| #!/usr/bin/env python3 | |
| """ | |
| deep_inspect_batch.py | |
| ===================== | |
| Deeply inspects module source code and extracts precise architectural components, | |
| exact forward-pass math/LaTeX, and calculates exact tensor parameters for all 4 models: | |
| - DistilBERT (Encoder, 6 layers, d=768, ffn=3072, h=12) | |
| - RoBERTa (Encoder, 12 layers, d=768, ffn=3072, h=12) | |
| - GPT-2 (Causal Decoder, 12 layers, d=768, ffn=3072, h=12) | |
| - Qwen (GQA Decoder, 24 layers, d=896, ffn=4864, h=14, kv_h=2) | |
| """ | |
| import os | |
| import sys | |
| import glob | |
| import ast | |
| import re | |
| import json | |
| import math | |
| BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) | |
| os.chdir(BASE_DIR) | |
| COMPONENTS_DIR = os.path.join(BASE_DIR, "Docs", "visualizer", "components") | |
| os.makedirs(COMPONENTS_DIR, exist_ok=True) | |
| MODEL_CONFIGS = { | |
| "distilbert": { | |
| "name": "DistilBERT-Base", | |
| "family": "Encoder", | |
| "hidden_size": 768, | |
| "intermediate_size": 3072, | |
| "num_layers": 6, | |
| "num_heads": 12, | |
| "num_kv_heads": 12, | |
| "vocab_size": 30522, | |
| "embeddings_params": 23834112, # Vocab (30522*768) + Pos (512*768) | |
| "base_attn_layer_params": 4 * 768 * 768, # 2,359,296 (4 separate linear projections) | |
| "base_ffn_layer_params": 2 * 768 * 3072, # 4,718,592 (lin1 + lin2) | |
| }, | |
| "roberta": { | |
| "name": "RoBERTa-Base", | |
| "family": "Encoder", | |
| "hidden_size": 768, | |
| "intermediate_size": 3072, | |
| "num_layers": 12, | |
| "num_heads": 12, | |
| "num_kv_heads": 12, | |
| "vocab_size": 50265, | |
| "embeddings_params": 38999040, # Vocab (50265*768) + Pos (514*768) + Token_type | |
| "base_attn_layer_params": 4 * 768 * 768, # 2,359,296 (Q, K, V, Dense) | |
| "base_ffn_layer_params": 2 * 768 * 3072, # 4,718,592 (intermediate + output) | |
| }, | |
| "gpt2": { | |
| "name": "GPT-2-Small", | |
| "family": "Causal Decoder", | |
| "hidden_size": 768, | |
| "intermediate_size": 3072, | |
| "num_layers": 12, | |
| "num_heads": 12, | |
| "num_kv_heads": 12, | |
| "vocab_size": 50257, | |
| "embeddings_params": 39383808, # Vocab (50257*768) + Pos (1024*768) | |
| "base_attn_layer_params": (768 * 2304) + (768 * 768), # 2,359,296 (Fused c_attn 3*d*d + c_proj d*d) | |
| "base_ffn_layer_params": (768 * 3072) + (3072 * 768), # 4,718,592 (c_fc + c_proj Conv1D) | |
| }, | |
| "qwen": { | |
| "name": "Qwen2.5-0.5B", | |
| "family": "GQA Decoder (SLM)", | |
| "hidden_size": 896, | |
| "intermediate_size": 4864, | |
| "num_layers": 24, | |
| "num_heads": 14, | |
| "num_kv_heads": 2, # Grouped-Query Attention (7:1 sharing) | |
| "head_dim": 64, | |
| "vocab_size": 151936, | |
| "embeddings_params": 136134656, # Vocab (151936*896) | |
| # Q (14*64*896 = 896*896) + K (2*64*896 = 128*896) + V (128*896) + Out (896*896) | |
| "base_attn_layer_params": (896 * 896) + (128 * 896) + (128 * 896) + (896 * 896), # 1,837,056 | |
| # SwiGLU: gate_proj + up_proj + down_proj (3 projections!) | |
| "base_ffn_layer_params": 3 * 896 * 4864, # 13,074,432 | |
| } | |
| } | |
| # Calculate baseline total model parameters | |
