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fix(ignite): making state saveable, param_groups modifieable #1235

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Jan 16, 2025
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fix(ignite): making state saveable, param_groups modifieable #1235

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0877779
ope): Brief description
sebffischer Jan 15, 2025
2590b23
cleanup previous commit
sebffischer Jan 15, 2025
b54cf6e
trigger ci
sebffischer Jan 16, 2025
f86edb2
tests: fix ignite sgd test
sebffischer Jan 16, 2025
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16 changes: 12 additions & 4 deletions R/ignite.R
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Original file line number Diff line number Diff line change
Expand Up @@ -117,7 +117,8 @@ OptimizerIgnite <- R6::R6Class(
#' The parameter groups of the optimizer.
param_groups = function(rhs) {
if (!missing(rhs)) {
prev_param_groups <- self$state_dict()$param_groups
prev_param_groups <- self$param_groups
all_params = unlist(lapply(prev_param_groups, function(x) x$params))
if (!is.list(rhs) && length(rhs) == length(prev_param_groups)) {
value_error("Parameter groups must be a list of the same length as the number of parameter groups.")
}
Expand All @@ -128,8 +129,16 @@ OptimizerIgnite <- R6::R6Class(
value_error("Parameter groups must have names {paste0(names(prev_param_group), collapse = ', ')} but got {paste0(names(new_param_group), collapse = ', ')}.")
}

if (!identical(prev_param_group$params, new_param_group$params)) {
value_error("Cannot change the indices of the parameter group, use `$add_param_group()` to add a new parameter group.")
param_cmp_value = if (is.integer(new_param_group$params)) {
all_params[new_param_group$params]
} else {
new_param_group$params
}

if (!identical(prev_param_group$params, param_cmp_value)) {
print(prev_param_group$params)
print(new_param_group$params)
value_error("Cannot change the parameter groups, use `$add_param_group()` to add a new parameter group.")
}

private$.set_param_group_options(self$ptr, rhs)
Expand Down Expand Up @@ -367,7 +376,6 @@ is_permutation <- function(vec1, vec2) {
if (length(vec1) != length(vec2)) {
return(FALSE)
}

