Add support for BLOOM (#331)

README.md

@@ -41,6 +41,7 @@ vLLM is flexible and easy to use with:
 
 vLLM seamlessly supports many Huggingface models, including the following architectures:
 
+- BLOOM (`bigscience/bloom`, `bigscience/bloomz`, etc.)
 - GPT-2 (`gpt2`, `gpt2-xl`, etc.)
 - GPT BigCode (`bigcode/starcoder`, `bigcode/gpt_bigcode-santacoder`, etc.)
 - GPT-NeoX (`EleutherAI/gpt-neox-20b`, `databricks/dolly-v2-12b`, `stabilityai/stablelm-tuned-alpha-7b`, etc.)

csrc/attention.cpp

@@ -1,4 +1,5 @@
 #include <torch/extension.h>
+#include <c10/util/Optional.h>
 
 void single_query_cached_kv_attention(
   torch::Tensor& out,
@@ -9,7 +10,8 @@ void single_query_cached_kv_attention(
   torch::Tensor& block_tables,
   torch::Tensor& context_lens,
   int block_size,
-  int max_context_len);
+  int max_context_len,
+  const c10::optional<torch::Tensor>& alibi_slopes);
 
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def(

csrc/attention/attention_kernels.cu

@@ -80,6 +80,7 @@ __global__ void single_query_cached_kv_attention_kernel(
   const int* __restrict__ block_tables,   // [num_seqs, max_num_blocks_per_seq]
   const int* __restrict__ context_lens,   // [num_seqs]
   const int max_num_blocks_per_seq,
+  const float* __restrict__ alibi_slopes, // [num_heads]
   const int q_stride) {
   constexpr int THREAD_GROUP_SIZE = MAX(WARP_SIZE / BLOCK_SIZE, 1);
   constexpr int NUM_TOKENS_PER_THREAD_GROUP = (BLOCK_SIZE + WARP_SIZE - 1) / WARP_SIZE;
@@ -91,6 +92,7 @@ __global__ void single_query_cached_kv_attention_kernel(
   const int head_idx = blockIdx.x;
   const int num_heads = gridDim.x;
   const int seq_idx = blockIdx.y;
+  const float alibi_slope = alibi_slopes == nullptr ? 0.f : alibi_slopes[head_idx];
 
   // A vector type to store a part of a key or a query.
   // The vector size is configured in such a way that the threads in a thread group
@@ -167,12 +169,14 @@ __global__ void single_query_cached_kv_attention_kernel(
 
       // Compute dot product.
       // This includes a reduction across the threads in the same thread group.
-      const float qk = scale * Qk_dot<scalar_t, THREAD_GROUP_SIZE>::dot(q_vecs, k_vecs);
-      const bool mask = token_idx >= context_len;
-    
+      float qk = scale * Qk_dot<scalar_t, THREAD_GROUP_SIZE>::dot(q_vecs, k_vecs);
+      // Add the ALiBi bias if slopes are given.
+      qk += (alibi_slope != 0) ? alibi_slope * (token_idx - context_len) : 0;
+
       if (thread_group_offset == 0) {
         // Store the partial reductions to shared memory.
         // NOTE(woosuk): It is required to zero out the masked logits.
+        const bool mask = token_idx >= context_len;
         logits[token_idx] = mask ? 0.f : qk;
         // Update the max value.
         qk_max = mask ? qk_max : fmaxf(qk_max, qk);
@@ -328,6 +332,7 @@ __global__ void single_query_cached_kv_attention_kernel(
     block_tables_ptr,                                                                         \
     context_lens_ptr,                                                                         \
     max_num_blocks_per_seq,                                                                   \
+    alibi_slopes_ptr,                                                                         \
     query_stride);
 
 // TODO(woosuk): Tune NUM_THREADS.
@@ -343,7 +348,8 @@ void single_query_cached_kv_attention_launcher(
   float scale,
   torch::Tensor& block_tables,
   torch::Tensor& context_lens,
-  int max_context_len) {
+  int max_context_len,
+  const c10::optional<torch::Tensor>& alibi_slopes) {
   int num_seqs = query.size(0);
   int num_heads = query.size(1);
   int head_size = query.size(2);
@@ -353,6 +359,11 @@ void single_query_cached_kv_attention_launcher(
   int thread_group_size = MAX(WARP_SIZE / BLOCK_SIZE, 1);
   assert(head_size % thread_group_size == 0);
 
