analytical-viz: interactive Streamlit tool for SIP transformer analysis
New tests/analytical_visualization/ module - an interactive dashboard for exploring memory / latency tradeoffs of transformer inference on the SIP architecture. Highlights: - 30+ model presets (Qwen, Llama 2/3/3.1, Mistral, Gemma 2, Phi 3, DeepSeek MLA, Mixtral/Qwen 3 MoE, Grok-1, ...) - Placement toggles: TP and CP each on PE-level vs cube-level - CP ring variant: K/V ring vs Q+O/m/l ring (prefill); in decode the O/m/l all-reduce is folded into S8 (no separate C1 row) - SIP interconnect: ring / mesh2d / torus2d with matching link drawing - Per-stage latency table with compute + memory + comm formulas, auto-scaled ns/us/ms, colored by dominant bound - Ring attention loop indicator on the pipeline diagram (purple arc over S5-S8 with 'xN hops' badge) - Tensor sharding view with optional physical PE/cube annotations - Replication-waste + optimization-hints panel - Save & compare configurations (config1, config2, ...): summary table plus side-by-side per-stage attention and FFN latency, best-in-row highlighting - Symbol glossary with current values for every symbol used in formulas Not tied to production sim_engine or runtime API; purely analytical tooling for design-space exploration. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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"""Model + machine parameters for the analytical visualization tool.
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All numbers are per-rank / per-PE unless noted. bytes are counted in
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bytes (b), FLOPs in raw ops, bandwidth in bytes/second, latency in
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seconds. Convert to μs / GB / GFLOP at display time only.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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@dataclass
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class ModelConfig:
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"""Transformer model dimensions."""
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name: str = "Qwen 3 8B"
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hidden: int = 4096
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ffn_dim: int = 12288
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h_q: int = 32 # Q heads
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h_kv: int = 8 # KV heads
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d_head: int = 128
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layers: int = 36
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bytes_per_elem: int = 2 # bf16
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@property
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def group_size(self) -> int:
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"""GQA group size = H_q / H_kv."""
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return self.h_q // self.h_kv
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@property
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def head_dim_total_q(self) -> int:
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return self.h_q * self.d_head
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@property
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def head_dim_total_kv(self) -> int:
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return self.h_kv * self.d_head
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@dataclass
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class TopologyConfig:
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"""Physical mapping — DP × PP × CP × TP (× EP for MoE)."""
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cp: int = 4 # inter-cube ring size (sequence shard)
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tp: int = 8 # PEs per cube (head/hidden shard)
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pp: int = 1 # pipeline stages (layer shard)
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dp: int = 1 # data-parallel replicas
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ep: int = 1 # expert-parallel degree (MoE only)
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s_kv: int = 4096
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mode: str = "decode" # "decode" or "prefill"
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kv_shard_mode: str = "split" # "split" or "replicate" (used when TP > H_kv)
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# FFN sharding scope: default TP+CP (across cube group) trades a small
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# extra AllReduce for lower per-PE weight bytes.
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ffn_shard_scope: str = "TP+CP" # "TP" | "TP+CP" | "TP+CP+DP"
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# Inter-SIP interconnect: how physical SIPs are wired together.
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# "ring" (1D wrap), "mesh2d" (grid, no wrap), "torus2d" (grid + wrap).
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sip_topology: str = "ring"
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# Placement of parallelism dims on the hierarchy.
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# "pe" -> dim lives on PEs within a single cube (intra-cube).
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# "cube" -> dim lives across cubes (inter-cube).
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tp_placement: str = "pe" # default: TP fills PEs of a cube
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cp_placement: str = "cube" # default: CP fills cubes of a SIP
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# CP ring variant: what gets passed around the CP ring each hop.
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# "kv" -> K and V shards rotate (Q,O,m,l stay local). Bytes/hop scales
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# with S_local * H_kv * d_h. Good for prefill (large T_q).
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# "qoml" -> Q and running (O, m, l) rotate; K,V stay local. Bytes/hop
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# scales with T_q * H_q * d_h. Cheap for decode (T_q=1).
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cp_ring_variant: str = "kv"
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pes_per_cube_hw: int = 8 # SIP hardware constant
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cubes_per_sip_hw: int = 16
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@property
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def pes_per_stage(self) -> int:
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"""PEs per pipeline stage (all stages have the same size)."""
