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Fix ACT layer gradient computation on CUDA #3128

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Merged
davisking merged 1 commit into from
Dec 28, 2025
Merged

Fix ACT layer gradient computation on CUDA #3128

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7 changes: 7 additions & 0 deletions dlib/cuda/cpu_dlib.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -3267,6 +3267,7 @@ namespace dlib
resizable_tensor& cumulative_halting,
resizable_tensor& remainders,
resizable_tensor& n_steps,
resizable_tensor& effective_weights,
long batch_size,
long seq_len,
long d_model,
Expand All @@ -3281,6 +3282,7 @@ namespace dlib
float* cum_halt = cumulative_halting.host();
float* remain = remainders.host();
float* steps = n_steps.host();
float* eff_weights = effective_weights.host();

for (long pos = 0; pos < batch_size * seq_len; ++pos) {
if (cum_halt[pos] < halt_threshold) {
Expand All @@ -3294,6 +3296,7 @@ namespace dlib
cum_halt[pos] += effective;
remain[pos] -= effective;
steps[pos] = static_cast<float>(current_step + 1);
eff_weights[pos] += effective;

for (long c = 0; c < num_channels; ++c) {
for (long d = 0; d < d_model; ++d) {
Expand All @@ -3309,6 +3312,7 @@ namespace dlib
resizable_tensor& output,
const tensor& input_data,
const tensor& remainders,
resizable_tensor& effective_weights,
long batch_size,
long seq_len,
long d_model,
Expand All @@ -3318,13 +3322,16 @@ namespace dlib
const float* in_ptr = input_data.host();
const float* remain = remainders.host();
float* out_ptr = output.host();
float* eff_weights = effective_weights.host();

for (long pos = 0; pos < batch_size * seq_len; ++pos) {
float r = remain[pos];
if (r > 1e-6f) {
const long n = pos / seq_len;
const long s = pos % seq_len;

eff_weights[pos] += r;

for (long c = 0; c < num_channels; ++c) {
for (long d = 0; d < d_model; ++d) {
const long idx = ((n * num_channels + c) * seq_len + s) * d_model + d;
Expand Down
2 changes: 2 additions & 0 deletions dlib/cuda/cpu_dlib.h
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Original file line number Diff line number Diff line change
Expand Up @@ -555,6 +555,7 @@ namespace dlib
resizable_tensor& cumulative_halting,
resizable_tensor& remainders,
resizable_tensor& n_steps,
resizable_tensor& effective_weights,
long batch_size,
long seq_len,
long d_model,
Expand All @@ -567,6 +568,7 @@ namespace dlib
resizable_tensor& output,
const tensor& input_data,
const tensor& remainders,
resizable_tensor& effective_weights,
long batch_size,
long seq_len,
long d_model,
Expand Down
9 changes: 9 additions & 0 deletions dlib/cuda/cuda_dlib.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -2819,6 +2819,7 @@ namespace dlib
float* cumulative_halting,
float* remainders,
float* n_steps,
float* effective_weights,
size_t batch_size,
size_t seq_len,
size_t d_model,
Expand All @@ -2841,6 +2842,7 @@ namespace dlib
cumulative_halting[pos] += effective;
remainders[pos] -= effective;
n_steps[pos] = static_cast<float>(current_step + 1);
effective_weights[pos] += effective;

for (size_t c = 0; c < num_channels; ++c) {
for (size_t d = 0; d < d_model; ++d) {
Expand All @@ -2859,6 +2861,7 @@ namespace dlib
resizable_tensor& cumulative_halting,
resizable_tensor& remainders,
resizable_tensor& n_steps,
resizable_tensor& effective_weights,
long batch_size,
long seq_len,
long d_model,
Expand All @@ -2877,6 +2880,7 @@ namespace dlib
cumulative_halting.device(),
remainders.device(),
n_steps.device(),
effective_weights.device(),
batch_size,
seq_len,
d_model,
Expand All @@ -2889,6 +2893,7 @@ namespace dlib
float* output,
const float* input_data,
const float* remainders,
float* effective_weights,
size_t batch_size,
size_t seq_len,
size_t d_model,
Expand All @@ -2902,6 +2907,8 @@ namespace dlib
const size_t n = pos / seq_len;
const size_t s = pos % seq_len;

effective_weights[pos] += r;

for (size_t c = 0; c < num_channels; ++c) {
for (size_t d = 0; d < d_model; ++d) {
const size_t idx = ((n * num_channels + c) * seq_len + s) * d_model + d;
Expand All @@ -2916,6 +2923,7 @@ namespace dlib
resizable_tensor& output,
const tensor& input_data,
const tensor& remainders,
resizable_tensor& effective_weights,
long batch_size,
long seq_len,
long d_model,
Expand All @@ -2929,6 +2937,7 @@ namespace dlib
output.device(),
input_data.device(),
remainders.device(),
effective_weights.device(),
batch_size,
seq_len,
d_model,
Expand Down
2 changes: 2 additions & 0 deletions dlib/cuda/cuda_dlib.h
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Original file line number Diff line number Diff line change
Expand Up @@ -627,6 +627,7 @@ namespace dlib
resizable_tensor& cumulative_halting,
resizable_tensor& remainders,
resizable_tensor& n_steps,
resizable_tensor& effective_weights,
long batch_size,
long seq_len,
long d_model,
Expand All @@ -639,6 +640,7 @@ namespace dlib
resizable_tensor& output,
const tensor& input_data,
const tensor& remainders,
resizable_tensor& effective_weights,
long batch_size,
long seq_len,
long d_model,
Expand Down
10 changes: 6 additions & 4 deletions dlib/cuda/tensor_tools.cpp
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Expand Up @@ -1440,6 +1440,7 @@ namespace dlib { namespace tt
resizable_tensor& cumulative_halting,
resizable_tensor& remainders,
resizable_tensor& n_steps,
resizable_tensor& effective_weights,
long batch_size,
long seq_len,
long d_model,
Expand All @@ -1450,28 +1451,29 @@ namespace dlib { namespace tt
{
#ifdef DLIB_USE_CUDA
cuda::update_act_state(output, input_data, halt_probs, cumulative_halting, remainders,
n_steps, batch_size, seq_len, d_model, num_channels, halt_threshold, current_step);
n_steps, effective_weights, batch_size, seq_len, d_model, num_channels, halt_threshold, current_step);
#else
cpu::update_act_state(output, input_data, halt_probs, cumulative_halting, remainders,
n_steps, batch_size, seq_len, d_model, num_channels, halt_threshold, current_step);
n_steps, effective_weights, batch_size, seq_len, d_model, num_channels, halt_threshold, current_step);
#endif
}

