[codegen] some cleaning for batched matmul
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@@ -40,7 +40,7 @@ perf_t do_bench(triton::driver::stream* stream, bool AT, bool BT, int32_t M, int
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hb[i] = static_cast<NumericT>((double)rand()/RAND_MAX);
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for(size_t i = 0; i < hc.size(); i++)
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hc[i] = static_cast<NumericT>((double)0);
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triton::driver::buffer* dc = triton::driver::buffer::create(context, hc.size()*dt_nbytes);
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triton::driver::buffer* dc = triton::driver::buffer::create(context, hc.size()*4);
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triton::driver::buffer* da = triton::driver::buffer::create(context, ha.size()*dt_nbytes);
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triton::driver::buffer* db = triton::driver::buffer::create(context, hb.size()*dt_nbytes);
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stream->write(da, true, 0, ha);
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@@ -49,7 +49,7 @@ perf_t do_bench(triton::driver::stream* stream, bool AT, bool BT, int32_t M, int
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stream->synchronize();
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triton::dnn::dot dot(M, N, K, AT, BT, ty, ty, ty, 8, 8, 8);
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// benchmark triton
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double triton_ns = triton::tools::bench([&]() { dot.enqueue(stream, {da, db, dc}, triton::dnn::NO_TUNING);}, stream);
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double triton_ns = triton::tools::bench([&]() { dot.enqueue(stream, {da, db, dc}, triton::dnn::PARTIAL_TUNING);}, stream);
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// benchmark cublas
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// NumericT alpha = 1;
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// NumericT beta = 0;
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@@ -77,7 +77,7 @@ perf_t do_bench(triton::driver::stream* stream, bool AT, bool BT, int32_t M, int
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std::vector<NumericT> rc(hc.size());
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dot.cpu_ref(rc, ha, hb);
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for(size_t i = 0; i < M*N; i++)
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if(std::isnan(hc[i]) || std::abs(hc[i] - rc[i])/std::max(hc[i], rc[i]) > 1e-2){
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if(std::isinf(hc[i]) || std::isnan(hc[i]) || std::abs(hc[i] - rc[i])/std::max(hc[i], rc[i]) > 1e-2){
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std::cout << i << " " << hc[i] << " " << rc[i] << std::endl;
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exit(EXIT_FAILURE);
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}
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@@ -111,8 +111,8 @@ int main() {
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// shapes to benchmark
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std::vector<config_t> configs = {
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// {false, false, 8192, 512, 512},
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// {false, true, 8192, 8192, 8192}
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{false, true, 128, 128, 128},
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{false, true, 128, 128, 128}
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// {false, true, 128, 128, 128},
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// {false, false, 128, 128, 128},
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// {true, false, 128, 128, 128},
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// {true, true, 128, 128, 128}
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