From 7995554ff272c4721a1e7ffbfc956a2141eff484 Mon Sep 17 00:00:00 2001 From: NCIA-AlphaSparse Date: Wed, 14 Oct 2026 15:23:55 +0800 Subject: [PATCH 1/9] 65 baseline --- conf/h800_reference.json | 5551 ++++------------- docs/ASCEND.md | 9 + docs/H800_REFERENCE.md | 235 +- docs/ILUVATAR.md | 9 +- docs/MACA.md | 7 + docs/MUSA.md | 11 + docs/debug/ASCEND.md | 37 + docs/debug/ILUVATAR.md | 18 +- docs/debug/MACA.md | 65 +- docs/debug/MUSA.md | 10 + run_flagsparse_pytest.py | 58 +- run_flagsparse_split_delivery.py | 112 +- .../sparse_operations/_spmv_csr_maca.py | 40 + .../sparse_operations/_spmv_csr_transpose.py | 25 +- .../sparse_operations/gather_scatter.py | 3 + .../sparse_operations/spgemm_csr.py | 7 +- src/flagsparse/sparse_operations/spmm_csc.py | 4 + src/flagsparse/sparse_operations/spmv_csr.py | 8 +- src/flagsparse/sparse_operations/spmv_sell.py | 5 + tests/ci/test_baseline_bound.py | 96 +- tests/ci/test_benchmark_policy.py | 7 + tests/ci/test_delivery_table.py | 31 + tests/ci/test_h800_reference.py | 115 +- tests/ci/test_runner_result_format.py | 99 + tests/ci/test_spgemm_compare.py | 83 + tests/ci/test_spgemm_tle_gate.py | 34 + tests/ci/test_split_delivery_scope.py | 138 + .../test_spmv_csr_transpose_complex_gather.py | 6 + .../pytest/test_rocm_sparse_reuse_accuracy.py | 21 +- tests/pytest/test_spmv_csr_maca_accuracy.py | 55 + tests/test_spgemm.py | 37 +- tools/baseline_bound.py | 150 +- tools/delivery_table.py | 14 +- tools/h800_reference.py | 133 +- 34 files changed, 2751 insertions(+), 4482 deletions(-) create mode 100644 src/flagsparse/sparse_operations/_spmv_csr_maca.py create mode 100644 tests/ci/test_spgemm_compare.py create mode 100644 tests/ci/test_spgemm_tle_gate.py create mode 100644 tests/pytest/test_spmv_csr_maca_accuracy.py diff --git a/conf/h800_reference.json b/conf/h800_reference.json index f28e3e10..99b6b240 100644 --- a/conf/h800_reference.json +++ b/conf/h800_reference.json @@ -1,430 +1,297 @@ { "schema": 1, -"reference": {"device": "NVIDIA H800", "device_count": 8, "peaks": "h800-sxm", "source_dir": "pytest_results_sparse_202609221038", "generated": "2026-09-28", "run_timestamp": "2026-09-22 18:01:14", "flagsparse_commit": "47a441e6c810a4e51c940412a25c564046d350a6+dirty", "torch": "2.8.0a0+5228986c39.nv25.05", "triton": "3.6.0", "note": "H800 wall times (ms). The vendor-library column (cusparse_ms / vendor_ms / *sparse_ms) is the baseline the scaled speedup divides into; see docs/H800_REFERENCE.md."}, +"reference": {"device": "NVIDIA H800", "device_count": 8, "peaks": "h800-sxm", "source_dir": "results_65_cuda_1014", "generated": "2026-10-14", "run_timestamp": "2026-10-14 10:17:48", "flagsparse_commit": "643e9c6b7e45099c11fafaa0e86ecd5dc8cba255+dirty", "torch": "2.8.0a0+5228986c39.nv25.05", "triton": "3.6.0", "note": "H800 wall times (ms). The vendor-library column (cusparse_ms / vendor_ms / *sparse_ms) is the baseline the scaled speedup divides into; see docs/H800_REFERENCE.md."}, "variants": { -"gather_c32_int": {"accuracy": "Passed", "accuracy_passed": 1, "accuracy_total": 1, "performance": "Passed", "speedup": {"complex64": 1.4637}}, -"gather_c64_int": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"complex128": 1.2147}}, -"gather_f16_int": {"accuracy": "Passed", "accuracy_passed": 1, "accuracy_total": 1, "performance": "Passed", "speedup": {"fp16": 1.1369}}, -"gather_f32_int": {"accuracy": "Passed", "accuracy_passed": 1, "accuracy_total": 1, "performance": "Passed", "speedup": {"fp32": 1.0548}}, -"gather_f64_int": {"accuracy": "Passed", "accuracy_passed": 1, "accuracy_total": 1, "performance": "Passed", "speedup": {"fp64": 1.4691}}, -"scatter_c32_int": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"complex64": 0.7596}}, -"scatter_c64_int": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"complex128": 0.8142}}, -"scatter_f16_int": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp16": 0.8074}}, -"scatter_f32_int": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 0.9824}}, -"scatter_f64_int": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp64": 0.9775}}, -"sddmm_csr_f32_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 3.2837}}, -"sddmm_csr_f64_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp64": 1.597}}, -"spmm_coo_f32_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 1, "accuracy_total": 1, "performance": "Passed", "speedup": {"fp32": 0.7627}}, -"spmm_coo_f64_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 1, "accuracy_total": 1, "performance": "Passed", "speedup": {"fp64": 3.9141}}, -"spmm_csr_f32_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 1.1328}}, -"spmm_csr_f64_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp64": 1.3815}}, -"spmv_coo_f32_int_non": {"accuracy": "Passed", "accuracy_passed": 1, "accuracy_total": 1, "performance": "Passed", "speedup": {"fp32": 0.9122}}, -"spmv_coo_f64_int_non": {"accuracy": "Passed", "accuracy_passed": 1, "accuracy_total": 1, "performance": "Passed", "speedup": {"fp64": 1.0774}}, -"spmv_csr_f32_int_non": {"accuracy": "Passed", "accuracy_passed": 57, "accuracy_total": 57, "performance": "Passed", "speedup": {"fp32": 0.3137}}, -"spmv_csr_f64_int_non": {"accuracy": "Passed", "accuracy_passed": 53, "accuracy_total": 53, "performance": "Passed", "speedup": {"fp64": 0.4054}} +"axpby_f16_int": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp16": 0.4097}}, +"gather_c32_int": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"complex64": 1.4753}}, +"gather_c64_int": {"accuracy": "Passed", "accuracy_passed": 3, "accuracy_total": 3, "performance": "Passed", "speedup": {"complex128": 1.2576}}, +"gather_f16_int": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp16": 1.1313}}, +"gather_f32_int": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 1.0569}}, +"gather_f64_int": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp64": 1.4691}}, +"scatter_c32_int": {"accuracy": "Passed", "accuracy_passed": 4, "accuracy_total": 4, "performance": "Passed", "speedup": {"complex64": 0.7552}}, +"scatter_c64_int": {"accuracy": "Passed", "accuracy_passed": 4, "accuracy_total": 4, "performance": "Passed", "speedup": {"complex128": 0.8157}}, +"scatter_f16_int": {"accuracy": "Passed", "accuracy_passed": 4, "accuracy_total": 4, "performance": "Passed", "speedup": {"fp16": 0.8081}}, +"scatter_f32_int": {"accuracy": "Passed", "accuracy_passed": 4, "accuracy_total": 4, "performance": "Passed", "speedup": {"fp32": 0.9858}}, +"scatter_f64_int": {"accuracy": "Passed", "accuracy_passed": 4, "accuracy_total": 4, "performance": "Passed", "speedup": {"fp64": 0.9828}}, +"scatter_i8_int": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"int8": 1.601}}, +"sddmm_csr_c32_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"complex64": 1.1862}}, +"sddmm_csr_f16_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp16": 3.1088}}, +"sddmm_csr_f32_int_non_non_col": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 2.4469}}, +"sddmm_csr_f32_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 4, "accuracy_total": 4, "performance": "Passed", "speedup": {"fp32": 1.4862}}, +"sddmm_csr_f32_int_non_trans_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 2.1518}}, +"sddmm_csr_f32_int_trans_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 1.5374}}, +"sddmm_csr_f64_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 4, "accuracy_total": 4, "performance": "Passed", "speedup": {"fp64": 1.1586}}, +"spgemm_csr_f32_int_non_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 0.6943}}, +"spmm_coo_c32_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"complex64": 0.3526}}, +"spmm_coo_f16_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp16": 0.908}}, +"spmm_coo_f32_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 1.169}}, +"spmm_coo_f64_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp64": 4.4969}}, +"spmm_coo_i8i32_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"int8": 0.2113}}, +"spmm_csc_c32_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"complex64": 0.1815}}, +"spmm_csc_f16_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp16": 0.5301}}, +"spmm_csc_f32_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 0.3975}}, +"spmm_csr_c32_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"complex64": 0.328}}, +"spmm_csr_f16_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp16": 2.2915}}, +"spmm_csr_f16f32_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp16": 1.1729}}, +"spmm_csr_f32_int_non_non_col": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 3.3306}}, +"spmm_csr_f32_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 4, "accuracy_total": 4, "performance": "Passed", "speedup": {"fp32": 3.4295}}, +"spmm_csr_f32_int_non_trans_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 4.4729}}, +"spmm_csr_f32_int_trans_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 1.6913}}, +"spmm_csr_f64_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 4, "accuracy_total": 4, "performance": "Passed", "speedup": {"fp64": 3.3412}}, +"spmm_csr_i8i32_int_non_non_row": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"int8": 1.2218}}, +"spmv_coo_c32_int_conj": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"complex64": 1.2982}}, +"spmv_coo_c32_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"complex64": 0.4925}}, +"spmv_coo_f16_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp16": 0.3413}}, +"spmv_coo_f16f32_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp16": 0.3336}}, +"spmv_coo_f32_int_non": {"accuracy": "Skipped", "accuracy_passed": 3, "accuracy_total": 5, "performance": "Passed", "speedup": {"fp32": 1.1938}}, +"spmv_coo_f32_int_trans": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 0.8966}}, +"spmv_coo_f64_int_non": {"accuracy": "Passed", "accuracy_passed": 3, "accuracy_total": 3, "performance": "Passed", "speedup": {"fp64": 1.2109}}, +"spmv_coo_i8i32_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"int8": 0.3573}}, +"spmv_csc_c32_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"complex64": 1.5693}}, +"spmv_csc_f16_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp16": 2.7169}}, +"spmv_csc_f32_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 0.9513}}, +"spmv_csr_c32_int_conj": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"complex64": 1.4317}}, +"spmv_csr_c32_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"complex64": 0.5986}}, +"spmv_csr_f16_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp16": 0.5416}}, +"spmv_csr_f16f32_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp16": 0.6245}}, +"spmv_csr_f32_int_non": {"accuracy": "Passed", "accuracy_passed": 59, "accuracy_total": 59, "performance": "Passed", "speedup": {"fp32": 0.5927}}, +"spmv_csr_f32_int_trans": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 1.023}}, +"spmv_csr_f32c32_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 0.6165}}, +"spmv_csr_f64_int_non": {"accuracy": "Passed", "accuracy_passed": 55, "accuracy_total": 55, "performance": "Passed", "speedup": {"fp64": 0.6704}}, +"spmv_csr_i8f32_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"int8": 0.5786}}, +"spmv_csr_i8i32_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"int8": 0.5501}}, +"spmv_sell_c32_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"complex64": 0.6629}}, +"spmv_sell_f16_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp16": 0.5312}}, +"spmv_sell_f32_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp32": 0.5764}}, +"spmv_sell_i8i32_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"int8": 0.4211}}, +"spvv_c32_int_conj": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"complex64": 0.3863}}, +"spvv_f16f32_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"fp16": 0.3978}}, +"spvv_i8i32_int_non": {"accuracy": "Passed", "accuracy_passed": 2, "accuracy_total": 2, "performance": "Passed", "speedup": {"int8": 0.4234}} }, "operators": { +"axpby": {"fields": ["variant", "case_id", "op", "value_dtype", "index_dtype", "dense_size", "nnz", "triton_ms", "cusparse_ms", "pytorch_ms", "triton_speedup_vs_cusparse", "triton_speedup_vs_pytorch", "cupy_ms", "triton_speedup_vs_cupy", "status"], +"rows": [ +["axpby_f16_int","axpby|float16|dense=32768|nnz=1024","non","float16","int32","32768","1024",0.0471477,0.007754602,0.0395162,0.1644747,0.8381363,null,null,"PASS"], +["axpby_f16_int","axpby|float16|dense=131072|nnz=4096","non","float16","int32","131072","4096",0.06650045,0.01100545,0.04235545,0.1654944,0.6369197,null,null,"PASS"], +["axpby_f16_int","axpby|float16|dense=524288|nnz=16384","non","float16","int32","524288","16384",0.05201865,0.0304333,0.041887,0.585046,0.8052305,null,null,"PASS"], +["axpby_f16_int","axpby|float16|dense=1048576|nnz=65536","non","float16","int32","1048576","65536",0.04328785,0.0313346,0.0340985,0.7238659,0.7877152,null,null,"PASS"], +["axpby_f32_int","axpby|float32|dense=32768|nnz=1024","non","float32","int32","32768","1024",0.045031,0.008190799,0.0272992,0.1818924,0.6062313,null,null,"PASS"], +["axpby_f32_int","axpby|float32|dense=131072|nnz=4096","non","float32","int32","131072","4096",0.051984,0.01104915,0.0340583,0.2125491,0.6551689,null,null,"PASS"], +["axpby_f32_int","axpby|float32|dense=524288|nnz=16384","non","float32","int32","524288","16384",0.05237895,0.01908425,0.03419395,0.3643496,0.6528186,null,null,"PASS"], +["axpby_f32_int","axpby|float32|dense=1048576|nnz=65536","non","float32","int32","1048576","65536",0.05282245,0.02017245,0.03458915,0.3818916,0.6548191,null,null,"PASS"], +["axpby_c32_int","axpby|complex64|dense=32768|nnz=1024","non","complex64","int32","32768","1024",0.06280305,0.00698675,0.0276693,0.1112486,0.4405725,null,null,"PASS"], 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+["amazon0601.mtx","int8","int32","non","row","q4","403394","403394","3387388","",1.140753,0.3172888,0.2516416,0.2781398,0.2205925,"PASS","spmm_coo_i8i32_int_non_non_row","int32","non","int32"], +["auto.mtx","complex64","int32","non","row","q4","448695","448695","6629222","",1.969502,0.6907881,0.6713028,0.3507424,0.3408489,"PASS","spmm_coo_c32_int_non_non_row","int32","non","complex64"], +["auto.mtx","float16","int32","non","row","q4","448695","448695","6629222","",0.4077001,0.511626,0.4528638,1.254908,1.110777,"PASS","spmm_coo_f16_int_non_non_row","int32","non","float16"], +["auto.mtx","int8","int32","non","row","q4","448695","448695","6629222","",2.325722,0.5106845,0.4314805,0.2195811,0.1855254,"PASS","spmm_coo_i8i32_int_non_non_row","int32","non","int32"], +["cage12.mtx","complex64","int32","non","row","q4","130228","130228","2032536","",0.5802872,0.1952641,0.1803167,0.3364957,0.310737,"PASS","spmm_coo_c32_int_non_non_row","int32","non","complex64"], +["cage12.mtx","float16","int32","non","row","q4","130228","130228","2032536","",0.1307886,0.1642238,0.1461255,1.255643,1.117264,"PASS","spmm_coo_f16_int_non_non_row","int32","non","float16"], +["cage12.mtx","int8","int32","non","row","q4","130228","130228","2032536","",0.7345365,0.1642832,0.1416829,0.2236556,0.1928875,"PASS","spmm_coo_i8i32_int_non_non_row","int32","non","int32"], +["cfd2.mtx","complex64","int32","non","row","q4","123440","123440","3087898","",0.6399909,0.2444568,0.2219107,0.3819693,0.3467404,"PASS","spmm_coo_c32_int_non_non_row","int32","non","complex64"], +["cfd2.mtx","float16","int32","non","row","q4","123440","123440","3087898","",0.1450151,0.20791,0.1960466,1.433712,1.351905,"PASS","spmm_coo_f16_int_non_non_row","int32","non","float16"], +["cfd2.mtx","int8","int32","non","row","q4","123440","123440","3087898","",1.139867,0.2080979,0.1906115,0.1825633,0.1672225,"PASS","spmm_coo_i8i32_int_non_non_row","int32","non","int32"], +["filter3D.mtx","complex64","int32","non","row","q4","106437","106437","2707179","",0.7133708,0.2277969,0.2136069,0.3193247,0.2994333,"PASS","spmm_coo_c32_int_non_non_row","int32","non","complex64"], +["filter3D.mtx","float16","int32","non","row","q4","106437","106437","2707179","",0.1328001,0.1909632,0.1779939,1.437974,1.340314,"PASS","spmm_coo_f16_int_non_non_row","int32","non","float16"], +["filter3D.mtx","int8","int32","non","row","q4","106437","106437","2707179","",0.9991255,0.1910493,0.1729088,0.1912165,0.1730602,"PASS","spmm_coo_i8i32_int_non_non_row","int32","non","int32"], +["roadNet-TX.mtx","complex64","int32","non","row","q4","1393383","1393383","3843320","",1.335102,0.6637893,0.531618,0.4971823,0.3981852,"PASS","spmm_coo_c32_int_non_non_row","int32","non","complex64"], +["roadNet-TX.mtx","float16","int32","non","row","q4","1393383","1393383","3843320","",1.145484,0.4843264,0.3659741,0.4228137,0.3194929,"PASS","spmm_coo_f16_int_non_non_row","int32","non","float16"], +["roadNet-TX.mtx","int8","int32","non","row","q4","1393383","1393383","3843320","",1.255465,0.4845132,0.3352028,0.3859234,0.266995,"PASS","spmm_coo_i8i32_int_non_non_row","int32","non","int32"], +["wave.mtx","complex64","int32","non","row","q4","156317","156317","2118662","",0.5554103,0.2038427,0.1814634,0.3670127,0.3267196,"PASS","spmm_coo_c32_int_non_non_row","int32","non","complex64"], +["wave.mtx","float16","int32","non","row","q4","156317","156317","2118662","",0.148903,0.172578,0.1536689,1.158996,1.032006,"PASS","spmm_coo_f16_int_non_non_row","int32","non","float16"], +["wave.mtx","int8","int32","non","row","q4","156317","156317","2118662","",0.7546795,0.1719898,0.1480534,0.2278978,0.1961806,"PASS","spmm_coo_i8i32_int_non_non_row","int32","non","int32"] ]}, -"spmm_csc": {"fields": ["matrix", "dtype", "index_dtype", "op", "layout", "alg", "n_rows", "n_cols", "nnz", "dense_cols", "ms", "pytorch_ms", "triton_speedup_vs_pytorch", "cusparse_ms", "cusparse_vs_alg_speedup", "status"], +"spmm_csc": {"fields": ["matrix", "dtype", "index_dtype", "op", "layout", "alg", "n_rows", "n_cols", "nnz", "dense_cols", "ms", "pytorch_ms", "triton_speedup_vs_pytorch", "cusparse_ms", "cusparse_vs_alg_speedup", "status", "variant", "indptr_dtype", "op_b", "out_dtype"], "rows": [ -["2cubes_sphere.mtx","float32","int32","non","row","spmm_csc_base","101492","101492","1647264","32",0.1497312,null,null,0.4996976,3.337298,"FAIL"], -["2cubes_sphere.mtx","float32","int32","trans","row","spmm_csc_base","101492","101492","1647264","32",0.1188512,null,null,null,null,"FAIL"], -["2cubes_sphere.mtx","float32","int32","conj","row","spmm_csc_base","101492","101492","1647264","32",0.1188784,null,null,null,null,"FAIL"], -["ASIC_680ks.mtx","float32","int32","non","row","spmm_csc_base","682712","682712","1693767","32",0.21316,null,null,0.2912272,1.366238,"PASS"], -["ASIC_680ks.mtx","float32","int32","trans","row","spmm_csc_base","682712","682712","1693767","32",0.2152336,null,null,null,null,"FAIL"], -["ASIC_680ks.mtx","float32","int32","conj","row","spmm_csc_base","682712","682712","1693767","32",0.2148336,null,null,null,null,"PASS"], -["CurlCurl_1.mtx","float32","int32","non","row","spmm_csc_base","226451","226451","2472071","32",0.2243808,null,null,0.3432,1.529543,"FAIL"], -["CurlCurl_1.mtx","float32","int32","trans","row","spmm_csc_base","226451","226451","2472071","32",0.255696,null,null,null,null,"FAIL"], -["CurlCurl_1.mtx","float32","int32","conj","row","spmm_csc_base","226451","226451","2472071","32",0.2555232,null,null,null,null,"FAIL"], -["GL7d14.mtx","float32","int32","non","row","spmm_csc_base","171375","47271","1831183","32",0.1604976,null,null,0.7156384,4.458873,"PASS"], -["GL7d14.mtx","float32","int32","trans","row","spmm_csc_base","171375","47271","1831183","32",0.1214992,null,null,null,null,"PASS"], -["GL7d14.mtx","float32","int32","conj","row","spmm_csc_base","171375","47271","1831183","32",0.121072,null,null,null,null,"PASS"], -["H2O.mtx","float32","int32","non","row","spmm_csc_base","67024","67024","2216736","32",0.1979248,null,null,0.6680048,3.375043,"FAIL"], -["H2O.mtx","float32","int32","trans","row","spmm_csc_base","67024","67024","2216736","32",0.0924288,null,null,null,null,"PASS"], -["H2O.mtx","float32","int32","conj","row","spmm_csc_base","67024","67024","2216736","32",0.0919328,null,null,null,null,"PASS"], -["NACA0015.mtx","float32","int32","non","row","spmm_csc_base","1039183","1039183","6229636","32",0.76104,null,null,4.37443,5.747964,"PASS"], -["NACA0015.mtx","float32","int32","trans","row","spmm_csc_base","1039183","1039183","6229636","32",0.7140064,null,null,null,null,"PASS"], -["NACA0015.mtx","float32","int32","conj","row","spmm_csc_base","1039183","1039183","6229636","32",0.7140752,null,null,null,null,"PASS"], -["Stanford.mtx","float32","int32","non","row","spmm_csc_base","281903","281903","2312497","32",0.28536,null,null,1.032446,3.618049,"FAIL"], -["Stanford.mtx","float32","int32","trans","row","spmm_csc_base","281903","281903","2312497","32",0.249424,null,null,null,null,"PASS"], -["Stanford.mtx","float32","int32","conj","row","spmm_csc_base","281903","281903","2312497","32",0.2494848,null,null,null,null,"PASS"], -["TF19.mtx","float32","int32","non","row","spmm_csc_base","241029","317955","4370721","32",0.3785104,null,null,1.395867,3.687791,"PASS"], -["TF19.mtx","float32","int32","trans","row","spmm_csc_base","241029","317955","4370721","32",0.3433312,null,null,null,null,"PASS"], -["TF19.mtx","float32","int32","conj","row","spmm_csc_base","241029","317955","4370721","32",0.3431936,null,null,null,null,"PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int32","non","row","spmm_csc_base","29214","29214","4400122","32",0.5117392,null,null,0.509712,0.9960386,"PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int32","trans","row","spmm_csc_base","29214","29214","4400122","32",0.2241552,null,null,null,null,"FAIL"], -["TSOPF_FS_b300_c1.mtx","float32","int32","conj","row","spmm_csc_base","29214","29214","4400122","32",0.22396,null,null,null,null,"PASS"], -["amazon0601.mtx","float32","int32","non","row","spmm_csc_base","403394","403394","3387388","32",0.3184928,null,null,0.9195248,2.887113,"PASS"], -["amazon0601.mtx","float32","int32","trans","row","spmm_csc_base","403394","403394","3387388","32",0.3789584,null,null,null,null,"PASS"], -["amazon0601.mtx","float32","int32","conj","row","spmm_csc_base","403394","403394","3387388","32",0.379856,null,null,null,null,"PASS"], -["auto.mtx","float32","int32","non","row","spmm_csc_base","448695","448695","6629222","32",0.57064,null,null,2.153792,3.774345,"PASS"], -["auto.mtx","float32","int32","trans","row","spmm_csc_base","448695","448695","6629222","32",0.5214096,null,null,null,null,"PASS"], -["auto.mtx","float32","int32","conj","row","spmm_csc_base","448695","448695","6629222","32",0.5214592,null,null,null,null,"PASS"], -["c8_mat11.mtx","float32","int32","non","row","spmm_csc_base","4562","5761","2462970","32",0.234472,null,null,0.4413584,1.88235,"FAIL"], -["c8_mat11.mtx","float32","int32","trans","row","spmm_csc_base","4562","5761","2462970","32",0.0593664,null,null,null,null,"FAIL"], -["c8_mat11.mtx","float32","int32","conj","row","spmm_csc_base","4562","5761","2462970","32",0.0595424,null,null,null,null,"FAIL"], -["cage12.mtx","float32","int32","non","row","spmm_csc_base","130228","130228","2032536","32",0.1811664,null,null,0.5581456,3.080845,"PASS"], -["cage12.mtx","float32","int32","trans","row","spmm_csc_base","130228","130228","2032536","32",0.1619328,null,null,null,null,"PASS"], -["cage12.mtx","float32","int32","conj","row","spmm_csc_base","130228","130228","2032536","32",0.1592416,null,null,null,null,"PASS"], -["cfd2.mtx","float32","int32","non","row","spmm_csc_base","123440","123440","3085406","32",0.2687488,null,null,0.4922112,1.831492,"PASS"], -["cfd2.mtx","float32","int32","trans","row","spmm_csc_base","123440","123440","3085406","32",0.1455872,null,null,null,null,"PASS"], -["cfd2.mtx","float32","int32","conj","row","spmm_csc_base","123440","123440","3085406","32",0.1447184,null,null,null,null,"PASS"], -["ecology1.mtx","float32","int32","non","row","spmm_csc_base","1000000","1000000","4996000","32",0.4928208,null,null,0.4972464,1.00898,"PASS"], -["ecology1.mtx","float32","int32","trans","row","spmm_csc_base","1000000","1000000","4996000","32",0.5307232,null,null,null,null,"PASS"], -["ecology1.mtx","float32","int32","conj","row","spmm_csc_base","1000000","1000000","4996000","32",0.529128,null,null,null,null,"PASS"], -["engine.mtx","float32","int32","non","row","spmm_csc_base","143571","143571","4706073","32",0.4228576,null,null,0.762208,1.802517,"FAIL"], -["engine.mtx","float32","int32","trans","row","spmm_csc_base","143571","143571","4706073","32",0.3286976,null,null,null,null,"PASS"], -["engine.mtx","float32","int32","conj","row","spmm_csc_base","143571","143571","4706073","32",0.3282144,null,null,null,null,"PASS"], -["filter3D.mtx","float32","int32","non","row","spmm_csc_base","106437","106437","2707179","32",0.2389616,null,null,0.805072,3.369043,"PASS"], -["filter3D.mtx","float32","int32","trans","row","spmm_csc_base","106437","106437","2707179","32",0.1649632,null,null,null,null,"PASS"], -["filter3D.mtx","float32","int32","conj","row","spmm_csc_base","106437","106437","2707179","32",0.1646704,null,null,null,null,"PASS"], -["mac_econ_fwd500.mtx","float32","int32","non","row","spmm_csc_base","206500","206500","1273389","32",0.128064,null,null,0.2944352,2.299125,"FAIL"], -["mac_econ_fwd500.mtx","float32","int32","trans","row","spmm_csc_base","206500","206500","1273389","32",0.1414688,null,null,null,null,"FAIL"], -["mac_econ_fwd500.mtx","float32","int32","conj","row","spmm_csc_base","206500","206500","1273389","32",0.1416176,null,null,null,null,"FAIL"], -["mip1.mtx","float32","int32","non","row","spmm_csc_base","66463","66463","10352819","32",1.070582,null,null,1.330397,1.242685,"FAIL"], -["mip1.mtx","float32","int32","trans","row","spmm_csc_base","66463","66463","10352819","32",0.5162784,null,null,null,null,"PASS"], -["mip1.mtx","float32","int32","conj","row","spmm_csc_base","66463","66463","10352819","32",0.5158544,null,null,null,null,"PASS"], -["msc10848.mtx","float32","int32","non","row","spmm_csc_base","10848","10848","1229776","32",0.1237712,null,null,0.1574848,1.272386,"FAIL"], -["msc10848.mtx","float32","int32","trans","row","spmm_csc_base","10848","10848","1229776","32",0.0645872,null,null,null,null,"FAIL"], -["msc10848.mtx","float32","int32","conj","row","spmm_csc_base","10848","10848","1229776","32",0.0636448,null,null,null,null,"FAIL"], -["neos3.mtx","float32","int32","non","row","spmm_csc_base","512209","518832","2055024","32",0.2603104,null,null,0.6305024,2.422118,"PASS"], -["neos3.mtx","float32","int32","trans","row","spmm_csc_base","512209","518832","2055024","32",0.3065072,null,null,null,null,"PASS"], -["neos3.mtx","float32","int32","conj","row","spmm_csc_base","512209","518832","2055024","32",0.3053632,null,null,null,null,"PASS"], -["net150.mtx","float32","int32","non","row","spmm_csc_base","43520","43520","3121200","32",0.3127104,null,null,0.7950704,2.542513,"PASS"], -["net150.mtx","float32","int32","trans","row","spmm_csc_base","43520","43520","3121200","32",0.2685024,null,null,null,null,"PASS"], -["net150.mtx","float32","int32","conj","row","spmm_csc_base","43520","43520","3121200","32",0.2679472,null,null,null,null,"PASS"], -["pkustk13.mtx","float32","int32","non","row","spmm_csc_base","94893","94893","6616827","32",0.6346432,null,null,1.267898,1.997812,"PASS"], -["pkustk13.mtx","float32","int32","trans","row","spmm_csc_base","94893","94893","6616827","32",0.5932448,null,null,null,null,"PASS"], -["pkustk13.mtx","float32","int32","conj","row","spmm_csc_base","94893","94893","6616827","32",0.5929552,null,null,null,null,"PASS"], -["roadNet-TX.mtx","float32","int32","non","row","spmm_csc_base","1393383","1393383","3843320","32",0.4300704,null,null,0.9825024,2.284515,"PASS"], -["roadNet-TX.mtx","float32","int32","trans","row","spmm_csc_base","1393383","1393383","3843320","32",0.4555952,null,null,null,null,"PASS"], -["roadNet-TX.mtx","float32","int32","conj","row","spmm_csc_base","1393383","1393383","3843320","32",0.455392,null,null,null,null,"PASS"], -["smt.mtx","float32","int32","non","row","spmm_csc_base","25710","25710","3749582","32",0.3744,null,null,0.5884528,1.571722,"FAIL"], -["smt.mtx","float32","int32","trans","row","spmm_csc_base","25710","25710","3749582","32",0.1641456,null,null,null,null,"FAIL"], -["smt.mtx","float32","int32","conj","row","spmm_csc_base","25710","25710","3749582","32",0.163904,null,null,null,null,"FAIL"], -["trdheim.mtx","float32","int32","non","row","spmm_csc_base","22098","22098","1935324","32",0.2003232,null,null,0.2240592,1.118489,"PASS"], -["trdheim.mtx","float32","int32","trans","row","spmm_csc_base","22098","22098","1935324","32",0.0647536,null,null,null,null,"PASS"], -["trdheim.mtx","float32","int32","conj","row","spmm_csc_base","22098","22098","1935324","32",0.0639312,null,null,null,null,"PASS"], -["water_tank.mtx","float32","int32","non","row","spmm_csc_base","60740","60740","2035281","32",0.1837008,null,null,0.3744832,2.03855,"FAIL"], -["water_tank.mtx","float32","int32","trans","row","spmm_csc_base","60740","60740","2035281","32",0.0838832,null,null,null,null,"FAIL"], -["water_tank.mtx","float32","int32","conj","row","spmm_csc_base","60740","60740","2035281","32",0.083688,null,null,null,null,"FAIL"], -["wave.mtx","float32","int32","non","row","spmm_csc_base","156317","156317","2118662","32",0.191144,null,null,0.409168,2.140627,"PASS"], -["wave.mtx","float32","int32","trans","row","spmm_csc_base","156317","156317","2118662","32",0.1856336,null,null,null,null,"PASS"], -["wave.mtx","float32","int32","conj","row","spmm_csc_base","156317","156317","2118662","32",0.1856352,null,null,null,null,"PASS"], -["wheel_601.mtx","float32","int32","non","row","spmm_csc_base","902103","723605","2170814","32",0.2946448,null,null,0.8581824,2.9126,"PASS"], -["wheel_601.mtx","float32","int32","trans","row","spmm_csc_base","902103","723605","2170814","32",0.2720944,null,null,null,null,"PASS"], -["wheel_601.mtx","float32","int32","conj","row","spmm_csc_base","902103","723605","2170814","32",0.2726368,null,null,null,null,"PASS"], -["wiki-Talk.mtx","float32","int32","non","row","spmm_csc_base","2394385","2394385","5021410","32",0.8435184,null,null,2.354582,2.791383,"FAIL"], -["wiki-Talk.mtx","float32","int32","trans","row","spmm_csc_base","2394385","2394385","5021410","32",0.6464256,null,null,null,null,"PASS"], -["wiki-Talk.mtx","float32","int32","conj","row","spmm_csc_base","2394385","2394385","5021410","32",0.6457952,null,null,null,null,"PASS"], -["2cubes_sphere.mtx","float32","int64","non","row","spmm_csc_base","101492","101492","1647264","32",0.1451936,null,null,0.4996128,3.441011,"FAIL"], -["2cubes_sphere.mtx","float32","int64","trans","row","spmm_csc_base","101492","101492","1647264","32",0.125616,null,null,null,null,"FAIL"], -["2cubes_sphere.mtx","float32","int64","conj","row","spmm_csc_base","101492","101492","1647264","32",0.12552,null,null,null,null,"FAIL"], -["ASIC_680ks.mtx","float32","int64","non","row","spmm_csc_base","682712","682712","1693767","32",0.2070784,null,null,0.2903152,1.401958,"PASS"], -["ASIC_680ks.mtx","float32","int64","trans","row","spmm_csc_base","682712","682712","1693767","32",0.2159856,null,null,null,null,"PASS"], -["ASIC_680ks.mtx","float32","int64","conj","row","spmm_csc_base","682712","682712","1693767","32",0.2158336,null,null,null,null,"PASS"], 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-["Stanford.mtx","float32","int64","conj","row","spmm_csc_base","281903","281903","2312497","32",0.2527104,null,null,null,null,"PASS"], -["TF19.mtx","float32","int64","non","row","spmm_csc_base","241029","317955","4370721","32",0.3691344,null,null,1.401126,3.795708,"PASS"], -["TF19.mtx","float32","int64","trans","row","spmm_csc_base","241029","317955","4370721","32",0.3619744,null,null,null,null,"PASS"], -["TF19.mtx","float32","int64","conj","row","spmm_csc_base","241029","317955","4370721","32",0.36216,null,null,null,null,"PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int64","non","row","spmm_csc_base","29214","29214","4400122","32",0.4683664,null,null,0.5497856,1.173837,"PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int64","trans","row","spmm_csc_base","29214","29214","4400122","32",0.2577904,null,null,null,null,"FAIL"], -["TSOPF_FS_b300_c1.mtx","float32","int64","conj","row","spmm_csc_base","29214","29214","4400122","32",0.2575504,null,null,null,null,"PASS"], 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-["cfd2.mtx","float32","int64","conj","row","spmm_csc_base","123440","123440","3085406","32",0.1556064,null,null,null,null,"PASS"], -["ecology1.mtx","float32","int64","non","row","spmm_csc_base","1000000","1000000","4996000","32",0.4723632,null,null,0.4973856,1.052973,"PASS"], -["ecology1.mtx","float32","int64","trans","row","spmm_csc_base","1000000","1000000","4996000","32",0.5352464,null,null,null,null,"PASS"], -["ecology1.mtx","float32","int64","conj","row","spmm_csc_base","1000000","1000000","4996000","32",0.5327472,null,null,null,null,"PASS"], -["engine.mtx","float32","int64","non","row","spmm_csc_base","143571","143571","4706073","32",0.4105424,null,null,0.7655584,1.864749,"FAIL"], -["engine.mtx","float32","int64","trans","row","spmm_csc_base","143571","143571","4706073","32",0.4540032,null,null,null,null,"PASS"], -["engine.mtx","float32","int64","conj","row","spmm_csc_base","143571","143571","4706073","32",0.4536752,null,null,null,null,"PASS"], -["filter3D.mtx","float32","int64","non","row","spmm_csc_base","106437","106437","2707179","32",0.2343056,null,null,0.8016256,3.421282,"PASS"], -["filter3D.mtx","float32","int64","trans","row","spmm_csc_base","106437","106437","2707179","32",0.1892176,null,null,null,null,"PASS"], -["filter3D.mtx","float32","int64","conj","row","spmm_csc_base","106437","106437","2707179","32",0.1877888,null,null,null,null,"PASS"], -["mac_econ_fwd500.mtx","float32","int64","non","row","spmm_csc_base","206500","206500","1273389","32",0.1238752,null,null,0.2945568,2.377851,"FAIL"], -["mac_econ_fwd500.mtx","float32","int64","trans","row","spmm_csc_base","206500","206500","1273389","32",0.1430992,null,null,null,null,"FAIL"], -["mac_econ_fwd500.mtx","float32","int64","conj","row","spmm_csc_base","206500","206500","1273389","32",0.1434192,null,null,null,null,"FAIL"], -["mip1.mtx","float32","int64","non","row","spmm_csc_base","66463","66463","10352819","32",1.030539,null,null,1.330862,1.291423,"FAIL"], -["mip1.mtx","float32","int64","trans","row","spmm_csc_base","66463","66463","10352819","32",0.5947424,null,null,null,null,"PASS"], -["mip1.mtx","float32","int64","conj","row","spmm_csc_base","66463","66463","10352819","32",0.59444,null,null,null,null,"PASS"], -["msc10848.mtx","float32","int64","non","row","spmm_csc_base","10848","10848","1229776","32",0.11568,null,null,0.15816,1.36722,"FAIL"], -["msc10848.mtx","float32","int64","trans","row","spmm_csc_base","10848","10848","1229776","32",0.0773408,null,null,null,null,"FAIL"], -["msc10848.mtx","float32","int64","conj","row","spmm_csc_base","10848","10848","1229776","32",0.0769312,null,null,null,null,"FAIL"], -["neos3.mtx","float32","int64","non","row","spmm_csc_base","512209","518832","2055024","32",0.2536896,null,null,0.6319328,2.490968,"PASS"], -["neos3.mtx","float32","int64","trans","row","spmm_csc_base","512209","518832","2055024","32",0.3085472,null,null,null,null,"PASS"], -["neos3.mtx","float32","int64","conj","row","spmm_csc_base","512209","518832","2055024","32",0.3083424,null,null,null,null,"PASS"], -["net150.mtx","float32","int64","non","row","spmm_csc_base","43520","43520","3121200","32",0.2966704,null,null,0.7890208,2.659587,"PASS"], -["net150.mtx","float32","int64","trans","row","spmm_csc_base","43520","43520","3121200","32",0.3541184,null,null,null,null,"PASS"], -["net150.mtx","float32","int64","conj","row","spmm_csc_base","43520","43520","3121200","32",0.3541728,null,null,null,null,"PASS"], -["pkustk13.mtx","float32","int64","non","row","spmm_csc_base","94893","94893","6616827","32",0.5979216,null,null,1.229955,2.057051,"PASS"], -["pkustk13.mtx","float32","int64","trans","row","spmm_csc_base","94893","94893","6616827","32",0.7703664,null,null,null,null,"PASS"], -["pkustk13.mtx","float32","int64","conj","row","spmm_csc_base","94893","94893","6616827","32",0.7701936,null,null,null,null,"PASS"], -["roadNet-TX.mtx","float32","int64","non","row","spmm_csc_base","1393383","1393383","3843320","32",0.41652,null,null,0.98232,2.358398,"PASS"], -["roadNet-TX.mtx","float32","int64","trans","row","spmm_csc_base","1393383","1393383","3843320","32",0.4596848,null,null,null,null,"PASS"], -["roadNet-TX.mtx","float32","int64","conj","row","spmm_csc_base","1393383","1393383","3843320","32",0.4596128,null,null,null,null,"PASS"], -["smt.mtx","float32","int64","non","row","spmm_csc_base","25710","25710","3749582","32",0.3512352,null,null,0.5913376,1.683594,"FAIL"], -["smt.mtx","float32","int64","trans","row","spmm_csc_base","25710","25710","3749582","32",0.2177344,null,null,null,null,"FAIL"], -["smt.mtx","float32","int64","conj","row","spmm_csc_base","25710","25710","3749582","32",0.2176832,null,null,null,null,"FAIL"], -["trdheim.mtx","float32","int64","non","row","spmm_csc_base","22098","22098","1935324","32",0.184488,null,null,0.2214432,1.200312,"PASS"], -["trdheim.mtx","float32","int64","trans","row","spmm_csc_base","22098","22098","1935324","32",0.07708,null,null,null,null,"PASS"], -["trdheim.mtx","float32","int64","conj","row","spmm_csc_base","22098","22098","1935324","32",0.0768,null,null,null,null,"PASS"], -["water_tank.mtx","float32","int64","non","row","spmm_csc_base","60740","60740","2035281","32",0.1815328,null,null,0.380616,2.096679,"FAIL"], -["water_tank.mtx","float32","int64","trans","row","spmm_csc_base","60740","60740","2035281","32",0.0906272,null,null,null,null,"FAIL"], -["water_tank.mtx","float32","int64","conj","row","spmm_csc_base","60740","60740","2035281","32",0.0905824,null,null,null,null,"FAIL"], -["wave.mtx","float32","int64","non","row","spmm_csc_base","156317","156317","2118662","32",0.1857392,null,null,0.4095344,2.20489,"PASS"], -["wave.mtx","float32","int64","trans","row","spmm_csc_base","156317","156317","2118662","32",0.1971856,null,null,null,null,"PASS"], -["wave.mtx","float32","int64","conj","row","spmm_csc_base","156317","156317","2118662","32",0.1968624,null,null,null,null,"PASS"], -["wheel_601.mtx","float32","int64","non","row","spmm_csc_base","902103","723605","2170814","32",0.2889392,null,null,0.8578912,2.969106,"PASS"], -["wheel_601.mtx","float32","int64","trans","row","spmm_csc_base","902103","723605","2170814","32",0.2747808,null,null,null,null,"PASS"], -["wheel_601.mtx","float32","int64","conj","row","spmm_csc_base","902103","723605","2170814","32",0.2745152,null,null,null,null,"PASS"], -["wiki-Talk.mtx","float32","int64","non","row","spmm_csc_base","2394385","2394385","5021410","32",0.8291792,null,null,2.341382,2.823735,"FAIL"], -["wiki-Talk.mtx","float32","int64","trans","row","spmm_csc_base","2394385","2394385","5021410","32",0.6497728,null,null,null,null,"PASS"], -["wiki-Talk.mtx","float32","int64","conj","row","spmm_csc_base","2394385","2394385","5021410","32",0.6493552,null,null,null,null,"PASS"], -["2cubes_sphere.mtx","float64","int32","non","row","spmm_csc_base","101492","101492","1647264","32",0.5111552,null,null,0.6716768,1.314037,"PASS"], -["2cubes_sphere.mtx","float64","int32","trans","row","spmm_csc_base","101492","101492","1647264","32",0.6200784,null,null,null,null,"PASS"], -["2cubes_sphere.mtx","float64","int32","conj","row","spmm_csc_base","101492","101492","1647264","32",0.6195328,null,null,null,null,"PASS"], -["ASIC_680ks.mtx","float64","int32","non","row","spmm_csc_base","682712","682712","1693767","32",0.64124,null,null,0.774336,1.20756,"PASS"], -["ASIC_680ks.mtx","float64","int32","trans","row","spmm_csc_base","682712","682712","1693767","32",0.6539296,null,null,null,null,"PASS"], -["ASIC_680ks.mtx","float64","int32","conj","row","spmm_csc_base","682712","682712","1693767","32",0.6545232,null,null,null,null,"PASS"], -["CurlCurl_1.mtx","float64","int32","non","row","spmm_csc_base","226451","226451","2472071","32",0.7669024,null,null,1.010355,1.31745,"PASS"], -["CurlCurl_1.mtx","float64","int32","trans","row","spmm_csc_base","226451","226451","2472071","32",0.8484272,null,null,null,null,"PASS"], -["CurlCurl_1.mtx","float64","int32","conj","row","spmm_csc_base","226451","226451","2472071","32",0.8484528,null,null,null,null,"PASS"], -["GL7d14.mtx","float64","int32","non","row","spmm_csc_base","171375","47271","1831183","32",0.5698432,null,null,0.7717328,1.35429,"PASS"], -["GL7d14.mtx","float64","int32","trans","row","spmm_csc_base","171375","47271","1831183","32",0.9538,null,null,null,null,"PASS"], -["GL7d14.mtx","float64","int32","conj","row","spmm_csc_base","171375","47271","1831183","32",0.9535968,null,null,null,null,"PASS"], -["H2O.mtx","float64","int32","non","row","spmm_csc_base","67024","67024","2216736","32",0.6858608,null,null,0.8780944,1.280281,"PASS"], -["H2O.mtx","float64","int32","trans","row","spmm_csc_base","67024","67024","2216736","32",0.5459328,null,null,null,null,"PASS"], -["H2O.mtx","float64","int32","conj","row","spmm_csc_base","67024","67024","2216736","32",0.5458784,null,null,null,null,"PASS"], -["NACA0015.mtx","float64","int32","non","row","spmm_csc_base","1039183","1039183","6229636","32",2.115803,null,null,6.494987,3.06975,"PASS"], -["NACA0015.mtx","float64","int32","trans","row","spmm_csc_base","1039183","1039183","6229636","32",2.317061,null,null,null,null,"PASS"], -["NACA0015.mtx","float64","int32","conj","row","spmm_csc_base","1039183","1039183","6229636","32",2.3136,null,null,null,null,"PASS"], -["Stanford.mtx","float64","int32","non","row","spmm_csc_base","281903","281903","2312497","32",1.161398,null,null,1.254542,1.0802,"PASS"], -["Stanford.mtx","float64","int32","trans","row","spmm_csc_base","281903","281903","2312497","32",0.8450736,null,null,null,null,"PASS"], -["Stanford.mtx","float64","int32","conj","row","spmm_csc_base","281903","281903","2312497","32",0.8433104,null,null,null,null,"PASS"], 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+["auto.mtx","complex128","int64","trans","row","spmm_csc_base","448695","448695","6629222","32",4.228696,null,null,null,null,"PASS","","","",""], +["auto.mtx","complex128","int64","conj","row","spmm_csc_base","448695","448695","6629222","32",4.245115,null,null,null,null,"PASS","","","",""], +["cage12.mtx","complex128","int64","non","row","spmm_csc_base","130228","130228","2032536","32",1.969362,null,null,1.868222,0.9486436,"PASS","","","",""], +["cage12.mtx","complex128","int64","trans","row","spmm_csc_base","130228","130228","2032536","32",1.272674,null,null,null,null,"PASS","","","",""], +["cage12.mtx","complex128","int64","conj","row","spmm_csc_base","130228","130228","2032536","32",1.276883,null,null,null,null,"PASS","","","",""], +["cfd2.mtx","complex128","int64","non","row","spmm_csc_base","123440","123440","3085406","32",2.930778,null,null,2.798107,0.954732,"PASS","","","",""], 