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Reduce the latency by increase the batch size for vision transformer #603

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Reduce the latency by increase the batch size for vision transformer #603

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25 changes: 16 additions & 9 deletions src/openpi/models/pi0.py
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Original file line number Diff line number Diff line change
Expand Up @@ -179,19 +179,25 @@ def embed_prefix(
ar_mask = []
tokens = []
# embed images
for name in obs.images:
image_tokens, _ = self.PaliGemma.img(obs.images[name], train=False)
# batched:
image_names = list(obs.images.keys())
stacked_images = jnp.stack(list(obs.images.values()), axis=1)

batch_size, num_cams = stacked_images.shape[:2]
reshaped_images = stacked_images.reshape(-1, *stacked_images.shape[2:])

all_image_tokens, _ = self.PaliGemma.img(reshaped_images, train=False)
all_image_tokens = all_image_tokens.reshape(batch_size, num_cams, all_image_tokens.shape[1], -1)

for i, name in enumerate(image_names):
image_tokens = all_image_tokens[:, i]
tokens.append(image_tokens)
input_mask.append(
einops.repeat(
obs.image_masks[name],
"b -> b s",
s=image_tokens.shape[1],
)
einops.repeat(obs.image_masks[name], "b -> b s", s=image_tokens.shape[1])
)
# image tokens attend to each other
ar_mask += [False] * image_tokens.shape[1]

# Set attention masks
ar_mask = [False] * (image_tokens.shape[1] * len(image_names))

# add language (aka tokenized inputs)
if obs.tokenized_prompt is not None:
Expand Down Expand Up @@ -323,3 +329,4 @@ def cond(carry):

x_0, _ = jax.lax.while_loop(cond, step, (noise, 1.0))
return x_0