mirror of
https://github.com/zama-ai/concrete.git
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test: add a test for distributed execution.
This commit is contained in:
Antoniu Pop
@@ -173,9 +173,11 @@ build-end-to-end-jit-lambda: build-initialized
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build-end-to-end-dataflow-tests: build-initialized
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cmake --build $(BUILD_DIR) --target end_to_end_jit_auto_parallelization
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cmake --build $(BUILD_DIR) --target end_to_end_jit_distributed
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run-end-to-end-dataflow-tests: build-end-to-end-dataflow-tests
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$(BUILD_DIR)/tools/concretelang/tests/end_to_end_tests/end_to_end_jit_auto_parallelization
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$(BUILD_DIR)/tools/concretelang/tests/end_to_end_tests/end_to_end_jit_distributed
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# benchmark
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@@ -64,4 +64,9 @@ if(CONCRETELANG_PARALLEL_EXECUTION_ENABLED)
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end_to_end_jit_auto_parallelization.cc
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globals.cc
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)
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add_concretecompiler_unittest(
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end_to_end_jit_distributed
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end_to_end_jit_distributed.cc
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globals.cc
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)
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endif()
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148
compiler/tests/end_to_end_tests/end_to_end_jit_distributed.cc
Normal file
148
compiler/tests/end_to_end_tests/end_to_end_jit_distributed.cc
Normal file
@@ -0,0 +1,148 @@
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#include <concretelang/Runtime/DFRuntime.hpp>
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#include <cstdint>
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#include <gtest/gtest.h>
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#include <tuple>
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#include <type_traits>
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#include "end_to_end_jit_test.h"
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///////////////////////////////////////////////////////////////////////////////
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// Auto-parallelize independent FHE ops /////////////////////////////////////
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///////////////////////////////////////////////////////////////////////////////
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std::vector<uint64_t> distributed_results;
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TEST(Distributed, nn_med_nested) {
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checkedJit(lambda, R"XXX(
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func @main(%arg0: tensor<200x4x!FHE.eint<4>>) -> tensor<200x8x!FHE.eint<4>> {
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%cst = arith.constant dense<"0x01010100010100000001010101000101010101010101010001000101000001010001010100000101000001000001010001000001010100010001000000010100010001010001000001000101010101000100010001000000000100010001000101000001000101010100010001000000000101000100000000000001000100000100000100000001010000010001000101000100010001000100000100000100010101010000000000000000010001010000000100000100010100000100000000010001000101000100000000000101010101000101010101010100010100010100000000000101010100000100010100000001000101000000010101000101000100000101010100010101010000010101010100010000000000000001010101000100010101000001010001010000010001010101000000000000000001000001000000010100000100000101010100010001000000000000010100010101000000010100000100010001010001000000000100010001000101010100010100000001010100010101010100010100010001000001000000000101000101010001000100000101010100000101010100000100010101000100000101000101010100010001000101010100010001010001010000010000010001010000000001000101010001000000000101000000010000010100010001000001000001010101000100010001010100000101000000010001000000000101000101000000010