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"""Tests for the python interface to the data generator library."""
# Copyright (c) 2023, ARM Limited.
# SPDX-License-Identifier: Apache-2.0
from pathlib import Path
import numpy as np
import pytest
from generator.datagenerator import GenerateError
from generator.datagenerator import GenerateLibrary
# NOTE: These tests are marked as POST COMMIT
# To run them, please build the reference_model in a local "build" directory
# (as per the README) and run them using: pytest -m "postcommit"
# Location of reference model binaries
REF_MODEL_BUILD_PATH = Path(__file__).resolve().parents[2] / "build" / "reference_model"
GENERATE_LIB = "libtosa_reference_generate_lib.so"
GENERATE_LIB_PATH = REF_MODEL_BUILD_PATH / GENERATE_LIB
TEST_DIR = Path(__file__).parent
@pytest.mark.postcommit
def test_generate_lib_built():
"""First test to check the library has been built."""
assert GENERATE_LIB_PATH.is_file()
@pytest.mark.postcommit
def test_checker_generate_load_fail():
with pytest.raises(GenerateError) as excinfo:
GenerateLibrary(Path("/place-that-does-not-exist"))
assert str(excinfo.value).startswith("Could not find generate library")
@pytest.mark.postcommit
def test_checker_generate_load():
glib = GenerateLibrary(GENERATE_LIB_PATH)
assert glib
JSON_DATAGEN_DOT_PRODUCT = {
"tosa_file": "test.json",
"ifm_name": ["input-0", "input-1"],
"ifm_file": ["input-0.npy", "input-1.npy"],
"ofm_name": ["result-0"],
"ofm_file": ["result-0.npy"],
"meta": {
"data_gen": {
"version": "0.1",
"tensors": {
"input-0": {
"generator": "DOT_PRODUCT",
"data_type": "FP32",
"input_type": "VARIABLE",
"shape": [3, 5, 4],
"input_pos": 0,
"op": "MATMUL",
"dot_product_info": {"s": 0, "ks": 4, "acc_type": "FP32"},
},
"input-1": {
"generator": "DOT_PRODUCT",
"data_type": "FP32",
"input_type": "VARIABLE",
"shape": [3, 4, 6],
"input_pos": 1,
"op": "MATMUL",
"dot_product_info": {"s": 0, "ks": 4, "acc_type": "FP32"},
},
},
}
},
}
@pytest.mark.postcommit
def test_generate_dot_product_check():
glib = GenerateLibrary(GENERATE_LIB_PATH)
assert glib
json_config = JSON_DATAGEN_DOT_PRODUCT
glib.set_config(json_config)
glib.write_numpy_files(TEST_DIR)
# Test the files exist and are the expected numpy files
for f, n in zip(json_config["ifm_file"], json_config["ifm_name"]):
file = TEST_DIR / f
assert file.is_file()
arr = np.load(file)
assert arr.shape == tuple(
json_config["meta"]["data_gen"]["tensors"][n]["shape"]
)
assert arr.dtype == np.float32
file.unlink()
@pytest.mark.postcommit
def test_generate_dot_product_check_fail_names():
glib = GenerateLibrary(GENERATE_LIB_PATH)
assert glib
# Fix up the JSON to have the wrong names
json_config = JSON_DATAGEN_DOT_PRODUCT.copy()
json_config["ifm_name"] = ["not-input0", "not-input1"]
glib.set_config(json_config)
with pytest.raises(GenerateError) as excinfo:
glib.write_numpy_files(TEST_DIR)
info = str(excinfo.value).split("\n")
for i, n in enumerate(json_config["ifm_name"]):
assert info[i].startswith(f"ERROR: Failed to create data for tensor {n}")
for f in json_config["ifm_file"]:
file = TEST_DIR / f
assert not file.is_file()
@pytest.mark.postcommit
def test_generate_tensor_data_check():
glib = GenerateLibrary(GENERATE_LIB_PATH)
assert glib
json_config = JSON_DATAGEN_DOT_PRODUCT["meta"]["data_gen"]
for n in JSON_DATAGEN_DOT_PRODUCT["ifm_name"]:
arr = glib.get_tensor_data(n, json_config)
assert arr.shape == tuple(json_config["tensors"][n]["shape"])
assert arr.dtype == np.float32
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