Merge tag 'v0.6.3.post1' into v0.6.3.post1-dev

vllm/model_executor/layers/quantization/awq_marlin.py

-from typing import Any, Dict, List, Optional
+from typing import Any, Callable, Dict, List, Optional
 
 import torch
+from torch.nn import Parameter
 
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
-from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.fused_moe.layer import (
+    FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
+from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
+                                               set_weight_attrs)
 from vllm.model_executor.layers.quantization.base_config import (
-    QuantizationConfig)
+    QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.utils import replace_parameter
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
     apply_awq_marlin_linear, awq_to_marlin_zero_points, check_marlin_supported,
-    marlin_make_empty_g_idx, marlin_make_workspace, marlin_permute_scales,
+    marlin_make_empty_g_idx, marlin_make_workspace, marlin_moe_permute_scales,
+    marlin_permute_scales, moe_awq_to_marlin_zero_points,
     verify_marlin_supported, verify_marlin_supports_shape)
 from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.parameter import (GroupQuantScaleParameter,
                                            PackedvLLMParameter)
+from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
 
 logger = init_logger(__name__)
@@ -35,12 +41,13 @@ class AWQMarlinConfig(QuantizationConfig):
         self.group_size = group_size
         self.has_zp = has_zp
         self.lm_head_quantized = lm_head_quantized
+        self.weight_bits = weight_bits
 
-        if weight_bits not in self.TYPE_MAP:
-            raise ValueError(f"Unsupported num_bits = {weight_bits}. "
+        if self.weight_bits not in self.TYPE_MAP:
+            raise ValueError(f"Unsupported num_bits = {self.weight_bits}. "
                              f"Supported num_bits = {self.TYPE_MAP.keys()}")
 
-        self.quant_type = self.TYPE_MAP[weight_bits]
+        self.quant_type = self.TYPE_MAP[self.weight_bits]
 
         verify_marlin_supported(self.quant_type,
                                 group_size=self.group_size,
@@ -98,10 +105,12 @@ class AWQMarlinConfig(QuantizationConfig):
         return None
 
     def get_quant_method(self, layer: torch.nn.Module,
-                         prefix: str) -> Optional["AWQMarlinLinearMethod"]:
+                         prefix: str) -> Optional["QuantizeMethodBase"]:
         if (isinstance(layer, LinearBase) or
             (isinstance(layer, ParallelLMHead) and self.lm_head_quantized)):
             return AWQMarlinLinearMethod(self)
+        elif isinstance(layer, FusedMoE):
+            return AWQMoEMethod(self)
         return None
 
     def get_scaled_act_names(self) -> List[str]:
@@ -115,6 +124,9 @@ class AWQMarlinConfig(QuantizationConfig):
         group_size = quant_config.get("group_size")
         has_zp = quant_config.get("zero_point")
 
+        if not current_platform.is_cuda():
+            return False
+
         if quant_method != "awq":
             return False
 
