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fix several typos in megatron/core/transformer/multi_token_prediction.py #1744

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fix several typos in megatron/core/transformer/multi_token_prediction.py #1744

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8 changes: 4 additions & 4 deletions megatron/core/transformer/multi_token_prediction.py
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Original file line number Diff line number Diff line change
Expand Up @@ -448,7 +448,7 @@ def __init__(
)

# For the linear projection at the (k - 1)-th MTP layer, the input is the concatenation
# of the i-th tocken's hidden states and the (i + K)-th tocken's decoder input,
# of the i-th token's hidden states and the (i + K)-th token's decoder input,
# so the input's shape is [s, b, 2*h].
# The output will be send to the following transformer layer,
# so the output's shape should be [s, b, h].
Expand Down Expand Up @@ -499,7 +499,7 @@ def forward(
decoder_input (Tensor): Input tensor of shape [s, b, h] where s is the
sequence length, b is the batch size, and h is the hidden size.
At the (k - 1)-th MTP module, the i-th element of decoder input is
the embedding of (i + K)-th tocken.
the embedding of (i + K)-th token.
attention_mask (Tensor): Boolean tensor of shape [1, 1, s, s] for masking
self-attention.
context (Tensor, optional): Context tensor for cross-attention.
Expand Down Expand Up @@ -545,8 +545,8 @@ def forward(
hidden_states = make_viewless_tensor(
inp=hidden_states, requires_grad=True, keep_graph=True
)
# At the (k - 1)-th MTP module, concatenates the i-th tocken's hidden_states
# and the (i + K)-th tocken's embedding, and combine them with linear projection.
# At the (k - 1)-th MTP module, concatenates the i-th token's hidden_states
# and the (i + K)-th token's embedding, and combine them with linear projection.
hidden_states = torch.cat((decoder_input, hidden_states), -1)
hidden_states, _ = self.eh_proj(hidden_states)
# For tensor parallel we need to gather the tensor across the model-parallel
Expand Down