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Enabling gradient checkpointing in eval() mode #9878

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merged 7 commits into from
Nov 8, 2024
Merged

Enabling gradient checkpointing in eval() mode #9878

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dea92b6
refactored
MikeTkachuk Nov 6, 2024
3beefa8
add grad check
MikeTkachuk Nov 7, 2024
b0ebd05
refactored
MikeTkachuk Nov 6, 2024
db85cf2
add grad check
MikeTkachuk Nov 7, 2024
7049d07
Merge remote-tracking branch 'origin/enable_grckpt_in_eval' into enab…
MikeTkachuk Nov 8, 2024
238b475
amend
MikeTkachuk Nov 8, 2024
f40937f
Merge branch 'main' into enable_grckpt_in_eval
MikeTkachuk Nov 8, 2024
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8 changes: 4 additions & 4 deletions examples/community/matryoshka.py
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Original file line number Diff line number Diff line change
Expand Up @@ -868,7 +868,7 @@ def forward(
blocks = list(zip(self.resnets, self.attentions))

for i, (resnet, attn) in enumerate(blocks):
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
Expand Down Expand Up @@ -1029,7 +1029,7 @@ def forward(

hidden_states = self.resnets[0](hidden_states, temb)
for attn, resnet in zip(self.attentions, self.resnets[1:]):
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
Expand Down Expand Up @@ -1191,7 +1191,7 @@ def forward(

hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
Expand Down Expand Up @@ -1364,7 +1364,7 @@ def forward(

# Blocks
for block in self.transformer_blocks:
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
Expand Down
2 changes: 1 addition & 1 deletion examples/research_projects/pixart/controlnet_pixart_alpha.py
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Original file line number Diff line number Diff line change
Expand Up @@ -215,7 +215,7 @@ def forward(

# 2. Blocks
for block_index, block in enumerate(self.transformer.transformer_blocks):
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:
# rc todo: for training and gradient checkpointing
print("Gradient checkpointing is not supported for the controlnet transformer model, yet.")
exit(1)
Expand Down
10 changes: 5 additions & 5 deletions src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py
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Original file line number Diff line number Diff line change
Expand Up @@ -420,7 +420,7 @@ def forward(
for i, resnet in enumerate(self.resnets):
conv_cache_key = f"resnet_{i}"

if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def create_forward(*inputs):
Expand Down Expand Up @@ -522,7 +522,7 @@ def forward(
for i, resnet in enumerate(self.resnets):
conv_cache_key = f"resnet_{i}"

if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def create_forward(*inputs):
Expand Down Expand Up @@ -636,7 +636,7 @@ def forward(
for i, resnet in enumerate(self.resnets):
conv_cache_key = f"resnet_{i}"

if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def create_forward(*inputs):
Expand Down Expand Up @@ -773,7 +773,7 @@ def forward(

hidden_states, new_conv_cache["conv_in"] = self.conv_in(sample, conv_cache=conv_cache.get("conv_in"))

if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def custom_forward(*inputs):
Expand Down Expand Up @@ -939,7 +939,7 @@ def forward(

hidden_states, new_conv_cache["conv_in"] = self.conv_in(sample, conv_cache=conv_cache.get("conv_in"))

if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def custom_forward(*inputs):
Expand Down
10 changes: 5 additions & 5 deletions src/diffusers/models/autoencoders/autoencoder_kl_mochi.py
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Original file line number Diff line number Diff line change
Expand Up @@ -206,7 +206,7 @@ def forward(
for i, (resnet, norm, attn) in enumerate(zip(self.resnets, self.norms, self.attentions)):
conv_cache_key = f"resnet_{i}"

if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def create_forward(*inputs):
Expand Down Expand Up @@ -311,7 +311,7 @@ def forward(
for i, (resnet, norm, attn) in enumerate(zip(self.resnets, self.norms, self.attentions)):
conv_cache_key = f"resnet_{i}"

if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def create_forward(*inputs):
Expand Down Expand Up @@ -392,7 +392,7 @@ def forward(
for i, resnet in enumerate(self.resnets):
conv_cache_key = f"resnet_{i}"

if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def create_forward(*inputs):
Expand Down Expand Up @@ -529,7 +529,7 @@ def forward(
hidden_states = self.proj_in(hidden_states)
hidden_states = hidden_states.permute(0, 4, 1, 2, 3)

if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def create_forward(*inputs):
Expand Down Expand Up @@ -646,7 +646,7 @@ def forward(
hidden_states = self.conv_in(hidden_states)

