mukesh
85f6716fc1
Extends the memory-only autosuggest to search the full 9-dimensional parallelism space (CP, TP, PP, DP, kv_shard_mode, ffn_shard_scope, tp_placement, cp_placement, cp_ring_variant) and rank feasible configs on a 3D Pareto frontier: (latency ↓, pes_used ↓, efficiency ↑). Throughput is stored on ConfigScore for display but is deliberately NOT a Pareto axis because for a single-request analysis it collapses to 1 / latency, which would collapse the frontier. Reuses existing physics (stage_latencies.all_stages + all_ffn_stages, memory_layout.compute_memory) — no new formulas. Single-request latency formula fix: PP does NOT reduce single-request decode/prefill latency because the request has to traverse every layer sequentially regardless of pipeline depth. The initial version had latency ~ per_layer × layers_per_stage, which incorrectly rewarded high PP. Corrected to latency ~ per_layer × model.layers. Enumerator prunes: - PP > model.layers - TP > 4 × h_q - ffn_shard_scope contains 'DP' when dp=1 (redundant) - cp_ring_variant='qoml' when cp=1 (no-op) Full sweep on Llama 3.1 70B: ~28,800 configs enumerated in ~7s, ~7k-10k feasible (varies with S_kv), 2-7 unique Pareto configs. Faster context lengths produce richer frontiers; at 1M, memory forces a single dominant config (128 PEs, HBM 85%). Verified: - 9 pytest tests pass (enumerate, score, Pareto, subset invariants) - Manual: Llama 70B decode at 8K/64K/128K/1M produces physically sensible Pareto (CP=8/TP=16 wins latency; smaller-PE options appear at longer context up to memory limits) Next: Streamlit tab UI in app.py + verification against more presets. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
338 lines
13 KiB
Python
338 lines
13 KiB
Python
"""Auto-explore parallelism configuration space and rank by Pareto frontier.
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Extends the existing ``autosuggest`` (memory-only, single winner) to the
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full 9-knob search (CP, TP, PP, DP, kv_shard_mode, ffn_shard_scope,
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tp_placement, cp_placement, cp_ring_variant) with four objectives:
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- min total_latency_ns (single-request decode/prefill step)
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- max throughput_tok_s (naive tokens/sec = 1 / latency for single-req)
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- max efficiency_score (geo-mean of compute + BW utilization)
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- min pes_used
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Reuses ``stage_latencies`` and ``memory_layout`` as the physics; adds
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enumeration + 4D Pareto sort on top.
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Analytical model runs in ~microseconds per config, so the full sweep
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(~5-10k feasible configs after pruning) completes in a few seconds.
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"""
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from __future__ import annotations
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import math
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from collections.abc import Iterator
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from dataclasses import dataclass, field, replace
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from .memory_layout import compute_memory
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from .model_config import FullConfig, MachineParams, ModelConfig, TopologyConfig
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from .stage_latencies import all_ffn_stages, all_stages
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# ── Search space ─────────────────────────────────────────────────────
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_CP_OPTIONS = (1, 2, 4, 8, 16, 32, 64, 96)
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_TP_OPTIONS = (1, 2, 4, 8, 16, 32)
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_PP_OPTIONS = (1, 2, 4, 8, 16)
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_DP_OPTIONS = (1, 2, 4)
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_KV_SHARD_MODES = ("split", "replicate")
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_FFN_SHARD_SCOPES = ("TP", "TP+CP", "TP+CP+DP")
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_TP_PLACEMENTS = ("pe", "cube")
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_CP_PLACEMENTS = ("cube", "pe")
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_CP_RING_VARIANTS = ("kv", "qoml")
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# ── Result types ─────────────────────────────────────────────────────
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@dataclass
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class ConfigScore:
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"""A single (config, computed-metrics) tuple. All floats in SI units
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(seconds, tokens/sec, dimensionless) unless suffixed otherwise."""
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# ── Config (the 9 knobs) ─────
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cp: int
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tp: int
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pp: int
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dp: int
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kv_shard_mode: str
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ffn_shard_scope: str
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tp_placement: str
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cp_placement: str
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cp_ring_variant: str
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# ── Objectives ─────
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total_latency_ns: float # ↓ minimize
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throughput_tok_s: float # ↑ maximize
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efficiency_score: float # ↑ maximize (0..1)
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pes_used: int # ↓ minimize
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# ── Info-only (not ranked on) ─────
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hbm_utilization: float # bytes_used / budget (0..1+; may exceed 1 if over-budget)
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weights_gb: float
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kv_gb: float
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transient_gb: float
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sips_used: int
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fits_memory: bool
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placement_valid: bool
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reason: str = ""
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@property
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def latency_us(self) -> float:
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return self.total_latency_ns / 1e3
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@property
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def latency_ms(self) -> float:
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return self.total_latency_ns / 1e6
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def as_topology(self, s_kv: int, mode: str) -> TopologyConfig:
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"""Reconstruct the TopologyConfig this score was computed for."""
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return TopologyConfig(
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cp=self.cp, tp=self.tp, pp=self.pp, dp=self.dp,
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s_kv=s_kv, mode=mode,
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kv_shard_mode=self.kv_shard_mode,
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ffn_shard_scope=self.ffn_shard_scope,
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tp_placement=self.tp_placement,
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cp_placement=self.cp_placement,
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cp_ring_variant=self.cp_ring_variant,
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)
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@dataclass
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class AutoExploreResult:
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model_name: str
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s_kv: int
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mode: str
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total_enumerated: int # after basic domain pruning
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total_feasible: int # after memory + placement checks
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all_scores: list[ConfigScore] = field(default_factory=list) # every feasible config
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pareto_scores: list[ConfigScore] = field(default_factory=list) # non-dominated set
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# ── Enumeration + pruning ────────────────────────────────────────────
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def enumerate_configs(
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model: ModelConfig,
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s_kv: int,
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mode: str,
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) -> Iterator[TopologyConfig]:
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"""Yield every domain-valid TopologyConfig.
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Domain rules (fast pruning; no memory check yet):
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- PP ≤ model.layers (can't have more stages than layers)
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- TP ≤ 4 × model.h_q (unrealistic head-dim splits above this)
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- Skip ffn_shard_scope containing 'DP' when DP=1 (redundant with plain 'TP+CP')
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- Skip cp_ring_variant='qoml' when CP=1 (no ring, variant is a no-op)
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"""
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for cp in _CP_OPTIONS:
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for tp in _TP_OPTIONS:
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if tp > 4 * model.h_q:
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continue
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for pp in _PP_OPTIONS:
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if pp > model.layers:
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continue
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for dp in _DP_OPTIONS:
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for kv_mode in _KV_SHARD_MODES:
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for ffn_scope in _FFN_SHARD_SCOPES:
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if "DP" in ffn_scope and dp == 1:
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continue
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for tp_place in _TP_PLACEMENTS:
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for cp_place in _CP_PLACEMENTS:
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for cp_ring in _CP_RING_VARIANTS:
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if cp == 1 and cp_ring == "qoml":
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continue
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yield TopologyConfig(
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cp=cp, tp=tp, pp=pp, dp=dp,
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s_kv=s_kv, mode=mode,
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kv_shard_mode=kv_mode,
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ffn_shard_scope=ffn_scope,
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tp_placement=tp_place,
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cp_placement=cp_place,
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cp_ring_variant=cp_ring,
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)
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# ── Scoring ──────────────────────────────────────────────────────────
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def _sum_visible_latency(cfg: FullConfig) -> float:
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"""Total single-request latency (seconds) across all model layers.
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A single request traverses every layer sequentially, whether the layers
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sit on one PP stage or are spread across many:
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- PP=1 : one rank holds all L layers, latency = L × per_layer
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- PP=K : K ranks each hold L/K layers, but request crosses all K
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stages sequentially → still L × per_layer
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PP therefore does NOT reduce single-request latency; it only improves
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throughput under batching. This function is the single-request cost, so
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we multiply by full model.layers regardless of PP.
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"""
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attn = sum(s.visible_s for s in all_stages(cfg))
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ffn = sum(s.visible_s for s in all_ffn_stages(cfg))
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per_layer = attn + ffn
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return per_layer * cfg.model.layers
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def _efficiency(cfg: FullConfig, latency_s: float) -> float:
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"""Geo-mean of compute-util and BW-util. Range ~ (0, 1].
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- compute_util = achieved_flops / (peak_flops × pes × latency)
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- bw_util = achieved_bytes / (peak_bw × pes × latency)
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"""
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if latency_s <= 0:
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return 0.0
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attn = all_stages(cfg)
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ffn = all_ffn_stages(cfg)
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layers = math.ceil(cfg.model.layers / cfg.topo.pp)
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total_flops = layers * sum(s.flops for s in attn + ffn)
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total_bytes = layers * sum(s.mem_bytes for s in attn + ffn)
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pes = cfg.topo.total_pes
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peak_flops = cfg.machine.peak_flops * pes
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peak_bw = cfg.machine.bw_hbm * pes
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compute_util = total_flops / (peak_flops * latency_s) if peak_flops > 0 else 0.0
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bw_util = total_bytes / (peak_bw * latency_s) if peak_bw > 0 else 0.0
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compute_util = min(1.0, max(0.0, compute_util))
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bw_util = min(1.0, max(0.0, bw_util))
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# Geo-mean; if either is 0 the score is 0 (avoids overrewarding lopsided configs).
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return math.sqrt(compute_util * bw_util)
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def score_config(cfg: FullConfig) -> ConfigScore:
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"""Compute all 4 objectives + info fields for one config.
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Feasibility (memory + placement) is stored but does NOT gate scoring —
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infeasible configs get returned with fits_memory=False so callers can
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filter or display them.
