rocm.py 20.7 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

4
import os
5
from datetime import timedelta
6
from functools import cache, lru_cache, wraps
7
from typing import TYPE_CHECKING, Optional
8
9

import torch
10
11
from torch.distributed import PrefixStore, ProcessGroup
from torch.distributed.distributed_c10d import is_nccl_available
12

13
import vllm.envs as envs
14
from vllm.logger import init_logger
15
from vllm.utils import cuda_device_count_stateless
16

17
from .interface import DeviceCapability, Platform, PlatformEnum, _Backend
18

19
from vllm.utils import SUPPORT_TC
zhuwenwen's avatar
zhuwenwen committed
20
21
22
23
24
25

if not SUPPORT_TC:
    os.environ['VLLM_USE_V1'] = '0'
    os.environ['VLLM_USE_FLASH_ATTN_PA'] = '0'
    os.environ['VLLM_USE_FLASH_MLA'] = '0'
    
zhuwenwen's avatar
zhuwenwen committed
26
    
27
if TYPE_CHECKING:
28
    from vllm.config import ModelConfig, VllmConfig
29

30
31
logger = init_logger(__name__)

32
try:
33
34
35
    from amdsmi import (AmdSmiException, amdsmi_get_gpu_asic_info,
                        amdsmi_get_processor_handles, amdsmi_init,
                        amdsmi_shut_down, amdsmi_topo_get_link_type)
36
37
38
except ImportError as e:
    logger.warning("Failed to import from amdsmi with %r", e)

39
40
41
42
43
44
try:
    import vllm._C  # noqa: F401
except ImportError as e:
    logger.warning("Failed to import from vllm._C with %r", e)

# import custom ops, trigger op registration
zhuwenwen's avatar
zhuwenwen committed
45
46
47
48
# try:
#     import vllm._rocm_C  # noqa: F401
# except ImportError as e:
#     logger.warning("Failed to import from vllm._rocm_C with %r", e)
49

50

51
# Models not supported by ROCm.
52
_ROCM_UNSUPPORTED_MODELS: list[str] = []
53
54
55

# Models partially supported by ROCm.
# Architecture -> Reason.
zhuwenwen's avatar
zhuwenwen committed
56
57
58
59
# _ROCM_SWA_REASON = ("Sliding window attention (SWA) is not yet supported in "
#                     "Triton flash attention. For half-precision SWA support, "
#                     "please use CK flash attention by setting "
#                     "`VLLM_USE_TRITON_FLASH_ATTN=0`")
60
_ROCM_PARTIALLY_SUPPORTED_MODELS: dict[str, str] = {
zhuwenwen's avatar
zhuwenwen committed
61
62
63
64
65
66
    # "Qwen2ForCausalLM":
    # _ROCM_SWA_REASON,
    # "MistralForCausalLM":
    # _ROCM_SWA_REASON,
    # "MixtralForCausalLM":
    # _ROCM_SWA_REASON,
zhuwenwen's avatar
zhuwenwen committed
67
68
69
    # "PaliGemmaForConditionalGeneration":
    # ("ROCm flash attention does not yet "
    #  "fully support 32-bit precision on PaliGemma"),
zhuwenwen's avatar
zhuwenwen committed
70
71
72
73
    # "Phi3VForCausalLM":
    # ("ROCm Triton flash attention may run into compilation errors due to "
    #  "excessive use of shared memory. If this happens, disable Triton FA "
    #  "by setting `VLLM_USE_TRITON_FLASH_ATTN=0`")
74
}
75
_ROCM_DEVICE_ID_NAME_MAP: dict[str, str] = {
76
77
78
79
80
81
82
83
    "0x74a0": "AMD_Instinct_MI300A",
    "0x74a1": "AMD_Instinct_MI300X",
    "0x74b5": "AMD_Instinct_MI300X",  # MI300X VF
    "0x74a5": "AMD_Instinct_MI325X",
    "0x74b9": "AMD_Instinct_MI325X",  # MI325X VF
    "0x74a9": "AMD_Instinct_MI300X_HF",
    "0x74bd": "AMD_Instinct_MI300X_HF",
}
84