| for m_key, cfg in MODEL_CONFIGS.items(): | |
| cfg["base_layer_params"] = cfg["base_attn_layer_params"] + cfg["base_ffn_layer_params"] | |
| cfg["total_baseline_params"] = cfg["embeddings_params"] + cfg["num_layers"] * cfg["base_layer_params"] | |
| def format_module_name(base_name): | |
| """Formats module identifier into human-readable publication title preserving acronyms.""" | |
| s = re.sub(r'([a-z])([A-Z])', r'\1 \2', base_name) | |
| s = s.replace('_', ' ').replace('-', ' ') | |
| words = s.split() | |
| acronyms = {'Svd', 'Soa', 'Moe', 'Mla', 'Ssm', 'Gqa', 'Fp8', 'Fp4', 'Lora', 'Hgrn', 'Eat', 'Bam', 'Rpc', 'Deltanet', 'Ssd', 'Kda', 'Tpa', 'Hla', 'Ada', 'Qkv'} | |
| formatted = [] | |
| for w in words: | |
| cap = w.capitalize() | |
| if cap in acronyms or w.upper() in ('SVD', 'SOA', 'MOE', 'MLA', 'SSM', 'GQA', 'FP8', 'FP4', 'LORA', 'HGRN', 'EAT', 'BAM', 'RPC', 'DELTANET', 'SSD', 'KDA', 'TPA', 'HLA', 'ADA', 'QKV'): | |
| formatted.append(w.upper()) | |
| else: | |
| formatted.append(cap) | |
| return " ".join(formatted) | |
| def extract_code_intelligence(file_path): | |
| """Deeply inspects Python source code for module components, submodules, rank, and math.""" | |
| base_name = os.path.splitext(os.path.basename(file_path))[0] | |
| is_attn = "attentions" in file_path | |
| with open(file_path, "r", encoding="utf-8", errors="ignore") as f: | |
| code = f.read() | |
| classes = [] | |
| methods = [] | |
| mod_docstring = "" | |
| main_class_docstring = "" | |
| rank_val = None | |
| experts_val = 4 | |
| prune_ratio = 0.45 | |
| quant_bits = 16 | |
| try: | |
| tree = ast.parse(code) | |
| mod_docstring = ast.get_docstring(tree) or "" | |
| for node in ast.walk(tree): | |
| if isinstance(node, ast.ClassDef): | |
| classes.append(node.name) | |
| c_doc = ast.get_docstring(node) | |
| if c_doc and (node.name.lower() in base_name.lower() or base_name.lower() in node.name.lower()): | |
| main_class_docstring = c_doc | |
| elif isinstance(node, ast.FunctionDef): | |
| methods.append(node.name) | |
| elif isinstance(node, ast.Assign): | |
| for target in node.targets: | |
| if isinstance(target, ast.Name): | |
| name = target.id.lower() | |
| if name in ('rank', 'r', 'lora_rank', 'default_rank') and isinstance(node.value, ast.Constant): | |
| if isinstance(node.value.value, int): | |
| rank_val = node.value.value | |
| elif name in ('num_experts', 'n_experts') and isinstance(node.value, ast.Constant): | |
| if isinstance(node.value.value, int): | |
| experts_val = node.value.value | |
| elif name in ('prune_ratio', 'sparsity_ratio') and isinstance(node.value, ast.Constant): | |
| if isinstance(node.value.value, (int, float)): | |
| prune_ratio = float(node.value.value) | |
| except Exception: | |
| pass | |
| # Fallback to class docstring if module docstring is empty | |
| chosen_doc = mod_docstring or main_class_docstring or "" | |
| # Hyperparameter extraction | |
| if rank_val is None: | |
| m_r = re.search(r'\b(?:rank|r|lora_r)\s*[:=]\s*(\d+)', code) | |
| rank_val = int(m_r.group(1)) if m_r else 64 | |
| # Determine paradigm & type accurately | |
| ident_lower = (base_name + " " + " ".join(classes)).lower() | |
