2f7dbc9b42
Signed-off-by: Bram Wasti <bwasti@meta.com> Signed-off-by: Bram Wasti <bwasti@fb.com> Co-authored-by: Wentao Ye <44945378+yewentao256@users.noreply.github.com>
1209 lines
52 KiB
Python
Executable File
1209 lines
52 KiB
Python
Executable File
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Attention layer with FlashInfer."""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import ClassVar, Optional, Union
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import numpy as np
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import torch
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from flashinfer import (BatchDecodeWithPagedKVCacheWrapper,
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BatchPrefillWithPagedKVCacheWrapper,
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MultiLevelCascadeAttentionWrapper)
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from flashinfer.decode import _get_range_buf, trtllm_batch_decode_with_kv_cache
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from flashinfer.prefill import trtllm_batch_context_with_kv_cache
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from flashinfer.utils import FP4Tensor
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from vllm import _custom_ops as ops
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from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
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AttentionType)
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from vllm.config import CUDAGraphMode, VllmConfig
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from vllm.logger import init_logger
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from vllm.model_executor.layers.batch_invariant import (
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vllm_kernel_override_batch_invariant)
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from vllm.model_executor.layers.quantization.utils.quant_utils import (
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QuantKey, kFp8StaticTensorSym, kNvfp4Quant)
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from vllm.platforms import current_platform
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from vllm.triton_utils import tl, triton
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from vllm.utils import cdiv, is_pin_memory_available
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from vllm.utils.flashinfer import (can_use_trtllm_attention,
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flashinfer_disable_q_quantization,
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supports_trtllm_attention,
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use_trtllm_attention)
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# yapf conflicts with isort for this block
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# yapf: disable
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from vllm.v1.attention.backends.utils import (AttentionCGSupport,
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AttentionMetadataBuilder,
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CommonAttentionMetadata,
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get_kv_cache_layout,
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get_per_layer_parameters,
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infer_global_hyperparameters,
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split_decodes_and_prefills)
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# yapf: enable
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from vllm.v1.kv_cache_interface import AttentionSpec
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FLASHINFER_WORKSPACE_BUFFER_SIZE = 256 * 1024 * 1024
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FLASHINFER_WORKSPACE_BUFFER_SIZE_BATCH_INVARIANT = 2048 * 1024 * 1024
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FP8_DTYPE = current_platform.fp8_dtype()
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FP4_DTYPE = torch.uint8
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logger = init_logger(__name__)
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trtllm_gen_workspace_buffer = None
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def _get_trtllm_gen_workspace_buffer():
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global trtllm_gen_workspace_buffer
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if trtllm_gen_workspace_buffer is None:
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trtllm_gen_workspace_buffer = torch.zeros(
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FLASHINFER_WORKSPACE_BUFFER_SIZE, dtype=torch.uint8, device='cuda')
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return trtllm_gen_workspace_buffer
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@triton.jit
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def _trtllm_prefill_attn_kvfp8_dequant(
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kv_cache_ptr,
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block_tables_prefill_ptr,
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block_table_stride,
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mock_kv_cache_ptr,
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k_scale_ptr,
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v_scale_ptr,
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K_CACHE_STRIDE: tl.constexpr,
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KV_CACHE_STRIDE: tl.constexpr,
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):
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batch_idx = tl.program_id(0).to(tl.int64)
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mock_block_table_idx = tl.program_id(1).to(tl.int64)
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orig_page_num = tl.load(block_tables_prefill_ptr +
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batch_idx * block_table_stride +
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mock_block_table_idx).to(tl.int64)
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if orig_page_num <= 0:
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return
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dequant_dtype = mock_kv_cache_ptr.dtype.element_ty
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# Dequantize K
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k_scale_val = tl.load(k_scale_ptr)
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offset = orig_page_num * KV_CACHE_STRIDE + tl.arange(0, K_CACHE_STRIDE)
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fp8_vals = tl.load(kv_cache_ptr + offset)
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dequantized_vals = fp8_vals.to(tl.float32) * k_scale_val
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mock_cache_offset = (batch_idx * block_table_stride + mock_block_table_idx
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+ 1) * KV_CACHE_STRIDE + tl.arange(0, K_CACHE_STRIDE)
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dequantized_vals = dequantized_vals.to(dequant_dtype)
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tl.store(mock_kv_cache_ptr + mock_cache_offset, dequantized_vals)
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# Dequantize V
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v_scale_val = tl.load(v_scale_ptr)
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offset = (orig_page_num * KV_CACHE_STRIDE + K_CACHE_STRIDE +
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tl.arange(0, K_CACHE_STRIDE))
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fp8_vals = tl.load(kv_cache_ptr + offset)
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dequantized_vals = fp8_vals.to(tl.float32) * v_scale_val
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mock_cache_offset = (
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(batch_idx * block_table_stride + mock_block_table_idx + 1) *
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KV_CACHE_STRIDE + K_CACHE_STRIDE + tl.arange(0, K_CACHE_STRIDE))
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dequantized_vals = dequantized_vals.to(dequant_dtype)
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tl.store(mock_kv_cache_ptr + mock_cache_offset, dequantized_vals)
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def trtllm_prefill_attn_kvfp8_dequant(
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kv_cache: torch.Tensor,
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block_tables_prefill: torch.Tensor,
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k_scale: torch.Tensor,
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v_scale: torch.Tensor,
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dequant_dtype: torch.dtype,
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) -> tuple[torch.Tensor, torch.Tensor]:
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batch_size, num_of_page_per_token = block_tables_prefill.shape
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s = kv_cache.shape
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assert s[1] == 2
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assert dequant_dtype in (torch.bfloat16, torch.float16)
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k_cache_stride = s[2] * s[3] * s[4]
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kv_cache_stride = k_cache_stride * s[1]
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new_s = (batch_size * num_of_page_per_token + 1, s[1], s[2], s[3], s[4])
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# mock kv cache contains just the pages needed by this prefill
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mock_kv_cache = torch.empty(new_s,
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dtype=dequant_dtype,
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device=kv_cache.device)
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# we simply sequentially index the pages needed by this prefill
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mock_block_table = torch.arange(
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start=1,
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end=batch_size * num_of_page_per_token + 1,
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dtype=torch.int32,
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device=block_tables_prefill.device,
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).reshape(batch_size, num_of_page_per_token)
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grid = (batch_size, num_of_page_per_token)
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_trtllm_prefill_attn_kvfp8_dequant[grid](
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kv_cache,
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block_tables_prefill,
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num_of_page_per_token,
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mock_kv_cache,
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k_scale,
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v_scale,
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k_cache_stride,
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kv_cache_stride,
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)
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return mock_kv_cache, mock_block_table
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class FlashInferBackend(AttentionBackend):
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accept_output_buffer: bool = True
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@classmethod
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def get_supported_dtypes(cls) -> list[torch.dtype]:
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return [torch.float16, torch.bfloat16]
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@classmethod
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def get_supported_head_sizes(cls) -> list[int]:
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# https://github.com/flashinfer-ai/flashinfer/blob/3d55c71a62052c590c130897d3a3db49b14fcc34/include/flashinfer/utils.cuh#L157
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return [64, 128, 256]
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@classmethod
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def validate_head_size(cls, head_size: int) -> None:
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supported_head_sizes = cls.get_supported_head_sizes()
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if head_size not in supported_head_sizes:
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attn_type = cls.__name__.removesuffix("Backend")
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raise ValueError(
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f"Head size {head_size} is not supported by {attn_type}. "
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f"Supported head sizes are: {supported_head_sizes}. "
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"Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION to use "
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"FlexAttention backend which supports all head sizes.")