@@ -271,4 +283,182 @@ class AWQMarlinLinearMethod(LinearMethodBase):
             quant_type=self.quant_config.quant_type,
             output_size_per_partition=layer.output_size_per_partition,
             input_size_per_partition=layer.input_size_per_partition,
-            bias=bias)
\ No newline at end of file
+            bias=bias)
+
+
+class AWQMoEMethod(FusedMoEMethodBase):
+
+    def __init__(self, quant_config: AWQMarlinConfig):
+        self.quant_config = quant_config
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+        extra_weight_attrs.update({
+            "is_transposed":
+            True,
+            "quant_method":
+            FusedMoeWeightScaleSupported.GROUP.value,
+        })
+
+        w13_qweight = Parameter(torch.empty(num_experts,
+                                            hidden_size,
+                                            2 * intermediate_size //
+                                            self.quant_config.pack_factor,
+                                            dtype=torch.int32),
+                                requires_grad=False)
+        layer.register_parameter("w13_qweight", w13_qweight)
+        set_weight_attrs(w13_qweight, extra_weight_attrs)
+
+        w2_qweight = Parameter(torch.empty(num_experts,
+                                           intermediate_size,
+                                           hidden_size //
+                                           self.quant_config.pack_factor,
+                                           dtype=torch.int32),
+                               requires_grad=False)
+        layer.register_parameter("w2_qweight", w2_qweight)
+        set_weight_attrs(w2_qweight, extra_weight_attrs)
+
+        num_groups_w13 = hidden_size // self.quant_config.group_size
+        num_groups_w2 = intermediate_size // self.quant_config.group_size
+
+        # WEIGHT_SCALES
+        # Allocate 2 scales for w1 and w3 respectively.
+        w13_scales = Parameter(torch.empty(num_experts,
+                                           num_groups_w13,
+                                           intermediate_size * 2,
+                                           dtype=params_dtype),
+                               requires_grad=False)
+        layer.register_parameter("w13_scales", w13_scales)
+        set_weight_attrs(w13_scales, extra_weight_attrs)
+
+        w2_scales = Parameter(torch.empty(num_experts,
+                                          num_groups_w2,
+                                          hidden_size,
+                                          dtype=params_dtype),
+                              requires_grad=False)
+        layer.register_parameter("w2_scales", w2_scales)
+        set_weight_attrs(w2_scales, extra_weight_attrs)
+
+        # WEIGHT_ZERO_POINT
+        # Allocate 2 zero points for w1 and w3 respectively.
+        w13_qzeros = Parameter(torch.empty(num_experts,
+                                           num_groups_w13,
+                                           2 * intermediate_size //
+                                           self.quant_config.pack_factor,
+                                           dtype=torch.int32),
+                               requires_grad=False)
+        layer.register_parameter("w13_qzeros", w13_qzeros)
+        set_weight_attrs(w13_qzeros, extra_weight_attrs)
+
+        w2_qzeros = Parameter(torch.empty(num_experts,
+                                          num_groups_w2,
+                                          hidden_size //
+                                          self.quant_config.pack_factor,
+                                          dtype=torch.int32),
+                              requires_grad=False)
+        layer.register_parameter("w2_qzeros", w2_qzeros)
+        set_weight_attrs(w2_qzeros, extra_weight_attrs)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        num_experts = layer.w13_qweight.shape[0]
+        device = layer.w13_qweight.device
+
+        layer.w13_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+            requires_grad=False,
+        )
+        layer.w2_g_idx_sort_indices = torch.nn.Parameter(
+            torch.empty((num_experts, 0), dtype=torch.int32, device=device),
+            requires_grad=False,
+        )
+
+        marlin_w13_qweight = ops.awq_marlin_moe_repack(
+            layer.w13_qweight,
+            layer.w13_g_idx_sort_indices,
+            size_k=layer.w13_qweight.shape[1],
+            size_n=layer.w13_qweight.shape[2] * self.quant_config.pack_factor,
+            num_bits=self.quant_config.weight_bits,
+        )
+        replace_parameter(layer, "w13_qweight", marlin_w13_qweight)
+
+        marlin_w2_qweight = ops.awq_marlin_moe_repack(
+            layer.w2_qweight,
+            layer.w2_g_idx_sort_indices,
+            size_k=layer.w2_qweight.shape[1],
+            size_n=layer.w2_qweight.shape[2] * self.quant_config.pack_factor,
+            num_bits=self.quant_config.weight_bits,
+        )
+        replace_parameter(layer, "w2_qweight", marlin_w2_qweight)
+
+        # Why does this take the intermediate size for size_k?
+        marlin_w13_scales = marlin_moe_permute_scales(
+            s=layer.w13_scales,
+            size_k=layer.intermediate_size_per_partition,
+            size_n=layer.w13_scales.shape[2],
+            group_size=self.quant_config.group_size,
+        )
+
+        replace_parameter(layer, "w13_scales", marlin_w13_scales)
+
+        marlin_w2_scales = marlin_moe_permute_scales(
+            s=layer.w2_scales,
+            size_k=layer.intermediate_size_per_partition,
+            size_n=layer.w2_scales.shape[2],
+            group_size=self.quant_config.group_size,
+        )
+        replace_parameter(layer, "w2_scales", marlin_w2_scales)
+
+        marlin_w13_zp = moe_awq_to_marlin_zero_points(
+            layer.w13_qzeros,
+            size_k=layer.w13_qzeros.shape[1],
+            size_n=layer.w13_qzeros.shape[2] * self.quant_config.pack_factor,
+            num_bits=self.quant_config.weight_bits)
+        replace_parameter(layer, "w13_qzeros", marlin_w13_zp)
+
+        marlin_w2_zp = moe_awq_to_marlin_zero_points(
+            layer.w2_qzeros,
+            size_k=layer.w2_qzeros.shape[1],
+            size_n=layer.w2_qzeros.shape[2] * self.quant_config.pack_factor,
+            num_bits=self.quant_config.weight_bits)
+        replace_parameter(layer, "w2_qzeros", marlin_w2_zp)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool = True,
+        use_grouped_topk: bool = False,
+        num_expert_group: Optional[int] = None,
+        topk_group: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+    ) -> torch.Tensor:
+
+        from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
+            fused_marlin_moe)
+
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function)
+
+        return fused_marlin_moe(
+            x,
+            layer.w13_qweight,
+            layer.w2_qweight,
+            layer.w13_scales,
+            layer.w2_scales,
+            router_logits,
+            topk_weights,
+            topk_ids,
+            w1_zeros=layer.w13_qzeros,
+            w2_zeros=layer.w2_qzeros,
+            num_bits=self.quant_config.weight_bits,
+        )

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/layers/quantization/compressed_tensors/compressed_tensors.py

 from typing import Any, Dict, List, Optional, cast
 
 import torch
+from compressed_tensors.config import CompressionFormat
+from compressed_tensors.quantization import (QuantizationArgs,
+                                             QuantizationStrategy,
+                                             QuantizationType)
 from pydantic import BaseModel
 
 from vllm.model_executor.layers.fused_moe import FusedMoE
@@ -16,8 +20,7 @@ from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsW8A8Fp8, CompressedTensorsW8A8Int8,
     CompressedTensorsW8A16Fp8, CompressedTensorsWNA16)
 from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
-    CompressionFormat, QuantizationArgs, QuantizationStrategy,
-    QuantizationType, find_matched_target, is_activation_quantization_format,
+    find_matched_target, is_activation_quantization_format,
     should_ignore_layer)
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
 from vllm.platforms import current_platform
@@ -138,10 +141,11 @@ class CompressedTensorsConfig(QuantizationConfig):
             or weight_quant.strategy == QuantizationStrategy.CHANNEL.value)
         is_tensor = (weight_strategy and input_quant.strategy
                      == QuantizationStrategy.TENSOR.value)
-        is_symmetric = weight_quant.symmetric and input_quant.symmetric
         is_static = not weight_quant.dynamic and not input_quant.dynamic
 
