add parallel_output for the opt model

colossalai/shardformer/modeling/opt.py

@@ -21,7 +21,7 @@ from transformers.utils import logging
 from colossalai.pipeline.stage_manager import PipelineStageManager
 from colossalai.shardformer.layer import ColoAttention
 from colossalai.shardformer.shard import ShardConfig
-
+from ..layer import cross_entropy_1d
 logger = logging.get_logger(__name__)
 
 
@@ -336,8 +336,22 @@ class OPTPipelineForwards:
                 shift_logits = logits[..., :-1, :].contiguous()
                 shift_labels = labels[..., 1:].contiguous()
                 # Flatten the tokens
-                loss_fct = CrossEntropyLoss()
+
-                loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
+                if shard_config.enable_tensor_parallelism and shard_config.parallel_output:
+                    new_vocab_size = 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,
+                    )
+                else:
+                    loss_fct = CrossEntropyLoss()
+                    shift_logits = shift_logits.view(-1, self.config.vocab_size)
+                    loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
+            
             if not return_dict:
                 output = (logits,) + outputs[1:]
                 return (loss,) + output if loss is not None else output
@@ -844,3 +858,148 @@ def get_jit_fused_opt_decoder_layer_forward():
         return outputs
 
     return forward
+
+
+def get_lm_forward_with_dist_cross_entropy(shard_config: ShardConfig):
+    def forward(
+        self: OPTForCausalLM,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = 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,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+                provide it.
+
+                Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+                [`PreTrainedTokenizer.__call__`] for details.
+
+                [What are input IDs?](../glossary#input-ids)
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **masked**.
+
+                [What are attention masks?](../glossary#attention-mask)
+            head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
+                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
+
+                - 1 indicates the head is **not masked**,
+                - 0 indicates the head is **masked**.
+
+            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+                Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
+                shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
+                tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
+
+                Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
+                cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+                than the model's internal embedding lookup matrix.
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                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, ..., config.vocab_size]`.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            output_hidden_states (`bool`, *optional*):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more detail.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, OPTForCausalLM
+
+        >>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m")
+        >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
+
+        >>> 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. I'm just a little bit of a weirdo."
+        ```"""
+
+        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 = self.model.decoder(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            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,
+        )
+
+        logits = self.lm_head(outputs[0]).contiguous()
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            new_vocab_size = logits.shape[-1]
+            shift_logits = shift_logits.view(-1, new_vocab_size)
+            loss = cross_entropy_1d(
+                shift_logits,
+                shift_labels,
+                process_group=shard_config.tensor_parallel_process_group,
+                vocab_size=self.lm_head.out_features,
+            )
+            #loss_fct = CrossEntropyLoss()
+            #loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
+
+        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,
+        )
+
+    return forward

colossalai/shardformer/policies/opt.py

@@ -23,6 +23,7 @@ from ..modeling.opt import (
     get_jit_fused_opt_decoder_layer_forward,
     get_opt_decoder_forward_for_flash_attention,
     get_opt_flash_attention_forward,
+    get_lm_forward_with_dist_cross_entropy
 )
 from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
 
@@ -269,12 +270,22 @@ class OPTForCausalLMPolicy(OPTPolicy):
                     suffix="lm_head",
                     target_module=VocabParallelLMHead1D,
                     kwargs=dict(
-                        gather_output=True, make_vocab_size_divisible_by=self.shard_config.make_vocab_size_divisible_by
+                        gather_output=not self.shard_config.parallel_output, 
+                        make_vocab_size_divisible_by=self.shard_config.make_vocab_size_divisible_by
                     ),
                 ),
                 policy=policy,
                 target_key=OPTForCausalLM,
             )
+            if self.shard_config.parallel_output:
+                method_replacement = {
+                    "forward": get_lm_forward_with_dist_cross_entropy(self.shard_config)
+                }
+                self.append_or_create_method_replacement(
+                    description=method_replacement, 
+                    policy=policy, 
+                    target_key=OPTForCausalLM
+                )
         else:
             self.append_or_create_submodule_replacement(
                 description=SubModuleReplacementDescription(