("gray", [128, 128, 128]),
]
- def __init__(self, device=torch.device("cpu")):
+ def __init__(
+ self,
+ max_nb_cached_chunks=None,
+ chunk_size=None,
+ nb_threads=-1,
+ ):
self.colors = torch.tensor([c for _, c in self.named_colors])
self.height = 10
self.width = 10
- self.device = device
+ self.cache_rec_coo = {}
+ super().__init__(max_nb_cached_chunks, chunk_size, nb_threads)
######################################################################
predicted_prompts=None,
predicted_answers=None,
nrow=4,
+ margin=8,
):
S = self.height * self.width
As = prompts[:, 0 * (S + 1) : 0 * (S + 1) + S].view(-1, self.height, self.width)
c = c.long()[:, None]
c = (
(1 - ((c == 1).long() + (c == 0).long() + (c == -1).long()))
- * torch.tensor([64, 64, 64], device=c.device)
- + (c == 1).long() * torch.tensor([0, 255, 0], device=c.device)
- + (c == 0).long() * torch.tensor([255, 255, 255], device=c.device)
- + (c == -1).long() * torch.tensor([255, 0, 0], device=c.device)
+ * torch.tensor([64, 64, 64])
+ + (c == 1).long() * torch.tensor([0, 255, 0])
+ + (c == 0).long() * torch.tensor([255, 255, 255])
+ + (c == -1).long() * torch.tensor([255, 0, 0])
)
y[...] = c[:, :, None, None]
return y
- margin = 8
-
img_prompts = torch.cat(
[
add_frame(
def nb_token_values(self):
return len(self.colors)
- # That's quite a tensorial spaghetti mess to sample
- # non-overlapping rectangles quickly, but made the generation of
- # 100k samples go from 1h50 with a lame pure python code to 3min30s
- # with this one.
- def rec_coo(self, nb_rec, min_height=3, min_width=3):
- nb_trials = 200
+ # @torch.compile
+ def rec_coo(
+ self,
+ nb_rec,
+ min_height=3,
+ min_width=3,
+ surface_max=None,
+ prevent_overlap=False,
+ ):
+ if surface_max is None:
+ surface_max = self.height * self.width // 2
+ signature = (nb_rec, min_height, min_width, surface_max)
+
+ try:
+ return self.cache_rec_coo[signature].pop()
+ except IndexError:
+ pass
+ except KeyError:
+ pass
+
+ N = 10000
while True:
- v = (
- (
- torch.rand(nb_trials * nb_rec, self.height + 1, device=self.device)
- .sort(dim=-1)
- .indices
- < 2
- )
- .long()
- .cumsum(dim=1)
- == 1
- ).long()
+ while True:
+ i = torch.randint(self.height, (N * nb_rec, 2)).sort(dim=-1).values
+ j = torch.randint(self.width, (N * nb_rec, 2)).sort(dim=-1).values
- h = (
- (
- torch.rand(nb_trials * nb_rec, self.width + 1, device=self.device)
- .sort(dim=-1)
- .indices
- < 2
+ big_enough = (
+ (i[:, 1] >= i[:, 0] + min_height)
+ & (j[:, 1] >= j[:, 0] + min_height)
+ & ((i[:, 1] - i[:, 0]) * (j[:, 1] - j[:, 0]) <= surface_max)
)
- .long()
- .cumsum(dim=1)
- == 1
- ).long()
-
- i = torch.logical_and(
- v.sum(dim=-1) >= min_height, h.sum(dim=-1) >= min_width
- )
- v, h = v[i], h[i]
- v = v[: v.size(0) - v.size(0) % nb_rec]
- h = h[: h.size(0) - h.size(0) % nb_rec]
- v = v.reshape(v.size(0) // nb_rec, nb_rec, -1)
- h = h.reshape(h.size(0) // nb_rec, nb_rec, -1)
+ i, j = i[big_enough], j[big_enough]
- r = v[:, :, :, None] * h[:, :, None, :]
+ n = i.size(0) - i.size(0) % nb_rec
