From 59f184a622be316b6a75ce053ee8e19e6a7b50ec Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=B8=B8=E9=9B=81?= Date: Tue, 7 Feb 2023 15:19:18 +0800 Subject: [PATCH] export model --- funasr/export/__init__.py | 0 funasr/export/export_model.py | 91 ++++++++ funasr/export/models/__init__.py | 91 ++++++++ funasr/export/models/decoder/__init__.py | 0 funasr/export/models/decoder/sanm_decoder.py | 155 +++++++++++++ funasr/export/models/e2e_asr_paraformer.py | 91 ++++++++ funasr/export/models/encoder/__init__.py | 0 funasr/export/models/encoder/sanm_encoder.py | 102 +++++++++ funasr/export/models/modules/__init__.py | 0 funasr/export/models/modules/decoder_layer.py | 43 ++++ funasr/export/models/modules/encoder_layer.py | 37 +++ funasr/export/models/modules/feedforward.py | 31 +++ funasr/export/models/modules/multihead_att.py | 135 +++++++++++ funasr/export/models/predictor/__init__.py | 0 funasr/export/models/predictor/cif.py | 168 ++++++++++++++ funasr/export/models/predictor/cif_test.py | 212 ++++++++++++++++++ funasr/export/utils/__init__.py | 0 funasr/export/utils/torch_function.py | 68 ++++++ 18 files changed, 1224 insertions(+) create mode 100644 funasr/export/__init__.py create mode 100644 funasr/export/export_model.py create mode 100644 funasr/export/models/__init__.py create mode 100644 funasr/export/models/decoder/__init__.py create mode 100644 funasr/export/models/decoder/sanm_decoder.py create mode 100644 funasr/export/models/e2e_asr_paraformer.py create mode 100644 funasr/export/models/encoder/__init__.py create mode 100644 funasr/export/models/encoder/sanm_encoder.py create mode 100644 funasr/export/models/modules/__init__.py create mode 100644 funasr/export/models/modules/decoder_layer.py create mode 100644 funasr/export/models/modules/encoder_layer.py create mode 100644 funasr/export/models/modules/feedforward.py create mode 100644 funasr/export/models/modules/multihead_att.py create mode 100644 funasr/export/models/predictor/__init__.py create mode 100644 funasr/export/models/predictor/cif.py create mode 100644 funasr/export/models/predictor/cif_test.py create mode 100644 funasr/export/utils/__init__.py create mode 100644 funasr/export/utils/torch_function.py diff --git a/funasr/export/__init__.py b/funasr/export/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/funasr/export/export_model.py b/funasr/export/export_model.py new file mode 100644 index 000000000..17bc1385a --- /dev/null +++ b/funasr/export/export_model.py @@ -0,0 +1,91 @@ +from typing import Union, Dict +from pathlib import Path +from typeguard import check_argument_types + +import os +import logging +import torch + +from funasr.bin.asr_inference_paraformer import Speech2Text +from funasr.export.models import get_model + + + +class ASRModelExportParaformer: + def __init__(self, cache_dir: Union[Path, str] = None, onnx: bool = True): + assert check_argument_types() + if cache_dir is None: + cache_dir = Path.home() / "cache" / "export" + + self.cache_dir = Path(cache_dir) + self.export_config = dict( + feats_dim=560, + onnx=onnx, + ) + logging.info("output dir: {}".format(self.cache_dir)) + self.onnx = onnx + + def export( + self, + model: Speech2Text, + tag_name: str = None, + verbose: bool = False, + ): + + export_dir = self.cache_dir / tag_name.replace(' ', '-') + os.makedirs(export_dir, exist_ok=True) + + # export encoder1 + self.export_config["model_name"] = "model" + model = get_model( + model, + self.export_config, + ) + if self.onnx: + self._export_onnx(model, verbose, export_dir) + + logging.info("output dir: {}".format(export_dir)) + + + def export_from_modelscope( + self, + tag_name: str = 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch', + ): + + from funasr.tasks.asr import ASRTaskParaformer as ASRTask + from modelscope.hub.snapshot_download import snapshot_download + + model_dir = snapshot_download(tag_name, cache_dir=self.cache_dir) + asr_train_config = os.path.join(model_dir, 'config.yaml') + asr_model_file = os.path.join(model_dir, 'model.pb') + cmvn_file = os.path.join(model_dir, 'am.mvn') + model, asr_train_args = ASRTask.build_model_from_file( + asr_train_config, asr_model_file, cmvn_file, 'cpu' + ) + self.export(model, tag_name) + + + + def _export_onnx(self, model, verbose, path, enc_size=None): + if enc_size: + dummy_input = model.get_dummy_inputs(enc_size) + else: + dummy_input = model.get_dummy_inputs() + + # model_script = torch.jit.script(model) + model_script = model #torch.jit.trace(model) + + torch.onnx.export( + model_script, + dummy_input, + os.path.join(path, f'{model.model_name}.onnx'), + verbose=verbose, + opset_version=12, + input_names=model.get_input_names(), + output_names=model.get_output_names(), + dynamic_axes=model.get_dynamic_axes() + ) + +if __name__ == '__main__': + export_model = ASRModelExportParaformer() + export_model.export_from_modelscope('damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch') \ No newline at end of file diff --git a/funasr/export/models/__init__.py b/funasr/export/models/__init__.py new file mode 100644 index 000000000..b21b08059 --- /dev/null +++ b/funasr/export/models/__init__.py @@ -0,0 +1,91 @@ +# from .ctc import CTC +# from .joint_network import JointNetwork +# +# # encoder +# from espnet2.asr.encoder.rnn_encoder import RNNEncoder as espnetRNNEncoder +# from espnet2.asr.encoder.vgg_rnn_encoder import VGGRNNEncoder as espnetVGGRNNEncoder +# from espnet2.asr.encoder.contextual_block_transformer_encoder import ContextualBlockTransformerEncoder as espnetContextualTransformer +# from espnet2.asr.encoder.contextual_block_conformer_encoder import ContextualBlockConformerEncoder as espnetContextualConformer +# from espnet2.asr.encoder.transformer_encoder import TransformerEncoder as espnetTransformerEncoder +# from espnet2.asr.encoder.conformer_encoder import ConformerEncoder as espnetConformerEncoder +# from funasr.export.models.encoder.rnn import RNNEncoder +# from funasr.export.models.encoders import TransformerEncoder +# from funasr.export.models.encoders import ConformerEncoder +# from funasr.export.models.encoder.contextual_block_xformer import ContextualBlockXformerEncoder +# +# # decoder +# from espnet2.asr.decoder.rnn_decoder import RNNDecoder as espnetRNNDecoder +# from espnet2.asr.transducer.transducer_decoder import TransducerDecoder as espnetTransducerDecoder +# from funasr.export.models.decoder.rnn import ( +# RNNDecoder +# ) +# from funasr.export.models.decoders import XformerDecoder +# from funasr.export.models.decoders import TransducerDecoder +# +# # lm +# from espnet2.lm.seq_rnn_lm import SequentialRNNLM as espnetSequentialRNNLM +# from espnet2.lm.transformer_lm import TransformerLM as espnetTransformerLM +# from .language_models.seq_rnn import SequentialRNNLM +# from .language_models.transformer import TransformerLM +# +# # frontend +# from espnet2.asr.frontend.s3prl import S3prlFrontend as espnetS3PRLModel +# from .frontends.s3prl import S3PRLModel +# +# from espnet2.asr.encoder.sanm_encoder import SANMEncoder_tf, SANMEncoderChunkOpt_tf +# from espnet_onnx.export.asr.models.encoders.transformer_sanm import TransformerEncoderSANM_tf +# from espnet2.asr.decoder.transformer_decoder import FsmnDecoderSCAMAOpt_tf +# from funasr.export.models.decoders import XformerDecoderSANM + +from funasr.models.e2e_asr_paraformer import Paraformer +from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export + +def get_model(model, export_config=None): + + if isinstance(model, Paraformer): + return Paraformer_export(model, **export_config) + else: + raise "The model is not exist!" + + +# def get_encoder(model, frontend, preencoder, predictor=None, export_config=None): +# if isinstance(model, espnetRNNEncoder) or isinstance(model, espnetVGGRNNEncoder): +# return RNNEncoder(model, frontend, preencoder, **export_config) +# elif isinstance(model, espnetContextualTransformer) or isinstance(model, espnetContextualConformer): +# return ContextualBlockXformerEncoder(model, **export_config) +# elif isinstance(model, espnetTransformerEncoder): +# return TransformerEncoder(model, frontend, preencoder, **export_config) +# elif isinstance(model, espnetConformerEncoder): +# return ConformerEncoder(model, frontend, preencoder, **export_config) +# elif isinstance(model, SANMEncoder_tf) or isinstance(model, SANMEncoderChunkOpt_tf): +# return TransformerEncoderSANM_tf(model, frontend, preencoder, predictor, **export_config) +# else: +# raise "The model is not exist!" + + +# +# def get_decoder(model, export_config): +# if isinstance(model, espnetRNNDecoder): +# return RNNDecoder(model, **export_config) +# elif isinstance(model, espnetTransducerDecoder): +# return TransducerDecoder(model, **export_config) +# elif isinstance(model, FsmnDecoderSCAMAOpt_tf): +# return XformerDecoderSANM(model, **export_config) +# else: +# return XformerDecoder(model, **export_config) +# +# +# def get_lm(model, export_config): +# if isinstance(model, espnetSequentialRNNLM): +# return SequentialRNNLM(model, **export_config) +# elif isinstance(model, espnetTransformerLM): +# return TransformerLM(model, **export_config) +# +# +# def get_frontend_models(model, export_config): +# if isinstance(model, espnetS3PRLModel): +# return S3PRLModel(model, **export_config) +# else: +# return None +# + \ No newline at end of file diff --git a/funasr/export/models/decoder/__init__.py b/funasr/export/models/decoder/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/funasr/export/models/decoder/sanm_decoder.py