utils.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Kernel test utils"""

import itertools
import random
from collections.abc import Sequence
from numbers import Number
from typing import Any, NamedTuple
from unittest.mock import patch

import torch
from torch._prims_common import TensorLikeType

from tests.kernels.quant_utils import native_w8a8_block_matmul
from vllm.attention.backends.abstract import AttentionType
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.fused_moe.utils import moe_kernel_quantize_input
from vllm.utils.torch_utils import make_tensor_with_pad

# For now, disable "test_aot_dispatch_dynamic" since there are some
# bugs related to this test in PyTorch 2.4.
DEFAULT_OPCHECK_TEST_UTILS: tuple[str, ...] = (
    "test_schema",
    "test_autograd_registration",
    "test_faketensor",
)

ALL_OPCHECK_TEST_UTILS: tuple[str, ...] = (
    "test_schema",
    "test_autograd_registration",
    "test_faketensor",
    "test_aot_dispatch_dynamic",
)


class QKVInputs(NamedTuple):
    """
    Data structure for representing unpacked attention inputs,
    query/key/values and their sequence lengths.

    Attributes:

        * {query,key,value}: unpacked (batch_size x padded_seq_len x
                             num_heads x head_size) attention inputs
        * q_seq_lens: query sequence lengths list
        * kv_seq_lens: shared key/value sequence lengths list
    """

    query: torch.Tensor
    key: torch.Tensor
    value: torch.Tensor
    q_seq_lens: list[int]
    kv_seq_lens: list[int]


class QKVO(NamedTuple):
    """
    Data structure for representing unpacked attention inputs,
    alongside unpacked known-correct attention output

    Attributes:

        * qkv: unpacked (batch_size x padded_seq_len x
                             num_heads x head_size) attention inputs
        * ideal_output: unpacked (batch_size x padded_seq_len x
                        num_heads x head_size) known-correct attention output
    """

    qkv: QKVInputs
    ideal_output: torch.Tensor


class PackedQKVInputs(NamedTuple):
    """
    Data structure for representing packed attention inputs

    Attributes:

        * {query,key,value}: packed (number_of_tokens x num_heads
                             x head_size) attention inputs
        * q_start_loc_list: list of query start locations within packed tensor
        * kv_start_loc_list: shared list of key/value start locations within
                             packed tensor
        * q_seq_lens: query sequence lengths list
        * kv_seq_lens: shared key/value sequence lengths list
    """

    query: torch.Tensor
    key: torch.Tensor
    value: torch.Tensor
    q_start_loc_list: list[int] | None
    kv_start_loc_list: list[int] | None
    q_seq_lens: list[int] | None
    kv_seq_lens: list[int] | None


class PackedQKVO(NamedTuple):
    """
    Data structure for representing packed attention inputs,
    alongside packed known-correct attention output

    Attributes:

        * packed_qkv: packed (number_of_tokens x num_heads
                      x head_size) attention inputs
        * ideal_output: packed (number_of_tokens x num_heads
                        x head_size) known-correct attention output
    """

    packed_qkv: PackedQKVInputs | None
    ideal_output: torch.Tensor


class KVMemoryMap(NamedTuple):
    """
    Data structure for encapsulating KV cache memory mapping.

    Attributes:

        * block_tables: KV cache block tables
        * slot_mapping: mapping of sequence offset to physical address
    """

    block_tables: torch.Tensor
    slot_mapping: torch.Tensor


class PhaseTestParameters(NamedTuple):
    """
    Data structure for encapsulating the test parameters
    for a given test "phase" (prefill or decode phase) and attention
    scenario (encoder, decoder-self, encoder/decoder-cross)

    Attributes:

        * packed_qkvo: packed (number_of_tokens x num_heads
                       x head_size) attention inputs & known-correct
                       output
        * kv_mmap: KV cache memory mapping, specific to this test phase &
                   attention scenario
    """

    packed_qkvo: PackedQKVO
    kv_mmap: KVMemoryMap | None


def maybe_make_int_tensor(
    _list: list[int] | None,
    device: torch.device | str,
) -> torch.Tensor:
    """
    Convert Python int list to a 1D int torch.Tensor on `device`

    Returns:

    * If _list is not None: 1D int torch.Tensor on `device`
    * None otherwise
    """
    return (
        None if _list is None else torch.tensor(_list, dtype=torch.int, device=device)
    )


def maybe_make_long_tensor(
    _list: list[int] | None,
    device: torch.device | str,
) -> torch.Tensor:
    """
    Convert Python int list to a 1D long torch.Tensor on `device`

