[Chore] Clean up pytorch helper functions in vllm.utils (vllm-project#26908)

Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Signed-off-by: isotr0py <2037008807@qq.com>
Signed-off-by: Alberto Perdomo <aperdomo@redhat.com>

Author: Isotr0py

benchmarks/kernels/bench_per_token_quant_fp8.py

@@ -10,7 +10,8 @@
 from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
 from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
 from vllm.triton_utils import triton
-from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
+from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 
 
 def with_triton_mode(fn):

benchmarks/kernels/benchmark_activation.py

@@ -10,7 +10,8 @@
 from vllm.model_executor.custom_op import CustomOp
 from vllm.platforms import current_platform
 from vllm.triton_utils import triton
-from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
+from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 
 batch_size_range = [1, 16, 32, 64, 128]
 seq_len_range = [1, 16, 64, 128, 256, 512, 1024, 2048, 4096]

benchmarks/kernels/benchmark_layernorm.py

@@ -7,7 +7,8 @@
 
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.platforms import current_platform
-from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
+from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 
 
 @torch.inference_mode()

benchmarks/kernels/benchmark_paged_attention.py

@@ -9,9 +9,9 @@
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils import (
+from vllm.utils import FlexibleArgumentParser
+from vllm.utils.torch_utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
-    FlexibleArgumentParser,
     create_kv_caches_with_random,
 )
 

benchmarks/kernels/benchmark_quant.py

@@ -7,7 +7,8 @@
 
 from vllm import _custom_ops as ops
 from vllm.platforms import current_platform
-from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser
+from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 
 
 @torch.inference_mode()

benchmarks/kernels/benchmark_reshape_and_cache.py

@@ -9,9 +9,9 @@
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils import (
+from vllm.utils import FlexibleArgumentParser
+from vllm.utils.torch_utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
-    FlexibleArgumentParser,
     create_kv_caches_with_random,
 )
 

benchmarks/kernels/benchmark_reshape_and_cache_flash.py

@@ -12,9 +12,9 @@
 )
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils import (
+from vllm.utils import FlexibleArgumentParser
+from vllm.utils.torch_utils import (
     STR_DTYPE_TO_TORCH_DTYPE,
-    FlexibleArgumentParser,
     create_kv_caches_with_random_flash,
 )
 

tests/compile/piecewise/test_full_cudagraph.py

@@ -11,7 +11,7 @@
 from vllm import LLM, SamplingParams
 from vllm.config import CompilationConfig
 from vllm.platforms import current_platform
-from vllm.utils import is_torch_equal_or_newer
+from vllm.utils.torch_utils import is_torch_equal_or_newer
 
 
 @contextlib.contextmanager

tests/compile/piecewise/test_multiple_graphs.py

@@ -20,7 +20,7 @@
     set_current_vllm_config,
 )
 from vllm.forward_context import BatchDescriptor, set_forward_context
-from vllm.utils import is_torch_equal_or_newer
+from vllm.utils.torch_utils import is_torch_equal_or_newer
 
 # This import automatically registers `torch.ops.silly.attention`
 from .. import silly_attention  # noqa: F401

tests/compile/piecewise/test_simple.py

@@ -19,7 +19,7 @@
     set_current_vllm_config,
 )
 from vllm.forward_context import BatchDescriptor, set_forward_context
-from vllm.utils import is_torch_equal_or_newer
+from vllm.utils.torch_utils import is_torch_equal_or_newer
 
 # This import automatically registers `torch.ops.silly.attention`
 from ..silly_attention import get_global_counter, reset_global_counter