fix bugs

deepspeech/__init__.py

@@ -22,7 +22,8 @@ import paddle
 from paddle import nn
 from paddle.nn import functional as F
 from paddle.nn import initializer as I
-
+#TODO(Hui Zhang): remove  fluid import
+from paddle.fluid import core
 logger = logging.getLogger(__name__)
 
 ########### hcak logging #############
@@ -44,10 +45,51 @@ paddle.int = 'int32'
 paddle.int64 = 'int64'
 paddle.long = 'int64'
 paddle.uint8 = 'uint8'
+paddle.uint16 = 'uint16'
 paddle.complex64 = 'complex64'
 paddle.complex128 = 'complex128'
 paddle.cdouble = 'complex128'
 
+
+def convert_dtype_to_string(tensor_dtype):
+    """
+    Convert the data type in numpy to the data type in Paddle
+    Args:
+        tensor_dtype(core.VarDesc.VarType): the data type in numpy.
+    Returns:
+        core.VarDesc.VarType: the data type in Paddle.
+    """
+    dtype = tensor_dtype
+    if dtype == core.VarDesc.VarType.FP32:
+        return paddle.float32
+    elif dtype == core.VarDesc.VarType.FP64:
+        return paddle.float64
+    elif dtype == core.VarDesc.VarType.FP16:
+        return paddle.float16
+    elif dtype == core.VarDesc.VarType.INT32:
+        return paddle.int32
+    elif dtype == core.VarDesc.VarType.INT16:
+        return paddle.int16
+    elif dtype == core.VarDesc.VarType.INT64:
+        return paddle.int64
+    elif dtype == core.VarDesc.VarType.BOOL:
+        return paddle.bool
+    elif dtype == core.VarDesc.VarType.BF16:
+        # since there is still no support for bfloat16 in NumPy,
+        # uint16 is used for casting bfloat16
+        return paddle.uint16
+    elif dtype == core.VarDesc.VarType.UINT8:
+        return paddle.uint8
+    elif dtype == core.VarDesc.VarType.INT8:
+        return paddle.int8
+    elif dtype == core.VarDesc.VarType.COMPLEX64:
+        return paddle.complex64
+    elif dtype == core.VarDesc.VarType.COMPLEX128:
+        return paddle.complex128
+    else:
+        raise ValueError("Not supported tensor dtype %s" % dtype)
+
+
 if not hasattr(paddle, 'softmax'):
     logger.warn("register user softmax to paddle, remove this when fixed!")
     setattr(paddle, 'softmax', paddle.nn.functional.softmax)
@@ -126,7 +168,9 @@ if not hasattr(paddle.Tensor, 'new_full'):
 
 
 def eq(xs: paddle.Tensor, ys: Union[paddle.Tensor, float]) -> paddle.Tensor:
-    return xs.equal(paddle.to_tensor(ys, dtype=xs.dtype, place=xs.place))
+    return xs.equal(
+        paddle.to_tensor(
+            ys, dtype=convert_dtype_to_string(xs.dtype), place=xs.place))
 
 
 if not hasattr(paddle.Tensor, 'eq'):
@@ -184,10 +228,21 @@ if not hasattr(paddle.Tensor, 'view_as'):
     paddle.Tensor.view_as = view_as
 
 
+def is_broadcastable(shp1, shp2):
+    for a, b in zip(shp1[::-1], shp2[::-1]):
+        if a == 1 or b == 1 or a == b:
+            pass
+        else:
+            return False
+    return True
+
+
 def masked_fill(xs: paddle.Tensor,
                 mask: paddle.Tensor,
                 value: Union[float, int]):
-    assert xs.shape == mask.shape
+    assert is_broadcastable(xs.shape, mask.shape) == True
+    bshape = paddle.broadcast_shape(xs.shape, mask.shape)
+    mask = mask.broadcast_to(bshape)
     trues = paddle.ones_like(xs) * value
     xs = paddle.where(mask, trues, xs)
     return xs
@@ -202,7 +257,9 @@ if not hasattr(paddle.Tensor, 'masked_fill'):
 def masked_fill_(xs: paddle.Tensor,
                  mask: paddle.Tensor,
                  value: Union[float, int]):
-    assert xs.shape == mask.shape
+    assert is_broadcastable(xs.shape, mask.shape) == True
+    bshape = paddle.broadcast_shape(xs.shape, mask.shape)
+    mask = mask.broadcast_to(bshape)
     trues = paddle.ones_like(xs) * value
     ret = paddle.where(mask, trues, xs)
     paddle.assign(ret, output=xs)
@@ -414,4 +471,4 @@ if not hasattr(paddle.nn, 'ConstantPad2d'):
 
