[Zerobubble] merge main. (#6142)
* [feat] add zerobubble pp (just a frame now); add POC test for dx_dw; add test for zerobubble;
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* [zerobubble]Support ZeroBubble Pipeline (#6034)
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* [feat] add dw test;
* [fix] fix weight not close;
* [update] update text;
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* [feat] add test for p & p grad;
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* [feat] moehybrid support zerobubble;
* [fix] fix zerobubble pp for shardformer type input;
* [feat] add more test;
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* [fix] fix dumb clone;
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* [fix] fix stage_indices;
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* [fix] fix zerobubble; support shardformer model type;
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* [fix] remove duplicate arg; rm comments;
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* [zerobubble]Support ZeroBubble Pipeline (#6034)
* [feat] add zerobubble pp (just a frame now); add POC test for dx_dw; add test for zerobubble;
* [feat] add dw test;
* [fix] fix weight not close;
* [update] update text;
* [feat] add test run_fwd_bwd automatic scheduling;
* [feat] split communication and calculation; fix pop empty send_bwd_buffer error;
* [feat] add test for p & p grad;
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* [fix] fix optim bwd;
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* [feat] zerobubble support moehybridplugin;
* [feat] update optimizer bwd; ä¸
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cast_to_fp8, cast_from_fp8, all_reduce_fp8
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* shardformer fp8
* fix rebase
* remove all to all
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* [fp8] add fp8 comm for low level zero
* [test] add zero fp8 test case
* [Feature] llama shardformer fp8 support (#5938)
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* Llama Shardformer Parity
* fix typo
* fix all reduce
* fix pytest failure
* fix reduce op and move function to fp8.py
* fix typo
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* fix dataloader
* remove debug code
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* fix style
* fix colossalai, transformers version
* fix colossalai, transformers version
* fix colossalai, transformers version
* fix torch colossalai version
* update transformers version
* [shardformer] DeepseekMoE support (#5871)
* [Feature] deepseek moe expert parallel implement
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* [misc] fix typo
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* [Feature] deepseek support & unit test
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* [Hoxfix] Fix CUDA_DEVICE_MAX_CONNECTIONS for comm overlap
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* [Feat] Diffusion Model(PixArtAlpha/StableDiffusion3) Support (#5838)
* Diffusion Model Inference support
* Stable Diffusion 3 Support
* pixartalpha support
* [HotFix] CI,import,requirements-test for #5838 (#5892)
* [Hot Fix] CI,import,requirements-test
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* [Feature] Enable PP + SP for llama (#5868)
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* fix typo
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---------
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* [ShardFormer] Add Ulysses Sequence Parallelism support for Command-R, Qwen2 and ChatGLM (#5897)
* add benchmark for sft, dpo, simpo, orpo. Add benchmarking result. Support lora with gradient checkpoint
* fix style
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix eval
* hotfix citation
* [zero] support all-gather overlap (#5898)
* [zero] support all-gather overlap
* [zero] add overlap all-gather flag
* [misc] fix typo
* [zero] update api
* fix orpo cross entropy loss
* [Auto Parallel]: Speed up intra-op plan generation by 44% (#5446)
* Remove unnecessary calls to deepcopy
* Build DimSpec's difference dict only once
This change considerably speeds up construction speed of DimSpec objects. The difference_dict is the same for each DimSpec object, so a single copy of it is enough.
