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MLA_config.json

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+{
+    "d_model": 4096,
+    "rms_norm_eps": 1e-05,
+    "vocab_size": null,
+    "d_inner": 4096,
+    "d_xb": 1024,
+    "intermediate_size": 14336,
+    "hidden_act": "silu",
+    "n_layer": 32,
+    "mla_to_mha": false,
+    "first_mha": false,
+    "attn_layers": [
+        1,
+        3,
+        5,
+        7,
+        9,
+        11,
+        13,
+        15,
+        17,
+        19,
+        21,
+        23,
+        25,
+        27,
+        29,
+        31
+    ]
+}

README.md

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+---
+base_model: meta-llama/Llama-3.1-8B-Instruct
+datasets:
+- JunxiongWang/sftdatasetv3
+model-index:
+- name: hybrid_8B_mla16_mamba16_Fix160_qr2048_qh64_stage2_data11B
+  results: []
+tags:
+- alignment-handbook
+- generated_from_trainer
+
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/mrgzadeh/huggingface/runs/y5dnnyl6)
+# hybrid_8B_mla16_mamba16_Fix160_qr2048_qh64_stage2_data11B
+
+This model is a fine-tuned version of [/workspace/amd-mla/llama3.1_8b-instruct](https://huggingface.co//workspace/amd-mla/llama3.1_8b-instruct) on the JunxiongWang/sftdatasetv3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 111.6995
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 4
+- eval_batch_size: 2
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 64
+- total_eval_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.01
+- num_epochs: 1.4
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss |
+|:-------------:|:-----:|:-----:|:---------------:|
+| 110.5648      | 1.0   | 51995 | 124.3641        |
+| 89.2123       | 1.4   | 72793 | 111.6995        |
+
+
+### Framework versions
+
+- Transformers 4.43.1
+- Pytorch 2.7.0a0+git6374332
+- Datasets 2.20.0
+- Tokenizers 0.19.1

all_results.json

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+{
+    "epoch": 1.4,
+    "eval_loss": 111.6994857788086,
+    "eval_runtime": 17.6372,
+    "eval_samples": 4096,
+    "eval_samples_per_second": 39.462,
+    "eval_steps_per_second": 2.495,
+    "total_flos": 4.721862248497152e+16,
+    "train_loss": 149.02320945810703,
+    "train_runtime": 264406.7938,
+    "train_samples": 19473081,
+    "train_samples_per_second": 17.62,
+    "train_steps_per_second": 0.275
+}

config.json

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+{
+    "_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 128000,
+    "eos_token_id": [
+        128001,
+        128008,
+        128009
+    ],
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 14336,
+    "max_position_embeddings": 131072,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 8,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": {
+        "factor": 8.0,
+        "high_freq_factor": 4.0,
+        "low_freq_factor": 1.0,
+        "original_max_position_embeddings": 8192,
+        "rope_type": "llama3"
+    },
+    "rope_theta": 500000.0,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.43.1",
+    "use_cache": true,
+    "vocab_size": 128256
+}

eval_results.json

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+{
+    "epoch": 1.4,
+    "eval_loss": 111.6994857788086,
+    "eval_runtime": 17.6372,
+    "eval_samples": 4096,
+    "eval_samples_per_second": 39.462,
+    "eval_steps_per_second": 2.495
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.43.1"
+}

hybrid_8B_mla16_mamba16_Fix160_qr2048_qh64_stage2_data11B.yaml

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+# Model arguments
+model_name_or_path: /workspace/amd-mla/llama3.1_8b-instruct
+teacher_model_name_or_path: /workspace/amd-mla/llama3.1_8b-instruct
+mamba_model_path: /workspace/amd-mla/HybridInLlama_mla0_mamba100_8B_stage1
+mla_model_path: /workspace/amd-mla/HybridInLlama_mla100_mamba0_8B_kv160_q2048_np64_rp64_stage1
+with_distill: false
+# mla_layers: [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]
+# mla_layers: [0,2,4,6,8,10,12,14]
+# mla_layers: [0,2,4,6,8,14]
+# mla_layers: [0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,31]
+mla_layers: [0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30]
+# mla_layers: []
+init_with_svd: false
+init_with_kqvo: false
+decontaminate: true
+torch_dtype: bfloat16
+use_flash_attention_2: true
+mla_to_mha: false
+mamba_to_mha: false
+first_mha: false
+
+# Data training arguments
+data_ratio: 0
+chat_template: "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"
+dataset_mixer:
+  # /home/data/datasets/sftdatasetv3: 1.0
+  JunxiongWang/sftdatasetv3: 1.0
+dataset_splits:
+- train
+- test
+preprocessing_num_workers: 128  
+
+# SFT trainer config
+bf16: true
+do_eval: true
+evaluation_strategy: epoch
+gradient_accumulation_steps: 2
+gradient_checkpointing: false
+gradient_checkpointing_kwargs:
+  use_reentrant: False
+learning_rate: 2.0e-05
+log_level: info
+logging_steps: 10
+logging_strategy: steps
+lr_scheduler_type: cosine
+max_seq_length: 2048
+max_steps: -1
+max_grad_norm: 1.0
+num_train_epochs: 1.4
+output_dir: hybrid_8B_mla16_mamba16_Fix160_qr2048_qh64_stage2_data11B
+# output_dir: /home/mingyyan/checkpoints/debug
+overwrite_output_dir: true
+per_device_eval_batch_size: 2
+per_device_train_batch_size: 4
+push_to_hub: false
+remove_unused_columns: true
+report_to:
+- wandb
+save_strategy: "steps"
+save_steps: 2000
+save_total_limit: 1
+seed: 42
+warmup_ratio: 0.01
+kl_weight: 1.0
+ce_weight: 0.0
+kl_hidden_weight: 0.0
+
+# MLA configs
+q_lora_rank: 2048
+qk_rope_head_dim: 64
+kv_lora_rank: 160
+v_head_dim: 128
+qk_nope_head_dim: 64
+freeze_non_mla: false
+use_lora_layer_norm: false
+use_full_kv_head: false

