> For the complete documentation index, see [llms.txt](https://deeplearning4j.konduit.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://deeplearning4j.konduit.ai/en-1.0.0-rewrite/nd4j/data-types.md). # Data Types One of the most significant changes in M2.1 is the move from a **global data type** to **per-array data types**. Every `INDArray` now carries its own `DataType`, and operations are type-aware. This page covers the full `DataType` enum, how to create typed arrays, how to cast between types, and how to take advantage of mixed precision training. *** ## The DataType Enum All numeric types in ND4J are represented by the enum `org.nd4j.linalg.api.buffer.DataType`. Import it with: ```java import org.nd4j.linalg.api.buffer.DataType; ``` ### Floating-Point Types Floating-point types are used for neural network weights, activations, and most numeric computation. | Enum Constant | Bits | Description | | ------------- | ---- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `DOUBLE` | 64 | IEEE 754 double-precision float. Highest precision, largest memory footprint. | | `FLOAT` | 32 | IEEE 754 single-precision float. Default type. Best balance of precision and performance on most hardware. | | `FLOAT16` | 16 | Half-precision float. Reduced memory and bandwidth; supported on modern NVIDIA GPUs and some CPUs. Same constant as the deprecated `HALF`. | | `BFLOAT16` | 16 | Brain float 16. Same 8-bit exponent range as `FLOAT`, but only 7 mantissa bits. Numerically safer than `FLOAT16` for training. Supported on TPUs, A100/H100 GPUs, and recent Intel CPUs. | ### Signed Integer Types | Enum Constant | Bits | Notes | | ------------- | ---- | -------------------------------------------------------------------------------- | | `INT64` | 64 | 64-bit signed integer. Same constant as the deprecated `LONG`. | | `INT32` | 32 | 32-bit signed integer. Same constant as the deprecated `INT`. | | `INT16` | 16 | 16-bit signed integer. Same constant as the deprecated `SHORT`. | | `INT8` | 8 | 8-bit signed integer, range −128 to 127. Same constant as the deprecated `BYTE`. | ### Unsigned Integer Types | Enum Constant | Bits | | ------------- | ------------------------------------------------------------ | | `UINT64` | 64 | | `UINT32` | 32 | | `UINT16` | 16 | | `UINT8` | 8 — range 0 to 255. Same constant as the deprecated `UBYTE`. | ### Other Types | Enum Constant | Description | | ------------- | -------------------------------------------------------------------------------------- | | `BOOL` | Boolean — stores `true`/`false` per element. Used by comparison ops that return masks. | | `UTF8` | Variable-length UTF-8 string data. Used for label arrays and metadata. | | `COMPRESSED` | Internal placeholder for compressed arrays. Not for direct use. | | `UNKNOWN` | Sentinel value. Indicates an uninitialized or invalid type. | ### Deprecated Aliases Several names from earlier releases are still present in the enum but are annotated `@Deprecated`. They map to the same underlying constant as their preferred replacement. | Deprecated Name | Preferred Name | | --------------- | -------------- | | `HALF` | `FLOAT16` | | `LONG` | `INT64` | | `INT` | `INT32` | | `SHORT` | `INT16` | | `BYTE` | `INT8` | | `UBYTE` | `UINT8` | `FLOAT16` and `HALF` are literally the same enum constant — `FLOAT16` is a static field on the enum that points to `HALF`. The same relationship holds for each deprecated/preferred pair. Both names work identically at runtime, but new code should use the preferred names to keep intent clear. *** ## Memory Footprint Every element of an `INDArray` occupies a fixed number of bytes determined by its `DataType`. Understanding this is essential for planning GPU memory budgets and comparing model footprints. | DataType | Bytes per element | | ------------------------------------------- | ----------------- | | `DOUBLE` / `INT64` / `UINT64` | 8 | | `FLOAT` / `INT32` / `UINT32` | 4 | | `FLOAT16` / `BFLOAT16` / `INT16` / `UINT16` | 2 | | `INT8` / `UINT8` / `BOOL` | 1 | A 1000-element array uses 