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[Whisper] patch float type on mps (#35295)

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* fix float type on mps

* make
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eustlb 2024-12-16 16:52:47 +01:00 committed by GitHub
parent d5b81e1ca1
commit 9feae5fb01
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1 changed files with 13 additions and 4 deletions
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17
src/transformers/models/whisper/generation_whisper.py
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@ -632,7 +632,9 @@ class WhisperGenerationMixin(GenerationMixin):
cur_bsz=cur_bsz,
batch_idx_map=batch_idx_map,
)
time_offset = seek.to(torch.float64) * time_precision / input_stride
time_offset = (
seek.to(torch.float32 if device.type == "mps" else torch.float64) * time_precision / input_stride
)
seek_num_frames = (max_frames - seek).clamp(max=num_segment_frames)
# 6.2 cut out next 30s segment from input features
@ -1805,6 +1807,7 @@ class WhisperGenerationMixin(GenerationMixin):
timestamp_segment_indices = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0]
timestamp_segment_indices.add_(1)
token_timestamps = seek_outputs[idx]["token_timestamps"] if return_token_timestamps else []
device = seek_sequence.device
# If whisper predicted a "end of segment" via a timestep token, let's go ever each
# "end of segment" prediction and slice the decoding into segments accordingly
@ -1828,8 +1831,12 @@ class WhisperGenerationMixin(GenerationMixin):
end_timestamp_pos = sliced_tokens[idx_sliced_tokens] - timestamp_begin
segments.append(
{
"start": time_offset[prev_idx] + start_timestamp_pos.to(torch.float64) * time_precision,
"end": time_offset[prev_idx] + end_timestamp_pos.to(torch.float64) * time_precision,
"start": time_offset[prev_idx]
+ start_timestamp_pos.to(torch.float32 if device.type == "mps" else torch.float64)
* time_precision,
"end": time_offset[prev_idx]
+ end_timestamp_pos.to(torch.float32 if device.type == "mps" else torch.float64)
* time_precision,
"tokens": sliced_tokens,
"result": seek_outputs[idx],
}
@ -1856,7 +1863,9 @@ class WhisperGenerationMixin(GenerationMixin):
last_timestamp_pos = int(seek_num_frames[prev_idx] * time_precision_features / time_precision)
if timestamps.numel() > 0 and timestamps[-1] != timestamp_begin:
# no consecutive timestamps but it has a timestamp; use the last one.
last_timestamp_pos = (timestamps[-1] - timestamp_begin).to(torch.float64)
last_timestamp_pos = (timestamps[-1] - timestamp_begin).to(
torch.float32 if device.type == "mps" else torch.float64
)
segments = [
{
"start": time_offset[prev_idx],