hybrid_config.json

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+{
+    "hidden_size": 4096,
+    "intermediate_size": 14336,
+    "hidden_act": "silu",
+    "n_layer": 32,
+    "mla_layers": [
+        0,
+        2,
+        4,
+        6,
+        8,
+        10,
+        12,
+	    14,
+	    16,
+	    18,
+	    20,
+	    22,
+	    24,
+	    26,
+	    28,
+        30
+    ],
+    "rms_norm_eps": 1e-05,
+    "num_attention_heads": 32,
+    "num_key_value_heads": 8,
+    "kv_lora_rank": 160,
+    "q_lora_rank": 2048,
+    "use_lora_layer_norm": false,
+    "use_fixed_rank_for_first_and_last_block": true,
+    "use_full_kv_head": false,
+    "layer_rank_list": {},
+    "qk_rope_head_dim": 64,
+    "v_head_dim": 128,
+    "qk_nope_head_dim": 64,
+    "q_energy_ratio": null,
+    "kv_energy_ratio": null,
+    "qkv_rank_divisor": 8,
+    "max_position_embeddings": 131072,
+    "rope_theta": 500000.0,
+    "rope_scaling": {
+        "factor": 8.0,
+        "high_freq_factor": 4.0,
+        "low_freq_factor": 1.0,
+        "original_max_position_embeddings": 8192,
+        "rope_type": "llama3"
+    },
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "rope_type": "yarn",
+    "d_model": 4096,
+    "ssm_cfg": {
+        "expand": 1,
+        "ngroups": 32,
+        "d_state": 128,
+        "repeat_kv_before_conv": false
+    },
+    "d_inner": 4096,
+    "d_xb": 1024
+}

