code/models/common/sampling/tt_sampling.py

# SPDX-FileCopyrightText: © 2025 Tenstorrent USA, Inc.

# SPDX-License-Identifier: Apache-2.0

import inspect
import sys

import torch
from loguru import logger

import ttnn
from models.common.lightweightmodule import LightweightModule
from models.common.sampling._utils import (
    is_default_value,
    is_power_of_2,
    topk_would_route_to_large_indices,
    upper_power_of_2,
)
from models.common.sampling.tt_log_probs import LogProbsCalculator
from models.common.sampling.vocab_padding import (
    build_invalid_vocab_mask,
    build_tail_invalid_vocab_mask,
    get_vocab_shard_dims,
)

# Greedy tie-break boost (see TTSampling._adjust_values_for_tiebreak).
# bfloat16 keeps an 8-bit mantissa, so the gap to the next representable value at magnitude |x| is
# between |x| * 2^-8 and |x| * 2^-7. Scaling the tied maximum by 2^-6 is therefore at least 2 ULP at
# every magnitude, whereas a fixed constant is not: 1.0 is below one ULP once |logit| >= 256 (bf16
# spacing there is 2.0), so 256 + 1 rounds straight back to 256 and the tie survives. Multiplying by
# a power of two is exact in bf16, so the boost is reproducible.
TIEBREAK_DELTA_SCALE = 2**-6
# Floor so the boost stays strictly positive when the tied maximum is exactly 0.0. Still a normal
# bf16 number (smallest normal ~1.18e-38) and orders of magnitude below one ULP of any real logit,
# so it never perturbs a non-zero maximum.
TIEBREAK_DELTA_FLOOR = 1e-30
# Added to the global index of every non-maximum candidate so the row min over the masked indices can
# only ever land on a tied maximum. A power of two, so the mask that carries it (built in bfloat16)
# holds it exactly; larger than any padded vocabulary size, which __init__ asserts; and small enough
# that sentinel + index cannot overflow the int32 the min reduce runs in.
TIEBREAK_INDEX_SENTINEL = 2**24

# Widest input ttnn.topk accepts in one call; vocabs beyond it must be cut into chunks.
TOPK_MAX_WIDTH = 64 * 1024


class TTSampling(LightweightModule):
    """
    On-device sampling module supporting top-k, top-p, and temperature-based sampling.

    This class implements high-performance on-device sampling that can work across different
    model implementations by accepting configuration parameters rather than assuming specific
    args structures.

    Multi-device sampling works by partitioning the vocabulary across devices. Each device
    computes top-k locally on its vocabulary partition, then all-gather operations combine
    the results across devices to perform global top-k selection before final sampling.

    Args:
        mesh_device: The device or MeshDevice for computations
        tt_ccl: CCL object for distributed operations (supports both line_all_gather and tt_all_gather)
        vocab_size: Vocabulary size of the model
        padded_vocab_size: Padded vocabulary size (must be divisible by num devices)
        max_batch_size: Maximum batch size supported
        max_top_k: Maximum number of top-k tokens to consider
        cluster_shape: Shape of the device cluster (rows, cols)
        sampling_all_gather_axis: Axis to all-gather over in 2D meshes (0=rows, 1=cols, default: 0)
        sub_core_grids: Sub-core grid configuration for operations
        sub_core_grid_topk: Sub-core grid configuration specifically for top-k operations
        start_core: Starting core coordinate for sampling operations
        num_gather_links: Number of links to use for all-gather operations (optional)
        sampling_memory_config: Memory configuration for sampling tensors (optional)
        k, p, temp: Initial sampling parameters (tensors of size max_batch_size)

    Note:
        Uses persistent buffers when CCL supports line_all_gather (llama3_70b_galaxy),
        otherwise uses standard all_gather where the CCL API handles memory allocation (tt-transformers).
    """

    @classmethod
    def num_single_device_vocab_splits(cls, padded_vocab_size):
        """Fewest power-of-two same-device chunks whose width fits ttnn.topk.

        Returns None when no tile-aligned cut exists, in which case the caller
        must fall back to host sampling instead of constructing TTSampling.
        """
        num_splits = 2
        while padded_vocab_size // num_splits > TOPK_MAX_WIDTH:
            num_splits *= 2
        chunk_width = padded_vocab_size // num_splits
        if padded_vocab_size % num_splits != 0 or chunk_width % ttnn.TILE_SIZE != 0:
            return None
        return num_splits

    @staticmethod
    def _untilize_chunk_count(width):
        """Fewest tile-aligned chunks of at most TOPK_MAX_WIDTH each, or 1 when
        the row is narrow enough (<= 2 * TOPK_MAX_WIDTH, the widest row known
        to untilize in one program).

        Prefer an even cut so every chunk is at least half of TOPK_MAX_WIDTH
        wide (the search is bounded to twice the minimum count).
        """

        if width <= 2 * TOPK_MAX_WIDTH:
            return 1
        min_chunks = (width + TOPK_MAX_WIDTH - 1) // TOPK_MAX_WIDTH  # ceil(width / TOPK_MAX_WIDTH)
        num_chunks = min_chunks
        max_chunks = 2 * num_chunks
        while num_chunks <= max_chunks:
            if width % num_chunks == 0 and (width // num_chunks) % ttnn.TILE_SIZE == 0:
                return num_chunks
            num_chunks += 1
        # INFO: split accepts an uneven trailing chunk.
        return min_chunks

    @staticmethod
    def _untilize_chunk_width(width, num_chunks):
        """Tile-aligned ttnn.split size that cuts `width` into `num_chunks` pieces
        (the last one shorter when the row does not divide evenly)."""
        per_chunk = (width + num_chunks - 1) // num_chunks  # ceil(width / num_chunks)
        return (per_chunk + ttnn.TILE_SIZE - 1) // ttnn.TILE_SIZE * ttnn.TILE_SIZE  # round up to a tile

    def _is_force_argmax_sampling(self, k, p, temp):
        """Detect whether all users request deterministic greedy decoding.

        When every user in the batch has k=1 (top-1), p=0.0 or p=1.0 (no top-p filter),
        and temp=1.0 (no temperature scaling), we can skip the full top-k / top-p /
        temperature / RNG pipeline and use a single all-gather + argmax instead.
        This is significantly faster because argmax needs only one all-gather of the
        full logits tensor vs. three gathers (values, indices, sampled tokens) in the
        normal path.

        Note: callers may represent greedy rows with p=1.0, while the
        device argmax-style representation uses p=0.0.
        The model config must also set allow_force_argmax=True for this to activate.

        Changing this state between decode steps invalidates captured traces, so
        SamplingGenerator maintains separate trace slots keyed by force_argmax.
        """
        calculator = getattr(self, "log_probs_calculator", None)
        needs_topk_logprobs = calculator is not None and calculator.enable_log_probs and calculator._use_topk_logprobs
        return (
            self._allow_force_argmax_sampling
            and not needs_topk_logprobs
            and is_default_value(k, 1)
            and (is_default_value(p, 1.0) or is_default_value(p, 0.0))
            and is_default_value(temp, 1.0)
        )

    @property
    def force_argmax_sampling(self) -> bool:
        return self._force_argmax_sampling

    def _normalize_device_params(self, k, temp):
        """Map k and temp onto the contract ttnn.sampling actually implements.

