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vllm.compilation.sequence_parallelism

logger module-attribute 

logger = init_logger(__name__)

AllReduceRMSNormPattern

Source code in vllm/compilation/sequence_parallelism.py 
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class AllReduceRMSNormPattern:

    def __init__(self, epsilon: float, dtype: torch.dtype, device: str):
        self.epsilon = epsilon
        self.dtype = dtype
        self.device = device

device instance-attribute 

device = device

dtype instance-attribute 

dtype = dtype

epsilon instance-attribute 

epsilon = epsilon

__init__ 

__init__(epsilon: float, dtype: dtype, device: str)
Source code in vllm/compilation/sequence_parallelism.py 
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def __init__(self, epsilon: float, dtype: torch.dtype, device: str):
    self.epsilon = epsilon
    self.dtype = dtype
    self.device = device

EmbeddingAllReduceRMSNormPattern

Bases: AllReduceRMSNormPattern

Source code in vllm/compilation/sequence_parallelism.py 
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class EmbeddingAllReduceRMSNormPattern(AllReduceRMSNormPattern):

    def get_inputs(self):
        arg2_1 = torch.empty([16, 4], device=self.device, dtype=self.dtype)
        mul_6 = torch.tensor([[3, 7, 1, 4, 9, 2, 5, 0]],
                             device=self.device,
                             dtype=torch.long)
        unsqueeze = torch.rand([1, 8, 1], device=self.device, \
            dtype=self.dtype) > 0.5
        full_default = torch.zeros([1, 8, 4], device=self.device, \
            dtype=self.dtype)
        permute = torch.empty([1, 8, 4], device=self.device, dtype=self.dtype)
        arg3_1 = torch.empty([4], device=self.device, dtype=self.dtype)

        return [arg2_1, mul_6, unsqueeze, full_default, permute, arg3_1]

    def register(self, pm_pass: PatternMatcherPass):

        def pattern(
            arg2_1: torch.Tensor,
            mul_6: torch.Tensor,
            unsqueeze: torch.Tensor,
            full_default: torch.Tensor,
            permute: torch.Tensor,
            arg3_1: torch.Tensor,
        ):
            embedding = torch.ops.aten.embedding.default(arg2_1, mul_6)
            where = torch.ops.aten.where.self(unsqueeze, full_default,
                                              embedding)
            all_reduce = tensor_model_parallel_all_reduce(where)
            rmsnorm = torch.ops.higher_order.auto_functionalized(
                torch.ops._C.rms_norm.default,
                result=permute,
                input=all_reduce,
                weight=arg3_1,
                epsilon=self.epsilon,
            )

            return rmsnorm[1], all_reduce

        def replacement(
            arg2_1: torch.Tensor,
            mul_6: torch.Tensor,
            unsqueeze: torch.Tensor,
            full_default: torch.Tensor,
            permute: torch.Tensor,
            arg3_1: torch.Tensor,
        ):
            embedding = torch.ops.aten.embedding.default(arg2_1, mul_6)
            where = torch.ops.aten.where.self(unsqueeze, full_default,
                                              embedding)

            tp = get_tp_group()
            tp_size = get_tensor_model_parallel_world_size()
            reduce_scatter = torch.ops.vllm.reduce_scatter.default(
                where, dim=0, world_size=tp_size, group_name=tp.unique_name)

            rmsnorm_result = torch.empty_like(reduce_scatter)
            rmsnorm = torch.ops.higher_order.auto_functionalized(
                torch.ops._C.rms_norm.default,
                result=rmsnorm_result,
                input=reduce_scatter,
                weight=arg3_1,
                epsilon=self.epsilon,
            )

            all_gather = torch.ops.vllm.all_gather.default(
                rmsnorm[1],
                dim=0,
                world_size=tp_size,
                group_name=tp.unique_name)

            return all_gather, reduce_scatter

        pm.register_replacement(pattern, replacement, self.get_inputs(),
                                pm.fwd_only, pm_pass)

get_inputs 

get_inputs()
Source code in vllm/compilation/sequence_parallelism.py 
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def get_inputs(self):
    arg2_1 = torch.empty([16, 4], device=self.device, dtype=self.dtype)
    mul_6 = torch.tensor([[3, 7, 1, 4, 9, 2, 5, 0]],
                         device=self.device,
                         dtype=torch.long)
    unsqueeze = torch.rand([1, 8, 1], device=self.device, \
        dtype=self.dtype) > 0.5
    full_default = torch.zeros([1, 8, 4], device=self.device, \
        dtype=self.dtype)
    permute = torch.empty([1, 8, 4], device=self.device, dtype=self.dtype)
    arg3_1 = torch.empty([4], device=self.device, dtype=self.dtype)

    return [arg2_1, mul_6, unsqueeze, full_default, permute, arg3_1]

