Jzone-solo-Legend commented 3 months ago

Notice: In order to resolve issues more efficiently, please raise issue following the template. （注意：为了更加高效率解决您遇到的问题，请按照模板提问，补充细节）

🐛 Bug

To Reproduce

Steps to reproduce the behavior (always include the command you ran):

Run examples/industrial_data_pretraining/paraformer/finetune.sh
See: File "/mnt/home/home/wxh/czl/fun-asr/funasr/models/paraformer/model.py", line 208, in forward loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att = self._calc_att_loss( File "/mnt/home/home/wxh/czl/fun-asr/funasr/models/paraformer/model.py", line 309, in _calc_att_loss sematic_embeds, decoder_out_1st = self.sampler( File "/mnt/home/home/wxh/czl/fun-asr/funasr/models/paraformer/model.py", line 350, in sampler decoder_outs = self.decoder( File "/home/wxh/anaconda3/envs/funasr/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, kwargs) File "/mnt/home/home/wxh/czl/fun-asr/funasr/models/paraformer/decoder.py", line 397, in forward x, tgt_mask, memory, memorymask, = self.decoders(x, tgt_mask, memory, memory_mask) File "/home/wxh/anaconda3/envs/funasr/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, kwargs) File "/mnt/home/home/wxh/czl/fun-asr/funasr/models/transformer/utils/repeat.py", line 32, in forward args = m(args) File "/home/wxh/anaconda3/envs/funasr/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(args, kwargs) File "/mnt/home/home/wxh/czl/fun-asr/funasr/models/paraformer/decoder.py", line 106, in forward x, _ = self.self_attn(tgt, tgt_mask) File "/home/wxh/anaconda3/envs/funasr/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, kwargs) File "/mnt/home/home/wxh/czl/fun-asr/funasr/models/sanm/attention.py", line 518, in forward inputs = inputs * mask RuntimeError: The size of tensor a (3) must match the size of tensor b (4) at non-singleton dimension 1

Code sample

Expected behavior

Environment

Linux
FunASR Version 1.0.27
ModelScope Version 1.15.0
PyTorch Version 2.0.0+cu118
How you installed funasr pip3 install -e ./
Python version: 3.8.0
GPU nvidia 3090
CUDA/cuDNN version cuda11.8
Docker version None
Any other relevant information:

Additional context

dtlzhuangz commented 3 months ago

Could you show me your cif_v1 code? Or git log to show your commit id?

Jzone-solo-Legend commented 3 months ago

I just pulled the latest branch (several minutes ago), and now it can work, tks!

dtlzhuangz commented 3 months ago

Could you try modify the cif via https://github.com/modelscope/FunASR/pull/1811/files . Because I found my bugfix commit has been discarded in https://github.com/modelscope/FunASR/commit/45d7aa9004763684fb748ee17942ecba81042201

dtlzhuangz commented 3 months ago

@LauraGPT Hello. Could it be possible that my bugfix commit was accidentally removed, leading to this issue? Because I cannot see my bug fix in the main code. It will lead to both training and onnx inference bug.

LauraGPT commented 3 months ago

Sorry, it is my mistake. I have fixed it: https://github.com/modelscope/FunASR/commit/93f9a424f2bc0607d31ef66b0c7c58dfac15ce25

FastSchnell commented 3 months ago

Linux FunASR Version 最新main ModelScope Version 最新版 PyTorch Version 2.3.1 Python version: 3.11.5 GPU nvidia 3090 Built on Thu_Nov_18_09:45:30_PST_2021 Cuda compilation tools, release 11.5, V11.5.119 Build cuda_11.5.r11.5/compiler.30672275_0

funasr_error

还是有报错

LauraGPT commented 3 months ago

@dtlzhuangz 请问可以看看这个问题么？最近反馈这个报错的有点多哈

dtlzhuangz commented 3 months ago

Hello, 现在main分支训练代码调用的还是原始版本的cif，而不是cif_v1。 @FastSchnell ,可以把更具体的报错信息，启动命令还有cif_predictor.py的文件给我吗？如果可以的话把报错的那条数据发一下。

dtlzhuangz commented 3 months ago

建议在 https://github.com/modelscope/FunASR/blob/main/funasr/models/paraformer/cif_predictor.py#L248 前打上断点，保存hidden和alphas

LauraGPT commented 3 months ago

抱歉，不知道是怎么被我覆盖的，请问，方便再pr一下么

FastSchnell commented 3 months ago

请你问你是哪个分支，我可以用你的版本试试，或者你的repo，发我一下

dtlzhuangz commented 3 months ago

我现在的repo试一下，哥们你clone完代码后有没有pip install -e . ？

dtlzhuangz commented 3 months ago

好的我提一下，但是讲道理原本的cif应该不会有这个报错的

FastSchnell commented 3 months ago

@dtlzhuangz funasr/bin/train.py ++model=paraformer-zh++train_data_set_list=train.jsonl ++valid_data_set_list=val.jsonl ++output_dir="./outputs"

!/usr/bin/env python3

-- encoding: utf-8 --

Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.

