taichi-forge 0.3.10

Taichi Forge - a community-maintained fork of the Taichi Programming Language (import name: taichi_forge).

Taichi Forge

A community-maintained fork of taichi focused on compile-time performance, modern toolchains (LLVM 20, VS 2026, Python 3.10-3.14), and tighter compile-time safety rails.

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Install

pip install taichi-forge

The import name is unchanged — existing code continues to work as-is:

import taichi as ti
ti.init(arch=ti.cuda)

Every public API from upstream Taichi 1.7.4 that we still ship behaves the same way.

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Why a fork?

Upstream Taichi 1.7.4 shipped against LLVM 15, Python ≤ 3.12, and the Visual Studio 2019/2022 toolchain. Since then the JIT ecosystem has moved on:

• LLVM 15 no longer compiles cleanly with current CUDA / NVPTX toolchains.
• Python 3.13 dropped distutils; 3.14 removes further deprecated stdlib APIs.
• Modern Windows developer setups default to VS 2026 (MSVC 14.50+), which rejects some headers hard-wired in the original build scripts.

Taichi Forge is the rolling result of those maintenance upgrades, along with compile-time performance improvements that reduce cold-start and warm-start latency.

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Headline feature: sparse SNode on Vulkan

Vanilla Taichi 1.7.4's Vulkan/SPIRV backend supports only dense + root. Every other SNode type — pointer, bitmasked, dynamic, hash — falls back to a TI_NOT_IMPLEMENTED on Vulkan, blocking macOS-via-MoltenVK, Linux-AMD-without-ROCm, and mobile / embedded users from running sparse data structures at all.

Taichi Forge 0.3.0 ships pointer / bitmasked / dynamic on Vulkan with end-to-end three-backend (cpu / cuda / vulkan) numerical equivalence. This is the fork's headline functional differentiator and is fully validated under the regression matrix in tests/p4/ (vulkan_pointer_*.py, vulkan_bitmasked_*.py, vulkan_dynamic_basic.py, g2_pool_fraction.py, g4_probe.py, g8_cache_compat.py).

SNode type	vanilla 1.7.4 Vulkan	Taichi Forge 0.3.0 Vulkan
dense	✅	✅
bitmasked	❌	✅
pointer	❌	✅
dynamic	❌	✅
quant_array / bit_struct	❌	⚠️ experimental (read + write + concurrent ti.atomic_add via CAS-loop; opt-in via vulkan_quant_experimental=True)
hash	❌	❌ (default-disabled in upstream Python frontend; see feasibility note below)

Highlights:

• No new public API — existing ti.root.pointer(...).dense(...).place(...) / ti.activate / ti.deactivate / ti.is_active / ti.length / ti.append Just Work on Vulkan with the same semantics as the LLVM backends.
• Static capacity by design — Vulkan has no device-side dynamic allocator, so each pointer / dynamic SNode reserves its worst-case cell pool in the root buffer at compile time. Out-of-capacity activates degrade silently rather than crashing (a cap_v guard verified by vulkan_pointer_race.py).
• Memory knob TI_VULKAN_POOL_FRACTION — opt-in env var (∈ (0, 1], default 1.0) shrinks the pointer pool to max(num_cells_per_container, round(total × fraction)). Combine with the G1.b deactivate-freelist (always on) to handle steady-state working sets far below the worst case. Verified at 0.25 against three-backend equivalence.
• dynamic uses a flat-array + length-suffix protocol on Vulkan instead of LLVM's chunk-list (no shader-side malloc exists). ti.append / ti.length / ti.deactivate preserve full LLVM semantics; total capacity is the static N.
• Offline cache cross-version safety — corrupt or version-mismatched ticache.tcb automatically triggers fallback recompile, never crashes (verified end-to-end by g8_cache_compat.py).
• Experimental quant_array / bit_struct on Vulkan — opt-in via ti.init(arch=ti.vulkan, vulkan_quant_experimental=True) or env var TI_VULKAN_QUANT=1. With the gate ON, QuantInt / QuantFixed member reads, writes, and concurrent multi-thread ti.atomic_add (via SPIR-V OpAtomicCompareExchange spin RMW) are byte-equivalent to the LLVM backends, including multi-field BitpackedFields packing (verified by tests/p4/g9_quant_baseline.py MPM-style 11/11/10 packing, tests/p4/g9_quant_array_baseline.py, and the same-word race baseline tests/p4/g9_quant_atomic_race.py). Default OFF preserves vanilla 1.7.4 behaviour exactly. QuantFloat shared-exponent and the non-add atomic ops (atomic_min/max/and/or/xor, identical restriction to LLVM) are explicitly out of scope and raise a clear TI_NOT_IMPLEMENTED rather than miscompiling.

