AWQ Qwen and Phi mappings (#1440)

SUMMARY:
I wanted to create a PR showing users how they can add more mappings to
AWQ to account for more models. Turns out qwen has the exact same as
Llama, so I added one for Phi as well. I also updated the naming and
used the infer pattern employed in SmoothQuant, rather than requiring
user to set it

TEST PLAN:
`examples/awq/llama_example.py` works on this branch for 

```python
MODEL_ID = "microsoft/Phi-4-mini-reasoning"
```


TODOs:
- [x] Merge in after #1451 lands

---------

Signed-off-by: Brian Dellabetta <bdellabe@redhat.com>

Author: brian-dellabetta

src/llmcompressor/modifiers/awq/base.py

@@ -16,6 +16,11 @@
 
 from llmcompressor.core import Event, EventType, State
 from llmcompressor.modifiers import Modifier
+from llmcompressor.modifiers.awq.mappings import (
+    AWQMapping,
+    ResolvedMapping,
+    get_layer_mappings_from_architecture,
+)
 from llmcompressor.modifiers.quantization.calibration import update_weight_zp_scale
 from llmcompressor.modifiers.quantization.quantization import QuantizationMixin
 from llmcompressor.modifiers.utils.hooks import HooksMixin
@@ -27,8 +32,6 @@
     get_parent_by_name,
 )
 
-from .mappings import AWQ_MAPPING_REGISTRY, AWQMapping, ResolvedMapping
-
 __all__ = ["AWQModifier"]
 
 
@@ -120,7 +123,7 @@ class AWQModifier(Modifier, QuantizationMixin):
 
     # User-provided vars (in addition to QuantizationMixin args)
     sequential_targets: Union[str, List[str], None] = None
-    mappings: List[AWQMapping] = AWQ_MAPPING_REGISTRY["Llama"]
+    mappings: Optional[List[AWQMapping]] = None
     max_chunk_memory: int = 1024 * 1024 * 1024
     duo_scaling: bool = True
 
@@ -212,6 +215,12 @@ def on_initialize(self, state: State, **kwargs) -> bool:
         if QuantizationMixin.has_config(self):
             QuantizationMixin.initialize_quantization(self, state.model)
 
+        if self.mappings is None:
+            logger.info("No AWQModifier.mappings provided, inferring from model...")
+            self.mappings = get_layer_mappings_from_architecture(
+                architecture=state.model.__class__.__name__
+            )
+
         self._set_resolved_mappings(state.model)
 
         self._set_module_kwargs(state.model, state.data.calib)
@@ -500,13 +509,9 @@ def smooth(module):
                             # in this case, default to scaling the last output features
                             # because the desired smooth layer is v_proj
                             # https://github.com/casper-hansen/AutoAWQ/blob/main/awq/quantize/scale.py#L123
-                            update_offload_parameter(
-                                module,
-                                "weight",
-                                module.weight[-scales.size(0) :].div_(
-                                    scales.view(-1, 1)
-                                ),
-                            )
+                            weight = module.weight
+                            weight[-scales.size(0) :].div_(scales.view(-1, 1))
+                            update_offload_parameter(module, "weight", weight)
                         if hasattr(module, "bias") and module.bias is not None:
                             update_offload_parameter(
                                 module,

src/llmcompressor/modifiers/awq/mappings.py

@@ -1,9 +1,10 @@
 from dataclasses import dataclass
 from typing import Dict, List, Optional
 
+from loguru import logger
 from torch.nn import Module
 
-__all__ = ["AWQMapping", "AWQ_MAPPING_REGISTRY"]
+__all__ = ["AWQMapping", "AWQ_MAPPING_REGISTRY", "get_layer_mappings_from_architecture"]
 
 
 @dataclass
@@ -22,24 +23,48 @@ class AWQMapping:
     balance_layers: list[str]
 
 
+_default_mappings = [
+    AWQMapping(
+        "re:.*input_layernorm",
+        ["re:.*q_proj", "re:.*k_proj", "re:.*v_proj"],
+    ),
+    AWQMapping("re:.*v_proj", ["re:.*o_proj"]),
+    AWQMapping(
+        "re:.*post_attention_layernorm",
+        ["re:.*gate_proj", "re:.*up_proj"],
+    ),
+    AWQMapping(
+        "re:.*up_proj",
+        ["re:.*down_proj"],
+    ),
+]
+
+# Phi merges
+#  q, k, and v proj layers into a single qkv_proj layer
+#  gate and up proj layers into a single gate_up_proj layer
+_phi_mappings = [
+    AWQMapping(
+        "re:.*input_layernorm",
+        ["re:.*qkv_proj"],
+    ),
+    AWQMapping("re:.*qkv_proj", ["re:.*o_proj"]),
+    AWQMapping(
+        "re:.*post_attention_layernorm",
+        ["re:.*gate_up_proj"],
+    ),
+    AWQMapping(
+        "re:.*gate_up_proj",
+        ["re:.*down_proj"],
+    ),
+]
+
 AWQ_MAPPING_REGISTRY: Dict[str, list[AWQMapping]] = {
-    "Llama": [
-        AWQMapping(
-            "re:.*input_layernorm",
-            ["re:.*q_proj", "re:.*k_proj", "re:.*v_proj"],
-        ),
-        AWQMapping("re:.*v_proj", ["re:.*o_proj"]),
-        AWQMapping(
-            "re:.*post_attention_layernorm",
-            ["re:.*gate_proj", "re:.*up_proj"],
-        ),
-        AWQMapping(
-            "re:.*up_proj",
-            ["re:.*down_proj"],
-        ),
-    ],
-    # TODO (Brian INFERENG-529) Add Qwen mappings
-    # "Qwen": [ ],
+    "LlamaForCausalLM": _default_mappings,
+    "Qwen2ForCausalLM": _default_mappings,
+    "Qwen3ForCausalLM": _default_mappings,
+    "MistralForCausalLM": _default_mappings,
+    "Phi3ForCausalLM": _phi_mappings,
+    "Phi3VForCausalLM": _phi_mappings,
 }
 
 
@@ -64,3 +89,18 @@ class ResolvedMapping:
     balance_names: Optional[List[str]] = None
     parent: Optional[Module] = None
     parent_name: Optional[str] = None
+
+
+def get_layer_mappings_from_architecture(architecture: str) -> List[AWQMapping]:
+    """
+    :param architecture: str: The architecture of the model
+    :return: list: The layer mappings for the given architecture
+    """
+
+    if architecture not in AWQ_MAPPING_REGISTRY:
+        logger.info(
+            f"Architecture {architecture} not found in mappings. "
+            f"Using default mappings: {_default_mappings}"
+        )
+
+    return AWQ_MAPPING_REGISTRY.get(architecture, _default_mappings)