[Model][Quant] Fix GLM, Fix fused module mappings for quantization (v…

…llm-project#12634)

Signed-off-by: mgoin <[email protected]>
Signed-off-by: Kyle Sayers <[email protected]>
Co-authored-by: mgoin <[email protected]>
Signed-off-by: Felix Marty <[email protected]>

vllm/model_executor/layers/quantization/base_config.py

@@ -2,7 +2,7 @@
 
 import inspect
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional, Type
+from typing import Any, Dict, List, Mapping, Optional, Type
 
 import torch
 from torch import nn
@@ -59,6 +59,7 @@ def method_has_implemented_embedding(
 
 class QuantizationConfig(ABC):
     """Base class for quantization configs."""
+    packed_modules_mapping: Mapping[str, List[str]] = dict()
 
     @abstractmethod
     def get_name(self) -> str:

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

@@ -83,7 +83,9 @@ def get_quant_method(
 
         # Check if the layer is skipped for quantization.
         # TODO (@robertgshaw2): support module names
-        if should_ignore_layer(prefix, ignore=self.ignore):
+        if should_ignore_layer(prefix,
+                               ignore=self.ignore,
+                               fused_mapping=self.packed_modules_mapping):
             return UnquantizedLinearMethod()
         if isinstance(layer, LinearBase):
             scheme = self.get_scheme(layer=layer, layer_name=prefix)
@@ -379,34 +381,29 @@ def get_scheme(self,
 
         # Will be empty for models with only sparsity
         weight_quant = input_quant = None
-        sparsity_scheme: Optional[SparsityCompressionConfig] = None
         if self.target_scheme_map:
             matched_target = find_matched_target(
                 layer_name=layer_name,
                 module=layer,
-                targets=self.target_scheme_map.keys())
+                targets=self.target_scheme_map.keys(),
+                fused_mapping=self.packed_modules_mapping)
 
             scheme_dict = self.target_scheme_map[matched_target]
             weight_quant = scheme_dict.get("weights")
             input_quant = scheme_dict.get("input_activations")
 
-        if self.sparsity_scheme_map:
-            is_ignored = False
-            with suppress(ValueError):
-                is_ignored = find_matched_target(
-                    layer_name=layer_name,
-                    module=layer,
-                    targets=self.sparsity_ignore_list)
-
-            # if the layer is in the sparsity ignore list,
-            # we should not apply any sparsity scheme
-
-            if not is_ignored:
-                matched_target = find_matched_target(
-                    layer_name=layer_name,
-                    module=layer,
-                    targets=self.sparsity_scheme_map.keys())
-                sparsity_scheme = self.sparsity_scheme_map.get(matched_target)
+        # Find the sparsity scheme of the layer
+        # assume that fused layers inerhit first component's sparsity scheme
+        sparsity_targets = (self.sparsity_scheme_map.keys() -
+                            set(self.sparsity_ignore_list))
+        sparsity_scheme: Optional[SparsityCompressionConfig] = None
+        with suppress(ValueError):
+            matched_target = find_matched_target(
+                layer_name=layer_name,
+                module=layer,
+                targets=sparsity_targets,
+                fused_mapping=self.packed_modules_mapping)
+            sparsity_scheme = self.sparsity_scheme_map[matched_target]
 
         if self.supports_cutlass_24(weight_quant=weight_quant,
                                     input_quant=input_quant,

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

@@ -1,14 +1,12 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import re
-from typing import Iterable, Optional
+from types import MappingProxyType
+from typing import Iterable, List, Mapping, Optional
 
 from compressed_tensors import CompressionFormat
 from torch.nn import Module
 
-from vllm.model_executor.layers.quantization.utils.quant_utils import (
-    FUSED_LAYER_NAME_MAPPING)
-
 
 def is_activation_quantization_format(format: str) -> bool:
     _ACTIVATION_QUANTIZATION_FORMATS = [
@@ -19,8 +17,11 @@ def is_activation_quantization_format(format: str) -> bool:
     return format in _ACTIVATION_QUANTIZATION_FORMATS
 
 
-def should_ignore_layer(layer_name: Optional[str],
-                        ignore: Iterable[str]) -> bool:
+def should_ignore_layer(
+    layer_name: Optional[str],
+    ignore: Iterable[str] = tuple(),
+    fused_mapping: Mapping[str, List[str]] = MappingProxyType({})
+) -> bool:
     if layer_name is None:
         return False
 
@@ -32,8 +33,8 @@ def should_ignore_layer(layer_name: Optional[str],
     # in the safetensors checkpoint. So, we convert the name
     # from the fused version to unfused + check to make sure that
     # each shard of the fused layer has the same scheme.
-    if proj_name in FUSED_LAYER_NAME_MAPPING and layer_name not in ignore:
-        shard_proj_names = FUSED_LAYER_NAME_MAPPING[proj_name]
+    if proj_name in fused_mapping and layer_name not in ignore:
+        shard_proj_names = fused_mapping[proj_name]
 