void finalize_act_output(
resizable_tensor& output,
const tensor& input_data,
const tensor& remainders,
resizable_tensor& effective_weights,
long batch_size,
long seq_len,
long d_model,
long num_channels
)
{
#ifdef DLIB_USE_CUDA
cuda::finalize_act_output(output, input_data, remainders,
cuda::finalize_act_output(output, input_data, remainders, effective_weights,
batch_size, seq_len, d_model, num_channels);
#else
cpu::finalize_act_output(output, input_data, remainders,
cpu::finalize_act_output(output, input_data, remainders, effective_weights,
batch_size, seq_len, d_model, num_channels);
#endif
}
Expand Down
9 changes: 6 additions & 3 deletions dlib/cuda/tensor_tools.h
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Original file line number Diff line number Diff line change
Expand Up @@ -2428,6 +2428,7 @@ namespace dlib { namespace tt
resizable_tensor& cumulative_halting,
resizable_tensor& remainders,
resizable_tensor& n_steps,
resizable_tensor& effective_weights,
long batch_size,
long seq_len,
long d_model,
Expand All @@ -2445,12 +2446,12 @@ namespace dlib { namespace tt
- input_data.nc() == d_model
- output has the same dimensions as input_data
- halt_probs.size() == batch_size * seq_len
- cumulative_halting.size() == remainders.size() == n_steps.size() == batch_size * seq_len
- cumulative_halting.size() == remainders.size() == n_steps.size() == effective_weights.size() == batch_size * seq_len
ensures
- Core ACT update step that accumulates weighted outputs:
- Updates ACT state for all positions
- Accumulates weighted outputs: output += α_t^n * input_data
- Updates cumulative_halting, remainders, and n_steps
- Updates cumulative_halting, remainders, n_steps, and effective_weights
- batch_size: number of samples in the batch
- seq_len: sequence length (number of positions to process)
- d_model: model dimension per channel
Expand All @@ -2463,6 +2464,7 @@ namespace dlib { namespace tt
resizable_tensor& output,
const tensor& input_data,
const tensor& remainders,
resizable_tensor& effective_weights,
long batch_size,
long seq_len,
long d_model,
Expand All @@ -2475,10 +2477,11 @@ namespace dlib { namespace tt
- input_data.nr() == seq_len
- input_data.nc() == d_model
- output has the same dimensions as input_data
- remainders.size() == batch_size * seq_len
- remainders.size() == effective_weights.size() == batch_size * seq_len
ensures
- Finalizes ACT output by adding remainder contributions:
- Adds final remainder contributions: output += ρ_t * input_data
- Updates effective_weights with remainder values
- Applied only to positions with significant remainder (> 1e-6)
- batch_size: number of samples in the batch
- seq_len: sequence length (number of positions to process)
Expand Down
30 changes: 2 additions & 28 deletions dlib/dnn/layers.h
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Original file line number Diff line number Diff line change
Expand Up @@ -5857,29 +5857,11 @@ namespace dlib
halting_probs_, logits_, input, params,
batch_size_, seq_len_, feature_dim_);

// Capture effective weights before state update
const float* p_halt = halting_probs_.host();
const float* cum_halt = cum_halt_ptr;
const float* remainders = remainders_ptr;
float* true_weights = true_effective_weights_.host();

for (long pos = 0; pos < total_positions; ++pos) {
if (cum_halt[pos] < halt_threshold_) {
float p = p_halt[pos];
float r = remainders[pos];

// Compute effective weight: alpha_t^n = min(p * rho, theta - h_t^(n-1))
float effective = std::min(p * r, halt_threshold_ - cum_halt[pos]);

// Store for backward pass
true_weights[pos] += effective;
}
}

// Update ACT state and accumulate weighted outputs
tt::update_act_state(
output, input, halting_probs_,
cumulative_halting_, remainders_, n_steps_,
true_effective_weights_,
batch_size_, seq_len_, d_model_, num_channels_,
halt_threshold_, step
);
Expand All @@ -5891,17 +5873,9 @@ namespace dlib
// Finalize with remainder contributions
tt::finalize_act_output(
output, input, remainders_,
true_effective_weights_,
batch_size_, seq_len_, d_model_, num_channels_);

// Add remainder weights for gradient computation
const float* final_remainders = remainders_.host();
float* true_weights = true_effective_weights_.host();
for (long pos = 0; pos < total_positions; ++pos) {
if (final_remainders[pos] > 1e-6f) {
true_weights[pos] += final_remainders[pos];
}
}

// Compute statistics for monitoring and regularization
compute_ponder_stats();
}
Expand Down
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