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Revert D30652629: use irange for loops

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Test Plan: revert-hammer

Differential Revision:
D30652629 (687c2267d4)

Original commit changeset: 0ae6c4bbbb55

fbshipit-source-id: 5c4f067b584a021c8c9656454d1ee60999600fb3
This commit is contained in:
Xue Li
2021-10-15 15:19:28 -07:00
committed by Facebook GitHub Bot
parent 1e2b2ee5ff
commit 2f099c7555
487 changed files with 21930 additions and 22184 deletions
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41
test/cpp/api/modules.cpp
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@ -1,6 +1,5 @@
#include <gtest/gtest.h>
#include <c10/util/irange.h>
#include <torch/torch.h>
#include <test/cpp/api/support.h>
@ -1149,7 +1148,7 @@ TEST_F(ModulesTest, LayerNorm) {
s.backward();
ASSERT_EQ(y.ndimension(), 2);
ASSERT_EQ(s.ndimension(), 0);
for (const auto i : c10::irange(2)) {
for (auto i = 0; i < 2; i++) {
ASSERT_EQ(y.size(i), 2);
}
@ -1167,7 +1166,7 @@ TEST_F(ModulesTest, GroupNorm) {
s.backward();
ASSERT_EQ(y.ndimension(), 2);
ASSERT_EQ(s.ndimension(), 0);
for (const auto i : c10::irange(2)) {
for (auto i = 0; i < 2; i++) {
ASSERT_EQ(y.size(i), 2);
}
@ -2596,7 +2595,7 @@ TEST_F(ModulesTest, Softmax) {
auto output = m(input);
auto sum = torch::sum(torch::exp(input), 1);
for (const auto i : c10::irange(2)) {
for (int i = 0; i < 2; i++) {
auto expected = torch::exp(input[i]) / sum[i];
ASSERT_TRUE(torch::allclose(output[i], expected));
}
@ -2608,7 +2607,7 @@ TEST_F(ModulesTest, Softmin) {
auto output = m(input);
auto sum = torch::sum(torch::exp(-input), 1);
for (const auto i : c10::irange(2)) {
for (int i = 0; i < 2; i++) {
auto expected = torch::exp(-input[i]) / sum[i];
ASSERT_TRUE(torch::allclose(output[i], expected));
}
@ -2620,7 +2619,7 @@ TEST_F(ModulesTest, LogSoftmax) {
auto output = m(input);
auto sum = torch::sum(torch::exp(input), 1);
for (const auto i : c10::irange(2)) {
for (int i = 0; i < 2; i++) {
auto expected = torch::log(torch::exp(input[i]) / sum[i]);
ASSERT_TRUE(torch::allclose(output[i], expected));
}
@ -2657,7 +2656,7 @@ TEST_F(ModulesTest, AdaptiveLogSoftmaxWithLoss) {
auto logprob_out = asfm->log_prob(x);
NLLLoss nll_loss;
for (const auto v : c10::irange(4)) {
for (int64_t v = 0; v < 4; ++v) {
auto y = torch::full({4}, v, torch::kLong);
auto asm_out = asfm(x, y);
auto out = asm_out.output;
@ -2676,10 +2675,10 @@ TEST_F(ModulesTest, Softmax2d) {
auto output = m(input);
auto sum = torch::sum(torch::exp(input), 1);
for (const auto i : c10::irange(1)) {
for (const auto j : c10::irange(2)) {
for (const auto k : c10::irange(3)) {
for (const auto l : c10::irange(4)) {
for (int i = 0; i < 1; i++) {
for (int j = 0; j < 2; j++) {
for (int k = 0; k < 3; k++) {
for (int l = 0; l < 4; l++) {
auto expected = torch::exp(input[i][j][k][l]) / sum[i][k][l];
ASSERT_TRUE(torch::allclose(output[i][j][k][l], expected));
}
@ -3390,8 +3389,8 @@ namespace detail {
TORCH_INTERNAL_ASSERT(a.size(0) == b.size(0));
TORCH_INTERNAL_ASSERT(a.size(1) == b.size(1));
auto retval = torch::zeros({a.size(0), a.size(1), a.size(2), b.size(3)}, torch::kFloat32);
for (const auto i : c10::irange(a.size(0))) {
for (const auto j : c10::irange(a.size(1))) {
for (int i = 0; i < a.size(0); i++) {
for (int j = 0; j < a.size(1); j++) {
retval[i][j] = torch::matmul(a[i][j], b[i][j]);
}
}
@ -3400,9 +3399,9 @@ namespace detail {
torch::Tensor _softmax(const torch::Tensor& x) {
auto output = torch::zeros(x.sizes());
for (const auto i : c10::irange(x.size(0))) {
for (const auto j : c10::irange(x.size(1))) {
for (const auto k : c10::irange(x.size(2))) {
for (int i = 0; i < x.size(0); i++) {
for (int j = 0; j < x.size(1); j++) {
for (int k = 0; k < x.size(2); k++) {
const auto& x_curr = x[i][j][k];
const auto e_x = torch::exp(x_curr - torch::max(x_curr));
output[i][j][k] = e_x / torch::sum(e_x);
@ -3425,10 +3424,10 @@ namespace detail {
const auto s1 = QKT.size(2);
const auto s2 = QKT.size(3);
if (unseen_mask.defined() || key_padding_mask.defined()) {
for (const auto i : c10::irange(b1)) {
for (const auto j : c10::irange(b2)) {
for (const auto m : c10::irange(s1)) {
for (const auto n : c10::irange(s2)) {
for (int i = 0; i < b1; i++) {
for (int j = 0; j < b2; j++) {
for (int m = 0; m < s1; m++) {
for (int n = 0; n < s2; n++) {
if (unseen_mask.defined() && unseen_mask[m][n].item<double>() == 0) {
QKT[i][j][m][n] = -std::numeric_limits<double>::infinity();
}
@ -3476,7 +3475,7 @@ namespace detail {
std::uniform_int_distribution<int> d_2_10(2, 10);
std::uniform_int_distribution<int> d_3_10(3, 10);
bool registration_checked = false;
for (const auto i : c10::irange(100)) {
for (int i = 0; i < 100; i++) {
const auto batch_sz = d_2_10(generator);
const auto seq_len = d_2_10(generator);
const auto d_head = d_3_10(generator);