register 

register(pm_pass: PatternMatcherPass)
Source code in vllm/compilation/sequence_parallelism.py 
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def register(self, pm_pass: PatternMatcherPass):

    def pattern(
        arg2_1: torch.Tensor,
        mul_6: torch.Tensor,
        unsqueeze: torch.Tensor,
        full_default: torch.Tensor,
        permute: torch.Tensor,
        arg3_1: torch.Tensor,
    ):
        embedding = torch.ops.aten.embedding.default(arg2_1, mul_6)
        where = torch.ops.aten.where.self(unsqueeze, full_default,
                                          embedding)
        all_reduce = tensor_model_parallel_all_reduce(where)
        rmsnorm = torch.ops.higher_order.auto_functionalized(
            torch.ops._C.rms_norm.default,
            result=permute,
            input=all_reduce,
            weight=arg3_1,
            epsilon=self.epsilon,
        )

        return rmsnorm[1], all_reduce

    def replacement(
        arg2_1: torch.Tensor,
        mul_6: torch.Tensor,
        unsqueeze: torch.Tensor,
        full_default: torch.Tensor,
        permute: torch.Tensor,
        arg3_1: torch.Tensor,
    ):
        embedding = torch.ops.aten.embedding.default(arg2_1, mul_6)
        where = torch.ops.aten.where.self(unsqueeze, full_default,
                                          embedding)

        tp = get_tp_group()
        tp_size = get_tensor_model_parallel_world_size()
        reduce_scatter = torch.ops.vllm.reduce_scatter.default(
            where, dim=0, world_size=tp_size, group_name=tp.unique_name)

        rmsnorm_result = torch.empty_like(reduce_scatter)
        rmsnorm = torch.ops.higher_order.auto_functionalized(
            torch.ops._C.rms_norm.default,
            result=rmsnorm_result,
            input=reduce_scatter,
            weight=arg3_1,
            epsilon=self.epsilon,
        )

        all_gather = torch.ops.vllm.all_gather.default(
            rmsnorm[1],
            dim=0,
            world_size=tp_size,
            group_name=tp.unique_name)

        return all_gather, reduce_scatter

    pm.register_replacement(pattern, replacement, self.get_inputs(),
                            pm.fwd_only, pm_pass)

LastAllReduceRMSNormPattern

Bases: AllReduceRMSNormPattern

Source code in vllm/compilation/sequence_parallelism.py 
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class LastAllReduceRMSNormPattern(AllReduceRMSNormPattern):

    def get_inputs(self):
        mm_1 = torch.empty([4, 4], device=self.device, dtype=self.dtype)

        residual = torch.empty([4, 4], device=self.device, dtype=self.dtype)
        rms_norm_weights = torch.empty([4, 4],
                                       device=self.device,
                                       dtype=self.dtype)

        return [
            residual,
            mm_1,
            rms_norm_weights,
        ]

    def register(self, pm_pass: PatternMatcherPass):

        def pattern(
            residual: torch.Tensor,
            mm_1: torch.Tensor,
            rms_norm_weights: torch.Tensor,
        ) -> tuple[torch.Tensor, torch.Tensor]:
            all_reduce = tensor_model_parallel_all_reduce(mm_1)

            rmsnorm = torch.ops.higher_order.auto_functionalized(
                torch.ops._C.fused_add_rms_norm.default,
                input=all_reduce,
                residual=residual,
                weight=rms_norm_weights,
                epsilon=self.epsilon,
            )

            return rmsnorm[1]

        def replacement(
            residual: torch.Tensor,
            mm_1: torch.Tensor,
            rms_norm_weights: torch.Tensor,
        ) -> tuple[torch.Tensor, torch.Tensor]:
            tp = get_tp_group()
            tp_size = get_tensor_model_parallel_world_size()
            reduce_scatter = torch.ops.vllm.reduce_scatter.default(
                mm_1, dim=0, world_size=tp_size, group_name=tp.unique_name)

            # TODO is it possible to extract epsilon from somewhere
            rmsnorm = torch.ops.higher_order.auto_functionalized(
                torch.ops._C.fused_add_rms_norm.default,
                input=reduce_scatter,
                residual=residual,
                weight=rms_norm_weights,
                epsilon=self.epsilon,
            )

            normalized = torch.ops.vllm.all_gather.default(
                rmsnorm[1],
                dim=0,
                world_size=tp_size,
                group_name=tp.unique_name)

            return normalized

        pm.register_replacement(pattern, replacement, self.get_inputs(),
                                pm.fwd_only, pm_pass)

get_inputs 

get_inputs()
Source code in vllm/compilation/sequence_parallelism.py 
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def get_inputs(self):
    mm_1 = torch.empty([4, 4], device=self.device, dtype=self.dtype)