000000001000101010100010001000000000001010000010001000001010101000101010101010100000000000001000100000100000001000000010101010101000000000101010101000100000101000100000000000001000100000101000101010100010000000101000000000100000100000101010000010100000000010000000000010001000100000101010001010101000000000000010000010101010001000000010001010001010000000000000101000000010101010101000001010101000001000001010100000000010001010100000100000101000101010100010001010001000001000100000101000100010100000100010000000101000000010000010001010101010000000101000000010101000001010100000100010001000000000001010000000100010000000000000000000000000001010101010101010101000001010101000001010100000001000101010101010000010101000101010100010101010000010101010100000100000000000101010000000000010101010000000001000000010100000100000001000101010000000001000001000001010001010000010001000101010001010001010101000100010000000100000100010101000000000101010101010001000100000000000101010000010101000001010001010000000001010100000101000001010000000001010101000100010000010101000000000001000101000001010101000101000001000001000000010100010001000101010100010001010000000101000000010001000001000100000101010001000001000001000101010000010001000001000101000000000000000101010000010000000101010100010100010001010101010000000000010001000101010000000001010100000000010001010100010001000001000101000000010100010000010000010001010100010000010001010100010000010100010101010001000100010100010101000100000101010100000100010100000100000000010101000000010001000001010000000101000100000100010101000000010100000101000001010001010100010000000101010000000001010001000000010100010101010001000100010001000001010101000000010001000100000100010101000000000000010100010000000100000000010100010000000100000101010000010101000100010000010100000001000100000000000100000001010101010101000100010001000000010101010100000001000001000001010001000101010100000001010001010100010101000101000000010001010100010101000100000101000101000001000001000001000101010100010001010000000100000101010100000001000000000000010101000100010001000001000001000000000000010100000100000001"> : tensor<200x8xi5>
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%cst_0 = arith.constant dense<[[1, 0, 0, 0, 1, 0, 0, 1], [0, 0, 1, 1, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0, 1, 1], [1, 1, 0, 0, 1, 0, 1, 1]]> : tensor<4x8xi5>
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%cst_1 = arith.constant dense<[0, 3, 7, 10, 14, 17, 21, 24, 28, 31, 35, 38, 42, 45, 49, 52]> : tensor<16xi64>
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%0 = "FHELinalg.matmul_eint_int"(%arg0, %cst_0) : (tensor<200x4x!FHE.eint<4>>, tensor<4x8xi5>) -> tensor<200x8x!FHE.eint<4>>
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%1 = "FHELinalg.add_eint_int"(%0, %cst) : (tensor<200x8x!FHE.eint<4>>, tensor<200x8xi5>) -> tensor<200x8x!FHE.eint<4>>
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%res = "FHE.zero_tensor"() : () -> tensor<200x8x!FHE.eint<4>>
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%slice_A = tensor.extract_slice %1[0, 0][25, 4][1, 1] : tensor<200x8x!FHE.eint<4>> to tensor<25x4x!FHE.eint<4>>
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%slice_B = tensor.extract_slice %1[25, 0][25, 4][1, 1] : tensor<200x8x!FHE.eint<4>> to tensor<25x4x!FHE.eint<4>>
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%slice_C = tensor.extract_slice %1[50, 0][25, 4][1, 1] : tensor<200x8x!FHE.eint<4>> to tensor<25x4x!FHE.eint<4>>
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%slice_D = tensor.extract_slice %1[75, 0][25, 4][1, 1] : tensor<200x8x!FHE.eint<4>> to tensor<25x4x!FHE.eint<4>>
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%slice_E = tensor.extract_slice %1[100, 0][25, 4][1, 1] : tensor<200x8x!FHE.eint<4>> to tensor<25x4x!FHE.eint<4>>
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%slice_F = tensor.extract_slice %1[125, 0][25, 4][1, 1] : tensor<200x8x!FHE.eint<4>> to tensor<25x4x!FHE.eint<4>>
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%slice_G = tensor.extract_slice %1[150, 0][25, 4][1, 1] : tensor<200x8x!FHE.eint<4>> to tensor<25x4x!FHE.eint<4>>
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%slice_H = tensor.extract_slice %1[175, 0][25, 4][1, 1] : tensor<200x8x!FHE.eint<4>> to tensor<25x4x!FHE.eint<4>>
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%slice_I = tensor.extract_slice %1[0, 4][25, 4][1, 1] : tensor<200x8x!FHE.eint<4>> to tensor<25x4x!FHE.eint<4>>
