Fix unused variable warning in LossCTC.cu (#71588)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/71588
Test Plan: Sandcastle
Reviewed By: malfet
Differential Revision: D33692622
fbshipit-source-id: b8ee83b775bc5b87f8ddc0936da2df24ab42420d
(cherry picked from commit b88934c5da4997fdef4f7f66b89fa8bee51c1525)
diff --git a/aten/src/ATen/native/cuda/LossCTC.cu b/aten/src/ATen/native/cuda/LossCTC.cu
index a22c53b..e6a6ab9 100644
--- a/aten/src/ATen/native/cuda/LossCTC.cu
+++ b/aten/src/ATen/native/cuda/LossCTC.cu
@@ -213,8 +213,6 @@
TORCH_CHECK(input_lengths.size() == batch_size, "input_lengths must be of size batch_size");
TORCH_CHECK(target_lengths.size() == batch_size, "target_lengths must be of size batch_size");
- int64_t lp_input_stride = log_probs.stride(0);
- int64_t lp_char_stride = log_probs.stride(2);
int64_t tg_target_stride;
int64_t max_target_length = 0;
@@ -418,7 +416,7 @@
ctc_loss_backward_collect_nonblank_gpu_kernel(scalar_t* __restrict__ gradient_data,
const scalar_t* __restrict__ grad_out_data, int64_t grad_out_batch_stride,
const scalar_t* __restrict__ log_alpha_data, const scalar_t* __restrict__ log_beta_data,
- const scalar_t*log_probs_data, const int64_t* __restrict__ input_lengths, int64_t max_input_length,
+ const scalar_t*log_probs_data, const int64_t* __restrict__ input_lengths,
const target_t* __restrict__ targets_data, const int64_t* __restrict__ target_lengths, int64_t max_target_length,
const scalar_t* __restrict__ neg_log_likelihood_data,
int64_t gr_input_stride, int64_t gr_batch_stride, int64_t gr_char_stride,
@@ -574,8 +572,6 @@
using target_t = typename std::conditional<target_scalar_type == kInt, int, int64_t>::type;
int64_t batch_size = log_probs.size(1);
int64_t num_labels = log_probs.size(2);
- int64_t lp_input_stride = log_probs.stride(0);
- int64_t lp_char_stride = log_probs.stride(2);
int64_t tg_target_stride;
int64_t max_target_length;
@@ -679,7 +675,7 @@
(grad.data_ptr<scalar_t>(),
grad_out.data_ptr<scalar_t>(), grad_out.stride(0),
log_alpha.data_ptr<scalar_t>(), log_beta.data_ptr<scalar_t>(),
- log_probs.data_ptr<scalar_t>(), input_lengths_t.data_ptr<int64_t>(), log_probs.size(0),
+ log_probs.data_ptr<scalar_t>(), input_lengths_t.data_ptr<int64_t>(),
targets.data_ptr<target_t>(), target_lengths_t.data_ptr<int64_t>(), max_target_length,
neg_log_likelihood.data_ptr<scalar_t>(),
grad.stride(0), grad.stride(1), grad.stride(2),