| doc_lower = (chosen_doc[:400]).lower() | |
| if any(k in ident_lower for k in ('moe', 'expert', 'router', 'switch')) or 'expert' in doc_lower: | |
| paradigm = 'moe' | |
| category = 'Mixture of Experts & Routing' | |
| icon = '🔀' | |
| elif any(k in ident_lower for k in ('mamba', 'ssm', 'state_space', 'selective_scan', 'bimamba', 'griffin', 'hgrn')): | |
| paradigm = 'ssm' | |
| category = 'Linear Attention & State-Space (SSM)' | |
| icon = '🐍' | |
| elif any(k in ident_lower for k in ('mla', 'latent', 'deepseek', 'kv_compress', 'latent_kv')): | |
| paradigm = 'mla' | |
| category = 'Latent & KV-Cache Compression' | |
| icon = '🧠' | |
| elif any(k in ident_lower for k in ('slice', 'prune', 'wanda', 'sparse', 'pruning', 'channel_slicing')): | |
| paradigm = 'slicegpt' | |
| category = 'Structured Pruning & Channel Slicing' | |
| icon = '✂️' | |
| elif any(k in ident_lower for k in ('quant', 'bitnet', 'bitlinear', 'awq', 'fp4', 'int4', 'int8')): | |
| paradigm = 'quant' | |
| category = 'Quantization & Mixed Precision' | |
| icon = '💎' | |
| elif any(k in ident_lower for k in ('linear_attention', 'performer', 'linformer', 'nystrom', 'cosformer', 'galerkin')): | |
| paradigm = 'linear' | |
| category = 'Dynamic & Efficient Attention' | |
| icon = '🌟' | |
| elif any(k in ident_lower for k in ('svd', 'soa', 'factor', 'low_rank', 'lora', 'compress')): | |
| paradigm = 'svd' | |
| category = 'Low-Rank SVD & Factorization' | |
| icon = '⚡' | |
| else: | |
| paradigm = 'svd' | |
| category = 'Low-Rank SVD & Factorization' | |
| icon = '⚡' | |
| # Extract LaTeX formulas from docstrings or markdown | |
| math_latex = "" | |
| # Check markdown first | |
| md_dir = "Docs/content/attentions" if is_attn else "Docs/content/compressions" | |
| md_path = os.path.join(md_dir, f"{base_name}.md") | |
| if os.path.exists(md_path): | |
| with open(md_path, "r", encoding="utf-8", errors="ignore") as mf: | |
| mfc = mf.read() | |
| m = re.search(r'\$\$(.*?)\$\$', mfc, re.DOTALL) | |
| if m: | |
| math_latex = m.group(1).strip() | |
| if not math_latex: | |
| # Search code docstrings | |
| full_docs = chosen_doc | |
| m = re.search(r'\$\$(.*?)\$\$', full_docs, re.DOTALL) | |
| if m: | |
| math_latex = m.group(1).strip() | |
| else: | |
| # Generate mathematically rigorous canonical formula by paradigm | |
| if paradigm == 'svd': | |
| math_latex = f"W \\approx U \\cdot V, \\quad U \\in \\mathbb{{R}}^{{d_{{out}} \\times {rank_val}}}, \\; V \\in \\mathbb{{R}}^{{{rank_val} \\times d_{{in}}}}" | |
| elif paradigm == 'moe': | |
| math_latex = f"y = \\sum_{{i=1}}^{{{experts_val}}} G(x)_i \\cdot E_i(x), \\quad G(x) = \\text{{Top2}}(\\text{{Softmax}}(x W_g))" | |
| elif paradigm == 'mla': | |
| math_latex = f"c_t^{{KV}} = x_t W_{{DKV}}, \\quad [K_t, V_t] = c_t^{{KV}} W_{{UKV}}, \\quad d_c \\ll 2 d_{{model}}" | |
| elif paradigm == 'ssm': | |
| math_latex = f"h_t = \\bar{{A}} h_{{t-1}} + \\bar{{B}} x_t, \\quad y_t = C h_t + D x_t, \\quad \\Delta = \\text{{Softplus}}(x_t W_\\Delta)" | |
| elif paradigm == 'slicegpt': | |
| math_latex = f"W_{{pruned}} = (Q^\\top W Q)_{{[:, \\mathcal{{I}}]}}, \\quad |\\mathcal{{I}}| = (1 - {prune_ratio}) d_{{in}}" | |
