From 8cc5bbf99a59694228aafcbe8712e09b9a4cb26b Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 27 二月 2023 17:01:48 +0800
Subject: [PATCH] Merge pull request #159 from alibaba-damo-academy/dev_dzh

---
 funasr/export/models/predictor/cif.py |   81 ++++++++++++++++++++++++++++++++++------
 1 files changed, 69 insertions(+), 12 deletions(-)

diff --git a/funasr/export/models/predictor/cif.py b/funasr/export/models/predictor/cif.py
index 5518cb8..cb26862 100644
--- a/funasr/export/models/predictor/cif.py
+++ b/funasr/export/models/predictor/cif.py
@@ -16,6 +16,11 @@
 	
 	return mask.type(dtype).to(device) if device is not None else mask.type(dtype)
 
+def sequence_mask_scripts(lengths, maxlen:int):
+	row_vector = torch.arange(0, maxlen, 1).type(lengths.dtype).to(lengths.device)
+	matrix = torch.unsqueeze(lengths, dim=-1)
+	mask = row_vector < matrix
+	return mask.type(torch.float32).to(lengths.device)
 
 class CifPredictorV2(nn.Module):
 	def __init__(self, model):
@@ -71,28 +76,76 @@
 		
 		return hidden, alphas, token_num_floor
 
+
+# @torch.jit.script
+# def cif(hidden, alphas, threshold: float):
+# 	batch_size, len_time, hidden_size = hidden.size()
+# 	threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
+#
+# 	# loop varss
+# 	integrate = torch.zeros([batch_size], device=hidden.device)
+# 	frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
+# 	# intermediate vars along time
+# 	list_fires = []
+# 	list_frames = []
+#
+# 	for t in range(len_time):
+# 		alpha = alphas[:, t]
+# 		distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
+#
+# 		integrate += alpha
+# 		list_fires.append(integrate)
+#
+# 		fire_place = integrate >= threshold
+# 		integrate = torch.where(fire_place,
+# 		                        integrate - torch.ones([batch_size], device=hidden.device),
+# 		                        integrate)
+# 		cur = torch.where(fire_place,
+# 		                  distribution_completion,
+# 		                  alpha)
+# 		remainds = alpha - cur
+#
+# 		frame += cur[:, None] * hidden[:, t, :]
+# 		list_frames.append(frame)
+# 		frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
+# 		                    remainds[:, None] * hidden[:, t, :],
+# 		                    frame)
+#
+# 	fires = torch.stack(list_fires, 1)
+# 	frames = torch.stack(list_frames, 1)
+# 	list_ls = []
+# 	len_labels = torch.floor(alphas.sum(-1)).int()
+# 	max_label_len = len_labels.max()
+# 	for b in range(batch_size):
+# 		fire = fires[b, :]
+# 		l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
+# 		pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], device=hidden.device)
+# 		list_ls.append(torch.cat([l, pad_l], 0))
+# 	return torch.stack(list_ls, 0), fires
+
+
 @torch.jit.script
 def cif(hidden, alphas, threshold: float):
 	batch_size, len_time, hidden_size = hidden.size()
 	threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
 	
 	# loop varss
-	integrate = torch.zeros([batch_size], device=hidden.device)
-	frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
+	integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=hidden.device)
+	frame = torch.zeros([batch_size, hidden_size], dtype=hidden.dtype, device=hidden.device)
 	# intermediate vars along time
 	list_fires = []
 	list_frames = []
 	
 	for t in range(len_time):
 		alpha = alphas[:, t]
-		distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
+		distribution_completion = torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device) - integrate
 		
 		integrate += alpha
 		list_fires.append(integrate)
 		
 		fire_place = integrate >= threshold
 		integrate = torch.where(fire_place,
-		                        integrate - torch.ones([batch_size], device=hidden.device),
+		                        integrate - torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device),
 		                        integrate)
 		cur = torch.where(fire_place,
 		                  distribution_completion,
@@ -107,12 +160,16 @@
 	
 	fires = torch.stack(list_fires, 1)
 	frames = torch.stack(list_frames, 1)
-	list_ls = []
-	len_labels = torch.round(alphas.sum(-1)).int()
-	max_label_len = len_labels.max()
+
+	fire_idxs = fires >= threshold
+	frame_fires = torch.zeros_like(hidden)
+	max_label_len = frames[0, fire_idxs[0]].size(0)
 	for b in range(batch_size):
-		fire = fires[b, :]
-		l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
-		pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], device=hidden.device)
-		list_ls.append(torch.cat([l, pad_l], 0))
-	return torch.stack(list_ls, 0), fires
+		frame_fire = frames[b, fire_idxs[b]]
+		frame_len = frame_fire.size(0)
+		frame_fires[b, :frame_len, :] = frame_fire
+	
+		if frame_len >= max_label_len:
+			max_label_len = frame_len
+	frame_fires = frame_fires[:, :max_label_len, :]
+	return frame_fires, fires

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