Merge branch 'r1.20.0' into spellmapper_fix_bugs

docs/source/asr/models.rst

@@ -215,6 +215,11 @@ It is recommended to train a model in streaming model with limited context for t
 
 You may find FastConformer variants of cache-aware streaming models under ``<NeMo_git_root>/examples/asr/conf/fastconformer/``.
 
+Note cache-aware streaming models are being exported without caching support by default.
+To include caching support, `model.set_export_config({'cache_support' : 'True'})` should be called before export.
+Or, if ``<NeMo_git_root>/scripts/export.py`` is being used:
+`python export.py cache_aware_conformer.nemo cache_aware_conformer.onnx --config cache_support=True`
+
 .. _LSTM-Transducer_model:
 
 LSTM-Transducer
@@ -291,6 +296,11 @@ Similar example configs for FastConformer variants of Hybrid models can be found
 ``<NeMo_git_root>/examples/asr/conf/fastconformer/hybrid_transducer_ctc/``
 ``<NeMo_git_root>/examples/asr/conf/fastconformer/hybrid_cache_aware_streaming/``
 
+Note Hybrid models are being exported as RNNT (encoder and decoder+joint parts) by default.
+To export as CTC (single encoder+decoder graph), `model.set_export_config({'decoder_type' : 'ctc'})` should be called before export.
+Or, if ``<NeMo_git_root>/scripts/export.py`` is being used:
+`python export.py hybrid_transducer.nemo hybrid_transducer.onnx --config decoder_type=ctc`
+
 .. _Conformer-HAT_model:
 
 Conformer-HAT (Hybrid Autoregressive Transducer)

docs/source/core/export.rst

@@ -177,6 +177,37 @@ Another common requirement for models that are being exported is to run certain
  # call base method for common set of modifications
  Exportable._prepare_for_export(self, **kwargs)
 
+Some models that require control flow, need to be exported in multiple parts. Typical examples are RNNT nets.
+To facilitate that, the hooks below are provided. To export, for example, 'encoder' and 'decoder' subnets of the model, overload list_export_subnets to return ['encoder', 'decoder'].
+
+.. code-block:: Python
+
+ def get_export_subnet(self, subnet=None):
+ """
+ Returns Exportable subnet model/module to export 
+ """
+
+
+ def list_export_subnets(self):
+ """
+ Returns default set of subnet names exported for this model
+ First goes the one receiving input (input_example)
+ """
+
+Some nertworks may be exported differently according to user-settable options (like ragged batch support for TTS or cache support for ASR). To facilitate that - `set_export_config()` method is provided by Exportable to set key/value pairs to predefined model.export_config dictionary, to be used during the export:
+
+.. code-block:: Python 
+ def set_export_config(self, args):
+ """
+ Sets/updates export_config dictionary
+ """
+Also, if an action hook on setting config is desired, this method may be overloaded by `Exportable` descendants to include one.
+An example can be found in ``<NeMo_git_root>/nemo/collections/asr/models/rnnt_models.py``.
+
+Here is example on now `set_export_config()` call is being tied to command line arguments in ``<NeMo_git_root>/scripts/export.py`` :
+
+.. code-block:: Python
+ python scripts/export.py hybrid_conformer.nemo hybrid_conformer.onnx --config decoder_type=ctc
 
