From c707cd4336ce8f02ddf996271e2acd6e562641f1 Mon Sep 17 00:00:00 2001 From: yh_cc Date: Tue, 3 Nov 2020 16:41:22 +0800 Subject: [PATCH 1/2] =?UTF-8?q?CRF=E7=BB=B4=E7=89=B9=E6=AF=94=E8=A7=A3?= =?UTF-8?q?=E7=A0=81BUG=E4=BF=AE=E5=A4=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- fastNLP/io/pipe/cws.py | 2 +- fastNLP/modules/decoder/crf.py | 51 +++-- test/data_for_tests/modules/decoder/crf.json | 1 + test/modules/decoder/test_CRF.py | 202 +++++++++++++------ 4 files changed, 179 insertions(+), 77 deletions(-) create mode 100644 test/data_for_tests/modules/decoder/crf.json diff --git a/fastNLP/io/pipe/cws.py b/fastNLP/io/pipe/cws.py index c3aab4e6..3849a34b 100644 --- a/fastNLP/io/pipe/cws.py +++ b/fastNLP/io/pipe/cws.py @@ -122,7 +122,7 @@ def _find_and_replace_digit_spans(line): otherwise unkdgt """ new_line = '' - pattern = '\d[\d\\.﹒·]*(?=[\u4e00-\u9fff ，％%,。！<－“])' + pattern = r'\d[\d\\.﹒·]*(?=[\u4e00-\u9fff ，％%,。！<－“])' prev_end = 0 for match in re.finditer(pattern, line): start, end = match.span() diff --git a/fastNLP/modules/decoder/crf.py b/fastNLP/modules/decoder/crf.py index b5ffa35d..0a05f6f4 100644 --- a/fastNLP/modules/decoder/crf.py +++ b/fastNLP/modules/decoder/crf.py @@ -198,8 +198,18 @@ class ConditionalRandomField(nn.Module): constrain = torch.zeros(num_tags + 2, num_tags + 2) else: constrain = torch.full((num_tags + 2, num_tags + 2), fill_value=-10000.0, dtype=torch.float) + has_start = False + has_end = False for from_tag_id, to_tag_id in allowed_transitions: constrain[from_tag_id, to_tag_id] = 0 + if from_tag_id==num_tags: + has_start = True + if to_tag_id==num_tags+1: + has_end = True + if not has_start: + constrain[num_tags, :].fill_(0) + if not has_end: + constrain[:, num_tags+1].fill_(0) self._constrain = nn.Parameter(constrain, requires_grad=False) initial_parameter(self, initial_method) @@ -290,14 +300,15 @@ class ConditionalRandomField(nn.Module): scores: torch.FloatTensor, size为(batch_size,), 对应每个最优路径的分数。 """ - batch_size, seq_len, n_tags = logits.size() + batch_size, max_len, n_tags = logits.size() + seq_len = mask.long().sum(1) logits = logits.transpose(0, 1).data # L, B, H mask = mask.transpose(0, 1).data.eq(True) # L, B flip_mask = mask.eq(False) # dp - vpath = logits.new_zeros((seq_len, batch_size, n_tags), dtype=torch.long) - vscore = logits[0] + vpath = logits.new_zeros((max_len, batch_size, n_tags), dtype=torch.long) + vscore = logits[0] # bsz x n_tags transitions = self._constrain.data.clone() transitions[:n_tags, :n_tags] += self.trans_m.data if self.include_start_end_trans: @@ -305,36 +316,44 @@ class ConditionalRandomField(nn.Module): transitions[:n_tags, n_tags + 1] += self.end_scores.data vscore += transitions[n_tags, :n_tags] + trans_score = transitions[:n_tags, :n_tags].view(1, n_tags, n_tags).data - for i in range(1, seq_len): + end_trans_score = transitions[:n_tags, n_tags+1].view(1, 1, n_tags).repeat(batch_size, 1, 1) # bsz, 1, n_tags + + # 针对长度为1的句子 + vscore += transitions[:n_tags, n_tags+1].view(1, n_tags).repeat(batch_size, 1) \ + .masked_fill(seq_len.ne(1).view(-1, 1), 0) + for i in range(1, max_len): prev_score = vscore.view(batch_size, n_tags, 1) cur_score = logits[i].view(batch_size, 1, n_tags) + trans_score - score = prev_score + cur_score.masked_fill(flip_mask[i].view(batch_size, 1, 1), 0) + score = prev_score + cur_score.masked_fill(flip_mask[i].view(batch_size, 1, 1), 0) # bsz x n_tag x n_tag + # 需要考虑当前位置是该序列的最后一个 + score += end_trans_score.masked_fill(seq_len.ne(i+1).view(-1, 1, 1), 0) + best_score, best_dst = score.max(1) vpath[i] = best_dst - vscore = best_score - - if self.include_start_end_trans: - vscore += transitions[:n_tags, n_tags + 1].view(1, -1) + # 由于最终是通过last_tags回溯，需要保持每个位置的vscore情况 + vscore = best_score.masked_fill(flip_mask[i].view(batch_size, 1), 0) + \ + vscore.masked_fill(mask[i].view(batch_size, 1), 0) # backtrace batch_idx = torch.arange(batch_size, dtype=torch.long, device=logits.device) - seq_idx = torch.arange(seq_len, dtype=torch.long, device=logits.device) - lens = (mask.long().sum(0) - 1) + seq_idx = torch.arange(max_len, dtype=torch.long, device=logits.device) + lens = (seq_len - 1) # idxes [L, B], batched idx from seq_len-1 to 0 - idxes = (lens.view(1, -1) - seq_idx.view(-1, 1)) % seq_len + idxes = (lens.view(1, -1) - seq_idx.view(-1, 1)) % max_len - ans = logits.new_empty((seq_len, batch_size), dtype=torch.long) + ans = logits.new_empty((max_len, batch_size), dtype=torch.long) ans_score, last_tags = vscore.max(1) ans[idxes[0], batch_idx] = last_tags - for i in range(seq_len - 1): + for i in range(max_len - 1): last_tags = vpath[idxes[i], batch_idx, last_tags] ans[idxes[i + 1], batch_idx] = last_tags ans = ans.transpose(0, 1) if unpad: paths = [] - for idx, seq_len in enumerate(lens): - paths.append(ans[idx, :seq_len + 1].tolist()) + for idx, max_len in enumerate(lens): + paths.append(ans[idx, :max_len + 1].tolist()) else: paths = ans return paths, ans_score diff --git a/test/data_for_tests/modules/decoder/crf.json b/test/data_for_tests/modules/decoder/crf.json new file mode 100644 index 00000000..ff2d6689 --- /dev/null +++ b/test/data_for_tests/modules/decoder/crf.json @@ -0,0 +1 @@ +{"bio_logits": [[[-1.8154915571212769, -1.3753865957260132, -10001.513671875, -1.619813084602356, -10001.79296875], [-1.742034673690796, -1.5048011541366577, -2.042131185531616, -1.2594754695892334, -1.6648437976837158], [-1.5522804260253906, -1.2926381826400757, -1.8607124090194702, -1.6692707538604736, -1.7734650373458862], [-1.6101375818252563, -1.3285458087921143, -1.7735439538955688, -1.5734118223190308, -1.8438279628753662], [-1.6522153615951538, -1.2640260457992554, -1.9092718362808228, -1.6192445755004883, -1.7168875932693481], [-1.4932769536972046, -1.4628725051879883, -1.9623159170150757, -1.497014045715332, -1.7177777290344238], [-1.8419824838638306, -2.1428799629211426, -1.4285861253738403, -1.2972710132598877, 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-16.7561], "bmes_path": [[3], [7, 3, 4, 6], [3], [3, 4, 5, 6, 7, 3, 4, 5, 6, 7]], "bmes_trans_m": [[0.47934335470199585, -0.2151593416929245, -0.12467780709266663, -0.44244644045829773, 0.16480575501918793, -0.006573359947651625, -1.187401294708252, -0.17424514889717102], [-0.03494556248188019, -0.8173441290855408, -0.2682552933692932, 0.18933893740177155, 0.2203899323940277, 0.3905894160270691, -0.007638207171112299, 0.19527725875377655], [-0.2779119908809662, -0.37053248286247253, 0.34394705295562744, -0.26433902978897095, -0.0001995275670196861, -0.39156094193458557, -0.035449881106615067, 0.02454843744635582], [-0.01391045656055212, 0.3419516384601593, -0.48559853434562683, -0.5893992781639099, 0.9119477272033691, 0.1731061041355133, -0.15039317309856415, 