def get_model_anchors_model2(doSharding,
doPipelining,
batchesPerStep,
doTraining,
doGradAccl=False,
gradAcclFactor=1,
doProfiling=False,
doDevicex=True,
anchorRestoredTensors=False,
returnRawInput=False,
labelArray=None):
np.random.seed(1234)
builder = popart.Builder()
micro_batch_size = batch_size // gradAcclFactor
shape_d0 = [micro_batch_size, 2, 4, 4]
shape_l0 = [batch_size]
d0 = builder.addInputTensor(popart.TensorInfo("FLOAT", shape_d0), "inp")
data_w0 = np.ones(shape=[2, 2, 3, 3]).astype(np.float32)
w0 = builder.addInitializedInputTensor(data_w0, "weights")
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],
debugPrefix="c0")
r0 = builder.reshape_const(builder.aiOnnx, [c0], [micro_batch_size, 32])
out = builder.aiOnnx.softmax([r0], axis=1, debugPrefix="sfm")
label_shape = [micro_batch_size]
l0 = builder.addInputTensor(popart.TensorInfo("INT32", label_shape),
"label")
nll = builder.aiGraphcore.nllloss([out, l0])
art = popart.AnchorReturnType("All")
anchor_map = {nll: art, w0: art, e0: art, s0: art, c0: 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
if doGradAccl is True:
anchor_map[popart.reservedAcclToUpdatePrefix() +
popart.reservedGradientPrefix() + w0] = art
opts = popart.SessionOptions()
opts.reportOptions = {"showExecutionSteps": "true"}
opts.enablePipelining = doPipelining
opts.enableGradientAccumulation = doGradAccl
opts.accumulationFactor = gradAcclFactor
if doSharding is False:
numIPUs = 1
else:
opts.virtualGraphMode = popart.VirtualGraphMode.Manual
numIPUs = 3
builder.virtualGraph(s0, 0)
builder.virtualGraph(e0, 1)
builder.virtualGraph(c0, 1)
builder.virtualGraph(r0, 2)
builder.virtualGraph(out, 2)
builder.virtualGraph(nll, 2)
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=tu.create_test_device(numIpus=numIPUs))
else:
session = popart.InferenceSession(
fnModel=builder.getModelProto(),
dataFlow=popart.DataFlow(batchesPerStep, anchor_map),
userOptions=opts,
deviceInfo=tu.create_test_device(numIpus=numIPUs))
if doDevicex is False:
return None
anchors = session.initAnchorArrays()
session.prepareDevice()
classes = np.prod(shape_d0) / (micro_batch_size * batchesPerStep)
label = np.random.randint(low=0, high=classes,
size=shape_l0).astype(np.int32)
outer_dim = 1
if batchesPerStep > 1:
# Add an outer dimension of batchesPerStep. We repeat the labels
# as we want consistency if we have different shape inputs between examples.
outer_dim *= batchesPerStep
label = np.repeat(label[np.newaxis], batchesPerStep, 0)
if gradAcclFactor > 1:
# Divide up the batches per step batches into gradAcclFactor * batchesPerStep
# samples.
outer_dim *= gradAcclFactor
label = label.reshape([gradAcclFactor * batchesPerStep, -1])
if outer_dim > 1:
# Add the gradAcclFactor * batchesPerStep dimension into the input.
shape_d0.insert(0, outer_dim)
data = np.ones(shape=shape_d0).astype(np.float32)
inputs = {d0: data, l0: label}
stepio = popart.PyStepIO(inputs, anchors)
session.weightsFromHost()
for i in range(6):
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
def get_model_anchors_model1(doSharding,
doPipelining,
batchesPerStep,
doTraining,
doGradAccl=False,
gradAcclFactor=1,
doProfiling=False,
doDevicex=True,
anchorRestoredTensors=False,
labelArray=None):
micro_batch_size = batch_size // gradAcclFactor
builder = popart.Builder()
input_shape = [micro_batch_size, hidden_size]
input_ = builder.addInputTensor(popart.TensorInfo("FLOAT", input_shape))
x = input_
with builder.virtualGraph(0):
for i in range(2):
w = builder.addInitializedInputTensor(
np.ones([hidden_size, hidden_size]).astype(np.float32),
f"weight_0_{i}")
x = builder.aiOnnx.matmul([x, w])
with builder.virtualGraph(1 if doSharding else 0):
for i in range(2):
w = builder.addInitializedInputTensor(
np.ones([hidden_size, hidden_size]).astype(np.float32),
f"weight_1_{i}")
x = builder.aiOnnx.matmul([x, w])
with builder.virtualGraph(2 if doSharding else 0):
for i in range(2):
w = builder.addInitializedInputTensor(
np.ones([hidden_size, hidden_size]).astype(np.float32),
f"weight_2_{i}")
if i == 1: w0 = w
x = builder.aiOnnx.matmul([x, w])
label = builder.addInputTensor("INT32", [micro_batch_size])
x = builder.aiGraphcore.nllloss([x, label])
output = x
builder.addOutputTensor(output)
art = popart.AnchorReturnType("All")
anchor_map = {x: art, w0: art}
if doTraining is True:
anchor_map[popart.reservedGradientPrefix() + x] = art
if doPipelining is True and anchorRestoredTensors is True:
anchor_map[popart.reservedRestoredPrefix() + x] = art
anchor_map[popart.reservedRestoredPrefix() + w0] = art
if doGradAccl is True:
anchor_map[popart.reservedAcclToUpdatePrefix() +
popart.reservedGradientPrefix() + w0] = art
opts = popart.SessionOptions()
opts.reportOptions = {"showExecutionSteps": "true"}
opts.enablePipelining = doPipelining
opts.enableGradientAccumulation = doGradAccl
opts.accumulationFactor = gradAcclFactor
opts.virtualGraphMode = popart.VirtualGraphMode.Manual
if doSharding is False:
numIPUs = 1
else:
numIPUs = 3
if doTraining is True:
session = popart.TrainingSession(
fnModel=builder.getModelProto(),
dataFlow=popart.DataFlow(batchesPerStep, anchor_map),
loss=output,
optimizer=popart.ConstSGD(0.01),
userOptions=opts,
deviceInfo=tu.create_test_device(numIpus=numIPUs))
else:
session = popart.InferenceSession(
fnModel=builder.getModelProto(),
dataFlow=popart.DataFlow(batchesPerStep, anchor_map),
userOptions=opts,
deviceInfo=tu.create_test_device(numIpus=numIPUs))
if doDevicex is False:
return None
anchors = session.initAnchorArrays()
session.prepareDevice()
outer_dim = 1
if batchesPerStep > 1:
# Add an outer dimension of batchesPerStep. We repeat the labels
# as we want consistency if we have different shape inputs between examples.
outer_dim *= batchesPerStep
labelArray = np.repeat(labelArray[np.newaxis], batchesPerStep, 0)
if gradAcclFactor > 1:
# Divide up the batches per step batches into gradAcclFactor * batchesPerStep
# samples.
outer_dim *= gradAcclFactor
labelArray = labelArray.reshape([gradAcclFactor * batchesPerStep, -1])
if outer_dim > 1:
# Add the gradAcclFactor * batchesPerStep dimension into the input.
input_shape = [outer_dim] + input_shape
stepio = popart.PyStepIO(
{
input_: np.ones(input_shape, np.float32),
label: labelArray.astype(np.int32)
}, anchors)
session.weightsFromHost()
session.run(stepio)
if doProfiling is True:
from gcprofile import save_popart_report
save_popart_report(session)
return anchors
def test_gradient_accumulation_anchors(tmpdir):
"""
Check that the accumulated gradients with gradient accumulation match
the gradients without gradient accumulation enabled.
"""
label_array = np.random.randint(0, hidden_size, batch_size)
#TODO T11866 larger batches-per-step, first without weight decay, then with weight decay
batches_per_step = 1
accl_initial_proto, accl_proto_filename, accl_anchor_arrays = run_mm_graph(
sgd_optimizer,
label_array=label_array,
accum_factor=4,
enable_accum=True,
batches_per_step=batches_per_step,
number_of_steps=1,
final_proto_filename=os.path.join(tmpdir,
"accl5batches3stepsAnchorsTest"),
enable_multi_ipu=False,
full_anchorage=True,
inference_mode=False)
no_accl_initial_proto, no_accl_proto_filename, no_accl_anchor_arrays = run_mm_graph(
sgd_optimizer,
label_array=label_array,
accum_factor=1,
enable_accum=False,
batches_per_step=batches_per_step,
number_of_steps=1,
final_proto_filename=os.path.join(tmpdir,
"noAccl5batches3stepsAnchorsTest"),
enable_multi_ipu=False,
full_anchorage=True,
inference_mode=False)
w0_tensor = onnx.load_from_string(accl_initial_proto).graph.initializer[0]
w0_name = w0_tensor.name
full_batch_grad = no_accl_anchor_arrays[popart.reservedGradientPrefix() +
w0_name]
accl_grad = accl_anchor_arrays[popart.reservedAcclToUpdatePrefix() +
w0_name]
print("full batch grad shape is ")
print(full_batch_grad.shape)
print("accl grad shape is ")
print(accl_grad.shape)
if (batches_per_step > 1):
#TODO T11866
raise RuntimeError("batches per step > 1 needs investigation")
for i in range(batches_per_step):
print("\nbatch %d" % (i, ))
print("Absolute accl grad %.3f" % (np.sum(np.abs(accl_grad[i]))))
print("Absolute no accl g %.3f" %
(np.sum(np.abs(full_batch_grad[i]))))
print("Absolute difference %.3f" %
(np.sum(np.abs(full_batch_grad[i] - accl_grad[i]))))
print("Absolute difference %.3f" %
(np.sum(np.abs(full_batch_grad[i] - adjusted_accl_grad[i]))))
else:
accl_grad_abs_sum = np.sum(np.abs(accl_grad))
print("Absolute accl grad %.3f" % (accl_grad_abs_sum))
# initialising as per equations. When velocity scaling != 1 this may need changing T12001
adjusted_accl_grad = accl_grad[-1].flatten().copy()
for i, v in enumerate(w0_tensor.float_data):
adjusted_accl_grad[i] -= wd * v
adjusted_accl_grad_abs_sum = np.sum(np.abs(adjusted_accl_grad))
print("Absolute accl grad, adjusted for weight decay %.3f" %
(adjusted_accl_grad_abs_sum))
full_batch_abs_sum = np.sum(np.abs(full_batch_grad))
print("Absolute no accl g %.3f" % (full_batch_abs_sum))
abs_diff = np.sum(
np.abs(full_batch_grad.flatten() - adjusted_accl_grad))
print("Absolute difference %.3f" % (abs_diff))
assert (abs_diff / (full_batch_abs_sum + accl_grad_abs_sum) < 1e-5)
def test_accumulators_names_dont_clash():
np.random.seed(1984)
builder = popart.Builder()
input_data = np.random.rand(4, 4).astype(np.float32)
weights = ['weight1', 'weight2', 'weight3']
d0 = builder.addInputTensor(popart.TensorInfo('FLOAT', [4, 4]), 'data0')
x = builder.aiOnnx.add([
d0,
builder.addInitializedInputTensor(
np.random.rand(4, 4).astype(np.float32), weights[0])
])
x = builder.aiOnnx.add([
x,
builder.addInitializedInputTensor(
np.random.rand(4, 4).astype(np.float32), weights[1])
])
x = builder.aiOnnx.add([
x,
builder.addInitializedInputTensor(
np.random.rand(4, 4).astype(np.float32), weights[2])
])
l1 = builder.aiGraphcore.l1loss([x], 1.0)
proto = builder.getModelProto()
dataFlow = popart.DataFlow(1, {})
opt = popart.SGD({
"defaultLearningRate": (0.1, True),
"defaultMomentum": (0.9, True),
"defaultDampening": (0, True)
})
session = popart.TrainingSession(
fnModel=proto,
dataFlow=dataFlow,
loss=l1,
optimizer=opt,
deviceInfo=tu.create_test_device(opts={"compileIPUCode": False}))
ir = json.loads(session._serializeIr(popart.IrSerializationFormat.JSON))
ops = ir["maingraph"]
tensors = set()
for op in ops:
for i in op["inputs"]:
tensors.add(i["name"])
for o in op["outputs"]:
tensors.add(o["name"])
prefixes = [
popart.reservedAcclPrefix(),
popart.reservedAcclToUpdatePrefix(),
popart.reservedAcclFinalOutPrefix()
]
for prefix, weight in itertools.product(prefixes, weights):
assert prefix + weight in tensors
def run_graph(optimizer, input_shape, initial_onnx_model, input_tensor_name,
output_tensor_name, label_tensor_name, label_array, accum_factor,
enable_accum, batches_per_step, number_of_steps,
final_proto_filename, enable_multi_ipu, full_anchorage,
inference_mode):
art = popart.AnchorReturnType("All")
anchorNames = {output_tensor_name: art}
if full_anchorage:
w0 = onnx.load_from_string(
initial_onnx_model).graph.initializer[0].name
anchorNames[popart.reservedGradientPrefix() + w0] = art
if enable_accum:
anchorNames[popart.reservedAcclPrefix() + w0] = art
anchorNames[popart.reservedAcclToUpdatePrefix() + w0] = art
opts = popart.SessionOptions()
opts.enableGradientAccumulation = enable_accum
opts.accumulationFactor = accum_factor
opts.enableOutlining = False
opts.virtualGraphMode = popart.VirtualGraphMode.Manual if enable_multi_ipu else popart.VirtualGraphMode.Off
if enable_multi_ipu:
device = tu.create_test_device(numIpus=num_ipus,
tilesPerIPU=testTilesPerIPU,
opts={"compileIPUCode": False})
opts.virtualGraphMode = popart.VirtualGraphMode.Manual
else:
device = tu.create_test_device(tilesPerIPU=testTilesPerIPU,
opts={"compileIPUCode": False})
opts.virtualGraphMode = popart.VirtualGraphMode.Off
# only for test purposes, inference with gradient_accumulation should never work
if inference_mode:
popart.InferenceSession(fnModel=initial_onnx_model,
dataFlow=popart.DataFlow(
batches_per_step, anchorNames),
userOptions=opts,
deviceInfo=device)
session = popart.TrainingSession(fnModel=initial_onnx_model,
dataFlow=popart.DataFlow(
batches_per_step, anchorNames),
deviceInfo=device,
loss=output_tensor_name,
optimizer=optimizer,
userOptions=opts)
session.prepareDevice()
session.weightsFromHost()
anchor_arrays = session.initAnchorArrays()
outer_dim = 1
if batches_per_step > 1:
outer_dim *= batches_per_step
label_array = np.repeat(label_array[np.newaxis], batches_per_step, 0)
if accum_factor > 1:
outer_dim *= accum_factor
label_array = label_array.reshape(
[accum_factor * batches_per_step, -1])
if outer_dim > 1:
input_shape = [outer_dim] + input_shape
stepio = popart.PyStepIO(
{
input_tensor_name:
(1.0 - xi * npr.rand(*input_shape)).astype(np.float32),
label_tensor_name:
label_array.astype(np.int32)
}, anchor_arrays)
for i in range(number_of_steps):
session.run(stepio)
final_proto_file = "{}.onnx".format(final_proto_filename)
session.modelToHost(final_proto_filename)
return final_proto_filename, anchor_arrays