* Fix documentation of DimSpec's difference method
* [ShardFormer] fix qwen2 sp (#5903)
* [compatibility] support torch 2.2 (#5875)
* Support Pytorch 2.2.2
* keep build_on_pr file and update .compatibility
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* [devops] add debug
* [devops] add debug
* [devops] add debug
* [devops] remove debug
* [devops] remove debug
* [release] update version (#5912)
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* [ColossalChat] Hotfix for ColossalChat (#5910)
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* add Qwen 1.5 32B
* refactor tokenization
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* cannot access local variable 'default_conversation' where it is not associated with a value
set default value for 'default_conversation'
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix test data
* refactor evaluation
* remove real data path
* remove real data path
* Add n_fused as an input from native_module (#5894)
* [FIX BUG] convert env param to int in (#5934)
* [Hotfix] Fix ZeRO typo #5936
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* [Feature] Add a switch to control whether the model checkpoint needs to be saved after each epoch ends (#5941)
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* fix style
* fix style
* fix style
* [shardformer] hotfix attn mask (#5945)
* [shardformer] hotfix attn mask (#5947)
* [Feat] Distrifusion Acceleration Support for Diffusion Inference (#5895)
* Distrifusion Support source
* comp comm overlap optimization
* sd3 benchmark
* pixart distrifusion bug fix
* sd3 bug fix and benchmark
* generation bug fix
* naming fix
* add docstring, fix counter and shape error
* add reference
* readme and requirement
* [zero] hotfix update master params (#5951)
* [release] update version (#5952)
* [Chat] Fix lora (#5946)
* fix merging
* remove filepath
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* remove readme
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* [test] add mixtral transformer test
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* moe sp support
* moe sp bug solve
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* [chore] minor fix
* [moe] init moe plugin comm setting with sp
* moe sp + ep bug fix
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* [chore] minor fix after rebase
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* [chore] solve moe ckpt test failure and some other arg pass failure
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* [bug] fix: somehow logger hangs the program
* [moe] deepseek moe sp support
* [test] add check
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* [misc] skip redunant test
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This reverts commit 2f9bce6686d1415a83d5726dc5ff02222c742582.
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* [misc] remove incompatible test config
* [misc] fix ci failure: change default value to false in moe plugin
* [misc] remove useless condition
* [chore] docstring
* [moe] remove force_overlap_comm flag and add warning instead
* [doc] add MoeHybridParallelPlugin docstring
* [moe] solve dp axis issue
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* support auto distributed data loader
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* fix tp error
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---------
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* [lora] lora support hybrid parallel plugin (#5956)
* lora support hybrid plugin
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cast_to_fp8, cast_from_fp8, all_reduce_fp8
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* fix
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* [fp8]support all2all fp8 (#5953)
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* [fp8] add fp8 linear (#5967)
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* [test] fix fp8 linear test condition
* [test] fix fp8 linear test condition
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* [fp8] support fp8 amp for hybrid parallel plugin
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* fix (#5976)
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* implement communication hook for FSDP params all-gather
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* [test ci]Feature/fp8 comm (#5981)
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* [ShardFormer] Add Ulysses Sequence Parallelism support for Command-R, Qwen2 and ChatGLM (#5897)
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* fix style
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* [chore] minor fix
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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* [fix] fix llama, mixtral benchmark zbv loss none bug; update mixtral & llama policy and modeling;
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* [fix] fix llama modeling policy;
* [fix] fix test_shard_llama ci;
* [fix] fix test zerobubble
* [fix] fix handle name; rm useless comments;
* [fix] fix send recv signature;
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* [fix] fix test case; moe error in second iter
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* [fix] rm debug info; update llama policy; update wait handle
* [fix] fix test_lora
* [fix] fix test_lora in llama policy
* [fix] fix wait handle in run_fwd_bwd
* [fix] remove debug info;
* [fix] rm unused comments
* [fix] fix fp8 overlap code
* [fix] fix yml file & v_schedule comments
* [fix] rm fwd only meta cache comments;
---------
Co-authored-by: flybird11111 <1829166702@qq.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: HangXu <hangxu0304@gmail.com>
