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@njroussel njroussel released this 21 Nov 09:24
· 34 commits to master since this release
v1.0.0
7ad3bc1

The 1.0 release of Dr.Jit marks a major new phase of this project. We addressed long-standing limitations and thoroughly documented every part of Dr.Jit. Due to the magnitude of the changes, some incompatibilities are unavoidable: bullet points with an exclamation mark highlight changes with an impact on source-level compatibility.

Here is what's new:

  • Python bindings: Dr.Jit comes with an all-new set of Python bindings created using the nanobind library. This has several consequences:

    • Tracing Dr.Jit code written in Python is now significantly faster (we've observed speedups by a factor of ~10-20×). This should help in situations where performance is limited by tracing rather than kernel evaluation.

    • Thorough type annotations improve static type checking and code completion in editors like VS Code.

    • Dr.Jit can now target Python 3.12's stable ABI. This means that binary wheels will work on future versions of Python without recompilation.

  • Natural syntax: vectorized loops and conditionals can now be expressed using natural Python syntax. To see what this means, consider the following function that computes an integer power of a floating point array:

    from drjit.cuda import Int, Float

@dr.syntax # <-- new!
def ipow(x: Float, n: Int):
    result = Float(1)

    while n != 0:       # <-- vectorized loop ('n' is an array)
        if n & 1 != 0:  # <-- vectorized conditional
            result *= x
        x *= x
        n >>= 1

    return result

    Given that this function processes arrays, we expect that condition of the if statement may disagree among elements. Also, each element may need a different number of loop iterations. However, such component-wise conditionals and loops aren't supported by normal Python. Previously, Dr.Jit provided ways of expressing such code using masking and a special dr.cuda.Loop object, but this was rather tedious.

    The new :py:func:@drjit.syntax <drjit.syntax> decorator greatly simplifies the development of programs with complex control flow. It performs an automatic source code transformation that replaces conditionals and loops with array-compatible variants (:py:func:drjit.while_loop, :py:func:drjit.if_stmt). The transformation leaves everything else as-is, including line number information that is relevant for debugging.

  • Differentiable control flow: symbolic control flow constructs (loops) previously failed with an error message when they detected differentiable variables. In the new version of Dr.Jit, symbolic operations (loops, function calls, and conditionals) are now differentiable in both forward and reverse modes, with one exception: the reverse-mode derivative of loops is still incomplete and will be added in the next version of Dr.Jit.

  • Documentation: every Dr.Jit function now comes with extensive reference documentation that clearly specifies its behavior and accepted inputs. The behavior with respect to tensors and arbitrary object graphs (referred to as :ref:"PyTrees" <pytrees>) was made consistent.

  • Half-precision arithmetic: Dr.Jit now provides float16-valued arrays and tensors on both the LLVM and CUDA backends (e.g., drjit.cuda.ad.TensorXf16 or drjit.llvm.Float16).

  • Mixed-precision optimization: Dr.Jit now maintains one global AD graph for all variables, enabling differentiation of computation combining single-, double, and half precision variables. Previously, there was a separate graph per type, and gradients did not propagate through casts between them.

  • Multi-framework computations: The @drjit.wrap decorator provides a differentiable bridge to other AD frameworks. In this new release of Dr.Jit, its capabilities were significantly revamped. Besides PyTorch, it now also supports JAX, and it consistently handles both forward and backward derivatives. The new interface admits functions with arbitrary fixed/variable-length positional and keyword arguments containing arbitrary PyTrees of differentiable and non-differentiable arrays, tensors, etc.

  • Debug mode: A new debug validation mode (drjit.JitFlag.Debug) inserts a number of additional checks to identify sources of undefined behavior. Enable it to catch out-of-bounds reads, writes, and calls to undefined callables. Such operations will trigger a warning that includes the responsible source code location.

    The following built-in assertion checks are also active in debug mode. They support both regular and symbolic inputs in a consistent fashion.

    • drjit.assert_true
    • drjit.assert_false
    • drjit.assert_equal

  • Symbolic print statement: A new high-level symbolic print operation drjit.print enables deferred printing from any symbolic context (i.e., within symbolic loops, conditionals, and function calls). It is compatible with Jupyter notebooks and displays arbitrary PyTrees in a structured manner. This operation replaces the function drjit.print_async() provided in previous releases.

  • Swizzling: swizzle access and assignment operator are now provided. You can use them to arbitrarily reorder, grow, or shrink the input array.

    a = Array4f(...), b = Array2f(...)
a.xyw = a.xzy + b.xyx

  • Scatter-reductions: the performance of atomic scatter-reductions (drjit.scatter_reduce, drjit.scatter_add) has been significantly improved. Both functions now provide a mode= parameter to select between different implementation strategies. The new strategy drjit.ReduceMode.Expand offers a speedup of over 10× on the LLVM backend compared to the previously used local reduction strategy. Furthermore, improved code generation for drjit.ReduceMode.Local brings a roughly 20-40% speedup on the CUDA backend. See the documentation section on atomic reductions for details and benchmarks with plots.

