fix orbax to hf converter

MaxText/llama_mistral_mixtral_orbax_to_hf.py

@@ -45,16 +45,15 @@
 import checkpointing
 from generate_param_only_checkpoint import _read_train_checkpoint
 import llama_or_mistral_ckpt
-from transformers import LlamaForCausalLM, MistralForCausalLM, AutoModelForCausalLM
+from transformers import LlamaForCausalLM, MistralForCausalLM, AutoModelForCausalLM, AutoConfig
 
 
 def unpermute_from_match_maxtext_rope(arr):
   """
   Function to get the RoPE values in correct ordering
   """
-  split_size = arr.shape[-1] // 2  # Assuming half for evens, half for odds
-  evens = arr[..., :split_size]
-  odds = arr[..., split_size:]
+  evens = arr[..., ::2]
+  odds = arr[..., 1::2]
   return jax.numpy.concatenate((evens, odds), axis=arr.ndim - 1)
 
 
@@ -77,6 +76,9 @@ def load_hf_model(model_size):
     model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
   elif model_size == "mixtral-8x7b":
     model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1", device_map="auto")
+  elif model_size == "llama3.1-8b":
+    config = AutoConfig.from_pretrained("meta-llama/Llama-3.1-8B")
+    model = AutoModelForCausalLM.from_config(config)
   else:
     raise NotImplementedError
   return model

MaxText/scratch_code/golden_llama3_1_export.py

@@ -0,0 +1,86 @@
+"""
+Copyright 2024 Google LLC
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+     https://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+Usage: python3 golden_llama3-70b_export.py --model-id meta-llama/Meta-Llama-3-70B --output-path llama3-70b/golden_logits/golden_data_llama3-70b.jsonl
+"""
+
+import os
+import torch
+import argparse
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import jsonlines
+from google.cloud import storage
+
+# Load the tokenizer and model from Hugging Face
+
+
+def upload_blob(bucket_name, source_file_name, destination_blob_name):
+  """Uploads a file to the bucket."""
+  storage_client = storage.Client()
+  bucket = storage_client.get_bucket(bucket_name)
+  blob = bucket.blob(destination_blob_name)
+
+  blob.upload_from_filename(source_file_name)
+
+
+def save_golden_logits(model_id, output_path):
+  tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B")
+  model = AutoModelForCausalLM.from_pretrained(
+      model_id,
+      torch_dtype=torch.float32,
+  )
+
+  # Your prompt text
+  prompt_texts = ["I love to"]
+  all_data_to_save = []
+
+  for prompt_text in prompt_texts:
+    # Encode the prompt text
+    input_ids = tokenizer.encode(prompt_text, return_tensors="pt")
+
+    # Get the logits for the prompt + completion
+    with torch.no_grad():
+      outputs = model(input_ids)
+      logits = outputs.logits
+
+      # Convert logits to fp32
+      logits = logits.cpu().numpy().astype("float32")
+
+      # Prepare data to be saved
+      data_to_save = {
+          "prompt": prompt_text,
+          "tokens": input_ids.tolist()[0],
+          "logits": logits.tolist()[0],  # Convert numpy array to list for JSON serialization
+      }
+      all_data_to_save.append(data_to_save)
+
+  with jsonlines.open(output_path, "w") as f:
+    f.write_all(all_data_to_save)
+
+  upload_blob("maxtext-llama", output_path, "Llama3_1_8B/golden-logits/" + output_path)
+  print("File {} uploaded to {}.".format(output_path, "Llama3_1_8B/golden-logits/" + output_path))
+  os.remove(output_path)
+
+
+def main(raw_args=None) -> None:
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--model-id", type=str, required=False, default="meta-llama/Llama-3.1-8B")
+  parser.add_argument("--output-path", type=str, required=True)
+  args = parser.parse_args(raw_args)
+  save_golden_logits(args.model_id, args.output_path)
+
+
+if __name__ == "__main__":
+  main()