-        return is_8_bits and is_tensor and is_symmetric and is_static
+        # Both symmetric and asymmetric input quantization supported.
+        # Only symmetric weight quantization supported.
+        return is_8_bits and is_tensor and weight_quant.symmetric and is_static
 
     def _is_dynamic_token_w8a8(self, weight_quant: BaseModel,
                                input_quant: BaseModel) -> bool:
@@ -151,10 +155,11 @@ class CompressedTensorsConfig(QuantizationConfig):
             or weight_quant.strategy == QuantizationStrategy.CHANNEL.value)
         is_token = (weight_strategy and input_quant.strategy
                     == QuantizationStrategy.TOKEN.value)
-        is_symmetric = weight_quant.symmetric and input_quant.symmetric
         is_dynamic = not weight_quant.dynamic and input_quant.dynamic
 
-        return is_8_bits and is_token and is_symmetric and is_dynamic
+        # Both symmetric and asymmetric input quantization supported.
+        # Only symmetric weight quantization supported.
+        return is_8_bits and is_token and weight_quant.symmetric and is_dynamic
 
     def _is_fp8_w8a8(self, weight_quant: BaseModel,
                      input_quant: BaseModel) -> bool:
@@ -265,12 +270,14 @@ class CompressedTensorsConfig(QuantizationConfig):
             if self._is_static_tensor_w8a8(weight_quant, input_quant):
                 return CompressedTensorsW8A8Int8(
                     strategy=weight_quant.strategy,
-                    is_static_input_scheme=True)
+                    is_static_input_scheme=True,
+                    input_symmetric=input_quant.symmetric)
 
             if self._is_dynamic_token_w8a8(weight_quant, input_quant):
                 return CompressedTensorsW8A8Int8(
                     strategy=weight_quant.strategy,
-                    is_static_input_scheme=False)
+                    is_static_input_scheme=False,
+                    input_symmetric=input_quant.symmetric)
 
         raise NotImplementedError(
             "No compressed-tensors compatible scheme was found.")

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -3,14 +3,14 @@ from enum import Enum
 from typing import Callable, List, Optional
 
 import torch
+from compressed_tensors import CompressionFormat
+from compressed_tensors.quantization import QuantizationStrategy
 
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
                                                   FusedMoeWeightScaleSupported)
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     WNA16_SUPPORTED_BITS)
-from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
-    CompressionFormat, QuantizationStrategy)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     all_close_1d, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize)
 from vllm.model_executor.utils import set_weight_attrs
@@ -498,14 +498,14 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
             x,
             layer.w13_weight_packed,
             layer.w2_weight_packed,
+            layer.w13_weight_scale,
+            layer.w2_weight_scale,
             router_logits,
-            layer.w13_g_idx,
-            layer.w2_g_idx,
-            layer.w13_g_idx_sort_indices,
-            layer.w2_g_idx_sort_indices,
             topk_weights,
             topk_ids,
-            w1_scale=layer.w13_weight_scale,
-            w2_scale=layer.w2_weight_scale,
+            g_idx1=layer.w13_g_idx,
+            g_idx2=layer.w2_g_idx,
+            sort_indices1=layer.w13_g_idx_sort_indices,
+            sort_indices2=layer.w2_g_idx_sort_indices,
             num_bits=self.num_bits,
         )

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a16_fp8.py

 from typing import Callable, List, Optional
 
 import torch
+from compressed_tensors.quantization import QuantizationStrategy
 
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
-from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
-    QuantizationStrategy)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
     apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py

 from typing import Callable, List, Optional
 
 import torch
+from compressed_tensors.quantization import QuantizationStrategy
 from torch.nn import Parameter
 
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
-from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
-    QuantizationStrategy)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     apply_fp8_linear, cutlass_fp8_supported, normalize_e4m3fn_to_e4m3fnuz,
     requantize_with_max_scale)

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py

 from typing import Callable, List, Optional
 
 import torch
+from compressed_tensors.quantization import QuantizationStrategy
 from torch.nn import Parameter
 
+from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
-from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
-    QuantizationStrategy)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     apply_int8_linear, convert_to_channelwise)
 from vllm.model_executor.parameter import (BasevLLMParameter,
@@ -14,12 +14,16 @@ from vllm.model_executor.parameter import (BasevLLMParameter,
                                            ModelWeightParameter,
                                            PerTensorScaleParameter)
 
+logger = init_logger(__name__)
+
 
 class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
 
-    def __init__(self, strategy: str, is_static_input_scheme: bool):
+    def __init__(self, strategy: str, is_static_input_scheme: bool,
+                 input_symmetric: bool):
         self.strategy = strategy
         self.is_static_input_scheme = is_static_input_scheme
+        self.input_symmetric = input_symmetric
 
     @classmethod
     def get_min_capability(cls) -> int:
@@ -46,10 +50,43 @@ class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
                                            requires_grad=False)
         # INPUT SCALE
         if self.is_static_input_scheme:
-            layer.input_scale = Parameter(layer.input_scale.max(),
-                                          requires_grad=False)
+            if self.input_symmetric:
+                layer.input_scale = Parameter(layer.input_scale.max(),
+                                              requires_grad=False)
+                layer.input_zero_point = None
+            else:
+                # reconstruct the ranges
+                int8_traits = torch.iinfo(torch.int8)
+                azps = layer.input_zero_point.to(dtype=torch.int32)
+                range_max = (layer.input_scale *
+                             (int8_traits.max - azps)).max()
+                range_min = (layer.input_scale *
+                             (int8_traits.min - azps)).min()
+
+                scale = (range_max - range_min) / (int8_traits.max -
+                                                   int8_traits.min)
+                layer.input_scale = Parameter(scale, requires_grad=False)
+
+                # AZP loaded as int8 but used as int32
+                azp = (int8_traits.min -
+                       range_min / scale).to(dtype=torch.int32)
+                layer.input_zero_point = Parameter(azp, requires_grad=False)
+
         else:
             layer.input_scale = None
+            layer.input_zero_point = None
+
+        # azp_adj is the AZP adjustment term, used to account for weights.
+        # It does not depend on scales or azp, so it is the same for
+        # static and dynamic quantization.
+        # For more details, see csrc/quantization/cutlass_w8a8/Epilogues.md
+        # https://github.com/vllm-project/vllm/blob/8d59dbb00044a588cab96bcdc028006ed922eb06/csrc/quantization/cutlass_w8a8/Epilogues.md
+        if not self.input_symmetric:
+            layer.azp_adj = layer.weight.sum(dim=0,
+                                             keepdim=True,
+                                             dtype=torch.int32)
+        else:
+            layer.azp_adj = None
 