latest

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+global_step72793

lm_harness_eval.txt

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+2025-06-12:13:21:08,578 INFO     [utils.py:146] Note: detected 96 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
+2025-06-12:13:21:08,578 INFO     [utils.py:149] Note: NumExpr detected 96 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
+2025-06-12:13:21:08,578 INFO     [utils.py:162] NumExpr defaulting to 16 threads.
+2025-06-12:13:21:08,721 INFO     [config.py:58] PyTorch version 2.7.0a0+git6374332 available.
+
+2025-06-12:13:21:18,795 INFO     [__main__.py:132] Verbosity set to INFO
+2025-06-12:13:21:23,277 INFO     [__main__.py:205] Selected Tasks: ['arc_challenge', 'arc_easy', 'hellaswag', 'mmlu', 'openbookqa', 'piqa', 'pubmedqa', 'race', 'winogrande']
+2025-06-12:13:21:23,282 WARNING  [evaluator.py:93] generation_kwargs specified through cli, these settings will be used over set parameters in yaml tasks.
+You are attempting to use Flash Attention 2.0 with a model not initialized on GPU. Make sure to move the model to GPU after initializing it on CPU with `model.to('cuda')`.
+Some weights of the model checkpoint at ./hybrid_8B_mla16_mamba16_Fix160_qr2048_qh64_stage2_data11B were not used when initializing LlamaForCausalLM: ['model.layers.0.mla.kv_a_proj_with_mqa.weight', 'model.layers.0.mla.kv_b_proj.weight', 'model.layers.0.mla.out_proj.weight', 'model.layers.0.mla.q_a_proj.weight', 'model.layers.0.mla.q_b_proj.weight', 'model.layers.1.mamba.A_log', 'model.layers.1.mamba.D', 'model.layers.1.mamba.conv1d.bias', 'model.layers.1.mamba.conv1d.weight', 'model.layers.1.mamba.dt_bias', 'model.layers.1.mamba.in_proj.weight', 'model.layers.1.mamba.norm.weight', 'model.layers.1.mamba.out_proj.weight', 'model.layers.10.mla.kv_a_proj_with_mqa.weight', 'model.layers.10.mla.kv_b_proj.weight', 'model.layers.10.mla.out_proj.weight', 'model.layers.10.mla.q_a_proj.weight', 'model.layers.10.mla.q_b_proj.weight', 'model.layers.11.mamba.A_log', 'model.layers.11.mamba.D', 'model.layers.11.mamba.conv1d.bias', 'model.layers.11.mamba.conv1d.weight', 'model.layers.11.mamba.dt_bias', 'model.layers.11.mamba.in_proj.weight', 'model.layers.11.mamba.norm.weight', 'model.layers.11.mamba.out_proj.weight', 'model.layers.12.mla.kv_a_proj_with_mqa.weight', 'model.layers.12.mla.kv_b_proj.weight', 'model.layers.12.mla.out_proj.weight', 'model.layers.12.mla.q_a_proj.weight', 'model.layers.12.mla.q_b_proj.weight', 'model.layers.13.mamba.A_log', 'model.layers.13.mamba.D', 'model.layers.13.mamba.conv1d.bias', 'model.layers.13.mamba.conv1d.weight', 'model.layers.13.mamba.dt_bias', 'model.layers.13.mamba.in_proj.weight', 'model.layers.13.mamba.norm.weight', 'model.layers.13.mamba.out_proj.weight', 'model.layers.14.mla.kv_a_proj_with_mqa.weight', 'model.layers.14.mla.kv_b_proj.weight', 'model.layers.14.mla.out_proj.weight', 'model.layers.14.mla.q_a_proj.weight', 'model.layers.14.mla.q_b_proj.weight', 'model.layers.15.mamba.A_log', 'model.layers.15.mamba.D', 'model.layers.15.mamba.conv1d.bias', 'model.layers.15.mamba.conv1d.weight', 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'model.layers.5.mamba.in_proj.weight', 'model.layers.5.mamba.norm.weight', 'model.layers.5.mamba.out_proj.weight', 'model.layers.6.mla.kv_a_proj_with_mqa.weight', 'model.layers.6.mla.kv_b_proj.weight', 'model.layers.6.mla.out_proj.weight', 'model.layers.6.mla.q_a_proj.weight', 'model.layers.6.mla.q_b_proj.weight', 'model.layers.7.mamba.A_log', 'model.layers.7.mamba.D', 'model.layers.7.mamba.conv1d.bias', 'model.layers.7.mamba.conv1d.weight', 'model.layers.7.mamba.dt_bias', 'model.layers.7.mamba.in_proj.weight', 'model.layers.7.mamba.norm.weight', 'model.layers.7.mamba.out_proj.weight', 'model.layers.8.mla.kv_a_proj_with_mqa.weight', 'model.layers.8.mla.kv_b_proj.weight', 'model.layers.8.mla.out_proj.weight', 'model.layers.8.mla.q_a_proj.weight', 'model.layers.8.mla.q_b_proj.weight', 'model.layers.9.mamba.A_log', 'model.layers.9.mamba.D', 'model.layers.9.mamba.conv1d.bias', 'model.layers.9.mamba.conv1d.weight', 'model.layers.9.mamba.dt_bias', 'model.layers.9.mamba.in_proj.weight', 'model.layers.9.mamba.norm.weight', 'model.layers.9.mamba.out_proj.weight']
+- This IS expected if you are initializing LlamaForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
+- This IS NOT expected if you are initializing LlamaForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