MIT License (https://opensource.org/licenses/MIT)

import torch import logging import numpy as np

from funasr.register import tables from funasr.train_utils.device_funcs import to_device from funasr.models.transformer.utils.nets_utils import make_pad_mask from torch.cuda.amp import autocast

@tables.register("predictor_classes", "CifPredictor") class CifPredictor(torch.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=0.45, ): super().init()

    self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0)
    self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1, groups=idim)
    self.cif_output = torch.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

def forward(
    self,
    hidden,
    target_label=None,
    mask=None,
    ignore_id=-1,
    mask_chunk_predictor=None,
    target_label_length=None,
):

    with autocast(False):
        h = hidden
        context = h.transpose(1, 2)
        queries = self.pad(context)
        memory = self.cif_conv1d(queries)
        output = memory + context
        output = self.dropout(output)
        output = output.transpose(1, 2)
        output = torch.relu(output)
        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

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()

@tables.register("predictor_classes", "CifPredictorV2") class CifPredictorV2(torch.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", tail_mask=True, ): super().init()

    self.pad = torch.nn.ConstantPad1d((l_order, r_order), 0)
    self.cif_conv1d = torch.nn.Conv1d(idim, idim, l_order + r_order + 1)
    self.cif_output = torch.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.tail_mask = tail_mask

def forward(
    self,
    hidden,
    target_label=None,
    mask=None,
    ignore_id=-1,
    mask_chunk_predictor=None,
    target_label_length=None,
):

    with autocast(False):
        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)
        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.squeeze(-1)
        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:
            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:
            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 forward_chunk(self, hidden, cache=None, **kwargs):
    is_final = kwargs.get("is_final", False)
    batch_size, len_time, hidden_size = hidden.shape
    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)

    token_length = []
    list_fires = []
    list_frames = []
    cache_alphas = []
    cache_hiddens = []

    if cache is not None and "chunk_size" in cache:
        alphas[:, : cache["chunk_size"][0]] = 0.0
        if not is_final:
            alphas[:, sum(cache["chunk_size"][:2]) :] = 0.0
    if cache is not None and "cif_alphas" in cache and "cif_hidden" in cache:
        cache["cif_hidden"] = to_device(cache["cif_hidden"], device=hidden.device)
        cache["cif_alphas"] = to_device(cache["cif_alphas"], device=alphas.device)
        hidden = torch.cat((cache["cif_hidden"], hidden), dim=1)
        alphas = torch.cat((cache["cif_alphas"], alphas), dim=1)
    if cache is not None and is_final:
        tail_hidden = torch.zeros((batch_size, 1, hidden_size), device=hidden.device)
        tail_alphas = torch.tensor([[self.tail_threshold]], device=alphas.device)
        tail_alphas = torch.tile(tail_alphas, (batch_size, 1))
        hidden = torch.cat((hidden, tail_hidden), dim=1)
        alphas = torch.cat((alphas, tail_alphas), dim=1)

    len_time = alphas.shape[1]
    for b in range(batch_size):
        integrate = 0.0
        frames = torch.zeros((hidden_size), device=hidden.device)
        list_frame = []
        list_fire = []
        for t in range(len_time):
            alpha = alphas[b][t]
            if alpha + integrate < self.threshold:
                integrate += alpha
                list_fire.append(integrate)
                frames += alpha * hidden[b][t]
            else:
                frames += (self.threshold - integrate) * hidden[b][t]
                list_frame.append(frames)
                integrate += alpha
                list_fire.append(integrate)
                integrate -= self.threshold
                frames = integrate * hidden[b][t]

        cache_alphas.append(integrate)
        if integrate > 0.0:
            cache_hiddens.append(frames / integrate)
        else:
            cache_hiddens.append(frames)

        token_length.append(torch.tensor(len(list_frame), device=alphas.device))
        list_fires.append(list_fire)
        list_frames.append(list_frame)

    cache["cif_alphas"] = torch.stack(cache_alphas, axis=0)
    cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
    cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
    cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)

    max_token_len = max(token_length)
    if max_token_len == 0:
        return hidden, torch.stack(token_length, 0), None, None
    list_ls = []
    for b in range(batch_size):
        pad_frames = torch.zeros(
            (max_token_len - token_length[b], hidden_size), device=alphas.device
        )
        if token_length[b] == 0:
            list_ls.append(pad_frames)
        else:
            list_frames[b] = torch.stack(list_frames[b])
            list_ls.append(torch.cat((list_frames[b], pad_frames), dim=0))

    cache["cif_alphas"] = torch.stack(cache_alphas, axis=0)
    cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
    cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
    cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)
    return torch.stack(list_ls, 0), torch.stack(token_length, 0), None, None