    residual = torch.empty([4, 4], device=self.device, dtype=self.dtype)
    rms_norm_weights = torch.empty([4, 4],
                                   device=self.device,
                                   dtype=self.dtype)

    return [
        residual,
        mm_1,
        rms_norm_weights,
    ]

register 

register(pm_pass: PatternMatcherPass)
Source code in vllm/compilation/sequence_parallelism.py 
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def register(self, pm_pass: PatternMatcherPass):

    def pattern(
        residual: torch.Tensor,
        mm_1: torch.Tensor,
        rms_norm_weights: torch.Tensor,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        all_reduce = tensor_model_parallel_all_reduce(mm_1)

        rmsnorm = torch.ops.higher_order.auto_functionalized(
            torch.ops._C.fused_add_rms_norm.default,
            input=all_reduce,
            residual=residual,
            weight=rms_norm_weights,
            epsilon=self.epsilon,
        )

        return rmsnorm[1]

    def replacement(
        residual: torch.Tensor,
        mm_1: torch.Tensor,
        rms_norm_weights: torch.Tensor,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        tp = get_tp_group()
        tp_size = get_tensor_model_parallel_world_size()
        reduce_scatter = torch.ops.vllm.reduce_scatter.default(
            mm_1, dim=0, world_size=tp_size, group_name=tp.unique_name)

        # TODO is it possible to extract epsilon from somewhere
        rmsnorm = torch.ops.higher_order.auto_functionalized(
            torch.ops._C.fused_add_rms_norm.default,
            input=reduce_scatter,
            residual=residual,
            weight=rms_norm_weights,
            epsilon=self.epsilon,
        )

        normalized = torch.ops.vllm.all_gather.default(
            rmsnorm[1],
            dim=0,
            world_size=tp_size,
            group_name=tp.unique_name)

        return normalized

    pm.register_replacement(pattern, replacement, self.get_inputs(),
                            pm.fwd_only, pm_pass)

MiddleAllReduceRMSNormPattern

Bases: AllReduceRMSNormPattern

Source code in vllm/compilation/sequence_parallelism.py 
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class MiddleAllReduceRMSNormPattern(AllReduceRMSNormPattern):

    def get_inputs(self):
        mm_1 = torch.empty([4, 4], device=self.device, dtype=self.dtype)

        residual = torch.empty([4, 4], device=self.device, dtype=self.dtype)
        rms_norm_weights = torch.empty([4, 4],
                                       device=self.device,
                                       dtype=self.dtype)

        return [
            residual,
            mm_1,
            rms_norm_weights,
        ]

    def register(self, pm_pass: PatternMatcherPass):

        def pattern(
            residual: torch.Tensor,
            mm_1: torch.Tensor,
            rms_norm_weights: torch.Tensor,
        ) -> tuple[torch.Tensor, torch.Tensor]:
            all_reduce = tensor_model_parallel_all_reduce(mm_1)

            rmsnorm = torch.ops.higher_order.auto_functionalized(
                torch.ops._C.fused_add_rms_norm.default,
                input=all_reduce,
                residual=residual,
                weight=rms_norm_weights,
                epsilon=self.epsilon,
            )

            return rmsnorm[1], rmsnorm[2]

        def replacement(
            residual: torch.Tensor,
            mm_1: torch.Tensor,
            rms_norm_weights: torch.Tensor,
        ) -> tuple[torch.Tensor, torch.Tensor]:
            tp = get_tp_group()
            tp_size = get_tensor_model_parallel_world_size()
            reduce_scatter = torch.ops.vllm.reduce_scatter.default(
                mm_1, dim=0, world_size=tp_size, group_name=tp.unique_name)

            # TODO is it possible to extract epsilon from somewhere
            rmsnorm = torch.ops.higher_order.auto_functionalized(
                torch.ops._C.fused_add_rms_norm.default,
                input=reduce_scatter,
                residual=residual,
                weight=rms_norm_weights,
                epsilon=self.epsilon,
            )

            all_gather = torch.ops.vllm.all_gather.default(
                rmsnorm[1],
                dim=0,
                world_size=tp_size,
                group_name=tp.unique_name)
            return all_gather, rmsnorm[2]

        pm.register_replacement(pattern, replacement, self.get_inputs(),
                                pm.fwd_only, pm_pass)

get_inputs 

get_inputs()
Source code in vllm/compilation/sequence_parallelism.py 
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def get_inputs(self):
    mm_1 = torch.empty([4, 4], device=self.device, dtype=self.dtype)

    residual = torch.empty([4, 4], device=self.device, dtype=self.dtype)
    rms_norm_weights = torch.empty([4, 4],
                                   device=self.device,
                                   dtype=self.dtype)

    return [
        residual,
        mm_1,
        rms_norm_weights,
    ]