+Some weights of LlamaForCausalLM were not initialized from the model checkpoint at ./hybrid_8B_mla16_mamba16_Fix160_qr2048_qh64_stage2_data11B and are newly initialized: ['model.layers.0.self_attn.k_proj.weight', 'model.layers.0.self_attn.o_proj.weight', 'model.layers.0.self_attn.q_proj.weight', 'model.layers.0.self_attn.v_proj.weight', 'model.layers.1.self_attn.k_proj.weight', 'model.layers.1.self_attn.o_proj.weight', 'model.layers.1.self_attn.q_proj.weight', 'model.layers.1.self_attn.v_proj.weight', 'model.layers.10.self_attn.k_proj.weight', 'model.layers.10.self_attn.o_proj.weight', 'model.layers.10.self_attn.q_proj.weight', 'model.layers.10.self_attn.v_proj.weight', 'model.layers.11.self_attn.k_proj.weight', 'model.layers.11.self_attn.o_proj.weight', 'model.layers.11.self_attn.q_proj.weight', 'model.layers.11.self_attn.v_proj.weight', 'model.layers.12.self_attn.k_proj.weight', 'model.layers.12.self_attn.o_proj.weight', 'model.layers.12.self_attn.q_proj.weight', 'model.layers.12.self_attn.v_proj.weight', 'model.layers.13.self_attn.k_proj.weight', 'model.layers.13.self_attn.o_proj.weight', 'model.layers.13.self_attn.q_proj.weight', 'model.layers.13.self_attn.v_proj.weight', 'model.layers.14.self_attn.k_proj.weight', 'model.layers.14.self_attn.o_proj.weight', 'model.layers.14.self_attn.q_proj.weight', 'model.layers.14.self_attn.v_proj.weight', 'model.layers.15.self_attn.k_proj.weight', 'model.layers.15.self_attn.o_proj.weight', 'model.layers.15.self_attn.q_proj.weight', 'model.layers.15.self_attn.v_proj.weight', 'model.layers.16.self_attn.k_proj.weight', 'model.layers.16.self_attn.o_proj.weight', 'model.layers.16.self_attn.q_proj.weight', 'model.layers.16.self_attn.v_proj.weight', 'model.layers.17.self_attn.k_proj.weight', 'model.layers.17.self_attn.o_proj.weight', 'model.layers.17.self_attn.q_proj.weight', 'model.layers.17.self_attn.v_proj.weight', 'model.layers.18.self_attn.k_proj.weight', 'model.layers.18.self_attn.o_proj.weight', 'model.layers.18.self_attn.q_proj.weight', 'model.layers.18.self_attn.v_proj.weight', 'model.layers.19.self_attn.k_proj.weight', 'model.layers.19.self_attn.o_proj.weight', 'model.layers.19.self_attn.q_proj.weight', 'model.layers.19.self_attn.v_proj.weight', 'model.layers.2.self_attn.k_proj.weight', 'model.layers.2.self_attn.o_proj.weight', 'model.layers.2.self_attn.q_proj.weight', 'model.layers.2.self_attn.v_proj.weight', 'model.layers.20.self_attn.k_proj.weight', 'model.layers.20.self_attn.o_proj.weight', 'model.layers.20.self_attn.q_proj.weight', 'model.layers.20.self_attn.v_proj.weight', 'model.layers.21.self_attn.k_proj.weight', 'model.layers.21.self_attn.o_proj.weight', 'model.layers.21.self_attn.q_proj.weight', 'model.layers.21.self_attn.v_proj.weight', 'model.layers.22.self_attn.k_proj.weight', 'model.layers.22.self_attn.o_proj.weight', 'model.layers.22.self_attn.q_proj.weight', 'model.layers.22.self_attn.v_proj.weight', 'model.layers.23.self_attn.k_proj.weight', 'model.layers.23.self_attn.o_proj.weight', 'model.layers.23.self_attn.q_proj.weight', 'model.layers.23.self_attn.v_proj.weight', 'model.layers.24.self_attn.k_proj.weight', 'model.layers.24.self_attn.o_proj.weight', 'model.layers.24.self_attn.q_proj.weight', 'model.layers.24.self_attn.v_proj.weight', 'model.layers.25.self_attn.k_proj.weight', 'model.layers.25.self_attn.o_proj.weight', 'model.layers.25.self_attn.q_proj.weight', 'model.layers.25.self_attn.v_proj.weight', 'model.layers.26.self_attn.k_proj.weight', 'model.layers.26.self_attn.o_proj.weight', 'model.layers.26.self_attn.q_proj.weight', 'model.layers.26.self_attn.v_proj.weight', 'model.layers.27.self_attn.k_proj.weight', 'model.layers.27.self_attn.o_proj.weight', 'model.layers.27.self_attn.q_proj.weight', 'model.layers.27.self_attn.v_proj.weight', 'model.layers.28.self_attn.k_proj.weight', 'model.layers.28.self_attn.o_proj.weight', 'model.layers.28.self_attn.q_proj.weight', 'model.layers.28.self_attn.v_proj.weight', 'model.layers.29.self_attn.k_proj.weight', 'model.layers.29.self_attn.o_proj.weight', 'model.layers.29.self_attn.q_proj.weight', 'model.layers.29.self_attn.v_proj.weight', 'model.layers.3.self_attn.k_proj.weight', 'model.layers.3.self_attn.o_proj.weight', 'model.layers.3.self_attn.q_proj.weight', 'model.layers.3.self_attn.v_proj.weight', 'model.layers.30.self_attn.k_proj.weight', 'model.layers.30.self_attn.o_proj.weight', 'model.layers.30.self_attn.q_proj.weight', 'model.layers.30.self_attn.v_proj.weight', 'model.layers.31.self_attn.k_proj.weight', 'model.layers.31.self_attn.o_proj.weight', 'model.layers.31.self_attn.q_proj.weight', 'model.layers.31.self_attn.v_proj.weight', 'model.layers.4.self_attn.k_proj.weight', 'model.layers.4.self_attn.o_proj.weight', 'model.layers.4.self_attn.q_proj.weight', 'model.layers.4.self_attn.v_proj.weight', 'model.layers.5.self_attn.k_proj.weight', 'model.layers.5.self_attn.o_proj.weight', 'model.layers.5.self_attn.q_proj.weight', 'model.layers.5.self_attn.v_proj.weight', 'model.layers.6.self_attn.k_proj.weight', 'model.layers.6.self_attn.o_proj.weight', 'model.layers.6.self_attn.q_proj.weight', 'model.layers.6.self_attn.v_proj.weight', 'model.layers.7.self_attn.k_proj.weight', 'model.layers.7.self_attn.o_proj.weight', 'model.layers.7.self_attn.q_proj.weight', 'model.layers.7.self_attn.v_proj.weight', 'model.layers.8.self_attn.k_proj.weight', 'model.layers.8.self_attn.o_proj.weight', 'model.layers.8.self_attn.q_proj.weight', 'model.layers.8.self_attn.v_proj.weight', 'model.layers.9.self_attn.k_proj.weight', 'model.layers.9.self_attn.o_proj.weight', 'model.layers.9.self_attn.q_proj.weight', 'model.layers.9.self_attn.v_proj.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+Traceback (most recent call last):
