Support moe_wna16 as well

Signed-off-by: mgoin <mgoin64@gmail.com>

vllm/_custom_ops.py

@@ -215,7 +215,7 @@ def rms_norm_dynamic_per_token_quant(
 def awq_dequantize(qweight: torch.Tensor, scales: torch.Tensor,
                    zeros: torch.Tensor, split_k_iters: int, thx: int,
                    thy: int) -> torch.Tensor:
-    if envs.VLLM_USE_TRITON_AWQ:
+    if envs.VLLM_USE_TRITON_AWQ or qweight.dtype != torch.float16:
         from vllm.model_executor.layers.quantization.awq_triton import (
             awq_dequantize_triton)
         return awq_dequantize_triton(qweight, scales, zeros)

vllm/attention/backends/mla/utils.py

@@ -18,6 +18,8 @@ from vllm.distributed import (get_tensor_model_parallel_world_size,
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                LinearBase, RowParallelLinear,
                                                UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization.awq_marlin import (
+    AWQMarlinLinearMethod)
 from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import (  # noqa: E501
     CompressedTensorsLinearMethod)
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
@@ -227,8 +229,9 @@ class MLACommonImpl(MLAAttentionImpl[T], Generic[T]):
                 and isinstance(layer.scheme, CompressedTensorsW8A8Fp8))
 
         def quantization_scheme_supported(layer: LinearBase) -> bool:
-            return isinstance(layer.quant_method, UnquantizedLinearMethod) or \
-                is_layer_fp8(layer)
+            return isinstance(layer.quant_method,
+                              (UnquantizedLinearMethod,
+                               AWQMarlinLinearMethod)) or is_layer_fp8(layer)
 
         # TODO(lucas) This is very gross, we need a more wide scale refactor of
         # all the FP8 code with a more standard way of
@@ -289,6 +292,8 @@ class MLACommonImpl(MLAAttentionImpl[T], Generic[T]):
 
                 return scaled_dequantize(weight, scales,
                                          weight_scale_group_shape)
+            elif isinstance(layer.quant_method, AWQMarlinLinearMethod):
+                return layer.quant_method.decompress_weights(layer).T
             else:
                 return layer.weight
 
@@ -296,12 +301,21 @@ class MLACommonImpl(MLAAttentionImpl[T], Generic[T]):
             quantization_scheme_supported(self.q_proj) and\
                 quantization_scheme_supported(self.o_proj)):
             raise NotImplementedError(
-                "Only FP8 and UnquantizedLinearMethod are supported for MLA"
+                "Only FP8, AWQ, and Unquantized are supported for MLA"
                 ", please run with VLLM_MLA_DISABLE=1")
 
-        weight_dtype = self.kv_b_proj.weight.dtype
-        assert self.o_proj.weight.dtype == weight_dtype
-        assert self.q_proj.weight.dtype == weight_dtype
+        def get_layer_dtype(layer):
+            if hasattr(layer, "weight"):
+                return layer.weight.dtype
+            elif hasattr(layer, "qweight"):
+                return layer.qweight.dtype
+            else:
+                raise AttributeError(
+                    f"Layer '{layer}' has neither weight nor qweight")
+
+        weight_dtype = get_layer_dtype(self.kv_b_proj)
+        assert get_layer_dtype(self.o_proj) == weight_dtype
+        assert get_layer_dtype(self.q_proj) == weight_dtype
 
         kv_b_proj_weight = get_and_maybe_dequant_weights(self.kv_b_proj).T
         assert kv_b_proj_weight.shape == (

vllm/config.py

@@ -990,7 +990,7 @@ class ModelConfig:
             return False
 
         if self.quantization is not None and self.quantization not in [\
-            "fp8", "compressed-tensors"]:
+            "fp8", "compressed-tensors", "awq_marlin", "moe_wna16"]:
             logger.warning(
                 "MLA is not supported with %s quantization. "
                 "Disabling MLA.", self.quantization)

vllm/model_executor/layers/quantization/awq_marlin.py

@@ -242,6 +242,16 @@ class AWQMarlinLinearMethod(LinearMethodBase):
         layer.output_size_per_partition = output_size_per_partition
         layer.num_groups = num_groups
 
+    def decompress_weights(self, layer: torch.nn.Module) -> torch.Tensor:
+        """
+        Decompress to recover the original unquantized weight.
+        NOTE: this is only to be used before process_weights_after_loading
+        """
+        # We can use AWQ's dequant since the unprocessed weights
+        # are in AWQ format
+        return ops.awq_dequantize(layer.qweight, layer.scales, layer.qzeros, 0,
+                                  0, 0)
+
     # TODO: Update this docs
     # Checkpoints are serialized in AutoAWQ format, which is different from the
     # marlin format. This function is called after the weights are loaded.

vllm/model_executor/model_loader/loader.py

@@ -153,6 +153,30 @@ def _initialize_model(
         return model_class(**kwargs)
 
 
+def _process_weights_after_loading(model: nn.Module, model_config: ModelConfig,
+                                   target_device: torch.device) -> None:
+    # Currently only used by MLA.
+    # NOTE: This intentionally happens before other modules so we can easily
+    # decompress the weights for MLA.
+    for _, module in model.named_modules():
+        if isinstance(module, Attention) and \
+            hasattr(module, "process_weights_after_loading"):
+            # TODO(lucas): see if there is a way to unify the signatures
+            # of process_weights_after_loading
+            module.process_weights_after_loading(model_config.dtype)
+
+    for _, module in model.named_modules():
+        quant_method = getattr(module, "quant_method", None)
+        if isinstance(quant_method, QuantizeMethodBase):
+            # When quant methods need to process weights after loading
+            # (for repacking, quantizing, etc), they expect parameters
+            # to be on the global target device. This scope is for the
+            # case where cpu offloading is used, where we will move the
+            # parameters onto device for processing and back off after.
+            with device_loading_context(module, target_device):
+                quant_method.process_weights_after_loading(module)
+
+
 class BaseModelLoader(ABC):
     """Base class for model loaders."""
 
