# Copyright (c) 2022 Graphcore Ltd. All rights reserved. import argparse import collections import json import math import os import sys from pathlib import Path from timeit import default_timer as timer import numpy as np import onnx import six import tokenization sys.path.append(str(Path(__file__).resolve().parent.parent)) import poprt from poprt.backend import get_session from poprt.converter import Converter RawResult = collections.namedtuple( "RawResult", ["unique_id", "start_logits", "end_logits"] ) Feature = collections.namedtuple( "Feature", [ "unique_id", "tokens", "example_index", "token_to_orig_map", "token_is_max_context", ], ) class SquadExample(object): """A single training/test example for simple sequence classification.""" def __init__( self, qas_id, question_text, doc_tokens, orig_answer_text=None, start_position=None, end_position=None, ): self.qas_id = qas_id self.question_text = question_text self.doc_tokens = doc_tokens self.orig_answer_text = orig_answer_text self.start_position = start_position self.end_position = end_position def __str__(self): return self.__repr__() def __repr__(self): s = [] s.append("qas_id: %s" % (tokenization.printable_text(self.qas_id))) s.append( "question_text: %s" % (tokenization.printable_text(self.question_text)) ) s.append("doc_tokens: [%s]" % (" ".join(self.doc_tokens))) if self.start_position: s.append("start_position: %d" % (self.start_position)) if self.start_position: s.append("end_position: %d" % (self.end_position)) return ", ".join(s) def _check_is_max_context(doc_spans, cur_span_index, position): """Check if this is the 'max context' doc span for the token.""" # Because of the sliding window approach taken to scoring documents, a single # token can appear in multiple documents. E.g. # Doc: the man went to the store and bought a gallon of milk # Span A: the man went to the # Span B: to the store and bought # Span C: and bought a gallon of # ... # # Now the word 'bought' will have two scores from spans B and C. We only # want to consider the score with "maximum context", which we define as # the *minimum* of its left and right context (the *sum* of left and # right context will always be the same, of course). # # In the example the maximum context for 'bought' would be span C since # it has 1 left context and 3 right context, while span B has 4 left context # and 0 right context. best_score = None best_span_index = None for span_index, doc_span in enumerate(doc_spans): end = doc_span.start + doc_span.length - 1 if position < doc_span.start: continue if position > end: continue num_left_context = position - doc_span.start num_right_context = end - position score = min(num_left_context, num_right_context) + 0.01 * doc_span.length if best_score is None or score > best_score: best_score = score best_span_index = span_index return cur_span_index == best_span_index def convert_examples_to_features( examples, tokenizer, max_seq_length, doc_stride, max_query_length ): """Load a data file into a list of `InputBatch`s.""" res_input_ids = [] res_input_mask = [] res_segment_ids = [] extra = [] unique_id = 0 for example_index, example in enumerate(examples): query_tokens = tokenizer.tokenize(example.question_text) if len(query_tokens) > max_query_length: query_tokens = query_tokens[0:max_query_length] tok_to_orig_index = [] orig_to_tok_index = [] all_doc_tokens = [] for i, token in enumerate(example.doc_tokens): orig_to_tok_index.append(len(all_doc_tokens)) sub_tokens = tokenizer.tokenize(token) for sub_token in sub_tokens: tok_to_orig_index.append(i) all_doc_tokens.append(sub_token) # The -3 accounts for [CLS], [SEP] and [SEP] max_tokens_for_doc = max_seq_length - len(query_tokens) - 3 # We can have documents that are longer than the maximum sequence length. # To deal with this we do a sliding window approach, where we take chunks # of the up to our max length with a stride of `doc_stride`. _DocSpan = collections.namedtuple("DocSpan", ["start", "length"]) doc_spans = [] start_offset = 0 while start_offset < len(all_doc_tokens): length = len(all_doc_tokens) - start_offset if length > max_tokens_for_doc: length = max_tokens_for_doc doc_spans.append(_DocSpan(start=start_offset, length=length)) if start_offset + length == len(all_doc_tokens): break start_offset += min(length, doc_stride) for doc_span_index, doc_span in enumerate(doc_spans): tokens = [] token_to_orig_map = {} token_is_max_context = {} segment_ids = [] tokens.append("[CLS]") segment_ids.append(0) for token in query_tokens: tokens.append(token) segment_ids.append(0) tokens.append("[SEP]") segment_ids.append(0) for i in range(doc_span.length): split_token_index = doc_span.start + i token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index] is_max_context = _check_is_max_context( doc_spans, doc_span_index, split_token_index ) token_is_max_context[len(tokens)] = is_max_context tokens.append(all_doc_tokens[split_token_index]) segment_ids.append(1) tokens.append("[SEP]") segment_ids.append(1) input_ids = tokenizer.convert_tokens_to_ids(tokens) # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. input_mask = [1] * len(input_ids) # Zero-pad up to the sequence length. while len(input_ids) < max_seq_length: input_ids.append(0) input_mask.append(0) segment_ids.append(0) res_input_ids.append(np.array(input_ids, dtype=np.int64)) res_input_mask.append(np.array(input_mask, dtype=np.int64)) res_segment_ids.append(np.array(segment_ids, dtype=np.int64)) feature = Feature( unique_id=unique_id, tokens=tokens, example_index=example_index, token_to_orig_map=token_to_orig_map, token_is_max_context=token_is_max_context, ) extra.append(feature) unique_id += 1 return ( np.array(res_input_ids), np.array(res_input_mask), np.array(res_segment_ids), extra, ) def read_squad_examples(input_file): """Read a SQuAD json file into a list of SquadExample.""" with open(input_file, "r") as f: input_data = json.load(f)["data"] def is_whitespace(c): if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F: return True return False examples = [] for idx, entry in enumerate(input_data): for paragraph in entry["paragraphs"]: paragraph_text = paragraph["context"] doc_tokens = [] char_to_word_offset = [] prev_is_whitespace = True for c in paragraph_text: if is_whitespace(c): prev_is_whitespace = True else: if prev_is_whitespace: doc_tokens.append(c) else: doc_tokens[-1] += c prev_is_whitespace = False char_to_word_offset.append(len(doc_tokens) - 1) for qa in paragraph["qas"]: qas_id = qa["id"] question_text = qa["question"] start_position = None end_position = None orig_answer_text = None example = SquadExample( qas_id=qas_id, question_text=question_text, doc_tokens=doc_tokens, orig_answer_text=orig_answer_text, start_position=start_position, end_position=end_position, ) examples.append(example) return examples def write_predictions( all_examples, all_features, all_results, n_best_size, max_answer_length, do_lower_case, output_prediction_file, output_nbest_file, ): """Write final predictions to the json file.""" example_index_to_features = collections.defaultdict(list) for feature in all_features: example_index_to_features[feature.example_index].append(feature) unique_id_to_result = {} for result in all_results: unique_id_to_result[result.unique_id] = result _PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name "PrelimPrediction", ["feature_index", "start_index", "end_index", "start_logit", "end_logit"], ) all_predictions = collections.OrderedDict() all_nbest_json = collections.OrderedDict() for example_index, example in enumerate(all_examples): features = example_index_to_features[example_index] prelim_predictions = [] for feature_index, feature in enumerate(features): if not feature.unique_id in unique_id_to_result: print("feature not in unique_Id", feature.unique_id) continue result = unique_id_to_result[feature.unique_id] start_indexes = _get_best_indexes(result.start_logits, n_best_size) end_indexes = _get_best_indexes(result.end_logits, n_best_size) for start_index in start_indexes: for end_index in end_indexes: # We could hypothetically create invalid predictions, e.g., predict # that the start of the span is in the question. We throw out all # invalid predictions. if start_index >= len(feature.tokens): continue if end_index >= len(feature.tokens): continue if start_index not in feature.token_to_orig_map: continue if end_index not in feature.token_to_orig_map: continue if not feature.token_is_max_context.get(start_index, False): continue if end_index < start_index: continue length = end_index - start_index + 1 if length > max_answer_length: continue prelim_predictions.append( _PrelimPrediction( feature_index=feature_index, start_index=start_index, end_index=end_index, start_logit=result.start_logits[start_index], end_logit=result.end_logits[end_index], ) ) prelim_predictions = sorted( prelim_predictions, key=lambda x: (x.start_logit + x.end_logit), reverse=True, ) _NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name "NbestPrediction", ["text", "start_logit", "end_logit"] ) seen_predictions = {} nbest = [] for pred in prelim_predictions: if len(nbest) >= n_best_size: break feature = features[pred.feature_index] tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)] orig_doc_start = feature.token_to_orig_map[pred.start_index] orig_doc_end = feature.token_to_orig_map[pred.end_index] orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)] tok_text = " ".join(tok_tokens) # De-tokenize WordPieces that have been split off. tok_text = tok_text.replace(" ##", "") tok_text = tok_text.replace("##", "") # Clean whitespace tok_text = tok_text.strip() tok_text = " ".join(tok_text.split()) orig_text = " ".join(orig_tokens) final_text = get_final_text(tok_text, orig_text, do_lower_case) if final_text in seen_predictions: continue seen_predictions[final_text] = True nbest.append( _NbestPrediction( text=final_text, start_logit=pred.start_logit, end_logit=pred.end_logit, ) ) # In very rare edge cases we could have no valid predictions. So we # just create a nonce prediction in this case to avoid failure. if not nbest: nbest.append(_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0)) assert len(nbest) >= 1 total_scores = [] for entry in nbest: total_scores.append(entry.start_logit + entry.end_logit) probs = _compute_softmax(total_scores) nbest_json = [] for i, entry in enumerate(nbest): output = collections.OrderedDict() output["text"] = entry.text output["probability"] = probs[i] output["start_logit"] = float(entry.start_logit) output["end_logit"] = float(entry.end_logit) nbest_json.append(output) all_predictions[example.qas_id] = nbest_json[0]["text"] all_nbest_json[example.qas_id] = nbest_json with open(output_prediction_file, "w") as writer: writer.write(json.dumps(all_predictions, indent=4) + "\n") with open(output_nbest_file, "w") as writer: writer.write(json.dumps(all_nbest_json, indent=4) + "\n") def get_final_text(pred_text, orig_text, do_lower_case): """Project the tokenized prediction back to the original text.""" # When we created the data, we kept track of the alignment between original # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So # now `orig_text` contains the span of our original text corresponding to the # span that we predicted. # # However, `orig_text` may contain extra characters that we don't want in # our prediction. # # For example, let's say: # pred_text = steve smith # orig_text = Steve Smith's # # We don't want to return `orig_text` because it contains the extra "'s". # # We don't want to return `pred_text` because it's already been normalized # (the SQuAD eval script also does punctuation stripping/lower casing but # our tokenizer does additional normalization like stripping accent # characters). # # What we really want to return is "Steve Smith". # # Therefore, we have to apply a semi-complicated alignment heruistic between # `pred_text` and `orig_text` to get a character-to-charcter alignment. This # can fail in certain cases in which case we just return `orig_text`. def _strip_spaces(text): ns_chars = [] ns_to_s_map = collections.OrderedDict() for i, c in enumerate(text): if c == " ": continue ns_to_s_map[len(ns_chars)] = i ns_chars.append(c) ns_text = "".join(ns_chars) return (ns_text, ns_to_s_map) # We first tokenize `orig_text`, strip whitespace from the result # and `pred_text`, and check if they are the same length. If they are # NOT the same length, the heuristic has failed. If they are the same # length, we assume the characters are one-to-one aligned. tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case) tok_text = " ".join(tokenizer.tokenize(orig_text)) start_position = tok_text.find(pred_text) if start_position == -1: return orig_text end_position = start_position + len(pred_text) - 1 (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text) (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text) if len(orig_ns_text) != len(tok_ns_text): return orig_text # We then project the characters in `pred_text` back to `orig_text` using # the character-to-character alignment. tok_s_to_ns_map = {} for i, tok_index in six.iteritems(tok_ns_to_s_map): tok_s_to_ns_map[tok_index] = i orig_start_position = None if start_position in tok_s_to_ns_map: ns_start_position = tok_s_to_ns_map[start_position] if ns_start_position in orig_ns_to_s_map: orig_start_position = orig_ns_to_s_map[ns_start_position] if orig_start_position is None: return orig_text orig_end_position = None if end_position in tok_s_to_ns_map: ns_end_position = tok_s_to_ns_map[end_position] if ns_end_position in orig_ns_to_s_map: orig_end_position = orig_ns_to_s_map[ns_end_position] if orig_end_position is None: return orig_text output_text = orig_text[orig_start_position : (orig_end_position + 1)] return output_text def _get_best_indexes(logits, n_best_size): """Get the n-best logits from a list.""" index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True) best_indexes = [] for i in range(len(index_and_score)): if i >= n_best_size: break best_indexes.append(index_and_score[i][0]) return best_indexes def _compute_softmax(scores): """Compute softmax probability over raw logits.""" if not scores: return [] max_score = None for score in scores: if max_score is None or score > max_score: max_score = score exp_scores = [] total_sum = 0.0 for score in