fusion.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Callable, NamedTuple, Optional

import torch
import torch._inductor.pattern_matcher as pm
from torch import fx
from torch._higher_order_ops.auto_functionalize import auto_functionalized
from torch._inductor.pattern_matcher import PatternMatcherPass
from torch._ops import OpOverload

from vllm.config import VllmConfig
from vllm.logger import init_logger
from vllm.model_executor.layers.quantization.utils.quant_utils import (
    GroupShape)
from vllm.platforms import current_platform

from .fx_utils import find_getitem_maybe
from .multi_output_match import MultiOutputMatch
from .vllm_inductor_pass import VllmInductorPass

logger = init_logger(__name__)
FP8_DTYPE = current_platform.fp8_dtype()


def empty_bf16(*args, **kwargs):
    return torch.empty(*args, **kwargs, dtype=torch.bfloat16, device="cuda")


def empty_fp32(*args, **kwargs):
    return torch.empty(*args, **kwargs, dtype=torch.float32, device="cuda")


RMS_OP = torch.ops._C.rms_norm.default
RMS_ADD_OP = torch.ops._C.fused_add_rms_norm.default


class QuantKey(NamedTuple):
    """
    Named tuple for identifying the type of quantization.
    dtype: quantized data type
    static: static quantization if True, dynamic if False
    group_shape: quantization group shape
    symmetric: symmetric if True, asymmetric if False

    TODO(luka) use QuantDescriptor once standardized:
    https://github.com/vllm-project/vllm/issues/8913

    """
    dtype: torch.dtype
    static: bool
    group_shape: GroupShape
    symmetric: bool = True

    def __str__(self):
        group_shape = ('per_tensor'
                       if self.group_shape == GroupShape.PER_TENSOR else
                       ('per_token' if self.group_shape == GroupShape.PER_TOKEN
                        else str(self.group_shape)))

        return (f"QuantKey({'static' if self.static else 'dynamic'},"
                f"{fx.graph.dtype_abbrs[self.dtype]},{group_shape},"
                f"{'a' if not self.symmetric else ''}symmetric)")


# kFp8StaticTensorSym = QuantKey(FP8_DTYPE, True, GroupShape.PER_TENSOR, True)
# kFp8DynamicTensorSym = QuantKey(FP8_DTYPE, False, GroupShape.PER_TENSOR, True)
# kFp8DynamicTokenSym = QuantKey(FP8_DTYPE, False, GroupShape.PER_TOKEN, True)

QUANT_OPS: dict[QuantKey, OpOverload] = {
    # kFp8StaticTensorSym:
    # torch.ops._C.static_scaled_fp8_quant.default,  # noqa: E501
    # kFp8DynamicTensorSym:
    # torch.ops._C.dynamic_scaled_fp8_quant.default,  # noqa: E501
    # kFp8DynamicTokenSym:
    # torch.ops._C.dynamic_per_token_scaled_fp8_quant.default,  # noqa: E501
}


class FusedRMSQuantKey(NamedTuple):
    """
    Named tuple for identifying the type of RMSNorm + quant fusion.
    quant: type of quantization
    fused_add: does the op also perform the residual add
    """
    quant: QuantKey
    fused_add: bool

    def __str__(self):
        return (f"FusedQuantKey({self.quant}, with"
                f"{'' if self.fused_add else 'out'} residual)")


FUSED_OPS: dict[FusedRMSQuantKey, OpOverload] = {
    # FusedRMSQuantKey(kFp8StaticTensorSym, False):
    # torch.ops._C.rms_norm_static_fp8_quant.default,  # noqa: E501
    # FusedRMSQuantKey(kFp8StaticTensorSym, True):
    # torch.ops._C.fused_add_rms_norm_static_fp8_quant.default,  # noqa: E501
    # FusedRMSQuantKey(kFp8DynamicTokenSym, False):
    # torch.ops._C.rms_norm_dynamic_per_token_quant.default,  # noqa: E501
    # FusedRMSQuantKey(kFp8DynamicTokenSym, True):
    # torch.ops._C.rms_norm_dynamic_per_token_quant.default,  # noqa: E501
}


class QuantMultiOutputMatch(MultiOutputMatch):

    def __init__(self, match: pm.Match, quant_op, fused_op):
        super().__init__(match)
        assert isinstance(quant_op, OpOverload)
        assert isinstance(fused_op, OpOverload)
        self.QUANT_OP = quant_op  # in-place quant op
        self.FUSED_OP = fused_op  # in-place fused quant op

    def insert_fused_node(self, fused_return_mapping: dict[int, tuple[fx.Node,
                                                                      int]],
                          **kwargs):
        """
        This utility function inserts an auto-functionalized node for FUSED_OP.
        It also correctly sets its meta value and rebinds the users of the
        unfused nodes to use the fused node instead.

