per-group and per-channel quantization (#14340)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14340

Pull Request resolved: https://github.com/pytorch/FBGEMM/pull/25

Per-group and per-channel quantization in fbgemm
This diff also cleans up explicit template instantiation using macro expansion
This diff also changes randFill interface which was easy to make mistakes of generating integer random numbers for floating point vectors.

Using this in DNNLOWP operators will be done in a separate diff.

Reviewed By: dskhudia

Differential Revision: D13176386

fbshipit-source-id: e46c53e31e21520bded71b8ed86e8b19e010e2dd

1 files changed, 19 insertions, 15 deletions

diff --git a/bench/FP16Benchmark.cc b/bench/FP16Benchmark.cc
index c03f18a..fd9de5b 100644
--- a/bench/FP16Benchmark.cc
+++ b/bench/FP16Benchmark.cc
@@ -73,20 +73,24 @@ void performance_test() {
     int n = s[1];
     int k = s[2];
 
-    aligned_vector<float> A(m * k, 0.f);
-    aligned_vector<float> B(k * n, 0.f);
-    aligned_vector<float> Cg(m * n, 1.f);
-    aligned_vector<float> Cp(m * n, NAN);
+    aligned_vector<float> C_ref(m * n, 1.f);
+    aligned_vector<float> C_fb(m * n, NAN);
 
     // initialize with small numbers
-    randFill(A, 0, 4);
+    aligned_vector<int> Aint(m * k);
+    randFill(Aint, 0, 4);
+    aligned_vector<float> A(Aint.begin(), Aint.end());
 
-    randFill(B, 0, 4);
+    aligned_vector<int> Bint(k * n);
+    randFill(Bint, 0, 4);
+    aligned_vector<float> B(Bint.begin(), Bint.end());
     PackedGemmMatrixFP16 Bp(btran, k, n, alpha, B.data());
 
     if (beta != 0.0f) {
-      randFill(Cg, 0, 4);
-      Cp = Cg;
+      aligned_vector<int> Cint(C_ref.size());
+      randFill(Cint, 0, 4);
+      C_ref.assign(Cint.begin(), Cint.end());
+      C_fb = C_ref;
     }
 
     double nflops = 2.0 * (double)m * (double)n * (double)k * (double)NITER;
@@ -111,17 +115,17 @@ void performance_test() {
           B.data(),
           (btran == matrix_op_t::NoTranspose) ? n : k,
           beta,
-          Cg.data(),
+          C_ref.data(),
           n);
 #endif
       cblas_gemm_compute(
-          matrix_op_t::NoTranspose, m, A.data(), Bp, beta, Cp.data());
+          matrix_op_t::NoTranspose, m, A.data(), Bp, beta, C_fb.data());
 
 #ifdef USE_MKL
       // Compare results
-      for (auto i = 0; i < Cg.size(); i++) {
-        // printf("%f %f\n", Cg[i], Cp[i]);
-        assert(std::abs(Cg[i] - Cp[i]) < 1e-3);
+      for (auto i = 0; i < C_ref.size(); i++) {
+        // printf("%f %f\n", C_ref[i], C_fb[i]);
+        assert(std::abs(C_ref[i] - C_fb[i]) < 1e-3);
       }
 #endif
     }
@@ -151,7 +155,7 @@ void performance_test() {
           B.data(),
           (btran == matrix_op_t::NoTranspose) ? n : k,
           beta,
-          Cg.data(),
+          C_ref.data(),
           n);
       t_end = chrono::system_clock::now();
       if (it >= 0) {
@@ -184,7 +188,7 @@ void performance_test() {
 
       t_begin = chrono::system_clock::now();
       cblas_gemm_compute(
-          matrix_op_t::NoTranspose, m, A.data(), Bp, beta, Cp.data());
+          matrix_op_t::NoTranspose, m, A.data(), Bp, beta, C_fb.data());
       t_end = chrono::system_clock::now();
 
       if (it >= 0) {