"""Streamlit app: interactive analytical model for transformer inference on the SIP architecture. Run: python -m streamlit run tests/analytical_visualization/app.py """ from __future__ import annotations import importlib import sys import matplotlib.pyplot as plt import pandas as pd import streamlit as st # Streamlit's hot-reload only re-runs THIS script; imported sub-modules stay # cached in sys.modules across reruns. When we edit auto_explore.py or # auto_hardware.py while the Streamlit process is alive, the app would # keep using the pre-edit versions until a full Ctrl+C + restart. Force # a reload of our own modules on every rerun so signature changes land # without a full restart. # Order matters: model_config first because everything else imports from it # (e.g. TopologyConfig). If we reload downstream modules while model_config # is stale, they end up holding the OLD dataclass definition and every # `cfg.topo.new_field` lookup blows up with AttributeError. for _mod_name in ( "tests.analytical_visualization.model_config", "tests.analytical_visualization.model_presets", "tests.analytical_visualization.autosuggest", "tests.analytical_visualization.memory_layout", "tests.analytical_visualization.stage_latencies", "tests.analytical_visualization.stage_shapes", "tests.analytical_visualization.chip_roofline", "tests.analytical_visualization.auto_explore", "tests.analytical_visualization.auto_hardware", "tests.analytical_visualization.pe_weight_layout", "tests.analytical_visualization.tensor_sharding", "tests.analytical_visualization.topology_map", "tests.analytical_visualization.pipeline_diagram", "tests.analytical_visualization.optimization_report", ): _m = sys.modules.get(_mod_name) if _m is not None: importlib.reload(_m) from tests.analytical_visualization.model_config import ( FullConfig, ModelConfig, TopologyConfig, MachineParams, ) from tests.analytical_visualization.model_presets import PRESETS from tests.analytical_visualization.stage_latencies import all_stages, all_ffn_stages from tests.analytical_visualization.stage_shapes import ( attn_stage_shape_rows, ffn_stage_shape_rows, ) from tests.analytical_visualization.chip_roofline import ( arithmetic_intensity, balance_context, bound_regime, critical_batch, good_batch, good_context, knee_batch, per_token_latency_curve, t_com, t_mem_long, t_mem_short, total_active_params, utilization_at, ) from tests.analytical_visualization.memory_layout import ( compute_memory, attention_weight_rows, ffn_weight_rows, kv_cache_rows, sum_bytes_all_layers, total_weight_bytes_full_model, total_kv_bytes_full_model, ) from tests.analytical_visualization.topology_map import draw_topology from tests.analytical_visualization.pe_weight_layout import ( draw_pe_layout, _per_pe_bytes, _q_heads_for_pe, _kv_heads_for_pe, ) from tests.analytical_visualization.tensor_sharding import draw_tensor_sharding from tests.analytical_visualization.pipeline_diagram import draw_pipeline from tests.analytical_visualization.optimization_report import ( replication_report, optimization_hints, ) from tests.analytical_visualization.autosuggest import auto_suggest st.set_page_config(page_title="SIP Attention Analytical Model", layout="wide") # Tighter spacing so more info fits per page. st.markdown( """ """, unsafe_allow_html=True, ) def _pick_in(container, label: str, options, default, key: str, help: str = ""): """Selectbox helper — returns the selected value from options.""" return container.selectbox( label, options, index=options.index(default) if default in options else 0, key=key, help=help, ) def _pick(label, options, default, key, help=""): return _pick_in(st.sidebar, label, options, default, key, help) # ── Sidebar ─ compact, guided layout ──────────────────────────── st.sidebar.markdown("### SIP config") st.sidebar.caption( "Pick a model, workload, parallelism plan, then tune hardware. " "Every change re-runs the analytical model live." ) with st.sidebar.expander("Model", expanded=True): preset_names = list(PRESETS.keys()) preset_name = st.selectbox( "Preset", preset_names, index=preset_names.index("Qwen 3 8B"), key="preset_select", label_visibility="collapsed", ) preset = PRESETS[preset_name] st.caption( f"{preset.family or '-'} - {preset.attn_type} - " f"H_q={preset.model.h_q}, H_kv={preset.model.h_kv}, " f"L={preset.model.layers}" ) with st.expander("Override dims", expanded=False): d1, d2 = st.columns(2) with d1: hidden_ = st.number_input("hidden", 128, 65536, value=preset.model.hidden, step=128) h_q_ = st.number_input("H_q", 1, 256, value=preset.model.h_q) d_head_ = st.number_input("d_head", 32, 512, value=preset.model.d_head, step=32) layers_ = st.number_input("layers", 1, 200, value=preset.model.layers) with d2: ffn_ = st.number_input("ffn_dim", 128, 131072, value=preset.model.ffn_dim, step=128) h_kv_ = st.number_input("H_kv", 1, 256, value=preset.model.h_kv) bpe_ = st.selectbox("dtype bytes", [1, 2, 4], index=1) model = ModelConfig( name=preset.model.name, hidden=hidden_, ffn_dim=ffn_, h_q=h_q_, h_kv=h_kv_, d_head=d_head_, layers=layers_, bytes_per_elem=bpe_, ) with st.sidebar.expander("Workload", expanded=True): w1, w2 = st.columns([2, 1]) with w1: s_kv = st.selectbox( "Context S_kv", [1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288, 1048576], index=10, key="s_kv", # default 1M ) with w2: mode = st.selectbox("Mode", ["decode", "prefill"], index=0, key="mode") b_batch = st.selectbox( "Batch B (concurrent requests)", [1, 2, 4, 8, 16, 32, 64, 128, 256], index=0, key="b_batch", help=("Number of concurrent requests processed in one decode/prefill " "step. KV cache scales linearly with B (each request holds its " "own slice). Compute + AR-comm bytes also scale with B, but " "weights are shared. Batching is the primary way to raise " "HBM-bandwidth efficiency for decode."), ) # Compute auto-suggest for defaults / apply button _default_machine = MachineParams() _default_suggestion = auto_suggest(model, _default_machine, s_kv, mode, b=b_batch) def _snap(v: int, opts: tuple) -> int: return min(opts, key=lambda o: abs(o - v)) _CP_OPTS = (1, 2, 4, 8, 16, 32, 48, 64, 96) _TP_OPTS = (1, 2, 4, 8, 16, 32) _PP_OPTS = (1, 2, 4, 8, 16, 32) _DP_OPTS = (1, 2, 4, 8, 16) _EP_OPTS = (1, 2, 4, 8, 16, 32) # Auto-reset dropdowns when model preset changes. if st.session_state.get("_last_preset") != preset_name: st.session_state["cp"] = _snap(_default_suggestion.cp, _CP_OPTS) st.session_state["tp"] = _snap(_default_suggestion.tp, _TP_OPTS) st.session_state["pp"] = _snap(_default_suggestion.pp, _PP_OPTS) st.session_state["dp"] = 1 st.session_state["ep"] = 1 # Apply the picked cp_placement so the sidebar reflects the packed # config (autosuggest may have chosen "pe" packing to minimize cubes). st.session_state["cp_placement"] = _default_suggestion.cp_placement st.session_state["_last_preset"] = preset_name with st.sidebar.expander("Parallelism", expanded=True): st.caption( f"Auto-suggest (memory-min): CP={_default_suggestion.cp}, " f"TP={_default_suggestion.tp}, PP={_default_suggestion.pp} " f"→ **{_default_suggestion.cubes_used} cube(s)**, " f"{_default_suggestion.pes_used} PEs " f"(cp_placement={_default_suggestion.cp_placement})" ) # Three scope-aware memory-min buttons. Each runs auto_suggest with a # different (include_attention, include_ffn) filter so the memory-fit # constraint counts only the relevant block: # - Attn → attention weights + KV cache (no FFN) # - FFN/MoE → FFN weights only (no attn / no KV) # - Attn+FFN → everything (the traditional autosuggest) st.caption( "**Apply memory-min** — smallest deployment that fits the chosen " "block. Each button sizes for a different memory footprint." ) _sb_scope_meta = { "attn": (True, False, "Attn"), "ffn": (False, True, "FFN/MoE"), "full": (True, True, "Attn+FFN"), } _sb_cols = st.columns(3) _sb_clicks = {} for _sb_scope, _sb_col in zip(("attn", "ffn", "full"), _sb_cols): with _sb_col: _sb_clicks[_sb_scope] = st.button( _sb_scope_meta[_sb_scope][2], width='stretch', key=f"_sb_apply_memmin_{_sb_scope}", help=(f"Memory-min autosuggest — memory budget counts only " f"{_sb_scope_meta[_sb_scope][2]} weights" f"{' + KV cache' if _sb_scope == 'attn' else ''}" f". Picks the smallest deployment that fits."), ) for _sb_scope, _sb_click in _sb_clicks.items(): if not _sb_click: continue _sb_ia, _sb_ff, _sb_lbl = _sb_scope_meta[_sb_scope] with st.spinner(f"Sizing memory-min for {_sb_lbl}..."): _sb_sug = auto_suggest( model, _default_machine, s_kv=s_kv, mode=mode, b=b_batch, include_attention=_sb_ia, include_ffn=_sb_ff, ) if not _sb_sug.fits: st.warning( f"{_sb_lbl} memory-min: no config fits at 10% slack; " f"best-effort {_sb_sug.pes_used} PEs, {_sb_sug.cubes_used} " f"cubes ({_sb_sug.reason})." ) st.session_state["cp"] = _snap(_sb_sug.cp, _CP_OPTS) st.session_state["tp"] = _snap(_sb_sug.tp, _TP_OPTS) st.session_state["pp"] = _snap(_sb_sug.pp, _PP_OPTS) st.session_state["dp"] = 1 st.session_state["ep"] = 1 st.session_state["cp_placement"] = _sb_sug.cp_placement # auto_suggest only explores (cp, tp, pp, cp_placement); reset # every other TopologyConfig field to its dataclass default so # the applied topology matches what memory-min actually scored # (else stale tp_placement="cube" etc. explodes cubes_used). st.session_state["tp_placement"] = "pe" st.session_state["kv_mode"] = "split" st.session_state["cp_ring_variant"] = "kv" # ffn_scope_label is a dynamic string (embeds current tp/cp) — # dropping the key lets the radio fall back to index=1 which is # "TP+CP", the TopologyConfig default. st.session_state.pop("ffn_scope_label", None) st.session_state["_pl_active_scope"] = _sb_scope st.rerun() # Sharding dims — arrange in 2x3 grid to save vertical space p_row1 = st.columns(3) with p_row1[0]: cp = st.selectbox( "CP", list(_CP_OPTS), index=_CP_OPTS.index(st.session_state.get( "cp", _snap(_default_suggestion.cp, _CP_OPTS))), key="cp", ) with p_row1[1]: tp = st.selectbox( "TP", list(_TP_OPTS), index=_TP_OPTS.index(st.session_state.get( "tp", _snap(_default_suggestion.tp, _TP_OPTS))), key="tp", ) with p_row1[2]: pp = st.selectbox( "PP", list(_PP_OPTS), index=_PP_OPTS.index(st.session_state.get( "pp", _snap(_default_suggestion.pp, _PP_OPTS))), key="pp", ) p_row2 = st.columns(2) with p_row2[0]: dp = st.selectbox( "DP", list(_DP_OPTS), index=_DP_OPTS.index(st.session_state.get("dp", 1)), key="dp", ) with p_row2[1]: ep = st.selectbox( "EP (MoE)", list(_EP_OPTS), index=_EP_OPTS.index(st.session_state.get("ep", 1)), key="ep", ) st.markdown("**Placement (PE-level vs cube-level)**") pl_col1, pl_col2 = st.columns(2) with pl_col1: tp_placement = st.radio( "TP on", options=["pe", "cube"], index=0, key="tp_placement", horizontal=True, help=("pe: TP shares PEs of one cube (intra-cube AllReduce). " "cube: each TP rank owns a whole cube (inter-cube AllReduce)."), ) with pl_col2: cp_placement = st.radio( "CP on", options=["pe", "cube"], index=1, key="cp_placement", horizontal=True, help=("pe: CP ring runs among PEs of one cube (intra-cube). " "cube: each CP rank owns cubes (inter-cube ring)."), ) st.markdown("**Sharding modes**") # FFN scope labels show the effective divisor (dynamic per CP/TP/DP). _ffn_labels = { "TP": f"TP only (div={max(1,tp)})", "TP+CP": f"TP*CP (div={max(1,tp*cp)})", "TP+CP+DP": f"TP*CP*DP (div={max(1,tp*cp*dp)})", } _ffn_choice = st.radio( "FFN shard scope", options=list(_ffn_labels.values()), index=1, key="ffn_scope_label", horizontal=True, help=("Divisor = number of ranks the FFN weights are split across. " "Larger divisor = less memory/PE, more AllReduce cost."), ) # Map label -> raw scope key. ffn_shard_scope = {v: k for k, v in _ffn_labels.items()}[_ffn_choice] sip_topology = st.radio( "SIP interconnect", options=["ring", "mesh2d", "torus2d"], index=0, key="sip_topo", horizontal=True, help=("ring: 1D chain + wrap. mesh2d: 2D grid, no wrap. " "torus2d: 2D grid + wrap."), ) cp_ring_variant = st.radio( "CP ring: what rotates", options=["kv", "qoml"], index=0, key="cp_ring_variant", horizontal=True, help=("kv: K,V shards rotate each hop (Q,O,m,l stay local). " "Bytes/hop scales with S_local*H_kv - good for prefill.