forked from mrq/DL-Art-School
672 lines
24 KiB
Python
672 lines
24 KiB
Python
import math
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import torch
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import torch.nn.functional as F
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from torch import nn
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from torch.cuda.amp import autocast
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from einops import rearrange, repeat
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from functools import partial
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from contextlib import contextmanager
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from local_attention import LocalAttention
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from axial_positional_embedding import AxialPositionalEmbedding
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from models.lucidrains.performer.reversible import ReversibleSequence, SequentialSequence
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from distutils.version import LooseVersion
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TORCH_GE_1_8_0 = LooseVersion(torch.__version__) >= LooseVersion('1.8.0')
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try:
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from apex import amp
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APEX_AVAILABLE = True
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except:
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APEX_AVAILABLE = False
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# helpers
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def exists(val):
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return val is not None
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def empty(tensor):
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return tensor.numel() == 0
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def default(val, d):
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return val if exists(val) else d
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@contextmanager
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def null_context():
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yield
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def cast_tuple(val):
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return (val,) if not isinstance(val, tuple) else val
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def get_module_device(module):
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return next(module.parameters()).device
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def find_modules(nn_module, type):
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return [module for module in nn_module.modules() if isinstance(module, type)]
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class Always(nn.Module):
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def __init__(self, val):
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super().__init__()
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self.val = val
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def forward(self, *args, **kwargs):
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return self.val
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# token shifting helper and classes
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def shift(t, amount, mask = None):
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if amount == 0:
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return t
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if exists(mask):
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t = t.masked_fill(~mask[..., None], 0.)
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return F.pad(t, (0, 0, amount, -amount), value = 0.)
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class PreShiftTokens(nn.Module):
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def __init__(self, shifts, fn):
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super().__init__()
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self.fn = fn
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self.shifts = tuple(shifts)
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def forward(self, x, **kwargs):
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mask = kwargs.get('mask', None)
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shifts = self.shifts
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segments = len(shifts)
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feats_per_shift = x.shape[-1] // segments
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splitted = x.split(feats_per_shift, dim = -1)
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segments_to_shift, rest = splitted[:segments], splitted[segments:]
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segments_to_shift = list(map(lambda args: shift(*args, mask = mask), zip(segments_to_shift, shifts)))
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x = torch.cat((*segments_to_shift, *rest), dim = -1)
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return self.fn(x, **kwargs)
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# kernel functions
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# transcribed from jax to pytorch from
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# https://github.com/google-research/google-research/blob/master/performer/fast_attention/jax/fast_attention.py
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def softmax_kernel(data, *, projection_matrix, is_query, normalize_data=True, eps=1e-4, device = None):
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b, h, *_ = data.shape
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data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1.
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ratio = (projection_matrix.shape[0] ** -0.5)
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projection = repeat(projection_matrix, 'j d -> b h j d', b = b, h = h)
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projection = projection.type_as(data)
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data_dash = torch.einsum('...id,...jd->...ij', (data_normalizer * data), projection)
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diag_data = data ** 2
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diag_data = torch.sum(diag_data, dim=-1)
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diag_data = (diag_data / 2.0) * (data_normalizer ** 2)
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diag_data = diag_data.unsqueeze(dim=-1)
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if is_query:
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data_dash = ratio * (
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torch.exp(data_dash - diag_data -
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torch.amax(data_dash, dim=-1, keepdim=True)) + eps)
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else:
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data_dash = ratio * (
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torch.exp(data_dash - diag_data - torch.amax(data_dash, dim=(-1, -2), keepdim=True)) + eps)
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return data_dash.type_as(data)
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def generalized_kernel(data, *, projection_matrix, kernel_fn = nn.ReLU(), kernel_epsilon = 0.001, normalize_data = True, device = None):
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b, h, *_ = data.shape
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data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1.
