def run(transposed):
bsize = 8
dsize = 10
builder = popart.Builder()
ip = builder.addInputTensor(
popart.TensorInfo("FLOAT", [bsize, dsize, dsize]))
if transposed:
# Explicitly specify the batch dimension for init
init = builder.aiGraphcore.init([dsize, dsize, bsize],
popart.DataType.FLOAT,
popart.InitType.Zero, 2)
else:
init = builder.aiGraphcore.init([bsize, dsize, dsize],
popart.DataType.FLOAT,
popart.InitType.Zero, 0)
def add_layer(in_id):
w = builder.addInitializedInputTensor(
np.ones([dsize, dsize], np.float32))
if transposed:
inputs = [w, in_id]
else:
inputs = [in_id, w]
matmul_id = builder.aiOnnx.matmul(inputs)
return matmul_id
if transposed:
ip_t = builder.aiOnnx.transpose([ip])
else:
ip_t = ip
m1 = add_layer(ip_t)
init = builder.aiOnnx.add([init, m1])
m2 = add_layer(m1)
init = builder.aiOnnx.add([init, m2])
m3 = add_layer(m2)
init = builder.aiOnnx.add([init, m3])
out = builder.aiGraphcore.l1loss([init], 0.1)
builder.addOutputTensor(out)
device = tu.create_test_device(1)
dfAnchors = {out: popart.AnchorReturnType("All")}
opts = popart.SessionOptions()
opts.enableOutlining = True
opts.batchSerializationSettings.factor = 4
proto = builder.getModelProto()
session = popart.InferenceSession(
fnModel=proto,
dataFlow=popart.DataFlow(1, dfAnchors),
patterns=popart.Patterns(popart.PatternsLevel.All),
userOptions=opts,
deviceInfo=device)
session.prepareDevice()
session.weightsFromHost()
anchors = session.initAnchorArrays()
ip_data = np.ones((bsize, dsize, dsize), dtype=np.float32)
stepio = popart.PyStepIO({ip: ip_data}, anchors)
session.run(stepio)
def _run_impl(torchWriter, patterns, outputdir, cifarInIndices, device,
device_hw_id, mode, syntheticData, transformations, epochs,
printAnchorArrays):
runIds = [-1] + [
int(x.split("runId")[1].split("_")[0])
for x in os.listdir(outputdir) if "runId" in x
]
baseId = 1 + max(runIds)
def getFnModel(framework, epoch):
return os.path.join(
outputdir,
"runId%d_%sModel_epoch%s.onnx" % (baseId, framework, epoch))
def getFnPopArt(epoch):
return getFnModel("PopArt", epoch)
def getFnTorch(epoch):
return getFnModel("Torch", epoch)
def getFnModel0():
return os.path.join(outputdir, "runId%d_model0.onnx" % (baseId, ))
dataFlow = torchWriter.dataFlow
inputShapeInfo = torchWriter.inputShapeInfo
validModes = ["infer", "train"]
if mode not in validModes:
raise Exception("mode must be one of " + str(validModes))
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# determine what the data directory is
datadir = "unset"
dir_path = os.path.dirname(os.path.realpath(__file__))
path_c10datadir = os.path.join(dir_path, "c10datadir.py")
if os.path.exists(path_c10datadir):
import c10datadir
datadir = c10datadir.c10datadir
else:
tmpdir = tempfile.gettempdir()
datadir = os.path.abspath(os.path.join(tmpdir, 'cifar10data'))
print("Using datadir=%s" % (datadir))
if (not os.path.exists(datadir)):
print(
"Specified datadir %s does not exist. Consider making it here with os.mkdir(datadir)"
% (datadir, ))
print("c10driver: getting data from", datadir)
trainset = datasets.CIFAR10(root=datadir,
train=True,
download=False,
transform=transform)
fnModel0 = getFnModel0()
# write ONNX Model to file
torchWriter.saveModel(fnModel=fnModel0)
stepLoader = torch.utils.data.DataLoader(
trainset,
# the amount of data loaded for each step.
# note this is not the batch size, it's the "step" size
# (samples per step)
batch_size=torchWriter.samplesPerBatch * dataFlow.batchesPerStep(),
#non-random data loading
shuffle=False,
num_workers=0)
deviceManager = popart.DeviceManager()
# Create a CPU device
if device == "cpu":
device = deviceManager.createCpuDevice()
# Create an IPU Model device
elif device == "ipu_model":
options = {"compileIPUCode": True, 'numIPUs': 1, 'tilesPerIPU': 4}
device = deviceManager.createIpuModelDevice(options)
# Create an Simulator
elif device == "sim":
options = {"numIpus": 1, "tilesPerIPU": 4}
device = deviceManager.createSimDevice(options)
# Get a Hardware Device
elif device == "hw":
# Get a hardware device that meets the reqirements,
# may throw if none are available.
# Will attach to the device
if device_hw_id:
device = deviceManager.acquireDeviceById(device_hw_id)
else:
device = tu.acquire_ipu()
# Enumerate available devices
print("Enumerating devices")
print("-------------------------------------")
for idx, d in enumerate(deviceManager.enumerateDevices()):
print('{0}. {1}'.format(idx, d))
print("")
opts = popart.SessionOptions()
opts.logDir = outputdir
if syntheticData == True:
opts.syntheticDataMode = popart.SyntheticDataMode.RandomNormal
modelProtoX = fnModel0
if transformations:
gc = popart.GraphTransformer(fnModel0)
for transformation in transformations:
print("Running %s transformation pass" % (transformation, ))
if transformation == "removeUnusedInputs":
gc.removeUnusedInputs()
elif transformation == "prepareNodesForTraining":
gc.prepareNodesForTraining()
else:
raise RuntimeError("Unrecognised transformation %s" %
(transformation, ))
modelProtoX = gc.getModelProto()
# Reads ONNX model from file and creates backwards graph,
# performs Ir optimisations
if mode == 'infer':
session = popart.InferenceSession(fnModel=modelProtoX,
inputShapeInfo=inputShapeInfo,
dataFlow=dataFlow,
patterns=patterns,
userOptions=opts,
deviceInfo=device)
else:
if len(torchWriter.outNames) != 1:
raise RuntimeError("Expecting single scalar loss tensor")
# Append output with an identity loss, to reduce to scalar if
# necessary
bder = popart.Builder(modelProtoX)
loss = bder.aiGraphcore.identityloss(
[torchWriter.outNames[0]], reduction=popart.ReductionType.Sum)
session = popart.TrainingSession(fnModel=bder.getModelProto(),
inputShapeInfo=inputShapeInfo,
dataFlow=dataFlow,
loss=loss,
optimizer=torchWriter.optimizer,
patterns=patterns,
userOptions=opts,
deviceInfo=device)
# get the tensor info for the anchors
anchorArrays = session.initAnchorArrays()
allDotPrefixes = [x[0:-4] for x in os.listdir(outputdir) if ".dot" in x]
print("Will generate graph pdfs for all of:")
print(allDotPrefixes)
import subprocess
# set generateFromDots to True to
# generate pdf figures of the Ir. It
# requires the 'dot' program
generateFromDots = False
if generateFromDots:
for name in allDotPrefixes:
dotfile = os.path.join(outputdir, "%s.dot" % (name, ))
outputfile = os.path.join(outputdir, "%s.pdf" % (name, ))
log = subprocess.call(
["dot", "-T", "pdf", "-o", outputfile, dotfile])
print("Exit status on `%s' was: %s" % (name, log))
print("Setting device to IPU, and preparing it")
session.prepareDevice()
if mode == "train":
print("Writing weights to device")
session.weightsFromHost()
print("Writing Optimizer tensors to device, if there are any")
def addStepDimension(data, batchesPerStep):
if batchesPerStep == 1:
return data
else:
dataShape = np.array(np.shape(data))
dataShape[0] //= batchesPerStep
dataShape = np.insert(dataShape, 0, batchesPerStep)
return np.reshape(data, dataShape)
def reportTensorError(tensorInd, result):
reportStr = str(tensorInd) + " :\n"
reportStr += " |pA - tA|^2 / (|pA||tA| + 1e-8) = " + str(
result) + "\n"
return reportStr
def getAnchorTensor(tId, anchorArrays):
assertStr = "Tensor" + tId + " must be specified as an anchor"
assert (tId in anchorArrays.keys()), assertStr
return anchorArrays[tId]
def subsampleBatches(array, refShape):
arrayShape = np.shape(array)
# Every Nth batch
if len(arrayShape) == len(refShape):
n = arrayShape[0] // refShape[0]
return array[n - 1::n]
# Last batch only
else:
return array[-1]
def getTensorError(tA, pA):
# pA, tA are corresponding tensors from two models
pA_shape = np.shape(pA)
tA_shape = np.shape(tA)
assert (pA_shape == tA_shape), "Arrays must be same shape"
ss_err = np.sum((np.array(pA) - np.array(tA))**2)
ss_pA = np.sum(np.array(pA)**2)
ss_tA = np.sum(np.array(tA)**2)
return ss_err / (math.sqrt(ss_pA * ss_tA) + 1.0e-8)
def checkResult(result, margin):
if np.isnan(result):
raise TestFailureError(str(result) + " is NaN")
elif (result > margin):
raise TestFailureError(
str(result) + " is greater than " + str(margin))
margin = 5.0e-7
numReports = []
for epoch in range(epochs): # loop over the dataset multiple times
print("Epoch is %d" % (epoch, ))
stepData = next(iter(stepLoader))
# Form the input map for one step's worth of data.
# Note: data from the torch DataLoader has shape:
# [stepSize * batchSize, sampleShape]
# whereas Popart expects input data of the shape:
# [stepSize, batchSize, sampleShape]
# so we reshape the input array before passing to the stepio
inputs = {}
for tenId in cifarInIndices.keys():
inputs[tenId] = \
addStepDimension(stepData[cifarInIndices[tenId]].numpy(),
session.dataFlow.batchesPerStep())
if mode == "train":
# take batchesPerStep passes (1 step), Torch
torchWriter.train(inputs)
# take batchesPerStep passes (1 step), PopArt
pystepio = popart.PyStepIO(inputs, anchorArrays)
session.run(pystepio)
if printAnchorArrays:
print(
"\nAnchor arrays (being printed as printAnchorArrays==True):"
)
for name in anchorArrays.keys():
arr = anchorArrays[name]
print("\nAnchored Array Name=", name, " and Size=",
arr.size)
if (arr.size < 10):
print("\nArray (of size < 10) values are")
print(arr)
if len(arr.shape) > 1:
for i, slice0 in enumerate(arr):
print("Sum along axis %d is Sum=%.15f" %
(i, slice0.sum()))
print("Total Sum is %.15f" % (arr.sum()))
# write models to file
fnTorchModel = getFnTorch(epoch)
fnPopArtModel = getFnPopArt(epoch)
torchWriter.saveModel(fnTorchModel)
session.modelToHost(fnPopArtModel)
print("Writing models to " + fnTorchModel + " and " +
fnPopArtModel)
# Compare parameters from updated Onnx models
print("Obtaining popart NumericsReport, A: Torch, B: Popart.")
if epoch is 0:
nr = popart.NumericsReport(fnModel0, fnTorchModel, fnModel0,
fnPopArtModel)
else:
nr = popart.NumericsReport(getFnTorch(epoch - 1), fnTorchModel,
getFnPopArt(epoch - 1),
fnPopArtModel)
print(nr.fullReport())
# One relative error calculated per weight tensor
for tId, relerror in nr.getRelativeErrors().items():
checkResult(relerror, margin)
elif mode == "infer":
# take batchesPerStep passes (1 step), Torch
# returns map of outputs for each sample
# Note: already are of dimension matching the
# anchors
torchOutputs = torchWriter.infer(inputs)
# take batchesPerStep passes (1 step), PopArt
pystepio = popart.PyStepIO(inputs, anchorArrays)
session.run(pystepio)
# Compare torch outputs tensors with popart output from
# anchor tensor maps
for nInd, outName in enumerate(torchWriter.outNames):
# Torch outputs returned for all samples, whereas
# anchors are returned as specified by the user.
# Subsample torch outputs to match dimensions
torchOuput = subsampleBatches(torchOutputs[outName],
np.shape(anchorArrays[outName]))
result = getTensorError(torchOuput, anchorArrays[outName])
print(reportTensorError(nInd, result))
checkResult(result, margin)
return anchorArrays
def get_model(input_shape: List[int], weight_array: np.array,
batches_per_step: int, replication_factor: int, batch_size: int,
channels: int, data_len: int, synthetic_data: bool,
buffer_streams: bool) -> Tuple:
"""Get a simple model for comparison with buffer streams on and off.
