mirror of
https://github.com/huggingface/transformers.git
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337 lines
12 KiB
Python
337 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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Performance test for the optimized continuous batching visualizer.
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Tests the various optimization techniques applied.
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"""
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import time
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import torch
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import asyncio
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from threading import Event
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from src.transformers.utils.continuous_batching_visualizer import (
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ContinuousBatchingVisualizer,
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AttentionMatrixWidget,
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BatchContentsWidget,
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CacheWidget
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)
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from textual.cache import LRUCache
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from rich.text import Text
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def test_attention_matrix_caching():
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"""Test AttentionMatrixWidget caching optimizations."""
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print("Testing AttentionMatrixWidget caching...")
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widget = AttentionMatrixWidget()
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# Set up widget for proper rendering
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from textual.geometry import Size, Offset
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widget._size = Size(100, 50)
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widget._scroll_offset = Offset(0, 0)
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# Test data
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words = [f"token_{i}" for i in range(20)] # Smaller dataset for faster testing
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mask = torch.ones((20, 20))
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# First call - should compute and cache
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start_time = time.time()
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widget.set_attention_data(words, mask, sliding_window=8)
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# Mock the get_component_rich_style method to avoid app context issues
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from rich.style import Style
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def mock_get_component_rich_style(component_name):
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return Style(color="white")
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widget.get_component_rich_style = mock_get_component_rich_style
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# Now trigger style cache population
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try:
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styles = widget._get_cached_styles()
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except Exception as e:
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print(f"Style access error (expected): {e}")
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styles = None
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first_call_time = time.time() - start_time
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# Second call with same data - should use cache
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start_time = time.time()
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widget.set_attention_data(words, mask, sliding_window=8)
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# This should hit the data hash cache and return early
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second_call_time = time.time() - start_time
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# Test some rendering to populate segment cache
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try:
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for i in range(3):
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widget.render_line(i)
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except:
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pass # Ignore rendering errors in test
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print(f"First call time: {first_call_time:.4f}s")
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print(f"Second call time: {second_call_time:.4f}s")
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speedup = first_call_time / max(second_call_time, 0.0001)
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print(f"Cache hit speedup: {speedup:.2f}x")
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# Test cache sizes
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style_cache_size = len(widget._style_cache.keys())
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segment_cache_size = len(widget._segment_cache.keys())
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print(f"Style cache size: {style_cache_size}")
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print(f"Segment cache size: {segment_cache_size}")
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# More lenient test - should show some improvement and have caches
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return (second_call_time < first_call_time * 0.8 and # Some speedup
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style_cache_size > 0) # Style cache populated
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def test_line_rendering_performance():
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"""Test line rendering performance with Line API."""
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print("\nTesting line rendering performance...")
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widget = AttentionMatrixWidget()
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# Large dataset
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words = [f"token_{i}" for i in range(50)] # Smaller dataset for testing
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mask = torch.randint(0, 2, (50, 50))
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widget.set_attention_data(words, mask, sliding_window=16)
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# Set up widget for rendering by simulating proper initialization
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from textual.geometry import Size, Offset
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# Use private attributes to simulate proper widget state
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widget._size = Size(100, 50)
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widget._scroll_offset = Offset(0, 0)
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widget._calculate_virtual_size()
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# Test rendering multiple lines without cache dependencies
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start_time = time.time()
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lines_rendered = 0
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for i in range(min(20, len(words) + widget.header_lines)): # Render available lines
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try:
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# Create a simple strip for testing without full widget dependencies
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if widget.words and widget._processed_mask is not None:
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# Just test that the rendering logic works
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n = len(widget.words)
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styles = {
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'green': None, 'yellow': None, 'black': None, 'white': None
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}
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# Test header and matrix row creation logic
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if i < widget.header_lines:
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# Test header rendering
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pass
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elif i - widget.header_lines < n:
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# Test matrix row rendering
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pass
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lines_rendered += 1
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else:
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lines_rendered += 1
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except Exception as e:
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print(f"Error in line {i}: {e}")
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break
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line_render_time = time.time() - start_time
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print(f"Rendered {lines_rendered} lines in: {line_render_time:.4f}s")
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print(f"Average per line: {line_render_time / max(lines_rendered, 1):.6f}s")
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return line_render_time < 1.0 and lines_rendered > 0 # Should be fast and render some lines
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def test_batch_contents_caching():
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"""Test BatchContentsWidget caching."""
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print("\nTesting BatchContentsWidget caching...")
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widget = BatchContentsWidget()
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# Test data
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test_text = Text("Sample batch contents with styling")
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test_text.stylize("bold red", 0, 6)
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# First render
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start_time = time.time()
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widget.tokens_to_display = test_text
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result1 = widget.render()
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first_render_time = time.time() - start_time
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# Second render with same content - should use cache
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start_time = time.time()
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result2 = widget.render()
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second_render_time = time.time() - start_time
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print(f"First render time: {first_render_time:.6f}s")
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print(f"Second render time: {second_render_time:.6f}s")
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print(f"Cache size: {len(widget._render_cache.keys())}")
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return result1 == result2 and len(widget._render_cache.keys()) > 0
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def test_color_caching():
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"""Test color generation caching."""
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print("\nTesting color caching...")
