187 lines
6.6 KiB
Python
187 lines
6.6 KiB
Python
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# Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
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# 2024 Alibaba Inc (authors: Xiang Lyu)
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# 2025 Alibaba Inc (authors: Xiang Lyu, Bofan Zhou)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Modified from ESPnet(https://github.com/espnet/espnet)
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"""Unility functions for Transformer."""
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import queue
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import random
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from typing import List
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import numpy as np
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import torch
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IGNORE_ID = -1
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def pad_list(xs: List[torch.Tensor], pad_value: int):
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"""Perform padding for the list of tensors.
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Args:
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xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].
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pad_value (float): Value for padding.
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Returns:
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Tensor: Padded tensor (B, Tmax, `*`).
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Examples:
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>>> x = [torch.ones(4), torch.ones(2), torch.ones(1)]
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>>> x
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[tensor([1., 1., 1., 1.]), tensor([1., 1.]), tensor([1.])]
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>>> pad_list(x, 0)
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tensor([[1., 1., 1., 1.],
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[1., 1., 0., 0.],
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[1., 0., 0., 0.]])
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"""
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max_len = max([len(item) for item in xs])
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batchs = len(xs)
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ndim = xs[0].ndim
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if ndim == 1:
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pad_res = torch.zeros(batchs,
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max_len,
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dtype=xs[0].dtype,
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device=xs[0].device)
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elif ndim == 2:
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pad_res = torch.zeros(batchs,
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max_len,
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xs[0].shape[1],
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dtype=xs[0].dtype,
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device=xs[0].device)
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elif ndim == 3:
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pad_res = torch.zeros(batchs,
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max_len,
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xs[0].shape[1],
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xs[0].shape[2],
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dtype=xs[0].dtype,
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device=xs[0].device)
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else:
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raise ValueError(f"Unsupported ndim: {ndim}")
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pad_res.fill_(pad_value)
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for i in range(batchs):
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pad_res[i, :len(xs[i])] = xs[i]
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return pad_res
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def th_accuracy(pad_outputs: torch.Tensor, pad_targets: torch.Tensor,
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ignore_label: int) -> torch.Tensor:
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"""Calculate accuracy.
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Args:
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pad_outputs (Tensor): Prediction tensors (B * Lmax, D).
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pad_targets (LongTensor): Target label tensors (B, Lmax).
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ignore_label (int): Ignore label id.
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Returns:
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torch.Tensor: Accuracy value (0.0 - 1.0).
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"""
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pad_pred = pad_outputs.view(pad_targets.size(0), pad_targets.size(1),
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pad_outputs.size(1)).argmax(2)
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mask = pad_targets != ignore_label
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numerator = torch.sum(
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pad_pred.masked_select(mask) == pad_targets.masked_select(mask))
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denominator = torch.sum(mask)
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return (numerator / denominator).detach()
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def get_padding(kernel_size, dilation=1):
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return int((kernel_size * dilation - dilation) / 2)
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def init_weights(m, mean=0.0, std=0.01):
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classname = m.__class__.__name__
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if classname.find("Conv") != -1:
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m.weight.data.normal_(mean, std)
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# Repetition Aware Sampling in VALL-E 2
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def ras_sampling(weighted_scores, decoded_tokens, sampling, top_p=0.8, top_k=25, win_size=10, tau_r=0.1):
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top_ids = nucleus_sampling(weighted_scores, top_p=top_p, top_k=top_k)
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rep_num = (torch.tensor(decoded_tokens[-win_size:]).to(weighted_scores.device) == top_ids).sum().item()
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if rep_num >= win_size * tau_r:
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top_ids = random_sampling(weighted_scores, decoded_tokens, sampling)
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return top_ids
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def nucleus_sampling(weighted_scores, top_p=0.8, top_k=25):
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prob, indices = [], []
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cum_prob = 0.0
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sorted_value, sorted_idx = weighted_scores.softmax(dim=0).sort(descending=True, stable=True)
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for i in range(len(sorted_idx)):
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# sampling both top-p and numbers.
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if cum_prob < top_p and len(prob) < top_k:
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cum_prob += sorted_value[i]
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prob.append(sorted_value[i])
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indices.append(sorted_idx[i])
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else:
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break
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prob = torch.tensor(prob).to(weighted_scores)
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indices = torch.tensor(indices, dtype=torch.long).to(weighted_scores.device)
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top_ids = indices[prob.multinomial(1, replacement=True)]
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return top_ids
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def random_sampling(weighted_scores, decoded_tokens, sampling):
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top_ids = weighted_scores.softmax(dim=0).multinomial(1, replacement=True)
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return top_ids
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def fade_in_out(fade_in_mel, fade_out_mel, window):
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device = fade_in_mel.device
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fade_in_mel, fade_out_mel = fade_in_mel.cpu(), fade_out_mel.cpu()
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mel_overlap_len = int(window.shape[0] / 2)
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if fade_in_mel.device == torch.device('cpu'):
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fade_in_mel = fade_in_mel.clone()
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fade_in_mel[..., :mel_overlap_len] = fade_in_mel[..., :mel_overlap_len] * window[:mel_overlap_len] + \
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fade_out_mel[..., -mel_overlap_len:] * window[mel_overlap_len:]
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return fade_in_mel.to(device)
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def set_all_random_seed(seed):
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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def mask_to_bias(mask: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
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assert mask.dtype == torch.bool
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assert dtype in [torch.float32, torch.bfloat16, torch.float16]
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mask = mask.to(dtype)
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# attention mask bias
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# NOTE(Mddct): torch.finfo jit issues
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# chunk_masks = (1.0 - chunk_masks) * torch.finfo(dtype).min
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mask = (1.0 - mask) * -1.0e+10
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return mask
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class TrtContextWrapper:
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def __init__(self, trt_engine, trt_concurrent=1, device='cuda:0'):
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self.trt_context_pool = queue.Queue(maxsize=trt_concurrent)
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self.trt_engine = trt_engine
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for _ in range(trt_concurrent):
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trt_context = trt_engine.create_execution_context()
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trt_stream = torch.cuda.stream(torch.cuda.Stream(device))
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assert trt_context is not None, 'failed to create trt context, maybe not enough CUDA memory, try reduce current trt concurrent {}'.format(trt_concurrent)
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self.trt_context_pool.put([trt_context, trt_stream])
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assert self.trt_context_pool.empty() is False, 'no avaialbe estimator context'
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def acquire_estimator(self):
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return self.trt_context_pool.get(), self.trt_engine
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def release_estimator(self, context, stream):
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self.trt_context_pool.put([context, stream])
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