CosyVoice

paddlespeech/__init__.py

@@ -13,3 +13,7 @@
 # limitations under the License.
 import _locale
 _locale._getdefaultlocale = (lambda *args: ['en_US', 'utf8'])
+
+
+
+

paddlespeech/audio/functional/mel_extract.py

@@ -0,0 +1,84 @@
+import numpy as np
+import paddle
+from librosa.filters import mel as librosa_mel_fn
+from scipy.io.wavfile import read
+
+MAX_WAV_VALUE = 32768.0
+
+
+def load_wav(full_path):
+    sampling_rate, data = read(full_path)
+    return data, sampling_rate
+
+
+def dynamic_range_compression(x, C=1, clip_val=1e-05):
+    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
+
+
+def dynamic_range_decompression(x, C=1):
+    return np.exp(x) / C
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-05):
+    return paddle.log(paddle.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression_torch(x, C=1):
+    return paddle.exp(x=x) / C
+
+
+def spectral_normalize_torch(magnitudes):
+    output = dynamic_range_compression_torch(magnitudes)
+    return output
+
+
+def spectral_de_normalize_torch(magnitudes):
+    output = dynamic_range_decompression_torch(magnitudes)
+    return output
+
+
+mel_basis = {}
+hann_window = {}
+
+
+def mel_spectrogram(
+    y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
+):
+    if paddle.compat.min(y) < -1.0:
+        print("min value is ", paddle.compat.min(y))
+    if paddle.compat.max(y) > 1.0:
+        print("max value is ", paddle.compat.max(y))
+    global mel_basis, hann_window
+    if f"{str(fmax)}_{str(y.place)}" not in mel_basis:
+        mel = librosa_mel_fn(
+            sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
+        )
+        mel_basis[str(fmax) + "_" + str(y.place)] = (
+            paddle.from_numpy(mel).float().to(y.place)
+        )
+        hann_window[str(y.place)] = paddle.audio.functional.get_window(
+            win_length=win_size, dtype="float32", window="hann"
+        ).to(y.place)
+    y = paddle.compat.pad(
+        y.unsqueeze(1),
+        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
+        mode="reflect",
+    )
+    y = y.squeeze(1)
+    spec = paddle.view_as_real(
+        paddle.signal.stft(
+            x=y,
+            n_fft=n_fft,
+            hop_length=hop_size,
+            win_length=win_size,
+            window=hann_window[str(y.place)],
+            center=center,
+            pad_mode="reflect",
+            normalized=False,
+            onesided=True,
+        )
+    )
+    spec = paddle.sqrt(spec.pow(2).sum(-1) + 1e-09)
+    spec = paddle.matmul(mel_basis[str(fmax) + "_" + str(y.place)], spec)
+    spec = spectral_normalize_torch(spec)
+    return spec

paddlespeech/t2s/models/CosyVoice/class_utils.py

@@ -0,0 +1,40 @@
+import paddle
+
+# from cosyvoice.cli.model import CosyVoice2Model, CosyVoiceModel
+# from cosyvoice.flow.flow import CausalMaskedDiffWithXvec, MaskedDiffWithXvec
+# from cosyvoice.hifigan.generator import HiFTGenerator
+# from cosyvoice.llm.llm import Qwen2LM, TransformerLM
+from paddlespeech.t2s.modules.transformer.activation import Swish
+from paddlespeech.t2s.modules.transformer.attention import RelPositionMultiHeadedAttention
+from paddlespeech.t2s.modules.transformer.embedding import EspnetRelPositionalEncoding
+from paddlespeech.t2s.modules.transformer.subsampling import LinearNoSubsampling
+                                               
+
+COSYVOICE_ACTIVATION_CLASSES = {
+    "swish": Swish
+}
+COSYVOICE_SUBSAMPLE_CLASSES = {
+    "linear": LinearNoSubsampling,
+}
+COSYVOICE_EMB_CLASSES = {
+    "rel_pos_espnet": EspnetRelPositionalEncoding,
+}
+COSYVOICE_ATTENTION_CLASSES = {
+    "rel_selfattn": RelPositionMultiHeadedAttention,
+}
+
+
+# def get_model_type(configs):
+#     if (
+#         isinstance(configs["llm"], TransformerLM)
+#         and isinstance(configs["flow"], MaskedDiffWithXvec)
+#         and isinstance(configs["hift"], HiFTGenerator)
+#     ):
+#         return CosyVoiceModel
+#     if (
+#         isinstance(configs["llm"], Qwen2LM)
+#         and isinstance(configs["flow"], CausalMaskedDiffWithXvec)
+#         and isinstance(configs["hift"], HiFTGenerator)
+#     ):
+#         return CosyVoice2Model
+#     raise TypeError("No valid model type found!")

paddlespeech/t2s/models/CosyVoice/common.py

@@ -0,0 +1,216 @@
+import paddle
+
+"""Unility functions for Transformer."""
+import queue
+import random
+from typing import List
+
+import numpy as np
+
+############################## 相关utils函数，如下 ##############################
+
+def device2str(type=None, index=None, *, device=None):
+    type = device if device else type
+    if isinstance(type, int):
+        type = f'gpu:{type}'
+    elif isinstance(type, str):
+        if 'cuda' in type:
+            type = type.replace('cuda', 'gpu')
+        if 'cpu' in type:
+            type = 'cpu'
+        elif index is not None:
+            type = f'{type}:{index}'
+    elif isinstance(type, paddle.CPUPlace) or (type is None):
+        type = 'cpu'
+    elif isinstance(type, paddle.CUDAPlace):
+        type = f'gpu:{type.get_device_id()}'
+
+    return type
+############################## 相关utils函数，如上 ##############################
+
+
+IGNORE_ID = -1
+
+
+def pad_list(xs: List[paddle.Tensor], pad_value: int):
+    """Perform padding for the list of tensors.
+
+    Args:
+        xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
+        pad_value (float): Value for padding.
+
+    Returns:
+        Tensor: Padded tensor (B, Tmax, `*`).
+
+    Examples:
+        >>> x = [torch.ones(4), torch.ones(2), torch.ones(1)]
+        >>> x
+        [tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])]
+        >>> pad_list(x, 0)
+        tensor([[1., 1., 1., 1.],
+                [1., 1., 0., 0.],
+                [1., 0., 0., 0.]])
+
+    """
+    max_len = max([len(item) for item in xs])
+    batchs = len(xs)
+    ndim = xs[0].ndim
+    if ndim == 1:
+        pad_res = paddle.zeros(batchs, max_len, dtype=xs[0].dtype, device=xs[0].place)
+    elif ndim == 2:
+        pad_res = paddle.zeros(
+            batchs, max_len, xs[0].shape[1], dtype=xs[0].dtype, device=xs[0].place
+        )
+    elif ndim == 3:
+        pad_res = paddle.zeros(
+            batchs,
+            max_len,
+            xs[0].shape[1],
+            xs[0].shape[2],
+            dtype=xs[0].dtype,
+            device=xs[0].place,
+        )
+    else:
+        raise ValueError(f"Unsupported ndim: {ndim}")
+    pad_res.fill_(pad_value)
+    for i in range(batchs):
+        pad_res[i, : len(xs[i])] = xs[i]
+    return pad_res
+
+
+def th_accuracy(
+    pad_outputs: paddle.Tensor, pad_targets: paddle.Tensor, ignore_label: int
+) -> paddle.Tensor:
+    """Calculate accuracy.
+
+    Args:
+        pad_outputs (Tensor): Prediction tensors (B * Lmax, D).
+        pad_targets (LongTensor): Target label tensors (B, Lmax).
+        ignore_label (int): Ignore label id.
+
+    Returns:
+        torch.Tensor: Accuracy value (0.0 - 1.0).
+
+    """
+    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(
+        pad_pred.masked_select(mask) == pad_targets.masked_select(mask)
+    )
+    denominator = paddle.sum(mask)
+    return (numerator / denominator).detach()
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def ras_sampling(
+    weighted_scores,
+    decoded_tokens,
+    sampling,
+    top_p=0.8,
+    top_k=25,
+    win_size=10,
+    tau_r=0.1,
+):
+    top_ids = nucleus_sampling(weighted_scores, top_p=top_p, top_k=top_k)
+    rep_num = (
+        (paddle.to_tensor(decoded_tokens[-win_size:],dtype = paddle.long).to(weighted_scores.place) == top_ids)
+        .sum()
+        .item()
+    )
+    print("top_ids:",top_ids)
+    if rep_num >= win_size * tau_r:
+        top_ids = random_sampling(weighted_scores, decoded_tokens, sampling)[0]
+    return top_ids
+
+
+def nucleus_sampling(weighted_scores, top_p=0.8, top_k=25):
+    prob, indices = [], []
+    cum_prob = 0.0
+    sorted_value, sorted_idx = paddle.sort(
+        descending=True, stable=True, x=weighted_scores.softmax(axis=0)
+    ), paddle.argsort(descending=True, stable=True, x=weighted_scores.softmax(axis=0))
+    
+    for i in range(len(sorted_idx)):
+        if cum_prob < top_p and len(prob) < top_k:
+            cum_prob += sorted_value[i]
+            prob.append(sorted_value[i])
+            indices.append(sorted_idx[i])
+        else:
+            break
+    prob = paddle.to_tensor(prob).cuda()
+    indices = paddle.to_tensor(indices, dtype=paddle.long).to(weighted_scores.place)
+    print("indices:",indices)
+    # top_ids = indices[prob.multinomial(num_samples=1, replacement=True)]
+    top_ids = indices[0]
+    return top_ids
+
+
+def random_sampling(weighted_scores, decoded_tokens, sampling):
+    top_ids = weighted_scores.softmax(axis=0).multinomial(
+        num_samples=1, replacement=True
+    )
+    print("random_sampling:",top_ids)
+    return top_ids
+
+
+def fade_in_out(fade_in_mel, fade_out_mel, window):
+    device = fade_in_mel.place
+    fade_in_mel, fade_out_mel = fade_in_mel.cpu(), fade_out_mel.cpu()
+    mel_overlap_len = int(window.shape[0] / 2)
+    if fade_in_mel.place == device2str("cpu"):
+        fade_in_mel = fade_in_mel.clone()
+    fade_in_mel[..., :mel_overlap_len] = (
+        fade_in_mel[..., :mel_overlap_len] * window[:mel_overlap_len]
+        + fade_out_mel[..., -mel_overlap_len:] * window[mel_overlap_len:]
+    )
+    return fade_in_mel.to(device)
+
+
+def set_all_random_seed(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    paddle.seed(seed)
+    paddle.seed(seed)
+
+
+def mask_to_bias(mask: paddle.Tensor, dtype: paddle.dtype) -> paddle.Tensor:
+    assert mask.dtype == paddle.bool
+    assert dtype in [paddle.float32, paddle.bfloat16, paddle.float16]
+    mask = mask.to(dtype)
+    mask = (1.0 - mask) * -10000000000.0
+    return mask
+
+
+class TrtContextWrapper:
+    def __init__(self, trt_engine, trt_concurrent=1, device="cuda:0"):
+        self.trt_context_pool = queue.Queue(maxsize=trt_concurrent)
+        self.trt_engine = trt_engine
+        for _ in range(trt_concurrent):
+            trt_context = trt_engine.create_execution_context()
+            trt_stream = paddle.device.stream_guard(
+                paddle.device.Stream(device=device2str(device))
+            )
+            assert (
+                trt_context is not None
+            ), "failed to create trt context, maybe not enough CUDA memory, try reduce current trt concurrent {}".format(
+                trt_concurrent
+            )
+            self.trt_context_pool.put([trt_context, trt_stream])
+        assert self.trt_context_pool.empty() is False, "no avaialbe estimator context"
+
+    def acquire_estimator(self):
+        return self.trt_context_pool.get(), self.trt_engine
+
+    def release_estimator(self, context, stream):
+        self.trt_context_pool.put([context, stream])

paddlespeech/t2s/models/CosyVoice/llm.py

@@ -220,7 +220,7 @@ class TransformerLM(paddle.nn.Layer):
         num_trials, max_trials = 0, 100
         while True:
             top_ids = self.sampling(weighted_scores, decoded_tokens, sampling)
-            if not ignore_eos or self.speech_token_size not in top_ids:
+            if (not ignore_eos) or (top_ids < self.speech_token_size):
                 break
             num_trials += 1
             if num_trials > max_trials:
@@ -325,15 +325,18 @@ class Qwen2Encoder(paddle.nn.Layer):
         )
         return outs.hidden_states[-1], masks.unsqueeze(1)
 
-    def forward_one_step(self, xs, masks, cache=None):
+    def forward_one_step(self, xs, masks, cache=None,idx = 0):
+
         input_masks = masks[:, -1, :]
         outs = self.model(
             inputs_embeds=xs,
             attention_mask=input_masks,
             output_hidden_states=True,
             return_dict=True,
+            output_attentions=False,
             use_cache=True,
             past_key_values=cache,
+            index =idx
         )
         xs = outs.hidden_states[-1]
         new_cache = outs.past_key_values
@@ -572,6 +575,7 @@ class Qwen2LM(TransformerLM):
             out_tokens = []
             cache = None
             for i in range(max_len):
+                
                 y_pred, cache = self.llm.forward_one_step(
                     lm_input,
                     masks=paddle.tril(
@@ -580,6 +584,7 @@ class Qwen2LM(TransformerLM):
                         )
                     ).to(paddle.bool),
                     cache=cache,
+                    idx = i
                 )
                 logp = F.log_softmax(self.llm_decoder(y_pred[:, -1]), axis = -1)
                 top_ids = self.sampling_ids(
@@ -587,15 +592,13 @@ class Qwen2LM(TransformerLM):
                     out_tokens,
                     sampling,
                     ignore_eos=True if i < min_len else False,
-                ).item()
-                if top_ids == self.speech_token_size:
+                )
+                if top_ids in self.stop_token_ids:
                     break
-                if top_ids > self.speech_token_size:
-                    continue
                 yield top_ids
                 out_tokens.append(top_ids)
                 lm_input = self.speech_embedding.weight[top_ids].reshape([1, 1, -1])
-
+            print(len(out_tokens))
     @paddle.no_grad()
     def inference_bistream(
         self,
@@ -735,3 +738,5 @@ class Qwen2LM(TransformerLM):
                     raise ValueError("should not get token {}".format(top_ids))
             yield top_ids
             lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1)
+
+

paddlespeech/t2s/models/CosyVoice/mask.py

@@ -0,0 +1,289 @@
+import paddle
+
+# ############################## 相关utils函数，如下 ##############################
+
+# def device2str(type=None, index=None, *, device=None):
+#     type = device if device else type
+#     if isinstance(type, int):
+#         type = f'gpu:{type}'
+#     elif isinstance(type, str):
+#         if 'cuda' in type:
+#             type = type.replace('cuda', 'gpu')
+#         if 'cpu' in type:
+#             type = 'cpu'
+#         elif index is not None:
+#             type = f'{type}:{index}'
+#     elif isinstance(type, paddle.CPUPlace) or (type is None):
+#         type = 'cpu'
+#     elif isinstance(type, paddle.CUDAPlace):
+#         type = f'gpu:{type.get_device_id()}'
+
+#     return type
+
+# def _Tensor_max(self, *args, **kwargs):
+#     if "other" in kwargs:
+#         kwargs["y"] = kwargs.pop("other")
+#         ret = paddle.maximum(self, *args, **kwargs)
+#     elif len(args) == 1 and isinstance(args[0], paddle.Tensor):
+#         ret = paddle.maximum(self, *args, **kwargs)
+#     else:
+#         if "dim" in kwargs:
+#             kwargs["axis"] = kwargs.pop("dim")
+
+#         if "axis" in kwargs or len(args) >= 1:
+#             ret = paddle.max(self, *args, **kwargs), paddle.argmax(self, *args, **kwargs)
+#         else:
+#             ret = paddle.max(self, *args, **kwargs)
+
+#     return ret
+
+# setattr(paddle.Tensor, "_max", _Tensor_max)
+# ############################## 相关utils函数，如上 ##############################
+
+
+# """
+# def subsequent_mask(
+#         size: int,
+#         device: torch.device = torch.device("cpu"),
+# ) -> torch.Tensor:
+#     ""\"Create mask for subsequent steps (size, size).
+
+#     This mask is used only in decoder which works in an auto-regressive mode.
+#     This means the current step could only do attention with its left steps.
+
+#     In encoder, fully attention is used when streaming is not necessary and
+#     the sequence is not long. In this  case, no attention mask is needed.
+
+#     When streaming is need, chunk-based attention is used in encoder. See
+#     subsequent_chunk_mask for the chunk-based attention mask.
+
+#     Args:
+#         size (int): size of mask
+#         str device (str): "cpu" or "cuda" or torch.Tensor.device
+#         dtype (torch.device): result dtype
+
+#     Returns:
+#         torch.Tensor: mask
+
+#     Examples:
+#         >>> subsequent_mask(3)
+#         [[1, 0, 0],
+#          [1, 1, 0],
+#          [1, 1, 1]]
+#     ""\"
+#     ret = torch.ones(size, size, device=device, dtype=torch.bool)
+#     return torch.tril(ret)
+# """
+
+
+# def subsequent_mask(
+#     size: int, device: paddle.device
+# ) -> paddle.Tensor:
+#     """Create mask for subsequent steps (size, size).
+
+#     This mask is used only in decoder which works in an auto-regressive mode.
+#     This means the current step could only do attention with its left steps.
+
+#     In encoder, fully attention is used when streaming is not necessary and
+#     the sequence is not long. In this  case, no attention mask is needed.
+
+#     When streaming is need, chunk-based attention is used in encoder. See
+#     subsequent_chunk_mask for the chunk-based attention mask.
+
+#     Args:
+#         size (int): size of mask
+#         str device (str): "cpu" or "cuda" or torch.Tensor.device
+#         dtype (torch.device): result dtype
+
+#     Returns:
+#         torch.Tensor: mask
+
+#     Examples:
+#         >>> subsequent_mask(3)
+#         [[1, 0, 0],
+#          [1, 1, 0],
+#          [1, 1, 1]]
+#     """
+#     arange = paddle.arange(size, device=device)
+#     mask = arange.expand(size, size)
+#     arange = arange.unsqueeze(-1)
+#     mask = mask <= arange
+#     return mask
+
+
+# def subsequent_chunk_mask_deprecated(
+#     size: int,
+#     chunk_size: int,
+#     num_left_chunks: int = -1,
+# >>>>>>    device: torch.device = device2str("cpu"),
+# ) -> paddle.Tensor:
+#     """Create mask for subsequent steps (size, size) with chunk size,
+#        this is for streaming encoder
+
+#     Args:
+#         size (int): size of mask
+#         chunk_size (int): size of chunk
+#         num_left_chunks (int): number of left chunks
+#             <0: use full chunk
+#             >=0: use num_left_chunks
+#         device (torch.device): "cpu" or "cuda" or torch.Tensor.device
+
+#     Returns:
+#         torch.Tensor: mask
+
+#     Examples:
+#         >>> subsequent_chunk_mask(4, 2)
+#         [[1, 1, 0, 0],
+#          [1, 1, 0, 0],
+#          [1, 1, 1, 1],
+#          [1, 1, 1, 1]]
+#     """
+#     ret = paddle.zeros(size, size, device=device, dtype=paddle.bool)
+#     for i in range(size):
+#         if num_left_chunks < 0:
+#             start = 0
+#         else:
+#             start = max((i // chunk_size - num_left_chunks) * chunk_size, 0)
+#         ending = min((i // chunk_size + 1) * chunk_size, size)
+#         ret[i, start:ending] = True
+#     return ret
+
+
+# def subsequent_chunk_mask(
+#     size: int,
+#     chunk_size: int,
+#     num_left_chunks: int = -1,
+# >>>>>>    device: torch.device = device2str("cpu"),
+# ) -> paddle.Tensor:
+#     """Create mask for subsequent steps (size, size) with chunk size,
+#        this is for streaming encoder
+
+#     Args:
+#         size (int): size of mask
+#         chunk_size (int): size of chunk
+#         num_left_chunks (int): number of left chunks
+#             <0: use full chunk
+#             >=0: use num_left_chunks
+#         device (torch.device): "cpu" or "cuda" or torch.Tensor.device
+
+#     Returns:
+#         torch.Tensor: mask
+
+#     Examples:
+#         >>> subsequent_chunk_mask(4, 2)
+#         [[1, 1, 0, 0],
+#          [1, 1, 0, 0],
+#          [1, 1, 1, 1],
+#          [1, 1, 1, 1]]
+#     """
+#     pos_idx = paddle.arange(size, device=device)
+#     block_value = (
+#         paddle.div(pos_idx, chunk_size, rounding_mode="trunc") + 1
+#     ) * chunk_size
+#     ret = pos_idx.unsqueeze(0) < block_value.unsqueeze(1)
+#     return ret
+
+
+def add_optional_chunk_mask(
+    xs: paddle.Tensor,
+    masks: paddle.Tensor,
+    use_dynamic_chunk: bool,
+    use_dynamic_left_chunk: bool,
+    decoding_chunk_size: int,
+    static_chunk_size: int,
+    num_decoding_left_chunks: int,
+    enable_full_context: bool = True,
+):
+    """Apply optional mask for encoder.
+
+    Args:
+        xs (torch.Tensor): padded input, (B, L, D), L for max length
+        mask (torch.Tensor): mask for xs, (B, 1, L)
+        use_dynamic_chunk (bool): whether to use dynamic chunk or not
+        use_dynamic_left_chunk (bool): whether to use dynamic left chunk for
+            training.
+        decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's
+            0: default for training, use random dynamic chunk.
+            <0: for decoding, use full chunk.
+            >0: for decoding, use fixed chunk size as set.
+        static_chunk_size (int): chunk size for static chunk training/decoding
+            if it's greater than 0, if use_dynamic_chunk is true,
+            this parameter will be ignored
+        num_decoding_left_chunks: number of left chunks, this is for decoding,
+            the chunk size is decoding_chunk_size.
+            >=0: use num_decoding_left_chunks
+            <0: use all left chunks
+        enable_full_context (bool):
+            True: chunk size is either [1, 25] or full context(max_len)
+            False: chunk size ~ U[1, 25]
+
+    Returns:
+        torch.Tensor: chunk mask of the input xs.
+    """
+    if use_dynamic_chunk:
+        max_len = xs.size(1)
+        if decoding_chunk_size < 0:
+            chunk_size = max_len
+            num_left_chunks = -1
+        elif decoding_chunk_size > 0:
+            chunk_size = decoding_chunk_size
+            num_left_chunks = num_decoding_left_chunks
+        else:
+            chunk_size = paddle.randint(low=1, high=max_len, shape=(1,)).item()
+            num_left_chunks = -1
+            if chunk_size > max_len // 2 and enable_full_context:
+                chunk_size = max_len
+            else:
+                chunk_size = chunk_size % 25 + 1
+                if use_dynamic_left_chunk:
+                    max_left_chunks = (max_len - 1) // chunk_size
+                    num_left_chunks = paddle.randint(
+                        low=0, high=max_left_chunks, shape=(1,)
+                    ).item()
+        chunk_masks = subsequent_chunk_mask(
+            xs.size(1), chunk_size, num_left_chunks, xs.place
+        )
+        chunk_masks = chunk_masks.unsqueeze(0)
+        chunk_masks = masks & chunk_masks
+    elif static_chunk_size > 0:
+        num_left_chunks = num_decoding_left_chunks
+        chunk_masks = subsequent_chunk_mask(
+            xs.size(1), static_chunk_size, num_left_chunks, xs.place
+        )
+        chunk_masks = chunk_masks.unsqueeze(0)
+        chunk_masks = masks & chunk_masks
+    else:
+        chunk_masks = masks
+    assert chunk_masks.dtype == paddle.bool
+    if (chunk_masks.sum(dim=-1) == 0).sum().item() != 0:
+        print(
+            "get chunk_masks all false at some timestep, force set to true, make sure they are masked in futuer computation!"
+        )
+        chunk_masks[chunk_masks.sum(dim=-1) == 0] = True
+    return chunk_masks
+
+
+def make_pad_mask(lengths: paddle.Tensor, max_len: int = 0) -> paddle.Tensor:
+    """Make mask tensor containing indices of padded part.
+
+    See description of make_non_pad_mask.
+
+    Args:
+        lengths (torch.Tensor): Batch of lengths (B,).
+    Returns:
+        torch.Tensor: Mask tensor containing indices of padded part.
+
+    Examples:
+        >>> lengths = [5, 3, 2]
+        >>> make_pad_mask(lengths)
+        masks = [[0, 0, 0, 0 ,0],
+                 [0, 0, 0, 1, 1],
+                 [0, 0, 1, 1, 1]]
+    """
+    batch_size = lengths.shape[0]
+    max_len = max_len if max_len > 0 else lengths.max().item()
+    seq_range = paddle.arange(0, max_len, dtype=paddle.int32)
+    seq_range_expand = seq_range.unsqueeze(0).expand([batch_size, max_len])
+    seq_length_expand = lengths.unsqueeze(-1)
+    mask = seq_range_expand >= seq_length_expand
+    return mask

paddlespeech/t2s/models/__init__.py

@@ -22,3 +22,4 @@ from .transformer_tts import *
 from .vits import *
 from .waveflow import *
 from .wavernn import *
+from .CosyVoice import *

paddlespeech/t2s/models/hifigan/__init__.py

@@ -13,3 +13,5 @@
 # limitations under the License.
 from .hifigan import *
 from .hifigan_updater import *
+from .cosy_hifigan import *
+from .f0_predictor import *

paddlespeech/t2s/models/hifigan/cosy_hifigan.py

@@ -0,0 +1,443 @@
+import paddle
+
+"""HIFI-GAN"""
+from typing import Dict, List, Optional
+
+import numpy as np
+from scipy.signal import get_window
+from paddlespeech.t2s.modules.transformer.activation import Snake
+from paddlespeech.t2s.models.CosyVoice.common import get_padding, init_weights
+
+"""hifigan based generator implementation.
+
+This code is modified from https://github.com/jik876/hifi-gan
+ ,https://github.com/kan-bayashi/ParallelWaveGAN and
+ https://github.com/NVIDIA/BigVGAN
+
+"""
+
+
+class ResBlock(paddle.nn.Layer):
+    """Residual block module in HiFiGAN/BigVGAN."""
+
+    def __init__(self, channels: int=512, kernel_size: int=3, dilations:
+        List[int]=[1, 3, 5]):
+        super(ResBlock, self).__init__()
+        self.convs1 = paddle.nn.LayerList()
+        self.convs2 = paddle.nn.LayerList()
+        for dilation in dilations:
+            self.convs1.append(paddle.nn.utils.weight_norm(layer=paddle.nn.
+                Conv1D(channels, channels, kernel_size, 1, dilation=
+                dilation, padding=get_padding(kernel_size, dilation))))
+            self.convs2.append(paddle.nn.utils.weight_norm(layer=paddle.nn.
+                Conv1D(channels, channels, kernel_size, 1, dilation=1,
+                padding=get_padding(kernel_size, 1))))
+        self.convs1.apply(init_weights)
+        self.convs2.apply(init_weights)
+        self.activations1 = paddle.nn.LayerList(sublayers=[Snake(channels,
+            alpha_logscale=False) for _ in range(len(self.convs1))])
+        self.activations2 = paddle.nn.LayerList(sublayers=[Snake(channels,
+            alpha_logscale=False) for _ in range(len(self.convs2))])
+
+    def forward(self, x: paddle.Tensor) ->paddle.Tensor:
+        for idx in range(len(self.convs1)):
+            xt = self.activations1[idx](x)
+            xt = self.convs1[idx](xt)
+            xt = self.activations2[idx](xt)
+            xt = self.convs2[idx](xt)
+            x = xt + x
+        return x
+
+    def remove_weight_norm(self):
+        for idx in range(len(self.convs1)):
+            paddle.nn.utils.remove_weight_norm(layer=self.convs1[idx])
+            paddle.nn.utils.remove_weight_norm(layer=self.convs2[idx])
+
+
+class SineGen(paddle.nn.Layer):
+    """ Definition of sine generator
+    SineGen(samp_rate, harmonic_num = 0,
+            sine_amp = 0.1, noise_std = 0.003,
+            voiced_threshold = 0,
+            flag_for_pulse=False)
+    samp_rate: sampling rate in Hz
+    harmonic_num: number of harmonic overtones (default 0)
+    sine_amp: amplitude of sine-wavefrom (default 0.1)
+    noise_std: std of Gaussian noise (default 0.003)
+    voiced_thoreshold: F0 threshold for U/V classification (default 0)
+    flag_for_pulse: this SinGen is used inside PulseGen (default False)
+    Note: when flag_for_pulse is True, the first time step of a voiced
+        segment is always sin(np.pi) or cos(0)
+    """
+
+    def __init__(self, samp_rate, harmonic_num=0, sine_amp=0.1, noise_std=
+        0.003, voiced_threshold=0):
+        super(SineGen, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = noise_std
+        self.harmonic_num = harmonic_num
+        self.sampling_rate = samp_rate
+        self.voiced_threshold = voiced_threshold
+
+    def _f02uv(self, f0):
+        uv = (f0 > self.voiced_threshold).astype(paddle.float32)
+        return uv
+
+    @paddle.no_grad()
+    def forward(self, f0):
+        """
+        :param f0: [B, 1, sample_len], Hz
+        :return: [B, 1, sample_len]
+        """
+        F_mat = paddle.zeros([f0.size(0), self.harmonic_num + 1, f0.size(-1)]).to(f0.place)
+        for i in range(self.harmonic_num + 1):
+            F_mat[:, i:i + 1, :] = f0 * (i + 1) / self.sampling_rate
+        theta_mat = 2 * np.pi * (paddle.cumsum(F_mat, axis=-1) % 1)
+        u_dist = paddle.distribution.Uniform(low=-np.pi, high=np.pi)
+        phase_vec = u_dist.sample(shape=(f0.size(0), self.harmonic_num + 1, 1)
+            ).to(F_mat.place)
+        phase_vec[:, 0, :] = 0
+        sine_waves = self.sine_amp * paddle.sin(theta_mat + phase_vec)
+        uv = self._f02uv(f0)
+        noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
+        noise = noise_amp * paddle.randn(shape=sine_waves.shape, dtype=
+            sine_waves.dtype)
+        sine_waves = sine_waves * uv + noise
+        return sine_waves, uv, noise
+
+
+class SourceModuleHnNSF(paddle.nn.Layer):
+    """ SourceModule for hn-nsf
+    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0)
+    sampling_rate: sampling_rate in Hz
+    harmonic_num: number of harmonic above F0 (default: 0)
+    sine_amp: amplitude of sine source signal (default: 0.1)
+    add_noise_std: std of additive Gaussian noise (default: 0.003)
+        note that amplitude of noise in unvoiced is decided
+        by sine_amp
+    voiced_threshold: threhold to set U/V given F0 (default: 0)
+    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+    F0_sampled (batchsize, length, 1)
+    Sine_source (batchsize, length, 1)
+    noise_source (batchsize, length 1)
+    uv (batchsize, length, 1)
+    """
+
+    def __init__(self, sampling_rate, upsample_scale, harmonic_num=0,
+        sine_amp=0.1, add_noise_std=0.003, voiced_threshod=0):
+        super(SourceModuleHnNSF, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = add_noise_std
+        self.l_sin_gen = SineGen(sampling_rate, harmonic_num, sine_amp,
+            add_noise_std, voiced_threshod)
+        self.l_linear = paddle.nn.Linear(in_features=harmonic_num + 1,
+            out_features=1)
+        self.l_tanh = paddle.nn.Tanh()
+
+    def forward(self, x):
+        """
+        Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+        F0_sampled (batchsize, length, 1)
+        Sine_source (batchsize, length, 1)
+        noise_source (batchsize, length 1)
+        """
+        with paddle.no_grad():
+            sine_wavs, uv, _ = self.l_sin_gen(paddle.transpose(x,perm=[0,2,1]))
+            sine_wavs = paddle.transpose(sine_wavs,perm=[0,2,1])
+            uv = paddle.transpose(uv,perm=[0,2,1])
+        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+
+        noise = paddle.randn(shape=uv.shape, dtype=uv.dtype
+            ) * self.sine_amp / 3
+        return sine_merge, noise, uv
+
+
+class SineGen2(paddle.nn.Layer):
+    """ Definition of sine generator
+    SineGen(samp_rate, harmonic_num = 0,
+            sine_amp = 0.1, noise_std = 0.003,
+            voiced_threshold = 0,
+            flag_for_pulse=False)
+    samp_rate: sampling rate in Hz
+    harmonic_num: number of harmonic overtones (default 0)
+    sine_amp: amplitude of sine-wavefrom (default 0.1)
+    noise_std: std of Gaussian noise (default 0.003)
+    voiced_thoreshold: F0 threshold for U/V classification (default 0)
+    flag_for_pulse: this SinGen is used inside PulseGen (default False)
+    Note: when flag_for_pulse is True, the first time step of a voiced
+        segment is always sin(np.pi) or cos(0)
+    """
+
+    def __init__(self, samp_rate, upsample_scale, harmonic_num=0, sine_amp=
+        0.1, noise_std=0.003, voiced_threshold=0, flag_for_pulse=False):
+        super(SineGen2, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = noise_std
+        self.harmonic_num = harmonic_num
+        self.axis = self.harmonic_num + 1
+        self.sampling_rate = samp_rate
+        self.voiced_threshold = voiced_threshold
+        self.flag_for_pulse = flag_for_pulse
+        self.upsample_scale = upsample_scale
+
+    def _f02uv(self, f0):
+        uv = (f0 > self.voiced_threshold).astype(paddle.float32)
+        return uv
+
+    def _f02sine(self, f0_values):
+        """ f0_values: (batchsize, length, axis)
+            where axis indicates fundamental tone and overtones
+        """
+        rad_values = f0_values / self.sampling_rate % 1
+        rand_ini = paddle.rand(shape=[f0_values.shape[0], f0_values.shape[2]])
+        rand_ini[:, 0] = 0
+        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+        if not self.flag_for_pulse:
+            x = paddle.transpose(rad_values,perm = [0,2,1])
+            
+            rad_values = paddle.transpose(paddle.nn.functional.interpolate(x=x, scale_factor=1 / self.upsample_scale, mode='linear'),perm = [0,2,1])
+            phase = paddle.cumsum(rad_values, axis=1) * 2 * np.pi
+            phase = paddle.transpose(paddle.nn.functional.interpolate(x=paddle.transpose(phase,perm = [0,2,1]) * self.upsample_scale, scale_factor=int(self.upsample_scale),mode='linear'),perm = [0,2,1])
+            sines = paddle.sin(phase)
+        else:
+            uv = self._f02uv(f0_values)
+            uv_1 = paddle.roll(uv, shifts=-1, axis=1)
+            uv_1[:, -1, :] = 1
+            u_loc = (uv < 1) * (uv_1 > 0)
+            tmp_cumsum = paddle.cumsum(rad_values, axis=1)
+            for idx in range(f0_values.shape[0]):
+                temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
+                temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
+                tmp_cumsum[idx, :, :] = 0
+                tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
+            i_phase = paddle.cumsum(rad_values - tmp_cumsum, axis=1)
+            sines = paddle.cos(i_phase * 2 * np.pi)
+        return sines
+
+    def forward(self, f0):
+        """ sine_tensor, uv = forward(f0)
+        input F0: tensor(batchsize=1, length, axis=1)
+                  f0 for unvoiced steps should be 0
+        output sine_tensor: tensor(batchsize=1, length, axis)
+        output uv: tensor(batchsize=1, length, 1)
+        """
+        paddle.seed(1986)
+        fn = paddle.multiply(f0, paddle.to_tensor([[range(1, self.harmonic_num +
+            2)]],dtype='float32',place=f0.place))
+
+        sine_waves = self._f02sine(fn) * self.sine_amp
+        uv = self._f02uv(f0)
+        noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
+        noise = noise_amp * paddle.randn(shape=sine_waves.shape, dtype=
+            sine_waves.dtype)
+        sine_waves = sine_waves * uv + noise
+
+        return sine_waves, uv, noise
+
+
+class SourceModuleHnNSF2(paddle.nn.Layer):
+    """ SourceModule for hn-nsf
+    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0)
+    sampling_rate: sampling_rate in Hz
+    harmonic_num: number of harmonic above F0 (default: 0)
+    sine_amp: amplitude of sine source signal (default: 0.1)
+    add_noise_std: std of additive Gaussian noise (default: 0.003)
+        note that amplitude of noise in unvoiced is decided
+        by sine_amp
+    voiced_threshold: threhold to set U/V given F0 (default: 0)
+    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+    F0_sampled (batchsize, length, 1)
+    Sine_source (batchsize, length, 1)
+    noise_source (batchsize, length 1)
+    uv (batchsize, length, 1)
+    """
+
+    def __init__(self, sampling_rate, upsample_scale, harmonic_num=0,
+        sine_amp=0.1, add_noise_std=0.003, voiced_threshod=0):
+        super(SourceModuleHnNSF2, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = add_noise_std
+        self.l_sin_gen = SineGen2(sampling_rate, upsample_scale,
+            harmonic_num, sine_amp, add_noise_std, voiced_threshod)
+        self.l_linear = paddle.nn.Linear(in_features=harmonic_num + 1,
+            out_features=1)
+        self.l_tanh = paddle.nn.Tanh()
+
+    def forward(self, x):
+        """
+        Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+        F0_sampled (batchsize, length, 1)
+        Sine_source (batchsize, length, 1)
+        noise_source (batchsize, length 1)
+        """
+        paddle.seed(1986)
+        with paddle.no_grad():
+            sine_wavs, uv, _ = self.l_sin_gen(x)
+        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+        noise = paddle.randn(shape=uv.shape, dtype=uv.dtype
+            ) * self.sine_amp / 3
+        return sine_merge, noise, uv
+
+
+class HiFTGenerator(paddle.nn.Layer):
+    """
+    HiFTNet Generator: Neural Source Filter + ISTFTNet
+    https://arxiv.org/abs/2309.09493
+    """
+
+    def __init__(self, in_channels: int=80, base_channels: int=512,
+        nb_harmonics: int=8, sampling_rate: int=22050, nsf_alpha: float=0.1,
+        nsf_sigma: float=0.003, nsf_voiced_threshold: float=10,
+        upsample_rates: List[int]=[8, 8], upsample_kernel_sizes: List[int]=
+        [16, 16], istft_params: Dict[str, int]={'n_fft': 16, 'hop_len': 4},
+        resblock_kernel_sizes: List[int]=[3, 7, 11],
+        resblock_dilation_sizes: List[List[int]]=[[1, 3, 5], [1, 3, 5], [1,
+        3, 5]], source_resblock_kernel_sizes: List[int]=[7, 11],
+        source_resblock_dilation_sizes: List[List[int]]=[[1, 3, 5], [1, 3, 
+        5]], lrelu_slope: float=0.1, audio_limit: float=0.99, f0_predictor:
+        paddle.nn.Layer=None):
+        super(HiFTGenerator, self).__init__()
+        self.out_channels = 1
+        self.nb_harmonics = nb_harmonics
+        self.sampling_rate = sampling_rate
+        self.istft_params = istft_params
+        self.lrelu_slope = lrelu_slope
+        self.audio_limit = audio_limit
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        this_SourceModuleHnNSF = (SourceModuleHnNSF if self.sampling_rate ==
+            22050 else SourceModuleHnNSF2)
+        self.m_source = this_SourceModuleHnNSF(sampling_rate=sampling_rate,
+            upsample_scale=np.prod(upsample_rates) * istft_params['hop_len'
+            ], harmonic_num=nb_harmonics, sine_amp=nsf_alpha, add_noise_std
+            =nsf_sigma, voiced_threshod=nsf_voiced_threshold)
+        self.f0_upsamp = paddle.nn.Upsample(scale_factor=(1,int(np.prod( upsample_rates) * istft_params['hop_len'])))
+        self.conv_pre = paddle.nn.utils.weight_norm(layer=paddle.nn.Conv1D(in_channels = in_channels, out_channels = base_channels, kernel_size=7, stride=1, padding=3))
+        self.ups = paddle.nn.LayerList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(paddle.nn.utils.weight_norm(layer=paddle.nn.
+                Conv1DTranspose(in_channels=base_channels // 2 ** i,
+                out_channels=base_channels // 2 ** (i + 1), kernel_size=k,
+                stride=u, padding=(k - u) // 2)))
+        self.source_downs = paddle.nn.LayerList()
+        self.source_resblocks = paddle.nn.LayerList()
+        downsample_rates = [1] + upsample_rates[::-1][:-1]
+        downsample_cum_rates = np.cumprod(downsample_rates)
+        for i, (u, k, d) in enumerate(zip(downsample_cum_rates[::-1],
+            source_resblock_kernel_sizes, source_resblock_dilation_sizes)):
+            if u == 1:
+                self.source_downs.append(paddle.nn.Conv1D(istft_params[
+                    'n_fft'] + 2, base_channels // 2 ** (i + 1), 1, 1))
+            else:
+                self.source_downs.append(paddle.nn.Conv1D(
+                    in_channels=istft_params['n_fft'] + 2,
+                    out_channels=base_channels // (2 ** (i + 1)),
+                    kernel_size=(u * 2,),
+                    stride=(u,),
+                    padding=int(u // 2),
+                ))
+            self.source_resblocks.append(ResBlock(base_channels // 2 ** (i +
+                1), k, d))
+        self.resblocks = paddle.nn.LayerList()
+        for i in range(len(self.ups)):
+            ch = base_channels // 2 ** (i + 1)
+            for _, (k, d) in enumerate(zip(resblock_kernel_sizes,
+                resblock_dilation_sizes)):
+                self.resblocks.append(ResBlock(ch, k, d))
+        self.conv_post = paddle.nn.utils.weight_norm(layer=paddle.nn.Conv1D
+            (ch, istft_params['n_fft'] + 2, 7, 1, padding=3))
+        self.ups.apply(init_weights)
+        self.conv_post.apply(init_weights)
+        self.reflection_pad = paddle.nn.Pad1D(padding=(1, 0), mode='reflect')
+        self.stft_window = paddle.to_tensor(get_window('hann',
+            istft_params['n_fft'], fftbins=True).astype(np.float32))
+        self.f0_predictor = f0_predictor
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            paddle.nn.utils.remove_weight_norm(layer=l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+        paddle.nn.utils.remove_weight_norm(layer=self.conv_pre)
+        paddle.nn.utils.remove_weight_norm(layer=self.conv_post)
+        self.m_source.remove_weight_norm()
+        for l in self.source_downs:
+            paddle.nn.utils.remove_weight_norm(layer=l)
+        for l in self.source_resblocks:
+            l.remove_weight_norm()
+
+    def _stft(self, x):
+        spec = paddle.signal.stft(x=x, n_fft=self.istft_params['n_fft'],
+            hop_length=self.istft_params['hop_len'], win_length=self.
+            istft_params['n_fft'], window=self.stft_window.to(x.place))
+        spec = paddle.as_real(spec)
+        return spec[..., 0], spec[..., 1]
+
+    def _istft(self, magnitude, phase):
+        magnitude = paddle.clip(magnitude, max=100.0)
+        real = magnitude * paddle.cos(phase)
+        img = magnitude * paddle.sin(phase)
+        inverse_transform = paddle.signal.istft(x=paddle.complex(real, img),
+            n_fft=self.istft_params['n_fft'], hop_length=self.istft_params[
+            'hop_len'], win_length=self.istft_params['n_fft'], window=self.
+            stft_window.to(magnitude.place))
+        return inverse_transform
+
+    def decode(self, x: paddle.Tensor, s: paddle.Tensor=paddle.zeros([1, 1, 0])
+        ) ->paddle.Tensor:
+        s_stft_real, s_stft_imag = self._stft(s.squeeze(1))
+        s_stft = paddle.cat([s_stft_real, s_stft_imag], dim=1)
+        x = self.conv_pre(x)
+        for i in range(self.num_upsamples):
+            x = paddle.nn.functional.leaky_relu(x=x, negative_slope=self.
+                lrelu_slope)
+            x = self.ups[i](x)
+            if i == self.num_upsamples - 1:
+                x = self.reflection_pad(x)
+            si = self.source_downs[i](s_stft)
+            si = self.source_resblocks[i](si)
+            x = x + si
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = paddle.nn.functional.leaky_relu(x=x)
+        x = self.conv_post(x)
+        magnitude = paddle.exp(x=x[:, :self.istft_params['n_fft'] // 2 + 1, :])
+        phase = paddle.sin(x[:, self.istft_params['n_fft'] // 2 + 1:, :])
+        x = self._istft(magnitude, phase)
+        x = paddle.clip(x, -self.audio_limit, self.audio_limit)
+        return x
+
+    def forward(self, batch: dict) ->Dict[str,
+        Optional[paddle.Tensor]]:
+        speech_feat = paddle.transpose(batch['speech_feat'],perm = [0,2,1]).to(device)
+        f0 = self.f0_predictor(speech_feat)
+        s = paddle.transpose(self.f0_upsamp(f0[:, None]),perm = [0,2,1])
+        s, _, _ = self.m_source(s)
+        s = paddle.transpose(s,perm = [0,2,1])
+        
+        generated_speech = self.decode(x=speech_feat, s=s)
+        return generated_speech, f0
+
+    @paddle.no_grad()
+    def inference(self, speech_feat: paddle.Tensor, cache_source: paddle.
+        Tensor=paddle.zeros([1, 1, 0])) ->paddle.Tensor:
+        paddle.seed(1986)
+        f0 = self.f0_predictor(speech_feat)
+        f0_4d = f0[:, None].unsqueeze(2)
+        s_4d = self.f0_upsamp(f0_4d)
+        s_3d = s_4d.squeeze(2)     
+        s = paddle.transpose(s_3d, perm=[0, 2, 1]) 
+        s, _, _ = self.m_source(s)
+        s = paddle.transpose(s,perm = [0,2,1])
+        if cache_source.shape[2] != 0:
+            s[:, :, :cache_source.shape[2]] = cache_source
+        generated_speech = self.decode(x=speech_feat, s=s)
+        return generated_speech, s

paddlespeech/t2s/models/hifigan/f0_predictor.py

@@ -0,0 +1,25 @@
+import paddle
+class ConvRNNF0Predictor(paddle.nn.Layer):
+
+    def __init__(self, num_class: int=1, in_channels: int=80, cond_channels:
+        int=512):
+        super().__init__()
+        self.num_class = num_class
+        self.condnet = paddle.nn.Sequential(paddle.nn.utils.weight_norm(
+            layer=paddle.nn.Conv1D(in_channels, cond_channels, kernel_size=
+            3, padding=1)), paddle.nn.ELU(), paddle.nn.utils.weight_norm(
+            layer=paddle.nn.Conv1D(cond_channels, cond_channels,
+            kernel_size=3, padding=1)), paddle.nn.ELU(), paddle.nn.utils.
+            weight_norm(layer=paddle.nn.Conv1D(cond_channels, cond_channels,
+            kernel_size=3, padding=1)), paddle.nn.ELU(), paddle.nn.utils.
+            weight_norm(layer=paddle.nn.Conv1D(cond_channels, cond_channels,
+            kernel_size=3, padding=1)), paddle.nn.ELU(), paddle.nn.utils.
+            weight_norm(layer=paddle.nn.Conv1D(cond_channels, cond_channels,
+            kernel_size=3, padding=1)), paddle.nn.ELU())
+        self.classifier = paddle.nn.Linear(in_features=cond_channels,
+            out_features=self.num_class)
+
+    def forward(self, x: paddle.Tensor) ->paddle.Tensor:
+        x = self.condnet(x)
+        x = paddle.transpose(x, perm=[0, 2, 1])
+        return paddle.abs(x=self.classifier(x).squeeze(-1))

paddlespeech/t2s/modules/conv.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+ # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -124,7 +124,7 @@ class Conv1dCell(nn.Conv1D):
         self._reshaped_weight = paddle.reshape(self.weight,
                                                (self._out_channels, -1))
 
-    def initialize_buffer(self, x_t):
+    def initialize_buffer(self, x_t): 
         """Initialize the buffer for the step input.
 
         Args:

paddlespeech/t2s/modules/flow/__init__.py

@@ -0,0 +1 @@
+from .flow import CausalMaskedDiffWithXvec

paddlespeech/t2s/modules/flow/attention.py

@@ -1,6 +1,93 @@
+from typing import Any, Dict, Optional
+import paddle
+from .attention_processor import Attention
 
-class BasicTransformerBlock(nn.Module):
-    r"""
+def _chunked_feed_forward(
+    ff: paddle.nn.Layer, hidden_states: paddle.Tensor, chunk_dim: int, chunk_size: int
+):
+    if hidden_states.shape[chunk_dim] % chunk_size != 0:
+        raise ValueError(
+            f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+        )
+    num_chunks = hidden_states.shape[chunk_dim] // chunk_size
+    ff_output = paddle.cat(
+        [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
+        dim=chunk_dim,
+    )
+    return ff_output
+
+class SinusoidalPositionalEmbedding(paddle.nn.Layer):
+    """Apply positional information to a sequence of embeddings.
+
+    Takes in a sequence of embeddings with shape (batch_size, seq_length, embed_dim) and adds positional embeddings to
+    them
+
+    Args:
+        embed_dim: (int): Dimension of the positional embedding.
+        max_seq_length: Maximum sequence length to apply positional embeddings
+
+    """
+
+    def __init__(self, embed_dim: int, max_seq_length: int = 32):
+        super().__init__()
+        position = paddle.arange(max_seq_length).unsqueeze(1)
+        div_term = paddle.exp(
+            x=paddle.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim)
+        )
+        pe = paddle.zeros(1, max_seq_length, embed_dim)
+        pe[0, :, 0::2] = paddle.sin(position * div_term)
+        pe[0, :, 1::2] = paddle.cos(position * div_term)
+        self.register_buffer(name="pe", tensor=pe)
+
+    def forward(self, x):
+        _, seq_length, _ = x.shape
+        x = x + self.pe[:, :seq_length]
+        return x
+
+class GatedSelfAttentionDense(paddle.nn.Layer):
+    """
+    A gated self-attention dense layer that combines visual features and object features.
+
+    Parameters:
+        query_dim (`int`): The number of channels in the query.
+        context_dim (`int`): The number of channels in the context.
+        n_heads (`int`): The number of heads to use for attention.
+        d_head (`int`): The number of channels in each head.
+    """
+
+    def __init__(self, query_dim: int, context_dim: int, n_heads: int, d_head: int):
+        super().__init__()
+        self.linear = paddle.nn.Linear(in_features=context_dim, out_features=query_dim)
+        self.attn = Attention(query_dim=query_dim, heads=n_heads, dim_head=d_head)
+        self.ff = FeedForward(query_dim, activation_fn="geglu")
+        self.norm1 = paddle.nn.LayerNorm(normalized_shape=query_dim)
+        self.norm2 = paddle.nn.LayerNorm(normalized_shape=query_dim)
+        self.add_parameter(
+            name="alpha_attn",
+            parameter=paddle.nn.parameter.Parameter(paddle.tensor(0.0)),
+        )
+        self.add_parameter(
+            name="alpha_dense",
+            parameter=paddle.nn.parameter.Parameter(paddle.tensor(0.0)),
+        )
+        self.enabled = True
+
+    def forward(self, x: paddle.Tensor, objs: paddle.Tensor) -> paddle.Tensor:
+        if not self.enabled:
+            return x
+        n_visual = x.shape[1]
+        objs = self.linear(objs)
+        x = (
+            x
+            + self.alpha_attn.tanh()
+            * self.attn(self.norm1(paddle.cat([x, objs], dim=1)))[:, :n_visual, :]
+        )
+        x = x + self.alpha_dense.tanh() * self.ff(self.norm2(x))
+        return x
+
+
+class BasicTransformerBlock(paddle.nn.Layer):
+    """
     A basic Transformer block.
 
     Parameters:
@@ -9,15 +96,29 @@ class BasicTransformerBlock(nn.Module):
         attention_head_dim (`int`): The number of channels in each head.
         dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
         cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
-        only_cross_attention (`bool`, *optional*):
-            Whether to use only cross-attention layers. In this case two cross attention layers are used.
-        double_self_attention (`bool`, *optional*):
-            Whether to use two self-attention layers. In this case no cross attention layers are used.
         activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
         num_embeds_ada_norm (:
             obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
         attention_bias (:
             obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
+        only_cross_attention (`bool`, *optional*):
+            Whether to use only cross-attention layers. In this case two cross attention layers are used.
+        double_self_attention (`bool`, *optional*):
+            Whether to use two self-attention layers. In this case no cross attention layers are used.
+        upcast_attention (`bool`, *optional*):
+            Whether to upcast the attention computation to float32. This is useful for mixed precision training.
+        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
+            Whether to use learnable elementwise affine parameters for normalization.
+        norm_type (`str`, *optional*, defaults to `"layer_norm"`):
+            The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
+        final_dropout (`bool` *optional*, defaults to False):
+            Whether to apply a final dropout after the last feed-forward layer.
+        attention_type (`str`, *optional*, defaults to `"default"`):
+            The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
+        positional_embeddings (`str`, *optional*, defaults to `None`):
+            The type of positional embeddings to apply to.
+        num_positional_embeddings (`int`, *optional*, defaults to `None`):
+            The maximum number of positional embeddings to apply.
     """
 
