From 3efa8eb2a935fa7dafd068eea3ec66564269e41a Mon Sep 17 00:00:00 2001 From: Quentin Bourgerie Date: Tue, 29 Nov 2022 11:41:37 +0100 Subject: [PATCH] refactor(tests): Use f-strings in linalg_apply_lookup_table generator --- ...nd_to_end_linalg_apply_lookup_table_gen.py | 26 ++++++++----------- 1 file changed, 11 insertions(+), 15 deletions(-) diff --git a/compiler/tests/end_to_end_fixture/end_to_end_linalg_apply_lookup_table_gen.py b/compiler/tests/end_to_end_fixture/end_to_end_linalg_apply_lookup_table_gen.py index a5a1c1bac..3526c4172 100644 --- a/compiler/tests/end_to_end_fixture/end_to_end_linalg_apply_lookup_table_gen.py +++ b/compiler/tests/end_to_end_fixture/end_to_end_linalg_apply_lookup_table_gen.py @@ -5,39 +5,35 @@ import argparse def generate(args): print("# /!\ DO NOT EDIT MANUALLY THIS FILE MANUALLY") - print("# /!\ THIS FILE HAS BEEN GENERATED THANKS THE end_to_end_levelled_gen.py scripts") + print("# /!\ THIS FILE HAS BEEN GENERATED") np.random.seed(0) for n_ct in args.n_ct: for p in range(args.min_bitwidth, args.max_bitwidth+1): max_value = (2 ** p) - 1 random_lut = np.random.randint(max_value+1, size=2**p) # identity_apply_lookup_table - print( - "description: apply_lookup_table_{0}bits_{1}ct".format(p, n_ct)) + print(f"description: apply_lookup_table_{p}bits_{n_ct}ct") print("program: |") print( - " func.func @main(%0: tensor<{1}x!FHE.eint<{0}>>) -> tensor<{1}x!FHE.eint<{0}>> {{".format(p, n_ct)) - print(" %tlu = arith.constant dense<[{0}]> : tensor<{1}xi64>".format( - ','.join(map(str, random_lut)), 2**p)) + f" func.func @main(%0: tensor<{n_ct}x!FHE.eint<{p}>>) -> tensor<{n_ct}x!FHE.eint<{p}>> {{") + print(f" %tlu = arith.constant dense<[{','.join(map(str, random_lut))}]> : tensor<{2**p}xi64>") for i in range(0, args.n_lut): - print( - " %{4} = \"FHELinalg.apply_lookup_table\"(%{3}, %tlu): (tensor<{2}x!FHE.eint<{0}>>, tensor<{1}xi64>) -> (tensor<{2}x!FHE.eint<{0}>>)".format(p, 2**p, n_ct, i, i+1)) - print(" return %{2}: tensor<{1}x!FHE.eint<{0}>>".format( - p, n_ct, args.n_lut)) + print(f" %{i+1} = \"FHELinalg.apply_lookup_table\"(%{i}, %tlu):") + print(f" (tensor<{n_ct}x!FHE.eint<{p}>>, tensor<{2**p}xi64>) -> (tensor<{n_ct}x!FHE.eint<{p}>>)") + print(f" return %{args.n_lut}: tensor<{n_ct}x!FHE.eint<{p}>>") print(" }") random_input = np.random.randint(max_value+1, size=n_ct) print("tests:") print(" - inputs:") - print( - " - tensor: [{0}]".format(','.join(map(str, random_input)))) - print(" shape: [{0}]".format(n_ct)) + print(f" - tensor: [{','.join(map(str, random_input))}]") + print(f" shape: [{n_ct}]") outputs = random_input for i in range(0, args.n_lut): outputs = [random_lut[v] for v in outputs] print(" outputs:") - print(" - tensor: [{0}]".format(','.join(map(str, outputs)))) - print(" shape: [{0}]".format(n_ct)) + print(f" - tensor: [{','.join(map(str, outputs))}]") + print(f" shape: [{n_ct}]") print("---")