[ROCm] AITER fused RoPE+KVCache (#33443)
Signed-off-by: Rohan138 <rohanpotdar138@gmail.com> Signed-off-by: charlifu <charlifu@amd.com> Signed-off-by: Rohan Potdar <66227218+Rohan138@users.noreply.github.com> Co-authored-by: charlifu <charlifu@amd.com> Co-authored-by: Luka Govedič <ProExpertProg@users.noreply.github.com> Co-authored-by: Douglas Lehr <91553416+dllehr-amd@users.noreply.github.com>
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Rohan Potdar
325
tests/compile/passes/test_rope_kvcache_fusion.py
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325
tests/compile/passes/test_rope_kvcache_fusion.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import pytest
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import torch
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import vllm.config
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from tests.compile.backend import TestBackend
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from tests.v1.attention.utils import BatchSpec, create_common_attn_metadata
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from vllm._aiter_ops import is_aiter_found_and_supported, rocm_aiter_ops
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from vllm.compilation.passes.fusion.matcher_utils import ROTARY_OP
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from vllm.compilation.passes.fusion.rope_kvcache_fusion import RopeKVCacheFusionPass
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from vllm.compilation.passes.utility.noop_elimination import NoOpEliminationPass
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from vllm.compilation.passes.utility.post_cleanup import PostCleanupPass
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from vllm.compilation.passes.utility.scatter_split_replace import (
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ScatterSplitReplacementPass,
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)
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from vllm.compilation.passes.utility.split_coalescing import SplitCoalescingPass
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from vllm.config import (
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CacheConfig,
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CompilationConfig,
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CompilationMode,
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ModelConfig,
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PassConfig,
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VllmConfig,
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)
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from vllm.forward_context import get_forward_context, set_forward_context
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from vllm.model_executor.layers.attention import Attention
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from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
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from vllm.platforms import current_platform
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from vllm.v1.attention.backend import (
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AttentionBackend,
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CommonAttentionMetadata,
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)
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from vllm.v1.attention.backends.registry import AttentionBackendEnum
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from vllm.v1.kv_cache_interface import AttentionSpec
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INDEX_SELECT_OP = torch.ops.aten.index.Tensor
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VLLM_UNIFIED_KV_CACHE_UPDATE_OP = torch.ops.vllm.unified_kv_cache_update
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FP8_DTYPE = current_platform.fp8_dtype()
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class QKRoPEKVCacheTestModel(torch.nn.Module):
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def __init__(
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self,
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vllm_config: VllmConfig,
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attn_backend: AttentionBackendEnum,
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num_heads: int,
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num_kv_heads: int,
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head_size: int,
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is_neox: bool,
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dtype: torch.dtype,
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device: torch.device,
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prefix: str = "model.layers.0.self_attn.attn",
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):
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super().__init__()
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self.num_heads = num_heads
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self.num_kv_heads = num_kv_heads
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self.head_size = head_size
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self.block_size = vllm_config.cache_config.block_size
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self.q_size = num_heads * head_size
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self.kv_size = num_kv_heads * head_size
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self.is_neox = is_neox
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self.dtype = dtype
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self.device = device
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self.layer_name = prefix
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self.rotary_emb = RotaryEmbedding(
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head_size,
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rotary_dim=head_size,
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max_position_embeddings=4096,
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base=10000,
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is_neox_style=is_neox,
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dtype=self.dtype,
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)
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# Whether to check for the RoPE custom op or component index_select
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self.enable_rope_custom_op = self.rotary_emb.enabled()
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# Register layer metadata for the fusion pass via Attention.
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self.attn = Attention(
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num_heads=num_heads,
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head_size=head_size,
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scale=1.0 / head_size**0.5,
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num_kv_heads=num_kv_heads,
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cache_config=vllm_config.cache_config,
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quant_config=vllm_config.quant_config,
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prefix=prefix,
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attn_backend=attn_backend.get_class(),
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)
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self.attn_backend: type[AttentionBackend] = self.attn.get_attn_backend()
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assert not self.attn_backend.forward_includes_kv_cache_update, (
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f"Attention backend {self.attn_backend} does not support fuse_rope_kvcache."