MaxText/tests/forward_pass_logit_checker.py

@@ -86,7 +86,10 @@ def main(config, test_args):
   model = models.Transformer(config, mesh=mesh, quant=quant)
   state, _ = max_utils.setup_decode_state(model, config, rng1, mesh, None)
 
-  input_golden_data_path = "MaxText/test_assets/golden_data_" + config.model_name + ".jsonl"
+  if test_args.golden_logits_path == "":
+    input_golden_data_path = "MaxText/test_assets/golden_data_" + config.model_name + ".jsonl"
+  else:
+    input_golden_data_path = test_args.golden_logits_path
   with jsonlines.open(input_golden_data_path, "r") as f:
     golden_data = list(f)
 
@@ -102,8 +105,8 @@ def main(config, test_args):
         rngs={"aqt": init_rng},
     )
     full_train_logits = jax.experimental.multihost_utils.process_allgather(full_train_logits)
-    max_logging.log(f"{golden_logits[0]=}")
-    max_logging.log(f"{full_train_logits[0, 0, :]=}")
+    max_logging.log(f"{golden_logits[2]=}")
+    max_logging.log(f"{full_train_logits[0, 2, :]=}")
     token_size = int(test_args.token_size) if test_args.token_size else golden_logits.shape[0]
     max_logging.log(
         f"Max Numerical Difference {np.max(np.subtract(full_train_logits[0, :token_size, :], golden_logits[:token_size, :]))}"
@@ -112,8 +115,8 @@ def main(config, test_args):
     model_probabilities = jax.nn.softmax(full_train_logits[0, :token_size, :], axis=-1)
     golden_probabilities = jax.nn.softmax(golden_logits[:token_size, :], axis=-1)
 
-    max_logging.log(f"{golden_probabilities[0]=}")
-    max_logging.log(f"{model_probabilities[0]=}")
+    max_logging.log(f"{golden_probabilities[1]=}")
+    max_logging.log(f"{model_probabilities[1]=}")
 
     kl_div = jax.numpy.sum(jax.scipy.special.kl_div(golden_probabilities, model_probabilities), axis=-1)
     max_logging.log(f"KL divergence = {kl_div}, max KL divergence = {jax.numpy.max(kl_div)}")
@@ -143,11 +146,12 @@ def main(config, test_args):
   parser.add_argument("--rtol", type=float, required=False, default=0.1)
   parser.add_argument("--token_size", type=int, required=False)
   parser.add_argument("--max_kl_div", type=float, required=False, default=None)
+  parser.add_argument("--golden_logits_path", type=str, required=False, default="")
   test_args, _ = parser.parse_known_args()
 
   # Remove args defined in this test file to avoid error from pyconfig
   model_args = sys.argv
-  to_remove_args = ["--atol", "--rtol", "--token_size", "--max_kl_div"]
+  to_remove_args = ["--atol", "--rtol", "--token_size", "--max_kl_div", "--golden_logits_path"]
   for arg in to_remove_args:
     model_args = [s for s in model_args if not s.startswith(arg)]
 

