From 28a19dbc4e85d3b8a4ec2ef7483bba64d422b43f Mon Sep 17 00:00:00 2001
From: aky15 <ankeyu.aky@11.17.44.249>
Date: 星期三, 12 四月 2023 18:03:06 +0800
Subject: [PATCH] Merge remote-tracking branch 'origin/main' into dev_aky
---
funasr/models/predictor/cif.py | 474 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++-
1 files changed, 463 insertions(+), 11 deletions(-)
diff --git a/funasr/models/predictor/cif.py b/funasr/models/predictor/cif.py
index 8199708..e80a915 100644
--- a/funasr/models/predictor/cif.py
+++ b/funasr/models/predictor/cif.py
@@ -1,10 +1,12 @@
import torch
from torch import nn
-
+import logging
+import numpy as np
from funasr.modules.nets_utils import make_pad_mask
+from funasr.modules.streaming_utils.utils import sequence_mask
class CifPredictor(nn.Module):
- def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0):
+ def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0, tail_threshold=0.45):
super(CifPredictor, self).__init__()
self.pad = nn.ConstantPad1d((l_order, r_order), 0)
@@ -14,6 +16,7 @@
self.threshold = threshold
self.smooth_factor = smooth_factor
self.noise_threshold = noise_threshold
+ self.tail_threshold = tail_threshold
def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,
target_label_length=None):
@@ -29,10 +32,12 @@
alphas = torch.sigmoid(output)
alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
if mask is not None:
- alphas = alphas * mask.transpose(-1, -2).float()
+ mask = mask.transpose(-1, -2).float()
+ alphas = alphas * mask
if mask_chunk_predictor is not None:
alphas = alphas * mask_chunk_predictor
alphas = alphas.squeeze(-1)
+ mask = mask.squeeze(-1)
if target_label_length is not None:
target_length = target_label_length
elif target_label is not None:
@@ -42,8 +47,40 @@
token_num = alphas.sum(-1)
if target_length is not None:
alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
+ elif self.tail_threshold > 0.0:
+ hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask)
+
acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
+
+ if target_length is None and self.tail_threshold > 0.0:
+ token_num_int = torch.max(token_num).type(torch.int32).item()
+ acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
+
return acoustic_embeds, token_num, alphas, cif_peak
+
+ def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
+ b, t, d = hidden.size()
+ tail_threshold = self.tail_threshold
+ if mask is not None:
+ zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
+ ones_t = torch.ones_like(zeros_t)
+ mask_1 = torch.cat([mask, zeros_t], dim=1)
+ mask_2 = torch.cat([ones_t, mask], dim=1)
+ mask = mask_2 - mask_1
+ tail_threshold = mask * tail_threshold
+ alphas = torch.cat([alphas, zeros_t], dim=1)
+ alphas = torch.add(alphas, tail_threshold)
+ else:
+ tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
+ tail_threshold = torch.reshape(tail_threshold, (1, 1))
+ alphas = torch.cat([alphas, tail_threshold], dim=1)
+ zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
+ hidden = torch.cat([hidden, zeros], dim=1)
+ token_num = alphas.sum(dim=-1)
+ token_num_floor = torch.floor(token_num)
+
+ return hidden, alphas, token_num_floor
+
def gen_frame_alignments(self,
alphas: torch.Tensor = None,
@@ -95,8 +132,19 @@
class CifPredictorV2(nn.Module):
- def __init__(self, idim, l_order, r_order, threshold=1.0, dropout=0.1, smooth_factor=1.0, noise_threshold=0,
