[Kernel] Support Microsoft Runtime Kernel Lib for our Low Precision Computation - BitBLAS (#6036)

Signed-off-by: xinyuxiao <xinyuxiao2024@gmail.com>
Co-authored-by: xinyuxiao <xinyuxiao2024@gmail.com>

benchmarks/kernels/benchmark_bitblas.py

+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
+    MINIMUM_BITBLAS_VERSION)
+
+try:
+    import bitblas
+    if bitblas.__version__ < MINIMUM_BITBLAS_VERSION:
+        raise ImportError("bitblas version is wrong. Please "
+                          f"install bitblas>={MINIMUM_BITBLAS_VERSION}")
+except ImportError as e:
+    bitblas_import_exception = e
+    raise ValueError("Trying to use the bitblas backend, but could not import"
+                     f"with the following error: {bitblas_import_exception}. "
+                     "Please install bitblas through the following command: "
+                     f"`pip install bitblas>={MINIMUM_BITBLAS_VERSION}`"
+                     ) from bitblas_import_exception
+
+from bitblas import Matmul, MatmulConfig, auto_detect_nvidia_target
+
+from vllm.utils import FlexibleArgumentParser
+
+parser = FlexibleArgumentParser(
+    description="Benchmark BitBLAS int4 on a specific target.")
+
+# Add arguments to the parser
+parser.add_argument(
+    "--target",
+    type=str,
+    default=auto_detect_nvidia_target(),
+    help="Specify the target device for benchmarking.",
+)
+parser.add_argument("--group_size",
+                    type=int,
+                    default=None,
+                    help="Group size for grouped quantization.")
+parser.add_argument(
+    "--A_dtype",
+    type=str,
+    default="float16",
+    choices=["float16", "float32", "float64", "int32", "int8"],
+    help="Data type of activation A.",
+)
+parser.add_argument(
+    "--W_dtype",
+    type=str,
+    default="int4",
+    choices=[
+        "float16",
+        "float32",
+        "float64",
+        "int32",
+        "int8",
+        "int4",
+        "int2",
+        "int1",
+        "nf4",
+        "fp4_e2m1",
+    ],
+    help="Data type of weight W.",
+)
+parser.add_argument(
+    "--accum_dtype",
+    type=str,
+    default="float16",
+    choices=["float16", "int32"],
+    help="Data type for accumulation.",
+)
+parser.add_argument(
+    "--out_dtype",
+    type=str,
+    default="float16",
+    choices=["float16", "float32", "int32", "int8"],
+    help="Data type for output.",
+)
+parser.add_argument(
+    "--layout",
+    type=str,
+    default="nt",
+    choices=["nt", "nn"],
+    help="Matrix layout, 'nt' for non-transpose A and transpose W.",
+)
+parser.add_argument("--with_bias",
+                    action="store_true",
+                    help="Include bias in the benchmark.")
+parser.add_argument(
+    "--with_scaling",
+    action="store_true",
+    help="Include scaling factor in the quantization.",
+)
+parser.add_argument("--with_zeros",
+                    action="store_true",
+                    help="Include zeros in the quantization.")
+parser.add_argument(
+    "--zeros_mode",
+    type=str,
+    default=None,
+    choices=["original", "rescale", "quantized"],
+    help="Specify the mode for calculating zeros.",
+)
+
+# Parse the arguments
+args = parser.parse_args()
+
+# Assign arguments to variables
+target = args.target
+A_dtype = args.A_dtype
+W_dtype = args.W_dtype
+accum_dtype = args.accum_dtype
+out_dtype = args.out_dtype
+layout = args.layout
+with_bias = args.with_bias
+group_size = args.group_size
+with_scaling = args.with_scaling
+with_zeros = args.with_zeros
+zeros_mode = args.zeros_mode
+
+# Define a list of shared arguments that repeat in every config
+shared_args = [
+    A_dtype,
+    W_dtype,
+    out_dtype,
+    accum_dtype,
+    layout,
+    with_bias,
+    group_size,
+    with_scaling,
+    with_zeros,
+    zeros_mode,
+]
+
+# Define just the (M, K, N) shapes in a more compact list
+shapes = [
+    # square test
+    (1, 16384, 16384),
+    # BLOOM-176B
+    (1, 43008, 14336),
+    (1, 14336, 14336),
+    (1, 57344, 14336),
+    (1, 14336, 57344),
+    # OPT-65B
+    (1, 9216, 9216),
+    (1, 36864, 9216),
+    (1, 9216, 36864),
+    (1, 22016, 8192),
+    # LLAMA-70B/65B
+    (1, 8192, 22016),
+    (1, 8192, 8192),
+    (1, 28672, 8192),
+    (1, 8192, 28672),
+    # square test
+    (16384, 16384, 16384),
+    # BLOOM-176B
+    (8192, 43008, 14336),
+    (8192, 14336, 14336),
+    (8192, 57344, 14336),
+    (8192, 14336, 57344),
+    # OPT-65B
+    (8192, 9216, 9216),
+    (8192, 36864, 9216),
+    (8192, 9216, 36864),
+    (8192, 22016, 8192),
+    # LLAMA-70B/65B
+    (8192, 8192, 22016),
+    (8192, 8192, 8192),
+    (8192, 28672, 8192),
+    (8192, 8192, 28672),
+]
+
+# Build test shapes with all the shared arguments
+test_shapes = [(MatmulConfig, Matmul, (*shape, *shared_args))
+               for shape in shapes]
+
+benchmark_sets = []
+benchmark_sets.extend(test_shapes)
+
+benchmark_results = {}
+for config_class, operator, input_args in benchmark_sets:
+    config = config_class(*input_args)
+    matmul = operator(config, target=target, enable_tuning=True)
+    kernel_latency = matmul.profile_latency()
+
+    print("Time cost is: {:.3f} ms".format(kernel_latency))
+
+    profile_config = {
+        f"{operator.__name__}-{'-'.join([str(i) for i in input_args])}": {
+            "BitBLAS_top20_latency": kernel_latency,
+        }
+    }
+
+    benchmark_results.update(profile_config)
+
+# Define headers for the table
+headers = [
+    "PrimFunc",
+    "Input Arguments",
+    "BitBLAS Top20 Latency",
+]
+
+# Calculate column widths for pretty printing
+col_widths = [0, 0, 0]
+for config_key, values in benchmark_results.items():
+    args_split = config_key.split("-")
+    func_name = args_split[0]
+    input_args_str = "-".join(args_split[1:])
+    col_widths[0] = max(col_widths[0], len(func_name) + 2, len(headers[0]) + 2)
+    col_widths[1] = max(col_widths[1],
+                        len(input_args_str) + 2,
+                        len(headers[1]) + 2)
+    col_widths[2] = max(col_widths[2],
+                        len(f"{values['BitBLAS_top20_latency']:.3f} ms") + 2,
+                        len(headers[2]) + 2)
+    # break only if you want to measure widths from a single example;
+    # otherwise, let it loop over all items.
+
+# Print header
+for i, header in enumerate(headers):
+    headers[i] = header.ljust(col_widths[i])
+print("".join(headers))
+print("-" * sum(col_widths))
+
+# Print rows
+for config_key, values in benchmark_results.items():
+    args_split = config_key.split("-")
+    func_name = args_split[0]
+    input_args_str = "-".join(args_split[1:])
+    row = [
+        func_name,
+        input_args_str,
+        f"{values['BitBLAS_top20_latency']:.3f} ms",
+    ]
+    row_str = "".join(
+        [str(cell).ljust(col_widths[idx]) for idx, cell in enumerate(row)])
+    print(row_str)

docs/source/features/quantization/bitblas.md

+# BitBLAS
+
+vLLM now supports [BitBLAS](https://github.com/microsoft/BitBLAS) for more efficient and flexible model inference. Compared to other quantization frameworks, BitBLAS provides more precision combinations.
+
+Below are the steps to utilize BitBLAS with vLLM.
+
+```console
+pip install bitblas>=0.1.0
+```
+
+vLLM reads the model's config file and supports pre-quantized checkpoints.
+
+You can find pre-quantized models on:
+
+- [Hugging Face (BitBLAS)](https://huggingface.co/models?other=bitblas)
+- [Hugging Face (GPTQ)](https://huggingface.co/models?other=gptq)
+
+Usually, these repositories have a `quantize_config.json` file that includes a `quantization_config` section.
+
+## Read bitblas format checkpoint
+
+```python
+from vllm import LLM
+import torch
+
+# "hxbgsyxh/llama-13b-4bit-g-1-bitblas" is a pre-quantized checkpoint.
+model_id = "hxbgsyxh/llama-13b-4bit-g-1-bitblas"
+llm = LLM(model=model_id, dtype=torch.bfloat16, trust_remote_code=True, quantization="bitblas")
+```
+
+## Read gptq format checkpoint
+
+```python
+from vllm import LLM
+import torch
+
+# "hxbgsyxh/llama-13b-4bit-g-1" is a pre-quantized checkpoint.
+model_id = "hxbgsyxh/llama-13b-4bit-g-1"
+llm = LLM(model=model_id, dtype=torch.float16, trust_remote_code=True, quantization="bitblas", max_model_len=1024)
+```