         # Convert fused_name --> [shard_names]
         shard_names = [
@@ -79,55 +80,12 @@ def check_equal_or_regex_match(layer_name: str,
     return False
 
 
-def _handle_fused_layers(func):
-    """
-    Decorator to handle fused layers by mapping vllm fused layer names
-    to their corresponding unfused layer names for quantization/pruning schemes.
-    """
-    # fused_layer_name -> unfused_layer_name
-    fused_layer_map = {
-        "qkv_proj": "q_proj",
-        "gate_up_proj": "up_proj",
-    }
-
-    def fused_layer_handler(layer_name: Optional[str], module: Module,
-                            targets: Iterable[str]) -> Optional[str]:
-        """
-        Wrapper function specifically designed to support the
-        find_matched_target function.
-
-        It handles cases where the provided layer name corresponds to a
-        fused layer in vllm, mapping it to its equivalent unfused layer name
-        based on the predefined fused_layer_map. If the original layer name
-        raises a ValueError in the wrapped function, this handler
-        will attempt to resolve the issue by substituting with unfused
-        layer name.
-
-        :param layer_name: Name of the layer, which may be fused.
-        :param module: An instance of torch.nn.Module.
-        :param targets: A list of target names or patterns to match.
-        :return: The result of the wrapped find_matched_target function with
-            the resolved layer name.
-        :raises ValueError: If the layer name cannot be resolved to a 
-            valid target.
-        """
-        try:
-            return func(layer_name, module, targets)
-        except ValueError:
-            if layer_name is None:
-                layer_name = ""
-            parent_name, fused_proj_name = layer_name.rsplit(".", 1)
-            unfused_proj_name = fused_layer_map.get(fused_proj_name,
-                                                    fused_proj_name)
-            new_layer_name = f"{parent_name}.{unfused_proj_name}"
-            return func(new_layer_name, module, targets)
-
-    return fused_layer_handler
-
-
-@_handle_fused_layers
-def find_matched_target(layer_name: Optional[str], module: Module,
-                        targets: Iterable[str]) -> str:
+def find_matched_target(
+    layer_name: Optional[str],
+    module: Module,
+    targets: Iterable[str],
+    fused_mapping: Mapping[str, List[str]] = MappingProxyType({})
+) -> str:
     """
     Helper function to look up which "target" in the compressed-tensors
     config that a layer corresponds to.
@@ -141,19 +99,25 @@ def find_matched_target(layer_name: Optional[str], module: Module,
 
     First, we try to match the layer_name with a target
     Second, we try to match the module's name with a target
+    Third, we try to map the layer_name to a list of fused module names.
+        *All* component module names must match in order for a match to be
+        successful. A successful match returns the first component target
 
     :param layer_name: layer name
     :param module: torch.nn.Module
     :param targets: list of targets to match the layer against
+    :param fused_mapping: map from fused layer names to its components
+    :param fused_strategy: either "all" or "any". If using "all", fused
+        layers match if "all" of its components match
     """
 
     if layer_name is None:
         layer_name = ""
 
-    matched_target = (_find_first_match(layer_name, targets)
-                      or _find_first_match(module.__class__.__name__, targets,
-                                           True)
-                      or _match_fused_layer(layer_name, targets))
+    matched_target = (
+        _find_first_match(layer_name, targets)
+        or _find_first_match(module.__class__.__name__, targets, True)
+        or _match_fused_layer(layer_name, targets, fused_mapping))
 
     if matched_target is None:
         raise ValueError(
@@ -205,39 +169,45 @@ def _is_equal_or_regex_match(value: str,
     return False
 
 
-def _match_fused_layer(layer_name: str,
-                       target_layers: Iterable[str]) -> Optional[str]:
+def _match_fused_layer(
+        layer_name: str, target_layers: Iterable[str],
+        fused_mapping: Mapping[str, List[str]]) -> Optional[str]:
     """
     Match a fused layer name to its corresponding individual layer in 
-    target_layers.
+    target_layers. Returns first value in fused_mapping which matches targets
+
+    Implements an "all" matching strategy where a fused layer matches iff
+    "all" of its components match
+
+    :param layer_name: layer name
+    :param target_layers: list of targets to match the layer against
+    :param fused_mapping: map from fused layer names to its components
 