- tail_threshold=0.0):
+ def __init__(self,
+ idim,
+ l_order,
+ r_order,
+ threshold=1.0,
+ dropout=0.1,
+ smooth_factor=1.0,
+ noise_threshold=0,
+ tail_threshold=0.0,
+ tf2torch_tensor_name_prefix_torch="predictor",
+ tf2torch_tensor_name_prefix_tf="seq2seq/cif",
+ tail_mask=True,
+ ):
super(CifPredictorV2, self).__init__()
self.pad = nn.ConstantPad1d((l_order, r_order), 0)
@@ -107,6 +155,9 @@
self.smooth_factor = smooth_factor
self.noise_threshold = noise_threshold
self.tail_threshold = tail_threshold
+ self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
+ self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
+ self.tail_mask = tail_mask
def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,
target_label_length=None):
@@ -120,10 +171,12 @@
alphas = torch.sigmoid(output)
alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
if mask is not None:
- alphas = alphas * mask.transpose(-1, -2).float()
+ mask = mask.transpose(-1, -2).float()
+ alphas = alphas * mask
if mask_chunk_predictor is not None:
alphas = alphas * mask_chunk_predictor
alphas = alphas.squeeze(-1)
+ mask = mask.squeeze(-1)
if target_label_length is not None:
target_length = target_label_length
elif target_label is not None:
@@ -134,7 +187,10 @@
if target_length is not None:
alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
elif self.tail_threshold > 0.0:
- hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num)
+ if self.tail_mask:
+ hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask)
+ else:
+ hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=None)
acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
if target_length is None and self.tail_threshold > 0.0:
@@ -143,12 +199,84 @@
return acoustic_embeds, token_num, alphas, cif_peak
- def tail_process_fn(self, hidden, alphas, token_num=None):
+ def forward_chunk(self, hidden, cache=None):
+ b, t, d = hidden.size()
+ h = hidden
+ context = h.transpose(1, 2)
+ queries = self.pad(context)
+ output = torch.relu(self.cif_conv1d(queries))
+ output = output.transpose(1, 2)
+ output = self.cif_output(output)
+ alphas = torch.sigmoid(output)
+ alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
+
+ alphas = alphas.squeeze(-1)
+ mask_chunk_predictor = None
+ if cache is not None:
+ mask_chunk_predictor = None
+ mask_chunk_predictor = torch.zeros_like(alphas)
+ mask_chunk_predictor[:, cache["pad_left"]:cache["stride"] + cache["pad_left"]] = 1.0
+
+ if mask_chunk_predictor is not None:
+ alphas = alphas * mask_chunk_predictor
+
+ if cache is not None:
+ if cache["is_final"]:
+ alphas[:, cache["stride"] + cache["pad_left"] - 1] += 0.45
+ if cache["cif_hidden"] is not None:
+ hidden = torch.cat((cache["cif_hidden"], hidden), 1)
+ if cache["cif_alphas"] is not None:
+ alphas = torch.cat((cache["cif_alphas"], alphas), -1)
+
+ token_num = alphas.sum(-1)
+ acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
+ len_time = alphas.size(-1)
+ last_fire_place = len_time - 1
+ last_fire_remainds = 0.0
+ pre_alphas_length = 0
+
+ mask_chunk_peak_predictor = None
+ if cache is not None:
+ mask_chunk_peak_predictor = None
+ mask_chunk_peak_predictor = torch.zeros_like(cif_peak)
+ if cache["cif_alphas"] is not None:
+ pre_alphas_length = cache["cif_alphas"].size(-1)
+ mask_chunk_peak_predictor[:, :pre_alphas_length] = 1.0