+  File "/workspace/amd-mla/benchmark/llm_eval/lm_harness_eval.py", line 407, in <module>
+    cli_evaluate()
+  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/lm_eval/__main__.py", line 207, in cli_evaluate
+    results = evaluator.simple_evaluate(
+  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/lm_eval/utils.py", line 402, in _wrapper
+    return fn(*args, **kwargs)
+  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/lm_eval/evaluator.py", line 102, in simple_evaluate
+    lm = lm_eval.api.registry.get_model(model).create_from_arg_string(
+  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/lm_eval/api/model.py", line 136, in create_from_arg_string
+    return cls(**args, **args2)
+  File "/workspace/amd-mla/benchmark/llm_eval/lm_harness_eval.py", line 312, in __init__
+    self._model = MLATransformerHybridModelWrapper.from_pretrained(pretrained, torch_dtype=dtype).model
+  File "/workspace/amd-mla/./mla/hybrid_wrapper.py", line 227, in from_pretrained
+    return MLATransformerHybridModelWrapper.from_pretrained_local(pretrained_model_name, torch_dtype, attn_implementation)
+  File "/workspace/amd-mla/./mla/hybrid_wrapper.py", line 210, in from_pretrained_local
+    return MLATransformerHybridModelWrapper(pretrained_model_name, transformer_model, MLA_config, MLA_config.attn_layers, torch_dtype, init_with_svd=False) 
+  File "/workspace/amd-mla/./mla/hybrid_wrapper.py", line 155, in __init__
+    self.model.load_state_dict(torch.load(f"{checkpoint_path}/pytorch_model.bin", map_location=torch.device("cpu")))
+  File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2591, in load_state_dict
+    raise RuntimeError(
+RuntimeError: Error(s) in loading state_dict for LlamaForCausalLM:
+	Missing key(s) in state_dict: "model.layers.1.self_attn.q_proj.weight", "model.layers.1.self_attn.k_proj.weight", "model.layers.1.self_attn.v_proj.weight", "model.layers.1.self_attn.o_proj.weight", "model.layers.3.self_attn.q_proj.weight", "model.layers.3.self_attn.k_proj.weight", "model.layers.3.self_attn.v_proj.weight", "model.layers.3.self_attn.o_proj.weight", "model.layers.5.self_attn.q_proj.weight", "model.layers.5.self_attn.k_proj.weight", "model.layers.5.self_attn.v_proj.weight", "model.layers.5.self_attn.o_proj.weight", "model.layers.7.self_attn.q_proj.weight", "model.layers.7.self_attn.k_proj.weight", "model.layers.7.self_attn.v_proj.weight", "model.layers.7.self_attn.o_proj.weight", "model.layers.9.self_attn.q_proj.weight", "model.layers.9.self_attn.k_proj.weight", "model.layers.9.self_attn.v_proj.weight", "model.layers.9.self_attn.o_proj.weight", "model.layers.11.self_attn.q_proj.weight", "model.layers.11.self_attn.k_proj.weight", "model.layers.11.self_attn.v_proj.weight", "model.layers.11.self_attn.o_proj.weight", "model.layers.13.self_attn.q_proj.weight", "model.layers.13.self_attn.k_proj.weight", "model.layers.13.self_attn.v_proj.weight", "model.layers.13.self_attn.o_proj.weight", "model.layers.15.self_attn.q_proj.weight", "model.layers.15.self_attn.k_proj.weight", "model.layers.15.self_attn.v_proj.weight", "model.layers.15.self_attn.o_proj.weight", "model.layers.17.self_attn.q_proj.weight", "model.layers.17.self_attn.k_proj.weight", "model.layers.17.self_attn.v_proj.weight", "model.layers.17.self_attn.o_proj.weight", "model.layers.19.self_attn.q_proj.weight", "model.layers.19.self_attn.k_proj.weight", "model.layers.19.self_attn.v_proj.weight", "model.layers.19.self_attn.o_proj.weight", "model.layers.21.self_attn.q_proj.weight", "model.layers.21.self_attn.k_proj.weight", "model.layers.21.self_attn.v_proj.weight", "model.layers.21.self_attn.o_proj.weight", "model.layers.23.self_attn.q_proj.weight", "model.layers.23.self_attn.k_proj.weight", "model.layers.23.self_attn.v_proj.weight", "model.layers.23.self_attn.o_proj.weight", "model.layers.25.self_attn.q_proj.weight", "model.layers.25.self_attn.k_proj.weight", "model.layers.25.self_attn.v_proj.weight", "model.layers.25.self_attn.o_proj.weight", "model.layers.27.self_attn.q_proj.weight", "model.layers.27.self_attn.k_proj.weight", "model.layers.27.self_attn.v_proj.weight", "model.layers.27.self_attn.o_proj.weight", "model.layers.29.self_attn.q_proj.weight", "model.layers.29.self_attn.k_proj.weight", "model.layers.29.self_attn.v_proj.weight", "model.layers.29.self_attn.o_proj.weight", "model.layers.31.self_attn.q_proj.weight", "model.layers.31.self_attn.k_proj.weight", "model.layers.31.self_attn.v_proj.weight", "model.layers.31.self_attn.o_proj.weight". 