    Returns:

    * If _list is not None: 1D long torch.Tensor on `device`
    * None otherwise
    """
    return (
        None if _list is None else torch.tensor(_list, dtype=torch.long, device=device)
    )


def maybe_max(_list: list | None) -> Number | None:
    """
    Returns:

    * If _list is not None: max(_list)
    * None otherwise
    """
    return None if _list is None else max(_list)


def make_causal_mask(
    q_max_seq_len: int,
    kv_max_seq_len: int,
) -> torch.Tensor:
    """
    Create a q_max_seq_len x kv_max_seq_len causal mask

    Arguments:

    * q_max_seq_len: query max seq len
    * kv_max_seq_len: key/value max seq len

    Returns:

    * 2D tensor, q_max_seq_len x kv_max_seq_len
    """

    # Create a matrix where entry (i, j) is True if i >= j
    mask = torch.triu(torch.ones(q_max_seq_len, kv_max_seq_len), diagonal=1)
    # Replace True with float('-inf') and False with 0
    mask = mask.masked_fill(mask == 1, float("-inf")).masked_fill(mask == 0, 0.0)
    return mask


def ref_masked_attention(
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    scale: float,
    custom_mask: torch.Tensor | None = None,
    q_seq_lens: list | None = None,
    kv_seq_lens: list | None = None,
) -> torch.Tensor:
    """
    "Golden" masked attention reference. Supports two types of masking:

    * Basic attention mask, utilizing {q,kv}_seq_lens args to mask out
      padding elements
    * Custom attention mask, which can force an arbitrary mask tensor, i.e.
      causal

    Arguments:

    * query: batch_size x q_padded_seq_len x num_heads x head_size
    * key: batch_size x kv_padded_seq_len x num_heads x head_size
    * value: batch_size x kv_padded_seq_len x num_heads x head_size
    * scale: Attention scale factor
    * custom_mask: custom attention mask; good place to inject a causal
      attention mask
    * q_seq_lens: list of unpadded query seq_lens for each batch index
    * kv_seq_lens: list of unpadded key/value seq_lens for each batch index

    Returns:

    * Attention result, batch_size x q_padded_seq_len x num_heads x head_size
    """

    assert q_seq_lens is not None
    assert kv_seq_lens is not None

    batch_size = query.shape[0]
    assert len(q_seq_lens) == batch_size
    assert len(kv_seq_lens) == batch_size

    attn_weights = scale * torch.einsum("bqhd,bkhd->bhqk", query, key).float()

    # Basic attention mask, derived from seq lens
    if (q_seq_lens is not None) or (kv_seq_lens is not None):
        attn_mask = torch.zeros_like(attn_weights)
        if q_seq_lens is not None:
            for bdx, plen in enumerate(q_seq_lens):
                attn_mask[bdx, :, plen:, :] = -torch.inf
        if kv_seq_lens is not None:
            for bdx, plen in enumerate(kv_seq_lens):
                attn_mask[bdx, :, :, plen:] = -torch.inf

        attn_weights = attn_weights + attn_mask.float()

    # Custom attention mask
    if custom_mask is not None:
        attn_weights = attn_weights + custom_mask.float()

    attn_weights = torch.softmax(attn_weights, dim=-1).to(value.dtype)
    out = torch.einsum("bhqk,bkhd->bqhd", attn_weights, value)
    return out


def make_qkv(
    batch_size: int,
    max_q_seq_len: int,
    max_kv_seq_len: int | None,
    num_heads: int,
    head_size: int,
    device: torch.device | str,
    force_kv_seq_lens: list[int] | None = None,
    attn_type: AttentionType = AttentionType.ENCODER_DECODER,
    force_max_len: bool = False,
) -> tuple[QKVInputs, QKVInputs, QKVInputs]:
    """
    Construct QKV test tensors for self- and cross-attention.

    Generates three query/key/value triplets:

    * "Baseline" query/key/value (for input to reference attention function)
    * "Prefill" query/key/value (last sequence offset zero'd out, for use as
      input to prefill kernel)
    * "Decode" query/key/value (only the last sequence offset  from baseline,
      for use as input to decode kernel)

    Each Q/K/V triplet is associated with a list of q seqlens and a list of k/v
    seqlens

    Arguments:

