Datasets:
shibing624
/
source_code

Dataset card Data Studio Files
xet
Community
2
text
stringlengths
0
2.2M
BufHandle ConvResultBuf("ConvResult", {1, 16, 32, 32}, kFloat);
BufHandle MatmulResultBuf("MatmulResult", {1, 16, 32, 32}, kFloat);
Tensor Input = Compute(
"Input",
{1, 16, 32, 32},
[&](const VarHandle& n,
const VarHandle& c,
const VarHandle& h,
const VarHandle& w) { return FloatImm::make(5.0f); });
Tensor Weight = Compute(
"Weight",
{16, 16, 1, 1},
[&](const VarHandle& n,
const VarHandle& c,
const VarHandle& h,
const VarHandle& w) { return FloatImm::make(6.0f); });
Tensor ConvResult = Tensor(
ConvResultBuf.node(),
ExternalCall::make(
ConvResultBuf,
"nnc_aten_conv2d",
{BufHandle(Input.buf()), BufHandle(Weight.buf())},
{}));
Tensor MatmulResult = Tensor(
MatmulResultBuf.node(),
ExternalCall::make(
MatmulResultBuf,
"nnc_aten_matmul",
{BufHandle(ConvResult.buf()), BufHandle(ConvResult.buf())},
{}));
Tensor Result = Compute(
"Result",
{1, 16, 32, 32},
[&](const VarHandle& n,
const VarHandle& c,
const VarHandle& h,
const VarHandle& w) {
return ConvResult.load(n, c, h, w) + MatmulResult.load(n, c, h, w);
});
LoopNest l({Input, Weight, ConvResult, MatmulResult, Result});
// Inlining should not inline anything here since all Bufs are either defined
// or used in ExternalCalls - we run it just for testing
l.inlineIntermediateBufs(true);
l.prepareForCodegen();
l.simplify();
auto options = at::TensorOptions()
.dtype(at::kFloat)
.layout(at::kStrided)
.device(at::kCPU)
.requires_grad(false);
at::Tensor input = at::ones({1, 16, 32, 32}, options) * 5.f;
at::Tensor weight = at::ones({16, 16, 1, 1}, options) * 6.f;
at::Tensor t = at::conv2d(input, weight);
at::Tensor t2 = at::matmul(t, t);
at::Tensor ref = t + t2;
at::Tensor nnc_result;
std::vector<float> input_buf(1 * 16 * 32 * 32, 5.f);
std::vector<float> weight_buf(16 * 16 * 1 * 1, 6.f);
std::vector<float> conv_result_buf(1 * 16 * 32 * 32, -1.f);
std::vector<float> matmul_result_buf(1 * 16 * 32 * 32, -1.f);
std::vector<float> result_buf(1 * 16 * 32 * 32, -1.f);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(
l.root_stmt(), {Input, Weight, ConvResult, MatmulResult, Result});
llvm_codegen.call(
{input_buf, weight_buf, conv_result_buf, matmul_result_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 16, 32, 32}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
#endif
SimpleIREvaluator ir_eval(
l.root_stmt(), {Input, Weight, ConvResult, MatmulResult, Result});
ir_eval.call(
{input_buf, weight_buf, conv_result_buf, matmul_result_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 16, 32, 32}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
}
TEST(ExternalCall, Inlining) {
// This test verifies that Tensors using external calls can be used by and
// can use Tensors built with Compute API.
BufHandle MatmulResultBuf("MatmulResult", {8, 8}, kFloat);
Tensor A = Compute("A", {8, 8}, [&](const VarHandle& i, const VarHandle& j) {
return FloatImm::make(5.0f);
});
Tensor B = Compute("B", {8, 8}, [&](const VarHandle& i, const VarHandle& j) {
return FloatImm::make(4.0f);