Do Not Merge: Integration Branch for GT4Py Next #1

philip-paul-mueller · 2025-04-30T10:04:09Z

This is the PR/branch that GT4Py.Next uses to pull DaCe.
It is essentially DaCe main together with our fixes that, for various reasons have not made it yet into DaCe main.

The process for updating this branch is is as follows there are no exceptions:

You start with current DaCe main.
Then you include the PR that enables automatic Python index update, by squash merge it.
Then squash merge the PRs that are listed below, check if they have been merged into DaCe proper and if so remove them from the list.
Then update the version.py file in the dace/ subfolder. Make sure that there is no new line at the end. For next we are using the epoch 43, cartesian would use 42. As version number the date is used. Thus the version (for next) would look something like: '43!YYYY.MM.DD'.
Force push your changes to this branch (gt4py-next-integration).
Create a tag with the pattern __gt4py-next-integration_YYYY_MM_DD and push it as well.
Make sure that the workflow has been triggered.

Afterwards you have to update GT4Py's pyproject.toml file.
For this you have to update the version requirement of DaCe in the dace-next group at the beginning of the file to the version you just created, i.e. change it to dace==43!YYYY.MM.DD.
Then you have to update the the source in the uv specific parts of the file, there you have to change the source to the new tag you have just created.
Then you have to update the uv look by running uv sync --extra next --group dace-next, if you have installed the precommit hooks then this will be done automatically.

NOTE: Once PR#2423 has been merged the second step, i.e. adapting the tag in the uv specific parts is no longer needed.

On top of DaCe/main we are using the following PRs:

No Longer Needed

Instead of pulling directly from the official DaCe repo, we now (for the time being) pull from [this PR](GridTools/dace#1). This became necessary as we have a lot of open PR in DaCe and need some custom fixes (that can in their current form not be merged into DaCe). In the long term however, we should switch back to the main DaCe repo.