- valid = r.sum(dim=1).flatten(1).max(dim=-1).values == 1
+ if n > 0:
+ break
- v = v[valid]
- h = h[valid]
+ i = i[:n].reshape(n // nb_rec, nb_rec, -1)
+ j = j[:n].reshape(n // nb_rec, nb_rec, -1)
+
+ if prevent_overlap:
+ can_fit = ((i[:, :, 1] - i[:, :, 0]) * (j[:, :, 1] - j[:, :, 0])).sum(
+ dim=-1
+ ) <= self.height * self.width
+ i, j = i[can_fit], j[can_fit]
+ if nb_rec == 2:
+ A_i1, A_i2, A_j1, A_j2 = (
+ i[:, 0, 0],
+ i[:, 0, 1],
+ j[:, 0, 0],
+ j[:, 0, 1],
+ )
+ B_i1, B_i2, B_j1, B_j2 = (
+ i[:, 1, 0],
+ i[:, 1, 1],
+ j[:, 1, 0],
+ j[:, 1, 1],
+ )
+ no_overlap = torch.logical_not(
+ (A_i1 >= B_i2)
+ & (A_i2 <= B_i1)
+ & (A_j1 >= B_j1)
+ & (A_j2 <= B_j1)
+ )
+ i, j = i[no_overlap], j[no_overlap]
+ elif nb_rec == 3:
+ A_i1, A_i2, A_j1, A_j2 = (
+ i[:, 0, 0],
+ i[:, 0, 1],
+ j[:, 0, 0],
+ j[:, 0, 1],
+ )
+ B_i1, B_i2, B_j1, B_j2 = (
+ i[:, 1, 0],
+ i[:, 1, 1],
+ j[:, 1, 0],
+ j[:, 1, 1],
+ )
+ C_i1, C_i2, C_j1, C_j2 = (
+ i[:, 2, 0],
+ i[:, 2, 1],
+ j[:, 2, 0],
+ j[:, 2, 1],
+ )
+ no_overlap = (
+ (
+ (A_i1 >= B_i2)
+ | (A_i2 <= B_i1)
+ | (A_j1 >= B_j2)
+ | (A_j2 <= B_j1)
+ )
+ & (
+ (A_i1 >= C_i2)
+ | (A_i2 <= C_i1)
+ | (A_j1 >= C_j2)
+ | (A_j2 <= C_j1)
+ )
+ & (
+ (B_i1 >= C_i2)
+ | (B_i2 <= C_i1)
+ | (B_j1 >= C_j2)
+ | (B_j2 <= C_j1)
+ )
+ )
+ i, j = (i[no_overlap], j[no_overlap])
+ else:
+ assert nb_rec == 1
- if v.size(0) > 0:
+ if i.size(0) > 1:
break
- av = torch.arange(v.size(2), device=self.device)[None, :]
- ah = torch.arange(h.size(2), device=self.device)[None, :]
-
- return [
- (i1.item(), j1.item(), i2.item() + 1, j2.item() + 1)
- for i1, j1, i2, j2 in zip(
- v.size(2) - (v[0] * (v.size(2) - av)).max(dim=-1).values,
- h.size(2) - (h[0] * (h.size(2) - ah)).max(dim=-1).values,
- (v[0] * av).max(dim=-1).values,
- (h[0] * ah).max(dim=-1).values,
- )
+ self.cache_rec_coo[signature] = [
+ [
+ (
+ i[n, k, 0].item(),
+ j[n, k, 0].item(),
+ i[n, k, 1].item(),
+ j[n, k, 1].item(),
+ )
+ for k in range(nb_rec)
+ ]
+ for n in range(i.size(0))
]
- def rec_coo_(self, x, n, min_height=3, min_width=3):
- collision = x.new(x.size())
- while True:
- collision[...] = 0
- result = []
- for _ in range(n):
- while True:
- i1, i2 = torch.randint(x.size(0), (2,))
- if i1 + min_height <= i2:
- break
- while True:
- j1, j2 = torch.randint(x.size(1), (2,))
- if j1 + min_width <= j2:
- break
- collision[i1:i2, j1:j2] += 1
- if collision.max() > 1:
- break
- result.append((i1, j1, i2, j2))
- if collision.max() == 1:
- break
- return result
+ return self.cache_rec_coo[signature].pop()
######################################################################
+ # @torch.compile
def task_replace_color(self, A, f_A, B, f_B):
nb_rec = 3
c = torch.randperm(len(self.colors) - 1)[: nb_rec + 1] + 1
for X, f_X in [(A, f_A), (B, f_B)]:
- r = self.rec_coo(nb_rec)
+ r = self.rec_coo(nb_rec, prevent_overlap=True)
for n in range(nb_rec):
i1, j1, i2, j2 = r[n]
X[i1:i2, j1:j2] = c[n]
f_X[i1:i2, j1:j2] = c[n if n > 0 else -1]
+ # @torch.compile
def task_translate(self, A, f_A, B, f_B):