@@ -376,7 +400,6 @@ class DefaultModelLoader(BaseModelLoader):
     def load_model(self, vllm_config: VllmConfig) -> nn.Module:
         device_config = vllm_config.device_config
         model_config = vllm_config.model_config
-
         target_device = torch.device(device_config.device)
         with set_default_torch_dtype(model_config.dtype):
             with target_device:
@@ -394,23 +417,8 @@ class DefaultModelLoader(BaseModelLoader):
                         "Following weights were not initialized from "
                         f"checkpoint: {weights_not_loaded}")
 
-            for _, module in model.named_modules():
-                quant_method = getattr(module, "quant_method", None)
-                if isinstance(quant_method, QuantizeMethodBase):
-                    # When quant methods need to process weights after loading
-                    # (for repacking, quantizing, etc), they expect parameters
-                    # to be on the global target device. This scope is for the
-                    # case where cpu offloading is used, where we will move the
-                    # parameters onto device for processing and back off after.
-                    with device_loading_context(module, target_device):
-                        quant_method.process_weights_after_loading(module)
-                if isinstance(module, Attention) and \
-                    hasattr(module, "process_weights_after_loading"):
-                    # When attention modules need to process weights after
-                    # currently only used by MLA
-                    # TODO(lucas): see if there is a way to unify the signatures
-                    # of process_weights_after_loading
-                    module.process_weights_after_loading(model_config.dtype)
+            _process_weights_after_loading(model, model_config, target_device)
+
         return model.eval()
 
 
@@ -429,29 +437,15 @@ class DummyModelLoader(BaseModelLoader):
     def load_model(self, vllm_config: VllmConfig) -> nn.Module:
         device_config = vllm_config.device_config
         model_config = vllm_config.model_config
+        target_device = torch.device(device_config.device)
         with set_default_torch_dtype(model_config.dtype):
-            with torch.device(device_config.device):
+            with target_device:
                 model = _initialize_model(vllm_config=vllm_config)
             # NOTE(woosuk): For accurate performance evaluation, we assign
             # random values to the weights.
             initialize_dummy_weights(model)
 
-            for _, module in model.named_modules():
-                quant_method = getattr(module, "quant_method", None)
-                if quant_method is not None:
-                    # When quant methods need to process weights after loading
-                    # (for repacking, quantizing, etc), they expect parameters
-                    # to be on the global target device. This scope is for the
-                    # case where cpu offloading is used, where we will move the
-                    # parameters onto device for processing and back off after.
-                    with device_loading_context(
-                            module, torch.device(device_config.device)):
-                        quant_method.process_weights_after_loading(module)
-                if isinstance(module, Attention) and \
-                    hasattr(module, "process_weights_after_loading"):
-                    # When attention modules need to process weights after
-                    # currently only used by MLA
-                    module.process_weights_after_loading(model_config.dtype)
+            _process_weights_after_loading(model, model_config, target_device)
         return model.eval()
 
 
@@ -632,6 +626,7 @@ class ShardedStateLoader(BaseModelLoader):
     def load_model(self, vllm_config: VllmConfig) -> nn.Module:
         device_config = vllm_config.device_config
         model_config = vllm_config.model_config
+        target_device = torch.device(device_config.device)
         from safetensors.torch import safe_open
 
         from vllm.distributed import get_tensor_model_parallel_rank
@@ -640,18 +635,10 @@ class ShardedStateLoader(BaseModelLoader):
                                                  model_config.revision)
 
         with set_default_torch_dtype(model_config.dtype):
-            with torch.device(device_config.device):
+            with target_device:
                 model = _initialize_model(vllm_config=vllm_config)
-                for _, module in model.named_modules():
-                    quant_method = getattr(module, "quant_method", None)
-                    if quant_method is not None:
-                        quant_method.process_weights_after_loading(module)
-                    if isinstance(module, Attention) and \
-                        hasattr(module, "process_weights_after_loading"):
-                        # When attention modules need to process weights after
-                        # currently only used by MLA
-                        module.process_weights_after_loading(
-                            model_config.dtype)
+                _process_weights_after_loading(model, model_config,
+                                               target_device)
             rank = get_tensor_model_parallel_rank()
             pattern = os.path.join(
                 local_model_path,
@@ -1401,16 +1388,7 @@ class RunaiModelStreamerLoader(BaseModelLoader):
                 self._get_weights_iterator(model_weights,
                                            model_config.revision))
 
-            for _, module in model.named_modules():
-                quant_method = getattr(module, "quant_method", None)
-                if quant_method is not None:
-                    with device_loading_context(module, target_device):
-                        quant_method.process_weights_after_loading(module)
-                if isinstance(module, Attention) and \
-                    hasattr(module, "process_weights_after_loading"):
-                    # When attention modules need to process weights after
-                    # currently only used by MLA
-                    module.process_weights_after_loading(model_config.dtype)
+            _process_weights_after_loading(model, model_config, target_device)
         return model.eval()