Merge pull request #6299 from wangbluo/upgrade_bloom

Upgrade bloom

colossalai/shardformer/modeling/bloom.py

@@ -6,7 +6,7 @@ import torch.distributed as dist
 from torch.distributed import ProcessGroup
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 from torch.nn import functional as F
-from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
+from transformers.cache_utils import Cache, DynamicCache
 from transformers.modeling_outputs import (
     BaseModelOutputWithPastAndCrossAttentions,
     CausalLMOutputWithCrossAttentions,
@@ -21,6 +21,7 @@ from transformers.models.bloom.modeling_bloom import (
     BloomForSequenceClassification,
     BloomForTokenClassification,
     BloomModel,
+    dropout_add,
 )
 from transformers.utils import logging
 
@@ -108,7 +109,7 @@ class BloomPipelineForwards:
     def bloom_model_forward(
         self: BloomModel,
         input_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+        past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]] = None,
         attention_mask: Optional[torch.Tensor] = None,
         head_mask: Optional[torch.LongTensor] = None,
         inputs_embeds: Optional[torch.LongTensor] = None,
@@ -116,6 +117,7 @@ class BloomPipelineForwards:
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
         stage_manager: Optional[PipelineStageManager] = None,
         hidden_states: Optional[torch.FloatTensor] = None,
         stage_index: Optional[List[int]] = None,
@@ -151,6 +153,8 @@ class BloomPipelineForwards:
         if use_cache:
             logger.warning_once("use_cache=True is not supported for pipeline models at the moment.")
             use_cache = False
+        past_key_values = None
+
         # Prepare head mask if needed
         # 1.0 in head_mask indicate we keep the head
         # attention_probs has shape batch_size x num_heads x N x N
@@ -161,46 +165,60 @@ class BloomPipelineForwards:
         # case: First stage of training
         if stage_manager.is_first_stage():
             # check input_ids and inputs_embeds
-            if input_ids is not None and inputs_embeds is not None:
+            if (input_ids is None) ^ (inputs_embeds is not None):
-                raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+                raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-            elif input_ids is not None:
+            if self.gradient_checkpointing and self.training and use_cache:
-                batch_size, seq_length = input_ids.shape
-            elif inputs_embeds is not None:
-                batch_size, seq_length, _ = inputs_embeds.shape
-            else:
-                raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-            if inputs_embeds is None:
-                inputs_embeds = self.word_embeddings(input_ids)
-
-            hidden_states = self.word_embeddings_layernorm(inputs_embeds)
-            # initialize in the first stage and then pass to the next stage
-        else:
-            input_shape = hidden_states.shape[:-1]
-            batch_size, seq_length = input_shape
-
-        # extra recording tensor should be generated in the first stage
-
-        presents = () if use_cache else None
-        all_self_attentions = () if output_attentions else None
-        all_hidden_states = () if output_hidden_states else None
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
                 logger.warning_once(
                     "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                 )
                 use_cache = False
 
-        if past_key_values is None:
+            if inputs_embeds is None:
-            past_key_values = tuple([None] * len(self.h))
+                inputs_embeds = self.word_embeddings(input_ids)
-        # Compute alibi tensor: check build_alibi_tensor documentation,build for every stage
+
-        seq_length_with_past = seq_length
+            hidden_states = self.word_embeddings_layernorm(inputs_embeds)
-        past_key_values_length = 0
+
-        if past_key_values[0] is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
-            past_key_values_length = past_key_values[0][0].shape[2]  # source_len
+            past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+            if cache_position is None:
+                cache_position = torch.arange(past_length, past_length + seq_length, device=inputs_embeds.device)
+            # initialize in the first stage and then pass to the next stage
+        else:
+            input_shape = hidden_states.shape[:-1]
+            batch_size, seq_length = input_shape
+            past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+            if cache_position is None:
+                cache_position = torch.arange(past_length, past_length + seq_length, device=hidden_states.device)
+
+        # extra recording tensor should be generated in the first stage
+
+        all_self_attentions = () if output_attentions else None
+        all_hidden_states = () if output_hidden_states else None
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+            )
+            use_cache = False
+
+        # kept for BC (non `Cache` `past_key_values` inputs)
+        return_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache):
+            return_legacy_cache = True
+            if past_key_values is None:
+                past_key_values = DynamicCache()
+            else:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+                logger.warning_once(
+                    "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
+                    "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
+                    "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
+                )
+
+        # Compute alibi tensor: check build_alibi_tensor documentation,build for every stage
+        past_length = 0
+        seq_length_with_past = seq_length + past_length
 
