v0.2.1.post1

.github/workflows/publish.yml

@@ -43,14 +43,14 @@ jobs:
     name: Build Wheel
     runs-on: ${{ matrix.os }}
     needs: release
-
+    
     strategy:
       fail-fast: false
       matrix:
           os: ['ubuntu-20.04']
           python-version: ['3.8', '3.9', '3.10', '3.11']
-          pytorch-version: ['2.1.0']
-          cuda-version: ['11.8', '12.1']
+          pytorch-version: ['2.0.1']
+          cuda-version: ['11.8'] # Github runner can't build anything older than 11.8
 
     steps:
       - name: Checkout
@@ -82,7 +82,7 @@ jobs:
           asset_name=${wheel_name//"linux"/"manylinux1"}
           echo "wheel_name=${wheel_name}" >> $GITHUB_ENV
           echo "asset_name=${asset_name}" >> $GITHUB_ENV
-
+      
       - name: Upload Release Asset
         uses: actions/upload-release-asset@v1
         env:

.github/workflows/scripts/build.sh

@@ -11,8 +11,5 @@ LD_LIBRARY_PATH=${cuda_home}/lib64:$LD_LIBRARY_PATH
 $python_executable -m pip install wheel packaging
 $python_executable -m pip install -r requirements.txt
 
-# Limit the number of parallel jobs to avoid OOM
-export MAX_JOBS=1
-
 # Build
 $python_executable setup.py bdist_wheel --dist-dir=dist

.github/workflows/scripts/cuda-install.sh

@@ -16,8 +16,3 @@ sudo apt clean
 # Test nvcc
 PATH=/usr/local/cuda-$1/bin:${PATH}
 nvcc --version
-
-# Log gcc, g++, c++ versions
-gcc --version
-g++ --version
-c++ --version

Dockerfile

@@ -1,72 +0,0 @@
-FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
-
-RUN apt-get update -y \
-    && apt-get install -y python3-pip
-
-WORKDIR /workspace
-
-# install build and runtime dependencies
-COPY requirements.txt requirements.txt
-RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install -r requirements.txt
-
-# install development dependencies
-COPY requirements-dev.txt requirements-dev.txt
-RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install -r requirements-dev.txt
-
-# image to build pytorch extensions
-FROM dev AS build
-
-# copy input files
-COPY csrc csrc
-COPY setup.py setup.py
-COPY requirements.txt requirements.txt
-COPY pyproject.toml pyproject.toml
-COPY vllm/__init__.py vllm/__init__.py
-
-# max jobs used by Ninja to build extensions
-ENV MAX_JOBS=$max_jobs
-RUN python3 setup.py build_ext --inplace
-
-# image to run unit testing suite
-FROM dev AS test
-
-# copy pytorch extensions separately to avoid having to rebuild
-# when python code changes
-COPY --from=build /workspace/vllm/*.so /workspace/vllm/
-COPY tests tests
-COPY vllm vllm
-
-ENTRYPOINT ["python3", "-m", "pytest", "tests"]
-
-# use CUDA base as CUDA runtime dependencies are already installed via pip
-FROM nvidia/cuda:12.1.0-base-ubuntu22.04 AS vllm-base
-
-# libnccl required for ray
-RUN apt-get update -y \
-    && apt-get install -y python3-pip
-
-WORKDIR /workspace
-COPY requirements.txt requirements.txt
-RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install -r requirements.txt
-
-FROM vllm-base AS vllm
-COPY --from=build /workspace/vllm/*.so /workspace/vllm/
-COPY vllm vllm
-
-EXPOSE 8000
-ENTRYPOINT ["python3", "-m", "vllm.entrypoints.api_server"]
-
-# openai api server alternative
-FROM vllm-base AS vllm-openai
-# install additional dependencies for openai api server
-RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install accelerate fschat
-
-COPY --from=build /workspace/vllm/*.so /workspace/vllm/
-COPY vllm vllm
-
-ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
-

README.md

@@ -49,7 +49,6 @@ vLLM seamlessly supports many Hugging Face models, including the following archi
 - Aquila & Aquila2 (`BAAI/AquilaChat2-7B`, `BAAI/AquilaChat2-34B`, `BAAI/Aquila-7B`, `BAAI/AquilaChat-7B`, etc.)
 - Baichuan (`baichuan-inc/Baichuan-7B`, `baichuan-inc/Baichuan-13B-Chat`, etc.)
 - BLOOM (`bigscience/bloom`, `bigscience/bloomz`, etc.)
-- ChatGLM (`THUDM/chatglm2-6b`, `THUDM/chatglm3-6b`, etc.)
 - Falcon (`tiiuae/falcon-7b`, `tiiuae/falcon-40b`, `tiiuae/falcon-rw-7b`, etc.)
 - GPT-2 (`gpt2`, `gpt2-xl`, etc.)
 - GPT BigCode (`bigcode/starcoder`, `bigcode/gpt_bigcode-santacoder`, etc.)
@@ -60,9 +59,7 @@ vLLM seamlessly supports many Hugging Face models, including the following archi
 - Mistral (`mistralai/Mistral-7B-v0.1`, `mistralai/Mistral-7B-Instruct-v0.1`, etc.)
 - MPT (`mosaicml/mpt-7b`, `mosaicml/mpt-30b`, etc.)
 - OPT (`facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc.)
-- Phi-1.5 (`microsoft/phi-1_5`, etc.)
 - Qwen (`Qwen/Qwen-7B`, `Qwen/Qwen-7B-Chat`, etc.)
-- Yi (`01-ai/Yi-6B`, `01-ai/Yi-34B`, etc.)
 
 Install vLLM with pip or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source):
 

benchmarks/benchmark_latency.py

@@ -70,7 +70,7 @@ if __name__ == '__main__':
     parser.add_argument('--tokenizer', type=str, default=None)
     parser.add_argument('--quantization',
                         '-q',
-                        choices=['awq', 'squeezellm', None],
+                        choices=['awq', None],
                         default=None)
     parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)
     parser.add_argument('--input-len', type=int, default=32)

benchmarks/benchmark_throughput.py

@@ -6,21 +6,18 @@ import time
 from typing import List, Optional, Tuple
 
 import torch
-from transformers import (AutoModelForCausalLM, AutoTokenizer,
-                          PreTrainedTokenizerBase)
+from transformers import AutoModelForCausalLM, PreTrainedTokenizerBase
 from tqdm import tqdm
 
+from vllm import LLM, SamplingParams
+from vllm.transformers_utils.tokenizer import get_tokenizer
+
 
 def sample_requests(
     dataset_path: str,
     num_requests: int,
     tokenizer: PreTrainedTokenizerBase,
-    fixed_output_len: Optional[int],
 ) -> List[Tuple[str, int, int]]:
-    if fixed_output_len is not None:
-        if fixed_output_len < 4:
-            raise ValueError("output_len too small")
-
     # Load the dataset.
     with open(dataset_path) as f:
         dataset = json.load(f)
@@ -38,8 +35,6 @@ def sample_requests(
     tokenized_dataset = []
     for i in range(len(dataset)):
         output_len = len(completion_token_ids[i])
-        if fixed_output_len is not None:
-            output_len = fixed_output_len
         tokenized_dataset.append((prompts[i], prompt_token_ids[i], output_len))
 
     # Filter out too long sequences.
@@ -71,7 +66,6 @@ def run_vllm(
     trust_remote_code: bool,
     dtype: str,
 ) -> float:
-    from vllm import LLM, SamplingParams
     llm = LLM(
         model=model,
         tokenizer=tokenizer,
@@ -100,7 +94,7 @@ def run_vllm(
         )
 
     start = time.perf_counter()
-    # FIXME(woosuk): Do not use internal method.
+    # FIXME(woosuk): Do use internal method.
     llm._run_engine(use_tqdm=True)
     end = time.perf_counter()
     return end - start
@@ -166,37 +160,14 @@ def run_hf(
     return end - start
 
 
-def run_mii(
-    requests: List[Tuple[str, int, int]],
-    model: str,
-    tensor_parallel_size: int,
-    output_len: int,
-) -> float:
-    from mii import pipeline
-    llm = pipeline(model, tensor_parallel=tensor_parallel_size)
-    prompts = [prompt for prompt, _, _ in requests]
-
-    start = time.perf_counter()
-    llm(prompts, max_new_tokens=output_len)
-    end = time.perf_counter()
-    return end - start
-
-
 def main(args: argparse.Namespace):
     print(args)
     random.seed(args.seed)
 
     # Sample the requests.
-    tokenizer = AutoTokenizer.from_pretrained(
-        args.tokenizer, trust_remote_code=args.trust_remote_code)
-    if args.dataset is None:
-        # Synthesize a prompt with the given input length.
-        prompt = "hi" * (args.input_len - 1)
-        requests = [(prompt, args.input_len, args.output_len)
-                    for _ in range(args.num_prompts)]
-    else:
-        requests = sample_requests(args.dataset, args.num_prompts, tokenizer,
-                                   args.output_len)
+    tokenizer = get_tokenizer(args.tokenizer,
+                              trust_remote_code=args.trust_remote_code)
+    requests = sample_requests(args.dataset, args.num_prompts, tokenizer)
 
     if args.backend == "vllm":
         elapsed_time = run_vllm(requests, args.model, args.tokenizer,
@@ -208,9 +179,6 @@ def main(args: argparse.Namespace):
         elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
                               args.use_beam_search, args.hf_max_batch_size,
                               args.trust_remote_code)
-    elif args.backend == "mii":
-        elapsed_time = run_mii(requests, args.model, args.tensor_parallel_size,
-                               args.output_len)
     else:
         raise ValueError(f"Unknown backend: {args.backend}")
     total_num_tokens = sum(prompt_len + output_len
@@ -223,26 +191,17 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Benchmark the throughput.")
     parser.add_argument("--backend",
                         type=str,
-                        choices=["vllm", "hf", "mii"],
+                        choices=["vllm", "hf"],
                         default="vllm")
     parser.add_argument("--dataset",
                         type=str,
-                        default=None,
+                        required=True,
                         help="Path to the dataset.")
-    parser.add_argument("--input-len",
-                        type=int,
-                        default=None,
-                        help="Input prompt length for each request")
-    parser.add_argument("--output-len",
-                        type=int,
-                        default=None,
-                        help="Output length for each request. Overrides the "
-                        "output length from the dataset.")
     parser.add_argument("--model", type=str, default="facebook/opt-125m")
     parser.add_argument("--tokenizer", type=str, default=None)
     parser.add_argument('--quantization',
                         '-q',
-                        choices=['awq', 'squeezellm', None],
+                        choices=['awq', None],
                         default=None)
     parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1)
     parser.add_argument("--n",
@@ -272,13 +231,6 @@ if __name__ == "__main__":
         'for FP32 and FP16 models, and BF16 precision '
         'for BF16 models.')
     args = parser.parse_args()
-    if args.tokenizer is None:
-        args.tokenizer = args.model
-    if args.dataset is None:
-        assert args.input_len is not None
-        assert args.output_len is not None
-    else:
-        assert args.input_len is None
 
     if args.backend == "vllm":
         if args.hf_max_batch_size is not None:
@@ -288,18 +240,7 @@ if __name__ == "__main__":
             raise ValueError("HF max batch size is required for HF backend.")
         if args.quantization is not None:
             raise ValueError("Quantization is only for vLLM backend.")
-    elif args.backend == "mii":
-        if args.dtype != "auto":
-            raise ValueError("dtype must be auto for MII backend.")
-        if args.n != 1:
-            raise ValueError("n must be 1 for MII backend.")
-        if args.use_beam_search:
-            raise ValueError("Beam search is not supported for MII backend.")
-        if args.quantization is not None:
-            raise ValueError("Quantization is only for vLLM backend.")
-        if args.hf_max_batch_size is not None:
-            raise ValueError("HF max batch size is only for HF backend.")
-        if args.tokenizer != args.model:
-            raise ValueError("Tokenizer must be the same as the model for MII "
-                             "backend.")
+    if args.tokenizer is None:
+        args.tokenizer = args.model
+
     main(args)

csrc/activation_kernels.cu

@@ -13,11 +13,11 @@ __device__ __forceinline__ T silu(const T& x) {
 
 template<typename scalar_t>
 __global__ void silu_and_mul_kernel(
-  scalar_t* __restrict__ out,               // [..., d]
-  const scalar_t* __restrict__ input,       // [..., 2, d]
+  scalar_t* __restrict__ out,               // [num_tokens, d]
+  const scalar_t* __restrict__ input,       // [num_tokens, 2, d]
   const int d) {
-  const int64_t token_idx = blockIdx.x;
-  for (int64_t idx = threadIdx.x; idx < d; idx += blockDim.x) {
+  const int token_idx = blockIdx.x;
+  for (int idx = threadIdx.x; idx < d; idx += blockDim.x) {
     const scalar_t x = __ldg(&input[token_idx * 2 * d + idx]);
     const scalar_t y = __ldg(&input[token_idx * 2 * d + d + idx]);
     out[token_idx * d + idx] = silu(x) * y;
@@ -27,11 +27,11 @@ __global__ void silu_and_mul_kernel(
 } // namespace vllm
 
 void silu_and_mul(
-  torch::Tensor& out,      // [..., d]
-  torch::Tensor& input)    // [..., 2 * d]
+  torch::Tensor& out,      // [num_tokens, d]
+  torch::Tensor& input)    // [num_tokens, 2 * d]
 {
-  int64_t num_tokens = input.numel() / input.size(-1);
-  int d = input.size(-1) / 2;
+  int num_tokens = input.size(0);
+  int d = input.size(1) / 2;
 
   dim3 grid(num_tokens);
   dim3 block(std::min(d, 1024));
@@ -52,11 +52,11 @@ namespace vllm {
 // Element-wise activation kernel template.
 template<typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&)>
 __global__ void activation_kernel(
-  scalar_t* __restrict__ out,               // [..., d]
-  const scalar_t* __restrict__ input,       // [..., d]
+  scalar_t* __restrict__ out,               // [num_tokens, d]
+  const scalar_t* __restrict__ input,       // [num_tokens, d]
   const int d) {
-  const int64_t token_idx = blockIdx.x;
-  for (int64_t idx = threadIdx.x; idx < d; idx += blockDim.x) {
+  const int token_idx = blockIdx.x;
+  for (int idx = threadIdx.x; idx < d; idx += blockDim.x) {
     const scalar_t x = __ldg(&input[token_idx * d + idx]);
     out[token_idx * d + idx] = ACT_FN(x);
   }
@@ -66,8 +66,8 @@ __global__ void activation_kernel(
 
 // Launch element-wise activation kernel.
 #define LAUNCH_ACTIVATION_KERNEL(KERNEL)                                                  \
-  int d = input.size(-1);                                                                 \
-  int64_t num_tokens = input.numel() / d;                                                 \
+  int num_tokens = input.size(0);                                                         \
+  int d = input.size(1);                                                                  \
   dim3 grid(num_tokens);                                                                  \
   dim3 block(std::min(d, 1024));                                                          \
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();                           \
@@ -100,15 +100,15 @@ __device__ __forceinline__ T gelu_fast_kernel(const T& x) {
 } // namespace vllm
 
 void gelu_new(
-  torch::Tensor& out,     // [..., d]
-  torch::Tensor& input)   // [..., d]
+  torch::Tensor& out,     // [num_tokens, d]
+  torch::Tensor& input)   // [num_tokens, d]
 {
   LAUNCH_ACTIVATION_KERNEL(vllm::gelu_new_kernel);
 }
 
 void gelu_fast(
-  torch::Tensor& out,     // [..., d]
-  torch::Tensor& input)   // [..., d]
+  torch::Tensor& out,     // [num_tokens, d]
+  torch::Tensor& input)   // [num_tokens, d]
 {
   LAUNCH_ACTIVATION_KERNEL(vllm::gelu_fast_kernel);
 }

csrc/attention/attention_kernels.cu

@@ -175,10 +175,7 @@ __device__ void paged_attention_kernel(
   // dot product with the query.
   const int* block_table = block_tables + seq_idx * max_num_blocks_per_seq;
   for (int block_idx = start_block_idx + warp_idx; block_idx < end_block_idx; block_idx += NUM_WARPS) {
-    // NOTE(woosuk): The block number is stored in int32. However, we cast it to int64
-    // because int32 can lead to overflow when this variable is multiplied by large numbers
-    // (e.g., kv_block_stride).
-    const int64_t physical_block_number = static_cast<int64_t>(block_table[block_idx]);
+    const int physical_block_number = block_table[block_idx];
 
     // Load a key to registers.
     // Each thread in a thread group has a different part of the key.
@@ -288,10 +285,7 @@ __device__ void paged_attention_kernel(
   scalar_t zero_value;
   zero(zero_value);
   for (int block_idx = start_block_idx + warp_idx; block_idx < end_block_idx; block_idx += NUM_WARPS) {
-    // NOTE(woosuk): The block number is stored in int32. However, we cast it to int64
-    // because int32 can lead to overflow when this variable is multiplied by large numbers
-    // (e.g., kv_block_stride).
-    const int64_t physical_block_number = static_cast<int64_t>(block_table[block_idx]);
+    const int physical_block_number = block_table[block_idx];
     const int physical_block_offset = (lane % NUM_V_VECS_PER_ROW) * V_VEC_SIZE;
     const int token_idx = block_idx * BLOCK_SIZE + physical_block_offset;
     L_vec logits_vec;

csrc/cache_kernels.cu

@@ -55,26 +55,26 @@ template<typename scalar_t>
 __global__ void copy_blocks_kernel(
   int64_t* key_cache_ptrs,
   int64_t* value_cache_ptrs,
-  const int64_t* __restrict__ block_mapping,
+  const int* __restrict__ block_mapping,
   const int numel_per_block) {
   const int layer_idx = blockIdx.x;
   const int pair_idx = blockIdx.y;
 
   scalar_t* key_cache = reinterpret_cast<scalar_t*>(key_cache_ptrs[layer_idx]);
   scalar_t* value_cache = reinterpret_cast<scalar_t*>(value_cache_ptrs[layer_idx]);
-  int64_t src_block_number = block_mapping[2 * pair_idx];
-  int64_t dst_block_number = block_mapping[2 * pair_idx + 1];
+  int src_block_number = block_mapping[2 * pair_idx];
+  int dst_block_number = block_mapping[2 * pair_idx + 1];
 
-  const int64_t src_block_offset = src_block_number * numel_per_block;
-  const int64_t dst_block_offset = dst_block_number * numel_per_block;
+  const int src_block_offset = src_block_number * numel_per_block;
+  const int dst_block_offset = dst_block_number * numel_per_block;
   for (int i = threadIdx.x; i < numel_per_block; i += blockDim.x) {
-    int64_t src_offset = src_block_offset + i;
-    int64_t dst_offset = dst_block_offset + i;
+    int src_offset = src_block_offset + i;
+    int dst_offset = dst_block_offset + i;
     key_cache[dst_offset] = key_cache[src_offset];
   }
   for (int i = threadIdx.x; i < numel_per_block; i += blockDim.x) {
-    int64_t src_offset = src_block_offset + i;
-    int64_t dst_offset = dst_block_offset + i;
+    int src_offset = src_block_offset + i;
+    int dst_offset = dst_block_offset + i;
     value_cache[dst_offset] = value_cache[src_offset];
   }
 }
@@ -102,15 +102,15 @@ void copy_blocks(
     value_cache_ptrs[layer_idx] = reinterpret_cast<int64_t>(value_caches[layer_idx].data_ptr());
   }
   // Create block mapping array.
-  std::vector<int64_t> block_mapping_vec;
+  std::vector<int> block_mapping_vec;
   for (const auto& pair : block_mapping) {
-    int64_t src_block_number = pair.first;
-    for (int64_t dst_block_number : pair.second) {
+    int src_block_number = pair.first;
+    for (int dst_block_number : pair.second) {
       block_mapping_vec.push_back(src_block_number);
       block_mapping_vec.push_back(dst_block_number);
     }
   }
-  int64_t* block_mapping_array = block_mapping_vec.data();
+  int* block_mapping_array = block_mapping_vec.data();
   int num_pairs = block_mapping_vec.size() / 2;
 
   // Move the data structures to the GPU.
@@ -120,7 +120,7 @@ void copy_blocks(
   torch::Tensor value_cache_ptrs_tensor = torch::from_blob(
     value_cache_ptrs, {num_layers}, torch::kInt64).to(cache_device);
   torch::Tensor block_mapping_tensor = torch::from_blob(
-    block_mapping_array, {2 * num_pairs}, torch::kInt64).to(cache_device);
+    block_mapping_array, {2 * num_pairs}, torch::kInt).to(cache_device);
 
   // Launch the kernel.
   const int numel_per_block = key_caches[0][0].numel();
@@ -132,7 +132,7 @@ void copy_blocks(
       vllm::copy_blocks_kernel<scalar_t><<<grid, block, 0, stream>>>(
         key_cache_ptrs_tensor.data_ptr<int64_t>(),
         value_cache_ptrs_tensor.data_ptr<int64_t>(),
-        block_mapping_tensor.data_ptr<int64_t>(),
+        block_mapping_tensor.data_ptr<int>(),
         numel_per_block);
     }));
 }
@@ -141,48 +141,43 @@ namespace vllm {
 
 template<typename scalar_t>
 __global__ void reshape_and_cache_kernel(
-  const scalar_t* __restrict__ key,           // [num_tokens, num_heads, head_size]
-  const scalar_t* __restrict__ value,         // [num_tokens, num_heads, head_size]
-  scalar_t* __restrict__ key_cache,           // [num_blocks, num_heads, head_size/x, block_size, x]
-  scalar_t* __restrict__ value_cache,         // [num_blocks, num_heads, head_size, block_size]
-  const int64_t* __restrict__ slot_mapping,   // [num_tokens]
+  const scalar_t* __restrict__ key,     // [num_tokens, num_heads, head_size]
+  const scalar_t* __restrict__ value,   // [num_tokens, num_heads, head_size]
+  scalar_t* __restrict__ key_cache,     // [num_blocks, num_heads, head_size/x, block_size, x]
+  scalar_t* __restrict__ value_cache,   // [num_blocks, num_heads, head_size, block_size]
+  const int* __restrict__ slot_mapping, // [num_tokens]
   const int key_stride,
   const int value_stride,
   const int num_heads,
   const int head_size,
   const int block_size,
   const int x) {
-  const int64_t token_idx = blockIdx.x;
-  const int64_t slot_idx = slot_mapping[token_idx];
-  if (slot_idx < 0) {
-    // Padding token that should be ignored.
-    return;
-  }
-
-  const int64_t block_idx = slot_idx / block_size;
-  const int64_t block_offset = slot_idx % block_size;
+  const int token_idx = blockIdx.x;
+  const int slot_idx = slot_mapping[token_idx];
+  const int block_idx = slot_idx / block_size;
+  const int block_offset = slot_idx % block_size;
 
   const int n = num_heads * head_size;
   for (int i = threadIdx.x; i < n; i += blockDim.x) {
-    const int64_t src_key_idx = token_idx * key_stride + i;
-    const int64_t src_value_idx = token_idx * value_stride + i;
+    const int src_key_idx = token_idx * key_stride + i;
+    const int src_value_idx = token_idx * value_stride + i;
 
     const int head_idx = i / head_size;
     const int head_offset = i % head_size;
     const int x_idx = head_offset / x;
     const int x_offset = head_offset % x;
 
-    const int64_t tgt_key_idx = block_idx * num_heads * (head_size / x) * block_size * x
-                                + head_idx * (head_size / x) * block_size * x
-                                + x_idx * block_size * x
-                                + block_offset * x
-                                + x_offset;
-    const int64_t tgt_value_idx = block_idx * num_heads * head_size * block_size
-                                  + head_idx * head_size * block_size
-                                  + head_offset * block_size
-                                  + block_offset;
-    key_cache[tgt_key_idx] = key[src_key_idx];
-    value_cache[tgt_value_idx] = value[src_value_idx];
+    const int tgt_key_idx = block_idx * num_heads * (head_size / x) * block_size * x
+                            + head_idx * (head_size / x) * block_size * x
+                            + x_idx * block_size * x
+                            + block_offset * x
+                            + x_offset;
+    const int tgt_value_idx = block_idx * num_heads * head_size * block_size
+                              + head_idx * head_size * block_size
+                              + head_offset * block_size
+                              + block_offset;
+    key_cache[tgt_key_idx] = __ldg(&key[src_key_idx]);
+    value_cache[tgt_value_idx] = __ldg(&value[src_value_idx]);
   }
 }
 
@@ -216,7 +211,7 @@ void reshape_and_cache(
         value.data_ptr<scalar_t>(),
         key_cache.data_ptr<scalar_t>(),
         value_cache.data_ptr<scalar_t>(),
-        slot_mapping.data_ptr<int64_t>(),
+        slot_mapping.data_ptr<int>(),
         key_stride,
         value_stride,
         num_heads,

csrc/layernorm.cpp

@@ -6,19 +6,9 @@ void rms_norm(
   torch::Tensor& weight,
   float epsilon);
 
-void fused_add_rms_norm(
-  torch::Tensor& input,
-  torch::Tensor& residual,
-  torch::Tensor& weight,
-  float epsilon);
-
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def(
     "rms_norm",
     &rms_norm,
     "Apply Root Mean Square (RMS) Normalization to the input tensor.");
-  m.def(
-    "fused_add_rms_norm",
-    &fused_add_rms_norm,
-    "In-place fused Add and RMS Normalization");
 }

csrc/layernorm_kernels.cu

@@ -9,8 +9,8 @@ namespace vllm {
 // TODO(woosuk): Further optimize this kernel.
 template<typename scalar_t>
 __global__ void rms_norm_kernel(
-  scalar_t* __restrict__ out,             // [..., hidden_size]
-  const scalar_t* __restrict__ input,     // [..., hidden_size]
+  scalar_t* __restrict__ out,             // [num_tokens, hidden_size]
+  const scalar_t* __restrict__ input,     // [num_tokens, hidden_size]
   const scalar_t* __restrict__ weight,    // [hidden_size]
   const float epsilon,
   const int num_tokens,
@@ -34,45 +34,15 @@ __global__ void rms_norm_kernel(
   }
 }
 
-// TODO: Further optimize this kernel.
-template<typename scalar_t>
-__global__ void fused_add_rms_norm_kernel(
-  scalar_t* __restrict__ input,           // [..., hidden_size]
-  scalar_t* __restrict__ residual,        // [..., hidden_size]
-  const scalar_t* __restrict__ weight,    // [hidden_size]
-  const float epsilon,
-  const int num_tokens,
-  const int hidden_size) {
-  __shared__ float s_variance;
-  float variance = 0.0f;
-
-  for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
-    float x = (float) input[blockIdx.x * hidden_size + idx];
-    x += (float) residual[blockIdx.x * hidden_size + idx];
-    variance += x * x;
-    residual[blockIdx.x * hidden_size + idx] = (scalar_t) x;
-  }
-  variance = blockReduceSum<float>(variance);
-  if (threadIdx.x == 0) {
-    s_variance = rsqrtf(variance / hidden_size + epsilon);
-  }
-  __syncthreads();
-
-  for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
-    float x = (float) residual[blockIdx.x * hidden_size + idx];
-    input[blockIdx.x * hidden_size + idx] = ((scalar_t) (x * s_variance)) * weight[idx];
-  }
-}
-
 } // namespace vllm
 
 void rms_norm(
-  torch::Tensor& out,      // [..., hidden_size]
-  torch::Tensor& input,    // [..., hidden_size]
+  torch::Tensor& out,      // [num_tokens, hidden_size]
+  torch::Tensor& input,    // [num_tokens, hidden_size]
   torch::Tensor& weight,   // [hidden_size]
   float epsilon) {
-  int hidden_size = input.size(-1);
-  int num_tokens = input.numel() / hidden_size;
+  int num_tokens = input.size(0);
+  int hidden_size = input.size(1);
 
   dim3 grid(num_tokens);
   dim3 block(std::min(hidden_size, 1024));
@@ -90,28 +60,3 @@ void rms_norm(
         hidden_size);
     });
 }
-
-void fused_add_rms_norm(
-  torch::Tensor& input,    // [..., hidden_size]
-  torch::Tensor& residual, // [..., hidden_size]
-  torch::Tensor& weight,   // [hidden_size]
-  float epsilon) {
-  int hidden_size = input.size(-1);
-  int num_tokens = input.numel() / hidden_size;
-
-  dim3 grid(num_tokens);
-  dim3 block(std::min(hidden_size, 1024));
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  VLLM_DISPATCH_FLOATING_TYPES(
-    input.scalar_type(),
-    "fused_add_rms_norm_kernel",
-    [&] {
-      vllm::fused_add_rms_norm_kernel<scalar_t><<<grid, block, 0, stream>>>(
-        input.data_ptr<scalar_t>(),
-        residual.data_ptr<scalar_t>(),
-        weight.data_ptr<scalar_t>(),
-        epsilon,
-        num_tokens,
-        hidden_size);
-    });
-}

csrc/pos_encoding_kernels.cu

@@ -37,9 +37,9 @@ inline __device__ void apply_rotary_embedding(
 
 template<typename scalar_t, bool IS_NEOX>
 __global__ void rotary_embedding_kernel(
-  const int64_t* __restrict__ positions,        // [batch_size, seq_len] or [num_tokens]
-  scalar_t* __restrict__ query,                 // [batch_size, seq_len, num_heads, head_size] or [num_tokens, num_heads, head_size]
-  scalar_t* __restrict__ key,                   // [batch_size, seq_len, num_kv_heads, head_size] or [num_tokens, num_kv_heads, head_size]
+  const int64_t* __restrict__ positions,        // [num_tokens]
+  scalar_t* __restrict__ query,                 // [num_tokens, num_heads, head_size]
+  scalar_t* __restrict__ key,                   // [num_tokens, num_kv_heads, head_size]
   const scalar_t* __restrict__ cos_sin_cache,   // [max_position, 2, rot_dim // 2]
   const int rot_dim,
   const int query_stride,
@@ -78,18 +78,18 @@ __global__ void rotary_embedding_kernel(
 } // namespace vllm
 
 void rotary_embedding(
-  torch::Tensor& positions,         // [batch_size, seq_len] or [num_tokens]
-  torch::Tensor& query,             // [batch_size, seq_len, num_heads * head_size] or [num_tokens, num_heads * head_size]
-  torch::Tensor& key,               // [batch_size, seq_len, num_kv_heads * head_size] or [num_tokens, num_kv_heads * head_size]
+  torch::Tensor& positions,         // [num_tokens]
+  torch::Tensor& query,             // [num_tokens, num_heads * head_size]
+  torch::Tensor& key,               // [num_tokens, num_kv_heads * head_size]
   int head_size,
   torch::Tensor& cos_sin_cache,     // [max_position, rot_dim]
   bool is_neox) {
-  int64_t num_tokens = query.numel() / query.size(-1);
+  int num_tokens = query.size(0);
   int rot_dim = cos_sin_cache.size(1);
-  int num_heads = query.size(-1) / head_size;
-  int num_kv_heads = key.size(-1) / head_size;
-  int query_stride = query.stride(-2);
-  int key_stride = key.stride(-2);
+  int num_heads = query.size(1) / head_size;
+  int num_kv_heads = key.size(1) / head_size;
+  int query_stride = query.stride(0);
+  int key_stride = key.stride(0);
 
   dim3 grid(num_tokens);
   dim3 block(std::min(num_heads * rot_dim / 2, 512));

csrc/quantization.cpp

@@ -7,13 +7,9 @@ torch::Tensor awq_gemm(
   torch::Tensor _zeros,
   int split_k_iters);
 
-void squeezellm_gemm(
-  torch::Tensor vec,
-  torch::Tensor mat,
-  torch::Tensor mul,
-  torch::Tensor lookup_table);
-
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("awq_gemm", &awq_gemm, "Quantized GEMM for AWQ");
-  m.def("squeezellm_gemm", &squeezellm_gemm, "Quantized GEMM for SqueezeLLM");
+  m.def(
+    "awq_gemm",
+    &awq_gemm,
+    "Quantized GEMM for AWQ");
 }

csrc/quantization/squeezellm/quant_cuda_kernel.cu

@@ -1,148 +0,0 @@
-#include <torch/all.h>
-#include <torch/python.h>
-#include <cuda.h>
-#include <cuda_runtime.h>
-#include <cuda_fp16.h>
-
-// half-tensor
-#include <c10/cuda/CUDAStream.h>
-#include <ATen/cuda/CUDATensorMethods.cuh>
-
-#define BLOCKWIDTH 128
-#define BLOCKHEIGHT4 16
-
-namespace vllm {
-namespace squeezellm {
-
-__device__ inline unsigned int as_unsigned(int i) {
-  return *reinterpret_cast<unsigned int*>(&i);
-}
-
-// 4-bit matvec kernel (LUT-based)
-__global__ void NUQ4MatMulKernel(
-    const  half2* __restrict__ vec,
-    const    int* __restrict__ mat,
-           half2* __restrict__ mul,
-    const  __half* __restrict__ lookup_table,
-    int height,
-    int width,
-    int batch,
-    int vec_height
-) {
-
-  const int blockwidth2 = BLOCKWIDTH / 2;
-
-  int row = BLOCKHEIGHT4 * blockIdx.x;
-  int col =  BLOCKWIDTH * blockIdx.y + threadIdx.x;
-
-  __shared__ half2 blockvec[blockwidth2];
-
-  __shared__ __half deq2[16][BLOCKWIDTH];
-  int off = threadIdx.x;
-  int column_offset = col * 16;
-  for (int val = 0; val < 16; val += 1) {
-    int lut_index = column_offset + val;
-    deq2[val][off] = lookup_table[lut_index];
-  }
-
-  __half res;
-  half2 res2;
-  half2 tmp2;
-
-  int i;
-  int k;
-
-  unsigned int tmp1;
-  unsigned int lut_index1, lut_index2;
-
-  for (int b = 0; b < batch; ++b){
-    i = width * row + col;
-    res = __int2half_rd(0);
-    k = 0;
-
-    __syncthreads();
-    if (threadIdx.x < blockwidth2)
-      blockvec[threadIdx.x] = vec[b * vec_height / 2 + (row / BLOCKHEIGHT4) * blockwidth2 + threadIdx.x];
-    __syncthreads();
-
-    while (k < blockwidth2) {
-      tmp1 = as_unsigned(mat[i]);
-
-      res2 = {};
-      tmp2 = {};
-
-      lut_index1 = tmp1 & 0xF;
-      lut_index2 = (tmp1 >> 4) & 0xF;
-      tmp2.x = deq2[lut_index1][off];
-      tmp2.y = deq2[lut_index2][off];
-      res2 = __hfma2(tmp2, blockvec[k + 0], res2);
-
-      lut_index1 = (tmp1 >> 8) & 0xF;
-      lut_index2 = (tmp1 >> 12) & 0xF;
-      tmp2.x = deq2[lut_index1][off];
-      tmp2.y = deq2[lut_index2][off];
-      res2 = __hfma2(tmp2, blockvec[k + 1], res2);
-
-      lut_index1 = (tmp1 >> 16) & 0xF;
-      lut_index2 = (tmp1 >> 20) & 0xF;
-      tmp2.x = deq2[lut_index1][off];
-      tmp2.y = deq2[lut_index2][off];
-      res2 = __hfma2(tmp2, blockvec[k + 2], res2);
-
-      lut_index1 = (tmp1 >> 24) & 0xF;
-      lut_index2 = (tmp1 >> 28) & 0xF;
-      tmp2.x = deq2[lut_index1][off];
-      tmp2.y = deq2[lut_index2][off];
-      res2 = __hfma2(tmp2, blockvec[k + 3], res2);
-
-      res = __hadd(__hadd(res2.x, res2.y), res);
-
-      i += width;
-      k += 4;
-    }
-
-    // col%2 -> only set one of the two values
-    half2 res3 = {};
-    if (col % 2 == 0) {
-      res3.x = res;
-    } else {
-      res3.y = res;
-    }
-
-    atomicAdd(&mul[b * width / 2 + col / 2], res3);
-  }
-}
-
-} // namespace squeezellm
-} // namespace vllm
-
-// 4-bit matvec kernel (LUT-based)
-void squeezellm_gemm(
-  torch::Tensor vec,
-  torch::Tensor mat,
-  torch::Tensor mul,
-  torch::Tensor lookup_table
-) {
-  int height = mat.size(0);
-  int width = mat.size(1);
-
-  int batch = vec.size(0);
-  int vec_height = vec.size(1);
-
-  dim3 blocks(
-    (height + BLOCKHEIGHT4 - 1) / BLOCKHEIGHT4,
-    (width + BLOCKWIDTH - 1) / BLOCKWIDTH
-  );
-  dim3 threads(BLOCKWIDTH);
-
-  vllm::squeezellm::NUQ4MatMulKernel<<<blocks, threads>>>(
-    (half2*) vec.data<at::Half>(),
-    mat.data_ptr<int>(),
-    (half2*) mul.data<at::Half>(),
-    (__half*) lookup_table.data<at::Half>(),
-    height, width, batch, vec_height
-  );
-}
-
-#undef BLOCKWIDTH
-#undef BLOCKHEIGHT4

docs/source/getting_started/quickstart.rst

@@ -40,16 +40,6 @@ Initialize vLLM's engine for offline inference with the ``LLM`` class and the `O
 
     llm = LLM(model="facebook/opt-125m")
 
-Use model from www.modelscope.cn
-
-.. code-block:: shell
-
-    export VLLM_USE_MODELSCOPE=True
-
-.. code-block:: python
-
-    llm = LLM(model="qwen/Qwen-7B-Chat", revision="v1.1.8", trust_remote_code=True)
-
 Call ``llm.generate`` to generate the outputs. It adds the input prompts to vLLM engine's waiting queue and executes the vLLM engine to generate the outputs with high throughput. The outputs are returned as a list of ``RequestOutput`` objects, which include all the output tokens.
 
 .. code-block:: python
@@ -77,16 +67,6 @@ Start the server:
 
     $ python -m vllm.entrypoints.api_server
 
-Use model from www.modelscope.cn
-
-.. code-block:: console
-
-    $ VLLM_USE_MODELSCOPE=True python -m vllm.entrypoints.api_server \
-    $    --model="qwen/Qwen-7B-Chat" \
-    $    --revision="v1.1.8" \
-    $    --trust-remote-code
-
-
 By default, this command starts the server at ``http://localhost:8000`` with the OPT-125M model.
 
 Query the model in shell:
@@ -115,13 +95,6 @@ Start the server:
     $ python -m vllm.entrypoints.openai.api_server \
     $     --model facebook/opt-125m
 
-Use model from www.modelscope.cn
-
-.. code-block:: console
-
-    $ VLLM_USE_MODELSCOPE=True python -m vllm.entrypoints.openai.api_server \
-    $     --model="qwen/Qwen-7B-Chat" --revision="v1.1.8" --trust-remote-code
-
 By default, it starts the server at ``http://localhost:8000``. You can specify the address with ``--host`` and ``--port`` arguments. The server currently hosts one model at a time (OPT-125M in the above command) and implements `list models <https://platform.openai.com/docs/api-reference/models/list>`_ and `create completion <https://platform.openai.com/docs/api-reference/completions/create>`_ endpoints. We are actively adding support for more endpoints.
 
 This server can be queried in the same format as OpenAI API. For example, list the models:

docs/source/index.rst

@@ -65,7 +65,6 @@ Documentation
    serving/distributed_serving
    serving/run_on_sky
    serving/deploying_with_triton
-   serving/deploying_with_docker
 
 .. toctree::
    :maxdepth: 1
@@ -73,9 +72,3 @@ Documentation
 
    models/supported_models
    models/adding_model
-
-.. toctree::
-   :maxdepth: 1
-   :caption: Quantization
-
-   quantization/auto_awq

docs/source/models/adding_model.rst

@@ -62,34 +62,31 @@ Next, you need to rewrite the :code:`forward` methods of your model by following
     +) -> SamplerOutput:
 
 3. Update the code by considering that :code:`input_ids` and :code:`positions` are now flattened tensors.
-4. Replace the attention operation with either :code:`PagedAttention`, :code:`PagedAttentionWithRoPE`, or :code:`PagedAttentionWithALiBi` depending on the model's architecture.
+4. Replace the attention operation with either :code:`GPTPagedAttention` or :code:`GPTNeoXPagedAttention`, depending on the model's architecture.
 
 .. note::
     Currently, vLLM supports the basic multi-head attention mechanism and its variant with rotary positional embeddings.
     If your model employs a different attention mechanism, you will need to implement a new attention layer in vLLM.
 
 
-3. (Optional) Implement tensor parallelism and quantization support
--------------------------------------------------------------------
+3. (Optional) Implement tensor parallelism support
+--------------------------------------------------
 
 If your model is too large to fit into a single GPU, you can use tensor parallelism to manage it.
 To do this, substitute your model's linear and embedding layers with their tensor-parallel versions.
-For the embedding layer, you can simply replace :code:`nn.Embedding` with :code:`VocabParallelEmbedding`. For the output LM head, you can use :code:`ParallelLMHead`.
-When it comes to the linear layers, we provide the following options to parallelize them:
+For the embedding layer, you can simply replace :code:`nn.Embedding` with :code:`VocabParallelEmbedding`.
+When it comes to the linear layers, you should use either :code:`RowParallelLinear` or :code:`ColumnParallelLinear`.
+Typically, :code:`ColumnParallelLinear` is used for QKV linear layers and the first linear layers of the MLP blocks.
+For the remaining linear layers, :code:`RowParallelLinear` is used.
 
-* :code:`ReplicatedLinear`: Replicates the inputs and weights across multiple GPUs. No memory saving.
-* :code:`RowParallelLinear`: The input tensor is partitioned along the hidden dimension. The weight matrix is partitioned along the rows (input dimension). An *all-reduce* operation is performed after the matrix multiplication to reduce the results. Typically used for the second FFN layer and the output linear transformation of the attention layer.
-* :code:`ColumnParallelLinear`: The input tensor is replicated. The weight matrix is partitioned along the columns (output dimension). The result is partitioned along the column dimension. Typically used for the first FFN layer and the separated QKV transformation of the attention layer in the original Transformer.
-* :code:`MergedColumnParallelLinear`: Column-parallel linear that merges multiple `ColumnParallelLinear` operators. Typically used for the first FFN layer with weighted activation functions (e.g., SiLU). This class handles the sharded weight loading logic of multiple weight matrices.
-* :code:`QKVParallelLinear`: Parallel linear layer for the query, key, and value projections of the multi-head and grouped-query attention mechanisms. When number of key/value heads are less than the world size, this class replicates the key/value heads properly. This class handles the weight loading and replication of the weight matrices.
-
-Note that all the linear layers above take `linear_method` as an input. vLLM will set this parameter according to different quantization schemes to support weight quantization.
 
 4. Implement the weight loading logic
 -------------------------------------
 
 You now need to implement the :code:`load_weights` method in your :code:`*ForCausalLM` class.
-This method should load the weights from the HuggingFace's checkpoint file and assign them to the corresponding layers in your model. Specifically, for `MergedColumnParallelLinear` and `QKVParallelLinear` layers, if the original model has separated weight matrices, you need to load the different parts separately.
+This method should load the weights from the HuggingFace's checkpoint file and assign them to the corresponding layers in your model.
+While the process is straightforward for most layers, the tensor-parallel layers necessitate some additional care as their weights should be partitioned to multiple GPUs.
+
 
 5. Register your model
 ----------------------

docs/source/models/supported_models.rst

@@ -20,9 +20,6 @@ Alongside each architecture, we include some popular models that use it.
   * - :code:`BaiChuanForCausalLM`
     - Baichuan
     - :code:`baichuan-inc/Baichuan-7B`, :code:`baichuan-inc/Baichuan-13B-Chat`, etc.
-  * - :code:`ChatGLMModel`
-    - ChatGLM
-    - :code:`THUDM/chatglm2-6b`, :code:`THUDM/chatglm3-6b`, etc.
   * - :code:`BloomForCausalLM`
     - BLOOM, BLOOMZ, BLOOMChat
     - :code:`bigscience/bloom`, :code:`bigscience/bloomz`, etc.
@@ -56,15 +53,9 @@ Alongside each architecture, we include some popular models that use it.
   * - :code:`OPTForCausalLM`
     - OPT, OPT-IML
     - :code:`facebook/opt-66b`, :code:`facebook/opt-iml-max-30b`, etc.
-  * - :code:`PhiForCausalLM`
-    - Phi-1.5
-    - :code:`microsoft/phi-1_5`, etc.
   * - :code:`QWenLMHeadModel`
     - Qwen
     - :code:`Qwen/Qwen-7B`, :code:`Qwen/Qwen-7B-Chat`, etc.
-  * - :code:`YiForCausalLM`
-    - Yi
-    - :code:`01-ai/Yi-6B`, :code:`01-ai/Yi-34B`, etc.
 