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if projection_matrix is None:
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return kernel_fn(data_normalizer * data) + kernel_epsilon
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projection = repeat(projection_matrix, 'j d -> b h j d', b = b, h = h)
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projection = projection.type_as(data)
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data_dash = torch.einsum('...id,...jd->...ij', (data_normalizer * data), projection)
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data_prime = kernel_fn(data_dash) + kernel_epsilon
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return data_prime.type_as(data)
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def orthogonal_matrix_chunk(cols, device = None):
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unstructured_block = torch.randn((cols, cols), device = device)
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if TORCH_GE_1_8_0:
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q, r = torch.linalg.qr(unstructured_block.cpu(), mode = 'reduced')
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else:
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q, r = torch.qr(unstructured_block.cpu(), some = True)
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q, r = map(lambda t: t.to(device), (q, r))
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return q.t()
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def gaussian_orthogonal_random_matrix(nb_rows, nb_columns, scaling = 0, device = None):
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nb_full_blocks = int(nb_rows / nb_columns)
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block_list = []
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for _ in range(nb_full_blocks):
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q = orthogonal_matrix_chunk(nb_columns, device = device)
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block_list.append(q)
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remaining_rows = nb_rows - nb_full_blocks * nb_columns
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if remaining_rows > 0:
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q = orthogonal_matrix_chunk(nb_columns, device = device)
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block_list.append(q[:remaining_rows])
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final_matrix = torch.cat(block_list)
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if scaling == 0:
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multiplier = torch.randn((nb_rows, nb_columns), device = device).norm(dim = 1)
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elif scaling == 1:
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multiplier = math.sqrt((float(nb_columns))) * torch.ones((nb_rows,), device = device)
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else:
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raise ValueError(f'Invalid scaling {scaling}')
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return torch.diag(multiplier) @ final_matrix
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# linear attention classes with softmax kernel
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# non-causal linear attention
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def linear_attention(q, k, v):
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k_cumsum = k.sum(dim = -2)
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D_inv = 1. / torch.einsum('...nd,...d->...n', q, k_cumsum.type_as(q))
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context = torch.einsum('...nd,...ne->...de', k, v)
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out = torch.einsum('...de,...nd,...n->...ne', context, q, D_inv)
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return out
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# efficient causal linear attention, created by EPFL
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# TODO: rewrite EPFL's CUDA kernel to do mixed precision and remove half to float conversion and back
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def causal_linear_attention(q, k, v, eps = 1e-6):
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from fast_transformers.causal_product import CausalDotProduct
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autocast_enabled = torch.is_autocast_enabled()
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is_half = isinstance(q, torch.cuda.HalfTensor)
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assert not is_half or APEX_AVAILABLE, 'half tensors can only be used if nvidia apex is available'
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cuda_context = null_context if not autocast_enabled else partial(autocast, enabled = False)
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causal_dot_product_fn = amp.float_function(CausalDotProduct.apply) if is_half else CausalDotProduct.apply
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k_cumsum = k.cumsum(dim=-2) + eps
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D_inv = 1. / torch.einsum('...nd,...nd->...n', q, k_cumsum.type_as(q))
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with cuda_context():
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if autocast_enabled:
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q, k, v = map(lambda t: t.float(), (q, k, v))
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out = causal_dot_product_fn(q, k, v)
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out = torch.einsum('...nd,...n->...nd', out, D_inv)
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return out
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# inefficient causal linear attention, without cuda code, for reader's reference
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# not being used
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def causal_linear_attention_noncuda(q, k, v, chunk_size = 128, eps = 1e-6):
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last_k_cumsum = 0
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last_context_cumsum = 0
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outs = []
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for q, k, v in zip(*map(lambda t: t.chunk(chunk_size, dim = -2), (q, k, v))):
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k_cumsum = last_k_cumsum + k.cumsum(dim=-2)
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D_inv = 1. / torch.einsum('...nd,...nd->...n', q, k_cumsum.type_as(q) + eps)
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context = torch.einsum('...nd,...ne->...nde', k, v)
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context_cumsum = last_context_cumsum + context.cumsum(dim=-3)
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out = torch.einsum('...nde,...nd,...n->...ne', context_cumsum, q, D_inv)
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last_k_cumsum = k_cumsum[:, :, -1:]
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last_context_cumsum = context_cumsum[:, :, -1:]
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outs.append(out)
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return torch.cat(outs, dim = -2)
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class FastAttention(nn.Module):