85
# Prevent use of clashing `{CUDA/HIP}_VISIBLE_DEVICES``
zhuwenwen's avatar
zhuwenwen committed
86
87
88
89
90
91
# if "HIP_VISIBLE_DEVICES" in os.environ:
#     val = os.environ["HIP_VISIBLE_DEVICES"]
#     if cuda_val := os.environ.get("CUDA_VISIBLE_DEVICES", None):
#         assert val == cuda_val
#     else:
#         os.environ["CUDA_VISIBLE_DEVICES"] = val
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111

# AMDSMI utils
# Note that NVML is not affected by `{CUDA/HIP}_VISIBLE_DEVICES`,
# all the related functions work on real physical device ids.
# the major benefit of using AMDSMI is that it will not initialize CUDA


def with_amdsmi_context(fn):

    @wraps(fn)
    def wrapper(*args, **kwargs):
        amdsmi_init()
        try:
            return fn(*args, **kwargs)
        finally:
            amdsmi_shut_down()

    return wrapper


112
113
114
115
116
117
118
119
120
def device_id_to_physical_device_id(device_id: int) -> int:
    if "CUDA_VISIBLE_DEVICES" in os.environ:
        device_ids = os.environ["CUDA_VISIBLE_DEVICES"].split(",")
        physical_device_id = device_ids[device_id]
        return int(physical_device_id)
    else:
        return device_id


121
122
123
124
@cache
def on_gfx1x() -> bool:
    GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName
    return any(arch in GPU_ARCH for arch in ["gfx11", "gfx12"])
125

126

127
@cache
128
def on_mi3xx() -> bool:
129
    GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName
130
    return any(arch in GPU_ARCH for arch in ["gfx942", "gfx950"])
131
132


133
@cache
134
135
136
def on_gfx9() -> bool:
    GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName
    return any(arch in GPU_ARCH for arch in ["gfx90a", "gfx942", "gfx950"])
137
138


139
@cache
140
141
142
143
144
145
146
147
148
def use_rocm_custom_paged_attention(
        qtype: torch.dtype,
        head_size: int,
        block_size: int,
        gqa_ratio: int,
        max_seq_len: int,
        sliding_window: int,
        kv_cache_dtype: str,
        alibi_slopes: Optional[torch.Tensor] = None) -> bool:
149

150
151
    GPU_ARCH = torch.cuda.get_device_properties("cuda").gcnArchName
    ON_GFX9 = any(arch in GPU_ARCH for arch in ["gfx90a", "gfx942", "gfx950"])
152
    ON_GFX11_GFX12 = any(arch in GPU_ARCH for arch in ["gfx11", "gfx12"])
153

154
155
    # custom paged attn always supported on V0. On V1, requires sliding window
    # disabled due to observed numerical discrepancy.
zhuwenwen's avatar
zhuwenwen committed
156
157
158
159
160
161
162
    # if ON_GFX9:
    #     return ((not envs.VLLM_USE_V1 or sliding_window == 0
    #              or sliding_window == (-1, -1))
    #             and (qtype == torch.half or qtype == torch.bfloat16)
    #             and (head_size == 64 or head_size == 128)
    #             and (block_size == 16 or block_size == 32)
    #             and (gqa_ratio >= 1 and gqa_ratio <= 16)
zhuwenwen's avatar
zhuwenwen committed
163
164
    #             and max_seq_len <= 128 * 1024
    #             and (envs.VLLM_ROCM_CUSTOM_PAGED_ATTN)
zhuwenwen's avatar
zhuwenwen committed
165
166
167
168
169
170
171
172
173
    #             and not (envs.VLLM_ROCM_USE_AITER_PAGED_ATTN
    #                      and envs.VLLM_ROCM_USE_AITER))

    # else:
    #     return (ON_GFX11_GFX12 and (not envs.VLLM_USE_V1 or sliding_window == 0
    #                                 or sliding_window == (-1, -1))
    #             and (qtype == torch.half or qtype == torch.bfloat16)
    #             and head_size == 128 and block_size == 16
    #             and (gqa_ratio >= 3 and gqa_ratio <= 16)
zhuwenwen's avatar
zhuwenwen committed
174
    #             and max_seq_len <= 128 * 1024 and alibi_slopes is None
zhuwenwen's avatar
zhuwenwen committed
175
176
    #             and kv_cache_dtype == "auto"
    #             and envs.VLLM_ROCM_CUSTOM_PAGED_ATTN)
177
    return False
178
179