+	Unexpected key(s) in state_dict: "model.layers.1.mamba.A_log", "model.layers.1.mamba.D", "model.layers.1.mamba.in_proj.weight", "model.layers.1.mamba.conv1d.weight", "model.layers.1.mamba.norm.weight", "model.layers.1.mamba.out_proj.weight", "model.layers.1.mamba.dt_bias", "model.layers.1.mamba.conv1d.bias", "model.layers.3.mamba.A_log", "model.layers.3.mamba.D", "model.layers.3.mamba.in_proj.weight", "model.layers.3.mamba.conv1d.weight", "model.layers.3.mamba.norm.weight", "model.layers.3.mamba.out_proj.weight", "model.layers.3.mamba.dt_bias", "model.layers.3.mamba.conv1d.bias", "model.layers.5.mamba.A_log", "model.layers.5.mamba.D", "model.layers.5.mamba.in_proj.weight", "model.layers.5.mamba.conv1d.weight", "model.layers.5.mamba.norm.weight", "model.layers.5.mamba.out_proj.weight", "model.layers.5.mamba.dt_bias", "model.layers.5.mamba.conv1d.bias", "model.layers.7.mamba.A_log", "model.layers.7.mamba.D", "model.layers.7.mamba.in_proj.weight", "model.layers.7.mamba.conv1d.weight", "model.layers.7.mamba.norm.weight", "model.layers.7.mamba.out_proj.weight", "model.layers.7.mamba.dt_bias", "model.layers.7.mamba.conv1d.bias", "model.layers.9.mamba.A_log", "model.layers.9.mamba.D", "model.layers.9.mamba.in_proj.weight", "model.layers.9.mamba.conv1d.weight", "model.layers.9.mamba.norm.weight", "model.layers.9.mamba.out_proj.weight", "model.layers.9.mamba.dt_bias", "model.layers.9.mamba.conv1d.bias", "model.layers.11.mamba.A_log", "model.layers.11.mamba.D", "model.layers.11.mamba.in_proj.weight", "model.layers.11.mamba.conv1d.weight", "model.layers.11.mamba.norm.weight", "model.layers.11.mamba.out_proj.weight", "model.layers.11.mamba.dt_bias", "model.layers.11.mamba.conv1d.bias", "model.layers.13.mamba.A_log", "model.layers.13.mamba.D", "model.layers.13.mamba.in_proj.weight", "model.layers.13.mamba.conv1d.weight", "model.layers.13.mamba.norm.weight", "model.layers.13.mamba.out_proj.weight", "model.layers.13.mamba.dt_bias", "model.layers.13.mamba.conv1d.bias", "model.layers.15.mamba.A_log", "model.layers.15.mamba.D", "model.layers.15.mamba.in_proj.weight", "model.layers.15.mamba.conv1d.weight", "model.layers.15.mamba.norm.weight", "model.layers.15.mamba.out_proj.weight", "model.layers.15.mamba.dt_bias", "model.layers.15.mamba.conv1d.bias", "model.layers.17.mamba.A_log", "model.layers.17.mamba.D", "model.layers.17.mamba.in_proj.weight", "model.layers.17.mamba.conv1d.weight", "model.layers.17.mamba.norm.weight", "model.layers.17.mamba.out_proj.weight", "model.layers.17.mamba.dt_bias", "model.layers.17.mamba.conv1d.bias", "model.layers.19.mamba.A_log", "model.layers.19.mamba.D", "model.layers.19.mamba.in_proj.weight", "model.layers.19.mamba.conv1d.weight", "model.layers.19.mamba.norm.weight", "model.layers.19.mamba.out_proj.weight", "model.layers.19.mamba.dt_bias", "model.layers.19.mamba.conv1d.bias", "model.layers.21.mamba.A_log", "model.layers.21.mamba.D", "model.layers.21.mamba.in_proj.weight", "model.layers.21.mamba.conv1d.weight", "model.layers.21.mamba.norm.weight", "model.layers.21.mamba.out_proj.weight", "model.layers.21.mamba.dt_bias", "model.layers.21.mamba.conv1d.bias", "model.layers.23.mamba.A_log", "model.layers.23.mamba.D", "model.layers.23.mamba.in_proj.weight", "model.layers.23.mamba.conv1d.weight", "model.layers.23.mamba.norm.weight", "model.layers.23.mamba.out_proj.weight", "model.layers.23.mamba.dt_bias", "model.layers.23.mamba.conv1d.bias", "model.layers.25.mamba.A_log", "model.layers.25.mamba.D", "model.layers.25.mamba.in_proj.weight", "model.layers.25.mamba.conv1d.weight", "model.layers.25.mamba.norm.weight", "model.layers.25.mamba.out_proj.weight", "model.layers.25.mamba.dt_bias", "model.layers.25.mamba.conv1d.bias", "model.layers.27.mamba.A_log", "model.layers.27.mamba.D", "model.layers.27.mamba.in_proj.weight", "model.layers.27.mamba.conv1d.weight", "model.layers.27.mamba.norm.weight", "model.layers.27.mamba.out_proj.weight", "model.layers.27.mamba.dt_bias", "model.layers.27.mamba.conv1d.bias", "model.layers.29.mamba.A_log", "model.layers.29.mamba.D", "model.layers.29.mamba.in_proj.weight", "model.layers.29.mamba.conv1d.weight", "model.layers.29.mamba.norm.weight", "model.layers.29.mamba.out_proj.weight", "model.layers.29.mamba.dt_bias", "model.layers.29.mamba.conv1d.bias", "model.layers.31.mamba.A_log", "model.layers.31.mamba.D", "model.layers.31.mamba.in_proj.weight", "model.layers.31.mamba.conv1d.weight", "model.layers.31.mamba.norm.weight", "model.layers.31.mamba.out_proj.weight", "model.layers.31.mamba.dt_bias", "model.layers.31.mamba.conv1d.bias". 

mamba_config.json

@@ -0,0 +1,34 @@
+{
+    "d_model": 4096,
+    "ssm_cfg": {
+        "expand": 1,
+        "ngroups": 32,
+        "d_state": 128
+    },
+    "rms_norm_eps": 1e-05,
+    "vocab_size": null,
+    "d_inner": 4096,
+    "d_xb": 1024,
+    "intermediate_size": 14336,
+    "hidden_act": "silu",
+    "n_layer": 32,
+    "mamba_to_mha": false,
+    "attn_layers": [
+        1,
+        3,
+        5,
+        7,
+        9,
+        11,
+        13,
+        15,
+        17,
+        19,
+        21,
+        23,
+        25,
+        27,
+        29,
+        31
+    ]
+}

mla_layer_config.json

@@ -0,0 +1,55 @@
+{
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "aux_loss_alpha": 0.001,
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "ep_size": 1,
+  "first_k_dense_replace": 3,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "kv_energy_ratio": null,
+  "kv_lora_rank": 160,
+  "kv_svd_norm": 2.0,
+  "layer_rank_list": {},
+  "max_position_embeddings": 131072,
+  "model_type": "deepseek_v3",
+  "n_group": 8,
+  "norm_topk_prob": true,
+  "num_attention_heads": 32,
+  "num_hidden_layers": 61,
+  "num_key_value_heads": 8,
+  "num_nextn_predict_layers": 1,
+  "pretraining_tp": 1,
+  "q_energy_ratio": null,
+  "q_lora_rank": 2048,
+  "q_svd_norm": 2.0,
+  "qk_nope_head_dim": 64,
+  "qk_rope_head_dim": 64,
+  "qkv_rank_divisor": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "rope_type": "yarn",
+  "routed_scaling_factor": 2.5,
+  "scoring_func": "sigmoid",
+  "seq_aux": true,
+  "tie_word_embeddings": false,
+  "topk_group": 4,
+  "topk_method": "noaux_tc",
+  "transformers_version": "4.43.1",
+  "use_cache": true,
+  "use_fixed_rank_for_first_and_last_block": true,
+  "use_full_kv_head": false,
+  "use_lora_layer_norm": false,
+  "v_head_dim": 128,
+  "vocab_size": 129280
+}

special_tokens_map.json

@@ -0,0 +1,17 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|eot_id|>"
+}

tokenizer_config.json

@@ -0,0 +1,2063 @@
+{
+  "added_tokens_decoder": {
+    "128000": {
+      "content": "<|begin_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128001": {
+      "content": "<|end_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128002": {
+      "content": "<|reserved_special_token_0|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128003": {
+      "content": "<|reserved_special_token_1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128004": {
+      "content": "<|finetune_right_pad_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128005": {
+      "content": "<|reserved_special_token_2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128006": {
+      "content": "<|start_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128007": {
+      "content": "<|end_header_id|>",
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+      "content": "<|reserved_special_token_244|>",
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+    "128253": {
+      "content": "<|reserved_special_token_245|>",
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+      "content": "<|reserved_special_token_246|>",
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+    "128255": {
+      "content": "<|reserved_special_token_247|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 2048,
+  "pad_token": "<|eot_id|>",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

