Cast - 6 vs 21¶
Next section compares an older to a newer version of the same operator after both definition are converted into markdown text. Green means an addition to the newer version, red means a deletion. Anything else is unchanged.
- Cast6 → Cast21 +68 -5
Cast6 → Cast21
RENAMED
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@@ -1 +1 @@
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The operator casts the elements of a given input tensor to a data type
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specified by the 'to' argument and returns an output tensor of the same size in
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the converted type. The 'to' argument must be one of the data types specified
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in the 'DataType' enum field in the TensorProto message.
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+
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Casting from string tensor in plain (e.g., "3.14" and "1000") and scientific numeric representations
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(e.g., "1e-5" and "1E8") to float types is supported. For example, converting string "100.5" to an integer may
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yield result 100. There are some string literals reserved for special floating-point values;
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"+INF" (and "INF"), "-INF", and "NaN" are positive infinity, negative infinity, and not-a-number, respectively.
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Any string which can exactly match "+INF" in a case-insensitive way would be mapped to positive infinite. Similarly,
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this case-insensitive rule is applied to "INF" and "NaN". When casting from numeric tensors
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to string tensors, plain floating-point representation (such as "314.15926") would be used.
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Converting non-numerical-literal string such as "Hello World!" is an undefined behavior. Cases
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of converting string representing floating-point arithmetic value, such as "2.718", to INT is an undefined behavior.
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Conversion from a numerical type to any numerical type is always allowed.
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User must be aware of precision loss and value change caused by range difference between two types.
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For example, a 64-bit float 3.1415926459 may be round to a 32-bit float 3.141592. Similarly, converting
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an integer 36 to Boolean may produce 1 because we truncate bits which can't be stored in the targeted type.
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+
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In more detail, the conversion among numerical types should follow these rules
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-
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if the destination type is not a float 8 type.
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* Casting from floating point to:
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* floating point: +/- infinity if OOR (out of range).
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* fixed point: undefined if OOR.
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* bool: +/- 0.0 to False; all else to True.
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* Casting from fixed point to:
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* floating point: +/- infinity if OOR. (+ infinity in the case of uint)
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* fixed point: when OOR, discard higher bits and reinterpret (with respect to two's complement representation for
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signed types). For example, 200 (int16) -> -56 (int8).
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* bool: zero to False; nonzero to True.
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* Casting from bool to:
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* floating point: {1.0, 0.0}.
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* fixed point: {1, 0}.
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* bool: no change.
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Float 8 type were introduced to speed up the training of
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deep models. By default the conversion of a float *x* obeys
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to the following rules. [x] means the value rounded to
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the target mantissa width.
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| x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ |
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|------|----|----|----|----|
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| 0 | 0 | 0 | 0 | 0 |
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|-0 | -0 | 0 | -0 | 0 |
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| NaN | NaN | NaN | NaN | NaN |
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| +/- Inf | +/- FLT_MAX | NaN | FLT_MAX | NaN |
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| [x] > FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX | FLT_MAX |
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| [x] < -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX | -FLT_MAX |
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| else | RNE | RNE | RNE | RNE |
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The behavior changes if the parameter 'saturate' is set to False.
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The rules then become:
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| x | E4M3FN | E4M3FNUZ | E5M2 | E5M2FNUZ |
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|------|----|----|----|----|
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| 0 | 0 | 0 | 0 | 0 |
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|-0 | -0 | 0 | -0 | 0 |
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| NaN | NaN | NaN | NaN | NaN |
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| +/- Inf | NaN | NaN | +/- Inf | NaN |
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| [x] > FLT_MAX | NaN | NaN | Inf | NaN |
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| [x] < -FLT_MAX | NaN | NaN | -Inf | NaN |
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| else | RNE | RNE | RNE | RNE |
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### Attributes
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* **saturate - INT** (default is '1'):
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+
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The parameter defines how the conversion behaves if an input value is out of range of the destination type. It only applies for float 8 conversion (float8e4m3fn, float8e4m3fnuz, float8e5m2, float8e5m2fnuz). It is true by default. All cases are fully described in two tables inserted in the operator description.
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* **to - INT** (required) :
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The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto
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### Inputs
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- **input** (heterogeneous) - **T1**:
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Input tensor to be cast.
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### Outputs
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- **output** (heterogeneous) - **T2**:
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Output tensor with the same shape as input with type specified by the 'to' argument
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### Type Constraints
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* **T1** in ( tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):
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* **T1** in ( tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int4), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint4), tensor(uint64), tensor(uint8) ):
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Constrain input types. Casting from
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Constrain input types. Casting from complex is not supported.
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* **T2** in ( tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):
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* **T2** in ( tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(float8e4m3fn), tensor(float8e4m3fnuz), tensor(float8e5m2), tensor(float8e5m2fnuz), tensor(int16), tensor(int32), tensor(int4), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint4), tensor(uint64), tensor(uint8) ):
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Constrain output types. Casting to
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Constrain output types. Casting to complex is not supported.? ^^
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