[ Misc ] non-uniform quantization via compressed-tensors for Llama (

vllm-project#6515)

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml

@@ -0,0 +1,11 @@
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-nonuniform-test -b auto -l 1000 -f 5 -t 1
+model_name: "nm-testing/Meta-Llama-3-8B-Instruct-nonuniform-test"
+tasks:
+- name: "gsm8k"
+  metrics:
+  - name: "exact_match,strict-match"
+    value: 0.758
+  - name: "exact_match,flexible-extract"
+    value: 0.759
+limit: 1000
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/models-small.txt

@@ -2,4 +2,5 @@ Meta-Llama-3-8B-Instruct.yaml
 Meta-Llama-3-8B-Instruct-FP8.yaml
 Meta-Llama-3-8B-Instruct-FP8-compressed-tensors.yaml
 Meta-Llama-3-8B-Instruct-INT8-compressed-tensors.yaml
+Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
 Qwen2-1.5B-Instruct-INT8-compressed-tensors.yaml

vllm/model_executor/layers/fused_moe/layer.py

@@ -158,6 +158,7 @@ def __init__(
         topk_group: Optional[int] = None,
         quant_config: Optional[QuantizationConfig] = None,
         tp_size: Optional[int] = None,
+        prefix: str = "",
     ):
         super().__init__()
 

vllm/model_executor/layers/linear.py

@@ -171,6 +171,8 @@ class ReplicatedLinear(LinearBase):
         skip_bias_add: If true, skip adding bias but instead return it.
         params_dtype: Data type for the parameters.
         quant_config: Quantization configure.
+        prefix: The name of the layer in the state dict, including all parents
+                        (e.g. model.layers.0.qkv_proj)
     """
 
     def __init__(self,
@@ -179,15 +181,19 @@ def __init__(self,
                  bias: bool = True,
                  skip_bias_add: bool = False,
                  params_dtype: Optional[torch.dtype] = None,
-                 quant_config: Optional[QuantizationConfig] = None):
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: Optional[str] = None):
         super().__init__(input_size, output_size, skip_bias_add, params_dtype,
                          quant_config)
 
         # All the linear layer supports quant method.
         assert self.quant_method is not None
-        self.quant_method.create_weights(self, self.input_size,
-                                         [self.output_size], self.input_size,
-                                         self.output_size, self.params_dtype)
+        self.quant_method.create_weights(self,
+                                         self.input_size, [self.output_size],
+                                         self.input_size,
+                                         self.output_size,
+                                         self.params_dtype,
+                                         prefix=prefix)
 
         if bias:
             self.bias = Parameter(
@@ -239,6 +245,8 @@ class ColumnParallelLinear(LinearBase):
         quant_config: Quantization configure.
         output_sizes: list of output sizes packed into one output, like for QKV
                        the list would be size 3.
+        prefix: The name of the layer in the state dict, including all parents
+                        (e.g. model.layers.0.qkv_proj) 
     """
 
     def __init__(self,
@@ -249,7 +257,8 @@ def __init__(self,
                  skip_bias_add: bool = False,
                  params_dtype: Optional[torch.dtype] = None,
                  quant_config: Optional[QuantizationConfig] = None,
-                 output_sizes: Optional[List[int]] = None):
+                 output_sizes: Optional[List[int]] = None,
+                 prefix: Optional[str] = None):
         super().__init__(input_size, output_size, skip_bias_add, params_dtype,
                          quant_config)
 
@@ -276,7 +285,8 @@ def __init__(self,
             input_size=self.input_size,
             output_size=self.output_size,
             params_dtype=self.params_dtype,
-            weight_loader=self.weight_loader)
+            weight_loader=self.weight_loader,
+            prefix=prefix)
         if bias:
             self.bias = Parameter(
                 torch.empty(self.output_size_per_partition,
@@ -348,6 +358,8 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
                        skip adding bias but instead return it.
         params_dtype: Data type for the parameters.
         quant_config: Quantization configure.
+        prefix: The name of the layer in the state dict, including all parents
+                        (e.g. model.layers.0.qkv_proj)
     """
 
     def __init__(self,
@@ -357,7 +369,8 @@ def __init__(self,
                  gather_output: bool = False,
                  skip_bias_add: bool = False,
                  params_dtype: Optional[torch.dtype] = None,
-                 quant_config: Optional[QuantizationConfig] = None):
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: Optional[str] = None):
         self.output_sizes = output_sizes
         tp_size = get_tensor_model_parallel_world_size()
         assert all(output_size % tp_size == 0 for output_size in output_sizes)
@@ -367,7 +380,8 @@ def __init__(self,
                          gather_output=gather_output,
                          skip_bias_add=skip_bias_add,
                          params_dtype=params_dtype,
-                         quant_config=quant_config)
+                         quant_config=quant_config,
+                         prefix=prefix)
 