+ mask_chunk_peak_predictor[:, pre_alphas_length + cache["pad_left"]:pre_alphas_length + cache["stride"] + cache["pad_left"]] = 1.0
+
+ if mask_chunk_peak_predictor is not None:
+ cif_peak = cif_peak * mask_chunk_peak_predictor.squeeze(-1)
+
+ for i in range(len_time):
+ if cif_peak[0][len_time - 1 - i] > self.threshold or cif_peak[0][len_time - 1 - i] == self.threshold:
+ last_fire_place = len_time - 1 - i
+ last_fire_remainds = cif_peak[0][len_time - 1 - i] - self.threshold
+ break
+ last_fire_remainds = torch.tensor([last_fire_remainds], dtype=alphas.dtype).to(alphas.device)
+ cache["cif_hidden"] = hidden[:, last_fire_place:, :]
+ cache["cif_alphas"] = torch.cat((last_fire_remainds.unsqueeze(0), alphas[:, last_fire_place+1:]), -1)
+ token_num_int = token_num.floor().type(torch.int32).item()
+ return acoustic_embeds[:, 0:token_num_int, :], token_num, alphas, cif_peak
+
+ def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
b, t, d = hidden.size()
tail_threshold = self.tail_threshold
- tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
- tail_threshold = torch.reshape(tail_threshold, (1, 1))
- alphas = torch.cat([alphas, tail_threshold], dim=1)
+ if mask is not None:
+ zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
+ ones_t = torch.ones_like(zeros_t)
+ mask_1 = torch.cat([mask, zeros_t], dim=1)
+ mask_2 = torch.cat([ones_t, mask], dim=1)
+ mask = mask_2 - mask_1
+ tail_threshold = mask * tail_threshold
+ alphas = torch.cat([alphas, zeros_t], dim=1)
+ alphas = torch.add(alphas, tail_threshold)
+ else:
+ tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
+ tail_threshold = torch.reshape(tail_threshold, (1, 1))
+ if b > 1:
+ alphas = torch.cat([alphas, tail_threshold.repeat(b, 1)], dim=1)
+ else:
+ alphas = torch.cat([alphas, tail_threshold], dim=1)
zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
hidden = torch.cat([hidden, zeros], dim=1)
token_num = alphas.sum(dim=-1)
@@ -203,6 +331,62 @@
predictor_alignments = index_div_bool_zeros_count_tile_out
predictor_alignments_length = predictor_alignments.sum(-1).type(encoder_sequence_length.dtype)
return predictor_alignments.detach(), predictor_alignments_length.detach()
+
+ def gen_tf2torch_map_dict(self):
+
+ tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
+ tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf
+ map_dict_local = {
+ ## predictor
+ "{}.cif_conv1d.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/conv1d/kernel".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": (2, 1, 0),
+ }, # (256,256,3),(3,256,256)
+ "{}.cif_conv1d.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/conv1d/bias".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ }, # (256,),(256,)
+ "{}.cif_output.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/conv1d_1/kernel".format(tensor_name_prefix_tf),
+ "squeeze": 0,
+ "transpose": (1, 0),
+ }, # (1,256),(1,256,1)
+ "{}.cif_output.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/conv1d_1/bias".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ }, # (1,),(1,)
+ }
+ return map_dict_local
+
+ def convert_tf2torch(self,
+ var_dict_tf,
+ var_dict_torch,
+ ):
+ map_dict = self.gen_tf2torch_map_dict()
+ var_dict_torch_update = dict()
+ for name in sorted(var_dict_torch.keys(), reverse=False):
+ names = name.split('.')
