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Test layers

Structure of the code and defaults

PULP-TrainLib's tests are organized as follows:

  • to verify the results of the code under test, PyTorch data (the so-called Golden Model or GM) is generated by utils/GM.py. You can find a GM under each test folder. Each GM generates a set of .h files containing the reference data (stored in the L2 memory);
  • the C code of each test is contained in net.c. net.h contains several useful definitions for each test. Furthemore, the stats.h file contains the macros to profile the execution of the C code. The main.c contains the code to launch the main task on the PULP cluster.

DNN layer tests are provided one for each data type. In case of tests related to a specific data format, the folder name ends with that specific format (e.g. test_linear_fp32/). Other tests may feature multiple data types. To verify this, look inside the Makefile and net.c, as well as net.h.

All the configuration variables in each test are specified inside the Makefile. Each Makefile has its own section for user mods (check the corresponding comments). To select the sizes you want to check, change these definitions as you wish.

Running the tests

To run a test, open you terminal and first source pulp-sdk/configs/pulp_open.sh. Then, move to the folder of your test (e.g. /tests/test_linear_fp32) and, from the same terminal, launch:

make clean all run

If the test you launch has a Golden Model, launch:

make clean get_golden all run

In case of tests of layers, which feature multiple training steps, launch instead:

make clean get_golden all run STEP='XXX'

where XXX is the step to be run.

The valid arguments are:

  • test_linear_fpXX/, test_conv2d_fpXX/: FORWARD, BACKWARD_GRAD, BACKWARD_ERROR
  • test_conv_pw_dw_fpXX/: DW_FORWARD, DW_BACKWARD_GRAD, DW_BACKWARD_ERROR, PW_FORWARD, PW_BACKWARD_GRAD, PW_BACKWARD_ERROR

You can see the valid arguments inside each user section of the Makefile. For example, certain tests, as for test_matmul, give the possibility to select the data type of the executed code. In this case, the parameter DATA_TYPE='XXX' (where XXX can be one between {float, fp16}) can be set by the user.

If the number of cores (NUM_CORES) is changed, make sure of manually deleting the BUILD/ folder before running the test with the new NUM_CORES to avoid behavioural issues. This applies also to other flags, like DEBUG/. For example, run:

rm -rf BUILD/ ; make clean get_golden all run STEP='XXX'

Useful mods

When launching a test, some flags or arguments can be set to modify the execution. They can be set as argument of the terminal command or can also be modified inside the Makefile, inside the "User Settings" section. Useful examples are:

  • DEBUG : activates printfs to show debug features of the code
  • NUM_CORES : select the number of cluster cores on which to parallelize the primitives

For other mods to the execution, please watch inside the Makefile of each test. Common useful parameters are inside the "User settings" section.

Also, in each test it can be possibile to modify the error sensitivity while checking tensors, by modifying the value of CHECK_TOLERANCE and ERROR_TOLERANCE, inside net.h.

Other mods to network sizes (and more) can be set by modifying the defaults inside utils/GM.py, which generates the golden model in each test.

Matmul profiling

test_matmul is a special test to profile the implemented matmuls on any matrix size. If you need to know which matmul performs better on given matrix sizes, run:

make clean get_golden all run IN_CH='size1' MID_CH='size2' OUT_CH='size3' > log.txt
make clean profile_fastest

size1, size2, size3 are user-defined integers (replace them with numbers of your choice). See Makefile's "User settings" section for more info. The sorted performances are written inside the fastest_matmul.txt file.

Running matmul optimizations

Each baseline version of a MM-based primitive (like Conv2D ones) can be launched by commenting the OPTIMIZE flag:

#APP_CFLAGS += -DOPTIMIZE

If you want to run the optimized version of the same primitive, you need to uncomment the OPTIMIZE flag and select the optimized MM you want:

APP_CFLAGS += -DOPTIMIZE
MATMUL_TYPE?=num_matmul

num_matmul is an integer number which refers to a specific optimized matmul algorithm. The MM selection is managed by the matmul_manager fucntion, defined in pulp_train_utils_fp32.c or pulp_train_utils_fp16.c, for each data format. You can see the code of each matmul inside the mm_manager_list.txt for fp32 and mm_manager_list_fp16.txt for fp16.

Running multiple simulations with different optimizations, varying sizes and matmul algorithms

To evaluate the fastest MM for your problem (layer size, step), please to refer to utils/profile_optimized.py under the layer test folders. This script launches multiple simulations and finds the fastest setup. To launch this, make sure to have:

APP_CFLAGS += -DOPTIMIZE
MATMUL_TYPE?=num_matmul

To launch the script, other options could be added, like IN_CH=... or OUT_CH=..., where ... is the size you desire. The sorted results, from the fastest to the slowest, are stored into runs.txt file.

An example to evaluate the fastest setup for a fully-connected layer (test_linear_fp32/) is:

make profile_all_optim STEP='BACKWARD_GRAD' IN_CH=1024 OUT_CH=8 NUM_CORES=8

Running multiple simulations with variable sizes

If you need to launch multiple simulations with variable layer size, launch the command:

make profile_all_sizes STEP='XXX' <options>

To adjust the sequence of sizes to be launched, modify the arrays inside utils/profile_sizes.py, inside the desired test folder. The sizes arrays are under the "USER SETTINGS" section of the code (on top of the file).

The output of the profiling is contained into runs.txt.

Adding new matmuls to the library

If a new matmul is added to the library, make sure to insert it in the correct function list inside the mm_manager function (inside pulp_train_utils_fpxx.c). Also, make sure that the names and lists of all matmuls in mm_manager_list.txt match exactly with the lists inside mm_manager before performing multiple simulations using profile_optimized.py (or the AutoTuner)!

PULP-TrainLib's Autotuner

PULP-TrainLib's Autotuner can be found inside pulp-trainlib/tools/AutoTuner. Please refer to the relative README for further info.