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def __init__(self, dim_heads, nb_features = None, ortho_scaling = 0, causal = False, generalized_attention = False, kernel_fn = nn.ReLU(), no_projection = False):
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super().__init__()
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nb_features = default(nb_features, int(dim_heads * math.log(dim_heads)))
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self.dim_heads = dim_heads
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self.nb_features = nb_features
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self.ortho_scaling = ortho_scaling
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self.create_projection = partial(gaussian_orthogonal_random_matrix, nb_rows = self.nb_features, nb_columns = dim_heads, scaling = ortho_scaling)
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projection_matrix = self.create_projection()
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self.register_buffer('projection_matrix', projection_matrix)
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self.generalized_attention = generalized_attention
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self.kernel_fn = kernel_fn
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# if this is turned on, no projection will be used
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# queries and keys will be softmax-ed as in the original efficient attention paper
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self.no_projection = no_projection
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self.causal = causal
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if causal:
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try:
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import fast_transformers.causal_product.causal_product_cuda
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self.causal_linear_fn = partial(causal_linear_attention)
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except ImportError:
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print('unable to import cuda code for auto-regressive Performer. will default to the memory inefficient non-cuda version')
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self.causal_linear_fn = causal_linear_attention_noncuda
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@torch.no_grad()
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def redraw_projection_matrix(self, device):
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projections = self.create_projection(device = device)
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self.projection_matrix.copy_(projections)
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del projections
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def forward(self, q, k, v):
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device = q.device
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if self.no_projection:
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q = q.softmax(dim = -1)
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k = torch.exp(k) if self.causal else k.softmax(dim = -2)
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elif self.generalized_attention:
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create_kernel = partial(generalized_kernel, kernel_fn = self.kernel_fn, projection_matrix = self.projection_matrix, device = device)
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q, k = map(create_kernel, (q, k))
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else:
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create_kernel = partial(softmax_kernel, projection_matrix = self.projection_matrix, device = device)
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q = create_kernel(q, is_query = True)
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k = create_kernel(k, is_query = False)
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attn_fn = linear_attention if not self.causal else self.causal_linear_fn
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out = attn_fn(q, k, v)
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return out
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# a module for keeping track of when to update the projections
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class ProjectionUpdater(nn.Module):
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def __init__(self, instance, feature_redraw_interval):
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super().__init__()
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self.instance = instance
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self.feature_redraw_interval = feature_redraw_interval
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self.register_buffer('calls_since_last_redraw', torch.tensor(0))
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def fix_projections_(self):
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self.feature_redraw_interval = None
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def redraw_projections(self):
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model = self.instance
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if not self.training:
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return
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if exists(self.feature_redraw_interval) and self.calls_since_last_redraw >= self.feature_redraw_interval:
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device = get_module_device(model)
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fast_attentions = find_modules(model, FastAttention)
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for fast_attention in fast_attentions:
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fast_attention.redraw_projection_matrix(device)
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self.calls_since_last_redraw.zero_()
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return
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self.calls_since_last_redraw += 1
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def forward(self, x):
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raise NotImplemented
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# classes
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class ReZero(nn.Module):
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def __init__(self, fn):
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super().__init__()
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self.g = nn.Parameter(torch.tensor(1e-3))
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self.fn = fn
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def forward(self, x, **kwargs):
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return self.fn(x, **kwargs) * self.g
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class PreScaleNorm(nn.Module):
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def __init__(self, dim, fn, eps=1e-5):
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super().__init__()
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self.fn = fn
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self.g = nn.Parameter(torch.ones(1))
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self.eps = eps
