Revert D25445815: [te] Add fast log approximation based on sleef
Test Plan: revert-hammer
Differential Revision:
D25445815 (https://github.com/pytorch/pytorch/commit/1329066b694a7a552352920b4775eaadeee1313e)
Original commit changeset: 20696eacd12a
fbshipit-source-id: 38830a6abd16260d60e5dd9a5594e65736a9c782
diff --git a/benchmarks/cpp/tensorexpr/bench_approx.cpp b/benchmarks/cpp/tensorexpr/bench_approx.cpp
deleted file mode 100644
index c978330..0000000
--- a/benchmarks/cpp/tensorexpr/bench_approx.cpp
+++ /dev/null
@@ -1,145 +0,0 @@
-#include <benchmark/benchmark.h>
-#include <torch/csrc/jit/tensorexpr/ir_simplifier.h>
-#include <torch/csrc/jit/tensorexpr/loopnest.h>
-#include <torch/csrc/jit/tensorexpr/tensor.h>
-#include <torch/csrc/jit/tensorexpr/llvm_codegen.h>
-#include <torch/torch.h>
-
-using namespace torch::jit::tensorexpr;
-
-static void log_sleef(benchmark::State& state) {
- KernelScope ks;
- auto N = VarHandle("N", kInt);
- Placeholder A("A", kFloat, {N});
- torch::jit::tensorexpr::Tensor* B =
- Compute("B", {N}, [&](const VarHandle& i) {
- return log(A.load(i));
- });
- LoopNest ln({B});
- ln.prepareForCodegen();
- ln.vectorizeInnerLoops();
- Stmt* s = ln.root_stmt();
- s = torch::jit::tensorexpr::IRSimplifier::simplify(s);
- std::vector<CodeGen::BufferArg> args;
- args.emplace_back(B);
- args.emplace_back(A);
- args.emplace_back(N);
- LLVMCodeGen cg(s, args);
- at::Tensor A_t = torch::abs(torch::randn({state.range(0)}));
- at::Tensor B_t = torch::randn({state.range(0)});
- auto B_ref = at::log(A_t);
- cg.call({B_t.data_ptr<float>(), A_t.data_ptr<float>(), state.range(0)});
- assert(at::allclose(B_t, B_ref));
- for (auto _ : state) {
- cg.call({B_t.data_ptr<float>(), A_t.data_ptr<float>(), state.range(0)});
- }
- state.counters["log/s"] = benchmark::Counter(
- uint64_t(state.range(0) * state.iterations()), benchmark::Counter::kIsRate);
-}
-
-static void log_fast(benchmark::State& state) {
- KernelScope ks;
- auto N = VarHandle("N", kInt);
- Placeholder A("A", kFloat, {N});
- torch::jit::tensorexpr::Tensor* B =
- Compute("B", {N}, [&](const VarHandle& i) {
- return fast_log(A.load(i));
- });
- LoopNest ln({B});
- ln.prepareForCodegen();
- ln.vectorizeInnerLoops();
- Stmt* s = ln.root_stmt();
- s = torch::jit::tensorexpr::IRSimplifier::simplify(s);
- std::vector<CodeGen::BufferArg> args;
- args.emplace_back(B);
- args.emplace_back(A);
- args.emplace_back(N);
- LLVMCodeGen cg(s, args);
- at::Tensor A_t = torch::abs(torch::randn({state.range(0)}));
- at::Tensor B_t = torch::randn({state.range(0)});
- auto B_ref = at::log(A_t);
- cg.call({B_t.data_ptr<float>(), A_t.data_ptr<float>(), state.range(0)});
- assert(at::allclose(B_t, B_ref));
- for (auto _ : state) {
- cg.call({B_t.data_ptr<float>(), A_t.data_ptr<float>(), state.range(0)});
- }
- state.counters["log/s"] = benchmark::Counter(
- uint64_t(state.range(0) * state.iterations()), benchmark::Counter::kIsRate);
-}
-
-static void log_aten(benchmark::State& state) {
- at::Tensor A_t = torch::abs(torch::randn({state.range(0)}));
- at::Tensor B_t = torch::randn({state.range(0)});
- for (auto _ : state) {
- at::native::log_out(B_t, A_t);
- }
- state.counters["log/s"] = benchmark::Counter(
- uint64_t(state.range(0) * state.iterations()), benchmark::Counter::kIsRate);
-}
-
-static void logit_fast(benchmark::State& state) {
- KernelScope ks;
- auto N = VarHandle("N", kInt);
- Placeholder A("A", kFloat, {N});
- torch::jit::tensorexpr::Tensor* B =
- Compute("B", {N}, [&](const VarHandle& i) {