-            seq_length_with_past = seq_length_with_past + past_key_values_length
         if attention_mask is None:
             attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
         else:
@@ -209,13 +227,10 @@ class BloomPipelineForwards:
         alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
 
         # causal_mask is constructed every stage and its input is passed through different stages
-        causal_mask = _prepare_4d_causal_attention_mask(
+        causal_mask = self._update_causal_mask(
-            attention_mask,
+            attention_mask, hidden_states, cache_position, past_key_values, output_attentions
-            input_shape=(batch_size, seq_length),
-            inputs_embeds=hidden_states,
-            past_key_values_length=past_key_values_length,
         )
-        causal_mask = causal_mask.bool()
+
         # split the input tensor along sequence dimension
         # [batch_size, seq_len, hidden_size] -> [batch_size, seq_len/TP_size, hidden_size]
         if shard_config and shard_config.enable_sequence_parallelism:
@@ -228,9 +243,7 @@ class BloomPipelineForwards:
                 )
 
         start_idx, end_idx = stage_index[0], stage_index[1]
-        for i, (block, layer_past) in enumerate(
+        for i, block in enumerate(self.h[start_idx:end_idx], start=start_idx):
-            zip(self.h[start_idx:end_idx], past_key_values[start_idx:end_idx]), start=start_idx
-        ):
             if output_hidden_states:
                 all_hidden_states = all_hidden_states + (hidden_states,)
 
@@ -240,26 +253,28 @@ class BloomPipelineForwards:
                     hidden_states,
                     alibi,
                     causal_mask,
-                    layer_past,
+                    past_key_values,
                     head_mask[i],
                     use_cache,
                     output_attentions,
+                    cache_position,
                 )
             else:
                 outputs = block(
                     hidden_states,
-                    layer_past=layer_past,
+                    layer_past=past_key_values,
                     attention_mask=causal_mask,
                     head_mask=head_mask[i],
                     use_cache=use_cache,
                     output_attentions=output_attentions,
                     alibi=alibi,
+                    cache_position=cache_position,
                 )
 
             hidden_states = outputs[0]
+            if use_cache:
+                next_decoder_cache = outputs[1]
 
-            if use_cache is True:
-                presents = presents + (outputs[1],)
             if output_attentions:
                 all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
 
@@ -277,20 +292,23 @@ class BloomPipelineForwards:
             # Add last hidden state
             hidden_states = self.ln_f(hidden_states)
 
-        # TODO(jianghai): deal with all_hidden_states, all_self_attentions, presents
         if output_hidden_states:
             all_hidden_states = all_hidden_states + (hidden_states,)
 
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
         if stage_manager.is_last_stage():
             if not return_dict:
                 return tuple(
-                    v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None
+                    v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None
                 )
 
             # attention_mask is not returned ; presents = past_key_values
             return BaseModelOutputWithPastAndCrossAttentions(
                 last_hidden_state=hidden_states,
-                past_key_values=presents,
+                past_key_values=next_cache,
                 hidden_states=all_hidden_states,
                 attentions=all_self_attentions,
             )
@@ -718,35 +736,24 @@ def get_jit_fused_bloom_attention_forward():
         head_mask: Optional[torch.Tensor] = None,
         use_cache: bool = False,
         output_attentions: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
     ):
+        batch_size, q_length, _ = hidden_states.shape
         fused_qkv = self.query_key_value(hidden_states)  # [batch_size, seq_length, 3 x hidden_size]
+        # 3 x [batch_size, num_heads, seq_length, head_dim]
+        query_layer, key_layer, value_layer = self._reshape(fused_qkv)
 
-        # 3 x [batch_size, seq_length, num_heads, head_dim]
-        (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
-
-        batch_size, q_length, _, _ = query_layer.shape
-
-        query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim)
-        key_layer = key_layer.permute(0, 2, 3, 1).reshape(batch_size * self.num_heads, self.head_dim, q_length)
-        value_layer = value_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim)
         if layer_past is not None:
-            past_key, past_value = layer_past
+            cache_kwargs = {"cache_position": cache_position}
-            # concatenate along seq_length dimension:
+            key_layer, value_layer = layer_past.update(key_layer, value_layer, self.layer_idx, cache_kwargs)
-            #  - key: [batch_size * self.num_heads, head_dim, kv_length]
-            #  - value: [batch_size * self.num_heads, kv_length, head_dim]
-            key_layer = torch.cat((past_key, key_layer), dim=2)
-            value_layer = torch.cat((past_value, value_layer), dim=1)
 