# Check if sorted elements are the same
identical(sort(vec1), sort(vec2))
}
Expand Down
29 changes: 17 additions & 12 deletions src/lantern/src/Ignite.cpp
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
#include <ostream>
#include <torch/optim/adamw.h>
#include <torch/types.h>
#define LANTERN_BUILD
#include <torch/torch.h>
#include "lantern/lantern.h"
Expand Down Expand Up @@ -113,7 +114,7 @@ void* _ignite_adagrad_get_states(void* optim) {
auto base_state = state_it->second.get();
auto adagrad_state = static_cast<torch::optim::AdagradParamState*>(base_state);
tensors.push_back(adagrad_state->sum().clone());
tensors.push_back(torch::scalar_tensor(adagrad_state->step(), torch::kLong));
tensors.push_back(torch::tensor({adagrad_state->step()}, torch::kLong));
}
}
}
Expand Down Expand Up @@ -201,9 +202,9 @@ void* _ignite_adam_get_states(void* optim) {
if (adam_state->max_exp_avg_sq().defined()) {
tensors.push_back(adam_state->max_exp_avg_sq().clone());
} else {
tensors.push_back(torch::Tensor());
tensors.push_back(torch::empty(0, torch::kFloat32));
}
tensors.push_back(torch::scalar_tensor(adam_state->step(), torch::kLong));
tensors.push_back(torch::tensor({adam_state->step()}, torch::kLong));
}
}
}
Expand All @@ -226,7 +227,7 @@ void _ignite_adam_set_states(void* optim, void* params,void* states_) {
auto* current_state = static_cast<torch::optim::AdamParamState*>(state_it->second.get());
current_state->exp_avg(states[i]);
current_state->exp_avg_sq(states[i + 1]);
if (states[i + 2].defined()) {
if (states[i + 2].numel() != 0) {
current_state->max_exp_avg_sq(states[i + 2]);
}
auto step = states[i + 3];
Expand Down Expand Up @@ -324,9 +325,9 @@ void* _ignite_adamw_get_states(void* optim) {
if (adamw_state->max_exp_avg_sq().defined()) {
tensors.push_back(adamw_state->max_exp_avg_sq().clone());
} else {
tensors.push_back(torch::Tensor());
tensors.push_back(torch::empty(0, torch::kFloat32));
}
tensors.push_back(torch::scalar_tensor(adamw_state->step(), torch::kLong));
tensors.push_back(torch::tensor({adamw_state->step()}, torch::kLong));
}
}
}
Expand Down Expand Up @@ -422,15 +423,15 @@ void* _ignite_rmsprop_get_states(void* optim) {
if (rmsprop_state->grad_avg().defined()) {
tensors.push_back(rmsprop_state->grad_avg().clone());
} else {
tensors.push_back(torch::Tensor());
tensors.push_back(torch::empty(0, torch::kFloat32));
}
tensors.push_back(rmsprop_state->square_avg().clone());
if (rmsprop_state->momentum_buffer().defined()) {
tensors.push_back(rmsprop_state->momentum_buffer().clone());
} else {
tensors.push_back(torch::Tensor());
tensors.push_back(torch::empty(0, torch::kFloat32));
}
tensors.push_back(torch::scalar_tensor(rmsprop_state->step(), torch::kLong));
tensors.push_back(torch::tensor({rmsprop_state->step()}, torch::kLong));
}
}
}
Expand All @@ -451,9 +452,13 @@ void _ignite_rmsprop_set_states(void* optim, void* params, void* states_) {
state_it = opt->state().find(param.unsafeGetTensorImpl());
}
auto* current_state = static_cast<torch::optim::RMSpropParamState*>(state_it->second.get());
current_state->grad_avg(states[i]);
if (states[i].numel() != 0) {
current_state->grad_avg(states[i]);
}
current_state->square_avg(states[i + 1]);
current_state->momentum_buffer(states[i + 2]);
if (states[i + 2].numel() != 0) {
current_state->momentum_buffer(states[i + 2]);
}
auto step = states[i + 3];
current_state->step(step.item<int64_t>());
i += 4;
Expand Down Expand Up @@ -519,7 +524,7 @@ void* _ignite_sgd_get_states(void* optim) {
if (sgd_state->momentum_buffer().defined()) {
tensors.push_back(sgd_state->momentum_buffer().clone());
} else {
tensors.push_back(torch::Tensor());
tensors.push_back(torch::empty(0, torch::kFloat32));
}
}
}
Expand Down
4 changes: 1 addition & 3 deletions tests/testthat/helper-ignite.R
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Original file line number Diff line number Diff line change
Expand Up @@ -75,14 +75,12 @@ expect_state_dict_works <- function(optimizer_fn, ...) {
}
replicate(2, s())
if (load) {
o$load_state_dict(o$state_dict())
o$load_state_dict(torch_load(torch_serialize(o$state_dict())))
}
replicate(2, s())
return(n$parameters)
}
torch_manual_seed(123)
w1 <- f(load = TRUE)
torch_manual_seed(123)
w2 <- f(load = FALSE)
expect_equal(w1, w2)
}
Expand Down
72 changes: 67 additions & 5 deletions tests/testthat/test-ignite.R
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Original file line number Diff line number Diff line change
Expand Up @@ -30,10 +30,10 @@ test_that("un-optimized parameters and state dict", {
states = sd$state
expect_equal(names(states), "1")
# all parameters are included in the state dict even when they don't have a state.
expect_false(cpp_tensor_is_undefined(states[[1]]$exp_avg))
expect_false(cpp_tensor_is_undefined(states[[1]]$exp_avg_sq))
expect_true(cpp_tensor_is_undefined(states[[1]]$max_exp_avg_sq))
expect_false(cpp_tensor_is_undefined(states[[1]]$step))
expect_false(is.null(states[[1]]$exp_avg))
expect_false(is.null(states[[1]]$exp_avg_sq))
expect_false(is.null(states[[1]]$max_exp_avg_sq))
expect_false(is.null(states[[1]]$step))
opt$load_state_dict(sd)
x1 = unlist(states)
x2 = unlist(opt$state_dict()$state)
Expand All @@ -58,6 +58,12 @@ test_that("adam", {
expect_ignite_can_change_param_groups(optim_ignite_adam)
expect_ignite_can_add_param_group(optim_ignite_adam)
do.call(expect_state_dict_works, c(list(optim_ignite_adam), defaults))
# can save adam even when one of the tensors in the state is undefined in C++
defaults$amsgrad <- FALSE
o <- do.call(make_ignite_adam, defaults)
prev <- o$state_dict()
o$load_state_dict(torch_load(torch_serialize(o$state_dict())))
expect_equal(prev, o$state_dict())
})

test_that("adamw", {
Expand All @@ -73,6 +79,13 @@ test_that("adamw", {
expect_ignite_can_change_param_groups(optim_ignite_adamw)
expect_ignite_can_add_param_group(optim_ignite_adamw)
do.call(expect_state_dict_works, c(list(optim_ignite_adamw), defaults))

