diff --git a/api_new_autoregressive.py b/api_new_autoregressive.py index 7a9d5ce..6fc67fd 100644 --- a/api_new_autoregressive.py +++ b/api_new_autoregressive.py @@ -134,8 +134,8 @@ class TextToSpeech: self.tokenizer = VoiceBpeTokenizer() download_models() - self.autoregressive = AutoregressiveCodegen(512, 12).cpu().eval() - self.autoregressive.load_state_dict(torch.load('D:\\dlas\\experiments\\train_autoregressive_codegen\\models\\23000_codegen_ema.pth')) + self.autoregressive = AutoregressiveCodegen(1024, 16).cpu().eval() + self.autoregressive.load_state_dict(torch.load('X:\\dlas\\experiments\\train_autoregressive_codegen\\models\\11000_codegen_ema.pth')) self.clip = VoiceCLIP(dim_text=512, dim_speech=512, dim_latent=512, num_text_tokens=256, text_enc_depth=12, text_seq_len=350, text_heads=8, diff --git a/models/new_autoregressive.py b/models/new_autoregressive.py index a6d8dee..0011f55 100644 --- a/models/new_autoregressive.py +++ b/models/new_autoregressive.py @@ -1,11 +1,9 @@ -import functools - import torch import torch.nn as nn import torch.nn.functional as F from transformers import GPT2PreTrainedModel, GPT2Config +from models.xtransformers import TransformerWrapper, Encoder, Decoder from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions -from x_transformers import TransformerWrapper, Encoder, Decoder from models.arch_util import AttentionBlock @@ -87,8 +85,8 @@ class InferenceModel(GPT2PreTrainedModel): assert labels is None # Training not supported by this inference model. return_dict = return_dict if return_dict is not None else self.config.use_return_dict - hidden_states = self.transformer.decoder(input_ids, context=self.context, return_embeddings=True) - logits = self.transformer.decoder.transformer.to_logits(hidden_states) + hidden_states = self.transformer.decoder(input_ids, full_context=self.context, return_embeddings=True) + logits = self.transformer.decoder.to_logits(hidden_states) if not return_dict: return (logits, ) @@ -157,54 +155,22 @@ class ConditioningEncoder(nn.Module): return h.mean(dim=2) -class CheckpointedLayer(nn.Module): - """ - Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses - checkpoint for all other args. - """ - def __init__(self, wrap): - super().__init__() - self.wrap = wrap - - def forward(self, x, *args, **kwargs): - for k, v in kwargs.items(): - assert not (isinstance(v, torch.Tensor) and v.requires_grad) # This would screw up checkpointing. - partial = functools.partial(self.wrap, **kwargs) - return torch.utils.checkpoint.checkpoint(partial, x, *args) - - -class CheckpointedXTransformerWrapper(nn.Module): - """ - Wraps a TransformerWrapper and applies CheckpointedLayer to each layer. - """ - def __init__(self, checkpoint=True, **xtransformer_kwargs): - super().__init__() - self.transformer = TransformerWrapper(**xtransformer_kwargs) - - if not checkpoint: - return - for i in range(len(self.transformer.attn_layers.layers)): - n, b, r = self.transformer.attn_layers.layers[i] - self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r]) - - def forward(self, x, **kwargs): - return self.transformer(x, **kwargs) - - class AutoregressiveCodegen(nn.Module): - def __init__(self, model_dim, depth, num_text_tokens=256, num_mel_tokens=8194, max_text_tokens=4000, - max_mel_tokens=4000, dropout=.1): + def __init__(self, model_dim, depth, num_text_tokens=256, num_mel_tokens=8194, dropout=.1): super().__init__() + assert depth >= 8 # This is the minimum bound to support the context interleaving that happens later. self.START_TOKEN=8192 self.STOP_TOKEN=8193 - self.max_mel_tokens = max_mel_tokens - self.minicoder = ConditioningEncoder(80, model_dim, do_checkpointing=False) - self.encoder = CheckpointedXTransformerWrapper( + self.max_text_token_id = num_text_tokens + self.max_mel_token_id = num_mel_tokens + self.mel_embedding = ConditioningEncoder(80, model_dim, do_checkpointing=False) + self.encoder = TransformerWrapper( num_tokens=num_text_tokens, - max_seq_len=max_text_tokens, + use_pos_emb=False, + max_seq_len=-1, attn_layers = Encoder( - depth=depth//2, + depth=depth, heads=model_dim//64, dim=model_dim, attn_dropout=dropout, @@ -213,11 +179,14 @@ class AutoregressiveCodegen(nn.Module): ff_glu=True, ff_mult=1, rotary_pos_emb=True, - rel_pos_bias=True, + attn_rel_pos_bias=True, )) - self.decoder = CheckpointedXTransformerWrapper( + self.encoder.norm = nn.Identity() # This layer and the next are unused. + self.encoder.to_logits = nn.Identity() + self.decoder = TransformerWrapper( num_tokens=num_mel_tokens, - max_seq_len=max_mel_tokens, + use_pos_emb=False, + max_seq_len=-1, attn_layers=Decoder( depth=depth, heads=model_dim//64, @@ -228,18 +197,21 @@ class AutoregressiveCodegen(nn.Module): ff_glu=True, ff_mult=1, rotary_pos_emb=True, - rel_pos_bias=True, cross_attend=True, + attn_rel_pos_bias=True, )) def get_grad_norm_parameter_groups(self): return { 'encoder': list(self.encoder.parameters()), 'decoder': list(self.decoder.parameters()), - 'minicoder': list(self.minicoder.parameters()), + 'minicoder': list(self.mel_embedding.parameters()), } def forward(self, text_codes, conditioning_signal, mel_codes, wav_lengths, return_loss=True): + assert text_codes.max() < self.max_text_token_id and text_codes.min() >= 0, f'Invalid text code encountered: {text_codes.max()}, {text_codes.min()}' + assert mel_codes.max() < self.max_mel_token_id and mel_codes.min() >= 0, f'Invalid mel code encountered: {mel_codes.max()}, {mel_codes.min()}' + # Format mel_codes with a stop token on the end. mel_lengths = wav_lengths // 1024 + 1 for b in range(mel_codes.shape[0]): @@ -251,43 +223,51 @@ class AutoregressiveCodegen(nn.Module): conditioning_signal = conditioning_signal.unsqueeze(1) cond_embs = [] for i in range(conditioning_signal.shape[1]): - cond_embs.append(self.minicoder(conditioning_signal[:, i])) + cond_embs.append(self.mel_embedding(conditioning_signal[:, i])) cond_emb = torch.stack(cond_embs, dim=1).mean(dim=1, keepdim=True) - enc_text = self.encoder(text_codes, return_embeddings=True) - context = torch.cat([cond_emb, enc_text], dim=1) + _, enc_text = self.encoder(text_codes, return_hiddens=True) + # Interleave cond_emb into the first few contexts. + full_context = enc_text + full_context[1] = cond_emb + full_context[3] = cond_emb + full_context[6] = cond_emb # Execute the decoder dec_inputs = F.pad(mel_codes, (1,0), value=self.START_TOKEN)[:, :-1] - dec = self.decoder(dec_inputs, context=context) + dec = self.decoder(dec_inputs, full_context=full_context) if not return_loss: return dec loss_mel = F.cross_entropy(dec.permute(0,2,1), mel_codes) return loss_mel - def generate(self, conditioning_signal, text_codes, **hf_generate_kwargs): - if not hasattr(self, 'inference_model'): - self.inference_model = InferenceModel(self) - + def generate(self, conditioning_signal, text_codes, max_tokens=256, **hf_generate_kwargs): + inference_model = InferenceModel(self) + # Build the context if len(conditioning_signal.shape) != 4: conditioning_signal = conditioning_signal.unsqueeze(1) cond_embs = [] for i in range(conditioning_signal.shape[1]): - cond_embs.append(self.minicoder(conditioning_signal[:, i])) + cond_embs.append(self.mel_embedding(conditioning_signal[:, i])) cond_emb = torch.stack(cond_embs, dim=1).mean(dim=1, keepdim=True) - enc_text = self.encoder(text_codes, return_embeddings=True) - context = torch.cat([cond_emb, enc_text], dim=1) - self.inference_model.store_context(context) + _, enc_text = self.encoder(text_codes, return_hiddens=True) + # Interleave cond_emb into the first few contexts. + full_context = enc_text + full_context[1] = cond_emb + full_context[3] = cond_emb + full_context[6] = cond_emb + inference_model.store_context(full_context) - gen = self.inference_model.generate(bos_token_id=self.START_TOKEN, pad_token_id=self.STOP_TOKEN, eos_token_id=self.STOP_TOKEN, - max_length=250, output_attentions=False, return_dict_in_generate=True, + gen = inference_model.generate(bos_token_id=self.START_TOKEN, pad_token_id=self.STOP_TOKEN, eos_token_id=self.STOP_TOKEN, + max_length=max_tokens, output_attentions=False, return_dict_in_generate=True, **hf_generate_kwargs) return gen.sequences if __name__ == '__main__': - codegen = AutoregressiveCodegen(1024, 20) - codegen.generate(torch.randn((1,80,120)), torch.randint(0,256,(1,200))) + codegen = AutoregressiveCodegen(256, 10) + torch.save(codegen.state_dict(), 'sample.pth') + #codegen.generate(torch.randn((1,80,120)), torch.randint(0,256,(1,200))) codegen(torch.randint(0,256, (2,200)), torch.randn(2,80,120), torch.randint(0,8192, (2,350)), - torch.tensor([192,350])) \ No newline at end of file + torch.tensor([192,350])) diff --git a/models/xtransformers.py b/models/xtransformers.py new file mode 100644 index 0000000..f203cb2 --- /dev/null +++ b/models/xtransformers.py @@ -0,0 +1,1259 @@ +import functools +import math +import torch +from torch import nn, einsum +import torch.nn.functional as F +from functools import partial +from inspect import isfunction +from collections import namedtuple + +from einops import rearrange, repeat, reduce +from einops.layers.torch import Rearrange + +from entmax import entmax15 +from torch.utils.checkpoint import checkpoint + +from x_transformers.autoregressive_wrapper import AutoregressiveWrapper + +DEFAULT_DIM_HEAD = 64 + +Intermediates = namedtuple('Intermediates', [ + 'pre_softmax_attn', + 'post_softmax_attn' +]) + +LayerIntermediates = namedtuple('Intermediates', [ + 'hiddens', + 'attn_intermediates' +]) + + +# helpers + +def exists(val): + return val is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def cast_tuple(val, depth): + return val if isinstance(val, tuple) else (val,) * depth + + +class always(): + def __init__(self, val): + self.val = val + + def __call__(self, *args, **kwargs): + return self.val + + +class not_equals(): + def __init__(self, val): + self.val = val + + def __call__(self, x, *args, **kwargs): + return x != self.val + + +class equals(): + def __init__(self, val): + self.val = val + + def __call__(self, x, *args, **kwargs): + return x == self.val + + +def max_neg_value(tensor): + return -torch.finfo(tensor.dtype).max + + +def l2norm(t): + return F.normalize(t, p=2, dim=-1) + + +# init helpers + +def init_zero_(layer): + nn.init.constant_(layer.weight, 0.) + if exists(layer.bias): + nn.init.constant_(layer.bias, 0.) + + +# keyword argument helpers + +def pick_and_pop(keys, d): + values = list(map(lambda key: d.pop(key), keys)) + return dict(zip(keys, values)) + + +def group_dict_by_key(cond, d): + return_val = [dict(), dict()] + for key in d.keys(): + match = bool(cond(key)) + ind = int(not match) + return_val[ind][key] = d[key] + return (*return_val,) + + +def string_begins_with(prefix, str): + return str.startswith(prefix) + + +def group_by_key_prefix(prefix, d): + return group_dict_by_key(partial(string_begins_with, prefix), d) + + +def groupby_prefix_and_trim(prefix, d): + kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d) + kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items()))) + return kwargs_without_prefix, kwargs + + +# activations + +class ReluSquared(nn.Module): + def forward(self, x): + return F.relu(x) ** 2 + + +# positional embeddings + +class AbsolutePositionalEmbedding(nn.Module): + def __init__(self, dim, max_seq_len): + super().