        :param fused_return_mapping: A dictionary, mapping from getitem indices
        of the fused node result to a tuple of the old node and a getitem index.
        :param kwargs: kwargs that get directly forwarded to the auto_fn node

        Example:
        If we want to replace this graph:
        _, x1, x2 = auto_fn(op1)
        _, y1, y2 = auto_fn(op2)

        with
        _, x1, y2, x2 = auto_fn(FUSED_OP)

        we would call:
        insert_fused_node({1: (op1_node, 1), 2: (op2_node, 2), 3: (op1_node, 2)}

        Note that the 0th element is None for auto-functionalized in-place ops.
        Hence, others appear 1-indexed.
        """
        fused_node = self.insert_auto_fn(self.FUSED_OP, kwargs)
        indices = fused_return_mapping.keys()
        getitem_nodes = self.insert_getitems(fused_node, indices)

        # Prepare the meta value, use a list so it's mutable
        meta_val = [None] * (max(indices) + 1)

        # Iterate through elements of the tuple produced by fused_node
        for idx, getitem_node in zip(indices, getitem_nodes):
            old_node, old_idx = fused_return_mapping[idx]

            # If the old value was never used, the old_getitem might not exist
            old_getitem = find_getitem_maybe(old_node, old_idx)
            if old_getitem is not None:
                # Rebind the users of match getitem nodes to use the new nodes.
                # The old nodes will be removed by DCE at the end of the pass.
                old_getitem.replace_all_uses_with(getitem_node)
                getitem_node.meta["val"] = old_getitem.meta["val"]

            # Extract the appropriate meta value
            # It is present even if the getitem node does not exist
            meta_val[idx] = old_node.meta["val"][old_idx]

        # Fix the meta value on the new fused node
        fused_node.meta["val"] = tuple(meta_val)


class RMSNormQuantPattern:

    def __init__(self, epsilon: float, key: FusedRMSQuantKey):
        self.epsilon = epsilon
        self.quant_dtype = key.quant.dtype

        assert key.quant in QUANT_OPS, \
            f"unsupported quantization scheme {key.quant}"
        self.QUANT_OP = QUANT_OPS[key.quant]

        assert key in FUSED_OPS, \
            f"unsupported fused rmsnorm+quant op for {key}"
        self.FUSED_OP = FUSED_OPS[key]


class RMSNormStaticQuantPattern(RMSNormQuantPattern):

    def __init__(self,
                 epsilon: float,
                 quant_dtype: torch.dtype,
                 symmetric=True):
        fused_key = FusedRMSQuantKey(fused_add=False,
                                     quant=QuantKey(
                                         dtype=quant_dtype,
                                         static=True,
                                         group_shape=GroupShape.PER_TENSOR,
                                         symmetric=symmetric))
        super().__init__(epsilon, fused_key)

    def register(self, pm_pass: PatternMatcherPass):
        # Cannot use methods, as the self argument affects tracing
        def pattern(result: torch.Tensor, result_rms: torch.Tensor,
                    input: torch.Tensor, weight: torch.Tensor,
                    scale: torch.Tensor):
            at1 = auto_functionalized(RMS_OP,
                                      result=result_rms,
                                      input=input,
                                      weight=weight,
                                      epsilon=self.epsilon)
            at2 = auto_functionalized(self.QUANT_OP,
                                      result=result,
                                      input=at1[1],
                                      scale=scale)

            # result
            return at2[1]

        def replacement(result: torch.Tensor, result_rms: torch.Tensor,
                        input: torch.Tensor, weight: torch.Tensor,
                        scale: torch.Tensor):
            at = auto_functionalized(self.FUSED_OP,
                                     result=result,
                                     input=input,
                                     weight=weight,
                                     scale=scale,
                                     epsilon=self.epsilon)

