[Bugfix] Try to handle older versions of pytorch (vllm-project#9086)

Signed-off-by: Alvant <[email protected]>

Author: bnellnm

tests/kernels/test_awq.py

@@ -1,11 +1,14 @@
 import os
 
+import pytest
 import torch
 
 from tests.kernels.utils import opcheck
 from vllm import _custom_ops as ops  # noqa: F401
 
 
+@pytest.mark.skipif(not hasattr(torch.ops._C, "awq_dequantize"),
+                    reason="AWQ is not supported on this GPU type.")
 def test_awq_dequantize_opcheck():
     os.environ["VLLM_USE_TRITON_AWQ"] = "0"
     qweight = torch.randint(-2000000000,
@@ -21,6 +24,8 @@ def test_awq_dequantize_opcheck():
             (qweight, scales, zeros, split_k_iters, thx, thy))
 
 
+@pytest.mark.skipif(not hasattr(torch.ops._C, "awq_gemm"),
+                    reason="AWQ is not supported on this GPU type.")
 def test_awq_gemm_opcheck():
     os.environ["VLLM_USE_TRITON_AWQ"] = "0"
     input = torch.rand((2, 8192), device='cuda', dtype=torch.float16)

tests/kernels/test_awq_marlin.py

@@ -7,6 +7,7 @@
 
 from tests.kernels.utils import (compute_max_diff, stack_and_dev, torch_moe,
                                  torch_moe_single)
+from vllm import _custom_ops as ops
 from vllm.model_executor.layers.fused_moe.fused_marlin_moe import (
     fused_marlin_moe, single_marlin_moe)
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_topk
@@ -21,6 +22,9 @@
 @pytest.mark.parametrize("e", [8, 64])
 @pytest.mark.parametrize("topk", [2, 6])
 @pytest.mark.parametrize("group_size", [-1, 32, 64, 128])
+@pytest.mark.skipif(not (ops.supports_moe_ops
+                         and hasattr(torch.ops._moe_C, "marlin_gemm_moe")),
+                    reason="Marlin is not supported on this GPU type.")
 def test_fused_marlin_moe_awq(
     m: int,
     n: int,

vllm/_custom_ops.py

@@ -1,8 +1,9 @@
 import contextlib
 import functools
-from typing import List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, List, Optional, Tuple, Union
 
 import torch
+import torch.library
 
 import vllm.envs as envs
 from vllm._core_ext import ScalarType
@@ -25,6 +26,16 @@
     import vllm._moe_C  # noqa: F401
     supports_moe_ops = True
 
+if TYPE_CHECKING:
+
+    def register_fake(fn):
+        return lambda name: fn
+else:
+    try:
+        from torch.library import register_fake
+    except ImportError:
+        from torch.library import impl_abstract as register_fake
+
 
 def hint_on_error(fn):
 
@@ -266,7 +277,7 @@ def gptq_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
 
 if hasattr(torch.ops._C, "gptq_gemm"):
 
-    @torch.library.register_fake("_C::gptq_gemm")
+    @register_fake("_C::gptq_gemm")
     def _gptq_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
                         b_gptq_qzeros: torch.Tensor,
                         b_gptq_scales: torch.Tensor, b_g_idx: torch.Tensor,
@@ -301,15 +312,15 @@ def gptq_marlin_24_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
 
 if hasattr(torch.ops._C, "gptq_marlin_24_gemm"):
 
-    @torch.library.register_fake("_C::gptq_marlin_24_gemm")
+    @register_fake("_C::gptq_marlin_24_gemm")
     def _gptq_marlin_24_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
                                   b_meta: torch.Tensor, b_scales: torch.Tensor,
                                   workspace: torch.Tensor,
                                   b_q_type: ScalarType, size_m: int,
                                   size_n: int, size_k: int) -> torch.Tensor:
         return torch.empty((size_m, size_n), device=a.device, dtype=a.dtype)
 