…cl#2164) # Pull Request: Machine Learning Integration for DaCe ## Overview This PR adds comprehensive machine learning capabilities to DaCe through three tightly integrated components: 1. **Automatic Differentiation (AD)** - Reverse-mode gradient computation for SDFGs 2. **ONNX Integration** - Import and execute neural network models 3. **PyTorch Integration** - Bidirectional interoperability with PyTorch's autograd system Together, these components enable DaCe to optimize and accelerate machine learning workloads, particularly neural network training and inference. ## High-Level Architecture ``` PyTorch Model ↓ ONNX Export ↓ DaCe SDFG (Forward) ↓ Automatic Differentiation ↓ DaCe SDFG (Backward) ↓ Compiled Code Generation ↓ PyTorch Operator (with Autograd) ``` ## Component 1: Automatic Differentiation (`dace/autodiff/`) ### Purpose Provides **reverse-mode automatic differentiation** for SDFGs, enabling gradient computation for any DaCe program. This is the foundation for neural network training and gradient-based optimization. ### Key Capabilities - **Full SDFG Support**: Differentiates maps, tasklets, nested SDFGs, loops, and library nodes - **Control Flow**: Handles loops (LoopRegion) and conditionals - **ONNX Operations**: 50+ backward implementations for ONNX operators - **Data Forwarding**: Flexible strategies (store vs. recompute) for memory/compute tradeoffs - **Extensible Registry**: Plugin-based system for adding backward rules ### Core Algorithm 1. **Forward Pass Execution**: Run original computation and identify required intermediates 2. **Backward Pass Generation**: Traverse computation graph in reverse, accumulating gradients 3. **Node Reversal**: Each forward node (Map, Tasklet, ONNXOp) has a registered backward implementation 4. **Gradient Accumulation**: Use write-conflict resolution (WCR) for multi-path gradients ### Key Files | File | Lines | Purpose | |------|-------|---------| | `backward_pass_generator.py` | ~800 | Core AD engine that orchestrates backward pass generation | | `implementations/onnx_ops.py` | ~2000 | Backward implementations for 50+ ONNX operations | | `implementations/dace_nodes.py` | ~600 | Backward rules for core SDFG elements (Tasklet, Map, etc.) | | `data_forwarding/manager.py` | ~300 | Store vs. recompute strategy coordination | --- ## Component 2: ONNX Integration (`dace/libraries/onnx/`) ### Purpose Enables **importing and executing ONNX neural network models** within DaCe. Converts ONNX graphs to optimized DaCe SDFGs for efficient execution on CPU/GPU. ### Key Capabilities - **Model Import**: Load ONNX models from files or protobuf objects - **100+ Operations**: Dynamically generated node classes for all ONNX ops - **Shape Inference**: Automatic symbolic and concrete shape computation - **Multi-Strategy Implementations**: Pure (correctness), optimized (performance), hardware-specific - **Type Safety**: Schema-based validation and type checking ### Core Architecture **Dynamic Node Generation**: - Registry system generates Python classes for all ONNX operations at import time - Each operation has schema, properties, connectors, and implementations - Example: `ONNXConv`, `ONNXMatMul`, `ONNXSoftmax` (100+ generated classes) **Implementation Strategies**: 1. **Pure Implementations** (`pure_implementations.py`): Reference implementations in Python/NumPy 2. **Optimized Implementations** (`img_op_implementations.py`): Hand-crafted SDFGs for performance 3. **Hardware-Specific**: Future GPU/FPGA specialized implementations **Import Pipeline**: ``` ONNX Model → Validation → Shape Inference → Simplification → SDFG Construction → Compilation ``` ### Key Files | File | Lines | Purpose | |------|-------|---------| | `onnx_importer.py` | 711 | Main entry point, orchestrates import pipeline | | `op_implementations/pure_implementations.py` | 3052 | Reference implementations for 40+ operations | | `nodes/onnx_op_registry.py` | 325 | Dynamic node class generation | | `schema.py` | 390 | Type system and validation | | `shape_inference/symbolic_shape_infer.py` | 1976 | Symbolic shape inference (Microsoft-sourced) | --- ## Component 3: PyTorch Integration (`dace/libraries/torch/`) ### Purpose Provides **bidirectional integration** between PyTorch and DaCe. Enables optimizing PyTorch models with DaCe while maintaining PyTorch's autograd compatibility. ### Key Capabilities - **Model Optimization**: Convert `torch.nn.Module` to optimized DaCe SDFGs - **Autograd Integration**: Backward pass generation integrates with PyTorch's autograd - **Dual Dispatch**: C++ extension (performance) or CTypes (flexibility) - **Zero-Copy Tensors**: DLPack protocol for efficient memory sharing - **Training Support**: Full forward + backward pass compilation ### Core Architecture **Integration Flow**: ``` PyTorch Model → ONNX Export → DaCe SDFG → Backward Generation → Compilation → PyTorch Operator ``` **Dispatcher Strategies**: 1. **C++ Extension** (`cpp_torch_extension.py`): Native PyTorch