I have in my env 2 A100s with 40GB each. They can be detected normally by using "nvidia-smi" command.
I can run Phind-CodeLlama 34B normally, redirecting its layers to GPU #0.
I'm trying, however, to split the load between the two GPUs. I'm doing this because later I'll try to load bigger models in this env.
However, it seems the second GPU is never detected and the load goes fully to the first detected one.
Please see below the screenshots of the code, and later the entire output of the test.
Python 3.10.12
llama_cpp_python 0.2.7
GPU 0: NVIDIA A100-PCIE-40GB (UUID: GPU-b4017bff-bba6-28c5-337e-f18951fff4a7)
MIG 7g.40gb Device 0: (UUID: MIG-d5255b50-686e-532f-9299-ccce9a125b8c)
GPU 1: NVIDIA A100-PCIE-40GB (UUID: GPU-a86a112f-8d85-572a-2907-15fa6c688e59)
MIG 7g.40gb Device 0: (UUID: MIG-45fc0598-d6e9-5a2b-b7dd-e7d2a0f516a5)
ggml_init_cublas: found 1 CUDA devices:
Device 0: NVIDIA A100-PCIE-40GB MIG 7g.40gb, compute capability 8.0
llama_model_loader: loaded meta data with 20 key-value pairs and 435 tensors from ./models/phind-codellama-34b-v2.Q8_0.gguf (version GGUF V2 (latest))
llama_model_loader: - tensor 0: token_embd.weight q8_0 [ 8192, 32000, 1, 1 ]
llama_model_loader: - tensor 1: blk.0.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 2: blk.0.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 3: blk.0.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 4: blk.0.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 5: blk.0.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 6: blk.0.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 7: blk.0.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 8: blk.0.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 9: blk.0.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 10: blk.1.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 11: blk.1.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 12: blk.1.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 13: blk.1.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 14: blk.1.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 15: blk.1.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 16: blk.1.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 17: blk.1.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 18: blk.1.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 19: blk.2.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 20: blk.2.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 21: blk.2.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 22: blk.2.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 23: blk.2.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 24: blk.2.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 25: blk.2.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 26: blk.2.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 27: blk.2.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 28: blk.3.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 29: blk.3.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 30: blk.3.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 31: blk.3.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 32: blk.3.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 33: blk.3.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 34: blk.3.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 35: blk.3.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 36: blk.3.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 37: blk.4.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 38: blk.4.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 39: blk.4.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 40: blk.4.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 41: blk.4.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 42: blk.4.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 43: blk.4.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 44: blk.4.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 45: blk.4.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 46: blk.5.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 47: blk.5.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 48: blk.5.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 49: blk.5.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 50: blk.5.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 51: blk.5.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 52: blk.5.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 53: blk.5.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 54: blk.5.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 55: blk.6.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 56: blk.6.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 57: blk.6.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 58: blk.6.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 59: blk.6.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 60: blk.6.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 61: blk.6.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 62: blk.6.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 63: blk.6.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 64: blk.7.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 65: blk.7.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 66: blk.7.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 67: blk.7.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 68: blk.7.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 69: blk.7.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 70: blk.7.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 71: blk.7.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 72: blk.7.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 73: blk.8.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 74: blk.8.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 75: blk.8.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 76: blk.8.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 77: blk.8.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 78: blk.8.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 79: blk.8.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 80: blk.8.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 81: blk.8.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 82: blk.9.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 83: blk.9.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 84: blk.9.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 85: blk.9.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 86: blk.9.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 87: blk.9.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 88: blk.9.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 89: blk.9.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 90: blk.9.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 91: blk.10.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 92: blk.10.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 93: blk.10.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 94: blk.10.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 95: blk.10.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 96: blk.10.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 97: blk.10.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 98: blk.10.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 99: blk.10.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 100: blk.11.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 101: blk.11.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 102: blk.11.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 103: blk.11.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 104: blk.11.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 105: blk.11.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 106: blk.11.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 107: blk.11.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 108: blk.11.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 109: blk.12.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 110: blk.12.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 111: blk.12.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 112: blk.12.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 113: blk.12.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 114: blk.12.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 115: blk.12.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 116: blk.12.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 117: blk.12.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 118: blk.13.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 119: blk.13.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 120: blk.13.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 121: blk.13.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 122: blk.13.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 123: blk.13.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 124: blk.13.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 125: blk.13.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 126: blk.13.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 127: blk.14.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 128: blk.14.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 129: blk.14.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 130: blk.14.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 131: blk.14.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 132: blk.14.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 133: blk.14.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 134: blk.14.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 135: blk.14.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 136: blk.15.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 137: blk.15.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 138: blk.15.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 139: blk.15.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 140: blk.15.