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gpu_runs)
print()
print("Custom Asymmetric (cuda 0 ) impl:")
print("input size: {0}".format(input_size))
run_profile(
AsymmetricQuantizer(QuantizerConfig(QuantizationParams(bits=NBITS))).cuda(),
input_size,
'cuda',
gpu_runs)
print()
print("Custom Asymmetric Per Weight Channel (cuda 0 ) impl")
print("input size: {0}".format(input_size))
run_profile(
AsymmetricQuantizer(QuantizerConfig(QuantizationParams(bits=NBITS),
input_shape=input_size,
per_channel=True,
is_weights=True)).cuda(),
input_size,
'cuda',
gpu_runs)
print()
print("Custom Asymmetric Per Activation Channel (cuda 0 ) impl")
print("input size: {0}".format(input_size))
run_profile(
AsymmetricQuantizer(QuantizerConfig(QuantizationParams(bits=NBITS),
input_shape=input_size,
per_channel=True,
is_weights=False)).cuda(),
input_size,
print("Custom Symmetric Per Activation Channel (cpu) impl")
print("input size: {0}".format(LOW_BATCH_INPUT_SIZE))
run_profile(
SymmetricQuantizer(QuantizerConfig(QuantizationParams(bits=NBITS),
input_shape=LOW_BATCH_INPUT_SIZE,
per_channel=True,
is_weights=False)),
LOW_BATCH_INPUT_SIZE,
'cpu',
CPU_RUNS)
print()
print("Custom Asymmetric (cpu) impl:")
print("input size: {0}".format(LOW_BATCH_INPUT_SIZE))
run_profile(
AsymmetricQuantizer(QuantizerConfig(QuantizationParams(bits=NBITS))),
LOW_BATCH_INPUT_SIZE,
'cpu',
CPU_RUNS)
print()
print("Custom Asymmetric Per Weight Channel (cpu) impl")
print("input size: {0}".format(LOW_BATCH_INPUT_SIZE))
run_profile(
AsymmetricQuantizer(QuantizerConfig(QuantizationParams(bits=NBITS),
input_shape=LOW_BATCH_INPUT_SIZE,
per_channel=True,
is_weights=True)),
LOW_BATCH_INPUT_SIZE,
'cpu',
CPU_RUNS)