📖 Full usage guide and limitations (bilingual): docs/forge/sparse_snode_on_vulkan.en.md / docs/forge/sparse_snode_on_vulkan.zh.md — covers static-capacity semantics, the TI_VULKAN_POOL_FRACTION knob, dynamic-protocol differences, troubleshooting, and the verification matrix.

📖 All fork-only knobs (compile / runtime / architecture / modernization options): docs/forge/forge_options.en.md / docs/forge/forge_options.zh.md.

hash SNode remains permanently deferred (no real-time physics or rendering pipeline depends on it; see the user guide §6.1 for the survey). quant_array / bit_struct ship experimental scaffolding on Vulkan in 0.3.0 — frontend gate is opt-in via vulkan_quant_experimental=True; codegen is incremental. See user guide §7.

Quick example (Vulkan-on-anything)

import taichi as ti
ti.init(arch=ti.vulkan)              # works on macOS via MoltenVK, Linux-AMD without ROCm, etc.

x = ti.field(ti.f32)
ti.root.pointer(ti.ij, 32).dense(ti.ij, 8).place(x)

@ti.kernel
def fill():
    for i, j in ti.ndrange(256, 256):
        if (i + j) % 17 == 0:
            x[i, j] = i * 0.1 + j * 0.01

fill()
print(x.to_numpy().sum())            # identical to ti.cpu / ti.cuda

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Supported toolchain

Area	Requirement
Python	3.10 – 3.14 (3.9 dropped)
Windows MSVC	VS 2026 (Visual Studio 17 2026, MSVC 14.50+)
LLVM	20.1.7 (included in the wheel)
CMake	3.20+
CUDA (optional)	NVCC 12.x

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Validated backends

End-to-end tested on Linux x86_64 and Windows x86_64:

• ✅ CPU (LLVM JIT)
• ✅ CUDA
• ✅ Vulkan
• ✅ OpenGL / GLES

Not yet regression-tested since the LLVM 20 migration:

• ⚠️ macOS (Apple Silicon / Intel) — Metal backend
• ⚠️ AMDGPU backend
• ⚠️ Android ARM64 (C-API)

Patches and reports welcome.

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New APIs and settings (fork-only)

All additions are strictly opt-in; default values preserve bit-identical behaviour vs. upstream 1.7.4.

New functions

Symbol	Purpose
ti.compile_kernels(kernels)	Pre-compile a list of kernels on a background thread pool before the hot loop. Accepts decorated kernels or (kernel, args_tuple) pairs. Returns the number of kernels submitted.
ti cache warmup script.py	CLI command — runs script.py once with the offline cache forced on, warming up kernel artifacts for subsequent cold starts.
ti.compile_profile()	Context manager — on exit, prints a per-pass timing report and optionally writes a CSV / Chrome trace.
@ti.kernel(opt_level=...)	Per-kernel LLVM optimization level override ("fast" / "balanced" / "full" or 0–3). Cache key is isolated per override.

