IQuest-Coder-V1-14B-Thinking / conversion_to_hf.log
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Loaded loader_megatron_core as the loader.
Loaded saver_llama2_hf_bf as the saver.
Starting saver...
Starting loader...
fused_indices_to_multihot has reached end of life. Please migrate to a non-experimental function.
/usr/local/lib/python3.12/dist-packages/modelopt/torch/utils/import_utils.py:31: UserWarning: Failed to import apex plugin due to: AttributeError("module 'transformers.modeling_utils' has no attribute 'Conv1D'"). You may ignore this warning if you do not need this plugin.
warnings.warn(
/usr/local/lib/python3.12/dist-packages/modelopt/torch/utils/import_utils.py:31: UserWarning: Failed to import huggingface plugin due to: AttributeError("module 'transformers.modeling_utils' has no attribute 'Conv1D'"). You may ignore this warning if you do not need this plugin.
warnings.warn(
/usr/local/lib/python3.12/dist-packages/modelopt/torch/utils/import_utils.py:31: UserWarning: Failed to import megatron plugin due to: AttributeError("module 'transformers.modeling_utils' has no attribute 'Conv1D'"). You may ignore this warning if you do not need this plugin.
warnings.warn(
Setting num_layers to 28 from checkpoint
Setting hidden_size to 5120 from checkpoint
Setting ffn_hidden_size to 27648 from checkpoint
Setting seq_length to 131072 from checkpoint
Setting num_attention_heads to 40 from checkpoint
Setting num_query_groups to 8 from checkpoint
Setting group_query_attention to True from checkpoint
Setting kv_channels to 128 from checkpoint
Setting max_position_embeddings to 131072 from checkpoint
Setting position_embedding_type to rope from checkpoint
Setting add_position_embedding to True from checkpoint
Setting use_rotary_position_embeddings to True from checkpoint
Setting rotary_base to 500000 from checkpoint
Setting rotary_percent to 1.0 from checkpoint
Setting rotary_interleaved to False from checkpoint
Setting add_bias_linear to False from checkpoint
Setting add_qkv_bias to False from checkpoint
Setting squared_relu to False from checkpoint
Setting swiglu to True from checkpoint
Setting untie_embeddings_and_output_weights to True from checkpoint
Setting apply_layernorm_1p to False from checkpoint
Setting normalization to RMSNorm from checkpoint
Setting apply_query_key_layer_scaling to False from checkpoint
Setting attention_dropout to 0.0 from checkpoint
Setting hidden_dropout to 0.0 from checkpoint
Checkpoint did not provide arguments hybrid_override_pattern
Checkpoint did not provide arguments spec
Setting hybrid_attention_ratio to 0.0 from checkpoint
Setting hybrid_mlp_ratio to 0.0 from checkpoint
Checkpoint did not provide arguments num_experts
Setting moe_layer_freq to 1 from checkpoint
Setting moe_router_topk to 2 from checkpoint
Setting moe_router_pre_softmax to False from checkpoint
Setting moe_grouped_gemm to False from checkpoint
Checkpoint did not provide arguments moe_shared_expert_intermediate_size
Setting mamba_state_dim to 128 from checkpoint
Setting mamba_head_dim to 64 from checkpoint
Setting mamba_num_groups to 8 from checkpoint
Checkpoint did not provide arguments mamba_num_heads
Setting is_hybrid_model to False from checkpoint
Checkpoint did not provide arguments heterogeneous_layers_config_path
Checkpoint did not provide arguments heterogeneous_layers_config_encoded_json
Setting tokenizer_type to SFTTokenizer from checkpoint
Setting tokenizer_model to /cpfs01/users/wzhang/iquest-coder-v1.1/RepoData-Ucoder-32B-128k-from2.5.2/97.09B_instruct_iquest-coder from checkpoint
Checkpoint did not provide arguments tiktoken_pattern
Setting padded_vocab_size to 76800 from checkpoint
INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1
WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it
building GPT model ...
(TP, PP) mismatch after resume ((1, 1) vs (8, 1) from checkpoint): RNG state will be ignored
sharded_state_dict metadata loaded from the checkpoint: {'distrib_optim_sharding_type': 'dp_reshardable', 'singleton_local_shards': False, 'chained_optim_avoid_prefix': True}
Job sharding has changed: Rerun state will be ignored
loading distributed checkpoint from /tmp/megatron_convert_iter1970_node0_pid360_a250e6f4 at iteration 1970
/volume/pt-train/users/wzhang/wjj-workspace/code-sft/src/training/Megatron-LM/megatron/core/dist_checkpointing/strategies/torch.py:956: FutureWarning: `load_state_dict` is deprecated and will be removed in future versions. Please use `load` instead.
checkpoint.load_state_dict(
/usr/local/lib/python3.12/dist-packages/torch/distributed/checkpoint/planner_helpers.py:406: FutureWarning: Please use DTensor instead and we are deprecating ShardedTensor.
device = getattr(value, "device", None)
/usr/local/lib/python3.12/dist-packages/torch/distributed/checkpoint/default_planner.py:454: FutureWarning: Please use DTensor instead and we are deprecating ShardedTensor.
and md.size != obj.size()
checkpoint version 3.0
successfully loaded checkpoint from /tmp/megatron_convert_iter1970_node0_pid360_a250e6f4 [ t 1/1, p 1/1 ] at iteration 1970
sending embeddings
sending transformer layer 0
sending transformer layer 1
sending transformer layer 2
sending transformer layer 3
sending transformer layer 4
sending transformer layer 5
sending transformer layer 6
sending transformer layer 7
sending transformer layer 8
sending transformer layer 9
sending transformer layer 10
sending transformer layer 11
sending transformer layer 12
sending transformer layer 13
sending transformer layer 14
sending transformer layer 15
sending transformer layer 16
sending transformer layer 17
sending transformer layer 18
sending transformer layer 19
sending transformer layer 20
sending transformer layer 21
sending transformer layer 22
sending transformer layer 23
sending transformer layer 24
sending transformer layer 25
sending transformer layer 26
sending transformer layer 27
sending final norm
sending output layer
Waiting for saver to complete...
fused_indices_to_multihot has reached end of life. Please migrate to a non-experimental function.
received embeddings
received transformer layer 0
received transformer layer 1
received transformer layer 2
received transformer layer 3
received transformer layer 4
received transformer layer 5
received transformer layer 6
received transformer layer 7
received transformer layer 8
received transformer layer 9
received transformer layer 10
received transformer layer 11
received transformer layer 12
received transformer layer 13
received transformer layer 14
received transformer layer 15
received transformer layer 16
received transformer layer 17
received transformer layer 18
received transformer layer 19
received transformer layer 20
received transformer layer 21
received transformer layer 22
received transformer layer 23
received transformer layer 24
received transformer layer 25
received transformer layer 26
received transformer layer 27
received final norm
received output layer
Saving model to disk ...