        Mirrors the rewrites format_sampling_params() applies, so the device is guarded even when a
        caller reaches reset_params() directly:

        * ``k`` outside [1, max_top_k]. ttnn.sampling walks k entries of each user's two-face
          candidate row. A k above max_top_k (callers pass vocab_size to mean "no top-k filter")
          runs the top-p scan and the draw off the end of that row into unrelated L1, and k < 1
          -- the documented "no restriction" encoding -- is worse: from_torch(dtype=uint32) turns
          a negative k into ~4.3e9. Both collapse to max_top_k.
        * ``temp == 0``. temp multiplies the logits before the softmax, so the greedy encoding
          flattens every candidate to an equal probability and turns the draw uniform. Greedy is
          the triple (temp=1, k=1), so rewrite k too -- temp alone would leave a caller passing
          temp=0 with k=50 sampling from the top 32 instead of taking the argmax.

        ``k`` and ``temp`` are required; None was only ever tolerated by the force_argmax path.
        """
        if k is None or temp is None:
            raise ValueError("k and temp are required; pass format_sampling_params() output.")
        k = torch.as_tensor(k)
        temp = torch.as_tensor(temp, dtype=torch.float32)
        k = torch.where(k < 1, torch.full_like(k, self.max_top_k), k).clamp(max=self.max_top_k)
        k = torch.where(temp == 0.0, torch.ones_like(k), k)
        temp = torch.where(temp == 0.0, torch.ones_like(temp), temp)
        return k, temp

    def __init__(
        self,
        mesh_device,
        tt_ccl,
        args,
        k=None,
        p=None,
        temp=None,
    ):
        super().__init__()
        self.mesh_device = mesh_device
        # Use the fast unstable top-k; _adjust_values_for_tiebreak guarantees the correctness
        # host-side. stable=True (lowest-index tie-break) is only cheap where the
        # multi-core factory can pack [bf16 value | u16 index] fused keys: otherwise a multi-step
        # path costs ~2.5-3x more, and additionally the BH topk_large_indices route currently
        # has no stable mode at all.
        self._topk_stable = False
        # Multi-step reduction is supported only on single device
        self.multi_step_reduction = list(mesh_device.shape) == [1, 1]
        self.tt_ccl = tt_ccl
        self._line_all_gather = getattr(self.tt_ccl, "line_all_gather", None)
        self._line_all_gather_supports_buffer_key = False
        self.pad_to_power_of_2 = getattr(args, "pad_logits_to_power_of_2", False)
        if callable(self._line_all_gather):
            try:
                line_all_gather_sig = inspect.signature(self._line_all_gather)
                line_all_gather_params = line_all_gather_sig.parameters
                self._line_all_gather_supports_buffer_key = "buffer_key" in line_all_gather_params or any(
                    param.kind == inspect.Parameter.VAR_KEYWORD for param in line_all_gather_params.values()
                )
            except (TypeError, ValueError):
                logger.warning("Unable to inspect line_all_gather signature; assuming no buffer_key support.")

        padded_vocab_size = getattr(args, "padded_vocab_size", None)
        self.padded_vocab_size = padded_vocab_size if padded_vocab_size is not None else args.vocab_size
        self.vocab_size = args.vocab_size

        # Single-device top-k runs the vocab in same-device chunks (multi-step reduction).
        # ttnn.topk handles at most TOPK_MAX_WIDTH elements per call, so use the fewest
        # power-of-two chunks whose width fits: two chunks preserve the historical behavior
        # for every vocab up to 128K, and larger vocabs get four -- e.g. Qwen3's 151936
        # (#53064) and Gemma-2's 256000. Callers gate on num_single_device_vocab_splits()
        # returning non-None (host-sampling fallback), so reaching None here is a caller
        # bug. Raise rather than assert: this guards a correctness invariant (misaligned
        # chunks silently corrupt global token indices) and must survive python -O.
        self._num_vocab_splits = 2
        if self.multi_step_reduction:
            self._num_vocab_splits = self.num_single_device_vocab_splits(self.padded_vocab_size)
            if self._num_vocab_splits is None:
                raise ValueError(
                    f"padded_vocab_size={self.padded_vocab_size} cannot be cut into "
                    f"tile-aligned single-device top-k chunks of at most {TOPK_MAX_WIDTH}"
                )
        # Round up to the next tile boundary (32) — device tensors must be tile-aligned.
        raw_batch = getattr(args, "max_batch_size", 32)
        self.max_batch_size = max(32, ((raw_batch + 31) // 32) * 32)
        self.max_top_k = getattr(args, "max_top_k", 32)
        self.cluster_shape = args.cluster_shape

        self.sampling_all_gather_axis = getattr(args, "sampling_all_gather_axis", 0)
        self.sub_core_grids = getattr(args, "sub_core_grids", None)
        self.sub_core_grid_topk = getattr(args, "sub_core_grid_topk", None)
        self.start_core = getattr(args, "start_core", ttnn.CoreCoord(0, 0))
        self._sampling_sub_core_grids = (
            ttnn.num_cores_to_corerangeset_in_subcoregrids(
                self.start_core, self.max_batch_size, self.sub_core_grids, row_wise=True
            )
            if self.sub_core_grids is not None
            else None
        )
        # Blackhole galaxy prefetcher (unfused-CCL) keeps a split senders/worker sub-device manager
        # loaded during prefill sampling (prefill samples in decode mode). Auto-multicore ops grab the
        # full 12-wide compute grid, which includes the dispatch column that no loaded sub-device covers
        # ("kernel group cores do not match sub device cores"). Pin the force-argmax untilize/argmax to
        # the worker sub-core grids so they stay inside the worker sub-device. Left None elsewhere so no
        # other arch's behaviour changes.
        self._force_argmax_sub_core_grids = self.sub_core_grids if getattr(args, "use_unfused_ccl", False) else None

        # sampling_dp > 1 when multiple mesh groups each sample users independently
        # (e.g. GPT-OSS on [4,8]: 4 rows × 32 users; Llama Galaxy on [8,4]: 4 cols × 8 users)
        self._sampling_dp = getattr(args, "sampling_dp", 1)
        # Shard params along the non-all-gather axis; replicate along the all-gather axis
        if self._sampling_dp > 1:
            if self.sampling_all_gather_axis == 0:
                self._param_dims = (None, 0)  # shard along cols
            else:
                self._param_dims = (0, None)  # shard along rows
        else:
            self._param_dims = (None, None)

        if hasattr(args, "model_config") and "GALAXY_NUM_LINKS" in args.model_config:
            # Calculate num_gather_links based on model config
            max_num_gather_links = args.model_config["GALAXY_NUM_LINKS"]
            self.num_gather_links = (
                args.max_top_k // 32 if args.max_top_k // 32 <= max_num_gather_links else max_num_gather_links
            )
        else:
            self.num_gather_links = 1
        if hasattr(args, "model_config") and "DECODE_SAMPLING_INPUT_MEMCFG" in args.model_config:
            self.sampling_memory_config = args.model_config["DECODE_SAMPLING_INPUT_MEMCFG"]
        else:
            self.sampling_memory_config = ttnn.DRAM_MEMORY_CONFIG

