From e65b1f701abca03bf3a1b5fbb200392aabd38c22 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 20 六月 2024 17:09:33 +0800
Subject: [PATCH] Dev gzf deepspeed (#1833)
---
funasr/models/paraformer/cif_predictor.py | 47 ++++++++++++++++++++++++-----------------------
1 files changed, 24 insertions(+), 23 deletions(-)
diff --git a/funasr/models/paraformer/cif_predictor.py b/funasr/models/paraformer/cif_predictor.py
index a6bfe65..0856eed 100644
--- a/funasr/models/paraformer/cif_predictor.py
+++ b/funasr/models/paraformer/cif_predictor.py
@@ -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
@@ -516,9 +518,7 @@
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)
frames = prefix_sum_hidden[fire_idxs]
shift_frames = torch.roll(frames, 1, dims=0)
@@ -530,9 +530,7 @@
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]
shift_remain_frames = torch.roll(remain_frames, 1, dims=0)
shift_remain_frames[shift_batch_idxs] = 0
@@ -541,13 +539,12 @@
max_label_len = batch_len.max()
- 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):
@@ -661,14 +658,13 @@
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)
@@ -682,10 +678,19 @@
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 = prefix_sum_hidden[fire_idxs]
shift_frames = torch.roll(frames, 1, dims=0)
@@ -697,9 +702,7 @@
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]
shift_remain_frames = torch.roll(remain_frames, 1, dims=0)
shift_remain_frames[shift_batch_idxs] = 0
@@ -708,9 +711,7 @@
max_label_len = batch_len.max()
- 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