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%slice_J = tensor.extract_slice %1[25, 4][25, 4][1, 1] : tensor<200x8x!FHE.eint<4>> to tensor<25x4x!FHE.eint<4>>
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%slice_K = tensor.extract_slice %1[50, 4][25, 4][1, 1] : tensor<200x8x!FHE.eint<4>> to tensor<25x4x!FHE.eint<4>>
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%slice_L = tensor.extract_slice %1[75, 4][25, 4][1, 1] : tensor<200x8x!FHE.eint<4>> to tensor<25x4x!FHE.eint<4>>
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%slice_M = tensor.extract_slice %1[100, 4][25, 4][1, 1] : tensor<200x8x!FHE.eint<4>> to tensor<25x4x!FHE.eint<4>>
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%slice_N = tensor.extract_slice %1[125, 4][25, 4][1, 1] : tensor<200x8x!FHE.eint<4>> to tensor<25x4x!FHE.eint<4>>
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%slice_O = tensor.extract_slice %1[150, 4][25, 4][1, 1] : tensor<200x8x!FHE.eint<4>> to tensor<25x4x!FHE.eint<4>>
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%slice_P = tensor.extract_slice %1[175, 4][25, 4][1, 1] : tensor<200x8x!FHE.eint<4>> to tensor<25x4x!FHE.eint<4>>
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%part_A = "FHELinalg.apply_lookup_table"(%slice_A, %cst_1) : (tensor<25x4x!FHE.eint<4>>, tensor<16xi64>) -> tensor<25x4x!FHE.eint<4>>
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%part_B = "FHELinalg.apply_lookup_table"(%slice_B, %cst_1) : (tensor<25x4x!FHE.eint<4>>, tensor<16xi64>) -> tensor<25x4x!FHE.eint<4>>
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%part_C = "FHELinalg.apply_lookup_table"(%slice_C, %cst_1) : (tensor<25x4x!FHE.eint<4>>, tensor<16xi64>) -> tensor<25x4x!FHE.eint<4>>
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%part_D = "FHELinalg.apply_lookup_table"(%slice_D, %cst_1) : (tensor<25x4x!FHE.eint<4>>, tensor<16xi64>) -> tensor<25x4x!FHE.eint<4>>
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%part_E = "FHELinalg.apply_lookup_table"(%slice_E, %cst_1) : (tensor<25x4x!FHE.eint<4>>, tensor<16xi64>) -> tensor<25x4x!FHE.eint<4>>
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%part_F = "FHELinalg.apply_lookup_table"(%slice_F, %cst_1) : (tensor<25x4x!FHE.eint<4>>, tensor<16xi64>) -> tensor<25x4x!FHE.eint<4>>
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%part_G = "FHELinalg.apply_lookup_table"(%slice_G, %cst_1) : (tensor<25x4x!FHE.eint<4>>, tensor<16xi64>) -> tensor<25x4x!FHE.eint<4>>
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%part_H = "FHELinalg.apply_lookup_table"(%slice_H, %cst_1) : (tensor<25x4x!FHE.eint<4>>, tensor<16xi64>) -> tensor<25x4x!FHE.eint<4>>
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%part_I = "FHELinalg.apply_lookup_table"(%slice_I, %cst_1) : (tensor<25x4x!FHE.eint<4>>, tensor<16xi64>) -> tensor<25x4x!FHE.eint<4>>
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%part_J = "FHELinalg.apply_lookup_table"(%slice_J, %cst_1) : (tensor<25x4x!FHE.eint<4>>, tensor<16xi64>) -> tensor<25x4x!FHE.eint<4>>
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%part_K = "FHELinalg.apply_lookup_table"(%slice_K, %cst_1) : (tensor<25x4x!FHE.eint<4>>, tensor<16xi64>) -> tensor<25x4x!FHE.eint<4>>
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%part_L = "FHELinalg.apply_lookup_table"(%slice_L, %cst_1) : (tensor<25x4x!FHE.eint<4>>, tensor<16xi64>) -> tensor<25x4x!FHE.eint<4>>
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%part_M = "FHELinalg.apply_lookup_table"(%slice_M, %cst_1) : (tensor<25x4x!FHE.eint<4>>, tensor<16xi64>) -> tensor<25x4x!FHE.eint<4>>
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%part_N = "FHELinalg.apply_lookup_table"(%slice_N, %cst_1) : (tensor<25x4x!FHE.eint<4>>, tensor<16xi64>) -> tensor<25x4x!FHE.eint<4>>
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%part_O = "FHELinalg.apply_lookup_table"(%slice_O, %cst_1) : (tensor<25x4x!FHE.eint<4>>, tensor<16xi64>) -> tensor<25x4x!FHE.eint<4>>
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%part_P = "FHELinalg.apply_lookup_table"(%slice_P, %cst_1) : (tensor<25x4x!FHE.eint<4>>, tensor<16xi64>) -> tensor<25x4x!FHE.eint<4>>
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%res_A = tensor.insert_slice %part_A into %res [0, 0][25, 4][1, 1] : tensor<25x4x!FHE.eint<4>> into tensor<200x8x!FHE.eint<4>>
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%res_B = tensor.insert_slice %part_B into %res_A[25, 0][25, 4][1, 1] : tensor<25x4x!FHE.eint<4>> into tensor<200x8x!FHE.eint<4>>
