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test_external_calls.cpp
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test_external_calls.cpp
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#include <gtest/gtest.h>
#include <test/cpp/tensorexpr/test_base.h>
#include <torch/csrc/jit/ir/irparser.h>
#include <torch/csrc/jit/passes/subgraph_rewrite.h>
#include <torch/csrc/jit/passes/tensorexpr_fuser.h>
#include <torch/csrc/jit/runtime/custom_operator.h>
#include <torch/csrc/jit/tensorexpr/kernel.h>
#include <test/cpp/tensorexpr/test_utils.h>
#include <torch/csrc/jit/runtime/operator.h>
#include <torch/csrc/jit/runtime/symbolic_shape_registry.h>
#include <torch/csrc/jit/tensorexpr/eval.h>
#include <torch/csrc/jit/tensorexpr/external_functions_registry.h>
#include <torch/csrc/jit/tensorexpr/ir.h>
#include <torch/csrc/jit/tensorexpr/ir_printer.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/tensor.h>
#include <torch/csrc/jit/testing/file_check.h>
#include <torch/jit.h>
#include <ATen/NativeFunctions.h>
#include <ATen/core/dispatch/Dispatcher.h>
#include <ATen/native/xnnpack/OpContext.h>
namespace torch {
namespace jit {
using namespace torch::jit::tensorexpr;
TEST(ExternalCall, Conv1d_float) {
BufHandle Input("Input", {1, 100, 115}, kFloat);
BufHandle Weight("Weight", {100, 1, 7}, kFloat);
BufHandle Bias("Bias", {100}, kFloat);
BufHandle ResultBuf("Result", {1, 100, 115}, kFloat);
int64_t stride = 1;
int64_t pad = 3;
int64_t dilation = 1;
int64_t groups = 100;
Tensor Result = Tensor(
ResultBuf.node(),
ExternalCall::make(
ResultBuf,
"nnc_aten_conv1d",
{Input, Weight, Bias},
{stride, pad, dilation, groups}));
LoopNest l({Result});
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, 100, 115}, options) * 5.f;
at::Tensor weight = at::ones({100, 1, 7}, options) * 6.f;
at::Tensor bias = at::ones({100}, options) * 11.f;
at::Tensor ref =
at::conv1d(input, weight, bias, {stride}, {pad}, {dilation}, groups);
at::Tensor nnc_result;
std::vector<float> input_buf(1 * 100 * 115, 5.f);
std::vector<float> weight_buf(100 * 1 * 7, 6.f);
std::vector<float> bias_buf(100, 11.f);
std::vector<float> result_buf(1 * 100 * 115, -1.f);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(l.root_stmt(), {Input, Weight, Bias, Result});
llvm_codegen.call({input_buf, weight_buf, bias_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 100, 115}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
#endif
SimpleIREvaluator ir_eval(l.root_stmt(), {Input, Weight, Bias, Result});
ir_eval.call({input_buf, weight_buf, bias_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 100, 115}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
}
TEST(ExternalCall, Conv1d_int) {
// A similar test, but now using kInt tensors
BufHandle Input("Input", {1, 100, 115}, kInt);
BufHandle Weight("Weight", {100, 1, 7}, kInt);
BufHandle Bias("Bias", {100}, kInt);
BufHandle ResultBuf("Result", {1, 100, 115}, kInt);
int64_t stride = 1;
int64_t pad = 3;
int64_t dilation = 1;
int64_t groups = 100;
Tensor Result = Tensor(
ResultBuf.node(),
ExternalCall::make(
ResultBuf,
"nnc_aten_conv1d",
{Input, Weight, Bias},
{stride, pad, dilation, groups}));
LoopNest l({Result});
l.prepareForCodegen();
l.simplify();
auto options = at::TensorOptions()
.dtype(at::kInt)
.layout(at::kStrided)
.device(at::kCPU)
.requires_grad(false);
at::Tensor input = at::ones({1, 100, 115}, options) * 5;
at::Tensor weight = at::ones({100, 1, 7}, options) * 6;
at::Tensor bias = at::ones({100}, options) * 11;
at::Tensor ref =
