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| # Copyright 2024 The YourMT3 Authors. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Please see the details in the LICENSE file. | |
| """ op.py """ | |
| import math | |
| from packaging.version import parse as VersionParse | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from einops import rearrange | |
| from transformers.models.t5.modeling_t5 import T5LayerNorm as RMSNorm | |
| def get_layer_norm(dim: int, layer_norm_type: str = "layer_norm", layer_norm_eps: float = 1e-5): | |
| """Get layer normalization layer. | |
| Args: | |
| dim (int): Feature dimension | |
| layer_norm_type (str): "layer_norm" or "rms_norm" | |
| layer_norm_eps (float): Epsilon value for numerical stability | |
| Returns: | |
| nn.Module: Layer normalization layer | |
| """ | |
| if layer_norm_type == "rms_norm": | |
| # T5LayerNorm is equivalent to RMSNorm. https://arxiv.org/abs/1910.07467 | |
| return RMSNorm(hidden_size=dim, eps=layer_norm_eps) | |
| else: | |
| return nn.LayerNorm(normalized_shape=dim, eps=layer_norm_eps) | |
| def check_all_elements_equal(x: torch.Tensor) -> bool: | |
| return x.eq(x[0]).all().item() | |
| def minmax_normalize(x: torch.Tensor, eps: float = 0.008) -> torch.FloatTensor: | |
| """Min-max normalization: | |
| x_norm = (x - x_min) / (x_max - x_min + eps) | |
| Args: | |
| x (torch.Tensor): (B, T, F) | |
| Returns: | |
| torch.Tensor: (B, T, F) with output range of [0, 1] | |
| """ | |
| x_max = rearrange(x, "b t f -> b (t f)").max(1, keepdim=True)[0] | |
| x_min = rearrange(x, "b t f -> b (f t)").min(1, keepdim=True)[0] | |
| x_max = x_max[:, None, :] # (B,1,1) | |
| x_min = x_min[:, None, :] # (B,1,1) | |
| return (x - x_min) / (x_max - x_min + eps) | |
| def count_parameters(model): | |
| num_trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) | |
| num_params = sum(p.numel() for p in model.parameters()) | |
| return num_trainable_params, num_params | |
| def adjust_b_to_gcd(a, b, min_gcd=16): | |
| """ | |
| Adjust the value of b to ensure the GCD(a, b) is at least min_gcd with minimum change to b. | |
| Parameters: | |
| - a (int): A positive integer | |
| - b (int): A positive integer | |
| - min_gcd (int): The minimum desired GCD | |
| Returns: | |
| - int: The adjusted value of b | |
| """ | |
| current_gcd = math.gcd(a, b) | |
| # If current GCD is already greater than or equal to min_gcd, return b as it is. | |
| if current_gcd >= min_gcd: | |
| return b | |
| # If a is less than min_gcd, then it's impossible to get a GCD of at least min_gcd. | |
| if a < min_gcd: | |
| raise ValueError("a must be at least as large as min_gcd.") | |
| # Adjust b by trying increments and decrements, preferring the smallest absolute change. | |
| adjusted_b_up = b | |
| adjusted_b_down = b | |
| while True: | |
| adjusted_b_up += 1 | |
| adjusted_b_down -= 1 | |
| if math.gcd(a, adjusted_b_up) >= min_gcd: | |
| return adjusted_b_up | |
| elif math.gcd(a, adjusted_b_down) >= min_gcd: | |
| return adjusted_b_down | |
| def optional_compiler_disable(func): | |
| if VersionParse(torch.__version__) >= VersionParse("2.1"): | |
| # If the version is 2.1 or higher, apply the torch.compiler.disable decorator. | |
| return torch.compiler.disable(func) | |
| else: | |
| # If the version is below 2.1, return the original function. | |
| return func | |
| def optional_compiler_dynamic(func): | |
| return torch.compile(func, dynamic=True) | |