[infer] Add Bloom inference policy and replaced methods (#4512)

* add bloom inference methods and policy

* enable pass BatchInferState from model forward

* revise bloom infer layers/policies

* add engine for inference (draft)

* add test for bloom infer

* fix bloom infer policy and flow

* revise bloom test

* fix bloom file path

* remove unused codes

* fix bloom modeling

* fix dir typo

* fix trivial

* fix policy

* clean pr

* trivial fix

colossalai/inference/tensor_parallel/__init__.py

@@ -1,6 +1,4 @@
-from .modeling.llama import LlamaInferenceForwards
-from .pollcies.llama import LlamaModelInferPolicy
 from .engine import TPInferEngine
 from .kvcache_manager import MemoryManager
- 
-__all__ = ['LlamaInferenceForwards', 'LlamaModelInferPolicy', 'MemoryManager', 'TPInferEngine']
+
+__all__ = ['MemoryManager', 'TPInferEngine']

colossalai/inference/tensor_parallel/engine.py

@@ -141,7 +141,6 @@ class TPInferEngine:
 
         outputs = self.sharded_model.generate(**input_tokens, **generate_kwargs, early_stopping=False)
 
-        print(f"outputs.shape {outputs.shape}")
         return outputs
 
     def prepare_batch_state(self, inputs) -> BatchInferState:
@@ -193,11 +192,7 @@ class TPInferEngine:
                 start_index += curr_seq_len
                 max_len_in_batch = curr_seq_len if curr_seq_len > max_len_in_batch else max_len_in_batch
 
-        print(" 666 ", max_len_in_batch)
-
-        block_loc = torch.empty((batch_size, self.max_input_len + self.max_output_len),
-                                dtype=torch.long,
-                                device='cuda')
+        block_loc = torch.empty((batch_size, self.max_input_len + self.max_output_len), dtype=torch.long, device='cuda')
         batch_infer_state = BatchInferState(batch_size, max_len_in_batch)
         batch_infer_state.seq_len = seq_lengths.to('cuda')    # might want to assign specific device
         batch_infer_state.start_loc = seq_start_indexes.to('cuda')
@@ -251,4 +246,4 @@ class TPInferEngine:
     #       => put information already recorded in batchinferstate and pass it to model forward
     #       => clear records in engine
     def add_request():
-        raise NotImplementedError()
+        raise NotImplementedError()

colossalai/inference/tensor_parallel/modeling/__init__.py

@@ -1,3 +1,4 @@
+from .bloom import BloomInferenceForwards
 from .llama import LlamaInferenceForwards
 
-__all__ = ['LlamaInferenceForwards']
+__all__ = ['BloomInferenceForwards', 'LlamaInferenceForwards']