- di, dj = torch.randint(3, (2,)) - 1
+ while True:
+ di, dj = torch.randint(3, (2,)) - 1
+ if di.abs() + dj.abs() > 0:
+ break
+
nb_rec = 3
c = torch.randperm(len(self.colors) - 1)[:nb_rec] + 1
for X, f_X in [(A, f_A), (B, f_B)]:
while True:
- r = self.rec_coo(nb_rec)
+ r = self.rec_coo(nb_rec, prevent_overlap=True)
i1, j1, i2, j2 = r[nb_rec - 1]
if (
i1 + di >= 0
else:
f_X[i1:i2, j1:j2] = c[n]
+ # @torch.compile
def task_grow(self, A, f_A, B, f_B):
di, dj = torch.randint(2, (2,)) * 2 - 1
nb_rec = 3
direction = torch.randint(2, (1,))
for X, f_X in [(A, f_A), (B, f_B)]:
while True:
- r = self.rec_coo(nb_rec)
+ r = self.rec_coo(nb_rec, prevent_overlap=True)
i1, j1, i2, j2 = r[nb_rec - 1]
if i1 + 3 < i2 and j1 + 3 < j2:
break
X[i1:i2, j1:j2] = c[n]
f_X[i1:i2, j1:j2] = c[n]
+ # @torch.compile
def task_color_grow(self, A, f_A, B, f_B):
di, dj = torch.randint(2, (2,)) * 2 - 1
nb_rec = 3
c = torch.randperm(len(self.colors) - 1)[: 2 * nb_rec] + 1
direction = torch.randint(4, (1,))
for X, f_X in [(A, f_A), (B, f_B)]:
- r = self.rec_coo(nb_rec)
+ r = self.rec_coo(nb_rec, prevent_overlap=True)
for n in range(nb_rec):
i1, j1, i2, j2 = r[n]
X[i1:i2, j1:j2] = c[2 * n]
else:
f_X[i1:i2, j : j + 1] = c[2 * n + 1]
+ # @torch.compile
def task_frame(self, A, f_A, B, f_B):
nb_rec = 3
c = torch.randperm(len(self.colors) - 1)[: nb_rec + 1] + 1
for X, f_X in [(A, f_A), (B, f_B)]:
- r = self.rec_coo(nb_rec)
+ r = self.rec_coo(nb_rec, prevent_overlap=True)
for n in range(nb_rec):
i1, j1, i2, j2 = r[n]
X[i1:i2, j1:j2] = c[n]
- f_X[i1:i2, j1:j2] = c[n]
if n == nb_rec - 1:
- f_X[i1 + 1 : i2 - 1, j1 + 1 : j2 - 1] = 0
+ f_X[i1:i2, j1] = c[n]
+ f_X[i1:i2, j2 - 1] = c[n]
+ f_X[i1, j1:j2] = c[n]
+ f_X[i2 - 1, j1:j2] = c[n]
+ else:
+ f_X[i1:i2, j1:j2] = c[n]
+ # @torch.compile
def task_detect(self, A, f_A, B, f_B):
nb_rec = 3
c = torch.randperm(len(self.colors) - 1)[: nb_rec + 1] + 1
for X, f_X in [(A, f_A), (B, f_B)]:
- r = self.rec_coo(nb_rec)
+ r = self.rec_coo(nb_rec, prevent_overlap=True)
for n in range(nb_rec):
i1, j1, i2, j2 = r[n]
X[i1:i2, j1:j2] = c[n]
if n < nb_rec - 1:
f_X[i1, j1] = c[-1]
+ # @torch.compile
def contact(self, X, i, j, q):
nq, nq_diag = 0, 0
no = 0
return no, nq, nq_diag
def task_count(self, A, f_A, B, f_B):
- N = torch.randint(4, (1,)) + 2
+ N = (torch.randint(4, (1,)) + 2).item()
c = torch.randperm(len(self.colors) - 1)[:N] + 1
for X, f_X in [(A, f_A), (B, f_B)]:
+ l_q = torch.randperm(self.height * self.width)[
+ : self.height * self.width // 20
+ ]
+ l_d = torch.randint(N, l_q.size())
nb = torch.zeros(N, dtype=torch.int64)
- q = torch.randint(N, (self.height * self.width,))
- k = torch.randperm(self.height * self.width)
- for p in range(self.height * self.width):
- i, j = k[p] % self.height, k[p] // self.height
- no, nq, nq_diag = self.contact(X, i, j, c[q[p]])
- if no == 0 and nq_diag == 0:
- if nq == 0:
- if nb[q[p]] < self.width:
- X[i, j] = c[q[p]]
- nb[q[p]] += 1
- if nq == 1:
- X[i, j] = c[q[p]]
-
- for n in range(N):
- for j in range(nb[n]):
- f_X[n, j] = c[n]
