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v0.16.1

20 Jun 18:02
@BenWeber42 BenWeber42
v0.16.1
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v0.16.1 Latest
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What's Changed

The main purpose of this release is to require NumPy < 2 for DaCe, since NumPy 2.0.0 contains breaking changes which aren't compatible with DaCe currently.

Recently, NumPy 2.0.0 has been released: https://numpy.org/news/#numpy-200-released

The release comes with documented breaking changes. Unfortunately, DaCe is currently not compatible with these changes. This also affects the recent 0.16 release of DaCe. Hence, we adjust our dependency requirements to use NumPy < 2 as a temporary work-around in this PR:

Fix numpy version to < 2.0 by @phschaad in #1601

Long term, we are tracking adding support for NumPy 2 in DaCe in this issue: #1602

Fix constant propagation failing due to invalid topological sort by @phschaad in #1589

This changeset has also landed in DaCe's development branch earlier. It fixes an issue where the ConstantPropagation pass can fail for certain graph structures.

Full Changelog: v0.16...v0.16.1

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phschaad
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v0.16

13 Jun 20:26
@BenWeber42 BenWeber42
v0.16
d6f481a
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v0.16

What's Changed

CI/CD pipeline for NOAA & NASA weather and climate model by @FlorianDeconinck & @BenWeber42 in #1460, #1478 & #1575

Our collaborators NOAA & NASA have successfully used DaCe as an optimization framework and back-end for some of the components of their climate and weather model. Particularly, the FV3 dycore and GFS physics parametrization have been ported to a combination of GT4Py Python DSL and DaCe. DaCe is used within their stack as a stencil backend and as a full-program optimizer integrating stencils and glue-code together.

With this CI/CD pipeline, we run various checks for those components on every change to DaCe. This is an important step for DaCe to ensure stability for real-world applications that utilize DaCe. We are very grateful for this contribution and the collaboration with NOAA & NASA.

Changed default of serialize_all_fields to False by @BenWeber42 in #1564

This feature was already implemented in the previous 0.15.1 release in #1452, but not enabled by default. In this release, we are changing the default so that only fields with non-default values are serialized. This generally leads to a reduction in file size for SDFGs.

Since each DaCe version stores the default values of each field, it is still possible to recover these missing values. Default values should rarely change across different DaCe versions. Nevertheless, we want to caution users & developers when using SDFG files with different DaCe versions.

Analysis passes for access range analysis by @tbennun in #1484

Adds two analysis passes to help with analyzing data access sets: access ranges and Reference sources. To enable constructing sets of memlets, this PR also reintroduces data descriptor names to memlet hashes.

Reference-to-View pass and comprehensive reference test suite by @tbennun in #1485

Implements a reference-to-view pass (converting references to views if they are only set to one particular subset). Also improves the simplify pipeline in the presence of Reference data descriptors and adds multiple tests that use references.

Ndarray strides by @alexnick83 in #1506

The PR adds support for custom strides to dace.ndarray. Furthermore, the stride unit is number of elements, in contrast to NumPy/CuPy, where it is number of bytes. Custom strides are not supported for numpy.ndarray and cupy.ndarray.

Structure Support to NestedSDFGs and Python Frontend by @alexnick83 in #1366

Adds basic support for nested data (Structures) to the Python frontend. It also resolves issues with the use of Structures in nested SDFG scopes (mostly code generation).

Generalize StructArrays to ContainerArrays and refactor View class structure by @tbennun in #1504

This PR enables the use of an array data descriptor that contains a nested data descriptor (e.g., ContainerArray of Arrays). Its contents can then be viewed normally with View or StructureView.
With this, concepts such as jagged arrays are natively supported in DaCe (see test for example).
Also adds support for using ctypes pointers and arrays as arguments to SDFGs.

This PR also refactors the notion of views to a View interface, and provides views to arrays, structures, and container arrays. It also adds a syntactic-sugar/helper API to define a view of an existing data descriptor.