 If your model uses one of the above model architectures, you can seamlessly run your model with vLLM.
 Otherwise, please refer to :ref:`Adding a New Model <adding_a_new_model>` for instructions on how to implement support for your model.
@@ -81,18 +72,4 @@ Alternatively, you can raise an issue on our `GitHub <https://github.com/vllm-pr
         output = llm.generate("Hello, my name is")
         print(output)
 
-    To use model from www.modelscope.cn
-
-    .. code-block:: shell
-
-       $ export VLLM_USE_MODELSCOPE=True
-
-    .. code-block:: python
-
-        from vllm import LLM
-
-        llm = LLM(model=..., revision=..., trust_remote_code=True)  # Name or path of your model
-        output = llm.generate("Hello, my name is")
-        print(output)
-
     If vLLM successfully generates text, it indicates that your model is supported.

docs/source/quantization/auto_awq.rst

@@ -1,69 +0,0 @@
-.. _auto_awq:
-
-AutoAWQ
-==================
-
-To create a new 4-bit quantized model, you can leverage `AutoAWQ <https://github.com/casper-hansen/AutoAWQ>`_. 
-Quantizing reduces the model's precision from FP16 to INT4 which effectively reduces the file size by ~70%.
-The main benefits are lower latency and memory usage.
-
-You can quantize your own models by installing AutoAWQ or picking one of the `400+ models on Huggingface <https://huggingface.co/models?sort=trending&search=awq>`_. 
-
-.. code-block:: console
-
-    $ pip install autoawq
-
-After installing AutoAWQ, you are ready to quantize a model. Here is an example of how to quantize Vicuna 7B v1.5:
-
-.. code-block:: python
-
-    from awq import AutoAWQForCausalLM
-    from transformers import AutoTokenizer
-
-    model_path = 'lmsys/vicuna-7b-v1.5'
-    quant_path = 'vicuna-7b-v1.5-awq'
-    quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" }
-
-    # Load model
-    model = AutoAWQForCausalLM.from_pretrained(model_path, **{"low_cpu_mem_usage": True})
-    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
-
-    # Quantize
-    model.quantize(tokenizer, quant_config=quant_config)
-
-    # Save quantized model
-    model.save_quantized(quant_path)
-    tokenizer.save_pretrained(quant_path)
-
-To run an AWQ model with vLLM, you can use `TheBloke/Llama-2-7b-Chat-AWQ <https://huggingface.co/TheBloke/Llama-2-7b-Chat-AWQ>`_ with the following command:
-
-.. code-block:: console
-
-    $ python examples/llm_engine_example.py --model TheBloke/Llama-2-7b-Chat-AWQ --quantization awq
-
-AWQ models are also supported directly through the LLM entrypoint:
-
-.. code-block:: python
-
-    from vllm import LLM, SamplingParams
-
-    # Sample prompts.
-    prompts = [
-        "Hello, my name is",
-        "The president of the United States is",
-        "The capital of France is",
-        "The future of AI is",
-    ]
-    # Create a sampling params object.
-    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
-
-    # Create an LLM.
-    llm = LLM(model="TheBloke/Llama-2-7b-Chat-AWQ", quantization="AWQ")
-    # Generate texts from the prompts. The output is a list of RequestOutput objects
-    # that contain the prompt, generated text, and other information.
-    outputs = llm.generate(prompts, sampling_params)
-    # Print the outputs.
-    for output in outputs:
-        prompt = output.prompt
-        generated_text = output.outputs[0].text
-        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

docs/source/serving/deploying_with_docker.rst

@@ -1,21 +0,0 @@
-.. _deploying_with_docker:
-
-Deploying with Docker
-============================
-
-You can build and run vLLM from source via the provided dockerfile. To build vLLM:
-
-.. code-block:: console
-
-    $ DOCKER_BUILDKIT=1 docker build . --target vllm --tag vllm --build-arg max_jobs=8
-
-To run vLLM:
-
-.. code-block:: console
-
-    $ docker run --runtime nvidia --gpus all \
-        -v ~/.cache/huggingface:/root/.cache/huggingface \
-        -p 8000:8000 \
-        --env "HUGGING_FACE_HUB_TOKEN=<secret>" \
-        vllm <args...>
-

examples/llm_engine_example.py

@@ -1,12 +1,15 @@
 import argparse
-from typing import List, Tuple
 
-from vllm import EngineArgs, LLMEngine, SamplingParams, RequestOutput
+from vllm import EngineArgs, LLMEngine, SamplingParams
 
 
-def create_test_prompts() -> List[Tuple[str, SamplingParams]]:
-    """Create a list of test prompts with their sampling parameters."""
-    return [
+def main(args: argparse.Namespace):
+    # Parse the CLI argument and initialize the engine.
+    engine_args = EngineArgs.from_cli_args(args)
+    engine = LLMEngine.from_engine_args(engine_args)
+
+    # Test the following prompts.
+    test_prompts = [
         ("A robot may not injure a human being",
          SamplingParams(temperature=0.0, logprobs=1, prompt_logprobs=1)),
         ("To be or not to be,",
@@ -22,36 +25,22 @@ def create_test_prompts() -> List[Tuple[str, SamplingParams]]:
                         temperature=0.0)),
     ]
 
-
-def process_requests(engine: LLMEngine,
-                     test_prompts: List[Tuple[str, SamplingParams]]):
-    """Continuously process a list of prompts and handle the outputs."""
+    # Run the engine by calling `engine.step()` manually.
     request_id = 0
-
-    while test_prompts or engine.has_unfinished_requests():
+    while True:
+        # To test continuous batching, we add one request at each step.
         if test_prompts:
             prompt, sampling_params = test_prompts.pop(0)
             engine.add_request(str(request_id), prompt, sampling_params)
             request_id += 1
 
-        request_outputs: List[RequestOutput] = engine.step()
-
+        request_outputs = engine.step()
         for request_output in request_outputs:
             if request_output.finished:
                 print(request_output)
 
-
-def initialize_engine(args: argparse.Namespace) -> LLMEngine:
-    """Initialize the LLMEngine from the command line arguments."""
-    engine_args = EngineArgs.from_cli_args(args)
-    return LLMEngine.from_engine_args(engine_args)
-
-
-def main(args: argparse.Namespace):
-    """Main function that sets up and runs the prompt processing."""
-    engine = initialize_engine(args)
-    test_prompts = create_test_prompts()
-    process_requests(engine, test_prompts)
+        if not (engine.has_unfinished_requests() or test_prompts):
+            break
 
 
 if __name__ == '__main__':

format.sh

@@ -93,43 +93,9 @@ echo 'vLLM yapf: Done'
 # echo 'vLLM mypy:'
 # mypy
 
-# Lint specified files
-lint() {
-    pylint "$@"
-}
-
-# Lint files that differ from main branch. Ignores dirs that are not slated
-# for autolint yet.
-lint_changed() {
-    # The `if` guard ensures that the list of filenames is not empty, which
-    # could cause pylint to receive 0 positional arguments, making it hang
-    # waiting for STDIN.
-    #
-    # `diff-filter=ACM` and $MERGEBASE is to ensure we only lint files that
-    # exist on both branches.
-    MERGEBASE="$(git merge-base origin/main HEAD)"
-
-    if ! git diff --diff-filter=ACM --quiet --exit-code "$MERGEBASE" -- '*.py' '*.pyi' &>/dev/null; then
-        git diff --name-only --diff-filter=ACM "$MERGEBASE" -- '*.py' '*.pyi' | xargs \
-             pylint
-    fi
-
-}
-
 # Run Pylint
 echo 'vLLM Pylint:'
-## This flag lints individual files. --files *must* be the first command line
-## arg to use this option.
-if [[ "$1" == '--files' ]]; then
-   lint "${@:2}"
-   # If `--all` is passed, then any further arguments are ignored and the
-   # entire python directory is linted.
-elif [[ "$1" == '--all' ]]; then
-   lint vllm tests
-else
-   # Format only the files that changed in last commit.
-   lint_changed
-fi
+pylint vllm tests
 
 if ! git diff --quiet &>/dev/null; then
     echo 'Reformatted files. Please review and stage the changes.'

pyproject.toml

@@ -3,7 +3,7 @@ requires = [
     "ninja",
     "packaging",
     "setuptools",
-    "torch >= 2.1.0",
+    "torch == 2.0.1",
     "wheel",
 ]
 build-backend = "setuptools.build_meta"

requirements-dev.txt

@@ -12,4 +12,3 @@ types-setuptools
 pytest
 pytest-forked
 pytest-asyncio
-

requirements.txt

@@ -5,10 +5,9 @@ pandas  # Required for Ray data.
 pyarrow  # Required for Ray data.
 sentencepiece  # Required for LLaMA tokenizer.
 numpy
-einops  # Required for phi-1_5
-torch >= 2.1.0
+torch == 2.0.1
 transformers >= 4.34.0  # Required for Mistral.
-xformers >= 0.0.22.post7  # Required for CUDA 12.1.
+xformers == 0.0.22  # Required for Mistral.
 fastapi
 uvicorn[standard]
-pydantic == 1.10.13  # Required for OpenAI server.
+pydantic < 2  # Required for OpenAI server.

setup.py

@@ -12,8 +12,6 @@ from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CUDA_HOME
 
 ROOT_DIR = os.path.dirname(__file__)
 
-MAIN_CUDA_VERSION = "12.1"
-
 # Supported NVIDIA GPU architectures.
 SUPPORTED_ARCHS = {"7.0", "7.5", "8.0", "8.6", "8.9", "9.0"}
 
@@ -202,7 +200,6 @@ quantization_extension = CUDAExtension(
     sources=[
         "csrc/quantization.cpp",
         "csrc/quantization/awq/gemm_kernels.cu",
-        "csrc/quantization/squeezellm/quant_cuda_kernel.cu",
     ],
     extra_compile_args={
         "cxx": CXX_FLAGS,
@@ -227,7 +224,7 @@ def get_path(*filepath) -> str:
     return os.path.join(ROOT_DIR, *filepath)
 
 
-def find_version(filepath: str) -> str:
+def find_version(filepath: str):
     """Extract version information from the given filepath.
 
     Adapted from https://github.com/ray-project/ray/blob/0b190ee1160eeca9796bc091e07eaebf4c85b511/python/setup.py
@@ -240,22 +237,9 @@ def find_version(filepath: str) -> str:
         raise RuntimeError("Unable to find version string.")
 
 
-def get_vllm_version() -> str:
-    version = find_version(get_path("vllm", "__init__.py"))
-    cuda_version = str(nvcc_cuda_version)
-    if cuda_version != MAIN_CUDA_VERSION:
-        cuda_version_str = cuda_version.replace(".", "")[:3]
-        version += f"+cu{cuda_version_str}"
-    return version
-
-
 def read_readme() -> str:
-    """Read the README file if present."""
-    p = get_path("README.md")
-    if os.path.isfile(p):
-        return io.open(get_path("README.md"), "r", encoding="utf-8").read()
-    else:
-        return ""
+    """Read the README file."""
+    return io.open(get_path("README.md"), "r", encoding="utf-8").read()
 
 
 def get_requirements() -> List[str]:
@@ -267,7 +251,7 @@ def get_requirements() -> List[str]:
 
 setuptools.setup(
     name="vllm",
-    version=get_vllm_version(),
+    version=find_version(get_path("vllm", "__init__.py")),
     author="vLLM Team",
     license="Apache 2.0",
     description=("A high-throughput and memory-efficient inference and "
@@ -293,5 +277,4 @@ setuptools.setup(
     install_requires=get_requirements(),
     ext_modules=ext_modules,
     cmdclass={"build_ext": BuildExtension},
-    package_data={"vllm": ["py.typed"]},
 )

tests/distributed/test_comm_ops.py

@@ -2,7 +2,7 @@
 
 Run `pytest tests/distributed/test_comm_ops.py --forked`.
 """
-from multiprocessing import Process, set_start_method
+from multiprocessing import Process
 
 import pytest
 import torch
@@ -70,7 +70,6 @@ def all_gather_test_worker(tensor_parallel_size: int, rank: int,
 @pytest.mark.parametrize("test_target",
                          [all_reduce_test_worker, all_gather_test_worker])
 def test_multi_process_tensor_parallel(tensor_parallel_size, test_target):
-    set_start_method("spawn", force=True)
     distributed_init_port = get_open_port()
     processes = []
     for rank in range(tensor_parallel_size):

tests/kernels/test_attention.py

@@ -13,7 +13,7 @@ FLOAT32_BYTES = torch.finfo(torch.float).bits // 8
 # This will change depending on the compute capability.
 # - 512 as a buffer
 MAX_SEQ_LEN = get_max_shared_memory_bytes() // FLOAT32_BYTES - 512
-NUM_BLOCKS = 40000  # Arbitrary values for testing
+NUM_BLOCKS = 128  # Arbitrary values for testing
 PARTITION_SIZE = 512
 
 DTYPES = [torch.half, torch.bfloat16, torch.float]

tests/kernels/test_cache.py

@@ -6,13 +6,13 @@ import torch
 from vllm import cache_ops
 
 DTYPES = [torch.half, torch.bfloat16, torch.float]
-NUM_TOKENS = [83]  # Arbitrary values for testing
-NUM_LAYERS = [1]  # Arbitrary values for testing
+NUM_TOKENS = [7, 83, 2048]  # Arbitrary values for testing
+NUM_LAYERS = [5]  # Arbitrary values for testing
 NUM_HEADS = [8]  # Arbitrary values for testing
 HEAD_SIZES = [64, 80, 96, 112, 128, 256]
 BLOCK_SIZES = [8, 16, 32]
-NUM_BLOCKS = [1024, 36000]  # Arbitrary values for testing
-NUM_MAPPINGS = [256]  # Arbitrary values for testing
+NUM_BLOCKS = [1024]  # Arbitrary values for testing
+NUM_MAPPINGS = [32, 256]  # Arbitrary values for testing
 SEEDS = [0]
 
 
@@ -69,9 +69,9 @@ def test_copy_blocks(
     for src, dsts in block_mapping.items():
         for dst in dsts:
             for cloned_key_cache in cloned_key_caches:
-                cloned_key_cache[dst].copy_(cloned_key_cache[src])
+                cloned_key_cache[dst] = cloned_key_cache[src]
             for cloned_value_cache in cloned_value_caches:
-                cloned_value_cache[dst].copy_(cloned_value_cache[src])
+                cloned_value_cache[dst] = cloned_value_cache[src]
 
     # Compare the results.
     for key_cache, cloned_key_cache in zip(key_caches, cloned_key_caches):
@@ -106,7 +106,7 @@ def test_reshape_and_cache(
     # Create a random slot mapping.
     num_slots = block_size * num_blocks
     slot_mapping = random.sample(range(num_slots), num_tokens)
-    slot_mapping = torch.tensor(slot_mapping, dtype=torch.long, device="cuda")
+    slot_mapping = torch.tensor(slot_mapping, dtype=torch.int, device="cuda")
 
     qkv = torch.randn(num_tokens,
                       3,

tests/models/test_models.py

@@ -6,16 +6,14 @@ import pytest
 
 MODELS = [
     "facebook/opt-125m",
-    "meta-llama/Llama-2-7b-hf",
-    "mistralai/Mistral-7B-v0.1",
-    "tiiuae/falcon-7b",
     "gpt2",
     "bigcode/tiny_starcoder_py",
     "EleutherAI/gpt-j-6b",
     "EleutherAI/pythia-70m",
     "bigscience/bloom-560m",
     "mosaicml/mpt-7b",
-    "microsoft/phi-1_5",
+    "tiiuae/falcon-7b",
+    "meta-llama/Llama-2-7b-hf",
 ]
 
 

tests/samplers/test_sampler.py

@@ -183,37 +183,3 @@ def test_sampler_mixed(seed: int):
             continue
         for nth_output in sequence_output.samples:
             assert nth_output.output_token in expected_tokens
-
-
-@pytest.mark.parametrize("seed", RANDOM_SEEDS)
-def test_sampler_logits_processors(seed: int):
-    set_random_seed(seed)
-    batch_size = random.randint(1, 256)
-    input_tensor, _, sampler, worker = _prepare_test(batch_size)
-
-    # This sample logits processor gives infinite score to the i-th token,
-    # where i is the length of the input sequence.
-    # We therefore expect the output token sequence to be [0, 1, 2, ...]
-    def pick_ith(token_ids, logits):
-        logits[len(token_ids)] = float("inf")
-        return logits
-
-    seq_group_metadata_list = []
-    for i in range(batch_size):
-        seq_group_metadata_list.append(
-            SequenceGroupMetadata(
-                request_id=f"test_{i}",
-                is_prompt=True,
-                seq_data={0: SequenceData([1, 2, 3])},
-                sampling_params=SamplingParams(temperature=0,
-                                               logits_processors=[pick_ith]),
-                block_tables={0: [1]},
-            ))
-
-    _, _, input_metadata = worker._prepare_inputs(seq_group_metadata_list)
-    sampler_output = sampler(embedding=None,
-                             hidden_states=input_tensor,
-                             input_metadata=input_metadata)
-    for i, sequence_output in enumerate(sampler_output):
-        for idx, nth_output in enumerate(sequence_output.samples):
-            assert nth_output.output_token == idx

tests/test_regression.py

@@ -1,27 +0,0 @@
-"""Containing tests that check for regressions in vLLM's behavior.
-
-It should include tests that are reported by users and making sure they
-will never happen again.
-
-"""
-from vllm import LLM, SamplingParams
-
-
-def test_duplicated_ignored_sequence_group():
-    """https://github.com/vllm-project/vllm/issues/1655"""
-
-    sampling_params = SamplingParams(temperature=0.01,
-                                     top_p=0.1,
-                                     max_tokens=256)
-    llm = LLM(model="facebook/opt-125m",
-              max_num_batched_tokens=4096,
-              tensor_parallel_size=1)
-    prompts = ["This is a short prompt", "This is a very long prompt " * 1000]
-    outputs = llm.generate(prompts, sampling_params=sampling_params)
-
-    assert len(prompts) == len(outputs)
-
-
-if __name__ == "__main__":
-    import pytest
-    pytest.main([__file__])

tests/worker/test_worker.py

@@ -1,44 +0,0 @@
-# pylint: disable=protected-access
-import random
-import torch
-
-from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
-from vllm.worker.worker import Worker
-
-
-def test_worker_prepare_inputs_for_prompt():
-    worker = Worker(None, None, None)
-    worker.block_size = 16
-    batch_size = random.randint(1, 256)
-    prompt_lens = []
-    seq_group_metadata_list = []
-    for i in range(batch_size):
-        # make sure all tokens fit into one block
-        prompt_len = i % (worker.block_size - 1) + 1
-        prompt_lens.append(prompt_len)
-        seq_data = list(range(prompt_len))
-        seq_group_metadata_list.append(
-            SequenceGroupMetadata(
-                request_id=f"test_{i}",
-                is_prompt=True,
-                seq_data={0: SequenceData(seq_data)},
-                sampling_params=SamplingParams(temperature=0),
-                block_tables={0: [1]},
-            ))
-    expected_selected_token_indices = []
-    selected_token_start_idx = 0
-    max_seq_len = max(prompt_lens)
-    for prompt_len in prompt_lens:
-        expected_selected_token_indices.append(selected_token_start_idx +
-                                               prompt_len - 1)
-        selected_token_start_idx += max_seq_len
-    input_tokens, input_positions, input_metadata = worker._prepare_inputs(
-        seq_group_metadata_list)
-    assert input_tokens.shape == input_positions.shape == (batch_size,
-                                                           max_seq_len)
-    torch.testing.assert_close(input_tokens, input_positions)
-    actual = input_metadata.selected_token_indices
-    expected = torch.tensor(expected_selected_token_indices,
-                            device=actual.device,
-                            dtype=actual.dtype)
-    torch.testing.assert_close(actual, expected)

vllm/__init__.py

@@ -8,7 +8,7 @@ from vllm.entrypoints.llm import LLM
 from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.sampling_params import SamplingParams
 
-__version__ = "0.2.2"
+__version__ = "0.2.1.post1"
 
 __all__ = [
     "LLM",

vllm/config.py

@@ -1,5 +1,4 @@
-from typing import Optional, Union
-import os
+from typing import Optional
 
 import torch
 from transformers import PretrainedConfig
@@ -59,7 +58,7 @@ class ModelConfig:
         trust_remote_code: bool,
         download_dir: Optional[str],
         load_format: str,
-        dtype: Union[str, torch.dtype],
+        dtype: str,
         seed: int,
         revision: Optional[str] = None,
         tokenizer_revision: Optional[str] = None,
@@ -77,18 +76,7 @@ class ModelConfig:
         self.tokenizer_revision = tokenizer_revision
         self.quantization = quantization
 
-        if os.environ.get("VLLM_USE_MODELSCOPE", "False").lower() == "true":
-            # download model from ModelScope hub,
-            # lazy import so that modelscope is not required for normal use.
-            from modelscope.hub.snapshot_download import snapshot_download  # pylint: disable=C
-            model_path = snapshot_download(model_id=model,
-                                           cache_dir=download_dir,
-                                           revision=revision)
-            self.model = model_path
-            self.download_dir = model_path
-            self.tokenizer = model_path
-
-        self.hf_config = get_config(self.model, trust_remote_code, revision)
+        self.hf_config = get_config(model, trust_remote_code, revision)
         self.dtype = _get_and_verify_dtype(self.hf_config, dtype)
         self.max_model_len = _get_and_verify_max_len(self.hf_config,
                                                      max_model_len)
@@ -115,31 +103,15 @@ class ModelConfig:
         self.tokenizer_mode = tokenizer_mode
 
     def _verify_quantization(self) -> None:
-        supported_quantization = ["awq", "squeezellm"]
-        if self.quantization is not None:
-            self.quantization = self.quantization.lower()
-
-        # Parse quantization method from the HF model config, if available.
-        hf_quant_config = getattr(self.hf_config, "quantization_config", None)
-        if hf_quant_config is not None:
-            hf_quant_method = str(hf_quant_config["quant_method"]).lower()
-            if self.quantization is None:
-                self.quantization = hf_quant_method
-            elif self.quantization != hf_quant_method:
-                raise ValueError(
-                    "Quantization method specified in the model config "
-                    f"({hf_quant_method}) does not match the quantization "
-                    f"method specified in the `quantization` argument "
-                    f"({self.quantization}).")
-
-        if self.quantization is not None:
-            if self.quantization not in supported_quantization:
-                raise ValueError(
-                    f"Unknown quantization method: {self.quantization}. Must "
-                    f"be one of {supported_quantization}.")
-            logger.warning(f"{self.quantization} quantization is not fully "
-                           "optimized yet. The speed can be slower than "
-                           "non-quantized models.")
+        supported_quantization = ["awq"]
+        if self.quantization is None:
+            return
+        quantization = self.quantization.lower()
+        if quantization not in supported_quantization:
+            raise ValueError(
+                f"Unknown quantization: {self.quantization}. Must be one of "
+                f"{supported_quantization}.")
+        self.quantization = quantization
 
     def verify_with_parallel_config(
         self,
@@ -168,8 +140,8 @@ class ModelConfig:
         # FIXME(woosuk): This may not be true for all models.
         return self.hf_config.hidden_size // self.hf_config.num_attention_heads
 
-    def get_total_num_kv_heads(self) -> int:
-        """Returns the total number of KV heads."""
+    def get_num_kv_heads(self, parallel_config: "ParallelConfig") -> int:
+        """Returns the number of KV heads per GPU worker."""
         # For GPTBigCode & Falcon:
         # NOTE: for falcon, when new_decoder_architecture is True, the
         # multi_query flag is ignored and we use n_head_kv for the number of
@@ -183,34 +155,19 @@ class ModelConfig:
             # Multi-query attention, only one KV head.
             # Currently, tensor parallelism is not supported in this case.
             return 1
-
-        attributes = [
-            # For Falcon:
-            "n_head_kv",
-            "num_kv_heads",
-            # For LLaMA-2:
-            "num_key_value_heads",
-            # For ChatGLM:
-            "multi_query_group_num",
-        ]
-        for attr in attributes:
-            num_kv_heads = getattr(self.hf_config, attr, None)
-            if num_kv_heads is not None:
-                return num_kv_heads
-
-        # For non-grouped-query attention models, the number of KV heads is
-        # equal to the number of attention heads.
-        return self.hf_config.num_attention_heads
-
-    def get_num_kv_heads(self, parallel_config: "ParallelConfig") -> int:
-        """Returns the number of KV heads per GPU."""
-        total_num_kv_heads = self.get_total_num_kv_heads()
-        # If tensor parallelism is used, we divide the number of KV heads by
-        # the tensor parallel size. We will replicate the KV heads in the
-        # case where the number of KV heads is smaller than the tensor
-        # parallel size so each GPU has at least one KV head.
-        return max(1,
-                   total_num_kv_heads // parallel_config.tensor_parallel_size)
+        # For Falcon:
+        if getattr(self.hf_config, "n_head_kv", None) is not None:
+            return (self.hf_config.n_head_kv //
+                    parallel_config.tensor_parallel_size)
+        if getattr(self.hf_config, "num_kv_heads", None) is not None:
+            return (self.hf_config.num_kv_heads //
+                    parallel_config.tensor_parallel_size)
+        # For LLaMA-2:
+        if getattr(self.hf_config, "num_key_value_heads", None) is not None:
+            return (self.hf_config.num_key_value_heads //
+                    parallel_config.tensor_parallel_size)
+        total_num_attention_heads = self.hf_config.num_attention_heads
+        return total_num_attention_heads // parallel_config.tensor_parallel_size
 
     def get_num_layers(self, parallel_config: "ParallelConfig") -> int:
         total_num_hidden_layers = self.hf_config.num_hidden_layers
@@ -311,7 +268,6 @@ class SchedulerConfig:
             iteration.
         max_model_len: Maximum length of a sequence (including prompt
             and generated text).
-        max_paddings: Maximum number of paddings to be added to a batch.
     """
 
     def __init__(
@@ -319,7 +275,6 @@ class SchedulerConfig:
         max_num_batched_tokens: Optional[int],
         max_num_seqs: int,
         max_model_len: int,
-        max_paddings: int,
     ) -> None:
         if max_num_batched_tokens is not None:
             self.max_num_batched_tokens = max_num_batched_tokens
@@ -329,7 +284,6 @@ class SchedulerConfig:
             self.max_num_batched_tokens = max(max_model_len, 2048)
         self.max_num_seqs = max_num_seqs
         self.max_model_len = max_model_len
-        self.max_paddings = max_paddings
         self._verify_args()
 
     def _verify_args(self) -> None:
@@ -359,7 +313,7 @@ _STR_DTYPE_TO_TORCH_DTYPE = {
 
 def _get_and_verify_dtype(
     config: PretrainedConfig,
-    dtype: Union[str, torch.dtype],
+    dtype: str,
 ) -> torch.dtype:
     # NOTE: getattr(config, "torch_dtype", torch.float32) is not correct
     # because config.torch_dtype can be None.
@@ -367,23 +321,17 @@ def _get_and_verify_dtype(
     if config_dtype is None:
         config_dtype = torch.float32
 
-    if isinstance(dtype, str):
-        dtype = dtype.lower()
-        if dtype == "auto":
-            if config_dtype == torch.float32:
-                # Following the common practice, we use float16 for float32
-                # models.
-                torch_dtype = torch.float16
-            else:
-                torch_dtype = config_dtype
+    dtype = dtype.lower()
+    if dtype == "auto":
+        if config_dtype == torch.float32:
+            # Following the common practice, we use float16 for float32 models.
+            torch_dtype = torch.float16
         else:
-            if dtype not in _STR_DTYPE_TO_TORCH_DTYPE:
-                raise ValueError(f"Unknown dtype: {dtype}")
-            torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
-    elif isinstance(dtype, torch.dtype):
-        torch_dtype = dtype
+            torch_dtype = config_dtype
     else:
-        raise ValueError(f"Unknown dtype: {dtype}")
+        if dtype not in _STR_DTYPE_TO_TORCH_DTYPE:
+            raise ValueError(f"Unknown dtype: {dtype}")
+        torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
 
     # Verify the dtype.
     if torch_dtype != config_dtype:
@@ -413,8 +361,6 @@ def _get_and_verify_max_len(
         "n_positions",
         # MPT
         "max_seq_len",
-        # ChatGLM2
-        "seq_length",
         # Others
         "max_sequence_length",
         "max_seq_length",
@@ -441,9 +387,6 @@ def _get_and_verify_max_len(
     if rope_scaling is not None:
         assert "factor" in rope_scaling
         scaling_factor = rope_scaling["factor"]
-        if rope_scaling["type"] == "yarn":
-            derived_max_model_len = rope_scaling[
-                "original_max_position_embeddings"]
         derived_max_model_len *= scaling_factor
 
     if max_model_len is None:

vllm/core/scheduler.py

@@ -131,8 +131,7 @@ class Scheduler:
             # requests in the generation phase.
             num_curr_seqs = sum(seq_group.get_max_num_running_seqs()
                                 for seq_group in self.running)
-            seq_lens: List[int] = []
-
+            num_batched_tokens = 0
             # Optimization: We do not sort the waiting queue since the preempted
             # sequence groups are added to the front and the new sequence groups
             # are added to the back.
@@ -158,9 +157,7 @@ class Scheduler:
                     break
 
                 # If the number of batched tokens exceeds the limit, stop.
-                new_seq_lens = seq_lens + [num_prompt_tokens]
-                num_batched_tokens = len(new_seq_lens) * max(new_seq_lens)
-                if (num_batched_tokens >
+                if (num_batched_tokens + num_prompt_tokens >
                         self.scheduler_config.max_num_batched_tokens):
                     break
 
@@ -171,14 +168,10 @@ class Scheduler:
                         self.scheduler_config.max_num_seqs):
                     break
 
-                num_paddings = num_batched_tokens - sum(new_seq_lens)
-                if num_paddings > self.scheduler_config.max_paddings:
-                    break
-                seq_lens = new_seq_lens
-
                 seq_group = self.waiting.pop(0)
                 self._allocate(seq_group)
                 self.running.append(seq_group)
+                num_batched_tokens += num_prompt_tokens
                 num_curr_seqs += num_new_seqs
                 scheduled.append(seq_group)
 
@@ -186,7 +179,7 @@ class Scheduler:
                 scheduler_outputs = SchedulerOutputs(
                     scheduled_seq_groups=scheduled,
                     prompt_run=True,
-                    num_batched_tokens=len(seq_lens) * max(seq_lens),
+                    num_batched_tokens=num_batched_tokens,
                     blocks_to_swap_in=blocks_to_swap_in,
                     blocks_to_swap_out=blocks_to_swap_out,
                     blocks_to_copy=blocks_to_copy,
@@ -275,7 +268,7 @@ class Scheduler:
         # Create input data structures.
         seq_group_metadata_list: List[SequenceGroupMetadata] = []
         for seq_group in scheduler_outputs.scheduled_seq_groups:
-            seq_data: Dict[int, SequenceData] = {}
+            seq_data: Dict[int, List[SequenceData]] = {}
             block_tables: Dict[int, List[int]] = {}
             for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
                 seq_id = seq.seq_id

vllm/engine/arg_utils.py

@@ -27,7 +27,6 @@ class EngineArgs:
     gpu_memory_utilization: float = 0.90
     max_num_batched_tokens: Optional[int] = None
     max_num_seqs: int = 256
-    max_paddings: int = 256
     disable_log_stats: bool = False
     revision: Optional[str] = None
     tokenizer_revision: Optional[str] = None
@@ -157,10 +156,6 @@ class EngineArgs:
                             type=int,
                             default=EngineArgs.max_num_seqs,
                             help='maximum number of sequences per iteration')
-        parser.add_argument('--max-paddings',
-                            type=int,
-                            default=EngineArgs.max_paddings,
-                            help='maximum number of paddings in a batch')
         parser.add_argument('--disable-log-stats',
                             action='store_true',
                             help='disable logging statistics')
@@ -168,7 +163,7 @@ class EngineArgs:
         parser.add_argument('--quantization',
                             '-q',
                             type=str,
-                            choices=['awq', 'squeezellm', None],
+                            choices=['awq', None],
                             default=None,
                             help='Method used to quantize the weights')
         return parser
@@ -198,8 +193,7 @@ class EngineArgs:
                                          self.worker_use_ray)
         scheduler_config = SchedulerConfig(self.max_num_batched_tokens,
                                            self.max_num_seqs,
-                                           model_config.max_model_len,
-                                           self.max_paddings)
+                                           model_config.max_model_len)
         return model_config, cache_config, parallel_config, scheduler_config
 
 

vllm/engine/async_llm_engine.py

@@ -142,10 +142,10 @@ class RequestTracker:
 
         self._request_streams[request_id].finish()
 
-    def get_new_and_finished_requests(self) -> Tuple[List[Dict], Set[str]]:
+    def get_new_and_finished_requests(self) -> Tuple[List[dict], Set[str]]:
         """Get the new requests and finished requests to be
         sent to the engine."""
-        new_requests: List[Dict] = []
+        new_requests: List[dict] = []
         finished_requests: Set[str] = set()
 
         while not self._finished_requests.empty():
@@ -206,17 +206,18 @@ class _AsyncLLMEngine(LLMEngine):
         **kwargs,
     ) -> Any:
         """Runs the given method on all workers."""
-        coros = []
+        all_outputs = []
         for worker in self.workers:
             if self.parallel_config.worker_use_ray:
-                coros.append(
-                    worker.execute_method.remote(method, *args, **kwargs))
+                executor = partial(worker.execute_method.remote, method)
             else:
                 executor = getattr(worker, method)
-                coros.append(asyncio.get_event_loop().run_in_executor(
-                    None, partial(executor, *args, **kwargs)))
 
-        all_outputs = await asyncio.gather(*coros)
+            output = executor(*args, **kwargs)
+            all_outputs.append(output)
+
+        if self.parallel_config.worker_use_ray:
+            all_outputs = await asyncio.gather(*all_outputs)
 
         if get_all_outputs:
             return all_outputs
@@ -483,7 +484,7 @@ class AsyncLLMEngine:
         distributed_init_method, placement_group = initialize_cluster(
             parallel_config, engine_args.engine_use_ray)
         # Create the async LLM engine.
-        engine = cls(parallel_config.worker_use_ray,
+        engine = cls(engine_args.worker_use_ray,
                      engine_args.engine_use_ray,
                      *engine_configs,
                      distributed_init_method,

vllm/engine/llm_engine.py

@@ -567,7 +567,7 @@ class LLMEngine:
             blocks_to_copy=scheduler_outputs.blocks_to_copy,
         )
 
-        return self._process_model_outputs(output, scheduler_outputs)
+        return self._process_model_outputs(output, scheduler_outputs) + ignored
 
     def _log_system_stats(
         self,
@@ -632,7 +632,8 @@ class LLMEngine:
                     f"CPU KV cache usage: {cpu_cache_usage * 100:.1f}%")
         self.last_logging_time = now
 
-    def _decode_sequence(self, seq: Sequence, prms: SamplingParams) -> None:
+    def _decode_sequence(self, seq: Sequence,
+                         sampling_params: SamplingParams) -> None:
         """Decodes the new token for a sequence."""
         (new_tokens, new_output_text, prefix_offset,
          read_offset) = detokenize_incrementally(
@@ -641,8 +642,7 @@ class LLMEngine:
              prev_tokens=seq.tokens,
              prefix_offset=seq.prefix_offset,
              read_offset=seq.read_offset,
-             skip_special_tokens=prms.skip_special_tokens,
-             spaces_between_special_tokens=prms.spaces_between_special_tokens,
+             skip_special_tokens=sampling_params.skip_special_tokens,
          )
         if seq.tokens is None:
             seq.tokens = new_tokens

vllm/entrypoints/api_server.py

@@ -17,12 +17,6 @@ app = FastAPI()
 engine = None
 
 
-@app.get("/health")
-async def health() -> Response:
-    """Health check."""
-    return Response(status_code=200)
-
-
 @app.post("/generate")
 async def generate(request: Request) -> Response:
     """Generate completion for the request.

vllm/entrypoints/openai/api_server.py

@@ -13,7 +13,7 @@ import uvicorn
 from fastapi import Request
 from fastapi.exceptions import RequestValidationError
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse, StreamingResponse, Response
+from fastapi.responses import JSONResponse, StreamingResponse
 from packaging import version
 
 from vllm.engine.arg_utils import AsyncEngineArgs
@@ -145,12 +145,6 @@ async def check_length(
         return input_ids, None
 
 
-@app.get("/health")
-async def health() -> Response:
-    """Health check."""
-    return Response(status_code=200)
-
-
 @app.get("/v1/models")
 async def show_available_models():
     """Show available models. Right now we only have one model."""
@@ -218,7 +212,6 @@ async def create_chat_completion(request: ChatCompletionRequest,
     request_id = f"cmpl-{random_uuid()}"
     created_time = int(time.monotonic())
     try:
-        spaces_between_special_tokens = request.spaces_between_special_tokens
         sampling_params = SamplingParams(
             n=request.n,
             presence_penalty=request.presence_penalty,
@@ -233,7 +226,6 @@ async def create_chat_completion(request: ChatCompletionRequest,
             ignore_eos=request.ignore_eos,
             use_beam_search=request.use_beam_search,
             skip_special_tokens=request.skip_special_tokens,
-            spaces_between_special_tokens=spaces_between_special_tokens,
         )
     except ValueError as e:
         return create_error_response(HTTPStatus.BAD_REQUEST, str(e))
@@ -245,7 +237,6 @@ async def create_chat_completion(request: ChatCompletionRequest,
         index: int,
         text: str,
         finish_reason: Optional[str] = None,
-        usage: Optional[UsageInfo] = None,
     ) -> str:
         choice_data = ChatCompletionResponseStreamChoice(
             index=index,
@@ -258,10 +249,7 @@ async def create_chat_completion(request: ChatCompletionRequest,
             model=model_name,
             choices=[choice_data],
         )
-        if usage is not None:
-            response.usage = usage
-        # exclude unset to leave details out of each sse
-        response_json = response.json(exclude_unset=True, ensure_ascii=False)
+        response_json = response.json(ensure_ascii=False)
 
         return response_json
 
@@ -287,25 +275,17 @@ async def create_chat_completion(request: ChatCompletionRequest,
                 i = output.index
                 delta_text = output.text[len(previous_texts[i]):]
                 previous_texts[i] = output.text
-                completion_tokens = len(output.token_ids)
-                previous_num_tokens[i] = completion_tokens
+                previous_num_tokens[i] = len(output.token_ids)
                 response_json = create_stream_response_json(
                     index=i,
                     text=delta_text,
                 )
                 yield f"data: {response_json}\n\n"
                 if output.finish_reason is not None:
-                    prompt_tokens = len(res.prompt_token_ids)
-                    final_usage = UsageInfo(
-                        prompt_tokens=prompt_tokens,
-                        completion_tokens=completion_tokens,
-                        total_tokens=prompt_tokens + completion_tokens,
-                    )
                     response_json = create_stream_response_json(
                         index=i,
                         text="",
                         finish_reason=output.finish_reason,
-                        usage=final_usage,
                     )
                     yield f"data: {response_json}\n\n"
         yield "data: [DONE]\n\n"
@@ -433,7 +413,6 @@ async def create_completion(request: CompletionRequest, raw_request: Request):
 
     created_time = int(time.monotonic())
     try:
-        spaces_between_special_tokens = request.spaces_between_special_tokens
         sampling_params = SamplingParams(
             n=request.n,
             best_of=request.best_of,
@@ -449,7 +428,6 @@ async def create_completion(request: CompletionRequest, raw_request: Request):
             logprobs=request.logprobs,
             use_beam_search=request.use_beam_search,
             skip_special_tokens=request.skip_special_tokens,
-            spaces_between_special_tokens=spaces_between_special_tokens,
         )
     except ValueError as e:
         return create_error_response(HTTPStatus.BAD_REQUEST, str(e))
@@ -474,7 +452,6 @@ async def create_completion(request: CompletionRequest, raw_request: Request):
         text: str,
         logprobs: Optional[LogProbs] = None,
         finish_reason: Optional[str] = None,
-        usage: Optional[UsageInfo] = None,
     ) -> str:
         choice_data = CompletionResponseStreamChoice(
             index=index,
@@ -488,9 +465,7 @@ async def create_completion(request: CompletionRequest, raw_request: Request):
             model=model_name,
             choices=[choice_data],
         )
-        if usage is not None:
-            response.usage = usage
-        response_json = response.json(exclude_unset=True, ensure_ascii=False)
+        response_json = response.json(ensure_ascii=False)
 
         return response_json
 
@@ -520,19 +495,11 @@ async def create_completion(request: CompletionRequest, raw_request: Request):
                 if output.finish_reason is not None:
                     logprobs = (LogProbs()
                                 if request.logprobs is not None else None)
-                    prompt_tokens = len(res.prompt_token_ids)
-                    completion_tokens = len(output.token_ids)
-                    final_usage = UsageInfo(
-                        prompt_tokens=prompt_tokens,
-                        completion_tokens=completion_tokens,
-                        total_tokens=prompt_tokens + completion_tokens,
-                    )
                     response_json = create_stream_response_json(
                         index=i,
                         text="",
                         logprobs=logprobs,
                         finish_reason=output.finish_reason,
-                        usage=final_usage,
                     )
                     yield f"data: {response_json}\n\n"
         yield "data: [DONE]\n\n"
@@ -648,10 +615,9 @@ if __name__ == "__main__":
     max_model_len = engine_model_config.max_model_len
 
     # A separate tokenizer to map token IDs to strings.
-    tokenizer = get_tokenizer(
-        engine_model_config.tokenizer,
-        tokenizer_mode=engine_model_config.tokenizer_mode,
-        trust_remote_code=engine_model_config.trust_remote_code)
+    tokenizer = get_tokenizer(engine_args.tokenizer,
+                              tokenizer_mode=engine_args.tokenizer_mode,
+                              trust_remote_code=engine_args.trust_remote_code)
 
     uvicorn.run(app,
                 host=args.host,

vllm/entrypoints/openai/protocol.py

@@ -72,7 +72,6 @@ class ChatCompletionRequest(BaseModel):
     use_beam_search: Optional[bool] = False
     stop_token_ids: Optional[List[int]] = Field(default_factory=list)
     skip_special_tokens: Optional[bool] = True
-    spaces_between_special_tokens: Optional[bool] = True
 
 
 class CompletionRequest(BaseModel):
@@ -99,7 +98,6 @@ class CompletionRequest(BaseModel):
     use_beam_search: Optional[bool] = False
     stop_token_ids: Optional[List[int]] = Field(default_factory=list)
     skip_special_tokens: Optional[bool] = True
-    spaces_between_special_tokens: Optional[bool] = True
 
 
 class LogProbs(BaseModel):
@@ -139,7 +137,6 @@ class CompletionStreamResponse(BaseModel):
     created: int = Field(default_factory=lambda: int(time.time()))
     model: str
     choices: List[CompletionResponseStreamChoice]
-    usage: Optional[UsageInfo]
 
 
 class ChatMessage(BaseModel):
@@ -179,5 +176,3 @@ class ChatCompletionStreamResponse(BaseModel):
     created: int = Field(default_factory=lambda: int(time.time()))
     model: str
     choices: List[ChatCompletionResponseStreamChoice]
-    usage: Optional[UsageInfo] = Field(
-        default=None, description="data about request and response")

vllm/logger.py

@@ -48,9 +48,4 @@ _setup_logger()
 
 
 def init_logger(name: str):
-    # Use the same settings as above for root logger
-    logger = logging.getLogger(name)
-    logger.setLevel(logging.DEBUG)
-    logger.addHandler(_default_handler)
-    logger.propagate = False
-    return logger
+    return logging.getLogger(name)

vllm/model_executor/input_metadata.py

@@ -3,7 +3,7 @@ from typing import Dict, List, Optional, Tuple
 import torch
 from xformers.ops import AttentionBias
 
-from vllm.sampling_params import SamplingParams, SamplingType
+from vllm.sampling_params import SamplingParams
 from vllm.sequence import SequenceData
 
 
@@ -29,8 +29,6 @@ class InputMetadata:
         context_lens: torch.Tensor,
         max_context_len: int,
         block_tables: torch.Tensor,
-        selected_token_indices: torch.Tensor,
-        categorized_sample_indices: Dict[SamplingType, torch.Tensor],
         sliding_window: Optional[int] = None,
     ) -> None:
         self.seq_groups = seq_groups
@@ -40,15 +38,12 @@ class InputMetadata:
         self.context_lens = context_lens
         self.max_context_len = max_context_len
         self.block_tables = block_tables
-        self.selected_token_indices = selected_token_indices
-        self.categorized_sample_indices = categorized_sample_indices
 
-        self.max_prompt_len = max(prompt_lens) if prompt_lens else 0
         self.to_cache = None
         if sliding_window is not None:
             # We need to keep the positions of sliding windows within
             # the key / value tables, this is helpful to know which
-            # elements we need to cache.
+            # elements we need to cache and where
             to_cache, start_idx = [], 0
             for prompt_len in self.prompt_lens:
                 to_cache.extend(
@@ -56,36 +51,36 @@ class InputMetadata:
                         start_idx + max(0, prompt_len - sliding_window),
                         start_idx + prompt_len,
                     ))
-                start_idx += self.max_prompt_len
+                start_idx += prompt_len
             to_cache.extend(range(start_idx, slot_mapping.shape[0]))
             self.to_cache = torch.tensor(to_cache,
                                          dtype=torch.int32,
                                          device=self.slot_mapping.device)
 
         self.num_prompts = len(prompt_lens)
-        self.num_prompt_tokens = self.num_prompts * self.max_prompt_len
+        self.num_prompt_tokens = sum(prompt_lens)
         self.num_generation_tokens = context_lens.shape[0]
+        self.num_valid_tokens = slot_mapping.shape[0]
         if block_tables.numel() > 0:
             self.max_num_blocks_per_seq = block_tables.shape[1]
         else:
             self.max_num_blocks_per_seq = 0
         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: Optional[AttentionBias] = None
+        self.attn_bias: List[AttentionBias] = []
 
     def __repr__(self) -> str:
         # Print only useful metadata.
-        return (
-            f'InputMetadata('
-            f'num_prompt_tokens={self.num_prompt_tokens}, '
-            f'num_prompts={self.num_prompts}, '
-            f'prompt_lens={self.prompt_lens}, '
-            f'num_generation_tokens={self.num_generation_tokens}, '
-            f'context_lens={self.context_lens}, '
-            f'max_context_len={self.max_context_len}), '
-            f'max_num_blocks_per_seq={self.max_num_blocks_per_seq}, '
-            f'block_tables={self.block_tables}, '
-            f'selected_token_indices={self.selected_token_indices}, '
-            f'categorized_sample_indices={self.categorized_sample_indices}, '
-            f'slot_mapping={self.slot_mapping})')
+        return (f'InputMetadata('
+                f'num_valid_tokens={self.num_valid_tokens}, '
+                f'num_prompt_tokens={self.num_prompt_tokens}, '
+                f'num_prompts={self.num_prompts}, '
+                f'prompt_lens={self.prompt_lens}, '
+                f'num_generation_tokens={self.num_generation_tokens}, '
+                f'context_lens={self.context_lens}, '
+                f'max_context_len={self.max_context_len}), '
+                f'max_num_blocks_per_seq={self.max_num_blocks_per_seq}, '
+                f'block_tables={self.block_tables}), '
+                f'slot_mapping={self.slot_mapping}')

vllm/model_executor/layers/activation.py

@@ -1,27 +1,24 @@
 """Custom activation functions."""
-from typing import Optional
-
 import torch
 import torch.nn as nn
 
 from vllm import activation_ops
-from vllm.model_executor.layers.quantization import QuantizationConfig
 
 
 class SiluAndMul(nn.Module):
     """An activation function for SwiGLU.
 
-    The function computes x -> silu(x[:d]) * x[d:] where d = x.shape[-1] // 2.
+    The function computes x -> silu(x[:d]) * x[d:] where d = x.shape[1] // 2.
 
