mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2026-01-15 18:44:28 +08:00
466 lines
16 KiB
Python
466 lines
16 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import os
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from functools import cache, lru_cache, wraps
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from typing import TYPE_CHECKING
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import torch
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import vllm.envs as envs
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from vllm.logger import init_logger
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from vllm.utils.torch_utils import cuda_device_count_stateless
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from .interface import DeviceCapability, Platform, PlatformEnum
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if TYPE_CHECKING:
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from vllm.attention.backends.registry import AttentionBackendEnum
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from vllm.config import VllmConfig
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else:
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AttentionBackendEnum = None
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logger = init_logger(__name__)
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try:
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from amdsmi import (
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AmdSmiException,
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amdsmi_get_gpu_asic_info,
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amdsmi_get_processor_handles,
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amdsmi_init,
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amdsmi_shut_down,
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amdsmi_topo_get_link_type,
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)
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except ImportError as e:
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logger.warning("Failed to import from amdsmi with %r", e)
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try:
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import vllm._C # noqa: F401
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except ImportError as e:
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logger.warning("Failed to import from vllm._C with %r", e)
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# import custom ops, trigger op registration
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try:
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import vllm._rocm_C # noqa: F401
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except ImportError as e:
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logger.warning("Failed to import from vllm._rocm_C with %r", e)
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# Models not supported by ROCm.
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_ROCM_UNSUPPORTED_MODELS: list[str] = []
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# Models partially supported by ROCm.
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# Architecture -> Reason.
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_ROCM_SWA_REASON = ()
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_ROCM_PARTIALLY_SUPPORTED_MODELS: dict[str, str] = {}
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_ROCM_DEVICE_ID_NAME_MAP: dict[str, str] = {
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"0x74a0": "AMD_Instinct_MI300A",
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"0x74a1": "AMD_Instinct_MI300X",
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"0x74b5": "AMD_Instinct_MI300X", # MI300X VF
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"0x74a2": "AMD_Instinct_MI308X",
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"0x74a5": "AMD_Instinct_MI325X",
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"0x74b9": "AMD_Instinct_MI325X", # MI325X VF
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"0x74a9": "AMD_Instinct_MI300X_HF",
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"0x74bd": "AMD_Instinct_MI300X_HF",
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"0x744c": "AMD_Radeon_RX7900XTX",
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}
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# Prevent use of clashing `{CUDA/HIP}_VISIBLE_DEVICES`
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if "HIP_VISIBLE_DEVICES" in os.environ:
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val = os.environ["HIP_VISIBLE_DEVICES"]
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if cuda_val := os.environ.get("CUDA_VISIBLE_DEVICES", None):
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assert val == cuda_val
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else:
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os.environ["CUDA_VISIBLE_DEVICES"] = val
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# AMDSMI utils
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# Note that NVML is not affected by `{CUDA/HIP}_VISIBLE_DEVICES`,
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# all the related functions work on real physical device ids.
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# the major benefit of using AMDSMI is that it will not initialize CUDA
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def with_amdsmi_context(fn):
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@wraps(fn)
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def wrapper(*args, **kwargs):
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amdsmi_init()
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try:
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return fn(*args, **kwargs)
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finally:
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amdsmi_shut_down()
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return wrapper
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@cache
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def on_gfx1x() -> bool:
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GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName
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return any(arch in GPU_ARCH for arch in ["gfx11", "gfx12"])
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@cache
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def on_mi3xx() -> bool:
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GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName
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return any(arch in GPU_ARCH for arch in ["gfx942", "gfx950"])
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@cache
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def on_gfx9() -> bool:
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GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName
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return any(arch in GPU_ARCH for arch in ["gfx90a", "gfx942", "gfx950"])
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@cache
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def on_gfx950() -> bool:
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GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName
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return any(arch in GPU_ARCH for arch in ["gfx950"])
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@cache
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def use_rocm_custom_paged_attention(
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qtype: torch.dtype,
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head_size: int,
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block_size: int,
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gqa_ratio: int,
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max_seq_len: int,
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sliding_window: int,
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kv_cache_dtype: str,
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alibi_slopes: torch.Tensor | None = None,
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sinks: torch.Tensor | None = None,
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) -> bool:
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from vllm._aiter_ops import rocm_aiter_ops
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GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName
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ON_GFX9 = any(arch in GPU_ARCH for arch in ["gfx90a", "gfx942", "gfx950"])
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ON_GFX11_GFX12 = any(arch in GPU_ARCH for arch in ["gfx11", "gfx12"])
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# custom paged attn always supported on V0. On V1, requires sliding window
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# disabled due to observed numerical discrepancy.
