diff --git a/extra_tests/regression_fits/central_16.exportgrid b/extra_tests/regression_fits/central_16.exportgrid index 503dbac86c..f7a1c4867c 100644 --- a/extra_tests/regression_fits/central_16.exportgrid +++ b/extra_tests/regression_fits/central_16.exportgrid @@ -1,590 +1,590 @@ labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT] pdfgrid: -- [0.0, 0.0, -2.3123602867126465, 0.09158137440681458, 0.06507625430822372, 0.06512340158224106, - -16.440601348876953, 0.06527390331029892, 0.06522823125123978, 0.09134119004011154, - -2.312361001968384, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.2394700050354004, 0.09345148503780365, 0.06588480621576309, 0.06593846529722214, - -16.451414108276367, 0.06610491126775742, 0.06605302542448044, 0.09318796545267105, - -2.2394700050354004, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.1688826084136963, 0.09551798552274704, 0.06684604287147522, 0.06690700352191925, - -16.46079444885254, 0.06709110736846924, 0.06703228503465652, 0.0952288955450058, - 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a/extra_tests/regression_fits/no_msr_92.json b/extra_tests/regression_fits/no_msr_92.json index c87ab8a66d..2c90ee4bf5 100644 --- a/extra_tests/regression_fits/no_msr_92.json +++ b/extra_tests/regression_fits/no_msr_92.json @@ -51,44 +51,44 @@ ], "stop_epoch": 1100, "best_epoch": 1099, - "erf_tr": 6.576577663421631, - "erf_vl": 5.858455181121826, - "chi2": 3.330012321472168, + "erf_tr": 6.576578617095947, + "erf_vl": 5.858458518981934, + "chi2": 3.3300085067749023, "pos_state": "POS_PASS", "arc_lengths": [ - 1.337021614754758, - 0.9939697379044121, - 1.1791486314894875, - 1.0013385222517424, - 1.1751097571490132 + 1.3370216884066382, + 0.9939697374308255, + 1.1791486738858517, + 1.0013385257857281, + 1.1751098009017222 ], "integrability": [ - 0.000991988403256927, - 0.000991988403257052, - 4.665317624120979e-05, - 0.4762776866555213, - 0.007486370741389338 + 0.0009919884178089394, + 0.0009919884178089672, + 4.6653176695957144e-05, + 0.4762776866555214, + 0.0074863710906354144 ], 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a/extra_tests/regression_fits/no_sumrules_18.exportgrid b/extra_tests/regression_fits/no_sumrules_18.exportgrid index feb23099da..0f7c0b8080 100644 --- a/extra_tests/regression_fits/no_sumrules_18.exportgrid +++ b/extra_tests/regression_fits/no_sumrules_18.exportgrid @@ -1,590 +1,590 @@ labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT] pdfgrid: -- [0.0, 0.0, -2.4447743892669678, 0.48206278681755066, 0.4548843801021576, 0.4549284875392914, - -0.536359429359436, 0.4549674689769745, 0.45492371916770935, 0.4819997251033783, - -2.4447743892669678, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.3672444820404053, 0.47239094972610474, 0.44413238763809204, 0.44418248534202576, - -0.5363330841064453, 0.4442256689071655, 0.44417592883110046, 0.4723217785358429, - -2.3672444820404053, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.292189836502075, 0.4632740914821625, 0.43389183282852173, 0.433948814868927, - -0.536237359046936, 0.4339965581893921, 0.4339400827884674, 0.4631982445716858, - 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replica: 18 diff --git a/extra_tests/regression_fits/no_sumrules_18.json b/extra_tests/regression_fits/no_sumrules_18.json index df7888cb29..8e5f95e4ae 100644 --- a/extra_tests/regression_fits/no_sumrules_18.json +++ b/extra_tests/regression_fits/no_sumrules_18.json @@ -51,44 +51,44 @@ ], "stop_epoch": 1100, "best_epoch": 1099, - "erf_tr": 5.3037943840026855, - "erf_vl": 5.3994460105896, - "chi2": 3.260413646697998, + "erf_tr": 5.303784370422363, + "erf_vl": 5.399442672729492, + "chi2": 3.2604198455810547, "pos_state": "POS_PASS", "arc_lengths": [ - 1.334838194261321, - 0.9938354337963408, - 1.0016686481364248, - 0.9905760062890283, - 1.0071507465033542 + 1.3348380879587267, + 0.9938354367951591, + 1.0016686524387772, + 0.9905760067696133, + 1.0071507400671937 ], "integrability": [ - 0.00022732430443259122, - 0.00022732430443293816, - 1.0915415287984787e-05, - 0.4772165119647979, - 0.0018179939506805104 + 0.000227324335355987, + 0.00022732433535575802, + 1.0915415316406496e-05, + 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file diff --git a/extra_tests/regression_fits/no_vsr_54.exportgrid b/extra_tests/regression_fits/no_vsr_54.exportgrid index b71ccbb37a..acd72ec4e5 100644 --- a/extra_tests/regression_fits/no_vsr_54.exportgrid +++ b/extra_tests/regression_fits/no_vsr_54.exportgrid @@ -1,590 +1,590 @@ labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT] pdfgrid: -- [0.0, 0.0, -2.3827309608459473, 0.17211303114891052, 0.14561547338962555, 0.14566178619861603, - -16.24198341369629, 0.14569853246212006, 0.14565253257751465, 0.17205394804477692, - -2.3827309608459473, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.3074989318847656, 0.17170847952365875, 0.1441579908132553, 0.1442105770111084, - -16.250526428222656, 0.14425119757652283, 0.14419899880886078, 0.17164362967014313, - -2.3074984550476074, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.2346484661102295, 0.1715724766254425, 0.14292648434638977, 0.1429862529039383, - -16.257551193237305, 0.14303116500377655, 0.1429719179868698, 0.1715013086795807, - 