fix bugs

deepspeech/__init__.py

@@ -168,6 +168,8 @@ if not hasattr(paddle.Tensor, 'new_full'):
 
 
 def eq(xs: paddle.Tensor, ys: Union[paddle.Tensor, float]) -> paddle.Tensor:
+    if convert_dtype_to_string(xs.dtype) == paddle.bool:
+        xs = xs.astype(paddle.int)
     return xs.equal(
         paddle.to_tensor(
             ys, dtype=convert_dtype_to_string(xs.dtype), place=xs.place))
@@ -262,7 +264,7 @@ def masked_fill_(xs: paddle.Tensor,
     mask = mask.broadcast_to(bshape)
     trues = paddle.ones_like(xs) * value
     ret = paddle.where(mask, trues, xs)
-    paddle.assign(ret, output=xs)
+    paddle.assign(ret.detach(), output=xs)
 
 
 if not hasattr(paddle.Tensor, 'masked_fill_'):
@@ -273,7 +275,7 @@ if not hasattr(paddle.Tensor, 'masked_fill_'):
 
 def fill_(xs: paddle.Tensor, value: Union[float, int]):
     val = paddle.full_like(xs, value)
-    paddle.assign(val, output=xs)
+    paddle.assign(val.detach(), output=xs)
 
 
 if not hasattr(paddle.Tensor, 'fill_'):

deepspeech/models/deepspeech2.py

@@ -162,8 +162,8 @@ class DeepSpeech2Model(nn.Layer):
         assert (self.encoder.output_size == rnn_size * 2)
 
         self.decoder = CTCDecoder(
-            enc_n_units=self.encoder.output_size,
             odim=dict_size + 1,  # <blank> is append after vocab
+            enc_n_units=self.encoder.output_size,
             blank_id=dict_size,  # last token is <blank>
             dropout_rate=0.0,
             reduction=True)

deepspeech/models/u2.py

@@ -112,7 +112,10 @@ class U2Model(nn.Module):
                                          text.shape, text_lengths.shape)
         # 1. Encoder
         encoder_out, encoder_mask = self.encoder(speech, speech_lengths)
-        encoder_out_lens = encoder_mask.squeeze(1).sum(1)  #[B, 1, T] -> [B]
+        #TODO(Hui Zhang): sum not support bool type
+        #encoder_out_lens = encoder_mask.squeeze(1).sum(1)  #[B, 1, T] -> [B]
+        encoder_out_lens = encoder_mask.squeeze(1).astype(paddle.int64).sum(
+            1)  #[B, 1, T] -> [B]
 
         # 2a. Attention-decoder branch
         loss_att = None

deepspeech/modules/conformer_convolution.py

@@ -139,7 +139,7 @@ class ConvolutionModule(nn.Layer):
             # It's better we just return None if no cache is requried,
             # However, for JIT export, here we just fake one tensor instead of
             # None.
-            new_cache = paddle.to_tensor([0.0], dtype=x.dtype, place=x.place)
+            new_cache = paddle.zeros([1], dtype=x.dtype)
 
         # GLU mechanism
         x = self.pointwise_conv1(x)  # (batch, 2*channel, dim)

deepspeech/modules/ctc.py

@@ -34,16 +34,16 @@ __all__ = ['CTCDecoder']
 
 class CTCDecoder(nn.Layer):
     def __init__(self,
-                 enc_n_units,
                  odim,
+                 enc_n_units,
                  blank_id=0,
                  dropout_rate: float=0.0,
                  reduction: bool=True):
         """CTC decoder
 
         Args:
+            odim ([int]): text vocabulary size
             enc_n_units ([int]): encoder output dimention
-            vocab_size ([int]): text vocabulary size
             dropout_rate (float): dropout rate (0.0 ~ 1.0)
             reduction (bool): reduce the CTC loss into a scalar
         """

deepspeech/modules/decoder.py

@@ -26,7 +26,7 @@ from deepspeech.modules.decoder_layer import DecoderLayer
 from deepspeech.modules.embedding import PositionalEncoding
 from deepspeech.modules.positionwise_feed_forward import PositionwiseFeedForward
 from deepspeech.modules.mask import subsequent_mask
-from deepspeech.modules.mask import make_pad_mask
+from deepspeech.modules.mask import make_non_pad_mask
 
 logger = logging.getLogger(__name__)
 
