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https://github.com/deepspeedai/DeepSpeed.git
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1c03d1b1bb
Fix invalid f-strings detected by ruff. --------- Signed-off-by: cyy <cyyever@outlook.com> Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com> Co-authored-by: Olatunji Ruwase <tunji.ruwase@snowflake.com> Co-authored-by: Michael Wyatt <michael.wyatt@snowflake.com>
88 lines
3.4 KiB
Python
88 lines
3.4 KiB
Python
# Copyright (c) Microsoft Corporation.
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# SPDX-License-Identifier: Apache-2.0
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# DeepSpeed Team
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import torch
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import os
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import time
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from deepspeed.ops.aio import AsyncIOBuilder
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from .test_ds_aio_utils import task_log, create_filename, create_file, create_page_locked_tensor
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from .ds_aio_constants import *
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from deepspeed.io import FastFileWriter
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class Torch_FastIO_Engine(object):
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def __init__(self, args, tid, read_op):
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assert read_op is False, 'Read operation is not currently supported'
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self.ctxt = self._create_context(args, tid, read_op)
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self.zipfile_serialization = not args.torch_legacy_save
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def fini(self):
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if self.ctxt[USE_CPU_LOCKED_TENSOR]:
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for buf in [BUFFER, FAST_IO_BUFFER]:
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self.ctxt[HANDLE].free_cpu_locked_tensor(self.ctxt[buf])
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self.ctxt[BUFFER].detach()
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self.ctxt[BUFFER] = None
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def read(self, args, tid):
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start_time = time.time()
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torch.load(f=self.ctxt[FILE], map_location=self.ctxt[BUFFER].device)
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end_time = time.time()
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self.ctxt[ELAPSED_SEC] += end_time - start_time
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def write(self, args, tid):
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# Avoid overwriting existing files as it could be artificially faster
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if os.path.isfile(self.ctxt[FILE]):
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os.remove(self.ctxt[FILE])
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ds_file_writer = FastFileWriter(file_path=self.ctxt[FILE],
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aio_handle=self.ctxt[HANDLE],
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pinned_tensor=self.ctxt[FAST_IO_BUFFER])
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start_time = time.time()
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torch.save(obj=self.ctxt[BUFFER], f=ds_file_writer, _use_new_zipfile_serialization=self.zipfile_serialization)
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ds_file_writer.close() # Force flush to storage
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end_time = time.time()
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self.ctxt[ELAPSED_SEC] += end_time - start_time
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ds_file_writer._dump_state()
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def _create_context(self, args, tid, read_op):
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io_string = "Read" if read_op else "Write"
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device_id, folder = args.mapping_list[tid]
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filename = create_filename(folder, args.read, args.io_size, tid)
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if args.read and not (os.path.isfile(filename) and os.path.getsize(filename) == args.io_size):
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create_file(filename, args.io_size)
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io_parallel = args.io_parallel if args.io_parallel else 1
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aio_handle = AsyncIOBuilder().load().aio_handle(args.block_size, args.queue_depth, args.single_submit,
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not args.sequential_requests, io_parallel)
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if args.gpu:
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buffer = torch.randint(high=128, size=(args.io_size, ), dtype=torch.uint8, device=f'cuda:{device_id}')
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else:
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buffer = create_page_locked_tensor(args.io_size, args.use_accelerator_pin_memory, aio_handle)
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task_log(tid, f'Allocate tensor of size {args.io_size} bytes')
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fast_io_buffer = create_page_locked_tensor(args.fast_io_size, args.use_accelerator_pin_memory, aio_handle)
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task_log(tid, 'created torch_fastio engine')
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ctxt = {}
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ctxt[FILE] = filename
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ctxt[NUM_BYTES] = args.io_size
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ctxt[BUFFER] = buffer
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ctxt[HANDLE] = aio_handle
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ctxt[FAST_IO_BUFFER] = fast_io_buffer
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ctxt[ELAPSED_SEC] = 0
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ctxt[USE_CPU_LOCKED_TENSOR] = not args.use_accelerator_pin_memory
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task_log(tid,
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f'{io_string} file {filename} of size {args.io_size} bytes from buffer on device {buffer.device}',
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force=True)
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return ctxt
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