\n" "qoml: Q + running (O,m,l) rotate (K,V stay local). " "Bytes/hop scales with T_q*H_q - much cheaper for decode."), ) kv_shard_mode = "split" if tp > model.h_kv: _rep_factor = max(1, tp // model.h_kv) kv_shard_mode = st.radio( f"KV mode (TP={tp} > H_kv={model.h_kv})", options=["split", "replicate"], index=0, key="kv_mode", horizontal=True, help=(f"split: fractional heads (KV/PE shrinks by {tp/max(1,model.h_kv):.1f}x, " f"adds Score AllReduce across {_rep_factor} ranks). " f"replicate: whole heads per PE, duplicated {_rep_factor}x, " f"no Score AllReduce."), ) with st.sidebar.expander("Hardware", expanded=False): hw_tab_pe, hw_tab_net = st.tabs(["Per-PE", "Interconnect"]) with hw_tab_pe: hp1, hp2 = st.columns(2) with hp1: pe_hbm_gb = st.selectbox( "PE HBM (GB)", [3.0, 6.0, 12.0, 24.0, 48.0, 96.0], index=1, key="pe_hbm", ) bw_hbm = st.selectbox( "HBM BW GB/s", [128.0, 256.0, 512.0, 1024.0, 2048.0], index=1, key="bw_hbm", ) with hp2: peak_tflops = st.selectbox( "TFLOPs/PE", [2.0, 4.0, 8.0, 16.0, 32.0, 64.0], index=2, key="tflops", ) compute_util = st.selectbox( "Compute util", [0.3, 0.5, 0.7, 0.8, 0.9, 1.0], index=3, key="util", ) with hw_tab_net: n1, n2 = st.columns(2) with n1: bw_intra = st.selectbox( "intra-cube GB/s", [128.0, 256.0, 512.0, 1024.0, 2048.0, 4096.0], index=2, key="bw_intra", ) bw_inter = st.selectbox( "inter-cube GB/s", [32.0, 64.0, 128.0, 256.0, 512.0, 900.0], index=2, key="bw_inter", ) bw_intersip = st.selectbox( "inter-SIP GB/s", [12.5, 25.0, 50.0, 100.0, 200.0, 400.0], index=2, key="bw_intersip", ) with n2: alpha_intra = st.selectbox( "a_intra ns", [5.0, 10.0, 20.0, 50.0, 100.0], index=2, key="a_intra", ) alpha_inter = st.selectbox( "a_inter ns", [50.0, 100.0, 200.0, 500.0, 1000.0], index=1, key="a_inter", ) alpha_intersip = st.selectbox( "a_intersip ns", [500.0, 1000.0, 2000.0, 5000.0, 10000.0], index=1, key="a_intersip", ) machine = MachineParams( pe_hbm_gb=pe_hbm_gb, peak_tflops_f16=peak_tflops, bw_hbm_gbs=bw_hbm, bw_intra_gbs=bw_intra, bw_inter_gbs=bw_inter, bw_intersip_gbs=bw_intersip, alpha_intra_ns=alpha_intra, alpha_inter_ns=alpha_inter, alpha_intersip_ns=alpha_intersip, compute_util=compute_util, ) topo = TopologyConfig(cp=cp, tp=tp, pp=pp, dp=dp, ep=ep, b=b_batch, s_kv=s_kv, mode=mode, kv_shard_mode=kv_shard_mode, ffn_shard_scope=ffn_shard_scope, sip_topology=sip_topology, tp_placement=tp_placement, cp_placement=cp_placement, cp_ring_variant=cp_ring_variant) cfg = FullConfig(model=model, topo=topo, machine=machine) # ── Main pane ───────────────────────────────────────────────────── st.title(f"SIP Analytical Model — {model.name}") st.caption( f"Family: **{preset.family or '-'}** | Attn: **{preset.attn_type}** | " f"H_q={model.h_q}, H_kv={model.h_kv}, hidden={model.hidden}, " f"ffn={model.ffn_dim}, layers={model.layers}" ) if preset.note: st.info(f"Note: {preset.note}") # Recompute auto-suggest with the *current* machine (in case user changed HBM etc.) suggestion = auto_suggest(model, machine, s_kv, mode, b=b_batch) # Compact top summary: auto-suggest + current in one row of tight bullets. _sug_status = "OK" if suggestion.fits else "NO FIT" _sug_line = ( f"**Auto-suggest:** CP={suggestion.cp} - TP={suggestion.tp} - " f"PP={suggestion.pp} - PEs={suggestion.pes_used} - SIPs={suggestion.sips_used} " f"({_sug_status}, slack {suggestion.slack_gb:.2f} GB / " f"{machine.pe_hbm_gb:.1f} GB budget)" ) _cur_line = ( f"**Current:** CP={cp}@{cp_placement} - TP={tp}@{tp_placement} - " f"PP={pp} - DP={dp} - EP={ep} - PEs={topo.total_pes} - " f"Cubes={topo.cubes_used} ({topo.pes_per_cube_used}/cube live) - " f"SIPs={topo.sips_used} - FFN={cfg.topo.ffn_shard_scope} - " f"SIPnet={cfg.topo.sip_topology}" ) sum_l, sum_r = st.columns([1, 1]) with sum_l: st.markdown(_sug_line) with sum_r: st.markdown(_cur_line) # Consolidated warnings (only if any apply) _warnings = [] if not topo.placement_valid: _spill_cubes = (topo.intra_cube_dims + topo.pes_per_cube_hw - 1) // topo.pes_per_cube_hw _warnings.append( f"Intra-cube demand = {topo.intra_cube_dims} PEs > " f"{topo.pes_per_cube_hw} PEs/cube -> spills to {_spill_cubes} cubes " f"per group (AllReduce/ring drops from intra-cube " f"{machine.bw_intra_gbs:.0f} to inter-cube " f"{machine.bw_inter_gbs:.0f} GB/s)." ) if tp > model.h_kv: # Compute per-PE KV bytes under each mode so the difference is visible. from tests.analytical_visualization.memory_layout import per_pe_kv_cache_bytes import copy as _copy _cfg_split = FullConfig(model=model, topo=TopologyConfig( **{**topo.__dict__, "kv_shard_mode": "split"}), machine=machine) _cfg_rep = FullConfig(model=model, topo=TopologyConfig( **{**topo.__dict__, "kv_shard_mode": "replicate"}), machine=machine) _kv_split_gb = per_pe_kv_cache_bytes(_cfg_split) / 1e9 _kv_rep_gb = per_pe_kv_cache_bytes(_cfg_rep) / 1e9 _kv_now = _kv_split_gb if kv_shard_mode == "split" else _kv_rep_gb _warnings.append( f"KV mode: **{kv_shard_mode}** (TP={tp} > H_kv={model.h_kv}) - " f"per-PE KV: split={_kv_split_gb:.2f} GB vs " f"replicate={_kv_rep_gb:.2f} GB (currently {_kv_now:.2f} GB). " f"Split adds one Score AllReduce over " f"{max(1, tp // model.h_kv)} ranks per hop." ) if cfg.kv_replication_needed: _warnings.append(f"Head-dim split: {cfg.head_dim_split_factor} ranks share one KV head") if topo.tp_spans_cubes > 1: _warnings.append(f"TP spans {topo.tp_spans_cubes} cubes " f"(AllReduce goes cross-cube)") if topo.cp_inter_sip_hops > 0: _warnings.append(f"CP ring crosses {topo.cp_inter_sip_hops} SIP boundary(ies) " f"at {machine.bw_intersip_gbs:.0f} GB/s") if _warnings: st.warning(" - ".join(_warnings)) # ── Tabs ───────────────────────────────────────────────────────── # One tab per auto feature; each has TWO sweep buttons (Attention only / # Attn + FFN/MoE) so the user can toggle scope without switching tabs. # Auto Suggest is renamed "Parallelism" since it only varies parallelism # knobs (hardware is held fixed at the sidebar values). (tab_layout, tab_auto, tab_memory, tab_stages, tab_compare, tab_hw, tab_roofline) = st.tabs([ "Physical layout", "Auto Suggest Parallelism", "Memory breakdown", "Per-stage latency", "Save & compare", "Auto Hardware", "Chip roofline & B*", ]) # ── TAB 1: Physical layout ────────────────────────────────────── with tab_layout: # Quick shortcut: three buttons that pick the memory-optimal # (smallest-fit) Pareto config for a chosen scope and load it into # the sidebar. Layout re-renders immediately on the next Streamlit # run with the new config. from tests.analytical_visualization.auto_explore import ( run_auto_explore as _run_auto_explore_layout, ) st.markdown( "**Quick auto-suggest layout** — pick a scope; the memory-optimal " "(smallest-fit) Pareto config for that scope is loaded into the " "sidebar and this layout redraws around it." ) _pl1, _pl2, _pl3, _pl4 = st.columns([1, 1, 1, 1]) with _pl1: _pl_run_attn = st.button("Run sweep — Attention", type="primary", width='stretch', key="_pl_run_attn") with _pl2: _pl_run_ffn = st.button("Run sweep — FFN/MoE", type="primary", width='stretch', key="_pl_run_ffn") with _pl3: _pl_run_full = st.button("Run sweep — Attn + FFN/MoE", type="primary", width='stretch', key="_pl_run_full") _pl_scope_meta = { "attn": (True, False, "Attention only"), "ffn": (False, True, "FFN / MoE only"), "full": (True, True, "Attn + FFN/MoE"), } _pl_button_click = { "attn": _pl_run_attn, "ffn": _pl_run_ffn, "full": _pl_run_full, } for _pl_scope, _pl_clicked in _pl_button_click.items(): if not _pl_clicked: continue _pl_ia, _pl_ff, _pl_lbl = _pl_scope_meta[_pl_scope] with st.spinner(f"Finding memory-optimal parallelism ({_pl_lbl})..."): _pl_res = _run_auto_explore_layout( model, machine, s_kv=s_kv, mode=mode, include_attention=_pl_ia, include_ffn=_pl_ff, b=b_batch, ) if not _pl_res.pareto_scores: st.error( f"No feasible parallelism for {_pl_lbl} — every config " "exceeds per-PE HBM. Try raising `pe_hbm_gb` or reducing " "`s_kv`." ) else: # Smallest-fit Pareto point (see sidebar apply comment). _pl_best = min(_pl_res.pareto_scores, key=lambda s: (s.pes_used, s.hbm_utilization, s.total_latency_ns)) # Load the winning config into the sidebar's session state. st.session_state["cp"] = _pl_best.cp st.session_state["tp"] = _pl_best.tp st.session_state["pp"] = _pl_best.pp st.session_state["dp"] = _pl_best.dp st.session_state["tp_placement"] = _pl_best.tp_placement st.session_state["cp_placement"] = _pl_best.cp_placement st.session_state["cp_ring_variant"] = _pl_best.cp_ring_variant st.session_state["kv_mode"] = _pl_best.kv_shard_mode _pl_tp, _pl_cp, _pl_dp = _pl_best.tp, _pl_best.cp, _pl_best.dp _pl_ffn_map = { "TP": f"TP only (div={max(1, _pl_tp)})", "TP+CP": f"TP*CP (div={max(1, _pl_tp * _pl_cp)})", "TP+CP+DP": f"TP*CP*DP (div={max(1, _pl_tp * _pl_cp * _pl_dp)})", } st.session_state["ffn_scope_label"] = \ _pl_ffn_map[_pl_best.ffn_shard_scope] # Persist the chosen scope so subsequent layout renders filter # stage tables (attn/ffn/full). st.session_state["_pl_active_scope"] = _pl_scope st.success( f"Loaded memory-optimal {_pl_lbl} config: CP={_pl_best.cp} " f"TP={_pl_best.tp} PP={_pl_best.pp} DP={_pl_best.dp} → " f"{_pl_best.latency_ms:.2f} ms, {_pl_best.pes_used} PEs. " "Redrawing