colossalai/inference/tensor_parallel/modeling/bloom.py

@@ -0,0 +1,559 @@
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.distributed as dist
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from torch.nn import functional as F
+from transformers.models.bloom.modeling_bloom import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BloomAttention,
+    BloomBlock,
+    BloomForCausalLM,
+    BloomModel,
+    CausalLMOutputWithCrossAttentions,
+)
+from transformers.utils import logging
+
+from colossalai.inference.tensor_parallel.batch_infer_state import BatchInferState
+from colossalai.kernel.triton.context_attention import bloom_context_attn_fwd
+from colossalai.kernel.triton.copy_kv_cache_dest import copy_kv_cache_to_dest
+from colossalai.kernel.triton.token_attention_kernel import token_attention_fwd
+
+
+def generate_alibi(n_head, dtype=torch.float16):
+    """
+    This method is originally the `build_alibi_tensor` function
+    in `transformers/models/bloom/modeling_bloom.py`
+    of the huggingface/transformers GitHub repository.
+
+    Copyright 2023 ModelTC Team
+    Copyright 2022 HuggingFace Inc. team and BigScience workshop
+
+    Licensed under the Apache License, Version 2.0 (the "License");
+    you may not use this file except in compliance with the License.
+    You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software
+    distributed under the License is distributed on an "AS IS" BASIS,
+    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+    See the License for the specific language governing permissions and
+    limitations under the License.
+    """
+
+    def get_slopes(n):
+
+        def get_slopes_power_of_2(n):
+            start = 2**(-(2**-(math.log2(n) - 3)))
+            ratio = start
+            return [start * ratio**i for i in range(n)]
+
+        if math.log2(n).is_integer():
+            return get_slopes_power_of_2(n)
+        else:
+            closest_power_of_2 = 2**math.floor(math.log2(n))
+            return (get_slopes_power_of_2(closest_power_of_2) +
+                    get_slopes(2 * closest_power_of_2)[0::2][:n - closest_power_of_2])
+
+    slopes = torch.Tensor(get_slopes(n_head))
+    head_alibi = slopes.to(dtype)
+    return head_alibi    # 1 * num_heads
+
+
+def generate_alibi_2(n_head, dtype=torch.float16):
+
+    def get_slopes_power_of_2(n):
+        start = 2**(-(2**-(math.log2(n) - 3)))
+        return [start * start**i for i in range(n)]
+
+    def get_slopes(n):
+        if math.log2(n).is_integer():
+            return get_slopes_power_of_2(n)
+        else:
+            closest_power_of_2 = 2**math.floor(math.log2(n))
+            slopes_power_of_2 = get_slopes_power_of_2(closest_power_of_2)
+            slopes_double = get_slopes(2 * closest_power_of_2)
+            slopes_combined = slopes_power_of_2 + slopes_double[0::2][:n - closest_power_of_2]
+            return slopes_combined
+
+    slopes = torch.tensor(get_slopes(n_head), dtype=dtype)
+    return slopes
+
+
+class BloomInferenceForwards:
+    """
+    This class serves a micro library for bloom inference forwards
+    """
+
+    @staticmethod
+    def bloom_model_forward(
+        self: BloomModel,
+        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.LongTensor] = None,
+        inputs_embeds: 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,
+        infer_state: Optional[BatchInferState] = None,
+        **deprecated_arguments,
+    ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
+
+        logger = logging.get_logger(__name__)
+
+        if deprecated_arguments.pop("position_ids", False) is not False:
+            # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
+            warnings.warn(
+                "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
+                " passing `position_ids`.",
+                FutureWarning,
+            )
+        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)
+        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:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        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")
+
+        # still need to keep past_key_values to fit original forward flow
+        if past_key_values is None:
+            past_key_values = tuple([None] * len(self.h))
+
+        # 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
+        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
+
+        # NOTE determine if BatchInferState is passed in via arg
+        #      if not, get the attr binded to the model
+        # We might wantto remove setattr later
+        if infer_state is None:
+            assert hasattr(self, 'infer_state')
+            infer_state = self.infer_state
+
+        # Compute alibi tensor: check build_alibi_tensor documentation
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+        # if self.cache_manager.past_key_values_length > 0:
+        if infer_state.cache_manager.past_key_values_length > 0:
+            # update the past key values length in cache manager,
+            # TODO use BatchInferState.past_key_values_length instead the one in cache manager
+            past_key_values_length = infer_state.cache_manager.past_key_values_length
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        # infer_state.cache_manager = self.cache_manager
+
+        if use_cache and seq_length != 1:
+            # prefill stage
+            infer_state.is_context_stage = True    # set prefill stage, notify attention layer
+            infer_state.context_mem_index = infer_state.cache_manager.alloc(infer_state.total_token_num)
+            BatchInferState.init_block_loc(infer_state.block_loc, infer_state.seq_len, seq_length,
+                                           infer_state.context_mem_index)
+        else:
+            infer_state.is_context_stage = False
+            alloc_mem = infer_state.cache_manager.alloc_contiguous(batch_size)
+            if alloc_mem is not None:
+                infer_state.decode_is_contiguous = True
+                infer_state.decode_mem_index = alloc_mem[0]
+                infer_state.decode_mem_start = alloc_mem[1]
+                infer_state.decode_mem_end = alloc_mem[2]
+                infer_state.block_loc[:, seq_length_with_past - 1] = infer_state.decode_mem_index
