0914ad0315
- New ensemble_reduce_kernel.cu: fused sigmoid→mean→weighted-sum in one kernel, single block, thread-per-model. Pure f32, no bf16. - build.rs: nvcc compilation without --use_fast_math - aggregate_logits_gpu: loads cubin, uploads raw f32 buffers, launches kernel, reads back single scalar. No GpuTensor/ActivationKernels. - Removed aggregate_logits_cpu (dead code, GPU-only system) - ml-explainability: removed unreachable dead code after stub return, prefixed unused vars. Zero warnings. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
40 lines
1.5 KiB
Plaintext
40 lines
1.5 KiB
Plaintext
// Fused ensemble reduce kernel: sigmoid + per-model mean + weighted sum.
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//
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// One thread per model. Each thread computes sigmoid(logit) for its segment,
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// averages, multiplies by the normalized weight, and atomically adds to a
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// single output scalar. The output is the weighted mean of sigmoid activations
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// across all models -- a value in [0, 1] that the host remaps to [-1, 1].
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//
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// All data is f32 (logits come from CPU-side Vec<f32>, not bf16 GpuTensor).
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extern "C" __global__
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void ensemble_sigmoid_mean_reduce(
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const float* __restrict__ logits, // [total_logits] concatenated
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const int* __restrict__ offsets, // [n_models] start offset per model
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const int* __restrict__ lengths, // [n_models] logit count per model
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const float* __restrict__ weights, // [n_models] normalized weights
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float* __restrict__ output, // [1] result scalar (must be zeroed)
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int n_models)
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{
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int model = blockIdx.x * blockDim.x + threadIdx.x;
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if (model >= n_models) return;
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int off = offsets[model];
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int len = lengths[model];
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if (len <= 0) return;
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// Fused sigmoid + accumulate for this model's segment
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float sum = 0.0f;
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for (int j = 0; j < len; ++j) {
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float x = logits[off + j];
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// sigmoid(x) = 1 / (1 + exp(-x))
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float s = 1.0f / (1.0f + expf(-x));
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sum += s;
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}
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float mean_sigmoid = sum / (float)len;
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// Weighted contribution (weights are pre-normalized on host)
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float contribution = mean_sigmoid * weights[model];
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atomicAdd(output, contribution);
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}
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