tests/kernels/conftest.py

from typing import List, Optional, Tuple, Union

import paged_attention as ops
import pytest
import torch


@pytest.fixture()
def kv_cache_factory():
    return create_kv_caches_with_random


@pytest.fixture()
def kv_cache_factory_flashinfer():
    return create_kv_caches_with_random_flash


STR_DTYPE_TO_TORCH_DTYPE = {
    "half": torch.half,
    "bfloat16": torch.bfloat16,
    "float": torch.float,
    "fp8": torch.uint8,
    "fp8_e4m3": torch.uint8,
    "fp8_e5m2": torch.uint8,
}


def create_kv_caches_with_random(
    num_blocks: int,
    block_size: int,
    num_layers: int,
    num_heads: int,
    head_size: int,
    cache_dtype: Optional[Union[str, torch.dtype]],
    model_dtype: Optional[Union[str, torch.dtype]] = None,
    seed: int = 0,
    device: Optional[str] = "cuda",
) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
    if cache_dtype == "fp8" and head_size % 16:
        raise ValueError(
            f"Does not support key cache of type fp8 with head_size {head_size}"
        )
    from paged_attention.platforms import current_platform

    current_platform.seed_everything(seed)

    torch_dtype = get_kv_cache_torch_dtype(cache_dtype, model_dtype)

    scale = head_size**-0.5
    x = 16 // torch.tensor([], dtype=torch_dtype).element_size()
    key_cache_shape = (num_blocks, num_heads, head_size // x, block_size, x)
    key_caches: List[torch.Tensor] = []
    for _ in range(num_layers):
        key_cache = torch.empty(size=key_cache_shape, dtype=torch_dtype, device=device)
        if cache_dtype in ["auto", "half", "bfloat16", "float"]:
            key_cache.uniform_(-scale, scale)
        elif cache_dtype == "fp8":
            _generate_random_fp8(key_cache, -scale, scale)
        else:
            raise ValueError(f"Does not support key cache of type {cache_dtype}")
        key_caches.append(key_cache)

    value_cache_shape = (num_blocks, num_heads, head_size, block_size)
    value_caches: List[torch.Tensor] = []
    for _ in range(num_layers):
        value_cache = torch.empty(
            size=value_cache_shape, dtype=torch_dtype, device=device
        )
        if cache_dtype in ["auto", "half", "bfloat16", "float"]:
            value_cache.uniform_(-scale, scale)
        elif cache_dtype == "fp8":
            _generate_random_fp8(value_cache, -scale, scale)
        else:
            raise ValueError(f"Does not support value cache of type {cache_dtype}")
        value_caches.append(value_cache)
    return key_caches, value_caches


def create_kv_caches_with_random_flash(
    num_blocks: int,
    block_size: int,
    num_layers: int,
    num_heads: int,
    head_size: int,
    cache_dtype: Optional[Union[str, torch.dtype]],
    model_dtype: Optional[Union[str, torch.dtype]] = None,
    seed: int = 0,
    device: Optional[str] = "cuda",
) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
    from paged_attention.platforms import current_platform

    current_platform.seed_everything(seed)

    torch_dtype = get_kv_cache_torch_dtype(cache_dtype, model_dtype)
    key_value_cache_shape = (num_blocks, 2, block_size, num_heads, head_size)
    scale = head_size**-0.5

    key_caches: List[torch.Tensor] = []
    value_caches: List[torch.Tensor] = []

    for _ in range(num_layers):
        key_value_cache = torch.empty(
            size=key_value_cache_shape, dtype=torch_dtype, device=device
        )
        if cache_dtype in ["auto", "half", "bfloat16", "float"]:
            key_value_cache.uniform_(-scale, scale)
        elif cache_dtype == "fp8":
            _generate_random_fp8(key_value_cache, -scale, scale)
        else:
            raise ValueError(f"Does not support key cache of type {cache_dtype}")
        key_caches.append(key_value_cache[:, 0])
        value_caches.append(key_value_cache[:, 1])
    return key_caches, value_caches


def get_kv_cache_torch_dtype(
    cache_dtype: Optional[Union[str, torch.dtype]],
    model_dtype: Optional[Union[str, torch.dtype]] = None,
) -> torch.dtype:
    if isinstance(cache_dtype, str):
        if cache_dtype == "auto":
            if isinstance(model_dtype, str):
                torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[model_dtype]
            elif isinstance(model_dtype, torch.dtype):
                torch_dtype = model_dtype
            else:
                raise ValueError(f"Invalid model dtype: {model_dtype}")
        elif cache_dtype in ["half", "bfloat16", "float"]:
            torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_dtype]
        elif cache_dtype == "fp8":
            torch_dtype = torch.uint8
        else:
            raise ValueError(f"Invalid kv cache dtype: {cache_dtype}")
    elif isinstance(cache_dtype, torch.dtype):
        torch_dtype = cache_dtype
    else:
        raise ValueError(f"Invalid kv cache dtype: {cache_dtype}")
    return torch_dtype


def _generate_random_fp8(
    tensor: torch.Tensor,
    low: float,
    high: float,
) -> None:
    # NOTE(zhaoyang): Due to NaN and Inf representation for fp8 data type,
    # it may occur Inf or NaN if we directly use torch.randint
    # to generate random data for fp8 data.
    # For example, s.11111.00 in fp8e5m2 format represents Inf.
    #     | E4M3        | E5M2
    # -----|-------------|-------------------
    # Inf | N/A         | s.11111.00
    # NaN | s.1111.111  | s.11111.{01,10,11}
    tensor_tmp = torch.empty_like(tensor, dtype=torch.float16)
    tensor_tmp.uniform_(low, high)
    ops.convert_fp8(tensor, tensor_tmp)
    del tensor_tmp