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+["roadNet-TX.mtx","complex128","int64","trans","row","spmm_csc_base","1393383","1393383","3843320","32",2.724741,null,null,null,null,"PASS","","","",""], +["roadNet-TX.mtx","complex128","int64","conj","row","spmm_csc_base","1393383","1393383","3843320","32",2.733867,null,null,null,null,"PASS","","","",""], +["wave.mtx","complex128","int64","non","row","spmm_csc_base","156317","156317","2118662","32",2.070922,null,null,1.953362,0.943233,"PASS","","","",""], +["wave.mtx","complex128","int64","trans","row","spmm_csc_base","156317","156317","2118662","32",1.300102,null,null,null,null,"PASS","","","",""], +["wave.mtx","complex128","int64","conj","row","spmm_csc_base","156317","156317","2118662","32",1.304941,null,null,null,null,"PASS","","","",""], +["ASIC_680ks.mtx","float16","int32","non","row","q4","682712","682712","2329176","",0.6390009,0.277146,0.4337176,0.3347743,0.5239027,"PASS","spmm_csc_f16_int_non_non_row","int32","non","float16"], +["ASIC_680ks.mtx","float32","int32","non","row","q4","682712","682712","2329176","",0.4976106,0.2771674,0.5569966,0.2267844,0.4557466,"PASS","spmm_csc_f32_int_non_non_row","int32","non","float32"], +["ASIC_680ks.mtx","complex64","int32","non","row","q4","682712","682712","2329176","",2.901383,0.3589267,0.1237088,0.5851418,0.2016768,"PASS","spmm_csc_c32_int_non_non_row","int32","non","complex64"], +["GL7d14.mtx","float16","int32","non","row","q4","171375","47271","1831183","",0.4488012,0.160662,0.3579804,0.208959,0.4655937,"PASS","spmm_csc_f16_int_non_non_row","int32","non","float16"], +["GL7d14.mtx","float32","int32","non","row","q4","171375","47271","1831183","",0.4130885,0.1599828,0.3872847,0.1208148,0.2924671,"PASS","spmm_csc_f32_int_non_non_row","int32","non","float32"], +["GL7d14.mtx","complex64","int32","non","row","q4","171375","47271","1831183","",2.190187,0.192139,0.0877272,0.3602863,0.1645002,"PASS","spmm_csc_c32_int_non_non_row","int32","non","complex64"], +["NACA0015.mtx","float16","int32","non","row","q4","1039183","1039183","6229636","",1.268787,0.6909494,0.5445745,0.7202488,0.567667,"PASS","spmm_csc_f16_int_non_non_row","int32","non","float16"], +["NACA0015.mtx","float32","int32","non","row","q4","1039183","1039183","6229636","",1.055349,0.6907631,0.6545352,0.6960851,0.6595782,"PASS","spmm_csc_f32_int_non_non_row","int32","non","float32"], +["NACA0015.mtx","complex64","int32","non","row","q4","1039183","1039183","6229636","",7.725805,0.9373228,0.1213236,1.758914,0.2276674,"PASS","spmm_csc_c32_int_non_non_row","int32","non","complex64"], +["amazon0601.mtx","float16","int32","non","row","q4","403394","403394","3387388","",0.6971643,0.3153974,0.4524004,0.4053833,0.5814746,"PASS","spmm_csc_f16_int_non_non_row","int32","non","float16"], +["amazon0601.mtx","float32","int32","non","row","q4","403394","403394","3387388","",0.5661747,0.3153343,0.5569559,0.2596192,0.4585498,"PASS","spmm_csc_f32_int_non_non_row","int32","non","float32"], +["amazon0601.mtx","complex64","int32","non","row","q4","403394","403394","3387388","",4.020243,0.4091397,0.1017699,0.7772878,0.1933435,"PASS","spmm_csc_c32_int_non_non_row","int32","non","complex64"], +["auto.mtx","float16","int32","non","row","q4","448695","448695","6629222","",0.9759502,0.5085846,0.5211173,0.6913009,0.7083362,"PASS","spmm_csc_f16_int_non_non_row","int32","non","float16"], +["auto.mtx","float32","int32","non","row","q4","448695","448695","6629222","",0.8609302,0.5085591,0.5907089,0.3780222,0.4390858,"PASS","spmm_csc_f32_int_non_non_row","int32","non","float32"], +["auto.mtx","complex64","int32","non","row","q4","448695","448695","6629222","",7.381707,0.6884855,0.09326915,1.331596,0.1803914,"PASS","spmm_csc_c32_int_non_non_row","int32","non","complex64"], +["cage12.mtx","float16","int32","non","row","q4","130228","130228","2032536","",0.5299113,0.16253,0.3067116,0.224832,0.4242824,"PASS","spmm_csc_f16_int_non_non_row","int32","non","float16"], +["cage12.mtx","float32","int32","non","row","q4","130228","130228","2032536","",0.4851122,0.1631766,0.3363687,0.1264649,0.260692,"PASS","spmm_csc_f32_int_non_non_row","int32","non","float32"], +["cage12.mtx","complex64","int32","non","row","q4","130228","130228","2032536","",2.427871,0.1936574,0.0797643,0.3756902,0.1547406,"PASS","spmm_csc_c32_int_non_non_row","int32","non","complex64"], +["cfd2.mtx","float16","int32","non","row","q4","123440","123440","3087898","",0.6189601,0.2061604,0.3330755,0.32366,0.5229093,"PASS","spmm_csc_f16_int_non_non_row","int32","non","float16"], +["cfd2.mtx","float32","int32","non","row","q4","123440","123440","3087898","",0.5789257,0.2057382,0.3553793,0.1711477,0.2956297,"PASS","spmm_csc_f32_int_non_non_row","int32","non","float32"], +["cfd2.mtx","complex64","int32","non","row","q4","123440","123440","3087898","",3.442889,0.2421511,0.07033371,0.5534191,0.1607427,"PASS","spmm_csc_c32_int_non_non_row","int32","non","complex64"], +["filter3D.mtx","float16","int32","non","row","q4","106437","106437","2707179","",0.5710044,0.1895466,0.3319528,0.2833078,0.496157,"PASS","spmm_csc_f16_int_non_non_row","int32","non","float16"], +["filter3D.mtx","float32","int32","non","row","q4","106437","106437","2707179","",0.5340975,0.1892041,0.3542501,0.1515551,0.2837593,"PASS","spmm_csc_f32_int_non_non_row","int32","non","float32"], +["filter3D.mtx","complex64","int32","non","row","q4","106437","106437","2707179","",3.080543,0.2260544,0.07338135,0.4977039,0.1615637,"PASS","spmm_csc_c32_int_non_non_row","int32","non","complex64"], +["roadNet-TX.mtx","float16","int32","non","row","q4","1393383","1393383","3843320","",0.9656484,0.4814884,0.4986168,0.537946,0.5570827,"PASS","spmm_csc_f16_int_non_non_row","int32","non","float16"], +["roadNet-TX.mtx","float32","int32","non","row","q4","1393383","1393383","3843320","",0.6897346,0.4817289,0.6984264,0.3759993,0.5451362,"PASS","spmm_csc_f32_int_non_non_row","int32","non","float32"], +["roadNet-TX.mtx","complex64","int32","non","row","q4","1393383","1393383","3843320","",4.69758,0.6616441,0.1408479,0.9732087,0.2071724,"PASS","spmm_csc_c32_int_non_non_row","int32","non","complex64"], +["wave.mtx","float16","int32","non","row","q4","156317","156317","2118662","",0.5321287,0.1701951,0.3198383,0.2415271,0.4538885,"PASS","spmm_csc_f16_int_non_non_row","int32","non","float16"], +["wave.mtx","float32","int32","non","row","q4","156317","156317","2118662","",0.4807525,0.1700293,0.3536733,0.1369351,0.2848348,"PASS","spmm_csc_f32_int_non_non_row","int32","non","float32"], +["wave.mtx","complex64","int32","non","row","q4","156317","156317","2118662","",2.478026,0.2020972,0.08155569,0.4048648,0.163382,"PASS","spmm_csc_c32_int_non_non_row","int32","non","complex64"] ]}, -"spmm_csr": {"fields": ["matrix", "value_dtype", "index_dtype", "op", "n_rows", "n_cols", "nnz", "triton_ms", "cusparse_ms", "pytorch_ms", "triton_speedup_vs_cusparse", "triton_speedup_vs_pytorch", "pt_status", "cu_status", "status"], +"spmm_csr": {"fields": ["matrix", "value_dtype", "index_dtype", "op", "n_rows", "n_cols", "nnz", "triton_ms", "cusparse_ms", "pytorch_ms", "triton_speedup_vs_cusparse", "triton_speedup_vs_pytorch", "pt_status", "cu_status", "status", "variant", "indptr_dtype", "op_b", "layout", "out_dtype"], "rows": [ -["2cubes_sphere.mtx","float32","int32","non","101492","101492","1647264",0.2864736,0.1824736,0.1361009,0.6369648,0.4750904,"FAIL","FAIL","FAIL"], -["ASIC_680ks.mtx","float32","int32","non","682712","682712","2329176",0.8234971,0.3521728,0.2785258,0.4276552,0.3382231,"PASS","PASS","PASS"], -["CurlCurl_1.mtx","float32","int32","non","226451","226451","2472071",0.3599983,0.24572,0.203153,0.6825589,0.5643168,"FAIL","FAIL","FAIL"], -["GL7d14.mtx","float32","int32","non","171375","47271","1831183",0.3211871,0.5118608,0.1599084,1.593653,0.497867,"PASS","PASS","PASS"], -["H2O.mtx","float32","int32","non","67024","67024","2216736",0.3591272,0.1697888,0.1517097,0.4727817,0.42244,"FAIL","FAIL","FAIL"], -["NACA0015.mtx","float32","int32","non","1039183","1039183","6229636",0.9303133,2.4388,0.6910339,2.621482,0.7427969,"PASS","PASS","PASS"], -["Stanford.mtx","float32","int32","non","281903","281903","2312497",1.343577,0.6482928,0.2478132,0.4825125,0.1844428,"FAIL","FAIL","FAIL"], -["TF19.mtx","float32","int32","non","241029","317955","4370721",0.4768013,0.63508,0.3346731,1.331959,0.7019131,"PASS","PASS","PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int32","non","29214","29214","4400122",1.178864,0.2935712,0.2454699,0.2490289,0.2082257,"FAIL","FAIL","FAIL"], -["amazon0601.mtx","float32","int32","non","403394","403394","3387388",0.4745653,0.6574384,0.315589,1.385349,0.6650065,"PASS","PASS","PASS"], -["auto.mtx","float32","int32","non","448695","448695","6629222",0.5749977,1.476515,0.5088828,2.567863,0.885017,"PASS","PASS","PASS"], -["c8_mat11.mtx","float32","int32","non","4562","5761","2462970",0.5798211,0.1305856,0.1290318,0.2252171,0.2225372,"FAIL","FAIL","FAIL"], -["cage12.mtx","float32","int32","non","130228","130228","2032536",0.3254322,0.1914528,0.1628352,0.5883033,0.500366,"PASS","PASS","PASS"], -["cfd2.mtx","float32","int32","non","123440","123440","3087898",0.296119,0.2058784,0.2062762,0.6952556,0.696599,"PASS","PASS","PASS"], -["ecology1.mtx","float32","int32","non","1000000","1000000","4996000",0.8838964,0.565096,0.4561169,0.6393238,0.5160297,"FAIL","FAIL","FAIL"], -["engine.mtx","float32","int32","non","143571","143571","4706073",0.4779449,0.320192,0.3152573,0.669935,0.65961,"FAIL","FAIL","FAIL"], -["filter3D.mtx","float32","int32","non","106437","106437","2707179",0.3821213,0.282168,0.1894618,0.7384252,0.4958159,"PASS","PASS","PASS"], -["mac_econ_fwd500.mtx","float32","int32","non","206500","206500","1273389",0.4383207,0.1608784,0.1455349,0.3670335,0.3320285,"FAIL","FAIL","FAIL"], -["mip1.mtx","float32","int32","non","66463","66463","10352819",1.86256,0.5371008,0.5329543,0.2883669,0.2861407,"FAIL","FAIL","FAIL"], -["msc10848.mtx","float32","int32","non","10848","10848","1229778",0.4447802,0.0831184,0.0770802,0.1868753,0.1732995,"FAIL","FAIL","FAIL"], -["neos3.mtx","float32","int32","non","512209","518832","2055024",1.119714,0.337416,0.2301829,0.3013411,0.2055729,"PASS","PASS","PASS"], -["net150.mtx","float32","int32","non","43520","43520","3121200",0.6069993,0.2603232,0.1926333,0.428869,0.3173535,"FAIL","FAIL","FAIL"], -["pkustk13.mtx","float32","int32","non","94893","94893","6616827",0.7285176,0.3982944,0.391024,0.5467189,0.5367392,"FAIL","FAIL","FAIL"], -["roadNet-TX.mtx","float32","int32","non","1393383","1393383","3843320",1.150439,0.8649152,0.4835879,0.751813,0.4203507,"PASS","PASS","PASS"], -["smt.mtx","float32","int32","non","25710","25710","3753184",0.6453576,0.2188208,0.2154376,0.3390691,0.3338267,"FAIL","FAIL","FAIL"], -["trdheim.mtx","float32","int32","non","22098","22098","1935324",0.3651802,0.1165104,0.1182982,0.319049,0.3239445,"FAIL","FAIL","FAIL"], -["water_tank.mtx","float32","int32","non","60740","60740","2035281",0.3059701,0.1317216,0.1393593,0.4305048,0.4554671,"FAIL","FAIL","FAIL"], -["wave.mtx","float32","int32","non","156317","156317","2118662",0.346267,0.2043872,0.1710619,0.590259,0.4940173,"PASS","PASS","PASS"], -["wheel_601.mtx","float32","int32","non","902103","723605","2170814",0.9151671,0.703224,0.3063451,0.7684105,0.3347423,"FAIL","FAIL","FAIL"], -["wiki-Talk.mtx","float32","int32","non","2394385","2394385","5021410",2.116426,3.40995,1.358185,1.611184,0.6417353,"FAIL","FAIL","FAIL"], -["2cubes_sphere.mtx","float32","int32","trans","101492","101492","1647264",0.1318471,0.183536,0.7531793,1.392037,5.712521,"FAIL","FAIL","FAIL"], -["ASIC_680ks.mtx","float32","int32","trans","682712","682712","2329176",0.849434,0.3530576,1.054096,0.4156387,1.240939,"PASS","PASS","PASS"], -["CurlCurl_1.mtx","float32","int32","trans","226451","226451","2472071",0.282803,0.2469744,1.032348,0.8733091,3.650415,"FAIL","FAIL","FAIL"], -["GL7d14.mtx","float32","int32","trans","171375","47271","1831183",0.105084,0.5128816,0.807944,4.880682,7.688554,"FAIL","FAIL","FAIL"], -["H2O.mtx","float32","int32","trans","67024","67024","2216736",0.1421858,0.1753856,0.9290695,1.233495,6.534191,"FAIL","FAIL","FAIL"], -["NACA0015.mtx","float32","int32","trans","1039183","1039183","6229636",1.260191,2.438283,2.563836,1.934852,2.034482,"PASS","PASS","PASS"], -["Stanford.mtx","float32","int32","trans","281903","281903","2312497",0.3841707,0.64072,2.433003,1.6678,6.33313,"FAIL","FAIL","FAIL"], -["TF19.mtx","float32","int32","trans","241029","317955","4370721",0.3945797,0.6308672,1.702054,1.598833,4.313586,"PASS","PASS","PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int32","trans","29214","29214","4400122",1.449754,0.295472,2.509124,0.2038083,1.730723,"FAIL","FAIL","FAIL"], -["amazon0601.mtx","float32","int32","trans","403394","403394","3387388",0.5182296,0.670464,1.404799,1.293759,2.710766,"FAIL","FAIL","FAIL"], -["auto.mtx","float32","int32","trans","448695","448695","6629222",0.5548774,1.478563,2.496555,2.664666,4.499292,"PASS","PASS","PASS"], -["c8_mat11.mtx","float32","int32","trans","4562","5761","2462970",0.1960005,0.153344,1.566834,0.7823653,7.994032,"FAIL","FAIL","FAIL"], -["cage12.mtx","float32","int32","trans","130228","130228","2032536",0.167709,0.1934464,0.8859418,1.153465,5.282615,"PASS","PASS","PASS"], -["cfd2.mtx","float32","int32","trans","123440","123440","3087898",0.1612202,0.208,1.220941,1.29016,7.573125,"PASS","PASS","PASS"], -["ecology1.mtx","float32","int32","trans","1000000","1000000","4996000",1.211482,0.5662416,1.886052,0.4673959,1.556814,"FAIL","FAIL","FAIL"], -["engine.mtx","float32","int32","trans","143571","143571","4706073",0.2526314,0.3200496,1.832576,1.266864,7.253953,"FAIL","FAIL","FAIL"], -["filter3D.mtx","float32","int32","trans","106437","106437","2707179",0.1625535,0.2827088,1.099417,1.739174,6.763419,"PASS","PASS","PASS"], -["mac_econ_fwd500.mtx","float32","int32","trans","206500","206500","1273389",0.2623732,0.1596624,0.6219835,0.6085318,2.370607,"FAIL","FAIL","FAIL"], -["mip1.mtx","float32","int32","trans","66463","66463","10352819",8.32288,0.538576,5.698206,0.06471029,0.6846435,"FAIL","FAIL","FAIL"], -["msc10848.mtx","float32","int32","trans","10848","10848","1229778",0.0826055,0.0828064,0.671067,1.002432,8.123757,"FAIL","FAIL","FAIL"], -["neos3.mtx","float32","int32","trans","512209","518832","2055024",0.7045182,0.4891328,1.227635,0.6942798,1.742518,"FAIL","FAIL","FAIL"], -["net150.mtx","float32","int32","trans","43520","43520","3121200",0.161523,0.2601152,1.404918,1.610392,8.697947,"FAIL","FAIL","FAIL"], -["pkustk13.mtx","float32","int32","trans","94893","94893","6616827",0.3242879,0.3986288,2.38,1.229244,7.339158,"FAIL","FAIL","FAIL"], -["roadNet-TX.mtx","float32","int32","trans","1393383","1393383","3843320",1.668456,0.8612608,1.644251,0.5162022,0.985492,"PASS","PASS","PASS"], -["smt.mtx","float32","int32","trans","25710","25710","3753184",0.1710774,0.218872,1.482057,1.279374,8.663078,"FAIL","FAIL","FAIL"], -["trdheim.mtx","float32","int32","trans","22098","22098","1935324",0.09361725,0.11788,0.8681299,1.25917,9.273183,"FAIL","FAIL","FAIL"], -["water_tank.mtx","float32","int32","trans","60740","60740","2035281",0.1068433,0.1344496,0.8615829,1.258381,8.063986,"FAIL","FAIL","FAIL"], -["wave.mtx","float32","int32","trans","156317","156317","2118662",0.1986891,0.2054704,0.9704458,1.03413,4.884242,"PASS","PASS","PASS"], -["wheel_601.mtx","float32","int32","trans","902103","723605","2170814",0.8775608,0.7125968,1.088781,0.8120199,1.24069,"PASS","PASS","PASS"], -["wiki-Talk.mtx","float32","int32","trans","2394385","2394385","5021410",2.897208,1.72892,6.004135,0.5967539,2.072387,"FAIL","FAIL","FAIL"], -["2cubes_sphere.mtx","float32","int32","conj","101492","101492","1647264",0.1315601,0.1852624,0.7532515,1.408197,5.725534,"FAIL","FAIL","FAIL"], -["ASIC_680ks.mtx","float32","int32","conj","682712","682712","2329176",0.8495412,0.3519088,1.053072,0.414234,1.239577,"PASS","PASS","PASS"], -["CurlCurl_1.mtx","float32","int32","conj","226451","226451","2472071",0.2825998,0.2463328,1.032942,0.8716665,3.655142,"FAIL","FAIL","FAIL"], -["GL7d14.mtx","float32","int32","conj","171375","47271","1831183",0.1053765,0.5110288,0.8024087,4.849552,7.614684,"PASS","FAIL","PASS"], -["H2O.mtx","float32","int32","conj","67024","67024","2216736",0.1416064,0.1721664,0.929016,1.21581,6.560554,"FAIL","FAIL","FAIL"], -["NACA0015.mtx","float32","int32","conj","1039183","1039183","6229636",1.260179,2.439646,2.569774,1.935952,2.039213,"PASS","PASS","PASS"], -["Stanford.mtx","float32","int32","conj","281903","281903","2312497",0.3837326,0.6415184,2.435779,1.671785,6.347596,"PASS","FAIL","PASS"], -["TF19.mtx","float32","int32","conj","241029","317955","4370721",0.3948568,0.6318656,1.699923,1.60024,4.305164,"PASS","FAIL","PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int32","conj","29214","29214","4400122",1.454832,0.3023296,2.600124,0.2078107,1.787233,"FAIL","FAIL","FAIL"], -["amazon0601.mtx","float32","int32","conj","403394","403394","3387388",0.5185229,0.6706368,1.3975,1.29336,2.695156,"FAIL","FAIL","FAIL"], -["auto.mtx","float32","int32","conj","448695","448695","6629222",0.5553239,1.477056,2.551172,2.65981,4.594027,"PASS","PASS","PASS"], -["c8_mat11.mtx","float32","int32","conj","4562","5761","2462970",0.1989816,0.1549456,1.572001,0.7786931,7.900234,"FAIL","FAIL","FAIL"], -["cage12.mtx","float32","int32","conj","130228","130228","2032536",0.1685247,0.1922656,0.8870648,1.140875,5.263708,"PASS","PASS","PASS"], -["cfd2.mtx","float32","int32","conj","123440","123440","3087898",0.1621977,0.2074384,1.22184,1.278923,7.533032,"PASS","PASS","PASS"], -["ecology1.mtx","float32","int32","conj","1000000","1000000","4996000",1.211982,0.566656,1.893108,0.4675448,1.561993,"FAIL","FAIL","FAIL"], -["engine.mtx","float32","int32","conj","143571","143571","4706073",0.2525257,0.322304,1.845211,1.276321,7.307022,"FAIL","FAIL","FAIL"], -["filter3D.mtx","float32","int32","conj","106437","106437","2707179",0.1620319,0.2820016,1.101444,1.740407,6.797694,"PASS","PASS","PASS"], -["mac_econ_fwd500.mtx","float32","int32","conj","206500","206500","1273389",0.2622336,0.1596208,0.623148,0.6086969,2.376308,"FAIL","FAIL","FAIL"], -["mip1.mtx","float32","int32","conj","66463","66463","10352819",8.321601,0.5381552,5.694089,0.06466967,0.684254,"FAIL","FAIL","FAIL"], -["msc10848.mtx","float32","int32","conj","10848","10848","1229778",0.0830431,0.08232,0.6756855,0.9912925,8.136564,"FAIL","FAIL","FAIL"], -["neos3.mtx","float32","int32","conj","512209","518832","2055024",0.7065396,0.4857312,1.227088,0.6874792,1.736758,"FAIL","FAIL","FAIL"], -["net150.mtx","float32","int32","conj","43520","43520","3121200",0.1617036,0.2623888,1.406197,1.622652,8.696136,"FAIL","FAIL","FAIL"], -["pkustk13.mtx","float32","int32","conj","94893","94893","6616827",0.3248918,0.3981408,2.382339,1.225457,7.332717,"FAIL","FAIL","FAIL"], -["roadNet-TX.mtx","float32","int32","conj","1393383","1393383","3843320",1.668768,0.8624128,1.649049,0.5167961,0.9881833,"PASS","PASS","PASS"], -["smt.mtx","float32","int32","conj","25710","25710","3753184",0.1713968,0.219216,1.476742,1.278997,8.61592,"FAIL","FAIL","FAIL"], -["trdheim.mtx","float32","int32","conj","22098","22098","1935324",0.09375695,0.1174544,0.8642383,1.252754,9.217858,"FAIL","FAIL","FAIL"], -["water_tank.mtx","float32","int32","conj","60740","60740","2035281",0.1071761,0.1313984,0.8685275,1.226005,8.103742,"FAIL","FAIL","FAIL"], -["wave.mtx","float32","int32","conj","156317","156317","2118662",0.1985697,0.2058576,0.9366634,1.036702,4.71705,"PASS","PASS","PASS"], -["wheel_601.mtx","float32","int32","conj","902103","723605","2170814",0.8774231,0.7129088,1.087578,0.8125028,1.239513,"PASS","PASS","PASS"], -["wiki-Talk.mtx","float32","int32","conj","2394385","2394385","5021410",3.27397,1.729269,5.944751,0.5281872,1.815762,"FAIL","FAIL","FAIL"], -["2cubes_sphere.mtx","float32","int64","non","101492","101492","1647264",0.2746487,0.1850096,0.1350093,0.6736227,0.4915709,"FAIL","FAIL","FAIL"], -["ASIC_680ks.mtx","float32","int64","non","682712","682712","2329176",0.8145699,0.3524544,0.2780808,0.4326877,0.3413837,"PASS","PASS","PASS"], -["CurlCurl_1.mtx","float32","int64","non","226451","226451","2472071",0.3869776,0.2462752,0.2034648,0.6364069,0.5257794,"FAIL","FAIL","FAIL"], -["GL7d14.mtx","float32","int64","non","171375","47271","1831183",0.3486083,0.5127776,0.1607703,1.470928,0.4611775,"PASS","PASS","PASS"], -["H2O.mtx","float32","int64","non","67024","67024","2216736",0.3989816,0.172016,0.1525529,0.4311376,0.3823557,"FAIL","FAIL","FAIL"], -["NACA0015.mtx","float32","int64","non","1039183","1039183","6229636",0.9328455,2.438218,0.6928077,2.613742,0.7426821,"PASS","PASS","PASS"], -["Stanford.mtx","float32","int64","non","281903","281903","2312497",1.480707,0.6493696,0.2486502,0.4385539,0.1679267,"FAIL","FAIL","FAIL"], -["TF19.mtx","float32","int64","non","241029","317955","4370721",0.5131616,0.636256,0.3366071,1.239875,0.6559475,"FAIL","FAIL","FAIL"], -["TSOPF_FS_b300_c1.mtx","float32","int64","non","29214","29214","4400122",1.252753,0.2947376,0.2467746,0.235272,0.1969858,"FAIL","FAIL","FAIL"], -["amazon0601.mtx","float32","int64","non","403394","403394","3387388",0.5071199,0.65808,0.3166471,1.297681,0.6244028,"PASS","PASS","PASS"], -["auto.mtx","float32","int64","non","448695","448695","6629222",0.6031762,1.47652,0.5110479,2.447908,0.8472614,"PASS","PASS","PASS"], -["c8_mat11.mtx","float32","int64","non","4562","5761","2462970",0.6257542,0.1316912,0.1299489,0.210452,0.2076676,"FAIL","FAIL","FAIL"], -["cage12.mtx","float32","int64","non","130228","130228","2032536",0.3583471,0.1925984,0.1631327,0.5374632,0.4552364,"PASS","PASS","PASS"], -["cfd2.mtx","float32","int64","non","123440","123440","3087898",0.3245567,0.2078096,0.2073847,0.6402874,0.6389784,"PASS","PASS","PASS"], -["ecology1.mtx","float32","int64","non","1000000","1000000","4996000",0.8886367,0.5667696,0.4578284,0.6377968,0.5152032,"FAIL","FAIL","FAIL"], -["engine.mtx","float32","int64","non","143571","143571","4706073",0.5081473,0.3218912,0.3156481,0.6334604,0.6211745,"FAIL","FAIL","FAIL"], -["filter3D.mtx","float32","int64","non","106437","106437","2707179",0.3773315,0.2824272,0.1899295,0.7484856,0.5033493,"PASS","PASS","PASS"], -["mac_econ_fwd500.mtx","float32","int64","non","206500","206500","1273389",0.43243,0.1602256,0.1456856,0.3705237,0.3368999,"FAIL","FAIL","FAIL"], -["mip1.mtx","float32","int64","non","66463","66463","10352819",1.880532,0.538384,0.5348168,0.2862934,0.2843965,"FAIL","FAIL","FAIL"], -["msc10848.mtx","float32","int64","non","10848","10848","1229778",0.4467393,0.0822816,0.077291,0.1841826,0.1730114,"FAIL","FAIL","FAIL"], -["neos3.mtx","float32","int64","non","512209","518832","2055024",1.087779,0.3373248,0.2310074,0.3101043,0.2123662,"PASS","PASS","PASS"], -["net150.mtx","float32","int64","non","43520","43520","3121200",0.6066121,0.2603712,0.1923181,0.429222,0.3170363,"FAIL","FAIL","FAIL"], -["pkustk13.mtx","float32","int64","non","94893","94893","6616827",0.770146,0.3983024,0.3911222,0.5171778,0.5078546,"FAIL","FAIL","FAIL"], -["roadNet-TX.mtx","float32","int64","non","1393383","1393383","3843320",1.31374,0.86304,0.4837236,0.6569334,0.3682034,"PASS","PASS","PASS"], -["smt.mtx","float32","int64","non","25710","25710","3753184",0.6438182,0.2191008,0.2156509,0.3403147,0.3349563,"FAIL","FAIL","FAIL"], -["trdheim.mtx","float32","int64","non","22098","22098","1935324",0.419024,0.116984,0.1172544,0.2791821,0.2798273,"PASS","PASS","PASS"], -["water_tank.mtx","float32","int64","non","60740","60740","2035281",0.3459844,0.1318816,0.1395626,0.3811779,0.4033785,"FAIL","FAIL","FAIL"], -["wave.mtx","float32","int64","non","156317","156317","2118662",0.377821,0.2051136,0.1711169,0.5428857,0.4529047,"PASS","PASS","PASS"], -["wheel_601.mtx","float32","int64","non","902103","723605","2170814",0.9521498,0.7033376,0.3062132,0.7386838,0.3216019,"FAIL","FAIL","FAIL"], -["wiki-Talk.mtx","float32","int64","non","2394385","2394385","5021410",2.165228,3.404682,1.354114,1.572436,0.6253909,"FAIL","FAIL","FAIL"], -["2cubes_sphere.mtx","float32","int64","trans","101492","101492","1647264",0.1321083,0.1833008,0.7562371,1.387504,5.724372,"FAIL","FAIL","FAIL"], -["ASIC_680ks.mtx","float32","int64","trans","682712","682712","2329176",0.8486682,0.3521856,1.055903,0.4149862,1.244188,"PASS","PASS","PASS"], -["CurlCurl_1.mtx","float32","int64","trans","226451","226451","2472071",0.2821803,0.246,1.030186,0.8717831,3.650806,"FAIL","FAIL","FAIL"], -["GL7d14.mtx","float32","int64","trans","171375","47271","1831183",0.1142202,0.5134528,0.8029457,4.495289,7.029805,"FAIL","FAIL","FAIL"], -["H2O.mtx","float32","int64","trans","67024","67024","2216736",0.1413986,0.1742784,0.9290245,1.232533,6.570255,"FAIL","FAIL","FAIL"], -["NACA0015.mtx","float32","int64","trans","1039183","1039183","6229636",1.366055,2.438648,2.558491,1.785176,1.872905,"PASS","PASS","PASS"], -["Stanford.mtx","float32","int64","trans","281903","281903","2312497",0.3815673,0.640744,2.425068,1.679243,6.355545,"PASS","PASS","PASS"], -["TF19.mtx","float32","int64","trans","241029","317955","4370721",0.3924826,0.631488,1.69529,1.608958,4.319402,"PASS","PASS","PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int64","trans","29214","29214","4400122",1.454811,0.2950384,2.508392,0.2028019,1.724205,"FAIL","FAIL","FAIL"], -["amazon0601.mtx","float32","int64","trans","403394","403394","3387388",0.5181344,0.6704336,1.397747,1.293938,2.697653,"FAIL","FAIL","FAIL"], -["auto.mtx","float32","int64","trans","448695","448695","6629222",0.5550467,1.478326,2.498102,2.663427,4.500707,"PASS","PASS","PASS"], -["c8_mat11.mtx","float32","int64","trans","4562","5761","2462970",0.2003341,0.1535104,1.568044,0.7662718,7.827144,"FAIL","FAIL","FAIL"], -["cage12.mtx","float32","int64","trans","130228","130228","2032536",0.1679855,0.1933232,0.8858464,1.150833,5.27335,"PASS","PASS","PASS"], -["cfd2.mtx","float32","int64","trans","123440","123440","3087898",0.1611708,0.2071008,1.219971,1.284977,7.569428,"PASS","PASS","PASS"], -["ecology1.mtx","float32","int64","trans","1000000","1000000","4996000",1.411552,0.5667888,1.910728,0.401536,1.353637,"FAIL","FAIL","FAIL"], -["engine.mtx","float32","int64","trans","143571","143571","4706073",0.2511845,0.3215504,1.844656,1.280136,7.343827,"FAIL","FAIL","FAIL"], -["filter3D.mtx","float32","int64","trans","106437","106437","2707179",0.1653981,0.2874336,1.135914,1.737829,6.867754,"PASS","PASS","PASS"], -["mac_econ_fwd500.mtx","float32","int64","trans","206500","206500","1273389",0.2639584,0.1607888,0.6240296,0.6091444,2.364121,"FAIL","FAIL","FAIL"], -["mip1.mtx","float32","int64","trans","66463","66463","10352819",8.288815,0.537152,5.670634,0.06480444,0.6841309,"FAIL","FAIL","FAIL"], -["msc10848.mtx","float32","int64","trans","10848","10848","1229778",0.08330785,0.0822512,0.6712196,0.9873163,8.057099,"FAIL","FAIL","FAIL"], -["neos3.mtx","float32","int64","trans","512209","518832","2055024",0.7035714,0.4886352,1.226654,0.6945069,1.743467,"FAIL","FAIL","FAIL"], -["net150.mtx","float32","int64","trans","43520","43520","3121200",0.1606184,0.2587744,1.397734,1.611113,8.702199,"FAIL","FAIL","FAIL"], -["pkustk13.mtx","float32","int64","trans","94893","94893","6616827",0.3239264,0.3976304,2.376477,1.227533,7.336473,"FAIL","FAIL","FAIL"], -["roadNet-TX.mtx","float32","int64","trans","1393383","1393383","3843320",2.214236,0.8624144,1.635801,0.3894862,0.7387655,"PASS","PASS","PASS"], -["smt.mtx","float32","int64","trans","25710","25710","3753184",0.1732227,0.2202576,1.480771,1.271528,8.548365,"FAIL","FAIL","FAIL"], -["trdheim.mtx","float32","int64","trans","22098","22098","1935324",0.0938769,0.119048,0.9544619,1.268129,10.16716,"PASS","FAIL","PASS"], -["water_tank.mtx","float32","int64","trans","60740","60740","2035281",0.1066475,0.135184,0.859055,1.267578,8.055088,"FAIL","FAIL","FAIL"], -["wave.mtx","float32","int64","trans","156317","156317","2118662",0.1971082,0.224712,0.9291393,1.140044,4.713855,"PASS","PASS","PASS"], -["wheel_601.mtx","float32","int64","trans","902103","723605","2170814",1.086717,0.714728,1.120904,0.6576949,1.031459,"PASS","PASS","PASS"], -["wiki-Talk.mtx","float32","int64","trans","2394385","2394385","5021410",3.999809,1.740395,5.884222,0.4351195,1.471126,"FAIL","FAIL","FAIL"], -["2cubes_sphere.mtx","float32","int64","conj","101492","101492","1647264",0.1329184,0.1855568,0.7576646,1.39602,5.700224,"FAIL","FAIL","FAIL"], 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-["wheel_601.mtx","complex128","int64","conj","902103","723605","2170814",12.07803,4.903595,5.264331,0.4059931,0.4358602,"PASS","PASS","PASS"], -["wiki-Talk.mtx","complex128","int64","conj","2394385","2394385","5021410",40.75005,12.05648,17.1485,0.2958642,0.4208216,"PASS","PASS","PASS"] +["ASIC_680ks.mtx","float32","int32","non","682712","682712","2329176",0.8314406,0.3517584,0.2785606,0.423071,0.3350336,"PASS","PASS","PASS","","","","",""], +["GL7d14.mtx","float32","int32","non","171375","47271","1831183",0.0682346,0.5114896,0.1608486,7.496044,2.357287,"PASS","PASS","PASS","","","","",""], +["NACA0015.mtx","float32","int32","non","1039183","1039183","6229636",0.9049552,2.436869,0.6931945,2.692806,0.7659987,"PASS","PASS","PASS","","","","",""], +["amazon0601.mtx","float32","int32","non","403394","403394","3387388",0.4772806,0.6598944,0.31687,1.382613,0.663907,"PASS","PASS","PASS","","","","",""], +["auto.mtx","float32","int32","non","448695","448695","6629222",0.1465618,1.473147,0.5113931,10.05137,3.489266,"PASS","FAIL","PASS","","","","",""], +["cage12.mtx","float32","int32","non","130228","130228","2032536",0.0894327,0.1936048,0.1637294,2.16481,1.830755,"PASS","PASS","PASS","","","","",""], +["cfd2.mtx","float32","int32","non","123440","123440","3087898",0.06709205,0.2058624,0.2073352,3.068358,3.09031,"PASS","PASS","PASS","","","","",""], +["filter3D.mtx","float32","int32","non","106437","106437","2707179",0.0895467,0.284352,0.19051,3.17546,2.127493,"PASS","PASS","PASS","","","","",""], +["roadNet-TX.mtx","float32","int32","non","1393383","1393383","3843320",1.129617,0.8622224,0.4839931,0.7632874,0.4284578,"PASS","PASS","PASS","","","","",""], +["wave.mtx","float32","int32","non","156317","156317","2118662",0.06646665,0.2045488,0.1710435,3.077465,2.573373,"PASS","FAIL","PASS","","","","",""], +["ASIC_680ks.mtx","float64","int32","non","682712","682712","2329176",1.808336,1.422398,0.5879046,0.7865786,0.3251081,"PASS","PASS","PASS","","","","",""], +["GL7d14.mtx","float64","int32","non","171375","47271","1831183",0.1906191,0.97272,0.343925,5.102951,1.804253,"PASS","PASS","PASS","","","","",""], +["NACA0015.mtx","float64","int32","non","1039183","1039183","6229636",2.439567,3.794323,1.243314,1.555327,0.5096453,"PASS","PASS","PASS","","","","",""], +["amazon0601.mtx","float64","int32","non","403394","403394","3387388",1.084663,1.836472,0.6407748,1.693126,0.5907591,"PASS","PASS","PASS","","","","",""], +["auto.mtx","float64","int32","non","448695","448695","6629222",0.6768235,3.413821,1.101341,5.043886,1.627221,"PASS","PASS","PASS","","","","",""], +["cage12.mtx","float64","int32","non","130228","130228","2032536",0.2078476,1.014176,0.3528225,4.87942,1.697505,"PASS","PASS","PASS","","","","",""], +["cfd2.mtx","float64","int32","non","123440","123440","3087898",0.2690108,1.333317,0.4925565,4.956368,1.830991,"PASS","PASS","PASS","","","","",""], +["filter3D.mtx","float64","int32","non","106437","106437","2707179",0.3031174,1.209694,0.4355275,3.990844,1.436828,"PASS","PASS","PASS","","","","",""], +["roadNet-TX.mtx","float64","int32","non","1393383","1393383","3843320",3.195771,2.542517,1.02282,0.795588,0.3200541,"PASS","PASS","PASS","","","","",""], +["wave.mtx","float64","int32","non","156317","156317","2118662",0.2423574,1.116666,0.3757417,4.607516,1.550362,"PASS","PASS","PASS","","","","",""], +["ASIC_680ks.mtx","float16","int32","non","682712","682712","2329176",0.4346594,0.2232198,0.2785271,0.5135512,0.640794,"","","PASS","spmm_csr_f16f32_int_non_non_row","int32","non","row","float32"], +["ASIC_680ks.mtx","float16","int32","non","682712","682712","2329176",0.1505282,0.2342322,0.278325,1.556069,1.848989,"","","PASS","spmm_csr_f16_int_non_non_row","int32","non","row","float16"], +["ASIC_680ks.mtx","float32","int32","non","682712","682712","2329176",0.2877124,0.2146619,0.3375299,0.7460989,1.17315,"","","PASS","spmm_csr_f32_int_non_non_col","int32","non","col","float32"], +["ASIC_680ks.mtx","float32","int32","non","682712","682712","2329176",0.2872296,0.2605468,0.335217,0.9071026,1.16707,"","","PASS","spmm_csr_f32_int_non_trans_row","int32","trans","row","float32"], +["ASIC_680ks.mtx","complex64","int32","non","682712","682712","2329176",0.8070076,0.2828069,0.3600938,0.350439,0.4462087,"","","PASS","spmm_csr_c32_int_non_non_row","int32","non","row","complex64"], +["ASIC_680ks.mtx","int8","int32","non","682712","682712","2329176",0.4322566,0.2201669,0.2787866,0.5093431,0.6449564,"","","PASS","spmm_csr_i8i32_int_non_non_row","int32","non","row","int32"], +["ASIC_680ks.mtx","float32","int32","trans","682712","682712","2329176",0.2114877,0.2274967,0.2786238,1.075697,1.317447,"","","PASS","spmm_csr_f32_int_trans_non_row","int32","non","row","float32"], +["GL7d14.mtx","float16","int32","non","171375","47271","1831183",0.1091252,0.1380884,0.16096,1.265413,1.475003,"","","PASS","spmm_csr_f16f32_int_non_non_row","int32","non","row","float32"], +["GL7d14.mtx","float16","int32","non","171375","47271","1831183",0.06589375,0.1425256,0.1597518,2.162961,2.424385,"","","PASS","spmm_csr_f16_int_non_non_row","int32","non","row","float16"], +["GL7d14.mtx","float32","int32","non","171375","47271","1831183",0.0746658,0.4895467,0.4986124,6.556505,6.677922,"","","PASS","spmm_csr_f32_int_non_non_col","int32","non","col","float32"], +["GL7d14.mtx","float32","int32","non","171375","47271","1831183",0.07656135,0.4816357,0.4904108,6.290846,6.405461,"","","PASS","spmm_csr_f32_int_non_trans_row","int32","trans","row","float32"], +["GL7d14.mtx","complex64","int32","non","171375","47271","1831183",0.5962483,0.1598696,0.1929202,0.2681259,0.3235568,"","","PASS","spmm_csr_c32_int_non_non_row","int32","non","row","complex64"], +["GL7d14.mtx","int8","int32","non","171375","47271","1831183",0.1091615,0.1449545,0.1609473,1.327891,1.474396,"","","PASS","spmm_csr_i8i32_int_non_non_row","int32","non","row","int32"], +["GL7d14.mtx","float32","int32","trans","171375","47271","1831183",0.1042068,0.1081748,0.1408077,1.038079,1.351234,"","","PASS","spmm_csr_f32_int_trans_non_row","int32","non","row","float32"], +["NACA0015.mtx","float16","int32","non","1039183","1039183","6229636",0.6308923,0.5790756,0.6933601,0.9178677,1.099015,"","","PASS","spmm_csr_f16f32_int_non_non_row","int32","non","row","float32"], +["NACA0015.mtx","float16","int32","non","1039183","1039183","6229636",0.2563394,0.5976324,0.6935344,2.33141,2.705531,"","","PASS","spmm_csr_f16_int_non_non_row","int32","non","row","float16"], +["NACA0015.mtx","float32","int32","non","1039183","1039183","6229636",0.4450384,2.236566,3.676975,5.025557,8.262152,"","","PASS","spmm_csr_f32_int_non_non_col","int32","non","col","float32"], +["NACA0015.mtx","float32","int32","non","1039183","1039183","6229636",0.4454012,3.753629,3.670961,8.427524,8.241919,"","","PASS","spmm_csr_f32_int_non_trans_row","int32","trans","row","float32"], +["NACA0015.mtx","complex64","int32","non","1039183","1039183","6229636",1.891864,0.8104705,0.9391424,0.4283978,0.4964111,"","","PASS","spmm_csr_c32_int_non_non_row","int32","non","row","complex64"], +["NACA0015.mtx","int8","int32","non","1039183","1039183","6229636",0.6309743,0.531481,0.6934343,0.8423179,1.09899,"","","PASS","spmm_csr_i8i32_int_non_non_row","int32","non","row","int32"], +["NACA0015.mtx","float32","int32","trans","1039183","1039183","6229636",0.3150281,0.6974114,0.6932107,2.213807,2.200472,"","","PASS","spmm_csr_f32_int_trans_non_row","int32","non","row","float32"], +["amazon0601.mtx","float16","int32","non","403394","403394","3387388",0.2492787,0.26589,0.3170582,1.066637,1.271903,"","","PASS","spmm_csr_f16f32_int_non_non_row","int32","non","row","float32"], +["amazon0601.mtx","float16","int32","non","403394","403394","3387388",0.1010447,0.2781986,0.3170513,2.753224,3.137734,"","","PASS","spmm_csr_f16_int_non_non_row","int32","non","row","float16"], +["amazon0601.mtx","float32","int32","non","403394","403394","3387388",0.162552,0.5777382,0.7852744,3.554175,4.830912,"","","PASS","spmm_csr_f32_int_non_non_col","int32","non","col","float32"], +["amazon0601.mtx","float32","int32","non","403394","403394","3387388",0.1616976,0.7982625,0.7836094,4.93676,4.84614,"","","PASS","spmm_csr_f32_int_non_trans_row","int32","trans","row","float32"], +["amazon0601.mtx","complex64","int32","non","403394","403394","3387388",0.9997769,0.3486459,0.4105793,0.3487237,0.4106709,"","","PASS","spmm_csr_c32_int_non_non_row","int32","non","row","complex64"], +["amazon0601.mtx","int8","int32","non","403394","403394","3387388",0.2491251,0.2632234,0.3177148,1.056591,1.275322,"","","PASS","spmm_csr_i8i32_int_non_non_row","int32","non","row","int32"], +["amazon0601.mtx","float32","int32","trans","403394","403394","3387388",0.517196,0.2481292,0.3266763,0.4797586,0.6316294,"","","PASS","spmm_csr_f32_int_trans_non_row","int32","non","row","float32"], +["auto.mtx","float16","int32","non","448695","448695","6629222",0.2934678,0.4324474,0.5107695,1.473577,1.740462,"","","PASS","spmm_csr_f16f32_int_non_non_row","int32","non","row","float32"], +["auto.mtx","float16","int32","non","448695","448695","6629222",0.133468,0.4602473,0.5109991,3.448374,3.828628,"","","PASS","spmm_csr_f16_int_non_non_row","int32","non","row","float16"], +["auto.mtx","float32","int32","non","448695","448695","6629222",0.2040051,1.385159,1.533348,6.789827,7.516226,"","","PASS","spmm_csr_f32_int_non_non_col","int32","non","col","float32"], +["auto.mtx","float32","int32","non","448695","448695","6629222",0.2031076,1.512458,1.519545,7.446585,7.481476,"","","PASS","spmm_csr_f32_int_non_trans_row","int32","trans","row","float32"], +["auto.mtx","complex64","int32","non","448695","448695","6629222",1.943896,0.5959608,0.6906396,0.3065806,0.3552864,"","","PASS","spmm_csr_c32_int_non_non_row","int32","non","row","complex64"], +["auto.mtx","int8","int32","non","448695","448695","6629222",0.2861341,0.4422665,0.5105253,1.545662,1.784217,"","","PASS","spmm_csr_i8i32_int_non_non_row","int32","non","row","int32"], +["auto.mtx","float32","int32","trans","448695","448695","6629222",0.1464976,0.3799067,0.5112454,2.593261,3.489785,"","","PASS","spmm_csr_f32_int_trans_non_row","int32","non","row","float32"], +["cage12.mtx","float16","int32","non","130228","130228","2032536",0.09213265,0.140378,0.1633288,1.523651,1.772757,"","","PASS","spmm_csr_f16f32_int_non_non_row","int32","non","row","float32"], +["cage12.mtx","float16","int32","non","130228","130228","2032536",0.0651852,0.1440056,0.163427,2.209176,2.507118,"","","PASS","spmm_csr_f16_int_non_non_row","int32","non","row","float16"], +["cage12.mtx","float32","int32","non","130228","130228","2032536",0.0752312,0.1631198,0.3085847,2.168247,4.101818,"","","PASS","spmm_csr_f32_int_non_non_col","int32","non","col","float32"], +["cage12.mtx","float32","int32","non","130228","130228","2032536",0.0766881,0.3144705,0.3060771,4.100642,3.991194,"","","PASS","spmm_csr_f32_int_non_trans_row","int32","trans","row","float32"], +["cage12.mtx","complex64","int32","non","130228","130228","2032536",0.5727358,0.1649396,0.1947055,0.2879856,0.339957,"","","PASS","spmm_csr_c32_int_non_non_row","int32","non","row","complex64"], +["cage12.mtx","int8","int32","non","130228","130228","2032536",0.0888486,0.144143,0.1631805,1.622344,1.836614,"","","PASS","spmm_csr_i8i32_int_non_non_row","int32","non","row","int32"], +["cage12.mtx","float32","int32","trans","130228","130228","2032536",0.06586815,0.127418,0.1634869,1.93444,2.482033,"","","PASS","spmm_csr_f32_int_trans_non_row","int32","non","row","float32"], +["cfd2.mtx","float16","int32","non","123440","123440","3087898",0.1161477,0.1808601,0.2070172,1.557157,1.782363,"","","PASS","spmm_csr_f16f32_int_non_non_row","int32","non","row","float32"], +["cfd2.mtx","float16","int32","non","123440","123440","3087898",0.067587,0.1891324,0.2071162,2.798355,3.064438,"","","PASS","spmm_csr_f16_int_non_non_row","int32","non","row","float16"], +["cfd2.mtx","float32","int32","non","123440","123440","3087898",0.07541335,0.1778959,0.3499815,2.358944,4.640843,"","","PASS","spmm_csr_f32_int_non_non_col","int32","non","col","float32"], +["cfd2.mtx","float32","int32","non","123440","123440","3087898",0.07493895,0.3220689,0.3497689,4.29775,4.667384,"","","PASS","spmm_csr_f32_int_non_trans_row","int32","trans","row","float32"], +["cfd2.mtx","complex64","int32","non","123440","123440","3087898",0.6304913,0.203105,0.2437163,0.3221377,0.3865497,"","","PASS","spmm_csr_c32_int_non_non_row","int32","non","row","complex64"], +["cfd2.mtx","int8","int32","non","123440","123440","3087898",0.1130938,0.1906531,0.2070742,1.685797,1.830996,"","","PASS","spmm_csr_i8i32_int_non_non_row","int32","non","row","int32"], +["cfd2.mtx","float32","int32","trans","123440","123440","3087898",0.0664222,0.1717134,0.2073295,2.58518,3.121388,"","","PASS","spmm_csr_f32_int_trans_non_row","int32","non","row","float32"], +["filter3D.mtx","float16","int32","non","106437","106437","2707179",0.1051523,0.1648742,0.1901051,1.567957,1.807903,"","","PASS","spmm_csr_f16f32_int_non_non_row","int32","non","row","float32"], +["filter3D.mtx","float16","int32","non","106437","106437","2707179",0.0801117,0.1686684,0.1903695,2.105415,2.376301,"","","PASS","spmm_csr_f16_int_non_non_row","int32","non","row","float16"], +["filter3D.mtx","float32","int32","non","106437","106437","2707179",0.0901443,0.2562809,0.4018116,2.843008,4.457427,"","","PASS","spmm_csr_f32_int_non_non_col","int32","non","col","float32"], +["filter3D.mtx","float32","int32","non","106437","106437","2707179",0.0909357,0.4063479,0.4033261,4.468519,4.435289,"","","PASS","spmm_csr_f32_int_non_trans_row","int32","trans","row","float32"], +["filter3D.mtx","complex64","int32","non","106437","106437","2707179",0.7054857,0.1929931,0.2273505,0.2735607,0.322261,"","","PASS","spmm_csr_c32_int_non_non_row","int32","non","row","complex64"], +["filter3D.mtx","int8","int32","non","106437","106437","2707179",0.1005377,0.1705059,0.1901714,1.69594,1.891544,"","","PASS","spmm_csr_i8i32_int_non_non_row","int32","non","row","int32"], +["filter3D.mtx","float32","int32","trans","106437","106437","2707179",0.0791819,0.1523869,0.1904561,1.924518,2.405298,"","","PASS","spmm_csr_f32_int_trans_non_row","int32","non","row","float32"], 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{"source_dir": "pytest_results_sparse_202609221038", "flagsparse_commit": "47a441e6c810a4e51c940412a25c564046d350a6+dirty", "run_timestamp": "2026-09-22 18:01:14", "device": "NVIDIA H800"}, +"fields": ["matrix", "value_dtype", "index_dtype", "op", "block_dim", "n_rows", "n_cols", "nnzb", "bsr_ms", "pytorch_ms", "bsr_speedup_vs_pytorch", "pytorch_padded_ms", "cusparse_ms", "status"], "rows": [ ["2cubes_sphere.mtx","float32","int32","non","4","101492","101492","1139017",0.2284384,1.125707,null,1.125707,null,"FAIL"], ["2cubes_sphere.mtx","float32","int32","non","4","101492","101492","1139017",0.5741968,1.135779,1.978031,1.135779,null,"PASS"], @@ -3835,2236 +2434,520 @@ ["wheel_601.mtx","complex128","int64","conj","4","902103","723605","1525348",49.66108,null,null,null,null,"PASS"], ["wiki-Talk.mtx","complex128","int64","conj","4","2394385","2394385","4466510",10037.73,null,null,null,null,"PASS"] ]}, -"spmv_coo": {"fields": ["matrix", "value_dtype", "index_dtype", "op", "n_rows", "n_cols", 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"value_dtype", "index_dtype", "op", "n_rows", "n_cols", "nnz", "csc_ms", "pytorch_ms", "csc_speedup_vs_pytorch", "cusparse_ms", "status", "variant", "indptr_dtype", "op_b", "layout", "out_dtype", "csc_speedup_vs_cusparse"], "rows": [ -["2cubes_sphere.mtx","float32","int32","non","101492","101492","1647264",0.7929456,0.0369952,0.05015005,0.06012,1.355583,1.625076,"FAIL","FAIL","FAIL"], -["ASIC_680ks.mtx","float32","int32","non","682712","682712","2329176",1.27303,0.0298224,0.03995875,0.077272,1.33989,2.591072,"PASS","PASS","PASS"], -["CurlCurl_1.mtx","float32","int32","non","226451","226451","2472071",1.057664,0.034664,0.0480608,0.0641648,1.386476,1.85105,"FAIL","FAIL","FAIL"], -["GL7d14.mtx","float32","int32","non","171375","47271","1831183",0.869816,0.0343264,0.0475173,0.0611072,1.384279,1.780181,"PASS","PASS","PASS"], -["H2O.mtx","float32","int32","non","67024","67024","2216736",0.8648608,0.0377648,0.04289215,0.0603568,1.135771,1.598229,"FAIL","FAIL","FAIL"], 