    * batch_size
    * max_q_seq_len: max query seq len
    * max_kv_seq_len: max key/value seq len
    * num_heads
    * head_size
    * is_encoder_decoder_attn: if True, query seqlen may differ from
      key/value seqlen (as is often the case for cross-attention);
      o/w, query/key/value seqlens match at each batch index
      (max_kv_seq_len is unused)
    * force_kv_seq_lens: if not None, overrides kv sequence lengths
    * attn_type: encoder, decoder self, or enc/dec cross attention
    * force_max_len: if True, all query seqlens are max_q_seq_len; o/w query
      seqlens are random in [2,max_q_seq_lens]. Same for key/value seqlens
      and max_kv_seq_len, unless forced by is_encoder_decoder_attn=False
    * device: CPU or CUDA device

    Returns:

    * Overall QKVInputs structure (containing full unpacked Q/K/V tensors)
    * Prefill QKVInputs structure (containing all but the last sequence offset)
    * Decode QKVInputs structure (containing all only the last sequence offset)
    """

    if force_max_len:
        q_seq_lens = [max_q_seq_len for _ in range(batch_size)]
    else:
        q_seq_lens = [random.randint(2, max_q_seq_len) for _ in range(batch_size)]
    kv_seq_lens = None
    if force_kv_seq_lens is not None:
        kv_seq_lens = force_kv_seq_lens
    elif attn_type != AttentionType.ENCODER_DECODER:
        # K,V seq lens match Q for self-attention
        kv_seq_lens = q_seq_lens
    else:
        # K,V seq lens are distinct from Q seq lens & random
        assert max_kv_seq_len is not None
        if force_max_len:
            kv_seq_lens = [max_kv_seq_len] * batch_size
        else:
            kv_seq_lens = [random.randint(2, max_kv_seq_len) for _ in range(batch_size)]

    query = torch.rand((batch_size, max_q_seq_len, num_heads, head_size)).to(device)
    key = torch.rand((batch_size, max_kv_seq_len, num_heads, head_size)).to(device)
    value = torch.rand((batch_size, max_kv_seq_len, num_heads, head_size)).to(device)

    prefill_query = torch.zeros((batch_size, max_q_seq_len, num_heads, head_size)).to(
        device
    )
    prefill_key = torch.zeros((batch_size, max_kv_seq_len, num_heads, head_size)).to(
        device
    )
    prefill_value = torch.zeros((batch_size, max_kv_seq_len, num_heads, head_size)).to(
        device
    )

    decode_query = torch.zeros((batch_size, 1, num_heads, head_size)).to(device)
    decode_key = torch.zeros((batch_size, 1, num_heads, head_size)).to(device)
    decode_value = torch.zeros((batch_size, 1, num_heads, head_size)).to(device)

    for bdx, (q_seq_len, kv_seq_len) in enumerate(zip(q_seq_lens, kv_seq_lens)):
        query[bdx, q_seq_len:, :, :] = 0
        key[bdx, kv_seq_len:, :, :] = 0
        value[bdx, kv_seq_len:, :, :] = 0

        prefill_query[bdx, 0 : (q_seq_len - 1), :, :] = query[
            bdx, 0 : (q_seq_len - 1), :, :
        ]
        prefill_key[bdx, 0 : (kv_seq_len - 1), :, :] = key[
            bdx, 0 : (kv_seq_len - 1), :, :
        ]
        prefill_value[bdx, 0 : (kv_seq_len - 1), :, :] = value[
            bdx, 0 : (kv_seq_len - 1), :, :
        ]

        decode_query[bdx, :, :, :] = query[bdx, (q_seq_len - 1) : q_seq_len, :, :]
        decode_key[bdx, :, :, :] = key[bdx, (kv_seq_len - 1) : kv_seq_len, :, :]
        decode_value[bdx, :, :, :] = value[bdx, (kv_seq_len - 1) : kv_seq_len, :, :]

    prefill_q_seq_lens = [plen - 1 for plen in q_seq_lens]
    prefill_kv_seq_lens = [plen - 1 for plen in kv_seq_lens]

    decode_q_seq_lens = [1 for _ in q_seq_lens]
    decode_kv_seq_lens = [1 for _ in kv_seq_lens]

    return (
        QKVInputs(
            query,  # Overall QKV inputs
            key,
            value,
            q_seq_lens,
            kv_seq_lens,
        ),
        QKVInputs(
            prefill_query,  # Prefill subset of QKV sequences
            prefill_key,
            prefill_value,
            prefill_q_seq_lens,
            prefill_kv_seq_lens,
        ),
        QKVInputs(
            decode_query,  # Decode subset of KV sequences
            decode_key,
            decode_value,
            decode_q_seq_lens,
            decode_kv_seq_lens,
        ),
    )


def pack_tensor(
    unpacked_tensor: torch.Tensor, seq_lens: list[int], device: torch.device | str
) -> tuple[torch.Tensor, list[int]]:
    """
    Pack a batch_size x padded_seq_len x num_heads x head_size tensor into an
    unpadded number_of_tokens x num_heads x head_size tensor, where
    number_of_tokens = sum(seq_lens)