- auto A_elem = A.load(i);
- return fast_log(A_elem / (FloatImm::make(1.0f) - A_elem));
- });
- LoopNest ln({B});
- ln.prepareForCodegen();
- ln.vectorizeInnerLoops();
- Stmt* s = ln.root_stmt();
- s = torch::jit::tensorexpr::IRSimplifier::simplify(s);
- std::vector<CodeGen::BufferArg> args;
- args.emplace_back(B);
- args.emplace_back(A);
- args.emplace_back(N);
- LLVMCodeGen cg(s, args);
- at::Tensor A_t = torch::abs(torch::randn({state.range(0)}));
- at::Tensor B_t = torch::randn({state.range(0)});
- auto B_ref = at::logit(A_t);
- cg.call({B_t.data_ptr<float>(), A_t.data_ptr<float>(), state.range(0)});
- assert(at::allclose(B_t, B_ref));
- for (auto _ : state) {
- cg.call({B_t.data_ptr<float>(), A_t.data_ptr<float>(), state.range(0)});
- }
- state.counters["logit/s"] = benchmark::Counter(
- uint64_t(state.range(0) * state.iterations()), benchmark::Counter::kIsRate);
-}
-
-static void logit_aten(benchmark::State& state) {
- at::Tensor A_t = torch::abs(torch::randn({state.range(0)}));
- at::Tensor B_t = torch::randn({state.range(0)});
- for (auto _ : state) {
- at::native::logit_out(B_t, A_t);
- }
- state.counters["logit/s"] = benchmark::Counter(
- uint64_t(state.range(0) * state.iterations()), benchmark::Counter::kIsRate);
-}
-
-BENCHMARK(log_sleef)
- ->Args({2<<5})
- ->Args({2<<8})
- ->Args({2<<12})
- ->Args({2<<14});
-BENCHMARK(log_fast)
- ->Args({2<<5})
- ->Args({2<<8})
- ->Args({2<<12})
- ->Args({2<<14});
-BENCHMARK(log_aten)
- ->Args({2<<5})
- ->Args({2<<8})
- ->Args({2<<12})
- ->Args({2<<14});
-BENCHMARK(logit_fast)
- ->Args({2<<5})
- ->Args({2<<8})
- ->Args({2<<12})
- ->Args({2<<14});
-BENCHMARK(logit_aten)
- ->Args({2<<5})
- ->Args({2<<8})
- ->Args({2<<12})
- ->Args({2<<14});
diff --git a/test/cpp/tensorexpr/test_aten.cpp b/test/cpp/tensorexpr/test_aten.cpp
index a87de81..39ddeb7 100644
--- a/test/cpp/tensorexpr/test_aten.cpp
+++ b/test/cpp/tensorexpr/test_aten.cpp
@@ -733,38 +733,6 @@
}
}
-TEST(ATen, fastLogFloat) {
- KernelScope kernel_scope;
- const int kTotalSize = 128 * 128;
- Placeholder a_buf(BufHandle("A", {ExprHandle(kTotalSize)}, kFloat));
- Placeholder b_buf(BufHandle("B", {ExprHandle(kTotalSize)}, kFloat));
-
- VarHandle index = VarHandle("index", kInt);
- ExprHandle load_a = a_buf.load(index);
- Stmt* store_b = b_buf.store({index}, fast_log(load_a));
- Stmt* stmt = For::make(index, 0, kTotalSize, store_b);
-
- PaddedBuffer<float> a_v(kTotalSize);
- PaddedBuffer<float> b_v(kTotalSize);
-
- for (int i = 0; i < kTotalSize; ++i) {
- a_v(i) = at::randn({1}).item().to<float>();
- }
-
- SimpleIREvaluator ir_eval(stmt, a_buf, b_buf);
- ir_eval(a_v, b_v);
-
- for (int i = 0; i < kTotalSize; ++i) {
- auto test = b_v(i);
- auto ref = std::log(a_v(i));
- if (std::isnan(ref)) {
- ASSERT_EQ(std::isnan(test), true);
- } else {
- ASSERT_FLOAT_EQ(test, ref);
- }
- }
-}
-
TEST(ATen, log10Float) {
KernelScope kernel_scope;
const int kTotalSize = 128;
diff --git a/test/cpp/tensorexpr/test_llvm.cpp b/test/cpp/tensorexpr/test_llvm.cpp
index 975ef96..c1d3392 100644
--- a/test/cpp/tensorexpr/test_llvm.cpp
+++ b/test/cpp/tensorexpr/test_llvm.cpp
@@ -217,38 +217,6 @@
}
}
-TEST(LLVM, fastLogFloat) {
- KernelScope kernel_scope;
- const int kTotalSize = 128 * 128;
- Placeholder a_buf(BufHandle("A", {ExprHandle(kTotalSize)}, kFloat));
- Placeholder b_buf(BufHandle("B", {ExprHandle(kTotalSize)}, kFloat));
-
- VarHandle index = VarHandle("index", kInt);
- ExprHandle load_a = a_buf.load(index);