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-["roadNet-TX.mtx","float32","int32","non","1393383","1393383","3843320",2.053896,0.0349232,0.0504328,0.107968,1.444106,3.091584,"PASS","PASS","PASS"], -["smt.mtx","float32","int32","non","25710","25710","3753184",1.185277,0.1220976,0.04752115,0.0751488,0.3892062,0.6154814,"FAIL","FAIL","FAIL"], -["trdheim.mtx","float32","int32","non","22098","22098","1935324",0.7737792,0.0536,0.0401856,0.0513776,0.7497313,0.9585373,"PASS","PASS","PASS"], -["water_tank.mtx","float32","int32","non","60740","60740","2035281",0.8151312,0.0359536,0.04229995,0.05216,1.176515,1.450759,"PASS","PASS","PASS"], -["wave.mtx","float32","int32","non","156317","156317","2118662",0.9064192,0.0294912,0.04137865,0.056088,1.403085,1.901855,"PASS","PASS","PASS"], -["wheel_601.mtx","float32","int32","non","902103","723605","2170814",1.377414,0.0302304,0.0410275,0.08016,1.35716,2.651635,"FAIL","FAIL","FAIL"], -["wiki-Talk.mtx","float32","int32","non","2394385","2394385","5021410",2.06556,0.4147072,0.3066327,0.5162608,0.7393956,1.24488,"FAIL","FAIL","FAIL"], -["2cubes_sphere.mtx","float32","int32","trans","101492","101492","1647264",0.8021904,0.0347424,0.07135055,0.9047568,2.053702,26.04186,"FAIL","FAIL","FAIL"], -["ASIC_680ks.mtx","float32","int32","trans","682712","682712","2329176",1.336389,0.0434032,0.07207775,1.314069,1.660655,30.27585,"PASS","PASS","PASS"], -["CurlCurl_1.mtx","float32","int32","trans","226451","226451","2472071",1.05683,0.0349024,0.0713657,1.348499,2.044722,38.63629,"FAIL","FAIL","FAIL"], -["GL7d14.mtx","float32","int32","trans","171375","47271","1831183",0.81948,0.0374896,0.07288125,0.9767056,1.944039,26.05271,"PASS","PASS","PASS"], -["H2O.mtx","float32","int32","trans","67024","67024","2216736",0.9107664,0.0365728,0.07208345,1.210051,1.970958,33.0861,"FAIL","FAIL","FAIL"], -["NACA0015.mtx","float32","int32","trans","1039183","1039183","6229636",2.367501,0.0795664,0.0856282,3.123664,1.076185,39.25858,"PASS","PASS","PASS"], -["Stanford.mtx","float32","int32","trans","281903","281903","2312497",1.040366,0.0356992,0.05998065,1.279016,1.680168,35.82758,"PASS","PASS","PASS"], -["TF19.mtx","float32","int32","trans","241029","317955","4370721",1.48907,0.0610928,0.068281,2.255758,1.11766,36.92348,"PASS","PASS","PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int32","trans","29214","29214","4400122",1.406971,0.1317472,0.088973,2.309698,0.6753312,17.53128,"PASS","PASS","PASS"], -["amazon0601.mtx","float32","int32","trans","403394","403394","3387388",1.360344,0.0543744,0.07340435,1.758659,1.34998,32.34351,"PASS","PASS","PASS"], -["auto.mtx","float32","int32","trans","448695","448695","6629222",2.115488,0.0904992,0.09788345,3.270957,1.081595,36.14349,"PASS","PASS","PASS"], -["c8_mat11.mtx","float32","int32","trans","4562","5761","2462970",0.9405264,0.0653872,0.0719289,1.356366,1.100046,20.74361,"FAIL","FAIL","FAIL"], -["cage12.mtx","float32","int32","trans","130228","130228","2032536",0.894536,0.0349856,0.07226175,1.08936,2.065471,31.13738,"PASS","PASS","PASS"], -["cfd2.mtx","float32","int32","trans","123440","123440","3087898",1.103477,0.0415648,0.0721497,1.624594,1.735837,39.0858,"PASS","PASS","PASS"], -["ecology1.mtx","float32","int32","trans","1000000","1000000","4996000",2.054174,0.043344,0.0602824,2.595402,1.39079,59.87914,"FAIL","FAIL","FAIL"], -["engine.mtx","float32","int32","trans","143571","143571","4706073",1.444155,0.0533216,0.0602286,2.428488,1.129535,45.54417,"PASS","FAIL","PASS"], -["filter3D.mtx","float32","int32","trans","106437","106437","2707179",0.9972448,0.0426704,0.0597716,1.445352,1.400774,33.87247,"PASS","PASS","PASS"], -["mac_econ_fwd500.mtx","float32","int32","trans","206500","206500","1273389",0.7695392,0.0345872,0.0713498,0.6987344,2.062896,20.20211,"FAIL","PASS","FAIL"], -["mip1.mtx","float32","int32","trans","66463","66463","10352819",2.968202,0.25288,0.1605075,5.126202,0.634718,20.27128,"FAIL","FAIL","FAIL"], -["msc10848.mtx","float32","int32","trans","10848","10848","1229778",0.6409296,0.0345248,0.06940995,0.7059056,2.010437,20.44633,"PASS","FAIL","PASS"], -["neos3.mtx","float32","int32","trans","512209","518832","2055024",1.123682,0.041984,0.0600724,1.129077,1.43084,26.89302,"PASS","PASS","PASS"], -["net150.mtx","float32","int32","trans","43520","43520","3121200",1.040014,0.0555024,0.06177545,1.64928,1.113023,29.71547,"PASS","FAIL","PASS"], -["pkustk13.mtx","float32","int32","trans","94893","94893","6616827",1.823888,0.0944432,0.06128305,3.279568,0.6488879,34.7253,"PASS","PASS","PASS"], -["roadNet-TX.mtx","float32","int32","trans","1393383","1393383","3843320",2.032552,0.0437456,0.0600984,1.98789,1.373816,45.44205,"PASS","PASS","PASS"], -["smt.mtx","float32","int32","trans","25710","25710","3753184",1.168149,0.0554912,0.063589,1.923936,1.14593,34.67101,"PASS","FAIL","PASS"], -["trdheim.mtx","float32","int32","trans","22098","22098","1935324",0.7726032,0.0294464,0.05857495,1.052594,1.989206,35.74609,"PASS","PASS","PASS"], -["water_tank.mtx","float32","int32","trans","60740","60740","2035281",0.8194896,0.0295088,0.0586497,1.079526,1.987533,36.5832,"FAIL","FAIL","FAIL"], -["wave.mtx","float32","int32","trans","156317","156317","2118662",0.9211968,0.0296736,0.0605145,1.157488,2.039338,39.00733,"PASS","PASS","PASS"], -["wheel_601.mtx","float32","int32","trans","902103","723605","2170814",1.276643,0.0336432,0.06064985,1.245406,1.802737,37.01807,"PASS","PASS","PASS"], 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-["wiki-Talk.mtx","float32","int64","non","2394385","2394385","5021410",2.082813,0.4212928,0.3036761,0.5161168,0.7208196,1.225079,"PASS","PASS","PASS"], -["2cubes_sphere.mtx","float32","int64","trans","101492","101492","1647264",0.7606112,0.0342256,0.07101065,0.8996256,2.074782,26.28517,"PASS","PASS","PASS"], -["ASIC_680ks.mtx","float32","int64","trans","682712","682712","2329176",1.241938,0.034648,0.0708126,1.310397,2.043772,37.82027,"PASS","PASS","PASS"], -["CurlCurl_1.mtx","float32","int64","trans","226451","226451","2472071",0.99612,0.034536,0.0697484,1.347118,2.019585,39.00621,"FAIL","FAIL","FAIL"], -["GL7d14.mtx","float32","int64","trans","171375","47271","1831183",0.7668288,0.039816,0.07063835,0.9730336,1.77412,24.43826,"PASS","PASS","PASS"], -["H2O.mtx","float32","int64","trans","67024","67024","2216736",0.8477968,0.036968,0.07160405,1.210626,1.93692,32.74793,"FAIL","FAIL","FAIL"], -["NACA0015.mtx","float32","int64","trans","1039183","1039183","6229636",2.365742,0.0884176,0.08589925,3.13031,0.9715175,35.4037,"PASS","PASS","PASS"], -["Stanford.mtx","float32","int64","trans","281903","281903","2312497",1.010069,0.043448,0.0702597,1.284123,1.617099,29.5554,"PASS","PASS","PASS"], -["TF19.mtx","float32","int64","trans","241029","317955","4370721",1.425066,0.0676096,0.07164995,2.259309,1.05976,33.41698,"PASS","PASS","PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int64","trans","29214","29214","4400122",1.334035,0.1365008,0.08876485,2.311699,0.6502881,16.93543,"PASS","PASS","PASS"], -["amazon0601.mtx","float32","int64","trans","403394","403394","3387388",1.294518,0.0599792,0.06150755,1.761984,1.025481,29.37658,"PASS","PASS","PASS"], -["auto.mtx","float32","int64","trans","448695","448695","6629222",1.973766,0.0995744,0.09625365,3.263349,0.9666506,32.77297,"PASS","PASS","PASS"], -["c8_mat11.mtx","float32","int64","trans","4562","5761","2462970",0.8536016,0.0712944,0.0605696,1.35408,0.8495703,18.9928,"PASS","FAIL","PASS"], -["cage12.mtx","float32","int64","trans","130228","130228","2032536",0.851688,0.0345056,0.0704252,1.090416,2.040979,31.60113,"PASS","PASS","PASS"], -["cfd2.mtx","float32","int64","trans","123440","123440","3087898",1.044254,0.0458,0.0705372,1.628787,1.540114,35.56304,"PASS","PASS","PASS"], -["ecology1.mtx","float32","int64","trans","1000000","1000000","4996000",1.960392,0.0536416,0.07088945,2.597139,1.321539,48.41651,"FAIL","FAIL","FAIL"], -["engine.mtx","float32","int64","trans","143571","143571","4706073",1.358027,0.0584432,0.06090905,2.431205,1.042192,41.59945,"FAIL","FAIL","FAIL"], -["filter3D.mtx","float32","int64","trans","106437","106437","2707179",0.966832,0.0478752,0.0716027,1.450728,1.495612,30.30229,"PASS","PASS","PASS"], -["mac_econ_fwd500.mtx","float32","int64","trans","206500","206500","1273389",0.7260432,0.034072,0.0696518,0.7004272,2.044253,20.55727,"FAIL","FAIL","FAIL"], -["mip1.mtx","float32","int64","trans","66463","66463","10352819",2.843402,0.2659408,0.1561803,5.122018,0.5872747,19.25999,"FAIL","FAIL","FAIL"], -["msc10848.mtx","float32","int64","trans","10848","10848","1229778",0.6132048,0.0343856,0.0699983,0.7048048,2.035686,20.49709,"PASS","PASS","PASS"], -["neos3.mtx","float32","int64","trans","512209","518832","2055024",1.10085,0.0518688,0.07097595,1.12823,1.368375,21.75162,"PASS","PASS","PASS"], -["net150.mtx","float32","int64","trans","43520","43520","3121200",1.002202,0.0620064,0.07150105,1.65345,1.153124,26.66579,"PASS","PASS","PASS"], -["pkustk13.mtx","float32","int64","trans","94893","94893","6616827",1.734238,0.0993072,0.07190055,3.278085,0.7240215,33.00954,"PASS","FAIL","PASS"], -["roadNet-TX.mtx","float32","int64","trans","1393383","1393383","3843320",1.965155,0.0522784,0.0713677,1.992056,1.365147,38.10476,"PASS","PASS","PASS"], -["smt.mtx","float32","int64","trans","25710","25710","3753184",1.114642,0.0606416,0.0703041,1.930885,1.159338,31.84093,"FAIL","FAIL","FAIL"], -["trdheim.mtx","float32","int64","trans","22098","22098","1935324",0.7624096,0.0342944,0.0689669,1.052019,2.011025,30.67612,"PASS","PASS","PASS"], -["water_tank.mtx","float32","int64","trans","60740","60740","2035281",0.8071296,0.0348048,0.06964515,1.087216,2.001021,31.23753,"PASS","PASS","PASS"], -["wave.mtx","float32","int64","trans","156317","156317","2118662",0.8842368,0.0351104,0.0696583,1.158968,1.983979,33.00925,"PASS","PASS","PASS"], -["wheel_601.mtx","float32","int64","trans","902103","723605","2170814",1.242336,0.04184,0.0712066,1.24499,1.701879,29.75599,"PASS","PASS","PASS"], -["wiki-Talk.mtx","float32","int64","trans","2394385","2394385","5021410",2.834837,0.0955248,0.4204822,2.642666,4.401811,27.66471,"PASS","PASS","PASS"], -["2cubes_sphere.mtx","float32","int64","conj","101492","101492","1647264",0.7636016,0.0339296,0.2282048,0.8999104,6.725832,26.52287,"FAIL","FAIL","FAIL"], -["ASIC_680ks.mtx","float32","int64","conj","682712","682712","2329176",1.262816,0.034816,0.2299471,1.313229,6.604639,37.71912,"PASS","PASS","PASS"], -["CurlCurl_1.mtx","float32","int64","conj","226451","226451","2472071",0.998816,0.0343792,0.2275384,1.345186,6.618492,39.12789,"FAIL","FAIL","FAIL"], -["GL7d14.mtx","float32","int64","conj","171375","47271","1831183",0.826976,0.04916,0.2853997,0.983616,5.805527,20.00846,"PASS","PASS","PASS"], -["H2O.mtx","float32","int64","conj","67024","67024","2216736",0.9003408,0.0483824,0.2908885,1.221899,6.01228,25.25503,"FAIL","FAIL","FAIL"], -["NACA0015.mtx","float32","int64","conj","1039183","1039183","6229636",2.369973,0.08872,0.2508267,3.131757,2.827172,35.29933,"PASS","PASS","PASS"], -["Stanford.mtx","float32","int64","conj","281903","281903","2312497",0.9896336,0.0433248,0.6732422,1.282706,15.53942,29.60673,"PASS","PASS","PASS"], -["TF19.mtx","float32","int64","conj","241029","317955","4370721",1.395693,0.0675216,0.2020764,2.254264,2.992767,33.38582,"PASS","PASS","PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int64","conj","29214","29214","4400122",1.305296,0.1362192,0.7029388,2.307362,5.16035,16.93859,"PASS","PASS","PASS"], -["amazon0601.mtx","float32","int64","conj","403394","403394","3387388",1.289819,0.0604112,0.2304026,1.761957,3.813906,29.16606,"PASS","PASS","PASS"], -["auto.mtx","float32","int64","conj","448695","448695","6629222",1.968221,0.0996944,0.2533163,3.260728,2.540928,32.70723,"PASS","PASS","PASS"], -["c8_mat11.mtx","float32","int64","conj","4562","5761","2462970",0.8522944,0.0711888,0.1968378,1.352824,2.765011,19.00333,"PASS","PASS","PASS"], -["cage12.mtx","float32","int64","conj","130228","130228","2032536",0.8214768,0.0304288,0.1927308,1.081954,6.333829,35.55689,"PASS","PASS","PASS"], -["cfd2.mtx","float32","int64","conj","123440","123440","3087898",1.016182,0.0455616,0.1921726,1.62364,4.217863,35.63615,"PASS","PASS","PASS"], -["ecology1.mtx","float32","int64","conj","1000000","1000000","4996000",1.934157,0.0533504,0.1927413,2.592475,3.612743,48.59336,"FAIL","FAIL","FAIL"], -["engine.mtx","float32","int64","conj","143571","143571","4706073",1.35565,0.05848,0.1925002,2.425768,3.291727,41.4803,"PASS","PASS","PASS"], -["filter3D.mtx","float32","int64","conj","106437","106437","2707179",0.9435888,0.0477616,0.1923676,1.449192,4.027662,30.3422,"PASS","PASS","PASS"], 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+["ASIC_680ks.mtx","complex128","int64","conj","682712","682712","2329176",0.114928,2.153272,18.73584,0.1109456,"PASS","","","","","",null], +["GL7d14.mtx","complex128","int64","conj","171375","47271","1831183",0.1067056,1.643792,15.40493,0.0810928,"PASS","","","","","",null], +["NACA0015.mtx","complex128","int64","conj","1039183","1039183","6229636",0.2691056,5.359042,19.91427,0.2538384,"PASS","","","","","",null], +["amazon0601.mtx","complex128","int64","conj","403394","403394","3387388",0.1544448,2.994955,19.39175,0.1396816,"PASS","","","","","",null], +["auto.mtx","complex128","int64","conj","448695","448695","6629222",0.2797216,5.650048,20.19883,0.2560592,"PASS","","","","","",null], +["cage12.mtx","complex128","int64","conj","130228","130228","2032536",0.1130176,1.835166,16.23788,0.0891408,"PASS","","","","","",null], +["cfd2.mtx","complex128","int64","conj","123440","123440","3087898",0.140016,2.74877,19.63183,0.1251152,"PASS","","","","","",null], 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+["GL7d14.mtx","float32","int32","non","171375","47271","1831183",0.03942445,0.02247995,0.5702033,0.03513245,"PASS","spmv_csc_f32_int_non","int32","non","row","float32",0.8911335], +["GL7d14.mtx","complex64","int32","non","171375","47271","1831183",0.0581941,0.0312775,0.5374686,0.05594275,"PASS","spmv_csc_c32_int_non","int32","non","row","complex64",0.9613131], +["GL7d14.mtx","float16","int32","non","171375","47271","1831183",0.05325035,0.0227758,0.4277117,0.1125639,"PASS","spmv_csc_f16_int_non","int32","non","row","float16",2.113862], +["NACA0015.mtx","float32","int32","non","1039183","1039183","6229636",0.0800174,0.05223725,0.6528236,0.0794885,"PASS","spmv_csc_f32_int_non","int32","non","row","float32",0.9933902], +["NACA0015.mtx","complex64","int32","non","1039183","1039183","6229636",0.0630997,0.0712137,1.12859,0.145389,"PASS","spmv_csc_c32_int_non","int32","non","row","complex64",2.304115], +["NACA0015.mtx","float16","int32","non","1039183","1039183","6229636",0.0865668,0.05228305,0.6039619,0.2216489,"PASS","spmv_csc_f16_int_non","int32","non","row","float16",2.560438], +["amazon0601.mtx","float32","int32","non","403394","403394","3387388",0.04662505,0.03205575,0.6875221,0.0471968,"PASS","spmv_csc_f32_int_non","int32","non","row","float32",1.012263], +["amazon0601.mtx","complex64","int32","non","403394","403394","3387388",0.04780385,0.03973425,0.8311935,0.0825132,"PASS","spmv_csc_c32_int_non","int32","non","row","complex64",1.726079], +["amazon0601.mtx","float16","int32","non","403394","403394","3387388",0.06163265,0.03257385,0.5285161,0.1544733,"PASS","spmv_csc_f16_int_non","int32","non","row","float16",2.506355], +["auto.mtx","float32","int32","non","448695","448695","6629222",0.09213455,0.0612509,0.6647984,0.0918403,"PASS","spmv_csc_f32_int_non","int32","non","row","float32",0.9968063], +["auto.mtx","complex64","int32","non","448695","448695","6629222",0.0775831,0.07118065,0.9174763,0.1662397,"PASS","spmv_csc_c32_int_non","int32","non","row","complex64",2.142731], +["auto.mtx","float16","int32","non","448695","448695","6629222",0.09246815,0.0604162,0.6533731,0.3238773,"PASS","spmv_csc_f16_int_non","int32","non","row","float16",3.502583], +["cage12.mtx","float32","int32","non","130228","130228","2032536",0.0345364,0.020217,0.5853824,0.03601685,"PASS","spmv_csc_f32_int_non","int32","non","row","float32",1.042866], +["cage12.mtx","complex64","int32","non","130228","130228","2032536",0.03668095,0.02463825,0.6716906,0.05840095,"PASS","spmv_csc_c32_int_non","int32","non","row","complex64",1.592133], +["cage12.mtx","float16","int32","non","130228","130228","2032536",0.05011555,0.0199799,0.3986767,0.1222861,"PASS","spmv_csc_f16_int_non","int32","non","row","float16",2.440082], +["cfd2.mtx","float32","int32","non","123440","123440","3087898",0.0467104,0.0235315,0.5037743,0.03698755,"PASS","spmv_csc_f32_int_non","int32","non","row","float32",0.7918483], +["cfd2.mtx","complex64","int32","non","123440","123440","3087898",0.05353375,0.0302964,0.5659309,0.0649829,"PASS","spmv_csc_c32_int_non","int32","non","row","complex64",1.213868], +["cfd2.mtx","float16","int32","non","123440","123440","3087898",0.05088265,0.02339095,0.4597038,0.2076412,"PASS","spmv_csc_f16_int_non","int32","non","row","float16",4.080787], +["filter3D.mtx","float32","int32","non","106437","106437","2707179",0.04589045,0.02420925,0.5275444,0.0441701,"PASS","spmv_csc_f32_int_non","int32","non","row","float32",0.9625118], +["filter3D.mtx","complex64","int32","non","106437","106437","2707179",0.05056075,0.03068875,0.6069679,0.072929,"PASS","spmv_csc_c32_int_non","int32","non","row","complex64",1.442404], +["filter3D.mtx","float16","int32","non","106437","106437","2707179",0.05116305,0.02407525,0.4705593,0.2432282,"PASS","spmv_csc_f16_int_non","int32","non","row","float16",4.753981], +["roadNet-TX.mtx","float32","int32","non","1393383","1393383","3843320",0.0438313,0.03935775,0.8979371,0.0498896,"PASS","spmv_csc_f32_int_non","int32","non","row","float32",1.138219], +["roadNet-TX.mtx","complex64","int32","non","1393383","1393383","3843320",0.0417554,0.0590324,1.413767,0.092157,"PASS","spmv_csc_c32_int_non","int32","non","row","complex64",2.207068], +["roadNet-TX.mtx","float16","int32","non","1393383","1393383","3843320",0.05120375,0.03926875,0.7669116,0.1127354,"PASS","spmv_csc_f16_int_non","int32","non","row","float16",2.201703], +["wave.mtx","float32","int32","non","156317","156317","2118662",0.0344818,0.0207466,0.6016682,0.03334375,"PASS","spmv_csc_f32_int_non","int32","non","row","float32",0.9669957], +["wave.mtx","complex64","int32","non","156317","156317","2118662",0.0372808,0.0248726,0.6671692,0.05609505,"PASS","spmv_csc_c32_int_non","int32","non","row","complex64",1.504663], +["wave.mtx","float16","int32","non","156317","156317","2118662",0.05120465,0.02043945,0.3991717,0.1081126,"PASS","spmv_csc_f16_int_non","int32","non","row","float16",2.111383] ]}, -"spmv_csc": {"fields": ["matrix", "value_dtype", "index_dtype", "op", "n_rows", "n_cols", "nnz", "csc_ms", "pytorch_ms", "csc_speedup_vs_pytorch", "cusparse_ms", "status"], +"spmv_csr": {"fields": ["matrix", "dtype", "index_dtype", "indptr_dtype", "op", "alg", "m", "n", "nnz", "status", "ms", "vendor_ms", "vendor_status", "speedup_vs_vendor", "variant", "op_b", "layout", "out_dtype", "pytorch_ms", "triton_speedup_vs_pytorch"], "rows": [ -["2cubes_sphere.mtx","float32","int32","non","101492","101492","1647264",0.0494368,0.0529824,1.07172,0.0518832,"PASS"], -["ASIC_680ks.mtx","float32","int32","non","682712","682712","2329176",0.0331696,0.076288,2.299937,0.0508224,"PASS"], -["CurlCurl_1.mtx","float32","int32","non","226451","226451","2472071",0.0333616,0.0654432,1.961633,0.0503968,"FAIL"], -["GL7d14.mtx","float32","int32","non","171375","47271","1831183",0.0351488,0.0554304,1.577021,0.0522112,"PASS"], -["H2O.mtx","float32","int32","non","67024","67024","2216736",0.055896,0.0599456,1.072449,0.061384,"FAIL"], -["NACA0015.mtx","float32","int32","non","1039183","1039183","6229636",0.0799152,0.1498368,1.874947,0.084608,"PASS"], -["Stanford.mtx","float32","int32","non","281903","281903","2312497",0.0805504,0.0795696,0.9878238,0.0880032,"PASS"], -["TF19.mtx","float32","int32","non","241029","317955","4370721",0.0621744,0.0951968,1.531125,0.06816,"PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int32","non","29214","29214","4400122",0.1378688,0.0824016,0.5976813,0.0872064,"PASS"], 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-["mac_econ_fwd500.mtx","float32","int32","non","206500","206500","1273389",0.0352928,0.0585584,1.659217,0.1060368,"PASS"], -["mip1.mtx","float32","int32","non","66463","66463","10352819",0.2451904,0.169216,0.6901412,0.1544368,"FAIL"], -["msc10848.mtx","float32","int32","non","10848","10848","1229778",0.0335584,0.0598384,1.783112,0.0523552,"FAIL"], -["neos3.mtx","float32","int32","non","512209","518832","2055024",0.0330848,0.0654448,1.978093,0.0497568,"PASS"], -["net150.mtx","float32","int32","non","43520","43520","3121200",0.0557088,0.0670688,1.203917,0.05608,"FAIL"], -["pkustk13.mtx","float32","int32","non","94893","94893","6616827",0.0936944,0.1205392,1.286514,0.0614864,"PASS"], -["roadNet-TX.mtx","float32","int32","non","1393383","1393383","3843320",0.0440704,0.1066928,2.420963,0.0605104,"PASS"], -["smt.mtx","float32","int32","non","25710","25710","3753184",0.0540416,0.0752608,1.392646,0.0612432,"FAIL"], -["trdheim.mtx","float32","int32","non","22098","22098","1935324",0.0397792,0.0603136,1.516209,0.0598704,"PASS"], -["water_tank.mtx","float32","int32","non","60740","60740","2035281",0.0471552,0.0600592,1.27365,0.06168,"PASS"], -["wave.mtx","float32","int32","non","156317","156317","2118662",0.0388944,0.0602672,1.549508,0.0613232,"PASS"], -["wheel_601.mtx","float32","int32","non","902103","723605","2170814",0.0403712,0.0801792,1.98605,0.06072,"PASS"], -["wiki-Talk.mtx","float32","int32","non","2394385","2394385","5021410",0.2897872,0.5146096,1.775819,0.2781568,"PASS"], -["2cubes_sphere.mtx","float32","int32","trans","101492","101492","1647264",0.0867472,0.8674928,10.00024,0.0380352,"PASS"], -["ASIC_680ks.mtx","float32","int32","trans","682712","682712","2329176",0.0868672,1.271861,14.64144,0.0561536,"PASS"], -["CurlCurl_1.mtx","float32","int32","trans","226451","226451","2472071",0.086472,1.308918,15.1369,0.04008,"FAIL"], -["GL7d14.mtx","float32","int32","trans","171375","47271","1831183",0.1083712,0.948968,8.756644,0.0463008,"PASS"], -["H2O.mtx","float32","int32","trans","67024","67024","2216736",0.1052656,1.183958,11.24734,0.0457184,"PASS"], -["NACA0015.mtx","float32","int32","trans","1039183","1039183","6229636",0.1567328,3.051342,19.46844,0.0511248,"PASS"], -["Stanford.mtx","float32","int32","trans","281903","281903","2312497",0.096952,1.243558,12.82654,0.0390096,"PASS"], -["TF19.mtx","float32","int32","trans","241029","317955","4370721",0.1086976,2.204963,20.2853,0.0454224,"PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int32","trans","29214","29214","4400122",0.1061888,2.258898,21.27247,0.0445504,"PASS"], -["amazon0601.mtx","float32","int32","trans","403394","403394","3387388",0.105512,1.714434,16.24871,0.0451392,"PASS"], -["auto.mtx","float32","int32","trans","448695","448695","6629222",0.15864,3.187698,20.09391,0.0573344,"PASS"], -["c8_mat11.mtx","float32","int32","trans","4562","5761","2462970",0.1042544,1.319112,12.65282,0.0451888,"PASS"], -["cage12.mtx","float32","int32","trans","130228","130228","2032536",0.1030336,1.058618,10.27449,0.0445008,"PASS"], -["cfd2.mtx","float32","int32","trans","123440","123440","3087898",0.1056592,1.583358,14.98552,0.0439264,"PASS"], -["ecology1.mtx","float32","int32","trans","1000000","1000000","4996000",0.127368,2.547595,20.00185,0.045688,"FAIL"], -["engine.mtx","float32","int32","trans","143571","143571","4706073",0.1126352,2.377069,21.10414,0.0462816,"FAIL"], -["filter3D.mtx","float32","int32","trans","106437","106437","2707179",0.1034224,1.413098,13.66336,0.0456368,"PASS"], -["mac_econ_fwd500.mtx","float32","int32","trans","206500","206500","1273389",0.1038096,0.675752,6.509533,0.045432,"PASS"], -["mip1.mtx","float32","int32","trans","66463","66463","10352819",0.2199088,5.010016,22.78224,0.0457664,"PASS"], -["msc10848.mtx","float32","int32","trans","10848","10848","1229778",0.088872,0.6771824,7.61975,0.0375728,"FAIL"], -["neos3.mtx","float32","int32","trans","512209","518832","2055024",0.0871968,1.093389,12.53932,0.0387664,"PASS"], -["net150.mtx","float32","int32","trans","43520","43520","3121200",0.0881376,1.606158,18.22331,0.0383376,"PASS"], -["pkustk13.mtx","float32","int32","trans","94893","94893","6616827",0.1450688,3.196386,22.03358,0.0542096,"PASS"], -["roadNet-TX.mtx","float32","int32","trans","1393383","1393383","3843320",0.1121072,1.941568,17.31885,0.0383264,"PASS"], -["smt.mtx","float32","int32","trans","25710","25710","3753184",0.087696,1.882328,21.46424,0.0400992,"PASS"], -["trdheim.mtx","float32","int32","trans","22098","22098","1935324",0.0860208,1.021978,11.88059,0.0393216,"PASS"], -["water_tank.mtx","float32","int32","trans","60740","60740","2035281",0.08584,1.045741,12.18244,0.0384464,"FAIL"], -["wave.mtx","float32","int32","trans","156317","156317","2118662",0.085336,1.12656,13.20146,0.0378368,"PASS"], -["wheel_601.mtx","float32","int32","trans","902103","723605","2170814",0.085328,1.2112,14.19464,0.038248,"PASS"], -["wiki-Talk.mtx","float32","int32","trans","2394385","2394385","5021410",0.1432,2.574333,17.97718,0.043024,"PASS"], -["2cubes_sphere.mtx","float32","int32","conj","101492","101492","1647264",0.1093344,0.8677008,7.93621,0.0372304,"FAIL"], -["ASIC_680ks.mtx","float32","int32","conj","682712","682712","2329176",0.1036096,1.273128,12.28774,0.044,"PASS"], -["CurlCurl_1.mtx","float32","int32","conj","226451","226451","2472071",0.103256,1.306453,12.65256,0.0395856,"FAIL"], -["GL7d14.mtx","float32","int32","conj","171375","47271","1831183",0.1030912,0.9536368,9.250419,0.045512,"PASS"], -["H2O.mtx","float32","int32","conj","67024","67024","2216736",0.1039984,1.182163,11.36713,0.0435152,"FAIL"], -["NACA0015.mtx","float32","int32","conj","1039183","1039183","6229636",0.1569408,3.053976,19.45941,0.0517264,"PASS"], -["Stanford.mtx","float32","int32","conj","281903","281903","2312497",0.1058912,1.249907,11.80369,0.0465504,"PASS"], -["TF19.mtx","float32","int32","conj","241029","317955","4370721",0.1082528,2.201354,20.3353,0.0380784,"PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int32","conj","29214","29214","4400122",0.090848,2.250187,24.7687,0.03884,"PASS"], -["amazon0601.mtx","float32","int32","conj","403394","403394","3387388",0.0876496,1.712398,19.53686,0.038464,"PASS"], -["auto.mtx","float32","int32","conj","448695","448695","6629222",0.1577072,3.183966,20.1891,0.0574032,"PASS"], -["c8_mat11.mtx","float32","int32","conj","4562","5761","2462970",0.0849792,1.323275,15.57175,0.0371664,"PASS"], -["cage12.mtx","float32","int32","conj","130228","130228","2032536",0.085488,1.053262,12.32059,0.0390848,"PASS"], -["cfd2.mtx","float32","int32","conj","123440","123440","3087898",0.0868304,1.580962,18.20747,0.0369216,"PASS"], -["ecology1.mtx","float32","int32","conj","1000000","1000000","4996000",0.1264048,2.535331,20.05724,0.0389216,"FAIL"], -["engine.mtx","float32","int32","conj","143571","143571","4706073",0.1114064,2.372693,21.29764,0.0382864,"PASS"], -["filter3D.mtx","float32","int32","conj","106437","106437","2707179",0.085832,1.407466,16.39791,0.0391936,"PASS"], -["mac_econ_fwd500.mtx","float32","int32","conj","206500","206500","1273389",0.0921888,0.6750512,7.322486,0.0376704,"PASS"], -["mip1.mtx","float32","int32","conj","66463","66463","10352819",0.219056,4.999511,22.82298,0.044344,"PASS"], -["msc10848.mtx","float32","int32","conj","10848","10848","1229778",0.0967632,0.6800464,7.027945,0.0377136,"PASS"], -["neos3.mtx","float32","int32","conj","512209","518832","2055024",0.0857408,1.091405,12.72912,0.0384064,"PASS"], -["net150.mtx","float32","int32","conj","43520","43520","3121200",0.0856144,1.606102,18.75972,0.039128,"PASS"], -["pkustk13.mtx","float32","int32","conj","94893","94893","6616827",0.1452752,3.191842,21.971,0.0392272,"PASS"], -["roadNet-TX.mtx","float32","int32","conj","1393383","1393383","3843320",0.1063056,1.942525,18.27302,0.0388192,"PASS"], -["smt.mtx","float32","int32","conj","25710","25710","3753184",0.1022112,1.884214,18.43452,0.0565824,"PASS"], -["trdheim.mtx","float32","int32","conj","22098","22098","1935324",0.098952,1.032574,10.4351,0.047816,"PASS"], -["water_tank.mtx","float32","int32","conj","60740","60740","2035281",0.086496,1.045909,12.09199,0.0376736,"PASS"], -["wave.mtx","float32","int32","conj","156317","156317","2118662",0.1771472,1.158869,6.541841,0.0713648,"PASS"], -["wheel_601.mtx","float32","int32","conj","902103","723605","2170814",0.1021792,1.215288,11.89369,0.0465056,"PASS"], -["wiki-Talk.mtx","float32","int32","conj","2394385","2394385","5021410",0.1424192,2.57612,18.08829,0.0450048,"PASS"], -["2cubes_sphere.mtx","float32","int64","non","101492","101492","1647264",0.0660896,0.0603552,0.9132329,0.0599792,"PASS"], -["ASIC_680ks.mtx","float32","int64","non","682712","682712","2329176",0.0403296,0.0760016,1.884512,0.0610752,"PASS"], -["CurlCurl_1.mtx","float32","int64","non","226451","226451","2472071",0.0328,0.0671232,2.046439,0.0502256,"FAIL"], -["GL7d14.mtx","float32","int64","non","171375","47271","1831183",0.0332464,0.054808,1.648539,0.051272,"PASS"], -["H2O.mtx","float32","int64","non","67024","67024","2216736",0.0338688,0.0581168,1.715939,0.051104,"FAIL"], -["NACA0015.mtx","float32","int64","non","1039183","1039183","6229636",0.0840848,0.1490432,1.772534,0.0850112,"PASS"], -["Stanford.mtx","float32","int64","non","281903","281903","2312497",0.0826272,0.0796976,0.9645443,0.0880544,"PASS"], -["TF19.mtx","float32","int64","non","241029","317955","4370721",0.064968,0.095208,1.46546,0.0682464,"PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int64","non","29214","29214","4400122",0.1370224,0.0823792,0.6012097,0.0873248,"PASS"], -["amazon0601.mtx","float32","int64","non","403394","403394","3387388",0.0494976,0.0922256,1.863234,0.0616688,"PASS"], -["auto.mtx","float32","int64","non","448695","448695","6629222",0.0952416,0.1536512,1.613278,0.0944112,"PASS"], -["c8_mat11.mtx","float32","int64","non","4562","5761","2462970",0.0688208,0.060192,0.8746193,0.0610192,"PASS"], -["cage12.mtx","float32","int64","non","130228","130228","2032536",0.0395968,0.0606256,1.531073,0.0594352,"PASS"], -["cfd2.mtx","float32","int64","non","123440","123440","3087898",0.0445664,0.071232,1.598334,0.0614272,"PASS"], -["ecology1.mtx","float32","int64","non","1000000","1000000","4996000",0.0490144,0.1183968,2.415551,0.0602528,"FAIL"], -["engine.mtx","float32","int64","non","143571","143571","4706073",0.055616,0.0947184,1.703078,0.0607872,"FAIL"], -["filter3D.mtx","float32","int64","non","106437","106437","2707179",0.045176,0.0682848,1.511528,0.0610784,"PASS"], -["mac_econ_fwd500.mtx","float32","int64","non","206500","206500","1273389",0.0480768,0.0601152,1.250399,0.059912,"FAIL"], -["mip1.mtx","float32","int64","non","66463","66463","10352819",0.2492896,0.1694992,0.6799288,0.155136,"FAIL"], -["msc10848.mtx","float32","int64","non","10848","10848","1229778",0.039768,0.0517808,1.302072,0.04912,"PASS"], -["neos3.mtx","float32","int64","non","512209","518832","2055024",0.0339984,0.065168,1.916796,0.0510528,"PASS"], -["net150.mtx","float32","int64","non","43520","43520","3121200",0.0590416,0.068016,1.152001,0.055624,"PASS"], -["pkustk13.mtx","float32","int64","non","94893","94893","6616827",0.0952976,0.120672,1.266265,0.060912,"PASS"], -["roadNet-TX.mtx","float32","int64","non","1393383","1393383","3843320",0.0481952,0.1073664,2.22774,0.0532032,"PASS"], -["smt.mtx","float32","int64","non","25710","25710","3753184",0.057336,0.0749776,1.307688,0.050272,"FAIL"], -["trdheim.mtx","float32","int64","non","22098","22098","1935324",0.033144,0.0514656,1.552788,0.0502992,"PASS"], -["water_tank.mtx","float32","int64","non","60740","60740","2035281",0.033848,0.0509424,1.505034,0.0505264,"FAIL"], -["wave.mtx","float32","int64","non","156317","156317","2118662",0.0343904,0.0565584,1.644599,0.0507968,"PASS"], -["wheel_601.mtx","float32","int64","non","902103","723605","2170814",0.038872,0.0796384,2.048734,0.0503664,"PASS"], -["wiki-Talk.mtx","float32","int64","non","2394385","2394385","5021410",0.2951056,0.5148464,1.744618,0.2772304,"FAIL"], -["2cubes_sphere.mtx","float32","int64","trans","101492","101492","1647264",0.0841952,0.8741712,10.38267,0.0377808,"PASS"], -["ASIC_680ks.mtx","float32","int64","trans","682712","682712","2329176",0.0882768,1.272869,14.41906,0.037352,"PASS"], -["CurlCurl_1.mtx","float32","int64","trans","226451","226451","2472071",0.1288112,1.310216,10.1716,0.055856,"FAIL"], -["GL7d14.mtx","float32","int64","trans","171375","47271","1831183",0.103752,1.039619,10.02023,0.0465168,"PASS"], -["H2O.mtx","float32","int64","trans","67024","67024","2216736",0.1024688,1.182139,11.53658,0.0436752,"FAIL"], -["NACA0015.mtx","float32","int64","trans","1039183","1039183","6229636",0.1598768,3.060778,19.1446,0.0856704,"PASS"], -["Stanford.mtx","float32","int64","trans","281903","281903","2312497",0.1038048,1.250602,12.04763,0.045248,"PASS"], -["TF19.mtx","float32","int64","trans","241029","317955","4370721",0.1282064,2.206312,17.20906,0.0452672,"PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int64","trans","29214","29214","4400122",0.105144,2.254554,21.44253,0.0456128,"PASS"], -["amazon0601.mtx","float32","int64","trans","403394","403394","3387388",0.1043808,1.722179,16.499,0.0563664,"PASS"], -["auto.mtx","float32","int64","trans","448695","448695","6629222",0.1584944,3.183624,20.08667,0.0575584,"PASS"], -["c8_mat11.mtx","float32","int64","trans","4562","5761","2462970",0.1024704,1.325235,12.93286,0.0458736,"PASS"], -["cage12.mtx","float32","int64","trans","130228","130228","2032536",0.1077984,1.06019,9.834936,0.0455648,"PASS"], -["cfd2.mtx","float32","int64","trans","123440","123440","3087898",0.1053856,1.586491,15.05416,0.044648,"PASS"], -["ecology1.mtx","float32","int64","trans","1000000","1000000","4996000",0.1293552,2.55373,19.74199,0.0581504,"FAIL"], -["engine.mtx","float32","int64","trans","143571","143571","4706073",0.112064,2.387152,21.30168,0.0469968,"FAIL"], -["filter3D.mtx","float32","int64","trans","106437","106437","2707179",0.1010832,1.410741,13.95623,0.037736,"PASS"], -["mac_econ_fwd500.mtx","float32","int64","trans","206500","206500","1273389",0.120752,0.6775168,5.610812,0.0439104,"PASS"], -["mip1.mtx","float32","int64","trans","66463","66463","10352819",0.218536,5.003482,22.89546,0.0453856,"FAIL"], -["msc10848.mtx","float32","int64","trans","10848","10848","1229778",0.10472,0.6835872,6.527762,0.0448336,"PASS"], -["neos3.mtx","float32","int64","trans","512209","518832","2055024",0.0884496,1.097664,12.41005,0.0377952,"PASS"], -["net150.mtx","float32","int64","trans","43520","43520","3121200",0.0885776,1.614677,18.22895,0.0385088,"PASS"], -["pkustk13.mtx","float32","int64","trans","94893","94893","6616827",0.1442928,3.193219,22.13014,0.0381216,"PASS"], -["roadNet-TX.mtx","float32","int64","trans","1393383","1393383","3843320",0.1069504,1.937603,18.11684,0.038632,"PASS"], -["smt.mtx","float32","int64","trans","25710","25710","3753184",0.1041568,1.880208,18.05171,0.0459536,"PASS"], -["trdheim.mtx","float32","int64","trans","22098","22098","1935324",0.1008112,1.024581,10.16336,0.045488,"PASS"], -["water_tank.mtx","float32","int64","trans","60740","60740","2035281",0.1014496,1.04865,10.33666,0.044272,"FAIL"], -["wave.mtx","float32","int64","trans","156317","156317","2118662",0.0857968,1.126238,13.12681,0.038728,"PASS"], -["wheel_601.mtx","float32","int64","trans","902103","723605","2170814",0.0869264,1.213408,13.95903,0.0387424,"PASS"], -["wiki-Talk.mtx","float32","int64","trans","2394385","2394385","5021410",0.1420144,2.576077,18.13955,0.0465088,"PASS"], -["2cubes_sphere.mtx","float32","int64","conj","101492","101492","1647264",0.0861776,0.8744128,10.14664,0.0401456,"FAIL"], -["ASIC_680ks.mtx","float32","int64","conj","682712","682712","2329176",0.0901936,1.279306,14.18399,0.0386496,"PASS"], -["CurlCurl_1.mtx","float32","int64","conj","226451","226451","2472071",0.1024192,1.306243,12.75389,0.0391936,"FAIL"], -["GL7d14.mtx","float32","int64","conj","171375","47271","1831183",0.0851616,0.9503088,11.15889,0.0393072,"PASS"], -["H2O.mtx","float32","int64","conj","67024","67024","2216736",0.0847104,1.177102,13.89561,0.0384096,"FAIL"], -["NACA0015.mtx","float32","int64","conj","1039183","1039183","6229636",0.1566672,3.053466,19.49014,0.0511296,"PASS"], -["Stanford.mtx","float32","int64","conj","281903","281903","2312497",0.0845024,1.248928,14.77979,0.0382576,"PASS"], -["TF19.mtx","float32","int64","conj","241029","317955","4370721",0.1084112,2.201632,20.30816,0.0378032,"PASS"], -["TSOPF_FS_b300_c1.mtx","float32","int64","conj","29214","29214","4400122",0.0907616,2.256512,24.86197,0.0379392,"PASS"], -["amazon0601.mtx","float32","int64","conj","403394","403394","3387388",0.087288,1.71389,19.63488,0.0377904,"PASS"], -["auto.mtx","float32","int64","conj","448695","448695","6629222",0.1580736,3.182378,20.13225,0.0575392,"PASS"], -["c8_mat11.mtx","float32","int64","conj","4562","5761","2462970",0.0858,1.3226,15.41492,0.037208,"PASS"], -["cage12.mtx","float32","int64","conj","130228","130228","2032536",0.0861936,1.054619,12.23547,0.0373088,"PASS"], -["cfd2.mtx","float32","int64","conj","123440","123440","3087898",0.0864096,1.581923,18.30726,0.0388368,"PASS"], -["ecology1.mtx","float32","int64","conj","1000000","1000000","4996000",0.1269216,2.544774,20.04997,0.039064,"FAIL"], -["engine.mtx","float32","int64","conj","143571","143571","4706073",0.1102192,2.37415,21.54026,0.039064,"FAIL"], -["filter3D.mtx","float32","int64","conj","106437","106437","2707179",0.0847328,1.40863,16.62438,0.0390608,"PASS"], -["mac_econ_fwd500.mtx","float32","int64","conj","206500","206500","1273389",0.0843376,0.677264,8.030393,0.0384416,"PASS"], -["mip1.mtx","float32","int64","conj","66463","66463","10352819",0.2177056,4.99932,22.96367,0.0440768,"PASS"], 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"47a441e6c810a4e51c940412a25c564046d350a6+dirty", "run_timestamp": "2026-09-22 18:01:14", "device": "NVIDIA H800"}, +"fields": ["matrix", "value_dtype", "index_dtype", "format", "n_rows", "n_cols", "nnz", "n_rhs", "FlagSparse_ms", "cuSPARSE_ms", "hipSPARSE_ms", "FlagSparse_vs_vendor_speedup", "status"], "rows": [ ["2cubes_sphere.mtx","float32","int32","coo","101492","101492","874378","1024",25.4585,471.1653,null,18.50719,"PASS"], ["ASIC_680ks.mtx","float32","int32","coo","682712","682712","1505944","1024",5.420813,26.09942,null,4.81467,"PASS"], @@ -6187,7 +3070,8 @@ ["wheel_601.mtx","complex128","int32","coo","902103","723605","N/A","1024",null,null,null,null,"SKIP"], ["wiki-Talk.mtx","complex128","int32","coo","ERR","ERR","ERR","1024",null,null,null,null,"ERROR"] ]}, -"spsv_coo": {"fields": ["matrix", "value_dtype", "index_dtype", "opA", "n_rows", "n_cols", "nnz", "FlagSparse_ms", "CuPy/cuSPARSE_ms", "PyTorch_ms", "FlagSparse_vs_CuPy/cuSPARSE_speedup", "FlagSparse_vs_PyTorch_speedup", "status"], +"spsv_coo": {"source": {"source_dir": "pytest_results_sparse_202609221038", "flagsparse_commit": "47a441e6c810a4e51c940412a25c564046d350a6+dirty", "run_timestamp": "2026-09-22 18:01:14", "device": "NVIDIA H800"}, +"fields": ["matrix", "value_dtype", "index_dtype", "opA", "n_rows", "n_cols", "nnz", "FlagSparse_ms", "CuPy/cuSPARSE_ms", "PyTorch_ms", "FlagSparse_vs_CuPy/cuSPARSE_speedup", "FlagSparse_vs_PyTorch_speedup", "status"], "rows": [ ["2cubes_sphere.mtx","float32","int32","NON","101492","101492","874378",43.59232,106.5705,null,2.444707,null,"PASS"], ["ASIC_680ks.mtx","float32","int32","NON","682712","682712","1469428",1.378938,1.006124,null,0.7296373,null,"PASS"], @@ -6910,7 +3794,8 @@ ["wheel_601.mtx","complex128","int64","CONJ","902103","723605","N/A",null,null,null,null,null,"SKIP"], ["wiki-Talk.mtx","complex128","int64","CONJ","2394385","2394385","3072221",30.92801,54.06928,null,1.74823,null,"PASS"] ]}, -"spsv_csr": {"fields": ["matrix", "value_dtype", "index_dtype", "opA", "n_rows", "n_cols", "nnz", "FlagSparse_ms", "CuPy/cuSPARSE_ms", "PyTorch_ms", "FlagSparse_vs_CuPy/cuSPARSE_speedup", "FlagSparse_vs_PyTorch_speedup", "status"], +"spsv_csr": {"source": {"source_dir": "pytest_results_sparse_202609221038", "flagsparse_commit": "47a441e6c810a4e51c940412a25c564046d350a6+dirty", "run_timestamp": "2026-09-22 18:01:14", "device": "NVIDIA H800"}, +"fields": ["matrix", "value_dtype", "index_dtype", "opA", "n_rows", "n_cols", "nnz", "FlagSparse_ms", "CuPy/cuSPARSE_ms", "PyTorch_ms", "FlagSparse_vs_CuPy/cuSPARSE_speedup", "FlagSparse_vs_PyTorch_speedup", "status"], "rows": [ ["2cubes_sphere.mtx","float32","int32","NON","101492","101492","874378",43.67669,109.5008,null,2.507077,null,"PASS"], ["ASIC_680ks.mtx","float32","int32","NON","682712","682712","1469428",1.268448,0.9837996,null,0.7755933,null,"PASS"], @@ -7633,7 +4518,8 @@ ["wheel_601.mtx","complex128","int64","CONJ","902103","723605","N/A",null,null,null,null,null,"SKIP"], ["wiki-Talk.mtx","complex128","int64","CONJ","2394385","2394385","3072221",30.90685,54.1385,null,1.751666,null,"PASS"] ]}, -"spsv_sell": {"fields": ["matrix", "value_dtype", "index_dtype", "opA", "n_rows", "n_cols", "nnz", "FlagSparse_ms", "cuSPARSE_ms", "PyTorch_ms", "FlagSparse_vs_cuSPARSE_speedup", "FlagSparse_vs_PyTorch_speedup", "status"], +"spsv_sell": {"source": {"source_dir": "pytest_results_sparse_202609221038", "flagsparse_commit": "47a441e6c810a4e51c940412a25c564046d350a6+dirty", "run_timestamp": "2026-09-22 18:01:14", "device": "NVIDIA H800"}, +"fields": ["matrix", "value_dtype", "index_dtype", "opA", "n_rows", "n_cols", "nnz", "FlagSparse_ms", "cuSPARSE_ms", "PyTorch_ms", "FlagSparse_vs_cuSPARSE_speedup", "FlagSparse_vs_PyTorch_speedup", "status"], "rows": [ ["2cubes_sphere.mtx","float32","int32","NON","101492","101492","874378",61.32829,140.0844,null,2.284173,null,"PASS"], ["ASIC_680ks.mtx","float32","int32","NON","682712","682712","1469428",123.9514,2.149722,null,0.01734327,null,"PASS"], @@ -7875,6 +4761,25 @@ ["wave.mtx","complex128","int64","NON","156317","156317","1215648",186.267,16.93561,null,0.09092114,null,"PASS"], ["wheel_601.mtx","complex128","int64","NON","902103","723605","",null,null,null,null,null,"SKIP"], ["wiki-Talk.mtx","complex128","int64","NON","2394385","2394385","3072221",356.8334,103.737,null,0.2907154,null,"PASS"] +]}, +"spvv": {"fields": ["variant", "case_id", "op", "value_dtype", "index_dtype", "dense_size", "nnz", "triton_ms", "cusparse_ms", "pytorch_ms", "triton_speedup_vs_cusparse", "triton_speedup_vs_pytorch", "cupy_ms", "triton_speedup_vs_cupy", "status"], +"rows": [ +["spvv_f16f32_int_non","spvv|float16|dense=32768|nnz=1024|non","non","float16","int32","32768","1024",0.04566235,0.01715185,0.040295,0.3756235,0.8824556,null,null,"PASS"], +["spvv_f16f32_int_non","spvv|float16|dense=131072|nnz=4096|non","non","float16","int32","131072","4096",0.0603285,0.023826,0.0507114,0.3949377,0.8405878,null,null,"PASS"], +["spvv_f16f32_int_non","spvv|float16|dense=524288|nnz=16384|non","non","float16","int32","524288","16384",0.04825675,0.0194971,0.0509996,0.4040285,1.056839,null,null,"PASS"], +["spvv_f16f32_int_non","spvv|float16|dense=1048576|nnz=65536|non","non","float16","int32","1048576","65536",0.0497734,0.02073785,0.05315585,0.4166452,1.067957,null,null,"PASS"], +["spvv_i8i32_int_non","spvv|int8|dense=32768|nnz=1024|non","non","int8","int32","32768","1024",0.06391705,0.02496465,0.0722056,0.3905789,1.129677,null,null,"PASS"], +["spvv_i8i32_int_non","spvv|int8|dense=131072|nnz=4096|non","non","int8","int32","131072","4096",0.0498493,0.0167849,0.06141295,0.3367129,1.231972,null,null,"PASS"], +["spvv_i8i32_int_non","spvv|int8|dense=524288|nnz=16384|non","non","int8","int32","524288","16384",0.05020435,0.0189676,0.0580167,0.3778079,1.155611,null,null,"PASS"], +["spvv_i8i32_int_non","spvv|int8|dense=1048576|nnz=65536|non","non","int8","int32","1048576","65536",0.04017415,0.023642,0.04264385,0.5884878,1.061475,null,null,"PASS"], +["spvv_c32_int_conj","spvv|complex64|dense=32768|nnz=1024|conj","conj","complex64","int32","32768","1024",0.0387864,0.0187766,0.03304715,0.4841027,0.8520294,null,null,"PASS"], +["spvv_c32_int_conj","spvv|complex64|dense=131072|nnz=4096|conj","conj","complex64","int32","131072","4096",0.0631848,0.02269435,0.0365464,0.3591742,0.578405,null,null,"PASS"], +["spvv_c32_int_conj","spvv|complex64|dense=524288|nnz=16384|conj","conj","complex64","int32","524288","16384",0.0756711,0.02520275,0.04504705,0.3330565,0.5953006,null,null,"PASS"], +["spvv_c32_int_conj","spvv|complex64|dense=1048576|nnz=65536|conj","conj","complex64","int32","1048576","65536",0.0623997,0.02302595,0.0358481,0.3690074,0.5744915,null,null,"PASS"], +["spvv_f32_int_non","spvv|float32|dense=32768|nnz=1024|non","non","float32","int32","32768","1024",0.0587349,0.01540965,0.03532525,0.2623593,0.6014355,null,null,"PASS"], +["spvv_f32_int_non","spvv|float32|dense=131072|nnz=4096|non","non","float32","int32","131072","4096",0.0505993,0.0246492,0.0360926,0.4871451,0.7133024,null,null,"PASS"], +["spvv_f32_int_non","spvv|float32|dense=524288|nnz=16384|non","non","float32","int32","524288","16384",0.0507663,0.0286352,0.03621985,0.5640592,0.7134625,null,null,"PASS"], +["spvv_f32_int_non","spvv|float32|dense=1048576|nnz=65536|non","non","float32","int32","1048576","65536",0.04924615,0.02776675,0.03709525,0.563836,0.7532619,null,null,"PASS"] ]} } } diff --git a/docs/ASCEND.md b/docs/ASCEND.md index 53fce38b..4d7d15e5 100644 --- a/docs/ASCEND.md +++ b/docs/ASCEND.md @@ -45,12 +45,21 @@ python3 -c "from flagsparse.sparse_operations import _common as c; print(c._back setsid timeout -s KILL 43200 python3 -u run_flagsparse_pytest.py \ --phase both --mode normal --delivery-only --gpus 6,7 --timeout 3600 \ --benchmark-input /home/matrix --benchmark-warmup 5 --benchmark-iters 20 \ + --h800-reference \ --results-dir pytest_results_ascend_delivery \ > pytest_results_ascend_delivery.log 2>&1 < /dev/null & python3 tools/delivery_table.py pytest_results_ascend_delivery # 跑完后：20 行结果，缺变体时退出码为 1 ``` +**`--h800-reference`（不带值）**：没有 PyTorch-NPU 基线的行，用仓库自带的 H800 结果按带宽折算出加速比 +（H800 cuSPARSE 时间 × 3050/1600；H800 也没有 cuSPARSE 时间的 `sddmm_csr_f16` 用 H800 PyTorch），交付表里标 +`(H800折算)` / `(H800 PyTorch折算)`。**有 PyTorch-NPU 基线的行不受影响。** 按 10-11 全量结果预演：有加速比的变体 +41 → 63，补上的 22 个大多在 0.001x–0.06x——昇腾上这些变体每次调用 0.3–2.4 秒（`timed=call`，PyTorch-NPU 自己 +在 legacy spmm_csr 上也要 20–44 ms），数是真的，不是折算出错。`spgemm_csr` 以前走探测脚本、测的是合成规模， +现在改为 `tests/test_spgemm.py` 在交付矩阵上测（**未在 NPU 上跑过**），重跑后才能折算。见 +[`H800_REFERENCE.md`](H800_REFERENCE.md)。 + **两个 timeout 不是一回事**：外层 `timeout -s KILL 43200` 是**整条命令**的总限时（12 小时），到点 直接强制杀掉整个进程；`--timeout 3600` 是**每个子任务**的限时，大多数算子是每个阶段（精度、性能） 各 3600 秒，`spmm_csr` / `sddmm_csr` 的性能是逐矩阵路径，则是每个矩阵 3600 秒。外层必须写， diff --git a/docs/H800_REFERENCE.md b/docs/H800_REFERENCE.md index b45eb853..e0867b46 100644 --- a/docs/H800_REFERENCE.md +++ b/docs/H800_REFERENCE.md @@ -5,13 +5,13 @@ ## 来源 - 设备：NVIDIA H800 × 8 -- 原始结果目录：`pytest_results_sparse_202609221038`（被 git 忽略，不随仓库） -- FlagSparse commit：`47a441e6c810+dirty`；torch 2.8.0a0+5228986c39.nv25.05；triton 3.6.0 -- 生成日期：2026-09-28 +- 原始结果目录：`results_65_cuda_1014`（被 git 忽略，不随仓库） +- FlagSparse commit：`643e9c6b7e45+dirty`；torch 2.8.0a0+5228986c39.nv25.05；triton 3.6.0 +- 生成日期：2026-10-14 ## 用途 -没有厂商 / PyTorch 基线的后端，用这里的 **H800 上 cuSPARSE 的时间**做基线： +**只用于没有任何基线的行**：目标卡上有厂商库（hipSPARSE、muSPARSE……）或 PyTorch 时间的行一律用实测基线，不会被折算覆盖。没有厂商 / PyTorch 基线的行，用这里的 **H800 上 cuSPARSE 的时间**做基线： ``` h800_scaled_ms = T_cuSPARSE@H800 × P_H800 / P_目标卡 (P = 实测显存带宽) @@ -20,64 +20,203 @@ speedup = h800_scaled_ms / T_FlagSparse@目标卡 (1.0 = 与折算后 ```bash # 不用再找目录：不带值即用本仓库自带的文件 -python3 run_flagsparse_pytest.py --h800-reference --vendor-card iluvatar-biv150 ... -python3 tools/baseline_bound.py --vendor <目标卡结果目录> --vendor-card iluvatar-biv150 +python3 run_flagsparse_pytest.py --h800-reference ... # 卡型按后端/设备名自动识别 +python3 run_flagsparse_split_delivery.py --h800-reference ... # C API 拆分交付（MUSA）：补 NoBaseline 变体 +python3 tools/baseline_bound.py --vendor <目标卡结果目录> # 已跑完的结果目录事后查看 ``` -这是**估算**，不是厂商实测：H800 与目标卡的访存效率、缓存、launch 开销并不成比例。 +这是**估算**，不是厂商实测：H800 与目标卡的访存效率、缓存、launch 开销并不成比例。折算出的加速比在 summary 里带 `baseline: h800_scaled`，`tools/delivery_table.py` 显示为 `(H800折算)`。 + +H800 上某个算例**没有** cuSPARSE 时间时（cuSPARSE SDDMM 不支持 fp16），退一步用同一行 H800 PyTorch 的时间（PyTorch 结果须通过校验），标 `baseline: h800_pytorch_scaled` / `(H800 PyTorch折算)`；cuSPARSE 跑了但结果不对的行不退。FlagSparse 自己在 H800 上的时间永远不当基线。 + +C API 拆分交付（MUSA）：带 `--h800-reference` 时 pytest 那一半也跑性能（与本参照同一 benchmark、同一规模），NoBaseline 变体优先取它；取不到再用 C API 时间按**变体 + 矩阵名**匹配（C API 的 SpMM `n=8`、SDDMM `k=16/64`、按矩阵测的向量类在这里没有对应规模，只有 SpMV 类能对上）。 ## 覆盖情况与限制 - 只有带 cuSPARSE（或 vendor）时间的行才能做基线；下表“可作基线的行”就是这个数。 为 0 的算子（当时只有 CuPy 基线、没有对应格式的库）**没有折算基线**，行会保持 `N/A`。 - FlagSparse commit 带 `+dirty` 表示那轮 H800 是在有未提交改动的工作树上跑的。 +- “来源”一列不是本轮的算子，是从上一份参考文件带过来的（新一轮没测它们），数据来自那一轮。 ## 各算子收录情况 -| 算子 | 行数 | 可作基线的行 | 保留的时间 / 加速比列 | -|---|---:|---:|---| -| `gather` | 48 | 40 | `triton_ms`, `pytorch_ms`, `cusparse_ms`, `triton_speedup_vs_pytorch`, `triton_speedup_vs_cusparse` | -| `scatter` | 40 | 40 | `triton_ms`, `pytorch_ms`, `cusparse_ms`, `triton_speedup_vs_pytorch`, `triton_speedup_vs_cusparse` | -| `sddmm_csr` | 240 | 240 | `triton_ms`, `pytorch_api_ms`, `triton_speedup_vs_pytorch_api`, `triton_speedup_vs_pytorch`, `cusparse_ms`, `pytorch_ms`, `triton_speedup_vs_cusparse` | -| `spgemm_csr` | 60 | 58 | `triton_ms`, `cusparse_ms`, `pytorch_ms`, `triton_speedup_vs_cusparse`, `triton_speedup_vs_pytorch` | -| `spmm_bell` | 240 | 0 | `ms`, `vendor_ms`, `torch_bell_ms`, `cupy_bell_ms` | -| `spmm_bsr` | 513 | 0 | `ms`, `torch_ms`, `cusparse_ms`, `torch_vs_alg_speedup`, `cusparse_vs_alg_speedup`, `scipy_vs_alg_speedup` | -| `spmm_coo` | 240 | 240 | `ms`, `torch_ms`, `cusparse_ms`, `triton_speedup_vs_pytorch`, `cusparse_vs_alg_speedup` | -| `spmm_csc` | 720 | 240 | `ms`, `pytorch_ms`, `triton_speedup_vs_pytorch`, `cusparse_ms`, `cusparse_vs_alg_speedup` | -| `spmm_csr` | 720 | 720 | `triton_ms`, `cusparse_ms`, `pytorch_ms`, `triton_speedup_vs_cusparse`, `triton_speedup_vs_pytorch` | -| `spmv_bsr` | 960 | 0 | `bsr_ms`, `pytorch_ms`, `bsr_speedup_vs_pytorch`, `pytorch_padded_ms`, `cusparse_ms` | -| `spmv_coo` | 720 | 720 | `base_ms`, `opt_ms`, `cusparse_ms`, `pytorch_ms`, `opt_speedup_vs_cusparse`, `opt_speedup_vs_pytorch` | -| `spmv_csc` | 720 | 720 | `csc_ms`, `pytorch_ms`, `csc_speedup_vs_pytorch`, `cusparse_ms` | -| `spmv_csr` | 780 | 780 | `ms`, `vendor_ms`, `speedup_vs_vendor` | -| `spsm_coo` | 120 | 92 | `FlagSparse_ms`, `cuSPARSE_ms`, `hipSPARSE_ms`, `FlagSparse_vs_vendor_speedup` | -| `spsv_coo` | 720 | 600 | `FlagSparse_ms`, `CuPy/cuSPARSE_ms`, `PyTorch_ms`, `FlagSparse_vs_CuPy/cuSPARSE_speedup`, `FlagSparse_vs_PyTorch_speedup` | -| `spsv_csr` | 720 | 600 | `FlagSparse_ms`, `CuPy/cuSPARSE_ms`, `PyTorch_ms`, `FlagSparse_vs_CuPy/cuSPARSE_speedup`, `FlagSparse_vs_PyTorch_speedup` | -| `spsv_sell` | 240 | 200 | `FlagSparse_ms`, `cuSPARSE_ms`, `PyTorch_ms`, `FlagSparse_vs_cuSPARSE_speedup`, `FlagSparse_vs_PyTorch_speedup` | - -## H800 上 20 个交付变体的结果 +| 算子 | 行数 | 可作基线的行 | 来源 | 保留的时间 / 加速比列 | +|---|---:|---:|---|---| +| `axpby` | 12 | 12 | 本轮 | `triton_ms`, `cusparse_ms`, `pytorch_ms`, `triton_speedup_vs_cusparse`, `triton_speedup_vs_pytorch`, `cupy_ms`, `triton_speedup_vs_cupy` | +| `gather` | 20 | 20 | 本轮 | `triton_ms`, `pytorch_ms`, `cusparse_ms`, `triton_speedup_vs_pytorch`, `triton_speedup_vs_cusparse` | +| `scatter` | 24 | 24 | 本轮 | `triton_ms`, `pytorch_ms`, `cusparse_ms`, `triton_speedup_vs_pytorch`, `triton_speedup_vs_cusparse` | +| `sddmm_csr` | 130 | 120 | 本轮 | `triton_ms`, `pytorch_api_ms`, `triton_speedup_vs_pytorch_api`, `triton_speedup_vs_pytorch`, `cusparse_ms`, `pytorch_ms`, `triton_speedup_vs_cusparse` | +| `spgemm_csr` | 20 | 20 | 本轮 | `triton_ms`, `cusparse_ms`, `pytorch_ms`, `triton_speedup_vs_cusparse`, `triton_speedup_vs_pytorch` | +| `spmm_bell` | 240 | 0 | `pytest_results_sparse_202609221038` (`47a441e6c810+dirty`) | `ms`, `vendor_ms`, `torch_bell_ms`, `cupy_bell_ms` | +| `spmm_bsr` | 513 | 0 | `pytest_results_sparse_202609221038` (`47a441e6c810+dirty`) | `ms`, `torch_ms`, `cusparse_ms`, `torch_vs_alg_speedup`, `cusparse_vs_alg_speedup`, `scipy_vs_alg_speedup` | +| `spmm_coo` | 50 | 50 | 本轮 | `ms`, `torch_ms`, `cusparse_ms`, `triton_speedup_vs_pytorch`, `cusparse_vs_alg_speedup` | +| `spmm_csc` | 270 | 110 | 本轮 | `ms`, `pytorch_ms`, `triton_speedup_vs_pytorch`, `cusparse_ms`, `cusparse_vs_alg_speedup` | +| `spmm_csr` | 90 | 90 | 本轮 | `triton_ms`, `cusparse_ms`, `pytorch_ms`, `triton_speedup_vs_cusparse`, `triton_speedup_vs_pytorch` | +| `spmv_bsr` | 960 | 0 | `pytest_results_sparse_202609221038` (`47a441e6c810+dirty`) | `bsr_ms`, `pytorch_ms`, `bsr_speedup_vs_pytorch`, `pytorch_padded_ms`, `cusparse_ms` | +| `spmv_coo` | 80 | 80 | 本轮 | `base_ms`, `opt_ms`, `cusparse_ms`, `pytorch_ms`, `opt_speedup_vs_cusparse`, `opt_speedup_vs_pytorch` | +| `spmv_csc` | 270 | 270 | 本轮 | `csc_ms`, `pytorch_ms`, `csc_speedup_vs_pytorch`, `cusparse_ms`, `csc_speedup_vs_cusparse` | +| `spmv_csr` | 100 | 100 | 本轮 | `ms`, `vendor_ms`, `speedup_vs_vendor`, `pytorch_ms`, `triton_speedup_vs_pytorch` | +| `spmv_sell` | 50 | 50 | 本轮 | `triton_ms`, `cusparse_ms`, `pytorch_ms`, `cupy_ms`, `triton_speedup_vs_cupy`, `triton_speedup_vs_cusparse`, `triton_speedup_vs_pytorch` | +| `spsm_coo` | 120 | 92 | `pytest_results_sparse_202609221038` (`47a441e6c810+dirty`) | `FlagSparse_ms`, `cuSPARSE_ms`, `hipSPARSE_ms`, `FlagSparse_vs_vendor_speedup` | +| `spsv_coo` | 720 | 600 | `pytest_results_sparse_202609221038` (`47a441e6c810+dirty`) | `FlagSparse_ms`, `CuPy/cuSPARSE_ms`, `PyTorch_ms`, `FlagSparse_vs_CuPy/cuSPARSE_speedup`, `FlagSparse_vs_PyTorch_speedup` | +| `spsv_csr` | 720 | 600 | `pytest_results_sparse_202609221038` (`47a441e6c810+dirty`) | `FlagSparse_ms`, `CuPy/cuSPARSE_ms`, `PyTorch_ms`, `FlagSparse_vs_CuPy/cuSPARSE_speedup`, `FlagSparse_vs_PyTorch_speedup` | +| `spsv_sell` | 240 | 200 | `pytest_results_sparse_202609221038` (`47a441e6c810+dirty`) | `FlagSparse_ms`, `cuSPARSE_ms`, `PyTorch_ms`, `FlagSparse_vs_cuSPARSE_speedup`, `FlagSparse_vs_PyTorch_speedup` | +| `spvv` | 16 | 16 | 本轮 | `triton_ms`, `cusparse_ms`, `pytorch_ms`, `triton_speedup_vs_cusparse`, `triton_speedup_vs_pytorch`, `cupy_ms`, `triton_speedup_vs_cupy` | + +## 各交付变体能否折算 + +按变体统计本轮 H800 上带厂商库时间的矩阵数；为 0 的变体只能退到 H800 PyTorch 的时间（见上）。 + +| 变体 | 可作基线的矩阵 / 总矩阵 | +|---|---| +| `axpby_c32_int` | 4 / 4 | +| `axpby_f16_int` | 4 / 4 | +| `axpby_f32_int` | 4 / 4 | +| `gather complex128 (legacy)` | 4 / 4 | +| `gather complex64 (legacy)` | 4 / 4 | +| `gather float16 (legacy)` | 4 / 4 | +| `gather float32 (legacy)` | 4 / 4 | +| `gather float64 (legacy)` | 4 / 4 | +| `scatter complex128 (legacy)` | 4 / 4 | +| `scatter complex64 (legacy)` | 4 / 4 | +| `scatter float16 (legacy)` | 4 / 4 | +| `scatter float32 (legacy)` | 4 / 4 | +| `scatter float64 (legacy)` | 4 / 4 | +| `scatter int8 (legacy)` | 4 / 4 | +| `sddmm_csr float32 (legacy)` | 40 / 40 | +| `sddmm_csr float64 (legacy)` | 40 / 40 | +| `sddmm_csr_c32_int_non_non_row` | 10 / 10 | +| `sddmm_csr_f16_int_non_non_row` | 0 / 10 **（无 cuSPARSE，用 H800 PyTorch）** | +| `sddmm_csr_f32_int_non_non_col` | 10 / 10 | +| `sddmm_csr_f32_int_non_trans_row` | 10 / 10 | +| `sddmm_csr_f32_int_trans_non_row` | 10 / 10 | +| `spgemm_csr float32 (legacy)` | 10 / 10 | +| `spgemm_csr float64 (legacy)` | 10 / 10 | +| `spmm_coo float32 (legacy)` | 10 / 10 | +| `spmm_coo float64 (legacy)` | 10 / 10 | +| `spmm_coo_c32_int_non_non_row` | 10 / 10 | +| `spmm_coo_f16_int_non_non_row` | 10 / 10 | +| `spmm_coo_i8i32_int_non_non_row` | 10 / 10 | +| `spmm_csc complex128 (legacy)` | 20 / 60 | +| `spmm_csc complex64 (legacy)` | 20 / 60 | +| `spmm_csc float32 (legacy)` | 20 / 60 | +| `spmm_csc float64 (legacy)` | 20 / 60 | +| `spmm_csc_c32_int_non_non_row` | 10 / 10 | +| `spmm_csc_f16_int_non_non_row` | 10 / 10 | +| `spmm_csc_f32_int_non_non_row` | 10 / 10 | +| `spmm_csr float32 (legacy)` | 8 / 10 | +| `spmm_csr float64 (legacy)` | 10 / 10 | +| `spmm_csr_c32_int_non_non_row` | 10 / 10 | +| `spmm_csr_f16_int_non_non_row` | 10 / 10 | +| `spmm_csr_f16f32_int_non_non_row` | 10 / 10 | +| `spmm_csr_f32_int_non_non_col` | 10 / 10 | +| `spmm_csr_f32_int_non_trans_row` | 10 / 10 | +| `spmm_csr_f32_int_trans_non_row` | 10 / 10 | +| `spmm_csr_i8i32_int_non_non_row` | 10 / 10 | +| `spmv_coo float32 (legacy)` | 10 / 10 | +| `spmv_coo float64 (legacy)` | 10 / 10 | +| `spmv_coo_c32_int_conj` | 10 / 10 | +| `spmv_coo_c32_int_non` | 10 / 10 | +| `spmv_coo_f16_int_non` | 10 / 10 | +| `spmv_coo_f16f32_int_non` | 10 / 10 | +| `spmv_coo_f32_int_trans` | 10 / 10 | +| `spmv_coo_i8i32_int_non` | 10 / 10 | +| `spmv_csc complex128 (legacy)` | 60 / 60 | +| `spmv_csc complex64 (legacy)` | 60 / 60 | +| `spmv_csc float32 (legacy)` | 60 / 60 | +| `spmv_csc float64 (legacy)` | 60 / 60 | +| `spmv_csc_c32_int_non` | 10 / 10 | +| `spmv_csc_f16_int_non` | 10 / 10 | +| `spmv_csc_f32_int_non` | 10 / 10 | +| `spmv_csr float32 (legacy)` | 10 / 10 | +| `spmv_csr float64 (legacy)` | 10 / 10 | +| `spmv_csr_c32_int_conj` | 10 / 10 | +| `spmv_csr_c32_int_non` | 10 / 10 | +| `spmv_csr_f16_int_non` | 10 / 10 | +| `spmv_csr_f16f32_int_non` | 10 / 10 | +| `spmv_csr_f32_int_trans` | 10 / 10 | +| `spmv_csr_f32c32_int_non` | 10 / 10 | +| `spmv_csr_i8f32_int_non` | 10 / 10 | +| `spmv_csr_i8i32_int_non` | 10 / 10 | +| `spmv_sell_c32_int_non` | 10 / 10 | +| `spmv_sell_f16_int_non` | 10 / 10 | +| `spmv_sell_f16f32_int_non` | 10 / 10 | +| `spmv_sell_f32_int_non` | 10 / 10 | +| `spmv_sell_i8i32_int_non` | 10 / 10 | +| `spvv_c32_int_conj` | 4 / 4 | +| `spvv_f16f32_int_non` | 4 / 4 | +| `spvv_f32_int_non` | 4 / 4 | +| `spvv_i8i32_int_non` | 4 / 4 | + +## H800 上 65 个交付变体的结果 | 变体 | 精度 | 性能 | 加速比（H800 vs cuSPARSE/PyTorch，runner 口径） | |---|---|---|---| -| `gather_c32_int` | Passed (1/1) | Passed | complex64: 1.464 | -| `gather_c64_int` | Passed (2/2) | Passed | complex128: 1.215 | -| `gather_f16_int` | Passed (1/1) | Passed | fp16: 1.137 | -| `gather_f32_int` | Passed (1/1) | Passed | fp32: 1.055 | -| `gather_f64_int` | Passed (1/1) | Passed | fp64: 1.469 | -| `scatter_c32_int` | Passed (2/2) | Passed | complex64: 0.760 | -| `scatter_c64_int` | Passed (2/2) | Passed | complex128: 0.814 | -| `scatter_f16_int` | Passed (2/2) | Passed | fp16: 0.807 | -| `scatter_f32_int` | Passed (2/2) | Passed | fp32: 0.982 | -| `scatter_f64_int` | Passed (2/2) | Passed | fp64: 0.978 | -| `sddmm_csr_f32_int_non_non_row` | Passed (2/2) | Passed | fp32: 3.284 | -| `sddmm_csr_f64_int_non_non_row` | Passed (2/2) | Passed | fp64: 1.597 | -| `spmm_coo_f32_int_non_non_row` | Passed (1/1) | Passed | fp32: 0.763 | -| `spmm_coo_f64_int_non_non_row` | Passed (1/1) | Passed | fp64: 3.914 | -| `spmm_csr_f32_int_non_non_row` | Passed (2/2) | Passed | fp32: 1.133 | -| `spmm_csr_f64_int_non_non_row` | Passed (2/2) | Passed | fp64: 1.381 | -| `spmv_coo_f32_int_non` | Passed (1/1) | Passed | fp32: 0.912 | -| `spmv_coo_f64_int_non` | Passed (1/1) | Passed | fp64: 1.077 | -| `spmv_csr_f32_int_non` | Passed (57/57) | Passed | fp32: 0.314 | -| `spmv_csr_f64_int_non` | Passed (53/53) | Passed | fp64: 0.405 | +| `axpby_f16_int` | Passed (2/2) | Passed | fp16: 0.410 | +| `gather_c32_int` | Passed (2/2) | Passed | complex64: 1.475 | +| `gather_c64_int` | Passed (3/3) | Passed | complex128: 1.258 | +| `gather_f16_int` | Passed (2/2) | Passed | fp16: 1.131 | +| `gather_f32_int` | Passed (2/2) | Passed | fp32: 1.057 | +| `gather_f64_int` | Passed (2/2) | Passed | fp64: 1.469 | +| `scatter_c32_int` | Passed (4/4) | Passed | complex64: 0.755 | +| `scatter_c64_int` | Passed (4/4) | Passed | complex128: 0.816 | +| `scatter_f16_int` | Passed (4/4) | Passed | fp16: 0.808 | +| `scatter_f32_int` | Passed (4/4) | Passed | fp32: 0.986 | +| `scatter_f64_int` | Passed (4/4) | Passed | fp64: 0.983 | +| `scatter_i8_int` | Passed (2/2) | Passed | int8: 1.601 | +| `sddmm_csr_c32_int_non_non_row` | Passed (2/2) | Passed | complex64: 1.186 | +| `sddmm_csr_f16_int_non_non_row` | Passed (2/2) | Passed | fp16: 3.109 | +| `sddmm_csr_f32_int_non_non_col` | Passed (2/2) | Passed | fp32: 2.447 | +| `sddmm_csr_f32_int_non_non_row` | Passed (4/4) | Passed | fp32: 1.486 | +| `sddmm_csr_f32_int_non_trans_row` | Passed (2/2) | Passed | fp32: 2.152 | +| `sddmm_csr_f32_int_trans_non_row` | Passed (2/2) | Passed | fp32: 1.537 | +| `sddmm_csr_f64_int_non_non_row` | Passed (4/4) | Passed | fp64: 1.159 | +| `spgemm_csr_f32_int_non_non` | Passed (2/2) | Passed | fp32: 0.694 | +| `spmm_coo_c32_int_non_non_row` | Passed (2/2) | Passed | complex64: 0.353 | +| `spmm_coo_f16_int_non_non_row` | Passed (2/2) | Passed | fp16: 0.908 | +| `spmm_coo_f32_int_non_non_row` | Passed (2/2) | Passed | fp32: 1.169 | +| `spmm_coo_f64_int_non_non_row` | Passed (2/2) | Passed | fp64: 4.497 | +| `spmm_coo_i8i32_int_non_non_row` | Passed (2/2) | Passed | int8: 0.211 | +| `spmm_csc_c32_int_non_non_row` | Passed (2/2) | Passed | complex64: 0.181 | +| `spmm_csc_f16_int_non_non_row` | Passed (2/2) | Passed | fp16: 0.530 | +| `spmm_csc_f32_int_non_non_row` | Passed (2/2) | Passed | fp32: 0.398 | +| `spmm_csr_c32_int_non_non_row` | Passed (2/2) | Passed | complex64: 0.328 | +| `spmm_csr_f16_int_non_non_row` | Passed (2/2) | Passed | fp16: 2.292 | +| `spmm_csr_f16f32_int_non_non_row` | Passed (2/2) | Passed | fp16: 1.173 | +| `spmm_csr_f32_int_non_non_col` | Passed (2/2) | Passed | fp32: 3.331 | +| `spmm_csr_f32_int_non_non_row` | Passed (4/4) | Passed | fp32: 3.429 | +| `spmm_csr_f32_int_non_trans_row` | Passed (2/2) | Passed | fp32: 4.473 | +| `spmm_csr_f32_int_trans_non_row` | Passed (2/2) | Passed | fp32: 1.691 | +| `spmm_csr_f64_int_non_non_row` | Passed (4/4) | Passed | fp64: 3.341 | +| `spmm_csr_i8i32_int_non_non_row` | Passed (2/2) | Passed | int8: 1.222 | +| `spmv_coo_c32_int_conj` | Passed (2/2) | Passed | complex64: 1.298 | +| `spmv_coo_c32_int_non` | Passed (2/2) | Passed | complex64: 0.492 | +| `spmv_coo_f16_int_non` | Passed (2/2) | Passed | fp16: 0.341 | +| `spmv_coo_f16f32_int_non` | Passed (2/2) | Passed | fp16: 0.334 | +| `spmv_coo_f32_int_non` | Skipped (3/5) | Passed | fp32: 1.194 | +| `spmv_coo_f32_int_trans` | Passed (2/2) | Passed | fp32: 0.897 | +| `spmv_coo_f64_int_non` | Passed (3/3) | Passed | fp64: 1.211 | +| `spmv_coo_i8i32_int_non` | Passed (2/2) | Passed | int8: 0.357 | +| `spmv_csc_c32_int_non` | Passed (2/2) | Passed | complex64: 1.569 | +| `spmv_csc_f16_int_non` | Passed (2/2) | Passed | fp16: 2.717 | +| `spmv_csc_f32_int_non` | Passed (2/2) | Passed | fp32: 0.951 | +| `spmv_csr_c32_int_conj` | Passed (2/2) | Passed | complex64: 1.432 | +| `spmv_csr_c32_int_non` | Passed (2/2) | Passed | complex64: 0.599 | +| `spmv_csr_f16_int_non` | Passed (2/2) | Passed | fp16: 0.542 | +| `spmv_csr_f16f32_int_non` | Passed (2/2) | Passed | fp16: 0.625 | +| `spmv_csr_f32_int_non` | Passed (59/59) | Passed | fp32: 0.593 | +| `spmv_csr_f32_int_trans` | Passed (2/2) | Passed | fp32: 1.023 | +| `spmv_csr_f32c32_int_non` | Passed (2/2) | Passed | fp32: 0.617 | +| `spmv_csr_f64_int_non` | Passed (55/55) | Passed | fp64: 0.670 | +| `spmv_csr_i8f32_int_non` | Passed (2/2) | Passed | int8: 0.579 | +| `spmv_csr_i8i32_int_non` | Passed (2/2) | Passed | int8: 0.550 | +| `spmv_sell_c32_int_non` | Passed (2/2) | Passed | complex64: 0.663 | +| `spmv_sell_f16_int_non` | Passed (2/2) | Passed | fp16: 0.531 | +| `spmv_sell_f32_int_non` | Passed (2/2) | Passed | fp32: 0.576 | +| `spmv_sell_i8i32_int_non` | Passed (2/2) | Passed | int8: 0.421 | +| `spvv_c32_int_conj` | Passed (2/2) | Passed | complex64: 0.386 | +| `spvv_f16f32_int_non` | Passed (2/2) | Passed | fp16: 0.398 | +| `spvv_i8i32_int_non` | Passed (2/2) | Passed | int8: 0.423 | ## 重新生成 diff --git a/docs/ILUVATAR.md b/docs/ILUVATAR.md index 495a598c..5123bd91 100644 --- a/docs/ILUVATAR.md +++ b/docs/ILUVATAR.md @@ -353,11 +353,14 @@ speedup_vs_h800_scaled = h800_scaled_ms / T_FlagSparse@BI-V150 # 1.0 = 和 ``` - 自带的数据在 [`conf/h800_reference.json`](../conf/h800_reference.json)（约 1 MB，一个文件），说明见 - [H800_REFERENCE.md](H800_REFERENCE.md)：H800 ×8，提交 `47a441e`（那轮是脏工作树），17 个算子的 7801 行， + [H800_REFERENCE.md](H800_REFERENCE.md)：2026-10-14 起是 H800 ×8 上**全部 65 个变体**的一轮（提交 `643e9c6`， + 脏工作树），13 个交付算子；spsv/spsm/bsr/bell 新一轮没测，沿用 09-22 那轮（`47a441e`）并在文件里标了来源。共 20 个算子 4645 行， 每行保留算例键、各 `*_ms` 时间、加速比和状态列。想换成自己的一轮 H800 结果，`--h800-reference 目录或json文件` 都行；重新生成用 `python3 tools/h800_reference.py `。 -- **有一处覆盖不到**：这轮 H800 数据里 `spmm_bsr`、`spmm_bell` 没有 cuSPARSE 时间（当时基线只有 CuPy，没有 - BSR / Blocked-ELL 的库），所以这两个算子没有折算基线，行保持 `N/A`；`spmm_csc` 只有 240/720 行有。 +- **H800 没有 cuSPARSE 时间的**：65 个交付变体里只有 `sddmm_csr_f16_int_non_non_row`（cuSPARSE SDDMM 不支持 + fp16），改用 H800 上 PyTorch 的时间折算，标 `(H800 PyTorch折算)`。非交付的 `spmm_bsr`、`spmm_bell` 两者都没有。 + 按 10-09 的天数结果预演，换成新参照后有加速比的变体从 25 个变为 55 个；剩下的是不测的 fp64 9 个，和那轮因 TLE + 编译失败全部 FAIL 的 `spgemm_csr`（已修，见 `debug/ILUVATAR.md` 改动记录，重跑后可折算）。 - 每行的 `performance.csv` 会多 5 列：`h800_vendor_ms`（H800 上 cuSPARSE 的用时）、`h800_scaled_ms`（折算后的时间， 即基线 1）、`h800_scaled_fs_ms`（天数上 FlagSparse 的实测）、`speedup_vs_h800_scaled`（加速比）、`h800_scaled_card`（用的卡）。 `summary.json` / `summary.csv` 里交付变体的 speedup 就是这个数，`delivery_table.py` 也照常显示。 diff --git a/docs/MACA.md b/docs/MACA.md index a414f037..208d9dcb 100644 --- a/docs/MACA.md +++ b/docs/MACA.md @@ -56,6 +56,7 @@ setsid timeout -s KILL 39600 python3 -u run_flagsparse_pytest.py \ --phase both --mode normal --delivery-only --gpus 0 --timeout 4500 \ --benchmark-input /root/gcx/matrix --benchmark-warmup 5 --benchmark-iters 20 \ --op-benchmark-args='sddmm_csr=--no-cusparse' \ + --h800-reference \ --results-dir pytest_results_metax_delivery \ > pytest_results_metax_delivery.log 2>&1 < /dev/null & ``` @@ -63,6 +64,12 @@ setsid timeout -s KILL 39600 python3 -u run_flagsparse_pytest.py \ - `--op-benchmark-args='sddmm_csr=--no-cusparse'`：C550 上没有可用的厂商稀疏库，且 SDDMM 的 `torch.sparse.sampled_addmm` 结果还是错的（7.3 节），因此只对 SDDMM 禁用该参考并改用 PyTorch。 `--benchmark-args` 会广播给所有性能脚本，而其他脚本不一定接受 `--no-cusparse`，所以用逐算子的形式； +- `--h800-reference`（不带值）：没有任何基线的行（目前是 legacy `spmv_csr` f32/f64、SDDMM 的 q4 变体、`spgemm_csr`）， + 用仓库自带的 H800 结果按带宽折算出加速比（`h800_scaled_ms = T_cuSPARSE@H800 × 3050/1440`），`delivery_table.py` + 里这些行后面会标 `(H800折算)`。**有 PyTorch 基线的行不受影响。** 这是估算，不是厂商实测，汇报时要注明； + `sddmm_csr_f16_int_non_non_row` 在 H800 上没有 cuSPARSE 时间（cuSPARSE SDDMM 不支持 fp16），改用 H800 上 + PyTorch 的时间折算，标 `(H800 PyTorch折算)`（C550 自己的 `sampled_addmm` 结果是错的，7.3 节）。 + 口径和覆盖见 [`H800_REFERENCE.md`](H800_REFERENCE.md)。 - `--timeout 4500`：`--delivery-only` 已把 spmv/spmm 收窄到 int32 + non，但 **SDDMM 的 4 个 K 值 不收窄**（交付名里没有 K），按 30 个矩阵实测推算全量至少 3660 秒（7.4 节；现在交付只用 10 个矩阵，这个数会小一截，没有重新实测）。只想快速出数，可以改用 `--timeout 1200 --op-benchmark-args='sddmm_csr=--k 64'`，但那样 SDDMM 的加速比只含 K=64，和 CUDA 等 diff --git a/docs/MUSA.md b/docs/MUSA.md index 64587f1d..00e9e8c8 100644 --- a/docs/MUSA.md +++ b/docs/MUSA.md @@ -45,10 +45,21 @@ mthreads-gmi # 单卡；跑之前确认没有别的任 ```bash setsid timeout -s KILL 43200 python3 -u run_flagsparse_split_delivery.py \ --mode normal --benchmark-input /root/gcx/matrix --timeout 3600 \ + --h800-reference \ --results-dir pytest_results_mthreads_split \ > pytest_results_mthreads_split.log 2>&1 < /dev/null & ``` +- `--h800-reference`（不带值）：muSPARSE 不支持、标 `NoBaseline` 的变体（2026-10-09 那轮是 26 个），用仓库自带的 + H800 结果按带宽折算出加速比（H800 时间 × 3050/1370），`delivery_table.py` 里标 `(H800折算)`。**muSPARSE 测得到的 + 变体不受影响。** 加了这个开关，pytest 那一半会从只跑精度变成**精度 + 性能**（多一轮 Python 性能，耗时会明显变长）， + 因为 C API 的规模（SpMM `n=8`、SDDMM `k=16/64`、向量类按矩阵）在 H800 上没有对应数据，而 Python 侧和 H800 参照 + 是同一个 benchmark、同样规模。合并时按顺序取：① C API 有 muSPARSE 基线 → C API；② Python 侧有折算加速比 → + Python（条目里 `source: python`，原 C API 结果留在 `capi_performance`）；③ 否则用 C API 时间对 H800 同变体同矩阵 + 折算（只有 SpMV 类能对上）。日志会打印三类各补了多少、还剩哪些。`sddmm_csr_f16` 在 H800 上没有 cuSPARSE 时间， + 用 H800 PyTorch 折算，标 `(H800 PyTorch折算)`。是估算，不是厂商实测，汇报时要注明；见 + [`H800_REFERENCE.md`](H800_REFERENCE.md)。 + `--timeout` 同时传给 pytest 的每个算子阶段和 C API 的 CTest（`-DFLAGSPARSE_CTEST_TIMEOUT`）。 首次运行会从零构建 C API（`capi/build`），配置日志里应当出现 `ctest baseline: MUSA -> /usr/local/musa/lib/libmusparse.so`；已经构建过可加 `--skip-capi-build`。 diff --git a/docs/debug/ASCEND.md b/docs/debug/ASCEND.md index e1056ff0..6e35220d 100644 --- a/docs/debug/ASCEND.md +++ b/docs/debug/ASCEND.md @@ -63,6 +63,33 @@ ## 上机要做的事，按优先级（工作量从小到大） +### 先做：65 变体全量重测（带 `--h800-reference`） + +```bash +source /usr/local/Ascend/ascend-toolkit/latest/set_env.sh +export PYTHONPATH="$PWD/src:$PWD" FLAGSPARSE_BACKEND=ascend FLAGSPARSE_ASCEND_VENDOR=torch +setsid timeout -s KILL 43200 python3 -u run_flagsparse_pytest.py \ + --phase both --mode normal --delivery-only --gpus 6,7 --timeout 3600 \ + --benchmark-input /home/matrix --benchmark-warmup 5 --benchmark-iters 20 \ + --h800-reference \ + --results-dir results_ascend_65_<日期> \ + > results_ascend_65_<日期>.log 2>&1 < /dev/null & +# 跑完后 +python3 tools/delivery_table.py results_ascend_65_<日期> +``` + +- 和 `pytest_results_ascend_full_20261011` 那轮是同一条命令，只多了 `--h800-reference`；`--results-dir` 用新目录。 +- `--h800-reference`（不带值）：没有 PyTorch-NPU 基线的行（10-11 那轮 25 个）用仓库自带的 H800 结果折算，交付表里标 + `(H800折算)` / `(H800 PyTorch折算)`，有 PyTorch-NPU 基线的行不受影响。卡型自动识别为 `ascend-910b`，日志里应出现 + `ascend-910b`；没有就加 `--vendor-card ascend-910b`。口径见 [`../H800_REFERENCE.md`](../H800_REFERENCE.md)。 +- 这轮要确认的三件事： + 1. `spgemm_csr` 的性能现在走 `tests/test_spgemm.py`（交付矩阵、按 dtype 分进程），**第一次在 NPU 上跑**。日志里 + 应有 `performance_float32.csv` / `performance_float64.csv`，`spgemm_csr_f32_int_non_non` 应有加速比； + 报错就贴 `spgemm_csr/performance_stderr.log` 原文。 + 2. `scatter_i8_int` 应在全量里出现（10-11 那轮是 NotFound，当时 dtype 列表还没 int8；v2 单跑是 0.999x）。 + 3. 折算补上的变体预演出来大多是 0.001x–0.06x：这些 q4 路径每次调用 0.3–2.4 秒（`timed=call`）。这是真实性能， + 回传时照实报，并附上 `delivery_table.py` 全表。 + ### 优先级 1：验证今天接上路由的 8 个变体（预计几分钟） ```bash @@ -343,3 +370,13 @@ git pull --ff-only origin main 之后，完整读一遍 docs/debug/ASCEND.md， - 为什么改（附报错原文）：在有 CUDA 的机器上，CPU 张量会触发 `ValueError: Expected a cuda device, but got: cpu`，9 个用例失败；CI 的 pre-commit 也会修改这个文件。 - 怎么验证的：有 GPU 和 `CUDA_VISIBLE_DEVICES=` 两种环境下都是 11 passed；连续两遍 `make pre-commit-check` 都通过。 - 其他后端是否也需要：否 + +### 2026-10-14 run_flagsparse_pytest.py::ASCEND_PERFORMANCE_COMMANDS["spgemm_csr"] +- 改了什么：昇腾 `spgemm_csr` 的性能从 `benchmark/benchmark_ascend_probe.py` 改为 `tests/test_spgemm.py`（与其他后端同一脚本）， + 并走按 dtype 分进程的 `_run_spgemm_split_dtypes`。 +- 为什么改：探测脚本测的是合成规模（`tiny_square`、`square_1k` 等 20 个），`pytest_results_ascend_full_20261011` 里 + `spgemm_csr_f32_int_non_non` 因此没有加速比，也无法和 H800 参照（交付矩阵）对上做折算。 +- 怎么验证的：只在 CUDA 机器上确认路由（`tests/ci/test_runner_result_format.py::test_ascend_spgemm_is_measured_on_the_delivery_matrices`）； + `tests/test_spgemm.py` 在 NPU 上能否跑通**未验证**，昇腾 spgemm 的精度用例是通过的（同一 `flagsparse_spgemm_csr`）。 +- 其他后端是否也需要：否。 + diff --git a/docs/debug/ILUVATAR.md b/docs/debug/ILUVATAR.md index 4db3c923..7b2ff3ee 100644 --- a/docs/debug/ILUVATAR.md +++ b/docs/debug/ILUVATAR.md @@ -44,6 +44,12 @@ python3 tools/delivery_table.py results_iluvatar_65_<日期> - 原 20 个变体里的 9 个 f64/c64 在本卡上照旧测不了，`delivery_table.py` 里显示 `NotFound` 是预期的； `spgemm_csr` 的 runner 会按 dtype 拆开分别跑 float32/float64，float64 那一组的结果不要当真，交付变体 只用 float32。 +- **`spgemm_csr` 这轮第一次真正能跑**：10-09 那轮 20 行全部是 TLE 编译错误 + （`iluvatar TLE alloc does not support nv_mma_shared_layout=True`），现在天数上改走 ESC（见文末改动记录）。 + 跑完确认 `spgemm_csr_f32_int_non_non` 精度 Passed、有 `(H800折算)` 加速比；还报 TLE 就说明没拉到新代码。 +- **`--h800-reference` 现在能补上 q4 变体**：10-09 那轮命令也带了它，但当时的参照只有 20 个变体，q4 全对不上， + 所以全是 `-`。参照已换成 H800 的 65 变体全量，按那轮数据预演，有加速比的变体 25 → 55（再加 spgemm 就是 56， + 其余 9 个是 fp64）。`sddmm_csr_f16` 标的是 `(H800 PyTorch折算)`，见 [`../H800_REFERENCE.md`](../H800_REFERENCE.md)。 - 外层 `timeout -s KILL` 是整条命令的总限时（内核卡死时 Ctrl-C 送不进去，只能靠 KILL）；`--timeout` 是 每个算子每个阶段的限时。这两个数是 20 变体时期定的，65 变体多了 6 个父算子，**没有在本机实测过总耗时**， @@ -131,4 +137,14 @@ python3 tools/delivery_table.py results_iluvatar_65_<日期> - 其他后端是否也需要：是 / 否 / 不确定 ``` -（暂无） +### 2026-10-14 src/flagsparse/sparse_operations/spgemm_csr.py::_spgemm_compute +- 改了什么：天数上 SpGEMM 不再走 TLE 共享内存哈希路径，和 MUSA、XPU 一样走纯 ESC（`tle_hash_safe` 加上 `_is_iluvatar_runtime()`）。 +- 为什么改（附报错原文）：天数的 FlagTree 能 import TLE，但编译哈希表时报 + `Memory allocation failed: iluvatar TLE alloc does not support nv_mma_shared_layout=True`， + `results_iluvatar_65_1009` 里 spgemm 的 20 行（f32/f64 × 10 矩阵）全部 FAIL，精度 2/2 失败。 + （那轮汇总把性能写成 Passed 是 `04135dd` 时 runner 的问题，当前 main 会判为 Failed。） +- 怎么验证的：CUDA 机器上 `FLAGSPARSE_BACKEND=iluvatar` 模拟：哈希路径调用 0 次，结果与稠密乘积一致； + `tests/pytest -m spgemm_csr` 11 passed；`tests/ci/test_spgemm_tle_gate.py`（旧代码上失败）。**未在 BI-V150 实测**， + ESC 在天数上的性能也还没有数。 +- 其他后端是否也需要：否（MUSA、XPU 早已排除）。 + diff --git a/docs/debug/MACA.md b/docs/debug/MACA.md index a1c3417b..93eca799 100644 --- a/docs/debug/MACA.md +++ b/docs/debug/MACA.md @@ -14,6 +14,7 @@ setsid timeout -s KILL 43200 python3 -u run_flagsparse_pytest.py \ --phase both --mode normal --gpus 0 --timeout 4500 \ --benchmark-input tests/data --benchmark-warmup 5 --benchmark-iters 20 \ --op-benchmark-args='sddmm_csr=--no-cusparse' \ + --h800-reference \ --results-dir results_metax_65_<日期> \ > results_metax_65_<日期>.log 2>&1 < /dev/null & # 跑完后 @@ -23,6 +24,12 @@ python3 tools/delivery_table.py results_metax_65_<日期> - `--benchmark-input tests/data`：仓库里的 `tests/data` 正好就是 `conf/operators.yaml` 的 10 个交付矩阵（在 git 里，拉下来就有）。这条命令没带 `--delivery-only`，runner 不会再筛矩阵，给 30 个矩阵的目录就会 30 个全跑， 汇总出来的加速比也会掺进非交付矩阵，所以不要换成别的目录。 +- `--h800-reference`（不带值）：没有任何基线的行（目前是 legacy `spmv_csr` f32/f64、SDDMM 的 q4 变体、`spgemm_csr`）， + 用仓库自带的 H800 结果按带宽折算出加速比（`h800_scaled_ms = T_cuSPARSE@H800 × 3050/1440`），`delivery_table.py` + 里这些行后面会标 `(H800折算)`。**有 PyTorch 基线的行不受影响。** 这是估算，不是厂商实测，汇报时要注明； + `sddmm_csr_f16_int_non_non_row` 在 H800 上没有 cuSPARSE 时间（cuSPARSE SDDMM 不支持 fp16），改用 H800 上 + PyTorch 的时间折算，标 `(H800 PyTorch折算)`（C550 自己的 `sampled_addmm` 结果是错的，7.3 节）。 + 口径和覆盖见 [`../H800_REFERENCE.md`](../H800_REFERENCE.md)。 - `sddmm_csr=--no-cusparse`、`--timeout 4500` 沿用 `../MACA.md` 0.5 节的 20 变体交付命令，原因见那一节 （C550 没有可用厂商稀疏库；SDDMM 的 4 个 K 值不收窄，单阶段耗时长）。 @@ -47,6 +54,25 @@ python3 tools/delivery_table.py results_metax_65_<日期> 一直用 `--op-benchmark-args='sddmm_csr=--no-cusparse'` 绕开。这次的新 sddmm 变体（c32、f16，还有 trans/col 方向）同样会撞上这个限制，SDDMM 的厂商列预期也是空的。 +## `spmm_csc` q4 性能汇总污染（已修复） + +2026-10-08 的首轮 C550 结果把三个 q4 交付变体 +`spmm_csc_f16_int_non_non_row`、`spmm_csc_f32_int_non_non_row`、 +`spmm_csc_c32_int_non_non_row` 的性能汇总为 `Failed`，但各变体的 10 条 q4 行实际都是 `PASS`。 + +真正失败的是同一个父性能进程里的传统行： +`amazon0601.mtx / float32 / int32 / trans / row / spmm_csc_base`，报 +`reason=correctness check failed`、`err_vs_pytorch=1.397478580078429`。脚本因这条记录以 +`returncode=1` 退出；旧汇总逻辑继承父阶段的 `FAIL`，从而污染了本应通过的 q4 投影。传统行的失败本身仍是 +真实失败，修复不会隐藏它。 + +上游 `982b985` 已修复这个通用 runner 问题。仅当以下条件同时成立时，目标变体才不继承父阶段失败：父阶段 +是普通 `exit 1`（不是 timeout 或信号退出）、stderr 无 Python traceback、目标 q4 变体的矩阵行完整、且存在 +其他变体的失败行可以解释该退出。目标自身出现 `FAIL` 时仍为失败。这些限制避免异常或半程结果被错误标绿。 + +复查旧产物时，不能只看 `delivery_table.py` 的三行 `Failed`；应按 `variant` 查看原始 CSV。若目标 q4 行均为 +`PASS`，而父阶段是上述可解释的 `exit 1`，则是历史汇总污染，需用包含 `982b985` 的 runner 重跑或重投影。 + ## 跑完要确认 1. `missing` 是不是 0。 @@ -109,4 +135,41 @@ python3 tools/delivery_table.py results_metax_65_<日期> - 其他后端是否也需要：是 / 否 / 不确定 ``` -（暂无） +### 2026-10-09 `run_flagsparse_pytest.py`：上游 `982b985` +- 改了什么：为 q4 和传统交付投影增加有条件的父阶段失败归因，保留父状态和解释它的原始失败行。 +- 为什么改（附报错原文）：`spmm_csc_base` 的 `amazon0601.mtx` 行报 + `reason=correctness check failed`、`err_vs_pytorch=1.397478580078429`，误伤三个 q4 交付变体。 +- 怎么验证的：`python3 -m pytest -q tests/ci/test_delivery_parent_failure.py tests/ci/test_delivery_algorithm_scope.py`，结果 `18 passed`；本机 65 变体复测为 65/65 精度 Passed、65/65 性能 Passed。 +- 其他后端是否也需要：是（通用 runner 汇总逻辑，不改变任何后端内核）。 + +### 2026-10-13 MACA C550 kernel tuning +- 改了什么：`spmv_csr` 的 MACA 专用 gather 计划和复数成对加载内核；`spmv_sell` 的 MACA + group/warp；复数 scatter 的 block size；`spmm_csc` 复数转置的 `BLOCK_NNZ`。所有分支均由 + `_is_maca_runtime()` 保护，CUDA/ROCm/MUSA/Ascend 路径不变。 +- 为什么改：C550 上原 CUDA 参数在部分稳定样本低于 0.8x（CSR、SELL、complex scatter，以及 + `amazon0601` CSC complex trans/conj）。重复测量同时观察到偶发 3--4 ms 运行时尖峰，未修改 + baseline、计时器或汇总逻辑。 +- 怎么验证的：CSR q4 80 行全部 `PASS`，最低 `speedup_vs_pytorch=1.007x`；complex scatter + 32 行全部 `PASS`，最低 `0.90x`；CSR/CSC 相关精度与缓存测试共 279 项通过。CSC complex + trans/conj 10 矩阵、2 索引类型共 40 行全部通过，PyTorch COO 参考下最低约 `20.5x`（本机 + 没有可用厂商 CSC baseline）。SELL 复测中位数均高于 0.8x；NACA complex64 有单次尖峰， + 10 次中位数约 `3.7x`，原始日志保存在 `results_metax_opt_20261013/`。 +- 其他后端是否也需要：否。 + +### 2026-10-14 SpGEMM 结果比较与 worker 可靠性 +- 改了什么：`tests/test_spgemm.py` 比较 CSR 结果前过滤显式零，避免与 SciPy 丢弃显式零后的结构差异被误报为 `nnz mismatch`；补充独立 matrix-worker CLI 和异常 stderr 归因。新增 `tests/ci/test_spgemm_compare.py` 以及对应的 CI 注册检查。 +- 为什么改：MACA 上 SpGEMM 的数学结果可能保留取消相消产生的显式零，而 SciPy 参考会丢弃这些项；原比较器按结构 nnz 直接失败，属于比较器误报，不是 kernel 精度错误。 +- 怎么验证的：SpGEMM comparator 回归与相关 CI 检查共 `15 passed`；MACA 10 个测试矩阵的 float32/float64 结果均通过精度检查。 +- 其他后端是否也需要：是（比较器和 runner 是通用逻辑；不改变后端 kernel）。 + +### 2026-10-14 MACA reuse / CSR gather accuracy coverage +- 改了什么：`tests/pytest/test_rocm_sparse_reuse_accuracy.py` 的门控扩展为 ROCm 或 MACA；新增 `tests/pytest/test_spmv_csr_maca_accuracy.py`，覆盖 MACA CSR gather 的 float16/complex64、int32/int64、non/trans/conj、空行和长行，以及 inference/live value 更新；CPU transpose 回归测试固定使用 portable launch 模拟。 +- 为什么改：这些测试验证的是共享的 preparation/cache-reuse 合约，原来只在 ROCm 执行会漏掉 MACA 专用 gather 路径；C550 的复数成对加载路径还需要直接精度回归。 +- 怎么验证的：MACA reuse 测试 `18 passed`；CSR/CSC 相关精度、缓存和 kernel 回归合计 `279 passed`。 +- 其他后端是否也需要：门控改动只扩大到 MACA；portable CPU 测试保持其他后端行为不变。 + +### 2026-10-14 MACA 性能 baseline 规则 +- 改了什么：MACA 性能交付统一使用 PyTorch 作为稀疏 baseline：`FLAGSPARSE_MACA_VENDOR=torch`。CuPy/cuSPARSE 可用时才切换到 `cupy_cusparse`；`none` 只用于明确不需要加速比的诊断运行。 +- 为什么改：C550 实机没有可用 CuPy/cuSPARSE。此前 65 算子命令继承了 `FLAGSPARSE_MACA_VENDOR=none`，导致 `spmv_csr_f32_int_non` 和 `spmv_csr_f64_int_non` 的 baseline/加速比显示 `N/A`；这是启动环境造成的，不是 CSR kernel 没有 baseline 实现。 +- 怎么验证的：设置 `FLAGSPARSE_MACA_VENDOR=torch` 后，`results_metax_spmv_csr_baseline_20261014/` 中两种 base CSR 变体均有 baseline 且性能 `Passed`；float32 聚合加速比 `295.033x`，float64 为 `284.441x`。规则和完整启动命令同步写在 `docs/MACA.md`。 +- 其他后端是否也需要：否；该环境变量和默认选择仅影响 MACA baseline。 diff --git a/docs/debug/MUSA.md b/docs/debug/MUSA.md index bfd4a2a4..10d6460a 100644 --- a/docs/debug/MUSA.md +++ b/docs/debug/MUSA.md @@ -10,6 +10,7 @@ ```bash setsid timeout -s KILL 43200 python3 -u run_flagsparse_split_delivery.py \ --mode normal --benchmark-input tests/data --timeout 3600 \ + --h800-reference \ --results-dir results_musa_split_65_<日期> \ > results_musa_split_65_<日期>.log 2>&1 < /dev/null & # 跑完后 @@ -21,6 +22,15 @@ python3 tools/delivery_table.py results_musa_split_65_<日期> 2026-10-08 已移植进 main（见下面"C API 现在覆盖全部 65 个变体"一节），每个变体按自己的 id 单独测一次。 muSPARSE 不支持的类型组合（fp16、int8、混合精度、SELL 等）显示 `NoBaseline`——跑通了、精度过了，只是没有 厂商对照，**这是预期的**，不是失败。 +- `--h800-reference`（不带值）：muSPARSE 不支持、标 `NoBaseline` 的变体（2026-10-09 那轮是 26 个），用仓库自带的 + H800 结果按带宽折算出加速比（H800 时间 × 3050/1370），`delivery_table.py` 里标 `(H800折算)`。**muSPARSE 测得到的 + 变体不受影响。** 加了这个开关，pytest 那一半会从只跑精度变成**精度 + 性能**（多一轮 Python 性能，耗时会明显变长）， + 因为 C API 的规模（SpMM `n=8`、SDDMM `k=16/64`、向量类按矩阵）在 H800 上没有对应数据，而 Python 侧和 H800 参照 + 是同一个 benchmark、同样规模。合并时按顺序取：① C API 有 muSPARSE 基线 → C API；② Python 侧有折算加速比 → + Python（条目里 `source: python`，原 C API 结果留在 `capi_performance`）；③ 否则用 C API 时间对 H800 同变体同矩阵 + 折算（只有 SpMV 类能对上）。日志会打印三类各补了多少、还剩哪些。`sddmm_csr_f16` 在 H800 上没有 cuSPARSE 时间， + 用 H800 PyTorch 折算，标 `(H800 PyTorch折算)`。是估算，不是厂商实测，汇报时要注明；见 + [`../H800_REFERENCE.md`](../H800_REFERENCE.md)。 - 拉到新代码后第一次跑**不要加 `--skip-capi-build`**，确保 C API 是重新编译的。 - `--timeout 3600` 沿用 `../MUSA.md` 的结论（`900` 不够），split 会把它同时传给 CTest。CUDA 上 SpMM 这一族 单独就要 18 分钟（65 个变体里 17 个是 SpMM，3 种宽度 × 10 个矩阵），MUSA 上 SpMM 只测第一种宽度。 diff --git a/run_flagsparse_pytest.py b/run_flagsparse_pytest.py index a228333d..27f2fd6c 100644 --- a/run_flagsparse_pytest.py +++ b/run_flagsparse_pytest.py @@ -300,7 +300,7 @@ def _delivery_accuracy_pytest_args(op: str, delivery_only: bool) -> list[str]: ("FlagSparse_vs_PyTorch_all_speedup", "pytorch_ms", "flagsparse_ms"), ("FlagSparse_vs_PyTorch_speedup", "pytorch_ms", "flagsparse_ms"), # LAST, so any vendor or PyTorch speedup a row has wins. Rows with neither get - # theirs from _apply_scaled_baseline: the same case's H800 FlagSparse time, + # theirs from _apply_scaled_baseline: the same case's H800 cuSPARSE time, # rescaled to this card by the bottleneck-unit peak ratio, is the baseline (1.0). ("speedup_vs_h800_scaled", "h800_scaled_ms", "h800_scaled_fs_ms"), ) @@ -905,12 +905,17 @@ def _probe_command(op: str) -> tuple[str, ...]: for op in ASCEND_PROBE_OPS if op not in ASCEND_PYTORCH_PERFORMANCE_OPS }, - # These three scripts are backend-neutral (their PyTorch baseline runs on + # These scripts are backend-neutral (their PyTorch baseline runs on # torch_npu; the cuSPARSE/cupy column comes back empty with a reason via # tests/cusparse_generic_baseline.py's skip_reason(), same as every other # non-CUDA backend), so Ascend reuses them as-is rather than needing its own - # benchmark_ascend.py routing. - **{op: PERFORMANCE_COMMANDS[op] for op in ("axpby", "spvv", "spmv_sell")}, + # benchmark_ascend.py routing. spgemm_csr was on the probe, which times + # synthetic shapes (tiny_square, square_1k, ...) instead of the delivery + # matrices -- no speedup and nothing an H800 reference could be matched to. + **{ + op: PERFORMANCE_COMMANDS[op] + for op in ("axpby", "spvv", "spmv_sell", "spgemm_csr") + }, } @@ -1584,6 +1589,10 @@ def _strict_flag_gems_perf_data(value: object) -> dict[str, object]: "details": details, "speedup": float(dtype_value.get("speedup") or 0.0), } + if dtype_value.get("baseline"): + normalized[_flag_gems_dtype(dtype)]["baseline"] = str( + dtype_value["baseline"] + ) return normalized @@ -2244,6 +2253,11 @@ def _run_spgemm_split_dtypes( "failed_dtypes": failed_dtypes, } if csv_path.exists(): + # Same as the other performance paths: rows with no vendor or PyTorch + # baseline get the scaled H800 one (rewrites the merged CSV when it fills). + rows, scaled_info = _apply_scaled_baseline(op, csv_path, rows) + if scaled_info is not None: + result["scaled_baseline"] = scaled_info write_benchmark_json_from_csv(op, csv_path, result_path, rows=rows) result.update( summarize_performance_csv( @@ -2399,10 +2413,16 @@ def _performance_schema(row: dict[str, str]) -> tuple[str, str | None, str | Non # spmm_csc reports it over pytorch_ms/ms -- so prefer an entry whose measurement # columns the row actually carries. Without this the first entry always wins and # the other operator's rows fail the completeness check and vanish from aggregates. + # The first pass wants NUMBERS: a backend with no vendor writes "N/A" into + # speedup_vs_vendor/vendor_ms, and taking that text as "carried" hid the + # scaled-H800 speedup further down the table (MACA spmv_csr f32/f64). for speedup_key, base_key, latency_key in PERFORMANCE_SPEEDUP_SCHEMAS: - if not _row_value(row, speedup_key): + if _to_float(_row_value(row, speedup_key)) is None: continue - if all(key is None or _row_value(row, key) for key in (base_key, latency_key)): + if all( + key is None or _to_float(_row_value(row, key)) is not None + for key in (base_key, latency_key) + ): return speedup_key, base_key, latency_key for speedup_key, base_key, latency_key in PERFORMANCE_SPEEDUP_SCHEMAS: if _row_value(row, speedup_key): @@ -2612,6 +2632,8 @@ def write_benchmark_json_from_csv( def _flaggems_perf_data(rows: list[dict[str, str]]) -> dict[str, object]: grouped: dict[str, dict[str, object]] = {} totals: dict[str, list[float]] = {} + scaled: dict[str, int] = {} + scaled_pytorch: set[str] = set() seen_by_dtype: dict[str, set[str]] = {} for index, row in enumerate(rows): dtype = _row_dtype(row) @@ -2637,10 +2659,27 @@ def _flaggems_perf_data(rows: list[dict[str, str]]) -> dict[str, object]: dtype_entry["details"][shape] = detail # type: ignore[index] if _performance_row_has_complete_speedup(row): totals.setdefault(dtype, []).append(speedup) + if speedup_key == _SCALED_SPEEDUP_KEY: + scaled[dtype] = scaled.get(dtype, 0) + 1 + if row.get("h800_baseline_kind") == "pytorch": + scaled_pytorch.add(dtype) for dtype, values in totals.items(): if values: grouped[dtype]["speedup"] = statistics.mean(values) + # An estimate against the rescaled H800 cuSPARSE time, not a measured + # baseline: say so, so a report can never pass it off as one. + if scaled.get(dtype): + # h800_pytorch_scaled: H800 had no vendor time for these cases (cuSPARSE + # SDDMM has no fp16), so the rescaled baseline is H800 PyTorch's. + label = ( + "h800_pytorch_scaled" + if dtype in scaled_pytorch + else _SCALED_BASELINE_LABEL + ) + grouped[dtype]["baseline"] = ( + label if scaled[dtype] == len(values) else f"partly_{label}" + ) return grouped @@ -2821,6 +2860,9 @@ def summarize_performance_csv( return summary +_SCALED_SPEEDUP_KEY = "speedup_vs_h800_scaled" +_SCALED_BASELINE_LABEL = "h800_scaled" + # Set by main() from --h800-reference; None leaves every report exactly as before. _SCALED_BASELINE: dict[str, str] | None = None @@ -2931,7 +2973,9 @@ def run_performance( # `not backend` covers an unexported FLAGSPARSE_BACKEND, which is how a CUDA # box usually runs -- it takes the generic per-operator script too. - if op == "spgemm_csr" and (not backend or backend in GENERIC_BENCHMARK_BACKENDS): + if op == "spgemm_csr" and ( + not backend or backend in GENERIC_BENCHMARK_BACKENDS or backend == "ascend" + ): return _run_spgemm_split_dtypes( project_root=project_root, op=op, diff --git a/run_flagsparse_split_delivery.py b/run_flagsparse_split_delivery.py index 0952dad4..6383596f 100644 --- a/run_flagsparse_split_delivery.py +++ b/run_flagsparse_split_delivery.py @@ -88,6 +88,16 @@ "iluvatar": "IX", } +# --h800-reference: the card each backend's C API run is rescaled to (the keys of +# tools/baseline_bound.py's VENDOR_BANDWIDTH_GBS). --vendor-card overrides it. +H800_CARD_BY_PYTEST_BACKEND = { + "mthreads": "musa-s5000", + "rocm": "dcu-bw1000", + "metax": "maca-c550", + "ascend": "ascend-910b", + "iluvatar": "iluvatar-biv150", +} + def log(message: str) -> None: print(f"[run_flagsparse_split_delivery] {message}", flush=True) @@ -110,12 +120,24 @@ def run_pytest_accuracy(args: argparse.Namespace, results_dir: Path) -> int: cmd = [ sys.executable, str(PROJECT_ROOT / "run_flagsparse_pytest.py"), - "--phase", "accuracy", + "--phase", "both" if args.h800_reference else "accuracy", "--mode", args.mode, "--delivery-only", "--gpus", args.gpus, "--results-dir", str(results_dir), ] + if args.h800_reference: + # The Python half also measures FlagSparse, in the SAME harness and sizes as + # the H800 reference (q4 bench N/K=32, the vector ops' synthetic sizes), so a + # variant the vendor library cannot measure gets a like-for-like scaled + # baseline -- the C API's own sizes (SpMM n=8 on MUSA, SDDMM k=16/64) have + # no H800 counterpart. See merge_summaries for which half wins. + cmd += [ + "--benchmark-input", str(args.benchmark_input), + "--h800-reference", str(args.h800_reference), + ] + if args.h800_card: + cmd += ["--vendor-card", args.h800_card] if args.timeout: cmd += ["--timeout", str(args.timeout)] if args.strict: @@ -244,7 +266,58 @@ def run_capi_benchmark(args: argparse.Namespace, bench_out: Path) -> int: "stdout_log_path", "stderr_log_path", "data_path", "reason", "command", ) -def merge_summaries(py_summary_path: Path, capi_summary_path: Path, out_path: Path) -> None: +def _has_speedup(performance: object) -> bool: + data = performance.get("data") if isinstance(performance, dict) else None + return isinstance(data, dict) and any( + isinstance(d, dict) and (d.get("speedup") or 0) > 0 for d in data.values() + ) + + +def apply_h800_scaled_baseline( + merged_result: dict, reference: str, card: str, py_result: dict | None = None +) -> dict: + """Fill the C API ``NoBaseline`` variants; a vendor-measured variant is never touched. + + In order of preference: + 1. the C API's own vendor baseline (muSPARSE ...) -- untouched; + 2. the Python half's performance for that variant, when it has a speedup (run + with --h800-reference it is against the H800 time of the SAME harness and + sizes) -- the C API entry is replaced and kept under ``capi_performance``; + 3. the C API's time against the H800 time of the same variant + matrix + (baseline_bound.scaled_capi_performance; only SpMV-type sizes match). + Returns {"python": n, "capi": n, "missing": [ids]}. + """ + from tools import baseline_bound + + index = baseline_bound._reference_by_variant(reference) + counts = {"python": 0, "capi": 0, "missing": []} + for variant_id, entry in merged_result.items(): + performance = entry.get("performance") + if not isinstance(performance, dict) or performance.get("status") != "NoBaseline": + continue + py_perf = ((py_result or {}).get(variant_id) or {}).get("performance") + if _has_speedup(py_perf) and str(py_perf.get("status")) == "Passed": + entry["capi_performance"] = performance + entry["performance"] = dict(py_perf, source="python") + counts["python"] += 1 + elif baseline_bound.scaled_capi_performance( + variant_id, performance, reference, card, _index=index + ): + performance["source"] = "capi" + counts["capi"] += 1 + else: + counts["missing"].append(variant_id) + return counts + + +def merge_summaries( + py_summary_path: Path, + capi_summary_path: Path, + out_path: Path, + *, + h800_reference: str | None = None, + h800_card: str | None = None, +) -> None: """One summary.json: accuracy from the pytest run, performance from the C API run. @@ -284,6 +357,15 @@ def merge_summaries(py_summary_path: Path, capi_summary_path: Path, out_path: Pa entry.setdefault("labels", py_entry.get("labels") or capi_entry.get("labels") or []) merged_result[variant_id] = entry + if h800_reference and h800_card: + counts = apply_h800_scaled_baseline( + merged_result, h800_reference, h800_card, py_result + ) + log(f"--h800-reference ({h800_card}): NoBaseline variants filled from the " + f"Python half={counts['python']}, from the C API time={counts['capi']}, " + f"still without a baseline={len(counts['missing'])} {counts['missing']} " + "(estimates, marked baseline=h800_scaled)") + merged = { "timestamp": capi_summary.get("timestamp") or py_summary.get("timestamp"), "env": {"pytest_accuracy": py_summary.get("env"), "capi_performance": capi_summary.get("env")}, @@ -397,11 +479,28 @@ def build_arg_parser() -> argparse.ArgumentParser: parser.add_argument("--skip-performance", action="store_true", help="Skip the C API performance phase.") parser.add_argument("--out", default=None, help="Merged summary.json path (default: /summary_split.json).") + parser.add_argument( + "--h800-reference", nargs="?", default=None, + const=str(PROJECT_ROOT / "conf" / "h800_reference.json"), + help="For variants the vendor library cannot measure (NoBaseline), compare " + "against the same variant+matrix's H800 cuSPARSE time rescaled to this " + "card by memory bandwidth. With no value: the bundled " + "conf/h800_reference.json. Variants with a vendor baseline are untouched.", + ) + parser.add_argument( + "--vendor-card", default=None, + help="Card for --h800-reference (default: from --backend, e.g. mthreads -> musa-s5000).", + ) return parser def main() -> int: args = build_arg_parser().parse_args() + args.h800_card = args.vendor_card or H800_CARD_BY_PYTEST_BACKEND.get(args.backend) + if args.h800_reference and not args.h800_card: + log(f"--h800-reference: no card known for backend {args.backend!r}; " + "pass --vendor-card") + return 2 results_dir = Path(args.results_dir) if args.results_dir else Path( f"pytest_results_{args.backend}_split_accuracy" @@ -427,7 +526,14 @@ def main() -> int: if not args.skip_accuracy and not args.skip_performance: out_path = Path(args.out) if args.out else results_dir / "summary_split.json" - merge_summaries(results_dir / "summary.json", bench_out / "summary.json", out_path) + card = args.h800_card + merge_summaries( + results_dir / "summary.json", + bench_out / "summary.json", + out_path, + h800_reference=args.h800_reference, + h800_card=card, + ) if accuracy_rc != 0: log(f"pytest accuracy phase exited {accuracy_rc}") diff --git a/src/flagsparse/sparse_operations/_spmv_csr_maca.py b/src/flagsparse/sparse_operations/_spmv_csr_maca.py new file mode 100644 index 00000000..0025df11 --- /dev/null +++ b/src/flagsparse/sparse_operations/_spmv_csr_maca.py @@ -0,0 +1,40 @@ +# Copyright 2026 FlagOS Contributors +# SPDX-License-Identifier: Apache-2.0 +"""MACA CSR complex gather with contiguous component loads.""" + +import triton +import triton.language as tl + + +@triton.jit +def complex_gather(V, I, P, ORDER, ROWS, X, Y, M: tl.constexpr, + CONJ: tl.constexpr, INDIRECT: tl.constexpr, + R: tl.constexpr, K: tl.constexpr): + ids = tl.program_id(0) * R + tl.arange(0, R) + valid_rows = ids < M + rows = tl.load(ROWS + ids, valid_rows, other=0) + starts = tl.load(P + rows, valid_rows, other=0) + ends = tl.load(P + rows + 1, valid_rows, other=0) + slots = tl.arange(0, K) + parts = tl.arange(0, 2) + re = tl.zeros((R, K), tl.float32) + im = tl.zeros((R, K), tl.float32) + for base in range(0, tl.max(ends - starts, 0), K): + pos = starts[:, None] + base + slots[None, :] + valid = valid_rows[:, None] & (pos < ends[:, None]) + col = tl.load(I + pos, valid, other=0) + source = tl.load(ORDER + pos, valid, other=0) if INDIRECT else pos + # Loading the adjacent components together lets MACA vectorize the + # memory operations instead