From feecfc9c1e176c6a9639a451d01d0a4891c121bd Mon Sep 17 00:00:00 2001 From: juacrumar Date: Mon, 3 Jun 2024 10:42:54 +0200 Subject: [PATCH 1/5] add shape to boolean mask --- n3fit/src/n3fit/backends/keras_backend/MetaLayer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/n3fit/src/n3fit/backends/keras_backend/MetaLayer.py b/n3fit/src/n3fit/backends/keras_backend/MetaLayer.py index ed083983ff..acdf01bc1d 100644 --- a/n3fit/src/n3fit/backends/keras_backend/MetaLayer.py +++ b/n3fit/src/n3fit/backends/keras_backend/MetaLayer.py @@ -8,8 +8,8 @@ For instance: np_to_tensor is just a call to K.constant """ -from tensorflow.keras.initializers import Constant, RandomUniform, glorot_normal, glorot_uniform -from tensorflow.keras.layers import Layer +from keras.initializers import Constant, RandomUniform, glorot_normal, glorot_uniform +from keras.layers import Layer # Define in this dictionary new initializers as well as the arguments they accept (with default values if needed be) initializers = { From e7b6a5b03f43178857554c0ab2e177f1ee758cfd Mon Sep 17 00:00:00 2001 From: Aron Date: Wed, 30 Aug 2023 14:50:38 +0200 Subject: [PATCH 2/5] Avoid TensorFlow overhead by making one step a batch rather than an epoch. Avoids memory overhead by only combining up to 100 steps into one epoch, and not changing anything when using only 1 replica (i.e. on CPU). --- .../n3fit/backends/keras_backend/MetaModel.py | 35 ++++++++++- .../n3fit/backends/keras_backend/callbacks.py | 58 ++++++++++++++++--- n3fit/src/n3fit/stopping.py | 6 +- 3 files changed, 87 insertions(+), 12 deletions(-) diff --git a/n3fit/src/n3fit/backends/keras_backend/MetaModel.py b/n3fit/src/n3fit/backends/keras_backend/MetaModel.py index 7ea2d5b9b1..9ae7356076 100644 --- a/n3fit/src/n3fit/backends/keras_backend/MetaModel.py +++ b/n3fit/src/n3fit/backends/keras_backend/MetaModel.py @@ -164,10 +164,36 @@ def perform_fit(self, x=None, y=None, epochs=1, **kwargs): x_params = self._parse_input(x) if y is None: y = self.target_tensors - history = super().fit(x=x_params, y=y, epochs=epochs, **kwargs) + + # Avoids Tensorflow overhead that happens at every epoch, by putting multiple steps in an epoch + steps_per_epoch = self.determine_steps_per_epoch(epochs) + + for k, v in x_params.items(): + x_params[k] = tf.repeat(v, steps_per_epoch, axis=0) + y = [tf.repeat(yi, steps_per_epoch, axis=0) for yi in y] + + history = super().fit( + x=x_params, y=y, epochs=epochs // steps_per_epoch, batch_size=1, **kwargs + ) loss_dict = history.history return loss_dict + def determine_steps_per_epoch(self, epochs): + num_replicas = self.output_shape[0][0] + # in this case we're most likely running on the CPU and this is not worth it + if num_replicas == 1: + return 1 + + # On the GPU, run with + for divisor in [10, 100]: + if epochs % divisor != 0: + steps_per_epoch = divisor // 10 + log.warning( + f"Epochs {epochs} not divisible by {divisor}, using {steps_per_epoch} steps per epoch" + ) + return steps_per_epoch + return 100 + def predict(self, x=None, **kwargs): """Call super().predict with the right input arguments""" x = self._parse_input(x) @@ -193,10 +219,15 @@ def compute_losses(self): out_names = [f"{i}_loss" for i in self.output_names] out_names.insert(0, "loss") + inputs = self._parse_input(None) + # get rid of the repetitions by number of epochs made in perform_fit + for k, v in inputs.items(): + inputs[k] = v[:1] + # Compile a evaluation function @tf.function def