operator with autograd - High performance - 64 parameter limit - Slower compilation 2. **CTypes Module** (`ctypes_module.py`): Pure Python dispatcher - Unlimited parameters - Faster compilation - Slight overhead **Zero-Copy Memory Sharing**: - DLPack protocol enables PyTorch tensors to view DaCe memory without copying - Bidirectional: DaCe → PyTorch (outputs) and PyTorch → DaCe (inputs) ### Key Files | File | Lines | Purpose | |------|-------|---------| | `dispatchers/cpp_torch_extension.py` | 717 | C++ code generation for PyTorch operators | | `dispatchers/ctypes_module.py` | 230 | CTypes-based dispatcher | | `dlpack.py` | 199 | Zero-copy tensor sharing via DLPack | | `environments/pytorch_env.py` | 94 | CMake build configuration | --- ## How Components Work Together ### Example: Training a PyTorch Model with DaCe ```python import torch from dace.frontend.python import DaceModule # 1. Define PyTorch model model = MyNeuralNetwork() optimizer = torch.optim.Adam(model.parameters()) # 2. Wrap with DaCe (compiles on first call) dace_model = DaceModule(model, dummy_inputs, backward=True) # 3. Training loop (standard PyTorch code) for inputs, labels in dataloader: optimizer.zero_grad() outputs = dace_model(inputs) # DaCe-optimized forward pass loss = criterion(outputs, labels) loss.backward() # DaCe-optimized backward pass optimizer.step() ``` **What Happens Internally**: 1. **First Call**: PyTorch model → ONNX export → DaCe SDFG (via ONNX integration) 2. **Backward Generation**: Forward SDFG → Backward SDFG (via autodiff) 3. **Compilation**: Both SDFGs compiled to optimized code 4. **Dispatcher**: C++ extension or CTypes wrapper created 5. **Forward Pass**: DaCe executes optimized forward computation 6. **Backward Pass**: DaCe executes generated backward computation 7. **Gradient Return**: Gradients flow back to PyTorch optimizer ### Data Flow ``` PyTorch Tensor (input) ↓ Zero-copy (DLPack) DaCe Array ↓ Optimized SDFG Execution DaCe Array (output) ↓ Zero-copy (DLPack) PyTorch Tensor (output) ↓ loss.backward() PyTorch Tensor (grad_output) ↓ Zero-copy (DLPack) DaCe Array (backward pass input) ↓ Backward SDFG Execution DaCe Array (grad_input) ↓ Zero-copy (DLPack) PyTorch Tensor (grad_input) ``` --- ## Testing Strategy ### Test Organization ``` tests/ ├── autodiff/ # AD correctness tests │ ├── test_single_state.py # Basic AD operations │ └── torch/ # PyTorch integration tests │ ├── test_training.py # End-to-end training │ ├── test_bert_encoder_backward.py # BERT model │ └── test_llama_decoder_backward.py # LLaMA model │ ├── onnx/ # ONNX import tests │ ├── test_python_frontend.py # Basic operations │ ├── test_bert_subgraphs.py # Real model subgraphs │ └── test_input_outputs.py # I/O handling │ └── torch/ # PyTorch integration tests │ ├── test_lenet.py # Simple CNN │ ├── test_bert_encoder.py # Transformer encoder │ └── test_llama_decoder.py # Decoder architecture │ └── npbench/ # AD tests on NPBench kernels ``` ### Test Coverage | Component | Test Files | Coverage | |-----------|-----------|----------| | Autodiff Core | 15+ files | Tasklets, maps, loops, nested SDFGs | | ONNX Integration | 20+ files | Import, execution, type handling | | PyTorch Integration | 15+ files | Forward, backward, training loops | ### Running Tests ```bash # All basic tests (excluding hardware-specific) pytest -m "(autodiff or torch or onnx) and not long" tests/ # AD tests only pytest tests/autodiff/ # ONNX tests only pytest tests/onnx/ # PyTorch tests only pytest tests/torch/ ``` --- ## Known Limitations and Future Work ### Current Limitations 1. **Recompute Strategy**: Experimental, not production-ready 2. **Control Flow**: Conditionals are inlined into state machine (not reversed as ControlFlowRegions) 3. **Second-Order Gradients**: Not yest tested --- ## Documentation Each component has detailed design documentation: - [`dace/autodiff/autodiff.md`](dace/autodiff/autodiff.md) - Complete AD system design - [`dace/libraries/onnx/onnx.md`](dace/libraries/onnx/onnx.md) - ONNX integration architecture - [`dace/libraries/torch/torch.md`](dace/libraries/torch/torch.md) - PyTorch integration details These documents provide: - Detailed component descriptions - Algorithm explanations - Code walkthrough - Extension points - Implementation notes --- ## Impact on DaCe ### Code Additions | Component | Lines of Code | Files | |-----------|--------------|-------| | Autodiff | ~8,000 | 15+ files | | ONNX | ~7,000 | 20+ files | | PyTorch | ~1,500 | 10+ files | | **Total** | **~16,500** | **45+ files** | ### Dependencies New dependencies (already in `setup.py`): - `onnx` - ONNX model format - `onnxsim` - ONNX graph simplification - `torch` - PyTorch framework (optional) - `protobuf` - Protocol buffers (for ONNX) - `jax` - For gradient numerical validation tests -`transformers` - For testing the Pytorch/ONNX frontends - `efficientnet_pytorch`- For testing EfficientNet --- --------- Co-authored-by: Oliver Rausch <oliverrausch99@gmail.com>

