def _fb_args(self, ef_items, ef_widxes, ef_valid_mask, ef_lab_idxes, enc_repr_ef,
evt_items, evt_widxes, evt_valid_mask, evt_lab_idxes, enc_repr_evt, margin):
# get the gold idxes
arg_linker = self.arg_linker
bsize, len_ef = ef_items.shape
bsize2, len_evt = evt_items.shape
assert bsize == bsize2
gold_idxes = np.zeros([bsize, len_ef, len_evt], dtype=np.long)
for one_gold_idxes, one_ef_items, one_evt_items in zip(gold_idxes, ef_items, evt_items):
# todo(note): check each pair
for ef_idx, one_ef in enumerate(one_ef_items):
if one_ef is None:
continue
role_map = {id(z.evt): z.role_idx for z in one_ef.links} # todo(note): since we get the original linked ones
for evt_idx, one_evt in enumerate(one_evt_items):
pairwise_role_hlidx = role_map.get(id(one_evt))
if pairwise_role_hlidx is not None:
pairwise_role_idx = arg_linker.hlidx2idx(pairwise_role_hlidx)
assert pairwise_role_idx > 0
one_gold_idxes[ef_idx, evt_idx] = pairwise_role_idx
# get loss
repr_ef = BK.gather_first_dims(enc_repr_ef, ef_widxes, -2) # [*, len-ef, D]
repr_evt = BK.gather_first_dims(enc_repr_evt, evt_widxes, -2) # [*, len-evt, D]
if np.prod(gold_idxes.shape) == 0:
# no instances!
return [[BK.zeros([]), BK.zeros([])]]
else:
gold_idxes_t = BK.input_idx(gold_idxes)
return arg_linker.loss(repr_ef, repr_evt, ef_lab_idxes, evt_lab_idxes, ef_valid_mask, evt_valid_mask,
gold_idxes_t, margin)
def _inference_args(self, ef_items, ef_widxes, ef_valid_mask, ef_lab_idxes, enc_repr_ef,
evt_items, evt_widxes, evt_valid_mask, evt_lab_idxes, enc_repr_evt):
arg_linker = self.arg_linker
repr_ef = BK.gather_first_dims(enc_repr_ef, ef_widxes, -2) # [*, len-ef, D]
repr_evt = BK.gather_first_dims(enc_repr_evt, evt_widxes, -2) # [*, len-evt, D]
role_logprobs, role_predictions = arg_linker.predict(repr_ef, repr_evt, ef_lab_idxes, evt_lab_idxes,
ef_valid_mask, evt_valid_mask)
# add them inplaced
roles_arr = BK.get_value(role_predictions) # [*, len-ef, len-evt]
logprobs_arr = BK.get_value(role_logprobs)
for bidx, one_roles_arr in enumerate(roles_arr):
one_ef_items, one_evt_items = ef_items[bidx], evt_items[bidx]
# =====
# todo(note): delete origin links!
for z in one_ef_items:
if z is not None:
z.links.clear()
for z in one_evt_items:
if z is not None:
z.links.clear()
# =====
one_logprobs = logprobs_arr[bidx]
for ef_idx, one_ef in enumerate(one_ef_items):
if one_ef is None:
continue
for evt_idx, one_evt in enumerate(one_evt_items):
if one_evt is None:
continue
one_role_idx = int(one_roles_arr[ef_idx, evt_idx])
if one_role_idx > 0: # link
this_hlidx = arg_linker.idx2hlidx(one_role_idx)
one_evt.add_arg(one_ef, role=str(this_hlidx), role_idx=this_hlidx,
score=float(one_logprobs[ef_idx, evt_idx]))
def _enc(self, input_lexi, input_expr, input_mask, sel_idxes):
if self.dmxnn:
bsize, slen = BK.get_shape(input_mask)
if sel_idxes is None:
sel_idxes = BK.arange_idx(slen).unsqueeze(
0) # select all, [1, slen]
ncand = BK.get_shape(sel_idxes, -1)
# enc_expr aug with PE
rel_dist = BK.arange_idx(slen).unsqueeze(0).unsqueeze(
0) - sel_idxes.unsqueeze(-1) # [*, ?, slen]
pe_embeds = self.posi_embed(rel_dist) # [*, ?, slen, Dpe]
aug_enc_expr = BK.concat([
pe_embeds.expand(bsize, -1, -1, -1),
input_expr.unsqueeze(1).expand(-1, ncand, -1, -1)
], -1) # [*, ?, slen, D+Dpe]
# [*, ?, slen, Denc]
hidden_expr = self.e_encoder(
aug_enc_expr.view(bsize * ncand, slen, -1),
input_mask.unsqueeze(1).expand(-1, ncand,
-1).contiguous().view(
bsize * ncand, slen))
hidden_expr = hidden_expr.view(bsize, ncand, slen, -1)
# dynamic max-pooling (dist<0, dist=0, dist>0)
NEG = Constants.REAL_PRAC_MIN
mp_hiddens = []
mp_masks = [rel_dist < 0, rel_dist == 0, rel_dist > 0]
for mp_mask in mp_masks:
float_mask = mp_mask.float() * input_mask.unsqueeze(
-2) # [*, ?, slen]
valid_mask = (float_mask.sum(-1) > 0.).float().unsqueeze(
-1) # [*, ?, 1]
mask_neg_val = (
1. - float_mask).unsqueeze(-1) * NEG # [*, ?, slen, 1]
