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https://github.com/tinygrad/tinygrad.git
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27613dd881
* BERT language modeling head + trunc normal initializers * add train loop + helpers * shuffle in dataloaders + slight changes in main loop * beam change * Minor changes * random.shuffle * HParam update * Use deque for dataloader * wandb bert project name * half fixes * BENCHMARK + remove epoch * cast + print() --------- Co-authored-by: chenyu <chenyu@fastmail.com>
279 lines
12 KiB
Python
279 lines
12 KiB
Python
from collections import OrderedDict
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import os, unicodedata, json, functools
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import numpy as np
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from tinygrad.nn import state
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from tinygrad.tensor import Tensor
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from tinygrad.dtype import dtypes
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#
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# checkpointing utils
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#
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def invert_dict(d): return {v: k for k, v in reversed(d.items())}
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def dedup_dict(d): return invert_dict(invert_dict(d))
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# store each tensor into the first key it appears in
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def get_training_state(model, optimizer, scheduler):
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# hack: let get_state_dict walk the tree starting with model, so that the checkpoint keys are
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# readable and can be loaded as a model for eval
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train_state = {'model': model, 'optimizer': optimizer, 'scheduler': scheduler}
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return dedup_dict(state.get_state_dict(train_state))
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def load_training_state(model, optimizer, scheduler, state_dict):
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# use fresh model to restore duplicate keys
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train_state = {'model': model, 'optimizer': optimizer, 'scheduler': scheduler}
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big_dict = state.get_state_dict(train_state)
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# hack: put back the dupes
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dupe_names = {}
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for k, v in big_dict.items():
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if v not in dupe_names:
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dupe_names[v] = k
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assert k in state_dict
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state_dict[k] = state_dict[dupe_names[v]]
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# scheduler contains optimizer and all params, load each weight only once
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scheduler_state = {'scheduler': scheduler}
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state.load_state_dict(scheduler_state, state_dict)
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def gaussian_kernel(n, std):
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from scipy import signal
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gaussian_1d = signal.windows.gaussian(n, std)
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gaussian_2d = np.outer(gaussian_1d, gaussian_1d)
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gaussian_3d = np.outer(gaussian_2d, gaussian_1d)
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gaussian_3d = gaussian_3d.reshape(n, n, n)
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gaussian_3d = np.cbrt(gaussian_3d)
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gaussian_3d /= gaussian_3d.max()
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return gaussian_3d
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def prepare_arrays(image, roi_shape=(128, 128, 128)):
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assert len(roi_shape) == 3 and any(roi_shape)
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image_shape = list(image.shape[2:])
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result = np.zeros((1, 3, *image_shape), dtype=image.dtype)
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norm_map = np.zeros_like(result)
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norm_patch = gaussian_kernel(roi_shape[0], 0.125 * roi_shape[0]).astype(norm_map.dtype)
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return result, norm_map, norm_patch
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def get_slice(image, roi_shape=(128, 128, 128), overlap_factor=0.5):
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assert len(roi_shape) == 3 and any(roi_shape)
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assert 0 < overlap_factor < 1
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image_shape, dim = list(image.shape[2:]), len(image.shape[2:])
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strides = [int(roi_shape[i] * (1 - overlap_factor)) for i in range(dim)]
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size = [(image_shape[i] - roi_shape[i]) // strides[i] + 1 for i in range(dim)]
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for i in range(0, strides[0] * size[0], strides[0]):
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for j in range(0, strides[1] * size[1], strides[1]):
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for k in range(0, strides[2] * size[2], strides[2]):
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yield i, j, k
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def _get_best_indices(logits, n_best_size):
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index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
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return list(map(lambda x: x[0], index_and_score))[:n_best_size]
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def _is_punctuation(char):
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if (cp := ord(char)) in range(33, 48) or cp in range(58, 65) or cp in range(91, 97) or cp in range(123, 127):
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return True
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return unicodedata.category(char).startswith("P")
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def _is_whitespace(char):
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if char == " " or char == "\t" or char == "\n" or char == "\r":
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return True
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return unicodedata.category(char) == "Zs"
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def _is_control(char):
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if char == "\t" or char == "\n" or char == "\r":
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return False
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return unicodedata.category(char).startswith("C")
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def _run_split_on_punc(text):
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if text in ("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]"):
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return [text]
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start_new_word = True
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output = []
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for i in range(len(text)):
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if _is_punctuation(char := text[i]):
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output.append([char])
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start_new_word = True
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else:
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if start_new_word:
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output.append([])