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%res_C = tensor.insert_slice %part_C into %res_B[50, 0][25, 4][1, 1] : tensor<25x4x!FHE.eint<4>> into tensor<200x8x!FHE.eint<4>>
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%res_D = tensor.insert_slice %part_D into %res_C[75, 0][25, 4][1, 1] : tensor<25x4x!FHE.eint<4>> into tensor<200x8x!FHE.eint<4>>
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%res_E = tensor.insert_slice %part_E into %res_D[100, 0][25, 4][1, 1] : tensor<25x4x!FHE.eint<4>> into tensor<200x8x!FHE.eint<4>>
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%res_F = tensor.insert_slice %part_F into %res_E[125, 0][25, 4][1, 1] : tensor<25x4x!FHE.eint<4>> into tensor<200x8x!FHE.eint<4>>
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%res_G = tensor.insert_slice %part_G into %res_F[150, 0][25, 4][1, 1] : tensor<25x4x!FHE.eint<4>> into tensor<200x8x!FHE.eint<4>>
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%res_H = tensor.insert_slice %part_H into %res_G[175, 0][25, 4][1, 1] : tensor<25x4x!FHE.eint<4>> into tensor<200x8x!FHE.eint<4>>
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%res_I = tensor.insert_slice %part_I into %res_H[0, 4][25, 4][1, 1] : tensor<25x4x!FHE.eint<4>> into tensor<200x8x!FHE.eint<4>>
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%res_J = tensor.insert_slice %part_J into %res_I[25, 4][25, 4][1, 1] : tensor<25x4x!FHE.eint<4>> into tensor<200x8x!FHE.eint<4>>
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%res_K = tensor.insert_slice %part_K into %res_J[50, 4][25, 4][1, 1] : tensor<25x4x!FHE.eint<4>> into tensor<200x8x!FHE.eint<4>>
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%res_L = tensor.insert_slice %part_L into %res_K[75, 4][25, 4][1, 1] : tensor<25x4x!FHE.eint<4>> into tensor<200x8x!FHE.eint<4>>
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%res_M = tensor.insert_slice %part_M into %res_L[100, 4][25, 4][1, 1] : tensor<25x4x!FHE.eint<4>> into tensor<200x8x!FHE.eint<4>>
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%res_N = tensor.insert_slice %part_N into %res_M[125, 4][25, 4][1, 1] : tensor<25x4x!FHE.eint<4>> into tensor<200x8x!FHE.eint<4>>
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%res_O = tensor.insert_slice %part_O into %res_N[150, 4][25, 4][1, 1] : tensor<25x4x!FHE.eint<4>> into tensor<200x8x!FHE.eint<4>>
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%res_P = tensor.insert_slice %part_P into %res_O[175, 4][25, 4][1, 1] : tensor<25x4x!FHE.eint<4>> into tensor<200x8x!FHE.eint<4>>
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return %res_P : tensor<200x8x!FHE.eint<4>>
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}
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)XXX",
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"main", false, true, true);
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const size_t numDim = 2;
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const size_t dim0 = 200;
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const size_t dim1 = 4;
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const size_t dim2 = 8;
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const int64_t dims[numDim]{dim0, dim1};
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const llvm::ArrayRef<int64_t> shape2D(dims, numDim);
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std::vector<uint8_t> input;
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input.reserve(dim0 * dim1);
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for (int i = 0; i < dim0 * dim1; ++i)
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input.push_back(i % 17 % 4);
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mlir::concretelang::TensorLambdaArgument<
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mlir::concretelang::IntLambdaArgument<uint8_t>>
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arg(input, shape2D);
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if (mlir::concretelang::dfr::_dfr_is_root_node()) {
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llvm::Expected<std::vector<uint64_t>> res =
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lambda.operator()<std::vector<uint64_t>>({&arg});
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ASSERT_EXPECTED_SUCCESS(res);
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ASSERT_EQ(res->size(), dim0 * dim2);
