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Convert eplb rebalance to numpy
Signed-off-by: ilmarkov <markovilya197@gmail.com>
This commit is contained in:
ilmarkov
parent
11c492ae81
commit
389f86e0c5
@ -1,6 +1,7 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import numpy as np
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import pytest
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import torch
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@ -312,9 +313,9 @@ if __name__ == "__main__":
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test_basic_rebalance()
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def _make_phy_replicas_idx_from_phy2log(phy2log: torch.Tensor) -> torch.Tensor:
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"""Create replicas indices mapping from phy2log"""
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pr = torch.zeros_like(phy2log)
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def _make_phy_replicas_idx_from_phy2log(phy2log: np.ndarray) -> np.ndarray:
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"""Create replicas indices mapping from phy2log."""
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pr = np.zeros_like(phy2log, dtype=np.int64)
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for layer in range(phy2log.shape[0]):
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seen: dict[int, int] = {}
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row = phy2log[layer].tolist()
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@ -326,11 +327,11 @@ def _make_phy_replicas_idx_from_phy2log(phy2log: torch.Tensor) -> torch.Tensor:
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def _validate_intragpu_rearrangement(
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old_global_expert_indices: torch.Tensor,
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new_phy2log: torch.Tensor,
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new_phy_replicas_idx: torch.Tensor,
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post_phy2log: torch.Tensor,
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post_phy_replicas_idx: torch.Tensor,
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old_global_expert_indices: np.ndarray,
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new_phy2log: np.ndarray,
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new_phy_replicas_idx: np.ndarray,
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post_phy2log: np.ndarray,
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post_phy_replicas_idx: np.ndarray,
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num_ranks: int,
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slots_per_gpu: int,
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):
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@ -345,7 +346,7 @@ def _validate_intragpu_rearrangement(
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post_rnk = post_phy_replicas_idx[0, start:end]
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# Pairwise equality for (expert, rank) pairs to ensure nothing is lost
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def sorted_pairs(seg: torch.Tensor, rnk: torch.Tensor):
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def sorted_pairs(seg, rnk):
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pairs = list(zip(seg.tolist(), rnk.tolist()))
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pairs.sort()
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return pairs
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@ -386,8 +387,8 @@ def _validate_intragpu_rearrangement(
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# GPU0 new -> [1,5,0,4]; GPU1 new -> [6,2,7,3]
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2,
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4,
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torch.tensor([[0, 1, 2, 3, 4, 5, 6, 7]]),
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torch.tensor([[1, 5, 0, 4, 6, 2, 7, 3]]),
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np.array([[0, 1, 2, 3, 4, 5, 6, 7]]),
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np.array([[1, 5, 0, 4, 6, 2, 7, 3]]),
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id="simple",
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),
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pytest.param(
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@ -401,8 +402,8 @@ def _validate_intragpu_rearrangement(
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# GPU1 new -> [6, 2, 3, 2, 1] (expert 2 duplicated)
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2,
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5,
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torch.tensor([[0, 1, 0, 2, 3, 4, 5, 6, 1, 2]]),
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torch.tensor([[0, 5, 4, 0, 1, 6, 2, 3, 2, 1]]),
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np.array([[0, 1, 0, 2, 3, 4, 5, 6, 1, 2]]),
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np.array([[0, 5, 4, 0, 1, 6, 2, 3, 2, 1]]),
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id="duplicates",
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),
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pytest.param(
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@ -418,8 +419,8 @@ def _validate_intragpu_rearrangement(
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# GPU2 new -> [1, 2, 3, 0]
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3,
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4,
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torch.tensor([[0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]]),
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torch.tensor([[0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0]]),
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np.array([[0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]]),
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np.array([[0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0]]),
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id="skewed_expert",
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),
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],
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@ -21,8 +21,8 @@ from .abstract import AbstractEplbPolicy
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class DefaultEplbPolicy(AbstractEplbPolicy):
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@classmethod
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def balanced_packing(
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cls, weight: torch.Tensor, num_packs: int
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) -> tuple[torch.Tensor, torch.Tensor]:
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cls, weight: np.ndarray, num_packs: int
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) -> tuple[np.ndarray, np.ndarray]:
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"""
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Pack n weighted objects to m packs, such that each bin contains exactly
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n/m objects and the weights of all packs are as balanced as possible.