Co-authored-by: GuangyaoZhang <xjtu521@qq.com>
Co-authored-by: Hongxin Liu <lhx0217@gmail.com>
Co-authored-by: YeAnbang <anbangy2@outlook.com>
Co-authored-by: Haze188 <haze188@qq.com>
Co-authored-by: Edenzzzz <wenxuan.tan@wisc.edu>
Co-authored-by: Edenzzzz <wtan45@wisc.edu>
Co-authored-by: Runyu Lu <77330637+LRY89757@users.noreply.github.com>
Co-authored-by: YeAnbang <44796419+YeAnbang@users.noreply.github.com>
Co-authored-by: Stephan Kö <stephankoe@users.noreply.github.com>
Co-authored-by: アマデウス <kurisusnowdeng@users.noreply.github.com>
Co-authored-by: Tong Li <tong.li352711588@gmail.com>
Co-authored-by: zhurunhua <1281592874@qq.com>
Co-authored-by: Insu Jang <insujang@umich.edu>
Co-authored-by: Gao, Ruiyuan <905370712@qq.com>
Co-authored-by: hxwang <wang1570@e.ntu.edu.sg>
Co-authored-by: Michelle <qianranma8@gmail.com>
Co-authored-by: Wang Binluo <32676639+wangbluo@users.noreply.github.com>
Co-authored-by: wangbluo <2538539015@qq.com>
Co-authored-by: root <root@notebook-8f919155-6035-47b4-9c6f-1be133b9e2c9-0.notebook-8f919155-6035-47b4-9c6f-1be133b9e2c9.colossal-ai.svc.cluster.local>
Co-authored-by: Camille Zhong <44392324+Camille7777@users.noreply.github.com>
2024-11-19 11:00:36 +00:00
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# Refer from Zero Bubble Pipeline Parallelism.
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# Github: https://github.com/sail-sg/zero-bubble-pipeline-parallelism
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# Paper: https://arxiv.org/abs/2401.10241
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# The following applies to all files unless otherwise noted:
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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions
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# are met:
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# * Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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# * Redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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# * Neither the name of NVIDIA CORPORATION nor the names of its
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# contributors may be used to endorse or promote products derived
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# from this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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from collections import deque
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from dataclasses import dataclass
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@dataclass(eq=True, frozen=True)
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class ScheduledNode:
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type: str
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chunk: int
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stage: int
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minibatch: int
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start_time: int = 0
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completion_time: int = 0
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rollback: bool = False
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class PipelineGraph(object):
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"""PipelineGraph"""
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def __init__(
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self,
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n_stage,
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n_micro,
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f_cost,
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b_cost,
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w_cost,
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c_cost,
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f_mem,
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b_mem,
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w_mem,
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max_mem=None,
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):
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self.n_node = 6 * n_stage * n_micro
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self.n_stage = n_stage
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self.n_micro = n_micro
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self.f_cost = f_cost
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self.b_cost = b_cost
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self.w_cost = w_cost
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self.c_cost = c_cost
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self.f_mem = f_mem
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self.b_mem = b_mem
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self.w_mem = w_mem
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self.fbw_cost = [f_cost, b_cost, w_cost]
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self.fbw_mem = [f_mem, b_mem, w_mem]
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self.max_mem = max_mem or f_mem * self.n_stage * 2
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def get_id(self, cat, chunk, stage, micro):
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return (
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cat * 2 * self.n_stage * self.n_micro + chunk * self.n_stage * self.n_micro + stage * self.n_micro + micro
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)
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def try_v_schedule(self, fill_f=True, fill_b=True, approved_bubble=None):