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distributed_results = *res;
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} else
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ASSERT_EXPECTED_FAILURE(lambda.operator()<std::vector<uint64_t>>());
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}
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TEST(Distributed, nn_med_sequential) {
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if (mlir::concretelang::dfr::_dfr_is_root_node()) {
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checkedJit(lambda, R"XXX(
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func @main(%arg0: tensor<200x4x!FHE.eint<4>>) -> tensor<200x8x!FHE.eint<4>> {
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%cst = arith.constant dense<"0x01010100010100000001010101000101010101010101010001000101000001010001010100000101000001000001010001000001010100010001000000010100010001010001000001000101010101000100010001000000000100010001000101000001000101010100010001000000000101000100000000000001000100000100000100000001010000010001000101000100010001000100000100000100010101010000000000000000010001010000000100000100010100000100000000010001000101000100000000000101010101000101010101010100010100010100000000000101010100000100010100000001000101000000010101000101000100000101010100010101010000010101010100010000000000000001010101000100010101000001010001010000010001010101000000000000000001000001000000010100000100000101010100010001000000000000010100010101000000010100000100010001010001000000000100010001000101010100010100000001010100010101010100010100010001000001000000000101000101010001000100000101010100000101010100000100010101000100000101000101010100010001000101010100010001010001010000010000010001010000000001000101010001000000000101000000010000010100010001000001000001010101000100010001010100000101000000010001000000000101000101000000010000000001000101010100010001000000000001010000010001000001010101000101010101010100000000000001000100000100000001000000010101010101000000000101010101000100000101000100000000000001000100000101000101010100010000000101000000000100000100000101010000010100000000010000000000010001000100000101010001010101000000000000010000010101010001000000010001010001010000000000000101000000010101010101000001010101000001000001010100000000010001010100000100000101000101010100010001010001000001000100000101000100010100000100010000000101000000010000010001010101010000000101000000010101000001010100000100010001000000000001010000000100010000000000000000000000000001010101010101010101000001010101000001010100000001000101010101010000010101000101010100010101010000010101010100000100000000000101010000000000010101010000000001000000010100000100000001000101010000000001000001000001010001010000010001000101010001010001010101000100010000000100000100010101000000000101010101010001000100000000000101010000010101000001010001010000000001010100000101000001010000000001010101000100010000010101000000000001000101000001010101000101000001000001000000010100010001000101010100010001010000000101000000010001000001000100000101010001000001000001000101010000010001000001000101000000000000000101010000010000000101010100010100010001010101010000000000010001000101010000000001010100000000010001010100010001000001000101000000010100010000010000010001010100010000010001010100010000010100010101010001000100010100010101000100000101010100000100010100000100000000010101000000010001000001010000000101000100000100010101000000010100000101000001010001010100010000000101010000000001010001000000010100010101010001000100010001000001010101000000010001000100000100010101000000000000010100010000000100000000010100010000000100000101010000010101000100010000010100000001000100000000000100000001010101010101000100010001000000010101010100000001000001000001010001000101010100000001010001010100010101000101000000010001010100010101000100000101000101000001000001000001000101010100010001010000000100000101010100000001000000000000010101000100010001000001000001000000000000010100000100000001"> : tensor<200x8xi5>
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%cst_0 = arith.constant dense<[[1, 0, 0, 0, 1, 0, 0, 1], [0, 0, 1, 1, 0, 0, 0, 0], [1, 1, 0, 1, 1, 0, 1, 1], [1, 1, 0, 0, 1, 0, 1, 1]]> : tensor<4x8xi5>
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%0 = "FHELinalg.matmul_eint_int"(%arg0, %cst_0) : (tensor<200x4x!FHE.eint<4>>, tensor<4x8xi5>) -> tensor<200x8x!FHE.eint<4>>
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%1 = "FHELinalg.add_eint_int"(%0, %cst) : (tensor<200x8x!FHE.eint<4>>, tensor<200x8xi5>) -> tensor<200x8x!FHE.eint<4>>