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)
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self.attn._k_scale = self.attn._k_scale.to(device)
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self.attn._v_scale = self.attn._v_scale.to(device)
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kv_cache_dtype_str = vllm_config.cache_config.cache_dtype
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self.kv_cache_dtype = (
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FP8_DTYPE if kv_cache_dtype_str.startswith("fp8") else self.dtype
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)
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# Initialize attn MetadataBuilder
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self.builder = self.attn.attn_backend.get_builder_cls()(
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kv_cache_spec=AttentionSpec(
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block_size=self.block_size,
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num_kv_heads=self.num_kv_heads,
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head_size=head_size,
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dtype=self.kv_cache_dtype,
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),
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layer_names=[self.attn.layer_name],
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vllm_config=vllm_config,
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device=device,
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)
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def build_attn_metadata(self, batch_size: int) -> CommonAttentionMetadata:
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"""Initialize attention metadata."""
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# Create common attn metadata
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batch_spec = BatchSpec(seq_lens=[1] * batch_size, query_lens=[1] * batch_size)
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common_attn_metadata = create_common_attn_metadata(
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batch_spec, self.block_size, self.device, arange_block_indices=True
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)
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max_blocks = (max(batch_spec.seq_lens) + self.block_size - 1) // self.block_size
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num_blocks = batch_size * max_blocks
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# Fetch the attention backend and kv cache shape and stride order
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attn_backend = self.attn.attn_backend
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kv_cache_shape = attn_backend.get_kv_cache_shape(
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num_blocks, self.block_size, self.num_kv_heads, self.head_size
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)
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try:
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kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
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except (AttributeError, NotImplementedError):
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kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
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kv_cache_shape = tuple(kv_cache_shape[i] for i in kv_cache_stride_order)
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inv_order = [
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kv_cache_stride_order.index(i) for i in range(len(kv_cache_stride_order))
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]
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# Create dummy KV cache
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raw_tensor = torch.zeros(
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2 * num_blocks * self.block_size * self.num_kv_heads * self.head_size,
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dtype=self.kv_cache_dtype,
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device=self.device,
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)
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raw_tensor = raw_tensor.view(kv_cache_shape)
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kv_cache = raw_tensor.permute(*inv_order)
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self.attn.kv_cache = [kv_cache]
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# Build attn metadata
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attn_metadata = self.builder.build(
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common_prefix_len=0, common_attn_metadata=common_attn_metadata
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)
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return attn_metadata
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def forward(
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self, qkv: torch.Tensor, positions: torch.Tensor
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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# Create copy so inplace ops do not modify the original tensors
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qkv = qkv.clone()
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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# Instead of a full forward pass, match only the KV cache update op here
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q = q.view(-1, self.num_heads, self.head_size)
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k = k.view(-1, self.num_kv_heads, self.head_size)
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v = v.view(-1, self.num_kv_heads, self.head_size)
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kv_cache_dummy_dep = torch.ops.vllm.unified_kv_cache_update(
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k, v, self.layer_name
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)
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return q, k, v, kv_cache_dummy_dep
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def ops_in_model_before(self) -> list[torch._ops.OpOverload]:
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ops = []
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if self.enable_rope_custom_op:
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ops.append(ROTARY_OP)
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else:
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ops.append(INDEX_SELECT_OP)
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ops.append(torch.ops.vllm.unified_kv_cache_update.default)
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return ops
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def ops_in_model_after(self) -> list[torch._ops.OpOverload]:
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return [torch.ops.vllm.fused_rope_and_unified_kv_cache_update.default]
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@pytest.mark.parametrize(
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"attn_backend",
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[
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AttentionBackendEnum.ROCM_AITER_UNIFIED_ATTN,
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AttentionBackendEnum.TRITON_ATTN,
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AttentionBackendEnum.ROCM_ATTN,
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],
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)
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@pytest.mark.parametrize("enable_rope_custom_op", [True]) # [True, False])
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@pytest.mark.parametrize("num_heads", [64])
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@pytest.mark.parametrize("num_kv_heads", [8])
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@pytest.mark.parametrize("head_size", [64])
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@pytest.mark.parametrize("block_size", [16])
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@pytest.mark.parametrize("is_neox", [True, False])
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@pytest.mark.parametrize("dtype", [torch.bfloat16])
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@pytest.mark.parametrize("kv_cache_dtype", ["auto", "fp8"])
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@pytest.mark.skipif(
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not is_aiter_found_and_supported(),
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reason="Only test on ROCm with AITER installed and supported",