+            else:
+                print(f" *** Encountered allocation non-contiguous")
+                print(
+                    f"    infer_state.cache_manager.past_key_values_length: {infer_state.cache_manager.past_key_values_length}"
+                )
+                infer_state.decode_is_contiguous = False
+                alloc_mem = infer_state.cache_manager.alloc(batch_size)
+                infer_state.decode_mem_index = alloc_mem
+                # infer_state.decode_key_buffer = torch.empty((batch_size, self.tp_head_num_, self.head_dim_), dtype=torch.float16, device="cuda")
+                # infer_state.decode_value_buffer = torch.empty((batch_size, self.tp_head_num_, self.head_dim_), dtype=torch.float16, device="cuda")
+                infer_state.block_loc[:, seq_length_with_past - 1] = infer_state.decode_mem_index
+
+        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)
+
+        # TODO revise: we might want to store a single 1D alibi(length is #heads) in model,
+        #      or store to BatchInferState to prevent re-calculating
+        #      When we have multiple process group (e.g. dp together with tp), we need to pass the pg to here
+        # alibi = generate_alibi(self.num_heads).contiguous().cuda()
+        tp_size = dist.get_world_size()
+        curr_tp_rank = dist.get_rank()
+        alibi = generate_alibi(self.num_heads * tp_size).contiguous()[curr_tp_rank * self.num_heads:(curr_tp_rank + 1) *
+                                                                      self.num_heads].cuda()
+        causal_mask = self._prepare_attn_mask(
+            attention_mask,
+            input_shape=(batch_size, seq_length),
+            past_key_values_length=past_key_values_length,
+        )
+
+        for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                # FIXME: currently our KV cache manager does not handle this condition
+                def create_custom_forward(module):
+
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
+
+                    return custom_forward
+
+                outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    alibi,
+                    causal_mask,
+                    layer_past,
+                    head_mask[i],
+                )
+            else:
+                outputs = block(
+                    hidden_states,
+                    layer_past=layer_past,
+                    attention_mask=causal_mask,
+                    head_mask=head_mask[i],
+                    use_cache=use_cache,
+                    output_attentions=output_attentions,
+                    alibi=alibi,
+                    infer_state=infer_state,
+                )
+
+            hidden_states = outputs[0]
+            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],)
+
+        # Add last hidden state
+        hidden_states = self.ln_f(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        # update indices of kv cache block
+        # TODO: might want to remove this part, instead, better to pass the BatchInferState from model forward,
+        #       and update these information in engine.generate after model foward called
+        infer_state.start_loc = infer_state.start_loc + torch.arange(0, batch_size, dtype=torch.int32, device="cuda")
+        infer_state.seq_len += 1
+        infer_state.decode_layer_id = 0
+
+        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 BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=presents,    # should always be (None, None, ..., None)
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+    @staticmethod
+    def bloom_for_causal_lm_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,
+                                    infer_state: Optional[BatchInferState] = None,
+                                    **deprecated_arguments):
+        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]`
+        """
+        logger = logging.get_logger(__name__)
+
+        if deprecated_arguments.pop("position_ids", False) is not False:
+            # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
+            warnings.warn(
+                "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
+                " passing `position_ids`.",
+                FutureWarning,
+            )
+        if len(deprecated_arguments) > 0:
+            raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = BloomInferenceForwards.bloom_model_forward(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,
+                                                                         infer_state=infer_state)
+        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()
+            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 not return_dict:
+            output = (lm_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return CausalLMOutputWithCrossAttentions(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+    @staticmethod
+    def bloom_for_causal_lm_prepare_inputs_for_generation(
+        self: BloomForCausalLM,
+        input_ids: torch.LongTensor,
+        past_key_values: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> dict:
+        # only last token for input_ids if past is not None
+        if past_key_values:
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+
+            # NOTE we won't use past key values here
+            # the cache may be in the stardard format (e.g. in contrastive search), convert to bloom's format if needed
+            # if past_key_values[0][0].shape[0] == input_ids.shape[0]:
+            #     past_key_values = self._convert_to_bloom_cache(past_key_values)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update({
+            "past_key_values": past_key_values,
+            "use_cache": kwargs.get("use_cache"),
+            "attention_mask": attention_mask,
+        })
+        return model_inputs
+
+    # replace decoder layer forward:
+    #   used to replace BloomBlock.forward
+    @staticmethod
+    def bloom_block_forward(
+        self: BloomBlock,
+        hidden_states: torch.Tensor,
+        alibi: torch.Tensor,
+        attention_mask: torch.Tensor,
+        layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        use_cache: bool = False,
+        output_attentions: bool = False,
