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Allow the model to be trained with most frameworks. #188

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Allow the model to be trained with most frameworks. #188

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23 changes: 20 additions & 3 deletions mamba_ssm/models/mixer_seq_simple.py
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Original file line number Diff line number Diff line change
Expand Up @@ -225,7 +225,7 @@ def tie_weights(self):
def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
return self.backbone.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype, **kwargs)

def forward(self, input_ids, position_ids=None, inference_params=None, num_last_tokens=0):
def forward(self, input_ids, position_ids=None, inference_params=None, num_last_tokens=0, attention_mask=None, labels=None):
"""
"position_ids" is just to be compatible with Transformer generation. We don't use it.
num_last_tokens: if > 0, only return the logits for the last n tokens
Expand All @@ -234,8 +234,25 @@ def forward(self, input_ids, position_ids=None, inference_params=None, num_last_
if num_last_tokens > 0:
hidden_states = hidden_states[:, -num_last_tokens:]
lm_logits = self.lm_head(hidden_states)
CausalLMOutput = namedtuple("CausalLMOutput", ["logits"])
return CausalLMOutput(logits=lm_logits)

# Source: https://github.com/huggingface/transformers/blob/80377eb018c077dba434bc8e7912bcaed3a64d09/src/transformers/models/llama/modeling_llama.py#L1196
if labels is not None:
logits = lm_logits
vocab_size = logits.shape[-1]
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = torch.nn.CrossEntropyLoss()
shift_logits = shift_logits.view(-1, vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
return (loss,)
else:
CausalLMOutput = namedtuple("CausalLMOutput", ["logits"])
return CausalLMOutput(logits=lm_logits)

@classmethod
def from_pretrained(cls, pretrained_model_name, device=None, dtype=None, **kwargs):
Expand Down