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%cst_1 = arith.constant dense<[0, 3, 7, 10, 14, 17, 21, 24, 28, 31, 35, 38, 42, 45, 49, 52]> : tensor<16xi64>
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%2 = "FHELinalg.apply_lookup_table"(%1, %cst_1) : (tensor<200x8x!FHE.eint<4>>, tensor<16xi64>) -> tensor<200x8x!FHE.eint<4>>
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return %2 : tensor<200x8x!FHE.eint<4>>
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}
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)XXX",
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"main", false, false, false);
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const size_t numDim = 2;
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const size_t dim0 = 200;
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const size_t dim1 = 4;
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const size_t dim2 = 8;
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const int64_t dims[numDim]{dim0, dim1};
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const llvm::ArrayRef<int64_t> shape2D(dims, numDim);
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std::vector<uint8_t> input;
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input.reserve(dim0 * dim1);
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for (int i = 0; i < dim0 * dim1; ++i)
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input.push_back(i % 17 % 4);
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mlir::concretelang::TensorLambdaArgument<
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mlir::concretelang::IntLambdaArgument<uint8_t>>
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arg(input, shape2D);
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llvm::Expected<std::vector<uint64_t>> res =
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lambda.operator()<std::vector<uint64_t>>({&arg});
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ASSERT_EXPECTED_SUCCESS(res);
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ASSERT_EQ(res->size(), dim0 * dim2);
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for (size_t i = 0; i < dim0 * dim2; i++)
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EXPECT_EQ(distributed_results[i], (*res)[i])
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<< "result differ at pos " << i;
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}
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}
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@@ -0,0 +1,23 @@
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#!/bin/bash
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#SBATCH --job-name=end_to_end_jit_distributed
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#SBATCH --mail-type=BEGIN,END,FAIL
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#SBATCH --mail-user=antoniu.pop@zama.ai
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#SBATCH --nodes=4
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#SBATCH --cpus-per-task=8
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#SBATCH --time=00:20:00
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#SBATCH --output=end_to_end_jit_distributed_%j.log
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echo "Date = $(date)"
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echo "Hostname = $(hostname -s)"
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echo "Working Directory = $(pwd)"
|
||||
echo ""
|
||||
echo "Number of Nodes Allocated = $SLURM_JOB_NUM_NODES"
|
||||
echo "Number of Tasks Allocated = $SLURM_NTASKS"
|
||||
echo "Number of Cores/Task Allocated = $SLURM_CPUS_PER_TASK"
|
||||
|
||||
export OMP_NUM_THREADS=8
|
||||
export DFR_NUM_THREADS=2
|
||||
|
||||
srun ./build/bin/end_to_end_jit_distributed
|
||||
|
||||
date
|
||||
@@ -32,6 +32,10 @@ static bool assert_expected_success(llvm::Expected<T> &&val) {
|
||||
template <typename T>
|
||||
static bool assert_expected_failure(llvm::Expected<T> &&val) {
|
||||
if (!((bool)val)) {
|
||||
if (!mlir::concretelang::dfr::_dfr_is_root_node()) {
|
||||
llvm::toString(val.takeError());
|
||||
return true;
|
||||
}
|
||||
// We need to consume the error, so let's do it here
|
||||
llvm::errs() << "assert_expected_failure: "
|
||||
<< llvm::toString(val.takeError()) << "\n";
|
||||
|
||||
Reference in New Issue
Block a user