8 KB as `DOUBLE`, 4 KB as `FLOAT`, and 2 KB as `FLOAT16` or `BFLOAT16`. For a 100M-parameter model this difference is 800 MB vs 400 MB vs 200 MB — a decisive factor in whether a model fits in GPU memory. *** ## Migration from Beta4 and Earlier In beta4 and all earlier releases, data type was a global, process-wide setting applied to all arrays: ```java // OLD beta4 code — does not compile in M2.1 import org.nd4j.linalg.api.buffer.DataBuffer; Nd4j.setDataType(DataBuffer.Type.DOUBLE); // All subsequent Nd4j.zeros(), Nd4j.rand() etc. would produce DOUBLE arrays ``` In M2.1, `DataBuffer.Type` has been removed entirely. Each `INDArray` carries its own `DataType`, and creation methods accept it as an explicit argument. The global concept still exists as a **default** (see below), but it is no longer the only mechanism. ### What Changed | Concept | Beta4 | M2.1 | | --------------- | ------------------------------------- | -------------------------------------------------- | | Type class | `DataBuffer.Type` | `DataType` | | Set global type | `Nd4j.setDataType(DataBuffer.Type.X)` | `Nd4j.setDefaultDataTypes(DataType.X, DataType.Y)` | | Per-array type | Not supported | Every `INDArray` has a `DataType` | | Type checking | N/A | `arr.dataType()` | | Casting | N/A | `arr.castTo(DataType.X)` | Replace every `DataBuffer.Type` reference in your codebase: ```java // OLD DataBuffer.Type.DOUBLE DataBuffer.Type.FLOAT // NEW DataType.DOUBLE DataType.FLOAT ``` *** ## Default Data Type The default type for newly created arrays is `FLOAT`. When you call a creation method without an explicit `DataType` argument, ND4J uses the current default. You can change the default at application startup with `Nd4j.setDefaultDataTypes`. It accepts two arguments: the default floating-point type and the default integer type. ```java import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.factory.Nd4j; // Set default float type to FLOAT, default integer type to INT32 Nd4j.setDefaultDataTypes(DataType.FLOAT, DataType.INT32); // From this point on, creation methods without an explicit DataType // will use FLOAT for floating-point arrays and INT32 for integer arrays INDArray a = Nd4j.zeros(3, 4); // DataType.FLOAT INDArray b = Nd4j.ones(2, 5); // DataType.FLOAT ``` Call `setDefaultDataTypes` once at startup, before any array creation. Changing it mid-run can produce inconsistent types that are hard to debug. *** ## Creating Typed Arrays All `Nd4j` creation methods accept a `DataType` as their first argument. Pass the type explicitly whenever you need a specific type, regardless of what the default is. ### Zeros, Ones, and Value-Filled Arrays ```java import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.factory.Nd4j; // All-zero arrays INDArray dblZeros = Nd4j.zeros(DataType.DOUBLE, 3, 4); // shape [3,4], DOUBLE INDArray fltZeros = Nd4j.zeros(DataType.FLOAT, 3, 4); // shape [3,4], FLOAT INDArray intZeros = Nd4j.zeros(DataType.INT32, 3, 4); // shape [3,4], INT32 // All-one arrays INDArray ones = Nd4j.ones(DataType.FLOAT, 2, 5); // Explicit-value creation INDArray mat = Nd4j.create(DataType.FLOAT, new long[]{3, 4}); // uninitialized, FLOAT INDArray imat = Nd4j.create(DataType.INT32, new long[]{3, 4}); // uninitialized, INT32 ``` ### Random Arrays ```java // Uniform random in [0, 1) — only meaningful for floating-point types INDArray uniformFloat = Nd4j.rand(DataType.FLOAT, 3, 4); INDArray uniformDouble = Nd4j.rand(DataType.DOUBLE, 3, 4); INDArray uniformHalf = Nd4j.rand(DataType.FLOAT16, 3, 4); // Standard normal N(0,1) INDArray gaussFloat = Nd4j.randn(DataType.FLOAT, 3, 4); INDArray gaussBf16 = Nd4j.randn(DataType.BFLOAT16, 3, 4); ``` ### Linspace ```java // 100 evenly-spaced points from 0.0 to 1.0, type DOUBLE INDArray lin = Nd4j.linspace(DataType.DOUBLE, 0.0, 1.0, 100); ``` ### From Java Arrays Type inference uses the Java primitive type when creating from Java arrays. To force a specific type, create with an explicit `DataType` and then fill, or create and cast: ```java // Inferred as FLOAT from