Merge branch 'vllm-project:main' into wye-refactor-quant-folder

.buildkite/nightly-benchmarks/tests/genai-perf-tests.json

@@ -12,7 +12,6 @@
         "vllm_server_parameters": {
             "disable_log_stats": "",
             "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },

.buildkite/nightly-benchmarks/tests/nightly-tests.json

@@ -36,7 +36,6 @@
         "vllm_server_parameters": {
             "disable_log_stats": "",
             "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },
@@ -90,7 +89,6 @@
         "vllm_server_parameters": {
             "disable_log_stats": "",
             "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },
@@ -144,7 +142,6 @@
         "vllm_server_parameters": {
             "disable_log_stats": "",
             "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },
@@ -195,7 +192,6 @@
         "vllm_server_parameters": {
             "disable_log_stats": "",
             "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },
@@ -248,7 +244,6 @@
         "vllm_server_parameters": {
             "disable_log_stats": "",
             "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },
@@ -301,7 +296,6 @@
         "vllm_server_parameters": {
             "disable_log_stats": "",
             "gpu_memory_utilization": 0.9,
-            "num_scheduler_steps": 10,
             "max_num_seqs": 512,
             "dtype": "bfloat16"
         },

.buildkite/test-pipeline.yaml

@@ -67,7 +67,6 @@ steps:
   - python3 standalone_tests/lazy_imports.py
   - pytest -v -s mq_llm_engine # MQLLMEngine
   - pytest -v -s async_engine # AsyncLLMEngine
-  - NUM_SCHEDULER_STEPS=4 pytest -v -s async_engine/test_async_llm_engine.py
   - pytest -v -s test_inputs.py
   - pytest -v -s test_outputs.py
   - pytest -v -s multimodal
@@ -773,27 +772,6 @@ steps:
   - pytest -v -s models/test_oot_registration.py # it needs a clean process
   - pytest -v -s plugins/lora_resolvers # unit tests for in-tree lora resolver plugins
 
-- label: Multi-step Tests (4 GPUs) # 36min
-  mirror_hardwares: [amdexperimental]
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  source_file_dependencies:
-  - vllm/model_executor/layers/sampler.py
-  - vllm/sequence.py
-  - vllm/worker/worker_base.py
-  - vllm/worker/worker.py
-  - vllm/worker/multi_step_worker.py
-  - vllm/worker/model_runner_base.py
-  - vllm/worker/model_runner.py
-  - vllm/worker/multi_step_model_runner.py
-  - vllm/engine
-  - tests/multi_step
-  commands:
-  # this test is quite flaky
-  # TODO: investigate and fix.
-  # - pytest -v -s multi_step/test_correctness_async_llm.py
-  - pytest -v -s multi_step/test_correctness_llm.py
-
 - label: Pipeline Parallelism Test # 45min
   mirror_hardwares: [amdexperimental]
   working_dir: "/vllm-workspace/tests"

.github/CODEOWNERS

@@ -36,7 +36,6 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @simon-mo @aarnphm
 /tests/kernels @tlrmchlsmth @WoosukKwon @yewentao256
 /tests/models @DarkLight1337 @ywang96
-/tests/multi_step @alexm-redhat @comaniac
 /tests/multimodal @DarkLight1337 @ywang96
 /tests/prefix_caching @comaniac @KuntaiDu
 /tests/quantization @mgoin @robertgshaw2-redhat @yewentao256

csrc/moe/topk_softmax_kernels.cu

@@ -188,7 +188,9 @@ __launch_bounds__(TPB) __global__ void moeTopK(
   It fuses the softmax, max and argmax into a single kernel.
 