+  // NOTE: alibi_slopes is optional.
+  const float* alibi_slopes_ptr = alibi_slopes ?
+    reinterpret_cast<const float*>(alibi_slopes.value().data_ptr())
+    : nullptr;
+
   T* out_ptr = reinterpret_cast<T*>(out.data_ptr());
   T* query_ptr = reinterpret_cast<T*>(query.data_ptr());
   T* key_cache_ptr = reinterpret_cast<T*>(key_cache.data_ptr());
@@ -411,7 +422,8 @@ void single_query_cached_kv_attention_launcher(
     scale,                                                          \
     block_tables,                                                   \
     context_lens,                                                   \
-    max_context_len);
+    max_context_len,                                                \
+    alibi_slopes);
 
 // NOTE(woosuk): To reduce the compilation time, we omitted block sizes
 // 1, 2, 4, 64, 128, 256.
@@ -458,7 +470,8 @@ void single_query_cached_kv_attention(
   torch::Tensor& block_tables,    // [num_seqs, max_num_blocks_per_seq]
   torch::Tensor& context_lens,    // [num_seqs]
   int block_size,
-  int max_context_len) {
+  int max_context_len,
+  const c10::optional<torch::Tensor>& alibi_slopes) {
   if (query.dtype() == at::ScalarType::Float) {
     CALL_KERNEL_LAUNCHER_BLOCK_SIZE(float);
   } else if (query.dtype() == at::ScalarType::Half) {

docs/source/models/supported_models.rst

@@ -14,6 +14,9 @@ Alongside each architecture, we include some popular models that use it.
   * - Architecture
     - Models
     - Example HuggingFace Models
+  * - :code:`BloomForCausalLM`
+    - BLOOM, BLOOMZ, BLOOMChat
+    - :code:`bigscience/bloom`, :code:`bigscience/bloomz`, etc.
   * - :code:`GPT2LMHeadModel`
     - GPT-2
     - :code:`gpt2`, :code:`gpt2-xl`, etc.

tests/kernels/test_attention.py

@@ -216,6 +216,7 @@ def run_single_query_cached_kv_attention(
         context_lens,
         block_size,
         max_context_len,
+        None,  # ALiBi slopes.
     )
 
     ref_output = torch.empty_like(query)

vllm/model_executor/input_metadata.py

@@ -1,7 +1,7 @@
 from typing import Dict, List, Tuple
 
 import torch
-from xformers.ops.fmha.attn_bias import BlockDiagonalCausalMask
+from xformers.ops import AttentionBias
 
 from vllm.sampling_params import SamplingParams
 from vllm.sequence import SequenceData
@@ -38,7 +38,6 @@ class InputMetadata:
         self.max_context_len = max_context_len
         self.block_tables = block_tables
 
-        self.attn_bias = BlockDiagonalCausalMask.from_seqlens(prompt_lens)
         self.num_prompts = len(prompt_lens)
         self.num_prompt_tokens = sum(prompt_lens)
         self.num_generation_tokens = context_lens.shape[0]
@@ -50,6 +49,9 @@ class InputMetadata:
         assert block_tables.shape[0] == self.num_generation_tokens
         assert context_lens.shape[0] == self.num_generation_tokens
 
+        # Set during the execution of the first attention op.
+        self.attn_bias: List[AttentionBias] = []
+
     def __repr__(self) -> str:
         # Print only useful metadata.
         return (f'InputMetadata('

vllm/model_executor/layers/attention.py

@@ -1,9 +1,11 @@
 """Multi-head attention."""
-from typing import Optional
+from typing import List, Optional
 
 import torch
 import torch.nn as nn
 from xformers import ops as xops
+from xformers.ops.fmha.attn_bias import (BlockDiagonalCausalMask,
+                                         LowerTriangularMaskWithTensorBias)
 
 from vllm import attention_ops
 from vllm import cache_ops
@@ -53,13 +55,21 @@ class PagedAttention(nn.Module):
             raise ValueError(f"head_size ({self.head_size}) is not supported. "
                              f"Supported head sizes: {_SUPPORTED_HEAD_SIZES}.")
 