+ if names[0] == self.tf2torch_tensor_name_prefix_torch:
+ name_tf = map_dict[name]["name"]
+ data_tf = var_dict_tf[name_tf]
+ if map_dict[name]["squeeze"] is not None:
+ data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])
+ if map_dict[name]["transpose"] is not None:
+ data_tf = np.transpose(data_tf, map_dict[name]["transpose"])
+ data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
+ assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
+ var_dict_torch[
+ name].size(),
+ data_tf.size())
+ var_dict_torch_update[name] = data_tf
+ logging.info(
+ "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,
+ var_dict_tf[name_tf].shape))
+
+ return var_dict_torch_update
class mae_loss(nn.Module):
@@ -264,3 +448,271 @@
pad_l = torch.zeros([max_label_len - l.size(0), hidden_size], device=hidden.device)
list_ls.append(torch.cat([l, pad_l], 0))
return torch.stack(list_ls, 0), fires
+
+
+def cif_wo_hidden(alphas, threshold):
+ batch_size, len_time = alphas.size()
+
+ # loop varss
+ integrate = torch.zeros([batch_size], device=alphas.device)
+ # intermediate vars along time
+ list_fires = []
+
+ for t in range(len_time):
+ alpha = alphas[:, t]
+
+ integrate += alpha
+ list_fires.append(integrate)
+
+ fire_place = integrate >= threshold
+ integrate = torch.where(fire_place,
+ integrate - torch.ones([batch_size], device=alphas.device),
+ integrate)
+
+ fires = torch.stack(list_fires, 1)
+ return fires
+
+
+class CifPredictorV3(nn.Module):
+ def __init__(self,
+ idim,
+ l_order,
+ r_order,
+ threshold=1.0,
+ dropout=0.1,
+ smooth_factor=1.0,
+ noise_threshold=0,
+ tail_threshold=0.0,
+ tf2torch_tensor_name_prefix_torch="predictor",
+ tf2torch_tensor_name_prefix_tf="seq2seq/cif",
+ smooth_factor2=1.0,
+ noise_threshold2=0,
+ upsample_times=5,
+ upsample_type="cnn",
+ use_cif1_cnn=True,
+ tail_mask=True,
+ ):
+ super(CifPredictorV3, self).__init__()
+
+ self.pad = nn.ConstantPad1d((l_order, r_order), 0)
+ self.cif_conv1d = nn.Conv1d(idim, idim, l_order + r_order + 1)
+ self.cif_output = nn.Linear(idim, 1)
+ self.dropout = torch.nn.Dropout(p=dropout)
+ self.threshold = threshold
+ self.smooth_factor = smooth_factor
+ self.noise_threshold = noise_threshold
+ self.tail_threshold = tail_threshold
+ self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
+ self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
+
+ self.upsample_times = upsample_times
+ self.upsample_type = upsample_type
+ self.use_cif1_cnn = use_cif1_cnn
+ if self.upsample_type == 'cnn':
+ self.upsample_cnn = nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
+ self.cif_output2 = nn.Linear(idim, 1)
+ elif self.upsample_type == 'cnn_blstm':
+ self.upsample_cnn = nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
+ self.blstm = nn.LSTM(idim, idim, 1, bias=True, batch_first=True, dropout=0.0, bidirectional=True)
+ self.cif_output2 = nn.Linear(idim*2, 1)
+ elif self.upsample_type == 'cnn_attn':
+ self.upsample_cnn = nn.ConvTranspose1d(idim, idim, self.upsample_times, self.upsample_times)
+ from funasr.models.encoder.transformer_encoder import EncoderLayer as TransformerEncoderLayer
+ from funasr.modules.attention import MultiHeadedAttention
+ from funasr.modules.positionwise_feed_forward import PositionwiseFeedForward
+ positionwise_layer_args = (
+ idim,
+ idim*2,
+ 0.1,
+ )
+ self.self_attn = TransformerEncoderLayer(
+ idim,
+ MultiHeadedAttention(
+ 4, idim, 0.1
+ ),
+ PositionwiseFeedForward(*positionwise_layer_args),
+ 0.1,
+ True, #normalize_before,
+ False, #concat_after,
+ )
+ self.cif_output2 = nn.Linear(idim, 1)
+ self.smooth_factor2 = smooth_factor2
+ self.noise_threshold2 = noise_threshold2
+