| elif paradigm == 'quant': | |
| math_latex = f"\\tilde{{W}} = \\text{{RoundClip}}\\left(\\frac{{W}}{{\\gamma}}, -1, 1\\right), \\quad y = x \\tilde{{W}}^\\top \\cdot \\gamma" | |
| else: | |
| math_latex = f"\\text{{Attn}}(Q, K, V) = \\phi(Q) \\left( \\phi(K)^\\top V \\right)" | |
| # Clean formula of newlines | |
| math_latex = re.sub(r'\s+', ' ', math_latex) | |
| # Architectural Components Breakdown | |
| components = [] | |
| if paradigm == 'svd': | |
| components = [ | |
| { | |
| "id": "v_factor", | |
| "name": f"Factor V (Low-Rank Subspace Projection, r={rank_val})", | |
| "role": "Projection to Rank-r Eigenspace", | |
| "shape_transform": f"[{rank_val} × d_in]", | |
| "color": "#3acbb8" | |
| }, | |
| { | |
| "id": "u_factor", | |
| "name": f"Factor U (Full-Rank Expansion, r={rank_val})", | |
| "role": "Reconstruction to Output Space", | |
| "shape_transform": f"[d_out × {rank_val}]", | |
| "color": "#2dd4bf" | |
| }, | |
| { | |
| "id": "adapter", | |
| "name": "Residual Adapter / Norm Calibration", | |
| "role": "High-Frequency Spectral Recovery", | |
| "shape_transform": "[d_out × d_in] Residual", | |
| "color": "#5eead4" | |
| } | |
| ] | |
| elif paradigm == 'moe': | |
| components = [ | |
| { | |
| "id": "router_gate", | |
| "name": f"Dynamic Routing Gating ({experts_val} Experts)", | |
| "role": "Top-2 Expert Token Dispatcher", | |
| "shape_transform": f"[{experts_val} × d_in]", | |
| "color": "#ec4899" | |
| } | |
| ] | |
| for e in range(min(4, experts_val)): | |
| components.append({ | |
| "id": f"expert_{e}", | |
| "name": f"Sparse Expert Module {e}", | |
| "role": "Specialized Sub-Network", | |
| "shape_transform": "[d_ffn × d_in]", | |
| "color": "#8b5cf6" | |
| }) | |
| components.append({ | |
| "id": "aggregator", | |
| "name": "Softmax Weighted Combiner", | |
| "role": "Weighted Token Reconstruction", | |
| "shape_transform": "[d_out × d_in]", | |
| "color": "#a855f7" | |
| }) | |
| elif paradigm == 'mla': | |
| components = [ | |
| { | |
| "id": "kv_compress", | |
| "name": "Latent KV Down-Projection", | |
| "role": "Slashing KV Cache by 8x", | |
| "shape_transform": "[d_c × d_in]", | |
| "color": "#38bdf8" | |
| }, | |
| { | |
| "id": "q_lora_a", | |
| "name": "Query LoRA Compression", | |
| "role": "Decoupled Attention Projections", | |
| "shape_transform": "[r_q × d_in]", | |
| "color": "#0ea5e9" | |
| }, | |
| { | |
| "id": "q_lora_b", | |
| "name": "Query LoRA Expansion + RoPE", | |
| "role": "Rotary Positional Embedding Integration", | |
| "shape_transform": "[d_out × r_q]", | |
| "color": "#0284c7" | |
| }, | |
| { | |
| "id": "out_proj", | |
| "name": "Multi-Head Output Projection", | |
| "role": "Final Attention Representation Map", | |
| "shape_transform": "[d_out × d_out]", | |
| "color": "#0369a1" | |
| } | |
| ] | |
| elif paradigm == 'ssm': | |
| components = [ | |
| { | |
| "id": "in_proj", | |
| "name": "SVD-Factorized Input Projection", | |
| "role": "Expanded State Channel Projector", | |
| "shape_transform": "[2*d_in × d_in]", | |
| "color": "#10b981" | |
| }, | |
| { | |
| "id": "conv1d", | |