layout..." ) st.rerun() # Determine the effective scope for filtering the tables below. Default # to "full" so a fresh page load / sidebar-driven config shows everything. _pl_active_scope = st.session_state.get("_pl_active_scope", "full") _pl_show_attn = _pl_active_scope in ("attn", "full") _pl_show_ffn = _pl_active_scope in ("ffn", "full") _pl_active_label = _pl_scope_meta[_pl_active_scope][2] st.markdown(f"### Layout scope: **{_pl_active_label}**") st.divider() # Row A: pipeline diagram (color-coded) + per-stage table (formulas). st.markdown("**Per-layer pipeline (color = dominant bound)**") fig_pipe = draw_pipeline(cfg) st.pyplot(fig_pipe, width='stretch') plt.close(fig_pipe) with st.expander("Per-stage latency (attention + FFN) with formulas", expanded=True): import html as _html _stages_attn = all_stages(cfg) _stages_ffn = all_ffn_stages(cfg) _bound_color = { "compute": "#d6eaf8", "memory": "#fdebd0", "comm": "#f5b7b1", "trivial": "#eaeded", } def _vsplit(formula: str) -> str: """Break 'X = Y = Z' into vertical `= X` / `= Y` / `= Z` lines. If the string contains a 'PURPOSE:' preamble ending with a line of '---', keep that preamble verbatim and only vsplit the tail. """ if not formula or formula in ("-", "0"): return formula _preamble = "" _body = formula if "PURPOSE:" in formula and "\n---\n" in formula: _preamble, _body = formula.split("\n---\n", 1) _preamble = _preamble.rstrip() + "\n---" pieces = [p.strip() for p in _body.split(" = ")] _vsplit_body = "\n".join(f"= {p}" for p in pieces) return f"{_preamble}\n{_vsplit_body}" if _preamble else _vsplit_body # Prefill: per-hop ring is concurrent with S5/S6/S7 compute - # annotate them. In decode the O/m/l all-reduce is already # folded into the S8 row, so no separate annotation needed. _per_hop_ring_us = 0.0 _ring_variant_desc = "" if topo.mode == "prefill" and topo.cp > 1: _c1 = next((_x for _x in _stages_attn if _x.name.startswith("C1 ")), None) if _c1 is not None and _c1.comm_s > 0: _hops = max(1, topo.cp - 1) _per_hop_ring_us = (_c1.comm_s * 1e6) / _hops _ring_variant_desc = ("K/V" if topo.cp_ring_variant == "kv" else "Q+O/m/l") def _fmt_time(sec: float) -> str: """Auto-scale time: ns for < 1 us, us for < 10 ms, else ms.""" if sec <= 0: return "0" ns = sec * 1e9 if ns < 1000: return f"{ns:.1f} ns" us = sec * 1e6 if us < 1000: return f"{us:.2f} us" ms = sec * 1e3 return f"{ms:.2f} ms" def _combined_formula(s): parts = [] if s.compute_s > 0 or (s.flops and s.flops > 0): parts.append( f"Compute:\n{_vsplit(s.flops_formula)}\n" f"-> {_fmt_time(s.compute_s)}" ) if s.memory_s > 0 or (s.mem_bytes and s.mem_bytes > 0): parts.append( f"Memory:\n{_vsplit(s.mem_formula)}\n" f"-> {_fmt_time(s.memory_s)}" ) if s.comm_s > 0 or (s.comm_bytes and s.comm_bytes > 0): # If this is a concurrent-comm stage (e.g. C1 CP ring in # prefill) that gets overlapped with compute, visible < raw. # Show both so the '0' in the Time column makes sense. _tail = f"-> raw comm: {_fmt_time(s.comm_s)}" if s.visible_s < s.comm_s * 0.999: _tail += ( f"\n-> visible: {_fmt_time(s.visible_s)} " f"(concurrent S5-S7 compute hides " f"{_fmt_time(s.comm_s - s.visible_s)} of it)" ) parts.append( f"Comm:\n{_vsplit(s.comm_formula)}\n{_tail}" ) # Prefill: S5/S6/S7 run concurrently with the CP ring. if (_per_hop_ring_us > 0 and s.name.startswith(("S5 ", "S6 ", "S7 "))): # convert us to seconds for _fmt_time _per_hop_s = _per_hop_ring_us / 1e6 parts.append( f"Concurrent CP ring ({_ring_variant_desc}, per hop):\n" f"~{_fmt_time(_per_hop_s)} in flight while this compute runs\n" f"(aggregated in C1 row; overlap kept only excess vs compute)" ) return "\n\n".join(parts) if parts else "-" # Compact CSS: rows auto-size to text; tight padding; columns fit content. _stage_table_css = """ """ def _render_table(stages, title): rows_html = [] for s in stages: bg = _bound_color.get(s.bound, "#eaeded") formula = _html.escape(_combined_formula(s)) rows_html.append( f'' f'{_html.escape(s.name)}' f'
{formula}
' f'{s.bound}' f'{_fmt_time(s.visible_s)}' f'' ) html = ( _stage_table_css + f'{_html.escape(title)}' + '' + '' '' + '' + ''.join(rows_html) + '
StageFormulaBoundTime
' ) # Prefer st.html (reliable) with a markdown fallback. if hasattr(st, "html"): st.html(html) else: st.markdown(html, unsafe_allow_html=True) _t = sum(s.visible_s for s in stages) st.caption( f"{title} total: **{_fmt_time(_t)}**. " f"Compute time = FLOPs / ({cfg.machine.peak_tflops_f16:.1f} TFLOPs " f"x {cfg.machine.compute_util:.0%} util). " f"Memory time = bytes / {cfg.machine.bw_hbm_gbs:.0f} GB/s HBM. " f"Bound = max(compute, memory, comm) - row color: " f"blue=compute, orange=memory, red=comm, grey=trivial." ) if _pl_show_attn: _render_table(_stages_attn, "Attention") if _pl_show_ffn: _render_table(_stages_ffn, "FFN") with st.expander("Symbol glossary (what does T_q, d_h, div... mean?)", expanded=False): _glossary = [ ("Model dims", [ ("d", f"hidden dim (current: {model.hidden})"), ("d_h", f"per-head dim (current: {model.d_head})"), ("H_q", f"total query heads (current: {model.h_q})"), ("H_kv", f"total KV heads (current: {model.h_kv})"), ("ffn", f"FFN intermediate dim (current: {model.ffn_dim})"), ("L", f"transformer layers (current: {model.layers})"), ("b", f"bytes per element (dtype size) " f"(current: {model.bytes_per_elem} -> " f"{'fp8' if model.bytes_per_elem == 1 else 'bf16/fp16' if model.bytes_per_elem == 2 else 'fp32'})"), ]), ("Workload", [ ("S_kv", f"global KV cache length / context (current: {topo.s_kv:,})"), ("S_local", f"per-CP-rank KV length = S_kv / CP " f"(current: {topo.s_local:,})"), ("T_q", f"query tokens processed this step " f"(decode=1, prefill=S_local; current: {topo.T_q})"), ("mode", f"decode | prefill (current: {topo.mode})"), ]), ("Parallelism (degrees)", [ ("TP", f"tensor parallelism (attn heads / FFN cols) " f"(current: {topo.tp}, on {topo.tp_placement})"), ("CP", f"context parallelism (sequence axis) " f"(current: {topo.cp}, on {topo.cp_placement})"), ("PP", f"pipeline parallelism (layer stages) (current: {topo.pp})"), ("DP", f"data parallelism (model replicas) (current: {topo.dp})"), ("EP", f"expert parallelism (MoE) (current: {topo.ep})"), ("div", f"FFN shard divisor = product of dims in " f"'FFN shard scope' (current: {cfg.ffn_shard_divisor})"), ("split", f"# ranks sharing one KV head via head-dim split " f"(TP/H_kv when TP > H_kv; current: " f"{cfg.head_dim_split_factor if cfg.kv_replication_needed else 1})"), ]), ("Hardware / machine", [ ("BW_HBM", f"per-PE HBM bandwidth " f"(current: {machine.bw_hbm_gbs:.0f} GB/s)"), ("BW_intra", f"intra-cube PE<->PE bandwidth " f"(current: {machine.bw_intra_gbs:.0f} GB/s)"), ("BW_inter", f"inter-cube (D2D) bandwidth " f"(current: {machine.bw_inter_gbs:.0f} GB/s)"), ("BW_intersip", f"inter-SIP (C2C) bandwidth " f"(current: {machine.bw_intersip_gbs:.0f} GB/s)"), ("alpha_intra", f"per-hop latency intra-cube " f"(current: {machine.alpha_intra_ns:.0f} ns)"), ("alpha_inter", f"per-hop latency inter-cube " f"(current: {machine.alpha_inter_ns:.0f} ns)"), ("alpha_intersip", f"per-hop latency inter-SIP " f"(current: {machine.alpha_intersip_ns:.0f} ns)"), ("peak TFLOPs", f"per-PE peak compute " f"(current: {machine.peak_tflops_f16:.1f} TFLOPs f16)"), ("util", f"achievable compute utilization " f"(current: {machine.compute_util:.0%})"), ]), ("Stage prefixes", [ ("S1 .. S10", "attention forward stages (RMSNorm, W_Q, W_K+W_V, " "KV append, Q.K^T, softmax, P.V, merge, " "normalize, W_O)"), ("F1 .. F5", "FFN forward stages (RMSNorm, W_gate, W_up, " "SwiGLU, W_down)"), ("C1", "CP K/V ring (K,V passed hop-by-hop)"), ("C2", "TP AllReduce on W_O output"), ("C3", "Score AllReduce when TP > H_kv with head-dim split"), ("CF1", "FFN AllReduce over the FFN shard scope"), ]), ("Notation in formulas", [ ("H_q / TP", "Q heads per PE (or per TP rank)"), ("H_kv / TP", "KV heads per PE (fractional allowed in split mode)"), ("ffn / div", "FFN cols/rows per PE after sharding"), ("2 * A * B * C", "GEMM FLOPs = 2 * M * K * N (multiply + add per fma)"), ("bytes * b", "byte count (b = bytes/element)"), ("-> X us", "resulting latency after dividing by BW or peak"), ]), ] for section, items in _glossary: st.markdown(f"**{section}**") _rows = "".join( f'{sym}' f'{desc}' for sym, desc in items ) st.html( f'{_rows}
' ) st.divider() # Row B: topology diagram (full width - it can be tall for multi-SIP) with st.expander( f"Physical layout - {topo.total_pes} PEs, {topo.cubes_used} cubes, " f"{topo.sips_used} SIP(s), inter-SIP: {topo.sip_topology}", expanded=True, ): fig_topo = draw_topology(cfg) st.pyplot(fig_topo, width='stretch') plt.close(fig_topo) l1, l2, l3, l4 = st.columns(4) l1.metric("Cubes", topo.cubes_used) l2.metric("SIPs", topo.sips_used) l3.metric("CP intra-SIP hops", topo.cp_intra_sip_hops) l4.metric("CP inter-SIP hops", topo.cp_inter_sip_hops) st.divider() # Row C: two-column - LEFT per-PE memory (pie + summary), RIGHT tensor sharding mem_layout = compute_memory(cfg) col_mem, col_shard = st.columns([1, 1], gap="medium") with col_mem: st.markdown("**Per-PE memory footprint**") labels = ["Weights", "KV cache", "Transient", "Slack"] vals = [mem_layout.weights_bytes, mem_layout.kv_cache_bytes, mem_layout.transient_bytes, mem_layout.slack_bytes] colors = ["#3a86ff", "#8338ec", "#ffbe0b", "#adb5bd"] fig_mem_l, ax_mem_l = plt.subplots(figsize=(4.2, 4.2)) ax_mem_l.pie(vals, labels=[f"{l}\n{v/1e9:.2f} GB" for l, v in zip(labels, vals)], colors=colors, autopct="%1.1f%%", startangle=90, textprops={"fontsize": 8}) ax_mem_l.set_title(f"Per-PE (budget: " f"{mem_layout.budget_bytes/1e9:.1f} GB)", fontsize=10) st.pyplot(fig_mem_l, width='stretch') plt.close(fig_mem_l) if mem_layout.over_budget: st.error(f"OVER BUDGET by " f"{(mem_layout.used_bytes - mem_layout.budget_bytes)/1e9:.2f} GB") else: st.caption( f"Weights {mem_layout.weights_bytes/1e9:.2f} GB - " f"KV {mem_layout.kv_cache_bytes/1e9:.2f} GB " f"(S_local={topo.s_local:,}) - " f"Transient {mem_layout.transient_bytes/1e9:.2f} GB - " f"Slack {mem_layout.slack_bytes/1e9:.2f} GB" ) with col_shard: st.markdown("**Tensor sharding (which dim is split)**") st.caption( "W_Q/W_K/W_V/W_gate/W_up: **output cols**. " "W_O/W_down: **input rows**. " "KV cache: **rows by CP** x **cols by TP**." ) s_c1, s_c2 = st.columns([1, 2]) with s_c1: my_pe_view = st.number_input("PE index", min_value=0, max_value=max(0, topo.tp - 1), value=0, key="my_pe_view") with s_c2: show_physical = st.checkbox( "Enlarge + show physical PE/cube mapping", value=False, key="tsv_physical", help=("Larger figure with each shard cell labelled by