            # result
            return at[1]

        inputs = [
            torch.empty(5, 4, device="cuda", dtype=self.quant_dtype),  # result
            empty_bf16(5, 4),  # result_rms
            empty_bf16(5, 4),  # input
            empty_bf16(1, 5),  # weight
            empty_fp32(1, 1)  # scale
        ]

        pm.register_replacement(pattern, replacement, inputs, pm.fwd_only,
                                pm_pass)


class FusedAddRMSNormStaticQuantPattern(RMSNormQuantPattern):

    def __init__(self,
                 epsilon: float,
                 quant_dtype: torch.dtype,
                 symmetric=True):
        key = FusedRMSQuantKey(fused_add=True,
                               quant=QuantKey(
                                   dtype=quant_dtype,
                                   static=True,
                                   group_shape=GroupShape.PER_TENSOR,
                                   symmetric=symmetric))
        super().__init__(epsilon, key)

    def register(self, pm_pass: PatternMatcherPass,
                 record_match: Callable[[MultiOutputMatch], bool]):

        def pattern(result: torch.Tensor, input: torch.Tensor,
                    residual: torch.Tensor, weight: torch.Tensor,
                    scale: torch.Tensor):
            at = auto_functionalized(RMS_ADD_OP,
                                     input=input,
                                     residual=residual,
                                     weight=weight,
                                     epsilon=self.epsilon)
            at1 = auto_functionalized(self.QUANT_OP,
                                      result=result,
                                      input=at[1],
                                      scale=scale)

            # result, residual
            return at1[1], at[2]

        def replacement(result: torch.Tensor, input: torch.Tensor,
                        residual: torch.Tensor, weight: torch.Tensor,
                        scale: torch.Tensor):
            at = auto_functionalized(self.FUSED_OP,
                                     result=result,
                                     input=input,
                                     residual=residual,
                                     weight=weight,
                                     scale=scale,
                                     epsilon=self.epsilon)

            # result, residual
            return at[1], at[2]

        inputs = [
            torch.empty(5, 4, device="cuda", dtype=self.quant_dtype),  # result
            empty_bf16(5, 4),  # input
            empty_bf16(5, 4),  # residual
            empty_bf16(1, 5),  # weight
            empty_fp32(1, 1)  # scale
        ]

        pm.register_replacement(
            pattern,
            replacement,
            inputs,
            pm.fwd_only,
            pm_pass,
            extra_check=lambda m: record_match(
                self.Match(m, self.QUANT_OP, self.FUSED_OP)))

    class Match(QuantMultiOutputMatch):

        def process(self):
            # Find the nodes in the match that we need to rebind
            rms_node = self.find_auto_fn(RMS_ADD_OP)
            quant_node = self.find_auto_fn(self.QUANT_OP)

            assert len(rms_node.users) == 2
            assert len(quant_node.users) == 1

            # First, insert a new auto_functionalized node for the fused op,
            # as well as getitem nodes to extract the result and residual.
            # The auto_fn node returns a tuple of (None, result, residual).
            #
            # The resulting graph looks like this:
            # at = auto_functionalized(torch.ops._C.fused_add_rms_norm_static_fp8_quant.default, ...)  # noqa
            # result_node_new = at[1]
            # residual_node_new = at[2]
            with self.inserting_after_match():
                # Missing epsilon, scalars cannot be inputs to the pattern
                kwargs = self.match.kwargs.copy()

                # 0 is always None
                fused_return_mapping = {1: (quant_node, 1), 2: (rms_node, 2)}
                self.insert_fused_node(fused_return_mapping,
                                       **kwargs,
                                       epsilon=rms_node.kwargs["epsilon"])


class RMSNormDynamicQuantPattern(RMSNormQuantPattern):

    def __init__(self,
                 epsilon: float,
                 quant_dtype: torch.dtype,
                 group_shape: GroupShape = GroupShape.PER_TOKEN,
                 symmetric=True):
        key = FusedRMSQuantKey(fused_add=False,
                               quant=QuantKey(dtype=quant_dtype,
                                              static=False,
                                              group_shape=group_shape,
                                              symmetric=symmetric))
        super().__init__(epsilon, key)

    def register(self, pm_pass: PatternMatcherPass,
                 record_match: Callable[[MultiOutputMatch], bool]):

        def pattern(result: torch.Tensor, result_rms: torch.Tensor,
                    input: torch.Tensor, weight: torch.Tensor,
                    scale: torch.Tensor):
            at1 = auto_functionalized(RMS_OP,
                                      result=result_rms,
                                      input=input,
                                      weight=weight,
                                      epsilon=self.epsilon)
            at2 = auto_functionalized(self.QUANT_OP,
                                      result=result,
                                      input=at1[1],
                                      scale=scale,
                                      scale_ub=None)