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def forward(self, x, **kwargs):
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n = torch.norm(x, dim=-1, keepdim=True).clamp(min=self.eps)
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x = x / n * self.g
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return self.fn(x, **kwargs)
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class PreLayerNorm(nn.Module):
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def __init__(self, dim, fn):
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super().__init__()
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self.norm = nn.LayerNorm(dim)
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self.fn = fn
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def forward(self, x, **kwargs):
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return self.fn(self.norm(x), **kwargs)
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class Chunk(nn.Module):
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def __init__(self, chunks, fn, along_dim = -1):
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super().__init__()
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self.dim = along_dim
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self.chunks = chunks
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self.fn = fn
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def forward(self, x, **kwargs):
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if self.chunks == 1:
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return self.fn(x, **kwargs)
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chunks = x.chunk(self.chunks, dim = self.dim)
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return torch.cat([self.fn(c, **kwargs) for c in chunks], dim = self.dim)
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class FeedForward(nn.Module):
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def __init__(self, dim, mult = 4, dropout = 0., activation = None, glu = False):
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super().__init__()
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activation = default(activation, nn.GELU)
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self.glu = glu
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self.w1 = nn.Linear(dim, dim * mult * (2 if glu else 1))
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self.act = activation()
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self.dropout = nn.Dropout(dropout)
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self.w2 = nn.Linear(dim * mult, dim)
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def forward(self, x, **kwargs):
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if not self.glu:
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x = self.w1(x)
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x = self.act(x)
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else:
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x, v = self.w1(x).chunk(2, dim=-1)
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x = self.act(x) * v
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x = self.dropout(x)
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x = self.w2(x)
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return x
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class Attention(nn.Module):
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def __init__(
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self,
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dim,
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causal = False,
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heads = 8,
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dim_head = 64,
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local_heads = 0,
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local_window_size = 256,
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nb_features = None,
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feature_redraw_interval = 1000,
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generalized_attention = False,
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kernel_fn = nn.ReLU(),
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dropout = 0.,
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no_projection = False,
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qkv_bias = False,
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attn_out_bias = True
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):
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super().__init__()
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assert dim % heads == 0, 'dimension must be divisible by number of heads'
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dim_head = default(dim_head, dim // heads)
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inner_dim = dim_head * heads
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self.fast_attention = FastAttention(dim_head, nb_features, causal = causal, generalized_attention = generalized_attention, kernel_fn = kernel_fn, no_projection = no_projection)
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self.heads = heads
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self.global_heads = heads - local_heads
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self.local_attn = LocalAttention(window_size = local_window_size, causal = causal, autopad = True, dropout = dropout, look_forward = int(not causal), rel_pos_emb_config = (dim_head, local_heads)) if local_heads > 0 else None
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self.to_q = nn.Linear(dim, inner_dim, bias = qkv_bias)
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self.to_k = nn.Linear(dim, inner_dim, bias = qkv_bias)
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self.to_v = nn.Linear(dim, inner_dim, bias = qkv_bias)
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self.to_out = nn.Linear(inner_dim, dim, bias = attn_out_bias)
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self.dropout = nn.Dropout(dropout)
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def forward(self, x, pos_emb = None, context = None, mask = None, context_mask = None, **kwargs):
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b, n, _, h, gh = *x.shape, self.heads, self.global_heads
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cross_attend = exists(context)
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context = default(context, x)
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context_mask = default(context_mask, mask) if not cross_attend else context_mask
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q, k, v = self.to_q(x), self.to_k(context), self.to_v(context)
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
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(q, lq), (k, lk), (v, lv) = map(lambda t: (t[:, :gh], t[:, gh:]), (q, k, v))
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attn_outs = []
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if not empty(q):
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if exists(context_mask):
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global_mask = context_mask[:, None, :, None]
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v.masked_fill_(~global_mask, 0.)
