[relay] use time_evaluator for measurement

tests/python/relay/benchmarking/benchmark_vm.py

@@ -21,16 +21,20 @@
 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)
@@ -41,60 +45,81 @@ def get_tvm_output(mod, data, params, target, ctx, dtype='float32'):
         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
     data = np.random.uniform(size=data_shape).astype(dtype)
     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():
@@ -112,7 +137,7 @@ def test_dcgan():
 
 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():
@@ -124,7 +149,7 @@ 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__':