losses_fun(): - predictions = self(self._parse_input(None)) + predictions = self(inputs) # If we only have one dataset the output changes if len(out_names) == 2: predictions = [predictions] diff --git a/n3fit/src/n3fit/backends/keras_backend/callbacks.py b/n3fit/src/n3fit/backends/keras_backend/callbacks.py index 05087dd640..911f069e5c 100644 --- a/n3fit/src/n3fit/backends/keras_backend/callbacks.py +++ b/n3fit/src/n3fit/backends/keras_backend/callbacks.py @@ -4,8 +4,12 @@ The callbacks defined in this module can be passed to the ``callbacks`` argument of the ``perform_fit`` method as a list. - For the most typical usage: ``on_epoch_end``, + For the most typical usage: ``on_batch_end``, they must take as input an epoch number and a log of the partial losses. + + Note: the terminology used everywhere refers to a single training step as a single epoch. + It turns out that to avoid tensorflow overhead, it is beneficial to write a step as a + single batch instead. So callbacks must use ``on_batch_end``. """ import logging @@ -18,7 +22,46 @@ log = logging.getLogger(__name__) -class TimerCallback(Callback): +class CallbackStep(Callback): + """ + Wrapper around the keras Callback that keeps track of how the steps are divided + between epochs and batches. + The callback will call ``on_step_end`` instead of ``on_batch_end``. + """ + + def __init__(self): + super().__init__() + self.steps_in_epoch = 0 + self.epochs_finished = 0 + self.steps_per_epoch = 0 # will be defined in the first epoch + self._previous_logs = {} + + def on_epoch_end(self, epoch, logs=None): + if self.steps_per_epoch == 0: + self.steps_per_epoch = self.steps_in_epoch + self.steps_in_epoch = 0 + self.epochs_finished += 1 + + def on_batch_end(self, batch, logs=None): + step_number = self.steps_in_epoch + self.epochs_finished * self.steps_per_epoch + self.on_step_end(step_number, logs) + self.steps_in_epoch += 1 + + def correct_logs(self, logs: dict) -> dict: + """ + The logs that get computed by default are an average over batches. + This converts it into the logs for the current step. + """ + corrected_logs = {} + for k in logs.keys(): + previous_total = self._previous_logs.get(k, 0.0) * self.steps_in_epoch + current_total = logs[k] * (self.steps_in_epoch + 1) + corrected_logs[k] = current_total - previous_total + self._previous_logs = logs + return corrected_logs + + +class TimerCallback(CallbackStep): """Callback to be used during debugging to time the fit""" def __init__(self, count_range=100): @@ -30,7 +73,7 @@ def __init__(self, count_range=100): self.starting_time = None self.last_time = 0 - def on_epoch_end(self, epoch, logs=None): + def on_step_end(self, epoch, logs=None): """At the end of every epoch it checks the time""" new_time = time() if epoch == 0: @@ -57,7 +100,7 @@ def on_train_end(self, logs=None): log.info(f"> > > Total time: {total_time/60:.5} min") -class StoppingCallback(Callback): +class StoppingCallback(CallbackStep): """ Given a ``stopping_object``, the callback will monitor the validation chi2 and will stop the training model when the conditions given by ``stopping_object`` @@ -77,7 +120,7 @@ def __init__(self, stopping_object, log_freq=100): self.log_freq = log_freq self.stopping_object = stopping_object - def on_epoch_end(self, epoch, logs=None): + def on_step_end(self, epoch, logs=None): """Function to be called at the end of every epoch Every ``log_freq`` number of epochs, the ``monitor_chi2`` method of the ``stopping_object`` will be called and the validation loss (broken down by experiment) will be logged. @@ -85,6 +128,7 @@ def on_epoch_end(self, epoch, logs=None): """ print_stats = ((epoch + 1) % self.log_freq) == 0 # Note that the input logs correspond to the fit before the weights are updated + logs = self.correct_logs(logs) self.stopping_object.monitor_chi2(logs, epoch, print_stats=print_stats) if self.stopping_object.stop_here(): self.model.stop_training = True @@ -97,7 +141,7 @@ def on_train_end(self, logs=None): self.stopping_object.make_stop() -class LagrangeCallback(Callback): +class LagrangeCallback(CallbackStep): """ Updates the given datasets with its respective multipliers each ``update_freq`` epochs @@ -137,7 +181,7 @@ def _update_weights(self): for w in ws: w.assign(w * multiplier) - def on_epoch_end(self, epoch, logs=None): + def on_step_end(self, epoch, logs=None): """Function to be called at the end of every epoch""" if (epoch + 1) % self.update_freq == 0: self._update_weights() diff --git a/n3fit/src/n3fit/stopping.py b/n3fit/src/n3fit/stopping.py index dec9236b55..e2f11a2579 100644 --- a/n3fit/src/n3fit/stopping.py +++ b/n3fit/src/n3fit/stopping.py @@ -496,12 +496,12 @@ def print_current_stats(self, epoch, fitstate): """ epoch_index = epoch + 1 vl_chi2 = fitstate.total_vl_chi2() - total_str = f"""Epoch {epoch_index}/{self.total_epochs}: loss: {fitstate.tr_loss:.7f} -Validation loss after training step: {vl_chi2:.7f}. -Validation chi2s: """ + total_str = f"Epoch {epoch_index}/{self.total_epochs}: loss: {fitstate.tr_loss:.7f}" + total_str += f"\nValidation loss after training step: {vl_chi2:.7f}." # The partial chi2 makes no sense for more than one replica at once: if self._n_replicas == 1: + total_str += "\nValidation chi2s: " partial_vl_chi2 = fitstate.total_partial_vl_chi2() partials = [] for experiment, chi2 in partial_vl_chi2.items(): From c0d1ad363bf5c82289166ab3421795ca594830d8 Mon Sep 17 00:00:00 2001 From: juacrumar Date: Thu, 6 Jun 2024 15:46:37 +0200 Subject: [PATCH 3/5] rebase + small changes --- .../n3fit/backends/keras_backend/MetaModel.py | 25 +++++++++---------- 1 file changed, 12 insertions(+), 13 deletions(-) diff --git a/n3fit/src/n3fit/backends/keras_backend/MetaModel.py b/n3fit/src/n3fit/backends/keras_backend/MetaModel.py index 9ae7356076..ea0136f655 100644 --- a/n3fit/src/n3fit/backends/keras_backend/MetaModel.py +++ b/n3fit/src/n3fit/backends/keras_backend/MetaModel.py @@ -166,7 +166,7 @@ def perform_fit(self, x=None, y=None, epochs=1, **kwargs): y = self.target_tensors # Avoids Tensorflow overhead that happens at every epoch, by putting multiple steps in an epoch - steps_per_epoch = self.determine_steps_per_epoch(epochs) + steps_per_epoch = self._determine_steps_per_epoch(epochs) for k, v in x_params.items(): x_params[k] = tf.repeat(v, steps_per_epoch, axis=0) @@ -178,20 +178,19 @@ def perform_fit(self, x=None, y=None, epochs=1, **kwargs): loss_dict = history.history return loss_dict - def determine_steps_per_epoch(self, epochs): - num_replicas = self.output_shape[0][0] - # in this case we're most likely running on the CPU and this is not worth it - if num_replicas == 1: + def _determine_steps_per_epoch(self, epochs): + """Determine how many step to run in every epoch. + When running a single replica (CPU) or when the number of epochs is < 100 default to 1. + Otherwise run 100 step per epoch. + + If the number of epochs requested is not divisible by 100 there will be a number + of extra training epochs being run equal to max_epochs % 100 in the worst case. + + """ + num_replicas = self.output_shape[0] + if num_replicas == 1 or epochs < 100: return 1 - # On the GPU, run with - for divisor in [10, 100]: - if epochs % divisor != 0: - steps_per_epoch = divisor // 10 - log.warning( - f"Epochs {epochs} not divisible by {divisor}, using {steps_per_epoch} steps per epoch" - ) - return steps_per_epoch return 100 def predict(self, x=None, **kwargs): From 3bf822f3b8e75947d08f7fb39d1ab1229eeede75 Mon Sep 17 00:00:00 2001 From: Redo regressions bot Date: Mon, 15 Jul 2024 09:23:55 +0000 Subject: [PATCH 4/5] Automatically regenerated regressions from PR 1939, branch avoid-idle-gpu. --- .../regression_fits/central_16.exportgrid | 1152 ++++++++-------- extra_tests/regression_fits/central_16.json | 50 +- .../regression_fits/diagonal_45.exportgrid | 1140 ++++++++-------- extra_tests/regression_fits/diagonal_45.json | 50 +- .../feature_scaling_81.exportgrid | 1146 ++++++++-------- .../regression_fits/feature_scaling_81.json | 50 +- .../regression_fits/flavour_29.exportgrid | 930 ++++++------- extra_tests/regression_fits/flavour_29.json | 50 +- .../regression_fits/no_csr_613.exportgrid | 1090 ++++++++-------- extra_tests/regression_fits/no_csr_613.json | 50 +- .../regression_fits/no_lagrange_27.exportgrid | 1144 ++++++++-------- .../regression_fits/no_lagrange_27.json | 52 +- .../regression_fits/no_msr_92.exportgrid | 1022 +++++++-------- extra_tests/regression_fits/no_msr_92.json | 50 +- .../regression_fits/no_sumrules_18.exportgrid | 1068 +++++++-------- .../regression_fits/no_sumrules_18.json | 50 +- .../regression_fits/no_vsr_54.exportgrid | 1142 ++++++++-------- extra_tests/regression_fits/no_vsr_54.json | 50 +- .../regression_fits/normal_fit_72.exportgrid | 1154 ++++++++--------- .../regression_fits/normal_fit_72.json | 54 +- .../regression_fits/polarized_evol_34.json | 24 +- .../single_dense_316.exportgrid | 1038 +++++++-------- .../regression_fits/single_dense_316.json | 50 +- .../trainable_prepro_61.exportgrid | 952 +++++++------- .../regression_fits/trainable_prepro_61.json | 50 +- 25 files changed, 6804 insertions(+), 6804 deletions(-) diff --git a/extra_tests/regression_fits/central_16.exportgrid b/extra_tests/regression_fits/central_16.exportgrid index 2017bd1e08..8a1c7ad742 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, -2.3123128414154053, 0.09169159829616547, 0.06518644094467163, 0.06523359566926956, - -16.369422912597656, 0.06538408994674683, 0.06533841788768768, 0.09145141392946243, - -2.3123133182525635, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.239423990249634, 0.09355919808149338, 0.06599249690771103, 0.06604615598917007, - -16.38018035888672, 0.06621259450912476, 0.06616071611642838, 0.09329568594694138, - 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"start": 0.0, - "replica_set": 0.23228165799999978, - "replica_fitted": 31.299169341000002, - "replica_set_to_replica_fitted": 31.066887683000004 + "replica_set": 0.23454409999999992, + "replica_fitted": 12.633997697, + "replica_set_to_replica_fitted": 12.399453597 } }, "version": { - "keras": "3.3.3", - "tensorflow": "2.16.1, mkl=False", + "keras": "3.4.1", + "tensorflow": "2.17.0, mkl=False", "numpy": "1.26.4", - "nnpdf": "4.0.9.post910.dev0+670d39e81", - "validphys": "4.0.9.post910.dev0+670d39e81" + "nnpdf": "4.0.9.post1053.dev0+e72ce3cc8", + "validphys": "4.0.9.post1053.dev0+e72ce3cc8" } } diff --git a/extra_tests/regression_fits/diagonal_45.exportgrid b/extra_tests/regression_fits/diagonal_45.exportgrid index 5bd55b2930..2508b9ff32 100644 --- a/extra_tests/regression_fits/diagonal_45.exportgrid +++ b/extra_tests/regression_fits/diagonal_45.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, 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+51,45 @@ ], "stop_epoch": 1100, "best_epoch": 1099, - "erf_tr": 3.7433674335479736, - "erf_vl": 3.06573224067688, - "chi2": 2.1767020225524902, + "erf_tr": 3.7433669567108154, + "erf_vl": 3.0657339096069336, + "chi2": 2.176706075668335, "pos_state": "POS_VETO", "arc_lengths": [ - 1.3174401174899257, - 1.5070530572454897, - 1.1761948219147023, - 1.0013647421970557, - 1.2246977365119018 + 1.31744014798244, + 1.5070503935489326, + 1.1761950076336678, + 1.0013647217831656, + 1.2246978602489684 ], "integrability": [ - 0.0008957563404708435, - 0.0008957563404695668, - 4.806763138198722e-05, - 0.4638940915465348, - 0.007004071318077343 + 0.0008957566533373817, + 0.0008957566533358552, + 4.806766594289691e-05, + 0.463894024491309, + 0.007004055194555159 ], "timing": { "walltime": { - "Total": 29.147084712982178, + "Total": 11.215790271759033, "start": 0.0, - "replica_set": 0.2392897605895996, - "replica_fitted": 29.146868228912354, - "replica_set_to_replica_fitted": 28.907578468322754 + 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-6.4617939905115e-13, 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 replica: 29 diff --git a/extra_tests/regression_fits/flavour_29.json b/extra_tests/regression_fits/flavour_29.json index 52a514f4f9..7721988ce9 100644 --- a/extra_tests/regression_fits/flavour_29.json +++ b/extra_tests/regression_fits/flavour_29.json @@ -51,45 +51,45 @@ ], "stop_epoch": 181, "best_epoch": 69, - "erf_tr": 4.977456092834473, - "erf_vl": 5.562390327453613, - "chi2": 3.896981954574585, + "erf_tr": 4.977453231811523, + "erf_vl": 5.5623860359191895, + "chi2": 3.8969779014587402, "pos_state": "POS_PASS", "arc_lengths": [ - 1.1047281554636061, - 1.2121893672897062, - 0.9906464941994304, - 0.9904916213693944, - 0.9907080935720707 + 1.1047281768455546, + 1.2121895138128305, + 0.9906464943638001, + 0.9904916210719534, + 0.9907080933270742 ], "integrability": [ - 0.4676448851823809, - 0.46764488518238057, - 0.0809117034077646, - 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"nnpdf": "4.0.9.post910.dev0+670d39e81", - "validphys": "4.0.9.post910.dev0+670d39e81" + "nnpdf": "4.0.9.post1053.dev0+e72ce3cc8", + "validphys": "4.0.9.post1053.dev0+e72ce3cc8" } } diff --git a/extra_tests/regression_fits/no_csr_613.exportgrid b/extra_tests/regression_fits/no_csr_613.exportgrid index daca363077..6a8cd6731b 100644 --- a/extra_tests/regression_fits/no_csr_613.exportgrid +++ b/extra_tests/regression_fits/no_csr_613.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, -1.4231513738632202, 1.0134621858596802, 1.0247423648834229, 1.0036934614181519, - -18.559612274169922, 1.0032085180282593, 1.0242578983306885, 1.0143146514892578, - -1.4230964183807373, 0.0, 0.0, 0.0] -- [0.0, 0.0, -1.3680644035339355, 0.9786251783370972, 0.9903812408447266, 0.968502402305603, - -18.337993621826172, 0.9679771661758423, 0.9898567199707031, 0.9795448780059814, +- [0.0, 0.0, -1.4231514930725098, 1.0134623050689697, 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b/extra_tests/regression_fits/no_csr_613.json index 2fc2d91e09..f2a7a525a0 100644 --- a/extra_tests/regression_fits/no_csr_613.json +++ b/extra_tests/regression_fits/no_csr_613.json @@ -57,45 +57,45 @@ ], "stop_epoch": 1100, "best_epoch": 1099, - "erf_tr": 50.92805862426758, - "erf_vl": 50.83631134033203, - "chi2": 29.386581420898438, + "erf_tr": 50.92805480957031, + "erf_vl": 50.836299896240234, + "chi2": 29.386579513549805, "pos_state": "POS_VETO", "arc_lengths": [ - 0.9916593109635676, - 1.8120037933717692, - 1.3579849560807138, - 1.0247125754536226, - 1.3362143325718208 + 0.9916593111240226, + 1.8120030443463793, + 1.3579847509208942, + 1.0247126070492014, + 1.3362150500155137 ], "integrability": [ - 0.0005673111809293141, - 0.002806156349833805, - 1.8679652213832165e-05, - 0.0024792357871781245, - 0.017847439507023055 + 0.0005673126142922702, + 0.0028061565826644486, + 1.8679654999048667e-05, + 0.002479236049112543, + 0.017847452545539044 ], "timing": { "walltime": { - "Total": 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diff --git a/extra_tests/regression_fits/no_lagrange_27.exportgrid b/extra_tests/regression_fits/no_lagrange_27.exportgrid index 0b2a127488..ddaa672ee5 100644 --- a/extra_tests/regression_fits/no_lagrange_27.exportgrid +++ b/extra_tests/regression_fits/no_lagrange_27.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, -2.2586166858673096, 0.03175506740808487, 0.00455773901194334, 0.004606186877936125, - -16.76580238342285, 0.004760659299790859, 0.004713739268481731, 0.0315084271132946, - -2.2586166858673096, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.187445640563965, 0.035123374313116074, 0.006836988031864166, 0.006892076693475246, - -16.77768898010254, 0.007062880787998438, 0.007009631954133511, 0.03485282510519028, - -2.187445640563965, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.1185190677642822, 0.03863407298922539, 0.009213710203766823, 0.009276362136006355, - -16.788166046142578, 0.009465261362493038, 0.00940482597798109, 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18 diff --git a/extra_tests/regression_fits/no_sumrules_18.json b/extra_tests/regression_fits/no_sumrules_18.json index 1a66d31fbb..074dc6e214 100644 --- a/extra_tests/regression_fits/no_sumrules_18.json +++ b/extra_tests/regression_fits/no_sumrules_18.json @@ -51,45 +51,45 @@ ], "stop_epoch": 1100, "best_epoch": 1099, - "erf_tr": 5.234591484069824, - "erf_vl": 5.933958530426025, - "chi2": 3.2721149921417236, + "erf_tr": 5.234589576721191, + "erf_vl": 5.933957099914551, + "chi2": 3.2721152305603027, "pos_state": "POS_PASS", "arc_lengths": [ - 1.3377847418174296, - 0.9938499771604175, - 1.0016840281484805, - 0.9905686298415103, - 1.0071484675459588 + 1.3377848138904636, + 0.9938499753341616, + 1.0016840225668207, + 0.990568630161653, + 1.0071484433088025 ], "integrability": [ - 0.00022827064094605581, - 0.00022827064094606275, - 1.0907395562656519e-05, - 0.47761861234903324, - 0.001831851579481382 + 0.0002282706336703827, + 0.0002282706336700635, + 1.0907395790099583e-05, + 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"4.0.9.post1053.dev0+e72ce3cc8", + "validphys": "4.0.9.post1053.dev0+e72ce3cc8" } } diff --git a/extra_tests/regression_fits/no_vsr_54.exportgrid b/extra_tests/regression_fits/no_vsr_54.exportgrid index d07089ddac..c643053359 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, -2.3508851528167725, 0.2019844502210617, 0.17533043026924133, 0.17537681758403778, - -16.43580436706543, 0.17541448771953583, 0.17536841332912445, 0.20192338526248932, - -2.3508851528167725, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.276609182357788, 0.2007421851158142, 0.1730288565158844, 0.17308156192302704, - -16.444120407104492, 0.17312322556972504, 0.17307090759277344, 0.20067520439624786, - -2.276609182357788, 0.0, 0.0, 0.0] -- [0.0, 0.0, -2.204685926437378, 0.19979439675807953, 0.1709788739681244, 0.17103877663612366, - 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+ "Total": 20.258057272, "start": 0.0, - "replica_set": 0.22130740499999924, - "replica_fitted": 88.582529111, - "replica_set_to_replica_fitted": 88.36122170600001 + "replica_set": 0.23266654400000064, + "replica_fitted": 20.25779319, + "replica_set_to_replica_fitted": 20.025126646 } }, "version": { - "keras": "3.3.3", - "tensorflow": "2.16.1, mkl=False", + "keras": "3.4.1", + "tensorflow": "2.17.0, mkl=False", "numpy": "1.26.4", - "nnpdf": "4.0.9.post910.dev0+670d39e81", - "validphys": "4.0.9.post910.dev0+670d39e81" + "nnpdf": "4.0.9.post1053.dev0+e72ce3cc8", + "validphys": "4.0.9.post1053.dev0+e72ce3cc8" } } diff --git a/extra_tests/regression_fits/polarized_evol_34.json b/extra_tests/regression_fits/polarized_evol_34.json index 1165450546..c86165a711 100644 --- a/extra_tests/regression_fits/polarized_evol_34.json +++ b/extra_tests/regression_fits/polarized_evol_34.json @@ -47,25 +47,25 @@ ], "timing": { "walltime": { - "Total": 16.877962350845337, + "Total": 8.326492309570312, 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a/extra_tests/regression_fits/single_dense_316.exportgrid b/extra_tests/regression_fits/single_dense_316.exportgrid index 2aaaa1825b..ee039f4e0d 100644 --- a/extra_tests/regression_fits/single_dense_316.exportgrid +++ b/extra_tests/regression_fits/single_dense_316.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, 0.6249072551727295, 0.9272689819335938, 0.9285414814949036, 0.9285261034965515, - 1.0354628562927246, 0.9288173317909241, 0.9288352131843567, 0.9272533655166626, - 0.6249072551727295, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.6072400212287903, 0.9036694169044495, 0.9050180315971375, 0.9050007462501526, - 1.0520607233047485, 0.9053239822387695, 0.9053443074226379, 0.9036529064178467, - 0.6072400212287903, 0.0, 0.0, 0.0] -- [0.0, 0.0, 0.5900651216506958, 0.8806789517402649, 0.8821075558662415, 0.8820883631706238, - 1.068817138671875, 0.8824471831321716, 0.8824701905250549, 0.88066166639328, 0.5900652408599854, 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a/extra_tests/regression_fits/single_dense_316.json +++ b/extra_tests/regression_fits/single_dense_316.json @@ -51,45 +51,45 @@ ], "stop_epoch": 1100, "best_epoch": 1099, - "erf_tr": 15.515103340148926, - "erf_vl": 16.148271560668945, - "chi2": 11.207619667053223, + "erf_tr": 15.51508903503418, + "erf_vl": 16.14827537536621, + "chi2": 11.207615852355957, "pos_state": "POS_VETO", "arc_lengths": [ - 1.0478482151392081, - 1.2771204978797341, - 1.0363260132325502, - 1.0001806040609758, - 1.0378799286869274 + 1.0478482082378506, + 1.2771206199216336, + 1.0363260020464442, + 1.0001806126564081, + 1.037880031773835 ], "integrability": [ - 0.005737940140534026, - 0.00573794014053397, - 7.455026388958341e-05, - 0.032392028253525273, - 0.00586189312162011 + 0.0057379395002511435, + 0.0057379395002497, + 7.455025911473623e-05, + 0.032392028719186894, + 0.005861893761903825 ], "timing": { "walltime": { - "Total": 30.10720419883728, + "Total": 10.993557214736938, "start": 0.0, - "replica_set": 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b/extra_tests/regression_fits/trainable_prepro_61.exportgrid index 266803857b..19879d10a7 100644 --- a/extra_tests/regression_fits/trainable_prepro_61.exportgrid +++ b/extra_tests/regression_fits/trainable_prepro_61.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, -2.007188558578491, 0.6650367975234985, 0.664764940738678, 0.6647703051567078, - -15.805450439453125, 0.6731981039047241, 0.673202395439148, 0.6733973026275635, + -15.805449485778809, 0.6731981039047241, 0.673202395439148, 0.6733973026275635, -2.007188558578491, 0.0, 0.0, 0.0] -- [0.0, 0.0, -1.9466973543167114, 0.6447261571884155, 0.6444252133369446, 0.6444316506385803, - -15.637470245361328, 0.6531933546066284, 0.6531983613967896, 0.653412401676178, - -1.9466973543167114, 0.0, 0.0, 0.0] -- [0.0, 0.0, -1.8882782459259033, 0.6251017451286316, 0.6247685551643372, 0.6247759461402893, - -15.469231605529785, 0.6338850855827332, 0.6338909268379211, 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1.5280262594614311, + 1.003308424887163, + 0.9979762366304027, + 1.4192810768228845 ], "integrability": [ - 0.11098539270460843, - 0.11098539270460639, - 0.00020276380928341275, - 0.008434453979133893, - 0.0018140714964819393 + 0.11098539642989813, + 0.11098539642989663, + 0.00020276380928346827, + 0.008434453862718572, + 0.0018140714964820503 ], "timing": { "walltime": { - "Total": 31.844950675964355, + "Total": 12.758163213729858, "start": 0.0, - "replica_set": 0.23360300064086914, - "replica_fitted": 31.844779014587402, - "replica_set_to_replica_fitted": 31.611176013946533 + "replica_set": 0.23384833335876465, + "replica_fitted": 12.758097887039185, + "replica_set_to_replica_fitted": 12.52424955368042 }, "cputime": { - "Total": 32.889014591, + "Total": 14.366323394000002, "start": 0.0, - "replica_set": 0.23132029399999965, - "replica_fitted": 32.888841699, - "replica_set_to_replica_fitted": 32.657521405 + "replica_set": 0.23262794400000075, + "replica_fitted": 14.366252531, + "replica_set_to_replica_fitted": 14.133624587 } }, "version": { - "keras": "3.3.3", - "tensorflow": "2.16.1, mkl=False", + "keras": "3.4.1", + "tensorflow": "2.17.0, mkl=False", "numpy": "1.26.4", - "nnpdf": "4.0.9.post910.dev0+670d39e81", - "validphys": "4.0.9.post910.dev0+670d39e81" + "nnpdf": "4.0.9.post1053.dev0+e72ce3cc8", + "validphys": "4.0.9.post1053.dev0+e72ce3cc8" } } From 1637dba7886aa7735c00bcddc34b9f3637e80461 Mon Sep 17 00:00:00 2001 From: "Juan M. Cruz-Martinez" Date: Mon, 15 Jul 2024 12:44:38 +0200 Subject: [PATCH 5/5] Update n3fit/src/n3fit/backends/keras_backend/MetaModel.py --- n3fit/src/n3fit/backends/keras_backend/MetaModel.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/n3fit/src/n3fit/backends/keras_backend/MetaModel.py b/n3fit/src/n3fit/backends/keras_backend/MetaModel.py index ea0136f655..d72208761e 100644 --- a/n3fit/src/n3fit/backends/keras_backend/MetaModel.py +++ b/n3fit/src/n3fit/backends/keras_backend/MetaModel.py @@ -181,7 +181,7 @@ def perform_fit(self, x=None, y=None, epochs=1, **kwargs): def _determine_steps_per_epoch(self, epochs): """Determine how many step to run in every epoch. When running a single replica (CPU) or when the number of epochs is < 100 default to 1. - Otherwise run 100 step per epoch. + Otherwise run 100 steps per epoch. If the number of epochs requested is not divisible by 100 there will be a number of extra training epochs being run equal to max_epochs % 100 in the worst case.