at::conv1d(input, weight, bias, {stride}, {pad}, {dilation}, groups);
at::Tensor nnc_result;
std::vector<int32_t> input_buf(1 * 100 * 115, 5);
std::vector<int32_t> weight_buf(100 * 1 * 7, 6);
std::vector<int32_t> bias_buf(100, 11);
std::vector<int32_t> result_buf(1 * 100 * 115, -1);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(l.root_stmt(), {Input, Weight, Bias, Result});
llvm_codegen.call({input_buf, weight_buf, bias_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 100, 115}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
#endif
SimpleIREvaluator ir_eval(l.root_stmt(), {Input, Weight, Bias, Result});
ir_eval.call({input_buf, weight_buf, bias_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 100, 115}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
}
TEST(ExternalCall, Conv1d_nobias_noargs) {
BufHandle Input("Input", {1, 1, 115}, kFloat);
BufHandle Weight("Weight", {10, 1, 7}, kFloat);
BufHandle ResultBuf("Result", {1, 10, 109}, kFloat);
Tensor Result = Tensor(
ResultBuf.node(),
ExternalCall::make(ResultBuf, "nnc_aten_conv1d", {Input, Weight}, {}));
LoopNest l({Result});
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, 1, 115}, options) * 5.f;
at::Tensor weight = at::ones({10, 1, 7}, options) * 6.f;
at::Tensor ref = at::conv1d(input, weight);
at::Tensor nnc_result;
std::vector<float> input_buf(1 * 1 * 115, 5.f);
std::vector<float> weight_buf(10 * 1 * 7, 6.f);
std::vector<float> result_buf(1 * 10 * 109, -1.f);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(l.root_stmt(), {Input, Weight, Result});
llvm_codegen.call({input_buf, weight_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 10, 109}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
#endif
SimpleIREvaluator ir_eval(l.root_stmt(), {Input, Weight, Result});
ir_eval.call({input_buf, weight_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 10, 109}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
}
TEST(ExternalCall, Conv2d_float) {
BufHandle Input("Input", {1, 3, 224, 224}, kFloat);
BufHandle Weight("Weight", {16, 3, 3, 3}, kFloat);
BufHandle Bias("Bias", {16}, kFloat);
BufHandle ResultBuf("Result", {1, 16, 112, 112}, kFloat);
int64_t stride = 2;
int64_t pad = 1;
int64_t dilation = 1;
int64_t groups = 1;
Tensor Result = Tensor(
ResultBuf.node(),
ExternalCall::make(
ResultBuf,
"nnc_aten_conv2d",
{Input, Weight, Bias},
{stride, stride, pad, pad, dilation, dilation, groups}));
LoopNest l({Result});
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, 3, 224, 224}, options) * 5.f;
at::Tensor weight = at::ones({16, 3, 3, 3}, options) * 6.f;
at::Tensor bias = at::ones({16}, options) * 11.f;
at::Tensor ref = at::conv2d(
input,
weight,
bias,
{stride, stride},
{pad, pad},
{dilation, dilation},
groups);
at::Tensor nnc_result;
std::vector<float> input_buf(1 * 3 * 224 * 224, 5.f);
std::vector<float> weight_buf(16 * 3 * 3 * 3, 6.f);
std::vector<float> bias_buf(16, 11.f);
std::vector<float> result_buf(1 * 16 * 112 * 112, -1.f);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(l.root_stmt(), {Input, Weight, Bias, Result});
llvm_codegen.call({input_buf, weight_buf, bias_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 16, 112, 112}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
#endif
SimpleIREvaluator ir_eval(l.root_stmt(), {Input, Weight, Bias, Result});
ir_eval.call({input_buf, weight_buf, bias_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 16, 112, 112}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
}
TEST(ExternalCall, Conv2d_int) {