-    @torch.library.register_fake("_C::gptq_marlin_gemm")
+    @register_fake("_C::gptq_marlin_gemm")
     def _gptq_marlin_gemm_fake(a: torch.Tensor,
                                b_q_weight: torch.Tensor,
                                b_scales: torch.Tensor,
@@ -326,12 +337,12 @@ def _gptq_marlin_gemm_fake(a: torch.Tensor,
                                use_fp32_reduce: bool = False) -> torch.Tensor:
         return torch.empty((size_m, size_n), device=a.device, dtype=a.dtype)
 
-    @torch.library.register_fake("_C::ggml_dequantize")
+    @register_fake("_C::ggml_dequantize")
     def _ggml_dequantize_fake(W: torch.Tensor, quant_type: int, m: int,
                               n: int) -> torch.Tensor:
         return torch.empty((m, n), dtype=torch.float16, device=W.device)
 
-    @torch.library.register_fake("_C::ggml_mul_mat_vec_a8")
+    @register_fake("_C::ggml_mul_mat_vec_a8")
     def _ggml_mul_mat_vec_a8_fake(
         W: torch.Tensor,
         X: torch.Tensor,
@@ -340,7 +351,7 @@ def _ggml_mul_mat_vec_a8_fake(
     ) -> torch.Tensor:
         return torch.empty((1, row), dtype=torch.float16, device=W.device)
 
-    @torch.library.register_fake("_C::ggml_mul_mat_a8")
+    @register_fake("_C::ggml_mul_mat_a8")
     def _ggml_mul_mat_a8_fake(
         W: torch.Tensor,
         X: torch.Tensor,
@@ -350,7 +361,7 @@ def _ggml_mul_mat_a8_fake(
         batch = X.size(0)
         return torch.empty((batch, row), dtype=torch.float16, device=W.device)
 
-    @torch.library.register_fake("_C::marlin_qqq_gemm")
+    @register_fake("_C::marlin_qqq_gemm")
     def _marlin_qqq_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
                               s_tok: torch.Tensor, s_ch: torch.Tensor,
                               s_group: torch.Tensor, workspace: torch.Tensor,
@@ -360,7 +371,7 @@ def _marlin_qqq_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
                            dtype=torch.float16,
                            device=a.device)
 
-    @torch.library.register_fake("_C::marlin_gemm")
+    @register_fake("_C::marlin_gemm")
     def _marlin_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
                           b_scales: torch.Tensor, workspace: torch.Tensor,
                           size_m: int, size_n: int,
@@ -369,7 +380,7 @@ def _marlin_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
                            dtype=torch.float16,
                            device=a.device)
 
-    @torch.library.register_fake("_C::awq_dequantize")
+    @register_fake("_C::awq_dequantize")
     def _awq_dequantize_fake(qweight: torch.Tensor, scales: torch.Tensor,
                              zeros: torch.Tensor, split_k_iters: int, thx: int,
                              thy: int) -> torch.Tensor:
@@ -380,7 +391,7 @@ def _awq_dequantize_fake(qweight: torch.Tensor, scales: torch.Tensor,
                            dtype=scales.dtype,
                            device=scales.device)
 
-    @torch.library.register_fake("_C::awq_gemm")
+    @register_fake("_C::awq_gemm")
     def _awq_gemm_fake(input: torch.Tensor, qweight: torch.Tensor,
                        qzeros: torch.Tensor, scales: torch.Tensor,
                        split_k_iters: int) -> torch.Tensor:
@@ -389,7 +400,7 @@ def _awq_gemm_fake(input: torch.Tensor, qweight: torch.Tensor,
                            dtype=input.dtype,
                            device=input.device).sum(0)
 
-    @torch.library.register_fake("_C::aqlm_gemm")
+    @register_fake("_C::aqlm_gemm")
     def _aqlm_gemm_fake(input: torch.Tensor, codes: torch.Tensor,
                         codebooks: torch.Tensor, scales: torch.Tensor,
                         codebook_partition_sizes: List[int],
@@ -405,7 +416,7 @@ def _aqlm_gemm_fake(input: torch.Tensor, codes: torch.Tensor,
         output_sizes.append(-1)
         return flat_output.reshape(tuple(output_sizes))
 