Adapted from prefetch_test.py as we require to test the validity of streams
here as well.
Args:
batches_per_step (int): Batches to run per step
replication_factor (int): Replicas to run
batch_size (int): Number of samples per model run
channels (int): Number of channels e.g. RGB = 3
data_len (int): Data size
synthetic_data (bool): Use synthetic data (zeros in this case)
buffer_streams (bool): The test option: whether to create ops
before the stream in order to schedule data loading as part of
graph scheduling. See T29603.
Returns:
Tuple: session, anchors, input_shape, label_shape required to run the model
"""
micro_batch_size = batch_size // (replication_factor)
builder = popart.Builder()
data_shape = popart.TensorInfo("FLOAT", input_shape)
lbl_shape = popart.TensorInfo("INT32", [micro_batch_size])
w = builder.addInitializedInputTensor(weight_array)
ip = builder.addInputTensor(data_shape, "main_input_123")
lb = builder.addInputTensor(lbl_shape, "label_input_456")
a = builder.aiOnnx.matmul([ip, w])
o = builder.reshape_const(
builder.aiOnnx, [a],
[micro_batch_size, channels * data_len * data_len])
relu = builder.aiOnnx.relu([o])
sm = builder.aiOnnx.softmax([relu], axis=0, debugContext="output")
builder.addOutputTensor(sm)
o = builder.aiGraphcore.nllloss([sm, lb],
reduction=popart.ReductionType.Mean)
art = popart.AnchorReturnType("All")
data_flow = popart.DataFlow(batches_per_step, {
ip: art,
lb: art,
o: art,
sm: art,
a: art,
relu: art
})
opts = popart.SessionOptions()
opts.useHostCopyOps = buffer_streams
# TODO: Fix outlining
opts.enableOutlining = False
ipus = 1
if replication_factor > 1:
opts.replicatedGraphCount = replication_factor
opts.enableReplicatedGraphs = True
ipus *= replication_factor
device = tu.create_test_device(ipus)
assert device
patterns = popart.Patterns(popart.PatternsLevel.Minimal).enablePattern(
"MatMulLhsGradOp", True).enablePattern("MatMulRhsGradOp", True)
patterns.InPlace = False
if synthetic_data:
opts.syntheticDataMode = popart.SyntheticDataMode.Zeros
session = popart.TrainingSession(fnModel=builder.getModelProto(),
dataFlow=data_flow,
loss=o,
optimizer=popart.ConstSGD(LR),
userOptions=opts,
deviceInfo=device,
patterns=patterns)
session.setRandomSeed(0)
session.prepareDevice()
label_shape = [micro_batch_size]
if replication_factor > 1:
input_shape = [replication_factor] + input_shape
label_shape = [replication_factor] + label_shape
if batches_per_step > 1:
input_shape = [batches_per_step] + input_shape
label_shape = [batches_per_step] + label_shape
anchors = session.initAnchorArrays()
return session, anchors, label_shape
def run_test(index, options):
builder = popart.Builder(opsets={
"ai.onnx": 9,
"ai.onnx.ml": 1,
"ai.graphcore": 1
})
mask = builder.addInputTensor(popart.TensorInfo("FLOAT", mask_shape),
"mask")
x_in = builder.addInputTensor(popart.TensorInfo("FLOAT", input_shape),
"x_in")
anchors = {}
x = x_in
for i in range(options['numLayers']):
qkv = builder.addInitializedInputTensor(qkv_data, f"qkv_{i}")
anchors[popart.reservedGradientPrefix() +
qkv] = popart.AnchorReturnType("All")
vgid = (i % 2) if options['pingPong'] else i
with builder.virtualGraph(vgid), builder.pingPongPhase(i):
x = builder.aiOnnx.matmul([x, qkv])
x = attention_onnx(builder, x, mask, batch_size,
sequence_length, hidden_size,
attention_heads, qkv_length)
vgid = ((options['numLayers'] - 1) %
2) if options['pingPong'] else options['numLayers'] - 1
with builder.virtualGraph(vgid):
l1 = builder.aiGraphcore.l1loss([x], 0.1)
proto = builder.getModelProto()
anchors[x] = popart.AnchorReturnType("All")
dataFlow = popart.DataFlow(batches_per_step, anchors)
opts = popart.SessionOptions()
opts.pingPongPhases = options['numLayers'] if options["pingPong"] else 0
opts.enableOutlining = options["outlining"]
# PingPong currently does its own recompute annotations
opts.autoRecomputation = (popart.RecomputationType.Standard
if options["explicitRecomputation"] else
popart.RecomputationType.NoRecompute)
opts.outlineThreshold = -np.inf
opts.enableOutliningCopyCostPruning = False
opts.virtualGraphMode = (popart.VirtualGraphMode.PingPong
if options["pingPong"] else
popart.VirtualGraphMode.Manual)
opts.explicitRecomputation = options["explicitRecomputation"]
opts.aliasZeroCopy = options["aliasZeroCopy"]
opts.batchSerializationFactor = options["batchSerialize"]
pat = popart.Patterns(popart.PatternsLevel.Default)
device = tu.create_test_device(2 if options["pingPong"] else 4,
pattern=popart.SyncPattern.Full)
session = popart.TrainingSession(fnModel=proto,
dataFlow=dataFlow,
userOptions=opts,
loss=l1,
optimizer=popart.ConstSGD(0.1),
patterns=pat,
deviceInfo=device)
session.prepareDevice()
session.weightsFromHost()
anchors = session.initAnchorArrays()
inputs = {x_in: input_data, mask: mask_data}
stepio = popart.PyStepIO(inputs, anchors)
for __ in range(10):
session.run(stepio)
session.modelToHost(str(tmpdir / f"pingpong_attention_{index}.onnx"))
return anchors
def bert_session_options(args, model):
options = popart.SessionOptions()
options.enableVirtualGraphs = True
options.virtualGraphMode = popart.VirtualGraphMode.Manual
options.enableFloatingPointChecks = args.floating_point_exceptions
options.enableStochasticRounding = args.stochastic_rounding
options.enableGroupedMatmuls = False
options.enableOutlining = not args.no_outlining
# Increasing the outlineThreshold prevents creating subgraphs of cheap Ops
# such as add or reshapeInplace.
# Instead only reusing ops with a highSubgraphValue such as matmul or normalisation.
options.outlineThreshold = 10.0
if args.execution_mode == "PIPELINE":
options.enablePipelining = True
options.autoRecomputation = popart.RecomputationType.Pipeline
if args.gradient_accumulation_factor > 1:
options.enableGradientAccumulation = True
options.accumulationFactor = args.gradient_accumulation_factor
if args.replication_factor > 1:
options.enableReplicatedGraphs = True
options.replicatedGraphCount = args.replication_factor
if args.engine_cache is not None:
options.enableEngineCaching = True
options.cachePath = args.engine_cache
if args.gc_profile:
options.reportOptions = {
"showVarStorage": "true",
"showPerIpuMemoryUsage": "true",
"showExecutionSteps": "true"
}
options.instrumentWithHardwareCycleCounter = args.report_hw_cycle_count
# Addition of momentum tensors causes merged copies to exceed max
# host translation table entries during the weightsFromHost program.
# With the addition of disableGradAccumulationTensorStreams no copy merging
# is needed but may be needed later when gradAccumulationTensorStreams are re-enabled
# FIXME when T11642 is resolved.
options.disableGradAccumulationTensorStreams = True
if args.max_copy_merge_size == -1:
logger.debug(f"No copy merge size limit applied")
else:
logger.warning(
f"Workaround for T11642: copy merge size limit set to {args.max_copy_merge_size}"
)
options.engineOptions = {
"opt.maxCopyMergeSize": str(args.max_copy_merge_size),
}
# Adding {"fullyConnectedPass", "TRAINING_BWD"} to some matmuls causes large
# transposes before operations.
# WARNING: This causes SQuAD 384 12-layer to go OOM
if args.disable_fully_connected_pass:
if args.task == "SQUAD" and args.sequence_length == 384:
logger.warning(
f"Fully connected pass has been disabled. This may cause SQuAD 384 12-layer to go OOM."
)
options.enableFullyConnectedPass = False
if args.inference and args.engine_cache is not None and not args.variable_weights_inference:
logger.warn(
"Using engine cache with constant weights. Checkpoint weights will be ignored. "
"Use the `--variable-weights-inference` flag if checkpoint weights should be used."
)
if args.variable_weights_inference:
options.constantWeights = False
return options
def run_py(proto: onnx.ModelProto,
data: Mapping[str, np.ndarray],
outputs: Optional[Union[str, Iterable[str]]],
loss: Optional[Union[popart.Loss, Iterable[popart.Loss]]] = None,
optimizer: Optional[popart.Optimizer] = None,
return_stats: bool = False,
log_dir: Optional[str] = None,
ipus: Optional[int] = None,
batches_per_step: int = 1,
user_options: Optional[Mapping[str, Any]] = None):
outputs = make_tuple(outputs)
if loss is not None:
loss = make_tuple(loss)
# Setting up the Session
data_flow = popart.DataFlow(
batches_per_step,
{output: popart.AnchorReturnType("ALL")
for output in outputs})
if user_options is None:
user_options = {}
options = popart.SessionOptions()
options.enableGroupedMatmuls = False
options.enableStochasticRounding = False
options.reportOptions = {"showVarStorage": "true"}
if ipus is not None and ipus > 1:
options.virtualGraphMode = popart.VirtualGraphMode.Manual
else:
ipus = 1
if return_stats:
options.engineOptions = {
"debug.allowOutOfMemory": "true",
"debug.instrument": "true"
}
for key, value in user_options.items():
setattr(options, key, value)
if ipus is not None:
options.enableVirtualGraphs = False
else:
ipus = 1
if return_stats:
options.engineOptions = {
"debug.allowOutOfMemory": "true",
"debug.instrument": "true"
}
request_ipus = pow(2, math.ceil(math.log2(ipus)))
device = popart.DeviceManager().acquireAvailableDevice(request_ipus)
# The cycle estimates of MSR_OPS codelets have not been validated
# so it is incorrect to use the IPU_MODEL.
if device is None:
raise Exception("Failed to acquire IPU.")