     def create_weights(self, layer: torch.nn.Module,
                        output_partition_sizes: List[int],
@@ -90,6 +127,15 @@ class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
                                             weight_loader=weight_loader)
             layer.register_parameter("input_scale", input_scale)
 
+            if not self.input_symmetric:
+                # Note: compressed-tensors stores the zp using the same dtype
+                # as the weights
+                # AZP loaded as int8 but used as int32
+                input_zero_point = BasevLLMParameter(
+                    data=torch.empty(1, dtype=torch.int8),
+                    weight_loader=weight_loader)
+                layer.register_parameter("input_zero_point", input_zero_point)
+
     def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor,
                       bias: Optional[torch.Tensor]) -> torch.Tensor:
 
@@ -97,4 +143,6 @@ class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
                                  weight=layer.weight,
                                  weight_scale=layer.weight_scale,
                                  input_scale=layer.input_scale,
+                                 input_zero_point=layer.input_zero_point,
+                                 azp_adj=layer.azp_adj,
                                  bias=bias)

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py

 from typing import Callable, List, Optional, Set
 
 import torch
+from compressed_tensors.quantization import ActivationOrdering
 
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
-from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
-    ActivationOrdering)
 from vllm.model_executor.layers.quantization.kernels import (
     MPLinearLayerConfig, choose_mp_linear_kernel)
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (

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

 import re
-from enum import Enum
-from typing import Any, Dict, Iterable, Optional, Union
+from typing import Iterable, Optional
 
-from pydantic import BaseModel, Field, field_validator
+from compressed_tensors import CompressionFormat
 from torch.nn import Module
 
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     FUSED_LAYER_NAME_MAPPING)
 
 
-class CompressionFormat(Enum):
-    dense = "dense"
-    sparse_bitmask = "sparse-bitmask"
-    naive_quantized = "naive-quantized"
-    float_quantized = "float-quantized"
-    int_quantized = "int-quantized"
-    pack_quantized = "pack-quantized"
-    marlin_24 = "marlin-24"
-
-
-class QuantizationType(str, Enum):
-    """
-    Enum storing quantization type options
-    """
-
-    INT = "int"
-    FLOAT = "float"
-
-
-class QuantizationStrategy(str, Enum):
-    """
-    Enum storing quantization strategy options
-    """
-
-    TENSOR = "tensor"
-    CHANNEL = "channel"
-    GROUP = "group"
-    BLOCK = "block"
-    TOKEN = "token"
-
-
-class ActivationOrdering(str, Enum):
-    """
-    Enum storing strategies for activation ordering
-
-    Group: reorder groups and weight\n
-    Weight: only reorder weight, not groups. Slightly lower latency and
-    accuracy compared to group actorder\n
-    """
-
-    GROUP = "group"
-    WEIGHT = "weight"
-
-
-class QuantizationArgs(BaseModel):
-    """
-    User facing arguments used to define a quantization config 
-    for weights or activations
-
-    :param num_bits: quantization bit depth
-    :param type: dtype to quantized to, either int or float
-    :param symmetric: whether or not quantization scale is symmetric
-    :param strategy: string determining the scope of scale/zero-point to apply
-    :param group_size: group length to use for the group strategy
-    :param block_structure: 2d block structure to use for the block 
-    strategy, must be of the format "2x4", "8x16", etc.
-    :param dynamic: set True to perform dynamic quantization -
-        values will not be calibrated during calibration phase, 
-        instead during inference new quantization ranges will be 
-        observed with every sample. Defaults to False for static
-        quantization. Note that enabling dynamic quantization 
-        will change the default observer to a memoryless one
-    :param actorder: whether to apply group quantization in decreasing order of
-        activation. Defaults to None for arbitrary ordering
-    """
-
-    num_bits: int = 8
-    type: QuantizationType = QuantizationType.INT
-    symmetric: bool = True
-    group_size: Optional[int] = None
-    strategy: Optional[QuantizationStrategy] = None
-    block_structure: Optional[str] = None
-    dynamic: bool = False
-    actorder: Union[ActivationOrdering, bool, None] = None
-    observer: str = Field(
-        default="minmax",
-        description=("The class to use to compute the quantization param - "
-                     "scale and zero-point'"),
-    )
-    observer_kwargs: Dict[str, Any] = Field(
-        default_factory=dict,
-        description=
-        ("optional dict of kwargs to be passed directly to torch quantization "
-         "Observers constructor excluding quantization range or symmetry"),
-    )
-
-    @field_validator("actorder", mode="before")
-    def validate_actorder(cls, value) -> Optional[ActivationOrdering]:
-        if isinstance(value, bool):
-            return ActivationOrdering.GROUP if value else None
-
-        if isinstance(value, str):
-            return ActivationOrdering(value.lower())
-
-        return value
-
-
 def is_activation_quantization_format(format: str) -> bool:
     _ACTIVATION_QUANTIZATION_FORMATS = [
         CompressionFormat.naive_quantized.value,

