mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-14 20:57:59 +00:00
067ad31210
Summary:
Fixes #{issue number}
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53420
Reviewed By: navahgar
Differential Revision: D26876784
Pulled By: Chillee
fbshipit-source-id: 05e7c782a72de5159879f88a104f1a273e0345eb
441 lines
15 KiB
C++
441 lines
15 KiB
C++
#include <gtest/gtest.h>
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#include <test/cpp/tensorexpr/test_base.h>
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#include <test/cpp/tensorexpr/test_utils.h>
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#include <torch/csrc/jit/tensorexpr/eval.h>
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#include <torch/csrc/jit/tensorexpr/ir.h>
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#include <torch/csrc/jit/tensorexpr/ir_printer.h>
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#include <torch/csrc/jit/tensorexpr/ir_simplifier.h>
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#include <torch/csrc/jit/tensorexpr/llvm_codegen.h>
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#include <torch/csrc/jit/tensorexpr/loopnest.h>
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#include <torch/csrc/jit/tensorexpr/tensor.h>
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#include <ATen/NativeFunctions.h>
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namespace torch {
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namespace jit {
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using namespace torch::jit::tensorexpr;
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TEST(ExternalCall, Conv2d_float) {
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KernelScope kernel_scope;
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Placeholder Input("Input", kFloat, {1, 3, 224, 224});
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Placeholder Weight("Weight", kFloat, {16, 3, 3, 3});
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Placeholder Bias("Bias", kFloat, {16});
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BufHandle ResultBuf("Result", {1, 16, 112, 112}, kFloat);
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int64_t stride = 2;
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int64_t pad = 1;
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int64_t dilation = 1;
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int64_t groups = 1;
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Tensor* Result = new Tensor(
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ResultBuf.node(),
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ExternalCall::make(
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ResultBuf,
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"nnc_aten_conv2d",
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{BufHandle(Input.data()),
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BufHandle(Weight.data()),
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BufHandle(Bias.data())},
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{stride, stride, pad, pad, dilation, dilation, groups}));
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LoopNest l({Result});
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l.prepareForCodegen();
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l.simplify();
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auto options = at::TensorOptions()
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.dtype(at::kFloat)
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.layout(at::kStrided)
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.device(at::kCPU)
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.requires_grad(false);
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at::Tensor input = at::ones({1, 3, 224, 224}, options) * 5.f;
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at::Tensor weight = at::ones({16, 3, 3, 3}, options) * 6.f;
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at::Tensor bias = at::ones({16}, options) * 11.f;
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at::Tensor ref = at::conv2d(
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input,
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weight,
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bias,
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{stride, stride},
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{pad, pad},
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{dilation, dilation},
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groups);
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at::Tensor nnc_result;
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std::vector<float> input_buf(1 * 3 * 224 * 224, 5.f);
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std::vector<float> weight_buf(16 * 3 * 3 * 3, 6.f);
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std::vector<float> bias_buf(16, 11.f);
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std::vector<float> result_buf(1 * 16 * 112 * 112, -1.f);
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#ifdef TORCH_ENABLE_LLVM
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LLVMCodeGen llvm_codegen(l.root_stmt(), {Input, Weight, Bias, Result});
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llvm_codegen.call({input_buf, weight_buf, bias_buf, result_buf});
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nnc_result = at::from_blob(result_buf.data(), {1, 16, 112, 112}, options);
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ASSERT_TRUE(at::allclose(nnc_result, ref));
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#endif
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SimpleIREvaluator ir_eval(l.root_stmt(), {Input, Weight, Bias, Result});
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ir_eval.call({input_buf, weight_buf, bias_buf, result_buf});
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nnc_result = at::from_blob(result_buf.data(), {1, 16, 112, 112}, options);
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ASSERT_TRUE(at::allclose(nnc_result, ref));
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}
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TEST(ExternalCall, Conv2d_int) {
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// A similar test, but now using kInt tensors
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KernelScope kernel_scope;
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Placeholder Input("Input", kInt, {1, 3, 224, 224});
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Placeholder Weight("Weight", kInt, {16, 3, 3, 3});
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Placeholder Bias("Bias", kInt, {16});
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BufHandle ResultBuf("Result", {1, 16, 112, 112}, kInt);
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int64_t stride = 2;
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int64_t pad = 1;
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int64_t dilation = 1;
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int64_t groups = 1;
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Tensor* Result = new Tensor(
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ResultBuf.node(),
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ExternalCall::make(
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ResultBuf,
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"nnc_aten_conv2d",
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{BufHandle(Input.data()),
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BufHandle(Weight.data()),
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BufHandle(Bias.data())},
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{stride, stride, pad, pad, dilation, dilation, groups}));
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LoopNest l({Result});
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l.prepareForCodegen();
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l.simplify();
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auto options = at::TensorOptions()
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.dtype(at::kInt)
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.layout(at::kStrided)
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.device(at::kCPU)
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.requires_grad(false);
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at::Tensor input = at::ones({1, 3, 224, 224}, options) * 5;
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at::Tensor weight = at::ones({16, 3, 3, 3}, options) * 6;
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at::Tensor bias = at::ones({16}, options) * 11;
