add do_trans in config & remove language catcher & support chaglm

llmc/compression/quantization/awq.py

@@ -203,6 +203,10 @@ def subset_transform(
         prev_op = subset['prev_op']
         input_name = subset['input'][0]
         inspect_module = subset['inspect']
+        do_trans = subset.get('do_trans', True)
+        if not do_trans:
+            logger.info('do_trans is set to False. Do not transform this subset.')
+            return
 
         if not check_do_quant(
             self.block_idx,
@@ -241,6 +245,7 @@ def subset_transform(
             if (
                 isinstance(prev_op[0], (nn.Linear, FakeQuantLinear))
                 and prev_op[0].out_features != layers[0].in_features * 3
+                and prev_op[0].out_features != layers[0].in_features * 2
                 and prev_op[0].out_features != layers[0].in_features
             ):
                 logger.info('Cannot apply scale. Do not transform this subset.')

llmc/compression/quantization/base_blockwise_quantization.py

@@ -598,6 +598,7 @@ 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:
+            logger.info('fc1.out_features == fc2.in_features * 3')
             num_heads = self.model.get_num_attention_heads()
             fc1.weight.t_()
             org_shape = fc1.weight.shape
@@ -616,13 +617,23 @@ def scale_fc_fc(self, fc1, fc2, scales):
                     fc1.bias[:, 2, :].shape
                 )
                 fc1.bias.data = fc1.bias.data.reshape(-1)
-        else:
+        elif fc1.out_features == fc2.in_features * 2:
+            logger.info('fc1.out_features == fc2.in_features * 2')
+            fc1.weight.data[fc1.weight.data.shape[0] // 2:].div_(scales.view(-1, 1))
+            if hasattr(fc1, 'bias') and fc1.bias is not None:
+                fc1.bias.data[fc1.bias.data.shape[0] // 2:].div_(scales.view(-1))
+        elif fc1.out_features == fc2.in_features:
+            logger.info('fc1.out_features == fc2.in_features')
             assert fc1.out_features == fc2.in_features
 
             if hasattr(fc1, 'bias') and fc1.bias is not None:
                 fc1.bias.div_(scales.view(-1))
 
             fc1.weight.div_(scales.view(-1, 1))
+        else:
+            logger.error(f'fc1.out_features: {fc1.out_features}')
+            logger.error(f'fc2.in_features: {fc2.in_features}')
+            raise Exception('Can not scale this fc-fc.')
 
         fc2.weight.mul_(scales.view(1, -1))
 

llmc/models/__init__.py

@@ -1,4 +1,5 @@
 from .bloom import Bloom
+from .chatglm import ChatGLM
 from .deepseekv2 import DeepseekV2
 from .falcon import Falcon
 from .gemma2 import Gemma2

llmc/models/base_model.py

@@ -106,8 +106,7 @@ def get_attention_rotary_layers(self):
     def batch_process(self):
         raise Exception('batch_process should not be called here.')
 
-    def get_vision_catcher(self, first_block_input):
-
+    def get_catcher(self, first_block_input):
         class Catcher(nn.Module):
             def __init__(self, module):
                 super().__init__()
@@ -125,24 +124,6 @@ def forward(self, *args, **kwargs):
                     kwargs.pop('output_router_logits')
                 first_block_input['kwargs'].append(kwargs)
                 raise ValueError
-
-        return Catcher
-
-    def get_language_catcher(self, first_block_input):
-
-        class Catcher(nn.Module):
-            def __init__(self, module):
-                super().__init__()
-                self.module = module
-
-            def forward(self, inp, **kwargs):
-                first_block_input['data'].append(inp)
-                if 'output_router_logits' in kwargs:
-                    assert kwargs['output_router_logits'] is False
-                    kwargs.pop('output_router_logits')
-                first_block_input['kwargs'].append(kwargs)
-                raise ValueError
-
         return Catcher
 
     def __str__(self):
@@ -184,10 +165,7 @@ def collect_first_block_input(self, calib_data, padding_mask=None,
         first_block_input = defaultdict(list)
 
         self.find_blocks(modality)
-        if modality == 'language':
-            Catcher = self.get_language_catcher(first_block_input)
-        elif modality == 'vision':
-            Catcher = self.get_vision_catcher(first_block_input)
+        Catcher = self.get_catcher(first_block_input)
 