ti.init(...) / CompileConfig knobs

Kwarg	Default	Purpose
compile_tier	"balanced"	"fast" lowers LLVM to -O0 (floor -O1 on NVPTX/AMDGCN) and SPIR-V optimizer to level 1. "full" preserves pre-fork behaviour.
llvm_opt_level	-1 (use tier)	Explicit LLVM -O override (0–3).
spv_opt_level	-1 (use tier)	Explicit SPIR-V spirv-opt optimization level override.
num_compile_threads	logical-core count	Thread pool size for ti.compile_kernels.
unrolling_hard_limit	0 (off)	Per-ti.static(for ...) unroll iteration cap. Aborts with TaichiCompilationError instead of silently burning seconds.
unrolling_kernel_hard_limit	0 (off)	Total unroll iteration cap across a single kernel.
func_inline_depth_limit	upstream default	Hard cap on @ti.func inline recursion depth.
cache_loop_invariant_global_vars	False	Set True to opt in to SNode loop-invariant caching in hot loops. (Default matches vanilla 1.7.4.)
use_fused_passes	False	Enable pipeline_dirty short-circuit for redundant full_simplify invocations. Numerically bit-identical to off.
tiered_full_simplify	True	Splits full_simplify into a local fixed-point pass followed by a single global round per iteration. Set False to match the legacy cadence.
compile_dag_scheduler	True	Anti-saturation scheduler for ti.compile_kernels batches; balances inner LLVM thread pool and outer kernel pool. Set False for the legacy two-tier model.
spirv_parallel_codegen	False	Opt-in task-level parallel SPIR-V codegen per kernel.
spirv_disabled_passes	[]	Per-call disable list for individual spirv-opt passes (e.g. ["loop-unroll"]).
auto_real_function	False	Auto-promote expensive @ti.func instances to is_real_function=True (LLVM-only, non-autodiff).
auto_real_function_threshold_us	1000	Promotion threshold in microseconds of estimated compile cost.
cuda_sparse_pool_size_GB	0.0 (use device_memory_GB)	CUDA-only. Explicit override for the sparse SNode dynamic-allocation pool, in GiB. 0 (default) preserves vanilla 1.7.4 semantics: the pool size equals device_memory_GB (or device_memory_fraction × total_VRAM). Set to a positive value to size the sparse pool independently of the dense allocation.
cuda_sparse_pool_auto_size	True	CUDA-only. When True, the sparse pool is auto-sized from the SNode tree geometry (capped by device_memory_GB). Heuristic now includes per-SNode num_cells_per_container as a physical upper bound, eliminating over-provisioning. Set False to restore vanilla 1.7.4 sizing.
cuda_sparse_pool_per_snode	True	CUDA-only. Carves per-SNode data regions from a single GPU buffer, isolating each gc-able SNode's allocation from the global metadata pool. Dramatically reduces peak VRAM for sparse workloads. Set False for legacy flat-pool behaviour.
cuda_sparse_pool_size_floor_MiB	0 (disabled)	CUDA-only. Optional safety floor for the auto-sized pool. Default 0 relies on the auto-hint mechanism; raise if your workload sees unexpected OOM.
max_active_hint (SNode parameter)	—	Per-SNode hint for the expected peak activation count. Pass to ti.root.pointer(...) or ti.root.dynamic(...) as max_active_hint=N. When set, overrides the automatic num_cells_per_container bound for tighter pool sizing.
spirv_listgen_subgroup_ballot	False	Vulkan/SPIR-V only. Aggregates the per-thread OpAtomicIAdd into one subgroup-ballot atomic per active subgroup in the listgen kernel. Reduces atomic contention on dense-active sparse struct-for. Output SPIR-V differs and is keyed into the offline cache hash.
listgen_static_grid_dim	False	CUDA / AMDGPU only. Launches sparse-listgen kernels with a grid_dim derived from the static upper bound on parent-element count, eliminating idle blocks on shallow sparse trees. Vulkan already computes the equivalent quantity, so this flag is a no-op for SPIR-V.

Compatibility note

• SNode.snode_tree_id — backported from upstream master (not in 1.7.4 release); available on all backends.
• offline_cache_l_sem — internal/testing flag, default off. Not for production use.

