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  "pre_change_reproduction": {
    "base_revision": "a0c1c65fe3cfef7dca3d6c36960712385d561504",
    "source_sha256": {
      "flepimop2-op_engine/src/flepimop2/engine/op_engine/__init__.py": "c2c2f292206e553532a18e5f6e33533c8b38325f8b460d34074106a5006e9183",
      "flepimop2-op_engine/src/flepimop2/engine/op_engine/config.py": "85dd751ad2edafbf16d48ada317593a72e5761b701fe3ec7828de8e755f514d3",
      "flepimop2-op_engine/benchmarks/prepared_execution.py": "741093a48a7438f235412fc52d90d0329fbcb8f4c2fcef9b812b621faecfc117"
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    "program": "import json\nimport jax\nimport jax.numpy as jnp\nimport numpy as np\nfrom flepimop2.axis import ResolvedShape\nfrom flepimop2.parameter.abc import ModelStateSpecification, ParameterValue\nfrom flepimop2.typing import StateChangeEnum\nfrom benchmarks.prepared_execution import _RateSystem, _configure_structured_options\nfrom flepimop2.engine.op_engine import OpEngineEngineConfig, OpEngineFlepimop2Engine\njax.config.update('jax_enable_x64', True)\nrows=[]\nfor layout in ['flat','pytree','block']:\n for loop in [None,'unroll','scan']:\n  kw={'method':'rk4','fixed_max_step':0.125,'state_layout':layout}\n  if layout=='block':kw['block_axis']='batch'\n  if loop is not None:kw['fixed_loop']=loop\n  config=OpEngineEngineConfig(**kw)\n  system=_RateSystem();_configure_structured_options(system,4)\n  shape=ResolvedShape(('batch',),(4,));rate=jnp.full((4,),-0.1);initial=jnp.ones(4)\n  engine=OpEngineFlepimop2Engine(state_change=StateChangeEnum.FLOW,config=config)\n  prepared=engine.prepare(system,np.asarray([0.,0.5,1.]),{'x':ParameterValue(initial,shape)},{'rate':ParameterValue(rate,shape)},model_state=ModelStateSpecification(parameter_names=('x',)))\n  def solve(r,y):return prepared({'x':y},{'rate':r})[-1,1:]\n  graph=jax.make_jaxpr(solve)(rate,initial)\n  value,gradient=jax.jit(jax.value_and_grad(lambda r:jnp.sum(solve(r,initial))))(rate)\n  rows.append({'layout':layout,'requested_loop':loop,'core_loop':config.to_run_config().fixed_loop,'top_level_scans':sum(e.primitive.name=='scan' for e in graph.jaxpr.eqns),'top_level_equations':len(graph.jaxpr.eqns),'value_error':float(abs(value-4*np.exp(-.1))),'gradient_error':float(np.max(np.abs(np.asarray(gradient)-np.exp(-.1))))})\nprint(json.dumps(rows,indent=2))\n",
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        "requested_loop": null,
        "core_loop": "unroll",
        "top_level_scans": 1,
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        "gradient_error": 9.134246692354964e-10
      },
      {
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        "top_level_equations": 21,
        "value_error": 7.440759119958784e-11,
        "gradient_error": 9.134246692354964e-10
      },
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        "core_loop": "unroll",
        "top_level_scans": 1,
        "top_level_equations": 21,
        "value_error": 7.440759119958784e-11,
        "gradient_error": 9.134246692354964e-10
      },
      {
        "layout": "pytree",
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        "core_loop": "unroll",
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        "top_level_equations": 12,
        "value_error": 7.440759119958784e-11,
        "gradient_error": 9.134246692354964e-10
      },
      {
        "layout": "pytree",
        "requested_loop": "unroll",
        "core_loop": "unroll",
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        "top_level_equations": 12,
        "value_error": 7.440759119958784e-11,
        "gradient_error": 9.134246692354964e-10
      },
      {
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        "value_error": 7.440759119958784e-11,
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        "value_error": 7.440759119958784e-11,
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      },
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  },
  "post_change_reproduction": [
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      "top_level_equations": 21,
      "value_error": 7.440759119958784e-11,
      "gradient_error": 9.134246692354964e-10
    },
    {
      "layout": "flat",
      "requested_loop": "unroll",
      "core_loop": "unroll",
      "top_level_scans": 0,
      "top_level_equations": 254,
      "value_error": 7.440759119958784e-11,
      "gradient_error": 9.134246692354964e-10
    },
    {
      "layout": "flat",
      "requested_loop": "scan",
      "core_loop": "scan",
      "top_level_scans": 1,
      "top_level_equations": 21,
      "value_error": 7.440759119958784e-11,
      "gradient_error": 9.134246692354964e-10
    },
    {
      "layout": "pytree",
      "requested_loop": null,
      "core_loop": "unroll",
      "top_level_scans": 1,
      "top_level_equations": 12,
      "value_error": 7.440759119958784e-11,
      "gradient_error": 9.134246692354964e-10
    },
    {
      "layout": "pytree",
      "requested_loop": "unroll",
      "core_loop": "unroll",
      "top_level_scans": 0,
      "top_level_equations": 181,
      "value_error": 7.440759119958784e-11,
      "gradient_error": 9.134246692354964e-10
    },
    {
      "layout": "pytree",
      "requested_loop": "scan",
      "core_loop": "scan",
      "top_level_scans": 1,
      "top_level_equations": 12,
      "value_error": 7.440759119958784e-11,
      "gradient_error": 9.134246692354964e-10
    },
    {
      "layout": "block",
      "requested_loop": null,
      "core_loop": "unroll",
      "top_level_scans": 1,
      "top_level_equations": 14,
      "value_error": 7.440759119958784e-11,
      "gradient_error": 9.134246692354964e-10
    },
    {
      "layout": "block",
      "requested_loop": "unroll",
      "core_loop": "unroll",
      "top_level_scans": 0,
      "top_level_equations": 181,
      "value_error": 7.440759119958784e-11,
      "gradient_error": 9.134246692354964e-10
    },
    {
      "layout": "block",
      "requested_loop": "scan",
      "core_loop": "scan",
      "top_level_scans": 1,
      "top_level_equations": 14,
      "value_error": 7.440759119958784e-11,
      "gradient_error": 9.134246692354964e-10
    }
  ],
  "limitations": [
    "Public synthetic CPU exponential fixture; no application/production workload or GPU measured.",
    "Three fresh workers per case, alternating/reversed case order; shared host with singleton CPU affinity and requested thread controls. Effective thread pools were not sampled.",
    "Within each worker ordinary runs before prepared; selected compilation follows tracing and backend setup, not whole-process cold startup.",
    "Trace and warm entries are within-worker medians of three samples; individual inner samples are not retained by this harness. All outer worker records are retained.",
    "Peak RSS includes both execution paths, tracing, compilation and backend startup. It is not per-path live solver memory. XLA temporary bytes are separate compiler estimates.",
    "Omitted provider settings already selected scans before the change. These compare execution choices; they are not a default-path implementation speedup or calibration evidence."
  ]
}