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Fix T5Gemma module structure #42145

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Merged
merged 2 commits into from
Nov 11, 2025
Merged

Fix T5Gemma module structure #42145

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8171e16
fix modular
Cyrilvallez Nov 11, 2025
2d5736a
oupsi typo
Cyrilvallez Nov 11, 2025
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32 changes: 29 additions & 3 deletions src/transformers/models/t5gemma/modeling_t5gemma.py
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Original file line number Diff line number Diff line change
Expand Up @@ -448,11 +448,28 @@ def forward(
return hidden_states


class T5GemmaDecoderLayer(T5GemmaEncoderLayer):
class T5GemmaDecoderLayer(GradientCheckpointingLayer):
"""Decoder sub-layer: an extra cross-attention layer."""

def __init__(self, config, layer_idx: int):
super().__init__(config, layer_idx)
super().__init__()
self.hidden_size = config.hidden_size
self.config = config
self.layer_idx = layer_idx
self.attention_type = config.layer_types[layer_idx]

self.self_attn = T5GemmaSelfAttention(
config=config,
layer_idx=layer_idx,
)
self.pre_self_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_self_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

self.mlp = T5GemmaMLP(config)
self.pre_feedforward_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_feedforward_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

self.dropout = nn.Dropout(config.dropout_rate)
self.cross_attn = T5GemmaCrossAttention(config=config, layer_idx=layer_idx)
self.pre_cross_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_cross_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
Expand Down Expand Up @@ -732,7 +749,7 @@ def forward(
)


class T5GemmaDecoder(T5GemmaEncoder):
class T5GemmaDecoder(T5GemmaPreTrainedModel):
_can_record_outputs = {
"attentions": OutputRecorder(T5GemmaSelfAttention, index=1),
"cross_attentions": OutputRecorder(T5GemmaCrossAttention, index=1),
Expand All @@ -741,11 +758,20 @@ class T5GemmaDecoder(T5GemmaEncoder):

def __init__(self, config):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size

self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.norm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False

self.layers = nn.ModuleList(
[T5GemmaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.dropout = nn.Dropout(config.dropout_rate)
self.rotary_emb = T5GemmaRotaryEmbedding(config=config)

# Initialize weights and apply final processing
self.post_init()

@check_model_inputs()
Expand Down
32 changes: 29 additions & 3 deletions src/transformers/models/t5gemma/modular_t5gemma.py
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Original file line number Diff line number Diff line change
Expand Up @@ -517,11 +517,28 @@ def forward(
return hidden_states


class T5GemmaDecoderLayer(T5GemmaEncoderLayer):
class T5GemmaDecoderLayer(GradientCheckpointingLayer):
"""Decoder sub-layer: an extra cross-attention layer."""

def __init__(self, config, layer_idx: int):
super().__init__(config, layer_idx)
super().__init__()
self.hidden_size = config.hidden_size
self.config = config
self.layer_idx = layer_idx
self.attention_type = config.layer_types[layer_idx]

self.self_attn = T5GemmaSelfAttention(
config=config,
layer_idx=layer_idx,
)
self.pre_self_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_self_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

self.mlp = T5GemmaMLP(config)
self.pre_feedforward_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_feedforward_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

self.dropout = nn.Dropout(config.dropout_rate)
self.cross_attn = T5GemmaCrossAttention(config=config, layer_idx=layer_idx)
self.pre_cross_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_cross_attn_layernorm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
Expand Down Expand Up @@ -770,7 +787,7 @@ def forward(
)


class T5GemmaDecoder(T5GemmaEncoder):
class T5GemmaDecoder(T5GemmaPreTrainedModel):
_can_record_outputs = {
"attentions": OutputRecorder(T5GemmaSelfAttention, index=1),
"cross_attentions": OutputRecorder(T5GemmaCrossAttention, index=1),
Expand All @@ -779,11 +796,20 @@ class T5GemmaDecoder(T5GemmaEncoder):

def __init__(self, config):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size

self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.norm = T5GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False

self.layers = nn.ModuleList(
[T5GemmaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.dropout = nn.Dropout(config.dropout_rate)
self.rotary_emb = T5GemmaRotaryEmbedding(config=config)

# Initialize weights and apply final processing
self.post_init()

@check_model_inputs()
Expand Down