add starcoder2 support (#406)

Co-authored-by: charrli <charrli@tencent.com>

awq/models/__init__.py

@@ -15,3 +15,4 @@
 from .mixtral import MixtralAWQForCausalLM
 from .qwen2 import Qwen2AWQForCausalLM
 from .gemma import GemmaAWQForCausalLM
+from .starcoder2 import Starcoder2AWQForCausalLM

awq/models/auto.py

@@ -24,6 +24,7 @@
     "llava": LlavaAWQForCausalLM,
     "qwen2": Qwen2AWQForCausalLM,
     "gemma": GemmaAWQForCausalLM,
+    "starcoder2": Starcoder2AWQForCausalLM,
 }
 
 

awq/models/base.py

@@ -68,6 +68,7 @@
     "llava": "AutoModelForVision2Seq",
     "qwen2": "AutoModelForCausalLM",
     "gemma": "AutoModelForCausalLM",
+    "starcoder2": "AutoModelForCausalLM",
 }
 
 

awq/models/starcoder2.py

@@ -0,0 +1,141 @@
+import tqdm
+from typing import List, Tuple
+from .base import BaseAWQForCausalLM
+from awq.utils.fused_utils import fuse_qkv
+from awq.modules.fused.block import LlamaLikeBlock
+from awq.modules.fused.model import LlamaLikeModel
+from transformers.models.starcoder2.modeling_starcoder2 import (
+    Starcoder2ForCausalLM as OldStarcoder2ForCausalLM,
+    Starcoder2DecoderLayer as OldStarcoder2DecoderLayer,
+)
+from awq.modules.fused.norm import FasterTransformerRMSNorm
+
+
+class Starcoder2AWQForCausalLM(BaseAWQForCausalLM):
+    layer_type = "Starcoder2DecoderLayer"
+    max_seq_len_key = "max_position_embeddings"
+
+    @staticmethod
+    def fuse_layers(model: OldStarcoder2ForCausalLM):
+        fuser = Starcoder2Fuser(model)
+        fuser.fuse_transformer()
+
+    @staticmethod
+    def get_model_layers(model: OldStarcoder2ForCausalLM):
+        return model.model.layers
+
+    @staticmethod
+    def get_act_for_scaling(module: OldStarcoder2DecoderLayer):
+        return dict(
+            is_scalable=True,
+            scale_name="mlp.act",
+            scale_layer=module.mlp.act,
+            scale_shape=module.mlp.c_fc.out_features,
+        )
+        # return dict(is_scalable=False)
+
+    @staticmethod
+    def move_embed(model: OldStarcoder2ForCausalLM, device):
+        model.model.embed_tokens = model.model.embed_tokens.to(device)
+
+    @staticmethod
+    def get_layers_for_scaling(module: OldStarcoder2DecoderLayer, input_feat, module_kwargs):
+        layers = []
+
+        # attention input
+        layers.append(
+            dict(
+                prev_op=module.input_layernorm,
+                layers=[
+                    module.self_attn.q_proj,
+                    module.self_attn.k_proj,
+                    module.self_attn.v_proj,
+                ],
+                inp=input_feat["self_attn.q_proj"],
+                module2inspect=module.self_attn,
+                kwargs=module_kwargs,
+            )
+        )
+
+        # attention out
+        if module.self_attn.v_proj.weight.shape == module.self_attn.o_proj.weight.shape:
+            layers.append(
+                dict(
+                    prev_op=module.self_attn.v_proj,
+                    layers=[module.self_attn.o_proj],
+                    inp=input_feat["self_attn.o_proj"],
+                )
+            )
+
+        # linear 1
+        layers.append(
+            dict(
+                prev_op=module.post_attention_layernorm,
+                layers=[module.mlp.c_fc],
+                inp=input_feat["mlp.c_fc"],
+                module2inspect=module.mlp,
+            )
+        )
+
+        # linear 2
+        layers.append(
+            dict(
+                prev_op=module.mlp.act,
+                layers=[module.mlp.c_proj],
+                inp=input_feat["mlp.c_proj"],
+            )
+        )
+
+        return layers
+
+class Starcoder2Fuser:
+    def __init__(self, model: OldStarcoder2ForCausalLM):
+        self.model = model
+
+        self.starcoder2_blocks: List[Tuple[str, OldStarcoder2DecoderLayer]] = [
+            (name, module)
+            for name, module in self.model.named_modules()
+            if "Starcoder2DecoderLayer".lower() in module.__class__.__name__.lower()
+        ]
+
+    def fuse_transformer(self):
+        blocks = []
+
+        module: OldStarcoder2DecoderLayer
+        for module in tqdm.tqdm(self.model.model.layers, desc="Fusing layers..."):
+            device = next(iter(module.state_dict().values())).device
+            qkv = fuse_qkv(
+                module,
+                module.self_attn.q_proj,
+                module.self_attn.k_proj,
+                module.self_attn.v_proj,
+            )
+            norm_1 = FasterTransformerRMSNorm(
+                module.input_layernorm.weight, module.input_layernorm.eps
+            )
+            norm_2 = FasterTransformerRMSNorm(
+                module.post_attention_layernorm.weight,
+                module.post_attention_layernorm.eps,
+            )
+            blocks.append(
+                LlamaLikeBlock(
+                    hidden_size=self.model.config.hidden_size,
+                    n_heads=self.model.config.num_attention_heads,
+                    n_kv_heads=self.model.config.num_key_value_heads,
+                    qkv_layer=qkv,
+                    o_proj=module.self_attn.o_proj,
+                    mlp=module.mlp,
+                    norm_1=norm_1,
+                    norm_2=norm_2,
+                    dev=device,
+                    max_seq_len=self.model.config.max_seq_len,
+                )
+            )
+
+        self.model.model = LlamaLikeModel(
+            self.model.config.vocab_size,
+            blocks,
+            self.model.model.embed_tokens,
+            self.model.model.norm,
+        )
+        setattr(self.model.model, "blocks", self.model.model.blocks)