b/funasr/export/models/decoder/sanm_decoder.py new file mode 100644 index 000000000..ca2563b7d --- /dev/null +++ b/funasr/export/models/decoder/sanm_decoder.py @@ -0,0 +1,155 @@ +import os + +import torch +import torch.nn as nn + + +# from espnet.nets.pytorch_backend.transformer.mask import subsequent_mask + +from funasr.export.utils.torch_function import MakePadMask + +from funasr.modules.attention import MultiHeadedAttentionSANMDecoder +from funasr.export.models.modules.multihead_att import MultiHeadedAttentionSANMDecoder as MultiHeadedAttentionSANMDecoder_export +from funasr.modules.attention import MultiHeadedAttentionCrossAtt +from funasr.export.models.modules.multihead_att import MultiHeadedAttentionCrossAtt as MultiHeadedAttentionCrossAtt_export +from funasr.modules.positionwise_feed_forward import PositionwiseFeedForwardDecoderSANM +from funasr.export.models.modules.feedforward import PositionwiseFeedForwardDecoderSANM as PositionwiseFeedForwardDecoderSANM_export +from funasr.export.models.modules.decoder_layer import DecoderLayerSANM as DecoderLayerSANM_export + + +class ParaformerSANMDecoder(nn.Module): + def __init__(self, model, + max_seq_len=512, + model_name='decoder'): + super().__init__() + # self.embed = model.embed #Embedding(model.embed, max_seq_len) + self.model = model + self.make_pad_mask = MakePadMask(max_seq_len, flip=False) + + for i, d in enumerate(self.model.decoders): + if isinstance(d.feed_forward, PositionwiseFeedForwardDecoderSANM): + d.feed_forward = PositionwiseFeedForwardDecoderSANM_export(d.feed_forward) + if isinstance(d.self_attn, MultiHeadedAttentionSANMDecoder): + d.self_attn = MultiHeadedAttentionSANMDecoder_export(d.self_attn) + if isinstance(d.src_attn, MultiHeadedAttentionCrossAtt): + d.src_attn = MultiHeadedAttentionCrossAtt_export(d.src_attn) + self.model.decoders[i] = DecoderLayerSANM_export(d) + + if self.model.decoders2 is not None: + for i, d in enumerate(self.model.decoders2): + if isinstance(d.feed_forward, PositionwiseFeedForwardDecoderSANM): + d.feed_forward = PositionwiseFeedForwardDecoderSANM_export(d.feed_forward) + if isinstance(d.self_attn, MultiHeadedAttentionSANMDecoder): + d.self_attn = MultiHeadedAttentionSANMDecoder_export(d.self_attn) + self.model.decoders2[i] = DecoderLayerSANM_export(d) + + for i, d in enumerate(self.model.decoders3): + if isinstance(d.feed_forward, PositionwiseFeedForwardDecoderSANM): + d.feed_forward = PositionwiseFeedForwardDecoderSANM_export(d.feed_forward) + self.model.decoders3[i] = DecoderLayerSANM_export(d) + + self.output_layer = model.output_layer + self.after_norm = model.after_norm + self.model_name = model_name + + def prepare_mask(self, mask): + mask_3d_btd = mask[:, :, None] + if len(mask.shape) == 2: + mask_4d_bhlt = 1 - mask[:, None, None, :] + elif len(mask.shape) == 3: + mask_4d_bhlt = 1 - mask[:, None, :] + mask_4d_bhlt = mask_4d_bhlt * -10000.0 + + return mask_3d_btd, mask_4d_bhlt + + def forward( + self, + hs_pad: torch.Tensor, + hlens: torch.Tensor, + ys_in_pad: torch.Tensor, + ys_in_lens: torch.Tensor, + ): + + tgt = ys_in_pad + tgt_mask = self.make_pad_mask(ys_in_lens) + tgt_mask, _ = self.prepare_mask(tgt_mask) + # tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None] + + memory = hs_pad + memory_mask = self.make_pad_mask(hlens) + _, memory_mask = self.prepare_mask(memory_mask) + # memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :] + + x = tgt + x, tgt_mask, memory, memory_mask, _ = self.model.decoders( + x, tgt_mask, memory, memory_mask + ) + if self.model.decoders2 is not None: + x, tgt_mask, memory, memory_mask, _ = self.model.decoders2( + x, tgt_mask, memory, memory_mask + ) + x, tgt_mask, memory, memory_mask, _ = self.model.decoders3( + x, tgt_mask, memory, memory_mask + ) + x = self.after_norm(x) + x = self.output_layer(x) + + return x, ys_in_lens + + + def get_dummy_inputs(self, enc_size): + tgt = torch.LongTensor([0]).unsqueeze(0) + memory = torch.randn(1, 100, enc_size) + pre_acoustic_embeds = torch.randn(1, 1, enc_size) + cache_num = len(self.model.decoders) + len(self.model.decoders2) + cache = [ + torch.zeros((1, self.model.decoders[0].size, self.model.decoders[0].self_attn.kernel_size)) + for _ in range(cache_num) + ] + return (tgt, memory, pre_acoustic_embeds, cache) + + def is_optimizable(self): + return True + + def get_input_names(self): + cache_num = len(self.model.decoders) + len(self.model.decoders2) + return ['tgt', 'memory', 'pre_acoustic_embeds'] \ + + ['cache_%d' % i for i in range(cache_num)] + + def get_output_names(self): + cache_num = len(self.model.decoders) + len(self.model.decoders2) + return ['y'] \ + + ['out_cache_%d' % i for