support some vlm models

configs/quantization/Awq/awq_w4a16_fakequant_eval_general.yml

@@ -34,4 +34,3 @@ quant:
 save:
     save_trans: False
     save_path: ./save
-    tokenizer_file_substring: ["token"]

configs/quantization/Awq/awq_w_only_mix_bits_1.yml

@@ -43,4 +43,3 @@ quant:
 save:
     save_trans: False
     save_path: ./save
-    tokenizer_file_substring: ["token"]

configs/quantization/Awq/awq_w_only_mix_bits_2.yml

@@ -46,4 +46,3 @@ quant:
 save:
     save_trans: False
     save_path: ./save
-    tokenizer_file_substring: ["token"]

llmc/compression/quantization/base_blockwise_quantization.py

@@ -365,7 +365,7 @@ def apply_shift(self, shifts, prev_op, layers):
     def scale_fc_fc(self, fc1, fc2, scales):
         scales = scales.to(fc1.weight.device)
         if fc1.out_features == fc2.in_features * 3:
-            num_heads = self.model.get_model_config().to_dict().get('n_head', None)
+            num_heads = self.model.get_num_attention_heads()
             fc1.weight.t_()
             org_shape = fc1.weight.shape
             fc1.weight.data = fc1.weight.data.reshape(org_shape[0] * num_heads, 3, -1)
@@ -798,7 +798,8 @@ def deploy(self, quant_format):
 
     @torch.no_grad()
     def copy_tokenizer(self, path):
-        for substring in self.config.save.get('tokenizer_file_substring', ['token']):
+        for substring in self.config.save.get('tokenizer_file_substring',
+                                              ['token', 'merges', 'vocab']):
             copy_files(self.config.model.path, path, substring)
         logger.info('copy tokenizer done --')
 
@@ -818,9 +819,9 @@ def save_model(self, path):
             return
         if self.online_rotate:
             self.contiguous_params()
-        if self.config.model.type == 'Llava':
-            self.model.llava_model.language_model = self.model.get_model()
-            self.model.llava_model.save_pretrained(path)
+        if self.config.model.type in ['Llava', 'InternVL2']:
+            self.model.vlm_model.language_model = self.model.get_model()
+            self.model.vlm_model.save_pretrained(path)
             logger.info('save model done --')
             self.copy_tokenizer(path)
             copy_files(self.config.model.path, path, 'preprocessor_config')

llmc/models/__init__.py

@@ -2,14 +2,17 @@
 from .falcon import Falcon
 from .gemma2 import Gemma2
 from .internlm2 import InternLM2
+from .internvl2 import InternVL2
 from .llama import Llama
 from .llava import Llava
 from .minicpm import MiniCPM
 from .mistral import Mistral
 from .mixtral import Mixtral
 from .opt import Opt
 from .phi import Phi
+from .qwen import Qwen
 from .qwen2 import Qwen2
+from .qwenvl import QwenVL
 from .smollm import SmolLM
 from .stablelm import StableLm
 from .starcoder import Starcoder

llmc/models/internvl2.py

@@ -0,0 +1,29 @@
+from loguru import logger
+from transformers import AutoConfig, AutoModelForCausalLM
+
+from llmc.utils.registry_factory import MODEL_REGISTRY
+
+from .internlm2 import InternLM2
+
+
+@MODEL_REGISTRY
+class InternVL2(InternLM2):
+    def __init__(self, model_path, torch_dtype):
+        super().__init__(model_path, torch_dtype)
+
+    def build_model(self):
+        self.vlm_model_config = AutoConfig.from_pretrained(
+            self.model_path, trust_remote_code=True
+        )
+        if hasattr(self.vlm_model_config, 'use_cache'):
+            self.vlm_model_config.use_cache = False
+        logger.info(f'self.vlm_model_config : {self.vlm_model_config}')
+        self.vlm_model = AutoModelForCausalLM.from_pretrained(
+            self.model_path,
+            config=self.vlm_model_config,
+            trust_remote_code=True,
+            torch_dtype=self.torch_dtype,
+            low_cpu_mem_usage=True,
+        )
+        self.model = self.vlm_model.language_model
+        self.model_config = self.vlm_model_config.llm_config

llmc/models/llava.py

@@ -20,14 +20,16 @@ def __init__(self, model_path, torch_dtype):
         super().__init__(model_path, torch_dtype)
 
