[Shardformer] Add parallel output for shardformer models(bloom, falcon) (#5702)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

* add parallel cross entropy output for falcon model & fix some typos in bloom.py

* fix module name error, self.model -> self.transformers in bloom, falcon model

* Fix the overflow bug of distributed cross entropy loss function when training with fp16

* add dtype to parallel cross entropy loss function

* fix dtype related typos adn prettify the loss.py

* fix grad dtype and update dtype mismatch error

* fix typo bugs

colossalai/shardformer/modeling/falcon.py

@@ -14,6 +14,7 @@ from transformers.modeling_attn_mask_utils import (
 from transformers.modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     CausalLMOutputWithCrossAttentions,
+    CausalLMOutputWithPast,
     QuestionAnsweringModelOutput,
     SequenceClassifierOutputWithPast,
     TokenClassifierOutput,
@@ -31,6 +32,8 @@ from transformers.utils import logging
 from colossalai.pipeline.stage_manager import PipelineStageManager
 from colossalai.shardformer.shard import ShardConfig
 
+from ..layer import cross_entropy_1d
+
 
 def build_falcon_alibi_tensor_fn(process_group: ProcessGroup) -> torch.Tensor:
     def build_falcon_alibi_tensor(
@@ -437,14 +440,28 @@ class FalconPipelineForwards:
             loss = None
             if labels is not None:
                 # Shift so that tokens < n predict n
+                labels = labels.to(lm_logits.device)
                 shift_logits = lm_logits[..., :-1, :].contiguous()
                 shift_labels = labels[..., 1:].contiguous()
                 batch_size, seq_length, vocab_size = shift_logits.shape
                 # Flatten the tokens
                 loss_fct = CrossEntropyLoss()
-                loss = loss_fct(
-                    shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)
-                )
+                if shard_config.enable_tensor_parallelism and shard_config.parallel_output:
+                    new_vocab_size = shift_logits.shape[-1]
+                    shift_logits = shift_logits.view(-1, new_vocab_size)
+                    shift_labels = shift_labels.view(-1)
+                    loss = cross_entropy_1d(
+                        shift_logits,
+                        shift_labels,
+                        process_group=shard_config.tensor_parallel_process_group,
+                        vocab_size=self.lm_head.out_features,
+                        dtype=self.transformer.dtype,
+                    )
+                else:
+                    loss = loss_fct(
+                        shift_logits.view(batch_size * seq_length, vocab_size),
+                        shift_labels.view(batch_size * seq_length),
+                    )
 
             if not return_dict:
                 output = (lm_logits,) + transformer_outputs[1:]
@@ -747,3 +764,79 @@ class FalconPipelineForwards:
         else:
             hidden_states = outputs.get("hidden_states")
             return {"hidden_states": hidden_states}
+
+
+def get_lm_forward_with_dist_cross_entropy(shard_config: ShardConfig):
+    from transformers import FalconForCausalLM
+
+    def forward(
+        self: FalconForCausalLM,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+        """
+        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
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        past_key_values = None
+        hidden_states = transformer_outputs[0]
+        lm_logits = self.lm_head(hidden_states)
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            labels = labels.to(lm_logits.device)
+            shift_logits = lm_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            batch_size, seq_length, vocab_size = shift_logits.shape
+            # Flatten the tokens
+            new_vocab_size = shift_logits.shape[-1]
+            shift_logits = shift_logits.view(-1, new_vocab_size)
+            shift_labels = shift_labels.view(-1)
+            loss = cross_entropy_1d(
+                shift_logits,
+                shift_labels,
+                process_group=shard_config.tensor_parallel_process_group,
+                vocab_size=self.lm_head.out_features,
+                dtype=self.transformer.dtype,
+            )
+
+        if not return_dict:
+            output = (lm_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+    return forward