Initial commit for vLLM-Kunlun Plugin

2025-12-10 12:05:39 +08:00
commit c728e52505
131 changed files with 28816 additions and 0 deletions
--- a/vllm_kunlun/ops/quantization/gptq.py
+++ b/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