i in range(cache_num)] + + def get_dynamic_axes(self): + ret = { + 'tgt': { + 0: 'tgt_batch', + 1: 'tgt_length' + }, + 'memory': { + 0: 'memory_batch', + 1: 'memory_length' + }, + 'pre_acoustic_embeds': { + 0: 'acoustic_embeds_batch', + 1: 'acoustic_embeds_length', + } + } + cache_num = len(self.model.decoders) + len(self.model.decoders2) + ret.update({ + 'cache_%d' % d: { + 0: 'cache_%d_batch' % d, + 2: 'cache_%d_length' % d + } + for d in range(cache_num) + }) + return ret + + def get_model_config(self, path): + return { + "dec_type": "XformerDecoder", + "model_path": os.path.join(path, f'{self.model_name}.onnx'), + "n_layers": len(self.model.decoders) + len(self.model.decoders2), + "odim": self.model.decoders[0].size + } diff --git a/funasr/export/models/e2e_asr_paraformer.py b/funasr/export/models/e2e_asr_paraformer.py new file mode 100644 index 000000000..162837a42 --- /dev/null +++ b/funasr/export/models/e2e_asr_paraformer.py @@ -0,0 +1,91 @@ +import logging + + +import torch +import torch.nn as nn + +from funasr.export.utils.torch_function import MakePadMask +from funasr.train.abs_espnet_model import AbsESPnetModel +from funasr.models.encoder.sanm_encoder import SANMEncoder +from funasr.export.models.encoder.sanm_encoder import SANMEncoder as SANMEncoder_export +from funasr.models.predictor.cif import CifPredictorV2 +from funasr.export.models.predictor.cif import CifPredictorV2 as CifPredictorV2_export +from funasr.models.decoder.sanm_decoder import ParaformerSANMDecoder +from funasr.export.models.decoder.sanm_decoder import ParaformerSANMDecoder as ParaformerSANMDecoder_export + +class Paraformer(nn.Module): + """ + Author: Speech Lab, Alibaba Group, China + Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition + https://arxiv.org/abs/2206.08317 + """ + + def __init__( + self, + model, + max_seq_len=512, + feats_dim=560, + model_name='model', + **kwargs, + ): + super().__init__() + if isinstance(model.encoder, SANMEncoder): + self.encoder = SANMEncoder_export(model.encoder) + if isinstance(model.predictor, CifPredictorV2): + self.predictor = CifPredictorV2_export(model.predictor) + if isinstance(model.decoder, ParaformerSANMDecoder): + self.decoder = ParaformerSANMDecoder_export(model.decoder) + self.make_pad_mask = MakePadMask(max_seq_len, flip=False) + self.feats_dim = feats_dim + self.model_name = model_name + self.onnx = False + if "onnx" in kwargs: + self.onnx = kwargs["onnx"] + + def forward( + self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + ): + # a. To device + batch = {"speech": speech, "speech_lengths": speech_lengths} + # batch = to_device(batch, device=self.device) + + enc, enc_len = self.encoder(**batch) + mask = self.make_pad_mask(enc_len)[:, None, :] + pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(enc, mask) + pre_token_length = pre_token_length.round().long() + + decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length) + decoder_out = torch.log_softmax(decoder_out, dim=-1) + + return decoder_out, pre_token_length + + # def get_output_size(self): + # return self.model.encoders[0].size + + def get_dummy_inputs(self): + speech = torch.randn(2, 30, self.feats_dim) + speech_lengths = torch.tensor([6, 30]).long() + return (speech, speech_lengths) + + def get_input_names(self): + return ['speech', 'speech_lengths'] + + def get_output_names(self): + return ['logits', 'token_num'] + + def get_dynamic_axes(self): + return { + 'speech': { + 0: 'batch_size', + 1: 'feats_length' + }, + 'speech_lengths': { + 0: 'batch_size', + }, + 'logits': { + 0: 'batch_size', + 1: 'logits_length' + }, + } \ No newline at end of file diff --git a/funasr/export/models/encoder/__init__.py b/funasr/export/models/encoder/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/funasr/export/models/encoder/sanm_encoder.py b/funasr/export/models/encoder/sanm_encoder.py new file mode 100644 index 000000000..ee4573287 --- /dev/null +++ b/funasr/export/models/encoder/sanm_encoder.py @@ -0,0 +1,102 @@ +import torch +import torch.nn as nn + +from funasr.export.utils.torch_function import MakePadMask +from funasr.modules.attention import MultiHeadedAttentionSANM +from funasr.export.models.modules.multihead_att import MultiHeadedAttentionSANM as MultiHeadedAttentionSANM_export +from funasr.export.models.modules.encoder_layer import EncoderLayerSANM as EncoderLayerSANM_export +from funasr.modules.positionwise_feed_forward import PositionwiseFeedForward +from funasr.export.models.modules.feedforward import PositionwiseFeedForward as PositionwiseFeedForward_export + +class SANMEncoder(nn.Module): + def __init__( + self, + model, + max_seq_len=512, + feats_dim=560, + model_name='encoder', + ): + super().