import ffutils
+# from blanket import blanket
+
# import memload
######################################################################
##############################
+class NaNChecker(nn.Module):
+ def __init__(self, name):
+ super().__init__()
+ self.name = name
+
+ def forward(self, bs):
+ x = bs.x if type(bs) is BracketedSequence else bs
+ assert not x.isnan().any(), f"${self.name} detected NaN"
+ assert not x.isinf().any(), f"${self.name} detected Inf"
+ return bs
+
+
class WithResidual(nn.Module):
def __init__(self, *f):
super().__init__()
self.w_qw = randw(nb_heads, dim_qk, dim_model)
self.w_qr = randw(nb_heads, dim_qk, dim_model)
- # self.w_k = randw(nb_heads, dim_qk, dim_model)
self.w_v = randw(nb_heads, dim_v, dim_model)
self.w_o = randw(dim_v * nb_heads, dim_model)
- def reset_inner_loss(self):
- self.acc_attention = 0
- self.acc_nb = 0
-
- def get_inner_loss(self):
- warnings.warn("l2 regularization", RuntimeWarning)
- return (self.acc_attention / self.acc_nb).pow(2).sum()
- # return torch.tensor([0], device=self.w_qw.device)
-
def forward(self, bs):
x_q, t0, t1 = bs.x, bs.first, bs.first + bs.nb
self.rec_v = x_q.new_zeros(
x_q.size(0), self.nb_lines, x_q.size(1), self.w_v.size(1)
)
- # self.rec_k = x_q.new_zeros(
- # x_q.size(0), self.nb_lines, x_q.size(1), self.w_k.size(1)
- # )
self.cache_y = x_q.new_zeros(x_q.size(0), x_q.size(1), self.w_o.size(1))
- ######################################################################
- # Prepare the keys
-
- k_star = self.k_star[:, None, :].expand(-1, t1 - t0, -1)
-
- warnings.warn("rotating key barrel", RuntimeWarning)
- k_star = self.k_star[:, None, :].expand(-1, x_q.size(1), -1)
- t_barrel = torch.arange(t0, t1, device=k_star.device)
- t_barrel = t_barrel[None, :].expand(k_star.size(0), t1 - t0)
- l_barrel = (
- torch.arange(k_star.size(0), device=k_star.device)[:, None] + t_barrel
- ) % k_star.size(0)
- k_star = k_star[l_barrel, t_barrel]
-
######################################################################
# Compute the recurrent state
qw = torch.einsum("ntc,hdc->nhtd", x_q[:, t0:t1], self.w_qw)
v = torch.einsum("ntc,hdc->nhtd", x_q[:, t0:t1], self.w_v)
- # k = torch.einsum("ntc,hdc->nhtd", x_q[:, t0:t1], self.w_k)
- aw = torch.einsum(
- "nhtd,ltd->nhlt",
- qw,
- k_star,
- ) / math.sqrt(self.w_qw.size(1))
+ aw = torch.einsum("nhtd,ld->nhlt", qw, self.k_star) / math.sqrt(
+ self.w_qw.size(1)
+ )
aw = aw.softmax(dim=2) # nhlt
- if self.train:
- self.acc_attention += aw.sum(dim=(0, 1, 3))
- self.acc_nb += aw.size(0) * aw.size(1) * aw.size(3)
-
aw = F.dropout(aw, self.attention_dropout, self.training)
A = 1 - aw.sum(dim=1) # nlt
V = torch.einsum("nhlt,nhtd->nltd", aw, v).contiguous()
- # K = torch.einsum("nhlt,nhtd->nltd", aw, k).contiguous()
if t0 == 0:
V0 = None
- # K0 = None
else:
V0 = self.rec_v[:, :, t0 - 1]
- # K0 = self.rec_k[:, :, t0 - 1]
self.rec_v[:, :, t0:t1] = pscan_shape(A, V, V0)
- # self.rec_k[:, :, t0:t1] = pscan_shape(A, K, K0)
######################################################################
# compute the readout
ar = torch.einsum(
"nhtd,ld->nhlt",
qr,
- # self.rec_k[:, :, t0:t1],
self.k_star,
) / math.sqrt(self.w_qr.size(1))
self.acc_nb = 0
def get_inner_loss(self):
- warnings.warn("l2 regularization", RuntimeWarning)
- return (self.acc_attention / self.acc_nb).pow(2).sum()
- # return torch.tensor([0], device=self.w_qw.device)
+ # warnings.warn("l2 regularization", RuntimeWarning)
+ # return (self.acc_attention / self.acc_nb).pow(2).sum()
+ return torch.tensor([0], device=self.w_qw.device)
