From aa3fe1a353bde71d106755d030d9e5300fbde328 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 22 七月 2024 19:02:15 +0800
Subject: [PATCH] python runtime
---
funasr/models/paraformer/cif_predictor.py | 80 ++++++++++++++++++++++++---------------
1 files changed, 49 insertions(+), 31 deletions(-)
diff --git a/funasr/models/paraformer/cif_predictor.py b/funasr/models/paraformer/cif_predictor.py
index a6bfe65..24145cd 100644
--- a/funasr/models/paraformer/cif_predictor.py
+++ b/funasr/models/paraformer/cif_predictor.py
@@ -80,7 +80,7 @@
hidden, alphas, token_num, mask=mask
)
- acoustic_embeds, cif_peak = cif_v1(hidden, alphas, self.threshold)
+ 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()
@@ -245,7 +245,7 @@
hidden, alphas, token_num, mask=None
)
- acoustic_embeds, cif_peak = cif_v1(hidden, alphas, self.threshold)
+ 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, :]
@@ -449,7 +449,7 @@
mask = mask.transpose(-1, -2).float()
mask = mask.squeeze(-1)
hidden, alphas, token_num = self.tail_process_fn(hidden, alphas, mask=mask)
- acoustic_embeds, cif_peak = cif_v1_export(hidden, alphas, self.threshold)
+ acoustic_embeds, cif_peak = cif_export(hidden, alphas, self.threshold)
return acoustic_embeds, token_num, alphas, cif_peak
@@ -494,6 +494,8 @@
token_num_floor = torch.floor(token_num)
return hidden, alphas, token_num_floor
+
+
@torch.jit.script
def cif_v1_export(hidden, alphas, threshold: float):
device = hidden.device
@@ -504,7 +506,10 @@
frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)
fires = torch.zeros(batch_size, len_time, dtype=dtype, device=device)
- prefix_sum = torch.cumsum(alphas, dim=1)
+ # prefix_sum = torch.cumsum(alphas, dim=1)
+ prefix_sum = torch.cumsum(alphas, dim=1, dtype=torch.float64).to(
+ torch.float32
+ ) # cumsum precision degradation cause wrong result in extreme
prefix_sum_floor = torch.floor(prefix_sum)
dislocation_prefix_sum = torch.roll(prefix_sum, 1, dims=1)
dislocation_prefix_sum_floor = torch.floor(dislocation_prefix_sum)
@@ -516,10 +521,8 @@
fires[fire_idxs] = 1
fires = fires + prefix_sum - prefix_sum_floor
- prefix_sum_hidden = torch.cumsum(
- alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1
- )
-
+ # prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)
+ prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)
frames = prefix_sum_hidden[fire_idxs]
shift_frames = torch.roll(frames, 1, dims=0)
@@ -530,24 +533,25 @@
shift_frames[shift_batch_idxs] = 0
remains = fires - torch.floor(fires)
- remain_frames = (
- remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
- )
+ # remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
+ remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
shift_remain_frames = torch.roll(remain_frames, 1, dims=0)
shift_remain_frames[shift_batch_idxs] = 0
frames = frames - shift_frames + shift_remain_frames - remain_frames
- max_label_len = batch_len.max()
+ # max_label_len = batch_len.max()
+ max_label_len = alphas.sum(dim=-1)
+ max_label_len = torch.floor(max_label_len).max().to(dtype=torch.int64)
- frame_fires = torch.zeros(
- batch_size, max_label_len, hidden_size, dtype=dtype, device=device
- )
+ # frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)
+ frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)
indices = torch.arange(max_label_len, device=device).expand(batch_size, -1)
frame_fires_idxs = indices < batch_len.unsqueeze(1)
frame_fires[frame_fires_idxs] = frames
return frame_fires, fires
+
@torch.jit.script
def cif_export(hidden, alphas, threshold: float):
@@ -661,17 +665,19 @@
return torch.stack(list_ls, 0), fires
-def cif_v1(hidden, alphas, threshold):
+def cif_wo_hidden_v1(alphas, threshold, return_fire_idxs=False):
+ batch_size, len_time = alphas.size()
+ device = alphas.device
+ dtype = alphas.dtype
- device = hidden.device
- dtype = hidden.dtype
- batch_size, len_time, hidden_size = hidden.size()
threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
- frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)
fires = torch.zeros(batch_size, len_time, dtype=dtype, device=device)
- prefix_sum = torch.cumsum(alphas, dim=1)
+ # prefix_sum = torch.cumsum(alphas, dim=1)
+ prefix_sum = torch.cumsum(alphas, dim=1, dtype=torch.float64).to(
+ torch.float32
+ ) # cumsum precision degradation cause wrong result in extreme
prefix_sum_floor = torch.floor(prefix_sum)
dislocation_prefix_sum = torch.roll(prefix_sum, 1, dims=1)
dislocation_prefix_sum_floor = torch.floor(dislocation_prefix_sum)
@@ -682,10 +688,21 @@
fire_idxs = dislocation_diff > 0
fires[fire_idxs] = 1
fires = fires + prefix_sum - prefix_sum_floor
+ if return_fire_idxs:
+ return fires, fire_idxs
+ return fires
- prefix_sum_hidden = torch.cumsum(
- alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1
- )
+
+def cif_v1(hidden, alphas, threshold):
+ fires, fire_idxs = cif_wo_hidden_v1(alphas, threshold, return_fire_idxs=True)
+
+ device = hidden.device
+ dtype = hidden.dtype
+ batch_size, len_time, hidden_size = hidden.size()
+ # frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)
+ # prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)
+ frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)
+ prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)
frames = prefix_sum_hidden[fire_idxs]
shift_frames = torch.roll(frames, 1, dims=0)
@@ -697,20 +714,21 @@
shift_frames[shift_batch_idxs] = 0
remains = fires - torch.floor(fires)
- remain_frames = (
- remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
- )
+ # remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
+ remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
shift_remain_frames = torch.roll(remain_frames, 1, dims=0)
shift_remain_frames[shift_batch_idxs] = 0
frames = frames - shift_frames + shift_remain_frames - remain_frames
- max_label_len = batch_len.max()
+ # max_label_len = batch_len.max()
+ max_label_len = (
+ torch.round(alphas.sum(-1)).int().max()
+ ) # torch.round to calculate the max length
- frame_fires = torch.zeros(
- batch_size, max_label_len, hidden_size, dtype=dtype, device=device
- )
+ # frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)
+ frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)
indices = torch.arange(max_label_len, device=device).expand(batch_size, -1)
frame_fires_idxs = indices < batch_len.unsqueeze(1)
frame_fires[frame_fires_idxs] = frames
--
Gitblit v1.9.1