        # Force argmax sampling
        if hasattr(args, "model_config") and "SAMPLING_AG_CONFIG" in args.model_config:
            # The model config may describe the fastest full-size Galaxy path, but
            # the actual CCL shape is resolved from the runtime mesh below.
            sampling_ag_config = args.model_config["SAMPLING_AG_CONFIG"]
            self._allow_force_argmax_sampling = sampling_ag_config["allow_force_argmax"]
            self.num_argmax_gather_links = sampling_ag_config["num_links"]
            self.argmax_chunks_per_sync = sampling_ag_config.get("chunks_per_sync", 10)
            self.argmax_num_workers_per_link = 1
            self.ag_topology = sampling_ag_config["topology"]
        else:
            self._allow_force_argmax_sampling = False
            self.num_argmax_gather_links = self.num_gather_links
            self.argmax_chunks_per_sync = 10
            self.argmax_num_workers_per_link = 1
            self.ag_topology = ttnn.Topology.Linear

        # Set defaults for sampling parameters if not provided
        # Default: k=1 (top-1), p=0 (effectively argmax), temp=1 (no temperature scaling)
        # When p=0, the sampling operation will select the token with highest probability (argmax)
        total_param_size = self.max_batch_size * self._sampling_dp
        if k is None:
            k = torch.ones(total_param_size)
        if p is None:
            p = torch.zeros(total_param_size)
        if temp is None:
            temp = torch.ones(total_param_size)

        k, temp = self._normalize_device_params(k, temp)
        self._force_argmax_sampling = self._is_force_argmax_sampling(k, p, temp)

        # Create sampling parameter tensors on device
        # When _sampling_dp > 1, dims=(0, None) shards the [128] tensor across 4 rows → [32] per row
        self.k_tensor = ttnn.from_torch(
            k,
            device=self.mesh_device,
            dtype=ttnn.uint32,
            layout=ttnn.ROW_MAJOR_LAYOUT,
            mesh_mapper=ttnn.ShardTensor2dMesh(self.mesh_device, dims=self._param_dims, mesh_shape=self.cluster_shape),
        )
        self.p_tensor = ttnn.from_torch(
            p,
            device=self.mesh_device,
            dtype=ttnn.bfloat16,
            layout=ttnn.ROW_MAJOR_LAYOUT,
            mesh_mapper=ttnn.ShardTensor2dMesh(self.mesh_device, dims=self._param_dims, mesh_shape=self.cluster_shape),
        )
        self.temp_tensor = ttnn.from_torch(
            temp,
            device=self.mesh_device,
            dtype=ttnn.bfloat16,
            layout=ttnn.ROW_MAJOR_LAYOUT,
            mesh_mapper=ttnn.ShardTensor2dMesh(self.mesh_device, dims=self._param_dims, mesh_shape=self.cluster_shape),
        )
        # The tie-break sentinel has to outrank every real global index. Vocabularies this large are
        # far beyond anything shipped, but exceeding it would corrupt the greedy token silently rather
        # than fail, so check it at construction. Raise rather than assert: this guards a correctness
        # invariant and must survive python -O.
        if self.padded_vocab_size > TIEBREAK_INDEX_SENTINEL:
            raise ValueError(
                f"padded_vocab_size {self.padded_vocab_size} exceeds the greedy tie-break sentinel "
                f"{TIEBREAK_INDEX_SENTINEL}; raise TIEBREAK_INDEX_SENTINEL (keeping it a power of two)"
            )
        # Persistent per-user ARGMAX mask [1,1,N,1] (1.0 where k==1), distributed like k_tensor. Used by
        # _adjust_values_for_tiebreak to boost the lowest-index tied-max for greedy users only. Built
        # host-side and kept in sync in reset_sampling_params (an on-device reshape of the [N] k_tensor
        # to [1,1,N,1] is not sub-device-safe). bfloat16 TILE so it broadcasts over the candidate width
        # and multiplies the (bfloat16) winner mask without a dtype mix; 0.0/1.0 are exact in bf16.
        self._greedy_col = ttnn.from_torch(
            (torch.as_tensor(k).reshape(1, 1, -1, 1) == 1).to(torch.bfloat16),
            device=self.mesh_device,
            dtype=ttnn.bfloat16,
            layout=ttnn.TILE_LAYOUT,
            mesh_mapper=ttnn.ShardTensor2dMesh(
                self.mesh_device, dims=self._greedy_col_dims(), mesh_shape=self.cluster_shape
            ),
        )

        # Create device offset indices for global indexing
        self._create_indices_tensors()
        self._create_invalid_vocab_mask()
        # Log-probs tensor to store the log-probs for the batch
        self.tt_log_probs = None
        self.log_probs_calculator = LogProbsCalculator(
            self.mesh_device,
            self.sub_core_grids,
            self.tt_ccl,
            batch_size=self.max_batch_size,
            use_topk_logprobs=getattr(args, "use_topk_logprobs", False),
        )

        # Seeds tensor: one RNG slot per user across all rows.
        # When sampling_dp > 1, shard across rows so each row gets its own slice.
        # user_ids tensor: core routing only (32 per row, replicated).
        self.seeds_tt_tensor = ttnn.from_torch(
            torch.arange(total_param_size).to(torch.uint32),
            device=self.mesh_device,
            dtype=ttnn.uint32,
            layout=ttnn.ROW_MAJOR_LAYOUT,
            mesh_mapper=(
                ttnn.ShardTensor2dMesh(self.mesh_device, dims=self._param_dims, mesh_shape=self.cluster_shape)
                if self._sampling_dp > 1
                else None
            ),
            memory_config=ttnn.DRAM_MEMORY_CONFIG,
        )
        self.user_ids_tt_tensor = ttnn.as_tensor(
            torch.arange(self.max_batch_size).to(torch.uint32),
            dtype=ttnn.uint32,
            layout=ttnn.ROW_MAJOR_LAYOUT,
            device=self.mesh_device,
            memory_config=ttnn.DRAM_MEMORY_CONFIG,
        )

    def _get_num_sampling_shards(self):
        if self.multi_step_reduction:
            return self._num_vocab_splits
        if 1 in self.cluster_shape:
            return max(self.cluster_shape[0], self.cluster_shape[1])

        if self.sampling_all_gather_axis not in (0, 1):
            raise ValueError(
                f"sampling_all_gather_axis must be 0 or 1 for 2D meshes, got {self.sampling_all_gather_axis}"
            )
        return self.cluster_shape[self.sampling_all_gather_axis]

    def _create_indices_tensors(self):
        """Create the per-shard index offsets added to the top-k indices after the gather."""
        num_devices_in_mesh = self._get_num_sampling_shards()
        indices_device_offsets = torch.ones(
            1, 1, self.max_batch_size, self.max_top_k * num_devices_in_mesh, dtype=torch.int64
        )
        padded_per_device = self.padded_vocab_size // num_devices_in_mesh

        for device_id in range(num_devices_in_mesh):
            indices_device_offsets[:, :, :, device_id * self.max_top_k : (device_id + 1) * self.max_top_k] = (
                device_id * padded_per_device
            )
        self.tt_indices_device_offsets = ttnn.from_torch(
            indices_device_offsets,
            device=self.mesh_device,
            dtype=ttnn.int32,
            layout=ttnn.TILE_LAYOUT,
            mesh_mapper=ttnn.ShardTensor2dMesh(self.mesh_device, dims=(None, None), mesh_shape=self.cluster_shape),
            memory_config=ttnn.DRAM_MEMORY_CONFIG,
        )

