w1.shape[2], "Hidden size mismatch"
     assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
     assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
-    #assert w1.is_contiguous(), "Expert weights1 must be contiguous"
-    #assert w2.is_contiguous(), "Expert weights2 must be contiguous"
+    assert w1.is_contiguous(), "Expert weights1 must be contiguous"
+    assert w2.is_contiguous(), "Expert weights2 must be contiguous"
     assert hidden_states.dtype in [
         torch.float32, torch.float16, torch.bfloat16
     ]

vllm/model_executor/layers/fused_moe/fused_moe_awq.py

@@ -3,8 +3,8 @@
 
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
-
-from .fused_moe import fused_experts, moe_align_block_size
+from vllm.model_executor.layers.fused_moe.fused_moe import (
+    fused_experts, moe_align_block_size)
 
 logger = init_logger(__name__)
 
@@ -43,12 +43,11 @@ def fused_experts_awq(
     # If large seq_len prefill, dequantize and use the fp16 MoE kernel.
     do_naive_dequant = hidden_states.shape[:-1].numel() >= NAIVE_THRESHOLD
     if do_naive_dequant:
-        # TODO: why is this not contiguous already?
-        # from @dsikka: because of the permutation operation
+        # NOTE: not contiguous because of the permutation operation
         dequant_w1 = ops.awq_dequantize(w1, w1_scales, w1_qzeros, 0, 0,
-                                        0).permute(0, 2, 1)
+                                        0).permute(0, 2, 1).contiguous()
         dequant_w2 = ops.awq_dequantize(w2, w2_scales, w2_qzeros, 0, 0,
-                                        0).permute(0, 2, 1)
+                                        0).permute(0, 2, 1).contiguous()
 
         return fused_experts(hidden_states, dequant_w1, dequant_w2,
                              topk_weights, topk_ids)

vllm/model_executor/layers/fused_moe/layer.py

@@ -225,9 +225,9 @@ def weight_loader(self, param: torch.nn.Parameter,
 
         # If transposed, weight is saved as [input_dim, output_dim]
         # Otherwise, weight is saved as     [output_dim, input_dim]
-        is_transposed = getattr(param, "is_transposed", False)
-        input_dim = 0 if is_transposed else 1
-        output_dim = 1 if is_transposed else 0
+        # Default is not transposed/input dim is dim 1
+        input_dim = getattr(param, "input_dim", 1)
+        output_dim = getattr(param, "output_dim", 0)
 
         # Index the loaded weight for tp sharding.
         # down_proj: "RowParallel" so tp sharding on input_dim

vllm/model_executor/layers/quantization/awq.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional
 
 import torch
 from torch.nn.parameter import Parameter
@@ -8,7 +8,7 @@
                                                   fused_experts_awq)
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+    QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.utils import set_weight_attrs
 
 
@@ -65,9 +65,8 @@ def from_config(cls, config: Dict[str, Any]) -> "AWQConfig":
         zero_point = cls.get_from_keys(config, ["zero_point"])
         return cls(weight_bits, group_size, zero_point)
 
-    def get_quant_method(
-            self, layer: torch.nn.Module,
-            prefix: str) -> Optional[Union["AWQMoEMethod", "AWQLinearMethod"]]:
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QuantizeMethodBase"]:
         if isinstance(layer, LinearBase):
             return AWQLinearMethod(self)
         elif isinstance(layer, FusedMoE):
@@ -202,7 +201,8 @@ def create_weights(self, layer: torch.nn.Module, num_experts: int,
             w13_qweight, {
                 "packed_dim": 1,
                 "pack_factor": self.quant_config.pack_factor,
-                "is_transposed": True,
+                "input_dim": 0,
+                "output_dim": 1,
                 **extra_weight_attrs
             })
 
@@ -217,7 +217,8 @@ def create_weights(self, layer: torch.nn.Module, num_experts: int,
             w2_qweight, {
                 "packed_dim": 1,
                 "pack_factor": self.quant_config.pack_factor,
-                "is_transposed": True,
+                "input_dim": 0,
+                "output_dim": 1,
                 **extra_weight_attrs
             })
 
@@ -231,7 +232,8 @@ def create_weights(self, layer: torch.nn.Module, num_experts: int,
                                requires_grad=False)
         layer.register_parameter("w13_scales", w13_scales)
         set_weight_attrs(w13_scales, {
-            "is_transposed": True,
+            "input_dim": 0,
+            "output_dim": 1,
             **extra_weight_attrs
         })
 
@@ -243,7 +245,8 @@ def create_weights(self, layer: torch.nn.Module, num_experts: int,
                               requires_grad=False)
         layer.register_parameter("w2_scales", w2_scales)
         set_weight_attrs(w2_scales, {
-            "is_transposed": True,
+            "input_dim": 0,
+            "output_dim": 1,
             **extra_weight_attrs
         })
 
@@ -260,7 +263,8 @@ def create_weights(self, layer: torch.nn.Module, num_experts: int,
             w13_qzeros, {
                 "packed_dim": 1,
                 "pack_factor": self.quant_config.pack_factor,
-                "is_transposed": True,
+                "input_dim": 0,
+                "output_dim": 1,
                 **extra_weight_attrs
             })
 
@@ -275,7 +279,8 @@ def create_weights(self, layer: torch.nn.Module, num_experts: int,
             w2_qzeros, {
                 "packed_dim": 1,
                 "pack_factor": self.quant_config.pack_factor,
-                "is_transposed": True,
+                "input_dim": 0,
+                "output_dim": 1,
                 **extra_weight_attrs
             })