forked from mrq/DL-Art-School
Modify x_transformers to do checkpointing and use relative positional biases
This commit is contained in:
James Betker
parent
09879b434d
commit
37bdfe82b2
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@ -1,17 +1,12 @@
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import functools
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from transformers import GPT2PreTrainedModel, GPT2Config
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from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
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from x_transformers import TransformerWrapper, Encoder, Decoder
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from data.audio.voice_tokenizer import VoiceBpeTokenizer
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from models.arch_util import AttentionBlock
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from scripts.audio.gen.speech_synthesis_utils import wav_to_mel
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from models.lucidrains.x_transformers import TransformerWrapper, Encoder, Decoder
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from trainer.networks import register_model
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from utils.util import load_audio
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class InferenceModel(GPT2PreTrainedModel):
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@ -92,7 +87,7 @@ class InferenceModel(GPT2PreTrainedModel):
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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hidden_states = self.transformer.decoder(input_ids, context=self.context, return_embeddings=True)
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logits = self.transformer.decoder.transformer.to_logits(hidden_states)
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logits = self.transformer.decoder.to_logits(hidden_states)
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if not return_dict:
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return (logits, )
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@ -161,40 +156,6 @@ class ConditioningEncoder(nn.Module):
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return h.mean(dim=2)
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class CheckpointedLayer(nn.Module):
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"""
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Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
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checkpoint for all other args.
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"""
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def __init__(self, wrap):
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super().__init__()
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self.wrap = wrap
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def forward(self, x, *args, **kwargs):
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for k, v in kwargs.items():
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assert not (isinstance(v, torch.Tensor) and v.requires_grad) # This would screw up checkpointing.
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partial = functools.partial(self.wrap, **kwargs)
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return torch.utils.checkpoint.checkpoint(partial, x, *args)
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class CheckpointedXTransformerWrapper(nn.Module):
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"""
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Wraps a TransformerWrapper and applies CheckpointedLayer to each layer.
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"""
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def __init__(self, checkpoint=True, **xtransformer_kwargs):
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super().__init__()
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self.transformer = TransformerWrapper(**xtransformer_kwargs)
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if not checkpoint:
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return
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for i in range(len(self.transformer.attn_layers.layers)):
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n, b, r = self.transformer.attn_layers.layers[i]
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self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
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def forward(self, x, **kwargs):
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return self.transformer(x, **kwargs)
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class AutoregressiveCodegen(nn.Module):
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def __init__(self, model_dim, depth, num_text_tokens=256, num_mel_tokens=8194, dropout=.1):
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super().__init__()
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@ -204,7 +165,7 @@ class AutoregressiveCodegen(nn.Module):
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self.max_text_token_id = num_text_tokens
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self.max_mel_token_id = num_mel_tokens
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self.mel_embedding = ConditioningEncoder(80, model_dim, do_checkpointing=False)
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self.encoder = CheckpointedXTransformerWrapper(
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self.encoder = TransformerWrapper(
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num_tokens=num_text_tokens,
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use_pos_emb=False,
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max_seq_len=-1,
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@ -218,9 +179,10 @@ class AutoregressiveCodegen(nn.Module):
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ff_glu=True,
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ff_mult=1,
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rotary_pos_emb=True,
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attn_rel_pos_bias=True,
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))
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self.encoder.transformer.to_logits = nn.Identity() # This is unused.
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self.decoder = CheckpointedXTransformerWrapper(
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self.encoder.to_logits = nn.Identity() # This is unused.
