support bitsandbytes quantization with more models (#9148)

tests/quantization/test_bitsandbytes.py

@@ -9,22 +9,22 @@ import pytest
 import torch
 
 from tests.quantization.utils import is_quant_method_supported
-
-from ..utils import fork_new_process_for_each_test
+from tests.utils import fork_new_process_for_each_test
 
 models_4bit_to_test = [
-    ('huggyllama/llama-7b', 'quantize model inflight'),
+    ("facebook/opt-125m", "quantize opt model inflight"),
 ]
 
 models_pre_qaunt_4bit_to_test = [
-    ('lllyasviel/omost-llama-3-8b-4bits',
-     'read pre-quantized 4-bit NF4 model'),
     ('PrunaAI/Einstein-v6.1-Llama3-8B-bnb-4bit-smashed',
      'read pre-quantized 4-bit FP4 model'),
+    ('poedator/opt-125m-bnb-4bit', 'read pre-quantized 4-bit NF4 opt model'),
 ]
 
 models_pre_quant_8bit_to_test = [
-    ('meta-llama/Llama-Guard-3-8B-INT8', 'read pre-quantized 8-bit model'),
+    ('meta-llama/Llama-Guard-3-8B-INT8',
+     'read pre-quantized llama 8-bit model'),
+    ("yec019/fbopt-350m-8bit", "read pre-quantized 8-bit opt model"),
 ]
 
 
@@ -133,6 +133,7 @@ def validate_generated_texts(hf_runner,
         hf_str = hf_log["generated_text"]
         vllm_str = vllm_log["generated_text"]
         prompt = hf_log["prompt"]
+
         assert hf_str == vllm_str, (f"Model: {model_name}"
                                     f"Mismatch between HF and vLLM outputs:\n"
                                     f"Prompt: {prompt}\n"

vllm/model_executor/layers/linear.py

@@ -336,8 +336,12 @@ class ColumnParallelLinear(LinearBase):
         if is_gguf_weight and isinstance(param, UninitializedParameter):
             param.materialize(loaded_weight.shape, dtype=loaded_weight.dtype)
 
+        use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False)
+
         param_data = param.data
-        if output_dim is not None:
+        # bitsandbytes loads the weights of the specific portion
+        # no need to narrow here
+        if output_dim is not None and not use_bitsandbytes_4bit:
             shard_size = param_data.shape[output_dim]
             start_idx = tp_rank * shard_size
             loaded_weight = loaded_weight.narrow(output_dim, start_idx,
@@ -821,6 +825,9 @@ class QKVParallelLinear(ColumnParallelLinear):
                 ("v", (self.total_num_heads + self.total_num_kv_heads) *
                  self.head_size, self.total_num_kv_heads * self.head_size),
             ]
+            use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit",
+                                            False)
+
             packed_dim = getattr(param, "packed_dim", None)
             for shard_id, shard_offset, shard_size in shard_offsets:
                 # Special case for Quantized Weights.
@@ -834,6 +841,23 @@ class QKVParallelLinear(ColumnParallelLinear):
                     shard_size, shard_offset = adjust_marlin_shard(
                         param, shard_size, shard_offset)
 
+                if use_bitsandbytes_4bit:
+                    orig_qkv_offsets = {
+                        "q": (0, self.total_num_heads * self.head_size),
+                        "k": (self.total_num_heads * self.head_size,
+                              self.total_num_kv_heads * self.head_size),
+                        "v":
+                        ((self.total_num_heads + self.total_num_kv_heads) *
+                         self.head_size,
+                         self.total_num_kv_heads * self.head_size),
+                        "total":
+                        ((self.total_num_heads + 2 * self.total_num_kv_heads) *
+                         self.head_size, 0)
+                    }
+
+                    shard_size, shard_offset = adjust_bitsandbytes_4bit_shard(
+                        param, orig_qkv_offsets, shard_id)
+
                 loaded_weight_shard = loaded_weight.narrow(
                     output_dim, shard_offset, shard_size)
                 self.weight_loader(param, loaded_weight_shard, shard_id)

vllm/model_executor/layers/quantization/bitsandbytes.py

@@ -108,7 +108,7 @@ class BitsAndBytesConfig(QuantizationConfig):
         return None
 
     def get_scaled_act_names(self) -> List[str]:
-        return ["gelu", "gelu_fast", "gelu_new", "gelu_pytorch_tanh"]
+        return []
 
 
 class BitsAndBytesLinearMethod(LinearMethodBase):
@@ -236,7 +236,7 @@ class BitsAndBytesLinearMethod(LinearMethodBase):
             if generation == 0 or generation == 1:
                 matmul_states[i] = MatmulLtState()
                 matmul_states[i].CB = qweight[offsets[i]:offsets[i + 1]]
-                matmul_states[i].SCB = quant_states[i]
+                matmul_states[i].SCB = quant_states[i].to(x.device)
                 matmul_states[i].threshold = (
                     self.quant_config.llm_int8_threshold)
                 matmul_states[i].has_fp16_weights = (

vllm/model_executor/model_loader/loader.py

@@ -736,15 +736,26 @@ class ShardedStateLoader(BaseModelLoader):
 class BitsAndBytesModelLoader(BaseModelLoader):
     """Model loader to load model weights with BitAndBytes quantization."""
 