__init__() + self.embed = model.embed + self.model = model + self.make_pad_mask = MakePadMask(max_seq_len, flip=False) + self.feats_dim = feats_dim + + if hasattr(model, 'encoders0'): + for i, d in enumerate(self.model.encoders0): + if isinstance(d.self_attn, MultiHeadedAttentionSANM): + d.self_attn = MultiHeadedAttentionSANM_export(d.self_attn) + if isinstance(d.feed_forward, PositionwiseFeedForward): + d.feed_forward = PositionwiseFeedForward_export(d.feed_forward) + self.model.encoders0[i] = EncoderLayerSANM_export(d) + + for i, d in enumerate(self.model.encoders): + if isinstance(d.self_attn, MultiHeadedAttentionSANM): + d.self_attn = MultiHeadedAttentionSANM_export(d.self_attn) + if isinstance(d.feed_forward, PositionwiseFeedForward): + d.feed_forward = PositionwiseFeedForward_export(d.feed_forward) + self.model.encoders[i] = EncoderLayerSANM_export(d) + + self.model_name = model_name + self.num_heads = model.encoders[0].self_attn.h + self.hidden_size = model.encoders[0].self_attn.linear_out.out_features + + + def prepare_mask(self, mask): + mask_3d_btd = mask[:, :, None] + if len(mask.shape) == 2: + mask_4d_bhlt = 1 - mask[:, None, None, :] + elif len(mask.shape) == 3: + mask_4d_bhlt = 1 - mask[:, None, :] + mask_4d_bhlt = mask_4d_bhlt * -10000.0 + + return mask_3d_btd, mask_4d_bhlt + + def forward(self, + speech: torch.Tensor, + speech_lengths: torch.Tensor, + ): + + mask = self.make_pad_mask(speech_lengths) + mask = self.prepare_mask(mask) + if self.embed is None: + xs_pad = speech + else: + xs_pad = self.embed(speech) + + encoder_outs = self.model.encoders0(xs_pad, mask) + xs_pad, masks = encoder_outs[0], encoder_outs[1] + + encoder_outs = self.model.encoders(xs_pad, mask) + xs_pad, masks = encoder_outs[0], encoder_outs[1] + + xs_pad = self.model.after_norm(xs_pad) + + return xs_pad, speech_lengths + + def get_output_size(self): + return self.model.encoders[0].size + + def get_dummy_inputs(self): + feats = torch.randn(1, 100, self.feats_dim) + return (feats) + + def get_input_names(self): + return ['feats'] + + def get_output_names(self): + return ['encoder_out', 'encoder_out_lens', 'predictor_weight'] + + def get_dynamic_axes(self): + return { + 'feats': { + 1: 'feats_length' + }, + 'encoder_out': { + 1: 'enc_out_length' + }, + 'predictor_weight':{ + 1: 'pre_out_length' + } + + } diff --git a/funasr/export/models/modules/__init__.py b/funasr/export/models/modules/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/funasr/export/models/modules/decoder_layer.py b/funasr/export/models/modules/decoder_layer.py new file mode 100644 index 000000000..bc306b1fd --- /dev/null +++ b/funasr/export/models/modules/decoder_layer.py @@ -0,0 +1,43 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import torch +from torch import nn + + +class DecoderLayerSANM(nn.Module): + + def __init__( + self, + model + ): + super().__init__() + self.self_attn = model.self_attn + self.src_attn = model.src_attn + self.feed_forward = model.feed_forward + self.norm1 = model.norm1 + self.norm2 = model.norm2 if hasattr(model, 'norm2') else None + self.norm3 = model.norm3 if hasattr(model, 'norm3') else None + self.size = model.size + + + def forward(self, tgt, tgt_mask, memory, memory_mask=None, cache=None): + + residual = tgt + tgt = self.norm1(tgt) + tgt = self.feed_forward(tgt) + + x = tgt + if self.self_attn is not None: + tgt = self.norm2(tgt) + x, cache = self.self_attn(tgt, tgt_mask, cache=cache) + x = residual + x + + if self.src_attn is not None: + residual = x + x = self.norm3(x) + x = residual + self.src_attn(x, memory, memory_mask) + + + return x, tgt_mask, memory, memory_mask, cache + diff --git a/funasr/export/models/modules/encoder_layer.py b/funasr/export/models/modules/encoder_layer.py new file mode 100644 index 000000000..800a4f784 --- /dev/null +++ b/funasr/export/models/modules/encoder_layer.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import torch +from torch import nn + + +class EncoderLayerSANM(nn.Module): + def __init__( + self, + model, + ): + """Construct an EncoderLayer object.""" + super().__init__() + self.self_attn = model.self_attn + self.feed_forward = model.feed_forward + self.norm1 = model.norm1 + self.norm2 = model.norm2 + self.size = model.size + + def forward(self, x, mask): + + residual = x + x = self.norm1(x) + x = self.self_attn(x, mask) + if x.size(2) == residual.size(2): + x = x + residual + residual = x + x = self.norm2(x) + x = self.feed_forward(x) + if x.size(2) == residual.size(2): + x = x + residual + + return x, mask + + + diff --git a/funasr/export/models/modules/feedforward.py b/funasr/export/models/modules/feedforward.py new file mode 100644 index 000000000..9388ae15f --- /dev/null +++ b/funasr/export/models/modules/feedforward.