70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
import json
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import seaborn as sns
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import matplotlib.pyplot as plt
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from matplotlib.colors import LinearSegmentedColormap
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model = "llama3.1-8B"
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dataset = "instructcode"
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method1 = "ngram"
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method2 = "eagle3"
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def get_datapath(method):
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datapath = f"/data/lily/batch-sd/data/{model}/{method}_{dataset}_acceptance_stats.jsonl"
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return datapath
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def cleanup(data):
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# Remove the prefill phase
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data = data[1:]
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# Cap the maximum value to 10
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data = [min(x, 10) for x in data]
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return data
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def load_data(datapath):
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acceptance_stats = {}
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with open(datapath, "r") as f:
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lines = f.readlines()
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for line in lines:
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data = json.loads(line)
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key = hash(tuple(data['prompt_token_ids']))
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acceptance_stats[key] = cleanup(data['acc'])
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# Pad the acceptance stats to the same length
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max_length = max(len(stats) for k, stats in acceptance_stats.items())
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for key in acceptance_stats:
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acceptance_stats[key] += [-2] * (max_length - len(acceptance_stats[key]))
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print(f"Load {len(acceptance_stats)} with max length {max_length} from {datapath}")
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return acceptance_stats
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def diff(acceptance_stats1, acceptance_stats2):
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diff = {}
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for key in acceptance_stats1:
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if key in acceptance_stats2:
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diff[key] = [a - b for a, b in zip(acceptance_stats1[key], acceptance_stats2[key])]
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return diff
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datapath_1 = get_datapath(method1)
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datapath_2 = get_datapath(method2)
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acceptance_stats_1 = load_data(datapath_1)
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acceptance_stats_2 = load_data(datapath_2)
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acceptance_stats_diff = diff(acceptance_stats_1, acceptance_stats_2)
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acceptance_stats = list(acceptance_stats_diff.values())
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fig, ax = plt.subplots()
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colors = ["red", "white", "blue"]
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custom_cmap = LinearSegmentedColormap.from_list("custom", colors, N=256)
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sns.heatmap(acceptance_stats, cmap=custom_cmap, center=0)
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plt.xlabel("Position")
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plt.ylabel("Request ID")
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# Add Y-axis labels on the right
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ax2 = ax.twinx()
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ax2.set_ylim(ax.get_ylim()) # Match y-axis range
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ax2.set_yticks([]) # Remove right tick marks if undesired
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ax2.set_ylabel("# of Accepted Tokens", labelpad=10) # Set right y-axis label
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plt.title(f"Diff between {method2} - {method1} acceptance stats for {dataset}")
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plt.tight_layout()
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plt.savefig(f"figures/{model}/diff_{method2}_{method1}_{dataset}_acceptance_stats.pdf")
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