print("Compiling graph")
if optimizer is not None:
session = popart.TrainingSession(fnModel=proto,
deviceInfo=device,
dataFeed=data_flow,
userOptions=options,
losses=loss,
optimizer=optimizer)
else:
session = popart.InferenceSession(fnModel=proto,
deviceInfo=device,
dataFeed=data_flow,
userOptions=options)
# Compile the Poplar Graph. If it fails, return the memory stats
try:
session.prepareDevice()
except popart.session.PrepareDeviceException as e:
if return_stats:
if log_dir:
import gcprofile
os.makedirs(log_dir, exist_ok=True)
reports = gcprofile.save_popart_report(session,
log_dir=log_dir,
exception=e)
graph_report = json.loads(reports["graph"])
else:
graph_report = json.loads(e.getGraphReport())
max_tile_memory = max(graph_report["memory"]["byTile"]["total"])
total_memory = np.sum(graph_report["memory"]["byTile"]["total"])
raise e
else:
raise e
print("Compilation complete")
session.weightsFromHost()
if optimizer is not None:
session.optimizerFromHost()
session.setRandomSeed(1984)
anchors = session.initAnchorArrays()
# Add a batches_per_step dimension if needed
if batches_per_step > 1:
data = {
k: np.repeat(v[np.newaxis], batches_per_step, 0)
for k, v in data.items()
}
stepio = popart.PyStepIO(data, anchors)
session.run(stepio)
with tempfile.TemporaryDirectory() as tmp:
file_path = os.path.join(tmp, "model.onnx")
session.modelToHost(file_path)
post_proto = onnx.load(file_path)
# Release device
device.detach()
if return_stats:
if log_dir:
import gcprofile
os.makedirs(log_dir, exist_ok=True)
reports = gcprofile.save_popart_report(session, log_dir=log_dir)
graph_report = json.loads(reports["graph"])
exec_report = json.loads(reports["execution"])
else:
graph_report = json.loads(session.getGraphReport())
exec_report = json.loads(session.getExecutionReport())
max_tile_memory = max(graph_report["memory"]["byTile"]["total"])
total_memory = np.sum(graph_report["memory"]["byTile"]["total"])
cycles = exec_report["simulation"]["cycles"]
return (anchors[output] for output in outputs
), post_proto, total_memory, max_tile_memory, cycles
return (anchors[output] for output in outputs), post_proto
def train(opts, model_file, ckpt_file) -> None:
"""
Train MNIST model using command line args.
Args:
opts: The command line options
model_file: Temporary file for holding the model
ckpt_file: Temporary file for holding the weights
"""
if not opts.test_mode:
max_value = NUM_TEST_SAMPLES // opts.batch_size
if max_value < opts.batches_per_step:
print(
"(batches-per-step * batch-size) is larger than test set!\n"
" Reduced batches-per-step to: {}\n".format(max_value)
)
opts.batches_per_step = max_value
# Construct MNIST data loaders
train_loader = get_data_loader(opts, is_train=True)
test_loader = get_data_loader(opts, is_train=False)
print("Creating ONNX model.")
data_in, output = create_model(opts.batch_size, model_file)
print("Converting model.")
proto, label_in, loss = convert_model(
opts.batch_size, model_file.name, output
)
# Describe how to run the model
anchor_desc = {
output: popart.AnchorReturnType("ALL"),
loss: popart.AnchorReturnType("ALL"),
}
dataFlow = popart.DataFlow(opts.batches_per_step, anchor_desc)
optimizer = popart.ConstSGD(0.01)
# Options
userOpts = popart.SessionOptions()
# Ensure weight tensors in the validation model are not modified by the IR
userOpts.constantWeights = False
# If requested, setup synthetic data
if opts.syn_data_type in ["random_normal", "zeros"]:
print(
"Running with Synthetic Data Type '{}'".format(opts.syn_data_type)
)
if opts.syn_data_type == "random_normal":
userOpts.syntheticDataMode = popart.SyntheticDataMode.RandomNormal
elif opts.syn_data_type == "zeros":
userOpts.syntheticDataMode = popart.SyntheticDataMode.Zeros
# Select a device
deviceManager = popart.DeviceManager()
if opts.simulation:
print("Running using IPU MODEL")
options = {
"compileIPUCode": True,
"numIPUs": 1,
"tilesPerIPU": TILES_PER_IPU,
}
device = deviceManager.createIpuModelDevice(options)
else:
print("Running using Hardware")
device = deviceManager.acquireAvailableDevice()
if device is None:
print("Failed to acquire IPU. Exiting.")
return
if opts.test_mode:
print(" IPU IDs: {}".format(device.driverIds))
def init_session(proto, loss, dataFlow, userOpts, device, training, opts):
# Create a session to compile and execute the graph
if opts.test_mode:
userOpts.instrumentWithHardwareCycleCounter = True
if training:
session = popart.TrainingSession(
fnModel=proto,
loss=loss,
optimizer=optimizer,
dataFlow=dataFlow,
userOptions=userOpts,
deviceInfo=device,
)
else:
session = popart.InferenceSession(
fnModel=proto,
dataFlow=dataFlow,
userOptions=userOpts,
deviceInfo=device,
)
print(
"Compiling the {} graph.".format(
"training" if training else "validation"
)
)
session.prepareDevice()
# Create buffers to receive results from the execution
anchors = session.initAnchorArrays()
Session = namedtuple("Session", ["session", "anchors"])
return Session(session, anchors)
training = init_session(proto, loss, dataFlow, userOpts, device, True, opts)
validation = init_session(
proto, loss, dataFlow, userOpts, device, False, opts
)
inputs_per_step = opts.batch_size * opts.batches_per_step
for i in range(opts.epochs):
# Training
if i > 0:
training.session.resetHostWeights(ckpt_file.name)
training.session.weightsFromHost()
for data, label in train_loader:
if len(label) != inputs_per_step:
continue
data, label = preprocess_data(data, label)
stepio = popart.PyStepIO(
{data_in: data, label_in: label}, training.anchors
)
if opts.test_mode == "training":
start = time()
training.session.run(stepio)
if opts.test_mode == "training":
duration = time() - start
report_string = "{:<8.3} sec/itr.".format(duration)
report_string += " " + iteration_report(opts, duration)
print(report_string)
print(
"Hardware cycle count per 'run':",
training.session.getCycleCount(),
)
print("Total time: {}".format(duration))
# Evaluation
aggregated_loss = 0
num_correct = 0
training.session.modelToHost(ckpt_file.name)
validation.session.resetHostWeights(ckpt_file.name)
validation.session.weightsFromHost()
for data, label in test_loader:
if len(label) != inputs_per_step:
continue
data, label = preprocess_data(data, label)
stepio = popart.PyStepIO(
{data_in: data, label_in: label}, validation.anchors
)
if opts.test_mode == "inference":
start = time()
validation.session.run(stepio)
if opts.test_mode == "inference":
duration = time() - start
report_string = "{:<8.3} sec/itr.".format(duration)
report_string += " " + iteration_report(opts, duration)
print(report_string)
print(
"Hardware cycle count per 'run':",
validation.session.getCycleCount(),
)
print("Total time: {}".format(duration))
aggregated_loss += np.mean(validation.anchors[loss])
results = np.argmax(
validation.anchors[output].reshape(
[inputs_per_step, NUM_CLASSES]
),
1,
)
score = results == label.reshape([inputs_per_step])
num_correct += np.sum(score)
aggregated_loss /= len(test_loader)
accuracy = num_correct / len(test_loader.dataset)
# Log statistics
print("Epoch #{}".format(i))
print(" Loss={0:.4f}".format(aggregated_loss))
print(" Accuracy={0:.2f}%".format(accuracy * 100))
def run_test(mode=None, verify=None):
builder = popart.Builder()
x_in = builder.addInputTensor(popart.TensorInfo("FLOAT", input_shape),
"x_in")
weight_1 = builder.addInitializedInputTensor(weight_data, "weight_1")
# We want a bwd pass that looks like:
#
# restore, op1, restore, op2, restore, op3
#
# Where op1, op2 & op3 are gradient operations that
# have implicit recompute inputs.
with builder.virtualGraph(0), builder.pipelineStage(0):
x = builder.aiOnnx.matmul([x_in, weight_1])
x = builder.checkpointOutput([x])[0]
x = builder.aiOnnx.add([x, x])
# Gelu is a unary operation that takes the fwd input
# activation. This satisfies our requirement above
# of needing an implicit recompute input.
x = builder.aiGraphcore.gelu([x])
x = builder.checkpointOutput([x])[0]
x = builder.aiOnnx.add([x, x])
x = builder.aiGraphcore.gelu([x])
x = builder.checkpointOutput([x])[0]
o = x
with builder.virtualGraph(1), builder.pipelineStage(1):
l1 = builder.aiGraphcore.l1loss([o], 0.1)
proto = builder.getModelProto()
dataFlow = popart.DataFlow(1, [
o,
popart.reservedGradientPrefix() + weight_1,
])
opts = popart.SessionOptions()
opts.enableOutlining = False
opts.enablePipelining = True
opts.enableGradientAccumulation = True
opts.accumulationFactor = gradient_accumulation
opts.optimizerStateTensorLocationSettings.location.storage = popart.TensorStorage.OffChip
if mode is not None:
opts.autoRecomputation = mode
opts.virtualGraphMode = popart.VirtualGraphMode.Manual
session = popart.TrainingSession(fnModel=proto,
dataFlow=dataFlow,
userOptions=opts,
loss=l1,
optimizer=popart.Adam({}),
deviceInfo=tu.create_test_device(
numIpus=2,
opts={"compileIPUCode": False}))
session.prepareDevice()
session.weightsFromHost()
anchors = session.initAnchorArrays()
inputs = {x_in: input_data}
stepio = popart.PyStepIO(inputs, anchors)
for _ in range(10):
session.run(stepio)
if verify is not None:
verify(session)
return anchors
def test_final_stage_recompute_0():
np.random.seed(0)
gradient_accumulation = 5
batch_size = 1
hidden_size = 16
input_shape = [batch_size, hidden_size]
weight_data = np.random.normal(0, 0.02, [hidden_size, hidden_size]).astype(
np.float32)
input_data = np.random.normal(
0, 0.02, [gradient_accumulation] + input_shape).astype(np.float32)
builder = popart.Builder()
x_in = builder.addInputTensor(popart.TensorInfo("FLOAT", input_shape),
"x_in")
with builder.virtualGraph(0), builder.pipelineStage(0):
weight_1 = builder.addInitializedInputTensor(weight_data, "weight_1")
x = builder.aiOnnx.matmul([x_in, weight_1])
with builder.virtualGraph(1), builder.pipelineStage(1):
weight_2 = builder.addInitializedInputTensor(weight_data, "weight_2")
x_recomp = builder.aiOnnx.matmul([x, weight_2])
# This MatMul should be recomputed
x = builder.checkpointOutput([x_recomp])[0]
weight_3 = builder.addInitializedInputTensor(weight_data, "weight_3")
# This MatMul should not be recomputed
x_no_recomp = builder.aiOnnx.matmul([x, weight_3])
l1 = builder.aiGraphcore.l1loss([x_no_recomp], 0.1)
proto = builder.getModelProto()
dataFlow = popart.DataFlow(1, [l1])
opts = popart.SessionOptions()
opts.enableOutlining = False
opts.enablePipelining = True
opts.enableGradientAccumulation = True
opts.accumulationFactor = gradient_accumulation
opts.optimizerStateTensorLocationSettings.location.storage = popart.TensorStorage.OffChip
opts.autoRecomputation = popart.RecomputationType.Pipeline
opts.virtualGraphMode = popart.VirtualGraphMode.Manual
session = popart.TrainingSession(fnModel=proto,
dataFlow=dataFlow,
userOptions=opts,
loss=l1,
optimizer=popart.Adam({}),
deviceInfo=tu.create_test_device(
numIpus=2,
opts={"compileIPUCode": False}))
''' Verify the the matmul in the main graphs is correct'''
ir = json.loads(session._serializeIr(popart.IrSerializationFormat.JSON))
for op in ir["maingraph"]:
if x_recomp in map(lambda out: out["name"], op["outputs"]):
assert op["attributes"]["recompute"] == "YES"
elif x_no_recomp in map(lambda out: out["name"], op["outputs"]):
assert op["attributes"]["recompute"] == "NO"
def get_model_anchors(doSharding,
doPipelining,
batchesPerStep,
doTraining,
