+ dropout_head * (1 - epsilon - G.detach())
- dropout_tail * G.detach()
)
+
+######################################################################
+
+2024 Jan 18 07:39:29 (from mygpt.py)
+
+class Calibrator:
+ def __init__(self, w=None, b=None):
+ self.w = w
+ self.b = b
+ self.s, self.s_sq, self.n = 0, 0, 0
+ self.mean, self.std = 0, 0
+
+ def update(self, X):
+ X = X.detach()
+ self.s += X.sum(dim=0)
+ self.s_sq += X.pow(2).sum(dim=0)
+ self.n += X.size(0)
+
+ def moments(self):
+ mean = self.s / self.n
+ std = (self.s_sq / self.n - mean * mean).sqrt()
+ return mean, std
+
+ def normalize(self):
+ mean, std = self.moments()
+ if self.b is not None:
+ self.b.sub_(mean)
+ if self.w is not None:
+ self.w.div_(std)
+ result = mean - self.mean, std - self.std
+ self.mean, self.std = mean, std
+ self.s, self.s_sq, self.n = 0, 0, 0
+ return result
+
+
+
+######################################################################
+
+2024 Jan 18 07:39:34 (from mygpt.py)
+
+ # self.calibrator_G = Calibrator()
+ # self.calibrator_rec_V = Calibrator()
+ # self.calibrator_rec_K = Calibrator()
+
+
+######################################################################
+
+2024 Jan 18 07:39:37 (from mygpt.py)
+
+ # self.calibrator_G.update(G.reshape(-1, G.size(-1)))
+
+
+######################################################################
+
+2024 Jan 18 07:39:42 (from mygpt.py)
+
+ # self.calibrator_rec_V.update(
+ # next_V.permute(0, 1, 3, 2).reshape(-1, next_V.size(2))
+ # )
+ # self.calibrator_rec_K.update(
+ # next_K.permute(0, 1, 3, 2).reshape(-1, next_K.size(2))
+ # )
+
+
+######################################################################
+
+2024 Jan 18 07:47:12 (from mygpt.py)
+
+ ######################################################################
+ # Roll the gating indexes
+
+ # warnings.warn("rotating barrel", RuntimeWarning)
+
+ # r_barrel = torch.arange(R, device=G.device)[None, None, :, None]
+ # t_barrel = torch.arange(t1 - t0, device=G.device)[None, None, None, :]
+ # r_barrel = (r_barrel + (t_barrel + t0) // L) % R
+ # G = G.gather(dim=2, index=r_barrel.expand_as(G))
+
+
+######################################################################
+
+2024 Jan 18 07:47:25 (from mygpt.py)
+
+ # warnings.warn("harmonic recurrence", RuntimeWarning)
+ # har = torch.arange(t0, t1, device = G.device).float() + 1
+ # A = har / (har + 1)
+ # G = G / har
+
import torch, torchvision
import torch.nn.functional as F
-name_shapes = ["A", "B", "C", "D", "E", "F"]
-
-name_colors = ["red", "yellow", "blue", "green", "white", "purple"]
-
######################################################################
max_nb_items=4,
max_nb_transformations=3,
nb_questions=4,
+ nb_shapes=6,
+ nb_colors=6,
):
assert size % 2 == 0
self.size = size
self.max_nb_items = max_nb_items
self.max_nb_transformations = max_nb_transformations
self.nb_questions = nb_questions
+ self.name_shapes = [chr(ord("A") + k) for k in range(nb_shapes)]
+ self.name_colors = [
+ "red",
+ "yellow",
+ "blue",
+ "green",
+ "white",
+ "black",
+ "maroon",
+ "dark_red",
+ "brown",
+ "firebrick",
+ "crimson",
+ "tomato",
+ "coral",
+ "indian_red",
+ "light_coral",
+ "dark_salmon",
+ "salmon",
+ "light_salmon",
+ "orange_red",
+ "dark_orange",
+ "orange",
+ "gold",
+ "dark_golden_rod",
+ "golden_rod",