docs/source/features/quantization/index.md

@@ -11,6 +11,7 @@ Quantization trades off model precision for smaller memory footprint, allowing l
 supported_hardware
 auto_awq
 bnb
+bitblas
 gguf
 gptqmodel
 int4

docs/source/features/quantization/supported_hardware.md

@@ -74,6 +74,17 @@ The table below shows the compatibility of various quantization implementations
   * ❌
   * ❌
   * ❌
+- * BitBLAS (GPTQ)
+  * ✅︎
+  * ✅︎
+  * ✅︎
+  * ✅︎
+  * ✅︎
+  * ✅︎
+  * ❌
+  * ❌
+  * ❌
+  * ❌
 - * AQLM
   * ✅︎
   * ✅︎

tests/models/test_bitblas.py

+# SPDX-License-Identifier: Apache-2.0
+"""Compare the outputs of a GPTQ model to a bitblas model.
+
+Note: GPTQ and bitblas do not have bitwise correctness.
+As a result, in this test, we just confirm that the top selected tokens of the
+bitblas/GPTQ models are in the top 3 selections of each other.
+
+Note: bitblas internally uses locks to synchronize the threads. This can
+result in very slight nondeterminism for bitblas. As a result, we re-run the 
+test up to 3 times to see if we pass.
+
+Run `pytest tests/models/test_bitblas.py`.
+"""
+from dataclasses import dataclass
+
+import pytest
+
+from .utils import check_logprobs_close
+
+
+@dataclass
+class ModelPair:
+    model_bitblas: str
+    model_gptq: str
+
+
+model_pairs = [
+    ModelPair(model_bitblas="hxbgsyxh/opt-125m-4bit-128g-bitblas",
+              model_gptq="hxbgsyxh/opt-125m-4bit-128g"),
+]
+
+
+@pytest.mark.flaky(reruns=2)
+@pytest.mark.skipif(True, reason="BitBLAS takes too much time for tuning.")
+@pytest.mark.parametrize("model_pair", model_pairs)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [32])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_models(
+    vllm_runner,
+    example_prompts,
+    model_pair: ModelPair,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+) -> None:
+    with vllm_runner(model_pair.model_bitblas,
+                     dtype=dtype,
+                     quantization="bitblas") as bitblas_model:
+        bitblas_outputs = bitblas_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+
+    with vllm_runner(model_pair.model_gptq, dtype=dtype,
+                     quantization="gptq") as gptq_model:
+        gptq_outputs = gptq_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+
+    check_logprobs_close(
+        outputs_0_lst=gptq_outputs,
+        outputs_1_lst=bitblas_outputs,
+        name_0="gptq",
+        name_1="bitblas",
+    )

tests/models/test_gptq_bitblas.py

+# SPDX-License-Identifier: Apache-2.0
+"""Compare the outputs of a GPTQ model to a bitblas model.
+
+Note: GPTQ and bitblas do not have bitwise correctness.
+As a result, in this test, we just confirm that the top selected tokens of the
+bitblas/GPTQ models are in the top 3 selections of each other.
+
+Note: bitblas internally uses locks to synchronize the threads. This can
+result in very slight nondeterminism for bitblas. As a result, we re-run the 
+test up to 3 times to see if we pass.
+
+Run `pytest tests/models/test_bitblas.py`.
+"""
+from dataclasses import dataclass
+
+import pytest
+
+from .utils import check_logprobs_close
+
+
+@dataclass
+class ModelPair:
+    model_gptq: str
+
+
+model_pairs = [
+    ModelPair(model_gptq="hxbgsyxh/opt-125m-4bit-128g"),
+]
+
+
+@pytest.mark.flaky(reruns=2)
+@pytest.mark.skipif(True, reason="BitBLAS takes too much time for tuning.")
+@pytest.mark.parametrize("model_pair", model_pairs)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [32])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_models(
+    vllm_runner,
+    example_prompts,
+    model_pair: ModelPair,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+) -> None:
+    with vllm_runner(model_pair.model_gptq,
+                     dtype=dtype,
+                     quantization="bitblas") as bitblas_model:
+        bitblas_outputs = bitblas_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+
+    with vllm_runner(model_pair.model_gptq, dtype=dtype,
+                     quantization="gptq") as gptq_model:
+        gptq_outputs = gptq_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+
+    check_logprobs_close(
+        outputs_0_lst=gptq_outputs,
+        outputs_1_lst=bitblas_outputs,
+        name_0="gptq",
+        name_1="gptq_bitblas",
+    )

vllm/config.py

@@ -750,7 +750,8 @@ class ModelConfig:
         optimized_quantization_methods = [
             "fp8", "marlin", "modelopt", "gptq_marlin_24", "gptq_marlin",
             "awq_marlin", "fbgemm_fp8", "compressed_tensors",
-            "compressed-tensors", "experts_int8", "quark", "nvfp4"
+            "compressed-tensors", "experts_int8", "quark", "nvfp4", "bitblas",
+            "gptq_bitblas"
         ]
         if self.quantization is not None:
             self.quantization = self.quantization.lower()

vllm/model_executor/layers/linear.py

@@ -31,6 +31,8 @@ logger = init_logger(__name__)
 
 WEIGHT_LOADER_V2_SUPPORTED = [
     "CompressedTensorsLinearMethod",
+    "BitBLASLinearMethod",
+    "GPTQBitBLASLinearMethod",
     "AWQMarlinLinearMethod",
     "AWQLinearMethod",
     "GPTQMarlinLinearMethod",
@@ -50,6 +52,15 @@ WEIGHT_LOADER_V2_SUPPORTED = [
 ]
 
 
+def adjust_bitblas_shard(param, shard_size, shard_offset):
+    bitblas_tile_size = getattr(param, "bitblas_tile_size", None)
+    if bitblas_tile_size is not None:
+        return (shard_size // bitblas_tile_size,
+                shard_offset // bitblas_tile_size)
+
+    return shard_size, shard_offset
+
+
 def adjust_marlin_shard(param, shard_size, shard_offset):
     marlin_tile_size = getattr(param, "marlin_tile_size", None)
     if marlin_tile_size is None:
@@ -615,6 +626,9 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
                     shard_size, shard_offset = adjust_marlin_shard(
                         param, shard_size, shard_offset)
 
+                shard_size, shard_offset = adjust_bitblas_shard(
+                    param, shard_size, shard_offset)
+
                 if use_bitsandbytes_4bit:
                     index = list(itertools.accumulate([0] + self.output_sizes))
                     orig_offsets = {
@@ -646,6 +660,8 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
                 # Special case for Marlin.
                 shard_size, shard_offset = adjust_marlin_shard(
                     param, shard_size, shard_offset)
+            shard_size, shard_offset = adjust_bitblas_shard(
+                param, shard_size, shard_offset)
 
             use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit",
                                             False)

vllm/model_executor/layers/quantization/__init__.py

@@ -18,9 +18,11 @@ QUANTIZATION_METHODS: List[str] = [
     # The order of gptq methods is important for config.py iteration over
     # override_quantization_method(..)
     "marlin",
+    "bitblas",
     "gguf",
     "gptq_marlin_24",
     "gptq_marlin",
+    "gptq_bitblas",
     "awq_marlin",
     "gptq",
     "compressed-tensors",
@@ -85,6 +87,7 @@ def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
     from .aqlm import AQLMConfig
     from .awq import AWQConfig
     from .awq_marlin import AWQMarlinConfig
+    from .bitblas import BitBLASConfig
     from .bitsandbytes import BitsAndBytesConfig
     from .compressed_tensors.compressed_tensors import (  # noqa: E501
         CompressedTensorsConfig)
@@ -94,6 +97,7 @@ def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
     from .fp8 import Fp8Config
     from .gguf import GGUFConfig
     from .gptq import GPTQConfig
+    from .gptq_bitblas import GPTQBitBLASConfig
     from .gptq_marlin import GPTQMarlinConfig
     from .gptq_marlin_24 import GPTQMarlin24Config
     from .hqq_marlin import HQQMarlinConfig
@@ -119,9 +123,11 @@ def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
         # The order of gptq methods is important for config.py iteration over
         # override_quantization_method(..)
         "marlin": MarlinConfig,
+        "bitblas": BitBLASConfig,
         "gguf": GGUFConfig,
         "gptq_marlin_24": GPTQMarlin24Config,
         "gptq_marlin": GPTQMarlinConfig,
+        "gptq_bitblas": GPTQBitBLASConfig,
         "awq_marlin": AWQMarlinConfig,
         "gptq": GPTQConfig,
         "compressed-tensors": CompressedTensorsConfig,
@@ -146,4 +152,4 @@ __all__ = [
     "QuantizationConfig",
     "get_quantization_config",
     "QUANTIZATION_METHODS",
-]
+]
\ No newline at end of file