    Arguments:

    * unpacked_tensor: batch_size x padded_seq_len x num_heads x head_size
    * seq_lens: list of token counts for each seq
    * device: CPU or CUDA device

    Returns

    * packed_tensor: number_of_tokens x num_heads x head_size
    * start_loc_list: start idx of each batch elt in packed_tensor; [0] +
      list(itertools.accumulate(seq_lens))
    """

    num_tok = sum(seq_lens)
    num_heads = unpacked_tensor.shape[-2]
    head_size = unpacked_tensor.shape[-1]
    start_loc_list = [0] + list(itertools.accumulate(seq_lens))
    packed_tensor = torch.zeros((num_tok, num_heads, head_size), device=device)

    for bdx, (seq_len, start_loc) in enumerate(zip(seq_lens, start_loc_list)):
        packed_tensor[start_loc : (start_loc + seq_len), :, :] = unpacked_tensor[
            bdx, :seq_len, :, :
        ]

    return packed_tensor, start_loc_list


def pack_qkv(qkv: QKVInputs, device: torch.device | str) -> PackedQKVInputs:
    """
    Individually pack each of Q, K and V, each with dimensions batch_size x
    padded_seq_len x num_heads x head_size, into respective number_of_tokens x
    num_heads x head_size tensors.

    For Q, number_of_tokens = sum(q_seq_lens).

    For K and V, number_of_tokens = sum(kv_seq_lens)

    Arguments:

    * qkv: Unpacked (batch_size x padded_seq_len x num_heads x head_size)
           attention inputs
    * device: CPU or CUDA device

    Returns

    * Packed (number_of_tokens x num_heads x head_size) QKV inputs
      derived from unpacked inputs
    """

    if qkv.query is None:
        packed_query = None
        q_start_loc_list = None
    else:
        packed_query, q_start_loc_list = pack_tensor(
            qkv.query, qkv.q_seq_lens, device=device
        )
    packed_key, kv_start_loc_list = pack_tensor(qkv.key, qkv.kv_seq_lens, device=device)
    packed_value, _ = pack_tensor(qkv.value, qkv.kv_seq_lens, device=device)
    return PackedQKVInputs(
        packed_query,
        packed_key,
        packed_value,
        q_start_loc_list,
        kv_start_loc_list,
        (None if q_start_loc_list is None else qkv.q_seq_lens),
        qkv.kv_seq_lens,
    )


def _make_metadata_tensors(
    seq_lens: list[int] | None,
    context_lens: list[int] | None,
    encoder_seq_lens: list[int] | None,
    device: torch.device | str,
) -> tuple[
    torch.Tensor,
    torch.Tensor,
    Any,
    Any,
    torch.Tensor | None,
    torch.Tensor,
    torch.Tensor,
    int | None,
]:
    """
    Build scalar & tensor values required to build attention metadata structure.

    Arguments:

    * seq_lens: list of token-counts for each decoder input seq
    * context_lens: list of context length values for each seq
    * encoder_seq_lens: list of token-counts for each encoder input seq
    * device: CPU or CUDA device

    Returns:

    * seq_lens_tensor: decoder seq_lens list, as tensor
    * context_lens_tensor: context_lens list, as tensor
    * max_context_len: max(context_lens)
    * max_seq_len: max(seq_lens)
    * seq_start_loc: start idx of each sequence
    * encoder_seq_lens_tensor: encoder seq_lens list, as tensor
    * encoder_seq_start_loc: start idx of each encoder sequence
    * max_encoder_seq_len: encoder seq_lens list, as tensor
    """
    seq_lens_tensor = maybe_make_int_tensor(seq_lens, device)
    context_lens_tensor = maybe_make_int_tensor(context_lens, device)
    max_context_len = maybe_max(context_lens)
    max_seq_len = maybe_max(seq_lens)

    encoder_seq_lens_tensor = maybe_make_int_tensor(encoder_seq_lens, device)
    max_encoder_seq_len = None if encoder_seq_lens is None else max(encoder_seq_lens)

    seq_start_loc = None

    if seq_lens_tensor is not None:
        seq_start_loc = torch.zeros(
            seq_lens_tensor.shape[0] + 1,
            dtype=torch.int32,
            device=seq_lens_tensor.device,
        )
        torch.cumsum(
            seq_lens_tensor, dim=0, dtype=seq_start_loc.dtype, out=seq_start_loc[1:]
        )

