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DiT now supports sequence conditions. #923

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merged 1 commit into from
Jan 14, 2025
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DiT now supports sequence conditions. #923

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71 changes: 46 additions & 25 deletions axlearn/common/dit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,8 @@

from typing import Optional, Union

import chex
import einops
import jax
import jax.numpy as jnp

Expand All @@ -31,7 +33,21 @@


def modulate(*, x, shift, scale):
return x * (1 + jnp.expand_dims(scale, 1)) + jnp.expand_dims(shift, 1)
"""Modulates the input x tensor.

Note: shift and scale must have the same shape.

Args:
x: input tensor with shape [batch_size, num_length, input_dim].
shift: shifting the norm tensor with shape [batch_size, 1|num_length, input_dim].
scale: scaling the norm tensor with shape [batch_size, 1|num_length, input_dim].

Returns:
A tensor with shape [batch_size, num_length, input_dim].
"""
chex.assert_equal_shape((shift, scale))
chex.assert_equal_rank((x, shift, scale))
return x * (1 + scale) + shift


class TimeStepEmbedding(BaseLayer):
Expand Down Expand Up @@ -211,15 +227,18 @@ def forward(self, input: Tensor) -> Tensor:
"""Generate the parameters for modulation.

Args:
input: A tensor with shape [batch_size, ..., dim].
input: A tensor with shape [batch_size, dim] or [batch_size, num_length, dim].

Returns:
A list of tensors with length num_outputs.
Each tensor has shape [batch_size, ..., dim].
Each tensor has shape [batch_size, 1|num_length, dim].
"""
cfg = self.config
x = get_activation_fn(cfg.activation)(input)
output = self.linear(x)
assert output.ndim in (2, 3)
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Raise a ValueError instead of assert (which should only be used to enforce internal logic errors and cannot be triggered by user error).

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As I clicked automerge, this your comment was not handled. I submitted #981 to handle it.

if output.ndim == 2:
output = einops.rearrange(output, "b d -> b 1 d")
output = jnp.split(output, cfg.num_outputs, axis=-1)
return output

Expand Down Expand Up @@ -292,14 +311,16 @@ def forward(self, *, input: Tensor, shift: Tensor, scale: Tensor, gate: Tensor)

Args:
input: input tensor with shape [batch_size, num_length, input_dim].
shift: shifting the norm tensor with shape [batch_size, input_dim].
scale: scaling the norm tensor with shape [batch_size, input_dim].
shift: shifting the norm tensor with shape [batch_size, 1|num_length, input_dim].
scale: scaling the norm tensor with shape [batch_size, 1|num_length, input_dim].
gate: applying before the residual addition with shape
[batch_size, input_dim].
[batch_size, 1|num_length, input_dim].

Returns:
A tensor with shape [batch_size, num_length, input_dim].
"""
chex.assert_equal_shape((shift, scale, gate))
chex.assert_equal_rank((input, shift))
cfg = self.config
remat_pt1 = "linear1_0"
remat_pt2 = "linear2"
Expand All @@ -325,7 +346,7 @@ def forward(self, *, input: Tensor, shift: Tensor, scale: Tensor, gate: Tensor)
x = self.postnorm(x)

x = self.dropout2(x)
x = x * jnp.expand_dims(gate, 1)
x = x * gate
x += input
return x

Expand Down Expand Up @@ -389,12 +410,12 @@ def forward(

Args:
input: input tensor with shape [batch_size, num_length, target_dim].
shift: If provided, shifting the norm tensor with shape [batch_size, target_dim] and
scale should be provided.
scale: If provided, scaling the norm tensor with shape [batch_size, target_dim] and
shift should be provided.
shift: If provided, shifting the norm tensor with shape [batch_size, 1|num_length,
target_dim] and scale should be provided.
scale: If provided, scaling the norm tensor with shape [batch_size, 1|num_length,
target_dim] and shift should be provided.
gate: If provided, applying before the residual addition with shape
[batch_size, target_dim].
[batch_size, 1|num_length, target_dim].
attention_logit_biases: Optional Tensor representing the self attention biases.

Returns:
Expand Down Expand Up @@ -426,7 +447,7 @@ def forward(
x = self.postnorm(x)

if gate is not None:
x = x * jnp.expand_dims(gate, 1)
x = x * gate

output = input + x
return output
Expand Down Expand Up @@ -463,12 +484,12 @@ def extend_step(
results of previous attentions, and index used for fast decoding. Contains
"attention" cached states.
target: target tensor with shape [batch_size, step_length, target_dim].
shift: If provided, shifting the norm tensor with shape [batch_size, target_dim] and
scale should be provided.
scale: If provided, scaling the norm tensor with shape [batch_size, target_dim] and
shift should be provided.
shift: If provided, shifting the norm tensor with shape [batch_size, 1|num_length,
target_dim] and scale should be provided.
scale: If provided, scaling the norm tensor with shape [batch_size, 1|num_length,
target_dim] and shift should be provided.
gate: If provided, applying before the residual addition with shape
[batch_size, target_dim].
[batch_size, 1|num_length, target_dim].

Returns:
A tuple (cached_states, output):
Expand Down Expand Up @@ -504,7 +525,7 @@ def extend_step(
x = self.postnorm(x)

if gate is not None:
x = x * jnp.expand_dims(gate, 1)
x = x * gate

output = target + x
return dict(attention=attn_states), output
Expand Down Expand Up @@ -542,8 +563,8 @@ def forward(self, *, input: Tensor, condition: Tensor) -> Tensor:

Args:
input: input tensor with shape [batch_size, num_length, input_dim].
condition: tensor with shape [batch_size, input_dim] for generating
layer norm shift, scale, and gate.
condition: tensor with shape [batch_size, input_dim] or [batch_size, num_length,
input_dim] for generating layer norm shift, scale, and gate.

Returns:
A tensor with shape [batch_size, num_length, input_dim].
Expand Down Expand Up @@ -584,8 +605,8 @@ def extend_step(
results of previous attentions, and index used for fast decoding. Contains
"attention" cached states.
target: target tensor with shape [batch_size, step_length, input_dim].
condition: tensor with shape [batch_size, input_dim] for generating
layer norm shift, scale, and gate.
condition: tensor with shape [batch_size, input_dim] or [batch_size, step_length,
input_dim] for generating layer norm shift, scale, and gate.

Returns:
A tuple (cached_states, output):
Expand Down Expand Up @@ -639,8 +660,8 @@ def forward(self, *, input: Tensor, condition: Tensor) -> Tensor:

Args:
input: input tensor with shape [batch_size, num_length, input_dim].
condition: tensor with shape [batch_size, input_dim] for generating
layer norm shift and scale.
condition: tensor with shape [batch_size, input_dim] or [batch_size, num_length,
input_dim] for generating layer norm shift and scale.

Returns:
A tensor with shape [batch_size, num_length, output_dim].
Expand Down
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