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#include <gtest/gtest.h>
#include <torch/csrc/jit/tensorexpr/ir_simplifier.h>
#include <torch/csrc/jit/tensorexpr/llvm_codegen.h>
#include <torch/csrc/jit/tensorexpr/loopnest.h>
#include <torch/csrc/jit/tensorexpr/operators/conv2d.h>
#include <torch/csrc/jit/tensorexpr/tensor.h>
#include <torch/torch.h>
namespace torch {
namespace jit {
namespace te = torch::jit::tensorexpr;
namespace F = torch::nn::functional;
#ifdef TORCH_ENABLE_LLVM
// Generate test data with few bits of precision, to minimize error
// accumulation from floating-point reordering.
static at::Tensor genTestData(c10::IntArrayRef args) {
return at::trunc(at::randn(args) * 256.0f) / 256.0f;
}
TEST(Conv, DepthwiseConv2D) {
constexpr int N = 1, C = 72, H = 56, W = 56;
constexpr int K = 72, R = 3, S = 3;
constexpr int kPad = 1, kStride = 2, kGroups = C;
constexpr int CperG = C / kGroups;
te::BufHandle input("input", {N, C, H, W}, te::kFloat);
te::BufHandle weight("weight", {K, CperG, R, S}, te::kFloat);
te::BufHandle bias("bias", {K}, te::kFloat);
te::Tensor output =
te::conv2d_depthwise(input, weight, bias, kStride, kPad, kGroups);
te::LoopNest loop({output});
loop.simplify();
loop.prepareForCodegen();
te::LLVMCodeGen cg(loop.root_stmt(), {input, weight, bias, output});
auto it = genTestData({N, C, H, W});
auto wt = genTestData({K, CperG, R, S});
auto bt = genTestData({K});
auto ref = at::conv2d(it, wt, bt, kStride, kPad, /*dilation=*/1, kGroups);
auto ot = at::zeros_like(ref);
cg.call(
{it.data_ptr<float>(),
wt.data_ptr<float>(),
bt.data_ptr<float>(),
ot.data_ptr<float>()});
ASSERT_TRUE(at::allclose(ref, ot));
}
TEST(Conv, DepthwiseConv2DNoBias) {
constexpr int N = 1, C = 72, H = 56, W = 56;
constexpr int K = 72, R = 3, S = 3;
constexpr int kPad = 1, kStride = 2, kGroups = C;
constexpr int CperG = C / kGroups;
te::BufHandle input("input", {N, C, H, W}, te::kFloat);
te::BufHandle weight("weight", {K, CperG, R, S}, te::kFloat);
te::Tensor output =
te::conv2d_depthwise(input, weight, kStride, kPad, kGroups);
te::LoopNest loop({output});
loop.simplify();
loop.prepareForCodegen();
te::LLVMCodeGen cg(loop.root_stmt(), {input, weight, output});
auto it = genTestData({N, C, H, W});
auto wt = genTestData({K, CperG, R, S});
auto ref =
at::conv2d(it, wt, at::Tensor(), kStride, kPad, /*dilation=*/1, kGroups);
auto ot = at::zeros_like(ref);
cg.call({it.data_ptr<float>(), wt.data_ptr<float>(), ot.data_ptr<float>()});
ASSERT_TRUE(at::allclose(ref, ot));
}
TEST(Conv, DepthwiseConv2DDynamicShapes) {
te::VarHandle N_var("N", te::kInt);
te::VarHandle C_var("C", te::kInt);
te::VarHandle H_var("H", te::kInt);
te::VarHandle W_var("W", te::kInt);
te::VarHandle K_var("K", te::kInt);
te::VarHandle CperG_var("CperG", te::kInt);
te::VarHandle R_var("R", te::kInt);
te::VarHandle S_var("S", te::kInt);
te::VarHandle kPad_var("kPad", te::kInt);
te::VarHandle kStride_var("kStride", te::kInt);
te::VarHandle kGroups_var("kGroups", te::kInt);
te::BufHandle input("input", {N_var, C_var, H_var, W_var}, te::kFloat);
te::BufHandle weight("weight", {K_var, CperG_var, R_var, S_var}, te::kFloat);