__init__() + self.scale = dim ** -0.5 + self.emb = nn.Embedding(max_seq_len, dim) + + def forward(self, x): + n = torch.arange(x.shape[1], device=x.device) + pos_emb = self.emb(n) + pos_emb = rearrange(pos_emb, 'n d -> () n d') + return pos_emb * self.scale + + +class FixedPositionalEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer('inv_freq', inv_freq) + + def forward(self, x, seq_dim=1, offset=0): + t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset + sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq) + emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1) + return rearrange(emb, 'n d -> () n d') + + +class RelativePositionBias(nn.Module): + def __init__(self, scale, causal=False, num_buckets=32, max_distance=128, heads=8): + super().__init__() + self.scale = scale + self.causal = causal + self.num_buckets = num_buckets + self.max_distance = max_distance + self.relative_attention_bias = nn.Embedding(num_buckets, heads) + + @staticmethod + def _relative_position_bucket(relative_position, causal=True, num_buckets=32, max_distance=128): + ret = 0 + n = -relative_position + if not causal: + num_buckets //= 2 + ret += (n < 0).long() * num_buckets + n = torch.abs(n) + else: + n = torch.max(n, torch.zeros_like(n)) + + max_exact = num_buckets // 2 + is_small = n < max_exact + + val_if_large = max_exact + ( + torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) + ).long() + val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1)) + + ret += torch.where(is_small, n, val_if_large) + return ret + + def forward(self, qk_dots): + i, j, device = *qk_dots.shape[-2:], qk_dots.device + q_pos = torch.arange(i, dtype=torch.long, device=device) + k_pos = torch.arange(j, dtype=torch.long, device=device) + rel_pos = k_pos[None, :] - q_pos[:, None] + rp_bucket = self._relative_position_bucket(rel_pos, causal=self.causal, num_buckets=self.num_buckets, + max_distance=self.max_distance) + values = self.relative_attention_bias(rp_bucket) + bias = rearrange(values, 'i j h -> () h i j') + return qk_dots + (bias * self.scale) + + +class AlibiPositionalBias(nn.Module): + def __init__(self, heads, **kwargs): + super().__init__() + self.heads = heads + slopes = torch.Tensor(self._get_slopes(heads)) + slopes = rearrange(slopes, 'h -> () h () ()') + self.register_buffer('slopes', slopes, persistent=False) + self.register_buffer('bias', None, persistent=False) + + @staticmethod + def _get_slopes(heads): + def get_slopes_power_of_2(n): + start = (2 ** (-2 ** -(math.log2(n) - 3))) + ratio = start + return [start * ratio ** i for i in range(n)] + + if math.log2(heads).is_integer(): + return get_slopes_power_of_2(heads) + + closest_power_of_2 = 2 ** math.floor(math.log2(heads)) + return get_slopes_power_of_2(closest_power_of_2) + get_slopes_power_of_2(2 * closest_power_of_2)[0::2][ + :heads - closest_power_of_2] + + def forward(self, qk_dots): + h, i, j, device = *qk_dots.shape[-3:], qk_dots.device + + if exists(self.bias) and self.bias.shape[-1] >= j: + return qk_dots + self.bias[..., :j] + + bias = torch.arange(j, device=device) + bias = rearrange(bias, 'j -> () () () j') + bias = bias * self.slopes + + num_heads_unalibied = h - bias.shape[1] + bias = F.pad(bias, (0, 0, 0, 0, 0, num_heads_unalibied)) + + self.register_buffer('bias', bias, persistent=False) + return qk_dots + self.bias + + +class LearnedAlibiPositionalBias(AlibiPositionalBias): + def __init__(self, heads, bidirectional=False): + super().__init__(heads) + los_slopes = torch.log(self.slopes) + self.learned_logslopes = nn.Parameter(los_slopes) + + self.bidirectional = bidirectional + if self.bidirectional: + self.learned_logslopes_future = nn.Parameter(los_slopes) + + def forward(self, qk_dots): + h, i, j, device = *qk_dots.shape[-3:], qk_dots.device + + def get_slopes(param): + return F.pad(param.exp(), (0, 0, 0, 0, 0, h - param.shape[1])) + + if exists(self.bias) and self.bias.shape[-1] >= j: + bias = self.bias[..., :i, :j] + else: + i_arange = torch.arange(i, device=device) + j_arange = torch.arange(j, device=device) + bias = rearrange(j_arange, 'j -> 1 1 1 j') - rearrange(i_arange, 'i -> 1 1 i 1') + self.register_buffer('bias', bias, persistent=False) + + if self.bidirectional: + past_slopes = get_slopes(self.learned_logslopes) + future_slopes = get_slopes(self.learned_logslopes_future) + bias = torch.tril(bias * past_slopes) + torch.triu(bias * future_slopes) + else: + slopes = get_slopes(self.learned_logslopes) + bias = bias * slopes + + return qk_dots + bias + + +class RotaryEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim)) + self.register_buffer('inv_freq', inv_freq) + + def forward(self, max_seq_len, device): + t = torch.arange(max_seq_len, device=device).type_as(self.inv_freq) + freqs = torch.einsum('i , j -> i j', t, self.inv_freq) + emb = torch.cat((freqs, freqs), dim=-1) + return rearrange(emb, 'n d -> () () n d') + + +def rotate_half(x): + x = rearrange(x, '... (j d) -> ... j d', j=2) + x1, x2 = x.unbind(dim=-2) + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(t, freqs): + seq_len = t.shape[-2] + freqs = freqs[:, :, -seq_len:] + return (t * freqs.cos()) + (rotate_half(t) * freqs.sin()) + + +# norms + +class Scale(nn.Module): + def __init__(self, value, fn): + super().