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Quick start

import taichi as ti

ti.init(arch=ti.cuda, compile_tier="fast")

@ti.kernel
def add(a: ti.types.ndarray(), b: ti.types.ndarray(), c: ti.types.ndarray()):
    for i in a:
        c[i] = a[i] + b[i]

import numpy as np
n = 1 << 20
a = np.random.rand(n).astype(np.float32)
b = np.random.rand(n).astype(np.float32)
c = np.empty_like(a)
add(a, b, c)

Pre-compiling a batch of kernels (fork-only)

import taichi as ti
ti.init(arch=ti.cuda)

@ti.kernel
def k1(x: ti.types.ndarray()): ...
@ti.kernel
def k2(x: ti.types.ndarray(), y: ti.types.ndarray()): ...

# Specialize + compile both on the thread pool before the hot loop.
ti.compile_kernels([k1, k2])

Command-line cache warmup (fork-only)

ti cache warmup train.py -- --epochs 1
# Subsequent `python train.py` runs start with a populated offline cache.

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Building from source

git clone https://github.com/fancifulland2718/taichi-forge/taichi.git
cd taichi
python -m pip install -r requirements_dev.txt
python -m pip install -e . --no-build-isolation -v

The build is driven entirely by pyproject.toml / scikit-build-core. On Windows, build a local LLVM 20 snapshot first:

.\scripts\build_llvm20_local.ps1   # produces dist\taichi-llvm-20\

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Versioning

Taichi Forge uses its own SemVer track starting at 0.1.2. Fork release numbers do not match upstream taichi versions.

• 0.1.x — LLVM 20 + VS 2026 + Python 3.14 + initial compile-performance improvements. Backends: Linux/Windows x86_64, CUDA, Vulkan, OpenGL, GLES, CPU.
• 0.2.x — deeper compile-time, runtime cache, and toolchain modernization. Stabilization line, superseded by 0.3.0.
• 0.3.x — sparse SNode (pointer / bitmasked / dynamic) on Vulkan + experimental quant_array scaffolding on Vulkan. Current line.

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Release notes

0.3.8 (current) — CUDA sparse pool overhaul

Phase 1 per-SNode pool + auto-hint + dynamic chunk sizing lands as default ON.

• cuda_sparse_pool_per_snode (new, default True) — carves per-SNode data regions from a single GPU buffer, isolating each gc-able SNode's allocation. Eliminates the legacy flat-pool over-provisioning for sparse workloads with multiple SNode types.
• Auto-hint from num_cells_per_container — the pool sizing heuristic now uses each SNode's physical cell capacity as the default activation upper bound, eliminating the need for manual max_active_hint tuning in most cases.
• Dynamic chunk_elements — per-SNode chunk size is tightened to ∼2× num_cells_per_container instead of a fixed 8192-slot default. For the MPM benchmark (495 pointer cells), this reduces pool VRAM from 404 MiB → 87 MiB.
• cuda_sparse_pool_size_floor_MiB default lowered from 128 → 0 (disabled). The auto-hint mechanism now provides precise worst-case sizing, making a defensive floor unnecessary.
• max_active_hint SNode parameter — users can pass ti.root.pointer(..., max_active_hint=N) to override the automatic bound with a tighter estimate.
• Host-side pool watermark query — ti.tools.get_sparse_pool_usage() returns per-SNode pool occupancy for profile-driven tuning.

VRAM improvement (MPM 88×33×69, 44K particles, CUDA sparse):

• Before: 1114 MiB
• After: 828 MiB (−26%)

Backward compatibility: cuda_sparse_pool_auto_size remains compatible; users who set it to False or explicitly pass cuda_sparse_pool_size_GB > 0 get vanilla 1.7.4 behaviour unchanged.

0.3.7 — sparse-pool sizing hotfix

Reverts the default behaviour change introduced in 0.3.5/0.3.6 that broke device_memory_GB semantics for sparse workloads.