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if exists(pos_emb) and not cross_attend:
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q, k = apply_rotary_pos_emb(q, k, pos_emb)
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out = self.fast_attention(q, k, v)
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attn_outs.append(out)
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if not empty(lq):
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assert not cross_attend, 'local attention is not compatible with cross attention'
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out = self.local_attn(lq, lk, lv, input_mask = mask)
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attn_outs.append(out)
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out = torch.cat(attn_outs, dim = 1)
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out = rearrange(out, 'b h n d -> b n (h d)')
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out = self.to_out(out)
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return self.dropout(out)
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class SelfAttention(Attention):
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def forward(self, *args, context = None, **kwargs):
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assert not exists(context), 'self attention should not receive context'
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return super().forward(*args, **kwargs)
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class CrossAttention(Attention):
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def forward(self, *args, context = None, **kwargs):
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assert exists(context), 'cross attention should receive context'
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return super().forward(*args, context = context, **kwargs)
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# positional embeddings
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class AbsolutePositionalEmbedding(nn.Module):
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def __init__(self, dim, max_seq_len):
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super().__init__()
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self.emb = nn.Embedding(max_seq_len, dim)
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def forward(self, x):
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t = torch.arange(x.shape[1], device=x.device)
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return self.emb(t)
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# rotary positional embedding helpers
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def rotate_every_two(x):
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x = rearrange(x, '... (d j) -> ... d j', j = 2)
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x1, x2 = x.unbind(dim = -1)
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x = torch.stack((-x2, x1), dim = -1)
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return rearrange(x, '... d j -> ... (d j)')
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def apply_rotary_pos_emb(q, k, sinu_pos):
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sinu_pos = rearrange(sinu_pos, '() n (j d) -> n j d', j = 2)
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sin, cos = sinu_pos.unbind(dim = -2)
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sin, cos = map(lambda t: repeat(t, 'b n -> b (n j)', j = 2), (sin, cos))
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q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k))
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return q, k
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# sinusoidal positional embeddings
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class FixedPositionalEmbedding(nn.Module):
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def __init__(self, dim, max_seq_len):
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super().__init__()
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inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
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position = torch.arange(0, max_seq_len, dtype=torch.float)
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sinusoid_inp = torch.einsum("i,j->ij", position, inv_freq)
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emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
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self.register_buffer('emb', emb)
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def forward(self, x):
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return self.emb[None, :x.shape[1], :].to(x)
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# performer
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class Performer(nn.Module):
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def __init__(
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self,
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dim,
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depth,
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heads,
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dim_head,
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local_attn_heads = 0,
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local_window_size = 256,
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causal = False,
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ff_mult = 4,
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nb_features = None,
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feature_redraw_interval = 1000,
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reversible = False,
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ff_chunks = 1,
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generalized_attention = False,
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kernel_fn = nn.ReLU(),
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use_scalenorm = False,
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use_rezero = False,
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ff_glu = False,
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ff_dropout = 0.,
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attn_dropout = 0.,
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cross_attend = False,
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no_projection = False,
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auto_check_redraw = True,
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qkv_bias = True,
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attn_out_bias = True,
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shift_tokens = False
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):
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super().__init__()
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layers = nn.ModuleList([])
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local_attn_heads = cast_tuple(local_attn_heads)
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local_attn_heads = local_attn_heads * depth if len(local_attn_heads) == 1 else local_attn_heads
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assert len(local_attn_heads) == depth, 'tuple specifying number of local attention heads per depth must be equal to the total depth'
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assert all(map(lambda n: n >= 0 and n <= heads, local_attn_heads)), 'local attention head value must be less than the total number of heads'
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if use_scalenorm:
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wrapper_fn = partial(PreScaleNorm, dim)
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elif use_rezero:
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wrapper_fn = ReZero
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else:
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wrapper_fn = partial(PreLayerNorm, dim)
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for _, local_heads in zip(range(depth), local_attn_heads):
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attn = SelfAttention(dim, causal = causal, heads = heads, dim_head = dim_head, local_heads = local_heads, local_window_size = local_window_size, nb_features = nb_features, generalized_attention = generalized_attention, kernel_fn = kernel_fn, dropout = attn_dropout, no_projection = no_projection, qkv_bias = qkv_bias, attn_out_bias = attn_out_bias)
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ff = Chunk(ff_chunks, FeedForward(dim, mult = ff_mult, dropout = ff_dropout, glu = ff_glu), along_dim = 1)
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if shift_tokens:
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shift = (0, 1) if causal else (-1, 0, 1)
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attn, ff = map(lambda t: PreShiftTokens(shift, t), (attn, ff))
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attn, ff = map(wrapper_fn, (attn, ff))