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@staticmethod
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def get_name() -> str:
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return "FLASHINFER"
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@staticmethod
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def get_impl_cls() -> type[FlashInferImpl]:
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return FlashInferImpl
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@staticmethod
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def get_metadata_cls() -> type[FlashInferMetadata]:
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return FlashInferMetadata
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@staticmethod
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def get_builder_cls() -> type[FlashInferMetadataBuilder]:
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return FlashInferMetadataBuilder
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@staticmethod
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def get_kv_cache_shape(
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num_blocks: int,
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block_size: int,
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num_kv_heads: int,
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head_size: int,
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cache_dtype_str: str = "auto",
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) -> tuple[int, ...]:
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return (num_blocks, 2, block_size, num_kv_heads, head_size)
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@staticmethod
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def get_kv_cache_stride_order() -> tuple[int, ...]:
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# `stride_order` indicates the permutation that gets us from
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# `get_kv_cache_shape` to the actual memory layout we want.
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cache_layout = get_kv_cache_layout()
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if cache_layout == "NHD":
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stride_order = (0, 1, 2, 3, 4)
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elif cache_layout == "HND":
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stride_order = (0, 1, 3, 2, 4)
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else:
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raise ValueError(f"Unknown cache layout format {cache_layout}.")
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return stride_order
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@staticmethod
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def get_fp8_dtype_for_flashinfer(kv_cache_dtype: str) -> torch.dtype:
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if kv_cache_dtype in ("fp8", "fp8_e4m3"):
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return torch.float8_e4m3fn
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elif kv_cache_dtype == "fp8_e5m2":
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return torch.float8_e5m2
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else:
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raise ValueError(f"Unrecognized FP8 dtype: {kv_cache_dtype}")
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@dataclass
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class FlashInferMetadata:
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num_actual_tokens: int # Number of tokens excluding padding.
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# The data type of the query
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q_data_type: torch.dtype
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slot_mapping: torch.Tensor
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# For flashinfer trtllm batch decode
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max_q_len: int
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max_q_len_prefill: int
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max_seq_len: int
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seq_lens: torch.Tensor
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block_table_tensor: torch.Tensor
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prefill_use_trtllm: bool
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decode_use_trtllm: bool
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# For handling prefill decode split
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num_decodes: int
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num_decode_tokens: int
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num_prefills: int
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num_prefill_tokens: int
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# For cascade attention (CPU for planning).
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use_cascade: bool
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prefill_wrapper: Optional[BatchPrefillWithPagedKVCacheWrapper] = None
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decode_wrapper: Optional[BatchDecodeWithPagedKVCacheWrapper] = None
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cascade_wrapper: Optional[MultiLevelCascadeAttentionWrapper] = None
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qo_indptr_gpu: Optional[torch.Tensor] = None
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paged_kv_indptr_gpu: Optional[torch.Tensor] = None
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class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
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cudagraph_support: ClassVar[AttentionCGSupport] = \
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AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
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reorder_batch_threshold: int = 1
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def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
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vllm_config: VllmConfig, device: torch.device):
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super().__init__(kv_cache_spec, layer_names, vllm_config, device)
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self.cache_config = vllm_config.cache_config
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self.model_config = vllm_config.model_config
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self._workspace_buffer = None
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self._prefill_wrapper = None # Wrapper for prefill/append
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self._decode_wrapper = None # Wrapper for decode (general shape)
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if vllm_kernel_override_batch_invariant():
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self.decode_fixed_split_size = 2048
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self.prefill_fixed_split_size = 4096
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self.disable_split_kv = True
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else:
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self.decode_fixed_split_size = -1
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self.prefill_fixed_split_size = -1
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self.disable_split_kv = False
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self.compilation_config = vllm_config.compilation_config
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max_num_pages_per_req = cdiv(self.model_config.max_model_len,
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self.kv_cache_spec.block_size)
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max_num_reqs = vllm_config.scheduler_config.max_num_seqs
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max_num_pages = max_num_reqs * max_num_pages_per_req
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self.enable_cuda_graph = (self.compilation_config.cudagraph_mode.\
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decode_mode() == CUDAGraphMode.FULL)
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if self.enable_cuda_graph:
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# For full cudagraph capture, one `decode_wrapper` for each batch
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# size is needed for FlashInfer.
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self._decode_wrappers_cudagraph: dict[
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int, BatchDecodeWithPagedKVCacheWrapper] = {}
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self._decode_cudagraph_max_bs = min(
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max_num_reqs, self.compilation_config.max_capture_size)
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self.num_qo_heads = self.model_config.get_num_attention_heads(
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self.vllm_config.parallel_config)
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self.num_kv_heads = self.kv_cache_spec.num_kv_heads
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self.head_dim = self.kv_cache_spec.head_size
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FlashInferBackend.validate_head_size(self.head_dim)
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self.page_size = self.kv_cache_spec.block_size
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self.cache_dtype = self.cache_config.cache_dtype
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if self.cache_dtype.startswith("fp8"):
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self.kv_cache_dtype = (
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FlashInferBackend.get_fp8_dtype_for_flashinfer(
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self.cache_dtype))
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else:
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assert self.kv_cache_spec.dtype == self.model_config.dtype
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self.kv_cache_dtype = self.kv_cache_spec.dtype
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# Use model dtype as q dtype when TRTLLM attn is not supported, or
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# VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION is set to 1. Otherwise, try to
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# use fp8 q if kv cache is fp8, and will fall back to model dtype
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# if TRTLLM attention kernel is not used when building attn metadata
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if supports_trtllm_attention() and \
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not flashinfer_disable_q_quantization():
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self.q_data_type = self.kv_cache_dtype
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else:
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self.q_data_type = self.model_config.dtype
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supports_spec_as_decode = \
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can_use_trtllm_attention(self.num_qo_heads, self.num_kv_heads)
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self._init_reorder_batch_threshold(1, supports_spec_as_decode)
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self._cascade_wrapper = None # Wrapper for cascade attention
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# Global hyperparameters shared by all attention layers
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# TODO: discard this for trtllm-gen backend
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self.global_hyperparameters = infer_global_hyperparameters(
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get_per_layer_parameters(vllm_config, layer_names, FlashInferImpl))
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self.sm_scale = self.global_hyperparameters.sm_scale
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self.window_left = self.global_hyperparameters.window_left
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self.logits_soft_cap = self.global_hyperparameters.logits_soft_cap
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self.has_sinks = self.global_hyperparameters.has_sinks
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if self.has_sinks and not supports_trtllm_attention():
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raise NotImplementedError(
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"FlashInfer backend currently does not support attention "
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"sinks, please use trtllm on blackwell or flash attention on "
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"earlier GPUs.")
