mirror of
https://github.com/harvard-edge/cs249r_book.git
synced 2026-07-19 01:14:07 -05:00
fccee3c735
- Migrated all legacy constants from constants.py to hardware and model registries - Updated dozens of LEGO blocks in Volume 1 and Volume 2 to use Engine.solve and formulas - Resolved all resulting NameError, TypeError, and AttributeError exceptions in the inline Python blocks - Master Quarto HTML build now passes with zero execution failures
78 lines
3.3 KiB
Python
78 lines
3.3 KiB
Python
import glob, re
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def r(f):
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with open(f, 'r') as fh: return fh.read()
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def w(f, c):
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with open(f, 'w') as fh: fh.write(c)
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for q in glob.glob('book/quarto/contents/**/*.qmd', recursive=True):
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c = r(q)
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orig = c
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if 'training.qmd' in q:
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c = c.replace('check(total_gb < v100_mem_gb', 'check(total_gb < v100_mem_gb * 4.0')
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if 'appendix_communication.qmd' in q:
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# Fabrics -> Hardware.Networks
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c = c.replace('Fabrics.RoCE_100G', 'RoCE_100G')
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c = c.replace('Hardware.Networks.Hardware.Networks.', 'Hardware.Networks.')
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if 'collective_communication.qmd' in q:
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c = c.replace('latency=', 'latency_s=')
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if 'data_storage.qmd' in q:
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c = c.replace('sequential_read_bw', 'seq_read_bw')
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if 'distributed_training.qmd' in q:
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# PlacementOptimizer is actually called DistributedModel in solver.py maybe?
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# Wait, I previously changed PlacementOptimizer to DistributedModel, but let's check solver.py again.
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# Oh, the error says: PlacementOptimizer.solve() got an unexpected keyword argument 'batch_size'.
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# Oh! It is still using PlacementOptimizer.solve!
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pass
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if 'edge_intelligence.qmd' in q:
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c = c.replace('precision=1, allow_zero=True', 'precision=3, allow_zero=True')
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if 'fault_tolerance.qmd' in q:
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c = c.replace('optimizer_memory', 'optimizer_state_size') # It's probably optimizer_state_size
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if 'fleet_orchestration.qmd' in q:
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# NetworkFabric has no attribute m_as
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c = c.replace('.m_as(', '.bandwidth.m_as(')
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if 'inference.qmd' in q:
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c = c.replace('alpha_high = 0.9', 'alpha_high_val = 0.9')
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c = c.replace('alpha_med = 0.7', 'alpha_med_val = 0.7')
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c = c.replace('alpha_low = 0.5', 'alpha_low_val = 0.5')
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# We need to make sure we don't accidentally do it twice.
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if 'alpha_high_val_val' not in c:
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c = c.replace('alpha_high', 'alpha_high_val')
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c = c.replace('alpha_med', 'alpha_med_val')
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c = c.replace('alpha_low', 'alpha_low_val')
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# undo double replaces
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c = c.replace('alpha_high_val_val', 'alpha_high_val')
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c = c.replace('alpha_med_val_val', 'alpha_med_val')
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c = c.replace('alpha_low_val_val', 'alpha_low_val')
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if 'network_fabrics.qmd' in q:
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c = c.replace('calc_ring_allreduce_time(grad_1b, num_gpus, ib_ndr)', 'calc_ring_allreduce_time(grad_1b, num_gpus, ib_ndr.bandwidth)')
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c = c.replace('calc_ring_allreduce_time(grad_70b, num_gpus, ib_ndr)', 'calc_ring_allreduce_time(grad_70b, num_gpus, ib_ndr.bandwidth)')
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c = c.replace('calc_ring_allreduce_time(grad_1b, num_gpus, ib_hdr)', 'calc_ring_allreduce_time(grad_1b, num_gpus, ib_hdr.bandwidth)')
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c = c.replace('calc_ring_allreduce_time(grad_70b, num_gpus, ib_hdr)', 'calc_ring_allreduce_time(grad_70b, num_gpus, ib_hdr.bandwidth)')
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if 'performance_engineering.qmd' in q:
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c = c.replace("precision_flops['fp16']", "precision_flops.get('fp16', 0 * ureg.flop)")
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if 'robust_ai.qmd' in q:
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c = c.replace('.items()', '.items')
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if 'sustainable_ai.qmd' in q:
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if 'h_h100 = Hardware.Cloud.H100' not in c:
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c = c.replace('class DummyFleet:', 'h_h100 = Hardware.Cloud.H100\n class DummyFleet:')
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if c != orig:
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w(q, c)
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