-        _, _, kv_length = key_layer.shape
+        # reshape qkv for further computations
-
+        query_layer = query_layer.reshape(batch_size * self.num_heads, -1, self.head_dim)
-        if use_cache is True:
+        key_layer = key_layer.reshape(batch_size * self.num_heads, -1, self.head_dim).transpose(-1, -2)
-            present = (key_layer, value_layer)
+        value_layer = value_layer.reshape(batch_size * self.num_heads, -1, self.head_dim)
-        else:
-            present = None
 
         # [batch_size * num_heads, q_length, kv_length]
-        # we use `torch.Tensor.baddbmm` instead of `torch.baddbmm` as the latter isn't supported by TorchScript v1.11
+        attention_scores = alibi.baddbmm(
-        matmul_result = alibi.baddbmm(
             batch1=query_layer,
             batch2=key_layer,
             beta=self.beta,
@@ -754,15 +761,13 @@ def get_jit_fused_bloom_attention_forward():
         )
 
         # change view to [batch_size, num_heads, q_length, kv_length]
-        attention_scores = matmul_result.view(batch_size, self.num_heads, q_length, kv_length)
+        attn_weights = attention_scores.view(batch_size, self.num_heads, q_length, -1)
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_layer.shape[-1]]
+            attn_weights = attn_weights + causal_mask
 
-        # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
+        # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype
-        input_dtype = attention_scores.dtype
+        attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_layer.dtype)
-        # `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
-        if input_dtype == torch.float16:
-            attention_scores = attention_scores.to(torch.float)
-        attn_weights = torch.masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
-        attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(input_dtype)
 
         # [batch_size, num_heads, q_length, kv_length]
         attention_probs = self.attention_dropout(attention_probs)
@@ -771,12 +776,12 @@ def get_jit_fused_bloom_attention_forward():
             attention_probs = attention_probs * head_mask
 
         # change view [batch_size x num_heads, q_length, kv_length]
-        attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, kv_length)
+        attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, -1)
 
         # matmul: [batch_size * num_heads, q_length, head_dim]
         context_layer = torch.bmm(attention_probs_reshaped, value_layer)
 
-        # change view [batch_size, num_heads, q_length, head_dim]
+        # change view [batch_size, q_length, num_heads * head_dim]
         context_layer = self._merge_heads(context_layer)
 
         # aggregate results across tp ranks. See here: https://github.com/pytorch/pytorch/issues/76232
@@ -791,9 +796,9 @@ def get_jit_fused_bloom_attention_forward():
         else:
             output_tensor = self.dense(context_layer)
 
-        output_tensor = self.dropout_add(output_tensor, residual, self.hidden_dropout, self.training)
+        output_tensor = dropout_add(output_tensor, residual, self.hidden_dropout, self.training)
 
-        outputs = (output_tensor, present)
+        outputs = (output_tensor, layer_past)
         if output_attentions:
             outputs += (attention_probs,)
 
@@ -839,13 +844,99 @@ def get_jit_fused_bloom_gelu_forward():
     return forward
 