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count = []
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for i in range(self.n_stage):
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count.append([0] * 6)
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end_time = [-1] * self.n_node
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cur_time = [0] * self.n_stage
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mem = [0] * self.n_stage
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stage_bubble = [0] * self.n_stage
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pending_w = [deque() for _ in range(self.n_stage)]
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schedule = [[] for _ in range(self.n_stage)]
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stage_str = [" " * i for i in range(self.n_stage)]
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if approved_bubble is None:
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approved_bubble = [-1] * self.n_stage
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max_approved_bubble = max(approved_bubble)
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def get_max_stage_bubble(stage=-1):
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max_stage_bubble = 0
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for bb in stage_bubble:
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max_stage_bubble = max(max_stage_bubble, bb)
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if stage >= 0:
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max_stage_bubble = max(max_stage_bubble, max_approved_bubble - approved_bubble[stage])
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return max_stage_bubble
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def put_w(stage):
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assert len(pending_w[stage]) > 0
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_, chunk_, _ = pending_w[stage].popleft()
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put(2, chunk_, stage)
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def put(cat, chunk, stage, assert_cnt=True):
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_tmp = _no_bubble = cur_time[stage] + self.fbw_cost[cat]
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_cnt = count[stage][cat * 2 + chunk]
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# assert _cnt < self.n_micro
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if _cnt >= self.n_micro:
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if not assert_cnt:
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stage_str[stage] += " "
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cur_time[stage] = _tmp # TODO
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return
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assert False
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assert mem[stage] + self.fbw_mem[cat] <= self.max_mem
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stage_str[stage] += "FfBbWw"[cat * 2 + chunk] + str(_cnt + 1) + " " * (3 - len(str(_cnt + 1)))
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if cat > 0 or chunk > 0:
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last_id = cat * 2 + chunk - 1
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if cat < 2:
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assert end_time[self.get_id(last_id // 2, last_id % 2, stage, _cnt)] >= 0
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else:
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assert end_time[self.get_id(1, chunk, stage, _cnt)] >= 0
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if chunk == 1 and cat < 2:
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if stage < self.n_stage - 1:
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_fa_id = self.get_id(cat, chunk, stage + 1, _cnt)
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assert end_time[_fa_id] >= 0
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_tmp = max(_tmp, end_time[_fa_id] + self.c_cost + self.fbw_cost[cat])
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if chunk == 0 and cat < 2:
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if stage > 0:
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_fa_id = self.get_id(cat, chunk, stage - 1, _cnt)
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assert end_time[_fa_id] >= 0, f"{cat}, {chunk}, {stage}, {_cnt}"
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_tmp = max(_tmp, end_time[_fa_id] + self.c_cost + self.fbw_cost[cat])
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_id = self.get_id(cat, chunk, stage, _cnt)
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if count[stage][0] > 0:
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stage_bubble[stage] += _tmp - _no_bubble
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end_time[_id] = _tmp
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cur_time[stage] = _tmp
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mem[stage] += self.fbw_mem[cat]
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# noinspection PyTypeChecker
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schedule[stage].append((cat, chunk, _cnt))
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if cat == 1:
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pending_w[stage].append((2, chunk, _cnt))
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count[stage][cat * 2 + chunk] += 1
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for i in range(self.n_stage):
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put(0, 0, i)