    def _create_invalid_vocab_mask(self):
        self.tt_invalid_vocab_mask = None
        self.tt_invalid_vocab_tail_mask = None
        self._invalid_vocab_tail_width = 0

        vocab_shard_dims = get_vocab_shard_dims(self.cluster_shape, self.sampling_all_gather_axis)
        # The compact tail-mask path masks only the padded tail, but it has to slice the
        # logits and concat them back. ttnn.concat only honours sub_core_grids when the
        # input is unsharded and the output is interleaved; otherwise it falls through to
        # the "massaged" untilize/transpose path, which is invoked without sub_core_grids
        # and so runs on the full Tensix grid. The sampling logits are width-sharded
        # (DECODE_LOGITS_MEMCFG), so on a sampling sub-core grid the concat escapes the
        # sub-device. Use the plain full-width additive mask (one elementwise add, no
        # reassembly) whenever a sub-core grid is in use.
        tail_mask = (
            build_tail_invalid_vocab_mask(
                self.vocab_size,
                self.padded_vocab_size,
                self.max_batch_size,
                self.cluster_shape,
                self.sampling_all_gather_axis,
                tile_size=ttnn.TILE_SIZE,
            )
            if self.sub_core_grids is None
            else None
        )
        if tail_mask is not None:
            self._invalid_vocab_tail_width = tail_mask.tail_width
            self.tt_invalid_vocab_tail_mask = ttnn.from_torch(
                tail_mask.mask,
                dtype=ttnn.bfloat16,
                layout=ttnn.TILE_LAYOUT,
                device=self.mesh_device,
                mesh_mapper=ttnn.ShardTensor2dMesh(
                    self.mesh_device,
                    dims=vocab_shard_dims,
                    mesh_shape=self.cluster_shape,
                ),
                memory_config=ttnn.DRAM_MEMORY_CONFIG,
            )
            return

        invalid_vocab_mask = build_invalid_vocab_mask(
            self.vocab_size,
            self.padded_vocab_size,
            self.max_batch_size,
        )
        if invalid_vocab_mask is None:
            return

        self.tt_invalid_vocab_mask = ttnn.from_torch(
            invalid_vocab_mask,
            dtype=ttnn.bfloat16,
            layout=ttnn.TILE_LAYOUT,
            device=self.mesh_device,
            mesh_mapper=ttnn.ShardTensor2dMesh(
                self.mesh_device,
                dims=vocab_shard_dims,
                mesh_shape=self.cluster_shape,
            ),
            memory_config=ttnn.DRAM_MEMORY_CONFIG,
        )

    def _mask_invalid_vocab_logits(self, logits):
        if self.tt_invalid_vocab_tail_mask is not None:
            return self._mask_invalid_vocab_tail_logits(logits)
        if self.tt_invalid_vocab_mask is None:
            return logits
        return ttnn.add(
            logits,
            self.tt_invalid_vocab_mask,
            memory_config=logits.memory_config(),
            sub_core_grids=self.sub_core_grids,
        )

    def _mask_invalid_vocab_tail_logits(self, logits):
        tail_width = self._invalid_vocab_tail_width
        local_width = logits.shape[-1]
        valid_width = local_width - tail_width
        if tail_width <= 0 or valid_width < 0:
            return self._mask_invalid_vocab_logits_fallback(logits)
        if valid_width == 0:
            return ttnn.add(
                logits,
                self.tt_invalid_vocab_tail_mask,
                memory_config=logits.memory_config(),
                sub_core_grids=self.sub_core_grids,
            )

        valid_logits = ttnn.slice(
            logits,
            [0, 0, 0, 0],
            [logits.shape[0], logits.shape[1], logits.shape[2], valid_width],
            memory_config=logits.memory_config(),
            sub_core_grids=self.sub_core_grids,
        )
        tail_logits = ttnn.slice(
            logits,
            [0, 0, 0, valid_width],
            [logits.shape[0], logits.shape[1], logits.shape[2], local_width],
            memory_config=logits.memory_config(),
            sub_core_grids=self.sub_core_grids,
        )
        masked_tail_logits = ttnn.add(
            tail_logits,
            self.tt_invalid_vocab_tail_mask,
            memory_config=logits.memory_config(),
            sub_core_grids=self.sub_core_grids,
        )
        masked_logits = ttnn.concat(
            [valid_logits, masked_tail_logits],
            dim=3,
            memory_config=logits.memory_config(),
            # Match the slices above: run concat on the sampling sub-device cores,
            # otherwise the concat program is placed on the full Tensix grid and
            # fails with "Kernel group cores do not match sub device cores"
            # (TT_FATAL num_intersections == num_cores).
            sub_core_grids=self.sub_core_grids,
        )
        ttnn.deallocate(valid_logits)
        ttnn.deallocate(tail_logits)
        ttnn.deallocate(masked_tail_logits)
        return masked_logits

    def _mask_invalid_vocab_logits_fallback(self, logits):
        if self.tt_invalid_vocab_mask is None:
            return logits
        return ttnn.add(
            logits,
            self.tt_invalid_vocab_mask,
            memory_config=logits.memory_config(),
            sub_core_grids=self.sub_core_grids,
        )

    def _can_slice_valid_vocab_for_argmax(self):
        return self.vocab_size < self.padded_vocab_size and self.vocab_size % ttnn.TILE_SIZE == 0

    def _slice_valid_vocab_for_argmax(self, logits):
        if not self._can_slice_valid_vocab_for_argmax() or logits.shape[-1] != self.padded_vocab_size:
            return logits
        return ttnn.slice(
            logits,
            [0, 0, 0, 0],
            [logits.shape[0], logits.shape[1], logits.shape[2], self.vocab_size],
            memory_config=logits.memory_config(),
            sub_core_grids=self.sub_core_grids,
        )

    def _perform_all_gather(self, tensor, dim, cluster_axis, memory_config, num_links, buffer_key=None):
        """
        Flexible all-gather that works across different CCL implementations.

        - If `tt_ccl` exposes `line_all_gather`, prefer it (enables persistent buffer usage on some stacks).
        - Otherwise fall back to `ttnn.all_gather`.
        """
        if callable(self._line_all_gather):
            # Some implementations accept `buffer_key` (for persistent buffers), others may not.
            line_all_gather_kwargs = {
                "dim": dim,
                "cluster_axis": cluster_axis,
                "memory_config": memory_config,
                "num_links": num_links,
            }
            if self._line_all_gather_supports_buffer_key and buffer_key is not None:
                line_all_gather_kwargs["buffer_key"] = buffer_key
            return self._line_all_gather(tensor, **line_all_gather_kwargs)

        return ttnn.all_gather(
            tensor,
            dim=dim,
            memory_config=memory_config,
            cluster_axis=cluster_axis,
        )

    def _get_sampling_cluster_axis(self):
        if self.mesh_device.get_num_devices() <= 1:
            return None
        # 1D submeshes should use the default CCL axis; forcing axis 1 can make
        # smaller Galaxy DP groups request routes outside the submesh.
        if 1 in self.cluster_shape:
            return None
        return self.sampling_all_gather_axis

    def _get_force_argmax_all_gather_config(self, cluster_axis):
        num_links = self.num_argmax_gather_links
        if hasattr(self.tt_ccl, "get_num_links"):
            # Clamp the tuned config to the links available on the actual submesh.
            num_links = min(num_links, self.tt_ccl.get_num_links(cluster_axis))

