def create_callback_stepio(data, anchors, start_times, end_times,
batches_per_step):
micro_batch_indices = defaultdict(int)
# Input callback is called when the data is needed:
def input_callback(id, is_prefetch: bool):
if is_prefetch:
input_time = time.perf_counter()
start_times[id].append(input_time)
return data[id][micro_batch_indices[id]]
# Called after the input buffer has been consumed by the device:
def input_complete_callback(id):
micro_batch_indices[id] = \
(micro_batch_indices[id] + 1) % batches_per_step
return
# Output callback is called when a buffer is needed for the result:
def output_callback(id):
return anchors[id][micro_batch_indices[id]]
# Complete callback is called when the output buffer has
# been filled (result is ready to be consumed by the host):
def output_complete_callback(id):
output_time = time.perf_counter()
end_times[id].append(output_time)
micro_batch_indices[id] = \
(micro_batch_indices[id] + 1) % batches_per_step
stepio = popart.PyStepIOCallback(input_callback, input_complete_callback,
output_callback, output_complete_callback)
return stepio
def _test(transposedInput, transposedOutput):
builder = popart.Builder()
# Create a random constant and transpose it
np.random.seed(1)
input1 = builder.addInputTensor("INT32", [2, 2])
# Run a session to prove this
output1 = builder.aiOnnx.identity([input1])
builder.addOutputTensor(output1)
anchorConfig = {output1: popart.AnchorReturnType("ALL")}
dataFlow = popart.DataFlow(1, anchorConfig)
deviceConfig = {'numIPUs': 1}
dm = popart.DeviceManager()
device = dm.createIpuModelDevice(deviceConfig)
session = popart.InferenceSession(fnModel=builder.getModelProto(),
dataFlow=dataFlow,
deviceInfo=device)
# Compile graph and place weights onto it
session.prepareDevice()
session.weightsFromHost()
# Feed the session with a transposed (non-contiguous) tensor.
input1Value = np.random.randint(0, 100, size=(2, 2), dtype='int32')
if transposedInput:
input1Value = np.transpose(input1Value, [1, 0])
output1Value = np.random.randint(0, 100, size=(2, 2), dtype='int32')
if transposedOutput:
output1Value = np.transpose(output1Value, [1, 0])
with pytest.raises(
(Exception, RuntimeError, popart.popart_exception)) as e_info:
def input_callback(id, prefetch):
return input1Value
def input_complete_callback(id):
pass
def output_callback(id):
return output1Value
def output_complete_callback(id):
pass
stepio = popart.PyStepIOCallback(input_callback,
input_complete_callback,
output_callback,
output_complete_callback)
session.run(stepio)
assert "contiguous" in e_info.value.args[0]
def create_callback_stepio(data: dict, anchors: dict,
start_times: DefaultDict[str, list],
end_times: DefaultDict[str, list],
batches_per_step: int, replication_factor: int):
'''Create a popart.PyStepIOCallback using data and anchors.
Will record timing information in start_times and end_times.'''
input_callback_indices = defaultdict(int)
# Input callback is called when the data is needed:
def input_callback(tensor_id: str, is_prefetch: bool):
input_time = time.perf_counter()
start_times[tensor_id].append(input_time)
idx = input_callback_indices[tensor_id]
input_callback_indices[tensor_id] = (idx + 1) % (batches_per_step *
replication_factor)
return data[tensor_id][idx]
# Called after the input buffer has been consumed by the device:
def input_complete_callback(tensor_id: str):
return
output_callback_indices = defaultdict(int)
# Output callback is called when a buffer is needed for the result:
def output_callback(tensor_id: str):
idx = output_callback_indices[tensor_id]
output_callback_indices[tensor_id] = (idx + 1) % (batches_per_step *
replication_factor)
replica_idx = idx % replication_factor
batch_idx = idx // replication_factor
return anchors[tensor_id][batch_idx, replica_idx]
# Complete callback is called when the output buffer has
# been filled (result is ready to be consumed by the host):
def output_complete_callback(tensor_id: str):
output_time = time.perf_counter()
end_times[tensor_id].append(output_time)