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at::Tensor ref = at::conv2d(
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input,
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weight,
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bias,
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{stride, stride},
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{pad, pad},
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{dilation, dilation},
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groups);
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at::Tensor nnc_result;
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std::vector<int32_t> input_buf(1 * 3 * 224 * 224, 5);
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std::vector<int32_t> weight_buf(16 * 3 * 3 * 3, 6);
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std::vector<int32_t> bias_buf(16, 11);
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std::vector<int32_t> result_buf(1 * 16 * 112 * 112, -1);
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#ifdef TORCH_ENABLE_LLVM
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LLVMCodeGen llvm_codegen(l.root_stmt(), {Input, Weight, Bias, Result});
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llvm_codegen.call({input_buf, weight_buf, bias_buf, result_buf});
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nnc_result = at::from_blob(result_buf.data(), {1, 16, 112, 112}, options);
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ASSERT_TRUE(at::allclose(nnc_result, ref));
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#endif
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SimpleIREvaluator ir_eval(l.root_stmt(), {Input, Weight, Bias, Result});
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ir_eval.call({input_buf, weight_buf, bias_buf, result_buf});
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nnc_result = at::from_blob(result_buf.data(), {1, 16, 112, 112}, options);
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ASSERT_TRUE(at::allclose(nnc_result, ref));
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}
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TEST(ExternalCall, Conv2d_nobias_noargs) {
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KernelScope kernel_scope;
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Placeholder Input("Input", kFloat, {1, 16, 112, 112});
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Placeholder Weight("Weight", kFloat, {16, 16, 1, 1});
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BufHandle ResultBuf("Result", {1, 16, 112, 112}, kFloat);
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Tensor* Result = new Tensor(
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ResultBuf.node(),
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ExternalCall::make(
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ResultBuf,
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"nnc_aten_conv2d",
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{BufHandle(Input.data()), BufHandle(Weight.data())},
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{}));
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LoopNest l({Result});
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l.prepareForCodegen();
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l.simplify();
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auto options = at::TensorOptions()
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.dtype(at::kFloat)
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.layout(at::kStrided)
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.device(at::kCPU)
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.requires_grad(false);
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at::Tensor input = at::ones({1, 16, 112, 112}, options) * 5.f;
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at::Tensor weight = at::ones({16, 16, 1, 1}, options) * 6.f;
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at::Tensor ref = at::conv2d(input, weight);
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at::Tensor nnc_result;
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std::vector<float> input_buf(1 * 16 * 112 * 112, 5.f);
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std::vector<float> weight_buf(16 * 16 * 1 * 1, 6.f);
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std::vector<float> result_buf(1 * 16 * 112 * 112, -1.f);
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#ifdef TORCH_ENABLE_LLVM
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LLVMCodeGen llvm_codegen(l.root_stmt(), {Input, Weight, Result});
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llvm_codegen.call({input_buf, weight_buf, result_buf});
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nnc_result = at::from_blob(result_buf.data(), {1, 16, 112, 112}, options);
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ASSERT_TRUE(at::allclose(nnc_result, ref));
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#endif
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SimpleIREvaluator ir_eval(l.root_stmt(), {Input, Weight, Result});
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ir_eval.call({input_buf, weight_buf, result_buf});
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nnc_result = at::from_blob(result_buf.data(), {1, 16, 112, 112}, options);
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ASSERT_TRUE(at::allclose(nnc_result, ref));
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}
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TEST(ExternalCall, BinaryFloat) {
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KernelScope kernel_scope;
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using TensorFunc = std::function<at::Tensor(at::Tensor, at::Tensor)>;
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using Test = std::tuple<
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std::vector<int64_t>,
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std::vector<int64_t>,
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std::vector<int64_t>,
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TensorFunc,
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std::string>;
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std::vector<Test> tests = {};
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tests.push_back(
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Test{{100, 200}, {200, 300}, {100, 300}, at::matmul, "nnc_aten_matmul"});
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tests.push_back(Test{{100, 300}, {300}, {100}, at::mv, "nnc_aten_mv"});
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tests.push_back(
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Test{{100, 200}, {200, 300}, {100, 300}, at::mm, "nnc_aten_mm"});
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for (auto curTest : tests) {
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std::vector<int64_t> aShape, bShape, resShape;
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TensorFunc torchFunc;
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std::string externCallName;
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std::tie(aShape, bShape, resShape, torchFunc, externCallName) = curTest;
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auto toExprHandleVec = [](std::vector<int64_t> v) {
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auto intV = std::vector<int>(v.begin(), v.end());
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return std::vector<ExprHandle>(intV.begin(), intV.end());
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};
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Placeholder A("A", kFloat, toExprHandleVec(aShape));
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Placeholder B("", kFloat, toExprHandleVec(bShape));
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BufHandle ResultBuf("Result", toExprHandleVec(resShape), kFloat);
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Tensor* Result = new Tensor(
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ResultBuf.node(),
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ExternalCall::make(
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ResultBuf,
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externCallName,
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{BufHandle(A.data()), BufHandle(B.data())},
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{}));
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LoopNest l({Result});
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l.prepareForCodegen();
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l.simplify();