vllm/model_executor/layers/quantization/bitblas.py

+# SPDX-License-Identifier: Apache-2.0
+from typing import Any, Dict, List, Optional
+
+import torch
+
+from vllm.logger import init_logger
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
+    BITBLAS_OPTIMIZE_FEATURES, BITBLAS_SUPPORTED_NUM_BITS,
+    BITBLAS_SUPPORTED_SYM, MINIMUM_BITBLAS_VERSION)
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           ChannelQuantScaleParameter,
+                                           GroupQuantScaleParameter,
+                                           PackedvLLMParameter)
+from vllm.model_executor.utils import set_weight_attrs
+
+logger = init_logger(__name__)
+
+
+class BitBLASConfig(QuantizationConfig):
+    """Config class for BitBLAS.
+
+    Reference: https://github.com/Microsoft/BitBLAS
+    """
+    TORCH_DTYPE = torch.float16
+    STORAGE_DTYPE = "int8"  # assume int8 storage
+    TORCH_STORAGE_DTYPE = getattr(torch, STORAGE_DTYPE)
+    # "original" or "rescale" or "quantized",
+    # gptq_with_bitblas prefer "quantized implementation"
+    ZEROS_MODE = "quantized"
+
+    def __init__(
+        self,
+        weight_bits: int,
+        group_size: Optional[int],
+        desc_act: Optional[bool],
+        is_sym: Optional[bool],
+        quant_method: Optional[str],
+        lm_head_quantized: bool,
+    ) -> None:
+        try:
+            import bitblas
+            if bitblas.__version__ < MINIMUM_BITBLAS_VERSION:
+                raise ImportError(
+                    "bitblas version is wrong. Please "
+                    f"install bitblas>={MINIMUM_BITBLAS_VERSION}")
+        except ImportError as e:
+            bitblas_import_exception = e
+            raise ValueError(
+                "Trying to use the bitblas backend, but could not import"
+                f"with the following error: {bitblas_import_exception}. "
+                "Please install bitblas through the following command: "
+                f"`pip install bitblas>={MINIMUM_BITBLAS_VERSION}`"
+            ) from bitblas_import_exception
+
+        if desc_act and group_size == -1:
+            # In this case, act_order == True is the same as act_order == False
+            # (since we have only one group per output channel)
+            desc_act = False
+
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.desc_act = desc_act
+        self.is_sym = is_sym
+        self.quant_method = quant_method
+        self.lm_head_quantized = lm_head_quantized
+
+        # Verify
+        if self.weight_bits not in BITBLAS_SUPPORTED_NUM_BITS:
+            raise ValueError(
+                f"BitBLAS does not support weight_bits = {self.weight_bits}. "
+                f"Only weight_bits = {BITBLAS_SUPPORTED_NUM_BITS} "
+                "are supported.")
+
+        if self.is_sym not in BITBLAS_SUPPORTED_SYM:
+            raise ValueError(
+                f"BitBLAS does not support is_sym = {self.is_sym}. "
+                f"Only sym = {BITBLAS_SUPPORTED_SYM} are supported.")
+
+        storage_dtype = self.STORAGE_DTYPE
+        storage_nbit = int("".join(c for c in storage_dtype if c.isdigit()))
+
+        self.storage_dtype = storage_dtype
+        self.storage_torch_dtype = self.TORCH_STORAGE_DTYPE
+        # 4 Bits packed into 32 bit datatype.
+        self.pack_factor = storage_nbit // weight_bits
+        self.nbits = weight_bits
+
+        # Zeros type for the quantized weights.
+        self.zeros_mode = self.ZEROS_MODE
+
+    def __repr__(self) -> str:
+        return (f"BitBLASConfig(weight_bits={self.weight_bits}, "
+                f"group_size={self.group_size}, "
+                f"desc_act={self.desc_act}, "
+                f"is_sym={self.is_sym}, "
+                f"quant_method={self.quant_method})")
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "bitblas"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.half, torch.bfloat16]
+
+    @classmethod
+    # Need to figure it out
+    def get_min_capability(cls) -> int:
+        return 70
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return ["quantize_config.json"]
+
+    @staticmethod
+    def get_from_keys(config: Dict[str, Any],
+                      keys: List[str],
+                      default: Any = None) -> Any:
+        """Get a value from the model's quantization config."""
+        for key in keys:
+            if key in config:
+                return config[key]
+        return default
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "BitBLASConfig":
+        weight_bits = cls.get_from_keys(config, ["bits"])
+        group_size = cls.get_from_keys(config, ["group_size"], -1)
+        desc_act = cls.get_from_keys(config, ["desc_act"], False)
+        is_sym = cls.get_from_keys(config, ["sym"], False)
+        quant_method = cls.get_from_keys(config, ["quant_method"])
+        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
+                                                 default=False)
+        return cls(weight_bits, group_size, desc_act, is_sym, quant_method,
+                   lm_head_quantized)
+
+    @classmethod
+    def override_quantization_method(cls, hf_quant_cfg,
+                                     user_quant) -> Optional[str]:
+        # compat: autogptq >=0.8.0 use checkpoint_format: str
+        # compat: autogptq <=0.7.1 is_bitblas_format: bool
+        is_bitblas_format = (hf_quant_cfg.get("checkpoint_format") == "bitblas"
+                             or hf_quant_cfg.get("is_bitblas_format", False))
+
+        is_valid_user_quant = (user_quant is None or user_quant == "gptq"
+                               or user_quant == "bitblas")
+
+        if is_bitblas_format and is_valid_user_quant:
+            msg = ("The model is serialized in {} format. Using {} kernel.".
+                   format(cls.get_name(), cls.get_name()))
+            logger.info(msg)
+            return cls.get_name()
+
+        return None
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["BitBLASLinearMethod"]:
+        if isinstance(layer, LinearBase) or (isinstance(layer, ParallelLMHead)
+                                             and self.lm_head_quantized):
+            return BitBLASLinearMethod(self)
+        return None
+
+
+class BitBLASLinearMethod(LinearMethodBase):
+    """Linear method for BitBLAS.
+
+    Args:
+        quant_config: The BitBLAS quantization config.
+    """
+    # USE BITBLAS_OPTIMIZE_FEATURES_CONTIGUOUS
+    # Instead of BITBLAS_OPTIMIZE_FEATURES
+    # If you want to high contiguous batching
+    # performance
+    OPT_FEATURES = BITBLAS_OPTIMIZE_FEATURES
+    ENABLE_TUNING = True
+    BITBLAS_DTYPES = {
+        torch.float32: "float32",
+        torch.float16: "float16",
+        torch.bfloat16: "bfloat16",
+        torch.half: "float16",
+        torch.int8: "int8",
+    }
+
+    def __init__(self, quant_config: BitBLASConfig):
+        self.quant_config = quant_config
+
+    def create_weights_gptq(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        """Creates quantized weights for use in linear operations.
+
+        The function initializes and returns a dictionary containing quantized 
+        weights, scales, and zeros
+        for performing quantized matrix multiplication operations.
+
+        Args:
+            input_size_per_partition: The size of the input partition.
+            output_size_per_partition: The size of the output partition.
+            input_size: The total size of the input (unused).
+            output_size: The total size of the output (unused).
+            params_dtype: 
+                The data type of the parameters (expected to be torch.float16).
+
+        Returns:
+            A dictionary containing the quantized weights ('qweight'), 
+            scales ('scales'), and zeros ('zeros').
+
+        Raises:
+            ValueError: If `params_dtype` is not `torch.float16` or if the 
+            input size per partition is not divisible by the group size in 
+            `quant_config`.
+        """
+        del input_size, output_size  # Unused arguments.
+        weight_loader = extra_weight_attrs["weight_loader"]
+
+        if params_dtype not in self.quant_config.get_supported_act_dtypes():
+            raise ValueError("Parameter data type must be torch.float16, "
+                             f"but got {params_dtype}")
+        group_size = self.quant_config.group_size
+        if group_size is None:
+            group_size = -1
+        # Validate output_size_per_partition
+        output_size_per_partition = sum(output_partition_sizes)
+        if (group_size != -1 and input_size_per_partition % group_size != 0):
+            raise ValueError(
+                f"Input size per partition ({input_size_per_partition}) must "
+                f"be divisible by group size ({group_size}).")
+
+        # Initialize or retrieve the BitBLAS matrix multiplication operator.
+        self._configure_bitblas_matmul(
+            input_size_per_partition,
+            output_size_per_partition,
+            params_dtype=params_dtype,
+            enable_tuning=self.ENABLE_TUNING,
+            bias=False,
+            layout="nt",
+            bits=self.quant_config.weight_bits,
+        )
+
+        # Initialize quantized weights with dimensions
+        # Quantized 4Bit weights packed.
+        qweight = PackedvLLMParameter(
+            data=torch.empty(
+                self.bitblas_matmul.retrieve_weight_shape(),
+                device="cuda",
+                dtype=self.quant_config.storage_torch_dtype,
+                requires_grad=False,
+            ),
+            input_dim=1,
+            output_dim=0,
+            packed_dim=1,
+            packed_factor=self.quant_config.pack_factor,
+            bitblas_tile_size=(self.bitblas_matmul.retrieve_weight_shape()[-2]
+                               if self.bitblas_matmul.propagate_b else None),
+            weight_loader=weight_loader,
+        )
+
+        # Compute the number of input groups for channel-wise quantization.
+        input_groups = (1 if group_size == -1 else input_size_per_partition //
+                        group_size)
+
+        # Initialize scales and zeros for the quantized weights.
+        weight_scale_args = {
+            "data":
+            torch.empty(
+                output_size_per_partition,
+                input_groups,
+                device="cuda",
+                dtype=params_dtype,
+            ),
+            "weight_loader":
+            weight_loader
+        }
+        if input_groups == 1:
+            scales = ChannelQuantScaleParameter(output_dim=0,
+                                                **weight_scale_args)
+        else:
+            scales = GroupQuantScaleParameter(output_dim=0,
+                                              input_dim=1,
+                                              **weight_scale_args)
+
+        if self.quant_config.zeros_mode == "quantized":
+            zeros = PackedvLLMParameter(
+                data=torch.empty(
+                    input_groups,
+                    output_size_per_partition // self.quant_config.pack_factor,
+                    device="cuda",
+                    dtype=self.quant_config.storage_torch_dtype,
+                    requires_grad=False,
+                ),
+                input_dim=0,
+                output_dim=1,
+                packed_dim=1,
+                packed_factor=self.quant_config.pack_factor,
+                weight_loader=weight_loader,
+            )
+
+        else:
+            zeros = BasevLLMParameter(
+                torch.empty(output_size_per_partition,
+                            input_groups,
+                            device="cuda",
+                            dtype=params_dtype),
+                weight_loader=weight_loader,
+            )
+            # Set attributes to indicate how scales and zeros are applied.
+            set_weight_attrs(zeros, {
+                "input_dim": None if input_groups == 1 else 1,
+                "output_dim": 0,
+            })
+
+        layer.register_parameter("qweight", qweight)
+        layer.register_parameter("scales", scales)
+        layer.register_parameter("zeros", zeros)
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        if self.quant_config.quant_method == "gptq":
+            return self.create_weights_gptq(layer, input_size_per_partition,
+                                            output_partition_sizes, input_size,
+                                            output_size, params_dtype,
+                                            **extra_weight_attrs)
+        else:
+            raise ValueError(
+                f"Unsupported quant_method {self.quant_config.quant_method}")
+
+    def _configure_bitblas_matmul(
+        self,
+        infeatures,
+        outfeatures,
+        params_dtype,
+        enable_tuning,
+        bias,
+        layout,
+        bits,
+        out_dtype="float16",
+    ):
+        from bitblas import MatmulConfig
+        bitblas_dtype = self.BITBLAS_DTYPES[params_dtype]
+
+        with_scaling = False
+        with_zeros = False
+        group_size = self.quant_config.group_size
+        zeros_mode = self.quant_config.zeros_mode
+        if self.quant_config.quant_method == "gptq":
+            with_scaling = True
+            with_zeros = True
+            W_dtype = f"uint{bits}"
+            if self.quant_config.is_sym:
+                with_zeros = False
+                W_dtype = f"int{bits}"
+        else:
+            raise ValueError(
+                f"Unsupported quant_method {self.quant_config.quant_method}")
+
+        matmul_config = MatmulConfig(
+            N=outfeatures,
+            K=infeatures,
+            A_dtype=bitblas_dtype,
+            W_dtype=W_dtype,
+            out_dtype=out_dtype,
+            accum_dtype="int32" if bitblas_dtype == "int8" else bitblas_dtype,
+            storage_dtype=self.quant_config.STORAGE_DTYPE,
+            with_scaling=with_scaling,
+            with_zeros=with_zeros,
+            group_size=group_size,
+            with_bias=bias,
+            layout=layout,
+            zeros_mode=zeros_mode,
+        )
+        self.bitblas_matmul = self._get_or_create_bitblas_operator(
+            matmul_config, enable_tuning)
+
+    def _get_or_create_bitblas_operator(self, config, enable_tuning):
+        from bitblas import Matmul, auto_detect_nvidia_target
+        from bitblas.cache import get_database_path, global_operator_cache
+        BITBLAS_DATABASE_PATH = get_database_path()
+        BITBLAS_TARGET = auto_detect_nvidia_target()
+        if global_operator_cache.size() == 0:
+            global_operator_cache.load_from_database(BITBLAS_DATABASE_PATH,
+                                                     BITBLAS_TARGET)
+
+        bitblas_matmul = global_operator_cache.get(config)
+        if bitblas_matmul is None:
+            bitblas_matmul = Matmul(config,
+                                    target=BITBLAS_TARGET,
+                                    enable_tuning=False)
+            if enable_tuning:
+                TUNING_MESSAGE = (f"BitBLAS Operator {config} is tuning ...")
+                logger.info(TUNING_MESSAGE)
+                bitblas_matmul.hardware_aware_finetune(topk=20)
+                global_operator_cache.add(config, bitblas_matmul)
+                global_operator_cache.save_into_database(
+                    BITBLAS_DATABASE_PATH, BITBLAS_TARGET)
+                TUNED_MESSAGE = (
+                    f"BitBLAS Operator {config} tuned and saved to database.")
+                logger.info(TUNED_MESSAGE)
+            else:
+                _message = f"BitBLAS Operator {config} created."
+                logger.info(_message)
+        else:
+            _message = (
+                f"BitBLAS Operator {config} found in global_operator_cache.")
+            logger.info(_message)
+        return bitblas_matmul
+
+    def apply_gptq(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        qweight = layer.qweight
+        scales = layer.scales
+        qzeros = layer.zeros
+
+        x_2d = x.view(-1, x.shape[-1])
+
+        if self.quant_config.is_sym:
+            output_2d = self.bitblas_matmul(x_2d, qweight, scales)
+        else:
+            output_2d = self.bitblas_matmul(x_2d, qweight, scales, qzeros)
+
+        output = output_2d.view(x.shape[:-1] + (output_2d.shape[1], ))
+
+        if bias is not None:
+            output.add_(bias)  # In-place add
+
+        return output
+
+    def apply(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> torch.Tensor:
+        if self.quant_config.quant_method == "gptq":
+            return self.apply_gptq(*args, **kwargs)
+        else:
+            raise ValueError(
+                f"Unsupported quant_method {self.quant_config.quant_method}")

