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Flax whisper gradient checkpointing #22897

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Flax whisper gradient checkpointing #22897

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1eefc67
Adding gradient_checkpointing to Flax Whisper
versae Apr 20, 2023
a38df46
Fixes issue with arguments
versae Apr 20, 2023
83fdd16
Update src/transformers/models/whisper/modeling_flax_whisper.py
versae Apr 21, 2023
010e6eb
Adding back kwargs to decoder_layer
versae Apr 21, 2023
7420ea4
Merge branch 'huggingface:main' into flax-whisper-gradient-checkpointing
versae Apr 21, 2023
f880f16
Merge branch 'huggingface:main' into flax-whisper-gradient-checkpointing
versae Apr 21, 2023
9204658
Setting internal attr self.gradient_checkpointing
versae Apr 21, 2023
93589f9
Merge branch 'huggingface:main' into flax-whisper-gradient-checkpointing
versae Apr 21, 2023
1774c69
Adding gradient_checkpointing to self to it's available in setup()
versae Apr 22, 2023
5bf5f8c
Adding back proper kwargs
versae Apr 22, 2023
4a4bad6
Removing kwargs to remat decoder layer
versae Apr 22, 2023
3dead7f
Black reformat
versae Apr 22, 2023
d1a6c74
Fix check copies
versae Apr 22, 2023
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84 changes: 58 additions & 26 deletions src/transformers/models/whisper/modeling_flax_whisper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
Expand Down Expand Up @@ -53,6 +54,8 @@
_CHECKPOINT_FOR_DOC = "openai/whisper-tiny"
_CONFIG_FOR_DOC = "WhisperConfig"

remat = nn_partitioning.remat


WHISPER_START_DOCSTRING = r"""
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
Expand Down Expand Up @@ -391,12 +394,20 @@ def __call__(
class FlaxWhisperEncoderLayerCollection(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False

def setup(self):
self.layers = [
FlaxWhisperEncoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.encoder_layers)
]
if self.gradient_checkpointing:
FlaxWhisperEncoderCheckpointLayer = remat(FlaxWhisperEncoderLayer, static_argnums=(2, 3))
self.layers = [
FlaxWhisperEncoderCheckpointLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.encoder_layers)
]
else:
self.layers = [
FlaxWhisperEncoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.encoder_layers)
]
self.layerdrop = self.config.encoder_layerdrop

def __call__(
Expand Down Expand Up @@ -535,12 +546,20 @@ def __call__(
class FlaxWhisperDecoderLayerCollection(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False

def setup(self):
self.layers = [
FlaxWhisperDecoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.decoder_layers)
]
if self.gradient_checkpointing:
FlaxWhisperDecoderCheckpointLayer = remat(FlaxWhisperDecoderLayer, static_argnums=(4, 5, 6))
self.layers = [
FlaxWhisperDecoderCheckpointLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.encoder_layers)
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]
else:
self.layers = [
FlaxWhisperDecoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.decoder_layers)
]
self.layerdrop = self.config.decoder_layerdrop

def __call__(
Expand Down Expand Up @@ -570,12 +589,12 @@ def __call__(
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
init_cache=init_cache,
output_attentions=output_attentions,
deterministic=deterministic,
attention_mask,
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encoder_hidden_states,
encoder_attention_mask,
init_cache,
output_attentions,
deterministic,
)

hidden_states = layer_outputs[0]
Expand Down Expand Up @@ -605,6 +624,7 @@ def __call__(
class FlaxWhisperEncoder(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False

def setup(self) -> None:
self.conv1 = nn.Conv(
Expand All @@ -628,6 +648,7 @@ def setup(self) -> None:
self.layers = FlaxWhisperEncoderLayerCollection(
self.config,
dtype=self.dtype,
gradient_checkpointing=self.gradient_checkpointing,
)
self.embed_positions = nn.Embed(self.config.max_source_positions, self.config.d_model, dtype=self.dtype)

Expand Down Expand Up @@ -689,12 +710,13 @@ def __call__(
class FlaxWhisperDecoder(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False

def setup(self) -> None:
self.embed_tokens = nn.Embed(self.config.vocab_size, self.config.d_model, dtype=self.dtype)
self.embed_positions = nn.Embed(self.config.max_target_positions, self.config.d_model, dtype=self.dtype)

self.layers = FlaxWhisperDecoderLayerCollection(self.config, dtype=self.dtype)
self.layers = FlaxWhisperDecoderLayerCollection(self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing)

self.dropout_layer = nn.Dropout(rate=self.config.dropout)

Expand All @@ -720,13 +742,13 @@ def __call__(

outputs = self.layers(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
attention_mask,
encoder_hidden_states,
deterministic,
init_cache,
output_attentions,
output_hidden_states,
return_dict,
)

last_hidden_states = outputs[0]
Expand All @@ -753,10 +775,11 @@ def __call__(
class FlaxWhisperModule(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False

def setup(self) -> None:
self.encoder = FlaxWhisperEncoder(self.config, dtype=self.dtype)
self.decoder = FlaxWhisperDecoder(self.config, dtype=self.dtype)
self.encoder = FlaxWhisperEncoder(self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing)
self.decoder = FlaxWhisperDecoder(self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing)

def __call__(
self,
Expand Down Expand Up @@ -821,11 +844,19 @@ def __init__(
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
gradient_checkpointing: bool = False,
**kwargs,
):
module = self.module_class(config=config, dtype=dtype, **kwargs)
module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs)
super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)

def enable_gradient_checkpointing(self):
self._module = self.module_class(
config=self.config,
dtype=self.dtype,
gradient_checkpointing=True,
)

def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_features = jnp.zeros(input_shape, dtype="f4")
Expand Down Expand Up @@ -1137,9 +1168,10 @@ class FlaxWhisperModel(FlaxWhisperPreTrainedModel):
class FlaxWhisperForConditionalGenerationModule(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
gradient_checkpointing: bool = False

def setup(self) -> None:
self.model = FlaxWhisperModule(config=self.config, dtype=self.dtype)
self.model = FlaxWhisperModule(config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing)
self.lm_head = nn.Dense(
self.config.vocab_size,
use_bias=False,
Expand Down