__init__() + self.value = value + self.fn = fn + + def forward(self, x, **kwargs): + out = self.fn(x, **kwargs) + scale_fn = lambda t: t * self.value + + if not isinstance(out, tuple): + return scale_fn(out) + + return (scale_fn(out[0]), *out[1:]) + + +class Rezero(nn.Module): + def __init__(self, fn): + super().__init__() + self.fn = fn + self.g = nn.Parameter(torch.zeros(1)) + + def forward(self, x, **kwargs): + out = self.fn(x, **kwargs) + rezero_fn = lambda t: t * self.g + + if not isinstance(out, tuple): + return rezero_fn(out) + + return (rezero_fn(out[0]), *out[1:]) + + +class ScaleNorm(nn.Module): + def __init__(self, dim, eps=1e-5): + super().__init__() + self.scale = dim ** -0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(1)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g + + +class RMSNorm(nn.Module): + def __init__(self, dim, eps=1e-8): + super().__init__() + self.scale = dim ** -0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(dim)) + + def forward(self, x): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + return x / norm.clamp(min=self.eps) * self.g + + +class RMSScaleShiftNorm(nn.Module): + def __init__(self, dim, eps=1e-8): + super().__init__() + self.scale = dim ** -0.5 + self.eps = eps + self.g = nn.Parameter(torch.ones(dim)) + self.scale_shift_process = nn.Linear(dim * 2, dim * 2) + + def forward(self, x, norm_scale_shift_inp): + norm = torch.norm(x, dim=-1, keepdim=True) * self.scale + norm = x / norm.clamp(min=self.eps) * self.g + + ss_emb = self.scale_shift_process(norm_scale_shift_inp) + scale, shift = torch.chunk(ss_emb, 2, dim=1) + h = norm * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + return h + + +# residual and residual gates + +class Residual(nn.Module): + def __init__(self, dim, scale_residual=False): + super().__init__() + self.residual_scale = nn.Parameter(torch.ones(dim)) if scale_residual else None + + def forward(self, x, residual): + if exists(self.residual_scale): + residual = residual * self.residual_scale + + return x + residual + + +class GRUGating(nn.Module): + def __init__(self, dim, scale_residual=False): + super().__init__() + self.gru = nn.GRUCell(dim, dim) + self.residual_scale = nn.Parameter(torch.ones(dim)) if scale_residual else None + + def forward(self, x, residual): + if exists(self.residual_scale): + residual = residual * self.residual_scale + + gated_output = self.gru( + rearrange(x, 'b n d -> (b n) d'), + rearrange(residual, 'b n d -> (b n) d') + ) + + return gated_output.reshape_as(x) + + +# token shifting + +def shift(t, amount, mask=None): + if amount == 0: + return t + + if exists(mask): + t = t.masked_fill(~mask[..., None], 0.) + + return F.pad(t, (0, 0, amount, -amount), value=0.) + + +class ShiftTokens(nn.Module): + def __init__(self, shifts, fn): + super().__init__() + self.fn = fn + self.shifts = tuple(shifts) + + def forward(self, x, **kwargs): + mask = kwargs.get('mask', None) + shifts = self.shifts + segments = len(shifts) + feats_per_shift = x.shape[-1] // segments + splitted = x.split(feats_per_shift, dim=-1) + segments_to_shift, rest = splitted[:segments], splitted[segments:] + segments_to_shift = list(map(lambda args: shift(*args, mask=mask), zip(segments_to_shift, shifts))) + x = torch.cat((*segments_to_shift, *rest), dim=-1) + return self.fn(x, **kwargs) + + +# feedforward + +class GLU(nn.Module): + def __init__(self, dim_in, dim_out, activation): + super().__init__() + self.act = activation + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * self.act(gate) + + +class FeedForward(nn.Module): + def __init__( + self, + dim, + dim_out=None, + mult=4, + glu=False, + relu_squared=False, + post_act_ln=False, + dropout=0., + zero_init_output=False + ): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + activation = ReluSquared() if relu_squared else nn.GELU() + + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + activation + ) if not glu else GLU(dim, inner_dim, activation) + + self.net = nn.Sequential( + project_in, + nn.LayerNorm(inner_dim) if post_act_ln else nn.Identity(), + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) + + # init last linear layer to 0 + if zero_init_output: + init_zero_(self.net[-1]) + + def forward(self, x): + return self.net(x) + + +# attention. + +class Attention(nn.Module): + def __init__( + self, + dim, + dim_head=DEFAULT_DIM_HEAD, + heads=8, + causal=False, + talking_heads=False, + head_scale=False, + collab_heads=False, + collab_compression=.3, + sparse_topk=None, + use_entmax15=False, + num_mem_kv=0, + dropout=0., + on_attn=False, + gate_values=False, + zero_init_output=False, + max_attend_past=None, + qk_norm=False, + scale_init_value=None, + rel_pos_bias=False, + rel_pos_num_buckets=32, + rel_pos_max_distance=128, + ): + super().