register 

register(pm_pass: PatternMatcherPass)
Source code in vllm/compilation/sequence_parallelism.py 
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def register(self, pm_pass: PatternMatcherPass):

    def pattern(
        residual: torch.Tensor,
        mm_1: torch.Tensor,
        rms_norm_weights: torch.Tensor,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        all_reduce = tensor_model_parallel_all_reduce(mm_1)

        rmsnorm = torch.ops.higher_order.auto_functionalized(
            torch.ops._C.fused_add_rms_norm.default,
            input=all_reduce,
            residual=residual,
            weight=rms_norm_weights,
            epsilon=self.epsilon,
        )

        return rmsnorm[1], rmsnorm[2]

    def replacement(
        residual: torch.Tensor,
        mm_1: torch.Tensor,
        rms_norm_weights: torch.Tensor,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        tp = get_tp_group()
        tp_size = get_tensor_model_parallel_world_size()
        reduce_scatter = torch.ops.vllm.reduce_scatter.default(
            mm_1, dim=0, world_size=tp_size, group_name=tp.unique_name)

        # TODO is it possible to extract epsilon from somewhere
        rmsnorm = torch.ops.higher_order.auto_functionalized(
            torch.ops._C.fused_add_rms_norm.default,
            input=reduce_scatter,
            residual=residual,
            weight=rms_norm_weights,
            epsilon=self.epsilon,
        )

        all_gather = torch.ops.vllm.all_gather.default(
            rmsnorm[1],
            dim=0,
            world_size=tp_size,
            group_name=tp.unique_name)
        return all_gather, rmsnorm[2]

    pm.register_replacement(pattern, replacement, self.get_inputs(),
                            pm.fwd_only, pm_pass)

SequenceParallelismPass

Bases: VllmInductorPass

Source code in vllm/compilation/sequence_parallelism.py 
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class SequenceParallelismPass(VllmInductorPass):

    def __init__(self, config: VllmConfig):
        super().__init__(config)

        self.patterns: PatternMatcherPass = PatternMatcherPass(
            pass_name="sequence_parallelism_pass")
        for epsilon in [1e-5, 1e-6]:
            EmbeddingAllReduceRMSNormPattern(
                epsilon, self.model_dtype, self.device).register(self.patterns)

            MiddleAllReduceRMSNormPattern(epsilon, self.model_dtype,
                                          self.device).register(self.patterns)

            LastAllReduceRMSNormPattern(epsilon, self.model_dtype,
                                        self.device).register(self.patterns)
            # 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 is_applicable_for_shape(self, shape: Optional[int]) -> bool:
        tp_size = get_tensor_model_parallel_world_size()
        return shape is not None and shape % tp_size == 0

    def __call__(self, graph: fx.Graph):
        self.begin()
        self.dump_graph(graph, "before_sequence_parallelism_pass")
        count = self.patterns.apply(graph)
        logger.debug("Replaced %s patterns", count)
        self.dump_graph(graph, "after_sequence_parallelism_pass")
        self.end_and_log()

patterns instance-attribute 

patterns: PatternMatcherPass = PatternMatcherPass(
    pass_name="sequence_parallelism_pass"
)

__call__ 

__call__(graph: Graph)
Source code in vllm/compilation/sequence_parallelism.py 
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def __call__(self, graph: fx.Graph):
    self.begin()
    self.dump_graph(graph, "before_sequence_parallelism_pass")
    count = self.patterns.apply(graph)
    logger.debug("Replaced %s patterns", count)
    self.dump_graph(graph, "after_sequence_parallelism_pass")
    self.end_and_log()

__init__ 

__init__(config: VllmConfig)
Source code in vllm/compilation/sequence_parallelism.py 
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def __init__(self, config: VllmConfig):
    super().__init__(config)

    self.patterns: PatternMatcherPass = PatternMatcherPass(
        pass_name="sequence_parallelism_pass")
    for epsilon in [1e-5, 1e-6]:
        EmbeddingAllReduceRMSNormPattern(
            epsilon, self.model_dtype, self.device).register(self.patterns)

        MiddleAllReduceRMSNormPattern(epsilon, self.model_dtype,
                                      self.device).register(self.patterns)

        LastAllReduceRMSNormPattern(epsilon, self.model_dtype,
                                    self.device).register(self.patterns)
        # WARNING: This is a hack to clear the pattern matcher cache
        # and allow multiple values of epsilon.
        torch._inductor.pattern_matcher._seen_patterns.clear()

is_applicable_for_shape 

is_applicable_for_shape(shape: Optional[int]) -> bool
Source code in vllm/compilation/sequence_parallelism.py 
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def is_applicable_for_shape(self, shape: Optional[int]) -> bool:
    tp_size = get_tensor_model_parallel_world_size()
    return shape is not None and shape % tp_size == 0