 
+# Fixed the q_length args when doing the sequence parallelism in bloom model.
+def get_bloom_sequence_parallel_attention_forward(shard_config: ShardConfig):
+    from transformers.models.bloom.modeling_bloom import BloomAttention
+
+    def forward(
+        self: BloomAttention,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor,
+        alibi: torch.Tensor,
+        attention_mask: torch.Tensor,
+        layer_past: Optional[Cache] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        use_cache: bool = False,
+        output_attentions: bool = False,
+        cache_position: Optional[torch.LongTensor] = None,
+    ):
+        batch_size, q_length, _ = hidden_states.shape
+        fused_qkv = self.query_key_value(hidden_states)  # [batch_size, seq_length, 3 x hidden_size]
+        # 3 x [batch_size, num_heads, seq_length, head_dim]
+        query_layer, key_layer, value_layer = self._reshape(fused_qkv)
+
+        if layer_past is not None:
+            cache_kwargs = {"cache_position": cache_position}
+            key_layer, value_layer = layer_past.update(key_layer, value_layer, self.layer_idx, cache_kwargs)
+
+        # reshape qkv for further computations
+        query_layer = query_layer.reshape(batch_size * self.num_heads, -1, self.head_dim)
+        key_layer = key_layer.reshape(batch_size * self.num_heads, -1, self.head_dim).transpose(-1, -2)
+        value_layer = value_layer.reshape(batch_size * self.num_heads, -1, self.head_dim)
+
+        # [batch_size * num_heads, q_length, kv_length]
+        attention_scores = alibi.baddbmm(
+            batch1=query_layer,
+            batch2=key_layer,
+            beta=self.beta,
+            alpha=self.inv_norm_factor,
+        )
+        if shard_config.enable_sequence_parallelism:
+            _, q_length, _ = query_layer.shape
+        # change view to [batch_size, num_heads, q_length, kv_length]
+        attn_weights = attention_scores.view(batch_size, self.num_heads, q_length, -1)
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_layer.shape[-1]]
+            attn_weights = attn_weights + causal_mask
+
+        # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype
+        attention_probs = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_layer.dtype)
+
+        # [batch_size, num_heads, q_length, kv_length]
+        attention_probs = self.attention_dropout(attention_probs)
+
+        if head_mask is not None:
+            attention_probs = attention_probs * head_mask
+
+        # change view [batch_size x num_heads, q_length, kv_length]
+        attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, -1)
+
+        # matmul: [batch_size * num_heads, q_length, head_dim]
+        context_layer = torch.bmm(attention_probs_reshaped, value_layer)
+
+        # change view [batch_size, q_length, num_heads * head_dim]
+        context_layer = self._merge_heads(context_layer)
+
+        # aggregate results across tp ranks. See here: https://github.com/pytorch/pytorch/issues/76232
+        if self.pretraining_tp > 1 and self.slow_but_exact:
+            slices = self.hidden_size / self.pretraining_tp
+            output_tensor = torch.zeros_like(context_layer)
+            for i in range(self.pretraining_tp):
+                output_tensor = output_tensor + F.linear(
+                    context_layer[:, :, int(i * slices) : int((i + 1) * slices)],
+                    self.dense.weight[:, int(i * slices) : int((i + 1) * slices)],
+                )
+        else:
+            output_tensor = self.dense(context_layer)
+
+        output_tensor = dropout_add(output_tensor, residual, self.hidden_dropout, self.training)
+
+        outputs = (output_tensor, layer_past)
+        if output_attentions:
+            outputs += (attention_probs,)
+
+        return outputs
+
+    return forward
+
+
 def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
     from transformers import BloomModel
 
     def forward(
         self: BloomModel,
         input_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+        past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]] = None,
         attention_mask: Optional[torch.Tensor] = None,
         head_mask: Optional[torch.LongTensor] = None,
         inputs_embeds: Optional[torch.LongTensor] = None,
@@ -853,6 +944,7 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
         **deprecated_arguments,
     ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
         if deprecated_arguments.pop("position_ids", False) is not False:
@@ -864,7 +956,6 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
             )
         if len(deprecated_arguments) > 0:
             raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
-
         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
@@ -872,62 +963,60 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
         use_cache = use_cache if use_cache is not None else self.config.use_cache
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
-        if input_ids is not None and inputs_embeds is not None:
+        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
-        elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape
-        elif inputs_embeds is not None:
-            batch_size, seq_length, _ = inputs_embeds.shape
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
 
-        if past_key_values is None:
+        if self.gradient_checkpointing and self.training and use_cache:
-            past_key_values = tuple([None] * len(self.h))
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+
+        # kept for BC (non `Cache` `past_key_values` inputs)
+        return_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache):
+            return_legacy_cache = True
+            if past_key_values is None:
+                past_key_values = DynamicCache()
+            else:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+                logger.warning_once(
+                    "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
+                    "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
+                    "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
+                )
+
+        batch_size, seq_length, _ = inputs_embeds.shape
+        past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+        seq_length_with_past = seq_length + past_length
+        if cache_position is None:
+            cache_position = torch.arange(past_length, past_length + seq_length, device=inputs_embeds.device)
 
         # Prepare head mask if needed
         # 1.0 in head_mask indicate we keep the head
         # attention_probs has shape batch_size x num_heads x N x N
         # head_mask has shape n_layer x batch x num_heads x N x N
         head_mask = self.get_head_mask(head_mask, self.config.n_layer)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-
         hidden_states = self.word_embeddings_layernorm(inputs_embeds)
 
