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e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
459 lines
15 KiB
Python
459 lines
15 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import random
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from typing import Optional
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import pytest
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import torch
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import torch.nn.functional as F
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class BlockDiagonalCausalFromBottomRightMask:
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@staticmethod
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def _from_seqlens(query_lens, seq_lens, block_size=None):
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from torch import logical_and, logical_or
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contexted = block_size is None
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context_lens = torch.tensor(seq_lens) - torch.tensor(query_lens)
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n_queries = sum(query_lens)
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num_seqs = len(query_lens)
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if contexted:
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key_lens_blockaligned = seq_lens
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else:
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n_blocks_per_seq = (context_lens + block_size - 1) // block_size
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offset_per_seq = n_blocks_per_seq * block_size
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key_lens_blockaligned = offset_per_seq[:num_seqs].tolist()
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n_keys = sum(key_lens_blockaligned)
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a = (torch.arange(n_queries).reshape(n_queries,
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1).expand(n_queries, n_keys))
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b = torch.arange(n_keys).reshape(1, n_keys).expand(n_queries, n_keys)
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q_cumsum = torch.tensor([0] + query_lens).cumsum(dim=0)
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k_cumsum = torch.tensor([0] + key_lens_blockaligned).cumsum(dim=0)
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prior_mask = torch.zeros(n_queries, n_keys)
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new_masks: list[torch.Tensor] = []
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for seq_id in range(num_seqs):
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ri = q_cumsum[seq_id]
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ci = k_cumsum[seq_id]
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nr = query_lens[seq_id]
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if contexted:
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nc = seq_lens[seq_id]
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a_offset = ci + nc - ri - nr
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new_mask = (a + a_offset) >= b
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else:
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nc = context_lens[seq_id]
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a_offset = ci + nc - 1
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new_mask = a_offset >= b
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left_mask = b >= ci
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top_mask = a >= ri
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bottom_mask = a < (ri + nr)
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new_mask = logical_and(
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logical_and(logical_and(new_mask, left_mask), top_mask),
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bottom_mask,
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)
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prior_mask = logical_or(prior_mask, new_mask)
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new_masks = new_masks + [new_mask]
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return prior_mask
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@staticmethod
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def from_seqlens(query_lens, seq_lens, block_size=None):
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contexted = block_size is None
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if contexted:
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prior_mask = BlockDiagonalCausalFromBottomRightMask._from_seqlens(
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query_lens, seq_lens)
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active_mask = None
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else:
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prior_mask = BlockDiagonalCausalFromBottomRightMask._from_seqlens(
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query_lens, seq_lens, block_size)
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active_mask = BlockDiagonalCausalFromBottomRightMask._from_seqlens(
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query_lens, query_lens)
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return prior_mask, active_mask
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def ref_softmax(x: torch.Tensor,
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dim: int,
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mixed_precision=False,
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return_max_reduce=False):
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max_value = torch.amax(x, dim=dim, keepdims=True)
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exp = torch.exp(x - max_value)
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if mixed_precision:
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sum_value = torch.sum(exp.astype(torch.float32),
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dim=dim,
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keepdims=True).astype(x.dtype)
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else:
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sum_value = torch.sum(exp, dim=dim, keepdims=True)
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if return_max_reduce:
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return exp / sum_value, max_value, torch.reciprocal(sum_value)
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return exp / sum_value
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def ref_masked_attention(
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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scale: float,
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attn_mask: Optional[torch.Tensor] = None,
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return_max_reduce: Optional[bool] = False,
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) -> torch.Tensor:
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scaled_qk = scale * torch.einsum("qhd,khd->hqk", query, key).float()
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if attn_mask is not None:
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masked_score = scaled_qk + attn_mask.float()
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if return_max_reduce:
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norm_score, cached_max, cached_sum_reciprocal = ref_softmax(
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masked_score, dim=-1, return_max_reduce=True)
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else:
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norm_score = ref_softmax(masked_score, dim=-1)
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out = torch.einsum("hqk,khd->qhd", norm_score, value)
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if return_max_reduce:
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return (
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out,