    encoder_seq_start_loc = torch.zeros(
        encoder_seq_lens_tensor.shape[0] + 1,
        dtype=torch.int32,
        device=encoder_seq_lens_tensor.device,
    )
    torch.cumsum(
        encoder_seq_lens_tensor,
        dim=0,
        dtype=encoder_seq_start_loc.dtype,
        out=encoder_seq_start_loc[1:],
    )

    return (
        seq_lens_tensor,
        context_lens_tensor,
        max_context_len,
        max_seq_len,
        seq_start_loc,
        encoder_seq_lens_tensor,
        encoder_seq_start_loc,
        max_encoder_seq_len,
    )


def make_kv_cache(
    num_blocks: int,
    num_heads: int,
    head_size: int,
    block_size: int,
    device: torch.device | str,
    backend: str,
    default_val: float = 0.0,
) -> torch.Tensor:
    """
    Create a fake KV cache.

    Arguments:

    * num_blocks: number of blocks in the KV cache
    * num_heads: number of attention heads
    * head_size: head dimension
    * block_size: number of offsets within a block
    * device: CPU or CUDA device
    * default_val: initialization value for KV cache elements

    Returns:

    * kv_cache: 2 x num_blocks x block_size x num_heads x head_size
    *     for backend 'FLASH_ATTN'
    """
    if backend != "FLASH_ATTN":
        raise ValueError(f"Unknown backend value: '{backend}'. Expected 'FLASH_ATTN'.")
    kv_cache = torch.rand((2, num_blocks, block_size, num_heads, head_size)).to(device)
    if default_val is not None:
        kv_cache[:, :, :] = default_val
    return kv_cache


def _num_tokens_to_min_blocks(num_tokens: int, block_size: int) -> int:
    """
    Compute the minimum number of blocks required to hold num_tokens tokens,
    given block_size
    """
    return (num_tokens + block_size) // block_size


def make_empty_slot_mapping_tensor(device: torch.device | str):
    return maybe_make_long_tensor([], device)


def make_empty_block_tables_tensor(device: torch.device | str):
    return torch.tensor([], device=device)


def split_slot_mapping(
    slot_mapping_list: torch.Tensor,
    seq_lens: list[int],
    device: torch.device | str,
):
    """
    Split a slot mapping into valid prefill- and decode-phase slot mappings.

    Context:
    * Your goal is to test (1) prefill of N prompts, with prompt-lengths
      {K_i \\forall i \\in [0,N)}, followed by (2) decoding of a single token
      for all N prompts (N tokens total); the resultant sequence lengths
      after decode would be {K_i + 1 for i \\in [0,N)}
    * The test you want to do requires (1) having the prefill slot mapping
      for all tokens present during prefill, the number of which is
      M = \\sum_i{K_i}, and (2) having the decode slot mapping for all N
      decoded tokens

    This function consumes a single 1D slot mapping, which is the
    concatenation of N slot mappings each of length K_i + 1 (corresponding
    to the  sequence lengths after decode), with a total length of
    P = \\sum_i{K_i + 1} = M + N

    The prefill-phase slot mapping results from excising the (K_i + 1)-th entry
    from each of the N subsequences in the slot mapping (i.e. omitting the
    decoded token's mapping.)

    The N excised entries are appended to obtain the decode-phase slot mapping

    Arguments:

    * slot_mapping_list: Length-P 1D slot mapping (as list) reflecting all N
      post-decode sequences
    * seq_lens: list of N post-decode sequence lengths (K_i + 1 in the
      description above)
    * device: cuda, cpu, etc.

    Returns:

    * prefill_slot_mapping: Length-M 1D slot mapping (as Tensor)
      reflecting all N prefill prompts
    * decode_slot_mapping: Length-N 1D slot mapping (as Tensor) reflecting
      all N decoded tokens
    """

    prefill_slot_mapping = []
    decode_slot_mapping = []

    base_idx = 0
    for seq_len in seq_lens:
        prefill_slot_mapping.extend(
            slot_mapping_list[base_idx : (base_idx + seq_len - 1)]
        )
        decode_slot_mapping.append(slot_mapping_list[base_idx + seq_len - 1])
        base_idx += seq_len

    return (
        maybe_make_long_tensor(prefill_slot_mapping, device),
        maybe_make_long_tensor(decode_slot_mapping, device),
    )


def make_block_tables_slot_mapping(
    block_size: int,
    seq_lens: list[int],
    device: torch.device | str,
    block_base_addr: int = 0,
) -> tuple[torch.Tensor, list[int], int]:
    """
    Construct fake block tables & slot mappings.

