Merge pull request #52 from liwei109/awq_gptq

[dev] support AWQ/GPTQ quantization for dense models

requirements.txt

@@ -9,7 +9,7 @@ blake3==1.0.5
 cachetools==6.1.0
 cbor2==5.7.0
 cloudpickle==3.1.1
-compressed-tensors==0.10.2
+compressed-tensors==0.11.0
 diskcache==5.6.3
 gguf==0.17.1
 mistral_common==1.8.3

vllm_kunlun/ops/__init__.py

@@ -16,4 +16,6 @@
 #
 
 import vllm_kunlun.ops.rotary_embedding
-import vllm_kunlun.ops.layernorm
+import vllm_kunlun.ops.layernorm
+import vllm_kunlun.ops.quantization.awq
+import vllm_kunlun.ops.quantization.gptq

vllm_kunlun/ops/quantization/awq.py

@@ -0,0 +1,128 @@
+#
+# Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
+# Author: Li Wei, Pan Xiakai, You Zeyu
+# Email: liwei157@baidu.com
+# This file is a part of the vllm-kunlun project.
+#
+# 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.
+
+import torch
+
+from typing import Optional
+from vllm.model_executor.layers.quantization.awq import AWQLinearMethod
+
+
+def repack_int4_for_kunlun(self, packed: torch.Tensor, num_bits: int = 4):
+    """Convert AWQ-packed int4 weights to Kunlun XPU format.
+    Input:  packed[N, K], dtype=int32, saved as AWQ order
+    Output: packed_reordered[N, K], dtype=int32, saved as Kunlun order
+    """
+    N, K = packed.shape
+    self.align_type = 1 if K % 8 == 0 else 0
+    assert num_bits == 4, "Only int4 supported now"
+    shifts = torch.arange(0, 32, num_bits, device=packed.device, dtype=torch.int32)
+
+    if self.align_type == 0:  # NORMAL MODE
+        # Unpack AWQ order:[0, 2, 4, 6, 1, 3, 5, 7]
+        unpacked_awq = (packed.unsqueeze(-1) >> shifts) & 0xF  # [N, K, 8]
+
+        # Reverse AWQ order and convert to KUNLUN order
+        AWQ_TO_KUNLUN_ORDER_NORMAL = [4, 0, 5, 1, 6, 2, 7, 3]
+        # [0,2,4,6,1,3,5,7] --> [1, 0, 3, 2, 5, 4, 7, 6]
+        unpacked_kunlun = unpacked_awq[..., AWQ_TO_KUNLUN_ORDER_NORMAL]  # [N, K, 8]
+
+        # Pack to int32, order[6, 7, 4, 5, 2, 3, 0, 1]
+        packed_kunlun = (unpacked_kunlun << shifts).sum(
+            dim=-1, dtype=torch.int32
+        )  # [N, K]
+    elif self.align_type == 1:  # FAST MODEL
+        # Unpack AWQ order
+        unpacked_awq = (
+            packed.view(N, K // 8, 8).unsqueeze(-1) >> shifts
+        ) & 0xF  # [N, K//8, 8, 8]
+
+        # Reverse AWQ order and convert to KUNLUN order
+        AWQ_TO_KUNLUN_ORDER_FAST = [
+            32, 0, 36, 4, 33, 1, 37, 5,
+            34, 2, 38, 6, 35, 3, 39, 7,
+            40, 8, 44, 12, 41, 9, 45, 13,
+            42, 10, 46, 14, 43, 11, 47, 15,
+            48, 16, 52, 20, 49, 17, 53, 21,
+            50, 18, 54, 22, 51, 19, 55, 23,
+            56, 24, 60, 28, 57, 25, 61, 29,
+            58, 26, 62, 30, 59, 27, 63, 31
+        ]
+        unpacked_awq = unpacked_awq.reshape(N, K // 8, 64)
+        unpacked_kunlun = unpacked_awq[..., AWQ_TO_KUNLUN_ORDER_FAST]  # [N, K//8, 64]
+
+        # Pack to int32
+        unpacked_kunlun = unpacked_kunlun.reshape(N, K // 8, 8, 8)
+        packed_kunlun = (
+            (unpacked_kunlun << shifts).sum(dim=-1, dtype=torch.int32).reshape(N, K)
+        )  # [N, K]
+    else:
+        raise NotImplementedError
+
+    return packed_kunlun
+
+
+def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+    layer.qweight = torch.nn.Parameter(
+        (
+            self.repack_int4_for_kunlun(layer.qweight.data)
+            if layer.qweight.data.dtype == torch.int32
+            else layer.qweight.data
+        ),
+        requires_grad=False,
+    )
+    layer.qzeros = torch.nn.Parameter(
+        (
+            self.repack_int4_for_kunlun(layer.qzeros.data)
+            if layer.qzeros.data.dtype == torch.int32
+            else 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])
+
+    # num_tokens >= threshold
+    FP16_MATMUL_HEURISTIC_CONDITION = x.shape[:-1].numel() >= 256
+
+    if FP16_MATMUL_HEURISTIC_CONDITION:
+        out = torch.ops._C.awq_dequantize(
+            qweight, scales, qzeros, quant_type=0, align_type=self.align_type
+        )
+        out = torch.matmul(reshaped_x, out)
+    else:
+        out = torch.ops._C.awq_gemm(
+            reshaped_x, qweight, scales, qzeros, align_type=self.align_type
+        )
+    if bias is not None:
+        out.add_(bias)
+    return out.reshape(out_shape)
+
+
+AWQLinearMethod.repack_int4_for_kunlun = repack_int4_for_kunlun
+AWQLinearMethod.process_weights_after_loading = process_weights_after_loading
+AWQLinearMethod.apply = apply

