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
56 lines
3.7 KiB
Python
56 lines
3.7 KiB
Python
import re
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filepath = 'mlsysim/mlsysim/core/constants.py'
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with open(filepath, 'r') as f:
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lines = f.readlines()
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mapping_keys = [
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'GPT2_PARAMS', 'GPT2_LAYERS', 'GPT2_HIDDEN_DIM', 'GPT2_HEADS',
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'GPT3_PARAMS', 'GPT3_LAYERS', 'GPT3_HIDDEN_DIM', 'GPT3_HEADS', 'GPT3_TRAINING_OPS',
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'GPT4_EST_PARAMS', 'GPT4_LAYERS', 'GPT4_HIDDEN_DIM', 'GPT4_HEADS',
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'BERT_BASE_PARAMS', 'BERT_BASE_LAYERS', 'BERT_BASE_HIDDEN_DIM', 'BERT_BASE_HEADS', 'BERT_BASE_FLOPs',
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'BERT_LARGE_PARAMS', 'BERT_LARGE_LAYERS', 'BERT_LARGE_HIDDEN_DIM', 'BERT_LARGE_HEADS', 'BERT_LARGE_FLOPs',
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'LLAMA2_70B_PARAMS', 'LLAMA2_70B_LAYERS', 'LLAMA2_70B_HIDDEN_DIM', 'LLAMA2_70B_HEADS',
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'LLAMA3_8B_PARAMS', 'LLAMA3_8B_LAYERS', 'LLAMA3_8B_HIDDEN_DIM', 'LLAMA3_8B_HEADS', 'LLAMA3_8B_KV_HEADS',
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'LLAMA3_70B_PARAMS', 'LLAMA3_70B_LAYERS', 'LLAMA3_70B_HIDDEN_DIM', 'LLAMA3_70B_HEADS', 'LLAMA3_70B_KV_HEADS',
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'RESNET50_PARAMS', 'RESNET50_FLOPs',
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'MOBILENETV2_PARAMS', 'MOBILENETV2_FLOPs',
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'ALEXNET_PARAMS', 'ALEXNET_FLOPs',
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'YOLOV8_NANO_PARAMS', 'YOLOV8_NANO_FLOPs', 'YOLOV8_NANO_LAYERS',
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'KWS_DSCNN_PARAMS', 'KWS_DSCNN_FLOPs',
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'WAKEVISION_PARAMS', 'WAKEVISION_FLOPs',
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'ANOMALY_MODEL_PARAMS',
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'DLRM_MODEL_SIZE_FP32',
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'MAMBA_130M_PARAMS', 'MAMBA_130M_LAYERS', 'MAMBA_130M_HIDDEN_DIM', 'MAMBA_130M_STATE_SIZE',
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'MAMBA_2_8B_PARAMS', 'MAMBA_2_8B_LAYERS', 'MAMBA_2_8B_HIDDEN_DIM', 'MAMBA_2_8B_STATE_SIZE',
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'STABLE_DIFFUSION_V1_5_PARAMS', 'STABLE_DIFFUSION_V1_5_RESOLUTION', 'STABLE_DIFFUSION_V1_5_STEPS', 'STABLE_DIFFUSION_V1_5_FLOPs_PER_STEP',
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'V100_FLOPS_FP16_TENSOR', 'V100_FLOPS_FP32', 'V100_MEM_BW', 'V100_MEM_CAPACITY', 'V100_TDP', 'V100_UNIT_COST',
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'A100_FLOPS_FP16_TENSOR', 'A100_FLOPS_TF32', 'A100_FLOPS_FP32', 'A100_TOPS_INT8', 'A100_MEM_BW', 'A100_MEM_CAPACITY', 'A100_TDP', 'A100_UNIT_COST',
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'H100_FLOPS_FP16_TENSOR', 'H100_FLOPS_FP8_TENSOR', 'H100_FLOPS_TF32', 'H100_TOPS_INT8', 'H100_MEM_BW', 'H100_MEM_CAPACITY', 'H100_TDP', 'H100_UNIT_COST',
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'H200_MEM_BW', 'H200_MEM_CAPACITY', 'H200_TDP', 'H200_UNIT_COST',
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'B200_FLOPS_FP16_TENSOR', 'B200_FLOPS_FP8_TENSOR', 'B200_FLOPS_FP4_TENSOR', 'B200_TOPS_INT4', 'B200_MEM_BW', 'B200_MEM_CAPACITY', 'B200_TDP', 'B200_UNIT_COST',
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'NVL72_GPUs', 'NVL72_FLOPS_FP16_TENSOR', 'NVL72_FLOPS_FP8_TENSOR', 'NVL72_FLOPS_FP4_TENSOR', 'NVL72_MEM_CAPACITY', 'NVL72_MEM_BW', 'NVL72_NVLINK_BW', 'NVL72_TDP', 'NVL72_UNIT_COST',
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'MI300X_FLOPS_FP16_TENSOR', 'MI300X_FLOPS_FP8', 'MI300X_TOPS_INT8', 'MI300X_FLOPS_FP32', 'MI300X_MEM_BW', 'MI300X_MEM_CAPACITY', 'MI300X_TDP', 'MI300X_UNIT_COST',
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'MI250X_FLOPS_FP16_TENSOR', 'MI250X_FLOPS_FP32', 'MI250X_TOPS_INT8', 'MI250X_MEM_BW', 'MI250X_MEM_CAPACITY', 'MI250X_TDP',
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'GAUDI2_FLOPS_BF16', 'GAUDI2_FLOPS_FP8', 'GAUDI2_MEM_BW', 'GAUDI2_MEM_CAPACITY', 'GAUDI2_TDP',
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'GAUDI3_FLOPS_BF16', 'GAUDI3_FLOPS_FP8', 'GAUDI3_MEM_BW', 'GAUDI3_MEM_CAPACITY', 'GAUDI3_TDP',
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'TRAINIUM2_FLOPS_BF16', 'TRAINIUM2_FLOPS_FP8', 'TRAINIUM2_MEM_BW', 'TRAINIUM2_MEM_CAPACITY', 'TRAINIUM2_TDP',
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'TPUV4_FLOPS_BF16', 'TPUV4_MEM_BW',
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'TPUV5P_FLOPS_BF16', 'TPUV5P_TOPS_INT8', 'TPUV5P_MEM_BW', 'TPUV5P_MEM_CAPACITY', 'TPUV5P_TDP',
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'TPUV6_FLOPS_BF16', 'TPUV6_MEM_BW', 'TPUV6_MEM_CAPACITY',
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'T4_FLOPS_FP16_TENSOR', 'T4_TOPS_INT8', 'T4_MEM_BW', 'T4_TDP', 'T4_UNIT_COST',
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'WSE3_FLOPS_FP16', 'WSE3_MEM_CAPACITY', 'WSE3_MEM_BW', 'WSE3_TDP', 'CEREBRAS_CS3_UNIT_COST',
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'MOBILE_NPU_TOPS_INT8', 'MOBILE_NPU_MEM_BW',
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]
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new_lines = []
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for line in lines:
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match = re.match(r'^([A-Z0-9_]+)\s*=', line)
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if match and match.group(1) in mapping_keys:
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continue # Skip this line
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new_lines.append(line)
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with open(filepath, 'w') as f:
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f.writelines(new_lines)
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print("Removed legacy constants from constants.py")
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