layer.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Attention layer."""
from typing import List, Optional

import torch
import torch.nn as nn
import torch.nn.functional as F

import vllm.envs as envs
from vllm.attention import AttentionType
from vllm.attention.backends.abstract import AttentionBackend
from vllm.attention.selector import backend_name_to_enum, get_attn_backend
from vllm.attention.utils.kv_sharing_utils import validate_kv_sharing_target
from vllm.config import CacheConfig, get_current_vllm_config
from vllm.distributed.kv_transfer import (get_kv_transfer_group,
                                          has_kv_transfer_group,
                                          is_v1_kv_transfer_group)
from vllm.forward_context import ForwardContext, get_forward_context
from vllm.logger import init_logger
from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
from vllm.model_executor.layers.linear import UnquantizedLinearMethod
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig)
from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
from vllm.model_executor.layers.quantization.utils.quant_utils import (
    GroupShape)
from vllm.model_executor.models.vision import get_vit_attn_backend
from vllm.platforms import _Backend, current_platform
from vllm.utils import GiB_bytes, direct_register_custom_op

logger = init_logger(__name__)
USE_XFORMERS_OPS = None
try:
    tag_cudagraph_unsafe = (torch._C.Tag.cudagraph_unsafe, )
except AttributeError:
    tag_cudagraph_unsafe = ()  # type: ignore[assignment]


def check_xformers_availability():
    global USE_XFORMERS_OPS
    if USE_XFORMERS_OPS is not None:
        return USE_XFORMERS_OPS

    if current_platform.is_cuda() and current_platform.has_device_capability(
            100):
        # Xformers FA is not compatible with B200
        USE_XFORMERS_OPS = False
    else:
        try:
            from importlib.util import find_spec

            find_spec("xformers.ops")
            USE_XFORMERS_OPS = True
        except ImportError:
            USE_XFORMERS_OPS = False

    # the warning only needs to be shown once
    if not USE_XFORMERS_OPS:
        logger.warning("Xformers is not available, falling back.")

    return USE_XFORMERS_OPS


def check_upstream_fa_availability(dtype: torch.dtype):
    if dtype in (torch.float16, torch.bfloat16) and current_platform.is_cuda(
    ) and current_platform.has_device_capability(80):
        from transformers.utils import is_flash_attn_2_available
        return is_flash_attn_2_available()
    return False


class Attention(nn.Module, AttentionLayerBase):
    """Attention layer.

    This class takes query, key, and value tensors as input. The input tensors
    can either contain prompt tokens or generation tokens.
    The class does the following:

    1. Store the input key and value tensors in the KV cache.
    2. Perform (multi-head/multi-query/grouped-query) attention.
    3. Return the output tensor.
    """

    def __init__(
        self,
        num_heads: int,
        head_size: int,
        scale: float,
        num_kv_heads: Optional[int] = None,
        alibi_slopes: Optional[List[float]] = None,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        logits_soft_cap: Optional[float] = None,
        per_layer_sliding_window: Optional[int] = None,
        use_mla: bool = False,
        prefix: str = "",
        attn_type: str = AttentionType.DECODER,
        kv_sharing_target_layer_name: Optional[str] = None,
        attn_backend: Optional[type[AttentionBackend]] = None,
        **extra_impl_args,
    ) -> None:
        """
        The KV cache is stored inside this class and is accessed via
        `self.kv_cache`.
        """
        super().__init__()
        if per_layer_sliding_window is not None:
            # per-layer sliding window
            sliding_window = per_layer_sliding_window
        elif cache_config is not None:
            # model-level sliding window
            sliding_window = cache_config.sliding_window
        else:
            sliding_window = None

        if cache_config is not None:
            kv_cache_dtype = cache_config.cache_dtype
            block_size = cache_config.block_size
            calculate_kv_scales = cache_config.calculate_kv_scales
        else:
            kv_cache_dtype = "auto"
            block_size = 16
            calculate_kv_scales = False
        if num_kv_heads is None:
            num_kv_heads = num_heads
        assert num_heads % num_kv_heads == 0, \
            f"num_heads ({num_heads}) is not " \
            f"divisible by num_kv_heads ({num_kv_heads})"

        # The default k/v_scale is set to 1.0. This is ignored
        # when kv-cache is not fp8, and should be used with
        # kv-cache in fp8_e5m2. For kv-cache in fp8_e4m3, we
        # expect the pre-quantized k/v_scale to be loaded along
        # with the model weights.
        self.kv_cache_dtype = kv_cache_dtype
        self.calculate_kv_scales = calculate_kv_scales
        self._k_scale = torch.tensor(1.0, dtype=torch.float32)
        self._v_scale = torch.tensor(1.0, dtype=torch.float32)
        # FlashAttn doesn't support quantizing the kv-cache only
        # but requires q to be quantized as well.
        self._q_scale = torch.tensor(1.0, dtype=torch.float32)
        self._prob_scale = torch.tensor(1.0, dtype=torch.float32)

