From 35a7052b61579cfe8df1a059d4cd3359310ec2d1 Mon Sep 17 00:00:00 2001 From: Roy Hvaara Date: Thu, 5 Jan 2023 19:02:10 +0100 Subject: [PATCH] [NumPy] Remove references to deprecated NumPy type aliases (#21022) [NumPy] Remove references to deprecated NumPy type aliases. This change replaces references to a number of deprecated NumPy type aliases (np.bool, np.int, np.float, np.complex, np.object, np.str) with their recommended replacement (bool, int, float, complex, object, str). NumPy 1.24 drops the deprecated aliases, so we must remove uses before updating NumPy. Co-authored-by: Peter Hawkins Co-authored-by: Peter Hawkins --- examples/legacy/seq2seq/utils.py | 2 +- examples/pytorch/benchmarking/plot_csv_file.py | 4 ++-- examples/research_projects/seq2seq-distillation/utils.py | 2 +- examples/tensorflow/benchmarking/plot_csv_file.py | 4 ++-- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/legacy/seq2seq/utils.py b/examples/legacy/seq2seq/utils.py index 2e0586a269b..e207e4d0dbd 100644 --- a/examples/legacy/seq2seq/utils.py +++ b/examples/legacy/seq2seq/utils.py @@ -371,7 +371,7 @@ def sortish_sampler_indices(data: List, bs: int, shuffle=True) -> np.array: ck_idx = [sort_idx[i : i + sz] for i in range(0, len(sort_idx), sz)] max_ck = np.argmax([key_fn(ck[0]) for ck in ck_idx]) # find the chunk with the largest key, ck_idx[0], ck_idx[max_ck] = ck_idx[max_ck], ck_idx[0] # then make sure it goes first. - sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=np.int) + sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=int) sort_idx = np.concatenate((ck_idx[0], sort_idx)) return sort_idx diff --git a/examples/pytorch/benchmarking/plot_csv_file.py b/examples/pytorch/benchmarking/plot_csv_file.py index 58dc50bb832..1a0ae735d8c 100644 --- a/examples/pytorch/benchmarking/plot_csv_file.py +++ b/examples/pytorch/benchmarking/plot_csv_file.py @@ -132,7 +132,7 @@ class Plot: if self.args.plot_along_batch: y_axis_array = np.asarray( [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results], - dtype=np.int, + dtype=int, ) else: y_axis_array = np.asarray( @@ -144,7 +144,7 @@ class Plot: ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz") ) - x_axis_array = np.asarray(x_axis_array, np.int)[: len(y_axis_array)] + x_axis_array = np.asarray(x_axis_array, int)[: len(y_axis_array)] plt.scatter( x_axis_array, y_axis_array, label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}" ) diff --git a/examples/research_projects/seq2seq-distillation/utils.py b/examples/research_projects/seq2seq-distillation/utils.py index b6994a1831d..a45194e6e05 100644 --- a/examples/research_projects/seq2seq-distillation/utils.py +++ b/examples/research_projects/seq2seq-distillation/utils.py @@ -353,7 +353,7 @@ def sortish_sampler_indices(data: List, bs: int, shuffle=True) -> np.array: ck_idx = [sort_idx[i : i + sz] for i in range(0, len(sort_idx), sz)] max_ck = np.argmax([key_fn(ck[0]) for ck in ck_idx]) # find the chunk with the largest key, ck_idx[0], ck_idx[max_ck] = ck_idx[max_ck], ck_idx[0] # then make sure it goes first. - sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=np.int) + sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=int) sort_idx = np.concatenate((ck_idx[0], sort_idx)) return sort_idx diff --git a/examples/tensorflow/benchmarking/plot_csv_file.py b/examples/tensorflow/benchmarking/plot_csv_file.py index 58dc50bb832..1a0ae735d8c 100644 --- a/examples/tensorflow/benchmarking/plot_csv_file.py +++ b/examples/tensorflow/benchmarking/plot_csv_file.py @@ -132,7 +132,7 @@ class Plot: if self.args.plot_along_batch: y_axis_array = np.asarray( [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results], - dtype=np.int, + dtype=int, ) else: y_axis_array = np.asarray( @@ -144,7 +144,7 @@ class Plot: ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz") ) - x_axis_array = np.asarray(x_axis_array, np.int)[: len(y_axis_array)] + x_axis_array = np.asarray(x_axis_array, int)[: len(y_axis_array)] plt.scatter( x_axis_array, y_axis_array, label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}" )