    For a sequence with num_tokens tokens the minimum number
    of required KV cache blocks is

    num_blocks = (num_tokens + block_size) // block_size

    Then the minimum KV cache size in blocks is

    total_cache_blocks = sum(num_blocks for all seqs)

    Then, the blocktable mapping counts downward from

    block_base_addr + total_cache_blocks

    to

    block_base_addr


    The constructed block-tables and slot-mapping are sized to the
    lengths of the sequences in their entirety (as reflected by seq_lens),
    i.e. the total of prefill prompt tokens + decoded tokens.

    Arguments:

    * block_size: number of offsets per block
    * seq_lens: list of token-counts for each sequence
    * block_base_addr: the block table base address
    * device: CPU or CUDA device

    Return:

    * block_tables_tensor: block table for sequence
    * slot_mapping_list: slot mapping for sequence
    * max_block_idx: the highest block address within this block table
    """

    # Provision minimum number of KV cache blocks
    num_blocks_list = [
        _num_tokens_to_min_blocks(num_tokens, block_size) for num_tokens in seq_lens
    ]
    max_block_table_len = max(num_blocks_list)
    block_table_pad_tokens = 10

    block_tables = []
    slot_mapping_list = []
    # Compute uppermost address of block table
    total_cache_blocks = sum(num_blocks_list)
    block_base_idx = block_base_addr + total_cache_blocks
    max_block_idx = block_base_idx
    for sdx, num_tokens in enumerate(seq_lens):
        num_blocks = num_blocks_list[sdx]
        block_table = list(range(block_base_idx, block_base_idx - num_blocks, -1))
        for idx in range(num_tokens):
            mapping_value = (idx % block_size) + block_table[
                idx // block_size
            ] * block_size
            slot_mapping_list.append(mapping_value)

        block_base_idx -= num_blocks
        block_tables.append(block_table)

    block_tables_tensor = make_tensor_with_pad(
        block_tables,
        max_len=max_block_table_len + block_table_pad_tokens,
        pad=0,
        dtype=torch.int,
        device=device,
    )

    return (block_tables_tensor, slot_mapping_list, max_block_idx)


def assert_actual_matches_ideal(
    test_params: PhaseTestParameters, output_under_test: torch.Tensor, backend: str
) -> None:
    """
    Assert that observed output matches the ideal output
    contained in the test parameters data structure.

    Arguments:

    * test_params: Test parameters including packed ideal output
    * output_under_test: actually observed output value
    """
    ideal_output = test_params.packed_qkvo.ideal_output
    if backend != "FLASH_ATTN":
        raise ValueError(f"Unknown backend value: '{backend}'. Expected 'FLASH_ATTN'.")
    # For FlashAttention override the accuracy thresholds to non default
    # values since we notice a higher difference between the ideal and
    # actual output.
    torch.testing.assert_close(
        ideal_output, output_under_test.view_as(ideal_output), atol=0.01, rtol=0.016
    )


# Copied/modified from torch._refs.__init__.py
def fp8_allclose(
    a: TensorLikeType,
    b: TensorLikeType,
    rtol: float = 1e-05,
    atol: float = 1e-08,
    equal_nan: bool = False,
) -> bool:
    """
    Reference implementation of torch.allclose
    """
    torch._refs._check_close_args(name="torch.allclose", a=a, b=b, rtol=rtol, atol=atol)

    return bool(
        torch.all(
            torch.isclose(
                a.double(), b.double(), rtol=rtol, atol=atol, equal_nan=equal_nan
            )
        ).item()
    )


# Marlin MoE test utils


def stack_and_dev(tensors: list[torch.Tensor]):
    dev = tensors[0].device
    return torch.stack(tensors, dim=0).to(dev)


def compute_max_diff(output, output_ref):
    return torch.mean(torch.abs(output - output_ref)) / torch.mean(
        torch.abs(output_ref)
    )


def torch_experts(
    a: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weight: torch.Tensor,
    topk_ids: torch.Tensor,
    global_num_experts: int = -1,
    b_bias1: torch.Tensor | None = None,
    b_bias2: torch.Tensor | None = None,
    expert_map: torch.Tensor | None = None,
    w1_scale: torch.Tensor | None = None,
    w2_scale: torch.Tensor | None = None,
    a1_scale: torch.Tensor | None = None,
    a2_scale: torch.Tensor | None = None,
    quant_dtype: torch.dtype | None = None,
    per_act_token_quant=False,
    block_shape: list[int] | None = None,
    apply_router_weights_on_input: bool = False,
) -> torch.Tensor:
    assert (
        global_num_experts == -1
        or (global_num_experts == w1.shape[0] and expert_map is None)
        or (expert_map is not None and global_num_experts == expert_map.shape[0])
    )

    if quant_dtype in [torch.float16, torch.bfloat16]:
        quant_dtype = None
    quant_input_only = quant_dtype is not None and w1_scale is None and w2_scale is None
    if quant_input_only:
        assert a1_scale is None and a2_scale is None
        assert per_act_token_quant

