Merge remote-tracking branch 'AzureGIT/main' into llava_devel

README.md

@@ -27,7 +27,7 @@ Easy, fast, and cheap LLM serving for everyone
 - [2023/06] We officially released vLLM! FastChat-vLLM integration has powered [LMSYS Vicuna and Chatbot Arena](https://chat.lmsys.org) since mid-April. Check out our [blog post](https://vllm.ai).
 
 ---
-
+## About
 vLLM is a fast and easy-to-use library for LLM inference and serving.
 
 vLLM is fast with:
@@ -54,6 +54,7 @@ vLLM seamlessly supports many Hugging Face models, including the following archi
 - Baichuan & Baichuan2 (`baichuan-inc/Baichuan2-13B-Chat`, `baichuan-inc/Baichuan-7B`, etc.)
 - BLOOM (`bigscience/bloom`, `bigscience/bloomz`, etc.)
 - ChatGLM (`THUDM/chatglm2-6b`, `THUDM/chatglm3-6b`, etc.)
+- DeciLM (`Deci/DeciLM-7B`, `Deci/DeciLM-7B-instruct`, 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.)

csrc/pos_encoding_kernels.cu

@@ -43,8 +43,8 @@ __global__ void rotary_embedding_kernel(
   scalar_t* __restrict__ key,                   // [batch_size, seq_len, num_kv_heads, head_size] or [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,
-  const int key_stride,
+  const int64_t query_stride,
+  const int64_t key_stride,
   const int num_heads,
   const int num_kv_heads,
   const int head_size) {
@@ -60,7 +60,7 @@ __global__ void rotary_embedding_kernel(
   const int nq = num_heads * embed_dim;
   for (int i = threadIdx.x; i < nq; i += blockDim.x) {
     const int head_idx = i / embed_dim;
-    const int token_head = token_idx * query_stride + head_idx * head_size;
+    const int64_t token_head = token_idx * query_stride + head_idx * head_size;
     const int rot_offset = i % embed_dim;
     apply_rotary_embedding<scalar_t, IS_NEOX>(query + token_head, cos_ptr,
                                               sin_ptr, rot_offset, embed_dim);
@@ -69,7 +69,7 @@ __global__ void rotary_embedding_kernel(
   const int nk = num_kv_heads * embed_dim;
   for (int i = threadIdx.x; i < nk; i += blockDim.x) {
     const int head_idx = i / embed_dim;
-    const int token_head = token_idx * key_stride + head_idx * head_size;
+    const int64_t token_head = token_idx * key_stride + head_idx * head_size;
     const int rot_offset = i % embed_dim;
     apply_rotary_embedding<scalar_t, IS_NEOX>(key + token_head, cos_ptr,
                                               sin_ptr, rot_offset, embed_dim);
@@ -89,8 +89,8 @@ void rotary_embedding(
   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);
+  int64_t query_stride = query.stride(-2);
+  int64_t key_stride = key.stride(-2);
 
   dim3 grid(num_tokens);
   dim3 block(std::min(num_heads * rot_dim / 2, 512));

csrc/quantization/gptq/q_gemm.cu

@@ -28,6 +28,7 @@ namespace gptq {
 #define DIVIDE(x, size) (((x) + (size) - 1) / (size))
 
 #if defined(USE_ROCM)
+#include <hipblas/hipblas.h>
 __host__ __forceinline__ hipblasStatus_t __compat_hipblasHgemm(hipblasHandle_t    handle,
                                                                hipblasOperation_t transA,
                                                                hipblasOperation_t transB,
@@ -520,12 +521,21 @@ __global__ void gemm_half_q_half_alt_kernel(
             zeros_tmp[tmp_k] = zero;
         }
         for (int m = 0; m < b_end; m++) {
+#ifndef USE_ROCM
             res2 = {};
+#else
+            res2.x = __half_as_ushort(__float2half(0));
+            res2.y = __half_as_ushort(__float2half(0));
+#endif
             res2 = __hfma2(__hfma2(deq2[(tmp >>  0) & 0xff][off], scales_tmp[0], zeros_tmp[0]), blockvec[m][k + 0], res2);
             res2 = __hfma2(__hfma2(deq2[(tmp >>  8) & 0xff][off], scales_tmp[1], zeros_tmp[1]), blockvec[m][k + 1], res2);
             res2 = __hfma2(__hfma2(deq2[(tmp >> 16) & 0xff][off], scales_tmp[2], zeros_tmp[2]), blockvec[m][k + 2], res2);
             res2 = __hfma2(__hfma2(deq2[(tmp >> 24) & 0xff][off], scales_tmp[3], zeros_tmp[3]), blockvec[m][k + 3], res2);
+#ifndef USE_ROCM
             res[m] = __hadd(res[m], __hadd(res2.x, res2.y));
+#else
+            res[m] = __hadd(res[m], __hadd(__ushort_as_half(res2.x), __ushort_as_half(res2.y)));
+#endif
         }
         i += width;
         k += 4;

docs/source/getting_started/amd-installation.rst

@@ -116,6 +116,7 @@ Alternatively, if you plan to install vLLM-ROCm on a local machine or start from
 