// A similar test, but now using kInt tensors
BufHandle Input("Input", {1, 3, 224, 224}, kInt);
BufHandle Weight("Weight", {16, 3, 3, 3}, kInt);
BufHandle Bias("Bias", {16}, kInt);
BufHandle ResultBuf("Result", {1, 16, 112, 112}, kInt);
int64_t stride = 2;
int64_t pad = 1;
int64_t dilation = 1;
int64_t groups = 1;
Tensor Result = Tensor(
ResultBuf.node(),
ExternalCall::make(
ResultBuf,
"nnc_aten_conv2d",
{Input, Weight, Bias},
{stride, stride, pad, pad, dilation, dilation, groups}));
LoopNest l({Result});
l.prepareForCodegen();
l.simplify();
auto options = at::TensorOptions()
.dtype(at::kInt)
.layout(at::kStrided)
.device(at::kCPU)
.requires_grad(false);
at::Tensor input = at::ones({1, 3, 224, 224}, options) * 5;
at::Tensor weight = at::ones({16, 3, 3, 3}, options) * 6;
at::Tensor bias = at::ones({16}, options) * 11;
at::Tensor ref = at::conv2d(
input,
weight,
bias,
{stride, stride},
{pad, pad},
{dilation, dilation},
groups);
at::Tensor nnc_result;
std::vector<int32_t> input_buf(1 * 3 * 224 * 224, 5);
std::vector<int32_t> weight_buf(16 * 3 * 3 * 3, 6);
std::vector<int32_t> bias_buf(16, 11);
std::vector<int32_t> result_buf(1 * 16 * 112 * 112, -1);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(l.root_stmt(), {Input, Weight, Bias, Result});
llvm_codegen.call({input_buf, weight_buf, bias_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 16, 112, 112}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
#endif
SimpleIREvaluator ir_eval(l.root_stmt(), {Input, Weight, Bias, Result});
ir_eval.call({input_buf, weight_buf, bias_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 16, 112, 112}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
}
TEST(ExternalCall, Conv2d_nobias_noargs) {
BufHandle Input("Input", {1, 16, 112, 112}, kFloat);
BufHandle Weight("Weight", {16, 16, 1, 1}, kFloat);
BufHandle ResultBuf("Result", {1, 16, 112, 112}, kFloat);
Tensor Result = Tensor(
ResultBuf.node(),
ExternalCall::make(ResultBuf, "nnc_aten_conv2d", {Input, Weight}, {}));
LoopNest l({Result});
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, 112, 112}, options) * 5.f;
at::Tensor weight = at::ones({16, 16, 1, 1}, options) * 6.f;
at::Tensor ref = at::conv2d(input, weight);
at::Tensor nnc_result;
std::vector<float> input_buf(1 * 16 * 112 * 112, 5.f);
std::vector<float> weight_buf(16 * 16 * 1 * 1, 6.f);
std::vector<float> result_buf(1 * 16 * 112 * 112, -1.f);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(l.root_stmt(), {Input, Weight, Result});
llvm_codegen.call({input_buf, weight_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 16, 112, 112}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
#endif
SimpleIREvaluator ir_eval(l.root_stmt(), {Input, Weight, Result});
ir_eval.call({input_buf, weight_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 16, 112, 112}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
}
TEST(ExternalCall, Addmm_float) {
BufHandle Input("Input", {100, 300}, kFloat);
BufHandle Mat1("Mat1", {100, 200}, kFloat);
BufHandle Mat2("Mat2", {200, 300}, kFloat);
BufHandle ResultBuf("Result", {100, 300}, kFloat);
int64_t beta = 2;
int64_t alpha = 2;
Tensor Result = Tensor(
ResultBuf.node(),
ExternalCall::make(
ResultBuf, "nnc_aten_addmm", {Input, Mat1, Mat2}, {beta, alpha}));
LoopNest l({Result});
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({100, 300}, options) * 5.f;
at::Tensor mat1 = at::ones({100, 200}, options) * 6.f;
at::Tensor mat2 = at::ones({200, 300}, options) * 11.f;
at::Tensor ref = at::addmm(input, mat1, mat2, beta, alpha);
