def init_builder(builder):
i1 = builder.addInputTensor(d1)
o = builder.aiOnnx.sign([i1], "test_sign")
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + o,
popart.reservedGradientPrefix() + i1,
]
def init_builder(builder):
i1 = builder.addInputTensor(d1)
o = builder.aiGraphcore.reshape(i1, shape=d2a)
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + i1,
popart.reservedGradientPrefix() + o,
]
def _get_ir(pingpong_enabled, virtualgraph_enabled, pipeline_enabled):
dsize = 10
builder = popart.Builder()
ip = builder.addInputTensor(popart.TensorInfo("FLOAT", [dsize, dsize]))
anchorIds = []
anchorIds.append(popart.reservedGradientPrefix() + ip)
def add_layer(in_id):
w = builder.addInitializedInputTensor(
np.ones([dsize, dsize], np.float32))
matmul_id = builder.aiOnnx.matmul([in_id, w])
return matmul_id
x = ip
for i in range(3):
w = builder.addInitializedInputTensor(
np.ones([dsize, dsize], np.float32))
x = builder.aiOnnx.matmul([x, w])
if pingpong_enabled:
builder.pingPongPhase(x, i)
if virtualgraph_enabled:
builder.virtualGraph(x, i)
if pipeline_enabled:
builder.pipelineStage(x, i)
anchorIds.append(popart.reservedGradientPrefix() + x)
out = builder.aiGraphcore.identityloss([x])
if virtualgraph_enabled:
builder.virtualGraph(out, 3)
device = tu.create_test_device()
dfAnchors = {}
for anchorId in anchorIds:
dfAnchors.update({anchorId: popart.AnchorReturnType("All")})
opts = popart.SessionOptions()
# disable outlining to make the ir easier to parse
opts.enableOutlining = False
proto = builder.getModelProto()
session = popart.TrainingSession(fnModel=proto,
dataFlow=popart.DataFlow(1, dfAnchors),
optimizer=popart.ConstSGD(0.1),
loss=out,
patterns=popart.Patterns(
popart.PatternsLevel.All),
userOptions=opts,
deviceInfo=device)
ir = json.loads(session._serializeIr(popart.IrSerializationFormat.JSON))
return ir
def run(model_file_name, enableOutlining):
dsize = 10
ratio = 0.5
builder = popart.Builder()
ip = builder.addInputTensor(popart.TensorInfo("FLOAT", [dsize, dsize]))
d__ip = popart.reservedGradientPrefix() + ip
def add_layer(in_id):
w = builder.addInitializedInputTensor(
np.ones([dsize, dsize], np.float32))
matmul_id = builder.aiOnnx.matmul([in_id, w])
return matmul_id
m1 = add_layer(ip)
m2 = add_layer(m1)
m3 = add_layer(m2)
anchorIds = []
for i in (ip, m1, m2, m3):
anchorIds.append(popart.reservedGradientPrefix() + i)
out = builder.aiGraphcore.identityloss([m3])
builder.addOutputTensor(out)
device = tu.create_test_device()
dfAnchors = {}
for anchorId in anchorIds:
dfAnchors.update({anchorId: popart.AnchorReturnType("All")})
opts = popart.SessionOptions()
opts.enableOutlining = enableOutlining
opts.separateCallOpPdfs = False
opts.subgraphCopyingStrategy = subgraphCopyingStrategy
proto = builder.getModelProto()
session = popart.TrainingSession(
fnModel=proto,
dataFlow=popart.DataFlow(1, dfAnchors),
optimizer=popart.ConstSGD(0.1),
loss=out,
patterns=popart.Patterns(popart.PatternsLevel.All),
userOptions=opts,
deviceInfo=device)
session.prepareDevice()
session.weightsFromHost()
anchors = session.initAnchorArrays()
ip_data = np.ones((dsize, dsize), dtype=np.float32)
stepio = popart.PyStepIO({ip: ip_data}, anchors)
session.run(stepio)
session.modelToHost(str(tmpdir / model_file_name))
def init_builder(builder):
D = builder.addInputTensor(src.numpy())
I = builder.addInputTensor(index.numpy().astype(np.uint32))
