rename init_tok2vec to resume

examples/experiments/onto-joint/pretrain.cfg

@@ -46,7 +46,6 @@ learn_rate = 0.001
 
 [pretraining]
 max_epochs = 1000
-start_epoch = 0
 min_length = 5
 max_length = 500
 dropout = 0.2
@@ -54,7 +53,6 @@ n_save_every = null
 batch_size = 3000
 seed = ${training:seed}
 use_pytorch_for_gpu_memory = ${training:use_pytorch_for_gpu_memory}
-init_tok2vec = null
 
 [pretraining.model]
 @architectures = "spacy.HashEmbedCNN.v1"

spacy/cli/pretrain.py

@@ -16,7 +16,6 @@ from ..tokens import Doc
 from ..attrs import ID, HEAD
 from .. import util
 from ..gold import Example
-from .deprecated_pretrain import _load_pretrained_tok2vec  # TODO
 
 
 @plac.annotations(
@@ -26,7 +25,10 @@ from .deprecated_pretrain import _load_pretrained_tok2vec  # TODO
     output_dir=("Directory to write models to on each epoch", "positional", None, Path),
     config_path=("Path to config file", "positional", None, Path),
     use_gpu=("Use GPU", "option", "g", int),
-    # fmt: on
+    resume_path=("Path to pretrained weights from which to resume pretraining", "option","r", Path),
+    epoch_resume=("The epoch to resume counting from when using '--resume_path'. Prevents unintended overwriting of existing weight files.","option", "er", int),
+
+# fmt: on
 )
 def pretrain(
     texts_loc,
@@ -34,6 +36,8 @@ def pretrain(
     config_path,
     output_dir,
     use_gpu=-1,
+    resume_path=None,
+    epoch_resume=None,
 ):
     """
     Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
@@ -66,11 +70,19 @@ def pretrain(
         use_pytorch_for_gpu_memory()
 
     if output_dir.exists() and [p for p in output_dir.iterdir()]:
-        msg.warn(
+        if resume_path:
-            "Output directory is not empty",
+            msg.warn(
-            "It is better to use an empty directory or refer to a new output path, "
+                "Output directory is not empty. ",
-            "then the new directory will be created for you.",
+                "If you're resuming a run from a previous "
-        )
+                "model, the old models for the consecutive epochs will be overwritten "
+                "with the new ones.",
+            )
+        else:
+            msg.warn(
+                "Output directory is not empty. ",
+                "It is better to use an empty directory or refer to a new output path, "
+                "then the new directory will be created for you.",
+            )
     if not output_dir.exists():
         output_dir.mkdir()
         msg.good(f"Created output directory: {output_dir}")
@@ -92,7 +104,7 @@ def pretrain(
         msg.good("Loaded input texts")
         random.shuffle(texts)
     else:  # reading from stdin
-        msg.text("Reading input text from stdin...")
+        msg.info("Reading input text from stdin...")
         texts = srsly.read_jsonl("-")
 
     with msg.loading(f"Loading model '{vectors_model}'..."):
@@ -101,35 +113,36 @@ def pretrain(
     tok2vec = pretrain_config["model"]
     model = create_pretraining_model(nlp, tok2vec)
     optimizer = pretrain_config["optimizer"]
-    init_tok2vec = pretrain_config["init_tok2vec"]
-    epoch_start = pretrain_config["epoch_start"]
 
-    # Load in pretrained weights - TODO test
+    # Load in pretrained weights to resume from
-    if init_tok2vec is not None:
+    if resume_path is not None:
-        components = _load_pretrained_tok2vec(nlp, init_tok2vec)
+        msg.info(f"Resume training tok2vec from: {resume_path}")
-        msg.text(f"Loaded pretrained tok2vec for: {components}")
+        with resume_path.open("rb") as file_:
+            weights_data = file_.read()
+            model.get_ref("tok2vec").from_bytes(weights_data)
         # Parse the epoch number from the given weight file
-        model_name = re.search(r"model\d+\.bin", str(init_tok2vec))
+        model_name = re.search(r"model\d+\.bin", str(resume_path))
         if model_name:
             # Default weight file name so read epoch_start from it by cutting off 'model' and '.bin'
-            epoch_start = int(model_name.group(0)[5:][:-4]) + 1
+            epoch_resume = int(model_name.group(0)[5:][:-4]) + 1
+            msg.info(f"Resuming from epoch: {epoch_resume}")
         else:
-            if not epoch_start:
+            if not epoch_resume:
                 msg.fail(
-                    "You have to use the epoch_start setting when using a renamed weight file for init_tok2vec",
+                    "You have to use the --epoch_resume setting when using a renamed weight file for --resume_path",
                     exits=True,
                 )
-            elif epoch_start < 0:
+            elif epoch_resume < 0:
                 msg.fail(
-                    f"The setting epoch_start has to be greater or equal to 0. {epoch_start} is invalid",
+                    f"The setting --epoch_resume has to be greater or equal to 0. {epoch_resume} is invalid",
                     exits=True,
                 )
     else:
-        # Without 'init-tok2vec' the 'epoch_start' setting is ignored
+        # Without 'resume_path' the 'epoch_resume' setting is ignored
-        epoch_start = 0
+        epoch_resume = 0
 
     tracker = ProgressTracker(frequency=10000)
-    msg.divider(f"Pre-training tok2vec layer - starting at epoch {epoch_start}")
+    msg.divider(f"Pre-training tok2vec layer - starting at epoch {epoch_resume}")
     row_settings = {"widths": (3, 10, 10, 6, 4), "aligns": ("r", "r", "r", "r", "r")}
     msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)
 
@@ -149,7 +162,7 @@ def pretrain(
 
     skip_counter = 0
     loss_func = pretrain_config["loss_func"]
-    for epoch in range(epoch_start, pretrain_config["max_epochs"]):
+    for epoch in range(epoch_resume, pretrain_config["max_epochs"]):
         examples = [Example(doc=text) for text in texts]
         batches = util.minibatch_by_words(examples, size=pretrain_config["batch_size"])
         for batch_id, batch in enumerate(batches):