ColossalAI/tests/test_infer_ops/triton/test_context_attn_unpad.py

import pytest
import torch
from packaging import version
from transformers.modeling_attn_mask_utils import AttentionMaskConverter

from colossalai.inference.modeling.layers.attention import PagedAttention
from colossalai.kernel.triton import context_attention_unpadded
from colossalai.utils import get_current_device
from tests.test_infer_ops.triton.kernel_utils import generate_caches_and_block_tables_v2, torch_attn_ref

try:
    import triton  # noqa

    HAS_TRITON = True
except ImportError:
    HAS_TRITON = False
    print("please install triton from https://github.com/openai/triton")

TRITON_CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.4")

HEAD_DIM = 32


def torch_attn_unpad(
    q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, context_lengths: torch.Tensor, num_heads: int, num_kv_heads: int
):
    # Process sequence one by one and concatenate them together.
    # q,k,v [num_tokens(sum(context_lengths)), num_heads, head_dim]
    assert context_lengths.dim() == 1, "context_lengths should be a 1D tensor"

    _, num_heads, head_dim = q.shape
    out_torch = []
    start_idx = 0
    for seq_i in range(len(context_lengths)):
        end_idx = start_idx + context_lengths[seq_i].item()
        seq_len = end_idx - start_idx
        mask = torch.tril(torch.ones(1, 1, seq_len, seq_len), diagonal=0).to(device=q.device)
        mask[mask == 0.0] = float("-inf")

        torch_attn_ref_out = torch_attn_ref(
            q[start_idx:end_idx].unsqueeze(0).transpose(1, 2),
            k[start_idx:end_idx].unsqueeze(0).transpose(1, 2),
            v[start_idx:end_idx].unsqueeze(0).transpose(1, 2),
            mask,
            1,  # set bsz as 1 as we're processing sequence one by one
            seq_len,
            seq_len,
            num_heads,
            num_kv_heads,
            head_dim,
        )
        out_torch.append(torch_attn_ref_out.squeeze(0))
        start_idx = end_idx

    return torch.cat(out_torch, dim=0)


@pytest.mark.skipif(not (HAS_TRITON and TRITON_CUDA_SUPPORT), reason="requires triton")
@pytest.mark.parametrize("bsz", [4, 7, 32])
@pytest.mark.parametrize("block_size", [16, 32, 64])
@pytest.mark.parametrize("max_num_blocks_per_seq", [8, 32])
@pytest.mark.parametrize("num_attn_heads", [16])
@pytest.mark.parametrize("kv_group_num", [1, 2, 16])
@pytest.mark.parametrize("same_context_len", [True, False])
def test_context_attention(
    bsz: int,
    block_size: int,
    max_num_blocks_per_seq: int,
    num_attn_heads: int,
    kv_group_num: int,
    same_context_len: bool,
):
    torch.manual_seed(123)
    # It's necessary to clear cache here.
    torch.cuda.empty_cache()
    torch.cuda.synchronize()
    torch.cuda.reset_peak_memory_stats()

    num_kv_heads = num_attn_heads // kv_group_num
    assert isinstance(num_kv_heads, int) and num_kv_heads > 0, "Invalid number of kv heads."
    max_seq_len = max_num_blocks_per_seq * block_size
    dtype = torch.float16
    device = get_current_device()

    if same_context_len:
        context_lengths = torch.tensor([max_seq_len for _ in range(bsz)], dtype=torch.int32, device=device)
    else:
        context_lengths = torch.randint(low=1, high=max_seq_len, size=(bsz,), dtype=torch.int32, device=device)
    num_tokens = torch.sum(context_lengths).item()

    qkv_size = (num_tokens, num_attn_heads + 2 * num_kv_heads, HEAD_DIM)
    qkv_unpad = torch.empty(size=qkv_size, dtype=dtype, device=device).normal_(mean=0.0, std=0.5)
    q_unpad, k_unpad, v_unpad = torch.split(qkv_unpad, [num_attn_heads, num_kv_heads, num_kv_heads], dim=-2)
    q_unpad = q_unpad.contiguous()

    k_cache_ref, v_cache_ref, block_tables = generate_caches_and_block_tables_v2(
        k_unpad, v_unpad, context_lengths, bsz, max_num_blocks_per_seq, block_size, dtype, device
    )
    block_tables = block_tables.to(device=device)
    k_cache_triton = torch.zeros_like(k_cache_ref)
    v_cache_triton = torch.zeros_like(v_cache_ref)

    out_triton = context_attention_unpadded(
        q_unpad, k_unpad, v_unpad, k_cache_triton, v_cache_triton, context_lengths, block_tables, block_size
    )

    out_torch = torch_attn_unpad(q_unpad, k_unpad, v_unpad, context_lengths, num_attn_heads, num_kv_heads)

    assert out_torch.shape == out_triton.shape
    assert torch.allclose(out_torch, out_triton, atol=1e-3)
    assert torch.equal(k_cache_ref, k_cache_triton)
    assert torch.equal(v_cache_ref, v_cache_triton)