        topology = self.ag_topology
        # Ring is available for T3K-like 8-device groups; smaller DP groups need
        # linear routing to avoid wraparound routes such as D0 -> D12.
        if self.mesh_device.get_num_devices() < 8:
            topology = ttnn.Topology.Linear

        return max(1, num_links), topology

    def reset_params(
        self,
        k,
        p,
        temp,
        enable_log_probs: bool | list[bool] = None,
        num_logprobs: int | list[int] = None,
        empty_slots: list[int] | None = None,
    ):
        """Update sampling parameters (k, p, temperature, logprobs) dynamically."""
        k, temp = self._normalize_device_params(k, temp)
        # Top-k reporting needs the candidate pipeline, even for greedy tokens.
        # Resolve reporting before choosing the path so its parameters update too.
        self.log_probs_calculator.set_log_probs_mode(
            enable_log_probs, num_logprobs=num_logprobs, empty_slots=empty_slots
        )
        self._force_argmax_sampling = self._is_force_argmax_sampling(k, p, temp)
        if not self._force_argmax_sampling:
            # When _sampling_dp > 1, create multi-device host tensors so
            # copy_host_to_device_tensor writes per-row shards correctly.
            if self._sampling_dp > 1:
                mapper = ttnn.ShardTensor2dMesh(self.mesh_device, dims=self._param_dims, mesh_shape=self.cluster_shape)
            else:
                mapper = None

            self.k_tensor_new = ttnn.from_torch(
                k,
                device=None,
                dtype=ttnn.uint32,
                layout=ttnn.ROW_MAJOR_LAYOUT,
                mesh_mapper=mapper,
            )
            self.p_tensor_new = ttnn.from_torch(
                torch.tensor(p),
                device=None,
                dtype=ttnn.bfloat16,
                layout=ttnn.ROW_MAJOR_LAYOUT,
                mesh_mapper=mapper,
            )
            self.temp_tensor_new = ttnn.from_torch(
                temp,
                device=None,
                dtype=ttnn.bfloat16,
                layout=ttnn.ROW_MAJOR_LAYOUT,
                mesh_mapper=mapper,
            )

            ttnn.copy_host_to_device_tensor(self.k_tensor_new, self.k_tensor)
            ttnn.copy_host_to_device_tensor(self.p_tensor_new, self.p_tensor)
            ttnn.copy_host_to_device_tensor(self.temp_tensor_new, self.temp_tensor)

            # Keep the greedy tie-break mask (1.0 where k==1) in sync with k, distributed like k_tensor.
            self._greedy_col_new = ttnn.from_torch(
                (k.reshape(1, 1, -1, 1) == 1).to(torch.bfloat16),
                device=None,
                dtype=ttnn.bfloat16,
                layout=ttnn.TILE_LAYOUT,
                mesh_mapper=(
                    ttnn.ShardTensor2dMesh(
                        self.mesh_device, dims=self._greedy_col_dims(), mesh_shape=self.cluster_shape
                    )
                    if self._sampling_dp > 1
                    else None
                ),
            )
            ttnn.copy_host_to_device_tensor(self._greedy_col_new, self._greedy_col)

    def _greedy_col_dims(self):
        """Map the 1-D k_tensor shard dims (self._param_dims, batch on dim0) to the [1,1,N,1] greedy
        mask's dims (batch on dim2), so self._greedy_col is distributed exactly like self.k_tensor."""
        return tuple(2 if d == 0 else d for d in self._param_dims)

    def _adjust_values_for_tiebreak(self, gathered_values, gathered_global_indices):
        """Return gathered_values with, for ARGMAX users (k==1) ONLY, the single lowest-GLOBAL-INDEX
        candidate among the tied maxima boosted just past the tie, so ttnn.sampling's argmax selects
        it deterministically. This fixes ttnn.sampling's array-position tie-break (it breaks exact
        value ties by all_gather/device order, which varies run-to-run/slot-to-slot and flips the
        greedy token) by correcting the sampling INPUT in the TILE domain -- avoiding an in-place
        write into the ROW_MAJOR output buffer, which NO ttnn op supports on a restricted
        sub-device. Random users (k>1) get boost==0 => their values are bit-identical => their
        sampling is byte-for-byte unchanged. All ops honor self.sub_core_grids.

        Stable top-k (`stable=True`) on EVERY local top-k call would make this pass redundant, but
        stable is not uniformly cheap: multicore-eligible bf16 calls take the fused-key engine
        (packed [bf16 value | u16 index] words on the plain unstable network, where otherwise costs
        ~2.5-3x on the SFPU sort stage and the BH topk_large_indices route has no stable mode at
        all. Sampling keeps the host-side pass as the stable-sort guarantee instead. Removing this
        method, `_greedy_col` and `_greedy_col_dims` in favour of `stable=True` becomes a pure win
        once the remaining routes implement a performant stable mode.

        KNOWN LIMITATION: this picks the lowest global id among the GATHERED candidates. If a single
        device shard holds more than `max_top_k` (32) maxima tied at the same value, its top-k
        breaks the tie by array position and drops all but 32 of them, so the true lowest-id token
        may never reach the gathered set and this pass cannot recover it. Enabling `stable=True`
        on every route (at the costs above) would close that window.

        is_winner = (value == rowmax) AND (global_index == lowest_index_among_maxima)  # exactly one candidate
            lowest_index_among_maxima = min(global_index + not_max * SENTINEL)          # == idx at maxima, huge else

        The value half runs in bfloat16 and the index half in int32, and neither dtype is incidental --
        see the comments on each block. Validated on a restricted active sub-device by
        tests/ttnn/unit_tests/operations/reduce/test_tiebreak_input_adjust.py.
        """
        scg = self.sub_core_grids
        # Every intermediate is deallocated as soon as its last reader is issued: this runs once per
        # decode step, so leaving them to Python refcounting would hold ~9 extra buffers per step.

        # Value domain, bfloat16: gathered_values is bf16, so the max reduce and the comparisons
        # against its result are exact -- bf16 survives the FPU's source-register truncation intact.
        maxv = ttnn.max(gathered_values, dim=3, keepdim=True, sub_core_grids=scg)  # [1,1,B,1] bf16
        is_max = ttnn.eq(gathered_values, maxv, sub_core_grids=scg)  # 1.0 at the (tied) maxima, exact
        not_max = ttnn.lt(gathered_values, maxv, sub_core_grids=scg)  # 1.0 strictly below max

        # Per-row boost, >= 2 bf16 ULP of that row's maximum whatever its magnitude or sign.
        abs_max = ttnn.abs(maxv, sub_core_grids=scg)
        ttnn.deallocate(maxv)
        delta_scaled = ttnn.multiply(abs_max, TIEBREAK_DELTA_SCALE, sub_core_grids=scg)
        ttnn.deallocate(abs_max)
        delta = ttnn.add(delta_scaled, TIEBREAK_DELTA_FLOOR, sub_core_grids=scg)  # [1,1,B,1] bf16
        ttnn.deallocate(delta_scaled)

