From f472b23f3a5add956c2176abbaf5df4ba3b99643 Mon Sep 17 00:00:00 2001 From: MaxFish <16432329+MaxMax2016@users.noreply.github.com> Date: Sat, 30 Sep 2023 10:18:24 +0800 Subject: [PATCH] revert RoPE --- grad/encoder.py | 155 +++++++++++++++++++++++------------------------- 1 file changed, 74 insertions(+), 81 deletions(-) diff --git a/grad/encoder.py b/grad/encoder.py index 3b9b539..6cd9347 100644 --- a/grad/encoder.py +++ b/grad/encoder.py @@ -1,10 +1,9 @@ import math import torch -from einops import rearrange from grad.base import BaseModule from grad.reversal import SpeakerClassifier -from grad.utils import sequence_mask +from grad.utils import sequence_mask, convert_pad_shape class LayerNorm(BaseModule): @@ -77,67 +76,8 @@ def calc_mean_std(self, x, mask=None): return mn, sd -class RotaryPositionalEmbeddings(BaseModule): - """ - ## RoPE module - https://github.com/labmlai/annotated_deep_learning_paper_implementations - - Rotary encoding transforms pairs of features by rotating in the 2D plane. - That is, it organizes the $d$ features as $\frac{d}{2}$ pairs. - Each pair can be considered a coordinate in a 2D plane, and the encoding will rotate it - by an angle depending on the position of the token. - """ - def __init__(self, d: int, base: int = 10_000): - r""" - * `d` is the number of features $d$ - * `base` is the constant used for calculating $\Theta$ - """ - super().__init__() - self.base = base - self.d = int(d) - self.cos_cached = None - self.sin_cached = None - - def _build_cache(self, x: torch.Tensor): - r""" - Cache $\cos$ and $\sin$ values - """ - # Return if cache is already built - if self.cos_cached is not None and x.shape[0] <= self.cos_cached.shape[0]: - return - # Get sequence length - seq_len = x.shape[0] - theta = 1.0 / (self.base ** (torch.arange(0, self.d, 2).float() / self.d)).to(x.device) - # Create position indexes `[0, 1, ..., seq_len - 1]` - seq_idx = torch.arange(seq_len, device=x.device).float().to(x.device) - # Calculate the product of position index and $\theta_i$ - idx_theta = torch.einsum("n,d->nd", seq_idx, theta) - # Concatenate so that for row $m$ we have - idx_theta2 = torch.cat([idx_theta, idx_theta], dim=1) - # Cache them - self.cos_cached = idx_theta2.cos()[:, None, None, :] - self.sin_cached = idx_theta2.sin()[:, None, None, :] - - def _neg_half(self, x: torch.Tensor): - d_2 = self.d // 2 - return torch.cat([-x[:, :, :, d_2:], x[:, :, :, :d_2]], dim=-1) - - def forward(self, x: torch.Tensor): - """ - * `x` is the Tensor at the head of a key or a query with shape `[seq_len, batch_size, n_heads, d]` - """ - x = rearrange(x, "b h t d -> t b h d") - self._build_cache(x) - # Split the features, we can choose to apply rotary embeddings only to a partial set of features. - x_rope, x_pass = x[..., : self.d], x[..., self.d :] - # Calculate - neg_half_x = self._neg_half(x_rope) - x_rope = (x_rope * self.cos_cached[: x.shape[0]]) + (neg_half_x * self.sin_cached[: x.shape[0]]) - return rearrange(torch.cat((x_rope, x_pass), dim=-1), "t b h d -> b h t d") - - class MultiHeadAttention(BaseModule): - def __init__(self, channels, out_channels, n_heads, + def __init__(self, channels, out_channels, n_heads, window_size=None, heads_share=True, p_dropout=0.0, proximal_bias=False, proximal_init=False): super(MultiHeadAttention, self).__init__() @@ -146,6 +86,7 @@ def __init__(self, channels, out_channels, n_heads, self.channels = channels self.out_channels = out_channels self.n_heads = n_heads + self.window_size = window_size self.heads_share = heads_share self.proximal_bias = proximal_bias self.p_dropout = p_dropout @@ -155,11 +96,13 @@ def __init__(self, channels, out_channels, n_heads, self.conv_q = torch.nn.Conv1d(channels, channels, 1) self.conv_k = torch.nn.Conv1d(channels, channels, 1) self.conv_v = torch.nn.Conv1d(channels, channels, 1) - - # from https://nn.labml.ai/transformers/rope/index.html - self.query_rotary_pe = RotaryPositionalEmbeddings(self.k_channels * 0.5) - self.key_rotary_pe = RotaryPositionalEmbeddings(self.k_channels * 0.5) - + if window_size is not None: + n_heads_rel = 1 if heads_share else n_heads + rel_stddev = self.k_channels**-0.5 + self.emb_rel_k = torch.nn.Parameter(torch.randn(n_heads_rel, + window_size * 2 + 1, self.k_channels) * rel_stddev) + self.emb_rel_v = torch.nn.Parameter(torch.randn(n_heads_rel, + window_size * 2 + 1, self.k_channels) * rel_stddev) self.conv_o = torch.nn.Conv1d(channels, out_channels, 1) self.drop = torch.nn.Dropout(p_dropout) @@ -169,12 +112,12 @@ def __init__(self, channels, out_channels, n_heads, self.conv_k.weight.data.copy_(self.conv_q.weight.data) self.conv_k.bias.data.copy_(self.conv_q.bias.data) torch.nn.init.xavier_uniform_(self.conv_v.weight) - + def forward(self, x, c, attn_mask=None): q = self.conv_q(x) k = self.conv_k(c) v = self.conv_v(c) - + x, self.attn = self.attention(q, k, v, mask=attn_mask) x = self.conv_o(x) @@ -182,15 +125,18 @@ def forward(self, x, c, attn_mask=None): def attention(self, query, key, value, mask=None): b, d, t_s, t_t = (*key.size(), query.size(2)) - query = rearrange(query, "b (h c) t-> b h t