File size: 13,079 Bytes
d596074 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 |
#!/usr/bin/env python3
# This was copied from /ceph-dan/torch-sampling/torch_sampling/sampling_ref.py,
# its git history is there.
import random
import timeit
from typing import Optional, Tuple
import torch
from scaling import ScaledLinear
from torch import Tensor, nn
from torch.cuda.amp import custom_bwd, custom_fwd
from torch_scheduled_sampling import sample_combined
from icefall.utils import create_grad_scaler, torch_autocast
# The main exports of this file are the module KnowledgeBaseLookup and the
# function create_knowledge_base.
def create_knowledge_base(M: int, N: int, D: int) -> nn.Parameter:
std = 0.1
a = (3**0.5) * std # this sqrt(3) thing is intended to get variance of
# 0.1 from uniform distribution
ans = nn.Parameter(torch.ones(M**N, D))
nn.init.uniform_(ans, -a, a)
return ans
def join_indexes(indexes: Tensor, M: int) -> Tensor:
"""
Combines N-tuples of indexes into single indexes that can be used for
lookup in the knowledge base. Args:
indexes: tensor of torch.int64 of shape (*, K, N), with elements in
{0..M-1}
M: the size of the original softmaxes, is upper bound on elements
in indexes
Returns:
joined_indexes: of shape (*, K), joined_indexes[...,k] equals
joined_indexes[...,0,k] + joined_indexes[...,1,k]*(M**1) ... + joined_indexes[...,1,k]*(M**(N-1))]
"""
N = indexes.shape[-1]
n_powers = M ** torch.arange(N, device=indexes.device) # [ 1, M, ..., M**(N-1) ]
return (indexes * n_powers).sum(dim=-1)
# Note, we don't use this, we
def weighted_matrix_lookup(
weights: Tensor, indexes: Tensor, knowledge_base: Tensor
) -> Tensor:
"""
Weighted combination of specified rows of a matrix.
weights: Tensor of shape (*, K), can contain any value but probably in [0..1].
indexes: Tensor of shape (*, K), with elements in [0..C-1]
knowledge_base: Tensor of shape (C-1, D), whose rows we'll be looking up
Returns:
tensor of shape (*, D), containing weighted sums of rows of
`knowledge_base`
"""
if True:
return WeightedMatrixLookupFunction.apply(weights, indexes, knowledge_base)
else:
# simpler but less memory-efficient implementation
lookup = torch.index_select(knowledge_base, dim=0, index=indexes.flatten())
D = knowledge_base.shape[-1]
weights = weights.unsqueeze(-2) # (*, 1, K)
lookup = lookup.reshape(*indexes.shape, D) # (*, K, D)
ans = torch.matmul(weights, lookup) # ans: (*, 1, D)
ans = ans.squeeze(-2)
assert list(ans.shape) == list(weights.shape[:-2]) + [D]
return ans
class WeightedMatrixLookupFunction(torch.autograd.Function):
@staticmethod
@custom_fwd
def forward(
ctx, weights: Tensor, indexes: Tensor, knowledge_base: Tensor
) -> Tensor:
"""
Weighted combination of specified rows of a matrix.
weights: Tensor of shape (*, K), can contain any value but probably in [0..1].
indexes: Tensor of shape (*, K), with elements in [0..C-1]
knowledge_base: Tensor of shape (C, D), whose rows we'll be looking up
Returns:
tensor of shape (*, D), containing weighted sums of rows of
`knowledge_base`
"""
if random.random() < 0.001:
print("dtype[1] = ", weights.dtype)
ctx.save_for_backward(
weights.detach(), indexes.detach(), knowledge_base.detach()
)
with torch.no_grad():
lookup = torch.index_select(knowledge_base, dim=0, index=indexes.flatten())
D = knowledge_base.shape[-1]
weights = weights.unsqueeze(-2) # (*, 1, K)
lookup = lookup.reshape(*indexes.shape, D) # (*, K, D)
ans = torch.matmul(weights, lookup) # ans: (*, 1, D)
ans = ans.squeeze(-2) # (*, D)
return ans
@staticmethod
@custom_bwd
def backward(ctx, ans_grad: Tensor) -> Tuple[Tensor, None, Tensor]:
# ans_grad: (*, D)
weights, indexes, knowledge_base = ctx.saved_tensors
knowledge_base.requires_grad = True
dtype = ans_grad.dtype
ans_grad = ans_grad.to(weights.dtype)
assert weights.requires_grad is False
D = knowledge_base.shape[-1]
with torch.enable_grad():