180
181
class RocmPlatform(Platform):
    _enum = PlatformEnum.ROCM
182
    device_name: str = "rocm"
183
    device_type: str = "cuda"
184
    dispatch_key: str = "CUDA"
185
    ray_device_key: str = "GPU"
186
187
    # rocm shares the same device control env var as CUDA
    device_control_env_var: str = "CUDA_VISIBLE_DEVICES"
188

189
    supported_quantization: list[str] = [
190
        "awq", "gptq", "fp8", "compressed-tensors", "fbgemm_fp8", "gguf",
191
        "quark", "ptpc_fp8", "moe_wna16", "blockwise_int8","slimquant_w4a8","awq_marlin",
zhuwenwen's avatar
zhuwenwen committed
192
193
        "slimquant_w4a8_marlin","slimquant_compressed_tensors_marlin",
        "slimquant_int8"
194
    ]
195

196
    @classmethod
197
    def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
198
199
                             kv_cache_dtype, block_size, use_v1,
                             use_mla) -> str:
200
        if use_mla:
zhuwenwen's avatar
zhuwenwen committed
201
202
203
204
205
            if selected_backend == _Backend.TRITON_MLA or block_size != 64:
                if use_v1:
                    logger.info_once("Using Triton MLA backend on V1 engine.")
                    return ("vllm.v1.attention.backends.mla."
                            "triton_mla.TritonMLABackend")
206

zhuwenwen's avatar
zhuwenwen committed
207
208
                else:
                    logger.info("Using Triton MLA backend.")
zhuwenwen's avatar
zhuwenwen committed
209
210
                    return "vllm.attention.backends.triton_mla.TritonMLABackend"   
            else:         
211
212
213
214
215
216
217
218
219
220
221
222
                if envs.VLLM_USE_FLASH_MLA:
                    from vllm.attention.backends.flashmla import (
                        is_flashmla_supported)
                    if not is_flashmla_supported()[0]:
                        logger.warning(
                            "FlashMLA backend is not supported due to %s",
                            is_flashmla_supported()[1])
                    elif block_size != 64:
                        logger.warning(
                            "FlashMLA backend is not supported for block size %d"
                            " (currently only supports block size 64).",
                            block_size)
zhuwenwen's avatar
zhuwenwen committed
223
                    else:
224
225
226
227
228
229
230
231
232
233
234
                        if use_v1:
                            logger.info_once(
                                "Using FlashMLA backend on V1 engine.")
                            return ("vllm.v1.attention.backends.mla."
                                    "flashmla.FlashMLABackend")
                        else:
                            logger.info("Using FlashMLA backend.")
                            return ("vllm.attention.backends."
                                    "flashmla.FlashMLABackend")
                else:
                    logger.info("Using Triton MLA backend (block size 64).")
zhuwenwen's avatar
zhuwenwen committed
235
                    return "vllm.attention.backends.triton_mla.TritonMLABackend"           
zhuwenwen's avatar
zhuwenwen committed
236
237
238
239
240
241
242
243
244
245
246

            # from vllm.attention.backends.rocm_aiter_mla import (
            #     is_aiter_mla_enabled)

            # if selected_backend is None:
            #     selected_backend = (_Backend.ROCM_AITER_MLA if
            #                         is_aiter_mla_enabled() or block_size == 1
            #                         else _Backend.TRITON_MLA)

