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t5 remove data dependency #22097

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Merged
sgugger merged 3 commits into from
Mar 15, 2023
Merged

t5 remove data dependency #22097

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c8e2044
t5 remove data dependency
Mar 10, 2023
fdbceae
make style
prathikr Mar 10, 2023
524731d
make fix-copies
prathikr Mar 10, 2023
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24 changes: 18 additions & 6 deletions src/transformers/models/mt5/modeling_mt5.py
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Original file line number Diff line number Diff line change
Expand Up @@ -566,8 +566,12 @@ def forward(
attention_outputs = self_attention_outputs[2:] # Keep self-attention outputs and relative position weights

# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
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hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

do_cross_attention = self.is_decoder and encoder_hidden_states is not None
Expand All @@ -593,8 +597,12 @@ def forward(
hidden_states = cross_attention_outputs[0]

# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

# Combine self attn and cross attn key value states
Expand All @@ -608,8 +616,12 @@ def forward(
hidden_states = self.layer[-1](hidden_states)

# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

outputs = (hidden_states,)
Expand Down
24 changes: 18 additions & 6 deletions src/transformers/models/t5/modeling_t5.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -703,8 +703,12 @@ def forward(
attention_outputs = self_attention_outputs[2:] # Keep self-attention outputs and relative position weights

# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

do_cross_attention = self.is_decoder and encoder_hidden_states is not None
Expand All @@ -730,8 +734,12 @@ def forward(
hidden_states = cross_attention_outputs[0]

# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

# Combine self attn and cross attn key value states
Expand All @@ -745,8 +753,12 @@ def forward(
hidden_states = self.layer[-1](hidden_states)

# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)

outputs = (hidden_states,)
Expand Down