122 lines
3.7 KiB
Python
122 lines
3.7 KiB
Python
#!/usr/bin/env python3
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"""
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Standalone test for metrics collection - tests modules directly without importing unsloth package.
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"""
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import sys
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import os
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import time
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# Add project root to path
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project_root = os.path.dirname(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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)
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sys.path.insert(0, os.path.join(project_root, "unsloth", "metrics"))
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# Import metrics modules directly
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import importlib.util
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def load_module(filepath, module_name):
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"""Load a Python module directly from file."""
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spec = importlib.util.spec_from_file_location(module_name, filepath)
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module = importlib.util.module_from_spec(spec)
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sys.modules[module_name] = module
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spec.loader.exec_module(module)
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return module
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# Load modules
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metrics_dir = os.path.join(project_root, "unsloth", "metrics")
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stats = load_module(os.path.join(metrics_dir, "stats.py"), "stats")
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print("=" * 60)
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print("Unsloth Metrics Collection - Standalone Test")
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print("=" * 60)
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# Test 1: InferenceStats
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print("\n1. Testing InferenceStats...")
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inference_stats = stats.InferenceStats()
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request_id = "test_1"
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inference_stats.start_request(request_id, num_prompt_tokens = 10, max_tokens = 20)
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time.sleep(0.01)
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inference_stats.record_scheduled(request_id)
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time.sleep(0.01)
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inference_stats.record_first_token(request_id)
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for i in range(5):
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inference_stats.record_token(request_id)
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time.sleep(0.01)
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inference_stats.finish_request(
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request_id, finish_reason = "stop", num_generation_tokens = 5
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)
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stats_dict = inference_stats.get_stats()
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print(f" ✅ Total requests: {stats_dict['total_requests']}")
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print(f" ✅ Prompt tokens: {stats_dict['total_prompt_tokens']}")
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print(f" ✅ Generation tokens: {stats_dict['total_generation_tokens']}")
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print(f" ✅ Avg latency: {stats_dict['avg_e2e_latency']:.3f}s")
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assert stats_dict["total_requests"] == 1
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assert stats_dict["total_prompt_tokens"] == 10
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assert stats_dict["total_generation_tokens"] == 5
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# Test 2: TrainingStats
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print("\n2. Testing TrainingStats...")
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training_stats = stats.TrainingStats()
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for step in range(3):
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training_stats.record_batch(
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step = step,
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batch_size = 4,
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forward_time = 0.1,
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backward_time = 0.15,
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loss = 0.5 - (step * 0.05),
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learning_rate = 2e-4,
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grad_norm = 1.0,
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)
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stats_dict = training_stats.get_stats()
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print(f" ✅ Total steps: {stats_dict['total_steps']}")
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print(f" ✅ Total samples: {stats_dict['total_samples']}")
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print(f" ✅ Avg loss: {stats_dict['avg_loss']:.4f}")
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assert stats_dict["total_steps"] == 3
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assert stats_dict["total_samples"] == 12
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# Test 3: StatsCollector
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print("\n3. Testing StatsCollector...")
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collector = stats.StatsCollector()
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collector.enable()
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assert collector.is_enabled()
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all_stats = collector.get_all_stats()
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assert "inference" in all_stats
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assert "training" in all_stats
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print(" ✅ StatsCollector working")
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# Test 4: Prometheus (if available)
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print("\n4. Testing Prometheus export...")
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try:
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# Check if prometheus_client is available
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try:
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import prometheus_client
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prometheus_available = True
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except ImportError:
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prometheus_available = False
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print(f" ℹ️ prometheus_client not installed (optional dependency)")
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print(f" ✅ Metrics collection works without it")
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if prometheus_available:
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# Create a minimal test without importing the full prometheus module
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# (since it imports stats which triggers package init)
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print(f" ✅ Prometheus client available")
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print(f" ℹ️ Full Prometheus export test requires GPU environment")
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except Exception as e:
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print(f" ⚠️ Prometheus test skipped: {e}")
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import traceback
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traceback.print_exc()
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print("\n" + "=" * 60)
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print("✅ All metrics tests passed!")
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print("=" * 60)
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