replicated_graph_count=1,
doProfiling=False,
doDropout=False,
doGradientAccl=False,
acclSteps=1,
doDevicex=True,
anchorRestoredTensors=False,
returnRawInput=False):
np.random.seed(seed=1)
builder = popart.Builder()
batchSize = 16
microBatchSize = batchSize // acclSteps
shape_d0 = [microBatchSize, 2, 4, 4]
shape_l0 = [microBatchSize]
d0 = builder.addInputTensor(popart.TensorInfo("FLOAT", shape_d0))
data_w0 = np.ones(shape=[2, 2, 3, 3]).astype(np.float32)
w0 = builder.addInitializedInputTensor(data_w0)
l0 = builder.addInputTensor(popart.TensorInfo("INT32", shape_l0))
s0 = builder.aiOnnx.sin([d0], "s0")
e0 = builder.aiOnnx.exp([s0], "e0")
c0 = builder.aiOnnx.conv([e0, w0],
dilations=[1, 1],
pads=[1, 1, 1, 1],
strides=[1, 1],
debugContext="c0")
r0 = builder.reshape_const(builder.aiOnnx, [c0], [microBatchSize, 32])
if doDropout:
do0 = builder.aiOnnx.dropout([r0], num_outputs=1, ratio=0.2)[0]
out = builder.aiOnnx.softmax([do0], axis=1, debugContext="sfm")
else:
out = builder.aiOnnx.softmax([r0], axis=1, debugContext="sfm")
nll = builder.aiGraphcore.nllloss([out, l0],
reduction=popart.ReductionType.Sum)
art = popart.AnchorReturnType("All")
anchor_map = {nll: art, w0: art, e0: art}
if doTraining is True:
anchor_map[popart.reservedGradientPrefix() + d0] = art
if doPipelining is True and anchorRestoredTensors is True:
anchor_map[popart.reservedRestoredPrefix() + e0] = art
anchor_map[d0] = art
anchor_map[popart.reservedRestoredPrefix() + d0] = art
opts = popart.SessionOptions()
opts.reportOptions = {"showExecutionSteps": "true"}
opts.enablePipelining = doPipelining
opts.enableGradientAccumulation = doGradientAccl
opts.accumulationFactor = acclSteps
opts.enableStochasticRounding = False
if doSharding is False:
numIpus = 1 * replicated_graph_count
else:
opts.virtualGraphMode = popart.VirtualGraphMode.Manual
numIpus = 2 * replicated_graph_count
builder.virtualGraph(s0, 0)
builder.virtualGraph(e0, 0)
builder.virtualGraph(c0, 0)
builder.virtualGraph(r0, 1)
if doDropout:
builder.virtualGraph(do0, 1)
builder.virtualGraph(out, 1)
builder.virtualGraph(nll, 1)
if replicated_graph_count > 1:
opts.replicatedGraphCount = replicated_graph_count
opts.enableReplicatedGraphs = True
device = tu.create_test_device(numIpus=numIpus)
if doTraining is True:
session = popart.TrainingSession(fnModel=builder.getModelProto(),
dataFlow=popart.DataFlow(
batchesPerStep, anchor_map),
loss=nll,
optimizer=popart.ConstSGD(0.01),
userOptions=opts,
deviceInfo=device)
else:
session = popart.InferenceSession(fnModel=builder.getModelProto(),
dataFlow=popart.DataFlow(
batchesPerStep, anchor_map),
userOptions=opts,
deviceInfo=device)
if doDevicex is False:
return None
session.prepareDevice()
anchors = session.initAnchorArrays()
session.setRandomSeed(0)
classes = np.prod(shape_d0) // (batchSize * batchesPerStep)
label = np.random.randint(low=0, high=classes,
size=shape_l0).astype(np.int32)
# With all options enabled return anchors are of the shape:
# [batches_per_step, accl_factor, repl_factor, micro_batch, *data_shape]
if acclSteps > 1:
shape_d0.insert(0, acclSteps)
label = label.reshape([acclSteps, -1])
if batchesPerStep > 1:
shape_d0.insert(0, batchesPerStep)
label = np.repeat(label[np.newaxis], batchesPerStep, 0)
data = np.random.random_sample(shape_d0).astype(np.float32)
# This is a slightly odd case - we want the same data to be input for both
# replicated graphs, but the dimension we need to repeat on is either the
# first or second (the replication dimension) depending on whether we
# have gradient accumulation enabled.
# If we are not testing, this is a lot simpler as we can split samples however
# we want.
if replicated_graph_count > 1:
if acclSteps > 1:
data = np.repeat(data[np.newaxis], replicated_graph_count, 2)
label = label.reshape([replicated_graph_count, -1])
else:
data = np.repeat(data[np.newaxis], replicated_graph_count, 1)
label = label.reshape([replicated_graph_count, -1])
inputs = {d0: data, l0: label}
stepio = popart.PyStepIO(inputs, anchors)
stepio.enableRuntimeAsserts(False)
session.weightsFromHost()
session.run(stepio)
if doProfiling is True:
from gcprofile import save_popart_report
save_popart_report(session)
if returnRawInput is True:
anchors["input_raw"] = data
return anchors
# Let's create a known tensor, but with an undefined shape
y = builder.aiOnnx.conv([leaky_relu, w],
dilations=[1, 1],
pads=[padding] * 4,
strides=[1, 1])
l1 = builder.aiGraphcore.l1loss([y], 1.0)
proto = builder.getModelProto()
art = popart.AnchorReturnType("All")
# Describe how to run the model
dataflow = popart.DataFlow(1, {y: art, leaky_relu: art, w: art, l1: art})
# Create a session to compile and execute the graph
options = popart.SessionOptions()
device = popart.DeviceManager().createIpuModelDevice({})
session = popart.TrainingSession(fnModel=proto,
dataFlow=dataflow,
loss=l1,
optimizer=popart.ConstSGD(0.001),
userOptions=options,
deviceInfo=device)
# Compile graph
session.prepareDevice()
# Create buffers to receive results from the execution
anchors = session.initAnchorArrays()
# Copy weights onto the IPU
session.weightsFromHost()
# Generate some random input data.
def run_test(enablePipelining):
popart.getLogger().setLevel("TRACE")
builder = popart.Builder()
i1 = builder.addInputTensor(
popart.TensorInfo("FLOAT", input_data.shape[1::]))
w0 = builder.addInitializedInputTensor(weight_data_0)
w1 = builder.addInitializedInputTensor(weight_data_1)
w2 = builder.addInitializedInputTensor(weight_data_2)
o0 = builder.aiOnnx.matmul([i1, w0])
if enablePipelining:
builder.virtualGraph(o0, 0)
o1 = builder.aiOnnx.matmul([o0, w1])
if enablePipelining:
builder.virtualGraph(o1, 1)
o2 = builder.aiOnnx.matmul([o1, w2])
if enablePipelining:
builder.virtualGraph(o2, 2)
o2l1 = builder.aiGraphcore.l1loss([o2], 0.1)
if enablePipelining:
builder.virtualGraph(o2l1, 2)
proto = builder.getModelProto()
anchorId = popart.reservedDefaultScaledLearningRate0Prefix() + "FLOAT"
# Need to anchor the output of the backward pass to stop it being pruned
dataFlow = popart.DataFlow(bps, [anchorId])
optimizer = popart.SGD({"defaultLearningRate": (1.0, False)})
opts = popart.SessionOptions()
if enablePipelining:
opts.virtualGraphMode = popart.VirtualGraphMode.Manual
opts.enablePipelining = enablePipelining
numIPUs = 1
if enablePipelining:
numIPUs = 3
session = popart.TrainingSession(
fnModel=proto,
dataFlow=dataFlow,
loss=o2l1,
optimizer=optimizer,
userOptions=opts,
deviceInfo=tu.create_test_device(numIpus=numIPUs))
session.prepareDevice()
anchors = session.initAnchorArrays()
inputs = {i1: input_data}
stepio = popart.PyStepIO(inputs, anchors)
session.weightsFromHost()
# run 2 steps, changing the optimizer halfway through
result = []
session.run(stepio)
result.append(np.copy(anchors[anchorId]))
session.updateOptimizerFromHost(
popart.SGD({"defaultLearningRate": (0.5, False)}))
session.run(stepio)
result.append(np.copy(anchors[anchorId]))
return result
def test_virtual_graph4():
builder = popart.Builder()
i1 = builder.addInputTensor(popart.TensorInfo("FLOAT", [1]))
i2 = builder.addInputTensor(popart.TensorInfo("FLOAT", [1]))
i3 = builder.addInputTensor(popart.TensorInfo("FLOAT", [1]))
with builder.virtualGraph(3):
o1 = builder.aiOnnx.add([i1, i2])
o1l1 = builder.aiGraphcore.l1loss([o1], 0.1)
o2 = builder.aiOnnx.add([i3, o1])
o2l1 = builder.aiGraphcore.l1loss([o2], 0.1)
with builder.virtualGraph(2):
o3 = builder.aiOnnx.mul([i1, i3])
o3l1 = builder.aiGraphcore.l1loss([o3], 0.1)
with builder.virtualGraph(3):
loss = builder.aiOnnx.sum([o1l1, o2l1, o3l1])
proto = builder.getModelProto()
# Need to anchor the output of the backward pass to stop it being pruned
dataFlow = popart.DataFlow(
1, {
o1: popart.AnchorReturnType("All"),
o2: popart.AnchorReturnType("All"),
o3: popart.AnchorReturnType("All"),
popart.reservedGradientPrefix() + i1:
popart.AnchorReturnType("All"),
popart.reservedGradientPrefix() + i2:
popart.AnchorReturnType("All"),
popart.reservedGradientPrefix() + i3:
popart.AnchorReturnType("All")
})
optimizer = popart.ConstSGD(0.01)
opts = popart.SessionOptions()
opts.virtualGraphMode = popart.VirtualGraphMode.Manual
s = popart.TrainingSession(fnModel=proto,
dataFlow=dataFlow,
loss=loss,
optimizer=optimizer,
userOptions=opts,
deviceInfo=tu.create_test_device(numIpus=4))
s.prepareDevice()
anchors = s.initAnchorArrays()
data1 = np.ones([1], dtype=np.float32)
data2 = np.ones([1], dtype=np.float32)
data3 = np.ones([1], dtype=np.float32)
inputs = {i1: data1, i2: data2, i3: data3}
stepio = popart.PyStepIO(inputs, anchors)
s.run(stepio)
s.weightsFromHost()
def test_virtual_graph3():
popart.getLogger().setLevel("TRACE")
builder = popart.Builder()
i1 = builder.addInputTensor(popart.TensorInfo("FLOAT", [1]))
i2 = builder.addInputTensor(popart.TensorInfo("FLOAT", [1]))
i3 = builder.addInputTensor(popart.TensorInfo("FLOAT", [1]))
i4 = builder.addInputTensor(popart.TensorInfo("FLOAT", [1]))
with builder.virtualGraph(3):
o1 = builder.aiOnnx.add([i1, i2])
o2 = builder.aiOnnx.add([i3, i4])
with builder.virtualGraph(2):
o3 = builder.aiOnnx.add([o1, o2])
o = builder.aiOnnx.add([i1, o3])
o = builder.aiGraphcore.l1loss([o], 0.1)
proto = builder.getModelProto()
# Need to anchor the output of the backward pass to stop it being pruned
dataFlow = popart.DataFlow(
1, {
o: popart.AnchorReturnType("All"),
popart.reservedGradientPrefix() + i1:
popart.AnchorReturnType("All"),
popart.reservedGradientPrefix() + i2:
popart.AnchorReturnType("All"),
popart.reservedGradientPrefix() + i3:
popart.AnchorReturnType("All"),
popart.reservedGradientPrefix() + i4:
popart.AnchorReturnType("All")
})
optimizer = popart.SGD({"defaultLearningRate": (0.01, True)})
opts = popart.SessionOptions()
opts.virtualGraphMode = popart.VirtualGraphMode.Manual
s = popart.TrainingSession(fnModel=proto,
dataFlow=dataFlow,
loss=o,
optimizer=optimizer,
userOptions=opts,
deviceInfo=tu.create_test_device(numIpus=4))
s.prepareDevice()
anchors = s.initAnchorArrays()
data1 = np.ones([1], dtype=np.float32)
data2 = np.ones([1], dtype=np.float32)
data3 = np.ones([1], dtype=np.float32)
data4 = np.ones([1], dtype=np.float32)
inputs = {i1: data1, i2: data2, i3: data3, i4: data4}
stepio = popart.PyStepIO(inputs, anchors)
s.run(stepio)
s.weightsFromHost()
def run_model(tmpdir, batches_per_step, accum_factor, replicas, tile_set,
exchange_strategy):
size = 64
proto, inputs, weights, labels, dataFlow, loss, sum = get_model(
size, batches_per_step, 4, 1, tile_set, exchange_strategy)
opts = popart.SessionOptions()
opts.enableExplicitMainLoops = True
opts.useHostCopyOps = True
opts.instrumentWithHardwareCycleCounter = False
opts.virtualGraphMode = popart.VirtualGraphMode.Auto
# Both true & false should work - testing with false to avoid
# host-cycle-overhead
opts.rearrangeAnchorsOnHost = False
opts.rearrangeStreamsOnHost = False
# Set session options to generate the report
tu.set_autoreport_options(opts, tmpdir, output_execution_profile=True)
if accum_factor > 1:
opts.enableGradientAccumulation = True
opts.accumulationFactor = accum_factor
if tile_set == popart.TileSet.IO:
opts.numIOTiles = 128
else:
opts.numIOTiles = 0
if replicas > 1:
opts.enableReplicatedGraphs = True
opts.replicatedGraphCount = replicas
pat = popart.Patterns(popart.PatternsLevel.Default)