     Shapes:
-        x: (batch_size, seq_len, 2 * d) or (num_tokens, 2 * d)
-        return: (batch_size, seq_len, d) or (num_tokens, d)
+        x: (num_tokens, 2 * d)
+        return: (num_tokens, d)
     """
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        d = x.shape[-1] // 2
-        output_shape = (x.shape[:-1] + (d, ))
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        num_tokens = x.shape[0]
+        d = x.shape[1] // 2
+        out = torch.empty(num_tokens, d, dtype=x.dtype, device=x.device)
         activation_ops.silu_and_mul(out, x)
         return out
 
@@ -29,7 +26,9 @@ class SiluAndMul(nn.Module):
 class NewGELU(nn.Module):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
+        num_tokens = x.shape[0]
+        d = x.shape[1]
+        out = torch.empty(num_tokens, d, dtype=x.dtype, device=x.device)
         activation_ops.gelu_new(out, x)
         return out
 
@@ -37,32 +36,13 @@ class NewGELU(nn.Module):
 class FastGELU(nn.Module):
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
-        out = torch.empty_like(x)
+        num_tokens = x.shape[0]
+        d = x.shape[1]
+        out = torch.empty(num_tokens, d, dtype=x.dtype, device=x.device)
         activation_ops.gelu_fast(out, x)
         return out
 
 
-class ScaledActivation(nn.Module):
-    """An activation function with post-scale parameters.
-
-    This is used for some quantization methods like AWQ.
-    """
-
-    def __init__(
-        self,
-        act_module: nn.Module,
-        hidden_size: int,
-        params_dtype: torch.dtype,
-    ):
-        super().__init__()
-        self.act = act_module
-        self.scales = nn.Parameter(
-            torch.empty(hidden_size, dtype=params_dtype, device="cuda"))
-
-    def forward(self, x: torch.Tensor):
-        return self.act(x) / self.scales
-
-
 _ACTIVATION_REGISTRY = {
     "gelu": nn.GELU(),
     "gelu_fast": FastGELU(),
@@ -72,27 +52,9 @@ _ACTIVATION_REGISTRY = {
 }
 
 
-def get_act_fn(
-    act_fn_name: str,
-    quant_config: Optional[QuantizationConfig] = None,
-    intermediate_size: Optional[int] = None,
-) -> nn.Module:
+def get_act_fn(act_fn: str) -> nn.Module:
     """Get an activation function by name."""
-    act_fn_name = act_fn_name.lower()
-    if act_fn_name not in _ACTIVATION_REGISTRY:
-        raise ValueError(
-            f"Activation function {act_fn_name!r} is not supported.")
-
-    act_fn = _ACTIVATION_REGISTRY[act_fn_name]
-    if quant_config is not None:
-        if act_fn_name in quant_config.get_scaled_act_names():
-            if intermediate_size is None:
-                raise ValueError(
-                    "intermediate_size must be specified for scaled "
-                    "activation functions.")
-            return ScaledActivation(
-                act_fn,
-                intermediate_size,
-                params_dtype=torch.get_default_dtype(),
-            )
-    return act_fn
+    act_fn = act_fn.lower()
+    if act_fn in _ACTIVATION_REGISTRY:
+        return _ACTIVATION_REGISTRY[act_fn]
+    raise ValueError(f"Activation function {act_fn!r} is not supported.")

vllm/model_executor/layers/attention.py

@@ -10,7 +10,9 @@ from xformers.ops.fmha.attn_bias import (BlockDiagonalCausalMask,
 from vllm import attention_ops
 from vllm import cache_ops
 from vllm.model_executor.input_metadata import InputMetadata
-from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.rotary_embedding import (
+    DynamicNTKScalingRotaryEmbedding, LinearScalingRotaryEmbedding,
+    RotaryEmbedding)
 
 _SUPPORTED_HEAD_SIZES = [64, 80, 96, 112, 128, 256]
 # Should be the same as PARTITION_SIZE in `paged_attention_v2_launcher`.
@@ -21,9 +23,25 @@ class PagedAttention(nn.Module):
     # pylint: disable=line-too-long
     """GPT-style multi-head PagedAttention.
 
-    This class takes query, key, and value tensors as input. The input tensors
-    can either contain prompt tokens or generation tokens, in addition to
-    paddings.
+    This class takes flattened 1D query, key, and value tensors as input. The
+    input 1D tensors can either contain prompt tokens or generation tokens, in
+    addition to paddings.
+
+    If the input tensors contain prompt tokens, the layout is as follows:
+
+    |<---------------------- num_valid_tokens ---------------------->|
+    |<--------------- num_prompt_tokens -------------->|
+    |<--prompt_0-->|<--prompt_1-->|...|<--prompt_N-1-->|<--padding-->|
+
+    Otherwise, the layout is as follows:
+
+    |<------------------ num_valid_tokens ------------------->|
+    |<------- num_generation_tokens (M) ------->|
+    |<--generation_0-->|...|<--generation_M-1-->|<--padding-->|
+
+    The prompts might have different lengths, while the generation tokens always
+    have length 1. The paddings are appended to make the input length a multiple
+    of 8, which is desirable for Tensor Cores.
 
     The class does the following:
     1. Perform multi_query_kv_attention for the prompts. This operation does
@@ -35,7 +53,7 @@ class PagedAttention(nn.Module):
     4. Perform single_query_cached_kv_attention for the generation tokens.
         This operation reads the previous key and value tensors from the KV
         cache.
-    5. Return the output tensor.
+    5. Output a flattened 1D tensor.
     """
 
     def __init__(self,
@@ -67,15 +85,14 @@ class PagedAttention(nn.Module):
         dtype: torch.dtype,
     ) -> None:
         del dtype  # Unused.
-        if input_metadata.attn_bias is not None:
+        if input_metadata.attn_bias:
             # Already set by a previous layer.
             return
-        prompt_lens = [input_metadata.max_prompt_len
-                       ] * input_metadata.num_prompts
+        prompt_lens = input_metadata.prompt_lens
         attn_bias = BlockDiagonalCausalMask.from_seqlens(prompt_lens)
         if self.sliding_window is not None:
             attn_bias = attn_bias.make_local_attention(self.sliding_window)
-        input_metadata.attn_bias = attn_bias
+        input_metadata.attn_bias.append(attn_bias)
 
     def multi_query_kv_attention(
         self,
@@ -94,6 +111,7 @@ class PagedAttention(nn.Module):
             value: shape = [num_prompt_tokens, num_kv_heads, head_size]
             input_metadata: metadata for paged attention.
         """
+
         if self.num_kv_heads != self.num_heads:
             # Project the key and value tensors to the desired number of heads.
             key = torch.repeat_interleave(key, self.num_queries_per_kv, dim=1)
@@ -106,7 +124,7 @@ class PagedAttention(nn.Module):
             query.unsqueeze(0),
             key.unsqueeze(0),
             value.unsqueeze(0),
-            attn_bias=input_metadata.attn_bias,
+            attn_bias=input_metadata.attn_bias[0],
             p=0.0,
             scale=self.scale,
         )
@@ -154,9 +172,7 @@ class PagedAttention(nn.Module):
         # sequences or heads is large, we use V1 since there is enough work
         # to parallelize.
         # TODO(woosuk): Tune this heuristic.
-        # For context len > 8192, use V2 kernel to avoid shared memory shortage.
-        use_v1 = input_metadata.max_context_len <= 8192 and (
-            max_num_partitions == 1 or num_seqs * num_heads > 512)
+        use_v1 = max_num_partitions == 1 or num_seqs * num_heads > 512
         if use_v1:
             # Run PagedAttention V1.
             attention_ops.paged_attention_v1(
@@ -216,12 +232,12 @@ class PagedAttention(nn.Module):
         """PagedAttention forward pass.
 
         NOTE: The query, key, and value tensors must be sliced from a qkv
-        tensor of shape [batch_size, seq_len, 3 * num_heads * head_size].
+        tensor of shape [num_tokens, 3 * num_heads * head_size].
 
         Args:
-            query: shape = [batch_size, seq_len, num_heads * head_size]
-            key: shape = [batch_size, seq_len, num_kv_heads * head_size]
-            value: shape = [batch_size, num_kv_heads * head_size]
+            query: shape = [num_tokens, num_heads * head_size]
+            key: shape = [num_tokens, num_kv_heads * head_size]
+            value: shape = [num_tokens, num_kv_heads * head_size]
             key_cache: shape = [num_blocks, num_kv_heads, head_size/x,
                 block_size, x]
             value_cache: shape = [num_blocks, num_kv_heads, head_size,
@@ -230,9 +246,9 @@ class PagedAttention(nn.Module):
             cache_event: event to wait for the cache operations to finish.
 
         Returns:
-            shape = [batch_size, seq_len, num_heads * head_size]
+            shape = [num_tokens, num_heads * head_size]
         """
-        batch_size, seq_len, _ = query.shape
+
         # Reshape the query, key, and value tensors.
         query = query.view(-1, self.num_heads, self.head_size)
         key = key.view(-1, self.num_kv_heads, self.head_size)
@@ -248,10 +264,10 @@ class PagedAttention(nn.Module):
             assert input_metadata.num_generation_tokens == 0
             self.set_attn_bias(input_metadata, dtype=query.dtype)
             self.multi_query_kv_attention(
-                output,
-                query,
-                key,
-                value,
+                output[:num_prompt_tokens],
+                query[:num_prompt_tokens],
+                key[:num_prompt_tokens],
+                value[:num_prompt_tokens],
                 input_metadata,
             )
 
@@ -262,10 +278,13 @@ class PagedAttention(nn.Module):
         # Reshape the keys and values and store them in the cache.
         # When key_cache and value_cache are not provided, the new key
         # and value vectors will not be cached.
-        if key_cache is not None and value_cache is not None:
-            key_to_cache = key
-            value_to_cache = value
-            slot_mapping = input_metadata.slot_mapping.view(-1)
+        num_valid_tokens = input_metadata.num_valid_tokens
+        if (num_valid_tokens > 0 and key_cache is not None
+                and value_cache is not None):
+            # The stride is 3 because the key and value are sliced from qkv.
+            key_to_cache = key[:num_valid_tokens]
+            value_to_cache = value[:num_valid_tokens]
+            slot_mapping = input_metadata.slot_mapping
             if input_metadata.to_cache is not None:
                 key_to_cache = key_to_cache[input_metadata.to_cache]
                 value_to_cache = value_to_cache[input_metadata.to_cache]
@@ -286,14 +305,14 @@ class PagedAttention(nn.Module):
                 "key_cache and value_cache must be provided when "
                 "generating tokens.")
             # Compute the attention op for generation tokens.
-            self.single_query_cached_kv_attention(output, query, key_cache,
-                                                  value_cache, input_metadata,
-                                                  self.get_alibi_slopes())
+            self.single_query_cached_kv_attention(
+                output[num_prompt_tokens:num_valid_tokens],
+                query[num_prompt_tokens:num_valid_tokens], key_cache,
+                value_cache, input_metadata, self.get_alibi_slopes())
 
         # Reshape the output tensor.
         # NOTE(woosuk): The output tensor may include paddings.
-        return output.view(batch_size, seq_len,
-                           self.num_heads * self.head_size)
+        return output.view(-1, self.num_heads * self.head_size)
 
 
 class PagedAttentionWithRoPE(PagedAttention):
@@ -317,8 +336,23 @@ class PagedAttentionWithRoPE(PagedAttention):
                          scale,
                          num_kv_heads,
                          sliding_window=sliding_window)
-        self.rotary_emb = get_rope(head_size, rotary_dim, max_position, base,
-                                   is_neox_style, rope_scaling)
+        if rope_scaling is None:
+            self.rotary_emb = RotaryEmbedding(head_size, rotary_dim,
+                                              max_position, base,
+                                              is_neox_style)
+        else:
+            scaling_type = rope_scaling["type"]
+            scaling_factor = rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = LinearScalingRotaryEmbedding(
+                    head_size, rotary_dim, max_position, base, is_neox_style,
+                    scaling_factor)
+            elif scaling_type == "dynamic":
+                self.rotary_emb = DynamicNTKScalingRotaryEmbedding(
+                    head_size, rotary_dim, max_position, base, is_neox_style,
+                    scaling_factor)
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
 
     def forward(
         self,
@@ -334,10 +368,10 @@ class PagedAttentionWithRoPE(PagedAttention):
         """ PagedAttention forward pass with rotary embedding.
 
         Args:
-            positions: shape = [batch_size, seq_len]
-            query: shape = [batch_size, seq_len, num_heads * head_size]
-            key: shape = [batch_size, seq_len, num_kv_heads * head_size]
-            value: shape = [batch_size, seq_len, num_kv_heads * head_size]
+            positions: shape = [num_tokens]
+            query: shape = [num_tokens, num_heads * head_size]
+            key: shape = [num_tokens, num_kv_heads * head_size]
+            value: shape = [num_tokens, num_kv_heads * head_size]
             key_cache: shape = [num_blocks, num_kv_heads, head_size/x,
                 block_size, x]
             value_cache: shape = [num_blocks, num_kv_heads, head_size,
@@ -346,7 +380,7 @@ class PagedAttentionWithRoPE(PagedAttention):
             cache_event: event to wait for the cache operations to finish.
 
         Returns:
-            shape = [batch_size, seq_len, num_heads * head_size]
+            shape = [num_tokens, num_heads * head_size]
         """
 
         # Apply rotary embedding to the query and key before passing them
@@ -380,34 +414,34 @@ class PagedAttentionWithALiBi(PagedAttention):
 
     def set_attn_bias(self, input_metadata: InputMetadata,
                       dtype: torch.dtype) -> None:
-        if input_metadata.attn_bias is not None:
+        if input_metadata.attn_bias:
             # Already set by a previous layer.
             return
-        # Generates ALiBi mask based on the max prompt length.
-        max_prompt_len = input_metadata.max_prompt_len
-        bias = torch.arange(max_prompt_len, dtype=dtype)
-        # NOTE(zhuohan): HF uses
-        #     `bias = bias[None, :].repeat(prompt_len, 1)`
-        # here. We find that both biases give the same results, but
-        # the bias below more accurately follows the original ALiBi
-        # paper.
-        bias = bias[None, :] - bias[:, None]
-        bias = bias.to(self.alibi_slopes.device)
+        # Generates ALiBi mask for each prompt.
+        for prompt_len in input_metadata.prompt_lens:
+            bias = torch.arange(prompt_len, dtype=dtype)
+            # NOTE(zhuohan): HF uses
+            #     `bias = bias[None, :].repeat(prompt_len, 1)`
+            # here. We find that both biases give the same results, but
+            # the bias below more accurately follows the original ALiBi
+            # paper.
+            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 = (max_prompt_len + 7) // 8 * 8
-        bias = torch.empty(
-            input_metadata.num_prompts,
-            self.num_heads,
-            max_prompt_len,
-            padded_len,
-            device=self.alibi_slopes.device,
-            dtype=dtype,
-        )[:, :, :, :max_prompt_len].copy_(bias)
-        bias.mul_(self.alibi_slopes[:, None, None])
-        attn_bias = LowerTriangularMaskWithTensorBias(bias)
-        input_metadata.attn_bias = attn_bias
+            # 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(
+                1,  # batch_size
+                self.num_heads,
+                prompt_len,
+                padded_len,
+                device=self.alibi_slopes.device,
+                dtype=dtype,
+            )[:, :, :, :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,
@@ -432,19 +466,24 @@ class PagedAttentionWithALiBi(PagedAttention):
             value = torch.repeat_interleave(value,
                                             self.num_queries_per_kv,
                                             dim=1)
-        batch_size = input_metadata.num_prompts
-        seq_len = input_metadata.max_prompt_len
 
-        out = xops.memory_efficient_attention_forward(
-            query.view(batch_size, seq_len, self.num_heads, self.head_size),
-            key.view(batch_size, seq_len, self.num_heads, self.head_size),
-            value.view(batch_size, seq_len, self.num_heads, self.head_size),
-            attn_bias=input_metadata.attn_bias,
-            p=0.0,
-            scale=self.scale,
-        )
-        # TODO(woosuk): Unnecessary copy. Optimize.
-        output.copy_(out.view(-1, self.num_heads, self.head_size))
+        # 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,
+            )
+            # TODO(woosuk): Unnecessary copy. Optimize.
+            output[start:end].copy_(out.squeeze(0))
+            start += prompt_len
         return output
 
     def get_alibi_slopes(self) -> Optional[torch.Tensor]:

vllm/model_executor/layers/layernorm.py

@@ -1,6 +1,4 @@
 """Custom normalization layers."""
-from typing import Optional, Tuple, Union
-
 import torch
 import torch.nn as nn
 
@@ -23,19 +21,7 @@ class RMSNorm(nn.Module):
         self.weight = nn.Parameter(torch.ones(hidden_size))
         self.variance_epsilon = eps
 
-    def forward(
-        self,
-        x: torch.Tensor,
-        residual: Optional[torch.Tensor] = None,
-    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
-        if residual is not None:
-            layernorm_ops.fused_add_rms_norm(
-                x,
-                residual,
-                self.weight.data,
-                self.variance_epsilon,
-            )
-            return x, residual
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         out = torch.empty_like(x)
         layernorm_ops.rms_norm(
             out,

vllm/model_executor/layers/linear.py

@@ -1,541 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Dict, List, Optional
-
-import torch
-import torch.nn.functional as F
-from torch.nn.parameter import Parameter
-
-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.communication_op import (
-    tensor_model_parallel_all_reduce, tensor_model_parallel_all_gather)
-from vllm.model_executor.parallel_utils.utils import (
-    divide, split_tensor_along_last_dim)
-from vllm.model_executor.utils import set_weight_attrs
-from vllm.logger import init_logger
-
-logger = init_logger(__name__)
-
-
-class LinearMethodBase(ABC):
-    """Base class for different (maybe quantized) linear methods."""
-
-    @abstractmethod
-    def create_weights(self, input_size: int, output_size: int,
-                       params_dtype: torch.dtype) -> Dict[str, torch.Tensor]:
-        """Create weights for a linear layer."""
-        raise NotImplementedError
-
-    @abstractmethod
-    def apply_weights(self,
-                      weights: Dict[str, torch.Tensor],
-                      x: torch.Tensor,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-        """Apply the weights to the input tensor."""
-        raise NotImplementedError
-
-
-class UnquantizedLinearMethod(LinearMethodBase):
-    """Linear method without quantization.
-
-    Args:
-        separate_bias_add: If true, add bias separately after matrix
-                           multiplication.
-    """
-
-    def __init__(self, separate_bias_add: bool = False):
-        self.separate_bias_add = separate_bias_add
-
-    def create_weights(self, input_size: int, output_size: int,
-                       params_dtype: torch.dtype) -> Dict[str, torch.Tensor]:
-        weight = Parameter(torch.empty(output_size,
-                                       input_size,
-                                       device=torch.cuda.current_device(),
-                                       dtype=params_dtype),
-                           requires_grad=False)
-        set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
-        return {"weight": weight}
-
-    def apply_weights(self,
-                      weights: Dict[str, torch.Tensor],
-                      x: torch.Tensor,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-        weight = weights["weight"]
-        if self.separate_bias_add:
-            if bias:
-                return F.linear(x, weight) + bias
-            return F.linear(x, weight)
-        return F.linear(x, weight, bias)
-
-
-class ReplicatedLinear(torch.nn.Module):
-    """Replicated linear layer.
-
-    Args:
-        input_size: input dimension of the linear layer.
-        output_size: output dimension of the linear layer.
-        bias: If true, add bias.
-        skip_bias_add: If true, skip adding bias but instead return it.
-        params_dtype: Data type for the parameters.
-        linear_method: (Maybe quantized) linear method.
-    """
-
-    def __init__(
-        self,
-        input_size: int,
-        output_size: int,
-        bias: bool = True,
-        skip_bias_add: bool = False,
-        params_dtype: Optional[torch.dtype] = None,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
-        super().__init__()
-
-        # Keep input parameters
-        self.input_size = input_size
-        self.output_size = output_size
-        self.skip_bias_add = skip_bias_add
-        if params_dtype is None:
-            params_dtype = torch.get_default_dtype()
-        self.params_dtype = params_dtype
-        if linear_method is None:
-            linear_method = UnquantizedLinearMethod()
-        self.linear_method = linear_method
-        self.linear_weights = self.linear_method.create_weights(
-            self.input_size, self.output_size, self.params_dtype)
-        for name, weight in self.linear_weights.items():
-            self.register_parameter(name, weight)
-        if bias:
-            self.bias = Parameter(
-                torch.empty(self.output_size,
-                            device=torch.cuda.current_device(),
-                            dtype=self.params_dtype))
-            set_weight_attrs(self.bias, {"output_dim": 0})
-        else:
-            self.register_parameter("bias", None)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        bias = self.bias if not self.skip_bias_add else None
-        output = self.linear_method.apply_weights(self.linear_weights, x, bias)
-        output_bias = self.bias if self.skip_bias_add else None
-        return output, output_bias
-
-
-class ColumnParallelLinear(torch.nn.Module):
-    """Linear layer with column parallelism.
-
-    The linear layer is defined as Y = XA + b. A is parallelized along
-    its second dimension as A = [A_1, ..., A_p].
-
-    Args:
-        input_size: first dimension of matrix A.
-        output_size: second dimension of matrix A.
-        bias: If true, add bias.
-        gather_output: If true, call all-gather on output and make Y available
-                       to all GPUs, otherwise, every GPU will have its output
-                       which is Y_i = XA_i
-        skip_bias_add: This was added to enable performance optimizations where
-                       bias can be fused with other element-wise operations. we
-                       skip adding bias but instead return it.
-        params_dtype: Data type for the parameters.
-        linear_method: (Maybe quantized) linear method.
-    """
-
-    def __init__(
-        self,
-        input_size: int,
-        output_size: int,
-        bias: bool = True,
-        gather_output: bool = False,
-        skip_bias_add: bool = False,
-        params_dtype: Optional[torch.dtype] = None,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
-        super().__init__()
-
-        # Keep input parameters
-        self.input_size = input_size
-        self.output_size = output_size
-        self.gather_output = gather_output
-        # Divide the weight matrix along the last dimension.
-        tp_size = get_tensor_model_parallel_world_size()
-        self.output_size_per_partition = divide(output_size, tp_size)
-        self.skip_bias_add = skip_bias_add
-        if params_dtype is None:
-            params_dtype = torch.get_default_dtype()
-        self.params_dtype = params_dtype
-        if linear_method is None:
-            linear_method = UnquantizedLinearMethod()
-        self.linear_method = linear_method
-        self.linear_weights = self.linear_method.create_weights(
-            self.input_size, self.output_size_per_partition, self.params_dtype)
-        for name, weight in self.linear_weights.items():
-            self.register_parameter(name, weight)
-            set_weight_attrs(weight, {"weight_loader": self.weight_loader})
-        if bias:
-            self.bias = Parameter(
-                torch.empty(self.output_size_per_partition,
-                            device=torch.cuda.current_device(),
-                            dtype=params_dtype))
-            set_weight_attrs(self.bias, {
-                "output_dim": 0,
-                "weight_loader": self.weight_loader,
-            })
-        else:
-            self.register_parameter("bias", None)
-
-    def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
-        tp_rank = get_tensor_model_parallel_rank()
-        output_dim = getattr(param, "output_dim", None)
-        param_data = param.data
-        if output_dim is not None:
-            shard_size = param_data.shape[output_dim]
-            start_idx = tp_rank * shard_size
-            loaded_weight = loaded_weight.narrow(output_dim, start_idx,
-                                                 shard_size)
-        assert param_data.shape == loaded_weight.shape
-        param_data.copy_(loaded_weight)
-
-    def forward(self, input_):
-        bias = self.bias if not self.skip_bias_add else None
-
-        # Matrix multiply.
-        output_parallel = self.linear_method.apply_weights(
-            self.linear_weights, input_, bias)
-        if self.gather_output:
-            # All-gather across the partitions.
-            output = tensor_model_parallel_all_gather(output_parallel)
-        else:
-            output = output_parallel
-        output_bias = self.bias if self.skip_bias_add else None
-        return output, output_bias
-
-
-class MergedColumnParallelLinear(ColumnParallelLinear):
-    """Packed linear layers with column parallelism.
-
-    Similar to ColumnParallelLinear, but the weight matrix is concatenated
-    along the output dimension. When the weight matrix is loaded, the
-    different partitions are sharded separately.
-
-    Args:
-        input_size: input dimension of the linear layer.
-        output_sizes: list of output dimensions of the linear layer.
-        bias: If true, add bias.
-        gather_output: If true, call all-gather on output and make the output
-                       available to all GPUs, otherwise, every GPU will have
-                       its own output.
-        skip_bias_add: This was added to enable performance optimizations where
-                       bias can be fused with other element-wise operations. we
-                       skip adding bias but instead return it.
-        params_dtype: Data type for the parameters.
-        linear_method: (Maybe quantized) linear method.
-    """
-
-    def __init__(
-        self,
-        input_size: int,
-        output_sizes: List[int],
-        bias: bool = True,
-        gather_output: bool = False,
-        skip_bias_add: bool = False,
-        params_dtype: Optional[torch.dtype] = None,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
-        self.output_sizes = output_sizes
-        tp_size = get_tensor_model_parallel_world_size()
-        assert all(output_size % tp_size == 0 for output_size in output_sizes)
-        super().__init__(input_size, sum(output_sizes), bias, gather_output,
-                         skip_bias_add, params_dtype, linear_method)
-
-    def weight_loader(self,
-                      param: Parameter,
-                      loaded_weight: torch.Tensor,
-                      loaded_shard_id: Optional[int] = None):
-        param_data = param.data
-        output_dim = getattr(param, "output_dim", None)
-        if loaded_shard_id is None:
-            # Loaded weight is already packed.
-            if output_dim is None:
-                assert param_data.shape == loaded_weight.shape
-                param_data.copy_(loaded_weight)
-                return
-            current_shard_offset = 0
-            shard_offsets = []
-            for i, output_size in enumerate(self.output_sizes):
-                shard_offsets.append((i, current_shard_offset, output_size))
-                current_shard_offset += output_size
-            packed_dim = getattr(param, "packed_dim", None)
-            for shard_id, shard_offset, shard_size in shard_offsets:
-                # If quantized, we need to adjust the offset and size to account
-                # for the packing.
-                if packed_dim == output_dim:
-                    shard_size = shard_size // param.pack_factor
-                    shard_offset = shard_offset // param.pack_factor
-                loaded_weight_shard = loaded_weight.narrow(
-                    output_dim, shard_offset, shard_size)
-                self.weight_loader(param, loaded_weight_shard, shard_id)
-            return
-
-        assert loaded_shard_id < len(self.output_sizes)
-        tp_rank = get_tensor_model_parallel_rank()
-        tp_size = get_tensor_model_parallel_world_size()
-        if output_dim is not None:
-            shard_offset = sum(self.output_sizes[:loaded_shard_id]) // tp_size
-            shard_size = self.output_sizes[loaded_shard_id] // tp_size
-            # If quantized, we need to adjust the offset and size to account
-            # for the packing.
-            packed_dim = getattr(param, "packed_dim", None)
-            if packed_dim == output_dim:
-                shard_size = shard_size // param.pack_factor
-                shard_offset = shard_offset // param.pack_factor
-            param_data = param_data.narrow(output_dim, shard_offset,
-                                           shard_size)
-            start_idx = tp_rank * shard_size
-            loaded_weight = loaded_weight.narrow(output_dim, start_idx,
-                                                 shard_size)
-        else:
-            logger.warning(
-                "Loading a weight without `output_dim` attribute in "
-                "MergedColumnParallelLinear, assume the weight is "
-                "the same for all partitions.")
-        assert param_data.shape == loaded_weight.shape
-        param_data.copy_(loaded_weight)
-
-
-class QKVParallelLinear(ColumnParallelLinear):
-    """Linear layers for the attention's QKV transformation.
-
-    Linear layers for the linear transformation of the query, key, and value
-    vectors in the attention layer. The weight matrix is concatenated along
-    the output dimension. The layer is parallelized along the head dimension.
-    When the number of key/value heads is smaller than the number of query
-    heads (e.g., multi-query/grouped-query attention), the key/value head may
-    be replicated while the query heads are partitioned.
-
-    Args:
-        hidden_size: input hidden state size of the transformer.
-        head_size: size of each attention head.
-        total_num_heads: total number of attention query heads.
-        total_num_kv_heads: total number of attention key/value heads. If
-                            None, assume total_num_kv_heads = total_num_heads.
-        bias: If true, add bias.
-        skip_bias_add: This was added to enable performance optimizations where
-                       bias can be fused with other element-wise operations. we
-                       skip adding bias but instead return it.
-        params_dtype: Data type for the parameters.
-        linear_method: (Maybe quantized) linear method.
-    """
-
-    def __init__(
-        self,
-        hidden_size: int,
-        head_size: int,
-        total_num_heads: int,
-        total_num_kv_heads: Optional[int] = None,
-        bias: bool = True,
-        skip_bias_add: bool = False,
-        params_dtype: Optional[torch.dtype] = None,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
-        self.hidden_size = hidden_size
-        self.head_size = head_size
-        self.total_num_heads = total_num_heads
-        if total_num_kv_heads is None:
-            total_num_kv_heads = total_num_heads
-        self.total_num_kv_heads = total_num_kv_heads
-        # Divide the weight matrix along the last dimension.
-        tp_size = get_tensor_model_parallel_world_size()
-        self.num_heads = divide(self.total_num_heads, tp_size)
-        if tp_size >= self.total_num_kv_heads:
-            self.num_kv_heads = 1
-            self.num_kv_head_replicas = divide(tp_size,
-                                               self.total_num_kv_heads)
-        else:
-            self.num_kv_heads = divide(self.total_num_kv_heads, tp_size)
-            self.num_kv_head_replicas = 1
-        input_size = self.hidden_size
-        output_size = (self.num_heads +
-                       2 * self.num_kv_heads) * tp_size * self.head_size
-        super().__init__(input_size, output_size, bias, False, skip_bias_add,
-                         params_dtype, linear_method)
-
-    def weight_loader(self,
-                      param: Parameter,
-                      loaded_weight: torch.Tensor,
-                      loaded_shard_id: Optional[str] = None):
-        param_data = param.data
-        output_dim = getattr(param, "output_dim", None)
-        if loaded_shard_id is None:
-            # Loaded weight is already packed.
-            if output_dim is None:
-                assert param_data.shape == loaded_weight.shape
-                param_data.copy_(loaded_weight)
-                return
-            shard_offsets = [
-                # (shard_id, shard_offset, shard_size)
-                ("q", 0, self.total_num_heads * self.head_size),
-                ("k", self.total_num_heads * self.head_size,
-                 self.total_num_kv_heads * self.head_size),
-                ("v", (self.total_num_heads + self.total_num_kv_heads) *
-                 self.head_size, self.total_num_kv_heads * self.head_size),
-            ]
-            packed_dim = getattr(param, "packed_dim", None)
-            for shard_id, shard_offset, shard_size in shard_offsets:
-                # If quantized, we need to adjust the offset and size to account
-                # for the packing.
-                if packed_dim == output_dim:
-                    shard_size = shard_size // param.pack_factor
-                    shard_offset = shard_offset // param.pack_factor
-                loaded_weight_shard = loaded_weight.narrow(
-                    output_dim, shard_offset, shard_size)
-                self.weight_loader(param, loaded_weight_shard, shard_id)
-            return
-
-        tp_rank = get_tensor_model_parallel_rank()
-        assert loaded_shard_id in ["q", "k", "v"]
-        if output_dim is not None:
-            if loaded_shard_id == "q":
-                shard_offset = 0
-                shard_size = self.num_heads * self.head_size
-            elif loaded_shard_id == "k":
-                shard_offset = self.num_heads * self.head_size
-                shard_size = self.num_kv_heads * self.head_size
-            elif loaded_shard_id == "v":
-                shard_offset = (self.num_heads +
-                                self.num_kv_heads) * self.head_size
-                shard_size = self.num_kv_heads * self.head_size
-            # If quantized, we need to adjust the offset and size to account
-            # for the packing.
-            packed_dim = getattr(param, "packed_dim", None)
-            if packed_dim == output_dim:
-                shard_size = shard_size // param.pack_factor
-                shard_offset = shard_offset // param.pack_factor
-            param_data = param_data.narrow(output_dim, shard_offset,
-                                           shard_size)
-            shard_id = tp_rank // self.num_kv_head_replicas
-            start_idx = shard_id * shard_size
-            loaded_weight = loaded_weight.narrow(output_dim, start_idx,
-                                                 shard_size)
-        else:
-            logger.warning(
-                "Loading a weight without `output_dim` attribute in "
-                "QKVParallelLinear, assume the weight is the same "
-                "for all partitions.")
-        assert param_data.shape == loaded_weight.shape
-        param_data.copy_(loaded_weight)
-
-
-class RowParallelLinear(torch.nn.Module):
-    """Linear layer with row parallelism.
-
-    The linear layer is defined as Y = XA + b. A is parallelized along
-    its first dimension and X along its second dimension as:
-               -   -
-              | A_1 |
-              | .   |
-          A = | .   |        X = [X_1, ..., X_p]
-              | .   |
-              | A_p |
-               -   -
-    Arguments:
-        input_size: first dimension of matrix A.
-        output_size: second dimension of matrix A.
-        bias: If true, add bias. Note that bias is not parallelized.
-        input_is_parallel: If true, we assume that the input is already
-                           split across the GPUs and we do not split
-                           again.
-        skip_bias_add: This was added to enable performance optimization where
-                       bias can be fused with other element-wise operations.
-                       We skip adding bias but instead return it.
-        params_dtype: Data type for the parameters.
-        linear_method: (Maybe quantized) linear method.
-    """
-
-    def __init__(
-        self,
-        input_size: int,
-        output_size: int,
-        bias: bool = True,
-        input_is_parallel: bool = True,
-        skip_bias_add: bool = False,
-        params_dtype: Optional[torch.dtype] = None,
-        reduce_results: bool = True,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
-        super().__init__()
-        # Keep input parameters
-        self.input_size = input_size
-        self.output_size = output_size
-        self.input_is_parallel = input_is_parallel
-        self.reduce_results = reduce_results
-        if params_dtype is None:
-            params_dtype = torch.get_default_dtype()
-        self.params_dtype = params_dtype
-
-        # Divide the weight matrix along the last dimension.
-        self.tp_size = get_tensor_model_parallel_world_size()
-        self.input_size_per_partition = divide(input_size, self.tp_size)
-        self.skip_bias_add = skip_bias_add
-        if linear_method is None:
-            linear_method = UnquantizedLinearMethod()
-        self.linear_method = linear_method
-        self.linear_weights = self.linear_method.create_weights(
-            self.input_size_per_partition, self.output_size, self.params_dtype)
-        for name, weight in self.linear_weights.items():
-            self.register_parameter(name, weight)
-            set_weight_attrs(weight, {"weight_loader": self.weight_loader})
-
-        if not reduce_results and (bias and not skip_bias_add):
-            raise ValueError("When not reduce the results, adding bias to the "
-                             "results can lead to incorrect results")
-
-        if bias:
-            self.bias = Parameter(
-                torch.empty(self.output_size,
-                            device=torch.cuda.current_device(),
-                            dtype=params_dtype))
-            set_weight_attrs(self.bias, {
-                "output_dim": 0,
-                "weight_loader": self.weight_loader,
-            })
-        else:
-            self.register_parameter("bias", None)
-
-    def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
-        tp_rank = get_tensor_model_parallel_rank()
-        input_dim = getattr(param, "input_dim", None)
-        param_data = param.data
-        if input_dim is not None:
-            shard_size = param_data.shape[input_dim]
-            start_idx = tp_rank * shard_size
-            loaded_weight = loaded_weight.narrow(input_dim, start_idx,
-                                                 shard_size)
-        assert param_data.shape == loaded_weight.shape
-        param_data.copy_(loaded_weight)
-
-    def forward(self, input_):
-        # Set up backprop all-reduce.
-        if self.input_is_parallel:
-            input_parallel = input_
-        else:
-            tp_rank = get_tensor_model_parallel_rank()
-            splitted_input = split_tensor_along_last_dim(
-                input_, num_partitions=self.tp_size)
-            input_parallel = splitted_input[tp_rank].contiguous()
-
-        # Matrix multiply.
-        output_parallel = self.linear_method.apply_weights(
-            self.linear_weights, input_parallel)
-        if self.reduce_results and self.tp_size > 1:
-            output_ = tensor_model_parallel_all_reduce(output_parallel)
-        else:
-            output_ = output_parallel
-
-        if not self.skip_bias_add:
-            output = output_ + self.bias if self.bias is not None else output_
-            output_bias = None
-        else:
-            output = output_
-            output_bias = self.bias
-        return output, output_bias

vllm/model_executor/layers/quantization/__init__.py

@@ -1,22 +0,0 @@
-from typing import Type
-
-from vllm.model_executor.layers.quantization.awq import AWQConfig
-from vllm.model_executor.layers.quantization.squeezellm import SqueezeLLMConfig
-from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
-
-_QUANTIZATION_CONFIG_REGISTRY = {
-    "awq": AWQConfig,
-    "squeezellm": SqueezeLLMConfig,
-}
-
-
-def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
-    if quantization not in _QUANTIZATION_CONFIG_REGISTRY:
-        raise ValueError(f"Invalid quantization method: {quantization}")
-    return _QUANTIZATION_CONFIG_REGISTRY[quantization]
-
-
-__all__ = [
-    "QuantizationConfig",
-    "get_quantization_config",
-]

vllm/model_executor/layers/quantization/awq.py

@@ -1,158 +0,0 @@
-from typing import Any, Dict, List, Optional
-
-import torch
-from torch.nn.parameter import Parameter
-
-from vllm import quantization_ops
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               set_weight_attrs)
-from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
-
-
-class AWQConfig(QuantizationConfig):
-    """Config class for AWQ.
-
-    Reference: https://arxiv.org/abs/2306.00978
-    """
-
-    def __init__(
-        self,
-        weight_bits: int,
-        group_size: int,
-        zero_point: bool,
-    ) -> None:
-        self.weight_bits = weight_bits
-        self.group_size = group_size
-        self.zero_point = zero_point
-
-        if self.weight_bits != 4:
-            raise ValueError(
-                "Currently, only 4-bit weight quantization is supported for "
-                f"AWQ, but got {self.weight_bits} bits.")
-        self.pack_factor = 32 // self.weight_bits
-
-    def __repr__(self) -> str:
-        return (f"AWQConfig(weight_bits={self.weight_bits}, "
-                f"group_size={self.group_size}, "
-                f"zero_point={self.zero_point})")
-
-    def get_name(self) -> str:
-        return "awq"
-
-    def get_supported_act_dtypes(self) -> List[torch.dtype]:
-        return [torch.half]
-
-    def get_min_capability(self) -> int:
-        # The AWQ kernel only supports Turing or newer GPUs.
-        return 75
-
-    @staticmethod
-    def get_config_filenames() -> List[str]:
-        return [
-            "quant_config.json",  # E.g., casperhansen/vicuna-7b-v1.5-awq
-            "quantize_config.json",  # E.g., abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq  # pylint: disable=line-too-long
-        ]
-
-    @classmethod
-    def from_config(cls, config: Dict[str, Any]) -> "AWQConfig":
-        weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
-        group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
-        zero_point = cls.get_from_keys(config, ["zero_point"])
-        return cls(weight_bits, group_size, zero_point)
-
-    def get_linear_method(self) -> "AWQLinearMethod":
-        return AWQLinearMethod(self)
-
-    def get_scaled_act_names(self) -> List[str]:
-        return ["gelu", "gelu_fast", "gelu_new", "gelu_pytorch_tanh"]
-
-
-class AWQLinearMethod(LinearMethodBase):
-    """Linear method for AWQ.
-
-    Args:
-        quant_config: The AWQ quantization config.
-    """
-
-    def __init__(self, quant_config: AWQConfig):
-        self.quant_config = quant_config
-
-    def create_weights(self, input_size: int, output_size: int,
-                       params_dtype: torch.dtype) -> Dict[str, torch.Tensor]:
-        if input_size % self.quant_config.group_size != 0:
-            raise ValueError(
-                "The input size is not aligned with the quantized "
-                "weight shape. This can be caused by too large "
-                "tensor parallel size.")
-        if output_size % self.quant_config.pack_factor != 0:
-            raise ValueError(
-                "The output size is not aligned with the quantized "
-                "weight shape. This can be caused by too large "
-                "tensor parallel size.")
-
-        qweight = Parameter(
-            torch.empty(
-                input_size,
-                output_size // self.quant_config.pack_factor,
-                device="cuda",
-                dtype=torch.int32,
-            ),
-            requires_grad=False,
-        )
-        set_weight_attrs(
-            qweight, {
-                "input_dim": 0,
-                "output_dim": 1,
-                "packed_dim": 1,
-                "pack_factor": self.quant_config.pack_factor,
-            })
-        qzeros = Parameter(
-            torch.empty(
-                input_size // self.quant_config.group_size,
-                output_size // self.quant_config.pack_factor,
-                device="cuda",
-                dtype=torch.int32,
-            ),
-            requires_grad=False,
-        )
-        set_weight_attrs(
-            qzeros, {
-                "input_dim": 0,
-                "output_dim": 1,
-                "packed_dim": 1,
-                "pack_factor": self.quant_config.pack_factor,
-            })
-        scales = Parameter(
-            torch.empty(
-                input_size // self.quant_config.group_size,
-                output_size,
-                device="cuda",
-                dtype=params_dtype,
-            ),
-            requires_grad=False,
-        )
-        set_weight_attrs(scales, {
-            "input_dim": 0,
-            "output_dim": 1,
-        })
-        return {
-            "qweight": qweight,
-            "qzeros": qzeros,
-            "scales": scales,
-        }
-
-    def apply_weights(self,
-                      weights: Dict[str, torch.Tensor],
-                      x: torch.Tensor,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-        qweight = weights["qweight"]
-        qzeros = weights["qzeros"]
-        scales = weights["scales"]
-        pack_factor = self.quant_config.pack_factor
-        out_shape = (x.shape[:-1] + (qweight.shape[-1] * pack_factor, ))
-        reshaped_x = x.reshape(-1, x.shape[-1])
-        out = quantization_ops.awq_gemm(reshaped_x, qweight, scales, qzeros,
-                                        pack_factor)
-        if bias is not None:
-            out = out + bias
-        return out.reshape(out_shape)

vllm/model_executor/layers/quantization/squeezellm.py

@@ -1,124 +0,0 @@
-from typing import Any, Dict, List, Optional
-
-import torch
-from torch.nn.parameter import Parameter
-
-from vllm import quantization_ops
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               set_weight_attrs)
-from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
-
-
-class SqueezeLLMConfig(QuantizationConfig):
-    """Config class for SqueezeLLM.
-
-    Reference: https://arxiv.org/pdf/2306.07629
-    """
-
-    def __init__(
-        self,
-        weight_bits: int,
-    ) -> None:
-        self.weight_bits = weight_bits
-
-        if self.weight_bits != 4:
-            raise ValueError(
-                "Currently, only 4-bit weight quantization is supported for "
-                f"SqueezeLLM, but got {self.weight_bits} bits.")
-
-        self.pack_factor = 32 // self.weight_bits
-
-    def __repr__(self) -> str:
-        return f"SqueezeLLMConfig(weight_bits={self.weight_bits})"
-
-    def get_name(self) -> str:
-        return "squeezellm"
-
-    def get_supported_act_dtypes(self) -> List[torch.dtype]:
-        return [torch.half]
-
-    def get_min_capability(self) -> int:
-        return 70
-
-    @staticmethod
-    def get_config_filenames() -> List[str]:
-        return ["quant_config.json"]
-
-    @classmethod
-    def from_config(cls, config: Dict[str, Any]) -> "SqueezeLLMConfig":
-        weight_bits = cls.get_from_keys(config, ["wbits"])
-        return cls(weight_bits)
-
-    def get_linear_method(self) -> "SqueezeLLMLinearMethod":
-        return SqueezeLLMLinearMethod(self)
-
-    def get_scaled_act_names(self) -> List[str]:
-        return []
-
-
-class SqueezeLLMLinearMethod(LinearMethodBase):
-    """Linear method for SqueezeLLM.
-
-    Args:
-        quant_config: The SqueezeLLM quantization config.
-    """
-
-    def __init__(self, quant_config: SqueezeLLMConfig):
-        self.quant_config = quant_config
-
-    def create_weights(self, input_size: int, output_size: int,
-                       params_dtype: torch.dtype) -> Dict[str, torch.Tensor]:
-        if input_size % self.quant_config.pack_factor != 0:
-            raise ValueError(
-                "The input size is not aligned with the quantized "
-                "weight shape. This can be caused by too large "
-                "tensor parallel size.")
-        qweight = Parameter(
-            torch.empty(
-                input_size // self.quant_config.pack_factor,
-                output_size,
-                device="cuda",
-                dtype=torch.int32,
-            ),
-            requires_grad=False,
-        )
-        set_weight_attrs(
-            qweight, {
-                "input_dim": 0,
-                "output_dim": 1,
-                "packed_dim": 0,
-                "pack_factor": self.quant_config.pack_factor,
-            })
-        lookup_table = Parameter(
-            torch.empty(
-                output_size,
-                self.quant_config.weight_bits**2,
-                device="cuda",
-                dtype=params_dtype,
-            ),
-            requires_grad=False,
-        )
-        set_weight_attrs(lookup_table, {
-            "output_dim": 0,
-        })
-        return {
-            "qweight": qweight,
-            "lookup_table": lookup_table,
-        }
-
-    def apply_weights(self,
-                      weights: Dict[str, torch.Tensor],
-                      x: torch.Tensor,
-                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-        qweight = weights["qweight"]
-        lookup_table = weights["lookup_table"]
-        out_shape = x.shape[:-1] + (qweight.shape[-1], )
-        reshaped_x = x.reshape(-1, x.shape[-1])
-        # NOTE: The output tensor should be zero-initialized.
-        out = torch.zeros(out_shape, device="cuda", dtype=torch.float16)
-        quantization_ops.squeezellm_gemm(reshaped_x, qweight, out,
-                                         lookup_table)
-
-        if bias is not None:
-            out = out + bias
-        return out.reshape(out_shape)

vllm/model_executor/layers/quantized_linear/__init__.py

@@ -0,0 +1,37 @@
+from vllm.model_executor.layers.quantized_linear.awq import (
+    AWQColumnParallelLinear, AWQRowParallelLinear)
+from vllm.model_executor.parallel_utils.layers import (ColumnParallelLinear,
+                                                       RowParallelLinear)
+
+_QUANTIZED_LINEAR_REGISTRY = {
+    "awq": (AWQColumnParallelLinear, AWQRowParallelLinear),
+}
+
+
+class ParallelLinear:
+
+    @classmethod
+    def column(cls, *args, **kwargs) -> ColumnParallelLinear:
+        quant_config = kwargs.get("quant_config", None)
+        if quant_config is None:
+            return ColumnParallelLinear(*args, **kwargs)
+
+        name = quant_config.get_name()
+        if name not in _QUANTIZED_LINEAR_REGISTRY:
+            raise ValueError(f"No quantized linear is found for {name}")
+
+        quant_linear_cls = _QUANTIZED_LINEAR_REGISTRY[name][0]
+        return quant_linear_cls(*args, **kwargs)
+
+    @classmethod
+    def row(cls, *args, **kwargs) -> RowParallelLinear:
+        quant_config = kwargs.get("quant_config", None)
+        if quant_config is None:
+            return RowParallelLinear(*args, **kwargs)
+
+        name = quant_config.get_name()
+        if name not in _QUANTIZED_LINEAR_REGISTRY:
+            raise ValueError(f"No quantized linear is found for {name}")
+
+        quant_linear_cls = _QUANTIZED_LINEAR_REGISTRY[name][1]
+        return quant_linear_cls(*args, **kwargs)

