574 lines
24 KiB
Python
574 lines
24 KiB
Python
"""AstrAI promo: Transformer GQA attention animation.
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Shows the Grouped-Query Attention (GQA) mechanism with orthogonal data-flow lines:
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Input → Q/K/V Projections → Repeat KV → SDPA → O Projection → Output
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"""
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from manim import *
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import numpy as np
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import math
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class Transformer(Scene):
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"""Animates the GQA attention mechanism with orthogonal connection lines."""
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def construct(self):
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title = Text("Grouped-Query Attention (GQA)", font_size=42, color=BLUE)
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title.to_edge(UP, buff=0.35)
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self.play(Write(title))
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# ── Helper: box ──
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def mk(name, color, w=2.6, h=0.72, fs=10):
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box = Rectangle(
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width=w, height=h, color=color, fill_opacity=0.12, stroke_width=1.5
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)
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lbl = Text(name, font_size=fs, color=color)
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return VGroup(box, lbl)
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# ── Layout ──
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inp = Text("x (hidden states)", font_size=15, color=GRAY)
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inp.move_to(UP * 2.6)
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y1 = 1.5
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q_grp = mk("Q Projection\n1536 → 24×64", YELLOW)
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k_grp = mk("K Projection\n1536 → 4×64", YELLOW)
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v_grp = mk("V Projection\n1536 → 4×64", YELLOW)
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q_grp.move_to(LEFT * 3.0 + UP * y1)
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k_grp.move_to(UP * y1)
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v_grp.move_to(RIGHT * 3.0 + UP * y1)
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y2 = 0.0
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repeat_grp = mk("Repeat KV\n4 heads → 24 heads", GREEN, 2.4, 0.68, 10)
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repeat_grp.move_to(UP * y2)
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y3 = -1.6
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sdpa_grp = mk(
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"Scaled Dot-Product\nAttention Q·K^T/√d", BLUE, 2.8, 0.74, 10
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)
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sdpa_grp.move_to(UP * y3)
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y4 = -2.9
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o_grp = mk("O Projection\n1536 → 1536", PURPLE, 2.2, 0.68, 10)
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o_grp.move_to(UP * y4)
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out = Text("x' (hidden states)", font_size=15, color=GRAY)
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out.next_to(o_grp, DOWN, buff=0.4)
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# ── Animate boxes ──
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self.play(Write(inp))
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all_boxes = [q_grp, k_grp, v_grp, repeat_grp, sdpa_grp, o_grp]
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for g in all_boxes:
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self.play(FadeIn(g, shift=UP * 0.1), run_time=0.2)
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# ── Input trunk → branch → Q/K/V (enter from directly above) ──
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trunk_bottom = np.array([0, q_grp.get_top()[1] + 0.35, 0])
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trunk = Line(inp.get_bottom(), trunk_bottom, color=GRAY, stroke_width=1.5)
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self.play(Create(trunk), run_time=0.15)
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branch_left = Line(
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np.array([q_grp.get_top()[0], trunk_bottom[1], 0]),
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np.array([k_grp.get_top()[0], trunk_bottom[1], 0]),
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color=GRAY, stroke_width=1.5,
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)
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branch_right = Line(
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np.array([k_grp.get_top()[0], trunk_bottom[1], 0]),
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np.array([v_grp.get_top()[0], trunk_bottom[1], 0]),