# can save adamw even when one of the tensors in the state is undefined in C++
defaults$amsgrad <- FALSE
o <- do.call(make_ignite_adamw, defaults)
prev <- o$state_dict()
o$load_state_dict(torch_load(torch_serialize(o$state_dict())))
expect_equal(prev, o$state_dict())
})

test_that("sgd", {
Expand All @@ -87,6 +100,13 @@ test_that("sgd", {
expect_ignite_can_change_param_groups(optim_ignite_sgd, lr = 0.1)
expect_ignite_can_add_param_group(optim_ignite_sgd)
do.call(expect_state_dict_works, c(list(optim_ignite_sgd), defaults))
o$load_state_dict(torch_load(torch_serialize(o$state_dict())))

# saving of state dict
o <- do.call(make_ignite_sgd, defaults)
prev <- o$state_dict()
o$load_state_dict(torch_load(torch_serialize(o$state_dict())))
expect_equal(prev, o$state_dict())
})

test_that("rmsprop", {
Expand All @@ -102,6 +122,11 @@ test_that("rmsprop", {
expect_ignite_can_change_param_groups(optim_ignite_rmsprop)
expect_ignite_can_add_param_group(optim_ignite_rmsprop)
do.call(expect_state_dict_works, c(list(optim_ignite_rmsprop), defaults))

o <- do.call(make_ignite_rmsprop, defaults)
prev <- o$state_dict()
o$load_state_dict(torch_load(torch_serialize(o$state_dict())))
expect_equal(prev, o$state_dict())
})

test_that("adagrad", {
Expand All @@ -117,6 +142,11 @@ test_that("adagrad", {
expect_ignite_can_change_param_groups(optim_ignite_adagrad)
expect_ignite_can_add_param_group(optim_ignite_adagrad)
do.call(expect_state_dict_works, c(list(optim_ignite_adagrad), defaults))

o <- do.call(make_ignite_adagrad, defaults)
prev <- o$state_dict()
o$load_state_dict(torch_load(torch_serialize(o$state_dict())))
expect_equal(prev, o$state_dict())
})

test_that("base class: can initialize optimizer with different options per param group", {
Expand Down Expand Up @@ -160,6 +190,17 @@ test_that("base class: params must have length > 1", {
expect_error(optim_ignite_adamw(list()), "must have length")
})

test_that("base class: can change values of param_groups", {
o = optim_ignite_adamw(list(torch_tensor(1, requires_grad = TRUE)), lr = 0.1)
o$param_groups[[1]]$lr = 1
expect_equal(o$param_groups[[1]]$lr, 1)
o$param_groups[[1]]$amsgrad = FALSE
expect_true(!o$param_groups[[1]]$amsgrad)
o$param_groups[[1]]$amsgrad = TRUE
expect_false(!o$param_groups[[1]]$amsgrad)
})


test_that("base class: error handling when loading state dict", {
o = make_ignite_adamw()
expect_error(o$load_state_dict(list()), "must be a list with elements")
Expand All @@ -174,7 +215,28 @@ test_that("base class: error handling when loading state dict", {
expect_error(o$load_state_dict(sd3), "but got params, weight_decay")
})

test_that("deep cloning not possible", {
test_that("base class: deep cloning not possible", {
o = make_ignite_adamw(steps = 0)
expect_error(o$clone(deep = TRUE), "OptimizerIgnite cannot be deep cloned")
})

test_that("base class: changing the learning rate has an effect", {
n1 = nn_linear(1, 1)
n2 = n1$clone(deep = TRUE)
o1 = optim_sgd(n1$parameters, lr = 0.1)
o2 = optim_sgd(n2$parameters, lr = 0.1)

s = function(n, o) {
o$zero_grad()
((n(torch_tensor(1)) - torch_tensor(1))^2)$backward()
o$step()
}

s(n1, o1)
s(n2, o2)
expect_true(torch_equal(n1$parameters[[1]], n2$parameters[[1]]) && torch_equal(n1$parameters[[2]], n2$parameters[[2]]))
o1$param_groups[[1]]$lr = 0.2
s(n1, o1)
s(n2, o2)
expect_false(torch_equal(n1$parameters[[1]], n2$parameters[[1]]) && torch_equal(n1$parameters[[2]], n2$parameters[[2]]))
})
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