out = builder.aiGraphcore.scatterreduce([D, I], axis_size=axsz)
builder.addOutputTensor(out)
return [
out,
popart.reservedGradientPrefix() + D,
popart.reservedGradientPrefix() + out
]
def init_builder(builder):
i0 = builder.addInputTensor(x)
i1 = builder.aiOnnxOpset11.constant(axis)
o = builder.aiOnnxOpset11.cumsum([i0, i1], reverse=1)
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + i0,
popart.reservedGradientPrefix() + o,
]
def init_builder(builder):
i1 = builder.addInputTensor(d1)
c = builder.aiOnnx.constant(d2)
o = builder.aiOnnx.tile([i1, c])
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + i1,
popart.reservedGradientPrefix() + o
]
def init_builder(builder):
i1 = builder.addInputTensor(d1)
c = builder.aiOnnx.cast([i1], builderDstType)
# Add an op that produces a gradient so we can test CastGrad properly
o = builder.aiOnnx.reducesum([c])
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + i1,
popart.reservedGradientPrefix() + o
]
def init_builder(builder):
i1 = builder.addInputTensor(d1)
i2 = builder.addInputTensor(d2)
o = builder.aiOnnx.pow([i1, i2])
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + i1,
popart.reservedGradientPrefix() + i2,
popart.reservedGradientPrefix() + o
]
def init_builder(builder):
P = builder.addInitializedInputTensor(data)
T = builder.addInputTensor(target.astype(np.int32))
logP = builder.aiOnnx.logsoftmax([P], axis=1)
nll = builder.aiGraphcore.nllloss([logP, T], inputIsLogProbability=1)
builder.addOutputTensor(nll)
return [
nll,
popart.reservedGradientPrefix() + P,
popart.reservedGradientPrefix() + nll,
]
def init_builder(builder):
i1 = builder.addInputTensor(d1)
o = builder.aiOnnxOpset11.depthtospace([i1],
blocksize=blocks,
mode="DCR")
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + i1,
popart.reservedGradientPrefix() + o,
]
def init_builder(builder):
i1 = builder.addInputTensor(indices)
i2 = builder.aiOnnx.constant(depth) # depth has to be a constant
i3 = builder.addInputTensor(values)
o = builder.aiOnnx.onehot([i1, i2, i3], 0, "test_onehot")
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + o,
popart.reservedGradientPrefix() + i3
]
def init_builder(builder):
i1 = builder.addInputTensor(data)
i2 = builder.addInputTensor(indices)
i3 = builder.addInputTensor(updates)
o = builder.aiOnnx.scatter([i1, i2, i3], axis)
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + i1,
popart.reservedGradientPrefix() + i3
]
def init_builder(builder):
i1 = builder.addInputTensor(input_y)
i2 = builder.addInputTensor(input_x)
o = builder.aiGraphcore.atan2([i1, i2])
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + i1,
popart.reservedGradientPrefix() + i2,
popart.reservedGradientPrefix() + o
]
def init_builder(builder):
tensor = builder.addInputTensor(data)
out = builder.aiGraphcore.swish(
[tensor],
debugContext='test_swish_grad',
)
builder.addOutputTensor(out)
return [
out,
popart.reservedGradientPrefix() + out,
popart.reservedGradientPrefix() + tensor,
]
def init_builder(builder):
d = builder.addInputTensor(data)
x = builder.addInputTensor(x_data)
s = builder.aiOnnx.constant(scales)
o = builder.aiOnnx.resize([d, s])
o = builder.aiOnnx.mul([o, x])
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + d,
popart.reservedGradientPrefix() + o,
]
def init_builder(builder):
i1 = builder.addInputTensor(d1)
axes = builder.aiOnnx.constant(axesV)
starts = builder.aiOnnx.constant(startsV)
ends = builder.aiOnnx.constant(endsV)
o = builder.aiOnnx.slice([i1, starts, ends, axes])
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + i1,
popart.reservedGradientPrefix() + o
]
def init_builder(builder):
i1 = builder.addInputTensor(data)
i2 = builder.addInputTensor(one_place)
s = builder.aiOnnx.softmax([i1], axis)
o = builder.aiOnnx.mul([s, i2])
builder.addOutputTensor(o)
return [
o, s,
popart.reservedGradientPrefix() + i1,
popart.reservedGradientPrefix() + o
]
def init_builder(builder):
i1 = builder.addInputTensor(A)
i2 = builder.addInputTensor(B)
i3 = builder.addInputTensor(C)
o = builder.aiOnnx.gemm([i1, i2, i3], alpha, beta, transA, transB)
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + i1,
popart.reservedGradientPrefix() + i2,
popart.reservedGradientPrefix() + i3,
popart.reservedGradientPrefix() + o
]
def init_builder(builder):
i = builder.addInputTensor(data)
a, b = builder.aiOnnx.split([i], 2)
o = builder.aiOnnx.sum([a, b])
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + i,
popart.reservedGradientPrefix() + o
]
def init_builder(builder):
i1 = builder.addInputTensor(input_data)
o = builder.aiOnnx.softplus([i1])
builder.addOutputTensor(o)
op_tester.setPatterns(['InPlace'], enableRuntimeAsserts=False)
# Set the result to
# ['Softplus:0', 'Gradient___input', 'Gradient___Softplus:0']
result = [
o,
popart.reservedGradientPrefix() + i1,
popart.reservedGradientPrefix() + o
]
return result
def run_test():
builder = popart.Builder()
lhs = builder.addInputTensor(popart.TensorInfo("FLOAT", lhs_shape),
"lhs")
rhs = builder.addInputTensor(popart.TensorInfo("FLOAT", rhs_shape),
"rhs")
z = builder.addInputTensor(popart.TensorInfo("FLOAT", [2]), "zero")
t1 = builder.aiOnnx.matmul([lhs, rhs])
o = builder.aiOnnx.add([z, t1])
o = builder.aiGraphcore.identityloss([o])
proto = builder.getModelProto()
dataFlow = popart.DataFlow(
1, {
o:
popart.AnchorReturnType("All"),
popart.reservedGradientPrefix() + lhs:
popart.AnchorReturnType("All"),
popart.reservedGradientPrefix() + rhs:
popart.AnchorReturnType("All"),
})
opts = popart.SessionOptions()
opts.reportOptions = {"showExecutionSteps": "true"}
pat = popart.Patterns(popart.PatternsLevel.Default)
session = popart.TrainingSession(
fnModel=proto,
dataFlow=dataFlow,
userOptions=opts,
loss=o,
optimizer=popart.ConstSGD(0.01),
patterns=pat,
deviceInfo=tu.create_test_device(opts={"compileIPUCode": False}))
session.prepareDevice()
anchors = session.initAnchorArrays()
inputs = {lhs: lhs_data, rhs: rhs_data, z: zero_data}
stepio = popart.PyStepIO(inputs, anchors)
session.run(stepio)
return anchors[o]
def init_builder(builder):
i1 = builder.addInputTensor(input_data)
o = builder.aiOnnx.hardsigmoid([i1], alpha=alpha, beta=beta)
builder.addOutputTensor(o)
result = [o]
if builder_settings is 'InPlace':
op_tester.setPatterns(['InPlace'], enableRuntimeAsserts=False)
elif builder_settings is 'backward':
result = [
o,
popart.reservedGradientPrefix() + i1,
popart.reservedGradientPrefix() + o
]
return result
def init_builder(builder):
i1 = builder.addInputTensor(A)
i2 = builder.addInitializedInputTensor(B)
i3 = builder.addInitializedInputTensor(C)
o = builder.aiOnnx.gemm([i1, i2, i3], alpha, beta, transA, transB)
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + i2, i2,