scores: x = math.exp(score - max_score) exp_scores.append(x) total_sum += x probs = [] for score in exp_scores: probs.append(score / total_sum) return probs def remove_redundant_inout_nodes(model_path): model = onnx.load(model_path) input = [i for i in model.graph.input if i.name == 'unique_ids_raw_output___9:0'] model.graph.input.remove(input[0]) output = [o for o in model.graph.output if o.name == 'unique_ids:0'] model.graph.output.remove(output[0]) node = [ n for n in model.graph.node if n.name == 'unique_ids_graph_outputs_Identity__10' ] model.graph.node.remove(node[0]) return model def main(): parser = argparse.ArgumentParser(description='onnx squad') parser.add_argument('--model', help='model') parser.add_argument('--model_save_path', help='model') parser.add_argument('--vocab_file', help='vocab_file') parser.add_argument('--bert_config_file', help='bert_config_file') parser.add_argument('--predict_file', help='predict_file') parser.add_argument('--output_dir', help='output dir') parser.add_argument( '--half_partial', action='store_true', help='enable inference with half partial' ) parser.add_argument( '--model_run_type', type=str, default='fp8', choices=['fp32', 'fp16', 'fp8', 'fp8_weight'], help='model run type', ) parser.add_argument( '--keep_precision_layer', type=str, help='layers that need to maintain the original data format', ) parser.add_argument( '--max_seq_length', type=int, default=256, help='max_seq_length' ) parser.add_argument( '--max_query_length', type=int, default=64, help='max_query_length' ) parser.add_argument( '--max_answer_length', type=int, default=30, help='max_answer_length' ) parser.add_argument('--n_best_size', type=int, default=20, help='n_best_size') parser.add_argument('--doc_stride', type=int, default=128, help='doc_stride') args = parser.parse_args() tokenizer = tokenization.FullTokenizer( vocab_file=args.vocab_file, do_lower_case=True ) eval_examples = read_squad_examples(input_file=args.predict_file) input_ids, input_mask, segment_ids, extra_data = convert_examples_to_features( eval_examples, tokenizer, args.max_seq_length, args.doc_stride, args.max_query_length, ) input_ids = input_ids.astype(np.int32) input_mask = input_mask.astype(np.int32) segment_ids = segment_ids.astype(np.int32) bert_fp32_save_path = args.model_save_path + '/bert_fp32.onnx' bert_fp16_save_path = args.model_save_path + '/bert_fp16.onnx' bert_fp8_save_path = args.model_save_path + '/bert_fp8.onnx' bert_fp32 = remove_redundant_inout_nodes(args.model) bert_fp32_layer_norm = poprt.optimizer.Optimizer( ['layer_norm_pattern', 'gelu_pattern'] ).run(bert_fp32) onnx.save(bert_fp32_layer_norm, bert_fp32_save_path) if args.model_run_type == 'fp32': bert_save_path = bert_fp32_save_path elif args.model_run_type == 'fp16': converter = Converter( convert_version=11, precision=args.model_run_type, fp16_skip_op_types='Gelu,GroupNormalization', ) bert_fp16 = converter.convert(bert_fp32_layer_norm) onnx.save(bert_fp16, bert_fp16_save_path) bert_save_path = bert_fp16_save_path else: converter = Converter( convert_version=11, precision=args.model_run_type, fp16_skip_op_types='Gelu,GroupNormalization', fp8_params='F143,F143,0,0', fp8_skip_op_names=args.keep_precision_layer, ) bert_fp8 = converter.convert(bert_fp32_layer_norm) onnx.save(bert_fp8, bert_fp8_save_path) bert_save_path = bert_fp8_save_path options = {'num_io_tiles': 32} if args.half_partial: options['partials_type'] = 'half' sess = get_session(bert_save_path, 1, "poprt", options=options).load() n = len(input_ids) bs = 1 all_results = [] start = timer() for idx in range(0, n, bs): feed_dicts = { "input_ids:0": input_ids[idx : idx + bs], "input_mask:0": input_mask[idx : idx + bs], "segment_ids:0": segment_ids[idx : idx + bs], } outputs = sess.run(['unstack:0', 'unstack:1'], feed_dicts) in_batch = outputs[0].shape[0] start_logits = [float(x) for x in outputs[0].flat] end_logits = [float(x) for x in outputs[1].flat] for i in range(0, in_batch): unique_id = len(all_results) all_results.append( RawResult( unique_id=unique_id, start_logits=start_logits, end_logits=end_logits, ) ) if unique_id > 0 and unique_id % 100 == 0: print( "at {} {}sec per item".format( unique_id, (timer() - start) / unique_id ) ) end = timer() print( "total time: {}sec, {}sec per item".format( end - start, (end - start) / len(all_results) ) ) if args.output_dir: if not os.path.exists(args.output_dir): os.mkdir(args.output_dir) output_prediction_file = os.path.join(args.output_dir, "predictions.json") output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json") write_predictions( eval_examples, extra_data, all_results, args.n_best_size, args.max_answer_length, True, output_prediction_file, output_nbest_file, ) return 0 if __name__ == "__main__": sys.exit(main())