Other operations

Returns the id of the current thread instance along the given axis.

Returns the dtype of a tensor or tensor descriptor.

Returns the size in bytes of a given Triton dtype. This is useful for dynamically computing memory sizes based on dtype, especially in barrier synchronization code.

Example:

    # Instead of hardcoding size values
    tlx.barrier_expect_bytes(barrier, 2 * BLOCK_M * BLOCK_K)  # Assumes float16

    # Use size_of for dtype-aware computation
    tlx.barrier_expect_bytes(barrier,
                           tlx.size_of(tlx.dtype_of(desc)) * BLOCK_M * BLOCK_K)

Returns the current 64-bit hardware clock value. E.g,

        start = tlx.clock64()
        # ... kernel code ...
        end = tlx.clock64()
        elapsed = end - start  # Number of clock cycles elapsed

Performs hardware-accelerated stochastic rounding for FP32→FP8/BF16/F16 conversions on Blackwell GPUs (compute capability ≥ 100). Uses PTX cvt.rs.satfinite instructions for probabilistic rounding.

Why Use Stochastic Rounding:

Performance Characteristics:

Parameters:

Example:

        # Generate random bits for entropy
        # n_rounds=7 provides sufficient randomness for unbiased stochastic rounding
        offsets = tl.arange(0, BLOCK_SIZE // 4)
        r0, r1, r2, r3 = tl.randint4x(seed, offsets, n_rounds=7)
        rbits = tl.join(tl.join(r0, r1), tl.join(r2, r3)).reshape(x.shape)

        # Apply stochastic rounding
        y = tlx.stoch_round(x, tlx.dtype_of(y_ptr), rbits)

Rubin packed x4 arithmetic

tlx.packed_add4, tlx.packed_sub4, tlx.packed_mul4, and tlx.packed_fma4 require [sm107+].

These operations apply one Rubin packed-arithmetic instruction to four low-precision lanes at a time. Operands must be block tensors containing FP8 E4M3/E5M2 values or packed FP4 E2M1 values represented as tl.int8.

dtype may be omitted when an FP8 result-typed operand, such as rhs or acc, determines it. Multiplying two packed-FP4 operands requires an explicit FP8 result dtype. For mixed FP4/FP8 operations, the packed-FP4 tensor has exactly one dimension at half the corresponding FP8 extent. For an all-FP4 packed_mul4, packing is inferred along the final dimension and the logical result doubles that dimension.

# fp4_values contains two packed E2M1 values per int8 element.
mixed = tlx.packed_mul4(fp4_values, fp8_values, dtype=tl.float8e4nv)
acc = tlx.packed_fma4(fp4_values, fp8_values, fp8_acc)

# An all-FP4 multiply needs an explicit FP8 result type.
product = tlx.packed_mul4(fp4_lhs, fp4_rhs, dtype=tl.float8e5)

The frontend lowers these APIs to ttng.packed_arith; the SM107 backend then emits the corresponding add, sub, mul, or fma x4 PTX instruction.

Collects a predicate from each thread in the warp and returns a 32-bit mask where each bit represents the predicate value from the corresponding lane. Only threads specified by mask participate in the vote.

        ballot_result = tlx.vote_ballot_sync(0xFFFFFFFF, pred)

Reduce one distributed predicate per physical lane to a warp-uniform scalar i1. Non-boolean inputs are compared with zero first. warp_all is true only if every lane contributes true; warp_any is true if at least one lane contributes true.

These are physical-lane votes, not logical tensor-axis reductions. tl.all and tl.max reduce a tensor dimension and retain tensor layout semantics, whereas these operations produce a scalar suitable for a uniform branch. The predicate must distribute exactly one element per lane; compilation fails if its resolved layout gives a lane zero or multiple elements.

    all_safe = tlx.warp_all(per_lane_safe)
    any_active = tlx.warp_any(per_lane_active)

Prefer these semantic reductions when code only needs an all/any result. They do not expose a hardware ballot bit mask.

Executes body(*inits, *args) with the hardware execution mask restricted to lanes where predicate is true. Active lanes receive the values returned by body; inactive lanes keep their corresponding values from inits. A single carried tensor is returned directly, while multiple carried tensors are returned as a tuple.

A scalar predicate controls its physical lane. For a tensor predicate, the elements owned by each lane are OR-reduced to form that lane's execution bit. Unlike tlx.warp_all and tlx.warp_any, this does not make the predicate uniform across the wave.

    @triton.jit
    def scale_active(acc, scale):
        return acc * scale

    acc = tlx.warp_predicate(
        active,
        acc,
        scale_active,
        args=(scale,),
    )

body must be an @triton.jit function that returns the same number and types of tensors as inits. It must contain straight-line computation and no cross-wave synchronization. Reductions, dots, and layout shuffles are allowed only with wave_uniform=True, which asserts that every lane in a wave observes the same predicate. Cross-warp reductions, nested dynamic control flow, and other nested regions are unsupported. This operation is currently lowered only by the AMD backend.

Additionally, if tensormap is specified to True, the API instead does a prefetch of tensor map object (TMA descriptor) and ignores other parameters other than pointer.

Example:

  offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
  mask = offsets < n_elements
  tlx.prefetch(input_ptr + offsets, level="L2", mask=mask)
  x = tl.load(input_ptr + offsets, mask=mask)

  ...
  # desc_in can be host side descriptor or device side like this:
  desc_in = tl.make_tensor_descriptor(
            input_ptr,
            shape=[M, N],
            strides=[N, 1],
            block_shape=[BLOCK_SIZE_M, BLOCK_SIZE_N],
        )
  tlx.prefetch(desc_in, tensormap=True)

tlx.assume_uniform

Assert that a scalar holds the same value in every lane of the wave.

value = tlx.assume_uniform(value)

Returns: value, unchanged.

The main use is buffer operations, which keep their base pointer in the scalar (SGPR) resource descriptor, so it has to be wave-uniform. When the backend cannot prove that it is — most commonly because the pointer was loaded from memory — it falls back to a per-lane waterfall loop around every access. tlx.assume_uniform tells the backend to take uniformity as given:

base = tl.load(ptr_array + gid).to(tl.pointer_type(tl.float16))
base = tlx.assume_uniform(base)
data = tlx.buffer_load(base, offsets)

Lowers to amdg.assume_uniform, which is eventually lowered to llvm.amdgcn.readfirstlane — that makes the result uniform by construction as far as LLVM's uniformity analysis is concerned. On non-AMD backends it is a no-op that returns its argument. Nothing verifies the assertion: if the value is not actually uniform, every lane silently gets lane 0's value.