Bug fix

• 0.3.5 and 0.3.6 unconditionally auto-sized the CUDA sparse pool from the SNode tree, treating device_memory_GB as a cap rather than the actual pool size. On MPM-shaped sparse trees the heuristic produced pools too small to accommodate runtime NodeAllocator activation, causing silent OOM on from_numpy / first-write kernels even when the user explicitly raised device_memory_GB.
• Auto-sizing is now opt-in via the new cuda_sparse_pool_auto_size=True kwarg (default False). Default behaviour matches vanilla taichi 1.7.4 exactly: the sparse pool size equals device_memory_GB (or device_memory_fraction × total_VRAM when set).
• cuda_sparse_pool_size_GB > 0 continues to act as an explicit override; it bypasses every other sizing path.
• cuda_sparse_pool_size_floor_MiB is now a no-op when cuda_sparse_pool_auto_size=False (the default).

Compatibility

• Default device_memory_GB semantics restored to vanilla 1.7.4.
• Users who relied on the 0.3.5/0.3.6 implicit auto-sizing should add cuda_sparse_pool_auto_size=True to their ti.init(...) call.
• Public Python and C-API surfaces unchanged versus 0.3.0.

0.3.5 / 0.3.6

Maintenance line on top of 0.3.0; introduced new sparse-pool sizing knobs and listgen optimisation flags. Note: the implicit sparse-pool auto-sizing default introduced in this line was reverted in 0.3.7 (see above) because it silently changed device_memory_GB semantics for sparse workloads.

Sparse struct-for performance (still active in 0.3.7)

• New spirv_listgen_subgroup_ballot knob (Vulkan/SPIR-V, opt-in) — aggregates the per-thread atomic in the listgen kernel into one subgroup-ballot atomic per subgroup, reducing contention on dense-active sparse struct-for. Output SPIR-V is offline-cache-keyed.
• New listgen_static_grid_dim knob (CUDA / AMDGPU, opt-in) — derives sparse-listgen grid_dim from the static parent-element upper bound, eliminating idle blocks on shallow sparse trees. No-op on Vulkan (which already computes the equivalent quantity).
• Both flags default OFF; output is bit-identical to the legacy path with the flag off, and offline-cache-keyed when on.

0.3.0

First release with sparse SNode on Vulkan as a public feature. Inherits the full 0.2.x compile-time, runtime-cache, IR-pass, and dependency-modernization stack (every knob below remains available and bit-identical to 0.2.4 with defaults off).

Sparse SNode on Vulkan (headline functional differentiator)

• pointer / bitmasked / dynamic SNodes now run end-to-end on the Vulkan/SPIRV backend, with three-backend (cpu / cuda / vulkan) numerical equivalence verified across 30+ tests in tests/p4/.
• Static-capacity model: pool size = total_num_cells_from_root (worst case), no shader-side dynamic allocator. Out-of-capacity activates degrade silently via the cap_v guard rather than crashing the device.
• New env var TI_VULKAN_POOL_FRACTION (∈ (0, 1], default 1.0) shrinks per-pointer pool capacity for memory-tight steady-state workloads. Combined with the deactivate-freelist (always on), supports re-activation cycles without leaking root-buffer space.
• dynamic SNode uses a flat-array + length-suffix protocol on Vulkan (length atomic-stored at cell_stride * N offset of each container) — full LLVM ti.append / ti.length / ti.deactivate semantics preserved, no chunk-list.
• Offline cache cross-version safety: corrupt or version-mismatched ticache.tcb triggers fallback recompile, never crashes (validated by g8_cache_compat.py three-phase test).
• Build-time guards: TI_VULKAN_POINTER / TI_VULKAN_DYNAMIC / TI_VULKAN_POINTER_POOL_FRACTION CMake flags (all default ON) allow byte-for-byte revert to vanilla 1.7.4 behaviour for regression bisecting.
• hash SNode remains unimplemented on every backend in this fork — vanilla taichi 1.7.4 itself default-disables ti.root.hash(...) in the Python frontend, and no real-world demo currently depends on it. See the user guide §6 for substitutes (pointer + TI_VULKAN_POOL_FRACTION, or user-level hash + dense buckets).
• Full user guide (limitations, env vars, troubleshooting, verification matrix): docs/forge/sparse_snode_on_vulkan.en.md / docs/forge/sparse_snode_on_vulkan.zh.md.