+ "pale_golden_rod",
+ "dark_khaki",
+ "khaki",
+ "olive",
+ "yellow_green",
+ "dark_olive_green",
+ "olive_drab",
+ "lawn_green",
+ "chartreuse",
+ "green_yellow",
+ "dark_green",
+ "forest_green",
+ "lime",
+ "lime_green",
+ "light_green",
+ "pale_green",
+ "dark_sea_green",
+ "medium_spring_green",
+ "spring_green",
+ "sea_green",
+ "medium_aqua_marine",
+ "medium_sea_green",
+ "light_sea_green",
+ "dark_slate_gray",
+ "teal",
+ "dark_cyan",
+ "aqua",
+ "cyan",
+ "light_cyan",
+ "dark_turquoise",
+ "turquoise",
+ "medium_turquoise",
+ "pale_turquoise",
+ "aqua_marine",
+ "powder_blue",
+ "cadet_blue",
+ "steel_blue",
+ "corn_flower_blue",
+ "deep_sky_blue",
+ "dodger_blue",
+ "light_blue",
+ "sky_blue",
+ "light_sky_blue",
+ "midnight_blue",
+ "navy",
+ "dark_blue",
+ "medium_blue",
+ "royal_blue",
+ "blue_violet",
+ "indigo",
+ "dark_slate_blue",
+ "slate_blue",
+ "medium_slate_blue",
+ "medium_purple",
+ "dark_magenta",
+ "dark_violet",
+ "dark_orchid",
+ "medium_orchid",
+ "purple",
+ "thistle",
+ "plum",
+ "violet",
+ "magenta",
+ "orchid",
+ "medium_violet_red",
+ "pale_violet_red",
+ "deep_pink",
+ "hot_pink",
+ "light_pink",
+ "pink",
+ "antique_white",
+ "beige",
+ "bisque",
+ "blanched_almond",
+ "wheat",
+ "corn_silk",
+ "lemon_chiffon",
+ "light_golden_rod_yellow",
+ "light_yellow",
+ "saddle_brown",
+ "sienna",
+ "chocolate",
+ "peru",
+ "sandy_brown",
+ "burly_wood",
+ "tan",
+ "rosy_brown",
+ "moccasin",
+ "navajo_white",
+ "peach_puff",
+ "misty_rose",
+ "lavender_blush",
+ "linen",
+ "old_lace",
+ "papaya_whip",
+ "sea_shell",
+ "mint_cream",
+ "slate_gray",
+ "light_slate_gray",
+ "light_steel_blue",
+ "lavender",
+ "floral_white",
+ "alice_blue",
+ "ghost_white",
+ "honeydew",
+ "ivory",
+ "azure",
+ "snow",
+ "silver",
+ "gainsboro",
+ "white_smoke",
+ ][:nb_colors]
def generate_scene(self):
nb_items = torch.randint(self.max_nb_items - 1, (1,)).item() + 2
col = torch.full((self.size * self.size,), -1)
shp = torch.full((self.size * self.size,), -1)
- a = torch.randperm(len(name_colors) * len(name_shapes))[:nb_items]
- col[:nb_items] = a % len(name_colors)
- shp[:nb_items] = a // len(name_colors)
+ a = torch.randperm(len(self.name_colors) * len(self.name_shapes))[:nb_items]
+ col[:nb_items] = a % len(self.name_colors)
+ shp[:nb_items] = a // len(self.name_colors)
i = torch.randperm(self.size * self.size)
col = col[i]
shp = shp[i]
# for i in range(self.size):
# for j in range(self.size):
# if col[i,j] >= 0:
- # print(f"at ({i},{j}) {name_colors[col[i,j]]} {name_shapes[shp[i,j]]}")
+ # print(f"at ({i},{j}) {self.name_colors[col[i,j]]} {self.name_shapes[shp[i,j]]}")
for i in range(self.size):
for j in range(self.size):
if col[i, j] >= 0:
- print(f"{name_colors[col[i,j]][0]}{name_shapes[shp[i,j]]}", end="")
+ print(
+ f"{self.name_colors[col[i,j]][0]}{self.name_shapes[shp[i,j]]}",
+ end="",
+ )
elif j == 0:
print(" +", end="")
else:
for i in range(self.size):
for j in range(self.size):
if col[i, j] >= 0:
- n = f"{name_colors[col[i,j]]} {name_shapes[shp[i,j]]}"
+ n = f"{self.name_colors[col[i,j]]} {self.name_shapes[shp[i,j]]}"
properties += [f"a {n} at {i} {j}"]
return properties
for i1 in range(self.size):
for j1 in range(self.size):
if col[i1, j1] >= 0:
- n1 = f"{name_colors[col[i1,j1]]} {name_shapes[shp[i1,j1]]}"