  • Packet memory operations: programs often gather or scatter several memory locations that are directly next to each other in memory. In principle, it should be possible to do such reads or writes more efficiently.

    Dr.Jit now features improved code generation to realize this optimization for calls to dr.gather() and dr.scatter() that access a power-of-two-sized chunk of contiguous array elements. On the CUDA backend, this operation leverages native package memory instruction, which can produce small speedups on the order of ~5-30%. On the LLVM backend, packet loads/stores now compile to aligned packet loads/stores with a transpose operation that brings data into the right shape. Speedups here are dramatic (up to >20× for scatters, 1.5 to 2× for gathers). See the drjit.JitFlag.PacketOps flag for details. On the LLVM backend, packet scatter-addition furthermore compose with the drjit.ReduceMode.Expand optimization explained in the last point, which combines the benefits of both steps. This is particularly useful when computing the reverse-mode derivative of packet reads.

  • Reductions: reduction operations previously existed as regular (e.g., drjit.all) and nested (e.g. drjit.all_nested) variants. Both are now subsumed by an optional axis argument similar to how this works in other array programming frameworks like NumPy. Reductions can now also process any number of axes on both regular Dr.Jit arrays and tensors.

    The reduction functions (drjit.all, drjit.any, drjit.sum, drjit.prod, drjit.min, drjit.max) have different default axis values depending on the input type. For tensors, axis=None by default and the reduction is performed along the entire underlying array recursively, analogous to the previous nested reduction. For all other types, the reduction is performed over the outermost axis (axis=0) by default.

    Aliases for the _nested function variants still exist to help porting but are deprecated and will be removed in a future release.

  • Prefix reductions: the functions drjit.cumsum, drjit.prefix_sum compute inclusive or exclusive prefix sums along arbitrary axes of a tensor or array. They wrap for the more general drjit.prefix_reduce that also supports other arithmetic operations (e.g. minimum/maximum/product/and/or reductions), reverse reductions, etc.

  • Block reductions: the new functions drjit.block_reduce and drjit.block_prefix_reduce compute reductions within contiguous blocks of an array.

  • Local memory: kernels can now allocate temporary thread-local memory and perform arbitrary indexed reads and writes. This is useful to implement a stack or other types of scratch space that might be needed by a calculation. See the separate documentation section about local memory for details.

  • DDA: a newly added digital differential analyzer (drjit.dda.dda) can be used to traverse the intersection of a ray segment and an n-dimensional grid. The function drjit.dda.integrate() builds on this functionality to compute analytic differentiable line integrals of bi- and trilinear interpolants.

  • Loop compression: the implementation of evaluated loops (previously referred to as wavefront mode) visits all entries of the loop state variables at every iteration, even when most of them have already finished executing the loop. Dr.Jit now provides an optional compress=True parameter in drjit.while_loop to prune away inactive entries and accelerate later loop iterations.

  • The new release has a strong focus on error resilience and leak avoidance. Exceptions raised in custom operations, function dispatch, symbolic loops, etc., should not cause failures or leaks. Both Dr.Jit and nanobind are very noisy if they detect that objects are still alive when the Python interpreter shuts down.

  • Terminology cleanup: Dr.Jit has two main ways of capturing control flow (conditionals, loops, function calls): it can evaluate each possible outcome eagerly, causing it to launch many small kernels (this is now called: evaluated mode). The second is to capture control flow and merge it into the same kernel (this is now called symbolic mode). Previously, inconsistent and rendering-specific terminology was used to refer to these two concepts.

    Several entries of the drjit.JitFlag enumeration were renamed to reflect this fact (for example, drjit.JitFlag.VCallRecord is now called drjit.JitFlag.SymbolicCalls). The former entries still exist as (deprecated) aliases.

  • Index reuse: variable indices (drjit.ArrayBase.index, drjit.ArrayBase.index_ad) used to monotonically increase as variables were being created. Internally, multiple hash tables were needed to associate these ever-growing indices with locations in an internal variable array, which which had a surprisingly large impact on tracing performance. Dr.Jit removes this mapping both at the AD and JIT levels and eagerly reuses variable indices.

    This change can be inconvenient for low-level debugging, where it was often helpful to inspect the history of operations involving a particular variable by searching a trace dump for mentions of its variable index. Such trace dumps were generated by setting drjit.set_log_level to a level of drjit.LogLevel.Debug or even drjit.LogLevel.Trace. A new flag was introduced to completely disable variable reuse and help such debugging workflows:

    dr.set_flag(dr.JitFlag.ReuseIndices, False)

    Note that this causes the internal variable array to steadily grow, hence this feature should only be used for brief debugging sessions.