of issuing separate component gathers. + values = tl.load(V + 2 * source[:, :, None] + parts[None, None, :], + valid[:, :, None], other=0.) + vector = tl.load(X + 2 * col[:, :, None] + parts[None, None, :], + valid[:, :, None], other=0.) + vr, vi = tl.split(values) + xr, xi = tl.split(vector) + if CONJ: + vi = -vi + re += vr * xr - vi * xi + im += vr * xi + vi * xr + result = tl.join(tl.sum(re, 1), tl.sum(im, 1)) + tl.store(Y + 2 * rows[:, None] + parts[None, :], result, valid_rows[:, None]) diff --git a/src/flagsparse/sparse_operations/_spmv_csr_transpose.py b/src/flagsparse/sparse_operations/_spmv_csr_transpose.py index e6c63f22..a42be1e8 100644 --- a/src/flagsparse/sparse_operations/_spmv_csr_transpose.py +++ b/src/flagsparse/sparse_operations/_spmv_csr_transpose.py @@ -6,7 +6,7 @@ import triton import triton.language as tl -from ._common import _gather_values +from ._common import _gather_values, _is_maca_runtime def prepare(indices, indptr, shape): @@ -31,6 +31,10 @@ def make_plan(cols, ptr, order, n): row_order = (torch.argsort(counts, stable=True) if avg <= 4 and maximum > 128 else torch.arange(n, device=ptr.device)).to(ptr.dtype) lanes = 4 if avg <= 4 else 8 if avg <= 8 else 16 if avg <= 24 else 32 + if _is_maca_runtime(): + # C550 favors a small one-wave tile over the CUDA 512-element tile. + lanes = 32 if avg >= 32 else 16 + return cols, ptr, order, row_order, lanes, 128 // lanes, {} return cols, ptr, order, row_order, lanes, 512 // lanes, {} @@ -79,6 +83,9 @@ def gather(values, x, plan, n_rows, conjugate=False, out=None): x = x.resolve_conj().contiguous() complex_values = values.is_complex() cols, ptr, order, rows, lanes, batch, cache = plan + maca = _is_maca_runtime() + if maca and not complex_values: + batch *= 2 # Torch in-place updates invalidate the materialized value order. Inference # tensors have no version counter and use live indirect loads instead. if order is not None: @@ -101,10 +108,18 @@ def gather(values, x, plan, n_rows, conjugate=False, out=None): v, vector, target = cache["component_view"], torch.view_as_real(x), torch.view_as_real(y) else: v, vector, target = values, x, y - _gather[(triton.cdiv(n_rows, batch),)]( - v, cols, ptr, order if order is not None else cols, rows, vector, target, - n_rows, complex_values, conjugate, order is not None, batch, lanes, num_warps=4, - ) + if maca and values.dtype == torch.complex64: + from ._spmv_csr_maca import complex_gather + complex_gather[(triton.cdiv(n_rows, batch),)]( + v, cols, ptr, order if order is not None else cols, rows, vector, target, + n_rows, conjugate, order is not None, batch, lanes, num_warps=1, + ) + else: + _gather[(triton.cdiv(n_rows, batch),)]( + v, cols, ptr, order if order is not None else cols, rows, vector, target, + n_rows, complex_values, conjugate, order is not None, batch, lanes, + num_warps=(1 if maca else 4), + ) return y diff --git a/src/flagsparse/sparse_operations/gather_scatter.py b/src/flagsparse/sparse_operations/gather_scatter.py index 21121dae..b7b99469 100644 --- a/src/flagsparse/sparse_operations/gather_scatter.py +++ b/src/flagsparse/sparse_operations/gather_scatter.py @@ -129,6 +129,9 @@ def _scatter_dtype_error_message(): def _resolve_scatter_block_size(block_size, value_dtype=None, nnz=None): """Choose the backend default while preserving explicit launch overrides.""" if block_size is None: + if _is_maca_runtime() and value_dtype in (torch.complex64, torch.complex128): + # C550 needs more programs to hide the complex indexed-store latency. + return 64 if _is_rocm_runtime(): # gfx936 needs different program counts for sparse puts: fp32's # 4K/64K cases favor 64 elements per program, while its 16K case diff --git a/src/flagsparse/sparse_operations/spgemm_csr.py b/src/flagsparse/sparse_operations/spgemm_csr.py index 015e509d..bc92d214 100644 --- a/src/flagsparse/sparse_operations/spgemm_csr.py +++ b/src/flagsparse/sparse_operations/spgemm_csr.py @@ -1146,7 +1146,12 @@ def _spgemm_compute(prepared): # CUDA and other backends retain the optimized TLE route. XPU's Triton # lowering cannot legalize TLE's shared-memory local_ptr/make_range path; # ESC uses ordinary device tensor operations and remains fully on-device. - tle_hash_safe = not (_is_mthreads_runtime() or _is_xpu_runtime()) + # Iluvatar's FlagTree imports TLE but cannot compile its shared-memory + # tables ("iluvatar TLE alloc does not support nv_mma_shared_layout=True", + # BI-V150, 2026-10-09), which failed every SpGEMM row there. + tle_hash_safe = not ( + _is_mthreads_runtime() or _is_xpu_runtime() or _is_iluvatar_runtime() + ) if tle_hash_safe and _TLE_AVAILABLE and prepared.a_data.dtype in (torch.float32, torch.float64): try: result = _spgemm_hash_hybrid_compute(prepared) diff --git a/src/flagsparse/sparse_operations/spmm_csc.py b/src/flagsparse/sparse_operations/spmm_csc.py index 6bdcb15a..14fb0020 100644 --- a/src/flagsparse/sparse_operations/spmm_csc.py +++ b/src/flagsparse/sparse_operations/spmm_csc.py @@ -522,6 +522,10 @@ def _select_spmm_csc_block_nnz(max_col_nnz, dtype, transposes=False): return 8 pow2 = 1 << max(0, int(max_col_nnz) - 1).bit_length() if transposes: + if _is_maca_runtime() and dtype == torch.complex64: + # C550 spills the CUDA-sized complex reduction tile. Stream eight + # entries per iteration instead of materializing 512 x BLOCK_N. + return 8 return max(8, min(512, pow2)) hi = 64 if _is_complex_dtype(dtype) else 128 return max(8, min(hi, pow2)) diff --git a/src/flagsparse/sparse_operations/spmv_csr.py b/src/flagsparse/sparse_operations/spmv_csr.py index 768556dd..880b658b 100644 --- a/src/flagsparse/sparse_operations/spmv_csr.py +++ b/src/flagsparse/sparse_operations/spmv_csr.py @@ -1052,8 +1052,14 @@ def prepare_spmv_csr( prepared.transpose_plan = _spmv_csr_transpose.prepare( kernel_indices, kernel_indptr, shape ) + # C550 uses row gathers for half values and for complex matrices with + # enough entries per row to amortize the reduction. Very sparse complex + # matrices retain segbin, which avoids long-row imbalance in this regime. elif (not transpose and requested_alg == "auto" and config is None - and _backend_name() == "cuda" and data.dtype in (torch.float16, torch.complex64)): + and data.dtype in (torch.float16, torch.complex64) + and (_backend_name() == "cuda" or + (_is_maca_runtime() and + (data.dtype == torch.float16 or data.numel() > 4 * n_rows)))): from . import _spmv_csr_transpose prepared.non_gather_plan = _spmv_csr_transpose.make_plan( kernel_indices, kernel_indptr, None, n_rows) diff --git a/src/flagsparse/sparse_operations/spmv_sell.py b/src/flagsparse/sparse_operations/spmv_sell.py index 6ab120bb..888abccf 100644 --- a/src/flagsparse/sparse_operations/spmv_sell.py +++ b/src/flagsparse/sparse_operations/spmv_sell.py @@ -358,6 +358,10 @@ def flagsparse_spmv_sell( group, num_warps = 4, 1 else: group, num_warps = 8, 2 + elif _is_maca_runtime() and values.dtype in (torch.float16, torch.float32, torch.int8): + # One slice per wave avoids C550's long serial slot loops and + # improves occupancy for both real and integer SELL gathers. + group, num_warps = 1, 1 else: num_warps = 4 # gfx936 wavefront occupancy is sensitive to how many slices one @@ -386,6 +390,7 @@ def flagsparse_spmv_sell( _spmv_sell_complex_parallel_kernel[(n_slices,)]( *complex_args, SLICE=slice_size, BLOCK_R=block_r, SLOTS=(8 if _is_rocm_runtime() else 4), + num_warps=(1 if _is_maca_runtime() and values.dtype == torch.complex64 else 4), ) elif _is_rocm_runtime() and not _is_complex_dtype(values.dtype): _rocm_sell_real_kernel( diff --git a/tests/ci/test_baseline_bound.py b/tests/ci/test_baseline_bound.py index dec924d4..11d84918 100644 --- a/tests/ci/test_baseline_bound.py +++ b/tests/ci/test_baseline_bound.py @@ -587,11 +587,103 @@ def test_a_reference_row_without_a_usable_library_time_gets_nothing(tmp_path): out, info = _scaled(tmp_path, ref, rows) assert info["filled"] == 1 and out[2]["speedup_vs_h800_scaled"] != "" assert info["skipped"] == { - "the reference row has no vendor-library (cuSPARSE) time": 1, + "the reference row has no vendor-library (cuSPARSE) time nor a PyTorch time": 1, "the reference's vendor-library result did not match FlagSparse": 1, } - # FlagSparse's own H800 time is NOT a fallback: there is no baseline to stand in for. + # FlagSparse's own H800 time is NOT a fallback (only H800 PyTorch's is, see + # test_pytorch_is_the_fallback_only_when_the_reference_has_no_library_time). assert ( out[0]["speedup_vs_h800_scaled"] == "" and out[1]["speedup_vs_h800_scaled"] == "" ) + + +def test_q4_variants_of_one_case_are_matched_to_their_own_reference_row(tmp_path): + # f16 and f16->f32 SpMV share matrix, dtype and op; only `variant`/`out_dtype` + # tell them apart. Each must take ITS H800 time, not be dropped as ambiguous. + ref = [ + _ref("a.mtx", "float16", t=1.0, variant="v_f16", out_dtype="float16"), + _ref("a.mtx", "float16", t=3.0, variant="v_f16f32", out_dtype="float32"), + ] + rows = [ + _ven("a.mtx", "float16", t=1.0, variant="v_f16", out_dtype="float16"), + _ven("a.mtx", "float16", t=1.0, variant="v_f16f32", out_dtype="float32"), + ] + out, info = _scaled(tmp_path, ref, rows) + assert info["filled"] == 2, info + assert [float(r["h800_vendor_ms"]) for r in out] == [1.0, 3.0] + + +def _capi_entry(details, result="NoBaseline"): + return { + "status": result, + "data": { + "fp16": { + "result": result, + "details": details, + "speedup": 0.0, + "reason": ["muSPARSE: unsupported"], + } + }, + } + + +def test_capi_nobaseline_entry_gets_the_rescaled_h800_time(tmp_path): + from tools.baseline_bound import scaled_capi_performance + + root = tmp_path / "h800" + _write_csv( + root / "spmv_csr" / "performance.csv", + [ + _ref("/x/A.mtx", "float16", t=1.0, variant="v1"), + _ref("/x/B.mtx", "float16", t=2.0, variant="v1"), + _ref("/x/A.mtx", "float16", t=9.0, variant="other"), + ], + ) + perf = _capi_entry( + { + "A": {"base": 0.0, "gems": 2.0, "speedup": 0.0}, + "B": {"base": 0.0, "gems": 1.0, "speedup": 0.0}, + "C_n=8": {"base": 0.0, "gems": 1.0, "speedup": 0.0}, # no H800 case + } + ) + info = scaled_capi_performance("v1", perf, str(root), "iluvatar-biv150") + assert info["filled"] == 2 + assert info["skipped"] == {"no such case in the reference run": 1} + dt = perf["data"]["fp16"] + assert dt["details"]["A"]["base"] == pytest.approx(1.0 * CARD_RATIO) + assert dt["details"]["A"]["speedup"] == pytest.approx(CARD_RATIO / 2.0) + assert dt["speedup"] == pytest.approx((CARD_RATIO / 2.0 + 2.0 * CARD_RATIO) / 2) + assert dt["result"] == "Passed" and dt["baseline"] == "h800_scaled" + assert dt["no_baseline_reason"] == ["muSPARSE: unsupported"] + assert perf["status"] == "Passed" + + +def test_capi_entry_with_a_vendor_baseline_is_left_alone(tmp_path): + from tools.baseline_bound import scaled_capi_performance + + root = tmp_path / "h800" + _write_csv(root / "spmv_csr" / "performance.csv", [_ref("A.mtx", variant="v1")]) + perf = _capi_entry({"A": {"base": 0.5, "gems": 1.0, "speedup": 0.5}}, "Passed") + before = json.loads(json.dumps(perf)) + assert scaled_capi_performance("v1", perf, str(root), "musa-s5000") is None + assert perf == before + + +def test_pytorch_is_the_fallback_only_when_the_reference_has_no_library_time(tmp_path): + ref = [ + # cuSPARSE has a time -> it is the baseline, PyTorch ignored + _ref("a.mtx", t=1.0, pytorch_ms=5.0, pytorch_max_error=1e-7), + # no cuSPARSE time (fp16 SDDMM) -> PyTorch's, checked and close + _ref("b.mtx", t=1.0, cusparse_ms="", pytorch_ms=4.0, pytorch_max_error=1e-7), + # no cuSPARSE time and PyTorch's result was wrong -> nothing + _ref("c.mtx", t=1.0, cusparse_ms="", pytorch_ms=4.0, pytorch_max_error=1.2), + # cuSPARSE ran and disagreed -> nothing, not a PyTorch fallback + _ref("d.mtx", t=1.0, cusparse_ms="", cu_status="FAIL", pytorch_ms=4.0), + ] + rows = [_ven(m, t=1.0) for m in ("a.mtx", "b.mtx", "c.mtx", "d.mtx")] + out, info = _scaled(tmp_path, ref, rows) + assert [r["h800_baseline_kind"] for r in out] == ["cusparse", "pytorch", "", ""] + assert float(out[0]["h800_vendor_ms"]) == 1.0 + assert float(out[1]["h800_vendor_ms"]) == 4.0 + assert info["filled"] == 2 and info["pytorch_fallback"] == 1 diff --git a/tests/ci/test_benchmark_policy.py b/tests/ci/test_benchmark_policy.py index ad016c19..e0ef6dbe 100644 --- a/tests/ci/test_benchmark_policy.py +++ b/tests/ci/test_benchmark_policy.py @@ -86,3 +86,10 @@ def test_spgemm_records_the_item_in_flight_for_crash_attribution(): # written before the work starts, cleared only after a clean finish assert "_record_spgemm_inflight(" in export assert "_clear_spgemm_inflight(csv_path)" in export + + +def test_spgemm_matrix_worker_cli_is_registered(): + source = (PROJECT_ROOT / "tests" / "test_spgemm.py").read_text(encoding="utf-8") + assert '"--_matrix-worker"' in source + assert "if args._matrix_worker:" in source + assert "_run_matrix_worker(args)" in source diff --git a/tests/ci/test_delivery_table.py b/tests/ci/test_delivery_table.py index a1eb3278..86b14926 100644 --- a/tests/ci/test_delivery_table.py +++ b/tests/ci/test_delivery_table.py @@ -46,3 +46,34 @@ def test_missing_variant_is_printed_and_fails(tmp_path): proc = _run(tmp_path, result) assert proc.returncode == 1 assert f"{ids[0]}" in proc.stdout and "MISSING" in proc.stdout + + +def test_a_speedup_against_the_scaled_h800_time_is_labelled(tmp_path): + ids = [v["id"] for v in load_delivery_variants()] + result = { + vid: { + "accuracy": {"status": "Passed"}, + "performance": {"status": "Passed", "data": {"fp32": {"speedup": 2.0}}}, + } + for vid in ids + } + result[ids[0]]["performance"]["data"]["fp32"]["baseline"] = "h800_scaled" + proc = _run(tmp_path, result) + assert proc.returncode == 0, proc.stderr + labelled = [line for line in proc.stdout.splitlines() if "(H800折算)" in line] + assert len(labelled) == 1 and ids[0] in labelled[0] + + +def test_a_pytorch_fallback_speedup_says_so(tmp_path): + ids = [v["id"] for v in load_delivery_variants()] + result = { + vid: { + "accuracy": {"status": "Passed"}, + "performance": {"status": "Passed", "data": {"fp32": {"speedup": 2.0}}}, + } + for vid in ids + } + result[ids[0]]["performance"]["data"]["fp32"]["baseline"] = "h800_pytorch_scaled" + proc = _run(tmp_path, result) + line = next(line for line in proc.stdout.splitlines() if ids[0] in line) + assert "(H800 PyTorch折算)" in line diff --git a/tests/ci/test_h800_reference.py b/tests/ci/test_h800_reference.py index 73119e19..2a924fe6 100644 --- a/tests/ci/test_h800_reference.py +++ b/tests/ci/test_h800_reference.py @@ -303,8 +303,11 @@ def test_bound_only_cli_defaults_to_the_bundled_file(tmp_path): capture_output=True, text=True, ) - assert proc.returncode == 0, proc.stderr + # Exit 1 only flags rows it could not judge: sddmm_csr_f16 has no cuSPARSE time on + # H800 (cuSPARSE SDDMM has no fp16), so those 10 rows are N/A -- not a failure. + assert proc.returncode in (0, 1), proc.stderr assert "sddmm_csr" in proc.stdout + assert "BOUND=120, N/A=10" in proc.stderr # --------------------------------------------------------------------------- @@ -360,58 +363,22 @@ def test_the_bundled_variants_are_a_subset_of_the_delivery_variants(bundled): assert set(bundled["variants"]) <= delivery_ids -# The bundle was built from the 2026-09-22 H800 run (conf/h800_reference.json's own -# "reference" block), which predates both the 45 q4 variants and the `capi` field -- -# it is NOT the same set as `capi: true` (all 65 since 2026-10-08, including -# spgemm_csr, column-major and complex variants the 2026-09-22 run never touched). -# Pinned here on purpose: a real H800 run adding or dropping an id must be a -# deliberate edit to this test too, not something a count or subset check would -# silently absorb. -H800_MEASURED_VARIANT_IDS = { - "gather_c32_int", - "gather_c64_int", - "gather_f16_int", - "gather_f32_int", - "gather_f64_int", - "scatter_c32_int", - "scatter_c64_int", - "scatter_f16_int", - "scatter_f32_int", - "scatter_f64_int", - "sddmm_csr_f32_int_non_non_row", - "sddmm_csr_f64_int_non_non_row", - "spmm_coo_f32_int_non_non_row", - "spmm_coo_f64_int_non_non_row", - "spmm_csr_f32_int_non_non_row", - "spmm_csr_f64_int_non_non_row", - "spmv_coo_f32_int_non", - "spmv_coo_f64_int_non", - "spmv_csr_f32_int_non", - "spmv_csr_f64_int_non", -} - - -def test_the_bundled_variants_are_exactly_the_2026_09_22_h800_run(bundled): - """As measured: the bundle is the fixed set `H800_MEASURED_VARIANT_IDS` above, - which is a strict subset of `capi: true` (not all of it -- `capi: true` also - covers variants the C API has implemented but no one has yet run on H800) and - unrelated to it structurally (a `capi: false` variant could be H800-measured - before it ever gets a C wrapper). An H800 run of one of the other 45 variants - should make this fail while leaving the subset test above green -- that is the - correct, expected way for it to fail; update the pinned set deliberately when - it does, same as DELIVERY_VARIANT_IDS-style registries elsewhere in tests/ci. - """ - assert set(bundled["variants"]) == H800_MEASURED_VARIANT_IDS - capi_ids = {v["id"] for v in load_delivery_variants() if v["capi"]} - assert H800_MEASURED_VARIANT_IDS <= capi_ids, ( - "every H800-measured variant should also be capi: true today -- if this " - "ever fails it means a variant was H800-measured before being C-wrapped, " - "which is fine, just update this comment and drop the assertion" - ) +# The bundle is built from the 2026-10-14 H800 run of the whole 65-variant registry +# (conf/h800_reference.json's own "reference" block). Pinned to the registry on +# purpose: a regenerated bundle that lost a variant must fail here, not shrink +# silently. +def test_the_bundled_variants_are_the_whole_delivery_registry(bundled): + delivery_ids = {v["id"] for v in load_delivery_variants()} + assert set(bundled["variants"]) == delivery_ids -def test_every_bundled_variant_has_passing_accuracy(bundled): - assert all(v["accuracy"] == "Passed" for v in bundled["variants"].values()) +def test_no_bundled_variant_failed(bundled): + # "Skipped" is allowed: on H800, spmv_coo_f32_int_non's accuracy phase skipped 2 + # of 5 cases (3 passed). A Failed variant must never become a baseline source. + assert all( + v["accuracy"] in ("Passed", "Skipped") and v["performance"] == "Passed" + for v in bundled["variants"].values() + ) def test_the_bundled_file_matches_its_source_directory_when_present(bundled): @@ -425,3 +392,49 @@ def test_the_bundled_file_matches_its_source_directory_when_present(bundled): def test_the_bundled_file_stays_small(bundled): assert BUNDLED.stat().st_size < 2 * 1024 * 1024 + + +def test_operators_the_new_run_lacks_are_carried_over_with_their_source(tmp_path): + old_run = _synthetic_run(tmp_path / "old") + _write_csv( + old_run / "spsv_csr" / "performance.csv", + [ + { + "matrix": "a.mtx", + "dtype": "float32", + "FlagSparse_ms": "1", + "cuSPARSE_ms": "2", + } + ], + ) + old = h800_reference.build(old_run) + new = h800_reference.build( + _synthetic_run(tmp_path / "new", [_sddmm_row(cusparse="7.0")]), carry_over=old + ) + # measured again -> the new run's table, untagged + assert "source" not in new["operators"]["sddmm_csr"] + assert ( + new["operators"]["sddmm_csr"]["rows"] != old["operators"]["sddmm_csr"]["rows"] + ) + # not measured again -> kept, tagged with the run it came from + carried = new["operators"]["spsv_csr"] + assert carried["rows"] == old["operators"]["spsv_csr"]["rows"] + assert carried["source"]["source_dir"] == "pytest_results_fake" + # and it survives a write/load round trip and reads like any other table + path = h800_reference.write(new, tmp_path / "ref.json") + assert h800_reference.load(path)["operators"]["spsv_csr"]["source"] + _, rows = h800_reference.tables(path)["spsv_csr"].read() + assert rows[0]["cuSPARSE_ms"] == "2.0" + assert "| `spsv_csr` |" in h800_reference.render_markdown(new) + + +def test_cli_carries_over_from_the_file_it_replaces_unless_told_not_to(tmp_path): + out = tmp_path / "ref.json" + old = h800_reference.build(_synthetic_run(tmp_path / "old")) + old["operators"]["spsv_csr"] = {"fields": ["matrix"], "rows": [["a.mtx"]]} + h800_reference.write(old, out) + run = _synthetic_run(tmp_path / "new") + h800_reference.main([str(run), "--out", str(out), "--doc", ""]) + assert "spsv_csr" in h800_reference.load(out)["operators"] + h800_reference.main([str(run), "--out", str(out), "--doc", "", "--carry-over", ""]) + assert "spsv_csr" not in h800_reference.load(out)["operators"] diff --git a/tests/ci/test_runner_result_format.py b/tests/ci/test_runner_result_format.py index c9c49fcf..e37507cc 100644 --- a/tests/ci/test_runner_result_format.py +++ b/tests/ci/test_runner_result_format.py @@ -1051,3 +1051,102 @@ def test_the_h800_flag_can_be_given_without_a_value(): assert "conf/h800_reference.json" in proc.stdout.replace("\n", " ").replace( " ", " " ) + + +def test_spgemm_split_path_also_applies_the_scaled_baseline(tmp_path, monkeypatch): + """SpGEMM has its own per-dtype path; it must fill no-baseline rows like the rest.""" + reference = tmp_path / "h800" / "spgemm_csr" + reference.mkdir(parents=True) + (reference / "performance.csv").write_text( + "matrix,value_dtype,index_dtype,triton_ms,cusparse_ms,cu_status,status\n" + "sample.mtx,float32,int32,9.0,4.0,PASS,PASS\n", + encoding="utf-8", + ) + monkeypatch.setattr( + runner, + "_SCALED_BASELINE", + { + "reference": str(tmp_path / "h800"), + "card": "maca-c550", + "reference_peaks": "h800-sxm", + }, + ) + + def fake_run_subprocess(cmd, **_kwargs): + csv_path = Path(cmd[cmd.index("--csv") + 1]) + dtype = cmd[cmd.index("--dtypes") + 1] + rows = "sample.mtx,float32,int32,2.0,,,,PASS\n" if dtype == "float32" else "" + csv_path.write_text( + "matrix,value_dtype,index_dtype,triton_ms,cusparse_ms,pytorch_ms," + "triton_speedup_vs_cusparse,status\n" + rows, + encoding="utf-8", + ) + return 0, "", "", 0.1, False + + monkeypatch.setattr(runner, "run_subprocess", fake_run_subprocess) + (tmp_path / "out").mkdir() + result = runner._run_spgemm_split_dtypes( + project_root=ROOT, + op="spgemm_csr", + gpu_id=0, + script_device=0, + template=("tests/test_spgemm.py", "--csv", "{csv}"), + op_dir=tmp_path / "out", + benchmark_input=None, + warmup=1, + iters=1, + extra_args=[], + timeout=10, + ) + assert result["scaled_baseline"]["filled"] == 1 + with (tmp_path / "out" / "performance.csv").open(encoding="utf-8") as handle: + row = next(csv.DictReader(handle)) + # H800 cuSPARSE 4.0 ms rescaled to C550 by bandwidth, against our 2.0 ms + assert float(row["speedup_vs_h800_scaled"]) == pytest.approx( + 4.0 * 3050 / 1440 / 2.0 + ) + + +def test_an_na_vendor_column_does_not_hide_the_scaled_speedup(): + # MACA/Iluvatar spmv_csr: no vendor, so speedup_vs_vendor and vendor_ms say + # "N/A" -- text, not a measurement. The scaled-H800 speedup must still count. + row = { + "matrix": "a.mtx", + "dtype": "float32", + "ms": "0.2", + "vendor_ms": "N/A", + "speedup_vs_vendor": "N/A", + "status": "PASS", + "h800_scaled_ms": "0.1", + "h800_scaled_fs_ms": "0.2", + "speedup_vs_h800_scaled": "0.5", + } + assert runner._performance_schema(row)[0] == "speedup_vs_h800_scaled" + assert runner._performance_row_has_complete_speedup(row) + data = runner._flaggems_perf_data([row]) + assert data["float32"]["speedup"] == 0.5 + assert data["float32"]["baseline"] == "h800_scaled" + + +def test_a_pytorch_fallback_scaled_speedup_is_labelled_as_such(): + row = { + "matrix": "a.mtx", + "value_dtype": "float16", + "triton_ms": "0.2", + "status": "PASS", + "h800_scaled_ms": "0.4", + "h800_scaled_fs_ms": "0.2", + "speedup_vs_h800_scaled": "2.0", + "h800_baseline_kind": "pytorch", + } + data = runner._flaggems_perf_data([row]) + assert data["float16"]["baseline"] == "h800_pytorch_scaled" + strict = runner._strict_flag_gems_perf_data(data) + assert strict["fp16"]["baseline"] == "h800_pytorch_scaled" + + +def test_ascend_spgemm_is_measured_on_the_delivery_matrices(monkeypatch): + # It used to run the probe, which times synthetic shapes (tiny_square, ...): + # no speedup, and nothing an H800 reference could be matched to. + command = runner.ASCEND_PERFORMANCE_COMMANDS["spgemm_csr"] + assert command[0] == "tests/test_spgemm.py" and "{input}" in command diff --git a/tests/ci/test_spgemm_compare.py b/tests/ci/test_spgemm_compare.py new file mode 100644 index 00000000..ca1e146c --- /dev/null +++ b/tests/ci/test_spgemm_compare.py @@ -0,0 +1,83 @@ +# Copyright 2026 FlagOS Contributors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""CPU regressions for the SpGEMM sparse-result comparator.""" + +import pytest +import torch + +from tests.test_spgemm import _spgemm_compare_metrics + + +def _csr(data, indices, indptr, shape, dtype): + return ( + torch.tensor(data, dtype=dtype), + torch.tensor(indices, dtype=torch.int32), + torch.tensor(indptr, dtype=torch.int64), + shape, + ) + + +@pytest.mark.parametrize("dtype", [torch.float32, torch.float64]) +def test_spgemm_comparison_ignores_explicit_zeros(dtype): + candidate = _csr( + [1.0, 0.0, 0.0, 2.0], + [0, 2, 1, 2], + [0, 2, 4], + (2, 3), + dtype, + ) + reference = _csr([1.0, 2.0], [0, 2], [0, 1, 2], (2, 3), dtype) + + metrics = _spgemm_compare_metrics(candidate, reference, dtype) + + assert metrics["pattern_ok"] + assert metrics["pass"] + assert metrics["reason"] == "ok" + + +@pytest.mark.parametrize("dtype", [torch.float32, torch.float64]) +def test_spgemm_comparison_accepts_empty_after_zero_elision(dtype): + candidate = _csr([0.0, 0.0], [0, 2], [0, 1, 2], (2, 3), dtype) + reference = _csr([], [], [0, 0, 0], (2, 3), dtype) + + metrics = _spgemm_compare_metrics(candidate, reference, dtype) + + assert metrics["pattern_ok"] + assert metrics["pass"] + assert metrics["err_ratio"] == 0.0 + + +@pytest.mark.parametrize("dtype", [torch.float32, torch.float64]) +def test_spgemm_comparison_still_rejects_nonzero_pattern_mismatch(dtype): + candidate = _csr([1.0], [0], [0, 1], (1, 3), dtype) + reference = _csr([1.0], [1], [0, 1], (1, 3), dtype) + + metrics = _spgemm_compare_metrics(candidate, reference, dtype) + + assert not metrics["pattern_ok"] + assert not metrics["pass"] + assert "sparsity pattern mismatch" in metrics["reason"] + + +@pytest.mark.parametrize("dtype", [torch.float32, torch.float64]) +def test_spgemm_comparison_still_checks_values(dtype): + candidate = _csr([1.0], [0], [0, 1], (1, 1), dtype) + reference = _csr([2.0], [0], [0, 1], (1, 1), dtype) + + metrics = _spgemm_compare_metrics(candidate, reference, dtype) + + assert metrics["pattern_ok"] + assert not metrics["pass"] + assert metrics["reason"] == "value mismatch" diff --git a/tests/ci/test_spgemm_tle_gate.py b/tests/ci/test_spgemm_tle_gate.py new file mode 100644 index 00000000..a89bf038 --- /dev/null +++ b/tests/ci/test_spgemm_tle_gate.py @@ -0,0 +1,34 @@ +# Copyright 2026 FlagOS Contributors +# SPDX-License-Identifier: Apache-2.0 +"""SpGEMM keeps the TLE shared-memory hash path off the backends that cannot run it.""" + +import importlib +from types import SimpleNamespace + +import pytest + +torch = pytest.importorskip("torch") +spgemm = importlib.import_module("flagsparse.sparse_operations.spgemm_csr") + +BACKENDS = ("_is_mthreads_runtime", "_is_xpu_runtime", "_is_iluvatar_runtime") + + +def _route(monkeypatch, backend): + for name in BACKENDS: + monkeypatch.setattr(spgemm, name, lambda value=(name == backend): value) + monkeypatch.setattr(spgemm, "_TLE_AVAILABLE", True) + monkeypatch.setattr(spgemm, "_spgemm_hash_hybrid_compute", lambda p: "hash") + monkeypatch.setattr(spgemm, "_spgemm_esc_compute_safe", lambda p: "esc") + prepared = SimpleNamespace(a_data=torch.zeros(1, dtype=torch.float32)) + return spgemm._spgemm_compute(prepared) + + +@pytest.mark.parametrize("backend", BACKENDS) +def test_backends_without_working_tle_use_esc(monkeypatch, backend): + # Iluvatar: "iluvatar TLE alloc does not support nv_mma_shared_layout=True" + # failed every BI-V150 SpGEMM row (2026-10-09). + assert _route(monkeypatch, backend) == "esc" + + +def test_other_backends_keep_the_hash_path(monkeypatch): + assert _route(monkeypatch, None) == "hash" diff --git a/tests/ci/test_split_delivery_scope.py b/tests/ci/test_split_delivery_scope.py index e967e652..c692925c 100644 --- a/tests/ci/test_split_delivery_scope.py +++ b/tests/ci/test_split_delivery_scope.py @@ -19,6 +19,7 @@ import sys from pathlib import Path +import pytest import yaml from tools.delivery_variants import load_delivery_variants @@ -238,3 +239,140 @@ def test_a_tag_outside_the_registry_is_flagged(tmp_path): path.write_text(json.dumps(doc), encoding="utf-8") out = _check(tmp_path, "delivery") assert "UNKNOWN VARIANT spmv_csr_f32_int_bogus" in out, out + + +def test_merge_fills_nobaseline_variants_from_the_h800_reference(tmp_path): + spec = importlib.util.spec_from_file_location( + "split_delivery", ROOT / "run_flagsparse_split_delivery.py" + ) + split = importlib.util.module_from_spec(spec) + spec.loader.exec_module(split) + + ref = tmp_path / "h800" / "spmv_csr" + ref.mkdir(parents=True) + (ref / "performance.csv").write_text( + "matrix,dtype,variant,ms,vendor_ms,status\n" + "/d/A.mtx,float16,spmv_csr_f16_int_non,0.5,1.0,PASS\n", + encoding="utf-8", + ) + nobase = { + "result": "NoBaseline", + "speedup": 0.0, + "details": {"A": {"base": 0.0, "gems": 2.0, "speedup": 0.0}}, + } + measured = { + "result": "Passed", + "speedup": 0.5, + "details": {"A": {"base": 1.0, "gems": 2.0, "speedup": 0.5}}, + } + capi = { + "result": { + "spmv_csr_f16_int_non": { + "performance": {"status": "NoBaseline", "data": {"fp16": nobase}} + }, + "spmv_csr_f32_int_non": { + "performance": {"status": "Passed", "data": {"fp32": measured}} + }, + } + } + py = {"result": {k: {"accuracy": {"status": "Passed"}} for k in capi["result"]}} + (tmp_path / "py.json").write_text(json.dumps(py), encoding="utf-8") + (tmp_path / "capi.json").write_text(json.dumps(capi), encoding="utf-8") + out = tmp_path / "merged.json" + split.merge_summaries( + tmp_path / "py.json", + tmp_path / "capi.json", + out, + h800_reference=str(tmp_path / "h800"), + h800_card="musa-s5000", + ) + merged = json.loads(out.read_text(encoding="utf-8"))["result"] + f16 = merged["spmv_csr_f16_int_non"]["performance"] + assert f16["status"] == "Passed" + assert f16["data"]["fp16"]["baseline"] == "h800_scaled" + assert f16["data"]["fp16"]["speedup"] == pytest.approx(1.0 * 3050 / 1370 / 2.0) + # a variant muSPARSE measured keeps its own baseline + assert merged["spmv_csr_f32_int_non"]["performance"]["data"]["fp32"] == measured + + +def test_merge_prefers_the_python_half_then_the_capi_time(tmp_path): + spec = importlib.util.spec_from_file_location( + "split_delivery", ROOT / "run_flagsparse_split_delivery.py" + ) + split = importlib.util.module_from_spec(spec) + spec.loader.exec_module(split) + + ref = tmp_path / "h800" / "spmv_csr" + ref.mkdir(parents=True) + (ref / "performance.csv").write_text( + "matrix,dtype,variant,ms,vendor_ms,status\n" + "/d/A.mtx,float16,v_capi,0.5,1.0,PASS\n", + encoding="utf-8", + ) + + def nobase(): + return { + "status": "NoBaseline", + "data": { + "fp16": { + "result": "NoBaseline", + "speedup": 0.0, + "details": {"A": {"base": 0.0, "gems": 2.0, "speedup": 0.0}}, + } + }, + } + + python_perf = { + "status": "Passed", + "data": {"fp16": {"speedup": 0.7, "baseline": "h800_scaled", "details": {}}}, + } + capi = { + "result": {v: {"performance": nobase()} for v in ("v_py", "v_capi", "v_none")} + } + py = { + "result": { + "v_py": {"accuracy": {"status": "Passed"}, "performance": python_perf}, + "v_capi": {"accuracy": {"status": "Passed"}}, + "v_none": {"accuracy": {"status": "Passed"}}, + } + } + (tmp_path / "py.json").write_text(json.dumps(py), encoding="utf-8") + (tmp_path / "capi.json").write_text(json.dumps(capi), encoding="utf-8") + out = tmp_path / "merged.json" + split.merge_summaries( + tmp_path / "py.json", + tmp_path / "capi.json", + out, + h800_reference=str(tmp_path / "h800"), + h800_card="musa-s5000", + ) + merged = json.loads(out.read_text(encoding="utf-8"))["result"] + # 2. the Python half (same harness as the H800 reference) wins over the C API + assert merged["v_py"]["performance"]["source"] == "python" + assert merged["v_py"]["performance"]["data"]["fp16"]["speedup"] == 0.7 + assert merged["v_py"]["capi_performance"]["status"] == "NoBaseline" + # 3. no Python speedup -> the C API time against the same variant + matrix + assert merged["v_capi"]["performance"]["source"] == "capi" + assert merged["v_capi"]["performance"]["status"] == "Passed" + # neither -> still NoBaseline, and said so + assert merged["v_none"]["performance"]["status"] == "NoBaseline" + + +def test_with_h800_reference_the_python_half_also_measures(tmp_path, monkeypatch): + spec = importlib.util.spec_from_file_location( + "split_delivery", ROOT / "run_flagsparse_split_delivery.py" + ) + split = importlib.util.module_from_spec(spec) + spec.loader.exec_module(split) + seen = [] + monkeypatch.setattr(split, "run", lambda cmd, **kw: seen.append(cmd) or 0) + args = split.build_arg_parser().parse_args(["--h800-reference"]) + args.h800_card = "musa-s5000" + split.run_pytest_accuracy(args, tmp_path) + cmd = seen[0] + assert cmd[cmd.index("--phase") + 1] == "both" + assert "--h800-reference" in cmd and "musa-s5000" in cmd + args = split.build_arg_parser().parse_args([]) + args.h800_card = None + split.run_pytest_accuracy(args, tmp_path) + assert seen[1][seen[1].index("--phase") + 1] == "accuracy" diff --git a/tests/ci/test_spmv_csr_transpose_complex_gather.py b/tests/ci/test_spmv_csr_transpose_complex_gather.py index e22f35ef..b06e7707 100644 --- a/tests/ci/test_spmv_csr_transpose_complex_gather.py +++ b/tests/ci/test_spmv_csr_transpose_complex_gather.py @@ -31,6 +31,12 @@ transpose = importlib.import_module("flagsparse.sparse_operations._spmv_csr_transpose") +@pytest.fixture(autouse=True) +def portable_host_cache(monkeypatch): + # These CPU tests simulate the portable/MUSA launch, even on a MACA host. + monkeypatch.setattr(transpose, "_is_maca_runtime", lambda: False) + + class MusaLikeTensor(torch.Tensor): """Raises on complex advanced indexing, as torch_musa does.""" diff --git a/tests/pytest/test_rocm_sparse_reuse_accuracy.py b/tests/pytest/test_rocm_sparse_reuse_accuracy.py index 99e9abec..1d16a99b 100644 --- a/tests/pytest/test_rocm_sparse_reuse_accuracy.py +++ b/tests/pytest/test_rocm_sparse_reuse_accuracy.py @@ -1,16 +1,31 @@ # Copyright 2026 FlagOS Contributors # SPDX-License-Identifier: Apache-2.0 -"""DCU-only sparse reuse: mutated inputs, inference tensors, and API outputs.""" +"""Sparse reuse: mutated inputs, inference tensors, and API outputs. + +Written for the DCU plan caches, but every case checks the public API against a +CPU result, so it is backend-neutral. It used to skip everywhere except ROCm, +which left MACA's spmv_coo (and the other operators here) without this coverage. +""" import pytest import torch import flagsparse as fs -from flagsparse.sparse_operations._common import _is_rocm_runtime +from flagsparse.sparse_operations._common import ( + _backend_name, + _is_maca_runtime, + _is_rocm_runtime, +) from tests.pytest.accuracy_utils import accelerator_available, accelerator_device +# Enabled where it has been run: ROCm (BW1000), MACA (C550, 18 passed) and CUDA. +# The runner's accuracy phase collects these by operator marker, so opening it to +# a backend that has never run it would change that backend's delivery result. +_VERIFIED_BACKEND = _is_rocm_runtime() or _is_maca_runtime() or _backend_name() == "cuda" + pytestmark = pytest.mark.skipif( - not accelerator_available() or not _is_rocm_runtime(), reason="requires ROCm" + not accelerator_available() or not _VERIFIED_BACKEND, + reason="reuse coverage not yet verified on this backend (ROCm, MACA, CUDA only)", ) diff --git a/tests/pytest/test_spmv_csr_maca_accuracy.py b/tests/pytest/test_spmv_csr_maca_accuracy.py new file mode 100644 index 00000000..98986e30 --- /dev/null +++ b/tests/pytest/test_spmv_csr_maca_accuracy.py @@ -0,0 +1,55 @@ +# Copyright 2026 FlagOS Contributors +# SPDX-License-Identifier: Apache-2.0 +"""MACA auto gathers must read updated and inference tensor values.""" + +import pytest +import torch + +from flagsparse.sparse_operations import spmv_csr as csr +from flagsparse.sparse_operations._common import _is_maca_runtime +from tests.pytest.accuracy_utils import accelerator_available, accelerator_device + +pytestmark = [pytest.mark.spmv_csr, pytest.mark.skipif( + not (_is_maca_runtime() and accelerator_available()), reason="MACA required" +)] + + +@pytest.mark.parametrize("dtype", [torch.float16, torch.complex64]) +@pytest.mark.parametrize("index_dtype", [torch.int32, torch.int64]) +@pytest.mark.parametrize("op", ["non", "trans", "conj"]) +@pytest.mark.parametrize("inference", [False, True]) +def test_maca_gather_live_values(dtype, index_dtype, op, inference): + dev = accelerator_device() + gen = torch.Generator().manual_seed(47) + # Empty rows, an uneven tail, duplicate indices and a long row exercise + # both masks and the streaming loop in the MACA component-pair kernel. + ptr = torch.tensor([0, 0, 3, 132, 134], dtype=index_dtype) + cols = torch.randint(0, 7, (134,), generator=gen).to(index_dtype) + values = torch.randn(134, generator=gen).to(dtype) + if dtype.is_complex: + values += 1j * torch.randn(134, generator=gen) + size = 7 if op == "non" else 4 + x_cpu = torch.randn(size, generator=gen).to(dtype) + if dtype.is_complex: + x_cpu += 1j * torch.randn(size, generator=gen) + with torch.inference_mode(inference): + values_d, cols_d, ptr_d = (t.to(dev) for t in (values, cols, ptr)) + x = x_cpu.to(dev) + prepared = csr.prepare_spmv_csr(values_d, cols_d, ptr_d, (4, 7), op=op) + out = torch.empty(4 if op == "non" else 7, dtype=dtype, device=dev) + for scale, x_scale in [(1, 1), (2, 1), (2, 0.5)]: + if scale == 2 and x_scale == 1: + values_d.mul_(2) + elif x_scale == 0.5: + x.mul_(0.5) + actual = csr.flagsparse_spmv_csr_run(prepared, x, out=out) + assert actual is out + wide = torch.complex128 if dtype.is_complex else torch.float64 + dense = torch.sparse_csr_tensor(ptr.long(), cols.long(), + values.to(wide) * scale, (4, 7)).to_dense() + if op != "non": + dense = dense.t() if op == "trans" else dense.conj().t() + gold = dense @ (x_cpu.to(wide) * x_scale) + torch.testing.assert_close(actual.cpu().to(wide), gold, + rtol=2e-3 if dtype == torch.float16 else 1e-4, + atol=2e-3 if dtype == torch.float16 else 1e-4) diff --git a/tests/test_spgemm.py b/tests/test_spgemm.py index 7a77ae66..3ea6ebda 100644 --- a/tests/test_spgemm.py +++ b/tests/test_spgemm.py @@ -243,6 +243,12 @@ def _csr_sorted_pairs_block(data, indices, indptr, n_cols, row_start, row_end): ) cols = indices[start:end].to(torch.int64) vals = data[start:end] + # Sparse products may retain structural entries whose value is exactly + # zero (explicit input zeros or cancellation). SciPy drops some of them. + # Compare the mathematical sparse result, keeping every nonzero entry and + # the existing numerical tolerances. Do this per block to bound memory. + nonzero = vals != 0 + rows, cols, vals = rows[nonzero], cols[nonzero], vals[nonzero] keys = rows * max(1, int(n_cols)) + cols order = torch.argsort(keys) return keys[order], vals[order] @@ -263,16 +269,7 @@ def _spgemm_compare_metrics(candidate, reference, value_dtype): c_nnz = int(c_indptr[-1].item()) if c_indptr.numel() > 0 else 0 r_nnz = int(r_indptr[-1].item()) if r_indptr.numel() > 0 else 0 - if c_nnz != r_nnz: - return { - "pattern_ok": False, - "pass": False, - "err_ratio": float("inf"), - "max_abs_error": float("inf"), - "max_relative_error": float("inf"), - "reason": f"nnz mismatch {c_nnz} vs {r_nnz}", - } - if c_nnz == 0: + if c_nnz == 0 and r_nnz == 0: return { "pattern_ok": True, "pass": True, @@ -785,7 +782,6 @@ def _run_matrix_worker(args): ref_isolated_retry=args.ref_isolated_retry, ref_cleanup=args.ref_cleanup, compare_device=args.compare_device, - isolate_matrices=ast_common._is_maca_runtime(), ) torch.save({"success": True, "entry": entry}, args._worker_output) return 0 @@ -859,7 +855,14 @@ def _run_matrix_worker_subprocess( if os.path.exists(tmp_path): payload = torch.load(tmp_path, map_location="cpu") except Exception as exc: - payload = {"success": False, "reason": f"failed to load matrix worker result: {exc}"} + stderr = proc.stderr.strip() if proc.stderr else "" + detail = f"{type(exc).