Modified the reloading scheme used by `ReloadableDLL`. If the library (of the compiled SDFG) is already loaded, through another instance of `CompiledSDFG` then `ReloadableDLL` will copy the SDFG library and try to load that until it founds a name that is free. In ICON4Py we noticed that this leads sometime to a segmentation fault on Linux, but not on MacOS X. We traced the main issue down to the fact that `ReloadableDLL` created a copy of the SDFG library without checking if the new name is already used, instead the file is simply overwritten. The new scheme changes this slightly, in the following ways: - If the new name is already taken, then no copy is performed and the class tries to use that file, that already exists. - Instead of copying library `n - 1` to `n` it always makes a copy from the initial library. --------- Co-authored-by: Philipp Schaad <schaad.phil@gmail.com>

Updated ignored paths and build notification settings.

Increased pytest timeout from 300 to 600 seconds.

## Refactor `dace/data.py` into `dace/data/` package ### Summary This PR refactors the monolithic `dace/data.py` file into a modular `dace/data/` package with separate files for different functionality, improving code organization and maintainability. ### Changes - [x] **`dace/data/core.py`**: Core data descriptor classes (`Data`, `Scalar`, `Array`, `ContainerArray`, `Stream`, `Structure`, `View`, `Reference` and their subclasses) - [x] **`dace/data/tensor.py`**: Tensor/sparse tensor support (`Tensor`, `TensorIndex*` classes) - [x] **`dace/data/creation.py`**: Data descriptor creation functions (`create_datadescriptor`, `make_array_from_descriptor`, `make_reference_from_descriptor`) - [x] **`dace/data/ctypes_interop.py`**: Ctypes interoperability (`make_ctypes_argument`) - [x] **`dace/data/ml.py`**: ML-related descriptors (`ParameterArray`) - [x] **`dace/data/__init__.py`**: Re-exports all public API for backward compatibility - [x] **`dace/utils.py`**: Utility functions (`find_new_name`, `deduplicate`, `prod`) - [x] **`dace/properties.py`**: Updated to handle circular import gracefully - [x] **`dace/autodiff/library/library.py`**: Updated to import `ParameterArray` from the new location - [x] **Deleted** old `dace/data.py` file - [x] **Removed** `Number` and `ArrayLike` from `dace/data/__init__.py` (other places import directly) - [x] **Moved** `_prod` to `dace/utils.py` as `prod` (kept `_prod` export for backward compat) - [x] **Fixed** broken imports in `data_report.py`, `data_layout_tuner.py`, and `cutout.py` ### Backward Compatibility All public APIs are re-exported from `dace.data`, ensuring backward compatibility with existing code.  <details> <summary>Original prompt</summary> > > ---- > > *This section details on the original issue you should resolve* > > <issue_title>Refactor `dace/data.py`</issue_title> > <issue_description>`data.py` is a monolithic file containing classes for core data containers (Data, Scalar, Array, Stream, View, Reference, and their subclasses `*{View, Reference}`; functionality to get data descriptors from arbitrary objects; derived objects for Tensors and sparse tensors; and other functions. > > This issue will be resolved once `data.py` is refactored to a `dace/data/*` folder, which will contain separate files for: > 1. core descriptor classes > 2. structures (the Structure class and similar functionality) > 3. tensors/sparse tensors > 4. descriptor creation > 5. ML-related data descriptors, such as parameter arrays (see `dace/autodiff/library/library.py`) > 6...N. Other functions and classes categorized by their semantic meaning. > > The code for `dace/data/*` will be refactored out of `data.py` (which should not exist at the end of this issue), `dtypes.py` (which may exist but be shorter), and other files that contain data descriptors (subclasses of Data/Array/Stream/Structure/View/Reference, such as ParameterArray. Try to find all such subclasses in the codebase barring tests/* and samples/*). > > Lastly, utility functions in `data.py` and `dtypes.py` (only those two files for this issue), such as `find_new_name` from data.py and `deduplicate` from dtypes.py, should find themselves in a new `dace/utils.py` file.</issue_description> > > ## Comments on the Issue (you are @copilot in this section) > > <comments> > </comments> > </details> - Fixes spcl#2244  --- 💡 You can make Copilot smarter by setting up custom instructions, customizing its development environment and configuring Model Context Protocol (MCP) servers. Learn more [Copilot coding agent tips](https://gh.io/copilot-coding-agent-tips) in the docs. --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: tbennun <8348955+tbennun@users.noreply.github.com>