 Exportable Model Code
 ~~~~~~~~~~~~~~~~~~~~~

nemo/collections/asr/models/asr_model.py

@@ -161,7 +161,7 @@ def output_module(self):
  @property
  def output_names(self):
  otypes = self.output_module.output_types
- if hasattr(self.input_module, 'export_cache_support') and self.input_module.export_cache_support:
+ if getattr(self.input_module, 'export_cache_support', False):
  in_types = self.input_module.output_types
  otypes = {n: t for (n, t) in list(otypes.items())[:1]}
  for (n, t) in list(in_types.items())[1:]:
@@ -174,7 +174,6 @@ def forward_for_export(
  """
  This forward is used when we need to export the model to ONNX format.
  Inputs cache_last_channel and cache_last_time are needed to be passed for exporting streaming models.
- When they are passed, it just passes the inputs through the encoder part and currently the ONNX conversion does not fully work for this case.
  Args:
  input: Tensor that represents a batch of raw audio signals,
  of shape [B, T]. T here represents timesteps.
@@ -187,49 +186,26 @@ def forward_for_export(
  Returns:
  the output of the model
  """
- if hasattr(self.input_module, 'forward_for_export'):
- if cache_last_channel is None and cache_last_time is None:
- encoder_output = self.input_module.forward_for_export(audio_signal=input, length=length)
- else:
- encoder_output = self.input_module.forward_for_export(
- audio_signal=input,
- length=length,
- cache_last_channel=cache_last_channel,
- cache_last_time=cache_last_time,
- cache_last_channel_len=cache_last_channel_len,
- )
+ enc_fun = getattr(self.input_module, 'forward_for_export', self.input_module.forward)
+ if cache_last_channel is None:
+ encoder_output = enc_fun(audio_signal=input, length=length)
+ if isinstance(encoder_output, tuple):
+ encoder_output = encoder_output[0]
  else:
- if cache_last_channel is None and cache_last_time is None:
- encoder_output = self.input_module(audio_signal=input, length=length)
- else:
- encoder_output = self.input_module(
- audio_signal=input,
- length=length,
- cache_last_channel=cache_last_channel,
- cache_last_time=cache_last_time,
- cache_last_channel_len=cache_last_channel_len,
- )
- if isinstance(encoder_output, tuple):
- decoder_input = encoder_output[0]
- else:
- decoder_input = encoder_output
- if hasattr(self.output_module, 'forward_for_export'):
- if cache_last_channel is None and cache_last_time is None:
- ret = self.output_module.forward_for_export(encoder_output=decoder_input)
- else:
- ret = self.output_module.forward_for_export(encoder_output=decoder_input)
- else:
- if cache_last_channel is None and cache_last_time is None:
- ret = self.output_module(encoder_output=decoder_input)
- else:
- ret = self.output_module(encoder_output=decoder_input)
- if cache_last_channel is None and cache_last_time is None:
- pass
- else:
- if isinstance(ret, tuple):
- ret = (ret[0], encoder_output[1], encoder_output[2], encoder_output[3], encoder_output[4])
- else:
- ret = (ret, encoder_output[1], encoder_output[2], encoder_output[3], encoder_output[4])
+ encoder_output, length, cache_last_channel, cache_last_time, cache_last_channel_len = enc_fun(
+ audio_signal=input,
+ length=length,
+ cache_last_channel=cache_last_channel,
+ cache_last_time=cache_last_time,
+ cache_last_channel_len=cache_last_channel_len,
+ )
+
+ dec_fun = getattr(self.output_module, 'forward_for_export', self.output_module.forward)
+ ret = dec_fun(encoder_output=encoder_output)
+ if isinstance(ret, tuple):
+ ret = ret[0]
+ if cache_last_channel is not None:
+ ret = (ret, length, cache_last_channel, cache_last_time, cache_last_channel_len)
  return cast_all(ret, from_dtype=torch.float16, to_dtype=torch.float32)
 
  @property
@@ -239,3 +215,11 @@ def disabled_deployment_input_names(self):
  @property
  def disabled_deployment_output_names(self):
  return self.encoder.disabled_deployment_output_names
+
+ def set_export_config(self, args):
+ if 'cache_support' in args:
+ enable = bool(args['cache_support'])
+ self.encoder.export_cache_support = enable
+ logging.info(f"Caching support enabled: {enable}")
+ self.encoder.setup_streaming_params()
+ super().set_export_config(args)

nemo/collections/asr/models/hybrid_rnnt_ctc_models.py

@@ -645,6 +645,20 @@ def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0):
  self.finalize_interctc_metrics(metrics, outputs, prefix="test_")
  return metrics
 
+ # EncDecRNNTModel is exported in 2 parts
