Checkpoint utility: add gpt-oss to_maxtext & refactor code

end_to_end/tpu/deepseek/v2-16b/test_deepseek.sh

@@ -10,7 +10,7 @@
 # Example Usage: export HF_TOKEN=<huggingface_access_token>; export BASE_OUTPUT_PATH=<GCS_bucket_path>; bash test_deepseek.sh
 
 # The golden logit can be generated by:
-# python3 -m MaxText.scratch_code.generate_hf_golden_logits --model-id=deepseek-ai/DeepSeek-V2-Lite --output-path=golden_data_deepseek2-16b.jsonl --prompts='I love to;Today is a;What is the' --hf-model-path=$local_bf16_path --not-trust-remote-code
+# python3 -m MaxText.scratch_code.generate_hf_golden_logits --model-id=deepseek-ai/DeepSeek-V2-Lite --output-path=golden_data_deepseek2-16b.jsonl --prompts='I love to;Today is a;What is the' --hf-model-path=$local_bf16_path --trust-remote-code=False
 
 set -ex
 

end_to_end/tpu/deepseek/v3-671b/2_test_deepseek.sh

@@ -8,7 +8,7 @@
 # 2. Run logit check, pre-training, fine-tuning, and decoding.
 
 # The golden logit can be generated by:
-# python3 -m MaxText.scratch_code.generate_hf_golden_logits --model-id=deepseek-ai/DeepSeek-V3 --output-path=golden_data_deepseek3-671b.jsonl --prompts='I love to' --hf-model-path=$local_bf16_path --not-trust-remote-code --hf-load-dtype=bfloat16 
+# python3 -m MaxText.scratch_code.generate_hf_golden_logits --model-id=deepseek-ai/DeepSeek-V3 --output-path=golden_data_deepseek3-671b.jsonl --prompts='I love to' --hf-model-path=$local_bf16_path --trust-remote-code=False --hf-load-dtype=bfloat16 
 
 set -ex
 

src/MaxText/scratch_code/generate_hf_golden_logits.py

@@ -47,6 +47,7 @@
 import numpy as np
 from google.cloud import storage
 from PIL import Image
+from MaxText.inference_utils import str2bool
 
 # Load the tokenizer and model from Hugging Face
 
@@ -184,11 +185,11 @@ def main(raw_args=None) -> None:
       default="float32",
       help="model_class.from_pretrained: dtype",
   )
-  # variable `args.trust_remote_code` is True by default, False only if with flag `--not-trust-remote-code`
   parser.add_argument(
-      "--not-trust-remote-code",
-      dest="trust_remote_code",
-      action="store_false",
+      "--trust-remote-code",
+      type=str2bool,
+      required=False,
+      default=True,
       help="model_class.from_pretrained: trust_remote_code",
   )
   parser.add_argument(

src/MaxText/utils/ckpt_conversion/README.md

@@ -6,10 +6,17 @@ This guide provides instructions for using the scripts that convert model checkp
 
 The following models are supported:
 
-- Gemma2 (2B, 9B, 27B).
-- Gemma3 multimodal (4B, 12B, 27B).
-- Qwen3 (0.6B, 4B, 8B, 14B, 32B).
-- Mixtral (8x7B, 8x22B).
+| Model Family | Sizes | HF $\to$ Orbax (scan) | HF $\to$ Orbax (unscan) | Orbax (scan) $\to$ HF | Orbax (unscan) $\to$ HF |
+| :--- | :--- | :---: | :---: | :---: | :---: |
+| **Gemma2** | 2B, 9B, 27B | √ | √ | √ | √ |
+| **Gemma3** (Multimodal) | 4B, 12B, 27B | - | √ | - | √ |
+| **Llama3.1** | 8B, 70B, 450B | √ | √ | √ | √ |
+| **Qwen3** | 0.6B, 4B, 8B, 14B, 32B | √ | √ | √ | √ |
+| **Qwen3 MoE** | 30B, 235B, 480B | √ | √ | √ | √ |
+| **Mixtral** | 8x7B, 8x22B | √ | √ | √ | √ |
+| **GPT-OSS** | 20B, 120B | √ | √ | √ | √ |
+| **DeepSeek3** | 671B | - | - | √ | - |
+
 
