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parallelizationPlanner.py
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# Copyright (c) 2021 MIT
#
# Permission to use, copy, modify, and distribute this software for any
# purpose with or without fee is hereby granted, provided that the above
# copyright notice and this permission notice appear in all copies.
#
# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR(S) DISCLAIM ALL WARRANTIES
# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL AUTHORS BE LIABLE FOR
# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
# OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
# from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from torch.autograd import Variable
import time
import collections
from typing import Type, Any, Callable, Union, List, Optional
from torch.nn.common_types import _size_1_t, _size_2_t, _size_3_t
import math
import jsonpickle
import json
import numpy
import graphviz
from array import array
from typing import Optional, IO, List, Any
from gpuProfiler import GpuProfiler
from jobDescription import TrainingJob
from jobDescription import Layer
import torch.cuda.profiler as profiler
import torch.cuda.nvtx as nvtx
import pyprof
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
class SearchContext:
def __init__(self, totalGpus: int, globalBatch: int, amplificationLimit: float = 2.0,
dataParallelBaseline = False, sampleSplit=True, spatialSplit=False, filterSplit=False,
doNotBench = False):
self.totalGpus = totalGpus
self.globalBatch = globalBatch
self.amplificationLimit = amplificationLimit
self.dataParallelBaseline = dataParallelBaseline
self.sampleSplit = sampleSplit
self.spatialSplit = spatialSplit
self.filterSplit = filterSplit
self.doNotBench = doNotBench
print("SearchContext: " + str(self.__dict__))
class CostSim:
def __init__(self, profiler: GpuProfiler, netBw = 1.25E4, verbose=False, gpuProfileLoc=None, gpuProfileLocSub=None):
self.profiler = profiler
self.layers: List[Layer] = []
self.NET_BANDWIDTH = netBw
self.NET_LATENCY = 240 #40
# self.NET_LATENCY = 40 #40
# self.NET_LATENCY = 400 #40
self.verbose = verbose
self.layerProfileCache = {}
if gpuProfileLocSub != None:
self.loadGpuProfile(gpuProfileLocSub)
if gpuProfileLoc != None:
self.loadGpuProfile(gpuProfileLoc)
else:
print("!!! gpuProfileLoc is not supplied for CostSim")
def loadGpuProfile(self, gpuProfileLoc):
with open(gpuProfileLoc, "r") as f:
for line in f:
items = line.strip().split()
if (len(items) != 5 and len(items) != 4) or items[0] == "#config":
continue
layerInfo = items[0]
avgTime = float(items[1]) * 1000
fwTime = float(items[2])
bwTime = float(items[3])
self.layerProfileCache[layerInfo] = {"avg": avgTime, "fwTime": fwTime, "bwTime": bwTime}
def getLayerIdentifier(self, layer, config: tuple):
layerInfo = layer.name +\
json.dumps(layer.params, sort_keys=True, separators=(',', ':')) +\
"[" + str(config[0]) + "]" +\
json.dumps(layer.inputDim, sort_keys=False, separators=(',', ':'))
return layerInfo
def queryLayerProfileCache(self, layer, config: tuple):
layerInfo = self.getLayerIdentifier(layer, config)
if layerInfo in self.layerProfileCache:
return self.layerProfileCache[layerInfo]["avg"]
else:
return 0
def queryFwBwTime(self, layer, config: tuple):
layerInfo = self.getLayerIdentifier(layer, config)
if layerInfo in self.layerProfileCache:
return (self.layerProfileCache[layerInfo]["fwTime"], self.layerProfileCache[layerInfo]["bwTime"])
else:
return (0, 0)
def generateModuleDescription(self, layerConfigs: list, globalBatch: int):
# gpuTimeSum = 0
# profiler.start()
maxGpuUsed = 0
for l, config in zip(self.layers, layerConfigs):
maxGpuUsed = max(maxGpuUsed, self.calcGpusNeeded(l, config, globalBatch))
# nvtx.range_push("l %d %s" % (l.id, l.name))
# gpuTime = self.benchGpuTime(l, config, profile=True)
# gpuTime = self.benchGpuTime(l, config)
# nvtx.range_pop()
# gpuTimeSum += gpuTime
# print("gpuTimeSum: ", gpuTimeSum)
# profiler.stop()
return TrainingJob("test", self.layers, layerConfigs, globalBatch, maxGpuUsed, "na")
# job.dumpSingleRunnableModule(15)
def printAllLayers(self, slient=False):
#TODO: topological sort of layers. Right now, assume it's sorted.
for i in range(len(self.layers)):
self.layers[i].id = i
for i in range(len(self.layers)):
layer = self.layers[i]
# layer.id = i
prevLayerIds = []
if layer.prevLayers != None:
for prevLayer in layer.prevLayers:
prevLayerIds.append(prevLayer.id)
nextLayerIds = []
for l in layer.nextLayers:
nextLayerIds.append(l.id)
if slient == False:
print("%3d %12s %20s %20s %s" % (i, layer.name, str(prevLayerIds), str(nextLayerIds), str(layer.params)) )
def computeInputDimensions(self, inputDim):
for i in range(len(self.layers)):
layer = self.layers[i]
if i == 0:
layer.inputDim = inputDim
else:
prevLayer = layer.prevLayers[0]
if len(layer.prevLayers) == 1:
layer.inputDim = prevLayer.outputDim
else:
if layer.name == "concat":
totalChannels = 0
for pl in layer.prevLayers:
totalChannels += pl.outputDim[0]
if prevLayer.outputDim[1] != pl.outputDim[1] or prevLayer.outputDim[2] != pl.outputDim[2]: # width and height must match.
print("prevLayer.outputDim: %15s, non-matching other input: %15s" % (prevLayer.outputDim, pl.outputDim))
layer.inputDim = (totalChannels, prevLayer.outputDim[1], prevLayer.outputDim[2])
if layer.name == "ReLU2d":
for pl in layer.prevLayers:
if prevLayer.outputDim != pl.outputDim: # this is only correct for additions in Resnet.