at::Tensor nnc_result;
std::vector<float> input_buf(100 * 300, 5.f);
std::vector<float> mat1_buf(100 * 200, 6.f);
std::vector<float> mat2_buf(200 * 300, 11.f);
std::vector<float> result_buf(100 * 300, -1.f);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(l.root_stmt(), {Input, Mat1, Mat2, Result});
llvm_codegen.call({input_buf, mat1_buf, mat2_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {100, 300}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
#endif
SimpleIREvaluator ir_eval(l.root_stmt(), {Input, Mat1, Mat2, Result});
ir_eval.call({input_buf, mat1_buf, mat2_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {100, 300}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
}
TEST(ExternalCall, Embedding) {
BufHandle Weight("Weight", {256, 100}, kFloat);
BufHandle Indices("Indices", {1, 115}, kLong);
BufHandle ResultBuf("Result", {1, 115, 100}, kFloat);
int64_t padding_idx = -1;
bool scale_grad_by_freq = false;
bool sparse = false;
Tensor Result = Tensor(
ResultBuf.node(),
ExternalCall::make(
ResultBuf,
"nnc_aten_embedding",
{Weight, Indices},
{padding_idx, (int64_t)scale_grad_by_freq, (int64_t)sparse}));
LoopNest l({Result});
l.prepareForCodegen();
l.simplify();
auto options = at::TensorOptions()
.layout(at::kStrided)
.device(at::kCPU)
.requires_grad(false);
at::Tensor weight = at::ones({256, 100}, options.dtype(at::kFloat)) * 5.f;
at::Tensor indices = at::ones({1, 115}, options.dtype(at::kLong)) * 6;
at::Tensor ref =
at::embedding(weight, indices, padding_idx, scale_grad_by_freq, sparse);
at::Tensor nnc_result;
std::vector<float> weight_buf(256 * 100, 5.f);
std::vector<int64_t> indices_buf(1 * 115, 6);
std::vector<float> result_buf(1 * 115 * 100, -1.f);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(l.root_stmt(), {Weight, Indices, Result});
llvm_codegen.call({weight_buf, indices_buf, result_buf});
nnc_result = at::from_blob(
result_buf.data(), {1, 115, 100}, options.dtype(at::kFloat));
ASSERT_TRUE(at::allclose(nnc_result, ref));
#endif
SimpleIREvaluator ir_eval(l.root_stmt(), {Weight, Indices, Result});
ir_eval.call({weight_buf, indices_buf, result_buf});
nnc_result = at::from_blob(
result_buf.data(), {1, 115, 100}, options.dtype(at::kFloat));
ASSERT_TRUE(at::allclose(nnc_result, ref));
}
TEST(ExternalCall, MaxReduction) {
BufHandle Input("Input", {1, 115, 152}, kFloat);
BufHandle ResultBuf("Result", {1, 152}, kFloat);
int64_t dim = 1;
bool keep_dim = false;
Tensor Result = Tensor(
ResultBuf.node(),
ExternalCall::make(
ResultBuf, "nnc_aten_max_red", {Input}, {dim, (int64_t)keep_dim}));
LoopNest l({Result});
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, 115, 152}, options) * 5.f;
at::Tensor ref = std::get<0>(at::max(input, dim, keep_dim));
at::Tensor nnc_result;
std::vector<float> input_buf(1 * 115 * 152, 5.f);
std::vector<float> result_buf(1 * 152, -1.f);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(l.root_stmt(), {Input, Result});
llvm_codegen.call({input_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 152}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
#endif
SimpleIREvaluator ir_eval(l.root_stmt(), {Input, Result});
ir_eval.call({input_buf, result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 152}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
}
#ifdef USE_XNNPACK
TEST(ExternalCall, Prepacked_Linear_float) {
using namespace at::native::xnnpack;
BufHandle Input("Input", {100, 200}, kFloat);
BufHandle ResultBuf("Result", {100, 300}, kFloat);
// Calculate reference result using at::linear.