__init__() + self.scale = dim_head ** -0.5 + + self.heads = heads + self.causal = causal + self.max_attend_past = max_attend_past + + qk_dim = v_dim = dim_head * heads + + # collaborative heads + self.collab_heads = collab_heads + if self.collab_heads: + qk_dim = int(collab_compression * qk_dim) + self.collab_mixing = nn.Parameter(torch.randn(heads, qk_dim)) + + self.to_q = nn.Linear(dim, qk_dim, bias=False) + self.to_k = nn.Linear(dim, qk_dim, bias=False) + self.to_v = nn.Linear(dim, v_dim, bias=False) + + self.dropout = nn.Dropout(dropout) + + # add GLU gating for aggregated values, from alphafold2 + self.to_v_gate = None + if gate_values: + self.to_v_gate = nn.Linear(dim, v_dim) + nn.init.constant_(self.to_v_gate.weight, 0) + nn.init.constant_(self.to_v_gate.bias, 1) + + # cosine sim attention + self.qk_norm = qk_norm + if qk_norm: + scale_init_value = default(scale_init_value, + -3) # if not provided, initialize as though it were sequence length of 1024 + self.scale = nn.Parameter(torch.ones(1, heads, 1, 1) * scale_init_value) + + # talking heads + self.talking_heads = talking_heads + if talking_heads: + self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads)) + self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads)) + + # head scaling + self.head_scale = head_scale + if head_scale: + self.head_scale_params = nn.Parameter(torch.ones(1, heads, 1, 1)) + + # explicit topk sparse attention + self.sparse_topk = sparse_topk + + # entmax + self.attn_fn = entmax15 if use_entmax15 else F.softmax + + # add memory key / values + self.num_mem_kv = num_mem_kv + if num_mem_kv > 0: + self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) + self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head)) + + # attention on attention + self.attn_on_attn = on_attn + self.to_out = nn.Sequential(nn.Linear(v_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(v_dim, dim) + + self.rel_pos_bias = rel_pos_bias + if rel_pos_bias: + assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance' + self.rel_pos = RelativePositionBias(scale=dim_head ** 0.5, causal=causal, heads=heads, + num_buckets=rel_pos_num_buckets, max_distance=rel_pos_max_distance) + + # init output projection 0 + if zero_init_output: + init_zero_(self.to_out) + + def forward( + self, + x, + context=None, + mask=None, + context_mask=None, + attn_mask=None, + sinusoidal_emb=None, + rotary_pos_emb=None, + prev_attn=None, + mem=None + ): + b, n, _, h, talking_heads, collab_heads, head_scale, scale, device, has_context = *x.shape, self.heads, self.talking_heads, self.collab_heads, self.head_scale, self.scale, x.device, exists( + context) + kv_input = default(context, x) + + q_input = x + k_input = kv_input + v_input = kv_input + + if exists(mem): + k_input = torch.cat((mem, k_input), dim=-2) + v_input = torch.cat((mem, v_input), dim=-2) + + if exists(sinusoidal_emb): + # in shortformer, the query would start at a position offset depending on the past cached memory + offset = k_input.shape[-2] - q_input.shape[-2] + q_input = q_input + sinusoidal_emb(q_input, offset=offset) + k_input = k_input + sinusoidal_emb(k_input) + + q = self.to_q(q_input) + k = self.to_k(k_input) + v = self.to_v(v_input) + + if not collab_heads: + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v)) + else: + q = einsum('b i d, h d -> b h i d', q, self.collab_mixing) + k = rearrange(k, 'b n d -> b () n d') + v = rearrange(v, 'b n (h d) -> b h n d', h=h) + + if exists(rotary_pos_emb) and not has_context: + l = rotary_pos_emb.shape[-1] + (ql, qr), (kl, kr), (vl, vr) = map(lambda t: (t[..., :l], t[..., l:]), (q, k, v)) + ql, kl, vl = map(lambda t: apply_rotary_pos_emb(t, rotary_pos_emb), (ql, kl, vl)) + q, k, v = map(lambda t: torch.cat(t, dim=-1), ((ql, qr), (kl, kr), (vl, vr))) + + input_mask = None + if any(map(exists, (mask, context_mask))): + q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool()) + k_mask = q_mask if not exists(context) else context_mask + k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool()) + q_mask = rearrange(q_mask, 'b i -> b () i ()') + k_mask = rearrange(k_mask, 'b j -> b () () j') + input_mask = q_mask * k_mask + + if self.num_mem_kv > 0: + mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v)) + k = torch.cat((mem_k, k), dim=-2) + v = torch.cat((mem_v, v), dim=-2) + if exists(input_mask): + input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True) + + if collab_heads: + k = k.expand(-1, h, -1, -1) + + if self.qk_norm: + q, k = map(l2norm, (q, k)) + scale = 1 / (self.scale.exp().clamp(min=1e-2)) + + dots = einsum('b h i d, b h j d -> b h i j', q, k) * scale + mask_value = max_neg_value(dots) + + if exists(prev_attn): + dots = dots + prev_attn + + pre_softmax_attn = dots.clone() + + if talking_heads: + dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous() + + if self.rel_pos_bias: + dots = self.rel_pos(dots) + + if exists(input_mask): + dots.masked_fill_(~input_mask, mask_value) + del input_mask + + if exists(attn_mask): + assert 2 <= attn_mask.ndim <= 4, 'attention mask must have greater than 2 dimensions but less than or equal to 4' + if attn_mask.ndim == 2: + attn_mask = rearrange(attn_mask, 'i j -> () () i j') + elif attn_mask.ndim == 3: + attn_mask = rearrange(attn_mask, 'h i j -> () h i j') + dots.masked_fill_(~attn_mask, mask_value) + + if exists(self.max_attend_past): + i, j = dots.shape[-2:] + range_q = torch.arange(j - i, j, device=device) + range_k = torch.arange(j, device=device) + dist = rearrange(range_q, 'i -> () () i ()') - rearrange(range_k, 'j -> () () () j') + mask = dist > self.max_attend_past + dots.masked_fill_(mask, mask_value) + del mask + + if self.causal: + i, j = dots.shape[-2:] + r = torch.arange(i, device=device) + mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j') + mask = F.pad(mask, (j - i, 0), value=False) + dots.masked_fill_(mask, mask_value) + del mask + + if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]: + top, _ = dots.topk(self.sparse_topk, dim=-1) + vk = top[..., -1].unsqueeze(-1).expand_as(dots) + mask = dots < vk + dots.masked_fill_(mask, mask_value) + del mask + + attn = self.attn_fn(dots, dim=-1) + post_softmax_attn = attn.clone() + + attn = self.dropout(attn) + + if talking_heads: + attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous() + + out = einsum('b h i j, b h j d -> b h i d', attn, v) + + if head_scale: + out = out * self.head_scale_params + + out = rearrange(out, 'b h n d -> b n (h d)') + + if exists(self.to_v_gate): + gates = self.to_v_gate(x) + out = out * gates.sigmoid() + + intermediates = Intermediates( + pre_softmax_attn=pre_softmax_attn, + post_softmax_attn=post_softmax_attn + ) + + return self.to_out(out), intermediates + + +class AttentionLayers(nn.Module): + def __init__( + self, + dim, + depth, + heads=8, + causal=False, + cross_attend=False, + only_cross=False, + use_scalenorm=False, + use_rms_scaleshift_norm=False, + use_rmsnorm=False, + use_rezero=False, + alibi_pos_bias=False, + alibi_num_heads=None, + alibi_learned=False, + position_infused_attn=False, + rotary_pos_emb=False, + rotary_emb_dim=None, + custom_layers=None, + sandwich_coef=None, + par_ratio=None, + residual_attn=False, + cross_residual_attn=False, + macaron=False, + pre_norm=True, + gate_residual=False, + scale_residual=False, + shift_tokens=0, + sandwich_norm=False, + use_qk_norm_attn=False, + qk_norm_attn_seq_len=None, + zero_init_branch_output=False, + **kwargs + ): + super().__init__() + ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs) + attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs) + + dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD) + + self.dim = dim + self.depth = depth + self.layers = nn.ModuleList([]) + + rel_pos_bias = 'rel_pos_bias' in attn_kwargs + self.has_pos_emb = position_infused_attn or rel_pos_bias or rotary_pos_emb + self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None + + rotary_emb_dim = max(default(rotary_emb_dim, dim_head // 2), 32) + self.rotary_pos_emb = RotaryEmbedding(rotary_emb_dim) if rotary_pos_emb else None + + assert not ( + alibi_pos_bias and rel_pos_bias), 'you can only choose Alibi positional bias or T5 relative positional bias, not both' + + if alibi_pos_bias: + alibi_num_heads = default(alibi_num_heads, heads) + assert alibi_num_heads <= heads, 'number of ALiBi heads must be less than the total number of heads' + alibi_pos_klass = LearnedAlibiPositionalBias if alibi_learned or not causal else AlibiPositionalBias + self.rel_pos = alibi_pos_klass(heads=alibi_num_heads, bidirectional=not causal) + else: + self.rel_pos = None + + assert not (not pre_norm and sandwich_norm), 'sandwich norm cannot be used when not using prenorm' + self.pre_norm = pre_norm + self.sandwich_norm = sandwich_norm + + self.residual_attn = residual_attn + self.cross_residual_attn = cross_residual_attn + self.cross_attend = cross_attend + + norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm + norm_class = RMSNorm if use_rmsnorm else norm_class + norm_class = RMSScaleShiftNorm if use_rms_scaleshift_norm else norm_class + norm_fn = partial(norm_class, dim) + + norm_fn = nn.Identity if use_rezero else norm_fn + branch_fn = Rezero if use_rezero else None + + if cross_attend and not only_cross: + default_block = ('a', 'c', 'f') + elif cross_attend and only_cross: + default_block = ('c', 'f') + else: + default_block = ('a', 'f') + + if macaron: + default_block = ('f',) + default_block + + # qk normalization + + if use_qk_norm_attn: + attn_scale_init_value = -math.log(math.log2(qk_norm_attn_seq_len ** 2 - qk_norm_attn_seq_len)) if exists( + qk_norm_attn_seq_len) else None + attn_kwargs = {**attn_kwargs, 'qk_norm': True, 'scale_init_value': attn_scale_init_value} + + # zero init + + if zero_init_branch_output: + attn_kwargs = {**attn_kwargs, 'zero_init_output': True} + ff_kwargs = {**ff_kwargs, 'zero_init_output': True} + + # calculate layer block order + + if exists(custom_layers): + layer_types = custom_layers + elif exists(par_ratio): + par_depth = depth * len(default_block) + assert 1 < par_ratio <= par_depth, 'par ratio out of range' + default_block = tuple(filter(not_equals('f'), default_block)) + par_attn = par_depth // par_ratio + depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper + par_width = (depth_cut + depth_cut // par_attn) // par_attn + assert len(default_block) <= par_width, 'default block is too large for par_ratio' + par_block = default_block + ('f',) * (par_width - len(default_block)) + par_head = par_block * par_attn + layer_types = par_head + ('f',) * (par_depth - len(par_head)) + elif exists(sandwich_coef): + assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth' + layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef + else: + layer_types = default_block * depth + + self.layer_types = layer_types + self.num_attn_layers = len(list(filter(equals('a'), layer_types))) + + # calculate token shifting + + shift_tokens = cast_tuple(shift_tokens, len(layer_types)) + + # iterate and construct layers + + for ind, (layer_type, layer_shift_tokens) in enumerate(zip(self.layer_types, shift_tokens)): + is_last_layer = ind == (len(self.layer_types) - 1) + + if layer_type == 'a': + layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs) + elif layer_type == 'c': + layer = Attention(dim, heads=heads, **attn_kwargs) + elif layer_type == 