+ def list_export_subnets(self):
+ if self.cur_decoder == 'rnnt':
+ return ['encoder', 'decoder_joint']
+ else:
+ return ['self']
+
+ @property
+ def output_module(self):
+ if self.cur_decoder == 'rnnt':
+ return self.decoder
+ else:
+ return self.ctc_decoder
+
  @classmethod
  def list_available_models(cls) -> Optional[PretrainedModelInfo]:
  """

nemo/collections/asr/models/rnnt_models.py

@@ -28,7 +28,7 @@
 from nemo.collections.asr.data.audio_to_text_dali import AudioToCharDALIDataset, DALIOutputs
 from nemo.collections.asr.losses.rnnt import RNNTLoss, resolve_rnnt_default_loss_name
 from nemo.collections.asr.metrics.rnnt_wer import RNNTWER, RNNTDecoding, RNNTDecodingConfig
-from nemo.collections.asr.models.asr_model import ASRModel
+from nemo.collections.asr.models.asr_model import ASRModel, ExportableEncDecModel
 from nemo.collections.asr.modules.rnnt import RNNTDecoderJoint
 from nemo.collections.asr.parts.mixins import ASRModuleMixin
 from nemo.collections.asr.parts.utils.audio_utils import ChannelSelectorType
@@ -39,7 +39,7 @@
 from nemo.utils import logging
 
 
-class EncDecRNNTModel(ASRModel, ASRModuleMixin, Exportable):
+class EncDecRNNTModel(ASRModel, ASRModuleMixin, ExportableEncDecModel):
  """Base class for encoder decoder RNNT-based models."""
 
  def __init__(self, cfg: DictConfig, trainer: Trainer = None):
@@ -960,6 +960,14 @@ def list_export_subnets(self):
  def decoder_joint(self):
  return RNNTDecoderJoint(self.decoder, self.joint)
 
+ def set_export_config(self, args):
+ if 'decoder_type' in args:
+ if hasattr(self, 'change_decoding_strategy'):
+ self.change_decoding_strategy(decoder_type=args['decoder_type'])
+ else:
+ raise Exception("Model does not have decoder type option")
+ super().set_export_config(args)
+
  @classmethod
  def list_available_models(cls) -> List[PretrainedModelInfo]:
  """

nemo/collections/asr/modules/conformer_encoder.py

@@ -505,11 +505,6 @@ def forward_internal(
  (audio_signal.size(0),), audio_signal.size(-1), dtype=torch.int64, device=audio_signal.device
  )
 
- if cache_last_time is not None:
- cache_last_time_next = torch.zeros_like(cache_last_time)
- else:
- cache_last_time_next = None
-
  # select a random att_context_size with the distribution specified by att_context_probs during training
  # for non-validation cases like test, validation or inference, it uses the first mode in self.att_context_size
  if self.training and len(self.att_context_size_all) > 1:
@@ -536,7 +531,6 @@ def forward_internal(
  if cache_last_channel is not None:
  cache_len = self.streaming_cfg.last_channel_cache_size
  cache_keep_size = max_audio_length - self.streaming_cfg.cache_drop_size
- cache_last_channel_next = torch.zeros_like(cache_last_channel)
  max_audio_length = max_audio_length + cache_len
  padding_length = length + cache_len
  offset = torch.neg(cache_last_channel_len) + cache_len
@@ -561,19 +555,32 @@ def forward_internal(
  pad_mask = pad_mask[:, cache_len:]
  if att_mask is not None:
  att_mask = att_mask[:, cache_len:]
+ # Convert caches from the tensor to list
+ cache_last_time_next = []
+ cache_last_channel_next = []
 
  for lth, (drop_prob, layer) in enumerate(zip(self.layer_drop_probs, self.layers)):
  original_signal = audio_signal
+ if cache_last_channel is not None:
+ cache_last_channel_cur = cache_last_channel[lth]
+ cache_last_time_cur = cache_last_time[lth]
+ else:
+ cache_last_channel_cur = None
+ cache_last_time_cur = None
  audio_signal = layer(
  x=audio_signal,
  att_mask=att_mask,
  pos_emb=pos_emb,
  pad_mask=pad_mask,
- cache_last_channel=cache_last_channel,
- cache_last_time=cache_last_time,
- cache_last_channel_next=cache_last_channel_next,
- cache_last_time_next=cache_last_time_next,
+ cache_last_channel=cache_last_channel_cur,
+ cache_last_time=cache_last_time_cur,
  )
+
+ if cache_last_channel_cur is not None:
+ (audio_signal, cache_last_channel_cur, cache_last_time_cur) = audio_signal
+ cache_last_channel_next.append(cache_last_channel_cur)
+ cache_last_time_next.append(cache_last_time_cur)
+
  # applying stochastic depth logic from https://arxiv.org/abs/2102.03216
  if self.training and drop_prob > 0.0:
  should_drop = torch.rand(1) < drop_prob
@@ -626,6 +633,8 @@ def forward_internal(
  length = length.to(dtype=torch.int64)
 
  if cache_last_channel is not None:
+ cache_last_channel_next = torch.stack(cache_last_channel_next, dim=0)