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# Preparing persistent buffers (device-side)
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self.paged_kv_indptr = torch.zeros(max_num_reqs + 1,
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dtype=torch.int32,
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device=self.device)
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self.paged_kv_indices = torch.zeros(
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max_num_pages, # max num pages possible
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dtype=torch.int32,
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device=self.device)
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self.paged_kv_last_page_len = torch.zeros(max_num_reqs,
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dtype=torch.int32,
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device=self.device)
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# host-side buffer
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pin_memory = is_pin_memory_available()
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self.paged_kv_indptr_cpu = torch.zeros(max_num_reqs + 1,
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dtype=torch.int32,
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device="cpu",
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pin_memory=pin_memory)
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self.paged_kv_indptr_np = self.paged_kv_indptr_cpu.numpy()
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self.paged_kv_indptr_buffer = torch.zeros_like(
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self.paged_kv_indptr_cpu, pin_memory=pin_memory)
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self.paged_kv_indices_cpu = torch.zeros(max_num_pages,
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dtype=torch.int32,
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device="cpu",
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pin_memory=pin_memory)
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self.paged_kv_last_page_len_cpu = torch.zeros(max_num_reqs,
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dtype=torch.int32,
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device="cpu",
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pin_memory=pin_memory)
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self.paged_kv_last_page_len_np = (
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self.paged_kv_last_page_len_cpu.numpy())
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def _get_workspace_buffer(self):
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if self._workspace_buffer is None:
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buffer_size = FLASHINFER_WORKSPACE_BUFFER_SIZE
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if vllm_kernel_override_batch_invariant():
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buffer_size = FLASHINFER_WORKSPACE_BUFFER_SIZE_BATCH_INVARIANT
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self._workspace_buffer = torch.zeros(buffer_size,
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dtype=torch.uint8,
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device=self.device)
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return self._workspace_buffer
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def _get_prefill_wrapper(self):
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if self._prefill_wrapper is None:
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self._prefill_wrapper = BatchPrefillWithPagedKVCacheWrapper(
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self._get_workspace_buffer(), get_kv_cache_layout())
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return self._prefill_wrapper
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def _get_decode_wrapper(self,
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batch_size: int,
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use_cudagraph: bool = False):
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if use_cudagraph:
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decode_wrapper = self._decode_wrappers_cudagraph.get(
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batch_size, None)
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else:
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decode_wrapper = self._decode_wrapper
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if decode_wrapper is None:
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if use_cudagraph:
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paged_kv_indptr = self.paged_kv_indptr[:batch_size + 1]
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paged_kv_indices = self.paged_kv_indices
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paged_kv_last_page_len = self.paged_kv_last_page_len[:
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batch_size]
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else:
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paged_kv_indptr = None
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paged_kv_indices = None
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paged_kv_last_page_len = None
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decode_wrapper = BatchDecodeWithPagedKVCacheWrapper(
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self._get_workspace_buffer(),
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get_kv_cache_layout(),
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use_cuda_graph=use_cudagraph,
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paged_kv_indptr_buffer=paged_kv_indptr,
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paged_kv_indices_buffer=paged_kv_indices,
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paged_kv_last_page_len_buffer=paged_kv_last_page_len,
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# Tensor cores are enabled by default because the perf would be
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# at least as good as cuda cores for all attention ops in latest
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# gpus.
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use_tensor_cores=True,
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)
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# save the decode wrapper
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if use_cudagraph:
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self._decode_wrappers_cudagraph[batch_size] = decode_wrapper
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else:
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self._decode_wrapper = decode_wrapper
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return decode_wrapper
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def _get_cascade_wrapper(self):
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if self._cascade_wrapper is None:
|
|
self._cascade_wrapper = MultiLevelCascadeAttentionWrapper(
|
|
2, self._get_workspace_buffer(), get_kv_cache_layout())
|
|
return self._cascade_wrapper
|
|
|
|
def build(self,
|
|
common_prefix_len: int,
|
|
common_attn_metadata: CommonAttentionMetadata,
|
|
fast_build: bool = False) -> FlashInferMetadata:
|
|
num_reqs = common_attn_metadata.num_reqs
|
|
num_actual_tokens = common_attn_metadata.num_actual_tokens
|
|
num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens =\
|
|
split_decodes_and_prefills(common_attn_metadata,
|
|
decode_threshold=self.reorder_batch_threshold,
|
|
require_uniform=True)
|
|
|
|
page_size = self.page_size
|
|
max_q_len = common_attn_metadata.max_query_len
|
|
max_seq_len = common_attn_metadata.max_seq_len
|
|
seq_lens = common_attn_metadata.seq_lens
|
|
seq_lens_cpu = common_attn_metadata.seq_lens_cpu
|
|
seq_lens_np = seq_lens_cpu.numpy()
|
|
block_table_tensor = common_attn_metadata.block_table_tensor
|
|
|
|
num_blocks_np = (seq_lens_np + (page_size - 1)) // page_size
|
|
|
|
use_cascade = common_prefix_len > 0
|
|
if use_cascade:
|
|
# Grab the blocks of the shared prefix from the first request.
|
|
assert common_prefix_len % page_size == 0
|
|
num_common_kv_blocks = common_prefix_len // page_size
|
|
|
|
# Create CPU versions directly for cascade (no GPU versions needed)
|
|
shared_qo_indptr_cpu = torch.tensor([0, num_actual_tokens],
|
|
dtype=torch.int32,
|
|
device='cpu')
|
|
shared_kv_page_indptr_cpu = torch.tensor([0, num_common_kv_blocks],
|
|
dtype=torch.int32,
|
|
device='cpu')
|
|
shared_kv_page_indices_cpu = block_table_tensor[
|
|
0, :num_common_kv_blocks]
|
|
shared_kv_last_page_len_cpu = torch.tensor([page_size],
|
|
dtype=torch.int32,
|
|
device='cpu')