vllm_kunlun/ops/quantization/gptq.py

@@ -0,0 +1,108 @@
+#
+# Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
+# Author: Li Wei, You Zeyu
+# Email: liwei157@baidu.com, youzeyu@baidu.com
+# This file is a part of the vllm-kunlun project.
+#
+# 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.
+
+import torch
+
+from torch.nn.parameter import Parameter
+from typing import Optional
+from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod, ExllamaState
+
+
+def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+    # for torch.compile
+    layer.qzeros = Parameter(
+        self.repack_int4_for_kunlun(layer.qzeros.data, self.quant_config.weight_bits)
+        if self.quant_config.weight_bits == 4 else layer.qzeros.data,
+        requires_grad=False
+    )
+    layer.qweight = Parameter(layer.qweight.data, requires_grad=False)
+    layer.g_idx = Parameter(layer.g_idx.data, requires_grad=False)
+    layer.scales = Parameter(layer.scales.data, requires_grad=False)
+
+    # exllama needs to shuffle the weight after the weight is loaded
+    # here we do the shuffle on first forward pass
+    if layer.exllama_state == ExllamaState.UNINITIALIZED:
+        if self.quant_config.desc_act:
+            layer.g_idx.data = torch.argsort(layer.g_idx).to(torch.int)
+        else:
+            layer.g_idx.data = torch.empty((0, ),
+                                            dtype=torch.int,
+                                            device=layer.g_idx.device)
+        layer.exllama_state = ExllamaState.READY
+
+        # No need shuffle on xpu
+        # ops.gptq_shuffle(layer.qweight, layer.g_idx,
+        #                  self.quant_config.weight_bits)
+
+
+def repack_int4_for_kunlun(self, packed: torch.Tensor, num_bits: int = 4):
+    N, K = packed.shape
+    assert num_bits == 4, "Only int4 supported now"
+    shifts = torch.arange(0, 32, num_bits, device=packed.device, dtype=torch.int32)
+
+    # Unpack int32 to int4 values
+    unpacked_gptq = (
+        packed.view(N, K // 8, 8).unsqueeze(-1) >> shifts
+    ) & 0xF  # [N, K//8, 8, 8]
+
+    # Convert to KUNLUN order
+    GPTQ_TO_KUNLUN_ORDER_FAST = [
+        32, 0, 33, 1, 34, 2, 35, 3,
+        36, 4, 37, 5, 38, 6, 39, 7,
+        40, 8, 41, 9, 42, 10, 43, 11,
+        44, 12, 45, 13, 46, 14, 47, 15,
+        48, 16, 49, 17, 50, 18, 51, 19,
+        52, 20, 53, 21, 54, 22, 55, 23,
+        56, 24, 57, 25, 58, 26, 59, 27,
+        60, 28, 61, 29, 62, 30, 63, 31,
+    ]
+    unpacked_gptq = unpacked_gptq.reshape(N, K // 8, 64)
+    unpacked_kunlun = unpacked_gptq[..., GPTQ_TO_KUNLUN_ORDER_FAST]  # [N, K//8, 64]
+
+    # Pack to int32
+    unpacked_kunlun = unpacked_kunlun.reshape(N, K // 8, 8, 8)
+    packed_kunlun = (
+        (unpacked_kunlun << shifts).sum(dim=-1, dtype=torch.int32).reshape(N, K)
+    )  # [N, K]
+
+    return packed_kunlun
+
+
+def apply(
+    self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None
+) -> torch.Tensor:
+    out_shape = x.shape[:-1] + (layer.qweight.shape[-1], )
+    reshaped_x = x.reshape(-1, x.shape[-1])
+
+    output = torch.ops.xspeedgate_ops.gptq_gemm(
+        reshaped_x,
+        layer.qweight,
+        layer.qzeros,
+        layer.scales,
+        layer.g_idx,
+        layer.exllama_state == ExllamaState.READY,
+        self.quant_config.weight_bits,
+    )
+    if bias is not None:
+        output.add_(bias)
+    return output.reshape(out_shape)
+
+
+GPTQLinearMethod.repack_int4_for_kunlun = repack_int4_for_kunlun
+GPTQLinearMethod.process_weights_after_loading = process_weights_after_loading
+GPTQLinearMethod.apply = apply

