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test_crt_aot_usmp.py
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test_crt_aot_usmp.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
""" This file contains test that use USMP + AoT using C runtime APIs"""
from collections import OrderedDict
import sys
import numpy as np
import pytest
import tvm
from tvm import relay, TVMError
from tvm.ir.module import IRModule
from tvm.relay import testing, transform
from tvm.relay.testing import byoc
from tvm.relay.op.annotation import compiler_begin, compiler_end
from tvm.relay.backend import Executor, Runtime
from aot_test_utils import (
AOTTestModel,
AOTTestRunner,
generate_ref_data,
convert_to_relay,
compile_and_run,
compile_models,
parametrize_aot_options,
run_and_check,
)
def check_for_no_tvm_backendallocworkspace_calls(mod: tvm.runtime.module):
"""This checker checks whether any c-source produced has TVMBackendAllocWorkspace calls.
If USMP is invoked, none of them should have TVMBAW calls"""
dso_modules = mod._collect_dso_modules()
for dso_mod in dso_modules:
assert (
dso_mod.type_key == "c"
), 'Current CRT AoT codegen flow should only produce type "c" runtime modules'
source = dso_mod.get_source()
source.count(
"TVMBackendAllocWorkspace"
) == 0, "This is failing because USMP was unable to plan for every tir.allocate node"
@pytest.mark.parametrize(
"workspace_byte_alignment,main_workspace_size",
[
(8, 17280),
(16, 17280),
(256, 17792),
],
)
def test_memory_planning(workspace_byte_alignment, main_workspace_size):
mod, params = tvm.relay.testing.synthetic.get_workload()
target = "c"
runtime = Runtime("crt")
executor = Executor(
"aot",
{
"workspace-byte-alignment": workspace_byte_alignment,
},
)
with tvm.transform.PassContext(
opt_level=3,
config={
"tir.disable_vectorize": True,
"tir.disable_storage_rewrite": True,
"tir.usmp.enable": True,
"tir.usmp.algorithm": "greedy_by_conflicts",
},
):
lib = tvm.relay.build(mod, target, executor=executor, runtime=runtime, params=params)
assert (
sum(lib.function_metadata["__tvm_main__"].workspace_sizes.values()) == main_workspace_size
)
@parametrize_aot_options
@pytest.mark.parametrize("groups,weight_shape", [(1, 32), (32, 1)])
def test_conv2d(interface_api, use_unpacked_api, test_runner, groups, weight_shape):
"""Test a subgraph with a single conv2d operator."""
dtype = "float32"
ishape = (1, 32, 14, 14)
wshape = (32, weight_shape, 3, 3)
pass_config = {"tir.usmp.enable": True}
test_runner = AOTTestRunner(
makefile=test_runner.makefile,
prologue=test_runner.prologue,
epilogue=test_runner.epilogue,
includes=test_runner.includes,
parameters=test_runner.parameters,
pass_config=pass_config,
)
data0 = relay.var("data", shape=ishape, dtype=dtype)
weight0 = relay.var("weight", shape=wshape, dtype=dtype)
out = relay.nn.conv2d(data0, weight0, kernel_size=(3, 3), padding=(1, 1), groups=groups)
main_f = relay.Function([data0, weight0], out)
mod = tvm.IRModule()
mod["main"] = main_f
mod = transform.InferType()(mod)
i_data = np.random.uniform(0, 1, ishape).astype(dtype)
w1_data = np.random.uniform(0, 1, wshape).astype(dtype)
inputs = OrderedDict([("data", i_data), ("weight", w1_data)])
output_list = generate_ref_data(mod, inputs)
compile_and_run(
AOTTestModel(module=mod, inputs=inputs, outputs=output_list),
test_runner,
interface_api,
use_unpacked_api,
)
compiled_test_mods = compile_models(
models=AOTTestModel(module=mod, inputs=inputs, outputs=output_list),
interface_api=interface_api,
use_unpacked_api=use_unpacked_api,
pass_config=test_runner.pass_config,
)
for compiled_model in compiled_test_mods:
check_for_no_tvm_backendallocworkspace_calls(compiled_model.executor_factory.lib)
run_and_check(
models=compiled_test_mods,
runner=test_runner,
interface_api=interface_api,
)
@pytest.mark.parametrize("merge_compiler_regions", [False, True])
def test_byoc_microtvm(merge_compiler_regions):
"""This is a simple test to check BYOC capabilities of AOT - with and without merging compiler regions to test for https://github.com/apache/tvm/issues/9036"""
use_unpacked_api = False
interface_api = "packed"
test_runner = AOTTestRunner(pass_config={"tir.usmp.enable": True})
x = relay.var("x", shape=(10, 10))
w0 = relay.var("w0", shape=(10, 10))
w1 = relay.var("w1", shape=(10, 10))
# z0 = x + w0
x_ = compiler_begin(x, "ccompiler")
w0_ = compiler_begin(w0, "ccompiler")
z0_ = relay.add(x_, w0_)
z0 = compiler_end(z0_, "ccompiler")
# z1 = z0 + w1
z0__ = compiler_begin(z0, "ccompiler")
w1_ = compiler_begin(w1, "ccompiler")
z1_ = relay.add(z0__, w1_)
z1 = compiler_end(z1_, "ccompiler")
# z2 = z0 + z1
z2 = relay.add(z0, z1)
f = relay.Function([x, w0, w1], z2)
mod = tvm.IRModule()
mod["main"] = f
if merge_compiler_regions:
mod = transform.MergeCompilerRegions()(mod)
mod = transform.PartitionGraph("mod_name")(mod)
mod = transform.InferType()(mod)
x_data = [("x", np.random.rand(10, 10).astype("float32"))]
w_data = [("w{}".format(i), np.random.rand(10, 10).astype("float32")) for i in range(2)]
map_inputs = OrderedDict(x_data + w_data)
output_list = generate_ref_data(mod, map_inputs)
compiled_test_mods = compile_models(
AOTTestModel(name="my_mod", module=mod, inputs=map_inputs, outputs=output_list),
interface_api=interface_api,
use_unpacked_api=use_unpacked_api,
pass_config=test_runner.pass_config,
)
for compiled_model in compiled_test_mods:
check_for_no_tvm_backendallocworkspace_calls(compiled_model.executor_factory.lib)
run_and_check(
models=compiled_test_mods,
runner=test_runner,
interface_api=interface_api,
)
MOBILENET_V1_URL = (
"https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz",
"mobilenet_v1_1.0_224_quant.tflite",
)
@pytest.mark.parametrize(
"model_url, usmp_algo, workspace_size,",
[
(MOBILENET_V1_URL, "greedy_by_size", 4845696),
(MOBILENET_V1_URL, "greedy_by_conflicts", 4444288),
],
)
def test_tflite_model(model_url, usmp_algo, workspace_size):
"""This checks for ML models and the memory used by them when using USMP with different algorithms"""
pytest.importorskip("tflite")
import tvm.relay.testing.tf as tf_testing
use_unpacked_api = True
interface_api = "c"
test_runner = AOTTestRunner(
pass_config={"tir.usmp.enable": True, "tir.usmp.algorithm": usmp_algo}
)
tflite_model_file = tf_testing.get_workload_official(
model_url[0],
model_url[1],
)
with open(tflite_model_file, "rb") as f:
tflite_model_buf = f.read()
data_shape = (1, 224, 224, 3)
in_min, in_max = (0, 255)
data = np.random.randint(in_min, high=in_max, size=data_shape, dtype="uint8")
mod, params = convert_to_relay(tflite_model_buf, data, "input")
inputs = {"input": data}
output_list = generate_ref_data(mod, inputs, params)
compiled_test_mods = compile_models(
AOTTestModel(module=mod, inputs=inputs, outputs=output_list, params=params),
interface_api=interface_api,
use_unpacked_api=use_unpacked_api,
pass_config=test_runner.pass_config,
)
for compiled_model in compiled_test_mods:
check_for_no_tvm_backendallocworkspace_calls(compiled_model.executor_factory.lib)
# Checking the workspace size
assert (
sum(
compiled_model.executor_factory.function_metadata[
"__tvm_main__"
].workspace_sizes.values()
)
== workspace_size
)
run_and_check(
models=compiled_test_mods,
runner=test_runner,
interface_api=interface_api,
)