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color=GRAY, stroke_width=1.5,
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)
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self.play(Create(branch_left), Create(branch_right), run_time=0.2)
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drop_q = Line(
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np.array([q_grp.get_top()[0], trunk_bottom[1], 0]),
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q_grp.get_top(),
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color=GRAY, stroke_width=1.5,
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)
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drop_k = Line(
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np.array([k_grp.get_top()[0], trunk_bottom[1], 0]),
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k_grp.get_top(),
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color=GRAY, stroke_width=1.5,
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)
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drop_v = Line(
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np.array([v_grp.get_top()[0], trunk_bottom[1], 0]),
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v_grp.get_top(),
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color=GRAY, stroke_width=1.5,
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)
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for ln in [drop_q, drop_k, drop_v]:
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self.play(Create(ln), run_time=0.12)
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input_lines = VGroup(trunk, branch_left, branch_right, drop_q, drop_k, drop_v)
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# ── K/V → Repeat KV (trunk-branch, enter from above) ──
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kv_junc_y = repeat_grp.get_top()[1] + 0.3
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drop_k2 = Line(
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k_grp.get_bottom(),
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np.array([k_grp.get_bottom()[0], kv_junc_y, 0]),
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color=GRAY, stroke_width=1.5,
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)
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drop_v2 = Line(
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v_grp.get_bottom(),
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np.array([v_grp.get_bottom()[0], kv_junc_y, 0]),
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color=GRAY, stroke_width=1.5,
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)
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kv_branch = Line(
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np.array([v_grp.get_bottom()[0], kv_junc_y, 0]),
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np.array([k_grp.get_bottom()[0], kv_junc_y, 0]),
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color=GRAY, stroke_width=1.5,
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)
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kv_trunk = Line(
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np.array([k_grp.get_bottom()[0], kv_junc_y, 0]),
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repeat_grp.get_top(),
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color=GRAY, stroke_width=1.5,
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)
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kv_lines = VGroup(drop_k2, drop_v2, kv_branch, kv_trunk)
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self.play(Create(kv_lines), run_time=0.3)
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# ── Q → SDPA (bypasses Repeat KV, from above) ──
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qs_junc_y = sdpa_grp.get_top()[1] + 0.3
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line_qs = VMobject(color=GRAY, stroke_width=1.5)
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line_qs.set_points_as_corners([
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q_grp.get_bottom(),
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np.array([q_grp.get_bottom()[0], qs_junc_y, 0]),
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np.array([sdpa_grp.get_top()[0], qs_junc_y, 0]),
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sdpa_grp.get_top(),
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])
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self.play(Create(line_qs), run_time=0.15)
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line_rs = orth_line(repeat_grp.get_bottom(), sdpa_grp.get_top(), GRAY)
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self.play(Create(line_rs), run_time=0.15)
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line_so = orth_line(sdpa_grp.get_bottom(), o_grp.get_top(), GRAY)
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self.play(Create(line_so), run_time=0.15)
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line_oo = orth_line(o_grp.get_bottom(), out.get_top(), GRAY)
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self.play(Create(line_oo), run_time=0.15)
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self.play(Write(out))
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self.wait(0.4)
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all_lines = VGroup(