float[] INDArray fromFloat = Nd4j.createFromArray(new float[]{1f, 2f, 3f}); // Inferred as DOUBLE from double[] INDArray fromDouble = Nd4j.createFromArray(new double[]{1.0, 2.0, 3.0}); // Inferred as INT32 from int[] INDArray fromInt = Nd4j.createFromArray(new int[]{1, 2, 3}); ``` *** ## Checking the Type of an Array Call `arr.dataType()` to retrieve the `DataType` of any `INDArray`: ```java INDArray a = Nd4j.zeros(DataType.DOUBLE, 3, 4); DataType t = a.dataType(); // DataType.DOUBLE System.out.println(t); // prints: DOUBLE System.out.println(t == DataType.DOUBLE); // true // Use in a conditional if (a.dataType() != DataType.FLOAT) { a = a.castTo(DataType.FLOAT); } ``` *** ## Casting Between Types To convert an `INDArray` from one `DataType` to another, call `castTo`. This always returns a **new array** (a copy) in the target type. The original is not modified. ```java INDArray original = Nd4j.rand(DataType.DOUBLE, 3, 4); System.out.println(original.dataType()); // DOUBLE // Cast to FLOAT — returns a new array, original unchanged INDArray floatCopy = original.castTo(DataType.FLOAT); System.out.println(floatCopy.dataType()); // FLOAT // Cast to FLOAT16 for reduced memory INDArray halfCopy = original.castTo(DataType.FLOAT16); System.out.println(halfCopy.dataType()); // FLOAT16 ``` ### Precision Loss Casting from a higher-precision type to a lower-precision type loses information: ```java INDArray precise = Nd4j.scalar(DataType.DOUBLE, Math.PI); // 3.141592653589793 INDArray half = precise.castTo(DataType.FLOAT16); System.out.println(half.getDouble(0)); // approximately 3.140625 — reduced precision ``` This is expected and intentional when using reduced precision. For training stability, keep high-precision master copies and cast only for the forward/backward pass. ### Integer Truncation Casting a floating-point array to an integer type truncates (not rounds) the fractional part: ```java INDArray floats = Nd4j.createFromArray(new float[]{1.9f, 2.1f, -0.7f}); INDArray ints = floats.castTo(DataType.INT32); // ints contains: [1, 2, 0] (truncation, not rounding) ``` *** ## Type Matching for Operations In M2.1, the two operands of a binary operation must have the same `DataType`. If types differ, ND4J throws an exception: ```java INDArray x = Nd4j.zeros(DataType.FLOAT, 3, 4); INDArray y = Nd4j.zeros(DataType.DOUBLE, 3, 4); // Throws: operands must have matching types INDArray result = x.add(y); // IllegalStateException ``` This is a deliberate design choice: silent implicit promotion (as in many scripting languages) hides bugs where the user accidentally mixes types and gets unexpected precision or memory use. **Solution**: cast one operand before the operation: ```java INDArray x = Nd4j.rand(DataType.FLOAT, 3, 4); INDArray y = Nd4j.rand(DataType.DOUBLE, 3, 4); // Cast x up to DOUBLE, then add INDArray result = x.castTo(DataType.DOUBLE).add(y); // result is DOUBLE // Or cast y down to FLOAT, then add INDArray result2 = x.add(y.castTo(DataType.FLOAT)); // result2 is FLOAT ``` Choose which direction to cast based on whether you need the precision of `DOUBLE` or the memory savings of `FLOAT`. ### Type-Safe Helper Pattern When writing utility code that must accept any numeric type, check and cast defensively: ```java public INDArray ensureFloat(INDArray arr) { if (arr.dataType() == DataType.FLOAT) { return arr; } return arr.castTo(DataType.FLOAT); } ``` *** ## Mixed Precision Training Mixed precision training uses lower-precision types (typically `FLOAT16` or `BFLOAT16`) for most computation while keeping higher-precision master weights to accumulate gradients accurately. The result is significantly lower GPU memory use and higher throughput on hardware with native half-precision support (NVIDIA Volta and later, AMD RDNA2+). ### Strategy 1. **Master weights** are stored as `FLOAT` (32-bit). 2. Before each forward pass, cast the master weights to `FLOAT16` or `BFLOAT16`. 3. The forward and backward passes execute in half precision. 4. Gradients are cast back to `FLOAT` and accumulated into the master weights. 