te::Tensor output = te::conv2d_depthwise(
input,
weight,
N_var,
C_var,
H_var,
W_var,
K_var,
CperG_var,
R_var,
S_var,
kStride_var,
kPad_var,
kGroups_var);
te::LoopNest loop({output});
loop.simplify();
loop.prepareForCodegen();
std::vector<te::CodeGen::BufferArg> buffer_args = {
input,
weight,
N_var,
C_var,
H_var,
W_var,
K_var,
CperG_var,
R_var,
S_var,
kPad_var,
kStride_var,
kGroups_var,
output};
te::LLVMCodeGen cg(loop.root_stmt(), buffer_args);
constexpr int N = 1, C = 72, H = 56, W = 56;
constexpr int K = 72, R = 3, S = 3;
constexpr int kPad = 1, kStride = 2, kGroups = C;
constexpr int CperG = C / kGroups;
auto it = genTestData({N, C, H, W});
auto wt = genTestData({K, CperG, R, S});
auto ref =
at::conv2d(it, wt, at::Tensor(), kStride, kPad, /*dilation=*/1, kGroups);
auto ot = at::zeros_like(ref);
std::vector<te::CodeGen::CallArg> call_args = {
it.data_ptr<float>(),
wt.data_ptr<float>(),
N,
C,
H,
W,
K,
CperG,
R,
S,
kPad,
kStride,
kGroups,
ot.data_ptr<float>()};
cg.call(call_args);
ASSERT_TRUE(at::allclose(ref, ot));
}
#endif
TEST(Conv, Conv2D) {
// Input dimensions.
constexpr int N = 1;
constexpr int C = 3;
constexpr int H = 11;
constexpr int W = 11;
// Filter dimensions.
constexpr int K = 8;
constexpr int R = 3;
constexpr int S = 3;
// Output dims.
constexpr int OH = H - R + 1;
constexpr int OW = W - S + 1;
// Compute reference result.
at::Tensor input = torch::randn({N, C, H, W});
at::Tensor filter = torch::randn({K, C, R, S});
at::Tensor ref = F::conv2d(input, filter);
// Double check the output size is as expected.
ASSERT_EQ(ref.size(0), N);
ASSERT_EQ(ref.size(1), K);
ASSERT_EQ(ref.size(2), OH);
ASSERT_EQ(ref.size(3), OW);
te::BufHandle inputB("input", {N, C, H, W}, te::kFloat);
te::BufHandle filterB("filter", {K, C, R, S}, te::kFloat);
te::Tensor conv = te::Reduce(
"conv",
{N, K, OH, OW},
te::Sum(),
// FIXME: We have to use a `std::vector` parameter here and then unpack
// it, because we don't have an overload allowing for an arbitrary number
// of ExprHandle/VarHandle parameters.
[&](const std::vector<te::VarHandle>& v) {
auto const& n = v[0];
auto const& k = v[1];
auto const& oh = v[2];
auto const& ow = v[3];
auto const& c = v[4];
auto const& r = v[5];
auto const& s = v[6];
// FIXME: We have to use `call` and construct a `std::vector` here
// because the `operator()` overload is only specialized for a small
// number of arguments.
return inputB.load(n, c, oh + r, ow + s) * filterB.load(k, c, r, s);
},
// FIXME: If you forget one of the reduction dims, you get a segfault.
// Could that be caught by a verifier?
{C, R, S});
// FIXME: It'd be nice to have a single header that pulls in things like
// LoopNest, IRSimplifier, etc.
te::LoopNest loop({conv});
loop.prepareForCodegen();
te::StmtPtr s = loop.root_stmt();
s = te::IRSimplifier::simplify(s);
at::Tensor result = at::empty_like(ref);
te::SimpleIREvaluator cg(s, {inputB, filterB, conv});
cg.call(
{input.data_ptr<float>(),
filter.data_ptr<float>(),
result.data_ptr<float>()});
ASSERT_TRUE(at::allclose(ref, result, 1e-3, 1e-3));
}
} // namespace jit
} // namespace torch
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