gpt_neox

awq/models/__init__.py

@@ -5,4 +5,5 @@ from .falcon import FalconAWQForCausalLM
 from .bloom import BloomAWQForCausalLM
 from .gptj import GPTJAWQForCausalLM
 from .gpt_bigcode import GptBigCodeAWQForCausalLM
-from .mistral import MistralAWQForCausalLM
\ No newline at end of file
+from .mistral import MistralAWQForCausalLM
+from .gpt_neox import GPTNeoXAWQForCausalLM

awq/models/auto.py

@@ -13,7 +13,8 @@ AWQ_CAUSAL_LM_MODEL_MAP = {
     "bloom": BloomAWQForCausalLM,
     "gptj": GPTJAWQForCausalLM,
     "gpt_bigcode": GptBigCodeAWQForCausalLM,
-    "mistral": MistralAWQForCausalLM
+    "mistral": MistralAWQForCausalLM,
+    "gpt_neox": GPTNeoXAWQForCausalLM,
 }
 
 def check_and_get_model_type(model_dir, trust_remote_code=True):

awq/models/gpt_neox.py

+from .base import BaseAWQForCausalLM
+from typing import Dict
+from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXLayer, GPTNeoXForCausalLM
+
+class GPTNeoXAWQForCausalLM(BaseAWQForCausalLM):
+    layer_type = "GPTNeoXDecoderLayer"
+    max_new_tokens_key = "max_position_embeddings"
+
+    @staticmethod
+    def get_model_layers(model: GPTNeoXForCausalLM):
+        return model.gpt_neox.layers
+    
+    @staticmethod
+    def get_act_for_scaling(module: GPTNeoXLayer):
+        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: GPTNeoXForCausalLM, device: str):
+        model.gpt_neox.embed_in = model.gpt_neox.embed_in.to(device)
+    
+    @staticmethod
+    def get_layers_for_scaling(module: GPTNeoXLayer, input_feat, module_kwargs):
+        layers = []
+
+        # attention input
+        layers.append(dict(
+            prev_op=module.input_layernorm,
+            layers=[module.attention.query_key_value],
+            inp=input_feat['attention.query_key_value'],
+        ))
+
+        # # attention out
+        # layers.append(dict(
+        #     prev_op=module.attention.query_key_value,
+        #     layers=[module.attention.dense],
+        #     inp=input_feat['attention.dense'],
+        # ))
+        
+        # NOTE: assumes "use_parallel_residual": false
+        # 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'],
+        ))
+
+        # linear 2
+        layers.append(dict(
+            prev_op=module.mlp.act,
+            layers=[module.mlp.dense_4h_to_h],
+            inp=input_feat['mlp.dense_4h_to_h'],
+        ))
+
+        return layers

awq/quantize/scale.py

@@ -5,10 +5,10 @@ from awq.modules.act import ScaledActivation
 from awq.utils.module import get_op_by_name, set_op_by_name
 from transformers.models.bloom.modeling_bloom import BloomGelu
 from transformers.models.llama.modeling_llama import LlamaRMSNorm
-from transformers.activations import NewGELUActivation, PytorchGELUTanh
+from transformers.activations import NewGELUActivation, PytorchGELUTanh, GELUActivation
 
 allowed_norms = [nn.LayerNorm, LlamaRMSNorm]
-allowed_act_fns = [nn.GELU, BloomGelu, NewGELUActivation, PytorchGELUTanh]
+allowed_act_fns = [nn.GELU, BloomGelu, NewGELUActivation, PytorchGELUTanh, GELUActivation]
 
 @torch.no_grad()
 def apply_clip(module, clip_list: Tuple[str, torch.Tensor]):