…to seq. maps inside GPU kernels or gpu dev. maps (spcl#2088) GPU codegen crashes and generates incorrect code with dynamic inputs to seq. maps inside GPU kernels or gpu dev. maps --------- Co-authored-by: alexnick83 <31545860+alexnick83@users.noreply.github.com> Co-authored-by: Tal Ben-Nun <tbennun@users.noreply.github.com>

…pcl#2246) Updated the documentation for proposed pass decomposition, including changes to pass names and descriptions for clarity.

The current version of DaCe only supports CUDA 12 since CUDA 13 has a few breaking changes. This PR updates the runtime headers according to https://nvidia.github.io/cccl/cccl/3.0_migration_guide.html, replacing classes that moved from cub to thrust. As I have no way of testing on CUDA 12, I have conservatively left the old code intact using preprocessor macros. Tested on CUDA 13.1

…spcl#2251) The values retrieved from `Config` are strings. For block_size configuration, the values need to be converted to `int` type. Co-authored-by: Philipp Schaad <schaad.phil@gmail.com> Co-authored-by: Tal Ben-Nun <tbennun@users.noreply.github.com>

As part of bringing our schedule tree branch from `v1/maintenance` to `main` (see spcl#2262), I have fixed a couple of typos. I'd like to pull these out into a separate PR to keep the schedule tree PR as small as possible (which will still be large once done). Co-authored-by: Roman Cattaneo <1116746+romanc@users.noreply.github.com>

The DaCe FPGA backend will be supported out-of-tree. This PR extracts the capabilities to a separate repository: https://github.com/spcl/dace-fpga This PR also reduces circular imports and encourages more object-oriented design for code generation.

- Added NVTX/rocTX markers to SDFGs, states, scopes and copies in the CPU code to analyze and measure easily the DaCe runtime using NVIDIA/AMD profiling tools - Added related test and documentation --------- Co-authored-by: Tal Ben-Nun <tbennun@users.noreply.github.com> Co-authored-by: Philip Müller <147368808+philip-paul-mueller@users.noreply.github.com> Co-authored-by: Philip Mueller <philip.paul.mueller@bluemain.ch> Co-authored-by: Tal Ben-Nun <tbennun@gmail.com>

commit 7e05c76 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Wed Jan 7 10:08:42 2026 +0100 Rename of the Index. commit 32224bb Author: Philip Mueller <philip.mueller@cscs.ch> Date: Thu Dec 18 09:53:57 2025 +0100 Updated the workflow file. commit ecb2785 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Wed Dec 17 08:19:40 2025 +0100 Updated the dace updater workflow file. commit f3198ef Author: Philip Mueller <philip.mueller@cscs.ch> Date: Wed Dec 17 07:41:26 2025 +0100 Made the update point to the correct repo. commit 96f963a Merge: 8b7cce5 387f1e8 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Wed Dec 17 07:37:48 2025 +0100 Merge remote-tracking branch 'spcl/main' into automatic_gt4py_deployment commit 8b7cce5 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Mon Dec 1 09:18:22 2025 +0100 Restored the original workflow files. commit 362ab70 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Mon Dec 1 07:41:40 2025 +0100 Now it has run once, so let's make it less runnable. commit 81b8cfa Author: Philip Mueller <philip.mueller@cscs.ch> Date: Mon Dec 1 07:39:09 2025 +0100 Made it run always. commit 6d71466 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Mon Dec 1 07:38:11 2025 +0100 Small update. commit eb31e6c Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Nov 21 15:23:33 2025 +0100 Empty commit in the branch containing the workflow file. commit 2970a75 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Nov 21 15:21:09 2025 +0100 Next step. commit f5d3d9d Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Nov 21 15:17:56 2025 +0100 Let's disable everything. commit 211e415 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Nov 21 15:10:43 2025 +0100 Disabled the kickstarter. commit d012c26 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Nov 21 15:05:38 2025 +0100 Updated everything.