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@ -39,19 +39,15 @@ class DefaultEplbPolicy(AbstractEplbPolicy):
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assert num_groups % num_packs == 0
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groups_per_pack = num_groups // num_packs
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device = weight.device
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if groups_per_pack == 1:
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pack_index = torch.arange(
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weight.size(-1), dtype=torch.int64, device=device
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).expand(weight.shape)
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rank_in_pack = torch.zeros_like(weight, dtype=torch.int64, device=device)
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return pack_index, rank_in_pack
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weight_np = weight.cpu().numpy()
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pack_index_np = np.tile(
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np.arange(num_groups, dtype=np.int64), (num_layers, 1)
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)
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rank_in_pack_np = np.zeros_like(pack_index_np, dtype=np.int64)
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return pack_index_np, rank_in_pack_np
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# Sort and get indices in decending order
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indices_np = np.argsort(-weight_np, axis=-1)
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indices = np.argsort(-weight, axis=-1)
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pack_index_np = np.full((num_layers, num_groups), -1, dtype=np.int64)
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rank_in_pack_np = np.full((num_layers, num_groups), -1, dtype=np.int64)
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@ -61,7 +57,7 @@ class DefaultEplbPolicy(AbstractEplbPolicy):
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pack_weights = [0.0] * num_packs
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pack_items = [0] * num_packs
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for group in indices_np[i]:
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for group in indices[i]:
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# Find a pack with capacity that has the lowest weight
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pack = min(
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(j for j in range(num_packs) if pack_items[j] < groups_per_pack),
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@ -71,18 +67,15 @@ class DefaultEplbPolicy(AbstractEplbPolicy):
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assert pack_items[pack] < groups_per_pack
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pack_index_np[i, group] = pack
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rank_in_pack_np[i, group] = pack_items[pack]
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pack_weights[pack] += weight_np[i, group]
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pack_weights[pack] += weight[i, group]
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pack_items[pack] += 1
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pack_index = torch.from_numpy(pack_index_np).to(device)
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rank_in_pack = torch.from_numpy(rank_in_pack_np).to(device)
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return pack_index, rank_in_pack
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return pack_index_np, rank_in_pack_np
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@classmethod
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def replicate_experts(
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cls, weight: torch.Tensor, num_phy: int
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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cls, weight: np.ndarray, num_phy: int
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) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
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"""
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Replicate `num_log` experts to `num_phy` replicas, such that the maximum
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load of all replicas is minimized.
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@ -99,13 +92,12 @@ class DefaultEplbPolicy(AbstractEplbPolicy):
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n, num_log = weight.shape
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num_redundant = num_phy - num_log
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assert num_redundant >= 0
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device = weight.device
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phy2log = torch.arange(num_phy, dtype=torch.int64, device=device).repeat(n, 1)
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replica_idx = torch.zeros(n, num_phy, dtype=torch.int64, device=device)
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logcnt = torch.ones(n, num_log, dtype=torch.int64, device=device)
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arangen = torch.arange(n, dtype=torch.int64, device=device)
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phy2log = np.tile(np.arange(num_phy, dtype=np.int64), (n, 1))
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replica_idx = np.zeros((n, num_phy), dtype=np.int64)
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logcnt = np.ones((n, num_log), dtype=np.int64)
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arangen = np.arange(n, dtype=np.int64)
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for i in range(num_log, num_phy):
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redundant_indices = (weight / logcnt).max(dim=-1).indices
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redundant_indices = np.argmax(weight / logcnt, axis=-1)
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phy2log[:, i] = redundant_indices
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replica_idx[:, i] = logcnt[arangen, redundant_indices]
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logcnt[arangen, redundant_indices] += 1
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@ -114,12 +106,12 @@ class DefaultEplbPolicy(AbstractEplbPolicy):
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@classmethod
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def rebalance_experts_hierarchical(
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cls,
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weight: torch.Tensor,
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weight: np.ndarray,
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num_physical_experts: int,
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num_groups: int,
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num_nodes: int,
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num_gpus: int,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
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"""
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Parameters:
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weight: [num_moe_layers, num_logical_experts]
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@ -146,35 +138,33 @@ class DefaultEplbPolicy(AbstractEplbPolicy):
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assert num_physical_experts % num_gpus == 0
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phy_experts_per_gpu = num_physical_experts // num_gpus
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def inverse(perm: torch.Tensor) -> torch.Tensor:
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inv = torch.empty_like(perm)
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inv.scatter_(
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1,
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perm,
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torch.arange(
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perm.size(1), dtype=torch.int64, device=perm.device
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).expand(perm.shape),
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)
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def inverse(perm: np.ndarray) -> np.ndarray:
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inv = np.empty_like(perm)
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row_idx = np.arange(perm.shape[0])[:, None]
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col_idx = np.arange(perm.shape[1], dtype=np.int64)
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inv[row_idx, perm] = col_idx
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return inv
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# Step 1: pack groups to nodes
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tokens_per_group = weight.unflatten(-1, (num_groups, group_size)).sum(-1)
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tokens_per_group = weight.reshape(num_layers, num_groups, group_size).sum(
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axis=-1
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)
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group_pack_index, group_rank_in_pack = cls.balanced_packing(
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tokens_per_group, num_nodes
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)
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# Map each logical expert into a node-local ordering based on packed groups.