        # We also keep q/k/v_scale on host (cpu) memory for attention
        # backends that require the scales to be on host instead of on device.
        # e.g. Flashinfer
        self._q_scale_float = 1.0
        self._k_scale_float = 1.0
        self._v_scale_float = 1.0

        # The output scale on host memory. This should be the input scale of
        # the quant op after this attention layer.
        self._o_scale_float: Optional[float] = None

        self.use_mla = use_mla
        self.num_heads = num_heads
        self.head_size = head_size
        self.num_kv_heads = num_kv_heads
        self.sliding_window = sliding_window
        self.has_sink = extra_impl_args.get("sinks") is not None

        quant_method = quant_config.get_quant_method(
            self, prefix=prefix) if quant_config else None
        if quant_method is not None and not isinstance(
                quant_method, UnquantizedLinearMethod):
            assert isinstance(quant_method, BaseKVCacheMethod)
            # TODO (mgoin): kv cache dtype should be specified in the FP8
            # checkpoint config and become the "auto" behavior
            if self.kv_cache_dtype == "fp8_e5m2":
                raise ValueError("fp8_e5m2 kv-cache is not supported with "
                                 "fp8 checkpoints.")
            # If quantization is enabled, we make "k_scale" and "v_scale"
            # parameters so that it can be loaded from the model checkpoint.
            # The k/v_scale will then be converted back to native float32
            # values after weight loading.
            self.quant_method = quant_method
            self.quant_method.create_weights(self)

        # During model initialization, the default dtype is set as the model
        # weight and activation dtype.
        dtype = torch.get_default_dtype()
        if attn_backend is None:
            self.attn_backend = get_attn_backend(head_size,
                                                 dtype,
                                                 kv_cache_dtype,
                                                 block_size,
                                                 use_mla=use_mla,
                                                 has_sink=self.has_sink)
        else:
            self.attn_backend = attn_backend

        impl_cls = self.attn_backend.get_impl_cls()
        self.impl = impl_cls(num_heads, head_size, scale, num_kv_heads,
                             alibi_slopes, sliding_window, kv_cache_dtype,
                             logits_soft_cap, attn_type,
                             kv_sharing_target_layer_name, **extra_impl_args)
        self.backend = backend_name_to_enum(self.attn_backend.get_name())
        self.dtype = dtype

        # For cuda-alike (CUDA and ROCM) and cpu platforms, we control how
        # torch.compile works by registering the attention as one giant
        # opaque custom op. For other platforms, we directly call them
        # and let torch.compile handle them.
        self.use_direct_call = not current_platform.opaque_attention_op()

        self.use_output = self.attn_backend.accept_output_buffer
        compilation_config = get_current_vllm_config().compilation_config
        if prefix in compilation_config.static_forward_context:
            raise ValueError(f"Duplicate layer name: {prefix}")
        compilation_config.static_forward_context[prefix] = self
        self.layer_name = prefix
        self.attn_type = attn_type

        if kv_sharing_target_layer_name is not None:
            validate_kv_sharing_target(
                prefix,
                kv_sharing_target_layer_name,
                compilation_config.static_forward_context,
            )
        self.kv_sharing_target_layer_name = kv_sharing_target_layer_name

        # use a placeholder kv cache tensor during init, which will be replaced
        # by bind_kv_cache
        # this variable will not be accessed if use_direct_call is True
        self.kv_cache = [
            torch.tensor([]) for _ in range(get_current_vllm_config(
            ).parallel_config.pipeline_parallel_size)
        ]

        try:
            self.q_range = torch.tensor(envs.Q_SCALE_CONSTANT,
                                        dtype=torch.float32)
            self.k_range = torch.tensor(envs.K_SCALE_CONSTANT,
                                        dtype=torch.float32)
            self.v_range = torch.tensor(envs.V_SCALE_CONSTANT,
                                        dtype=torch.float32)
        except torch.cuda.OutOfMemoryError as e:
            logger.error(
                "Failed to initialize attention q/k/v range constants: %s", e)
            if torch.cuda.is_available():
                logger.debug("CUDA device: %s", torch.cuda.current_device())
                logger.debug("Allocated: %.2f GiB",
                             torch.cuda.memory_allocated() / GiB_bytes)
                logger.debug("Reserved: %.2f GiB",
                             torch.cuda.memory_reserved() / GiB_bytes)
            raise RuntimeError(
                "Failed to initialize q/k/v range constants. "
                "This may be caused by insufficient memory to allocate "
                "kv cache.") from e

