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# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from nemo.collections.speechlm2.models.salm import replace_placeholders_and_build_targets
def test_replace_placeholders():
# fmt: off
PAD = 0
AUDIO = 100
input_ids = torch.tensor([
[7 , AUDIO, 1, 2 , AUDIO, 1],
[PAD, PAD, 3, AUDIO, 4 , 5] # note: left padding required
])
loss_mask = torch.tensor([
[False, False, False, False, False, True], # predict last token
[False, False, False, False, True , True] # predict last two tokens
])
embeds = torch.ones(2, 6, 2)
embeds[1, :2] = 0 # note: indicate left padding
# 3 embedding sequences with varying shapes, corresponding to 3 AUDIO tokens
replacements = [
torch.full((4, 2), fill_value=2.0),
torch.full((3, 2), fill_value=3.0),
torch.full((2, 2), fill_value=4.0),
]
embeds_r, targets_r, attention_mask_r = replace_placeholders_and_build_targets(
input_ids=input_ids,
embeds=embeds,
padding_id=PAD,
placeholder_id=AUDIO,
replacements=replacements,
target_ids=input_ids.where(loss_mask, -100)
)
assert embeds_r.shape == (2, 11, 2)
# batch item 0
assert (embeds_r[0, 0] == 1.0).all() # 1=orig
assert (embeds_r[0, 1:5] == 2.0).all() # 2=repl
assert (embeds_r[0, 5:7] == 1.0).all() # 1=orig
assert (embeds_r[0, 7:10] == 3.0).all() # 3=repl
assert (embeds_r[0, 10] == 1.0).all() # 1=orig
# batch item 1
assert (embeds_r[1, :6] == 0.0).all() # 0=pad
assert (embeds_r[1, 6:7] == 1.0).all() # 1=orig
assert (embeds_r[1, 7:9] == 4.0).all() # 4=repl
assert (embeds_r[1, 9:] == 1.0).all() # 1=orig
assert targets_r.shape == (2, 11)
torch.testing.assert_close(
targets_r,
torch.tensor([
[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 1],
[-100, -100, -100, -100, -100, -100, -100, -100, -100, 4 , 5],
])
)
assert attention_mask_r.shape == (2, 11)
torch.testing.assert_close(
attention_mask_r,
torch.tensor([
[True, True, True, True, True, True, True, True, True, True, True],
[False, False, False, False, False, False, True, True, True, True, True],
])
)
# fmt: on
def test_replace_placeholders_removes_excessive_left_padding():
# fmt: off
PAD = 0
AUDIO = 100
input_ids = torch.tensor([
[7 , AUDIO, 1 , 2],
[PAD, PAD, AUDIO, 3] # note: left padding required
])
loss_mask = torch.tensor([
[False, False, True , True], # predict last two tokens
[False, False, False, True] # predict last token
])
embeds = torch.ones(2, 4, 2)
embeds[1, :2] = 0 # note: indicate left padding
# 3 embedding sequences with varying shapes, corresponding to 3 AUDIO tokens
replacements = [
torch.full((3, 2), fill_value=2.0),
torch.full((5, 2), fill_value=4.0),
]
embeds_r, targets_r, attention_mask_r = replace_placeholders_and_build_targets(
input_ids=input_ids,
embeds=embeds,
padding_id=PAD,
placeholder_id=AUDIO,
replacements=replacements,
target_ids=input_ids.where(loss_mask, -100)
)
assert embeds_r.shape == (2, 6, 2)
# batch item 0
assert (embeds_r[0, 0 ] == 1.0).all() # 1=orig
assert (embeds_r[0, 1:4] == 2.0).all() # 2=repl
assert (embeds_r[0, 4: ] == 1.0).all() # 1=orig
# batch item 1
assert (embeds_r[1, :5] == 4.0).all() # 4=repl
assert (embeds_r[1, 5 ] == 1.0).all() # 1=orig
assert targets_r.shape == (2, 6)
torch.testing.assert_close(
targets_r,
torch.tensor([
[-100, -100, -100, -100, 1, 2],
[-100, -100, -100, -100, -100, 3],
])
)
assert attention_mask_r.shape == (2, 6)
torch.testing.assert_close(
attention_mask_r,
torch.tensor([
[True, True, True, True, True, True],
[True, True, True, True, True, True],
])
)
# fmt: on
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