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start_new_word = False
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output[-1].append(char)
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return ["".join(x) for x in output]
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def _run_strip_accents(text):
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output = []
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for char in unicodedata.normalize("NFD", text):
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if unicodedata.category(char) != "Mn":
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output.append(char)
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return "".join(output)
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def _clean_text(text):
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output = []
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for char in text:
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if not ((cp := ord(char)) == 0 or cp == 0xfffd or _is_control(char)):
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output.append(" " if _is_whitespace(char) else char)
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return "".join(output)
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def _get_final_text(pred_text, orig_text):
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def _strip_spaces(text):
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ns_text = ""
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ns_to_s_map = OrderedDict()
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for i, c in enumerate(text):
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if c == " ":
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continue
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ns_to_s_map[len(ns_text)] = i
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ns_text += c
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return ns_text, ns_to_s_map
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orig_tokens = _clean_text(orig_text).strip().split()
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split_tokens = []
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for token in orig_tokens:
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if token not in ("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]"):
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token = token.lower()
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token = _run_strip_accents(token)
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split_tokens.extend(_run_split_on_punc(token))
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tok_text = " ".join(" ".join(split_tokens).strip().split())
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start_position = tok_text.find(pred_text)
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if start_position == -1:
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return orig_text
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end_position = start_position + len(pred_text) - 1
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orig_ns_text, orig_ns_to_s_map = _strip_spaces(orig_text)
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tok_ns_text, tok_ns_to_s_map = _strip_spaces(tok_text)
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if len(orig_ns_text) != len(tok_ns_text):
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return orig_text
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tok_s_to_ns_map = {v: k for k, v in tok_ns_to_s_map.items()}
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orig_start_position = None
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if start_position in tok_s_to_ns_map:
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if (ns_start_position := tok_s_to_ns_map[start_position]) in orig_ns_to_s_map:
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orig_start_position = orig_ns_to_s_map[ns_start_position]
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if orig_start_position is None:
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return orig_text
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orig_end_position = None
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if end_position in tok_s_to_ns_map:
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if (ns_end_position := tok_s_to_ns_map[end_position]) in orig_ns_to_s_map:
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orig_end_position = orig_ns_to_s_map[ns_end_position]
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if orig_end_position is None:
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return orig_text
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output_text = orig_text[orig_start_position:(orig_end_position + 1)]
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return output_text
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def get_bert_qa_prediction(features, example, start_end_logits):
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prelim_predictions = []
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for i, feature in enumerate(features):
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for start_index in _get_best_indices(start_end_logits[i][0], 20):
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for end_index in _get_best_indices(start_end_logits[i][1], 20):
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if start_index >= len(feature["tokens"]) or end_index >= len(feature["tokens"]):
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continue
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if start_index not in feature["token_to_orig_map"] or end_index not in feature["token_to_orig_map"]:
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continue
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if not feature["token_is_max_context"].get(start_index, False):
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continue
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if end_index < start_index or end_index - start_index + 1 > 30:
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continue
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prelim_predictions.append({
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"feature_index": i,
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"start_index": start_index,
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"end_index": end_index,
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"start_logit": start_end_logits[i][0, start_index],
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"end_logit": start_end_logits[i][1, end_index]
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})
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predictions = sorted(prelim_predictions, key=lambda x: (x["start_logit"] + x["end_logit"]), reverse=True)
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if len(predictions) > 0:
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feature = features[predictions[0]["feature_index"]]
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tok_tokens = feature["tokens"][predictions[0]["start_index"]:(predictions[0]["end_index"] + 1)]
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orig_doc_start = feature["token_to_orig_map"][predictions[0]["start_index"]]
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orig_doc_end = feature["token_to_orig_map"][predictions[0]["end_index"]]
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orig_tokens = example["context"][orig_doc_start:(orig_doc_end + 1)]
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tok_text = " ".join(tok_tokens).replace(" ##", "").replace("##", "")
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tok_text = " ".join(tok_text.strip().split())
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orig_text = " ".join(orig_tokens)
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return _get_final_text(tok_text, orig_text)
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return "empty"
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def get_mlperf_bert_model(config_path:str):
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from extra.models import bert
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from examples.mlperf.initializers import LinearBert, EmbeddingBert, LayerNormBert