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cached_max,
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cached_sum_reciprocal,
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norm_score,
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masked_score,
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scaled_qk,
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)
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else:
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return out
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def ref_context_attention(
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query,
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key,
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value,
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query_lens,
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seq_lens,
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head_size,
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num_kv_heads,
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num_heads,
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num_queries_per_kv,
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return_max_reduce=False,
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):
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scale = float(1.0 / (head_size**0.5))
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if num_queries_per_kv > 1:
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# Handle MQA and GQA
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key = torch.repeat_interleave(key, num_queries_per_kv, dim=1)
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value = torch.repeat_interleave(value, num_queries_per_kv, dim=1)
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attn_mask, _ = BlockDiagonalCausalFromBottomRightMask.from_seqlens(
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query_lens, seq_lens)
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# convert binary mask to -inf values
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attn_mask = torch.logical_not(attn_mask)
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attn_mask = attn_mask.float() * -30000
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output, cached_max, cached_sum_reciprocal, lse, masked_score, scaled_qk = (
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ref_masked_attention(
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query,
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key,
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value,
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scale,
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attn_mask,
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return_max_reduce=return_max_reduce,
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))
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output = output.unsqueeze(1)
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if return_max_reduce:
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return (
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output,
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cached_max,
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cached_sum_reciprocal,
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lse,
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masked_score,
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scaled_qk,
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)
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else:
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return output
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@pytest.mark.parametrize(
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"num_heads,num_queries_per_kv,head_size,mixed_precision",
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[
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(4, 2, 8, False),
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(4, 2, 8, True),
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(32, 8, 64, True),
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],
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)
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@torch.inference_mode()
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def test_contexted_kv_attention(
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num_heads: int,
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num_queries_per_kv: int,
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head_size: int,
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mixed_precision: bool,
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) -> None:
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import os
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import torch_xla.core.xla_model as xm
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from vllm.attention.ops.nki_flash_attn import flash_attn_varlen_nkifunc
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device = xm.xla_device()
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os.environ["NEURON_CC_FLAGS"] = (
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" --model-type=transformer -O1 "
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" --internal-hlo2tensorizer-options='--verify-hlo' ")
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random.seed(0)
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torch.manual_seed(0)
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torch.set_printoptions(sci_mode=False)
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min_ctx_len = 2
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max_ctx_len = 64
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min_query_len = 2
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max_query_len = 64
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prefill_batch_size = 2
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decode_batch_size = 6
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batch_size = prefill_batch_size + decode_batch_size
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block_size = 32
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max_model_len = (max_query_len + max_ctx_len) * 4
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max_block_per_request = max_model_len // block_size
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dtype = torch.float32
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cache_size = (batch_size * max_block_per_request) + 2
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ctx_lens = [
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random.randint(min_ctx_len, max_ctx_len)
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for _ in range(prefill_batch_size)
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] + [
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random.randint(min_ctx_len, max_ctx_len)
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for _ in range(decode_batch_size)
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]
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query_lens = [
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random.randint(min_query_len, max_query_len)
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for _ in range(prefill_batch_size)
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] + [1 for _ in range(decode_batch_size)]
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seq_lens = [a + b for a, b in zip(query_lens, ctx_lens)]
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num_kv_heads = num_heads // num_queries_per_kv
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num_tokens = sum(query_lens)
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query = torch.empty(num_tokens, num_heads, head_size, dtype=dtype)
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query.uniform_(-1, 1)
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torch.empty(num_tokens, num_heads, head_size, dtype=dtype)
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kv = torch.empty(sum(seq_lens), 2, num_kv_heads, head_size, dtype=dtype)