            # if selected_backend == _Backend.TRITON_MLA:
            #     if block_size != 1:
zhuwenwen's avatar
zhuwenwen committed
247
248
249
250
251
252
253
254
            #         if use_v1:
            #             logger.info_once(
            #                 "Using Triton MLA backend on V1 engine.")
            #             return ("vllm.v1.attention.backends.mla."
            #                     "triton_mla.TritonMLABackend")
            #         else:
            #             logger.info("Using Triton MLA backend.")
            #             return "vllm.attention.backends.triton_mla.TritonMLABackend"  # noqa: E501
zhuwenwen's avatar
zhuwenwen committed
255
256
257
258
            #     else:
            #         raise ValueError(
            #             f" The selected backend, {selected_backend.name},"
            #             f"does not support block size {block_size}.")
zhuwenwen's avatar
zhuwenwen committed
259
260
            # elif selected_backend == _Backend.ROCM_AITER_MLA \
            #     or selected_backend == _Backend.ROCM_AITER_MLA_VLLM_V1:
zhuwenwen's avatar
zhuwenwen committed
261
            #     if block_size == 1:
zhuwenwen's avatar
zhuwenwen committed
262
263
264
265
266
267
            #         if use_v1:
            #             logger.info("Using AITER MLA backend on V1 engine.")
            #             return "vllm.v1.attention.backends.mla.rocm_aiter_mla.AiterMLABackend"  # noqa: E501
            #         else:
            #             logger.info("Using AITER MLA backend")
            #             return "vllm.attention.backends.rocm_aiter_mla.AiterMLABackend"  # noqa: E501
zhuwenwen's avatar
zhuwenwen committed
268
269
270
271
272
273
274
275
276
            #     else:
            #         raise ValueError(
            #             f" The selected backend, {selected_backend.name},"
            #             f"does not support block size {block_size}."
            #             "(currently only supports block size 1)")
            # else:
            #     raise ValueError(
            #         f" The selected backend, {selected_backend.name},"
            #         f"is not MLA type while requested for MLA backend.")
277
278
279
      
        if selected_backend is None or selected_backend == _Backend.FLASH_ATTN:
            selected_backend = _Backend.ROCM_FLASH
280

281
        if envs.VLLM_USE_V1:
zhuwenwen's avatar
zhuwenwen committed
282
283
284
285
286
287
            TRITON_ATTN_VLLM_V1 = "vllm.v1.attention.backends.triton_attn.TritonAttentionBackend"  # noqa: E501
            FLASH_ATTN_V1 = "vllm.v1.attention.backends.flash_attn.FlashAttentionBackend"  # noqa: E501
            # if selected_backend == _Backend.TRITON_ATTN_VLLM_V1:
            #     logger.info_once("Using Triton backend on V1 engine.")
            #     return TRITON_ATTN_VLLM_V1
            
zhuwenwen's avatar
zhuwenwen committed
288
            if envs.VLLM_USE_FLASH_ATTN_PA and block_size == 64:
zhuwenwen's avatar
zhuwenwen committed
289
290
                logger.info_once("Using Flash Attention backend on V1 engine. (only supports block size 64)")
                return FLASH_ATTN_V1
291
            else:
292
                os.environ['VLLM_USE_FLASH_ATTN_PA'] = '0'
zhuwenwen's avatar
zhuwenwen committed
293
294
295
                logger.info_once("Using Triton backend on V1 engine.")
                return TRITON_ATTN_VLLM_V1
            
296
297
298
299
        if selected_backend == _Backend.DUAL_CHUNK_FLASH_ATTN:
            logger.info("Using DualChunkFlashAttention backend.")
            return ("vllm.attention.backends.dual_chunk_flash_attn."
                    "DualChunkFlashAttentionBackend")
300
301
302
303
304
305
306
307
        if selected_backend == _Backend.ROCM_FLASH:
            if not cls.has_device_capability(90):
                # not Instinct series GPUs.
                logger.info("flash_attn is not supported on NAVI GPUs.")
        else:
            logger.info("%s is not supported in AMD GPUs.", selected_backend)
        logger.info("Using ROCmFlashAttention backend.")
        return "vllm.attention.backends.rocm_flash_attn.ROCmFlashAttentionBackend"  # noqa: E501
308

309

310
    @classmethod
311
    @lru_cache(maxsize=8)
312
313
314
    def get_device_capability(cls,
                              device_id: int = 0
                              ) -> Optional[DeviceCapability]:
315
316
        major, minor = torch.cuda.get_device_capability(device_id)
        return DeviceCapability(major=major, minor=minor)
317