owning " "cube/PE. Especially useful for KV cache when CP is " "split across cubes and PEs."), ) fig_shard = draw_tensor_sharding(cfg, my_pe=my_pe_view, show_physical=show_physical) st.pyplot(fig_shard, width='stretch') plt.close(fig_shard) st.divider() # Row D: PE-level view (wide, full-width) inside an expander with st.expander( f"PE-level view of one CP group (TP={topo.tp}, " f"H_q={model.h_q}, H_kv={model.h_kv})", expanded=False, ): st.caption( f"One CP rank's {topo.tp} PE(s). Attention weights replicated " f"across CP groups; KV sharded by CP (S/{topo.cp} tokens) x TP heads; " f"FFN by TP x EP." ) fig_pe = draw_pe_layout(cfg) st.pyplot(fig_pe, width='stretch') plt.close(fig_pe) # Row E: Replication + Optimization panel (side-by-side) st.divider() col_rep, col_opt = st.columns([1, 1], gap="medium") with col_rep: st.markdown("**Replicated / duplicated storage**") rep_entries = replication_report(cfg) if not rep_entries: st.caption("No duplicated storage detected - every tensor is uniquely sharded.") else: rep_rows = [] total_waste = 0 for e in rep_entries: rep_rows.append({ "Tensor": e.tensor, "Per-PE (GB)": round(e.per_pe_bytes / 1e9, 3), "Replicated": e.replicated_across, "Copies": e.copies, "Waste/PE (GB)": round(e.wasted_bytes / 1e9, 3), }) total_waste += e.wasted_bytes st.dataframe(pd.DataFrame(rep_rows), width='stretch', hide_index=True) st.caption( f"Total per-PE duplicated bytes: " f"**{total_waste/1e9:.2f} GB** " f"(vs {mem_layout.budget_bytes/1e9:.1f} GB budget). " f"Each 'copy' represents one identical block stored on " f"another rank." ) with col_opt: st.markdown("**Optimization opportunities**") hints = optimization_hints(cfg) if not hints: st.caption("No obvious improvements at this configuration.") else: _icons = {"warn": ":warning:", "info": ":bulb:", "good": ":white_check_mark:"} _cats = {"space": "SPACE", "comm": "COMM", "compute": "COMPUTE", "layout": "LAYOUT"} for h in hints: icon = _icons.get(h.severity, ":bulb:") cat = _cats.get(h.category, h.category.upper()) st.markdown(f"{icon} **[{cat}]** {h.message}") st.divider() # Row F: Per-PE breakdown table (collapsible) with st.expander("Per-PE breakdown table", expanded=False): rows = [] for pe_id in range(topo.tp): q_heads = _q_heads_for_pe(pe_id, topo.tp, model.h_q) kv_heads, kv_note = _kv_heads_for_pe( pe_id, topo.tp, model.h_kv, topo.kv_shard_mode, ) bytes_ = _per_pe_bytes(cfg, pe_id) weights_bytes = sum(v for k, v in bytes_.items() if k not in ("KV cache", "Transient")) rows.append({ "PE": pe_id, "Q heads": (f"{q_heads[0]}-{q_heads[-1]}" if len(q_heads) > 1 else str(q_heads[0]) if q_heads else "-"), "KV heads": (str(kv_heads[0]) + (f" ({kv_note})" if kv_note else "") if kv_heads else "-"), "W_Q (MB)": round(bytes_["W_Q"] / 1e6, 2), "W_K (MB)": round(bytes_["W_K"] / 1e6, 2), "W_V (MB)": round(bytes_["W_V"] / 1e6, 2), "W_O (MB)": round(bytes_["W_O"] / 1e6, 2), "FFN (MB)": round((bytes_["W_gate"] + bytes_["W_up"] + bytes_["W_down"]) / 1e6, 2), "Weights total (GB)": round(weights_bytes / 1e9, 3), "KV cache (GB)": round(bytes_["KV cache"] / 1e9, 3), "Transient (MB)": round(bytes_["Transient"] / 1e6, 2), }) st.dataframe(pd.DataFrame(rows), width='stretch', hide_index=True) # Row G: All tensor shapes (per PE) — one collapsed expander with # weight, KV, and per-stage shapes for anyone who wants them. with st.expander("All tensor shapes (per PE)", expanded=False): st.caption( "Every tensor this deployment holds, per PE. Weights + KV " "show global vs per-PE shape (one row per tensor, per layer). " "Per-stage shapes show input / weight / output tensors each " "stage of the forward pass operates on." ) _lp = (model.layers + topo.pp - 1) // topo.pp st.markdown(f"**Attention weights** (per layer, layers/stage = {_lp})") _attn_rows_layout = attention_weight_rows(cfg) _display_attn_layout = [ {k: v for k, v in r.items() if not k.startswith("_")} for r in _attn_rows_layout ] st.dataframe(pd.DataFrame(_display_attn_layout), width='stretch', hide_index=True) st.markdown(f"**FFN weights** (per layer, layers/stage = {_lp})") _ffn_rows_layout = ffn_weight_rows(cfg) _display_ffn_layout = [ {k: v for k, v in r.items() if not k.startswith("_")} for r in _ffn_rows_layout ] st.dataframe(pd.DataFrame(_display_ffn_layout), width='stretch', hide_index=True) st.markdown(f"**KV cache** (per layer, S_kv = {s_kv:,})") _kv_rows_layout = kv_cache_rows(cfg) _display_kv_layout = [ {k: v for k, v in r.items() if not k.startswith("_")} for r in _kv_rows_layout ] st.dataframe(pd.DataFrame(_display_kv_layout), width='stretch', hide_index=True) st.markdown("**Per-stage attention shapes**") st.dataframe(pd.DataFrame(attn_stage_shape_rows(cfg)), width='stretch', hide_index=True) st.markdown("**Per-stage FFN shapes**") st.dataframe(pd.DataFrame(ffn_stage_shape_rows(cfg)), width='stretch', hide_index=True) # ── TAB 2: Memory breakdown ───────────────────────────────────── with tab_memory: mem = compute_memory(cfg) layers_per_stage = (model.layers + pp - 1) // pp parallelism_str = ( f"CP={cp}, TP={tp}, PP={pp}, DP={dp}, EP={ep}" f" | layers={model.layers}, layers/stage={layers_per_stage}" f" | KV mode: {kv_shard_mode}" ) st.caption(f"**Parallelism:** {parallelism_str}") # ─── ATTENTION (per-layer) ───────────────────────────────── st.subheader(f"Attention weights (per layer) — {parallelism_str}") attn_rows = attention_weight_rows(cfg) display_attn = [{k: v for k, v in r.items() if not k.startswith("_")} for r in attn_rows] st.dataframe(pd.DataFrame(display_attn), width='stretch', hide_index=True) attn_bytes_1L_global = sum(r["_p_global"] for r in attn_rows) * model.bytes_per_elem attn_bytes_1L_pe = sum(r["_p_per_pe"] for r in attn_rows) * model.bytes_per_elem attn_bytes_all_global = attn_bytes_1L_global * model.layers attn_bytes_all_pe = attn_bytes_1L_pe * layers_per_stage ac1, ac2, ac3, ac4 = st.columns(4) ac1.metric("Attn / layer (global)", f"{attn_bytes_1L_global/1e6:.2f} MB") ac2.metric(f"Attn all {model.layers} layers", f"{attn_bytes_all_global/1e9:.3f} GB") ac3.metric("Attn / layer (per PE)", f"{attn_bytes_1L_pe/1e6:.2f} MB") ac4.metric(f"Attn per PE ({layers_per_stage} layers/stage)", f"{attn_bytes_all_pe/1e9:.3f} GB") # ─── FFN / MoE (per-layer) ───────────────────────────────── is_moe = "MoE" in (preset.family + preset.note) ffn_label = "FFN (activated experts approx)" if is_moe else "FFN" st.subheader(f"{ffn_label} weights (per layer) — {parallelism_str}") ffn_rows = ffn_weight_rows(cfg) display_ffn = [{k: v for k, v in r.items() if not k.startswith("_")} for r in ffn_rows] st.dataframe(pd.DataFrame(display_ffn), width='stretch', hide_index=True) ffn_bytes_1L_global = sum(r["_p_global"] for r in ffn_rows) * model.bytes_per_elem ffn_bytes_1L_pe = sum(r["_p_per_pe"] for r in ffn_rows) * model.bytes_per_elem ffn_bytes_all_global = ffn_bytes_1L_global * model.layers ffn_bytes_all_pe = ffn_bytes_1L_pe * layers_per_stage fc1, fc2, fc3, fc4 = st.columns(4) fc1.metric("FFN / layer (global)", f"{ffn_bytes_1L_global/1e6:.2f} MB") fc2.metric(f"FFN all {model.layers} layers", f"{ffn_bytes_all_global/1e9:.3f} GB") fc3.metric("FFN / layer (per PE)", f"{ffn_bytes_1L_pe/1e6:.2f} MB") fc4.metric(f"FFN per PE ({layers_per_stage} layers/stage)", f"{ffn_bytes_all_pe/1e9:.3f} GB") # ─── KV cache (per-layer) ───────────────────────────────── st.subheader(f"KV cache (per layer, S_kv = {s_kv:,}) — {parallelism_str}") kv_rows = kv_cache_rows(cfg) display_kv = [{k: v for k, v in r.items() if not k.startswith("_")} for r in kv_rows] st.dataframe(pd.DataFrame(display_kv), width='stretch', hide_index=True) kv_bytes_1L_global = sum(r["_p_global"] for r in kv_rows) * model.bytes_per_elem kv_bytes_1L_pe = sum(r["_p_per_pe"] for r in kv_rows) * model.bytes_per_elem kv_bytes_all_global = kv_bytes_1L_global * model.layers kv_bytes_all_pe = kv_bytes_1L_pe * layers_per_stage kc1, kc2, kc3, kc4 = st.columns(4) kc1.metric("KV / layer (global)", f"{kv_bytes_1L_global/1e6:.2f} MB") kc2.metric(f"KV all {model.layers} layers", f"{kv_bytes_all_global/1e9:.3f} GB") kc3.metric("KV / layer (per PE)", f"{kv_bytes_1L_pe/1e6:.2f} MB") kc4.metric(f"KV per PE ({layers_per_stage} layers/stage)", f"{kv_bytes_all_pe/1e9:.3f} GB") st.divider() # ─── FULL-MODEL TOTALS ─────────────────────────────────── st.subheader("Full-model totals (all layers)") total_w_global = attn_bytes_all_global + ffn_bytes_all_global total_all_global = total_w_global + kv_bytes_all_global total_per_pe = attn_bytes_all_pe + ffn_bytes_all_pe + kv_bytes_all_pe tc1, tc2, tc3, tc4 = st.columns(4) tc1.metric("Weights (all layers, global)", f"{total_w_global/1e9:.2f} GB") tc2.metric("KV (all layers, global)", f"{kv_bytes_all_global/1e9:.2f} GB") tc3.metric("Weights + KV (global)", f"{total_all_global/1e9:.2f} GB") tc4.metric("Per-PE total (W+KV, layers/stage)", f"{total_per_pe/1e9:.2f} GB") # ─── Per-PE memory pie ─────────────────────────────────── st.subheader("Per-PE memory footprint (with transient + slack)") pie_col, info_col = st.columns([1, 1]) with pie_col: labels = ["Weights", "KV cache", "Transient", "Slack"] vals = [mem.weights_bytes, mem.kv_cache_bytes, mem.transient_bytes, mem.slack_bytes] colors = ["#3a86ff", "#8338ec", "#ffbe0b", "#adb5bd"] fig_mem, ax_mem = plt.subplots(figsize=(6, 6)) ax_mem.pie(vals, labels=[f"{l}\n{v/1e9:.2f} GB" for l, v in zip(labels, vals)], colors=colors, autopct="%1.1f%%", startangle=90) ax_mem.set_title(f"Per-PE (budget: {mem.budget_bytes/1e9:.1f} GB)") st.pyplot(fig_mem, width='stretch') plt.close(fig_mem) with info_col: if mem.over_budget: st.error(f"OVER BUDGET by " f"{(mem.used_bytes - mem.budget_bytes)/1e9:.2f} GB") else: st.success(f"{mem.slack_bytes/1e9:.2f} GB slack") st.markdown( f"- Weights: **{mem.weights_bytes/1e9:.2f} GB** " f"(layers/PP = {layers_per_stage})\n" f"- KV cache: **{mem.kv_cache_bytes/1e9:.2f} GB** " f"(S_local = {topo.s_local:,})\n" f"- Transient: {mem.transient_bytes/1e9:.2f} GB\n" f"- Slack: {mem.slack_bytes/1e9:.2f} GB" ) # ── TAB 3: Per-stage latency ──────────────────────────────────── with tab_stages: st.subheader("Per-stage attention latency") stages = all_stages(cfg) rows = [] for s in stages: rows.append({ "Stage": s.name, "Bound": s.bound, "Compute (us)": round(s.compute_s * 1e6, 3), "Memory (us)": round(s.memory_s * 1e6, 3), "Comm (us)": round(s.comm_s * 1e6, 3), "Visible (us)": round(s.visible_s * 1e6, 3), "Formula": s.formula, }) df = pd.DataFrame(rows) st.dataframe(df, width='stretch', hide_index=True) total_visible_s = sum(s.visible_s for s in stages) total_layers = total_visible_s * model.layers st.markdown( f"**Per-layer attention (analytical): {total_visible_s*1e6:.2f} us** \n" f"**Full-model