|
|
|
|
# Remove the blocks of the shared prefix from all requests.
|
|
block_table_tensor = block_table_tensor[:, num_common_kv_blocks:]
|
|
num_blocks_np -= num_common_kv_blocks
|
|
else:
|
|
shared_qo_indptr_cpu = None
|
|
shared_kv_page_indptr_cpu = None
|
|
shared_kv_page_indices_cpu = None
|
|
shared_kv_last_page_len_cpu = None
|
|
|
|
# write self.paged_kv_indptr_cpu inplace (0-index is always 0)
|
|
np.cumsum(
|
|
num_blocks_np,
|
|
dtype=np.int32,
|
|
out=self.paged_kv_indptr_np[1:num_reqs + 1],
|
|
)
|
|
# NOTE(woosuk): Because self.paged_kv_indptr_cpu can be modified
|
|
# after this line (e.g., for cuda graphs), we need to copy the data to
|
|
# self.paged_kv_indptr_buffer to avoid race condition.
|
|
self.paged_kv_indptr_buffer[:num_reqs +
|
|
1] = (self.paged_kv_indptr_cpu[:num_reqs +
|
|
1])
|
|
paged_kv_indptr = self.paged_kv_indptr[:num_reqs + 1]
|
|
paged_kv_indptr.copy_(self.paged_kv_indptr_buffer[:num_reqs + 1],
|
|
non_blocking=True)
|
|
|
|
# write self.paged_kv_indices inplace
|
|
num_actual_pages = self.paged_kv_indptr_np[num_reqs]
|
|
paged_kv_indices = self.paged_kv_indices[:num_actual_pages]
|
|
_copy_page_indices_kernel[(num_reqs, )](
|
|
paged_kv_indices,
|
|
block_table_tensor,
|
|
block_table_tensor.stride(0),
|
|
paged_kv_indptr,
|
|
BLOCK_SIZE=1024,
|
|
)
|
|
|
|
# write self.paged_kv_last_page_len_cpu inplace
|
|
paged_kv_last_page_len_np = seq_lens_np % page_size
|
|
self.paged_kv_last_page_len_np[:num_reqs] = np.where(
|
|
paged_kv_last_page_len_np == 0,
|
|
page_size,
|
|
paged_kv_last_page_len_np,
|
|
)
|
|
|
|
uses_spec_reorder = self.reorder_batch_threshold > 1
|
|
prefill_use_trtllm = use_trtllm_attention(self.num_qo_heads,
|
|
self.num_kv_heads,
|
|
num_prefill_tokens,
|
|
max_seq_len,
|
|
self.cache_dtype,
|
|
self.q_data_type,
|
|
is_prefill=True,
|
|
has_sinks=self.has_sinks,
|
|
has_spec=uses_spec_reorder)
|
|
decode_use_trtllm = use_trtllm_attention(self.num_qo_heads,
|
|
self.num_kv_heads,
|
|
num_decode_tokens,
|
|
max_seq_len,
|
|
self.cache_dtype,
|
|
self.q_data_type,
|
|
is_prefill=False,
|
|
has_sinks=self.has_sinks,
|
|
has_spec=uses_spec_reorder)
|
|
if self.has_sinks and not (prefill_use_trtllm and decode_use_trtllm):
|
|
raise NotImplementedError(
|
|
"FlashInfer backend currently does not support attention "
|
|
"sinks, please use trtllm on blackwell or flash attention on "
|
|
"earlier GPUs.")
|
|
|
|
# If TRTLLM attention is not used, the q quantization is not supported.
|
|
# Fall back to use model dtype.
|
|
if not (prefill_use_trtllm and decode_use_trtllm):
|
|
self.q_data_type = self.model_config.dtype
|
|
|
|
attn_metadata = FlashInferMetadata(
|
|
num_actual_tokens=num_actual_tokens,
|
|
q_data_type=self.q_data_type,
|
|
slot_mapping=common_attn_metadata.slot_mapping,
|
|
max_q_len=max_q_len,
|
|
max_q_len_prefill=max_q_len,
|
|
max_seq_len=max_seq_len,
|
|
seq_lens=seq_lens,
|
|
block_table_tensor=block_table_tensor,
|
|
prefill_use_trtllm=prefill_use_trtllm,
|
|
decode_use_trtllm=decode_use_trtllm,
|
|
num_decodes=num_decodes,
|
|
num_decode_tokens=num_decode_tokens,
|
|
num_prefills=num_prefills,
|
|
num_prefill_tokens=num_prefill_tokens,
|
|
use_cascade=use_cascade,
|
|
)
|
|
|
|
qo_indptr_cpu = common_attn_metadata.query_start_loc_cpu
|
|
paged_kv_indptr_cpu = self.paged_kv_indptr_cpu[:1 + num_reqs]
|
|
paged_kv_last_page_len_cpu = self.paged_kv_last_page_len_cpu[:num_reqs]
|
|
|
|
if attn_metadata.use_cascade:
|
|
attn_metadata.cascade_wrapper = self._get_cascade_wrapper()
|
|
attn_metadata.cascade_wrapper.plan(
|
|
[shared_qo_indptr_cpu, qo_indptr_cpu],
|
|
[shared_kv_page_indptr_cpu, paged_kv_indptr_cpu],
|
|
[shared_kv_page_indices_cpu, paged_kv_indices],
|
|
[shared_kv_last_page_len_cpu, paged_kv_last_page_len_cpu],
|
|
self.num_qo_heads,
|
|
self.num_kv_heads,
|
|
self.head_dim,
|
|
self.page_size,
|
|
causal=True,
|
|
sm_scale=self.sm_scale,
|
|
window_left=self.window_left,
|
|
logits_soft_cap=self.logits_soft_cap,
|
|
q_data_type=self.q_data_type,
|
|
kv_data_type=self.kv_cache_dtype,
|
|
)
|
|
else:
|
|
# Regular attention (common case).
|
|
# Decodes are at the front and prefills are at the back,
|
|
# according to reorder_batch()
|
|
num_prefills = attn_metadata.num_prefills
|
|
num_decodes = attn_metadata.num_decodes
|
|
if num_prefills > 0:
|
|
# Decodes are first so prefills start after the last decode
|
|
prefill_start = num_decodes
|
|
attn_metadata.prefill_wrapper = self._get_prefill_wrapper()
|
|
assert qo_indptr_cpu[prefill_start:].shape[
|
|
0] == num_prefills + 1
|
|
assert paged_kv_indptr_cpu[prefill_start:].shape[
|
|
0] == num_prefills + 1
|
|
assert paged_kv_last_page_len_cpu[prefill_start:].shape[
|
|
0] == num_prefills
|
|
# Since prefill_wrapper.run() will be called with
|
|
# query[num_decode_tokens:] we need to adjust the qo_indptr
|
|
# to be relative to the start of the prefill queries.
|
|
qo_indptr_cpu = qo_indptr_cpu[prefill_start:] - qo_indptr_cpu[
|
|
prefill_start]
|
|
paged_kv_indptr_cpu = paged_kv_indptr_cpu[prefill_start:]
|
|
|
|
# Recompute max_q_len for the slice of requests we are using
|
|
# for prefills. This can be different from max_q_len when
|
|
# we have a non-uniform batch with some short decodes offloaded
|
|
# to the prefill pathway
|
|
query_lens_prefill = qo_indptr_cpu[1:] - qo_indptr_cpu[:-1]
|
|
attn_metadata.max_q_len_prefill = \
|
|
int(query_lens_prefill.max().item())
|
|
|
|
if not attn_metadata.prefill_use_trtllm:
|
|
attn_metadata.prefill_wrapper.plan(
|
|
qo_indptr_cpu,
|
|
paged_kv_indptr_cpu,
|
|
paged_kv_indices,
|
|
paged_kv_last_page_len_cpu[prefill_start:],
|
|
self.num_qo_heads,
|
|
self.num_kv_heads,
|
|
self.head_dim,
|
|
self.page_size,
|
|
causal=True,
|
|
sm_scale=self.sm_scale,
|
|
window_left=self.window_left,
|
|
logits_soft_cap=self.logits_soft_cap,
|
|
q_data_type=self.q_data_type,
|
|
kv_data_type=self.kv_cache_dtype,
|
|
fixed_split_size=self.prefill_fixed_split_size,
|
|
disable_split_kv=self.disable_split_kv,
|
|
)
|
|
else:
|
|
attn_metadata.qo_indptr_gpu = qo_indptr_cpu.to(
|
|
self.device, non_blocking=True)
|
|
attn_metadata.paged_kv_indptr_gpu = paged_kv_indptr_cpu.to(
|
|
self.device, non_blocking=True)
|
|
|
|
if num_decodes > 0:
|
|
pure_decode = num_prefills == 0
|
|
# possible required padding for cudagraph replay
|
|
use_cudagraph = (self.enable_cuda_graph and pure_decode and
|
|
num_decodes <= self._decode_cudagraph_max_bs)
|
|
if use_cudagraph:
|
|
num_input_tokens = (
|
|
self.vllm_config.pad_for_cudagraph(num_decode_tokens))
|
|
# Carefully fulfill the padding region with reasonable value
|
|
# on cpu.
|
|
# Make sure paged_kv_indptr_cpu is not decreasing
|
|
self.paged_kv_indptr_cpu[1 + num_decodes:1 +
|
|
num_input_tokens].fill_(
|
|
paged_kv_indptr_cpu[-1])
|
|
# Fill the remaining paged_kv_last_page_len_cpu with 1.
|
|
# This is because flashinfer treats 0 as a full page
|
|
# instead of empty.
|
|
self.paged_kv_last_page_len_cpu[
|
|
num_decodes:num_input_tokens].fill_(1)
|
|
|
|
else:
|
|
num_input_tokens = num_decode_tokens
|
|
|
|
attn_metadata.decode_wrapper = self._get_decode_wrapper(
|
|
num_input_tokens, use_cudagraph)
|
|
if not attn_metadata.decode_use_trtllm:
|
|
# Use the persistent buffer with padding length,
|
|
# instead of the same address but chunked version
|
|
# in atten_metadata when using cudagraph.
|
|
fast_plan_decode(
|
|
attn_metadata.decode_wrapper,
|
|
self.paged_kv_indptr_cpu[:num_input_tokens + 1],
|
|
paged_kv_indices,
|
|
self.paged_kv_last_page_len_cpu[:num_input_tokens],
|
|
seq_lens_cpu[:num_input_tokens],
|
|
self.num_qo_heads,
|
|
self.num_kv_heads,
|
|
self.head_dim,
|
|
self.page_size,
|
|
# Disable flashinfer's pos encoding and use vllm's rope.
|
|
pos_encoding_mode="NONE",
|
|
sm_scale=self.sm_scale,
|
|
window_left=self.window_left,
|
|
logits_soft_cap=self.logits_soft_cap,
|
|
q_data_type=self.q_data_type,
|
|
kv_data_type=self.kv_cache_dtype,
|
|
fixed_split_size=self.decode_fixed_split_size,
|
|
disable_split_kv=self.disable_split_kv,
|
|
)
|
|
return attn_metadata
|
|
|
|
def use_cascade_attention(self, *args, **kwargs) -> bool:
|
|
if self.kv_cache_spec.dtype != self.vllm_config.model_config.dtype:
|
|
# TODO: The cascade wrapper currently does not support setting
|
|
# kv cache dtype to something different from query dtype.
|
|
return False
|
|
# TODO: Cascade attention doesn't work, disable it for now
|
|
# return use_cascade_attention(*args, **kwargs)
|
|
return False
|
|
|
|
|
|
class FlashInferImpl(AttentionImpl):
|
|
|
|
def __init__(
|
|
self,
|
|
num_heads: int,
|
|
head_size: int,
|
|
scale: float,
|
|
num_kv_heads: int,
|
|
alibi_slopes: Optional[list[float]],
|
|
sliding_window: Optional[int],
|
|
kv_cache_dtype: str,
|
|
logits_soft_cap: Optional[float] = None,
|
|
attn_type: AttentionType = AttentionType.DECODER,
|
|
kv_sharing_target_layer_name: Optional[int] = None,
|
|
sinks: Optional[torch.Tensor] = None,
|
|
) -> None:
|
|
self.num_heads = num_heads
|
|
self.head_size = head_size
|
|
self.scale = float(scale)
|
|
self.num_kv_heads = num_kv_heads
|
|
if alibi_slopes is not None:
|
|
alibi_slopes = torch.tensor(alibi_slopes, dtype=torch.float32)
|
|
self.alibi_slopes = alibi_slopes
|
|
if sliding_window is None:
|
|
self.sliding_window = (-1, -1)
|
|
else:
|
|
self.sliding_window = (sliding_window - 1, 0)
|
|
self.window_left = (self.sliding_window[0]
|
|
if self.sliding_window is not None else -1)
|
|
self.kv_cache_dtype = kv_cache_dtype
|
|
self.logits_soft_cap = logits_soft_cap
|
|
self.kv_sharing_target_layer_name = kv_sharing_target_layer_name
|
|
|
|
self.num_queries_per_kv = self.num_heads // self.num_kv_heads
|
|
|
|
if attn_type != AttentionType.DECODER:
|
|
raise NotImplementedError("Encoder self-attention and "
|
|
"encoder/decoder cross-attention "
|
|
"are not implemented for "
|
|
"FlashInferImpl")
|
|
|
|
self.sinks: Optional[torch.Tensor] = None
|
|
if sinks is not None:
|
|
if sinks.shape[0] != num_heads:
|
|
raise ValueError(
|
|
"Sinks must have the same number of heads as the number of "
|
|
f"heads in the layer. Expected {num_heads}, but got "
|
|
f"{sinks.shape[0]}.")