 if not hasattr(paddle.jit, 'export'):
     logger.warn("register user export to paddle.jit, remove this when fixed!")
-    setattr(paddle.jit, 'export', paddle.jit.to_static)
+    setattr(paddle.jit, 'export', paddle.jit.to_static)

deepspeech/models/u2.py

@@ -39,7 +39,7 @@ from deepspeech.modules.encoder import ConformerEncoder
 from deepspeech.modules.encoder import TransformerEncoder
 from deepspeech.modules.ctc import CTCDecoder
 from deepspeech.modules.decoder import TransformerDecoder
-from deepspeech.modules.label_smoothing_loss import LabelSmoothingLoss
+from deepspeech.modules.loss import LabelSmoothingLoss
 
 from deepspeech.frontend.utility import load_cmvn
 
@@ -633,7 +633,7 @@ class U2Model(nn.Module):
 
 
 class U2TransformerModel(U2Model):
-    def __init__(configs: dict):
+    def __init__(self, configs: dict):
         if configs['cmvn_file'] is not None:
             mean, istd = load_cmvn(configs['cmvn_file'],
                                    configs['cmvn_file_type'])
@@ -655,7 +655,7 @@ class U2TransformerModel(U2Model):
                                      **configs['decoder_conf'])
         ctc = CTCDecoder(vocab_size, encoder.output_size())
 
-        self.__init__(
+        super().__init__(
             vocab_size=vocab_size,
             encoder=encoder,
             decoder=decoder,
@@ -664,7 +664,7 @@ class U2TransformerModel(U2Model):
 
 
 class U2ConformerModel(U2Model):
-    def __init__(configs: dict):
+    def __init__(self, configs: dict):
         if configs['cmvn_file'] is not None:
             mean, istd = load_cmvn(configs['cmvn_file'],
                                    configs['cmvn_file_type'])
@@ -686,7 +686,7 @@ class U2ConformerModel(U2Model):
                                      **configs['decoder_conf'])
         ctc = CTCDecoder(vocab_size, encoder.output_size())
 
-        self.__init__(
+        super().__init__(
             vocab_size=vocab_size,
             encoder=encoder,
             decoder=decoder,

deepspeech/modules/attention.py

@@ -217,11 +217,11 @@ class RelPositionMultiHeadedAttention(MultiHeadedAttention):
         # first compute matrix a and matrix c
         # as described in https://arxiv.org/abs/1901.02860 Section 3.3
         # (batch, head, time1, time2)
-        matrix_ac = torch.matmul(q_with_bias_u, k.transpose([0, 1, 3, 2]))
+        matrix_ac = paddle.matmul(q_with_bias_u, k.transpose([0, 1, 3, 2]))
 
         # compute matrix b and matrix d
         # (batch, head, time1, time2)
-        matrix_bd = torch.matmul(q_with_bias_v, p.transpose([0, 1, 3, 2]))
+        matrix_bd = paddle.matmul(q_with_bias_v, p.transpose([0, 1, 3, 2]))
         # Remove rel_shift since it is useless in speech recognition,
         # and it requires special attention for streaming.
         # matrix_bd = self.rel_shift(matrix_bd)

deepspeech/modules/embedding.py

@@ -48,7 +48,7 @@ class PositionalEncoding(nn.Layer):
         self.max_len = max_len
         self.xscale = paddle.to_tensor(math.sqrt(self.d_model))
         self.dropout = nn.Dropout(p=dropout_rate)
-        self.pe = paddle.zeros(self.max_len, self.d_model)  #[T,D]
+        self.pe = paddle.zeros([self.max_len, self.d_model])  #[T,D]
 
