Support Falcon 7B+40B

awq/models/__init__.py

 from .mpt import MptAWQForCausalLM
 from .llama import LlamaAWQForCausalLM
 from .opt import OptAWQForCausalLM
+from .falcon import FalconAWQForCausalLM
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awq/models/auto.py

@@ -5,7 +5,9 @@ from awq.models.base import BaseAWQForCausalLM
 AWQ_CAUSAL_LM_MODEL_MAP = {
     "mpt": MptAWQForCausalLM,
     "llama": LlamaAWQForCausalLM,
-    "opt": OptAWQForCausalLM
+    "opt": OptAWQForCausalLM,
+    "RefinedWeb": FalconAWQForCausalLM,
+    "RefinedWebModel": FalconAWQForCausalLM
 }
 
 def check_and_get_model_type(model_dir, trust_remote_code=True):

awq/models/falcon.py

+from .base import BaseAWQForCausalLM
+from transformers.models.falcon.modeling_falcon import FalconDecoderLayer, FalconForCausalLM
+
+class FalconAWQForCausalLM(BaseAWQForCausalLM):
+    layer_type = "FalconDecoderLayer"
+
+    @staticmethod
+    def get_model_layers(model: FalconForCausalLM):
+        return model.transformer.h
+    
+    @staticmethod
+    def get_act_for_scaling(module: FalconDecoderLayer):
+        return dict(
+            is_scalable=True,
+            scale_name="mlp.act",
+            scale_layer=module.mlp.act,
+            scale_shape=module.mlp.dense_h_to_4h.out_features
+        )
+    
+    @staticmethod
+    def move_embed(model: FalconForCausalLM, device):
+        model.transformer.word_embeddings = model.transformer.word_embeddings.to(device)
+    
+    @staticmethod
+    def get_layers_for_scaling(module: FalconDecoderLayer, input_feat, module_kwargs):
+        layers = []
+        
+        # Falcon 7B (older architecture)
+        if module.config.num_attention_heads == 71:
+            # linear 1 + attention
+            layers.append(dict(
+                prev_op=module.input_layernorm,
+                layers=[module.mlp.dense_h_to_4h, module.self_attention.query_key_value],
+                inp=input_feat['self_attention.query_key_value'],
+                module2inspect=module,
+                kwargs=module_kwargs,
+            ))
+
+        # Falcon 40B (newer architecture)
+        else:
+            # linear 1 + attention
+            layers.append(dict(
+                prev_op=module.ln_attn,
+                layers=[module.self_attention.query_key_value],
+                inp=input_feat['self_attention.query_key_value'],
+                module2inspect=module,
+                kwargs=module_kwargs,
+            ))
+
+            # linear 2
+            layers.append(dict(
+                prev_op=module.ln_mlp,
+                layers=[module.mlp.dense_h_to_4h],
+                inp=input_feat['mlp.dense_h_to_4h'],
+                module2inspect=module,
+                kwargs=module_kwargs,
+            ))
+
+        return layers
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