+        infer_state: Optional[BatchInferState] = None,
+    ):
+        # hidden_states: [batch_size, seq_length, hidden_size]
+
+        # Layer norm at the beginning of the transformer layer.
+        layernorm_output = self.input_layernorm(hidden_states)
+
+        # Layer norm post the self attention.
+        if self.apply_residual_connection_post_layernorm:
+            residual = layernorm_output
+        else:
+            residual = hidden_states
+
+        # Self attention.
+        attn_outputs = self.self_attention(
+            layernorm_output,
+            residual,
+            layer_past=layer_past,
+            attention_mask=attention_mask,
+            alibi=alibi,
+            head_mask=head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            infer_state=infer_state,
+        )
+
+        attention_output = attn_outputs[0]
+
+        outputs = attn_outputs[1:]
+
+        layernorm_output = self.post_attention_layernorm(attention_output)
+
+        # Get residual
+        if self.apply_residual_connection_post_layernorm:
+            residual = layernorm_output
+        else:
+            residual = attention_output
+
+        # MLP.
+        output = self.mlp(layernorm_output, residual)
+
+        if use_cache:
+            outputs = (output,) + outputs
+        else:
+            outputs = (output,) + outputs[1:]
+
+        return outputs    # hidden_states, present, attentions
+
+    # replace attention forward:
+    #   used to replace BloomAttention.forward
+    @staticmethod
+    def bloom_attention_forward(
+        self: BloomAttention,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor,
+        alibi: torch.Tensor,
+        attention_mask: torch.Tensor,
+        layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        use_cache: bool = False,
+        output_attentions: bool = False,
+        infer_state: Optional[BatchInferState] = None,
+    ):
+
+        fused_qkv = self.query_key_value(hidden_states)    # [batch_size, seq_length, 3 x hidden_size]
+
+        # 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, H, D_HEAD = query_layer.shape
+        k = key_layer.reshape(-1, H, D_HEAD)    # batch_size * q_length, H, D_HEAD, q_lenth == 1
+        v = value_layer.reshape(-1, H, D_HEAD)    # batch_size * q_length, H, D_HEAD, q_lenth == 1
+
+        mem_manager = infer_state.cache_manager
+        layer_id = infer_state.decode_layer_id
+
+        if infer_state.is_context_stage:
+            # context process
+            max_input_len = q_length
+            b_start_loc = infer_state.start_loc
+            b_seq_len = infer_state.seq_len[:batch_size]
+            q = query_layer.reshape(-1, H, D_HEAD)
+
+            copy_kv_cache_to_dest(k, infer_state.context_mem_index, mem_manager.key_buffer[layer_id])
+            copy_kv_cache_to_dest(v, infer_state.context_mem_index, mem_manager.value_buffer[layer_id])
+
+            # output = self.output[:batch_size*q_length, :, :]
+            output = torch.empty_like(q)
+
+            bloom_context_attn_fwd(q, k, v, output, b_start_loc, b_seq_len, max_input_len, alibi)
+
+            context_layer = output.view(batch_size, q_length, H * D_HEAD)
+            # record the length of past key values cache when entering the first attention layer in bloom block,
+            # since we won't return past_key_value_cache right now
+            if layer_id == 0:    # once per model.forward
+                infer_state.cache_manager.past_key_values_length = q_length    # seq_len
+        else:
+            # query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim)
+            # need shape: batch_size, H, D_HEAD (q_length == 1), input q shape : (batch_size, q_length(1), H, D_HEAD)
+            assert q_length == 1, "for non-context process, we only support q_length == 1"
+            q = query_layer.reshape(-1, H, D_HEAD)
+
+            if infer_state.decode_is_contiguous:
+                # if decode is contiguous, then we copy to key cache and value cache in cache manager directly
+                cache_k = infer_state.cache_manager.key_buffer[layer_id][
+                    infer_state.decode_mem_start:infer_state.decode_mem_end, :, :]
+                cache_v = infer_state.cache_manager.value_buffer[layer_id][
+                    infer_state.decode_mem_start:infer_state.decode_mem_end, :, :]
+                cache_k.copy_(k)
+                cache_v.copy_(v)
+            else:
+                # if decode is not contiguous, use triton kernel to copy key and value cache
+                # k, v shape: [batch_size, num_heads, head_dim/embed_size_per_head]
+                copy_kv_cache_to_dest(k, infer_state.decode_mem_index, mem_manager.key_buffer[layer_id])
+                copy_kv_cache_to_dest(v, infer_state.decode_mem_index, mem_manager.value_buffer[layer_id])
+
+            b_start_loc = infer_state.start_loc[:batch_size]
+            b_loc = infer_state.block_loc[:batch_size, :]
+            b_seq_len = infer_state.seq_len[:batch_size]
+            max_len_in_batch = mem_manager.past_key_values_length + q_length
+            output = torch.empty_like(q)
+            token_attention_fwd(q, mem_manager.key_buffer[layer_id], mem_manager.value_buffer[layer_id], output, b_loc,
+                                b_start_loc, b_seq_len, max_len_in_batch, alibi)
+
+            context_layer = output.view(batch_size, q_length, H * D_HEAD)
+
+            if layer_id == 0:    # once per model.forward
+                assert infer_state.cache_manager.past_key_values_length != 0
+                infer_state.cache_manager.past_key_values_length += q_length    # += 1
+
+        # update layer id
+        infer_state.decode_layer_id += 1
+
+        # NOTE: always set present as none for now, instead of returning past key value to the next decoding,
+        #       we create the past key value pair from the cache manager
+        present = None
+
+        # 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)
+
+        # dropout is not required here during inference
+        output_tensor = residual + output_tensor
+
+        outputs = (output_tensor, present)
+        assert output_attentions is False, "we do not support output_attentions at this time"
+
+        return outputs