vllm/model_executor/layers/quantization/gptq_bitblas.py

+# SPDX-License-Identifier: Apache-2.0
+from typing import Any, Dict, List, Optional, Set
+
+import torch
+from torch.nn.parameter import Parameter
+
+from vllm.logger import init_logger
+from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
+                                               set_weight_attrs)
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.quantization.kernels.mixed_precision import (
+    BitBLASLinearKernel, MPLinearLayerConfig)
+from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
+    BITBLAS_SUPPORTED_NUM_BITS as GPTQ_BITBLAS_SUPPORTED_NUM_BITS)
+from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
+    BITBLAS_SUPPORTED_SYM as GPTQ_BITBLAS_SUPPORTED_SYM)
+from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
+    MINIMUM_BITBLAS_VERSION, bitblas_repeat_scales_on_all_ranks,
+    check_bitblas_supported, verify_bitblas_supported)
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
+from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
+                                           GroupQuantScaleParameter,
+                                           PackedColumnParameter,
+                                           PackedvLLMParameter,
+                                           RowvLLMParameter)
+from vllm.scalar_type import scalar_types
+
+logger = init_logger(__name__)
+
+
+class GPTQBitBLASConfig(QuantizationConfig):
+    """Config class for GPTQ BitBLAS"""
+
+    # (num_bits, is_sym) -> quant_type
+    TYPE_MAP = {
+        (4, True): scalar_types.uint4b8,
+        (8, True): scalar_types.uint8b128,
+    }
+
+    TORCH_DTYPE = torch.float16
+    GPTQ_CKPT_STORAGE_DTYPE = (
+        "int32"  # GPTQ Default Checkpoints use int32 as storage dtype
+    )
+    GPTQ_BITBLAS_STORAGE_DTYPE = "int8"  # BitBLAS uses int8 as storage dtype
+    TORCH_BITBLAS_STORAGE_DTYPE = getattr(torch, GPTQ_BITBLAS_STORAGE_DTYPE)
+    # "original" or "rescale" or "quantized",
+    # the gptq_bitblas prefer "quantized"
+    ZEROS_MODE = "quantized"
+
+    def __init__(
+        self,
+        weight_bits: int,
+        group_size: int,
+        desc_act: bool,
+        is_sym: bool,
+        quant_method: Optional[str],
+        lm_head_quantized: bool,
+    ) -> None:
+
+        try:
+            import bitblas
+            if bitblas.__version__ < MINIMUM_BITBLAS_VERSION:
+                raise ImportError(
+                    "bitblas version is wrong. Please "
+                    f"install bitblas>={MINIMUM_BITBLAS_VERSION}")
+        except ImportError as e:
+            bitblas_import_exception = e
+            raise ValueError(
+                "Trying to use the bitblas backend, but could not import"
+                f"with the following error: {bitblas_import_exception}. "
+                "Please install bitblas through the following command: "
+                f"`pip install bitblas>={MINIMUM_BITBLAS_VERSION}`"
+            ) from bitblas_import_exception
+
+        if desc_act and group_size == -1:
+            # In this case, act_order == True is the same as act_order == False
+            # (since we have only one group per output channel)
+            desc_act = False
+
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.desc_act = desc_act
+        self.is_sym = is_sym
+        self.quant_method = quant_method
+        self.lm_head_quantized = lm_head_quantized
+
+        # Verify
+        if self.weight_bits not in GPTQ_BITBLAS_SUPPORTED_NUM_BITS:
+            raise ValueError(
+                f"BitBLAS does not support weight_bits = {self.weight_bits}. "
+                f"Only weight_bits = {GPTQ_BITBLAS_SUPPORTED_NUM_BITS} "
+                "are supported.")
+
+        if self.is_sym not in GPTQ_BITBLAS_SUPPORTED_SYM:
+            raise ValueError(
+                f"BitBLAS does not support is_sym = {self.is_sym}. "
+                f"Only sym = {GPTQ_BITBLAS_SUPPORTED_SYM} are supported.")
+
+        self.storage_dtype = self.GPTQ_BITBLAS_STORAGE_DTYPE
+
+        storage_nbit = int("".join(c for c in self.GPTQ_CKPT_STORAGE_DTYPE
+                                   if c.isdigit()))
+
+        # 4 Bits packed into 32 bit datatype.
+        self.pack_factor = storage_nbit // weight_bits
+        self.nbits = weight_bits
+
+        # Zeros type for the quantized weights.
+        self.zeros_mode = self.ZEROS_MODE
+
+        if (weight_bits, is_sym) not in self.TYPE_MAP:
+            raise ValueError("Unsupported quantization config: "
+                             f"bits={weight_bits}, sym={is_sym}")
+
+        self.quant_type = self.TYPE_MAP[(weight_bits, is_sym)]
+
+    def __repr__(self) -> str:
+        return (f"GPTQBitBLASConfig(weight_bits={self.weight_bits}, "
+                f"group_size={self.group_size}, "
+                f"desc_act={self.desc_act})"
+                f"is_sym={self.is_sym}, "
+                f"quant_method={self.quant_method})")
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "gptq_bitblas"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.half, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 70
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return ["quantize_config.json"]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "GPTQBitBLASConfig":
+        weight_bits = cls.get_from_keys(config, ["bits"])
+        group_size = cls.get_from_keys(config, ["group_size"])
+        desc_act = cls.get_from_keys(config, ["desc_act"])
+        is_sym = cls.get_from_keys(config, ["sym"])
+        quant_method = cls.get_from_keys(config, ["quant_method"])
+        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
+                                                 default=False)
+        return cls(weight_bits, group_size, desc_act, is_sym, quant_method,
+                   lm_head_quantized)
+
+    @classmethod
+    def override_quantization_method(cls, hf_quant_cfg,
+                                     user_quant) -> Optional[str]:
+        can_convert = cls.is_gptq_bitblas_compatible(hf_quant_cfg)
+
+        is_valid_user_quant = (user_quant is None or user_quant == "bitblas"
+                               or user_quant == "gptq_bitblas")
+
+        if can_convert and is_valid_user_quant:
+            msg = ("The model is convertible to {} during runtime."
+                   " Using {} kernel.".format(cls.get_name(), cls.get_name()))
+            logger.info(msg)
+            return cls.get_name()
+
+        if can_convert and user_quant == "gptq":
+            logger.info("Detected that the model can run with gptq_bitblas"
+                        ", however you specified quantization=gptq explicitly,"
+                        " so forcing gptq. Use quantization=gptq_bitblas for"
+                        " faster inference")
+        return None
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["GPTQBitBLASLinearMethod"]:
+        if isinstance(layer, LinearBase) or (isinstance(layer, ParallelLMHead)
+                                             and self.lm_head_quantized):
+            return GPTQBitBLASLinearMethod(self)
+        return None
+
+    @property
+    def torch_storage_dtype(self) -> torch.dtype:
+        return self.TORCH_BITBLAS_STORAGE_DTYPE
+
+    @classmethod
+    def is_gptq_bitblas_compatible(cls, quant_config: Dict[str, Any]):
+        # Extract data from quant config.
+        num_bits = quant_config.get("bits")
+        group_size = quant_config.get("group_size")
+        sym = quant_config.get("sym")
+        desc_act = quant_config.get("desc_act")
+
+        # If we cannot find the info needed in the config, cannot convert.
+        if (num_bits is None or group_size is None or sym is None
+                or desc_act is None):
+            return False
+
+        if (num_bits, sym) not in cls.TYPE_MAP:
+            return False
+
+        # If the capability of the device is too low, cannot convert.
+        major, minor = torch.cuda.get_device_capability()
+        device_capability = major * 10 + minor
+        if device_capability < cls.get_min_capability():
+            return False
+
+        # Otherwise, can convert if model satisfies bitblas constraints.
+        return check_bitblas_supported(quant_type=cls.TYPE_MAP[(num_bits,
+                                                                sym)],
+                                       group_size=group_size)
+
+
+class GPTQBitBLASLinearMethod(LinearMethodBase):
+    """Linear method for GPTQ BitBLAS.
+
+    Args:
+        quant_config: The GPTQ BitBLAS quantization config.
+    """
+
+    kernel_type = BitBLASLinearKernel
+    _kernel_backends_being_used: Set[str] = set()
+
+    def __init__(self, quant_config: GPTQBitBLASConfig) -> None:
+        self.quant_config = quant_config
+        # Verify supported on platform.
+        verify_bitblas_supported(quant_type=self.quant_config.quant_type,
+                                 group_size=self.quant_config.group_size)
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ) -> None:
+        """Creates quantized weights for use in linear operations.
+
+        The function initializes and returns a dictionary containing 
+        quantized weights, scales, and zeros
+        for performing quantized matrix multiplication operations.
+
+        Args:
+            input_size_per_partition: The size of the input partition.
+            output_partition_sizes: The size of the output partition.
+            input_size: The total size of the input (unused).
+            output_size: The total size of the output (unused).
+            params_dtype: 
+                The data type of the parameters (expected to be torch.float16).
+
+        Returns:
+            A dictionary containing the quantized weights ('qweight'), 
+            scales ('scales'), and zeros ('zeros').
+
+        Raises:
+            ValueError: If `params_dtype` is not `torch.float16` or 
+            if the input size per partition is not divisible by the 
+            group size in `quant_config`.
+        """
+        if params_dtype != torch.float16:
+            raise ValueError("Parameter data type must be torch.float16, "
+                             f"but got {params_dtype}")
+
+        # Normalize group_size
+        if self.quant_config.group_size != -1:
+            group_size = self.quant_config.group_size
+        else:
+            group_size = input_size
+
+        if input_size_per_partition % group_size != 0:
+            raise ValueError(
+                f"Input size per partition ({input_size_per_partition}) must "
+                f"be divisible by group size ({self.quant_config.group_size})."
+            )
+
+        kernel_type = self.kernel_type
+        # Validate output_size_per_partition
+        output_size_per_partition = sum(output_partition_sizes)
+
+        is_row_parallel = input_size != input_size_per_partition
+        weight_loader = extra_weight_attrs.get("weight_loader")
+
+        mp_linear_kernel_config = MPLinearLayerConfig(
+            full_weight_shape=(input_size, output_size),
+            partition_weight_shape=\
+                (input_size_per_partition, output_size_per_partition),
+            weight_type=self.quant_config.quant_type,
+            act_type=params_dtype,
+            group_size=self.quant_config.group_size,
+            zero_points=False,
+            has_g_idx=self.quant_config.desc_act
+        )
+
+        if kernel_type.__name__ not in self._kernel_backends_being_used:
+            logger.info("Using %s for GPTQBitBLASLinearMethod",
+                        kernel_type.__name__)
+            self._kernel_backends_being_used.add(kernel_type.__name__)
+
+        # Normalize group_size
+        if self.quant_config.group_size != -1:
+            group_size = self.quant_config.group_size
+        else:
+            group_size = input_size
+
+        # Determine sharding
+        if bitblas_repeat_scales_on_all_ranks(self.quant_config.desc_act,
+                                              self.quant_config.group_size,
+                                              is_row_parallel):
+            # By setting scale_dim == None, weight_loader will
+            # repeat the scales on each GPU in TP>1 case.
+            scales_and_zp_input_dim = None
+            scales_and_zp_size = input_size // group_size
+        else:
+            # By setting scale_dim == 0, weight_loader will
+            # shard the scales in TP>1 case.
+            scales_and_zp_input_dim = 0
+            scales_and_zp_size = input_size_per_partition // group_size
+
+        # Init buffers
+        # Quantized weights
+        qweight = PackedvLLMParameter(
+            data=torch.empty(
+                input_size_per_partition // self.quant_config.pack_factor,
+                output_size_per_partition,
+                dtype=torch.int32,
+            ),
+            input_dim=0,
+            output_dim=1,
+            packed_dim=0,
+            packed_factor=self.quant_config.pack_factor,
+            weight_loader=weight_loader)
+
+        # Activation order
+        # Ignore warning from fused linear layers such as QKVParallelLinear.
+        g_idx = RowvLLMParameter(data=torch.empty(
+            input_size_per_partition,
+            dtype=torch.int32,
+        ),
+                                 input_dim=0,
+                                 weight_loader=weight_loader)
+
+        # Scales
+        scales = Parameter(
+            torch.empty(
+                scales_and_zp_size,
+                output_size_per_partition,
+                dtype=params_dtype,
+            ),
+            requires_grad=False,
+        )
+        set_weight_attrs(
+            scales,
+            {
+                **extra_weight_attrs,
+                "input_dim": scales_and_zp_input_dim,
+                "output_dim": 1,
+            },
+        )
+
+        # Quantized zero-points
+        qzeros_args = {
+            "data":
+            torch.empty(
+                scales_and_zp_size,
+                output_size_per_partition // self.quant_config.pack_factor,
+                dtype=torch.int32,
+            ),
+            "weight_loader":
+            weight_loader
+        }
+        weight_scale_args = {
+            "data":
+            torch.empty(
+                scales_and_zp_size,
+                output_size_per_partition,
+                dtype=params_dtype,
+            ),
+            "weight_loader":
+            weight_loader
+        }
+
+        if scales_and_zp_input_dim is None:
+            scales = ChannelQuantScaleParameter(output_dim=1,
+                                                **weight_scale_args)
+            qzeros = PackedColumnParameter(
+                output_dim=1,
+                packed_dim=1,
+                packed_factor=self.quant_config.pack_factor,
+                **qzeros_args)
+
+        else:
+            scales = GroupQuantScaleParameter(output_dim=1,
+                                              input_dim=0,
+                                              **weight_scale_args)
+            qzeros = PackedvLLMParameter(
+                input_dim=0,
+                output_dim=1,
+                packed_dim=1,
+                packed_factor=self.quant_config.pack_factor,
+                **qzeros_args)
+
+        layer.register_parameter("qweight", qweight)
+        layer.register_parameter("g_idx", g_idx)
+        layer.register_parameter("scales", scales)
+        layer.register_parameter("qzeros", qzeros)
+
+        self.kernel = kernel_type(
+            mp_linear_kernel_config,
+            w_q_param_name="qweight",
+            w_s_param_name="scales",
+            w_zp_param_name="qzeros",
+            w_gidx_param_name="g_idx",
+            bitblas_quant_config=self.quant_config,
+        )
+
+        # Initialize or retrieve the BitBLAS matrix multiplication operator.
+        self.kernel.configure_bitblas_matmul(
+            input_size_per_partition,
+            output_size_per_partition,
+            params_dtype=params_dtype,
+            bias=False,
+        )
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        self.kernel.process_weights_after_loading(layer)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        out = self.kernel.apply_gptq_bitblas_linear(layer, x)
+        if bias is not None:
+            out.add_(bias)
+        return out