vllm/model_executor/layers/quantization/gguf.py

@@ -86,15 +86,16 @@ class GGUFLinearMethod(LinearMethodBase):
         output_size_per_partition = sum(output_partition_sizes)
 
         tensor_shape = (output_size_per_partition, input_size_per_partition)
-        qweight = UninitializedParameter(requires_grad=False)
+        qweight = GGUFUninitializedParameter(requires_grad=False)
         set_weight_attrs(
             qweight, {
                 "input_dim": 1,
                 "output_dim": 0,
                 "tensor_shape": tensor_shape,
                 "is_gguf_weight": True,
-                "shard_size": {},
+                "data_container": [],
                 "shard_id": [],
+                "shard_id_map": {},
             })
         set_weight_attrs(qweight, extra_weight_attrs)
         layer.register_parameter("qweight", qweight)
@@ -116,21 +117,17 @@ class GGUFLinearMethod(LinearMethodBase):
               layer: torch.nn.Module,
               x: torch.Tensor,
               bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-        shard_size = getattr(layer.qweight, "shard_size", None)
         shard_id = getattr(layer.qweight, "shard_id", None)
 
-        if shard_id and shard_size:
-            result = []
-            offset = 0
+        if shard_id:
             # dequantize shard weights respectively
             shard_id = ["q", "k", "v"] if "q" in shard_id else shard_id
+            qweight = layer.qweight.unbind(0)
+            result = []
             for id in shard_id:
-                shard_weight = layer.qweight[
-                    offset:offset +
-                    shard_size[id][0], :shard_size[id][1]].contiguous()
+                q_idx = layer.qweight.shard_id_map[id]
                 qweight_type = layer.qweight_type.shard_weight_type[id]
-                result.append(_fuse_mul_mat(x, shard_weight, qweight_type))
-                offset += shard_size[id][0]
+                result.append(_fuse_mul_mat(x, qweight[q_idx], qweight_type))
             out = torch.cat(result, axis=1)
         else:
             qweight = layer.qweight
@@ -162,3 +159,20 @@ class GGUFEmbeddingMethod(GGUFLinearMethod):
         dequant = ops.ggml_dequantize(quant, qweight_type, hidden_size,
                                       x_flat.shape[0])
         return dequant.view(*x.shape, hidden_size)
+
+
+class GGUFUninitializedParameter(UninitializedParameter):
+    cls_to_become = Parameter
+    data_container: List[torch.Tensor]
+
+    def materialize_nested(self) -> Parameter:
+        nested_data = torch.nested.nested_tensor(self.data_container,
+                                                 device=self.device,
+                                                 dtype=torch.uint8)
+        self.data_container.clear()
+        param = torch.Tensor._make_subclass(self.cls_to_become,
+                                            nested_data,
+                                            require_grad=False)
+        for k, v in self.__dict__.items():
+            setattr(param, k, v)
+        return param

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -557,14 +557,14 @@ class GPTQMarlinMoEMethod(FusedMoEMethodBase):
             x,
             layer.w13_qweight,
             layer.w2_qweight,
+            layer.w13_scales,
+            layer.w2_scales,
             router_logits,
-            layer.w13_g_idx,
-            layer.w2_g_idx,
-            layer.w13_g_idx_sort_indices,
-            layer.w2_g_idx_sort_indices,
             topk_weights,
             topk_ids,
-            w1_scale=layer.w13_scales,
-            w2_scale=layer.w2_scales,
+            g_idx1=layer.w13_g_idx,
+            g_idx2=layer.w2_g_idx,
+            sort_indices1=layer.w13_g_idx_sort_indices,
+            sort_indices2=layer.w2_g_idx_sort_indices,
             num_bits=self.quant_config.quant_type.size_bits,
         ).to(orig_dtype)