print("prevLayer.outputDim: %15s, non-matching other input: %15s" % (prevLayer.outputDim, pl.outputDim))
layer.inputDim = prevLayer.outputDim
if layer.name in ["conv2d", "maxPool2d", "avgPool2d"]:
paddingW = layer.params["padding"][0] if type(layer.params["padding"]) is tuple else layer.params["padding"]
paddingH = layer.params["padding"][1] if type(layer.params["padding"]) is tuple else layer.params["padding"]
strideW = layer.params["stride"][0] if type(layer.params["stride"]) is tuple else layer.params["stride"]
strideH = layer.params["stride"][1] if type(layer.params["stride"]) is tuple else layer.params["stride"]
kernelSizeW = layer.params["kernel_size"][0] if type(layer.params["kernel_size"]) is tuple else layer.params["kernel_size"]
kernelSizeH = layer.params["kernel_size"][1] if type(layer.params["kernel_size"]) is tuple else layer.params["kernel_size"]
outWidth = int((layer.inputDim[1] + paddingW * 2 - kernelSizeW) / strideW + 1)
outHeight = int((layer.inputDim[2] + paddingH * 2 - kernelSizeH) / strideH + 1)
if layer.name == "conv2d":
outChannel = layer.params["out_channels"]
elif layer.name in ["maxPool2d", "avgPool2d"]:
outChannel = layer.inputDim[0]
layer.outputDim = (outChannel, outWidth, outHeight)
elif layer.name == "adAvgPool2d":
layer.outputDim = (layer.inputDim[0], layer.params["output_width"], layer.params["output_height"])
elif layer.name == "linear":
layer.outputDim = (layer.params["out_features"])
elif layer.name in ["ReLU2d", "ReLU1d", "ReLU"]:
layer.outputDim = layer.inputDim
elif layer.name == "flatten":
layer.outputDim = int(numpy.prod(layer.inputDim))
elif layer.name == "concat":
layer.outputDim = layer.inputDim
else:
inputSize = [1] + (list(layer.inputDim) if type(layer.inputDim) == tuple else [layer.inputDim])
fakeIn = torch.empty(inputSize)
outSize = list(layer.module(fakeIn).size())[1:]
layer.outputDim = outSize[0] if len(outSize) == 1 else tuple(outSize)
# print("Computed outputDim: ", layer.outputDim)
# print("%3d %11s %20s %20s %s" % (i, layer.name, str(layer.inputDim), str(layer.outputDim), str(layer.params)) )
def calcInputXfer(self, srcLayer: Layer, destLayer: Layer, srcConfig: tuple, destConfig: tuple, noGpuOverlap = False):
namesIn2d = ["conv2d", "maxPool2d", "avgPool2d", "adAvgPool2d", "ReLU2d", "concat"]
namesIn1d = ["linear", "ReLU1d"]
# Don't allow dynamic scaling from real layers to relu.
reluMultiple = 1
if destLayer.name in ["ReLU2d", "ReLU1d", "flatten"]:
reluMultiple = 1000
if srcLayer.name in namesIn2d and \
destLayer.name in namesIn2d + ["flatten"]:
actTime = self.calc2dActivationTime(srcLayer, destLayer, srcConfig, destConfig, noGpuOverlap)
return (actTime[0] * reluMultiple, actTime[1])
elif srcLayer.name in namesIn1d + ["flatten"] and \
destLayer.name in namesIn1d:
actTime = self.calcLinearActivationTime(srcLayer, destLayer, srcConfig, destConfig, noGpuOverlap)
return (actTime[0] * reluMultiple, actTime[1])
else:
actTime = self.calcGeneralActivationTime(srcLayer, destLayer, srcConfig, destConfig, noGpuOverlap)
return (actTime[0] * reluMultiple, actTime[1])
# print("Can't compute input transfer time from %s to %s." % (srcLayer.name, destLayer.name))
def calcSyncTime(self, layer, config, ctx):
if layer.name in ["conv2d"]:
syncTime = self.calcConv2dSyncTime(layer, config)
elif layer.name in ["linear"]:
syncTime = self.calcLinearSyncTime(config, ctx.globalBatch)
else:
syncTime = 0
return syncTime
def calcConv2dSyncTime(self, layer, config, bytesPerParam=4):
filterCount = config[4]
kernelSizeW = layer.params["kernel_size"][0] if type(layer.params["kernel_size"]) is tuple else layer.params["kernel_size"]
kernelSizeH = layer.params["kernel_size"][1] if type(layer.params["kernel_size"]) is tuple else 1
params = kernelSizeW * kernelSizeH * filterCount + kernelSizeW * kernelSizeH
size = params * bytesPerParam
return size / self.NET_BANDWIDTH # Returns microseconds.
def calc2dActivationTime(self, srcLayer: Layer, destLayer: Layer, srcConfig: tuple, destConfig: tuple, noGpuOverlap = False):
bytesPerParam = 4
# Compute output dimension of previous and current layer.
srcS = srcConfig[0]
srcW = srcConfig[1] * srcLayer.outputDim[1] // srcLayer.inputDim[1] # Adjusts based on input/output ratio.
srcH = srcConfig[2] * srcLayer.outputDim[2] // srcLayer.inputDim[2] # It's necessary for pool or conv2d with stride > 1
srcOutChannel = srcConfig[4] if len(srcConfig) >= 5 else srcConfig[3] # non-convolutional 2d layers don't have filter.
destS = destConfig[0]
destW = destConfig[1]
destH = destConfig[2]
# destInChannel = destConfig[3]
destInChannel = srcOutChannel # TODO: temporary hack to handle Inception V3's concat.
srcNoIndependent = True
destNoIndependent = True
if hasattr(srcLayer, 'gpuAssignment') and hasattr(destLayer, 'gpuAssignment'):
for r in srcLayer.gpuAssignment:
if r not in destLayer.gpuAssignment:
srcNoIndependent = False
break
for r in destLayer.gpuAssignment:
if r not in srcLayer.gpuAssignment:
destNoIndependent = False
break
else:
# Compute "estimated" number of gpus used for src and dest. This is used only for comparing which side might unshared gpus.
srcGpus = self.calcGpusNeeded(srcLayer, srcConfig, srcS * destS)
destGpus = self.calcGpusNeeded(destLayer, destConfig, srcS * destS)
if srcGpus > destGpus:
srcNoIndependent = False # no src node is stopped used in dest.
if srcGpus < destGpus:
destNoIndependent = False # no dest node is newly used.