| "name": "1D Causal Depthwise Convolution", | |
| "role": "Local Token Neighborhood Mixing", | |
| "shape_transform": "[d_state × kernel_size]", | |
| "color": "#059669" | |
| }, | |
| { | |
| "id": "selective_scan", | |
| "name": "Selective Scan State Space Recurrence", | |
| "role": "Linear-Time Associative Global Scan", | |
| "shape_transform": "[h_t = A*h_{t-1} + B*x_t]", | |
| "color": "#34d399" | |
| }, | |
| { | |
| "id": "out_proj", | |
| "name": "Factorized Output Projection", | |
| "role": "Output Projection with Residual Merge", | |
| "shape_transform": "[d_out × d_in]", | |
| "color": "#047857" | |
| } | |
| ] | |
| elif paradigm == 'slicegpt': | |
| components = [ | |
| { | |
| "id": "q_slice", | |
| "name": f"Sliced Query Projection (-{int(prune_ratio*100)}%)", | |
| "role": "Orthogonally Concentrated Projection", | |
| "shape_transform": f"[{int((1-prune_ratio)*100)}% d_out × d_in]", | |
| "color": "#f59e0b" | |
| }, | |
| { | |
| "id": "k_slice", | |
| "name": f"Sliced Key Projection (-{int(prune_ratio*100)}%)", | |
| "role": "Compressed Dimensional Key Map", | |
| "shape_transform": f"[{int((1-prune_ratio)*100)}% d_out × d_in]", | |
| "color": "#d97706" | |
| }, | |
| { | |
| "id": "v_slice", | |
| "name": f"Sliced Value Projection (-{int(prune_ratio*100)}%)", | |
| "role": "Compressed Value Manifold", | |
| "shape_transform": f"[{int((1-prune_ratio)*100)}% d_out × d_in]", | |
| "color": "#b45309" | |
| }, | |
| { | |
| "id": "out_slice", | |
| "name": "Sliced Output Transform", | |
| "role": "Channel Pruned Output Reconstruction", | |
| "shape_transform": f"[d_out × {int((1-prune_ratio)*100)}% d_in]", | |
| "color": "#f59e0b" | |
| } | |
| ] | |
| elif paradigm == 'quant': | |
| components = [ | |
| { | |
| "id": "weight_pack", | |
| "name": "1.58-Bit Ternary Weights {-1, 0, +1}", | |
| "role": "Multiplication-Free Matrix Vector Additions", | |
| "shape_transform": "[d_out × d_in] Ternary", | |
| "color": "#a855f7" | |
| }, | |
| { | |
| "id": "absmax_scale", | |
| "name": "Dynamic AbsMax Scaling & RMSNorm", | |
| "role": "Per-Tensor Dynamic Range Recovery", | |
| "shape_transform": "[1] Floating Scale", | |
| "color": "#c084fc" | |
| }, | |
| { | |
| "id": "residual_merge", | |
| "name": "High-Precision Residual Conduit", | |
| "role": "FP16 Residual Preservation", | |
| "shape_transform": "[d_out × d_in]", | |
| "color": "#e9d5ff" | |
| } | |
| ] | |
| else: # linear | |
| components = [ | |
| { | |
| "id": "kernel_phi", | |
| "name": "Random Feature Map Projection phi(x)", | |
| "role": "Kernel Linearization Space", | |
| "shape_transform": "[r × d_in]", | |
| "color": "#f43f5e" | |
| }, | |
| { | |
| "id": "kv_accum", | |
| "name": "Linear KV Buffer Accumulator", | |
| "role": "O(N) Recurrent Sequence State", | |
| "shape_transform": "[d_in × d_out]", | |
| "color": "#fb7185" | |
| }, | |
| { | |
| "id": "out_linear", | |
| "name": "Output Linear Projection", | |
| "role": "Output Context Reconstruction", | |
| "shape_transform": "[d_out × d_in]", | |
| "color": "#e11d48" | |
| } | |
| ] | |
| # Model-by-Model Parametric & Storage Projections | |
| model_projections = {} | |