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for i in range(self.n_stage - 1, -1, -1):
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if i == self.n_stage - 1:
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put(0, 1, i)
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continue
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tmp = end_time[self.get_id(0, 1, i + 1, 0)] + self.c_cost
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while (
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mem[i] + self.fbw_mem[0] * (2 + i * 2) <= self.max_mem
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and cur_time[i] + self.fbw_cost[0] <= tmp
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and count[i][0] < self.n_micro
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):
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for j in range(i + 1):
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put(0, 0, j)
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put(0, 1, i)
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iter_chunk_ = 0
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end_tmp = 0
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for i in range(self.n_stage):
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if i == 0:
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end_tmp = cur_time[0] + self.fbw_cost[1]
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continue
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tmp = end_tmp + self.c_cost
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while (
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count[i][0] + count[i][1] < count[i - 1][0] + count[i - 1][1]
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or count[i][1] <= count[i - 1][1] < self.n_micro
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):
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for j in range(self.n_stage - 1, i - 1, -1):
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if count[j][iter_chunk_] < self.n_micro:
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put(0, iter_chunk_, j)
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iter_chunk_ = 1 - iter_chunk_
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for _ in range(2 * self.n_micro):
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# check mem before putting b
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for i in range(self.n_stage):
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while mem[i] + self.fbw_mem[1] > self.max_mem:
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assert len(pending_w[i]) > 0
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put_w(i)
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b0_ranks, b1_ranks = [], []
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for i in range(self.n_stage):
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if count[i][3] >= count[i][2]:
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b0_ranks.append(i)
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elif i == self.n_stage - 1:
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b1_ranks.append(i)
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else:
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fa_id = self.get_id(1, 1, i + 1, count[i][3])
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if end_time[fa_id] >= 0 or count[i][2] >= self.n_micro:
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b1_ranks.append(i)
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else:
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b0_ranks.append(i)
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b_ranks = []
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# put b1
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for i in reversed(b1_ranks):
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b_ranks.append((i, 1))
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# put b0
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for i in b0_ranks:
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b_ranks.append((i, 0))
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for i, _chunk_ in b_ranks:
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fa_id = -1
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if _chunk_ == 1 and i < self.n_stage - 1:
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fa_id = self.get_id(1, 1, i + 1, count[i][3])
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if _chunk_ == 0 and i > 0:
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fa_id = self.get_id(1, 0, i - 1, count[i][2])
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while (
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len(pending_w[i]) > 0
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and fa_id >= 0
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and end_time[fa_id] + self.c_cost >= cur_time[i] + self.fbw_cost[2]
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):
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# fill the bubble
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put_w(i)
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if (
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len(pending_w[i]) > 0
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and end_time[fa_id] + self.c_cost - cur_time[i] > get_max_stage_bubble(i) - stage_bubble[i]
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):
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if _chunk_ == 1:
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put_w(i)