 - `ROCm <https://rocm.docs.amd.com/en/latest/deploy/linux/index.html>`_
 - `Pytorch <https://pytorch.org/>`_
+- `hipBLAS <https://rocm.docs.amd.com/projects/hipBLAS/en/latest/install.html>`_
 
 1. Install `flash attention for ROCm <https://github.com/ROCmSoftwarePlatform/flash-attention/tree/flash_attention_for_rocm>`_
 

docs/source/getting_started/installation.rst

@@ -42,6 +42,10 @@ You can install vLLM using pip:
         $ pip uninstall torch -y
         $ pip install torch --upgrade --index-url https://download.pytorch.org/whl/cu118
 
+        $ # Re-install xFormers with CUDA 11.8.
+        $ pip uninstall xformers -y
+        $ pip install --upgrade xformers --index-url https://download.pytorch.org/whl/cu118
+
 
 .. _build_from_source:
 

docs/source/models/engine_args.rst

@@ -89,9 +89,11 @@ Below, you can find an explanation of every engine argument for vLLM:
 
     CPU swap space size (GiB) per GPU.
 
-.. option:: --gpu-memory-utilization <percentage>
+.. option:: --gpu-memory-utilization <fraction>
 
-    The percentage of GPU memory to be used for the model executor.
+    The fraction of GPU memory to be used for the model executor, which can range from 0 to 1. 
+    For example, a value of 0.5 would imply 50% GPU memory utilization.
+    If unspecified, will use the default value of 0.9.
 
 .. option:: --max-num-batched-tokens <tokens>
 

docs/source/models/supported_models.rst

@@ -23,6 +23,9 @@ Alongside each architecture, we include some popular models that use it.
   * - :code:`ChatGLMModel`
     - ChatGLM
     - :code:`THUDM/chatglm2-6b`, :code:`THUDM/chatglm3-6b`, etc.
+  * - :code:`DeciLMForCausalLM`
+    - DeciLM
+    - :code:`Deci/DeciLM-7B`, :code:`Deci/DeciLM-7B-instruct`, etc.
   * - :code:`BloomForCausalLM`
     - BLOOM, BLOOMZ, BLOOMChat
     - :code:`bigscience/bloom`, :code:`bigscience/bloomz`, etc.
@@ -90,7 +93,7 @@ Alternatively, you can raise an issue on our `GitHub <https://github.com/vllm-pr
     If vLLM successfully generates text, it indicates that your model is supported.
 
 .. tip::
-    To use models from `ModelScope <www.modelscope.cn>`_ instead of HuggingFace Hub, set an environment variable:
+    To use models from `ModelScope <https://www.modelscope.cn>`_ instead of HuggingFace Hub, set an environment variable:
 
     .. code-block:: shell
 

docs/source/serving/serving_with_langchain.rst

@@ -28,4 +28,4 @@ To run inference on a single or multiple GPUs, use ``VLLM`` class from ``langcha
 
     print(llm("What is the capital of France ?"))
 
-Please refer to this `Tutorial <https://github.com/langchain-ai/langchain/blob/master/docs/extras/integrations/llms/vllm.ipynb>`_ for more details.
+Please refer to this `Tutorial <https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/llms/vllm.ipynb>`_ for more details.

setup.py

@@ -219,13 +219,13 @@ def get_torch_arch_list() -> Set[str]:
     "csrc/activation_kernels.cu",
     "csrc/layernorm_kernels.cu",
     "csrc/quantization/squeezellm/quant_cuda_kernel.cu",
+    "csrc/quantization/gptq/q_gemm.cu",
     "csrc/cuda_utils_kernels.cu",
     "csrc/pybind.cpp",
 ]
 
 if _is_cuda():
     vllm_extension_sources.append("csrc/quantization/awq/gemm_kernels.cu")
-    vllm_extension_sources.append("csrc/quantization/gptq/q_gemm.cu")
 
 vllm_extension = CUDAExtension(
     name="vllm._C",

tests/async_engine/test_api_server.py

@@ -44,13 +44,14 @@ def test_api_server(api_server):
     """
     with Pool(32) as pool:
         # Wait until the server is ready
-        prompts = ["Hello world"] * 1
+        prompts = ["warm up"] * 1
         result = None
         while not result:
             try:
-                for _ in pool.map(_query_server, prompts):
+                for r in pool.map(_query_server, prompts):
+                    result = r
                     break
-            except Exception:
+            except requests.exceptions.ConnectionError:
                 time.sleep(1)
 
