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[NumPy] Remove references to deprecated NumPy type aliases (#21022)

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[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 <phawkins@google.com>

Co-authored-by: Peter Hawkins <phawkins@google.com>
This commit is contained in:
Roy Hvaara 2023-01-05 19:02:10 +01:00 committed by GitHub
parent 1d21471c78
commit 35a7052b61
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 6 additions and 6 deletions
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2
examples/legacy/seq2seq/utils.py
View File

@ -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

4
examples/pytorch/benchmarking/plot_csv_file.py
View File

@ -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}"
)

2
examples/research_projects/seq2seq-distillation/utils.py
View File

@ -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

4
examples/tensorflow/benchmarking/plot_csv_file.py
View File

@ -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}"
)