+    def set_attn_bias(self, input_metadata: InputMetadata) -> None:
+        if input_metadata.attn_bias:
+            # Already set by a previous layer.
+            return
+        prompt_lens = input_metadata.prompt_lens
+        attn_bias = BlockDiagonalCausalMask.from_seqlens(prompt_lens)
+        input_metadata.attn_bias.append(attn_bias)
+
     def multi_query_kv_attention(
         self,
         output: torch.Tensor,
         query: torch.Tensor,
         key: torch.Tensor,
         value: torch.Tensor,
-        attn_bias: xops.AttentionBias,
+        input_metadata: InputMetadata,
     ) -> torch.Tensor:
         """Normal attention for the prompt tokens.
 
@@ -68,13 +78,14 @@ class PagedAttention(nn.Module):
             query: shape = [num_prompt_tokens, num_heads, head_size]
             key: shape = [num_prompt_tokens, num_heads, head_size]
             value: shape = [num_prompt_tokens, num_heads, head_size]
+            input_metadata: metadata for paged attention.
         """
         # TODO(woosuk): The unsqueeze op may incur some CPU overhead. Optimize.
         out = xops.memory_efficient_attention_forward(
             query.unsqueeze(0),
             key.unsqueeze(0),
             value.unsqueeze(0),
-            attn_bias=attn_bias,
+            attn_bias=input_metadata.attn_bias[0],
             p=0.0,
             scale=self.scale,
             op=self.attn_op,
@@ -112,6 +123,7 @@ class PagedAttention(nn.Module):
             input_metadata.context_lens,
             block_size,
             input_metadata.max_context_len,
+            None,  # alibi_slopes
         )
 
     def forward(
@@ -154,12 +166,13 @@ class PagedAttention(nn.Module):
         # Compute the attention op for prompts.
         num_prompt_tokens = input_metadata.num_prompt_tokens
         if num_prompt_tokens > 0:
+            self.set_attn_bias(input_metadata)
             self.multi_query_kv_attention(
                 output[:num_prompt_tokens],
                 query[:num_prompt_tokens],
                 key[:num_prompt_tokens],
                 value[:num_prompt_tokens],
-                input_metadata.attn_bias,
+                input_metadata,
             )
 
         # Wait until the cache op is done.
@@ -219,7 +232,8 @@ class PagedAttentionWithRoPE(PagedAttention):
         cache = torch.cat((cos, sin), dim=-1)
 