vllm/model_executor/layers/quantized_linear/awq.py

@@ -0,0 +1,102 @@
+from typing import Optional
+
+import torch
+from torch.nn.parameter import Parameter
+
+from vllm import quantization_ops
+from vllm.model_executor.parallel_utils.layers import (ColumnParallelLinear,
+                                                       RowParallelLinear)
+
+
+class AWQColumnParallelLinear(ColumnParallelLinear):
+
+    def create_weights(self, dtype: torch.dtype) -> None:
+        assert self.input_size % self.quant_config.weight_bits == 0
+        assert (self.output_size_per_partition %
+                self.quant_config.pack_factor == 0)
+        self.qweight = Parameter(
+            torch.empty(
+                self.input_size,
+                self.output_size_per_partition //
+                self.quant_config.pack_factor,
+                device="cuda",
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        self.qzeros = Parameter(
+            torch.empty(
+                self.input_size // self.quant_config.group_size,
+                self.output_size_per_partition //
+                self.quant_config.pack_factor,
+                device="cuda",
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        self.scales = Parameter(
+            torch.empty(
+                self.input_size // self.quant_config.group_size,
+                self.output_size_per_partition,
+                device="cuda",
+                dtype=dtype,
+            ),
+            requires_grad=False,
+        )
+
+    def apply_weights(
+        self,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        pack_factor = self.quant_config.pack_factor
+        out_shape = (x.shape[-2], self.qweight.shape[-1] * pack_factor)
+        reshaped_x = x.reshape(-1, x.shape[-1])
+        out = quantization_ops.awq_gemm(reshaped_x, self.qweight, self.scales,
+                                        self.qzeros, pack_factor)
+        if bias is not None:
+            out = out + bias
+        return out.reshape(out_shape)
+
+
+class AWQRowParallelLinear(RowParallelLinear):
+
+    def create_weights(self, dtype: torch.dtype) -> None:
+        assert (self.input_size_per_partition %
+                self.quant_config.weight_bits == 0)
+        assert self.output_size % self.quant_config.pack_factor == 0
+        self.qweight = Parameter(
+            torch.empty(
+                self.input_size_per_partition,
+                self.output_size // self.quant_config.pack_factor,
+                device="cuda",
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        self.qzeros = Parameter(
+            torch.empty(
+                self.input_size_per_partition // self.quant_config.group_size,
+                self.output_size // self.quant_config.pack_factor,
+                device="cuda",
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        self.scales = Parameter(
+            torch.empty(
+                self.input_size_per_partition // self.quant_config.group_size,
+                self.output_size,
+                device="cuda",
+                dtype=dtype,
+            ),
+            requires_grad=False,
+        )
+
+    def apply_weights(self, x: torch.Tensor) -> torch.Tensor:
+        pack_factor = self.quant_config.pack_factor
+        out_shape = (x.shape[-2], self.qweight.shape[-1] * pack_factor)
+        reshaped_x = x.reshape(-1, x.shape[-1])
+        out = quantization_ops.awq_gemm(reshaped_x, self.qweight, self.scales,
+                                        self.qzeros, pack_factor)
+        return out.reshape(out_shape)

vllm/model_executor/layers/rotary_embedding.py

@@ -21,8 +21,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Rotary Positional Embeddings."""
-import math
-from typing import Any, Dict, Optional, Tuple, Union
+from typing import Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -168,149 +167,3 @@ class DynamicNTKScalingRotaryEmbedding(RotaryEmbedding):
         sin = freqs.sin()
         cache = torch.cat((cos, sin), dim=-1)
         return cache
-
-
-# Inverse dim formula to find dim based on number of rotations
-def _yarn_find_correction_dim(num_rotations: int,
-                              dim: int,
-                              base: float = 10000,
-                              max_position_embeddings: int = 2048) -> float:
-    return (dim * math.log(max_position_embeddings /
-                           (num_rotations * 2 * math.pi))) / (2 *
-                                                              math.log(base))
-
-
-# Find dim range bounds based on rotations
-def _yarn_find_correction_range(low_rot: int,
-                                high_rot: int,
-                                dim: int,
-                                base: float = 10000,
-                                max_position_embeddings: int = 2048) -> int:
-    low = math.floor(
-        _yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings))
-    high = math.ceil(
-        _yarn_find_correction_dim(high_rot, dim, base,
-                                  max_position_embeddings))
-    return max(low, 0), min(high, dim - 1)  # Clamp values just in case
-
-
-def _yarn_linear_ramp_mask(low: float, high: float, dim: int,
-                           dtype: torch.dtype,
-                           device: torch.device) -> torch.Tensor:
-    if low == high:
-        high += 0.001  # Prevent singularity
-
-    linear_func = (torch.arange(dim, dtype=dtype, device=device) -
-                   low) / (high - low)
-    ramp_func = torch.clamp(linear_func, 0, 1)
-    return ramp_func
-
-
-def _yarn_get_mscale(scale: float = 1) -> float:
-    if scale <= 1:
-        return 1.0
-    return 0.1 * math.log(scale) + 1.0
-
-
-class YaRNScalingRotaryEmbedding(RotaryEmbedding):
-    """RotaryEmbedding extended with YaRN method.
-
-    Credits to Peng et al. github.com/jquesnelle/yarn
-    """
-
-    def __init__(
-        self,
-        head_size: int,
-        rotary_dim: int,
-        max_position_embeddings: int,
-        base: int,
-        is_neox_style: bool,
-        scaling_factor: float,
-        *,
-        extrapolation_factor: float = 1,
-        attn_factor: float = 1,
-        beta_fast: float = 32,
-        beta_slow: float = 1,
-    ) -> None:
-        self.scaling_factor = scaling_factor
-        self.extrapolation_factor = extrapolation_factor
-        self.attn_factor = attn_factor
-        self.beta_fast = beta_fast
-        self.beta_slow = beta_slow
-        # Get n-d magnitude scaling corrected for interpolation
-        self.mscale = float(
-            _yarn_get_mscale(self.scaling_factor) * attn_factor)
-        super().__init__(head_size, rotary_dim, max_position_embeddings, base,
-                         is_neox_style)
-
-    def _compute_inv_freq(self, scaling_factor: float) -> torch.Tensor:
-        pos_freqs = self.base**(torch.arange(
-            0, self.rotary_dim, 2, dtype=torch.float, device="cuda") /
-                                self.rotary_dim)
-        inv_freq_extrapolation = 1.0 / pos_freqs
-        inv_freq_interpolation = 1.0 / (scaling_factor * pos_freqs)
-
-        low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow,
-                                                self.rotary_dim, self.base,
-                                                self.max_position_embeddings)
-        # Get n-d rotational scaling corrected for extrapolation
-        inv_freq_mask = (1 - _yarn_linear_ramp_mask(
-            low, high, self.rotary_dim // 2, dtype=torch.float,
-            device="cuda")) * self.extrapolation_factor
-        inv_freq = inv_freq_interpolation * (
-            1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask
-        return inv_freq
-
-    def _compute_cos_sin_cache(self) -> torch.Tensor:
-        inv_freq = self._compute_inv_freq(self.scaling_factor)
-        t = torch.arange(self.max_position_embeddings * self.scaling_factor,
-                         device="cuda",
-                         dtype=torch.float32)
-        freqs = torch.einsum("i,j -> ij", t, inv_freq)
-        cos = (freqs.cos() * self.mscale)
-        sin = (freqs.sin() * self.mscale)
-        cache = torch.cat((cos, sin), dim=-1)
-        return cache
-
-
-def get_rope(
-    head_size: int,
-    rotary_dim: int,
-    max_position: int,
-    base: int,
-    is_neox_style: bool,
-    rope_scaling: Optional[Dict[str, Any]],
-) -> RotaryEmbedding:
-    if rope_scaling is None:
-        rotary_emb = RotaryEmbedding(head_size, rotary_dim, max_position, base,
-                                     is_neox_style)
-    else:
-        scaling_type = rope_scaling["type"]
-        scaling_factor = rope_scaling["factor"]
-        if scaling_type == "linear":
-            rotary_emb = LinearScalingRotaryEmbedding(head_size, rotary_dim,
-                                                      max_position, base,
-                                                      is_neox_style,
-                                                      scaling_factor)
-        elif scaling_type == "dynamic":
-            rotary_emb = DynamicNTKScalingRotaryEmbedding(
-                head_size, rotary_dim, max_position, base, is_neox_style,
-                scaling_factor)
-        elif scaling_type == "yarn":
-            original_max_position = rope_scaling[
-                "original_max_position_embeddings"]
-            assert max_position == original_max_position * scaling_factor
-            extra_kwargs = {
-                k: v
-                for k, v in rope_scaling.items()
-                if k in ("extrapolation_factor", "attn_factor", "beta_fast",
-                         "beta_slow")
-            }
-            rotary_emb = YaRNScalingRotaryEmbedding(head_size, rotary_dim,
-                                                    original_max_position,
-                                                    base, is_neox_style,
-                                                    scaling_factor,
-                                                    **extra_kwargs)
-        else:
-            raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
-    return rotary_emb

vllm/model_executor/layers/sampler.py

@@ -47,18 +47,15 @@ class Sampler(nn.Module):
         logits = _get_logits(hidden_states, embedding, embedding_bias,
                              self.vocab_size)
 
-        # Apply logits processors (if any).
-        logits = _apply_logits_processors(logits, input_metadata)
         # Apply presence and frequency penalties.
         output_tokens = _get_output_tokens(input_metadata)
         assert len(output_tokens) == logits.shape[0]
-        presence_penalties, frequency_penalties, repetition_penalties = (
-            _get_penalties(input_metadata))
+        presence_penalties, frequency_penalties = _get_penalties(
+            input_metadata)
         assert len(presence_penalties) == logits.shape[0]
         assert len(frequency_penalties) == logits.shape[0]
-        assert len(repetition_penalties) == logits.shape[0]
         logits = _apply_penalties(logits, output_tokens, presence_penalties,
-                                  frequency_penalties, repetition_penalties)
+                                  frequency_penalties)
 
         # Apply temperature scaling.
         temperatures = _get_temperatures(input_metadata)
@@ -71,18 +68,13 @@ class Sampler(nn.Module):
             logits.div_(t.unsqueeze(dim=1))
 
         # Apply top-p and top-k truncation.
-        top_ps, top_ks, min_ps = _get_top_p_top_k_min_p(
-            input_metadata, self.vocab_size)
+        top_ps, top_ks = _get_top_p_top_k(input_metadata, self.vocab_size)
         assert len(top_ps) == len(top_ks) == logits.shape[0]
         do_top_p = any(p < 1.0 - _SAMPLING_EPS for p in top_ps)
         do_top_k = any(k != self.vocab_size for k in top_ks)
         if do_top_p or do_top_k:
             logits = _apply_top_p_top_k(logits, top_ps, top_ks)
 
-        do_min_p = any(mp > _SAMPLING_EPS for mp in min_ps)
-        if do_min_p:
-            logits = _apply_min_p(logits, min_ps)
-
         # We use float32 for probabilities and log probabilities.
         # Compute the probabilities.
         probs = torch.softmax(logits, dim=-1, dtype=torch.float)
@@ -116,22 +108,39 @@ def _prune_hidden_states(
     hidden_states: torch.Tensor,
     input_metadata: InputMetadata,
 ) -> torch.Tensor:
-    hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
-    return hidden_states.index_select(0, input_metadata.selected_token_indices)
+    selected_token_indices: List[int] = []
+    start_idx = 0
+    for i, seq_group in enumerate(input_metadata.seq_groups):
+        seq_ids, sampling_params = seq_group
+        if i < input_metadata.num_prompts:
+            assert len(seq_ids) == 1, "Prompt input should have only one seq."
+            prompt_len = input_metadata.prompt_lens[i]
+            if sampling_params.prompt_logprobs is not None:
+                selected_token_indices.extend(
+                    range(start_idx, start_idx + prompt_len - 1))
+            selected_token_indices.append(start_idx + prompt_len - 1)
+            start_idx += prompt_len
+        else:
+            num_seqs = len(seq_ids)
+            selected_token_indices.extend(
+                range(start_idx, start_idx + num_seqs))
+            start_idx += num_seqs
+
+    selected_token_indices = torch.tensor(selected_token_indices,
+                                          dtype=torch.long,
+                                          device=hidden_states.device)
+    return hidden_states.index_select(0, selected_token_indices)
 
 
 def _get_penalties(
-    input_metadata: InputMetadata
-) -> Tuple[List[float], List[float], List[float]]:
+        input_metadata: InputMetadata) -> Tuple[List[float], List[float]]:
     # Collect the presence and frequency penalties.
     presence_penalties: List[float] = []
     frequency_penalties: List[float] = []
-    repetition_penalties: List[float] = []
     for i, seq_group in enumerate(input_metadata.seq_groups):
         seq_ids, sampling_params = seq_group
         p = sampling_params.presence_penalty
         f = sampling_params.frequency_penalty
-        r = sampling_params.repetition_penalty
         if (i < input_metadata.num_prompts
                 and sampling_params.prompt_logprobs is not None):
             # NOTE: We do not apply presence and frequency penalties for the
@@ -139,11 +148,9 @@ def _get_penalties(
             prompt_len = input_metadata.prompt_lens[i]
             presence_penalties += [0] * (prompt_len - 1)
             frequency_penalties += [0] * (prompt_len - 1)
-            repetition_penalties += [1] * (prompt_len - 1)
         presence_penalties += [p] * len(seq_ids)
         frequency_penalties += [f] * len(seq_ids)
-        repetition_penalties += [r] * len(seq_ids)
-    return presence_penalties, frequency_penalties, repetition_penalties
+    return presence_penalties, frequency_penalties
 
 
 def _get_output_tokens(input_metadata: InputMetadata) -> List[List[int]]:
@@ -162,34 +169,11 @@ def _get_output_tokens(input_metadata: InputMetadata) -> List[List[int]]:
     return output_tokens
 
 
-def _apply_logits_processors(logits: torch.Tensor,
-                             input_metadata: InputMetadata) -> torch.Tensor:
-    logits_row_idx = 0
-    found_logits_processors = False
-    for seq_ids, sampling_params in input_metadata.seq_groups:
-        logits_processors = sampling_params.logits_processors
-        if logits_processors:
-            found_logits_processors = True
-            for seq_id in seq_ids:
-                logits_row = logits[logits_row_idx]
-                token_ids = input_metadata.seq_data[seq_id].output_token_ids
-                for logits_processor in logits_processors:
-                    logits_row = logits_processor(token_ids, logits_row)
-                logits[logits_row_idx] = logits_row
-                logits_row_idx += 1
-        else:
-            logits_row_idx += len(seq_ids)
-    if found_logits_processors:
-        assert logits_row_idx == logits.shape[0]
-    return logits
-
-
 def _apply_penalties(
     logits: torch.Tensor,
     output_tokens: List[List[int]],
     presence_penalties: List[float],
     frequency_penalties: List[float],
-    repetition_penalties: List[float],
 ) -> torch.Tensor:
     num_seqs, vocab_size = logits.shape
     for i in range(num_seqs):
@@ -197,9 +181,7 @@ def _apply_penalties(
             continue
         p = presence_penalties[i]
         f = frequency_penalties[i]
-        r = repetition_penalties[i]
-        if abs(p) < _SAMPLING_EPS and abs(f) < _SAMPLING_EPS and abs(
-                r - 1.0) < _SAMPLING_EPS:
+        if abs(p) < _SAMPLING_EPS and abs(f) < _SAMPLING_EPS:
             continue
         break
     else:
@@ -223,11 +205,7 @@ def _apply_penalties(
     bin_counts.scatter_add_(1, output_tokens_tensor,
                             torch.ones_like(output_tokens_tensor))
     bin_counts = bin_counts[:, :vocab_size]  # Remove the padding bin.
-    mask = bin_counts > 0
 
-    repetition_penalties = torch.tensor(repetition_penalties,
-                                        dtype=logits.dtype,
-                                        device=logits.device)
     frequency_penalties = torch.tensor(frequency_penalties,
                                        dtype=logits.dtype,
                                        device=logits.device)
@@ -235,15 +213,10 @@ def _apply_penalties(
                                       dtype=logits.dtype,
                                       device=logits.device)
 
-    repetition_penalties = repetition_penalties[:, None].repeat(1, vocab_size)
-    repetition_penalties[~mask] = 1.0
-    logits = torch.where(logits > 0, logits / repetition_penalties,
-                         logits * repetition_penalties)
-
     # We follow the definition in OpenAI API.
     # Refer to https://platform.openai.com/docs/api-reference/parameter-details
     logits -= frequency_penalties.unsqueeze(dim=1) * bin_counts
-    logits -= presence_penalties.unsqueeze(dim=1) * mask
+    logits -= presence_penalties.unsqueeze(dim=1) * (bin_counts > 0)
     return logits
 
 
@@ -266,17 +239,15 @@ def _get_temperatures(input_metadata: InputMetadata) -> List[float]:
     return temperatures
 
 
-def _get_top_p_top_k_min_p(
+def _get_top_p_top_k(
     input_metadata: InputMetadata,
     vocab_size: int,
-) -> Tuple[List[float], List[int], List[float]]:
+) -> Tuple[List[float], List[int]]:
     top_ps: List[float] = []
     top_ks: List[int] = []
-    min_ps: List[float] = []
     for i, seq_group in enumerate(input_metadata.seq_groups):
         seq_ids, sampling_params = seq_group
         top_p = sampling_params.top_p
-        min_p = sampling_params.min_p
         # k should not be greater than the vocab size.
         top_k = min(sampling_params.top_k, vocab_size)
         # k=-1 means no truncation.
@@ -286,11 +257,9 @@ def _get_top_p_top_k_min_p(
             prompt_len = input_metadata.prompt_lens[i]
             top_ps += [top_p] * (prompt_len - 1)
             top_ks += [top_k] * (prompt_len - 1)
-            min_ps += [min_p] * (prompt_len - 1)
         top_ps += [top_p] * len(seq_ids)
         top_ks += [top_k] * len(seq_ids)
-        min_ps += [min_p] * len(seq_ids)
-    return top_ps, top_ks, min_ps
+    return top_ps, top_ks
 
 
 def _apply_top_p_top_k(
@@ -322,24 +291,6 @@ def _apply_top_p_top_k(
     return logits
 
 
-def _apply_min_p(
-    logits: torch.Tensor,
-    min_ps: List[float],
-) -> torch.Tensor:
-    """
-    Adapted from
-    https://github.com/oobabooga/text-generation-webui/blob/3146124ec01f02c8fb1650a6517cf1b60b537aaf/modules/sampler_hijack.py#L16C17-L16C17
-    """
-    min_p = torch.tensor(min_ps, dtype=logits.dtype, device=logits.device)
-    probs = torch.softmax(logits, dim=-1)
-    top_probs, _ = probs.max(dim=-1, keepdim=True)
-    scaled_min_p = min_p.unsqueeze(dim=1) * top_probs
-    tokens_to_remove = probs < scaled_min_p
-    logits = logits.masked_fill(tokens_to_remove, -float("inf"))
-
-    return logits
-
-
 def _greedy_sample(
     selected_seq_groups: List[Tuple[List[int], SamplingParams]],
     logprobs: torch.Tensor,
@@ -456,11 +407,21 @@ def _sample(
     input_metadata: InputMetadata,
 ) -> List[Tuple[List[int], List[int]]]:
     categorized_seq_group_ids = {t: [] for t in SamplingType}
-    categorized_sample_indices = input_metadata.categorized_sample_indices
+    categorized_sample_indices = {t: [] for t in SamplingType}
+    start_idx = 0
     for i, seq_group in enumerate(input_metadata.seq_groups):
-        _, sampling_params = seq_group
+        seq_ids, sampling_params = seq_group
         sampling_type = sampling_params.sampling_type
+        if (i < input_metadata.num_prompts
+                and sampling_params.prompt_logprobs is not None):
+            # NOTE: prompt token positions do not need sample, skip
+            prompt_len = input_metadata.prompt_lens[i]
+            start_idx += prompt_len - 1
         categorized_seq_group_ids[sampling_type].append(i)
+        num_seqs = len(seq_ids)
+        categorized_sample_indices[sampling_type].extend(
+            range(start_idx, start_idx + num_seqs))
+        start_idx += num_seqs
 
     sample_results_dict: Dict[int, Tuple[List[int], List[int]]] = {}
     for sampling_type in SamplingType:

vllm/model_executor/layers/vocab_parallel_embedding.py

@@ -1,139 +0,0 @@
-from typing import Optional, Sequence
-
-import torch
-import torch.nn.functional as F
-from torch.nn.parameter import Parameter
-
-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.utils import divide
-from vllm.model_executor.parallel_utils.communication_op import (
-    tensor_model_parallel_all_reduce)
-from vllm.model_executor.utils import set_weight_attrs
-
-
-def pad_vocab_size(vocab_size: int, pad_to: int = 64) -> int:
-    """Pad the vocab size to the given value."""
-    return ((vocab_size + pad_to - 1) // pad_to) * pad_to
-
-
-def vocab_range_from_per_partition_vocab_size(per_partition_vocab_size: int,
-                                              rank: int) -> Sequence[int]:
-    index_f = rank * per_partition_vocab_size
-    index_l = index_f + per_partition_vocab_size
-    return index_f, index_l
-
-
-def vocab_range_from_global_vocab_size(global_vocab_size: int, rank: int,
-                                       world_size: int) -> Sequence[int]:
-    per_partition_vocab_size = divide(global_vocab_size, world_size)
-    return vocab_range_from_per_partition_vocab_size(per_partition_vocab_size,
-                                                     rank)
-
-
-class VocabParallelEmbedding(torch.nn.Module):
-    """Embedding parallelized in the vocabulary dimension.
-
-    Adapted from torch.nn.Embedding, note that we pad the vocabulary size to
-    make sure it is divisible by the number of model parallel GPUs.
-
-    Args:
-        num_embeddings: vocabulary size.
-        embedding_dim: size of hidden state.
-        params_dtype: type of the parameters.
-    """
-
-    def __init__(self,
-                 num_embeddings: int,
-                 embedding_dim: int,
-                 params_dtype: Optional[torch.dtype] = None):
-        super().__init__()
-
-        # Keep the input dimensions.
-        self.num_embeddings = num_embeddings
-        self.num_embeddings_padded = pad_vocab_size(num_embeddings)
-        self.embedding_dim = embedding_dim
-        if params_dtype is None:
-            params_dtype = torch.get_default_dtype()
-        self.tp_size = get_tensor_model_parallel_world_size()
-        # Divide the weight matrix along the vocaburaly dimension.
-        self.vocab_start_index, self.vocab_end_index = (
-            vocab_range_from_global_vocab_size(
-                self.num_embeddings_padded, get_tensor_model_parallel_rank(),
-                self.tp_size))
-        self.num_embeddings_per_partition = (self.vocab_end_index -
-                                             self.vocab_start_index)
-        self.weight = Parameter(
-            torch.empty(self.num_embeddings_per_partition,
-                        self.embedding_dim,
-                        device=torch.cuda.current_device(),
-                        dtype=params_dtype))
-        set_weight_attrs(self.weight, {
-            "parallel_dim": 0,
-            "weight_loader": self.weight_loader
-        })
-
-    def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
-        parallel_dim = param.parallel_dim
-        assert loaded_weight.shape[parallel_dim] == self.num_embeddings
-        loaded_weight = loaded_weight[self.vocab_start_index:self.
-                                      vocab_end_index]
-        param[:loaded_weight.shape[0]].data.copy_(loaded_weight)
-
-    def forward(self, input_):
-        if self.tp_size > 1:
-            # Build the mask.
-            input_mask = ((input_ < self.vocab_start_index) |
-                          (input_ >= self.vocab_end_index))
-            # Mask the input.
-            masked_input = input_.clone() - self.vocab_start_index
-            masked_input[input_mask] = 0
-        else:
-            masked_input = input_
-            # Get the embeddings.
-        output_parallel = F.embedding(masked_input, self.weight)
-        # Mask the output embedding.
-        if self.tp_size > 1:
-            output_parallel[input_mask, :] = 0.0
-        # Reduce across all the model parallel GPUs.
-        output = tensor_model_parallel_all_reduce(output_parallel)
-        return output
-
-
-class ParallelLMHead(VocabParallelEmbedding):
-    """Parallelized LM head.
-
-    Output logits weight matrices used in the Sampler. The weight and bias
-    tensors are padded to make sure they are divisible by the number of
-    model parallel GPUs.
-
-    Args:
-        num_embeddings: vocabulary size.
-        embedding_dim: size of hidden state.
-        bias: whether to use bias.
-        params_dtype: type of the parameters.
-    """
-
-    def __init__(self,
-                 num_embeddings: int,
-                 embedding_dim: int,
-                 bias: bool = False,
-                 params_dtype: Optional[torch.dtype] = None):
-        super().__init__(num_embeddings, embedding_dim, params_dtype)
-        if bias:
-            self.bias = Parameter(
-                torch.empty(self.num_embeddings_per_partition,
-                            device=torch.cuda.current_device(),
-                            dtype=params_dtype))
-            set_weight_attrs(self.bias, {
-                "parallel_dim": 0,
-                "weight_loader": self.weight_loader
-            })
-        else:
-            self.register_parameter("bias", None)
-
-    def forward(self, input_):
-        del input_
-        raise RuntimeError("LMHead's weights should be used in the sampler.")

vllm/model_executor/model_loader.py

@@ -18,7 +18,6 @@ _MODEL_REGISTRY = {
     "BaiChuanForCausalLM": BaiChuanForCausalLM,  # baichuan-7b
     "BaichuanForCausalLM": BaichuanForCausalLM,  # baichuan-13b
     "BloomForCausalLM": BloomForCausalLM,
-    "ChatGLMModel": ChatGLMForCausalLM,
     "FalconForCausalLM": FalconForCausalLM,
     "GPT2LMHeadModel": GPT2LMHeadModel,
     "GPTBigCodeForCausalLM": GPTBigCodeForCausalLM,
@@ -28,16 +27,18 @@ _MODEL_REGISTRY = {
     "LlamaForCausalLM": LlamaForCausalLM,
     "LLaMAForCausalLM": LlamaForCausalLM,  # For decapoda-research/llama-*
     "MistralForCausalLM": MistralForCausalLM,
-    # transformers's mpt class has lower case
-    "MptForCausalLM": MPTForCausalLM,
     "MPTForCausalLM": MPTForCausalLM,
     "OPTForCausalLM": OPTForCausalLM,
-    "PhiForCausalLM": PhiForCausalLM,
     "QWenLMHeadModel": QWenLMHeadModel,
     "RWForCausalLM": FalconForCausalLM,
-    "YiForCausalLM": YiForCausalLM,
 }
 
+# FIXME(woosuk): Remove this once all models support quantization.
+_MODEL_CLASSES_SUPPORT_QUANTIZATION = [
+    LlamaForCausalLM,
+    MistralForCausalLM,
+]
+
 
 @contextlib.contextmanager
 def _set_default_torch_dtype(dtype: torch.dtype):
@@ -61,12 +62,14 @@ def _get_model_architecture(config: PretrainedConfig) -> Type[nn.Module]:
 def get_model(model_config: ModelConfig) -> nn.Module:
     model_class = _get_model_architecture(model_config.hf_config)
 
-    # Get the (maybe quantized) linear method.
-    linear_method = None
+    # Get the quantization config.
+    quant_config = None
     if model_config.quantization is not None:
+        if model_class not in _MODEL_CLASSES_SUPPORT_QUANTIZATION:
+            raise ValueError(
+                f"Quantization is not supported for {model_class}.")
         quant_config = get_quant_config(model_config.quantization,
                                         model_config.model,
-                                        model_config.hf_config,
                                         model_config.download_dir)
         capability = torch.cuda.get_device_capability()
         capability = capability[0] * 10 + capability[1]
@@ -82,12 +85,14 @@ def get_model(model_config: ModelConfig) -> nn.Module:
                 f"{model_config.dtype} is not supported for quantization "
                 f"method {model_config.quantization}. Supported dtypes: "
                 f"{supported_dtypes}")
-        linear_method = quant_config.get_linear_method()
 
     with _set_default_torch_dtype(model_config.dtype):
         # Create a model instance.
         # The weights will be initialized as empty tensors.
-        model = model_class(model_config.hf_config, linear_method)
+        if model_class in _MODEL_CLASSES_SUPPORT_QUANTIZATION:
+            model = model_class(model_config.hf_config, quant_config)
+        else:
+            model = model_class(model_config.hf_config)
         if model_config.load_format == "dummy":
             model = model.cuda()
             # NOTE(woosuk): For accurate performance evaluation, we assign

vllm/model_executor/models/__init__.py

@@ -12,17 +12,13 @@ from vllm.model_executor.models.llama import LlamaForCausalLM
 from vllm.model_executor.models.mistral import MistralForCausalLM
 from vllm.model_executor.models.mpt import MPTForCausalLM
 from vllm.model_executor.models.opt import OPTForCausalLM
-from vllm.model_executor.models.phi_1_5 import PhiForCausalLM
 from vllm.model_executor.models.qwen import QWenLMHeadModel
-from vllm.model_executor.models.chatglm import ChatGLMForCausalLM
-from vllm.model_executor.models.yi import YiForCausalLM
 
 __all__ = [
     "AquilaForCausalLM",
     "BaiChuanForCausalLM",
     "BaichuanForCausalLM",
     "BloomForCausalLM",
-    "ChatGLMForCausalLM",
     "FalconForCausalLM",
     "GPT2LMHeadModel",
     "GPTBigCodeForCausalLM",
@@ -32,8 +28,6 @@ __all__ = [
     "LlamaForCausalLM",
     "MPTForCausalLM",
     "OPTForCausalLM",
-    "PhiForCausalLM",
     "QWenLMHeadModel",
     "MistralForCausalLM",
-    "YiForCausalLM",
 ]

vllm/model_executor/models/aquila.py

@@ -25,7 +25,7 @@
 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.
 """
-from typing import Any, Dict, List, Optional, Tuple
+from typing import List, Optional, Tuple
 
 import torch
 from torch import nn
@@ -33,17 +33,15 @@ from torch import nn
 from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.weight_utils import (
+    hf_model_weights_iterator, load_padded_tensor_parallel_vocab,
+    load_tensor_parallel_weights)
 from vllm.model_executor.parallel_utils.parallel_state import (
-    get_tensor_model_parallel_world_size)
-from vllm.model_executor.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.layers import (VocabParallelEmbedding,
+                                                       ColumnParallelLinear,
+                                                       RowParallelLinear)
 from vllm.sequence import SamplerOutput
 from vllm.transformers_utils.configs.aquila import AquilaConfig
 
@@ -57,17 +55,20 @@ class AquilaMLP(nn.Module):
         hidden_size: int,
         intermediate_size: int,
         hidden_act: str,
-        linear_method: Optional[LinearMethodBase] = None,
     ):
         super().__init__()
-        self.gate_up_proj = MergedColumnParallelLinear(
-            hidden_size, [intermediate_size] * 2,
+        self.gate_up_proj = ColumnParallelLinear(
+            hidden_size,
+            2 * intermediate_size,
             bias=False,
-            linear_method=linear_method)
-        self.down_proj = RowParallelLinear(intermediate_size,
-                                           hidden_size,
-                                           bias=False,
-                                           linear_method=linear_method)
+            gather_output=False,
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            input_is_parallel=True,
+        )
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
                              "Only silu is supported for now.")
@@ -109,8 +110,6 @@ class AquilaAttention(nn.Module):
         num_kv_heads: int,
         rope_theta: float = 10000,
         max_position_embeddings: int = 8192,
-        rope_scaling: Optional[Dict[str, Any]] = None,
-        linear_method: Optional[LinearMethodBase] = None,
     ):
         super().__init__()
         self.hidden_size = hidden_size
@@ -128,29 +127,28 @@ class AquilaAttention(nn.Module):
         self.rope_theta = rope_theta
         self.max_position_embeddings = max_position_embeddings
 
-        self.qkv_proj = QKVParallelLinear(
+        self.qkv_proj = ColumnParallelLinear(
             hidden_size,
+            (self.total_num_heads + 2 * self.total_num_kv_heads) *
             self.head_dim,
-            self.total_num_heads,
-            self.total_num_kv_heads,
             bias=False,
-            linear_method=linear_method,
+            gather_output=False,
         )
         self.o_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=False,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
         self.attn = PagedAttentionWithRoPE(
             self.num_heads,
             self.head_dim,
             self.scaling,
+            rotary_dim=self.head_dim,
             base=self.rope_theta,
             max_position=self.max_position_embeddings,
-            rotary_dim=self.head_dim,
             num_kv_heads=self.num_kv_heads,
-            rope_scaling=rope_scaling)
+        )
 
     def forward(
         self,
@@ -171,15 +169,10 @@ class AquilaAttention(nn.Module):
 
 class AquilaDecoderLayer(nn.Module):
 
-    def __init__(
-        self,
-        config: AquilaConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: AquilaConfig):
         super().__init__()
         self.hidden_size = config.hidden_size
         rope_theta = getattr(config, "rope_theta", 10000)
-        rope_scaling = getattr(config, "rope_scaling", None)
         max_position_embeddings = getattr(config, "max_position_embeddings",
                                           8192)
         self.self_attn = AquilaAttention(
@@ -188,14 +181,11 @@ class AquilaDecoderLayer(nn.Module):
             num_kv_heads=config.num_key_value_heads,
             rope_theta=rope_theta,
             max_position_embeddings=max_position_embeddings,
-            rope_scaling=rope_scaling,
-            linear_method=linear_method,
         )
         self.mlp = AquilaMLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,
             hidden_act=config.hidden_act,
-            linear_method=linear_method,
         )
         self.input_layernorm = AquilaRMSNorm(config.hidden_size,
                                              eps=config.rms_norm_eps)
@@ -232,22 +222,19 @@ class AquilaDecoderLayer(nn.Module):
 
 class AquilaModel(nn.Module):
 
-    def __init__(
-        self,
-        config: AquilaConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: AquilaConfig):
         super().__init__()
         self.config = config
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
+
+        #vocab_size = ((config.vocab_size + 63) // 64) * 64
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
         )
         self.layers = nn.ModuleList([
-            AquilaDecoderLayer(config, linear_method)
-            for _ in range(config.num_hidden_layers)
+            AquilaDecoderLayer(config) for _ in range(config.num_hidden_layers)
         ])
         self.norm = AquilaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
@@ -280,16 +267,17 @@ class AquilaModel(nn.Module):
 
 class AquilaForCausalLM(nn.Module):
 
-    def __init__(
-        self,
-        config,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = AquilaModel(config, linear_method)
-        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.model = AquilaModel(config)
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.lm_head = ColumnParallelLinear(
+            config.hidden_size,
+            vocab_size,
+            bias=False,
+            gather_output=False,
+        )
         self.sampler = Sampler(config.vocab_size)
 
     def forward(
@@ -306,33 +294,79 @@ class AquilaForCausalLM(nn.Module):
                                    input_metadata)
         return next_tokens
 
+    _column_parallel_weights = [
+        "qkv_proj.weight", "gate_proj.weight", "up_proj.weight"
+    ]
+    _row_parallel_weights = ["o_proj.weight", "down_proj.weight"]
+
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      load_format: str = "auto",
                      revision: Optional[str] = None):
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("qkv_proj", "q_proj", "q"),
-            ("qkv_proj", "k_proj", "k"),
-            ("qkv_proj", "v_proj", "v"),
-            ("gate_up_proj", "gate_proj", 0),
-            ("gate_up_proj", "up_proj", 1),
+        tp_size = get_tensor_model_parallel_world_size()
+        tensor_model_parallel_rank = get_tensor_model_parallel_rank()
+        q_proj_shard_size = (self.config.hidden_size // tp_size)
+        kv_proj_shard_size = (self.config.hidden_size //
+                              self.config.num_attention_heads *
+                              self.config.num_key_value_heads // tp_size)
+        attention_weight_specs = [
+            # (weight_name, shard_size, offset)
+            ("q_proj", q_proj_shard_size, 0),
+            ("k_proj", kv_proj_shard_size, q_proj_shard_size),
+            ("v_proj", kv_proj_shard_size,
+             q_proj_shard_size + kv_proj_shard_size),
         ]
-        params_dict = dict(self.named_parameters())
+        state_dict = self.state_dict()
+
         for name, loaded_weight in hf_model_weights_iterator(
                 model_name_or_path, cache_dir, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
-            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+
+            is_attention_weight = False
+            for weight_name, shard_size, offset in attention_weight_specs:
                 if weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
+                param = state_dict[name.replace(weight_name, "qkv_proj")]
+
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[offset:offset + shard_size]
+                assert param_slice.shape == loaded_weight.shape
+
+                param_slice.copy_(loaded_weight)
+                is_attention_weight = True
                 break
-            else:
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader",
-                                        default_weight_loader)
-                weight_loader(param, loaded_weight)
+            if is_attention_weight:
+                continue
+
+            is_gate_up_weight = False
+            for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]):
+                if weight_name not in name:
+                    continue
+                param = state_dict[name.replace(weight_name, "gate_up_proj")]
+                shard_size = param.shape[0] // 2
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_gate_up_weight = True
+                break
+            if is_gate_up_weight:
+                continue
+
+            param = state_dict[name]
+            if "embed_tokens" in name or "lm_head" in name:
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tensor_model_parallel_rank)
+                continue
+
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         self._column_parallel_weights,
+                                         self._row_parallel_weights,
+                                         tensor_model_parallel_rank)

vllm/model_executor/models/baichuan.py

@@ -30,20 +30,18 @@ from torch import nn
 
 from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.attention import (PagedAttentionWithRoPE,
                                                   PagedAttentionWithALiBi)
-from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.weight_utils import (
+    convert_pyslice_to_tensor, hf_model_weights_iterator,
+    load_padded_tensor_parallel_vocab, 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.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
+from vllm.model_executor.parallel_utils.layers import (VocabParallelEmbedding,
+                                                       ColumnParallelLinear,
+                                                       RowParallelLinear)
 from vllm.sequence import SamplerOutput
 from vllm.transformers_utils.configs.baichuan import BaiChuanConfig
 
@@ -82,17 +80,20 @@ class BaiChuanMLP(nn.Module):
         hidden_size: int,
         intermediate_size: int,
         hidden_act: str,
-        linear_method: Optional[LinearMethodBase] = None,
     ):
         super().__init__()
-        self.gate_up_proj = MergedColumnParallelLinear(
-            hidden_size, [intermediate_size] * 2,
+        self.gate_up_proj = ColumnParallelLinear(
+            hidden_size,
+            2 * intermediate_size,
             bias=False,
-            linear_method=linear_method)
-        self.down_proj = RowParallelLinear(intermediate_size,
-                                           hidden_size,
-                                           bias=False,
-                                           linear_method=linear_method)
+            gather_output=False,
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            input_is_parallel=True,
+        )
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
                              "Only silu is supported for now.")
@@ -115,7 +116,6 @@ class BaiChuanAttention(nn.Module):
         position_embedding: str,
         rope_theta: float = 10000,
         max_position_embeddings: int = 8192,
-        linear_method: Optional[LinearMethodBase] = None,
     ):
         super().__init__()
         self.hidden_size = hidden_size
@@ -131,19 +131,17 @@ class BaiChuanAttention(nn.Module):
         self.max_position_embeddings = max_position_embeddings
 
         # pylint: disable=invalid-name
-        self.W_pack = QKVParallelLinear(
+        self.W_pack = ColumnParallelLinear(
             hidden_size,
-            self.head_dim,
-            self.total_num_heads,
-            self.total_num_heads,
+            3 * hidden_size,
             bias=False,
-            linear_method=linear_method,
+            gather_output=False,
         )
         self.o_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=False,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
         # Create the alibi slopes and slice them.
         if self.postion_embedding == "ALIBI":
@@ -190,10 +188,7 @@ class BaiChuanAttention(nn.Module):
 
 class BaiChuanDecoderLayer(nn.Module):
 
-    def __init__(self,
-                 config: BaiChuanConfig,
-                 position_embedding: str,
-                 linear_method: Optional[LinearMethodBase] = None):
+    def __init__(self, config: BaiChuanConfig, position_embedding: str):
         super().__init__()
         self.hidden_size = config.hidden_size
         rope_theta = getattr(config, "rope_theta", 10000)
@@ -205,13 +200,11 @@ class BaiChuanDecoderLayer(nn.Module):
             position_embedding=position_embedding,
             rope_theta=rope_theta,
             max_position_embeddings=max_position_embeddings,
-            linear_method=linear_method,
         )
         self.mlp = BaiChuanMLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,
             hidden_act=config.hidden_act,
-            linear_method=linear_method,
         )
         self.input_layernorm = RMSNorm(config.hidden_size,
                                        eps=config.rms_norm_eps)
@@ -225,15 +218,10 @@ class BaiChuanDecoderLayer(nn.Module):
         kv_cache: KVCache,
         input_metadata: InputMetadata,
         cache_event: Optional[torch.cuda.Event],
-        residual: Optional[torch.Tensor],
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+    ) -> torch.Tensor:
         # Self Attention
-        if residual is None:
-            residual = hidden_states
-            hidden_states = self.input_layernorm(hidden_states)
-        else:
-            hidden_states, residual = self.input_layernorm(
-                hidden_states, residual)
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
         hidden_states = self.self_attn(
             positions=positions,
             hidden_states=hidden_states,
@@ -241,20 +229,19 @@ class BaiChuanDecoderLayer(nn.Module):
             input_metadata=input_metadata,
             cache_event=cache_event,
         )
+        hidden_states = residual + hidden_states
 
         # Fully Connected
-        hidden_states, residual = self.post_attention_layernorm(
-            hidden_states, residual)
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
         hidden_states = self.mlp(hidden_states)
-        return hidden_states, residual
+        hidden_states = residual + hidden_states
+        return hidden_states
 
 
 class BaiChuanModel(nn.Module):
 
-    def __init__(self,
-                 config: BaiChuanConfig,
-                 position_embedding: str,
-                 linear_method: Optional[LinearMethodBase] = None):
+    def __init__(self, config: BaiChuanConfig, position_embedding: str):
         super().__init__()
         self.config = config
         self.padding_idx = config.pad_token_id
@@ -265,7 +252,7 @@ class BaiChuanModel(nn.Module):
             config.hidden_size,
         )
         self.layers = nn.ModuleList([
-            BaiChuanDecoderLayer(config, position_embedding, linear_method)
+            BaiChuanDecoderLayer(config, position_embedding)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -279,36 +266,35 @@ class BaiChuanModel(nn.Module):
         cache_events: Optional[List[torch.cuda.Event]],
     ) -> torch.Tensor:
         hidden_states = self.embed_tokens(input_ids)
-        residual = None
         for i in range(len(self.layers)):
             if cache_events is None:
                 cache_event = None
             else:
                 cache_event = cache_events[i]
             layer = self.layers[i]
-            hidden_states, residual = layer(
+            hidden_states = layer(
                 positions,
                 hidden_states,
                 kv_caches[i],
                 input_metadata,
                 cache_event,
-                residual,
             )
-        hidden_states, _ = self.norm(hidden_states, residual)
+        hidden_states = self.norm(hidden_states)
         return hidden_states
 
 
 class BaiChuanBaseForCausalLM(nn.Module):
 
-    def __init__(self,
-                 config,
-                 position_embedding: str,
-                 linear_method: Optional[LinearMethodBase] = None):
+    def __init__(self, config, position_embedding: str):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = BaiChuanModel(config, position_embedding, linear_method)
-        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.model = BaiChuanModel(config, position_embedding)
+        self.lm_head = ColumnParallelLinear(
+            config.hidden_size,
+            config.vocab_size,
+            bias=False,
+            gather_output=False,
+        )
         self.sampler = Sampler(config.vocab_size)
 
     def forward(
@@ -325,46 +311,79 @@ class BaiChuanBaseForCausalLM(nn.Module):
                                    input_metadata)
         return next_tokens
 
+    _column_parallel_weights = []
+    _row_parallel_weights = ["o_proj.weight", "down_proj.weight"]
+
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      load_format: str = "auto",
                      revision: Optional[str] = None):
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("gate_up_proj", "gate_proj", 0),
-            ("gate_up_proj", "up_proj", 1),
-        ]
-        params_dict = dict(self.named_parameters())
+        tp_world_size = get_tensor_model_parallel_world_size()
+        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, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
-            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+
+            loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+
+            if "W_pack" in name:
+                total_num_heads = self.config.num_attention_heads
+                hidden_size = self.config.hidden_size
+                head_size = hidden_size // total_num_heads
+                num_heads = total_num_heads // tp_world_size
+                head_start = tp_rank * num_heads
+                head_end = (tp_rank + 1) * num_heads
+
+                loaded_weight = loaded_weight.view(3, total_num_heads,
+                                                   head_size, hidden_size)
+                loaded_weight = loaded_weight[:, head_start:head_end, :, :]
+                loaded_weight = loaded_weight.reshape(-1, hidden_size)
+
+            is_gate_up_weight = False
+            for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]):
                 if weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
+                param = state_dict[name.replace(weight_name, "gate_up_proj")]
+                shard_size = param.shape[0] // 2
+                loaded_weight = loaded_weight[shard_size * tp_rank:shard_size *
+                                              (tp_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_gate_up_weight = True
                 break
-            else:
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader",
-                                        default_weight_loader)
-                weight_loader(param, loaded_weight)
+            if is_gate_up_weight:
+                continue
+
+            param = state_dict[name]
+
+            if "embed_tokens" in name or "lm_head" in name:
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tp_rank)
+                continue
+
+            load_tensor_parallel_weights(
+                param,
+                loaded_weight,
+                name,
+                self._column_parallel_weights,
+                self._row_parallel_weights,
+                tp_rank,
+            )
 
 
 class BaichuanForCausalLM(BaiChuanBaseForCausalLM):  # baichuan 13b
 
-    def __init__(self,
-                 config,
-                 linear_method: Optional[LinearMethodBase] = None):
-        super().__init__(config, "ALIBI", linear_method)
+    def __init__(self, config):
+        super().__init__(config, "ALIBI")
 
 
 class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):  # baichuan 7b
 
-    def __init__(self,
-                 config,
-                 linear_method: Optional[LinearMethodBase] = None):
-        super().__init__(config, "ROPE", linear_method)
+    def __init__(self, config):
+        super().__init__(config, "ROPE")

vllm/model_executor/models/bloom.py

@@ -30,17 +30,14 @@ 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.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding)
+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.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
+from vllm.model_executor.parallel_utils.layers import (VocabParallelEmbedding,
+                                                       ColumnParallelLinear,
+                                                       RowParallelLinear)
 from vllm.sequence import SamplerOutput
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
@@ -73,11 +70,7 @@ def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
 
 class BloomAttention(nn.Module):
 