   Limitations:
-  1) This implementation is intended for when the number of experts is a small power of 2.
+  1) This implementation is optimized for when the number of experts is a small power of 2.
+     Additionally it also supports when number of experts is multiple of 64 which is still
+     faster than the computing softmax and topK separately (only tested on CUDA yet).
   2) This implementation assumes k is small, but will work for any k.
 */
 
@@ -198,8 +200,6 @@ __launch_bounds__(WARPS_PER_CTA* WARP_SIZE_PARAM) __global__
         int* source_rows, const int k, const int start_expert, const int end_expert)
 {
     // We begin by enforcing compile time assertions and setting up compile time constants.
-    static_assert(VPT == (VPT & -VPT), "VPT must be power of 2");
-    static_assert(NUM_EXPERTS == (NUM_EXPERTS & -NUM_EXPERTS), "NUM_EXPERTS must be power of 2");
     static_assert(BYTES_PER_LDG == (BYTES_PER_LDG & -BYTES_PER_LDG), "BYTES_PER_LDG must be power of 2");
     static_assert(BYTES_PER_LDG <= 16, "BYTES_PER_LDG must be leq 16");
 
@@ -407,12 +407,10 @@ struct TopkConstants
 };
 } // namespace detail
 
-template <int EXPERTS, int WARPS_PER_TB, int WARP_SIZE_PARAM, typename IndType>
+template <int EXPERTS, int WARPS_PER_TB, int WARP_SIZE_PARAM, int MAX_BYTES_PER_LDG, typename IndType>
 void topkGatingSoftmaxLauncherHelper(const float* input, const bool* finished, float* output, IndType* indices,
     int* source_row, const int num_rows, const int k, const int start_expert, const int end_expert, cudaStream_t stream)
 {
-    static constexpr std::size_t MAX_BYTES_PER_LDG = 16;
-
     static constexpr int BYTES_PER_LDG = MIN(MAX_BYTES_PER_LDG, sizeof(float) * EXPERTS);
     using Constants = detail::TopkConstants<EXPERTS, BYTES_PER_LDG, WARP_SIZE_PARAM>;
     static constexpr int VPT = Constants::VPT;
@@ -425,21 +423,12 @@ void topkGatingSoftmaxLauncherHelper(const float* input, const bool* finished, f
         input, finished, output, num_rows, indices, source_row, k, start_expert, end_expert);
 }
 
-#define LAUNCH_SOFTMAX(NUM_EXPERTS, WARPS_PER_TB)                                \
-    switch (warpSize) {                                                          \
-        case 32:                                                                 \
-            topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 32>(      \
-                gating_output, nullptr, topk_weights, topk_indices,              \
-                token_expert_indices, num_tokens, topk, 0, num_experts, stream); \
-            break;                                                               \
-        case 64:                                                                 \
-            topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, 64>(      \
-                gating_output, nullptr, topk_weights, topk_indices,              \
-                token_expert_indices, num_tokens, topk, 0, num_experts, stream); \
-            break;                                                               \
-        default:                                                                 \
-            TORCH_CHECK(false, "Unsupported warp size: ", warpSize);             \
-    }
+#define LAUNCH_SOFTMAX(NUM_EXPERTS, WARPS_PER_TB, MAX_BYTES)                          \
+    static_assert(WARP_SIZE == 32 || WARP_SIZE == 64,                                 \
+                  "Unsupported warp size. Only 32 and 64 are supported.");            \
+    topkGatingSoftmaxLauncherHelper<NUM_EXPERTS, WARPS_PER_TB, WARP_SIZE, MAX_BYTES>( \
+        gating_output, nullptr, topk_weights, topk_indices,                           \
+        token_expert_indices, num_tokens, topk, 0, num_experts, stream);
 