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +import torch +import torch.nn as nn + + +class PositionwiseFeedForward(nn.Module): + def __init__(self, model): + super().__init__() + self.w_1 = model.w_1 + self.w_2 = model.w_2 + self.activation = model.activation + + def forward(self, x): + x = self.activation(self.w_1(x)) + x = self.w_2(x) + return x + + +class PositionwiseFeedForwardDecoderSANM(nn.Module): + def __init__(self, model): + super().__init__() + self.w_1 = model.w_1 + self.w_2 = model.w_2 + self.activation = model.activation + self.norm = model.norm + + def forward(self, x): + x = self.activation(self.w_1(x)) + x = self.w_2(self.norm(x)) + return x \ No newline at end of file diff --git a/funasr/export/models/modules/multihead_att.py b/funasr/export/models/modules/multihead_att.py new file mode 100644 index 000000000..377b979d4 --- /dev/null +++ b/funasr/export/models/modules/multihead_att.py @@ -0,0 +1,135 @@ +import os +import math + +import torch +import torch.nn as nn + +class MultiHeadedAttentionSANM(nn.Module): + def __init__(self, model): + super().__init__() + self.d_k = model.d_k + self.h = model.h + self.linear_out = model.linear_out + self.linear_q_k_v = model.linear_q_k_v + self.fsmn_block = model.fsmn_block + self.pad_fn = model.pad_fn + + self.attn = None + self.all_head_size = self.h * self.d_k + + def forward(self, x, mask): + mask_3d_btd, mask_4d_bhlt = mask + q_h, k_h, v_h, v = self.forward_qkv(x) + fsmn_memory = self.forward_fsmn(v, mask_3d_btd) + q_h = q_h * self.d_k**(-0.5) + scores = torch.matmul(q_h, k_h.transpose(-2, -1)) + att_outs = self.forward_attention(v_h, scores, mask_4d_bhlt) + return att_outs + fsmn_memory + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + (self.h, self.d_k) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward_qkv(self, x): + + q_k_v = self.linear_q_k_v(x) + q, k, v = torch.split(q_k_v, int(self.h * self.d_k), dim=-1) + q_h = self.transpose_for_scores(q) + k_h = self.transpose_for_scores(k) + v_h = self.transpose_for_scores(v) + return q_h, k_h, v_h, v + + def forward_fsmn(self, inputs, mask): + + # b, t, d = inputs.size() + # mask = torch.reshape(mask, (b, -1, 1)) + inputs = inputs * mask + x = inputs.transpose(1, 2) + x = self.pad_fn(x) + x = self.fsmn_block(x) + x = x.transpose(1, 2) + x = x + inputs + x = x * mask + return x + + + def forward_attention(self, value, scores, mask): + scores = scores + mask + + self.attn = torch.softmax(scores, dim=-1) + context_layer = torch.matmul(self.attn, value) # (batch, head, time1, d_k) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + return self.linear_out(context_layer) # (batch, time1, d_model) + +class MultiHeadedAttentionSANMDecoder(nn.Module): + def __init__(self, model): + super().__init__() + self.fsmn_block = model.fsmn_block + self.pad_fn = model.pad_fn + self.kernel_size = model.kernel_size + self.attn = None + + def forward(self, inputs, mask, cache=None): + + # b, t, d = inputs.size() + # mask = torch.reshape(mask, (b, -1, 1)) + inputs = inputs * mask + + x = inputs.transpose(1, 2) + if cache is None: + x = self.pad_fn(x) + else: + x = torch.cat((cache[:, :, 1:], x), dim=2) + cache = x + x = self.fsmn_block(x) + x = x.transpose(1, 2) + + x = x + inputs + x = x * mask + return x, cache + +class MultiHeadedAttentionCrossAtt(nn.Module): + def __init__(self, model): + super().__init__() + self.d_k = model.d_k + self.h = model.h + self.linear_q = model.linear_q + self.linear_k_v = model.linear_k_v + self.linear_out = model.linear_out + self.attn = None + self.all_head_size = self.h * self.d_k + + def forward(self, x, memory, memory_mask): + q, k, v = self.forward_qkv(x, memory) + scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) + return self.forward_attention(v, scores, memory_mask) + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + (self.h, self.d_k) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward_qkv(self, x, memory): + q = self.linear_q(x) + + k_v = self.linear_k_v(memory) + k, v = torch.split(k_v, int(self.h * self.d_k), dim=-1) + q = self.transpose_for_scores(q) + k = self.transpose_for_scores(k) + v = self.transpose_for_scores(v) + return q, k, v + + def forward_attention(self, value, scores, mask): + scores = scores + mask + + self.attn = torch.softmax(scores, dim=-1) + context_layer = torch.matmul(self.attn, value) # (batch, head, time1, d_k) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + return self.linear_out(context_layer) # (batch, time1, d_model) diff --git a/funasr/export/models/predictor/__init__.py b/funasr/export/models/predictor/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/funasr/export/models/predictor/cif.py b/funasr/export/models/predictor/cif.py new file mode 100644 index 000000000..32a3c1395 --- /dev/null +++ b/funasr/export/models/predictor/cif.