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return self.cp * self.tp
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@property
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def pes_per_replica(self) -> int:
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"""PEs per one full model replica (CP*TP*PP)."""
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return self.cp * self.tp * self.pp
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@property
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def total_pes(self) -> int:
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return self.pes_per_replica * self.dp
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# ── Placement-aware layout ────────────────────────────────
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@property
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def intra_cube_dims(self) -> int:
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"""Product of dims placed on PE level (must fit in one cube, else spills)."""
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n = 1
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if self.tp_placement == "pe":
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n *= self.tp
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if self.cp_placement == "pe":
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n *= self.cp
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return n
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@property
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def inter_cube_dims(self) -> int:
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"""Product of dims placed at cube level = distinct cube groups per stage."""
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n = 1
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if self.tp_placement == "cube":
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n *= self.tp
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if self.cp_placement == "cube":
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n *= self.cp
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return n
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@property
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def pes_per_cube_used(self) -> int:
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"""How many PEs are live in each cube."""
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return min(self.intra_cube_dims, self.pes_per_cube_hw)
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@property
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def cubes_per_stage(self) -> int:
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"""Cubes needed per PP stage under current placement.
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If intra-cube demand exceeds PEs/cube, the intra dim spills to cubes."""
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spill = max(1, (self.intra_cube_dims + self.pes_per_cube_hw - 1)
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// self.pes_per_cube_hw)
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return self.inter_cube_dims * spill
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@property
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def cubes_used(self) -> int:
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"""Physical cubes used across all PP stages and DP replicas."""
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return self.cubes_per_stage * self.pp * self.dp
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@property
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def sips_used(self) -> int:
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"""Number of SIPs used (each SIP = pes_per_cube_hw × cubes_per_sip_hw)."""
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cubes_per_sip = self.cubes_per_sip_hw
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return max(1, (self.cubes_used + cubes_per_sip - 1) // cubes_per_sip)
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@property
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def placement_valid(self) -> bool:
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"""False when intra-cube demand exceeds one cube (dim spills to cubes)."""
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return self.intra_cube_dims <= self.pes_per_cube_hw
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@property
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def tp_spans_cubes(self) -> int:
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"""How many cubes one TP group spans (>=1). Depends on placement:
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- tp_placement=pe: 1 if TP fits in a cube, else spill.
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- tp_placement=cube: TP itself is the number of cubes per TP group.
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"""
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if self.tp_placement == "cube":
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return max(1, self.tp)
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return max(1, (self.tp + self.pes_per_cube_hw - 1) // self.pes_per_cube_hw)
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@property
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def cp_intra_sip_hops(self) -> int:
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"""CP ring hops that stay within one SIP."""
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if self.cp <= 1:
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return 0
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sips_in_ring = self.sips_used # one PP stage's CP ring may span SIPs
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return max(0, (self.cp - 1) - (sips_in_ring - 1))
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@property
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def cp_inter_sip_hops(self) -> int:
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"""CP ring hops that cross SIP boundaries."""
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if self.cp <= 1:
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return 0
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# Approximation: one inter-SIP hop per SIP boundary in the ring.
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# For CP=32 across 2 SIPs → 1 inter-SIP + 30 intra-SIP hops.
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return max(0, self.sips_used - 1)
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@property
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def T_q(self) -> int:
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return 1 if self.mode == "decode" else self.s_kv // self.cp
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@property
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def s_local(self) -> int:
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return self.s_kv // self.cp
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@property
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def layers_per_stage(self) -> str:
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"""Layers per PP stage. Requires model.layers to render — leave as fn."""
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return "N_layers / PP"
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def tp_link_tier(self) -> str:
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"""Return which BW tier a TP AllReduce uses: 'intra' | 'inter' | 'intersip'.
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- tp_placement=pe and TP fits in one cube -> 'intra'
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- tp_placement=cube and TP fits in one SIP -> 'inter'
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- otherwise -> 'intersip'
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"""
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if self.tp_placement == "pe":
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if self.tp <= self.pes_per_cube_hw:
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return "intra"
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# spilled to multiple cubes: cross-cube
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if self.tp_spans_cubes <= self.cubes_per_sip_hw:
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return "inter"
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return "intersip"
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else: # tp_placement == "cube"
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if self.tp <= self.cubes_per_sip_hw:
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return "inter"
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return "intersip"
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def cp_link_tier(self) -> str:
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"""Return BW tier for the CP ring: 'intra' | 'inter' | 'intersip'."""