        # Index domain, int32: lowest global index among the maxima. Push the non-maxima above every
        # real index by TIEBREAK_INDEX_SENTINEL, then take the row min.
        #
        # This min MUST run in int32. ttnn.min/ttnn.max on a FLOAT32 tensor go to the FPU -- only
        # int32 and the opt-in accurate fp32 mean take the SFPU reduce path (see
        # ttnn/cpp/ttnn/operations/reduction/generic/device/common.hpp:use_sfpu_reduce_path) -- and the
        # FPU truncates its source registers to a 10-bit mantissa. Every index above 2**11 therefore
        # comes back rounded to a value that equals NO actual index, the winner mask comes out empty,
        # and the boost silently degrades to a no-op: the tie survives and the greedy token keeps
        # flipping. That is exactly how the first version of this pass failed on device. Int32 min/max
        # are integer-exact at every index. Keep the reduce, the mask arithmetic and the equality all
        # in int32 -- routing any of them through float32 (or uint32, which is on the FPU path too)
        # reintroduces the same rounding. forward() hands us uint32, so cast once here.
        idx = ttnn.typecast(gathered_global_indices, ttnn.int32, sub_core_grids=scg)
        offset = ttnn.multiply(not_max, TIEBREAK_INDEX_SENTINEL, sub_core_grids=scg)  # bf16, exact (2**24)
        ttnn.deallocate(not_max)
        offset_i32 = ttnn.typecast(offset, ttnn.int32, sub_core_grids=scg)
        ttnn.deallocate(offset)
        masked_idx = ttnn.add(idx, offset_i32, sub_core_grids=scg)  # int32
        ttnn.deallocate(offset_i32)
        greedy_i = ttnn.min(masked_idx, dim=3, keepdim=True, sub_core_grids=scg)  # [1,1,B,1] int32
        ttnn.deallocate(masked_idx)

        is_lowidx_i32 = ttnn.eq(idx, greedy_i, sub_core_grids=scg)  # broadcast over W
        ttnn.deallocate(idx)
        ttnn.deallocate(greedy_i)
        is_lowidx = ttnn.typecast(is_lowidx_i32, ttnn.bfloat16, sub_core_grids=scg)  # 1/0 -> 1.0/0.0
        ttnn.deallocate(is_lowidx_i32)
        is_winner = ttnn.multiply(is_max, is_lowidx, sub_core_grids=scg)  # 1.0 at exactly one candidate
        ttnn.deallocate(is_max)
        ttnn.deallocate(is_lowidx)

        # gate by k==1 (self._greedy_col [1,1,B,1] bf16); random users get boost 0 => values unchanged
        winner_gated = ttnn.multiply(is_winner, self._greedy_col, sub_core_grids=scg)
        ttnn.deallocate(is_winner)
        boost = ttnn.multiply(winner_gated, delta, sub_core_grids=scg)  # delta broadcasts over W
        ttnn.deallocate(winner_gated)
        ttnn.deallocate(delta)

        adjusted = ttnn.add(gathered_values, boost, sub_core_grids=scg)
        ttnn.deallocate(boost)
        return adjusted

    def _untilize_for_argmax(self, x):
        """Untilize a vocabulary row in bounded chunks while preserving its exact width."""
        num_untilize_chunks = self._untilize_chunk_count(x.shape[-1])
        # DRAM-interleaved ttnn.split/slice does not honor sub_core_grids (same
        # senders-column spill as the vocab-trim slice above).
        if num_untilize_chunks > 1 and self._force_argmax_sub_core_grids is None:
            # Untilizing the full row in one program needs a static circular-buffer
            # region proportional to the row width with past  around 150K elements it clashes
            # with the model's resident L1 buffers at compile.
            x_chunks = ttnn.split(x, self._untilize_chunk_width(x.shape[-1], num_untilize_chunks), dim=3)
            untilized_chunks = []
            for chunk in x_chunks:
                # Free each tiled chunk as soon as its row-major copy exists,
                # so peak memory holds around 1 full-vocab buffer less than freeing
                # after the loop.
                untilized_chunks.append(
                    ttnn.untilize(
                        chunk,
                        use_multicore=True,
                        sub_core_grids=self._force_argmax_sub_core_grids,
                    )
                )
                chunk.deallocate()
            x_untilized = ttnn.concat(
                untilized_chunks,
                dim=3,
                sub_core_grids=self._force_argmax_sub_core_grids,
            )
            for chunk in untilized_chunks:
                ttnn.deallocate(chunk)
        else:
            x_untilized = ttnn.untilize(x, use_multicore=True, sub_core_grids=self._force_argmax_sub_core_grids)
        return x_untilized

    def forward(
        self,
        x: ttnn.Tensor,
        tt_out_tok: ttnn.Tensor = None,
    ):
        """
        Perform on-device sampling on logits tensor.
        The logits are sharded over the devices in the cluster.
        We perform local top-k on each device, then all-gather the top-k values and indices across all devices.
        We then convert the gathered values and indices to the appropriate format, add the device offsets to get the global vocabulary indices,
        and perform the actual sampling with top-k, top-p, and temperature.

        Args:
            x: Input logits tensor
            tt_out_tok: Optional output tensor to write results to

        Returns:
            Sampled token indices tensor
        """
        if self._force_argmax_sampling:
            logger.info("Forcing argmax sampling")
            # Logprobs consume the original vocabulary shards, before argmax's
            # gather/slice. Keep token selection identical when reporting toggles.
            logits_for_log_probs = x
            # BH galaxy prefetcher (unfused-CCL) keeps a split senders/worker sub-device manager
            # loaded during decode. The vocab-trim ttnn.slice auto-grids a 32-core block from origin
            # (0,0) on the DRAM-interleaved path (it does not honor sub_core_grids there) and spills
            # into the uncovered senders-column tail -> "kernel group cores do not match sub device
            # cores". Skip that slice here. Still apply the full-width additive invalid-vocab mask
            # (already selected whenever sub_core_grids is set; one elementwise add, no slice/concat)
            # so a 0-padded logit cannot win argmax when every valid logit is negative. untilize/
            # argmax stay pinned to the worker sub-core grid via _force_argmax_sub_core_grids.
            force_argmax_skip_vocab_trim = self._force_argmax_sub_core_grids is not None
            slice_valid_vocab = self._can_slice_valid_vocab_for_argmax() and not force_argmax_skip_vocab_trim
            if not slice_valid_vocab:
                x = self._mask_invalid_vocab_logits(x)
            # Gather the output across all devices and untilize the tensor (for argmax)
            num_devices = self.mesh_device.get_num_devices()
            if num_devices > 1:
                cluster_axis = self._get_sampling_cluster_axis()
                num_links, topology = self._get_force_argmax_all_gather_config(cluster_axis)
                logger.debug(
                    f"Force argmax sampling all-gather: cluster_axis={cluster_axis}, "
                    f"num_links={num_links}, topology={topology}"
                )
                # NOTE: do NOT pass a persistent_output_buffer here. The all_gather_async factory
                # disables its barrier semaphore when persistent buffers are used, and the barrier
                # is what bounds cross-invocation skew: without it a fast device's next-invocation
                # writes/increments can reach a peer before that peer's reader has reset its
                # out_ready semaphore, leaving residual counts that make later waits pass early
                # (argmax then reads the previous step's logits). Under trace the fresh output
                # allocation is frozen at capture, so the address is stable anyway.
                barrier_accessor = getattr(
                    self.tt_ccl,
                    "get_sampling_barrier_semaphore_handle",
                    self.tt_ccl.get_and_cycle_barrier_semaphore_handle,
                )
                # Pin the gather to the worker sub-device only on the BH unfused path, where the
                # downstream untilize/argmax are themselves confined via _force_argmax_sub_core_grids.
                # Without this, all_gather_async defaults to sub-device 0 (prefetcher/senders under
                # the galaxy decode manager). Dispatch only serializes within a sub-device, so a
                # gather on 0 races the worker-grid untilize and under trace replay returns the
                # previous step's tokens. Eager mode masks this via host dispatch latency.
                # Left unpinned on Wormhole: untilize/argmax are not sub-core-grid confined there
                # and still use the default sub-device 0, matching pre-existing WH behaviour.
                ag_sub_device_id = (
                    getattr(self.tt_ccl, "worker_sub_device_id", None)
                    if self._force_argmax_sub_core_grids is not None
                    else None
                )
                x = ttnn.experimental.all_gather_async(
                    x,
                    persistent_output_buffer=None,
                    dim=3,
                    multi_device_global_semaphore=self.tt_ccl.get_and_cycle_ag_semaphore_handles(cluster_axis),
                    num_links=num_links,
                    memory_config=x.memory_config(),
                    cluster_axis=cluster_axis,
                    topology=topology,
                    barrier_semaphore=barrier_accessor(cluster_axis),
                    chunks_per_sync=self.argmax_chunks_per_sync,
                    num_workers_per_link=self.argmax_num_workers_per_link,
                    num_buffers_per_channel=2,
                    subdevice_id=ag_sub_device_id,
                )
            if slice_valid_vocab:
                x = self._slice_valid_vocab_for_argmax(x)
            x_untilized = self._untilize_for_argmax(x)
            tt_out_tok = ttnn.argmax(
                x_untilized,
                dim=-1,
                output_tensor=tt_out_tok,
                keepdim=False,
                sub_core_grids=self._force_argmax_sub_core_grids,
            )
            if self.log_probs_calculator.enable_log_probs:
                # argmax drops the final axis; the calculator takes the ordinary
                # sampler's [1,1,1,B] ROW_MAJOR index layout.
                logprob_indices = (
                    ttnn.reshape(tt_out_tok, (1, 1, 1, tt_out_tok.shape[-1]))
                    if len(tt_out_tok.shape) == 3
                    else tt_out_tok
                )
                self.tt_log_probs = self.log_probs_calculator.calculate_log_probs(logits_for_log_probs, logprob_indices)
            else:
                self.tt_log_probs = None
            return tt_out_tok, self.tt_log_probs