        # for attn backends supporting query quantization
        self.query_quant = None
        if self.kv_cache_dtype.startswith(
                "fp8") and self.attn_backend.supports_quant_query_input:
            self.query_quant = QuantFP8(static=True,
                                        group_shape=GroupShape.PER_TENSOR)

    def forward(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        # For some alternate attention backends like MLA the attention output
        # shape does not match the query shape, so we optionally let the model
        # definition specify the output tensor shape.
        output_shape: Optional[torch.Size] = None,
    ) -> torch.Tensor:
        """
        The KV cache is stored inside this class and is accessed via
        `self.kv_cache`.

        Attention metadata (`attn_metadata`) is set using a context manager in
        the model runner's `execute_model` method. It is accessed via forward
        context using
        `vllm.forward_context.get_forward_context().attn_metadata`.
        """
        if self.calculate_kv_scales:
            attn_metadata = get_forward_context().attn_metadata
            if attn_metadata.enable_kv_scales_calculation:
                self.calc_kv_scales(query, key, value)

        output_dtype = query.dtype
        if self.query_quant is not None:
            # quantizing with a simple torch operation enables
            # torch.compile to fuse this into previous ops
            # which reduces overheads during decoding.
            # Otherwise queries are quantized using custom ops
            # which causes decoding overheads
            assert self.kv_cache_dtype in {"fp8", "fp8_e4m3"}
            query, _ = self.query_quant(query, self._q_scale)

        if self.use_output:
            output_shape = (output_shape
                            if output_shape is not None else query.shape)
            output = torch.zeros(output_shape,
                                 dtype=output_dtype,
                                 device=query.device)
            hidden_size = output_shape[-1]
            # We skip reshaping query, key and value tensors for the MLA
            # backend since these tensors have different semantics and are
            # processed differently.
            if not self.use_mla:
                # Reshape the query, key, and value tensors.
                # NOTE(woosuk): We do this outside the custom op to minimize the
                # CPU overheads from the non-CUDA-graph regions.
                query = query.view(-1, self.num_heads, self.head_size)
                output = output.view(-1, self.num_heads, self.head_size)
                if key is not None:
                    key = key.view(-1, self.num_kv_heads, self.head_size)
                if value is not None:
                    value = value.view(-1, self.num_kv_heads, self.head_size)
            if self.use_direct_call:
                forward_context: ForwardContext = get_forward_context()
                attn_metadata = forward_context.attn_metadata
                if isinstance(attn_metadata, dict):
                    attn_metadata = attn_metadata[self.layer_name]
                self_kv_cache = self.kv_cache[forward_context.virtual_engine]
                self.impl.forward(self,
                                  query,
                                  key,
                                  value,
                                  self_kv_cache,
                                  attn_metadata,
                                  output=output)
            else:
                torch.ops.vllm.unified_attention_with_output(
                    query, key, value, output, self.layer_name)
            return output.view(-1, hidden_size)
        else:
            if self.use_direct_call:
                forward_context = get_forward_context()
                attn_metadata = forward_context.attn_metadata
                if isinstance(attn_metadata, dict):
                    attn_metadata = attn_metadata[self.layer_name]
                self_kv_cache = self.kv_cache[forward_context.virtual_engine]
                return self.impl.forward(self, query, key, value,
                                         self_kv_cache, attn_metadata)
            else:
                return torch.ops.vllm.unified_attention(
                    query, key, value, self.layer_name)

    def calc_kv_scales(self, query, key, value):
        self._q_scale.copy_(torch.abs(query).max() / self.q_range)
        self._k_scale.copy_(torch.abs(key).max() / self.k_range)
        self._v_scale.copy_(torch.abs(value).max() / self.v_range)
        self._q_scale_float = self._q_scale.item()
        self._k_scale_float = self._k_scale.item()
        self._v_scale_float = self._v_scale.item()
        # We only calculate the scales once
        self.calculate_kv_scales = False

    def extra_repr(self) -> str:
        s = f"head_size={self.impl.head_size}"  # type: ignore
        s += f", num_heads={self.impl.num_heads}"  # type: ignore
        s += f", num_kv_heads={self.impl.num_kv_heads}"  # type: ignore
        s += f", scale={self.impl.scale}"  # type: ignore
        s += f", backend={self.impl.__class__.__name__}"
        return s

    def process_weights_after_loading(self, act_dtype: torch.dtype):
        if hasattr(self.impl, "process_weights_after_loading"):
            self.impl.process_weights_after_loading(act_dtype)

