Add vectorization in elementwise_util #9432
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Stack from ghstack (oldest at bottom): |
swolchok
requested review from
JacobSzwejbka,
lucylq and
manuelcandales
as code owners
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/9432
Note: Links to docs will display an error until the docs builds have been completed. ⏳ 2 Pending, 2 Unrelated FailuresAs of commit a7876b5 with merge base 0cf2d53 ( BROKEN TRUNK - The following jobs failed but were present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
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| template <typename T> \ | ||
| auto func_name(at::vec::Vectorized<T> vec) { \ | ||
| if constexpr (!::executorch::runtime::is_floating_point<T>::value) { \ | ||
| return at::vec::convert<float>(vec).func_name(); \ |
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is this a valid thing to do? that is convert say an instance of at::vec::Vectorized<int8_t> to float and apply the func_name? Maybe i am misunderstanding how this works
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hmm, if i'm reading https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/cpu/vec/vec_convert.h correctly, this is not quite correct. good catch! (it will silently drop the int8_t instances that don't fit in the float vector; the right thing to do is convert to VectorizedN, do the computation, and then convert back.) better make sure we have tests here.
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this should be fixed in the latest update.
| */ | ||
| #define ET_INTERNAL_VECTORIZED_FLOAT_UNARY_FUNC(func_name) \ | ||
| namespace executorch { \ | ||
| inline namespace math { \ |
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what does inline to namespace do?
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in short, it causes the namespace to not actually exist in a lot of circumstances. see https://en.cppreference.com/w/cpp/language/namespace
| * corresponding operator is a "float op" in TensorIterator parlance | ||
| * (i.e., uses something like build_borrowing_binary_float_op()), |
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I dont know if anyone reading this code would understand what this means?
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But this provides answer to my earlier question
kernels/portable/cpu/op_where.cpp
Outdated
| @@ -47,7 +47,7 @@ Tensor& where_out( | |||
| CTYPE_COMPUTE, | |||
| op_name, | |||
| utils::SupportedTensorDtypes::SAME_AS_COMMON>( | |||
| [](const auto val_a, const auto val_b, const auto val_c) { | |||
| [](const CTYPE_COMPUTE val_a, const CTYPE_COMPUTE val_b, const CTYPE_COMPUTE val_c) { | |||
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is this unrelated change?
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no, we can't vectorize this op
| @@ -51,6 +56,34 @@ inline int64_t scalar_to<int64_t>(const Scalar& s) { | |||
| } | |||
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| namespace internal { | |||
| template <typename Ignore, typename T> | |||
| using ignore_first_yield_second = T; | |||
| if constexpr (std::is_invocable_v< | ||
| Op, | ||
| ignore_first_yield_second<Args, Vec>...>) { | ||
| // For bool, we will get a false positive if we rely on only the |
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you mean bool return type?
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bool ctype_compute
| ((inputs.first->scalar_type() == | ||
| CppTypeToScalarType<CTYPE_COMPUTE>::value) && | ||
| ...)); |
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Man, this is a good reminder of all the template meta programming magic
| ...); | ||
| if (!any_is_broadcasted) { | ||
| using Vec = at::vec::Vectorized<CTYPE_COMPUTE>; | ||
| ::executorch::extension::parallel_for( |
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I think doing this blindly for each op is a bit risky in that, no all multithreading is always better. some ops benefit from smaller grain size while others with larger
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IIRC xnnpack blindly parallelizes absolutely everything; we're doing strictly better here
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yeah I am not comparing with xnnpack. In fact one bit part of the reason why we ended up leveraging optimized op lib for some of the llama stuff for exactly that reason. That it blindly parallelized everything and that actually hurt perf
| const auto vectorized_begin = | ||
| begin + (Vec::size() - begin % Vec::size()) % Vec::size(); |
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Feels like something that has chances of bug. Hope we test this enough. I would doubt if our test cases will exercise this code path.
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although I do see you treat scalar left overs of both head and tails separately
| inputs.first->sizes(), out.sizes()) && | ||
| ...); | ||
| if (!any_is_broadcasted) { | ||
| using Vec = at::vec::Vectorized<CTYPE_COMPUTE>; |
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So the one point of contention for me is that why do we need vectorized_math.h which largely is doing trampoline to underlying vectorized methods. Mainly you dont even need to use can_use_vectorized, because on non accelerated platforms Vectorized falls back to scalar impl even if Vec::size() != `. Maybe you said that the generated code would be worse if forced Vectorized, but I am not sure why. Rest makes sense.
However, place where I can potentially see that being useful is for dtype that Vectorized doesnt support but for float I am not sure. So maybe if you can clarify that it would help.
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why do we need vectorized_math.h which largely is doing trampoline to underlying vectorized methods
without it, you can't take the same lambda you already wrote for scalars and reuse it for Vectorized (the change isn't zero because you have to point at executorch::math, but crucially it doesn't require separate code)
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Ok we synced offline. So the major value prop as I understood: You can write your lambdas without having to explicitly use Vectorized. Why explicitly using Vectorized might not be good? Because maybe Vectorized does not have everything you need to implement your lambda. So as op author you dont have to worry about Vectorized when writing your lambda (although do have to use executorch::math ops). And later if Vectorized added support for the ::math.. ops you get vectorization for free without having to go back and rewrite your lambda with Vectorized. So this is nice. |
Mixed dtype should be uncommon. Here is how we can specialize for the common case. Prepares us to tackle #9241 . Test Plan: automated tests on this PR verify we didn't break the now-deprecated runtime_out_dtypes mode; tests on the next PR will verify that everything works after migration. Also included migration for exactly one operator, op_mul, to verify that the new code compiles. To check performance, I edited examples/models/toy_model/model.py so that MulModule used inputs of size 3000, 2000 instead of 3, 2. I exported it with `python3 -m examples.portable.scripts.export --model_name mul` and saved the resulting `mul.pte`. Then I built in release mode with optimized kernels on, but with mul.out removed from kernels/optimized/optimized.yaml, so that we would use the optimized_portable_kernels build of kernels/portable/op_mul.cpp. Finally, I ran 3 trials on my M1 Macbook Pro using `cmake-out/executor_runner --model_path mul3kby2k.pte --num_executions 1000 --cpu_threads 2`. Resulting times for 1000 iterations in ms: Previous diff: 8295, 8187, 8139 This diff: 2953, 2806, 2861 (For comparison, the actual optimized mul kernel took around 1000 ms to run 1000 iterations, and #9432 later in the stack arrived at similar numbers.)
swolchok
added
the
release notes: ops & kernels
swolchok
requested review from
jathu,
larryliu0820 and
kirklandsign
as code owners
This is a first cut at #9241 . In this PR I've vectorized a small initial set of ops: atan2, fmod_Scalar, maximum, minimum, mul, pow, and sigmoid. I've left covering ops that use the
unary_ufunc_*utilities in pattern.h for a follow-up push, because pattern.h and elementwise_util need some work before we can migrate pattern.h's utilities to be backed by elementwise_util.