+ cache_last_time_next = torch.stack(cache_last_time_next, dim=0)
  return (
  audio_signal,
  length,
@@ -860,20 +869,12 @@ def setup_streaming_params(
  else:
  streaming_cfg.drop_extra_pre_encoded = streaming_cfg.pre_encode_cache_size // self.subsampling_factor
 
- # counting the number of the layers need caching
- streaming_cfg.last_channel_num = 0
- streaming_cfg.last_time_num = 0
  for m in self.layers.modules():
  if hasattr(m, "_max_cache_len"):
  if isinstance(m, MultiHeadAttention):
- m._cache_id = streaming_cfg.last_channel_num
  m.cache_drop_size = streaming_cfg.cache_drop_size
- streaming_cfg.last_channel_num += 1
-
  if isinstance(m, CausalConv1D):
- m._cache_id = streaming_cfg.last_time_num
  m.cache_drop_size = streaming_cfg.cache_drop_size
- streaming_cfg.last_time_num += 1
 
  self.streaming_cfg = streaming_cfg
 
@@ -886,19 +887,12 @@ def get_initial_cache_state(self, batch_size=1, dtype=torch.float32, device=None
  create_tensor = torch.zeros
  last_time_cache_size = self.conv_context_size[0]
  cache_last_channel = create_tensor(
- (
- self.streaming_cfg.last_channel_num,
- batch_size,
- self.streaming_cfg.last_channel_cache_size,
- self.d_model,
- ),
+ (len(self.layers), batch_size, self.streaming_cfg.last_channel_cache_size, self.d_model,),
  device=device,
  dtype=dtype,
  )
  cache_last_time = create_tensor(
- (self.streaming_cfg.last_time_num, batch_size, self.d_model, last_time_cache_size),
- device=device,
- dtype=dtype,
+ (len(self.layers), batch_size, self.d_model, last_time_cache_size), device=device, dtype=dtype,
  )
  if max_dim > 0:
  cache_last_channel_len = torch.randint(

nemo/collections/asr/parts/submodules/adapters/multi_head_attention_adapter_module.py

@@ -147,26 +147,26 @@ def __init__(
  # reset parameters for Q to be identity operation
  self.reset_parameters()
 
- def forward(self, query, key, value, mask, pos_emb=None, cache=None, cache_next=None):
+ def forward(self, query, key, value, mask, pos_emb=None, cache=None):
  """Compute 'Scaled Dot Product Attention'.
  Args:
  query (torch.Tensor): (batch, time1, size)
  key (torch.Tensor): (batch, time2, size)
  value(torch.Tensor): (batch, time2, size)
  mask (torch.Tensor): (batch, time1, time2)
- cache (torch.Tensor) : (cache_nums, batch, time_cache, size)
- cache_next (torch.Tensor) : (cache_nums, batch, time_cache_next, size)
+ cache (torch.Tensor) : (batch, time_cache, size)
 
  returns:
  output (torch.Tensor): transformed `value` (batch, time1, d_model) weighted by the query dot key attention
+ cache (torch.Tensor) : (batch, time_cache_next, size)
  """
  # Need to perform duplicate computations as at this point the tensors have been
  # separated by the adapter forward
  query = self.pre_norm(query)
  key = self.pre_norm(key)
  value = self.pre_norm(value)
 
- return super().forward(query, key, value, mask, pos_emb, cache=cache, cache_next=cache_next)
+ return super().forward(query, key, value, mask, pos_emb, cache=cache)
 
  def reset_parameters(self):
  with torch.no_grad():
@@ -242,26 +242,26 @@ def __init__(
  # reset parameters for Q to be identity operation
  self.reset_parameters()
 
- def forward(self, query, key, value, mask, pos_emb, cache=None, cache_next=None):
+ def forward(self, query, key, value, mask, pos_emb, cache=None):
  """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
  Args:
  query (torch.Tensor): (batch, time1, size)
  key (torch.Tensor): (batch, time2, size)
  value(torch.Tensor): (batch, time2, size)
  mask (torch.Tensor): (batch, time1, time2)
  pos_emb (torch.Tensor) : (batch, time1, size)
- cache (torch.Tensor) : (cache_nums, batch, time_cache, size)
- cache_next (torch.Tensor) : (cache_nums, batch, time_cache_next, size)
+ cache (torch.Tensor) : (batch, time_cache, size)
  Returns:
  output (torch.Tensor): transformed `value` (batch, time1, d_model) weighted by the query dot key attention
+ cache_next (torch.Tensor) : (batch, time_cache_next, size)
  """
  # Need to perform duplicate computations as at this point the tensors have been
  # separated by the adapter forward
  query = self.pre_norm(query)
  key = self.pre_norm(key)
  value = self.pre_norm(value)
 
- return super().forward(query, key, value, mask, pos_emb, cache=cache, cache_next=cache_next)
+ return super().forward(query, key, value, mask, pos_emb, cache=cache)
 
  def reset_parameters(self):
  with torch.no_grad():