[Bugfix] Fix triton import with local TritonPlaceholder (#17446)

Signed-off-by: Mengqing Cao <cmq0113@163.com>

benchmarks/kernels/benchmark_moe.py

@@ -10,12 +10,12 @@ from typing import Any, TypedDict
 
 import ray
 import torch
-import triton
 from ray.experimental.tqdm_ray import tqdm
 from transformers import AutoConfig
 
 from vllm.model_executor.layers.fused_moe.fused_moe import *
 from vllm.platforms import current_platform
+from vllm.triton_utils import triton
 from vllm.utils import FlexibleArgumentParser
 
 FP8_DTYPE = current_platform.fp8_dtype()

benchmarks/kernels/benchmark_rmsnorm.py

@@ -4,11 +4,11 @@ import itertools
 from typing import Optional, Union
 
 import torch
-import triton
 from flashinfer.norm import fused_add_rmsnorm, rmsnorm
 from torch import nn
 
 from vllm import _custom_ops as vllm_ops
+from vllm.triton_utils import triton
 
 
 class HuggingFaceRMSNorm(nn.Module):

benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py

@@ -6,13 +6,13 @@ import time
 # Import DeepGEMM functions
 import deep_gemm
 import torch
-import triton
 from deep_gemm import calc_diff, ceil_div, get_col_major_tma_aligned_tensor
 
 # Import vLLM functions
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     per_token_group_quant_fp8, w8a8_block_fp8_matmul)
+from vllm.triton_utils import triton
 
 
 # Copied from

tests/kernels/attention/test_flashmla.py

@@ -5,11 +5,11 @@ import random
 
 import pytest
 import torch
-import triton
 
 from vllm.attention.ops.flashmla import (flash_mla_with_kvcache,
                                          get_mla_metadata,
                                          is_flashmla_supported)
+from vllm.triton_utils import triton
 
 
 def cal_diff(x: torch.Tensor, y: torch.Tensor, name: str) -> None:

tests/test_triton_utils.py

@@ -0,0 +1,92 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import sys
+import types
+from unittest import mock
+
+from vllm.triton_utils.importing import (TritonLanguagePlaceholder,
+                                         TritonPlaceholder)
+
+
+def test_triton_placeholder_is_module():
+    triton = TritonPlaceholder()
+    assert isinstance(triton, types.ModuleType)
+    assert triton.__name__ == "triton"
+
+
+def test_triton_language_placeholder_is_module():
+    triton_language = TritonLanguagePlaceholder()
+    assert isinstance(triton_language, types.ModuleType)
+    assert triton_language.__name__ == "triton.language"
+
+
+def test_triton_placeholder_decorators():
+    triton = TritonPlaceholder()
+
+    @triton.jit
+    def foo(x):
+        return x
+
+    @triton.autotune
+    def bar(x):
+        return x
+
+    @triton.heuristics
+    def baz(x):
+        return x
+
+    assert foo(1) == 1
+    assert bar(2) == 2
+    assert baz(3) == 3
+
+
+def test_triton_placeholder_decorators_with_args():
+    triton = TritonPlaceholder()
+
+    @triton.jit(debug=True)
+    def foo(x):
+        return x
+
+    @triton.autotune(configs=[], key="x")
+    def bar(x):
+        return x
+
+    @triton.heuristics(
+        {"BLOCK_SIZE": lambda args: 128 if args["x"] > 1024 else 64})
+    def baz(x):
+        return x
+
+    assert foo(1) == 1
+    assert bar(2) == 2
+    assert baz(3) == 3
+
+
+def test_triton_placeholder_language():
+    lang = TritonLanguagePlaceholder()
+    assert isinstance(lang, types.ModuleType)
+    assert lang.__name__ == "triton.language"
+    assert lang.constexpr is None
+    assert lang.dtype is None
+    assert lang.int64 is None
+
+
+def test_triton_placeholder_language_from_parent():
+    triton = TritonPlaceholder()
+    lang = triton.language
+    assert isinstance(lang, TritonLanguagePlaceholder)
+
+
+def test_no_triton_fallback():
+    # clear existing triton modules
+    sys.modules.pop("triton", None)
+    sys.modules.pop("triton.language", None)
+    sys.modules.pop("vllm.triton_utils", None)
+    sys.modules.pop("vllm.triton_utils.importing", None)
+
+    # mock triton not being installed
+    with mock.patch.dict(sys.modules, {"triton": None}):
+        from vllm.triton_utils import HAS_TRITON, tl, triton
+        assert HAS_TRITON is False
+        assert triton.__class__.__name__ == "TritonPlaceholder"
+        assert triton.language.__class__.__name__ == "TritonLanguagePlaceholder"
+        assert tl.__class__.__name__ == "TritonLanguagePlaceholder"

vllm/attention/ops/blocksparse_attention/blocksparse_attention_kernel.py

@@ -1,8 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import torch
-import triton
+
-import triton.language as tl
+from vllm.triton_utils import tl, triton
 