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input_lines, kv_lines, line_qs,
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line_rs, line_so, line_oo,
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)
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# ── RoPE highlight ──
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rope_q = SurroundingRectangle(q_grp, color=TEAL, buff=0.12)
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rope_k = SurroundingRectangle(k_grp, color=TEAL, buff=0.12)
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rope_t = Text(
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"RoPE: rotary position encoding\napplied to Q and K",
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font_size=13, color=TEAL,
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)
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rope_t.next_to(VGroup(rope_q, rope_k), UP, buff=0.25)
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self.play(Create(rope_q), Create(rope_k), Write(rope_t))
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self.wait(1.5)
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self.play(FadeOut(rope_q), FadeOut(rope_k), FadeOut(rope_t))
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# ── GQA ratio highlight ──
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gqa_h = SurroundingRectangle(
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VGroup(q_grp, k_grp, v_grp), color=YELLOW, buff=0.2
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)
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gqa_t = Text(
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"GQA 6:1 — 24 Q-heads → 4 KV-heads\nKV cache reduced by 83%",
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font_size=13, color=YELLOW,
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)
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gqa_t.next_to(gqa_h, RIGHT, buff=0.5)
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self.play(Create(gqa_h), Write(gqa_t))
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self.wait(1.8)
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self.play(FadeOut(gqa_h), FadeOut(gqa_t))
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# ── Repeat KV highlight ──
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kv_h = SurroundingRectangle(
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VGroup(k_grp, v_grp), color=GREEN, buff=0.12
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)
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kv_t = Text(
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"repeat_kv(): broadcast\n4 heads → 24 heads",
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font_size=12, color=GREEN,
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)
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kv_t.next_to(kv_h, RIGHT, buff=0.5)
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self.play(Create(kv_h), Write(kv_t))
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self.wait(1.5)
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# ── Fade all ──
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self.play(
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*[FadeOut(g) for g in all_boxes],
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FadeOut(all_lines),
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FadeOut(kv_h), FadeOut(kv_t),
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FadeOut(inp), FadeOut(out), FadeOut(title),
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)
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# ═══════════════════════════════════════════════════
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# 12. Scaled Dot-Product Attention — full formula + breakdown
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# ═══════════════════════════════════════════════════
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qkv_title = Text("Scaled Dot-Product Attention", font_size=34, color=BLUE)
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qkv_title.to_edge(UP, buff=0.35)
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self.play(Write(qkv_title))
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# Full formula first, stays on screen
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full_eq = MathTex(
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r"\operatorname{Attention}(Q,K,V)=\operatorname{softmax}\!\left(\frac{QK^\top}{\sqrt{d_k}}\right)\!V",
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font_size=36, color=WHITE,
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)
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full_eq.next_to(qkv_title, DOWN, buff=0.5)
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self.play(Write(full_eq))
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self.wait(0.6)
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# Q / K / V — brief meanings
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q_txt = Text("Q = Query", font_size=18, color=YELLOW)
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k_txt = Text("K = Key", font_size=18, color=ORANGE)
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v_txt = Text("V = Value", font_size=18, color=GREEN)