    M, K = a.shape
    topk = topk_ids.shape[1]

    if apply_router_weights_on_input:
        assert topk == 1
        a = a * topk_weight.to(a.dtype)

    a = a.view(M, -1, K).repeat(1, topk, 1).reshape(-1, K)

    out = torch.zeros(M * topk, w2.shape[1], dtype=a.dtype, device=a.device)

    if a1_scale:
        assert not per_act_token_quant and block_shape is None
    a, a_scale = moe_kernel_quantize_input(
        a, a1_scale, quant_dtype, per_act_token_quant, block_shape
    )

    if quant_input_only:
        a = (a.float() * a_scale.view(-1, 1)).to(w1.dtype)

    num_experts = w1.shape[0]

    topk_ids = topk_ids.view(-1)
    if expert_map is not None:
        topk_ids = expert_map[topk_ids]

    f32 = torch.float32

    for i in range(num_experts):
        mask = topk_ids == i
        if mask.sum():
            if quant_dtype is None:
                tmp1 = a[mask] @ w1[i].transpose(0, 1)
                if b_bias1 is not None:
                    tmp1 = tmp1 + b_bias1[i].view(1, -1).to(tmp1.dtype)
                tmp2 = SiluAndMul()(tmp1)
                out[mask] = tmp2 @ w2[i].transpose(0, 1)
                if b_bias2 is not None:
                    out[mask] = out[mask] + b_bias2[i].view(1, -1).to(tmp1.dtype)
            elif quant_input_only:
                tmp1 = a[mask] @ w1[i].transpose(0, 1)
                tmp2 = SiluAndMul()(tmp1)
                tmp2, tmp2_scale = moe_kernel_quantize_input(
                    tmp2, None, quant_dtype, per_act_token_quant
                )
                tmp2 = (tmp2.float() * tmp2_scale.view(-1, 1)).to(w2.dtype)
                out[mask] = tmp2 @ w2[i].transpose(0, 1)
            elif block_shape is not None:
                # block quantized
                assert (
                    a_scale is not None
                    and w1_scale is not None
                    and w2_scale is not None
                )
                tmp1 = native_w8a8_block_matmul(
                    a[mask], w1[i], a_scale[mask], w1_scale[i], block_shape, out.dtype
                )
                if b_bias1 is not None:
                    tmp1 = tmp1 + b_bias1[i].view(1, -1).to(tmp1.dtype)
                tmp2 = SiluAndMul()(tmp1)
                tmp2, b_scale = moe_kernel_quantize_input(
                    tmp2, a2_scale, quant_dtype, per_act_token_quant, block_shape
                )

                out[mask] = native_w8a8_block_matmul(
                    tmp2, w2[i], b_scale, w2_scale[i], block_shape, out.dtype
                )
                if b_bias2 is not None:
                    out[mask] = out[mask] + b_bias2[i].view(1, -1).to(tmp1.dtype)
            else:
                assert (
                    a_scale is not None
                    and w1_scale is not None
                    and w2_scale is not None
                )
                scales = a_scale if a_scale.numel() == 1 else a_scale[mask]

                tmp1 = a[mask].to(f32) * scales
                w1_dq = (w1[i].to(f32) * w1_scale[i]).transpose(0, 1)
                tmp1 = (tmp1 @ w1_dq).to(out.dtype)
                if b_bias1 is not None:
                    tmp1 = tmp1 + b_bias1[i].view(1, -1).to(out.dtype)

                tmp2 = SiluAndMul()(tmp1).to(out.dtype)

                tmp2, b_scale = moe_kernel_quantize_input(
                    tmp2, a2_scale, quant_dtype, per_act_token_quant, block_shape
                )
                assert b_scale is not None

                tmp2 = tmp2.to(f32) * b_scale
                w2_dq = (w2[i].to(f32) * w2_scale[i]).transpose(0, 1)
                out[mask] = (tmp2 @ w2_dq).to(out.dtype)
                if b_bias2 is not None:
                    out[mask] = out[mask] + b_bias2[i].view(1, -1).to(out.dtype)

