daca 0.1.3

DaVinci Accelerated Compute Architecture - Ascend NPU Platform Library

DACA - DaVinci Accelerated Compute Architecture

DACA is to Ascend what CUDA is to NVIDIA and ROCm is to AMD.

A compute platform library that makes Ascend 910ProA NPUs fully usable for AI workloads by closing operator gaps, fixing CANN bugs, optimizing performance, and enabling CUDA-ecosystem code to run on Ascend.

Features

• Pure Python - No sudo, no custom kernels, runs on OpenI virtual machines
• Training Ready - All NN modules are nn.Cell with full backward pass support
• bf16 Shim - Transparent bf16 → fp16 conversion (hardware doesn't support bf16)
• LayerNorm Fix - fp32 upcast workaround for CANN fusion bug
• Missing Operators - SiLU, SwiGLU, and other missing ops implemented
• FlashAttention - Chunked online softmax (pure MindSpore, full autograd)
• Graph Mode - Safe environment variables for stable compilation
• MindFormers Compatible - Patches for seamless integration

Quick Start

# Install
pip install -e .

# Verify installation
python -c "import daca; daca.info()"

Basic Usage

import daca

# Apply all compatibility patches
daca.patch()

# Your MindSpore code now works on Ascend
import mindspore as ms
from mindspore import ops

# bf16 automatically converted to fp16
x = ms.Tensor([1.0, 2.0, 3.0], ms.bfloat16)  # → fp16 internally

# SiLU now available
y = ops.silu(x)  # Works via x * sigmoid(x)

# LayerNorm won't crash
from daca.nn import LayerNorm
ln = LayerNorm(hidden_size, epsilon=1e-5)
normalized = ln(x)  # Uses fp32 upcast internally

# Use FlashAttention
from daca.nn import FlashAttention
attn = FlashAttention(num_heads=32, num_kv_heads=8, head_dim=64)  # GQA-ready
output = attn(query, key, value)  # Full autograd support (nn.Cell)

# When done, restore original state
daca.unpatch()

Why DACA?

The Problem

Huawei Ascend 910ProA is powerful hardware (256 TFLOPS FP16, 32GB HBM2), but the software stack has gaps:

Issue	Impact
bf16 unsupported	Crashes with aclnnCastGetWorkspaceSize error
ops.SiLU missing	Code expecting SiLU fails
LayerNorm fp16 broken	CANN routes through FlashAttention, crashes
CANN auto-fusion bugs	Aggressive fusion causes rank mismatches

The Solution

DACA patches these issues at the Python level:

def patch():
    """Apply all DACA compatibility patches."""
    runtime.dtype.enable_bf16_shim()      # bf16 → fp16
    nn.activations.inject_silu()          # Add SiLU to ops namespace
    nn.layernorm.enable_fp32_upcast()     # LayerNorm fp32 fast-path
    compile.fusion.disable_fa_fusion()    # Disable broken CANN fusions
    compile.graph_mode.set_safe_env()     # Graph mode env vars
    compat.mindspore_patches.apply_all()  # MindSpore namespace patches
    compat.mindformers_patches.apply_all() # MindFormers fixes

Hardware Requirements

• NPU: Ascend 910ProA (TSMC N7+)
• Cores: 32× DaVinci cores per NPU
• Memory: 32GB HBM2 per NPU
• Performance: 256 TFLOPS FP16
• CANN: 8.3.RC1.alpha003
• MindSpore: 2.7.1+
• Platform: aarch64-linux, no root required

Installation

From Source

git clone https://github.com/changcheng967/DACA.git
cd DACA
pip install -e .

With MindSpore

pip install -e ".[mindspore]"

Development

pip install -e ".[dev]"

API Reference

Core Functions

import daca

# Show DACA info banner
daca.info()

# Apply all patches
daca.patch()

# Remove all patches
daca.unpatch()

# Check if patched
daca.is_patched()  # → bool

# Run benchmarks
daca.benchmark()

Runtime Module

from daca.runtime import (
    detect_npu,          # Detect Ascend NPUs
    get_npu_info,        # Get NPU specs
    check_cann_version,  # Verify CANN compatibility
    is_openi_env,        # Detect OpenI VM
    set_device,          # Set active device
    device_count,        # Get NPU count
    MemoryTracker,       # Track memory usage
)

# Detect hardware
if detect_npu():
    info = get_npu_info()
    print(f"Found {info['count']} NPUs")
    print(f"Memory: {info['memory_gb']}GB each")

NN Module

from daca.nn import (
    FlashAttention,   # FlashAttention wrapper
    LayerNorm,        # fp32 upcast LayerNorm
    RMSNorm,          # Manual RMSNorm decomposition
    silu,             # x * sigmoid(x)
    swiglu,           # SwiGLU activation
    RotaryEmbedding,  # Rotary position embeddings
    Embedding,        # Embedding wrapper
    softmax,          # Numerically stable softmax
)