auto options = at::TensorOptions()
.dtype(at::kFloat)
.layout(at::kStrided)
.device(at::kCPU)
.requires_grad(false);
at::Tensor input =
at::linspace(-10.0, 10.0, 100 * 200, options).resize_({100, 200});
at::Tensor weight =
at::linspace(-10.0, 10.0, 300 * 200, options).resize_({300, 200});
at::Tensor bias = at::linspace(-10.0, 10.0, 300, options);
at::Tensor ref = at::linear(input, weight, bias);
// Create prepacked xnnpack context object.
auto linear_clamp_prepack_op =
c10::Dispatcher::singleton()
.findSchemaOrThrow("prepacked::linear_clamp_prepack", "")
.typed<c10::intrusive_ptr<LinearOpContext>(
at::Tensor,
std::optional<at::Tensor>,
const std::optional<at::Scalar>&,
const std::optional<at::Scalar>&)>();
auto prepacked = linear_clamp_prepack_op.call(
weight, bias, std::optional<at::Scalar>(), std::optional<at::Scalar>());
BufHandle DummyPrepacked("DummyPrepacked", {1}, kFloat);
Tensor Result = Tensor(
ResultBuf.node(),
ExternalCall::make(
ResultBuf,
"nnc_prepacked_linear_clamp_run",
{Input, DummyPrepacked},
{}));
LoopNest l({Result});
l.prepareForCodegen();
l.simplify();
at::Tensor nnc_result;
std::vector<float> input_buf(
input.data_ptr<float>(), input.data_ptr<float>() + 100 * 200);
std::vector<float> result_buf(100 * 300, -1.f);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(l.root_stmt(), {Input, DummyPrepacked, Result});
llvm_codegen.call({input_buf, prepacked.get(), result_buf});
nnc_result = at::from_blob(result_buf.data(), {100, 300}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
#endif
SimpleIREvaluator ir_eval(l.root_stmt(), {Input, DummyPrepacked, Result});
ir_eval.call({input_buf, prepacked.get(), result_buf});
nnc_result = at::from_blob(result_buf.data(), {100, 300}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
}
TEST(ExternalCall, Prepacked_Conv2d_float) {
using namespace at::native::xnnpack;
BufHandle Input("Input", {1, 3, 224, 224}, kFloat);
BufHandle ResultBuf("Result", {1, 16, 112, 112}, kFloat);
int64_t stride = 2;
int64_t pad = 1;
int64_t dilation = 1;
int64_t groups = 1;
// Calculate reference result using at::conv2d.
auto options = at::TensorOptions()
.dtype(at::kFloat)
.layout(at::kStrided)
.device(at::kCPU)
.requires_grad(false);
at::Tensor input = at::linspace(-10.0, 10.0, 1 * 3 * 224 * 224, options)
.resize_({1, 3, 224, 224});
at::Tensor weight =
at::linspace(-10.0, 10.0, 16 * 3 * 3 * 3, options).resize_({16, 3, 3, 3});
at::Tensor bias = at::linspace(-10.0, 10.0, 16, options);
at::Tensor ref = at::conv2d(
input,
weight,
bias,
{stride, stride},
{pad, pad},
{dilation, dilation},
groups);
// Create prepacked xnnpack context object.