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elif fill_b:
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put_w(i)
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put(1, _chunk_, i)
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# put f
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for i in range(self.n_stage):
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if count[i][1] >= self.n_micro:
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continue
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put_item = None
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if count[i][1] >= count[i][0]:
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put_item = 0
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elif i == self.n_stage - 1:
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put_item = 1
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else:
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if end_time[self.get_id(0, 1, i + 1, count[i][1])] >= 0:
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put_item = 1
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elif count[i][0] < self.n_micro:
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if i == 0:
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put_item = 0
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elif end_time[self.get_id(0, 0, i - 1, count[i][0])] >= 0:
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put_item = 0
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if put_item is None:
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continue
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# check mem before putting f
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while mem[i] + self.fbw_mem[0] > self.max_mem:
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assert len(pending_w[i]) > 0
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put_w(i)
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fa_id = -1
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if put_item == 0 and i > 0:
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fa_id = self.get_id(0, 0, i - 1, count[i][0])
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if put_item == 1 and i < self.n_stage - 1:
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fa_id = self.get_id(0, 1, i + 1, count[i][1])
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while (
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len(pending_w[i]) > 0
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and fa_id >= 0
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and end_time[fa_id] + self.c_cost >= cur_time[i] + self.fbw_cost[2]
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):
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# fill the bubble
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put_w(i)
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if (
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len(pending_w[i]) > 0
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and end_time[fa_id] + self.c_cost - cur_time[i] > get_max_stage_bubble(i) - stage_bubble[i]
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):
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if fill_f:
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put_w(i)
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put(0, put_item, i)
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for i in range(self.n_stage):
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while len(pending_w[i]) > 0:
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put_w(i)
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max_bubble = get_max_stage_bubble()
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expected_time = sum(self.fbw_cost) * self.n_micro * 2
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max_bubble / expected_time
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if max_approved_bubble < 0 or max_bubble < max_approved_bubble:
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_schedule, _end_time, _max_bubble = self.try_v_schedule(
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fill_f=fill_f,
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fill_b=fill_b,
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approved_bubble=stage_bubble,
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)
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if _max_bubble < max_bubble:
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return _schedule, _end_time, _max_bubble
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return schedule, end_time, max_bubble
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def print_details(self, end_time, print_scaling=1):
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for stage in range(self.n_stage):
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stage_str = ["."] * int(max(end_time) / print_scaling)
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for _cat in range(3):
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for _chunk in range(2):
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for _micro in range(self.n_micro):
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_id = self.get_id(_cat, _chunk, stage, _micro)
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if end_time[_id] < 0:
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continue
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end = int(end_time[_id] / print_scaling)
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start = int((end_time[_id] - self.fbw_cost[_cat]) / print_scaling)
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for j in range(start, end):
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|
|
|
if j == start or j == end - 1:
|
|
|
|
stage_str[j] = "FfBbWw"[_cat * 2 + _chunk]
|
|
|
|
elif j == start + 1:
|
|
|
|
if _micro >= 10:
|
|
|
|
stage_str[j] = str(_micro // 10)
|
|
|
|
else:
|
|
|
|
stage_str[j] = str(_micro)
|
|
|
|
elif j == start + 2 and _micro >= 10:
|
|
|
|
stage_str[j] = str(_micro % 10)
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|
|
|
else:
|
|
|
|
stage_str[j] = "-"
|
|
|
|
_str = ""
|
|
|
|
for _c in stage_str:
|
|
|
|
_str += _c
|
|
|
|
print(_str)
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|
|
|
|
|
|
|
def get_v_schedule(self, only_run_time=False):
|
|
|
|
schedule, end_time, max_bubble = None, None, None
|
|
|
|
expected_time = sum(self.fbw_cost) * self.n_micro * 2
|
|
|
|
for fill_b in [True, False]:
|
|
|
|
for fill_f in [True, False]:
|
|
|
|
_schedule, _end_time, _max_bubble = self.try_v_schedule(fill_b=fill_b, fill_f=fill_f)
|
|
|
|
if max_bubble is None or _max_bubble < max_bubble:
|
|
|
|
max_bubble = _max_bubble
|
|
|
|
schedule = _schedule
|
|
|
|
end_time = _end_time
|
|
|
|
if only_run_time:
|
|
|
|
return max_bubble + expected_time
|
|
|
|
max_bubble / (expected_time + max_bubble)
|
|
|
|
local_order = [[] for _ in range(self.n_stage)]
|
|
|
|
comm_id = {}
|
|
|
|
comm_id_counter = 0
|
|
|
|
post_validation_time = 0
|
|
|
|
for i in range(self.n_stage - 1, -1, -1):
|
|
|
|
pv_id = min(2 * (self.n_stage - 1 - i), self.n_micro - 1)
|
|
|
|
post_validation_time = max(
|
|
|
|
post_validation_time, end_time[self.get_id(0, 0, i, pv_id)] - self.fbw_cost[0] - self.c_cost
|
|
|
|
)
|
|
|
|
# post_validation_time = 0
|
|
|
|
for it in ["RECV_", "SEND_", ""]:
|
|
|
|
if i == 0 and it == "SEND_":
|
|
|
|
continue
|
|
|
|
if i == self.n_stage - 1 and it == "RECV_":
|
|
|
|
continue
|
|
|
|
# stage_ = i - 1 if it == "RECV_" else i
|
|
|
|
stage_ = i
|
|
|
|
local_order[stage_].append(
|
|
|
|
ScheduledNode(
|
|
|
|
type=it + "POST_VALIDATION",
|
|
|
|
chunk=0,
|
|
|
|
stage=stage_,
|
|
|
|
minibatch=0,
|
|
|
|
start_time=post_validation_time,
|
|
|
|
completion_time=post_validation_time,
|
|
|
|
)
|
|
|
|
)
|
|
|
|
comm_id[local_order[stage_][-1]] = comm_id_counter
|
|
|
|
comm_id_counter += 1
|
|
|
|
for i in range(self.n_stage):
|
|
|
|
for _cat_, _chunk_, _micro_ in schedule[i]:
|
|
|
|
complete_time = end_time[self.get_id(_cat_, _chunk_, i, _micro_)]
|
|
|
|
local_order[i].append(
|
|
|
|
ScheduledNode(
|
|
|
|
type="FBW"[_cat_],
|
|
|
|
chunk=_chunk_ if _cat_ == 0 else 1 - _chunk_,
|
|
|
|
stage=i,
|
|
|
|
minibatch=_micro_,
|
|
|
|
start_time=complete_time - self.fbw_cost[_cat_],
|
|
|
|
completion_time=complete_time,
|
|
|
|
)
|
|
|
|
)
|
|
|
|
if _cat_ == 2: # no communication for W
|
|
|
|
continue
|
|
|
|
cat_str = "FORWARD" if _cat_ == 0 else "BACKWARD"
|
|
|
|
|
|
|
|
def communicate(send_recv, stage_):
|
|
|
|
# noinspection PyTypeChecker
|
|
|
|
local_order[stage_].append(
|
|
|
|
ScheduledNode(
|
|
|
|
type=send_recv + cat_str,
|
|
|
|
chunk=_chunk_ if _cat_ == 0 else 1 - _chunk_,
|
|
|
|
stage=stage_,
|
|
|
|
minibatch=_micro_,
|
|
|
|
start_time=complete_time,
|
|
|
|
completion_time=complete_time,
|
|
|
|
)
|
|
|
|
)
|
|
|
|
comm_id[local_order[stage_][-1]] = comm_id_counter
|
|
|
|
|
|
|
|
if _chunk_ == 1 and i > 0:
|
|
|
|
communicate("SEND_", i)
|
|
|
|
communicate("RECV_", i - 1)
|
|
|
|
if _chunk_ == 0 and i < self.n_stage - 1:
|
|
|
|
communicate("SEND_", i)
|
|
|
|
communicate("RECV_", i + 1)
|
|
|
|
comm_id_counter += 1
|
|
|
|
for rank in range(self.n_stage):
|
|
|
|
# For nodes with the same timestamp on the same stage, communication will be prioritized.
|
|
|
|
def even_breaker(x: ScheduledNode):
|
|
|
|
# Compute nodes are always delayed.
|
|
|
|
if x.type in ["F", "B", "W"]:
|
|
|
|
return comm_id_counter
|
|
|
|
# For comm nodes, order by their unique comm id
|
|
|
|
return comm_id[x]
|
|
|
|
|
|
|
|
local_order[rank] = list(sorted(local_order[rank], key=lambda x: (x.start_time, even_breaker(x))))
|
|
|
|
# If a recv with intersects with previous computation, reorder them so that recv
|
|
|
|
# is executed before computation and hence can be overlapped.
|
|
|
|
for i in range(len(local_order[rank])):
|
|
|
|
if (
|
|
|
|
i > 0
|
|
|
|
and local_order[rank][i - 1].type in {"F", "B", "W"}
|
|
|
|
and local_order[rank][i].type.startswith("RECV")
|
|
|
|
and "POST_VALIDATION" not in local_order[rank][i].type
|
|
|
|
and local_order[rank][i].start_time <= local_order[rank][i - 1].completion_time
|
|
|
|
):
|
|
|
|
local_order[rank][i], local_order[rank][i - 1] = local_order[rank][i - 1], local_order[rank][i]
|
|
|
|
|
|
|
|
local_order_with_rollback = [[] for _ in range(self.n_stage)]
|
|
|
|
for rank in range(self.n_stage):
|
|
|
|
rollback_comm = set()
|
|
|
|
if rank > 0:
|
|
|
|
for node in local_order[rank - 1]:
|
|
|
|
if node.type == "POST_VALIDATION":
|
|
|
|
break
|
|
|
|
if node.type == "SEND_FORWARD":
|
|
|
|
assert node.chunk == 0
|
|
|
|
rollback_comm.add(node.minibatch)
|
|
|
|
for node in local_order[rank]:
|
|
|
|
if node.type == "RECV_FORWARD" and node.chunk == 0 and node.minibatch in rollback_comm:
|
|
|
|
rollback = True
|
|
|
|
rollback_comm.remove(node.minibatch)
|
|
|
|
else:
|
|
|
|
rollback = False
|
|
|
|
local_order_with_rollback[rank].append(
|
|
|
|
ScheduledNode(
|
|
|
|
type=node.type,
|
|
|
|
chunk=node.chunk,
|
|
|
|
stage=node.stage,
|
|
|
|
minibatch=node.minibatch,
|
|
|
|
start_time=node.start_time,
|
|
|
|
completion_time=node.completion_time,
|
|
|
|
rollback=rollback,
|
|
|
|
)
|
|
|
|
)
|
|
|
|
assert len(rollback_comm) == 0
|
|
|
|
|
|
|
|
return local_order_with_rollback
|