     def __init__(
@@ -26,202 +127,260 @@ class BasicTransformerBlock(nn.Module):
         num_attention_heads: int,
         attention_head_dim: int,
         dropout=0.0,
-        activation_fn: str = "geglu",
         cross_attention_dim: Optional[int] = None,
+        activation_fn: str = "geglu",
         num_embeds_ada_norm: Optional[int] = None,
         attention_bias: bool = False,
+        only_cross_attention: bool = False,
         double_self_attention: bool = False,
         upcast_attention: bool = False,
         norm_elementwise_affine: bool = True,
         norm_type: str = "layer_norm",
+        norm_eps: float = 1e-05,
         final_dropout: bool = False,
+        attention_type: str = "default",
+        positional_embeddings: Optional[str] = None,
+        num_positional_embeddings: Optional[int] = None,
+        ada_norm_continous_conditioning_embedding_dim: Optional[int] = None,
+        ada_norm_bias: Optional[int] = None,
+        ff_inner_dim: Optional[int] = None,
+        ff_bias: bool = True,
+        attention_out_bias: bool = True,
     ):
         super().__init__()
-        self.use_ada_layer_norm_zero = (num_embeds_ada_norm is not None) and norm_type == "ada_norm_zero"
-        self.use_ada_layer_norm = (num_embeds_ada_norm is not None) and norm_type == "ada_norm"
-
+        self.only_cross_attention = only_cross_attention
+        self.use_ada_layer_norm_zero = (
+            num_embeds_ada_norm is not None and norm_type == "ada_norm_zero"
+        )
+        self.use_ada_layer_norm = (
+            num_embeds_ada_norm is not None and norm_type == "ada_norm"
+        )
+        self.use_ada_layer_norm_single = norm_type == "ada_norm_single"
+        self.use_layer_norm = norm_type == "layer_norm"
+        self.use_ada_layer_norm_continuous = norm_type == "ada_norm_continuous"
         if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
             raise ValueError(
-                f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
-                f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
+                f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
+            )
+        self.norm_type = norm_type
+        self.num_embeds_ada_norm = num_embeds_ada_norm
+        if positional_embeddings and num_positional_embeddings is None:
+            raise ValueError(
+                "If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined."
             )
-        # Define 3 blocks. Each block has its own normalization layer.
-        # 1. Self-Attn
-        self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
+        if positional_embeddings == "sinusoidal":
+            self.pos_embed = SinusoidalPositionalEmbedding(
+                dim, max_seq_length=num_positional_embeddings
+            )
+        else:
+            self.pos_embed = None
+        
+
+        self.norm1 = paddle.nn.LayerNorm(
+            normalized_shape=dim,
+            weight_attr=norm_elementwise_affine,
+            bias_attr=norm_elementwise_affine,
+            epsilon=norm_eps,
+        )
         self.attn1 = Attention(
             query_dim=dim,
             heads=num_attention_heads,
             dim_head=attention_head_dim,
             dropout=dropout,
             bias=attention_bias,
-            cross_attention_dim=None
-            upcast_attention=False
+            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+            upcast_attention=upcast_attention,
+            out_bias=attention_out_bias,
         )
-        # 2. Cross-Attn
-        self.norm2 = None
-        self.attn2 = None
-        self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
-        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)
-
-        # let chunk size default to None
+        if cross_attention_dim is not None or double_self_attention:
+            if norm_type == "ada_norm":
+                self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm)
+            elif norm_type == "ada_norm_continuous":
+                self.norm2 = AdaLayerNormContinuous(
+                    dim,
+                    ada_norm_continous_conditioning_embedding_dim,
+                    norm_elementwise_affine,
+                    norm_eps,
+                    ada_norm_bias,
+                    "rms_norm",
+                )
+            else:
+                self.norm2 = paddle.nn.LayerNorm(
+                    normalized_shape=dim,
+                    epsilon=norm_eps,
+                    weight_attr=norm_elementwise_affine,
+                    bias_attr=norm_elementwise_affine,
+                )
+            self.attn2 = Attention(
+                query_dim=dim,
+                cross_attention_dim=cross_attention_dim
+                if not double_self_attention
+                else None,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                upcast_attention=upcast_attention,
+                out_bias=attention_out_bias,
+            )
+        else:
+            self.norm2 = None
+            self.attn2 = None
+        if norm_type == "ada_norm_continuous":
+            self.norm3 = AdaLayerNormContinuous(
+                dim,
+                ada_norm_continous_conditioning_embedding_dim,
+                norm_elementwise_affine,
+                norm_eps,
+                ada_norm_bias,
+                "layer_norm",
+            )
+        elif norm_type in [
+            "ada_norm_zero",
+            "ada_norm",
+            "layer_norm",
+            "ada_norm_continuous",
+        ]:
+            self.norm3 = paddle.nn.LayerNorm(
+                normalized_shape=dim,
+                epsilon=norm_eps,
+                weight_attr=norm_elementwise_affine,
+                bias_attr=norm_elementwise_affine,
+            )
+        elif norm_type == "layer_norm_i2vgen":
+            self.norm3 = None
+        self.ff = FeedForward(
+            dim,
+            dropout=dropout,
+            activation_fn=activation_fn,
+            final_dropout=final_dropout,
+            inner_dim=ff_inner_dim,
+            bias=ff_bias,
+        )
+        if attention_type == "gated" or attention_type == "gated-text-image":
+            self.fuser = GatedSelfAttentionDense(
+                dim, cross_attention_dim, num_attention_heads, attention_head_dim
+            )
+        if norm_type == "ada_norm_single":
+            self.scale_shift_table = paddle.nn.parameter.Parameter(
+                paddle.randn(6, dim) / dim**0.5
+            )
         self._chunk_size = None
         self._chunk_dim = 0
-    def forward(self,hidden_states):
-        norm_hidden_states = self.norm1(hidden_states)
-        cross_attention_kwargs = {}
+
+    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
+        self._chunk_size = chunk_size
+        self._chunk_dim = dim
+
+    def forward(
+        self,
+        hidden_states: paddle.Tensor,
+        attention_mask: Optional[paddle.Tensor] = None,
+        encoder_hidden_states: Optional[paddle.Tensor] = None,
+        encoder_attention_mask: Optional[paddle.Tensor] = None,
+        timestep: Optional[paddle.Tensor] = None,
+        cross_attention_kwargs: Dict[str, Any] = None,
+        class_labels: Optional[paddle.Tensor] = None,
+        added_cond_kwargs: Optional[Dict[str, paddle.Tensor]] = None,
+    ) -> paddle.Tensor:
+        if cross_attention_kwargs is not None:
+            if cross_attention_kwargs.get("scale", None) is not None:
+                logger.warning(
+                    "Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored."
+                )
+        batch_size = hidden_states.shape[0]
+        if self.norm_type == "ada_norm":
+            norm_hidden_states = self.norm1(hidden_states, timestep)
+        elif self.norm_type == "ada_norm_zero":
+            (norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp) = self.norm1(
+                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+            )
+        elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]:
+            norm_hidden_states = self.norm1(hidden_states)
+        elif self.norm_type == "ada_norm_continuous":
+            norm_hidden_states = self.norm1(
+                hidden_states, added_cond_kwargs["pooled_text_emb"]
+            )
+        elif self.norm_type == "ada_norm_single":
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+                self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
+            ).chunk(6, dim=1)
+            norm_hidden_states = self.norm1(hidden_states)
+            norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
+            norm_hidden_states = norm_hidden_states.squeeze(1)
+        else:
+            raise ValueError("Incorrect norm used")
+        if self.pos_embed is not None:
+            norm_hidden_states = self.pos_embed(norm_hidden_states)
+        cross_attention_kwargs = (
+            cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
+        )
+        gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
         attn_output = self.attn1(
             norm_hidden_states,
-            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
-            attention_mask=encoder_attention_mask if self.only_cross_attention else attention_mask,
+            encoder_hidden_states=encoder_hidden_states
+            if self.only_cross_attention
+            else None,
+            attention_mask=attention_mask,
             **cross_attention_kwargs,
         )
+        if self.norm_type == "ada_norm_zero":
+            attn_output = gate_msa.unsqueeze(1) * attn_output
+        elif self.norm_type == "ada_norm_single":
+            attn_output = gate_msa * attn_output
         hidden_states = attn_output + hidden_states
-        norm_hidden_states = self.norm3(hidden_states)
-        ff_output = self.ff(norm_hidden_states)
-        hidden_states = ff_output + hidden_states
-        return hidden_states
-
-class FeedForward(nn.Layer):
-
-    def __init__(
-        self,
-        dim: int,
-        dim_out: Optional[int] = None,
-        mult: int = 4,
-        dropout: float = 0.0,
-        activation_fn: str = "geglu",
-        final_dropout: bool = False,
-    ):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        dim_out = dim_out if dim_out is not None else dim
-
-        if activation_fn == "gelu":
-            act_fn = GELU(dim, inner_dim, approximate=False)
-        elif activation_fn == "gelu-approximate":
-            act_fn = GELU(dim, inner_dim, approximate=True)
-        elif activation_fn == "geglu":
-            act_fn = GEGLU(dim, inner_dim)
-        elif activation_fn == "geglu-approximate":
-            act_fn = ApproximateGELU(dim, inner_dim)
-        elif activation_fn == "snakebeta":
-            act_fn = SnakeBeta(dim, inner_dim)
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+        if gligen_kwargs is not None:
+            hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
+        if self.attn2 is not None:
+            if self.norm_type == "ada_norm":
+                norm_hidden_states = self.norm2(hidden_states, timestep)
+            elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]:
+                norm_hidden_states = self.norm2(hidden_states)
+            elif self.norm_type == "ada_norm_single":
+                norm_hidden_states = hidden_states
+            elif self.norm_type == "ada_norm_continuous":
+                norm_hidden_states = self.norm2(
+                    hidden_states, added_cond_kwargs["pooled_text_emb"]
+                )
+            else:
+                raise ValueError("Incorrect norm")
+            if self.pos_embed is not None and self.norm_type != "ada_norm_single":
+                norm_hidden_states = self.pos_embed(norm_hidden_states)
+            attn_output = self.attn2(
+                norm_hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+                **cross_attention_kwargs,
+            )
+            hidden_states = attn_output + hidden_states
+        if self.norm_type == "ada_norm_continuous":
+            norm_hidden_states = self.norm3(
+                hidden_states, added_cond_kwargs["pooled_text_emb"]
+            )
+        elif not self.norm_type == "ada_norm_single":
+            norm_hidden_states = self.norm3(hidden_states)
+        if self.norm_type == "ada_norm_zero":
+            norm_hidden_states = (
+                norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+            )
+        if self.norm_type == "ada_norm_single":
+            norm_hidden_states = self.norm2(hidden_states)
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
+        if self._chunk_size is not None:
+            ff_output = _chunked_feed_forward(
+                self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size
+            )
         else:
-            act_fn = GEGLU(dim, inner_dim)
-
-        self.net = nn.LayerList()
-        self.net.append(act_fn)
-        self.net.append(nn.Dropout(dropout))
-        self.net.append(LoRACompatibleLinear(inner_dim, dim_out))
-        
-        if final_dropout:
-            self.net.append(nn.Dropout(dropout))
-
-    def forward(self, hidden_states):
-        for module in self.net:
-            hidden_states = module(hidden_states)
+            ff_output = self.ff(norm_hidden_states)
+        if self.norm_type == "ada_norm_zero":
+            ff_output = gate_mlp.unsqueeze(1) * ff_output
+        elif self.norm_type == "ada_norm_single":
+            ff_output = gate_mlp * ff_output
+        hidden_states = ff_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
         return hidden_states
 
-query_dim=dim,
-heads=num_attention_heads,
-dim_head=attention_head_dim,
-dropout=dropout,
-bias=attention_bias,
-cross_attention_dim=None,
-upcast_attention=upcast_attention,
-class Attention(nn.Module):
-    def __init__(
-        self,
-        query_dim: int,
-        cross_attention_dim: Optional[int] = None,
-        heads: int = 8,
-        dim_head: int = 64,
-        dropout: float = 0.0,
-        bias: bool = False,
-        upcast_attention: bool = False,
-        upcast_softmax: bool = False,
-        cross_attention_norm: Optional[str] = None,
-        cross_attention_norm_num_groups: int = 32,
-        qk_norm: Optional[str] = None,
-        norm_num_groups: Optional[int] = None,
-        spatial_norm_dim: Optional[int] = None,
-        out_bias: bool = True,
-        scale_qk: bool = True,
-        only_cross_attention: bool = False,
-        eps: float = 1e-5,
-        rescale_output_factor: float = 1.0,
-        processor: Optional["AttnProcessor"] = None,
-        out_dim: int = None,
-    ):
-        super().__init__()
-        self.inner_dim = out_dim if out_dim is not None else dim_head * heads
-        self.query_dim = query_dim
-        self.use_bias = bias
-        self.is_cross_attention = cross_attention_dim is not None
-        self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
-        self.upcast_attention = upcast_attention
-        self.upcast_softmax = upcast_softmax
-        self.rescale_output_factor = rescale_output_factor
-        self.dropout = dropout
-        self.fused_projections = False
-        self.out_dim = out_dim if out_dim is not None else query_dim
-        # we make use of this private variable to know whether this class is loaded
-        # with an deprecated state dict so that we can convert it on the fly
-
-        self.scale_qk = scale_qk
-        self.scale = dim_head**-0.5 if self.scale_qk else 1.0
-
-        self.heads = out_dim // dim_head if out_dim is not None else heads
-        # for slice_size > 0 the attention score computation
-        # is split across the batch axis to save memory
-        # You can set slice_size with `set_attention_slice`
-        self.sliceable_head_dim = heads
-        self.to_q = nn.Linear(query_dim, self.inner_dim, bias=bias)
-        self.to_k = nn.Linear(self.cross_attention_dim, self.inner_dim, bias=bias)
-        self.to_v = nn.Linear(self.cross_attention_dim, self.inner_dim, bias=bias)
-        self.to_out = nn.ModuleList([])
-        self.to_out.append(nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))
-        self.to_out.append(nn.Dropout(dropout))
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        temb: Optional[torch.Tensor] = None,
-        ):
-        residual = hidden_states
-        input_ndim = hidden_states.ndim
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-        query = attn.to_q(hidden_states)
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-        hidden_states = hidden_states.to(query.dtype)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-        hidden_states = hidden_states / attn.rescale_output_factor
-        return hidden_states
 

paddlespeech/t2s/modules/flow/attention_processor.py

@@ -0,0 +1,625 @@
+import inspect
+import math
+from typing import Callable, List, Optional, Union
+import paddle
+
+class Attention(paddle.nn.Layer):
+    """
+    A cross attention layer.
+
+    Parameters:
+        query_dim (`int`):
+            The number of channels in the query.
+        cross_attention_dim (`int`, *optional*):
+            The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
+        heads (`int`,  *optional*, defaults to 8):
+            The number of heads to use for multi-head attention.
+        dim_head (`int`,  *optional*, defaults to 64):
+            The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability to use.
+        bias (`bool`, *optional*, defaults to False):
+            Set to `True` for the query, key, and value linear layers to contain a bias parameter.
+        upcast_attention (`bool`, *optional*, defaults to False):
+            Set to `True` to upcast the attention computation to `float32`.
+        upcast_softmax (`bool`, *optional*, defaults to False):
+            Set to `True` to upcast the softmax computation to `float32`.
+        cross_attention_norm (`str`, *optional*, defaults to `None`):
+            The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`.
+        cross_attention_norm_num_groups (`int`, *optional*, defaults to 32):
+            The number of groups to use for the group norm in the cross attention.
+        added_kv_proj_dim (`int`, *optional*, defaults to `None`):
+            The number of channels to use for the added key and value projections. If `None`, no projection is used.
+        norm_num_groups (`int`, *optional*, defaults to `None`):
+            The number of groups to use for the group norm in the attention.
+        spatial_norm_dim (`int`, *optional*, defaults to `None`):
+            The number of channels to use for the spatial normalization.
+        out_bias (`bool`, *optional*, defaults to `True`):
+            Set to `True` to use a bias in the output linear layer.
+        scale_qk (`bool`, *optional*, defaults to `True`):
+            Set to `True` to scale the query and key by `1 / sqrt(dim_head)`.
+        only_cross_attention (`bool`, *optional*, defaults to `False`):
+            Set to `True` to only use cross attention and not added_kv_proj_dim. Can only be set to `True` if
+            `added_kv_proj_dim` is not `None`.
+        eps (`float`, *optional*, defaults to 1e-5):
+            An additional value added to the denominator in group normalization that is used for numerical stability.
+        rescale_output_factor (`float`, *optional*, defaults to 1.0):
+            A factor to rescale the output by dividing it with this value.
+        residual_connection (`bool`, *optional*, defaults to `False`):
+            Set to `True` to add the residual connection to the output.
+        _from_deprecated_attn_block (`bool`, *optional*, defaults to `False`):
+            Set to `True` if the attention block is loaded from a deprecated state dict.
+        processor (`AttnProcessor`, *optional*, defaults to `None`):
+            The attention processor to use. If `None`, defaults to `AttnProcessor2_0` if `torch 2.x` is used and
+            `AttnProcessor` otherwise.
+    """
+
+    def __init__(
+        self,
+        query_dim: int,
+        cross_attention_dim: Optional[int] = None,
+        heads: int = 8,
+        dim_head: int = 64,
+        dropout: float = 0.0,
+        bias: bool = False,
+        upcast_attention: bool = False,
+        upcast_softmax: bool = False,
+        cross_attention_norm: Optional[str] = None,
+        cross_attention_norm_num_groups: int = 32,
+        qk_norm: Optional[str] = None,
+        added_kv_proj_dim: Optional[int] = None,
+        norm_num_groups: Optional[int] = None,
+        spatial_norm_dim: Optional[int] = None,
+        out_bias: bool = True,
+        scale_qk: bool = True,
+        only_cross_attention: bool = False,
+        eps: float = 1e-05,
+        rescale_output_factor: float = 1.0,
+        residual_connection: bool = False,
+        _from_deprecated_attn_block: bool = False,
+        processor: Optional["AttnProcessor"] = None,
+        out_dim: int = None,
+        context_pre_only=None,
+    ):
+        super().__init__()
+        self.inner_dim = out_dim if out_dim is not None else dim_head * heads
+        self.query_dim = query_dim
+        self.use_bias = bias
+        self.is_cross_attention = cross_attention_dim is not None
+        self.cross_attention_dim = (
+            cross_attention_dim if cross_attention_dim is not None else query_dim
+        )
+        self.upcast_attention = upcast_attention
+        self.upcast_softmax = upcast_softmax
+        self.rescale_output_factor = rescale_output_factor
+        self.residual_connection = residual_connection
+        self.dropout = dropout
+        self.fused_projections = False
+        self.out_dim = out_dim if out_dim is not None else query_dim
+        self.context_pre_only = context_pre_only
+        self._from_deprecated_attn_block = _from_deprecated_attn_block
+        self.scale_qk = scale_qk
+        self.scale = dim_head**-0.5 if self.scale_qk else 1.0
+        self.heads = out_dim // dim_head if out_dim is not None else heads
+        self.sliceable_head_dim = heads
+        self.added_kv_proj_dim = added_kv_proj_dim
+        self.only_cross_attention = only_cross_attention
+        if self.added_kv_proj_dim is None and self.only_cross_attention:
+            raise ValueError(
+                "`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`."
+            )
+        if norm_num_groups is not None:
+            self.group_norm = paddle.nn.GroupNorm(
+                num_channels=query_dim,
+                num_groups=norm_num_groups,
+                epsilon=eps,
+                weight_attr=True,
+                bias_attr=True,
+            )
+        else:
+            self.group_norm = None
+        if spatial_norm_dim is not None:
+            self.spatial_norm = SpatialNorm(
+                f_channels=query_dim, zq_channels=spatial_norm_dim
+            )
+        else:
+            self.spatial_norm = None
+        if qk_norm is None:
+            self.norm_q = None
+            self.norm_k = None
+        elif qk_norm == "layer_norm":
+            self.norm_q = paddle.nn.LayerNorm(normalized_shape=dim_head, epsilon=eps)
+            self.norm_k = paddle.nn.LayerNorm(normalized_shape=dim_head, epsilon=eps)
+        else:
+            raise ValueError(
+                f"unknown qk_norm: {qk_norm}. Should be None or 'layer_norm'"
+            )
+        if cross_attention_norm is None:
+            self.norm_cross = None
+        elif cross_attention_norm == "layer_norm":
+            self.norm_cross = paddle.nn.LayerNorm(
+                normalized_shape=self.cross_attention_dim
+            )
+        elif cross_attention_norm == "group_norm":
+            if self.added_kv_proj_dim is not None:
+                norm_cross_num_channels = added_kv_proj_dim
+            else:
+                norm_cross_num_channels = self.cross_attention_dim
+            self.norm_cross = paddle.nn.GroupNorm(
+                num_channels=norm_cross_num_channels,
+                num_groups=cross_attention_norm_num_groups,
+                epsilon=1e-05,
+                weight_attr=True,
+                bias_attr=True,
+            )
+        else:
+            raise ValueError(
+                f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'"
+            )
+        self.to_q = paddle.nn.Linear(
+            in_features=query_dim, out_features=self.inner_dim, bias_attr=bias
+        )
+        if not self.only_cross_attention:
+            self.to_k = paddle.nn.Linear(
+                in_features=self.cross_attention_dim,
+                out_features=self.inner_dim,
+                bias_attr=bias,
+            )
+            self.to_v = paddle.nn.Linear(
+                in_features=self.cross_attention_dim,
+                out_features=self.inner_dim,
+                bias_attr=bias,
+            )
+        else:
+            self.to_k = None
+            self.to_v = None
+        if self.added_kv_proj_dim is not None:
+            self.add_k_proj = paddle.nn.Linear(
+                in_features=added_kv_proj_dim, out_features=self.inner_dim
+            )
+            self.add_v_proj = paddle.nn.Linear(
+                in_features=added_kv_proj_dim, out_features=self.inner_dim
+            )
+            if self.context_pre_only is not None:
+                self.add_q_proj = paddle.nn.Linear(
+                    in_features=added_kv_proj_dim, out_features=self.inner_dim
+                )
+        self.to_out = paddle.nn.LayerList(sublayers=[])
+        self.to_out.append(
+            paddle.nn.Linear(
+                in_features=self.inner_dim,
+                out_features=self.out_dim,
+                bias_attr=out_bias,
+            )
+        )
+        self.to_out.append(paddle.nn.Dropout(p=dropout))
+        if self.context_pre_only is not None and not self.context_pre_only:
+            self.to_add_out = paddle.nn.Linear(
+                in_features=self.inner_dim,
+                out_features=self.out_dim,
+                bias_attr=out_bias,
+            )
+            processor = AttnProcessor()
+        processor = AttnProcessor2_0()
+        self.set_processor(processor)
+
+    def set_processor(self, processor: "AttnProcessor") -> None:
+        """
+        Set the attention processor to use.
+
+        Args:
+            processor (`AttnProcessor`):
+                The attention processor to use.
+        """
+        if (
+            hasattr(self, "processor")
+            and isinstance(self.processor, paddle.nn.Layer)
+            and not isinstance(processor, paddle.nn.Layer)
+        ):
+            self._modules.pop("processor")
+        self.processor = processor
+
+    def forward(
+        self,
+        hidden_states: paddle.Tensor,
+        encoder_hidden_states: Optional[paddle.Tensor] = None,
+        attention_mask: Optional[paddle.Tensor] = None,
+        **cross_attention_kwargs,
+    ) -> paddle.Tensor:
+        """
+        The forward method of the `Attention` class.
+
+        Args:
+            hidden_states (`torch.Tensor`):
+                The hidden states of the query.
+            encoder_hidden_states (`torch.Tensor`, *optional*):
+                The hidden states of the encoder.
+            attention_mask (`torch.Tensor`, *optional*):
+                The attention mask to use. If `None`, no mask is applied.
+            **cross_attention_kwargs:
+                Additional keyword arguments to pass along to the cross attention.
+
+        Returns:
+            `torch.Tensor`: The output of the attention layer.
+        """
+        attn_parameters = set(
+            inspect.signature(self.processor.__call__).parameters.keys()
+        )
+        quiet_attn_parameters = {"ip_adapter_masks"}
+        unused_kwargs = [
+            k
+            for k, _ in cross_attention_kwargs.items()
+            if k not in attn_parameters and k not in quiet_attn_parameters
+        ]
+        if len(unused_kwargs) > 0:
+            logger.warning(
+                f"cross_attention_kwargs {unused_kwargs} are not expected by {self.processor.__class__.__name__} and will be ignored."
+            )
+        cross_attention_kwargs = {
+            k: w for k, w in cross_attention_kwargs.items() if k in attn_parameters
+        }
+        return self.processor(
+            self,
+            hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+            attention_mask=attention_mask,
+            **cross_attention_kwargs,
+        )
+
+    def batch_to_head_dim(self, tensor: paddle.Tensor) -> paddle.Tensor:
+        """
+        Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads`
+        is the number of heads initialized while constructing the `Attention` class.
+
+        Args:
+            tensor (`torch.Tensor`): The tensor to reshape.
+
+        Returns:
+            `torch.Tensor`: The reshaped tensor.
+        """
+        head_size = self.heads
+        batch_size, seq_len, dim = tensor.shape
+        tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
+        tensor = tensor.permute(0, 2, 1, 3).reshape(
+            batch_size // head_size, seq_len, dim * head_size
+        )
+        return tensor
+
+    def head_to_batch_dim(
+        self, tensor: paddle.Tensor, out_dim: int = 3
+    ) -> paddle.Tensor:
+        """
+        Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is
+        the number of heads initialized while constructing the `Attention` class.
+
+        Args:
+            tensor (`torch.Tensor`): The tensor to reshape.
+            out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is
+                reshaped to `[batch_size * heads, seq_len, dim // heads]`.
+
+        Returns:
+            `torch.Tensor`: The reshaped tensor.
+        """
+        head_size = self.heads
+        if tensor.ndim == 3:
+            batch_size, seq_len, dim = tensor.shape
+            extra_dim = 1
+        else:
+            batch_size, extra_dim, seq_len, dim = tensor.shape
+        tensor = tensor.reshape(
+            batch_size, seq_len * extra_dim, head_size, dim // head_size
+        )
+        tensor = tensor.permute(0, 2, 1, 3)
+        if out_dim == 3:
+            tensor = tensor.reshape(
+                batch_size * head_size, seq_len * extra_dim, dim // head_size
+            )
+        return tensor
+
+    def get_attention_scores(
+        self,
+        query: paddle.Tensor,
+        key: paddle.Tensor,
+        attention_mask: paddle.Tensor = None,
+    ) -> paddle.Tensor:
+        """
+        Compute the attention scores.
+
+        Args:
+            query (`torch.Tensor`): The query tensor.
+            key (`torch.Tensor`): The key tensor.
+            attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied.
+
+        Returns:
+            `torch.Tensor`: The attention probabilities/scores.
+        """
+        dtype = query.dtype
+        if self.upcast_attention:
+            query = query.float()
+            key = key.float()
+        if attention_mask is None:
+            baddbmm_input = paddle.empty(
+                query.shape[0],
+                query.shape[1],
+                key.shape[1],
+                dtype=query.dtype,
+                device=query.place,
+            )
+            beta = 0
+        else:
+            baddbmm_input = attention_mask
+            beta = 1
+        attention_scores = paddle.baddbmm(
+            input=baddbmm_input,
+            x=query,
+            y=key.transpose(-1, -2),
+            beta=beta,
+            alpha=self.scale,
+        )
+        del baddbmm_input
+        if self.upcast_softmax:
+            attention_scores = attention_scores.float()
+        attention_probs = attention_scores.softmax(dim=-1)
+        del attention_scores
+        attention_probs = attention_probs.to(dtype)
+        return attention_probs
+
+    def prepare_attention_mask(
+        self,
+        attention_mask: paddle.Tensor,
+        target_length: int,
+        batch_size: int,
+        out_dim: int = 3,
+    ) -> paddle.Tensor:
+        """
+        Prepare the attention mask for the attention computation.
+
+        Args:
+            attention_mask (`torch.Tensor`):
+                The attention mask to prepare.
+            target_length (`int`):
+                The target length of the attention mask. This is the length of the attention mask after padding.
+            batch_size (`int`):
+                The batch size, which is used to repeat the attention mask.
+            out_dim (`int`, *optional*, defaults to `3`):
+                The output dimension of the attention mask. Can be either `3` or `4`.
+
+        Returns:
+            `torch.Tensor`: The prepared attention mask.
+        """
+        head_size = self.heads
+        if attention_mask is None:
+            return attention_mask
+        current_length: int = attention_mask.shape[-1]
+        if current_length != target_length:
+            if attention_mask.device.type == "mps":
+                padding_shape = (
+                    attention_mask.shape[0],
+                    attention_mask.shape[1],
+                    target_length,
+                )
+                padding = paddle.zeros(
+                    padding_shape,
+                    dtype=attention_mask.dtype,
+                    device=attention_mask.place,
+                )
+                attention_mask = paddle.cat([attention_mask, padding], dim=2)
+            else:
+                attention_mask = paddle.compat.pad(
+                    attention_mask, (0, target_length), value=0.0
+                )
+        if out_dim == 3:
+            if attention_mask.shape[0] < batch_size * head_size:
+                attention_mask = attention_mask.repeat_interleave(head_size, axis=0)
+        elif out_dim == 4:
+            attention_mask = attention_mask.unsqueeze(1)
+            attention_mask = attention_mask.repeat_interleave(head_size, dim=1)
+        return attention_mask
+
+    def norm_encoder_hidden_states(
+        self, encoder_hidden_states: paddle.Tensor
+    ) -> paddle.Tensor:
+        """
+        Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the
+        `Attention` class.
+
+        Args:
+            encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder.
+
+        Returns:
+            `torch.Tensor`: The normalized encoder hidden states.
+        """
+        assert (
+            self.norm_cross is not None
+        ), "self.norm_cross must be defined to call self.norm_encoder_hidden_states"
+        if isinstance(self.norm_cross, paddle.nn.LayerNorm):
+            encoder_hidden_states = self.norm_cross(encoder_hidden_states)
+        elif isinstance(self.norm_cross, paddle.nn.GroupNorm):
+            encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
+            encoder_hidden_states = self.norm_cross(encoder_hidden_states)
+            encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
+        else:
+            assert False
+        return encoder_hidden_states
+
+    @paddle.no_grad()
+    def fuse_projections(self, fuse=True):
+        device = self.to_q.weight.data.place
+        dtype = self.to_q.weight.data.dtype
+        if not self.is_cross_attention:
+            concatenated_weights = paddle.cat(
+                [self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data]
+            )
+            in_features = concatenated_weights.shape[1]
+            out_features = concatenated_weights.shape[0]
+            self.to_qkv = paddle.nn.Linear(
+                in_features=in_features,
+                out_features=out_features,
+                bias_attr=self.use_bias,
+            )
+            self.to_qkv.weight.copy_(concatenated_weights)
+            if self.use_bias:
+                concatenated_bias = paddle.cat(
+                    [self.to_q.bias.data, self.to_k.bias.data, self.to_v.bias.data]
+                )
+                self.to_qkv.bias.copy_(concatenated_bias)
+        else:
+            concatenated_weights = paddle.cat(
+                [self.to_k.weight.data, self.to_v.weight.data]
+            )
+            in_features = concatenated_weights.shape[1]
+            out_features = concatenated_weights.shape[0]
+            self.to_kv = paddle.nn.Linear(
+                in_features=in_features,
+                out_features=out_features,
+                bias_attr=self.use_bias,
+            )
+            self.to_kv.weight.copy_(concatenated_weights)
+            if self.use_bias:
+                concatenated_bias = paddle.cat(
+                    [self.to_k.bias.data, self.to_v.bias.data]
+                )
+                self.to_kv.bias.copy_(concatenated_bias)
+        self.fused_projections = fuse
+
+
+class AttnProcessor:
+    """
+    Default processor for performing attention-related computations.
+    """
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: paddle.Tensor,
+        encoder_hidden_states: Optional[paddle.Tensor] = None,
+        attention_mask: Optional[paddle.Tensor] = None,
+        temb: Optional[paddle.Tensor] = None,
+        *args,
+        **kwargs,
+    ) -> paddle.Tensor:
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+        input_ndim = hidden_states.ndim
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+        batch_size, sequence_length, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+        attention_mask = attn.prepare_attention_mask(
+            attention_mask, sequence_length, batch_size
+        )
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
+                1, 2
+            )
+        query = attn.to_q(hidden_states)
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(
+                encoder_hidden_states
+            )
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+        query = attn.head_to_batch_dim(query)
+        key = attn.head_to_batch_dim(key)
+        value = attn.head_to_batch_dim(value)
+        attention_probs = attn.get_attention_scores(query, key, attention_mask)
+        hidden_states = paddle.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+        hidden_states = attn.to_out[0](hidden_states)
+        hidden_states = attn.to_out[1](hidden_states)
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+        hidden_states = hidden_states / attn.rescale_output_factor
+        return hidden_states
+
+class AttnProcessor2_0:
+    """
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(self):
+        pass
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: paddle.Tensor,
+        encoder_hidden_states: Optional[paddle.Tensor] = None,
+        attention_mask: Optional[paddle.Tensor] = None,
+        temb: Optional[paddle.Tensor] = None,
+        *args,
+        **kwargs,
+    ) -> paddle.Tensor:
+        residual = hidden_states
+        if attn.spatial_norm is not None:
+            hidden_states = attn.spatial_norm(hidden_states, temb)
+        input_ndim = hidden_states.ndim
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+        batch_size, sequence_length, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+        if attention_mask is not None:
+            attention_mask = attn.prepare_attention_mask(
+                attention_mask, sequence_length, batch_size
+            )
+            attention_mask = attention_mask.view(
+                [batch_size, attn.heads, -1, attention_mask.shape[-1]]
+            )
+        if attn.group_norm is not None:
+            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
+                1, 2
+            )
+        query = attn.to_q(hidden_states)
+        if encoder_hidden_states is None:
+            encoder_hidden_states = hidden_states
+        elif attn.norm_cross:
+            encoder_hidden_states = attn.norm_encoder_hidden_states(
+                encoder_hidden_states
+            )
+        key = attn.to_k(encoder_hidden_states)
+        value = attn.to_v(encoder_hidden_states)
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+        query = paddle.transpose(query.view([batch_size, -1, attn.heads, head_dim]),perm = [0,2,1])
+        key = paddle.transpose(key.view([batch_size, -1, attn.heads, head_dim]),perm = [0,2,1])
+        value =paddle.transpose(value.view([batch_size, -1, attn.heads, head_dim]),perm = [0,2,1])
+        hidden_states = paddle.nn.functional.scaled_dot_product_attention(
+            query.transpose([0, 2, 1, 3]),
+            key.transpose([0, 2, 1, 3]),
+            value.transpose([0, 2, 1, 3]),
+            attn_mask=attention_mask,
+            dropout_p=0.0,
+            is_causal=False,
+        ).transpose([0, 2, 1, 3])
+        hidden_states = paddle.transpose(hidden_states,perm = [0,2,1]).reshape(
+            [batch_size, -1, attn.heads * head_dim]
+        )
+        hidden_states = hidden_states.to(query.dtype)
+        hidden_states = attn.to_out[0](hidden_states)
+        hidden_states = attn.to_out[1](hidden_states)
+        if input_ndim == 4:
+            hidden_states =paddle.transpose(hidden_states,perm = [0,1,3,2]).reshape(
+                [batch_size, channel, height, width]
+            )
+        if attn.residual_connection:
+            hidden_states = hidden_states + residual
+        hidden_states = hidden_states / attn.rescale_output_factor
+        return hidden_states