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bert.Linear = LinearBert
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bert.Embedding = EmbeddingBert
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bert.LayerNorm = LayerNormBert
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from extra.models.bert import BertForMLPerf
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with open(config_path, "r") as f:
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config = json.load(f)
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return BertForMLPerf(
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config["hidden_size"],
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config["intermediate_size"],
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config["max_position_embeddings"],
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config["num_attention_heads"],
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config["num_hidden_layers"],
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config["type_vocab_size"],
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config["vocab_size"],
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config["attention_probs_dropout_prob"],
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config["hidden_dropout_prob"]
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)
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@functools.lru_cache(maxsize=None)
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def load_tf_weights_to_dict(checkpoint_path):
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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import tensorflow as tf
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reader = tf.train.load_checkpoint(checkpoint_path)
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var_to_shape_map = reader.get_variable_to_shape_map()
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weights_dict = {}
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for key in sorted(var_to_shape_map):
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weights_dict[key] = reader.get_tensor(key)
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return weights_dict
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def tt(tf_tensor): return Tensor(tf_tensor, dtype=dtypes.float32)
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def load_from_tf2_ckpt(key: str, ckpt_dir: str):
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p = "model/layer-3/"
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s = "/.ATTRIBUTES/VARIABLE_VALUE"
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tf_dict = load_tf_weights_to_dict(ckpt_dir)
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if key.startswith("model.embeddings"):
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if key.endswith("word_embeddings.weight"): return tt(tf_dict[p+"layer-1/embeddings"+s])
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elif key.endswith("position_embeddings.weight"): return tt(tf_dict[p+"layer-3/embeddings"+s])
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elif key.endswith("token_type_embeddings.weight"): return tt(tf_dict[p+"layer-4/embeddings"+s])
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elif key.endswith("LayerNorm.weight"): return tt(tf_dict[p+"layer-6/gamma"+s])
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elif key.endswith("LayerNorm.bias"): return tt(tf_dict[p+"layer-6/beta"+s])
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else: raise ValueError(f"Unknown key: {key}")
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elif key.startswith("model.encoder.layer"):
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l_id = str(int(key.split(".")[3]) + 10)
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if ".attention." in key:
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if key.endswith("self.query.weight"): return tt(tf_dict[p+f"layer-{l_id}/_attention_layer/_query_dense/kernel"+s])
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elif key.endswith("self.query.bias"): return tt(tf_dict[p+f"layer-{l_id}/_attention_layer/_query_dense/bias"+s])
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elif key.endswith("self.key.weight"): return tt(tf_dict[p+f"layer-{l_id}/_attention_layer/_key_dense/kernel"+s])
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elif key.endswith("self.key.bias"): return tt(tf_dict[p+f"layer-{l_id}/_attention_layer/_key_dense/bias"+s])
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elif key.endswith("self.value.weight"): return tt(tf_dict[p+f"layer-{l_id}/_attention_layer/_value_dense/kernel"+s])
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elif key.endswith("self.value.bias"): return tt(tf_dict[p+f"layer-{l_id}/_attention_layer/_value_dense/bias"+s])
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# Attention output
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elif key.endswith("output.dense.weight"): return tt(tf_dict[p+f"layer-{l_id}/_attention_output_dense/kernel"+s])
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elif key.endswith("output.dense.bias"): return tt(tf_dict[p+f"layer-{l_id}/_attention_output_dense/bias"+s])
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elif key.endswith("output.LayerNorm.weight"): return tt(tf_dict[p+f"layer-{l_id}/_attention_layer_norm/gamma"+s])
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elif key.endswith("output.LayerNorm.bias"): return tt(tf_dict[p+f"layer-{l_id}/_attention_layer_norm/beta"+s])
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else: raise ValueError(f"Unknown key: {key}")
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elif ".intermediate." in key:
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if key.endswith("dense.weight"): return tt(tf_dict[p+f"layer-{l_id}/_intermediate_dense/kernel"+s])
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elif key.endswith("dense.bias"): return tt(tf_dict[p+f"layer-{l_id}/_intermediate_dense/bias"+s])
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else: raise ValueError(f"Unknown key: {key}")
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elif ".output." in key:
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if key.endswith("dense.weight"): return tt(tf_dict[p+f"layer-{l_id}/_output_dense/kernel"+s])
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elif key.endswith("dense.bias"): return tt(tf_dict[p+f"layer-{l_id}/_output_dense/bias"+s])
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elif key.endswith("LayerNorm.weight"): return tt(tf_dict[p+f"layer-{l_id}/_output_layer_norm/gamma"+s])
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elif key.endswith("LayerNorm.bias"): return tt(tf_dict[p+f"layer-{l_id}/_output_layer_norm/beta"+s])
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else: raise ValueError(f"Unknown key: {key}")
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elif key.startswith("clsf_pooler.weight"): return tt(tf_dict[f"model/layer-3/layer-35/kernel"+s])
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elif key.startswith("clsf_pooler.bias"): return tt(tf_dict[f"model/layer-3/layer-35/bias"+s])
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elif key.startswith("clsf_output.weight"): return tt(tf_dict[f"model/layer-6/layer-1/kernel"+s])
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elif key.startswith("clsf_output.bias"): return tt(tf_dict[f"model/layer-6/layer-1/bias"+s])
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elif key.startswith("lm_transform.weight"): return tt(tf_dict[f"model/layer-5/layer-3/kernel"+s])
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elif key.startswith("lm_transform.bias"): return tt(tf_dict[f"model/layer-5/layer-3/bias"+s])
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elif key.startswith("lm_norm.weight"): return tt(tf_dict[f"model/layer-5/layer-4/gamma"+s])
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elif key.startswith("lm_norm.bias"): return tt(tf_dict[f"model/layer-5/layer-4/beta"+s])
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elif key.startswith("lm_output_bias"): return tt(tf_dict[f"model/layer-5/layer-6/bias"+s])
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else: raise ValueError(f"Unknown key: {key}") |