         position = paddle.arange(
             0, self.max_len, dtype=paddle.float32).unsqueeze(1)  #[T, 1]
@@ -70,11 +70,9 @@ class PositionalEncoding(nn.Layer):
             paddle.Tensor: Encoded tensor. Its shape is (batch, time, ...)
             paddle.Tensor: for compatibility to RelPositionalEncoding, (batch=1, time, ...)
         """
-        T = paddle.shape(x)[1]
-        assert offset + T < self.max_len
-        #assert offset + x.size(1) < self.max_len
-        #self.pe = self.pe.to(x.device)
-        #pos_emb = self.pe[:, offset:offset + x.size(1)]
+        T = x.shape[1]
+        assert offset + x.size(1) < self.max_len
+        #TODO(Hui Zhang): using T = x.size(1), __getitem__ not support Tensor
         pos_emb = self.pe[:, offset:offset + T]
         x = x * self.xscale + pos_emb
         return self.dropout(x), self.dropout(pos_emb)
@@ -119,11 +117,8 @@ class RelPositionalEncoding(PositionalEncoding):
             paddle.Tensor: Encoded tensor (batch, time, `*`).
             paddle.Tensor: Positional embedding tensor (1, time, `*`).
         """
-        #T = paddle.shape()[1]
-        #assert offset + T < self.max_len
         assert offset + x.size(1) < self.max_len
-        #self.pe = self.pe.to(x.device)
         x = x * self.xscale
-        pos_emb = self.pe[:, offset:offset + x.size(1)]
-        #pos_emb = self.pe[:, offset:offset + T]
+        #TODO(Hui Zhang): using x.size(1), __getitem__ not support Tensor
+        pos_emb = self.pe[:, offset:offset + x.shape[1]]
         return self.dropout(x), self.dropout(pos_emb)

deepspeech/modules/encoder.py

@@ -23,7 +23,7 @@ from paddle.nn import initializer as I
 
 from deepspeech.modules.attention import MultiHeadedAttention
 from deepspeech.modules.attention import RelPositionMultiHeadedAttention
-from deepspeech.modules.convolution import ConvolutionModule
+from deepspeech.modules.conformer_convolution import ConvolutionModule
 from deepspeech.modules.embedding import PositionalEncoding
 from deepspeech.modules.embedding import RelPositionalEncoding
 from deepspeech.modules.encoder_layer import TransformerEncoderLayer
@@ -33,7 +33,7 @@ from deepspeech.modules.subsampling import Conv2dSubsampling4
 from deepspeech.modules.subsampling import Conv2dSubsampling6
 from deepspeech.modules.subsampling import Conv2dSubsampling8
 from deepspeech.modules.subsampling import LinearNoSubsampling
-from deepspeech.modules.mask import make_pad_mask
+from deepspeech.modules.mask import make_non_pad_mask
 from deepspeech.modules.mask import add_optional_chunk_mask
 from deepspeech.modules.activation import get_activation
 
@@ -155,10 +155,12 @@ 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)
-        xs, pos_emb, masks = self.embed(xs, masks, offset=0)
-        mask_pad = ~masks
+        #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)
         chunk_masks = add_optional_chunk_mask(
             xs, masks, self.use_dynamic_chunk, self.use_dynamic_left_chunk,
             decoding_chunk_size, self.static_chunk_size,
@@ -380,7 +382,7 @@ class ConformerEncoder(BaseEncoder):
             concat_after: bool=False,
             static_chunk_size: int=0,
             use_dynamic_chunk: bool=False,
-            global_cmvn: torch.nn.Module=None,
+            global_cmvn: nn.Layer=None,
             use_dynamic_left_chunk: bool=False,
             positionwise_conv_kernel_size: int=1,
             macaron_style: bool=True,
@@ -431,7 +433,7 @@ class ConformerEncoder(BaseEncoder):
         self.encoders = nn.ModuleList([
             ConformerEncoderLayer(
                 size=output_size,
-                eself_attn=ncoder_selfattn_layer(*encoder_selfattn_layer_args),
+                self_attn=encoder_selfattn_layer(*encoder_selfattn_layer_args),
                 feed_forward=positionwise_layer(*positionwise_layer_args),
                 feed_forward_macaron=positionwise_layer(
                     *positionwise_layer_args) if macaron_style else None,

deepspeech/modules/encoder_layer.py

@@ -127,7 +127,7 @@ class TransformerEncoderLayer(nn.Layer):
         if output_cache is not None:
             x = paddle.concat([output_cache, x], axis=1)
 