vllm_kunlun/vllm_utils_wrapper.py

@@ -1149,3 +1149,175 @@ def fake_moe_post(
     return None
 
 moe_post.register_fake(fake_moe_post)
+
+
+##################################################
+# --------------- awq_dequantize -----------------
+##################################################
+@custom_op("_C::awq_dequantize", mutates_args=())
+def awq_dequantize(
+    qweight: torch.Tensor,
+    scales: torch.Tensor,
+    zeros: torch.Tensor,
+    quant_type: int = 0,
+    align_type: int = 1,
+) -> torch.Tensor:
+    weight = torch.empty(
+        (qweight.shape[0], qweight.shape[1] * 8),
+        dtype=torch.float16,
+        device=qweight.device,
+    )
+    group_m = int(qweight.shape[0] / scales.shape[0])
+    xtorch_ops.awq_dequantize(
+        qweight=qweight,
+        scales=scales,
+        zeros=zeros,
+        weight=weight,
+        group_m=group_m,
+        quant_type=quant_type,
+        align_type=align_type,
+    )
+    return weight
+
+
+@impl("_C::awq_dequantize", "CUDA")
+def awq_dequantize_cuda(
+    qweight: torch.Tensor,
+    scales: torch.Tensor,
+    zeros: torch.Tensor,
+    quant_type: int = 0,
+    align_type: int = 1,
+) -> torch.Tensor:
+    weight = torch.empty(
+        (qweight.shape[0], qweight.shape[1] * 8),
+        dtype=torch.float16,
+        device=qweight.device,
+    )
+    group_m = int(qweight.shape[0] / scales.shape[0])
+    out = xtorch_ops.awq_dequantize(
+        qweight=qweight,
+        scales=scales,
+        zeros=zeros,
+        weight=weight,
+        group_m=group_m,
+        quant_type=quant_type,
+        align_type=align_type,
+    )
+    return weight
+
+
+def _fake_awq_dequantize(
+    qweight: torch.Tensor,
+    scales: torch.Tensor,
+    zeros: torch.Tensor,
+    quant_type: int = 0,
+    align_type: int = 1,
+) -> torch.Tensor:
+    weight = torch.empty(
+        (qweight.shape[0], qweight.shape[1] * 8),
+        dtype=torch.float16,
+        device=qweight.device,
+    )
+    return weight
+
+
+awq_dequantize.register_fake(_fake_awq_dequantize)
+
+
+##################################################
+# ------------------ awq_gemm -------------------
+##################################################
+@custom_op("_C::awq_gemm", mutates_args=())
+def awq_gemm(
+    x: torch.Tensor,
+    qweight: torch.Tensor,
+    scale: torch.Tensor,
+    zeros: torch.Tensor,
+    align_type: int = 1,
+) -> torch.Tensor:
+    out = torch.empty(
+        (x.shape[0], qweight.shape[1] * 8), dtype=torch.float16, device=x.device
+    )
+    group_size = int(qweight.shape[0] / scale.shape[0])
+    xtorch_ops.awq_gemm(
+        x=x,
+        w=qweight,
+        scale=scale,
+        zeros=zeros,
+        out=out,
+        align_type=align_type,
+        group_size=group_size,
+    )
+    return out
+
+
+@impl("_C::awq_gemm", "CUDA")
+def awq_gemm_cuda(
+    x: torch.Tensor,
+    qweight: torch.Tensor,
+    scale: torch.Tensor,
+    zeros: torch.Tensor,
+    align_type: int = 1,
+) -> torch.Tensor:
+    out = torch.empty(
+        (x.shape[0], qweight.shape[1] * 8), dtype=torch.float16, device=x.device
+    )
+    group_size = int(qweight.shape[0] / scale.shape[0])
+    xtorch_ops.awq_gemm(
+        x=x,
+        w=qweight,
+        scale=scale,
+        zeros=zeros,
+        out=out,
+        align_type=align_type,
+        group_size=group_size,
+    )
+    return out
+
+
+def _fake_awq_gemm(
+    x: torch.Tensor,
+    qweight: torch.Tensor,
+    scale: torch.Tensor,
+    zeros: torch.Tensor,
+    align_type: int = 1,
+) -> torch.Tensor:
+    out = torch.empty(
+        (x.shape[0], qweight.shape[1] * 8), dtype=torch.float16, device=x.device
+    )
+    return out
+
+
+awq_gemm.register_fake(_fake_awq_gemm)
+
+
+##################################################
+# ---------------- gptq_shuffle ------------------
+##################################################
+@custom_op("_C::gptq_shuffle", mutates_args=())
+def gptq_shuffle(
+    q_weight: torch.Tensor,
+    q_perm: torch.Tensor,
+    bit: int,
+) -> None:
+    xtorch_ops.gptq_shuffle(weight=q_weight, perm=q_perm, bit=bit)
+
+
+@impl("_C::gptq_shuffle", "CUDA")
+def gptq_shuffle_cuda(
+    q_weight: torch.Tensor,
+    q_perm: torch.Tensor,
+    bit: int,
+) -> None:
+    xtorch_ops.gptq_shuffle(weight=q_weight, perm=q_perm, bit=bit)
+
+
+def _fake_gptq_shuffle(
+    q_weight: torch.Tensor,
+    q_perm: torch.Tensor,
+    bit: int,
+) -> None:
+    return None
+
+
+gptq_shuffle.register_fake(_fake_gptq_shuffle)