paddlespeech/t2s/modules/flow/convolution.py

@@ -0,0 +1,99 @@
+import paddle
+
+"""ConvolutionModule definition."""
+from typing import Tuple
+
+
+class ConvolutionModule(paddle.nn.Layer):
+    """ConvolutionModule in Conformer model."""
+
+    def __init__(
+        self,
+        channels: int,
+        kernel_size: int = 15,
+        activation: paddle.nn.Layer = paddle.nn.ReLU(),
+        norm: str = "batch_norm",
+        causal: bool = False,
+        bias: bool = True,
+    ):
+        """Construct an ConvolutionModule object.
+        Args:
+            channels (int): The number of channels of conv layers.
+            kernel_size (int): Kernel size of conv layers.
+            causal (int): Whether use causal convolution or not
+        """
+        super().__init__()
+        self.pointwise_conv1 = paddle.nn.Conv1d(
+            channels, 2 * channels, kernel_size=1, stride=1, padding=0, bias=bias
+        )
+        if causal:
+            padding = 0
+            self.lorder = kernel_size - 1
+        else:
+            assert (kernel_size - 1) % 2 == 0
+            padding = (kernel_size - 1) // 2
+            self.lorder = 0
+        self.depthwise_conv = paddle.nn.Conv1d(
+            channels,
+            channels,
+            kernel_size,
+            stride=1,
+            padding=padding,
+            groups=channels,
+            bias=bias,
+        )
+        assert norm in ["batch_norm", "layer_norm"]
+        if norm == "batch_norm":
+            self.use_layer_norm = False
+            self.norm = paddle.nn.BatchNorm1D(num_features=channels)
+        else:
+            self.use_layer_norm = True
+            self.norm = paddle.nn.LayerNorm(normalized_shape=channels)
+        self.pointwise_conv2 = paddle.nn.Conv1d(
+            channels, channels, kernel_size=1, stride=1, padding=0, bias=bias
+        )
+        self.activation = activation
+
+    def forward(
+        self,
+        x: paddle.Tensor,
+        mask_pad: paddle.Tensor = paddle.ones((0, 0, 0), dtype=paddle.bool),
+        cache: paddle.Tensor = paddle.zeros((0, 0, 0)),
+    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
+        """Compute convolution module.
+        Args:
+            x (torch.Tensor): Input tensor (#batch, time, channels).
+            mask_pad (torch.Tensor): used for batch padding (#batch, 1, time),
+                (0, 0, 0) means fake mask.
+            cache (torch.Tensor): left context cache, it is only
+                used in causal convolution (#batch, channels, cache_t),
+                (0, 0, 0) meas fake cache.
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, channels).
+        """
+        x = x.transpose(1, 2)
+        if mask_pad.size(2) > 0:
+            x.masked_fill_(~mask_pad, 0.0)
+        if self.lorder > 0:
+            if cache.size(2) == 0:
+                x = paddle.compat.pad(x, (self.lorder, 0), "constant", 0.0)
+            else:
+                assert cache.size(0) == x.size(0)
+                assert cache.size(1) == x.size(1)
+                x = paddle.cat((cache, x), dim=2)
+            assert x.size(2) > self.lorder
+            new_cache = x[:, :, -self.lorder :]
+        else:
+            new_cache = paddle.zeros((0, 0, 0), dtype=x.dtype, device=x.place)
+        x = self.pointwise_conv1(x)
+        x = paddle.nn.functional.glu(x=x, axis=1)
+        x = self.depthwise_conv(x)
+        if self.use_layer_norm:
+            x = x.transpose(1, 2)
+        x = self.activation(self.norm(x))
+        if self.use_layer_norm:
+            x = x.transpose(1, 2)
+        x = self.pointwise_conv2(x)
+        if mask_pad.size(2) > 0:
+            x.masked_fill_(~mask_pad, 0.0)
+        return x.transpose(1, 2), new_cache

paddlespeech/t2s/modules/flow/decoder.py

@@ -11,15 +11,15 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Tuple
+from typing import Tuple, Any, Dict, Optional
 import paddle
+import math
 from paddle import nn
 import paddle.nn.functional as F
 from einops import pack, rearrange, repeat
-from cosyvoice.utils.common import mask_to_bias
-from cosyvoice.utils.mask import add_optional_chunk_mask
-from matcha.models.components.decoder import SinusoidalPosEmb, Block1D, ResnetBlock1D, Downsample1D, TimestepEmbedding, Upsample1D
-from .attention import BasicTransformerBlock
+from paddlespeech.t2s.models.CosyVoice.common import mask_to_bias
+from paddlespeech.t2s.models.CosyVoice.mask import add_optional_chunk_mask
+from .matcha_transformer import BasicTransformerBlock
 
 def get_activation(act_fn):
     if act_fn == "silu":
@@ -110,14 +110,15 @@ class TimestepEmbedding(nn.Layer):
         if condition is not None and self.cond_proj is not None:
             sample = sample + self.cond_proj(condition)
         sample = self.linear_1(sample)
-
+        # print("sample2:",sample)
         if self.act is not None:
             sample = self.act(sample)
-
+        # print("sample3:",sample)
         sample = self.linear_2(sample)
-
+        # print("sample4:",sample)
         if self.post_act is not None:
             sample = self.post_act(sample)
+        # print("sample5:",sample)
         return sample
 
 class Upsample1D(nn.Layer):
@@ -160,17 +161,17 @@ class Upsample1D(nn.Layer):
 
         return outputs
 
-class Transpose(nn.Module):
+class Transpose(nn.Layer):
     def __init__(self, dim0: int, dim1: int):
         super().__init__()
         self.dim0 = dim0
         self.dim1 = dim1
 
     def forward(self, x: paddle.Tensor) -> paddle.Tensor:
-        x = paddle.transpose(x, (self.dim0, self.dim1))
+        x = paddle.transpose(x, [0, self.dim1, self.dim0])
         return x
 
-class CausalConv1d(nn.Conv1d):
+class CausalConv1d(nn.Conv1D):
     def __init__(
         self,
         in_channels: int,
@@ -332,8 +333,7 @@ def add_optional_chunk_mask(xs: paddle.Tensor,
         chunk_masks = masks & chunk_masks  # (B, L, L)
     else:
         chunk_masks = masks
-    
-    assert chunk_masks.dtype == 'bool'
+    assert chunk_masks.dtype == paddle.bool
     if (chunk_masks.sum(axis=-1) == 0).sum().item() != 0:
         print('get chunk_masks all false at some timestep, force set to true, make sure they are masked in future computation!')
         all_false_mask = chunk_masks.sum(axis=-1) == 0
@@ -342,8 +342,8 @@ def add_optional_chunk_mask(xs: paddle.Tensor,
     return chunk_masks
 
 def mask_to_bias(mask: paddle.Tensor, dtype: str) -> paddle.Tensor:
-    assert mask.dtype == 'bool', "Input mask must be of boolean type"
-    assert dtype in ['float32', 'bfloat16', 'float16'], f"Unsupported dtype: {dtype}"
+    assert mask.dtype == paddle.bool, "Input mask must be of boolean type"
+    assert dtype in [paddle.float32, paddle.bfloat16, paddle.float16], f"Unsupported dtype: {dtype}"
     mask = mask.astype(dtype)
     mask = (1.0 - mask) * -1.0e+10
     
@@ -489,7 +489,6 @@ class ConditionalDecoder(nn.Layer):
         t = self.time_mlp(t)
 
         x = pack([x, mu], "b * t")[0]
-
         if spks is not None:
             spks = repeat(spks, "b c -> b c t", t=x.shape[-1])
             x = pack([x, spks], "b * t")[0]
@@ -667,15 +666,16 @@ class CausalConditionalDecoder(nn.Layer):
             if isinstance(m, nn.Conv1D):
                 nn.initializer.KaimingNormal(m.weight, nonlinearity='relu')
                 if m.bias is not None:
-                    nn.initializer.Constant(m.bias, value=0)
+                    initializer = nn.initializer.Constant(value=0)
+                    initializer(m.bias)
             elif isinstance(m, nn.GroupNorm):
                 nn.initializer.Constant(m.weight, value=1)
                 nn.initializer.Constant(m.bias, value=0)
             elif isinstance(m, nn.Linear):
                 nn.initializer.KaimingNormal(m.weight, nonlinearity='relu')
                 if m.bias is not None:
-                    nn.initializer.Constant(m.bias, value=0)
-
+                    initializer = nn.initializer.Constant(value=0)
+                    initializer(m.bias)
     def forward(self, x, mask, mu, t, spks=None, cond=None, streaming=False):
         """Forward pass of the UNet1DConditional model.
 
@@ -693,9 +693,7 @@ class CausalConditionalDecoder(nn.Layer):
         """
         t = self.time_embeddings(t).astype(t.dtype)  # 使用 astype 代替 .to(t.dtype)
         t = self.time_mlp(t)
-
         x = pack([x, mu], "b * t")[0]  # 假设 pack 函数已实现
-
         if spks is not None:
             spks = repeat(spks, "b c -> b c t", t=x.shape[-1])  # 假设 repeat 函数已实现
             x = pack([x, spks], "b * t")[0]

paddlespeech/t2s/modules/flow/diffusers_activatioins.py

@@ -0,0 +1,132 @@
+import paddle
+
+from ..utils import deprecate
+from ..utils.import_utils import is_torch_npu_available
+
+if is_torch_npu_available():
+    import torch_npu
+ACTIVATION_FUNCTIONS = {
+    "swish": paddle.nn.SiLU(),
+    "silu": paddle.nn.SiLU(),
+    "mish": paddle.nn.Mish(),
+    "gelu": paddle.nn.GELU(),
+    "relu": paddle.nn.ReLU(),
+}
+
+
+def get_activation(act_fn: str) -> paddle.nn.Layer:
+    """Helper function to get activation function from string.
+
+    Args:
+        act_fn (str): Name of activation function.
+
+    Returns:
+        nn.Module: Activation function.
+    """
+    act_fn = act_fn.lower()
+    if act_fn in ACTIVATION_FUNCTIONS:
+        return ACTIVATION_FUNCTIONS[act_fn]
+    else:
+        raise ValueError(f"Unsupported activation function: {act_fn}")
+
+
+class FP32SiLU(paddle.nn.Layer):
+    """
+    SiLU activation function with input upcasted to torch.float32.
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, inputs: paddle.Tensor) -> paddle.Tensor:
+        return paddle.nn.functional.silu(inputs.float(), inplace=False).to(inputs.dtype)
+
+
+class GELU(paddle.nn.Layer):
+    """
+    GELU activation function with tanh approximation support with `approximate="tanh"`.
+
+    Parameters:
+        dim_in (`int`): The number of channels in the input.
+        dim_out (`int`): The number of channels in the output.
+        approximate (`str`, *optional*, defaults to `"none"`): If `"tanh"`, use tanh approximation.
+        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
+    """
+
+    def __init__(
+        self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True
+    ):
+        super().__init__()
+        self.proj = paddle.nn.Linear(
+            in_features=dim_in, out_features=dim_out, bias_attr=bias
+        )
+        self.approximate = approximate
+
+    def gelu(self, gate: paddle.Tensor) -> paddle.Tensor:
+        if gate.device.type != "mps":
+            return paddle.nn.functional.gelu(gate, approximate=self.approximate)
+        return paddle.nn.functional.gelu(
+            gate.to(dtype=paddle.float32), approximate=self.approximate
+        ).to(dtype=gate.dtype)
+
+    def forward(self, hidden_states):
+        hidden_states = self.proj(hidden_states)
+        hidden_states = self.gelu(hidden_states)
+        return hidden_states
+
+
+class GEGLU(paddle.nn.Layer):
+    """
+    A [variant](https://arxiv.org/abs/2002.05202) of the gated linear unit activation function.
+
+    Parameters:
+        dim_in (`int`): The number of channels in the input.
+        dim_out (`int`): The number of channels in the output.
+        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
+    """
+
+    def __init__(self, dim_in: int, dim_out: int, bias: bool = True):
+        super().__init__()
+        self.proj = paddle.nn.Linear(
+            in_features=dim_in, out_features=dim_out * 2, bias_attr=bias
+        )
+
+    def gelu(self, gate: paddle.Tensor) -> paddle.Tensor:
+        if gate.device.type != "mps":
+            return paddle.nn.functional.gelu(gate)
+        return paddle.nn.functional.gelu(gate.to(dtype=paddle.float32)).to(
+            dtype=gate.dtype
+        )
+
+    def forward(self, hidden_states, *args, **kwargs):
+        if len(args) > 0 or kwargs.get("scale", None) is not None:
+            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
+            deprecate("scale", "1.0.0", deprecation_message)
+        hidden_states = self.proj(hidden_states)
+        if is_torch_npu_available():
+            return torch_npu.npu_geglu(hidden_states, dim=-1, approximate=1)[0]
+        else:
+            hidden_states, gate = hidden_states.chunk(2, dim=-1)
+            return hidden_states * self.gelu(gate)
+
+
+class ApproximateGELU(paddle.nn.Layer):
+    """
+    The approximate form of the Gaussian Error Linear Unit (GELU). For more details, see section 2 of this
+    [paper](https://arxiv.org/abs/1606.08415).
+
+    Parameters:
+        dim_in (`int`): The number of channels in the input.
+        dim_out (`int`): The number of channels in the output.
+        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
+    """
+
+    def __init__(self, dim_in: int, dim_out: int, bias: bool = True):
+        super().__init__()
+        self.proj = paddle.nn.Linear(
+            in_features=dim_in, out_features=dim_out, bias_attr=bias
+        )
+
+    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
+        x = self.proj(x)
+        return x * paddle.nn.functional.sigmoid(1.702 * x)

paddlespeech/t2s/modules/flow/encoder_layer.py

@@ -0,0 +1,205 @@
+import paddle
+
+"""Encoder self-attention layer definition."""
+from typing import Optional, Tuple
+
+
+class TransformerEncoderLayer(paddle.nn.Layer):
+    """Encoder layer module.
+
+    Args:
+        size (int): Input dimension.
+        self_attn (torch.nn.Module): Self-attention module instance.
+            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
+            instance can be used as the argument.
+        feed_forward (torch.nn.Module): Feed-forward module instance.
+            `PositionwiseFeedForward`, instance can be used as the argument.
+        dropout_rate (float): Dropout rate.
+        normalize_before (bool):
+            True: use layer_norm before each sub-block.
+            False: to use layer_norm after each sub-block.
+    """
+
+    def __init__(
+        self,
+        size: int,
+        self_attn: paddle.nn.Layer,
+        feed_forward: paddle.nn.Layer,
+        dropout_rate: float,
+        normalize_before: bool = True,
+    ):
+        """Construct an EncoderLayer object."""
+        super().__init__()
+        self.self_attn = self_attn
+        self.feed_forward = feed_forward
+        self.norm1 = paddle.nn.LayerNorm(normalized_shape=size, epsilon=1e-12)
+        self.norm2 = paddle.nn.LayerNorm(normalized_shape=size, epsilon=1e-12)
+        self.dropout = paddle.nn.Dropout(p=dropout_rate)
+        self.size = size
+        self.normalize_before = normalize_before
+
+    def forward(
+        self,
+        x: paddle.Tensor,
+        mask: paddle.Tensor,
+        pos_emb: paddle.Tensor,
+        mask_pad: paddle.Tensor = paddle.ones((0, 0, 0), dtype=paddle.bool),
+        att_cache: paddle.Tensor = paddle.zeros((0, 0, 0, 0)),
+        cnn_cache: paddle.Tensor = paddle.zeros((0, 0, 0, 0)),
+    ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]:
+        """Compute encoded features.
+
+        Args:
+            x (torch.Tensor): (#batch, time, size)
+            mask (torch.Tensor): Mask tensor for the input (#batch, time，time),
+                (0, 0, 0) means fake mask.
+            pos_emb (torch.Tensor): just for interface compatibility
+                to ConformerEncoderLayer
+            mask_pad (torch.Tensor): does not used in transformer layer,
+                just for unified api with conformer.
+            att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
+                (#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
+            cnn_cache (torch.Tensor): Convolution cache in conformer layer
+                (#batch=1, size, cache_t2), not used here, it's for interface
+                compatibility to ConformerEncoderLayer.
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, size).
+            torch.Tensor: Mask tensor (#batch, time, time).
+            torch.Tensor: att_cache tensor,
+                (#batch=1, head, cache_t1 + time, d_k * 2).
+            torch.Tensor: cnn_cahce tensor (#batch=1, size, cache_t2).
+
+        """
+        residual = x
+        if self.normalize_before:
+            x = self.norm1(x)
+        x_att, new_att_cache = self.self_attn(
+            x, x, x, mask, pos_emb=pos_emb, cache=att_cache
+        )
+        x = residual + self.dropout(x_att)
+        if not self.normalize_before:
+            x = self.norm1(x)
+        residual = x
+        if self.normalize_before:
+            x = self.norm2(x)
+        x = residual + self.dropout(self.feed_forward(x))
+        if not self.normalize_before:
+            x = self.norm2(x)
+        fake_cnn_cache = paddle.zeros((0, 0, 0), dtype=x.dtype, device=x.place)
+        return x, mask, new_att_cache, fake_cnn_cache
+
+
+class ConformerEncoderLayer(paddle.nn.Layer):
+    """Encoder layer module.
+    Args:
+        size (int): Input dimension.
+        self_attn (torch.nn.Module): Self-attention module instance.
+            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
+            instance can be used as the argument.
+        feed_forward (torch.nn.Module): Feed-forward module instance.
+            `PositionwiseFeedForward` instance can be used as the argument.
+        feed_forward_macaron (torch.nn.Module): Additional feed-forward module
+             instance.
+            `PositionwiseFeedForward` instance can be used as the argument.
+        conv_module (torch.nn.Module): Convolution module instance.
+            `ConvlutionModule` instance can be used as the argument.
+        dropout_rate (float): Dropout rate.
+        normalize_before (bool):
+            True: use layer_norm before each sub-block.
+            False: use layer_norm after each sub-block.
+    """
+
+    def __init__(
+        self,
+        size: int,
+        self_attn: paddle.nn.Layer,
+        feed_forward: Optional[paddle.nn.Layer] = None,
+        feed_forward_macaron: Optional[paddle.nn.Layer] = None,
+        conv_module: Optional[paddle.nn.Layer] = None,
+        dropout_rate: float = 0.1,
+        normalize_before: bool = True,
+    ):
+        """Construct an EncoderLayer object."""
+        super().__init__()
+        self.self_attn = self_attn
+        self.feed_forward = feed_forward
+        self.feed_forward_macaron = feed_forward_macaron
+        self.conv_module = conv_module
+        self.norm_ff = paddle.nn.LayerNorm(normalized_shape=size, epsilon=1e-12)
+        self.norm_mha = paddle.nn.LayerNorm(normalized_shape=size, epsilon=1e-12)
+        if feed_forward_macaron is not None:
+            self.norm_ff_macaron = paddle.nn.LayerNorm(
+                normalized_shape=size, epsilon=1e-12
+            )
+            self.ff_scale = 0.5
+        else:
+            self.ff_scale = 1.0
+        if self.conv_module is not None:
+            self.norm_conv = paddle.nn.LayerNorm(normalized_shape=size, epsilon=1e-12)
+            self.norm_final = paddle.nn.LayerNorm(normalized_shape=size, epsilon=1e-12)
+        self.dropout = paddle.nn.Dropout(p=dropout_rate)
+        self.size = size
+        self.normalize_before = normalize_before
+
+    def forward(
+        self,
+        x: paddle.Tensor,
+        mask: paddle.Tensor,
+        pos_emb: paddle.Tensor,
+        mask_pad: paddle.Tensor = paddle.ones((0, 0, 0), dtype=paddle.bool),
+        att_cache: paddle.Tensor = paddle.zeros((0, 0, 0, 0)),
+        cnn_cache: paddle.Tensor = paddle.zeros((0, 0, 0, 0)),
+    ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]:
+        """Compute encoded features.
+
+        Args:
+            x (torch.Tensor): (#batch, time, size)
+            mask (torch.Tensor): Mask tensor for the input (#batch, time，time),
+                (0, 0, 0) means fake mask.
+            pos_emb (torch.Tensor): positional encoding, must not be None
+                for ConformerEncoderLayer.
+            mask_pad (torch.Tensor): batch padding mask used for conv module.
+                (#batch, 1，time), (0, 0, 0) means fake mask.
+            att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
+                (#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
+            cnn_cache (torch.Tensor): Convolution cache in conformer layer
+                (#batch=1, size, cache_t2)
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, size).
+            torch.Tensor: Mask tensor (#batch, time, time).
+            torch.Tensor: att_cache tensor,
+                (#batch=1, head, cache_t1 + time, d_k * 2).
+            torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2).
+        """
+        if self.feed_forward_macaron is not None:
+            residual = x
+            if self.normalize_before:
+                x = self.norm_ff_macaron(x)
+            x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
+            if not self.normalize_before:
+                x = self.norm_ff_macaron(x)
+        residual = x
+        if self.normalize_before:
+            x = self.norm_mha(x)
+        x_att, new_att_cache = self.self_attn(x, x, x, mask, pos_emb, att_cache)
+        x = residual + self.dropout(x_att)
+        if not self.normalize_before:
+            x = self.norm_mha(x)
+        new_cnn_cache = paddle.zeros((0, 0, 0), dtype=x.dtype, device=x.place)
+        if self.conv_module is not None:
+            residual = x
+            if self.normalize_before:
+                x = self.norm_conv(x)
+            x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)
+            x = residual + self.dropout(x)
+            if not self.normalize_before:
+                x = self.norm_conv(x)
+        residual = x
+        if self.normalize_before:
+            x = self.norm_ff(x)
+        x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
+        if not self.normalize_before:
+            x = self.norm_ff(x)
+        if self.conv_module is not None:
+            x = self.norm_final(x)
+        return x, mask, new_att_cache, new_cnn_cache

paddlespeech/t2s/modules/flow/flow.py

@@ -5,7 +5,7 @@ from typing import Dict, Optional
 import paddle
 from omegaconf import DictConfig
 
