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import pytest
import torch
from sgl_kernel import FusedSetKVBufferArg, apply_rope_with_cos_sin_cache_inplace
from sgl_kernel.testing.rotary_embedding import (
FlashInferRotaryEmbedding,
MHATokenToKVPool,
RotaryEmbedding,
SglKernelRotaryEmbedding,
create_inputs,
)
@pytest.mark.parametrize(
"head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype, device, batch_size, seq_len, num_q_heads, num_kv_heads, save_kv_cache",
[
# GPT-OSS cases
*[
(
64,
64,
4096,
8000,
True,
torch.bfloat16,
"cuda",
batch_size,
seq_len,
64,
8,
save_kv_cache,
)
for batch_size, seq_len in (
(1, 1),
(32, 1),
(128, 1),
(512, 1),
(2, 512),
(4, 4096),
)
for save_kv_cache in (False, True)
],
# Other cases
(64, 64, 32, 8000, True, torch.bfloat16, "cuda", 32, 32, 1, 1, False),
(256, 128, 4096, 10000, True, torch.bfloat16, "cuda", 2, 512, 4, 2, False),
(512, 128, 311, 10000, True, torch.bfloat16, "cuda", 3, 39, 4, 2, False),
(128, 128, 2048, 10000, False, torch.bfloat16, "cuda", 2, 512, 32, 8, False),
(128, 128, 2048, 10000, False, torch.bfloat16, "cuda", 2, 512, 16, 4, False),
(512, 128, 311, 10000, False, torch.bfloat16, "cuda", 3, 39, 4, 2, False),
(64, 64, 32, 8000, True, torch.float32, "cuda", 32, 32, 1, 1, False),
(256, 128, 4096, 10000, True, torch.float32, "cuda", 2, 512, 4, 2, False),
(512, 128, 311, 10000, True, torch.float32, "cuda", 3, 39, 4, 2, False),
(128, 128, 2048, 10000, False, torch.float32, "cuda", 2, 512, 32, 8, False),
(128, 128, 2048, 10000, False, torch.float32, "cuda", 2, 512, 16, 4, False),
(512, 128, 311, 10000, False, torch.float32, "cuda", 3, 39, 4, 2, False),
],
)
def test_correctness(
head_size: int,
rotary_dim: int,
max_position_embeddings: int,
base: int,
is_neox_style: bool,
dtype: torch.dtype,
device: str,
batch_size: int,
seq_len: int,
num_q_heads: int,
num_kv_heads: int,
save_kv_cache: bool,
):
config = dict(
head_size=head_size,
rotary_dim=rotary_dim,
max_position_embeddings=max_position_embeddings,
base=base,
is_neox_style=is_neox_style,
dtype=dtype,
)
rope_ref = RotaryEmbedding(**config).to(device)
rope_flashinfer = FlashInferRotaryEmbedding(**config).to(device)
rope_sglkernel = SglKernelRotaryEmbedding(**config).to(device)
inputs = create_inputs(
head_size=head_size,
batch_size=batch_size,
seq_len=seq_len,
device=device,
dtype=dtype,
num_q_heads=num_q_heads,
num_kv_heads=num_kv_heads,
)
if save_kv_cache:
pool_ref_for_flashinfer = MHATokenToKVPool(
head_num=num_kv_heads, head_dim=head_size
)
pool_flashinfer = MHATokenToKVPool(head_num=num_kv_heads, head_dim=head_size)
query_ref, key_ref = inputs["query"].clone(), inputs["key"].clone()
query_flashinfer, key_flashinfer = inputs["query"].clone(), inputs["key"].clone()
query_sglkernel, key_sglkernel = inputs["query"].clone(), inputs["key"].clone()
# This is to align with the flashinfer implementation, flashinfer uses float32 cos/sin cache
query_ref_for_flashinfer_out, key_ref_for_flashinfer_out = rope_ref.forward_native(
inputs["pos_ids"], query_ref.to(torch.float32), key_ref.to(torch.float32)
)
query_ref_for_sglkernel_out, key_ref_for_sglkernel_out = rope_ref.forward_native(
inputs["pos_ids"], query_ref, key_ref
)
if save_kv_cache:
pool_ref_for_flashinfer.set_kv_buffer(
loc=inputs["out_cache_loc"],
cache_k=key_ref_for_flashinfer_out.view(-1, num_kv_heads, head_size),
cache_v=inputs["value"].view(-1, num_kv_heads, head_size),
)
query_flashinfer_out, key_flashinfer_out = rope_flashinfer.forward_cuda(
inputs["pos_ids"],
query_flashinfer,
key_flashinfer,
fused_set_kv_buffer_arg=(
FusedSetKVBufferArg(
value=inputs["value"],
k_buffer=pool_flashinfer.k_buffer[0].view(-1, num_kv_heads * head_size),
v_buffer=pool_flashinfer.v_buffer[0].view(-1, num_kv_heads * head_size),
k_scale=None,
v_scale=None,
cache_loc=inputs["out_cache_loc"],
)
if save_kv_cache
else None
),
)
query_sglkernel_out, key_sglkernel_out = rope_sglkernel.forward_cuda(
inputs["pos_ids"],
query_sglkernel,
key_sglkernel,
)
torch.testing.assert_close(
query_ref_for_flashinfer_out, query_flashinfer_out, atol=1e-2, rtol=1e-2
)
torch.testing.assert_close(
key_ref_for_flashinfer_out, key_flashinfer_out, atol=1e-2, rtol=1e-2
)
torch.testing.assert_close(
query_ref_for_sglkernel_out, query_sglkernel_out, atol=1e-2, rtol=1e-2
)
torch.testing.assert_close(
key_ref_for_sglkernel_out, key_sglkernel_out, atol=1e-2, rtol=1e-2
)
if save_kv_cache:
for field in ["k_buffer", "v_buffer"]:
x_ref = getattr(pool_ref_for_flashinfer, field)[0]
x_flashinfer = getattr(pool_flashinfer, field)[0]
torch.testing.assert_close(x_ref, x_flashinfer, atol=1e-2, rtol=1e-2)
nonzero_ref = x_ref != 0
nonzero_flashinfer = x_ref != 0
assert torch.all(nonzero_ref == nonzero_flashinfer)
if __name__ == "__main__":
pytest.main([__file__])
|