 template <typename IndType>
 void topkGatingSoftmaxKernelLauncher(
@@ -453,38 +442,62 @@ void topkGatingSoftmaxKernelLauncher(
     const int topk,
     cudaStream_t stream) {
     static constexpr int WARPS_PER_TB = 4;
-    auto warpSize = WARP_SIZE;
+    static constexpr int BYTES_PER_LDG_POWER_OF_2 = 16;
+    static constexpr int BYTES_PER_LDG_MULTIPLE_64 = 8;
     switch (num_experts) {
         case 1:
-            LAUNCH_SOFTMAX(1, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(1, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 2:
-            LAUNCH_SOFTMAX(2, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(2, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 4:
-            LAUNCH_SOFTMAX(4, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(4, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 8:
-            LAUNCH_SOFTMAX(8, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(8, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 16:
-            LAUNCH_SOFTMAX(16, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(16, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 32:
-            LAUNCH_SOFTMAX(32, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(32, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 64:
-            LAUNCH_SOFTMAX(64, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(64, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 128:
-            LAUNCH_SOFTMAX(128, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(128, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
         case 256:
-            LAUNCH_SOFTMAX(256, WARPS_PER_TB);
+            LAUNCH_SOFTMAX(256, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
             break;
+        case 512:
+            LAUNCH_SOFTMAX(512, WARPS_PER_TB, BYTES_PER_LDG_POWER_OF_2);
+            break;
+        // (CUDA only) support multiples of 64 when num_experts is not power of 2.
+        // ROCm uses WARP_SIZE 64 so 8 bytes loading won't fit for some of num_experts,
+        // alternatively we can test 4 bytes loading and enable it in future.
+#ifndef USE_ROCM
+        case 192:
+            LAUNCH_SOFTMAX(192, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
+            break;
+        case 320:
+            LAUNCH_SOFTMAX(320, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
+            break;
+        case 384:
+            LAUNCH_SOFTMAX(384, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
+            break;
+        case 448:
+            LAUNCH_SOFTMAX(448, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
+            break;
+        case 576:
+            LAUNCH_SOFTMAX(576, WARPS_PER_TB, BYTES_PER_LDG_MULTIPLE_64);
+            break;
+#endif
         default: {
             TORCH_CHECK(softmax_workspace != nullptr,
-                "softmax_workspace must be provided for num_experts that are not a power of 2.");
+                "softmax_workspace must be provided for num_experts that are not a power of 2 or multiple of 64.");
             static constexpr int TPB = 256;
             moeSoftmax<TPB><<<num_tokens, TPB, 0, stream>>>(
                 gating_output, nullptr, softmax_workspace, num_experts);

docker/Dockerfile

@@ -432,7 +432,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 
 # Install DeepGEMM from source
 ARG DEEPGEMM_GIT_REPO="https://github.com/deepseek-ai/DeepGEMM.git"
-ARG DEEPGEMM_GIT_REF="187656694f7f69e3e7975617a68bc3387680a7e1"
+ARG DEEPGEMM_GIT_REF="7b6b5563b9d4c1ae07ffbce7f78ad3ac9204827c"
 RUN --mount=type=cache,target=/root/.cache/uv bash - <<'BASH'
   . /etc/environment
     CUDA_MAJOR="${CUDA_VERSION%%.*}"