'f': + layer = FeedForward(dim, **ff_kwargs) + layer = layer if not macaron else Scale(0.5, layer) + else: + raise Exception(f'invalid layer type {layer_type}') + + if layer_shift_tokens > 0: + shift_range_upper = layer_shift_tokens + 1 + shift_range_lower = -layer_shift_tokens if not causal else 0 + layer = ShiftTokens(range(shift_range_lower, shift_range_upper), layer) + + if exists(branch_fn): + layer = branch_fn(layer) + + residual_fn = GRUGating if gate_residual else Residual + residual = residual_fn(dim, scale_residual=scale_residual) + + layer_uses_qk_norm = use_qk_norm_attn and layer_type in ('a', 'c') + + pre_branch_norm = norm_fn() if pre_norm and not layer_uses_qk_norm else None + post_branch_norm = norm_fn() if sandwich_norm or layer_uses_qk_norm else None + post_main_norm = norm_fn() if not pre_norm and not is_last_layer else None + + norms = nn.ModuleList([ + pre_branch_norm, + post_branch_norm, + post_main_norm + ]) + + self.layers.append(nn.ModuleList([ + norms, + layer, + residual + ])) + + def forward( + self, + x, + context=None, + full_context=None, # for passing a list of hidden states from an encoder + mask=None, + context_mask=None, + attn_mask=None, + mems=None, + return_hiddens=False, + norm_scale_shift_inp=None, + ): + + assert not (self.cross_attend ^ (exists(context) or exists( + full_context))), 'context must be passed in if cross_attend is set to True' + assert context is None or full_context is None, 'only one of full_context or context can be provided' + + hiddens = [] + intermediates = [] + prev_attn = None + prev_cross_attn = None + + mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers + norm_args = {} + if exists(norm_scale_shift_inp): + norm_args['norm_scale_shift_inp'] = norm_scale_shift_inp + + rotary_pos_emb = None + if exists(self.rotary_pos_emb): + max_rotary_emb_length = max(list(map(lambda m: (m.shape[1] if exists(m) else 0) + x.shape[1], mems))) + rotary_pos_emb = self.rotary_pos_emb(max_rotary_emb_length, x.device) + + cross_attn_count = 0 + for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)): + if layer_type == 'a': + layer_mem = mems.pop(0) if mems else None + + residual = x + + pre_branch_norm, post_branch_norm, post_main_norm = norm + + if exists(pre_branch_norm): + x = pre_branch_norm(x, **norm_args) + + if layer_type == 'a': + out, inter = checkpoint(block, x, None, mask, None, attn_mask, self.pia_pos_emb, rotary_pos_emb, + prev_attn, layer_mem) + elif layer_type == 'c': + if exists(full_context): + out, inter = checkpoint(block, x, full_context[cross_attn_count], mask, context_mask, None, None, + None, prev_attn) + else: + out, inter = checkpoint(block, x, context, mask, context_mask, None, None, None, prev_attn) + elif layer_type == 'f': + out = checkpoint(block, x) + + if exists(post_branch_norm): + out = post_branch_norm(out, **norm_args) + + x = residual_fn(out, residual) + + if layer_type in ('a', 'c'): + intermediates.append(inter) + + if layer_type == 'a' and self.residual_attn: + prev_attn = inter.pre_softmax_attn + elif layer_type == 'c' and self.cross_residual_attn: + prev_cross_attn = inter.pre_softmax_attn + + if exists(post_main_norm): + x = post_main_norm(x, **norm_args) + + if layer_type == 'c': + cross_attn_count += 1 + + if layer_type == 'f': + hiddens.append(x) + + if return_hiddens: + intermediates = LayerIntermediates( + hiddens=hiddens, + attn_intermediates=intermediates + ) + + return x, intermediates + + return x + + +class Encoder(AttentionLayers): + def __init__(self, **kwargs): + assert 'causal' not in kwargs, 'cannot set causality on encoder' + super().__init__(causal=False, **kwargs) + + +class Decoder(AttentionLayers): + def __init__(self, **kwargs): + assert 'causal' not in kwargs, 'cannot set causality on decoder' + super().__init__(causal=True, **kwargs) + + +class CrossAttender(AttentionLayers): + def __init__(self, **kwargs): + super().__init__(cross_attend=True, only_cross=True, **kwargs) + + +class ViTransformerWrapper(nn.Module): + def __init__( + self, + *, + image_size, + patch_size, + attn_layers, + num_classes=None, + dropout=0., + emb_dropout=0. + ): + super().__init__() + assert isinstance(attn_layers, Encoder), 'attention layers must be an Encoder' + assert image_size % patch_size == 0, 'image dimensions must be divisible by the patch size' + dim = attn_layers.dim + num_patches = (image_size // patch_size) ** 2 + patch_dim = 3 * patch_size ** 2 + + self.patch_size = patch_size + + self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim)) + self.patch_to_embedding = nn.Linear(patch_dim, dim) + self.cls_token = nn.Parameter(torch.randn(1, 1, dim)) + self.dropout = nn.Dropout(emb_dropout) + + self.attn_layers = attn_layers + self.norm = nn.LayerNorm(dim) + self.mlp_head = FeedForward(dim, dim_out=num_classes, dropout=dropout) if exists(num_classes) else None + + def forward( + self, + img, + return_embeddings=False + ): + p = self.patch_size + + x = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p) + x = self.patch_to_embedding(x) + b, n, _ = x.shape + + cls_tokens = repeat(self.cls_token, '() n d -> b n d', b=b) + x = torch.cat((cls_tokens, x), dim=1) + x = x + self.pos_embedding[:, :(n + 1)] + x = self.dropout(x) + + x = self.attn_layers(x) + x = self.norm(x) + + if not exists(self.mlp_head) or return_embeddings: + return x + + return self.mlp_head(x[:, 0]) + + +class TransformerWrapper(nn.Module): + def __init__( + self, + *, + num_tokens, + max_seq_len, + attn_layers, + emb_dim=None, + max_mem_len=0., + shift_mem_down=0, + emb_dropout=0., + num_memory_tokens=None, + tie_embedding=False, + use_pos_emb=True + ): + super().