-    # TODO: these module names are for Llama only,
-    # change so that it works with other models as well
+    possible_config_file_names = ["adapter_config.json"]
+
     default_target_modules = [
-        "gate_proj", "down_proj", "up_proj", "q_proj", "k_proj", "v_proj",
-        "o_proj"
+        ".gate_proj.",
+        ".down_proj.",
+        ".up_proj.",
+        ".q_proj.",
+        ".k_proj.",
+        ".v_proj.",
+        ".o_proj.",
+        '.fc1.',
+        '.fc2.',
+        '.dense.',
+        '.query_key_value.',
+        '.qkv_proj.',
+        '.dense_h_to_4h.',
+        '.dense_4h_to_h.',
+        '.out_proj.',
     ]
 
-    possible_config_file_names = ["adapter_config.json"]
-
     def __init__(self, load_config: LoadConfig):
         super().__init__(load_config)
 
@@ -754,7 +765,7 @@ class BitsAndBytesModelLoader(BaseModelLoader):
         if (not load_config.model_loader_extra_config
                 or "qlora_adapter_name_or_path"
                 not in load_config.model_loader_extra_config):
-            self.target_modules = self.default_target_modules
+            self.target_modules = []
             return
 
         qlora_adapter = load_config.model_loader_extra_config[
@@ -901,10 +912,11 @@ class BitsAndBytesModelLoader(BaseModelLoader):
         for weight_name, weight_tensor in self._hf_weight_iter(
                 hf_weights_files, use_safetensors):
 
-            if not weight_name.endswith(".weight"):
+            if not weight_name.endswith((".weight", ".bias")):
                 continue
 
             qweight_name = weight_name.replace(".weight", ".qweight")
+
             if qweight_name in quant_state_dict:
                 set_weight_attrs(weight_tensor, {"load_in_8bit": True})
                 yield qweight_name, weight_tensor
@@ -920,7 +932,7 @@ class BitsAndBytesModelLoader(BaseModelLoader):
                                                use_safetensors)
         temp_state_dict = {}
         for weight_name, weight_tensor in weight_iterator:
-            if weight_name.endswith(".weight"):
+            if weight_name.endswith((".weight", ".bias")):
                 continue
             # bitsandbytes library requires
             # weight.quant_state.bitsandbytes__* in CPU
@@ -943,9 +955,10 @@ class BitsAndBytesModelLoader(BaseModelLoader):
         # pre quantized weights would have a quant_state
         for weight_name, weight_tensor in self._hf_weight_iter(
                 hf_weights_files, use_safetensors):
-            # Filter out all weights whose suffix is not ".weight"
-            if not weight_name.endswith(".weight"):
+
+            if not weight_name.endswith((".weight", ".bias")):
                 continue
+
             if (f"{weight_name}.quant_state.bitsandbytes__nf4" \
                     in temp_state_dict) or \
             (f"{weight_name}.quant_state.bitsandbytes__fp4" \
@@ -965,15 +978,14 @@ class BitsAndBytesModelLoader(BaseModelLoader):
 
         for weight_name, weight_tensor in self._hf_weight_iter(
                 hf_weights_files, use_safetensors):
-            if any(target_module in weight_name
-                   for target_module in self.target_modules):
+
+            if any(target_module in weight_name for target_module in
+                   self.target_modules) and weight_name.endswith(".weight"):
                 weight_name = weight_name.replace(".weight", ".qweight")
 
-                # weight partitions of different modules occur at
-                # different dimensions
-                # TODO: these module names are for Llama only,
-                # change so that it works with other models as well
-                if 'down_proj' in weight_name or 'o_proj' in weight_name:
+                if any(module in weight_name
+                       for module in self.column_parallel_weights_modules):
+
                     total_size = weight_tensor.size(-1)
                     start_index = total_size // tp_size * tp_rank
                     end_index = total_size // tp_size * (tp_rank + 1)
@@ -1022,6 +1034,20 @@ class BitsAndBytesModelLoader(BaseModelLoader):
                 f"Model {type(model).__name__} does not support BitsAndBytes "
                 "quantization yet.")
 