commit ef4aba7 Merge: 68ffa3b 387f1e8 Author: Philip Müller <147368808+philip-paul-mueller@users.noreply.github.com> Date: Fri Dec 19 06:27:48 2025 +0100 Merge branch 'main' into make_construct_args_public commit 68ffa3b Merge: c069546 d99ad29 Author: Philipp Schaad <schaad.phil@gmail.com> Date: Sun Nov 30 06:17:32 2025 -0600 Merge branch 'main' into make_construct_args_public commit c069546 Merge: 41902c3 408a481 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Tue Nov 4 11:22:14 2025 +0100 Merge remote-tracking branch 'spcl/main' into make_construct_args_public commit 41902c3 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 16:01:26 2025 +0100 Fixed a bug. commit 65725f9 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 15:15:03 2025 +0100 This should be enough for bug compatibility. commit daf90e9 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 12:58:25 2025 +0100 Updated the thing a bit more. commit 2ddabbd Merge: 4da0c4e b44aeb0 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 12:54:19 2025 +0100 Merge remote-tracking branch 'spcl/main' into make_construct_args_public commit 4da0c4e Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 12:53:48 2025 +0100 Made some additional check. commit 69960ce Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 12:00:30 2025 +0100 Forgot to do this. commit 6e1a9ff Merge: c1214fa 1bf2173 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 11:25:46 2025 +0100 Merge remote-tracking branch 'spcl/main' into make_construct_args_public commit c1214fa Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 09:50:41 2025 +0100 Updated the tests and made it clear that you can not return a scalar from an SDFG. commit 9397a23 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 09:40:29 2025 +0100 Implemented the proper handling of tuples of size one. commit e8d909e Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 09:30:48 2025 +0100 Removed that stupid sclar return value feature that CAN NOT WORK. However, I saw that it also, under the hood sometimes tests if the argument is a pyobject. Since that thing is a pointer it is possible and I implemented it for that. But it was again not implemented properly, since for the case when the return value is passed as a regular argument, it was not checking that, only for managed return values. commit ab110d2 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 09:24:45 2025 +0100 Updated the description. commit 899b2a0 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 09:24:32 2025 +0100 Fixed some old stuff. commit 7f17e13 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 09:08:49 2025 +0100 Fixed a bug, but in a way I do not like. commit c2c1116 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 08:40:47 2025 +0100 Removed a missleading comment. commit ded5df8 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 08:04:38 2025 +0100 Made some refactoring to remove some strange DaCe behaviour. commit b029828 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 08:02:28 2025 +0100 Fixed an issue in safe_call commit b09c9fc Author: Philip Mueller <philip.mueller@cscs.ch> Date: Fri Oct 31 07:17:36 2025 +0100 Included the first bunch of Tal's changes. commit e138b06 Author: Philip Mueller <philip.mueller@cscs.ch> Date: Thu Oct 30 15:12:23 2025 +0100 Made the 'passed as positional and named argument'-error more explicit. commit f901a3d Author: Philip Mueller <philip.mueller@cscs.ch> Date: Thu Oct 30 15:05:00 2025 +0100 Fixed a bug in a unit test. Due to the refactoring the case that a variable is passed once as positional and as named argument is not detected and asserted. This test however, passed `a` always as positional argument and if `symbolic` is `True` also as named argument. commit 767260d Author: Philip Mueller <philip.mueller@cscs.ch> Date: Thu Oct 30 14:19:44 2025 +0100 Clarified a comment. commit 2b8123a Author: Philip Mueller <philip.mueller@cscs.ch> Date: Thu Oct 30 13:56:20 2025 +0100 Made the construct argumt vector function publich and also refactored some things.