# todo(+2): or do we simply multiply mask?
mp_hid0 = (hidden_expr + mask_neg_val).max(-2)[0]
mp_hid = mp_hid0 * valid_mask # [*, ?, Denc]
mp_hiddens.append(self.special_drop(mp_hid))
# mp_hiddens.append(mp_hid)
final_hiddens = mp_hiddens
else:
hidden_expr = self.e_encoder(input_expr,
input_mask) # [*, slen, D']
if sel_idxes is None:
hidden_expr1 = hidden_expr
else:
hidden_expr1 = BK.gather_first_dims(hidden_expr, sel_idxes,
-2) # [*, ?, D']
final_hiddens = [self.special_drop(hidden_expr1)]
if self.lab_f_use_lexi:
final_hiddens.append(
BK.gather_first_dims(input_lexi, sel_idxes,
-2)) # [*, ?, DLex]
ret_expr = self.lab_f(final_hiddens) # [*, ?, DLab]
return ret_expr
def loss(self, repr_t, pred_mask_repl_arr, pred_idx_arr):
mask_idxes, mask_valids = BK.mask2idx(
BK.input_real(pred_mask_repl_arr)) # [bsize, ?]
if BK.get_shape(mask_idxes, -1) == 0: # no loss
zzz = BK.zeros([])
return [[zzz, zzz, zzz]]
else:
target_reprs = BK.gather_first_dims(repr_t, mask_idxes,
1) # [bsize, ?, *]
target_hids = self.hid_layer(target_reprs)
target_scores = self.pred_layer(target_hids) # [bsize, ?, V]
pred_idx_t = BK.input_idx(pred_idx_arr) # [bsize, slen]
target_idx_t = pred_idx_t.gather(-1, mask_idxes) # [bsize, ?]
target_idx_t[(mask_valids <
1.)] = 0 # make sure invalid ones in range
# get loss
pred_losses = BK.loss_nll(target_scores,
target_idx_t) # [bsize, ?]
pred_loss_sum = (pred_losses * mask_valids).sum()
pred_loss_count = mask_valids.sum()
# argmax
_, argmax_idxes = target_scores.max(-1)
pred_corrs = (argmax_idxes == target_idx_t).float() * mask_valids
pred_corr_count = pred_corrs.sum()
return [[pred_loss_sum, pred_loss_count, pred_corr_count]]
def _step(self, input_expr, input_mask, hard_coverage, prev_state, force_widx, force_lidx, free_beam_size):
conf = self.conf
free_mode = (force_widx is None)
prev_state_h = prev_state[0]
# =====
# collect att scores
key_up = self.affine_k([input_expr, hard_coverage.unsqueeze(-1)]) # [*, slen, h]
query_up = self.affine_q([self.repos.unsqueeze(0), prev_state_h.unsqueeze(-2)]) # [*, R, h]
orig_scores = BK.matmul(key_up, query_up.transpose(-2, -1)) # [*, slen, R]
orig_scores += (1.-input_mask).unsqueeze(-1) * Constants.REAL_PRAC_MIN # [*, slen, R]
# first maximum across the R dim (this step is hard max)
maxr_scores, maxr_idxes = orig_scores.max(-1) # [*, slen]
if conf.zero_eos_score:
# use mask to make it able to be backward
tmp_mask = BK.constants(BK.get_shape(maxr_scores), 1.)
tmp_mask.index_fill_(-1, BK.input_idx(0), 0.)
maxr_scores *= tmp_mask
# then select over the slen dim (this step is prob based)
maxr_logprobs = BK.log_softmax(maxr_scores) # [*, slen]
if free_mode:
cur_beam_size = min(free_beam_size, BK.get_shape(maxr_logprobs, -1))
sel_tok_logprobs, sel_tok_idxes = maxr_logprobs.topk(cur_beam_size, dim=-1, sorted=False) # [*, beam]
else:
sel_tok_idxes = force_widx.unsqueeze(-1) # [*, 1]
sel_tok_logprobs = maxr_logprobs.gather(-1, sel_tok_idxes) # [*, 1]
# then collect the info and perform labeling
lf_input_expr = BK.gather_first_dims(input_expr, sel_tok_idxes, -2) # [*, ?, ~]
lf_coverage = hard_coverage.gather(-1, sel_tok_idxes).unsqueeze(-1) # [*, ?, 1]
lf_repos = self.repos[maxr_idxes.gather(-1, sel_tok_idxes)] # [*, ?, ~] # todo(+3): using soft version?
lf_prev_state = prev_state_h.unsqueeze(-2) # [*, 1, ~]
lab_hid_expr = self.lab_f([lf_input_expr, lf_coverage, lf_repos, lf_prev_state]) # [*, ?, ~]
# final predicting labels
# todo(+N): here we select only max at labeling part, only beam at previous one
if free_mode:
sel_lab_logprobs, sel_lab_idxes, sel_lab_embeds = self.hl.predict(lab_hid_expr, None) # [*, ?]
else:
sel_lab_logprobs, sel_lab_idxes, sel_lab_embeds = self.hl.predict(lab_hid_expr, force_lidx.unsqueeze(-1))
# no lab-logprob (*=0) for eos (sel_tok==0)
sel_lab_logprobs *= (sel_tok_idxes>0).float()
# compute next-state [*, ?, ~]
# todo(note): here we flatten the first two dims
tmp_rnn_dims = BK.get_shape(sel_tok_idxes) + [-1]
tmp_rnn_input = BK.concat([lab_hid_expr, sel_lab_embeds], -1)
tmp_rnn_input = tmp_rnn_input.view(-1, BK.get_shape(tmp_rnn_input, -1))
tmp_rnn_hidden = [z.unsqueeze(-2).expand(tmp_rnn_dims).contiguous().view(-1, BK.get_shape(z, -1))
for z in prev_state] # [*, ?, ?, D]
next_state = self.rnn_unit(tmp_rnn_input, tmp_rnn_hidden, None)
next_state = [z.view(tmp_rnn_dims) for z in next_state]
return sel_tok_idxes, sel_tok_logprobs, sel_lab_idxes, sel_lab_logprobs, sel_lab_embeds, next_state
def _exclude_nil(self,
sel_idxes,
sel_valid_mask,
sel_lab_idxes,
sel_lab_embeds,
sel_logprobs=None,
sel_items_arr=None):
# todo(note): assure that nil is 0
sel_valid_mask = sel_valid_mask * (sel_lab_idxes !=
0).float() # not inplaced
# idx on idx
s2_idxes, s2_valid_mask = BK.mask2idx(sel_valid_mask)
sel_idxes = sel_idxes.gather(-1, s2_idxes)
sel_valid_mask = s2_valid_mask
sel_lab_idxes = sel_lab_idxes.gather(-1, s2_idxes)
sel_lab_embeds = BK.gather_first_dims(sel_lab_embeds, s2_idxes, -2)
sel_logprobs = None if sel_logprobs is None else sel_logprobs.gather(
-1, s2_idxes)
sel_items_arr = None if sel_items_arr is None \
else sel_items_arr[np.arange(len(sel_items_arr))[:, np.newaxis], BK.get_value(s2_idxes)]
return sel_idxes, sel_valid_mask, sel_lab_idxes, sel_lab_embeds, sel_logprobs, sel_items_arr
def forward_batch(self,
batched_ids: List,
batched_starts: List,
batched_typeids: List,
training: bool,
other_inputs: List[List] = None):
conf = self.bconf
tokenizer = self.tokenizer
PAD_IDX = tokenizer.pad_token_id
MASK_IDX = tokenizer.mask_token_id
CLS_IDX = tokenizer.cls_token_id
SEP_IDX = tokenizer.sep_token_id
if other_inputs is None:
other_inputs = []
# =====
# batch: here add CLS and SEP
bsize = len(batched_ids)
max_len = max(len(z) for z in batched_ids) + 2 # plus [CLS] and [SEP]
input_shape = (bsize, max_len)
# first collect on CPU
input_ids_arr = np.full(input_shape, PAD_IDX, dtype=np.int64)
input_ids_arr[:, 0] = CLS_IDX
input_mask_arr = np.full(input_shape, 0, dtype=np.float32)
input_is_start_arr = np.full(input_shape, 0, dtype=np.int64)
input_typeids = None if batched_typeids is None else np.full(
input_shape, 0, dtype=np.int64)
other_input_arrs = [
np.full(input_shape, 0, dtype=np.int64) for _ in other_inputs
]
if conf.bert2_retinc_cls: # act as the ROOT word
input_is_start_arr[:, 0] = 1
training_mask_rate = conf.bert2_training_mask_rate if training else 0.
