Add AWQ class

vllm_hpu_extension/awq_hpu.py

@@ -0,0 +1,241 @@
+# Copyright (c) 2025 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, Dict, List, Optional
+
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.parameter import (GroupQuantScaleParameter,
+                                           PackedvLLMParameter)
+
+
+class AWQHPUConfig(QuantizationConfig):
+    """Config class for AWQ.
+
+    Reference: https://arxiv.org/abs/2306.00978
+    """
+
+    def __init__(
+        self,
+        weight_bits: int,
+        group_size: int,
+        zero_point: bool,
+    ) -> None:
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.zero_point = zero_point
+
+        if self.weight_bits != 4:
+            raise ValueError(
+                "Currently, only 4-bit weight quantization is supported for "
+                f"AWQ, but got {self.weight_bits} bits.")
+        self.pack_factor = 32 // self.weight_bits
+
+    def __repr__(self) -> str:
+        return (f"AWQConfig(weight_bits={self.weight_bits}, "
+                f"group_size={self.group_size}, "
+                f"zero_point={self.zero_point})")
+
+    def get_name(self) -> str:
+        return "awq_hpu"
+
+    def get_supported_act_dtypes(self) -> List[torch.dtype]:
+        return [torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # The AWQ kernel only supports Turing or newer GPUs.
+        return 0
+
+    @staticmethod
+    def get_config_filenames() -> List[str]:
+        return [
+            "quant_config.json",  # E.g., casperhansen/vicuna-7b-v1.5-awq
+            # E.g., abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq
+            "quantize_config.json",
+        ]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "AWQHPUConfig":
+        weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
+        group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
+        zero_point = cls.get_from_keys(config, ["zero_point"])
+        return cls(weight_bits, group_size, zero_point)
+
+    @classmethod
+    def override_quantization_method(cls, hf_quant_cfg,
+                                     user_quant) -> Optional[str]:
+
+        is_valid_user_quant = user_quant == "awq_hpu"
+
+        if is_valid_user_quant:
+            return cls.get_name(cls)
+
+        return None
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["AWQHPULinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return AWQHPULinearMethod(self)
+        return None
+
+    def get_scaled_act_names(self) -> List[str]:
+        return ["gelu", "gelu_fast", "gelu_new", "gelu_pytorch_tanh"]
+
+
+class AWQHPULinearMethod(LinearMethodBase):
+    """Linear method for AWQ.
+
+    Args:
+        quant_config: The AWQ quantization config.
+    """
+
+    def __init__(self, quant_config: AWQHPUConfig):
+        self.quant_config = quant_config
+
+    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 input_size_per_partition % self.quant_config.group_size != 0:
+            raise ValueError(
+                "The input size is not aligned with the quantized "
+                "weight shape. This can be caused by too large "
+                "tensor parallel size.")
+
+        output_size_per_partition = sum(output_partition_sizes)
+        if output_size_per_partition % self.quant_config.pack_factor != 0:
+            raise ValueError(
+                "The output size is not aligned with the quantized "
+                "weight shape. This can be caused by too large "
+                "tensor parallel size.")
+
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        qweight = PackedvLLMParameter(
+            data=torch.empty(
+                input_size_per_partition,
+                output_size_per_partition // self.quant_config.pack_factor,
+                dtype=torch.int32,
+            ),
+            input_dim=0,
+            output_dim=1,
+            packed_dim=1,
+            packed_factor=self.quant_config.pack_factor,
+            weight_loader=weight_loader)
+
+        qzeros = PackedvLLMParameter(
+            data=torch.empty(
+                input_size_per_partition // self.quant_config.group_size,
+                output_size_per_partition // self.quant_config.pack_factor,
+                dtype=torch.int32,
+            ),
+            input_dim=0,
+            output_dim=1,
+            packed_dim=1,
+            packed_factor=self.quant_config.pack_factor,
+            weight_loader=weight_loader)
+
+        scales = GroupQuantScaleParameter(data=torch.empty(
+            input_size_per_partition // self.quant_config.group_size,
+            output_size_per_partition,
+            dtype=params_dtype,
+        ),
+                                          input_dim=0,
+                                          output_dim=1,
+                                          weight_loader=weight_loader)
+
+        layer.register_parameter("qweight", qweight)
+        layer.register_parameter("qzeros", qzeros)
+        layer.register_parameter("scales", scales)
+
+    def pack_tensor(self, x):
+        wf = torch.tensor(list(range(0, 32, self.quant_config.weight_bits)),
+                          dtype=torch.int32).unsqueeze(0)
+        xp = torch.sum(torch.bitwise_left_shift(
+            x.reshape(x.shape[0], -1, (32 // self.quant_config.weight_bits)),
+            wf.unsqueeze(0)),
+                       dim=-1).to(torch.int32)
+        return xp
+
+    def unpack_tensor(self, xp):
+        wf = torch.tensor(list(range(0, 32, self.quant_config.weight_bits)),
+                          dtype=torch.int32).unsqueeze(0)
+        x = torch.bitwise_right_shift(
+            torch.unsqueeze(xp,
+                            -1).expand(xp.shape[0], -1,
+                                       32 // self.quant_config.weight_bits),
+            wf.unsqueeze(0)).to(torch.int8)
+        x = torch.bitwise_and(x, (2**self.quant_config.weight_bits) - 1)
+        x = x.reshape((x.shape[0], -1))
+        return x
+
+    def awq_order(self, x):
+
+        order = [0, 4, 1, 5, 2, 6, 3, 7]
+        idx = torch.arange(
+            x.shape[-1],
+            dtype=torch.int32,
+            device=x.device,
+        )
+        idx = idx.view(-1, 32 // self.quant_config.weight_bits)
+        idx = idx[:, order]
+        idx = idx.view(-1)
+
+        x = x[:, idx]
+        return x
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+
+        # This unpacking-packing is required because HPU dequant kernel
+        # is not compatible with AWQ format
+        device = layer.qweight.device
+        wq = layer.qweight.cpu()
+        zq = layer.qzeros.cpu()
+        wqu = self.awq_order(self.unpack_tensor(wq))
+        zu = self.awq_order(self.unpack_tensor(zq))
+        layer.qweight.data = self.pack_tensor(wqu).to(device)
+        layer.qzeros.data = self.pack_tensor(zu).to(device)
+
+        layer.qweight = torch.nn.Parameter(layer.qweight.data,
+                                           requires_grad=False)
+        layer.qzeros = torch.nn.Parameter(layer.qzeros.data,
+                                          requires_grad=False)
+        layer.scales = torch.nn.Parameter(layer.scales.data,
+                                          requires_grad=False)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        qweight = layer.qweight
+        scales = layer.scales
+        qzeros = layer.qzeros
+        pack_factor = self.quant_config.pack_factor
+        out_shape = (x.shape[:-1] + (qweight.shape[-1] * pack_factor, ))
+        reshaped_x = x.reshape(-1, x.shape[-1])
+
+        weight = torch.ops.hpu.convert_from_uint4(qweight,
+                                                scales,
+                                                qzeros,
+                                                x.dtype)
+        out = torch.matmul(reshaped_x, weight)
+
+        if bias is not None:
+            out.add_(bias)
+        return out.reshape(out_shape)