Add support for distributed compilation in DaceProgram by @kotsaloscv in #1551 & #1555

Adds configurable support for distributed compilation (MPI) to the Python front-end (via mpi4py). Distributed compilation can be enabled with the distributed_compilation parameter in the dace.program decorator.

Fixes and other improvements:

New Contributors

Full Changelog: v0.15.1...v0.16

Contributors

tim0s, FlorianDeconinck, and 14 other contributors
Assets 2
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v0.15.1

07 Dec 18:20
@BenWeber42 BenWeber42
v0.15.1
7056675
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v0.15.1

What's Changed

Highlights

Fixes and other improvements:

New Contributors

Full Changelog: v0.15...v0.15.1rc1

Contributors

mcopik, tim0s, and 14 other contributors
Assets 2
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v0.15

16 Oct 17:32
@tbennun tbennun
v0.15
0755385
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v0.15

What's Changed

Work-Depth / Average Parallelism Analysis by @hodelcl in #1363 and #1327

A new analysis engine allows SDFGs to be statically analyzed for work and depth / average parallelism. The analysis allows specifying a series of assumptions about symbolic program parameters that can help simplify and improve the analysis results. For an example on how to use the analysis, see the following example:

from dace.sdfg.work_depth_analysis import work_depth

# A dictionary mapping each SDFG element to a tuple (work, depth)
work_depth_map = {}
# Assumptions about symbolic parameters
assumptions = ['N>5', 'M<200', 'K>N']
work_depth.analyze_sdfg(mysdfg, work_depth_map, work_depth.get_tasklet_work_depth, assumptions)

# A dictionary mapping each SDFG element to its average parallelism
average_parallelism_map = {}
work_depth.analyze_sdfg(mysdfg, average_parallelism_map, work_depth.get_tasklet_avg_par, assumptions)

Symbol parameter reduction in generated code (#1338, #1344)

To improve our integration with external codes, we limit the symbolic parameters generated by DaCe to only the used symbols. Take the following code for example:

@dace
def addone(a: dace.float64[N]):
  for i in dace.map[0:10]:
    a[i] += 1

Since the internal code does not actually need N to process the array, it will not appear in the generated code. Before this release the signature of the generated code would be:

DACE_EXPORTED void __program_addone(addone_t *__state, double * __restrict__ a, int N);

After this release it is:

DACE_EXPORTED void __program_addone(addone_t *__state, double * __restrict__ a);

Note that this is a major, breaking change that requires users who manually interact with the generated .so files to adapt to.

Externally-allocated memory (workspace) support (#1294)

A new allocation lifetime, dace.AllocationLifetime.External, has been introduced into DaCe. Now you can use your DaCe code with external memory allocators (such as PyTorch) and ask DaCe for: (a) how much transient memory it will need; and (b) to use a specific pre-allocated pointer. Example:

@dace
def some_workspace(a: dace.float64[N]):
  workspace = dace.ndarray([N], dace.float64, lifetime=dace.AllocationLifetime.External)
  workspace[:] = a
  workspace += 1
  a[:] = workspace

csdfg = some_workspace.to_sdfg().compile()

sizes = csdfg.get_workspace_sizes()  # Returns {dace.StorageType.CPU_Heap: N*8}
wsp = # ...Allocate externally...
csdfg.set_workspace(dace.StorageType.CPU_Heap, wsp)

The same interface is available in the generated code:

size_t __dace_get_external_memory_size_CPU_Heap(programname_t *__state, int N);
void __dace_set_external_memory_CPU_Heap(programname_t *__state, char *ptr, int N);
// or GPU_Global...

