add option to select backends TF/PT

dpgen/generator/arginfo.py

@@ -87,6 +87,9 @@ def training_args() -> list[Argument]:
  list[dargs.Argument]
  List of training arguments.
  """
+ doc_train_backend = (
+ "The backend of the training. Currently only support tensorflow and pytorch."
+ )
  doc_numb_models = "Number of models to be trained in 00.train. 4 is recommend."
  doc_training_iter0_model_path = "The model used to init the first iter training. Number of element should be equal to numb_models."
  doc_training_init_model = "Iteration > 0, the model parameters will be initilized from the model trained at the previous iteration. Iteration == 0, the model parameters will be initialized from training_iter0_model_path."
@@ -123,6 +126,13 @@ def training_args() -> list[Argument]:
  doc_training_finetune_model = "At interation 0, finetune the model parameters from the given frozen models. Number of element should be equal to numb_models."
 
  return [
+ Argument(
+ "train_backend",
+ str,
+ optional=True,
+ default="tensorflow",
+ doc=doc_train_backend,
+ ),
  Argument("numb_models", int, optional=False, doc=doc_numb_models),
  Argument(
  "training_iter0_model_path",

dpgen/generator/run.py

@@ -125,6 +125,19 @@
 run_opt_file = os.path.join(ROOT_PATH, "generator/lib/calypso_run_opt.py")
 
 
+def _get_model_suffix(jdata) -> str:
+ """Return the model suffix based on the backend."""
+ suffix_map = {"tensorflow": ".pb", "pytorch": ".pth"}
+ backend = jdata.get("train_backend", "tensorflow")
+ if backend in suffix_map:
+ suffix = suffix_map[backend]
+ else:
+ raise ValueError(
+ f"The backend {backend} is not available. Supported backends are: 'tensorflow', 'pytorch'."
+ )
+ return suffix
+
+
 def get_job_names(jdata):
  jobkeys = []
  for ii in jdata.keys():
@@ -172,7 +185,7 @@
  return all(empty_sys)
 
 
-def copy_model(numb_model, prv_iter_index, cur_iter_index):
+def copy_model(numb_model, prv_iter_index, cur_iter_index, suffix=".pb"):
  cwd = os.getcwd()
  prv_train_path = os.path.join(make_iter_name(prv_iter_index), train_name)
  cur_train_path = os.path.join(make_iter_name(cur_iter_index), train_name)
@@ -184,7 +197,8 @@
  os.chdir(cur_train_path)
  os.symlink(os.path.relpath(prv_train_task), train_task_fmt % ii)
  os.symlink(
- os.path.join(train_task_fmt % ii, "frozen_model.pb"), "graph.%03d.pb" % ii
+ os.path.join(train_task_fmt % ii, f"frozen_model{suffix}"),
+ "graph.%03d%s" % (ii, suffix),
  )
  os.chdir(cwd)
  with open(os.path.join(cur_train_path, "copied"), "w") as fp:
@@ -315,18 +329,19 @@
  number_old_frames = 0
  number_new_frames = 0
 
+ suffix = _get_model_suffix(jdata)
  model_devi_engine = jdata.get("model_devi_engine", "lammps")
  if iter_index > 0 and _check_empty_iter(iter_index - 1, fp_task_min):
  log_task("prev data is empty, copy prev model")
- copy_model(numb_models, iter_index - 1, iter_index)
+ copy_model(numb_models, iter_index - 1, iter_index, suffix)
  return
  elif (
  model_devi_engine != "calypso"
  and iter_index > 0
  and _check_skip_train(model_devi_jobs[iter_index - 1])
  ):
  log_task("skip training at step %d " % (iter_index - 1))
- copy_model(numb_models, iter_index - 1, iter_index)
+ copy_model(numb_models, iter_index - 1, iter_index, suffix)
  return
  else:
  iter_name = make_iter_name(iter_index)
@@ -647,7 +662,9 @@
  )
  if copied_models is not None:
  for ii in range(len(copied_models)):
- _link_old_models(work_path, [copied_models[ii]], ii, basename="init.pb")
+ _link_old_models(
+ work_path, [copied_models[ii]], ii, basename=f"init{suffix}"
+ )
  # Copy user defined forward files
  symlink_user_forward_files(mdata=mdata, task_type="train", work_path=work_path)
  # HDF5 format for training data
@@ -699,6 +716,7 @@
  # print("debug:run_train:mdata", mdata)
  # load json param
  numb_models = jdata["numb_models"]
+ suffix = _get_model_suffix(jdata)
  # train_param = jdata['train_param']
  train_input_file = default_train_input_file
  training_reuse_iter = jdata.get("training_reuse_iter")
@@ -730,7 +748,11 @@
  "training_init_model, training_init_frozen_model, and training_finetune_model are mutually exclusive."
  )
 