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"""
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mem = compute_memory(cfg)
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placement_ok = cfg.topo.placement_valid
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latency_s = _sum_visible_latency(cfg)
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throughput = 1.0 / latency_s if latency_s > 0 else 0.0
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efficiency = _efficiency(cfg, latency_s) if latency_s > 0 else 0.0
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fits = not mem.over_budget
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reason = ""
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if not fits:
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reason = (f"weights+KV+transient ({mem.used_bytes/1e9:.2f} GB) "
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f"exceeds per-PE budget ({mem.budget_bytes/1e9:.2f} GB)")
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elif not placement_ok:
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reason = (f"intra-cube demand ({cfg.topo.intra_cube_dims}) "
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f"exceeds PEs/cube ({cfg.topo.pes_per_cube_hw})")
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return ConfigScore(
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cp=cfg.topo.cp, tp=cfg.topo.tp, pp=cfg.topo.pp, dp=cfg.topo.dp,
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kv_shard_mode=cfg.topo.kv_shard_mode,
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ffn_shard_scope=cfg.topo.ffn_shard_scope,
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tp_placement=cfg.topo.tp_placement,
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cp_placement=cfg.topo.cp_placement,
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cp_ring_variant=cfg.topo.cp_ring_variant,
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total_latency_ns=latency_s * 1e9,
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throughput_tok_s=throughput,
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efficiency_score=efficiency,
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pes_used=cfg.topo.total_pes,
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hbm_utilization=mem.used_bytes / mem.budget_bytes if mem.budget_bytes > 0 else 0.0,
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weights_gb=mem.weights_bytes / 1e9,
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kv_gb=mem.kv_cache_bytes / 1e9,
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transient_gb=mem.transient_bytes / 1e9,
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sips_used=cfg.topo.sips_used,
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fits_memory=fits,
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placement_valid=placement_ok,
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reason=reason,
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)
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# ── Pareto sort ──────────────────────────────────────────────────────
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def _dominates(a: ConfigScore, b: ConfigScore) -> bool:
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"""True iff a is no worse than b on every axis AND strictly better on ≥ 1.
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Axes (with direction) — 3D Pareto:
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total_latency_ns ↓ (a ≤ b)
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pes_used ↓ (a ≤ b) — proxy for cost / deployment size
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efficiency_score ↑ (a ≥ b) — geo-mean of compute+BW utilization
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Note: ``throughput_tok_s`` is deliberately NOT a Pareto axis. For a
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single-request analysis, throughput = 1 / latency, so it's collinear
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with latency and would collapse the frontier. It stays on ConfigScore
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for display but doesn't participate in domination.
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"""
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no_worse = (
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a.total_latency_ns <= b.total_latency_ns
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and a.pes_used <= b.pes_used
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and a.efficiency_score >= b.efficiency_score
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)
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if not no_worse:
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return False
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strictly_better = (
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a.total_latency_ns < b.total_latency_ns
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or a.pes_used < b.pes_used
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or a.efficiency_score > b.efficiency_score
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)
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return strictly_better
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def pareto_frontier(scores: list[ConfigScore]) -> list[ConfigScore]:
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"""Extract non-dominated set on (latency↓, pe↓, throughput↑, efficiency↑).
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Only feasible configs (fits_memory AND placement_valid) participate; the
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non-feasible are excluded from the frontier (they can still be returned in
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``all_scores`` for informational display).
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O(N²) — fine for N up to ~50k with early exits.
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"""
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feasible = [s for s in scores if s.fits_memory and s.placement_valid]
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frontier: list[ConfigScore] = []
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for i, a in enumerate(feasible):
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dominated = False
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for j, b in enumerate(feasible):
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if i == j:
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continue
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if _dominates(b, a):
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dominated = True
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break
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if not dominated:
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frontier.append(a)
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return frontier
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# ── Top-level driver ─────────────────────────────────────────────────
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def run_auto_explore(
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model: ModelConfig,
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machine: MachineParams,
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s_kv: int,
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mode: str = "decode",
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) -> AutoExploreResult:
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"""Enumerate all configs, score each, extract Pareto frontier.
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Returns both the full ``all_scores`` list (for the table view) and the
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``pareto_scores`` subset (for the scatter/highlight view). Both are sorted
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by total_latency_ns ascending.
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"""
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all_scores: list[ConfigScore] = []
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total_enumerated = 0
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for topo in enumerate_configs(model, s_kv, mode):
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total_enumerated += 1
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cfg = FullConfig(model=model, topo=topo, machine=machine)
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all_scores.append(score_config(cfg))
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all_scores.sort(key=lambda s: s.total_latency_ns)
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pareto = pareto_frontier(all_scores)
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pareto.sort(key=lambda s: s.total_latency_ns)
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feasible_count = sum(1 for s in all_scores if s.fits_memory and s.placement_valid)
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return AutoExploreResult(
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model_name=model.name,
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s_kv=s_kv,
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mode=mode,
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total_enumerated=total_enumerated,
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total_feasible=feasible_count,
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all_scores=all_scores,
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pareto_scores=pareto,
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)
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