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +import torch +from torch import nn +import logging +import numpy as np + + +def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None): + if maxlen is None: + maxlen = lengths.max() + row_vector = torch.arange(0, maxlen, 1).to(lengths.device) + matrix = torch.unsqueeze(lengths, dim=-1) + mask = row_vector < matrix + mask = mask.detach() + + return mask.type(dtype).to(device) if device is not None else mask.type(dtype) + + +class CifPredictorV2(nn.Module): + def __init__(self, model): + 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, + ): + 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) + + acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold) + + 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 + + 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, 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 + +@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.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([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 + + +def CifPredictorV2_test(): + x = torch.rand([2, 21, 2]) + x_len = torch.IntTensor([6, 21]) + + mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype) + x = x * mask[:, :, None] + + predictor_scripts = torch.jit.script(CifPredictorV2(2, 1, 1)) + # cif_output, cif_length, alphas, cif_peak = predictor_scripts(x, mask=mask[:, None, :]) + predictor_scripts.save('test.pt') + loaded = torch.jit.load('test.pt') + cif_output, cif_length, alphas, cif_peak = loaded(x, mask=mask[:, None, :]) + # print(cif_output) + print(predictor_scripts.code) + # predictor = CifPredictorV2(2, 1, 1) + # cif_output, cif_length, alphas, cif_peak = predictor(x, mask=mask[:, None, :]) + print(cif_output) + + +def CifPredictorV2_export_test(): + x = torch.rand([2, 21, 2]) + x_len = torch.IntTensor([6, 21]) + + mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype) + x = x * mask[:, :, None] + + # predictor_scripts = torch.jit.script(CifPredictorV2(2, 1, 1)) + # cif_output, cif_length, alphas, cif_peak = predictor_scripts(x, mask=mask[:, None, :]) + predictor = CifPredictorV2(2, 1, 1) + predictor_trace = torch.jit.trace(predictor, (x, mask[:, None, :])) + predictor_trace.save('test_trace.pt') + loaded = torch.jit.load('test_trace.pt') + + x = torch.rand([3, 30, 2]) + x_len = torch.IntTensor([6, 20, 30]) + mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype) + x = x * mask[:, :, None] + cif_output, cif_length, alphas, cif_peak = loaded(x, mask=mask[:, None, :]) + print(cif_output) + # print(predictor_trace.code) + # predictor = CifPredictorV2(2, 1, 1) + # cif_output, cif_length, alphas, cif_peak = predictor(x, mask=mask[:, None, :]) + # print(cif_output) + + +if __name__ == '__main__': + # CifPredictorV2_test() + CifPredictorV2_export_test() \ No newline at end of file diff --git a/funasr/export/models/predictor/cif_test.py b/funasr/export/models/predictor/cif_test.py new file mode 100644 index 000000000..954c43417 --- /dev/null +++ b/funasr/export/models/predictor/cif_test.py @@ -0,0 +1,212 @@ +import torch +from torch import nn +import logging +import numpy as np + + +def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None): + if maxlen is None: + maxlen = lengths.max() + row_vector = torch.arange(0, maxlen, 1).to(lengths.device) + matrix = torch.unsqueeze(lengths, dim=-1) + mask = row_vector < matrix + mask = mask.detach() + + return mask.type(dtype).to(device) if device is not None else mask.type(dtype) + + +def make_pad_mask(lengths, xs=None, length_dim=-1, maxlen=None): + + if length_dim == 0: + raise ValueError("length_dim cannot be 0: {}".format(length_dim)) + + if not isinstance(lengths, list): + lengths = lengths.tolist() + bs = int(len(lengths)) + if maxlen is None: + if xs is None: + maxlen = int(max(lengths)) + else: + maxlen = xs.size(length_dim) + else: + assert xs is None + assert maxlen >= int(max(lengths)) + + seq_range = torch.arange(0, maxlen, dtype=torch.int64) + seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen) + seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1) + mask = seq_range_expand >= seq_length_expand + + if xs is not None: + assert xs.size(0) == bs, (xs.size(0), bs) + + if length_dim < 0: + length_dim = xs.dim() + length_dim + # ind = (:, None, ..., None, :, , None, ..., None) + ind = tuple( + slice(None) if i in (0, length_dim) else None for i in range(xs.dim()) + ) + mask = mask[ind].expand_as(xs).to(xs.device) + return mask + + + +class CifPredictorV2(nn.Module): + def __init__(self, + idim: int, + l_order: int, + r_order: int, + threshold: float = 1.0, + dropout: float = 0.1, + smooth_factor: float = 1.0, + noise_threshold: float = 0, + tail_threshold: float = 0.0, + ): + super(CifPredictorV2, self).