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log2mlog = (
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(
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(group_pack_index * groups_per_node + group_rank_in_pack) * group_size
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).unsqueeze(-1)
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+ torch.arange(
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group_size, dtype=torch.int64, device=group_pack_index.device
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(group_pack_index * groups_per_node + group_rank_in_pack)[..., None]
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* group_size
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)
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).flatten(-2)
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+ np.arange(group_size, dtype=np.int64)
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).reshape(num_layers, num_logical_experts)
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mlog2log = inverse(log2mlog)
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# Step 2: construct redundant experts within nodes
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# [num_layers * num_nodes, num_logical_experts // num_nodes]
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tokens_per_mlog = weight.gather(-1, mlog2log).view(
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# Reorder weights into the node-local layout so replication is done per node.
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tokens_per_mlog = np.take_along_axis(weight, mlog2log, axis=1).reshape(
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-1, num_logical_experts // num_nodes
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)
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phy2mlog, replicas_idx, mlogcnt = cls.replicate_experts(
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@ -182,39 +172,43 @@ class DefaultEplbPolicy(AbstractEplbPolicy):
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)
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# Step 3: pack physical_experts to GPUs
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# [num_layers * num_nodes, num_physical_experts // num_nodes]
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tokens_per_phy = (tokens_per_mlog / mlogcnt).gather(-1, phy2mlog)
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# Effective per-physical load = logical load divided by replica count.
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tokens_per_phy = np.take_along_axis(tokens_per_mlog / mlogcnt, phy2mlog, axis=1)
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pack_index, rank_in_pack = cls.balanced_packing(
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tokens_per_phy, num_gpus // num_nodes
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)
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phy2pphy = pack_index * phy_experts_per_gpu + rank_in_pack
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pphy2phy = inverse(phy2pphy)
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pphy2mlog = phy2mlog.gather(
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-1, pphy2phy
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) # [num_layers * num_nodes, num_log_per_nodes]
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# Reorder node-local logical indices into the post-packing physical order.
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pphy2mlog = np.take_along_axis(phy2mlog, pphy2phy, axis=1)
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pphy2mlog = (
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pphy2mlog.view(num_layers, num_nodes, -1)
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+ torch.arange(
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pphy2mlog.reshape(num_layers, num_nodes, -1)
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+ np.arange(
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0,
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num_logical_experts,
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num_logical_experts // num_nodes,
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device=group_pack_index.device,
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).view(1, -1, 1)
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).flatten(-2)
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pphy2log = mlog2log.gather(-1, pphy2mlog)
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pphy_replicas_idx = replicas_idx.gather(-1, pphy2phy).view(num_layers, -1)
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logcnt = mlogcnt.view(num_layers, -1).gather(-1, log2mlog)
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dtype=np.int64,
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)[None, :, None]
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).reshape(num_layers, -1)
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# Map node-local logical indices back to global logical expert ids.
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pphy2log = np.take_along_axis(mlog2log, pphy2mlog, axis=1)
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# Reorder replica ranks to the post-packing physical ordering.