docs/README.md

@@ -1,3 +1,9 @@
+---
+hide:
+  - navigation
+  - toc
+---
+
 # Welcome to vLLM
 
 <figure markdown="span">

docs/features/lora.md

@@ -351,3 +351,22 @@ vllm serve ibm-granite/granite-speech-3.3-2b \
 ```
 
 Note: Default multimodal LoRAs are currently only available for `.generate` and chat completions.
+
+## Using Tips
+
+### Configuring `max_lora_rank`
+
+The `--max-lora-rank` parameter controls the maximum rank allowed for LoRA adapters. This setting affects memory allocation and performance:
+
+- **Set it to the maximum rank** among all LoRA adapters you plan to use
+- **Avoid setting it too high** - using a value much larger than needed wastes memory and can cause performance issues
+
+For example, if your LoRA adapters have ranks [16, 32, 64], use `--max-lora-rank 64` rather than 256
+
+```bash
+# Good: matches actual maximum rank
+vllm serve model --enable-lora --max-lora-rank 64
+
+# Bad: unnecessarily high, wastes memory
+vllm serve model --enable-lora --max-lora-rank 256
+```

docs/getting_started/installation/README.md

@@ -14,3 +14,16 @@ vLLM supports the following hardware platforms:
 - [Google TPU](google_tpu.md)
 - [Intel Gaudi](intel_gaudi.md)
 - [AWS Neuron](aws_neuron.md)
+
+## Hardware Plugins
+
+The backends below live **outside** the main `vllm` repository and follow the
+[Hardware-Pluggable RFC](../design/plugin_system.md).
+
+| Accelerator | PyPI / package | Repository |
+|-------------|----------------|------------|
+| Ascend NPU | `vllm-ascend` | <https://github.com/vllm-project/vllm-ascend> |
+| Intel Gaudi (HPU) | N/A, install from source | <https://github.com/vllm-project/vllm-gaudi> |
+| MetaX MACA GPU | N/A, install from source | <https://github.com/MetaX-MACA/vLLM-metax> |
+| Rebellions ATOM / REBEL NPU | `vllm-rbln` | <https://github.com/rebellions-sw/vllm-rbln> |
+| IBM Spyre AIU | `vllm-spyre` | <https://github.com/vllm-project/vllm-spyre> |

docs/models/supported_models.md

@@ -615,7 +615,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `Gemma3nForConditionalGeneration` | Gemma 3n | T + I + A | `google/gemma-3n-E2B-it`, `google/gemma-3n-E4B-it`, etc. | | | ✅︎ |
 | `GLM4VForCausalLM`<sup>^</sup> | GLM-4V | T + I | `zai-org/glm-4v-9b`, `zai-org/cogagent-9b-20241220`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `Glm4vForConditionalGeneration` | GLM-4.1V-Thinking | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.1V-9B-Thinking`, etc. | ✅︎ | ✅︎ | ✅︎ |
-| `Glm4vMoeForConditionalGeneration` | GLM-4.5V | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.5V`, etc. | ✅︎ | ✅︎ | ✅︎ |
+| `Glm4vMoeForConditionalGeneration` | GLM-4.5V | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.5V`, etc. | | ✅︎ | ✅︎ |
 | `GraniteSpeechForConditionalGeneration` | Granite Speech | T + A | `ibm-granite/granite-speech-3.3-8b` | ✅︎ | ✅︎ | ✅︎ |
 | `H2OVLChatModel` | H2OVL | T + I<sup>E+</sup> | `h2oai/h2ovl-mississippi-800m`, `h2oai/h2ovl-mississippi-2b`, etc. | | ✅︎ | ✅︎ |
 | `Idefics3ForConditionalGeneration` | Idefics3 | T + I | `HuggingFaceM4/Idefics3-8B-Llama3`, etc. | ✅︎ | | ✅︎ |

examples/online_serving/openai_embedding_long_text/README.md

@@ -0,0 +1,186 @@
+# Long Text Embedding with Chunked Processing
+
+This directory contains examples for using vLLM's **chunked processing** feature to handle long text embedding that exceeds the model's maximum context length.
+
+## 🚀 Quick Start
+
+### Start the Server
+
+Use the provided script to start a vLLM server with chunked processing enabled:
+
+```bash
+# Basic usage (supports very long texts up to ~3M tokens)
+./service.sh
+
+# Custom configuration with different models
+MODEL_NAME="jinaai/jina-embeddings-v3" \
+MAX_EMBED_LEN=1048576 \
+./service.sh
+
+# For extremely long documents
+MODEL_NAME="intfloat/multilingual-e5-large" \
+MAX_EMBED_LEN=3072000 \
+./service.sh
+```
+
+### Test Long Text Embedding
+
+Run the comprehensive test client:
+
+```bash
+python client.py
+```
+
+## 📁 Files
+
+| File | Description |
+|------|-------------|
+| `service.sh` | Server startup script with chunked processing enabled |
+| `client.py` | Comprehensive test client for long text embedding |
+
+## ⚙️ Configuration
+
+### Server Configuration
+
+The key parameters for chunked processing are in the `--override-pooler-config`:
+
+```json
+{
+  "pooling_type": "auto",