-from cosyvoice.utils.mask import make_pad_mask
+from paddlespeech.t2s.models.CosyVoice.mask import make_pad_mask
 
 
 class MaskedDiffWithXvec(paddle.nn.Layer):
@@ -78,7 +78,7 @@ class MaskedDiffWithXvec(paddle.nn.Layer):
         self.only_mask_loss = only_mask_loss
 
     def forward(
->>>>>>        self, batch: dict, device: torch.device
+        self, batch: dict, device: paddle.device
     ) -> Dict[str, Optional[paddle.Tensor]]:
         token = batch["speech_token"].to(device)
         token_len = batch["speech_token_len"].to(device)
@@ -88,7 +88,7 @@ class MaskedDiffWithXvec(paddle.nn.Layer):
         embedding = paddle.nn.functional.normalize(x=embedding, axis=1)
         embedding = self.spk_embed_affine_layer(embedding)
         mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
-        token = self.input_embedding(paddle.clamp(token, min=0)) * mask
+        token = self.input_embedding(paddle.clip(token, min=0)) * mask
         h, h_lengths = self.encoder(token, token_len)
         h = self.encoder_proj(h)
         h, h_lengths = self.length_regulator(h, feat_len)
@@ -98,12 +98,12 @@ class MaskedDiffWithXvec(paddle.nn.Layer):
                 continue
             index = random.randint(0, int(0.3 * j))
             conds[i, :index] = feat[i, :index]
-        conds = conds.transpose(1, 2)
+        conds = paddle.transpose(conds, perm=[0, 2, 1])
         mask = (~make_pad_mask(feat_len)).to(h)
         loss, _ = self.decoder.compute_loss(
-            feat.transpose(1, 2).contiguous(),
+            paddle.transpose(feat, perm=[0, 2, 1]),
             mask.unsqueeze(1),
-            h.transpose(1, 2).contiguous(),
+            paddle.transpose(h, perm=[0, 2, 1]),
             embedding,
             cond=conds,
         )
@@ -130,7 +130,7 @@ class MaskedDiffWithXvec(paddle.nn.Layer):
             prompt_token_len + token_len,
         )
         mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
-        token = self.input_embedding(paddle.clamp(token, min=0)) * mask
+        token = self.input_embedding(paddle.clip(token, min=0)) * mask
         h, h_lengths = self.encoder(token, token_len)
         h = self.encoder_proj(h)
         mel_len1, mel_len2 = prompt_feat.shape[1], int(
@@ -147,10 +147,11 @@ class MaskedDiffWithXvec(paddle.nn.Layer):
             [1, mel_len1 + mel_len2, self.output_size], device=token.place
         ).to(h.dtype)
         conds[:, :mel_len1] = prompt_feat
-        conds = conds.transpose(1, 2)
+        conds = paddle.transpose(conds, perm=[0, 2, 1])
+        
         mask = (~make_pad_mask(paddle.tensor([mel_len1 + mel_len2]))).to(h)
         feat, flow_cache = self.decoder(
-            mu=h.transpose(1, 2).contiguous(),
+            mu=paddle.transpose(h, perm=[0, 2, 1]),
             mask=mask.unsqueeze(1),
             spks=embedding,
             cond=conds,
@@ -235,7 +236,7 @@ class CausalMaskedDiffWithXvec(paddle.nn.Layer):
         self.pre_lookahead_len = pre_lookahead_len
 
     def forward(
->>>>>>        self, batch: dict, device: torch.device
+        self, batch: dict, device: paddle.device
     ) -> Dict[str, Optional[paddle.Tensor]]:
         token = batch["speech_token"].to(device)
         token_len = batch["speech_token_len"].to(device)
@@ -246,7 +247,7 @@ class CausalMaskedDiffWithXvec(paddle.nn.Layer):
         embedding = paddle.nn.functional.normalize(x=embedding, axis=1)
         embedding = self.spk_embed_affine_layer(embedding)
         mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
-        token = self.input_embedding(paddle.clamp(token, min=0)) * mask
+        token = self.input_embedding(paddle.clip(token, min=0)) * mask
         h, h_lengths = self.encoder(token, token_len, streaming=streaming)
         h = self.encoder_proj(h)
         conds = paddle.zeros(feat.shape, device=token.place)
@@ -255,12 +256,13 @@ class CausalMaskedDiffWithXvec(paddle.nn.Layer):
                 continue
             index = random.randint(0, int(0.3 * j))
             conds[i, :index] = feat[i, :index]
-        conds = conds.transpose(1, 2)
+        conds = paddle.transpose(conds, perm=[0, 2, 1])
+        
         mask = (~make_pad_mask(h_lengths.sum(dim=-1).squeeze(dim=1))).to(h)
         loss, _ = self.decoder.compute_loss(
-            feat.transpose(1, 2).contiguous(),
+            paddle.transpose(feat, perm=[0, 2, 1]).contiguous(),
             mask.unsqueeze(1),
-            h.transpose(1, 2).contiguous(),
+            paddle.transpose(h, perm=[0, 2, 1]).contiguous(),
             embedding,
             cond=conds,
             streaming=streaming,
@@ -283,12 +285,13 @@ class CausalMaskedDiffWithXvec(paddle.nn.Layer):
         assert token.shape[0] == 1
         embedding = paddle.nn.functional.normalize(x=embedding, axis=1)
         embedding = self.spk_embed_affine_layer(embedding)
+
         token, token_len = (
             paddle.cat([prompt_token, token], dim=1),
             prompt_token_len + token_len,
         )
         mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
-        token = self.input_embedding(paddle.clamp(token, min=0)) * mask
+        token = self.input_embedding(paddle.clip(token, min=0)) * mask
         if finalize is True:
             h, h_lengths = self.encoder(token, token_len, streaming=streaming)
         else:
@@ -302,19 +305,20 @@ class CausalMaskedDiffWithXvec(paddle.nn.Layer):
         mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1]
         h = self.encoder_proj(h)
         conds = paddle.zeros(
-            [1, mel_len1 + mel_len2, self.output_size], device=token.place
+            [1, mel_len1 + mel_len2, self.output_size]
         ).to(h.dtype)
         conds[:, :mel_len1] = prompt_feat
-        conds = conds.transpose(1, 2)
-        mask = (~make_pad_mask(paddle.tensor([mel_len1 + mel_len2]))).to(h)
+        conds = paddle.transpose(conds, perm=[0, 2, 1])
+        mask = (~make_pad_mask(paddle.to_tensor([mel_len1 + mel_len2],dtype='int32'))).to(h)
         feat, _ = self.decoder(
-            mu=h.transpose(1, 2).contiguous(),
+            mu=paddle.transpose(h, perm=[0, 2, 1]).contiguous(),
             mask=mask.unsqueeze(1),
             spks=embedding,
             cond=conds,
             n_timesteps=10,
             streaming=streaming,
         )
+        paddle.save(feat,'/root/paddlejob/workspace/zhangjinghong/CosyVoice/feat.pdparams')
         feat = feat[:, :, mel_len1:]
         assert feat.shape[2] == mel_len2
         return feat.float(), None

paddlespeech/t2s/modules/flow/flow_matching.py

@@ -1,8 +1,100 @@
 import paddle
-from matcha.models.components.flow_matching import BASECFM
+from abc import ABC
+from paddlespeech.t2s.models.CosyVoice.common import set_all_random_seed
 
-from cosyvoice.utils.common import set_all_random_seed
+class BASECFM(paddle.nn.Layer, ABC):
+    def __init__(self, n_feats, cfm_params, n_spks=1, spk_emb_dim=128):
+        super().__init__()
+        self.n_feats = n_feats
+        self.n_spks = n_spks
+        self.spk_emb_dim = spk_emb_dim
+        self.solver = cfm_params.solver
+        if hasattr(cfm_params, "sigma_min"):
+            self.sigma_min = cfm_params.sigma_min
+        else:
+            self.sigma_min = 0.0001
+        self.estimator = None
+
+    @paddle.no_grad()
+    def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None):
+        """Forward diffusion
+
+        Args:
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            n_timesteps (int): number of diffusion steps
+            temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+            cond: Not used but kept for future purposes
 
+        Returns:
+            sample: generated mel-spectrogram
+                shape: (batch_size, n_feats, mel_timesteps)
+        """
+        z = paddle.randn(shape=mu.shape, dtype=mu.dtype) * temperature
+        t_span = paddle.linspace(start=0, stop=1, num=n_timesteps + 1)
+        return self.solve_euler(
+            z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond
+        )
+
+    def solve_euler(self, x, t_span, mu, mask, spks, cond):
+        """
+        Fixed euler solver for ODEs.
+        Args:
+            x (torch.Tensor): random noise
+            t_span (torch.Tensor): n_timesteps interpolated
+                shape: (n_timesteps + 1,)
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+            cond: Not used but kept for future purposes
+        """
+        t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
+        sol = []
+        for step in range(1, len(t_span)):
+            dphi_dt = self.estimator(x, mask, mu, t, spks, cond)
+            x = x + dt * dphi_dt
+            t = t + dt
+            sol.append(x)
+            if step < len(t_span) - 1:
+                dt = t_span[step + 1] - t
+        return sol[-1]
+
+    def compute_loss(self, x1, mask, mu, spks=None, cond=None):
+        """Computes diffusion loss
+
+        Args:
+            x1 (torch.Tensor): Target
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): target mask
+                shape: (batch_size, 1, mel_timesteps)
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            spks (torch.Tensor, optional): speaker embedding. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+
+        Returns:
+            loss: conditional flow matching loss
+            y: conditional flow
+                shape: (batch_size, n_feats, mel_timesteps)
+        """
+        b, _, t = mu.shape
+        t = paddle.rand(shape=[b, 1, 1], dtype=mu.dtype)
+        z = paddle.randn(shape=x1.shape, dtype=x1.dtype)
+        y = (1 - (1 - self.sigma_min) * t) * z + t * x1
+        u = x1 - (1 - self.sigma_min) * z
+        loss = paddle.nn.functional.mse_loss(
+            input=self.estimator(y, mask, mu, t.squeeze(), spks),
+            label=u,
+            reduction="sum",
+        ) / (paddle.sum(mask) * u.shape[1])
+        return loss, y
 
 class ConditionalCFM(BASECFM):
     def __init__(
@@ -35,7 +127,7 @@ class ConditionalCFM(BASECFM):
         spks=None,
         cond=None,
         prompt_len=0,
-        cache=paddle.zeros(1, 80, 0, 2),
+        cache=paddle.zeros([1, 80, 0, 2]),
     ):
         """Forward diffusion
 
@@ -62,9 +154,9 @@ class ConditionalCFM(BASECFM):
         if cache_size != 0:
             z[:, :, :cache_size] = cache[:, :, :, 0]
             mu[:, :, :cache_size] = cache[:, :, :, 1]
-        z_cache = paddle.cat([z[:, :, :prompt_len], z[:, :, -34:]], dim=2)
-        mu_cache = paddle.cat([mu[:, :, :prompt_len], mu[:, :, -34:]], dim=2)
-        cache = paddle.stack([z_cache, mu_cache], dim=-1)
+        z_cache = paddle.cat([z[:, :, :prompt_len], z[:, :, -34:]], axis=2)
+        mu_cache = paddle.cat([mu[:, :, :prompt_len], mu[:, :, -34:]], axis=2)
+        cache = paddle.stack([z_cache, mu_cache], axis=-1)
         t_span = paddle.linspace(start=0, stop=1, num=n_timesteps + 1, dtype=mu.dtype)
         if self.t_scheduler == "cosine":
             t_span = 1 - paddle.cos(t_span * 0.5 * paddle.pi)
@@ -89,14 +181,14 @@ class ConditionalCFM(BASECFM):
             cond: Not used but kept for future purposes
         """
         t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
-        t = t.unsqueeze(dim=0)
+        t = t.unsqueeze(axis=0)
         sol = []
-        x_in = paddle.zeros([2, 80, x.size(2)], device=x.place, dtype=x.dtype)
-        mask_in = paddle.zeros([2, 1, x.size(2)], device=x.place, dtype=x.dtype)
-        mu_in = paddle.zeros([2, 80, x.size(2)], device=x.place, dtype=x.dtype)
-        t_in = paddle.zeros([2], device=x.place, dtype=x.dtype)
-        spks_in = paddle.zeros([2, 80], device=x.place, dtype=x.dtype)
-        cond_in = paddle.zeros([2, 80, x.size(2)], device=x.place, dtype=x.dtype)
+        x_in = paddle.zeros([2, 80, x.shape[2]],  dtype=x.dtype)
+        mask_in = paddle.zeros([2, 1, x.shape[2]],  dtype=x.dtype)
+        mu_in = paddle.zeros([2, 80, x.shape[2]],  dtype=x.dtype)
+        t_in = paddle.zeros([2], dtype=x.dtype)
+        spks_in = paddle.zeros([2, 80], dtype=x.dtype)
+        cond_in = paddle.zeros([2, 80, x.shape[2]], dtype=x.dtype)
         for step in range(1, len(t_span)):
             x_in[:] = x
             mask_in[:] = mask
@@ -107,9 +199,7 @@ class ConditionalCFM(BASECFM):
             dphi_dt = self.forward_estimator(
                 x_in, mask_in, mu_in, t_in, spks_in, cond_in, streaming
             )
-            dphi_dt, cfg_dphi_dt = paddle.compat.split(
-                dphi_dt, [x.size(0), x.size(0)], dim=0
-            )
+            dphi_dt, cfg_dphi_dt = paddle.split(dphi_dt, [x.shape[0], x.shape[0]], axis=0)
             dphi_dt = (
                 1.0 + self.inference_cfg_rate
             ) * dphi_dt - self.inference_cfg_rate * cfg_dphi_dt
@@ -127,12 +217,12 @@ class ConditionalCFM(BASECFM):
             [estimator, stream], trt_engine = self.estimator.acquire_estimator()
             paddle.device.current_stream().synchronize()
             with stream:
-                estimator.set_input_shape("x", (2, 80, x.size(2)))
-                estimator.set_input_shape("mask", (2, 1, x.size(2)))
-                estimator.set_input_shape("mu", (2, 80, x.size(2)))
+                estimator.set_input_shape("x", (2, 80, x.shape[2]))
+                estimator.set_input_shape("mask", (2, 1, x.shape[2]))
+                estimator.set_input_shape("mu", (2, 80, x.shape[2]))
                 estimator.set_input_shape("t", (2,))
                 estimator.set_input_shape("spks", (2, 80))
-                estimator.set_input_shape("cond", (2, 80, x.size(2)))
+                estimator.set_input_shape("cond", (2, 80, x.shape[2]))
                 data_ptrs = [
                     x.contiguous().data_ptr(),
                     mask.contiguous().data_ptr(),
@@ -201,9 +291,8 @@ class CausalConditionalCFM(ConditionalCFM):
         estimator: paddle.nn.Layer = None,
     ):
         super().__init__(in_channels, cfm_params, n_spks, spk_emb_dim, estimator)
-        set_all_random_seed(0)
+        set_all_random_seed(42)
         self.rand_noise = paddle.randn([1, 80, 50 * 300])
-
     @paddle.no_grad()
     def forward(
         self,
@@ -232,7 +321,9 @@ class CausalConditionalCFM(ConditionalCFM):
             sample: generated mel-spectrogram
                 shape: (batch_size, n_feats, mel_timesteps)
         """
-        z = self.rand_noise[:, :, : mu.size(2)].to(mu.place).to(mu.dtype) * temperature
+        
+        z = self.rand_noise[:, :, : mu.shape[2]].to(mu.place).to(mu.dtype) * temperature
+        
         t_span = paddle.linspace(start=0, stop=1, num=n_timesteps + 1, dtype=mu.dtype)
         if self.t_scheduler == "cosine":
             t_span = 1 - paddle.cos(t_span * 0.5 * paddle.pi)

paddlespeech/t2s/modules/flow/flow_matching_matcha.py

@@ -0,0 +1,124 @@
+from abc import ABC
+
+import paddle
+from matcha.models.components.decoder import Decoder
+from matcha.utils.pylogger import get_pylogger
+
+log = get_pylogger(__name__)
+
+
+class BASECFM(paddle.nn.Layer, ABC):
+    def __init__(self, n_feats, cfm_params, n_spks=1, spk_emb_dim=128):
+        super().__init__()
+        self.n_feats = n_feats
+        self.n_spks = n_spks
+        self.spk_emb_dim = spk_emb_dim
+        self.solver = cfm_params.solver
+        if hasattr(cfm_params, "sigma_min"):
+            self.sigma_min = cfm_params.sigma_min
+        else:
+            self.sigma_min = 0.0001
+        self.estimator = None
+
+    @paddle.no_grad()
+    def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None):
+        """Forward diffusion
+
+        Args:
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            n_timesteps (int): number of diffusion steps
+            temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+            cond: Not used but kept for future purposes
+
+        Returns:
+            sample: generated mel-spectrogram
+                shape: (batch_size, n_feats, mel_timesteps)
+        """
+        z = paddle.randn(shape=mu.shape, dtype=mu.dtype) * temperature
+        t_span = paddle.linspace(start=0, stop=1, num=n_timesteps + 1)
+        return self.solve_euler(
+            z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond
+        )
+
+    def solve_euler(self, x, t_span, mu, mask, spks, cond):
+        """
+        Fixed euler solver for ODEs.
+        Args:
+            x (torch.Tensor): random noise
+            t_span (torch.Tensor): n_timesteps interpolated
+                shape: (n_timesteps + 1,)
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+            cond: Not used but kept for future purposes
+        """
+        t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
+        sol = []
+        for step in range(1, len(t_span)):
+            dphi_dt = self.estimator(x, mask, mu, t, spks, cond)
+            x = x + dt * dphi_dt
+            t = t + dt
+            sol.append(x)
+            if step < len(t_span) - 1:
+                dt = t_span[step + 1] - t
+        return sol[-1]
+
+    def compute_loss(self, x1, mask, mu, spks=None, cond=None):
+        """Computes diffusion loss
+
+        Args:
+            x1 (torch.Tensor): Target
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): target mask
+                shape: (batch_size, 1, mel_timesteps)
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            spks (torch.Tensor, optional): speaker embedding. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+
+        Returns:
+            loss: conditional flow matching loss
+            y: conditional flow
+                shape: (batch_size, n_feats, mel_timesteps)
+        """
+        b, _, t = mu.shape
+        t = paddle.rand(shape=[b, 1, 1], dtype=mu.dtype)
+        z = paddle.randn(shape=x1.shape, dtype=x1.dtype)
+        y = (1 - (1 - self.sigma_min) * t) * z + t * x1
+        u = x1 - (1 - self.sigma_min) * z
+        loss = paddle.nn.functional.mse_loss(
+            input=self.estimator(y, mask, mu, t.squeeze(), spks),
+            label=u,
+            reduction="sum",
+        ) / (paddle.sum(mask) * u.shape[1])
+        return loss, y
+
+
+class CFM(BASECFM):
+    def __init__(
+        self,
+        in_channels,
+        out_channel,
+        cfm_params,
+        decoder_params,
+        n_spks=1,
+        spk_emb_dim=64,
+    ):
+        super().__init__(
+            n_feats=in_channels,
+            cfm_params=cfm_params,
+            n_spks=n_spks,
+            spk_emb_dim=spk_emb_dim,
+        )
+        in_channels = in_channels + (spk_emb_dim if n_spks > 1 else 0)
+        self.estimator = Decoder(
+            in_channels=in_channels, out_channels=out_channel, **decoder_params
+        )

paddlespeech/t2s/modules/flow/lora.py

@@ -0,0 +1,123 @@
+from typing import Optional, Tuple, Union
+
+import paddle
+
+
+class LoRALinearLayer(paddle.nn.Layer):
+    """
+    A linear layer that is used with LoRA.
+
+    Parameters:
+        in_features (`int`):
+            Number of input features.
+        out_features (`int`):
+            Number of output features.
+        rank (`int`, `optional`, defaults to 4):
+            The rank of the LoRA layer.
+        network_alpha (`float`, `optional`, defaults to `None`):
+            The value of the network alpha used for stable learning and preventing underflow. This value has the same
+            meaning as the `--network_alpha` option in the kohya-ss trainer script. See
+            https://github.com/darkstorm2150/sd-scripts/blob/main/docs/train_network_README-en.md#execute-learning
+        device (`torch.device`, `optional`, defaults to `None`):
+            The device to use for the layer's weights.
+        dtype (`torch.dtype`, `optional`, defaults to `None`):
+            The dtype to use for the layer's weights.
+    """
+
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        rank: int = 4,
+        network_alpha: Optional[float] = None,
+        device: Optional[Union[paddle.CPUPlace, paddle.CUDAPlace, str]] = None,
+        dtype: Optional[paddle.dtype] = None,
+    ):
+        super().__init__()
+        self.down = paddle.nn.Linear(
+            in_features=in_features, out_features=rank, bias_attr=False
+        )
+        self.up = paddle.nn.Linear(
+            in_features=rank, out_features=out_features, bias_attr=False
+        )
+        self.network_alpha = network_alpha
+        self.rank = rank
+        self.out_features = out_features
+        self.in_features = in_features
+        paddle.nn.init.normal_(self.down.weight, std=1 / rank)
+        paddle.nn.init.zeros_(self.up.weight)
+
+    def forward(self, hidden_states: paddle.Tensor) -> paddle.Tensor:
+        orig_dtype = hidden_states.dtype
+        dtype = self.down.weight.dtype
+        down_hidden_states = self.down(hidden_states.to(dtype))
+        up_hidden_states = self.up(down_hidden_states)
+        if self.network_alpha is not None:
+            up_hidden_states *= self.network_alpha / self.rank
+        return up_hidden_states.to(orig_dtype)
+
+
+
+class LoRACompatibleLinear(paddle.nn.Linear):
+    """
+    A Linear layer that can be used with LoRA.
+    """
+
+    def __init__(self, *args, lora_layer: Optional[LoRALinearLayer] = None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.lora_layer = lora_layer
+
+    def set_lora_layer(self, lora_layer: Optional[LoRALinearLayer]):
+        self.lora_layer = lora_layer
+
+    def _fuse_lora(self, lora_scale: float = 1.0, safe_fusing: bool = False):
+        if self.lora_layer is None:
+            return
+        dtype, device = self.weight.data.dtype, self.weight.data.place
+        w_orig = self.weight.data.float()
+        w_up = self.lora_layer.up.weight.data.float()
+        w_down = self.lora_layer.down.weight.data.float()
+        if self.lora_layer.network_alpha is not None:
+            w_up = w_up * self.lora_layer.network_alpha / self.lora_layer.rank
+        fused_weight = (
+            w_orig + lora_scale * paddle.bmm(w_up[None, :], w_down[None, :])[0]
+        )
+        if safe_fusing and paddle.isnan(fused_weight).any().item():
+            raise ValueError(
+                f"This LoRA weight seems to be broken. Encountered NaN values when trying to fuse LoRA weights for {self}.LoRA weights will not be fused."
+            )
+        self.weight.data = fused_weight.to(device=device, dtype=dtype)
+        self.lora_layer = None
+        self.w_up = w_up.cpu()
+        self.w_down = w_down.cpu()
+        self._lora_scale = lora_scale
+
+    def _unfuse_lora(self):
+        if not (
+            getattr(self, "w_up", None) is not None
+            and getattr(self, "w_down", None) is not None
+        ):
+            return
+        fused_weight = self.weight.data
+        dtype, device = fused_weight.dtype, fused_weight.place
+        w_up = self.w_up.to(device=device).float()
+        w_down = self.w_down.to(device).float()
+        unfused_weight = (
+            fused_weight.float()
+            - self._lora_scale * paddle.bmm(w_up[None, :], w_down[None, :])[0]
+        )
+        self.weight.data = unfused_weight.to(device=device, dtype=dtype)
+        self.w_up = None
+        self.w_down = None
+
+    def forward(
+        self, hidden_states: paddle.Tensor, scale: float = 1.0
+    ) -> paddle.Tensor:
+        if self.lora_layer is None:
+            out = super().forward(hidden_states)
+            return out
+        else:
+            out = super().forward(hidden_states) + scale * self.lora_layer(
+                hidden_states
+            )
+            return out

paddlespeech/t2s/modules/flow/mask.py

@@ -0,0 +1,287 @@
+import paddle
+
+def device2str(type=None, index=None, *, device=None):
+    type = device if device else type
+    if isinstance(type, int):
+        type = f'gpu:{type}'
+    elif isinstance(type, str):
+        if 'cuda' in type:
+            type = type.replace('cuda', 'gpu')
+        if 'cpu' in type:
+            type = 'cpu'
+        elif index is not None:
+            type = f'{type}:{index}'
+    elif isinstance(type, paddle.CPUPlace) or (type is None):
+        type = 'cpu'
+    elif isinstance(type, paddle.CUDAPlace):
+        type = f'gpu:{type.get_device_id()}'
+
+    return type
+
+def _Tensor_max(self, *args, **kwargs):
+    if "other" in kwargs:
+        kwargs["y"] = kwargs.pop("other")
+        ret = paddle.maximum(self, *args, **kwargs)
+    elif len(args) == 1 and isinstance(args[0], paddle.Tensor):
+        ret = paddle.maximum(self, *args, **kwargs)
+    else:
+        if "dim" in kwargs:
+            kwargs["axis"] = kwargs.pop("dim")
+
+        if "axis" in kwargs or len(args) >= 1:
+            ret = paddle.max(self, *args, **kwargs), paddle.argmax(self, *args, **kwargs)
+        else:
+            ret = paddle.max(self, *args, **kwargs)
+
+    return ret
+
+setattr(paddle.Tensor, "_max", _Tensor_max)
+
+
+
+"""
+def subsequent_mask(
+        size: int,
+        device: torch.device = torch.device("cpu"),
+) -> torch.Tensor:
+    ""\"Create mask for subsequent steps (size, size).
+
+    This mask is used only in decoder which works in an auto-regressive mode.
+    This means the current step could only do attention with its left steps.
+
+    In encoder, fully attention is used when streaming is not necessary and
+    the sequence is not long. In this  case, no attention mask is needed.
+
+    When streaming is need, chunk-based attention is used in encoder. See
+    subsequent_chunk_mask for the chunk-based attention mask.
+
+    Args:
+        size (int): size of mask
+        str device (str): "cpu" or "cuda" or torch.Tensor.device
+        dtype (torch.device): result dtype
+
+    Returns:
+        torch.Tensor: mask
+
+    Examples:
+        >>> subsequent_mask(3)
+        [[1, 0, 0],
+         [1, 1, 0],
+         [1, 1, 1]]
+    ""\"
+    ret = torch.ones(size, size, device=device, dtype=torch.bool)
+    return torch.tril(ret)
+"""
+
+
+def subsequent_mask(
+>>>>>>    size: int, device: torch.device = device2str("cpu")
+) -> paddle.Tensor:
+    """Create mask for subsequent steps (size, size).
+
+    This mask is used only in decoder which works in an auto-regressive mode.
+    This means the current step could only do attention with its left steps.
+
+    In encoder, fully attention is used when streaming isnot necessary and
+    the sequence is not long. In this  case, no attention mask is needed.
+
+    When streaming is need, chunk-based attention is used in encoder. See
+    subsequent_chunk_mask for the chunk-based attention mask.
+
+    Args:
+        size (int): size of mask
+        str device (str): "cpu" or "cuda" or torch.Tensor.device
+        dtype (torch.device): result dtype
+
+    Returns:
+        torch.Tensor: mask
+
+    Examples:
+        >>> subsequent_mask(3)
+        [[1, 0, 0],
+         [1, 1, 0],
+         [1, 1, 1]]
+    """
+    arange = paddle.arange(size, device=device)
+    mask = arange.expand(size, size)
+    arange = arange.unsqueeze(-1)
+    mask = mask <= arange
+    return mask
+
+
+def subsequent_chunk_mask_deprecated(
+    size: int,
+    chunk_size: int,
+    num_left_chunks: int = -1,
+>>>>>>    device: torch.device = device2str("cpu"),
+) -> paddle.Tensor:
+    """Create mask for subsequent steps (size, size) with chunk size,
+       this is for streaming encoder
+
+    Args:
+        size (int): size of mask
+        chunk_size (int): size of chunk
+        num_left_chunks (int): number of left chunks
+            <0: use full chunk
+            >=0: use num_left_chunks
+        device (torch.device): "cpu" or "cuda" or torch.Tensor.device
+
+    Returns:
+        torch.Tensor: mask
+
+    Examples:
+        >>> subsequent_chunk_mask(4, 2)
+        [[1, 1, 0, 0],
+         [1, 1, 0, 0],
+         [1, 1, 1, 1],
+         [1, 1, 1, 1]]
+    """
+    ret = paddle.zeros(size, size, device=device, dtype=paddle.bool)
+    for i in range(size):
+        if num_left_chunks < 0:
+            start = 0
+        else:
+            start = max((i // chunk_size - num_left_chunks) * chunk_size, 0)
+        ending = min((i // chunk_size + 1) * chunk_size, size)
+        ret[i, start:ending] = True
+    return ret
+
+
+def subsequent_chunk_mask(
+    size: int,
+    chunk_size: int,
+    num_left_chunks: int = -1,
+>>>>>>    device: torch.device = device2str("cpu"),
+) -> paddle.Tensor:
+    """Create mask for subsequent steps (size, size) with chunk size,
+       this is for streaming encoder
+
+    Args:
+        size (int): size of mask
+        chunk_size (int): size of chunk
+        num_left_chunks (int): number of left chunks
+            <0: use full chunk
+            >=0: use num_left_chunks
+        device (torch.device): "cpu" or "cuda" or torch.Tensor.device
+
+    Returns:
+        torch.Tensor: mask
+
+    Examples:
+        >>> subsequent_chunk_mask(4, 2)
+        [[1, 1, 0, 0],
+         [1, 1, 0, 0],
+         [1, 1, 1, 1],
+         [1, 1, 1, 1]]
+    """
+    pos_idx = paddle.arange(size, device=device)
+    block_value = (
+        paddle.div(pos_idx, chunk_size, rounding_mode="trunc") + 1
+    ) * chunk_size
+    ret = pos_idx.unsqueeze(0) < block_value.unsqueeze(1)
+    return ret
+
+
+def add_optional_chunk_mask(
+    xs: paddle.Tensor,
+    masks: paddle.Tensor,
+    use_dynamic_chunk: bool,
+    use_dynamic_left_chunk: bool,
+    decoding_chunk_size: int,
+    static_chunk_size: int,
+    num_decoding_left_chunks: int,
+    enable_full_context: bool = True,
+):
+    """Apply optional mask for encoder.
+
+    Args:
+        xs (torch.Tensor): padded input, (B, L, D), L for max length
+        mask (torch.Tensor): mask for xs, (B, 1, L)
+        use_dynamic_chunk (bool): whether to use dynamic chunk or not
+        use_dynamic_left_chunk (bool): whether to use dynamic left chunk for
+            training.
+        decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's
+            0: default for training, use random dynamic chunk.
+            <0: for decoding, use full chunk.
+            >0: for decoding, use fixed chunk size as set.
+        static_chunk_size (int): chunk size for static chunk training/decoding
+            if it's greater than 0, if use_dynamic_chunk is true,
+            this parameter will be ignored
+        num_decoding_left_chunks: number of left chunks, this is for decoding,
+            the chunk size is decoding_chunk_size.
+            >=0: use num_decoding_left_chunks
+            <0: use all left chunks
+        enable_full_context (bool):
+            True: chunk size is either [1, 25] or full context(max_len)
+            False: chunk size ~ U[1, 25]
+
+    Returns:
+        torch.Tensor: chunk mask of the input xs.
+    """
+    if use_dynamic_chunk:
+        max_len = xs.size(1)
+        if decoding_chunk_size < 0:
+            chunk_size = max_len
+            num_left_chunks = -1
+        elif decoding_chunk_size > 0:
+            chunk_size = decoding_chunk_size
+            num_left_chunks = num_decoding_left_chunks
+        else:
+            chunk_size = paddle.randint(low=1, high=max_len, shape=(1,)).item()
+            num_left_chunks = -1
+            if chunk_size > max_len // 2 and enable_full_context:
+                chunk_size = max_len
+            else:
+                chunk_size = chunk_size % 25 + 1
+                if use_dynamic_left_chunk:
+                    max_left_chunks = (max_len - 1) // chunk_size
+                    num_left_chunks = paddle.randint(
+                        low=0, high=max_left_chunks, shape=(1,)
+                    ).item()
+        chunk_masks = subsequent_chunk_mask(
+            xs.size(1), chunk_size, num_left_chunks, xs.place
+        )
+        chunk_masks = chunk_masks.unsqueeze(0)
+        chunk_masks = masks & chunk_masks
+    elif static_chunk_size > 0:
+        num_left_chunks = num_decoding_left_chunks
+        chunk_masks = subsequent_chunk_mask(
+            xs.size(1), static_chunk_size, num_left_chunks, xs.place
+        )
+        chunk_masks = chunk_masks.unsqueeze(0)
+        chunk_masks = masks & chunk_masks
+    else:
+        chunk_masks = masks
+    assert chunk_masks.dtype == paddle.bool
+    if (chunk_masks.sum(dim=-1) == 0).sum().item() != 0:
+        print(
+            "get chunk_masks all false at some timestep, force set to true, make sure they are masked in futuer computation!"
+        )
+        chunk_masks[chunk_masks.sum(dim=-1) == 0] = True
+    return chunk_masks
+
+
+def make_pad_mask(lengths: paddle.Tensor, max_len: int = 0) -> paddle.Tensor:
+    """Make mask tensor containing indices of padded part.
+
+    See description of make_non_pad_mask.
+
+    Args:
+        lengths (torch.Tensor): Batch of lengths (B,).
+    Returns:
+        torch.Tensor: Mask tensor containing indices of padded part.
+
+    Examples:
+        >>> lengths = [5, 3, 2]
+        >>> make_pad_mask(lengths)
+        masks = [[0, 0, 0, 0 ,0],
+                 [0, 0, 0, 1, 1],
+                 [0, 0, 1, 1, 1]]
+    """
+    batch_size = lengths.size(0)
+    max_len = max_len if max_len > 0 else lengths._max().item()
+    seq_range = paddle.arange(0, max_len, dtype=paddle.int64, device=lengths.place)
+    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
+    seq_length_expand = lengths.unsqueeze(-1)
+    mask = seq_range_expand >= seq_length_expand
+    return mask