Schedule Trees (EXPERIMENTAL, #1145)

An experimental feature that allows you to analyze your SDFGs in a schedule-oriented format. It takes in SDFGs (even after applying transformations) and outputs a tree of elements that can be printed out in a Python-like syntax. For example:

@dace.program
def matmul(A: dace.float32[10, 10], B: dace.float32[10, 10], C: dace.float32[10, 10]):
  for i in range(10):
   for j in dace.map[0:10]:
     atile = dace.define_local([10], dace.float32)
     atile[:] = A[i]
     for k in range(10):
       with dace.tasklet:
         # ...
sdfg = matmul.to_sdfg()

from dace.sdfg.analysis.schedule_tree.sdfg_to_tree import as_schedule_tree
stree = as_schedule_tree(sdfg)
print(stree.as_string())

will print:

for i = 0; (i < 10); i = i + 1:
  map j in [0:10]:
    atile = copy A[i, 0:10]
    for k = 0; (k < 10); k = (k + 1):
      C[i, j] = tasklet(atile[k], B(10) [k, j], C[i, j])

There are some new transformation classes and passes in dace.sdfg.analysis.schedule_tree.passes, for example, to remove empty control flow scopes:

class RemoveEmptyScopes(tn.ScheduleNodeTransformer):
  def visit_scope(self, node: tn.ScheduleTreeScope):
    if len(node.children) == 0:
      return None
    return self.generic_visit(node)

We hope you find new ways to analyze and optimize DaCe programs with this feature!

Other Major Changes

Minor Changes

Fixes and Smaller Changes:

  • Fix transient bug in test with array_equal of empty arrays by @tbennun in #1374
  • Fixes GPUTransform bug when data are already in GPU memory by @alexnick83 in #1291
  • Fixed erroneous parsing of data slices when the data are defined inside a nested scope by @alexnick83 in #1287
  • Disable OpenMP sections by default by @tbennun in #1282
  • Make SDFG.name a proper property by @phschaad in #1289
  • Refactor and fix performance regression with GPU runtime checks by @tbennun in #1292
  • Fixed RW dependency violation when accessing data attributes by @alexnick83 in #1296
  • Externally-managed memory lifetime by @tbennun in #1294
  • External interaction fixes by @tbennun in #1301
  • Improvements to RefineNestedAccess by @alexnick83 and @Sajohn-CH in #1310
  • Fixed erroneous parsing of while-loop conditions by @alexnick83 in #1313
  • Improvements to MapFusion when the Map bodies contain NestedSDFGs by @alexnick83 in #1312
  • Fixed erroneous code generation of indirected accesses by @alexnick83 in #1302
  • RefineNestedAccess take indices into account when checking for missing free symbols by @Sajohn-CH in #1317
  • Fixed SubgraphFusion erroneously removing/merging intermediate data nodes by @alexnick83 in #1307
  • Fixed SDFG DFS traversal missing InterstateEdges by @alexnick83 in #1320
  • Frontend now uses the AST nodes' context to infer read/write accesses by @alexnick83 in #1297
  • Added capability for non-strict shape validation by @alexnick83 in #1321
  • Fixes for persistent schedule and GPUPersistentFusion transformation by @tbennun in #1322
  • Relax test for inter-state edges in default schedules by @tbennun in #1326
  • Improvements to inference of an SDFGState's read and write sets by @Sajohn-CH in #1325 and #1329
  • Fixed ArrayElimination pass trying to eliminate data that were already removed in #1314
  • Bump certifi from 2023.5.7 to 2023.7.22 by @dependabot in #1332
  • Fix some underlying issues with tensor core sample by @computablee in #1336
  • Updated hlslib to support Xilinx Vitis >=2022.2 by @carljohnsen in #1340
  • Docs: mention FPGA backend tested with Intel Quartus PRO by @TizianoDeMatteis in #1335
  • Improved validation of NestedSDFG connectors by @alexnick83 in #1333
  • Remove unused global data descriptor shapes from arguments by @tbennun in #1338
  • Fixed Scalar data validation in NestedSDFGs by @alexnick83 in #1341
  • Fix for None set properties by @tbennun in #1345
  • Add Object to defined types in code generation and some documentation by @tbennun in #1343
  • Fix symbolic parsing for ternary operators by @tbennun in #1346
  • Fortran fix memlet indices by @Sajohn-CH in #1342
  • Have memory type as argument for fpga auto interleave by @TizianoDeMatteis in #1352
  • Eliminate extraneous branch-end gotos in code generation by @tbennun in #1355
  • TaskletFusion: Fix additional edges in case of none-connectors by @lukastruemper in #1360
  • Fix dynamic memlet propagation condition by @tbennun in #1364
  • Configurable GPU thread/block index types, minor fixes to integer code generation and GPU runtimes by @tbennun in #1357

