Intel® oneAPI Deep Neural Network Developer Guide and Reference
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Primitive Attributes: floating-point math mode
For some applications, it can be beneficial to allow down-conversions to speedup computations without noticeable impact on accuracy.
This section describes how the default numerical behavior of oneDNN (described in Data Types) can be altered to allow implicit down-conversions of floating-point types.
The floating-point math mode attribute.
When passed to a primitive creation, the dnnl::fpmath_mode primitive attribute specifies which implicit down-conversions are allowed for that given primitive. Only down-conversions from f32 to narrower data-types (f16, bf16, or tf32) are currently allowed. Furthermore these down-conversions are allowed only during computation, and do not affect the storage datatype (which must remain f32).
The dnnl::fpmath_mode primitive attribute can take 3 types of values:
the strict mode disables any down-conversion.
the any mode allows all conversions from f32 to a smaller floating-point datatype (f16, bf16, or tf32).
a specific datatype (f16, bf16, or tf32) which specifically allows down-conversion only from f32 to a datatype at least as accurate as the specified data-type (at least same number of exponent and mantissa bits).
This attribute is ignored if a primitive computation data-type is integral.
A note on default floating-point math mode
The default floating-point mode is strict, which means no implicit down-conversion is allowed. However, this default behavior can be changed with the ONEDNN_DEFAULT_FPMATH_MODE environment variable, the dnnl_set_default_fpmath_mode (C API) or the dnnl::set_default_fpmath_mode (C++ API) functions.