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import gradio as gr |
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import hand_schedule |
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import adaptive_schedule |
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import interleaved_variant |
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import type2 |
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import schedule1f1bv |
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from PIL import Image |
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from svg_event import render_manual_graph |
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import pathlib |
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def percentage(x): |
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return f"{x*100:.2f}%" |
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def get_schedule_time(result): |
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result = [ |
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list(filter(lambda x: x.type in {'F', 'B', 'W'}, r)) for r in result |
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] |
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time = max( |
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[ |
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max([x.completion_time for x in stage]) - min([x.start_time for x in stage]) for stage in result |
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] |
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) |
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return time |
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def get_memory_usage(result): |
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max_mem = 0 |
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has_w = False |
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for r in result: |
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for x in r: |
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if x.type in ('W', 'w'): |
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has_w = True |
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for r in result: |
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cur = 0 |
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for x in r: |
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if x.type in ('F', 'f'): |
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cur += 1 |
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if x.type in ('W', 'w'): |
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cur -= 1 |
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if has_w == False and x.type in ('B', 'b'): |
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cur -= 1 |
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max_mem = max(max_mem, cur) |
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return max_mem |
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img_queue = [] |
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def get_schedule_image(result, max_time): |
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result = [ |
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list(filter(lambda x: x.type in {'F', 'B', 'W'}, r)) for r in result |
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] |
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svg = render_manual_graph(result, max_time, len(result[0]) <= 72) |
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img_queue.append(svg) |
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if len(img_queue) > 32: |
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poped = img_queue.pop(0) |
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pathlib.Path(poped).unlink() |
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return pathlib.Path(svg) |
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def calculate(p, m, f, b, w, c, mem): |
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def get_bubble_rate(_time): |
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return 1 - ((f + b + w) * m / _time) |
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baseline_result = hand_schedule.get_hand_schedule(p, m, f, b + w, 0, c) |
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baseline_result = [ |
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list(filter(lambda x: x.type in {'F', 'B'}, r)) for r in baseline_result |
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] |
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baseline_time = get_schedule_time(baseline_result) |
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baseline_bubble=percentage(get_bubble_rate(baseline_time)) |
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baseline_mem = get_memory_usage(baseline_result) |
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baseline_acceleration=percentage(0) |
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adapt_result = adaptive_schedule.schedule( |
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p, |
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m, |
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[f/2, b/2, w/2, c], |
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max_mem=mem * 2 |
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) |
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adapt_time = get_schedule_time(adapt_result) |
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adapt_mem = get_memory_usage(adapt_result) / 2 |
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adapt_bubble=percentage(get_bubble_rate(adapt_time)) |
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adapt_acceleration=percentage(baseline_time/adapt_time - 1) if baseline_time is not None else None |
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schedule1f1bv_result = schedule1f1bv.schedule( |
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p, |
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m, |
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[f / 2, b / 2, w / 2, c] |
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) |
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schedule1f1bv_time = get_schedule_time(schedule1f1bv_result) |
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schedule1f1bv_mem = get_memory_usage(schedule1f1bv_result) / 2 |
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schedule1f1bv_bubble=percentage(get_bubble_rate(schedule1f1bv_time)) |
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schedule1f1bv_acceleration=percentage(baseline_time/schedule1f1bv_time - 1) if baseline_time is not None else None |
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type2_result = type2.schedule( |
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p, |
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m, |
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[f, b, w, c] |
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) |
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type2_time = get_schedule_time(type2_result) |
