Upstream Sync

.pre-commit-config.yaml

@@ -125,8 +125,6 @@ repos:
     name: Update Dockerfile dependency graph
     entry: tools/update-dockerfile-graph.sh
     language: script
-    files: ^docker/Dockerfile$
-    pass_filenames: false
   # Keep `suggestion` last
   - id: suggestion
     name: Suggestion

benchmarks/benchmark_dataset.py

@@ -315,13 +315,15 @@ def sample(
         )
 
         vocab_size = tokenizer.vocab_size
+        num_special_tokens = tokenizer.num_special_tokens_to_add()
+        real_input_len = input_len - num_special_tokens
 
         prefix_token_ids = (np.random.randint(
             0, vocab_size, size=prefix_len).tolist() if prefix_len > 0 else [])
 
         # New sampling logic: [X * (1 - b), X * (1 + b)]
-        input_low = int(input_len * (1 - range_ratio))
-        input_high = int(input_len * (1 + range_ratio))
+        input_low = int(real_input_len * (1 - range_ratio))
+        input_high = int(real_input_len * (1 + range_ratio))
         output_low = int(output_len * (1 - range_ratio))
         output_high = int(output_len * (1 + range_ratio))
 
@@ -344,6 +346,17 @@ def sample(
                          vocab_size).tolist()
             token_sequence = prefix_token_ids + inner_seq
             prompt = tokenizer.decode(token_sequence)
+            # After decoding the prompt we have to encode and decode it again.
+            # This is done because in some cases N consecutive tokens
+            # give a string tokenized into != N number of tokens.
+            # For example for GPT2Tokenizer:
+            # [6880, 6881] -> ['Ġcalls', 'here'] ->
+            # [1650, 939, 486] -> ['Ġcall', 'sh', 'ere']
+            # To avoid uncontrolled change of the prompt length,
+            # the encoded sequence is truncated before being decode again.
+            re_encoded_sequence = tokenizer.encode(
+                prompt, add_special_tokens=False)[:input_lens[i]]
+            prompt = tokenizer.decode(re_encoded_sequence)
             total_input_len = prefix_len + int(input_lens[i])
             requests.append(
                 SampleRequest(
@@ -874,6 +887,94 @@ def sample(self,
         return sampled_requests
 
 
+# -----------------------------------------------------------------------------
+# Next Edit Prediction Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+zeta_prompt = """### Instruction:
+You are a code completion assistant and your task is to analyze user edits and then rewrite an excerpt that the user provides, suggesting the appropriate edits within the excerpt, taking into account the cursor location.
+
+### User Edits:
+
+{}
+
+### User Excerpt:
+
+{}
+
+### Response:
+
+""" # noqa: E501
+
+
+def _format_zeta_prompt(
+        sample: dict,
+        original_start_marker: str = "<|editable_region_start|>") -> dict:
+    """Format the zeta prompt for the Next Edit Prediction (NEP) dataset.
+
+    This function formats examples from the NEP dataset 
+    into prompts and expected outputs. It could be 
+    further extended to support more NEP datasets.
+
+    Args:
+        sample: The dataset sample containing events, 
+            inputs, and outputs.
+        original_start_marker: The marker indicating the 
+            start of the editable region. Defaults to 
+            "<|editable_region_start|>".
+
+    Returns:
+        A dictionary with the formatted prompts and expected outputs.
+    """
+    events = sample["events"]
+    input = sample["input"]
+    output = sample["output"]
+    prompt = zeta_prompt.format(events, input)
+
+    # following the original implementation, extract the focused region
+    # from the raw output
+    output_start_index = output.find(original_start_marker)
+    output_focused_region = output[output_start_index:]
+    expected_output = output_focused_region
+
+    return {"prompt": prompt, "expected_output": expected_output}
+
+
+class NextEditPredictionDataset(HuggingFaceDataset):
+    """
+    Dataset class for processing a Next Edit Prediction dataset.
+    """
+
+    SUPPORTED_DATASET_PATHS = {
+        "zed-industries/zeta",
+    }
+    MAPPING_PROMPT_FUNCS = {
+        "zed-industries/zeta": _format_zeta_prompt,
+    }
+
+    def sample(self, tokenizer: PreTrainedTokenizerBase, num_requests: int,
+               **kwargs):
+        formatting_prompt_func = self.MAPPING_PROMPT_FUNCS.get(
+            self.dataset_path)
+        if formatting_prompt_func is None:
+            raise ValueError(f"Unsupported dataset path: {self.dataset_path}")
+        samples = []
+        for sample in self.data:
+            sample = formatting_prompt_func(sample)
+            samples.append(
+                SampleRequest(
+                    prompt=sample["prompt"],
+                    prompt_len=len(tokenizer(sample["prompt"]).input_ids),
+                    expected_output_len=len(
+                        tokenizer(sample["expected_output"]).input_ids),
+                ))
+            if len(samples) >= num_requests:
+                break
+        self.maybe_oversample_requests(samples, num_requests)
+        return samples
+
+
 # -----------------------------------------------------------------------------
 # ASR Dataset Implementation
 # -----------------------------------------------------------------------------