paddlespeech/t2s/modules/flow/matcha_decoder.py

@@ -0,0 +1,445 @@
+import math
+from typing import Optional
+
+import einops
+import paddle
+from conformer import ConformerBlock
+from matcha.models.components.transformer import BasicTransformerBlock
+ACTIVATION_FUNCTIONS = {
+    "swish": paddle.nn.SiLU(),
+    "silu": paddle.nn.SiLU(),
+    "mish": paddle.nn.Mish(),
+    "gelu": paddle.nn.GELU(),
+    "relu": paddle.nn.ReLU(),
+}
+def get_activation(act_fn: str) -> paddle.nn.Layer:
+    """Helper function to get activation function from string.
+
+    Args:
+        act_fn (str): Name of activation function.
+
+    Returns:
+        nn.Module: Activation function.
+    """
+    act_fn = act_fn.lower()
+    if act_fn in ACTIVATION_FUNCTIONS:
+        return ACTIVATION_FUNCTIONS[act_fn]
+    else:
+        raise ValueError(f"Unsupported activation function: {act_fn}")
+
+class SinusoidalPosEmb(paddle.nn.Layer):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+        assert self.dim % 2 == 0, "SinusoidalPosEmb requires dim to be even"
+
+    def forward(self, x, scale=1000):
+        if x.ndim < 1:
+            x = x.unsqueeze(0)
+        device = x.place
+        half_dim = self.dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = paddle.exp(x=paddle.arange(half_dim, device=device).float() * -emb)
+        emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
+        emb = paddle.cat((emb.sin(), emb.cos()), dim=-1)
+        return emb
+
+
+class Block1D(paddle.nn.Layer):
+    def __init__(self, dim, dim_out, groups=8):
+        super().__init__()
+        self.block = paddle.nn.Sequential(
+            paddle.nn.Conv1d(dim, dim_out, 3, padding=1),
+            paddle.nn.GroupNorm(num_groups=groups, num_channels=dim_out),
+            paddle.nn.Mish(),
+        )
+
+    def forward(self, x, mask):
+        output = self.block(x * mask)
+        return output * mask
+
+
+class ResnetBlock1D(paddle.nn.Layer):
+    def __init__(self, dim, dim_out, time_emb_dim, groups=8):
+        super().__init__()
+        self.mlp = paddle.nn.Sequential(
+            paddle.nn.Mish(),
+            paddle.nn.Linear(in_features=time_emb_dim, out_features=dim_out),
+        )
+        self.block1 = Block1D(dim, dim_out, groups=groups)
+        self.block2 = Block1D(dim_out, dim_out, groups=groups)
+        self.res_conv = paddle.nn.Conv1d(dim, dim_out, 1)
+
+    def forward(self, x, mask, time_emb):
+        h = self.block1(x, mask)
+        h += self.mlp(time_emb).unsqueeze(-1)
+        h = self.block2(h, mask)
+        output = h + self.res_conv(x * mask)
+        return output
+
+
+class Downsample1D(paddle.nn.Layer):
+    def __init__(self, dim):
+        super().__init__()
+        self.conv = paddle.nn.Conv1d(dim, dim, 3, 2, 1)
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+class TimestepEmbedding(paddle.nn.Layer):
+    def __init__(
+        self,
+        in_channels: int,
+        time_embed_dim: int,
+        act_fn: str = "silu",
+        out_dim: int = None,
+        post_act_fn: Optional[str] = None,
+        cond_proj_dim=None,
+    ):
+        super().__init__()
+        self.linear_1 = paddle.nn.Linear(
+            in_features=in_channels, out_features=time_embed_dim
+        )
+        if cond_proj_dim is not None:
+            self.cond_proj = paddle.nn.Linear(
+                in_features=cond_proj_dim, out_features=in_channels, bias_attr=False
+            )
+        else:
+            self.cond_proj = None
+        self.act = get_activation(act_fn)
+        if out_dim is not None:
+            time_embed_dim_out = out_dim
+        else:
+            time_embed_dim_out = time_embed_dim
+        self.linear_2 = paddle.nn.Linear(
+            in_features=time_embed_dim, out_features=time_embed_dim_out
+        )
+        if post_act_fn is None:
+            self.post_act = None
+        else:
+            self.post_act = get_activation(post_act_fn)
+
+    def forward(self, sample, condition=None):
+        if condition is not None:
+            sample = sample + self.cond_proj(condition)
+        sample = self.linear_1(sample)
+        if self.act is not None:
+            sample = self.act(sample)
+        sample = self.linear_2(sample)
+        if self.post_act is not None:
+            sample = self.post_act(sample)
+        return sample
+
+
+class Upsample1D(paddle.nn.Layer):
+    """A 1D upsampling layer with an optional convolution.
+
+    Parameters:
+        channels (`int`):
+            number of channels in the inputs and outputs.
+        use_conv (`bool`, default `False`):
+            option to use a convolution.
+        use_conv_transpose (`bool`, default `False`):
+            option to use a convolution transpose.
+        out_channels (`int`, optional):
+            number of output channels. Defaults to `channels`.
+    """
+
+    def __init__(
+        self,
+        channels,
+        use_conv=False,
+        use_conv_transpose=True,
+        out_channels=None,
+        name="conv",
+    ):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_conv_transpose = use_conv_transpose
+        self.name = name
+        self.conv = None
+        if use_conv_transpose:
+            self.conv = paddle.nn.Conv1DTranspose(
+                in_channels=channels,
+                out_channels=self.out_channels,
+                kernel_size=4,
+                stride=2,
+                padding=1,
+            )
+        elif use_conv:
+            self.conv = paddle.nn.Conv1d(self.channels, self.out_channels, 3, padding=1)
+
+    def forward(self, inputs):
+        assert inputs.shape[1] == self.channels
+        if self.use_conv_transpose:
+            return self.conv(inputs)
+        outputs = paddle.nn.functional.interpolate(
+            x=inputs, scale_factor=2.0, mode="nearest"
+        )
+        if self.use_conv:
+            outputs = self.conv(outputs)
+        return outputs
+
+
+class ConformerWrapper(ConformerBlock):
+    def __init__(
+        self,
+        *,
+        dim,
+        dim_head=64,
+        heads=8,
+        ff_mult=4,
+        conv_expansion_factor=2,
+        conv_kernel_size=31,
+        attn_dropout=0,
+        ff_dropout=0,
+        conv_dropout=0,
+        conv_causal=False,
+    ):
+        super().__init__(
+            dim=dim,
+            dim_head=dim_head,
+            heads=heads,
+            ff_mult=ff_mult,
+            conv_expansion_factor=conv_expansion_factor,
+            conv_kernel_size=conv_kernel_size,
+            attn_dropout=attn_dropout,
+            ff_dropout=ff_dropout,
+            conv_dropout=conv_dropout,
+            conv_causal=conv_causal,
+        )
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        timestep=None,
+    ):
+        return super().forward(x=hidden_states, mask=attention_mask.bool())
+
+
+class Decoder(paddle.nn.Layer):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        channels=(256, 256),
+        dropout=0.05,
+        attention_head_dim=64,
+        n_blocks=1,
+        num_mid_blocks=2,
+        num_heads=4,
+        act_fn="snake",
+        down_block_type="transformer",
+        mid_block_type="transformer",
+        up_block_type="transformer",
+    ):
+        super().__init__()
+        channels = tuple(channels)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.time_embeddings = SinusoidalPosEmb(in_channels)
+        time_embed_dim = channels[0] * 4
+        self.time_mlp = TimestepEmbedding(
+            in_channels=in_channels, time_embed_dim=time_embed_dim, act_fn="silu"
+        )
+        self.down_blocks = paddle.nn.LayerList(sublayers=[])
+        self.mid_blocks = paddle.nn.LayerList(sublayers=[])
+        self.up_blocks = paddle.nn.LayerList(sublayers=[])
+        output_channel = in_channels
+        for i in range(len(channels)):
+            input_channel = output_channel
+            output_channel = channels[i]
+            is_last = i == len(channels) - 1
+            resnet = ResnetBlock1D(
+                dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim
+            )
+            transformer_blocks = paddle.nn.LayerList(
+                sublayers=[
+                    self.get_block(
+                        down_block_type,
+                        output_channel,
+                        attention_head_dim,
+                        num_heads,
+                        dropout,
+                        act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+            downsample = (
+                Downsample1D(output_channel)
+                if not is_last
+                else paddle.nn.Conv1d(output_channel, output_channel, 3, padding=1)
+            )
+            self.down_blocks.append(
+                paddle.nn.LayerList(sublayers=[resnet, transformer_blocks, downsample])
+            )
+        for i in range(num_mid_blocks):
+            input_channel = channels[-1]
+            out_channels = channels[-1]
+            resnet = ResnetBlock1D(
+                dim=input_channel, dim_out=output_channel, time_emb_dim=time_embed_dim
+            )
+            transformer_blocks = paddle.nn.LayerList(
+                sublayers=[
+                    self.get_block(
+                        mid_block_type,
+                        output_channel,
+                        attention_head_dim,
+                        num_heads,
+                        dropout,
+                        act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+            self.mid_blocks.append(
+                paddle.nn.LayerList(sublayers=[resnet, transformer_blocks])
+            )
+        channels = channels[::-1] + (channels[0],)
+        for i in range(len(channels) - 1):
+            input_channel = channels[i]
+            output_channel = channels[i + 1]
+            is_last = i == len(channels) - 2
+            resnet = ResnetBlock1D(
+                dim=2 * input_channel,
+                dim_out=output_channel,
+                time_emb_dim=time_embed_dim,
+            )
+            transformer_blocks = paddle.nn.LayerList(
+                sublayers=[
+                    self.get_block(
+                        up_block_type,
+                        output_channel,
+                        attention_head_dim,
+                        num_heads,
+                        dropout,
+                        act_fn,
+                    )
+                    for _ in range(n_blocks)
+                ]
+            )
+            upsample = (
+                Upsample1D(output_channel, use_conv_transpose=True)
+                if not is_last
+                else paddle.nn.Conv1d(output_channel, output_channel, 3, padding=1)
+            )
+            self.up_blocks.append(
+                paddle.nn.LayerList(sublayers=[resnet, transformer_blocks, upsample])
+            )
+        self.final_block = Block1D(channels[-1], channels[-1])
+        self.final_proj = paddle.nn.Conv1d(channels[-1], self.out_channels, 1)
+        self.initialize_weights()
+
+    @staticmethod
+    def get_block(block_type, dim, attention_head_dim, num_heads, dropout, act_fn):
+        if block_type == "conformer":
+            block = ConformerWrapper(
+                dim=dim,
+                dim_head=attention_head_dim,
+                heads=num_heads,
+                ff_mult=1,
+                conv_expansion_factor=2,
+                ff_dropout=dropout,
+                attn_dropout=dropout,
+                conv_dropout=dropout,
+                conv_kernel_size=31,
+            )
+        elif block_type == "transformer":
+            block = BasicTransformerBlock(
+                dim=dim,
+                num_attention_heads=num_heads,
+                attention_head_dim=attention_head_dim,
+                dropout=dropout,
+                activation_fn=act_fn,
+            )
+        else:
+            raise ValueError(f"Unknown block type {block_type}")
+        return block
+
+    def initialize_weights(self):
+        for m in self.sublayers():
+            if isinstance(m, paddle.nn.Conv1d):
+                paddle.nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
+                if m.bias is not None:
+                    paddle.nn.init.constant_(m.bias, 0)
+            elif isinstance(m, paddle.nn.GroupNorm):
+                paddle.nn.init.constant_(m.weight, 1)
+                paddle.nn.init.constant_(m.bias, 0)
+            elif isinstance(m, paddle.nn.Linear):
+                paddle.nn.init.kaiming_normal_(m.weight, nonlinearity="relu")
+                if m.bias is not None:
+                    paddle.nn.init.constant_(m.bias, 0)
+
+    def forward(self, x, mask, mu, t, spks=None, cond=None):
+        """Forward pass of the UNet1DConditional model.
+
+        Args:
+            x (torch.Tensor): shape (batch_size, in_channels, time)
+            mask (_type_): shape (batch_size, 1, time)
+            t (_type_): shape (batch_size)
+            spks (_type_, optional): shape: (batch_size, condition_channels). Defaults to None.
+            cond (_type_, optional): placeholder for future use. Defaults to None.
+
+        Raises:
+            ValueError: _description_
+            ValueError: _description_
+
+        Returns:
+            _type_: _description_
+        """
+        t = self.time_embeddings(t)
+        t = self.time_mlp(t)
+        x = einops.pack([x, mu], "b * t")[0]
+        if spks is not None:
+            spks = einops.repeat(spks, "b c -> b c t", t=x.shape[-1])
+            x = einops.pack([x, spks], "b * t")[0]
+        hiddens = []
+        masks = [mask]
+        for resnet, transformer_blocks, downsample in self.down_blocks:
+            mask_down = masks[-1]
+            x = resnet(x, mask_down, t)
+            x = einops.rearrange(x, "b c t -> b t c")
+            mask_down = einops.rearrange(mask_down, "b 1 t -> b t")
+            for transformer_block in transformer_blocks:
+                x = transformer_block(
+                    hidden_states=x, attention_mask=mask_down, timestep=t
+                )
+            x = einops.rearrange(x, "b t c -> b c t")
+            mask_down = einops.rearrange(mask_down, "b t -> b 1 t")
+            hiddens.append(x)
+            x = downsample(x * mask_down)
+            masks.append(mask_down[:, :, ::2])
+        masks = masks[:-1]
+        mask_mid = masks[-1]
+        for resnet, transformer_blocks in self.mid_blocks:
+            x = resnet(x, mask_mid, t)
+            x = einops.rearrange(x, "b c t -> b t c")
+            mask_mid = einops.rearrange(mask_mid, "b 1 t -> b t")
+            for transformer_block in transformer_blocks:
+                x = transformer_block(
+                    hidden_states=x, attention_mask=mask_mid, timestep=t
+                )
+            x = einops.rearrange(x, "b t c -> b c t")
+            mask_mid = einops.rearrange(mask_mid, "b t -> b 1 t")
+        for resnet, transformer_blocks, upsample in self.up_blocks:
+            mask_up = masks.pop()
+            x = resnet(einops.pack([x, hiddens.pop()], "b * t")[0], mask_up, t)
+            x = einops.rearrange(x, "b c t -> b t c")
+            mask_up = einops.rearrange(mask_up, "b 1 t -> b t")
+            for transformer_block in transformer_blocks:
+                x = transformer_block(
+                    hidden_states=x, attention_mask=mask_up, timestep=t
+                )
+            x = einops.rearrange(x, "b t c -> b c t")
+            mask_up = einops.rearrange(mask_up, "b t -> b 1 t")
+            x = upsample(x * mask_up)
+        x = self.final_block(x, mask_up)
+        output = self.final_proj(x * mask_up)
+        return output * mask

paddlespeech/t2s/modules/flow/matcha_transformer.py

@@ -0,0 +1,318 @@
+from typing import Any, Dict, Optional
+from .attention_processor import Attention
+import paddle
+from paddle import nn
+from paddlespeech.t2s.modules.flow.lora import LoRACompatibleLinear
+class SnakeBeta(paddle.nn.Layer):
+    """
+    A modified Snake function which uses separate parameters for the magnitude of the periodic components
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+    Parameters:
+        - alpha - trainable parameter that controls frequency
+        - beta - trainable parameter that controls magnitude
+    References:
+        - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+        https://arxiv.org/abs/2006.08195
+    Examples:
+        >>> a1 = snakebeta(256)
+        >>> x = torch.randn(256)
+        >>> x = a1(x)
+    """
+
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        alpha=1.0,
+        alpha_trainable=True,
+        alpha_logscale=True,
+    ):
+        """
+        Initialization.
+        INPUT:
+            - in_features: shape of the input
+            - alpha - trainable parameter that controls frequency
+            - beta - trainable parameter that controls magnitude
+            alpha is initialized to 1 by default, higher values = higher-frequency.
+            beta is initialized to 1 by default, higher values = higher-magnitude.
+            alpha will be trained along with the rest of your model.
+        """
+        super().__init__()
+        self.in_features = (
+            out_features if isinstance(out_features, list) else [out_features]
+        )
+        self.proj = LoRACompatibleLinear(
+            in_features, out_features
+        )
+        self.alpha_logscale = alpha_logscale
+        if self.alpha_logscale:
+            self.alpha = paddle.nn.parameter.Parameter(
+                paddle.zeros(self.in_features) * alpha
+            )
+            self.beta = paddle.nn.parameter.Parameter(
+                paddle.zeros(self.in_features) * alpha
+            )
+        else:
+            self.alpha = paddle.nn.parameter.Parameter(
+                paddle.ones(self.in_features) * alpha
+            )
+            self.beta = paddle.nn.parameter.Parameter(
+                paddle.ones(self.in_features) * alpha
+            )
+        self.alpha.stop_gradient = not alpha_trainable
+        self.beta.stop_gradient = not alpha_trainable
+        self.no_div_by_zero = 1e-09
+
+    def forward(self, x):
+        """
+        Forward pass of the function.
+        Applies the function to the input elementwise.
+        SnakeBeta ∶= x + 1/b * sin^2 (xa)
+        """
+        x = self.proj(x)
+        if self.alpha_logscale:
+            alpha = paddle.exp(x=self.alpha)
+            beta = paddle.exp(x=self.beta)
+        else:
+            alpha = self.alpha
+            beta = self.beta
+        x = x + 1.0 / (beta + self.no_div_by_zero) * paddle.pow(
+            paddle.sin(x * alpha), 2
+        )
+        return x
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+class GELU(nn.Layer):
+    def __init__(self, dim_in: int, dim_out: int, approximate: str = "none", bias: bool = True):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out, bias_attr=bias)
+        self.approximate = approximate
+
+    def gelu(self, gate: paddle.Tensor) -> paddle.Tensor:
+        if self.approximate == "tanh":
+            approximate_bool = True
+        else:
+            approximate_bool = False
+
+        if gate.dtype == paddle.float16:
+            return F.gelu(gate.astype(paddle.float32), approximate=approximate_bool).astype(paddle.float16)
+        else:
+            return F.gelu(gate, approximate=approximate_bool)
+
+    def forward(self, hidden_states: paddle.Tensor) -> paddle.Tensor:
+        hidden_states = self.proj(hidden_states)
+        hidden_states = self.gelu(hidden_states) 
+        return hidden_states
+
+class FeedForward(paddle.nn.Layer):
+    """
+    A feed-forward layer.
+
+    Parameters:
+        dim (`int`): The number of channels in the input.
+        dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
+        mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        dim_out: Optional[int] = None,
+        mult: int = 4,
+        dropout: float = 0.0,
+        activation_fn: str = "geglu",
+        final_dropout: bool = False,
+    ):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = dim_out if dim_out is not None else dim
+        act_fn = GELU(dim, inner_dim)
+#         if activation_fn == "gelu":
+# >>>>>>            act_fn = diffusers.models.attention.GELU(dim, inner_dim)
+#         if activation_fn == "gelu-approximate":
+# >>>>>>            act_fn = diffusers.models.attention.GELU(dim, inner_dim, approximate="tanh")
+#         elif activation_fn == "geglu":
+# >>>>>>            act_fn = diffusers.models.attention.GEGLU(dim, inner_dim)
+#         elif activation_fn == "geglu-approximate":
+#             act_fn = diffusers.models.attention.ApproximateGELU(dim, inner_dim)
+#         elif activation_fn == "snakebeta":
+            # act_fn = SnakeBeta(dim, inner_dim)
+        self.net = paddle.nn.LayerList(sublayers=[])
+        self.net.append(act_fn)
+        self.net.append(paddle.nn.Dropout(p=dropout))
+        self.net.append(LoRACompatibleLinear(inner_dim, dim_out))
+        if final_dropout:
+            self.net.append(paddle.nn.Dropout(p=dropout))
+
+    def forward(self, hidden_states):
+        for module in self.net:
+            hidden_states = module(hidden_states)
+        return hidden_states
+
+
+
+class BasicTransformerBlock(paddle.nn.Layer):
+    """
+    A basic Transformer block.
+
+    Parameters:
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
+        only_cross_attention (`bool`, *optional*):
+            Whether to use only cross-attention layers. In this case two cross attention layers are used.
+        double_self_attention (`bool`, *optional*):
+            Whether to use two self-attention layers. In this case no cross attention layers are used.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        num_embeds_ada_norm (:
+            obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
+        attention_bias (:
+            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        dropout=0.0,
+        cross_attention_dim: Optional[int] = None,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        attention_bias: bool = False,
+        only_cross_attention: bool = False,
+        double_self_attention: bool = False,
+        upcast_attention: bool = False,
+        norm_elementwise_affine: bool = True,
+        norm_type: str = "layer_norm",
+        final_dropout: bool = False,
+    ):
+        super().__init__()
+        self.only_cross_attention = only_cross_attention
+        self.use_ada_layer_norm_zero = (
+            num_embeds_ada_norm is not None and norm_type == "ada_norm_zero"
+        )
+        self.use_ada_layer_norm = (
+            num_embeds_ada_norm is not None and norm_type == "ada_norm"
+        )
+        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
+            raise ValueError(
+                f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
+            )
+        
+        self.norm1 = paddle.nn.LayerNorm(
+            normalized_shape=dim,
+            weight_attr=norm_elementwise_affine,
+            bias_attr=norm_elementwise_affine,
+        )
+        self.attn1 = Attention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            dropout=dropout,
+            bias=attention_bias,
+            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+            upcast_attention=upcast_attention,
+        )
+        self.norm2 = None
+        self.attn2 = None
+        self.norm3 = paddle.nn.LayerNorm(
+            normalized_shape=dim,
+            weight_attr=norm_elementwise_affine,
+            bias_attr=norm_elementwise_affine,
+        )
+        self.ff = FeedForward(
+            dim,
+            dropout=dropout,
+            activation_fn=activation_fn,
+            final_dropout=final_dropout,
+        )
+        self._chunk_size = None
+        self._chunk_dim = 0
+
+    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):
+        self._chunk_size = chunk_size
+        self._chunk_dim = dim
+
+    def forward(
+        self,
+        hidden_states: paddle.Tensor,
+        attention_mask: Optional[paddle.Tensor] = None,
+        encoder_hidden_states: Optional[paddle.Tensor] = None,
+        encoder_attention_mask: Optional[paddle.Tensor] = None,
+        timestep: Optional[paddle.Tensor] = None,
+        cross_attention_kwargs: Dict[str, Any] = None,
+        class_labels: Optional[paddle.Tensor] = None,
+    ):
+        if self.use_ada_layer_norm:
+            norm_hidden_states = self.norm1(hidden_states, timestep)
+        elif self.use_ada_layer_norm_zero:
+            (norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp) = self.norm1(
+                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+            )
+        else:
+            norm_hidden_states = self.norm1(hidden_states)
+        cross_attention_kwargs = (
+            cross_attention_kwargs if cross_attention_kwargs is not None else {}
+        )
+        attn_output = self.attn1(
+            norm_hidden_states,
+            encoder_hidden_states=encoder_hidden_states
+            if self.only_cross_attention
+            else None,
+            attention_mask=encoder_attention_mask
+            if self.only_cross_attention
+            else attention_mask,
+            **cross_attention_kwargs,
+        )
+        if self.use_ada_layer_norm_zero:
+            attn_output = gate_msa.unsqueeze(1) * attn_output
+        hidden_states = attn_output + hidden_states
+        if self.attn2 is not None:
+            norm_hidden_states = (
+                self.norm2(hidden_states, timestep)
+                if self.use_ada_layer_norm
+                else self.norm2(hidden_states)
+            )
+            attn_output = self.attn2(
+                norm_hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+                **cross_attention_kwargs,
+            )
+            hidden_states = attn_output + hidden_states
+        norm_hidden_states = self.norm3(hidden_states)
+        if self.use_ada_layer_norm_zero:
+            norm_hidden_states = (
+                norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+            )
+        if self._chunk_size is not None:
+            if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
+                raise ValueError(
+                    f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+                )
+            num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
+            ff_output = paddle.cat(
+                [
+                    self.ff(hid_slice)
+                    for hid_slice in norm_hidden_states.chunk(
+                        num_chunks, dim=self._chunk_dim
+                    )
+                ],
+                dim=self._chunk_dim,
+            )
+        else:
+            ff_output = self.ff(norm_hidden_states)
+        if self.use_ada_layer_norm_zero:
+            ff_output = gate_mlp.unsqueeze(1) * ff_output
+        hidden_states = ff_output + hidden_states
+        return hidden_states

