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[Kernel][Model] logits_soft_cap for Gemma2 with flashinfer (#6051)
Co-authored-by: Simon Mo <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,248 @@ | ||
| from typing import List, Optional, Tuple | ||
|
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| import flashinfer | ||
| import pytest | ||
| import torch | ||
|
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| NUM_HEADS = [(16, 16), (32, 8), (64, 8)] | ||
| HEAD_SIZES = [128, 256] | ||
| BLOCK_SIZES = [16, 32] | ||
| DTYPES = [torch.float16, torch.bfloat16] | ||
| NUM_BLOCKS = 32768 # Large enough to test overflow in index calculation. | ||
|
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| def ref_paged_attn( | ||
| query: torch.Tensor, | ||
| key_cache: torch.Tensor, | ||
| value_cache: torch.Tensor, | ||
| query_lens: List[int], | ||
| kv_lens: List[int], | ||
| block_tables: torch.Tensor, | ||
| scale: float, | ||
| sliding_window: Optional[int] = None, | ||
| soft_cap: Optional[float] = None, | ||
| ) -> torch.Tensor: | ||
| num_seqs = len(query_lens) | ||
| block_tables = block_tables.cpu().numpy() | ||
| _, block_size, num_kv_heads, head_size = key_cache.shape | ||
|
|
||
| outputs: List[torch.Tensor] = [] | ||
| start_idx = 0 | ||
| for i in range(num_seqs): | ||
| query_len = query_lens[i] | ||
| kv_len = kv_lens[i] | ||
| q = query[start_idx:start_idx + query_len] | ||
| q *= scale | ||
|
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| num_kv_blocks = (kv_len + block_size - 1) // block_size | ||
| block_indices = block_tables[i, :num_kv_blocks] | ||
|
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| k = key_cache[block_indices].view(-1, num_kv_heads, head_size) | ||
| k = k[:kv_len] | ||
| v = value_cache[block_indices].view(-1, num_kv_heads, head_size) | ||
| v = v[:kv_len] | ||
|
|
||
| if q.shape[1] != k.shape[1]: | ||
| k = torch.repeat_interleave(k, q.shape[1] // k.shape[1], dim=1) | ||
| v = torch.repeat_interleave(v, q.shape[1] // v.shape[1], dim=1) | ||
| attn = torch.einsum("qhd,khd->hqk", q, k).float() | ||
| empty_mask = torch.ones(query_len, kv_len) | ||
| mask = torch.triu(empty_mask, diagonal=kv_len - query_len + 1).bool() | ||
| if sliding_window is not None: | ||
| sliding_window_mask = torch.triu(empty_mask, | ||
| diagonal=kv_len - | ||
| (query_len + sliding_window) + | ||
| 1).bool().logical_not() | ||
| mask |= sliding_window_mask | ||
| if soft_cap is not None: | ||
| attn = soft_cap * torch.tanh(attn / soft_cap) | ||
| attn.masked_fill_(mask, float("-inf")) | ||
| attn = torch.softmax(attn, dim=-1).to(v.dtype) | ||
| out = torch.einsum("hqk,khd->qhd", attn, v) | ||
|
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| outputs.append(out) | ||
| start_idx += query_len | ||
|
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| return torch.cat(outputs, dim=0) | ||
|
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| @pytest.mark.parametrize("kv_lens", [[1328, 18, 463], [1, 54, 293, 70]]) | ||
| @pytest.mark.parametrize("num_heads", NUM_HEADS) | ||
| @pytest.mark.parametrize("head_size", HEAD_SIZES) | ||
| @pytest.mark.parametrize("block_size", BLOCK_SIZES) | ||
| @pytest.mark.parametrize("dtype", DTYPES) | ||
| @pytest.mark.parametrize("soft_cap", [None, 30.0, 50.0]) | ||
| @torch.inference_mode | ||
| def test_flashinfer_decode_with_paged_kv(kv_lens: List[int], | ||
| num_heads: Tuple[int, | ||
| int], head_size: int, | ||
| dtype: torch.dtype, block_size: int, | ||
| soft_cap: Optional[float]) -> None: | ||
| torch.set_default_device("cuda") | ||
| torch.cuda.manual_seed_all(0) | ||
| num_seqs = len(kv_lens) | ||
| num_query_heads = num_heads[0] | ||
| num_kv_heads = num_heads[1] | ||
| assert num_query_heads % num_kv_heads == 0 | ||
| max_kv_len = max(kv_lens) | ||
| scale = head_size**-0.5 | ||
|
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| query = torch.randn(num_seqs, num_query_heads, head_size, dtype=dtype) | ||
| key_value_cache = torch.randn(NUM_BLOCKS, | ||
| 2, | ||
| block_size, | ||
| num_kv_heads, | ||
| head_size, | ||
| dtype=dtype) | ||
| key_cache = key_value_cache[:, 0, :, :, :].squeeze(1) | ||
| value_cache = key_value_cache[:, 1, :, :, :].squeeze(1) | ||
|
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| max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size | ||
| block_tables = torch.randint(0, | ||
| NUM_BLOCKS, | ||
| (num_seqs, max_num_blocks_per_seq), | ||
| dtype=torch.int32) | ||
|
|
||
| kv_indptr = [0] | ||
| kv_indices = [] | ||
| kv_last_page_lens = [] | ||
| for i in range(num_seqs): | ||
| seq_len = kv_lens[i] | ||
| assert seq_len > 0 | ||
| num_blocks = (seq_len + block_size - 1) // block_size | ||
| kv_indices.extend(block_tables[i, :num_blocks]) | ||
| kv_indptr.append(kv_indptr[-1] + num_blocks) | ||
| kv_last_page_len = seq_len % block_size | ||
| if kv_last_page_len == 0: | ||
| kv_last_page_len = block_size | ||
| kv_last_page_lens.append(kv_last_page_len) | ||
|