         # Actual tests start here
@@ -63,13 +64,14 @@ def test_api_server(api_server):
         assert num_aborted_requests == 0
 
         # Try with 100 prompts
-        prompts = ["Hello world"] * 100
+        prompts = ["test prompt"] * 100
         for result in pool.map(_query_server, prompts):
             assert result
 
         # Cancel requests
+        prompts = ["canceled requests"] * 100
         pool.map_async(_query_server, prompts)
-        time.sleep(0.01)
+        time.sleep(0.001)
         pool.terminate()
         pool.join()
 
@@ -81,6 +83,6 @@ def test_api_server(api_server):
     # check that server still runs after cancellations
     with Pool(32) as pool:
         # Try with 100 prompts
-        prompts = ["Hello world"] * 100
+        prompts = ["test prompt after canceled"] * 100
         for result in pool.map(_query_server, prompts):
             assert result

tests/conftest.py

@@ -8,8 +8,9 @@
 from vllm import LLM, SamplingParams
 from vllm.transformers_utils.tokenizer import get_tokenizer
 
-_TEST_PROMPTS = ["prompts/example.txt"]
-_LONG_PROMPTS = ["prompts/summary.txt"]
+_TEST_DIR = os.path.dirname(__file__)
+_TEST_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "example.txt")]
+_LONG_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "summary.txt")]
 
 
 def _read_prompts(filename: str) -> str:
@@ -24,15 +25,15 @@ def _read_prompts(filename: str) -> str:
 def example_prompts() -> List[str]:
     prompts = []
     for filename in _TEST_PROMPTS:
-        prompts += _read_prompts(os.path.join("tests", filename))
+        prompts += _read_prompts(filename)
     return prompts
 
 
 @pytest.fixture
 def example_long_prompts() -> List[str]:
     prompts = []
     for filename in _LONG_PROMPTS:
-        prompts += _read_prompts(os.path.join("tests", filename))
+        prompts += _read_prompts(filename)
     return prompts
 
 

tests/distributed/test_comm_ops.py

@@ -8,7 +8,7 @@
 import torch
 
 from vllm.config import ParallelConfig
-from vllm.engine.ray_utils import get_open_port
+from vllm.utils import get_open_port
 from vllm.model_executor.parallel_utils.communication_op import (
     tensor_model_parallel_all_reduce,
     tensor_model_parallel_all_gather,

tests/models/test_models.py

@@ -8,6 +8,7 @@
     "facebook/opt-125m",
     "meta-llama/Llama-2-7b-hf",
     "mistralai/Mistral-7B-v0.1",
+    "Deci/DeciLM-7b",
     "tiiuae/falcon-7b",
     "gpt2",
     "bigcode/tiny_starcoder_py",

vllm/__init__.py

@@ -9,7 +9,7 @@
 from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.sampling_params import SamplingParams
 
-__version__ = "0.2.5"
+__version__ = "0.2.6"
 
 __all__ = [
     "LLM",

vllm/config.py

@@ -112,24 +112,20 @@ def _verify_load_format(self) -> None:
         supported_load_format = [
             "auto", "pt", "safetensors", "npcache", "dummy"
         ]
-        rocm_not_supported_load_format = ["safetensors"]
+        rocm_not_supported_load_format = []
         if load_format not in supported_load_format:
             raise ValueError(
                 f"Unknown load format: {self.load_format}. Must be one of "
                 "'auto', 'pt', 'safetensors', 'npcache', or 'dummy'.")
-        if is_hip():
-            if load_format in ["safetensors"]:
-                rocm_supported_load_format = [
-                    f for f in supported_load_format
-                    if (f not in rocm_not_supported_load_format)
-                ]
-                raise ValueError(
-                    f"load format \'{load_format}\' is not supported in ROCm. "
-                    f"Supported load format are "
-                    f"{rocm_supported_load_format}")
-            # Force ROCm to load from pt weights if nothing specific is set
-            if load_format == "auto":
-                load_format = "pt"
+        if is_hip() and load_format in rocm_not_supported_load_format:
+            rocm_supported_load_format = [
+                f for f in supported_load_format
+                if (f not in rocm_not_supported_load_format)
+            ]
+            raise ValueError(
+                f"load format \'{load_format}\' is not supported in ROCm. "
+                f"Supported load format are "
+                f"{rocm_supported_load_format}")
 