- train_command = mdata.get("train_command", "dp")
+ train_command = mdata.get("train_command", "dp").strip()
+ # assert train_command == "dp", "The 'train_command' should be 'dp'" # the tests should be updated to run this command
+ if suffix == ".pth":
+ train_command += " --pt"
+
  train_resources = mdata["train_resources"]
 
  # paths
@@ -761,9 +783,9 @@
  if training_init_model:
  init_flag = " --init-model old/model.ckpt"
  elif training_init_frozen_model is not None:
- init_flag = " --init-frz-model old/init.pb"
+ init_flag = f" --init-frz-model old/init{suffix}"
  elif training_finetune_model is not None:
- init_flag = " --finetune old/init.pb"
+ init_flag = f" --finetune old/init{suffix}"
  command = f"{train_command} train {train_input_file}{extra_flags}"
  command = f"{{ if [ ! -f model.ckpt.index ]; then {command}{init_flag}; else {command} --restart model.ckpt; fi }}"
  command = f"/bin/sh -c {shlex.quote(command)}"
@@ -792,23 +814,35 @@
  if "srtab_file_path" in jdata.keys():
  forward_files.append(zbl_file)
  if training_init_model:
- forward_files += [
- os.path.join("old", "model.ckpt.meta"),
- os.path.join("old", "model.ckpt.index"),
- os.path.join("old", "model.ckpt.data-00000-of-00001"),
- ]
+ if suffix == ".pb":
+ forward_files += [
+ os.path.join("old", "model.ckpt.meta"),
+ os.path.join("old", "model.ckpt.index"),
+ os.path.join("old", "model.ckpt.data-00000-of-00001"),
+ ]
+ elif suffix == ".pth":
+ forward_files += [os.path.join("old", "model.ckpt.pt")]
  elif training_init_frozen_model is not None or training_finetune_model is not None:
- forward_files.append(os.path.join("old", "init.pb"))
+ forward_files.append(os.path.join("old", f"init{suffix}"))
 
- backward_files = ["frozen_model.pb", "lcurve.out", "train.log"]
- backward_files += [
- "model.ckpt.meta",
- "model.ckpt.index",
- "model.ckpt.data-00000-of-00001",
+ backward_files = [
+ f"frozen_model{suffix}",
+ "lcurve.out",
+ "train.log",
  "checkpoint",
  ]
  if jdata.get("dp_compress", False):
- backward_files.append("frozen_model_compressed.pb")
+ backward_files.append(f"frozen_model_compressed{suffix}")
+
+ if suffix == ".pb":
+ backward_files += [
+ "model.ckpt.meta",
+ "model.ckpt.index",
+ "model.ckpt.data-00000-of-00001",
+ ]
+ elif suffix == ".pth":
+ backward_files += ["model.ckpt.pt"]
+
  if not jdata.get("one_h5", False):
  init_data_sys_ = jdata["init_data_sys"]
  init_data_sys = []
@@ -879,13 +913,14 @@
  log_task("copied model, do not post train")
  return
  # symlink models
+ suffix = _get_model_suffix(jdata)
  for ii in range(numb_models):
- if not jdata.get("dp_compress", False):
- model_name = "frozen_model.pb"
- else:
- model_name = "frozen_model_compressed.pb"
+ model_name = f"frozen_model{suffix}"
+ if jdata.get("dp_compress", False):
+ model_name = f"frozen_model_compressed{suffix}"
+
+ ofile = os.path.join(work_path, "graph.%03d%s" % (ii, suffix))
  task_file = os.path.join(train_task_fmt % ii, model_name)
- ofile = os.path.join(work_path, "graph.%03d.pb" % ii)
  if os.path.isfile(ofile):
  os.remove(ofile)
  os.symlink(task_file, ofile)
@@ -1124,7 +1159,8 @@
  iter_name = make_iter_name(iter_index)
  train_path = os.path.join(iter_name, train_name)
  train_path = os.path.abspath(train_path)
- models = sorted(glob.glob(os.path.join(train_path, "graph*pb")))
+ suffix = _get_model_suffix(jdata)
+ models = sorted(glob.glob(os.path.join(train_path, f"graph*{suffix}")))
  work_path = os.path.join(iter_name, model_devi_name)
  create_path(work_path)
  if model_devi_engine == "calypso":
@@ -1305,7 +1341,8 @@
  iter_name = make_iter_name(iter_index)
  train_path = os.path.join(iter_name, train_name)
  train_path = os.path.abspath(train_path)
- models = sorted(glob.glob(os.path.join(train_path, "graph*pb")))
+ suffix = _get_model_suffix(jdata)
+ models = sorted(glob.glob(os.path.join(train_path, f"graph*{suffix}")))
  task_model_list = []
  for ii in models:
  task_model_list.append(os.path.join("..", os.path.basename(ii)))
@@ -1502,7 +1539,8 @@
  iter_name = make_iter_name(iter_index)
  train_path = os.path.join(iter_name, train_name)
  train_path = os.path.abspath(train_path)
- models = glob.glob(os.path.join(train_path, "graph*pb"))
+ suffix = _get_model_suffix(jdata)
+ models = sorted(glob.glob(os.path.join(train_path, f"graph*{suffix}")))
  task_model_list = []
  for ii in models:
  task_model_list.append(os.path.join("..", os.path.basename(ii)))
@@ -1644,7 +1682,8 @@
  iter_name = make_iter_name(iter_index)
  train_path = os.path.join(iter_name, train_name)
  train_path = os.path.abspath(train_path)
- models = glob.glob(os.path.join(train_path, "graph*pb"))
+ suffix = _get_model_suffix(jdata)
+ models = sorted(glob.glob(os.path.join(train_path, f"graph*{suffix}")))
  task_model_list = []
  for ii in models:
  task_model_list.append(os.path.join("..", os.path.basename(ii)))
@@ -1827,7 +1866,8 @@
  .replace("@qm_theory@", jdata["low_level"])
  .replace("@rcut@", str(jdata["cutoff"]))
  )
- models = sorted(glob.glob(os.path.join(train_path, "graph.*.pb")))
+ suffix = _get_model_suffix(jdata)
+ models = sorted(glob.glob(os.path.join(train_path, f"graph.*{suffix}")))
  task_model_list = []
  for ii in models:
  task_model_list.append(os.path.join("..", os.path.basename(ii)))
@@ -1935,7 +1975,9 @@
  run_tasks = [os.path.basename(ii) for ii in run_tasks_]
  # dlog.info("all_task is ", all_task)
  # dlog.info("run_tasks in run_model_deviation",run_tasks_)
- all_models = glob.glob(os.path.join(work_path, "graph*pb"))
+
+ suffix = _get_model_suffix(jdata)
+ all_models = glob.glob(os.path.join(work_path, f"graph*{suffix}"))
  model_names = [os.path.basename(ii) for ii in all_models]
 
  model_devi_engine = jdata.get("model_devi_engine", "lammps")

dpgen/simplify/simplify.py

@@ -31,6 +31,7 @@
  record_iter,
 )
 from dpgen.generator.run import (
+ _get_model_suffix,
  data_system_fmt,
  fp_name,
  fp_task_fmt,
@@ -186,7 +187,9 @@
  # link the model
  train_path = os.path.join(iter_name, train_name)
  train_path = os.path.abspath(train_path)
- models = glob.glob(os.path.join(train_path, "graph*pb"))
+ suffix = _get_model_suffix(jdata)
+ models = glob.glob(os.path.join(train_path, f"graph*{suffix}"))
+
  for mm in models:
  model_name = os.path.basename(mm)
  os.symlink(mm, os.path.join(work_path, model_name))