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8530c65e25
Test Plan: Sandcastle and visual inspection. Reviewed By: igorsugak Differential Revision: D25849205 fbshipit-source-id: ef664c1ad4b3ee92d5c020a5511b4ef9837a09a0
88 lines
2.7 KiB
C++
88 lines
2.7 KiB
C++
#include <gtest/gtest.h>
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#include <torch/csrc/jit/tensorexpr/ir_simplifier.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 <torch/torch.h>
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namespace torch {
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namespace jit {
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namespace te = torch::jit::tensorexpr;
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namespace F = torch::nn::functional;
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TEST(Conv, Conv2D) {
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te::KernelScope kernel_scope;
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// Input dimensions.
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constexpr int N = 1;
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constexpr int C = 3;
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constexpr int H = 11;
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constexpr int W = 11;
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// Filter dimensions.
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constexpr int K = 8;
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constexpr int R = 3;
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constexpr int S = 3;
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// Output dims.
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constexpr int OH = H - R + 1;
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constexpr int OW = W - S + 1;
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// Compute reference result.
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at::Tensor input = torch::randn({N, C, H, W});
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at::Tensor filter = torch::randn({K, C, R, S});
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at::Tensor ref = F::conv2d(input, filter);
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// Double check the output size is as expected.
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ASSERT_EQ(ref.size(0), N);
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ASSERT_EQ(ref.size(1), K);
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ASSERT_EQ(ref.size(2), OH);
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ASSERT_EQ(ref.size(3), OW);
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te::Placeholder inputB(te::BufHandle("input", {N, C, H, W}, te::kFloat));
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te::Placeholder filterB(te::BufHandle("filter", {K, C, R, S}, te::kFloat));
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te::Tensor* conv = te::Reduce(
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"conv",
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{{N, "n"}, {K, "k"}, {OH, "oh"}, {OW, "ow"}},
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te::Sum(),
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// FIXME: We have to use a `std::vector` parameter here and then unpack
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// it, because we don't have an overload allowing for an arbitrary number
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// of ExprHandle/VarHandle parameters.
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[&](const std::vector<te::VarHandle>& v) {
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auto const& n = v[0];
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auto const& k = v[1];
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auto const& oh = v[2];
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auto const& ow = v[3];
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auto const& c = v[4];
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auto const& r = v[5];
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auto const& s = v[6];
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// FIXME: We have to use `call` and construct a `std::vector` here
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// because the `operator()` overload is only specialized for a small
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// number of arguments.
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return inputB.load(n, c, oh + r, ow + s) * filterB.load(k, c, r, s);
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},
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// FIXME: If you forget one of the reduction dims, you get a segfault.
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// Could that be caught by a verifier?
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{{C, "c"}, {R, "r"}, {S, "s"}});
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// FIXME: It'd be nice to have a single header that pulls in things like
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// LoopNest, IRSimplifier, etc.
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te::LoopNest loop({conv});
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loop.prepareForCodegen();
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te::Stmt* s = loop.root_stmt();
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s = te::IRSimplifier::simplify(s);
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at::Tensor result = at::empty_like(ref);
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te::SimpleIREvaluator cg(s, {inputB, filterB, conv});
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cg.call(
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{input.data_ptr<float>(),
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filter.data_ptr<float>(),
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result.data_ptr<float>()});
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ASSERT_TRUE(at::allclose(ref, result, 1e-3, 1e-3));
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}
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} // namespace jit
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} // namespace torch
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