c", h=self.n_heads) - key = rearrange(key, "b (h c) t-> b h t c", h=self.n_heads) - value = rearrange(value, "b (h c) t-> b h t c", h=self.n_heads) - - query = self.query_rotary_pe(query) - key = self.key_rotary_pe(key) + query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) + key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) + value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels) - + if self.window_size is not None: + assert t_s == t_t, "Relative attention is only available for self-attention." + key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) + rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings) + rel_logits = self._relative_position_to_absolute_position(rel_logits) + scores_local = rel_logits / math.sqrt(self.k_channels) + scores = scores + scores_local if self.proximal_bias: assert t_s == t_t, "Proximal bias is only available for self-attention." scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, @@ -200,9 +146,52 @@ def attention(self, query, key, value, mask=None): p_attn = torch.nn.functional.softmax(scores, dim=-1) p_attn = self.drop(p_attn) output = torch.matmul(p_attn, value) + if self.window_size is not None: + relative_weights = self._absolute_position_to_relative_position(p_attn) + value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) + output = output + self._matmul_with_relative_values(relative_weights, + value_relative_embeddings) output = output.transpose(2, 3).contiguous().view(b, d, t_t) return output, p_attn + def _matmul_with_relative_values(self, x, y): + ret = torch.matmul(x, y.unsqueeze(0)) + return ret + + def _matmul_with_relative_keys(self, x, y): + ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) + return ret + + def _get_relative_embeddings(self, relative_embeddings, length): + pad_length = max(length - (self.window_size + 1), 0) + slice_start_position = max((self.window_size + 1) - length, 0) + slice_end_position = slice_start_position + 2 * length - 1 + if pad_length > 0: + padded_relative_embeddings = torch.nn.functional.pad( + relative_embeddings, convert_pad_shape([[0, 0], + [pad_length, pad_length], [0, 0]])) + else: + padded_relative_embeddings = relative_embeddings + used_relative_embeddings = padded_relative_embeddings[:, + slice_start_position:slice_end_position] + return used_relative_embeddings + + def _relative_position_to_absolute_position(self, x): + batch, heads, length, _ = x.size() + x = torch.nn.functional.pad(x, convert_pad_shape([[0,0],[0,0],[0,0],[0,1]])) + x_flat = x.view([batch, heads, length * 2 * length]) + x_flat = torch.nn.functional.pad(x_flat, convert_pad_shape([[0,0],[0,0],[0,length-1]])) + x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:] + return x_final + + def _absolute_position_to_relative_position(self, x): + batch, heads, length, _ = x.size() + x = torch.nn.functional.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]])) + x_flat = x.view([batch, heads, length**2 + length*(length - 1)]) + x_flat = torch.nn.functional.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]])) + x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:] + return x_final + def _attention_bias_proximal(self, length): r = torch.arange(length, dtype=torch.float32) diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) @@ -235,7 +224,7 @@ def forward(self, x, x_mask): class Encoder(BaseModule): def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, - kernel_size=1, p_dropout=0.0, **kwargs): + kernel_size=1, p_dropout=0.0, window_size=None, **kwargs): super(Encoder, self).__init__() self.hidden_channels = hidden_channels self.filter_channels = filter_channels @@ -243,6 +232,7 @@ def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, self.n_layers = n_layers self.kernel_size = kernel_size self.p_dropout = p_dropout + self.window_size = window_size self.drop = torch.nn.Dropout(p_dropout) self.attn_layers = torch.nn.ModuleList() @@ -250,8 +240,8 @@ def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, self.ffn_layers = torch.nn.ModuleList() self.norm_layers_2 = torch.nn.ModuleList() for _ in range(self.n_layers): - self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, - n_heads, p_dropout=p_dropout)) + self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, + n_heads, window_size=window_size, p_dropout=p_dropout)) self.norm_layers_1.append(LayerNorm(hidden_channels)) self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout)) @@ -278,7 +268,8 @@ def __init__(self, n_vecs, n_mels, n_embs, n_heads=2, n_layers=6, kernel_size=3, - p_dropout=0.1): + p_dropout=0.1, + window_size=4): super(TextEncoder, self).__init__() self.n_vecs = n_vecs self.n_mels = n_mels @@ -289,6 +280,7 @@ def __init__(self, n_vecs, n_mels, n_embs, self.n_layers = n_layers self.kernel_size = kernel_size self.p_dropout = p_dropout + self.window_size = window_size self.prenet = ConvReluNorm(n_vecs, n_channels, @@ -307,7 +299,8 @@ def __init__(self, n_vecs, n_mels, n_embs, n_heads, n_layers, kernel_size, - p_dropout) + p_dropout, + window_size=window_size) self.proj_m = torch.nn.Conv1d(n_channels + n_embs + n_embs, n_mels, 1)