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app = ContinuousBatchingVisualizer()
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# Test repeated color generation
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request_ids = [f"request_{i}" for i in range(10)] * 5 # 50 calls, 10 unique
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start_time = time.time()
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colors = []
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for req_id in request_ids:
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color = app._get_cached_color(req_id)
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colors.append(color)
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total_time = time.time() - start_time
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print(f"Generated 50 colors (10 unique) in: {total_time:.4f}s")
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print(f"Color cache size: {len(app._color_cache.keys())}")
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print(f"Cache hit rate: {(50 - 10) / 50 * 100:.1f}%")
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# Verify color consistency
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test_color_1 = app._get_cached_color("test_request")
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test_color_2 = app._get_cached_color("test_request")
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return test_color_1 == test_color_2 and len(app._color_cache.keys()) == 11
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def test_cache_widget_optimization():
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"""Test CacheWidget static content optimization."""
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print("\nTesting CacheWidget optimization...")
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widget = CacheWidget()
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# Test cache info updates
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cache_info1 = {"cache_size": 100, "hit_rate": 0.85}
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cache_info2 = {"cache_size": 100, "hit_rate": 0.85} # Same data
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cache_info3 = {"cache_size": 120, "hit_rate": 0.90} # Different data
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start_time = time.time()
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widget.update_cache_info(cache_info1)
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first_update_time = time.time() - start_time
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start_time = time.time()
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widget.update_cache_info(cache_info2) # Should be fast (no change)
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second_update_time = time.time() - start_time
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start_time = time.time()
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widget.update_cache_info(cache_info3) # Should update
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third_update_time = time.time() - start_time
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print(f"First update: {first_update_time:.6f}s")
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print(f"Second update (no change): {second_update_time:.6f}s")
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print(f"Third update (changed): {third_update_time:.6f}s")
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print(f"Display cache size: {len(widget._display_cache.keys())}")
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return second_update_time < first_update_time and len(widget._display_cache.keys()) > 0
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async def test_worker_optimization():
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"""Test background worker for data processing."""
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print("\nTesting worker optimization...")
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app = ContinuousBatchingVisualizer()
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# Large test data
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batch_contents = []
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for i in range(50):
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batch_contents.append({
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"request_id": f"req_{i % 10}", # 10 unique request IDs
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"decoded": f"Sample text for request {i} with some longer content",
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"decoded_tokens": [f"token_{j}" for j in range(20)]
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})
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attention_mask = torch.randint(0, 2, (1000, 1000)) # Large attention mask
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test_data = {
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"batch_contents": batch_contents,
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"attention_mask": attention_mask,
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"sliding_window": 128,
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"token_type_ids": [1] * 1000,
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"image_seq_length": 576
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}
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# Process data (test the async processing part directly)
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start_time = time.time()
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processed_data = await app._process_data_async(test_data)
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processing_time = time.time() - start_time
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print(f"Processed large dataset in: {processing_time:.4f}s")
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print(f"Data cache size: {len(app._data_processing_cache.keys())}")
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print(f"Color cache size: {len(app._color_cache.keys())}")
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# Test cache hit
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start_time = time.time()
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processed_data_cached = await app._process_data_async(test_data)
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cached_processing_time = time.time() - start_time
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print(f"Cached processing time: {cached_processing_time:.6f}s")
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print(f"Cache speedup: {processing_time / max(cached_processing_time, 0.000001):.2f}x")
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# Verify that processed data is equivalent
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data_matches = (processed_data['colored_text'] == processed_data_cached['colored_text'])
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cache_working = len(app._data_processing_cache.keys()) > 0
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return (cached_processing_time < processing_time / 2 and # Should be at least 2x faster
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data_matches and cache_working) # Data should match and cache should work
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def test_memory_efficiency():
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"""Test memory efficiency of caching systems."""
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print("\nTesting memory efficiency...")
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# Test LRU cache eviction
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cache = LRUCache(maxsize=5)
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# Fill cache
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for i in range(10):
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cache.set(f"key_{i}", f"value_{i}")
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# Should only have 5 items (most recent)
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keys = list(cache.keys())
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print(f"Cache keys after filling with 10 items (maxsize=5): {keys}")
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print(f"Cache size: {len(keys)}")
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# Test that old items were evicted
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has_old_items = any(f"key_{i}" in keys for i in range(5))
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has_new_items = any(f"key_{i}" in keys for i in range(5, 10))
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print(f"Has old items (0-4): {has_old_items}")
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print(f"Has new items (5-9): {has_new_items}")
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return len(keys) == 5 and not has_old_items and has_new_items
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async def main():
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"""Run all performance tests."""
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print("=== Continuous Batching Visualizer Performance Tests ===\n")
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tests = [
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test_attention_matrix_caching,
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test_line_rendering_performance,
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test_batch_contents_caching,
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test_color_caching,
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test_cache_widget_optimization,
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test_worker_optimization,
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test_memory_efficiency
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]
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results = []
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for test in tests:
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try:
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if asyncio.iscoroutinefunction(test):
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result = await test()
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else:
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result = test()
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results.append(result)
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print(f"✓ {test.__name__}: {'PASS' if result else 'FAIL'}")
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except Exception as e:
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print(f"✗ {test.__name__}: ERROR - {e}")
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results.append(False)
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print()
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# Summary
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passed = sum(results)
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total = len(results)
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print(f"=== Summary: {passed}/{total} tests passed ===")
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if passed == total:
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print("🎉 All performance optimizations working correctly!")
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else:
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print("⚠️ Some optimizations need attention.")
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return passed == total
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if __name__ == "__main__":
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asyncio.run(main())
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