+  "normalize": true,
+  "enable_chunked_processing": true,
+  "max_embed_len": 3072000
+}
+```
+
+!!! note
+    `pooling_type` sets the model's own pooling strategy for processing within each chunk. The cross-chunk aggregation automatically uses MEAN strategy when input exceeds the model's native maximum length.
+
+#### Chunked Processing Behavior
+
+Chunked processing uses **MEAN aggregation** for cross-chunk combination when input exceeds the model's native maximum length:
+
+| Component | Behavior | Description |
+|-----------|----------|-------------|
+| **Within chunks** | Model's native pooling | Uses the model's configured pooling strategy |
+| **Cross-chunk aggregation** | Always MEAN | Weighted averaging based on chunk token counts |
+| **Performance** | Optimal | All chunks processed for complete semantic coverage |
+
+### Environment Variables
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `MODEL_NAME` | `intfloat/multilingual-e5-large` | Embedding model to use (supports multiple models) |
+| `PORT` | `31090` | Server port |
+| `GPU_COUNT` | `1` | Number of GPUs to use |
+| `MAX_EMBED_LEN` | `3072000` | Maximum embedding input length (supports very long documents) |
+| `POOLING_TYPE` | `auto` | Model's native pooling type: `auto`, `MEAN`, `CLS`, `LAST` (only affects within-chunk pooling, not cross-chunk aggregation) |
+| `API_KEY` | `EMPTY` | API key for authentication |
+
+## 🔧 How It Works
+
+1. **Enhanced Input Validation**: `max_embed_len` allows accepting inputs longer than `max_model_len` without environment variables
+2. **Smart Chunking**: Text is split based on `max_position_embeddings` to maintain semantic integrity
+3. **Unified Processing**: All chunks processed separately through the model using its configured pooling strategy
+4. **MEAN Aggregation**: When input exceeds model's native length, results combined using token count-based weighted averaging across all chunks
+5. **Consistent Output**: Final embeddings maintain the same dimensionality as standard processing
+
+### Input Length Handling
+
+- **Within max_embed_len**: Input is accepted and processed (up to 3M+ tokens)
+- **Exceeds max_position_embeddings**: Chunked processing is automatically triggered
+- **Exceeds max_embed_len**: Input is rejected with clear error message
+- **No environment variables required**: Works without `VLLM_ALLOW_LONG_MAX_MODEL_LEN`
+
+### Extreme Long Text Support
+
+With `MAX_EMBED_LEN=3072000`, you can process:
+
+- **Academic papers**: Full research papers with references
+- **Legal documents**: Complete contracts and legal texts  
+- **Books**: Entire chapters or small books
+- **Code repositories**: Large codebases and documentation
+
+## 📊 Performance Characteristics
+
+### Chunked Processing Performance
+
+| Aspect | Behavior | Performance |
+|--------|----------|-------------|
+| **Chunk Processing** | All chunks processed with native pooling | Consistent with input length |
+| **Cross-chunk Aggregation** | MEAN weighted averaging | Minimal overhead |
+| **Memory Usage** | Proportional to number of chunks | Moderate, scalable |
+| **Semantic Quality** | Complete text coverage | Optimal for long documents |
+
+## 🧪 Test Cases
+
+The test client demonstrates:
+
+- ✅ **Short text**: Normal processing (baseline)
+- ✅ **Medium text**: Single chunk processing
+- ✅ **Long text**: Multi-chunk processing with aggregation
+- ✅ **Very long text**: Many chunks processing
+- ✅ **Extreme long text**: Document-level processing (100K+ tokens)
+- ✅ **Batch processing**: Mixed-length inputs in one request
+- ✅ **Consistency**: Reproducible results across runs
+
+## 🐛 Troubleshooting
+
+### Common Issues
+
+1. **Chunked processing not enabled**:
+
+   ```log
+   ValueError: This model's maximum position embeddings length is 4096 tokens...
+   ```
+
+   **Solution**: Ensure `enable_chunked_processing: true` in pooler config
+
+2. **Input exceeds max_embed_len**:
+
+   ```log
+   ValueError: This model's maximum embedding input length is 3072000 tokens...
+   ```
+
+   **Solution**: Increase `max_embed_len` in pooler config or reduce input length
+
+3. **Memory errors**:
+  
+   ```log
+   RuntimeError: CUDA out of memory
+   ```
+  
+   **Solution**: Reduce chunk size by adjusting model's `max_position_embeddings` or use fewer GPUs