self_sample_stream = self.random_sample_stream
for bidx in range(bsize):
cur_ids, cur_starts = batched_ids[bidx], batched_starts[bidx]
cur_end = len(cur_ids) + 2 # plus CLS and SEP
if training_mask_rate > 0.:
# input dropout
input_ids_arr[bidx, 1:cur_end] = [
(MASK_IDX
if next(self_sample_stream) < training_mask_rate else z)
for z in cur_ids
] + [SEP_IDX]
else:
input_ids_arr[bidx, 1:cur_end] = cur_ids + [SEP_IDX]
input_is_start_arr[bidx, 1:cur_end - 1] = cur_starts
input_mask_arr[bidx, :cur_end] = 1.
if batched_typeids is not None and batched_typeids[
bidx] is not None:
input_typeids[bidx, 1:cur_end - 1] = batched_typeids[bidx]
for one_other_input_arr, one_other_input_list in zip(
other_input_arrs, other_inputs):
one_other_input_arr[bidx,
1:cur_end - 1] = one_other_input_list[bidx]
# arr to tensor
input_ids_t = BK.input_idx(input_ids_arr)
input_mask_t = BK.input_real(input_mask_arr)
input_is_start_t = BK.input_idx(input_is_start_arr)
input_typeid_t = None if input_typeids is None else BK.input_idx(
input_typeids)
other_input_ts = [BK.input_idx(z) for z in other_input_arrs]
# =====
# forward (maybe need multiple times to fit maxlen constraint)
MAX_LEN = 510 # save two for [CLS] and [SEP]
BACK_LEN = 100 # for splitting cases, still remaining some of previous sub-tokens for context
if max_len <= MAX_LEN:
# directly once
final_outputs = self.forward_features(
input_ids_t, input_mask_t, input_typeid_t,
other_input_ts) # [bs, slen, *...]
start_idxes, start_masks = BK.mask2idx(
input_is_start_t.float()) # [bsize, ?]
else:
all_outputs = []
cur_sub_idx = 0
slice_size = [bsize, 1]
slice_cls, slice_sep = BK.constants(slice_size,
CLS_IDX,
dtype=BK.int64), BK.constants(
slice_size,
SEP_IDX,
dtype=BK.int64)
while cur_sub_idx < max_len - 1: # minus 1 to ignore ending SEP
cur_slice_start = max(1, cur_sub_idx - BACK_LEN)
cur_slice_end = min(cur_slice_start + MAX_LEN, max_len - 1)
cur_input_ids_t = BK.concat([
slice_cls, input_ids_t[:, cur_slice_start:cur_slice_end],
slice_sep
], 1)
# here we simply extend extra original masks
cur_input_mask_t = input_mask_t[:, cur_slice_start -
1:cur_slice_end + 1]
cur_input_typeid_t = None if input_typeid_t is None else input_typeid_t[:,
cur_slice_start
-
1:
cur_slice_end
+
1]
cur_other_input_ts = [
z[:, cur_slice_start - 1:cur_slice_end + 1]
for z in other_input_ts
]
cur_outputs = self.forward_features(cur_input_ids_t,
cur_input_mask_t,
cur_input_typeid_t,
cur_other_input_ts)
# only include CLS in the first run, no SEP included
if cur_sub_idx == 0:
# include CLS, exclude SEP
all_outputs.append(cur_outputs[:, :-1])
else:
# include only new ones, discard BACK ones, exclude CLS, SEP
all_outputs.append(cur_outputs[:, cur_sub_idx -
cur_slice_start + 1:-1])
zwarn(
f"Add multiple-seg range: [{cur_slice_start}, {cur_sub_idx}, {cur_slice_end})] "
f"for all-len={max_len}")
cur_sub_idx = cur_slice_end
final_outputs = BK.concat(all_outputs, 1) # [bs, max_len-1, *...]
