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Fix Gemma2 Attention Args #11365

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merged 4 commits into from
Nov 21, 2024
Merged

Fix Gemma2 Attention Args #11365

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8adc8be
fix gemma2 attention args
suiyoubi Nov 21, 2024
ace2b37
pylint
suiyoubi Nov 21, 2024
63a124a
kwargs
suiyoubi Nov 21, 2024
2393856
Apply isort and black reformatting
suiyoubi Nov 21, 2024
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14 changes: 13 additions & 1 deletion nemo/collections/nlp/models/language_modeling/megatron/gemma2/gemma2_modules.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,8 @@ class Gemma2DotProductAttention(MegatronModule):
Region where selective activation recomputation is applied.
This region is memory intensive but less compute intensive which
makes activation checkpointing more efficient for LLMs (20B+).
See Reducing Activation Recomputation in Large Transformer Models: https://arxiv.org/abs/2205.05198 for more details.
See Reducing Activation Recomputation in Large Transformer Models:
https://arxiv.org/abs/2205.05198 for more details.

We use the following notation:
h: hidden size
Expand Down Expand Up @@ -126,7 +127,12 @@ def forward(
attention_mask: Tensor,
attn_mask_type: AttnMaskType = None,
packed_seq_params: PackedSeqParams = None,
**kwargs,
):
"""Forward.
Modified from mcore.transformer.dot_product_attention to support Gemma2-specific
final_logit_softcapping.
"""
assert packed_seq_params is None, (
"Packed sequence is not supported by DotProductAttention." "Please use TEDotProductAttention instead."
)
Expand Down Expand Up @@ -243,6 +249,8 @@ def forward(


class TERowParallelLinearLayerNorm(TERowParallelLinear):
"""Modified From TERowParallelLinear with an additional Post-LN."""

def __init__(
self,
input_size: int,
Expand Down Expand Up @@ -270,12 +278,16 @@ def __init__(
self.post_layernorm = TENorm(config, output_size)

def forward(self, x):
"""Forward with additional Post LN on output"""
output, bias = super().forward(x)
return self.post_layernorm(output), bias


class Gemma2OutputLayer(ColumnParallelLinear):
"""Extends from ColumnParallelLinear with logit soft capping."""

def forward(self, *args, **kwargs):
"""Forward with logit soft capping."""
output, bias = super().forward(*args, **kwargs)
output = logit_softcapping(output, self.config.final_logit_softcapping)
return output, bias
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