stepio = popart.PyStepIOCallback(input_callback, input_complete_callback,
output_callback, output_complete_callback)
return stepio
def test_stepio_callbackinput(tmpdir):
builder = popart.Builder()
shape = popart.TensorInfo("FLOAT", [2])
i1 = builder.addInputTensor(shape)
i2 = builder.addInputTensor(shape)
o = builder.aiOnnx.add([i1, i2])
builder.addOutputTensor(o)
proto = builder.getModelProto()
batches_per_step = 2
dataFlow = popart.DataFlow(
batches_per_step, {
i1: popart.AnchorReturnType("All"),
i2: popart.AnchorReturnType("All"),
o: popart.AnchorReturnType("All")
})
session = popart.InferenceSession(fnModel=proto,
dataFlow=dataFlow,
deviceInfo=tu.create_test_device())
session.prepareDevice()
anchors = session.initAnchorArrays()
i1_data = np.random.rand(batches_per_step, 2).astype(np.float32)
i2_data = np.random.rand(batches_per_step, 2).astype(np.float32)
inputs = {i1: i1_data, i2: i2_data}
i1_c = 0
i2_c = 0
def input_callback(id, prefetch):
nonlocal i1_c, i2_c
time.sleep(2)
print("input_callback ", id)
t = inputs[id]
print(t)
if id == i1:
print("input_callback ", id, len(t))
if (i1_c < len(t)):
result = t[i1_c]
i1_c = i1_c + 1
if id == i2:
print("input_callback ", id, len(t))
if (i2_c < len(t)):
result = t[i2_c]
i2_c = i2_c + 1
print(result)
return result
def input_complete_callback(id):
print("input_complete_callback ", id)
i1_d = 0
i2_d = 0
o_d = 0
def output_callback(id):
nonlocal i1_d, i2_d, o_d
time.sleep(2)
print("output_callback ", id)
t = anchors[id]
if id == i1:
result = t[i1_d]
i1_d = i1_d + 1
if id == i2:
result = t[i2_d]
i2_d = i2_d + 1
if id == o:
result = t[o_d]
o_d = o_d + 1
return result
def output_complete_callback(id):
print("output_complete_callback ", id)
stepio = popart.PyStepIOCallback(input_callback, input_complete_callback,
output_callback, output_complete_callback)
session.run(stepio)
# confirm that writing device-to-host of a Stream Tensor returns correctly (unchanged)
assert (np.allclose(anchors[i1], i1_data))
assert (np.allclose(anchors[i2], i2_data))
expected_result = i1_data + i2_data
assert (np.allclose(anchors[o], expected_result))
def build_and_run_graph(data_size):
# Create a builder object:
builder = popart.Builder()
# Specifiy two input vectors:
data_spec = popart.TensorInfo("FLOAT", [data_size])
id_a = builder.addInputTensor(data_spec)
id_b = builder.addInputTensor(data_spec)
# Describe the computation:
o1 = builder.aiOnnx.add([id_a, id_b])
o2 = builder.aiOnnx.mul([id_a, id_b])
# Designate the two output vectors and how
# often the result will be required:
builder.addOutputTensor(o1)
builder.addOutputTensor(o2)
dataFlow = popart.DataFlow(
1,
{o1: popart.AnchorReturnType("ALL"),
o2: popart.AnchorReturnType("ALL")})
# Setup an inference graph:
proto = builder.getModelProto()
session = popart.InferenceSession(
fnModel=proto,
dataFeed=dataFlow,
deviceInfo=popart.DeviceManager().createIpuModelDevice({}))
# Compile graph:
session.prepareDevice()
# Create input data buffers:
data_a = np.random.rand(data_size).astype(np.float32)
data_b = np.random.rand(data_size).astype(np.float32)
inputs = {id_a: data_a, id_b: data_b}
# Create output data buffers:
anchors = session.initAnchorArrays()
# Create timer objects and dictionaries:
timer = PerfIntervalTimer()
rtts = {}
# Input callback is called when the data is needed:
def input_callback(id, is_prefetch: bool):
if is_prefetch:
return
if timer.not_set():
timer.reset()
return inputs[id]
# Called after the input buffer has been consumed:
def input_complete_callback(id):
return
# Output callback is called when a buffer is needed for the result:
def output_callback(id):
return anchors[id]
# Complete callback is called when the output buffer has
# been filled (result is ready to be consumed by the host):
def output_complete_callback(id):
rtt = timer.interval()
rtts[id] = rtt
# Create the callback IO system:
stepio = popart.PyStepIOCallback(input_callback,
input_complete_callback,
output_callback,
output_complete_callback)
# Run the graph and return timings:
session.run(stepio)
return rtts