MarkBase Admin
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- E4B-MarkBase model (42 layers, 4.4GB) loaded successfully - All Phase 1-6 tests passed (model loading, forward pass, vision/audio towers, token generation, performance) - All stress tests passed (5/5 in 127.6s) - Concurrent inference - Memory stress (67.5 tok/s, 0 NaN) - Continuous generation - Batch processing - Long-running stability - Swift Metal inference engine with multimodal support
258 lines
10 KiB
Python
258 lines
10 KiB
Python
#!/usr/bin/env python3
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"""Debug E4B inference using numpy/safetensors to compare with Swift Metal."""
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import numpy as np
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import json
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from pathlib import Path
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from safetensors.numpy import safe_open
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MODEL_PATH = Path("/Users/accusys/MarkBase12B/models/E4B-MarkBase")
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def load_config():
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with open(MODEL_PATH / "config.json") as f:
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return json.load(f)
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def bf16_to_f32(bf16_data):
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"""Convert BF16 uint16 array to float32."""
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bf16 = bf16_data.view(np.uint16)
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f32_bits = bf16.astype(np.uint32) << 16
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return f32_bits.view(np.float32)
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def load_tensor(name):
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"""Load tensor from safetensors, converting BF16 to F32."""
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model_file = MODEL_PATH / "model.safetensors"
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with safe_open(model_file, framework="numpy") as f:
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data = f.get_tensor(name)
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# Check if dtype name contains 'bfloat' or is uint16 for scales/biases
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dtype_str = str(data.dtype)
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if 'bfloat' in dtype_str.lower() or dtype_str == 'e4m3bfloat16':
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return bf16_to_f32(data)
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if data.dtype == np.uint32:
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return data # Keep uint32 for weights
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return data.astype(np.float32)
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def debug_inference():
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print("=" * 50)
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print("NumPy E4B Debug Inference")
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print("=" * 50)
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config = load_config()
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text_config = config["text_config"]
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print(f"\nConfig: hidden={text_config['hidden_size']}, layers={text_config['num_hidden_layers']}")
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print(f" head_dim={text_config['head_dim']}, n_heads={text_config['num_attention_heads']}")
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print(f" n_kv_heads={text_config['num_key_value_heads']}")
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hidden_size = text_config['hidden_size']
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n_heads = text_config['num_attention_heads']
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n_kv_heads = text_config['num_key_value_heads']
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head_dim = text_config['head_dim']
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print("\n--- Loading weights ---")
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# Embedding
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embed_weight = load_tensor("language_model.model.embed_tokens.weight")
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embed_scales = load_tensor("language_model.model.embed_tokens.scales")
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embed_biases = load_tensor("language_model.model.embed_tokens.biases")
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print(f"embed_weight: shape={embed_weight.shape}, dtype={embed_weight.dtype}")
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print(f"embed_scales: shape={embed_scales.shape}, dtype={embed_scales.dtype}")
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print(f"embed_biases: shape={embed_biases.shape}, dtype={embed_biases.dtype}")
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# BOS token = 2
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bos_token = 2
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vocab_size, packed_dim = embed_weight.shape
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num_groups = hidden_size // 64
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print(f"\n--- Dequantizing BOS embedding (token={bos_token}) ---")
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w_row = embed_weight[bos_token].astype(np.uint32)
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s_row = embed_scales[bos_token]
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b_row = embed_biases[bos_token]
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embedding = np.zeros(hidden_size, dtype=np.float32)
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for g in range(num_groups):
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scale = s_row[g]
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bias = b_row[g]
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for j in range(64):
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packed_idx = g * 8 + j // 8
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shift = (j % 8) * 4
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qval = int((w_row[packed_idx] >> shift) & 0xF)
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embedding[g * 64 + j] = qval * scale + bias
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print(f"embedding: shape={embedding.shape}")
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print(f" min={embedding.min():.6f}, max={embedding.max():.6f}")
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print(f" mean={embedding.mean():.6f}")
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print(f" first 20: {embedding[:20].tolist()}")
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# Embedding scale
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embed_scale = np.sqrt(float(hidden_size))
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embedding_scaled = embedding * embed_scale
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print(f"\nAfter scaling (scale={embed_scale:.2f}):")
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print(f" min={embedding_scaled.min():.6f}, max={embedding_scaled.max():.6f}")
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print(f" first 20: {embedding_scaled[:20].tolist()}")
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# Layer 0 weights
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print("\n--- Layer 0 weights ---")
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prefix = "language_model.model.layers.0"
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input_norm_w = load_tensor(f"{prefix}.input_layernorm.weight")
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print(f"input_norm: shape={input_norm_w.shape}, first 5: {input_norm_w[:5].tolist()}")
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q_proj_w = load_tensor(f"{prefix}.self_attn.q_proj.weight")
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q_proj_s = load_tensor(f"{prefix}.self_attn.q_proj.scales")
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q_proj_b = load_tensor(f"{prefix}.self_attn.q_proj.biases")
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k_proj_w = load_tensor(f"{prefix}.self_attn.k_proj.weight")
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k_proj_s = load_tensor(f"{prefix}.self_attn.k_proj.scales")
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k_proj_b = load_tensor(f"{prefix}.self_attn.k_proj.biases")
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v_proj_w = load_tensor(f"{prefix}.self_attn.v_proj.weight")
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v_proj_s = load_tensor(f"{prefix}.self_attn.v_proj.scales")
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v_proj_b = load_tensor(f"{prefix}.self_attn.v_proj.biases")
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print(f"q_proj: w={q_proj_w.shape}, s={q_proj_s.shape}, b={q_proj_b.shape}")
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print(f"k_proj: w={k_proj_w.shape}, s={k_proj_s.shape}, b={k_proj_b.shape}")
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print(f"v_proj: w={v_proj_w.shape}, s={v_proj_s.shape}, b={v_proj_b.shape}")
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q_norm_w = load_tensor(f"{prefix}.self_attn.q_norm.weight")
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k_norm_w = load_tensor(f"{prefix}.self_attn.k_norm.weight")
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print(f"q_norm: shape={q_norm_w.shape}, first 5: {q_norm_w[:5].tolist()}")
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print(f"k_norm: shape={k_norm_w.shape}, first 5: {k_norm_w[:5].tolist()}")
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o_proj_w = load_tensor(f"{prefix}.self_attn.o_proj.weight")
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o_proj_s = load_tensor(f"{prefix}.self_attn.o_proj.scales")
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o_proj_b = load_tensor(f"{prefix}.self_attn.o_proj.biases")
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print(f"o_proj: w={o_proj_w.shape}, s={o_proj_s.shape}, b={o_proj_b.shape}")
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# --- Forward ---
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print("\n--- Forward pass ---")
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hidden = embedding_scaled
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# 1. Input RMS norm
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eps = 1e-6
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ss = np.sum(hidden * hidden) / hidden_size
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rms = np.sqrt(ss + eps)
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hidden_normed = hidden / rms * input_norm_w
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print(f"\nInput norm: rms={rms:.6f}")
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print(f" min={hidden_normed.min():.6f}, max={hidden_normed.max():.6f}")
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print(f" first 20: {hidden_normed[:20].tolist()}")
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# 2. Q, K, V projections
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def dequantize_matmul(x, w, s, b):
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out_dim, packed_dim = w.shape
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in_dim = packed_dim * 8
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num_groups = in_dim // 64
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result = np.zeros(out_dim, dtype=np.float32)
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for out_idx in range(out_dim):
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sum_val = 0.0
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for g in range(num_groups):
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scale = s[out_idx, g]
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bias_q = b[out_idx, g]
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for j in range(64):
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packed_idx = g * 8 + j // 8
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shift = (j % 8) * 4
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qval = int((w[out_idx, packed_idx] >> shift) & 0xF)
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dq = qval * scale + bias_q
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sum_val += dq * x[g * 64 + j]
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result[out_idx] = sum_val
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return result
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print("\nQ projection...")
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q = dequantize_matmul(hidden_normed, q_proj_w.astype(np.uint32), q_proj_s, q_proj_b)
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print(f"Q: shape={q.shape}")
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print(f" min={q.min():.6f}, max={q.max():.6f}")
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print(f" first 20: {q[:20].tolist()}")
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print("\nK projection...")
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k = dequantize_matmul(hidden_normed, k_proj_w.astype(np.uint32), k_proj_s, k_proj_b)
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print(f"K: shape={k.shape}")
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print(f" min={k.min():.6f}, max={k.max():.6f}")
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print(f" first 20: {k[:20].tolist()}")
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print("\nV projection...")
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v = dequantize_matmul(hidden_normed, v_proj_w.astype(np.uint32), v_proj_s, v_proj_b)
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print(f"V: shape={v.shape}")
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print(f" min={v.min():.6f}, max={v.max():.6f}")
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print(f" first 20: {v[:20].tolist()}")
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# 3. Q, K norms
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print("\nQ norm (per-head)...")
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q_reshaped = q.reshape(n_heads, head_dim)
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q_normed = np.zeros_like(q_reshaped)
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for h in range(n_heads):
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h_q = q_reshaped[h]
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ss = np.sum(h_q * h_q) / head_dim
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rms = np.sqrt(ss + eps)
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q_normed[h] = h_q / rms * q_norm_w
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q_normed = q_normed.flatten()
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print(f"Q normed: shape={q_normed.shape}")
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print(f" min={q_normed.min():.6f}, max={q_normed.max():.6f}")
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print(f" first 20: {q_normed[:20].tolist()}")
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print("\nK norm (per-head)...")
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k_reshaped = k.reshape(n_kv_heads, head_dim)
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k_normed = np.zeros_like(k_reshaped)
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for h in range(n_kv_heads):
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h_k = k_reshaped[h]
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ss = np.sum(h_k * h_k) / head_dim
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rms = np.sqrt(ss + eps)
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k_normed[h] = h_k / rms * k_norm_w
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k_normed = k_normed.flatten()
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print(f"K normed: shape={k_normed.shape}")
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print(f" min={k_normed.min():.6f}, max={k_normed.max():.6f}")
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print(f" first 20: {k_normed[:20].tolist()}")
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# 4. Attention (position 0, only attend to self)
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print("\n--- Attention at position 0 ---")
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attn_scale = 1.0 / np.sqrt(float(head_dim))
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attn_out = np.zeros(n_heads * head_dim, dtype=np.float32)
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for h in range(n_heads):
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kv_head = h % n_kv_heads
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h_q = q_normed[h * head_dim : (h+1) * head_dim]
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h_k = k_normed[kv_head * head_dim : (kv_head+1) * head_dim]
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h_v = v[kv_head * head_dim : (kv_head+1) * head_dim]
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score = float(np.sum(h_q * h_k) * attn_scale)
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print(f" Head {h}: score={score:.6f}")
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# At position 0, softmax(score) = 1.0 for self-attention
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attn_out[h * head_dim : (h+1) * head_dim] = h_v
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print(f"\nAttention output: shape={attn_out.shape}")
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print(f" min={attn_out.min():.6f}, max={attn_out.max():.6f}")
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print(f" first 20: {attn_out[:20].tolist()}")
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# 5. O projection
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print("\nO projection...")
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o_out = dequantize_matmul(attn_out, o_proj_w.astype(np.uint32), o_proj_s, o_proj_b)
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print(f"O output: shape={o_out.shape}")
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print(f" min={o_out.min():.6f}, max={o_out.max():.6f}")
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print(f" first 20: {o_out[:20].tolist()}")
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# 6. Residual
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hidden_after_attn = hidden + o_out
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print(f"\nAfter attention residual:")
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print(f" min={hidden_after_attn.min():.6f}, max={hidden_after_attn.max():.6f}")
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print(f" first 20: {hidden_after_attn[:20].tolist()}")
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print("\n" + "=" * 50)
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print("KEY VALUES TO COMPARE WITH SWIFT:")
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print("=" * 50)
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print("\n1. Embedding (scaled):")
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print(f" first 5: {embedding_scaled[:5].tolist()}")
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print("\n2. After input norm:")
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print(f" first 5: {hidden_normed[:5].tolist()}")
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print("\n3. Q after projection:")
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print(f" first 5: {q[:5].tolist()}")
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print("\n4. Q after norm:")
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print(f" first 5: {q_normed[:5].tolist()}")
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print("\n5. Attention output:")
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print(f" first 5: {attn_out[:5].tolist()}")
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print("\n6. O projection output:")
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print(f" first 5: {o_out[:5].tolist()}")
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print("\n7. Hidden after attention:")
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print(f" first 5: {hidden_after_attn[:5].tolist()}")
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if __name__ == "__main__":
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debug_inference() |