+        if len(self.target_modules) == 0:
+            if hasattr(model, 'default_bitsandbytes_target_modules'):
+                self.target_modules = model.default_bitsandbytes_target_modules
+            else:
+                self.target_modules = self.default_target_modules
+
+        if hasattr(model, 'column_parallel_weights_modules'):
+            self.column_parallel_weights_modules = \
+                model.column_parallel_weights_modules
+        else:
+            self.column_parallel_weights_modules = []
+
+        self.model_type = type(model).__name__
+
         logger.info("Loading weights with BitsAndBytes quantization. "
                     " May take a while ...")
 

vllm/model_executor/models/falcon.py

@@ -391,6 +391,17 @@ class FalconModel(nn.Module):
 
 class FalconForCausalLM(nn.Module, SupportsPP):
 
+    # BitandBytes specific attributes
+    bitsandbytes_stacked_params_mapping = {}
+    default_bitsandbytes_target_modules = [
+        ".query_key_value.",
+        ".dense.",
+        ".dense_h_to_4h.",
+        ".dense_4h_to_h.",
+    ]
+    # in TP, these weights are partitioned along the column dimension (dim=-1)
+    column_parallel_weights_modules = [".dense_4h_to_h.", ".dense."]
+
     def __init__(
         self,
         config: FalconConfig,

vllm/model_executor/models/gemma.py

@@ -332,6 +332,28 @@ class GemmaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         "gate_up_proj",
         "down_proj",
     ]
+
+    # BitandBytes specific attributes
+    default_bitsandbytes_target_modules = [
+        ".gate_proj.",
+        ".down_proj.",
+        ".up_proj.",
+        ".q_proj.",
+        ".k_proj.",
+        ".v_proj.",
+        ".o_proj.",
+    ]
+    # in TP, these weights are partitioned along the column dimension (dim=-1)
+    column_parallel_weights_modules = [".down_proj.", ".o_proj."]
+    bitsandbytes_stacked_params_mapping = {
+        # shard_name, weight_name, index
+        "q_proj": ("qkv_proj", 0),
+        "k_proj": ("qkv_proj", 1),
+        "v_proj": ("qkv_proj", 2),
+        "gate_proj": ("gate_up_proj", 0),
+        "up_proj": ("gate_up_proj", 1),
+    }
+
     # Gemma does not apply LoRA to the embedding layer.
     embedding_modules = {}
     embedding_padding_modules = []

vllm/model_executor/models/gemma2.py

@@ -375,6 +375,19 @@ class Gemma2ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
     # Gemma does not apply LoRA to the embedding layer.
     embedding_modules = {}
     embedding_padding_modules = []
+
+    # BitandBytes specific attributes
+    default_bitsandbytes_target_modules = [
+        ".gate_proj.",
+        ".down_proj.",
+        ".up_proj.",
+        ".q_proj.",
+        ".k_proj.",
+        ".v_proj.",
+        ".o_proj.",
+    ]
+    # in TP, these weights are partitioned along the column dimension (dim=-1)
+    column_parallel_weights_modules = [".down_proj.", ".o_proj."]
     bitsandbytes_stacked_params_mapping = {
         # shard_name, weight_name, index
         "q_proj": ("qkv_proj", 0),

vllm/model_executor/models/llama.py

@@ -449,6 +449,19 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         "lm_head": "output_embeddings"
     }
     embedding_padding_modules = ["lm_head"]
+
+    # BitandBytes specific attributes
+    default_bitsandbytes_target_modules = [
+        ".gate_proj.",
+        ".down_proj.",
+        ".up_proj.",
+        ".q_proj.",
+        ".k_proj.",
+        ".v_proj.",
+        ".o_proj.",
+    ]
+    # in TP, these weights are partitioned along the column dimension (dim=-1)
+    column_parallel_weights_modules = [".down_proj.", ".o_proj."]
     bitsandbytes_stacked_params_mapping = {
         # shard_name, weight_name, index
         "q_proj": ("qkv_proj", 0),

vllm/model_executor/models/opt.py

@@ -315,6 +315,19 @@ class OPTModel(nn.Module):
 
 class OPTForCausalLM(nn.Module, SupportsPP):
 
+    # BitandBytes specific attributes
+    bitsandbytes_stacked_params_mapping = {
+        # shard_name, weight_name, index
+        "q_proj": ("qkv_proj", 0),
+        "k_proj": ("qkv_proj", 1),
+        "v_proj": ("qkv_proj", 2),
+    }
+    default_bitsandbytes_target_modules = [
+        ".q_proj.", ".k_proj.", ".v_proj.", ".out_proj.", ".fc1.", ".fc2."
+    ]
+    # in TP, these weights are partitioned along the column dimension (dim=-1)
+    column_parallel_weights_modules = [".out_proj.", ".fc2."]
+
     def __init__(
         self,
         config: OPTConfig,

vllm/model_executor/models/phi.py

@@ -260,6 +260,20 @@ class PhiForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         "fc1",
         "fc2",
     ]
+
+    # BitandBytes specific attributes
+    bitsandbytes_stacked_params_mapping = {
+        # shard_name, weight_name, index
+        "q_proj": ("qkv_proj", 0),
+        "k_proj": ("qkv_proj", 1),
+        "v_proj": ("qkv_proj", 2),
+    }
+    default_bitsandbytes_target_modules = [
+        ".q_proj.", ".k_proj.", ".v_proj.", ".fc1.", ".fc2.", ".dense."
+    ]
+    # in TP, these weights are partitioned along the column dimension (dim=-1)
+    column_parallel_weights_modules = [".fc2.", ".dense."]
+
     embedding_modules = {}
     embedding_padding_modules = []