    if apply_router_weights_on_input:
        return out
    else:
        return (
            (out.view(M, -1, w2.shape[1]).to(f32) * topk_weight.view(M, -1, 1))
            .sum(dim=1)
            .to(out.dtype)
        )


def torch_moe(
    a: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    score: torch.Tensor,
    topk: int,
    b_bias1: torch.Tensor | None = None,
    b_bias2: torch.Tensor | None = None,
    global_num_experts: int = -1,
    expert_map: torch.Tensor | None = None,
) -> torch.Tensor:
    score = torch.softmax(score, dim=-1, dtype=torch.float32)
    topk_weight, topk_ids = torch.topk(score, topk)
    return torch_experts(
        a,
        w1,
        w2,
        topk_weight,
        topk_ids,
        global_num_experts,
        b_bias1,
        b_bias2,
        expert_map,
    )


def torch_moe_single(a, w, score, topk):
    B, D = a.shape
    a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
    out = torch.zeros(B * topk, w.shape[1], dtype=a.dtype, device=a.device)
    score = torch.softmax(score, dim=-1, dtype=torch.float32)
    _, topk_ids = torch.topk(score, topk)
    topk_ids = topk_ids.view(-1)
    for i in range(w.shape[0]):
        mask = topk_ids == i
        if mask.sum():
            out[mask] = a[mask] @ w[i].transpose(0, 1)
    return (out.view(B, -1, w.shape[1])).sum(dim=1)


# A special version of op check that has a restricted default set of test_utils
# and a patched version of allclose that supports fp8 types.
def opcheck(
    op: torch._ops.OpOverload
    | torch._ops.OpOverloadPacket
    | torch._library.custom_ops.CustomOpDef,
    args: tuple[Any, ...],
    kwargs: dict[str, Any] | None = None,
    *,
    test_utils: str | Sequence[str] = ALL_OPCHECK_TEST_UTILS,
    raise_exception: bool = True,
    cond: bool = True,
) -> dict[str, str]:
    with patch("torch.allclose", new=fp8_allclose):
        return (
            torch.library.opcheck(
                op, args, kwargs, test_utils=test_utils, raise_exception=raise_exception
            )
            if cond
            else {}
        )


# For testing quantized linear kernels
def to_fp8(tensor: torch.Tensor):
    finfo = torch.finfo(torch.float8_e4m3fn)
    return torch.round(tensor.clamp(min=finfo.min, max=finfo.max)).to(
        dtype=torch.float8_e4m3fn
    )


def to_int8(tensor: torch.Tensor):
    return torch.round(tensor.clamp(min=-128, max=127)).to(dtype=torch.int8)


def baseline_scaled_mm(
    a: torch.Tensor,
    b: torch.Tensor,
    scale_a: torch.Tensor,
    scale_b: torch.Tensor,
    out_dtype: type[torch.dtype],
    bias: torch.Tensor | None = None,
) -> torch.Tensor:
    # We treat N-dimensional group scaling as extended numpy-style broadcasting
    # in numpy simply stretches dimensions with an extent of 1 to match
    # the target shape by repeating the data along that dimension (broadcasting)
    # , we extend these semantics to say if the extent of a dimension in the
    # source shape is not 1 and does not match the target shape we repeat each
    # element along that dimension src_shape[dim] // target_shape[dim] times
    # example if we have:
    #       a = [[1, 2], and target_shape = (2, 4)
    #            [3, 4]]
    # then we would expand a to:
    #       a = [[1, 1, 2, 2],
    #            [3, 3, 4, 4]]
    # NOTE this function does not explicitly broadcast dimensions
    # with an extent of 1, since this can be done implicitly by pytorch
    def group_broadcast(t, shape):
        for i, s in enumerate(shape):
            if t.shape[i] != s and t.shape[i] != 1:
                assert s % t.shape[i] == 0
                t = (
                    t.unsqueeze(i + 1)
                    .expand(*t.shape[: i + 1], s // t.shape[i], *t.shape[i + 1 :])
                    .flatten(i, i + 1)
                )
        return t

    scale_a = group_broadcast(scale_a, a.shape)
    scale_b = group_broadcast(scale_b, b.shape)

    output = torch.mm(
        (scale_a * a.to(dtype=torch.float32)), (scale_b * b.to(dtype=torch.float32))
    ).to(out_dtype)

    if bias is not None:
        output = output + bias

    return output