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layers.append(nn.ModuleList([attn, ff]))
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if not cross_attend:
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continue
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layers.append(nn.ModuleList([
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wrapper_fn(CrossAttention(dim, heads = heads, dim_head = dim_head, nb_features = nb_features, generalized_attention = generalized_attention, kernel_fn = kernel_fn, dropout = attn_dropout, no_projection = no_projection, qkv_bias = qkv_bias, attn_out_bias = attn_out_bias)),
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wrapper_fn(Chunk(ff_chunks, FeedForward(dim, mult = ff_mult, dropout = ff_dropout, glu = ff_glu), along_dim = 1))
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]))
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execute_type = ReversibleSequence if reversible else SequentialSequence
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route_attn = ((True, False),) * depth * (2 if cross_attend else 1)
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route_context = ((False, False), (True, False)) * depth
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attn_route_map = {'mask': route_attn, 'pos_emb': route_attn}
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context_route_map = {'context': route_context, 'context_mask': route_context} if cross_attend else {}
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self.net = execute_type(layers, args_route = {**attn_route_map, **context_route_map})
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# keeping track of when to redraw projections for all attention layers
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self.auto_check_redraw = auto_check_redraw
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self.proj_updater = ProjectionUpdater(self.net, feature_redraw_interval)
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def fix_projection_matrices_(self):
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self.proj_updater.feature_redraw_interval = None
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def forward(self, x, **kwargs):
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if self.auto_check_redraw:
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self.proj_updater.redraw_projections()
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return self.net(x, **kwargs)
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class PerformerLM(nn.Module):
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def __init__(
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self,
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*,
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num_tokens,
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max_seq_len,
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dim,
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depth,
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heads,
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dim_head = 64,
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local_attn_heads = 0,
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local_window_size = 256,
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causal = False,
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ff_mult = 4,
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nb_features = None,
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feature_redraw_interval = 1000,
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reversible = False,
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ff_chunks = 1,
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ff_glu = False,
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emb_dropout = 0.,
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ff_dropout = 0.,
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attn_dropout = 0.,
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generalized_attention = False,
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kernel_fn = nn.ReLU(),
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use_scalenorm = False,
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use_rezero = False,
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cross_attend = False,
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no_projection = False,
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tie_embed = False,
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rotary_position_emb = True,
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axial_position_emb = False,
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axial_position_shape = None,
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auto_check_redraw = True,
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qkv_bias = False,
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attn_out_bias = False,
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shift_tokens = False
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):
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super().__init__()
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local_attn_heads = cast_tuple(local_attn_heads)
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self.max_seq_len = max_seq_len
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self.token_emb = nn.Embedding(num_tokens, dim)
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if rotary_position_emb:
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self.pos_emb = FixedPositionalEmbedding(dim, max_seq_len)
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self.layer_pos_emb = FixedPositionalEmbedding(dim_head, max_seq_len)
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elif axial_position_emb:
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axial_position_shape = default(axial_position_shape, (math.ceil(max_seq_len / 64), 64))
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self.pos_emb = AxialPositionalEmbedding(dim, axial_position_shape)
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self.layer_pos_emb = Always(None)
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else:
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self.pos_emb = AbsolutePositionalEmbedding(dim, max_seq_len)
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self.layer_pos_emb = Always(None)
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self.dropout = nn.Dropout(emb_dropout)
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self.performer = Performer(dim, depth, heads, dim_head, local_attn_heads, local_window_size, causal, ff_mult, nb_features, feature_redraw_interval, reversible, ff_chunks, generalized_attention, kernel_fn, use_scalenorm, use_rezero, ff_glu, ff_dropout, attn_dropout, cross_attend, no_projection, auto_check_redraw, qkv_bias, attn_out_bias, shift_tokens)
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self.norm = nn.LayerNorm(dim)
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self.to_out = nn.Linear(dim, num_tokens) if not tie_embed else None
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def check_redraw_projections(self):
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self.performer.check_redraw_projections()
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def fix_projection_matrices_(self):
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self.performer.fix_projection_matrices_()
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def forward(self, x, return_encodings = False, **kwargs):
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b, n, device = *x.shape, x.device
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assert n <= self.max_seq_len, f'sequence length {n} must be less than the max sequence length {self.max_seq_len}'
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# token and positional embeddings
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x = self.token_emb(x)
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x += self.pos_emb(x)
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x = self.dropout(x)
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# performer layers
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layer_pos_emb = self.layer_pos_emb(x)
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x = self.performer(x, pos_emb = layer_pos_emb, **kwargs)
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# norm and to logits
|
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x = self.norm(x)
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if return_encodings:
|
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return x
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if exists(self.to_out):
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return self.to_out(x)
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|
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return x @ self.token_emb.weight.t()
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