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qkv_labels = VGroup(q_txt, k_txt, v_txt).arrange(RIGHT, buff=1.5)
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qkv_labels.next_to(full_eq, DOWN, buff=0.5)
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self.play(Write(qkv_labels))
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q_desc = Text("\"what am I looking for?\"", font_size=11, color=YELLOW) \
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.next_to(q_txt, DOWN, buff=0.10)
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k_desc = Text("\"what do I have?\"", font_size=11, color=ORANGE) \
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.next_to(k_txt, DOWN, buff=0.10)
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v_desc = Text("\"what do I contribute?\"", font_size=11, color=GREEN) \
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.next_to(v_txt, DOWN, buff=0.10)
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self.play(Write(q_desc), Write(k_desc), Write(v_desc))
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self.wait(2.0)
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self.play(FadeOut(qkv_labels), FadeOut(q_desc), FadeOut(k_desc), FadeOut(v_desc))
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# Step-by-step decomposition — full formula stays visible above
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steps = [
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(MathTex(r"\text{(1) } S = QK^\top", font_size=26, color=YELLOW),
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Text("score matrix — pairwise token similarity", font_size=12, color=GRAY)),
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(MathTex(r"\text{(2) } S \mathbin{/} \sqrt{d_k}", font_size=26, color=ORANGE),
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Text("scale — prevents gradient explosion", font_size=12, color=GRAY)),
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(MathTex(r"\text{(3) } \operatorname{softmax}\!\left(S \mathbin{/} \sqrt{d_k}\right)", font_size=26, color=GREEN),
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Text("normalize — each row sums to 1 (probability)", font_size=12, color=GRAY)),
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(MathTex(r"\text{(4) } \operatorname{softmax}\!\left(S \mathbin{/} \sqrt{d_k}\right) \cdot V", font_size=26, color=BLUE),
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Text("weighted sum — aggregate values by attention", font_size=12, color=GRAY)),
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]
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step_group = VGroup()
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steps_mobj = VGroup()
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for eq, desc in steps:
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sg = VGroup(eq, desc).arrange(DOWN, buff=0.06)
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step_group.add(sg)
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steps_mobj.add(eq)
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step_group.arrange(DOWN, buff=0.22, aligned_edge=LEFT)
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step_group.next_to(full_eq, DOWN, buff=0.6)
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for sg in step_group:
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self.play(Write(sg), run_time=0.3)
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self.wait(2.5)
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self.play(FadeOut(step_group), FadeOut(full_eq))
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# ═══════════════════════════════════════════════════
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# 13. Attention score heatmap — concrete example
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# ═══════════════════════════════════════════════════
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hm_title = Text("Attention Score Heatmap", font_size=34, color=BLUE)
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hm_title.to_edge(UP, buff=0.35)
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hm_sub = Text("\"The cat sat on the mat\" — causal, per-token attention weights",
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font_size=14, color=GRAY).next_to(hm_title, DOWN, buff=0.12)
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self.play(FadeOut(qkv_title), Write(hm_title), Write(hm_sub))
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tokens = ["<s>", "The", "cat", "sat", "on", "the", "mat"]
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n = len(tokens)
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cell_size = 0.52
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gap = 0.04
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grid_high = n * cell_size + (n - 1) * gap
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grid_left = -grid_high / 2
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grid_top = 1.4
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# pre-mask raw scores (QK^T / sqrt(d_k)) — random-varied, distance-biased
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pre_scores = [
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[2.8, 1.5, 0.3, 0.1, 0.0, 0.0, 0.0],
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[1.2, 3.5, 1.8, 0.5, 0.2, 0.1, 0.0],