     def weight_loader(self,
                       param: Parameter,
@@ -487,6 +501,8 @@ class QKVParallelLinear(ColumnParallelLinear):
                        skip adding bias but instead return it.
         params_dtype: Data type for the parameters.
         quant_config: Quantization configure.
+        prefix: The name of the layer in the state dict, including all parents
+                        (e.g. model.layers.0.qkv_proj)
     """
 
     def __init__(self,
@@ -497,7 +513,8 @@ def __init__(self,
                  bias: bool = True,
                  skip_bias_add: bool = False,
                  params_dtype: Optional[torch.dtype] = None,
-                 quant_config: Optional[QuantizationConfig] = None):
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: Optional[str] = None):
         self.hidden_size = hidden_size
         self.head_size = head_size
         self.total_num_heads = total_num_heads
@@ -529,7 +546,8 @@ def __init__(self,
                          gather_output=False,
                          skip_bias_add=skip_bias_add,
                          params_dtype=params_dtype,
-                         quant_config=quant_config)
+                         quant_config=quant_config,
+                         prefix=prefix)
 
     def weight_loader(self,
                       param: Parameter,
@@ -688,7 +706,8 @@ def __init__(self,
                  skip_bias_add: bool = False,
                  params_dtype: Optional[torch.dtype] = None,
                  reduce_results: bool = True,
-                 quant_config: Optional[QuantizationConfig] = None):
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: Optional[str] = None):
         super().__init__(input_size, output_size, skip_bias_add, params_dtype,
                          quant_config)
 
@@ -706,7 +725,8 @@ def __init__(self,
             input_size=self.input_size,
             output_size=self.output_size,
             params_dtype=self.params_dtype,
-            weight_loader=self.weight_loader)
+            weight_loader=self.weight_loader,
+            prefix=prefix)
         if not reduce_results and (bias and not skip_bias_add):
             raise ValueError("When not reduce the results, adding bias to the "
                              "results can lead to incorrect results")

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -8,23 +8,25 @@
     QuantizationConfig)
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     W4A16SPARSE24_SUPPORTED_BITS, WNA16_SUPPORTED_BITS,
-    CompressedTensorsScheme, CompressedTensorsW4A16Sparse24,
-    CompressedTensorsW8A8Fp8, CompressedTensorsW8A8Int8,
-    CompressedTensorsWNA16)
+    CompressedTensorsScheme, CompressedTensorsUnquantized,
+    CompressedTensorsW4A16Sparse24, CompressedTensorsW8A8Fp8,
+    CompressedTensorsW8A8Int8, CompressedTensorsWNA16)
 from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
     CompressionFormat, QuantizationArgs, QuantizationStrategy,
-    QuantizationType, find_first_name_or_class_match,
-    is_activation_quantization_format)
+    QuantizationType, find_matched_target, is_activation_quantization_format,
+    should_ignore_layer)
 from vllm.platforms import current_platform
 
 
 class CompressedTensorsConfig(QuantizationConfig):
 
-    def __init__(self, layer_quant_details: Dict[str, Any], ignore: List[str],
+    def __init__(self, target_scheme_map: Dict[str, Any], ignore: List[str],
                  quant_format: str):
+
         self.ignore = ignore
-        self.layer_quant_details = layer_quant_details
         self.quant_format = quant_format
+        # Map from [target -> scheme]
+        self.target_scheme_map = target_scheme_map
 
     def get_linear_method(self) -> "CompressedTensorsLinearMethod":
         return CompressedTensorsLinearMethod(self)
@@ -51,7 +53,7 @@ def get_quant_method(
 
     @classmethod
     def from_config(cls, config: Dict[str, Any]) -> "CompressedTensorsConfig":
-        layer_quant_details: Dict[str, Any] = dict()
+        target_scheme_map: Dict[str, Any] = dict()
         ignore: List[str] = config.get("ignore", None)
         quant_format: str = config.get("format", None)
 
@@ -63,21 +65,21 @@ def from_config(cls, config: Dict[str, Any]) -> "CompressedTensorsConfig":
         # details follow the structure defined by the QuantizationArgs
         # pydantic model, which is used to verify the structure of the
         # quant_config and also store the details for later use.
-        for key, quant_config in config["config_groups"].items():
+        for _, quant_config in config["config_groups"].items():
             targets = quant_config.get("targets")
             for target in targets:
-                layer_quant_details[target] = {}
-                layer_quant_details[target][
+                target_scheme_map[target] = {}
+                target_scheme_map[target][
                     "weights"] = QuantizationArgs.parse_obj(
                         quant_config.get("weights"))
                 try:
-                    layer_quant_details[target][
+                    target_scheme_map[target][
                         "input_activations"] = QuantizationArgs.parse_obj(
                             quant_config.get("input_activations"))
                 except Exception:
-                    layer_quant_details[target]["input_activations"] = None
+                    target_scheme_map[target]["input_activations"] = None
 
