73c8d677e5
Co-authored-by: alexm <alexm@neuralmagic.com> Co-authored-by: mgoin <michael@neuralmagic.com>
30 lines
1.3 KiB
Python
30 lines
1.3 KiB
Python
def check_logprobs_close(outputs_0_lst, outputs_1_lst, name_0, name_1):
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"""Compare the logprobs of two sequences generated by different models,
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which should be similar but not necessarily equal.
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"""
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# Loop through responses to each prompt.
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for prompt_idx, (outputs_0,
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outputs_1) in enumerate(zip(outputs_0_lst,
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outputs_1_lst)):
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output_ids_0, output_str_0, logprobs_0 = outputs_0
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output_ids_1, output_str_1, logprobs_1 = outputs_1
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# Loop through generated tokens.
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for idx, (output_id_0,
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output_id_1) in enumerate(zip(output_ids_0, output_ids_1)):
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# If generated tokens don't match, then
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if output_id_0 != output_id_1:
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# Each predicted token must be in top N logprobs of the other
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assert output_id_0 in logprobs_1[idx], (
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f"Test{prompt_idx}:"
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f"\n{name_0}:\t{output_str_0!r}"
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f"\n{name_1}:\t{output_str_1!r}")
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assert output_id_1 in logprobs_0[idx], (
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f"Test{prompt_idx}:"
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f"\n{name_0}:\t{output_str_0!r}"
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f"\n{name_1}:\t{output_str_1!r}")
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# Break out since sequences will now diverge.
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break
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