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-0.012848636135458946, -0.004151065833866596, -0.0024993254337459803, 0.014644378796219826, - -0.006841970607638359, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.007751834113150835, -0.026106029748916626, 0.017945600673556328, 0.016360444948077202, + -0.015295428223907948, -0.004575177561491728, -0.0025066977832466364, 0.016251521185040474, + -0.007751834113150835, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.006841971538960934, -0.023371243849396706, 0.01592409797012806, 0.014652988873422146, + -0.012848636135458946, -0.004151066765189171, -0.0024993258994072676, 0.01464438159018755, + -0.006841971538960934, 0.0, 0.0, 0.0] - [0.0, 0.0, -0.0059868404641747475, -0.020732462406158447, 0.014006377197802067, - 0.01300437655299902, -0.010635212995111942, -0.0037378263659775257, -0.002441328950226307, + 0.01300437655299902, -0.010635213926434517, -0.0037378263659775257, -0.002441328950226307, 0.013089039362967014, -0.005986839532852173, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.005186258815228939, -0.018198547884821892, 0.012194514274597168, 0.011420144699513912, - -0.008654520846903324, -0.0033361692912876606, -0.0023385800886899233, 0.011588728986680508, - -0.0051862578839063644, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.004440126474946737, -0.015778575092554092, 0.01049072202295065, 0.009906020946800709, - -0.006904028356075287, -0.002946865512058139, -0.002197000663727522, 0.010146994143724442, - -0.004440126474946737, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.0037484238855540752, -0.013481860049068928, 0.008897373452782631, - 0.008467930369079113, -0.005379220936447382, -0.002570768352597952, -0.002022573258727789, - 0.008767697960138321, -0.0037484238855540752, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.003111243946477771, -0.011318105272948742, 0.007417107932269573, 0.007112081628292799, - -0.004073586780577898, -0.002208887366577983, -0.0018213908188045025, 0.007455188315361738, - -0.003111243946477771, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.005186258349567652, -0.01819855161011219, 0.012194515205919743, 0.011420145630836487, + -0.008654520846903324, -0.00333617115393281, -0.0023385819513350725, 0.011588731780648232, + -0.005186257418245077, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.004440127406269312, -0.015778575092554092, 0.01049072202295065, 0.009906021878123283, + -0.006904028356075287, -0.0029468650463968515, -0.0021970001980662346, 0.010146994143724442, + -0.004440127406269312, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.0037484238855540752, -0.013481860980391502, 0.008897374384105206, + 0.008467930369079113, -0.005379220470786095, -0.0025707699824124575, -0.002022574422881007, + 0.00876769982278347, -0.0037484238855540752, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.0031112441793084145, -0.011318106204271317, 0.0074171083979308605, + 0.007112081628292799, -0.004073587246239185, -0.00220888783223927, -0.0018213912844657898, + 0.007455189246684313, -0.0031112441793084145, 0.0, 0.0, 0.0] - [0.0, 0.0, -0.0025288208853453398, -0.009297503158450127, 0.0060528917238116264, 0.005845014471560717, -0.0029785633087158203, -0.0018624247750267386, -0.0015996773727238178, 0.006214385852217674, -0.0025288208853453398, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.0020015647169202566, -0.007430892903357744, 0.0048081353306770325, - 0.0046736979857087135, -0.0020834477618336678, -0.0015328616136685014, -0.0013638486852869391, - 0.005050962325185537, -0.0020015647169202566, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.0015301386592909694, -0.005730059463530779, 0.003686889773234725, - 0.0036057105753570795, -0.001375332591123879, -0.001222081365995109, -0.001120609580539167, - 0.00397163350135088, -0.0015301386592909694, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.002001565182581544, -0.0074308933690190315, 0.004808136261999607, + 0.004673698917031288, -0.0020834477618336678, -0.0015328616136685014, -0.0013638486852869391, + 0.005050962790846825, -0.002001565182581544, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.0015301386592909694, -0.005730059929192066, 0.0036868900060653687, + 0.0036057105753570795, -0.0013753327075392008, -0.0012220815988257527, -0.0011206098133698106, + 0.003971633967012167, -0.0015301386592909694, 0.0, 0.0, 0.0] - [0.0, 0.0, -0.0011155317770317197, -0.0042080446146428585, 0.002694044029340148, 0.002649407833814621, -0.0008389418944716454, -0.0009325185092166066, -0.0008770745480433106, 0.0029845056124031544, -0.0011155317770317197, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.0007592430920340121, -0.002879832172766328, 0.0018357853405177593, - 0.0018143567722290754, -0.0004564860137179494, -0.0006674372707493603, -0.000641007733065635, - 0.0020997701212763786, -0.0007592432084493339, 0.0, 0.0, 0.0] -- [0.0, 0.0, -0.0004635540535673499, -0.0017633828101679683, 0.0011202655732631683, - 0.001111964462324977, -0.00020737350860144943, -0.0004313824465498328, -0.0004212285275571048, - 0.001330826198682189, -0.000463553995359689, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.0007592430920340121, -0.0028798324055969715, 0.0018357854569330812, + 0.0018143568886443973, -0.0004564860137179494, -0.0006674373871646821, -0.0006410078494809568, + 0.0020997703541070223, -0.0007592432084493339, 0.0, 0.0, 0.0] +- [0.0, 0.0, -0.000463553995359689, -0.0017633828101679683, 0.0011202655732631683, + 0.001111964462324977, -0.00020737350860144943, -0.0004313824756536633, -0.0004212286148685962, + 0.0013308260822668672, -0.0004635539080481976, 0.0, 0.0, 0.0] - [0.0, 0.0, -0.00023213210806716233, -0.0008818600908853114, 0.0005590427899733186, 0.0005568687338382006, -6.779083923902363e-05, -0.0002311105781700462, -0.00022847899526823312, 0.0006966661894693971, -0.00023213210806716233, 0.0, 0.0, 0.0] - [0.0, 0.0, -7.16399954399094e-05, -0.00026940598036162555, 0.00017080882389564067, - 0.00017058996309060603, -9.925640370056499e-06, -7.798225124133751e-05, -7.772142998874187e-05, + 0.00017058996309060603, -9.925639460561797e-06, -7.798225124133751e-05, -7.772142998874187e-05, 0.0002281411871081218, -7.16399954399094e-05, 0.0, 0.0, 0.0] - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] q20: 2.7224999999999997 diff --git a/extra_tests/regression_fits/trainable_prepro_61.json b/extra_tests/regression_fits/trainable_prepro_61.json index db5b3f5ba8..8442536b24 100644 --- a/extra_tests/regression_fits/trainable_prepro_61.json +++ b/extra_tests/regression_fits/trainable_prepro_61.json @@ -51,44 +51,44 @@ ], "stop_epoch": 1100, "best_epoch": 1099, - "erf_tr": 83.88552856445312, - "erf_vl": 88.89352416992188, - "chi2": 59.15236282348633, + "erf_tr": 83.88551330566406, + "erf_vl": 88.89353942871094, + "chi2": 59.15235900878906, "pos_state": "POS_VETO", "arc_lengths": [ - 1.0440971720839172, - 1.5280262072729438, - 1.0033084215844166, - 0.9979762330411598, - 1.4192811360785857 + 1.0440971854791903, + 1.5280261822454786, + 1.0033084216442367, + 0.9979762378240656, + 1.4192810423200604 ], "integrability": [ - 0.11098538525402664, - 0.11098538525402585, - 0.0002027638274733623, - 0.00843445386271846, - 0.0018140712927551261 + 0.11098538525402746, + 0.11098538525402579, + 0.00020276382838291251, + 0.008434453979133893, + 0.0018140712345475762 ], "timing": { "walltime": { - "Total": 19.603114128112793, + "Total": 21.60775899887085, "start": 0.0, - "replica_set": 0.3588278293609619, - "replica_fitted": 19.60303783416748, - "replica_set_to_replica_fitted": 19.24421000480652 + "replica_set": 0.3911454677581787, + "replica_fitted": 21.60759949684143, + "replica_set_to_replica_fitted": 21.216454029083252 }, "cputime": { - "Total": 21.8009634, + "Total": 23.8026402, "start": 0.0, - "replica_set": 0.6759696999999996, - "replica_fitted": 21.800885700000002, - "replica_set_to_replica_fitted": 21.124916 + "replica_set": 0.7076198999999992, + "replica_fitted": 23.8024797, + "replica_set_to_replica_fitted": 23.094859800000002 } }, "version": { "tensorflow": "2.15.0, mkl=??", "numpy": "1.24.4", - "nnpdf": "4.0.8.post297.dev0+234efa01e", - "validphys": "4.0.8.post297.dev0+234efa01e" + "nnpdf": "4.0.8.post339.dev0+bc76939f2", + "validphys": "4.0.8.post339.dev0+bc76939f2" } } \ No newline at end of file diff --git a/n3fit/src/n3fit/backends/keras_backend/operations.py b/n3fit/src/n3fit/backends/keras_backend/operations.py index 5d7638537f..fe62dfd24e 100644 --- a/n3fit/src/n3fit/backends/keras_backend/operations.py +++ b/n3fit/src/n3fit/backends/keras_backend/operations.py @@ -212,6 +212,12 @@ def flatten(x): return tf.reshape(x, (-1,)) +@tf.function +def reshape(x, shape): + """reshape tensor x""" + return tf.reshape(x, shape) + + def boolean_mask(*args, **kwargs): """ Applies a boolean mask to a tensor diff --git a/n3fit/src/n3fit/checks.py b/n3fit/src/n3fit/checks.py index 36ae24d52c..e1df9e2865 100644 --- a/n3fit/src/n3fit/checks.py +++ b/n3fit/src/n3fit/checks.py @@ -380,11 +380,6 @@ def check_consistent_parallel(parameters, parallel_models, same_trvl_per_replica """ if not parallel_models: return - if not same_trvl_per_replica: - raise CheckError( - "Replicas cannot be run in parallel with different training/validation " - " masks, please set `same_trvl_per_replica` to True in the runcard" - ) if parameters.get("layer_type") != "dense": raise CheckError("Parallelization has only been tested with layer_type=='dense'") diff --git a/n3fit/src/n3fit/layers/losses.py b/n3fit/src/n3fit/layers/losses.py index 4315919907..1d330ef8f5 100644 --- a/n3fit/src/n3fit/layers/losses.py +++ b/n3fit/src/n3fit/layers/losses.py @@ -8,6 +8,7 @@ """ import numpy as np + from n3fit.backends import MetaLayer from n3fit.backends import operations as op @@ -38,9 +39,6 @@ class LossInvcovmat(MetaLayer): """ def __init__(self, invcovmat, y_true, mask=None, covmat=None, **kwargs): - # If we have a diagonal matrix, padd with 0s and hope it's not too heavy on memory - if len(invcovmat.shape) == 1: - invcovmat = np.diag(invcovmat) self._invcovmat = op.numpy_to_tensor(invcovmat) self._covmat = covmat self._y_true = op.numpy_to_tensor(y_true) @@ -56,9 +54,7 @@ def build(self, input_shape): """Transform the inverse covmat and the mask into weights of the layers""" init = MetaLayer.init_constant(self._invcovmat) - self.kernel = self.builder_helper( - "invcovmat", (self._ndata, self._ndata), init, trainable=False - ) + self.kernel = self.builder_helper("invcovmat", self._invcovmat.shape, init, trainable=False) mask_shape = (1, 1, self._ndata) if self._mask is None: init_mask = MetaLayer.init_constant(np.ones(mask_shape)) @@ -79,17 +75,24 @@ def update_mask(self, new_mask): self.mask.assign(new_mask) def call(self, y_pred, **kwargs): - tmp_raw = self._y_true - y_pred + obs_diff_raw = self._y_true - y_pred # TODO: most of the time this is a y * I multiplication and can be skipped # benchmark how much time (if any) is lost in this in actual fits for the benefit of faster kfolds - tmp = op.op_multiply([tmp_raw, self.mask]) - if tmp.shape[1] == 1: - # einsum is not well suited for CPU, so use tensordot if not multimodel - right_dot = op.tensor_product(self.kernel, tmp[0, 0, :], axes=1) - res = op.tensor_product(tmp[0, :, :], right_dot, axes=1) + obs_diff = op.op_multiply([obs_diff_raw, self.mask]) + + # The experimental loss doesn't depend on replicas, so it doesn't have a replica axis and + # must be treated separately + experimental_loss = len(self.kernel.shape) == 2 + one_replica = obs_diff.shape[1] == 1 + + if one_replica: # einsum is not well suited for CPU, so use tensordot if single replica + kernel = self.kernel if experimental_loss else self.kernel[0] + right_dot = op.tensor_product(kernel, obs_diff[0, 0, :], axes=1) + loss = op.tensor_product(obs_diff[0, :, :], right_dot, axes=1) else: - res = op.einsum("bri, ij, brj -> r", tmp, self.kernel, tmp) - return res + einstr = "bri, ij, brj -> r" if experimental_loss else "bri, rij, brj -> r" + loss = op.einsum(einstr, obs_diff, self.kernel, obs_diff) + return loss class LossLagrange(MetaLayer): diff --git a/n3fit/src/n3fit/layers/mask.py b/n3fit/src/n3fit/layers/mask.py index 877df3b502..2b0bff6c00 100644 --- a/n3fit/src/n3fit/layers/mask.py +++ b/n3fit/src/n3fit/layers/mask.py @@ -1,10 +1,12 @@ +from numpy import count_nonzero + from n3fit.backends import MetaLayer from n3fit.backends import operations as op class Mask(MetaLayer): """ - This layers applies a boolean mask to a rank-1 input tensor. + This layers applies a boolean mask to an input tensor. The mask admit a multiplier for all outputs which will be internally saved as a weight so it can be updated during trainig. @@ -14,34 +16,50 @@ class Mask(MetaLayer): Parameters ---------- - bool_mask: np.array + bool_mask: np.array of shape (n_replicas, n_features) numpy array with the boolean mask to be applied c: float constant multiplier for every output - axis: int - axis in which to apply the mask """ - def __init__(self, bool_mask=None, c=None, axis=None, **kwargs): + def __init__(self, bool_mask=None, c=None, **kwargs): if bool_mask is None: self.mask = None + self.last_dim = -1 else: self.mask = op.numpy_to_tensor(bool_mask, dtype=bool) + self.last_dim = count_nonzero(bool_mask[0, ...]) self.c = c - self.axis = axis + self.masked_output_shape = None super().__init__(**kwargs) def build(self, input_shape): if self.c is not None: initializer = MetaLayer.init_constant(value=self.c) - self.kernel = self.builder_helper( - "mask", (1,), initializer, trainable=False - ) + self.kernel = self.builder_helper("mask", (1,), initializer, trainable=False) + # Make sure reshape will succeed: set the last dimension to the unmasked data length and before-last to + # the number of replicas + if self.mask is not None: + self.masked_output_shape = [-1 if d is None else d for d in input_shape] + self.masked_output_shape[-1] = self.last_dim + self.masked_output_shape[-2] = self.mask.shape[-2] super(Mask, self).build(input_shape) def call(self, ret): + """ + Apply the mask to the input tensor, and multiply by the constant if present. + + Parameters + ---------- + ret: Tensor of shape (batch_size, n_replicas, n_features) + + Returns + ------- + Tensor of shape (batch_size, n_replicas, n_features) + """ if self.mask is not None: - ret = op.boolean_mask(ret, self.mask, axis=self.axis) + flat_res = op.boolean_mask(ret, self.mask, axis=1) + ret = op.reshape(flat_res, shape=self.masked_output_shape) if self.c is not None: ret = ret * self.kernel return ret diff --git a/n3fit/src/n3fit/model_gen.py b/n3fit/src/n3fit/model_gen.py index 5c338135a1..219bbdfc11 100644 --- a/n3fit/src/n3fit/model_gen.py +++ b/n3fit/src/n3fit/model_gen.py @@ -32,6 +32,7 @@ AddPhoton, FkRotation, FlavourToEvolution, + Mask, ObsRotation, Preprocessing, losses, @@ -56,6 +57,7 @@ class ObservableWrapper: name: str observables: list + trvl_mask_layer: Mask dataset_xsizes: list invcovmat: np.array = None covmat: np.array = None @@ -69,8 +71,22 @@ def _generate_loss(self, mask=None): """Generates the corresponding loss function depending on the values the wrapper was initialized with""" if self.invcovmat is not None: + if self.rotation: + # If we have a matrix diagonal only, padd with 0s and hope it's not too heavy on memory + invcovmat_matrix = np.eye(self.invcovmat.shape[-1]) * self.invcovmat[..., np.newaxis] + if self.covmat is not None: + covmat_matrix = np.eye(self.covmat.shape[-1]) * self.covmat[..., np.newaxis] + else: + covmat_matrix = self.covmat + else: + covmat_matrix = self.covmat + invcovmat_matrix = self.invcovmat loss = losses.LossInvcovmat( - self.invcovmat, self.data, mask, covmat=self.covmat, name=self.name + invcovmat_matrix, + self.data, + mask, + covmat=covmat_matrix, + name=self.name ) elif self.positivity: loss = losses.LossPositivity(name=self.name, c=self.multiplier) @@ -95,10 +111,15 @@ def _generate_experimental_layer(self, pdf): else: output_layers = [obs(pdf) for obs in self.observables] - # Finally concatenate all observables (so that experiments are one single entitiy) - ret = op.concatenate(output_layers, axis=2) + # Finally concatenate all observables (so that experiments are one single entity) + ret = op.concatenate(output_layers, axis=-1) + if self.rotation is not None: ret = self.rotation(ret) + + if self.trvl_mask_layer is not None: + ret = self.trvl_mask_layer(ret) + return ret def __call__(self, pdf_layer, mask=None): @@ -108,7 +129,14 @@ def __call__(self, pdf_layer, mask=None): def observable_generator( - spec_dict, positivity_initial=1.0, integrability=False + spec_dict, + mask_array=None, + training_data=None, + validation_data=None, + invcovmat_tr=None, + invcovmat_vl=None, + positivity_initial=1.0, + integrability=False, ): # pylint: disable=too-many-locals """ This function generates the observable models for each experiment. @@ -157,9 +185,7 @@ def observable_generator( spec_name = spec_dict["name"] dataset_xsizes = [] model_inputs = [] - model_obs_tr = [] - model_obs_vl = [] - model_obs_ex = [] + model_observables = [] # The first step is to compute the observable for each of the datasets for dataset in spec_dict["datasets"]: # Get the generic information of the dataset @@ -180,50 +206,14 @@ def observable_generator( # list of validphys.coredata.FKTableData objects # these will then be used to check how many different pdf inputs are needed # (and convolutions if given the case) - - if spec_dict["positivity"]: - # Positivity (and integrability, which is a special kind of positivity...) - # enters only at the "training" part of the models - obs_layer_tr = Obs_Layer( - dataset.fktables_data, - dataset.training_fktables(), - operation_name, - name=f"dat_{dataset_name}", - ) - obs_layer_ex = obs_layer_vl = None - elif spec_dict.get("data_transformation_tr") is not None: - # Data transformation needs access to the full array of output data - obs_layer_ex = Obs_Layer( - dataset.fktables_data, - dataset.fktables(), - operation_name, - name=f"exp_{dataset_name}", - ) - obs_layer_tr = obs_layer_vl = obs_layer_ex - else: - obs_layer_tr = Obs_Layer( - dataset.fktables_data, - dataset.training_fktables(), - operation_name, - name=f"dat_{dataset_name}", - ) - obs_layer_ex = Obs_Layer( - dataset.fktables_data, - dataset.fktables(), - operation_name, - name=f"exp_{dataset_name}", - ) - obs_layer_vl = Obs_Layer( - dataset.fktables_data, - dataset.validation_fktables(), - operation_name, - name=f"val_{dataset_name}", - ) + obs_layer = Obs_Layer( + dataset.fktables_data, dataset.fktables(), operation_name, name=f"dat_{dataset_name}" + ) # If the observable layer found that all input grids are equal, the splitting will be None # otherwise the different xgrids need to be stored separately # Note: for pineappl grids, obs_layer_tr.splitting should always be None - if obs_layer_tr.splitting is None: + if obs_layer.splitting is None: xgrid = dataset.fktables_data[0].xgrid model_inputs.append(xgrid) dataset_xsizes.append(len(xgrid)) @@ -232,9 +222,7 @@ def observable_generator( model_inputs += xgrids dataset_xsizes.append(sum([len(i) for i in xgrids])) - model_obs_tr.append(obs_layer_tr) - model_obs_vl.append(obs_layer_vl) - model_obs_ex.append(obs_layer_ex) + model_observables.append(obs_layer) # Check whether all xgrids of all observables in this experiment are equal # if so, simplify the model input @@ -245,11 +233,25 @@ def observable_generator( # Reshape all inputs arrays to be (1, nx) model_inputs = np.concatenate(model_inputs).reshape(1, -1) - full_nx = sum(dataset_xsizes) + # Make the mask layers... + if mask_array is not None: + tr_mask_layer = Mask(mask_array, name=f"trmask_{spec_name}") + vl_mask_layer = Mask(~mask_array, name=f"vlmask_{spec_name}") + else: + tr_mask_layer = None + vl_mask_layer = None + + # Make rotations of the final data (if any) + if spec_dict.get("data_transformation") is not None: + obsrot = ObsRotation(spec_dict.get("data_transformation")) + else: + obsrot = None + if spec_dict["positivity"]: out_positivity = ObservableWrapper( spec_name, - model_obs_tr, + model_observables, + tr_mask_layer, dataset_xsizes, multiplier=positivity_initial, positivity=not integrability, @@ -259,38 +261,33 @@ def observable_generator( layer_info = { "inputs": model_inputs, "output_tr": out_positivity, - "experiment_xsize": full_nx, + "experiment_xsize": sum(dataset_xsizes), } # For positivity we end here return layer_info - # Generate the loss function and rotations of the final data (if any) - if spec_dict.get("data_transformation_tr") is not None: - obsrot_tr = ObsRotation(spec_dict.get("data_transformation_tr")) - obsrot_vl = ObsRotation(spec_dict.get("data_transformation_vl")) - else: - obsrot_tr = None - obsrot_vl = None - out_tr = ObservableWrapper( spec_name, - model_obs_tr, + model_observables, + tr_mask_layer, dataset_xsizes, - invcovmat=spec_dict["invcovmat"], - data=spec_dict["expdata"], - rotation=obsrot_tr, + invcovmat=invcovmat_tr, + data=training_data, + rotation=obsrot, ) out_vl = ObservableWrapper( f"{spec_name}_val", - model_obs_vl, + model_observables, + vl_mask_layer, dataset_xsizes, - invcovmat=spec_dict["invcovmat_vl"], - data=spec_dict["expdata_vl"], - rotation=obsrot_vl, + invcovmat=invcovmat_vl, + data=validation_data, + rotation=obsrot, ) out_exp = ObservableWrapper( f"{spec_name}_exp", - model_obs_ex, + model_observables, + None, dataset_xsizes, invcovmat=spec_dict["invcovmat_true"], covmat=spec_dict["covmat"], @@ -303,7 +300,7 @@ def observable_generator( "output": out_exp, "output_tr": out_tr, "output_vl": out_vl, - "experiment_xsize": full_nx, + "experiment_xsize": sum(dataset_xsizes), } return layer_info diff --git a/n3fit/src/n3fit/model_trainer.py b/n3fit/src/n3fit/model_trainer.py index acbcc8b3cd..59acff377a 100644 --- a/n3fit/src/n3fit/model_trainer.py +++ b/n3fit/src/n3fit/model_trainer.py @@ -15,9 +15,8 @@ import numpy as np from n3fit import model_gen -from n3fit.backends import MetaModel, callbacks, clear_backend_state +from n3fit.backends import NN_LAYER_ALL_REPLICAS, MetaModel, callbacks, clear_backend_state from n3fit.backends import operations as op -from n3fit.backends import NN_LAYER_ALL_REPLICAS import n3fit.hyper_optimization.penalties import n3fit.hyper_optimization.rewards from n3fit.scaler import generate_scaler @@ -143,15 +142,10 @@ def __init__( list with the replicas ids to be fitted """ # Save all input information - self.exp_info = exp_info - if pos_info is None: - pos_info = [] - self.pos_info = pos_info - self.integ_info = integ_info - if self.integ_info is not None: - self.all_info = exp_info + pos_info + integ_info - else: - self.all_info = exp_info + pos_info + self.exp_info = list(exp_info) + self.pos_info = [] if pos_info is None else pos_info + self.integ_info = [] if integ_info is None else integ_info + self.all_info = self.exp_info[0] + self.pos_info + self.integ_info self.flavinfo = flavinfo self.fitbasis = fitbasis self._nn_seeds = nnseeds @@ -219,6 +213,7 @@ def __init__( "posdatasets": [], } self.experimental = {"output": [], "expdata": [], "ndata": 0, "model": None, "folds": []} + self.tr_masks = [] self._fill_the_dictionaries() @@ -268,7 +263,7 @@ def _fill_the_dictionaries(self): - ``name``: names of the experiment - ``ndata``: number of experimental points """ - for exp_dict in self.exp_info: + for exp_dict in self.exp_info[0]: self.training["expdata"].append(exp_dict["expdata"]) self.validation["expdata"].append(exp_dict["expdata_vl"]) self.experimental["expdata"].append(exp_dict["expdata_true"]) @@ -293,10 +288,10 @@ def _fill_the_dictionaries(self): self.training["posdatasets"].append(pos_dict["name"]) self.validation["expdata"].append(pos_dict["expdata"]) self.validation["posdatasets"].append(pos_dict["name"]) - if self.integ_info is not None: - for integ_dict in self.integ_info: - self.training["expdata"].append(integ_dict["expdata"]) - self.training["integdatasets"].append(integ_dict["name"]) + + for integ_dict in self.integ_info: + self.training["expdata"].append(integ_dict["expdata"]) + self.training["integdatasets"].append(integ_dict["name"]) def _xgrid_generation(self): """ @@ -483,7 +478,7 @@ def _reset_observables(self): self.experimental[key] = [] ############################################################################ - # # Parametizable functions # + # # Parameterizable functions # # # # The functions defined in this block accept a 'params' dictionary which # # defines the fit and the behaviours of the Neural Networks # @@ -523,12 +518,44 @@ def _generate_observables( self._reset_observables() log.info("Generating layers") + # We need to transpose Experimental data, stacking over replicas + experiment_data = { + "trmask": [], + "expdata": [], + "expdata_vl": [], + "invcovmat": [], + "invcovmat_vl": [], + } + + # Loop over datasets + for i in range(len(self.exp_info[0])): + # Loop over data fields + for key, value in experiment_data.items(): + replica_data = [] + # Loop over replicas + for replica in self.exp_info: + if key in ["expdata", "expdata_vl"]: + # Save the data with shape (ndata) instead of (1, ndata) + replica_data.append(replica[i][key][0]) + else: + replica_data.append(replica[i][key]) + # Stack + value.append(np.stack(replica_data)) + # Now we need to loop over all dictionaries (First exp_info, then pos_info and integ_info) - for exp_dict in self.exp_info: + for i, exp_dict in enumerate(self.exp_info[0]): if not self.mode_hyperopt: log.info("Generating layers for experiment %s", exp_dict["name"]) - exp_layer = model_gen.observable_generator(exp_dict) + # Stacked tr-vl mask array for all replicas for this dataset + exp_layer = model_gen.observable_generator( + exp_dict, + mask_array=experiment_data["trmask"][i], + training_data=experiment_data["expdata"][i], + validation_data=experiment_data["expdata_vl"][i], + invcovmat_tr=experiment_data["invcovmat"][i], + invcovmat_vl=experiment_data["invcovmat_vl"][i], + ) # Save the input(s) corresponding to this experiment self.input_list.append(exp_layer["inputs"]) @@ -549,8 +576,17 @@ def _generate_observables( pos_initial, pos_multiplier = _LM_initial_and_multiplier( all_pos_initial, all_pos_multiplier, max_lambda, positivity_steps ) - - pos_layer = model_gen.observable_generator(pos_dict, positivity_initial=pos_initial) + num_experiments = len(self.exp_info) + replica_masks = np.stack([pos_dict["trmask"]] * num_experiments) + training_data = np.stack([pos_dict["expdata"].flatten()] * num_experiments) + + pos_layer = model_gen.observable_generator( + pos_dict, + positivity_initial=pos_initial, + mask_array=replica_masks, + training_data=training_data, + validation_data=training_data, + ) # The input list is still common self.input_list.append(pos_layer["inputs"]) @@ -562,28 +598,27 @@ def _generate_observables( self.training["posinitials"].append(pos_initial) # Finally generate the integrability penalty - if self.integ_info is not None: - for integ_dict in self.integ_info: - if not self.mode_hyperopt: - log.info("Generating integrability penalty for %s", integ_dict["name"]) - - integrability_steps = int(epochs / PUSH_INTEGRABILITY_EACH) - max_lambda = integ_dict["lambda"] - - integ_initial, integ_multiplier = _LM_initial_and_multiplier( - all_integ_initial, all_integ_multiplier, max_lambda, integrability_steps - ) - - integ_layer = model_gen.observable_generator( - integ_dict, positivity_initial=integ_initial, integrability=True - ) - # The input list is still common - self.input_list.append(integ_layer["inputs"]) - - # The integrability all falls to the training - self.training["output"].append(integ_layer["output_tr"]) - self.training["integmultipliers"].append(integ_multiplier) - self.training["integinitials"].append(integ_initial) + for integ_dict in self.integ_info: + if not self.mode_hyperopt: + log.info("Generating integrability penalty for %s", integ_dict["name"]) + + integrability_steps = int(epochs / PUSH_INTEGRABILITY_EACH) + max_lambda = integ_dict["lambda"] + + integ_initial, integ_multiplier = _LM_initial_and_multiplier( + all_integ_initial, all_integ_multiplier, max_lambda, integrability_steps + ) + + integ_layer = model_gen.observable_generator( + integ_dict, positivity_initial=integ_initial, integrability=True + ) + # The input list is still common + self.input_list.append(integ_layer["inputs"]) + + # The integrability all falls to the training + self.training["output"].append(integ_layer["output_tr"]) + self.training["integmultipliers"].append(integ_multiplier) + self.training["integinitials"].append(integ_initial) # Store a reference to the interpolator as self._scaler if interpolation_points: diff --git a/n3fit/src/n3fit/performfit.py b/n3fit/src/n3fit/performfit.py index ee38aad4ea..6514029ca2 100644 --- a/n3fit/src/n3fit/performfit.py +++ b/n3fit/src/n3fit/performfit.py @@ -143,49 +143,40 @@ def performfit( from n3fit.io.writer import WriterWrapper from n3fit.model_trainer import ModelTrainer - # Note: there are three possible scenarios for the loop of replicas: - # 1.- Only one replica is being run, in this case the loop is only evaluated once - # 2.- Many replicas being run, in this case each will have a replica_number, seed, etc - # and they will be fitted sequentially - # 3.- Many replicas being run in parallel. In this case the loop will be evaluated just once - # but a model per replica will be generated + # Note that this can be run in sequence or in parallel + # To do both cases in the same loop, we uniformize the replica information as: + # - sequential: a list over replicas, each entry containing tuples of length 1 + # - parallel: a list of length 1, containing tuples over replicas # - # In the main scenario (1) replicas_nnseed_fitting_data_dict is a list of just one element - # case (3) is similar but the one element of replicas_nnseed_fitting_data_dict will be modified - # to be ( - # [list of all replica idx], - # one experiment with data=(replicas, ndata), - # [list of all NN seeds] - # ) - # - n_models = len(replicas_nnseed_fitting_data_dict) - if parallel_models and n_models != 1: - replicas, replica_experiments, nnseeds = zip(*replicas_nnseed_fitting_data_dict) - # Parse the experiments so that the output data contain information for all replicas - # as the only different from replica to replica is the experimental training/validation data - all_experiments = copy.deepcopy(replica_experiments[0]) - for i_exp in range(len(all_experiments)): - training_data = [] - validation_data = [] - for i_rep in range(n_models): - training_data.append(replica_experiments[i_rep][i_exp]['expdata']) - validation_data.append(replica_experiments[i_rep][i_exp]['expdata_vl']) - all_experiments[i_exp]['expdata'] = np.concatenate(training_data, axis=0) - all_experiments[i_exp]['expdata_vl'] = np.concatenate(validation_data, axis=0) + # Add inner tuples + replicas_info = [ + ((replica,), (experiment,), (nnseed,)) + for replica, experiment, nnseed in replicas_nnseed_fitting_data_dict + ] + + n_models = len(replicas_info) + if parallel_models: + # Move replicas from outer list to inner tuples + replicas, experiments, nnseeds = [], [], [] + + for replica, experiment, nnseed in replicas_info: + replicas.extend(replica) + experiments.extend(experiment) + nnseeds.extend(nnseed) + + replicas_info = [(tuple(replicas), tuple(experiments), tuple(nnseeds))] log.info( "Starting parallel fits from replica %d to %d", replicas[0], replicas[0] + n_models - 1 ) - replicas_info = [(replicas, all_experiments, nnseeds)] else: - replicas_info = replicas_nnseed_fitting_data_dict + log.info( + "Starting sequential fits from replica %d to %d", + replicas[0], + replicas[0] + n_models - 1, + ) for replica_idxs, exp_info, nnseeds in replicas_info: - if not parallel_models or n_models == 1: - # Cases 1 and 2 above are a special case of 3 where the replica idx and the seed should - # be a list of just one element - replica_idxs = [replica_idxs] - nnseeds = [nnseeds] - log.info("Starting replica fit %d", replica_idxs[0]) + log.info("Starting replica fit " + str(replica_idxs)) # Generate a ModelTrainer object # this object holds all necessary information to train a PDF (up to the NN definition) diff --git a/n3fit/src/n3fit/stopping.py b/n3fit/src/n3fit/stopping.py index 8884e7f01b..380c5445d7 100644 --- a/n3fit/src/n3fit/stopping.py +++ b/n3fit/src/n3fit/stopping.py @@ -384,7 +384,7 @@ def e_best_chi2(self): @property def stop_epoch(self): """Epoch in which the fit is stopped""" - return self._history.final_epoch + 1 + return -1 if self._history.final_epoch is None else self._history.final_epoch + 1 @property def positivity_status(self): diff --git a/n3fit/src/n3fit/tests/test_layers.py b/n3fit/src/n3fit/tests/test_layers.py index 1cae6c6c6a..6ff4887d64 100644 --- a/n3fit/src/n3fit/tests/test_layers.py +++ b/n3fit/src/n3fit/tests/test_layers.py @@ -240,8 +240,9 @@ def test_rotation_evol(): def test_mask(): """Test the mask layer""" - SIZE = 100 - fi = np.random.rand(SIZE) + batch_size, replicas, points = 1, 1, 100 + shape = (batch_size, replicas, points) + fi = np.random.rand(*shape) # Check that the multiplier works vals = [0.0, 2.0, np.random.rand()] for val in vals: @@ -249,10 +250,10 @@ def test_mask(): ret = masker(fi) np.testing.assert_allclose(ret, val * fi, rtol=1e-5) # Check that the boolean works - np_mask = np.random.randint(0, 2, size=SIZE, dtype=bool) + np_mask = np.random.randint(0, 2, size=shape[1:], dtype=bool) masker = layers.Mask(bool_mask=np_mask) ret = masker(fi) - masked_fi = fi[np_mask] + masked_fi = fi[np.newaxis, :, np_mask] np.testing.assert_allclose(ret, masked_fi, rtol=1e-5) # Check that the combination works! rn_val = vals[-1] diff --git a/validphys2/src/validphys/filters.py b/validphys2/src/validphys/filters.py index 931d5b4214..566b372c6d 100644 --- a/validphys2/src/validphys/filters.py +++ b/validphys2/src/validphys/filters.py @@ -180,9 +180,7 @@ def _filter_real_data(filter_path, data): return total_data_points, total_cut_data_points -def _filter_closure_data( - filter_path, data, fakepdf, fakenoise, filterseed, data_index, sep_mult -): +def _filter_closure_data(filter_path, data, fakepdf, fakenoise, filterseed, data_index, sep_mult): """ This function is accessed within a closure test only, that is, the fakedata namespace has to be True (If fakedata = False, the _filter_real_data function @@ -395,14 +393,7 @@ class Rule: numpy_functions = {"sqrt": np.sqrt, "log": np.log, "fabs": np.fabs} - def __init__( - self, - initial_data: dict, - *, - defaults: dict, - theory_parameters: dict, - loader=None, - ): + def __init__(self, initial_data: dict, *, defaults: dict, theory_parameters: dict, loader=None): self.dataset = None self.process_type = None self._local_variables_code = {} @@ -453,13 +444,7 @@ def __init__( self.rule_string = self.rule self.defaults = defaults self.theory_params = theory_parameters - ns = { - *self.numpy_functions, - *self.defaults, - *self.variables, - "idat", - "central_value", - } + ns = {*self.numpy_functions, *self.defaults, *self.variables, "idat", "central_value"} for k, v in self.local_variables.items(): try: self._local_variables_code[k] = lcode = compile( @@ -536,11 +521,7 @@ def __call__(self, dataset, idat): return eval( self.rule, self.numpy_functions, - { - **{"idat": idat, "central_value": central_value}, - **self.defaults, - **ns, - }, + {**{"idat": idat, "central_value": central_value}, **self.defaults, **ns}, ) except Exception as e: # pragma: no cover raise FatalRuleError(f"Error when applying rule {self.rule_string!r}: {e}") from e diff --git a/validphys2/src/validphys/n3fit_data.py b/validphys2/src/validphys/n3fit_data.py index 38f946248d..ced700f98f 100644 --- a/validphys2/src/validphys/n3fit_data.py +++ b/validphys2/src/validphys/n3fit_data.py @@ -74,7 +74,7 @@ def __iter__(self): yield m -def tr_masks(data, replica_trvlseed): +def tr_masks(data, replica_trvlseed, parallel_models=False, replica=1, replicas=(1,)): """Generate the boolean masks used to split data into training and validation points. Returns a list of 1-D boolean arrays, one for each dataset. Each array has length equal to N_data, the datapoints which @@ -97,8 +97,16 @@ def tr_masks(data, replica_trvlseed): # We do this so that a given dataset will always have the same number of points masked trmax = int(ndata * frac) if trmax == 0: - # If that number is 0, then get 1 point with probability frac - trmax = int(rng.random() < frac) + if parallel_models: + if replica == replicas[0]: + log.warning( + f'Single-datapoint dataset {dataset.name} encountered in parallel multi-replica fit: ' + 'all replicas will include it in their training data' + ) + trmax = 1 + else: + # If that number is 0, then get 1 point with probability frac + trmax = int(rng.random() < frac) mask = np.concatenate([np.ones(trmax, dtype=bool), np.zeros(ndata - trmax, dtype=bool)]) rng.shuffle(mask) trmask_partial.append(mask) @@ -311,6 +319,7 @@ def fitting_data_dict( "folds": folds, "data_transformation_tr": dt_trans_tr, "data_transformation_vl": dt_trans_vl, + "data_transformation": dt_trans, } return dict_out