attention ({model.layers} layers): " f"{total_layers*1e6:.2f} us = {total_layers*1e3:.2f} ms**" ) st.subheader("Stage cost breakdown") fig_bar, ax_bar = plt.subplots(figsize=(12, 5)) names = [s.name for s in stages] cmp_vals = [s.compute_s * 1e6 for s in stages] mem_vals = [s.memory_s * 1e6 for s in stages] comm_vals = [s.comm_s * 1e6 for s in stages] x = range(len(names)) ax_bar.bar(x, cmp_vals, label="Compute", color="#3a86ff") ax_bar.bar(x, mem_vals, bottom=cmp_vals, label="Memory", color="#ffbe0b") ax_bar.bar(x, comm_vals, bottom=[c + m for c, m in zip(cmp_vals, mem_vals)], label="Comm", color="#d90429") ax_bar.set_xticks(x) ax_bar.set_xticklabels(names, rotation=45, ha="right", fontsize=8) ax_bar.set_ylabel("Time (us)") ax_bar.set_title(f"Stage breakdown - mode={mode}, T_q={topo.T_q}") ax_bar.legend() ax_bar.grid(axis="y", alpha=0.3) plt.tight_layout() st.pyplot(fig_bar, width='stretch') plt.close(fig_bar) # ── TAB 4: Save & compare ─────────────────────────────────────── def _snapshot_current_config(): """Capture a compact snapshot of the current cfg + key metrics.""" _stages_attn = all_stages(cfg) _stages_ffn = all_ffn_stages(cfg) attn_us = sum(s.visible_s for s in _stages_attn) * 1e6 ffn_us = sum(s.visible_s for s in _stages_ffn) * 1e6 mem = compute_memory(cfg) # Per-stage latencies keyed by short prefix (S1, S2, ..., C1, F1, ..., CF1). # Value = seconds (raw), so display can format uniformly across snapshots. _by_prefix_attn = {s.name.split()[0]: s.visible_s for s in _stages_attn} _by_prefix_ffn = {s.name.split()[0]: s.visible_s for s in _stages_ffn} # Preserve the full descriptive name too so a hover/second column can # show what the abbreviation means for this particular snapshot. _fullname_attn = {s.name.split()[0]: s.name for s in _stages_attn} _fullname_ffn = {s.name.split()[0]: s.name for s in _stages_ffn} return { "model": model.name, "mode": topo.mode, "s_kv": topo.s_kv, "cp": topo.cp, "tp": topo.tp, "pp": topo.pp, "dp": topo.dp, "ep": topo.ep, "cp_placement": topo.cp_placement, "tp_placement": topo.tp_placement, "ffn_scope": topo.ffn_shard_scope, "kv_mode": topo.kv_shard_mode, "sip_topology": topo.sip_topology, "cp_ring_variant": topo.cp_ring_variant, "pes": topo.total_pes, "cubes": topo.cubes_used, "sips": topo.sips_used, "pe_hbm_gb": machine.pe_hbm_gb, "peak_tflops": machine.peak_tflops_f16, "bw_hbm_gbs": machine.bw_hbm_gbs, "bw_inter_gbs": machine.bw_inter_gbs, "bw_intersip_gbs": machine.bw_intersip_gbs, "weights_gb": round(mem.weights_bytes / 1e9, 3), "kv_gb": round(mem.kv_cache_bytes / 1e9, 3), "transient_gb": round(mem.transient_bytes / 1e9, 3), "used_gb": round(mem.used_bytes / 1e9, 3), "slack_gb": round(mem.slack_bytes / 1e9, 3), "over_budget": mem.over_budget, "attn_us": round(attn_us, 3), "ffn_us": round(ffn_us, 3), "layer_us": round(attn_us + ffn_us, 3), "stages_attn": _by_prefix_attn, "stages_ffn": _by_prefix_ffn, "stage_names_attn": _fullname_attn, "stage_names_ffn": _fullname_ffn, } with tab_compare: st.markdown( "Save the current configuration under a name, then load it back " "later or compare multiple saved configs side-by-side." ) if "saved_cfgs" not in st.session_state: st.session_state["saved_cfgs"] = {} # name -> snapshot dict # Default name: next free config1, config2, ... def _next_config_name(): i = 1 while f"config{i}" in st.session_state.get("saved_cfgs", {}): i += 1 return f"config{i}" sv_c1, sv_c2, sv_c3 = st.columns([3, 1, 1]) with sv_c1: _save_name = st.text_input( "Config name", value=_next_config_name(), key=f"cfg_save_name_{len(st.session_state.get('saved_cfgs', {}))}", ) with sv_c2: if st.button("Save current", width='stretch'): if _save_name.strip(): st.session_state["saved_cfgs"][_save_name.strip()] = \ _snapshot_current_config() st.success(f"Saved '{_save_name.strip()}'") st.rerun() else: st.warning("Give it a name first.") with sv_c3: if st.button("Clear all", width='stretch'): st.session_state["saved_cfgs"] = {} st.rerun() _saved_all = st.session_state.get("saved_cfgs", {}) if not _saved_all: st.info("No saved configurations yet.") else: st.markdown(f"**{len(_saved_all)} saved configuration(s)** - " f"pick which to compare below") # Pick which configs to include in the comparison (default all) _picked = st.multiselect( "Compare", options=list(_saved_all.keys()), default=list(_saved_all.keys()), key="compare_picked", ) _saved = {n: _saved_all[n] for n in _picked} # Delete individual with st.expander("Manage (delete individual)", expanded=False): for _n in list(_saved_all.keys()): d1, d2, d3 = st.columns([5, 1, 1]) d1.text(_n) if d2.button("Rename", key=f"rn_btn_{_n}"): st.session_state[f"rn_active_{_n}"] = True if d3.button("Delete", key=f"del_{_n}"): del st.session_state["saved_cfgs"][_n] st.rerun() if st.session_state.get(f"rn_active_{_n}"): new_name = st.text_input( f"New name for '{_n}'", value=_n, key=f"rn_txt_{_n}", ) if st.button("Confirm rename", key=f"rn_ok_{_n}"): if new_name and new_name != _n and new_name not in _saved_all: st.session_state["saved_cfgs"][new_name] = \ st.session_state["saved_cfgs"].pop(_n) st.session_state[f"rn_active_{_n}"] = False st.rerun() if not _saved: st.info("No configurations selected. Tick some above to compare.") st.stop() # Comparison table: rows = metrics, columns = config names _key_order = [ ("Model / workload", ["model", "mode", "s_kv"]), ("Parallelism", ["cp", "tp", "pp", "dp", "ep", "cp_placement", "tp_placement", "ffn_scope", "kv_mode", "cp_ring_variant", "sip_topology"]), ("Physical", ["pes", "cubes", "sips"]), ("Hardware knobs", ["pe_hbm_gb", "peak_tflops", "bw_hbm_gbs", "bw_inter_gbs", "bw_intersip_gbs"]), ("Memory / PE (GB)", ["weights_gb", "kv_gb", "transient_gb", "used_gb", "slack_gb", "over_budget"]), ("Per-layer latency (us)", ["attn_us", "ffn_us", "layer_us"]), ] # Build one wide DataFrame - metrics x saved names _names = list(_saved.keys()) _rows_out = [] for _sec, _keys in _key_order: _rows_out.append({"metric": f"= {_sec}", **{n: "" for n in _names}}) for k in _keys: _rows_out.append({ "metric": k, **{n: _saved[n].get(k, "-") for n in _names}, }) _df_cmp = pd.DataFrame(_rows_out) # Highlight best (lowest) for latency + used_gb rows, worst (over_budget True) red. def _cmp_style(row): styles = [""] * len(row) m = row["metric"] vals = row[1:] try: nums = {k: float(v) for k, v in vals.items() if isinstance(v, (int, float)) and not isinstance(v, bool)} except Exception: nums = {} if m in ("layer_us", "attn_us", "ffn_us", "used_gb", "weights_gb", "kv_gb"): if nums: best = min(nums, key=nums.get) for i, (k, v) in enumerate(vals.items(), start=1): if k == best: styles[i] = "background-color: #d4edda; font-weight:600;" if m == "slack_gb" and nums: best = max(nums, key=nums.get) for i, (k, v) in enumerate(vals.items(), start=1): if k == best: styles[i] = "background-color: #d4edda; font-weight:600;" if m == "over_budget": for i, (k, v) in enumerate(vals.items(), start=1): if v is True or v == "True": styles[i] = "background-color: #f8d7da; font-weight:600;" if m.startswith("= "): styles = ["background-color: #e9ecef; font-weight:600;"] * len(row) return styles _styled_cmp = _df_cmp.style.apply(_cmp_style, axis=1) st.dataframe(_styled_cmp, width='stretch', hide_index=True, row_height=28) st.caption( "Green cell = best value in that row (lowest latency / memory, " "highest slack). Red = over-budget. Grey rows = section headers." ) # ── Per-stage side-by-side comparison ──────────────────── _fmt = _fmt_time # reuse the ns/us/ms auto-formatter _short_label = { "S1": "S1 RMSNorm", "S2": "S2 W_Q", "S3": "S3 W_K+W_V", "S4": "S4 KV append", "S5": "S5 Q.K^T", "S6": "S6 softmax", "S7": "S7 P.V", "S8": "S8 merge (+ AR if decode)", "S9": "S9 norm O/l", "S10": "S10 W_O", "C1": "C1 CP ring (prefill only)", "C2": "C2 TP AR (W_O)", "C3": "C3 Score AR (head-split)", "F1": "F1 RMSNorm", "F2": "F2 W_gate", "F3": "F3 W_up", "F4": "F4 SwiGLU", "F5": "F5 W_down", "CF1": "CF1 FFN AR", } def _stage_rows_across(prefix_order, key): """Build one row per stage prefix, with each saved config as a col.""" rows = [] for pfx in prefix_order: row = {"stage": _short_label.get(pfx, pfx)} sec_values = {} for n, snap in _saved.items(): v = snap.get(key, {}).get(pfx) if v is None: row[n] = "-" else: row[n] = _fmt(v) sec_values[n] = v rows.append(row) return rows def _stage_style_row(row): styles = [""] * len(row) # Try to find min seconds — reconstruct from formatted strings # is fragile; recompute from saved secs directly. pfx = row["stage"].split()[0] secs = {} for n, snap in _saved.items(): key = "stages_attn" if pfx in _attn_prefixes else "stages_ffn" v = snap.get(key, {}).get(pfx) if v is not None and v > 0: secs[n] = v if secs: best = min(secs, key=secs.get) for i, name in enumerate(row.index[1:], start=1): if name == best: styles[i] = "background-color: #d4edda; font-weight:600;" return styles _attn_prefixes = ["S1", "S2", "S3", "S4", "S5", "S6", "S7", "S8", "S9", "S10", "C1", "C2", "C3"] _ffn_prefixes = ["F1", "F2", "F3", "F4", "F5", "CF1"] st.markdown("**Attention per-stage latency (side-by-side)**") _df_attn = pd.DataFrame(_stage_rows_across(_attn_prefixes, "stages_attn")) st.dataframe(_df_attn.style.apply(_stage_style_row, axis=1), width='stretch', hide_index=True, row_height=28) st.markdown("**FFN per-stage latency (side-by-side)**") _df_ffn = pd.DataFrame(_stage_rows_across(_ffn_prefixes, "stages_ffn")) st.dataframe(_df_ffn.style.apply(_stage_style_row, axis=1), width='stretch', hide_index=True, row_height=28) st.caption( "Times auto-scale (ns/us/ms). Green cell = fastest for that stage. " "'-' means the stage is absent for that config (e.g. C1 in " "decode, since the O/m/l all-reduce is folded into S8)." ) # ── TAB (Auto Suggest Parallelism) — one tab, two sweep buttons ── def _render_auto_explore_tab(): """Render the Auto Suggest Parallelism tab body. Two sweep buttons — Attention-only and Attn+FFN/MoE. Each caches its result under its own session_state key. Whichever button was last clicked drives the display below; both caches persist so re-clicking either without changing model/workload just re-shows the cached view. """ from tests.analytical_visualization.auto_explore import ( compute_parallelism_sensitivity, run_auto_explore, ) st.markdown( "Sweep every valid parallelism config (CP, TP, PP, DP, kv_shard, " "ffn_scope, tp_placement, cp_placement, cp_ring) for the currently-" "selected model + workload and extract the 3D Pareto frontier on " "(latency ↓, PEs ↓, efficiency ↑). Throughput = 1/latency for a " "single request so it collapses with latency and is shown for info