# FlashAttention
attn = FlashAttention(head_dim=64, num_heads=32, dropout=0.0)
output = attn(query, key, value, mask=mask)

# LayerNorm (fp32 upcast internally)
ln = LayerNorm(hidden_size=768, epsilon=1e-6)
normalized = ln(hidden_states)

# RMSNorm
rms = RMSNorm(hidden_size=768, epsilon=1e-6)
normalized = rms(hidden_states)

# Rotary embeddings
rotary = RotaryEmbedding(dim=64, max_seq_len=2048)
cos, sin = rotary(seq_len)
q_rotated = apply_rotary_pos_emb(query, cos, sin)

BLAS Module

from daca.blas import (
    matmul,        # MatMul with workspace handling
    bmm,           # BatchMatMul
    batch_matmul,  # Alias for bmm
)

# 2D MatMul
result = matmul(a, b)

# 4D attention shapes
q = ms.Tensor(shape=(batch, heads, seq, dim))
k = ms.Tensor(shape=(batch, heads, seq, dim))
scores = matmul(q, k.transpose(0, 1, 3, 2))

# BatchMatMul
result = bmm(a, b)  # (b, n, m) @ (b, m, p) → (b, n, p)

Compile Module

from daca.compile import (
    enable_graph_mode,           # Graph mode with safe env vars
    disable_flash_attention_fusion,  # Disable CANN FA fusion
    FusionConfig,                # Granular fusion control
)

# Enable graph mode safely
enable_graph_mode()

# Disable broken fusions
disable_flash_attention_fusion()

Compatibility Module

from daca.compat import (
    rewrite_config,  # bf16 → fp16 in dicts
    ConfigRewriter,  # JSON/YAML config rewriting
)

# Rewrite config dict
config = {"dtype": "bfloat16", "hidden_size": 768}
rewritten = rewrite_config(config)  # dtype → float16

# Rewrite config file
rewriter = ConfigRewriter()
rewriter.rewrite_file("config.json", "config_fp16.json")

Probing Hardware

# Run hardware capability probe
python tools/probe.py

# Output: probe_data.json with test results

Example output:

{
  "ops": {
    "bf16_cast": {"ok": false, "err": "aclnnCastGetWorkspaceSize call failed"},
    "silu": {"ok": false, "err": "module 'mindspore.ops' has no attribute 'SiLU'"},
    "sigmoid": {"ok": true, "ms": 95.3},
    "fa_native": {"ok": true, "ms": 1803.1},
    "ln_fp32": {"ok": true, "ms": 9904.2}
  }
}

Diagnosing Issues

# Run environment diagnostics
python tools/doctor.py

Running Tests

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ -v --cov=daca

Running Benchmarks

# Run all benchmarks
python benchmarks/bench_all.py

# Run specific benchmark
python benchmarks/bench_matmul.py
python benchmarks/bench_attention.py

Project Structure

DACA/
├── daca/
│   ├── __init__.py          # Main entry point
│   ├── runtime/             # Hardware detection, device mgmt
│   ├── blas/                # Matrix operations
│   ├── nn/                  # Neural network layers
│   ├── comm/                # Multi-NPU communication
│   ├── compile/             # Graph mode, fusion control
│   ├── compat/              # Third-party patches
│   ├── autotune/            # Benchmarking
│   └── docs/                # Documentation
├── tests/                   # Test suite
├── benchmarks/              # Performance benchmarks
├── examples/                # Usage examples
├── tools/                   # probe.py, doctor.py
├── setup.py
├── pyproject.toml
├── LICENSE
└── README.md

Known Limitations

1. bf16 completely unsupported - Hardware limitation, shim converts to fp16
2. LayerNorm fp16 broken - CANN fusion bug, workaround uses fp32 upcast
3. CANN auto-fusion bugs - Aggressive fusion causes crashes, disabled by DACA

Available vs Missing Operators

Available (Native)

Sigmoid, GeLU, ReLU, Tanh, Mish, FastGeLU, HSigmoid, HSwish, PReLU, SeLU, Softmax, RMSNorm, RotaryEmbedding, FlashAttentionScore, MatMul, BatchMatMul

Not Available (Shimmed)

SiLU → x * sigmoid(x), SwiGLU → manual split/silu/mul, bf16 → fp16

Contributing

Contributions are welcome! Please read our contributing guidelines.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests: pytest tests/ -v
5. Submit a pull request

License

Apache License 2.0 - see LICENSE for details.

Acknowledgments

• Huawei Ascend team for the hardware
• MindSpore community
• OpenI platform for providing access to Ascend hardware