 
 def blocksparse_flash_attn_varlen_fwd(

vllm/attention/ops/blocksparse_attention/utils.py

@@ -8,7 +8,8 @@ from functools import lru_cache
 
 import numpy as np
 import torch
-import triton
+
+from vllm.triton_utils import triton
 
 
 class csr_matrix:

vllm/attention/ops/chunked_prefill_paged_decode.py

@@ -7,11 +7,10 @@
 #  - Thomas Parnell <tpa@zurich.ibm.com>
 
 import torch
-import triton
-import triton.language as tl
 
 from vllm import _custom_ops as ops
 from vllm.platforms.rocm import use_rocm_custom_paged_attention
+from vllm.triton_utils import tl, triton
 
 from .prefix_prefill import context_attention_fwd
 

vllm/attention/ops/prefix_prefill.py

@@ -4,10 +4,9 @@
 # https://github.com/ModelTC/lightllm/blob/main/lightllm/models/llama/triton_kernel/context_flashattention_nopad.py
 
 import torch
-import triton
-import triton.language as tl
 
 from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
 
 # Static kernels parameters
 BASE_BLOCK = 128 if current_platform.has_device_capability(80) else 64

vllm/attention/ops/triton_decode_attention.py

@@ -30,10 +30,8 @@ It supports page size >= 1.
 
 import logging
 
-import triton
-import triton.language as tl
-
 from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
 
 is_hip_ = current_platform.is_rocm()
 

vllm/attention/ops/triton_flash_attention.py

@@ -25,11 +25,10 @@ Currently only the forward kernel is supported, and contains these features:
 from typing import Optional
 
 import torch
-import triton
-import triton.language as tl
 
 from vllm import _custom_ops as ops
 from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
 
 SUPPORTED_LAYOUTS = ['thd', 'bhsd', 'bshd']
 

vllm/attention/ops/triton_merge_attn_states.py

@@ -2,8 +2,8 @@
 from typing import Optional
 
 import torch
-import triton
+
-import triton.language as tl
+from vllm.triton_utils import tl, triton
 
 
 # Implements section 2.2 of https://www.arxiv.org/pdf/2501.01005

vllm/lora/ops/triton_ops/kernel_utils.py

@@ -2,8 +2,7 @@
 """
 Utilities for Punica kernel construction.
 """
-import triton
+from vllm.triton_utils import tl, triton
-import triton.language as tl
 
 
 @triton.jit

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -6,8 +6,6 @@ import os
 from typing import Any, Callable, Dict, List, Optional, Tuple
 
 import torch
-import triton
-import triton.language as tl
 
 import vllm.envs as envs
 from vllm import _custom_ops as ops
@@ -21,6 +19,7 @@ from vllm.model_executor.layers.quantization.utils.fp8_utils import (
 from vllm.model_executor.layers.quantization.utils.int8_utils import (
     per_token_group_quant_int8, per_token_quant_int8)
 from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
 from vllm.utils import direct_register_custom_op
 
 from .rocm_aiter_fused_moe import is_rocm_aiter_moe_enabled

vllm/model_executor/layers/fused_moe/moe_align_block_size.py

@@ -2,11 +2,10 @@
 from typing import Optional, Tuple
 
 import torch
-import triton
-import triton.language as tl
 
 import vllm.envs as envs
 from vllm import _custom_ops as ops
+from vllm.triton_utils import tl, triton
 from vllm.utils import round_up
 
 

vllm/model_executor/layers/lightning_attn.py

@@ -1,9 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 import torch
-import triton
-import triton.language as tl
 from einops import rearrange
 
+from vllm.triton_utils import tl, triton
+
 
 @triton.jit
 def _fwd_diag_kernel(Q, K, V, Out, S, b: tl.constexpr, h: tl.constexpr, n,

vllm/model_executor/layers/mamba/ops/mamba_ssm.py

@@ -4,13 +4,11 @@
 # Adapted from https://github.com/state-spaces/mamba/blob/v2.2.4/mamba_ssm/ops/triton/selective_state_update.py
 
 import torch
-import triton
-import triton.language as tl
 from packaging import version
 
 from vllm import _custom_ops as ops
 from vllm.attention.backends.utils import PAD_SLOT_ID
-from vllm.triton_utils import HAS_TRITON
+from vllm.triton_utils import HAS_TRITON, tl, triton
 
 TRITON3 = HAS_TRITON and (version.parse(triton.__version__)
                           >= version.parse("3.0.0"))

vllm/model_executor/layers/mamba/ops/ssd_bmm.py

@@ -8,8 +8,8 @@
 import math
 
 import torch
-import triton
+
-import triton.language as tl
+from vllm.triton_utils import tl, triton
 
 
 @triton.autotune(

vllm/model_executor/layers/mamba/ops/ssd_chunk_scan.py

@@ -6,10 +6,10 @@
 # ruff: noqa: E501,SIM102
 
 import torch
-import triton
-import triton.language as tl
 from packaging import version
 