only." ) st.caption( "**Attention only**: drop FFN / MoE from the summed latency — " "useful when isolating attention-kernel tuning (matches what our " "kernbench attention sim measures). \n" "**Attn + FFN/MoE**: sum every stage — the full per-token cost." ) st.markdown( f"**Context:** {model.name} | S_kv=**{s_kv:,}** | mode=**{mode}** | " f"per-PE HBM = **{machine.pe_hbm_gb:.1f} GB**" ) _b1, _b2, _b3, _b4 = st.columns([1, 1, 1, 1]) with _b1: _run_attn = st.button("Run sweep — Attention", type="primary", width='stretch', key="_auto_run_attn") with _b2: _run_ffn = st.button("Run sweep — FFN/MoE", type="primary", width='stretch', key="_auto_run_ffn") with _b3: _run_full = st.button("Run sweep — Attn + FFN/MoE", type="primary", width='stretch', key="_auto_run_full") _scope_meta = { "attn": (True, False, "Attention only"), "ffn": (False, True, "FFN / MoE only"), "full": (True, True, "Attn + FFN/MoE"), } _button_click = {"attn": _run_attn, "ffn": _run_ffn, "full": _run_full} for _scope, _clicked in _button_click.items(): if not _clicked: continue _ia, _ff, _lbl = _scope_meta[_scope] with st.spinner(f"Sweeping ~28k configs ({_lbl})..."): _r = run_auto_explore( model, machine, s_kv=s_kv, mode=mode, include_attention=_ia, include_ffn=_ff, b=b_batch, ) st.session_state[f"_auto_explore_result_{_scope}"] = _r st.session_state[f"_auto_explore_ctx_{_scope}"] = ( model.name, s_kv, mode, machine.pe_hbm_gb, ) st.session_state["_auto_explore_active"] = _scope _active = st.session_state.get("_auto_explore_active") if _active is None: st.info( "Click one of the run buttons above to enumerate valid " "parallelism configurations and rank them on the Pareto " "frontier. Takes ~5–10 s per scope. All button results are " "cached so you can flip between scopes without re-running." ) return # Downstream code expects _suffix, _label, include_attention, include_ffn, # _res, _ctx_key. include_attention, include_ffn, _label = _scope_meta[_active] _suffix = f"_{_active}" _result_key = f"_auto_explore_result{_suffix}" _ctx_key = f"_auto_explore_ctx{_suffix}" _res = st.session_state.get(_result_key) if _res is None: st.info(f"Click **Run sweep — {_label.split(' ')[0]}** to compute this view.") return st.markdown(f"### Currently viewing: **{_label}**") # Warn if the cached result is stale relative to the current config. _cached_ctx = st.session_state.get(_ctx_key) _cur_ctx = (model.name, s_kv, mode, machine.pe_hbm_gb) if _cached_ctx != _cur_ctx: st.warning( f"Cached {_label} result is from a different model/workload. " f"Click **Run sweep — {_label.split(' ')[0]}** to refresh." ) _m1, _m2, _m3, _m4 = st.columns(4) _m1.metric("Enumerated", f"{_res.total_enumerated:,}") _m2.metric("Feasible", f"{_res.total_feasible:,}") _m3.metric("Pareto configs", len(_res.pareto_scores)) if _res.pareto_scores: _best = min(_res.pareto_scores, key=lambda s: (s.total_latency_ns, s.pes_used, s.hbm_utilization)) _m4.metric("Best latency", f"{_best.latency_ms:.2f} ms") if not _res.pareto_scores: st.error( "No feasible configs — every parallelism choice exceeds " "per-PE HBM. Try raising `pe_hbm_gb` in the sidebar or " "reducing `s_kv`." ) return # ── Pareto scatter: latency vs PEs, coloured by efficiency ─ import matplotlib.pyplot as _plt _fig, _axs = _plt.subplots(1, 2, figsize=(10, 3.5)) _feas = [s for s in _res.all_scores if s.fits_memory and s.placement_valid] _axs[0].scatter( [s.pes_used for s in _feas], [s.latency_ms for s in _feas], c="lightgrey", s=8, alpha=0.4, label="feasible", ) _pareto_sorted = sorted(_res.pareto_scores, key=lambda s: s.pes_used) _sc = _axs[0].scatter( [s.pes_used for s in _pareto_sorted], [s.latency_ms for s in _pareto_sorted], c=[s.efficiency_score for s in _pareto_sorted], cmap="viridis", s=60, edgecolors="black", linewidths=0.8, label="Pareto", ) _axs[0].plot( [s.pes_used for s in _pareto_sorted], [s.latency_ms for s in _pareto_sorted], "k--", linewidth=0.8, alpha=0.5, ) _axs[0].set_xlabel("PEs used"); _axs[0].set_ylabel("Latency (ms)") _axs[0].set_xscale("log", base=2) _axs[0].set_yscale("log") _axs[0].set_title("Pareto: latency vs PEs") _axs[0].grid(alpha=0.3) _axs[0].legend(loc="upper right", fontsize=8) _fig.colorbar(_sc, ax=_axs[0], label="efficiency") _pareto_by_lat = sorted(_res.pareto_scores, key=lambda s: s.total_latency_ns) _axs[1].scatter( [s.hbm_utilization * 100 for s in _pareto_by_lat], [s.latency_ms for s in _pareto_by_lat], c=[s.pes_used for s in _pareto_by_lat], cmap="plasma", s=60, edgecolors="black", linewidths=0.8, ) _axs[1].set_xlabel("HBM utilization (%)") _axs[1].set_ylabel("Latency (ms)") _axs[1].set_yscale("log") _axs[1].set_title("HBM usage vs latency (Pareto)") _axs[1].grid(alpha=0.3) _sc2 = _axs[1].collections[0] _fig.colorbar(_sc2, ax=_axs[1], label="PEs") _fig.tight_layout() st.pyplot(_fig, width='stretch') _plt.close(_fig) # ── Pareto table ──────────────────────────────────────────── st.markdown("**Pareto configurations** (sorted by latency)") _rows = [] for i, s in enumerate(_pareto_by_lat): _rows.append({ "#": i, "CP": s.cp, "TP": s.tp, "PP": s.pp, "DP": s.dp, "kv": s.kv_shard_mode, "ffn": s.ffn_shard_scope, "tp_place": s.tp_placement, "cp_place": s.cp_placement, "cp_ring": s.cp_ring_variant, "lat (ms)": round(s.latency_ms, 3), "eff": round(s.efficiency_score, 4), "PEs": s.pes_used, "SIPs": s.sips_used, "HBM %": round(s.hbm_utilization * 100, 1), }) _df = pd.DataFrame(_rows) st.dataframe(_df, width='stretch', hide_index=True) # ── Top-N by memory (smallest → largest) ───────────────────── st.markdown( "**Top 10 configurations by memory footprint** " "(smallest HBM% first). Useful when memory pressure is the " "real binding constraint of your deployment." ) _pareto_by_mem = sorted( _res.pareto_scores, key=lambda s: (s.hbm_utilization, s.pes_used, s.total_latency_ns), )[:10] _mem_rows = [] for i, s in enumerate(_pareto_by_mem): _mem_rows.append({ "#": i, "HBM %": round(s.hbm_utilization * 100, 1), "weights (GB)": round(s.weights_gb, 2), "KV (GB)": round(s.kv_gb, 2), "PEs": s.pes_used, "SIPs": s.sips_used, "lat (ms)": round(s.latency_ms, 3), "CP": s.cp, "TP": s.tp, "PP": s.pp, "DP": s.dp, "kv": s.kv_shard_mode, "ffn": s.ffn_shard_scope, "tp_place": s.tp_placement, "cp_place": s.cp_placement, }) _df_mem = pd.DataFrame(_mem_rows) st.dataframe(_df_mem, width='stretch', hide_index=True) # ── Parallelism sensitivity subplots ────────────────────── st.markdown( "**Parallelism sensitivity** — for the *baseline* config picked " "below, vary each knob one at a time (holding others fixed). " "Solid line = fits memory; red X = infeasible." ) _sens_c1, _sens_c2 = st.columns([1, 4]) with _sens_c1: _sens_row = st.number_input( "Baseline row #", min_value=0, max_value=len(_pareto_by_lat) - 1, value=0, step=1, key=f"_auto_sens_row{_suffix}", help="Which Pareto row is the sensitivity computed around?", ) _baseline_for_sens = _pareto_by_lat[int(_sens_row)] _sens_rows = compute_parallelism_sensitivity( _baseline_for_sens, model, machine, s_kv=s_kv, mode=mode, include_attention=include_attention, include_ffn=include_ffn, ) _fig_sens, _axes_sens = _plt.subplots(1, 5, figsize=(14, 3.2)) for _ax, _row in zip(_axes_sens, _sens_rows): _fit_vals, _fit_lats = [], [] _bad_vals = [] for _v, _lat_ns, _fit in zip(_row.values, _row.latencies_ns, _row.fits_flags): if _lat_ns != _lat_ns: continue if _fit: _fit_vals.append(_v) _fit_lats.append(_lat_ns / 1e6) else: _bad_vals.append((_v, _lat_ns / 1e6)) if _fit_vals: _ax.plot(_fit_vals, _fit_lats, "o-", color="#2c3e50", linewidth=1.4, markersize=4) if _bad_vals: _ax.scatter( [v for v, _ in _bad_vals], [l for _, l in _bad_vals], marker="x", color="#c0392b", s=40, alpha=0.6, label="no fit", ) _ax.axvline(_row.baseline_value, color="#3498db", linestyle=":", linewidth=1.0) _ax.set_xscale("log") _ax.set_yscale("log") _ax.set_title( f"{_row.knob.upper()} (baseline={_row.baseline_value})", fontsize=10, ) _ax.set_xlabel("value") _ax.set_ylabel("lat (ms)") _ax.grid(alpha=0.3) if _bad_vals: _ax.legend(loc="best", fontsize=7) _fig_sens.tight_layout() st.pyplot(_fig_sens, width='stretch') _plt.close(_fig_sens) # ── Load-into-sidebar ─────────────────────────────────────── st.markdown("**Load a Pareto config into the main sidebar sliders**") _lc1, _lc2 = st.columns([1, 3]) with _lc1: _pick = st.number_input( "Row #", min_value=0, max_value=len(_pareto_by_lat) - 1, value=0, step=1, key=f"_auto_pick_row{_suffix}", ) with _lc2: if st.button("Load into sidebar", type="secondary", key=f"_auto_load_btn{_suffix}"): _s = _pareto_by_lat[int(_pick)] st.session_state["cp"] = _s.cp st.session_state["tp"] = _s.tp st.session_state["pp"] = _s.pp st.session_state["dp"] = _s.dp st.session_state["tp_placement"] = _s.tp_placement st.session_state["cp_placement"] = _s.cp_placement st.session_state["cp_ring_variant"] = _s.cp_ring_variant st.session_state["kv_mode"] = _s.kv_shard_mode _tp, _cp, _dp = _s.tp, _s.cp, _s.dp _ffn_label_map = { "TP": f"TP only (div={max(1, _tp)})", "TP+CP": f"TP*CP (div={max(1, _tp * _cp)})", "TP+CP+DP": f"TP*CP*DP (div={max(1, _tp * _cp * _dp)})", } st.session_state["ffn_scope_label"] = \ _ffn_label_map[_s.ffn_shard_scope] st.success( f"Loaded row {_pick}: CP={_s.cp} TP={_s.tp} PP={_s.pp} " f"DP={_s.dp} → latency {_s.latency_ms:.2f} ms, " f"{_s.pes_used} PEs. Flip to another tab to see the " f"full breakdown." ) st.rerun() with tab_auto: _render_auto_explore_tab() # ── TAB (Auto Hardware) — one tab, two sweep buttons ───────────── def _render_auto_hardware_tab(): """Render the Auto Hardware tab body. Two sweep buttons — Attention-only and Attn+FFN/MoE. Each caches its result. Whichever button was last clicked drives the display; both caches persist for fast toggling.""" from tests.analytical_visualization.auto_hardware import ( _HW_KNOB_DEFAULTS, joint_explore, ) st.markdown( "Sweep hardware knobs (PE HBM, HBM BW, TFLOPs, PE↔PE / D2D / C2C " "interconnect BW) **together** with parallelism. For each hardware " "candidate, we pick the latency-minimum parallelism that fits, then " "Pareto-rank the joint (latency ↓, hardware cost ↓) space. Also " "computes per-knob sensitivity: which HW investment gives the " "biggest speedup?" ) st.caption( "**Attention only**: FFN / MoE stages are excluded from the summed " "latency (both in the joint search and sensitivity). \n" "**Attn + FFN/MoE**: full per-token cost." ) hx_l, hx_m = st.columns([1, 1]) with hx_l: st.markdown( f"**Context:** {model.name} | S_kv=**{s_kv:,}** | mode=**{mode}**" ) with hx_m: _depth = st.radio( "Sweep depth", options=["two_stage", "balanced", "coarse"], index=1, key="_hw_depth", horizontal=True, help=("two_stage: 1 HW candidate (defaults) + autosuggest " "parallelism, ~1s. Great for sensitivity alone.