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type2_mem = get_memory_usage(type2_result) |
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type2_bubble=percentage(get_bubble_rate(type2_time)) |
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type2_acceleration=percentage(baseline_time/type2_time - 1) if baseline_time is not None else None |
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interleaved_result = interleaved_variant.get_interleaved_variation( |
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p, |
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m, |
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[f/2, b/2, w/2, c] |
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) |
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interleaved_time = get_schedule_time(interleaved_result) |
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interleaved_mem = get_memory_usage(interleaved_result) / 2 |
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interleaved_bubble=percentage(get_bubble_rate(interleaved_time)) |
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interleaved_acceleration=percentage(baseline_time/interleaved_time - 1) if baseline_time is not None else None |
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max_time = max(filter(lambda x: x is not None, [baseline_time, adapt_time, interleaved_time, type2_time, schedule1f1bv_time])) |
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print(max_time) |
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if baseline_result is not None: |
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baseline_image = get_schedule_image(baseline_result, max_time) |
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if adapt_result is not None: |
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adapt_image = get_schedule_image(adapt_result, max_time) |
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if interleaved_result is not None: |
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interleaved_image = get_schedule_image(interleaved_result, max_time) |
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if type2_result is not None: |
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type2_image = get_schedule_image(type2_result, max_time) |
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if schedule1f1bv_result is not None: |
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schedule1f1bv_image = get_schedule_image(schedule1f1bv_result, max_time) |
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return [baseline_acceleration, baseline_mem, baseline_bubble, baseline_image, |
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adapt_acceleration, adapt_mem, adapt_bubble, adapt_image, |
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schedule1f1bv_acceleration, schedule1f1bv_mem, schedule1f1bv_bubble, schedule1f1bv_image, |
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type2_acceleration, type2_mem, type2_bubble, type2_image, |
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interleaved_acceleration, interleaved_mem, interleaved_bubble, interleaved_image] |
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with gr.Blocks() as demo: |
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gr.Markdown(open("description1.md").read()) |
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gr.Markdown("# Pipeline Scheduler Playground") |
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presets = { |
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'Default Case': (4, 10, 100, 110, 90, 5, 'V-Half (1/2)'), |
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'Ideal Case': (4, 10, 20, 20, 20, 0, 'V-Min (1/3)'), |
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'Real Case': (4, 10, 1049, 1122, 903, 79, 'V-Half (1/2)'), |
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'Zero Bubble Case': (4, 10, 1049, 1122, 903, 79, 'V-ZB (1)') |
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} |
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preset_buttons = {} |
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with gr.Group(): |
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gr.Markdown("Preset Setups") |
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with gr.Row(): |
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for (k, v) in presets.items(): |
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preset_buttons[k] = gr.Button(k, variant="secondary") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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with gr.Group(): |
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gr.Markdown("Basic Parameters") |
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with gr.Row(): |
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p=gr.Number(label="Number of stages (p)", value=4, interactive=True, precision=0) |
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m=gr.Number(label="Number of microbatches (m)", value=10, interactive=True, precision=0) |
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with gr.Column(scale=2): |
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with gr.Group(): |
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gr.Markdown("Costs. All costs are used as integers. For chunked schedules, this is the time of two virtual stages on a stage combined.") |
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with gr.Row(): |
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f=gr.Number(label="Time of F", value=100, interactive=True, precision=0) |
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b=gr.Number(label="Time of B", value=110, interactive=True, precision=0) |
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w=gr.Number(label="Time of W", value=90, interactive=True, precision=0) |
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c=gr.Number(label="Time of one P2P communication", value=5, interactive=True, precision=0) |
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with gr.Group(): |
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gr.Markdown("Activation memory limit.") |
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def update_mem(p, s, mem): |
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print("update") |
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if s == "custom": |
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return mem |
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if s == "V-Min (1/3)": |
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return (p + 4) // 3 |
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if s == "V-Half (1/2)": |
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return (p + 2) // 2 |
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if s == "V-ZB (1)": |
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return p |
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assert False |
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memsel=gr.Radio(choices=["V-Min (1/3)", "V-Half (1/2)", "V-ZB (1)", "custom"], value="V-Half (1/2)") |