New Contributors

Full Changelog: v0.14.4...v0.15

Contributors

mcopik, carljohnsen, and 11 other contributors
Assets 2
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DaCe 0.14.4

12 Jun 07:24
@tbennun tbennun
v0.14.4
b7a8b5f
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DaCe 0.14.4

Minor release; adds support for Python 3.11.

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DaCe 0.14.3

08 Jun 18:52
@phschaad phschaad
v0.14.3
37b58bb
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DaCe 0.14.3

What's Changed

Scope Schedules

The schedule type of a scope (e.g., a Map) is now also determined by the surrounding storage. If the surrounding storage is ambiguous, dace will fail with a nice exception. This means that codes such as the one below:

@dace.program
def add(a: dace.float32[10, 10] @ dace.StorageType.GPU_Global, 
        b: dace.float32[10, 10] @ dace.StorageType.GPU_Global):
    return a + b @ b

will now automatically run the + and @ operators on the GPU.

(#1262 by @tbennun)

DaCe Profiler

Easier interface for profiling applications: dace.profile and dace.instrument can now be used within Python with a simple API:

with dace.profile(repetitions=100) as profiler:
    some_program(...)
    # ...
    other_program(...)

# Print all execution times of the last called program (other_program)
print(profiler.times[-1])

Where instrumentation is applied can be controlled with filters in the form of strings and wildcards, or with a function:

with dace.instrument(dace.InstrumentationType.GPU_Events, 
                     filter='*add??') as profiler:
    some_program(...)
    # ...
    other_program(...)

# Print instrumentation report for last call
print(profiler.reports[-1])

With dace.builtin_hooks.instrument_data, the same technique can be applied to instrument data containers.

(#1197 by @tbennun)

Improved Data Instrumentation

Data container instrumentation can further now be used conditionally, allowing saving and restoring of data container contents only if certain conditions are met. In addition to this, data instrumentation now saves the SDFG's symbol values at the time of dumping data, allowing an entire SDFG's state / context to be restored from data reports.

(#1202, #1208 by @phschaad)

Restricted SSA for Scalars and Symbols

Two new passes (ScalarFission and StrictSymbolSSA) allow fissioning of scalar data containers (or arrays of size 1) and symbols into separate containers and symbols respectively, based on the scope or reach of writes to them. This is a form of restricted SSA, which performs SSA wherever possible without introducing Phi-nodes. This change is made possible by a set of new analysis passes that provide the scope or reach of each write to scalars or symbols.

(#1198, #1214 by @phschaad)

Extending Cutout Capabilities

SDFG Cutouts can now be taken from more than one state.

Additionally, taking cutouts that only access a subset of a data containre (e.g., A[2:5] from a data container A of size N) results in the cutout receiving an "Alibi Node" to represent only that subset of the data (A_cutout[0:3] -> A[2:5], where A_cutout is of size 4). This allows cutouts to be significantly smaller and have a smaller memory footprint, simplifying debugging and localized optimization.

Finally, cutouts now contain an exact description of their input and output configuration. The input configuration is anything that may influence a cutout's behavior and may contain data before the cutout is executed in the context of the original SDFG. Similarly, the output configuration is anything that a cutout writes to, that may be read externally or may influence the behavior of the remaining SDFG. This allows isolating all side effects of changes to a particular cutout, allowing transformations to be tested and verified in isolation and simplifying debugging.

