Adds Ascend platform adaptation code

deepmd/entrypoints/__init__.py

@@ -12,6 +12,7 @@
 from ..infer.model_devi import make_model_devi
 from .convert import convert
 from .neighbor_stat import neighbor_stat
+from .transfer_to_ascend import transfer_to_ascend
 
 __all__ = [
     "config",
@@ -25,4 +26,5 @@
     "make_model_devi",
     "convert",
     "neighbor_stat",
+    "transfer_to_ascend",
 ]

deepmd/entrypoints/convert.py

@@ -1,4 +1,4 @@
-from deepmd.utils.convert import convert_012_to_21, convert_10_to_21, convert_20_to_21, convert_13_to_21, convert_12_to_21 
+from deepmd.utils.convert import convert_012_to_21, convert_10_to_21, convert_20_to_21, convert_13_to_21, convert_12_to_21
 
 def convert(
     *,

deepmd/entrypoints/main.py

@@ -18,6 +18,7 @@
     make_model_devi,
     convert,
     neighbor_stat,
+    transfer_to_ascend
 )
 from deepmd.loggers import set_log_handles
 
@@ -139,6 +140,14 @@ def main_parser() -> argparse.ArgumentParser:
         type=str,
         help="the model after passing parameters",
     )
+    parser_transfer.add_argument(
+        "-a",
+        "--ascend-graph",
+        default="",
+        type=str,
+        help="the model with constant natoms input, which is onle used for Ascend platform",
+    )
+
 
     # * config parser ******************************************************************
     parser_train = subparsers.add_parser(
@@ -513,6 +522,48 @@ def main_parser() -> argparse.ArgumentParser:
         choices=['s1', 's2'],
         help="steps to train model of NVNMD: s1 (train CNN), s2 (train QNN)"
     )
+
+    # * transfer to ascend models  ***********************************************************
+    parser_trans_to_ascend = subparsers.add_parser(
+        'transfer-to-ascend',
+        parents=[parser_log, parser_mpi_log],
+        help='transfer original model to ascend NPU supported mix precision version',
+    )
+    parser_trans_to_ascend.add_argument(
+        'TO',
+        type = str,
+        default = 'mix_precision',
+        choices = ['mix_precision'],
+        help="The transfer type of transfer-to-ascend module",
+    )
+    parser_trans_to_ascend.add_argument(
+        '-i',
+        "--input-model",
+        default = "model.pb",
+        type=str, 
+		help = "the input model",
+    )
+    parser_trans_to_ascend.add_argument(
+        "-o",
+        "--output-model",
+        default = "Ascend_transfer.pb",
+        type=str, 
+		help='the output model',
+    )
+    parser_trans_to_ascend.add_argument(
+        "-c",
+        "--checkpoint-folder",
+        default = "model-transfer",
+        type=str, 
+		help='path to checkpoint folder',
+    )
+    parser_trans_to_ascend.add_argument(
+        "-t",
+        "--training-script",
+        type=str,
+        default=None,
+        help="The training script of the input frozen model",
+    )
     return parser
 
 
@@ -580,6 +631,8 @@ def main():
         neighbor_stat(**dict_args)
     elif args.command == "train-nvnmd":  # nvnmd
         train_nvnmd(**dict_args)
+    elif args.command == "transfer-to-ascend":
+        transfer_to_ascend(**dict_args)
     elif args.command is None:
         pass
     else:

deepmd/entrypoints/train.py

@@ -146,11 +146,14 @@ def _do_work(jdata: Dict[str, Any], run_opt: RunOptions, is_compress: bool = Fal
 
     # setup data modifier
     modifier = get_modifier(jdata["model"].get("modifier", None))
+
+    # get transfer info
+    is_ascend_transfer = jdata["model"].get("transfered_from_model", None)
 
     # decouple the training data from the model compress process
     train_data = None
     valid_data = None
-    if not is_compress:
+    if not is_compress and not is_ascend_transfer:
         # init data
         train_data = get_data(jdata["training"]["training_data"], rcut, ipt_type_map, modifier)
         train_data.print_summary("training")
@@ -162,7 +165,10 @@ def _do_work(jdata: Dict[str, Any], run_opt: RunOptions, is_compress: bool = Fal
     stop_batch = j_must_have(jdata["training"], "numb_steps")
     model.build(train_data, stop_batch)
 
-    if not is_compress:
+    if is_ascend_transfer:
+        model.save_transfered()
+        log.info("finished transfering")
+    elif not is_compress:
         # train the model with the provided systems in a cyclic way
         start_time = time.time()
         model.train(train_data, valid_data)

deepmd/entrypoints/transfer.py

@@ -5,6 +5,7 @@
 import re
 import numpy as np
 import logging
+import os
 
 __all__ = ["transfer"]
 
@@ -67,6 +68,12 @@ def transfer(*, old_model: str, raw_model: str, output: str, **kwargs):
     with tf.gfile.GFile(output, mode="wb") as f:
         f.write(new_graph_def.SerializeToString())
     log.info("the output model is saved in " + output)
+    dp_float_prec = os.environ.get("DP_INTERFACE_PREC", "high").lower()
+    if kwargs['ascend_graph']:
+        const_graph_def = modify_const_op(new_graph_def)
+        with tf.gfile.GFile(kwargs['ascend_graph'], mode="wb") as f:
+            f.write(const_graph_def.SerializeToString())
+        log.info("the dp test model is saved in " + kwargs['ascend_graph'])
 
 
 def load_graph(graph_name: str) -> tf.Graph:
@@ -89,6 +96,37 @@ def load_graph(graph_name: str) -> tf.Graph:
         tf.import_graph_def(graph_def, name="")
         return graph
 