paddlespeech/t2s/modules/flow/normalization.py

@@ -0,0 +1,276 @@
+import numbers
+from typing import Dict, Optional, Tuple
+
+import paddle
+from paddle import nn
+from .activations import get_activation
+from .embeddings import (CombinedTimestepLabelEmbeddings,
+                         PixArtAlphaCombinedTimestepSizeEmbeddings)
+def get_activation(act_fn):
+    if act_fn == "silu":
+        return nn.Silu()
+    elif act_fn == "mish":
+        return nn.Mish()
+    elif act_fn == "relu":
+        return nn.ReLU()
+    elif act_fn == "gelu":
+        return nn.GELU()
+    else:
+        raise ValueError(f"Unsupported activation function: {act_fn}")
+    
+class AdaLayerNorm(paddle.nn.Layer):
+    """
+    Norm layer modified to incorporate timestep embeddings.
+
+    Parameters:
+        embedding_dim (`int`): The size of each embedding vector.
+        num_embeddings (`int`): The size of the embeddings dictionary.
+    """
+
+    def __init__(self, embedding_dim: int, num_embeddings: int):
+        super().__init__()
+        self.emb = paddle.nn.Embedding(num_embeddings, embedding_dim)
+        self.silu = paddle.nn.SiLU()
+        self.linear = paddle.nn.Linear(
+            in_features=embedding_dim, out_features=embedding_dim * 2
+        )
+        self.norm = paddle.nn.LayerNorm(
+            normalized_shape=embedding_dim, weight_attr=False, bias_attr=False
+        )
+
+    def forward(self, x: paddle.Tensor, timestep: paddle.Tensor) -> paddle.Tensor:
+        emb = self.linear(self.silu(self.emb(timestep)))
+        scale, shift = paddle.chunk(emb, 2)
+        x = self.norm(x) * (1 + scale) + shift
+        return x
+
+
+class AdaLayerNormZero(paddle.nn.Layer):
+    """
+    Norm layer adaptive layer norm zero (adaLN-Zero).
+
+    Parameters:
+        embedding_dim (`int`): The size of each embedding vector.
+        num_embeddings (`int`): The size of the embeddings dictionary.
+    """
+
+    def __init__(self, embedding_dim: int, num_embeddings: Optional[int] = None):
+        super().__init__()
+        if num_embeddings is not None:
+            self.emb = CombinedTimestepLabelEmbeddings(num_embeddings, embedding_dim)
+        else:
+            self.emb = None
+        self.silu = paddle.nn.SiLU()
+        self.linear = paddle.nn.Linear(
+            in_features=embedding_dim, out_features=6 * embedding_dim, bias_attr=True
+        )
+        self.norm = paddle.nn.LayerNorm(
+            normalized_shape=embedding_dim,
+            weight_attr=False,
+            bias_attr=False,
+            epsilon=1e-06,
+        )
+
+    def forward(
+        self,
+        x: paddle.Tensor,
+        timestep: Optional[paddle.Tensor] = None,
+        class_labels: Optional[paddle.LongTensor] = None,
+        hidden_dtype: Optional[paddle.dtype] = None,
+        emb: Optional[paddle.Tensor] = None,
+    ) -> Tuple[
+        paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor
+    ]:
+        if self.emb is not None:
+            emb = self.emb(timestep, class_labels, hidden_dtype=hidden_dtype)
+        emb = self.linear(self.silu(emb))
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk(
+            6, dim=1
+        )
+        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
+        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
+
+
+class AdaLayerNormSingle(paddle.nn.Layer):
+    """
+    Norm layer adaptive layer norm single (adaLN-single).
+
+    As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3).
+
+    Parameters:
+        embedding_dim (`int`): The size of each embedding vector.
+        use_additional_conditions (`bool`): To use additional conditions for normalization or not.
+    """
+
+    def __init__(self, embedding_dim: int, use_additional_conditions: bool = False):
+        super().__init__()
+        self.emb = PixArtAlphaCombinedTimestepSizeEmbeddings(
+            embedding_dim,
+            size_emb_dim=embedding_dim // 3,
+            use_additional_conditions=use_additional_conditions,
+        )
+        self.silu = paddle.nn.SiLU()
+        self.linear = paddle.nn.Linear(
+            in_features=embedding_dim, out_features=6 * embedding_dim, bias_attr=True
+        )
+
+    def forward(
+        self,
+        timestep: paddle.Tensor,
+        added_cond_kwargs: Optional[Dict[str, paddle.Tensor]] = None,
+        batch_size: Optional[int] = None,
+        hidden_dtype: Optional[paddle.dtype] = None,
+    ) -> Tuple[
+        paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor
+    ]:
+        embedded_timestep = self.emb(
+            timestep,
+            **added_cond_kwargs,
+            batch_size=batch_size,
+            hidden_dtype=hidden_dtype,
+        )
+        return self.linear(self.silu(embedded_timestep)), embedded_timestep
+
+
+class AdaGroupNorm(paddle.nn.Layer):
+    """
+    GroupNorm layer modified to incorporate timestep embeddings.
+
+    Parameters:
+        embedding_dim (`int`): The size of each embedding vector.
+        num_embeddings (`int`): The size of the embeddings dictionary.
+        num_groups (`int`): The number of groups to separate the channels into.
+        act_fn (`str`, *optional*, defaults to `None`): The activation function to use.
+        eps (`float`, *optional*, defaults to `1e-5`): The epsilon value to use for numerical stability.
+    """
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        out_dim: int,
+        num_groups: int,
+        act_fn: Optional[str] = None,
+        eps: float = 1e-05,
+    ):
+        super().__init__()
+        self.num_groups = num_groups
+        self.eps = eps
+        if act_fn is None:
+            self.act = None
+        else:
+            self.act = get_activation(act_fn)
+        self.linear = paddle.nn.Linear(
+            in_features=embedding_dim, out_features=out_dim * 2
+        )
+
+    def forward(self, x: paddle.Tensor, emb: paddle.Tensor) -> paddle.Tensor:
+        if self.act:
+            emb = self.act(emb)
+        emb = self.linear(emb)
+        emb = emb[:, :, None, None]
+        scale, shift = emb.chunk(2, dim=1)
+        x = paddle.nn.functional.group_norm(
+            x=x, num_groups=self.num_groups, epsilon=self.eps
+        )
+        x = x * (1 + scale) + shift
+        return x
+
+
+class AdaLayerNormContinuous(paddle.nn.Layer):
+    def __init__(
+        self,
+        embedding_dim: int,
+        conditioning_embedding_dim: int,
+        elementwise_affine=True,
+        eps=1e-05,
+        bias=True,
+        norm_type="layer_norm",
+    ):
+        super().__init__()
+        self.silu = paddle.nn.SiLU()
+        self.linear = paddle.nn.Linear(
+            in_features=conditioning_embedding_dim,
+            out_features=embedding_dim * 2,
+            bias_attr=bias,
+        )
+        if norm_type == "layer_norm":
+            self.norm = LayerNorm(embedding_dim, eps, elementwise_affine, bias)
+        elif norm_type == "rms_norm":
+            self.norm = RMSNorm(embedding_dim, eps, elementwise_affine)
+        else:
+            raise ValueError(f"unknown norm_type {norm_type}")
+
+    def forward(
+        self, x: paddle.Tensor, conditioning_embedding: paddle.Tensor
+    ) -> paddle.Tensor:
+        emb = self.linear(self.silu(conditioning_embedding).to(x.dtype))
+        scale, shift = paddle.chunk(emb, 2, dim=1)
+        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
+        return x
+
+LayerNorm = paddle.nn.LayerNorm
+
+
+class LayerNorm(paddle.nn.Layer):
+    def __init__(
+        self,
+        dim,
+        eps: float = 1e-05,
+        elementwise_affine: bool = True,
+        bias: bool = True,
+    ):
+        super().__init__()
+        self.eps = eps
+        if isinstance(dim, numbers.Integral):
+            dim = (dim,)
+        self.dim = paddle.Size(dim)
+        if elementwise_affine:
+            self.weight = paddle.nn.parameter.Parameter(paddle.ones(dim))
+            self.bias = (
+                paddle.nn.parameter.Parameter(paddle.zeros(dim)) if bias else None
+            )
+        else:
+            self.weight = None
+            self.bias = None
+
+    def forward(self, input):
+        return paddle.nn.functional.layer_norm(
+            input, self.dim, self.weight, self.bias, self.eps
+        )
+
+
+class RMSNorm(paddle.nn.Layer):
+    def __init__(self, dim, eps: float, elementwise_affine: bool = True):
+        super().__init__()
+        self.eps = eps
+        if isinstance(dim, numbers.Integral):
+            dim = (dim,)
+        self.dim = paddle.Size(dim)
+        if elementwise_affine:
+            self.weight = paddle.nn.parameter.Parameter(paddle.ones(dim))
+        else:
+            self.weight = None
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        variance = hidden_states.to(paddle.float32).pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * paddle.rsqrt(variance + self.eps)
+        if self.weight is not None:
+            if self.weight.dtype in [paddle.float16, paddle.bfloat16]:
+                hidden_states = hidden_states.to(self.weight.dtype)
+            hidden_states = hidden_states * self.weight
+        else:
+            hidden_states = hidden_states.to(input_dtype)
+        return hidden_states
+
+
+class GlobalResponseNorm(paddle.nn.Layer):
+    def __init__(self, dim):
+        super().__init__()
+        self.gamma = paddle.nn.parameter.Parameter(paddle.zeros(1, 1, 1, dim))
+        self.beta = paddle.nn.parameter.Parameter(paddle.zeros(1, 1, 1, dim))
+
+    def forward(self, x):
+        gx = paddle.norm(x, p=2, dim=(1, 2), keepdim=True)
+        nx = gx / (gx.mean(dim=-1, keepdim=True) + 1e-06)
+        return self.gamma * (x * nx) + self.beta + x

paddlespeech/t2s/modules/predictor/length_regulator.py

@@ -108,7 +108,6 @@ class LengthRegulator(nn.Layer):
         Returns:
             Tensor: replicated input tensor based on durations (B, T*, D).
         """
-
         if alpha != 1.0:
             assert alpha > 0
             ds = paddle.round(ds.cast(dtype=paddle.float32) * alpha)

paddlespeech/t2s/modules/transformer/activation.py

@@ -0,0 +1,82 @@
+import paddle
+
+"""Swish() activation function for Conformer."""
+
+
+class Swish(paddle.nn.Layer):
+    """Construct an Swish object."""
+
+    def forward(self, x: paddle.Tensor) -> paddle.Tensor:
+        """Return Swish activation function."""
+        return x * paddle.nn.functional.sigmoid(x)
+
+
+class Snake(paddle.nn.Layer):
+    '''
+    Implementation of a sine-based periodic activation function
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+    Parameters:
+        - alpha - trainable parameter
+    References:
+        - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+        https://arxiv.org/abs/2006.08195
+    Examples:
+        >>> a1 = Snake(256)
+        >>> x = paddle.randn([1, 256, 100])  # Example input
+        >>> x = a1(x)
+    '''
+    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
+        '''
+        Initialization.
+        INPUT:
+            - in_features: number of input features (channel dimension)
+            - alpha: trainable parameter
+            alpha is initialized to 1 by default, higher values = higher-frequency.
+            alpha will be trained along with the rest of your model.
+        '''
+        super(Snake, self).__init__()
+        self.in_features = in_features
+        self.alpha_logscale = alpha_logscale
+        
+        # 避免除零的小常数
+        self.no_div_by_zero = 1e-9
+
+        # 初始化alpha的值：对数尺度下初始为0，线性尺度下初始为alpha
+        if self.alpha_logscale:
+            initial_value = 0.0  # 对数尺度下，初始化为0，前向传播中会进行exp运算
+        else:
+            initial_value = alpha  # 线性尺度下，直接初始化为alpha
+
+        # 创建可训练参数alpha - 使用PaddlePaddle的方式
+        # 注意：这里使用self.create_parameter而不是paddle.create_parameter
+        self.alpha = self.create_parameter(
+            shape=[in_features],  # 参数形状为[in_features]
+            dtype='float32',       # 数据类型
+            default_initializer=paddle.nn.initializer.Constant(value=initial_value)  # 初始化器
+        )
+        
+        # 设置参数是否需要梯度更新（是否可训练）
+        # 在PaddlePaddle中，通过设置stop_gradient来控制
+        self.alpha.stop_gradient = not alpha_trainable
+
+    def forward(self, x):
+        '''
+        Forward pass of the function.
+        Applies the function to the input elementwise.
+        Snake ∶= x + 1/a * sin^2 (xa)
+        '''
+        # 调整alpha的维度以匹配输入x: [B, C, T] -> alpha需要变为[1, C, 1]
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1)  # 从[C]变为[1, C, 1]
+        
+        # 如果使用对数尺度，对alpha取指数
+        if self.alpha_logscale:
+            alpha = paddle.exp(alpha)
+        
+        # 计算Snake激活函数
+        # 公式: x + (1.0 / (alpha + epsilon)) * sin(x * alpha)^2
+        sin_term = paddle.sin(x * alpha)
+        result = x + (1.0 / (alpha + self.no_div_by_zero)) * (sin_term ** 2)
+        
+        return result

paddlespeech/t2s/modules/transformer/attention.py

@@ -199,7 +199,7 @@ class RelPositionMultiHeadedAttention(MultiHeadedAttention):
             x = x * paddle.tril(ones, t2 - t1)[None, None, :, :]
         return x
 
-    def forward(self, query, key, value, pos_emb, mask):
+    def forward(self, query, key, value, pos_emb, mask, cache):
         """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
 
         Args:
@@ -220,6 +220,11 @@ class RelPositionMultiHeadedAttention(MultiHeadedAttention):
         q, k, v = self.forward_qkv(query, key, value)
         # (batch, time1, head, d_k)
         q = q.transpose([0, 2, 1, 3])
+        if cache is not None and cache.shape[0] > 0:
+            key_cache, value_cache = paddle.split(cache, num_or_sections=2, axis=-1)
+            k = paddle.concat([key_cache, k], axis=2)
+            v = paddle.concat([value_cache, v], axis=2)
+        new_cache = paddle.concat([k, v], axis=-1)
         n_batch_pos = paddle.shape(pos_emb)[0]
         p = self.linear_pos(pos_emb).reshape(
             [n_batch_pos, -1, self.h, self.d_k])
@@ -243,7 +248,7 @@ class RelPositionMultiHeadedAttention(MultiHeadedAttention):
         # (batch, head, time1, time2)
         scores = (matrix_ac + matrix_bd) / math.sqrt(self.d_k)
 
-        return self.forward_attention(v, scores, mask)
+        return self.forward_attention(v, scores, mask), new_cache
 
 
 class LegacyRelPositionMultiHeadedAttention(MultiHeadedAttention):

paddlespeech/t2s/modules/transformer/convolution.py

@@ -0,0 +1,99 @@
+import paddle
+
+"""ConvolutionModule definition."""
+from typing import Tuple
+
+
+class ConvolutionModule(paddle.nn.Layer):
+    """ConvolutionModule in Conformer model."""
+
+    def __init__(
+        self,
+        channels: int,
+        kernel_size: int = 15,
+        activation: paddle.nn.Layer = paddle.nn.ReLU(),
+        norm: str = "batch_norm",
+        causal: bool = False,
+        bias: bool = True,
+    ):
+        """Construct an ConvolutionModule object.
+        Args:
+            channels (int): The number of channels of conv layers.
+            kernel_size (int): Kernel size of conv layers.
+            causal (int): Whether use causal convolution or not
+        """
+        super().__init__()
+        self.pointwise_conv1 = paddle.nn.Conv1d(
+            channels, 2 * channels, kernel_size=1, stride=1, padding=0, bias=bias
+        )
+        if causal:
+            padding = 0
+            self.lorder = kernel_size - 1
+        else:
+            assert (kernel_size - 1) % 2 == 0
+            padding = (kernel_size - 1) // 2
+            self.lorder = 0
+        self.depthwise_conv = paddle.nn.Conv1d(
+            channels,
+            channels,
+            kernel_size,
+            stride=1,
+            padding=padding,
+            groups=channels,
+            bias=bias,
+        )
+        assert norm in ["batch_norm", "layer_norm"]
+        if norm == "batch_norm":
+            self.use_layer_norm = False
+            self.norm = paddle.nn.BatchNorm1D(num_features=channels)
+        else:
+            self.use_layer_norm = True
+            self.norm = paddle.nn.LayerNorm(normalized_shape=channels)
+        self.pointwise_conv2 = paddle.nn.Conv1d(
+            channels, channels, kernel_size=1, stride=1, padding=0, bias=bias
+        )
+        self.activation = activation
+
+    def forward(
+        self,
+        x: paddle.Tensor,
+        mask_pad: paddle.Tensor = paddle.ones((0, 0, 0), dtype=paddle.bool),
+        cache: paddle.Tensor = paddle.zeros((0, 0, 0)),
+    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
+        """Compute convolution module.
+        Args:
+            x (torch.Tensor): Input tensor (#batch, time, channels).
+            mask_pad (torch.Tensor): used for batch padding (#batch, 1, time),
+                (0, 0, 0) means fake mask.
+            cache (torch.Tensor): left context cache, it is only
+                used in causal convolution (#batch, channels, cache_t),
+                (0, 0, 0) meas fake cache.
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, channels).
+        """
+        x = x.transpose(1, 2)
+        if mask_pad.size(2) > 0:
+            x.masked_fill_(~mask_pad, 0.0)
+        if self.lorder > 0:
+            if cache.size(2) == 0:
+                x = paddle.compat.pad(x, (self.lorder, 0), "constant", 0.0)
+            else:
+                assert cache.size(0) == x.size(0)
+                assert cache.size(1) == x.size(1)
+                x = paddle.cat((cache, x), dim=2)
+            assert x.size(2) > self.lorder
+            new_cache = x[:, :, -self.lorder :]
+        else:
+            new_cache = paddle.zeros((0, 0, 0), dtype=x.dtype, device=x.place)
+        x = self.pointwise_conv1(x)
+        x = paddle.nn.functional.glu(x=x, axis=1)
+        x = self.depthwise_conv(x)
+        if self.use_layer_norm:
+            x = x.transpose(1, 2)
+        x = self.activation(self.norm(x))
+        if self.use_layer_norm:
+            x = x.transpose(1, 2)
+        x = self.pointwise_conv2(x)
+        if mask_pad.size(2) > 0:
+            x.masked_fill_(~mask_pad, 0.0)
+        return x.transpose(1, 2), new_cache

paddlespeech/t2s/modules/transformer/embedding.py

@@ -14,7 +14,7 @@
 # Modified from espnet(https://github.com/espnet/espnet)
 """Positional Encoding Module."""
 import math
-
+from typing import Union
 import paddle
 from paddle import nn
 
@@ -131,6 +131,108 @@ class ScaledPositionalEncoding(PositionalEncoding):
         x = x + self.alpha * self.pe[:, :T]
         return self.dropout(x)
 
+class EspnetRelPositionalEncoding(paddle.nn.Layer):
+    """Relative positional encoding module (new implementation).
+
+    Details can be found in https://github.com/espnet/espnet/pull/2816.
+
+    See : Appendix B in https://arxiv.org/abs/1901.02860
+
+    Args:
+        d_model (int): Embedding dimension.
+        dropout_rate (float): Dropout rate.
+        max_len (int): Maximum input length.
+
+    """
+
+    def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000):
+        """Construct an PositionalEncoding object."""
+        super(EspnetRelPositionalEncoding, self).__init__()
+        self.d_model = d_model
+        self.xscale = math.sqrt(self.d_model)
+        self.dropout = paddle.nn.Dropout(p=dropout_rate)
+        self.pe = None
+        self.extend_pe(paddle.to_tensor([0.0]).expand([1, max_len]))
+
+    def extend_pe(self, x: paddle.Tensor):
+        """Reset the positional encodings."""
+        if self.pe is not None:
+            if self.pe.shape[1] >= x.shape[1] * 2 - 1:
+                if self.pe.dtype != x.dtype or self.pe.place != x.place:
+                    self.pe = self.pe.to(dtype=x.dtype, device=x.place)
+                return
+        pe_positive = paddle.zeros([x.shape[1], self.d_model])
+        pe_negative = paddle.zeros([x.shape[1], self.d_model])
+        position = paddle.arange(0, x.shape[1], dtype=paddle.float32).unsqueeze(1)
+        div_term = paddle.exp(
+            x=paddle.arange(0, self.d_model, 2, dtype=paddle.float32)
+            * -(math.log(10000.0) / self.d_model)
+        )
+        pe_positive[:, 0::2] = paddle.sin(position * div_term)
+        pe_positive[:, 1::2] = paddle.cos(position * div_term)
+        pe_negative[:, 0::2] = paddle.sin(-1 * position * div_term)
+        pe_negative[:, 1::2] = paddle.cos(-1 * position * div_term)
+        pe_positive = paddle.flip(x=pe_positive, axis=[0]).unsqueeze(0)
+        pe_negative = pe_negative[1:].unsqueeze(0)
+        pe = paddle.cat([pe_positive, pe_negative], dim=1)
+        self.pe = pe.to(device=x.place, dtype=x.dtype)
+
+    def forward(
+        self, x: paddle.Tensor, offset: Union[int, paddle.Tensor] = 0
+    ) -> tuple[paddle.Tensor, paddle.Tensor]:
+        """Add positional encoding.
+
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, `*`).
+
+        Returns:
+            torch.Tensor: Encoded tensor (batch, time, `*`).
+
+        """
+        self.extend_pe(x)
+        x = x * self.xscale
+        pos_emb = self.position_encoding(size=x.shape[1], offset=offset)
+        return self.dropout(x), self.dropout(pos_emb)
+
+    def position_encoding(
+        self, offset: Union[int, paddle.Tensor], size: int
+    ) -> paddle.Tensor:
+        """For getting encoding in a streaming fashion
+
+        Attention!!!!!
+        we apply dropout only once at the whole utterance level in a none
+        streaming way, but will call this function several times with
+        increasing input size in a streaming scenario, so the dropout will
+        be applied several times.
+
+        Args:
+            offset (int or torch.tensor): start offset
+            size (int): required size of position encoding
+
+        Returns:
+            torch.Tensor: Corresponding encoding
+        """
+        if isinstance(offset, int):
+            pos_emb = self.pe[
+                :,
+                self.pe.shape[1] // 2
+                - size
+                - offset
+                + 1 : self.pe.shape[1] // 2
+                + size
+                + offset,
+            ]
+        elif isinstance(offset, paddle.Tensor):
+            pos_emb = self.pe[
+                :,
+                self.pe.shape[1] // 2
+                - size
+                - offset
+                + 1 : self.pe.shape[1] // 2
+                + size
+                + offset,
+            ]
+        return pos_emb
 
 class RelPositionalEncoding(nn.Layer):
     """Relative positional encoding module (new implementation).

paddlespeech/t2s/modules/transformer/encoder_layer.py

@@ -15,7 +15,7 @@
 """Encoder self-attention layer definition."""
 import paddle
 from paddle import nn
-
+from typing import Optional
 
 class EncoderLayer(nn.Layer):
     """Encoder layer module.
@@ -111,3 +111,118 @@ class EncoderLayer(nn.Layer):
             x = paddle.concat([cache, x], axis=1)
 
         return x, mask
+        
+class ConformerEncoderLayer(paddle.nn.Layer):
+    """Encoder layer module.
+    Args:
+        size (int): Input dimension.
+        self_attn (torch.nn.Module): Self-attention module instance.
+            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention`
+            instance can be used as the argument.
+        feed_forward (torch.nn.Module): Feed-forward module instance.
+            `PositionwiseFeedForward` instance can be used as the argument.
+        feed_forward_macaron (torch.nn.Module): Additional feed-forward module
+             instance.
+            `PositionwiseFeedForward` instance can be used as the argument.
+        conv_module (torch.nn.Module): Convolution module instance.
+            `ConvlutionModule` instance can be used as the argument.
+        dropout_rate (float): Dropout rate.
+        normalize_before (bool):
+            True: use layer_norm before each sub-block.
+            False: use layer_norm after each sub-block.
+    """
+
+    def __init__(
+        self,
+        size: int,
+        self_attn: paddle.nn.Layer,
+        feed_forward: Optional[paddle.nn.Layer] = None,
+        feed_forward_macaron: Optional[paddle.nn.Layer] = None,
+        conv_module: Optional[paddle.nn.Layer] = None,
+        dropout_rate: float = 0.1,
+        normalize_before: bool = True,
+    ):
+        """Construct an EncoderLayer object."""
+        super().__init__()
+        self.self_attn = self_attn
+        self.feed_forward = feed_forward
+        self.feed_forward_macaron = feed_forward_macaron
+        self.conv_module = conv_module
+        self.norm_ff = paddle.nn.LayerNorm(normalized_shape=size, epsilon=1e-12)
+        self.norm_mha = paddle.nn.LayerNorm(normalized_shape=size, epsilon=1e-12)
+        if feed_forward_macaron is not None:
+            self.norm_ff_macaron = paddle.nn.LayerNorm(
+                normalized_shape=size, epsilon=1e-12
+            )
+            self.ff_scale = 0.5
+        else:
+            self.ff_scale = 1.0
+        if self.conv_module is not None:
+            self.norm_conv = paddle.nn.LayerNorm(normalized_shape=size, epsilon=1e-12)
+            self.norm_final = paddle.nn.LayerNorm(normalized_shape=size, epsilon=1e-12)
+        self.dropout = paddle.nn.Dropout(p=dropout_rate)
+        self.size = size
+        self.normalize_before = normalize_before
+
+    def forward(
+        self,
+        x: paddle.Tensor,
+        mask: paddle.Tensor,
+        pos_emb: paddle.Tensor,
+        mask_pad: paddle.Tensor = paddle.ones((0, 0, 0), dtype=paddle.bool),
+        att_cache: paddle.Tensor = paddle.zeros((0, 0, 0, 0)),
+        cnn_cache: paddle.Tensor = paddle.zeros((0, 0, 0, 0)),
+    ) -> tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor, paddle.Tensor]:
+        """Compute encoded features.
+
+        Args:
+            x (torch.Tensor): (#batch, time, size)
+            mask (torch.Tensor): Mask tensor for the input (#batch, time，time),
+                (0, 0, 0) means fake mask.
+            pos_emb (torch.Tensor): positional encoding, must not be None
+                for ConformerEncoderLayer.
+            mask_pad (torch.Tensor): batch padding mask used for conv module.
+                (#batch, 1，time), (0, 0, 0) means fake mask.
+            att_cache (torch.Tensor): Cache tensor of the KEY & VALUE
+                (#batch=1, head, cache_t1, d_k * 2), head * d_k == size.
+            cnn_cache (torch.Tensor): Convolution cache in conformer layer
+                (#batch=1, size, cache_t2)
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, size).
+            torch.Tensor: Mask tensor (#batch, time, time).
+            torch.Tensor: att_cache tensor,
+                (#batch=1, head, cache_t1 + time, d_k * 2).
+            torch.Tensor: cnn_cahce tensor (#batch, size, cache_t2).
+        """
+        if self.feed_forward_macaron is not None:
+            residual = x
+            if self.normalize_before:
+                x = self.norm_ff_macaron(x)
+            x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
+            if not self.normalize_before:
+                x = self.norm_ff_macaron(x)
+        residual = x
+        if self.normalize_before:
+            x = self.norm_mha(x)
+        x_att, new_att_cache = self.self_attn(x, x, x,  pos_emb, mask,att_cache)
+        x = residual + self.dropout(x_att)
+        if not self.normalize_before:
+            x = self.norm_mha(x)
+        new_cnn_cache = paddle.zeros([0, 0, 0], dtype=x.dtype)
+        if self.conv_module is not None:
+            residual = x
+            if self.normalize_before:
+                x = self.norm_conv(x)
+            x, new_cnn_cache = self.conv_module(x, mask_pad, cnn_cache)
+            x = residual + self.dropout(x)
+            if not self.normalize_before:
+                x = self.norm_conv(x)
+        residual = x
+        if self.normalize_before:
+            x = self.norm_ff(x)
+        x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
+        if not self.normalize_before:
+            x = self.norm_ff(x)
+        if self.conv_module is not None:
+            x = self.norm_final(x)
+        return x, mask, new_att_cache, new_cnn_cache

