mirror of https://github.com/hpcaitech/ColossalAI
113 lines
4.3 KiB
Python
113 lines
4.3 KiB
Python
import pytest
|
|
import torch
|
|
from packaging import version
|
|
|
|
from colossalai.kernel.triton import context_attention_unpadded
|
|
from colossalai.utils import get_current_device
|
|
from tests.test_infer_ops.triton.kernel_utils import mock_alloc_block_table_and_kvcache, 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")
|
|
|
|
|
|
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),
|
|
k[start_idx:end_idx].unsqueeze(0),
|
|
v[start_idx:end_idx].unsqueeze(0),
|
|
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."
|
|
head_dim = 32
|
|
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 = torch.empty(size=qkv_size, dtype=dtype, device=device).normal_(mean=0.0, std=0.5)
|
|
q, k, v = torch.split(qkv, [num_attn_heads, num_kv_heads, num_kv_heads], dim=-2)
|
|
|
|
cache_shape = (bsz * max_num_blocks_per_seq, num_kv_heads, head_dim, block_size)
|
|
k_cache_torch = torch.zeros(size=cache_shape, dtype=dtype, device=device)
|
|
k_cache_triton = torch.zeros_like(k_cache_torch)
|
|
v_cache_torch = torch.zeros(size=cache_shape, dtype=dtype, device=device)
|
|
v_cache_triton = torch.zeros_like(v_cache_torch)
|
|
|
|
# Mock allocation on block tables
|
|
block_tables = mock_alloc_block_table_and_kvcache(
|
|
k, v, k_cache_torch, v_cache_torch, context_lengths, bsz, max_num_blocks_per_seq, block_size
|
|
)
|
|
block_tables = block_tables.to(device=device)
|
|
out_triton = context_attention_unpadded(
|
|
q, k, v, k_cache_triton, v_cache_triton, context_lengths, block_tables, block_size
|
|
)
|
|
|
|
out_torch = torch_attn_unpad(q, k, v, 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, rtol=1e-4)
|
|
assert torch.allclose(k_cache_torch, k_cache_triton)
|
|
assert torch.allclose(v_cache_torch, v_cache_triton)
|