auto conv2d_clamp_prepack_op =
c10::Dispatcher::singleton()
.findSchemaOrThrow("prepacked::conv2d_clamp_prepack", "")
.typed<c10::intrusive_ptr<Conv2dOpContext>(
at::Tensor,
std::optional<at::Tensor>,
std::vector<int64_t>,
std::vector<int64_t>,
std::vector<int64_t>,
int64_t,
const std::optional<at::Scalar>&,
const std::optional<at::Scalar>&)>();
auto prepacked = conv2d_clamp_prepack_op.call(
weight,
bias,
{stride, stride},
{pad, pad},
{dilation, dilation},
groups,
std::optional<at::Scalar>(),
std::optional<at::Scalar>());
BufHandle DummyPrepacked("DummyPrepacked", {1}, kFloat);
Tensor Result = Tensor(
ResultBuf.node(),
ExternalCall::make(
ResultBuf,
"nnc_prepacked_conv2d_clamp_run",
{Input, DummyPrepacked},
{}));
LoopNest l({Result});
l.prepareForCodegen();
l.simplify();
at::Tensor nnc_result;
std::vector<float> input_buf(
input.data_ptr<float>(), input.data_ptr<float>() + 1 * 3 * 224 * 224);
std::vector<float> result_buf(1 * 16 * 112 * 112, -1.f);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(l.root_stmt(), {Input, DummyPrepacked, Result});
llvm_codegen.call({input_buf, prepacked.get(), result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 16, 112, 112}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref, 1e-03, 1e-03));
#endif
SimpleIREvaluator ir_eval(l.root_stmt(), {Input, DummyPrepacked, Result});
ir_eval.call({input_buf, prepacked.get(), result_buf});
nnc_result = at::from_blob(result_buf.data(), {1, 16, 112, 112}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref, 1e-03, 1e-03));
}
#endif // USE_XNNPACK
TEST(ExternalCall, BinaryFloat) {
using TensorFunc = std::function<at::Tensor(at::Tensor, at::Tensor)>;
using Test = std::tuple<
std::vector<int64_t>,
std::vector<int64_t>,
std::vector<int64_t>,
TensorFunc,
std::string>;
std::vector<Test> tests = {};
tests.push_back(
Test{{100, 200}, {200, 300}, {100, 300}, at::matmul, "nnc_aten_matmul"});
tests.push_back(Test{{100, 300}, {300}, {100}, at::mv, "nnc_aten_mv"});
tests.push_back(
Test{{100, 200}, {200, 300}, {100, 300}, at::mm, "nnc_aten_mm"});
for (auto curTest : tests) {
auto [aShape, bShape, resShape, torchFunc, externCallName] = curTest;
auto toExprHandleVec = [](std::vector<int64_t> v) {
auto intV = std::vector<int>(v.begin(), v.end());
return std::vector<ExprHandle>(intV.begin(), intV.end());
};
BufHandle A("A", toExprHandleVec(aShape), kFloat);
BufHandle B("B", toExprHandleVec(bShape), kFloat);
BufHandle ResultBuf("Result", toExprHandleVec(resShape), kFloat);
Tensor Result = Tensor(
ResultBuf.node(),
ExternalCall::make(ResultBuf, externCallName, {A, B}, {}));
LoopNest l({Result});
l.prepareForCodegen();
l.simplify();
auto options = at::TensorOptions()
.dtype(at::kFloat)
.layout(at::kStrided)
.device(at::kCPU)
.requires_grad(false);
at::Tensor a = at::ones(c10::IntArrayRef(aShape), options) * 5.f;
at::Tensor b = at::ones(c10::IntArrayRef(bShape), options) * 6.f;
at::Tensor ref = torchFunc(a, b);
auto prod = [](std::vector<int64_t> v) {
// NOLINTNEXTLINE(modernize-use-transparent-functors)
return std::accumulate(v.begin(), v.end(), 1, std::multiplies<int64_t>());