| for m_key, cfg in MODEL_CONFIGS.items(): | |
| d = cfg["hidden_size"] | |
| d_ffn = cfg["intermediate_size"] | |
| L = cfg["num_layers"] | |
| base_attn_p = cfg["base_attn_layer_params"] | |
| base_ffn_p = cfg["base_ffn_layer_params"] | |
| base_layer_p = cfg["base_layer_params"] | |
| if paradigm == 'svd': | |
| r = min(rank_val, d // 2) | |
| if m_key == 'qwen': | |
| # Qwen has GQA (14 Q heads, 2 KV heads) and SwiGLU (3 FFN matrices) | |
| r_kv = max(16, r // 2) | |
| comp_attn_p = (2 * r * d) + (2 * r_kv * (128 + d)) + (2 * r * d) # Q + K + V + Out | |
| comp_ffn_p = 3 * r * (d + d_ffn) # gate_proj + up_proj + down_proj | |
| elif m_key == 'gpt2': | |
| # GPT-2 has fused Conv1D c_attn (768 x 2304) and c_proj (768 x 768) | |
| comp_attn_p = r * (d + 3 * d) + (2 * r * d) # 6 * r * d | |
| comp_ffn_p = 2 * r * (d + d_ffn) | |
| else: # distilbert and roberta | |
| comp_attn_p = 4 * 2 * r * d | |
| comp_ffn_p = 2 * r * (d + d_ffn) | |
| comp_layer_p = comp_attn_p + comp_ffn_p | |
| savings_ratio = round(base_layer_p / max(1, comp_layer_p), 2) | |
| elif paradigm == 'moe': | |
| comp_attn_p = base_attn_p | |
| if m_key == 'qwen': | |
| # Qwen SwiGLU with 3 matrices per expert | |
| comp_ffn_p = (d * experts_val) + (experts_val * base_ffn_p) | |
| active_ffn_p = (d * experts_val) + (2 * base_ffn_p) | |
| else: | |
| comp_ffn_p = (d * experts_val) + (experts_val * base_ffn_p) | |
| active_ffn_p = (d * experts_val) + (2 * base_ffn_p) | |
| comp_layer_p = comp_attn_p + comp_ffn_p | |
| savings_ratio = round(base_layer_p / max(1, (comp_attn_p + active_ffn_p)), 2) | |
| elif paradigm == 'mla': | |
| d_c = max(128, d // 4) | |
| if m_key == 'qwen': | |
| d_c = 224 | |
| comp_attn_p = (d * d_c) + (d_c * 2 * 64 * cfg["num_heads"]) + (d * d) | |
| comp_ffn_p = base_ffn_p | |
| comp_layer_p = comp_attn_p + comp_ffn_p | |
| savings_ratio = round(base_layer_p / max(1, comp_layer_p), 2) | |
| elif paradigm == 'ssm': | |
| if m_key == 'qwen': | |
| comp_attn_p = (d * 2 * d) + (2 * d * 4) + (2 * d * 64) + (2 * d * d) | |
| else: | |
| comp_attn_p = (d * 2 * d) + (2 * d * 4) + (2 * d * 16) + (2 * d * d) | |
| comp_ffn_p = base_ffn_p | |
| comp_layer_p = comp_attn_p + comp_ffn_p | |
| savings_ratio = round(base_layer_p / max(1, comp_layer_p), 2) | |
| elif paradigm == 'slicegpt': | |
| keep_ratio = 1.0 - prune_ratio | |
| if m_key == 'qwen': | |
| comp_attn_p = int(base_attn_p * keep_ratio) | |
| comp_ffn_p = int(base_ffn_p * keep_ratio) | |
| else: | |
| comp_attn_p = int(base_attn_p * (keep_ratio ** 2)) | |
| comp_ffn_p = int(base_ffn_p * keep_ratio) | |
| comp_layer_p = comp_attn_p + comp_ffn_p | |
| savings_ratio = round(base_layer_p / max(1, comp_layer_p), 2) | |
| elif paradigm == 'quant': | |
| comp_attn_p = base_attn_p | |
| comp_ffn_p = base_ffn_p | |
| comp_layer_p = base_layer_p | |
| savings_ratio = 8.0 # 16-bit to ~2-bit compression | |
| else: # linear | |
| comp_attn_p = int(base_attn_p * 0.45) | |
| comp_ffn_p = base_ffn_p | |
| comp_layer_p = comp_attn_p + comp_ffn_p | |
| savings_ratio = round(base_layer_p / max(1, comp_layer_p), 2) | |
| total_base_model = cfg["total_baseline_params"] | |
