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Diffstat (limited to 'tensorflow/docs_src/performance/xla/operation_semantics.md')
| -rw-r--r-- | tensorflow/docs_src/performance/xla/operation_semantics.md | 24 |
1 files changed, 13 insertions, 11 deletions
diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md index 02af71f8a3..fad9fd57f1 100644 --- a/tensorflow/docs_src/performance/xla/operation_semantics.md +++ b/tensorflow/docs_src/performance/xla/operation_semantics.md @@ -1877,19 +1877,19 @@ See also [`XlaBuilder::RngNormal`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Constructs an output of a given shape with random numbers generated following -the $$N(\mu, \sigma)$$ normal distribution. The parameters `mu` and `sigma`, and -output shape have to have elemental type F32. The parameters furthermore have to -be scalar valued. +the $$N(\mu, \sigma)$$ normal distribution. The parameters $$\mu$$ and +$$\sigma$$, and output shape have to have a floating point elemental type. The +parameters furthermore have to be scalar valued. -<b>`RngNormal(mean, sigma, shape)`</b> +<b>`RngNormal(mu, sigma, shape)`</b> | Arguments | Type | Semantics | | --------- | ------- | --------------------------------------------------- | -| `mu` | `XlaOp` | Scalar of type F32 specifying mean of generated | -: : : numbers : -| `sigma` | `XlaOp` | Scalar of type F32 specifying standard deviation of | +| `mu` | `XlaOp` | Scalar of type T specifying mean of generated | +: : : numbers : +| `sigma` | `XlaOp` | Scalar of type T specifying standard deviation of | : : : generated numbers : -| `shape` | `Shape` | Output shape of type F32 | +| `shape` | `Shape` | Output shape of type T | ## RngUniform @@ -1898,9 +1898,11 @@ See also Constructs an output of a given shape with random numbers generated following the uniform distribution over the interval $$[a,b)$$. The parameters and output -shape may be either F32, S32 or U32, but the types have to be consistent. -Furthermore, the parameters need to be scalar valued. If $$b <= a$$ the result -is implementation-defined. +element type have to be a boolean type, an integral type or a floating point +types, and the types have to be consistent. The CPU and GPU backends currently +only support F64, F32, F16, BF16, S64, U64, S32 and U32. Furthermore, the +parameters need to be scalar valued. If $$b <= a$$ the result is +implementation-defined. <b>`RngUniform(a, b, shape)`</b> |