5. The optimizer step runs in `FLOAT`. ```java // Master weights stored in full precision INDArray masterWeights = Nd4j.rand(DataType.FLOAT, 512, 512); // Half-precision copy for forward/backward pass INDArray halfWeights = masterWeights.castTo(DataType.FLOAT16); // ... forward and backward pass using halfWeights ... // Assume gradientsHalf is the gradient computed in FLOAT16 INDArray gradientsHalf = computeGradients(halfWeights); // Cast gradients back to FLOAT for the optimizer update INDArray gradientsFloat = gradientsHalf.castTo(DataType.FLOAT); // Optimizer step on master weights masterWeights.subi(gradientsFloat.muli(learningRate)); ``` ### FLOAT16 vs BFLOAT16 Both use 2 bytes per element, but they allocate those bits differently: | Type | Exponent bits | Mantissa bits | Max value | | ---------- | ------------- | ------------- | ----------------------------- | | `FLOAT16` | 5 | 10 | \~65,504 | | `BFLOAT16` | 8 | 7 | \~3.4 × 10^38 (same as FLOAT) | `FLOAT16` offers higher precision for values in its range, but overflows easily — activations or gradients that exceed \~65,504 become `Inf`. This requires **loss scaling** (multiplying the loss by a large constant before the backward pass) to keep gradients in range. `BFLOAT16` has the same exponent range as `FLOAT`, so it does not overflow under conditions that `FLOAT` would not overflow. Gradients stay finite without loss scaling in most cases. The trade-off is lower mantissa precision (\~2 decimal digits vs \~3 for `FLOAT16`). **Recommendation**: prefer `BFLOAT16` when your hardware supports it. It requires less engineering overhead to use safely. ### Setting a Default for Half-Precision Training ```java // Use BFLOAT16 as the default for all new arrays Nd4j.setDefaultDataTypes(DataType.BFLOAT16, DataType.INT32); // Create compute arrays without specifying type explicitly INDArray input = Nd4j.rand(3, 512); // DataType.BFLOAT16 INDArray kernel = Nd4j.rand(512, 256); // DataType.BFLOAT16 ``` *** ## Reference: DataType Summary | DataType | Bytes | Floating-point? | Notes | | ---------- | -------- | --------------- | ------------------------------------------------------------------------------------------------ | | `DOUBLE` | 8 | Yes | 64-bit IEEE 754. Use for scientific computing where precision matters. | | `FLOAT` | 4 | Yes | 32-bit IEEE 754. Default. Good choice for most training workloads. | | `FLOAT16` | 2 | Yes | 16-bit half. GPU-accelerated mixed precision. Requires loss scaling. `HALF` is deprecated alias. | | `BFLOAT16` | 2 | Yes | Brain float. Same exponent range as FLOAT. Preferred for training without loss scaling. | | `INT64` | 8 | No | 64-bit signed. `LONG` is deprecated alias. | | `INT32` | 4 | No | 32-bit signed. `INT` is deprecated alias. | | `INT16` | 2 | No | 16-bit signed. `SHORT` is deprecated alias. | | `INT8` | 1 | No | 8-bit signed, −128 to 127. `BYTE` is deprecated alias. | | `UINT64` | 8 | No | 64-bit unsigned. | | `UINT32` | 4 | No | 32-bit unsigned. | | `UINT16` | 2 | No | 16-bit unsigned. | | `UINT8` | 1 | No | 8-bit unsigned, 0 to 255. `UBYTE` is deprecated alias. | | `BOOL` | 1 | No | Boolean. Returned by comparison ops. | | `UTF8` | variable | No | String data. Used for label arrays. | *** ## Key API Signatures ```java // Set process-wide defaults (call once at startup) Nd4j.setDefaultDataTypes(DataType floatType, DataType intType); // Check default DataType defaultFP = Nd4j.defaultFloatingPointType(); // default: FLOAT // Get the type of an existing array DataType t = arr.dataType(); // Cast to a new type (returns copy, never in-place) INDArray cast = arr.castTo(DataType.FLOAT); // Creation methods accepting DataType Nd4j.zeros(DataType, long... shape); Nd4j.ones(DataType, long... shape); Nd4j.create(DataType, long... shape); Nd4j.rand(DataType, long... shape); Nd4j.randn(DataType, long... shape); Nd4j.linspace(DataType, double from, double to, long length); ``` --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. 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