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if ON_GFX9:
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return (
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(sliding_window == 0 or sliding_window == (-1, -1))
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and (qtype == torch.half or qtype == torch.bfloat16)
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and (head_size == 64 or head_size == 128)
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and (block_size == 16 or block_size == 32)
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and (gqa_ratio >= 1 and gqa_ratio <= 16)
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and max_seq_len <= 128 * 1024
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and (envs.VLLM_ROCM_CUSTOM_PAGED_ATTN)
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and not (rocm_aiter_ops.is_pa_attn_enabled())
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and sinks is None
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)
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else:
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return (
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ON_GFX11_GFX12
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and (sliding_window == 0 or sliding_window == (-1, -1))
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and (qtype == torch.half or qtype == torch.bfloat16)
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and head_size == 128
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and block_size == 16
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and (gqa_ratio >= 3 and gqa_ratio <= 16)
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and max_seq_len <= 128 * 1024
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and alibi_slopes is None
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and kv_cache_dtype == "auto"
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and envs.VLLM_ROCM_CUSTOM_PAGED_ATTN
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and sinks is None
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)
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class RocmPlatform(Platform):
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_enum = PlatformEnum.ROCM
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device_name: str = "rocm"
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device_type: str = "cuda"
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dispatch_key: str = "CUDA"
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ray_device_key: str = "GPU"
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dist_backend: str = "nccl"
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# rocm shares the same device control env var as CUDA
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device_control_env_var: str = "CUDA_VISIBLE_DEVICES"
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supported_quantization: list[str] = [
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"awq",
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"gptq",
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"fp8",
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"compressed-tensors",
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"fbgemm_fp8",
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"gguf",
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"quark",
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"ptpc_fp8",
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"mxfp4",
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"petit_nvfp4",
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"torchao",
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]
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@classmethod
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def get_vit_attn_backend(
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cls, head_size: int, dtype: torch.dtype
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) -> AttentionBackendEnum:
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from importlib.util import find_spec
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from vllm._aiter_ops import rocm_aiter_ops
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from vllm.attention.backends.registry import AttentionBackendEnum
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if rocm_aiter_ops.is_mha_enabled():
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# Note: AITER FA is only supported for Qwen-VL models.
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# TODO: Add support for other VL models in their model class.
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return AttentionBackendEnum.ROCM_AITER_FA
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if on_gfx9() and find_spec("flash_attn") is not None:
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return AttentionBackendEnum.FLASH_ATTN
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return AttentionBackendEnum.TORCH_SDPA
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@classmethod
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def get_attn_backend_cls(
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cls,
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selected_backend,
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head_size,
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dtype,
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kv_cache_dtype,
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block_size,
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use_mla,
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has_sink,
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use_sparse,
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) -> str:
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from vllm._aiter_ops import rocm_aiter_ops
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from vllm.attention.backends.registry import AttentionBackendEnum
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if use_sparse:
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raise NotImplementedError("Sparse Attention is not supported on ROCm.")
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if use_mla:
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if selected_backend is None:
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selected_backend = (
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AttentionBackendEnum.ROCM_AITER_MLA
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if rocm_aiter_ops.is_mla_enabled() or block_size == 1
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else AttentionBackendEnum.TRITON_MLA
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)
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if selected_backend == AttentionBackendEnum.TRITON_MLA:
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if block_size != 1:
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logger.info_once("Using Triton MLA backend.")
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return AttentionBackendEnum.TRITON_MLA.get_path()
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raise ValueError(
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f" The selected backend, {selected_backend.name},"
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f"does not support block size {block_size}."
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)
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if selected_backend == AttentionBackendEnum.ROCM_AITER_MLA:
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logger.info("Using AITER MLA backend.")
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return AttentionBackendEnum.ROCM_AITER_MLA.get_path()
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raise ValueError(
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f" The selected backend, {selected_backend.name},"
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f"is not MLA type while requested for MLA backend."