+ def forward(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,
+ target_label_length=None):
+ h = hidden
+ context = h.transpose(1, 2)
+ queries = self.pad(context)
+ output = torch.relu(self.cif_conv1d(queries))
+
+ # alphas2 is an extra head for timestamp prediction
+ if not self.use_cif1_cnn:
+ _output = context
+ else:
+ _output = output
+ if self.upsample_type == 'cnn':
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1,2)
+ elif self.upsample_type == 'cnn_blstm':
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1,2)
+ output2, (_, _) = self.blstm(output2)
+ elif self.upsample_type == 'cnn_attn':
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1,2)
+ output2, _ = self.self_attn(output2, mask)
+ # import pdb; pdb.set_trace()
+ alphas2 = torch.sigmoid(self.cif_output2(output2))
+ alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)
+ # repeat the mask in T demension to match the upsampled length
+ if mask is not None:
+ mask2 = mask.repeat(1, self.upsample_times, 1).transpose(-1, -2).reshape(alphas2.shape[0], -1)
+ mask2 = mask2.unsqueeze(-1)
+ alphas2 = alphas2 * mask2
+ alphas2 = alphas2.squeeze(-1)
+ token_num2 = alphas2.sum(-1)
+
+ output = output.transpose(1, 2)
+
+ output = self.cif_output(output)
+ alphas = torch.sigmoid(output)
+ alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
+ if mask is not None:
+ mask = mask.transpose(-1, -2).float()
+ alphas = alphas * mask
+ if mask_chunk_predictor is not None:
+ alphas = alphas * mask_chunk_predictor
+ alphas = alphas.squeeze(-1)
+ mask = mask.squeeze(-1)
+ if target_label_length is not None:
+ target_length = target_label_length
+ elif target_label is not None:
+ target_length = (target_label != ignore_id).float().sum(-1)
+ else:
+ target_length = None
+ token_num = alphas.sum(-1)
+
+ if target_length is not None:
+ alphas *= (target_length / token_num)[:, None].repeat(1, alphas.size(1))
+ elif self.tail_threshold > 0.0:
+ hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, token_num, mask=mask)
+
+ acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
+ if target_length is None and self.tail_threshold > 0.0:
+ token_num_int = torch.max(token_num).type(torch.int32).item()
+ acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
+ return acoustic_embeds, token_num, alphas, cif_peak, token_num2
+
+ def get_upsample_timestamp(self, hidden, mask=None, token_num=None):
+ h = hidden
+ b = hidden.shape[0]
+ context = h.transpose(1, 2)
+ queries = self.pad(context)
+ output = torch.relu(self.cif_conv1d(queries))
+
+ # alphas2 is an extra head for timestamp prediction
+ if not self.use_cif1_cnn:
+ _output = context
+ else:
+ _output = output
+ if self.upsample_type == 'cnn':
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1,2)
+ elif self.upsample_type == 'cnn_blstm':
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1,2)
+ output2, (_, _) = self.blstm(output2)
+ elif self.upsample_type == 'cnn_attn':
+ output2 = self.upsample_cnn(_output)
+ output2 = output2.transpose(1,2)
+ output2, _ = self.self_attn(output2, mask)
+ alphas2 = torch.sigmoid(self.cif_output2(output2))
+ alphas2 = torch.nn.functional.relu(alphas2 * self.smooth_factor2 - self.noise_threshold2)
+ # repeat the mask in T demension to match the upsampled length
+ if mask is not None:
+ mask2 = mask.repeat(1, self.upsample_times, 1).transpose(-1, -2).reshape(alphas2.shape[0], -1)
+ mask2 = mask2.unsqueeze(-1)
+ alphas2 = alphas2 * mask2
+ alphas2 = alphas2.squeeze(-1)
+ _token_num = alphas2.sum(-1)
+ if token_num is not None:
+ alphas2 *= (token_num / _token_num)[:, None].repeat(1, alphas2.size(1))
+ # re-downsample
+ ds_alphas = alphas2.reshape(b, -1, self.upsample_times).sum(-1)
