fix: remove cleaving
#13
by
Markus28
- opened
- modeling_bert.py +4 -23
modeling_bert.py
CHANGED
@@ -166,25 +166,6 @@ class BertEncoder(nn.Module):
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[create_block(config, layer_idx=i) for i in range(config.num_hidden_layers)]
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)
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self._grad_checkpointing = False
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self._last_layer_idx = len(self.layers) - 1
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@property
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def last_layer_idx(self):
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return self._last_layer_idx
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@last_layer_idx.setter
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def last_layer_idx(self, idx: int):
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assert 0 <= idx < len(self.layers)
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self._last_layer_idx = idx
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@property
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def cleaved_layers(self):
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return len(self.layers) - self.last_layer_idx - 1
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@cleaved_layers.setter
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def cleaved_layers(self, n: int):
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assert 0 <= n < len(self.layers)
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self.last_layer_idx = len(self.layers) - n - 1
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@property
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def gradient_checkpointing(self):
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@@ -205,7 +186,7 @@ class BertEncoder(nn.Module):
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mixer_kwargs = (
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{"key_padding_mask": key_padding_mask.bool()} if key_padding_mask is not None else None
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)
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-
for layer in self.layers
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hidden_states = layer(hidden_states, mixer_kwargs=mixer_kwargs)
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if subset_mask is not None:
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hidden_states = hidden_states[subset_mask]
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@@ -216,11 +197,11 @@ class BertEncoder(nn.Module):
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)
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mixer_kwargs = {"cu_seqlens": cu_seqlens, "max_seqlen": max_seqlen_in_batch}
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if subset_mask is None:
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for layer in self.layers
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hidden_states = layer(hidden_states, mixer_kwargs=mixer_kwargs)
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hidden_states = pad_input(hidden_states, indices, batch, seqlen)
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else:
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for layer in self.layers[
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hidden_states = layer(hidden_states, mixer_kwargs=mixer_kwargs)
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if key_padding_mask is not None:
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subset_idx = torch.nonzero(
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@@ -247,7 +228,7 @@ class BertEncoder(nn.Module):
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"cu_seqlens_k": cu_seqlens,
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"max_seqlen_k": max_seqlen_in_batch,
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}
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hidden_states = self.layers[
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return hidden_states
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[create_block(config, layer_idx=i) for i in range(config.num_hidden_layers)]
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)
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self._grad_checkpointing = False
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@property
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def gradient_checkpointing(self):
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mixer_kwargs = (
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{"key_padding_mask": key_padding_mask.bool()} if key_padding_mask is not None else None
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)
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+
for layer in self.layers:
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hidden_states = layer(hidden_states, mixer_kwargs=mixer_kwargs)
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if subset_mask is not None:
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hidden_states = hidden_states[subset_mask]
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)
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mixer_kwargs = {"cu_seqlens": cu_seqlens, "max_seqlen": max_seqlen_in_batch}
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if subset_mask is None:
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+
for layer in self.layers:
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hidden_states = layer(hidden_states, mixer_kwargs=mixer_kwargs)
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hidden_states = pad_input(hidden_states, indices, batch, seqlen)
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else:
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+
for layer in self.layers[:-1]:
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hidden_states = layer(hidden_states, mixer_kwargs=mixer_kwargs)
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if key_padding_mask is not None:
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subset_idx = torch.nonzero(
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"cu_seqlens_k": cu_seqlens,
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"max_seqlen_k": max_seqlen_in_batch,
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}
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+
hidden_states = self.layers[-1](hidden_states_subset, mixer_kwargs=mixer_kwargs)
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return hidden_states
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