mukesh
9fdde44922
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>
331 lines
13 KiB
Python
331 lines
13 KiB
Python
"""Pictorial view of how each weight/KV tensor is sharded across ranks.
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Every tensor is drawn as a rectangle labelled with its (rows, cols)
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shape. Grid lines show shard boundaries; one shard (this PE's share)
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is highlighted so it's obvious which dim the split is along.
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Sharding pattern:
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- W_Q (hidden, H_q*d_head) : split on **output cols** across TP
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- W_K (hidden, H_kv*d_head): split on **output cols** across TP
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- W_V (hidden, H_kv*d_head): split on **output cols** across TP
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- W_O (H_q*d_head, hidden) : split on **input rows** across TP
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- W_gate/up (hidden, ffn_dim): split on **output cols** across FFN scope
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- W_down (ffn_dim, hidden) : split on **input rows** across FFN scope
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- K/V cache (S_kv, H_kv*d_head): split on **rows (S axis)** by CP AND
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**cols (head axis)** by TP -> 2D shard grid
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"""
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from __future__ import annotations
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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from .model_config import FullConfig
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_UNSHARDED_FACE = "#e9ecef"
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_UNSHARDED_EDGE = "#adb5bd"
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_MY_SHARD_FACE = "#3a86ff"
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_MY_SHARD_EDGE = "#0057b3"
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_TP_LINE_COLOR = "#d90429" # TP split lines
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_CP_LINE_COLOR = "#0f9d58" # CP split lines
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_FFN_LINE_COLOR = "#e37400" # FFN-scope split lines
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def _draw_tensor(ax, x, y, w, h, name, shape_str,
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row_splits: int = 1, col_splits: int = 1,
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my_row_shard: int = 0, my_col_shard: int = 0,
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row_line_color: str = _TP_LINE_COLOR,
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col_line_color: str = _TP_LINE_COLOR,
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row_label: str = "", col_label: str = "",
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note: str = "",
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cell_annot=None):
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"""Draw a tensor with sharding grid.
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cell_annot: optional callable (row, col) -> str returning the physical
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label to render inside shard cell (r, c). If it returns "", cell is not
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annotated.
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"""
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"""Draw a tensor as a rectangle with grid lines showing shards."""
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# Base rectangle (unsharded background)
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rect = patches.Rectangle(
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(x, y), w, h,
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facecolor=_UNSHARDED_FACE, edgecolor=_UNSHARDED_EDGE, linewidth=1.0,
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)
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ax.add_patch(rect)
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# Highlight this PE's shard
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shard_w = w / max(1, col_splits)
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shard_h = h / max(1, row_splits)
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sx = x + my_col_shard * shard_w
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sy = y + (row_splits - 1 - my_row_shard) * shard_h
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highlight = patches.Rectangle(
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(sx, sy), shard_w, shard_h,
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facecolor=_MY_SHARD_FACE, edgecolor=_MY_SHARD_EDGE, linewidth=1.5,
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alpha=0.8,
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)
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ax.add_patch(highlight)
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# Horizontal grid lines (row splits)
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for r in range(1, row_splits):
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yy = y + r * shard_h
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ax.plot([x, x + w], [yy, yy],
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color=row_line_color, linewidth=1.2, linestyle="--")
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# Vertical grid lines (col splits)
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for c in range(1, col_splits):
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xx = x + c * shard_w
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ax.plot([xx, xx], [y, y + h],
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color=col_line_color, linewidth=1.2, linestyle="--")
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# Per-cell physical annotations (e.g. "cube3 PE5") if provided.
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if cell_annot is not None:
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for r in range(row_splits):
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for c in range(col_splits):
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text = cell_annot(r, c)
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if not text:
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continue
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cx = x + c * shard_w + shard_w / 2
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cy = y + (row_splits - 1 - r) * shard_h + shard_h / 2
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ax.text(cx, cy, text,
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ha="center", va="center",
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fontsize=6.0, color="#0d1b2a",
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alpha=0.85)
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# Stacked labels ABOVE rectangle: note (italic, top), shape, name (bold, closest)
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if note:
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ax.text(x + w / 2, y + h + 0.55, note,
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ha="center", va="bottom", fontsize=6.5,
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color="#555", style="italic")
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ax.text(x + w / 2, y + h + 0.30, shape_str,
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ha="center", va="bottom", fontsize=7, color="#666")
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ax.text(x + w / 2, y + h + 0.08, name,
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ha="center", va="bottom",
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fontsize=10, fontweight="bold")
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# Split labels BELOW rectangle (stacked if both present)
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if row_label and col_label:
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ax.text(x + w / 2, y - 0.10, row_label,
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ha="center", va="top", fontsize=6.5,
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color=row_line_color, fontweight="bold")
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ax.text(x + w / 2, y - 0.35, col_label,
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ha="center", va="top", fontsize=6.5,
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color=col_line_color, fontweight="bold")
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elif row_label:
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ax.text(x + w / 2, y - 0.10, row_label,
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ha="center", va="top", fontsize=6.5,
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color=row_line_color, fontweight="bold")
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elif col_label:
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ax.text(x + w / 2, y - 0.10, col_label,
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ha="center", va="top", fontsize=6.5,
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color=col_line_color, fontweight="bold")
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def _physical_label(cfg: FullConfig, dim: str, rank: int) -> str:
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"""Where does rank `rank` of dim (`tp`|`cp`|`ffn`) live physically?