 ## Prerequisites
 - Hugging Face requires Pytorch.
@@ -42,8 +49,9 @@ python3 -m MaxText.utils.ckpt_conversion.to_maxtext src/MaxText/configs/base.yml
   * `use_multimodal`: Indicates if multimodality is used, important for Gemma3.
   * `hf_access_token`: Your Hugging Face token.
   * `base_output_directory`: The path where the converted Orbax checkpoint will be stored; it can be Googld Cloud Storage (GCS) or local. If not set, the default output directory is `Maxtext/tmp`.
+  * `--lazy_load_tensors` (optional): If `true`, loads Hugging Face weights on-demand to minimize RAM usage.
+  * `--hf_model_path` (optional): Specifies a local directory containing the model weights. If unspecified, we use the [default Hugging Face repository ID](https://github.com/AI-Hypercomputer/maxtext/blob/2f77e7b5fcc4b580bc2d109525c362f3d9056ec9/src/MaxText/utils/ckpt_conversion/utils/utils.py#L54-L82) (e.g., openai/gpt-oss-20b). This is necessary for locally dequantized models like GPT-OSS or DeepSeek. 
 
-\*\**It only converts the official version of Hugging Face model. You can refer the supported official version in HF_IDS in `src/MaxText/utils/ckpt_conversion/utils/utils.py`*
 
 ## MaxText to Hugging Face
 
@@ -62,6 +70,7 @@ python3 -m MaxText.utils.ckpt_conversion.to_huggingface src/MaxText/configs/base
     scan_layers=false \
     use_multimodal=false \
     hf_access_token=<your-hf-token> \
+    weight_dtype=bfloat16
 ```
 
 **Key arguments:**
@@ -72,6 +81,7 @@ python3 -m MaxText.utils.ckpt_conversion.to_huggingface src/MaxText/configs/base
   * `hf_access_token`: Your Hugging Face token.
   * `use_multimodal`: Indicates if multimodality is used, important for Gemma3.
   * `base_output_directory`: The path where the converted Orbax checkpoint will be stored; it can be Googld Cloud Storage (GCS), Hugging Face Hub or local. If not set, the default output directory is `Maxtext/tmp`.
+  * `weight_dtype`: dtype for MaxText weights. It affects the resulting HF weight dtype. Default value is `float32`. We recommend using `bfloat16` to save memory and speed up conversion.
 
 
 ## Verifying conversion correctness
@@ -87,11 +97,11 @@ python3 -m tests.forward_pass_logit_checker src/MaxText/configs/base.yml \
     model_name=<MODEL_NAME> \
     scan_layers=false \
     max_prefill_predict_length=4 \
-     max_target_length=8 \
+    max_target_length=8 \
     use_multimodal=false \
     --run_hf_model=True \
     --hf_model_path=<path-to-HF-checkpoint> \
-    --max_kl_div=0.015 \
+    --max_kl_div=0.015
 ```
 
 **Key arguments:**

src/MaxText/utils/ckpt_conversion/to_huggingface.py

@@ -72,8 +72,12 @@
 )
 from MaxText.utils.ckpt_conversion.utils.hf_shape import HF_SHAPE
 from MaxText.utils.ckpt_conversion.utils.hf_model_configs import HF_MODEL_CONFIGS
-from MaxText.utils.ckpt_conversion.utils.utils import process_maxtext_param, save_model_files, HF_IDS
-
+from MaxText.utils.ckpt_conversion.utils.utils import (
+    validate_and_filter_param_map_keys,
+    process_maxtext_param,
+    save_model_files,
+    HF_IDS,
+)
 
 os.environ["JAX_PLATFORMS"] = "cpu"
 os.environ["XLA_FLAGS"] = "--xla_force_host_platform_device_count=16"
@@ -107,59 +111,6 @@ def _get_model_mappings(
   }
 