start_idxes, start_masks = BK.mask2idx(
input_is_start_t[:, :-1].float()) # [bsize, ?]
start_expr = BK.gather_first_dims(final_outputs, start_idxes,
1) # [bsize, ?, *...]
return start_expr, start_masks # [bsize, ?, ...], [bsize, ?]
def loss(self,
repr_ts,
input_erase_mask_arr,
orig_map: Dict,
active_hid=True,
**kwargs):
conf = self.conf
_tie_input_embeddings = conf.tie_input_embeddings
# prepare idxes for the masked ones
if self.add_root_token: # offset for the special root added in embedder
mask_idxes, mask_valids = BK.mask2idx(
BK.input_real(input_erase_mask_arr),
padding_idx=-1) # [bsize, ?]
repr_mask_idxes = mask_idxes + 1
mask_idxes.clamp_(min=0)
else:
mask_idxes, mask_valids = BK.mask2idx(
BK.input_real(input_erase_mask_arr)) # [bsize, ?]
repr_mask_idxes = mask_idxes
# get the losses
if BK.get_shape(mask_idxes, -1) == 0: # no loss
return self._compile_component_loss("mlm", [])
else:
if not isinstance(repr_ts, (List, Tuple)):
repr_ts = [repr_ts]
target_word_scores, target_pos_scores = [], []
target_pos_scores = None # todo(+N): for simplicity, currently ignore this one!!
for layer_idx in conf.loss_layers:
# calculate scores
target_reprs = BK.gather_first_dims(repr_ts[layer_idx],
repr_mask_idxes,
1) # [bsize, ?, *]
if self.hid_layer and active_hid: # todo(+N): sometimes, we only want last softmax, need to ensure dim at outside!
target_hids = self.hid_layer(target_reprs)
else:
target_hids = target_reprs
if _tie_input_embeddings:
pred_W = self.inputter_word_node.E.E[:self.
pred_word_size] # [PSize, Dim]
target_word_scores.append(BK.matmul(
target_hids, pred_W.T)) # List[bsize, ?, Vw]
else:
target_word_scores.append(self.pred_word_layer(
target_hids)) # List[bsize, ?, Vw]
# gather the losses
all_losses = []
for pred_name, target_scores, loss_lambda, range_min, range_max in \
zip(["word", "pos"], [target_word_scores, target_pos_scores], [conf.lambda_word, conf.lambda_pos],
[conf.min_pred_rank, 0], [min(conf.max_pred_rank, self.pred_word_size-1), self.pred_pos_size-1]):
if loss_lambda > 0.:
seq_idx_t = BK.input_idx(
orig_map[pred_name]) # [bsize, slen]
target_idx_t = seq_idx_t.gather(-1,
mask_idxes) # [bsize, ?]
ranged_mask_valids = mask_valids * (
target_idx_t >= range_min).float() * (
target_idx_t <= range_max).float()
target_idx_t[(ranged_mask_valids <
1.)] = 0 # make sure invalid ones in range
# calculate for each layer
all_layer_losses, all_layer_scores = [], []
for one_layer_idx, one_target_scores in enumerate(
target_scores):
# get loss: [bsize, ?]
one_pred_losses = BK.loss_nll(
one_target_scores,
target_idx_t) * conf.loss_weights[one_layer_idx]
all_layer_losses.append(one_pred_losses)
# get scores
one_pred_scores = BK.log_softmax(
one_target_scores,
-1) * conf.loss_weights[one_layer_idx]
all_layer_scores.append(one_pred_scores)
# combine all layers
pred_losses = self.loss_comb_f(all_layer_losses)
pred_loss_sum = (pred_losses * ranged_mask_valids).sum()
pred_loss_count = ranged_mask_valids.sum()
# argmax
_, argmax_idxes = self.score_comb_f(all_layer_scores).max(
-1)
pred_corrs = (argmax_idxes
== target_idx_t).float() * ranged_mask_valids
pred_corr_count = pred_corrs.sum()
# compile leaf loss
r_loss = LossHelper.compile_leaf_info(
pred_name,
pred_loss_sum,
pred_loss_count,
loss_lambda=loss_lambda,
corr=pred_corr_count)
all_losses.append(r_loss)
return self._compile_component_loss("mlm", all_losses)