session = popart.TrainingSession(
fnModel=proto,
dataFlow=dataFlow,
userOptions=opts,
loss=loss,
optimizer=popart.ConstSGD(1e-6),
patterns=pat,
# Trying to use less than all the tiles throw an error like
# popart_core.poplar_exception: Trying to access tile 72 on IPU
# 0 but the virtual graph only covers the following tiles on
# that IPU: 0-63
# The error happens in a call to poplar made by gcl::perIPUTiles.
deviceInfo=tu.create_test_device(numIpus=replicas,
tilesPerIPU=tu.USE_ALL_TILES))
anchors = session.initAnchorArrays()
session.prepareDevice()
np.random.seed(224488)
session.weightsFromHost()
warmup_iterations = 1
calc_iterations = 1
for i in range(warmup_iterations + calc_iterations):
datainputs = {
input: (np.random.normal(
0, 0.05, (replicas * batches_per_step * accum_factor, 1, size,
size)).astype(np.float32))
for input in inputs
}
datainputs[labels] = np.random.randint(
0, size, (replicas * batches_per_step * accum_factor, 1, size))
stepio = popart.PyStepIO(datainputs, anchors)
session.run(stepio)
session.weightsToHost()
weights_data = {
w: np.zeros((1, size, size), dtype=np.float32)
for w in weights
}
weights_read = popart.PyWeightsIO(weights_data)
session.readWeights(weights_read)
for w in weights_data:
assert np.count_nonzero(np.isnan(weights_data[w])) == 0
report = session.getReport()
overlapPercentage = get_compute_io_overlap_percentage(
report, warmup_iterations)
return overlapPercentage, weights_data
def run_test(mode=None, verify=None):
builder = popart.Builder()
def norm(input_x):
gamma = builder.addInitializedInputTensor(
np.ones(hidden_size, np.float32), "Gamma")
beta = builder.addInitializedInputTensor(
np.zeros(hidden_size, np.float32), "Beta")
return builder.aiGraphcore.groupnormalization(
[input_x, gamma, beta], 1)[0]
x_in = builder.addInputTensor(popart.TensorInfo("FLOAT", input_shape),
"x_in")
weight_1 = builder.addInitializedInputTensor(weight_data, "weight_1")
weight_2 = builder.addInitializedInputTensor(weight_data, "weight_2")
weight_3 = builder.addInitializedInputTensor(weight_data, "weight_3")
with builder.virtualGraph(0), builder.pipelineStage(0):
x_0 = builder.aiOnnx.matmul([x_in, weight_1])
x_0 = norm(x_0)
# If recomputeOutputs was used directly on `x_0` all 3 outputs
# of groupnormalization would be stashed.
# By using a checkpointOutput only 1 output will be stashed and the
# rest will be recomputed.
x_0 = builder.checkpointOutput([x_0])[0]
x_1 = builder.aiOnnx.matmul([x_0, weight_2])
x_1 = norm(x_1)
x_1 = builder.aiOnnx.add([x_0, x_1])
# This checkpoint should be redundant as x_1 will be stashed
# at the start of stage1 on ipu1.
x_1 = builder.checkpointOutput([x_1])[0]
with builder.virtualGraph(1), builder.pipelineStage(1):
o = builder.aiOnnx.matmul([x_1, weight_3])
l1 = builder.aiGraphcore.l1loss([o], 0.1)
proto = builder.getModelProto()
dataFlow = popart.DataFlow(1, [
o,
popart.reservedGradientPrefix() + weight_1,
popart.reservedGradientPrefix() + weight_2,
popart.reservedGradientPrefix() + weight_3,
])
opts = popart.SessionOptions()
opts.enableOutlining = False
opts.enablePipelining = True
opts.enableGradientAccumulation = True
opts.accumulationFactor = gradient_accumulation
opts.optimizerStateTensorLocationSettings.location.storage = popart.TensorStorage.OffChip
if mode is not None:
opts.autoRecomputation = mode
opts.virtualGraphMode = popart.VirtualGraphMode.Manual
session = popart.TrainingSession(fnModel=proto,
dataFlow=dataFlow,
userOptions=opts,
loss=l1,
optimizer=popart.Adam({}),
deviceInfo=tu.create_test_device(
numIpus=2,
opts={"compileIPUCode": False}))
session.prepareDevice()
session.weightsFromHost()
anchors = session.initAnchorArrays()
inputs = {x_in: input_data}
stepio = popart.PyStepIO(inputs, anchors)
for _ in range(10):
session.run(stepio)
if verify is not None:
verify(session, x_0)
return anchors
def matmul_avail_memory(capfd, apply_to_conv=True, avail_mem_prop=0.9):
os.environ["POPLIBS_LOG_LEVEL"] = "DEBUG"
builder = popart.Builder()
input_shape = popart.TensorInfo("FLOAT", [2, 4])
weight_shape = popart.TensorInfo("FLOAT", [4, 8])
weight_data = np.ones(weight_shape.shape(), np.float32)
input_ = builder.addInputTensor(input_shape)
weights = builder.addInitializedInputTensor(weight_data)
act = builder.aiOnnx.matmul([input_, weights])
o = builder.aiOnnx.relu([act])
loss = builder.aiGraphcore.identityloss([o])
# Apply the setAvailableMemoryProportion to the matmul
if apply_to_conv:
builder.setAvailableMemoryProportion(act, avail_mem_prop)
# For the test_conv_avail_memory_error_2 test we try to apply the
# setAvailableMemoryProportion to the relu op defined above, rather
# than the expected convolution op, and expect an error.
else:
builder.setAvailableMemoryProportion(o, avail_mem_prop)
anchor_names = [
o,
popart.reservedGradientPrefix() + input_,
popart.reservedGradientPrefix() + weights
]
training_dataFlow = popart.DataFlow(
1, {
anchor_names[0]: popart.AnchorReturnType("All"),
anchor_names[1]: popart.AnchorReturnType("All"),
anchor_names[2]: popart.AnchorReturnType("All")
})
opts = popart.SessionOptions()
opts.constantWeights = False # Allow the weights to be updated
# Create the device
device = tu.create_test_device(1, opts={"compileIPUCode": True})
device.attach()
# Prepare the input data
input_data = np.random.random_sample(input_shape.shape()).astype(
np.float32)
# Prepare the Training session
training_session = popart.TrainingSession(fnModel=builder.getModelProto(),
dataFlow=training_dataFlow,
loss=loss,
optimizer=popart.ConstSGD(0.01),
userOptions=opts,
deviceInfo=device)
# Compile the training graph
training_session.prepareDevice()
# Run the training session
training_session.weightsFromHost()
training_anchors = training_session.initAnchorArrays()
training_inputs = {input_: input_data}
training_session.run(popart.PyStepIO(training_inputs, training_anchors))
captured = capfd.readouterr()
os.environ["POPLIBS_LOG_LEVEL"] = "NONE"
return captured.err
def test_pipeline_stage_merging():
np.random.seed(0)
# With 3 stages the minimum pipeline cycles is 5
# With 2 stages the minimum pipeline cycles is 3
# So if the consecutive stages aren't fused an error will be thrown.
gradient_accumulation = 3
batch_size = 1
hidden_size = 16
input_shape = [batch_size, hidden_size]
weight_data = np.random.normal(0, 0.02, [hidden_size, hidden_size]).astype(
np.float32)
input_data = np.random.normal(0, 0.02, [gradient_accumulation] +
input_shape).astype(np.float32)
builder = popart.Builder()
x_in = builder.addInputTensor(popart.TensorInfo("FLOAT", input_shape),
"x_in")
weight_1 = builder.addInitializedInputTensor(weight_data, "weight_1")
weight_2 = builder.addInitializedInputTensor(weight_data, "weight_2")
weight_3 = builder.addInitializedInputTensor(weight_data, "weight_3")
# Pipelining should combine stage 0 and 1.
with builder.virtualGraph(0), builder.pipelineStage(0):
x_0 = builder.aiOnnx.matmul([x_in, weight_1])
with builder.virtualGraph(0), builder.pipelineStage(1):
x_1 = builder.aiOnnx.matmul([x_0, weight_2])
with builder.virtualGraph(1), builder.pipelineStage(2):
o = builder.aiOnnx.matmul([x_1, weight_3])
l1 = builder.aiGraphcore.l1loss([o], 0.1)
proto = builder.getModelProto()
dataFlow = popart.DataFlow(1, [o])
opts = popart.SessionOptions()
opts.enableOutlining = False
opts.enablePipelining = True
opts.enableGradientAccumulation = True
opts.accumulationFactor = gradient_accumulation
opts.autoRecomputation = popart.RecomputationType.Pipeline
opts.virtualGraphMode = popart.VirtualGraphMode.Manual
session = popart.TrainingSession(fnModel=proto,
dataFlow=dataFlow,
userOptions=opts,
loss=l1,
optimizer=popart.ConstSGD(1e-9),
deviceInfo=tu.create_test_device(
numIpus=2,
opts={"compileIPUCode": False}))
ir = json.loads(session._serializeIr(popart.IrSerializationFormat.JSON))
stashes = [op for op in ir["maingraph"] if op["type"] == "Stash"]
stashedTensors = [stash["inputs"][0]["name"] for stash in stashes]
assert {'x_in'} == set(stashedTensors)
def test_reset_host_weights_with_extra_tensor_in_onnx_model():
"""
1. Create a training session, and a corresponding validation session
2. The training session must contain some feauture that means when writing
the ONNX model back to the host, it contains extra initializers compared
with the original (builder-generated) model. In this case we achieve this
by using an SGD optimizer with momentum.