-    def __init__(
-        self,
-        config: BloomConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: BloomConfig):
         super().__init__()
         self.hidden_size = config.hidden_size
         self.total_num_heads = config.n_head
@@ -88,18 +81,17 @@ class BloomAttention(nn.Module):
         assert self.total_num_heads % tp_world_size == 0
         self.num_heads = self.total_num_heads // tp_world_size
 
-        self.query_key_value = QKVParallelLinear(
+        self.query_key_value = ColumnParallelLinear(
             self.hidden_size,
-            self.head_dim,
-            self.total_num_heads,
+            3 * self.hidden_size,
             bias=True,
-            linear_method=linear_method,
+            gather_output=False,
         )
         self.dense = RowParallelLinear(
             self.hidden_size,
             self.hidden_size,
             bias=True,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
 
         # Create the alibi slopes and slice them.
@@ -133,49 +125,40 @@ class BloomAttention(nn.Module):
 
 class BloomMLP(nn.Module):
 
-    def __init__(
-        self,
-        config: BloomConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: BloomConfig):
         super().__init__()
         hidden_size = config.hidden_size
         self.dense_h_to_4h = ColumnParallelLinear(
             hidden_size,
             4 * hidden_size,
-            linear_method=linear_method,
+            gather_output=False,
         )
-        quant_config = getattr(linear_method, "quant_config", None)
-        self.gelu_impl = get_act_fn("gelu", quant_config, 4 * hidden_size)
+        self.act = get_act_fn("gelu")
         self.dense_4h_to_h = RowParallelLinear(
             4 * hidden_size,
             hidden_size,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         x, _ = self.dense_h_to_4h(x)
-        x = self.gelu_impl(x)
+        x = self.act(x)
         x, _ = self.dense_4h_to_h(x)
         return x
 
 
 class BloomBlock(nn.Module):
 
-    def __init__(
-        self,
-        config: BloomConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    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, linear_method)
+        self.self_attention = BloomAttention(config)
         self.post_attention_layernorm = nn.LayerNorm(
             hidden_size, eps=config.layer_norm_epsilon)
-        self.mlp = BloomMLP(config, linear_method)
+        self.mlp = BloomMLP(config)
         self.apply_residual_connection_post_layernorm = (
             config.apply_residual_connection_post_layernorm)
 
@@ -220,11 +203,7 @@ class BloomBlock(nn.Module):
 
 class BloomModel(nn.Module):
 
-    def __init__(
-        self,
-        config: BloomConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: BloomConfig):
         super().__init__()
         self.embed_dim = config.hidden_size
 
@@ -237,10 +216,8 @@ class BloomModel(nn.Module):
             self.embed_dim, eps=config.layer_norm_epsilon)
 
         # Transformer blocks
-        self.h = nn.ModuleList([
-            BloomBlock(config, linear_method)
-            for _ in range(config.num_hidden_layers)
-        ])
+        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)
@@ -274,15 +251,12 @@ class BloomModel(nn.Module):
 
 class BloomForCausalLM(nn.Module):
 
-    def __init__(
-        self,
-        config: BloomConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: BloomConfig):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.transformer = BloomModel(config, linear_method)
+        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)
 
@@ -300,36 +274,55 @@ class BloomForCausalLM(nn.Module):
                                    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,
                      load_format: str = "auto",
                      revision: Optional[str] = None):
-        params_dict = dict(self.named_parameters(remove_duplicate=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, load_format, revision):
             if name == "lm_head.weight":
-                continue
-            if not name.startswith("transformer."):
-                name = "transformer." + name
-            param = params_dict[name]
+                # Since hidden_states are parallelized, we need to
+                # load lm_head.weight in parallel.
+                self._column_parallel_weights.append(name)
+                # If lm_head is provided, use it instead.
+                param = self.lm_head_weight
+            else:
+                if not name.startswith("transformer."):
+                    name = "transformer." + name
+                param = state_dict[name]
 
             if "query_key_value" in name:
-                # NOTE: BLOOM's fused QKV's output_dim has the shape of
-                # (num_heads * 3 * head_size), while the
-                # required shape is (3 * num_heads * head_size).
+                # 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.
-                output_dim = getattr(param, "output_dim", None)
-                num_heads = self.config.num_attention_heads
-                if output_dim is not None:
-                    loaded_weight_shape = loaded_weight.shape
-                    loaded_weight = loaded_weight.view(
-                        loaded_weight_shape[:output_dim] + (num_heads, 3, -1) +
-                        loaded_weight_shape[output_dim + 1:])
-                    loaded_weight = loaded_weight.transpose(
-                        output_dim, output_dim + 1)
-                    loaded_weight = loaded_weight.reshape(loaded_weight_shape)
+                shard_size = param.shape[0]
+                start = shard_size * tp_rank
+                end = shard_size * (tp_rank + 1)
+                loaded_weight = loaded_weight[start:end]
 
-            weight_loader = getattr(param, "weight_loader",
-                                    default_weight_loader)
-            weight_loader(param, loaded_weight)
+                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/chatglm.py

@@ -1,376 +0,0 @@
-# coding=utf-8
-# Adapted from
-# https://github.com/THUDM/ChatGLM2-6B
-"""Inference-only ChatGLM model compatible with THUDM 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.
-"""
-from typing import List, Optional, Tuple
-
-import torch
-from torch import nn
-from torch.nn import LayerNorm
-
-from vllm.model_executor.input_metadata import InputMetadata
-from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
-from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
-from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding, ParallelLMHead)
-from vllm.model_executor.parallel_utils.parallel_state import (
-    get_tensor_model_parallel_world_size)
-from vllm.model_executor.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
-from vllm.sequence import SamplerOutput
-from vllm.transformers_utils.configs import ChatGLMConfig
-
-KVCache = Tuple[torch.Tensor, torch.Tensor]
-
-
-class GLMAttention(nn.Module):
-
-    def __init__(
-        self,
-        config,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        tp_size = get_tensor_model_parallel_world_size()
-        self.total_num_heads = config.num_attention_heads
-        assert self.total_num_heads % tp_size == 0
-        self.num_heads = self.total_num_heads // tp_size
-        self.multi_query_attention = config.multi_query_attention
-        self.total_num_kv_heads = (config.multi_query_group_num
-                                   if config.multi_query_attention else
-                                   config.num_attention_heads)
-        if self.total_num_kv_heads >= tp_size:
-            # Number of KV heads is greater than TP size, so we partition
-            # the KV heads across multiple tensor parallel GPUs.
-            assert self.total_num_kv_heads % tp_size == 0
-        else:
-            # Number of KV heads is less than TP size, so we replicate
-            # the KV heads across multiple tensor parallel GPUs.
-            assert tp_size % self.total_num_kv_heads == 0
-        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
-        self.head_dim = config.hidden_size // self.total_num_heads
-        self.q_size = self.num_heads * self.head_dim
-        self.kv_size = self.num_kv_heads * self.head_dim
-        self.scaling = self.head_dim**-0.5
-
-        self.query_key_value = QKVParallelLinear(
-            self.hidden_size,
-            self.head_dim,
-            self.total_num_heads,
-            self.total_num_kv_heads,
-            bias=config.add_bias_linear or config.add_qkv_bias,
-            linear_method=linear_method,
-        )
-        self.dense = RowParallelLinear(
-            self.total_num_heads * self.head_dim,
-            config.hidden_size,
-            bias=config.add_bias_linear,
-            linear_method=linear_method,
-        )
-
-        self.attn = PagedAttentionWithRoPE(
-            self.num_heads,
-            self.head_dim,
-            self.scaling,
-            rotary_dim=self.head_dim // 2,
-            num_kv_heads=self.num_kv_heads,
-            is_neox_style=False,
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_ids: torch.Tensor,
-        kv_cache: KVCache,
-        input_metadata: InputMetadata,
-        cache_event: Optional[torch.cuda.Event],
-    ) -> torch.Tensor:
-        qkv, _ = self.query_key_value(hidden_states)
-        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
-        key_cache, value_cache = kv_cache
-
-        context_layer = self.attn(
-            position_ids,
-            q,
-            k,
-            v,
-            key_cache,
-            value_cache,
-            input_metadata,
-            cache_event,
-        )
-
-        attn_output, _ = self.dense(context_layer)
-
-        return attn_output
-
-
-class GLMMLP(nn.Module):
-    """MLP.
-
-    MLP will take the input with h hidden state, project it to 4*h
-    hidden dimension, perform nonlinear transformation, and project the
-    state back into h hidden dimension.
-    """
-
-    def __init__(
-        self,
-        config,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
-        super().__init__()
-
-        self.add_bias = config.add_bias_linear
-
-        # Project to 4h.
-        self.dense_h_to_4h = MergedColumnParallelLinear(
-            config.hidden_size,
-            [config.ffn_hidden_size] * 2,
-            bias=config.add_bias_linear,
-            linear_method=linear_method,
-        )
-
-        self.activation_func = SiluAndMul()
-
-        # Project back to h.
-        self.dense_4h_to_h = RowParallelLinear(
-            config.ffn_hidden_size,
-            config.hidden_size,
-            bias=config.add_bias_linear,
-            linear_method=linear_method,
-        )
-
-    def forward(self, hidden_states):
-        # [s, b, 4hp]
-        intermediate_parallel, _ = self.dense_h_to_4h(hidden_states)
-        intermediate_parallel = self.activation_func(intermediate_parallel)
-        # [s, b, h]
-        output, _ = self.dense_4h_to_h(intermediate_parallel)
-        return output
-
-
-class GLMBlock(nn.Module):
-    """A single transformer layer.
-
-    Transformer layer takes input with size [s, b, h] and returns an
-    output of the same size.
-    """
-
-    def __init__(
-        self,
-        config,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
-        super().__init__()
-        self.apply_residual_connection_post_layernorm = (
-            config.apply_residual_connection_post_layernorm)
-
-        self.fp32_residual_connection = config.fp32_residual_connection
-
-        layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
-        # Layernorm on the input data.
-        self.input_layernorm = layer_norm_func(config.hidden_size,
-                                               eps=config.layernorm_epsilon)
-
-        # Self attention.
-        self.self_attention = GLMAttention(config, linear_method)
-        self.hidden_dropout = config.hidden_dropout
-
-        # Layernorm on the attention output
-        self.post_attention_layernorm = layer_norm_func(
-            config.hidden_size, eps=config.layernorm_epsilon)
-
-        # MLP
-        self.mlp = GLMMLP(config, linear_method)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_ids: torch.Tensor,
-        kv_cache: KVCache,
-        input_metadata: InputMetadata,
-        cache_event: Optional[torch.cuda.Event],
-    ) -> torch.Tensor:
-        # hidden_states: [num_tokens, h]
-        # Layer norm at the beginning of the transformer layer.
-        layernorm_output = self.input_layernorm(hidden_states)
-        # Self attention.
-        attention_output = self.self_attention(
-            hidden_states=layernorm_output,
-            position_ids=position_ids,
-            kv_cache=kv_cache,
-            input_metadata=input_metadata,
-            cache_event=cache_event,
-        )
-
-        # Residual connection.
-        if self.apply_residual_connection_post_layernorm:
-            residual = layernorm_output
-        else:
-            residual = hidden_states
-
-        layernorm_input = residual + attention_output
-
-        # Layer norm post the self attention.
-        layernorm_output = self.post_attention_layernorm(layernorm_input)
-
-        # Second residual connection.
-        if self.apply_residual_connection_post_layernorm:
-            residual = layernorm_output
-        else:
-            residual = layernorm_input
-
-        output = self.mlp(layernorm_output) + residual
-
-        return output
-
-
-class GLMTransformer(nn.Module):
-    """Transformer class."""
-
-    def __init__(
-        self,
-        config,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
-        super().__init__()
-        self.post_layer_norm = config.post_layer_norm
-
-        # Number of layers.
-        self.num_layers = config.num_layers
-
-        # Transformer layers.
-        self.layers = nn.ModuleList(
-            [GLMBlock(config, linear_method) for i in range(self.num_layers)])
-
-        if self.post_layer_norm:
-            layer_norm_func = RMSNorm if config.rmsnorm else LayerNorm
-            # Final layer norm before output.
-            self.final_layernorm = layer_norm_func(
-                config.hidden_size, eps=config.layernorm_epsilon)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        position_ids: torch.Tensor,
-        kv_caches: List[KVCache],
-        input_metadata: InputMetadata,
-        cache_events: Optional[List[torch.cuda.Event]],
-    ) -> torch.Tensor:
-        for i in range(self.num_layers):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
-            layer = self.layers[i]
-            hidden_states = layer(
-                hidden_states=hidden_states,
-                position_ids=position_ids,
-                kv_cache=kv_caches[i],
-                input_metadata=input_metadata,
-                cache_event=cache_event,
-            )
-        # Final layer norm.
-        if self.post_layer_norm:
-            hidden_states = self.final_layernorm(hidden_states)
-
-        return hidden_states
-
-
-class ChatGLMModel(nn.Module):
-
-    def __init__(
-        self,
-        config,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
-        super().__init__()
-
-        self.embedding = VocabParallelEmbedding(config.padded_vocab_size,
-                                                config.hidden_size)
-
-        self.num_layers = config.num_layers
-        self.multi_query_group_num = config.multi_query_group_num
-        self.kv_channels = config.kv_channels
-        self.encoder = GLMTransformer(config, linear_method)
-
-        self.output_layer = ParallelLMHead(config.padded_vocab_size,
-                                           config.hidden_size)
-
-    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]],
-    ):
-        inputs_embeds = self.embedding(input_ids)
-
-        # Run encoder.
-        hidden_states = self.encoder(
-            hidden_states=inputs_embeds,
-            position_ids=position_ids,
-            kv_caches=kv_caches,
-            input_metadata=input_metadata,
-            cache_events=cache_events,
-        )
-
-        return hidden_states
-
-
-class ChatGLMForCausalLM(nn.Module):
-
-    def __init__(
-        self,
-        config: ChatGLMConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
-        super().__init__()
-        self.config: ChatGLMConfig = config
-        self.linear_method = linear_method
-        self.transformer = ChatGLMModel(config, linear_method)
-        self.lm_head_weight = self.transformer.output_layer.weight
-        self.sampler = Sampler(config.padded_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]],
-    ) -> SamplerOutput:
-        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
-
-    def load_weights(self,
-                     model_name_or_path: str,
-                     cache_dir: Optional[str] = None,
-                     load_format: str = "auto",
-                     revision: Optional[str] = None):
-        params_dict = dict(self.named_parameters(remove_duplicate=False))
-        for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format, revision):
-            if "rotary_pos_emb.inv_freq" in name:
-                continue
-            if "word_embeddings" in name:
-                name = name.replace(".word_embeddings", "")
-            param = params_dict[name]
-            weight_loader = getattr(param, "weight_loader",
-                                    default_weight_loader)
-            weight_loader(param, loaded_weight)

vllm/model_executor/models/falcon.py

@@ -27,23 +27,20 @@ from torch.nn import LayerNorm
 from transformers import FalconConfig as HF_FalconConfig
 
 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 (PagedAttention,
                                                   PagedAttentionWithALiBi,
                                                   PagedAttentionWithRoPE)
-from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding, ParallelLMHead)
-from vllm.model_executor.parallel_utils.communication_op import (
-    tensor_model_parallel_all_reduce)
+from vllm.model_executor.weight_utils import (convert_pyslice_to_tensor,
+                                              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.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
+from vllm.model_executor.parallel_utils.layers import (VocabParallelEmbedding,
+                                                       ColumnParallelLinear,
+                                                       RowParallelLinear)
+from vllm.model_executor.parallel_utils.communication_op import (
+    tensor_model_parallel_all_reduce)
 from vllm.sequence import SamplerOutput
 from vllm.transformers_utils.configs import RWConfig
 
@@ -51,6 +48,19 @@ KVCache = Tuple[torch.Tensor, torch.Tensor]
 FalconConfig = Union[HF_FalconConfig, RWConfig]
 
 
+# NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during
+# training, this means that there's one additional quantization to bfloat16
+# between the operations. In order not to degrade the quality of our HF-port,
+# we keep these characteristics in the final model.
+class FalconLinear(nn.Linear):
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        hidden_states = x @ self.weight.T
+        if self.bias is None:
+            return hidden_states
+        return hidden_states + self.bias
+
+
 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))),
@@ -76,11 +86,7 @@ def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
 
 class FalconAttention(nn.Module):
 
-    def __init__(
-        self,
-        config: FalconConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: FalconConfig):
         super().__init__()
 
         self.hidden_size = config.hidden_size
@@ -97,29 +103,41 @@ class FalconAttention(nn.Module):
 
         if self.new_decoder_architecture:
             self.total_num_kv_heads = config.num_kv_heads
+            assert self.total_num_heads % tp_size == 0
+            self.num_kv_heads = self.total_num_kv_heads // tp_size
+            self.query_key_value = ColumnParallelLinear(
+                self.hidden_size,
+                (self.total_num_heads + 2 * self.total_num_kv_heads) *
+                self.head_dim,
+                bias=config.bias,
+                gather_output=False,
+                skip_bias_add=True,
+            )
         elif self.multi_query:
             self.total_num_kv_heads = 1
+            self.num_kv_heads = 1
+            self.query = ColumnParallelLinear(
+                self.hidden_size,
+                self.total_num_heads * self.head_dim,
+                bias=config.bias,
+                gather_output=False,
+                skip_bias_add=True,
+            )
+            self.key_value = FalconLinear(self.hidden_size,
+                                          2 * self.head_dim,
+                                          bias=config.bias)
         else:
             self.total_num_kv_heads = self.total_num_heads
-        if self.total_num_kv_heads >= tp_size:
-            # Number of KV heads is greater than TP size, so we partition
-            # the KV heads across multiple tensor parallel GPUs.
-            assert self.total_num_kv_heads % tp_size == 0
-        else:
-            # Number of KV heads is less than TP size, so we replicate
-            # the KV heads across multiple tensor parallel GPUs.
-            assert tp_size % self.total_num_kv_heads == 0
-        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+            self.num_kv_heads = self.num_heads
+            self.query_key_value = ColumnParallelLinear(
+                self.hidden_size,
+                (self.total_num_heads + 2 * self.total_num_kv_heads) *
+                self.head_dim,
+                bias=config.bias,
+                gather_output=False,
+                skip_bias_add=True,
+            )
 
-        self.query_key_value = QKVParallelLinear(
-            self.hidden_size,
-            self.head_dim,
-            self.total_num_heads,
-            self.total_num_kv_heads,
-            bias=config.bias,
-            skip_bias_add=True,
-            linear_method=linear_method,
-        )
         self.q_size = self.num_heads * self.head_dim
         self.kv_size = self.num_kv_heads * self.head_dim
 
@@ -131,8 +149,8 @@ class FalconAttention(nn.Module):
             self.hidden_size,
             self.hidden_size,
             bias=config.bias,
+            input_is_parallel=True,
             skip_bias_add=True,
-            linear_method=linear_method,
             reduce_results=self.reduce_row_parallel_results)
 
         self.use_rotary = config.rotary
@@ -178,10 +196,18 @@ class FalconAttention(nn.Module):
         input_metadata: InputMetadata,
         cache_event: Optional[torch.cuda.Event],
     ) -> torch.Tensor:
-        qkv, bias = self.query_key_value(hidden_states)
-        if bias is not None:
-            qkv += bias
-        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        if not self.new_decoder_architecture and self.multi_query:
+            q, bias = self.query(hidden_states)
+            if bias is not None:
+                q += bias
+            kv = self.key_value(hidden_states)
+            k, v = kv.split([self.kv_size, self.kv_size], dim=-1)
+        else:
+            qkv, bias = self.query_key_value(hidden_states)
+            if bias is not None:
+                qkv += bias
+            q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size],
+                                dim=-1)
         k_cache, v_cache = kv_cache
         if self.use_rotary:
             attn_output = self.attn(positions, q, k, v, k_cache, v_cache,
@@ -195,30 +221,25 @@ class FalconAttention(nn.Module):
 
 class FalconMLP(nn.Module):
 
-    def __init__(
-        self,
-        config: FalconConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: FalconConfig):
         super().__init__()
         hidden_size = config.hidden_size
 
         self.dense_h_to_4h = ColumnParallelLinear(hidden_size,
                                                   4 * hidden_size,
                                                   bias=config.bias,
-                                                  skip_bias_add=True,
-                                                  linear_method=linear_method)
-        quant_config = getattr(linear_method, "quant_config", None)
-        self.act = get_act_fn("gelu", quant_config, 4 * hidden_size)
+                                                  gather_output=False,
+                                                  skip_bias_add=True)
+        self.act = nn.GELU()
         self.reduce_row_parallel_results = not (config.new_decoder_architecture
                                                 or config.parallel_attn)
         self.dense_4h_to_h = RowParallelLinear(
             4 * hidden_size,
             hidden_size,
             bias=config.bias,
+            input_is_parallel=True,
             skip_bias_add=True,
-            reduce_results=self.reduce_row_parallel_results,
-            linear_method=linear_method)
+            reduce_results=self.reduce_row_parallel_results)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         # NOTE(zhuohan): Following huggingface, we do not fuse bias add here.
@@ -232,16 +253,12 @@ class FalconMLP(nn.Module):
 
 class FalconDecoderLayer(nn.Module):
 
-    def __init__(
-        self,
-        config: FalconConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: FalconConfig):
         super().__init__()
         hidden_size = config.hidden_size
         self.num_heads = config.num_attention_heads
-        self.self_attention = FalconAttention(config, linear_method)
-        self.mlp = FalconMLP(config, linear_method)
+        self.self_attention = FalconAttention(config)
+        self.mlp = FalconMLP(config)
         self.config = config
 
         if config.new_decoder_architecture:
@@ -317,11 +334,7 @@ class FalconDecoderLayer(nn.Module):
 
 class FalconModel(nn.Module):
 
-    def __init__(
-        self,
-        config: FalconConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: FalconConfig):
         super().__init__()
         self.config = config
         self.embed_dim = config.hidden_size
@@ -336,8 +349,7 @@ class FalconModel(nn.Module):
 
         # Transformer blocks
         self.h = nn.ModuleList([
-            FalconDecoderLayer(config, linear_method)
-            for _ in range(config.num_hidden_layers)
+            FalconDecoderLayer(config) for _ in range(config.num_hidden_layers)
         ])
 
         # Final Layer Norm
@@ -371,18 +383,15 @@ class FalconModel(nn.Module):
 
 class FalconForCausalLM(nn.Module):
 
-    def __init__(
-        self,
-        config: FalconConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: FalconConfig):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.transformer = FalconModel(config, linear_method)
-        self.lm_head = ParallelLMHead(
-            config.vocab_size,
+        self.transformer = FalconModel(config)
+        self.lm_head = ColumnParallelLinear(
             config.hidden_size,
+            config.vocab_size,
+            bias=False,
+            gather_output=False,
         )
         self.sampler = Sampler(config.vocab_size)
 
@@ -406,44 +415,89 @@ class FalconForCausalLM(nn.Module):
 
         return next_tokens
 
+    _column_parallel_weights = [
+        "word_embeddings.weight", "lm_head.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,
                      load_format: str = "auto",
                      revision: Optional[str] = None):
+        tp_size = (get_tensor_model_parallel_world_size())
+        tp_rank = get_tensor_model_parallel_rank()
+
+        hidden_size = self.config.hidden_size
         total_num_heads = self.config.num_attention_heads
+        num_heads = total_num_heads // tp_size
+        head_size = hidden_size // total_num_heads
+        head_start = tp_rank * num_heads
+        head_end = (tp_rank + 1) * num_heads
         if self.config.new_decoder_architecture:
             total_num_kv_heads = self.config.num_kv_heads
+            num_kv_heads = total_num_kv_heads // tp_size
+            separated_q_kv = False
+            kv_head_start = tp_rank * num_kv_heads
+            kv_head_end = (tp_rank + 1) * num_kv_heads
         elif self.config.multi_query:
             total_num_kv_heads = 1
+            num_kv_heads = 1
+            separated_q_kv = True
+            kv_head_start = 0
+            kv_head_end = 1
         else:
             total_num_kv_heads = total_num_heads
+            num_kv_heads = total_num_kv_heads // tp_size
+            separated_q_kv = False
+            kv_head_start = tp_rank * num_kv_heads
+            kv_head_end = (tp_rank + 1) * num_kv_heads
         num_query_heads_per_kv_head = total_num_heads // total_num_kv_heads
-        params_dict = dict(self.named_parameters())
+        state_dict = self.state_dict()
+
         for name, loaded_weight in hf_model_weights_iterator(
                 model_name_or_path, cache_dir, load_format, revision):
-            param = params_dict[name]
             if "query_key_value" in name:
-                output_dim = getattr(param, "output_dim", None)
-                loaded_weight_shape = loaded_weight.shape
+                loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+                loaded_weight_size = loaded_weight.size()
                 loaded_weight = loaded_weight.view(
-                    loaded_weight_shape[:output_dim] +
-                    (total_num_kv_heads, num_query_heads_per_kv_head + 2, -1) +
-                    loaded_weight_shape[output_dim + 1:])
-                wq = loaded_weight.narrow(
-                    output_dim + 1, 0, num_query_heads_per_kv_head).reshape(
-                        *loaded_weight_shape[:output_dim], -1,
-                        *loaded_weight_shape[output_dim + 1:])
-                wk = loaded_weight.narrow(
-                    output_dim + 1, num_query_heads_per_kv_head,
-                    1).reshape(*loaded_weight_shape[:output_dim], -1,
-                               *loaded_weight_shape[output_dim + 1:])
-                wv = loaded_weight.narrow(
-                    output_dim + 1, num_query_heads_per_kv_head + 1,
-                    1).reshape(*loaded_weight_shape[:output_dim], -1,
-                               *loaded_weight_shape[output_dim + 1:])
-                loaded_weight = torch.cat([wq, wk, wv], dim=output_dim)
+                    total_num_kv_heads, num_query_heads_per_kv_head + 2,
+                    head_size, *loaded_weight_size[1:])
 
-            weight_loader = getattr(param, "weight_loader",
-                                    default_weight_loader)
-            weight_loader(param, loaded_weight)
+                wq = loaded_weight[:, :-2].reshape(-1, *loaded_weight_size[1:])
+                wk = loaded_weight[:, [-2]].reshape(-1,
+                                                    *loaded_weight_size[1:])
+                wv = loaded_weight[:, [-1]].reshape(-1,
+                                                    *loaded_weight_size[1:])
+
+                wq = wq[head_size * head_start:head_size * head_end]
+                wk = wk[head_size * kv_head_start:head_size * kv_head_end]
+                wv = wv[head_size * kv_head_start:head_size * kv_head_end]
+
+                if separated_q_kv:
+                    loaded_weight_q = wq
+                    loaded_weight_kv = torch.cat([wk, wv], dim=0)
+                    q_weight_name = name.replace("query_key_value", "query")
+                    kv_weight_name = name.replace("query_key_value",
+                                                  "key_value")
+                    load_tensor_parallel_weights(state_dict[q_weight_name],
+                                                 loaded_weight_q,
+                                                 q_weight_name,
+                                                 self._column_parallel_weights,
+                                                 self._row_parallel_weights,
+                                                 tp_rank)
+                    load_tensor_parallel_weights(state_dict[kv_weight_name],
+                                                 loaded_weight_kv,
+                                                 kv_weight_name,
+                                                 self._column_parallel_weights,
+                                                 self._row_parallel_weights,
+                                                 tp_rank)
+                    continue
+                else:
+                    loaded_weight = torch.cat([wq, wk, wv], dim=0)
+
+            param = state_dict[name]
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         self._column_parallel_weights,
+                                         self._row_parallel_weights, tp_rank)

vllm/model_executor/models/gpt2.py

@@ -30,17 +30,15 @@ from transformers import GPT2Config
 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 PagedAttention
-from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding)
+from vllm.model_executor.weight_utils import (
+    convert_pyslice_to_tensor, hf_model_weights_iterator,
+    load_padded_tensor_parallel_vocab, load_tensor_parallel_weights)
 from vllm.model_executor.parallel_utils.parallel_state import (
-    get_tensor_model_parallel_world_size)
-from vllm.model_executor.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.layers import (VocabParallelEmbedding,
+                                                       ColumnParallelLinear,
+                                                       RowParallelLinear)
 from vllm.sequence import SamplerOutput
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
@@ -48,11 +46,7 @@ KVCache = Tuple[torch.Tensor, torch.Tensor]
 
 class GPT2Attention(nn.Module):
 
-    def __init__(
-        self,
-        config: GPT2Config,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: GPT2Config):
         super().__init__()
         self.hidden_size = config.hidden_size
         total_num_heads = config.num_attention_heads
@@ -63,18 +57,17 @@ class GPT2Attention(nn.Module):
         self.head_dim = self.hidden_size // total_num_heads
         self.scale = self.head_dim**-0.5
 
-        self.c_attn = QKVParallelLinear(
+        self.c_attn = ColumnParallelLinear(
             self.hidden_size,
-            self.head_dim,
-            total_num_heads,
+            3 * self.hidden_size,
             bias=True,
-            linear_method=linear_method,
+            gather_output=False,
         )
         self.c_proj = RowParallelLinear(
             self.hidden_size,
             self.hidden_size,
             bias=True,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
         self.attn = PagedAttention(self.num_heads,
                                    self.head_dim,
@@ -102,7 +95,6 @@ class GPT2MLP(nn.Module):
         self,
         intermediate_size: int,
         config: GPT2Config,
-        linear_method: Optional[LinearMethodBase] = None,
     ):
         super().__init__()
         hidden_size = config.hidden_size
@@ -110,17 +102,15 @@ class GPT2MLP(nn.Module):
             hidden_size,
             intermediate_size,
             bias=True,
-            linear_method=linear_method,
+            gather_output=False,
         )
         self.c_proj = RowParallelLinear(
             intermediate_size,
             hidden_size,
             bias=True,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
-        quant_config = getattr(linear_method, "quant_config", None)
-        self.act = get_act_fn(config.activation_function, quant_config,
-                              intermediate_size)
+        self.act = get_act_fn(config.activation_function)
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         hidden_states, _ = self.c_fc(hidden_states)
@@ -131,20 +121,16 @@ class GPT2MLP(nn.Module):
 
 class GPT2Block(nn.Module):
 
-    def __init__(
-        self,
-        config: GPT2Config,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: GPT2Config):
         super().__init__()
         hidden_size = config.hidden_size
         inner_dim = (config.n_inner if config.n_inner is not None else 4 *
                      hidden_size)
 
         self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        self.attn = GPT2Attention(config, linear_method)
+        self.attn = GPT2Attention(config)
         self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        self.mlp = GPT2MLP(inner_dim, config, linear_method)
+        self.mlp = GPT2MLP(inner_dim, config)
 
     def forward(
         self,
@@ -174,23 +160,24 @@ class GPT2Block(nn.Module):
 
 class GPT2Model(nn.Module):
 
-    def __init__(
-        self,
-        config: GPT2Config,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: GPT2Config):
         super().__init__()
         self.config = config
         assert not config.add_cross_attention
         assert not config.scale_attn_by_inverse_layer_idx
         assert not config.reorder_and_upcast_attn
         self.embed_dim = config.hidden_size
-        self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
+
+        # Optimization: While the vocab size of GPT-2 is 50257, we extend it
+        # to 50304 in order to make it divisible by 64.
+        # This improves performance since GPUs are faster if the dimension
+        # is divisible by 64. In addition, it allows us to shard the embedding
+        # layer across 2, 4, 8, or more GPUs.
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.wte = VocabParallelEmbedding(vocab_size, self.embed_dim)
         self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
-        self.h = nn.ModuleList([
-            GPT2Block(config, linear_method)
-            for _ in range(config.num_hidden_layers)
-        ])
+        self.h = nn.ModuleList(
+            [GPT2Block(config) for _ in range(config.num_hidden_layers)])
         self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
 
     def forward(
@@ -220,15 +207,12 @@ class GPT2Model(nn.Module):
 
 class GPT2LMHeadModel(nn.Module):
 
-    def __init__(
-        self,
-        config: GPT2Config,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: GPT2Config):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.transformer = GPT2Model(config, linear_method)
+        self.transformer = GPT2Model(config)
+        # TODO(zhuohan): create a new weight after implementing pipeline
+        #                parallelism
         self.lm_head_weight = self.transformer.wte.weight
         self.sampler = Sampler(config.vocab_size)
 
@@ -246,12 +230,19 @@ class GPT2LMHeadModel(nn.Module):
                                    input_metadata)
         return next_tokens
 
+    _column_parallel_weights = ["c_fc.weight", "c_fc.bias"]
+    _row_parallel_weights = ["c_proj.weight"]
+
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      load_format: str = "auto",
                      revision: Optional[str] = None):
-        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
+        tensor_model_parallel_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, load_format, revision):
             if "lm_head.weight" in name:
@@ -262,19 +253,53 @@ class GPT2LMHeadModel(nn.Module):
                 # Skip attention mask.
                 # NOTE: "c_attn.bias" should not be skipped.
                 continue
+
             if not name.startswith("transformer."):
                 name = "transformer." + name
-            param = params_dict[name]
+
+            loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+
             # The HF's GPT-2 implementation uses Conv1D instead of Linear.
             # Because of this, we need to transpose the weights.
-            # Note(zhuohan): the logic below might break quantized models.
             for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]:
                 if conv1d_weight_name not in name:
                     continue
                 if not name.endswith(".weight"):
                     continue
                 loaded_weight = loaded_weight.t()
+            param = state_dict[name]
 
-            weight_loader = getattr(param, "weight_loader",
-                                    default_weight_loader)
-            weight_loader(param, loaded_weight)
+            if name == "transformer.wte.weight":
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tensor_model_parallel_rank)
+                continue
+
+            # For the fused QKV linear layer, manually shard the weights.
+            if "c_attn" in name:
+                # GPT-2's fused QKV has the shape of
+                # [3 * num_heads * head_size, hidden_size].
+                # When tensor parallelism is used, we shard the weights along
+                # the head dimension.
+                total_num_heads = self.config.num_attention_heads
+                hidden_size = self.config.hidden_size
+                head_size = hidden_size // total_num_heads
+                num_heads = total_num_heads // tensor_model_parallel_world_size
+                head_start = tensor_model_parallel_rank * num_heads
+                head_end = (tensor_model_parallel_rank + 1) * num_heads
+
+                if name.endswith(".weight"):
+                    loaded_weight = loaded_weight.view(3, total_num_heads,
+                                                       head_size, hidden_size)
+                    loaded_weight = loaded_weight[:, head_start:head_end, :, :]
+                    loaded_weight = loaded_weight.reshape(-1, hidden_size)
+                elif name.endswith(".bias"):
+                    loaded_weight = loaded_weight.view(3, total_num_heads,
+                                                       head_size)
+                    loaded_weight = loaded_weight[:, head_start:head_end, :]
+                    loaded_weight = loaded_weight.reshape(-1)
+                else:
+                    raise ValueError(f"Unexpected parameter name {name}")
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         self._column_parallel_weights,
+                                         self._row_parallel_weights,
+                                         tensor_model_parallel_rank)

vllm/model_executor/models/gpt_bigcode.py

@@ -31,17 +31,15 @@ from transformers import GPTBigCodeConfig
 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 PagedAttention
-from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding)
+from vllm.model_executor.weight_utils import (
+    convert_pyslice_to_tensor, hf_model_weights_iterator,
+    load_padded_tensor_parallel_vocab, load_tensor_parallel_weights)
 from vllm.model_executor.parallel_utils.parallel_state import (
-    get_tensor_model_parallel_world_size)
-from vllm.model_executor.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.layers import (VocabParallelEmbedding,
+                                                       ColumnParallelLinear,
+                                                       RowParallelLinear)
 from vllm.sequence import SamplerOutput
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
@@ -49,11 +47,7 @@ KVCache = Tuple[torch.Tensor, torch.Tensor]
 
 class GPTBigCodeAttention(nn.Module):
 
-    def __init__(
-        self,
-        config: GPTBigCodeConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: GPTBigCodeConfig):
         super().__init__()
         self.hidden_size = config.hidden_size
         total_num_heads = config.num_attention_heads
@@ -67,26 +61,32 @@ class GPTBigCodeAttention(nn.Module):
 
         self.multi_query = config.multi_query
         if self.multi_query:
-            total_num_kv_heads = 1
             self.num_kv_heads = 1
+            self.kv_dim = self.head_dim
+            self.c_attn_q = ColumnParallelLinear(
+                self.hidden_size,
+                self.hidden_size,
+                bias=True,
+                gather_output=False,
+            )
+            self.c_attn_kv = nn.Linear(self.hidden_size,
+                                       2 * self.kv_dim,
+                                       bias=True)
         else:
-            total_num_kv_heads = total_num_heads
             self.num_kv_heads = self.num_heads
-        self.kv_dim = self.head_dim * self.num_kv_heads
-        self.c_attn = QKVParallelLinear(
-            self.hidden_size,
-            self.head_dim,
-            total_num_heads,
-            total_num_kv_heads,
-            bias=True,
-            linear_method=linear_method,
-        )
+            self.kv_dim = self.num_kv_heads * self.head_dim
+            self.c_attn = ColumnParallelLinear(
+                self.hidden_size,
+                self.hidden_size + 2 * self.kv_dim,
+                bias=True,
+                gather_output=False,
+            )
 
         self.c_proj = RowParallelLinear(
             self.hidden_size,
             self.hidden_size,
             bias=True,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
         self.attn = PagedAttention(self.num_heads,
                                    self.head_dim,
@@ -100,14 +100,17 @@ class GPTBigCodeAttention(nn.Module):
         input_metadata: InputMetadata,
         cache_event: Optional[torch.cuda.Event],
     ) -> torch.Tensor:
-        qkv, _ = self.c_attn(hidden_states)
-        q, k, v = qkv.split(
-            [
+        if self.multi_query:
+            q, _ = self.c_attn_q(hidden_states)
+            kv = self.c_attn_kv(hidden_states)
+            k, v = kv.split([self.kv_dim, self.kv_dim], dim=-1)
+        else:
+            qkv, _ = self.c_attn(hidden_states)
+            q, k, v = qkv.split([
                 self.hidden_size // self.tensor_model_parallel_world_size,
                 self.kv_dim, self.kv_dim
             ],
-            dim=-1,
-        )
+                                dim=-1)
         key_cache, value_cache = kv_cache
         attn_output = self.attn(q, k, v, key_cache, value_cache,
                                 input_metadata, cache_event)
@@ -121,7 +124,6 @@ class GPTBigMLP(nn.Module):
         self,
         intermediate_size: int,
         config: GPTBigCodeConfig,
-        linear_method: Optional[LinearMethodBase] = None,
     ):
         super().__init__()
         hidden_size = config.hidden_size
@@ -129,17 +131,15 @@ class GPTBigMLP(nn.Module):
             hidden_size,
             intermediate_size,
             bias=True,
-            linear_method=linear_method,
+            gather_output=False,
         )
         self.c_proj = RowParallelLinear(
             intermediate_size,
             hidden_size,
             bias=True,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
-        quant_config = getattr(linear_method, "quant_config", None)
-        self.act = get_act_fn(config.activation_function, quant_config,
-                              intermediate_size)
+        self.act = get_act_fn(config.activation_function)
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         hidden_states, _ = self.c_fc(hidden_states)
@@ -150,20 +150,16 @@ class GPTBigMLP(nn.Module):
 
 class GPTBigCodeBlock(nn.Module):
 
-    def __init__(
-        self,
-        config: GPTBigCodeConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: GPTBigCodeConfig):
         super().__init__()
         hidden_size = config.hidden_size
         inner_dim = (config.n_inner if config.n_inner is not None else 4 *
                      hidden_size)
 
         self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        self.attn = GPTBigCodeAttention(config, linear_method)
+        self.attn = GPTBigCodeAttention(config)
         self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        self.mlp = GPTBigMLP(inner_dim, config, linear_method)
+        self.mlp = GPTBigMLP(inner_dim, config)
 
     def forward(
         self,
@@ -193,23 +189,23 @@ class GPTBigCodeBlock(nn.Module):
 
 class GPTBigCodeModel(nn.Module):
 
-    def __init__(
-        self,
-        config: GPTBigCodeConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: GPTBigCodeConfig):
         super().__init__()
         self.config = config
         assert not config.add_cross_attention
 
         self.embed_dim = config.hidden_size
 
-        self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
+        # Optimization: While the vocab size of GPT-2 is 50257, we extend it
+        # to 50304 in order to make it divisible by 64.
+        # This improves performance since GPUs are faster if the dimension
+        # is divisible by 64. In addition, it allows us to shard the embedding
+        # layer across 2, 4, 8, or more GPUs.
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.wte = VocabParallelEmbedding(vocab_size, self.embed_dim)
         self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
-        self.h = nn.ModuleList([
-            GPTBigCodeBlock(config, linear_method)
-            for _ in range(config.num_hidden_layers)
-        ])
+        self.h = nn.ModuleList(
+            [GPTBigCodeBlock(config) for _ in range(config.num_hidden_layers)])
         self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
 
     def forward(
@@ -239,15 +235,12 @@ class GPTBigCodeModel(nn.Module):
 
 class GPTBigCodeForCausalLM(nn.Module):
 
-    def __init__(
-        self,
-        config: GPTBigCodeConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: GPTBigCodeConfig):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.transformer = GPTBigCodeModel(config, linear_method)
+        self.transformer = GPTBigCodeModel(config)
+        # TODO(zhuohan): create a new weight after implementing pipeline
+        #                parallelism
         self.lm_head_weight = self.transformer.wte.weight
         self.sampler = Sampler(config.vocab_size)
 
@@ -265,21 +258,89 @@ class GPTBigCodeForCausalLM(nn.Module):
                                    input_metadata)
         return next_tokens
 
+    _column_parallel_weights = ["c_fc.weight", "c_fc.bias"]
+    _row_parallel_weights = ["c_proj.weight"]
+
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      load_format: str = "auto",
                      revision: Optional[str] = None):
-        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
+        tensor_model_parallel_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, load_format, revision):
             if "lm_head.weight" in name:
+                # GPT-2 ties the weights of the embedding layer and the final
+                # linear layer.
                 continue
             if ".attn.bias" in name:
                 # Skip attention mask.
                 # NOTE: "c_attn.bias" should not be skipped.
                 continue
-            param = params_dict[name]
-            weight_loader = getattr(param, "weight_loader",
-                                    default_weight_loader)
-            weight_loader(param, loaded_weight)
+
+            if not name.startswith("transformer."):
+                name = "transformer." + name
+
+            # For the fused QKV linear layer, manually shard the weights.
+            if "c_attn" in name:
+                # GPT-2's fused QKV has the shape of
+                # [3 * num_heads * head_size, hidden_size].
+                # When tensor parallelism is used, we shard the weights along
+                # the head dimension.
+                total_num_heads = self.config.num_attention_heads
+                total_num_kv_heads = (1 if self.config.multi_query else
+                                      total_num_heads)
+                hidden_size = self.config.hidden_size
+                head_size = hidden_size // total_num_heads
+                total_kv_size = head_size * total_num_kv_heads
+                num_heads = total_num_heads // tensor_model_parallel_world_size
+                head_start = tensor_model_parallel_rank * num_heads
+                head_end = (tensor_model_parallel_rank + 1) * num_heads
+
+                loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+                wq, wk, wv = torch.split(
+                    loaded_weight, [hidden_size, total_kv_size, total_kv_size],
+                    dim=0)
+
+                wq = wq[head_size * head_start:head_size * head_end]
+                if not self.config.multi_query:
+                    # Split the heads when using normal multi-head attention
+                    wk = wk[head_size * head_start:head_size * head_end]
+                    wv = wv[head_size * head_start:head_size * head_end]
+                    loaded_weight = torch.cat([wq, wk, wv], dim=0)
+                else:
+                    # For multi-query attention, we split the query
+                    # but replicate the key and value.
+                    loaded_weight_q = wq
+                    loaded_weight_kv = torch.cat([wk, wv], dim=0)
+                    q_weight_name = name.replace("c_attn", "c_attn_q")
+                    kv_weight_name = name.replace("c_attn", "c_attn_kv")
+                    load_tensor_parallel_weights(state_dict[q_weight_name],
+                                                 loaded_weight_q,
+                                                 q_weight_name,
+                                                 self._column_parallel_weights,
+                                                 self._row_parallel_weights,
+                                                 tensor_model_parallel_rank)
+                    load_tensor_parallel_weights(state_dict[kv_weight_name],
+                                                 loaded_weight_kv,
+                                                 kv_weight_name,
+                                                 self._column_parallel_weights,
+                                                 self._row_parallel_weights,
+                                                 tensor_model_parallel_rank)
+                    continue
+
+            param = state_dict[name]
+
+            if name == "transformer.wte.weight":
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tensor_model_parallel_rank)
+                continue
+
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         self._column_parallel_weights,
+                                         self._row_parallel_weights,
+                                         tensor_model_parallel_rank)

vllm/model_executor/models/gpt_j.py

@@ -29,17 +29,14 @@ from transformers import GPTJConfig
 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 PagedAttentionWithRoPE
-from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding, ParallelLMHead)
+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_world_size)
-from vllm.model_executor.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.layers import (VocabParallelEmbedding,
+                                                       ColumnParallelLinear,
+                                                       RowParallelLinear)
 from vllm.sequence import SamplerOutput
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
@@ -47,28 +44,23 @@ KVCache = Tuple[torch.Tensor, torch.Tensor]
 
 class GPTJAttention(nn.Module):
 
-    def __init__(
-        self,
-        config: GPTJConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: GPTJConfig):
         super().__init__()
         self.total_num_heads = config.num_attention_heads
         self.hidden_size = config.hidden_size
         self.head_size = self.hidden_size // self.total_num_heads
 
-        self.qkv_proj = QKVParallelLinear(
+        self.qkv_proj = ColumnParallelLinear(
             config.hidden_size,
-            self.head_size,
-            self.total_num_heads,
+            3 * config.hidden_size,
             bias=False,
-            linear_method=linear_method,
+            gather_output=False,
         )
         self.out_proj = RowParallelLinear(
             config.hidden_size,
             config.hidden_size,
             bias=False,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
 
         tp_world_size = get_tensor_model_parallel_world_size()
@@ -110,27 +102,20 @@ class GPTJAttention(nn.Module):
 
 class GPTJMLP(nn.Module):
 