+ n1 = (
+ f"{self.name_colors[col[i1,j1]]} {self.name_shapes[shp[i1,j1]]}"
+ )
properties += [f"there is a {n1}"]
if i1 < self.size // 2:
properties += [f"a {n1} is in the top half"]
for i2 in range(self.size):
for j2 in range(self.size):
if col[i2, j2] >= 0:
- n2 = f"{name_colors[col[i2,j2]]} {name_shapes[shp[i2,j2]]}"
+ n2 = f"{self.name_colors[col[i2,j2]]} {self.name_shapes[shp[i2,j2]]}"
if i1 > i2:
properties += [f"a {n1} is below a {n2}"]
if i1 < i2:
parser.add_argument("--grid_size", type=int, default=6)
+parser.add_argument("--grid_nb_colors", type=int, default=6)
+
+parser.add_argument("--grid_nb_shapes", type=int, default=6)
+
##############################
# picoclvr options
nb_test_samples=args.nb_test_samples,
batch_size=args.batch_size,
size=args.grid_size,
+ nb_shapes=args.grid_nb_shapes,
+ nb_colors=args.grid_nb_colors,
logger=log_string,
device=device_data,
)
##############################
-for input in task.batches(split="train", desc="calibrate"):
- input = input.to(device)
- output = model(mygpt.BracketedSequence(input)).x
-
-for n, m in model.named_modules():
- for a in dir(m):
- x = getattr(m, a)
- if isinstance(x, mygpt.Calibrator):
- print(f"####### ${n} | ${a} ########################")
- mean, std = x.moments()
- print("mean\n", mean, "\n")
- print("std\n", std, "\n")
- print(f"############################################\n\n")
-
-exit(0)
+if "calibrate" in sup_args:
+ for input in task.batches(split="train", desc="calibrate"):
+ input = input.to(device)
+ output = model(mygpt.BracketedSequence(input)).x
+
+ for n, m in model.named_modules():
+ for a in dir(m):
+ x = getattr(m, a)
+ if isinstance(x, mygpt.Calibrator):
+ print(f"####### ${n} | ${a} ########################")
+ mean, std = x.moments()
+ print("mean\n", mean, "\n")
+ print("std\n", std, "\n")
+ print(f"############################################\n\n")
+
+ exit(0)
##############################
import pscan
-
# X is /.../xTxD A is /.../xT Y_init is /.../xD
return Y
+def pscan_rgrad(grad_Y, A, X, Y_init, dim=-2, eps=1e-2):
+ with torch.no_grad():
+ s_A, s_X = 0, 0
+ for t in range(X.size(dim) - 1, 0, -1):
+ delta = (grad_Y[t] - s_A) / A[t].grad
+ s_A += A[t].grad * delta
+ A[t].grad = delta
+ delta = (grad_Y[t] - s_X) / X[t].grad
+ s_X += X[t].grad * delta
+ X[t].grad = delta
+
+
def pscan_shape(A, X, Y_init):
s = X.size()
A = A.reshape(-1, s[-2])
##############################
-class Calibrator:
- def __init__(self, w=None, b=None):
- self.w = w
- self.b = b
- self.s, self.s_sq, self.n = 0, 0, 0
- self.mean, self.std = 0, 0
-
- def update(self, X):
- X = X.detach()
- self.s += X.sum(dim=0)
- self.s_sq += X.pow(2).sum(dim=0)
- self.n += X.size(0)
-
- def moments(self):
- mean = self.s / self.n
- std = (self.s_sq / self.n - mean * mean).sqrt()
- return mean, std
-
- def normalize(self):
- mean, std = self.moments()
- if self.b is not None:
- self.b.sub_(mean)
- if self.w is not None:
- self.w.div_(std)
- result = mean - self.mean, std - self.std
- self.mean, self.std = mean, std
- self.s, self.s_sq, self.n = 0, 0, 0
- return result
-
-
class Caterpillar(nn.Module):
def __init__(
self,
dim_v,
)
- self.calibrator_G = Calibrator()
- self.calibrator_rec_V = Calibrator()
- self.calibrator_rec_K = Calibrator()
-
def reset_inner_loss(self):
self.acc_attention = 0