3. Try resetting the weights of the validation session using the ONNX model
with the additional momentum tensor (call resetHostWeights)
4. Observe that a PopART exception is thrown
5. Try again, but with ignoreWeightsInModelWithoutCorrespondingHostWeight.
6. Observe that it succeeds
"""
def getModelWithRandomWeights():
builder = popart.Builder()
dShape = [2, 2]
i0 = builder.addInputTensor(popart.TensorInfo("FLOAT", dShape))
wData = np.random.rand(*dShape).astype(np.float32)
w0 = builder.addInitializedInputTensor(wData)
o = builder.aiOnnx.matmul([i0, w0])
loss = builder.aiGraphcore.l1loss([o], 0.1)
builder.addOutputTensor(loss)
return builder
device = tu.create_test_device()
tr_builder = getModelWithRandomWeights()
o = tr_builder.getOutputTensorIds()[0]
# 1. & 2.
# Training
tr_opt = popart.SGD({"defaultMomentum": (0.01, True)})
tr_sess = popart.TrainingSession(fnModel=tr_builder.getModelProto(),
dataFlow=popart.DataFlow(1, []),
loss=o,
optimizer=tr_opt,
deviceInfo=device)
tr_sess.prepareDevice()
tmpfile = os.path.join(tempfile.mkdtemp(), "tr_model.onnx")
tr_sess.modelToHost(tmpfile)
# Validation (with different model proto weights)
va_builder = getModelWithRandomWeights()
va_opts = popart.SessionOptions()
va_opts.constantWeights = False
va_sess = popart.InferenceSession(fnModel=va_builder.getModelProto(),
dataFlow=popart.DataFlow(1, [o]),
deviceInfo=device,
userOptions=va_opts)
va_sess.prepareDevice()
# 3. Try reset validation weights with training weights
wId = [
w for w in va_builder.getInputTensorIds()
if va_builder.isInitializer(w)
][0]
missing_tensor_name = popart.reservedAcclToAccumulatorPrefix(
) + popart.reservedGradientPrefix() + wId
with pytest.raises(popart.popart_exception) as e_info:
va_sess.resetHostWeights(tmpfile)
# 4.
assert e_info.value.args[
0] == "resetWeights, no tensor '" + missing_tensor_name + "' in tensors"
# 5. & 6. Try again, but this time ignore the missing tensor
va_sess.resetHostWeights(
tmpfile, ignoreWeightsInModelWithoutCorrespondingHostWeight=True)
def test_manual_serialization():
# Basic model:
#
# X: data input if shape (N, C0)
# W: weight input of shape (C0, C1)
#
# Y = matmul(X, W)
# Z = relu(Y)
# loss = l1Loss(Z)
#
# With array dimensions
N = 12
C0 = 244
C1 = 286
# In this test, we manually serialise the matmul, converting
# matmul ((N,C0) , (C0,C1))
#
# into a sequence of factor-f smaller matmuls
# matmul (N,C0/f),(C0/f,C1))
#
# reapeated and accumulated f times, where f is
f = 4
assert (C0 % f == 0)
# Constructing the model
builder = popart.Builder()
# NOTE: T22702 For some seeds this test fails.
np.random.seed(0)
wVals = np.array(npr.randn(C0, C1), dtype=np.float32)
W = builder.addInitializedInputTensor(wVals)
xInfo = popart.TensorInfo("FLOAT", [N, C0])
X = builder.addInputTensor(xInfo)
axesV = np.array([0, 1]).astype(np.int32)
axes = builder.addInitializedInputTensor(axesV)
for i in range(f):
# the lower index of the i'th slice
lwr = int(i * C0 / f)
# the upper index of the i'th slice
upp = int((i + 1) * C0 / f)
# Take a slice of size (N,C0/f) out of X
s0 = builder.addInitializedInputTensor(
np.array([0, lwr]).astype(np.int32))
e0 = builder.addInitializedInputTensor(
np.array([N, upp]).astype(np.int32))
X_slice = builder.aiOnnx.slice([X, s0, e0, axes])
# Take a slice of size (C0/f,C1) out of W
s1 = builder.addInitializedInputTensor(
np.array([lwr, 0]).astype(np.int32))
e1 = builder.addInitializedInputTensor(
np.array([upp, C1]).astype(np.int32))
W_slice = builder.aiOnnx.slice([W, s1, e1, axes])
# Multiply the slices together, and accumulate as necessary
mm_part = builder.aiOnnx.matmul([X_slice, W_slice])
if i == 0:
Y = mm_part
else:
Y = builder.aiOnnx.add([mm_part, Y])
# Finally, the non-linearity
Z = builder.aiOnnx.relu([Y])
# This boiler-plate is currently necessary with opset-10 slice
graph_transformer = popart.GraphTransformer(builder.getModelProto())
graph_transformer.convertAllFixedPointInitializersToConstants()
builder = popart.Builder(graph_transformer.getModelProto())
l1 = builder.aiGraphcore.l1loss([Z], 0.2)
dataFlow = popart.DataFlow(1, {})
device = tu.create_test_device()
userOptions = popart.SessionOptions()
# To obtain the final dot graph, uncomment this:
# userOptions.dotChecks = {"Final"};
patterns = popart.Patterns()
session = popart.TrainingSession(fnModel=builder.getModelProto(),
dataFlow=dataFlow,
optimizer=popart.SGD(
{"defaultLearningRate": (0.1, True)}),
loss=l1,
patterns=patterns,
userOptions=userOptions,
deviceInfo=device)
session.prepareDevice()
session.weightsFromHost()
inputVals = np.array(npr.randn(1 * N * C0), dtype=np.float32)
stepio = popart.PyStepIO({X: inputVals}, {})
session.run(stepio)
session.weightsToHost()
w0R = np.array(-777.0 * np.ones(C0 * C1), dtype=np.float32)
weightsRead = popart.PyWeightsIO({W: w0R})
session.readWeights(weightsRead)
# A pytorch version to confirm numerical correctness:
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.w0 = torch.nn.Parameter(torch.from_numpy(wVals.copy()))
def forward(self, x):
return torch.relu(torch.matmul(x, self.w0))
net = Net()
optimizer = optim.SGD(net.parameters(), lr=0.1)
out = net(torch.from_numpy(inputVals.reshape([N, C0])))
loss = 0.2 * torch.mean(torch.abs(out))
optimizer.zero_grad()
loss.backward()
optimizer.step()
baseline0 = np.sum(
np.abs(net.w0.detach().numpy().flatten() - wVals.flatten()))
baseline1 = np.sum(np.abs(w0R - wVals.flatten()))
error = np.sum(np.abs(np.abs(net.w0.detach().numpy().flatten() - w0R)))
assert (error / (baseline0 + baseline1) < 1e-6)
def main(argv):
FLAGS = flags.FLAGS
print(f"micro batch size is {FLAGS.micro_batch_size}")
print(f"batch size is {FLAGS.batch_size}")
print(f"batches_per_step is {FLAGS.batches_per_step}")
proto, data, outputs, output_id = graph_builder()
print(f"Model: {FLAGS.model_name}")
if not FLAGS.synthetic:
print(f"Data_dir: {FLAGS.data_dir}")
else:
print(f"Using synthetic data")
print(f"Data_sub_dir for this process: {FLAGS.data_sub_dir}")
print(f"num_workers: {FLAGS.num_workers}")
print(f"batches per step: {FLAGS.batches_per_step}")
dataFlow = popart.DataFlow(FLAGS.batches_per_step, outputs)
# Create a session to compile and execute the graph
options = popart.SessionOptions()
if FLAGS.synthetic:
options.syntheticDataMode = popart.SyntheticDataMode.Zeros
options.instrumentWithHardwareCycleCounter = FLAGS.report_hw_cycle_count
# Configure precision of convolutions and MatMuls
if FLAGS.half_partials:
options.convolutionOptions = {'partialsType': 'half'}
options.partialsTypeMatMuls = "half"
# Select a device
deviceManager = popart.DeviceManager()
device = deviceManager.acquireAvailableDevice(1)
print(f"{device}\n")
if device is None:
raise Exception("Not enough IPUs available.")
session = popart.InferenceSession(fnModel=proto,
deviceInfo=device,
dataFlow=dataFlow,
userOptions=options)
print("Compiling...")
start = time.time()
try:
session.prepareDevice()
except popart.PrepareDeviceException as e:
import gcprofile
gcprofile.save_popart_report(session, exception=e)
sys.exit(1)
compilation_duration = time.time() - start
print("Time to compile: {:.3f} seconds\n".format(compilation_duration))
# Create buffers to receive results from the execution
anchors = session.initAnchorArrays()
# Copy weights and optimisation parameters onto the device
session.weightsFromHost()
def report_time(duration, data_duration=None, compute_duration=None):
report_string = "Total {:<8.3} sec.".format(duration)
if data_duration:
report_string += " Preprocessing {:<8.3} sec ({:4.3}%).".format(
data_duration, 100 * (data_duration / duration))
if compute_duration:
report_string += " Compute {:<8.3} sec ({:4.3}%).".format(
compute_duration, 100 * (compute_duration / duration))
report_string += " {:5f} images/sec.".format(
int(FLAGS.micro_batch_size * FLAGS.batches_per_step / duration))
print(report_string)
if FLAGS.report_hw_cycle_count:
print("Hardware cycle count per 'run':", session.getCycleCount())
print("Executing...")
average_batches_per_sec = 0
# Run
start = time.time()
durations = []
if FLAGS.synthetic:
for i in range(FLAGS.iterations):
stepio = popart.PyStepIO(data, anchors)
data_time = time.time()
data_d = data_time - start
# Run compute
session.run(stepio)
# Calc compute duration
results = anchors[output_id]
comp_d = time.time() - data_time
# Calc total duration
t = time.time() - start
report_time(t, data_d, comp_d)
durations.append(t)
start = time.time()
duration = np.mean(durations)
else:
for d in data:
stepio = popart.PyStepIO(d, anchors)
# Calc data duration
data_time = time.time()
data_d = data_time - start
# Run compute
session.run(stepio)
# Calc compute duration
results = anchors[output_id]
comp_d = time.time() - data_time
# Calc total duration
t = time.time() - start
report_time(t, data_d, comp_d)
durations.append(t)
start = time.time()
duration = np.mean(durations)
def run_py(proto: onnx.ModelProto,
data: Mapping[str, np.ndarray],
outputs: Optional[Union[str, Iterable[str]]],
loss: Optional[str] = None,
optimizer: Optional[popart.Optimizer] = None,
patterns: Optional[popart.Patterns] = None,
user_options: Optional[Mapping[str, Any]] = None,
skip_execution: bool = False):
batches_per_step = 1
outputs = make_tuple(outputs)
# Setting up the Session
data_flow = popart.DataFlow(
batches_per_step,
{output: popart.AnchorReturnType("ALL")
for output in outputs})
if user_options is None:
user_options = {}
options = popart.SessionOptions()
options.reportOptions = {"showVarStorage": "true"}
options.enableStochasticRounding = False
options.constantWeights = True
options.outlineThreshold = 10.0
for key, value in user_options.items():
if key not in ["batchSerializationFactor", "executionPhases"]:
setattr(options, key, value)
replicas = user_options.get("replicatedGraphCount", 1)
request_ipus = pow(2, math.ceil(math.log2(replicas)))
device = tu.create_test_device(numIpus=request_ipus)
print("Compiling graph")
if optimizer is not None:
session = popart.TrainingSession(fnModel=proto,
deviceInfo=device,
dataFlow=data_flow,
userOptions=options,
loss=loss,
optimizer=optimizer,
patterns=patterns)
else:
session = popart.InferenceSession(fnModel=proto,
deviceInfo=device,
dataFlow=data_flow,
userOptions=options,
patterns=patterns)
if skip_execution:
device.detach()
return session
# Compile the Poplar Graph. If it fails, return the memory stats
try:
session.prepareDevice()
except popart.session.OutOfMemoryException as e:
device.detach()
raise e
print("Compilation complete")
session.weightsFromHost()
