eliminate expensive long cast in average tensors

src/zeroband/csrc/compress.cpp

@@ -3,6 +3,12 @@
 #include <cmath>
 #include <immintrin.h>
 
+#include <thread>
+#include <vector>
+#include <algorithm>
+#include <chrono>
+#include <iostream>
+
 namespace py = pybind11;
 
 constexpr int n_bins = 256;  // 8-bit quantization
@@ -105,17 +111,16 @@ torch::Tensor average_buckets(const torch::Tensor& tensor, const torch::Tensor&
 }
 
 torch::Tensor average_buckets_multithread(const torch::Tensor& tensor, const torch::Tensor& quant_weight, int64_t n_bins) {
-    std::cout << quant_weight.sizes() <<  tensor.sizes() << std::endl;
     torch::NoGradGuard no_grad;
     auto flat_tensor = tensor.flatten().contiguous();
-    auto flat_quant_weight = quant_weight.flatten().to(torch::kLong).contiguous();
+    auto flat_quant_weight = quant_weight.flatten().contiguous();
     auto options = flat_tensor.options();
     auto bin_sums = torch::zeros({n_bins}, options);
     auto bin_counts = torch::zeros({n_bins}, options.dtype(torch::kLong));
 
     // Get raw pointers
     float* tensor_data = flat_tensor.data_ptr<float>();
-    int64_t* quant_data = flat_quant_weight.data_ptr<int64_t>();
+    uint8_t* quant_data = flat_quant_weight.data_ptr<uint8_t>();
     float* sums_data = bin_sums.data_ptr<float>();
     int64_t* counts_data = bin_counts.data_ptr<int64_t>();
     int64_t numel = flat_tensor.numel();
@@ -133,8 +138,8 @@ torch::Tensor average_buckets_multithread(const torch::Tensor& tensor, const tor
         std::vector<int64_t> local_counts(n_bins, 0);
 
         for (int64_t i = start; i < end; ++i) {
-            int64_t bin = quant_data[i];
-            if (bin >= 0 && bin < n_bins) {
+            uint8_t bin = quant_data[i];
+            if (bin < n_bins) {  // No need to check for >= 0 as uint8_t is always non-negative
                 local_sums[bin] += tensor_data[i];
                 local_counts[bin]++;
             }
@@ -188,8 +193,7 @@ std::tuple<torch::Tensor, torch::Tensor> uniform_8bit_quantize(torch::Tensor ten
     // Call average_buckets to create the lookup table
     torch::Tensor lookup = average_buckets_multithread(tensor, quantized_tensor, n_bins);
 
-    return std::make_tuple(centered_tensor, centered_tensor);
-    //return std::make_tuple(quantized_tensor, lookup);
+    return std::make_tuple(quantized_tensor, lookup);
 }