318
    @classmethod
319
    @with_amdsmi_context
320
    def is_fully_connected(cls, physical_device_ids: list[int]) -> bool:
zhuwenwen's avatar
zhuwenwen committed
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
        """
        Query if the set of gpus are fully connected by xgmi (1 hop)
        """
        handles = [
            amdsmi_get_processor_handles()[i] for i in physical_device_ids
        ]
        for i, handle in enumerate(handles):
            for j, peer_handle in enumerate(handles):
                if i < j:
                    try:
                        link_type = amdsmi_topo_get_link_type(
                            handle, peer_handle)
                        # type is 2 for XGMI
                        if link_type["hops"] != 1 or link_type["type"] != 2:
                            return False
                    except AmdSmiException as error:
                        logger.error("AMD 1 hop XGMI detection failed.",
                                     exc_info=error)
                        return False
        return True
341

342
    @classmethod
343
    @with_amdsmi_context
344
    @lru_cache(maxsize=8)
345
    def get_device_name(cls, device_id: int = 0) -> str:
346
        physical_device_id = device_id_to_physical_device_id(device_id)
347
        handle = amdsmi_get_processor_handles()[physical_device_id]
348
349
        # return amdsmi_get_gpu_asic_info(handle)["market_name"]
        return torch.cuda.get_device_name(device_id)
350

zhuwenwen's avatar
zhuwenwen committed
351
352
353
354
    @classmethod
    def get_device_total_memory(cls, device_id: int = 0) -> int:
        device_props = torch.cuda.get_device_properties(device_id)
        return device_props.total_memory
355

356
357
358
359
360
361
362
363
364
365
    @classmethod
    def is_async_output_supported(cls, enforce_eager: Optional[bool]) -> bool:
        if enforce_eager:
            logger.warning(
                "To see benefits of async output processing, enable CUDA "
                "graph. Since, enforce-eager is enabled, async output "
                "processor cannot be used")
            return False
        return True

366
    @classmethod
367
    def check_and_update_config(cls, vllm_config: "VllmConfig") -> None:
368
369
370
371
        cache_config = vllm_config.cache_config
        if cache_config and cache_config.block_size is None:
            cache_config.block_size = 16

372
373
374
375
        parallel_config = vllm_config.parallel_config
        scheduler_config = vllm_config.scheduler_config
        if parallel_config.worker_cls == "auto":
            if scheduler_config.is_multi_step:
376
377
378
                if envs.VLLM_USE_V1:
                    raise NotImplementedError(
                        "Multi-step scheduling is not supported (and not "
379
                        "needed) on vLLM V1. Please launch without "
380
381
382
383
                        "--num-scheduler-steps.")
                else:
                    parallel_config.worker_cls = \
                        "vllm.worker.multi_step_worker.MultiStepWorker"
384
            elif vllm_config.speculative_config:
385
                if envs.VLLM_USE_V1:
zhuwenwen's avatar
zhuwenwen committed
386
387
388
389
390
                    # raise NotImplementedError(
                    #     "Speculative decoding is not yet supported on vLLM V1."
                    # )
                    parallel_config.worker_cls = \
                            "vllm.v1.worker.gpu_worker.Worker"
391
392
393
394
395
                else:
                    parallel_config.worker_cls = \
                        "vllm.spec_decode.spec_decode_worker.create_spec_worker"
                    parallel_config.sd_worker_cls = \
                        "vllm.worker.worker.Worker"
396
            else:
397
398
399
400
401
                if envs.VLLM_USE_V1:
                    parallel_config.worker_cls = \
                            "vllm.v1.worker.gpu_worker.Worker"
                else:
                    parallel_config.worker_cls = "vllm.worker.worker.Worker"
402

403
404
405
406
407
408
409
410
411
412
413
414
    @classmethod
    def verify_model_arch(cls, model_arch: str) -> None:
        if model_arch in _ROCM_UNSUPPORTED_MODELS:
            raise ValueError(f"Model architecture '{model_arch}' is not "
                             "supported by ROCm for now.")