# warnings.warn("side regularization", RuntimeWarning)
# return (
# (0.5 / self.nb_lines - self.acc_attention / self.acc_nb).clamp(min=0).sum()
k_star = self.k_star[:, None, :].expand(-1, t1 - t0, -1)
- warnings.warn("rotating key barrel", RuntimeWarning)
+ # warnings.warn("rotating key barrel", RuntimeWarning)
k_star = self.k_star[:, None, :].expand(-1, x_q.size(1), -1)
t_barrel = torch.arange(t0, t1, device=k_star.device)
t_barrel = t_barrel[None, :].expand(k_star.size(0), t1 - t0)
l_barrel = (
- torch.arange(k_star.size(0), device=k_star.device)[:, None] + t_barrel
+ torch.arange(k_star.size(0), device=k_star.device)[:, None] # + t_barrel
) % k_star.size(0)
k_star = k_star[l_barrel, t_barrel]
self.caterpillar_height = caterpillar_height
self.attention_dropout = attention_dropout
- self.gate_dropout_proba = args.gate_dropout_proba
- self.gate_dropout_sync = args.gate_dropout_sync
- self.gate_dropout_replace = args.gate_dropout_replace
-
######################################################################
- self.w_G = randw(nb_heads, caterpillar_height, dim_model, factor=1.0)
+ self.w_G = randw(nb_heads, caterpillar_height, dim_model)
self.b_G = nn.Parameter(torch.full((nb_heads, caterpillar_height), 0.0))
self.w_K = randw(nb_heads, dim_qk, dim_model)
- self.w_V = randw(nb_heads, dim_v, dim_model, factor=1)
+ self.w_V = randw(nb_heads, dim_v, dim_model)
self.w_Q = randw(nb_heads, dim_qk, dim_model)
self.w_O = randw(dim_v * nb_heads, dim_model)
# Clip the gating to avoid values greater than 1 when several
# heads hit the same row
- # G = G / G.sum(1, keepdim=True).clamp(min=1)
-
- H = (1 - G).log().sum(1, keepdim=True).exp()
+ G = G / G.sum(1, keepdim=True).clamp(min=1)
######################################################################
- def recurrence(G, V, K):
- # We prepare the arguments for the parallel scan
-
- A = H
-
- gated_V = torch.einsum("nhrt,nhtd->nrtd", H * G / (1 - G), V)
- gated_K = torch.einsum("nhrt,nhtd->nrtd", H * G / (1 - G), K)
-
- # We start from cached values, which matters in inference
-
- init_rec_V = self.rec_V[:, :, t0 - L : t0]
- init_rec_K = self.rec_K[:, :, t0 - L : t0]
-
- # Here there is a trick: Since the stack at position t is
- # computed by updating that at position t-L, the parallel
- # scan operates with a period of L. To do so we split the
- # sequence indexing in two axes, the second of size L, and
- # run the parallel scan using the first as the sequence index.
-
- A = A.unflatten(2, (-1, L))
- gated_V = gated_V.unflatten(2, (-1, L))
- gated_K = gated_K.unflatten(2, (-1, L))
-
- next_V = pscan_dim(A, gated_V, init_rec_V, dim=2).flatten(2, 3)
- next_K = pscan_dim(A, gated_K, init_rec_K, dim=2).flatten(2, 3)
-
- return next_V, next_K
+ A = 1 - G.sum(dim=1)
- #################################################################
+ gated_V = torch.einsum("nhrt,nhtd->nrtd", G, V)
+ gated_K = torch.einsum("nhrt,nhtd->nrtd", G, K)
- next_V, next_K = recurrence(G, V, K)
+ # We start from cached values, which matters in inference
- if self.training and self.gate_dropout_proba > 0.0:
- # G is NxHxRxT where r is the caterpillar's row.
+ init_rec_V = self.rec_V[:, :, t0 - L : t0]
+ init_rec_K = self.rec_K[:, :, t0 - L : t0]
- warnings.warn("gate dropout", RuntimeWarning)
+ # Here there is a trick: Since the stack at position t is
+ # computed by updating that at position t-L, the parallel
+ # scan operates with a period of L. To do so we split the
+ # sequence indexing in two axes, the second of size L, and
+ # run the parallel scan using the first as the sequence index.