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))
        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)
    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()

@tables.register("predictor_classes", "CifPredictorV2Export") class CifPredictorV2Export(torch.nn.Module): def init(self, model, **kwargs): super().init()

    self.pad = model.pad
    self.cif_conv1d = model.cif_conv1d
    self.cif_output = model.cif_output
    self.threshold = model.threshold
    self.smooth_factor = model.smooth_factor
    self.noise_threshold = model.noise_threshold
    self.tail_threshold = model.tail_threshold

def forward(
    self,
    hidden: torch.Tensor,
    mask: torch.Tensor,
):
    alphas, token_num = self.forward_cnn(hidden, mask)
    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)

    return acoustic_embeds, token_num, alphas, cif_peak

def forward_cnn(
    self,
    hidden: torch.Tensor,
    mask: torch.Tensor,
):
    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)
    mask = mask.transpose(-1, -2).float()
    alphas = alphas * mask
    alphas = alphas.squeeze(-1)
    token_num = alphas.sum(-1)

    return alphas, token_num

def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
    b, t, d = hidden.size()
    tail_threshold = self.tail_threshold

    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)

    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

@torch.jit.script def cif_v1_export(hidden, alphas, threshold: float): 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, 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)

dislocation_prefix_sum_floor[:, 0] = 0
dislocation_diff = prefix_sum_floor - dislocation_prefix_sum_floor

fire_idxs = dislocation_diff > 0
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)

batch_len = fire_idxs.sum(1)
batch_idxs = torch.cumsum(batch_len, dim=0)
shift_batch_idxs = torch.roll(batch_idxs, 1, dims=0)
shift_batch_idxs[0] = 0
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

frames = frames - shift_frames + shift_remain_frames - remain_frames

# 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)
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): 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], 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], 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], dtype=alphas.dtype, 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)

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):
    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

class mae_loss(torch.nn.Module):

def __init__(self, normalize_length=False):
    super(mae_loss, self).__init__()
    self.normalize_length = normalize_length
    self.criterion = torch.nn.L1Loss(reduction="sum")

def forward(self, token_length, pre_token_length):
    loss_token_normalizer = token_length.size(0)
    if self.normalize_length:
        loss_token_normalizer = token_length.sum().type(torch.float32)
    loss = self.criterion(token_length, pre_token_length)
    loss = loss / loss_token_normalizer
    return loss

def cif(hidden, alphas, threshold): batch_size, len_time, hidden_size = hidden.size()

# 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.round(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([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_v1(alphas, threshold, return_fire_idxs=False): batch_size, len_time = alphas.size() device = alphas.device dtype = alphas.dtype

threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.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, 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)

dislocation_prefix_sum_floor[:, 0] = 0
dislocation_diff = prefix_sum_floor - dislocation_prefix_sum_floor

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

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)

batch_len = fire_idxs.sum(1)
batch_idxs = torch.cumsum(batch_len, dim=0)
shift_batch_idxs = torch.roll(batch_idxs, 1, dims=0)
shift_batch_idxs[0] = 0
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

frames = frames - shift_frames + shift_remain_frames - remain_frames

# 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)
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

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) * threshold,
        integrate,
    )

fires = torch.stack(list_fires, 1)
return fires

dtlzhuangz commented 3 months ago

@FastSchnell 我的repo用了如果没问题说一下哈，没问题我再提pr改一下

FastSchnell commented 3 months ago

https://drive.google.com/file/d/1ssUYvoCvwQZhLUhhuOBzabVeirjE2Ze3/view?usp=sharing 也不行，是不是我这套数据集有问题 File "/root/miniconda3/envs/myenv/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl return forward_call(*args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/miniconda3/envs/myenv/lib/python3.11/site-packages/funasr/models/sanm/attention.py", line 518, in forward inputs = inputs mask


RuntimeError: The size of tensor a (13) must match the size of tensor b (14) at non-singleton dimension 1

> @FastSchnell 我的repo用了如果没问题说一下哈，没问题我再提pr改一下

dtlzhuangz commented 3 months ago

你代码调用的不是clone下来的代码吧，clone下来后有没有pip install -e .呢

FastSchnell commented 3 months ago

下载main分支的zip包到服务器跑的，pip install -e .打成pip包也试过一样报错，我怀疑是我数据集问题，因为用这个项目的测试数据不报错

dtlzhuangz commented 3 months ago

你把微信发我邮箱吧，我加你

dtlzhuangz commented 3 months ago

@LauraGPT 这个问题解决了，最新的代码没安装上

modelscope / FunASR

RuntimeError: The size of tensor a (3) must match the size of tensor b (4) at non-singleton dimension 1 #1837

🐛 Bug

To Reproduce

Code sample

Expected behavior

Environment

Additional context

!/usr/bin/env python3

-- encoding: utf-8 --

Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.

MIT License (https://opensource.org/licenses/MIT)