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mem=gr.Number(label="Custom memory limit in terms of pending F on a stage. For chunked schedules, this is relative to two virtual stages on a stage combined.", value=(p.value + 2) // 2, interactive=True, precision=0) |
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memsel.change(update_mem, inputs=[p, memsel, mem], outputs=mem) |
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p.change(update_mem, inputs=[p, memsel, mem], outputs=mem) |
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button=gr.Button("Calculate", variant="primary") |
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with gr.Group(): |
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gr.Markdown("1F1B") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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baseline_acceleration=gr.Textbox("", label="Acceleration compared to 1F1B") |
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baseline_mem=gr.Textbox("", label="Maximum memory usage") |
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baseline_bubble=gr.Textbox("", label="Bubble Rate") |
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with gr.Column(scale=4): |
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baseline_image=gr.Image(None, interactive=False, label="Schedule Image", show_label=False) |
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with gr.Group(): |
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gr.Markdown("Adaptive Scheduler") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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adapt_acceleration=gr.Textbox("", label="Acceleration compared to 1F1B") |
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adapt_mem=gr.Textbox("", label="Maximum memory usage") |
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adapt_bubble=gr.Textbox("", label="Bubble Rate") |
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with gr.Column(scale=4): |
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adapt_image=gr.Image(None, interactive=False, label="Schedule Image", show_label=False) |
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gr.Markdown(open("description2.md").read()) |
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with gr.Group(): |
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gr.Markdown("1F1B-V Schedule") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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schedule1f1bv_acceleration=gr.Textbox("", label="Acceleration compared to 1F1B") |
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schedule1f1bv_mem=gr.Textbox("", label="Maximum memory usage") |
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schedule1f1bv_bubble=gr.Textbox("", label="Bubble Rate") |
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with gr.Column(scale=4): |
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schedule1f1bv_image=gr.Image(None, interactive=False, label="Schedule Image", show_label=False) |
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with gr.Group(): |
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gr.Markdown("Zero bubble schedule with 2/3 1F1B memory") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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type2_acceleration=gr.Textbox("", label="Acceleration compared to 1F1B") |
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type2_mem=gr.Textbox("", label="Maximum memory usage") |
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type2_bubble=gr.Textbox("", label="Bubble Rate") |
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with gr.Column(scale=4): |
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type2_image=gr.Image(None, interactive=False, label="Schedule Image", show_label=False) |
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with gr.Group(): |
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gr.Markdown("Variation of Interleaved 1F1B Schedule") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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interleaved_acceleration=gr.Textbox("", label="Acceleration compared to 1F1B") |
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interleaved_mem=gr.Textbox("", label="Maximum memory usage") |
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interleaved_bubble=gr.Textbox("", label="Bubble Rate") |
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with gr.Column(scale=4): |
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interleaved_image=gr.Image(None, interactive=False, label="Schedule Image", show_label=False) |
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button.click(calculate, inputs=[p, m, f, b, w, c, mem], outputs=[baseline_acceleration, baseline_mem, baseline_bubble, baseline_image, |
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adapt_acceleration, adapt_mem, adapt_bubble, adapt_image, |
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schedule1f1bv_acceleration, schedule1f1bv_mem, schedule1f1bv_bubble, schedule1f1bv_image, |
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type2_acceleration, type2_mem, type2_bubble, type2_image, |
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interleaved_acceleration, interleaved_mem, interleaved_bubble, interleaved_image]) |
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gr.Markdown(open("description3.md").read()) |
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for (k, v) in presets.items(): |
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def update_preset(pb, p, m, f, b, w, c, mem): |
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print(pb) |
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print(presets[pb]) |
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print(presets[pb][-1]) |
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return *presets[pb],*calculate(*presets[pb][:-1], update_mem(p, presets[pb][-1], -1)) |
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preset_buttons[k].click( |
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update_preset, |
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inputs=[preset_buttons[k], p, m, f, b, w, c, mem], |
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outputs=[p, m, f, b, w, c, memsel, |
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baseline_acceleration, baseline_mem, baseline_bubble, baseline_image, |
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adapt_acceleration, adapt_mem, adapt_bubble, adapt_image, |
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schedule1f1bv_acceleration, schedule1f1bv_mem, schedule1f1bv_bubble, schedule1f1bv_image, |
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type2_acceleration, type2_mem, type2_bubble, type2_image, |
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interleaved_acceleration, interleaved_mem, interleaved_bubble, interleaved_image]) |
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demo.launch() |
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