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Returns a short "cubeX PEy" style label based on the placement config.
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"""
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topo = cfg.topo
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if dim == "tp":
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if topo.tp_placement == "pe":
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# TP fills PEs within one cube (spilling if tp > 8)
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pe_in_cube = rank % topo.pes_per_cube_hw
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cube = rank // topo.pes_per_cube_hw
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return f"c{cube}p{pe_in_cube}"
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else: # tp on cube
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return f"cube{rank}"
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if dim == "cp":
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if topo.cp_placement == "pe":
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pe_in_cube = rank % topo.pes_per_cube_hw
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cube = rank // topo.pes_per_cube_hw
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return f"c{cube}p{pe_in_cube}"
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else: # cp on cube
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return f"cube{rank}"
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return ""
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def _kv_cell_annot(cfg: FullConfig):
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"""Return an annotation function for KV cache cells: (cp_r, tp_r) -> label."""
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def _ann(r, c):
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cp_lbl = _physical_label(cfg, "cp", r)
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tp_lbl = _physical_label(cfg, "tp", c)
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# Two lines if both are non-trivial; otherwise a single line
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if cp_lbl and tp_lbl:
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return f"{cp_lbl}\n{tp_lbl}"
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return cp_lbl or tp_lbl
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return _ann
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def _tp_cell_annot(cfg: FullConfig):
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"""Col-parallel tensors (W_Q/W_K/W_V/W_gate/W_up): each col = one TP rank."""
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def _ann(r, c):
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return _physical_label(cfg, "tp", c)
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return _ann
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def _tp_row_cell_annot(cfg: FullConfig):
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"""Row-parallel tensors split by TP (W_O): each row = one TP rank."""
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def _ann(r, c):
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return _physical_label(cfg, "tp", r)
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return _ann
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def draw_tensor_sharding(cfg: FullConfig, ax=None, my_pe: int = 0,
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show_physical: bool = False):
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"""Draw all tensors with their sharding overlays for PE `my_pe`.
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show_physical: annotate each shard cell with the owning cube/PE.
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"""
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m = cfg.model
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tp = cfg.topo.tp
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cp = cfg.topo.cp
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# PE's assigned shard index for each dim
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q_shard = my_pe % tp # W_Q col shard
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kv_shard_col = min(my_pe % tp, m.h_kv - 1) if m.h_kv >= tp else 0
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ffn_shard = my_pe % cfg.ffn_shard_divisor
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kv_row_shard = 0 # per-CP; showing CP=0 view for clarity
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# Enlarged mode gives more room for physical annotations.
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_fig_w, _fig_h = (22, 13) if show_physical else (15, 9)
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_cell_w = 3.4 if show_physical else 2.6
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_cell_h = 3.0 if show_physical else 2.2
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_gap_x = 0.8 if show_physical else 0.55
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_gap_y = 2.0 if show_physical else 1.7
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if ax is None:
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fig, ax = plt.subplots(figsize=(_fig_w, _fig_h))
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else:
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fig = ax.figure
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# Grid layout: 2 rows x 4 cols of tensor rects
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# Attention row: W_Q, W_K, W_V, W_O
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# FFN + KV row: W_gate, W_up, W_down, KV cache
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cell_w = _cell_w
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cell_h = _cell_h
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gap_x = _gap_x
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gap_y = _gap_y
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_kv_ann = _kv_cell_annot(cfg) if show_physical else None
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_tp_col_ann = _tp_cell_annot(cfg) if show_physical else None
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_tp_row_ann = _tp_row_cell_annot(cfg) if show_physical else None
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positions = [
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(0, 1), # W_Q at row 0
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(1, 1), # W_K
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(2, 1), # W_V
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(3, 1), # W_O
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(0, 0), # W_gate at row 1
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(1, 0), # W_up
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(2, 0), # W_down
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(3, 0), # KV cache
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]
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# ── attention tensors ────────────────────────
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tp_txt = f"TP={tp}"
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ffn_txt = f"{cfg.topo.ffn_shard_scope}={cfg.ffn_shard_divisor}"
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cp_txt = f"CP={cp}"
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def px(col): return col * (cell_w + gap_x)
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def py(row): return row * (cell_h + gap_y)
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# W_Q: (hidden, H_q*d_head), split on COL by TP
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_draw_tensor(ax, px(0), py(1), cell_w, cell_h,
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"W_Q", f"({m.hidden}, {m.h_q * m.d_head})",
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row_splits=1, col_splits=tp,
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my_col_shard=q_shard,