\n" "balanced (default): 64 HW × ~2k parallelism, ~10-20s.\n" "coarse: 729 HW × ~2k parallelism, ~2-5 min."), ) _bh1, _bh2, _bh3, _bh4 = st.columns([1, 1, 1, 1]) with _bh1: _run_hw_attn = st.button("Run joint sweep — Attention", type="primary", width='stretch', key="_hw_run_attn") with _bh2: _run_hw_ffn = st.button("Run joint sweep — FFN/MoE", type="primary", width='stretch', key="_hw_run_ffn") with _bh3: _run_hw_full = st.button("Run joint sweep — Attn + FFN/MoE", type="primary", width='stretch', key="_hw_run_full") _scope_meta_hw = { "attn": (True, False, "Attention only"), "ffn": (False, True, "FFN / MoE only"), "full": (True, True, "Attn + FFN/MoE"), } _button_click_hw = { "attn": _run_hw_attn, "ffn": _run_hw_ffn, "full": _run_hw_full, } for _scope, _clicked in _button_click_hw.items(): if not _clicked: continue _ia, _ff, _lbl = _scope_meta_hw[_scope] with st.spinner(f"Sweeping HW × parallelism ({_depth}, {_lbl})..."): _r = joint_explore(model, s_kv=s_kv, mode=mode, depth=_depth, include_attention=_ia, include_ffn=_ff, b=b_batch) st.session_state[f"_hw_result_{_scope}"] = _r st.session_state[f"_hw_ctx_{_scope}"] = ( model.name, s_kv, mode, _depth, ) st.session_state["_hw_active"] = _scope _active = st.session_state.get("_hw_active") if _active is None: st.info( "Click one of the run buttons to explore hardware × parallelism " "co-design for this model + workload. All button results are " "cached so you can flip between scopes without re-running." ) return include_attention, include_ffn, _label = _scope_meta_hw[_active] _suffix = f"_{_active}" _hw_result_key = f"_hw_result{_suffix}" _hw_ctx_key = f"_hw_ctx{_suffix}" _hw_res = st.session_state.get(_hw_result_key) if _hw_res is None: st.info(f"Click **Run joint sweep — {_label.split(' ')[0]}** to compute this view.") return st.markdown(f"### Currently viewing: **{_label}**") _cached_ctx = st.session_state.get(_hw_ctx_key) _cur_ctx = (model.name, s_kv, mode, _depth) if _cached_ctx != _cur_ctx: st.warning( f"Cached {_label} result is from a different model/workload/depth. " f"Click **Run joint sweep — {_label.split(' ')[0]}** to refresh." ) _hm1, _hm2, _hm3, _hm4 = st.columns(4) _hm1.metric("HW candidates", _hw_res.total_hw) _hm2.metric("Feasible joint", _hw_res.total_joint) _hm3.metric("Pareto configs", len(_hw_res.pareto_scores)) if _hw_res.pareto_scores: _best = min(_hw_res.pareto_scores, key=lambda s: (s.total_latency_ns, s.parallelism.pes_used, s.parallelism.hbm_utilization)) _hm4.metric("Best latency", f"{_best.latency_ms:.2f} ms") if not _hw_res.pareto_scores: st.error( "No feasible joint configurations — every HW+parallelism " "combo exceeds per-PE HBM at this workload." ) return if True: # ── Panel 1: latency vs cost Pareto ──────────────────────── import matplotlib.pyplot as _plt _fig1, _ax1 = _plt.subplots(figsize=(8, 4)) _feas = _hw_res.all_scores _ax1.scatter( [s.cost_score for s in _feas], [s.latency_ms for s in _feas], c="lightgrey", s=15, alpha=0.5, label="feasible", ) _pareto_by_cost = sorted(_hw_res.pareto_scores, key=lambda s: s.cost_score) _sc = _ax1.scatter( [s.cost_score for s in _pareto_by_cost], [s.latency_ms for s in _pareto_by_cost], c=[s.parallelism.pes_used for s in _pareto_by_cost], cmap="viridis", s=90, edgecolors="black", linewidths=1.0, label="Pareto", ) _ax1.plot( [s.cost_score for s in _pareto_by_cost], [s.latency_ms for s in _pareto_by_cost], "k--", linewidth=1.0, alpha=0.6, ) _ax1.set_xlabel("Hardware cost proxy (unitless — 6.0 = all defaults)") _ax1.set_ylabel("Latency (ms)") _ax1.set_yscale("log") _ax1.set_title("Optimal HW+parallelism: latency vs cost") _ax1.grid(alpha=0.3) _ax1.legend(loc="upper right", fontsize=9) _fig1.colorbar(_sc, ax=_ax1, label="PEs") _fig1.tight_layout() st.pyplot(_fig1, width='stretch') _plt.close(_fig1) # ── Panel 2: per-knob sensitivity ────────────────────────── st.markdown( "**Per-knob sensitivity** (doubling each HW knob from the " "latency-optimal baseline). Higher bar = more valuable " "investment for this workload." ) _fig2, _ax2 = _plt.subplots(figsize=(8, 3.5)) _knobs = [r.knob for r in _hw_res.sensitivity] _speedups = [r.rel_speedup * 100 for r in _hw_res.sensitivity] _colors = ["#2ecc71" if v > 0 else "#95a5a6" for v in _speedups] _ax2.barh(_knobs, _speedups, color=_colors, edgecolor="black", linewidth=0.5) _ax2.invert_yaxis() # biggest speedup on top _ax2.set_xlabel("Speedup when knob is doubled (%)") _ax2.set_title("Sensitivity — where to invest next") _ax2.grid(alpha=0.3, axis="x") for i, r in enumerate(_hw_res.sensitivity): _ax2.text( r.rel_speedup * 100 + 0.5, i, f"{r.baseline_value:g} → {r.doubled_value:g}", va="center", fontsize=8, ) _fig2.tight_layout() st.pyplot(_fig2, width='stretch') _plt.close(_fig2) # ── Panel 3: Pareto table (HW spec + parallelism) ────────── st.markdown("**Pareto joint configurations** (sorted by latency)") _pareto_by_lat = sorted(_hw_res.pareto_scores, key=lambda s: s.total_latency_ns) _rows = [] for i, s in enumerate(_pareto_by_lat): p = s.parallelism h = s.hardware _rows.append({ "#": i, "lat (ms)": round(s.latency_ms, 3), "cost": round(s.cost_score, 2), "PEs": p.pes_used, "CP": p.cp, "TP": p.tp, "PP": p.pp, "DP": p.dp, "kv": p.kv_shard_mode, "PE HBM": f"{h.pe_hbm_gb:g} GB", "HBM BW": f"{h.bw_hbm_gbs:g} GB/s", "TFLOPs": f"{h.peak_tflops_f16:g}", "PE↔PE": f"{h.bw_intra_gbs:g}", "D2D": f"{h.bw_inter_gbs:g}", "C2C": f"{h.bw_intersip_gbs:g}", }) _df_hw = pd.DataFrame(_rows) st.dataframe(_df_hw, width='stretch', hide_index=True) # ── Load hardware into sidebar ───────────────────────────── st.markdown( "**Load a Pareto HW config into the main sidebar** " "(overwrites the Hardware→Per-PE and Hardware→Interconnect " "sliders; also loads the paired parallelism)." ) _lch1, _lch2 = st.columns([1, 3]) with _lch1: _pick_hw = st.number_input( "Row #", min_value=0, max_value=len(_pareto_by_lat) - 1, value=0, step=1, key=f"_hw_pick_row{_suffix}", ) with _lch2: if st.button("Load into sidebar", type="secondary", key=f"_hw_load_btn{_suffix}"): _s = _pareto_by_lat[int(_pick_hw)] _h = _s.hardware _p = _s.parallelism # HW knobs (only load if the value is in the selectbox # options; otherwise leave as-is). _apply_if_in = { "pe_hbm": ([3.0, 6.0, 12.0, 24.0, 48.0, 96.0], _h.pe_hbm_gb), "bw_hbm": ([128.0, 256.0, 512.0, 1024.0, 2048.0], _h.bw_hbm_gbs), "tflops": ([2.0, 4.0, 8.0, 16.0, 32.0, 64.0], _h.peak_tflops_f16), "bw_intra": ([128.0, 256.0, 512.0, 1024.0, 2048.0, 4096.0], _h.bw_intra_gbs), "bw_inter": ([32.0, 64.0, 128.0, 256.0, 512.0, 900.0], _h.bw_inter_gbs), "bw_intersip": ([12.5, 25.0, 50.0, 100.0, 200.0, 400.0], _h.bw_intersip_gbs), } for _key, (_opts, _val) in _apply_if_in.items(): if _val in _opts: st.session_state[_key] = _val # Parallelism knobs st.session_state["cp"] = _p.cp st.session_state["tp"] = _p.tp st.session_state["pp"] = _p.pp st.session_state["dp"] = _p.dp st.session_state["tp_placement"] = _p.tp_placement st.session_state["cp_placement"] = _p.cp_placement st.session_state["cp_ring_variant"] = _p.cp_ring_variant st.session_state["kv_mode"] = _p.kv_shard_mode # Ffn_scope_label reconstruction (same as auto_explore tab) _tp2, _cp2, _dp2 = _p.tp, _p.cp, _p.dp _ffn_label_map = { "TP": f"TP only (div={max(1, _tp2)})", "TP+CP": f"TP*CP (div={max(1, _tp2 * _cp2)})", "TP+CP+DP": f"TP*CP*DP (div={max(1, _tp2 * _cp2 * _dp2)})", } st.session_state["ffn_scope_label"] = \ _ffn_label_map[_p.ffn_shard_scope] st.success( f"Loaded row {_pick_hw}: latency {_s.latency_ms:.2f} " f"ms, cost {_s.cost_score:.2f}, " f"{_p.pes_used} PEs. Flip to another tab to see the " f"full breakdown." ) st.rerun() with tab_hw: _render_auto_hardware_tab() # ── TAB 7: Chip roofline & B* ──────────────────────────────────── with tab_roofline: st.subheader("Chip roofline & critical batch size (B*)") st.caption( "Back-of-envelope answer to 'is my chip well-matched to this " "model?' — see how big the batch must be before decode becomes " "compute-bound, and how big the context can grow before the KV " "bandwidth wall kills your utilization. All numbers per PE, " "peak roofline (no compute-util factor), no comm." ) _ai = arithmetic_intensity(_default_machine) _b_star = critical_batch(_default_machine, model) _l_star = balance_context(_default_machine, model) _n_active = total_active_params(model) # ── Local S_kv knob — override sidebar for exploring the plots # without changing the rest of the app's config. st.markdown("**Explore: override S_kv for the plots below**") _skv_min = 128 _skv_max = max(int(20 * _l_star), 4 * s_kv, 1_000_000) _rf_skv = st.slider( "S_kv (context length) for roofline plots", min_value=_skv_min, max_value=_skv_max, value=int(s_kv), step=max(1, _skv_min), key="_rf_skv_override", help=("Local to this tab. Drag to see how KV-read time grows " "and where the knee disappears past L*."), ) st.caption( f"Using S_kv = **{_rf_skv:,}** tokens for all plots on this tab. " f"L* on your chip is **{_l_star:,.0f}** tokens → " f"S_kv / L* = **{_rf_skv/_l_star:.2f}**." ) _regime_now = bound_regime(_default_machine, model, batch=max(1, b_batch), s_kv=_rf_skv) # ── KPI cards ───────────────────────────────────────────────── _r1, _r2, _r3, _r4 = st.columns(4) _r1.metric("AI (FLOPs/byte)", f"{_ai:.1f}", help="C/W. Peak FLOPs per byte of HBM bandwidth. " "Chip-only — no model-dependent factor.") _r2.metric("B* (dense)", f"{_b_star:.0f}", help="C*b/(2*W). Batch size where weight-fetch time " "equals compute time. Below this you're memory-bound.") _r3.metric("L* (tokens)", f"{_l_star:,.0f}", help="Context length where KV-read time equals compute " "time. Above this, no batch size gets you back to " "compute-bound.") _r4.metric(f"Regime @ (B={b_batch}, S_kv={_rf_skv:,})", _regime_now.replace("-bound", ""), help="Which term dominates cost at the current (B, S_kv) " "you've selected.") # ── Recommended B and L (heuristics) ────────────────────────── _rec_b = good_batch(_default_machine, model, sparsity=1.0) _rec_l = good_context(_default_machine, model, s_kv=_rf_skv) _g1, _g2, _g3 = st.columns(3) _g1.metric("Good B (dense)", f"{_rec_b.effective:.0f}", help=_rec_b.reason) _g2.metric("Good L ceiling", f"{_rec_l.max_efficient:,.0f} tok", help="Stay at or below L* to keep decode compute-bound.") _g3.metric(f"Utilization @ S_kv={_rf_skv:,}", f"{_rec_l.utilization_at*100:.1f}%", help="Peak compute utilization at this context: " "1 / (1 + S_kv/L*).") # Optional MoE B*: only surface if the preset flags MoE. _is_moe = "MoE" in (preset.family + preset.note) if _is_moe: st.info( "This preset is dense-approximated as an MoE. Reiner