bert weight loading from tf

convert_tf_checkpoint_to_pytorch.py

@@ -26,35 +26,14 @@ import numpy as np
 
 from modeling import BertConfig, BertModel
 
-parser = argparse.ArgumentParser()
 
-## Required parameters
-parser.add_argument("--tf_checkpoint_path",
-                    default = None,
-                    type = str,
-                    required = True,
-                    help = "Path the TensorFlow checkpoint path.")
-parser.add_argument("--bert_config_file",
-                    default = None,
-                    type = str,
-                    required = True,
-                    help = "The config json file corresponding to the pre-trained BERT model. \n"
-                        "This specifies the model architecture.")
-parser.add_argument("--pytorch_dump_path",
-                    default = None,
-                    type = str,
-                    required = True,
-                    help = "Path to the output PyTorch model.")
-
-args = parser.parse_args()
-
-def convert():
+def convert(config_path, ckpt_path, out_path=None):
     # Initialise PyTorch model
-    config = BertConfig.from_json_file(args.bert_config_file)
+    config = BertConfig.from_json_file(config_path)
     model = BertModel(config)
 
     # Load weights from TF model
-    path = args.tf_checkpoint_path
+    path = ckpt_path
     print("Converting TensorFlow checkpoint from {}".format(path))
 
     init_vars = tf.train.list_variables(path)
@@ -99,7 +78,32 @@ def convert():
         pointer.data = torch.from_numpy(array)
 
     # Save pytorch-model
-    torch.save(model.state_dict(), args.pytorch_dump_path)
+    if out_path is not None:
+        torch.save(model.state_dict(), out_path)
+    return model
+
 
 if __name__ == "__main__":
-    convert()
+    parser = argparse.ArgumentParser()
+
+    ## Required parameters
+    parser.add_argument("--tf_checkpoint_path",
+                        default=None,
+                        type=str,
+                        required=True,
+                        help="Path the TensorFlow checkpoint path.")
+    parser.add_argument("--bert_config_file",
+                        default=None,
+                        type=str,
+                        required=True,
+                        help="The config json file corresponding to the pre-trained BERT model. \n"
+                             "This specifies the model architecture.")
+    parser.add_argument("--pytorch_dump_path",
+                        default=None,
+                        type=str,
+                        required=False,
+                        help="Path to the output PyTorch model.")
+
+    args = parser.parse_args()
+    print(args)
+    convert(args.bert_config_file, args.tf_checkpoint_path, args.pytorch_dump_path)

modeling.py

@@ -355,7 +355,7 @@ class BertModel(nn.Module):
         all_encoder_layers = self.encoder(embedding_output, extended_attention_mask)
         sequence_output = all_encoder_layers[-1]
         pooled_output = self.pooler(sequence_output)
-        return all_encoder_layers, pooled_output
+        return [embedding_output] + all_encoder_layers, pooled_output
 
 class BertForSequenceClassification(nn.Module):
     """BERT model for classification.

tests/mytest.py

+import unittest
+import json
+import random
+
+import torch
+import numpy as np
+
+import modeling
+import convert_tf_checkpoint_to_pytorch
+
+import grouch
+
+
+class MyTest(unittest.TestCase):
+    def test_loading_and_running(self):
+        bertpath = "../../grouch/data/bert/bert-base/"
+        configpath = bertpath + "bert_config.json"
+        ckptpath = bertpath + "bert_model.ckpt"
+        m = convert_tf_checkpoint_to_pytorch.convert(configpath, ckptpath)
+        m.eval()
+        # print(m)
+
+        input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+        input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+        token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+
+        all_y, pool_y = m(input_ids, token_type_ids, input_mask)
+        print(pool_y.shape)
+        # np.save("_bert_ref_pool_out.npy", pool_y.detach().numpy())
+        # np.save("_bert_ref_all_out.npy", torch.stack(all_y, 0).detach().numpy())
+
+        config = grouch.TransformerBERT.load_config(configpath)
+        gm = grouch.TransformerBERT.init_from_config(config)
+        gm.load_weights_from_tf_checkpoint(ckptpath)
+        gm.eval()
+
+        g_all_y, g_pool_y = gm(input_ids, token_type_ids, input_mask)
+        print(g_pool_y.shape)
+
+        # check embeddings
+        # print(m.embeddings)
+        # print(gm.emb)
+        # hugging_emb = m.embeddings(input_ids, token_type_ids)
+        # grouch_emb = gm.emb(input_ids, token_type_ids)
+
+        print((all_y[0] - g_all_y[0]).norm())
+        # print(all_y[0][:, :, :10] - g_all_y[0][:, :, :10])
+        self.assertTrue(np.allclose(all_y[0].detach().numpy(), g_all_y[0].detach().numpy(), atol=1e-7))
+        print("embeddings good")
+
+        print(m.encoder.layer[0])
+        print(gm.encoder.layers[0])
+        print("norm of diff at layer 1", (all_y[1] - g_all_y[1]).norm())
+        # print(all_y[1][:, :, :10] - g_all_y[1][:, :, :10])
+        self.assertTrue(np.allclose(all_y[1].detach().numpy(), g_all_y[1].detach().numpy(), atol=1e-6))
+
+        # hugging_layer = m.encoder.layer[0]
+        # grouch_layer = gm.encoder.layers[0]
+        # print("comparing weights")
+        # print((hugging_layer.attention.self.query.weight - grouch_layer.slf_attn.q_proj.weight).norm())
+        # print((hugging_layer.attention.self.query.bias - grouch_layer.slf_attn.q_proj.bias).norm())
+        # print((hugging_layer.attention.self.key.weight - grouch_layer.slf_attn.k_proj.weight).norm())
+        # print((hugging_layer.attention.self.key.bias - grouch_layer.slf_attn.k_proj.bias).norm())
+        # print((hugging_layer.attention.self.value.weight - grouch_layer.slf_attn.v_proj.weight).norm())
+        # print((hugging_layer.attention.self.value.bias - grouch_layer.slf_attn.v_proj.bias).norm())
+        # print((hugging_layer.attention.output.dense.weight - grouch_layer.slf_attn.vw_proj.weight).norm())
+        # print((hugging_layer.attention.output.dense.bias - grouch_layer.slf_attn.vw_proj.bias).norm())
+
+        print("norm of diff at last layer", (all_y[-1] - g_all_y[-1]).norm())
+        # print(all_y[-1][:, :, :10] - g_all_y[-1][:, :, :10])
+        self.assertTrue(np.allclose(all_y[-1].detach().numpy(), g_all_y[-1].detach().numpy(), atol=1e-4))
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