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auto options = at::TensorOptions()
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.dtype(at::kFloat)
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.layout(at::kStrided)
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.device(at::kCPU)
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.requires_grad(false);
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at::Tensor a = at::ones(c10::IntArrayRef(aShape), options) * 5.f;
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at::Tensor b = at::ones(c10::IntArrayRef(bShape), options) * 6.f;
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at::Tensor ref = torchFunc(a, b);
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auto prod = [](std::vector<int64_t> v) {
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return std::accumulate(v.begin(), v.end(), 1, std::multiplies<int64_t>());
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};
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at::Tensor nnc_result;
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std::vector<float> a_buf(prod(aShape), 5.f);
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std::vector<float> b_buf(prod(bShape), 6.f);
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std::vector<float> result_buf(prod(resShape), -1.f);
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#ifdef TORCH_ENABLE_LLVM
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LLVMCodeGen llvm_codegen(l.root_stmt(), {A, B, Result});
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llvm_codegen.call({a_buf, b_buf, result_buf});
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nnc_result =
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at::from_blob(result_buf.data(), c10::IntArrayRef(resShape), options);
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ASSERT_TRUE(at::allclose(nnc_result, ref));
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#endif
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SimpleIREvaluator ir_eval(l.root_stmt(), {A, B, Result});
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ir_eval.call({a_buf, b_buf, result_buf});
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nnc_result =
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at::from_blob(result_buf.data(), c10::IntArrayRef(resShape), options);
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ASSERT_TRUE(at::allclose(nnc_result, ref));
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}
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}
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TEST(ExternalCall, UnaryFloat) {
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KernelScope kernel_scope;
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using TensorFunc = std::function<at::Tensor(at::Tensor)>;
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auto toExprHandleVec = [](std::vector<int64_t> v) {
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auto intV = std::vector<int>(v.begin(), v.end());
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return std::vector<ExprHandle>(intV.begin(), intV.end());
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};
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using Test = std::tuple<
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std::vector<int64_t>,
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std::vector<int64_t>,
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TensorFunc,
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std::string,
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std::vector<ExprHandle>>;
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std::vector<Test> tests = {};
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tests.push_back(Test{
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{1, 64, 8, 9},
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{1, 64, 5, 7},
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[](at::Tensor x) {
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return at::adaptive_avg_pool2d(x, {5, 7});
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},
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"nnc_aten_adaptive_avg_pool2d",
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toExprHandleVec({5, 7})});
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tests.push_back(Test{
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{100, 200},
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{100},
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[](at::Tensor x) { return at::mean(x, {1}); },
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"nnc_aten_mean",
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toExprHandleVec({1})});
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for (auto curTest : tests) {
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std::vector<int64_t> aShape, resShape;
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TensorFunc torchFunc;
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std::string externCallName;
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std::vector<ExprHandle> externCallArgs;
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std::tie(aShape, resShape, torchFunc, externCallName, externCallArgs) =
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curTest;
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Placeholder A("A", kFloat, toExprHandleVec(aShape));
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BufHandle ResultBuf("Result", toExprHandleVec(resShape), kFloat);
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Tensor* Result = new Tensor(
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ResultBuf.node(),
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ExternalCall::make(
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ResultBuf, externCallName, {BufHandle(A.data())}, externCallArgs));
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LoopNest l({Result});
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l.prepareForCodegen();
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l.simplify();
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auto options = at::TensorOptions()
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.dtype(at::kFloat)
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.layout(at::kStrided)
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.device(at::kCPU)
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.requires_grad(false);
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at::Tensor a = at::ones(c10::IntArrayRef(aShape), options) * 5.f;
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at::Tensor ref = torchFunc(a);
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auto prod = [](std::vector<int64_t> v) {
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return std::accumulate(v.begin(), v.end(), 1, std::multiplies<int64_t>());
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};
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at::Tensor nnc_result;
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std::vector<float> a_buf(prod(aShape), 5.f);
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std::vector<float> result_buf(prod(resShape), -1.f);
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#ifdef TORCH_ENABLE_LLVM
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LLVMCodeGen llvm_codegen(l.root_stmt(), {A, Result});
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llvm_codegen.call({a_buf, result_buf});
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nnc_result =
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at::from_blob(result_buf.data(), c10::IntArrayRef(resShape), options);
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ASSERT_TRUE(at::allclose(nnc_result, ref));
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#endif
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SimpleIREvaluator ir_eval(l.root_stmt(), {A, Result});
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ir_eval.call({a_buf, result_buf});
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nnc_result =
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at::from_blob(result_buf.data(), c10::IntArrayRef(resShape), options);
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ASSERT_TRUE(at::allclose(nnc_result, ref));
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}
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}
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TEST(ExternalCall, ComputeInterop) {
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// This test verifies that Tensors using external calls can be used by and can
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// use Tensors built with Compute API.