benchmarks/benchmark_serving.py

@@ -53,8 +53,9 @@
 from benchmark_dataset import (AIMODataset, ASRDataset, BurstGPTDataset,
                                ConversationDataset, HuggingFaceDataset,
                                InstructCoderDataset, MTBenchDataset,
-                               RandomDataset, SampleRequest, ShareGPTDataset,
-                               SonnetDataset, VisionArenaDataset)
+                               NextEditPredictionDataset, RandomDataset,
+                               SampleRequest, ShareGPTDataset, SonnetDataset,
+                               VisionArenaDataset)
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
 
 MILLISECONDS_TO_SECONDS_CONVERSION = 1000
@@ -603,6 +604,9 @@ def main(args: argparse.Namespace):
         elif args.dataset_path in AIMODataset.SUPPORTED_DATASET_PATHS:
             dataset_class = AIMODataset
             args.hf_split = "train"
+        elif args.dataset_path in NextEditPredictionDataset.SUPPORTED_DATASET_PATHS:  # noqa: E501
+            dataset_class = NextEditPredictionDataset
+            args.hf_split = "train"
         elif args.dataset_path in ASRDataset.SUPPORTED_DATASET_PATHS:
             dataset_class = ASRDataset
             args.hf_split = "train"

benchmarks/kernels/benchmark_moe.py

@@ -10,12 +10,12 @@
 
 import ray
 import torch
-import triton
 from ray.experimental.tqdm_ray import tqdm
 from transformers import AutoConfig
 
 from vllm.model_executor.layers.fused_moe.fused_moe import *
 from vllm.platforms import current_platform
+from vllm.triton_utils import triton
 from vllm.utils import FlexibleArgumentParser
 
 FP8_DTYPE = current_platform.fp8_dtype()

benchmarks/kernels/benchmark_rmsnorm.py

@@ -4,11 +4,11 @@
 from typing import Optional, Union
 
 import torch
-import triton
 from flashinfer.norm import fused_add_rmsnorm, rmsnorm
 from torch import nn
 
 from vllm import _custom_ops as vllm_ops
+from vllm.triton_utils import triton
 
 
 class HuggingFaceRMSNorm(nn.Module):

benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py

@@ -6,13 +6,13 @@
 # Import DeepGEMM functions
 import deep_gemm
 import torch
-import triton
 from deep_gemm import calc_diff, ceil_div, get_col_major_tma_aligned_tensor
 
 # Import vLLM functions
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     per_token_group_quant_fp8, w8a8_block_fp8_matmul)
+from vllm.triton_utils import triton
 
 
 # Copied from

docs/source/deployment/frameworks/index.md

@@ -11,6 +11,7 @@ helm
 lws
 modal
 open-webui
+retrieval_augmented_generation
 skypilot
 streamlit
 triton