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)
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if selected_backend == AttentionBackendEnum.FLEX_ATTENTION:
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logger.info("Using FlexAttention backend.")
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return "vllm.v1.attention.backends.flex_attention.FlexAttentionBackend"
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if (
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rocm_aiter_ops.is_mha_enabled()
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) or selected_backend == AttentionBackendEnum.ROCM_AITER_FA:
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logger.info("Using Aiter Flash Attention backend.")
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return AttentionBackendEnum.ROCM_AITER_FA.get_path()
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if (
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rocm_aiter_ops.is_triton_unified_attn_enabled()
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) or selected_backend == AttentionBackendEnum.ROCM_AITER_UNIFIED_ATTN:
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logger.info("Using Aiter Unified Attention backend.")
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return AttentionBackendEnum.ROCM_AITER_UNIFIED_ATTN.get_path()
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if (
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envs.VLLM_V1_USE_PREFILL_DECODE_ATTENTION
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or selected_backend == AttentionBackendEnum.ROCM_ATTN
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):
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# rocm specific backend, with aiter and/or
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# triton prefix-prefill
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logger.info("Using Rocm Attention backend.")
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return AttentionBackendEnum.ROCM_ATTN.get_path()
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# default case, using triton unified attention
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logger.info("Using Triton Attention backend.")
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return AttentionBackendEnum.TRITON_ATTN.get_path()
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@classmethod
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def set_device(cls, device: torch.device) -> None:
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"""
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Set the device for the current platform.
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"""
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torch.cuda.set_device(device)
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@classmethod
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@lru_cache(maxsize=8)
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def get_device_capability(cls, device_id: int = 0) -> DeviceCapability | None:
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major, minor = torch.cuda.get_device_capability(device_id)
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return DeviceCapability(major=major, minor=minor)
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@classmethod
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@with_amdsmi_context
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def is_fully_connected(cls, physical_device_ids: list[int]) -> bool:
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"""
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Query if the set of gpus are fully connected by xgmi (1 hop)
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"""
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handles = [amdsmi_get_processor_handles()[i] for i in physical_device_ids]
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for i, handle in enumerate(handles):
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for j, peer_handle in enumerate(handles):
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if i < j:
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try:
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link_type = amdsmi_topo_get_link_type(handle, peer_handle)
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# type is 2 for XGMI
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if link_type["hops"] != 1 or link_type["type"] != 2:
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return False
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except AmdSmiException as error:
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logger.error("AMD 1 hop XGMI detection failed.", exc_info=error)
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return False
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return True
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@classmethod
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@with_amdsmi_context
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@lru_cache(maxsize=8)
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def get_device_name(cls, device_id: int = 0) -> str:
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physical_device_id = cls.device_id_to_physical_device_id(device_id)
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handle = amdsmi_get_processor_handles()[physical_device_id]
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asic_info = amdsmi_get_gpu_asic_info(handle)
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device_name: str = asic_info["device_id"]
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if device_name in _ROCM_DEVICE_ID_NAME_MAP:
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return _ROCM_DEVICE_ID_NAME_MAP[device_name]
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return asic_info["market_name"]
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@classmethod
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def get_device_total_memory(cls, device_id: int = 0) -> int:
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device_props = torch.cuda.get_device_properties(device_id)
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return device_props.total_memory
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@classmethod
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def check_and_update_config(cls, vllm_config: "VllmConfig") -> None:
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from vllm._aiter_ops import rocm_aiter_ops
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from vllm.config.compilation import CUDAGraphMode
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cache_config = vllm_config.cache_config
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compilation_config = vllm_config.compilation_config
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parallel_config = vllm_config.parallel_config
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is_eager_execution = compilation_config == CUDAGraphMode.NONE
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use_aiter_rms_norm = rocm_aiter_ops.is_rmsnorm_enabled()
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if cache_config and cache_config.block_size is None:
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cache_config.block_size = 16
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if parallel_config.worker_cls == "auto":
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parallel_config.worker_cls = "vllm.v1.worker.gpu_worker.Worker"
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# Aiter rms norm perform best when CUDA Graph capture is enabled.