- Stmt* store_b = b_buf.store({index}, fast_log(load_a));
- Stmt* stmt = For::make(index, 0, kTotalSize, store_b);
-
- PaddedBuffer<float> a_v(kTotalSize);
- PaddedBuffer<float> b_v(kTotalSize);
-
- for (int i = 0; i < kTotalSize; ++i) {
- a_v(i) = at::randn({1}).item().to<float>();
- }
-
- LLVMCodeGen ir_eval(stmt, {a_buf, b_buf});
- ir_eval.call({a_v, b_v});
-
- for (int i = 0; i < kTotalSize; ++i) {
- auto test = b_v(i);
- auto ref = std::log(a_v(i));
- if (std::isnan(ref)) {
- ASSERT_EQ(std::isnan(test), true);
- } else {
- ASSERT_FLOAT_EQ(test, ref);
- }
- }
-}
-
TEST(LLVM, LetTest01) {
KernelScope kernel_scope;
diff --git a/torch/csrc/jit/tensorexpr/eval.h b/torch/csrc/jit/tensorexpr/eval.h
index a9b04c5..f01c496 100644
--- a/torch/csrc/jit/tensorexpr/eval.h
+++ b/torch/csrc/jit/tensorexpr/eval.h
@@ -337,12 +337,9 @@
std::vector<T> result_v(lhs_v.size());
for (size_t i = 0; i < lhs_v.size(); i++) {
switch (op_type) {
- case IRNodeType::kLshift: {
- typename std::make_unsigned<T>::type a =
- static_cast<typename std::make_unsigned<T>::type>(lhs_v[i]);
- result_v[i] = a << rhs_v[i];
+ case IRNodeType::kLshift:
+ result_v[i] = lhs_v[i] << rhs_v[i];
break;
- }
case IRNodeType::kRshift:
result_v[i] = lhs_v[i] >> rhs_v[i];
break;
diff --git a/torch/csrc/jit/tensorexpr/expr.cpp b/torch/csrc/jit/tensorexpr/expr.cpp
index 267120d..f8a9bfe 100644
--- a/torch/csrc/jit/tensorexpr/expr.cpp
+++ b/torch/csrc/jit/tensorexpr/expr.cpp
@@ -128,40 +128,6 @@
return Intrinsics::make(kFabs, v);
}
-ExprHandle fast_log(const ExprHandle& v) {
- // this implementation is taken from sleef:
- // https://github.com/shibatch/sleef/blob/master/src/libm/sleefsp.c#L1131
- // to generate coefficients, this tool is provided
- // https://github.com/shibatch/sleef/blob/master/src/gencoef/gencoef.txt
- auto ilogb2kf = [](ExprHandle x) {
- auto y = (bitcast<int32_t>(x) >> IntImm::make(23)) & IntImm::make(0xff);
- return y - IntImm::make(0x7f);
- };
-
- auto ldexp3kf = [](ExprHandle x, ExprHandle e) {
- return bitcast<float>(bitcast<int32_t>(x) + (e << IntImm::make(23)));
- };
- auto e = ilogb2kf(v * FloatImm::make(1.0 / 0.75));
- auto m = ldexp3kf(v, IntImm::make(-1) * e);
- auto one = FloatImm::make(1.0f);
- auto x = (m - one) / (m + one);
- auto x2 = x * x;
-
- auto mlaf = [](ExprHandle x, ExprHandle y, float z) {
- return x * y + FloatImm::make(z);
- };
-
- auto t = FloatImm::make(0.2392828464508056640625);
- t = mlaf(t, x2, 0.28518211841583251953125);
- t = mlaf(t, x2, 0.400005877017974853515625);
- t = mlaf(t, x2, 0.666666686534881591796875);
- t = mlaf(t, x2, 2.0);
- x = x * t + FloatImm::make(0.693147180559945286226764) * e;
- x = IfThenElse::make(v < FloatImm::make(0), FloatImm::make(std::numeric_limits<float>::quiet_NaN()), x);
- x = IfThenElse::make(v == FloatImm::make(0), FloatImm::make(-std::numeric_limits<float>::infinity()), x);
- return x;
-}
-
ExprHandle log(const ExprHandle& v) {
return Intrinsics::make(kLog, v);
}
diff --git a/torch/csrc/jit/tensorexpr/expr.h b/torch/csrc/jit/tensorexpr/expr.h
index b269349..8ba9966 100644
--- a/torch/csrc/jit/tensorexpr/expr.h
+++ b/torch/csrc/jit/tensorexpr/expr.h
@@ -290,7 +290,6 @@
TORCH_API ExprHandle expm1(const ExprHandle& v);
TORCH_API ExprHandle fabs(const ExprHandle& v);
TORCH_API ExprHandle log(const ExprHandle& v);
-TORCH_API ExprHandle fast_log(const ExprHandle& v);
TORCH_API ExprHandle log2(const ExprHandle& v);
TORCH_API ExprHandle log10(const ExprHandle& v);
TORCH_API ExprHandle log1p(const ExprHandle& v);