-        presents = () if use_cache else None
+        next_decoder_cache = None
         all_self_attentions = () if output_attentions else None
         all_hidden_states = () if output_hidden_states else None
 
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                )
-                use_cache = False
-
         # Compute alibi tensor: check build_alibi_tensor documentation
-        seq_length_with_past = seq_length
-        past_key_values_length = 0
-        if past_key_values[0] is not None:
-            past_key_values_length = past_key_values[0][0].shape[2]
-            seq_length_with_past = seq_length_with_past + past_key_values_length
         if attention_mask is None:
             attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
         else:
             attention_mask = attention_mask.to(hidden_states.device)
 
         alibi = self.build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
-
+        causal_mask = self._update_causal_mask(
-        causal_mask = _prepare_4d_causal_attention_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
-            attention_mask,
-            input_shape=(batch_size, seq_length),
-            inputs_embeds=hidden_states,
-            past_key_values_length=past_key_values_length,
         )
-        causal_mask = causal_mask.bool()
+
-        # split the input tensor along sequence dimension
-        # [batch_size, seq_len, hidden_size] -> [batch_size, seq_len/TP_size, hidden_size]
         hidden_states = split_forward_gather_backward(
             hidden_states,
             dim=1,
@@ -935,7 +1024,7 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
             fp8_communication=shard_config.fp8_communication,
         )
 
-        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
+        for i, block in enumerate(self.h):
             if output_hidden_states:
                 all_hidden_states = all_hidden_states + (hidden_states,)
 
@@ -945,25 +1034,27 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
                     hidden_states,
                     alibi,
                     causal_mask,
-                    layer_past,
+                    past_key_values,
                     head_mask[i],
                     use_cache,
                     output_attentions,
+                    cache_position,
                 )
             else:
                 outputs = block(
                     hidden_states,
-                    layer_past=layer_past,
+                    layer_past=past_key_values,
                     attention_mask=causal_mask,
                     head_mask=head_mask[i],
                     use_cache=use_cache,
                     output_attentions=output_attentions,
                     alibi=alibi,
+                    cache_position=cache_position,
                 )
 
             hidden_states = outputs[0]
-            if use_cache is True:
+            if use_cache:
-                presents = presents + (outputs[1],)
+                next_decoder_cache = outputs[1]
 
             if output_attentions:
                 all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
@@ -975,18 +1066,25 @@ def get_bloom_sequence_parallel_forward_fn(shard_config: ShardConfig):
             process_group=shard_config.tensor_parallel_process_group,
             fp8_communication=shard_config.fp8_communication,
         )
+
         # Add last hidden state
         hidden_states = self.ln_f(hidden_states)
 
         if output_hidden_states:
             all_hidden_states = all_hidden_states + (hidden_states,)
 
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
         if not return_dict:
-            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
+            return tuple(
+                v for v in [hidden_states, next_cache, all_hidden_states, all_self_attentions] if v is not None
+            )
 
         return BaseModelOutputWithPastAndCrossAttentions(
             last_hidden_state=hidden_states,
-            past_key_values=presents,
+            past_key_values=next_cache,
             hidden_states=all_hidden_states,
             attentions=all_self_attentions,
         )

colossalai/shardformer/policies/bloom.py

@@ -11,6 +11,7 @@ import colossalai.shardformer.layer as col_nn
 from ..modeling.bloom import (
     BloomPipelineForwards,
     build_bloom_alibi_tensor_fn,
+    get_bloom_sequence_parallel_attention_forward,
     get_bloom_sequence_parallel_forward_fn,
     get_jit_fused_bloom_attention_forward,
     get_jit_fused_bloom_gelu_forward,
@@ -61,6 +62,15 @@ class BloomPolicy(Policy):
 
         use_zbv = self.pipeline_stage_manager is not None and self.pipeline_stage_manager.use_zbv
 
+        if self.shard_config.enable_sequence_parallelism:
+            self.append_or_create_method_replacement(
+                description={
+                    "forward": get_bloom_sequence_parallel_attention_forward(self.shard_config),
+                },
+                policy=policy,
+                target_key=BloomAttention,
+            )
+
         if self.shard_config.enable_tensor_parallelism:
             assert (
                 self.model.config.n_head % self.shard_config.tensor_parallel_size == 0