-    def __init__(
-        self,
-        intermediate_size: int,
-        config: GPTJConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, intermediate_size: int, config: GPTJConfig):
         super().__init__()
         hidden_size = config.n_embd
         self.fc_in = ColumnParallelLinear(
             hidden_size,
             intermediate_size,
-            linear_method=linear_method,
+            gather_output=False,
         )
         self.fc_out = RowParallelLinear(
             intermediate_size,
             hidden_size,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
-        quant_config = getattr(linear_method, "quant_config", None)
-        self.act = get_act_fn(config.activation_function, quant_config,
-                              intermediate_size)
+        self.act = get_act_fn(config.activation_function)
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         hidden_states, _ = self.fc_in(hidden_states)
@@ -141,19 +126,15 @@ class GPTJMLP(nn.Module):
 
 class GPTJBlock(nn.Module):
 
-    def __init__(
-        self,
-        config: GPTJConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: GPTJConfig):
         super().__init__()
         if config.n_inner is None:
             inner_dim = 4 * config.n_embd
         else:
             inner_dim = config.n_inner
         self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
-        self.attn = GPTJAttention(config, linear_method)
-        self.mlp = GPTJMLP(inner_dim, config, linear_method)
+        self.attn = GPTJAttention(config)
+        self.mlp = GPTJMLP(inner_dim, config)
 
     def forward(
         self,
@@ -179,11 +160,7 @@ class GPTJBlock(nn.Module):
 
 class GPTJModel(nn.Module):
 
-    def __init__(
-        self,
-        config: GPTJConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: GPTJConfig):
         super().__init__()
         self.config = config
         self.embed_dim = config.n_embd
@@ -192,7 +169,7 @@ class GPTJModel(nn.Module):
             self.embed_dim,
         )
         self.h = nn.ModuleList(
-            [GPTJBlock(config, linear_method) for _ in range(config.n_layer)])
+            [GPTJBlock(config) for _ in range(config.n_layer)])
         self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
 
     def forward(
@@ -223,20 +200,15 @@ class GPTJModel(nn.Module):
 
 class GPTJForCausalLM(nn.Module):
 
-    def __init__(
-        self,
-        config: GPTJConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: GPTJConfig):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
         assert not config.tie_word_embeddings
-        self.transformer = GPTJModel(config, linear_method)
-        self.lm_head = ParallelLMHead(
-            config.vocab_size,
+        self.transformer = GPTJModel(config)
+        self.lm_head = ColumnParallelLinear(
             config.n_embd,
-            bias=True,
+            config.vocab_size,
+            gather_output=False,
         )
         self.sampler = Sampler(config.vocab_size)
 
@@ -254,33 +226,43 @@ class GPTJForCausalLM(nn.Module):
                                    input_metadata, self.lm_head.bias)
         return next_tokens
 
+    _column_parallel_weights = [
+        "wte.weight", "fc_in.weight", "fc_in.bias", "lm_head.weight",
+        "lm_head.bias"
+    ]
+    _row_parallel_weights = ["out_proj.weight", "fc_out.weight"]
+
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      load_format: str = "auto",
                      revision: Optional[str] = None):
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("qkv_proj", "q_proj", "q"),
-            ("qkv_proj", "k_proj", "k"),
-            ("qkv_proj", "v_proj", "v"),
-            ("gate_up_proj", "gate_proj", 0),
-            ("gate_up_proj", "up_proj", 1),
-        ]
-        params_dict = dict(self.named_parameters())
+        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, load_format, revision):
             if "attn.bias" in name or "attn.masked_bias" in name:
                 continue
-            for (param_name, weight_name, shard_id) in stacked_params_mapping:
-                if weight_name not in name:
+
+            is_attention_weight = False
+            for stride_id, att_weight_name in enumerate(
+                ["q_proj", "k_proj", "v_proj"]):
+                if att_weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
+                param = state_dict[name.replace(att_weight_name, "qkv_proj")]
+                shard_size = param.shape[1]
+                loaded_weight = loaded_weight[shard_size * tp_rank:shard_size *
+                                              (tp_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_attention_weight = True
                 break
-            else:
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader",
-                                        default_weight_loader)
-                weight_loader(param, loaded_weight)
+            if is_attention_weight:
+                continue
+
+            param = state_dict[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

@@ -29,17 +29,14 @@ from transformers import GPTNeoXConfig
 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 PagedAttentionWithRoPE
-from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding, ParallelLMHead)
+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_world_size)
-from vllm.model_executor.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.layers import (VocabParallelEmbedding,
+                                                       ColumnParallelLinear,
+                                                       RowParallelLinear)
 from vllm.sequence import SamplerOutput
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
@@ -47,11 +44,7 @@ KVCache = Tuple[torch.Tensor, torch.Tensor]
 
 class GPTNeoXAttention(nn.Module):
 
-    def __init__(
-        self,
-        config: GPTNeoXConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: GPTNeoXConfig):
         super().__init__()
         self.total_num_heads = config.num_attention_heads
         self.hidden_size = config.hidden_size
@@ -63,16 +56,15 @@ class GPTNeoXAttention(nn.Module):
         self.num_heads = (self.total_num_heads //
                           tensor_model_parallel_world_size)
 
-        self.query_key_value = QKVParallelLinear(
+        self.query_key_value = ColumnParallelLinear(
             config.hidden_size,
-            self.head_size,
-            self.total_num_heads,
-            linear_method=linear_method,
+            3 * config.hidden_size,
+            gather_output=False,
         )
         self.dense = RowParallelLinear(
             config.hidden_size,
             config.hidden_size,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
 
         scaling = self.head_size**-0.5
@@ -108,25 +100,19 @@ class GPTNeoXAttention(nn.Module):
 
 class GPTNeoXMLP(nn.Module):
 
-    def __init__(
-        self,
-        config: GPTNeoXConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: GPTNeoXConfig):
         super().__init__()
         self.dense_h_to_4h = ColumnParallelLinear(
             config.hidden_size,
             config.intermediate_size,
-            linear_method=linear_method,
+            gather_output=False,
         )
         self.dense_4h_to_h = RowParallelLinear(
             config.intermediate_size,
             config.hidden_size,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
-        quant_config = getattr(linear_method, "quant_config", None)
-        self.act = get_act_fn(config.hidden_act, quant_config,
-                              config.intermediate_size)
+        self.act = get_act_fn(config.hidden_act)
 
     def forward(self, hidden_states):
         hidden_states, _ = self.dense_h_to_4h(hidden_states)
@@ -137,19 +123,15 @@ class GPTNeoXMLP(nn.Module):
 
 class GPTNeoXLayer(nn.Module):
 
-    def __init__(
-        self,
-        config: GPTNeoXConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: GPTNeoXConfig):
         super().__init__()
         self.use_parallel_residual = config.use_parallel_residual
         self.input_layernorm = nn.LayerNorm(config.hidden_size,
                                             eps=config.layer_norm_eps)
         self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
                                                      eps=config.layer_norm_eps)
-        self.attention = GPTNeoXAttention(config, linear_method)
-        self.mlp = GPTNeoXMLP(config, linear_method)
+        self.attention = GPTNeoXAttention(config)
+        self.mlp = GPTNeoXMLP(config)
 
     def forward(
         self,
@@ -187,11 +169,7 @@ class GPTNeoXLayer(nn.Module):
 
 class GPTNeoXModel(nn.Module):
 
-    def __init__(
-        self,
-        config: GPTNeoXConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: GPTNeoXConfig):
         super().__init__()
         self.config = config
 
@@ -199,10 +177,8 @@ class GPTNeoXModel(nn.Module):
             config.vocab_size,
             config.hidden_size,
         )
-        self.layers = nn.ModuleList([
-            GPTNeoXLayer(config, linear_method)
-            for _ in range(config.num_hidden_layers)
-        ])
+        self.layers = nn.ModuleList(
+            [GPTNeoXLayer(config) for _ in range(config.num_hidden_layers)])
         self.final_layer_norm = nn.LayerNorm(config.hidden_size,
                                              eps=config.layer_norm_eps)
 
@@ -234,18 +210,15 @@ class GPTNeoXModel(nn.Module):
 
 class GPTNeoXForCausalLM(nn.Module):
 
-    def __init__(
-        self,
-        config,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.gpt_neox = GPTNeoXModel(config, linear_method)
-        self.embed_out = ParallelLMHead(
-            config.vocab_size,
+        self.gpt_neox = GPTNeoXModel(config)
+        self.embed_out = ColumnParallelLinear(
             config.hidden_size,
+            config.vocab_size,
+            bias=False,
+            gather_output=False,
         )
         self.sampler = Sampler(config.vocab_size)
 
@@ -263,35 +236,50 @@ class GPTNeoXForCausalLM(nn.Module):
                                    input_metadata)
         return next_tokens
 
+    _column_parallel_weights = [
+        "embed_in.weight", "embed_out.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,
                      load_format: str = "auto",
                      revision: Optional[str] = None):
-        params_dict = dict(self.named_parameters())
+        tensor_model_parallel_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, load_format, revision):
             if ("attention.bias" in name or "attention.masked_bias" in name
                     or "rotary_emb.inv_freq" in name):
                 continue
-            param = params_dict[name]
-
+            param = state_dict[name]
             if "query_key_value" in name:
-                # NOTE: GPT-NeoX's fused QKV's output_dim has the shape of
-                # (num_heads * 3 * head_size), while the
-                # required shape is (3 * num_heads * head_size).
+                # NOTE(woosuk): GPT-NeoX'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.
-                output_dim = getattr(param, "output_dim", None)
-                num_heads = self.config.num_attention_heads
-                if output_dim is not None:
-                    loaded_weight_shape = loaded_weight.shape
-                    loaded_weight = loaded_weight.view(
-                        loaded_weight_shape[:output_dim] + (num_heads, 3, -1) +
-                        loaded_weight_shape[output_dim + 1:])
-                    loaded_weight = loaded_weight.transpose(
-                        output_dim, output_dim + 1)
-                    loaded_weight = loaded_weight.reshape(loaded_weight_shape)
+                shard_size = param.shape[0]
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
 
-            weight_loader = getattr(param, "weight_loader",
-                                    default_weight_loader)
-            weight_loader(param, loaded_weight)
+                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,
+                                         tensor_model_parallel_rank)

vllm/model_executor/models/internlm.py

@@ -9,17 +9,15 @@ from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding, ParallelLMHead)
 from vllm.model_executor.parallel_utils.parallel_state import (
-    get_tensor_model_parallel_world_size)
-from vllm.model_executor.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.layers import (ColumnParallelLinear,
+                                                       RowParallelLinear,
+                                                       VocabParallelEmbedding)
+from vllm.model_executor.weight_utils import (
+    hf_model_weights_iterator, load_padded_tensor_parallel_vocab,
+    load_tensor_parallel_weights)
 from vllm.sequence import SamplerOutput
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
@@ -32,17 +30,20 @@ class InternLMMLP(nn.Module):
         hidden_size: int,
         intermediate_size: int,
         hidden_act: str,
-        linear_method: Optional[LinearMethodBase] = None,
     ):
         super().__init__()
-        self.gate_up_proj = MergedColumnParallelLinear(
-            hidden_size, [intermediate_size] * 2,
+        self.gate_up_proj = ColumnParallelLinear(
+            hidden_size,
+            2 * intermediate_size,
             bias=False,
-            linear_method=linear_method)
-        self.down_proj = RowParallelLinear(intermediate_size,
-                                           hidden_size,
-                                           bias=False,
-                                           linear_method=linear_method)
+            gather_output=False,
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            input_is_parallel=True,
+        )
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
                              "Only silu is supported for now.")
@@ -61,10 +62,8 @@ class InternLMAttention(nn.Module):
         self,
         hidden_size: int,
         num_heads: int,
-        bias: bool,
         rope_theta: float = 10000,
         max_position_embeddings: int = 8192,
-        linear_method: Optional[LinearMethodBase] = None,
     ):
         super().__init__()
         self.hidden_size = hidden_size
@@ -79,18 +78,17 @@ class InternLMAttention(nn.Module):
         self.rope_theta = rope_theta
         self.max_position_embeddings = max_position_embeddings
 
-        self.qkv_proj = QKVParallelLinear(
+        self.qkv_proj = ColumnParallelLinear(
             hidden_size,
-            self.head_dim,
-            self.total_num_heads,
-            bias=bias,
-            linear_method=linear_method,
+            3 * self.total_num_heads * self.head_dim,
+            bias=True,
+            gather_output=False,
         )
         self.o_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             hidden_size,
-            bias=bias,
-            linear_method=linear_method,
+            bias=True,
+            input_is_parallel=True,
         )
         self.attn = PagedAttentionWithRoPE(
             self.num_heads,
@@ -119,11 +117,7 @@ class InternLMAttention(nn.Module):
 
 class InternLMDecoderLayer(nn.Module):
 
-    def __init__(
-        self,
-        config: LlamaConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: LlamaConfig):
         super().__init__()
         self.hidden_size = config.hidden_size
         rope_theta = getattr(config, "rope_theta", 10000)
@@ -132,16 +126,13 @@ class InternLMDecoderLayer(nn.Module):
         self.self_attn = InternLMAttention(
             hidden_size=self.hidden_size,
             num_heads=config.num_attention_heads,
-            bias=config.bias,
             rope_theta=rope_theta,
             max_position_embeddings=max_position_embeddings,
-            linear_method=linear_method,
         )
         self.mlp = InternLMMLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,
             hidden_act=config.hidden_act,
-            linear_method=linear_method,
         )
         self.input_layernorm = RMSNorm(config.hidden_size,
                                        eps=config.rms_norm_eps)
@@ -155,15 +146,10 @@ class InternLMDecoderLayer(nn.Module):
         kv_cache: KVCache,
         input_metadata: InputMetadata,
         cache_event: Optional[torch.cuda.Event],
-        residual: Optional[torch.Tensor],
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+    ) -> torch.Tensor:
         # Self Attention
-        if residual is None:
-            residual = hidden_states
-            hidden_states = self.input_layernorm(hidden_states)
-        else:
-            hidden_states, residual = self.input_layernorm(
-                hidden_states, residual)
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
         hidden_states = self.self_attn(
             positions=positions,
             hidden_states=hidden_states,
@@ -171,21 +157,19 @@ class InternLMDecoderLayer(nn.Module):
             input_metadata=input_metadata,
             cache_event=cache_event,
         )
+        hidden_states = residual + hidden_states
 
         # Fully Connected
-        hidden_states, residual = self.post_attention_layernorm(
-            hidden_states, residual)
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
         hidden_states = self.mlp(hidden_states)
-        return hidden_states, residual
+        hidden_states = residual + hidden_states
+        return hidden_states
 
 
 class InternLMModel(nn.Module):
 
-    def __init__(
-        self,
-        config: LlamaConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: LlamaConfig):
         super().__init__()
         self.config = config
         self.padding_idx = config.pad_token_id
@@ -197,7 +181,7 @@ class InternLMModel(nn.Module):
             config.hidden_size,
         )
         self.layers = nn.ModuleList([
-            InternLMDecoderLayer(config, linear_method)
+            InternLMDecoderLayer(config)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -211,37 +195,36 @@ class InternLMModel(nn.Module):
         cache_events: Optional[List[torch.cuda.Event]],
     ) -> torch.Tensor:
         hidden_states = self.embed_tokens(input_ids)
-        residual = None
         for i in range(len(self.layers)):
             if cache_events is None:
                 cache_event = None
             else:
                 cache_event = cache_events[i]
             layer = self.layers[i]
-            hidden_states, residual = layer(
+            hidden_states = layer(
                 positions,
                 hidden_states,
                 kv_caches[i],
                 input_metadata,
                 cache_event,
-                residual,
             )
-        hidden_states, _ = self.norm(hidden_states, residual)
+        hidden_states = self.norm(hidden_states)
         return hidden_states
 
 
 class InternLMForCausalLM(nn.Module):
 
-    def __init__(
-        self,
-        config,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = InternLMModel(config, linear_method)
-        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.model = InternLMModel(config)
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.lm_head = ColumnParallelLinear(
+            config.hidden_size,
+            vocab_size,
+            bias=False,
+            gather_output=False,
+        )
         self.sampler = Sampler(config.vocab_size)
 
     def forward(
@@ -258,33 +241,69 @@ class InternLMForCausalLM(nn.Module):
                                    input_metadata)
         return next_tokens
 
+    _column_parallel_weights = [
+        "qkv_proj.weight", "gate_proj.weight", "up_proj.weight"
+    ]
+    _row_parallel_weights = ["o_proj.weight", "down_proj.weight"]
+
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      load_format: str = "auto",
                      revision: Optional[str] = None):
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("qkv_proj", "q_proj", "q"),
-            ("qkv_proj", "k_proj", "k"),
-            ("qkv_proj", "v_proj", "v"),
-            ("gate_up_proj", "gate_proj", 0),
-            ("gate_up_proj", "up_proj", 1),
-        ]
-        params_dict = dict(self.named_parameters())
+        tensor_model_parallel_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, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
-            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+
+            if "embed_tokens" in name or "lm_head" in name:
+                param = state_dict[name]
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tensor_model_parallel_rank)
+                continue
+
+            is_attention_weight = False
+            for stride_id, att_weight_name in enumerate(
+                ["q_proj", "k_proj", "v_proj"]):
+                if att_weight_name not in name:
+                    continue
+                param = state_dict[name.replace(att_weight_name, "qkv_proj")]
+                shard_size = param.shape[0] // 3
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_attention_weight = True
+                break
+            if is_attention_weight:
+                continue
+
+            is_gate_up_weight = False
+            for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]):
                 if weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
+                param = state_dict[name.replace(weight_name, "gate_up_proj")]
+                shard_size = param.shape[0] // 2
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_gate_up_weight = True
                 break
-            else:
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader",
-                                        default_weight_loader)
-                weight_loader(param, loaded_weight)
+            if is_gate_up_weight:
+                continue
+
+            param = state_dict[name]
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         self._column_parallel_weights,
+                                         self._row_parallel_weights,
+                                         tensor_model_parallel_rank)

vllm/model_executor/models/llama.py

@@ -33,19 +33,17 @@ from transformers import LlamaConfig
 
 from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
+from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.layers.quantized_linear import ParallelLinear
 from vllm.model_executor.parallel_utils.parallel_state import (
-    get_tensor_model_parallel_world_size)
-from vllm.model_executor.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.layers import VocabParallelEmbedding
+from vllm.model_executor.quantization_utils import QuantizationConfig
+from vllm.model_executor.weight_utils import (
+    convert_pyslice_to_tensor, hf_model_weights_iterator,
+    load_tensor_parallel_weights, load_padded_tensor_parallel_vocab)
 from vllm.sequence import SamplerOutput
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
@@ -58,17 +56,19 @@ class LlamaMLP(nn.Module):
         hidden_size: int,
         intermediate_size: int,
         hidden_act: str,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
-        self.gate_up_proj = MergedColumnParallelLinear(
-            hidden_size, [intermediate_size] * 2,
-            bias=False,
-            linear_method=linear_method)
-        self.down_proj = RowParallelLinear(intermediate_size,
-                                           hidden_size,
-                                           bias=False,
-                                           linear_method=linear_method)
+        self.gate_up_proj = ParallelLinear.column(hidden_size,
+                                                  2 * intermediate_size,
+                                                  bias=False,
+                                                  gather_output=False,
+                                                  quant_config=quant_config)
+        self.down_proj = ParallelLinear.row(intermediate_size,
+                                            hidden_size,
+                                            bias=False,
+                                            input_is_parallel=True,
+                                            quant_config=quant_config)
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
                              "Only silu is supported for now.")
@@ -91,7 +91,7 @@ class LlamaAttention(nn.Module):
         rope_theta: float = 10000,
         rope_scaling: Optional[Dict[str, Any]] = None,
         max_position_embeddings: int = 8192,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = hidden_size
@@ -109,6 +109,7 @@ class LlamaAttention(nn.Module):
             # the KV heads across multiple tensor parallel GPUs.
             assert tp_size % self.total_num_kv_heads == 0
         self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        num_kv_heads_replicas = max(1, tp_size // self.total_num_kv_heads)
         self.head_dim = hidden_size // self.total_num_heads
         self.q_size = self.num_heads * self.head_dim
         self.kv_size = self.num_kv_heads * self.head_dim
@@ -116,19 +117,21 @@ class LlamaAttention(nn.Module):
         self.rope_theta = rope_theta
         self.max_position_embeddings = max_position_embeddings
 
-        self.qkv_proj = QKVParallelLinear(
+        self.qkv_proj = ParallelLinear.column(
             hidden_size,
+            (self.total_num_heads +
+             2 * self.total_num_kv_heads * num_kv_heads_replicas) *
             self.head_dim,
-            self.total_num_heads,
-            self.total_num_kv_heads,
             bias=False,
-            linear_method=linear_method,
+            gather_output=False,
+            quant_config=quant_config,
         )
-        self.o_proj = RowParallelLinear(
+        self.o_proj = ParallelLinear.row(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=False,
-            linear_method=linear_method,
+            input_is_parallel=True,
+            quant_config=quant_config,
         )
         self.attn = PagedAttentionWithRoPE(
             self.num_heads,
@@ -162,10 +165,11 @@ class LlamaDecoderLayer(nn.Module):
     def __init__(
         self,
         config: LlamaConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
+        # Requires transformers > 4.32.0
         rope_theta = getattr(config, "rope_theta", 10000)
         rope_scaling = getattr(config, "rope_scaling", None)
         max_position_embeddings = getattr(config, "max_position_embeddings",
@@ -177,13 +181,13 @@ class LlamaDecoderLayer(nn.Module):
             rope_theta=rope_theta,
             rope_scaling=rope_scaling,
             max_position_embeddings=max_position_embeddings,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.mlp = LlamaMLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,
             hidden_act=config.hidden_act,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.input_layernorm = RMSNorm(config.hidden_size,
                                        eps=config.rms_norm_eps)
@@ -197,15 +201,10 @@ class LlamaDecoderLayer(nn.Module):
         kv_cache: KVCache,
         input_metadata: InputMetadata,
         cache_event: Optional[torch.cuda.Event],
-        residual: Optional[torch.Tensor],
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+    ) -> torch.Tensor:
         # Self Attention
-        if residual is None:
-            residual = hidden_states
-            hidden_states = self.input_layernorm(hidden_states)
-        else:
-            hidden_states, residual = self.input_layernorm(
-                hidden_states, residual)
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
         hidden_states = self.self_attn(
             positions=positions,
             hidden_states=hidden_states,
@@ -213,12 +212,14 @@ class LlamaDecoderLayer(nn.Module):
             input_metadata=input_metadata,
             cache_event=cache_event,
         )
+        hidden_states = residual + hidden_states
 
         # Fully Connected
-        hidden_states, residual = self.post_attention_layernorm(
-            hidden_states, residual)
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
         hidden_states = self.mlp(hidden_states)
-        return hidden_states, residual
+        hidden_states = residual + hidden_states
+        return hidden_states
 
 
 class LlamaModel(nn.Module):
@@ -226,18 +227,20 @@ class LlamaModel(nn.Module):
     def __init__(
         self,
         config: LlamaConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
+
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
         self.embed_tokens = VocabParallelEmbedding(
-            config.vocab_size,
+            vocab_size,
             config.hidden_size,
         )
         self.layers = nn.ModuleList([
-            LlamaDecoderLayer(config, linear_method)
+            LlamaDecoderLayer(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -251,22 +254,20 @@ class LlamaModel(nn.Module):
         cache_events: Optional[List[torch.cuda.Event]],
     ) -> torch.Tensor:
         hidden_states = self.embed_tokens(input_ids)
-        residual = None
         for i in range(len(self.layers)):
             if cache_events is None:
                 cache_event = None
             else:
                 cache_event = cache_events[i]
             layer = self.layers[i]
-            hidden_states, residual = layer(
+            hidden_states = layer(
                 positions,
                 hidden_states,
                 kv_caches[i],
                 input_metadata,
                 cache_event,
-                residual,
             )
-        hidden_states, _ = self.norm(hidden_states, residual)
+        hidden_states = self.norm(hidden_states)
         return hidden_states
 
 
@@ -275,13 +276,19 @@ class LlamaForCausalLM(nn.Module):
     def __init__(
         self,
         config: LlamaConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = LlamaModel(config, linear_method)
-        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.quant_config = quant_config
+        self.model = LlamaModel(config, quant_config)
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        # NOTE: The LM head is not quantized.
+        self.lm_head = ParallelLinear.column(config.hidden_size,
+                                             vocab_size,
+                                             bias=False,
+                                             gather_output=False,
+                                             quant_config=None)
         self.sampler = Sampler(config.vocab_size)
 
     def forward(
@@ -298,33 +305,118 @@ class LlamaForCausalLM(nn.Module):
                                    input_metadata)
         return next_tokens
 
+    _column_parallel_layers = []
+    _row_parallel_layers = ["o_proj", "down_proj"]
+
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      load_format: str = "auto",
                      revision: Optional[str] = None):
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("qkv_proj", "q_proj", "q"),
-            ("qkv_proj", "k_proj", "k"),
-            ("qkv_proj", "v_proj", "v"),
-            ("gate_up_proj", "gate_proj", 0),
-            ("gate_up_proj", "up_proj", 1),
+        if self.quant_config is None:
+            weight_suffixes = ["weight"]
+        else:
+            weight_suffixes = self.quant_config.get_tp_tensor_names()
+
+        column_parallel_weights: List[str] = []
+        for layer in self._column_parallel_layers:
+            for suffix in weight_suffixes:
+                column_parallel_weights.append(f"{layer}.{suffix}")
+        row_parallel_weights: List[str] = []
+        for layer in self._row_parallel_layers:
+            for suffix in weight_suffixes:
+                row_parallel_weights.append(f"{layer}.{suffix}")
+
+        tp_size = get_tensor_model_parallel_world_size()
+        tp_rank = get_tensor_model_parallel_rank()
+        q_proj_shard_size = (self.config.hidden_size // tp_size)
+        num_kv_heads_replicas = max(1,
+                                    tp_size // self.config.num_key_value_heads)
+        num_kv_heads_per_gpu = max(1,
+                                   self.config.num_key_value_heads // tp_size)
+        kv_proj_shard_size = (self.config.hidden_size //
+                              self.config.num_attention_heads *
+                              num_kv_heads_per_gpu)
+        attention_weight_specs = [
+            # (weight_name, shard_size, offset)
+            ("q_proj", q_proj_shard_size, 0),
+            ("k_proj", kv_proj_shard_size, q_proj_shard_size),
+            ("v_proj", kv_proj_shard_size,
+             q_proj_shard_size + kv_proj_shard_size),
         ]
-        params_dict = dict(self.named_parameters())
+        state_dict = self.state_dict()
+
         for name, loaded_weight in hf_model_weights_iterator(
                 model_name_or_path, cache_dir, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
-            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+
+            is_packed = False
+            is_transposed = False
+            if self.quant_config is not None:
+                is_packed = self.quant_config.is_packed(name)
+                is_transposed = self.quant_config.is_transposed(name)
+            if is_transposed:
+                loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+                loaded_weight = loaded_weight.T
+
+            is_attention_weight = False
+            for weight_name, shard_size, offset in attention_weight_specs:
                 if weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
+                param = state_dict[name.replace(weight_name, "qkv_proj")]
+                if is_transposed:
+                    param = param.T
+
+                if is_packed:
+                    shard_size //= self.quant_config.pack_factor
+                    offset //= self.quant_config.pack_factor
+
+                if weight_name in ["k_proj", "v_proj"]:
+                    shard_id = tp_rank // num_kv_heads_replicas
+                else:
+                    shard_id = tp_rank
+                loaded_weight = loaded_weight[shard_size *
+                                              shard_id:shard_size *
+                                              (shard_id + 1)]
+                param_slice = param.data[offset:offset + shard_size]
+                assert param_slice.shape == loaded_weight.shape
+
+                param_slice.copy_(loaded_weight)
+                is_attention_weight = True
                 break
-            else:
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader",
-                                        default_weight_loader)
-                weight_loader(param, loaded_weight)
+            if is_attention_weight:
+                continue
+
+            is_gate_up_weight = False
+            for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]):
+                if weight_name not in name:
+                    continue
+                param = state_dict[name.replace(weight_name, "gate_up_proj")]
+                if is_transposed:
+                    param = param.T
+
+                shard_size = param.shape[0] // 2
+                loaded_weight = loaded_weight[shard_size * tp_rank:shard_size *
+                                              (tp_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_gate_up_weight = True
+                break
+            if is_gate_up_weight:
+                continue
+
+            param = state_dict[name]
+            if is_transposed:
+                param = param.T
+
+            if "embed_tokens" in name or "lm_head" in name:
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tp_rank)
+                continue
+
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         column_parallel_weights,
+                                         row_parallel_weights, tp_rank)

vllm/model_executor/models/mistral.py

@@ -33,19 +33,17 @@ from transformers import MistralConfig
 
 from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
+from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.layers.quantized_linear import ParallelLinear
 from vllm.model_executor.parallel_utils.parallel_state import (
-    get_tensor_model_parallel_world_size)
-from vllm.model_executor.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.layers import VocabParallelEmbedding
+from vllm.model_executor.quantization_utils import QuantizationConfig
+from vllm.model_executor.weight_utils import (
+    convert_pyslice_to_tensor, hf_model_weights_iterator,
+    load_tensor_parallel_weights, load_padded_tensor_parallel_vocab)
 from vllm.sequence import SamplerOutput
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
@@ -58,17 +56,19 @@ class MistralMLP(nn.Module):
         hidden_size: int,
         intermediate_size: int,
         hidden_act: str,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
-        self.gate_up_proj = MergedColumnParallelLinear(
-            hidden_size, [intermediate_size] * 2,
-            bias=False,
-            linear_method=linear_method)
-        self.down_proj = RowParallelLinear(intermediate_size,
-                                           hidden_size,
-                                           bias=False,
-                                           linear_method=linear_method)
+        self.gate_up_proj = ParallelLinear.column(hidden_size,
+                                                  2 * intermediate_size,
+                                                  bias=False,
+                                                  gather_output=False,
+                                                  quant_config=quant_config)
+        self.down_proj = ParallelLinear.row(intermediate_size,
+                                            hidden_size,
+                                            bias=False,
+                                            input_is_parallel=True,
+                                            quant_config=quant_config)
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
                              "Only silu is supported for now.")
@@ -89,7 +89,7 @@ class MistralAttention(nn.Module):
                  num_kv_heads: int,
                  max_position: int = 4096 * 32,
                  rope_theta: float = 10000,
-                 linear_method: Optional[LinearMethodBase] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
                  sliding_window: Optional[int] = None) -> None:
         super().__init__()
         self.hidden_size = hidden_size
@@ -98,15 +98,8 @@ class MistralAttention(nn.Module):
         assert self.total_num_heads % tp_size == 0
         self.num_heads = self.total_num_heads // tp_size
         self.total_num_kv_heads = num_kv_heads
-        if self.total_num_kv_heads >= tp_size:
-            # Number of KV heads is greater than TP size, so we partition
-            # the KV heads across multiple tensor parallel GPUs.
-            assert self.total_num_kv_heads % tp_size == 0
-        else:
-            # Number of KV heads is less than TP size, so we replicate
-            # the KV heads across multiple tensor parallel GPUs.
-            assert tp_size % self.total_num_kv_heads == 0
-        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        assert self.total_num_kv_heads % tp_size == 0
+        self.num_kv_heads = self.total_num_kv_heads // tp_size
         self.head_dim = hidden_size // self.total_num_heads
         self.q_size = self.num_heads * self.head_dim
         self.kv_size = self.num_kv_heads * self.head_dim
@@ -114,19 +107,20 @@ class MistralAttention(nn.Module):
         self.rope_theta = rope_theta
         self.sliding_window = sliding_window
 
-        self.qkv_proj = QKVParallelLinear(
+        self.qkv_proj = ParallelLinear.column(
             hidden_size,
+            (self.total_num_heads + 2 * self.total_num_kv_heads) *
             self.head_dim,
-            self.total_num_heads,
-            self.total_num_kv_heads,
             bias=False,
-            linear_method=linear_method,
+            gather_output=False,
+            quant_config=quant_config,
         )
-        self.o_proj = RowParallelLinear(
+        self.o_proj = ParallelLinear.row(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=False,
-            linear_method=linear_method,
+            input_is_parallel=True,
+            quant_config=quant_config,
         )
         self.attn = PagedAttentionWithRoPE(self.num_heads,
                                            self.head_dim,
@@ -159,7 +153,7 @@ class MistralDecoderLayer(nn.Module):
     def __init__(
         self,
         config: MistralConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
@@ -171,13 +165,13 @@ class MistralDecoderLayer(nn.Module):
             max_position=config.max_position_embeddings,
             num_kv_heads=config.num_key_value_heads,
             rope_theta=rope_theta,
-            linear_method=linear_method,
+            quant_config=quant_config,
             sliding_window=config.sliding_window)
         self.mlp = MistralMLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,
             hidden_act=config.hidden_act,
-            linear_method=linear_method,
+            quant_config=quant_config,
         )
         self.input_layernorm = RMSNorm(config.hidden_size,
                                        eps=config.rms_norm_eps)
@@ -191,15 +185,10 @@ class MistralDecoderLayer(nn.Module):
         kv_cache: KVCache,
         input_metadata: InputMetadata,
         cache_event: Optional[torch.cuda.Event],
-        residual: Optional[torch.Tensor],
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+    ) -> torch.Tensor:
         # Self Attention
-        if residual is None:
-            residual = hidden_states
-            hidden_states = self.input_layernorm(hidden_states)
-        else:
-            hidden_states, residual = self.input_layernorm(
-                hidden_states, residual)
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
         hidden_states = self.self_attn(
             positions=positions,
             hidden_states=hidden_states,
@@ -207,12 +196,14 @@ class MistralDecoderLayer(nn.Module):
             input_metadata=input_metadata,
             cache_event=cache_event,
         )
+        hidden_states = residual + hidden_states
 
         # Fully Connected
-        hidden_states, residual = self.post_attention_layernorm(
-            hidden_states, residual)
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
         hidden_states = self.mlp(hidden_states)
-        return hidden_states, residual
+        hidden_states = residual + hidden_states
+        return hidden_states
 
 
 class MistralModel(nn.Module):
@@ -220,19 +211,20 @@ class MistralModel(nn.Module):
     def __init__(
         self,
         config: MistralConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
         self.embed_tokens = VocabParallelEmbedding(
-            config.vocab_size,
+            vocab_size,
             config.hidden_size,
         )
         self.layers = nn.ModuleList([
-            MistralDecoderLayer(config, linear_method)
+            MistralDecoderLayer(config, quant_config)
             for _ in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@@ -246,22 +238,20 @@ class MistralModel(nn.Module):
         cache_events: Optional[List[torch.cuda.Event]],
     ) -> torch.Tensor:
         hidden_states = self.embed_tokens(input_ids)
-        residual = None
         for i in range(len(self.layers)):
             if cache_events is None:
                 cache_event = None
             else:
                 cache_event = cache_events[i]
             layer = self.layers[i]
-            hidden_states, residual = layer(
+            hidden_states = layer(
                 positions,
                 hidden_states,
                 kv_caches[i],
                 input_metadata,
                 cache_event,
-                residual,
             )
-        hidden_states, _ = self.norm(hidden_states, residual)
+        hidden_states = self.norm(hidden_states)
         return hidden_states
 
 
@@ -270,13 +260,19 @@ class MistralForCausalLM(nn.Module):
     def __init__(
         self,
         config: MistralConfig,
-        linear_method: Optional[LinearMethodBase] = None,
+        quant_config: Optional[QuantizationConfig] = None,
     ) -> None:
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = MistralModel(config, linear_method)
-        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.quant_config = quant_config
+        self.model = MistralModel(config, quant_config)
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        # NOTE: The LM head is not quantized.
+        self.lm_head = ParallelLinear.column(config.hidden_size,
+                                             vocab_size,
+                                             bias=False,
+                                             gather_output=False,
+                                             quant_config=None)
         self.sampler = Sampler(config.vocab_size)
 
     def forward(
@@ -293,33 +289,112 @@ class MistralForCausalLM(nn.Module):
                                    input_metadata)
         return next_tokens
 
+    _column_parallel_layers = []
+    _row_parallel_layers = ["o_proj", "down_proj"]
+
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      load_format: str = "auto",
                      revision: Optional[str] = None):
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("qkv_proj", "q_proj", "q"),
-            ("qkv_proj", "k_proj", "k"),
-            ("qkv_proj", "v_proj", "v"),
-            ("gate_up_proj", "gate_proj", 0),
-            ("gate_up_proj", "up_proj", 1),
+        if self.quant_config is None:
+            weight_suffixes = ["weight"]
+        else:
+            weight_suffixes = self.quant_config.get_tp_tensor_names()
+
+        column_parallel_weights: List[str] = []
+        for layer in self._column_parallel_layers:
+            for suffix in weight_suffixes:
+                column_parallel_weights.append(f"{layer}.{suffix}")
+        row_parallel_weights: List[str] = []
+        for layer in self._row_parallel_layers:
+            for suffix in weight_suffixes:
+                row_parallel_weights.append(f"{layer}.{suffix}")
+
+        tp_size = get_tensor_model_parallel_world_size()
+        tensor_model_parallel_rank = get_tensor_model_parallel_rank()
+        q_proj_shard_size = (self.config.hidden_size // tp_size)
+        kv_proj_shard_size = (self.config.hidden_size //
+                              self.config.num_attention_heads *
+                              self.config.num_key_value_heads // tp_size)
+        attention_weight_specs = [
+            # (weight_name, shard_size, offset)
+            ("q_proj", q_proj_shard_size, 0),
+            ("k_proj", kv_proj_shard_size, q_proj_shard_size),
+            ("v_proj", kv_proj_shard_size,
+             q_proj_shard_size + kv_proj_shard_size),
         ]
-        params_dict = dict(self.named_parameters())
+        state_dict = self.state_dict()
+
         for name, loaded_weight in hf_model_weights_iterator(
                 model_name_or_path, cache_dir, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
-            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+
+            is_packed = False
+            is_transposed = False
+            if self.quant_config is not None:
+                is_packed = self.quant_config.is_packed(name)
+                is_transposed = self.quant_config.is_transposed(name)
+            if is_transposed:
+                loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+                loaded_weight = loaded_weight.T
+
+            is_attention_weight = False
+            for weight_name, shard_size, offset in attention_weight_specs:
                 if weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
+                param = state_dict[name.replace(weight_name, "qkv_proj")]
+                if is_transposed:
+                    param = param.T
+
+                if is_packed:
+                    shard_size //= self.quant_config.pack_factor
+                    offset //= self.quant_config.pack_factor
+
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[offset:offset + shard_size]
+                assert param_slice.shape == loaded_weight.shape
+
+                param_slice.copy_(loaded_weight)
+                is_attention_weight = True
                 break
-            else:
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader",
-                                        default_weight_loader)
-                weight_loader(param, loaded_weight)
+            if is_attention_weight:
+                continue
+
+            is_gate_up_weight = False
+            for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]):
+                if weight_name not in name:
+                    continue
+                param = state_dict[name.replace(weight_name, "gate_up_proj")]
+                if is_transposed:
+                    param = param.T
+
+                shard_size = param.shape[0] // 2
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_gate_up_weight = True
+                break
+            if is_gate_up_weight:
+                continue
+
+            param = state_dict[name]
+            if is_transposed:
+                param = param.T
+
+            if "embed_tokens" in name or "lm_head" in name:
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tensor_model_parallel_rank)
+                continue
+
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         column_parallel_weights,
+                                         row_parallel_weights,
+                                         tensor_model_parallel_rank)

vllm/model_executor/models/mpt.py

@@ -9,17 +9,15 @@ import torch.nn as nn
 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.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding)
+from vllm.model_executor.weight_utils import (convert_pyslice_to_tensor,
+                                              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.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
+from vllm.model_executor.parallel_utils.layers import (VocabParallelEmbedding,
+                                                       ColumnParallelLinear,
+                                                       RowParallelLinear)
 from vllm.sequence import SamplerOutput
 from vllm.transformers_utils.configs.mpt import MPTConfig
 
@@ -41,11 +39,7 @@ def _get_alibi_slopes(
 
 class MPTAttention(nn.Module):
 
-    def __init__(
-        self,
-        config: MPTConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: MPTConfig):
         super().__init__()
         self.d_model = config.d_model
         self.total_num_heads = config.n_heads
@@ -55,13 +49,11 @@ class MPTAttention(nn.Module):
         assert not config.attn_config["prefix_lm"]
         assert config.attn_config["alibi"]
 
-        # pylint: disable=invalid-name
-        self.Wqkv = QKVParallelLinear(
+        self.qkv_proj = ColumnParallelLinear(
             self.d_model,
-            self.d_model // self.total_num_heads,
-            self.total_num_heads,
+            3 * self.d_model,
             bias=not config.no_bias,
-            linear_method=linear_method,
+            gather_output=False,
         )
         if self.qk_ln:
             self.q_ln = nn.LayerNorm(self.d_model)
@@ -70,7 +62,7 @@ class MPTAttention(nn.Module):
             self.d_model,
             self.d_model,
             bias=not config.no_bias,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
 
         tp_world_size = get_tensor_model_parallel_world_size()
@@ -99,7 +91,7 @@ class MPTAttention(nn.Module):
         cache_event: Optional[torch.cuda.Event],
     ) -> torch.Tensor:
         del position_ids  # unused.
-        qkv, _ = self.Wqkv(hidden_states)
+        qkv, _ = self.qkv_proj(hidden_states)
         if self.clip_qkv is not None:
             qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
         q, k, v = qkv.chunk(chunks=3, dim=-1)
@@ -115,11 +107,7 @@ class MPTAttention(nn.Module):
 
 class MPTMLP(nn.Module):
 
-    def __init__(
-        self,
-        config: MPTConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: MPTConfig):
         super().__init__()
         hidden_size = config.d_model
         expansion_ratio = config.expansion_ratio
@@ -128,15 +116,14 @@ class MPTMLP(nn.Module):
             hidden_size,
             intermediate_size,
             bias=not config.no_bias,
-            linear_method=linear_method,
+            gather_output=False,
         )
-        quant_config = getattr(linear_method, "quant_config", None)
-        self.act = get_act_fn("gelu", quant_config, intermediate_size)
+        self.act = get_act_fn("gelu")
         self.down_proj = RowParallelLinear(
             intermediate_size,
             hidden_size,
             bias=not config.no_bias,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -148,17 +135,13 @@ class MPTMLP(nn.Module):
 
 class MPTBlock(nn.Module):
 
-    def __init__(
-        self,
-        config: MPTConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: MPTConfig):
         super().__init__()
         hidden_size = config.d_model
         self.norm_1 = nn.LayerNorm(hidden_size)
-        self.attn = MPTAttention(config, linear_method)
+        self.attn = MPTAttention(config)
         self.norm_2 = nn.LayerNorm(hidden_size)
-        self.ffn = MPTMLP(config, linear_method)
+        self.ffn = MPTMLP(config)
 
     def forward(
         self,
@@ -185,11 +168,7 @@ class MPTBlock(nn.Module):
 
 class MPTModel(nn.Module):
 
-    def __init__(
-        self,
-        config: MPTConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: MPTConfig):
         super().__init__()
         assert config.embedding_fraction == 1.0
         assert config.norm_type == "low_precision_layernorm"
@@ -199,7 +178,7 @@ class MPTModel(nn.Module):
             config.d_model,
         )
         self.blocks = nn.ModuleList(
-            [MPTBlock(config, linear_method) for _ in range(config.n_layers)])
+            [MPTBlock(config) for _ in range(config.n_layers)])
         self.norm_f = nn.LayerNorm(config.d_model)
         if config.no_bias:
             for module in self.modules():
@@ -236,17 +215,14 @@ class MPTModel(nn.Module):
 
 class MPTForCausalLM(nn.Module):
 
-    def __init__(
-        self,
-        config: MPTConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: MPTConfig):
         super().__init__()
         self.config = config
         assert config.tie_word_embeddings
-        self.linear_method = linear_method
 
-        self.transformer = MPTModel(config, linear_method)
+        self.transformer = MPTModel(config)
+        # TODO(zhuohan): create a new weight after implementing pipeline
+        #                parallelism
         self.lm_head_weight = self.transformer.wte.weight
         self.sampler = Sampler(config.vocab_size)
 
@@ -264,15 +240,45 @@ class MPTForCausalLM(nn.Module):
                                    input_metadata)
         return next_tokens
 
+    _column_parallel_weights = ["wte.weight", "up_proj.weight", "up_proj.bias"]
+    _row_parallel_weights = ["out_proj.weight", "down_proj.weight"]
+
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      load_format: str = "auto",
                      revision: Optional[str] = None):
-        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        tp_world_size = get_tensor_model_parallel_world_size()
+        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, load_format, revision):
-            param = params_dict[name]
-            weight_loader = getattr(param, "weight_loader",
-                                    default_weight_loader)
-            weight_loader(param, loaded_weight)
+            if "Wqkv" in name:
+                # NOTE(woosuk): MPT's fused QKV has the shape of
+                # [3 * num_heads * head_size, hidden_size].
+                # When tensor model parallelism is used, we need to shard
+                # the weight along the hidden dimension.
+                total_num_heads = self.config.num_attention_heads
+                hidden_size = self.config.hidden_size
+                head_size = hidden_size // total_num_heads
+                num_heads = total_num_heads // tp_world_size
+                head_start = tp_rank * num_heads
+                head_end = (tp_rank + 1) * num_heads
+                loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+                if name.endswith(".weight"):
+                    loaded_weight = loaded_weight.view(3, total_num_heads,
+                                                       head_size, hidden_size)
+                    loaded_weight = loaded_weight[:, head_start:head_end, :, :]
+                    loaded_weight = loaded_weight.reshape(-1, hidden_size)
+                elif name.endswith(".bias"):
+                    loaded_weight = loaded_weight.view(3, total_num_heads,
+                                                       head_size)
+                    loaded_weight = loaded_weight[:, head_start:head_end, :]
+                    loaded_weight = loaded_weight.reshape(-1)
+                else:
+                    raise ValueError(f"Unexpected parameter name {name}")
+                name = name.replace("Wqkv", "qkv_proj")
+            param = state_dict[name]
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         self._column_parallel_weights,
+                                         self._row_parallel_weights, tp_rank)

vllm/model_executor/models/opt.py

@@ -30,18 +30,14 @@ from transformers import OPTConfig
 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 PagedAttention
-from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
-                                               QKVParallelLinear,
-                                               ReplicatedLinear,
-                                               RowParallelLinear)
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding)
+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_world_size)
-from vllm.model_executor.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.layers import (VocabParallelEmbedding,
+                                                       ColumnParallelLinear,
+                                                       RowParallelLinear)
 from vllm.sequence import SamplerOutput
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
@@ -67,7 +63,6 @@ class OPTAttention(nn.Module):
         embed_dim: int,
         num_heads: int,
         bias: bool = True,
-        linear_method: Optional[LinearMethodBase] = None,
     ) -> None:
         super().__init__()
         self.embed_dim = embed_dim
@@ -79,18 +74,17 @@ class OPTAttention(nn.Module):
         self.head_dim = embed_dim // total_num_heads
         self.scaling = self.head_dim**-0.5
 
