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a7c2243 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | # 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.parts.label_prep import maybe_prepend_prompt_tokens
def test_maybe_prepend_prompt_tokens_with_source_tokens():
"""Test that prompt tokens are correctly prepended to all sequences and lengths are updated."""
B, T_src, T_tgt, H = 2, 6, 6, 4
PAD = 0
prompt_len_0, prompt_len_1 = 3, 2
# Prompt token IDs (will be passed through embed_fn)
max_prompt_len = max(prompt_len_0, prompt_len_1)
prompt_tokens = torch.full((B, max_prompt_len), PAD, dtype=torch.long)
prompt_tokens[0, :prompt_len_0] = torch.tensor([10, 11, 12])
prompt_tokens[1, :prompt_len_1] = torch.tensor([20, 21])
# Use a simple embedding: token_id -> [token_id, token_id, token_id, token_id]
def embed_fn(token_ids):
return token_ids.unsqueeze(-1).expand(-1, -1, H).float()
# Source encoded (audio features)
source_encoded = torch.arange(B * T_src * H).reshape(B, T_src, H).float()
source_encoded_lens = torch.tensor([5, 4])
# Target tokens
target_tokens = torch.tensor(
[
[1, 100, 101, 102, 2, 0], # BOS, tokens, EOS, PAD
[1, 200, 201, 2, 0, 0],
]
)
target_token_lens = torch.tensor([5, 4])
# Source tokens (for ASR head)
source_tokens = torch.tensor(
[
[1, 50, 51, 52, 2, 0],
[1, 60, 61, 2, 0, 0],
]
)
source_token_lens = torch.tensor([5, 4])
batch = {
"prompt_tokens": prompt_tokens,
"prompt_token_lens": torch.tensor([prompt_len_0, prompt_len_1]),
"target_tokens": target_tokens,
"target_token_lens": target_token_lens,
"source_tokens": source_tokens,
"source_token_lens": source_token_lens,
}
new_source_encoded, new_source_encoded_lens, new_target_tokens = maybe_prepend_prompt_tokens(
batch=batch,
embed_fn=embed_fn,
source_encoded=source_encoded,
source_encoded_lens=source_encoded_lens,
text_pad_id=PAD,
)
# Check output shapes are extended by max_prompt_len
assert new_source_encoded.shape == (B, max_prompt_len + T_src, H)
assert new_target_tokens.shape == (B, max_prompt_len + T_tgt)
assert batch["source_tokens"].shape == (B, max_prompt_len + T_tgt)
# Check lengths are updated: original_len + prompt_len
assert new_source_encoded_lens[0].item() == 5 + prompt_len_0
assert new_source_encoded_lens[1].item() == 4 + prompt_len_1
assert batch["target_token_lens"][0].item() == 5 + prompt_len_0
assert batch["target_token_lens"][1].item() == 4 + prompt_len_1
assert batch["source_token_lens"][0].item() == 5 + prompt_len_0
assert batch["source_token_lens"][1].item() == 4 + prompt_len_1
# Check prompt embeddings are at the beginning of source_encoded
# embed_fn maps token_id -> [token_id]*H, so prompt region should match
for h in range(H):
assert new_source_encoded[0, 0, h].item() == 10.0
assert new_source_encoded[0, 1, h].item() == 11.0
assert new_source_encoded[0, 2, h].item() == 12.0
assert new_source_encoded[1, 0, h].item() == 20.0
assert new_source_encoded[1, 1, h].item() == 21.0
# Check original audio features follow the prompt
for t in range(5): # source_encoded_lens[0] was 5
assert torch.equal(new_source_encoded[0, prompt_len_0 + t], source_encoded[0, t])
for t in range(4): # source_encoded_lens[1] was 4
assert torch.equal(new_source_encoded[1, prompt_len_1 + t], source_encoded[1, t])
# Check target tokens are shifted by prompt_len
assert new_target_tokens[0, :prompt_len_0].tolist() == [PAD] * prompt_len_0
assert new_target_tokens[0, prompt_len_0 : prompt_len_0 + 5].tolist() == [1, 100, 101, 102, 2]
assert new_target_tokens[1, :prompt_len_1].tolist() == [PAD] * prompt_len_1
assert new_target_tokens[1, prompt_len_1 : prompt_len_1 + 4].tolist() == [1, 200, 201, 2]
# Check source tokens are shifted by prompt_len
assert batch["source_tokens"][0, :prompt_len_0].tolist() == [PAD] * prompt_len_0
assert batch["source_tokens"][0, prompt_len_0 : prompt_len_0 + 5].tolist() == [1, 50, 51, 52, 2]
assert batch["source_tokens"][1, :prompt_len_1].tolist() == [PAD] * prompt_len_1
assert batch["source_tokens"][1, prompt_len_1 : prompt_len_1 + 4].tolist() == [1, 60, 61, 2]
def test_maybe_prepend_prompt_tokens_no_prompt():
"""Test that without prompt_tokens in batch, inputs are returned unchanged."""
B, T_src, H = 1, 4, 4
source_encoded = torch.randn(B, T_src, H)
source_encoded_lens = torch.tensor([3])
target_tokens = torch.tensor([[1, 100, 2, 0]])
batch = {
"target_tokens": target_tokens,
"target_token_lens": torch.tensor([3]),
}
out_encoded, out_lens, out_tokens = maybe_prepend_prompt_tokens(
batch=batch,
embed_fn=lambda x: x.unsqueeze(-1).expand(-1, -1, H).float(),
source_encoded=source_encoded,
source_encoded_lens=source_encoded_lens,
text_pad_id=0,
)
assert torch.equal(out_encoded, source_encoded)
assert torch.equal(out_lens, source_encoded_lens)
assert torch.equal(out_tokens, target_tokens)
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