# NOTE: If we ever use a model with random ops, we would need to call this
# here, using the same seed given to numpy.
# session.setRandomSeed(1984)
anchors = session.initAnchorArrays()
rf = user_options.get("replicatedGraphCount")
if rf is not None and rf > 1:
data = {k: np.repeat(v[np.newaxis], rf, 0) for k, v in data.items()}
# Add a gradient accumulation factor dimension if needed
af = user_options.get("accumulationFactor")
if af is not None and af > 1:
data = {k: np.repeat(v[np.newaxis], af, 0) for k, v in data.items()}
stepio = popart.PyStepIO(data, anchors)
session.run(stepio)
with tempfile.TemporaryDirectory() as tmp:
file_path = os.path.join(tmp, "model.onnx")
session.modelToHost(file_path)
post_proto = onnx.load(file_path)
# Release device
device.detach()
return (anchors[output] for output in outputs), post_proto, outputs
def train(opts):
# Do not require the mnist data to be present if running with synthetic data
train_data, train_labels, test_data, test_labels = load_dummy(opts) \
if opts.syn_data_type in ["random_normal", "zeros"] else load_mnist()
if not opts.test_mode:
max_value = len(test_data) // opts.batch_size
if max_value < opts.batches_per_step:
print("(batches-per-step * batch-size) is larger than test set!\n"
" Reduced batches-per-step to: {}\n".format(max_value))
opts.batches_per_step = max_value
training_set = DataSet(opts.batch_size, opts.batches_per_step, train_data,
train_labels)
test_set = DataSet(opts.batch_size, opts.batches_per_step, test_data,
test_labels)
print("Creating ONNX model.")
proto, data_in, labels_in, output, loss = create_model(
opts.samples_per_device)
# Describe how to run the model
anchor_desc = {
output: popart.AnchorReturnType("ALL"),
loss: popart.AnchorReturnType("ALL")
}
dataFlow = popart.DataFlow(opts.batches_per_step, anchor_desc)
# Options
userOpts = popart.SessionOptions()
# The validation graph by default will be optimized to change all variables to constants
# This prevents that, which allows for checkpoints to be loaded into the model without recompiling
userOpts.constantWeights = False
# If requested, setup synthetic data
if opts.syn_data_type in ["random_normal", "zeros"]:
print("Running with Synthetic Data Type '{}'".format(
opts.syn_data_type))
if opts.syn_data_type == "random_normal":
userOpts.syntheticDataMode = popart.SyntheticDataMode.RandomNormal
elif opts.syn_data_type == "zeros":
userOpts.syntheticDataMode = popart.SyntheticDataMode.Zeros
# Enable auto-sharding
if opts.num_ipus > opts.replication_factor:
userOpts.virtualGraphMode = popart.VirtualGraphMode.Auto
# Enable pipelining
if opts.pipeline:
userOpts.enablePipelining = True
# Enable replication
if opts.replication_factor > 1:
userOpts.enableReplicatedGraphs = True
userOpts.replicatedGraphCount = opts.replication_factor
# A single device is shared between training and validation sessions
device = get_device(opts.num_ipus, opts.simulation)
training = init_session(proto,
loss,
dataFlow,
userOpts,
device,
training=True)
validation = init_session(proto,
loss,
dataFlow,
userOpts,
device,
training=False)
# Make weight transfer file
_, onnx_file_name = tempfile.mkstemp()
print("Running training loop.")
for i in range(opts.epochs):
# Training
if i > 0:
training.session.resetHostWeights(onnx_file_name)
training.session.weightsFromHost()
for step, (data, labels) in enumerate(training_set):
stepio = popart.PyStepIO({
data_in: data,
labels_in: labels
}, training.anchors)
start = time()
training.session.run(
stepio, 'Epoch ' + str(i) + ' training step' + str(step))
if opts.test_mode == "training":
log_run_info(training, start, opts)
training.session.modelToHost(onnx_file_name)
if not opts.validation_final_epoch or i == opts.epochs - 1:
aggregated_loss = 0
aggregated_accuracy = 0
validation.session.resetHostWeights(onnx_file_name)
validation.session.weightsFromHost()
# Evaluation
for step, (data, labels) in enumerate(test_set):
stepio = popart.PyStepIO({
data_in: data,
labels_in: labels
}, validation.anchors)
start = time()
validation.session.run(
stepio,
'Epoch ' + str(i) + ' evaluation step ' + str(step))
if opts.test_mode == "inference":
log_run_info(validation, start, opts)
# Loss
aggregated_loss += np.mean(validation.anchors[loss])
# Accuracy
results = np.argmax(
validation.anchors[output].reshape(
[test_set.inputs_per_step, 10]), 1)
num_correct = np.sum(
results == labels.reshape([test_set.inputs_per_step]))
aggregated_accuracy += num_correct / test_set.inputs_per_step
# Log statistics
aggregated_loss /= len(test_set)
aggregated_accuracy /= len(test_set)
print("Epoch #{}".format(i + 1))
print(" Loss={0:.4f}".format(aggregated_loss))
print(" Accuracy={0:.2f}%".format(aggregated_accuracy * 100))
# Remove weight transfer file
os.remove(onnx_file_name)
def run(benchmark, opts):
proto, data, outputs, losses, optimizer = benchmark.graph_builder(opts)
if opts.save_graph:
with open('model.onnx', "wb") as f:
f.write(proto)
print("Written to file: model.onnx")
dataFlow = popart.DataFlow(opts.batches_per_step, outputs)
# Create a session to compile and execute the graph
options = popart.SessionOptions()
if not opts.use_generated_data:
options.syntheticDataMode = popart.SyntheticDataMode.Zeros
options.instrumentWithHardwareCycleCounter = opts.report_hw_cycle_count
options.engineOptions = {
"debug.instrumentCompute": "true" if opts.report else "false"
}
if opts.convolution_options:
options.convolutionOptions = json.loads(opts.convolution_options)
if opts.shards > 1:
if opts.auto_sharding:
options.virtualGraphMode = popart.VirtualGraphMode.Auto
else:
options.virtualGraphMode = popart.VirtualGraphMode.Manual
options.enablePipelining = opts.pipeline
# Select a device
deviceManager = popart.DeviceManager()
if opts.simulation:
deviceOptions = {
"compileIPUCode": True,
'numIPUs': opts.shards,
"tilesPerIPU": 1216
}
device = deviceManager.createIpuModelDevice(deviceOptions)
else:
device = deviceManager.acquireAvailableDevice(opts.shards)
if device is None:
raise OSError("Failed to acquire IPU.")
if opts.mode == 'train':
session = popart.TrainingSession(fnModel=proto,
loss=losses,
deviceInfo=device,
optimizer=optimizer,
dataFlow=dataFlow,
userOptions=options)
else:
session = popart.InferenceSession(fnModel=proto,
deviceInfo=device,
dataFlow=dataFlow,
userOptions=options)
print("Compiling...")
start = time.time()
session.prepareDevice()
compilation_duration = time.time() - start
print("Duration: {:.3f} seconds\n".format(compilation_duration))
if opts.tensor_tile_mapping:
with open("tile_mapping.json", 'w') as f:
json.dump(session.getTensorTileMap(), f)
print("Written to file: tile_mapping.json")
# Create buffers to receive results from the execution
anchors = session.initAnchorArrays()
# Copy weights and optimization parameters onto the device
session.weightsFromHost()
# Add a batches_per_step dimension if needed
if opts.batches_per_step > 1:
data = {
k: np.repeat(v[np.newaxis], opts.batches_per_step, 0)
for k, v in data.items()
}
stepio = popart.PyStepIO(data, anchors)
print("Executing...")
average_batches_per_sec = 0
# Steps
for __ in range(opts.steps):
# Run
start = time.time()
session.run(stepio)
duration = time.time() - start
if opts.report:
return save_reports(opts, session)
average_batches_per_sec += (opts.batches_per_step /
duration) / opts.steps
report_string = "{:<8.3} sec/itr.".format(duration)
report_string += " " + benchmark.iteration_report(opts, duration)
print(report_string)
if opts.report_hw_cycle_count:
print("Hardware cycle count per 'run':", session.getCycleCount())
return compilation_duration, average_batches_per_sec
ip = builder.addInputTensor(data_shape)
lb = builder.addInputTensor(lbl_shape)
w = builder.addInitializedInputTensor(np.ones([2, 2], np.float16))
b = builder.addInitializedInputTensor(np.ones([2], np.float16))
o = builder.aiOnnx.gemm([ip, w, b], 1., 1., False, False)
o = builder.aiOnnx.relu([o])
o = builder.aiOnnx.softmax([o])
o = builder.aiGraphcore.nllloss([o, lb])
dataFlow = popart.DataFlow(1, {o: popart.AnchorReturnType("All")})
# Create a session to compile and the graph for inference
#------------------------------------------------------------------------------
inferenceOptions = popart.SessionOptions()
# Need to compile the inference graph with variable weights we they can be updated
# before execution
inferenceOptions.constantWeights = False
inferenceSession = popart.InferenceSession(
fnModel=builder.getModelProto(),
dataFlow=dataFlow,
userOptions=inferenceOptions,
deviceInfo=popart.DeviceManager().createIpuModelDevice({}))
# Compile graph
inferenceSession.prepareDevice()
# Create buffers to receive results from the execution
inferenceAnchors = inferenceSession.initAnchorArrays()
def test(config, iteration, true_scaling, test_case):
builder = popart.Builder()
w0name = "weight_0"
w1name = "weight_1"
w2name = "weight_2"
input0Shape = [1, 1, 1]
input0 = builder.addInputTensor(
popart.TensorInfo("FLOAT", input0Shape), "input0")
w0data = np.array([test_case[0][0]], dtype=np.float32)
w0R = np.empty([1, ], dtype=np.float32)
w0Id = builder.addInitializedInputTensor(w0data, w0name)
w1data = np.array([test_case[1][0]], dtype=np.float32)
w1R = np.empty([1, ], dtype=np.float32)
w1Id = builder.addInitializedInputTensor(w1data, w1name)
w2data = np.array([test_case[2][0]], dtype=np.float32)
w2R = np.empty([1, ], dtype=np.float32)
w2Id = builder.addInitializedInputTensor(w2data, w2name)
add0 = builder.aiOnnx.add([w0Id, input0])
add1 = builder.aiOnnx.add([w1Id, add0])
add2 = builder.aiOnnx.add([w2Id, add1])
loss = builder.aiGraphcore.l1loss([add2], 1.0, debugPrefix="l1LossVal")
builder.addOutputTensor(add2)
proto = builder.getModelProto()
dataFlow = popart.DataFlow(1, {})
opts = popart.SessionOptions()
opts.reportOptions = {"showExecutionSteps": "true"}
opts.enableGroupedMatmuls = False
pat = popart.Patterns(popart.PatternsLevel.Default)
device = popart.DeviceManager().acquireAvailableDevice(1)
if device is None:
raise OSError("Failed to acquire IPU.")