commit 5b068e7 Author: Affifboudaoud <hk_boudaoud@esi.dz> Date: Sun Nov 23 22:50:46 2025 +0100 Add visited set to avoid visiting same node multiple times

commit 71cc2ae Merge: c0f4a87 b88b82b Author: Ioannis Magkanaris <ioannis.magkanaris@cscs.ch> Date: Mon Jan 12 15:02:19 2026 +0100 Merge remote-tracking branch 'origin/main' into fix_gpu_reduce_templates commit c0f4a87 Author: Ioannis Magkanaris <ioannis.magkanaris@cscs.ch> Date: Fri Jan 9 15:35:53 2026 +0200 Add template keyword as well commit f52f72e Author: Ioannis Magkanaris <ioannis.magkanaris@cscs.ch> Date: Fri Jan 9 15:14:43 2026 +0200 Fix reduction templates

commit 5b8e401 Author: Ioannis Magkanaris <ioannis.magkanaris@cscs.ch> Date: Mon Jan 12 15:41:28 2026 +0100 Added test commit d71a8e8 Author: Ioannis Magkanaris <iomagkanaris@gmail.com> Date: Mon Dec 8 17:49:17 2025 +0100 Enable loop unrolling of LoopRegions

commit 585e86d Author: Edoardo Paone <edoardo.paone@cscs.ch> Date: Mon Jan 12 18:33:28 2026 +0100 hip mempool

philip-paul-mueller marked this pull request as draft April 30, 2025 10:04

This was referenced Apr 30, 2025

Do Not Merge: Integration Branch for GT4Py philip-paul-mueller/dace#4

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build[dace][next]: Changed DaCe Source GridTools/gt4py#2012

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Do Not Merge: Changes to DaCe CUDA compilation flags #2

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build[dace][next]: Updated DaCe GridTools/gt4py#2064

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philip-paul-mueller mentioned this pull request Jul 11, 2025

feat[cartesian]: DaCe bridge refactor: OIR -> TreeIR -> ScheduleTree -> SDFG GridTools/gt4py#2067

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edopao force-pushed the gt4py-next-integration branch from 178037a to 9114985 Compare July 14, 2025 08:42

philip-paul-mueller changed the title ~~Do Not Merge: Integration Branch for GT4Py~~ Do Not Merge: Integration Branch for GT4Py Next Jul 15, 2025

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philip-paul-mueller mentioned this pull request Dec 1, 2025

DO NOT MERGE: Automatic Deployment #12

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affifboudaoud and others added 9 commits December 2, 2025 09:38

Update .coveragerc

076fd31

Modify codecov.yml to change ignored files and builds

e19e785

Updated ignored paths and build notification settings.

Increase timeout for ML tests (spcl#2243)

0efa622

Increased pytest timeout from 300 to 600 seconds.

Support dace.map syntax for struct fields (spcl#2187)

cc59d77

Modular Code Generation Docs: Add LowerConsume and remove numbering (s…

312f37f

…pcl#2246) Updated the documentation for proposed pass decomposition, including changes to pass names and descriptions for clarity.

edopao force-pushed the gt4py-next-integration branch from 3eec6f6 to ab9eaef Compare December 11, 2025 13:27

Update C++ standard to C++20 (spcl#2253)

387f1e8

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zero9178 and others added 5 commits December 22, 2025 10:13

edopao force-pushed the gt4py-next-integration branch from cd52c4b to 0362ff3 Compare January 12, 2026 14:46

edopao added 7 commits January 12, 2026 23:29

Squashed commit of the following:

28b0070

commit 5b068e7 Author: Affifboudaoud <hk_boudaoud@esi.dz> Date: Sun Nov 23 22:50:46 2025 +0100 Add visited set to avoid visiting same node multiple times

Squashed commit of the following:

d177bce

commit 585e86d Author: Edoardo Paone <edoardo.paone@cscs.ch> Date: Mon Jan 12 18:33:28 2026 +0100 hip mempool

Set version file

54b3349

edopao force-pushed the gt4py-next-integration branch from 0362ff3 to 54b3349 Compare January 12, 2026 22:40

Relocated the version file.

513e824

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Do Not Merge: Integration Branch for GT4Py Next #1

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philip-paul-mueller commented Apr 30, 2025 •

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