        # FlashInfer requires attention sinks to be float32
        if (self.backend == _Backend.FLASHINFER
                and hasattr(self.impl, 'sinks')):
            from vllm.v1.attention.backends.flashinfer import FlashInferImpl
            assert isinstance(self.impl, FlashInferImpl)
            if (self.impl.sinks is not None
                    and self.impl.sinks.dtype != torch.float32):
                self.impl.sinks = self.impl.sinks.to(torch.float32)

    def get_attn_backend(self) -> type[AttentionBackend]:
        return self.attn_backend


class MultiHeadAttention(nn.Module):
    """Multi-headed attention without any cache, used for ViT."""

    def __init__(
        self,
        num_heads: int,
        head_size: int,
        scale: float,
        num_kv_heads: Optional[int] = None,
    ):
        super().__init__()
        self.num_heads = num_heads
        self.head_size = head_size
        self.scale = scale
        self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads

        assert self.num_heads % self.num_kv_heads == 0, \
            f"num_heads ({self.num_heads}) is not " \
            f"divisible by num_kv_heads ({self.num_kv_heads})"
        self.num_queries_per_kv = self.num_heads // self.num_kv_heads

        # During model initialization, the default dtype is set as the model
        # weight and activation dtype.
        dtype = torch.get_default_dtype()

        # Determine the attention backend
        backend = get_vit_attn_backend(head_size=head_size, dtype=dtype)

        # Some auto-selected backends can be upgraded
        # to upstream flash attention if available.
        # If vllm native fa is selected, we use it directly.
        use_upstream_fa = False
        if backend != _Backend.FLASH_ATTN and check_upstream_fa_availability(
                dtype):
            backend = _Backend.FLASH_ATTN
            use_upstream_fa = True

        if current_platform.is_rocm() or current_platform.is_xpu():
            # currently, only torch_sdpa is supported on rocm/xpu
            self.attn_backend = _Backend.TORCH_SDPA
        else:

            self.attn_backend = backend if backend in {
                _Backend.TORCH_SDPA,
                _Backend.XFORMERS,
                _Backend.PALLAS,
                _Backend.ROCM_AITER_FA,
                _Backend.FLASH_ATTN,
            } else _Backend.TORCH_SDPA

        if (self.attn_backend == _Backend.XFORMERS
                and not check_xformers_availability()):
            self.attn_backend = _Backend.TORCH_SDPA

        if self.attn_backend == _Backend.FLASH_ATTN:
            if use_upstream_fa:
                from flash_attn import flash_attn_varlen_func
                self._flash_attn_varlen_func = flash_attn_varlen_func
            else:
                from vllm.vllm_flash_attn import flash_attn_varlen_func
                self._flash_attn_varlen_func = flash_attn_varlen_func

        logger.info_once(
            f"MultiHeadAttention attn_backend: {self.attn_backend}, "
            f"use_upstream_fa: {use_upstream_fa}")

    def forward(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
    ) -> torch.Tensor:
        """Input shape: 
        (batch_size x seq_len x hidden_size) or
        (batch_size x seq_len x num_heads x head_size)
        """
        bsz, q_len = query.size()[:2]
        kv_len = key.size(1)

        query = query.view(bsz, q_len, self.num_heads, self.head_size)
        key = key.view(bsz, kv_len, self.num_kv_heads, self.head_size)
        value = value.view(bsz, kv_len, self.num_kv_heads, self.head_size)

        if (num_repeat := self.num_queries_per_kv) > 1:
            # Handle MQA and GQA
            key = torch.repeat_interleave(key, num_repeat, dim=2)
            value = torch.repeat_interleave(value, num_repeat, dim=2)

        if self.attn_backend == _Backend.FLASH_ATTN:
            cu_seqlens_q = torch.arange(0, (bsz + 1) * q_len,
                                        step=q_len,
                                        dtype=torch.int32,
                                        device=query.device)
            cu_seqlens_k = torch.arange(0, (bsz + 1) * kv_len,
                                        step=kv_len,
                                        dtype=torch.int32,
                                        device=key.device)

            out = self._flash_attn_varlen_func(
                query.flatten(0, 1),
                key.flatten(0, 1),
                value.flatten(0, 1),
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_k=cu_seqlens_k,
                max_seqlen_q=q_len,
                max_seqlen_k=kv_len,
                softmax_scale=self.scale,
            )
        elif self.attn_backend == _Backend.XFORMERS:
            from xformers import ops as xops