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if (
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use_aiter_rms_norm
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and not is_eager_execution
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and "-rms_norm" not in compilation_config.custom_ops
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):
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compilation_config.custom_ops.append("+rms_norm")
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@classmethod
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def verify_model_arch(cls, model_arch: str) -> None:
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if model_arch in _ROCM_UNSUPPORTED_MODELS:
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raise ValueError(
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f"Model architecture '{model_arch}' is not supported by ROCm for now."
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)
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if model_arch in _ROCM_PARTIALLY_SUPPORTED_MODELS:
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msg = _ROCM_PARTIALLY_SUPPORTED_MODELS[model_arch]
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logger.warning(
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"Model architecture '%s' is partially supported by ROCm: %s",
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model_arch,
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msg,
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)
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@classmethod
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def verify_quantization(cls, quant: str) -> None:
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super().verify_quantization(quant)
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if quant == "awq" and not envs.VLLM_USE_TRITON_AWQ:
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logger.warning(
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"Using AWQ quantization with ROCm, but VLLM_USE_TRITON_AWQ"
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" is not set, enabling VLLM_USE_TRITON_AWQ."
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)
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os.environ["VLLM_USE_TRITON_AWQ"] = "1"
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@classmethod
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def get_punica_wrapper(cls) -> str:
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return "vllm.lora.punica_wrapper.punica_gpu.PunicaWrapperGPU"
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@classmethod
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def get_current_memory_usage(
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cls, device: torch.types.Device | None = None
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) -> float:
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torch.cuda.reset_peak_memory_stats(device)
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return torch.cuda.mem_get_info(device)[1] - torch.cuda.mem_get_info(device)[0]
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@classmethod
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def get_device_communicator_cls(cls) -> str:
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return (
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"vllm.distributed.device_communicators.cuda_communicator.CudaCommunicator" # noqa
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)
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@classmethod
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def supports_mx(cls) -> bool:
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gcn_arch = torch.cuda.get_device_properties(0).gcnArchName
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return any(gfx in gcn_arch for gfx in ["gfx95"])
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@classmethod
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def supports_fp8(cls) -> bool:
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gcn_arch = torch.cuda.get_device_properties(0).gcnArchName
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return any(gfx in gcn_arch for gfx in ["gfx94", "gfx95", "gfx12"])
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@classmethod
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def is_fp8_fnuz(cls) -> bool:
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# only device 0 is checked, this assumes MI300 platforms are homogeneous
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return "gfx94" in torch.cuda.get_device_properties(0).gcnArchName
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@classmethod
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def fp8_dtype(cls) -> torch.dtype:
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if cls.is_fp8_fnuz():
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return torch.float8_e4m3fnuz
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else:
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return torch.float8_e4m3fn
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@classmethod
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def use_custom_allreduce(cls) -> bool:
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# We only enable custom allreduce for MI300 series
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gcn_arch = torch.cuda.get_device_properties(0).gcnArchName
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supported_archs = ["gfx94", "gfx95"]
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return any(gfx in gcn_arch for gfx in supported_archs)
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@classmethod
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def opaque_attention_op(cls) -> bool:
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return True
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@classmethod
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def is_navi(cls) -> bool:
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return "gfx1" in torch.cuda.get_device_properties(0).gcnArchName
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@classmethod
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def get_static_graph_wrapper_cls(cls) -> str:
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return "vllm.compilation.cuda_graph.CUDAGraphWrapper"
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@classmethod
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def device_count(cls) -> int:
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return cuda_device_count_stateless()
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@classmethod
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def check_if_supports_dtype(cls, dtype: torch.dtype):
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if dtype == torch.bfloat16: # noqa: SIM102
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if not cls.has_device_capability(80):
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capability = cls.get_device_capability()
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gpu_name = cls.get_device_name()
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if capability is None:
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compute_str = "does not have a compute capability"
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else:
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version_str = capability.as_version_str()
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compute_str = f"has compute capability {version_str}"
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raise ValueError(
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"Bfloat16 is only supported on GPUs "
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"with compute capability of at least 8.0. "
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f"Your {gpu_name} GPU {compute_str}. "
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"You can use float16 instead by explicitly setting the "
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"`dtype` flag in CLI, for example: --dtype=half."
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)
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@classmethod
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def support_hybrid_kv_cache(cls) -> bool:
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return True
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@classmethod
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def support_static_graph_mode(cls) -> bool:
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return True
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