@@ -124,7 +124,9 @@ class TransformerDecoder(nn.Module):
         # m: (1, L, L)
         m = subsequent_mask(tgt_mask.size(-1)).unsqueeze(0)
         # tgt_mask: (B, L, L)
-        tgt_mask = tgt_mask & m
+        # TODO(Hui Zhang): not support & for tensor
+        #tgt_mask = tgt_mask & m
+        tgt_mask = tgt_mask.logical_and(m)
 
         x, _ = self.embed(tgt)
         for layer in self.decoders:
@@ -135,7 +137,9 @@ class TransformerDecoder(nn.Module):
         if self.use_output_layer:
             x = self.output_layer(x)
 
-        olens = tgt_mask.sum(1)
+        #TODO(Hui Zhang): reduce_sum not support bool type
+        #olens = tgt_mask.sum(1)
+        olens = tgt_mask.astype(paddle.int).sum(1)
         return x, olens
 
     def forward_one_step(

deepspeech/modules/encoder.py

@@ -155,12 +155,15 @@ class BaseEncoder(nn.Layer):
             encoder output tensor, lens and mask
         """
         masks = make_non_pad_mask(xs_lens).unsqueeze(1)  # (B, 1, L)
-        #TODO(Hui Zhang): mask_pad = ~masks
-        mask_pad = masks.logical_not()
+
         if self.global_cmvn is not None:
             xs = self.global_cmvn(xs)
         #TODO(Hui Zhang): self.embed(xs, masks, offset=0), stride_slice not support bool tensor
         xs, pos_emb, masks = self.embed(xs, masks.type_as(xs), offset=0)
+        #TODO(Hui Zhang): remove mask.astype, stride_slice not support bool tensor
+        masks = masks.astype(paddle.bool)
+        #TODO(Hui Zhang): mask_pad = ~masks
+        mask_pad = masks.logical_not()
         chunk_masks = add_optional_chunk_mask(
             xs, masks, self.use_dynamic_chunk, self.use_dynamic_left_chunk,
             decoding_chunk_size, self.static_chunk_size,

deepspeech/modules/loss.py

@@ -117,13 +117,12 @@ class LabelSmoothingLoss(nn.Layer):
         B, T, D = paddle.shape(x)
         assert D == self.size
         x = x.reshape((-1, self.size))
-        target = target.reshape(-1)
+        target = target.reshape([-1])
 
         # use zeros_like instead of torch.no_grad() for true_dist,
         # since no_grad() can not be exported by JIT
         true_dist = paddle.full_like(x, self.smoothing / (self.size - 1))
         ignore = target == self.padding_idx  # (B,)
-        ignore = ignore.cast(target.dtype)
 
         #target = target * (1 - ignore)  # avoid -1 index
         target = target.masked_fill(ignore, 0)  # avoid -1 index
@@ -131,7 +130,9 @@ class LabelSmoothingLoss(nn.Layer):
 
         kl = self.criterion(F.log_softmax(x, axis=1), true_dist)
 
-        total = len(target) - int(ignore.sum())
+        #TODO(Hui Zhang): sum not support bool type
+        #total = len(target) - int(ignore.sum())
+        total = len(target) - int(ignore.type_as(target).sum())
         denom = total if self.normalize_length else B
         #numer = (kl * (1 - ignore)).sum()
         numer = kl.masked_fill(ignore.unsqueeze(1), 0).sum()

deepspeech/modules/mask.py

@@ -97,6 +97,7 @@ def make_non_pad_mask(lengths: paddle.Tensor) -> paddle.Tensor:
                  [1, 1, 1, 0, 0],
                  [1, 1, 0, 0, 0]]
     """
+    #TODO(Hui Zhang): return ~make_pad_mask(lengths), not support ~
     return make_pad_mask(lengths).logical_not()
 
 
@@ -119,7 +120,12 @@ def subsequent_mask(size: int) -> paddle.Tensor:
          [1, 1, 1]]
     """
     ret = paddle.ones([size, size], dtype=paddle.bool)
-    return paddle.tril(ret)
+    #TODO(Hui Zhang): tril not support bool
+    #return paddle.tril(ret)
+    ret = ret.astype(paddle.float)
+    ret = paddle.tril(ret)
+    ret = ret.astype(paddle.bool)
+    return ret
 