BATCH = 16
BLOCK_SIZE = 32
SAME_LEN = True
WARM_UPS = 10
REPS = 100
configs = [
    triton.testing.Benchmark(
        x_names=["KV_LEN"],
        x_vals=[2**i for i in range(8, 13)],
        # x_vals=[x for x in range(256, 8192, 256)],
        line_arg="provider",
        line_vals=["torch", "triton"],
        line_names=["Torch", "Triton"],
        styles=[("red", "-"), ("blue", "-")],
        ylabel="ms",
        plot_name=f"context_attn-block_size-{BLOCK_SIZE}-batch{BATCH}",
        args={"bsz": BATCH, "block_size": BLOCK_SIZE, "same_context_len": SAME_LEN, "kv_group_num": 1},
    )
]


@triton.testing.perf_report(configs)
def bench_kernel(
    bsz,
    KV_LEN,
    provider,
    block_size: int,
    kv_group_num: int,
    same_context_len: bool,
):
    num_attn_heads = 16
    max_num_blocks_per_seq = triton.cdiv(KV_LEN, block_size)
    max_seq_len = block_size * max_num_blocks_per_seq

    num_kv_heads = num_attn_heads // kv_group_num
    assert isinstance(num_kv_heads, int) and num_kv_heads > 0, "Invalid number of kv heads."
    dtype = torch.float16
    device = get_current_device()

    if same_context_len:
        context_lengths = torch.tensor([max_seq_len for _ in range(bsz)], dtype=torch.int32, device=device)
    else:
        context_lengths = torch.randint(low=1, high=max_seq_len, size=(bsz,), dtype=torch.int32, device=device)
    num_tokens = torch.sum(context_lengths).item()

    qkv_size = (num_tokens, num_attn_heads + 2 * num_kv_heads, HEAD_DIM)
    qkv_unpad = torch.empty(size=qkv_size, dtype=dtype, device=device).normal_(mean=0.0, std=0.5)
    q_unpad, k_unpad, v_unpad = torch.split(qkv_unpad, [num_attn_heads, num_kv_heads, num_kv_heads], dim=-2)
    q_unpad = q_unpad.contiguous()
    k_cache_ref, v_cache_ref, block_tables = generate_caches_and_block_tables_v2(
        k_unpad, v_unpad, context_lengths, bsz, max_num_blocks_per_seq, block_size, dtype, device
    )
    block_tables = block_tables.to(device=device)

    quantiles = [0.5, 0.2, 0.8]
    if provider == "torch":
        q_padded = PagedAttention.pad_and_reshape(q_unpad, context_lengths, max_seq_len, num_attn_heads, HEAD_DIM)
        k_padded = PagedAttention.pad_and_reshape(k_unpad, context_lengths, max_seq_len, num_kv_heads, HEAD_DIM)
        v_padded = PagedAttention.pad_and_reshape(v_unpad, context_lengths, max_seq_len, num_kv_heads, HEAD_DIM)
        q_padded, k_padded, v_padded = (
            q_padded.to(device=device),
            k_padded.to(device=device),
            v_padded.to(device=device),
        )
        q_padded = q_padded.transpose(1, 2)
        k_padded = PagedAttention.repeat_kv(k_padded.transpose(1, 2), kv_group_num)
        v_padded = PagedAttention.repeat_kv(v_padded.transpose(1, 2), kv_group_num)
        # This benchmark ignores the padding mask. *Only* use the-same-length inputs for benchmarkings
        attn_mask = AttentionMaskConverter._make_causal_mask(
            (bsz, max_seq_len), q_padded.dtype, q_padded.device, past_key_values_length=0
        )
        attn_mask = attn_mask.to(device=q_padded.device)
        fn = lambda: torch_attn_ref(
            q_padded,
            k_padded,
            v_padded,
            attn_mask,
            bsz,
            max_seq_len,
            max_seq_len,
            num_attn_heads,
            num_kv_heads,
            HEAD_DIM,
        )
        ms, min_ms, max_ms = triton.testing.do_bench(fn, warmup=WARM_UPS, rep=REPS, quantiles=quantiles)
    if provider == "triton":
        k_cache_triton = torch.zeros_like(k_cache_ref)
        v_cache_triton = torch.zeros_like(v_cache_ref)
        fn = lambda: context_attention_unpadded(
            q_unpad, k_unpad, v_unpad, k_cache_triton, v_cache_triton, context_lengths, block_tables, block_size
        )
        ms, min_ms, max_ms = triton.testing.do_bench(fn, warmup=WARM_UPS, rep=REPS, quantiles=quantiles)

    return ms, min_ms, max_ms


if __name__ == "__main__":
    test_context_attention(4, 32, 8, 16, 1, True)
    # bench_kernel.run(save_path=".", print_data=True)