# 1. Mid
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def create_forward(*inputs):
Expand Down
2 changes: 1 addition & 1 deletion src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -95,7 +95,7 @@ def forward(
sample = self.conv_in(sample)

upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def custom_forward(*inputs):
Expand Down
10 changes: 5 additions & 5 deletions src/diffusers/models/autoencoders/vae.py
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Original file line number Diff line number Diff line change
Expand Up @@ -142,7 +142,7 @@ def forward(self, sample: torch.Tensor) -> torch.Tensor:

sample = self.conv_in(sample)

if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def custom_forward(*inputs):
Expand Down Expand Up @@ -291,7 +291,7 @@ def forward(
sample = self.conv_in(sample)

upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def custom_forward(*inputs):
Expand Down Expand Up @@ -544,7 +544,7 @@ def forward(
sample = self.conv_in(sample)

upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def custom_forward(*inputs):
Expand Down Expand Up @@ -876,7 +876,7 @@ def __init__(

def forward(self, x: torch.Tensor) -> torch.Tensor:
r"""The forward method of the `EncoderTiny` class."""
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def custom_forward(*inputs):
Expand Down Expand Up @@ -962,7 +962,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
# Clamp.
x = torch.tanh(x / 3) * 3

if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def custom_forward(*inputs):
Expand Down
4 changes: 2 additions & 2 deletions src/diffusers/models/controlnet_flux.py
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Original file line number Diff line number Diff line change
Expand Up @@ -329,7 +329,7 @@ def forward(

block_samples = ()
for index_block, block in enumerate(self.transformer_blocks):
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
Expand Down Expand Up @@ -363,7 +363,7 @@ def custom_forward(*inputs):

single_block_samples = ()
for index_block, block in enumerate(self.single_transformer_blocks):
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
Expand Down
2 changes: 1 addition & 1 deletion src/diffusers/models/controlnet_sd3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -324,7 +324,7 @@ def forward(
block_res_samples = ()

for block in self.transformer_blocks:
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
Expand Down
6 changes: 3 additions & 3 deletions src/diffusers/models/controlnet_xs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1465,7 +1465,7 @@ def custom_forward(*inputs):
h_ctrl = torch.cat([h_ctrl, b2c(h_base)], dim=1)

# apply base subblock
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
h_base = torch.utils.checkpoint.checkpoint(
create_custom_forward(b_res),
Expand All @@ -1488,7 +1488,7 @@ def custom_forward(*inputs):

# apply ctrl subblock
if apply_control:
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
h_ctrl = torch.utils.checkpoint.checkpoint(
create_custom_forward(c_res),
Expand Down Expand Up @@ -1897,7 +1897,7 @@ def maybe_apply_freeu_to_subblock(hidden_states, res_h_base):
hidden_states, res_h_base = maybe_apply_freeu_to_subblock(hidden_states, res_h_base)
hidden_states = torch.cat([hidden_states, res_h_base], dim=1)

if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:
ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
hidden_states = torch.utils.checkpoint.checkpoint(
create_custom_forward(resnet),
Expand Down
4 changes: 2 additions & 2 deletions src/diffusers/models/transformers/auraflow_transformer_2d.py
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Expand Up @@ -466,7 +466,7 @@ def forward(

# MMDiT blocks.
for index_block, block in enumerate(self.joint_transformer_blocks):
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
Expand Down Expand Up @@ -497,7 +497,7 @@ def custom_forward(*inputs):
combined_hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)

for index_block, block in enumerate(self.single_transformer_blocks):
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
Expand Down
2 changes: 1 addition & 1 deletion src/diffusers/models/transformers/cogvideox_transformer_3d.py
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Expand Up @@ -452,7 +452,7 @@ def forward(

# 3. Transformer blocks
for i, block in enumerate(self.transformer_blocks):
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:
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oh thanks! why do we also removed the torch.is_grad_enabled() check? gradient checkpointing isn't meaningful without gradient being computed, no?