- if self.gate_dropout_sync:
- shape_kill = (N, 1, 1)
- else:
- shape_kill = (N, H, R)
-
- # Pick a point in each of the NxHxR timeline and set this
- # entry and the following to 1
- kill = (
- torch.rand(*shape_kill, t1 - t0, device=G.device).sort(dim=3).indices
- == 0
- ).cumsum(dim=3)
-
- # Keep these mask for only some of the NxHxR
- kill = kill * (
- torch.rand(*shape_kill, 1, device=G.device) <= self.gate_dropout_proba
- )
-
- # The coefficient to keep are the complementary
- mask = 1 - kill
-
- masked_next_V, masked_next_K = recurrence(G * mask, V, K)
-
- if self.gate_dropout_replace:
- next_V = next_V.detach()
- next_K = next_K.detach()
+ A = A.unflatten(2, (-1, L))
+ gated_V = gated_V.unflatten(2, (-1, L))
+ gated_K = gated_K.unflatten(2, (-1, L))
- warnings.warn("the rescaling is probably a bad idea", RuntimeWarning)
-
- next_V = next_V + (masked_next_V - masked_next_V.detach()) / (
- 1 - self.gate_dropout_proba
- )
- next_K = next_K + (masked_next_K - masked_next_K.detach()) / (
- 1 - self.gate_dropout_proba
- )
+ next_V = pscan_dim(A, gated_V, init_rec_V, dim=2).flatten(2, 3)
+ next_K = pscan_dim(A, gated_K, init_rec_K, dim=2).flatten(2, 3)
self.rec_V[:, :, t0:t1] = next_V
self.rec_K[:, :, t0:t1] = next_K
Q = torch.einsum("ntc,hdc->nhtd", X, self.w_Q)
+ # Q = blanket(Q)
+
# We build tensors NxHxTxRxL where N is the sample index, H
# the head, T the time, R the row in the caterpillar, and L
# the column in the caterpillar
windowed_V,
).flatten(2)
- # Compute the final output
-
self.cache_Y[:, t0:t1] = Y @ self.w_O
return BracketedSequence(self.cache_Y, t0, t1 - t0, bs.init_cache)
dim_v,
nb_heads=1,
causal=False,
+ horizon=None,
attention_dropout=0.0,
logger=print,
args=None,
return nn.Parameter(torch.randn(*d) / math.sqrt(d[-1]))
self.causal = causal
+ self.horizon = horizon
self.attention_dropout = attention_dropout
self.record_attention = False
torch.arange(x_q.size(1), device=q.device)[None, None, :, None]
< torch.arange(x_q.size(1), device=q.device)[None, None, None, :]
)
+
+ if self.horizon is not None:
+ self.cache_attzero = torch.logical_or(
+ self.cache_attzero,
+ torch.arange(x_q.size(1), device=q.device)[None, None, :, None]
+ >= torch.arange(x_q.size(1), device=q.device)[
+ None, None, None, :
+ ]
+ + self.horizon,
+ )
+
a = a.masked_fill(
self.cache_attzero[
:, :, bs.first : bs.first + bs.nb, : bs.first + bs.nb
):
super().__init__()
+ self.vocabulary_size = vocabulary_size
+
assert attention_layer in {
"mha",
"dumbrec",
"kvrec",
"caterpillar",
+ "attcat",
}, f"Unknown attention operator {attention_layer}."
- if attention_layer == "caterpillar":
+ if attention_layer == "caterpillar" or attention_layer == "attcat":
assert nb_lines % caterpillar_height == 0
self.caterpillar_length = nb_lines // caterpillar_height
self.caterpillar_height = caterpillar_height
def attlayer():
if attention_layer == "mha":
- return QKVAttention(
- dim_model=dim_model,
- dim_qk=dim_keys,
- dim_v=dim_model // nb_heads,
- nb_heads=nb_heads,
- causal=causal,
- attention_dropout=dropout,
- logger=logger,
- args=args,
+ return WithResidual(
+ CacheWrapper(nn.LayerNorm((dim_model,))),
+ QKVAttention(
+ dim_model=dim_model,
+ dim_qk=dim_keys,
+ dim_v=dim_model // nb_heads,
+ nb_heads=nb_heads,
+ causal=causal,
+ attention_dropout=dropout,
+ logger=logger,
+ args=args,
+ ),
)
elif attention_layer == "dumbrec":
- return DumbRec(
- dim_model=dim_model,
- dim_qk=dim_keys,
- dim_v=dim_model // nb_heads,
- nb_heads=nb_heads,
- nb_lines=nb_lines,
- attention_dropout=dropout,
- logger=logger,
- args=args,
+ return WithResidual(
+ CacheWrapper(nn.LayerNorm((dim_model,))),
+ DumbRec(
+ dim_model=dim_model,
+ dim_qk=dim_keys,
+ dim_v=dim_model // nb_heads,
+ nb_heads=nb_heads,
+ nb_lines=nb_lines,