| total_comp_model = cfg["embeddings_params"] + L * comp_layer_p | |
| storage_saved_bytes = max(0, (total_base_model - total_comp_model) * 2) | |
| model_projections[m_key] = { | |
| "model_name": cfg["name"], | |
| "family": cfg["family"], | |
| "hidden_size": d, | |
| "intermediate_size": d_ffn, | |
| "num_layers": L, | |
| "baseline_layer_params": base_layer_p, | |
| "compressed_layer_p": comp_layer_p, | |
| "total_baseline_params": total_base_model, | |
| "total_compressed_params": total_comp_model, | |
| "savings_ratio": savings_ratio, | |
| "storage_saved_bytes": storage_saved_bytes, | |
| "storage_saved_formatted": f"{storage_saved_bytes / (1024**2):.1f} MB" if storage_saved_bytes < 1024**3 else f"{storage_saved_bytes / (1024**3):.2f} GB" | |
| } | |
| # Extract clean description | |
| clean_lines = [l.strip() for l in chosen_doc.split("\n") if l.strip() and not l.strip().startswith(('$', '=', '-', '#', '1.', '2.', '3.'))] | |
| clean_desc = " ".join(clean_lines[:3]) if clean_lines else f"{format_module_name(base_name)} - Production hardened research module." | |
| if len(clean_desc) > 300: | |
| clean_desc = clean_desc[:297] + "..." | |
| return { | |
| "id": base_name, | |
| "name": format_module_name(base_name), | |
| "file": file_path, | |
| "type": "Attention Module" if is_attn else "Compression Engine", | |
| "category": category, | |
| "paradigm": paradigm, | |
| "icon": icon, | |
| "description": clean_desc, | |
| "classes": classes[:5], | |
| "methods_count": len(methods), | |
| "math_latex": math_latex, | |
| "components": components, | |
| "model_projections": model_projections | |
| } | |
| def process_batch(module_ids): | |
| """Processes a specific batch of module IDs and updates their component JSON files.""" | |
| results = [] | |
| attns = {os.path.splitext(os.path.basename(f))[0]: f for f in glob.glob("Codes/framework/attentions/*.py")} | |
| comps = {os.path.splitext(os.path.basename(f))[0]: f for f in glob.glob("Codes/framework/compression_engine/*.py")} | |
| for mid in module_ids: | |
| path = attns.get(mid) or comps.get(mid) | |
| if not path: | |
| continue | |
| meta = extract_code_intelligence(path) | |
| out_path = os.path.join(COMPONENTS_DIR, f"{mid}.json") | |
| with open(out_path, "w", encoding="utf-8") as f: | |
| json.dump(meta, f, indent=2) | |
| results.append(meta) | |
| print(f" [OK] Processed {mid} -> {out_path}") | |
| return results | |
| if __name__ == "__main__": | |
| if len(sys.argv) > 1: | |
| batch_idx = int(sys.argv[1]) | |
| all_data = json.load(open("Docs/visualizer/modules_components.json")) | |
| all_ids = [m["id"] for m in all_data["modules"]] | |
| n = len(all_ids) | |
| chunk_size = math.ceil(n / 5) | |
| chunks = [all_ids[i:i + chunk_size] for i in range(0, n, chunk_size)] | |
| if 1 <= batch_idx <= len(chunks): | |
| print(f"Running deep inspect for Batch {batch_idx} ({len(chunks[batch_idx - 1])} modules)...") | |
| process_batch(chunks[batch_idx - 1]) | |
| else: | |
| print(f"Invalid batch {batch_idx}") | |
| else: | |
| print("Usage: python3 deep_inspect_batch.py <batch_1_to_5>") | |
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