     def build_model(self):
-        self.model_config = AutoConfig.from_pretrained(
+        self.vlm_model_config = AutoConfig.from_pretrained(
             self.model_path, trust_remote_code=True
         )
-        self.model_config.text_config.use_cache = False
-        self.llava_model = LlavaForConditionalGeneration.from_pretrained(
+        self.vlm_model_config.text_config.use_cache = False
+        logger.info(f'self.vlm_model_config : {self.vlm_model_config}')
+        self.vlm_model = LlavaForConditionalGeneration.from_pretrained(
             self.model_path,
-            config=self.model_config,
+            config=self.vlm_model_config,
             torch_dtype=self.torch_dtype,
             low_cpu_mem_usage=True,
         )
-        self.model = self.llava_model.language_model
+        self.model = self.vlm_model.language_model
+        self.model_config = self.vlm_model_config.text_config

llmc/models/qwen.py

@@ -0,0 +1,85 @@
+from llmc.utils.registry_factory import MODEL_REGISTRY
+
+from .base_model import BaseModel
+
+
+@MODEL_REGISTRY
+class Qwen(BaseModel):
+    def __init__(self, model_path, torch_dtype):
+        super().__init__(model_path, torch_dtype)
+
+    def find_blocks(self):
+        self.blocks = self.model.transformer.h
+
+    def find_embed_layers(self):
+        self.wte = self.model.transformer.wte
+        self.rotary_emb = self.model.transformer.rotary_emb
+
+    def find_block_name(self):
+        self.block_name_prefix = 'transformer.h'
+
+    def get_embed_layers(self):
+        return [self.wte, self.rotary_emb]
+
+    def get_head_layers(self):
+        return [self.model.lm_head]
+
+    def get_pre_head_layernorm_layers(self):
+        return [self.model.transformer.ln_f]
+
+    def get_layers_except_blocks(self):
+        return [self.wte,
+                self.rotary_emb,
+                self.model.transformer.ln_f,
+                self.model.lm_head]
+
+    def has_bias(self):
+        return False
+
+    def get_layernorms_in_block(self, block):
+        return {
+            'ln_1': block.ln_1,
+            'ln_2': block.ln_2,
+        }
+
+    def get_num_attention_heads(self):
+        return self.model_config.num_attention_heads
+
+    def get_subsets_in_block(self, block):
+        return [
+            {
+                'layers': {
+                    'attn.c_attn': block.attn.c_attn
+                },
+                'prev_op': [block.ln_1],
+                'input': ['attn.c_attn'],
+                'inspect': block.attn,
+                'has_kwargs': True,
+            },
+            {
+                'layers': {'attn.c_proj': block.attn.c_proj},
+                'prev_op': [block.attn.c_attn],
+                'input': ['attn.c_proj'],
+                'inspect': block.attn.c_proj,
+                'has_kwargs': False,
+            },
+            {
+                'layers': {
+                    'mlp.w1': block.mlp.w1,
+                    'mlp.w2': block.mlp.w2,
+                },
+                'prev_op': [block.ln_2],
+                'input': ['mlp.w1'],
+                'inspect': block.mlp,
+                'has_kwargs': False,
+                'is_mlp': True,
+            },
+            {
+                'layers': {'mlp.c_proj': block.mlp.c_proj},
+                'prev_op': [block.mlp.w1],
+                'input': ['mlp.c_proj'],
+                'inspect': block.mlp.c_proj,
+                'has_kwargs': False,
+                'is_mlp': True,
+            },
+        ]

llmc/models/qwenvl.py

@@ -0,0 +1,30 @@
+from loguru import logger
+from transformers import AutoConfig, AutoModelForCausalLM
+
+from llmc.utils.registry_factory import MODEL_REGISTRY
+
+from .qwen import Qwen
+
+
+@MODEL_REGISTRY
+class QwenVL(Qwen):
+    def __init__(self, model_path, torch_dtype):
+        super().__init__(model_path, torch_dtype)
+
+    def build_model(self):
+        self.vlm_model_config = AutoConfig.from_pretrained(
+            self.model_path, trust_remote_code=True
+        )
+        if hasattr(self.vlm_model_config, 'use_cache'):
+            self.vlm_model_config.use_cache = False
+        logger.info(f'self.vlm_model_config : {self.vlm_model_config}')
+        self.vlm_model = AutoModelForCausalLM.from_pretrained(
+            self.model_path,
+            config=self.vlm_model_config,
+            trust_remote_code=True,
+            torch_dtype=self.torch_dtype,
+            low_cpu_mem_usage=True,
+        )
+        self.model = self.vlm_model
+        self.model_config = self.vlm_model_config
+        self.vision_model = self.vlm_model.transformer.visual