+ for q, e in zip(l_q, l_d):
+ d = c[e]
+ i, j = q % self.height, q // self.height
+ if (
+ nb[e] < self.width
+ and X[max(0, i - 1) : i + 2, max(0, j - 1) : j + 2] == 0
+ ).all():
+ X[i, j] = d
+ nb[e] += 1
+
+ l_q = torch.randperm((self.height - 2) * (self.width - 2))[
+ : self.height * self.width // 2
+ ]
+ l_d = torch.randint(N, l_q.size())
+ for q, e in zip(l_q, l_d):
+ d = c[e]
+ i, j = q % (self.height - 2) + 1, q // (self.height - 2) + 1
+ a1, a2, a3 = X[i - 1, j - 1 : j + 2]
+ a8, a4 = X[i, j - 1], X[i, j + 1]
+ a7, a6, a5 = X[i + 1, j - 1 : j + 2]
+ if (
+ X[i, j] == 0
+ and nb[e] < self.width
+ and (a2 == 0 or a2 == d)
+ and (a4 == 0 or a4 == d)
+ and (a6 == 0 or a6 == d)
+ and (a8 == 0 or a8 == d)
+ and (a1 == 0 or a2 == d or a8 == d)
+ and (a3 == 0 or a4 == d or a2 == d)
+ and (a5 == 0 or a6 == d or a4 == d)
+ and (a7 == 0 or a8 == d or a6 == d)
+ ):
+ o = (
+ (a2 != 0).long()
+ + (a4 != 0).long()
+ + (a6 != 0).long()
+ + (a8 != 0).long()
+ )
+ if o <= 1:
+ X[i, j] = d
+ nb[e] += 1 - o
+
+ for e in range(N):
+ for j in range(nb[e]):
+ f_X[e, j] = c[e]
+
+ # @torch.compile
def task_trajectory(self, A, f_A, B, f_B):
c = torch.randperm(len(self.colors) - 1)[:2] + 1
for X, f_X in [(A, f_A), (B, f_B)]:
f_X[i + k * di, j + k * dj] = c[min(k, 1)]
k += 1
+ # @torch.compile
def task_bounce(self, A, f_A, B, f_B):
c = torch.randperm(len(self.colors) - 1)[:3] + 1
for X, f_X in [(A, f_A), (B, f_B)]:
-
+ # @torch.compile
def free(i, j):
return (
i >= 0
if l > 3:
break
+ # @torch.compile
def task_scale(self, A, f_A, B, f_B):
c = torch.randperm(len(self.colors) - 1)[:2] + 1
X[i, j] = c[1]
f_X[0:2, 0:2] = c[1]
+ # @torch.compile
def task_symbols(self, A, f_A, B, f_B):
nb_rec = 4
c = torch.randperm(len(self.colors) - 1)[: nb_rec + 1] + 1
f_X[i[0] : i[0] + delta, j[0] : j[0] + delta] = c[q]
+ # @torch.compile
+ def task_ortho(self, A, f_A, B, f_B):
+ nb_rec = 3
+ di, dj = torch.randint(3, (2,)) - 1
+ o = torch.tensor([[0.0, 1.0], [-1.0, 0.0]])
+ m = torch.eye(2)
+ for _ in range(torch.randint(4, (1,))):
+ m = m @ o
+ if torch.rand(1) < 0.5:
+ m[0, :] = -m[0, :]
+
+ ci, cj = (self.height - 1) / 2, (self.width - 1) / 2
+
+ for X, f_X in [(A, f_A), (B, f_B)]:
+ while True:
+ X[...] = 0
+ f_X[...] = 0
+
+ c = torch.randperm(len(self.colors) - 1)[:nb_rec] + 1
+
+ for r in range(nb_rec):
+ while True:
+ i1, i2 = torch.randint(self.height - 2, (2,)) + 1
+ j1, j2 = torch.randint(self.width - 2, (2,)) + 1
+ if (
+ i2 >= i1
+ and j2 >= j1
+ and max(i2 - i1, j2 - j1) >= 2
+ and min(i2 - i1, j2 - j1) <= 3
+ ):
+ break
+ X[i1 : i2 + 1, j1 : j2 + 1] = c[r]
+
+ i1, j1, i2, j2 = i1 - ci, j1 - cj, i2 - ci, j2 - cj
+
+ i1, j1 = m[0, 0] * i1 + m[0, 1] * j1, m[1, 0] * i1 + m[1, 1] * j1
+ i2, j2 = m[0, 0] * i2 + m[0, 1] * j2, m[1, 0] * i2 + m[1, 1] * j2
+
+ i1, j1, i2, j2 = i1 + ci, j1 + cj, i2 + ci, j2 + cj
+ i1, i2 = i1.long() + di, i2.long() + di
+ j1, j2 = j1.long() + dj, j2.long() + dj
+ if i1 > i2:
+ i1, i2 = i2, i1
+ if j1 > j2:
+ j1, j2 = j2, j1
+
+ f_X[i1 : i2 + 1, j1 : j2 + 1] = c[r]
+