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if self.cp_placement == "pe":
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return "intra"
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# cp_placement == "cube"
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if self.cp <= self.cubes_per_sip_hw:
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return "inter"
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return "intersip"
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@dataclass
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class MachineParams:
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"""SIP hardware parameters — all user-adjustable via sliders.
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Bandwidth tiers, from fastest to slowest:
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HBM (per-PE local memory)
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> intra-cube (PE↔PE on same die)
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> inter-cube (UCIe D2D between dies on same SIP)
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> inter-SIP (C2C / RDMA-class, across SIPs / servers)
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"""
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# Per-PE compute peak (TFLOPs f16).
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peak_tflops_f16: float = 8.0
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# Per-PE HBM budget (GB) — the memory ceiling per PE.
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pe_hbm_gb: float = 6.0
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# HBM per-PE bandwidth (GB/s).
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bw_hbm_gbs: float = 256.0
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# Intra-cube PE↔PE link BW (GB/s).
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bw_intra_gbs: float = 512.0
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# Inter-cube (UCIe D2D) BW (GB/s).
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bw_inter_gbs: float = 128.0
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# Inter-SIP (C2C / RDMA) BW (GB/s).
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bw_intersip_gbs: float = 50.0
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# Per-hop latencies (ns).
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alpha_intra_ns: float = 20.0
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alpha_inter_ns: float = 100.0
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alpha_intersip_ns: float = 1000.0
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# Achievable utilization for compute-bound stages (0-1).
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compute_util: float = 0.8
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@property
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def peak_flops(self) -> float:
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return self.peak_tflops_f16 * 1e12
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@property
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def bw_hbm(self) -> float:
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return self.bw_hbm_gbs * 1e9
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@property
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def bw_intra(self) -> float:
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return self.bw_intra_gbs * 1e9
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@property
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def bw_inter(self) -> float:
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return self.bw_inter_gbs * 1e9
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@property
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def bw_intersip(self) -> float:
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return self.bw_intersip_gbs * 1e9
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@property
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def alpha_intra(self) -> float:
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return self.alpha_intra_ns * 1e-9
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@property
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def alpha_inter(self) -> float:
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return self.alpha_inter_ns * 1e-9
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@property
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def alpha_intersip(self) -> float:
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return self.alpha_intersip_ns * 1e-9
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@property
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def pe_budget_bytes(self) -> int:
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return int(self.pe_hbm_gb * 1e9)
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@dataclass
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class FullConfig:
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"""Bundled config for one analysis run."""
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model: ModelConfig = field(default_factory=ModelConfig)
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topo: TopologyConfig = field(default_factory=TopologyConfig)
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machine: MachineParams = field(default_factory=MachineParams)
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@property
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def h_q_per_pe(self) -> int:
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return max(1, self.model.h_q // self.topo.tp)
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@property
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def h_kv_per_pe(self) -> float:
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"""Fractional if TP > H_kv (head-dim split needed)."""
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return self.model.h_kv / self.topo.tp
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@property
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def d_per_pe(self) -> int:
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return self.model.hidden // self.topo.tp
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@property
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def ffn_per_pe(self) -> int:
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return self.model.ffn_dim // self.topo.tp
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@property
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def kv_replication_needed(self) -> bool:
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"""True if TP > H_kv (each KV head shared across TP/H_kv ranks)."""
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return self.topo.tp > self.model.h_kv
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@property
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def head_dim_split_factor(self) -> int:
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"""Number of TP ranks sharing one head (via d_head split)."""
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return max(1, self.topo.tp // self.model.h_kv)
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@property
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def ffn_shard_divisor(self) -> int:
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"""How much the FFN dim is sharded across ranks."""
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scope = self.topo.ffn_shard_scope
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d = self.topo.tp
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if "CP" in scope:
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d *= self.topo.cp
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if "DP" in scope:
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d *= self.topo.dp
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return max(1, d)
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