__init__() + assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder' + + dim = attn_layers.dim + emb_dim = default(emb_dim, dim) + + self.max_seq_len = max_seq_len + self.max_mem_len = max_mem_len + self.shift_mem_down = shift_mem_down + + self.token_emb = nn.Embedding(num_tokens, emb_dim) + self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if ( + use_pos_emb and not attn_layers.has_pos_emb) else always(0) + self.emb_dropout = nn.Dropout(emb_dropout) + + self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity() + self.attn_layers = attn_layers + self.norm = nn.LayerNorm(dim) + + self.init_() + + self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t() + + # memory tokens (like [cls]) from Memory Transformers paper + num_memory_tokens = default(num_memory_tokens, 0) + self.num_memory_tokens = num_memory_tokens + if num_memory_tokens > 0: + self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim)) + + def init_(self): + nn.init.kaiming_normal_(self.token_emb.weight) + + def forward( + self, + x, + return_embeddings=False, + mask=None, + return_hiddens=False, + return_attn=False, + mems=None, + **kwargs + ): + b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens + x = self.token_emb(x) + x = x + self.pos_emb(x) + x = self.emb_dropout(x) + + x = self.project_emb(x) + + if num_mem > 0: + mem = repeat(self.memory_tokens, 'n d -> b n d', b=b) + x = torch.cat((mem, x), dim=1) + + # auto-handle masking after appending memory tokens + if exists(mask): + mask = F.pad(mask, (num_mem, 0), value=True) + + if self.shift_mem_down and exists(mems): + mems_l, mems_r = mems[:self.shift_mem_down], mems[self.shift_mem_down:] + mems = [*mems_r, *mems_l] + + x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs) + x = self.norm(x) + + mem, x = x[:, :num_mem], x[:, num_mem:] + + out = self.to_logits(x) if not return_embeddings else x + + if return_hiddens: + hiddens = intermediates.hiddens + return out, hiddens + + if return_attn: + attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates)) + return out, attn_maps + + return out + + +class ContinuousTransformerWrapper(nn.Module): + def __init__( + self, + *, + max_seq_len, + attn_layers, + dim_in=None, + dim_out=None, + emb_dim=None, + emb_dropout=0., + use_pos_emb=True + ): + super().__init__() + assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder' + + dim = attn_layers.dim + + self.max_seq_len = max_seq_len + + self.pos_emb = AbsolutePositionalEmbedding(dim, max_seq_len) if ( + use_pos_emb and not attn_layers.has_pos_emb) else always(0) + self.emb_dropout = nn.Dropout(emb_dropout) + + self.project_in = nn.Linear(dim_in, dim) if exists(dim_in) else nn.Identity() + + self.attn_layers = attn_layers + self.norm = nn.LayerNorm(dim) + + self.project_out = nn.Linear(dim, dim_out) if exists(dim_out) else nn.Identity() + + def forward( + self, + x, + return_embeddings=False, + mask=None, + return_attn=False, + mems=None, + **kwargs + ): + b, n, _, device = *x.shape, x.device + + x = self.project_in(x) + x = x + self.pos_emb(x) + x = self.emb_dropout(x) + + x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs) + x = self.norm(x) + + out = self.project_out(x) if not return_embeddings else x + + if return_attn: + attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates)) + return out, attn_maps + + return out + + +class XTransformer(nn.Module): + def __init__( + self, + *, + dim, + tie_token_emb=False, + **kwargs + ): + super().__init__() + enc_kwargs, kwargs = groupby_prefix_and_trim('enc_', kwargs) + dec_kwargs, kwargs = groupby_prefix_and_trim('dec_', kwargs) + + assert 'dim' not in enc_kwargs and 'dim' not in dec_kwargs, 'dimension of either encoder or decoder must be set with `dim` keyword' + enc_transformer_kwargs = pick_and_pop(['num_tokens', 'max_seq_len'], enc_kwargs) + enc_transformer_kwargs['emb_dropout'] = enc_kwargs.pop('emb_dropout', 0) + enc_transformer_kwargs['num_memory_tokens'] = enc_kwargs.pop('num_memory_tokens', None) + enc_transformer_kwargs['use_pos_emb'] = enc_kwargs.pop('use_pos_emb', True) + + dec_transformer_kwargs = pick_and_pop(['num_tokens', 'max_seq_len'], dec_kwargs) + dec_transformer_kwargs['emb_dropout'] = dec_kwargs.pop('emb_dropout', 0) + dec_transformer_kwargs['use_pos_emb'] = dec_kwargs.pop('use_pos_emb', True) + + self.encoder = TransformerWrapper( + **enc_transformer_kwargs, + attn_layers=Encoder(dim=dim, **enc_kwargs) + ) + + self.decoder = TransformerWrapper( + **dec_transformer_kwargs, + attn_layers=Decoder(dim=dim, cross_attend=True, **dec_kwargs) + ) + + if tie_token_emb: + self.decoder.token_emb = self.encoder.token_emb + + self.decoder = AutoregressiveWrapper(self.decoder) + + @torch.no_grad() + def generate(self, seq_in, seq_out_start, seq_len, src_mask=None, src_attn_mask=None, **kwargs): + encodings = self.encoder(seq_in, mask=src_mask, attn_mask=src_attn_mask, return_embeddings=True) + return self.decoder.generate(seq_out_start, seq_len, context=encodings, context_mask=src_mask, **kwargs) + + def forward(self, src, tgt, src_mask=None, tgt_mask=None, src_attn_mask=None): + enc = self.encoder(src, mask=src_mask, attn_mask=src_attn_mask, return_embeddings=True) + out = self.decoder(tgt, context=enc, mask=tgt_mask, context_mask=src_mask) + return out