vllm/model_executor/layers/quantization/kernels/mixed_precision/__init__.py

@@ -5,6 +5,8 @@ from typing import List, Optional, Type
 import vllm.envs as envs
 from vllm.model_executor.layers.quantization.kernels.mixed_precision.allspark import (  # noqa: E501
     AllSparkLinearKernel)
+from vllm.model_executor.layers.quantization.kernels.mixed_precision.bitblas import (  # noqa: E501
+    BitBLASLinearKernel)
 from vllm.model_executor.layers.quantization.kernels.mixed_precision.exllama import (  # noqa: E501
     ExllamaLinearKernel)
 from vllm.model_executor.layers.quantization.kernels.mixed_precision.machete import (  # noqa: E501
@@ -20,6 +22,7 @@ _POSSIBLE_KERNELS: List[Type[MPLinearKernel]] = [
     MacheteLinearKernel,
     AllSparkLinearKernel,
     MarlinLinearKernel,
+    BitBLASLinearKernel,
     ExllamaLinearKernel,
 ]
 
@@ -76,4 +79,4 @@ def choose_mp_linear_kernel(
     raise ValueError(
         "Failed to find a kernel that can implement the "\
         "WNA16 linear layer. Reasons: \n"
-        + '\n'.join(failure_reasons))
+        + '\n'.join(failure_reasons))
\ No newline at end of file