# we'll use torch's autograd to differentiate this operation, which
# is nontrivial [and anyway we need `lookup` to compute weight grad.
# We don't save `lookup` because it's large, that is the reason
# we override Torch autograd.
lookup = torch.index_select(knowledge_base, dim=0, index=indexes.flatten())
lookup = lookup.reshape(*indexes.shape, D) # (*, K, D)
weights = weights.unsqueeze(-1) # (*, K, 1)
# forward pass: was:
## ans = torch.matmul(weights, lookup)
## ans: (*, 1, D)
## ans = ans.squeeze(-2) # ans, ans_grad: (*, D)
weights_grad = torch.matmul(
lookup, ans_grad.unsqueeze(-1) # (*, K, D)
) # (*, D, 1)
weights_grad = weights_grad.squeeze(-1) # (*, K, 1) -> (*, K)
lookup_grad = weights * ans_grad.unsqueeze(
-2
) # (*, K, 1) * (*, 1, D) = (*, K, D)
lookup.backward(gradient=lookup_grad)
return weights_grad.to(dtype), None, knowledge_base.grad.to(dtype)
class PenalizeNegentropyFunction(torch.autograd.Function):
"""
Function that does nothing in forward pass, but in backprop, it is as
if you had added: `- tot_entropy * alpha` to the loss function, where
tot_entropy is the the entropy of the average of the input distributions,
times the number of input distributions. (We multiply by this because
our overall loss function is proportional to the number of frames).
This will tend to make the entropy want to become as large as possible,
making (-tot_entropy * alpha) as negative as possible.
Args:
logprobs: Tensor of shape (*, num_classes), should be the result of
calling some_tensor.log_softmax(dim=-1)
Returns:
logprobs
"""
@staticmethod
def forward(ctx, logprobs: Tensor, alpha: float):
ctx.save_for_backward(logprobs.detach())
ctx.alpha = alpha
return logprobs
@staticmethod
def backward(ctx, logprobs_grad: Tensor) -> Tuple[Tensor, None]:
(logprobs,) = ctx.saved_tensors
with torch.enable_grad():
logprobs.requires_grad = True
# `negentropy` is the negative entropy of the average distribution.
# distributions. It will be <= 0.
l = logprobs.reshape(-1, logprobs.shape[-1]) # noqa: E741
scale = ctx.alpha * l.shape[0]
avg_dist = l.exp().mean(dim=0)
negentropy = (avg_dist * (avg_dist + 1.0e-20).log()).sum()
if random.random() < 0.0005:
negentropy_individual = (l * l.exp()).sum(dim=-1).mean()
print(
"Negentropy[individual,combined] = ",
negentropy_individual.item(),
", ",
negentropy.item(),
)
loss = negentropy * scale
loss.backward()
return logprobs_grad + logprobs.grad, None
class KnowledgeBaseLookup(nn.Module):
"""
Create knowledge-base lookup module. (The knowledge-base parameter, which is
large, is shared between these modules).
Args:
M: int, softmax size, e.g. in [32..128]
N: int, number of softmaxes, in [2..3]
D: int, embedding dimension in knowledge base, e.g. 256
K: number of samples (affects speed/accuracy tradeoff), e.g. 16.
embedding_dim: the dimension to project from and to, e.g. the
d_model of the conformer.
"""
def __init__(
self,
M: int,
N: int,
D: int,
K: int,
embedding_dim: int,
knowledge_base: nn.Parameter,
negentropy_penalty: float = 0.001,
):
super(KnowledgeBaseLookup, self).__init__()
self.knowledge_base = knowledge_base # shared!
self.in_proj = ScaledLinear(embedding_dim, M * N, initial_scale=1.0)
# initial_scale = 4.0 because the knowlege_base activations are
# quite small -- if we use our optimizer they'll have stddev <= 0.1.
self.out_proj = ScaledLinear(D, embedding_dim, initial_scale=4.0)
self.M = M
self.N = N
self.K = K
self.negentropy_penalty = negentropy_penalty
def forward(self, x: Tensor) -> Tensor:
"""
Forward function that does knowledge-base lookup.
