Support Bloom models

awq/models/__init__.py

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

@@ -7,7 +7,8 @@ AWQ_CAUSAL_LM_MODEL_MAP = {
     "llama": LlamaAWQForCausalLM,
     "opt": OptAWQForCausalLM,
     "RefinedWeb": FalconAWQForCausalLM,
-    "RefinedWebModel": FalconAWQForCausalLM
+    "RefinedWebModel": FalconAWQForCausalLM,
+    "bloom": BloomAWQForCausalLM
 }
 
 def check_and_get_model_type(model_dir, trust_remote_code=True):

awq/models/bloom.py

+from .base import BaseAWQForCausalLM
+from transformers.models.bloom.modeling_bloom import BloomForCausalLM, BloomBlock
+
+class BloomAWQForCausalLM(BaseAWQForCausalLM):
+    layer_type = "BloomBlock"
+
+    @staticmethod
+    def get_model_layers(model: BloomForCausalLM):
+        return model.transformer.h
+    
+    @staticmethod
+    def get_act_for_scaling(module: BloomBlock):
+        return dict(
+            is_scalable=True,
+            scale_name="mlp.gelu_impl",
+            scale_layer=module.mlp.gelu_impl,
+            scale_shape=module.mlp.dense_h_to_4h.out_features
+        )
+    
+    @staticmethod
+    def move_embed(model: BloomForCausalLM, device: str):
+        model.transformer.word_embeddings = model.transformer.word_embeddings.to(device)
+        model.transformer.word_embeddings_layernorm = model.transformer.word_embeddings_layernorm.to(device)
+    
+    @staticmethod
+    def get_layers_for_scaling(module: BloomBlock, input_feat, module_kwargs):
+        layers = []
+
+        # attention input
+        layers.append(dict(
+            prev_op=module.input_layernorm,
+            layers=[module.self_attention.query_key_value],
+            inp=input_feat['self_attention.query_key_value'],
+            module2inspect=module, kwargs=module_kwargs,
+        ))
+        # attention out
+        # Please refer to https://github.com/mit-han-lab/llm-awq/issues/2#issuecomment-1606297469
+        """
+        scales_list.append(_auto_get_scale(
+            prev_op=module.self_attention.query_key_value,
+            layers=[module.self_attention.dense],
+            inp=input_feat['self_attention.dense'],
+        ))
+        """
+        # linear 1
+        layers.append(dict(
+            prev_op=module.post_attention_layernorm,
+            layers=[module.mlp.dense_h_to_4h],
+            inp=input_feat['mlp.dense_h_to_4h'],
+            module2inspect=module, kwargs=module_kwargs,
+        ))
+        # linear 2
+        layers.append(dict(
+            prev_op=module.mlp.gelu_impl,
+            layers=[module.mlp.dense_4h_to_h],
+            inp=input_feat['mlp.dense_4h_to_h'],
+        ))
+
+        return layers
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