-        self.qkv_proj = QKVParallelLinear(
+        self.qkv_proj = ColumnParallelLinear(
             embed_dim,
-            self.head_dim,
-            total_num_heads,
+            3 * embed_dim,
             bias=bias,
-            linear_method=linear_method,
+            gather_output=False,
         )
         self.out_proj = RowParallelLinear(
             embed_dim,
             embed_dim,
             bias=bias,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
         self.attn = PagedAttention(self.num_heads,
                                    self.head_dim,
@@ -114,11 +108,7 @@ class OPTAttention(nn.Module):
 
 class OPTDecoderLayer(nn.Module):
 
-    def __init__(
-        self,
-        config: OPTConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: OPTConfig):
         super().__init__()
         self.config = config
         self.embed_dim = config.hidden_size
@@ -126,12 +116,9 @@ class OPTDecoderLayer(nn.Module):
             embed_dim=self.embed_dim,
             num_heads=config.num_attention_heads,
             bias=config.enable_bias,
-            linear_method=linear_method,
         )
         self.do_layer_norm_before = config.do_layer_norm_before
-        quant_config = getattr(linear_method, "quant_config", None)
-        self.activation_fn = get_act_fn(config.activation_function,
-                                        quant_config, config.ffn_dim)
+        self.activation_fn = get_act_fn(config.activation_function)
 
         self.self_attn_layer_norm = nn.LayerNorm(
             self.embed_dim,
@@ -140,13 +127,13 @@ class OPTDecoderLayer(nn.Module):
             self.embed_dim,
             config.ffn_dim,
             bias=config.enable_bias,
-            linear_method=linear_method,
+            gather_output=False,
         )
         self.fc2 = RowParallelLinear(
             config.ffn_dim,
             self.embed_dim,
             bias=config.enable_bias,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
         self.final_layer_norm = nn.LayerNorm(
             self.embed_dim,
@@ -190,11 +177,7 @@ class OPTDecoderLayer(nn.Module):
 
 class OPTDecoder(nn.Module):
 
-    def __init__(
-        self,
-        config: OPTConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: OPTConfig):
         super().__init__()
         self.config = config
         self.padding_idx = config.pad_token_id
@@ -211,18 +194,16 @@ class OPTDecoder(nn.Module):
 
         # Project out & in will be replicated if they exist.
         if config.word_embed_proj_dim != config.hidden_size:
-            self.project_out = ReplicatedLinear(config.hidden_size,
-                                                config.word_embed_proj_dim,
-                                                bias=False,
-                                                linear_method=linear_method)
+            self.project_out = nn.Linear(config.hidden_size,
+                                         config.word_embed_proj_dim,
+                                         bias=False)
         else:
             self.project_out = None
 
         if config.word_embed_proj_dim != config.hidden_size:
-            self.project_in = ReplicatedLinear(config.word_embed_proj_dim,
-                                               config.hidden_size,
-                                               bias=False,
-                                               linear_method=linear_method)
+            self.project_in = nn.Linear(config.word_embed_proj_dim,
+                                        config.hidden_size,
+                                        bias=False)
         else:
             self.project_in = None
 
@@ -237,10 +218,8 @@ class OPTDecoder(nn.Module):
         else:
             self.final_layer_norm = None
 
-        self.layers = nn.ModuleList([
-            OPTDecoderLayer(config, linear_method)
-            for _ in range(config.num_hidden_layers)
-        ])
+        self.layers = nn.ModuleList(
+            [OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)])
 
     def forward(
         self,
@@ -253,7 +232,7 @@ class OPTDecoder(nn.Module):
         inputs_embeds = self.embed_tokens(input_ids)
         pos_embeds = self.embed_positions(positions)
         if self.project_in is not None:
-            inputs_embeds, _ = self.project_in(inputs_embeds)
+            inputs_embeds = self.project_in(inputs_embeds)
         hidden_states = inputs_embeds + pos_embeds
 
         for i in range(len(self.layers)):
@@ -268,19 +247,15 @@ class OPTDecoder(nn.Module):
         if self.final_layer_norm is not None:
             hidden_states = self.final_layer_norm(hidden_states)
         if self.project_out is not None:
-            hidden_states, _ = self.project_out(hidden_states)
+            hidden_states = self.project_out(hidden_states)
         return hidden_states
 
 
 class OPTModel(nn.Module):
 
-    def __init__(
-        self,
-        config: OPTConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: OPTConfig):
         super().__init__()
-        self.decoder = OPTDecoder(config, linear_method)
+        self.decoder = OPTDecoder(config)
 
     def forward(
         self,
@@ -296,15 +271,12 @@ class OPTModel(nn.Module):
 
 class OPTForCausalLM(nn.Module):
 
-    def __init__(
-        self,
-        config,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.model = OPTModel(config, linear_method)
+        self.model = OPTModel(config)
+        # TODO(zhuohan): create a new weight after implementing pipeline
+        #                parallelism
         self.lm_head_weight = self.model.decoder.embed_tokens.weight
         self.sampler = Sampler(config.vocab_size)
 
@@ -322,31 +294,48 @@ class OPTForCausalLM(nn.Module):
                                    input_metadata)
         return next_tokens
 
+    _column_parallel_weights = [
+        "embed_tokens.weight", "fc1.weight", "fc1.bias"
+    ]
+    _row_parallel_weights = ["out_proj.weight", "fc2.weight"]
+
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      load_format: str = "auto",
                      revision: Optional[str] = None):
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("qkv_proj", "q_proj", "q"),
-            ("qkv_proj", "k_proj", "k"),
-            ("qkv_proj", "v_proj", "v"),
-        ]
-        params_dict = dict(self.named_parameters(remove_duplicate=False))
+        tensor_model_parallel_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, load_format, revision):
             if "lm_head.weight" in name:
                 continue
-            for (param_name, weight_name, shard_id) in stacked_params_mapping:
-                if weight_name not in name:
+
+            if name.startswith("decoder."):
+                name = "model." + name
+
+            is_attention_weight = False
+            for stride_id, att_weight_name in enumerate(
+                ["q_proj", "k_proj", "v_proj"]):
+                if att_weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
+                param = state_dict[name.replace(att_weight_name, "qkv_proj")]
+                shard_size = param.shape[0] // 3
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_attention_weight = True
                 break
-            else:
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader",
-                                        default_weight_loader)
-                weight_loader(param, loaded_weight)
+            if is_attention_weight:
+                continue
+
+            param = state_dict[name]
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         self._column_parallel_weights,
+                                         self._row_parallel_weights,
+                                         tensor_model_parallel_rank)

vllm/model_executor/models/phi_1_5.py

@@ -1,316 +0,0 @@
-# coding=utf-8
-# Adapted from
-# https://huggingface.co/microsoft/phi-1_5/blob/main/modeling_phi.py
-# Copyright 2023 The vLLM team.
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
-#
-# BSD 3-Clause License
-#
-# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
-# All rights reserved.
-#
-# Redistribution and use in source and binary forms, with or without
-# modification, are permitted provided that the following conditions are met:
-#
-# * Redistributions of source code must retain the above copyright notice, this
-#   list of conditions and the following disclaimer.
-#
-# * Redistributions in binary form must reproduce the above copyright notice,
-#   this list of conditions and the following disclaimer in the documentation
-#   and/or other materials provided with the distribution.
-#
-# * Neither the name of the copyright holder nor the names of its
-#   contributors may be used to endorse or promote products derived from
-#   this software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-"""Inference-only Phi-1.5 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.
-"""
-from typing import List, Optional, Tuple
-
-import torch
-from torch import nn
-from transformers import PretrainedConfig
-
-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 PagedAttentionWithRoPE
-from vllm.model_executor.layers.linear import (ColumnParallelLinear,
-                                               LinearMethodBase,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
-from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding, ParallelLMHead)
-from vllm.model_executor.parallel_utils.parallel_state import (
-    get_tensor_model_parallel_world_size)
-from vllm.model_executor.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
-from vllm.sequence import SamplerOutput
-
-KVCache = Tuple[torch.Tensor, torch.Tensor]
-
-
-class PhiEmbedding(nn.Module):
-
-    def __init__(self, config: PretrainedConfig):
-        super().__init__()
-
-        self.wte = VocabParallelEmbedding(
-            config.vocab_size,
-            config.hidden_size,
-        )
-
-    def forward(self, input_ids: torch.LongTensor):
-        return self.wte(input_ids)
-
-
-class PhiAttention(nn.Module):
-
-    def __init__(self,
-                 config: PretrainedConfig,
-                 linear_method: Optional[LinearMethodBase] = None):
-        super().__init__()
-        self.total_num_heads = config.num_attention_heads
-        self.hidden_size = config.hidden_size
-        self.head_size = self.hidden_size // self.total_num_heads
-
-        tensor_model_parallel_world_size = (
-            get_tensor_model_parallel_world_size())
-        assert self.total_num_heads % tensor_model_parallel_world_size == 0
-        self.num_heads = (self.total_num_heads //
-                          tensor_model_parallel_world_size)
-
-        # pylint: disable=C0103
-        self.Wqkv = QKVParallelLinear(
-            self.hidden_size,
-            self.head_size,
-            self.total_num_heads,
-            linear_method=linear_method,
-        )
-        self.qkv_proj = QKVParallelLinear(
-            config.hidden_size,
-            self.head_size,
-            self.total_num_heads,
-            bias=False,
-            linear_method=linear_method,
-        )
-        self.out_proj = RowParallelLinear(
-            self.hidden_size,
-            self.hidden_size,
-            linear_method=linear_method,
-        )
-
-        scaling = self.head_size**-0.5
-        rotary_dim = config.rotary_dim
-        assert rotary_dim % 2 == 0
-
-        # pylint: disable=C0301
-        # Refer to:
-        # https://huggingface.co/microsoft/phi-1_5/blob/d212a789620c380ff32ca1d1ee9943a777360987/modeling_phi.py#L518
-        rope_theta = 10000
-        max_position_embeddings = getattr(config, "n_positions", 2048)
-        self.attn = PagedAttentionWithRoPE(
-            self.num_heads,
-            self.head_size,
-            scaling,
-            rotary_dim,
-            base=rope_theta,
-            max_position=max_position_embeddings)
-
-    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:
-        qkv, _ = self.Wqkv(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,
-                                input_metadata, cache_event)
-        output, _ = self.out_proj(attn_output)
-        return output
-
-
-class PhiMLP(nn.Module):
-
-    def __init__(self,
-                 config: PretrainedConfig,
-                 linear_method: Optional[LinearMethodBase] = None):
-        super().__init__()
-
-        n_inner = getattr(config, "n_inner", None)
-        n_inner = n_inner if n_inner is not None else 4 * config.hidden_size
-
-        self.fc1 = ColumnParallelLinear(
-            config.hidden_size,
-            n_inner,
-            linear_method=linear_method,
-        )
-        self.fc2 = RowParallelLinear(
-            n_inner,
-            config.hidden_size,
-            linear_method=linear_method,
-        )
-        quant_config = getattr(linear_method, "quant_config", None)
-        self.act = get_act_fn(config.activation_function, quant_config,
-                              n_inner)
-
-    def forward(self, hidden_states):
-        hidden_states, _ = self.fc1(hidden_states)
-        hidden_states = self.act(hidden_states)
-        hidden_states, _ = self.fc2(hidden_states)
-        return hidden_states
-
-
-class PhiLayer(nn.Module):
-
-    def __init__(self,
-                 config: PretrainedConfig,
-                 linear_method: Optional[LinearMethodBase] = None):
-        super().__init__()
-        self.ln = nn.LayerNorm(config.hidden_size,
-                               eps=config.layer_norm_epsilon)
-        self.mixer = PhiAttention(config, linear_method)
-        self.mlp = PhiMLP(config, linear_method)
-
-    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:
-        residual = hidden_states
-        hidden_states = self.ln(hidden_states)
-        attn_outputs = self.mixer(
-            position_ids=position_ids,
-            hidden_states=hidden_states,
-            kv_cache=kv_cache,
-            input_metadata=input_metadata,
-            cache_event=cache_event,
-        )
-        feed_forward_hidden_states = self.mlp(hidden_states)
-        hidden_states = attn_outputs + feed_forward_hidden_states + residual
-        return hidden_states
-
-
-class PhiCausalLMHead(nn.Module):
-
-    def __init__(self, config: PretrainedConfig):
-        super().__init__()
-        self.ln = nn.LayerNorm(config.hidden_size,
-                               eps=config.layer_norm_epsilon)
-        self.linear = ParallelLMHead(config.vocab_size,
-                                     config.hidden_size,
-                                     bias=True)
-        self.sampler = Sampler(config.vocab_size)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        input_metadata: InputMetadata,
-    ):
-        hidden_states = self.ln(hidden_states)
-        next_tokens = self.sampler(self.linear.weight, hidden_states,
-                                   input_metadata, self.linear.bias)
-        return next_tokens
-
-
-class PhiModel(nn.Module):
-
-    def __init__(self,
-                 config: PretrainedConfig,
-                 linear_method: Optional[LinearMethodBase] = None):
-        super().__init__()
-        self.config = config
-        self.linear_method = linear_method
-        self.embd = PhiEmbedding(config)
-        self.h = nn.ModuleList([
-            PhiLayer(config, linear_method)
-            for _ in range(config.num_hidden_layers)
-        ])
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        kv_caches: List[KVCache],
-        input_metadata: InputMetadata,
-        cache_events: Optional[List[torch.cuda.Event]],
-    ) -> SamplerOutput:
-        hidden_states = self.embd(input_ids)
-        for i in range(self.config.num_hidden_layers):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
-            layer = self.h[i]
-            hidden_states = layer(
-                positions,
-                hidden_states,
-                kv_caches[i],
-                input_metadata,
-                cache_event,
-            )
-        return hidden_states
-
-
-class PhiForCausalLM(nn.Module):
-
-    def __init__(self,
-                 config: PretrainedConfig,
-                 linear_method: Optional[LinearMethodBase] = None):
-        super().__init__()
-        self.config = config
-        self.linear_method = linear_method
-
-        self.transformer = PhiModel(config, linear_method)
-        self.lm_head = PhiCausalLMHead(config)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        kv_caches: List[KVCache],
-        input_metadata: InputMetadata,
-        cache_events: Optional[List[torch.cuda.Event]],
-    ) -> SamplerOutput:
-        hidden_states = self.transformer(input_ids, positions, kv_caches,
-                                         input_metadata, cache_events)
-        lm_logits = self.lm_head(hidden_states, input_metadata)
-        return lm_logits
-
-    def load_weights(self,
-                     model_name_or_path: str,
-                     cache_dir: Optional[str] = None,
-                     load_format: str = "auto",
-                     revision: Optional[str] = None):
-        params_dict = dict(self.named_parameters())
-        for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format, revision):
-            if "rotary_emb.inv_freq" in name:
-                continue
-
-            # pylint: disable=E1136
-            param = params_dict[name]
-            weight_loader = getattr(param, "weight_loader",
-                                    default_weight_loader)
-            weight_loader(param, loaded_weight)

vllm/model_executor/models/qwen.py

@@ -15,19 +15,24 @@ from torch import nn
 
 from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
+from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.weight_utils import (
+    convert_pyslice_to_tensor,
+    hf_model_weights_iterator,
+    load_padded_tensor_parallel_vocab,
+    load_tensor_parallel_weights,
+)
 from vllm.model_executor.parallel_utils.parallel_state import (
-    get_tensor_model_parallel_world_size)
-from vllm.model_executor.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+from vllm.model_executor.parallel_utils.layers import (
+    VocabParallelEmbedding,
+    ColumnParallelLinear,
+    RowParallelLinear,
+)
 from vllm.sequence import SamplerOutput
 from vllm.transformers_utils.configs.qwen import QWenConfig
 
@@ -41,17 +46,20 @@ class QWenMLP(nn.Module):
         hidden_size: int,
         intermediate_size: int,
         hidden_act: str = "silu",
-        linear_method: Optional[LinearMethodBase] = None,
     ):
         super().__init__()
-        self.gate_up_proj = MergedColumnParallelLinear(
-            hidden_size, [intermediate_size] * 2,
+        self.gate_up_proj = ColumnParallelLinear(
+            hidden_size,
+            2 * intermediate_size,
             bias=False,
-            linear_method=linear_method)
-        self.c_proj = RowParallelLinear(intermediate_size,
-                                        hidden_size,
-                                        bias=False,
-                                        linear_method=linear_method)
+            gather_output=False,
+        )
+        self.c_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            input_is_parallel=True,
+        )
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
                              "Only silu is supported for now.")
@@ -66,15 +74,12 @@ class QWenMLP(nn.Module):
 
 class QWenAttention(nn.Module):
 
-    def __init__(
-        self,
-        hidden_size: int,
-        num_heads: int,
-        max_position_embeddings: int,
-        rope_theta: float = 10000,
-        rope_scaling: Optional[Dict[str, Any]] = None,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self,
+                 hidden_size: int,
+                 num_heads: int,
+                 max_position_embeddings: int,
+                 rope_theta: float = 10000,
+                 rope_scaling: Optional[Dict[str, Any]] = None):
         super().__init__()
         self.hidden_size = hidden_size
         tensor_model_parallel_world_size = get_tensor_model_parallel_world_size(
@@ -85,18 +90,18 @@ class QWenAttention(nn.Module):
                           tensor_model_parallel_world_size)
         self.head_dim = hidden_size // self.total_num_heads
 
-        self.c_attn = QKVParallelLinear(
+        # pylint: disable=invalid-name
+        self.c_attn = ColumnParallelLinear(
             hidden_size,
-            self.head_dim,
-            self.total_num_heads,
+            3 * hidden_size,
             bias=True,
-            linear_method=linear_method,
+            gather_output=False,
         )
         self.c_proj = RowParallelLinear(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=False,
-            linear_method=linear_method,
+            input_is_parallel=True,
         )
         self.scaling = self.head_dim**-0.5
         self.attn = PagedAttentionWithRoPE(
@@ -129,11 +134,7 @@ class QWenAttention(nn.Module):
 
 class QWenBlock(nn.Module):
 
-    def __init__(
-        self,
-        config: QWenConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: QWenConfig):
         super().__init__()
         self.ln_1 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
 
@@ -143,14 +144,11 @@ class QWenBlock(nn.Module):
                                   config.num_attention_heads,
                                   config.max_position_embeddings,
                                   rope_theta=rope_theta,
-                                  rope_scaling=rope_scaling,
-                                  linear_method=linear_method)
+                                  rope_scaling=rope_scaling)
 
         self.ln_2 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
 
-        self.mlp = QWenMLP(config.hidden_size,
-                           config.intermediate_size // 2,
-                           linear_method=linear_method)
+        self.mlp = QWenMLP(config.hidden_size, config.intermediate_size // 2)
 
     def forward(
         self,
@@ -159,14 +157,10 @@ class QWenBlock(nn.Module):
         kv_cache: KVCache,
         input_metadata: InputMetadata,
         cache_event: Optional[torch.cuda.Event],
-        residual: Optional[torch.Tensor],
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+    ) -> torch.Tensor:
         # Self Attention
-        if residual is None:
-            residual = hidden_states
-            hidden_states = self.ln_1(hidden_states)
-        else:
-            hidden_states, residual = self.ln_1(hidden_states, residual)
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states)
         hidden_states = self.attn(
             positions=positions,
             hidden_states=hidden_states,
@@ -174,32 +168,30 @@ class QWenBlock(nn.Module):
             input_metadata=input_metadata,
             cache_event=cache_event,
         )
+        hidden_states = residual + hidden_states
 
         # Fully Connected
-        hidden_states, residual = self.ln_2(hidden_states, residual)
+        residual = hidden_states
+        hidden_states = self.ln_2(hidden_states)
         hidden_states = self.mlp(hidden_states)
-        return hidden_states, residual
+        hidden_states = residual + hidden_states
+        return hidden_states
 
 
 class QWenModel(nn.Module):
 
-    def __init__(
-        self,
-        config: QWenConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: QWenConfig):
         super().__init__()
         self.config = config
         self.vocab_size = config.vocab_size
 
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
         self.wte = VocabParallelEmbedding(
-            config.vocab_size,
+            vocab_size,
             config.hidden_size,
         )
-        self.h = nn.ModuleList([
-            QWenBlock(config, linear_method)
-            for _ in range(config.num_hidden_layers)
-        ])
+        self.h = nn.ModuleList(
+            [QWenBlock(config) for _ in range(config.num_hidden_layers)])
         self.ln_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
 
     def forward(
@@ -211,37 +203,36 @@ class QWenModel(nn.Module):
         cache_events: Optional[List[torch.cuda.Event]],
     ) -> torch.Tensor:
         hidden_states = self.wte(input_ids)
-        residual = None
         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, residual = layer(
+            hidden_states = layer(
                 positions,
                 hidden_states,
                 kv_caches[i],
                 input_metadata,
                 cache_event,
-                residual,
             )
-        hidden_states, _ = self.ln_f(hidden_states, residual)
+        hidden_states = self.ln_f(hidden_states)
         return hidden_states
 
 
 class QWenLMHeadModel(nn.Module):
 
-    def __init__(
-        self,
-        config: QWenConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ):
+    def __init__(self, config: QWenConfig):
         super().__init__()
         self.config = config
-        self.linear_method = linear_method
-        self.transformer = QWenModel(config, linear_method)
-        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.transformer = QWenModel(config)
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.lm_head = ColumnParallelLinear(
+            config.hidden_size,
+            vocab_size,
+            bias=False,
+            gather_output=False,
+        )
         self.sampler = Sampler(config.vocab_size)
 
     def forward(
@@ -258,30 +249,75 @@ class QWenLMHeadModel(nn.Module):
                                    input_metadata)
         return next_tokens
 
-    def load_weights(self,
-                     model_name_or_path: str,
-                     cache_dir: Optional[str] = None,
-                     load_format: str = "auto",
-                     revision: Optional[str] = None):
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("gate_up_proj", "w2", 0),
-            ("gate_up_proj", "w1", 1),
-        ]
-        params_dict = dict(self.named_parameters())
+    _column_parallel_weights = []
+    _row_parallel_weights = ["c_proj.weight"]
+
+    def load_weights(
+        self,
+        model_name_or_path: str,
+        cache_dir: Optional[str] = None,
+        load_format: str = "auto",
+        revision: Optional[str] = None,
+    ):
+        tp_world_size = get_tensor_model_parallel_world_size()
+        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, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
-            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+
+            loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+
+            if "c_attn" in name:
+                total_num_heads = self.config.num_attention_heads
+                hidden_size = self.config.hidden_size
+                head_size = hidden_size // total_num_heads
+                num_heads = total_num_heads // tp_world_size
+                head_start = tp_rank * num_heads
+                head_end = (tp_rank + 1) * num_heads
+
+                if "weight" in name:
+                    loaded_weight = loaded_weight.view(3, total_num_heads,
+                                                       head_size, hidden_size)
+                    loaded_weight = loaded_weight[:, head_start:head_end, :, :]
+                    loaded_weight = loaded_weight.reshape(-1, hidden_size)
+                elif "bias" in name:
+                    loaded_weight = loaded_weight.view(3, total_num_heads,
+                                                       head_size)
+                    loaded_weight = loaded_weight[:, head_start:head_end, :]
+                    loaded_weight = loaded_weight.reshape(-1)
+
+            is_gate_up_weight = False
+            for stride_id, weight_name in enumerate(["w2", "w1"]):
                 if weight_name not in name:
                     continue
-                param = params_dict[name.replace(weight_name, param_name)]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
+                param = state_dict[name.replace(weight_name, "gate_up_proj")]
+                shard_size = param.shape[0] // 2
+                loaded_weight = loaded_weight[shard_size * tp_rank:shard_size *
+                                              (tp_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_gate_up_weight = True
                 break
-            else:
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader",
-                                        default_weight_loader)
-                weight_loader(param, loaded_weight)
+            if is_gate_up_weight:
+                continue
+
+            param = state_dict[name]
+
+            if "wte" in name or "lm_head" in name:
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tp_rank)
+                continue
+
+            load_tensor_parallel_weights(
+                param,
+                loaded_weight,
+                name,
+                self._column_parallel_weights,
+                self._row_parallel_weights,
+                tp_rank,
+            )

vllm/model_executor/models/yi.py

@@ -1,326 +0,0 @@
-# coding=utf-8
-# Adapted from
-# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
-# Copyright 2023 The vLLM team.
-# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
-# and OPT implementations in this library. It has been modified from its
-# original forms to accommodate minor architectural differences compared
-# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
-#
-# 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 Yi model (https://01.ai) 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.
-"""
-from typing import Any, Dict, List, Optional, Tuple
-
-import torch
-from torch import nn
-from vllm.transformers_utils.configs.yi import YiConfig
-
-from vllm.model_executor.input_metadata import InputMetadata
-from vllm.model_executor.layers.activation import SiluAndMul
-from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
-from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.linear import (LinearMethodBase,
-                                               MergedColumnParallelLinear,
-                                               QKVParallelLinear,
-                                               RowParallelLinear)
-from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import (
-    VocabParallelEmbedding, ParallelLMHead)
-from vllm.model_executor.parallel_utils.parallel_state import (
-    get_tensor_model_parallel_world_size)
-from vllm.model_executor.weight_utils import (default_weight_loader,
-                                              hf_model_weights_iterator)
-from vllm.sequence import SamplerOutput
-
-KVCache = Tuple[torch.Tensor, torch.Tensor]
-
-
-class YiMLP(nn.Module):
-
-    def __init__(
-        self,
-        hidden_size: int,
-        intermediate_size: int,
-        hidden_act: str,
-        linear_method: Optional[LinearMethodBase] = None,
-    ) -> None:
-        super().__init__()
-        self.gate_up_proj = MergedColumnParallelLinear(
-            hidden_size, [intermediate_size] * 2,
-            bias=False,
-            linear_method=linear_method)
-        self.down_proj = RowParallelLinear(intermediate_size,
-                                           hidden_size,
-                                           bias=False,
-                                           linear_method=linear_method)
-        if hidden_act != "silu":
-            raise ValueError(f"Unsupported activation: {hidden_act}. "
-                             "Only silu is supported for now.")
-        self.act_fn = SiluAndMul()
-
-    def forward(self, x):
-        gate_up, _ = self.gate_up_proj(x)
-        x = self.act_fn(gate_up)
-        x, _ = self.down_proj(x)
-        return x
-
-
-class YiAttention(nn.Module):
-
-    def __init__(
-        self,
-        hidden_size: int,
-        num_heads: int,
-        num_kv_heads: int,
-        rope_theta: float = 10000,
-        rope_scaling: Optional[Dict[str, Any]] = None,
-        max_position_embeddings: int = 8192,
-        linear_method: Optional[LinearMethodBase] = None,
-    ) -> None:
-        super().__init__()
-        self.hidden_size = hidden_size
-        tp_size = get_tensor_model_parallel_world_size()
-        self.total_num_heads = num_heads
-        assert self.total_num_heads % tp_size == 0
-        self.num_heads = self.total_num_heads // tp_size
-        self.total_num_kv_heads = num_kv_heads
-        if self.total_num_kv_heads >= tp_size:
-            # Number of KV heads is greater than TP size, so we partition
-            # the KV heads across multiple tensor parallel GPUs.
-            assert self.total_num_kv_heads % tp_size == 0
-        else:
-            # Number of KV heads is less than TP size, so we replicate
-            # the KV heads across multiple tensor parallel GPUs.
-            assert tp_size % self.total_num_kv_heads == 0
-        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
-        self.head_dim = hidden_size // self.total_num_heads
-        self.q_size = self.num_heads * self.head_dim
-        self.kv_size = self.num_kv_heads * self.head_dim
-        self.scaling = self.head_dim**-0.5
-        self.rope_theta = rope_theta
-        self.max_position_embeddings = max_position_embeddings
-
-        self.qkv_proj = QKVParallelLinear(
-            hidden_size,
-            self.head_dim,
-            self.total_num_heads,
-            self.total_num_kv_heads,
-            bias=False,
-            linear_method=linear_method,
-        )
-        self.o_proj = RowParallelLinear(
-            self.total_num_heads * self.head_dim,
-            hidden_size,
-            bias=False,
-            linear_method=linear_method,
-        )
-        self.attn = PagedAttentionWithRoPE(
-            self.num_heads,
-            self.head_dim,
-            self.scaling,
-            base=self.rope_theta,
-            max_position=self.max_position_embeddings,
-            rotary_dim=self.head_dim,
-            num_kv_heads=self.num_kv_heads,
-            rope_scaling=rope_scaling)
-
-    def forward(
-        self,
-        positions: torch.Tensor,
-        hidden_states: torch.Tensor,
-        kv_cache: KVCache,
-        input_metadata: InputMetadata,
-        cache_event: Optional[torch.cuda.Event],
-    ) -> torch.Tensor:
-        qkv, _ = self.qkv_proj(hidden_states)
-        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
-        k_cache, v_cache = kv_cache
-        attn_output = self.attn(positions, q, k, v, k_cache, v_cache,
-                                input_metadata, cache_event)
-        output, _ = self.o_proj(attn_output)
-        return output
-
-
-class YiDecoderLayer(nn.Module):
-
-    def __init__(
-        self,
-        config: YiConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ) -> None:
-        super().__init__()
-        self.hidden_size = config.hidden_size
-        rope_theta = getattr(config, "rope_theta", 10000)
-        rope_scaling = getattr(config, "rope_scaling", None)
-        max_position_embeddings = getattr(config, "max_position_embeddings",
-                                          8192)
-        self.self_attn = YiAttention(
-            hidden_size=self.hidden_size,
-            num_heads=config.num_attention_heads,
-            num_kv_heads=config.num_key_value_heads,
-            rope_theta=rope_theta,
-            rope_scaling=rope_scaling,
-            max_position_embeddings=max_position_embeddings,
-            linear_method=linear_method,
-        )
-        self.mlp = YiMLP(
-            hidden_size=self.hidden_size,
-            intermediate_size=config.intermediate_size,
-            hidden_act=config.hidden_act,
-            linear_method=linear_method,
-        )
-        self.ln1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.ln2 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-    def forward(
-        self,
-        positions: torch.Tensor,
-        hidden_states: torch.Tensor,
-        kv_cache: KVCache,
-        input_metadata: InputMetadata,
-        cache_event: Optional[torch.cuda.Event],
-        residual: Optional[torch.Tensor],
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        # Self Attention
-        if residual is None:
-            residual = hidden_states
-            hidden_states = self.ln1(hidden_states)
-        else:
-            hidden_states, residual = self.ln1(hidden_states, residual)
-        hidden_states = self.self_attn(
-            positions=positions,
-            hidden_states=hidden_states,
-            kv_cache=kv_cache,
-            input_metadata=input_metadata,
-            cache_event=cache_event,
-        )
-
-        # Fully Connected
-        hidden_states, residual = self.ln2(hidden_states, residual)
-        hidden_states = self.mlp(hidden_states)
-        return hidden_states, residual
-
-
-class YiModel(nn.Module):
-
-    def __init__(
-        self,
-        config: YiConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ) -> None:
-        super().__init__()
-        self.config = config
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-        self.embed_tokens = VocabParallelEmbedding(
-            config.vocab_size,
-            config.hidden_size,
-        )
-        self.layers = nn.ModuleList([
-            YiDecoderLayer(config, linear_method)
-            for _ in range(config.num_hidden_layers)
-        ])
-        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        kv_caches: List[KVCache],
-        input_metadata: InputMetadata,
-        cache_events: Optional[List[torch.cuda.Event]],
-    ) -> torch.Tensor:
-        hidden_states = self.embed_tokens(input_ids)
-        residual = None
-        for i in range(len(self.layers)):
-            if cache_events is None:
-                cache_event = None
-            else:
-                cache_event = cache_events[i]
-            layer = self.layers[i]
-            hidden_states, residual = layer(
-                positions,
-                hidden_states,
-                kv_caches[i],
-                input_metadata,
-                cache_event,
-                residual,
-            )
-        hidden_states, _ = self.norm(hidden_states, residual)
-        return hidden_states
-
-
-class YiForCausalLM(nn.Module):
-
-    def __init__(
-        self,
-        config: YiConfig,
-        linear_method: Optional[LinearMethodBase] = None,
-    ) -> None:
-        super().__init__()
-        self.config = config
-        self.linear_method = linear_method
-        self.model = YiModel(config, linear_method)
-        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
-        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]],
-    ) -> SamplerOutput:
-        hidden_states = self.model(input_ids, positions, kv_caches,
-                                   input_metadata, cache_events)
-        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
-                                   input_metadata)
-        return next_tokens
-
-    def load_weights(self,
-                     model_name_or_path: str,
-                     cache_dir: Optional[str] = None,
-                     load_format: str = "auto",
-                     revision: Optional[str] = None):
-        stacked_params_mapping = [
-            # (param_name, shard_name, shard_id)
-            ("qkv_proj", "q_proj", "q"),
-            ("qkv_proj", "k_proj", "k"),
-            ("qkv_proj", "v_proj", "v"),
-            ("gate_up_proj", "gate_proj", 0),
-            ("gate_up_proj", "up_proj", 1),
-        ]
-        params_dict = dict(self.named_parameters())
-        for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format, revision):
-            if "rotary_emb.inv_freq" in name:
-                continue
-            for (param_name, weight_name, shard_id) in stacked_params_mapping:
-                if weight_name not in name:
-                    continue
-                param = params_dict[name.replace(weight_name, param_name)]
-                weight_loader = param.weight_loader
-                weight_loader(param, loaded_weight, shard_id)
-                break
-            else:
-                param = params_dict[name]
-                weight_loader = getattr(param, "weight_loader",
-                                        default_weight_loader)
-                weight_loader(param, loaded_weight)

vllm/model_executor/parallel_utils/layers.py

@@ -0,0 +1,303 @@
+# Copyright 2023 The vLLM team.
+# Adapted from
+# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/tensor_parallel/layers.py
+# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+
+# Parts of the code here are adapted from PyTorch
+# repo: https://github.com/pytorch/pytorch
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch.nn.parameter import Parameter
+
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+from vllm.model_executor.quantization_utils import QuantizationConfig
+from vllm.model_executor.parallel_utils.communication_op import (
+    tensor_model_parallel_all_reduce, tensor_model_parallel_all_gather)
+
+from vllm.model_executor.parallel_utils.utils import (
+    divide,
+    VocabUtility,
+    split_tensor_along_last_dim,
+)
+
+
+class VocabParallelEmbedding(torch.nn.Module):
+    """Embedding parallelized in the vocabulary dimension.
+
+    This is mainly adapted from torch.nn.Embedding and all the default
+    values are kept.
+    Arguments:
+        num_embeddings: vocabulary size.
+        embedding_dim: size of hidden state.
+        params_dtype: type of the parameters.
+    """
+
+    def __init__(self,
+                 num_embeddings: int,
+                 embedding_dim: int,
+                 params_dtype: Optional[torch.dtype] = None):
+        super().__init__()
+
+        # Keep the input dimensions.
+        self.num_embeddings = num_embeddings
+        self.embedding_dim = embedding_dim
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+
+        self.tp_size = get_tensor_model_parallel_world_size()
+        # TODO: Handle vocab padding here.
+        # Divide the weight matrix along the vocaburaly dimension.
+        self.vocab_start_index, self.vocab_end_index = (
+            VocabUtility.vocab_range_from_global_vocab_size(
+                self.num_embeddings, get_tensor_model_parallel_rank(),
+                self.tp_size))
+        self.num_embeddings_per_partition = (self.vocab_end_index -
+                                             self.vocab_start_index)
+
+        self.weight = Parameter(
+            torch.empty(self.num_embeddings_per_partition,
+                        self.embedding_dim,
+                        device=torch.cuda.current_device(),
+                        dtype=params_dtype))
+
+    def forward(self, input_):
+        if self.tp_size > 1:
+            # Build the mask.
+            input_mask = ((input_ < self.vocab_start_index) |
+                          (input_ >= self.vocab_end_index))
+            # Mask the input.
+            masked_input = input_.clone() - self.vocab_start_index
+            masked_input[input_mask] = 0
+        else:
+            masked_input = input_
+            # Get the embeddings.
+        output_parallel = F.embedding(masked_input, self.weight)
+        # Mask the output embedding.
+        if self.tp_size > 1:
+            output_parallel[input_mask, :] = 0.0
+        # Reduce across all the model parallel GPUs.
+        output = tensor_model_parallel_all_reduce(output_parallel)
+        return output
+
+
+class ColumnParallelLinear(torch.nn.Module):
+    """Linear layer with column parallelism.
+
+    The linear layer is defined as Y = XA + b. A is parallelized along
+    its second dimension as A = [A_1, ..., A_p].
+
+    Arguments:
+        input_size: first dimension of matrix A.
+        output_size: second dimension of matrix A.
+
+    Keyword Arguments
+        bias: If true, add bias
+        gather_output: If true, call all-gather on output and make Y available
+                       to all GPUs, otherwise, every GPU will have its output
+                       which is Y_i = XA_i
+        skip_bias_add: This was added to enable performance optimizations where
+                       bias can be fused with other element-wise operations. we
+                       skip adding bias but instead return it.
+        params_dtype: Data type for the parameters.
+        quant_config: Quantization configuration.
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        output_size: int,
+        bias: bool = True,
+        gather_output: bool = True,
+        skip_bias_add: bool = False,
+        params_dtype: Optional[torch.dtype] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+
+        # Keep input parameters
+        self.input_size = input_size
+        self.output_size = output_size
+        self.gather_output = gather_output
+        # Divide the weight matrix along the last dimension.
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.output_size_per_partition = divide(output_size, self.tp_size)
+        self.skip_bias_add = skip_bias_add
+        self.quant_config = quant_config
+
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+
+        # Parameters.
+        # NOTE: torch.nn.functional.linear performs XA^T + b and as a result
+        # we allocate the transpose.
+        self.create_weights(params_dtype)
+
+        if bias:
+            self.bias = Parameter(
+                torch.empty(self.output_size_per_partition,
+                            device=torch.cuda.current_device(),
+                            dtype=params_dtype))
+        else:
+            self.register_parameter('bias', None)
+
+    def create_weights(self, dtype: torch.dtype) -> None:
+        self.weight = Parameter(
+            torch.empty(self.output_size_per_partition,
+                        self.input_size,
+                        device=torch.cuda.current_device(),
+                        dtype=dtype))
+
+    def apply_weights(
+        self,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        return F.linear(x, self.weight, bias)
+
+    def forward(self, input_):
+        """Forward of ColumnParallelLinear
+
+        Args:
+            input_: Tensor whose last dimension is `input_size`.
+
+        Returns:
+            - output
+            - bias
+        """
+        bias = self.bias if not self.skip_bias_add else None
+
+        input_parallel = input_
+        # Matrix multiply.
+        output_parallel = self.apply_weights(input_parallel, bias)
+        if self.gather_output:
+            # All-gather across the partitions.
+            output = tensor_model_parallel_all_gather(output_parallel)
+        else:
+            output = output_parallel
+        output_bias = self.bias if self.skip_bias_add else None
+        return output, output_bias
+
+
+class RowParallelLinear(torch.nn.Module):
+    """Linear layer with row parallelism.
+
+    The linear layer is defined as Y = XA + b. A is parallelized along
+    its first dimension and X along its second dimension as:
+               -   -
+              | A_1 |
+              | .   |
+          A = | .   |        X = [X_1, ..., X_p]
+              | .   |
+              | A_p |
+               -   -
+    Arguments:
+        input_size: first dimension of matrix A.
+        output_size: second dimension of matrix A.
+
+    Keyword Arguments:
+        bias: If true, add bias. Note that bias is not parallelized.
+        input_is_parallel: If true, we assume that the input is already
+                           split across the GPUs and we do not split
+                           again.
+        skip_bias_add: This was added to enable performance optimization where
+                       bias can be fused with other element-wise operations.
+                       We skip adding bias but instead return it.
+        params_dtype: Data type for the parameters.
+        quant_config: Quantization configuration.
+    """
+
+    def __init__(
+        self,
+        input_size: int,
+        output_size: int,
+        bias: bool = True,
+        input_is_parallel: bool = False,
+        skip_bias_add: bool = False,
+        params_dtype: Optional[torch.dtype] = None,
+        reduce_results: bool = True,
+        quant_config: Optional[QuantizationConfig] = None,
+    ):
+        super().__init__()
+        # Keep input parameters
+        self.input_size = input_size
+        self.output_size = output_size
+        self.input_is_parallel = input_is_parallel
+        self.reduce_results = reduce_results
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+
+        # Divide the weight matrix along the last dimension.
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.input_size_per_partition = divide(input_size, self.tp_size)
+        self.skip_bias_add = skip_bias_add
+        self.quant_config = quant_config
+
+        self.create_weights(params_dtype)
+
+        if not reduce_results and (bias and not skip_bias_add):
+            raise ValueError('When not reduce the results, adding bias to the '
+                             'results can lead to incorrect results')
+
+        if bias:
+            self.bias = Parameter(
+                torch.empty(self.output_size,
+                            device=torch.cuda.current_device(),
+                            dtype=params_dtype))
+
+            # Always initialize bias to zero.
+            with torch.no_grad():
+                self.bias.zero_()
+        else:
+            self.register_parameter('bias', None)
+
+    def create_weights(self, dtype: torch.dtype) -> None:
+        self.weight = Parameter(
+            torch.empty(self.output_size,
+                        self.input_size_per_partition,
+                        device=torch.cuda.current_device(),
+                        dtype=dtype))
+
+    def apply_weights(self, x: torch.Tensor) -> torch.Tensor:
+        return F.linear(x, self.weight)
+
+    def forward(self, input_):
+        """Forward of RowParallelLinear
+
+        Args:
+            input_: tensor whose last dimension is `input_size`. If
+                    `input_is_parallel` is set, then the last dimension
+                    is `input_size // tp_size`.
+
+        Returns:
+            - output
+            - bias
+        """
+        # Set up backprop all-reduce.
+        if self.input_is_parallel:
+            input_parallel = input_
+        else:
+            # TODO: simplify code below
+            tp_rank = get_tensor_model_parallel_rank()
+            splitted_input = split_tensor_along_last_dim(
+                input_, num_partitions=self.tp_size)
+            input_parallel = splitted_input[tp_rank].contiguous()
+
+        # Matrix multiply.
+        output_parallel = self.apply_weights(input_parallel)
+        if self.reduce_results and self.tp_size > 1:
+            output_ = tensor_model_parallel_all_reduce(output_parallel)
+        else:
+            output_ = output_parallel
+
+        if not self.skip_bias_add:
+            output = output_ + self.bias if self.bias is not None else output_
+            output_bias = None
+        else:
+            output = output_
+            output_bias = self.bias
+        return output, output_bias

vllm/model_executor/parallel_utils/utils.py

@@ -2,7 +2,7 @@
 # Adapted from
 # https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/core/tensor_parallel/utils.py
 # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
-from typing import Sequence
+from typing import List, Sequence
 
 import torch
 
@@ -24,7 +24,7 @@ def split_tensor_along_last_dim(
     tensor: torch.Tensor,
     num_partitions: int,
     contiguous_split_chunks: bool = False,
-) -> Sequence[torch.Tensor]:
+) -> List[torch.Tensor]:
     """ Split a tensor along its last dimension.
 
         Arguments:
@@ -46,3 +46,25 @@ def split_tensor_along_last_dim(
         return tuple(chunk.contiguous() for chunk in tensor_list)
 
     return tensor_list
+
+
+class VocabUtility:
+    """ Split the vocabulary into `world_size` chunks and return the first
+        and last index of the vocabulary belonging to the `rank`
+        partition: Note that indices in [fist, last)
+
+    """
+
+    @staticmethod
+    def vocab_range_from_per_partition_vocab_size(
+            per_partition_vocab_size: int, rank: int) -> Sequence[int]:
+        index_f = rank * per_partition_vocab_size
+        index_l = index_f + per_partition_vocab_size
+        return index_f, index_l
+
+    @staticmethod
+    def vocab_range_from_global_vocab_size(global_vocab_size: int, rank: int,
+                                           world_size: int) -> Sequence[int]:
+        per_partition_vocab_size = divide(global_vocab_size, world_size)
+        return VocabUtility.vocab_range_from_per_partition_vocab_size(
+            per_partition_vocab_size, rank)

vllm/model_executor/quantization_utils/__init__.py

@@ -0,0 +1,20 @@
+from typing import Type
+
+from vllm.model_executor.quantization_utils.awq import AWQConfig
+from vllm.model_executor.quantization_utils.base import QuantizationConfig
+
+_QUANTIZATION_REGISTRY = {
+    "awq": AWQConfig,
+}
+
+
+def get_quant_class(quantization: str) -> Type[QuantizationConfig]:
+    if quantization not in _QUANTIZATION_REGISTRY:
+        raise ValueError(f"Invalid quantization method: {quantization}")
+    return _QUANTIZATION_REGISTRY[quantization]
+
+
+__all__ = [
+    "QuantizationConfig",
+    "get_quant_class",
+]

vllm/model_executor/quantization_utils/awq.py

@@ -0,0 +1,72 @@
+from typing import Any, Dict, List
+
+import torch
+
+from vllm.model_executor.quantization_utils.base import QuantizationConfig
+
+
+class AWQConfig(QuantizationConfig):
+    """Config class for AWQ.
+
+    Reference: https://arxiv.org/abs/2306.00978
+    """
+
+    def __init__(
+        self,
+        weight_bits: int,
+        group_size: int,
+        zero_point: bool,
+    ) -> None:
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.zero_point = zero_point
+
+        if self.weight_bits != 4:
+            raise ValueError(
+                "Currently, only 4-bit weight quantization is supported for "
+                f"AWQ, but got {self.weight_bits} bits.")
+        self.pack_factor = 32 // self.weight_bits
+
+    def __repr__(self) -> str:
+        return (f"AWQConfig(weight_bits={self.weight_bits}, "
+                f"group_size={self.group_size}, "
+                f"zero_point={self.zero_point})")
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "awq"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.half]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # The AWQ kernel only supports Turing or newer GPUs.
+        return 75
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return [
+            "quant_config.json",  # E.g., casperhansen/vicuna-7b-v1.5-awq
+            "quantize_config.json",  # E.g., abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq  # pylint: disable=line-too-long
+        ]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "AWQConfig":
+        weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
+        group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
+        zero_point = cls.get_from_keys(config, ["zero_point"])
+        return cls(weight_bits, group_size, zero_point)
+
+    @classmethod
+    def get_packed_tensor_names(cls) -> List[str]:
+        return ["qweight", "qzeros"]
+
+    @classmethod
+    def get_transposed_tensor_names(cls) -> List[str]:
+        return ["qweight", "qzeros", "scales"]
+
+    @classmethod
+    def get_tp_tensor_names(cls) -> List[str]:
+        return ["qweight", "qzeros", "scales"]

vllm/model_executor/quantization_utils/base.py

@@ -1,26 +1,22 @@
-from abc import ABC, abstractmethod
 from typing import Any, Dict, List
 
 import torch
 
-from vllm.model_executor.layers.linear import LinearMethodBase
 
+class QuantizationConfig:
 
-class QuantizationConfig(ABC):
-    """Base class for quantization configs."""
-
-    @abstractmethod
-    def get_name(self) -> str:
+    @classmethod
+    def get_name(cls) -> str:
         """Name of the quantization method."""
         raise NotImplementedError
 
-    @abstractmethod
-    def get_supported_act_dtypes(self) -> List[torch.dtype]:
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
         """List of supported activation dtypes."""
         raise NotImplementedError
 
-    @abstractmethod
-    def get_min_capability(self) -> int:
+    @classmethod
+    def get_min_capability(cls) -> int:
         """Minimum GPU capability to support the quantization method.
 