popart.reservedGradientPrefix() + i3, i3,
"scaledLearningRate0___default___FLOAT",
"weightDecayScaleFactor0___default___FLOAT"
]
def init_builder(builder):
i1 = builder.addInputTensor(d1)
(o, ) = builder.aiOnnx.maxpool([i1],
num_outputs=1,
kernel_shape=[2, 2],
pads=[0, 0, 0, 0],
storage_order=0,
strides=[2, 2])
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + i1,
popart.reservedGradientPrefix() + o
]
def init_builder(builder):
i1 = builder.addInputTensor(d1)
c = builder.aiOnnx.constant(d2)
o = builder.aiOnnx.expand([i1, c])
if inplace:
o_identity = builder.aiOnnx.identity([o])
else:
o_identity = o
builder.addOutputTensor(o_identity)
return [
o_identity,
popart.reservedGradientPrefix() + o,
popart.reservedGradientPrefix() + i1
]
def init_builder(builder):
i1 = builder.addInputTensor(input_data)
o = builder.aiOnnx.elu([i1], alpha=alpha)
builder.addOutputTensor(o)
result = [o]
if builder_settings is 'InPlace':
op_tester.patterns = ['InPlace']
elif builder_settings is 'backward':
result = [
o,
popart.reservedGradientPrefix() + i1,
popart.reservedGradientPrefix() + o
]
return result
def init_builder(builder):
i1 = builder.addInputTensor(input_data)
if (alpha == None):
o = builder.aiOnnx.leakyrelu([i1])
else:
o = builder.aiOnnx.leakyrelu([i1], alpha=alpha)
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + i1,
popart.reservedGradientPrefix() + o
]
def init_builder(builder):
tData = builder.addInputTensor(data, data_id)
tIW = builder.addInitializedInputTensor(input_weights,
input_weights_id)
tOW = builder.addInitializedInputTensor(output_weights,
output_weights_id)
tBiases = builder.addInitializedInputTensor(biases, biases_id)
tInitH = builder.addInputTensor(initial_h, init_h_id)
tInitC = builder.addInputTensor(initial_c, init_c_id)
def reshape_weights(w):
ws = builder.aiOnnx.split([w], 4, 1, [hidden_size] * 4)
ws = [builder.aiOnnx.transpose([i], [0, 2, 1]) for i in ws]
ws = builder.aiOnnx.concat([ws[i] for i in (2, 0, 3, 1)], 0)
return ws
tIW = reshape_weights(tIW)
tOW = reshape_weights(tOW)
# NB shape inference is not yet possible with aiOnnx.split
tWeights = builder.aiOnnx.concat([tIW, tOW], 1)
tBiases = builder.aiOnnx.split([tBiases], 8, 1, [hidden_size] * 8)
tBiases0 = builder.aiOnnx.concat(
[tBiases[i] for i in (2, 0, 3, 1)], 0)
tBiases1 = builder.aiOnnx.concat(
[tBiases[i] for i in (6, 4, 7, 5)], 0)
tBiases = builder.aiOnnx.add([tBiases0, tBiases1])
tInitState = builder.aiOnnx.concat([tInitH, tInitC], 0)
input_ids = [tData, tWeights, tBiases, tInitState]
out, cell_state = builder.aiGraphcore.lstm(input_ids)
assert builder.getTensorDtypeString(out) == "float32"
assert builder.getTensorDtypeString(cell_state) == "float32"
if sum_outputs:
out = builder.aiOnnx.add([out, cell_state],
"sum_out_and_cell_state")
loss = builder.aiGraphcore.identityloss([out])
return [
loss,
popart.reservedGradientPrefix() + data_id,
popart.reservedGradientPrefix() + input_weights_id,
popart.reservedGradientPrefix() + output_weights_id,
popart.reservedGradientPrefix() + biases_id,
]
def init_builder_manual_padding(builder):
i1 = builder.addInputTensor(d1)
(o, ) = builder.aiOnnxOpset10.maxpool([i1],
num_outputs=1,
kernel_shape=[3, 3],
ceil_mode=0,
pads=[0, 0, 1, 1],
storage_order=0,
strides=[2, 2])
builder.addOutputTensor(o)
return [
o,
popart.reservedGradientPrefix() + i1,
popart.reservedGradientPrefix() + o
]