-        fake_cnn_cache = paddle.to_tensor([0.0], dtype=x.dtype, place=x.place)
+        fake_cnn_cache = paddle.zeros([1], dtype=x.dtype)
         return x, mask, fake_cnn_cache
 
 
@@ -253,7 +253,7 @@ class ConformerEncoderLayer(nn.Layer):
 
         # convolution module
         # Fake new cnn cache here, and then change it in conv_module
-        new_cnn_cache = paddle.to_tensor([0.0], dtype=x.dtype, place=x.place)
+        new_cnn_cache = paddle.zeros([1], dtype=x.dtype)
         if self.conv_module is not None:
             residual = x
             if self.normalize_before:

deepspeech/utils/layer_tools.py

@@ -24,13 +24,18 @@ __all__ = [
 
 def summary(layer: nn.Layer, print_func=print):
     num_params = num_elements = 0
-    print_func("layer summary:")
+    if print_func:
+        print_func(f"{layer.__class__.__name__} summary:")
     for name, param in layer.state_dict().items():
-        print_func("{}|{}|{}".format(name, param.shape, np.prod(param.shape)))
+        if print_func:
+            print_func(
+                "{} | {} | {}".format(name, param.shape, np.prod(param.shape)))
         num_elements += np.prod(param.shape)
         num_params += 1
-    print_func("layer has {} parameters, {} elements.".format(num_params,
-                                                              num_elements))
+    if print_func:
+        print_func(
+            f"{layer.__class__.__name__} has {num_params} parameters, {num_elements} elements."
+        )
 
 
 def gradient_norm(layer: nn.Layer):

deepspeech/utils/tensor_utils.py

@@ -122,7 +122,7 @@ def add_sos_eos(ys_pad: paddle.Tensor, sos: int, eos: int,
     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_list(ys_in, eos), pad_list(ys_out, ignore_id)
+    return pad_sequence(ys_in, padding_value=eos), pad_sequence(ys_out, padding_value=ignore_id)
 
 
 def th_accuracy(pad_outputs: paddle.Tensor,

tests/u2_model_test.py

@@ -20,6 +20,7 @@ from yacs.config import CfgNode as CN
 
 from deepspeech.models.u2 import U2TransformerModel
 from deepspeech.models.u2 import U2ConformerModel
+from deepspeech.utils.layer_tools import summary
 
 
 class TestU2Model(unittest.TestCase):
@@ -27,8 +28,9 @@ class TestU2Model(unittest.TestCase):
         paddle.set_device('cpu')
 
         self.batch_size = 2
-        self.feat_dim = 161
+        self.feat_dim = 83
         self.max_len = 64
+        self.vocab_size = 4239
 
         #(B, T, D)
         audio = np.random.randn(self.batch_size, self.max_len, self.feat_dim)
@@ -77,8 +79,15 @@ class TestU2Model(unittest.TestCase):
                 length_normalized_loss: false
         """
         cfg = CN().load_cfg(conf_str)
-        print(cfg)
-        model = U2TransformerModel()
+        cfg.input_dim = self.feat_dim
+        cfg.output_dim = self.vocab_size
+        cfg.cmvn_file = None
+        cfg.cmvn_file_type = 'npz'
+        cfg.freeze()
+        model = U2TransformerModel(cfg)
+        summary(model, None)
+        output = model(self.audio, self.audio_len, self.text, self.text_len)
+        print(output)
 
     def test_conformer(self):
         conf_str = """
@@ -119,8 +128,15 @@ class TestU2Model(unittest.TestCase):
                 length_normalized_loss: false
         """
         cfg = CN().load_cfg(conf_str)
-        print(cfg)
-        model = U2ConformerModel()
+        cfg.input_dim = self.feat_dim
+        cfg.output_dim = self.vocab_size
+        cfg.cmvn_file = None
+        cfg.cmvn_file_type = 'npz'
+        cfg.freeze()
+        model = U2ConformerModel(cfg)
+        summary(model, None)
+        output = model(self.audio, self.audio_len, self.text, self.text_len)
+        print(output)
 
 
 if __name__ == '__main__':