From 1c697b40116ddb5276450bf0dc4e6b870f06205d Mon Sep 17 00:00:00 2001
From: Paul O'Leary McCann <polm@dampfkraft.com>
Date: Tue, 8 Mar 2022 18:13:09 +0900
Subject: [PATCH] Remove references to config

Replaced with model arguments
---
 spacy/ml/models/coref.py | 43 ++++++++++++++++++++++------------------
 1 file changed, 24 insertions(+), 19 deletions(-)

diff --git a/spacy/ml/models/coref.py b/spacy/ml/models/coref.py
index 2e291aa2b..bfaa97060 100644
--- a/spacy/ml/models/coref.py
+++ b/spacy/ml/models/coref.py
@@ -461,34 +461,43 @@ import .coref_util_wl as utils
 # TODO rename to plain coref
 @registry.architectures("spacy.WLCoref.v1")
 def build_wl_coref_model(
-        #TODO add other hyperparams
     tok2vec: Model[List[Doc], List[Floats2d]],
+    embedding_size: int = 20,
+    hidden_size: int = 1024,
+    n_hidden_layers: int = 1, # TODO rename to "depth"?
+    dropout: float = 0.3,
+    # pairs to keep per mention after rough scoring
+    # TODO change to meaningful name
+    rough_k: int = 50,
+    # TODO is this not a training loop setting?
+    a_scoring_batch_size: int = 512,
+    # span predictor embeddings
+    sp_embedding_size: int = 64,
     ):
     
-    # TODO change to use passed in values for config
-    config = utils._load_config("/dev/null")
     with Model.define_operators({">>": chain}):
-
-        coref_scorer, span_predictor = configure_pytorch_modules(config)
         # TODO chain tok2vec with these models
+        # TODO fix device - should be automatic
+        device = "gpu:0"
         coref_scorer = PyTorchWrapper(
             CorefScorer(
-                config.device,
-                config.embedding_size,
-                config.hidden_size,
-                config.n_hidden_layers,
-                config.dropout_rate,
-                config.rough_k,
-                config.a_scoring_batch_size
+                device,
+                embedding_size,
+                hidden_size,
+                n_hidden_layers,
+                dropout_rate,
+                rough_k,
+                a_scoring_batch_size
             ),
             convert_inputs=convert_coref_scorer_inputs,
             convert_outputs=convert_coref_scorer_outputs
         )
         span_predictor = PyTorchWrapper(
             SpanPredictor(
-                1024,
-                config.sp_embedding_size,
-                config.device
+                # TODO this was hardcoded to 1024, check
+                hidden_size,
+                sp_embedding_size,
+                device
             ),
             convert_inputs=convert_span_predictor_inputs
         )
@@ -597,8 +606,6 @@ class CorefScorer(torch.nn.Module):
     """Combines all coref modules together to find coreferent spans.
 
     Attributes:
-        config (coref.config.Config): the model's configuration,
-            see config.toml for the details
         epochs_trained (int): number of epochs the model has been trained for
 
     Submodules (in the order of their usage in the pipeline):
@@ -622,8 +629,6 @@ class CorefScorer(torch.nn.Module):
         A newly created model is set to evaluation mode.
 
         Args:
-            config_path (str): the path to the toml file with the configuration
-            section (str): the selected section of the config file
             epochs_trained (int): the number of epochs finished
                 (useful for warm start)
         """