-    @torch.library.register_fake("_C::aqlm_dequant")
+    @register_fake("_C::aqlm_dequant")
     def _aqlm_dequant_fake(
             codes: torch.Tensor, codebooks: torch.Tensor,
             codebook_partition_sizes: List[int]) -> torch.Tensor:
@@ -415,14 +426,14 @@ def _aqlm_dequant_fake(
                            dtype=codebooks.dtype,
                            device=codebooks.device)
 
-    @torch.library.register_fake("_C::fp8_marlin_gemm")
+    @register_fake("_C::fp8_marlin_gemm")
     def _fp8_marlin_gemm_fake(a: torch.Tensor, b_q_weight: torch.Tensor,
                               b_scales: torch.Tensor, workspace: torch.Tensor,
                               num_bits: int, size_m: int, size_n: int,
                               size_k: int) -> torch.Tensor:
         return torch.empty((size_m, size_n), dtype=a.dtype, device=a.device)
 
-    @torch.library.register_fake("_C::machete_gemm")
+    @register_fake("_C::machete_gemm")
     def machete_gemm_fake(
         a: torch.Tensor,
         # Should be the tensor returned by machete_prepack_B
@@ -440,13 +451,13 @@ def machete_gemm_fake(
         n = b_q.size(1)
         return torch.empty((m, n), device=a.device, dtype=a.dtype)
 
-    @torch.library.register_fake("_C::machete_prepack_B")
+    @register_fake("_C::machete_prepack_B")
     def machete_prepack_B_fake(b_q_weight: torch.Tensor,
                                b_type: ScalarType) -> torch.Tensor:
         return torch.empty_like(b_q_weight,
                                 memory_format=torch.contiguous_format)
 
-    @torch.library.register_fake("_C::causal_conv1d_fwd")
+    @register_fake("_C::causal_conv1d_fwd")
     def causal_conv1d_fwd_fake(x: torch.Tensor, weight: torch.Tensor,
                                bias_: Optional[torch.Tensor],
                                conv_states: Optional[torch.Tensor],
@@ -456,15 +467,15 @@ def causal_conv1d_fwd_fake(x: torch.Tensor, weight: torch.Tensor,
                                silu_activation: bool) -> torch.Tensor:
         return torch.empty_like(x)
 
-    @torch.library.register_fake("_C::causal_conv1d_update")
+    @register_fake("_C::causal_conv1d_update")
     def causal_conv1d_update_fake(
             x: torch.Tensor, conv_state: torch.Tensor, weight: torch.Tensor,
             bias_: Optional[torch.Tensor], silu_activation: bool,
             cache_seqlens: Optional[torch.Tensor],
             conv_state_indices: Optional[torch.Tensor]) -> torch.Tensor:
         return torch.empty_like(x)
 
-    @torch.library.register_fake("_C::selective_scan_fwd")
+    @register_fake("_C::selective_scan_fwd")
     def selective_scan_fwd_fake(u: torch.Tensor, delta: torch.Tensor,
                                 A: torch.Tensor, B: torch.Tensor,
                                 C: torch.Tensor, D_: Optional[torch.Tensor],
@@ -639,7 +650,7 @@ def machete_prepack_B(b_q_weight: torch.Tensor,
 
 if hasattr(torch.ops._C, "permute_cols"):
 
-    @torch.library.register_fake("_C::permute_cols")
+    @register_fake("_C::permute_cols")
     def _permute_cols_fake(a: torch.Tensor,
                            perm: torch.Tensor) -> torch.Tensor:
         return torch.empty_like(a)
@@ -837,7 +848,7 @@ def topk_softmax(topk_weights: torch.Tensor, topk_ids: torch.Tensor,
 
 if supports_moe_ops and hasattr(torch.ops._moe_C, "marlin_gemm_moe"):
 
-    @torch.library.register_fake("_moe_C::marlin_gemm_moe")
+    @register_fake("_moe_C::marlin_gemm_moe")
     def marlin_gemm_moe_fake(a: torch.Tensor, b_q_weights: torch.Tensor,
                              sorted_ids: torch.Tensor,
                              topk_weights: torch.Tensor,