            # result, scale
            return at2[1], at2[2]

        def replacement(result: torch.Tensor, result_rms: torch.Tensor,
                        input: torch.Tensor, weight: torch.Tensor,
                        scale: torch.Tensor):
            at = auto_functionalized(self.FUSED_OP,
                                     result=result,
                                     input=input,
                                     weight=weight,
                                     scale=scale,
                                     epsilon=self.epsilon,
                                     scale_ub=None,
                                     residual=None)

            # result, scale
            return at[1], at[2]

        inputs = [
            torch.empty(5, 4, device="cuda", dtype=self.quant_dtype),  # result
            empty_bf16(5, 4),  # result_rms
            empty_bf16(5, 4),  # input
            empty_bf16(1, 5),  # weight
            empty_fp32(1, 1)  # scale
        ]

        pm.register_replacement(
            pattern,
            replacement,
            inputs,
            pm.fwd_only,
            pm_pass,
            extra_check=lambda m: record_match(
                self.Match(m, self.QUANT_OP, self.FUSED_OP)))

    class Match(QuantMultiOutputMatch):

        def process(self):
            # Find the nodes in the match that we need to rebind
            rms_node = self.find_auto_fn(RMS_OP)
            quant_node = self.find_auto_fn(self.QUANT_OP)

            assert len(rms_node.users) == 1
            assert len(quant_node.users) == 2

            # First, insert a new auto_functionalized node for the fused op,
            # as well as getitem nodes to extract the result and scale.
            # The auto_fn node returns a tuple of (None, result, scale).
            #
            # The resulting graph looks like this:
            # at = auto_functionalized(torch.ops._C.rms_norm_dynamic_per_token_quant.default, ...)  # noqa
            # result_node_new = at[1]
            # scale_node_new = at[2]
            with self.inserting_after_match():
                # Missing epsilon, scalars cannot be inputs to the pattern
                kwargs = self.match.kwargs.copy()
                del kwargs["result_rms"]  # not used in the fused op

                fused_return_mapping = {1: (quant_node, 1), 2: (quant_node, 2)}
                self.insert_fused_node(
                    fused_return_mapping,
                    epsilon=rms_node.kwargs["epsilon"],
                    scale_ub=None,  # not used but required
                    residual=None,  # not used but required
                    **kwargs)


class FusedAddRMSNormDynamicQuantPattern(RMSNormQuantPattern):

    def __init__(self,
                 epsilon: float,
                 quant_dtype: torch.dtype,
                 group_shape: GroupShape = GroupShape.PER_TOKEN,
                 symmetric=True):
        key = FusedRMSQuantKey(fused_add=True,
                               quant=QuantKey(dtype=quant_dtype,
                                              static=False,
                                              group_shape=group_shape,
                                              symmetric=symmetric))
        super().__init__(epsilon, key)

    def register(self, pm_pass: PatternMatcherPass,
                 record_match: Callable[[MultiOutputMatch], bool]):

        def pattern(result: torch.Tensor, input: torch.Tensor,
                    residual: torch.Tensor, weight: torch.Tensor,
                    scale: torch.Tensor):
            at = auto_functionalized(RMS_ADD_OP,
                                     input=input,
                                     residual=residual,
                                     weight=weight,
                                     epsilon=self.epsilon)
            at1 = auto_functionalized(self.QUANT_OP,
                                      result=result,
                                      input=at[1],
                                      scale=scale,
                                      scale_ub=None)

            # result, residual, scale
            return at1[1], at[2], at1[2]

        def replacement(result: torch.Tensor, input: torch.Tensor,
                        residual: torch.Tensor, weight: torch.Tensor,
                        scale: torch.Tensor):
            at = auto_functionalized(self.FUSED_OP,
                                     result=result,
                                     input=input,
                                     weight=weight,
                                     scale=scale,
                                     epsilon=self.epsilon,
                                     scale_ub=None,
                                     residual=residual)

            # result, residual, scale
            return at[1], at[3], at[2]

        inputs = [
            torch.empty(5, 4, device="cuda", dtype=self.quant_dtype),  # result
            empty_bf16(5, 4),  # input
            empty_bf16(5, 4),  # residual
            empty_bf16(1, 5),  # weight
            empty_fp32(1, 1)  # scale
        ]

        pm.register_replacement(
            pattern,
            replacement,
            inputs,
            pm.fwd_only,
            pm_pass,
            extra_check=lambda m: record_match(
                self.Match(m, self.QUANT_OP, self.FUSED_OP)))

    class Match(QuantMultiOutputMatch):

        def process(self):
            # Find the nodes in the match that we need to rebind
            rms_node = self.find_auto_fn(RMS_ADD_OP)
            quant_node = self.find_auto_fn(self.QUANT_OP)

            assert len(rms_node.users) == 2
            assert len(quant_node.users) == 2

            # First, insert a new auto_functionalized node for the fused op,
            # as well as getitem nodes to extract result, scale, and residual.
            # The auto_fn node returns a tuple (None, result, scale, residual).
            #
            # The resulting graph looks like this:
            # at = auto_functionalized(torch.ops._C.rms_norm_dynamic_per_token_quant.default, ...)  # noqa
            # result_node_new = at[1]
            # scale_node_new = at[2]
            # residual_node_new = at[3]
            with self.inserting_after_match():
                # Missing epsilon, scalars cannot be inputs to the pattern
                kwargs = self.match.kwargs.copy()

                fused_return_mapping = {
                    1: (quant_node, 1),  # result
                    2: (quant_node, 2),  # scale
                    3: (rms_node, 2),  # residual
                }
                self.insert_fused_node(
                    fused_return_mapping,
                    epsilon=rms_node.kwargs["epsilon"],
                    scale_ub=None,  # not used but required
                    **kwargs)


class FusionPass(VllmInductorPass):
    """
    This pass fuses a pre-defined set of custom ops into fused ops.
    It uses the torch pattern matcher to find the patterns and replace them.
    It also manually processes multi-output matches, as those are broken in
    the torch pattern matcher.

    Because patterns can only be registered once, the pass is a singleton.
    This will be addressed in a future version of PyTorch:
    https://github.com/pytorch/pytorch/pull/139321#issuecomment-2452354980
    """

    _instance: 'Optional[FusionPass]' = None

    @classmethod
    def instance(cls, config: VllmConfig):
        """
        Get the singleton instance of the FusionPass.
        If the instance exists, the config is updated but
        initialization is not repeated.
        """
        if cls._instance is None:
            cls._instance = FusionPass(config)
        else:
            cls._instance.pass_config = config.compilation_config.pass_config
        return cls._instance

    def __init__(self, config: VllmConfig):
        assert self.__class__._instance is None, \
            "FusionPass singleton instance already exists"
        super().__init__(config)

        self.matches: list[MultiOutputMatch] = []
        self.patterns: PatternMatcherPass = PatternMatcherPass(
            pass_name="fusion_pass")

        for epsilon in [1e-5, 1e-6]:
            # Fuse rms_norm + static fp8 quant
            # RMSNormStaticQuantPattern(epsilon,
            #                           FP8_DTYPE).register(self.patterns)

            # Matches for patterns below have 2 or more outputs,
            # so we need to process them manually (see process_matches)

            # Fuse rms_norm + static fp8 quant
            # FusedAddRMSNormStaticQuantPattern(epsilon, FP8_DTYPE).register(
            #     self.patterns, self.record_match)

            # Fuse rms_norm + dynamic per-token fp8 quant
            # RMSNormDynamicQuantPattern(epsilon, FP8_DTYPE).register(
            #     self.patterns, self.record_match)

            # Fuse fused_add_rms_norm + dynamic per-token fp8 quant
            # FusedAddRMSNormDynamicQuantPattern(epsilon, FP8_DTYPE).register(
            #     self.patterns, self.record_match)

            # WARNING: This is a hack to clear the pattern matcher cache
            # and allow multiple values of epsilon.
            torch._inductor.pattern_matcher._seen_patterns.clear()

    def record_match(self, match: MultiOutputMatch) -> bool:
        # Hijack the extra_check to record the match and
        # save it for post-processing.
        self.matches.append(match)

        # Return False to prevent automatic replacement.
        return False

    def process_matches(self, graph: fx.Graph):
        """
        Manually process multi-output matches and replace them with fused nodes.
        See MultiOutputMatch for more details.
        """
        for match in self.matches:
            match.process()

        # Finally, remove matched nodes
        graph.eliminate_dead_code()
        assert all(node not in graph.nodes for match in self.matches
                   for node in match.match.nodes)

    def __call__(self, graph: fx.Graph):
        self.begin()
        self.dump_graph(graph, "before_fusion")

        count = self.patterns.apply(graph)
        logger.debug("Replaced %s patterns", count)
        self.dump_graph(graph, "after_pattern_match")

        # Manually process multi-output matches (and run DCE)
        self.process_matches(graph)
        logger.debug("Post-processed %s matches", len(self.matches))
        self.dump_graph(graph, "after_fusion")
        self.matches.clear()
        self.end_and_log()