__init__() + + self.pad = nn.ConstantPad1d((l_order, r_order), 0.0) + 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 + + def forward(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) + + acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold) + + 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 + + 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, 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 + +@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.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([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 + + +def CifPredictorV2_test(): + x = torch.rand([2, 21, 2]) + x_len = torch.IntTensor([6, 21]) + + mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype) + x = x * mask[:, :, None] + + predictor_scripts = torch.jit.script(CifPredictorV2(2, 1, 1)) + # cif_output, cif_length, alphas, cif_peak = predictor_scripts(x, mask=mask[:, None, :]) + predictor_scripts.save('test.pt') + loaded = torch.jit.load('test.pt') + cif_output, cif_length, alphas, cif_peak = loaded(x, mask=mask[:, None, :]) + # print(cif_output) + print(predictor_scripts.code) + # predictor = CifPredictorV2(2, 1, 1) + # cif_output, cif_length, alphas, cif_peak = predictor(x, mask=mask[:, None, :]) + print(cif_output) + + +def CifPredictorV2_export_test(): + x = torch.rand([2, 21, 2]) + x_len = torch.IntTensor([6, 21]) + + mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype) + x = x * mask[:, :, None] + + # predictor_scripts = torch.jit.script(CifPredictorV2(2, 1, 1)) + # cif_output, cif_length, alphas, cif_peak = predictor_scripts(x, mask=mask[:, None, :]) + predictor = CifPredictorV2(2, 1, 1) + predictor_trace = torch.jit.trace(predictor, (x, mask[:, None, :])) + predictor_trace.save('test_trace.pt') + loaded = torch.jit.load('test_trace.pt') + + x = torch.rand([3, 30, 2]) + x_len = torch.IntTensor([6, 20, 30]) + mask = sequence_mask(x_len, maxlen=x.size(1), dtype=x.dtype) + x = x * mask[:, :, None] + cif_output, cif_length, alphas, cif_peak = loaded(x, mask=mask[:, None, :]) + print(cif_output) + # print(predictor_trace.code) + # predictor = CifPredictorV2(2, 1, 1) + # cif_output, cif_length, alphas, cif_peak = predictor(x, mask=mask[:, None, :]) + # print(cif_output) + + +if __name__ == '__main__': + # CifPredictorV2_test() + CifPredictorV2_export_test() \ No newline at end of file diff --git a/funasr/export/utils/__init__.py b/funasr/export/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/funasr/export/utils/torch_function.py b/funasr/export/utils/torch_function.py new file mode 100644 index 000000000..e8e5e1af8 --- /dev/null +++ b/funasr/export/utils/torch_function.py @@ -0,0 +1,68 @@ +from typing import Optional + +import torch +import torch.nn as nn + +import numpy as np + + +class MakePadMask(nn.Module): + def __init__(self, max_seq_len=512, flip=True): + super().__init__() + if flip: + self.mask_pad = torch.Tensor(1 - np.tri(max_seq_len)).type(torch.bool) + else: + self.mask_pad = torch.Tensor(np.tri(max_seq_len)).type(torch.bool) + + def forward(self, lengths, xs=None, length_dim=-1, maxlen=None): + """Make mask tensor containing indices of padded part. + This implementation creates the same mask tensor with original make_pad_mask, + which can be converted into onnx format. + Dimension length of xs should be 2 or 3. + """ + if length_dim == 0: + raise ValueError("length_dim cannot be 0: {}".format(length_dim)) + + if xs is not None and len(xs.shape) == 3: + if length_dim == 1: + lengths = lengths.unsqueeze(1).expand( + *xs.transpose(1, 2).shape[:2]) + else: + lengths = lengths.unsqueeze(1).expand(*xs.shape[:2]) + + if maxlen is not None: + m = maxlen + elif xs is not None: + m = xs.shape[-1] + else: + m = torch.max(lengths) + + mask = self.mask_pad[lengths - 1][..., :m].type(torch.float32) + + if length_dim == 1: + return mask.transpose(1, 2) + else: + return mask + + +def normalize(input: torch.Tensor, p: float = 2.0, dim: int = 1, out: Optional[torch.Tensor] = None) -> torch.Tensor: + if out is None: + denom = input.norm(p, dim, keepdim=True).expand_as(input) + return input / denom + else: + denom = input.norm(p, dim, keepdim=True).expand_as(input) + return torch.div(input, denom, out=out) + +def subsequent_mask(size: torch.Tensor): + return torch.ones(size, size).tril() + + +def MakePadMask_test(): + feats_length = torch.tensor([10]).type(torch.long) + mask_fn = MakePadMask() + mask = mask_fn(feats_length) + print(mask) + + +if __name__ == '__main__': + MakePadMask_test() \ No newline at end of file