        if model_arch in _ROCM_PARTIALLY_SUPPORTED_MODELS:
            msg = _ROCM_PARTIALLY_SUPPORTED_MODELS[model_arch]
            logger.warning(
                "Model architecture '%s' is partially "
                "supported by ROCm: %s", model_arch, msg)

415
416
417
418
419
420
    @classmethod
    def verify_quantization(cls, quant: str) -> None:
        super().verify_quantization(quant)
        if quant == "awq" and not envs.VLLM_USE_TRITON_AWQ:
            logger.warning(
                "Using AWQ quantization with ROCm, but VLLM_USE_TRITON_AWQ"
421
422
                " is not set, disabling VLLM_USE_TRITON_AWQ.")
            envs.VLLM_USE_TRITON_AWQ = False
423
424
425
426

    @classmethod
    def get_punica_wrapper(cls) -> str:
        return "vllm.lora.punica_wrapper.punica_gpu.PunicaWrapperGPU"
427
428
429
430
431
432

    @classmethod
    def get_current_memory_usage(cls,
                                 device: Optional[torch.types.Device] = None
                                 ) -> float:
        torch.cuda.reset_peak_memory_stats(device)
zhuwenwen's avatar
zhuwenwen committed
433
434
435
        # return torch.cuda.mem_get_info(device)[1] - torch.cuda.mem_get_info(
        #     device)[0]
        return torch.cuda.max_memory_allocated(device)
436
437
438
439

    @classmethod
    def get_device_communicator_cls(cls) -> str:
        return "vllm.distributed.device_communicators.cuda_communicator.CudaCommunicator"  # noqa
440

441
442
443
444
445
    @classmethod
    def supports_mx(cls) -> bool:
        gcn_arch = torch.cuda.get_device_properties(0).gcnArchName
        return any(gfx in gcn_arch for gfx in ["gfx95"])

446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
    @classmethod
    def supports_fp8(cls) -> bool:
        gcn_arch = torch.cuda.get_device_properties(0).gcnArchName
        return any(gfx in gcn_arch for gfx in ['gfx94', 'gfx95', 'gfx12'])

    @classmethod
    def is_fp8_fnuz(cls) -> bool:
        # only device 0 is checked, this assumes MI300 platforms are homogeneous
        return 'gfx94' in torch.cuda.get_device_properties(0).gcnArchName

    @classmethod
    def fp8_dtype(cls) -> torch.dtype:
        if cls.is_fp8_fnuz():
            return torch.float8_e4m3fnuz
        else:
            return torch.float8_e4m3fn
462
463

    @classmethod
464
    def supports_v1(cls, model_config: "ModelConfig") -> bool:
465
466
        # V1 support on AMD gpus is experimental
        return True
467
468
469
470
471

    @classmethod
    def use_custom_allreduce(cls) -> bool:
        # We only enable custom allreduce for MI300 series
        gcn_arch = torch.cuda.get_device_properties(0).gcnArchName
472
        supported_archs = ['gfx94', 'gfx95']
473
        return any(gfx in gcn_arch for gfx in supported_archs)
474
475
476
477

    @classmethod
    def get_cu_count(cls, device_id: int = 0) -> int:
        return torch.cuda.get_device_properties(
478
            device_id).multi_processor_count
479
480
481
482

    @classmethod
    def is_navi(cls) -> bool:
        return 'gfx1' in torch.cuda.get_device_properties(0).gcnArchName
483
484
485
486

    @classmethod
    def get_piecewise_backend_cls(cls) -> str:
        return "vllm.compilation.cuda_piecewise_backend.CUDAPiecewiseBackend"  # noqa
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516

    @classmethod
    def stateless_init_device_torch_dist_pg(
        cls,
        backend: str,
        prefix_store: PrefixStore,
        group_rank: int,
        group_size: int,
        timeout: timedelta,
    ) -> ProcessGroup:
        assert is_nccl_available()
        pg: ProcessGroup = ProcessGroup(
            prefix_store,
            group_rank,
            group_size,
        )
        from torch.distributed.distributed_c10d import ProcessGroupNCCL

        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
517
518
519
520

    @classmethod
    def device_count(cls) -> int:
        return cuda_device_count_stateless()