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[0.4, 2.0, 3.0, 1.5, 0.6, 0.2, 0.1],
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[0.1, 0.6, 2.5, 2.8, 1.2, 0.4, 0.1],
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[0.0, 0.2, 0.8, 2.0, 2.5, 1.5, 0.3],
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[0.0, 0.1, 0.3, 0.9, 1.8, 2.5, 1.2],
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[0.0, 0.0, 0.1, 0.4, 0.8, 1.5, 3.0],
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]
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# compute post-softmax weights with causal mask (j > i → -inf)
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post_weights = []
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for i in range(n):
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row = pre_scores[i]
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masked = [-float('inf') if j > i else row[j] for j in range(n)]
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exps = [math.exp(v) for v in masked]
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exp_sum = sum(exps)
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post_weights.append([e / exp_sum for e in exps])
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flat_pre = [w for row in pre_scores for w in row]
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pre_min, pre_max = min(flat_pre), max(flat_pre)
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flat_post = [w for row in post_weights for w in row]
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post_min, post_max = min(flat_post), max(flat_post)
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cells = VGroup()
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masked_cells = VGroup()
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for i in range(n):
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for j in range(n):
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pw = pre_scores[i][j]
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pw_normed = (pw - pre_min) / (pre_max - pre_min)
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pw_color = interpolate_color(BLUE, RED, pw_normed)
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sq = Square(
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side_length=cell_size, fill_color=pw_color,
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fill_opacity=0.75, stroke_width=0.5,
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stroke_color=GRAY,
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)
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x = grid_left + j * (cell_size + gap) + cell_size / 2
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y = grid_top - i * (cell_size + gap) - cell_size / 2
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sq.move_to([x, y, 0])
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cells.add(sq)
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if j > i:
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masked_cells.add(sq)
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self.play(FadeIn(sq, scale=0.6), run_time=0.015)
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# row labels (query) on the left
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row_lbls = VGroup()
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for i, tok in enumerate(tokens):
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lbl = Text(tok, font_size=12, color=GRAY)
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y = grid_top - i * (cell_size + gap) - cell_size / 2
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lbl.next_to([grid_left - 0.15, y, 0], LEFT, buff=0.08)
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row_lbls.add(lbl)
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q_label = Text("Q", font_size=11, color=WHITE, weight=BOLD)
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q_label.move_to(row_lbls[0].get_left() + LEFT * 0.3).shift(UP * 0.15)
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self.play(*[Write(l) for l in row_lbls], Write(q_label))
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# column labels (key) on top
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col_lbls = VGroup()
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for j, tok in enumerate(tokens):
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lbl = Text(tok, font_size=9, color=GRAY).rotate(PI / 6)
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x = grid_left + j * (cell_size + gap) + cell_size / 2
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lbl.next_to([x, grid_top + 0.06, 0], UP, buff=0.04)
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col_lbls.add(lbl)
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k_label = Text("K", font_size=11, color=WHITE, weight=BOLD)
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k_label.next_to(col_lbls[0], UP, buff=0.06)
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self.play(*[Write(l) for l in col_lbls], Write(k_label))
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self.wait(1.0)
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# causal mask + softmax — zero out future tokens, recompute weights
