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fda8fb3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | from __future__ import annotations
from typing import Any
import torch
from app.analysis.sentence_split import split_sentences
from app.analysis.summaries import (
compute_incoming_importance,
compute_outgoing_importance,
compute_top_edges,
)
from app.analysis.suppression import compute_attribution_matrix
from app.analysis.token_boundaries import tokenize_with_sentence_ranges, truncate_text_to_token_limit
from app.analysis.validation import validate_top_edges
from app.core.config import get_settings
from app.core.runtime import load_model_bundle
from app.core.schemas import AnalysisResult, GenerationResult
from app.generation.service import generate_answer_and_trace
def compute_attribution_analysis(
*,
question: str,
model_name: str | None = None,
take_log: bool | None = None,
max_sentences: int | None = None,
max_trace_tokens: int | None = None,
validate_top_k: int = 0,
max_new_tokens: int = 512,
temperature: float = 0.6,
top_p: float = 0.95,
device_preference: str | None = None,
dtype_preference: str | None = None,
attn_implementation: str | None = None,
trust_remote_code: bool | None = None,
low_cpu_mem_usage: bool | None = None,
) -> AnalysisResult:
generation = None
return analyze_generation_result(
question=question,
generation=generation,
model_name=model_name,
take_log=take_log,
max_sentences=max_sentences,
max_trace_tokens=max_trace_tokens,
validate_top_k=validate_top_k,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_p=top_p,
device_preference=device_preference,
dtype_preference=dtype_preference,
attn_implementation=attn_implementation,
trust_remote_code=trust_remote_code,
low_cpu_mem_usage=low_cpu_mem_usage,
)
def analyze_generation_result(
*,
question: str,
generation: GenerationResult | None = None,
model_name: str | None = None,
take_log: bool | None = None,
max_sentences: int | None = None,
max_trace_tokens: int | None = None,
validate_top_k: int = 0,
max_new_tokens: int = 512,
temperature: float = 0.6,
top_p: float = 0.95,
device_preference: str | None = None,
dtype_preference: str | None = None,
attn_implementation: str | None = None,
trust_remote_code: bool | None = None,
low_cpu_mem_usage: bool | None = None,
) -> AnalysisResult:
settings = get_settings()
resolved_model_name = model_name or settings.model_name
resolved_take_log = settings.take_log if take_log is None else take_log
resolved_max_sentences = max_sentences or settings.max_sentences
resolved_max_trace_tokens = max_trace_tokens or settings.max_trace_tokens
resolved_device = device_preference or settings.device_preference
resolved_dtype = dtype_preference or settings.dtype_preference
resolved_attn_implementation = attn_implementation or settings.attn_implementation
resolved_trust_remote_code = settings.trust_remote_code if trust_remote_code is None else trust_remote_code
resolved_low_cpu_mem_usage = (
settings.low_cpu_mem_usage if low_cpu_mem_usage is None else low_cpu_mem_usage
)
bundle = load_model_bundle(
resolved_model_name,
device_preference=resolved_device,
dtype_preference=resolved_dtype,
attn_implementation=resolved_attn_implementation,
trust_remote_code=resolved_trust_remote_code,
low_cpu_mem_usage=resolved_low_cpu_mem_usage,
)
if not bundle.capability.supports_attribution:
reason = bundle.capability.reason or "Model does not support attribution analysis."
raise RuntimeError(reason)
if generation is None:
generation = generate_answer_and_trace(
question=question,
model_name=resolved_model_name,
model=bundle.model,
tokenizer=bundle.tokenizer,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_p=top_p,
)
truncated_text = truncate_text_to_token_limit(
generation.normalized_trace_text,
bundle.tokenizer,
resolved_max_trace_tokens,
)
sentence_spans = split_sentences(truncated_text)
if resolved_max_sentences > 0 and len(sentence_spans) > resolved_max_sentences:
sentence_spans = sentence_spans[:resolved_max_sentences]
truncated_text = truncated_text[: sentence_spans[-1].end_char]
sentence_spans = split_sentences(truncated_text)
if not sentence_spans:
raise RuntimeError("Trace normalization produced no analyzable sentences.")
mapping = tokenize_with_sentence_ranges(truncated_text, sentence_spans, bundle.tokenizer)
input_ids = mapping.input_ids.to(bundle.device)
computation = compute_attribution_matrix(
input_ids=input_ids,
token_ranges=mapping.token_ranges,
model=bundle.model,
take_log=resolved_take_log,
max_trace_tokens=resolved_max_trace_tokens,
max_sentences=resolved_max_sentences,
)
outgoing = compute_outgoing_importance(computation.raw_matrix)
incoming = compute_incoming_importance(computation.raw_matrix)
top_edges = compute_top_edges(computation.raw_matrix, top_k=10)
validation = validate_top_edges(
model=bundle.model,
input_ids=input_ids,
token_ranges=mapping.token_ranges,
top_edges=top_edges,
baseline_token_nll=computation.token_nll,
top_k=validate_top_k,
)
return AnalysisResult(
question=question,
model_name=resolved_model_name,
answer=generation.answer,
raw_trace_text=generation.raw_trace_text,
normalized_trace_text=truncated_text,
sentences=[span.text for span in sentence_spans],
sentence_token_ranges=mapping.token_ranges,
suppression_matrix=computation.matrix.tolist(),
raw_suppression_matrix=computation.raw_matrix.tolist(),
outgoing_importance=outgoing,
incoming_importance=incoming,
top_edges=top_edges,
runtime_metadata=computation.runtime_metadata,
validation_metadata=validation,
extra_metadata={
"raw_generation_text": generation.raw_generation_text,
"generation_metadata": generation.generation_metadata.model_dump(),
"effective_runtime": {
"device_preference": resolved_device,
"dtype_preference": resolved_dtype,
"attn_implementation": resolved_attn_implementation,
"trust_remote_code": resolved_trust_remote_code,
"low_cpu_mem_usage": resolved_low_cpu_mem_usage,
},
},
)
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