[shardformer] support pipeline for deepseek v3 and optimize lora save (#6188)

* [shardformer] support pipeline for deepseek v3

* [checkpointio] fix lora save

* [devops] update ci env

* [booster] optimize lora

* fix test

* fix test

colossalai/shardformer/modeling/deepseek_v3.py

@@ -4,9 +4,10 @@ import numpy as np
 import torch
 import torch.distributed as dist
 from torch.distributed import ProcessGroup
+from torch.nn import CrossEntropyLoss
 from transformers.cache_utils import Cache, DynamicCache
 from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
-from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 
 from colossalai.lazy import LazyInitContext
 from colossalai.moe._operation import (
@@ -16,6 +17,7 @@ from colossalai.moe._operation import (
     EPGradScalerOut,
     all_to_all_uneven,
 )
+from colossalai.pipeline.stage_manager import PipelineStageManager
 from colossalai.shardformer.layer.linear import ParallelModule
 from colossalai.shardformer.shard.utils import set_tensors_to_none
 from colossalai.tensor.moe_tensor.api import set_moe_tensor_ep_group
@@ -167,6 +169,9 @@ def deepseek_v3_model_forward(
     output_attentions: Optional[bool] = None,
     output_hidden_states: Optional[bool] = None,
     return_dict: Optional[bool] = None,
+    stage_manager: Optional[PipelineStageManager] = None,
+    stage_index: Optional[List[int]] = None,
+    hidden_states_internal: Optional[torch.Tensor] = None,
 ) -> Union[Tuple, BaseModelOutputWithPast]:
     output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
     output_hidden_states = (
@@ -203,8 +208,11 @@ def deepseek_v3_model_forward(
         )
         position_ids = position_ids.unsqueeze(0)
 
-    if inputs_embeds is None:
-        inputs_embeds = self.embed_tokens(input_ids)
+    if stage_manager is None or stage_manager.is_first_stage():
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+    else:
+        inputs_embeds = hidden_states_internal
 
     if self._use_flash_attention_2:
         # 2d mask is passed through the layers
@@ -226,7 +234,11 @@ def deepseek_v3_model_forward(
     all_self_attns = () if output_attentions else None
     next_decoder_cache = None
 
-    for i, decoder_layer in enumerate(self.layers):
+    if stage_index is not None:
+        start_idx, end_idx = stage_index
+    else:
+        start_idx, end_idx = 0, len(self.layers)
+    for i, decoder_layer in enumerate(self.layers[start_idx:end_idx], start=start_idx):
         if output_hidden_states:
             all_hidden_states += (hidden_states,)
 
@@ -258,7 +270,8 @@ def deepseek_v3_model_forward(
         if output_attentions:
             all_self_attns += (layer_outputs[1],)
 
-    hidden_states = self.norm(hidden_states)
+    if stage_manager is None or stage_manager.is_last_stage():
+        hidden_states = self.norm(hidden_states)
 
     # add hidden states from the last decoder layer
     if output_hidden_states:
@@ -267,6 +280,10 @@ def deepseek_v3_model_forward(
     next_cache = None
     if use_cache:
         next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+    if stage_manager is not None and not stage_manager.is_last_stage():
+        return {
+            "hidden_states_internal": hidden_states,
+        }
     if not return_dict:
         return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
     return BaseModelOutputWithPast(
@@ -275,3 +292,94 @@ def deepseek_v3_model_forward(
         hidden_states=all_hidden_states,
         attentions=all_self_attns,
     )
+
+
+def deepseek_v3_for_causal_lm_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[List[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    labels: Optional[torch.LongTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+    stage_manager: Optional[PipelineStageManager] = None,
+    stage_index: Optional[List[int]] = None,
+    hidden_states_internal: Optional[torch.Tensor] = None,
+) -> Union[Tuple, CausalLMOutputWithPast]:
+    r"""
+    Args:
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers.,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`.
+    Returns:
+    Example:
+    ```python
+    >>> from transformers import AutoTokenizer, DeepseekV3ForCausalLM
+    >>> model = DeepseekV3ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+    >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+    >>> prompt = "Hey, are you conscious? Can you talk to me?"
+    >>> inputs = tokenizer(prompt, return_tensors="pt")
+    >>> # Generate
+    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+    ```"""
+    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+    output_hidden_states = (
+        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+    )
+    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+    outputs = deepseek_v3_model_forward(
+        self.model,
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_attentions=output_attentions,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+        stage_manager=stage_manager,
+        stage_index=stage_index,
+        hidden_states_internal=hidden_states_internal,
+    )
+    if stage_manager is not None and not stage_manager.is_last_stage():
+        return outputs
+
+    hidden_states = outputs[0]
+
+    logits = self.lm_head(hidden_states)
+    logits = logits.float()
+
+    loss = None
+    if labels is not None:
+        # Shift so that tokens < n predict n
+        shift_logits = logits[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+        # Flatten the tokens
+        loss_fct = CrossEntropyLoss()
+        shift_logits = shift_logits.view(-1, self.config.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)
+
+    if not return_dict:
+        output = (logits,) + outputs[1:]
+        return (loss,) + output if loss is not None else output
+
+    return CausalLMOutputWithPast(
+        loss=loss,
+        logits=logits,
+        past_key_values=outputs.past_key_values,
+        hidden_states=outputs.hidden_states,
+        attentions=outputs.attentions,
+    )