|
|
self.sinks = sinks
|
|
|
|
self.support_trtllm_attn = (supports_trtllm_attention()
|
|
and num_heads % num_kv_heads == 0)
|
|
self.bmm1_scale: Optional[float] = None
|
|
self.bmm2_scale: Optional[float] = None
|
|
self.o_sf_scale: Optional[float] = None
|
|
|
|
def fused_output_quant_supported(self, quant_key: QuantKey):
|
|
return (self.support_trtllm_attn
|
|
and self.kv_cache_dtype.startswith("fp8")
|
|
and quant_key in (kFp8StaticTensorSym, kNvfp4Quant))
|
|
|
|
def forward(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
kv_cache: torch.Tensor,
|
|
attn_metadata: FlashInferMetadata,
|
|
output: Optional[torch.Tensor] = None,
|
|
output_scale: Optional[torch.Tensor] = None,
|
|
output_block_scale: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
"""Forward pass with FlashInfer.
|
|
|
|
Args:
|
|
query: shape = [num_tokens, num_heads, head_size]
|
|
key: shape = [num_tokens, num_kv_heads, head_size]
|
|
value: shape = [num_tokens, num_kv_heads, head_size]
|
|
kv_cache: KV cache tensor with different possible shapes:
|
|
- NHD: [num_blocks, 2, block_size, num_kv_heads, head_size]
|
|
- HND: [num_blocks, 2, num_kv_heads, block_size, head_size]
|
|
attn_metadata: Metadata for attention.
|
|
Returns:
|
|
shape = [num_tokens, num_heads * head_size]
|
|
"""
|
|
assert output is not None, "Output tensor must be provided."
|
|
|
|
if attn_metadata is None:
|
|
# Profiling run.
|
|
return output
|
|
|
|
if self.bmm1_scale is None:
|
|
self.bmm1_scale = (layer._q_scale_float * layer._k_scale_float *
|
|
self.scale)
|
|
|
|
if self.bmm2_scale is None:
|
|
self.bmm2_scale = layer._v_scale_float
|
|
|
|
# The attn+quant fusion happens when output_scale is provided.
|
|
if output_scale is None:
|
|
assert output_block_scale is None, "output_block_scale "\
|
|
"is not supported when fusion has not happened"
|
|
else:
|
|
assert attn_metadata.q_data_type == FP8_DTYPE, \
|
|
"Query must be FP8 when attn+quant fusion happened."
|
|
assert (attn_metadata.prefill_use_trtllm and
|
|
attn_metadata.decode_use_trtllm), "Must use TRT-LLM attn"
|
|
|
|
if output.dtype == FP8_DTYPE:
|
|
assert output_block_scale is None, \
|
|
"output_block_scale should not be provided for fp8 output"
|
|
elif output.dtype == FP4_DTYPE:
|
|
assert output_block_scale is not None, \
|
|
"output_block_scale is required for nvfp4 output"
|
|
else:
|
|
raise ValueError(f"Unsupported output dtype: {output.dtype}")
|
|
|
|
# TRTLLM attn kernel requires to scale to pass as a host scalar,
|
|
# store the o scale as a host scalar in warmup run with cuda graph
|
|
# not enabled
|
|
if layer._o_scale_float is None:
|
|
layer._o_scale_float = output_scale.cpu().item()
|
|
if output.dtype == FP8_DTYPE:
|
|
self.bmm2_scale = self.bmm2_scale / layer._o_scale_float
|
|
elif output.dtype == FP4_DTYPE:
|
|
self.o_sf_scale = layer._o_scale_float
|
|
|
|
# Insert FP8 quant for query
|
|
if attn_metadata.q_data_type == FP8_DTYPE:
|
|
num_tokens, num_heads, head_size = query.shape
|
|
query, _ = ops.scaled_fp8_quant(
|
|
query.reshape(
|
|
(num_tokens, num_heads * head_size)).contiguous(),
|
|
layer._q_scale)
|
|
query = query.reshape((num_tokens, num_heads, head_size))
|
|
|
|
# IMPORTANT!
|
|
# NOTE(woosuk): With piece-wise CUDA graphs, this method is executed in
|
|
# eager-mode PyTorch. Thus, we need to be careful about any CPU overhead
|
|
# in this method. For example, `view` and `slice` (or `[:n]`) operations
|
|
# are surprisingly slow even in the case they do not invoke any GPU ops.
|
|
# Minimize the PyTorch ops in this method as much as possible.
|
|
# Whenever making a change in this method, please benchmark the
|
|
# performance to make sure it does not introduce any overhead.
|
|
|
|
num_actual_tokens = attn_metadata.num_actual_tokens
|
|
|
|
if self.kv_sharing_target_layer_name is None:
|
|
# Reshape the input keys and values and store them in the cache.
|
|
# Skip this if sharing KV cache with an earlier attention layer.
|
|
# NOTE(woosuk): Here, key and value are padded while slot_mapping is
|
|
# not padded. However, we don't need to do key[:num_actual_tokens]
|
|
# and value[:num_actual_tokens] because the reshape_and_cache_flash
|
|
# op uses the slot_mapping's shape to determine the number of
|
|
# actual tokens.
|
|
torch.ops._C_cache_ops.reshape_and_cache_flash(
|
|
key,
|
|
value,
|
|
kv_cache[:, 0],
|
|
kv_cache[:, 1],
|
|
attn_metadata.slot_mapping,
|
|
self.kv_cache_dtype,
|
|
layer._k_scale,
|
|
layer._v_scale,
|
|
)
|
|
|
|
# The FlashInfer api requires data to be in fp8_e4m3 or fp8_e5m2
|
|
# to process the cache when the kv_cache_dtype is fp8
|
|
if self.kv_cache_dtype.startswith("fp8"):
|
|
torch_dtype = FlashInferBackend.get_fp8_dtype_for_flashinfer(
|
|
self.kv_cache_dtype)
|
|
kv_cache = kv_cache.view(torch_dtype)
|
|
|
|
# Inputs and outputs may be padded for CUDA graphs
|
|
query = query[:num_actual_tokens]
|
|
output_padded = output
|
|
output = output[:num_actual_tokens]
|
|
|
|
if attn_metadata.use_cascade:
|
|
# Cascade attention (rare case).
|
|
assert attn_metadata.cascade_wrapper is not None
|
|
output.copy_(attn_metadata.cascade_wrapper.run(query, kv_cache))
|
|
return output
|
|
|
|
# When using spec decoding, num_decodes can be < num_decode_tokens
|
|
# because some decode requests may have more than one query token.
|
|
num_decodes = attn_metadata.num_decodes
|
|
num_decode_tokens = attn_metadata.num_decode_tokens
|
|
num_prefill_tokens = attn_metadata.num_prefill_tokens
|
|
|
|
stride_order = FlashInferBackend.get_kv_cache_stride_order()
|
|
kv_cache_permute = kv_cache.permute(*stride_order)