MaxText/weight_inspector.py

@@ -0,0 +1,70 @@
+"""
+ Copyright 2024 Google LLC
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+      https://www.apache.org/licenses/LICENSE-2.0
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ """
+
+r"""This is to inspect/analyze two weights with the same structure to find differences.
+This assumes weights are dumped in a pickle file
+
+Usage:
+
+python MaxText/weight_inspector.py --lhs left_hand.pkl --rhs right_hand.pkl
+
+"""
+
+import argparse
+import pickle
+import numpy as np
+import torch
+import max_logging
+
+
+def inspect_weights(left_path, right_path):
+  """Load the pickle files and compare contents."""
+  with open(left_path, "rb") as file:
+    left_weights = pickle.load(file)
+
+  with open(right_path, "rb") as file:
+    right_weights = pickle.load(file)
+  assert sorted(left_weights.keys()) == sorted(
+      right_weights.keys()
+  ), f"Weights structure does not match! {list(set(left_weights.keys()).symmetric_difference(right_weights.keys()))}"
+
+  mismatched_keys = []
+  # Iterate through keys common to both dictionaries
+  for key in left_weights.keys() & right_weights.keys():  # Intersection of keys
+    if ".0." in key:  # check only layer 0 of the model
+      assert (
+          left_weights[key].shape == right_weights[key].shape
+      ), f"Mismatched shapes left {left_weights[key].shape}, right right_weights[key].shape"
+      if not np.allclose(
+          left_weights[key].type(torch.float16).numpy(), right_weights[key].type(torch.float16).numpy(), atol=1e-8
+      ):
+        mismatched_keys.append(key)
+
+  if mismatched_keys:
+    max_logging.log("Contents of mismatched keys")
+    for key in mismatched_keys:
+      max_logging.log(f"Key: {key}")
+      max_logging.log(f"{left_weights[key]=}")
+      max_logging.log(f"{right_weights[key]=}")
+
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--lhs", type=str, required=True)
+  parser.add_argument("--rhs", type=str, required=True)
+
+  args = parser.parse_args()
+
+  inspect_weights(args.lhs, args.rhs)
+
+  args = parser.parse_args()

end_to_end/tpu/test_orbax_to_hf.sh

@@ -0,0 +1,36 @@
+#!/bin/bash
+
+# This script is to test the flow of MaxText/llama_mistral_mixtral_orbax_to_hf.py.
+# Steps in the script:
+# 1. Convert MaxText orbax ckpt to HF using MaxText/llama_mistral_mixtral_orbax_to_hf.py
+# 2. Confirm the logits match for MaxText orbax ckpt and the new HF ckpt created in step 2.
+set -ex
+export MODEL_VARIATION='llama3.1-8b'
+
+export BASE_OUTPUT_PATH=gs://runner-maxtext-logs/2024-12-18-17-35
+
+export CONVERTED_CHECKPOINT=${BASE_OUTPUT_PATH}/${MODEL_VARIATION}/scanned_chkpt/0/items
+export RUN_NAME=unscann_llama3.1
+# We defined path to unscanned checkpoint created in 1_test_llama3.1_8b.sh
+export UNSCANNED_CKPT_PATH=${BASE_OUTPUT_PATH}/${RUN_NAME}/checkpoints/0/items
+
+# converting MaxText orbax ckpt to HF
+
+JAX_PLATFORMS=cpu python3 MaxText/llama_mistral_mixtral_orbax_to_hf.py MaxText/configs/base.yml base_output_directory=gs://runner-maxtext-logs \
+    load_parameters_path=gs://runner-maxtext-logs/2024-12-18-17-35/llama3.1-8b/scanned_chkpt/0/items run_name=convert_to_hf \
+    model_name=llama3.1-8b hf_model_path=/home/mohitkhatwani/maxtext/hf_llama3.1_new/
+
+
+python MaxText/scratch_code/golden_llama3-70b_export.py --model-id /home/mohitkhatwani/maxtext/hf_llama3.1_new/ --output-path golden_data_new_llama3_1_8b.jsonl
+
+pushd MaxText/test_assets
+gcloud storage cp gs://maxtext-llama/Llama3_1_8B/golden-logits/golden_data_new_llama3_1_8b.jsonl .
+popd
+
+# comparing logits of the HF ckpt above
+
+python3 MaxText/tests/forward_pass_logit_checker.py MaxText/configs/base.yml base_output_directory=${BASE_OUTPUT_PATH} \
+    tokenizer_path=assets/tokenizer_llama3.tiktoken load_parameters_path=${UNSCANNED_CKPT_PATH} \
+    run_name=forward_pass_test per_device_batch_size=1 model_name=${MODEL_VARIATION} max_prefill_predict_length=3 max_target_length=4 \
+    dataset_type=synthetic dtype=float32 activations_in_float32=true matmul_precision=float32 async_checkpointing=false \
+    scan_layers=false --golden_logits_path="MaxText/test_assets/golden_data_new_llama3_1_8b.jsonl"