        # Decode logits normally arrive in bfloat16 already; a bf16->bf16 ttnn.typecast is
        # semantically a no-op but still dispatches a full copy program, so skip it.
        x_bf16 = (
            x if x.dtype == ttnn.bfloat16 else ttnn.typecast(x, dtype=ttnn.bfloat16, sub_core_grids=self.sub_core_grids)
        )
        x_bf16 = self._mask_invalid_vocab_logits(x_bf16)

        # The single-device split below exists only because the stock top-k factories cap
        # out near 64K columns. When ttnn.topk would take the Blackhole topk_large_indices
        # composite for the FULL row, the authoritative C++ route query lets one call
        # replace the split/per-chunk-topk/offset-add/concat pipeline; its indices are
        # already global vocab positions. Calls constrained to a sub-grid never relax.
        route_full_row = (
            self.multi_step_reduction
            and self.sub_core_grid_topk is None
            and topk_would_route_to_large_indices(x_bf16, self._num_vocab_splits * self.max_top_k)
        )
        if route_full_row:
            # stable dropped for the same reason as the chunked path below:
            # _adjust_values_for_tiebreak guarantees the greedy pick (#33492).
            #
            # k is multiplied by the split count the chunked path would have used, so the
            # candidate set keeps that path's exact width (num_splits x max_top_k): every
            # downstream shape is unchanged, and sampling sees a full-row top-(num_splits*k)
            # that is a strict quality upgrade over the union of the per-chunk top-ks. At the
            # production max_top_k=32 this also keeps the candidate row two tiles wide --
            # ttnn.sampling deadlocks on a SINGLE-tile candidate row whose values all tie
            # (compute-internal CB deadlock, writer CWFW; tenstorrent/tt-metal#53781).
            topk_values_gathered_bf16_interleaved, topk_indices_gathered = ttnn.topk(
                x_bf16,
                k=self._num_vocab_splits * self.max_top_k,
                dim=-1,
                stable=False,
            )
        elif self.multi_step_reduction:
            x_bf16_list = ttnn.split(x_bf16, x_bf16.shape[-1] // self._num_vocab_splits, dim=3)
            topk_values_list = []
            topk_indices_list = []

            # Drop stable=True ONLY when the authoritative C++ route query says
            # ttnn.topk will take the Blackhole topk_large_indices composite for
            # these halves once the custom arguments are absent.
            # Sampling opts out of stable topk for decode perf (_topk_stable is
            # False): these chunks route to the single-core factory,
            # where stable pays ~2-3x on the SFPU sort stage.
            # _adjust_values_for_tiebreak is what guarantees the greedy
            # pick after the gather, regardless of per-device tie order. Calls
            # that would not route keep today's arguments bit-for-bit, and a
            # call the model constrained to a sub-grid is never relaxed.
            use_routed_topk = self.sub_core_grid_topk is None and topk_would_route_to_large_indices(
                x_bf16_list[0], self.max_top_k
            )

            for i in range(len(x_bf16_list)):
                # Chunks are not padded to a power of two here: an A/B on this path
                # (PR #53167) measured no end-to-end decode benefit from steering
                # ttnn.topk to the multi-core factory, so single-core chunks stay.
                topk_values, topk_indices = ttnn.topk(
                    x_bf16_list[i],
                    k=self.max_top_k,
                    dim=-1,
                    sub_core_grids=self.sub_core_grid_topk,
                    # _topk_stable is False: the fast unstable network is used and
                    # _adjust_values_for_tiebreak guarantees the greedy pick. The routed-topk
                    # relax (unstable where the call would take the BH topk_large_indices
                    # composite) is kept so re-enabling stable stays a one-line change.
                    stable=False if use_routed_topk else self._topk_stable,
                )
                topk_values_list.append(topk_values)
                topk_indices_list.append(topk_indices)
                x_bf16_list[i].deallocate()

            topk_values_gathered_bf16_interleaved = ttnn.concat(topk_values_list, dim=3)
            topk_indices_gathered = ttnn.concat(topk_indices_list, dim=3)

            for i in range(len(topk_indices_list)):
                ttnn.deallocate(topk_values_list[i])
                ttnn.deallocate(topk_indices_list[i])

        else:
            # apply padding to the input tensor if needed
            # if number is not power of 2, pad to upper power of 2
            # pad only last dimension with float::min value to upper_power_of_2
            # This is necessary to use full optimization in the topk operation.
            if self.pad_to_power_of_2 and not is_power_of_2(x_bf16.shape[-1]):
                padded_value = upper_power_of_2(x_bf16.shape[-1])
                x_bf16 = ttnn.pad(
                    x_bf16,
                    [(0, 0), (0, 0), (0, 0), (0, padded_value - x_bf16.shape[-1])],
                    value=-sys.float_info.max,
                    sub_core_grids=self.sub_core_grids,
                )
            # Perform local top-k on each device. Drop stable=True ONLY when the
            # authoritative C++ route query says the relaxed call will take the
            # Blackhole topk_large_indices composite -- sampling keeps every call
            # unstable (_topk_stable is False): this multicore-eligible call would
            # usually get the ~free fused-key stable engine, but the greedy
            # guarantee must hold on ALL routes (see _topk_stable), and
            # _adjust_values_for_tiebreak already provides it host-side.
            # Sub-grid-constrained calls never relax.
            use_routed_topk = self.sub_core_grid_topk is None and topk_would_route_to_large_indices(
                x_bf16, self.max_top_k
            )
            topk_values, topk_indices = ttnn.topk(
                x_bf16,
                k=self.max_top_k,
                dim=-1,
                sub_core_grids=self.sub_core_grid_topk,
                # _topk_stable is False: the fast unstable network is used and
                # _adjust_values_for_tiebreak guarantees the greedy pick. The routed-topk
                # relax (unstable where the call would take the BH topk_large_indices
                # composite) is kept so re-enabling stable stays a one-line change.
                stable=False if use_routed_topk else self._topk_stable,
            )