vllm/model_executor/layers/quantization/ipex_quant.py

+from typing import Any, Dict, List, Optional
+
+import torch
+
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.awq import AWQLinearMethod
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.platforms import current_platform
+
+
+class IPEXConfig(QuantizationConfig):
+    """INT8 quantization config class using IPEX for the CPU backend,
+    including AWQ.
+    """
+
+    IPEX_QUANT_METHOD_MAP = {
+        "awq": 1,
+        "gptq": 2,
+    }
+
+    def __init__(
+        self,
+        method: str,
+        weight_bits: int,
+        group_size: int,
+    ) -> None:
+        self.method = method
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.pack_factor = 32 // self.weight_bits
+
+        if self.weight_bits not in [4]:
+            raise ValueError(f"IPEX quantization supports weight bits [4], "
+                             f"but got {self.weight_bits}.")
+
+        if self.method == "awq":
+            self.quant_method = IPEXAWQLinearMethod
+        else:
+            raise ValueError(f"IPEX quantization supports [awq], "
+                             f"but got {self.method}.")
+
+    def __repr__(self) -> str:
+        return (f"IPEXConfig(method={self.method}"
+                f"weight_bits={self.weight_bits}, "
+                f"group_size={self.group_size}")
+
+    def get_ipex_quant_method_id(self) -> int:
+        return IPEXConfig.IPEX_QUANT_METHOD_MAP[self.method]
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "ipex"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return -1
+
+    @staticmethod
+    def get_config_filenames() -> List[str]:
+        return [
+            "quant_config.json",
+            "quantize_config.json",
+        ]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "IPEXConfig":
+        method = cls.get_from_keys(config, ["quant_method"]).lower()
+        weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
+        group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
+        return cls(method, weight_bits, group_size)
+
+    @classmethod
+    def override_quantization_method(cls, hf_quant_cfg,
+                                     user_quant) -> Optional[str]:
+        if not current_platform.is_cpu():
+            return None
+
+        quant_method = hf_quant_cfg.get("quant_method", "").lower()
+
+        if quant_method in ["awq"]:
+            return cls.get_name()
+
+        return None
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["LinearMethodBase"]:
+        if isinstance(layer, LinearBase):
+            return self.quant_method(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        if self.method == "awq":
+            return ["gelu", "gelu_fast", "gelu_new", "gelu_pytorch_tanh"]
+        else:
+            return []
+
+
+class IPEXAWQLinearMethod(AWQLinearMethod):
+    """AWQ linear method using IPEX for the CPU backend.
+    """
+
+    def __init__(self, quant_config: IPEXConfig):
+        self.quant_config = quant_config  # type: ignore
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        super().process_weights_after_loading(layer=layer)
+
+        bias = layer.bias if not layer.skip_bias_add else None
+
+        try:
+            import intel_extension_for_pytorch as ipex
+            if ipex.__version__ < "2.4.0":
+                raise ImportError("intel_extension_for_pytorch version is "
+                                  "wrong. Please install "
+                                  "intel_extension_for_pytorch>=2.4.0.")
+        except ImportError as err:
+            raise ImportError(
+                "Please install "
+                "intel_extension_for_pytorch>=2.4.0 via "
+                "`pip install intel_extension_for_pytorch>=2.4.0`"
+                " to use IPEX-AWQ linear method.") from err
+
+        # Using the compute dtype (lowp_mode) as INT8 to leverage instructions
+        # with better performance.
+        lowp_mode = ipex.quantization.WoqLowpMode.INT8
+        # The weight will be de-packed from INT4 to INT8.
+        weight_dtype = ipex.quantization.WoqWeightDtype.INT4
+        # The float activation will be quantized (dynamic, per-token) to INT8.
+        act_quant_mode = ipex.quantization.WoqActQuantMode.PER_BATCH
+
+        qconfig = ipex.quantization.get_weight_only_quant_qconfig_mapping(
+            weight_dtype=weight_dtype,
+            lowp_mode=lowp_mode,
+            act_quant_mode=act_quant_mode,
+            group_size=self.quant_config.group_size,
+        )
+
+        layer.ipex_output_size = layer.qweight.size(
+            1) * self.quant_config.pack_factor
+        layer.ipex_qlinear = ipex.nn.modules.weight_only_quantization.\
+            WeightOnlyQuantizedLinear.from_weight(
+                layer.qweight,
+                layer.scales,
+                layer.qzeros,
+                layer.qweight.size(0),
+                layer.ipex_output_size,
+                qconfig=qconfig,
+                bias=bias,
+                group_size=self.quant_config.group_size,
+                quant_method=
+                    self.quant_config.get_ipex_quant_method_id() # type: ignore
+            )
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        reshaped_x = x.reshape(-1, x.shape[-1])
+        out = layer.ipex_qlinear(reshaped_x)
+
+        return out.reshape(x.shape[:-1] + (layer.ipex_output_size, ))

vllm/model_executor/layers/quantization/kernels/MPLinearKernel.py

@@ -42,6 +42,10 @@ class MPLinearKernel(ABC):
         self.config = c
         self.w_q_name = w_q_param_name
         self.w_s_name = w_s_param_name
+        if c.zero_points:
+            assert w_zp_param_name is not None
+        if c.has_g_idx:
+            assert w_gidx_param_name is not None
         self.w_zp_name = w_zp_param_name
         self.w_gidx_name = w_gidx_param_name
 

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

-import os
 from typing import List, Optional, Type
 
+import vllm.envs as envs
+from vllm.model_executor.layers.quantization.kernels.exllama import (
+    ExllamaLinearKernel)
 from vllm.model_executor.layers.quantization.kernels.machete import (
     MacheteLinearKernel)
 from vllm.model_executor.layers.quantization.kernels.marlin import (
@@ -13,6 +15,7 @@ from vllm.platforms import current_platform
 _POSSIBLE_KERNELS: List[Type[MPLinearKernel]] = [
     MacheteLinearKernel,
     MarlinLinearKernel,
+    ExllamaLinearKernel,
 ]
 
 
@@ -45,8 +48,7 @@ def choose_mp_linear_kernel(
 
     failure_reasons = []
     for kernel in _POSSIBLE_KERNELS:
-        if kernel.__name__ in os.environ.get("VLLM_DISABLED_KERNELS", "")\
-            .split(","):
+        if kernel.__name__ in envs.VLLM_DISABLED_KERNELS:
             failure_reasons.append(
                 f' {kernel.__name__} disabled by environment variable')
             continue