vllm/model_executor/layers/quantization/kernels/mixed_precision/bitblas.py

+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Dict, List, Optional, Tuple
+
+import torch
+
+from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.layers.quantization.utils import replace_parameter
+from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
+    BITBLAS_OPTIMIZE_FEATURES, BITBLAS_SUPPORTED_GROUP_SIZES,
+    MINIMUM_BITBLAS_VERSION, bitblas_make_empty_g_idx, bitblas_sort_g_idx,
+    check_bitblas_supports_shape, query_bitblas_supported_quant_types,
+    unpack_gptq_qweight, unpack_gptq_qzeros)
+
+from .MPLinearKernel import MPLinearKernel, MPLinearLayerConfig
+
+logger = init_logger(__name__)
+
+
+class BitBLASLinearKernel(MPLinearKernel):
+
+    OPT_FEATURES: List[int] = BITBLAS_OPTIMIZE_FEATURES
+    ENABLE_TUNING: bool = True
+    MATMUL_LAYOUT: str = "nt"
+    BITBLAS_DTYPES: Dict[torch.dtype, str] = {
+        torch.float32: "float32",
+        torch.float16: "float16",
+        torch.bfloat16: "bfloat16",
+        torch.half: "float16",
+        torch.int8: "int8",
+    }
+    bitblas_matmul: object = None
+
+    def __init__(
+        self,
+        c: MPLinearLayerConfig,
+        w_q_param_name: str,
+        w_s_param_name: str,
+        w_zp_param_name: Optional[str] = None,
+        w_gidx_param_name: Optional[str] = None,
+        bitblas_quant_config: Optional[QuantizationConfig] = None,
+    ):
+        self.quant_config = bitblas_quant_config
+        super().__init__(c, w_q_param_name, w_s_param_name, w_zp_param_name,
+                         w_gidx_param_name)
+
+    def repack_bitblas_from_gptq(
+        self,
+        b_q_weight: torch.Tensor,
+        scales: torch.Tensor,
+        qzeros: Optional[torch.Tensor] = None,
+    ):
+        from bitblas.quantization.utils import general_compress
+        assert self.bitblas_matmul is not None, "bitblas_matmul is None"
+
+        quant_config = self.quant_config
+        # qweight in gptq old quant linear stored with
+        # (outfeatures, infeatures), should be transposed.
+        qweight = b_q_weight.T.contiguous().view(
+            quant_config.torch_storage_dtype)  # type: ignore[union-attr]
+        intweight = unpack_gptq_qweight(
+            qweight,
+            quant_config.weight_bits).contiguous()  # type: ignore[union-attr]
+        if self.bitblas_matmul.weight_transform is not None:  # type: ignore[attr-defined]
+            qweight = self.bitblas_matmul.weight_transform(  # type: ignore[attr-defined]
+                intweight.cpu()).cuda()
+        # scales in gptq old quant linear stored with
+        # (infeatures // group_size, outfeatures), should be transposed.
+        scales = scales.T.contiguous()
+
+        if qzeros is None:
+            return qweight, scales, None
+
+        # qzeros should be de-quantized to int zeros.
+        weight_bits = quant_config.weight_bits  # type: ignore[union-attr]
+        intzeros = unpack_gptq_qzeros(qzeros, weight_bits).T.contiguous()
+        zeros: Optional[torch.Tensor] = None
+        zeros_mode = self.bitblas_matmul.config.zeros_mode  # type: ignore[attr-defined]
+        if zeros_mode == "original":
+            zeros = intzeros.to(torch.float16).contiguous()
+        elif zeros_mode == "rescale":
+            assert zeros is not None, "zeros should not be None"
+            zeros[:, :] = intzeros.to(torch.float16)[:, :] * scales[:, :]
+        elif zeros_mode == "quantized":
+            zeros = (
+                torch.Tensor(
+                    general_compress(
+                        intzeros.T.contiguous().cpu().numpy(),
+                        weight_bits,
+                    )).to(qweight.device).
+                to(quant_config.torch_storage_dtype  # type: ignore[union-attr]
+                   ).contiguous())
+        else:
+            raise ValueError("Unsupported zeros type: {}".format(zeros_mode))
+
+        return qweight, scales, zeros
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 70
+
+    @classmethod
+    def can_implement(cls,
+                      c: MPLinearLayerConfig) -> Tuple[bool, Optional[str]]:
+
+        is_bitblas_installed = True
+
+        try:
+            import bitblas
+            if bitblas.__version__ < MINIMUM_BITBLAS_VERSION:
+                raise ImportError(
+                    "bitblas version is wrong. Please "
+                    f"install bitblas>={MINIMUM_BITBLAS_VERSION}")
+        except ImportError:
+            is_bitblas_installed = False
+
+        if not is_bitblas_installed:
+            return False, "bitblas is not installed. Please install bitblas "\
+                          "by running `pip install bitblas>="\
+                           f"{MINIMUM_BITBLAS_VERSION}`"
+
+        quant_types = query_bitblas_supported_quant_types(c.zero_points)
+        if c.weight_type not in quant_types:
+            return False, (f"Quant type ({c.weight_type}) not supported by"
+                           f"  BitBLAS, supported types are: {quant_types}")
+
+        if c.group_size not in BITBLAS_SUPPORTED_GROUP_SIZES:
+            return False, (f"Group size ({c.group_size}) not supported by "
+                           "BitBLAS, supported group sizes are: "
+                           f"{BITBLAS_SUPPORTED_GROUP_SIZES}")
+
+        return check_bitblas_supports_shape(
+            c.partition_weight_shape[1],  # out_features
+            c.partition_weight_shape[0],  # in_features
+            c.full_weight_shape[0],  # in_features
+            c.group_size)
+
+    # note assumes that
+    #  `weight_packed` is: {input_dim = 0, output_dim = 1, packed_dim = 0}
+    #  `weight_scale` is: {input_dim = 0, output_dim = 1}
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        device = getattr(layer, self.w_q_name).device
+        c = self.config
+        quant_config = self.quant_config
+
+        # Default names since bitblas requires empty parameters for these,
+        # TODO: remove this requirement from bitblas (allow optional tensors)
+        if self.w_gidx_name is None:
+            self.w_gidx_name = "g_idx"
+        if self.w_zp_name is None:
+            self.w_zp_name = "qzeros"
+
+        if c.has_g_idx:
+            g_idx, g_idx_sort_indices = bitblas_sort_g_idx(
+                getattr(layer, self.w_gidx_name))
+            self._transform_param(layer, self.w_gidx_name, lambda _: g_idx)
+            layer.g_idx_sort_indices = g_idx_sort_indices
+        else:
+            setattr(layer, self.w_gidx_name, bitblas_make_empty_g_idx(device))
+            layer.g_idx_sort_indices = bitblas_make_empty_g_idx(device)
+
+        if c.zero_points:
+            raise NotImplementedError("Zero points not supported by BitBLAS")
+        else:
+            setattr(layer, self.w_zp_name, bitblas_make_empty_g_idx(device))
+
+        # Repack weights
+        bitblas_qweight, bitblas_scales, bitblas_qzeros = (
+            self.repack_bitblas_from_gptq(
+                layer.qweight,
+                layer.scales,
+                None if quant_config.is_sym else  # type: ignore[union-attr]
+                layer.qzeros,  # type: ignore[union-attr]
+            ))
+        replace_parameter(layer, self.w_q_name, bitblas_qweight)
+        replace_parameter(layer, self.w_s_name, bitblas_scales)
+        if bitblas_qzeros is not None:
+            replace_parameter(layer, self.w_zp_name, bitblas_qzeros)
+
+    def configure_bitblas_matmul(
+        self,
+        infeatures: int,
+        outfeatures: int,
+        params_dtype: torch.dtype,
+        bias: bool,
+    ) -> None:
+        enable_tuning = self.ENABLE_TUNING
+        layout = self.MATMUL_LAYOUT
+        bits = self.quant_config.weight_bits  # type: ignore[union-attr]
+        self._configure_bitblas_matmul(
+            infeatures,
+            outfeatures,
+            params_dtype,
+            enable_tuning,
+            bias,
+            layout,
+            bits,
+        )
+
+    def _configure_bitblas_matmul(
+        self,
+        infeatures,
+        outfeatures,
+        params_dtype,
+        enable_tuning,
+        bias,
+        layout,
+        bits,
+    ):
+        from bitblas import MatmulConfig
+        bitblas_dtype = self.BITBLAS_DTYPES[params_dtype]
+        quant_config = self.quant_config
+        with_scaling = False
+        with_zeros = False
+        group_size = quant_config.group_size  # type: ignore[union-attr]
+        zeros_mode = quant_config.zeros_mode  # type: ignore[union-attr]
+        if quant_config.quant_method == "gptq":  # type: ignore[union-attr]
+            with_scaling = True
+            with_zeros = True
+            W_dtype = f"uint{bits}"
+            if quant_config.is_sym:  # type: ignore[union-attr]
+                with_zeros = False
+                W_dtype = f"int{bits}"
+        else:
+            raise ValueError(
+                f"Unsupported quant_method {quant_config.quant_method}"  # type: ignore[union-attr]
+            )  # type: ignore[union-attr]
+
+        matmul_config = MatmulConfig(
+            M=self.OPT_FEATURES,
+            N=outfeatures,
+            K=infeatures,
+            A_dtype=bitblas_dtype,
+            W_dtype=W_dtype,
+            out_dtype=bitblas_dtype,
+            accum_dtype="int32" if bitblas_dtype == "int8" else bitblas_dtype,
+            storage_dtype=quant_config.  # type: ignore[union-attr]
+            storage_dtype,  # type: ignore[union-attr]
+            with_scaling=with_scaling,
+            with_zeros=with_zeros,
+            group_size=group_size,
+            with_bias=bias,
+            layout=layout,
+            zeros_mode=zeros_mode,
+        )
+        self.bitblas_matmul = self._get_or_create_bitblas_operator(
+            matmul_config, enable_tuning)
+
+    def _get_or_create_bitblas_operator(self, config, enable_tuning):
+        from bitblas import Matmul, auto_detect_nvidia_target
+        from bitblas.cache import get_database_path, global_operator_cache
+        BITBLAS_DATABASE_PATH = get_database_path()
+        BITBLAS_TARGET = auto_detect_nvidia_target()
+
+        if global_operator_cache.size() == 0:
+            global_operator_cache.load_from_database(BITBLAS_DATABASE_PATH,
+                                                     BITBLAS_TARGET)
+
+        bitblas_matmul = global_operator_cache.get(config)
+        if bitblas_matmul is None:
+            bitblas_matmul = Matmul(config,
+                                    target=BITBLAS_TARGET,
+                                    enable_tuning=False)
+            if enable_tuning:
+                bitblas_matmul.hardware_aware_finetune(topk=20)
+                global_operator_cache.add(config, bitblas_matmul)
+                global_operator_cache.save_into_database(
+                    BITBLAS_DATABASE_PATH, BITBLAS_TARGET)
+                TUNING_MESSAGE = (
+                    f"BitBLAS Operator {config} tuned and saved to database.")
+                logger.info(TUNING_MESSAGE)
+            else:
+                _message = f"BitBLAS Operator {config} created without tuning. "
+                logger.info(_message)
+        else:
+            _message = f"BitBLAS Operator {config} retrieved from cache."
+            logger.info(_message)
+        return bitblas_matmul
+
+    def apply_gptq_bitblas_linear(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+    ) -> torch.Tensor:
+        output_size_per_partition = self.config.partition_weight_shape[1]
+        out_shape = x.shape[:-1] + (output_size_per_partition, )
+        args = [x, layer.qweight, layer.scales]
+        if self.bitblas_matmul.config.with_zeros:  # type: ignore[attr-defined]
+            args.append(layer.qzeros)
+        output = self.bitblas_matmul(*args)  # type: ignore[operator]
+        return output.view(out_shape)
+
+    def apply_weights(self, layer, x, bias=None):
+        NOT_IMPLEMENT_MESSAGE = (
+            f"{self.__class__.__name__}.apply_weights is not implemented. "
+            "Please use BitBLASLinearKernel.apply_gptq_bitblas_linear instead")
+        raise NotImplementedError(NOT_IMPLEMENT_MESSAGE)