|
|
# Regular attention (common case).
|
|
# Decodes are at the front and prefills are at the back,
|
|
# according to reorder_batch()
|
|
if num_prefill_tokens > 0:
|
|
prefill_wrapper = attn_metadata.prefill_wrapper
|
|
prefill_query = query[num_decode_tokens:]
|
|
assert prefill_query.shape[0] == num_prefill_tokens
|
|
assert prefill_wrapper is not None
|
|
|
|
if not attn_metadata.prefill_use_trtllm:
|
|
assert prefill_wrapper._causal
|
|
assert prefill_wrapper._window_left == self.window_left
|
|
assert prefill_wrapper._logits_soft_cap == (
|
|
self.logits_soft_cap or 0.0)
|
|
assert prefill_wrapper._sm_scale == self.scale
|
|
prefill_wrapper.run(
|
|
prefill_query,
|
|
kv_cache_permute,
|
|
k_scale=layer._k_scale_float,
|
|
v_scale=layer._v_scale_float,
|
|
out=output[num_decode_tokens:],
|
|
)
|
|
else:
|
|
# prefill_query may be non-contiguous
|
|
prefill_query = prefill_query.contiguous()
|
|
workspace_buffer = _get_trtllm_gen_workspace_buffer()
|
|
block_tables_prefill = attn_metadata.block_table_tensor[
|
|
num_decodes:]
|
|
seq_lens_prefill = attn_metadata.seq_lens[num_decodes:]
|
|
|
|
# This path needs to be enabled with VLLM_KV_CACHE_LAYOUT = HND
|
|
assert get_kv_cache_layout() == "HND"
|
|
assert prefill_query.is_contiguous()
|
|
assert kv_cache_permute.is_contiguous()
|
|
assert workspace_buffer.is_contiguous()
|
|
assert block_tables_prefill.is_contiguous()
|
|
assert seq_lens_prefill.is_contiguous()
|
|
|
|
if output.dtype == FP4_DTYPE:
|
|
assert self.o_sf_scale is not None
|
|
out = FP4Tensor(data=output[num_decode_tokens:],
|
|
scale=output_block_scale,
|
|
scale_start_index=num_decode_tokens,
|
|
original_shape=prefill_query.shape)
|
|
else:
|
|
assert self.o_sf_scale is None
|
|
out = output[num_decode_tokens:]
|
|
|
|
if attn_metadata.q_data_type != FP8_DTYPE \
|
|
and self.kv_cache_dtype.startswith("fp8"):
|
|
# TRTLLM prefill attention does not support BF16 Q
|
|
# and fp8 kv cache. So to enable prefill attention
|
|
# with fp8 kv cache, we can construct a mock block
|
|
# and mock kv cache with BF16 KV involved in the prefill
|
|
mock_kv_cache, mock_block_table = (
|
|
trtllm_prefill_attn_kvfp8_dequant(
|
|
kv_cache_permute,
|
|
block_tables_prefill,
|
|
layer._k_scale,
|
|
layer._v_scale,
|
|
attn_metadata.q_data_type,
|
|
))
|
|
else:
|
|
mock_kv_cache = kv_cache_permute
|
|
mock_block_table = block_tables_prefill
|
|
|
|
trtllm_batch_context_with_kv_cache(
|
|
query=prefill_query,
|
|
kv_cache=mock_kv_cache,
|
|
workspace_buffer=workspace_buffer,
|
|
block_tables=mock_block_table,
|
|
seq_lens=seq_lens_prefill,
|
|
max_q_len=attn_metadata.max_q_len_prefill,
|
|
max_kv_len=attn_metadata.max_seq_len,
|
|
bmm1_scale=self.bmm1_scale,
|
|
bmm2_scale=self.bmm2_scale,
|
|
batch_size=attn_metadata.num_prefills,
|
|
cum_seq_lens_q=attn_metadata.qo_indptr_gpu,
|
|
cum_seq_lens_kv=attn_metadata.paged_kv_indptr_gpu,
|
|
window_left=self.window_left,
|
|
sinks=self.sinks,
|
|
o_sf_scale=self.o_sf_scale,
|
|
out=out,
|
|
)
|
|
|
|
if num_decode_tokens > 0:
|
|
decode_wrapper = attn_metadata.decode_wrapper
|
|
decode_query = query[:num_decode_tokens]
|
|
assert decode_query.shape[0] == num_decode_tokens
|
|
assert decode_wrapper is not None
|
|
|
|
if not attn_metadata.decode_use_trtllm:
|
|
assert decode_wrapper._window_left == self.window_left
|
|
assert decode_wrapper._logits_soft_cap == (self.logits_soft_cap
|
|
or 0.0)
|
|
assert decode_wrapper._sm_scale == self.scale
|
|
decode_wrapper.run(
|
|
decode_query,
|
|
kv_cache_permute,
|
|
k_scale=layer._k_scale_float,
|
|
v_scale=layer._v_scale_float,
|
|
out=output[:num_decode_tokens],
|
|
)
|
|
else:
|
|
# decode_query may be non-contiguous
|
|
decode_query = decode_query.contiguous()
|
|
workspace_buffer = _get_trtllm_gen_workspace_buffer()
|
|
block_tables_decode = attn_metadata.\
|
|
block_table_tensor[:num_decode_tokens]
|
|
seq_lens_decode = attn_metadata.seq_lens[:num_decode_tokens]
|
|
|
|
# This path needs to be enabled with VLLM_KV_CACHE_LAYOUT = HND
|
|
assert get_kv_cache_layout() == "HND"
|
|
assert decode_query.is_contiguous()
|
|
assert kv_cache_permute.is_contiguous()
|
|
assert workspace_buffer.is_contiguous()
|
|
assert block_tables_decode.is_contiguous()
|
|
assert seq_lens_decode.is_contiguous()
|
|
|
|
if output.dtype == FP4_DTYPE:
|
|
assert self.o_sf_scale is not None
|
|
out = FP4Tensor(data=output[:num_decode_tokens],
|
|
scale=output_block_scale,
|
|
scale_start_index=0,
|
|
original_shape=decode_query.shape)
|
|
else:
|
|
assert self.o_sf_scale is None
|
|
out = output[:num_decode_tokens]
|
|
|
|
if num_decode_tokens % attn_metadata.num_decodes != 0:
|
|
# This gets triggered when the dummy_run forces
|
|
# attention to be initialized with q_len = 0
|
|
q_len_per_req = 1
|
|
else:
|
|
q_len_per_req = \
|
|
num_decode_tokens // attn_metadata.num_decodes
|
|
|
|
trtllm_batch_decode_with_kv_cache(
|
|
query=decode_query,
|
|
kv_cache=kv_cache_permute,
|
|
workspace_buffer=workspace_buffer,
|
|
block_tables=block_tables_decode,
|
|
seq_lens=seq_lens_decode,
|
|
max_seq_len=attn_metadata.max_seq_len,
|
|
bmm1_scale=self.bmm1_scale,
|
|
bmm2_scale=self.bmm2_scale,
|
|
window_left=self.window_left,
|
|
sinks=self.sinks,
|
|
o_sf_scale=self.o_sf_scale,
|
|
out=out,
|
|
q_len_per_req=q_len_per_req)
|
|
return output_padded
|
|
|
|
|
|
def fast_plan_decode(
|
|
self, # decode wrapper
|
|
indptr_cpu: torch.Tensor,
|
|
indices: torch.Tensor,
|
|
last_page_len_cpu: torch.Tensor,
|
|
seq_lens_cpu: torch.Tensor,
|
|
num_qo_heads: int,
|
|
num_kv_heads: int,
|
|
head_dim: int,
|
|
page_size: int,
|
|
pos_encoding_mode: str = "NONE",
|
|
window_left: int = -1,
|
|
logits_soft_cap: Optional[float] = None,
|
|
q_data_type: Optional[Union[str, torch.dtype]] = "float16",
|
|
kv_data_type: Optional[Union[str, torch.dtype]] = None,
|
|
data_type: Optional[Union[str, torch.dtype]] = None,
|
|
sm_scale: Optional[float] = None,
|
|
rope_scale: Optional[float] = None,
|
|
rope_theta: Optional[float] = None,
|
|
non_blocking: bool = True,
|
|
fixed_split_size: int = -1,
|
|
disable_split_kv: bool = False,
|
|
) -> None:
|
|
"""
|
|
A faster version of BatchDecodeWithPagedKVCacheWrapper::plan used for
|
|
cudagraph capture/replay, while the no cudagraph version turns back
|
|
to the original plan.