vllm/model_executor/layers/quantization/kernels/exllama.py

+from typing import Optional, Tuple
+
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    pack_quantized_values_into_int32)
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           permute_param_layout_)
+from vllm.scalar_type import scalar_types
+
+from .MPLinearKernel import MPLinearKernel, MPLinearLayerConfig
+
+
+class ExllamaLinearKernel(MPLinearKernel):
+    SUPPORTED_QUANT_TYPES = [scalar_types.uint4b8, scalar_types.uint8b128]
+    # In theory supports `scalar_types.uint2b2, scalar_types.uint3b4` too but
+    # currently untested so not added to the list
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 60
+
+    @classmethod
+    def can_implement(cls,
+                      c: MPLinearLayerConfig) -> Tuple[bool, Optional[str]]:
+        if c.has_g_idx and\
+            c.partition_weight_shape[0] != c.full_weight_shape[0]:
+            return False, "Act reordering currently not supported by Exllama, "\
+                          "when the input features are partitioned across "\
+                          "devices"
+
+        if c.partition_weight_shape[1] % (32 // c.weight_type.size_bits) != 0:
+            return False, "Output features must be a multiple of the pack " \
+                            "factor (32 / num_bits) so that we can correctly " \
+                            "pack the zero points"
+
+        if c.act_type != torch.float16:
+            return False, "Exllama only supports float16 activations"
+
+        if c.weight_type not in cls.SUPPORTED_QUANT_TYPES:
+            return False, f"Quant type ({c.weight_type}) not supported by "\
+                           "Exllama, supported types are: "\
+                           f"{cls.SUPPORTED_QUANT_TYPES}"
+
+        if c.full_weight_shape[0] % c.group_size != 0:
+            return False, f"Group size ({c.group_size}) does not evenly divide"\
+                           " the number of input features "\
+                           f"({c.full_weight_shape[0]})"
+
+        return True, None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module):
+        c = self.config
+
+        # For Exllama, we need to set a zero-point tensor if there is not one
+        if not c.zero_points:
+            self.w_zp_name = "qzeros"
+            device = getattr(layer, self.w_q_name).device
+            groups = c.partition_weight_shape[0] // c.group_size
+            out_features = c.partition_weight_shape[1]
+
+            if c.weight_type.has_bias():
+                # if the type has a bias we have to create a zeros tensor that
+                # contains the bias values repeated for each group (-1 due to
+                # a bug in the original GPTQ checkpoint format leading to
+                # exllama kernel adding 1 to the zero points during inference)
+                # Documentation of the bug can be found here:
+                #  https://garden.danieldk.eu/GPTQ-Checkpoint-Format
+                zeros = torch.full((groups, out_features),
+                                   c.weight_type.bias - 1,
+                                   dtype=torch.int32,
+                                   device=device)
+            else:
+                raise NotImplementedError(
+                    "A 0 zero-point is not supported by Exllama due to "
+                    "a bug in the original GPTQ checkpoint format leading to "
+                    "exllama kernel adding 1 to the zero points during "
+                    "inference")
+            zeros = pack_quantized_values_into_int32(zeros,
+                                                     c.weight_type,
+                                                     packed_dim=1)
+            setattr(layer, self.w_zp_name,
+                    torch.nn.Parameter(zeros, requires_grad=False))
+
+        if c.has_g_idx:
+
+            def transform_w_g_idx(x):
+                # Exllama wants the permutation array instead of the group
+                # indices
+                return torch.argsort(x).to(torch.int)
+
+            self._transform_param(layer, self.w_gidx_name, transform_w_g_idx)
+        else:
+            self.w_gidx_name = "g_idx"
+            empty_g_idx = torch.nn.Parameter(torch.empty((0, ),
+                                                         dtype=torch.int,
+                                                         device=device),
+                                             requires_grad=False)
+            setattr(layer, self.w_gidx_name, empty_g_idx)
+
+        def transform_w_q(x):
+            assert isinstance(x, BasevLLMParameter)
+            assert self.w_gidx_name is not None
+            g_idx = getattr(layer, self.w_gidx_name)
+
+            permute_param_layout_(x, input_dim=0, output_dim=1, packed_dim=0)
+            x_cont = x.data.contiguous()
+            ops.gptq_shuffle(x_cont, g_idx, c.weight_type.size_bits)
+            return x_cont
+
+        def transform_w_s(x):
+            assert isinstance(x, BasevLLMParameter)
+            permute_param_layout_(x, input_dim=0, output_dim=1)
+            x.data = x.data.contiguous()
+            return x.to(dtype=c.act_type)
+
+        # Repack weights and scales for Machete
+        self._transform_param(layer, self.w_q_name, transform_w_q)
+        self._transform_param(layer, self.w_s_name, transform_w_s)
+
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        c = self.config
+
+        x_2d = x.reshape(-1, x.shape[-1])
+        out_shape = x.shape[:-1] + (c.partition_weight_shape[1], )
+
+        w_q, w_s, w_zp, w_g_idx = self._get_weight_params(layer)
+
+        assert w_zp is not None, "Zero points are required by Exllama"
+        assert w_g_idx is not None, "Group index is required by Exllama"
+        output = ops.gptq_gemm(x_2d, w_q, w_zp, w_s, w_g_idx, True,
+                               c.weight_type.size_bits)
+
+        if bias is not None:
+            output.add_(bias)
+        return output.reshape(out_shape)

vllm/model_executor/layers/quantization/kernels/machete.py

@@ -8,7 +8,7 @@ from vllm.model_executor.layers.quantization.utils.machete_utils import (
     MACHETE_SUPPORTED_GROUP_SIZES, check_machete_supports_shape,
     query_machete_supported_quant_types)
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
-    pack_weights_into_int32, unpack_weights_into_int32)
+    pack_quantized_values_into_int32, unpack_quantized_values_into_int32)
 from vllm.model_executor.parameter import (BasevLLMParameter,
                                            permute_param_layout_)
 