train_results.json

@@ -0,0 +1,9 @@
+{
+    "epoch": 1.4,
+    "total_flos": 4.721862248497152e+16,
+    "train_loss": 149.02320945810703,
+    "train_runtime": 264406.7938,
+    "train_samples": 19473081,
+    "train_samples_per_second": 17.62,
+    "train_steps_per_second": 0.275
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a555ab498a1b857719f50fc47d4e99c63658af3be8d2a2a1818f2dbd5ebccb90
+size 7608

zero_to_fp32.py

@@ -0,0 +1,589 @@
+#!/usr/bin/env python
+
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
+# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
+# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
+# application.
+#
+# todo: update the examples
+# example: python zero_to_fp32.py . pytorch_model.bin
+
+import argparse
+import torch
+import glob
+import math
+import os
+import re
+from collections import OrderedDict
+from dataclasses import dataclass
+
+# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
+# DeepSpeed data structures it has to be available in the current python environment.
+from deepspeed.utils import logger
+from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
+                                            FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
+                                            FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
+
+
+@dataclass
+class zero_model_state:
+    buffers: dict()
+    param_shapes: dict()
+    shared_params: list
+    ds_version: int
+    frozen_param_shapes: dict()
+    frozen_param_fragments: dict()
+
+
+debug = 0
+
+# load to cpu
+device = torch.device('cpu')
+
+
+def atoi(text):
+    return int(text) if text.isdigit() else text
+
+
+def natural_keys(text):
+    '''
+    alist.sort(key=natural_keys) sorts in human order
+    http://nedbatchelder.com/blog/200712/human_sorting.html
+    (See Toothy's implementation in the comments)
+    '''
+    return [atoi(c) for c in re.split(r'(\d+)', text)]
+
+
+def get_model_state_file(checkpoint_dir, zero_stage):
+    if not os.path.isdir(checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
+
+    # there should be only one file
+    if zero_stage <= 2:
+        file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
+    elif zero_stage == 3:
+        file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
+
+    if not os.path.exists(file):
+        raise FileNotFoundError(f"can't find model states file at '{file}'")
+
+    return file
+
+
+def get_checkpoint_files(checkpoint_dir, glob_pattern):
+    # XXX: need to test that this simple glob rule works for multi-node setup too
+    ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
+
+    if len(ckpt_files) == 0:
+        raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
+
+    return ckpt_files
+
+
+def get_optim_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
+
+
+def get_model_state_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device)
+
+        if BUFFER_NAMES not in state_dict:
+            raise ValueError(f"{file} is not a model state checkpoint")
+        buffer_names = state_dict[BUFFER_NAMES]
+        if debug:
+            print("Found buffers:", buffer_names)
+
+        # recover just the buffers while restoring them to fp32 if they were saved in fp16
+        buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
+        param_shapes = state_dict[PARAM_SHAPES]
+
+        # collect parameters that are included in param_shapes
+        param_names = []
+        for s in param_shapes:
+            for name in s.keys():
+                param_names.append(name)
+
+        # update with frozen parameters
+        frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
+        if frozen_param_shapes is not None:
+            if debug:
+                print(f"Found frozen_param_shapes: {frozen_param_shapes}")
+            param_names += list(frozen_param_shapes.keys())
+
+        # handle shared params
+        shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
+
+        ds_version = state_dict.get(DS_VERSION, None)
+
+        frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
+
+        z_model_state = zero_model_state(buffers=buffers,
+                                         param_shapes=param_shapes,
+                                         shared_params=shared_params,
+                                         ds_version=ds_version,
+                                         frozen_param_shapes=frozen_param_shapes,
+                                         frozen_param_fragments=frozen_param_fragments)
+        zero_model_states.append(z_model_state)
+
+    return zero_model_states
+
+
+def parse_optim_states(files, ds_checkpoint_dir):
+
+    total_files = len(files)
+    state_dicts = []
+    for f in files:
+        state_dict = torch.load(f, map_location=device, weights_only=False)
+        # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
+        # and also handle the case where it was already removed by another helper script
+        state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
+        state_dicts.append(state_dict)
+
+    if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
+        raise ValueError(f"{files[0]} is not a zero checkpoint")
+    zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
+    world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
+
+    # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
+    # parameters can be different from data parallelism for non-expert parameters. So we can just
+    # use the max of the partition_count to get the dp world_size.
+
+    if type(world_size) is list:
+        world_size = max(world_size)
+
+    if world_size != total_files:
+        raise ValueError(
+            f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
+            "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
+        )
+
+    # the groups are named differently in each stage
+    if zero_stage <= 2:
+        fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
+    elif zero_stage == 3:
+        fp32_groups_key = FP32_FLAT_GROUPS
+    else:
+        raise ValueError(f"unknown zero stage {zero_stage}")
+
+    if zero_stage <= 2:
+        fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
+    elif zero_stage == 3:
+        # if there is more than one param group, there will be multiple flattened tensors - one
+        # flattened tensor per group - for simplicity merge them into a single tensor
+        #
+        # XXX: could make the script more memory efficient for when there are multiple groups - it
+        # will require matching the sub-lists of param_shapes for each param group flattened tensor
+
+        fp32_flat_groups = [
+            torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
+        ]
+
+    return zero_stage, world_size, fp32_flat_groups
+
+
+def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
+    """
+    Returns fp32 state_dict reconstructed from ds checkpoint
+
+    Args:
+        - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
+
+    """
+    print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
+
+    optim_files = get_optim_files(ds_checkpoint_dir)
+    zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
+    print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
+
+    model_files = get_model_state_files(ds_checkpoint_dir)
+
+    zero_model_states = parse_model_states(model_files)
+    print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
+
+    if zero_stage <= 2:
+        return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states)
+    elif zero_stage == 3:
+        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states)
+
+
+def _zero2_merge_frozen_params(state_dict, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+    frozen_param_fragments = zero_model_states[0].frozen_param_fragments
+
+    if debug:
+        num_elem = sum(s.numel() for s in frozen_param_shapes.values())
+        print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        state_dict[name] = frozen_param_fragments[name]
+
+        if debug:
+            print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+
+    # Reconstruction protocol:
+    #
+    # XXX: document this
+
+    if debug:
+        for i in range(world_size):
+            for j in range(len(fp32_flat_groups[0])):
+                print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
+
+    # XXX: memory usage doubles here (zero2)
+    num_param_groups = len(fp32_flat_groups[0])
+    merged_single_partition_of_fp32_groups = []
+    for i in range(num_param_groups):
+        merged_partitions = [sd[i] for sd in fp32_flat_groups]
+        full_single_fp32_vector = torch.cat(merged_partitions, 0)
+        merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
+    avail_numel = sum(
+        [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
+
+    if debug:
+        wanted_params = sum([len(shapes) for shapes in param_shapes])
+        wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
+        # not asserting if there is a mismatch due to possible padding
+        print(f"Have {avail_numel} numels to process.")
+        print(f"Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    total_numel = 0
+    total_params = 0
+    for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
+        offset = 0
+        avail_numel = full_single_fp32_vector.numel()
+        for name, shape in shapes.items():
+
+            unpartitioned_numel = shape.numel()
+            total_numel += unpartitioned_numel
+            total_params += 1
+
+            if debug:
+                print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+            state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
+            offset += unpartitioned_numel
+
+        # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
+        # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
+        # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
+        # live optimizer object, so we are checking that the numbers are within the right range
+        align_to = 2 * world_size
+
+        def zero2_align(x):
+            return align_to * math.ceil(x / align_to)
+
+        if debug:
+            print(f"original offset={offset}, avail_numel={avail_numel}")
+
+        offset = zero2_align(offset)
+        avail_numel = zero2_align(avail_numel)
+
+        if debug:
+            print(f"aligned  offset={offset}, avail_numel={avail_numel}")
+
+        # Sanity check
+        if offset != avail_numel:
+            raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    _zero2_merge_frozen_params(state_dict, zero_model_states)
+
+    _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def zero3_partitioned_param_info(unpartitioned_numel, world_size):
+    remainder = unpartitioned_numel % world_size
+    padding_numel = (world_size - remainder) if remainder else 0
+    partitioned_numel = math.ceil(unpartitioned_numel / world_size)
+    return partitioned_numel, padding_numel
+
+
+def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    if debug:
+        for i in range(world_size):
+            num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
+            print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in zero_model_states[0].frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
+        state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
+
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+    avail_numel = fp32_flat_groups[0].numel() * world_size
+    # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
+    # param, re-consolidating each param, while dealing with padding if any
+
+    # merge list of dicts, preserving order
+    param_shapes = {k: v for d in param_shapes for k, v in d.items()}
+
+    if debug:
+        for i in range(world_size):
+            print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
+
+        wanted_params = len(param_shapes)
+        wanted_numel = sum(shape.numel() for shape in param_shapes.values())
+        # not asserting if there is a mismatch due to possible padding
+        avail_numel = fp32_flat_groups[0].numel() * world_size
+        print(f"Trainable params: Have {avail_numel} numels to process.")
+        print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    offset = 0
+    total_numel = 0
+    total_params = 0
+    for name, shape in param_shapes.items():
+
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+        total_params += 1
+
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+        # XXX: memory usage doubles here
+        state_dict[name] = torch.cat(
+            tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
+            0).narrow(0, 0, unpartitioned_numel).view(shape)
+        offset += partitioned_numel
+
+    offset *= world_size
+
+    # Sanity check
+    if offset != avail_numel:
+        raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
+
+    _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
+    ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
+    via a model hub.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    Note: this approach may not work if your application doesn't have sufficient free CPU memory and
+    you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint.
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        # do the training and checkpoint saving
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
+        model = model.cpu() # move to cpu
+        model.load_state_dict(state_dict)
+        # submit to model hub or save the model to share with others
+
+    In this example the ``model`` will no longer be usable in the deepspeed context of the same
+    application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
+
+    """
+    if tag is None:
+        latest_path = os.path.join(checkpoint_dir, 'latest')
+        if os.path.isfile(latest_path):
+            with open(latest_path, 'r') as fd:
+                tag = fd.read().strip()
+        else:
+            raise ValueError(f"Unable to find 'latest' file at {latest_path}")
+
+    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
+
+    if not os.path.isdir(ds_checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
+
+    return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir)
+
+
+def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
+    loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+    """
+
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+    print(f"Saving fp32 state dict to {output_file}")
+    torch.save(state_dict, output_file)
+
+
+def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
+    """
+    1. Put the provided model to cpu
+    2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
+    3. Load it into the provided model
+
+    Args:
+        - ``model``: the model object to update
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+
+    Returns:
+        - ``model`: modified model
+
+    Make sure you have plenty of CPU memory available before you call this function. If you don't
+    have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
+    conveniently placed for you in the checkpoint folder.
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
+        model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
+        # submit to model hub or save the model to share with others
+
+    Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
+    of the same application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    """
+    logger.info(f"Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info(f"Overwriting model with fp32 weights")
+    model = model.cpu()
+    model.load_state_dict(state_dict, strict=False)
+
+    return model
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("checkpoint_dir",
+                        type=str,
+                        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
+    parser.add_argument(
+        "--output_file",
+        default="pytorch_model.bin",
+        type=str,
+        help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
+    parser.add_argument("-t",
+                        "--tag",
+                        type=str,
+                        default=None,
+                        help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
+    parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
+    args = parser.parse_args()
+
+    debug = args.debug
+
+    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file, tag=args.tag)