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causal_txt = Text("causal mask + softmax\n(future tokens → 0)", font_size=11, color=RED) \
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.next_to(cells[n - 1], UP, buff=0.25).align_to(cells[n - 1], RIGHT)
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anims = [sq.animate.set_fill(DARK_GRAY, 0.15) for sq in masked_cells]
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for i in range(n):
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for j in range(n):
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if j <= i:
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idx = i * n + j
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aw = post_weights[i][j]
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aw_normed = (aw - post_min) / (post_max - post_min)
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aw_color = interpolate_color(BLUE, RED, aw_normed)
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anims.append(cells[idx].animate.set_fill(aw_color, 0.75))
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self.play(*anims, Write(causal_txt))
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self.wait(1.2)
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self.play(FadeOut(causal_txt))
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# highlight key patterns
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h1 = SurroundingRectangle(cells[2 * n + 1], color=ORANGE, stroke_width=2, buff=0.04)
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h2 = SurroundingRectangle(cells[3 * n + 2], color=ORANGE, stroke_width=2, buff=0.04)
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h3 = SurroundingRectangle(cells[4 * n + 3], color=ORANGE, stroke_width=2, buff=0.04)
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h4 = SurroundingRectangle(cells[5 * n + 4], color=ORANGE, stroke_width=2, buff=0.04)
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h5 = SurroundingRectangle(cells[6 * n + 5], color=ORANGE, stroke_width=2, buff=0.04)
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hl_text = Text("previous token attends to next\n(causal sequence learning)", font_size=11, color=ORANGE) \
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.next_to(cells[(n - 1) * n + (n - 1)], RIGHT, buff=0.8)
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self.play(Create(h1), Create(h2), Create(h3), Create(h4), Create(h5), Write(hl_text))
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self.wait(2.0)
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self.play(FadeOut(h1), FadeOut(h2), FadeOut(h3), FadeOut(h4), FadeOut(h5), FadeOut(hl_text))
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# fade all heatmap
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self.play(
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FadeOut(hm_title), FadeOut(hm_sub),
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FadeOut(cells), FadeOut(row_lbls), FadeOut(col_lbls),
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FadeOut(q_label), FadeOut(k_label),
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)
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# ═══════════════════════════════════════════════════
|
||
# Auto-regressive Generation Demo (v2: full I/O pipeline)
|
||
def tok_card(text, fill=DARK_BLUE, stroke=GRAY):
|
||
t = Text(text, font_size=10, color=WHITE, weight=BOLD)
|
||
box = RoundedRectangle(
|
||
width=t.width + 0.2, height=t.height + 0.1,
|
||
corner_radius=0.04, fill_color=fill, fill_opacity=0.5,
|
||
stroke_color=stroke, stroke_width=0.5,
|
||
)
|
||
t.move_to(box)
|
||
return VGroup(box, t)
|
||
|
||
gen_tokens = ["<s>", "The", "cat", "sat", "on", "the", "mat"]
|
||
|
||
gen_title = Text("Auto-regressive Generation", font_size=34, color=BLUE)
|
||
gen_title.to_edge(UP, buff=0.35)
|
||
self.play(Write(gen_title))
|
||
|
||
# ── Layout constants ──
|
||
BLK_W = 1.8
|
||
BLK_H = 0.28
|
||
TFR_H = 0.48
|
||
CX_BLK = -1.0
|
||
Y_EMB = 1.40
|
||
Y_NORM1 = 0.95
|
||
Y_TFR = 0.32
|
||
Y_NORM2 = -0.25
|
||
Y_HEAD = -0.70
|
||
|
||
def mkblk(w, h, y, txt, color, fs=10):
|
||
b = RoundedRectangle(width=w, height=h, corner_radius=0.06,
|
||
fill_color=DARK_BLUE, fill_opacity=0.25,
|
||
stroke_color=color, stroke_width=1.5)
|
||
l = Text(txt, font_size=fs, color=color, weight=BOLD).move_to(b)
|
||
return VGroup(b, l).move_to([CX_BLK, y, 0])
|
||
|
||
emb_node = mkblk(BLK_W, BLK_H, Y_EMB, "Embedding", YELLOW, 9)
|
||
norm1 = mkblk(BLK_W, BLK_H, Y_NORM1, "RMS Norm", GREEN, 9)
|
||
tfr_node = mkblk(BLK_W, TFR_H, Y_TFR, "Transformer", PURPLE, 11)
|
||
norm2 = mkblk(BLK_W, BLK_H, Y_NORM2, "RMS Norm", GREEN, 9)
|
||
head = mkblk(BLK_W, BLK_H, Y_HEAD, "LM Head", RED, 9)
|
||
pipeline = VGroup(emb_node, norm1, tfr_node, norm2, head)
|
||
|
||
# Arrows between blocks
|
||
arrows = VGroup()
|
||
for a, b in zip(pipeline[:-1], pipeline[1:]):
|
||
arr = Arrow(a.get_bottom(), b.get_top(),
|
||
color=GRAY, stroke_width=1.2, tip_length=0.07)
|
||
arrows.add(arr)
|
||
|
||
# ── KV Cache (right of Transformer) ──
|
||
KV_X = 1.8
|
||
kv_size = 0.16
|
||
kv_gap = 0.04
|
||
K_Y = Y_TFR + 0.05
|
||
V_Y = Y_TFR - 0.22
|
||
cache_lbl = Text("KV Cache", font_size=8, color=GRAY).move_to([KV_X, Y_TFR + TFR_H / 2 + 0.2, 0])
|
||
k_hdr = Text("K:", font_size=7, color=YELLOW).move_to([KV_X - 0.65, K_Y, 0])
|
||