self.acc_nb = 0
torch.einsum("ntc,hrc->nhrt", X, self.w_G) + self.b_G[None, :, :, None]
).sigmoid()
- self.calibrator_G.update(G.reshape(-1, G.size(-1)))
-
# warnings.warn("softmax gating", RuntimeWarning)
# G = (
G = alpha * (1 - kill)
- ######################################################################
- # Clip the gating to avoid values greater than 1 when several
- # heads hit the same row
+ def recurrence(G, V, K):
+ # 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)
+ G = G / G.sum(1, keepdim=True).clamp(min=1)
- ######################################################################
- # Roll the gating indexes
-
- # warnings.warn("rotating barrel", RuntimeWarning)
+ # We prepare the arguments for the parallel scan
- # r_barrel = torch.arange(R, device=G.device)[None, None, :, None]
- # t_barrel = torch.arange(t1 - t0, device=G.device)[None, None, None, :]
- # r_barrel = (r_barrel + (t_barrel + t0) // L) % R
- # G = G.gather(dim=2, index=r_barrel.expand_as(G))
+ A = 1 - G.sum(1)
- # We prepare the arguments for the parallel scan
+ gated_V = torch.einsum("nhrt,nhtd->nrtd", G, V)
+ gated_K = torch.einsum("nhrt,nhtd->nrtd", G, K)
- A = 1 - G.sum(1)
+ # We start from cached values, which matters in inference
- # warnings.warn("harmonic recurrence", RuntimeWarning)
- # har = torch.arange(t0, t1, device = G.device).float() + 1
- # A = har / (har + 1)
- # G = G / har
+ init_rec_V = self.rec_V[:, :, t0 - L : t0]
+ init_rec_K = self.rec_K[:, :, t0 - L : t0]
- gated_V = torch.einsum("nhrt,nhtd->nrtd", G, V)
- gated_K = torch.einsum("nhrt,nhtd->nrtd", G, K)
+ # Associative scan
- # We start from cached values, which matters in inference
+ # 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.
- init_rec_V = self.rec_V[:, :, t0 - L : t0]
- init_rec_K = self.rec_K[:, :, t0 - L : t0]
-
- #################################################################
- # Associative scan
+ A = A.unflatten(2, (-1, L))
+ gated_V = gated_V.unflatten(2, (-1, L))
+ gated_K = gated_K.unflatten(2, (-1, L))
- # 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.
+ next_V = pscan_dim(A, gated_V, init_rec_V, dim=2)
+ next_K = pscan_dim(A, gated_K, init_rec_K, dim=2)
- A = A.unflatten(2, (-1, L))
- gated_V = gated_V.unflatten(2, (-1, L))
- gated_K = gated_K.unflatten(2, (-1, L))
+ next_V = next_V.flatten(2, 3)
+ next_K = next_K.flatten(2, 3)
- next_V = pscan_dim(A, gated_V, init_rec_V, dim=2)
- next_K = pscan_dim(A, gated_K, init_rec_K, dim=2)
+ return next_V, next_K
- next_V = next_V.flatten(2, 3)
- next_K = next_K.flatten(2, 3)
+ #################################################################
- self.calibrator_rec_V.update(
- next_V.permute(0, 1, 3, 2).reshape(-1, next_V.size(2))
- )
- self.calibrator_rec_K.update(
- next_K.permute(0, 1, 3, 2).reshape(-1, next_K.size(2))
- )
+ next_V, next_K = recurrence(G, V, K)
self.rec_V[:, :, t0:t1] = next_V
self.rec_K[:, :, t0:t1] = next_K
nb_test_samples,
batch_size,
size,
+ nb_shapes,
+ nb_colors,
logger=None,
device=torch.device("cpu"),
):
self.device = device
self.batch_size = batch_size
- self.grid_factory = grid.GridFactory(size=size)
+ self.grid_factory = grid.GridFactory(
+ size=size, nb_shapes=nb_shapes, nb_colors=nb_colors
+ )
if logger is not None:
logger(
projects / mygptrnn.git / commitdiff