[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

Author: Hz188

colossalai/shardformer/layer/loss.py

@@ -22,6 +22,7 @@ def forward(
         ignore_index: int,
         process_group: ProcessGroup,
         vocab_size: int,
+        dtype=torch.float32,
     ):
         r"""
         Calculate the cross entropy loss before gather, the origin loss function is as follows:
@@ -34,7 +35,7 @@ def forward(
         Args:
             vocab_logits (:class:`torch.Tensor`): The logits of the vocabulary, shape is
               [batch_size, seq_len, vocab_size]
-            labels (:class:`torch.Tensor`): The labels of the vocabulary, shape is
+            target (:class:`torch.Tensor`): The labels of the vocabulary, shape is
               [batch_size, seq_len]
 
         Returns:
@@ -86,7 +87,7 @@ def forward(
         dist.all_reduce(pred_logits, op=dist.ReduceOp.SUM, group=process_group)
         exp_logits = vocab_logits
         torch.exp(vocab_logits, out=exp_logits)
-        sum_exp_logits = torch.sum(exp_logits, dim=-1)
+        sum_exp_logits = torch.sum(exp_logits, dim=-1, dtype=torch.float32)
         dist.all_reduce(sum_exp_logits, op=dist.ReduceOp.SUM, group=process_group)
 
         # calculate the loss
@@ -97,9 +98,10 @@ def forward(
         loss = torch.sum(loss).div_(num_non_zero)
 
         # calculate the softmax
-        exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
+        exp_logits = exp_logits.div(sum_exp_logits.unsqueeze(dim=-1)).to(dtype)
         exp_logits[target == ignore_index] = 0.0
         ctx.save_for_backward(exp_logits, mask, masked_target_1d)
+        ctx.dtype = dtype
 
         return loss
 
@@ -114,11 +116,11 @@ def backward(ctx, grad_output):
         partion_vocab_size = grad_logits.shape[-1]
         grad_logits_2d = grad_logits.view(-1, partion_vocab_size)
 
-        update = 1.0 - mask.view(-1).float()
+        update = 1.0 - mask.view(-1).float().to(ctx.dtype)
         grad_logits_2d[torch.arange(0, grad_logits_2d.shape[0]), masked_target_1d] -= update
 
         grad_logits.mul_(grad_output.unsqueeze(dim=-1))
-        return grad_logits, None, None, None, None
+        return grad_logits, None, None, None, None, None
 
 
 def cross_entropy_1d(
@@ -127,5 +129,6 @@ def cross_entropy_1d(
     ignore_index: int = -100,
     process_group: ProcessGroup = None,
     vocab_size: int = None,
+    dtype: torch.dtype = None,
 ) -> torch.Tensor:
-    return DistCrossEntropy.apply(vocab_logits, labels, ignore_index, process_group, vocab_size)
+    return DistCrossEntropy.apply(vocab_logits, labels, ignore_index, process_group, vocab_size, dtype)

colossalai/shardformer/modeling/bloom.py

@@ -10,6 +10,7 @@
 from transformers.modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     CausalLMOutputWithCrossAttentions,
+    CausalLMOutputWithPast,
     QuestionAnsweringModelOutput,
     SequenceClassifierOutputWithPast,
     TokenClassifierOutput,
@@ -27,6 +28,8 @@
 from colossalai.shardformer.layer._operation import gather_forward_split_backward, split_forward_gather_backward
 from colossalai.shardformer.shard import ShardConfig
 
+from ..layer import cross_entropy_1d
+
 logger = logging.get_logger(__name__)
 
 
@@ -354,7 +357,7 @@ def bloom_for_causal_lm_forward(
         past_key_values = None
         if stage_manager.is_last_stage():
             hidden_states = transformer_outputs[0]
-            lm_logits = self.lm_head(hidden_states)
+            lm_logits = self.lm_head(hidden_states).contiguous()
 
             loss = None
             if labels is not None:
@@ -365,10 +368,21 @@ def bloom_for_causal_lm_forward(
                 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 = lm_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_fct = CrossEntropyLoss()
+                    shift_logits = shift_logits.view(-1, self.config.vocab_size)
+                    loss = loss_fct(shift_logits, shift_labels.view(-1))
 
             if not return_dict:
                 output = (lm_logits,) + transformer_outputs[1:]
@@ -1065,3 +1079,79 @@ def forward(
         )
 
     return forward
+
+
+def get_lm_forward_with_dist_cross_entropy(shard_config: ShardConfig):
+    from transformers import BloomForCausalLM
+
+    def forward(
+        self: BloomForCausalLM,
+        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=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:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(lm_logits.device)
+            # Shift so that tokens < n predict n
+            shift_logits = lm_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            new_vocab_size = lm_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

colossalai/shardformer/modeling/falcon.py

@@ -14,6 +14,7 @@
 from transformers.modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     CausalLMOutputWithCrossAttentions,
+    CausalLMOutputWithPast,
     QuestionAnsweringModelOutput,
     SequenceClassifierOutputWithPast,
     TokenClassifierOutput,
@@ -31,6 +32,8 @@
 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 @@ def falcon_for_causal_lm_forward(
             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 @@ def falcon_for_question_answering_forward(
         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

colossalai/shardformer/modeling/gpt2.py

@@ -389,6 +389,7 @@ def gpt2_lmhead_model_forward(
                     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, shift_labels)
@@ -1294,6 +1295,7 @@ def forward(
                 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:

colossalai/shardformer/modeling/llama.py

@@ -332,6 +332,7 @@ def llama_for_causal_lm_forward(
                         shift_labels,
                         process_group=shard_config.tensor_parallel_process_group,
                         vocab_size=self.lm_head.out_features,
+                        dtype=self.model.dtype,
                     )
                 else:
                     shift_logits = shift_logits.view(-1, self.config.vocab_size)
@@ -768,6 +769,7 @@ def forward(
                 shift_labels,
                 process_group=shard_config.tensor_parallel_process_group,
                 vocab_size=self.lm_head.out_features,
+                dtype=self.model.dtype,
             )
 
         if not return_dict:

colossalai/shardformer/modeling/mistral.py

@@ -281,6 +281,7 @@ def mistral_for_causal_lm_forward(
                         shift_labels,
                         process_group=shard_config.tensor_parallel_process_group,
                         vocab_size=self.lm_head.out_features,
+                        dtype=self.model.dtype,
                     )
                 else:
                     shift_logits = shift_logits.view(-1, self.config.vocab_size)
@@ -701,6 +702,7 @@ def forward(
                 shift_labels,
                 process_group=shard_config.tensor_parallel_process_group,
                 vocab_size=self.lm_head.out_features,
+                dtype=self.model.dtype,
             )
 
         if not return_dict:

colossalai/shardformer/modeling/opt.py

@@ -348,6 +348,7 @@ def opt_for_causal_lm_forward(
                         shift_labels,
                         process_group=shard_config.tensor_parallel_process_group,
                         vocab_size=self.lm_head.out_features,
+                        dtype=self.model.decoder.dtype,
                     )
                 else:
                     loss_fct = CrossEntropyLoss()
@@ -988,6 +989,7 @@ def forward(
                 shift_labels,
                 process_group=shard_config.tensor_parallel_process_group,
                 vocab_size=self.lm_head.out_features,
+                dtype=self.model.decoder.dtype,
             )
 
         if not return_dict: