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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,11 @@ | ||
| # SPDX-License-Identifier: LGPL-3.0-or-later | ||
| import tensorflow as tf | ||
|
|
||
| if not tf.executing_eagerly(): | ||
| # TF disallow temporary eager execution | ||
| raise RuntimeError( | ||
| "Unfortunatly, jax2tf (requires eager execution) cannot be used with the " | ||
| "TensorFlow backend (disables eager execution). " | ||
| "If you are converting a model between different backends, " | ||
| "considering converting to the `.dp` format first." | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,172 @@ | ||
| # SPDX-License-Identifier: LGPL-3.0-or-later | ||
| import json | ||
|
|
||
| import tensorflow as tf | ||
| from jax.experimental import ( | ||
| jax2tf, | ||
| ) | ||
|
|
||
| from deepmd.jax.model.base_model import ( | ||
| BaseModel, | ||
| ) | ||
|
|
||
|
|
||
| def deserialize_to_file(model_file: str, data: dict) -> None: | ||
| """Deserialize the dictionary to a model file. | ||
| Parameters | ||
| ---------- | ||
| model_file : str | ||
| The model file to be saved. | ||
| data : dict | ||
| The dictionary to be deserialized. | ||
| """ | ||
| if model_file.endswith(".savedmodel"): | ||
| model = BaseModel.deserialize(data["model"]) | ||
| model_def_script = data["model_def_script"] | ||
| call_lower = model.call_lower | ||
|
|
||
| tf_model = tf.Module() | ||
|
|
||
| def exported_whether_do_atomic_virial(do_atomic_virial): | ||
| def call_lower_with_fixed_do_atomic_virial( | ||
| coord, atype, nlist, mapping, fparam, aparam | ||
| ): | ||
| return call_lower( | ||
| coord, | ||
| atype, | ||
| nlist, | ||
| mapping, | ||
| fparam, | ||
| aparam, | ||
| do_atomic_virial=do_atomic_virial, | ||
| ) | ||
|
|
||
| return jax2tf.convert( | ||
| call_lower_with_fixed_do_atomic_virial, | ||
| polymorphic_shapes=[ | ||
| "(nf, nloc + nghost, 3)", | ||
| "(nf, nloc + nghost)", | ||
| f"(nf, nloc, {model.get_nnei()})", | ||
| "(nf, nloc + nghost)", | ||
| f"(nf, {model.get_dim_fparam()})", | ||
| f"(nf, nloc, {model.get_dim_aparam()})", | ||
| ], | ||
| with_gradient=True, | ||
| ) | ||
|
|
||
| # Save a function that can take scalar inputs. | ||
| # We need to explicit set the function name, so C++ can find it. | ||
| @tf.function( | ||
| autograph=False, | ||
| input_signature=[ | ||
| tf.TensorSpec([None, None, 3], tf.float64), | ||
| tf.TensorSpec([None, None], tf.int32), | ||
| tf.TensorSpec([None, None, model.get_nnei()], tf.int64), | ||
| tf.TensorSpec([None, None], tf.int64), | ||
| tf.TensorSpec([None, model.get_dim_fparam()], tf.float64), | ||
| tf.TensorSpec([None, None, model.get_dim_aparam()], tf.float64), | ||
| ], | ||
| ) | ||
| def call_lower_without_atomic_virial( | ||
| coord, atype, nlist, mapping, fparam, aparam | ||
| ): | ||
| return exported_whether_do_atomic_virial(do_atomic_virial=False)( | ||
| coord, atype, nlist, mapping, fparam, aparam | ||
| ) | ||
|
|
||
| tf_model.call_lower = call_lower_without_atomic_virial | ||
|
|
||
| @tf.function( | ||
| autograph=False, | ||
| input_signature=[ | ||
| tf.TensorSpec([None, None, 3], tf.float64), | ||
| tf.TensorSpec([None, None], tf.int32), | ||
| tf.TensorSpec([None, None, model.get_nnei()], tf.int64), | ||
| tf.TensorSpec([None, None], tf.int64), | ||
| tf.TensorSpec([None, model.get_dim_fparam()], tf.float64), | ||
| tf.TensorSpec([None, None, model.get_dim_aparam()], tf.float64), | ||
| ], | ||
| ) | ||
| def call_lower_with_atomic_virial(coord, atype, nlist, mapping, fparam, aparam): | ||
| return exported_whether_do_atomic_virial(do_atomic_virial=True)( | ||
| coord, atype, nlist, mapping, fparam, aparam | ||
| ) | ||
|
|
||
| tf_model.call_lower_atomic_virial = call_lower_with_atomic_virial | ||
|
|
||
| # set functions to export other attributes | ||
| @tf.function | ||
| def get_type_map(): | ||
| return tf.constant(model.get_type_map(), dtype=tf.string) | ||
|
|
||
| tf_model.get_type_map = get_type_map | ||
|
|
||
| @tf.function | ||
| def get_rcut(): | ||
| return tf.constant(model.get_rcut(), dtype=tf.double) | ||
|
|
||
| tf_model.get_rcut = get_rcut | ||
|
|
||
| @tf.function | ||
| def get_dim_fparam(): | ||
| return tf.constant(model.get_dim_fparam(), dtype=tf.int64) | ||
|
|
||
| tf_model.get_dim_fparam = get_dim_fparam | ||
|
|
||
| @tf.function | ||
| def get_dim_aparam(): | ||
| return tf.constant(model.get_dim_aparam(), dtype=tf.int64) | ||
|
|
||
| tf_model.get_dim_aparam = get_dim_aparam | ||
|
|
||
| @tf.function | ||
| def get_sel_type(): | ||
| return tf.constant(model.get_sel_type(), dtype=tf.int64) | ||
|
|
||
| tf_model.get_sel_type = get_sel_type | ||
|
|
||
| @tf.function | ||
| def is_aparam_nall(): | ||
| return tf.constant(model.is_aparam_nall(), dtype=tf.bool) | ||
|
|
||
| tf_model.is_aparam_nall = is_aparam_nall | ||
|
|
||
| @tf.function | ||
| def model_output_type(): | ||
| return tf.constant(model.model_output_type(), dtype=tf.string) | ||
|
|
||
| tf_model.model_output_type = model_output_type | ||
|
|
||
| @tf.function | ||
| def mixed_types(): | ||
| return tf.constant(model.mixed_types(), dtype=tf.bool) | ||
|
|
||
| tf_model.mixed_types = mixed_types | ||
|
|
||
| if model.get_min_nbor_dist() is not None: | ||
|
|
||
| @tf.function | ||
| def get_min_nbor_dist(): | ||
| return tf.constant(model.get_min_nbor_dist(), dtype=tf.double) | ||
|
|
||
| tf_model.get_min_nbor_dist = get_min_nbor_dist | ||
|
|
||
| @tf.function | ||
| def get_sel(): | ||
| return tf.constant(model.get_sel(), dtype=tf.int64) | ||
|
|
||
| tf_model.get_sel = get_sel | ||
|
|
||
| @tf.function | ||
| def get_model_def_script(): | ||
| return tf.constant( | ||
| json.dumps(model_def_script, separators=(",", ":")), dtype=tf.string | ||
| ) | ||
|
|
||
| tf_model.get_model_def_script = get_model_def_script | ||
| tf.saved_model.save( | ||
| tf_model, | ||
| model_file, | ||
| options=tf.saved_model.SaveOptions(experimental_custom_gradients=True), | ||
| ) |
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