# The stage->tensor map would come from the Bert model in reality
# (see model.tensors)
mock_tensor_map = {
0: [w0Id],
1: [w1Id],
2: [w2Id]
}
factory = ScheduledOptimizerFactory(
config, iteration, tensors=mock_tensor_map)
assert_scaled_lr(factory, true_scaling)
optimizer_step0 = factory.create()
session = popart.TrainingSession(
fnModel=proto,
dataFlow=dataFlow,
userOptions=opts,
loss=loss,
optimizer=optimizer_step0,
patterns=pat,
deviceInfo=device)
session.prepareDevice()
session.weightsFromHost()
anchors = session.initAnchorArrays()
input_data = np.array([3.1415], dtype=np.float32)
stepio = popart.PyStepIO({input0: input_data}, anchors)
for step in range(iteration.total_steps):
session.run(stepio)
session.weightsToHost()
weightsRead = popart.PyWeightsIO({w0Id: w0R, w1Id: w1R, w2Id: w2R})
session.readWeights(weightsRead)
assert (np.isclose(test_case[0][step+1], w0R))
assert (np.isclose(test_case[1][step+1], w1R))
assert (np.isclose(test_case[2][step+1], w2R))
iteration.count += 1
if factory.should_update(iteration):
optimizer_step1 = factory.update_and_create(iteration)
assert_scaled_lr(factory, true_scaling)
session.updateOptimizerFromHost(optimizer_step1)
def main(args):
# Model parameters
np.random.seed(1971)
input_rows = 28
input_columns = 28
num_classes = 10
batch_size = 2048
input_shape = [batch_size, input_rows * input_columns]
labels_shape = [batch_size]
# Create model
x0, labels, model_proto, anchor_map, loss = create_model(
num_features=input_columns * input_rows,
num_classes=num_classes,
batch_size=batch_size,
force_recompute=True if args.recomputing == 'ON' else False)
# Save model (optional)
if args.export:
with open(args.export, 'wb') as model_path:
model_path.write(model_proto)
# Session options
num_ipus = 1
opts = popart.SessionOptions()
opts.reportOptions = {"showExecutionSteps": "true"}
opts.engineOptions = {"debug.instrument": "true"}
if args.recomputing == 'AUTO':
opts.autoRecomputation = popart.RecomputationType.Standard
# Create session
session = popart.TrainingSession(
fnModel=model_proto,
dataFeed=popart.DataFlow(1, anchor_map),
losses=[loss],
optimizer=popart.ConstSGD(0.01),
userOptions=opts,
deviceInfo=popart.DeviceManager().acquireAvailableDevice(num_ipus))
anchors = session.initAnchorArrays()
session.prepareDevice()
# Synthetic data input
data_in = np.random.uniform(low=0.0, high=1.0,
size=input_shape).astype(np.float32)
labels_in = np.random.randint(low=0, high=num_classes,
size=labels_shape).astype(np.int32)
# Run session
inputs = {x0: data_in, labels: labels_in}
stepio = popart.PyStepIO(inputs, anchors)
session.weightsFromHost()
session.optimizerFromHost()
session.run(stepio)
# Save report and return session object (optional)
if args.report:
from gcprofile import save_popart_report
save_popart_report(session)
if args.test:
return session
def get_ir(model_file_name='model.onnx',
enable_executionphases=True,
enable_matmul_serialization=False,
enable_outlining=False,
activation_tensor_location_settings=None,
weight_tensor_location_settings=None,
optimizer_state_tensor_location_settings=None,
accumulator_tensor_location_settings=None,
tensor_location_setting_override={},
num_layers=3,
dsize=48,
batch_size=1,
num_iterations=1,
num_replicas=1,
accumulation_factor=2,
optimizer=popart.SGD({"defaultLearningRate": (0.5, False)})):
np.random.seed(10911)
matmul_serialization_mode = 'output_channels'
matmul_serialization_factor = 2
builder = popart.Builder()
ip = builder.addInputTensor(
popart.TensorInfo("FLOAT", [batch_size, dsize, dsize]))
def add_layer(index, in_id):
w = builder.addInitializedInputTensor(
np.random.rand(dsize, dsize).astype(np.float32), f"W{index}")
matmul_id = builder.aiOnnx.matmul([in_id, w])
if enable_matmul_serialization:
builder.setSerializeMatMul({matmul_id}, matmul_serialization_mode,
matmul_serialization_factor)
return matmul_id
out = ip
for i in range(num_layers):
with builder.executionPhase(i):
out = add_layer(i, out)
l1 = builder.aiGraphcore.l1loss([out], 0.1)
anchorIds = []
builder.addOutputTensor(out)
device = tu.create_test_device(num_replicas *
(2 if enable_executionphases else 1),
pattern=popart.SyncPattern.Full)
dfAnchors = {}
for anchorId in anchorIds:
dfAnchors.update({anchorId: popart.AnchorReturnType("All")})
opts = popart.SessionOptions()
opts.enableOutlining = enable_outlining
opts.enableReplicatedGraphs = True if num_replicas > 1 else False
opts.replicatedGraphCount = num_replicas
if accumulation_factor > 1:
opts.enableGradientAccumulation = True
opts.accumulationFactor = accumulation_factor
if activation_tensor_location_settings is not None:
opts.activationTensorLocationSettings = activation_tensor_location_settings
if weight_tensor_location_settings is not None:
opts.weightTensorLocationSettings = weight_tensor_location_settings
if optimizer_state_tensor_location_settings is not None:
opts.optimizerStateTensorLocationSettings = optimizer_state_tensor_location_settings
if accumulator_tensor_location_settings is not None:
opts.accumulatorTensorLocationSettings = accumulator_tensor_location_settings
opts.tensorLocationSettingsOverride = tensor_location_setting_override
if (enable_executionphases):
opts.executionPhaseSettings.phases = num_layers
opts.autoRecomputation = popart.RecomputationType.NoRecompute
opts.virtualGraphMode = popart.VirtualGraphMode.ExecutionPhases
opts.explicitRecomputation = False
proto = builder.getModelProto()
session = popart.TrainingSession(fnModel=proto,
dataFlow=popart.DataFlow(1, dfAnchors),
optimizer=optimizer,
loss=l1,
patterns=popart.Patterns(
popart.PatternsLevel.All),
userOptions=opts,
deviceInfo=device)
session.prepareDevice()
session.weightsFromHost()
anchors = session.initAnchorArrays()
for i in range(num_iterations):
ip_data = np.random.rand(num_replicas, accumulation_factor, batch_size,
dsize, dsize).astype(np.float32)
stepio = popart.PyStepIO({ip: ip_data}, anchors)
session.run(stepio)
ir = json.loads(session._serializeIr(popart.IrSerializationFormat.JSON))
return ir
def create_model_pipelined(bufferStreams: bool = False,
pipelining: bool = False) -> Dict:
"""Create a simple model with optional pipeliing to test buffering streams
Args:
bufferStreams (bool, optional): Whether bufferStreamCopiesToDevice is on or off.
Defaults to False.
pipelining (bool, optional): Whether to pipeline the model in 2 parts.
Defaults to False.
Returns:
Dict: A dict of session, stepio, anchors and out tensor name required
to run and test the model.
"""
builder = popart.Builder()
data_shape = popart.TensorInfo("FLOAT16", [8, 2])
lbl_shape = popart.TensorInfo("INT32", [8])
ip = builder.addInputTensor(data_shape, "input_data")
lb = builder.addInputTensor(lbl_shape, "label")
w = builder.addInitializedInputTensor(w_init)
b = builder.addInitializedInputTensor(bias_init)
gemm = builder.aiOnnx.gemm([ip, w, b], 1., 1., False, False)
relu = builder.aiOnnx.relu([gemm])
sm = builder.aiOnnx.softmax([relu])
nll = builder.aiGraphcore.nllloss([sm, lb])
builder.addOutputTensor(sm)
art = popart.AnchorReturnType("All")
dataFlow = popart.DataFlow(BPS, {sm: art, nll: art})
opts = popart.SessionOptions()
opts.enableOutlining = True
opts.useHostCopyOps = bufferStreams
numIPUs = 1
if pipelining:
opts.enablePipelining = True
opts.virtualGraphMode = popart.VirtualGraphMode.Manual
builder.pipelineStage(gemm, 0)
builder.virtualGraph(gemm, 0)
builder.pipelineStage(relu, 0)
builder.virtualGraph(relu, 0)
builder.pipelineStage(sm, 1)
builder.virtualGraph(sm, 1)
builder.pipelineStage(nll, 1)
builder.virtualGraph(nll, 1)
numIPUs = 2
device = tu.create_test_device(numIPUs)
session = popart.TrainingSession(fnModel=builder.getModelProto(),
dataFlow=dataFlow,
loss=nll,
optimizer=popart.ConstSGD(0.1),
userOptions=opts,
deviceInfo=device)
session.prepareDevice()
# 2 host load ops for input
check_ops(session, bufferStreams, 2)
anchors = session.initAnchorArrays()
stepio = popart.PyStepIO({
ip: trainingData,
lb: trainingDataLables
}, anchors)
return {
"session": session,
"stepio": stepio,
"anchors": anchors,
"out": sm
}
def test_groupHostSync():
builder = popart.Builder()
a = builder.addInputTensor(popart.TensorInfo("FLOAT16", [1]))
w = builder.addInitializedInputTensor(np.ones([1], np.float16))
o = builder.aiOnnx.add([w, a])
l1 = builder.aiGraphcore.l1loss([o], 0.1)
anchor_config = {
o: popart.AnchorReturnType("All"),
l1: popart.AnchorReturnType("All")
}
dataFlow = popart.DataFlow(1, anchor_config)
options = popart.SessionOptions()
options.engineOptions = {
"debug.instrumentCompute": "true",
"debug.instrumentExternalExchange": "true"
}
options.groupHostSync = True #The option we are testing
options.reportOptions = {
"showVarStorage": "true",
"showPerIpuMemoryUsage": "true",
"showExecutionSteps": "true"
}
session = popart.InferenceSession(fnModel=builder.getModelProto(),
dataFlow=dataFlow,
deviceInfo=tu.create_test_device(),
userOptions=options)
session.prepareDevice()
session.weightsFromHost()
anchors = session.initAnchorArrays()
input_a = np.array([1.4], dtype=np.float16)
stepio = popart.PyStepIO({a: input_a}, anchors)
session.run(stepio)
summaryReport = session.getSummaryReport()
lines = summaryReport.split('\n')
order = []
first = False
countStreams = 0
countSeq = 0
# Analyse a sequence:
# default order :
# StreamCopy (FromHost) x2
# Add
# StreamCopy(ToHost) x2
# Absolute
# Reduce
# StreamCopy(ToHost) x2
# with the option:
# StreamCopy (FromHost) x2
# Add
# Absolute
# Reduce
# StreamCopy(ToHost) x2
for l in lines:
if re.search(r"Sequence", l):
countSeq += 1
if countSeq >= 7:
break
if re.search(r"OnTileExecute: 104/Op/Add", l):
order.append(1)
first = True
if re.search(r"OnTileExecute: 101/abs/Op/Absolute", l):
order.append(2)
if re.search(r"101/add/ReduceExpression", l):
order.append(3)
if re.search(r"StreamCopy", l) and first:
order.append(4)
countStreams += 1
# The streamcopy to host should only happen at the end (after ReduceExpression)
# Expected list with the option enabled: [1,2,3,4,4]
# Expected list without the option: [1,4,4,2,3,4,4]
assert (order[1] == 2)
assert (order[2] == 3)
assert (order[3] == 4)
# The number of Streamcopies happening in total
# (start counting from Add) should be 2.
assert (countStreams == 2)