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kv.uniform_(-1, 1)
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key, value = kv.unbind(dim=1)
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k_cache = torch.zeros(cache_size,
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block_size,
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num_kv_heads,
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head_size,
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dtype=dtype)
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v_cache = torch.zeros(cache_size,
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block_size,
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num_kv_heads,
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head_size,
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dtype=dtype)
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k = torch.zeros(sum(query_lens), num_kv_heads, head_size, dtype=dtype)
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v = torch.zeros(sum(query_lens), num_kv_heads, head_size, dtype=dtype)
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values = torch.arange(0, cache_size, dtype=torch.long)
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values = values[torch.randperm(cache_size)]
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block_table = values[:batch_size * max_block_per_request].view(
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batch_size, max_block_per_request)
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torch.tensor(seq_lens, dtype=torch.long)
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b_ctx_len = torch.tensor(ctx_lens, dtype=torch.long)
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b_start_loc = torch.cumsum(torch.tensor([0] + query_lens[:-1],
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dtype=torch.long),
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dim=0)
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# copy kv to cache
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b_seq_start_loc = torch.cumsum(torch.tensor([0] + seq_lens[:-1],
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dtype=torch.long),
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dim=0)
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for i in range(batch_size):
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for j in range(query_lens[i]):
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k[b_start_loc[i] + j].copy_(key[b_seq_start_loc[i] + b_ctx_len[i] +
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j])
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v[b_start_loc[i] + j].copy_(value[b_seq_start_loc[i] +
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b_ctx_len[i] + j])
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cur_ctx = 0
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block_id = 0
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while cur_ctx < b_ctx_len[i]:
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start_loc = b_seq_start_loc[i] + cur_ctx
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if cur_ctx + block_size > b_ctx_len[i]:
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end_loc = b_seq_start_loc[i] + b_ctx_len[i]
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else:
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end_loc = start_loc + block_size
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start_slot = block_table[i, block_id] * block_size
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end_slot = start_slot + end_loc - start_loc
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k_cache.view(-1, num_kv_heads,
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head_size)[start_slot:end_slot].copy_(
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key[start_loc:end_loc])
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v_cache.view(-1, num_kv_heads,
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head_size)[start_slot:end_slot].copy_(
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value[start_loc:end_loc])
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cur_ctx += block_size
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block_id += 1
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(
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output_ref,
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cached_max,
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cached_sum_reciprocal,
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lse,
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masked_score,
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scaled_qk,
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) = ref_context_attention(
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query,
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key,
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value,
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query_lens,
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seq_lens,
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head_size,
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num_kv_heads,
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num_heads,
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num_queries_per_kv,
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return_max_reduce=True,
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)
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# build neuron program
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return_debug_tensors = False
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B_P_SIZE = 128
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LARGE_TILE_SZ = 2048
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max_num_queries = (
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(sum(query_lens) + block_size - 1) // block_size) * block_size
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def get_active_block_tables(block_tables, query_lens, seq_lens, block_size,
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num_blocks):
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context_lens = seq_lens - query_lens
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blocks_per_seq = (context_lens + block_size - 1) // block_size
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num_seqs = len(seq_lens)
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active_blocks: list[int] = []
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for seq_id in range(num_seqs):
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active_blocks = (
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active_blocks +
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block_tables[seq_id, :blocks_per_seq[seq_id]].tolist())
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return F.pad(
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torch.tensor(active_blocks),
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(0, num_blocks - len(active_blocks)),
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"constant",
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0,
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)
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def shift_bit_length(x):
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return 1 << (x - 1).bit_length()
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# calculate input shapes
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max_num_queries_shifted = shift_bit_length(max_num_queries)
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max_num_queries_factor = B_P_SIZE // max_num_queries_shifted
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max_num_queries_padded = max_num_queries_shifted * max_num_queries_factor
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assert (max_num_queries_padded == B_P_SIZE
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), "invalid {max_num_queries_padded=}"
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head_size_padded = B_P_SIZE