};
at::Tensor nnc_result;
std::vector<float> a_buf(prod(aShape), 5.f);
std::vector<float> b_buf(prod(bShape), 6.f);
std::vector<float> result_buf(prod(resShape), -1.f);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(l.root_stmt(), {A, B, Result});
llvm_codegen.call({a_buf, b_buf, result_buf});
nnc_result =
at::from_blob(result_buf.data(), c10::IntArrayRef(resShape), options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
#endif
SimpleIREvaluator ir_eval(l.root_stmt(), {A, B, Result});
ir_eval.call({a_buf, b_buf, result_buf});
nnc_result =
at::from_blob(result_buf.data(), c10::IntArrayRef(resShape), options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
}
}
TEST(ExternalCall, UnaryFloat) {
using TensorFunc = std::function<at::Tensor(at::Tensor)>;
auto toExprHandleVec = [](std::vector<int64_t> v) {
auto intV = std::vector<int>(v.begin(), v.end());
return std::vector<ExprHandle>(intV.begin(), intV.end());
};
using Test = std::tuple<
std::vector<int64_t>,
std::vector<int64_t>,
TensorFunc,
std::string,
std::vector<ExprHandle>>;
std::vector<Test> tests = {};
tests.push_back(Test{// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
{1, 64, 8, 9},
{1, 64, 5, 7},
[](at::Tensor x) {
return at::adaptive_avg_pool2d(x, {5, 7});
},
"nnc_aten_adaptive_avg_pool2d",
toExprHandleVec({5, 7})});
tests.push_back(Test{// NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers)
{100, 200},
{100},
[](at::Tensor x) { return at::mean(x, {1}); },
"nnc_aten_mean",
toExprHandleVec({1, /*keepdim=*/0})});
for (auto curTest : tests) {
auto [aShape, resShape, torchFunc, externCallName, externCallArgs] =
curTest;
BufHandle A("A", toExprHandleVec(aShape), kFloat);
BufHandle ResultBuf("Result", toExprHandleVec(resShape), kFloat);
Tensor Result = Tensor(
ResultBuf.node(),
ExternalCall::make(ResultBuf, externCallName, {A}, externCallArgs));
LoopNest l({Result});
l.prepareForCodegen();
l.simplify();
auto options = at::TensorOptions()
.dtype(at::kFloat)
.layout(at::kStrided)
.device(at::kCPU)
.requires_grad(false);
at::Tensor a = at::ones(c10::IntArrayRef(aShape), options) * 5.f;
at::Tensor ref = torchFunc(a);
auto prod = [](std::vector<int64_t> v) {
// NOLINTNEXTLINE(modernize-use-transparent-functors)
return std::accumulate(v.begin(), v.end(), 1, std::multiplies<int64_t>());
};
at::Tensor nnc_result;
std::vector<float> a_buf(prod(aShape), 5.f);
std::vector<float> result_buf(prod(resShape), -1.f);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(l.root_stmt(), {A, Result});
llvm_codegen.call({a_buf, result_buf});
nnc_result =
at::from_blob(result_buf.data(), c10::IntArrayRef(resShape), options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
#endif
SimpleIREvaluator ir_eval(l.root_stmt(), {A, Result});
ir_eval.call({a_buf, result_buf});
nnc_result =
at::from_blob(result_buf.data(), c10::IntArrayRef(resShape), options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
}
}
TEST(ExternalCall, ComputeInterop) {
// This test verifies that Tensors using external calls can be used by and can
// use Tensors built with Compute API.