         # TODO: Remove this check once HF updates the pt weights of Mixtral.
         architectures = getattr(self.hf_config, "architectures", [])
@@ -149,7 +145,7 @@ def _verify_tokenizer_mode(self) -> None:
 
     def _verify_quantization(self) -> None:
         supported_quantization = ["awq", "gptq", "squeezellm"]
-        rocm_not_supported_quantization = ["awq", "gptq"]
+        rocm_not_supported_quantization = ["awq"]
         if self.quantization is not None:
             self.quantization = self.quantization.lower()
 
@@ -185,10 +181,11 @@ def _verify_cuda_graph(self) -> None:
             self.max_context_len_to_capture = self.max_model_len
         self.max_context_len_to_capture = min(self.max_context_len_to_capture,
                                               self.max_model_len)
-        if self.quantization == "gptq" and not self.enforce_eager:
+        if (self.quantization in ["gptq", "squeezellm"]
+                and not self.enforce_eager):
             # Related issue: https://github.com/vllm-project/vllm/issues/2147
-            logger.warning("GPTQ does not support CUDA graph yet. Disabling "
-                           "CUDA graph.")
+            logger.warning(f"{self.quantization} does not support CUDA graph "
+                           "yet. Disabling CUDA graph.")
             self.enforce_eager = True
 
     def verify_with_parallel_config(

vllm/core/block_manager.py

@@ -103,7 +103,7 @@ def __init__(
     def can_allocate(self, seq_group: SequenceGroup) -> AllocStatus:
         # FIXME(woosuk): Here we assume that all sequences in the group share
         # the same prompt. This may not be true for preempted sequences.
-        seq = seq_group.get_seqs()[0]
+        seq = seq_group.get_seqs(status=SequenceStatus.WAITING)[0]
         num_required_blocks = len(seq.logical_token_blocks)
         if self.block_sliding_window is not None:
             num_required_blocks = min(num_required_blocks,
@@ -122,7 +122,7 @@ def can_allocate(self, seq_group: SequenceGroup) -> AllocStatus:
     def allocate(self, seq_group: SequenceGroup) -> None:
         # NOTE: Here we assume that all sequences in the group have the same
         # prompt.
-        seq = seq_group.get_seqs()[0]
+        seq = seq_group.get_seqs(status=SequenceStatus.WAITING)[0]
 
         # Allocate new physical token blocks that will store the prompt tokens.
         block_table: BlockTable = []
@@ -137,7 +137,7 @@ def allocate(self, seq_group: SequenceGroup) -> None:
             block_table.append(block)
 
         # Assign the block table for each sequence.
-        for seq in seq_group.get_seqs():
+        for seq in seq_group.get_seqs(status=SequenceStatus.WAITING):
             self.block_tables[seq.seq_id] = block_table.copy()
 
     def can_append_slot(self, seq_group: SequenceGroup) -> bool:

vllm/core/scheduler.py

@@ -139,15 +139,17 @@ def _schedule(self) -> SchedulerOutputs:
             while self.waiting:
                 seq_group = self.waiting[0]
 
-                assert seq_group.num_seqs() == 1, (
+                waiting_seqs = seq_group.get_seqs(
+                    status=SequenceStatus.WAITING)
+                assert len(waiting_seqs) == 1, (
                     "Waiting sequence group should have only one prompt "
                     "sequence.")
-                num_prompt_tokens = seq_group.get_seqs()[0].get_len()
+                num_prompt_tokens = waiting_seqs[0].get_len()
                 if num_prompt_tokens > self.prompt_limit:
                     logger.warning(
                         f"Input prompt ({num_prompt_tokens} tokens) is too long"
                         f" and exceeds limit of {self.prompt_limit}")
-                    for seq in seq_group.get_seqs():
+                    for seq in waiting_seqs:
                         seq.status = SequenceStatus.FINISHED_IGNORED
                     ignored_seq_groups.append(seq_group)
                     self.waiting.pop(0)
@@ -161,7 +163,7 @@ def _schedule(self) -> SchedulerOutputs:
                     logger.warning(
                         f"Input prompt ({num_prompt_tokens} tokens) is too long"
                         f" and exceeds the capacity of block_manager")
-                    for seq in seq_group.get_seqs():
+                    for seq in waiting_seqs:
                         seq.status = SequenceStatus.FINISHED_IGNORED
                     ignored_seq_groups.append(seq_group)
                     self.waiting.pop(0)
@@ -317,7 +319,7 @@ def free_finished_seq_groups(self) -> None:
 
     def _allocate(self, seq_group: SequenceGroup) -> None:
         self.block_manager.allocate(seq_group)
-        for seq in seq_group.get_seqs():
+        for seq in seq_group.get_seqs(status=SequenceStatus.WAITING):
             seq.status = SequenceStatus.RUNNING
 
     def _append_slot(