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pphy_replicas_idx = np.take_along_axis(replicas_idx, pphy2phy, axis=1).reshape(
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num_layers, -1
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)
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# Convert replica counts back to the original logical ordering.
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logcnt = np.take_along_axis(mlogcnt.reshape(num_layers, -1), log2mlog, axis=1)
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return pphy2log, pphy_replicas_idx, logcnt
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@classmethod
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def preserve_intragpu_slots(
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cls,
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phy2log: torch.Tensor,
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phy_replicas_idx: torch.Tensor,
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phy2log: np.ndarray,
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phy_replicas_idx: np.ndarray,
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num_ranks: int,
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old_global_expert_indices: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor]:
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old_phy2log: np.ndarray,
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) -> tuple[np.ndarray, np.ndarray]:
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"""
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Reorder the new mapping per GPU so that experts that remain on the same GPU
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keep their previous slot positions when possible. Incoming experts to that GPU
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@ -222,29 +216,24 @@ class DefaultEplbPolicy(AbstractEplbPolicy):
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is unchanged and the slots per GPU remain the same between
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the old and new mappings.
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"""
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device = phy2log.device
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num_phy_experts = phy2log.shape[1]
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if num_ranks <= 0 or num_phy_experts % num_ranks != 0:
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return phy2log, phy_replicas_idx
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# Move to CPU and convert to NumPy for processing
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new_phy2log_np = phy2log.cpu().numpy()
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replicas_idx_np = phy_replicas_idx.cpu().numpy()
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old_phy2log_np = old_global_expert_indices.cpu().numpy()
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slots_per_gpu = num_phy_experts // num_ranks
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num_layers = new_phy2log_np.shape[0]
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num_layers = phy2log.shape[0]
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post_phy2log_np = new_phy2log_np.copy()
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post_phy_replicas_idx_np = replicas_idx_np.copy()
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post_phy2log = phy2log.copy()
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post_phy_replicas_idx = phy_replicas_idx.copy()
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for gpu_idx in range(num_ranks):
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start = gpu_idx * slots_per_gpu
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end = start + slots_per_gpu
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# Experts across all layers for this GPU
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old_local = old_phy2log_np[:, start:end] # [layers, slots]
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new_local = new_phy2log_np[:, start:end] # [layers, slots]
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new_ridx = replicas_idx_np[:, start:end] # [layers, slots]
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old_local = old_phy2log[:, start:end] # [layers, slots]
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new_local = phy2log[:, start:end] # [layers, slots]
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new_ridx = phy_replicas_idx[:, start:end] # [layers, slots]
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used_new_indices = np.zeros((num_layers, slots_per_gpu), dtype=bool)
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preserved_positions = np.zeros((num_layers, slots_per_gpu), dtype=bool)
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@ -261,12 +250,12 @@ class DefaultEplbPolicy(AbstractEplbPolicy):
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first_idx = np.argmax(matches, axis=1)
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layer_indices = np.nonzero(has_any)[0]
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matched_new_positions = first_idx[layer_indices]
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post_phy2log_np[layer_indices, start + slot_idx] = new_local[
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post_phy2log[layer_indices, start + slot_idx] = new_local[
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layer_indices, matched_new_positions
