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https://github.com/pytorch/pytorch.git
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382fbcc1e4
Summary: Continuing the work from https://github.com/pytorch/pytorch/pull/146427 Adds the `torch.float8_e8m0fnu` dtype to PyTorch, as detailed in https://github.com/pytorch/pytorch/issues/146414 . Please see the issue for a detailed definition of the format. Example of basic functionality: ```python import torch # round trip x0 = torch.randn(4, 4, dtype=torch.float32) x1 = x0.to(torch.float8_e8m0fnu) # RNE rounding x2 = x1.to(torch.float32) # 2 ** exponent # creation with empty x0 = torch.empty(4, 4, dtype=torch.float8_e8m0fnu) # printing print(x0) ``` Done in this PR: * numerical correctness * op coverage (except for `torch._scaled_mm`): create tensor, cast to/from float32 * printing a tensor works For future PRs: * performance optimizations for casting * torch._scaled_mm * PT2 * various cleanups (detailed in comments with issue numbers) Test Plan: ``` pytest test/quantization/core/experimental/test_float8.py -s ``` Reviewers: Subscribers: Tasks: Tags: Pull Request resolved: https://github.com/pytorch/pytorch/pull/147466 Approved by: https://github.com/drisspg
76 lines
2.9 KiB
C++
76 lines
2.9 KiB
C++
#define TORCH_ASSERT_NO_OPERATORS
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#include <ATen/Dispatch_v2.h>
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#include <ATen/Parallel.h>
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#include <ATen/cpu/vec/vec.h>
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#include <ATen/cpu/vec/functional.h>
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#include <ATen/native/TensorIterator.h>
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#include <ATen/native/cpu/Loops.h>
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#include <ATen/native/Fill.h>
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#include <c10/core/Scalar.h>
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namespace at::native {
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namespace {
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template <typename scalar_t>
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void fill_non_native_type(TensorIterator& iter, const Scalar& value_scalar) {
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auto value = value_scalar.to<scalar_t>().x;
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using H = typename std::make_signed_t<decltype(value)>; // Signed type has more acceleration
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// Reserve the representation of value. static_cast<H>(value) is implementation defined.
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H val = *reinterpret_cast<H*>(std::addressof(value));
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cpu_kernel_vec</*check_dynamic_cast=*/false>(
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iter,
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[val]() -> H { return val; },
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[val]() { return Vectorized<H>(val); });
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}
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template <>
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void fill_non_native_type<c10::complex<at::Half>>(TensorIterator& iter, const Scalar& value_scalar) {
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static_assert(sizeof(c10::complex<at::Half>) == sizeof(int32_t), "Size of ComplexHalf should be 32-bits");
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auto value = c10::complex<at::Half>(value_scalar.to<c10::complex<float>>());
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auto val = *reinterpret_cast<int32_t*>(std::addressof(value));
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cpu_kernel_vec</*check_dynamic_cast=*/false>(
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iter,
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[val]() -> int32_t { return val; },
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[val]() { return Vectorized<int32_t>(val); });
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}
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void fill_kernel(TensorIterator& iter, const Scalar& value_scalar) {
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if (iter.dtype() == ScalarType::Half) {
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fill_non_native_type<at::Half>(iter, value_scalar);
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} else if (iter.dtype() == ScalarType::BFloat16) {
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fill_non_native_type<at::BFloat16>(iter, value_scalar);
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} else if (iter.dtype() == ScalarType::ComplexHalf) {
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fill_non_native_type<c10::complex<at::Half>>(iter, value_scalar);
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} else if (iter.dtype() == ScalarType::Float8_e4m3fn) {
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fill_non_native_type<at::Float8_e4m3fn>(iter, value_scalar);
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} else if (iter.dtype() == ScalarType::Float8_e5m2) {
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fill_non_native_type<at::Float8_e5m2>(iter, value_scalar);
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} else if (iter.dtype() == ScalarType::Float8_e4m3fnuz) {
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fill_non_native_type<at::Float8_e4m3fnuz>(iter, value_scalar);
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} else if (iter.dtype() == ScalarType::Float8_e5m2fnuz) {
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fill_non_native_type<at::Float8_e5m2fnuz>(iter, value_scalar);
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} else if (iter.dtype() == ScalarType::Float8_e8m0fnu) {
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// TODO(#146647): use macro here instead of spelling out each float8 dtype
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fill_non_native_type<at::Float8_e8m0fnu>(iter, value_scalar);
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} else {
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AT_DISPATCH_V2(
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iter.dtype(), "fill_cpu", AT_WRAP([&]() {
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scalar_t value = value_scalar.to<scalar_t>();
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cpu_kernel_vec(
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iter,
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[=]() -> scalar_t { return value; },
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[=]() { return Vectorized<scalar_t>(value); });
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}),
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AT_EXPAND(AT_ALL_TYPES_AND_COMPLEX), kBool, AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES)
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);
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}
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}
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} // namespace
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REGISTER_DISPATCH(fill_stub, &fill_kernel)
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} // namespace at::native
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