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[ragged-paged-attn] Use hidden states in kv cache and support any num… #8891

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[ragged-paged-attn] Use hidden states in kv cache and support any num… #8891

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4 changes: 2 additions & 2 deletions test/test_pallas.py
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Original file line number Diff line number Diff line change
Expand Up @@ -135,9 +135,9 @@ def _ragged_pagedattention_generate_qkv(
"constant", 0)
q = torch.randn((max_num_batched_tokens, num_q_heads, head_dim),
dtype=dtype)
k_pages = torch.randn((num_pages, page_size, num_kv_heads, head_dim),
k_pages = torch.randn((num_pages, page_size, num_kv_heads * head_dim),
dtype=dtype)
v_pages = torch.randn((num_pages, page_size, num_kv_heads, head_dim),
v_pages = torch.randn((num_pages, page_size, num_kv_heads * head_dim),
dtype=dtype)
page_indices = torch.randint(
0, num_pages, (max_num_seqs, pages_per_seq), dtype=torch.int32)
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80 changes: 10 additions & 70 deletions torch_xla/experimental/custom_kernel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -893,70 +893,10 @@ def flash_attention(
sm_scale, ab, partition_spec, mesh)


def ceil_div(a, b):
assert b != 0
return (a + b - 1) // b


def validate_ragged_paged_attention_inputs(
q, # [max_num_batched_tokens, num_q_heads, head_dim]
k_pages, # [total_num_pages, page_size, num_kv_heads, head_dim]
v_pages, # [total_num_pages, page_size, num_kv_heads, head_dim]
kv_lens, # i32[max_num_seqs]
page_indices, # i32[max_num_seqs, pages_per_seq]
cu_q_lens, # i32[max_num_seqs + 1]
num_seqs, # i32[1]
):
_, num_q_heads, head_dim = q.shape
_, _, num_kv_heads, head_dim_k = k_pages.shape
max_num_seqs, _ = page_indices.shape
if k_pages.shape != v_pages.shape:
raise ValueError(
f"{k_pages.shape=} and {v_pages.shape=} must have the same shape.")
if head_dim_k != head_dim:
raise ValueError(
f"Q head_dim {head_dim} must be the same as that of K/V {head_dim_k}.")
if kv_lens.shape != (max_num_seqs,):
raise ValueError(f"Expected {kv_lens.shape=} to be ({max_num_seqs},) where"
" `max_num_seqs` is `page_indices.shape[0]`.")
if cu_q_lens.shape != (max_num_seqs + 1,):
raise ValueError(
f"Expected {cu_q_lens.shape=} to be ({max_num_seqs + 1},) where"
" `max_num_seqs` is `page_indices.shape[0]`.")
if (kv_lens.dtype != torch.int32 or page_indices.dtype != torch.int32 or
cu_q_lens.dtype != torch.int32):
raise ValueError(
"The dtype of `kv_lens`, `page_indices`, and `cu_q_lens` must be"
f" int32. Got {kv_lens.dtype=}, {page_indices.dtype=},"
f" {cu_q_lens.dtype=}.")
if num_q_heads % num_kv_heads != 0:
raise ValueError(f"{num_q_heads=} must be divisible by {num_kv_heads=}")

# Must check below on runtime!
# if num_seqs > max_num_seqs:
# raise ValueError(f"{num_seqs=} must be less or equal to {max_num_seqs=}")
# max_kv_len = torch.max(kv_lens)
# min_pages_per_seq = ceil_div(max_kv_len, page_size)
# if pages_per_seq < min_pages_per_seq:
# raise ValueError(
# f"{pages_per_seq=} must be greater or equal to"
# f" {min_pages_per_seq=} given {max_kv_len=} and {page_size=}.")
# if cu_q_lens[num_seqs] > max_num_batched_tokens:
# raise ValueError(
# f"Total q tokens {cu_q_lens[num_seqs]} must be less or equal to"
# f" {max_num_batched_tokens=}.")
# for i in range(num_seqs):
# q_len = cu_q_lens[i + 1] - cu_q_lens[i]
# kv_len = kv_lens[i]
# if q_len > kv_len:
# raise ValueError(
# f"{q_len=} must be less or equal to {kv_len=} at sequence {i}.")


def _ragged_paged_attention_nonkernel(
queries, # [max_num_batched_tokens, num_q_heads, head_dim]
k_pages, # [total_num_pages, page_size, num_kv_heads, head_dim]
v_pages, # [total_num_pages, page_size, num_kv_heads, head_dim]
k_pages, # [total_num_pages, page_size, num_kv_heads * head_dim]
v_pages, # [total_num_pages, page_size, num_kv_heads * head_dim]
kv_lens, # i32[max_num_seqs]
page_indices, # i32[max_num_seqs, pages_per_seq]
cu_q_lens, # i32[max_num_seqs + 1]
Expand All @@ -965,8 +905,9 @@ def _ragged_paged_attention_nonkernel(
sm_scale=1.0,
mask_value=DEFAULT_MASK_VALUE,
):
_, _, num_kv_heads, head_dim = k_pages.shape
num_q_heads = queries.shape[1]
_, num_q_heads, head_dim = queries.shape
_, _, kv_model_dim = k_pages.shape
num_kv_heads = kv_model_dim // head_dim
assert num_q_heads % num_kv_heads == 0
num_query_per_kv = num_q_heads // num_kv_heads
outputs = []
Expand All @@ -977,8 +918,8 @@ def _ragged_paged_attention_nonkernel(
kv_len = kv_lens[i]
indices = page_indices[i]
q = queries[q_start:q_end]
k = k_pages[indices, :, :, :].reshape(-1, num_kv_heads, head_dim)[:kv_len]
v = v_pages[indices, :, :, :].reshape(-1, num_kv_heads, head_dim)[:kv_len]
k = k_pages[indices, :, :].reshape(-1, num_kv_heads, head_dim)[:kv_len]
v = v_pages[indices, :, :].reshape(-1, num_kv_heads, head_dim)[:kv_len]
k = torch.repeat_interleave(k, num_query_per_kv, dim=1)
v = torch.repeat_interleave(v, num_query_per_kv, dim=1)
attn = torch.einsum("qhd,khd->hqk", q, k)
Expand All @@ -998,8 +939,8 @@ def _ragged_paged_attention_nonkernel(
@requires_jax
def ragged_paged_attention(
q, # [max_num_batched_tokens, num_q_heads, head_dim]
k_pages, # [total_num_pages, page_size, num_kv_heads, head_dim]
v_pages, # [total_num_pages, page_size, num_kv_heads, head_dim]
k_pages, # [total_num_pages, page_size, num_kv_heads * head_dim]
v_pages, # [total_num_pages, page_size, num_kv_heads * head_dim]
kv_lens, # i32[max_num_seqs]
page_indices, # i32[max_num_seqs, pages_per_seq]
cu_q_lens, # i32[max_num_seqs + 1]
Expand All @@ -1014,8 +955,7 @@ def ragged_paged_attention(
):
if mask_value is None:
mask_value = DEFAULT_MASK_VALUE
validate_ragged_paged_attention_inputs(q, k_pages, v_pages, kv_lens,
page_indices, cu_q_lens, num_seqs)

if not use_kernel:
return _ragged_paged_attention_nonkernel(
q,
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