 
 def subsequent_chunk_mask(

deepspeech/utils/tensor_utils.py

@@ -115,14 +115,28 @@ def add_sos_eos(ys_pad: paddle.Tensor, sos: int, eos: int,
                 [ 4,  5,  6, 11, -1, -1],
                 [ 7,  8,  9, 11, -1, -1]])
     """
-    _sos = paddle.to_tensor(
-        [sos], dtype=paddle.long, stop_gradient=True, place=ys_pad.place)
-    _eos = paddle.to_tensor(
-        [eos], dtype=paddle.long, stop_gradient=True, place=ys_pad.place)
-    ys = [y[y != ignore_id] for y in ys_pad]  # parse padded ys
-    ys_in = [paddle.cat([_sos, y], dim=0) for y in ys]
-    ys_out = [paddle.cat([y, _eos], dim=0) for y in ys]
-    return pad_sequence(ys_in, padding_value=eos), pad_sequence(ys_out, padding_value=ignore_id)
+    # TODO(Hui Zhang): using comment code, 
+    #_sos = paddle.to_tensor(
+    #    [sos], dtype=paddle.long, stop_gradient=True, place=ys_pad.place)
+    #_eos = paddle.to_tensor(
+    #    [eos], dtype=paddle.long, stop_gradient=True, place=ys_pad.place)
+    #ys = [y[y != ignore_id] for y in ys_pad]  # parse padded ys
+    #ys_in = [paddle.cat([_sos, y], dim=0) for y in ys]
+    #ys_out = [paddle.cat([y, _eos], dim=0) for y in ys]
+    #return pad_sequence(ys_in, padding_value=eos), pad_sequence(ys_out, padding_value=ignore_id)
+    B = ys_pad.size(0)
+    _sos = paddle.ones([B, 1], dtype=ys_pad.dtype) * sos
+    _eos = paddle.ones([B, 1], dtype=ys_pad.dtype) * eos
+    ys_in = paddle.cat([_sos, ys_pad], dim=1)
+    mask_pad = (ys_in == ignore_id)
+    ys_in = ys_in.masked_fill(mask_pad, eos)
+
+    ys_out = paddle.cat([ys_pad, _eos], dim=1)
+    ys_out = ys_out.masked_fill(mask_pad, eos)
+    mask_eos = (ys_in == ignore_id)
+    ys_out = ys_out.masked_fill(mask_eos, eos)
+    ys_out = ys_out.masked_fill(mask_pad, ignore_id)
+    return ys_in, ys_out
 
 
 def th_accuracy(pad_outputs: paddle.Tensor,
@@ -139,7 +153,13 @@ def th_accuracy(pad_outputs: paddle.Tensor,
     pad_pred = pad_outputs.view(
         pad_targets.size(0), pad_targets.size(1), pad_outputs.size(1)).argmax(2)
     mask = pad_targets != ignore_label
-    numerator = paddle.sum(
+    #TODO(Hui Zhang): sum not support bool type
+    # numerator = paddle.sum(
+    #     pad_pred.masked_select(mask) == pad_targets.masked_select(mask))
+    numerator = (
         pad_pred.masked_select(mask) == pad_targets.masked_select(mask))
-    denominator = paddle.sum(mask)
+    numerator = paddle.sum(numerator.type_as(pad_targets))
+    #TODO(Hui Zhang): sum not support bool type
+    # denominator = paddle.sum(mask)
+    denominator = paddle.sum(mask.type_as(pad_targets))
     return float(numerator) / float(denominator)

tests/u2_model_test.py

@@ -86,8 +86,11 @@ class TestU2Model(unittest.TestCase):
         cfg.freeze()
         model = U2TransformerModel(cfg)
         summary(model, None)
-        output = model(self.audio, self.audio_len, self.text, self.text_len)
-        print(output)
+        total_loss, attention_loss, ctc_loss = model(self.audio, self.audio_len,
+                                                     self.text, self.text_len)
+        self.assertEqual(total_loss.numel(), 1)
+        self.assertEqual(attention_loss.numel(), 1)
+        self.assertEqual(ctc_loss.numel(), 1)
 
     def test_conformer(self):
         conf_str = """
@@ -135,8 +138,11 @@ class TestU2Model(unittest.TestCase):
         cfg.freeze()
         model = U2ConformerModel(cfg)
         summary(model, None)
-        output = model(self.audio, self.audio_len, self.text, self.text_len)
-        print(output)
+        total_loss, attention_loss, ctc_loss = model(self.audio, self.audio_len,
+                                                     self.text, self.text_len)
+        self.assertEqual(total_loss.numel(), 1)
+        self.assertEqual(attention_loss.numel(), 1)
+        self.assertEqual(ctc_loss.numel(), 1)
 
 
 if __name__ == '__main__':