From c02684d1faf6f889313e9696c3d8753253a53aae Mon Sep 17 00:00:00 2001 From: Jae-Won Chung Date: Thu, 12 Oct 2023 19:31:03 -0400 Subject: [PATCH] Release v0.1.0 --- examples/perseus.py | 246 ------------------------------- examples/wide-resnet-peresus.gif | Bin 2967234 -> 0 bytes 2 files changed, 246 deletions(-) delete mode 100644 examples/perseus.py delete mode 100644 examples/wide-resnet-peresus.gif diff --git a/examples/perseus.py b/examples/perseus.py deleted file mode 100644 index ec090d3..0000000 --- a/examples/perseus.py +++ /dev/null @@ -1,246 +0,0 @@ -# Copyright (C) 2023 Jae-Won Chung -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Example script of running Perseus energy scheduling.""" - -from __future__ import annotations - -import time -import itertools -import logging -from pathlib import Path -from typing import Union, Type -from collections import defaultdict -from dataclasses import dataclass - -import tyro -import pandas as pd -import networkx as nx -import matplotlib.pyplot as plt - -from lowtime.operation import ( - CandidateExecutionOptions, - OperationSpec, - ExecutionOption, -) -from lowtime.cost_model import ExponentialModel -from lowtime.perseus.instruction import ( - Instruction, - Forward, - Backward, - forward_dep, - backward_dep, -) -from lowtime.solver import PhillipsDessouky -from lowtime.graph_utils import add_source_node, add_sink_node, DependencyResolver -from lowtime.perseus.schedule import Synchronous1F1B -from lowtime.perseus.visualizer import PipelineVisualizer, ANNOTATE_ARGS, LINE_ARGS - -logger = logging.getLogger() - - -@dataclass -class Args: - # Path to instruction profile results - inst_profile: str - # GPU power consumption while blocking on P2P communication, in Watts - p2p_power: float - # Directory to output results - output_dir: Path - # Number of microbatchs - num_mbs: int - # Number of stages - num_stages: int - # Interval to draw the state of the pipeline - plot_interval: int = 100 - # The unit of reduction for each iteration, in seconds - unit_time: float = 0.001 - - -def main(args: Args) -> None: - """Perseus time-cost tradeoff optimization.""" - # Setup logging and output. - output_dir = Path(args.output_dir) - output_dir.mkdir(parents=True, exist_ok=False) - log_path = output_dir / "job.log" - - logging.basicConfig( - format="%(asctime)s %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s", - level=logging.INFO, - handlers=[logging.FileHandler(log_path, mode="a"), logging.StreamHandler()], - ) - logger.info("Arguments: %s", args) - - # Instruction offline profiling results. - inst_df = pd.read_csv(args.inst_profile) - - #################### - # Execution Option # - #################### - # Construct the OperationSpec object of each pipeline instruction in each stage. - op_spec_map: dict[int, dict[Type[Instruction], OperationSpec]] = defaultdict(dict) - for instruction in [Forward, Backward]: - inst_name = instruction.__name__ - for stage_id in range(args.num_stages): - logger.info("Processing %s stage %d", inst_name, stage_id) - options = [] - _df = inst_df.query( - f"stage == {stage_id} and instruction == '{inst_name.lower()}'" - ) - for _, row in _df.iterrows(): - row = row.to_dict() - options.append( - ExecutionOption[int]( - real_time=row["time"], - unit_time=args.unit_time, - cost=row["energy"], - knob=int(row["frequency"]), - ) - ) - - # Get the Preto frontier, quantize time, and deduplicate time. - cand_options = CandidateExecutionOptions[int](options=options) - - # Map the cost to be effective computation energy. - # Everything from now on is in terms of effective energy. - for option in cand_options.options: - option.cost -= args.p2p_power * option.quant_time * option.unit_time - - # Fit the cost model. - model = ExponentialModel(cand_options) - - # Draw the cost model. - fig, ax = plt.subplots(figsize=(8, 8), tight_layout=True) - model.draw(ax, cand_options) - fig.savefig(f"{output_dir}/{inst_name.lower()}_{stage_id}.png") - - # Initialize the operation spec. - op_spec = OperationSpec[int](options=cand_options, cost_model=model) - op_spec_map[stage_id][instruction] = op_spec - - #################### - # DAG construction # - #################### - dag = nx.DiGraph() - - # Generate and add all instructions to the DAG. - # Reserve 0 for dummy source and 1 for dummy sink. - node_id = 2 - instructions: list[list[Instruction]] = [] - for stage_id in range(args.num_stages): - # Generate instructions for each stage. - stage_insts: list[Instruction] = [] - stage_node_ids: list[int] = [] - for inst in Synchronous1F1B( - num_stages=args.num_stages, - num_micro_batches=args.num_mbs, - stage_id=stage_id, - operation_spec_map=op_spec_map[stage_id], - ): - dag.add_node(node_id, op=inst) - stage_insts.append(inst) - stage_node_ids.append(node_id) - node_id += 1 - instructions.append(stage_insts) - - # Add dependencies between adjacent instructions in the same stage. - for node_id1, node_id2 in zip(stage_node_ids, stage_node_ids[1:]): - dag.add_edge(node_id1, node_id2) - - # Add dependencies between dependent pipeline instructions. - insts = dag.nodes(data=True) - resolver = DependencyResolver( - dependency_rules=[forward_dep, backward_dep], - node_type=Instruction, - ) - for (id1, data1), (id2, data2) in itertools.product(insts, insts): - if resolver.is_dependent(data1["op"], data2["op"]): - dag.add_edge(id1, id2) - - # Add source and sink nodes. - add_source_node(dag, 0) - add_sink_node(dag, 1) - dag.graph["source_node"] = 0 - dag.graph["sink_node"] = 1 - - ################################### - # Time-cost tradeoff optimization # - ################################### - def annotation_hook(inst: Instruction) -> str: - return f"{type(inst).__name__[0]}\n{inst.micro_batch_id}" - - def draw(dag: nx.DiGraph, iteration: int, xlim: int) -> None: - ANNOTATE_ARGS[Forward]["fontsize"] = 11.0 - ANNOTATE_ARGS[Backward]["fontsize"] = 11.0 - ANNOTATE_ARGS[Forward]["color"] = "black" - ANNOTATE_ARGS[Backward]["color"] = "black" - LINE_ARGS["linewidth"] = 3.0 - - fig, ax = plt.subplots(figsize=(args.num_mbs, 4), tight_layout=True) - - vis = PipelineVisualizer(dag) - vis.draw( - ax, - draw_time_axis=True, - p2p_power=args.p2p_power, - annotation_hook=annotation_hook, - power_color="RdBu_r", - normalizer_range=(-200, 550), - ) - vis.draw_critical_path(ax) - - # Fix xlim so that we can visually see the pipeline width shrink. - ax.set_xlim(0.0, xlim) - ax.set_title(f"Iteration {iteration:4d}") - fig.savefig(f"{output_dir}/pipeline_{iteration:05d}.png") - plt.close(fig) - - - solver = PhillipsDessouky(dag) - - draw_xlim = None - iteration = 0 - for iteration, result in enumerate(solver.run()): - # Maybe draw the pipeline. - if iteration % args.plot_interval == 0: - if draw_xlim is None: - draw_xlim = int(result.real_time) + 1 - draw(dag, iteration, draw_xlim) - - # Write the frequency assignment Python file. - f = open(args.output_dir / f"freqs_pipeline_{iteration:05d}.py", "w") - f.write("[\n") - for stage_id, stage_insts in enumerate(instructions): - stage_freq: list[tuple[str, int]] = [] - for inst in stage_insts: - stage_freq.append((type(inst).__name__.lower(), inst.assigned_knob)) - f.write(f"{stage_freq},\n") - f.write("]\n") - - iter_str = f"# Iteration {iteration} " - real_cost = result.cost + args.num_stages + result.real_time * args.p2p_power - f.write(iter_str + f"cost change: {result.cost_change}\n") - f.write(iter_str + 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