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 141: blk.15.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 142: blk.15.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 143: blk.15.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 144: blk.15.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 145: blk.16.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 146: blk.16.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 147: blk.16.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 148: blk.16.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 149: blk.16.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 150: blk.16.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 151: blk.16.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 152: blk.16.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 153: blk.16.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 154: blk.17.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 155: blk.17.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 156: blk.17.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 157: blk.17.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 158: blk.17.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 159: blk.17.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 160: blk.17.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 161: blk.17.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 162: blk.17.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 163: blk.18.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 164: blk.18.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 165: blk.18.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 166: blk.18.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 167: blk.18.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 168: blk.18.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 169: blk.18.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 170: blk.18.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 171: blk.18.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 172: blk.19.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 173: blk.19.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 174: blk.19.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 175: blk.19.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 176: blk.19.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 177: blk.19.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 178: blk.19.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 179: blk.19.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 180: blk.19.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 181: blk.20.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 182: blk.20.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 183: blk.20.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 184: blk.20.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 185: blk.20.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 186: blk.20.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 187: blk.20.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 188: blk.20.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 189: blk.20.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 190: blk.21.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 191: blk.21.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 192: blk.21.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 193: blk.21.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 194: blk.21.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 195: blk.21.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 196: blk.21.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 197: blk.21.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 198: blk.21.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 199: blk.22.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 200: blk.22.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 201: blk.22.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 202: blk.22.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
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llama_model_loader: - tensor 413: blk.45.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 414: blk.45.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 415: blk.46.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 416: blk.46.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 417: blk.46.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 418: blk.46.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 419: blk.46.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 420: blk.46.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 421: blk.46.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 422: blk.46.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 423: blk.46.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 424: blk.47.attn_q.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 425: blk.47.attn_k.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 426: blk.47.attn_v.weight q8_0 [ 8192, 1024, 1, 1 ]
llama_model_loader: - tensor 427: blk.47.attn_output.weight q8_0 [ 8192, 8192, 1, 1 ]
llama_model_loader: - tensor 428: blk.47.ffn_gate.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 429: blk.47.ffn_up.weight q8_0 [ 8192, 22016, 1, 1 ]
llama_model_loader: - tensor 430: blk.47.ffn_down.weight q8_0 [ 22016, 8192, 1, 1 ]
llama_model_loader: - tensor 431: blk.47.attn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 432: blk.47.ffn_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 433: output_norm.weight f32 [ 8192, 1, 1, 1 ]
llama_model_loader: - tensor 434: output.weight q8_0 [ 8192, 32000, 1, 1 ]
llama_model_loader: - kv 0: general.architecture str
llama_model_loader: - kv 1: general.name str
llama_model_loader: - kv 2: llama.context_length u32
llama_model_loader: - kv 3: llama.embedding_length u32
llama_model_loader: - kv 4: llama.block_count u32
llama_model_loader: - kv 5: llama.feed_forward_length u32
llama_model_loader: - kv 6: llama.rope.dimension_count u32
llama_model_loader: - kv 7: llama.attention.head_count u32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32
llama_model_loader: - kv 10: llama.rope.freq_base f32
llama_model_loader: - kv 11: general.file_type u32
llama_model_loader: - kv 12: tokenizer.ggml.model str
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr
llama_model_loader: - kv 14: tokenizer.ggml.scores arr
llama_model_loader: - kv 15: tokenizer.ggml.token_type arr
llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32
llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32
llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32
llama_model_loader: - kv 19: general.quantization_version u32
llama_model_loader: - type f32: 97 tensors
llama_model_loader: - type q8_0: 338 tensors
llm_load_print_meta: format = GGUF V2 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 16384
llm_load_print_meta: n_ctx = 512
llm_load_print_meta: n_embd = 8192
llm_load_print_meta: n_head = 64
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 48
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_gqa = 8
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: n_ff = 22016
llm_load_print_meta: freq_base = 10000.0
llm_load_print_meta: freq_scale = 1
llm_load_print_meta: model type = 34B
llm_load_print_meta: model ftype = mostly Q8_0
llm_load_print_meta: model params = 33.74 B
llm_load_print_meta: model size = 33.39 GiB (8.50 BPW)
llm_load_print_meta: general.name = phind_phind-codellama-34b-v2
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.14 MB
llm_load_tensors: using CUDA for GPU acceleration
llm_load_tensors: mem required = 265.76 MB (+ 96.00 MB per state)
llm_load_tensors: offloading 48 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloading v cache to GPU
llm_load_tensors: offloading k cache to GPU
llm_load_tensors: offloaded 51/51 layers to GPU
llm_load_tensors: VRAM used: 34025 MB
....................................................................................................
llama_new_context_with_model: kv self size = 96.00 MB
llama_new_context_with_model: compute buffer total size = 119.47 MB
llama_new_context_with_model: VRAM scratch buffer: 118.00 MB
AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 |
User:
write hello world in rust
llama_print_timings: load time = 518.91 ms
llama_print_timings: sample time = 26.40 ms / 21 runs ( 1.26 ms per token, 795.54 tokens per second)
llama_print_timings: prompt eval time = 518.79 ms / 49 tokens ( 10.59 ms per token, 94.45 tokens per second)
llama_print_timings: eval time = 697.18 ms / 20 runs ( 34.86 ms per token, 28.69 tokens per second)
llama_print_timings: total time = 1310.95 ms
AI:
{'id': 'cmpl-2239d429-adc9-49bb-9cfb-e049c5751220', 'object': 'text_completion', 'created': 1695738691, 'model': './models/phind-codellama-34b-v2.Q8_0.gguf', 'choices': [{'text': '\n### System Prompt\nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### User Message\nwrite hello world in rust\n\n### Assistant\n```rust\nfn main() {\n println!("Hello, world!"));\n}\n```', 'index': 0, 'logprobs': None, 'finish_reason': 'stop'}], 'usage': {'prompt_tokens': 49, 'completion_tokens': 20, 'total_tokens': 69}}
bye!What am I missing, for being able to properly split the workload between the two GPUs (which are clearly detected by "nvidia-smi", from the output)?
I have in my env 2 A100s with 40GB each. They can be detected normally by using "nvidia-smi" command.
I can run Phind-CodeLlama 34B normally, redirecting its layers to GPU #0.
I'm trying, however, to split the load between the two GPUs. I'm doing this because later I'll try to load bigger models in this env.
However, it seems the second GPU is never detected and the load goes fully to the first detected one.
Please see below the screenshots of the code, and later the entire output of the test.
Initial script (left side) and the python code (right side)
Output screenshot (beginning)
Output screenshot (end)
Full output text
What am I missing, for being able to properly split the workload between the two GPUs (which are clearly detected by "nvidia-smi", from the output)?