+
+4. **Slow processing**:
+   **Expected**: Long text takes more time due to multiple inference calls
+
+### Debug Information
+
+Server logs show chunked processing activity:
+
+```log
+INFO: Input length 150000 exceeds max_position_embeddings 4096, will use chunked processing
+INFO: Split input of 150000 tokens into 37 chunks (max_chunk_size: 4096)
+```
+
+## 🤝 Contributing
+
+To extend chunked processing support to other embedding models:
+
+1. Check model compatibility with the pooling architecture
+2. Test with various text lengths
+3. Validate embedding quality compared to single-chunk processing
+4. Submit PR with test cases and documentation updates
+
+## 🆕 Enhanced Features
+
+### max_embed_len Parameter
+
+The new `max_embed_len` parameter provides:
+
+- **Simplified Configuration**: No need for `VLLM_ALLOW_LONG_MAX_MODEL_LEN` environment variable
+- **Flexible Input Validation**: Accept inputs longer than `max_model_len` up to `max_embed_len`
+- **Extreme Length Support**: Process documents with millions of tokens
+- **Clear Error Messages**: Better feedback when inputs exceed limits
+- **Backward Compatibility**: Existing configurations continue to work

examples/online_serving/openai_embedding_long_text/client.py

@@ -0,0 +1,366 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+"""
+Example script demonstrating long text embedding with chunked processing in vLLM.
+
+This example shows how to use vLLM's chunked processing feature to handle text
+inputs that exceed the model's maximum token length. The feature automatically
+splits long text into chunks and handles different pooling types optimally.
+
+Prerequisites:
+1. Start vLLM server with chunked processing enabled:
+   
+   # MEAN pooling (processes all chunks, recommended for complete coverage)
+   vllm serve intfloat/multilingual-e5-large \
+     --override-pooler-config \
+      '{"pooling_type": "MEAN", "normalize": true, ' \
+      '"enable_chunked_processing": true, "max_embed_len": 3072000}' \
+     --served-model-name multilingual-e5-large \
+     --trust-remote-code \
+     --port 31090 \
+     --api-key your-api-key
+
+   # OR CLS pooling (native CLS within chunks, MEAN aggregation across chunks)
+   vllm serve BAAI/bge-large-en-v1.5 \
+     --override-pooler-config \
+      '{"pooling_type": "CLS", "normalize": true, ' \
+      '"enable_chunked_processing": true, "max_embed_len": 1048576}' \
+     --served-model-name bge-large-en-v1.5 \
+     --trust-remote-code \
+     --port 31090 \
+     --api-key your-api-key
+
+2. Install required dependencies:
+   pip install openai requests
+"""
+
+import time
+
+import numpy as np
+from openai import OpenAI
+
+# Configuration
+API_KEY = "your-api-key"  # Replace with your actual API key
+BASE_URL = "http://localhost:31090/v1"
+MODEL_NAME = "multilingual-e5-large"
+
+
+def generate_long_text(base_text: str, repeat_count: int) -> str:
+    """Generate long text by repeating base text."""
+    return base_text * repeat_count
+
+
+def test_embedding_with_different_lengths():
+    """Test embedding generation with different text lengths."""
+    client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
+
+    # Test cases with different text lengths
+    test_cases = [
+        {
+            "name": "Short Text",
+            "text": "Hello, this is a short text for embedding.",
+            "expected_chunks": 1,
+        },
+        {
+            "name": "Medium Text",
+            "text": generate_long_text(
+                "This is a medium-length text that should fit within the "
+                "model's context window. " * 20,
+                2,
+            ),
+            "expected_chunks": 1,
+        },
+        {
+            "name": "Long Text (2 chunks)",
+            "text": generate_long_text(
+                "This is a very long text that will exceed the model's "
+                "maximum context length and trigger chunked processing. " * 50,
+                5,
+            ),
+            "expected_chunks": 2,
+        },
+        {
+            "name": "Very Long Text (3+ chunks)",
+            "text": generate_long_text(
+                "This text is extremely long and will definitely "
+                "require multiple chunks for processing. " * 100,
+                10,
+            ),