|
|
using original plan after passing host-side buffers:
|
|
- only host-to-device copy of indptr and last_page_len buffers
|
|
Modifications for cudagraph:
|
|
- only host-to-device copy of indptr and last_page_len buffers.
|
|
- avoid device-to-device copy of indices buffer.
|
|
|
|
Part of the code get inspiration from the original plan from FlashInfer repo
|
|
and the implementation of fast_decode_plan for FlashInfer in SGlang repo.
|
|
"""
|
|
# Warm up with the original plan if it is first call, and always run the
|
|
# original plan if we run for dynamic shape. For fixed shape (cudagraph),
|
|
# this warm up is to generate the _cached_module for the decode wrapper.
|
|
if not self.is_cuda_graph_enabled or \
|
|
getattr(self, "vllm_first_call", True):
|
|
self.plan(
|
|
indptr_cpu,
|
|
indices,
|
|
last_page_len_cpu,
|
|
num_qo_heads,
|
|
num_kv_heads,
|
|
head_dim,
|
|
page_size,
|
|
pos_encoding_mode,
|
|
window_left,
|
|
logits_soft_cap,
|
|
q_data_type,
|
|
kv_data_type,
|
|
data_type,
|
|
sm_scale,
|
|
rope_scale,
|
|
rope_theta,
|
|
non_blocking,
|
|
None, # block_tables
|
|
None, # seq_lens
|
|
fixed_split_size,
|
|
disable_split_kv,
|
|
)
|
|
self.vllm_first_call = False
|
|
return
|
|
|
|
assert self.is_cuda_graph_enabled, "Should be cudagraph only here"
|
|
|
|
batch_size = len(last_page_len_cpu)
|
|
if logits_soft_cap is None:
|
|
logits_soft_cap = 0.0
|
|
|
|
# Handle data types consistently
|
|
if data_type is not None:
|
|
if q_data_type is None:
|
|
q_data_type = data_type
|
|
if kv_data_type is None:
|
|
kv_data_type = data_type
|
|
elif q_data_type is None:
|
|
q_data_type = "float16"
|
|
|
|
if kv_data_type is None:
|
|
kv_data_type = q_data_type
|
|
q_data_type = getattr(torch, q_data_type) if isinstance(
|
|
q_data_type, str) else q_data_type
|
|
kv_data_type = getattr(torch, kv_data_type) if isinstance(
|
|
kv_data_type, str) else kv_data_type
|
|
|
|
if batch_size != self._fixed_batch_size:
|
|
raise ValueError(
|
|
"The batch size should be fixed in cudagraph mode, the runtime "
|
|
"batch size {} mismatches the batch size set during "
|
|
"initialization {}".format(batch_size, self._fixed_batch_size))
|
|
if len(indices) > len(self._paged_kv_indices_buf):
|
|
raise ValueError(
|
|
"The size of indices should be less than or equal to the "
|
|
"allocated buffer")
|
|
|
|
# host-to-device copy for the indptr buffer
|
|
self._paged_kv_indptr_buf.copy_(indptr_cpu, non_blocking=True)
|
|
# host-to-device copy for the last_page_len buffer
|
|
self._paged_kv_last_page_len_buf.copy_(last_page_len_cpu,
|
|
non_blocking=True)
|
|
|
|
qo_indptr_host = _get_range_buf(batch_size + 1, "cpu")
|
|
|
|
try:
|
|
# Make sure we pass exactly 18 arguments for tensor core version
|
|
self._plan_info = self._cached_module.plan(
|
|
self._float_workspace_buffer,
|
|
self._int_workspace_buffer,
|
|
self._pin_memory_int_workspace_buffer,
|
|
qo_indptr_host,
|
|
indptr_cpu,
|
|
seq_lens_cpu,
|
|
batch_size, # total_num_rows
|
|
batch_size,
|
|
num_qo_heads,
|
|
num_kv_heads,
|
|
page_size,
|
|
self.is_cuda_graph_enabled,
|
|
head_dim,
|
|
head_dim,
|
|
False, # causal
|
|
window_left,
|
|
fixed_split_size,
|
|
disable_split_kv,
|
|
)
|
|
except Exception as e:
|
|
raise RuntimeError(f"Error in tensor core plan: {e}") from e
|
|
|
|
self._pos_encoding_mode = pos_encoding_mode
|
|
self._window_left = window_left
|
|
self._logits_soft_cap = logits_soft_cap
|
|
self._sm_scale = sm_scale
|
|
self._rope_scale = rope_scale
|
|
self._rope_theta = rope_theta
|
|
|
|
|
|
@triton.jit
|
|
def _copy_page_indices_kernel(
|
|
page_indices,
|
|
block_table,
|
|
block_table_stride,
|
|
cu_num_blocks,
|
|
BLOCK_SIZE: tl.constexpr,
|
|
):
|
|
req_idx = tl.program_id(0)
|
|
row_ptr = block_table + req_idx * block_table_stride
|
|
start_idx = tl.load(cu_num_blocks + req_idx)
|
|
end_idx = tl.load(cu_num_blocks + req_idx + 1)
|
|
num_blocks = end_idx - start_idx
|
|
|
|
offset = tl.arange(0, BLOCK_SIZE)
|
|
for i in tl.range(0, num_blocks, BLOCK_SIZE):
|
|
block_ids = tl.load(row_ptr + i + offset, mask=i + offset < num_blocks)
|
|
tl.store(page_indices + start_idx + i + offset,
|
|
block_ids,
|
|
mask=i + offset < num_blocks)
|