from tvm.contrib import graph_runtime
from tvm import relay
from tvm.relay import testing
+from tvm.relay import vm
+from tvm.relay import vmobj as _obj
def benchmark_execution(mod,
params,
- measure=False,
+ measure=True,
data_shape=(1, 3, 224, 224),
out_shape=(1, 1000),
- dtype='float32'):
- def get_tvm_output(mod, data, params, target, ctx, dtype='float32'):
- with relay.build_config(opt_level=1):
+ dtype='float32',
+ model="unknown"):
+ def get_graph_runtime_output(mod, data, params, target, ctx,
+ dtype='float32', number=2, repeat=20):
+ with relay.build_config(opt_level=3):
graph, lib, params = relay.build(mod, target, params=params)
m = graph_runtime.create(graph, lib, ctx)
out = m.get_output(0, tvm.nd.empty(out_shape, dtype))
if measure:
- print("Evaluate graph runtime inference time cost...")
+ print("Evaluate graph runtime inference cost of {} on "
+ "{}".format(model, repr(ctx)))
ftimer = m.module.time_evaluator("run", ctx, number=1, repeat=20)
# Measure in millisecond.
prof_res = np.array(ftimer().results) * 1000
- print("Mean inference time (std dev): %.2f ms (%.2f ms)" %
+ print("Mean graph runtime inference time (std dev): %.2f ms (%.2f ms)" %
(np.mean(prof_res), np.std(prof_res)))
return out.asnumpy()
- def get_tvm_vm_output(mod, data, params, target, ctx, dtype='float32'):
- ex = relay.create_executor('vm', mod=mod, ctx=ctx)
- result = ex.evaluate()(data, **params)
+ def get_vm_output(mod, data, params, target, ctx, dtype='float32',
+ number=2, repeat=20):
+ with relay.build_config(opt_level=3):
+ exe = vm.compile(mod, target, params=params)
+ rly_vm = vm.VirtualMachine(exe)
+ rly_vm.init(ctx)
+ result = rly_vm.run(data)
+
+ if measure:
+ print("Evaluate vm inference cost of {} on {}".format(model,
+ repr(ctx)))
+ ftimer = rly_vm.mod.time_evaluator("invoke", ctx, number=number,
+ repeat=repeat)
+ # Measure in millisecond.
+ prof_res = np.array(ftimer("main", _obj.Tensor(data)).results) * 1000
+ print("Mean vm inference time (std dev): %.2f ms (%.2f ms)" %
+ (np.mean(prof_res), np.std(prof_res)))
+
return result.asnumpy().astype(dtype)
# random input
target = "llvm"
ctx = tvm.cpu(0)
- tvm_out = get_tvm_output(mod, tvm.nd.array(data.astype(dtype)), params,
- target, ctx, dtype)
- vm_out = get_tvm_vm_output(mod, tvm.nd.array(data.astype(dtype)), params,
- target, ctx, dtype)
+ tvm_out = get_graph_runtime_output(mod, tvm.nd.array(data.astype(dtype)),
+ params, target, ctx, dtype)
+ vm_out = get_vm_output(mod, tvm.nd.array(data.astype(dtype)), params,
+ target, ctx, dtype)
tvm.testing.assert_allclose(vm_out, tvm_out, rtol=1e-5, atol=1e-5)
def test_mlp():
image_shape = (1, 1, 28, 28)
mod, params = testing.mlp.get_workload(1)
- benchmark_execution(mod, params, data_shape=image_shape, out_shape=(1, 10))
+ benchmark_execution(mod, params, data_shape=image_shape, out_shape=(1, 10),
+ model="mlp")
def test_vgg():
for n in [11, 16]:
mod, params = testing.vgg.get_workload(1, num_layers=n)
- benchmark_execution(mod, params)
+ model = "vgg" + str(n)
+ benchmark_execution(mod, params, model=model)
def test_resnet():
for n in [18, 50]:
mod, params = testing.resnet.get_workload(batch_size=1, num_layers=n)
- benchmark_execution(mod, params, True)
+ model = "resnet" + str(n)
+ benchmark_execution(mod, params, model=model)
def test_squeezenet():
for version in ['1.0', '1.1']:
mod, params = testing.squeezenet.get_workload(version=version)
- benchmark_execution(mod, params)
+ model = "squeezenet" + version
+ benchmark_execution(mod, params, model=model)
def test_inception_v3():
image_shape = (3, 299, 299)
mod, params = testing.inception_v3.get_workload(image_shape=image_shape)
- benchmark_execution(mod, params, data_shape=(1, 3, 299, 299))
+ benchmark_execution(mod, params, data_shape=(1, 3, 299, 299),
+ model="inception_v3")
def test_dqn():
def test_mobilenet():
mod, params = testing.mobilenet.get_workload(batch_size=1)
- benchmark_execution(mod, params)
+ benchmark_execution(mod, params, model="mobilenet")
# TODO: enable when the low building performance (several minutes) fixed.
def test_mobilenet_nhwc():
def test_densenet():
mod, params = testing.densenet.get_workload(batch_size=1)
- benchmark_execution(mod, params)
+ benchmark_execution(mod, params, model="densenet")
if __name__ == '__main__':