Getting Started With PAPI

Getting Started With PAPI#

The Performance Application Programming Interface (PAPI) is a library that provides performance counters on a variety of platforms. Performance counters provide accurate low-level information about processors behavior during a given execution run. This information can contain simple metrics like total cycle count, cache misses, and instructions executed as well as more high level information like total FLOPS and warp occupancy. PAPI makes these metrics available while profiling.

Installing PAPI#

PAPI can either be installed using your package manager (apt-get install libpapi-dev on Ubuntu), or from source here: icl-utk-edu/papi.

Pulling the latest version of PAPI from source has caused build issues before. Therefore, it is recommended to checkout tagged version papi-6-0-0-1-t.

Building TVM With PAPI#

To include PAPI in your build of TVM, set the following line in you config.cmake:

set(USE_PAPI ON)

If PAPI is installed in a non-standard place, you can specify where it is like so:

set(USE_PAPI path/to/papi.pc)

Using PAPI While Profiling#

If TVM has been built with PAPI (see above), then you can pass a tvm.runtime.profiling.PAPIMetricCollector to tvm.runtime.GraphModule.profile() to collect performance metrics. Here is an example:

import tvm
from tvm import relay
from tvm.relay.testing import mlp
from tvm.runtime import profiler_vm
import numpy as np

target = "llvm"
dev = tvm.cpu()
mod, params = mlp.get_workload(1)

exe = relay.vm.compile(mod, target, params=params)
vm = profiler_vm.VirtualMachineProfiler(exe, dev)

data = tvm.nd.array(np.random.rand(1, 1, 28, 28).astype("float32"), device=dev)
report = vm.profile(
    data,
    func_name="main",
    collectors=[tvm.runtime.profiling.PAPIMetricCollector()],
)
print(report)

Name                                    perf::CACHE-MISSES   perf::CYCLES  perf::STALLED-CYCLES-BACKEND  perf::INSTRUCTIONS  perf::STALLED-CYCLES-FRONTEND
fused_nn_dense_nn_bias_add_nn_relu                   2,494      1,570,698                        85,608             675,564                         39,583
fused_nn_dense_nn_bias_add_nn_relu_1                 1,149        655,101                        13,278             202,297                         21,380
fused_nn_dense_nn_bias_add                             288        600,184                         8,321             163,446                         19,513
fused_nn_batch_flatten                                 301        587,049                         4,636             158,636                         18,565
fused_nn_softmax                                       154        575,143                         8,018             160,738                         18,995
----------
Sum                                                  4,386      3,988,175                       119,861           1,360,681                        118,036
Total                                               10,644      8,327,360                       179,310           2,660,569                        270,044

You can also change which metrics are collected:

report = vm.profile(
    data,
    func_name="main",
    collectors=[tvm.runtime.profiling.PAPIMetricCollector({dev: ["PAPI_FP_OPS"]})],
)

Name                                  PAPI_FP_OPS
fused_nn_dense_nn_bias_add_nn_relu        200,832
fused_nn_dense_nn_bias_add_nn_relu_1       16,448
fused_nn_dense_nn_bias_add                  1,548
fused_nn_softmax                              160
fused_nn_batch_flatten                          0
----------
Sum                                       218,988
Total                                     218,988

You can find a list of available metrics by running the papi_avail and papi_native_avail commands.