2a7ca101d7
Sparse-checkout from NVIDIA/cccl main branch to complete cccl_upstream: Added: - python/cuda_cccl/ (226 files) — Python bindings for device-level algorithms Critical for muh toolchain: cuda.compute.reduce_into, scan, radix_sort, etc. Includes 204 .py files with full test coverage for all 27 algorithms - ci/ (163 files) — Build/test infrastructure build_cub.sh, test_cub.sh, build_and_test_targets.sh, matrix.yaml Directly maps to our [INFRA-CI] and [INFRA-BUILD] items - .agent/skills/ (7 files) — NVIDIA's own agent skills for CCCL cccl-style/SKILL.md, cccl-test/SKILL.md, sass-diff/SKILL.md - docs/ (491 files) — Official CCCL documentation CI references, CMake guides, Python compute docs, libcudacxx PTX docs - test/ (12 files) — Top-level integration tests (cuda_smoke, stdpar) - Root configs: .clang-format, .clang-tidy, CONTRIBUTING.md, pyproject.toml - CLAUDE.md symlink → AGENTS.md (NVIDIA's standard) cccl_upstream now mirrors full NVIDIA/cccl structure: Before: 42M (cub + thrust + libcudacxx + cudax + c + examples + benchmarks) After: 53M (+python +ci +docs +.agent +test +configs) This completes the CCCL base needed for: - [muh-bench] items: ci/util/build_and_test_targets.sh for targeted builds - [CCCL-verify] items: python/cuda_cccl/tests/ as reference implementations - [CCCL-test] items: ci/test_cub.sh, ci/test_thrust.sh - Agent workflow: .agent/skills/ for consistent style and test patterns
81 lines
2.6 KiB
Bash
Executable File
81 lines
2.6 KiB
Bash
Executable File
#!/usr/bin/env bash
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set -euo pipefail
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die() {
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local message="$1"
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local code="${2:-2}"
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echo "${message}" >&2
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exit "${code}"
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}
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usage() {
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cat <<EOF
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Usage: $0 [bench-yaml-path]
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Parse ci/bench.yaml and emit a GitHub Actions strategy matrix JSON object:
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{"include":[...]}
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Each include entry maps one enabled GPU to a benchmark workflow invocation.
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EOF
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}
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if [[ "${1:-}" == "-h" || "${1:-}" == "--help" ]]; then
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usage
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exit 0
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fi
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bench_yaml_path="${1:-ci/bench.yaml}"
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if [[ ! -f "${bench_yaml_path}" ]]; then
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die "Benchmark config file not found: ${bench_yaml_path}"
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fi
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command -v "yq" >/dev/null 2>&1 || die "'yq' is required to parse ${bench_yaml_path}."
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command -v "jq" >/dev/null 2>&1 || die "'jq' is required to build the dispatch matrix."
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if ! bench_cfg_json="$(yq -o=json '.benchmarks // {}' "${bench_yaml_path}" 2>&1)"; then
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die "Failed to parse ${bench_yaml_path} as YAML: ${bench_cfg_json}"
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fi
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# Extract CUB and Python filter arrays (default to empty arrays).
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cub_filters_json="$(jq -c '.filters.cub // []' <<<"${bench_cfg_json}")"
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python_filters_json="$(jq -c '.filters.python // []' <<<"${bench_cfg_json}")"
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has_cub_filters="$(jq -e 'type == "array" and length > 0 and all(.[]; type == "string")' <<<"${cub_filters_json}" >/dev/null 2>&1 && echo true || echo false)"
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has_python_filters="$(jq -e 'type == "array" and length > 0 and all(.[]; type == "string")' <<<"${python_filters_json}" >/dev/null 2>&1 && echo true || echo false)"
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if [[ "${has_cub_filters}" != "true" && "${has_python_filters}" != "true" ]]; then
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die "${bench_yaml_path} must define at least one string entry in benchmarks.filters.cub or benchmarks.filters.python."
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fi
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cub_filters_arg=""
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if [[ "${has_cub_filters}" == "true" ]]; then
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cub_filters_arg="$(jq -r '.filters.cub | map(@sh) | join(" ")' <<<"${bench_cfg_json}")"
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fi
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python_filters_arg=""
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if [[ "${has_python_filters}" == "true" ]]; then
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python_filters_arg="$(jq -r '.filters.python | map(@sh) | join(" ")' <<<"${bench_cfg_json}")"
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fi
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jq -cn \
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--argjson cfg "${bench_cfg_json}" \
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--arg cub_filters "${cub_filters_arg}" \
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--arg python_filters "${python_filters_arg}" \
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'{
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"include": [
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($cfg.gpus // [])[] as $gpu
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"gpu": $gpu,
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"launch_args": ($cfg.launch_args // ""),
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"arch": ($cfg.arch // "native"),
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"base_ref": ($cfg.base_ref // "origin/main"),
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"test_ref": ($cfg.test_ref // "HEAD"),
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"cub_filters": $cub_filters,
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"python_filters": $python_filters,
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"nvbench_args": ($cfg.nvbench_args // ""),
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"nvbench_compare_args": ($cfg.nvbench_compare_args // "")
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}
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]
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}'
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