[Quantization][V1] BitsAndBytes support V1 (#15611)

Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>

tests/models/encoder_decoder/vision_language/test_mllama.py

@@ -425,7 +425,6 @@ def test_bnb_regression(
         max_model_len=4096,
         max_num_seqs=2,
         quantization="bitsandbytes",
-        load_format="bitsandbytes",
     )
     sampling_params = SamplingParams(
         temperature=0,

tests/models/test_transformers.py

@@ -72,7 +72,6 @@ def test_distributed(
         "meta-llama/Llama-3.2-1B-Instruct",
         {
             "quantization": "bitsandbytes",
-            "load_format": "bitsandbytes",
         },
     ),
 ])

tests/quantization/test_bitsandbytes.py

@@ -101,8 +101,6 @@ def test_load_pp_4bit_bnb_model(model_name, description) -> None:
         "--enable-prefix-caching",
         "--quantization",
         "bitsandbytes",
-        "--load-format",
-        "bitsandbytes",
         "--gpu-memory-utilization",
         "0.7",
     ]
@@ -137,7 +135,6 @@ def validate_generated_texts(hf_runner,
     # when using distributed inference
     with vllm_runner(model_name,
                      quantization='bitsandbytes',
-                     load_format='bitsandbytes',
                      tensor_parallel_size=vllm_tp_size,
                      enforce_eager=False) as llm:
         vllm_outputs = llm.generate_greedy(prompts, 8)

vllm/config.py

@@ -682,8 +682,9 @@ class ModelConfig:
 
     def _verify_bnb_config(self) -> None:
         """
-        The current version of bitsandbytes (0.44.0) with 8-bit models does not
+        The current version of bitsandbytes (0.45.3) with 8-bit models does not
         yet support CUDA graph.
+        # TODO Remove this when bitsandbytes supports.
         """
         is_bitsandbytes = self.quantization == "bitsandbytes"
         has_quantization_config = (getattr(self.hf_config,
@@ -698,8 +699,9 @@ class ModelConfig:
                 not self.enforce_eager,
         ]):
             logger.warning(
-                "CUDA graph is not supported on BitAndBytes 8bit yet, "
+                "CUDA graph is not supported on BitsAndBytes 8bit yet, "
                 "fallback to the eager mode.")
+
             self.enforce_eager = True
 
     def _verify_with_expert_parallelism(self) -> None:

vllm/engine/arg_utils.py

@@ -1616,7 +1616,7 @@ class EngineArgs:
             return False
 
         # Some quantization is not compatible with torch.compile.
-        V1_UNSUPPORTED_QUANT = ["bitsandbytes", "gguf"]
+        V1_UNSUPPORTED_QUANT = ["gguf"]
         if model_config.quantization in V1_UNSUPPORTED_QUANT:
             _raise_or_fallback(
                 feature_name=f"--quantization {model_config.quantization}",

vllm/model_executor/layers/quantization/bitsandbytes.py

@@ -9,6 +9,7 @@ from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                set_weight_attrs)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.utils import direct_register_custom_op
 
 
 class BitsAndBytesConfig(QuantizationConfig):
@@ -321,9 +322,6 @@ class BitsAndBytesLinearMethod(LinearMethodBase):
             x: torch.Tensor,
             bias: Optional[torch.Tensor] = None) -> torch.Tensor:
 
-        # only load the bitsandbytes module when needed
-        from bitsandbytes import matmul_4bit
-
         original_type = x.dtype
         original_shape = x.shape
         reshape_after_matmul = False
@@ -343,19 +341,7 @@ class BitsAndBytesLinearMethod(LinearMethodBase):
                           out_dim_1,
                           dtype=torch.bfloat16,
                           device=x.device)
-
-        current_index = 0
-        for i in range(len(quant_states)):
-            output_size = quant_states[i].shape[0]
-            # It is more efficient to use out kwarg like
-            # matmul_4bit(..., out = ...).  Infeasible now due to the bug
-            # https://github.com/TimDettmers/bitsandbytes/issues/1235.
-            # Need to change  after the bug is fixed.
-            out[:, current_index:current_index + output_size] = matmul_4bit(
-                bf_x, qweight[offsets[i]:offsets[i + 1]].t(), quant_states[i])
-
-            current_index += output_size
-
+        apply_bnb_4bit(bf_x, qweight, offsets, out)
         out = out.to(original_type)
 
         if reshape_after_matmul:
@@ -365,3 +351,46 @@ class BitsAndBytesLinearMethod(LinearMethodBase):
             out += bias
 
         return out
+
+
+def _apply_bnb_4bit(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    offsets: torch.Tensor,
+    out: torch.Tensor,
+) -> None:
+    # only load the bitsandbytes module when needed
+    from bitsandbytes import matmul_4bit
+    quant_states = weight.bnb_quant_state
+    current_index = 0
+    for i in range(len(quant_states)):
+        output_size = quant_states[i].shape[0]
+        # It is more efficient to use out kwarg like
+        # matmul_4bit(..., out = ...).  Infeasible now due to the bug
+        # https://github.com/TimDettmers/bitsandbytes/issues/1235.
+        # Need to change  after the bug is fixed.
+        out[:, current_index:current_index + output_size] = matmul_4bit(
+            x, weight[offsets[i]:offsets[i + 1]].t(), quant_states[i])
+        current_index += output_size
+
+
+def _apply_bnb_4bit_fake(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    offsets: torch.Tensor,
+    out: torch.Tensor,
+) -> None:
+    return
+
+
+try:
+    direct_register_custom_op(
+        op_name="apply_bnb_4bit",
+        op_func=_apply_bnb_4bit,
+        mutates_args=["out"],
+        fake_impl=_apply_bnb_4bit_fake,
+    )
+    apply_bnb_4bit = torch.ops.vllm.apply_bnb_4bit
+
+except AttributeError as error:
+    raise error

vllm/model_executor/model_loader/loader.py

@@ -1259,6 +1259,8 @@ class BitsAndBytesModelLoader(BaseModelLoader):
                                          pack_ratio)
 
                 offsets = np.concatenate(([0], np.cumsum(num_elements)))
+                # Make torch infer_schema happy
+                offsets = torch.tensor(offsets).cpu()
                 set_weight_attrs(param, {"bnb_shard_offsets": offsets})
 
                 if load_8bit: