71683ca6f6
Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu> Signed-off-by: Woosuk Kwon <woosuk@thinkingmachines.ai>
464 lines
18 KiB
Python
464 lines
18 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 datetime import timedelta
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from functools import cache, lru_cache, wraps
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from typing import TYPE_CHECKING, Optional
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import torch
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from torch.distributed import PrefixStore, ProcessGroup
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from torch.distributed.distributed_c10d import is_nccl_available
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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 import cuda_device_count_stateless
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from .interface import DeviceCapability, Platform, PlatformEnum, _Backend
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if TYPE_CHECKING:
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from vllm.config import ModelConfig, VllmConfig
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logger = init_logger(__name__)
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try:
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from amdsmi import (AmdSmiException, amdsmi_get_gpu_asic_info,
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amdsmi_get_processor_handles, amdsmi_init,
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amdsmi_shut_down, amdsmi_topo_get_link_type)
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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 = ("Sliding window attention (SWA) is not yet supported in "
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"Triton flash attention. For half-precision SWA support, "
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"please use CK flash attention by setting "
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"`VLLM_USE_TRITON_FLASH_ATTN=0`")
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_ROCM_PARTIALLY_SUPPORTED_MODELS: dict[str, str] = {
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"Qwen2ForCausalLM":
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_ROCM_SWA_REASON,
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"MistralForCausalLM":
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_ROCM_SWA_REASON,
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"MixtralForCausalLM":
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_ROCM_SWA_REASON,
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"PaliGemmaForConditionalGeneration":
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("ROCm flash attention does not yet "
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"fully support 32-bit precision on PaliGemma"),
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"Phi3VForCausalLM":
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("ROCm Triton flash attention may run into compilation errors due to "
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"excessive use of shared memory. If this happens, disable Triton FA "
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"by setting `VLLM_USE_TRITON_FLASH_ATTN=0`")
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}
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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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"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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}
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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 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: Optional[torch.Tensor] = None,
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sinks: Optional[torch.Tensor] = None) -> bool:
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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 ((not envs.VLLM_USE_V1 or sliding_window == 0
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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 (envs.VLLM_ROCM_USE_AITER_PAGED_ATTN
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and envs.VLLM_ROCM_USE_AITER) and sinks is None)
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else:
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return (ON_GFX11_GFX12 and (not envs.VLLM_USE_V1 or sliding_window == 0
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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 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 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 and sinks is None)
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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", "gptq", "fp8", "compressed-tensors", "fbgemm_fp8", "gguf",
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"quark", "ptpc_fp8", "mxfp4"
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]
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@classmethod
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def get_vit_attn_backend(cls, support_fa: bool = False) -> _Backend:
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if support_fa:
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if (envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_MHA
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and on_gfx9()):
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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 _Backend.ROCM_AITER_FA
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if on_gfx9():
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return _Backend.FLASH_ATTN
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return _Backend.TORCH_SDPA
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@classmethod
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def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
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kv_cache_dtype, block_size, use_v1, use_mla,
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has_sink) -> str:
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if use_mla:
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from vllm.attention.backends.rocm_aiter_mla import (
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is_aiter_mla_enabled)
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if selected_backend is None:
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selected_backend = (_Backend.ROCM_AITER_MLA if
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is_aiter_mla_enabled() or block_size == 1
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else _Backend.TRITON_MLA)
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if selected_backend == _Backend.TRITON_MLA:
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if block_size != 1:
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if use_v1:
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logger.info_once(
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"Using Triton MLA backend on V1 engine.")
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return ("vllm.v1.attention.backends.mla."
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"triton_mla.TritonMLABackend")
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else:
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logger.info("Using Triton MLA backend.")
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return "vllm.attention.backends.triton_mla.TritonMLABackend" # noqa: E501
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else:
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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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elif selected_backend == _Backend.ROCM_AITER_MLA \
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or selected_backend == _Backend.ROCM_AITER_MLA_VLLM_V1:
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if block_size == 1:
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if use_v1:
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logger.info("Using AITER MLA backend on V1 engine.")
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return "vllm.v1.attention.backends.mla.rocm_aiter_mla.AiterMLABackend" # noqa: E501
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else:
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logger.info("Using AITER MLA backend")
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return "vllm.attention.backends.rocm_aiter_mla.AiterMLABackend" # noqa: E501
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else:
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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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"(currently only supports block size 1)")
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else:
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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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if selected_backend is None or selected_backend == _Backend.FLASH_ATTN:
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selected_backend = _Backend.ROCM_FLASH
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if envs.VLLM_USE_V1:
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if envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_MHA \
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and on_gfx9():
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logger.info("Using Flash Attention backend on V1 engine.")
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return ("vllm.v1.attention.backends."
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"rocm_aiter_fa.AiterFlashAttentionBackend")
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else:
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logger.info("Using Triton Attention backend on V1 engine.")
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return ("vllm.v1.attention.backends."
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"triton_attn.TritonAttentionBackend")
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if selected_backend == _Backend.ROCM_FLASH:
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if not cls.has_device_capability(90):
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# not Instinct series GPUs.
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logger.info("flash_attn is not supported on NAVI GPUs.")
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else:
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logger.info("%s is not supported in AMD GPUs.", selected_backend)
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logger.info("Using ROCmFlashAttention backend.")
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return "vllm.attention.backends.rocm_flash_attn.ROCmFlashAttentionBackend" # noqa: E501
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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,
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device_id: int = 0
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) -> Optional[DeviceCapability]:
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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 = [
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amdsmi_get_processor_handles()[i] for i in physical_device_ids
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]
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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(
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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.",
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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 is_async_output_supported(cls, enforce_eager: Optional[bool]) -> bool:
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if enforce_eager and not envs.VLLM_USE_V1:
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logger.warning(
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"To see benefits of async output processing, enable CUDA "
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"graph. Since, enforce-eager is enabled, async output "
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"processor cannot be used")
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return False
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return True
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@classmethod
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def check_and_update_config(cls, vllm_config: "VllmConfig") -> None:
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cache_config = vllm_config.cache_config
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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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parallel_config = vllm_config.parallel_config
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if parallel_config.worker_cls == "auto":
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if vllm_config.speculative_config:
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if not envs.VLLM_USE_V1:
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raise NotImplementedError(
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"Speculative decoding is not supported on vLLM V0.")
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parallel_config.worker_cls = "vllm.v1.worker.gpu_worker.Worker"
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else:
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if envs.VLLM_USE_V1:
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parallel_config.worker_cls = \
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"vllm.v1.worker.gpu_worker.Worker"
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else:
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parallel_config.worker_cls = "vllm.worker.worker.Worker"
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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(f"Model architecture '{model_arch}' is not "
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"supported by ROCm for now.")
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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 "
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"supported by ROCm: %s", model_arch, msg)
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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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envs.VLLM_USE_TRITON_AWQ = True
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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(cls,
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device: Optional[torch.types.Device] = 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(
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device)[0]
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@classmethod
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def get_device_communicator_cls(cls) -> str:
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return "vllm.distributed.device_communicators.cuda_communicator.CudaCommunicator" # noqa
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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 supports_v1(cls, model_config: "ModelConfig") -> bool:
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# V1 support on AMD gpus is experimental
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return True
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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 get_cu_count(cls, device_id: int = 0) -> int:
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return torch.cuda.get_device_properties(
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device_id).multi_processor_count
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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_piecewise_backend_cls(cls) -> str:
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return "vllm.compilation.cuda_piecewise_backend.CUDAPiecewiseBackend" # noqa
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@classmethod
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def stateless_init_device_torch_dist_pg(
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cls,
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backend: str,
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prefix_store: PrefixStore,
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group_rank: int,
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group_size: int,
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timeout: timedelta,
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) -> ProcessGroup:
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assert is_nccl_available()
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pg: ProcessGroup = ProcessGroup(
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prefix_store,
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group_rank,
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group_size,
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)
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from torch.distributed.distributed_c10d import ProcessGroupNCCL
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backend_options = ProcessGroupNCCL.Options()
|
|
backend_options._timeout = timeout
|
|
|
|
backend_class = ProcessGroupNCCL(prefix_store, group_rank, group_size,
|
|
backend_options)
|
|
backend_type = ProcessGroup.BackendType.NCCL
|
|
device = torch.device("cuda")
|
|
pg._set_default_backend(backend_type)
|
|
backend_class._set_sequence_number_for_group()
|
|
|
|
pg._register_backend(device, backend_type, backend_class)
|
|
return pg
|
|
|
|
@classmethod
|
|
def device_count(cls) -> int:
|
|
return cuda_device_count_stateless()
|
|
|
|
@classmethod
|
|
def is_kv_cache_dtype_supported(cls, kv_cache_dtype: str) -> bool:
|
|
return True
|