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added it back, thanks for pointing it out.
it does not break anything, but found that it throws an annoying warning when use_reentrant=True,

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but found that it throws an annoying warning when use_reentrant=True,

what do you mean by that?

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use_reentrant is an argument passed to torch.utils.checkpoint.checkpoint

if True one of the checks will print this to stderr
warnings.warn(
"None of the inputs have requires_grad=True. Gradients will be None"
)
but diffusers are using use_reentrant=False anyway

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oh got thanks, so the warning is specific to when we use gradient checkpointing when gradient is not enabled


def create_custom_forward(module):
def custom_forward(*inputs):
Expand Down
2 changes: 1 addition & 1 deletion src/diffusers/models/transformers/dit_transformer_2d.py
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Expand Up @@ -184,7 +184,7 @@ def forward(

# 2. Blocks
for block in self.transformer_blocks:
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
Expand Down
4 changes: 2 additions & 2 deletions src/diffusers/models/transformers/latte_transformer_3d.py
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Expand Up @@ -238,7 +238,7 @@ def forward(
for i, (spatial_block, temp_block) in enumerate(
zip(self.transformer_blocks, self.temporal_transformer_blocks)
):
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:
hidden_states = torch.utils.checkpoint.checkpoint(
spatial_block,
hidden_states,
Expand Down Expand Up @@ -271,7 +271,7 @@ def forward(
if i == 0 and num_frame > 1:
hidden_states = hidden_states + self.temp_pos_embed

if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:
hidden_states = torch.utils.checkpoint.checkpoint(
temp_block,
hidden_states,
Expand Down
2 changes: 1 addition & 1 deletion src/diffusers/models/transformers/pixart_transformer_2d.py
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Expand Up @@ -386,7 +386,7 @@ def forward(

# 2. Blocks
for block in self.transformer_blocks:
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
Expand Down
2 changes: 1 addition & 1 deletion src/diffusers/models/transformers/stable_audio_transformer.py
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Expand Up @@ -414,7 +414,7 @@ def forward(
attention_mask = torch.cat([prepend_mask, attention_mask], dim=-1)

for block in self.transformer_blocks:
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
Expand Down
2 changes: 1 addition & 1 deletion src/diffusers/models/transformers/transformer_2d.py
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Expand Up @@ -415,7 +415,7 @@ def forward(

# 2. Blocks
for block in self.transformer_blocks:
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
Expand Down
2 changes: 1 addition & 1 deletion src/diffusers/models/transformers/transformer_cogview3plus.py
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Expand Up @@ -341,7 +341,7 @@ def forward(
hidden_states = hidden_states[:, text_seq_length:]

for index_block, block in enumerate(self.transformer_blocks):
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def custom_forward(*inputs):
Expand Down
4 changes: 2 additions & 2 deletions src/diffusers/models/transformers/transformer_flux.py
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Expand Up @@ -480,7 +480,7 @@ def forward(
image_rotary_emb = self.pos_embed(ids)

for index_block, block in enumerate(self.transformer_blocks):
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
Expand Down Expand Up @@ -525,7 +525,7 @@ def custom_forward(*inputs):
hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)

for index_block, block in enumerate(self.single_transformer_blocks):
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
Expand Down
2 changes: 1 addition & 1 deletion src/diffusers/models/transformers/transformer_mochi.py
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Expand Up @@ -350,7 +350,7 @@ def forward(
)

for i, block in enumerate(self.transformer_blocks):
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module):
def custom_forward(*inputs):
Expand Down
2 changes: 1 addition & 1 deletion src/diffusers/models/transformers/transformer_sd3.py
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Expand Up @@ -317,7 +317,7 @@ def forward(
encoder_hidden_states = self.context_embedder(encoder_hidden_states)

for index_block, block in enumerate(self.transformer_blocks):
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:

def create_custom_forward(module, return_dict=None):
def custom_forward(*inputs):
Expand Down
2 changes: 1 addition & 1 deletion src/diffusers/models/transformers/transformer_temporal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -340,7 +340,7 @@ def forward(

# 2. Blocks
for block, temporal_block in zip(self.transformer_blocks, self.temporal_transformer_blocks):
if self.training and self.gradient_checkpointing:
if self.gradient_checkpointing:
hidden_states = torch.utils.checkpoint.checkpoint(
block,
hidden_states,
Expand Down
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