         E.g., 70 for Volta, 75 for Turing, 80 for Ampere.
@@ -29,14 +25,12 @@ class QuantizationConfig(ABC):
         """
         raise NotImplementedError
 
-    @staticmethod
-    @abstractmethod
-    def get_config_filenames() -> List[str]:
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
         """List of filenames to search for in the model directory."""
         raise NotImplementedError
 
     @classmethod
-    @abstractmethod
     def from_config(cls, config: Dict[str, Any]) -> "QuantizationConfig":
         """Create a config class from the model's quantization config."""
         raise NotImplementedError
@@ -50,15 +44,32 @@ class QuantizationConfig(ABC):
         raise ValueError(f"Cannot find any of {keys} in the model's "
                          "quantization config.")
 
-    @abstractmethod
-    def get_linear_method(self) -> LinearMethodBase:
-        """Get the linear method to use for the quantized linear layer."""
+    @classmethod
+    def get_packed_tensor_names(cls) -> List[str]:
         raise NotImplementedError
 
-    @abstractmethod
-    def get_scaled_act_names(self) -> List[str]:
-        """Returns the activation function names that should be post-scaled.
+    @classmethod
+    def is_packed(cls, tensor_name: str) -> bool:
+        """Returns True if a tensor is packed.
 
-        For now, this is only used by AWQ.
+        A tensor is considered packed if each element in the tensor is a
+        packed representation of multiple elements in the original tensor.
+        For example, an INT32 element in the tensor may represent 8 INT4
+        elements in the original tensor.
         """
+        return any(tag in tensor_name for tag in cls.get_packed_tensor_names())
+
+    @classmethod
+    def get_transposed_tensor_names(cls) -> List[str]:
+        raise NotImplementedError
+
+    @classmethod
+    def is_transposed(cls, tensor_name: str) -> bool:
+        """Returns True if a tensor is transposed relative to nn.Linear.weight.
+        """
+        return any(tag in tensor_name
+                   for tag in cls.get_transposed_tensor_names())
+
+    @classmethod
+    def get_tp_tensor_names(cls) -> List[str]:
         raise NotImplementedError

vllm/model_executor/utils.py

@@ -1,6 +1,5 @@
 """Utils for model executor."""
 import random
-from typing import Any, Dict, Optional
 
 import numpy as np
 import torch
@@ -12,24 +11,3 @@ def set_random_seed(seed: int) -> None:
     torch.manual_seed(seed)
     if torch.cuda.is_available():
         torch.cuda.manual_seed_all(seed)
-
-
-def set_weight_attrs(
-    weight: torch.Tensor,
-    weight_attrs: Optional[Dict[str, Any]],
-):
-    """Set attributes on a weight tensor.
-
-    This method is used to set attributes on a weight tensor. This method
-    will not overwrite existing attributes.
-
-    Args:
-        weight: The weight tensor.
-        weight_attrs: A dictionary of attributes to set on the weight tensor.
-    """
-    if weight_attrs is None:
-        return
-    for key, value in weight_attrs.items():
-        assert not hasattr(
-            weight, key), (f"Overwriting existing tensor attribute: {key}")
-        setattr(weight, key, value)

vllm/model_executor/weight_utils.py

@@ -7,15 +7,14 @@ from collections import defaultdict
 from typing import Any, Iterator, List, Optional, Tuple
 
 from huggingface_hub import snapshot_download
-import numpy as np
 from safetensors.torch import load_file, save_file, safe_open
+import numpy as np
 import torch
-from transformers import PretrainedConfig
 from tqdm.auto import tqdm
 
 from vllm.logger import init_logger
-from vllm.model_executor.layers.quantization import (get_quantization_config,
-                                                     QuantizationConfig)
+from vllm.model_executor.quantization_utils import get_quant_class
+from vllm.model_executor.quantization_utils.base import QuantizationConfig
 
 logger = init_logger(__name__)
 
@@ -85,15 +84,8 @@ def convert_bin_to_safetensor_file(
 def get_quant_config(
     quantization: str,
     model_name_or_path: str,
-    hf_config: PretrainedConfig,
     cache_dir: Optional[str] = None,
 ) -> QuantizationConfig:
-    quant_cls = get_quantization_config(quantization)
-    # Read the quantization config from the HF model config, if available.
-    hf_quant_config = getattr(hf_config, "quantization_config", None)
-    if hf_quant_config is not None:
-        return quant_cls.from_config(hf_quant_config)
-
     is_local = os.path.isdir(model_name_or_path)
     if not is_local:
         # Download the config files.
@@ -106,6 +98,7 @@ def get_quant_config(
         hf_folder = model_name_or_path
     config_files = glob.glob(os.path.join(hf_folder, "*.json"))
 
+    quant_cls = get_quant_class(quantization)
     quant_config_files = [
         f for f in config_files if any(
             f.endswith(x) for x in quant_cls.get_config_filenames())
@@ -243,7 +236,7 @@ def hf_model_weights_iterator(
         for st_file in hf_weights_files:
             with safe_open(st_file, framework="pt") as f:
                 for name in f.keys():
-                    param = f.get_tensor(name)
+                    param = f.get_slice(name)
                     yield name, param
     else:
         for bin_file in hf_weights_files:
@@ -269,10 +262,46 @@ def convert_pyslice_to_tensor(x: Any) -> torch.Tensor:
     return x
 
 
-def default_weight_loader(param: torch.Tensor,
-                          loaded_weight: torch.Tensor) -> None:
-    """Default weight loader."""
-    assert param.size() == loaded_weight.size()
+def load_padded_tensor_parallel_vocab(
+    param: torch.Tensor,
+    loaded_weight: Any,  # `torch.Tensor` or `PySafeSlice`
+    tensor_model_parallel_rank: int,
+) -> None:
+    shard_size = param.shape[0]
+    start_idx = tensor_model_parallel_rank * shard_size
+    end_idx = (tensor_model_parallel_rank + 1) * shard_size
+    loaded_weight = loaded_weight[start_idx:end_idx]
+    loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+    param[:loaded_weight.shape[0]].copy_(loaded_weight)
+
+
+def load_tensor_parallel_weights(
+    param: torch.Tensor,
+    loaded_weight: Any,  # `torch.Tensor` or `PySafeSlice`
+    param_name: str,
+    column_parallel_weight_names: List[str],
+    row_parallel_weight_names: List[str],
+    tensor_model_parallel_rank: int,
+) -> None:
+    for p in column_parallel_weight_names:
+        if p in param_name:
+            shard_size = param.shape[0]
+            start_idx = tensor_model_parallel_rank * shard_size
+            end_idx = (tensor_model_parallel_rank + 1) * shard_size
+            loaded_weight = loaded_weight[start_idx:end_idx]
+            break
+    for p in row_parallel_weight_names:
+        if p in param_name:
+            shard_size = param.shape[1]
+            start_idx = tensor_model_parallel_rank * shard_size
+            end_idx = (tensor_model_parallel_rank + 1) * shard_size
+            loaded_weight = loaded_weight[:, start_idx:end_idx]
+            break
+
+    loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+    assert param.shape == loaded_weight.shape, (
+        f"{param_name} shape mismatch between model and checkpoint: "
+        f"{param.shape} != {loaded_weight.shape}")
     param.data.copy_(loaded_weight)
 
 

vllm/outputs.py

@@ -53,7 +53,6 @@ class RequestOutput:
         request_id: The unique ID of the request.
         prompt: The prompt string of the request.
         prompt_token_ids: The token IDs of the prompt.
-        prompt_logprobs: The log probabilities to return per prompt token.
         outputs: The output sequences of the request.
         finished: Whether the whole request is finished.
     """

vllm/py.typed

@@ -1,2 +0,0 @@
-# Marker file for PEP 561.
-# The vllm package uses inline types.

vllm/sampling_params.py

@@ -1,8 +1,7 @@
 """Sampling parameters for text generation."""
 from enum import IntEnum
 from functools import cached_property
-from typing import Callable, List, Optional, Union
-import torch
+from typing import List, Optional, Union
 
 _SAMPLING_EPS = 1e-5
 
@@ -13,12 +12,6 @@ class SamplingType(IntEnum):
     BEAM = 2
 
 
-LogitsProcessor = Callable[[List[int], torch.Tensor], torch.Tensor]
-"""LogitsProcessor is a function that takes a list of previously generated
-tokens and a tensor of the logits for the next token, and returns a modified
-tensor of logits to sample from."""
-
-
 class SamplingParams:
     """Sampling parameters for text generation.
 
@@ -41,10 +34,6 @@ class SamplingParams:
             frequency in the generated text so far. Values > 0 encourage the
             model to use new tokens, while values < 0 encourage the model to
             repeat tokens.
-        repetition_penalty: Float that penalizes new tokens based on whether
-            they appear in the generated text so far. Values > 1 encourage the
-            model to use new tokens, while values < 1 encourage the model to
-            repeat tokens.
         temperature: Float that controls the randomness of the sampling. Lower
             values make the model more deterministic, while higher values make
             the model more random. Zero means greedy sampling.
@@ -52,9 +41,6 @@ class SamplingParams:
             to consider. Must be in (0, 1]. Set to 1 to consider all tokens.
         top_k: Integer that controls the number of top tokens to consider. Set
             to -1 to consider all tokens.
-        min_p: Float that represents the minimum probability for a token to be
-            considered, relative to the probability of the most likely token.
-            Must be in [0, 1]. Set to 0 to disable this.
         use_beam_search: Whether to use beam search instead of sampling.
         length_penalty: Float that penalizes sequences based on their length.
             Used in beam search.
@@ -81,10 +67,6 @@ class SamplingParams:
             `logprobs+1` elements in the response.
         prompt_logprobs: Number of log probabilities to return per prompt token.
         skip_special_tokens: Whether to skip special tokens in the output.
-        spaces_between_special_tokens: Whether to add spaces between special
-            tokens in the output.  Defaults to True.
-        logits_processors: List of functions that modify logits based on
-            previously generated tokens.
     """
 
     def __init__(
@@ -93,11 +75,9 @@ class SamplingParams:
         best_of: Optional[int] = None,
         presence_penalty: float = 0.0,
         frequency_penalty: float = 0.0,
-        repetition_penalty: float = 1.0,
         temperature: float = 1.0,
         top_p: float = 1.0,
         top_k: int = -1,
-        min_p: int = 0.0,
         use_beam_search: bool = False,
         length_penalty: float = 1.0,
         early_stopping: Union[bool, str] = False,
@@ -108,18 +88,14 @@ class SamplingParams:
         logprobs: Optional[int] = None,
         prompt_logprobs: Optional[int] = None,
         skip_special_tokens: bool = True,
-        spaces_between_special_tokens: bool = True,
-        logits_processors: Optional[List[LogitsProcessor]] = None,
     ) -> None:
         self.n = n
         self.best_of = best_of if best_of is not None else n
         self.presence_penalty = presence_penalty
         self.frequency_penalty = frequency_penalty
-        self.repetition_penalty = repetition_penalty
         self.temperature = temperature
         self.top_p = top_p
         self.top_k = top_k
-        self.min_p = min_p
         self.use_beam_search = use_beam_search
         self.length_penalty = length_penalty
         self.early_stopping = early_stopping
@@ -138,8 +114,7 @@ class SamplingParams:
         self.logprobs = logprobs
         self.prompt_logprobs = prompt_logprobs
         self.skip_special_tokens = skip_special_tokens
-        self.spaces_between_special_tokens = spaces_between_special_tokens
-        self.logits_processors = logits_processors
+
         self._verify_args()
         if self.use_beam_search:
             self._verify_beam_search()
@@ -161,9 +136,6 @@ class SamplingParams:
         if not -2.0 <= self.frequency_penalty <= 2.0:
             raise ValueError("frequency_penalty must be in [-2, 2], got "
                              f"{self.frequency_penalty}.")
-        if not 0.0 < self.repetition_penalty <= 2.0:
-            raise ValueError("repetition_penalty must be in (0, 2], got "
-                             f"{self.repetition_penalty}.")
         if self.temperature < 0.0:
             raise ValueError(
                 f"temperature must be non-negative, got {self.temperature}.")
@@ -172,9 +144,6 @@ class SamplingParams:
         if self.top_k < -1 or self.top_k == 0:
             raise ValueError(f"top_k must be -1 (disable), or at least 1, "
                              f"got {self.top_k}.")
-        if not 0.0 <= self.min_p <= 1.0:
-            raise ValueError("min_p must be in [0, 1], got "
-                             f"{self.min_p}.")
         if self.max_tokens < 1:
             raise ValueError(
                 f"max_tokens must be at least 1, got {self.max_tokens}.")
@@ -232,11 +201,9 @@ class SamplingParams:
                 f"best_of={self.best_of}, "
                 f"presence_penalty={self.presence_penalty}, "
                 f"frequency_penalty={self.frequency_penalty}, "
-                f"repetition_penalty={self.repetition_penalty}, "
                 f"temperature={self.temperature}, "
                 f"top_p={self.top_p}, "
                 f"top_k={self.top_k}, "
-                f"min_p={self.min_p}, "
                 f"use_beam_search={self.use_beam_search}, "
                 f"length_penalty={self.length_penalty}, "
                 f"early_stopping={self.early_stopping}, "
@@ -245,6 +212,4 @@ class SamplingParams:
                 f"max_tokens={self.max_tokens}, "
                 f"logprobs={self.logprobs}, "
                 f"prompt_logprobs={self.prompt_logprobs}, "
-                f"skip_special_tokens={self.skip_special_tokens}, "
-                "spaces_between_special_tokens="
-                f"{self.spaces_between_special_tokens})")
+                f"skip_special_tokens={self.skip_special_tokens})")

vllm/transformers_utils/config.py

@@ -5,14 +5,12 @@ from transformers import AutoConfig, PretrainedConfig
 from vllm.transformers_utils.configs import *  # pylint: disable=wildcard-import
 
 _CONFIG_REGISTRY = {
-    "aquila": AquilaConfig,
-    "baichuan": BaiChuanConfig,
-    "chatglm": ChatGLMConfig,
     "mpt": MPTConfig,
+    "baichuan": BaiChuanConfig,
+    "aquila": AquilaConfig,
     "qwen": QWenConfig,
     "RefinedWeb": RWConfig,  # For tiiuae/falcon-40b(-instruct)
     "RefinedWebModel": RWConfig,  # For tiiuae/falcon-7b(-instruct)
-    "yi": YiConfig,
 }
 
 

vllm/transformers_utils/configs/__init__.py

@@ -1,20 +1,16 @@
-from vllm.transformers_utils.configs.aquila import AquilaConfig
-from vllm.transformers_utils.configs.baichuan import BaiChuanConfig
-from vllm.transformers_utils.configs.chatglm import ChatGLMConfig
 from vllm.transformers_utils.configs.mpt import MPTConfig
+from vllm.transformers_utils.configs.baichuan import BaiChuanConfig
+from vllm.transformers_utils.configs.aquila import AquilaConfig
 from vllm.transformers_utils.configs.qwen import QWenConfig
 # RWConfig is for the original tiiuae/falcon-40b(-instruct) and
 # tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
 # `FalconConfig` class from the official HuggingFace transformers library.
 from vllm.transformers_utils.configs.falcon import RWConfig
-from vllm.transformers_utils.configs.yi import YiConfig
 
 __all__ = [
-    "AquilaConfig",
-    "BaiChuanConfig",
-    "ChatGLMConfig",
     "MPTConfig",
+    "BaiChuanConfig",
+    "AquilaConfig",
     "QWenConfig",
     "RWConfig",
-    "YiConfig",
 ]

vllm/transformers_utils/configs/chatglm.py

@@ -1,68 +0,0 @@
-# coding=utf-8
-# Adapted from
-# https://github.com/THUDM/ChatGLM2-6B
-from transformers import PretrainedConfig
-
-
-class ChatGLMConfig(PretrainedConfig):
-    model_type = "chatglm"
-    attribute_map = {
-        "num_hidden_layers": "num_layers",
-        "n_head_kv": "multi_query_group_num",
-    }
-
-    def __init__(self,
-                 num_layers=28,
-                 padded_vocab_size=65024,
-                 hidden_size=4096,
-                 ffn_hidden_size=13696,
-                 kv_channels=128,
-                 num_attention_heads=32,
-                 seq_length=2048,
-                 hidden_dropout=0.0,
-                 attention_dropout=0.0,
-                 layernorm_epsilon=1e-5,
-                 rmsnorm=True,
-                 apply_residual_connection_post_layernorm=False,
-                 post_layer_norm=True,
-                 add_bias_linear=False,
-                 add_qkv_bias=False,
-                 interleaved_qkv=False,
-                 bias_dropout_fusion=True,
-                 multi_query_attention=False,
-                 multi_query_group_num=1,
-                 apply_query_key_layer_scaling=True,
-                 attention_softmax_in_fp32=True,
-                 fp32_residual_connection=False,
-                 quantization_bit=0,
-                 pre_seq_len=None,
-                 prefix_projection=False,
-                 **kwargs):
-        self.num_layers = num_layers
-        self.vocab_size = padded_vocab_size
-        self.padded_vocab_size = padded_vocab_size
-        self.hidden_size = hidden_size
-        self.ffn_hidden_size = ffn_hidden_size
-        self.kv_channels = kv_channels
-        self.num_attention_heads = num_attention_heads
-        self.seq_length = seq_length
-        self.hidden_dropout = hidden_dropout
-        self.attention_dropout = attention_dropout
-        self.layernorm_epsilon = layernorm_epsilon
-        self.rmsnorm = rmsnorm
-        self.apply_residual_connection_post_layernorm = (
-            apply_residual_connection_post_layernorm)
-        self.post_layer_norm = post_layer_norm
-        self.add_bias_linear = add_bias_linear
-        self.add_qkv_bias = add_qkv_bias
-        self.bias_dropout_fusion = bias_dropout_fusion
-        self.multi_query_attention = multi_query_attention
-        self.multi_query_group_num = multi_query_group_num
-        self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
-        self.attention_softmax_in_fp32 = attention_softmax_in_fp32
-        self.fp32_residual_connection = fp32_residual_connection
-        self.quantization_bit = quantization_bit
-        self.pre_seq_len = pre_seq_len
-        self.prefix_projection = prefix_projection
-        self.interleaved_qkv = interleaved_qkv
-        super().__init__(**kwargs)

vllm/transformers_utils/configs/mpt.py

@@ -1,124 +1,52 @@
-# coding=utf-8
-# Copied from
+# Adapted from
 # https://huggingface.co/mosaicml/mpt-7b/blob/main/configuration_mpt.py
-"""A HuggingFace-style model configuration."""
-import warnings
 from typing import Any, Dict, Optional, Union
+
 from transformers import PretrainedConfig
 
-attn_config_defaults: Dict = {
-    'attn_type': 'multihead_attention',
-    'attn_pdrop': 0.0,
-    'attn_impl': 'triton',
-    'qk_ln': False,
-    'clip_qkv': None,
-    'softmax_scale': None,
-    'prefix_lm': False,
-    'attn_uses_sequence_id': False,
-    'alibi': False,
-    'alibi_bias_max': 8
-}
-ffn_config_defaults: Dict = {'ffn_type': 'mptmlp'}
-init_config_defaults: Dict = {
-    'name': 'kaiming_normal_',
-    'fan_mode': 'fan_in',
-    'init_nonlinearity': 'relu',
-    'init_div_is_residual': True,
-    'emb_init_std': None,
-    'emb_init_uniform_lim': None,
-    'init_std': None,
-    'init_gain': 0.0
+_ATTN_CONFIG_DEFAULTS = {
+    "attn_type": "multihead_attention",
+    "attn_pdrop": 0.0,
+    "attn_impl": "triton",
+    "qk_ln": False,
+    "clip_qkv": None,
+    "softmax_scale": None,
+    "prefix_lm": False,
+    "attn_uses_sequence_id": False,
+    "alibi": False,
+    "alibi_bias_max": 8,
 }
 
 
 class MPTConfig(PretrainedConfig):
-    model_type = 'mpt'
+    model_type = "mpt"
     attribute_map = {
-        'num_attention_heads': 'n_heads',
-        'hidden_size': 'd_model',
-        'num_hidden_layers': 'n_layers',
+        "hidden_size": "d_model",
+        "num_attention_heads": "n_heads",
+        "num_hidden_layers": "n_layers",
     }
 
-    # pylint: disable=dangerous-default-value
-    def __init__(self,
-                 d_model: int = 2048,
-                 n_heads: int = 16,
-                 n_layers: int = 24,
-                 expansion_ratio: int = 4,
-                 max_seq_len: int = 2048,
-                 vocab_size: int = 50368,
-                 resid_pdrop: float = 0.0,
-                 emb_pdrop: float = 0.0,
-                 learned_pos_emb: bool = True,
-                 attn_config: Dict = attn_config_defaults,
-                 ffn_config: Dict = ffn_config_defaults,
-                 init_device: str = 'cpu',
-                 logit_scale: Optional[Union[float, str]] = None,
-                 no_bias: bool = False,
-                 embedding_fraction: float = 1.0,
-                 norm_type: str = 'low_precision_layernorm',
-                 use_cache: bool = False,
-                 init_config: Dict = init_config_defaults,
-                 fc_type: str = 'torch',
-                 verbose: Optional[int] = None,
-                 **kwargs: Any):
-        # pylint: disable=line-too-long
-        """The MPT configuration class.
-        Args:
-            d_model (int): The size of the embedding dimension of the model.
-            n_heads (int): The number of attention heads.
-            n_layers (int): The number of layers in the model.
-            expansion_ratio (int): The ratio of the up/down scale in the ffn.
-            max_seq_len (int): The maximum sequence length of the model.
-            vocab_size (int): The size of the vocabulary.
-            resid_pdrop (float): The dropout probability applied to the attention output before combining with residual.
-            emb_pdrop (float): The dropout probability for the embedding layer.
-            learned_pos_emb (bool): Whether to use learned positional embeddings
-            attn_config (Dict): A dictionary used to configure the model's attention module:
-                attn_type (str): type of attention to use. Options: multihead_attention, multiquery_attention, grouped_query_attention
-                attn_pdrop (float): The dropout probability for the attention layers.
-                attn_impl (str): The attention implementation to use. One of 'torch', 'flash', or 'triton'.
-                qk_ln (bool): Whether to apply layer normalization to the queries and keys in the attention layer.
-                clip_qkv (Optional[float]): If not None, clip the queries, keys, and values in the attention layer to
-                    this value.
-                softmax_scale (Optional[float]): If not None, scale the softmax in the attention layer by this value. If None,
-                    use the default scale of ``1/sqrt(d_keys)``.
-                prefix_lm (Optional[bool]): Whether the model should operate as a Prefix LM. This requires passing an
-                    extra `prefix_mask` argument which indicates which tokens belong to the prefix. Tokens in the prefix
-                    can attend to one another bi-directionally. Tokens outside the prefix use causal attention.
-                attn_uses_sequence_id (Optional[bool]): Whether to restrict attention to tokens that have the same sequence_id.
-                    When the model is in `train` mode, this requires passing an extra `sequence_id` argument which indicates
-                    which sub-sequence each token belongs to.
-                    Defaults to ``False`` meaning any provided `sequence_id` will be ignored.
-                alibi (bool): Whether to use the alibi bias instead of position embeddings.
-                alibi_bias_max (int): The maximum value of the alibi bias.
-                kv_n_heads (Optional[int]): For grouped_query_attention only, allow user to specify number of kv heads.
-            ffn_config (Dict): A dictionary used to configure the model's ffn module:
-                ffn_type (str): type of ffn to use. Options: mptmlp, te_ln_mlp
-            init_device (str): The device to use for parameter initialization.
-            logit_scale (Optional[Union[float, str]]): If not None, scale the logits by this value.
-            no_bias (bool): Whether to use bias in all layers.
-            verbose (int): The verbosity level. 0 is silent.
-            embedding_fraction (float): The fraction to scale the gradients of the embedding layer by.
-            norm_type (str): choose type of norm to use
-            use_cache (bool): Whether or not the model should return the last key/values attentions
-            init_config (Dict): A dictionary used to configure the model initialization:
-                init_config.name: The parameter initialization scheme to use. Options: 'default_', 'baseline_',
-                    'kaiming_uniform_', 'kaiming_normal_', 'neox_init_', 'small_init_', 'xavier_uniform_', or
-                    'xavier_normal_'. These mimic the parameter initialization methods in PyTorch.
-                init_div_is_residual (Union[int, float, str, bool]): Value to divide initial weights by if ``module._is_residual`` is True.
-                emb_init_std (Optional[float]): The standard deviation of the normal distribution used to initialize the embedding layer.
-                emb_init_uniform_lim (Optional[Union[Tuple[float, float], float]]): The lower and upper limits of the uniform distribution
-                    used to initialize the embedding layer. Mutually exclusive with ``emb_init_std``.
-                init_std (float): The standard deviation of the normal distribution used to initialize the model,
-                    if using the baseline_ parameter initialization scheme.
-                init_gain (float): The gain to use for parameter initialization with kaiming or xavier initialization schemes.
-                fan_mode (str): The fan mode to use for parameter initialization with kaiming initialization schemes.
-                init_nonlinearity (str): The nonlinearity to use for parameter initialization with kaiming initialization schemes.
-                ---
-                See llmfoundry.models.utils.param_init_fns.py for info on other param init config options
-            fc_type (str): choose fc layer implementation. Options: torch and te. te layers support fp8 when using H100 GPUs.
-        """
+    def __init__(
+        self,
+        d_model: int = 2048,
+        n_heads: int = 16,
+        n_layers: int = 24,
+        expansion_ratio: int = 4,
+        max_seq_len: int = 2048,
+        vocab_size: int = 50368,
+        resid_pdrop: float = 0.0,
+        emb_pdrop: float = 0.0,
+        learned_pos_emb: bool = True,
+        attn_config: Optional[Dict[str, Any]] = None,
+        init_device: str = "cpu",
+        logit_scale: Optional[Union[float, str]] = None,
+        no_bias: bool = False,
+        verbose: int = 0,
+        embedding_fraction: float = 1.0,
+        norm_type: str = "low_precision_layernorm",
+        use_cache: bool = False,
+        **kwargs,
+    ) -> None:
         self.d_model = d_model
         self.n_heads = n_heads
         self.n_layers = n_layers
@@ -128,105 +56,19 @@ class MPTConfig(PretrainedConfig):
         self.resid_pdrop = resid_pdrop
         self.emb_pdrop = emb_pdrop
         self.learned_pos_emb = learned_pos_emb
-        self.attn_config = attn_config
-        self.ffn_config = ffn_config
+        if attn_config is None:
+            self.attn_config = _ATTN_CONFIG_DEFAULTS
+        else:
+            self.attn_config = attn_config
         self.init_device = init_device
         self.logit_scale = logit_scale
         self.no_bias = no_bias
+        self.verbose = verbose
         self.embedding_fraction = embedding_fraction
         self.norm_type = norm_type
         self.use_cache = use_cache
-        self.init_config = init_config
-        self.fc_type = fc_type
-        if verbose is not None:
-            warnings.warn(
-                DeprecationWarning(
-                    'verbose argument for MPTConfig is now ignored and will be removed. Use python_log_level instead.'
-                ))
-        if 'name' in kwargs:
-            del kwargs['name']
-        if 'loss_fn' in kwargs:
-            del kwargs['loss_fn']
-        if self.attn_config.get('alibi', False):
-            self.learned_pos_emb = False
-            warnings.warn(
-                f'alibi is turned on, setting `learned_pos_emb` to {self.learned_pos_emb}`'
-            )
+        if "name" in kwargs:
+            del kwargs["name"]
+        if "loss_fn" in kwargs:
+            del kwargs["loss_fn"]
         super().__init__(**kwargs)
-        self._validate_config()
-
-    def _set_config_defaults(
-            self, config: Dict[str, Any],
-            config_defaults: Dict[str, Any]) -> Dict[str, Any]:
-        for (k, v) in config_defaults.items():
-            if k not in config:
-                config[k] = v
-        return config
-
-    def _validate_config(self) -> None:
-        self.attn_config = self._set_config_defaults(self.attn_config,
-                                                     attn_config_defaults)
-        self.ffn_config = self._set_config_defaults(self.ffn_config,
-                                                    ffn_config_defaults)
-        self.init_config = self._set_config_defaults(self.init_config,
-                                                     init_config_defaults)
-        if self.d_model % self.n_heads != 0:
-            raise ValueError('d_model must be divisible by n_heads')
-        if any((
-                prob < 0 or prob > 1 for prob in
-            [self.attn_config['attn_pdrop'], self.resid_pdrop, self.emb_pdrop]
-        )):
-            raise ValueError(
-                "self.attn_config['attn_pdrop'], resid_pdrop, emb_pdrop are probabilities and must be between 0 and 1"  # pylint: disable=line-too-long
-            )
-        if self.attn_config['attn_impl'] not in ['torch', 'flash', 'triton']:
-            raise ValueError(
-                f"Unknown attn_impl={self.attn_config['attn_impl']}")
-        if self.attn_config['prefix_lm'] and self.attn_config[
-                'attn_impl'] not in ['torch', 'triton']:
-            raise NotImplementedError(
-                'prefix_lm only implemented with torch and triton attention.')
-        if self.attn_config['alibi'] and self.attn_config['attn_impl'] not in [
-                'torch', 'triton'
-        ]:
-            raise NotImplementedError(
-                'alibi only implemented with torch and triton attention.')
-        if self.attn_config['attn_uses_sequence_id'] and self.attn_config[
-                'attn_impl'] not in ['torch', 'triton']:
-            raise NotImplementedError(
-                'attn_uses_sequence_id only implemented with torch and triton attention.'  # pylint: disable=line-too-long
-            )
-        if self.embedding_fraction > 1 or self.embedding_fraction <= 0:
-            raise ValueError(
-                'model.embedding_fraction must be between 0 (exclusive) and 1 (inclusive)!'  # pylint: disable=line-too-long
-            )
-        if isinstance(self.logit_scale,
-                      str) and self.logit_scale != 'inv_sqrt_d_model':
-            raise ValueError(
-                f"self.logit_scale={self.logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'."  # pylint: disable=line-too-long
-            )
-        if self.init_config.get('name', None) is None:
-            raise ValueError(
-                f"self.init_config={self.init_config!r} 'name' needs to be set."
-            )
-        if not self.learned_pos_emb and (not self.attn_config['alibi']):
-            warnings.warn(
-                'Positional information not being provided to the model.')
-        if self.fc_type == 'te' or self.ffn_config['ffn_type'] == 'te_ln_mlp':
-            try:
-                # pylint: disable=import-outside-toplevel
-                import transformer_engine.pytorch as te
-                del te
-            except Exception as exc:
-                raise ImportError(
-                    # pylint: disable=line-too-long
-                    'TransformerEngine import fail. `fc_type: te` requires TransformerEngine be installed. '
-                    +
-                    'The required version of transformer_engine also requires FlashAttention v1.0.6 is installed:\n'
-                    + 'pip install flash-attn==1.0.6 --no-build-isolation \n' +
-                    'pip install git+https://github.com/NVIDIA/TransformerEngine.git@144e4888b2cdd60bd52e706d5b7a79cb9c1a7156'
-                ) from exc
-        if self.ffn_config['ffn_type'] == 'mptmlp':
-            self.ffn_config['fc_type'] = self.fc_type
-        elif self.ffn_config['ffn_type'] == 'te_ln_mlp':
-            self.ffn_config['bias'] = not self.no_bias

vllm/transformers_utils/configs/yi.py

@@ -1,64 +0,0 @@
-""" Yi model configuration"""
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-Yi_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
-
-
-class YiConfig(PretrainedConfig):
-    r"""
-        Reference:
-        https://huggingface.co/01-ai/Yi-6B/blob/main/configuration_yi.py
-    """
-    model_type = "Yi"
-    keys_to_ignore_at_inference = ["past_key_values"]
-
-    def __init__(
-        self,
-        vocab_size=64000,
-        hidden_size=4096,
-        intermediate_size=11008,
-        num_hidden_layers=32,
-        num_attention_heads=32,
-        num_key_value_heads=4,
-        hidden_act="silu",
-        max_position_embeddings=4096,
-        initializer_range=0.02,
-        rms_norm_eps=1e-5,
-        use_cache=True,
-        pad_token_id=0,
-        bos_token_id=1,
-        eos_token_id=2,
-        tie_word_embeddings=False,
-        output_attentions=False,
-        rope_theta=5000000.0,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-
-        # for backward compatibility
-        if num_key_value_heads is None:
-            num_key_value_heads = num_attention_heads
-
-        self.num_key_value_heads = num_key_value_heads
-        self.hidden_act = hidden_act
-        self.initializer_range = initializer_range
-        self.rms_norm_eps = rms_norm_eps
-        self.use_cache = use_cache
-        self.output_attentions = output_attentions
-        self.rope_theta = rope_theta
-
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            tie_word_embeddings=tie_word_embeddings,
-            **kwargs,
-        )

vllm/transformers_utils/tokenizer.py

@@ -73,7 +73,6 @@ def _convert_tokens_to_string_with_added_encoders(
     tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
     output_tokens: List[str],
     skip_special_tokens: bool,
-    spaces_between_special_tokens: bool,
 ) -> str:
     # Adapted from
     # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/tokenization_utils.py#L921
@@ -97,10 +96,7 @@ def _convert_tokens_to_string_with_added_encoders(
     if current_sub_text:
         sub_text = tokenizer.convert_tokens_to_string(current_sub_text)
         sub_texts.append(sub_text)
-    if spaces_between_special_tokens:
-        return " ".join(sub_texts)
-    else:
-        return "".join(sub_texts)
+    return " ".join(sub_texts)
 
 
 # Based on
@@ -113,7 +109,6 @@ def detokenize_incrementally(
     prefix_offset: int = 0,
     read_offset: int = 0,
     skip_special_tokens: bool = False,
-    spaces_between_special_tokens: bool = True,
 ) -> Tuple[List[str], str, int, int]:
     new_token_id = all_input_ids[-1]
     # This is the first iteration for this sequence
@@ -125,11 +120,7 @@ def detokenize_incrementally(
         # tokenizers (bigger = more conservative).
         # Subtract 1 extra to account for the generated token.
         prefix_offset = max(len(output_tokens) - 6, 0)
-        # If the first new token is a special token, we can't skip 1 extra token
-        if skip_special_tokens and new_token_id in tokenizer.all_special_ids:
-            read_offset = max(len(output_tokens), 0)
-        else:
-            read_offset = max(len(output_tokens) - 1, 0)
+        read_offset = max(len(output_tokens) - 1, 0)
     else:
         # Put new_token_id in a list so skip_special_tokens is respected
         new_tokens = tokenizer.convert_ids_to_tokens(
@@ -148,15 +139,11 @@ def detokenize_incrementally(
         prefix_text = _convert_tokens_to_string_with_added_encoders(
             tokenizer,
             output_tokens[prefix_offset:read_offset],
-            skip_special_tokens=skip_special_tokens,
-            spaces_between_special_tokens=spaces_between_special_tokens,
-        )
+            skip_special_tokens=skip_special_tokens)
         new_text = _convert_tokens_to_string_with_added_encoders(
             tokenizer,
             output_tokens[prefix_offset:],
-            skip_special_tokens=skip_special_tokens,
-            spaces_between_special_tokens=spaces_between_special_tokens,
-        )
+            skip_special_tokens=skip_special_tokens)
 
     if len(new_text) > len(prefix_text) and not new_text.endswith("<EFBFBD>"):
         # utf-8 char at the end means it's a potential unfinished byte sequence

vllm/worker/worker.py

@@ -10,10 +10,10 @@ from vllm.config import (CacheConfig, ModelConfig, ParallelConfig,
 from vllm.model_executor import get_model, InputMetadata, set_random_seed
 from vllm.model_executor.parallel_utils.parallel_state import (
     initialize_model_parallel)
-from vllm.sampling_params import SamplingParams, SamplingType
+from vllm.sampling_params import SamplingParams
 from vllm.sequence import SamplerOutput, SequenceData, SequenceGroupMetadata
 from vllm.worker.cache_engine import CacheEngine
-from vllm.utils import get_gpu_memory
+from vllm.utils import get_gpu_memory, get_max_shared_memory_bytes
 
 
 class Worker:
@@ -141,6 +141,13 @@ class Worker:
         self.block_size = cache_config.block_size
         self.sliding_window = cache_config.sliding_window
 
+        if self.sliding_window is None:
+            max_seq_len = self.scheduler_config.max_model_len
+        else:
+            max_seq_len = min(self.scheduler_config.max_model_len,
+                              self.sliding_window)
+        _check_if_can_support_max_seq_len(max_seq_len, self.block_size)
+
         self.cache_engine = CacheEngine(self.cache_config, self.model_config,
                                         self.parallel_config)
         self.cache_events = self.cache_engine.events
@@ -151,13 +158,9 @@ class Worker:
         seq_group_metadata_list: List[SequenceGroupMetadata],
     ) -> Tuple[torch.Tensor, torch.Tensor, InputMetadata]:
         seq_groups: List[Tuple[List[int], SamplingParams]] = []
-        input_tokens: List[List[int]] = []
-        input_positions: List[List[int]] = []
-        slot_mapping: List[List[int]] = []
-        selected_token_indices: List[int] = []
-        selected_token_start_idx = 0
-        categorized_sample_indices = {t: [] for t in SamplingType}
-        categorized_sample_indices_start_idx = 0
+        input_tokens: List[int] = []
+        input_positions: List[int] = []
+        slot_mapping: List[int] = []
 
         # Add prompt tokens.
         prompt_lens: List[int] = []
@@ -177,82 +180,48 @@ class Worker:
             prompt_len = len(prompt_tokens)
             prompt_lens.append(prompt_len)
 
-            if sampling_params.prompt_logprobs is not None:
-                # NOTE: prompt token positions do not need sample, skip
-                categorized_sample_indices_start_idx += prompt_len - 1
-
-            categorized_sample_indices[sampling_params.sampling_type].append(
-                categorized_sample_indices_start_idx)
-            categorized_sample_indices_start_idx += 1
-
-            input_tokens.append(prompt_tokens)
+            input_tokens.extend(prompt_tokens)
             # NOTE(woosuk): Here we assume that the first token in the prompt
             # is always the first token in the sequence.
-            input_positions.append(list(range(prompt_len)))
+            input_positions.extend(range(len(prompt_tokens)))
 
             if seq_group_metadata.block_tables is None:
                 # During memory profiling, the block tables are not initialized
                 # yet. In this case, we just use a dummy slot mapping.
-                slot_mapping.append([0] * prompt_len)
+                slot_mapping.extend([0] * prompt_len)
                 continue
 
             # Compute the slot mapping.
-            slot_mapping.append([])
             block_table = seq_group_metadata.block_tables[seq_id]
             for i in range(prompt_len):
                 block_number = block_table[i // self.block_size]
                 block_offset = i % self.block_size
                 slot = block_number * self.block_size + block_offset
-                slot_mapping[-1].append(slot)
+                slot_mapping.append(slot)
 
         # Add generation tokens.
         max_context_len = 0
         max_num_blocks_per_seq = 0
         context_lens: List[int] = []
         generation_block_tables: List[List[int]] = []
-        max_seq_len = max(prompt_lens) if prompt_lens else 1
-        for i, seq_group_metadata in enumerate(seq_group_metadata_list):
+        for seq_group_metadata in seq_group_metadata_list:
             if seq_group_metadata.is_prompt:
-                # We need to do this in this loop as we need to know max_seq_len
-                assert len(
-                    seq_ids) == 1, "Prompt input should have only one seq."
-                sampling_params = seq_group_metadata.sampling_params
-                assert len(prompt_lens) == len(seq_group_metadata_list)
-                prompt_len = prompt_lens[i]
-                if sampling_params.prompt_logprobs is not None:
-                    selected_token_indices.extend(
-                        range(selected_token_start_idx,
-                              selected_token_start_idx + prompt_len - 1))
-                selected_token_indices.append(selected_token_start_idx +
-                                              prompt_len - 1)
-                selected_token_start_idx += max_seq_len
                 continue
 
             seq_ids = list(seq_group_metadata.seq_data.keys())
             sampling_params = seq_group_metadata.sampling_params
             seq_groups.append((seq_ids, sampling_params))
 
-            num_seqs = len(seq_ids)
-            selected_token_indices.extend(
-                range(selected_token_start_idx,
-                      selected_token_start_idx + num_seqs))
-            selected_token_start_idx += num_seqs
-
-            categorized_sample_indices[sampling_params.sampling_type].extend(
-                range(categorized_sample_indices_start_idx,
-                      categorized_sample_indices_start_idx + num_seqs))
-            categorized_sample_indices_start_idx += num_seqs
-
             for seq_id in seq_ids:
                 seq_data = seq_group_metadata.seq_data[seq_id]
                 generation_token = seq_data.get_last_token_id()
-                input_tokens.append([generation_token])
+                input_tokens.append(generation_token)
 
                 context_len = seq_data.get_len()
                 position = context_len - 1
                 if self.sliding_window is not None:
                     context_len = min(context_len, self.sliding_window)
-                input_positions.append([position])
+                input_positions.append(position)
 
                 block_table = seq_group_metadata.block_tables[seq_id]
 
@@ -264,7 +233,7 @@ class Worker:
                 block_number = block_table[position // self.block_size]
                 block_offset = position % self.block_size
                 slot = block_number * self.block_size + block_offset
-                slot_mapping.append([slot])
+                slot_mapping.append(slot)
 
                 if self.sliding_window is not None:
                     sliding_window_blocks = (self.sliding_window //
@@ -272,42 +241,28 @@ class Worker:
                     block_table = block_table[-sliding_window_blocks:]
                 generation_block_tables.append(block_table)
 
-        padded_input_tokens = [
-            _pad_to_max(tokens, max_seq_len, pad=0) for tokens in input_tokens
-        ]
-        padded_input_positions = [
-            _pad_to_max(positions, max_seq_len, pad=0)
-            for positions in input_positions
-        ]
-        padded_slot_mapping = [
-            _pad_to_max(mapping, max_seq_len, pad=-1)
-            for mapping in slot_mapping
-        ]
-        padded_block_tables = [
-            _pad_to_max(block_table, max_num_blocks_per_seq, pad=0)
-            for block_table in generation_block_tables
-        ]
+        # Optimization: Pad the input length to be a multiple of 8.
+        # This is required for utilizing the Tensor Cores in NVIDIA GPUs.
+        input_tokens = _pad_to_alignment(input_tokens, multiple_of=8)
+        input_positions = _pad_to_alignment(input_positions, multiple_of=8)
 
         # Convert to tensors.
-        tokens_tensor = torch.tensor(padded_input_tokens,
+        tokens_tensor = torch.tensor(input_tokens,
                                      dtype=torch.long,
                                      device="cuda")
-        positions_tensor = torch.tensor(padded_input_positions,
+        positions_tensor = torch.tensor(input_positions,
                                         dtype=torch.long,
                                         device="cuda")
-        slot_mapping_tensor = torch.tensor(padded_slot_mapping,
-                                           dtype=torch.long,
+        slot_mapping_tensor = torch.tensor(slot_mapping,
+                                           dtype=torch.int,
                                            device="cuda")
         context_lens_tensor = torch.tensor(context_lens,
                                            dtype=torch.int,
                                            device="cuda")
-        selected_token_indices = torch.tensor(selected_token_indices,
-                                              dtype=torch.long,
-                                              device="cuda")
-        categorized_sample_indices = {
-            t: torch.tensor(seq_ids, dtype=torch.int, device="cuda")
-            for t, seq_ids in categorized_sample_indices.items()
-        }
+        padded_block_tables = [
+            _pad_to_max(block_table, max_num_blocks_per_seq)
+            for block_table in generation_block_tables
+        ]
         block_tables_tensor = torch.tensor(padded_block_tables,
                                            dtype=torch.int,
                                            device="cuda")
@@ -324,8 +279,6 @@ class Worker:
             context_lens=context_lens_tensor,
             max_context_len=max_context_len,
             block_tables=block_tables_tensor,
-            selected_token_indices=selected_token_indices,
-            categorized_sample_indices=categorized_sample_indices,
             sliding_window=self.sliding_window,
         )
         return tokens_tensor, positions_tensor, input_metadata
@@ -408,12 +361,32 @@ def _init_distributed_environment(
                               parallel_config.pipeline_parallel_size)
 
 
-def _pad_to_alignment(x: List[int], multiple_of: int, pad: int) -> List[int]:
-    return x + [pad] * ((-len(x)) % multiple_of)
+def _pad_to_alignment(x: List[int], multiple_of: int) -> List[int]:
+    return x + [0] * ((-len(x)) % multiple_of)
 
 
-def _pad_to_max(x: List[int], max_len: int, pad: int) -> List[int]:
-    return x + [pad] * (max_len - len(x))
+def _pad_to_max(x: List[int], max_len: int) -> List[int]:
+    return x + [0] * (max_len - len(x))
+
+
+def _check_if_can_support_max_seq_len(max_seq_len: int,
+                                      block_size: int) -> None:
+    # Follows the logic in
+    # attention_kernels.cu::single_query_cached_kv_attention_launcher
+    max_shared_mem = get_max_shared_memory_bytes()
+    float32_bytes = torch.finfo(torch.float).bits // 8
+    padded_max_seq_len = (
+        (max_seq_len + block_size - 1) / block_size) * block_size
+    # padded_max_seq_len + extra buffer
+    required_shared_mem = (padded_max_seq_len + 512) * float32_bytes
+    if padded_max_seq_len * float32_bytes > max_shared_mem:
+        raise RuntimeError(
+            f"vLLM cannot currently support max_model_len={max_seq_len} "
+            f"with block_size={block_size} on GPU with compute "
+            f"capability {torch.cuda.get_device_capability()} "
+            f"(required shared memory {required_shared_mem} > "
+            f"available shared memory {max_shared_mem}). "
+            "This will be fixed in a future release.")
 
 
 def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):