@@ -71,13 +71,13 @@ class MacheteLinearKernel(MPLinearKernel):
             assert isinstance(x, BasevLLMParameter)
             permute_param_layout_(x, input_dim=0, output_dim=1, packed_dim=0)
             if c.has_g_idx:
-                x_unpacked = unpack_weights_into_int32(x.data,
-                                                       c.weight_type,
-                                                       packed_dim=0)
+                x_unpacked = unpack_quantized_values_into_int32(x.data,
+                                                                c.weight_type,
+                                                                packed_dim=0)
                 x_perm = x_unpacked[perm, :]
-                x.data = pack_weights_into_int32(x_perm,
-                                                 c.weight_type,
-                                                 packed_dim=0)
+                x.data = pack_quantized_values_into_int32(x_perm,
+                                                          c.weight_type,
+                                                          packed_dim=0)
             x.data = ops.machete_prepack_B(x.data.t().contiguous().t(),
                                            self.config.weight_type)
             return x

vllm/model_executor/layers/quantization/kernels/marlin.py

@@ -38,10 +38,11 @@ class MarlinLinearKernel(MPLinearKernel):
                             "Marlin, supported group sizes are: "\
                             f"{MARLIN_SUPPORTED_GROUP_SIZES}"
 
-        return check_marlin_supports_shape(c.partition_weight_shape[0],
-                                           c.partition_weight_shape[1],
-                                           c.full_weight_shape[1],
-                                           c.group_size)
+        return check_marlin_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}

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

@@ -208,6 +208,7 @@ def marlin_moe_permute_scales(
         device=s.device,
         dtype=s.dtype,
     )
+
     for e in range(num_experts):
         output[e] = marlin_permute_scales(s[e], size_k, size_n, group_size)
     return output
@@ -258,6 +259,20 @@ def awq_to_marlin_zero_points(q_zp_packed: torch.Tensor, size_k: int,
     return marlin_zp
 
 
+def moe_awq_to_marlin_zero_points(q_zp_packed: torch.Tensor, size_k: int,
+                                  size_n: int, num_bits: int):
+    num_experts = q_zp_packed.shape[0]
+    output = torch.empty(
+        (num_experts, q_zp_packed.shape[1], q_zp_packed.shape[2]),
+        device=q_zp_packed.device,
+        dtype=q_zp_packed.dtype,
+    )
+    for e in range(num_experts):
+        output[e] = awq_to_marlin_zero_points(q_zp_packed[e], size_k, size_n,
+                                              num_bits)
+    return output
+
+
 def apply_gptq_marlin_linear(
         input: torch.Tensor,
         weight: torch.Tensor,

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

@@ -20,9 +20,9 @@ FUSED_LAYER_NAME_MAPPING = {
 }
 
 
-def pack_weights_into_int32(w_q: torch.Tensor,
-                            wtype: ScalarType,
-                            packed_dim: int = 0):
+def pack_quantized_values_into_int32(w_q: torch.Tensor,
+                                     wtype: ScalarType,
+                                     packed_dim: int = 0):
     # move dim to pack to the end
     perm = (*[i for i in range(len(w_q.shape)) if i != packed_dim], packed_dim)
     inv_perm = tuple(perm.index(i) for i in range(len(perm)))
@@ -42,9 +42,9 @@ def pack_weights_into_int32(w_q: torch.Tensor,
     return res.permute(inv_perm)
 
 
-def unpack_weights_into_int32(w_q: torch.Tensor,
-                              wtype: ScalarType,
-                              packed_dim: int = 0):
+def unpack_quantized_values_into_int32(w_q: torch.Tensor,
+                                       wtype: ScalarType,
+                                       packed_dim: int = 0):
     # move dim to pack to the end
     perm = (*[i for i in range(len(w_q.shape)) if i != packed_dim], packed_dim)
     inv_perm = tuple(perm.index(i) for i in range(len(perm)))

vllm/model_executor/layers/quantization/utils/w8a8_utils.py

@@ -159,7 +159,8 @@ def apply_fp8_linear(
 
             # Making sure the dummy tensor is on the same device as the weight
             global TORCH_DEVICE_IDENTITY
-            if TORCH_DEVICE_IDENTITY.device != weight.device:
+            if (TORCH_DEVICE_IDENTITY is not None
+                    and TORCH_DEVICE_IDENTITY.device != weight.device):
                 TORCH_DEVICE_IDENTITY = TORCH_DEVICE_IDENTITY.to(weight.device)
 
             # GEMM
@@ -191,13 +192,28 @@ def apply_int8_linear(
     weight: torch.Tensor,
     weight_scale: torch.Tensor,
     input_scale: Optional[torch.Tensor] = None,
+    input_zero_point: Optional[torch.Tensor] = None,
+    azp_adj: Optional[torch.Tensor] = None,
     bias: Optional[torch.Tensor] = None,
 ):
     # ops.scaled_int8_quant supports both dynamic and static quant.
     # * dynamic, layer.input_scale is None and x_scale computed from x.
     # * static, layer.input_scale is scalar and x_scale is input_scale.
-    x_q, x_scale, _ = ops.scaled_int8_quant(input, input_scale)
-
+    symmetric = azp_adj is None
+    x_q, x_scale, x_zp = ops.scaled_int8_quant(input,
+                                               input_scale,
+                                               input_zero_point,
+                                               symmetric=symmetric)
+
+    if x_zp is not None:
+        return ops.cutlass_scaled_mm_azp(x_q,
+                                         weight,
+                                         scale_a=x_scale,
+                                         scale_b=weight_scale,
+                                         out_dtype=input.dtype,
+                                         azp_adj=azp_adj,
+                                         azp=x_zp,
+                                         bias=bias)
     return ops.cutlass_scaled_mm(x_q,
                                  weight,
                                  scale_a=x_scale,