vllm/model_executor/layers/quantization/utils/bitblas_utils.py

+# SPDX-License-Identifier: Apache-2.0
+from typing import Optional, Tuple
+
+import torch
+
+from vllm.platforms import current_platform
+from vllm.scalar_type import ScalarType, scalar_types
+
+MINIMUM_BITBLAS_VERSION = "0.1.0"
+
+BITBLAS_MIN_WEIGHT_SIZE_N = 16
+BITBLAS_MIN_WEIGHT_SIZE_K = 16
+GPTQ_BITBLAS_MAX_PARALLEL = 16
+
+BITBLAS_SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128]
+
+# For dynamic shape code generation
+BITBLAS_OPTIMIZE_FEATURES = [1, 16, 32, 64, 128, 256, 512, 1024]
+# If want to enable high performance for contiguous batching
+# Please use the following values
+BITBLAS_OPTIMIZE_FEATURES_CONTIGUOUS = [16, 32, 64, 128, 256, 512, 1024]
+
+BITBLAS_SUPPORTED_NUM_BITS = [1, 2, 4, 8]
+BITBLAS_SUPPORTED_SYM = [False, True]
+
+
+# Determines the supported quantization types for BitBLAS based on the
+# device's capability and whether zero-point (zp) is used.
+def query_bitblas_supported_quant_types(has_zp: bool,
+                                        device_capability: Optional[int] = None
+                                        ):
+    if device_capability is None:
+        capability_tuple = current_platform.get_device_capability()
+        device_capability = (-1 if capability_tuple is None else
+                             capability_tuple.to_int())
+
+    if device_capability < 70:
+        return []
+
+    if has_zp:
+        # AWQ style, unsigned + runtime zero-point
+        return [scalar_types.uint4, scalar_types.uint8]
+    else:
+        # GPTQ style, unsigned + symmetric bias
+        # TODO: once fp8_bitblas is merged into "gptq_bitblas" we should be able
+        #  to add `scalar_types.float8_e4m3fn` here
+        return [scalar_types.uint4b8, scalar_types.uint8b128]
+
+
+def _check_bitblas_supported(
+        quant_type: ScalarType,
+        group_size: Optional[int],
+        has_zp: bool,
+        device_capability: Optional[int] = None) -> Tuple[bool, Optional[str]]:
+
+    if device_capability is None:
+        capability_tuple = current_platform.get_device_capability()
+        device_capability = (-1 if capability_tuple is None else
+                             capability_tuple.to_int())
+
+    supported_types = query_bitblas_supported_quant_types(
+        has_zp, device_capability)
+
+    if quant_type not in supported_types:
+        return (False, f"BitBLAS does not support weight_bits = {quant_type}. "
+                f"Only types = {supported_types} "
+                f"are supported (for group_size = {group_size}, "
+                f"device_capability = {device_capability}, zp = {has_zp}).")
+    if (group_size is None or group_size not in BITBLAS_SUPPORTED_GROUP_SIZES):
+        return (False, f"BitBLAS does not support group_size = {group_size}. "
+                f"Only group_sizes = {BITBLAS_SUPPORTED_GROUP_SIZES} "
+                "are supported.")
+
+    return True, None
+
+
+def check_bitblas_supported(quant_type: ScalarType,
+                            group_size: int,
+                            has_zp: bool = False,
+                            device_capability: Optional[int] = None) -> bool:
+    cond, _ = _check_bitblas_supported(quant_type, group_size, has_zp,
+                                       device_capability)
+    return cond
+
+
+def verify_bitblas_supported(quant_type: ScalarType,
+                             group_size: int,
+                             has_zp: bool = False) -> None:
+    cond, err_msg = _check_bitblas_supported(quant_type, group_size, has_zp)
+    if not cond:
+        assert err_msg is not None
+        raise ValueError(err_msg)
+
+
+def verify_bitblas_supports_shape(output_size_per_partition: int,
+                                  input_size_per_partition: int,
+                                  input_size: int, group_size: int) -> None:
+
+    # Validate output_size_per_partition
+    if output_size_per_partition % BITBLAS_MIN_WEIGHT_SIZE_N != 0:
+        raise ValueError(f"Weight output_size_per_partition = "
+                         f"{output_size_per_partition} is not divisible by "
+                         f" min_thread_n = {BITBLAS_MIN_WEIGHT_SIZE_N}. "
+                         "Consider reducing tensor_parallel_size or running "
+                         "with --quantization gptq.")
+
+    # Validate input_size_per_partition
+    if input_size_per_partition % BITBLAS_MIN_WEIGHT_SIZE_K != 0:
+        raise ValueError(f"Weight input_size_per_partition = "
+                         f"{input_size_per_partition} is not divisible "
+                         f"by min_thread_k = {BITBLAS_MIN_WEIGHT_SIZE_K}. "
+                         "Consider reducing tensor_parallel_size or running "
+                         "with --quantization gptq.")
+
+    if (group_size < input_size
+            and input_size_per_partition % group_size != 0):
+        raise ValueError(
+            f"Weight input_size_per_partition = {input_size_per_partition}"
+            f" is not divisible by group_size = {group_size}."
+            "Consider reducing tensor_parallel_size or running "
+            "with --quantization gptq.")
+
+
+def check_bitblas_supports_shape(output_size_per_partition: int,
+                                input_size_per_partition: int,
+                                input_size: int, group_size: int) \
+                                    -> Tuple[bool, Optional[str]]:
+    try:
+        verify_bitblas_supports_shape(output_size_per_partition,
+                                      input_size_per_partition, input_size,
+                                      group_size)
+    except ValueError as e:
+        return False, e.__str__()
+    return True, None
+
+
+def bitblas_is_k_full(act_order: bool, is_row_parallel: bool) -> bool:
+    return (not act_order) or (act_order and not is_row_parallel)
+
+
+def bitblas_repeat_scales_on_all_ranks(act_order: bool, group_size: int,
+                                       is_row_parallel: bool) -> bool:
+    # Need to repeat scales on every rank if act_ordering or
+    # channelwise and RowParallelLinear
+    is_channelwise = group_size == -1
+    return act_order or (is_channelwise and is_row_parallel)
+
+
+def bitblas_make_empty_g_idx(device: torch.device) -> torch.Tensor:
+    return torch.nn.Parameter(torch.empty(0, dtype=torch.int, device=device),
+                              requires_grad=False)
+
+
+def bitblas_make_empty_zp(device: torch.device) -> torch.Tensor:
+    return torch.nn.Parameter(torch.empty(0, dtype=torch.int, device=device),
+                              requires_grad=False)
+
+
+def bitblas_sort_g_idx(
+        g_idx: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    g_idx_sort_indices = torch.argsort(g_idx).to(torch.int)
+    return g_idx[g_idx_sort_indices], g_idx_sort_indices
+
+
+def unpack_gptq_qzeros(qzeros, bits, is_gptq_v2=False) -> torch.Tensor:
+    qzeros = qzeros.view(torch.int32)
+    elems_per_int32 = 32 // bits
+    unpacked_zeros = torch.zeros(
+        (qzeros.shape[0], qzeros.shape[1] * elems_per_int32),
+        dtype=torch.int8,
+        device=qzeros.device,
+        requires_grad=False,
+    )
+
+    for col in range(unpacked_zeros.shape[1]):
+        i = col % elems_per_int32
+        unpacked_zeros[:, col] = (qzeros[:, col // elems_per_int32] >>
+                                  (bits * i)) & 0xF
+    if not is_gptq_v2:
+        return unpacked_zeros + 1
+    return unpacked_zeros
+
+
+def unpack_gptq_qweight(qweight, bits):
+    qweight = qweight.view(torch.int8)
+    elems_per_int8 = 8 // bits
+    unpacked_weight = torch.zeros(
+        (qweight.shape[0], qweight.shape[1] * elems_per_int8),
+        dtype=torch.int8,
+        device=qweight.device,
+        requires_grad=False,
+    )
+    for col in range(unpacked_weight.shape[1]):
+        i = col % elems_per_int8
+        unpacked_weight[:, col] = (qweight[:, col // elems_per_int8] >>
+                                   (bits * i))
+
+    return torch.bitwise_and(unpacked_weight, 2**bits - 1)

vllm/model_executor/parameter.py

@@ -282,10 +282,12 @@ class PackedColumnParameter(_ColumnvLLMParameter):
                  packed_factor: Union[int, Fraction],
                  packed_dim: int,
                  marlin_tile_size: Optional[int] = None,
+                 bitblas_tile_size: Optional[int] = None,
                  **kwargs):
         self._packed_factor = packed_factor
         self._packed_dim = packed_dim
         self._marlin_tile_size = marlin_tile_size
+        self._bitblas_tile_size = bitblas_tile_size
         super().__init__(**kwargs)
 
     @property
@@ -300,12 +302,17 @@ class PackedColumnParameter(_ColumnvLLMParameter):
     def marlin_tile_size(self):
         return self._marlin_tile_size
 
+    @property
+    def bitblas_tile_size(self):
+        return self._bitblas_tile_size
+
     def adjust_shard_indexes_for_packing(self, shard_size, shard_offset):
         return _adjust_shard_indexes_for_packing(
             shard_size=shard_size,
             shard_offset=shard_offset,
             packed_factor=self.packed_factor,
-            marlin_tile_size=self.marlin_tile_size)
+            marlin_tile_size=self.marlin_tile_size,
+            bitblas_tile_size=self.bitblas_tile_size)
 
 
 class PackedvLLMParameter(ModelWeightParameter):
@@ -323,10 +330,12 @@ class PackedvLLMParameter(ModelWeightParameter):
                  packed_factor: Union[int, Fraction],
                  packed_dim: int,
                  marlin_tile_size: Optional[int] = None,
+                 bitblas_tile_size: Optional[int] = None,
                  **kwargs):
         self._packed_factor = packed_factor
         self._packed_dim = packed_dim
         self._marlin_tile_size = marlin_tile_size
+        self._bitblas_tile_size = bitblas_tile_size
         super().__init__(**kwargs)
 
     @property
@@ -341,12 +350,17 @@ class PackedvLLMParameter(ModelWeightParameter):
     def marlin_tile_size(self):
         return self._marlin_tile_size
 
+    @property
+    def bitblas_tile_size(self):
+        return self._bitblas_tile_size
+
     def adjust_shard_indexes_for_packing(self, shard_size, shard_offset):
         return _adjust_shard_indexes_for_packing(
             shard_size=shard_size,
             shard_offset=shard_offset,
             packed_factor=self.packed_factor,
-            marlin_tile_size=self.marlin_tile_size)
+            marlin_tile_size=self.marlin_tile_size,
+            bitblas_tile_size=self.bitblas_tile_size)
 
 
 class BlockQuantScaleParameter(_ColumnvLLMParameter, RowvLLMParameter):
@@ -421,8 +435,13 @@ def _adjust_shard_indexes_for_marlin(shard_size, shard_offset,
     return shard_size * marlin_tile_size, shard_offset * marlin_tile_size
 
 
+def _adjust_shard_indexes_for_bitblas(shard_size, shard_offset,
+                                      bitblas_tile_size):
+    return shard_size // bitblas_tile_size, shard_offset // bitblas_tile_size
+
+
 def _adjust_shard_indexes_for_packing(shard_size, shard_offset, packed_factor,
-                                      marlin_tile_size):
+                                      marlin_tile_size, bitblas_tile_size):
     shard_size = shard_size // packed_factor
     shard_offset = shard_offset // packed_factor
     if marlin_tile_size is not None:
@@ -430,4 +449,10 @@ def _adjust_shard_indexes_for_packing(shard_size, shard_offset, packed_factor,
             shard_size=shard_size,
             shard_offset=shard_offset,
             marlin_tile_size=marlin_tile_size)
-    return shard_size, shard_offset
+    elif bitblas_tile_size is not None:
+        return _adjust_shard_indexes_for_bitblas(
+            shard_size=shard_size,
+            shard_offset=shard_offset,
+            bitblas_tile_size=bitblas_tile_size)
+
+    return shard_size, shard_offset
\ No newline at end of file