Compile-time performance

• Fused-pass driver: use_fused_passes adds a pipeline_dirty short-circuit around full_simplify so that no-op iterations are skipped. Measured ~48.6% of full_simplify invocations are observably no-op on representative workloads.
• Tiered full_simplify (tiered_full_simplify, default on): splits the legacy fixed-point loop into a local fixed-point phase plus a single global round per outer iteration, while preserving final IR.
• DAG-aware scheduler for ti.compile_kernels (compile_dag_scheduler, default on): balances the inner LLVM thread pool against the outer kernel pool to avoid thread oversubscription on batch warm-up.
• Single-offload bypass on the LLVM CPU path: removes the prior 0.89× CPU regression introduced by earlier batch-compile work.
• Per-kernel opt_level= override and compile_tier="fast"|"balanced"|"full" presets, with isolated cache keys so mixed-tier batches do not poison each other.
• SPIR-V pipeline gains a per-call spirv_disabled_passes allowlist, with cache-key isolation. Disabling loop-unroll alone gives ~54% SPIR-V codegen wall-time reduction on the validated Vulkan suite; disabling the three heaviest passes gives ~61%, with byte-identical kernel results.
• Optional task-level parallel SPIR-V codegen per kernel (spirv_parallel_codegen).
• Auto real-function promotion (auto_real_function + auto_real_function_threshold_us) and budget-aware inlining fallback in the LLVM-only path; both default off.

Offline cache and runtime caches

• Parallel disk-read for offline cache: metadata-hit but ckd-miss path now reads outside the cache mutex and serializes duplicate requests via an in-progress key set. Validated 12-kernel × Vulkan double-process cold start: 290.1 ms (prime) → 83.1 ms (hit), 3.49× faster with byte-identical per-kernel artifacts.
• CompileConfig key audit + offline-cache schema versioning: unrecognized cache versions now fall back to recompile cleanly instead of crashing.
• rhi_cache.bin now uses atomic write-then-rename to eliminate half-written cache files after abrupt termination.

IR / passes

• pipeline_dirty is now explicit and OR-combined across the five mutating passes that can dirty the pipeline, removing spurious dirty marks at no-op call sites. Validated across CPU / CUDA / Vulkan smoke matrices with no regression.
• Defensive assert + "type-query forbidden zone" notes on linking_context_data->llvm_context to catch accidental cross-context type queries early.

Toolchain and third-party libraries

• spdlog 1.14.1 → 1.15.3.
• Vulkan-Headers / volk / SPIRV-Headers / SPIRV-Tools aligned to Vulkan SDK 1.4.341 as a single coordinated bump.
• googletest 1.10.0 → 1.17.0 (test-only, no runtime impact).
• glm 0.9.9.8+187 → 1.0.3.
• imgui v1.84 (WIP) → v1.91.9b (non-docking branch). The Vulkan backend was migrated to the new ImGui_ImplVulkan_InitInfo layout (RenderPass + ApiVersion fields, self-managed font texture, LoadFunctions(api_version, loader) signature). GGUI visual-regression suite: 90 / 90 passing on Vulkan + CUDA backends.

Compatibility

• All public Python and C-API surfaces from upstream Taichi 1.7.4 remain unchanged. New configuration knobs are additive; their defaults preserve pre-fork behaviour.
• Build toolchain: LLVM 20.1.7, MSVC 14.50+ (VS 2026), Python 3.10–3.14 — unchanged from 0.1.x.

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License

Apache 2.0, same as upstream. See LICENSE. All upstream copyright notices are preserved.

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Acknowledgements

Taichi Forge is built on top of the work of the upstream Taichi developers at taichi-dev/taichi. The core compiler, runtime, and the vast majority of the Python frontend are theirs. This fork carries only the delta described above.