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| kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32) | ||
| kv_indices = torch.tensor(kv_indices, dtype=torch.int32) | ||
| kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32) | ||
|
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| workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8) | ||
| wrapper = flashinfer.\ | ||
| BatchDecodeWithPagedKVCacheWrapper(workspace_buffer, "NHD") | ||
| wrapper.begin_forward(kv_indptr, | ||
| kv_indices, | ||
| kv_last_page_lens, | ||
| num_query_heads, | ||
| num_kv_heads, | ||
| head_size, | ||
| block_size, | ||
| "NONE", | ||
| data_type=dtype) | ||
|
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| output = wrapper.forward(query, key_value_cache, logits_soft_cap=soft_cap) | ||
|
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| ref_output = ref_paged_attn(query=query, | ||
| key_cache=key_cache, | ||
| value_cache=value_cache, | ||
| query_lens=[1] * num_seqs, | ||
| kv_lens=kv_lens, | ||
| block_tables=block_tables, | ||
| scale=scale, | ||
| soft_cap=soft_cap) | ||
| assert torch.allclose(output, ref_output, atol=1e-2, rtol=1e-2), \ | ||
| f"{torch.max(torch.abs(output - ref_output))}" | ||
|
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| @pytest.mark.parametrize("seq_lens", [[(1, 1328), (5, 18), (129, 463)]]) | ||
| @pytest.mark.parametrize("num_heads", NUM_HEADS) | ||
| @pytest.mark.parametrize("head_size", HEAD_SIZES) | ||
| @pytest.mark.parametrize("block_size", BLOCK_SIZES) | ||
| @pytest.mark.parametrize("dtype", DTYPES) | ||
| @pytest.mark.parametrize("soft_cap", [None, 30.0, 50.0]) | ||
| @torch.inference_mode | ||
| def test_flashinfer_prefill_with_paged_kv(seq_lens: List[Tuple[int, int]], | ||
| num_heads: Tuple[int, int], | ||
| head_size: int, dtype: torch.dtype, | ||
| block_size: int, | ||
| soft_cap: Optional[float]) -> None: | ||
| torch.set_default_device("cuda") | ||
| torch.cuda.manual_seed_all(0) | ||
| num_seqs = len(seq_lens) | ||
| query_lens = [x[0] for x in seq_lens] | ||
| kv_lens = [x[1] for x in seq_lens] | ||
| num_query_heads = num_heads[0] | ||
| num_kv_heads = num_heads[1] | ||
| assert num_query_heads % num_kv_heads == 0 | ||
| max_kv_len = max(kv_lens) | ||
| scale = head_size**-0.5 | ||
|
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||
| query = torch.randn(sum(query_lens), | ||
| num_query_heads, | ||
| head_size, | ||
| dtype=dtype) | ||
| key_value_cache = torch.randn(NUM_BLOCKS, | ||
| 2, | ||
| block_size, | ||
| num_kv_heads, | ||
| head_size, | ||
| dtype=dtype) | ||
| key_cache = key_value_cache[:, 0, :, :, :].squeeze(1) | ||
| value_cache = key_value_cache[:, 1, :, :, :].squeeze(1) | ||
|
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| # Normalize the scale of the key and value caches to mitigate | ||
| # numerical instability. | ||
| key_cache /= head_size**0.5 | ||
| value_cache /= head_size**0.5 | ||
|
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||
| max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size | ||
| block_tables = torch.randint(0, | ||
| NUM_BLOCKS, | ||
| (num_seqs, max_num_blocks_per_seq), | ||
| dtype=torch.int32) | ||
|
|
||
| qo_indptr = [0] | ||
| kv_indptr = [0] | ||
| kv_indices = [] | ||
| kv_last_page_lens = [] | ||
| for i in range(num_seqs): | ||
| seq_len = kv_lens[i] | ||
| assert seq_len > 0 | ||
| num_blocks = (seq_len + block_size - 1) // block_size | ||
| kv_indices.extend(block_tables[i, :num_blocks]) | ||
| kv_indptr.append(kv_indptr[-1] + num_blocks) | ||
| kv_last_page_len = seq_len % block_size | ||
| if kv_last_page_len == 0: | ||
| kv_last_page_len = block_size | ||
| kv_last_page_lens.append(kv_last_page_len) | ||
| qo_indptr.append(qo_indptr[-1] + query_lens[i]) | ||
|
|
||
| qo_indptr = torch.tensor(qo_indptr, dtype=torch.int32) | ||
| kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32) | ||
| kv_indices = torch.tensor(kv_indices, dtype=torch.int32) | ||
| kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32) | ||
|
|
||
| workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8) | ||
| wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper( | ||
| workspace_buffer, "NHD") | ||
| wrapper.begin_forward( | ||
| qo_indptr, | ||
| kv_indptr, | ||
| kv_indices, | ||
| kv_last_page_lens, | ||
| num_query_heads, | ||
| num_kv_heads, | ||
| head_size, | ||
| block_size, | ||
| ) | ||
|
|
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| output = wrapper.forward( | ||
| query, | ||
| key_value_cache, | ||
| logits_soft_cap=soft_cap, | ||
| ) | ||
|
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| ref_output = ref_paged_attn(query=query, | ||
| key_cache=key_cache, | ||
| value_cache=value_cache, | ||
| query_lens=query_lens, | ||
| kv_lens=kv_lens, | ||
| block_tables=block_tables, | ||
| scale=scale, | ||
| soft_cap=soft_cap) | ||
| assert torch.allclose(output, ref_output, atol=1e-2, rtol=1e-2), \ | ||
| f"{torch.max(torch.abs(output - ref_output))}" |
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