+ attention_dropout=dropout,
+ logger=logger,
+ args=args,
+ ),
)
elif attention_layer == "kvrec":
- return KVRec(
- dim_model=dim_model,
- dim_qk=dim_keys,
- dim_v=dim_model // nb_heads,
- nb_heads=nb_heads,
- nb_lines=nb_lines,
- attention_dropout=dropout,
- logger=logger,
- args=args,
+ return WithResidual(
+ CacheWrapper(nn.LayerNorm((dim_model,))),
+ KVRec(
+ dim_model=dim_model,
+ dim_qk=dim_keys,
+ dim_v=dim_model // nb_heads,
+ nb_heads=nb_heads,
+ nb_lines=nb_lines,
+ attention_dropout=dropout,
+ logger=logger,
+ args=args,
+ ),
)
elif attention_layer == "caterpillar":
- return Caterpillar(
- dim_model=dim_model,
- dim_qk=dim_keys,
- dim_v=dim_model // nb_heads,
- nb_heads=nb_heads,
- caterpillar_length=self.caterpillar_length,
- caterpillar_height=self.caterpillar_height,
- attention_dropout=dropout,
- logger=logger,
- args=args,
+ return WithResidual(
+ CacheWrapper(nn.LayerNorm((dim_model,))),
+ Caterpillar(
+ dim_model=dim_model,
+ dim_qk=dim_keys,
+ dim_v=dim_model // nb_heads,
+ nb_heads=nb_heads,
+ caterpillar_length=self.caterpillar_length,
+ caterpillar_height=self.caterpillar_height,
+ attention_dropout=dropout,
+ logger=logger,
+ args=args,
+ ),
+ )
+ elif attention_layer == "attcat":
+ return nn.Sequential(
+ WithResidual(
+ CacheWrapper(nn.LayerNorm((dim_model,))),
+ QKVAttention(
+ dim_model=dim_model,
+ dim_qk=dim_keys,
+ dim_v=dim_model // nb_heads,
+ nb_heads=nb_heads,
+ causal=causal,
+ horizon=self.caterpillar_length,
+ attention_dropout=dropout,
+ logger=logger,
+ args=args,
+ ),
+ ),
+ WithResidual(
+ CacheWrapper(nn.LayerNorm((dim_model,))),
+ Caterpillar(
+ dim_model=dim_model,
+ dim_qk=dim_keys,
+ dim_v=dim_model // nb_heads,
+ nb_heads=nb_heads,
+ caterpillar_length=self.caterpillar_length,
+ caterpillar_height=self.caterpillar_height,
+ attention_dropout=dropout,
+ logger=logger,
+ args=args,
+ ),
+ ),
)
else:
raise ValueError(f"Unknown attention type {attention_layer}.")
for b in range(nb_blocks):
trunk_blocks += [
- WithResidual(
- CacheWrapper(nn.LayerNorm((dim_model,))),
- attlayer(),
- ),
+ attlayer(),
WithResidual(
CacheWrapper(
nn.LayerNorm((dim_model,)),
# t = np.arange(dt, 20.0, dt)
# ax.semilogx(t, np.exp(-t / 5.0))
# ax.grid()
+ ax.set_yscale("log")
######################################################################
- for label, model in [
- # ("nn.Linear", linear),
- ("mygpy.QKVAttention", qkv),
- ("mygpt.Caterpillar", caterpillar),
+ for label, model, thickness in [
+ ("nn.Linear", linear, 0.2),
+ ("mygpy.QKVAttention", qkv, 1),
+ ("mygpt.Caterpillar", caterpillar, 2),
]:
y = model(BracketedSequence(x, 32, x.size(1) - 32, init_cache=True)).x
- data = []
- for t in range(y.size(1)):
- for d in torch.randperm(y.size(2))[:8]:
- g = torch.autograd.grad(y[0, t, d], x, retain_graph=True)[0]
- sg = g.pow(2).sum().item()
- # sg = 0
- # for p in model.parameters():
- # g = torch.autograd.grad(y[0, t, d], p, retain_graph=True)[0]
- # sg = sg + g.pow(2).sum().item()
- data.append([t, sg])
-
- data = torch.tensor(data)
- ax.scatter(
- data[:, 0], data[:, 1], s=1, label=label
- ) # , color='gray', label='Input')
+ for n, p in [("input", x)] + list(model.named_parameters()):
+ print(f"Processing {model}.{n}")
+ data = []
+ for t in range(y.size(1)):
+ sg = 0
+ for d in torch.randperm(y.size(2))[:8]:
+ sg += torch.autograd.grad(y[0, t, d], p, retain_graph=True)[0]
+ assert not sg.isinf().any()
+ assert not sg.isnan().any()
+ data.append([t, sg.sum().item()])
+
+ data = torch.tensor(data)
+ # cx, cy = data[:, 0], data[:, 1]
+ cy = data[:, 1].sort().values
+ cx = torch.linspace(0, 1, cy.size(0))
+ ax.plot(
+ cx, cy, label=label + "." + n, linewidth=thickness
+ ) # , color='gray', label='Input')
# ax.legend(frameon=False, loc="top right")
projects / mygptrnn.git / blobdiff