+ n = F.one_hot(X.flatten()).sum(dim=0)[1:]
+ if (
+ n.sum() > self.height * self.width // 4
+ and (n > 0).long().sum() == nb_rec
+ ):
+ break
+
+ # @torch.compile
def task_islands(self, A, f_A, B, f_B):
pass
self.task_bounce,
self.task_scale,
self.task_symbols,
+ self.task_ortho,
# self.task_islands,
]
S = self.height * self.width
Bs = prompts[:, 2 * (S + 1) : 2 * (S + 1) + S]
f_Bs = answers
- return (B_s == f_Bs).long().min(dim=-1).values > 0
+ return (Bs == f_Bs).long().min(dim=-1).values > 0
- def generate_prompts_and_answers(self, nb, tasks=None, device="cpu"):
+ def generate_prompts_and_answers_(self, nb, tasks=None, progress_bar=False):
if tasks is None:
tasks = self.all_tasks()
prompts = torch.zeros(nb, 3 * S + 2, dtype=torch.int64)
answers = torch.zeros(nb, S, dtype=torch.int64)
- for prompt, answer in tqdm.tqdm(
- zip(prompts, answers),
- dynamic_ncols=True,
- desc="world generation",
- total=prompts.size(0),
- ):
+ bunch = zip(prompts, answers)
+
+ if progress_bar:
+ bunch = tqdm.tqdm(
+ bunch,
+ dynamic_ncols=True,
+ desc="world generation",
+ total=prompts.size(0),
+ )
+
+ for prompt, answer in bunch:
A = prompt[0 * (S + 1) : 0 * (S + 1) + S].view(self.height, self.width)
f_A = prompt[1 * (S + 1) : 1 * (S + 1) + S].view(self.height, self.width)
B = prompt[2 * (S + 1) : 2 * (S + 1) + S].view(self.height, self.width)
if __name__ == "__main__":
import time
- nb = 48
-
+ # grids = Grids(max_nb_cached_chunks=5, chunk_size=100, nb_threads=4)
grids = Grids()
- # for t in grids.all_tasks():
- for t in [grids.task_islands]:
+ # nb = 1000
+ # grids = problem.MultiThreadProblem(
+ # grids, max_nb_cached_chunks=50, chunk_size=100, nb_threads=1
+ # )
+ # time.sleep(10)
+ # start_time = time.perf_counter()
+ # prompts, answers = grids.generate_prompts_and_answers(nb)
+ # delay = time.perf_counter() - start_time
+ # print(f"{prompts.size(0)/delay:02f} seq/s")
+ # exit(0)
+
+ # if True:
+ # nb,nrow = 72,4
+ nb, nrow = 8, 2
+
+ for t in grids.all_tasks():
+ # for t in [grids.task_replace_color]:
print(t.__name__)
- prompts, answers = grids.generate_prompts_and_answers(nb, tasks=[t])
- grids.save_quizzes("/tmp", t.__name__, prompts[:nb], answers[:nb], nrow=4)
+ prompts, answers = grids.generate_prompts_and_answers_(nb, tasks=[t])
+ grids.save_quizzes("/tmp", t.__name__, prompts[:nb], answers[:nb], nrow=nrow)
- exit(0)
+ nb = 1000
- nb = 72
+ for t in grids.all_tasks():
+ # for t in [ grids.task_replace_color ]: #grids.all_tasks():
+ start_time = time.perf_counter()
+ prompts, answers = grids.generate_prompts_and_answers_(nb, tasks=[t])
+ delay = time.perf_counter() - start_time
+ print(f"{t.__name__} {prompts.size(0)/delay:02f} seq/s")
- start_time = time.perf_counter()
- prompts, answers = grids.generate_prompts_and_answers(nb)
- delay = time.perf_counter() - start_time
- print(f"{prompts.size(0)/delay:02f} seq/s")
+ exit(0)
m = torch.randint(2, (prompts.size(0),))
predicted_prompts = m * (torch.randint(2, (prompts.size(0),)) * 2 - 1)
projects / culture.git / blobdiff