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);
});
Tensor MatmulResult = Tensor(
MatmulResultBuf.node(),
ExternalCall::make(
MatmulResultBuf,
"nnc_aten_matmul",
{BufHandle(A.buf()), BufHandle(B.buf())},
{}));
Tensor Result =
Compute("Result", {8, 8}, [&](const VarHandle& i, const VarHandle& j) {
return MatmulResult.load(i, j) + FloatImm::make(3.0f);
});
StmtPtr root_stmt = alloc<torch::jit::tensorexpr::Block>(std::vector<StmtPtr>(
{A.stmt(), B.stmt(), MatmulResult.stmt(), Result.stmt()}));
LoopNest l(root_stmt, {Result.buf()});
// Inlining should not inline anything here since all Bufs are either
// defined or used in ExternalCalls
l.inlineIntermediateBufs(false);
l.prepareForCodegen();
l.simplify();
auto options = at::TensorOptions()
.dtype(at::kFloat)
.layout(at::kStrided)
.device(at::kCPU)
.requires_grad(false);
at::Tensor a = at::ones({8, 8}, options) * 5.f;
at::Tensor b = at::ones({8, 8}, options) * 4.f;
at::Tensor t = at::matmul(a, b);
at::Tensor ref = t + 3.f;
at::Tensor nnc_result;
std::vector<float> result_buf(8 * 8);
#ifdef TORCH_ENABLE_LLVM
LLVMCodeGen llvm_codegen(l.root_stmt(), {Result});
llvm_codegen.call({result_buf});
nnc_result = at::from_blob(result_buf.data(), {8, 8}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
#endif
SimpleIREvaluator ir_eval(l.root_stmt(), {Result});
ir_eval.call({result_buf});
nnc_result = at::from_blob(result_buf.data(), {8, 8}, options);
ASSERT_TRUE(at::allclose(nnc_result, ref));
}
TEST(ExternalCall, JitCustomFusionOp) {
const char* custom_op_schema_literal =
"nnc_custom::add_mul(Tensor a, Tensor b, Tensor c) -> Tensor";
const char* external_func_name = "nnc_add_mul";
auto add_mul_lowering_func =
[external_func_name](
const std::vector<torch::jit::tensorexpr::ArgValue>& inputs,
const std::vector<torch::jit::tensorexpr::ExprHandle>& output_shape,
const std::vector<torch::jit::tensorexpr::ExprHandle>& output_strides,
const std::optional<torch::jit::tensorexpr::ScalarType>& output_type,
at::Device device) {
auto output_dtype = Dtype(*output_type);
torch::jit::tensorexpr::BufHandle result_buf(
"nnc_add_mul_res_buf", output_shape, output_dtype);
const torch::jit::tensorexpr::BufHandle& a =
std::get<torch::jit::tensorexpr::BufHandle>(inputs[0]);
const torch::jit::tensorexpr::BufHandle& b =
std::get<torch::jit::tensorexpr::BufHandle>(inputs[1]);
const torch::jit::tensorexpr::BufHandle& c =
std::get<torch::jit::tensorexpr::BufHandle>(inputs[1]);
torch::jit::tensorexpr::StmtPtr s =
torch::jit::tensorexpr::ExternalCall::make(
result_buf, external_func_name, {a, b, c}, {});
return Tensor(result_buf.node(), s);
};
auto add_mul_external_func = [](int64_t bufs_num,
void** buf_data,
int64_t* buf_ranks,
int64_t* buf_dims,
int64_t* buf_strides,
int8_t* buf_dtypes,
int64_t args_num,
int64_t* extra_args) {};
torch::jit::RegisterOperators reg({Operator(
custom_op_schema_literal,
[](const Node* node) -> Operation {
return [](Stack& _stack) {
auto a = std::move(peek(_stack, 0, 3)).toTensor();
auto b = std::move(peek(_stack, 1, 3)).toTensor();
auto c = std::move(peek(_stack, 2, 3)).toTensor();
drop(_stack, 3);
auto result = (a + b) * c;
pack(_stack, std::move(result));
return 0;
};
},
c10::AliasAnalysisKind::FROM_SCHEMA)});
auto& custom_operator_set = torch::jit::tensorexpr::getCustomOperatorSet();
custom_operator_set.insert({custom_op_schema_literal});
auto& te_lowering_registry = torch::jit::tensorexpr::getNNCLoweringRegistry();
te_lowering_registry.insert(
parseSchema(custom_op_schema_literal), add_mul_lowering_func);
auto& te_nnc_func_registry = torch::jit::tensorexpr::getNNCFunctionRegistry();
te_nnc_func_registry[external_func_name] = add_mul_external_func;
std::string graph_string = R"IR(
graph(%a : Float(10, 20, strides=[20, 1], device=cpu),
%b : Float(10, 20, strides=[20, 1], device=cpu),
%c : Float(10, 20, strides=[20, 1], device=cpu)):
%res : Float(10, 20, strides=[20, 1], device=cpu) = nnc_custom::add_mul(%a, %b, %c)
return (%res))IR";
auto graph = std::make_shared<Graph>();
torch::jit::parseIR(graph_string, graph.get());