         # FIXME(woosuk): This assumes that we configure the default dtype when
-        # initializing the model. Make it more robust.
+        # initializing the model.
+        # TODO(woosuk): Make it more robust.
         torch_dtype = torch.get_default_dtype()
         cache = cache.to(torch_dtype)
         # Embedding size: [max_position, rotary_dim]
@@ -271,3 +285,112 @@ class PagedAttentionWithRoPE(PagedAttention):
             input_metadata,
             cache_event,
         )
+
+
+class PagedAttentionWithALiBi(PagedAttention):
+    """PagedAttention with ALiBi attention bias."""
+
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        scale: float,
+        slopes: List[float],
+    ) -> None:
+        super().__init__(num_heads, head_size, scale)
+        assert len(slopes) == num_heads
+
+        slopes = torch.tensor(slopes, dtype=torch.float32)
+        self.register_buffer("alibi_slopes", slopes, persistent=False)
+
+    def set_attn_bias(self, input_metadata: InputMetadata) -> None:
+        if input_metadata.attn_bias:
+            # Already set by a previous layer.
+            return
+        # Generates ALiBi mask for each prompt.
+        for prompt_len in input_metadata.prompt_lens:
+            bias = torch.arange(prompt_len)
+            bias = bias[None, :] - bias[:, None]
+            bias = bias.to(self.alibi_slopes.device)
+
+            # When using custom attention bias, xformers requires the bias to
+            # be sliced from a tensor whose length is a multiple of 8.
+            padded_len = (prompt_len + 7) // 8 * 8
+            bias = torch.empty(
+                self.num_heads,
+                padded_len,
+                padded_len,
+                device=self.alibi_slopes.device,
+            )[:, :prompt_len, :prompt_len].copy_(bias)
+            bias.mul_(self.alibi_slopes[:, None, None])
+            attn_bias = LowerTriangularMaskWithTensorBias(bias)
+            input_metadata.attn_bias.append(attn_bias)
+
+    def multi_query_kv_attention(
+        self,
+        output: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        """Attention with ALiBi bias for the prompt tokens.
+
+        Args:
+            output: shape = [num_prompt_tokens, num_heads, head_size]
+            query: shape = [num_prompt_tokens, num_heads, head_size]
+            key: shape = [num_prompt_tokens, num_heads, head_size]
+            value: shape = [num_prompt_tokens, num_heads, head_size]
+            input_metadata: metadata for paged attention.
+        """
+        # FIXME(woosuk): Because xformers does not support dynamic sequence
+        # lengths with custom attention bias, we process each prompt one by
+        # one. This is inefficient, especially when we have many short prompts.
+        start = 0
+        for i, prompt_len in enumerate(input_metadata.prompt_lens):
+            end = start + prompt_len
+            out = xops.memory_efficient_attention_forward(
+                query[None, start:end],
+                key[None, start:end],
+                value[None, start:end],
+                attn_bias=input_metadata.attn_bias[i],
+                p=0.0,
+                scale=self.scale,
+                op=self.attn_op,
+            )
+            # TODO(woosuk): Unnecessary copy. Optimize.
+            output[start:end].copy_(out.squeeze(0))
+            start += prompt_len
+        return output
+
+    def single_query_cached_kv_attention(
+        self,
+        output: torch.Tensor,
+        query: torch.Tensor,
+        key_cache: torch.Tensor,
+        value_cache: torch.Tensor,
+        input_metadata: InputMetadata,
+    ) -> None:
+        """PagedAttention with ALiBi bias for the generation tokens.
+
+        Args:
+            output: shape = [num_generation_tokens, num_heads, head_size]
+            query: shape = [num_generation_tokens, num_heads, head_size]
+            key_cache: shape = [num_blocks, num_heads, head_size/x,
+                block_size, x]
+            value_cache: shape = [num_blocks, num_heads, head_size, block_size]
+            input_metadata: metadata for paged attention.
+        """
+        block_size = value_cache.shape[3]
+        attention_ops.single_query_cached_kv_attention(
+            output,
+            query,
+            key_cache,
+            value_cache,
+            self.scale,
+            input_metadata.block_tables,
+            input_metadata.context_lens,
+            block_size,
+            input_metadata.max_context_len,
+            self.alibi_slopes,
+        )

vllm/model_executor/model_loader.py

@@ -6,13 +6,12 @@ import torch.nn as nn
 from transformers import PretrainedConfig
 
 from vllm.config import ModelConfig
-from vllm.model_executor.models import (GPT2LMHeadModel, GPTBigCodeForCausalLM,
-                                        GPTNeoXForCausalLM, LlamaForCausalLM,
-                                        OPTForCausalLM)
+from vllm.model_executor.models import *  # pylint: disable=wildcard-import
 from vllm.model_executor.weight_utils import initialize_dummy_weights
 
 # TODO(woosuk): Lazy-load the model classes.
 _MODEL_REGISTRY = {
+    "BloomForCausalLM": BloomForCausalLM,
     "GPT2LMHeadModel": GPT2LMHeadModel,
     "GPTBigCodeForCausalLM": GPTBigCodeForCausalLM,
     "GPTNeoXForCausalLM": GPTNeoXForCausalLM,

vllm/model_executor/models/__init__.py

@@ -1,3 +1,4 @@
+from vllm.model_executor.models.bloom import BloomForCausalLM
 from vllm.model_executor.models.gpt2 import GPT2LMHeadModel
 from vllm.model_executor.models.gpt_bigcode import GPTBigCodeForCausalLM
 from vllm.model_executor.models.gpt_neox import GPTNeoXForCausalLM
@@ -5,6 +6,7 @@ from vllm.model_executor.models.llama import LlamaForCausalLM
 from vllm.model_executor.models.opt import OPTForCausalLM
 