v_hdr = Text("V:", font_size=7, color=ORANGE).move_to([KV_X - 0.65, V_Y, 0])
|
||
|
||
kv_k = VGroup()
|
||
kv_v = VGroup()
|
||
k0 = Square(kv_size, fill_color=YELLOW, fill_opacity=0.4,
|
||
stroke_color=YELLOW, stroke_width=0.5).move_to([KV_X, K_Y, 0])
|
||
v0 = Square(kv_size, fill_color=ORANGE, fill_opacity=0.4,
|
||
stroke_color=ORANGE, stroke_width=0.5).move_to([KV_X, V_Y, 0])
|
||
kv_k.add(k0); kv_v.add(v0)
|
||
kv_group = VGroup(cache_lbl, k_hdr, v_hdr, kv_k, kv_v)
|
||
|
||
# ── Distribution builder ──
|
||
def build_dist(probs, y_center, max_w=2.8):
|
||
bars = VGroup(); lbls = VGroup()
|
||
bh = 0.12; bg = 0.02; lx = CX_BLK - max_w / 2
|
||
items = list(probs.items())
|
||
n = len(items)
|
||
y_top = y_center + (n * bh + (n - 1) * bg) / 2
|
||
for i, (tok, pct) in enumerate(items):
|
||
w = max_w * pct / 100
|
||
y = y_top - i * (bh + bg)
|
||
bar = Rectangle(width=w, height=bh,
|
||
fill_color=interpolate_color(BLUE, RED, pct / 100),
|
||
fill_opacity=0.85, stroke_color=LIGHT_GRAY, stroke_width=0.3)
|
||
bar.move_to([lx + w / 2, y, 0])
|
||
lbl = Text(f"{tok} {pct}%", font_size=7, color=WHITE)
|
||
lbl.next_to(bar, RIGHT, buff=0.05)
|
||
bars.add(bar); lbls.add(lbl)
|
||
return VGroup(bars, lbls)
|
||
|
||
dists = [
|
||
{"The": 72, "cat": 8, "sat": 6, "on": 4, "<unk>": 10},
|
||
{"cat": 65, "sat": 12, "was": 8, "is": 6, "<unk>": 9},
|
||
{"sat": 58, "slept": 15, "ran": 8, "jumped": 6, "<unk>": 13},
|
||
{"on": 55, "down": 12, "quietly": 8, "and": 6, "<unk>": 19},
|
||
{"the": 60, "a": 12, "top": 8, "floor": 5, "<unk>": 15},
|
||
{"mat": 50, "table": 15, "chair": 8, "floor": 6, "<unk>": 21},
|
||
]
|
||
Y_DIST = -1.45
|
||
|
||
# ── Token sequence row ──
|
||
SX = -4.0; Y_SEQ = 2.0; GAP = 0.55
|
||
seq = VGroup()
|
||
sos = tok_card("<s>", BLUE, BLUE).move_to([SX, Y_SEQ, 0])
|
||
seq.add(sos)
|
||
|
||
# Step label (below everything)
|
||
step_lbl = Text("Step 0 — [<s>] → ?", font_size=9, color=GRAY)
|
||
step_lbl.move_to([0, -2.1, 0])
|
||
|
||
# ── Show static elements ──
|
||
self.play(FadeIn(pipeline), FadeIn(arrows))
|
||
self.play(FadeIn(kv_group))
|
||
self.play(FadeIn(sos))
|
||
self.play(Write(step_lbl))
|
||
self.wait(0.5)
|
||
|
||
# ── Generation loop ──
|
||
for i, tok in enumerate(gen_tokens[1:], start=1):
|
||
input_str = " ".join(gen_tokens[:i + 1])
|
||
|
||
# 1. Highlight last token (the input being processed)
|
||
last = seq[-1]
|
||
hl = SurroundingRectangle(last, color=YELLOW, stroke_width=2, buff=0.04)
|
||
self.play(Create(hl), run_time=0.12)
|
||
|
||
# 2. Arrow from last token → Embedding
|
||
in_arr = Arrow(last.get_bottom(), emb_node.get_top(), color=YELLOW,
|
||
stroke_width=2, tip_length=0.08)
|
||
self.play(FadeIn(in_arr, scale=0.5), run_time=0.1)
|
||
|
||
# 3. Cascade through pipeline
|
||
pipes = [emb_node, norm1, tfr_node, norm2, head]
|
||
self.play(
|
||
*[p[0].animate.set_fill_opacity(0.6) for p in pipes],
|
||
run_time=0.12
|
||
)
|
||
self.play(
|
||
*[p[0].animate.set_fill_opacity(0.25) for p in pipes],
|
||
run_time=0.1
|
||
)
|
||
self.play(FadeOut(in_arr), FadeOut(hl), run_time=0.08)
|
||
|
||
# 4. Show probability distribution
|
||
dist_arr = Arrow(head.get_bottom(), head.get_bottom() + DOWN * 0.22,
|
||
color=GRAY, stroke_width=1, tip_length=0.06)
|
||
dist = build_dist(dists[i - 1], Y_DIST)
|
||
self.play(FadeIn(dist_arr, scale=0.5), FadeIn(dist, scale=0.8), run_time=0.25)
|
||
|
||
# 5. Sampling: highlight top bar
|
||
top_bar = dist[0][0]
|
||
sample_hl = SurroundingRectangle(top_bar, color=YELLOW, stroke_width=1.5, buff=0.02)
|
||
samp_lbl = Text("\u2713 argmax", font_size=7, color=YELLOW)
|
||
samp_lbl.next_to(dist, DOWN, buff=0.05)
|
||
self.play(Create(sample_hl), Write(samp_lbl), run_time=0.2)
|
||
self.wait(0.15)
|
||
|
||
# 6. Predicted token appears below distribution
|
||
pred = tok_card(tok, YELLOW, YELLOW)
|
||
target_x = SX + len(seq) * GAP
|
||
pred.move_to([CX_BLK, Y_DIST - 0.45, 0])
|
||
self.play(FadeIn(pred, scale=0.4), run_time=0.15)
|
||
|
||
# 7. Token rises to join sequence at top
|
||
self.play(pred.animate.move_to([target_x, Y_SEQ, 0]), run_time=0.3)
|
||
self.play(
|
||
pred[0].animate.set_fill(DARK_BLUE, 0.5).set_stroke(GRAY, 0.5),
|
||
pred[1].animate.set_color(WHITE),
|
||
FadeOut(sample_hl), FadeOut(samp_lbl),
|
||
)
|
||
seq.add(pred)
|
||
|
||
# 8. KV Cache: add K,V of this predicted token
|
||
kv_x = KV_X + i * (kv_size + kv_gap)
|
||
k_sq = Square(kv_size, fill_color=YELLOW, fill_opacity=0.4,
|
||
stroke_color=YELLOW, stroke_width=0.5).move_to([kv_x, K_Y, 0])
|
||
v_sq = Square(kv_size, fill_color=ORANGE, fill_opacity=0.4,
|
||
stroke_color=ORANGE, stroke_width=0.5).move_to([kv_x, V_Y, 0])
|
||
self.play(FadeIn(k_sq, scale=1.5), FadeIn(v_sq, scale=1.5), run_time=0.15)
|
||
kv_k.add(k_sq); kv_v.add(v_sq)
|
||
|
||
# 9. Remove distribution and arrow
|
||
self.play(FadeOut(dist), FadeOut(dist_arr), run_time=0.08)
|
||
|
||
# 10. Update step label
|
||
new_lbl = Text(f"Step {i} — [{input_str}]", font_size=9, color=GRAY)
|
||
new_lbl.move_to(step_lbl)
|
||
self.play(Transform(step_lbl, new_lbl))
|
||
self.wait(0.3)
|
||
|
||
self.wait(2.0)
|
||
|
||
|
||
def orth_line(start, end, color=GRAY):
|
||
"""Create an L-shaped orthogonal line from start to end."""
|
||
mid = np.array([start[0], end[1], 0])
|
||
path = VMobject(color=color, stroke_width=1.5)
|
||
path.set_points_as_corners([start, mid, end])
|
||
return path
|