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context_lens = torch.tensor(seq_lens) - torch.tensor(query_lens)
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num_active_blocks_shifted = shift_bit_length(
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((context_lens + block_size - 1) // block_size).sum().item())
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num_active_blocks_factor = (LARGE_TILE_SZ // block_size //
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num_active_blocks_shifted)
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num_active_blocks = num_active_blocks_shifted * num_active_blocks_factor
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assert (num_active_blocks *
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block_size) == LARGE_TILE_SZ, "invalid {num_active_blocks=}"
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context_kv_len = num_active_blocks * block_size
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assert context_kv_len == LARGE_TILE_SZ, f"invalid {context_kv_len=}"
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# pad QKV tensors
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pad_dims = (
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0,
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head_size_padded - query.shape[2],
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0,
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0,
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0,
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max_num_queries_padded - query.shape[0],
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)
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query = F.pad(query, pad_dims, "constant", 0)
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k = F.pad(k, pad_dims, "constant", 0)
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v = F.pad(v, pad_dims, "constant", 0)
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k_cache = F.pad(k_cache, (0, head_size_padded - head_size), "constant", 0)
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v_cache = F.pad(v_cache, (0, head_size_padded - head_size), "constant", 0)
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# permute QKV tensors
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# query: (1, n_heads, d, seq_q)
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# key: (1, n_kv_heads, d, seq_k)
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# value: (1, n_kv_heads, seq_v, d)
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query = query.unsqueeze(0).permute(0, 2, 3, 1).contiguous()
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k = k.unsqueeze(0).permute(0, 2, 3, 1).contiguous()
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v = v.unsqueeze(0).permute(0, 2, 1, 3).contiguous()
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# transform block table
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active_block_table = get_active_block_tables(
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block_table,
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torch.tensor(query_lens),
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torch.tensor(seq_lens),
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block_size,
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num_active_blocks,
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)
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# Build attention masks
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prior_mask, active_mask = (
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BlockDiagonalCausalFromBottomRightMask.from_seqlens(
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query_lens, seq_lens, block_size=block_size))
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attn_mask = torch.concat(
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[
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F.pad(
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prior_mask,
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(
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0,
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context_kv_len - prior_mask.shape[1],
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0,
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B_P_SIZE - prior_mask.shape[0],
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),
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"constant",
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0,
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).bool(),
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F.pad(
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active_mask,
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(
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0,
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B_P_SIZE - active_mask.shape[1],
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0,
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B_P_SIZE - active_mask.shape[0],
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),
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"constant",
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0,
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).bool(),
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],
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dim=1,
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)
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input_args = (
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query.to(device=device),
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k.to(device=device),
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v.to(device=device),
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k_cache.to(device=device),
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v_cache.to(device=device),
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active_block_table.to(torch.int32).to(device=device),
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attn_mask.to(device=device),
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)
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input_kwargs = dict(
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n_kv_head=num_kv_heads,
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head_size=head_size,
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mixed_precision=mixed_precision,
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)
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if return_debug_tensors:
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output_nki, *debug_tensors = flash_attn_varlen_nkifunc(
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*input_args, **input_kwargs)
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else:
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output_nki = flash_attn_varlen_nkifunc(*input_args, **input_kwargs)
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debug_tensors = []
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output_nki = torch.tensor(output_nki).cpu()
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debug_tensors = [torch.tensor(dt).cpu() for dt in debug_tensors]
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num_actual_tokens = sum(query_lens)
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print(f"{num_actual_tokens=}")
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# - o: shape (bs, n_heads, seq_q, d) -> (bs, seq_q, n_heads, d)
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output_nki = output_nki.permute(
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0, 2, 1, 3)[:, :, :, :head_size].cpu()[0, :num_actual_tokens, :, :]
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output_ref_padded = F.pad(
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output_ref,
|
|
(0, 0, 0, 0, 0, 0, 0, max_num_queries_padded - output_ref.shape[0]),
|
|
"constant",
|
|
0,
|
|
)
|
|
output_ref = output_ref_padded.transpose(0, 1)[0, :num_actual_tokens, :, :]
|
|
|
|
torch.testing.assert_close(output_nki, output_ref, atol=1e-2, rtol=0)
|