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col_line_color=_TP_LINE_COLOR,
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col_label=f"cols split by {tp_txt}",
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note="col-parallel (output dim)",
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cell_annot=_tp_col_ann)
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# W_K, W_V: (hidden, H_kv*d_head)
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for (name, col_idx) in [("W_K", 1), ("W_V", 2)]:
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splits = min(tp, m.h_kv) if m.h_kv >= tp else tp
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_draw_tensor(ax, px(col_idx), py(1), cell_w, cell_h,
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name, f"({m.hidden}, {m.h_kv * m.d_head})",
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row_splits=1, col_splits=splits,
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my_col_shard=min(q_shard, splits - 1),
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col_line_color=_TP_LINE_COLOR,
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col_label=f"cols split by {tp_txt}",
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note="col-parallel (heads)",
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cell_annot=_tp_col_ann)
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# W_O: (H_q*d_head, hidden), split on ROW by TP
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_draw_tensor(ax, px(3), py(1), cell_w, cell_h,
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"W_O", f"({m.h_q * m.d_head}, {m.hidden})",
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row_splits=tp, col_splits=1,
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my_row_shard=q_shard,
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row_line_color=_TP_LINE_COLOR,
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row_label=f"rows split by {tp_txt}",
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note="row-parallel (input dim)",
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cell_annot=_tp_row_ann)
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# ── FFN tensors ──────────────────────────────
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# W_gate, W_up: (hidden, ffn_dim), split on COL by FFN scope
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for (name, col_idx) in [("W_gate", 0), ("W_up", 1)]:
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_draw_tensor(ax, px(col_idx), py(0), cell_w, cell_h,
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name, f"({m.hidden}, {m.ffn_dim})",
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row_splits=1, col_splits=cfg.ffn_shard_divisor,
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my_col_shard=ffn_shard,
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col_line_color=_FFN_LINE_COLOR,
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col_label=f"cols split by {ffn_txt}",
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note="col-parallel")
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# W_down: (ffn_dim, hidden), split on ROW by FFN scope
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_draw_tensor(ax, px(2), py(0), cell_w, cell_h,
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"W_down", f"({m.ffn_dim}, {m.hidden})",
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row_splits=cfg.ffn_shard_divisor, col_splits=1,
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my_row_shard=ffn_shard,
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row_line_color=_FFN_LINE_COLOR,
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row_label=f"rows split by {ffn_txt}",
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note="row-parallel")
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# ── KV cache: (S_kv, H_kv*d_head), 2D shard ─
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kv_row_splits = cp
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kv_col_splits = min(tp, m.h_kv) if m.h_kv >= tp else tp
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_draw_tensor(ax, px(3), py(0), cell_w, cell_h,
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"KV cache (K and V)",
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f"({m.h_kv * m.d_head}, S_kv={cfg.topo.s_kv:,})",
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row_splits=kv_row_splits,
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col_splits=kv_col_splits if show_physical else 1,
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my_row_shard=kv_row_shard,
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row_line_color=_CP_LINE_COLOR,
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row_label=f"rows: S split by {cp_txt}",
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col_label=f"cols: heads split by {tp_txt}",
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col_line_color=_TP_LINE_COLOR,
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note="2D shard: seq axis (CP) x head axis (TP)",
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cell_annot=_kv_ann)
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# In non-physical mode, KV was drawn with col_splits=1; overlay TP col
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# splits as dotted lines to hint at the 2D nature. In physical mode
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# the _draw_tensor call already drew proper vertical dashes.
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if not show_physical:
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kv_x = px(3)
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kv_y = py(0)
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shard_col_w = cell_w / max(1, kv_col_splits)
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for c in range(1, kv_col_splits):
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xx = kv_x + c * shard_col_w
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ax.plot([xx, xx], [kv_y, kv_y + cell_h],
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color=_TP_LINE_COLOR, linewidth=1.2, linestyle=":")
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ax.set_xlim(-0.3, 4 * (cell_w + gap_x) - gap_x + 0.3)
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ax.set_ylim(-0.9, 2 * (cell_h + gap_y) - gap_y + 0.9)
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ax.set_aspect("auto")
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ax.axis("off")
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ax.set_title(
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f"Tensor sharding view (PE {my_pe}) | TP={tp}, CP={cp}, "
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f"FFN scope={cfg.topo.ffn_shard_scope} | "
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f"blue = this PE's shard",
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fontsize=11, fontweight="bold",
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)
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# Legend for line colors
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ax.plot([], [], color=_TP_LINE_COLOR, linewidth=1.2, linestyle="--",
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label=f"TP split ({tp} ranks)")
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ax.plot([], [], color=_CP_LINE_COLOR, linewidth=1.2, linestyle="--",
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label=f"CP split ({cp} ranks, sequence dim)")
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ax.plot([], [], color=_FFN_LINE_COLOR, linewidth=1.2, linestyle="--",
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label=f"FFN scope split ({cfg.ffn_shard_divisor} ranks)")
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ax.legend(loc="lower center", bbox_to_anchor=(0.5, -0.05),
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ncol=3, fontsize=8, frameon=False)
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return fig
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