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Merge MLI feature branch into v1.0 branch (#754)
Create a v1.0 branch to combine ongoing efforts in `mli-feature` and `smartsim-refactor` feature branches --------- Co-authored-by: Alyssa Cote <[email protected]> Co-authored-by: Al Rigazzi <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,77 @@ | ||
| import os | ||
| import base64 | ||
| import cloudpickle | ||
| import sys | ||
| from smartsim import Experiment | ||
| from smartsim._core.mli.infrastructure.worker.torch_worker import TorchWorker | ||
| from smartsim.status import TERMINAL_STATUSES | ||
| from smartsim.settings import DragonRunSettings | ||
| import time | ||
| import typing as t | ||
|
|
||
| DEVICE = "gpu" | ||
| NUM_RANKS = 4 | ||
| NUM_WORKERS = 1 | ||
| filedir = os.path.dirname(__file__) | ||
| worker_manager_script_name = os.path.join(filedir, "standalone_worker_manager.py") | ||
| app_script_name = os.path.join(filedir, "mock_app.py") | ||
| model_name = os.path.join(filedir, f"resnet50.{DEVICE}.pt") | ||
|
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| transport: t.Literal["hsta", "tcp"] = "hsta" | ||
|
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| os.environ["SMARTSIM_DRAGON_TRANSPORT"] = transport | ||
|
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| exp_path = os.path.join(filedir, f"MLI_proto_{transport.upper()}") | ||
| os.makedirs(exp_path, exist_ok=True) | ||
| exp = Experiment("MLI_proto", launcher="dragon", exp_path=exp_path) | ||
|
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| torch_worker_str = base64.b64encode(cloudpickle.dumps(TorchWorker)).decode("ascii") | ||
|
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| worker_manager_rs: DragonRunSettings = exp.create_run_settings( | ||
| sys.executable, | ||
| [ | ||
| worker_manager_script_name, | ||
| "--device", | ||
| DEVICE, | ||
| "--worker_class", | ||
| torch_worker_str, | ||
| "--batch_size", | ||
| str(NUM_RANKS//NUM_WORKERS), | ||
| "--batch_timeout", | ||
| str(0.00), | ||
| "--num_workers", | ||
| str(NUM_WORKERS) | ||
| ], | ||
| ) | ||
|
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| aff = [] | ||
|
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| worker_manager_rs.set_cpu_affinity(aff) | ||
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| worker_manager = exp.create_model("worker_manager", run_settings=worker_manager_rs) | ||
| worker_manager.attach_generator_files(to_copy=[worker_manager_script_name]) | ||
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| app_rs: DragonRunSettings = exp.create_run_settings( | ||
| sys.executable, | ||
| exe_args=[app_script_name, "--device", DEVICE, "--log_max_batchsize", str(6)], | ||
| ) | ||
| app_rs.set_tasks_per_node(NUM_RANKS) | ||
|
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| app = exp.create_model("app", run_settings=app_rs) | ||
| app.attach_generator_files(to_copy=[app_script_name], to_symlink=[model_name]) | ||
|
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| exp.generate(worker_manager, app, overwrite=True) | ||
| exp.start(worker_manager, app, block=False) | ||
|
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| while True: | ||
| if exp.get_status(app)[0] in TERMINAL_STATUSES: | ||
| time.sleep(10) | ||
| exp.stop(worker_manager) | ||
| break | ||
| if exp.get_status(worker_manager)[0] in TERMINAL_STATUSES: | ||
| time.sleep(10) | ||
| exp.stop(app) | ||
| break | ||
|
|
||
| print("Exiting.") |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,142 @@ | ||
| # BSD 2-Clause License | ||
| # | ||
| # Copyright (c) 2021-2024, Hewlett Packard Enterprise | ||
| # All rights reserved. | ||
| # | ||
| # Redistribution and use in source and binary forms, with or without | ||
| # modification, are permitted provided that the following conditions are met: | ||
| # | ||
| # 1. Redistributions of source code must retain the above copyright notice, this | ||
| # list of conditions and the following disclaimer. | ||
| # | ||
| # 2. Redistributions in binary form must reproduce the above copyright notice, | ||
| # this list of conditions and the following disclaimer in the documentation | ||
| # and/or other materials provided with the distribution. | ||
| # | ||
| # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | ||
| # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | ||
| # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE | ||
| # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE | ||
| # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL | ||
| # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR | ||
| # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER | ||
| # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, | ||
| # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | ||
| # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | ||
|
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||
| # isort: off | ||
| import dragon | ||
| from dragon import fli | ||
| from dragon.channels import Channel | ||
| import dragon.channels | ||
| from dragon.data.ddict.ddict import DDict | ||
| from dragon.globalservices.api_setup import connect_to_infrastructure | ||
| from dragon.utils import b64decode, b64encode | ||
|
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| # isort: on | ||
|
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| import argparse | ||
| import io | ||
|
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| import torch | ||
|
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| from smartsim.log import get_logger | ||
|
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| torch.set_num_interop_threads(16) | ||
| torch.set_num_threads(1) | ||
|
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| logger = get_logger("App") | ||
| logger.info("Started app") | ||
|
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| from collections import OrderedDict | ||
|
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| from smartsim.log import get_logger, log_to_file | ||
| from smartsim._core.mli.client.protoclient import ProtoClient | ||
|
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| logger = get_logger("App") | ||
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| CHECK_RESULTS_AND_MAKE_ALL_SLOWER = False | ||
|
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|
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| class ResNetWrapper: | ||
| """Wrapper around a pre-rained ResNet model.""" | ||
| def __init__(self, name: str, model: str): | ||
| """Initialize the instance. | ||
| :param name: The name to use for the model | ||
| :param model: The path to the pre-trained PyTorch model""" | ||
| self._model = torch.jit.load(model) | ||
| self._name = name | ||
| buffer = io.BytesIO() | ||
| scripted = torch.jit.trace(self._model, self.get_batch()) | ||
| torch.jit.save(scripted, buffer) | ||
| self._serialized_model = buffer.getvalue() | ||
|
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| def get_batch(self, batch_size: int = 32): | ||
| """Create a random batch of data with the correct dimensions to | ||
| invoke a ResNet model. | ||
| :param batch_size: The desired number of samples to produce | ||
| :returns: A PyTorch tensor""" | ||
| return torch.randn((batch_size, 3, 224, 224), dtype=torch.float32) | ||
|
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| @property | ||
| def model(self) -> bytes: | ||
| """The content of a model file. | ||
| :returns: The model bytes""" | ||
| return self._serialized_model | ||
|
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| @property | ||
| def name(self) -> str: | ||
| """The name applied to the model. | ||
| :returns: The name""" | ||
| return self._name | ||
|
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|
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| if __name__ == "__main__": | ||
|
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| parser = argparse.ArgumentParser("Mock application") | ||
| parser.add_argument("--device", default="cpu", type=str) | ||
| parser.add_argument("--log_max_batchsize", default=8, type=int) | ||
| args = parser.parse_args() | ||
|
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| resnet = ResNetWrapper("resnet50", f"resnet50.{args.device}.pt") | ||
|
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| client = ProtoClient(timing_on=True) | ||
| client.set_model(resnet.name, resnet.model) | ||
|
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| if CHECK_RESULTS_AND_MAKE_ALL_SLOWER: | ||
| # TODO: adapt to non-Nvidia devices | ||
| torch_device = args.device.replace("gpu", "cuda") | ||
| pt_model = torch.jit.load(io.BytesIO(initial_bytes=(resnet.model))).to( | ||
| torch_device | ||
| ) | ||
|
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| TOTAL_ITERATIONS = 100 | ||
|
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| for log2_bsize in range(args.log_max_batchsize + 1): | ||
| b_size: int = 2**log2_bsize | ||
| logger.info(f"Batch size: {b_size}") | ||
| for iteration_number in range(TOTAL_ITERATIONS + int(b_size == 1)): | ||
| logger.info(f"Iteration: {iteration_number}") | ||
| sample_batch = resnet.get_batch(b_size) | ||
| remote_result = client.run_model(resnet.name, sample_batch) | ||
| logger.info(client.perf_timer.get_last("total_time")) | ||
| if CHECK_RESULTS_AND_MAKE_ALL_SLOWER: | ||
| local_res = pt_model(sample_batch.to(torch_device)) | ||
| err_norm = torch.linalg.vector_norm( | ||
| torch.flatten(remote_result).to(torch_device) | ||
| - torch.flatten(local_res), | ||
| ord=1, | ||
| ).cpu() | ||
| res_norm = torch.linalg.vector_norm(remote_result, ord=1).item() | ||
| local_res_norm = torch.linalg.vector_norm(local_res, ord=1).item() | ||
| logger.info( | ||
| f"Avg norm of error {err_norm.item()/b_size} compared to result norm of {res_norm/b_size}:{local_res_norm/b_size}" | ||
| ) | ||
| torch.cuda.synchronize() | ||
|
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| client.perf_timer.print_timings(to_file=True) |
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