refactor: remove unstable Option<&T> branch by returning owned Tensor
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@@ -232,11 +232,11 @@ impl MiniCPMModel {
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.embed_tokens
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.forward(input_ids)?
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.affine(self.cfg.scale_emb, 0.0)?;
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let attention_mask: Option<&Tensor> = {
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let attention_mask: Option<Tensor> = {
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if seq_len <= 1 {
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None
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} else {
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Some(&prepare_causal_attention_mask(
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Some(prepare_causal_attention_mask(
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bs,
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seq_len,
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0,
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@@ -248,7 +248,8 @@ impl MiniCPMModel {
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let (cos, sin) = self.rope_emb.forward(position_id, seq_len)?;
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let mut hidden_states = input_embeds;
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for decode_layer in &self.layers {
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hidden_states = decode_layer.forward(&hidden_states, &cos, &sin, attention_mask)?;
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hidden_states =
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decode_layer.forward(&hidden_states, &cos, &sin, attention_mask.as_ref())?;
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}
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hidden_states = self.norm.forward(&hidden_states)?;
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let hidden_state = hidden_states.narrow(1, seq_len - 1, 1)?;
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@@ -266,11 +267,11 @@ impl MiniCPMModel {
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.embed_tokens
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.forward(input_ids)?
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.affine(self.cfg.scale_emb, 0.0)?;
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let attention_mask: Option<&Tensor> = {
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let attention_mask: Option<Tensor> = {
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if seq_len <= 1 {
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None
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} else {
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Some(&prepare_causal_attention_mask(
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Some(prepare_causal_attention_mask(
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bs,
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seq_len,
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0,
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@@ -281,8 +282,12 @@ impl MiniCPMModel {
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let (cos, sin) = self.rope_emb.forward(position_id, seq_len)?;
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let mut hidden_states = input_embeds;
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for decode_layer in &mut self.layers {
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hidden_states =
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decode_layer.forward_with_cache(&hidden_states, &cos, &sin, attention_mask)?;
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hidden_states = decode_layer.forward_with_cache(
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&hidden_states,
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&cos,
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&sin,
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attention_mask.as_ref(),
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)?;
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}
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hidden_states = self.norm.forward(&hidden_states)?;
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let hidden_state = hidden_states.narrow(1, seq_len - 1, 1)?;
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