Args:
x: input, of shape (*, E) where E is embedding_dim
as passed to constructor
y: output of knowledge-base lookup, of shape (*, E)
# TODO: later we can try multiplying by a projection of x or something like that.
"""
x = self.in_proj(x) # now (*, M*N)
x = x.reshape(*x.shape[:-1], self.N, self.M) # now (*, N, M)
x = x.log_softmax(dim=-1) # now normalized logprobs, dim= (*, N, M)
x = PenalizeNegentropyFunction.apply(x, self.negentropy_penalty)
_, indexes, weights = sample_combined(x, self.K, input_is_log=True)
x = weighted_matrix_lookup(weights, indexes, self.knowledge_base) # now (*, D)
x = self.out_proj(x) # now (*, self.embedding_dim)
return x
def _test_knowledge_base_lookup():
K = 16
N = 2
M = 128
D = 256
E = 255
knowledge_base: nn.Parameter = create_knowledge_base(M, N, D)
m = KnowledgeBaseLookup(M, N, D, K, E, knowledge_base)
B = 30
T = 40
x = torch.randn(B, T, E)
x.requires_grad = True
y = m(x)
assert y.shape == x.shape
y.sum().backward() # make sure backward doesn't crash..
print("y = ", y)
print("x.grad = ", x.grad)
print("knowlege_base.grad norm = ", knowledge_base.grad.norm())
dtype = torch.float32
device = torch.device("cuda")
train_pairs = [
(
torch.randn(B, T, E, device=device, dtype=dtype),
torch.randn(B, T, E, device=device, dtype=dtype),
)
for _ in range(10)
]
from optim import Eve
optimizer = Eve(m.parameters(), lr=0.005, eps=1.0e-04)
m = m.to(device).to(dtype)
start = timeit.default_timer()
# Epoch 0, batch 0, loss 1.0109944343566895
# Epoch 10, batch 0, loss 1.0146660804748535
# Epoch 20, batch 0, loss 1.0119813680648804
# Epoch 30, batch 0, loss 1.0105408430099487
# Epoch 40, batch 0, loss 1.0077732801437378
# Epoch 50, batch 0, loss 1.0050103664398193
# Epoch 60, batch 0, loss 1.0033129453659058
# Epoch 70, batch 0, loss 1.0014232397079468
# Epoch 80, batch 0, loss 0.9977912306785583
# Epoch 90, batch 0, loss 0.8274348974227905
# Epoch 100, batch 0, loss 0.3368612825870514
# Epoch 110, batch 0, loss 0.11323091387748718
# Time taken: 17.591704960912466
for epoch in range(150):
for n, (x, y) in enumerate(train_pairs):
y_out = m(x)
loss = ((y_out - y) ** 2).mean() * 100.0
if n % 10 == 0 and epoch % 10 == 0:
print(f"Epoch {epoch}, batch {n}, loss {loss.item()}")
loss.backward()
optimizer.step()
optimizer.zero_grad()
stop = timeit.default_timer()
print("Time taken: ", stop - start)
def _test_knowledge_base_lookup_autocast():
K = 16
N = 2
M = 128
D = 256
E = 255
knowledge_base: nn.Parameter = create_knowledge_base(M, N, D)
m = KnowledgeBaseLookup(M, N, D, K, E, knowledge_base)
B = 30
T = 40
x = torch.randn(B, T, E)
x.requires_grad = True
y = m(x)
assert y.shape == x.shape
y.sum().backward() # make sure backward doesn't crash..
print("y = ", y)
print("x.grad = ", x.grad)
print("knowlege_base.grad norm = ", knowledge_base.grad.norm())
device = torch.device("cuda")
train_pairs = [
(torch.randn(B, T, E, device=device), torch.randn(B, T, E, device=device))
for _ in range(10)
]
from optim import Eve
optimizer = Eve(m.parameters(), lr=0.005, eps=1.0e-04)
m = m.to(device)
scaler = create_grad_scaler(enabled=True)
start = timeit.default_timer()
for epoch in range(150):
for n, (x, y) in enumerate(train_pairs):
y_out = m(x)
with torch_autocast(enabled=True):
loss = ((y_out - y) ** 2).mean() * 100.0
if n % 10 == 0 and epoch % 10 == 0:
print(f"Epoch {epoch}, batch {n}, loss {loss.item()}")
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad()
stop = timeit.default_timer()
print("Time taken: ", stop - start)
if __name__ == "__main__":
_test_knowledge_base_lookup()
_test_knowledge_base_lookup_autocast()
|