(#1201 by @phschaad)

Bug Fixes, Compatability Improvements, and Other Changes

Full Changelog: v0.14.2...v0.14.3

Please let us know if there are any regressions with this new release.

Contributors

tbennun, phschaad, and 4 other contributors
Assets 2
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DaCe 0.14.2

22 Feb 15:58
@phschaad phschaad
v0.14.2
da95a40
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DaCe 0.14.2

What's Changed

Full Changelog: v0.14.1...v0.14.2

Contributors

carljohnsen, TizianoDeMatteis, and 7 other contributors
Assets 2
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DaCe 0.14.1

14 Oct 17:08
@tbennun tbennun
v0.14.1
1580f0f
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DaCe 0.14.1

This release of DaCe offers mostly stability fixes for the Python frontend, transformations, and callbacks.

Full Changelog: v0.14...v0.14.1

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DaCe 0.14

26 Aug 00:48
@tbennun tbennun
v0.14
5636931
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DaCe 0.14

What's Changed

This release brings forth a major change to how SDFGs are simplified in DaCe, using the Simplify pass pipeline. This both improves the performance of DaCe's transformations and introduces new types of simplification, such as dead dataflow elimination.

Please let us know if there are any regressions with this new release.

Features

  • Breaking change: The experimental dace.constant type hint has now achieved stable status and was renamed to dace.compiletime
  • Major change: Only modified configuration entries are now stored in ~/.dace.conf. The SDFG build folders still include the full configuration file. Old .dace.conf files are detected and migrated automatically.
  • Detailed, multi-platform performance counters are now available via native LIKWID instrumentation (by @lukastruemper in #1063). To use, set .instrument to dace.InstrumentationType.LIKWID_Counters
  • GPU Memory Pools are now supported through CUDA's mallocAsync API. To enable, set desc.pool = True on any GPU data descriptor.
  • Map schedule and array storage types can now be annotated directly in Python code (by @orausch in #1088). For example:
import dace
from dace.dtypes import StorageType, ScheduleType

N = dace.symbol('N')

@dace
def add_on_gpu(a: dace.float64[N] @ StorageType.GPU_Global,
               b: dace.float64[N] @ StorageType.GPU_Global):
  # This map will become a GPU kernel
  for i in dace.map[0:N] @ ScheduleType.GPU_Device:
    b[i] = a[i] + 1.0
  • Customizing GPU block dimension and OpenMP threading properties per map is now supported
  • Optional arrays (i.e., arrays that can be None) can now be annotated in the code. The simplification pipeline also infers non-optional arrays from their use and can optimize code by eliminating branches. For example:
@dace
def optional(maybe: Optional[dace.float64[20]], always: dace.float64[20]):
  always += 1  # "always" is always used, so it will not be optional
  if maybe is None:  # This condition will stay in the code
    return 1
  if always is None:  # This condition will be eliminated in simplify
    return 2
  return 3

Minor changes

  • Miscellaneous fixes to transformations and passes
  • Fixes for string literal ("string") use in the Python frontend
  • einsum is now a library node
  • If CMake is already installed, it is now detected and will not be installed through pip
  • Add kernel detection flag by @TizianoDeMatteis in #1061
  • Better support for __array_interface__ objects by @gronerl in #1071
  • Replacements look up base classes by @tbennun in #1080

Full Changelog: v0.13.3...v0.14

Contributors

TizianoDeMatteis, tbennun, and 3 other contributors
Assets 2
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DaCe 0.13.3

30 Jun 22:17
@tbennun tbennun
v0.13.3
6e89314
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DaCe 0.13.3

What's Changed

  • Better integration with Visual Studio Code: Calling sdfg.view() inside a VSCode console or debug session will open the file directly in the editor!
  • Code generator for the Snitch RISC-V architecture (by @Noah95 and @AM-Ivanov)
  • Minor hotfixes to Python frontend, transformations, and code generation (with @orausch)

Full Changelog: v0.13.2...v0.13.3

Contributors

huettern, and-ivanov, and orausch
Assets 2
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