     Examples:
         layer_name = "model.layers.0.self_attn.qkv_proj"
         target_layers = ["model.layers.0.self_attn.q_proj",
                         "model.layers.0.self_attn.k_proj",
                         "model.layers.0.self_attn.v_proj"]
     """
-    # Split into parent path and layer type
-    # e.g., "model.layers.0.self_attn" and "qkv_proj"
-    parent_path = ".".join(layer_name.split(".")[:-1])
-    layer_type = layer_name.split(".")[-1]
-
-    if layer_type not in FUSED_LAYER_NAME_MAPPING:
+    # find layer_name in mapping
+    fused = next((key for key in fused_mapping if layer_name.endswith(key)),
+                 None)
+    if fused is None:
         return None
 
-    possible_layer_types = FUSED_LAYER_NAME_MAPPING[layer_type]
-
-    # Look for a target layer that:
-    # 1. Has the same parent path
-    # 2. Ends with one of the possible individual layer types
-    for target in target_layers:
-        is_same_parent = parent_path in target
-        is_matching_type = any(type_suffix in target
-                               for type_suffix in possible_layer_types)
-
-        if is_same_parent and is_matching_type and all(
-            (f"{parent_path}.{type_suffix}" in target_layers)
-                for type_suffix in possible_layer_types):
-            return target
+    # expand path of unfused components
+    unfused_paths = [
+        layer_name.replace(fused, unfused) for unfused in fused_mapping[fused]
+    ]
 
-    return None
+    # for each unfused component, find a match in targets
+    unfused_matches: List[Optional[str]] = []
+    for unfused in unfused_paths:
+        for target in target_layers:
+            if _is_equal_or_regex_match(unfused, target):
+                unfused_matches.append(target)
+                break
+        else:
+            unfused_matches.append(None)
+
+    return unfused_matches[0] if all(unfused_matches) else None

vllm/model_executor/layers/quantization/quark/quark.py

@@ -18,8 +18,6 @@
     QuarkScheme, QuarkW8A8Fp8, QuarkW8A8Int8)
 from vllm.model_executor.layers.quantization.quark.utils import (
     deep_compare, should_ignore_layer)
-from vllm.model_executor.layers.quantization.utils.quant_utils import (
-    FUSED_LAYER_NAME_MAPPING)
 from vllm.platforms import current_platform
 
 __all__ = ["QuarkLinearMethod"]
@@ -58,7 +56,9 @@ def get_quant_method(self, layer: torch.nn.Module,
 
         # Check if the layer is skipped for quantization.
         exclude_layers = cast(List[str], self.quant_config.get("exclude"))
-        if should_ignore_layer(prefix, ignore=exclude_layers):
+        if should_ignore_layer(prefix,
+                               ignore=exclude_layers,
+                               fused_mapping=self.packed_modules_mapping):
             return UnquantizedLinearMethod()
         if isinstance(layer, LinearBase):
             scheme = self.get_scheme(layer=layer, layer_name=prefix)
@@ -201,8 +201,8 @@ def _find_matched_config(self, layer_name: str,
                              module: torch.nn.Module) -> Dict[str, Any]:
 
         proj_name = layer_name.split(".")[-1]
-        if proj_name in FUSED_LAYER_NAME_MAPPING:
-            shard_proj_names = FUSED_LAYER_NAME_MAPPING[proj_name]
+        if proj_name in self.packed_modules_mapping:
+            shard_proj_names = self.packed_modules_mapping[proj_name]
 
             # Convert fused_name --> [shard_names]
             shard_names = [

vllm/model_executor/layers/quantization/quark/utils.py

@@ -1,10 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import re
-from typing import Any, Iterable, Optional
-
-from vllm.model_executor.layers.quantization.utils.quant_utils import (
-    FUSED_LAYER_NAME_MAPPING)
+from types import MappingProxyType
+from typing import Any, Iterable, List, Mapping, Optional
 
 
 def deep_compare(dict1: Any, dict2: Any) -> bool:
@@ -20,8 +18,11 @@ def deep_compare(dict1: Any, dict2: Any) -> bool:
         return dict1 == dict2
 
 
-def should_ignore_layer(layer_name: Optional[str],
-                        ignore: Iterable[str]) -> bool:
+def should_ignore_layer(
+    layer_name: Optional[str],
+    ignore: Iterable[str],
+    fused_mapping: Mapping[str, List[str]] = MappingProxyType({})
+) -> bool:
     if layer_name is None:
         return False
 
@@ -33,8 +34,8 @@ def should_ignore_layer(layer_name: Optional[str],
     # in the safetensors checkpoint. So, we convert the name
     # from the fused version to unfused + check to make sure that
     # each shard of the fused layer has the same scheme.
-    if proj_name in FUSED_LAYER_NAME_MAPPING:
-        shard_proj_names = FUSED_LAYER_NAME_MAPPING[proj_name]
+    if proj_name in fused_mapping:
+        shard_proj_names = fused_mapping[proj_name]
 
         # Convert fused_name --> [shard_names]
         shard_names = [