  • The drjit.empty function used to immediate allocate an array of the desired shape (compared to, say, drjit.zero which creates a literal constant array that consumes no device memory). Users found this surprising, so the behavior was changed so that drjit.empty similarly delays allocation.

  • Fast math: Dr.Jit now has an optimization flag named drjit.JitFlag.FastMath that is reminiscent of -ffast-math in C/C++ compilers. It enables program simplifications such as a*0 == 0 that are not always valid. For example, equality in this example breaks when a is infinite or equal to NaN. The flag is on by default since it can considerably improve performance especially when targeting GPUs.

⚠️ Compatibility ⚠️

  • Symbolic loop syntax: the old "recorded loop" syntax is no longer supported. Existing code will need adjustments to use drjit.while_loop.

  • Comparison operators: The == and != comparisons previously reduced the result of to a single Python bool. They now return an array of component-wise comparisons to be more consistent with other array programming frameworks. Use :py:func:dr.all(a == b) <all> or dr.all(a == b, axis=None) <all> to get the previous behavior.

    The functions drjit.eq() and drjit.neq() for element-wise equality and inequality tests were removed, as their behavior is now subsumed by the builtin == and != operators.

  • Matrix layout: The Dr.Jit matrix type switched from column-major to row-major storage. Your code will need to be updated if it indexes into matrices first by column and then row (matrix[col][row]) instead of specifying the complete location matrix[row, col]. The latter convention is consistent between both versions.

Internals

This section documents lower level changes that don't directly impact the Python API.

  • Compilation of Dr.Jit is faster and produces smaller binaries. Downstream projects built on top of Dr.Jit will also see improvements on both metrics.

  • Dr.Jit now builds a support library (libdrjit-extra.so) containing large amounts of functionality that used to be implemented using templates. The disadvantage of the previous template-heavy approach was that this code ended up getting compiled over and over again especially when Dr.Jit was used within larger projects such as Mitsuba 3, where this caused very long compilation times.

    The following features were moved into this library:

    • Transcendental functions (drjit.log, drjit.atan2, etc.) now have pre-compiled implementations for Jit arrays. Automatic differentiation of such operations was also moved into libdrjit-extra.so.

    • The AD layer was rewritten to reduce the previous backend (drjit/autodiff.h) into a thin wrapper around functionality in libdrjit-extra.so. The previous AD-related shared library libdrjit-autodiff.so no longer exists.

    • The template-based C++ interface to perform vectorized method calls on instance arrays (drjit/vcall.h, drjit/vcall_autodiff.h, drjit/vcall_jit_reduce.h, drjit/vcall_jit_record.h) was removed and turned into generic implementation within the libdrjit-extra.so library. All functionality (symbolic/evaluated model, automatic differentiation) is now exposed through a single statically precompiled function (ad_call). The same function is also used to realize the Python interface (drjit.switch, drjit.dispatch).

      To de-emphasize C++ virtual method calls (the interface is more broadly about calling things in parallel), the header file was renamed to drjit/call.h. All macro uses of DRJIT_VCALL_* should be renamed to DRJIT_CALL_*.

    • Analogous to function calls, the Python and C++ interfaces to symbolic/evaluated loops and conditionals are each implemented through a single top-level function (ad_loop and ad_cond) in libdrjit-extra.so. This removes large amounts of template code and accelerates compilation.

  • Improvements to CUDA and LLVM backends kernel launch configurations that more effectively use the available parallelism.

  • The packet mode backend (include/drjit/packet.h) now includes support for aarch64 processors via NEON intrinsics. This is actually an old feature from a predecessor project (Enoki) that was finally revived.

  • The nb::set_attr() function that was previously used to update modified fields queried by a getter no longer exists. Dr.Jit now uses a simpler way to deal with getters. The technical reason that formerly required the presence of this function doesn't exist anymore.

Removals

  • Packet-mode virtual function call dispatch (drjit/vcall_packet.h) was removed.

  • The legacy string-based IR in Dr.Jit-core has been removed.

  • The ability to instantiate a differentiable array on top of a non-JIT-compiled type (e.g., dr::DiffArray<float>) was removed. This was in any case too inefficient to be useful besides debugging.

Other minor technical improvements

  • drjit.switch and drjit.dispatch now support all standard Python calling conventions (positional, keyword, variable length).

  • There is a new C++ interface named drjit::dispatch that works analogously to the Python version.

  • The drjit.reinterpret_array_v function was renamed to drjit.reinterpret_array.

  • The drjit.llvm.PCG32.seed function (and other backend variants) were modified to add the lane counter to both initseq and initstate. Previously, the counter was only added to the former, which led to noticeable correlation artifacts.

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