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self.decoder = TransformerWrapper(
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num_tokens=num_mel_tokens,
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use_pos_emb=False,
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max_seq_len=-1,
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@ -235,6 +197,7 @@ class AutoregressiveCodegen(nn.Module):
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ff_mult=1,
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rotary_pos_emb=True,
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cross_attend=True,
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attn_rel_pos_bias=True,
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))
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def get_grad_norm_parameter_groups(self):
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@ -289,7 +252,7 @@ class AutoregressiveCodegen(nn.Module):
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gen = self.inference_model.generate(bos_token_id=self.START_TOKEN, pad_token_id=self.STOP_TOKEN, eos_token_id=self.STOP_TOKEN,
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max_length=max_tokens, output_attentions=False, return_dict_in_generate=True,
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**hf_generate_kwargs)
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return gen
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return gen.sequences
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@register_model
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@ -1,3 +1,4 @@
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import functools
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import math
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import torch
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from torch import nn, einsum
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@ -14,6 +15,7 @@ from entmax import entmax15
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from x_transformers.autoregressive_wrapper import AutoregressiveWrapper
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# constants
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from utils.util import checkpoint
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DEFAULT_DIM_HEAD = 64
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@ -445,7 +447,10 @@ class Attention(nn.Module):
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zero_init_output = False,
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max_attend_past = None,
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qk_norm = False,
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scale_init_value = None
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scale_init_value = None,
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rel_pos_bias = False,
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rel_pos_num_buckets = 32,
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rel_pos_max_distance = 128,
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):
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super().__init__()
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self.scale = dim_head ** -0.5
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@ -508,6 +513,11 @@ class Attention(nn.Module):
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self.attn_on_attn = on_attn
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self.to_out = nn.Sequential(nn.Linear(v_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(v_dim, dim)
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self.rel_pos_bias = rel_pos_bias
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if rel_pos_bias:
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assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
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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)
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# init output projection 0
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if zero_init_output:
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init_zero_(self.to_out)
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@ -519,7 +529,6 @@ class Attention(nn.Module):
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mask = None,
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context_mask = None,
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attn_mask = None,
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rel_pos = None,
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sinusoidal_emb = None,
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rotary_pos_emb = None,
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prev_attn = None,
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@ -593,8 +602,8 @@ class Attention(nn.Module):
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if talking_heads:
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dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
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if exists(rel_pos):
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dots = rel_pos(dots)
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if self.rel_pos_bias:
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dots = self.rel_pos(dots)
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if exists(input_mask):
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dots.masked_fill_(~input_mask, mask_value)
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@ -673,9 +682,6 @@ class AttentionLayers(nn.Module):
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alibi_pos_bias = False,
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alibi_num_heads = None,
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alibi_learned = False,
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rel_pos_bias = False,
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rel_pos_num_buckets = 32,
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rel_pos_max_distance = 128,
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position_infused_attn = False,
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rotary_pos_emb = False,
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rotary_emb_dim = None,
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@ -705,6 +711,7 @@ class AttentionLayers(nn.Module):
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self.depth = depth
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self.layers = nn.ModuleList([])
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rel_pos_bias = 'rel_pos_bias' in attn_kwargs
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self.has_pos_emb = position_infused_attn or rel_pos_bias or rotary_pos_emb
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self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
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@ -712,11 +719,8 @@ class AttentionLayers(nn.Module):
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self.rotary_pos_emb = RotaryEmbedding(rotary_emb_dim) if rotary_pos_emb else None
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assert not (alibi_pos_bias and rel_pos_bias), 'you can only choose Alibi positional bias or T5 relative positional bias, not both'
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assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
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if rel_pos_bias:
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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)
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elif alibi_pos_bias:
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if alibi_pos_bias:
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alibi_num_heads = default(alibi_num_heads, heads)
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assert alibi_num_heads <= heads, 'number of ALiBi heads must be less than the total number of heads'
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alibi_pos_klass = LearnedAlibiPositionalBias if alibi_learned or not causal else AlibiPositionalBias
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@ -858,8 +862,6 @@ class AttentionLayers(nn.Module):
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rotary_pos_emb = self.rotary_pos_emb(max_rotary_emb_length, x.device)
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for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
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is_last = ind == (len(self.layers) - 1)
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if layer_type == 'a':
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hiddens.append(x)
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layer_mem = mems.pop(0) if mems else None
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@ -872,11 +874,13 @@ class AttentionLayers(nn.Module):
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x = pre_branch_norm(x)
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if layer_type == 'a':
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out, inter = block(x, mask = mask, attn_mask = attn_mask, sinusoidal_emb = self.pia_pos_emb, rel_pos = self.rel_pos, rotary_pos_emb = rotary_pos_emb, prev_attn = prev_attn, mem = layer_mem)
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block_fn = functools.partial(block, mask = mask, attn_mask = attn_mask, sinusoidal_emb = self.pia_pos_emb, rotary_pos_emb = rotary_pos_emb, prev_attn = prev_attn, mem = layer_mem)
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out, inter = checkpoint(block_fn, x)
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elif layer_type == 'c':
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out, inter = block(x, context, mask = mask, context_mask = context_mask, prev_attn = prev_cross_attn)
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block_fn = functools.partial(block, mask = mask, context_mask = context_mask, prev_attn = prev_cross_attn)
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out, inter = checkpoint(block_fn, x)
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elif layer_type == 'f':
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out = block(x)
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out = checkpoint(block, x)
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if exists(post_branch_norm):
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out = post_branch_norm(out)
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