Pope's " "'300 × sparsity' rule: for real sparsity k = N_total/N_active, " f"B*_moe ≈ {_b_star:.0f} × k. E.g. DeepSeek 32-of-256 experts " f"(k=8) → B* ≈ {_b_star*8:.0f}." ) st.caption( f"**Full-model attention+FFN params:** {_n_active/1e9:.2f} B. " f"**Chip:** {_default_machine.peak_tflops_f16:.0f} TFLOPs BF16, " f"{_default_machine.bw_hbm_gbs:.0f} GB/s HBM. " f"Change these in the sidebar Hardware panel to see the roofline shift." ) st.divider() # ── Plot 1: cost vs B at current S_kv ───────────────────────── st.markdown(f"**Per-token latency vs batch size** (S_kv = {_rf_skv:,})") _b_range = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] _pts = per_token_latency_curve(_default_machine, model, _b_range, s_kv=_rf_skv) _fig1, _ax1 = plt.subplots(figsize=(9, 4.5)) _xs = [p.batch for p in _pts] _ax1.plot(_xs, [p.weight_s * 1e3 for p in _pts], "o-", label="Weight fetch (1/B)", color="#ffbe0b") _ax1.axhline(_pts[0].compute_s * 1e3, linestyle="--", color="#3a86ff", label="Compute floor (peak)") _ax1.axhline(_pts[0].kv_s * 1e3, linestyle=":", color="#d90429", label="KV read floor") _ax1.plot(_xs, [p.total_s * 1e3 for p in _pts], "-", color="#212529", linewidth=2, label="Total") _ax1.axvline(_b_star, linestyle=":", color="#2e7d32", label=f"B* = {_b_star:.0f}") _ax1.set_xscale("log", base=2) _ax1.set_yscale("log") _ax1.set_xlabel("Batch size (sequences in flight)") _ax1.set_ylabel("Per-token step time (ms)") _ax1.set_title(f"Cost curve at S_kv = {_rf_skv:,} tokens") _ax1.grid(True, which="both", alpha=0.3) _ax1.legend(fontsize=8, loc="upper right") plt.tight_layout() st.pyplot(_fig1, width='stretch') plt.close(_fig1) st.caption( "**Below B\\*:** cost is dominated by weight fetch; doubling B " "halves cost per token. **At B\\*:** weight fetch = compute. " "**Above 2-3× B\\*:** you've captured most amortization; further " "batching mostly buys latency you don't want." ) st.divider() # ── Plot 2: cost curves at multiple context lengths ─────────── st.markdown("**The 'no-knee' phenomenon — cost vs B at different S_kv**") _fig2, _ax2 = plt.subplots(figsize=(9, 4.5)) _l_ratios = [0.25, 0.5, 1.0, 2.0, 5.0] _colors = ["#3a86ff", "#2e7d32", "#ffbe0b", "#ef6c00", "#d90429"] for _r, _col in zip(_l_ratios, _colors): _skv_at = max(1, int(_r * _l_star)) _pts_r = per_token_latency_curve(_default_machine, model, _b_range, s_kv=_skv_at) _label = f"S_kv = {_r:.2g} × L* ({_skv_at:,} tok)" _ax2.plot(_xs, [p.total_s * 1e3 for p in _pts_r], "-", color=_col, label=_label, linewidth=1.6) _ax2.axhline(_pts[0].compute_s * 1e3, linestyle="--", color="#888", label="Compute floor") _ax2.axvline(_b_star, linestyle=":", color="#2e7d32", label=f"B* = {_b_star:.0f}") _ax2.set_xscale("log", base=2) _ax2.set_yscale("log") _ax2.set_xlabel("Batch size") _ax2.set_ylabel("Per-token step time (ms)") _ax2.set_title("Cost curves at several context lengths " f"(L* = {_l_star:,.0f} tokens)") _ax2.grid(True, which="both", alpha=0.3) _ax2.legend(fontsize=8, loc="upper right") plt.tight_layout() st.pyplot(_fig2, width='stretch') plt.close(_fig2) st.caption( "**S_kv < L\\*:** curve bends at B_knee = B\\* / (1 − S_kv/L\\*), " "then flattens onto the compute floor. **S_kv = L\\*:** curves " "become parallel — you approach the floor but never touch it. " "**S_kv > L\\*:** KV read is above the compute floor at all B; " "no batch size recovers utilization. This is the algebraic core " "of 'at 1M context, there's no B that gets you compute-bound'." ) st.divider() # ── Plot 3: knee vs context ─────────────────────────────────── st.markdown("**Effective knee B_knee(S_kv) = B\\* / (1 − S_kv/L\\*)**") _skv_range = [int(_r * _l_star) for _r in (0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.85, 0.9, 0.95, 0.98)] _knees = [knee_batch(_default_machine, model, s) for s in _skv_range] _fig3, _ax3 = plt.subplots(figsize=(9, 3.5)) _ax3.plot(_skv_range, _knees, "o-", color="#7b1fa2", linewidth=2) _ax3.axhline(_b_star, linestyle=":", color="#2e7d32", label=f"B* = {_b_star:.0f}") _ax3.axvline(_l_star, linestyle=":", color="#d90429", label=f"L* = {_l_star:,.0f}") _ax3.set_yscale("log") _ax3.set_xlabel("Context length S_kv (tokens)") _ax3.set_ylabel("Effective knee batch size") _ax3.set_title("How far right the cost-curve knee slides as context grows") _ax3.grid(True, which="both", alpha=0.3) _ax3.legend(fontsize=8, loc="upper left") plt.tight_layout() st.pyplot(_fig3, width='stretch') plt.close(_fig3) st.caption( "The knee starts at B\\* for short context and slides right as " "S_kv/L\\* grows. It diverges at S_kv = L\\* and disappears " "beyond that. Sparse attention (e.g. DeepSeek-style) replaces " "S_kv with ~√S_kv in the KV term — bending this curve back down." ) st.divider() # ── Plot 4: decode-step latency decomposition ───────────────── st.markdown(f"**Latency per decode step — decomposition** " f"(S_kv = {_rf_skv:,})") _fig4, _ax4 = plt.subplots(figsize=(9, 4.5)) _weight_vals = [p.weight_s * 1e3 for p in _pts] _compute_vals = [p.compute_s * 1e3 for p in _pts] _kv_vals = [p.kv_s * 1e3 for p in _pts] _mem_total = [w + k for w, k in zip(_weight_vals, _kv_vals)] _total_vals = [p.total_s * 1e3 for p in _pts] _ax4.plot(_xs, _compute_vals, "--", color="#3a86ff", label="Compute", linewidth=1.6) _ax4.plot(_xs, _weight_vals, "^-", color="#ffbe0b", label="Weight fetch (shrinks 1/B)", linewidth=1.6) _ax4.plot(_xs, _kv_vals, "s-", color="#d90429", label="KV fetch (flat)", linewidth=1.6) _ax4.plot(_xs, _mem_total, "-", color="#7b1fa2", label="Memory total (weights + KV)", linewidth=2) _ax4.plot(_xs, _total_vals, "-", color="#212529", label="Total (compute + memory)", linewidth=2.5) _ax4.axvline(_b_star, linestyle=":", color="#2e7d32", label=f"B* = {_b_star:.0f}") _ax4.set_xscale("log", base=2) _ax4.set_yscale("log") _ax4.set_xlabel("Batch size (sequences in flight)") _ax4.set_ylabel("Per-token step time (ms)") _ax4.set_title("Where the time goes: compute vs weight fetch vs KV fetch") _ax4.grid(True, which="both", alpha=0.3) _ax4.legend(fontsize=8, loc="upper right") plt.tight_layout() st.pyplot(_fig4, width='stretch') plt.close(_fig4) st.caption( "**Compute** is flat in B. **Weight fetch** shrinks like 1/B " "(the amortization win). **KV fetch** is flat in B — each " "sequence reads its own KV, so batching does nothing for it. " "**Memory total** is weight+KV summed. Which of compute or " "memory-total is bigger at your operating B tells you the " "regime; if memory-total's floor (= KV alone at large B) " "already sits above compute, you're past L*." ) st.divider() # ── Regime formulas (short vs long context) ─────────────────── st.markdown("**Cost-term formulas by regime**") _t_com_val = t_com(_default_machine, model) * 1e3 # ms _t_mem_s_at_bstar = t_mem_short(_default_machine, model, int(round(_b_star))) * 1e3 _t_mem_l_at_skv = t_mem_long(_default_machine, model, _rf_skv) * 1e3 _regime_rows = [ {"Term": "t_com (compute)", "Short context (S_kv < L*)": "2 · N / C", "Long context (S_kv > L*)": "2 · N / C (same)", "Value now (ms)": f"{_t_com_val:.3f}"}, {"Term": "t_mem — weight fetch", "Short context (S_kv < L*)": "N · b / (W · B)", "Long context (S_kv > L*)": "N · b / (W · B) — becomes small", "Value now (ms)": f"{_t_mem_s_at_bstar:.3f} at B = B*"}, {"Term": "t_mem — KV fetch", "Short context (S_kv < L*)": "S_kv · kv_bpt / W — small", "Long context (S_kv > L*)": "S_kv · kv_bpt / W — dominates", "Value now (ms)": f"{_t_mem_l_at_skv:.3f}"}, {"Term": "Bottleneck", "Short context (S_kv < L*)": "Weights (B < B*), else Compute (B ≥ B*)", "Long context (S_kv > L*)": "KV bandwidth (regardless of B)", "Value now (ms)": _regime_now.replace("-bound", "")}, ] st.dataframe(pd.DataFrame(_regime_rows), width='stretch', hide_index=True) st.caption( "N = active params, b = bytes/elem, W = HBM BW, C = peak FLOPs, " "kv_bpt = KV bytes per token = 2 · H_kv · d_head · b · layers. " "'Value now' uses the S_kv slider above and B = B\\*." ) st.divider() # ── Recommended B and L (recap) ─────────────────────────────── st.markdown("**How to pick a good B and a good S_kv**") st.markdown( f"- **Good batch**: `B_target = 2 · B* = {_rec_b.effective:.0f}`. " "Below B\\*: memory-bound (doubling B halves cost/token). " "Above 3·B\\*: diminishing returns; latency keeps rising. " "Also cap by your HBM budget: `B_max ≤ (HBM − weights_per_PE) / " "(S_kv · kv_bpt_per_PE)` — check the Memory Breakdown tab.\n" f"- **Good S_kv**: stay ≤ **L\\* = {_l_star:,.0f} tokens** to " "keep decode compute-bound (peak utilization). Past L\\*, " f"utilization = 1/(1 + S_kv/L\\*). At S_kv = {_rf_skv:,}, " f"you're at ~**{_rec_l.utilization_at*100:.1f}%** utilization." ) st.divider() # ── Interpretation table ─────────────────────────────────────── st.markdown("**Formulas & interpretation**") _C = _default_machine.peak_flops # FLOPs / s _W = _default_machine.bw_hbm # bytes / s _b_elem = model.bytes_per_elem # bytes / weight _kv_bpt_full = 2 * model.h_kv * model.d_head * model.bytes_per_elem * model.layers _knee_now = knee_batch(_default_machine, model, _rf_skv) if _knee_now is None: _sub_bknee = ( f"{_b_star:.0f} / (1 − {_rf_skv:,}/{_l_star:,.0f}) = " f"{_b_star:.0f} / (1 − {_rf_skv/_l_star:.3f}) [≤ 0]" ) _bknee_val = "no knee (S_kv ≥ L*)" else: _sub_bknee = ( f"{_b_star:.0f} / (1 − {_rf_skv:,}/{_l_star:,.0f}) = " f"{_b_star:.0f} / {1 - _rf_skv/_l_star:.3f} = " f"{_knee_now:.0f}" ) _bknee_val = f"{_knee_now:.0f}" _formula_rows = [ {"Symbol": "AI", "Formula": "C / W", "With numbers": f"{_C:.2e} / {_W:.2e}", "Meaning": "FLOPs per byte of HBM BW. Chip-only.", "Value": f"{_ai:.2f} FLOPs/byte"}, {"Symbol": "B*", "Formula": "C · b / (2 · W)", "With numbers": f"{_C:.2e} · {_b_elem} / (2 · {_W:.2e})", "Meaning": ("Batch where weight-fetch = compute. Below: " "memory-bound. Above: amortized."), "Value": f"{_b_star:.0f}"}, {"Symbol": "B*_moe", "Formula": "B* · (N_total / N_active)", "With numbers": (f"{_b_star:.0f} · 8 (k=8 example)" if _is_moe else "N/A (dense preset)"), "Meaning": "MoE fetches all experts; computes on active only.", "Value": (f"{_b_star * 8:.0f}" if _is_moe else "1× (dense)")}, {"Symbol": "L*", "Formula": "2 · N_active · W / (C · kv_bpt)", "With numbers": (f"2 · {_n_active:.2e} · {_W:.2e} / " f"({_C:.2e} · {_kv_bpt_full:,})"), "Meaning": ("Context where KV-read = compute. Above: " "KV-bound regardless of B."), "Value": f"{_l_star:,.0f} tokens"}, {"Symbol": "B_knee", "Formula": "B* / (1 − S_kv/L*)", "With numbers": _sub_bknee, "Meaning": ("Batch where cost curve bends onto its floor. " "Diverges at S_kv = L*."), "Value": _bknee_val}, ] st.dataframe(pd.DataFrame(_formula_rows), width='stretch', hide_index=True)