 __all__ = [
+    "BloomForCausalLM",
     "GPT2LMHeadModel",
     "GPTBigCodeForCausalLM",
     "GPTNeoXForCausalLM",

vllm/model_executor/models/bloom.py

@@ -0,0 +1,316 @@
+# coding=utf-8
+# Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/bloom/modeling_bloom.py
+# Copyright 2023 The CacheFlow 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.
+"""Inference-only BLOOM model compatible with HuggingFace weights.
+
+The input of the model is flattened to a 1D tensor of tokens. The model uses
+InputMetadata to extract the original 2D shape of the input.
+"""
+import math
+from typing import Dict, List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import BloomConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention import PagedAttentionWithALiBi
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
+                                              load_tensor_parallel_weights)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.tensor_parallel import (
+    VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
+from vllm.sequence import SequenceOutputs
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
+    closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
+    base = torch.tensor(
+        2**(-(2**-(math.log2(closest_power_of_2) - 3))),
+        dtype=torch.float32,
+    )
+    powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
+    slopes = torch.pow(base, powers)
+
+    if closest_power_of_2 != total_num_heads:
+        extra_base = torch.tensor(
+            2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
+            dtype=torch.float32,
+        )
+        num_remaining_heads = min(closest_power_of_2,
+                                  total_num_heads - closest_power_of_2)
+        extra_powers = torch.arange(start=1,
+                                    end=1 + 2 * num_remaining_heads,
+                                    step=2,
+                                    dtype=torch.int32)
+        slopes = torch.cat(
+            [slopes, torch.pow(extra_base, extra_powers)], dim=0)
+    return slopes
+
+
+class BloomAttention(nn.Module):
+
+    def __init__(self, config: BloomConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.total_num_heads = config.n_head
+        self.head_dim = self.hidden_size // self.total_num_heads
+        assert self.head_dim * self.total_num_heads == self.hidden_size
+
+        tp_world_size = get_tensor_model_parallel_world_size()
+        assert self.total_num_heads % tp_world_size == 0
+        self.num_heads = self.total_num_heads // tp_world_size
+
+        self.query_key_value = ColumnParallelLinear(
+            self.hidden_size,
+            3 * self.hidden_size,
+            bias=True,
+            gather_output=False,
+            perform_initialization=False,
+        )
+        self.dense = RowParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=True,
+            input_is_parallel=True,
+            perform_initialization=False,
+        )
+
+        # Create the alibi slopes and slice them.
+        tp_rank = get_tensor_model_parallel_rank()
+        head_start = tp_rank * self.num_heads
+        head_end = (tp_rank + 1) * self.num_heads
+        alibi_slopes = _get_alibi_slopes(self.total_num_heads)
+        alibi_slopes = alibi_slopes[head_start:head_end].tolist()
+
+        scaling = self.head_dim**-0.5
+        self.attn = PagedAttentionWithALiBi(self.num_heads, self.head_dim,
+                                            scaling, alibi_slopes)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        del position_ids  # Unused.
+        qkv, _ = self.query_key_value(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata,
+                                cache_event)
+        output, _ = self.dense(attn_output)
+        return output
+
+
+class BloomMLP(nn.Module):
+
+    def __init__(self, config: BloomConfig):
+        super().__init__()
+        hidden_size = config.hidden_size
+        self.dense_h_to_4h = ColumnParallelLinear(hidden_size,
+                                                  4 * hidden_size,
+                                                  gather_output=False,
+                                                  perform_initialization=False)
+        self.act = get_act_fn("gelu")
+        self.dense_4h_to_h = RowParallelLinear(4 * hidden_size,
+                                               hidden_size,
+                                               input_is_parallel=True,
+                                               perform_initialization=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x, _ = self.dense_h_to_4h(x)
+        x = self.act(x)
+        x, _ = self.dense_4h_to_h(x)
+        return x
+
+
+class BloomBlock(nn.Module):
+
+    def __init__(self, config: BloomConfig):
+        super().__init__()
+        hidden_size = config.hidden_size
+
+        self.input_layernorm = nn.LayerNorm(hidden_size,
+                                            eps=config.layer_norm_epsilon)
+        self.self_attention = BloomAttention(config)
+        self.post_attention_layernorm = nn.LayerNorm(
+            hidden_size, eps=config.layer_norm_epsilon)