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)
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def test_rope_kvcache_fusion(
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attn_backend: AttentionBackendEnum,
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enable_rope_custom_op: bool,
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num_heads: int,
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num_kv_heads: int,
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head_size: int,
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block_size: int,
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is_neox: bool,
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dtype: torch.dtype,
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kv_cache_dtype: str,
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monkeypatch: pytest.MonkeyPatch,
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):
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torch.set_default_device("cuda")
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torch.set_default_dtype(dtype)
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torch.manual_seed(0)
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custom_ops: list[str] = []
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if enable_rope_custom_op:
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custom_ops.append("+rotary_embedding")
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vllm_config = VllmConfig(
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model_config=ModelConfig(dtype=dtype),
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cache_config=CacheConfig(
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block_size=block_size,
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cache_dtype=kv_cache_dtype,
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),
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compilation_config=CompilationConfig(
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mode=CompilationMode.VLLM_COMPILE,
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custom_ops=custom_ops,
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pass_config=PassConfig(
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fuse_rope_kvcache=True,
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eliminate_noops=True,
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),
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),
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)
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with vllm.config.set_current_vllm_config(vllm_config), monkeypatch.context() as m:
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m.setenv("VLLM_ROCM_USE_AITER", "1")
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rocm_aiter_ops.refresh_env_variables()
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model = QKRoPEKVCacheTestModel(
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vllm_config=vllm_config,
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attn_backend=attn_backend,
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num_heads=num_heads,
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num_kv_heads=num_kv_heads,
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head_size=head_size,
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is_neox=is_neox,
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dtype=dtype,
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device=torch.get_default_device(),
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)
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fusion_pass = RopeKVCacheFusionPass(vllm_config)
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passes = [
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NoOpEliminationPass(vllm_config),
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SplitCoalescingPass(vllm_config),
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ScatterSplitReplacementPass(vllm_config),
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fusion_pass,
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PostCleanupPass(vllm_config),
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]
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backend = TestBackend(*passes)
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T = 5
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qkv = torch.randn(
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T, num_heads * head_size + 2 * num_kv_heads * head_size, dtype=dtype
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)
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pos = torch.arange(T, dtype=torch.long)
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qkv_unfused = qkv.clone()
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pos_unfused = pos.clone()
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with set_forward_context(None, vllm_config):
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forward_context = get_forward_context()
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attn_metadata = model.build_attn_metadata(T)
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forward_context.slot_mapping = {
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model.layer_name: attn_metadata.slot_mapping
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}
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q_unfused, k_unfused, v_unfused, dummy = model(qkv_unfused, pos_unfused)
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attn_layer = forward_context.no_compile_layers[model.layer_name]
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kv_cache_unfused = attn_layer.kv_cache[forward_context.virtual_engine]
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del dummy
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torch._dynamo.mark_dynamic(qkv, 0)
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torch._dynamo.mark_dynamic(pos, 0)
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with set_forward_context(None, vllm_config):
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model_fused = torch.compile(model, backend=backend)
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forward_context = get_forward_context()
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attn_metadata = model_fused.build_attn_metadata(T)
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forward_context.slot_mapping = {
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model.layer_name: attn_metadata.slot_mapping
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}
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q_fused, k_fused, v_fused, dummy = model_fused(qkv, pos)
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attn_layer = forward_context.no_compile_layers[model.layer_name]
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kv_cache_fused = attn_layer.kv_cache[forward_context.virtual_engine]
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del dummy
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assert fusion_pass.matched_count == 1
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backend.check_before_ops(model.ops_in_model_before())
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backend.check_after_ops(model.ops_in_model_after())
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if dtype == torch.float16:
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ATOL, RTOL = (2e-3, 2e-3)
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else:
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ATOL, RTOL = (1e-2, 1e-2)
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torch.testing.assert_close(q_unfused, q_fused, atol=ATOL, rtol=RTOL)
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torch.testing.assert_close(k_unfused, k_fused, atol=ATOL, rtol=RTOL)
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torch.testing.assert_close(v_unfused, v_fused, atol=ATOL, rtol=RTOL)
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# Cannot compare fp8_* directly here, cast to model dtype instead
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torch.testing.assert_close(
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kv_cache_unfused.view(dtype),
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kv_cache_fused.view(dtype),
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atol=ATOL,
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rtol=RTOL,
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)
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