sugar

run_test.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-import os, sys
+import contextlib, os, sys
 
 STORE_WEIGHTS = False
 DEDUPLICATE_SAFETENSORS = True
@@ -12,6 +12,8 @@ user, model = model_id.split('/')
 prompt = 'Once upon a time,'
 fn = f'{user}_{model}_{revision}.{"logits-and-weights" if STORE_WEIGHTS else "logits"}.{"DEDUPLICATED.safetensors" if DEDUPLICATE_SAFETENSORS else "safetensors"}'
 
+
+## Increase determinism, from torch docs
 import torch, numpy as np, random
 torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False
 torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
@@ -23,12 +25,34 @@ os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
 torch.manual_seed(0)
 random.seed(0)
 np.random.seed(0)
+
+
 if FAKE_H100:
     torch.cuda.is_available = lambda: True
     torch.cuda.get_device_capability = lambda: [9,0]
+
 import accelerate, safetensors.torch, transformers, tqdm
 from _safetensors import WritingSafeTensors
 
+
+## Show progress for slow internal functions (model layer enumerations)
+import builtins
+builtin_range = range
+def range_progress(*params):
+    return tqdm.tqdm(builtin_range(*params), leave=False, desc=range_progress.desc + ' range('+','.join([repr(p)for p in params])+')')
+@contextlib.contextmanager
+def range_progress_description(str):
+    range_progress.desc = str
+    try:
+        yield
+    finally:
+        range_progress.desc = ''
+range_progress.desc = ''
+builtins.range = range_progress
+tqdm.std.range = builtin_range
+import sre_parse; sre_parse.range = builtin_range
+
+
 config = transformers.AutoConfig.from_pretrained(model_id, revision=revision, trust_remote_code=True)
 tokenizer = transformers.AutoTokenizer.from_pretrained(model_id, revision=revision, trust_remote_code=True)
 Model = transformers.AutoModelForCausalLM
@@ -43,12 +67,14 @@ max_memory['cpu'] //= 3
 model_kwparams = dict(pretrained_model_name_or_path=model_id, revision=revision, trust_remote_code=True,
     torch_dtype=getattr(torch, TORCH_DTYPE, TORCH_DTYPE), use_safetensors=True, low_cpu_mem_usage=True,
     device_map=DEVICE_MAP, max_memory=max_memory, offload_state_dict=True, offload_buffers=True)
-model = Model.from_pretrained(**model_kwparams)
+with range_progress_description('constructing model'):
+    model = Model.from_pretrained(**model_kwparams)
 
 if config.model_type == 'deepseek_v3':
     model._supports_cache_class = False
 
-pipe = transformers.pipeline('text-generation', model=model, config=config, tokenizer=tokenizer)
+with range_progress_description('constructing pipeline'):
+    pipe = transformers.pipeline('text-generation', model=model, config=config, tokenizer=tokenizer)
 
 SafeTensors = WritingSafeTensors(
         fn,