moved bert to qelos-util
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11
modeling.py
11
modeling.py
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@ -34,6 +34,10 @@ def gelu(x):
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return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
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def swish(x):
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return x * torch.sigmoid(x)
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class BertConfig(object):
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"""Configuration class to store the configuration of a `BertModel`.
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"""
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@ -60,7 +64,7 @@ class BertConfig(object):
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intermediate_size: The size of the "intermediate" (i.e., feed-forward)
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layer in the Transformer encoder.
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hidden_act: The non-linear activation function (function or string) in the
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encoder and pooler.
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encoder and pooler. If string, "gelu", "relu" and "swish" supported.
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hidden_dropout_prob: The dropout probabilitiy for all fully connected
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layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob: The dropout ratio for the attention
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@ -237,7 +241,8 @@ class BERTIntermediate(nn.Module):
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def __init__(self, config):
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super(BERTIntermediate, self).__init__()
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self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
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self.intermediate_act_fn = gelu
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act2fn = {"gelu": gelu, "relu": torch.nn.ReLU, "swish": swish}
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self.intermediate_act_fn = act2fn[config.hidden_act] if isinstance(config.hidden_act, str) else config.hidden_act
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def forward(self, hidden_states):
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hidden_states = self.dense(hidden_states)
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@ -355,7 +360,7 @@ class BertModel(nn.Module):
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all_encoder_layers = self.encoder(embedding_output, extended_attention_mask)
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sequence_output = all_encoder_layers[-1]
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pooled_output = self.pooler(sequence_output)
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return [embedding_output] + all_encoder_layers, pooled_output
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return all_encoder_layers, pooled_output
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class BertForSequenceClassification(nn.Module):
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"""BERT model for classification.
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@ -1,71 +0,0 @@
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import unittest
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import json
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import random
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import torch
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import numpy as np
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import modeling
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import convert_tf_checkpoint_to_pytorch
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import grouch
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class MyTest(unittest.TestCase):
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def test_loading_and_running(self):
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bertpath = "../../grouch/data/bert/bert-base/"
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configpath = bertpath + "bert_config.json"
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ckptpath = bertpath + "bert_model.ckpt"
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m = convert_tf_checkpoint_to_pytorch.convert(configpath, ckptpath)
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m.eval()
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# print(m)
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input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
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input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
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token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
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all_y, pool_y = m(input_ids, token_type_ids, input_mask)
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print(pool_y.shape)
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# np.save("_bert_ref_pool_out.npy", pool_y.detach().numpy())
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# np.save("_bert_ref_all_out.npy", torch.stack(all_y, 0).detach().numpy())
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config = grouch.TransformerBERT.load_config(configpath)
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gm = grouch.TransformerBERT.init_from_config(config)
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gm.load_weights_from_tf_checkpoint(ckptpath)
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gm.eval()
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g_all_y, g_pool_y = gm(input_ids, token_type_ids, input_mask)
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print(g_pool_y.shape)
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# check embeddings
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# print(m.embeddings)
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# print(gm.emb)
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# hugging_emb = m.embeddings(input_ids, token_type_ids)
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# grouch_emb = gm.emb(input_ids, token_type_ids)
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print((all_y[0] - g_all_y[0]).norm())
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# print(all_y[0][:, :, :10] - g_all_y[0][:, :, :10])
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self.assertTrue(np.allclose(all_y[0].detach().numpy(), g_all_y[0].detach().numpy(), atol=1e-7))
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print("embeddings good")
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print(m.encoder.layer[0])
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print(gm.encoder.layers[0])
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print("norm of diff at layer 1", (all_y[1] - g_all_y[1]).norm())
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# print(all_y[1][:, :, :10] - g_all_y[1][:, :, :10])
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self.assertTrue(np.allclose(all_y[1].detach().numpy(), g_all_y[1].detach().numpy(), atol=1e-6))
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# hugging_layer = m.encoder.layer[0]
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# grouch_layer = gm.encoder.layers[0]
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# print("comparing weights")
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# print((hugging_layer.attention.self.query.weight - grouch_layer.slf_attn.q_proj.weight).norm())
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# print((hugging_layer.attention.self.query.bias - grouch_layer.slf_attn.q_proj.bias).norm())
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# print((hugging_layer.attention.self.key.weight - grouch_layer.slf_attn.k_proj.weight).norm())
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# print((hugging_layer.attention.self.key.bias - grouch_layer.slf_attn.k_proj.bias).norm())
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# print((hugging_layer.attention.self.value.weight - grouch_layer.slf_attn.v_proj.weight).norm())
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# print((hugging_layer.attention.self.value.bias - grouch_layer.slf_attn.v_proj.bias).norm())
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# print((hugging_layer.attention.output.dense.weight - grouch_layer.slf_attn.vw_proj.weight).norm())
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# print((hugging_layer.attention.output.dense.bias - grouch_layer.slf_attn.vw_proj.bias).norm())
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print("norm of diff at last layer", (all_y[-1] - g_all_y[-1]).norm())
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# print(all_y[-1][:, :, :10] - g_all_y[-1][:, :, :10])
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self.assertTrue(np.allclose(all_y[-1].detach().numpy(), g_all_y[-1].detach().numpy(), atol=1e-4))
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