         self.move_embed_to_device('cuda')
         if data_type == 'img_txt':

llmc/models/chatglm.py

@@ -0,0 +1,88 @@
+import inspect
+
+import torch.nn as nn
+
+from llmc.utils.registry_factory import MODEL_REGISTRY
+
+from .base_model import BaseModel
+
+
+@MODEL_REGISTRY
+class ChatGLM(BaseModel):
+    def __init__(self, config, device_map=None, use_cache=False):
+        super().__init__(config, device_map, use_cache)
+
+    def find_blocks(self, modality='language'):
+        self.blocks = self.model.transformer.encoder.layers
+
+    def find_embed_layers(self):
+        self.embedding = self.model.transformer.embedding
+        self.rotary_pos_emb = self.model.transformer.rotary_pos_emb
+
+    def find_block_name(self):
+        self.block_name_prefix = 'transformer.encoder.layers'
+
+    def get_embed_layers(self):
+        return [self.embedding]
+
+    def get_attention_rotary_layers(self):
+        return [self.rotary_pos_emb]
+
+    def get_head_layers(self):
+        return [self.model.transformer.output_layer]
+
+    def get_pre_head_layernorm_layers(self):
+        return [self.model.transformer.encoder.final_layernorm]
+
+    def get_layers_except_blocks(self):
+        return [self.embedding, self.rotary_pos_emb, self.model.transformer.output_layer, self.model.transformer.encoder.final_layernorm] # noqa
+
+    def skip_layer_name(self):
+        return ['final_layernorm']
+
+    def has_bias(self):
+        return False
+
+    def get_layernorms_in_block(self, block):
+        return {
+            'input_layernorm': block.input_layernorm,
+            'post_attention_layernorm': block.post_attention_layernorm,
+        }
+
+    def get_subsets_in_block(self, block):
+        return [
+            {
+                'layers': {
+                    'self_attention.query_key_value': block.self_attention.query_key_value
+                },
+                'prev_op': [block.input_layernorm],
+                'input': ['self_attention.query_key_value'],
+                'inspect': block.self_attention,
+                'has_kwargs': True,
+            },
+            {
+                'layers': {'self_attention.dense': block.self_attention.dense},
+                'prev_op': [block.self_attention.query_key_value],
+                'input': ['self_attention.dense'],
+                'inspect': block.self_attention.dense,
+                'has_kwargs': False,
+            },
+            {
+                'layers': {
+                    'mlp.dense_h_to_4h': block.mlp.dense_h_to_4h
+                },
+                'prev_op': [block.post_attention_layernorm],
+                'input': ['mlp.dense_h_to_4h'],
+                'inspect': block.mlp,
+                'has_kwargs': False,
+                'is_mlp': True,
+            },
+            {
+                'layers': {'mlp.down_proj': block.mlp.dense_4h_to_h},
+                'prev_op': [block.mlp.dense_h_to_4h],
+                'input': ['mlp.dense_4h_to_h'],
+                'inspect': block.mlp.dense_4h_to_h,
+                'has_kwargs': False,
+                'is_mlp': True,
+            },
+        ]

llmc/models/opt.py

@@ -85,5 +85,6 @@ def get_subsets_in_block(self, block):
                 'inspect': block.fc2,
                 'has_kwargs': False,
                 'is_mlp': True,
+                'do_trans': False
             },
         ]

llmc/models/vit.py

@@ -78,28 +78,6 @@ def batch_process(self, imgs):
             samples.append(sample)
         return samples
 
-    def get_catcher(self, first_block_input):
-
-        class Catcher(nn.Module):
-            def __init__(self, module):
-                super().__init__()
-                self.module = module
-                self.signature = inspect.signature(module.forward)
-
-            def forward(self, *args, **kwargs):
-                params = list(self.signature.parameters.keys())
-                for i, arg in enumerate(args):
-                    if i > 0:
-                        kwargs[params[i]] = arg
-                first_block_input['data'].append(args[0])
-                if 'output_router_logits' in kwargs:
-                    assert kwargs['output_router_logits'] is False
-                    kwargs.pop('output_router_logits')
-                first_block_input['kwargs'].append(kwargs)
-                raise ValueError
-
-        return Catcher
-
     def get_subsets_in_block(self, block):
         return [
             {