+        self.mlp = BloomMLP(config)
+        self.apply_residual_connection_post_layernorm = (
+            config.apply_residual_connection_post_layernorm)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        # 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.
+        attention_output = self.self_attention(
+            position_ids=position_ids,
+            hidden_states=layernorm_output,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+            cache_event=cache_event,
+        )
+        attention_output = attention_output + residual
+        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
+        return output
+
+
+class BloomModel(nn.Module):
+
+    def __init__(self, config: BloomConfig):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+
+        # Embedding + LN Embedding
+        self.word_embeddings = VocabParallelEmbedding(
+            config.vocab_size, self.embed_dim, perform_initialization=False)
+        self.word_embeddings_layernorm = nn.LayerNorm(
+            self.embed_dim, eps=config.layer_norm_epsilon)
+
+        # Transformer blocks
+        self.h = nn.ModuleList(
+            [BloomBlock(config) for _ in range(config.num_hidden_layers)])
+
+        # Final Layer Norm
+        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> torch.Tensor:
+        hidden_states = self.word_embeddings(input_ids)
+        hidden_states = self.word_embeddings_layernorm(hidden_states)
+        for i in range(len(self.h)):
+            if cache_events is None:
+                cache_event = None
+            else:
+                cache_event = cache_events[i]
+            layer = self.h[i]
+            hidden_states = layer(
+                position_ids,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                cache_event,
+            )
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+
+class BloomForCausalLM(nn.Module):
+
+    def __init__(self, config: BloomConfig):
+        super().__init__()
+        self.config = config
+        self.transformer = BloomModel(config)
+        # TODO(zhuohan): create a new weight after implementing pipeline
+        #                parallelism
+        self.lm_head_weight = self.transformer.word_embeddings.weight
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> Dict[int, SequenceOutputs]:
+        hidden_states = self.transformer(input_ids, positions, kv_caches,
+                                         input_metadata, cache_events)
+        next_tokens = self.sampler(self.lm_head_weight, hidden_states,
+                                   input_metadata)
+        return next_tokens
+
+    _column_parallel_weights = [
+        "word_embeddings.weight", "dense_h_to_4h.weight", "dense_h_to_4h.bias"
+    ]
+    _row_parallel_weights = ["dense.weight", "dense_4h_to_h.weight"]
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     use_np_cache: bool = False):
+        tp_rank = get_tensor_model_parallel_rank()
+        state_dict = self.state_dict()
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, use_np_cache):
+            if not name.startswith("transformer."):
+                name = "transformer." + name
+
+            param = state_dict[name]
+            if "query_key_value" in name:
+                # NOTE(woosuk): BLOOM's fused QKV has the shape of
+                # [num_heads * 3 * head_size, hidden_size], while the
+                # required shape is [3 * num_heads * head_size, hidden_size].
+                # Thus, we need weight conversion.
+                shard_size = param.shape[0]
+                start = shard_size * tp_rank
+                end = shard_size * (tp_rank + 1)
+                loaded_weight = loaded_weight[start:end]
+
+                num_heads = self.config.num_attention_heads
+                hidden_size = self.config.hidden_size
+                head_size = hidden_size // num_heads
+                if "query_key_value.weight" in name:
+                    loaded_weight = loaded_weight.view(-1, 3, head_size,
+                                                       hidden_size)
+                    loaded_weight = loaded_weight.transpose(0, 1)
+                    loaded_weight = loaded_weight.reshape(-1, hidden_size)
+                elif "query_key_value.bias" in name:
+                    loaded_weight = loaded_weight.view(-1, 3, head_size)
+                    loaded_weight = loaded_weight.transpose(0, 1)
+                    loaded_weight = loaded_weight.reshape(-1)
+                else:
+                    raise ValueError(f"Unexpected weight name: {name}")
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         self._column_parallel_weights,
+                                         self._row_parallel_weights, tp_rank)

vllm/model_executor/models/gpt_neox.py

@@ -80,7 +80,6 @@ class GPTNeoXAttention(nn.Module):
         cache_event: Optional[torch.cuda.Event],
     ) -> torch.Tensor:
         qkv, _ = self.query_key_value(hidden_states)
-
         q, k, v = qkv.chunk(chunks=3, dim=-1)
         k_cache, v_cache = kv_cache
         attn_output = self.attn(position_ids, q, k, v, k_cache, v_cache,