__name__}: {exc}" + if stderr: + detail = f"{detail}; worker stderr: {stderr[-1000:]}" + payload = { + "success": False, + "reason": f"failed to load matrix worker result: {detail}", + } finally: if os.path.exists(tmp_path): os.remove(tmp_path) @@ -2309,6 +2312,11 @@ def main(): default=None, help=argparse.SUPPRESS, ) + parser.add_argument( + "--_matrix-worker", + action="store_true", + help=argparse.SUPPRESS, + ) parser.add_argument("--_worker-mtx", type=str, default=None, help=argparse.SUPPRESS) parser.add_argument( "--_worker-output", type=str, default=None, help=argparse.SUPPRESS @@ -2336,6 +2344,11 @@ def main(): raise SystemExit("worker mode requires --_worker-mtx and --_worker-output") raise SystemExit(_run_reference_worker(args)) + if args._matrix_worker: + if not args._worker_mtx or not args._worker_output: + raise SystemExit("matrix worker mode requires --_worker-mtx and --_worker-output") + raise SystemExit(_run_matrix_worker(args)) + if not ACCEL.is_available(): print("CUDA is not available.") return diff --git a/tools/baseline_bound.py b/tools/baseline_bound.py index abd3c885..cd99e811 100644 --- a/tools/baseline_bound.py +++ b/tools/baseline_bound.py @@ -180,7 +180,18 @@ _OP_KEYS = ("op", "opa", "transpose") # Case axes joined into the key when BOTH sides carry the column; one side alone # (ROCm spmv_csr has `alg`, CUDA does not) would make every key miss. -_OPTIONAL_KEYS = ("alg", "layout", "mode", "dense_cols", "k") +# variant/op_b/out_dtype: q4 rows of one matrix and dtype differ only there (f16 vs +# f16->f32 SpMV, SDDMM non_non vs non_trans), so without them two cases share a key. +_OPTIONAL_KEYS = ( + "alg", + "layout", + "mode", + "dense_cols", + "k", + "variant", + "op_b", + "out_dtype", +) _PASS = {"PASS", "PASSED", "OK", "SUCCESS"} _TEMPLATE_SIDE = { "name": None, @@ -569,7 +580,40 @@ def _bound(out: dict, row: dict[str, str], ref_col: str | None, args, peaks) -> "h800_scaled_fs_ms", "speedup_vs_h800_scaled", "h800_scaled_card", + "h800_baseline_kind", ) +# Which H800 time the scaled baseline is: the vendor library's, or -- only when the +# H800 run has none for that case (cuSPARSE SDDMM has no fp16) -- PyTorch's. +BASELINE_KIND_LIBRARY = "cusparse" +BASELINE_KIND_PYTORCH = "pytorch" +_PYTORCH_TIME_COLUMNS = ("pytorch_ms", "torch_ms") +_PYTORCH_MAX_ERROR = 1e-2 # q4_variant_bench's own gate for using a baseline + + +def _reference_baseline(row: dict[str, str]) -> tuple[float | None, str, str]: + """``(time, kind, why-not)`` of a reference row's baseline for the scaled speedup. + + The vendor library's time first. Only when the row has NONE -- not when it has + one that failed its check -- the same row's PyTorch time, provided PyTorch's own + result was checked and close (q4 rows record ``pytorch_max_error``). Never + FlagSparse's own time. + """ + t_ref, why = _reference_time(row, None) + if t_ref is not None and t_ref > 0: + return t_ref, BASELINE_KIND_LIBRARY, "" + if not _baseline_ok(row): # the library ran and disagreed: no fallback + return None, "", why + status = str(row.get("pt_status") or "").strip().upper() + if any(bad in status for bad in ("FAIL", "ERROR")): + return None, "", why + err = _num(row.get("pytorch_max_error")) + if err is not None and err > _PYTORCH_MAX_ERROR: + return None, "", why + for column in _PYTORCH_TIME_COLUMNS: + t_pt = _num(row.get(column)) + if t_pt is not None and t_pt > 0: + return t_pt, BASELINE_KIND_PYTORCH, "" + return None, "", why + " nor a PyTorch time" def _usable(row: dict[str, str]) -> bool: @@ -663,8 +707,8 @@ def skip(reason: str, count: int = 1) -> None: if not _usable(ref): skip("the reference row did not pass") continue - t_ref, why = _reference_time(ref, None) - if t_ref is None or t_ref <= 0: + t_ref, kind, why = _reference_baseline(ref) + if t_ref is None: skip(why) continue resource, _ = _resource_for(ref, "auto", "mem") @@ -680,10 +724,110 @@ def skip(reason: str, count: int = 1) -> None: row["h800_scaled_fs_ms"] = repr(t_own) row["speedup_vs_h800_scaled"] = repr(scaled / t_own) row["h800_scaled_card"] = card + row["h800_baseline_kind"] = kind + if kind == BASELINE_KIND_PYTORCH: + info["pytorch_fallback"] = info.get("pytorch_fallback", 0) + 1 info["filled"] += 1 return out, info +def _reference_by_variant(reference_run: str) -> dict[str, dict[str, dict[str, str]]]: + """{variant id: {matrix stem (lowercase): reference row}} over every operator.""" + out: dict[str, dict[str, dict[str, str]]] = {} + for path in _collect(reference_run).values(): + _, rows = _read_csv(path) + for row in rows: + variant = str(row.get("variant") or "").strip() + matrix = Path(str(row.get("matrix") or "").strip()).stem.lower() + if variant and matrix: + out.setdefault(variant, {})[matrix] = row + return out + + +def scaled_capi_performance( + variant_id: str, + performance: dict, + reference_run: str, + card: str, + *, + reference: str = "h800-sxm", + _index: dict | None = None, +) -> dict | None: + """Fill a C API ``NoBaseline`` performance entry from the rescaled H800 time. + + ``performance`` is one variant's entry in capi/tools/write_summary.py's schema + (``data[dtype]["details"][matrix] = {base, gems, speedup}``, base 0 when the + vendor library had nothing). Only ``NoBaseline`` dtypes are touched; a matrix + gets ``base = T_cuSPARSE@H800(variant, matrix) * P_h800 / P_card`` and + ``speedup = base / gems``, and the dtype speedup is their mean, as + write_summary.py computes it. Matching is by variant id and matrix name, so + the vector operators (H800 measured synthetic sizes, not matrices) and the + legacy, untagged variants never match and keep ``NoBaseline``. + + Returns the info dict (filled/skipped counts) when anything was filled, else + ``None``; ``performance`` is updated in place. + """ + data = performance.get("data") + if not isinstance(data, dict): + return None + index = _index if _index is not None else _reference_by_variant(reference_run) + by_matrix = index.get(variant_id) or {} + p_ref = REFERENCE_PEAKS[reference] + p_v = {"mem_bw_gbs": VENDOR_BANDWIDTH_GBS[card]} + info = {"card": card, "reference": str(reference_run), "filled": 0, "skipped": {}} + + def skip(reason: str) -> None: + info["skipped"][reason] = info["skipped"].get(reason, 0) + 1 + + for dtype_entry in data.values(): + if not isinstance(dtype_entry, dict) or dtype_entry.get("result") != "NoBaseline": + continue + speedups, kinds = [], set() + for matrix, detail in (dtype_entry.get("details") or {}).items(): + gems = _num(detail.get("gems")) + if gems is None or gems <= 0 or _num(detail.get("base")): + continue + ref = by_matrix.get(Path(str(matrix)).stem.lower()) + if ref is None: + skip("no such case in the reference run") + continue + if not _usable(ref): + skip("the reference row did not pass") + continue + t_ref, kind, why = _reference_baseline(ref) + if t_ref is None: + skip(why) + continue + kinds.add(kind) + resource, _ = _resource_for(ref, "auto", "mem") + try: + scaled = t_ref * ( + _peak(p_ref, resource, _dtype(ref)) / _peak(p_v, resource, _dtype(ref)) + ) + except KeyError as exc: + skip(f"peak missing: {exc.args[0]}") + continue + detail["base"] = scaled + detail["speedup"] = scaled / gems + speedups.append(scaled / gems) + if speedups: + dtype_entry["no_baseline_reason"] = dtype_entry.pop("reason", None) + dtype_entry["result"] = "Passed" + dtype_entry["speedup"] = sum(speedups) / len(speedups) + dtype_entry["baseline"] = ( + "h800_pytorch_scaled" if BASELINE_KIND_PYTORCH in kinds else "h800_scaled" + ) + info["filled"] += len(speedups) + if not info["filled"]: + return None + if all( + not isinstance(d, dict) or d.get("result") != "NoBaseline" for d in data.values() + ): + performance["status"] = "Passed" + performance["scaled_baseline"] = info + return info + + def _evaluate_op(op: str, ref_path: Path, vendor_path: Path | None, args, peaks): ref_fields, ref_rows = _read_csv(ref_path) ref_col = ( diff --git a/tools/delivery_table.py b/tools/delivery_table.py index ede15279..ad250cb1 100644 --- a/tools/delivery_table.py +++ b/tools/delivery_table.py @@ -41,11 +41,23 @@ def _summary_path(target: Path) -> Path: def _speedup(performance: dict) -> str: data = performance.get("data") values = [] + labels = set() if isinstance(data, dict): for entry in data.values(): if isinstance(entry, dict) and entry.get("speedup"): values.append(float(entry["speedup"])) - return f"{sum(values) / len(values):.3f}x" if values else "-" + labels.add(str(entry.get("baseline") or "")) + if not values: + return "-" + # Against a rescaled H800 time (no baseline on this card): an estimate, never + # to be read as a measured vendor speedup. H800 PyTorch only where H800's + # cuSPARSE had no time for the case. + note = "" + if any("h800_pytorch_scaled" in label for label in labels): + note = " (H800 PyTorch折算)" + elif any("h800_scaled" in label for label in labels): + note = " (H800折算)" + return f"{sum(values) / len(values):.3f}x" + note def main() -> int: diff --git a/tools/h800_reference.py b/tools/h800_reference.py index 2fc58f18..7050af99 100644 --- a/tools/h800_reference.py +++ b/tools/h800_reference.py @@ -15,10 +15,15 @@ * every ``*_ms`` wall-time column and every ``*speedup*`` column, as numbers; * the ``status`` columns (a row whose vendor call failed must not become a baseline). +Operators the new run did not measure are carried over from the previous file +(``--carry-over``, default: the file being replaced), each tagged with the run it came +from, so a narrower re-run never silently drops a reference. + Usage:: - python3 tools/h800_reference.py pytest_results_sparse_202609221038 # regenerate + python3 tools/h800_reference.py # regenerate python3 tools/h800_reference.py DIR --out conf/h800_reference.json --doc docs/H800_REFERENCE.md + python3 tools/h800_reference.py DIR --carry-over '' # new run only """ from __future__ import annotations @@ -68,6 +73,10 @@ "nnzb", "n_rhs", "format", + # q4 rows: the variant name and the axes that tell same-matrix variants apart + "variant", + "op_b", + "out_dtype", ) # `*_ms` columns that are breakdowns / host-side / conversion cost, not the timed operator. _DROP_MS = re.compile( @@ -146,8 +155,19 @@ def _variant_summary(summary): return variants -def build(results_dir, when=None): - """Read a pytest results directory and return the reference document (a dict).""" +def _source(reference): + return { + k: reference.get(k) + for k in ("source_dir", "flagsparse_commit", "run_timestamp", "device") + } + + +def build(results_dir, when=None, carry_over=None): + """Read a pytest results directory and return the reference document (a dict). + + ``carry_over`` is a previous reference document: its operators that this run did + not measure are kept, tagged with the run they came from (``source``). + """ results_dir = Path(results_dir) summary_path = results_dir / "summary.json" if not summary_path.is_file(): @@ -176,9 +196,7 @@ def build(results_dir, when=None): if fields and rows: operators[op_dir.name] = {"fields": fields, "rows": rows} - return { - "schema": SCHEMA, - "reference": { + reference = { "device": device, "device_count": torch_env.get("device_count"), "peaks": "h800-sxm", @@ -193,7 +211,20 @@ def build(results_dir, when=None): "*sparse_ms) is the baseline the scaled speedup divides into; see " "docs/H800_REFERENCE.md." ), - }, + } + if carry_over: + old_source = _source(carry_over.get("reference") or {}) + for op, table in (carry_over.get("operators") or {}).items(): + if op not in operators: + operators[op] = { + "fields": table["fields"], + "rows": table["rows"], + "source": table.get("source") or old_source, + } + operators = dict(sorted(operators.items())) + return { + "schema": SCHEMA, + "reference": reference, "variants": _variant_summary(summary), "operators": operators, } @@ -220,8 +251,13 @@ def write(doc, path): json.dumps(r, ensure_ascii=False, separators=(",", ":")) for r in table["rows"] ) + source = ( + f'"source": {json.dumps(table["source"], ensure_ascii=False)},\n' + if table.get("source") + else "" + ) op_chunks.append( - f'{json.dumps(name)}: {{"fields": {json.dumps(table["fields"])},\n"rows": [\n{body}\n]}}' + f'{json.dumps(name)}: {{{source}"fields": {json.dumps(table["fields"])},\n"rows": [\n{body}\n]}}' ) lines.append(",\n".join(op_chunks)) lines.append("}") @@ -304,6 +340,30 @@ def _usable_rows(op, table): return sum(1 for row in rows if baseline_bound._baseline_ms(row)) +def _variant_coverage(doc): + """{variant: (rows with a vendor time, rows)} for the q4-tagged reference rows. + + Legacy variants carry no ``variant`` column; they are reported under their + operator/dtype (`` (legacy)``) instead. + """ + from tools import baseline_bound + + coverage = {} + for op, table in doc["operators"].items(): + if table.get("source"): + continue + _, rows = ReferenceTable("", op, table["fields"], table["rows"]).read() + for row in rows: + name = (row.get("variant") or "").strip() + if not name: + dtype = baseline_bound._dtype(row) + name = f"{op} {dtype} (legacy)" + usable, total = coverage.get(name, (0, 0)) + ok = bool(baseline_bound._baseline_ms(row)) and baseline_bound._baseline_ok(row) + coverage[name] = (usable + ok, total + 1) + return dict(sorted(coverage.items())) + + def _short_commit(commit): commit = commit or "?" sha, plus, tail = commit.partition("+") @@ -330,7 +390,7 @@ def render_markdown(doc): "", "## 用途", "", - "没有厂商 / PyTorch 基线的后端，用这里的 **H800 上 cuSPARSE 的时间**做基线：", + "**只用于没有任何基线的行**：目标卡上有厂商库（hipSPARSE、muSPARSE……）或 PyTorch 时间的行一律用实测基线，不会被折算覆盖。没有厂商 / PyTorch 基线的行，用这里的 **H800 上 cuSPARSE 的时间**做基线：", "", "```", "h800_scaled_ms = T_cuSPARSE@H800 × P_H800 / P_目标卡 (P = 实测显存带宽)", @@ -339,33 +399,62 @@ def render_markdown(doc): "", "```bash", "# 不用再找目录：不带值即用本仓库自带的文件", - "python3 run_flagsparse_pytest.py --h800-reference --vendor-card iluvatar-biv150 ...", - "python3 tools/baseline_bound.py --vendor <目标卡结果目录> --vendor-card iluvatar-biv150", + "python3 run_flagsparse_pytest.py --h800-reference ... # 卡型按后端/设备名自动识别", + "python3 run_flagsparse_split_delivery.py --h800-reference ... # C API 拆分交付（MUSA）：补 NoBaseline 变体", + "python3 tools/baseline_bound.py --vendor <目标卡结果目录> # 已跑完的结果目录事后查看", "```", "", - "这是**估算**，不是厂商实测：H800 与目标卡的访存效率、缓存、launch 开销并不成比例。", + "这是**估算**，不是厂商实测：H800 与目标卡的访存效率、缓存、launch 开销并不成比例。" + "折算出的加速比在 summary 里带 `baseline: h800_scaled`，`tools/delivery_table.py` 显示为 `(H800折算)`。", + "", + "H800 上某个算例**没有** cuSPARSE 时间时（cuSPARSE SDDMM 不支持 fp16），退一步用同一行 H800 PyTorch 的时间" + "（PyTorch 结果须通过校验），标 `baseline: h800_pytorch_scaled` / `(H800 PyTorch折算)`；cuSPARSE 跑了但结果" + "不对的行不退。FlagSparse 自己在 H800 上的时间永远不当基线。", + "", + "C API 拆分交付（MUSA）：带 `--h800-reference` 时 pytest 那一半也跑性能（与本参照同一 benchmark、同一规模），" + "NoBaseline 变体优先取它；取不到再用 C API 时间按**变体 + 矩阵名**匹配（C API 的 SpMM `n=8`、SDDMM `k=16/64`、" + "按矩阵测的向量类在这里没有对应规模，只有 SpMV 类能对上）。", "", "## 覆盖情况与限制", "", "- 只有带 cuSPARSE（或 vendor）时间的行才能做基线；下表“可作基线的行”就是这个数。", " 为 0 的算子（当时只有 CuPy 基线、没有对应格式的库）**没有折算基线**，行会保持 `N/A`。", "- FlagSparse commit 带 `+dirty` 表示那轮 H800 是在有未提交改动的工作树上跑的。", + "- “来源”一列不是本轮的算子，是从上一份参考文件带过来的（新一轮没测它们），数据来自那一轮。", "", "## 各算子收录情况", "", - "| 算子 | 行数 | 可作基线的行 | 保留的时间 / 加速比列 |", - "|---|---:|---:|---|", + "| 算子 | 行数 | 可作基线的行 | 来源 | 保留的时间 / 加速比列 |", + "|---|---:|---:|---|---|", ] for op, table in doc["operators"].items(): fields = table["fields"] numeric = [f for f in fields if _is_number_column(f)] cols = ", ".join(f"`{c}`" for c in numeric) + src = table.get("source") + origin = ( + f"`{src.get('source_dir')}` (`{_short_commit(src.get('flagsparse_commit'))}`)" + if src + else "本轮" + ) out.append( - f"| `{op}` | {len(table['rows'])} | {_usable_rows(op, table)} | {cols} |" + f"| `{op}` | {len(table['rows'])} | {_usable_rows(op, table)} | {origin} | {cols} |" ) out += [ "", - "## H800 上 20 个交付变体的结果", + "## 各交付变体能否折算", + "", + "按变体统计本轮 H800 上带厂商库时间的矩阵数；为 0 的变体只能退到 H800 PyTorch 的时间（见上）。", + "", + "| 变体 | 可作基线的矩阵 / 总矩阵 |", + "|---|---|", + ] + for name, (usable, total) in _variant_coverage(doc).items(): + mark = "" if usable else " **（无 cuSPARSE，用 H800 PyTorch）**" + out.append(f"| `{name}` | {usable} / {total}{mark} |") + out += [ + "", + f"## H800 上 {len(doc['variants'])} 个交付变体的结果", "", "| 变体 | 精度 | 性能 | 加速比（H800 vs cuSPARSE/PyTorch，runner 口径） |", "|---|---|---|---|", @@ -404,9 +493,19 @@ def main(argv=None): parser.add_argument( "--doc", default=str(DEFAULT_DOC), help="output markdown summary ('' to skip)" ) + parser.add_argument( + "--carry-over", + default=None, + help=( + "previous reference JSON whose operators missing from this run are kept " + "(default: the --out file if it exists; '' to keep nothing)" + ), + ) args = parser.parse_args(argv) - doc = build(args.results_dir) + carry_path = args.out if args.carry_over is None else args.carry_over + carry = load(carry_path) if carry_path and Path(carry_path).is_file() else None + doc = build(args.results_dir, carry_over=carry) out = write(doc, args.out) rows = sum(len(t["rows"]) for t in doc["operators"].values()) print( From 78e97e16cce8369a2423e23fb52521440f6939e7 Mon Sep 17 00:00:00 2001 From: NCIA-AlphaSparse Date: Wed, 14 Oct 2026 15:39:05 +0800 Subject: [PATCH 2/9] ci checks --- docs/debug/ASCEND.md | 1 - docs/debug/ILUVATAR.md | 1 - run_flagsparse_pytest.py | 5 +++++ tests/ci/test_runner_result_format.py | 23 +++++++++++++++++++++++ 4 files changed, 28 insertions(+), 2 deletions(-) diff --git a/docs/debug/ASCEND.md b/docs/debug/ASCEND.md index 6e35220d..52b2c6a2 100644 --- a/docs/debug/ASCEND.md +++ b/docs/debug/ASCEND.md @@ -379,4 +379,3 @@ git pull --ff-only origin main 之后，完整读一遍 docs/debug/ASCEND.md， - 怎么验证的：只在 CUDA 机器上确认路由（`tests/ci/test_runner_result_format.py::test_ascend_spgemm_is_measured_on_the_delivery_matrices`）； `tests/test_spgemm.py` 在 NPU 上能否跑通**未验证**，昇腾 spgemm 的精度用例是通过的（同一 `flagsparse_spgemm_csr`）。 - 其他后端是否也需要：否。 - diff --git a/docs/debug/ILUVATAR.md b/docs/debug/ILUVATAR.md index 7b2ff3ee..150e4910 100644 --- a/docs/debug/ILUVATAR.md +++ b/docs/debug/ILUVATAR.md @@ -147,4 +147,3 @@ python3 tools/delivery_table.py results_iluvatar_65_<日期> `tests/pytest -m spgemm_csr` 11 passed；`tests/ci/test_spgemm_tle_gate.py`（旧代码上失败）。**未在 BI-V150 实测**， ESC 在天数上的性能也还没有数。 - 其他后端是否也需要：否（MUSA、XPU 早已排除）。 - diff --git a/run_flagsparse_pytest.py b/run_flagsparse_pytest.py index 27f2fd6c..21353257 100644 --- a/run_flagsparse_pytest.py +++ b/run_flagsparse_pytest.py @@ -1650,6 +1650,11 @@ def parse_accuracy_json(path: Path) -> dict[str, object]: "external matrix regression directory not configured", "legacy bucket requires int32 column indices", "ascend complex gather/scatter capability unavailable", + # tests/pytest/test_rocm_sparse_reuse_accuracy.py runs only where it has been + # verified (ROCm, MACA, CUDA). Its mixed-COO cases carry float32/float16/int8 + # tokens, so an unexpected skip turned spmv_coo_f32_int_non into "Skipped" on + # every other backend (seen on H800 when the gate was still ROCm-only). + "reuse coverage not yet verified on this backend", ) diff --git a/tests/ci/test_runner_result_format.py b/tests/ci/test_runner_result_format.py index e37507cc..e4b76c00 100644 --- a/tests/ci/test_runner_result_format.py +++ b/tests/ci/test_runner_result_format.py @@ -1150,3 +1150,26 @@ def test_ascend_spgemm_is_measured_on_the_delivery_matrices(monkeypatch): # no speedup, and nothing an H800 reference could be matched to. command = runner.ASCEND_PERFORMANCE_COMMANDS["spgemm_csr"] assert command[0] == "tests/test_spgemm.py" and "{input}" in command + + +def test_the_reuse_suite_backend_gate_does_not_demote_a_delivery_variant(): + # On MUSA/Iluvatar/Ascend the reuse cases skip by design; their float32 token + # lands them in spmv_coo_f32_int_non's slice, which must still read Passed. + gate = ( + "Skipped: reuse coverage not yet verified on this backend " + "(ROCm, MACA, CUDA only)" + ) + raw = { + "tests/pytest/test_spmv_coo_accuracy.py::test_ok[float32]": { + "params": {"dtype": "float32"}, + "result": "passed", + }, + "tests/pytest/test_rocm_sparse_reuse_accuracy.py::test_mixed[f32]": { + "params": {"out_dtype": "torch.float32"}, + "result": "skipped", + "reason": gate, + }, + } + summary = runner.summarize_accuracy_cases(raw) + assert summary["status"] == "Passed" + assert summary["skipped"] == 1 # still reported, just not a demotion From d7d191904925a46d58758498568a36be147a87ac Mon Sep 17 00:00:00 2001 From: NCIA-AlphaSparse Date: Wed, 14 Oct 2026 15:49:15 +0800 Subject: [PATCH 3/9] ci again --- tests/ci/test_spgemm_compare.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/tests/ci/test_spgemm_compare.py b/tests/ci/test_spgemm_compare.py index ca1e146c..f151b3e1 100644 --- a/tests/ci/test_spgemm_compare.py +++ b/tests/ci/test_spgemm_compare.py @@ -15,9 +15,13 @@ """CPU regressions for the SpGEMM sparse-result comparator.""" import pytest -import torch -from tests.test_spgemm import _spgemm_compare_metrics +# CI's tests/ci job runs without torch (or triton); skip there like the rest of +# tests/ci instead of failing collection. +torch = pytest.importorskip("torch") +_spgemm_compare_metrics = pytest.importorskip( + "tests.test_spgemm" +)._spgemm_compare_metrics def _csr(data, indices, indptr, shape, dtype): From 6a000f1faec8536ddfec6ad79e268d10754db9b7 Mon Sep 17 00:00:00 2001 From: NCIA-AlphaSparse Date: Wed, 14 Oct 2026 16:06:55 +0800 Subject: [PATCH 4/9] rerun pro --- docs/debug/ASCEND.md | 61 +++++++++++++++++++++++++++++------------- docs/debug/ILUVATAR.md | 46 +++++++++++++++---------------- docs/debug/MACA.md | 48 ++++++++++++++++++--------------- docs/debug/MUSA.md | 51 +++++++++++++++++------------------ 4 files changed, 118 insertions(+), 88 deletions(-) diff --git a/docs/debug/ASCEND.md b/docs/debug/ASCEND.md index 52b2c6a2..3df55fbc 100644 --- a/docs/debug/ASCEND.md +++ b/docs/debug/ASCEND.md @@ -90,6 +90,9 @@ python3 tools/delivery_table.py results_ascend_65_<日期> 3. 折算补上的变体预演出来大多是 0.001x–0.06x：这些 q4 路径每次调用 0.3–2.4 秒（`timed=call`）。这是真实性能， 回传时照实报，并附上 `delivery_table.py` 全表。 +> **下面的优先级 1–3 和"带回来的东西"是 2026-10-08 的历史任务清单**，到 10-11 已基本完成（45/45 派发、 +> 10-11 全量 64/65）。这一轮复测只做上面的"先做：65 变体全量重测"，按"跑完要确认"一节回报。 + ### 优先级 1：验证今天接上路由的 8 个变体（预计几分钟） ```bash @@ -141,33 +144,55 @@ sweep、单次 `index_add`）。涉及对 CSC 格式和 SpGEMM 哈希表算法 报错，或者判 TRITON 但其实某个新版本 CANN 能编过），这个工具本身需要更新——它是 CUDA 模拟，不是 NPU 本身的真值。 +## 跑完要确认（2026-10-14 之后这一轮） + +1. `missing` 是不是 0；`scatter_i8_int` 这次应在全量里出现（10-11 那轮 NotFound）。 +2. `spgemm_csr` 的性能这次第一次走 `tests/test_spgemm.py`（交付矩阵、float32/float64 分进程）： + 应有 `spgemm_csr/performance_float32.csv`、`performance_float64.csv`，`spgemm_csr_f32_int_non_non` 应有加速比。 + 失败就贴 `spgemm_csr/performance_stderr.log` 最后 50 行原文。 +3. 有加速比的变体个数（预演 64–65）。带 `(H800折算)` / `(H800 PyTorch折算)` 的变体原样列出；日志里卡型应是 `ascend-910b`。 +4. 折算补上的变体预演大多是 0.001x–0.06x（q4 call 路径每次 0.3–2.4 秒）。如实带回，并挑 3 个最慢的变体贴 + `performance.csv` 原始行（含 `timed`、`triton_ms` 列）。 + +把这些结果写回来，不用额外分析，原始数据最有用。 + ## 交给 Codex 的 prompt 上机时把下面整段原样贴给 Codex（或其他代理），不用改任何内容。 ````text -你在昇腾 910B 实机上做 FlagSparse 65 变体的剩余工作。仓库在当前目录，main 分支。 -git pull --ff-only origin main 之后，完整读一遍 docs/debug/ASCEND.md，按里面的优先级做： - -1. 先跑 PYTHONPATH=src python3 tools/q4_ascend_dispatch_check.py。它的结论是在 CUDA 上模拟出来的 - （38/45 OK），本机结果如果不一样（判 OK 但真机报错，或者判 TRITON 但真机能编过），本身就是重要发现。 -2. 做优先级 1（8 个变体，命令在文档里），带回 delivery_table.py 的输出。 -3. 优先级 2 只挑 spmv_csr_f16_int_non 一个变体试着接通，接通后先停下来汇报，不要一次接完 30 个。 -4. 优先级 3 不做。 +你在 昇腾 910B（Ascend） 实机上复测 FlagSparse 的 65 个交付变体（2026-10-14 之后的这一轮）。仓库在当前目录，main 分支。 + +背景：这台机器上一轮已经跑过 65 项。之后仓库改了两类东西，这轮就是验证它们： +- 没有基线的变体，现在用仓库自带的 H800 结果按带宽折算出加速比（命令里的 --h800-reference），交付表里会标 + (H800折算) 或 (H800 PyTorch折算)。有本机基线的变体不受影响。 +- 本后端相关的修复，见 docs/debug/ASCEND.md "跑完要确认"一节逐条列出的内容。 +主要目的是收集数据，不是修代码。 + +步骤： +1. git pull --ff-only origin main；git log -1 --oneline 的提交应不早于 d7d1919（"ci again"）。 +2. 按 docs/ASCEND.md 配好环境，做完它的环境自检。 +3. 确认导入的是仓库源码：python3 -c "import flagsparse; print(flagsparse.__file__)" + 输出必须在当前目录的 src/ 下。如果指向 site-packages / dist-packages，先停下来汇报，不要继续。 +4. 完整读一遍 docs/debug/ASCEND.md，照"上机要做的事"里"先做：65 变体全量重测"一节的命令原样跑（已带 --h800-reference，不要去掉）。 + - --results-dir 用新目录（<日期> 填今天），不要复用旧目录，也不要往旧目录里补跑。 + - 命令本身已经用 setsid 放到后台，定期看日志进度，不要中途打断。 + - 不要用 pytest --forked，它在 GPU 上会让所有用例失败。 +5. 跑完执行 python3 tools/delivery_table.py <结果目录>。 规矩： -- 不要为了"让它通过"去改 src/ 或 tests/ 下的代码。确实要改（优先级 2 本身就要改 benchmark_ascend.py） - 时，每改一个文件，都按 docs/debug/ASCEND.md 末尾"实机改动记录"一节的格式追加一条记录，并和代码改动 - 放在同一个 commit 里。没改就在那一节写"无"。 -- 不要猜根因。遇到报错就给出完整报错原文。 -- --results-dir 用新目录，不要复用旧目录。不要用 pytest --forked。 +- 不要为了"让它通过"去改 src/ 或 tests/ 下的代码。确实非改不可（比如环境适配）时，每改一个文件， + 都按 docs/debug/ASCEND.md 末尾"实机改动记录"一节的格式追加一条记录，并和代码改动放在同一个 commit 里。 +- 不要猜根因。遇到报错就给出完整报错原文，以及出错变体在结果目录里那一行的 reason/error 字段。 +- 折算出来的加速比是估算，低也照实报，不要为了数字好看换基线或改参数。 +- 结果目录整个打包带回（至少 summary.json、summary.csv、各算子的 performance.csv 和 *_stderr.log）。 汇报包含： -1. q4_ascend_dispatch_check.py 的完整输出。 -2. 优先级 1 的 delivery_table.py 输出（8 行），以及非 Passed 变体的 reason/error 原文。 -3. 如果做了优先级 2：那个变体第一次在真机上跑出的 performance.csv 原始行。 -4. 环境指纹：torch、torch_npu、CANN 版本，设备名。 -5. docs/debug/ASCEND.md "实机改动记录"一节的内容。 +1. delivery_table.py 的完整输出，尤其是第一行 "N registered variants, M missing"。 +2. 所有非 Passed 的变体、以及加速比为 "-" 的变体：变体名、阶段（精度/性能）、reason 或 error 原文。 +3. 逐条回答 docs/debug/ASCEND.md "跑完要确认"一节。 +4. 环境指纹：torch、triton 版本，设备名，驱动/SDK 版本，git log -1 --oneline，整轮总耗时。 +5. docs/debug/ASCEND.md "实机改动记录"一节的内容（没改写"无"）。 ```` ## 实机改动记录 diff --git a/docs/debug/ILUVATAR.md b/docs/debug/ILUVATAR.md index 150e4910..9cb66da7 100644 --- a/docs/debug/ILUVATAR.md +++ b/docs/debug/ILUVATAR.md @@ -73,53 +73,53 @@ python3 tools/delivery_table.py results_iluvatar_65_<日期> `cusparseScsrmv`/`cusparseScsrmm`，且只覆盖 fp32 + int32 + 不转置。45 个新变体里的转置/共轭/ 混合精度/int8 变体大概率没有厂商基线，这是预期行为。 -## 跑完要确认 +## 跑完要确认（2026-10-14 之后这一轮） -1. `missing` 是不是 0。 -2. 任何一行的 PyTorch 列数字是不是"看起来正常但可疑"（极快、或者跟其他后端差异巨大）——按上面第二条 - 风险点，这张卡的 PyTorch 列历史上出过静默错误的先例，数字本身不能直接当证据，需要交叉核对。 -3. int8/混合精度变体的精度是不是真的通过了。 +1. `missing` 是不是 0。9 个 f64/c64 变体 `NotFound` 是预期的，其余 56 个精度应 Passed。 +2. `spgemm_csr_f32_int_non_non`：精度 Passed、有加速比；日志/CSV 里不应再出现 + `iluvatar TLE alloc does not support nv_mma_shared_layout=True`（出现就说明没拉到新代码）。 +3. 有加速比的变体个数（预演 56 = 10-09 预演的 55 + spgemm）。带 `(H800折算)` / `(H800 PyTorch折算)` 的原样列出。 +4. 任何一行的 PyTorch 列"看起来正常但可疑"（这张卡的 torch.sparse 有静默算错的先例）；int8/混合精度变体精度是否真的通过。 -把这三条的结果写回来，不用额外分析，原始数据最有用。 +把这些结果写回来，不用额外分析，原始数据最有用。 ## 交给 Codex 的 prompt -上机时把下面整段原样贴给 Codex（或其他代理）。不用改任何内容。 +上机时把下面整段原样贴给 Codex（或其他代理），不用改任何内容。 ````text -你在 天数智芯 BI-V150（Iluvatar） 实机上复测 FlagSparse 的 65 个交付变体。仓库在当前目录，main 分支。 +你在 天数智芯 BI-V150（Iluvatar） 实机上复测 FlagSparse 的 65 个交付变体（2026-10-14 之后的这一轮）。仓库在当前目录，main 分支。 -背景：65 个变体（原 20 个 + 新合并的 45 个）只在 CUDA 上实测过，本机从没跑过。 -任务是在本机跑一遍，把原始结果带回来。主要目的是收集数据，不是修代码。 +背景：这台机器上一轮已经跑过 65 项。之后仓库改了两类东西，这轮就是验证它们： +- 没有基线的变体，现在用仓库自带的 H800 结果按带宽折算出加速比（命令里的 --h800-reference），交付表里会标 + (H800折算) 或 (H800 PyTorch折算)。有本机基线的变体不受影响。 +- 本后端相关的修复，见 docs/debug/ILUVATAR.md "跑完要确认"一节逐条列出的内容。 +主要目的是收集数据，不是修代码。 步骤： -1. git pull --ff-only origin main。 +1. git pull --ff-only origin main；git log -1 --oneline 的提交应不早于 d7d1919（"ci again"）。 2. 按 docs/ILUVATAR.md 配好环境，做完它的环境自检。 3. 确认导入的是仓库源码：python3 -c "import flagsparse; print(flagsparse.__file__)" 输出必须在当前目录的 src/ 下。如果指向 site-packages / dist-packages，先停下来汇报，不要继续。 - 旧安装包会让基线列全部变成 N/A，看起来像正常结果。 -4. 完整读一遍 docs/debug/ILUVATAR.md，照"要做的事"一节的命令原样跑。 - 不要自己删减或改写参数，每个参数的来由那一节都写了。 - - --results-dir 用新目录（<日期> 填今天），不要复用旧目录。 +4. 完整读一遍 docs/debug/ILUVATAR.md，照"要做的事"一节的命令原样跑（已带 --h800-reference，不要去掉）。 + - --results-dir 用新目录（<日期> 填今天），不要复用旧目录，也不要往旧目录里补跑。 - 命令本身已经用 setsid 放到后台，定期看日志进度，不要中途打断。 - 不要用 pytest --forked，它在 GPU 上会让所有用例失败。 5. 跑完执行 python3 tools/delivery_table.py <结果目录>。 规矩： - 不要为了"让它通过"去改 src/ 或 tests/ 下的代码。确实非改不可（比如环境适配）时，每改一个文件， - 都按 docs/debug/ILUVATAR.md 末尾"实机改动记录"一节的格式追加一条记录，并和代码改动放在同一个 - commit 里。没改就在那一节写"无"。 + 都按 docs/debug/ILUVATAR.md 末尾"实机改动记录"一节的格式追加一条记录，并和代码改动放在同一个 commit 里。 - 不要猜根因。遇到报错就给出完整报错原文，以及出错变体在结果目录里那一行的 reason/error 字段。 -- 9 个 f64/c64 变体显示 NotFound 是预期的（本卡 fp64 不可用，命令已经挡掉了）。 -- 新变体的 PyTorch 列出现 PyTorch result off by ... 也是预期的，统计有多少行即可。 - 原 20 个变体的 PyTorch 数字不要当真。 +- 折算出来的加速比是估算，低也照实报，不要为了数字好看换基线或改参数。 +- 结果目录整个打包带回（至少 summary.json、summary.csv、各算子的 performance.csv 和 *_stderr.log）。 汇报包含： 1. delivery_table.py 的完整输出，尤其是第一行 "N registered variants, M missing"。 -2. 所有非 Passed 的变体：变体名、阶段（精度/性能）、reason 或 error 原文。 +2. 所有非 Passed 的变体、以及加速比为 "-" 的变体：变体名、阶段（精度/性能）、reason 或 error 原文。 3. 逐条回答 docs/debug/ILUVATAR.md "跑完要确认"一节。 -4. 环境指纹：torch、triton 版本，设备名，warp size，驱动/SDK 版本，整轮总耗时。 -5. docs/debug/ILUVATAR.md "实机改动记录"一节的内容。 +4. 环境指纹：torch、triton 版本，设备名，驱动/SDK 版本，git log -1 --oneline，整轮总耗时。 +5. docs/debug/ILUVATAR.md "实机改动记录"一节的内容（没改写"无"）。 ```` ## 实机改动记录 diff --git a/docs/debug/MACA.md b/docs/debug/MACA.md index 93eca799..06080e13 100644 --- a/docs/debug/MACA.md +++ b/docs/debug/MACA.md @@ -73,51 +73,57 @@ python3 tools/delivery_table.py results_metax_65_<日期> 复查旧产物时，不能只看 `delivery_table.py` 的三行 `Failed`；应按 `variant` 查看原始 CSV。若目标 q4 行均为 `PASS`，而父阶段是上述可解释的 `exit 1`，则是历史汇总污染，需用包含 `982b985` 的 runner 重跑或重投影。 -## 跑完要确认 +## 跑完要确认（2026-10-14 之后这一轮） -1. `missing` 是不是 0。 -2. 65 行里有没有 `FAIL`（不是"没有基线所以是 N/A"，是真的算错了）。 -3. 复数变体和 SDDMM 变体有没有卡死或者 OOM（对应上面两条风险点）。 +1. `missing` 是不是 0；65 个变体精度是不是全 Passed（`spmv_coo_f32_int_non` 不应再是 Skipped）。 +2. 有加速比的变体是不是 65/65。哪些带 `(H800折算)`、哪些带 `(H800 PyTorch折算)`，原样列出。按 10-13 那轮预演： + SDDMM 的 q4 变体、`spgemm_csr` 应带 `(H800折算)`，`sddmm_csr_f16_int_non_non_row` 带 `(H800 PyTorch折算)`（预演 1.595x）。 +3. `spmv_csr_f32_int_non` / `spmv_csr_f64_int_non` 的加速比和它的基线是什么：`spmv_csr/performance.csv` 里 + `vendor_backend`、`vendor_ms`、`speedup_vs_vendor`、`h800_scaled_ms` 各一行原样贴回（用 PyTorch 基线会是几百倍， + 用 H800 折算预演是 0.68x；这一项待定，先如实带回）。 +4. `spgemm_csr` 每一行都不应是 `MATRIX_WORKER_FAILED`（按矩阵隔离的子进程以前在 main 上从没跑通过，这轮第一次能跑）。 +5. MACA 专用调参（spmv_csr gather / SELL / 复数 scatter / spmm_csc）合进来后第一次全量跑：和 10-13 那轮比， + 加速比掉了一半以上的变体列出来。 -把这三条的结果写回来，不用额外分析，原始数据最有用。 +把这些结果写回来，不用额外分析，原始数据最有用。 ## 交给 Codex 的 prompt -上机时把下面整段原样贴给 Codex（或其他代理）。不用改任何内容。 +上机时把下面整段原样贴给 Codex（或其他代理），不用改任何内容。 ````text -你在 MetaX C550（MACA） 实机上复测 FlagSparse 的 65 个交付变体。仓库在当前目录，main 分支。 +你在 MetaX C550（MACA） 实机上复测 FlagSparse 的 65 个交付变体（2026-10-14 之后的这一轮）。仓库在当前目录，main 分支。 -背景：65 个变体（原 20 个 + 新合并的 45 个）只在 CUDA 上实测过，本机从没跑过。 -任务是在本机跑一遍，把原始结果带回来。主要目的是收集数据，不是修代码。 +背景：这台机器上一轮已经跑过 65 项。之后仓库改了两类东西，这轮就是验证它们： +- 没有基线的变体，现在用仓库自带的 H800 结果按带宽折算出加速比（命令里的 --h800-reference），交付表里会标 + (H800折算) 或 (H800 PyTorch折算)。有本机基线的变体不受影响。 +- 本后端相关的修复，见 docs/debug/MACA.md "跑完要确认"一节逐条列出的内容。 +主要目的是收集数据，不是修代码。 步骤： -1. git pull --ff-only origin main。 +1. git pull --ff-only origin main；git log -1 --oneline 的提交应不早于 d7d1919（"ci again"）。 2. 按 docs/MACA.md 配好环境，做完它的环境自检。 3. 确认导入的是仓库源码：python3 -c "import flagsparse; print(flagsparse.__file__)" 输出必须在当前目录的 src/ 下。如果指向 site-packages / dist-packages，先停下来汇报，不要继续。 - 旧安装包会让基线列全部变成 N/A，看起来像正常结果。 -4. 完整读一遍 docs/debug/MACA.md，照"要做的事"一节的命令原样跑。 - 不要自己删减或改写参数，每个参数的来由那一节都写了。 - - --results-dir 用新目录（<日期> 填今天），不要复用旧目录。 +4. 完整读一遍 docs/debug/MACA.md，照"要做的事"一节的命令原样跑（已带 --h800-reference，不要去掉）。 + - --results-dir 用新目录（<日期> 填今天），不要复用旧目录，也不要往旧目录里补跑。 - 命令本身已经用 setsid 放到后台，定期看日志进度，不要中途打断。 - 不要用 pytest --forked，它在 GPU 上会让所有用例失败。 5. 跑完执行 python3 tools/delivery_table.py <结果目录>。 规矩： - 不要为了"让它通过"去改 src/ 或 tests/ 下的代码。确实非改不可（比如环境适配）时，每改一个文件， - 都按 docs/debug/MACA.md 末尾"实机改动记录"一节的格式追加一条记录，并和代码改动放在同一个 - commit 里。没改就在那一节写"无"。 + 都按 docs/debug/MACA.md 末尾"实机改动记录"一节的格式追加一条记录，并和代码改动放在同一个 commit 里。 - 不要猜根因。遇到报错就给出完整报错原文，以及出错变体在结果目录里那一行的 reason/error 字段。 -- 本机没有厂商稀疏库，厂商列为空是预期的。复数变体如果报 memory size or pointer value too large， - 把这行之前的 MACA 运行时提示一起带回来。 +- 折算出来的加速比是估算，低也照实报，不要为了数字好看换基线或改参数。 +- 结果目录整个打包带回（至少 summary.json、summary.csv、各算子的 performance.csv 和 *_stderr.log）。 汇报包含： 1. delivery_table.py 的完整输出，尤其是第一行 "N registered variants, M missing"。 -2. 所有非 Passed 的变体：变体名、阶段（精度/性能）、reason 或 error 原文。 +2. 所有非 Passed 的变体、以及加速比为 "-" 的变体：变体名、阶段（精度/性能）、reason 或 error 原文。 3. 逐条回答 docs/debug/MACA.md "跑完要确认"一节。 -4. 环境指纹：torch、triton 版本，设备名，warp size，驱动/SDK 版本，整轮总耗时。 -5. docs/debug/MACA.md "实机改动记录"一节的内容。 +4. 环境指纹：torch、triton 版本，设备名，驱动/SDK 版本，git log -1 --oneline，整轮总耗时。 +5. docs/debug/MACA.md "实机改动记录"一节的内容（没改写"无"）。 ```` ## 实机改动记录 diff --git a/docs/debug/MUSA.md b/docs/debug/MUSA.md index 10d6460a..b4ed6f4a 100644 --- a/docs/debug/MUSA.md +++ b/docs/debug/MUSA.md @@ -54,16 +54,17 @@ python3 tools/delivery_table.py results_musa_split_65_<日期> - **muDNN 的 gemv 不支持 fp64/复数**（gemm 支持）。这条主要影响原有 fp64 变体，新变体里的 fp64 相关 路径（如果有间接依赖 gemv 的）也要留意。 -## 跑完要确认 +## 跑完要确认（2026-10-14 之后这一轮） -1. 65 个变体精度是不是全 Passed，`missing` 是不是 0。 -2. 性能列只应该出现 `Passed`（有 muSPARSE 加速比）和 `NoBaseline`，**不应该再有 `NOT_CONFIGURED`/`NotFound`**。 - 和下面那张 2026-10-06 q4 实测表对照：那张表里有加速比的 21 个变体这次也应该有；原来 20 个上次实机都有加速比， - 少了就是回归。 -3. 所有复数变体（c32）是不是真的跑通了，还是报了 `IndexMusa` 之类的错误；以及有没有哪个变体的耗时可疑（比如 - 慢几百倍，通常意味着走了某种 fallback 循环，而不是真正的 kernel）。 +1. `missing` 是不是 0；65 个变体精度是不是全 Passed（`spmv_coo_f32_int_non` 不应再是 Skipped）。 +2. 日志里 `--h800-reference (musa-s5000): NoBaseline variants filled from the Python half=…, from the C API time=…, + still without a baseline=…` 这一行原样贴回。按设计"still without"应该是 0；不是 0 就列出是哪些变体、为什么。 +3. 交付表里还有没有 `NoBaseline`；带 `(H800折算)` / `(H800 PyTorch折算)` 的变体原样列出。 +4. 这轮 pytest 那一半第一次跑性能（加了 `--h800-reference` 的代价），报一下总耗时，以及 Python 性能阶段里有没有 + 哪个算子超时或崩溃（`<结果目录>/<算子>/performance_stderr.log`）。 +5. 复数变体有没有 `IndexMusa not implemented`；有没有慢几百倍的可疑变体。 -把这三条的结果写回来，不用额外分析，原始数据最有用。 +把这些结果写回来，不用额外分析，原始数据最有用。 ## C API 现在覆盖全部 65 个变体（2026-10-08 在 CUDA 上完成移植） @@ -165,43 +166,41 @@ C API 精度 10/10 测试族通过。唯一的精度瑕疵是 `spmm_csc_f32_int_ ## 交给 Codex 的 prompt -上机时把下面整段原样贴给 Codex（或其他代理）。不用改任何内容。 +上机时把下面整段原样贴给 Codex（或其他代理），不用改任何内容。 ````text -你在 摩尔线程 S5000（MUSA） 实机上复测 FlagSparse 的 65 个交付变体。仓库在当前目录，main 分支。 +你在 摩尔线程 S5000（MUSA） 实机上复测 FlagSparse 的 65 个交付变体（2026-10-14 之后的这一轮）。仓库在当前目录，main 分支。 -背景：65 个变体（原 20 个 + 新合并的 45 个）只在 CUDA 上实测过，本机从没跑过。 -任务是在本机跑一遍，把原始结果带回来。主要目的是收集数据，不是修代码。 +背景：这台机器上一轮已经跑过 65 项。之后仓库改了两类东西，这轮就是验证它们： +- 没有基线的变体，现在用仓库自带的 H800 结果按带宽折算出加速比（命令里的 --h800-reference），交付表里会标 + (H800折算) 或 (H800 PyTorch折算)。有本机基线的变体不受影响。 +- 本后端相关的修复，见 docs/debug/MUSA.md "跑完要确认"一节逐条列出的内容。 +主要目的是收集数据，不是修代码。 步骤： -1. git pull --ff-only origin main。 +1. git pull --ff-only origin main；git log -1 --oneline 的提交应不早于 d7d1919（"ci again"）。 2. 按 docs/MUSA.md 配好环境，做完它的环境自检。 3. 确认导入的是仓库源码：python3 -c "import flagsparse; print(flagsparse.__file__)" 输出必须在当前目录的 src/ 下。如果指向 site-packages / dist-packages，先停下来汇报，不要继续。 - 旧安装包会让基线列全部变成 N/A，看起来像正常结果。 -4. 完整读一遍 docs/debug/MUSA.md，照"要做的事"一节的命令原样跑。 - 不要自己删减或改写参数，每个参数的来由那一节都写了。 - - --results-dir 用新目录（<日期> 填今天），不要复用旧目录。 +4. 完整读一遍 docs/debug/MUSA.md，照"要做的事"一节的命令原样跑（已带 --h800-reference，不要去掉）。 + - --results-dir 用新目录（<日期> 填今天），不要复用旧目录，也不要往旧目录里补跑。 - 命令本身已经用 setsid 放到后台，定期看日志进度，不要中途打断。 - 不要用 pytest --forked，它在 GPU 上会让所有用例失败。 5. 跑完执行 python3 tools/delivery_table.py <结果目录>。 规矩： - 不要为了"让它通过"去改 src/ 或 tests/ 下的代码。确实非改不可（比如环境适配）时，每改一个文件， - 都按 docs/debug/MUSA.md 末尾"实机改动记录"一节的格式追加一条记录，并和代码改动放在同一个 - commit 里。没改就在那一节写"无"。 + 都按 docs/debug/MUSA.md 末尾"实机改动记录"一节的格式追加一条记录，并和代码改动放在同一个 commit 里。 - 不要猜根因。遇到报错就给出完整报错原文，以及出错变体在结果目录里那一行的 reason/error 字段。 -- 性能列出现 NoBaseline 是预期的（muSPARSE 不支持 fp16、int8、混合精度、SELL 等），不应该再出现 - NOT_CONFIGURED / NotFound。拿结果对照 docs/debug/MUSA.md 里 q4 那张 2026-10-06 的实测表， - 逐个列出和那张表差别明显的变体（那张表有加速比、这次没有，或者加速比差两倍以上）。 -- 复数变体如果报 IndexMusa not implemented，列出是哪些变体。 +- 折算出来的加速比是估算，低也照实报，不要为了数字好看换基线或改参数。 +- 结果目录整个打包带回（至少 summary.json、summary.csv、各算子的 performance.csv 和 *_stderr.log）。 汇报包含： 1. delivery_table.py 的完整输出，尤其是第一行 "N registered variants, M missing"。 -2. 所有非 Passed 的变体：变体名、阶段（精度/性能）、reason 或 error 原文。 +2. 所有非 Passed 的变体、以及加速比为 "-" 的变体：变体名、阶段（精度/性能）、reason 或 error 原文。 3. 逐条回答 docs/debug/MUSA.md "跑完要确认"一节。 -4. 环境指纹：torch、triton 版本，设备名，warp size，驱动/SDK 版本，整轮总耗时。 -5. docs/debug/MUSA.md "实机改动记录"一节的内容。 +4. 环境指纹：torch、triton 版本，设备名，驱动/SDK 版本，git log -1 --oneline，整轮总耗时。 +5. docs/debug/MUSA.md "实机改动记录"一节的内容（没改写"无"）。 ```` ## 实机改动记录 From 6bce409951a549154f5e09d82043196be26bbee8 Mon Sep 17 00:00:00 2001 From: NCIA-AlphaSparse Date: Wed, 14 Oct 2026 16:24:58 +0800 Subject: [PATCH 5/9] test sets --- docs/debug/ASCEND.md | 23 +++++++++++++++++++---- docs/debug/ILUVATAR.md | 17 +++++++++++++++-- docs/debug/MACA.md | 17 +++++++++++++++-- docs/debug/MUSA.md | 17 +++++++++++++++-- 4 files changed, 64 insertions(+), 10 deletions(-) diff --git a/docs/debug/ASCEND.md b/docs/debug/ASCEND.md index 3df55fbc..28c9b1bf 100644 --- a/docs/debug/ASCEND.md +++ b/docs/debug/ASCEND.md @@ -70,7 +70,7 @@ source /usr/local/Ascend/ascend-toolkit/latest/set_env.sh export PYTHONPATH="$PWD/src:$PWD" FLAGSPARSE_BACKEND=ascend FLAGSPARSE_ASCEND_VENDOR=torch setsid timeout -s KILL 43200 python3 -u run_flagsparse_pytest.py \ --phase both --mode normal --delivery-only --gpus 6,7 --timeout 3600 \ - --benchmark-input /home/matrix --benchmark-warmup 5 --benchmark-iters 20 \ + --benchmark-input tests/data --benchmark-warmup 5 --benchmark-iters 20 \ --h800-reference \ --results-dir results_ascend_65_<日期> \ > results_ascend_65_<日期>.log 2>&1 < /dev/null & @@ -78,7 +78,9 @@ setsid timeout -s KILL 43200 python3 -u run_flagsparse_pytest.py \ python3 tools/delivery_table.py results_ascend_65_<日期> ``` -- 和 `pytest_results_ascend_full_20261011` 那轮是同一条命令，只多了 `--h800-reference`；`--results-dir` 用新目录。 +- 和 `pytest_results_ascend_full_20261011` 那轮相比：多了 `--h800-reference`；矩阵目录从本机的 `/home/matrix` + （30 个，靠 `--delivery-only` 挑出 10 个）改成仓库自带的 `tests/data`（正好这 10 个，和其他后端一致）。 + `--results-dir` 用新目录。 - `--h800-reference`（不带值）：没有 PyTorch-NPU 基线的行（10-11 那轮 25 个）用仓库自带的 H800 结果折算，交付表里标 `(H800折算)` / `(H800 PyTorch折算)`，有 PyTorch-NPU 基线的行不受影响。卡型自动识别为 `ascend-910b`，日志里应出现 `ascend-910b`；没有就加 `--vendor-card ascend-910b`。口径见 [`../H800_REFERENCE.md`](../H800_REFERENCE.md)。 @@ -174,11 +176,24 @@ sweep、单次 `index_add`）。涉及对 CSC 格式和 SpGEMM 哈希表算法 2. 按 docs/ASCEND.md 配好环境，做完它的环境自检。 3. 确认导入的是仓库源码：python3 -c "import flagsparse; print(flagsparse.__file__)" 输出必须在当前目录的 src/ 下。如果指向 site-packages / dist-packages，先停下来汇报，不要继续。 -4. 完整读一遍 docs/debug/ASCEND.md，照"上机要做的事"里"先做：65 变体全量重测"一节的命令原样跑（已带 --h800-reference，不要去掉）。 +4. 核对测试矩阵：这轮只用仓库自带的 10 个交付矩阵（conf/operators.yaml 的 delivery_matrices），路径和大小： + tests/data/ASIC_680ks.mtx 69268748 字节 + tests/data/GL7d14.mtx 26726293 字节 + tests/data/NACA0015.mtx 43158387 字节 + tests/data/amazon0601.mtx 44469712 字节 + tests/data/auto.mtx 44768883 字节 + tests/data/cage12.mtx 61548649 字节 + tests/data/cfd2.mtx 41954205 字节 + tests/data/filter3D.mtx 47472525 字节 + tests/data/roadNet-TX.mtx 27699282 字节 + tests/data/wave.mtx 13325709 字节 + 用 ls -l tests/data/*.mtx 核对：恰好这 10 个文件、大小一致。缺文件或大小不对（例如 git lfs 指针、下载不全） + 先停下来汇报，不要换别的矩阵目录。命令里的 --benchmark-input tests/data 指的就是它们。 +5. 完整读一遍 docs/debug/ASCEND.md，照"上机要做的事"里"先做：65 变体全量重测"一节的命令原样跑（已带 --h800-reference，不要去掉）。 - --results-dir 用新目录（<日期> 填今天），不要复用旧目录，也不要往旧目录里补跑。 - 命令本身已经用 setsid 放到后台，定期看日志进度，不要中途打断。 - 不要用 pytest --forked，它在 GPU 上会让所有用例失败。 -5. 跑完执行 python3 tools/delivery_table.py <结果目录>。 +6. 跑完执行 python3 tools/delivery_table.py <结果目录>。 规矩： - 不要为了"让它通过"去改 src/ 或 tests/ 下的代码。确实非改不可（比如环境适配）时，每改一个文件， diff --git a/docs/debug/ILUVATAR.md b/docs/debug/ILUVATAR.md index 9cb66da7..8e6d1682 100644 --- a/docs/debug/ILUVATAR.md +++ b/docs/debug/ILUVATAR.md @@ -101,11 +101,24 @@ python3 tools/delivery_table.py results_iluvatar_65_<日期> 2. 按 docs/ILUVATAR.md 配好环境，做完它的环境自检。 3. 确认导入的是仓库源码：python3 -c "import flagsparse; print(flagsparse.__file__)" 输出必须在当前目录的 src/ 下。如果指向 site-packages / dist-packages，先停下来汇报，不要继续。 -4. 完整读一遍 docs/debug/ILUVATAR.md，照"要做的事"一节的命令原样跑（已带 --h800-reference，不要去掉）。 +4. 核对测试矩阵：这轮只用仓库自带的 10 个交付矩阵（conf/operators.yaml 的 delivery_matrices），路径和大小： + tests/data/ASIC_680ks.mtx 69268748 字节 + tests/data/GL7d14.mtx 26726293 字节 + tests/data/NACA0015.mtx 43158387 字节 + tests/data/amazon0601.mtx 44469712 字节 + tests/data/auto.mtx 44768883 字节 + tests/data/cage12.mtx 61548649 字节 + tests/data/cfd2.mtx 41954205 字节 + tests/data/filter3D.mtx 47472525 字节 + tests/data/roadNet-TX.mtx 27699282 字节 + tests/data/wave.mtx 13325709 字节 + 用 ls -l tests/data/*.mtx 核对：恰好这 10 个文件、大小一致。缺文件或大小不对（例如 git lfs 指针、下载不全） + 先停下来汇报，不要换别的矩阵目录。命令里的 --benchmark-input tests/data 指的就是它们。 +5. 完整读一遍 docs/debug/ILUVATAR.md，照"要做的事"一节的命令原样跑（已带 --h800-reference，不要去掉）。 - --results-dir 用新目录（<日期> 填今天），不要复用旧目录，也不要往旧目录里补跑。 - 命令本身已经用 setsid 放到后台，定期看日志进度，不要中途打断。 - 不要用 pytest --forked，它在 GPU 上会让所有用例失败。 -5. 跑完执行 python3 tools/delivery_table.py <结果目录>。 +6. 跑完执行 python3 tools/delivery_table.py <结果目录>。 规矩： - 不要为了"让它通过"去改 src/ 或 tests/ 下的代码。确实非改不可（比如环境适配）时，每改一个文件， diff --git a/docs/debug/MACA.md b/docs/debug/MACA.md index 06080e13..57323751 100644 --- a/docs/debug/MACA.md +++ b/docs/debug/MACA.md @@ -105,11 +105,24 @@ python3 tools/delivery_table.py results_metax_65_<日期> 2. 按 docs/MACA.md 配好环境，做完它的环境自检。 3. 确认导入的是仓库源码：python3 -c "import flagsparse; print(flagsparse.__file__)" 输出必须在当前目录的 src/ 下。如果指向 site-packages / dist-packages，先停下来汇报，不要继续。 -4. 完整读一遍 docs/debug/MACA.md，照"要做的事"一节的命令原样跑（已带 --h800-reference，不要去掉）。 +4. 核对测试矩阵：这轮只用仓库自带的 10 个交付矩阵（conf/operators.yaml 的 delivery_matrices），路径和大小： + tests/data/ASIC_680ks.mtx 69268748 字节 + tests/data/GL7d14.mtx 26726293 字节 + tests/data/NACA0015.mtx 43158387 字节 + tests/data/amazon0601.mtx 44469712 字节 + tests/data/auto.mtx 44768883 字节 + tests/data/cage12.mtx 61548649 字节 + tests/data/cfd2.mtx 41954205 字节 + tests/data/filter3D.mtx 47472525 字节 + tests/data/roadNet-TX.mtx 27699282 字节 + tests/data/wave.mtx 13325709 字节 + 用 ls -l tests/data/*.mtx 核对：恰好这 10 个文件、大小一致。缺文件或大小不对（例如 git lfs 指针、下载不全） + 先停下来汇报，不要换别的矩阵目录。命令里的 --benchmark-input tests/data 指的就是它们。 +5. 完整读一遍 docs/debug/MACA.md，照"要做的事"一节的命令原样跑（已带 --h800-reference，不要去掉）。 - --results-dir 用新目录（<日期> 填今天），不要复用旧目录，也不要往旧目录里补跑。 - 命令本身已经用 setsid 放到后台，定期看日志进度，不要中途打断。 - 不要用 pytest --forked，它在 GPU 上会让所有用例失败。 -5. 跑完执行 python3 tools/delivery_table.py <结果目录>。 +6. 跑完执行 python3 tools/delivery_table.py <结果目录>。 规矩： - 不要为了"让它通过"去改 src/ 或 tests/ 下的代码。确实非改不可（比如环境适配）时，每改一个文件， diff --git a/docs/debug/MUSA.md b/docs/debug/MUSA.md index b4ed6f4a..7382eac3 100644 --- a/docs/debug/MUSA.md +++ b/docs/debug/MUSA.md @@ -182,11 +182,24 @@ C API 精度 10/10 测试族通过。唯一的精度瑕疵是 `spmm_csc_f32_int_ 2. 按 docs/MUSA.md 配好环境，做完它的环境自检。 3. 确认导入的是仓库源码：python3 -c "import flagsparse; print(flagsparse.__file__)" 输出必须在当前目录的 src/ 下。如果指向 site-packages / dist-packages，先停下来汇报，不要继续。 -4. 完整读一遍 docs/debug/MUSA.md，照"要做的事"一节的命令原样跑（已带 --h800-reference，不要去掉）。 +4. 核对测试矩阵：这轮只用仓库自带的 10 个交付矩阵（conf/operators.yaml 的 delivery_matrices），路径和大小： + tests/data/ASIC_680ks.mtx 69268748 字节 + tests/data/GL7d14.mtx 26726293 字节 + tests/data/NACA0015.mtx 43158387 字节 + tests/data/amazon0601.mtx 44469712 字节 + tests/data/auto.mtx 44768883 字节 + tests/data/cage12.mtx 61548649 字节 + tests/data/cfd2.mtx 41954205 字节 + tests/data/filter3D.mtx 47472525 字节 + tests/data/roadNet-TX.mtx 27699282 字节 + tests/data/wave.mtx 13325709 字节 + 用 ls -l tests/data/*.mtx 核对：恰好这 10 个文件、大小一致。缺文件或大小不对（例如 git lfs 指针、下载不全） + 先停下来汇报，不要换别的矩阵目录。命令里的 --benchmark-input tests/data 指的就是它们。 +5. 完整读一遍 docs/debug/MUSA.md，照"要做的事"一节的命令原样跑（已带 --h800-reference，不要去掉）。 - --results-dir 用新目录（<日期> 填今天），不要复用旧目录，也不要往旧目录里补跑。 - 命令本身已经用 setsid 放到后台，定期看日志进度，不要中途打断。 - 不要用 pytest --forked，它在 GPU 上会让所有用例失败。 -5. 跑完执行 python3 tools/delivery_table.py <结果目录>。 +6. 跑完执行 python3 tools/delivery_table.py <结果目录>。 规矩： - 不要为了"让它通过"去改 src/ 或 tests/ 下的代码。确实非改不可（比如环境适配）时，每改一个文件， From 5da882a62c7600e9fabc2c9440a403c8364e7640 Mon Sep 17 00:00:00 2001 From: NCIA-AlphaSparse Date: Wed, 14 Oct 2026 16:47:16 +0800 Subject: [PATCH 6/9] fp64 test removed --- tests/pytest/test_q4_variants_accuracy.py | 20 ----------- tests/pytest/test_spmv_sell_out_accuracy.py | 39 +++++++++++++++++++++ 2 files changed, 39 insertions(+), 20 deletions(-) create mode 100644 tests/pytest/test_spmv_sell_out_accuracy.py diff --git a/tests/pytest/test_q4_variants_accuracy.py b/tests/pytest/test_q4_variants_accuracy.py index 52093776..e09ebf0c 100644 --- a/tests/pytest/test_q4_variants_accuracy.py +++ b/tests/pytest/test_q4_variants_accuracy.py @@ -377,23 +377,3 @@ def test_q4_variant_matches_cpu_golden(name, builder, dtype, shape): gen = torch.Generator().manual_seed(zlib.crc32(name.encode())) result, reference, expected_dtype = builder(dtype, shape, gen) _check(result, reference, expected_dtype) - - -@pytest.mark.spmv_sell -@pytest.mark.parametrize("dtype", [torch.float32, torch.float64], ids=str) -@pytest.mark.parametrize("strided_out", [False, True], ids=["dense_out", "strided_out"]) -def test_spmv_sell_writes_caller_out(dtype, strided_out): - # out= must be filled on every backend (Ascend computes into a fresh tensor), - # including a strided view, and FP64 values must keep FP64 accumulation. - gen = torch.Generator().manual_seed(7) - m, k = 257, 129 - A, x = _sparse(m, k, dtype, gen), _rand((k,), dtype, gen) - vals, cols, offsets = _dev(*fs.dense_to_sell(A, 8)) - x = _dev(x)[0] - storage = torch.full((2 * m,), -7.0, dtype=dtype, device=x.device) - out = storage[::2] if strided_out else storage[:m] - y = fs.flagsparse_spmv_sell(vals, cols, offsets, x, (m, k), slice_size=8, out=out) - assert y is out - _check(out, _wide(A) @ _wide(x.cpu()), dtype) - if strided_out: - assert bool((storage[1::2] == -7.0).all()) diff --git a/tests/pytest/test_spmv_sell_out_accuracy.py b/tests/pytest/test_spmv_sell_out_accuracy.py new file mode 100644 index 00000000..01850abc --- /dev/null +++ b/tests/pytest/test_spmv_sell_out_accuracy.py @@ -0,0 +1,39 @@ +# Copyright 2026 FlagOS Contributors +# SPDX-License-Identifier: Apache-2.0 +"""SpMV SELL writes the caller's ``out`` on every backend. + +Kept OUT of test_q4_variants_accuracy.py on purpose: Iluvatar's runner command selects +that whole module with ``-k "test_q4_variant or ..."``, so a float64 case placed there +bypasses its ``not float64`` filter -- and BI-V150's fp64 H2D copy returns zeros. +""" + +import pytest +import torch + +import flagsparse as fs +from tests.pytest.accuracy_utils import ACCELERATOR_REQUIRED, accelerator_available +from tests.pytest.test_q4_variants_accuracy import _check, _dev, _rand, _sparse, _wide + +pytestmark = pytest.mark.skipif( + not accelerator_available(), reason=ACCELERATOR_REQUIRED +) + + +@pytest.mark.spmv_sell +@pytest.mark.parametrize("dtype", [torch.float32, torch.float64], ids=str) +@pytest.mark.parametrize("strided_out", [False, True], ids=["dense_out", "strided_out"]) +def test_spmv_sell_writes_caller_out(dtype, strided_out): + # out= must be filled on every backend (Ascend computes into a fresh tensor), + # including a strided view, and FP64 values must keep FP64 accumulation. + gen = torch.Generator().manual_seed(7) + m, k = 257, 129 + A, x = _sparse(m, k, dtype, gen), _rand((k,), dtype, gen) + vals, cols, offsets = _dev(*fs.dense_to_sell(A, 8)) + x = _dev(x)[0] + storage = torch.full((2 * m,), -7.0, dtype=dtype, device=x.device) + out = storage[::2] if strided_out else storage[:m] + y = fs.flagsparse_spmv_sell(vals, cols, offsets, x, (m, k), slice_size=8, out=out) + assert y is out + _check(out, _wide(A) @ _wide(x.cpu()), dtype) + if strided_out: + assert bool((storage[1::2] == -7.0).all()) From dd7805832b3a3a7559448d3b57cfc289e22b8781 Mon Sep 17 00:00:00 2001 From: NCIA-AlphaSparse Date: Wed, 14 Oct 2026 17:17:35 +0800 Subject: [PATCH 7/9] biv spgemm fp64 no --- docs/ILUVATAR.md | 4 ++- docs/debug/ILUVATAR.md | 6 +++-- run_flagsparse_pytest.py | 36 ++++++++++++++++++++++++- tests/ci/test_runner_result_format.py | 38 +++++++++++++++++++++++++++ 4 files changed, 80 insertions(+), 4 deletions(-) diff --git a/docs/ILUVATAR.md b/docs/ILUVATAR.md index 5123bd91..74c988f6 100644 --- a/docs/ILUVATAR.md +++ b/docs/ILUVATAR.md @@ -201,7 +201,8 @@ setsid timeout -s KILL 14400 python3 -u run_flagsparse_pytest.py \ `allclose` 里面）； - **多卡机器先确认 `--gpus 0` 真的生效**：runner 靠 `CUDA_VISIBLE_DEVICES` 选卡，CoreX 是否遵守这个 变量未验证，跑起来用 `ixsmi` 看负载落在哪张卡； -- **不用跑 `spsv` / `spsm` / `spgemm`**，它们不在交付清单里，`--delivery-only` 也选不到。 +- **不用跑 `spsv` / `spsm`**，它们不在交付清单里，`--delivery-only` 也选不到。`spgemm_csr` 现在是交付变体 + （`spgemm_csr_f32_int_non_non`），命令里用 `spgemm_csr=--dtypes float32` 只跑 f32（本卡 fp64 不可用）。 --- @@ -231,6 +232,7 @@ setsid timeout -s KILL 43200 \ --op-benchmark-args='spmm_csr=--dtypes float16,float32' \ --op-benchmark-args='spmm_coo=--dtypes float16,float32' \ --op-benchmark-args='sddmm_csr=--dtype float32' \ + --op-benchmark-args='spgemm_csr=--dtypes float32' \ --results-dir pytest_results_iluvatar_delivery \ > pytest_results_iluvatar_delivery.log 2>&1 < /dev/null & ``` diff --git a/docs/debug/ILUVATAR.md b/docs/debug/ILUVATAR.md index 8e6d1682..fdd4f375 100644 --- a/docs/debug/ILUVATAR.md +++ b/docs/debug/ILUVATAR.md @@ -26,6 +26,7 @@ setsid timeout -s KILL 43200 \ --op-benchmark-args='spmm_coo=--dtypes float16,float32' \ --op-benchmark-args='spmm_csc=--dtypes float32,complex64' \ --op-benchmark-args='sddmm_csr=--dtype float32' \ + --op-benchmark-args='spgemm_csr=--dtypes float32' \ --results-dir results_iluvatar_65_<日期> \ > results_iluvatar_65_<日期>.log 2>&1 < /dev/null & # 跑完后 @@ -42,8 +43,9 @@ python3 tools/delivery_table.py results_iluvatar_65_<日期> - **新增 `spmv_csc`/`spmm_csc` 的 dtype 限制**：这两个脚本默认会跑 float64/complex128，先跑 fp64 万一 卡死或崩溃，排在最后追加的新变体行就丢了；它们的交付变体只要 f32/c32/f16，f16 由新变体那一路单独测。 - 原 20 个变体里的 9 个 f64/c64 在本卡上照旧测不了，`delivery_table.py` 里显示 `NotFound` 是预期的； - `spgemm_csr` 的 runner 会按 dtype 拆开分别跑 float32/float64，float64 那一组的结果不要当真，交付变体 - 只用 float32。 + `spgemm_csr` 的性能 runner 默认按 dtype 拆开分别跑 float32/float64；命令里的 + `--op-benchmark-args='spgemm_csr=--dtypes float32'` 让它只跑 float32（交付变体只有 f32，fp64 那个子进程在本卡 + 只会失败，崩溃时还会把整个 spgemm 性能阶段判成 FAIL）。精度侧的 fp64 用例已被 `-k` 挡掉。 - **`spgemm_csr` 这轮第一次真正能跑**：10-09 那轮 20 行全部是 TLE 编译错误 （`iluvatar TLE alloc does not support nv_mma_shared_layout=True`），现在天数上改走 ESC（见文末改动记录）。 跑完确认 `spgemm_csr_f32_int_non_non` 精度 Passed、有 `(H800折算)` 加速比；还报 TLE 就说明没拉到新代码。 diff --git a/run_flagsparse_pytest.py b/run_flagsparse_pytest.py index 21353257..45ceff1f 100644 --- a/run_flagsparse_pytest.py +++ b/run_flagsparse_pytest.py @@ -2133,6 +2133,39 @@ def _merge_csv_rows(paths: list[Path], destination: Path) -> list[dict[str, str] return rows +_SPGEMM_SPLIT_DTYPES = ("float32", "float64") + + +def _spgemm_split_dtypes(extra_args: list[str]) -> tuple[tuple[str, ...], list[str]]: + """The dtypes to run one by one, and ``extra_args`` without their ``--dtypes``. + + Defaults to float32 then float64. An ``--op-benchmark-args='spgemm_csr=--dtypes + float32'`` narrows it: Iluvatar's fp64 H2D copy returns zeros, so its float64 child + could only fail -- and a crashed child fails the whole phase, float32 included. + Passing the user's ``--dtypes`` through unchanged would not work: each child gets + its own ``--dtypes `` and argparse keeps the last. + """ + args = list(extra_args) + wanted = None + for index, arg in enumerate(args): + if arg == "--dtypes" and index + 1 < len(args): + wanted, args = args[index + 1], args[:index] + args[index + 2 :] + break + if arg.startswith("--dtypes="): + wanted, args = arg.split("=", 1)[1], args[:index] + args[index + 1 :] + break + if wanted is None: + return _SPGEMM_SPLIT_DTYPES, args + requested = {part.strip() for part in wanted.split(",") if part.strip()} + unknown = requested - set(_SPGEMM_SPLIT_DTYPES) + if unknown: + raise ValueError( + f"spgemm_csr --dtypes {wanted!r}: unsupported {sorted(unknown)}; " + f"choose from {', '.join(_SPGEMM_SPLIT_DTYPES)}" + ) + return tuple(d for d in _SPGEMM_SPLIT_DTYPES if d in requested), args + + def _run_spgemm_split_dtypes( *, project_root: Path, @@ -2164,7 +2197,8 @@ def _run_spgemm_split_dtypes( failed_dtypes: list[str] = [] total_duration = 0.0 - for dtype in ("float32", "float64"): + dtypes, extra_args = _spgemm_split_dtypes(extra_args) + for dtype in dtypes: csv_path = op_dir / f"performance_{dtype}.csv" cmd, csv_path = render_performance_command( template, diff --git a/tests/ci/test_runner_result_format.py b/tests/ci/test_runner_result_format.py index e4b76c00..6cef3550 100644 --- a/tests/ci/test_runner_result_format.py +++ b/tests/ci/test_runner_result_format.py @@ -1173,3 +1173,41 @@ def test_the_reuse_suite_backend_gate_does_not_demote_a_delivery_variant(): summary = runner.summarize_accuracy_cases(raw) assert summary["status"] == "Passed" assert summary["skipped"] == 1 # still reported, just not a demotion + + +def test_spgemm_split_path_honours_a_dtypes_override(tmp_path, monkeypatch): + # Iluvatar passes spgemm_csr=--dtypes float32: its fp64 child could only fail. + seen = [] + + def fake_run_subprocess(cmd, **_kwargs): + dtype = cmd[cmd.index("--dtypes") + 1] + seen.append((dtype, cmd.count("--dtypes"))) + Path(cmd[cmd.index("--csv") + 1]).write_text( + "matrix,value_dtype,triton_ms,status\nsample.mtx,float32,1.0,PASS\n", + encoding="utf-8", + ) + return 0, "", "", 0.1, False + + monkeypatch.setattr(runner, "run_subprocess", fake_run_subprocess) + result = runner._run_spgemm_split_dtypes( + project_root=ROOT, + op="spgemm_csr", + gpu_id=0, + script_device=0, + template=("tests/test_spgemm.py", "--csv", "{csv}"), + op_dir=tmp_path, + benchmark_input=None, + warmup=1, + iters=1, + extra_args=["--dtypes", "float32"], + timeout=10, + ) + assert seen == [("float32", 1)] # one child, one --dtypes + assert result["status"] == "Passed" and not result["failed_dtypes"] + assert runner._spgemm_split_dtypes([]) == (("float32", "float64"), []) + assert runner._spgemm_split_dtypes(["--dtypes=float64,float32", "--x"]) == ( + ("float32", "float64"), + ["--x"], + ) + with pytest.raises(ValueError): + runner._spgemm_split_dtypes(["--dtypes", "float16"]) From a330b62b46df3a2048bd14c95650eb4f82698185 Mon Sep 17 00:00:00 2001 From: NCIA-AlphaSparse Date: Wed, 14 Oct 2026 20:26:12 +0800 Subject: [PATCH 8/9] delever bug fix --- docs/ASCEND.md | 2 +- docs/H800_REFERENCE.md | 4 +- docs/ILUVATAR.md | 2 +- docs/MACA.md | 6 +- docs/MUSA.md | 4 +- docs/debug/ASCEND.md | 10 +- docs/debug/ILUVATAR.md | 10 +- docs/debug/MACA.md | 14 +-- docs/debug/MUSA.md | 10 +- .../sparse_operations/benchmarks.py | 59 ++++++------ tests/ci/test_delivery_table.py | 32 +++---- tests/ci/test_musa_benchmark_references.py | 94 +++++++++++++++++++ tests/ci/test_spgemm_reference_fp64.py | 35 +++++++ tests/test_scatter.py | 4 + tests/test_spgemm.py | 5 +- tests/test_spmm_csc.py | 32 ++++++- tools/delivery_table.py | 54 +++++++---- tools/h800_reference.py | 4 +- 18 files changed, 283 insertions(+), 98 deletions(-) create mode 100644 tests/ci/test_musa_benchmark_references.py create mode 100644 tests/ci/test_spgemm_reference_fp64.py diff --git a/docs/ASCEND.md b/docs/ASCEND.md index 4d7d15e5..6703e1e6 100644 --- a/docs/ASCEND.md +++ b/docs/ASCEND.md @@ -54,7 +54,7 @@ python3 tools/delivery_table.py pytest_results_ascend_delivery # 跑完后：2 **`--h800-reference`（不带值）**：没有 PyTorch-NPU 基线的行，用仓库自带的 H800 结果按带宽折算出加速比 （H800 cuSPARSE 时间 × 3050/1600；H800 也没有 cuSPARSE 时间的 `sddmm_csr_f16` 用 H800 PyTorch），交付表里标 -`(H800折算)` / `(H800 PyTorch折算)`。**有 PyTorch-NPU 基线的行不受影响。** 按 10-11 全量结果预演：有加速比的变体 +只显示数字（`--show-baseline` 可看来源）。**有 PyTorch-NPU 基线的行不受影响。** 按 10-11 全量结果预演：有加速比的变体 41 → 63，补上的 22 个大多在 0.001x–0.06x——昇腾上这些变体每次调用 0.3–2.4 秒（`timed=call`，PyTorch-NPU 自己 在 legacy spmm_csr 上也要 20–44 ms），数是真的，不是折算出错。`spgemm_csr` 以前走探测脚本、测的是合成规模， 现在改为 `tests/test_spgemm.py` 在交付矩阵上测（**未在 NPU 上跑过**），重跑后才能折算。见 diff --git a/docs/H800_REFERENCE.md b/docs/H800_REFERENCE.md index e0867b46..2101d734 100644 --- a/docs/H800_REFERENCE.md +++ b/docs/H800_REFERENCE.md @@ -25,9 +25,9 @@ python3 run_flagsparse_split_delivery.py --h800-reference ... # C API 拆分交 python3 tools/baseline_bound.py --vendor <目标卡结果目录> # 已跑完的结果目录事后查看 ``` -这是**估算**，不是厂商实测：H800 与目标卡的访存效率、缓存、launch 开销并不成比例。折算出的加速比在 summary 里带 `baseline: h800_scaled`，`tools/delivery_table.py` 显示为 `(H800折算)`。 +这是**估算**，不是厂商实测：H800 与目标卡的访存效率、缓存、launch 开销并不成比例。折算出的加速比在 summary 里带 `baseline: h800_scaled`；`tools/delivery_table.py` 只显示数字，加 `--show-baseline` 才显示来源。 -H800 上某个算例**没有** cuSPARSE 时间时（cuSPARSE SDDMM 不支持 fp16），退一步用同一行 H800 PyTorch 的时间（PyTorch 结果须通过校验），标 `baseline: h800_pytorch_scaled` / `(H800 PyTorch折算)`；cuSPARSE 跑了但结果不对的行不退。FlagSparse 自己在 H800 上的时间永远不当基线。 +H800 上某个算例**没有** cuSPARSE 时间时（cuSPARSE SDDMM 不支持 fp16），退一步用同一行 H800 PyTorch 的时间（PyTorch 结果须通过校验），标 `baseline: h800_pytorch_scaled`；cuSPARSE 跑了但结果不对的行不退。FlagSparse 自己在 H800 上的时间永远不当基线。 C API 拆分交付（MUSA）：带 `--h800-reference` 时 pytest 那一半也跑性能（与本参照同一 benchmark、同一规模），NoBaseline 变体优先取它；取不到再用 C API 时间按**变体 + 矩阵名**匹配（C API 的 SpMM `n=8`、SDDMM `k=16/64`、按矩阵测的向量类在这里没有对应规模，只有 SpMV 类能对上）。 diff --git a/docs/ILUVATAR.md b/docs/ILUVATAR.md index 74c988f6..1de67bc3 100644 --- a/docs/ILUVATAR.md +++ b/docs/ILUVATAR.md @@ -360,7 +360,7 @@ speedup_vs_h800_scaled = h800_scaled_ms / T_FlagSparse@BI-V150 # 1.0 = 和 每行保留算例键、各 `*_ms` 时间、加速比和状态列。想换成自己的一轮 H800 结果，`--h800-reference 目录或json文件` 都行；重新生成用 `python3 tools/h800_reference.py `。 - **H800 没有 cuSPARSE 时间的**：65 个交付变体里只有 `sddmm_csr_f16_int_non_non_row`（cuSPARSE SDDMM 不支持 - fp16），改用 H800 上 PyTorch 的时间折算，标 `(H800 PyTorch折算)`。非交付的 `spmm_bsr`、`spmm_bell` 两者都没有。 + fp16），改用 H800 上 PyTorch 的时间折算。非交付的 `spmm_bsr`、`spmm_bell` 两者都没有。 按 10-09 的天数结果预演，换成新参照后有加速比的变体从 25 个变为 55 个；剩下的是不测的 fp64 9 个，和那轮因 TLE 编译失败全部 FAIL 的 `spgemm_csr`（已修，见 `debug/ILUVATAR.md` 改动记录，重跑后可折算）。 - 每行的 `performance.csv` 会多 5 列：`h800_vendor_ms`（H800 上 cuSPARSE 的用时）、`h800_scaled_ms`（折算后的时间， diff --git a/docs/MACA.md b/docs/MACA.md index 208d9dcb..ce6a7359 100644 --- a/docs/MACA.md +++ b/docs/MACA.md @@ -65,10 +65,10 @@ setsid timeout -s KILL 39600 python3 -u run_flagsparse_pytest.py \ `torch.sparse.sampled_addmm` 结果还是错的（7.3 节），因此只对 SDDMM 禁用该参考并改用 PyTorch。 `--benchmark-args` 会广播给所有性能脚本，而其他脚本不一定接受 `--no-cusparse`，所以用逐算子的形式； - `--h800-reference`（不带值）：没有任何基线的行（目前是 legacy `spmv_csr` f32/f64、SDDMM 的 q4 变体、`spgemm_csr`）， - 用仓库自带的 H800 结果按带宽折算出加速比（`h800_scaled_ms = T_cuSPARSE@H800 × 3050/1440`），`delivery_table.py` - 里这些行后面会标 `(H800折算)`。**有 PyTorch 基线的行不受影响。** 这是估算，不是厂商实测，汇报时要注明； + 用仓库自带的 H800 结果按带宽折算出加速比（`h800_scaled_ms = T_cuSPARSE@H800 × 3050/1440`），交付表里只显示数字； + 要看哪些是折算的用 `delivery_table.py --show-baseline`。**有 PyTorch 基线的行不受影响。** 这是估算，不是厂商实测，汇报时要注明； `sddmm_csr_f16_int_non_non_row` 在 H800 上没有 cuSPARSE 时间（cuSPARSE SDDMM 不支持 fp16），改用 H800 上 - PyTorch 的时间折算，标 `(H800 PyTorch折算)`（C550 自己的 `sampled_addmm` 结果是错的，7.3 节）。 + PyTorch 的时间折算（`--show-baseline` 显示 `h800_pytorch_scaled`；C550 自己的 `sampled_addmm` 结果是错的，7.3 节）。 口径和覆盖见 [`H800_REFERENCE.md`](H800_REFERENCE.md)。 - `--timeout 4500`：`--delivery-only` 已把 spmv/spmm 收窄到 int32 + non，但 **SDDMM 的 4 个 K 值 不收窄**（交付名里没有 K），按 30 个矩阵实测推算全量至少 3660 秒（7.4 节；现在交付只用 10 个矩阵，这个数会小一截，没有重新实测）。只想快速出数，可以改用 diff --git a/docs/MUSA.md b/docs/MUSA.md index 00e9e8c8..ae82e25d 100644 --- a/docs/MUSA.md +++ b/docs/MUSA.md @@ -51,13 +51,13 @@ setsid timeout -s KILL 43200 python3 -u run_flagsparse_split_delivery.py \ ``` - `--h800-reference`（不带值）：muSPARSE 不支持、标 `NoBaseline` 的变体（2026-10-09 那轮是 26 个），用仓库自带的 - H800 结果按带宽折算出加速比（H800 时间 × 3050/1370），`delivery_table.py` 里标 `(H800折算)`。**muSPARSE 测得到的 + H800 结果按带宽折算出加速比（H800 时间 × 3050/1370），交付表里只显示数字（`--show-baseline` 可看来源）。**muSPARSE 测得到的 变体不受影响。** 加了这个开关，pytest 那一半会从只跑精度变成**精度 + 性能**（多一轮 Python 性能，耗时会明显变长）， 因为 C API 的规模（SpMM `n=8`、SDDMM `k=16/64`、向量类按矩阵）在 H800 上没有对应数据，而 Python 侧和 H800 参照 是同一个 benchmark、同样规模。合并时按顺序取：① C API 有 muSPARSE 基线 → C API；② Python 侧有折算加速比 → Python（条目里 `source: python`，原 C API 结果留在 `capi_performance`）；③ 否则用 C API 时间对 H800 同变体同矩阵 折算（只有 SpMV 类能对上）。日志会打印三类各补了多少、还剩哪些。`sddmm_csr_f16` 在 H800 上没有 cuSPARSE 时间， - 用 H800 PyTorch 折算，标 `(H800 PyTorch折算)`。是估算，不是厂商实测，汇报时要注明；见 + 用 H800 PyTorch 折算。是估算，不是厂商实测，汇报时要注明；见 [`H800_REFERENCE.md`](H800_REFERENCE.md)。 `--timeout` 同时传给 pytest 的每个算子阶段和 C API 的 CTest（`-DFLAGSPARSE_CTEST_TIMEOUT`）。 diff --git a/docs/debug/ASCEND.md b/docs/debug/ASCEND.md index 28c9b1bf..c510e301 100644 --- a/docs/debug/ASCEND.md +++ b/docs/debug/ASCEND.md @@ -81,8 +81,8 @@ python3 tools/delivery_table.py results_ascend_65_<日期> - 和 `pytest_results_ascend_full_20261011` 那轮相比：多了 `--h800-reference`；矩阵目录从本机的 `/home/matrix` （30 个，靠 `--delivery-only` 挑出 10 个）改成仓库自带的 `tests/data`（正好这 10 个，和其他后端一致）。 `--results-dir` 用新目录。 -- `--h800-reference`（不带值）：没有 PyTorch-NPU 基线的行（10-11 那轮 25 个）用仓库自带的 H800 结果折算，交付表里标 - `(H800折算)` / `(H800 PyTorch折算)`，有 PyTorch-NPU 基线的行不受影响。卡型自动识别为 `ascend-910b`，日志里应出现 +- `--h800-reference`（不带值）：没有 PyTorch-NPU 基线的行（10-11 那轮 25 个）用仓库自带的 H800 结果折算，交付表里只显示数字 + （`--show-baseline` 可看来源），有 PyTorch-NPU 基线的行不受影响。卡型自动识别为 `ascend-910b`，日志里应出现 `ascend-910b`；没有就加 `--vendor-card ascend-910b`。口径见 [`../H800_REFERENCE.md`](../H800_REFERENCE.md)。 - 这轮要确认的三件事： 1. `spgemm_csr` 的性能现在走 `tests/test_spgemm.py`（交付矩阵、按 dtype 分进程），**第一次在 NPU 上跑**。日志里 @@ -152,7 +152,7 @@ sweep、单次 `index_add`）。涉及对 CSC 格式和 SpGEMM 哈希表算法 2. `spgemm_csr` 的性能这次第一次走 `tests/test_spgemm.py`（交付矩阵、float32/float64 分进程）： 应有 `spgemm_csr/performance_float32.csv`、`performance_float64.csv`，`spgemm_csr_f32_int_non_non` 应有加速比。 失败就贴 `spgemm_csr/performance_stderr.log` 最后 50 行原文。 -3. 有加速比的变体个数（预演 64–65）。带 `(H800折算)` / `(H800 PyTorch折算)` 的变体原样列出；日志里卡型应是 `ascend-910b`。 +3. 有加速比的变体个数（预演 64–65）。`python3 tools/delivery_table.py <结果目录> --show-baseline` 的输出原样贴回；日志里卡型应是 `ascend-910b`。 4. 折算补上的变体预演大多是 0.001x–0.06x（q4 call 路径每次 0.3–2.4 秒）。如实带回，并挑 3 个最慢的变体贴 `performance.csv` 原始行（含 `timed`、`triton_ms` 列）。 @@ -166,8 +166,8 @@ sweep、单次 `index_add`）。涉及对 CSC 格式和 SpGEMM 哈希表算法 你在 昇腾 910B（Ascend） 实机上复测 FlagSparse 的 65 个交付变体（2026-10-14 之后的这一轮）。仓库在当前目录，main 分支。 背景：这台机器上一轮已经跑过 65 项。之后仓库改了两类东西，这轮就是验证它们： -- 没有基线的变体，现在用仓库自带的 H800 结果按带宽折算出加速比（命令里的 --h800-reference），交付表里会标 - (H800折算) 或 (H800 PyTorch折算)。有本机基线的变体不受影响。 +- 没有基线的变体，现在用仓库自带的 H800 结果按带宽折算出加速比（命令里的 --h800-reference），交付表里只显示数字， + delivery_table.py --show-baseline 的最后一列说明哪些是折算的。有本机基线的变体不受影响。 - 本后端相关的修复，见 docs/debug/ASCEND.md "跑完要确认"一节逐条列出的内容。 主要目的是收集数据，不是修代码。 diff --git a/docs/debug/ILUVATAR.md b/docs/debug/ILUVATAR.md index fdd4f375..492cc38d 100644 --- a/docs/debug/ILUVATAR.md +++ b/docs/debug/ILUVATAR.md @@ -48,10 +48,10 @@ python3 tools/delivery_table.py results_iluvatar_65_<日期> 只会失败，崩溃时还会把整个 spgemm 性能阶段判成 FAIL）。精度侧的 fp64 用例已被 `-k` 挡掉。 - **`spgemm_csr` 这轮第一次真正能跑**：10-09 那轮 20 行全部是 TLE 编译错误 （`iluvatar TLE alloc does not support nv_mma_shared_layout=True`），现在天数上改走 ESC（见文末改动记录）。 - 跑完确认 `spgemm_csr_f32_int_non_non` 精度 Passed、有 `(H800折算)` 加速比；还报 TLE 就说明没拉到新代码。 + 跑完确认 `spgemm_csr_f32_int_non_non` 精度 Passed、有加速比；还报 TLE 就说明没拉到新代码。 - **`--h800-reference` 现在能补上 q4 变体**：10-09 那轮命令也带了它，但当时的参照只有 20 个变体，q4 全对不上， 所以全是 `-`。参照已换成 H800 的 65 变体全量，按那轮数据预演，有加速比的变体 25 → 55（再加 spgemm 就是 56， - 其余 9 个是 fp64）。`sddmm_csr_f16` 标的是 `(H800 PyTorch折算)`，见 [`../H800_REFERENCE.md`](../H800_REFERENCE.md)。 + 其余 9 个是 fp64）。`sddmm_csr_f16` 用的是 H800 PyTorch 折算，见 [`../H800_REFERENCE.md`](../H800_REFERENCE.md)。 - 外层 `timeout -s KILL` 是整条命令的总限时（内核卡死时 Ctrl-C 送不进去，只能靠 KILL）；`--timeout` 是 每个算子每个阶段的限时。这两个数是 20 变体时期定的，65 变体多了 6 个父算子，**没有在本机实测过总耗时**， @@ -80,7 +80,7 @@ python3 tools/delivery_table.py results_iluvatar_65_<日期> 1. `missing` 是不是 0。9 个 f64/c64 变体 `NotFound` 是预期的，其余 56 个精度应 Passed。 2. `spgemm_csr_f32_int_non_non`：精度 Passed、有加速比；日志/CSV 里不应再出现 `iluvatar TLE alloc does not support nv_mma_shared_layout=True`（出现就说明没拉到新代码）。 -3. 有加速比的变体个数（预演 56 = 10-09 预演的 55 + spgemm）。带 `(H800折算)` / `(H800 PyTorch折算)` 的原样列出。 +3. 有加速比的变体个数（预演 56 = 10-09 预演的 55 + spgemm）。`python3 tools/delivery_table.py <结果目录> --show-baseline` 的输出原样贴回。 4. 任何一行的 PyTorch 列"看起来正常但可疑"（这张卡的 torch.sparse 有静默算错的先例）；int8/混合精度变体精度是否真的通过。 把这些结果写回来，不用额外分析，原始数据最有用。 @@ -93,8 +93,8 @@ python3 tools/delivery_table.py results_iluvatar_65_<日期> 你在 天数智芯 BI-V150（Iluvatar） 实机上复测 FlagSparse 的 65 个交付变体（2026-10-14 之后的这一轮）。仓库在当前目录，main 分支。 背景：这台机器上一轮已经跑过 65 项。之后仓库改了两类东西，这轮就是验证它们： -- 没有基线的变体，现在用仓库自带的 H800 结果按带宽折算出加速比（命令里的 --h800-reference），交付表里会标 - (H800折算) 或 (H800 PyTorch折算)。有本机基线的变体不受影响。 +- 没有基线的变体，现在用仓库自带的 H800 结果按带宽折算出加速比（命令里的 --h800-reference），交付表里只显示数字， + delivery_table.py --show-baseline 的最后一列说明哪些是折算的。有本机基线的变体不受影响。 - 本后端相关的修复，见 docs/debug/ILUVATAR.md "跑完要确认"一节逐条列出的内容。 主要目的是收集数据，不是修代码。 diff --git a/docs/debug/MACA.md b/docs/debug/MACA.md index 57323751..66c625f9 100644 --- a/docs/debug/MACA.md +++ b/docs/debug/MACA.md @@ -25,10 +25,10 @@ python3 tools/delivery_table.py results_metax_65_<日期> 里，拉下来就有）。这条命令没带 `--delivery-only`，runner 不会再筛矩阵，给 30 个矩阵的目录就会 30 个全跑， 汇总出来的加速比也会掺进非交付矩阵，所以不要换成别的目录。 - `--h800-reference`（不带值）：没有任何基线的行（目前是 legacy `spmv_csr` f32/f64、SDDMM 的 q4 变体、`spgemm_csr`）， - 用仓库自带的 H800 结果按带宽折算出加速比（`h800_scaled_ms = T_cuSPARSE@H800 × 3050/1440`），`delivery_table.py` - 里这些行后面会标 `(H800折算)`。**有 PyTorch 基线的行不受影响。** 这是估算，不是厂商实测，汇报时要注明； + 用仓库自带的 H800 结果按带宽折算出加速比（`h800_scaled_ms = T_cuSPARSE@H800 × 3050/1440`），交付表里只显示数字； + 要看哪些是折算的用 `delivery_table.py --show-baseline`。**有 PyTorch 基线的行不受影响。** 这是估算，不是厂商实测，汇报时要注明； `sddmm_csr_f16_int_non_non_row` 在 H800 上没有 cuSPARSE 时间（cuSPARSE SDDMM 不支持 fp16），改用 H800 上 - PyTorch 的时间折算，标 `(H800 PyTorch折算)`（C550 自己的 `sampled_addmm` 结果是错的，7.3 节）。 + PyTorch 的时间折算（`--show-baseline` 显示 `h800_pytorch_scaled`；C550 自己的 `sampled_addmm` 结果是错的，7.3 节）。 口径和覆盖见 [`../H800_REFERENCE.md`](../H800_REFERENCE.md)。 - `sddmm_csr=--no-cusparse`、`--timeout 4500` 沿用 `../MACA.md` 0.5 节的 20 变体交付命令，原因见那一节 （C550 没有可用厂商稀疏库；SDDMM 的 4 个 K 值不收窄，单阶段耗时长）。 @@ -76,8 +76,8 @@ python3 tools/delivery_table.py results_metax_65_<日期> ## 跑完要确认（2026-10-14 之后这一轮） 1. `missing` 是不是 0；65 个变体精度是不是全 Passed（`spmv_coo_f32_int_non` 不应再是 Skipped）。 -2. 有加速比的变体是不是 65/65。哪些带 `(H800折算)`、哪些带 `(H800 PyTorch折算)`，原样列出。按 10-13 那轮预演： - SDDMM 的 q4 变体、`spgemm_csr` 应带 `(H800折算)`，`sddmm_csr_f16_int_non_non_row` 带 `(H800 PyTorch折算)`（预演 1.595x）。 +2. 有加速比的变体是不是 65/65。`python3 tools/delivery_table.py <结果目录> --show-baseline` 的输出原样贴回。按 10-13 那轮预演：SDDMM 的 q4 变体、 + `spgemm_csr` 应为 `h800_scaled`，`sddmm_csr_f16_int_non_non_row` 为 `h800_pytorch_scaled`（预演 1.595x）。 3. `spmv_csr_f32_int_non` / `spmv_csr_f64_int_non` 的加速比和它的基线是什么：`spmv_csr/performance.csv` 里 `vendor_backend`、`vendor_ms`、`speedup_vs_vendor`、`h800_scaled_ms` 各一行原样贴回（用 PyTorch 基线会是几百倍， 用 H800 折算预演是 0.68x；这一项待定，先如实带回）。 @@ -95,8 +95,8 @@ python3 tools/delivery_table.py results_metax_65_<日期> 你在 MetaX C550（MACA） 实机上复测 FlagSparse 的 65 个交付变体（2026-10-14 之后的这一轮）。仓库在当前目录，main 分支。 背景：这台机器上一轮已经跑过 65 项。之后仓库改了两类东西，这轮就是验证它们： -- 没有基线的变体，现在用仓库自带的 H800 结果按带宽折算出加速比（命令里的 --h800-reference），交付表里会标 - (H800折算) 或 (H800 PyTorch折算)。有本机基线的变体不受影响。 +- 没有基线的变体，现在用仓库自带的 H800 结果按带宽折算出加速比（命令里的 --h800-reference），交付表里只显示数字， + delivery_table.py --show-baseline 的最后一列说明哪些是折算的。有本机基线的变体不受影响。 - 本后端相关的修复，见 docs/debug/MACA.md "跑完要确认"一节逐条列出的内容。 主要目的是收集数据，不是修代码。 diff --git a/docs/debug/MUSA.md b/docs/debug/MUSA.md index 7382eac3..f4d4248d 100644 --- a/docs/debug/MUSA.md +++ b/docs/debug/MUSA.md @@ -23,13 +23,13 @@ python3 tools/delivery_table.py results_musa_split_65_<日期> muSPARSE 不支持的类型组合（fp16、int8、混合精度、SELL 等）显示 `NoBaseline`——跑通了、精度过了，只是没有 厂商对照，**这是预期的**，不是失败。 - `--h800-reference`（不带值）：muSPARSE 不支持、标 `NoBaseline` 的变体（2026-10-09 那轮是 26 个），用仓库自带的 - H800 结果按带宽折算出加速比（H800 时间 × 3050/1370），`delivery_table.py` 里标 `(H800折算)`。**muSPARSE 测得到的 + H800 结果按带宽折算出加速比（H800 时间 × 3050/1370），交付表里只显示数字（`--show-baseline` 可看来源）。**muSPARSE 测得到的 变体不受影响。** 加了这个开关，pytest 那一半会从只跑精度变成**精度 + 性能**（多一轮 Python 性能，耗时会明显变长）， 因为 C API 的规模（SpMM `n=8`、SDDMM `k=16/64`、向量类按矩阵）在 H800 上没有对应数据，而 Python 侧和 H800 参照 是同一个 benchmark、同样规模。合并时按顺序取：① C API 有 muSPARSE 基线 → C API；② Python 侧有折算加速比 → Python（条目里 `source: python`，原 C API 结果留在 `capi_performance`）；③ 否则用 C API 时间对 H800 同变体同矩阵 折算（只有 SpMV 类能对上）。日志会打印三类各补了多少、还剩哪些。`sddmm_csr_f16` 在 H800 上没有 cuSPARSE 时间， - 用 H800 PyTorch 折算，标 `(H800 PyTorch折算)`。是估算，不是厂商实测，汇报时要注明；见 + 用 H800 PyTorch 折算。是估算，不是厂商实测，汇报时要注明；见 [`../H800_REFERENCE.md`](../H800_REFERENCE.md)。 - 拉到新代码后第一次跑**不要加 `--skip-capi-build`**，确保 C API 是重新编译的。 - `--timeout 3600` 沿用 `../MUSA.md` 的结论（`900` 不够），split 会把它同时传给 CTest。CUDA 上 SpMM 这一族 @@ -59,7 +59,7 @@ python3 tools/delivery_table.py results_musa_split_65_<日期> 1. `missing` 是不是 0；65 个变体精度是不是全 Passed（`spmv_coo_f32_int_non` 不应再是 Skipped）。 2. 日志里 `--h800-reference (musa-s5000): NoBaseline variants filled from the Python half=…, from the C API time=…, still without a baseline=…` 这一行原样贴回。按设计"still without"应该是 0；不是 0 就列出是哪些变体、为什么。 -3. 交付表里还有没有 `NoBaseline`；带 `(H800折算)` / `(H800 PyTorch折算)` 的变体原样列出。 +3. 交付表里还有没有 `NoBaseline`；`python3 tools/delivery_table.py <结果目录> --show-baseline` 的输出原样贴回（最后一列说明哪些加速比是折算的）。 4. 这轮 pytest 那一半第一次跑性能（加了 `--h800-reference` 的代价），报一下总耗时，以及 Python 性能阶段里有没有 哪个算子超时或崩溃（`<结果目录>/<算子>/performance_stderr.log`）。 5. 复数变体有没有 `IndexMusa not implemented`；有没有慢几百倍的可疑变体。 @@ -172,8 +172,8 @@ C API 精度 10/10 测试族通过。唯一的精度瑕疵是 `spmm_csc_f32_int_ 你在 摩尔线程 S5000（MUSA） 实机上复测 FlagSparse 的 65 个交付变体（2026-10-14 之后的这一轮）。仓库在当前目录，main 分支。 背景：这台机器上一轮已经跑过 65 项。之后仓库改了两类东西，这轮就是验证它们： -- 没有基线的变体，现在用仓库自带的 H800 结果按带宽折算出加速比（命令里的 --h800-reference），交付表里会标 - (H800折算) 或 (H800 PyTorch折算)。有本机基线的变体不受影响。 +- 没有基线的变体，现在用仓库自带的 H800 结果按带宽折算出加速比（命令里的 --h800-reference），交付表里只显示数字， + delivery_table.py --show-baseline 的最后一列说明哪些是折算的。有本机基线的变体不受影响。 - 本后端相关的修复，见 docs/debug/MUSA.md "跑完要确认"一节逐条列出的内容。 主要目的是收集数据，不是修代码。 diff --git a/src/flagsparse/sparse_operations/benchmarks.py b/src/flagsparse/sparse_operations/benchmarks.py index de365047..b535d9ef 100644 --- a/src/flagsparse/sparse_operations/benchmarks.py +++ b/src/flagsparse/sparse_operations/benchmarks.py @@ -311,11 +311,15 @@ def benchmark_scatter_case( fallback_reason = fallback_reason or prep_meta["fallback_reason"] base_out = _build_random_dense(dense_size, sparse_values.dtype, device) + # The reference is computed on the CPU so it never depends on the backend's own + # torch ops: MUSA's muDNN rejects int8 there ("Now muDNN only support float64, + # float32, half, bfloat16, int32, and int64"), which used to fail the whole case + # and lose the FlagSparse time with it. expected = _pytorch_scatter_impl( - sparse_values, - indices, + sparse_values.cpu(), + indices.cpu(), dense_size, - out=base_out.clone(), + out=base_out.cpu().clone(), reset_output=reset_output, ) @@ -375,6 +379,10 @@ def benchmark_scatter_case( # at parity (measured 1.11x at 32K/1024 and 0.96x at 1M/65536 once the Python # overhead is amortised out). The ops write into preallocated buffers, so the # values are read back from those after timing, exactly as gather does. + # A backend whose torch cannot run the PyTorch scatter (MUSA int8) has no + # PyTorch baseline for that case; FlagSparse is still timed and checked. + pytorch_ms = None + pytorch_reason = None try: pytorch_ms = _benchmark_cuda_graph_op( pytorch_op, @@ -383,23 +391,29 @@ def benchmark_scatter_case( repeats=iters, ) except Exception as exc: - raise RuntimeError(f"PyTorch CUDA Graph timing failed: {exc}") from exc + pytorch_reason = f"PyTorch scatter baseline unavailable: {exc}" try: triton_ms = _benchmark_scatter_triton_op( triton_op, warmup=warmup, iters=iters ) except Exception as exc: raise RuntimeError(f"Triton CUDA Graph timing failed: {exc}") from exc - pytorch_values = pytorch_out + pytorch_values = pytorch_out if pytorch_ms is not None else expected triton_values = triton_out atol, rtol = _tolerance_for_dtype(effective_value_dtype) - triton_match = torch.allclose(triton_values, expected, atol=atol, rtol=rtol) - triton_max_error = ( - float(torch.max(torch.abs(triton_values - expected)).item()) - if dense_size > 0 - else 0.0 - ) + + def _compare(values): + # On the CPU, against the CPU reference; int8 is widened first so the + # difference cannot wrap around. + got, want = values.cpu(), expected + if not (got.is_floating_point() or got.is_complex()): + got, want = got.to(torch.int64), want.to(torch.int64) + match = torch.allclose(got, want, atol=atol, rtol=rtol) + error = float(torch.max(torch.abs(got - want)).item()) if dense_size > 0 else 0.0 + return match, error + + triton_match, triton_max_error = _compare(triton_values) cusparse_ms = None cusparse_match = None @@ -419,14 +433,7 @@ def benchmark_scatter_case( warmup=warmup, iters=iters, ) - cusparse_match = torch.allclose( - cusparse_values, expected, atol=atol, rtol=rtol - ) - cusparse_max_error = ( - float(torch.max(torch.abs(cusparse_values - expected)).item()) - if dense_size > 0 - else 0.0 - ) + cusparse_match, cusparse_max_error = _compare(cusparse_values) except Exception as exc: cusparse_reason = str(exc) elif run_cusparse: @@ -468,19 +475,14 @@ def benchmark_scatter_case( cusparse_values = cusparse_plan.dense_vector finally: cusparse_plan.close() - cusparse_match = torch.allclose( - cusparse_values, expected, atol=atol, rtol=rtol - ) - cusparse_max_error = ( - float(torch.max(torch.abs(cusparse_values - expected)).item()) - if dense_size > 0 - else 0.0 - ) + cusparse_match, cusparse_max_error = _compare(cusparse_values) except Exception as exc: cusparse_reason = str(exc) triton_speedup_vs_pytorch = ( - pytorch_ms / triton_ms if triton_ms > 0 else float("inf") + None + if pytorch_ms is None + else (pytorch_ms / triton_ms if triton_ms > 0 else float("inf")) ) triton_speedup_vs_cusparse = ( cusparse_ms / triton_ms if (cusparse_ms is not None and triton_ms > 0) else None @@ -519,6 +521,7 @@ def benchmark_scatter_case( }, "backend_status": { "cusparse_unavailable_reason": cusparse_reason, + "pytorch_unavailable_reason": pytorch_reason, "fallback_reason": fallback_reason, "index_fallback_applied": bool(triton_index_meta["index_fallback_applied"]), "index_fallback_reason": triton_index_meta["index_fallback_reason"], diff --git a/tests/ci/test_delivery_table.py b/tests/ci/test_delivery_table.py index 86b14926..f2085d5c 100644 --- a/tests/ci/test_delivery_table.py +++ b/tests/ci/test_delivery_table.py @@ -48,7 +48,7 @@ def test_missing_variant_is_printed_and_fails(tmp_path): assert f"{ids[0]}" in proc.stdout and "MISSING" in proc.stdout -def test_a_speedup_against_the_scaled_h800_time_is_labelled(tmp_path): +def test_a_scaled_speedup_shows_only_the_number_unless_asked(tmp_path): ids = [v["id"] for v in load_delivery_variants()] result = { vid: { @@ -58,22 +58,20 @@ def test_a_speedup_against_the_scaled_h800_time_is_labelled(tmp_path): for vid in ids } result[ids[0]]["performance"]["data"]["fp32"]["baseline"] = "h800_scaled" + result[ids[1]]["performance"]["data"]["fp32"]["baseline"] = "h800_pytorch_scaled" proc = _run(tmp_path, result) assert proc.returncode == 0, proc.stderr - labelled = [line for line in proc.stdout.splitlines() if "(H800折算)" in line] - assert len(labelled) == 1 and ids[0] in labelled[0] - - -def test_a_pytorch_fallback_speedup_says_so(tmp_path): - ids = [v["id"] for v in load_delivery_variants()] - result = { - vid: { - "accuracy": {"status": "Passed"}, - "performance": {"status": "Passed", "data": {"fp32": {"speedup": 2.0}}}, - } - for vid in ids - } - result[ids[0]]["performance"]["data"]["fp32"]["baseline"] = "h800_pytorch_scaled" - proc = _run(tmp_path, result) + assert "H800" not in proc.stdout and "h800" not in proc.stdout line = next(line for line in proc.stdout.splitlines() if ids[0] in line) - assert "(H800 PyTorch折算)" in line + assert line.rstrip().endswith("2.000x") + # --show-baseline says where each denominator came from + shown = subprocess.run( + [sys.executable, str(TOOL), str(tmp_path), "--show-baseline"], + capture_output=True, + text=True, + cwd=ROOT, + ).stdout.splitlines() + by_id = {vid: next(line for line in shown if vid in line) for vid in ids[:3]} + assert by_id[ids[0]].rstrip().endswith("h800_scaled") + assert by_id[ids[1]].rstrip().endswith("h800_pytorch_scaled") + assert by_id[ids[2]].rstrip().endswith("-") diff --git a/tests/ci/test_musa_benchmark_references.py b/tests/ci/test_musa_benchmark_references.py new file mode 100644 index 00000000..1707e5bc --- /dev/null +++ b/tests/ci/test_musa_benchmark_references.py @@ -0,0 +1,94 @@ +# Copyright 2026 FlagOS Contributors +# SPDX-License-Identifier: Apache-2.0 +"""Benchmark scripts must not lose the FlagSparse time to a broken torch op (MUSA). + +results_musa_split_65_20261014 left two variants NoBaseline because the Python half +measured nothing: tests/test_spmm_csc.py crashed on torch.sparse.mm (MUSA registers +no sparse matmul) and benchmark_scatter_case raised on muDNN's missing int8 support. +Both run here on the CPU, no accelerator needed. +""" + +import pytest + +torch = pytest.importorskip("torch") +pytest.importorskip("scipy") +pytest.importorskip("triton") +test_spmm_csc = pytest.importorskip("test_spmm_csc") +benchmarks = pytest.importorskip("flagsparse.sparse_operations.benchmarks") + + +@pytest.mark.parametrize("op", ["non", "trans", "conj"]) +@pytest.mark.parametrize("dtype", [torch.float32, torch.complex64, torch.float16]) +def test_scipy_csc_reference_matches_dense(op, dtype): + gen = torch.Generator().manual_seed(3) + dense = torch.randn(5, 4, generator=gen) + dense[dense.abs() < 0.6] = 0 + if dtype.is_complex: + dense = torch.complex(dense, torch.randn(5, 4, generator=gen) * (dense != 0)) + dense = dense.to(dtype) + csc = dense.to(torch.float64 if not dtype.is_complex else torch.complex128) + csc = csc.to_sparse_csc() + data = csc.values().to(dtype) + indices, indptr = csc.row_indices().int(), csc.ccol_indices() + a_op = {"non": dense, "trans": dense.t(), "conj": dense.conj().t()}[op] + B = torch.randn(a_op.shape[1], 3, generator=gen).to(dtype) + got = test_spmm_csc._scipy_spmm_csc_reference( + data, indices, indptr, B, (5, 4), dtype, op + ) + wide = torch.complex128 if dtype.is_complex else torch.float64 + want = (a_op.to(wide) @ B.to(wide)).to(dtype) + assert got.dtype == dtype and got.shape == want.shape + torch.testing.assert_close(got, want, atol=2e-3, rtol=2e-3) + + +def test_scatter_benchmark_keeps_the_flagsparse_time_without_a_torch_baseline( + monkeypatch, +): + # The device is the CPU; FlagSparse's kernel is replaced by an exact CPU + # scatter, and the PyTorch timing raises the way muDNN does for int8. + monkeypatch.setattr(benchmarks, "_ACCEL_DEVICE_TYPE", "cpu") + + def fake_triton(values, indices, dense_size, out=None, reset_output=True, **kw): + if reset_output: + out.zero_() + out.index_copy_(0, indices.to(torch.int64), values) + meta = { + "index_fallback_applied": False, + "index_fallback_reason": None, + "kernel_index_dtype": "int32", + } + return (out, meta) if kw.get("return_metadata") else out + + def mudnn(*_a, **_k): + raise RuntimeError( + "Now muDNN only support float64, float32, half, bfloat16, int32, and int64" + ) + + def prepare(values, indices, dense_size, **_kw): + meta = { + "effective_value_dtype": values.dtype, + "fallback_applied": False, + "fallback_reason": None, + } + return values, indices, indices, dense_size, meta + + monkeypatch.setattr(benchmarks, "_prepare_scatter_inputs", prepare) + monkeypatch.setattr(benchmarks, "_triton_scatter_impl", fake_triton) + monkeypatch.setattr(benchmarks, "_benchmark_cuda_graph_op", mudnn) + monkeypatch.setattr( + benchmarks, "_benchmark_scatter_triton_op", lambda op, **kw: (op(), 0.5)[1] + ) + result = benchmarks.benchmark_scatter_case( + dense_size=256, + nnz=64, + value_dtype=torch.int8, + warmup=1, + iters=1, + run_cusparse=False, + ) + assert result["performance"]["triton_ms"] == 0.5 + assert result["performance"]["pytorch_ms"] is None + assert result["performance"]["triton_speedup_vs_pytorch"] is None + assert "muDNN" in result["backend_status"]["pytorch_unavailable_reason"] + assert result["verification"]["triton_match_pytorch"] is True + assert result["verification"]["triton_max_error"] == 0.0 diff --git a/tests/ci/test_spgemm_reference_fp64.py b/tests/ci/test_spgemm_reference_fp64.py new file mode 100644 index 00000000..b0da0022 --- /dev/null +++ b/tests/ci/test_spgemm_reference_fp64.py @@ -0,0 +1,35 @@ +# Copyright 2026 FlagOS Contributors +# SPDX-License-Identifier: Apache-2.0 +"""The SpGEMM SciPy reference never moves fp64 data to the device. + +BI-V150 (CoreX) zeroes an fp64 H2D copy. tests/test_spgemm.py built its reference +with ``as_torch(data, float64, device).to(float32)``, so on Iluvatar every value +arrived as 0, was dropped as an explicit zero, and all ten rows failed with +"sparsity pattern mismatch" (results_iluvatar_65_1014_spgemm_f32). +""" + +import pytest + +torch = pytest.importorskip("torch") +pytest.importorskip("scipy") +reference_utils = pytest.importorskip("reference_utils") +test_spgemm = pytest.importorskip("tests.test_spgemm") + + +def test_reference_survives_a_device_that_zeroes_fp64(monkeypatch): + real = reference_utils.as_torch + + def biv150_as_torch(array, dtype, device): + out = real(array, dtype, device) + return torch.zeros_like(out) if dtype == torch.float64 else out + + monkeypatch.setattr(reference_utils, "as_torch", biv150_as_torch) + a = torch.tensor([[1.0, 2.0], [0.0, 3.0]]).to_sparse_csr() + data, indices, indptr = a.values(), a.col_indices().int(), a.crow_indices() + result = test_spgemm._build_scipy_spgemm_reference( + data, indices, indptr, (2, 2), data, indices, indptr, (2, 2) + ) + values = result[0][0] if isinstance(result[0], tuple) else result[0] + # A @ A = [[1, 8], [0, 9]]: three stored values, none of them zero + assert values.dtype == torch.float32 + assert sorted(values.tolist()) == [1.0, 8.0, 9.0] diff --git a/tests/test_scatter.py b/tests/test_scatter.py index 107405d1..435fa1a2 100644 --- a/tests/test_scatter.py +++ b/tests/test_scatter.py @@ -314,6 +314,9 @@ def run_cli(args): "cusparse_unavailable_reason": backend.get( "cusparse_unavailable_reason" ), + "pytorch_unavailable_reason": backend.get( + "pytorch_unavailable_reason" + ), "fallback_reason": backend.get("fallback_reason"), "index_fallback_reason": backend.get( "index_fallback_reason" @@ -393,6 +396,7 @@ def run_cli(args): "triton_max_error", "cusparse_max_error", "cusparse_unavailable_reason", + "pytorch_unavailable_reason", "fallback_reason", "index_fallback_reason", "status", diff --git a/tests/test_spgemm.py b/tests/test_spgemm.py index 3ea6ebda..4e9dcf73 100644 --- a/tests/test_spgemm.py +++ b/tests/test_spgemm.py @@ -427,7 +427,10 @@ def _build_scipy_spgemm_reference( product = reference_utils.spgemm(a_csr, b_csr) product.sort_indices() device = a_data.device - data = reference_utils.as_torch(product.data, ref_dtype, device).to(a_data.dtype) + # as_torch_at, not as_torch(...).to(): BI-V150 zeroes an fp64 H2D copy, so the + # reference arrived as zeros and every Iluvatar SpGEMM row failed with + # "sparsity pattern mismatch" (the zeros are dropped as explicit zeros). + data = reference_utils.as_torch_at(product.data, a_data.dtype, device) indices = torch.as_tensor( product.indices, dtype=torch.int32, device=device ).contiguous() diff --git a/tests/test_spmm_csc.py b/tests/test_spmm_csc.py index f728138e..53060ba0 100644 --- a/tests/test_spmm_csc.py +++ b/tests/test_spmm_csc.py @@ -38,6 +38,7 @@ sys.path.insert(0, str(_SRC_ROOT)) import flagsparse as fs +import reference_utils from flagsparse.sparse_operations import _common as fs_common from flagsparse.sparse_operations import spmm_csr as spmm_ops @@ -361,6 +362,35 @@ def _logical_out_rows(shape, op): return int(shape[1]) if _op_transposes(op) else int(shape[0]) +def _scipy_spmm_csc_reference(data, indices, indptr, B, shape, dtype, op): + """Same value as _torch_spmm_coo_reference, computed on the CPU with SciPy. + + The CSC arrays of A are the CSR arrays of A.T. The result is cast on the CPU and + moved once at the output dtype (as_torch_at): CoreX zeroes an fp64 H2D copy. + """ + n_rows, n_cols = int(shape[0]), int(shape[1]) + ref_dtype = reference_utils.reference_dtype(dtype) + a_t = reference_utils.scipy_csr(data, indices, indptr, (n_cols, n_rows), ref_dtype) + product = reference_utils.spmm(a_t.transpose().tocsr(), B, ref_dtype, op=op) + return reference_utils.as_torch_at(product, dtype, B.device) + + +def _correctness_reference(data, indices, indptr, B, shape, dtype, op): + """The value the kernel is compared against, per backend policy. + + MUSA registers no sparse matmul at all ("Could not run 'aten::addmm' with + arguments from the 'Sparsemusa' backend"): the torch reference crashed the + script on its first case, so spmm_csc measured nothing there -- including the + q4 rows appended after the classic sweep. + """ + builder = ( + _scipy_spmm_csc_reference + if fs_common._use_scipy_accuracy_reference() + else _torch_spmm_coo_reference + ) + return builder(data, indices, indptr, B, shape, dtype, op) + + def _torch_spmm_coo_reference(data, indices, indptr, B, shape, dtype, op): ref_dtype = _reference_dtype(dtype) A = _csc_to_torch_coo(data.to(ref_dtype), indices, indptr, shape) @@ -557,7 +587,7 @@ def _run_case( _random_values((_logical_b_rows(shape, op), int(dense_cols)), dtype, data.device) * 0.125, layout, ) - ref = _torch_spmm_coo_reference(data, indices, indptr, B, shape, dtype, op) + ref = _correctness_reference(data, indices, indptr, B, shape, dtype, op) logical_out_rows = _logical_out_rows(shape, op) row = { "matrix": matrix_name, diff --git a/tools/delivery_table.py b/tools/delivery_table.py index ad250cb1..ddbfc813 100644 --- a/tools/delivery_table.py +++ b/tools/delivery_table.py @@ -41,23 +41,27 @@ def _summary_path(target: Path) -> Path: def _speedup(performance: dict) -> str: data = performance.get("data") values = [] - labels = set() if isinstance(data, dict): for entry in data.values(): if isinstance(entry, dict) and entry.get("speedup"): values.append(float(entry["speedup"])) - labels.add(str(entry.get("baseline") or "")) - if not values: - return "-" - # Against a rescaled H800 time (no baseline on this card): an estimate, never - # to be read as a measured vendor speedup. H800 PyTorch only where H800's - # cuSPARSE had no time for the case. - note = "" - if any("h800_pytorch_scaled" in label for label in labels): - note = " (H800 PyTorch折算)" - elif any("h800_scaled" in label for label in labels): - note = " (H800折算)" - return f"{sum(values) / len(values):.3f}x" + note + return f"{sum(values) / len(values):.3f}x" if values else "-" + + +def _baseline(performance: dict) -> str: + """Where the speedup's denominator came from (--show-baseline only). + + ``h800_scaled`` / ``h800_pytorch_scaled``: the card had no baseline, so the + H800 cuSPARSE (or, where cuSPARSE has no time, PyTorch) time rescaled by + bandwidth stands in -- see docs/H800_REFERENCE.md. Empty: measured on the card. + """ + data = performance.get("data") + labels = { + str(entry.get("baseline")) + for entry in (data.values() if isinstance(data, dict) else ()) + if isinstance(entry, dict) and entry.get("speedup") and entry.get("baseline") + } + return ",".join(sorted(labels)) or "-" def main() -> int: @@ -66,6 +70,11 @@ def main() -> int: parser.add_argument( "--markdown", action="store_true", help="print a Markdown table" ) + parser.add_argument( + "--show-baseline", + action="store_true", + help="add a column saying which speedups are against the rescaled H800 time", + ) args = parser.parse_args() path = _summary_path(args.target) @@ -76,7 +85,7 @@ def main() -> int: for variant in variants: entry = result.get(variant["id"]) if entry is None: - rows.append((variant["id"], "MISSING", "MISSING", "-")) + rows.append((variant["id"], "MISSING", "MISSING", "-", "-")) continue accuracy = entry.get("accuracy") or {} performance = entry.get("performance") or {} @@ -86,20 +95,29 @@ def main() -> int: str(accuracy.get("status", "-")), str(performance.get("status", "-")), _speedup(performance), + _baseline(performance), ) ) - header = ("variant", "accuracy", "performance", "speedup") + header = ("variant", "accuracy", "performance", "speedup", "baseline") + if not args.show_baseline: + header = header[:4] + rows = [row[:4] for row in rows] if args.markdown: print("| # | " + " | ".join(header) + " |") - print("|---:|---|---|---|---:|") + print("|---:|---|---|---|---:|" + "---|" * (len(header) - 4)) for index, row in enumerate(rows, 1): print(f"| {index} | `{row[0]}` | " + " | ".join(row[1:]) + " |") else: width = max(len(row[0]) for row in rows) - print(f"{'#':>3} {header[0]:<{width}} {header[1]:<10} {header[2]:<12} {header[3]}") + + def line(index, row): + text = f"{index:>3} {row[0]:<{width}} {row[1]:<10} {row[2]:<12} " + return text + (f"{row[3]:<10} {row[4]}" if len(row) > 4 else row[3]) + + print(line("#", header)) for index, row in enumerate(rows, 1): - print(f"{index:>3} {row[0]:<{width}} {row[1]:<10} {row[2]:<12} {row[3]}") + print(line(index, row)) missing = sum(1 for row in rows if row[1] == "MISSING") accuracy_counts = Counter(row[1] for row in rows) diff --git a/tools/h800_reference.py b/tools/h800_reference.py index 7050af99..cf083164 100644 --- a/tools/h800_reference.py +++ b/tools/h800_reference.py @@ -405,10 +405,10 @@ def render_markdown(doc): "```", "", "这是**估算**，不是厂商实测：H800 与目标卡的访存效率、缓存、launch 开销并不成比例。" - "折算出的加速比在 summary 里带 `baseline: h800_scaled`，`tools/delivery_table.py` 显示为 `(H800折算)`。", + "折算出的加速比在 summary 里带 `baseline: h800_scaled`；`tools/delivery_table.py` 只显示数字，加 `--show-baseline` 才显示来源。", "", "H800 上某个算例**没有** cuSPARSE 时间时（cuSPARSE SDDMM 不支持 fp16），退一步用同一行 H800 PyTorch 的时间" - "（PyTorch 结果须通过校验），标 `baseline: h800_pytorch_scaled` / `(H800 PyTorch折算)`；cuSPARSE 跑了但结果" + "（PyTorch 结果须通过校验），标 `baseline: h800_pytorch_scaled`；cuSPARSE 跑了但结果" "不对的行不退。FlagSparse 自己在 H800 上的时间永远不当基线。", "", "C API 拆分交付（MUSA）：带 `--h800-reference` 时 pytest 那一半也跑性能（与本参照同一 benchmark、同一规模），" From 4d5ab61e1cd736507b05ae6e97da44545482e2ff Mon Sep 17 00:00:00 2001 From: NCIA-AlphaSparse Date: Thu, 15 Oct 2026 14:24:54 +0800 Subject: [PATCH 9/9] maca opt --- docs/MACA.md | 27 +- docs/debug/MACA.md | 240 +++++++++++++++- run_flagsparse_pytest.py | 3 + .../sparse_operations/_spgemm_maca.py | 262 ++++++++++++++++++ src/flagsparse/sparse_operations/sddmm_csr.py | 86 ++++++ .../sparse_operations/spgemm_csr.py | 16 ++ tests/ci/test_runner_result_format.py | 18 ++ tests/ci/test_spgemm_maca_baseline.py | 126 +++++++++ tests/ci/test_spgemm_maca_dispatch.py | 56 ++++ tests/pytest/test_spgemm_maca_accuracy.py | 114 ++++++++ tests/test_spgemm.py | 77 ++++- 11 files changed, 1021 insertions(+), 4 deletions(-) create mode 100644 src/flagsparse/sparse_operations/_spgemm_maca.py create mode 100644 tests/ci/test_spgemm_maca_baseline.py create mode 100644 tests/ci/test_spgemm_maca_dispatch.py create mode 100644 tests/pytest/test_spgemm_maca_accuracy.py diff --git a/docs/MACA.md b/docs/MACA.md index ce6a7359..e17e202c 100644 --- a/docs/MACA.md +++ b/docs/MACA.md @@ -64,7 +64,7 @@ setsid timeout -s KILL 39600 python3 -u run_flagsparse_pytest.py \ - `--op-benchmark-args='sddmm_csr=--no-cusparse'`：C550 上没有可用的厂商稀疏库，且 SDDMM 的 `torch.sparse.sampled_addmm` 结果还是错的（7.3 节），因此只对 SDDMM 禁用该参考并改用 PyTorch。 `--benchmark-args` 会广播给所有性能脚本，而其他脚本不一定接受 `--no-cusparse`，所以用逐算子的形式； -- `--h800-reference`（不带值）：没有任何基线的行（目前是 legacy `spmv_csr` f32/f64、SDDMM 的 q4 变体、`spgemm_csr`）， +- `--h800-reference`（不带值）：没有任何基线的行（例如 SDDMM 的 q4 变体，或本机 baseline 校验失败的行）， 用仓库自带的 H800 结果按带宽折算出加速比（`h800_scaled_ms = T_cuSPARSE@H800 × 3050/1440`），交付表里只显示数字； 要看哪些是折算的用 `delivery_table.py --show-baseline`。**有 PyTorch 基线的行不受影响。** 这是估算，不是厂商实测，汇报时要注明； `sddmm_csr_f16_int_non_non_row` 在 H800 上没有 cuSPARSE 时间（cuSPARSE SDDMM 不支持 fp16），改用 H800 上 @@ -613,6 +613,31 @@ PYTHONPATH=src python -u run_flagsparse_pytest.py --phase both --mode normal --g 该命令在前台运行；如需脱离终端，可由调用方以 `setsid` 或 `tmux` 包裹，命令本身不依赖后台参数。 +### 7.3.1 SpGEMM 本机 PyTorch CSR baseline + +MACA 的矩阵 runner 在 `FLAGSPARSE_MACA_VENDOR=torch` 且默认 SciPy 精度参考下，独立计时 +`torch.sparse.mm(A_csr, B_csr)`，支持 float32/float64。CSR 描述符构造在 baseline 计时窗口外； +FlagSparse 沿用原有单次调用口径，包含 prepare 和输出写回，不因接入 baseline 改变计时范围。 +两侧使用相同输入、warmup 和 iters。基线计时后的输出必须通过 SciPy 校验，才填入 `pytorch_ms` +及 `triton_speedup_vs_pytorch`；校验失败或调用异常只影响 baseline，不替代算子的 SciPy 精度判定。 +CSV 的 `pytorch_baseline_status`、`pytorch_baseline_format`、`pytorch_baseline_max_error` +和 `pytorch_reason` 记录基线状态；原有 `pt_status` 在默认参考下仍表示 FlagSparse 对 SciPy 的精度结果。 +`--no-cusparse` 不禁用此 PyTorch baseline；显式设置 `FLAGSPARSE_MACA_VENDOR=none` 才不测本机基线。 + +```bash +env PYTHONPATH=src FLAGSPARSE_BACKEND=metax FLAGSPARSE_MACA_VENDOR=torch \ + python3 -u run_flagsparse_pytest.py \ + --ops spgemm_csr --phase both --mode normal --gpus 0 --timeout 2400 \ + --benchmark-input tests/data --benchmark-warmup 5 --benchmark-iters 20 \ + --results-dir results_metax_spgemm_<日期> +``` + +本节 SpGEMM 只使用本机 PyTorch CSR baseline，不开启 H800 折算。MACA 的 float32/float64 +计算路径为纯 Triton：metadata、行内排序归并、长行全局哈希、CSR 前缀扫描和结果压紧均不依赖 +TLE，也不调用 `torch.sparse.mm` 或旧的 PyTorch ESC。PyTorch 仅用于存储分配和独立 baseline； +公共接口仍执行原有输入校验、输入 contiguous 和 out 写回。 +CUDA、ROCm 及其他后端的 baseline 路径不变。 + ### 7.4 交付性能：只跑交付范围（2026-09-18 实测） 7.1–7.3 是**全量** sweep。交付的 20 个变体只要 `int32` 索引和 `non` 操作，而默认 sweep 远大于此， diff --git a/docs/debug/MACA.md b/docs/debug/MACA.md index 66c625f9..57cbe340 100644 --- a/docs/debug/MACA.md +++ b/docs/debug/MACA.md @@ -24,7 +24,7 @@ python3 tools/delivery_table.py results_metax_65_<日期> - `--benchmark-input tests/data`：仓库里的 `tests/data` 正好就是 `conf/operators.yaml` 的 10 个交付矩阵（在 git 里，拉下来就有）。这条命令没带 `--delivery-only`，runner 不会再筛矩阵，给 30 个矩阵的目录就会 30 个全跑， 汇总出来的加速比也会掺进非交付矩阵，所以不要换成别的目录。 -- `--h800-reference`（不带值）：没有任何基线的行（目前是 legacy `spmv_csr` f32/f64、SDDMM 的 q4 变体、`spgemm_csr`）， +- `--h800-reference`（不带值）：没有任何基线的行（例如 SDDMM 的 q4 变体，或本机 baseline 校验失败的行）， 用仓库自带的 H800 结果按带宽折算出加速比（`h800_scaled_ms = T_cuSPARSE@H800 × 3050/1440`），交付表里只显示数字； 要看哪些是折算的用 `delivery_table.py --show-baseline`。**有 PyTorch 基线的行不受影响。** 这是估算，不是厂商实测，汇报时要注明； `sddmm_csr_f16_int_non_non_row` 在 H800 上没有 cuSPARSE 时间（cuSPARSE SDDMM 不支持 fp16），改用 H800 上 @@ -77,7 +77,9 @@ python3 tools/delivery_table.py results_metax_65_<日期> 1. `missing` 是不是 0；65 个变体精度是不是全 Passed（`spmv_coo_f32_int_non` 不应再是 Skipped）。 2. 有加速比的变体是不是 65/65。`python3 tools/delivery_table.py <结果目录> --show-baseline` 的输出原样贴回。按 10-13 那轮预演：SDDMM 的 q4 变体、 - `spgemm_csr` 应为 `h800_scaled`，`sddmm_csr_f16_int_non_non_row` 为 `h800_pytorch_scaled`（预演 1.595x）。 + `sddmm_csr_f16_int_non_non_row` 为 `h800_pytorch_scaled`（预演 1.595x）。2026-10-15 起 SpGEMM 在 + `FLAGSPARSE_MACA_VENDOR=torch` 下独立测量并校验本机 PyTorch CSR baseline（见 `../MACA.md` 7.3.1）； + 有效时应使用 `pytorch_ms`，不再引用历史 `h800_scaled` 作为本机加速比。 3. `spmv_csr_f32_int_non` / `spmv_csr_f64_int_non` 的加速比和它的基线是什么：`spmv_csr/performance.csv` 里 `vendor_backend`、`vendor_ms`、`speedup_vs_vendor`、`h800_scaled_ms` 各一行原样贴回（用 PyTorch 基线会是几百倍， 用 H800 折算预演是 0.68x；这一项待定，先如实带回）。 @@ -192,3 +194,237 @@ python3 tools/delivery_table.py results_metax_65_<日期> - 为什么改：C550 实机没有可用 CuPy/cuSPARSE。此前 65 算子命令继承了 `FLAGSPARSE_MACA_VENDOR=none`，导致 `spmv_csr_f32_int_non` 和 `spmv_csr_f64_int_non` 的 baseline/加速比显示 `N/A`；这是启动环境造成的，不是 CSR kernel 没有 baseline 实现。 - 怎么验证的：设置 `FLAGSPARSE_MACA_VENDOR=torch` 后，`results_metax_spmv_csr_baseline_20261014/` 中两种 base CSR 变体均有 baseline 且性能 `Passed`；float32 聚合加速比 `295.033x`，float64 为 `284.441x`。规则和完整启动命令同步写在 `docs/MACA.md`。 - 其他后端是否也需要：否；该环境变量和默认选择仅影响 MACA baseline。 + +### 2026-10-15 MACA SDDMM / SpGEMM 调优 +- 改了什么：`sddmm_csr.py` 为 MACA complex64 选择 `(BLOCK_P, BLOCK_K, num_warps)=(16,16,2)`，并保留 MACA pair-load 复数 kernel 和非连续输入 contiguous 路径；`spgemm_csr.py` 保留 float32 宽行时的 MACA PyTorch CSR dispatch，并让 dispatch 对缺少 `n_rows` 的轻量 prepared/mock 对象安全回退。改动均限定在 MACA runtime 分支。 +- 为什么改：C550 SDDMM 复数宽 tile 的寄存器/地址计算开销较高，实测窄 tile 更快；SpGEMM 的宽行 PyTorch CSR 路径在部分矩阵略快，但目前整体仍未达到 H800 折算 0.8x，不把局部改善当作完成。 +- 怎么验证的：`tests/pytest/test_q4_variants_accuracy.py -k 'sddmm_csr'` 为 `10 passed`，`-k 'spgemm_csr'` 为 `2 passed`；`tests/ci/test_spgemm_tle_gate.py tests/ci/test_sddmm_sweep.py` 为 `15 passed`。C550 的 10 矩阵 q4 SDDMM 实测按现有 H800 折算分母计算，转置 float32 算术平均约 `2.17x`，complex64 约 `1.14x`。SpGEMM float32 的 10 矩阵精度均通过；现有 65 算子结果中 SpGEMM H800 折算加速比算术平均约 `0.15x`，仍需继续优化。TLE fill-table A/B 因本机 `_TLE_AVAILABLE=False` 未执行到 kernel，结果作废，不据此改参数。 +- 其他后端是否也需要：否；运行时分支只影响 MACA。轻量 mock 兼容只避免非 MACA dispatch 测试因属性缺失崩溃。 + +### 2026-10-15 `tests/test_spgemm.py`：MACA PyTorch CSR baseline +- 改了什么：在 MACA、`FLAGSPARSE_MACA_VENDOR=torch` 和 SciPy 精度参考模式下，独立计时 `torch.sparse.mm(A_csr, B_csr)` 的 float32/float64 baseline；新增 baseline 状态、格式和误差字段。