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Support offloading encode, for generate() with much less VRAM #269

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Support offloading encode, for generate() with much less VRAM #269

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40 changes: 27 additions & 13 deletions src/dalle_mini/model/modeling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1612,6 +1612,7 @@ def generate(
condition_scale: Optional[float] = 1.0,
input_ids_uncond: Optional[jnp.ndarray] = None,
attention_mask_uncond: Optional[jnp.ndarray] = None,
model_kwargs_uncond: Optional[Dict[str, jnp.ndarray]] = None,
**model_kwargs,
):
"""Edit: Allow super conditioning."""
Expand Down Expand Up @@ -1651,33 +1652,46 @@ def generate(
params,
{"attention_mask": attention_mask, **model_kwargs_input},
)
if condition_scale != 1.0:
assert (
input_ids_uncond is not None
), "`input_ids_uncond` has to be defined for super conditioning."
assert (
do_sample is True
), "`do_sample` has to be True for super conditioning."
assert (
num_beams == 1
), "`num_beams` has to be 1 for super conditioning."
else:
# model_kwargs was passed in, doesn't include named parameter yet
model_kwargs["attention_mask"] = attention_mask
if condition_scale != 1.0:
model_kwargs_uncond = model_kwargs_uncond or {}
if model_kwargs_uncond.get("encoder_outputs") is None:
model_kwargs_uncond = (
self._prepare_encoder_decoder_kwargs_for_generation(
input_ids_uncond,
params,
{
"attention_mask": attention_mask_uncond,
**model_kwargs_input,
**model_kwargs_uncond,
},
)
)
else:
model_kwargs_uncond = None
# since the null prompt is usually constant, allow passing only one for the whole batch
model_kwargs_uncond = jax.tree_util.tree_map(
lambda x, y: jnp.broadcast_to(x, y.shape),
model_kwargs_uncond,
model_kwargs,
)
else:
model_kwargs_uncond = None

# prepare decoder_input_ids for generation
input_ids = (
jnp.ones((input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
)

if condition_scale != 1.0:
assert (input_ids_uncond is not None) or (
model_kwargs_uncond is not None
and model_kwargs_uncond.get("encoder_outputs") is not None
), '`input_ids_uncond` or `model_kwargs_uncond["encoder_outputs"]` has to be defined for super conditioning.'
assert (
do_sample is True
), "`do_sample` has to be True for super conditioning."
assert num_beams == 1, "`num_beams` has to be 1 for super conditioning."

if not do_sample and num_beams == 1:
logits_processor = self._get_logits_processor(
no_repeat_ngram_size,
Expand Down