            out = xops.memory_efficient_attention_forward(query,
                                                          key,
                                                          value,
                                                          scale=self.scale)
        elif self.attn_backend == _Backend.TORCH_SDPA:
            query, key, value = (x.transpose(1, 2)
                                 for x in (query, key, value))
            out = F.scaled_dot_product_attention(query,
                                                 key,
                                                 value,
                                                 scale=self.scale)
            out = out.transpose(1, 2)
        elif self.attn_backend == _Backend.PALLAS:
            query, key, value = (x.transpose(1, 2)
                                 for x in (query, key, value))
            from torch_xla.experimental.custom_kernel import flash_attention
            out = flash_attention(query, key, value, sm_scale=self.scale)
            out = out.transpose(1, 2)
        elif self.attn_backend == _Backend.ROCM_AITER_FA:
            from aiter import flash_attn_varlen_func

            # ROCm Flash Attention expects (batch, seq, heads, head_dim)
            out = flash_attn_varlen_func(query,
                                         key,
                                         value,
                                         softmax_scale=self.scale)
        else:
            # ViT attention hasn't supported this backend yet
            raise NotImplementedError(
                f"ViT attention hasn't supported {self.attn_backend} "
                f"backend yet.")

        return out.reshape(bsz, q_len, -1)


def wait_for_kv_layer_from_connector(layer_name: str):
    if not has_kv_transfer_group() or not is_v1_kv_transfer_group():
        return

    connector = get_kv_transfer_group()

    forward_context: ForwardContext = get_forward_context()
    attn_metadata = forward_context.attn_metadata
    if attn_metadata is None:
        return
    assert isinstance(attn_metadata, dict)
    connector.wait_for_layer_load(layer_name)


def maybe_save_kv_layer_to_connector(
    layer_name: str,
    kv_cache_layer: List[torch.Tensor],
):
    if not has_kv_transfer_group() or not is_v1_kv_transfer_group():
        return

    connector = get_kv_transfer_group()

    forward_context: ForwardContext = get_forward_context()
    attn_metadata = forward_context.attn_metadata
    if attn_metadata is None:
        return
    assert isinstance(attn_metadata, dict)
    connector.save_kv_layer(layer_name, kv_cache_layer,
                            attn_metadata[layer_name])


def unified_attention(
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    layer_name: str,
) -> torch.Tensor:
    wait_for_kv_layer_from_connector(layer_name)

    forward_context: ForwardContext = get_forward_context()
    attn_metadata = forward_context.attn_metadata
    if isinstance(attn_metadata, dict):
        attn_metadata = attn_metadata[layer_name]
    self = forward_context.no_compile_layers[layer_name]
    kv_cache = self.kv_cache[forward_context.virtual_engine]
    output = self.impl.forward(self, query, key, value, kv_cache,
                               attn_metadata)

    maybe_save_kv_layer_to_connector(layer_name, kv_cache)
    return output


def unified_attention_fake(
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    layer_name: str,
) -> torch.Tensor:
    return torch.empty_like(query).contiguous()


direct_register_custom_op(
    op_name="unified_attention",
    op_func=unified_attention,
    fake_impl=unified_attention_fake,
    tags=tag_cudagraph_unsafe,
)


def unified_attention_with_output(
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    output: torch.Tensor,
    layer_name: str,
    output_scale: Optional[torch.Tensor] = None,
    output_block_scale: Optional[torch.Tensor] = None,
) -> None:
    wait_for_kv_layer_from_connector(layer_name)
    forward_context: ForwardContext = get_forward_context()
    attn_metadata = forward_context.attn_metadata
    if isinstance(attn_metadata, dict):
        attn_metadata = attn_metadata[layer_name]
    self = forward_context.no_compile_layers[layer_name]
    kv_cache = self.kv_cache[forward_context.virtual_engine]
    self.impl.forward(self,
                      query,
                      key,
                      value,
                      kv_cache,
                      attn_metadata,
                      output=output,
                      output_scale=output_scale,
                      output_block_scale=output_block_scale)

    maybe_save_kv_layer_to_connector(layer_name, kv_cache)


def unified_attention_with_output_fake(
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    output: torch.Tensor,
    layer_name: str,
    output_scale: Optional[torch.Tensor] = None,
    output_block_scale: Optional[torch.Tensor] = None,
) -> None:
    return


direct_register_custom_op(
    op_name="unified_attention_with_output",
    op_func=unified_attention_with_output,
    mutates_args=["output", "output_block_scale"],
    fake_impl=unified_attention_with_output_fake,
    tags=tag_cudagraph_unsafe,
)