            # For 1D meshes use `cluster_axis=None`. For 2D meshes, use the configured gather axis.
            sampling_cluster_axis = self._get_sampling_cluster_axis()

            # Gather top-k values across all devices
            topk_values_gathered = self._perform_all_gather(
                topk_values,
                dim=3,
                cluster_axis=sampling_cluster_axis,
                memory_config=ttnn.DRAM_MEMORY_CONFIG,
                num_links=self.num_gather_links,
                buffer_key="SAMPLING_VALUES",
            )

            ttnn.deallocate(topk_values)

            # Convert gathered values to appropriate format
            if self.sampling_memory_config != ttnn.DRAM_MEMORY_CONFIG:
                topk_values_gathered_bf16 = ttnn.to_memory_config(
                    topk_values_gathered,
                    memory_config=self.sampling_memory_config,
                    dtype=ttnn.bfloat16,
                )
                topk_values_gathered_bf16_interleaved = ttnn.to_memory_config(
                    topk_values_gathered_bf16, memory_config=ttnn.DRAM_MEMORY_CONFIG
                )
                ttnn.deallocate(topk_values_gathered_bf16)
            else:
                topk_values_gathered_bf16_interleaved = topk_values_gathered

            # Gather top-k indices across all devices
            topk_indices_gathered = self._perform_all_gather(
                topk_indices,
                dim=3,
                cluster_axis=sampling_cluster_axis,
                memory_config=ttnn.DRAM_MEMORY_CONFIG,
                num_links=self.num_gather_links,
                buffer_key="SAMPLING_INDICES",
            )
            ttnn.deallocate(topk_indices)

        # Convert indices to appropriate data types

        if route_full_row:
            # Full-row top-k indices are already global vocab positions; there are no
            # per-chunk offsets to add. The routed composite emits uint16/uint32 on the
            # stock op's 16-bit width boundary, so widen only when needed.
            if topk_indices_gathered.dtype == ttnn.uint32:
                topk_global_indices_interleaved = topk_indices_gathered
            else:
                topk_global_indices_interleaved = ttnn.typecast(
                    topk_indices_gathered, dtype=ttnn.uint32, sub_core_grids=self.sub_core_grids
                )
                ttnn.deallocate(topk_indices_gathered)
        else:
            topk_indices_gathered_int32 = ttnn.typecast(
                topk_indices_gathered, dtype=ttnn.int32, sub_core_grids=self.sub_core_grids
            )

            if self.sampling_memory_config != ttnn.DRAM_MEMORY_CONFIG:
                topk_indices_gathered_int32_sharded = ttnn.to_memory_config(
                    topk_indices_gathered_int32, self.sampling_memory_config
                )
                ttnn.deallocate(topk_indices_gathered_int32)
            else:
                topk_indices_gathered_int32_sharded = topk_indices_gathered_int32

            # Add device offsets to get global vocabulary indices
            topk_global_indices = ttnn.add(
                self.tt_indices_device_offsets,
                topk_indices_gathered_int32_sharded,
                dtype=ttnn.uint32,
                memory_config=self.sampling_memory_config,
            )

            ttnn.deallocate(topk_indices_gathered_int32_sharded)

            topk_global_indices_interleaved = ttnn.to_memory_config(topk_global_indices, ttnn.DRAM_MEMORY_CONFIG)

        # Untilize indices for sampling operation
        topk_global_indices_interleaved_untilised = ttnn.untilize(
            topk_global_indices_interleaved, use_multicore=True, sub_core_grids=self.sub_core_grids
        )
        # Perform the actual sampling with top-k, top-p, and temperature.
        # Host-side tie-break (in lieu of stable=True on every top-k route; see _topk_stable):
        # for argmax users (k==1) only, boost the single lowest-GLOBAL-INDEX tied maximum in the
        # sampling INPUT so ttnn.sampling's argmax picks it regardless of how the top-k network
        # ordered the tied candidates. Random users are byte-for-byte unchanged. Correcting the INPUT
        # (not the RM output buffer) is required: no ttnn op writes an interleaved ROW_MAJOR tensor
        # in-place on a restricted sub-device. See _adjust_values_for_tiebreak for the full rationale
        # and its known limitation (>max_top_k maxima tied within one device shard).
        sampling_values = self._adjust_values_for_tiebreak(
            topk_values_gathered_bf16_interleaved, topk_global_indices_interleaved
        )
        # Seed immediately before the draw. The tie-break's int32 ops run on the
        # SFPU (use_sfpu_reduce_path admits INT32 MIN/MAX/SUM) on the same sub-core grid,
        # and rand_tile's PRNG/LREG state is programmed by manual_seed -- so any SFPU work
        # between seeding and drawing can perturb the draw. The original's fp32 tie-break
        # took the FPU path and never disturbed it.
        ttnn.manual_seed(
            seeds=self.seeds_tt_tensor,
            user_ids=self.user_ids_tt_tensor,
            sub_core_grids=self._sampling_sub_core_grids,
        )
        tt_out_tok = ttnn.sampling(
            sampling_values,
            topk_global_indices_interleaved_untilised,
            k=self.k_tensor,
            p=self.p_tensor,
            temp=self.temp_tensor,
            sub_core_grids=self._sampling_sub_core_grids,
            output_tensor=tt_out_tok,
        )

        # Compute logprobs if enabled
        if self.log_probs_calculator.enable_log_probs and self.log_probs_calculator._use_topk_logprobs:
            # New path: top-K logprobs for gpt-oss-120b
            self.tt_log_probs = self.log_probs_calculator.calculate_topk_log_probs(
                logits_tensor=x,
                topk_values=topk_values_gathered_bf16_interleaved,
                topk_global_indices=topk_global_indices_interleaved,
                sub_core_grid_topk=self.sub_core_grid_topk,
            )
        elif self.log_probs_calculator.enable_log_probs:
            # Old path: single sampled-token logprob
            self.tt_log_probs = self.log_probs_calculator.calculate_log_probs(x, tt_out_tok)
        else:
            self.tt_log_probs = None

        ttnn.deallocate(sampling_values)
        ttnn.deallocate(topk_values_gathered_bf16_interleaved)
        ttnn.deallocate(topk_global_indices_interleaved)
        ttnn.deallocate(topk_global_indices_interleaved_untilised)

        return tt_out_tok, self.tt_log_probs