+def modify_const_op(new_graph_def: tf.Graph) -> tf.Graph:
+    """modify natoms to constant.
+
+    Parameters
+    ----------
+    new_graph : tf.Graph 
+        orginal new graph
+    Returns :
+    -------
+    tf.Graph
+        natoms transfer to a const op for Ascend platform
+    """
+    for node in new_graph_def.node:
+        if "t_natoms" in node.name:
+            node.op = "Const"
+            natoms_shape = node.attr["shape"]
+            shape_val = [dim.size for dim in natoms_shape.shape.dim]
+            try:
+                natoms_list = np.loadtxt("natoms_val.txt")
+            except Exception as e:
+                explanation = "natoms_val.txt file is not exist, one shold put padding natoms in it. Values are separated by SPACE." 
+                log.error(explanation)
+                raise FileNotFoundError from e
+
+            assert shape_val[0] == len(natoms_list)
+            del node.attr["shape"]
+            node.attr["value"].CopyFrom(tf.AttrValue(tensor=tf.make_tensor_proto([int(i) for i in natoms_list],
+             tf.int32, [shape_val[0]])))
+            log.info(f"{node.name} is passed from a placeholder to a const")
+
+    return new_graph_def
 
 def transform_graph(raw_graph: tf.Graph, old_graph: tf.Graph) -> tf.Graph:
     """Trasform old graph into new.
@@ -131,15 +169,16 @@ def transform_graph(raw_graph: tf.Graph, old_graph: tf.Graph) -> tf.Graph:
             if old_graph_dtype == np.float64 or old_graph_dtype == np.float32:
                 if (len(tensor_shape) != 1) or (tensor_shape[0] != 1):
                     tensor = np.frombuffer(old_node.tensor_content, dtype = old_graph_dtype)
-                    tensor = tensor.astype(raw_graph_dtype)
-                    cp_attr.from_str(tensor)
                 else:
                     tensor = load_tensor(old_node, old_graph_dtype, raw_graph_dtype)
-                    cp_attr.from_array(tensor, tf.float16, [1])
 
-            elif old_graph_dtype[1] == "float16":
-                tensor = convertMatrix(np.array(old_node.half_val), tensor_shape)
-                cp_attr.from_array(tensor, raw_graph_dtype)
+                tensor = tf.make_tensor_proto(tensor, raw_graph_dtype, tensor_shape)
+                for i in range(len(tensor.half_val)):
+                    raw_node.half_val[i] = tensor.half_val[i]
+
+            elif old_graph_dtype == np.float16:
+                for i in range(len(old_node.half_val)):
+                    raw_node.half_val[i] = old_node.half_val[i]
 
         elif raw_graph_dtype == np.float64 or raw_graph_dtype == np.float32:
             if old_graph_dtype == np.float64 or old_graph_dtype == np.float32:

deepmd/entrypoints/transfer_to_ascend.py

@@ -0,0 +1,13 @@
+from deepmd.utils.transfer_to_ascend import mix_precision
+
+def transfer_to_ascend(
+    *,
+    TO: str,
+    input_model: str,
+    output_model: str,
+    **kwargs,
+):
+    if TO == 'mix_precision':
+        mix_precision(input_model, output_model, **kwargs)
+    else:
+        raise RuntimeError('unsupported transfering type' + FROM)

deepmd/env.py

@@ -32,6 +32,7 @@
     "GLOBAL_TF_FLOAT_PRECISION",
     "GLOBAL_NP_FLOAT_PRECISION",
     "GLOBAL_ENER_FLOAT_PRECISION",
+    "GLOBAL_ASCEND_OUT_PRECISION",
     "global_float_prec",
     "global_cvt_2_tf_float",
     "global_cvt_2_ener_float",
@@ -367,10 +368,15 @@ def _get_package_constants(
     GLOBAL_NP_FLOAT_PRECISION = np.float32
     GLOBAL_ENER_FLOAT_PRECISION = np.float64
     global_float_prec = "float"
+elif dp_float_prec == "ascend_mix":
+    GLOBAL_TF_FLOAT_PRECISION = tf.float32
+    GLOBAL_NP_FLOAT_PRECISION = np.float32
+    GLOBAL_ENER_FLOAT_PRECISION = np.float64
+    global_float_prec = "float"
 else:
     raise RuntimeError(
         "Unsupported float precision option: %s. Supported: high,"
-        "low. Please set precision with environmental variable "
+        "low and ascend_mix. Please set precision with environmental variable "
         "DP_INTERFACE_PREC." % dp_float_prec
     )