docs/source/deployment/frameworks/retrieval_augmented_generation.md

@@ -0,0 +1,84 @@
+(deployment-retrieval-augmented-generation)=
+
+# Retrieval-Augmented Generation
+
+[Retrieval-augmented generation (RAG)](https://en.wikipedia.org/wiki/Retrieval-augmented_generation) is a technique that enables generative artificial intelligence (Gen AI) models to retrieve and incorporate new information. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to supplement information from its pre-existing training data. This allows LLMs to use domain-specific and/or updated information. Use cases include providing chatbot access to internal company data or generating responses based on authoritative sources.
+
+Here are the integrations:
+- vLLM + [langchain](https://github.com/langchain-ai/langchain) + [milvus](https://github.com/milvus-io/milvus)
+- vLLM + [llamaindex](https://github.com/run-llama/llama_index) + [milvus](https://github.com/milvus-io/milvus)
+
+## vLLM + langchain
+
+### Prerequisites
+
+- Setup vLLM and langchain environment
+
+```console
+pip install -U vllm \
+            langchain_milvus langchain_openai \
+            langchain_community beautifulsoup4 \
+            langchain-text-splitters
+```
+
+### Deploy
+
+- Start the vLLM server with the supported embedding model, e.g.
+
+```console
+# Start embedding service (port 8000)
+vllm serve ssmits/Qwen2-7B-Instruct-embed-base
+```
+
+- Start the vLLM server with the supported chat completion model, e.g.
+
+```console
+# Start chat service (port 8001)
+vllm serve qwen/Qwen1.5-0.5B-Chat --port 8001
+```
+
+- Use the script: <gh-file:examples/online_serving/retrieval_augmented_generation_with_langchain.py>
+
+- Run the script
+
+```python
+python retrieval_augmented_generation_with_langchain.py
+```
+
+## vLLM + llamaindex
+
+### Prerequisites
+
+- Setup vLLM and llamaindex environment
+
+```console
+pip install vllm \
+            llama-index llama-index-readers-web \
+            llama-index-llms-openai-like    \
+            llama-index-embeddings-openai-like \
+            llama-index-vector-stores-milvus \
+```
+
+### Deploy
+
+- Start the vLLM server with the supported embedding model, e.g.
+
+```console
+# Start embedding service (port 8000)
+vllm serve ssmits/Qwen2-7B-Instruct-embed-base
+```
+
+- Start the vLLM server with the supported chat completion model, e.g.
+
+```console
+# Start chat service (port 8001)
+vllm serve qwen/Qwen1.5-0.5B-Chat --port 8001
+```
+
+- Use the script: <gh-file:examples/online_serving/retrieval_augmented_generation_with_llamaindex.py>
+
+- Run the script
+
+```python
+python retrieval_augmented_generation_with_llamaindex.py
+```

docs/source/features/tool_calling.md

@@ -141,9 +141,9 @@ Known issues:
 much shorter than what vLLM generates. Since an exception is thrown when this condition
 is not met, the following additional chat templates are provided:
 
-* `examples/tool_chat_template_mistral.jinja` - this is the "official" Mistral chat template, but tweaked so that
+* <gh-file:examples/tool_chat_template_mistral.jinja> - this is the "official" Mistral chat template, but tweaked so that
 it works with vLLM's tool call IDs (provided `tool_call_id` fields are truncated to the last 9 digits)
-* `examples/tool_chat_template_mistral_parallel.jinja` - this is a "better" version that adds a tool-use system prompt
+* <gh-file:examples/tool_chat_template_mistral_parallel.jinja> - this is a "better" version that adds a tool-use system prompt
 when tools are provided, that results in much better reliability when working with parallel tool calling.
 
 Recommended flags: `--tool-call-parser mistral --chat-template examples/tool_chat_template_mistral_parallel.jinja`
@@ -170,15 +170,15 @@ Known issues:
 
 VLLM provides two JSON based chat templates for Llama 3.1 and 3.2:
 
-* `examples/tool_chat_template_llama3.1_json.jinja` - this is the "official" chat template for the Llama 3.1
+* <gh-file:examples/tool_chat_template_llama3.1_json.jinja> - this is the "official" chat template for the Llama 3.1
 models, but tweaked so that it works better with vLLM.
-* `examples/tool_chat_template_llama3.2_json.jinja` - this extends upon the Llama 3.1 chat template by adding support for
+* <gh-file:examples/tool_chat_template_llama3.2_json.jinja> - this extends upon the Llama 3.1 chat template by adding support for
 images.
 
 Recommended flags: `--tool-call-parser llama3_json --chat-template {see_above}`
 
 VLLM also provides a JSON based chat template for Llama 4:
-* `examples/tool_chat_template_llama4_json.jinja` - this is based on the "official" chat template for the Llama 4
+* <gh-file:examples/tool_chat_template_llama4_json.jinja> - this is based on the "official" chat template for the Llama 4
 models, but tweaked so that it works better with vLLM.
 