# Compute common part that doesn't have to move.
commonSize = bytesPerParam * min(srcS, destS) * min(srcW, destW) * min(srcH, destH) * min(srcOutChannel, destInChannel)
if noGpuOverlap:
commonSize = 0
# Halo exchange
if "kernel_size" in destLayer.params: # TODO: hack for adaptive avgPool2D.
if type(destLayer.params["kernel_size"]) is tuple:
haloW = int((destLayer.params["kernel_size"][0] - 1) / 2)
haloH = int((destLayer.params["kernel_size"][1] - 1) / 2)
else:
haloW = int((destLayer.params["kernel_size"] - 1) / 2)
haloH = int((destLayer.params["kernel_size"] - 1) / 2)
else:
haloW = 0
haloH = 0
haloPixels = 2 * haloW * ((destH + haloH) if destW != destLayer.inputDim[1] else 0)\
+ 2 * haloH * ((destW + haloW) if destH != destLayer.inputDim[2] else 0)
haloSize = bytesPerParam * min(srcS, destS) * haloPixels * min(srcOutChannel, destInChannel)
# compute times
egressBytes = bytesPerParam * srcS * srcW * srcH * srcOutChannel + (haloSize - commonSize) if srcNoIndependent else bytesPerParam * srcS * srcW * srcH * srcOutChannel + haloSize
ingressBytes = bytesPerParam * destS * destW * destH * destInChannel + (haloSize - commonSize) if destNoIndependent else bytesPerParam * destS * destW * destH * destInChannel + haloSize
activationTime = max(egressBytes, ingressBytes) / self.NET_BANDWIDTH
activationTime += self.NET_LATENCY if activationTime > 0 else 0
return (2 * activationTime, (egressBytes, ingressBytes, haloSize)) # double to count both forward and backward passes.
def calcLinearSyncTime(self, config, globalBatch, bytesPerParam=4, alwaysPaySyncTime=False):
if not alwaysPaySyncTime and config[0] == globalBatch: # No split.
return 0
inFeatures = config[1]
outFeatures = config[2]
params = inFeatures * outFeatures + outFeatures
size = params * bytesPerParam
return size / self.NET_BANDWIDTH # Returns microseconds.
def calcLinearActivationTime(self, srcLayer: Layer, destLayer: Layer, srcConfig: tuple, destConfig: tuple, noGpuOverlap: bool):
bytesPerParam = 4
# Prepare variables.
prevOutFeatures = 0
if len(srcConfig) >= 4: # prev layer was conv2d.
# print("%s to %s" % (srcLayer.name, destLayer.name))
srcS = srcConfig[0]
srcW = srcConfig[1] * 1 if srcLayer.name == "flatten" else (srcLayer.outputDim[1] // srcLayer.inputDim[1]) # Adjusts based on input/output ratio.
srcH = srcConfig[2] * 1 if srcLayer.name == "flatten" else (srcLayer.outputDim[2] // srcLayer.inputDim[2]) # It's necessary for pool or conv2d with stride > 1
srcOutChannel = srcConfig[4] if len(srcConfig) >= 5 else srcConfig[3] # non-convolutional 2d layers don't have filter.
srcOutFeatures = srcW * srcH * srcOutChannel
splitFactor = 1
elif len(srcConfig) == 3:
srcS = srcConfig[0]
srcOutFeatures = srcConfig[2]
splitFactor = srcLayer.inputDim / srcConfig[1] # This much output must be added up to get the final output.
else:
print("[calcLinearActivationTime] error! srcConfig dimensions is not correct.")
destS = destConfig[0]
destInFeatures = destConfig[1]
commonSize = bytesPerParam * min(srcS, destS) * min(srcOutFeatures, destInFeatures)
if noGpuOverlap:
commonSize = 0
# compute times
egressBytes = bytesPerParam * srcS * srcOutFeatures * splitFactor - commonSize
ingressBytes = bytesPerParam * destS * destInFeatures * splitFactor - commonSize
activationTime = max(egressBytes, ingressBytes) / self.NET_BANDWIDTH
activationTime += self.NET_LATENCY if activationTime > 0 else 0
# print("activationTime:%.1f, egressBytes:%d, ingressBytes:%d, splitFactor: %d, commonSize: %d" %\
# (activationTime, egressBytes, ingressBytes, splitFactor, commonSize))
return (2 * activationTime, (egressBytes, ingressBytes, splitFactor)) # double to count both forward and backward passes.
def calcGeneralActivationTime(self, srcLayer: Layer, destLayer: Layer, srcConfig: tuple, destConfig: tuple, noGpuOverlap: bool):
# print("srcConfig: ", srcConfig)
bytesPerParam = 4
# Prepare variables.
srcS = srcConfig[0]
srcOutFeatures = numpy.prod(srcLayer.outputDim)
destS = destConfig[0]
destInFeatures = numpy.prod(destLayer.inputDim)
splitFactor = 1
commonSize = bytesPerParam * min(srcS, destS) * min(srcOutFeatures, destInFeatures)
if noGpuOverlap:
commonSize = 0
# compute times
egressBytes = bytesPerParam * srcS * srcOutFeatures * splitFactor - commonSize
ingressBytes = bytesPerParam * destS * destInFeatures * splitFactor - commonSize
activationTime = max(egressBytes, ingressBytes) / self.NET_BANDWIDTH
activationTime += self.NET_LATENCY if activationTime > 0 else 0
# print("activationTime:%.1f, egressBytes:%d, ingressBytes:%d, splitFactor: %d, commonSize: %d" %\
# (activationTime, egressBytes, ingressBytes, splitFactor, commonSize))
return (2 * activationTime, (egressBytes, ingressBytes, splitFactor)) # double to count both forward and backward passes.
def Conv2d(self,
in_channels: int,
out_channels: int,
kernel_size,
stride: _size_2_t = 1,
padding: _size_2_t = 0,
dilation: _size_2_t = 1,
groups: int = 1,
bias: bool = True,
padding_mode: str = 'zeros',
custom_previous_layers: list = None):
module = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode)
if custom_previous_layers == None and len(self.layers) > 0:
custom_previous_layers = [self.layers[-1]]
layer = Layer(module, "conv2d",
{"in_channels": in_channels, "out_channels": out_channels, "kernel_size": kernel_size, "stride": stride, "padding": padding},
prevLayers = custom_previous_layers)
self.layers.append(layer)
return module
def MaxPool2d(self,
kernel_size: _size_2_t,
stride: _size_2_t,
padding: _size_2_t = 0,
# dilation: _size_2_t = 1,
custom_previous_layers: list = None):
module = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=padding) #, dilation=dilation)
if custom_previous_layers == None and len(self.layers) > 0:
custom_previous_layers = [self.layers[-1]]
layer = Layer(module, "maxPool2d",
{"kernel_size": kernel_size, "stride": stride, "padding": padding},
prevLayers = custom_previous_layers)
self.layers.append(layer)
return module
def AdaptiveAvgPool2d(self,
output_size,
custom_previous_layers: list = None):
module = nn.AdaptiveAvgPool2d(output_size)
if custom_previous_layers == None and len(self.layers) > 0:
custom_previous_layers = [self.layers[-1]]
# stride = (input_size//output_size)
# kernel_size = input_size - (output_size-1)*stride
# padding = 0
layer = Layer(module, "adAvgPool2d",
{"output_width": output_size[0], "output_height": output_size[1]},
prevLayers = custom_previous_layers)
self.layers.append(layer)
return module
def AvgPool2d(self, kernel_size: _size_2_t, stride: Optional[_size_2_t] = None, padding: _size_2_t = 0,
ceil_mode: bool = False, count_include_pad: bool = True, divisor_override: bool = None,
custom_previous_layers: list = None):
module = nn.AvgPool2d(kernel_size=kernel_size, stride=stride, padding=padding, ceil_mode=ceil_mode,
count_include_pad=count_include_pad, divisor_override=divisor_override)
if custom_previous_layers == None and len(self.layers) > 0:
custom_previous_layers = [self.layers[-1]]
layer = Layer(module, "avgPool2d",
{"kernel_size": kernel_size, "stride": stride, "padding": padding},
prevLayers = custom_previous_layers)
self.layers.append(layer)
return module
def Linear(self, in_features: int, out_features: int, bias: bool = True, custom_previous_layers: list = None):
module = nn.Linear(in_features, out_features, bias)
if custom_previous_layers == None and len(self.layers) > 0:
custom_previous_layers = [self.layers[-1]]
layer = Layer(module, "linear",
{"in_features": in_features, "out_features": out_features, "bias": bias},
prevLayers = custom_previous_layers)
self.layers.append(layer)
return module
def ReLU(self, inplace: bool = False, custom_previous_layers: list = None):
module = nn.ReLU(inplace=inplace)
if custom_previous_layers == None and len(self.layers) > 0:
custom_previous_layers = [self.layers[-1]]
if custom_previous_layers[0].name in ["conv2d", "maxPool2d", "avgPool2d", "adAvgPool2d", "concat"]:
name = "ReLU2d"
elif custom_previous_layers[0].name in ["linear"]:
name = "ReLU1d"
else:
name = "ReLU"
layer = Layer(module, name, {"inplace": inplace, "kernel_size": 1, "stride": 1, "padding": 0}, prevLayers = custom_previous_layers)
self.layers.append(layer)
return module
def Flatten(self, custom_previous_layers: list = None):
module = nn.Flatten(start_dim=1)
if custom_previous_layers == None and len(self.layers) > 0:
custom_previous_layers = [self.layers[-1]]
layer = Layer(module, "flatten", {"kernel_size": 1}, prevLayers = custom_previous_layers)
self.layers.append(layer)
return
class ConcatInputs(nn.Module):
def __init__(self, dim: int = 1):
super(CostSim.ConcatInputs, self).__init__()
self.dim = dim
def forward(self, inputList: List[torch.Tensor]):
out = torch.cat(inputList, dim=self.dim)
return out
def Concat(self, custom_previous_layers: list = None): # concatenates tensors on channel dimension only.
module = CostSim.ConcatInputs(dim=1)
if custom_previous_layers == None and len(self.layers) > 0:
custom_previous_layers = [self.layers[-1]]
layer = Layer(module, "concat", {"kernel_size": 1}, prevLayers = custom_previous_layers)
self.layers.append(layer)
return
def Dropout(self, dropout, inplace: bool = False, custom_previous_layers: list = None):
module = nn.Dropout(dropout, inplace=inplace)
if custom_previous_layers == None and len(self.layers) > 0:
custom_previous_layers = [self.layers[-1]]
layer = Layer(module, "dropout", {"dropout": dropout}, prevLayers = custom_previous_layers)
self.layers.append(layer)
return module
def LayerNorm(self, dim, custom_previous_layers: list = None):
module = nn.LayerNorm(dim)
if custom_previous_layers == None and len(self.layers) > 0:
custom_previous_layers = [self.layers[-1]]
layer = Layer(module, "layerNorm", {"dim": dim}, prevLayers = custom_previous_layers)
self.layers.append(layer)
return module
def GeneralLayer(self, module, name, params, custom_previous_layers: list = None, mustTrace=False):
if custom_previous_layers == None and len(self.layers) > 0:
custom_previous_layers = [self.layers[-1]]
layer = Layer(module, name, params, prevLayers = custom_previous_layers)
layer.must_trace = mustTrace
self.layers.append(layer)
return
def getInitialConfig(self, layer, globalBatch: int):
if layer.name in ["conv2d"]:
initCfg = (globalBatch, layer.inputDim[1], layer.inputDim[2], layer.inputDim[0], layer.outputDim[2]) # (batch, width, height, channel, filter)
elif layer.name in ["linear", "ReLU1d"]:
if type(layer.inputDim) == tuple:
initCfg = (globalBatch, *layer.inputDim, layer.outputDim)
else:
initCfg = (globalBatch, layer.inputDim, layer.outputDim)
elif layer.name in ["flatten", "maxPool2d", "avgPool2d", "adAvgPool2d", "ReLU2d", "concat"]:
initCfg = (globalBatch, layer.inputDim[1], layer.inputDim[2], layer.inputDim[0]) # (batch, width, height, channel, filter)
else:
initCfg = (globalBatch, *layer.inputDim) if type(layer.inputDim) == tuple else (globalBatch, layer.inputDim) # (batch, width, height, channel)
return initCfg
def listConfigOptions(self, layer, globalBatch: int, totalGpus: int, samplePo2=True, sampleSplit=True, spatialSplit=True, filterSplit=False, pruneHeuristics=False, dataParallelBaseline=False):
initCfg = self.getInitialConfig(layer, globalBatch)
totalSplits = int(math.log(totalGpus, 2))
# generate config candidates.
sampleSplitOptions = range(totalSplits + 1) if sampleSplit else [0]
if dataParallelBaseline:
sampleSplitOptions = [totalSplits]
if layer.name in ["conv2d"]:
configCandidates = [(int(initCfg[0] / 2**bs), math.ceil(initCfg[1] / 2**int(whs/2)), math.ceil(initCfg[1] / 2**int(whs/2+0.5)), initCfg[3], math.ceil(initCfg[4] / 2**fs) )
for bs in sampleSplitOptions \
for whs in (range(totalSplits - bs + 1) if spatialSplit else [0]) \
for fs in (range(totalSplits - bs - whs + 1) if filterSplit else [0]) ]
elif layer.name in ["linear", "ReLU1d"]:
configCandidates = [(int(initCfg[0] / 2**bs), int(initCfg[1] / 2**ins), int(initCfg[2] / 2**outs) )
for bs in sampleSplitOptions \
for ins in (range(totalSplits - bs + 1) if filterSplit else [0]) \
for outs in (range(totalSplits - bs - ins + 1) if filterSplit else [0]) ]
elif layer.name in ["flatten", "maxPool2d", "avgPool2d", "adAvgPool2d", "ReLU2d", "concat"]:
configCandidates = [(int(initCfg[0] / 2**bs), math.ceil(initCfg[1] / 2**int(whs/2)), math.ceil(initCfg[1] / 2**int(whs/2+0.5)), initCfg[3] )
for bs in sampleSplitOptions \
for whs in (range(totalSplits - bs + 1) if spatialSplit else [0]) ]
else:
configCandidates = [(int(initCfg[0] / 2**bs), *initCfg[1:] )
for bs in sampleSplitOptions ]
# if layer.name in ["conv2d"]:
# configCandidates = [(math.ceil(initCfg[0] / replicas), math.ceil(initCfg[1] / 2**int(whs/2)), math.ceil(initCfg[1] / 2**int(whs/2+0.5)), initCfg[3], math.ceil(initCfg[4] / 2**fs) )
# for whs in (range(totalSplits + 1) if spatialSplit else [0]) \
# for fs in (range(totalSplits - whs + 1) if filterSplit else [0]) \
# for replicas in (([2**ss for ss in range(totalSplits - whs - fs + 1)] if samplePo2 else range(1, 2**(totalSplits - whs - fs) + 1)) if sampleSplit else [1]) ]
# elif layer.name in ["linear", "ReLU1d"]:
# configCandidates = [(math.ceil(initCfg[0] / replicas), math.ceil(initCfg[1] / 2**ins), math.ceil(initCfg[2] / 2**outs) )
# for ins in (range(totalSplits + 1) if filterSplit else [0]) \
# for outs in (range(totalSplits - ins + 1) if filterSplit else [0]) \
# for replicas in (([2**ss for ss in range(totalSplits - ins - outs + 1)] if samplePo2 else range(1, 2**(totalSplits - ins - outs) + 1)) if sampleSplit else [1]) ]
# # for replicas in (range(1, 2**(totalSplits - ins - outs) + 1) if sampleSplit else [1]) ]
# elif layer.name in ["flatten", "maxPool2d", "avgPool2d", "adAvgPool2d", "ReLU2d", "concat"]:
# configCandidates = [(math.ceil(initCfg[0] / replicas), math.ceil(initCfg[1] / 2**int(whs/2)), math.ceil(initCfg[1] / 2**int(whs/2+0.5)), initCfg[3] )
# for whs in (range(totalSplits + 1) if spatialSplit else [0]) \
# for replicas in (([2**ss for ss in range(totalSplits - whs + 1)] if samplePo2 else range(1, 2**(totalSplits - whs) + 1)) if sampleSplit else [1]) ]
validConfigs = []
for config in configCandidates:
invalidConfig = False
for dim in range(len(config)):
if config[dim] < 1:
invalidConfig = True
break
# add some other rules..
if not invalidConfig:
validConfigs.append(config)
if pruneHeuristics:
bestGpuTimeByGpusUsed = {}
prunedConfigs = []
for config in validConfigs:
gpuCount = self.calcGpusNeeded(layer, config, globalBatch)
if gpuCount not in bestGpuTimeByGpusUsed:
bestGpuTimeByGpusUsed[gpuCount] = 999999999
gpuTime = self.benchGpuTime(layer, config)
if bestGpuTimeByGpusUsed[gpuCount] > gpuTime:
bestGpuTimeByGpusUsed[gpuCount] = gpuTime
for config in validConfigs:
gpuCount = self.calcGpusNeeded(layer, config, globalBatch)
gpuTime = self.benchGpuTime(layer, config)
if gpuTime <= bestGpuTimeByGpusUsed[gpuCount] * 1.5:
prunedConfigs.append(config)
validConfigs = prunedConfigs
return validConfigs
def displayConfigSizeGrowth(self, layer, maxNumGpus):
options = [(True, True, True), (True, True, False), (True, False, False)]
totalSplits = int(math.log(maxNumGpus, 2))
globalBatch = maxNumGpus * 4
for sampleSplit, spatialSplit, filterSplit in options:
print("Gpus Sample Spatial filter configListSize prunedConfigs")
for allowedSplits in range(totalSplits+1):
totalGpus = 2 ** allowedSplits
configs = self.listConfigOptions(layer, globalBatch, totalGpus, sampleSplit=sampleSplit, spatialSplit=spatialSplit, filterSplit=filterSplit)
prunedConfigs = self.listConfigOptions(layer, globalBatch, totalGpus, sampleSplit=sampleSplit, spatialSplit=spatialSplit, filterSplit=filterSplit, pruneHeuristics=True)
print( "%3d %5s %5s %5s : %11d %11d" % (totalGpus, str(sampleSplit), str(spatialSplit), str(filterSplit), len(configs), len(prunedConfigs) ))
def calcGpusNeeded(self, layer, config: tuple, globalBatch: int):
initCfg = self.getInitialConfig(layer, globalBatch)
gpuCount = 1
# if len(config) != len(initCfg):
# print("[calcGpusNeeded] dimension of configs doesn't match!! %20s layer len(config):%d != len(initCfg):%d" % (layer.name, len(config), len(initCfg)))
for i in range(len(initCfg)):
gpuCount *= int(initCfg[i] / config[i])
return gpuCount
def isConfigDataParallelOnly(self, layer, config: tuple, globalBatch: int):
initCfg = self.getInitialConfig(layer, globalBatch)
dpOnly = True
for i in range(1, len(config)):
if config[i] != initCfg[i]:
dpOnly = False
return dpOnly
def benchGpuTime(self, layer, config: tuple, profile=False, ctx=None):
cached = self.queryLayerProfileCache(layer, config)
if cached > 0:
return cached
else:
return 1
# This is a hack for quickly generating a plan for initial layer profiling.
if ctx != None and ctx.doNotBench:
assert ctx.totalGpus == 1
return 1
if layer.name in ["conv2d"]:
gpuTime = self.profiler.runConv2dBench(config, layer.params, profile)
print(" Something bad happened!! Missed queryLayerProfileCache")
elif layer.name in ["linear"]:
gpuTime = self.profiler.runLinearBench(config, profile)
print(" Something bad happened!! Missed queryLayerProfileCache")
else:
gpuTime = 0
return gpuTime
def runMultiChainZhihao(self, startLayer, startConfig, globalBatch: int, totalGpus: int):
k = len(startLayer.nextLayers)
llist = [[startLayer] for j in range(k)]
endLayer = None
for j in range(k):
l = startLayer.nextLayers[j]
while len(l.prevLayers) == 1: # Until join happens.
llist[j].append(l)
if len(l.nextLayers) > 1:
print("[searchMultiChain] ERROR! nested multi-chain. TODO; implement handling of this.")
l = l.nextLayers[0]
if endLayer == None:
endLayer = l
else:
assert(endLayer == l)
print("Found %d chains, branching at %d-th layer, joining at %d-th layer" % (k, startLayer.id, endLayer.id))
noParallelTimeSum = 0
t = [[ [] for i in range(len(llist[j])) ] for j in range(k)] # [layer] = list of (config, cumulativeTime, prevConfigIndex)
for branchIdx in range(k):
length = len(llist[branchIdx])
bestConfigList = []
bestTimeList = []
bestDataParallelTimeList = []
for idx in range(length):
layer = llist[branchIdx][idx]
initCfg = self.getInitialConfig(layer, globalBatch)
bestTime = self.benchGpuTime(layer, initCfg)
bestDataParallelTime = bestTime
noParallelTimeSum += bestTime
bestConfig = initCfg
for config in (self.listConfigOptions(layer, globalBatch, totalGpus) if idx > 0 else [startConfig]):
gpuTime = self.benchGpuTime(layer, config)
# Computer all-reduce time
# if layer.name in ["conv2d"]:
# syncTime = self.calcConv2dSyncTime(config)
# elif layer.name in ["linear"]:
# syncTime = self.calcLinearSyncTime(config, globalBatch)
# else:
# syncTime = 0
syncTime = 0
if idx == 0:
t[branchIdx][idx].append((config, syncTime, None, (0, 0, 0, syncTime, (0)) ))
else:
bestPrevCfgIdx = 0
bestCumulativeTime = 99999999999
bestTimeComposition = None
# WARNING!! Following main branch only!!
prevLayer = layer.prevLayers[0]
for prevCfgIdx in range(len(t[branchIdx][idx-1])):
prevCfg, cumulativeTime, prevConfigIndexOfPrev, timeComposition = t[branchIdx][idx-1][prevCfgIdx]
activationTime, activationSizeMatrix = self.calcInputXfer(prevLayer, layer, prevCfg, config)
newTime = cumulativeTime + activationTime + gpuTime + syncTime
if newTime < bestCumulativeTime:
bestCumulativeTime = newTime
bestTimeComposition = (cumulativeTime, activationTime, gpuTime, syncTime, activationSizeMatrix)
bestPrevCfgIdx = prevCfgIdx
t[branchIdx][idx].append((config, bestCumulativeTime, bestPrevCfgIdx, bestTimeComposition ))
if gpuTime < bestTime:
bestTime = gpuTime
bestConfig = config
bestConfigList.append(bestConfig)
bestTimeList.append(bestTime)
bestDataParallelTimeList.append(bestDataParallelTime)
# join at the endLayer. Sum up all times of all branches.
configToTimeDict = {}
for endConfig in self.listConfigOptions(endLayer, globalBatch, totalGpus):
gpuTime = self.benchGpuTime(endLayer, endConfig)
bestTime = 9999999999999
optionWithBestTime = []
sumOfBestTime = 0
for branchIdx in range(k):
bestPrevCfgIdx = 0
bestCumulativeTime = 99999999999
bestTimeComposition = None
prevLayer = llist[branchIdx][-1]
for prevCfgIdx in range(len(t[branchIdx][-1])):
prevCfg, cumulativeTime, prevConfigIndexOfPrev, timeComposition = t[branchIdx][-1][prevCfgIdx]
activationTime, activationSizeMatrix = self.calcInputXfer(prevLayer, endLayer, prevCfg, endConfig)
newTime = cumulativeTime + activationTime + gpuTime + syncTime
if newTime < bestCumulativeTime:
bestCumulativeTime = newTime
bestTimeComposition = (cumulativeTime, activationTime, gpuTime, syncTime, activationSizeMatrix)
bestPrevCfgIdx = prevCfgIdx
sumOfBestTime += bestCumulativeTime
optionWithBestTime.append(bestPrevCfgIdx)
configToTimeDict[endConfig] = (sumOfBestTime, tuple(optionWithBestTime))
return (endLayer, configToTimeDict, t)
def searchMultiChain(self, startLayer, startConfig, globalBatch: int, totalGpus: int, verbose=True):
maximumOptionListSize = 0
k = len(startLayer.nextLayers)
llist = [[startLayer] for j in range(k)]
endLayer = None
for j in range(k):
l = startLayer.nextLayers[j]
while len(l.prevLayers) == 1: # Until join happens.
llist[j].append(l)
if len(l.nextLayers) > 1:
print("[searchMultiChain] ERROR! nested multi-chain. TODO; implement handling of this.")
l = l.nextLayers[0]
if endLayer == None:
endLayer = l
else:
assert(endLayer == l)
print("Found %d chains, branching at %d-th layer, joining at %d-th layer" % (k, startLayer.id, endLayer.id))
# Start dynamic programming.
time_start = time.time()
def generateAllConfigs(k: int, llist: list):
if k == 0:
return [[]]
configs = []
for laterPart in generateAllConfigs(k-1, llist[1:]):
configs.append([(0, startConfig)] + laterPart)
for nextIndex in range(1, len(llist[0])):
for config in self.listConfigOptions(llist[0][nextIndex], globalBatch, totalGpus):
laterPartList = generateAllConfigs(k-1, llist[1:])
# print("[generateAllConfigs] for k=%d, (%d, %s), got %s" % (k, nextIndex, str(config), str(laterPartList)))
for laterPart in laterPartList:
completePart = [(nextIndex, config)] + laterPart
configs.append(completePart)
return configs
# allCombinedIdx = generateAllConfigs(k, llist)
print("Total # of cell in table prefore config pruning: %d" % len(generateAllConfigs(k, llist)))
# TODO: replace this with carefully pruned config matrix.
configOptionLists = []
for j in range(k):
prevTotalStateCombo = 1
configOptionLists.append( [ [startConfig] ] )
print("[Pruning configs] %d-th chain, before prune: [" % j, end="")
for idx in range(1, len(llist[j])):
configOptionLists[j].append(self.listConfigOptions(llist[j][idx], globalBatch, totalGpus))
print(" %2d" % len(configOptionLists[j][idx]), end="")
prevTotalStateCombo *= len(configOptionLists[j][idx])
configOptionLists[j].append(self.listConfigOptions(endLayer, globalBatch, totalGpus))
print(" ] (%d combos)" % prevTotalStateCombo, end="")
for prunIter in range(3):
print(" ==> after prune (pass %d): [" % (prunIter + 1), end="")
totalStateCombo = 1
for idx in range(1, len(llist[j])):
prevLayer = llist[j][idx-1]
layer = llist[j][idx]
nextLayer = llist[j][idx + 1] if (idx + 1 < len(llist[j])) else endLayer
selectedConfigs = []
for nextConfig in configOptionLists[j][idx + 1]:
for prevConfig in configOptionLists[j][idx - 1]:
bestConfigByGpuCount = {}
for config in configOptionLists[j][idx]:
gpusUsed = self.calcGpusNeeded(layer, config, globalBatch)
activationTime1, activationSizeMatrix = self.calcInputXfer(prevLayer, layer, prevConfig, config)
activationTime2, activationSizeMatrix = self.calcInputXfer(layer, nextLayer, config, nextConfig)
gpuTime = self.benchGpuTime(layer, config)
totalTime = activationTime1 + activationTime2 + gpuTime
if gpusUsed not in bestConfigByGpuCount or \
totalTime < bestConfigByGpuCount[gpusUsed][0]:
bestConfigByGpuCount[gpusUsed] = (totalTime, config)
gpuCountList = bestConfigByGpuCount.keys()
previousSelectedTime = 999999999
for gpuCount in sorted(gpuCountList):
if bestConfigByGpuCount[gpuCount][0] < previousSelectedTime * 0.9: # Don't use more GPUs unless it reduce time by 10% or more.
previousSelectedTime = bestConfigByGpuCount[gpuCount][0]
if bestConfigByGpuCount[gpuCount][1] not in selectedConfigs:
selectedConfigs.append(bestConfigByGpuCount[gpuCount][1])
configOptionLists[j][idx] = selectedConfigs
print(" %2d" % len(configOptionLists[j][idx]), end="")
totalStateCombo *= len(configOptionLists[j][idx])
print(" ] (%d combos)" % totalStateCombo, end="")
# Stop prunning if it is converged.
if totalStateCombo == prevTotalStateCombo:
break
prevTotalStateCombo = totalStateCombo
print("")
configOptionLists[j].pop() # remove configs of endLayer
def generatePrunedCombo(branchIdx: int, configOptionLists: list):
if branchIdx == len(configOptionLists):
return [[]]
combos = []
for idx in range(len(configOptionLists[branchIdx])):
for config in configOptionLists[branchIdx][idx]:
laterPartList = generatePrunedCombo(branchIdx + 1, configOptionLists)
# print("[generateAllConfigs] for k=%d, (%d, %s), got %s" % (k, nextIndex, str(config), str(laterPartList)))
for laterPart in laterPartList:
completePart = [(idx, config)] + laterPart
combos.append(completePart)
return combos
allCombinedIdx = generatePrunedCombo(0, configOptionLists)
initialIdx = tuple(allCombinedIdx[0])
t = {}
# t[initialIdx] = [(numpy.zeros(k, dtype=numpy.int32), numpy.zeros(totalGpus, dtype=numpy.int32), (0, startConfig))]
t[initialIdx] = [([0 for j in range(k)], [0 for j in range(totalGpus)], (-1, startConfig, 0))]
# t[initialIdx] = [(array('i', [0 for j in range(k)]), [0 for j in range(totalGpus)], (0, startConfig))]
optionsConsideredTotal = 0
optionsAfterMinTotal = 0
# configOptionLists = [[] for j in range(k)]
# for j in range(k):
# configOptionLists[j].append([startConfig])
# for idx in range(1, len(llist[j])):
# configOptionLists[j].append(self.listConfigOptions(llist[j][idx], globalBatch, totalGpus))
if verbose:
print("Total # of cells in table: %d, configGeneration took: %d sec" % (len(allCombinedIdx), time.time() - time_start))
for combinedIdxAndConfig in allCombinedIdx[1:]:
# print(combinedIdx)
combined = tuple(combinedIdxAndConfig)
t[combined] = []
prefilteredOptions = []
for j in range(k):
prevIdx = combinedIdxAndConfig[j][0] - 1
if prevIdx < 0:
continue
currentIdx = combinedIdxAndConfig[j][0]
layer = llist[j][currentIdx]
prevLayer = llist[j][prevIdx]
currentConfig = combinedIdxAndConfig[j][1]
gpuTime = int(self.benchGpuTime(layer, currentConfig))
for prevConfig in configOptionLists[j][prevIdx]: #prevConfigList:
prevCombinedIdxAndConfig = combinedIdxAndConfig.copy()
prevCombinedIdxAndConfig[j] = (prevIdx, prevConfig)
prevCombined = tuple(prevCombinedIdxAndConfig)
for optionIdx in range(len(t[prevCombined])):
prevTimeVec, prevGpuReady, prevStep = t[prevCombined][optionIdx]
# for (prevTimeVec, prevGpuReady, prevStep) in t[prevCombined]:
# compute time.
activationTime, activationSizeMatrix = self.calcInputXfer(prevLayer, layer, prevConfig, currentConfig)
# First, compute the earliest time it can finish j-th chain.
prevBranchReady = prevTimeVec[j]
gpusNeeded = self.calcGpusNeeded(layer, currentConfig, globalBatch)
newGpuReadyTimeVec = sorted(prevGpuReady, reverse=True)
prevGpuReadyTime = newGpuReadyTimeVec[totalGpus - gpusNeeded]
newStartTime = max(prevBranchReady, prevGpuReadyTime)
# newReadyTime = int(newStartTime + activationTime + gpuTime)
newReadyTime = newStartTime + int(activationTime) + gpuTime
newTimeVec = prevTimeVec.copy()
newTimeVec[j] = newReadyTime
gpusAssigned = 0
for i in range(totalGpus):
if newGpuReadyTimeVec[i] <= newStartTime:
newGpuReadyTimeVec[i] = newReadyTime
gpusAssigned += 1
if gpusAssigned >= gpusNeeded:
break
# # Experiment... bump always.
# bumpToTime = min(newGpuReadyTimeVec)
# bumpedTimeVec = [ max(timeElem, bumpToTime) for timeElem in newTimeVec ]
# prefilteredOptions.append((bumpedTimeVec, newGpuReadyTimeVec, (j, prevConfig, optionIdx) ))
prefilteredOptions.append((newTimeVec, newGpuReadyTimeVec, (j, prevConfig, optionIdx) ))
optionsConsideredTotal += len(prefilteredOptions)
# Filter by lamportMin.
skipIdicesForEqual = []
filteredIndices = []
for optionIdx in range(len(prefilteredOptions)):
if optionIdx in skipIdicesForEqual:
continue
(newTimeVec, newGpuReadyTimeVec, step) = prefilteredOptions[optionIdx]
bumpToTime = min(newGpuReadyTimeVec)
bumpedTimeVec = [ max(timeElem, bumpToTime) for timeElem in newTimeVec ]
# check if there's any other result that's clearly better than this.
foundBetterOption = False
for compareAgainstIdx in range(len(prefilteredOptions)):
if (optionIdx == compareAgainstIdx) or (compareAgainstIdx in filteredIndices):
continue
(cmpTimeVec, cmpGpuReadyTimeVec, cmpStep) = prefilteredOptions[compareAgainstIdx]
better = True
equal = True
for i in range(k):
if bumpedTimeVec[i] < cmpTimeVec[i]:
better = False
if bumpedTimeVec[i] != cmpTimeVec[i]:
equal = False
if better and (not equal):
foundBetterOption = True
if equal:
skipIdicesForEqual.append(compareAgainstIdx)
if foundBetterOption:
filteredIndices.append(optionIdx)
else:
t[combined].append(prefilteredOptions[optionIdx])
# print("[MultiChain] t[%50s] (%3d->%3d options)= %s" %
# (str(combined), len(prefilteredOptions), len(t[combined]), str(t[combined])))
if len(t[combined]) == 0:
print(" ****** prefilteredOptions: %s" % str(prefilteredOptions))
maximumOptionListSize = max(maximumOptionListSize, len(t[combined]))
# optionListSizeList.append(len(t[combined]))
optionsAfterMinTotal += len(t[combined])
# TODO: Final join to end layer.
configToTimeDict = {}
def generatePrunedCombo(branchIdx: int, configOptionLists: list):
if branchIdx == len(configOptionLists):
return [[]]
combos = []
for idx in range(len(configOptionLists[branchIdx])):
for config in configOptionLists[branchIdx][idx]:
laterPartList = generatePrunedCombo(branchIdx + 1, configOptionLists)
# print("[generateAllConfigs] for k=%d, (%d, %s), got %s" % (k, nextIndex, str(config), str(laterPartList)))
for laterPart in laterPartList:
completePart = [(idx, config)] + laterPart
combos.append(completePart)
return combos
# def generateFinalConfigs(k: int, llist: list):
# if k == 0:
# return [[]]
# configs = []
# for config in self.listConfigOptions(llist[0][-1], globalBatch, totalGpus):