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stringlengths 0
2.2M
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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, Embedding) {
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BufHandle Weight("Weight", {256, 100}, kFloat);
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BufHandle Indices("Indices", {1, 115}, kLong);
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BufHandle ResultBuf("Result", {1, 115, 100}, kFloat);
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int64_t padding_idx = -1;
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bool scale_grad_by_freq = false;
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bool sparse = false;
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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_aten_embedding",
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{Weight, Indices},
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{padding_idx, (int64_t)scale_grad_by_freq, (int64_t)sparse}));
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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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.layout(at::kStrided)
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.device(at::kCPU)
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.requires_grad(false);
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at::Tensor weight = at::ones({256, 100}, options.dtype(at::kFloat)) * 5.f;
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at::Tensor indices = at::ones({1, 115}, options.dtype(at::kLong)) * 6;
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at::Tensor ref =
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at::embedding(weight, indices, padding_idx, scale_grad_by_freq, sparse);
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at::Tensor nnc_result;
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std::vector<float> weight_buf(256 * 100, 5.f);
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std::vector<int64_t> indices_buf(1 * 115, 6);
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std::vector<float> result_buf(1 * 115 * 100, -1.f);
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#ifdef TORCH_ENABLE_LLVM
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LLVMCodeGen llvm_codegen(l.root_stmt(), {Weight, Indices, Result});
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llvm_codegen.call({weight_buf, indices_buf, result_buf});
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nnc_result = at::from_blob(
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result_buf.data(), {1, 115, 100}, options.dtype(at::kFloat));
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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(), {Weight, Indices, Result});
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ir_eval.call({weight_buf, indices_buf, result_buf});
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nnc_result = at::from_blob(
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result_buf.data(), {1, 115, 100}, options.dtype(at::kFloat));
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ASSERT_TRUE(at::allclose(nnc_result, ref));
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}
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TEST(ExternalCall, MaxReduction) {
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BufHandle Input("Input", {1, 115, 152}, kFloat);
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BufHandle ResultBuf("Result", {1, 152}, kFloat);
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int64_t dim = 1;
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bool keep_dim = false;
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Tensor Result = Tensor(
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ResultBuf.node(),
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ExternalCall::make(
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ResultBuf, "nnc_aten_max_red", {Input}, {dim, (int64_t)keep_dim}));
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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, 115, 152}, options) * 5.f;
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at::Tensor ref = std::get<0>(at::max(input, dim, keep_dim));
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at::Tensor nnc_result;
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std::vector<float> input_buf(1 * 115 * 152, 5.f);
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std::vector<float> result_buf(1 * 152, -1.f);
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#ifdef TORCH_ENABLE_LLVM
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LLVMCodeGen llvm_codegen(l.root_stmt(), {Input, Result});
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llvm_codegen.call({input_buf, result_buf});
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nnc_result = at::from_blob(result_buf.data(), {1, 152}, 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, Result});
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ir_eval.call({input_buf, result_buf});
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nnc_result = at::from_blob(result_buf.data(), {1, 152}, options);
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ASSERT_TRUE(at::allclose(nnc_result, ref));
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}
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#ifdef USE_XNNPACK
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TEST(ExternalCall, Prepacked_Linear_float) {
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