-        return cls(layer_quant_details=layer_quant_details,
+        return cls(target_scheme_map=target_scheme_map,
                    ignore=ignore,
                    quant_format=quant_format)
 
@@ -167,8 +169,9 @@ def _is_wNa16_group_channel(self, weight_quant: BaseModel,
         return (is_channel_group and input_quant_none and is_symmetric
                 and is_static)
 
-    def _get_schema(self, weight_quant: BaseModel,
-                    input_quant: BaseModel) -> "CompressedTensorsScheme":
+    def _get_scheme_from_parts(
+            self, weight_quant: BaseModel,
+            input_quant: BaseModel) -> "CompressedTensorsScheme":
 
         # Detect If Mixed Precision
         if self._is_wNa16_group_channel(weight_quant, input_quant):
@@ -205,26 +208,47 @@ def _get_schema(self, weight_quant: BaseModel,
         raise NotImplementedError(
             "No compressed-tensors compatible scheme was found.")
 
-    def get_scheme(self, layer: torch.nn.Module) -> "CompressedTensorsScheme":
+    def get_scheme(
+            self,
+            layer: torch.nn.Module,
+            layer_name: Optional[str] = None) -> "CompressedTensorsScheme":
+        """
+        compressed-tensors supports non uniform in the following way:
+
+        ignore: List of layer_names or nn.Module names to be ignored.
+        targets of config_groups: There can be N config_groups which each
+            have a quantization scheme. Each config_group has a list of targets
+            which can be a full layer_name, a regex for a layer_name, or
+            an nn.Module name.
 
-        layer_type_name = find_first_name_or_class_match(
-            name="",
-            module=layer,
-            targets=self.layer_quant_details.keys(),
-            check_contains=True)
+        We first check whether a layer is in the ignore group and use
+        CompressedTensorsUnquantized (i.e. fp16/bf16) scheme for the layer
 
-        if layer_type_name is None:
-            raise ValueError(f"Could not matching target for layer {layer}")
+        We then detect whether a layer_name is found in any target and
+        use the quantization scheme corresponding to the matched target
+        to select the CompressedTensorsScheme used for infernece.
+        """
+
+        # Check if the layer is skipped for quantization.
+        # TODO (@robertgshaw2): support module names
+        if should_ignore_layer(layer_name, ignore=self.ignore):
+            return CompressedTensorsUnquantized()
+
+        # Find the "target" in the compressed-tensors config
+        # that our layer conforms to.
+        # TODO (@robertgshaw): add compressed-tensors as dep
+        # so we do not have to re-write these functions
+        matched_target = find_matched_target(
+            layer_name=layer_name,
+            module=layer,
+            targets=self.target_scheme_map.keys())
 
-        layer_quant_details: Dict[str, Any] = self.layer_quant_details.get(
-            layer_type_name, None)
-        if layer_quant_details is None:
-            raise ValueError(
-                f"Could not find quantization details for {layer}.")
+        # Find the quant_scheme
+        scheme = self.target_scheme_map[matched_target]
 
-        scheme = self._get_schema(
-            weight_quant=layer_quant_details["weights"],
-            input_quant=layer_quant_details["input_activations"])
+        return self._get_scheme_from_parts(
+            weight_quant=scheme["weights"],
+            input_quant=scheme["input_activations"])
 
         # Raise error if device does not support the scheme
         # (e.g. fp8 needs ada lovelace)
@@ -250,11 +274,11 @@ def create_weights(self, layer: torch.nn.Module,
         Use the CompressedTensorsScheme associated with each layer to create 
         the necessary parameters for the layer. See LinearMethodBase for param
         details
-
         """
         weight_loader = extra_weight_attrs.get("weight_loader")
+        layer_name = extra_weight_attrs.get("prefix")
 
-        scheme = self.quantization_config.get_scheme(layer=layer)
+        scheme = self.quantization_config.get_scheme(layer, layer_name)
         scheme.create_weights(
             layer=layer,
             input_size=input_size,

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_unquantized.py

@@ -33,7 +33,6 @@ def create_weights(self, layer: torch.nn.Module,
 
         weight = Parameter(torch.empty(sum(output_partition_sizes),
                                        input_size_per_partition,
-                                       device="cuda",
                                        dtype=params_dtype),
                            requires_grad=False)