colossalai/inference/tensor_parallel/policies/__init__.py

@@ -0,0 +1,4 @@
+from .bloom import BloomModelInferPolicy
+from .llama import LlamaModelInferPolicy
+
+__all__ = ['BloomModelInferPolicy', 'LlamaModelInferPolicy']

colossalai/inference/tensor_parallel/policies/bloom.py

@@ -0,0 +1,44 @@
+from colossalai.shardformer.policies.bloom import BloomForCausalLMPolicy
+
+from ..modeling.bloom import BloomInferenceForwards
+
+
+class BloomModelInferPolicy(BloomForCausalLMPolicy):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def module_policy(self):
+        from transformers.models.bloom.modeling_bloom import BloomAttention, BloomBlock, BloomForCausalLM, BloomModel
+        policy = super().module_policy()
+        # NOTE set inference mode to shard config
+        self.shard_config._infer()
+
+        if self.shard_config.enable_tensor_parallelism:
+
+            method_replacement = {
+                'forward':
+                    BloomInferenceForwards.bloom_for_causal_lm_forward,
+                'prepare_inputs_for_generation':
+                    BloomInferenceForwards.bloom_for_causal_lm_prepare_inputs_for_generation
+            }
+            self.append_or_create_method_replacement(description=method_replacement,
+                                                     policy=policy,
+                                                     target_key=BloomForCausalLM)
+
+            method_replacement = {'forward': BloomInferenceForwards.bloom_model_forward}
+            self.append_or_create_method_replacement(description=method_replacement,
+                                                     policy=policy,
+                                                     target_key=BloomModel)
+
+            method_replacement = {'forward': BloomInferenceForwards.bloom_block_forward}
+            self.append_or_create_method_replacement(description=method_replacement,
+                                                     policy=policy,
+                                                     target_key=BloomBlock)
+
+            method_replacement = {'forward': BloomInferenceForwards.bloom_attention_forward}
+            self.append_or_create_method_replacement(description=method_replacement,
+                                                     policy=policy,
+                                                     target_key=BloomAttention)
+
+        return policy

colossalai/inference/tensor_parallel/policies/llama.py

@@ -2,7 +2,8 @@ from functools import partial
 
 from colossalai.shardformer.policies.llama import LlamaForCausalLMPolicy
 
-from ..modeling.llama import LlamaInferenceForwards 
+from ..modeling.llama import LlamaInferenceForwards
+
 
 class LlamaModelInferPolicy(LlamaForCausalLMPolicy):
 