计时输出必须通过独立 SciPy 校验，错误值、NaN/Inf、异常或无效时延均不发布 `pytorch_ms` 和本机加速比，不污染 FlagSparse 的精度结果。 +- 为什么改（附报错原文）：原 vendor 分支返回 `PyTorch CSR SpGEMM baseline is not wired for this runner`，但本机 PyTorch CSR 乘法实际可执行。仅接入性能参考，不修改 FlagSparse 的 prepare、计算和输出写回计时范围。 +- 怎么验证的：新增 `tests/ci/test_spgemm_maca_baseline.py`，18 项覆盖 float32/float64、异常和错误输出、非有限值、无效时延、运行时门控及精度/性能隔离；与 dispatch、比较器、FP64 参考、TLE 门控、结果格式回归一起运行，共 `94 passed`。首轮 `results_metax_spgemm_baseline_20261015/` 的精度阶段 `12 passed`，10 矩阵 × 2 dtype 的算子精度及 baseline 校验均 `PASS`。 +- 其他后端是否也需要：否；baseline 新路径仅在 MACA 启用，其余后端原行为不变。 + +### 2026-10-15 `src/flagsparse/sparse_operations/spgemm_csr.py`：MACA FP64 native dispatch +- 改了什么：MACA 宽行 PyTorch CSR dispatch 从 float32 扩展到 float32/float64；A 平均每行至少 10 项，或至少 800000 行且平均每行至少 5.5 项时启用。短行保留原 hash/ESC，其他后端完全保留原 dispatch。 +- 为什么改：接入真实 baseline 后，首轮 float64 的 10 矩阵算术平均仅 `0.798037x`；其中 `cfd2`、`filter3D` 的 ESC 加速比为 `0.278583x`、`0.286582x`，而本机 PyTorch CSR 的 FP64 输出通过 SciPy 校验。 +- 怎么验证的：新增 `tests/ci/test_spgemm_maca_dispatch.py`，18 项覆盖两种 dtype、阈值边界、空行、非 MACA 路由及 native 路径 live value 更新；连同 baseline 和现有相关回归共 `94 passed`。完整矩阵 A/B 和后续 runner 数据单独记录，不覆盖首轮结果。 +- 其他后端是否也需要：否；仅影响 MACA runtime 分支。 + +### 2026-10-15 `docs/MACA.md`、`docs/debug/MACA.md`：baseline 口径同步 +- 改了什么：增加 SpGEMM 本机 PyTorch CSR baseline 的复现命令、CSV 字段和失败策略，更新旧的“SpGEMM 一律无 baseline”说明，并记录上述 MACA dispatch 的 dtype 和阈值。 +- 为什么改：本机有效 PyTorch baseline 应优先使用；它与 H800 带宽折算是两种不同评估口径，不能把基线补齐当成 kernel 加速，也不能把 native dispatch 的约 1x 宣称为纯 Triton 性能。 +- 怎么验证的：首轮交付投影 `spgemm_csr_f32_int_non_non` 为精度/性能 `Passed`，本机加速比 `1.059200x`，原始数据在 `spgemm_csr/performance.csv`。使用 `tools/baseline_bound.py --all-rows` 另存 `h800_bound_all_rows.csv`，首轮 float32/float64 的 H800 折算算术平均为 `0.283576x` / `0.158081x`，仍未达 `0.8x`。 +- 其他后端是否也需要：否；本节描述 MACA 实机行为。 + +## 2026-10-15 SpGEMM baseline 接线后的受控 A/B + +结果保存在 `results_metax_spgemm_baseline_20261015/spgemm_ab.csv` 和 +`spgemm_ab_summary.json`，复现脚本为同目录的 `spgemm_ab.py`。该脚本不修改仓库源码； +只在自己的进程中临时切换 `_spgemm_compute`，并确认本机 `_TLE_AVAILABLE=False`， +因此“原路径”明确指本机原先实际执行的 ESC，而不是没有运行过的 TLE kernel。 + +```bash +env PYTHONPATH=src FLAGSPARSE_BACKEND=metax FLAGSPARSE_MACA_VENDOR=torch \ + python3 -u results_metax_spgemm_baseline_20261015/spgemm_ab.py --dtypes float32 float64 +``` + +使用 `tests/data` 的 10 个矩阵，warmup=5、iters=20；按矩阵交替测量顺序。 +FlagSparse 两条路径均包含 prepare、计算和输出写回；PyTorch baseline 单独计时 CSR mm。 +原 ESC 和当前 dispatch 的 20 个输出均通过独立 SciPy 校验，20 个 baseline 全部有效。 +下表的数字均为 10 矩阵逐行比值的算术平均，不是总时延之比。 + +| dtype | 原 ESC / 当前路径 | PyTorch / 原 ESC | PyTorch / 当前路径 | +|---|---:|---:|---:| +| float32 | 1.672425x | 0.792104x | 1.039500x | +| float64 | 1.613562x | 0.795328x | 1.034217x | + +本机 PyTorch baseline 下两种 dtype 的平均值均超过 0.8x。宽行路径调用的是同一个 +PyTorch CSR 后端，不把约 1x 解释为自研 Triton kernel 已达标；H800 带宽折算独立评估， +不因本机 baseline 补齐而删除或替换历史未达标结论。 + +## 2026-10-15 SpGEMM 最终版本正式 runner 复测 + +结果目录：`results_metax_spgemm_baseline_20261015_opt/`；整轮日志为同名 `.log`。 +使用 `docs/MACA.md` 7.3.1 的命令，仅将结果目录替换为该新目录；输入为 +`tests/data` 的 10 个交付矩阵，warmup=5、iters=20，float32/float64、int32 索引。 +本机环境为 MetaX C550，torch `2.10.0+metax3.8.1.0`、triton `3.6.0`， +源码基于 `a330b62` 加本节记录的未提交改动。 + +- 精度阶段：`12 passed, 4241 deselected`，无失败、无 skipped。 +- 性能阶段：20/20 行 `status=PASS`，20/20 行 `pytorch_baseline_status=PASS`，runner 正常退出。 +- 正式性能阶段耗时约 650.81 秒；prepare 和输出写回始终包含在 FlagSparse 时延内。 +- 单算子运行的交付投影 `spgemm_csr_f32_int_non_non` 为精度/性能 `Passed`、`1.045125x`。 + 其他 64 个变体为 `NotFound`，本轮没有复测它们，不能作为新一轮 65 算子全通过的证据。 + +| dtype | 本机 PyTorch 平均加速比 | H800 带宽折算平均加速比 | +|---|---:|---:| +| float32 | 1.045125x | 0.284665x | +| float64 | 1.033220x | 0.283301x | + +平均值均按 10 个矩阵逐行比值做算术平均。本机结果来自 `spgemm_csr/performance.csv`； +另用以下命令对全部 20 行计算 H800 折算，结果保存为 `h800_bound_all_rows.csv`， +不覆盖正式 runner 的本机 baseline 和汇总。 + +```bash +python3 tools/baseline_bound.py \ + --vendor results_metax_spgemm_baseline_20261015_opt --vendor-card maca-c550 \ + --all-rows --ops spgemm_csr \ + --csv results_metax_spgemm_baseline_20261015_opt/h800_bound_all_rows.csv +``` + +该工具逐行判定为 1 PASS / 19 FAIL，退出码 1 是 H800 折算未达标，不是算子精度失败。 +折算保持原有带宽比例 `3050/1440` 和默认 memory bottleneck 假定，属于估算。 +float64 本机平均从首轮 `0.798037x` 提升到 `1.033220x`；但两种 dtype 的 H800 折算 +平均仍远低于 0.8x，不能宣称全部性能目标已完成。后续要达该目标需要进一步优化 +MACA SpGEMM 实现，不能仅补 baseline 或调整汇总口径。 + +## 2026-10-15 后续：仅 MACA 的纯 Triton SpGEMM + +本节取代上面的 MACA native/PyTorch 计算 dispatch；历史结果保留，不代表当前实现。 +用户要求计算使用纯 Triton，baseline 仅用本机 PyTorch，不再评估 H800 折算。 + +### `src/flagsparse/sparse_operations/_spgemm_maca.py` +- 改了什么：新增不依赖 TLE 的 MACA SpGEMM。Triton 计算逐行展开量和产品前缀，按 64/256/1024/4096 大小桶在行内排序、分段求和；更长行使用全局显存开放寻址哈希。递归前缀扫描生成 int64 CSR 指针，再压紧输出。仅通过 `torch.empty` 分配存储，不调用 PyTorch 稀疏乘法、sort、cumsum、index_add 或旧 ESC；形状分配需要读取两个计数标量，不缓存数值结果。 +- 为什么改（附报错原文）：本机没有 TLE；动态 `tl.gather` 编译报 `custom op 'maca.shfl.sync' is unknown`。因此携带产品位置做标量排序，再直接重索引输入，使用反向扫描定位分段边界，避免动态跨 lane gather。C550 128 元素多 warp CAS/add 布局在长行测试出现一半列数值错误，改为 64 元素/单 warp 后，float32/float64 及两种指针类型均通过。没有通过 Torch fallback 隐藏错误。 +- 怎么验证的：合成精度、长行强碰撞、空维度、重复列、空 RHS 行、live value、out 复用、跨块递归扫描及 int32 最大列索引测试共 `39 passed`；正式 runner 精度同为 `39 passed`。所有 10 个交付矩阵的最大单行展开量不超过 3340，均走行内排序归并；长行哈希另由强制边界用例验证。 +- 其他后端是否也需要：否；模块只从 MACA runtime 分支导入和调用。临时存储按逐行展开上界分配，不使用旧 ESC 的分块回退；非常大的展开量仍可能需要进一步限制峰值显存，不能视为已验证所有超大稀疏矩阵。 + +### `src/flagsparse/sparse_operations/spgemm_csr.py` +- 改了什么：移除 `_maca_torch_spgemm_compute` 及原宽行阈值；MACA 的真实 `SpGEMMPrepared` 一律调用纯 Triton 实现，prepare 的 metadata 也走 MACA Triton kernel。公共输入校验、contiguous、输出写回和端到端计时口径不变。非 MACA 的 TLE/hash/ESC 分支保持原样。 +- 为什么改：不能把与 baseline 相同的 PyTorch CSR 实现作为待优化 kernel；需要以独立实现与 PyTorch 做真实比较。 +- 怎么验证的：正式精度 `39 passed`；`tests/ci/test_spgemm_maca_dispatch.py` 验证 MACA/非 MACA、TLE 可用/不可用时路由隔离；相关 baseline、比较器和 FP64 参考回归共 `36 passed`。 +- 其他后端是否也需要：否。 + +### `tests/ci/test_spgemm_maca_dispatch.py` +- 改了什么：将旧 native dispatch 测试改为纯 Triton 路由测试，新增 AST 检查，约束 MACA 计算模块的 Torch 调用只能是存储分配，并确认旧 `_maca_torch_spgemm_compute` 已移除。 +- 为什么改：防止后续重新接入 PyTorch sparse 或张量算法而被误报为纯 Triton 性能。 +- 怎么验证的：与 baseline、比较器、FP64 参考、TLE 门控回归合计 `36 passed`。 +- 其他后端是否也需要：否；只约束 MACA 新路径，同时检查其他后端路由不变。 + +### `tests/pytest/test_spgemm_maca_accuracy.py` +- 改了什么：新增 MACA 专用纯 Triton 边界精度测试；包括长行/强碰撞、float32/float64、int32/int64 指针、空行/空维度、重复列、空 B 行、prepared 值更新、out 复用、三级递归扫描及最大 int32 列索引。核心测试将 `torch.sparse.mm` 和旧 ESC 替换为抛异常，防止隐式 fallback。 +- 为什么改：交付矩阵不能覆盖超过 4096 个产品的行，必须单独验证全局哈希路径和扫描跨块边界。 +- 怎么验证的：新增 27 项测试全部通过，连同既有 SpGEMM 精度/reuse/q4 共 `39 passed`。 +- 其他后端是否也需要：否；门控为 MACA。 + +### `docs/MACA.md`、`docs/debug/MACA.md` +- 改了什么：7.3.1 的 SpGEMM runner 命令去掉 `--h800-reference`，说明本机 PyTorch CSR 是唯一性能 baseline；更新纯 Triton 计算路径与验证记录。本次正式结果使用新目录 `results_metax_spgemm_triton_20261015/`。 +- 为什么改：按用户要求不做折算，也不混用上轮 native/PyTorch 计算结果；其他算子的历史 H800 文档保留，不借本次任务改变它们的测试口径。 +- 怎么验证的：本轮启动命令与文档一致，warmup=5、iters=20，输入为 `tests/data` 的 10 个矩阵；结果在本轮完成后按 dtype 单独记录。 +- 其他后端是否也需要：否。 + +### 纯 Triton 最终实测结果 + +结果目录为 `results_metax_spgemm_triton_20261015/`，启动命令如下；不带 `--h800-reference`。 + +```bash +env PYTHONPATH=src FLAGSPARSE_BACKEND=metax FLAGSPARSE_MACA_VENDOR=torch \ + python3 -u run_flagsparse_pytest.py \ + --ops spgemm_csr --phase both --mode normal --gpus 0 --timeout 2400 \ + --benchmark-input tests/data --benchmark-warmup 5 --benchmark-iters 20 \ + --results-dir results_metax_spgemm_triton_20261015 +``` + +环境：MetaX C550、torch `2.10.0+metax3.8.1.0`、triton `3.6.0`，基于 `a330b62` +加上述未提交改动。精度阶段 `39 passed, 4241 deselected`，无失败或 skipped；性能 +20/20 行 `status=PASS`，20/20 行 `pytorch_baseline_status=PASS`，runner 正常退出。 +精度阶段约 18.66 秒，性能阶段约 719.99 秒。相关回归和结果格式验证共 `81 passed`。 + +所有加速比均为本机独立测得的 `pytorch_ms / triton_ms`，CSV 没有 H800 折算列。 +两侧输入一致，warmup=5、iters=20；FlagSparse 时延仍包括 prepare、计算和输出写回。 + +| 矩阵 | float32 | float64 | +|---|---:|---:| +| ASIC_680ks | 2.136395x | 2.092398x | +| GL7d14 | 2.090525x | 2.113907x | +| NACA0015 | 0.998436x | 1.100590x | +| amazon0601 | 2.374381x | 2.389323x | +| auto | 1.895073x | 1.896815x | +| cage12 | 1.424595x | 1.441711x | +| cfd2 | 0.839985x | 0.841748x | +| filter3D | 0.464664x | 0.476849x | +| roadNet-TX | 1.301999x | 1.244056x | +| wave | 2.195355x | 2.224231x | +| 10 矩阵算术平均 | 1.572141x | 1.582163x | + +按用户“平均超过 0.8x，不要求每个矩阵都高”的标准，两种 dtype 均达标。 +`filter3D` 单矩阵仍低于 0.8x，不能表述为逐矩阵全部达标；后续可继续细分排序桶降低该矩阵 +的 4096 桶开销。本轮仅复测 SpGEMM，不代表另外 64 个交付变体重新测过。 +原始 20 行数据、baseline 状态和误差见 `spgemm_csr/performance.csv`；不要用本节数字 +覆盖上轮 native/PyTorch 计算路径的 CSV。 + +## 2026-10-15 修改文件交付与复测范围确认 + +### 另外两项低于 0.8x 的 SDDMM 变体优化 + +除 SpGEMM 外，本轮还优化了以下两个交付变体。它们都在 +`src/flagsparse/sparse_operations/sddmm_csr.py` 中实现，不能因只有一个源码文件就漏记 +其中一个变体。优化前数据来自 `results_metax_65_20261014/summary.json` 和 +`sddmm_csr/performance.csv`；优化后数值为前述调优阶段的 10 矩阵算术平均记录， +不是新一轮完整 65 变体 runner 的结果。 + +| 交付变体 | 优化前平均加速比 | 调优阶段平均加速比 | baseline 口径 | +|---|---:|---:|---| +| `sddmm_csr_f32_int_trans_non_row` | 0.662x | 约 2.17x | H800 带宽折算 | +| `sddmm_csr_c32_int_non_non_row` | 0.413x | 约 1.14x | H800 带宽折算 | + +#### `sddmm_csr_f32_int_trans_non_row` + +- 改了什么：`_apply_sddmm_dense_ops` 在完成 op/布局转换后，仅在 MACA、float32 或 + complex64 且 K 维步长不为 1 时，对对应的 X/Y 调用 `contiguous()`。已经连续的输入 + 不复制；同一分支也覆盖列主序及其他转置组合，不为某个矩阵缓存计算结果。 +- 为什么改：转置视图使 K 维加载跨大步长，C550 上 nnz 并行 sampled-dot 的访存代价 + 很高。先连续化再执行 kernel，可减少后续重复的跨步访问；连续化开销仍保留在调用路径， + 不通过移出计时范围改善数字。 +- 怎么验证的：既有 q4 SDDMM 合成精度为 `10 passed`，覆盖转置和布局变体；10 个交付 + 矩阵调优阶段的平均折算加速比约 `2.17x`，超过用户按算术平均 `0.8x` 的目标。 +- 其他后端是否也需要：否；分支由 `_is_maca_runtime()` 保护，其他后端保留零复制步长路径。 + +#### `sddmm_csr_c32_int_non_non_row` + +- 改了什么：新增 `_sddmm_csr_complex_kernel_maca`，将交错 complex64 的实部/虚部 + 成对加载后用 `tl.split` 分离，计算复数乘积并沿 K 求和，保留 alpha/beta 和输入累加 + 语义；`_resolve_sddmm_launch_config` 为 MACA complex64 固定选择 + `(BLOCK_P, BLOCK_K, num_warps)=(16,16,2)`。仅 MACA complex64 的默认 + `baseline` kernel 路由到该实现，诊断 kernel 和其他后端路由不变。 +- 为什么改：原复数宽 tile 在 MACA lowering 下寄存器和地址计算开销较高；成对加载与 + 窄 tile 减轻这些开销，提高可驻留 program 数量。这里的 `baseline` 是内部 kernel + 变体名，不代表调用 PyTorch 计算。 +- 怎么验证的：同组 q4 SDDMM 合成精度 `10 passed`；相关 SDDMM sweep 与 SpGEMM + TLE 门控回归合计 `15 passed`。10 矩阵调优阶段的平均折算加速比约 `1.14x`，超过 + 算术平均 `0.8x` 目标，不代表每个矩阵都超过 0.8x。 +- 其他后端是否也需要：否；kernel 选择与参数覆盖均只在 MACA runtime 生效。 + +以上是已有优化和验证记录的补全，本次没有新增 kernel 修改或重新测试。SDDMM 这两项 +仍沿用历史 H800 折算评估，不能写成本机 PyTorch 实测加速比；SpGEMM 最新结果则仅 +使用本机 PyTorch CSR baseline，两者必须分开汇报。全部当前修改的 65 变体复测仍待执行。 + +### 压缩包内容与最新复测范围 + +当前基于 `a330b62b46df3a2048bd14c95650eb4f82698185` 的未提交修改打包为 +`flagsparse_maca_changes_20261015.tar.gz`，保留仓库相对路径。包内为当前完整文件， +不是 diff；仅收录下列 9 个源码、测试和文档文件，不包含历史测试结果、诊断脚本、 +矩阵数据或已有压缩包。 + +- `docs/MACA.md` +- `docs/debug/MACA.md` +- `src/flagsparse/sparse_operations/sddmm_csr.py` +- `src/flagsparse/sparse_operations/spgemm_csr.py` +- `src/flagsparse/sparse_operations/_spgemm_maca.py`（新增） +- `tests/test_spgemm.py` +- `tests/ci/test_spgemm_maca_baseline.py`（新增） +- `tests/ci/test_spgemm_maca_dispatch.py`（新增） +- `tests/pytest/test_spgemm_maca_accuracy.py`（新增） + +本次交付保留已有 MACA SDDMM 调优、SpGEMM PyTorch baseline 接线，以及最新纯 +Triton SpGEMM 实现和回归测试；kernel 和 dispatch 的新增行为限定在 MACA 后端。 +本次仅更新文档并打包，没有重新运行测试。 + +截至此次打包，最新正式复测仍是 `results_metax_spgemm_triton_20261015/`： +SpGEMM 合成精度 `39 passed`，10 矩阵 × float32/float64 的 20 行性能及本机 PyTorch +baseline 均通过，平均加速比分别为 `1.572141x`、`1.582163x`。`39` 是精度用例数， +不是算子数；另外 64 个交付变体未用当前修改重新测试。历史 +`results_metax_65_20261014/` 不能作为当前修改的全量 65 算子验证结果。 diff --git a/run_flagsparse_pytest.py b/run_flagsparse_pytest.py index 45ceff1f..0ececb83 100644 --- a/run_flagsparse_pytest.py +++ b/run_flagsparse_pytest.py @@ -1655,6 +1655,9 @@ def parse_accuracy_json(path: Path) -> dict[str, object]: # tokens, so an unexpected skip turned spmv_coo_f32_int_non into "Skipped" on # every other backend (seen on H800 when the gate was still ROCm-only). "reuse coverage not yet verified on this backend", + # tests/pytest/test_spmv_csr_maca_accuracy.py is a MACA-only regression; its + # float16/complex64 cases turned spmv_csr_f16 / _c64 "Skipped" elsewhere. + "maca required", ) diff --git a/src/flagsparse/sparse_operations/_spgemm_maca.py b/src/flagsparse/sparse_operations/_spgemm_maca.py new file mode 100644 index 00000000..2c7b90f7 --- /dev/null +++ b/src/flagsparse/sparse_operations/_spgemm_maca.py @@ -0,0 +1,262 @@ +# Copyright 2026 FlagOS Contributors +# SPDX-License-Identifier: Apache-2.0 +"""TLE-free MACA SpGEMM. Torch supplies storage, not sparse computation.""" + +import torch +import triton +import triton.language as tl + + +@triton.jit +def _metadata(ai, ap, bp, work, pref, BLOCK: tl.constexpr): + row = tl.program_id(0) + start = tl.load(ap + row).to(tl.int64) + stop = tl.load(ap + row + 1).to(tl.int64) + total = tl.full((), 0, tl.int64) + lane = tl.arange(0, BLOCK) + for base in range(start, stop, BLOCK): + pos = base + lane + col = tl.load(ai + pos, pos < stop, 0) + begin = tl.load(bp + col, pos < stop, 0).to(tl.int64) + end = tl.load(bp + col + 1, pos < stop, 0).to(tl.int64) + length = end - begin + prefix = tl.cumsum(length, 0) + tl.store(pref + pos, total + prefix - length, pos < stop) + total += tl.sum(length, 0) + tl.store(work + row, total) + + +@triton.jit +def _scan_blocks(counts, ptr, sums, N: tl.constexpr, BLOCK: tl.constexpr): + block = tl.program_id(0) + pos = block * BLOCK + tl.arange(0, BLOCK) + values = tl.load(counts + pos, pos < N, 0).to(tl.int64) + prefix = tl.cumsum(values, 0) + tl.store(ptr + pos + 1, prefix, pos < N) + tl.store(sums + block, tl.sum(values, 0)) + if block == 0: + tl.store(ptr, 0) + + +@triton.jit +def _scan_add(ptr, block_ptr, N: tl.constexpr, BLOCK: tl.constexpr): + block = tl.program_id(0) + pos = block * BLOCK + tl.arange(0, BLOCK) + carry = tl.load(block_ptr + block) + value = tl.load(ptr + pos + 1, pos < N, 0) + tl.store(ptr + pos + 1, value + carry, pos < N) + + +def _prefix_ptr(counts): + n = counts.numel() + ptr = torch.empty(n + 1, dtype=torch.int64, device=counts.device) + blocks = max(1, triton.cdiv(n, 1024)) + sums = torch.empty(blocks, dtype=torch.int64, device=counts.device) + _scan_blocks[(blocks,)](counts, ptr, sums, n, 1024) + if blocks > 1: + block_ptr = _prefix_ptr(sums) + _scan_add[(blocks,)](ptr, block_ptr, n, 1024) + return ptr + + +@triton.jit +def _capacity(work, sizes, counts, N: tl.constexpr, COLS: tl.constexpr, LIMIT: tl.constexpr, + BLOCK: tl.constexpr): + row = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK) + w = tl.load(work + row, row < N, 0) + # Long rows use a global hash table with guaranteed load <= 1/2. Short + # rows need only their product bound for a compact intermediate result. + h = tl.minimum(w, COLS) * 2 + h = tl.maximum(h, 1) - 1 + h = h | (h >> 1) + h = h | (h >> 2) + h = h | (h >> 4) + h = h | (h >> 8) + h = h | (h >> 16) + h = h | (h >> 32) + tl.store(sizes + row, tl.where(w <= LIMIT, w, h + 1), row < N) + tl.store(counts + row, 0, row < N) + + +@triton.jit +def _products(ad, ai, ap, pref, bd, bi, bp, row, work, q, BLOCK: tl.constexpr): + lo = tl.full((BLOCK,), tl.load(ap + row), tl.int64) + stop = tl.load(ap + row + 1).to(tl.int64) + hi = tl.full((BLOCK,), stop, tl.int64) + while tl.sum((hi - lo > 1).to(tl.int32), 0) > 0: + mid = (lo + hi) // 2 + p = tl.load(pref + mid, mid < stop, 0x7fffffffffffffff) + take = p <= q + lo = tl.where(take, mid, lo) + hi = tl.where(take, hi, mid) + col = tl.load(ai + lo, q < work, 0) + p = tl.load(pref + lo, q < work, 0) + bpos = tl.load(bp + col, q < work, 0) + q - p + key = tl.load(bi + bpos, q < work, -1) + av = tl.load(ad + lo, q < work, 0.0) + bv = tl.load(bd + bpos, q < work, 0.0) + return key, av * bv + + +@triton.jit +def _segment_add(ka, va, kb, vb): + return kb, tl.where(ka == kb, va + vb, vb) + + +@triton.jit +def _scan_last_two(ka, next_a, na, kb, next_b, nb): + return kb, tl.where(nb > 1, next_b, ka), na + nb + + +@triton.jit +def _sort_rows(ad, ai, ap, pref, bd, bi, bp, work, slots, keys, values, counts, + LOWER: tl.constexpr, BLOCK: tl.constexpr): + row = tl.program_id(0) + w = tl.load(work + row) + if (w > LOWER) & (w <= BLOCK): + q = tl.arange(0, BLOCK) + key, _ = _products(ad, ai, ap, pref, bd, bi, bp, row, w, q, BLOCK) + # MACA cannot lower dynamic cross-lane gather. Carry product positions + # through scalar sort, then load operands directly in the sorted order. + order = tl.sort((key.to(tl.uint32).to(tl.uint64) << 32) | q.to(tl.uint64), descending=False) + key = (order >> 32).to(tl.int32) + _, value = _products(ad, ai, ap, pref, bd, bi, bp, row, w, + (order & 0xffffffff).to(tl.int32), BLOCK) + _, total = tl.associative_scan((key, value), 0, _segment_add) + _, following, _ = tl.associative_scan( + (key, key, tl.full((BLOCK,), 1, tl.int32)), 0, _scan_last_two, reverse=True + ) + last = (key >= 0) & ((q == BLOCK - 1) | (key != following)) + rank = tl.cumsum(last.to(tl.int32), 0) - 1 + base = tl.load(slots + row) + tl.store(keys + base + rank, key, last) + tl.store(values + base + rank, total, last) + tl.store(counts + row, tl.sum(last.to(tl.int32), 0)) + + +@triton.jit +def _hash_init(work, slots, keys, values, LIMIT: tl.constexpr, BLOCK: tl.constexpr): + row = tl.program_id(0) + if tl.load(work + row) > LIMIT: + start = tl.load(slots + row) + end = tl.load(slots + row + 1) + for base in range(start, end, BLOCK): + pos = base + tl.arange(0, BLOCK) + tl.store(keys + pos, -1, pos < end) + tl.store(values + pos, 0.0, pos < end) + + +@triton.jit +def _hash_rows(ad, ai, ap, pref, bd, bi, bp, work, slots, keys, values, counts, + LIMIT: tl.constexpr, BLOCK: tl.constexpr): + row = tl.program_id(0) + w = tl.load(work + row) + if w > LIMIT: + start = tl.load(slots + row) + cap = tl.load(slots + row + 1) - start + a_start = tl.load(ap + row).to(tl.int64) + a_end = tl.load(ap + row + 1).to(tl.int64) + count = 0 + for base in range(0, w, BLOCK): + q = base + tl.arange(0, BLOCK) + valid = q < w + lo = tl.full((BLOCK,), a_start, tl.int64) + hi = tl.full((BLOCK,), a_end, tl.int64) + while tl.sum((hi - lo > 1).to(tl.int32), 0) > 0: + mid = (lo + hi) // 2 + p = tl.load(pref + mid, mid < a_end, 0x7fffffffffffffff) + take = p <= q + lo = tl.where(take, mid, lo) + hi = tl.where(take, hi, mid) + col = tl.load(ai + lo, valid, 0) + p = tl.load(pref + lo, valid, 0) + bpos = tl.load(bp + col, valid, 0) + q - p + key = tl.load(bi + bpos, valid, -1) + av = tl.load(ad + lo, valid, 0.0) + bv = tl.load(bd + bpos, valid, 0.0) + prod = av * bv + h = (key.to(tl.uint32) * 2654435761).to(tl.int64) & (cap - 1) + done = ~valid + while tl.sum((~done).to(tl.int32), 0) > 0: + found = tl.atomic_cas( + keys + start + h, tl.full((BLOCK,), -1, tl.int32), + tl.where(done, -1, key).to(tl.int32), sem="relaxed" + ) + hit = (found == -1) | (found == key) + count += tl.sum(((~done) & (found == -1)).to(tl.int32), 0) + tl.atomic_add(values + start + h, prod, (~done) & hit, sem="relaxed") + done = done | hit + h = (h + 1) & (cap - 1) + tl.store(counts + row, count) + + +@triton.jit +def _compact(work, slots, keys, values, counts, ptr, ci, cd, + LIMIT: tl.constexpr, BLOCK: tl.constexpr): + row = tl.program_id(0) + base = tl.load(slots + row) + dst = tl.load(ptr + row) + count = tl.load(counts + row) + if tl.load(work + row) <= LIMIT: + for offset in range(0, count, BLOCK): + lane = offset + tl.arange(0, BLOCK) + key = tl.load(keys + base + lane, lane < count, 0) + value = tl.load(values + base + lane, lane < count, 0.0) + tl.store(ci + dst + lane, key, lane < count) + tl.store(cd + dst + lane, value, lane < count) + else: + cap = tl.load(slots + row + 1) - base + emitted = 0 + for offset in range(0, cap, BLOCK): + lane = offset + tl.arange(0, BLOCK) + key = tl.load(keys + base + lane, lane < cap, -1) + value = tl.load(values + base + lane, lane < cap, 0.0) + valid = (lane < cap) & (key >= 0) + rank = tl.cumsum(valid.to(tl.int32), 0) - 1 + tl.store(ci + dst + emitted + rank, key, valid) + tl.store(cd + dst + emitted + rank, value, valid) + emitted += tl.sum(valid.to(tl.int32), 0) + + +def build_metadata(indices, indptr, b_indptr): + work = torch.empty(indptr.numel() - 1, dtype=torch.int64, device=indices.device) + pref = torch.empty(indices.numel(), dtype=torch.int64, device=indices.device) + if work.numel(): + _metadata[(work.numel(),)](indices, indptr, b_indptr, work, pref, 32, num_warps=1) + return work, pref + + +def compute(prepared): + rows = prepared.n_rows + device = prepared.a_data.device + sizes = torch.empty(rows, dtype=torch.int64, device=device) + counts = torch.empty(rows, dtype=torch.int64, device=device) + limit = 4096 + if rows: + _capacity[(triton.cdiv(rows, 256),)]( + prepared.a_row_work, sizes, counts, rows, prepared.n_cols, limit, 256 + ) + slots = _prefix_ptr(sizes) + total = int(slots[-1].item()) + keys = torch.empty(total, dtype=torch.int32, device=device) + values = torch.empty(total, dtype=prepared.a_data.dtype, device=device) + args = (prepared.a_data, prepared.a_indices, prepared.a_indptr, prepared.a_pref, + prepared.b_data, prepared.b_indices, prepared.b_indptr, prepared.a_row_work, + slots, keys, values, counts) + if rows: + lower = 0 + for block, warps in ((64, 1), (256, 2), (1024, 4), (4096, 8)): + _sort_rows[(rows,)](*args, lower, block, num_warps=warps, enable_fp_fusion=False) + lower = block + _hash_init[(rows,)](prepared.a_row_work, slots, keys, values, limit, 256) + # C550 CAS/add loses lanes with a 128-element multi-warp layout; keep + # each probing step within its native 64-lane warp (tested for FP64). + _hash_rows[(rows,)](*args, limit, 64, num_warps=1, enable_fp_fusion=False) + ptr = _prefix_ptr(counts) + nnz = int(ptr[-1].item()) + data = torch.empty(nnz, dtype=prepared.a_data.dtype, device=device) + indices = torch.empty(nnz, dtype=torch.int32, device=device) + if rows: + _compact[(rows,)](prepared.a_row_work, slots, keys, values, counts, ptr, + indices, data, limit, 256, num_warps=4) + return data, indices, ptr diff --git a/src/flagsparse/sparse_operations/sddmm_csr.py b/src/flagsparse/sparse_operations/sddmm_csr.py index 69eb0676..1ddfd817 100644 --- a/src/flagsparse/sparse_operations/sddmm_csr.py +++ b/src/flagsparse/sparse_operations/sddmm_csr.py @@ -456,6 +456,13 @@ def _resolve_sddmm_launch_config(k, mean_row_len=None, value_dtype=None): if _is_iluvatar_runtime() and value_dtype == torch.float32: return 64, 16, 4 + # MACA's complex lowering has a substantially higher register and address + # calculation cost than its real path. The narrower tile keeps enough + # resident programs to hide the gathered CSR loads on C550. This branch is + # intentionally runtime-local; CUDA/ROCm retain their measured profiles. + if _is_maca_runtime() and value_dtype == torch.complex64: + return 16, 16, 2 + block_k = 32 if k >= 32 else max(1, triton.next_power_of_2(int(k))) if ( value_dtype != torch.float64 @@ -799,6 +806,72 @@ def _sddmm_csr_complex_kernel( tl.store(out_ri_ptr + offs_p * 2 + 1, out_im, mask=mask_p) +@triton.jit +def _sddmm_csr_complex_kernel_maca( + indices_ptr, + row_ids_ptr, + x_ri_ptr, + y_ri_ptr, + in_ri_ptr, + out_ri_ptr, + nnz, + k_dim, + stride_xm, + stride_xk, + stride_ym, + stride_yk, + alpha, + beta, + HAS_IN: tl.constexpr, + BLOCK_P: tl.constexpr, + BLOCK_K: tl.constexpr, + ACC_DTYPE: tl.constexpr, +): + """MACA complex SDDMM with pair loads for interleaved complex64.""" + pid = tl.program_id(0) + offs_p = pid * BLOCK_P + tl.arange(0, BLOCK_P) + mask_p = offs_p < nnz + rows = tl.load(row_ids_ptr + offs_p, mask=mask_p, other=0) + cols = tl.load(indices_ptr + offs_p, mask=mask_p, other=0) + parts = tl.arange(0, 2) + acc_re = tl.zeros([BLOCK_P], dtype=ACC_DTYPE) + acc_im = tl.zeros([BLOCK_P], dtype=ACC_DTYPE) + + for k0 in tl.range(0, k_dim, BLOCK_K): + offs_k = k0 + tl.arange(0, BLOCK_K) + mask = mask_p[:, None, None] & (offs_k[None, :, None] < k_dim) + x_base = (rows[:, None] * stride_xm + offs_k[None, :] * stride_xk) * 2 + y_base = (cols[:, None] * stride_ym + offs_k[None, :] * stride_yk) * 2 + x_pair = tl.load( + x_ri_ptr + x_base[:, :, None] + parts[None, None, :], + mask=mask, + other=0.0, + ).to(ACC_DTYPE) + y_pair = tl.load( + y_ri_ptr + y_base[:, :, None] + parts[None, None, :], + mask=mask, + other=0.0, + ).to(ACC_DTYPE) + x_re, x_im = tl.split(x_pair) + y_re, y_im = tl.split(y_pair) + acc_re += tl.sum(x_re * y_re - x_im * y_im, axis=1) + acc_im += tl.sum(x_re * y_im + x_im * y_re, axis=1) + + out_re = acc_re * alpha + out_im = acc_im * alpha + if HAS_IN: + in_pair = tl.load( + in_ri_ptr + offs_p[:, None] * 2 + parts[None, :], + mask=mask_p[:, None], + other=0.0, + ).to(ACC_DTYPE) + in_re, in_im = tl.split(in_pair) + out_re += in_re * beta + out_im += in_im * beta + tl.store(out_ri_ptr + offs_p * 2, out_re, mask=mask_p) + tl.store(out_ri_ptr + offs_p * 2 + 1, out_im, mask=mask_p) + + def _validate_sddmm_dense_inputs(data, prepared, x, y): if x.ndim != 2 or y.ndim != 2: raise ValueError("x and y must be 2D dense tensors") @@ -932,6 +1005,8 @@ def _run_sddmm_prepared( k_dim, mean_row_len=mean_row_len, value_dtype=x.dtype ) kernel, acc_dtype = _resolve_sddmm_diagnostic_kernel(variant, x.dtype) + if _is_maca_runtime() and x.dtype == torch.complex64 and variant == "baseline": + kernel = _sddmm_csr_complex_kernel_maca grid = (triton.cdiv(nnz, block_p),) fallback_used = False # Strides captured in COMPLEX-element units before view_as_real (which adds @@ -1038,6 +1113,17 @@ def _apply_sddmm_dense_ops(x, y, op_a, op_b, dense_layout): x = x.transpose(0, 1) if _normalize_sddmm_dense_op(op_b, "trans") == "non": y = y.transpose(0, 1) + + # MetaX C550 pays a large penalty for the strided views produced by the + # cuSPARSE-style transpose flags (and by column-major dense inputs). The + # SDDMM kernel is nnz-parallel, so each lane otherwise walks a large stride + # in the K dimension. Materialize only these non-contiguous views on MACA; + # other runtimes keep the original zero-copy stride behavior. + if _is_maca_runtime() and x.dtype in (torch.float32, torch.complex64): + if x.ndim == 2 and x.stride(1) != 1: + x = x.contiguous() + if y.ndim == 2 and y.stride(1) != 1: + y = y.contiguous() return x, y diff --git a/src/flagsparse/sparse_operations/spgemm_csr.py b/src/flagsparse/sparse_operations/spgemm_csr.py index bc92d214..4afc3baf 100644 --- a/src/flagsparse/sparse_operations/spgemm_csr.py +++ b/src/flagsparse/sparse_operations/spgemm_csr.py @@ -244,6 +244,11 @@ def prepare_spgemm_csr( row_work = torch.empty(0, dtype=torch.int32, device=a_data.device) a_pref = torch.empty(0, dtype=torch.int32, device=a_data.device) row_work_ready = True + elif _is_maca_runtime(): + from ._spgemm_maca import build_metadata + + row_work, a_pref = build_metadata(a_indices, a_indptr, b_indptr) + row_work_ready = True elif analyze_rows: row_work, a_pref = _build_row_product_metadata(a_indices, a_indptr, b_indptr) row_work_ready = True @@ -842,6 +847,12 @@ def _spgemm_esc_compute_safe(prepared): ) +def _maca_triton_spgemm_compute(prepared): + from ._spgemm_maca import compute + + return compute(prepared) + + def _spgemm_hash_hybrid_compute(prepared): """Hybrid: TLE shared-memory hash for rows that fit a table, chunked ESC for the rest. Returns ``None`` when hashing does not apply (caller then @@ -1149,6 +1160,11 @@ def _spgemm_compute(prepared): # Iluvatar's FlagTree imports TLE but cannot compile its shared-memory # tables ("iluvatar TLE alloc does not support nv_mma_shared_layout=True", # BI-V150, 2026-10-09), which failed every SpGEMM row there. + # C550 has no TLE. Its product, reductions and CSR construction use the + # MACA-only standard Triton implementation, never torch.sparse.mm or ESC. + if _is_maca_runtime() and isinstance(prepared, SpGEMMPrepared): + return _maca_triton_spgemm_compute(prepared) + tle_hash_safe = not ( _is_mthreads_runtime() or _is_xpu_runtime() or _is_iluvatar_runtime() ) diff --git a/tests/ci/test_runner_result_format.py b/tests/ci/test_runner_result_format.py index 6cef3550..4bc4302f 100644 --- a/tests/ci/test_runner_result_format.py +++ b/tests/ci/test_runner_result_format.py @@ -1175,6 +1175,24 @@ def test_the_reuse_suite_backend_gate_does_not_demote_a_delivery_variant(): assert summary["skipped"] == 1 # still reported, just not a demotion +def test_the_maca_only_spmv_csr_gate_does_not_demote_a_delivery_variant(): + # Seen on CUDA 2026-10-15: spmv_csr accuracy read "Skipped" (0 failed). + raw = { + "tests/pytest/test_spmv_csr_accuracy.py::test_ok[float16]": { + "params": {"dtype": "torch.float16"}, + "result": "passed", + }, + "tests/pytest/test_spmv_csr_maca_accuracy.py::test_live[float16]": { + "params": {"dtype": "torch.float16"}, + "result": "skipped", + "reason": "Skipped: MACA required", + }, + } + summary = runner.summarize_accuracy_cases(raw) + assert summary["status"] == "Passed" + assert summary["skipped"] == 1 + + def test_spgemm_split_path_honours_a_dtypes_override(tmp_path, monkeypatch): # Iluvatar passes spgemm_csr=--dtypes float32: its fp64 child could only fail. seen = [] diff --git a/tests/ci/test_spgemm_maca_baseline.py b/tests/ci/test_spgemm_maca_baseline.py new file mode 100644 index 00000000..cb6d41a2 --- /dev/null +++ b/tests/ci/test_spgemm_maca_baseline.py @@ -0,0 +1,126 @@ +# Copyright 2026 FlagOS Contributors +# SPDX-License-Identifier: Apache-2.0 +"""MACA performance references stay independent of the SciPy accuracy oracle.""" + +import pytest + +torch = pytest.importorskip("torch") +bench = pytest.importorskip("tests.test_spgemm") + + +def _case(dtype=torch.float32): + a = torch.tensor([[1.0, 2.0], [0.0, 3.0]], dtype=dtype).to_sparse_csr() + args = (a.values(), a.col_indices().int(), a.crow_indices(), (2, 2)) + oracle = bench._build_scipy_spgemm_reference(*args, *args) + return args, oracle + + +def _timer(monkeypatch): + monkeypatch.setattr( + bench.ast_ops, "_benchmark_cuda_op", lambda op, warmup, iters: (op(), 2.0) + ) + + +@pytest.mark.parametrize("dtype", [torch.float32, torch.float64]) +def test_csr_baseline_is_timed_and_validated(monkeypatch, dtype): + _timer(monkeypatch) + args, oracle = _case(dtype) + state = bench._benchmark_maca_torch_spgemm_baseline(*args, *args, oracle, 1, 3) + assert state["pytorch_baseline_status"] == "PASS" + assert state["pytorch_baseline_format"] == "CSR" + assert state["pytorch_ms"] == 2.0 + assert state["pytorch_reason"] is None + + +@pytest.mark.parametrize("value", [99.0, float("nan"), float("inf")]) +def test_wrong_baseline_never_publishes_latency(monkeypatch, value): + _timer(monkeypatch) + args, oracle = _case() + bad = torch.full((2, 2), value).to_sparse_csr() + monkeypatch.setattr(torch.sparse, "mm", lambda a, b: bad) + state = bench._benchmark_maca_torch_spgemm_baseline(*args, *args, oracle, 0, 1) + assert state["pytorch_baseline_status"] == "FAIL" + assert state["pytorch_ms"] is None + assert state["pytorch_reason"] + + +def test_baseline_exception_is_reported(monkeypatch): + _timer(monkeypatch) + args, oracle = _case() + + def fail(*args): + raise RuntimeError("sparse multiply failed") + + monkeypatch.setattr(torch.sparse, "mm", fail) + state = bench._benchmark_maca_torch_spgemm_baseline(*args, *args, oracle, 0, 1) + assert state["pytorch_ms"] is None + assert state["pytorch_baseline_status"] == "ERROR" + assert state["pytorch_reason"] == "RuntimeError: sparse multiply failed" + + +@pytest.mark.parametrize("latency", [None, 0.0, -1.0, float("nan"), float("inf")]) +def test_invalid_latency_is_not_a_baseline(monkeypatch, latency): + args, oracle = _case() + monkeypatch.setattr( + bench.ast_ops, "_benchmark_cuda_op", lambda op, warmup, iters: (op(), latency) + ) + state = bench._benchmark_maca_torch_spgemm_baseline(*args, *args, oracle, 0, 1) + assert state["pytorch_ms"] is None + assert state["pytorch_baseline_status"] == "ERROR" + + +def test_baseline_validates_the_timed_output(monkeypatch): + args, oracle = _case() + bad = torch.ones((2, 2)).to_sparse_csr() + + def timer(op, warmup, iters): + op() + return bad, 2.0 + + monkeypatch.setattr(bench.ast_ops, "_benchmark_cuda_op", timer) + state = bench._benchmark_maca_torch_spgemm_baseline(*args, *args, oracle, 1, 1) + assert state["pytorch_ms"] is None + assert state["pytorch_baseline_status"] == "FAIL" + + +@pytest.mark.parametrize( + "maca,vendor,enabled", + [(True, "torch", True), (True, None, False), (False, "torch", False)], +) +@pytest.mark.parametrize("baseline_ok", [True, False]) +def test_matrix_runner_keeps_accuracy_separate( + monkeypatch, maca, vendor, enabled, baseline_ok +): + args, oracle = _case() + monkeypatch.setattr(bench, "accelerator_device", lambda: torch.device("cpu")) + monkeypatch.setattr(bench, "load_mtx_to_csr_torch", lambda *a, **kw: args) + monkeypatch.setattr(bench.ast_common, "_use_scipy_accuracy_reference", lambda: True) + monkeypatch.setattr(bench.ast_common, "_is_maca_runtime", lambda: maca) + monkeypatch.setattr( + bench.ast_common, "_expected_vendor_sparse_backend", lambda: vendor + ) + monkeypatch.setattr(bench, "_cleanup_reference_pools", lambda: None) + monkeypatch.setattr( + bench, "_benchmark_flagsparse_spgemm", lambda *a, **kw: (oracle, 1.0, 1.0, {}) + ) + calls = [] + + def baseline(*a): + calls.append(a) + return { + "pytorch_ms": 2.0 if baseline_ok else None, + "pytorch_baseline_status": "PASS" if baseline_ok else "FAIL", + "pytorch_reason": None if baseline_ok else "wrong baseline", + } + + monkeypatch.setattr(bench, "_benchmark_maca_torch_spgemm_baseline", baseline) + result = bench.run_one_mtx("mock.mtx", run_cusparse=False) + assert bool(calls) == enabled + assert result["status"] == "PASS" + assert result["triton_ok_pt"] + assert result["error"] is None + assert result["pytorch_ms"] == (2.0 if enabled and baseline_ok else None) + row = bench._spgemm_csv_row(result, torch.float32, torch.int32) + assert row["triton_speedup_vs_pytorch"] == ( + 2.0 if enabled and baseline_ok else None + ) diff --git a/tests/ci/test_spgemm_maca_dispatch.py b/tests/ci/test_spgemm_maca_dispatch.py new file mode 100644 index 00000000..35326af3 --- /dev/null +++ b/tests/ci/test_spgemm_maca_dispatch.py @@ -0,0 +1,56 @@ +# Copyright 2026 FlagOS Contributors +# SPDX-License-Identifier: Apache-2.0 +"""Only MACA dispatches to its TLE-free Triton product implementation.""" + +import ast +import importlib +from pathlib import Path +from types import SimpleNamespace + +import pytest + +torch = pytest.importorskip("torch") +spgemm = importlib.import_module("flagsparse.sparse_operations.spgemm_csr") + + +@pytest.mark.parametrize("maca", [True, False]) +@pytest.mark.parametrize("tle", [True, False]) +def test_triton_dispatch_is_maca_only(monkeypatch, maca, tle): + monkeypatch.setattr(spgemm, "_is_maca_runtime", lambda: maca) + for name in ("_is_mthreads_runtime", "_is_xpu_runtime", "_is_iluvatar_runtime"): + monkeypatch.setattr(spgemm, name, lambda: False) + monkeypatch.setattr(spgemm, "_TLE_AVAILABLE", tle) + monkeypatch.setattr(spgemm, "_maca_triton_spgemm_compute", lambda p: "maca_triton") + monkeypatch.setattr(spgemm, "_spgemm_esc_compute_safe", lambda p: "esc") + monkeypatch.setattr(spgemm, "_spgemm_hash_hybrid_compute", lambda p: "hash") + prepared = object.__new__(spgemm.SpGEMMPrepared) + prepared.a_data = SimpleNamespace(dtype=torch.float64) + assert spgemm._spgemm_compute(prepared) == ( + "maca_triton" if maca else "hash" if tle else "esc" + ) + + +def test_maca_module_does_not_compute_with_torch(): + path = Path(spgemm.__file__).with_name("_spgemm_maca.py") + tree = ast.parse(path.read_text()) + # Storage allocation is allowed; numerical work belongs to Triton kernels. + for node in ast.walk(tree): + if isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute): + root = node.func + attributes = [] + while isinstance(root, ast.Attribute): + attributes.append(root.attr) + root = root.value + if isinstance(root, ast.Name) and root.id == "torch": + assert attributes == ["empty"], ast.unparse(node.func) + if node.func.attr in ( + "sort", + "argsort", + "cumsum", + "index_add_", + "scatter_add_", + ): + assert isinstance(root, ast.Name) and root.id == "tl", ast.unparse( + node.func + ) + assert not hasattr(spgemm, "_maca_torch_spgemm_compute") diff --git a/tests/pytest/test_spgemm_maca_accuracy.py b/tests/pytest/test_spgemm_maca_accuracy.py new file mode 100644 index 00000000..86d2d240 --- /dev/null +++ b/tests/pytest/test_spgemm_maca_accuracy.py @@ -0,0 +1,114 @@ +# Copyright 2026 FlagOS Contributors +# SPDX-License-Identifier: Apache-2.0 +"""The pure Triton MACA product never falls back to torch sparse or ESC.""" + +import pytest +import torch + +from flagsparse.sparse_operations import spgemm_csr as csr +from flagsparse.sparse_operations._common import _is_maca_runtime +from tests.pytest.accuracy_utils import accelerator_available, accelerator_device + +pytestmark = [pytest.mark.spgemm_csr, pytest.mark.skipif( + not (_is_maca_runtime() and accelerator_available()), reason="MACA required" +)] + + +def _triple(matrix, device, ptr_dtype): + sparse = matrix.to_sparse_csr() + return (sparse.values().to(device), sparse.col_indices().int().to(device), + sparse.crow_indices().to(device=device, dtype=ptr_dtype), tuple(matrix.shape)) + + +@pytest.mark.parametrize("dtype", [torch.float32, torch.float64]) +@pytest.mark.parametrize("ptr_dtype", [torch.int32, torch.int64]) +@pytest.mark.parametrize("shape", [(0, 7, 9), (7, 0, 9), (1, 128, 128), (1100, 3, 5)]) +def test_pure_triton_edges_and_live_values(monkeypatch, dtype, ptr_dtype, shape): + device = accelerator_device() + m, k, n = shape + gen = torch.Generator().manual_seed(91) + a = torch.randn(m, k, dtype=dtype, generator=gen) + b = torch.randn(k, n, dtype=dtype, generator=gen) + if m > 1: + a[0] = 0 + args_a, args_b = _triple(a, device, ptr_dtype), _triple(b, device, ptr_dtype) + prepared = csr.prepare_spgemm_csr(*args_a, *args_b) + + def forbidden(*args, **kwargs): + raise AssertionError("non-Triton sparse compute was called") + + monkeypatch.setattr(torch.sparse, "mm", forbidden) + monkeypatch.setattr(csr, "_spgemm_esc_compute_safe", forbidden) + for scale in (1.0, 2.0): + if scale == 2: + args_a[0].mul_(2) + data, indices, ptr, result_shape = csr.flagsparse_spgemm_csr(prepared=prepared) + result = torch.sparse_csr_tensor(ptr.cpu(), indices.cpu(), data.cpu(), result_shape) + torch.testing.assert_close(result.to_dense(), (a * scale) @ b, + atol=1e-5 if dtype == torch.float32 else 1e-11, + rtol=1e-4 if dtype == torch.float32 else 1e-11) + out = (torch.empty_like(data), torch.empty_like(indices), torch.empty_like(ptr)) + got = csr.flagsparse_spgemm_csr(prepared=prepared, out=out) + for actual, expected in zip(got[:3], out): + assert actual is expected + torch.testing.assert_close(got[0], data) + + +@pytest.mark.parametrize("dtype", [torch.float32, torch.float64]) +def test_duplicate_columns_and_empty_rhs_rows(dtype): + device = accelerator_device() + a = (torch.tensor([1., -1., 2., 3.], dtype=dtype, device=device), + torch.tensor([0, 0, 1, 2], dtype=torch.int32, device=device), + torch.tensor([0, 3, 4], dtype=torch.int64, device=device), (2, 3)) + b = (torch.tensor([4., -2.], dtype=dtype, device=device), + torch.tensor([1, 1], dtype=torch.int32, device=device), + torch.tensor([0, 0, 2, 2], dtype=torch.int64, device=device), (3, 4)) + data, indices, ptr, shape = csr.flagsparse_spgemm_csr(*a, *b) + result = torch.sparse_csr_tensor(ptr.cpu(), indices.cpu(), data.cpu(), shape) + expected = torch.tensor([[0., 4., 0., 0.], [0., 0., 0., 0.]], dtype=dtype) + torch.testing.assert_close(result.to_dense(), expected) + + +@pytest.mark.parametrize("dtype", [torch.float32, torch.float64]) +def test_long_row_hash_collisions(dtype): + device = accelerator_device() + inner, width = 128, 64 + # Work=8192 gives 16384 hash slots. Every distinct column hashes to the + # same slot, exercising probing and duplicate insertion across iterations. + columns = torch.arange(width, dtype=torch.int32, device=device) * 16384 + ad = torch.ones(inner, dtype=dtype, device=device) + ai = torch.arange(inner, dtype=torch.int32, device=device) + ap = torch.tensor([0, inner], dtype=torch.int64, device=device) + bd = torch.ones(inner * width, dtype=dtype, device=device) + bi = columns.repeat(inner) + bp = torch.arange(inner + 1, dtype=torch.int64, device=device) * width + data, indices, ptr, _ = csr.flagsparse_spgemm_csr( + ad, ai, ap, (1, inner), bd, bi, bp, (inner, 1048576) + ) + assert ptr.cpu().tolist() == [0, width] + assert sorted(indices.cpu().tolist()) == columns.cpu().tolist() + torch.testing.assert_close(data.cpu(), torch.full((width,), float(inner), dtype=dtype)) + + +@pytest.mark.parametrize("n", [0, 1, 1023, 1024, 1025, 1048579]) +def test_recursive_prefix_scan(n): + from flagsparse.sparse_operations._spgemm_maca import _prefix_ptr + + values = torch.arange(n, dtype=torch.int64) % 7 + got = _prefix_ptr(values.to(accelerator_device())).cpu() + expected = torch.cat((torch.zeros(1, dtype=torch.int64), values.cumsum(0))) + torch.testing.assert_close(got, expected) + + +def test_largest_int32_column_is_not_padding(): + device = accelerator_device() + one = torch.ones(1, device=device) + ap = torch.tensor([0, 1], dtype=torch.int64, device=device) + ai = torch.zeros(1, dtype=torch.int32, device=device) + bi = torch.tensor([2147483647], dtype=torch.int32, device=device) + data, indices, ptr, _ = csr.flagsparse_spgemm_csr( + one, ai, ap, (1, 1), one, bi, ap, (1, 2147483648) + ) + assert ptr.cpu().tolist() == [0, 1] + assert indices.cpu().tolist() == [2147483647] + assert data.cpu().tolist() == [1.0] diff --git a/tests/test_spgemm.py b/tests/test_spgemm.py index 4e9dcf73..d085a9c0 100644 --- a/tests/test_spgemm.py +++ b/tests/test_spgemm.py @@ -22,6 +22,7 @@ import gc import glob import json +import math import os import subprocess import sys @@ -481,6 +482,56 @@ def _build_torch_spgemm_reference( return ast_ops._torch_sparse_to_csr(ref_sparse), ref_format, op +def _benchmark_maca_torch_spgemm_baseline( + a_data, a_indices, a_indptr, a_shape, + b_data, b_indices, b_indptr, b_shape, + oracle, warmup, iters, +): + """Time native CSR multiplication; accept only a SciPy-validated result.""" + state = { + "pytorch_ms": None, + "pytorch_baseline_status": "ERROR", + "pytorch_baseline_format": "CSR", + "pytorch_baseline_max_error": None, + "pytorch_reason": None, + } + try: + # Match the candidate's CSR types. Descriptor setup is outside timing, + # just like the existing PyTorch reference; FlagSparse stays single-shot. + a = torch.sparse_csr_tensor( + a_indptr, a_indices, a_data, size=a_shape, + device=a_data.device, check_invariants=False, + ) + b = torch.sparse_csr_tensor( + b_indptr, b_indices, b_data, size=b_shape, + device=b_data.device, check_invariants=False, + ) + output, ms = ast_ops._benchmark_cuda_op( + lambda: torch.sparse.mm(a, b), warmup=warmup, iters=iters + ) + candidate = _convert_result_for_compare( + ast_ops._torch_sparse_to_csr(output), "cpu", a_data.device + ) + reference = _convert_result_for_compare(oracle, "cpu", a_data.device) + if not bool(torch.isfinite(candidate[0]).all()): + state.update(pytorch_baseline_status="FAIL", pytorch_reason="PyTorch CSR baseline returned non-finite values") + return state + metrics = _compare_spgemm_cpu(candidate, reference, a_data.dtype) + state["pytorch_baseline_max_error"] = metrics["err_ratio"] + if not metrics["pass"]: + state.update( + pytorch_baseline_status="FAIL", + pytorch_reason=f"PyTorch CSR baseline failed SciPy validation: {metrics['reason']} (err_ratio={metrics['err_ratio']})", + ) + elif ms is None or not math.isfinite(ms) or ms <= 0: + state["pytorch_reason"] = "PyTorch CSR baseline returned invalid latency" + else: + state.update(pytorch_ms=ms, pytorch_baseline_status="PASS") + except Exception as exc: + state["pytorch_reason"] = f"{type(exc).__name__}: {exc}" + return state + + def _build_torch_spgemm_reference_blocked( a_data, a_indices, @@ -1339,7 +1390,25 @@ def run_one_mtx( if ref_cleanup: _cleanup_reference_pools() - if run_cusparse: + maca_torch_baseline = ( + ast_common._is_maca_runtime() + and ast_common._expected_vendor_sparse_backend() == "torch" + and ast_common._use_scipy_accuracy_reference() + ) + if maca_torch_baseline and pt_ref_success: + _log_stage(mtx_path, "pytorch-csr-baseline", case_start) + result.update(_benchmark_maca_torch_spgemm_baseline( + a_data, a_indices, a_indptr, a_shape, + b_data, b_indices, b_indptr, b_shape, + pt_ref_result, ref_warmup, ref_iters, + )) + + if maca_torch_baseline: + # This backend's native baseline was measured above, not via CuPy. + result["cusparse_reason"] = "MACA PyTorch CSR baseline is reported in pytorch_ms" + result["cu_exec_mode"] = "disabled" + result["attempted_modes_cu"] = "disabled" + elif run_cusparse: # Vendor baseline per backend: hipSPARSE on DCU/ROCm, cuSPARSE via CuPy on CUDA. vendor_backend, _vendor_reason = ast_ops._spgemm_csr_sparse_ref_backend( value_dtype, a_indices.dtype, a_indptr.dtype, b_indices.dtype, b_indptr.dtype @@ -1744,6 +1813,9 @@ def print_mtx_results(results, value_dtype, index_dtype): "triton_ms", "cusparse_ms", "pytorch_ms", + "pytorch_baseline_status", + "pytorch_baseline_format", + "pytorch_baseline_max_error", "triton_speedup_vs_cusparse", "triton_speedup_vs_pytorch", "pt_status", @@ -1805,6 +1877,9 @@ def _spgemm_csv_row(entry, value_dtype, index_dtype): "triton_ms": entry.get("triton_ms"), "cusparse_ms": entry.get("cusparse_ms"), "pytorch_ms": entry.get("pytorch_ms"), + "pytorch_baseline_status": entry.get("pytorch_baseline_status"), + "pytorch_baseline_format": entry.get("pytorch_baseline_format"), + "pytorch_baseline_max_error": entry.get("pytorch_baseline_max_error"), "triton_speedup_vs_cusparse": _speedup_ratio( entry.get("cusparse_ms"), entry.get("triton_ms") ),