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using namespace at::native::xnnpack;
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BufHandle Input("Input", {100, 200}, kFloat);
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BufHandle ResultBuf("Result", {100, 300}, kFloat);
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// Calculate reference result using at::linear.
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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 =
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at::linspace(-10.0, 10.0, 100 * 200, options).resize_({100, 200});
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at::Tensor weight =
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at::linspace(-10.0, 10.0, 300 * 200, options).resize_({300, 200});
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at::Tensor bias = at::linspace(-10.0, 10.0, 300, options);
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at::Tensor ref = at::linear(input, weight, bias);
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// Create prepacked xnnpack context object.
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auto linear_clamp_prepack_op =
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c10::Dispatcher::singleton()
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.findSchemaOrThrow("prepacked::linear_clamp_prepack", "")
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.typed<c10::intrusive_ptr<LinearOpContext>(
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at::Tensor,
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c10::optional<at::Tensor>,
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const c10::optional<at::Scalar>&,
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const c10::optional<at::Scalar>&)>();
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auto prepacked = linear_clamp_prepack_op.call(
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weight, bias, c10::optional<at::Scalar>(), c10::optional<at::Scalar>());
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BufHandle DummyPrepacked("DummyPrepacked", {1}, kFloat);
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Tensor Result = Tensor(
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ResultBuf.node(),
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ExternalCall::make(
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ResultBuf,
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"nnc_prepacked_linear_clamp_run",
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{Input, DummyPrepacked},
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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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at::Tensor nnc_result;
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std::vector<float> input_buf(
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input.data_ptr<float>(), input.data_ptr<float>() + 100 * 200);
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std::vector<float> result_buf(100 * 300, -1.f);
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#ifdef TORCH_ENABLE_LLVM
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LLVMCodeGen llvm_codegen(l.root_stmt(), {Input, DummyPrepacked, Result});
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llvm_codegen.call({input_buf, prepacked.get(), result_buf});
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nnc_result = at::from_blob(result_buf.data(), {100, 300}, 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, DummyPrepacked, Result});
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ir_eval.call({input_buf, prepacked.get(), result_buf});
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nnc_result = at::from_blob(result_buf.data(), {100, 300}, options);
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ASSERT_TRUE(at::allclose(nnc_result, ref));
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}
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TEST(ExternalCall, Prepacked_Conv2d_float) {
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using namespace at::native::xnnpack;
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BufHandle Input("Input", {1, 3, 224, 224}, kFloat);
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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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// Calculate reference result using at::conv2d.
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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::linspace(-10.0, 10.0, 1 * 3 * 224 * 224, options)
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.resize_({1, 3, 224, 224});
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at::Tensor weight =
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at::linspace(-10.0, 10.0, 16 * 3 * 3 * 3, options).resize_({16, 3, 3, 3});
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at::Tensor bias = at::linspace(-10.0, 10.0, 16, options);
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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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// Create prepacked xnnpack context object.
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auto conv2d_clamp_prepack_op =
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c10::Dispatcher::singleton()
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.findSchemaOrThrow("prepacked::conv2d_clamp_prepack", "")
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.typed<c10::intrusive_ptr<Conv2dOpContext>(
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at::Tensor,
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c10::optional<at::Tensor>,
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std::vector<int64_t>,
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