+ ds_cif_peak = cif_wo_hidden(ds_alphas, self.threshold - 1e-4)
+ # upsampled alphas and cif_peak
+ us_alphas = alphas2
+ us_cif_peak = cif_wo_hidden(us_alphas, self.threshold - 1e-4)
+ return ds_alphas, ds_cif_peak, us_alphas, us_cif_peak
+
+ def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
+ b, t, d = hidden.size()
+ tail_threshold = self.tail_threshold
+ if mask is not None:
+ zeros_t = torch.zeros((b, 1), dtype=torch.float32, device=alphas.device)
+ ones_t = torch.ones_like(zeros_t)
+ mask_1 = torch.cat([mask, zeros_t], dim=1)
+ mask_2 = torch.cat([ones_t, mask], dim=1)
+ mask = mask_2 - mask_1
+ tail_threshold = mask * tail_threshold
+ alphas = torch.cat([alphas, zeros_t], dim=1)
+ alphas = torch.add(alphas, tail_threshold)
+ else:
+ tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
+ tail_threshold = torch.reshape(tail_threshold, (1, 1))
+ alphas = torch.cat([alphas, tail_threshold], dim=1)
+ zeros = torch.zeros((b, 1, d), dtype=hidden.dtype).to(hidden.device)
+ hidden = torch.cat([hidden, zeros], dim=1)
+ token_num = alphas.sum(dim=-1)
+ token_num_floor = torch.floor(token_num)
+
+ return hidden, alphas, token_num_floor
+
+ def gen_frame_alignments(self,
+ alphas: torch.Tensor = None,
+ encoder_sequence_length: torch.Tensor = None):
+ batch_size, maximum_length = alphas.size()
+ int_type = torch.int32
+
+ is_training = self.training
+ if is_training:
+ token_num = torch.round(torch.sum(alphas, dim=1)).type(int_type)
+ else:
+ token_num = torch.floor(torch.sum(alphas, dim=1)).type(int_type)
+
+ max_token_num = torch.max(token_num).item()
+
+ alphas_cumsum = torch.cumsum(alphas, dim=1)
+ alphas_cumsum = torch.floor(alphas_cumsum).type(int_type)
+ alphas_cumsum = alphas_cumsum[:, None, :].repeat(1, max_token_num, 1)
+
+ index = torch.ones([batch_size, max_token_num], dtype=int_type)
+ index = torch.cumsum(index, dim=1)
+ index = index[:, :, None].repeat(1, 1, maximum_length).to(alphas_cumsum.device)
+
+ index_div = torch.floor(torch.true_divide(alphas_cumsum, index)).type(int_type)
+ index_div_bool_zeros = index_div.eq(0)
+ index_div_bool_zeros_count = torch.sum(index_div_bool_zeros, dim=-1) + 1
+ index_div_bool_zeros_count = torch.clamp(index_div_bool_zeros_count, 0, encoder_sequence_length.max())
+ token_num_mask = (~make_pad_mask(token_num, maxlen=max_token_num)).to(token_num.device)
+ index_div_bool_zeros_count *= token_num_mask
+
+ index_div_bool_zeros_count_tile = index_div_bool_zeros_count[:, :, None].repeat(1, 1, maximum_length)
+ ones = torch.ones_like(index_div_bool_zeros_count_tile)
+ zeros = torch.zeros_like(index_div_bool_zeros_count_tile)
+ ones = torch.cumsum(ones, dim=2)
+ cond = index_div_bool_zeros_count_tile == ones
+ index_div_bool_zeros_count_tile = torch.where(cond, zeros, ones)
+
+ index_div_bool_zeros_count_tile_bool = index_div_bool_zeros_count_tile.type(torch.bool)
+ index_div_bool_zeros_count_tile = 1 - index_div_bool_zeros_count_tile_bool.type(int_type)
+ index_div_bool_zeros_count_tile_out = torch.sum(index_div_bool_zeros_count_tile, dim=1)
+ index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out.type(int_type)
+ predictor_mask = (~make_pad_mask(encoder_sequence_length, maxlen=encoder_sequence_length.max())).type(
+ int_type).to(encoder_sequence_length.device)
+ index_div_bool_zeros_count_tile_out = index_div_bool_zeros_count_tile_out * predictor_mask
+
+ predictor_alignments = index_div_bool_zeros_count_tile_out
+ predictor_alignments_length = predictor_alignments.sum(-1).type(encoder_sequence_length.dtype)
+ return predictor_alignments.detach(), predictor_alignments_length.detach()
--
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