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KernelScope kernel_scope;
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BufHandle ConvResultBuf("ConvResult", {1, 16, 112, 112}, kFloat);
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BufHandle MatmulResultBuf("MatmulResult", {1, 16, 112, 112}, kFloat);
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Tensor* Input = Compute(
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"Input",
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{{1, "n"}, {16, "c"}, {112, "h"}, {112, "w"}},
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[&](const VarHandle& n,
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const VarHandle& c,
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const VarHandle& h,
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const VarHandle& w) { return FloatImm::make(5.0f); });
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Tensor* Weight = Compute(
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"Weight",
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{{16, "n"}, {16, "c"}, {1, "kh"}, {1, "kw"}},
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[&](const VarHandle& n,
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const VarHandle& c,
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const VarHandle& h,
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const VarHandle& w) { return FloatImm::make(6.0f); });
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Tensor* ConvResult = new Tensor(
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ConvResultBuf.node(),
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ExternalCall::make(
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ConvResultBuf,
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"nnc_aten_conv2d",
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{BufHandle(Input->buf()), BufHandle(Weight->buf())},
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{}));
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Tensor* MatmulResult = new Tensor(
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MatmulResultBuf.node(),
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ExternalCall::make(
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MatmulResultBuf,
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"nnc_aten_matmul",
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{BufHandle(ConvResult->buf()), BufHandle(ConvResult->buf())},
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{}));
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Tensor* Result = Compute(
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"Result",
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{{1, "n"}, {16, "c"}, {112, "h"}, {112, "w"}},
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[&](const VarHandle& n,
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const VarHandle& c,
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const VarHandle& h,
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const VarHandle& w) {
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return ConvResult->call(n, c, h, w) + MatmulResult->call(n, c, h, w);
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});
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LoopNest l({Input, Weight, ConvResult, MatmulResult, Result});
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// Inlining should not inline anything here since all Bufs are either defined
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// or used in ExternalCalls - we run it just for testing
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l.inlineIntermediateBufs(true);
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l.prepareForCodegen();
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l.simplify();
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auto options = at::TensorOptions()
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.dtype(at::kFloat)
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.layout(at::kStrided)
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.device(at::kCPU)
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.requires_grad(false);
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at::Tensor input = at::ones({1, 16, 112, 112}, options) * 5.f;
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at::Tensor weight = at::ones({16, 16, 1, 1}, options) * 6.f;
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at::Tensor t = at::conv2d(input, weight);
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at::Tensor t2 = at::matmul(t, t);
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at::Tensor ref = t + t2;
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at::Tensor nnc_result;
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std::vector<float> input_buf(1 * 16 * 112 * 112, 5.f);
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std::vector<float> weight_buf(16 * 16 * 1 * 1, 6.f);
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std::vector<float> conv_result_buf(1 * 16 * 112 * 112, -1.f);
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std::vector<float> matmul_result_buf(1 * 16 * 112 * 112, -1.f);
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std::vector<float> result_buf(1 * 16 * 112 * 112, -1.f);
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#ifdef TORCH_ENABLE_LLVM
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LLVMCodeGen llvm_codegen(
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l.root_stmt(), {Input, Weight, ConvResult, MatmulResult, Result});
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llvm_codegen.call(
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{input_buf, weight_buf, conv_result_buf, matmul_result_buf, result_buf});
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nnc_result = at::from_blob(result_buf.data(), {1, 16, 112, 112}, options);
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ASSERT_TRUE(at::allclose(nnc_result, ref));
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#endif
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SimpleIREvaluator ir_eval(
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l.root_stmt(), {Input, Weight, ConvResult, MatmulResult, Result});
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ir_eval.call(
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{input_buf, weight_buf, conv_result_buf, matmul_result_buf, result_buf});
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nnc_result = at::from_blob(result_buf.data(), {1, 16, 112, 112}, options);
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ASSERT_TRUE(at::allclose(nnc_result, ref));
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}
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} // namespace jit
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} // namespace torch
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