+from vllm.triton_utils import tl, triton
+
 TRITON_22 = version.parse(triton.__version__) >= version.parse('2.2.0')
 
 

vllm/model_executor/layers/mamba/ops/ssd_chunk_state.py

@@ -8,8 +8,8 @@
 import math
 
 import torch
-import triton
+
-import triton.language as tl
+from vllm.triton_utils import tl, triton
 
 from .mamba_ssm import softplus
 

vllm/model_executor/layers/mamba/ops/ssd_combined.py

@@ -6,10 +6,11 @@
 # ruff: noqa: E501
 
 import torch
-import triton
 from einops import rearrange
 from packaging import version
 
+from vllm.triton_utils import triton
+
 from .ssd_bmm import _bmm_chunk_fwd
 from .ssd_chunk_scan import _chunk_scan_fwd
 from .ssd_chunk_state import (_chunk_cumsum_fwd, _chunk_state_fwd,

vllm/model_executor/layers/mamba/ops/ssd_state_passing.py

@@ -6,8 +6,8 @@
 # ruff: noqa: E501
 
 import torch
-import triton
+
-import triton.language as tl
+from vllm.triton_utils import tl, triton
 
 
 @triton.autotune(

vllm/model_executor/layers/quantization/awq_triton.py

@@ -1,8 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import torch
-import triton
+
-import triton.language as tl
+from vllm.triton_utils import tl, triton
 
 AWQ_TRITON_SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128]
 

vllm/model_executor/layers/quantization/compressed_tensors/triton_scaled_mm.py

@@ -3,8 +3,8 @@
 from typing import Optional, Type
 
 import torch
-import triton
+
-import triton.language as tl
+from vllm.triton_utils import tl, triton
 
 
 def is_weak_contiguous(x: torch.Tensor):

vllm/model_executor/layers/quantization/utils/fp8_utils.py

@@ -7,8 +7,6 @@ import os
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
-import triton
-import triton.language as tl
 
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
@@ -17,6 +15,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     CUTLASS_BLOCK_FP8_SUPPORTED)
 from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
 from vllm.utils import direct_register_custom_op
 
 logger = init_logger(__name__)

vllm/model_executor/layers/quantization/utils/int8_utils.py

@@ -8,10 +8,9 @@ import os
 from typing import Any, Dict, List, Optional, Tuple
 
 import torch
-import triton
-import triton.language as tl
 
 from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
 
 logger = logging.getLogger(__name__)
 

vllm/triton_utils/__init__.py

@@ -1,5 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
 
-from vllm.triton_utils.importing import HAS_TRITON
+from vllm.triton_utils.importing import (HAS_TRITON, TritonLanguagePlaceholder,
+                                         TritonPlaceholder)
 
-__all__ = ["HAS_TRITON"]
+if HAS_TRITON:
+    import triton
+    import triton.language as tl
+else:
+    triton = TritonPlaceholder()
+    tl = TritonLanguagePlaceholder()
+
+__all__ = ["HAS_TRITON", "triton", "tl"]

vllm/triton_utils/importing.py

@@ -16,6 +16,7 @@ if not HAS_TRITON:
     logger.info("Triton not installed or not compatible; certain GPU-related"
                 " functions will not be available.")
 
+
 class TritonPlaceholder(types.ModuleType):
 
     def __init__(self):
@@ -31,13 +32,14 @@ if not HAS_TRITON:
 
     def _dummy_decorator(self, name):
 
-            def decorator(func=None, **kwargs):
+        def decorator(*args, **kwargs):
-                if func is None:
+            if args and callable(args[0]):
+                return args[0]
             return lambda f: f
-                return func
 
         return decorator
 
+
 class TritonLanguagePlaceholder(types.ModuleType):
 
     def __init__(self):

vllm/v1/sample/rejection_sampler.py

@@ -3,10 +3,9 @@ from typing import Optional
 
 import torch
 import torch.nn as nn
-import triton
-import triton.language as tl
 
 from vllm.logger import init_logger
+from vllm.triton_utils import tl, triton
 from vllm.v1.sample.metadata import SamplingMetadata
 from vllm.v1.sample.ops.topk_topp_sampler import apply_top_k_top_p
 from vllm.v1.spec_decode.metadata import SpecDecodeMetadata

vllm/v1/spec_decode/eagle.py

@@ -1,8 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 import torch
 import torch.nn as nn
-import triton
-import triton.language as tl
 
 from vllm.config import CompilationLevel, VllmConfig, set_current_vllm_config
 from vllm.forward_context import set_forward_context
@@ -11,6 +9,7 @@ from vllm.model_executor.model_loader.loader import get_model_loader
 from vllm.model_executor.model_loader.utils import set_default_torch_dtype
 from vllm.model_executor.models import ModelRegistry
 from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
+from vllm.triton_utils import tl, triton
 from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata
 from vllm.v1.sample.metadata import SamplingMetadata