@@ -23,13 +24,17 @@ class LlamaModelInferPolicy(LlamaForCausalLMPolicy):
         infer_forward = LlamaInferenceForwards.llama_model_forward
         method_replacement = {'forward': partial(infer_forward)}
         self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=LlamaModel)
-        
+
         infer_forward = LlamaInferenceForwards.llama_decoder_layer_forward
         method_replacement = {'forward': partial(infer_forward)}
-        self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=LlamaDecoderLayer)
-        
+        self.append_or_create_method_replacement(description=method_replacement,
+                                                 policy=policy,
+                                                 target_key=LlamaDecoderLayer)
+
         infer_forward = LlamaInferenceForwards.llama_flash_attn_kvcache_forward
         method_replacement = {'forward': partial(infer_forward)}
-        self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=LlamaAttention)
+        self.append_or_create_method_replacement(description=method_replacement,
+                                                 policy=policy,
+                                                 target_key=LlamaAttention)
 
-        return policy
+        return policy

colossalai/inference/tensor_parallel/pollcies/__init__.py

@@ -1,3 +0,0 @@
-from .llama import LlamaModelInferPolicy
-
-__all__ = ['LlamaModelInferPolicy']

colossalai/shardformer/policies/auto_policy.py

@@ -137,6 +137,11 @@ _INFER_POLICY_LIST = {
         PolicyLocation(file_name="llama", class_name="LlamaModelInferPolicy"),
     "transformers.models.llama.modeling_llama.LlamaForCausalLM":
         PolicyLocation(file_name="llama", class_name="LlamaModelInferPolicy"),
+    # Bloom
+    "transformers.models.bloom.modeling_bloom.BloomModel":
+        PolicyLocation(file_name="bloom", class_name="BloomModelInferPolicy"),
+    "transformers.models.bloom.modeling_bloom.BloomForCausalLM":
+        PolicyLocation(file_name="bloom", class_name="BloomModelInferPolicy"),
 }
 
 
@@ -144,9 +149,8 @@ def import_policy(policy_location: PolicyLocation, inference_only: Optional[bool
     """
     Dynamically import a Policy class based on the policy location.
     """
-    
     if inference_only:
-        module_name = f"colossalai.inference.tensor_parallel.pollcies.{policy_location.file_name}"
+        module_name = f"colossalai.inference.tensor_parallel.policies.{policy_location.file_name}"
     else:
         module_name = f"colossalai.shardformer.policies.{policy_location.file_name}"
     module = importlib.import_module(module_name)

tests/test_infer/test_bloom_infer.py

@@ -0,0 +1,60 @@
+import pytest
+import torch
+import torch.distributed as dist
+from transformers import AutoModelForCausalLM, AutoTokenizer, BloomForCausalLM
+
+import colossalai
+from colossalai.inference.tensor_parallel import TPInferEngine
+from colossalai.logging import disable_existing_loggers
+from colossalai.shardformer import ShardConfig, ShardFormer
+from colossalai.testing import clear_cache_before_run, rerun_if_address_is_in_use, spawn
+
+TP_SIZE = 2
+MAX_BATCH_SIZE = 4
+MAX_INPUT_LEN = 16
+MAX_OUTPUT_LEN = 32
+
+
+def run():
+
+    model_path = "/data3/data/model_eval_for_commerical_use/phoenix-inst-chat-7b"
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    tokenizer.pad_token = tokenizer.eos_token
+
+    text = "Introduce some landmarks in Beijing"
+    input_ids = tokenizer.batch_encode_plus([text], return_tensors='pt')
+
+    model = BloomForCausalLM.from_pretrained(model_path, pad_token_id=tokenizer.eos_token_id)
+    model = model.half()
+    model.to(torch.cuda.current_device())
+
+    shard_config = ShardConfig(enable_tensor_parallelism=True, inference_only=True)
+    shardformer = ShardFormer(shard_config=shard_config)
+
+    infer_engine = TPInferEngine(model, MAX_BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN)
+    infer_engine.prepare_with_shard_config(shard_config=shard_config)
+    infer_engine.shard_model_by(shardformer)
+
+    generate_kwargs = dict(do_sample=False)
+    outputs = infer_engine.generate(input_ids, generate_kwargs)
+
+    if not dist.is_initialized() or dist.get_rank() == 0:
+        output_text = tokenizer.decode(outputs[0])
+        print(output_text)
+
+
+def check_engine(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    run()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_engine_infer():
+    spawn(check_engine, TP_SIZE)
+
+
+if __name__ == '__main__':
+    test_engine_infer()