skip ggml_dequantize

vllm/_custom_ops.py

@@ -2,7 +2,7 @@ import contextlib
 import functools
 
 import importlib
-from typing import TYPE_CHECKING, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, List, Optional, Tuple, Union, Type
 
 import torch
 import torch.library
@@ -863,32 +863,32 @@ if hasattr(torch.ops._C, "gptq_marlin_24_gemm"):
                                 memory_format=torch.contiguous_format)
 
 
-if hasattr(torch.ops._C, "ggml_dequantize"):
+# if hasattr(torch.ops._C, "ggml_dequantize"):
 
-    @register_fake("_C::ggml_dequantize")
-    def _ggml_dequantize_fake(W: torch.Tensor, quant_type: int,
-                              m: torch.SymInt,
-                              n: torch.SymInt) -> torch.Tensor:
-        return torch.empty((m, n), dtype=torch.float16, device=W.device)
+#     @register_fake("_C::ggml_dequantize")
+#     def _ggml_dequantize_fake(W: torch.Tensor, quant_type: int,
+#                               m: torch.SymInt,
+#                               n: torch.SymInt) -> torch.Tensor:
+#         return torch.empty((m, n), dtype=torch.float16, device=W.device)
 
-    @register_fake("_C::ggml_mul_mat_vec_a8")
-    def _ggml_mul_mat_vec_a8_fake(
-        W: torch.Tensor,
-        X: torch.Tensor,
-        quant_type: int,
-        row: torch.SymInt,
-    ) -> torch.Tensor:
-        return torch.empty((1, row), dtype=torch.float16, device=W.device)
-
-    @register_fake("_C::ggml_mul_mat_a8")
-    def _ggml_mul_mat_a8_fake(
-        W: torch.Tensor,
-        X: torch.Tensor,
-        quant_type: int,
-        row: torch.SymInt,
-    ) -> torch.Tensor:
-        batch = X.size(0)
-        return torch.empty((batch, row), dtype=torch.float16, device=W.device)
+#     @register_fake("_C::ggml_mul_mat_vec_a8")
+#     def _ggml_mul_mat_vec_a8_fake(
+#         W: torch.Tensor,
+#         X: torch.Tensor,
+#         quant_type: int,
+#         row: torch.SymInt,
+#     ) -> torch.Tensor:
+#         return torch.empty((1, row), dtype=torch.float16, device=W.device)
+
+#     @register_fake("_C::ggml_mul_mat_a8")
+#     def _ggml_mul_mat_a8_fake(
+#         W: torch.Tensor,
+#         X: torch.Tensor,
+#         quant_type: int,
+#         row: torch.SymInt,
+#     ) -> torch.Tensor:
+#         batch = X.size(0)
+#         return torch.empty((batch, row), dtype=torch.float16, device=W.device)
 
 
 # cutlass
@@ -1231,9 +1231,9 @@ def marlin_qqq_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
 
 
 # gguf
-def ggml_dequantize(W: torch.Tensor, quant_type: int, m: int,
-                    n: int) -> torch.Tensor:
-    return torch.ops._C.ggml_dequantize(W, quant_type, m, n)
+# def ggml_dequantize(W: torch.Tensor, quant_type: int, m: int,
+#                     n: int) -> torch.Tensor:
+#     return torch.ops._C.ggml_dequantize(W, quant_type, m, n)
 
 
 def ggml_mul_mat_vec_a8(

vllm/worker/worker_base.py

@@ -15,6 +15,7 @@ from vllm.platforms import current_platform
 from vllm.sequence import ExecuteModelRequest, IntermediateTensors
 from vllm.utils import (enable_trace_function_call_for_thread,
                         resolve_obj_by_qualname, update_environment_variables)
+from vllm.worker.cache_engine import CacheEngine
 from vllm.worker.model_runner_base import (BroadcastableModelInput,
                                            ModelRunnerBase,
                                            ModelRunnerInputBase)