 
-def _check_param_map_keys(param_map_keys, maxtext_state_keys):
-  """Validates map coverage, handles N-to-1 mappings, and filters unused keys.
-
-  Ensures every MaxText checkpoint key (`maxtext_state_keys`) is covered by
-  the flattened parameter map. Keys in the map that are not present in the
-  checkpoint (common for multi-variant maps like gemma3, qwen3, deepseek) are skipped.
-
-  Tuple keys represent N-to-1 mappings (multiple MaxText keys combining into one
-  target key) and are only returned if all constituent keys exist in the checkpoint.
-
-  Args:
-    param_map_keys: Keys from the parameter mapping (strings or N-to-1 tuples).
-    maxtext_state_keys: Set of parameter keys loaded from the MaxText checkpoint.
-
-  Returns:
-    A list of 'filtered' mapping keys (strings or tuples) that are fully present
-    and valid based on `maxtext_state_keys`.
-
-  Raises:
-    ValueError: If `maxtext_state_keys` is NOT a subset of the flattened
-      `param_map_keys`.
-  """
-  flattened_map_keys = set()
-  for key in param_map_keys:
-    if isinstance(key, tuple):
-      flattened_map_keys.update(key)
-    else:
-      flattened_map_keys.add(key)
-
-  # every maxtext state key must be covered by param map
-  missing_keys = maxtext_state_keys - flattened_map_keys
-  if missing_keys:
-    raise ValueError(
-        "maxtext_state_dict must be a subset of flattened param_map"
-        + f"\nparam map\n{param_map_keys}"
-        + f"\nmaxtext:\n{maxtext_state_keys}"
-    )
-
-  # param map may have extra keys
-  extra_keys = flattened_map_keys - maxtext_state_keys
-  if extra_keys:
-    max_logging.log(f"Warning: extra keys in param_map are skipped: {extra_keys}")
-
-  # skip extra keys in param map
-  filtered_map_keys = []
-  for key in param_map_keys:
-    if (isinstance(key, str) and key in maxtext_state_keys) or (
-        isinstance(key, tuple) and all(k in maxtext_state_keys for k in key)
-    ):
-      filtered_map_keys.append(key)
-  return filtered_map_keys
-
-
 def main(argv: Sequence[str]) -> None:
   """Main function to convert a MaxText checkpoint to HuggingFace format.
 
@@ -180,6 +131,7 @@ def main(argv: Sequence[str]) -> None:
       config.load_full_state_path == ""
   ), "This script expects parameters, not a full state. Use generate_param_only_checkpoint first if needed."
   max_utils.print_system_information()
+  overall_start = time.time()
 
   # Load Maxtext checkpoint
   max_logging.log("\nLoading Orbax checkpoint...")
@@ -189,7 +141,7 @@ def main(argv: Sequence[str]) -> None:
   rng, rng_load_params = jax.random.split(rng)
   # load params from maxengine
   loaded_params_from_engine = engine.load_params(rng_load_params)
-  max_logging.log(f"Elapse: {(time.time() - start) / 60:.2f} min")
+  max_logging.log(f"Elapse for checkpoint load: {(time.time() - start) / 60:.2f} min")
 
   if not config.base_output_directory:
     output_directory = f"tmp/{config.run_name}"
@@ -239,7 +191,7 @@ def main(argv: Sequence[str]) -> None:
   # The param_map may contain tuples as keys, which represent N-to-1 mappings from maxtext to huggingface
   # Check maxtext_state_dict is a subset of flattened param_map
   # Skip extra keys from param_map
-  filtered_map_keys = _check_param_map_keys(param_map.keys(), maxtext_state_dict.keys())
+  filtered_map_keys = validate_and_filter_param_map_keys(param_map.keys(), maxtext_state_dict.keys())
 
   # Iterate through the parameter map to transform and collect weights.
   # This loop handles both simple 1-to-1 mappings and complex N-to-1 mappings
@@ -260,7 +212,7 @@ def main(argv: Sequence[str]) -> None:
     processed_params_list.extend(processed_params)
 
   transformed_hf_weights = dict(processed_params_list)
-  max_logging.log(f"Elapse: {(time.time() - start) / 60:.2f} min")
+  max_logging.log(f"Elapse for transform: {(time.time() - start) / 60:.2f} min")
 
   # 5. Save in HuggingFace Format
   if not transformed_hf_weights:
@@ -277,7 +229,8 @@ def main(argv: Sequence[str]) -> None:
       output_dir=output_directory,
   )
   max_logging.log(f"✅ MaxText model successfully saved in HuggingFace format at {output_directory}")
-  max_logging.log(f"Elapse: {(time.time() - start) / 60:.2f} min")
+  max_logging.log(f"Elapse for save: {(time.time() - start) / 60:.2f} min")
+  max_logging.log(f"Overall Elapse: {(time.time() - overall_start) / 60:.2f} min")
 
 
 if __name__ == "__main__":