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moved bert to qelos-util

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lukovnikov 2018-11-06 18:21:44 +01:00
parent 4e52188433
commit bd91ae654f
3 changed files with 8 additions and 74 deletions
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0
hf_bert/__init__.py Normal file
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11
modeling.py
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@ -34,6 +34,10 @@ def gelu(x):
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
def swish(x):
return x * torch.sigmoid(x)
class BertConfig(object): class BertConfig(object):
"""Configuration class to store the configuration of a `BertModel`. """Configuration class to store the configuration of a `BertModel`.
""" """
@ -60,7 +64,7 @@ class BertConfig(object):
intermediate_size: The size of the "intermediate" (i.e., feed-forward) intermediate_size: The size of the "intermediate" (i.e., feed-forward)
layer in the Transformer encoder. layer in the Transformer encoder.
hidden_act: The non-linear activation function (function or string) in the hidden_act: The non-linear activation function (function or string) in the
encoder and pooler. encoder and pooler. If string, "gelu", "relu" and "swish" supported.
hidden_dropout_prob: The dropout probabilitiy for all fully connected hidden_dropout_prob: The dropout probabilitiy for all fully connected
layers in the embeddings, encoder, and pooler. layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob: The dropout ratio for the attention attention_probs_dropout_prob: The dropout ratio for the attention
@ -237,7 +241,8 @@ class BERTIntermediate(nn.Module):
def __init__(self, config): def __init__(self, config):
super(BERTIntermediate, self).__init__() super(BERTIntermediate, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size) self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
self.intermediate_act_fn = gelu act2fn = {"gelu": gelu, "relu": torch.nn.ReLU, "swish": swish}
self.intermediate_act_fn = act2fn[config.hidden_act] if isinstance(config.hidden_act, str) else config.hidden_act
def forward(self, hidden_states): def forward(self, hidden_states):
hidden_states = self.dense(hidden_states) hidden_states = self.dense(hidden_states)
@ -355,7 +360,7 @@ class BertModel(nn.Module):
all_encoder_layers = self.encoder(embedding_output, extended_attention_mask) all_encoder_layers = self.encoder(embedding_output, extended_attention_mask)
sequence_output = all_encoder_layers[-1] sequence_output = all_encoder_layers[-1]
pooled_output = self.pooler(sequence_output) pooled_output = self.pooler(sequence_output)
return [embedding_output] + all_encoder_layers, pooled_output return all_encoder_layers, pooled_output
class BertForSequenceClassification(nn.Module): class BertForSequenceClassification(nn.Module):
"""BERT model for classification. """BERT model for classification.

71
tests/mytest.py
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@ -1,71 +0,0 @@
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))