paddlespeech/t2s/modules/transformer/espnet.py

@@ -0,0 +1,275 @@
+import paddle
+
+"""Positonal Encoding Module."""
+import math
+from typing import Tuple, Union
+
+import numpy as np
+
+
+class PositionalEncoding(paddle.nn.Layer):
+    """Positional encoding.
+
+    :param int d_model: embedding dim
+    :param float dropout_rate: dropout rate
+    :param int max_len: maximum input length
+
+    PE(pos, 2i)   = sin(pos/(10000^(2i/dmodel)))
+    PE(pos, 2i+1) = cos(pos/(10000^(2i/dmodel)))
+    """
+
+    def __init__(
+        self,
+        d_model: int,
+        dropout_rate: float,
+        max_len: int = 5000,
+        reverse: bool = False,
+    ):
+        """Construct an PositionalEncoding object."""
+        super().__init__()
+        self.d_model = d_model
+        self.xscale = math.sqrt(self.d_model)
+        self.dropout = paddle.nn.Dropout(p=dropout_rate)
+        self.max_len = max_len
+        self.pe = paddle.zeros(self.max_len, self.d_model)
+        position = paddle.arange(0, self.max_len, dtype=paddle.float32).unsqueeze(1)
+        div_term = paddle.exp(
+            x=paddle.arange(0, self.d_model, 2, dtype=paddle.float32)
+            * -(math.log(10000.0) / self.d_model)
+        )
+        self.pe[:, 0::2] = paddle.sin(position * div_term)
+        self.pe[:, 1::2] = paddle.cos(position * div_term)
+        self.pe = self.pe.unsqueeze(0)
+
+    def forward(
+        self, x: paddle.Tensor, offset: Union[int, paddle.Tensor] = 0
+    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
+        """Add positional encoding.
+
+        Args:
+            x (torch.Tensor): Input. Its shape is (batch, time, ...)
+            offset (int, torch.tensor): position offset
+
+        Returns:
+            torch.Tensor: Encoded tensor. Its shape is (batch, time, ...)
+            torch.Tensor: for compatibility to RelPositionalEncoding
+        """
+        self.pe = self.pe.to(x.place)
+        pos_emb = self.position_encoding(offset, x.size(1), False)
+        x = x * self.xscale + pos_emb
+        return self.dropout(x), self.dropout(pos_emb)
+
+    def position_encoding(
+        self, offset: Union[int, paddle.Tensor], size: int, apply_dropout: bool = True
+    ) -> paddle.Tensor:
+        """For getting encoding in a streaming fashion
+
+        Attention!!!!!
+        we apply dropout only once at the whole utterance level in a none
+        streaming way, but will call this function several times with
+        increasing input size in a streaming scenario, so the dropout will
+        be applied several times.
+
+        Args:
+            offset (int or torch.tensor): start offset
+            size (int): required size of position encoding
+
+        Returns:
+            torch.Tensor: Corresponding encoding
+        """
+        if isinstance(offset, int):
+            assert offset + size <= self.max_len
+            pos_emb = self.pe[:, offset : offset + size]
+        elif isinstance(offset, paddle.Tensor) and offset.dim() == 0:
+            assert offset + size <= self.max_len
+            pos_emb = self.pe[:, offset : offset + size]
+        else:
+            assert paddle.compat.max(offset) + size <= self.max_len
+            index = offset.unsqueeze(1) + paddle.arange(0, size).to(offset.place)
+            flag = index > 0
+            index = index * flag
+            pos_emb = paddle.nn.functional.embedding(index, self.pe[0])
+        if apply_dropout:
+            pos_emb = self.dropout(pos_emb)
+        return pos_emb
+
+
+class RelPositionalEncoding(PositionalEncoding):
+    """Relative positional encoding module.
+    See : Appendix B in https://arxiv.org/abs/1901.02860
+    Args:
+        d_model (int): Embedding dimension.
+        dropout_rate (float): Dropout rate.
+        max_len (int): Maximum input length.
+    """
+
+    def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000):
+        """Initialize class."""
+        super().__init__(d_model, dropout_rate, max_len, reverse=True)
+
+    def forward(
+        self, x: paddle.Tensor, offset: Union[int, paddle.Tensor] = 0
+    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
+        """Compute positional encoding.
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, `*`).
+        Returns:
+            torch.Tensor: Encoded tensor (batch, time, `*`).
+            torch.Tensor: Positional embedding tensor (1, time, `*`).
+        """
+        self.pe = self.pe.to(x.place)
+        x = x * self.xscale
+        pos_emb = self.position_encoding(offset, x.size(1), False)
+        return self.dropout(x), self.dropout(pos_emb)
+
+
+class WhisperPositionalEncoding(PositionalEncoding):
+    """Sinusoids position encoding used in openai-whisper.encoder"""
+
+    def __init__(self, d_model: int, dropout_rate: float, max_len: int = 1500):
+        super().__init__(d_model, dropout_rate, max_len)
+        self.xscale = 1.0
+        log_timescale_increment = np.log(10000) / (d_model // 2 - 1)
+        inv_timescales = paddle.exp(
+            x=-log_timescale_increment * paddle.arange(d_model // 2)
+        )
+        scaled_time = (
+            paddle.arange(max_len)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+        )
+        pe = paddle.cat([paddle.sin(scaled_time), paddle.cos(scaled_time)], dim=1)
+        delattr(self, "pe")
+        self.register_buffer(name="pe", tensor=pe.unsqueeze(0))
+
+
+class LearnablePositionalEncoding(PositionalEncoding):
+    """Learnable position encoding used in openai-whisper.decoder"""
+
+    def __init__(self, d_model: int, dropout_rate: float, max_len: int = 448):
+        super().__init__(d_model, dropout_rate, max_len)
+        self.pe = paddle.nn.parameter.Parameter(paddle.empty(1, max_len, d_model))
+        self.xscale = 1.0
+
+
+class NoPositionalEncoding(paddle.nn.Layer):
+    """No position encoding"""
+
+    def __init__(self, d_model: int, dropout_rate: float):
+        super().__init__()
+        self.d_model = d_model
+        self.dropout = paddle.nn.Dropout(p=dropout_rate)
+
+    def forward(
+        self, x: paddle.Tensor, offset: Union[int, paddle.Tensor] = 0
+    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
+        """Just return zero vector for interface compatibility"""
+        pos_emb = paddle.zeros(1, x.size(1), self.d_model).to(x.place)
+        return self.dropout(x), pos_emb
+
+    def position_encoding(
+        self, offset: Union[int, paddle.Tensor], size: int
+    ) -> paddle.Tensor:
+        return paddle.zeros(1, size, self.d_model)
+
+
+class EspnetRelPositionalEncoding(paddle.nn.Layer):
+    """Relative positional encoding module (new implementation).
+
+    Details can be found in https://github.com/espnet/espnet/pull/2816.
+
+    See : Appendix B in https://arxiv.org/abs/1901.02860
+
+    Args:
+        d_model (int): Embedding dimension.
+        dropout_rate (float): Dropout rate.
+        max_len (int): Maximum input length.
+
+    """
+
+    def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000):
+        """Construct an PositionalEncoding object."""
+        super(EspnetRelPositionalEncoding, self).__init__()
+        self.d_model = d_model
+        self.xscale = math.sqrt(self.d_model)
+        self.dropout = paddle.nn.Dropout(p=dropout_rate)
+        self.pe = None
+        self.extend_pe(paddle.tensor(0.0).expand(1, max_len))
+
+    def extend_pe(self, x: paddle.Tensor):
+        """Reset the positional encodings."""
+        if self.pe is not None:
+            if self.pe.size(1) >= x.size(1) * 2 - 1:
+                if self.pe.dtype != x.dtype or self.pe.place != x.place:
+                    self.pe = self.pe.to(dtype=x.dtype, device=x.place)
+                return
+        pe_positive = paddle.zeros(x.size(1), self.d_model)
+        pe_negative = paddle.zeros(x.size(1), self.d_model)
+        position = paddle.arange(0, x.size(1), dtype=paddle.float32).unsqueeze(1)
+        div_term = paddle.exp(
+            x=paddle.arange(0, self.d_model, 2, dtype=paddle.float32)
+            * -(math.log(10000.0) / self.d_model)
+        )
+        pe_positive[:, 0::2] = paddle.sin(position * div_term)
+        pe_positive[:, 1::2] = paddle.cos(position * div_term)
+        pe_negative[:, 0::2] = paddle.sin(-1 * position * div_term)
+        pe_negative[:, 1::2] = paddle.cos(-1 * position * div_term)
+        pe_positive = paddle.flip(x=pe_positive, axis=[0]).unsqueeze(0)
+        pe_negative = pe_negative[1:].unsqueeze(0)
+        pe = paddle.cat([pe_positive, pe_negative], dim=1)
+        self.pe = pe.to(device=x.place, dtype=x.dtype)
+
+    def forward(
+        self, x: paddle.Tensor, offset: Union[int, paddle.Tensor] = 0
+    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
+        """Add positional encoding.
+
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, `*`).
+
+        Returns:
+            torch.Tensor: Encoded tensor (batch, time, `*`).
+
+        """
+        self.extend_pe(x)
+        x = x * self.xscale
+        pos_emb = self.position_encoding(size=x.size(1), offset=offset)
+        return self.dropout(x), self.dropout(pos_emb)
+
+    def position_encoding(
+        self, offset: Union[int, paddle.Tensor], size: int
+    ) -> paddle.Tensor:
+        """For getting encoding in a streaming fashion
+
+        Attention!!!!!
+        we apply dropout only once at the whole utterance level in a none
+        streaming way, but will call this function several times with
+        increasing input size in a streaming scenario, so the dropout will
+        be applied several times.
+
+        Args:
+            offset (int or torch.tensor): start offset
+            size (int): required size of position encoding
+
+        Returns:
+            torch.Tensor: Corresponding encoding
+        """
+        if isinstance(offset, int):
+            pos_emb = self.pe[
+                :,
+                self.pe.size(1) // 2
+                - size
+                - offset
+                + 1 : self.pe.size(1) // 2
+                + size
+                + offset,
+            ]
+        elif isinstance(offset, paddle.Tensor):
+            pos_emb = self.pe[
+                :,
+                self.pe.size(1) // 2
+                - size
+                - offset
+                + 1 : self.pe.size(1) // 2
+                + size
+                + offset,
+            ]
+        return pos_emb

paddlespeech/t2s/modules/transformer/mask.py

@@ -50,3 +50,106 @@ def target_mask(ys_in_pad, ignore_id, dtype=paddle.bool):
     ys_mask = ys_in_pad != ignore_id
     m = subsequent_mask(ys_mask.shape[-1]).unsqueeze(0)
     return ys_mask.unsqueeze(-2) & m
+
+def make_pad_mask(lengths: paddle.Tensor, max_len: int = 0) -> paddle.Tensor:
+    """Make mask tensor containing indices of padded part.
+
+    See description of make_non_pad_mask.
+
+    Args:
+        lengths (torch.Tensor): Batch of lengths (B,).
+    Returns:
+        torch.Tensor: Mask tensor containing indices of padded part.
+
+    Examples:
+        >>> lengths = [5, 3, 2]
+        >>> make_pad_mask(lengths)
+        masks = [[0, 0, 0, 0 ,0],
+                 [0, 0, 0, 1, 1],
+                 [0, 0, 1, 1, 1]]
+    """
+    batch_size = lengths.shape[0]
+    max_len = max_len if max_len > 0 else lengths._max().item()
+    seq_range = paddle.arange(0, max_len, dtype=paddle.int32)
+    seq_range_expand = seq_range.unsqueeze(0).expand([batch_size, max_len])
+    seq_length_expand = lengths.unsqueeze(-1)
+    mask = seq_range_expand >= seq_length_expand
+    return mask
+
+def add_optional_chunk_mask(
+    xs: paddle.Tensor,
+    masks: paddle.Tensor,
+    use_dynamic_chunk: bool,
+    use_dynamic_left_chunk: bool,
+    decoding_chunk_size: int,
+    static_chunk_size: int,
+    num_decoding_left_chunks: int,
+    enable_full_context: bool = True,
+):
+    """Apply optional mask for encoder.
+
+    Args:
+        xs (torch.Tensor): padded input, (B, L, D), L for max length
+        mask (torch.Tensor): mask for xs, (B, 1, L)
+        use_dynamic_chunk (bool): whether to use dynamic chunk or not
+        use_dynamic_left_chunk (bool): whether to use dynamic left chunk for
+            training.
+        decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's
+            0: default for training, use random dynamic chunk.
+            <0: for decoding, use full chunk.
+            >0: for decoding, use fixed chunk size as set.
+        static_chunk_size (int): chunk size for static chunk training/decoding
+            if it's greater than 0, if use_dynamic_chunk is true,
+            this parameter will be ignored
+        num_decoding_left_chunks: number of left chunks, this is for decoding,
+            the chunk size is decoding_chunk_size.
+            >=0: use num_decoding_left_chunks
+            <0: use all left chunks
+        enable_full_context (bool):
+            True: chunk size is either [1, 25] or full context(max_len)
+            False: chunk size ~ U[1, 25]
+
+    Returns:
+        torch.Tensor: chunk mask of the input xs.
+    """
+    if use_dynamic_chunk:
+        max_len = xs.size(1)
+        if decoding_chunk_size < 0:
+            chunk_size = max_len
+            num_left_chunks = -1
+        elif decoding_chunk_size > 0:
+            chunk_size = decoding_chunk_size
+            num_left_chunks = num_decoding_left_chunks
+        else:
+            chunk_size = paddle.randint(low=1, high=max_len, shape=(1,)).item()
+            num_left_chunks = -1
+            if chunk_size > max_len // 2 and enable_full_context:
+                chunk_size = max_len
+            else:
+                chunk_size = chunk_size % 25 + 1
+                if use_dynamic_left_chunk:
+                    max_left_chunks = (max_len - 1) // chunk_size
+                    num_left_chunks = paddle.randint(
+                        low=0, high=max_left_chunks, shape=(1,)
+                    ).item()
+        chunk_masks = subsequent_chunk_mask(
+            xs.size(1), chunk_size, num_left_chunks, xs.place
+        )
+        chunk_masks = chunk_masks.unsqueeze(0)
+        chunk_masks = masks & chunk_masks
+    elif static_chunk_size > 0:
+        num_left_chunks = num_decoding_left_chunks
+        chunk_masks = subsequent_chunk_mask(
+            xs.size(1), static_chunk_size, num_left_chunks, xs.place
+        )
+        chunk_masks = chunk_masks.unsqueeze(0)
+        chunk_masks = masks & chunk_masks
+    else:
+        chunk_masks = masks
+    assert chunk_masks.dtype == paddle.bool
+    if (chunk_masks.sum(axis=-1) == 0).sum().item() != 0:
+        print(
+            "get chunk_masks all false at some timestep, force set to true, make sure they are masked in futuer computation!"
+        )
+        chunk_masks[chunk_masks.sum(axis=-1) == 0] = True
+    return chunk_masks

paddlespeech/t2s/modules/transformer/subsampling.py

@@ -15,10 +15,66 @@
 """Subsampling layer definition."""
 import paddle
 from paddle import nn
-
+from typing import Union
 from paddlespeech.t2s.modules.transformer.embedding import PositionalEncoding
 
+class BaseSubsampling(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.right_context = 0
+        self.subsampling_rate = 1
+
+    def position_encoding(
+        self, offset: Union[int, paddle.Tensor], size: int
+    ) -> paddle.Tensor:
+        return self.pos_enc.position_encoding(offset, size)
+class LinearNoSubsampling(BaseSubsampling):
+    """Linear transform the input without subsampling
+
+    Args:
+        idim (int): Input dimension.
+        odim (int): Output dimension.
+        dropout_rate (float): Dropout rate.
+
+    """
+
+    def __init__(
+        self, idim: int, odim: int, dropout_rate: float, pos_enc_class: paddle.nn.Layer
+    ):
+        """Construct an linear object."""
+        super().__init__()
+        self.out = paddle.nn.Sequential(
+            paddle.nn.Linear(in_features=idim, out_features=odim),
+            paddle.nn.LayerNorm(normalized_shape=odim, epsilon=1e-05),
+            paddle.nn.Dropout(p=dropout_rate),
+        )
+        self.pos_enc = pos_enc_class
+        self.right_context = 0
+        self.subsampling_rate = 1
+
+    def forward(
+        self,
+        x: paddle.Tensor,
+        x_mask: paddle.Tensor,
+        offset: Union[int, paddle.Tensor] = 0,
+    ) -> tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
+        """Input x.
+
+        Args:
+            x (paddle.Tensor): Input tensor (#batch, time, idim).
+            x_mask (torch.Tensor): Input mask (#batch, 1, time).
+
+        Returns:
+            paddle.Tensor: linear input tensor (#batch, time', odim),
+                where time' = time .
+            paddle.Tensor: linear input mask (#batch, 1, time'),
+                where time' = time .
+
+        """
 
+        x = self.out(x)
+        x, pos_emb = self.pos_enc(x, offset)
+        return x, pos_emb, x_mask
 class Conv2dSubsampling(nn.Layer):
     """Convolutional 2D subsampling (to 1/4 length).
 

paddlespeech/t2s/modules/transformer/upsample_encoder.py

@@ -0,0 +1,343 @@
+import paddle
+
+"""Encoder definition."""
+from typing import Tuple
+import paddle.nn.functional as F
+from paddlespeech.t2s.modules.transformer.convolution import ConvolutionModule
+from paddlespeech.t2s.modules.transformer.encoder_layer import ConformerEncoderLayer
+from paddlespeech.t2s.modules.transformer.positionwise_feed_forward import PositionwiseFeedForward
+from paddlespeech.t2s.models.CosyVoice.class_utils import (COSYVOICE_ACTIVATION_CLASSES,
+                                         COSYVOICE_ATTENTION_CLASSES,
+                                         COSYVOICE_EMB_CLASSES,
+                                         COSYVOICE_SUBSAMPLE_CLASSES)
+from paddlespeech.t2s.modules.transformer.mask import add_optional_chunk_mask, make_pad_mask
+
+
+class Upsample1D(paddle.nn.Layer):
+    """A 1D upsampling layer with an optional convolution.
+
+    Parameters:
+        channels (`int`):
+            number of channels in the inputs and outputs.
+        use_conv (`bool`, default `False`):
+            option to use a convolution.
+        use_conv_transpose (`bool`, default `False`):
+            option to use a convolution transpose.
+        out_channels (`int`, optional):
+            number of output channels. Defaults to `channels`.
+    """
+
+    def __init__(self, channels: int, out_channels: int, stride: int = 2):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels
+        self.stride = stride
+        self.conv = paddle.nn.Conv1D(
+            self.channels, self.out_channels, stride * 2 + 1, stride=1, padding=0
+        )
+
+    def forward(
+        self, inputs: paddle.Tensor, input_lengths: paddle.Tensor
+    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
+        inputs = inputs.unsqueeze(2)
+        outputs = paddle.nn.functional.interpolate(
+            x=inputs, scale_factor=[1, float(self.stride)],mode="nearest"
+        )
+        outputs = outputs.squeeze(2)
+        outputs = F.pad(outputs, [self.stride * 2, 0], value=0.0)
+        outputs = self.conv(outputs)
+        return outputs, input_lengths * self.stride
+
+
+class PreLookaheadLayer(paddle.nn.Layer):
+    def __init__(self, channels: int, pre_lookahead_len: int = 1):
+        super().__init__()
+        self.channels = channels
+        self.pre_lookahead_len = pre_lookahead_len
+        self.conv1 = paddle.nn.Conv1D(
+            channels, channels, kernel_size=pre_lookahead_len + 1, stride=1, padding=0
+        )
+        self.conv2 = paddle.nn.Conv1D(
+            channels, channels, kernel_size=3, stride=1, padding=0
+        )
+
+    def forward(
+        self, inputs: paddle.Tensor, context: paddle.Tensor = paddle.zeros([0, 0, 0])
+    ) -> paddle.Tensor:
+        """
+        inputs: (batch_size, seq_len, channels)
+        """
+        outputs =  paddle.transpose(inputs, perm=[0, 2, 1]).contiguous()
+        context = paddle.transpose(context, perm=[0, 2, 1]).contiguous()
+        if context.shape[2] == 0:
+            outputs = F.pad(
+                outputs, [0, self.pre_lookahead_len], mode="constant", value=0.0
+            )
+        else:
+            assert (
+                self.training is False
+            ), "you have passed context, make sure that you are running inference mode"
+            assert context.shape[2] == self.pre_lookahead_len
+            outputs = F.pad(
+                paddle.cat([outputs, context], dim=2),
+                [0, self.pre_lookahead_len - context.shape[2]],
+                mode="constant",
+                value=0.0,
+            )
+        outputs = paddle.nn.functional.leaky_relu(x=self.conv1(outputs))
+        outputs = F.pad(
+            outputs, [self.conv2._kernel_size[0] - 1, 0], mode="constant", value=0.0
+        )
+        outputs = self.conv2(outputs)
+        outputs = paddle.transpose(outputs, perm=[0, 2, 1]).contiguous()
+
+        outputs = outputs + inputs
+        return outputs
+
+
+class UpsampleConformerEncoder(paddle.nn.Layer):
+    def __init__(
+        self,
+        input_size: int,
+        output_size: int = 256,
+        attention_heads: int = 4,
+        linear_units: int = 2048,
+        num_blocks: int = 6,
+        dropout_rate: float = 0.1,
+        positional_dropout_rate: float = 0.1,
+        attention_dropout_rate: float = 0.0,
+        input_layer: str = "conv2d",
+        pos_enc_layer_type: str = "rel_pos",
+        normalize_before: bool = True,
+        static_chunk_size: int = 0,
+        use_dynamic_chunk: bool = False,
+        global_cmvn: paddle.nn.Layer = None,
+        use_dynamic_left_chunk: bool = False,
+        positionwise_conv_kernel_size: int = 1,
+        macaron_style: bool = True,
+        selfattention_layer_type: str = "rel_selfattn",
+        activation_type: str = "swish",
+        use_cnn_module: bool = True,
+        cnn_module_kernel: int = 15,
+        causal: bool = False,
+        cnn_module_norm: str = "batch_norm",
+        key_bias: bool = True,
+        gradient_checkpointing: bool = False,
+    ):
+        """
+        Args:
+            input_size (int): input dim
+            output_size (int): dimension of attention
+            attention_heads (int): the number of heads of multi head attention
+            linear_units (int): the hidden units number of position-wise feed
+                forward
+            num_blocks (int): the number of decoder blocks
+            dropout_rate (float): dropout rate
+            attention_dropout_rate (float): dropout rate in attention
+            positional_dropout_rate (float): dropout rate after adding
+                positional encoding
+            input_layer (str): input layer type.
+                optional [linear, conv2d, conv2d6, conv2d8]
+            pos_enc_layer_type (str): Encoder positional encoding layer type.
+                opitonal [abs_pos, scaled_abs_pos, rel_pos, no_pos]
+            normalize_before (bool):
+                True: use layer_norm before each sub-block of a layer.
+                False: use layer_norm after each sub-block of a layer.
+            static_chunk_size (int): chunk size for static chunk training and
+                decoding
+            use_dynamic_chunk (bool): whether use dynamic chunk size for
+                training or not, You can only use fixed chunk(chunk_size > 0)
+                or dyanmic chunk size(use_dynamic_chunk = True)
+            global_cmvn (Optional[torch.nn.Module]): Optional GlobalCMVN module
+            use_dynamic_left_chunk (bool): whether use dynamic left chunk in
+                dynamic chunk training
+            key_bias: whether use bias in attention.linear_k, False for whisper models.
+            gradient_checkpointing: rerunning a forward-pass segment for each
+                checkpointed segment during backward.
+        """
+        super().__init__()
+        self._output_size = output_size
+        self.global_cmvn = global_cmvn
+        self.embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer](
+            input_size,
+            output_size,
+            dropout_rate,
+            COSYVOICE_EMB_CLASSES[pos_enc_layer_type](
+                output_size, positional_dropout_rate
+            ),
+        )
+        self.normalize_before = normalize_before
+        self.after_norm = paddle.nn.LayerNorm(
+            normalized_shape=output_size, epsilon=1e-05
+        )
+        self.static_chunk_size = static_chunk_size
+        self.use_dynamic_chunk = use_dynamic_chunk
+        self.use_dynamic_left_chunk = use_dynamic_left_chunk
+        self.gradient_checkpointing = gradient_checkpointing
+        activation = COSYVOICE_ACTIVATION_CLASSES[activation_type]()
+        encoder_selfattn_layer_args = (
+            attention_heads,
+            output_size,
+            attention_dropout_rate,
+            False,
+        )
+        positionwise_layer_args = (output_size, linear_units, dropout_rate, activation)
+        convolution_layer_args = (
+            output_size,
+            cnn_module_kernel,
+            activation,
+            cnn_module_norm,
+            causal,
+        )
+        self.pre_lookahead_layer = PreLookaheadLayer(channels=512, pre_lookahead_len=3)
+        self.encoders = paddle.nn.LayerList(
+            sublayers=[
+                ConformerEncoderLayer(
+                    output_size,
+                    COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type](
+                        *encoder_selfattn_layer_args
+                    ),
+                    PositionwiseFeedForward(*positionwise_layer_args),
+                    PositionwiseFeedForward(*positionwise_layer_args)
+                    if macaron_style
+                    else None,
+                    ConvolutionModule(*convolution_layer_args)
+                    if use_cnn_module
+                    else None,
+                    dropout_rate,
+                    normalize_before,
+                )
+                for _ in range(num_blocks)
+            ]
+        )
+        self.up_layer = Upsample1D(channels=512, out_channels=512, stride=2)
+        self.up_embed = COSYVOICE_SUBSAMPLE_CLASSES[input_layer](
+            input_size,
+            output_size,
+            dropout_rate,
+            COSYVOICE_EMB_CLASSES[pos_enc_layer_type](
+                output_size, positional_dropout_rate
+            ),
+        )
+        self.up_encoders = paddle.nn.LayerList(
+            sublayers=[
+                ConformerEncoderLayer(
+                    output_size,
+                    COSYVOICE_ATTENTION_CLASSES[selfattention_layer_type](
+                        *encoder_selfattn_layer_args
+                    ),
+                    PositionwiseFeedForward(*positionwise_layer_args),
+                    PositionwiseFeedForward(*positionwise_layer_args)
+                    if macaron_style
+                    else None,
+                    ConvolutionModule(*convolution_layer_args)
+                    if use_cnn_module
+                    else None,
+                    dropout_rate,
+                    normalize_before,
+                )
+                for _ in range(4)
+            ]
+        )
+
+    def output_size(self) -> int:
+        return self._output_size
+
+    def forward(
+        self,
+        xs: paddle.Tensor,
+        xs_lens: paddle.Tensor,
+        context: paddle.Tensor = paddle.zeros([0, 0, 0]),
+        decoding_chunk_size: int = 0,
+        num_decoding_left_chunks: int = -1,
+        streaming: bool = False,
+    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
+        """Embed positions in tensor.
+
+        Args:
+            xs: padded input tensor (B, T, D)
+            xs_lens: input length (B)
+            decoding_chunk_size: decoding chunk size for dynamic chunk
+                0: default for training, use random dynamic chunk.
+                <0: for decoding, use full chunk.
+                >0: for decoding, use fixed chunk size as set.
+            num_decoding_left_chunks: number of left chunks, this is for decoding,
+            the chunk size is decoding_chunk_size.
+                >=0: use num_decoding_left_chunks
+                <0: use all left chunks
+        Returns:
+            encoder output tensor xs, and subsampled masks
+            xs: padded output tensor (B, T' ~= T/subsample_rate, D)
+            masks: torch.Tensor batch padding mask after subsample
+                (B, 1, T' ~= T/subsample_rate)
+        NOTE(xcsong):
+            We pass the `__call__` method of the modules instead of `forward` to the
+            checkpointing API because `__call__` attaches all the hooks of the module.
+            https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2
+        """
+        T = xs.shape[1]
+        masks = ~make_pad_mask(xs_lens, T).unsqueeze(1)
+        if self.global_cmvn is not None:
+            xs = self.global_cmvn(xs)
+        xs, pos_emb, masks = self.embed(xs, masks)
+        if context.shape[1] != 0:
+            assert (
+                self.training is False
+            ), "you have passed context, make sure that you are running inference mode"
+            context_masks = paddle.ones(1, 1, context.shape[1]).to(masks)
+            context, _, _ = self.embed(context, context_masks, offset=xs.shape[1])
+        mask_pad = masks
+        chunk_masks = add_optional_chunk_mask(
+            xs,
+            masks,
+            False,
+            False,
+            0,
+            self.static_chunk_size if streaming is True else 0,
+            -1,
+        )
+        xs = self.pre_lookahead_layer(xs, context=context)
+        xs = self.forward_layers(xs, chunk_masks, pos_emb, mask_pad)
+        # 
+        xs = paddle.transpose(xs, perm=[0, 2, 1]).contiguous()
+        xs, xs_lens = self.up_layer(xs, xs_lens)
+        xs = paddle.transpose(xs, perm=[0, 2, 1]).contiguous()
+        T = xs.shape[1]
+        masks = ~make_pad_mask(xs_lens, T).unsqueeze(1)
+        xs, pos_emb, masks = self.up_embed(xs, masks)
+        mask_pad = masks
+        chunk_masks = add_optional_chunk_mask(
+            xs,
+            masks,
+            False,
+            False,
+            0,
+            self.static_chunk_size * self.up_layer.stride if streaming is True else 0,
+            -1,
+        )
+        xs = self.forward_up_layers(xs, chunk_masks, pos_emb, mask_pad)
+        if self.normalize_before:
+            xs = self.after_norm(xs)
+        return xs, masks
+
+    def forward_layers(
+        self,
+        xs: paddle.Tensor,
+        chunk_masks: paddle.Tensor,
+        pos_emb: paddle.Tensor,
+        mask_pad: paddle.Tensor,
+    ) -> paddle.Tensor:
+        for layer in self.encoders:
+            xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
+        return xs
+
+    def forward_up_layers(
+        self,
+        xs: paddle.Tensor,
+        chunk_masks: paddle.Tensor,
+        pos_emb: paddle.Tensor,
+        mask_pad: paddle.Tensor,
+    ) -> paddle.Tensor:
+        for layer in self.up_encoders:
+            xs, chunk_masks, _, _ = layer(xs, chunk_masks, pos_emb, mask_pad)
+        return xs