 For Llama 4 use `--tool-call-parser llama4_json examples/tool_chat_template_llama4_json.jinja`.
@@ -191,7 +191,7 @@ Supported models:
 
 Recommended flags: `--tool-call-parser granite --chat-template examples/tool_chat_template_granite.jinja`
 
-`examples/tool_chat_template_granite.jinja`: this is a modified chat template from the original on Huggingface. Parallel function calls are supported.
+<gh-file:examples/tool_chat_template_granite.jinja>: this is a modified chat template from the original on Huggingface. Parallel function calls are supported.
 
 * `ibm-granite/granite-3.1-8b-instruct`
 
@@ -203,7 +203,7 @@ The chat template from Huggingface can be used directly. Parallel function calls
 
 Recommended flags: `--tool-call-parser granite-20b-fc --chat-template examples/tool_chat_template_granite_20b_fc.jinja`
 
-`examples/tool_chat_template_granite_20b_fc.jinja`: this is a modified chat template from the original on Huggingface, which is not vLLM compatible. It blends function description elements from the Hermes template and follows the same system prompt as "Response Generation" mode from [the paper](https://arxiv.org/abs/2407.00121). Parallel function calls are supported.
+<gh-file:examples/tool_chat_template_granite_20b_fc.jinja>: this is a modified chat template from the original on Huggingface, which is not vLLM compatible. It blends function description elements from the Hermes template and follows the same system prompt as "Response Generation" mode from [the paper](https://arxiv.org/abs/2407.00121). Parallel function calls are supported.
 
 ### InternLM Models (`internlm`)
 
@@ -253,12 +253,12 @@ Limitations:
 
 Example supported models:
 
-* `meta-llama/Llama-3.2-1B-Instruct`\* (use with `examples/tool_chat_template_llama3.2_pythonic.jinja`)
-* `meta-llama/Llama-3.2-3B-Instruct`\* (use with `examples/tool_chat_template_llama3.2_pythonic.jinja`)
-* `Team-ACE/ToolACE-8B` (use with `examples/tool_chat_template_toolace.jinja`)
-* `fixie-ai/ultravox-v0_4-ToolACE-8B` (use with `examples/tool_chat_template_toolace.jinja`)
-* `meta-llama/Llama-4-Scout-17B-16E-Instruct`\* (use with `examples/tool_chat_template_llama4_pythonic.jinja`)
-* `meta-llama/Llama-4-Maverick-17B-128E-Instruct`\* (use with `examples/tool_chat_template_llama4_pythonic.jinja`)
+* `meta-llama/Llama-3.2-1B-Instruct`\* (use with <gh-file:examples/tool_chat_template_llama3.2_pythonic.jinja>)
+* `meta-llama/Llama-3.2-3B-Instruct`\* (use with <gh-file:examples/tool_chat_template_llama3.2_pythonic.jinja>)
+* `Team-ACE/ToolACE-8B` (use with <gh-file:examples/tool_chat_template_toolace.jinja>)
+* `fixie-ai/ultravox-v0_4-ToolACE-8B` (use with <gh-file:examples/tool_chat_template_toolace.jinja>)
+* `meta-llama/Llama-4-Scout-17B-16E-Instruct`\* (use with <gh-file:examples/tool_chat_template_llama4_pythonic.jinja>)
+* `meta-llama/Llama-4-Maverick-17B-128E-Instruct`\* (use with <gh-file:examples/tool_chat_template_llama4_pythonic.jinja>)
 
 Flags: `--tool-call-parser pythonic --chat-template {see_above}`
 
@@ -270,7 +270,7 @@ Llama's smaller models frequently fail to emit tool calls in the correct format.
 
 ## How to write a tool parser plugin
 
-A tool parser plugin is a Python file containing one or more ToolParser implementations. You can write a ToolParser similar to the `Hermes2ProToolParser` in vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py.
+A tool parser plugin is a Python file containing one or more ToolParser implementations. You can write a ToolParser similar to the `Hermes2ProToolParser` in <gh-file:vllm/entrypoints/openai/tool_parsers/hermes_tool_parser.py>.
 
 Here is a summary of a plugin file: