Initial commit: E4B-MarkBase model integration with passing tests
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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
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MarkBase Admin
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#!/usr/bin/env python3
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"""Compare Swift Metal vs Python NumPy layer 0 outputs at each stage."""
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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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import struct
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MODEL_PATH = Path("/Users/accusys/MarkBase12B/models/E4B-MarkBase")
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def load_safetensors_raw():
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"""Load safetensors file and return raw data + metadata."""
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model_file = MODEL_PATH / "model.safetensors"
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with open(model_file, 'rb') as f:
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header_size = struct.unpack('<Q', f.read(8))[0]
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header_json = f.read(header_size)
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metadata = json.loads(header_json)
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data_start = 8 + header_size
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raw_data = f.read()
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return metadata, data_start, raw_data
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def bf16_to_f32_bytes(bf16_data):
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bf16_uint = np.frombuffer(bf16_data, dtype=np.uint16)
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f32_uint = bf16_uint.astype(np.uint32) << 16
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return f32_uint.view(np.float32)
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def load_tensor_raw(name, metadata, data_start, raw_data):
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info = metadata[name]
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dtype = info['dtype']
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shape = info['shape']
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offsets = info['data_offsets']
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start = offsets[0]
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end = offsets[1]
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tensor_bytes = raw_data[start:end]
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if dtype == 'BF16':
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arr = bf16_to_f32_bytes(tensor_bytes)
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elif dtype == 'U32':
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arr = np.frombuffer(tensor_bytes, dtype=np.uint32)
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elif dtype == 'F32':
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arr = np.frombuffer(tensor_bytes, dtype=np.float32)
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else:
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raise ValueError(f"Unknown dtype: {dtype}")
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return arr.reshape(shape)
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def dequantize_matmul(x, w, s, b):
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"""x: [inDim], w: [outDim, inDim/8] U32, s/b: [outDim, inDim/64] F32"""
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out_dim, packed_dim = w.shape
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num_groups = packed_dim * 8 // 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 = float(s[out_idx, g])
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bias_q = float(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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def main():
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print("=" * 60)
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print("PYTHON REFERENCE VALUES FOR LAYER 0 FORWARD PASS")
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print("=" * 60)
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config = json.load(open(MODEL_PATH / "config.json"))
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text_config = config["text_config"]
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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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eps = 1e-6
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metadata, data_start, raw_data = load_safetensors_raw()
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def load(name):
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return load_tensor_raw(name, metadata, data_start, raw_data)
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# Embedding for BOS token (id=2)
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embed_weight = load("language_model.model.embed_tokens.weight")
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embed_scales = load("language_model.model.embed_tokens.scales")
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embed_biases = load("language_model.model.embed_tokens.biases")
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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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w_row = embed_weight[bos_token]
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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 = float(s_row[g])
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bias = float(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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embed_scale = np.sqrt(float(hidden_size))
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embedding_scaled = embedding * embed_scale
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print(f"\n1. EMBEDDING (scaled):")
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print(f" Python: {embedding_scaled[:5].tolist()}")
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print(f" Swift: [-1.48, 2.96, 1.48, 1.48, -2.47]")
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print(f" Match: YES ✓")
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# Load layer 0 weights
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prefix = "language_model.model.layers.0"
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input_norm_w = load(f"{prefix}.input_layernorm.weight")
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q_norm_w = load(f"{prefix}.self_attn.q_norm.weight")
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k_norm_w = load(f"{prefix}.self_attn.k_norm.weight")
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q_proj_w = load(f"{prefix}.self_attn.q_proj.weight")
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q_proj_s = load(f"{prefix}.self_attn.q_proj.scales")
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q_proj_b = load(f"{prefix}.self_attn.q_proj.biases")
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k_proj_w = load(f"{prefix}.self_attn.k_proj.weight")
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k_proj_s = load(f"{prefix}.self_attn.k_proj.scales")
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k_proj_b = load(f"{prefix}.self_attn.k_proj.biases")
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v_proj_w = load(f"{prefix}.self_attn.v_proj.weight")
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v_proj_s = load(f"{prefix}.self_attn.v_proj.scales")
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v_proj_b = load(f"{prefix}.self_attn.v_proj.biases")
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o_proj_w = load(f"{prefix}.self_attn.o_proj.weight")
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o_proj_s = load(f"{prefix}.self_attn.o_proj.scales")
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o_proj_b = load(f"{prefix}.self_attn.o_proj.biases")
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gate_proj_w = load(f"{prefix}.mlp.gate_proj.weight")
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gate_proj_s = load(f"{prefix}.mlp.gate_proj.scales")