0.1523006409406662], [0.4866299033164978, 0.28264448046684265, -0.25895795226097107, 0.0404033362865448, -0.060920555144548416, 0.12364576756954193, 0.1294233351945877, 0.2434755265712738], [-0.04159824922680855, 0.25353407859802246, 0.12913571298122406, -0.036356933414936066, -0.18522876501083374, -0.5329958200454712, 0.2505933344364166, 0.26512718200683594], [-0.2509276270866394, 0.3572998046875, 0.01873799040913582, -0.30620086193084717, -0.09893298894166946, -0.37399813532829285, -0.6530448198318481, -0.17514197528362274], [-0.29702028632164, 0.680363118648529, -0.6010262370109558, 0.17669369280338287, 0.45010149478912354, -0.1026386097073555, 0.34120017290115356, -0.04910941794514656]], "bmes_seq_lens": [1, 4, 1, 10]} \ No newline at end of file diff --git a/test/modules/decoder/test_CRF.py b/test/modules/decoder/test_CRF.py index 85173669..55548a41 100644 --- a/test/modules/decoder/test_CRF.py +++ b/test/modules/decoder/test_CRF.py @@ -132,68 +132,150 @@ class TestCRF(unittest.TestCase): self.assertSetEqual(expected_res, set( allowed_transitions(vocab, include_start_end=True))) + # def test_case2(self): + # # 测试CRF能否避免解码出非法跃迁, 使用allennlp做了验证。 + # pass + # import torch + # from fastNLP import seq_len_to_mask + # + # labels = ['O'] + # for label in ['X', 'Y']: + # for tag in 'BI': + # labels.append('{}-{}'.format(tag, label)) + # id2label = {idx: label for idx, label in enumerate(labels)} + # num_tags = len(id2label) + # max_len = 10 + # batch_size = 4 + # bio_logits = torch.nn.functional.softmax(torch.rand(size=(batch_size, max_len, num_tags)), dim=-1).log() + # from allennlp.modules.conditional_random_field import ConditionalRandomField, allowed_transitions + # allen_CRF = ConditionalRandomField(num_tags=num_tags, constraints=allowed_transitions('BIO', id2label), + # include_start_end_transitions=False) + # bio_trans_m = allen_CRF.transitions + # bio_seq_lens = torch.randint(1, max_len, size=(batch_size,)) + # bio_seq_lens[0] = 1 + # bio_seq_lens[-1] = max_len + # mask = seq_len_to_mask(bio_seq_lens) + # allen_res = allen_CRF.viterbi_tags(bio_logits, mask) + # + # from fastNLP.modules.decoder.crf import ConditionalRandomField, allowed_transitions + # fast_CRF = ConditionalRandomField(num_tags=num_tags, allowed_transitions=allowed_transitions(id2label, + # include_start_end=True)) + # fast_CRF.trans_m = bio_trans_m + # fast_res = fast_CRF.viterbi_decode(bio_logits, mask, unpad=True) + # bio_scores = [round(score, 4) for _, score in allen_res] + # # score equal + # self.assertListEqual(bio_scores, [round(s, 4) for s in fast_res[1].tolist()]) + # # seq equal + # bio_path = [_ for _, score in allen_res] + # self.assertListEqual(bio_path, fast_res[0]) + # + # labels = [] + # for label in ['X', 'Y']: + # for tag in 'BMES': + # labels.append('{}-{}'.format(tag, label)) + # id2label = {idx: label for idx, label in enumerate(labels)} + # num_tags = len(id2label) + # + # from allennlp.modules.conditional_random_field import ConditionalRandomField, allowed_transitions + # allen_CRF = ConditionalRandomField(num_tags=num_tags, constraints=allowed_transitions('BMES', id2label), + # include_start_end_transitions=False) + # bmes_logits = torch.nn.functional.softmax(torch.rand(size=(batch_size, max_len, num_tags)), dim=-1).log() + # bmes_trans_m = allen_CRF.transitions + # bmes_seq_lens = torch.randint(1, max_len, size=(batch_size,)) + # bmes_seq_lens[0] = 1 + # bmes_seq_lens[-1] = max_len + # mask = seq_len_to_mask(bmes_seq_lens) + # allen_res = allen_CRF.viterbi_tags(bmes_logits, mask) + # + # from fastNLP.modules.decoder.crf import ConditionalRandomField, allowed_transitions + # fast_CRF = ConditionalRandomField(num_tags=num_tags, allowed_transitions=allowed_transitions(id2label, + # encoding_type='BMES', + # include_start_end=True)) + # fast_CRF.trans_m = bmes_trans_m + # fast_res = fast_CRF.viterbi_decode(bmes_logits, mask, unpad=True) + # # score equal + # bmes_scores = [round(score, 4) for _, score in allen_res] + # self.assertListEqual(bmes_scores, [round(s, 4) for s in fast_res[1].tolist()]) + # # seq equal + # bmes_path = [_ for _, score in allen_res] + # self.assertListEqual(bmes_path, fast_res[0]) + # + # data = { + # 'bio_logits': bio_logits.tolist(), + # 'bio_scores': bio_scores, + # 'bio_path': bio_path, + # 'bio_trans_m': bio_trans_m.tolist(), + # 'bio_seq_lens': bio_seq_lens.tolist(), + # 'bmes_logits': bmes_logits.tolist(), + # 'bmes_scores': bmes_scores, + # 'bmes_path': bmes_path, + # 'bmes_trans_m': bmes_trans_m.tolist(), + # 'bmes_seq_lens': bmes_seq_lens.tolist(), + # } + # + # with open('weights.json', 'w') as f: + # import json + # json.dump(data, f) def test_case2(self): - # 测试CRF能否避免解码出非法跃迁, 使用allennlp做了验证。 - pass - # import torch - # from fastNLP.modules.decoder.crf import seq_len_to_byte_mask - # - # labels = ['O'] - # for label in ['X', 'Y']: - # for tag in 'BI': - # labels.append('{}-{}'.format(tag, label)) - # id2label = {idx: label for idx, label in enumerate(labels)} - # num_tags = len(id2label) - # - # from allennlp.modules.conditional_random_field import ConditionalRandomField, allowed_transitions - # allen_CRF = ConditionalRandomField(num_tags=num_tags, constraints=allowed_transitions('BIO', id2label), - # include_start_end_transitions=False) - # batch_size = 3 - # logits = torch.nn.functional.softmax(torch.rand(size=(batch_size, 20, num_tags))).log() - # trans_m = allen_CRF.transitions - # seq_lens = torch.randint(1, 20, size=(batch_size,)) - # seq_lens[-1] = 20 - # mask = seq_len_to_byte_mask(seq_lens) - # allen_res = allen_CRF.viterbi_tags(logits, mask) - # - # from fastNLP.modules.decoder.crf import ConditionalRandomField, allowed_transitions - # fast_CRF = ConditionalRandomField(num_tags=num_tags, allowed_transitions=allowed_transitions(id2label)) - # fast_CRF.trans_m = trans_m - # fast_res = fast_CRF.viterbi_decode(logits, mask, get_score=True, unpad=True) - # # score equal - # self.assertListEqual([score for _, score in allen_res], fast_res[1]) - # # seq equal - # self.assertListEqual([_ for _, score in allen_res], fast_res[0]) - # - # - # labels = [] - # for label in ['X', 'Y']: - # for tag in 'BMES': - # labels.append('{}-{}'.format(tag, label)) - # id2label = {idx: label for idx, label in enumerate(labels)} - # num_tags = len(id2label) - # - # from allennlp.modules.conditional_random_field import ConditionalRandomField, allowed_transitions - # allen_CRF = ConditionalRandomField(num_tags=num_tags, constraints=allowed_transitions('BMES', id2label), - # include_start_end_transitions=False) - # batch_size = 3 - # logits = torch.nn.functional.softmax(torch.rand(size=(batch_size, 20, num_tags))).log() - # trans_m = allen_CRF.transitions - # seq_lens = torch.randint(1, 20, size=(batch_size,)) - # seq_lens[-1] = 20 - # mask = seq_len_to_byte_mask(seq_lens) - # allen_res = allen_CRF.viterbi_tags(logits, mask) - # - # from fastNLP.modules.decoder.crf import ConditionalRandomField, allowed_transitions - # fast_CRF = ConditionalRandomField(num_tags=num_tags, allowed_transitions=allowed_transitions(id2label, - # encoding_type='BMES')) - # fast_CRF.trans_m = trans_m - # fast_res = fast_CRF.viterbi_decode(logits, mask, get_score=True, unpad=True) - # # score equal - # self.assertListEqual([score for _, score in allen_res], fast_res[1]) - # # seq equal - # self.assertListEqual([_ for _, score in allen_res], fast_res[0]) + # 测试CRF是否正常work。 + import json + import torch + from fastNLP import seq_len_to_mask + + with open('test/data_for_tests/modules/decoder/crf.json', 'r') as f: + data = json.load(f) + + bio_logits = torch.FloatTensor(data['bio_logits']) + bio_scores = data['bio_scores'] + bio_path = data['bio_path'] + bio_trans_m = torch.FloatTensor(data['bio_trans_m']) + bio_seq_lens = torch.LongTensor(data['bio_seq_lens']) + + bmes_logits = torch.FloatTensor(data['bmes_logits']) + bmes_scores = data['bmes_scores'] + bmes_path = data['bmes_path'] + bmes_trans_m = torch.FloatTensor(data['bmes_trans_m']) + bmes_seq_lens = torch.LongTensor(data['bmes_seq_lens']) + + labels = ['O'] + for label in ['X', 'Y']: + for tag in 'BI': + labels.append('{}-{}'.format(tag, label)) + id2label = {idx: label for idx, label in enumerate(labels)} + num_tags = len(id2label) + + mask = seq_len_to_mask(bio_seq_lens) + + from fastNLP.modules.decoder.crf import ConditionalRandomField, allowed_transitions + fast_CRF = ConditionalRandomField(num_tags=num_tags, allowed_transitions=allowed_transitions(id2label, + include_start_end=True)) + fast_CRF.trans_m.data = bio_trans_m + fast_res = fast_CRF.viterbi_decode(bio_logits, mask, unpad=True) + # score equal + self.assertListEqual(bio_scores, [round(s, 4) for s in fast_res[1].tolist()]) + # seq equal + self.assertListEqual(bio_path, fast_res[0]) + + labels = [] + for label in ['X', 'Y']: + for tag in 'BMES': + labels.append('{}-{}'.format(tag, label)) + id2label = {idx: label for idx, label in enumerate(labels)} + num_tags = len(id2label) + + mask = seq_len_to_mask(bmes_seq_lens) + + from fastNLP.modules.decoder.crf import ConditionalRandomField, allowed_transitions + fast_CRF = ConditionalRandomField(num_tags=num_tags, allowed_transitions=allowed_transitions(id2label, + encoding_type='BMES', + include_start_end=True)) + fast_CRF.trans_m.data = bmes_trans_m + fast_res = fast_CRF.viterbi_decode(bmes_logits, mask, unpad=True) + # score equal + self.assertListEqual(bmes_scores, [round(s, 4) for s in fast_res[1].tolist()]) + # seq equal + self.assertListEqual(bmes_path, fast_res[0]) def test_case3(self): # 测试crf的loss不会出现负数 From 850561728b40fede38713952c5f06e605bc19146 Mon Sep 17 00:00:00 2001 From: willqvq Date: Fri, 6 Nov 2020 17:46:53 +0800 Subject: [PATCH 2/2] update the .Jenkinsfile --- .Jenkinsfile | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/.Jenkinsfile b/.Jenkinsfile index b7dd29a0..7c0a64fd 100644 --- a/.Jenkinsfile +++ b/.Jenkinsfile @@ -36,10 +36,13 @@ pipeline { } } post { - always { - sh 'post' + failure { + sh 'post 1' + } + success { + sh 'post 0' + sh 'post github' } - } } \ No newline at end of file