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]
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post_phy_replicas_idx[layer_indices, start + slot_idx] = new_ridx[
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layer_indices, matched_new_positions
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]
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post_phy_replicas_idx_np[layer_indices, start + slot_idx] = (
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new_ridx[layer_indices, matched_new_positions]
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)
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used_new_indices[layer_indices, matched_new_positions] = True
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preserved_positions[layer_indices, slot_idx] = True
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@ -295,16 +284,13 @@ class DefaultEplbPolicy(AbstractEplbPolicy):
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continue
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src_pos = remaining_indices[layer_idx, :k]
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dst_pos = fill_indices[layer_idx, :k]
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post_phy2log_np[layer_idx, start + dst_pos] = new_local[
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post_phy2log[layer_idx, start + dst_pos] = new_local[
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layer_idx, src_pos
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]
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post_phy_replicas_idx_np[layer_idx, start + dst_pos] = new_ridx[
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post_phy_replicas_idx[layer_idx, start + dst_pos] = new_ridx[
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layer_idx, src_pos
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]
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# Convert back to torch and move to original device
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post_phy2log = torch.from_numpy(post_phy2log_np).to(device)
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post_phy_replicas_idx = torch.from_numpy(post_phy_replicas_idx_np).to(device)
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return post_phy2log, post_phy_replicas_idx
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@classmethod
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@ -340,40 +326,51 @@ class DefaultEplbPolicy(AbstractEplbPolicy):
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logcnt: [layers, num_logical_experts], number of
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physical replicas for each logical expert
|
||||
"""
|
||||
device = weight.device
|
||||
num_layers, num_logical_experts = weight.shape
|
||||
weight = weight.float()
|
||||
weight_np = weight.float().cpu().numpy()
|
||||
old_phy2log_np = (
|
||||
old_global_expert_indices.cpu().numpy()
|
||||
if old_global_expert_indices is not None
|
||||
else None
|
||||
)
|
||||
|
||||
if num_groups % num_nodes == 0:
|
||||
# use hierarchical load-balance policy
|
||||
phy2log, phy_replicas_idx, logcnt = cls.rebalance_experts_hierarchical(
|
||||
weight, num_replicas, num_groups, num_nodes, num_ranks
|
||||
phy2log_np, phy_replicas_idx_np, logcnt_np = (
|
||||
cls.rebalance_experts_hierarchical(
|
||||
weight_np, num_replicas, num_groups, num_nodes, num_ranks
|
||||
)
|
||||
)
|
||||
else:
|
||||
# use global load-balance policy
|
||||
phy2log, phy_replicas_idx, logcnt = cls.rebalance_experts_hierarchical(
|
||||
weight, num_replicas, 1, 1, num_ranks
|
||||
phy2log_np, phy_replicas_idx_np, logcnt_np = (
|
||||
cls.rebalance_experts_hierarchical(
|
||||
weight_np, num_replicas, 1, 1, num_ranks
|
||||
)
|
||||
)
|
||||
|
||||
# Optional postprocessing to preserve slots for experts moving
|
||||
# within the same GPU
|
||||
# Only apply when the number of GPUs and slots per GPU remain unchanged.
|
||||
# Helps to avoid unnecessary weight copying when experts move
|
||||
# within the same GPU.
|
||||
if old_global_expert_indices is not None:
|
||||
phy2log, phy_replicas_idx = cls.preserve_intragpu_slots(
|
||||
phy2log, phy_replicas_idx, num_ranks, old_global_expert_indices
|
||||
phy2log_np, phy_replicas_idx_np = cls.preserve_intragpu_slots(
|
||||
phy2log_np, phy_replicas_idx_np, num_ranks, old_phy2log_np
|
||||
)
|
||||
num_redundant_experts = num_replicas - num_logical_experts
|
||||
maxlogcnt = num_redundant_experts + 1
|
||||
log2phy: torch.Tensor = torch.full(
|
||||
(num_layers, num_logical_experts, maxlogcnt),
|
||||
-1,
|
||||
dtype=torch.int64,
|
||||
device=logcnt.device,
|
||||
log2phy_np = np.full(
|
||||
(num_layers, num_logical_experts, maxlogcnt), -1, dtype=np.int64
|
||||
)
|
||||
log2phy.view(num_layers, -1).scatter_(
|
||||
-1,
|
||||
phy2log * maxlogcnt + phy_replicas_idx,
|
||||
torch.arange(num_replicas, dtype=torch.int64, device=log2phy.device).expand(
|
||||
num_layers, -1
|
||||
),
|
||||
layer_indices = np.arange(num_layers)[:, None]
|
||||
replica_indices = np.tile(
|
||||
np.arange(num_replicas, dtype=np.int64), (num_layers, 1)
|
||||
)
|
||||
log2phy_np[layer_indices, phy2log_np, phy_replicas_idx_np] = replica_indices
|
||||
|
||||
phy2log = torch.from_numpy(phy2log_np).to(device)
|
||||
log2phy = torch.from_numpy(log2phy_np).to(device)
|
||||
logcnt = torch.from_numpy(logcnt_np).to(device)
|
||||
return phy2log, log2phy, logcnt
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user