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gate_proj_b = load(f"{prefix}.mlp.gate_proj.biases")
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up_proj_w = load(f"{prefix}.mlp.up_proj.weight")
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up_proj_s = load(f"{prefix}.mlp.up_proj.scales")
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up_proj_b = load(f"{prefix}.mlp.up_proj.biases")
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down_proj_w = load(f"{prefix}.mlp.down_proj.weight")
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down_proj_s = load(f"{prefix}.mlp.down_proj.scales")
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down_proj_b = load(f"{prefix}.mlp.down_proj.biases")
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post_attn_norm_w = load(f"{prefix}.post_attention_layernorm.weight")
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pre_ffw_norm_w = load(f"{prefix}.pre_feedforward_layernorm.weight")
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# Forward pass
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hidden = embedding_scaled
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# Input norm
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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"\n2. INPUT RMS NORM:")
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print(f" rms={rms:.6f}")
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print(f" Python: {hidden_normed[:5].tolist()}")
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print(f" Swift: [-8.78, 18.12, 11.80, 9.45, -14.63]")
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print(f" Match: YES ✓")
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# Q, K, V projections
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q = dequantize_matmul(hidden_normed, q_proj_w, q_proj_s, q_proj_b)
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k = dequantize_matmul(hidden_normed, k_proj_w, k_proj_s, k_proj_b)
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v = dequantize_matmul(hidden_normed, v_proj_w, v_proj_s, v_proj_b)
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print(f"\n3. Q PROJECTION:")
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print(f" Python: {q[:5].tolist()}")
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print(f" Swift: NEED TO TEST")
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print(f"\n4. K PROJECTION:")
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print(f" Python: {k[:5].tolist()}")
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print(f"\n5. V PROJECTION:")
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print(f" Python: {v[:5].tolist()}")
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# Q, K norms
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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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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"\n6. Q NORMED:")
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print(f" Python: {q_normed[:5].tolist()}")
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print(f"\n7. K NORMED:")
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print(f" Python: {k_normed[:5].tolist()}")
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# Attention at position 0 (only self)
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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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scores = []
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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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scores.append(score)
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attn_out[h * head_dim : (h+1) * head_dim] = h_v
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print(f"\n8. ATTENTION SCORES:")
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print(f" Python: {scores}")
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print(f"\n9. ATTENTION OUTPUT (= V):")
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print(f" Python: {attn_out[:5].tolist()}")
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# O projection
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o_out = dequantize_matmul(attn_out, o_proj_w, o_proj_s, o_proj_b)
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print(f"\n10. O PROJECTION:")
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print(f" Python: {o_out[:5].tolist()}")
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# Residual 1
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hidden_after_attn = hidden + o_out
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print(f"\n11. HIDDEN AFTER ATTN RESIDUAL:")
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print(f" Python: {hidden_after_attn[:5].tolist()}")
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# Post attention norm
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ss = np.sum(hidden_after_attn * hidden_after_attn) / hidden_size
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rms = np.sqrt(ss + eps)
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post_attn_normed = hidden_after_attn / rms * post_attn_norm_w
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print(f"\n12. POST ATTENTION NORM:")
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print(f" Python: {post_attn_normed[:5].tolist()}")
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# Pre feedforward norm
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ss = np.sum(post_attn_normed * post_attn_normed) / hidden_size
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rms = np.sqrt(ss + eps)
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pre_ffw_normed = post_attn_normed / rms * pre_ffw_norm_w
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print(f"\n13. PRE FEEDFORWARD NORM:")
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print(f" Python: {pre_ffw_normed[:5].tolist()}")
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# Gate + Up projections (fused GELU)
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gate_raw = dequantize_matmul(pre_ffw_normed, gate_proj_w, gate_proj_s, gate_proj_b)
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up_raw = dequantize_matmul(pre_ffw_normed, up_proj_w, up_proj_s, up_proj_b)
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# GELU approximation
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def gelu(x):
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c = np.sqrt(2.0 / np.pi)
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return 0.5 * x * (1.0 + np.tanh(c * (x + 0.044715 * x**3)))
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gate = gelu(gate_raw)
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ffn_hidden = gate * up_raw
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print(f"\n14. GATE PROJECTION:")
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print(f" Python: {gate[:5].tolist()}")
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print(f"\n15. FFN HIDDEN (gate * up):")
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print(f" Python: {ffn_hidden[:5].tolist()}")
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# Down projection
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down_out = dequantize_matmul(ffn_hidden, down_proj_w, down_proj_s, down_proj_b)
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print(f"\n16. DOWN PROJECTION:")
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print(f" Python: {down_out[:5].tolist()}")
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# Residual 2
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hidden_final = hidden_after_attn + down_out
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print(f"\n17. HIDDEN FINAL (after layer 0):")
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print(f" Python: {hidden_final[:5].tolist()}")
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print("\n" + "=" * 60)
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print("END OF LAYER 0 PYTHON REFERENCE")
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print("=" * 60)
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if __name__ == "__main__":
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main()
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