+            "expected_chunks": 3,
+        },
+    ]
+
+    print("🧪 Testing vLLM Long Text Embedding with Chunked Processing")
+    print("=" * 70)
+
+    for i, test_case in enumerate(test_cases, 1):
+        print(f"\n📝 Test {i}: {test_case['name']}")
+        print(f"Text length: {len(test_case['text'])} characters")
+
+        try:
+            start_time = time.time()
+
+            response = client.embeddings.create(
+                input=test_case["text"], model=MODEL_NAME, encoding_format="float"
+            )
+
+            end_time = time.time()
+            processing_time = end_time - start_time
+
+            # Extract embedding data
+            embedding = response.data[0].embedding
+            embedding_dim = len(embedding)
+
+            print("✅ Success!")
+            print(f"   - Embedding dimension: {embedding_dim}")
+            print(f"   - Processing time: {processing_time:.2f}s")
+            print(f"   - Expected chunks: ~{test_case['expected_chunks']}")
+            print(f"   - First 5 values: {embedding[:5]}")
+
+        except Exception as e:
+            print(f"❌ Failed: {str(e)}")
+
+
+def test_batch_embedding():
+    """Test batch embedding with mixed-length inputs."""
+    client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
+
+    print("\n🔄 Testing Batch Embedding with Mixed Lengths")
+    print("=" * 50)
+
+    # Mix of short and long texts
+    batch_inputs = [
+        "Short text 1",
+        generate_long_text("Medium length text that fits in one chunk. " * 20, 1),
+        "Another short text",
+        generate_long_text("Long text requiring chunked processing. " * 100, 5),
+    ]
+
+    try:
+        start_time = time.time()
+
+        response = client.embeddings.create(
+            input=batch_inputs, model=MODEL_NAME, encoding_format="float"
+        )
+
+        end_time = time.time()
+        processing_time = end_time - start_time
+
+        print("✅ Batch processing successful!")
+        print(f"   - Number of inputs: {len(batch_inputs)}")
+        print(f"   - Number of embeddings: {len(response.data)}")
+        print(f"   - Total processing time: {processing_time:.2f}s")
+        print(
+            f"   - Average time per input: {processing_time / len(batch_inputs):.2f}s"
+        )
+
+        for i, data in enumerate(response.data):
+            input_length = len(batch_inputs[i])
+            embedding_dim = len(data.embedding)
+            print(
+                f"   - Input {i + 1}: {input_length} chars → {embedding_dim}D embedding"
+            )
+
+    except Exception as e:
+        print(f"❌ Batch processing failed: {str(e)}")
+
+
+def test_multiple_long_texts_batch():
+    """Test batch processing with multiple long texts to verify chunk ID uniqueness."""
+    client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
+
+    print("\n🔧 Testing Multiple Long Texts in Batch (Chunk ID Fix Verification)")
+    print("=" * 70)
+
+    # Create multiple distinct long texts that will all require chunking
+    # Note: All pooling types now use MEAN aggregation across chunks:
+    # - Native pooling (MEAN/CLS/LAST) is used within each chunk
+    # - MEAN aggregation combines results across all chunks
+    # - Full semantic coverage for all pooling types
+    long_texts = [
+        generate_long_text(
+            "First long document about artificial intelligence and machine learning. "
+            * 80,
+            6,
+        ),
+        generate_long_text(
+            "Second long document about natural language processing and transformers. "
+            * 80,
+            6,
+        ),
+        generate_long_text(
+            "Third long document about computer vision and neural networks. " * 80, 6
+        ),
+    ]
+
+    # Add some short texts to mix things up
+    batch_inputs = [
+        "Short text before long texts",
+        long_texts[0],
+        "Short text between long texts",
+        long_texts[1],
+        long_texts[2],
+        "Short text after long texts",
+    ]
+
+    print("📊 Batch composition:")
+    for i, text in enumerate(batch_inputs):
+        length = len(text)
+        text_type = "Long (will be chunked)" if length > 5000 else "Short"
+        print(f"   - Input {i + 1}: {length} chars ({text_type})")
+
+    try:
+        start_time = time.time()
+
+        response = client.embeddings.create(
+            input=batch_inputs, model=MODEL_NAME, encoding_format="float"
+        )
+
+        end_time = time.time()
+        processing_time = end_time - start_time
+
+        print("\n✅ Multiple long texts batch processing successful!")
+        print(f"   - Number of inputs: {len(batch_inputs)}")
+        print(f"   - Number of embeddings returned: {len(response.data)}")
+        print(f"   - Total processing time: {processing_time:.2f}s")
+
+        # Verify each embedding is different (no incorrect aggregation)
+        embeddings = [data.embedding for data in response.data]
+
+        if len(embeddings) >= 3:
+            import numpy as np
+
+            # Compare embeddings of the long texts (indices 1, 3, 4)
+            long_embeddings = [
+                np.array(embeddings[1]),  # First long text
+                np.array(embeddings[3]),  # Second long text
+                np.array(embeddings[4]),  # Third long text
+            ]
+
+            print("\n🔍 Verifying embedding uniqueness:")
+            for i in range(len(long_embeddings)):
+                for j in range(i + 1, len(long_embeddings)):
+                    cosine_sim = np.dot(long_embeddings[i], long_embeddings[j]) / (
+                        np.linalg.norm(long_embeddings[i])
+                        * np.linalg.norm(long_embeddings[j])
+                    )
+                    print(
+                        f"   - Similarity between long text {i + 1} and {j + 1}: "
+                        f"{cosine_sim:.4f}"
+                    )
+
+                    if (
+                        cosine_sim < 0.9
+                    ):  # Different content should have lower similarity
+                        print("     ✅ Good: Embeddings are appropriately different")
+                    else:
+                        print(
+                            "     ⚠️ High similarity - may indicate chunk "
+                            "aggregation issue"
+                        )
+
+        print("\n📋 Per-input results:")
+        for i, data in enumerate(response.data):
+            input_length = len(batch_inputs[i])
+            embedding_dim = len(data.embedding)
+            embedding_norm = np.linalg.norm(data.embedding)
+            print(
+                f"   - Input {i + 1}: {input_length} chars → {embedding_dim}D "
+                f"embedding (norm: {embedding_norm:.4f})"
+            )
+
+        print(
+            "\n✅ This test verifies the fix for chunk ID collisions in "
+            "batch processing"
+        )
+        print("   - Before fix: Multiple long texts would have conflicting chunk IDs")
+        print("   - After fix: Each prompt's chunks have unique IDs with prompt index")
+
+    except Exception as e:
+        print(f"❌ Multiple long texts batch test failed: {str(e)}")
+        print("   This might indicate the chunk ID collision bug is present!")
+
+
+def test_embedding_consistency():
+    """Test that chunked processing produces consistent results."""
+    client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
+
+    print("\n🔍 Testing Embedding Consistency")
+    print("=" * 40)
+
+    # Use the same long text multiple times
+    long_text = generate_long_text(
+        "Consistency test text for chunked processing validation. " * 50, 3
+    )
+
+    embeddings = []
+
+    try:
+        for i in range(3):
+            response = client.embeddings.create(
+                input=long_text, model=MODEL_NAME, encoding_format="float"
+            )
+            embeddings.append(response.data[0].embedding)
+            print(f"   - Generated embedding {i + 1}")
+
+        # Check consistency (embeddings should be identical)
+        if len(embeddings) >= 2:
+            # Calculate similarity between first two embeddings
+
+            emb1 = np.array(embeddings[0])
+            emb2 = np.array(embeddings[1])
+
+            # Cosine similarity
+            cosine_sim = np.dot(emb1, emb2) / (
+                np.linalg.norm(emb1) * np.linalg.norm(emb2)
+            )
+
+            print("✅ Consistency test completed!")
+            print(f"   - Cosine similarity between runs: {cosine_sim:.6f}")
+            print("   - Expected: ~1.0 (identical embeddings)")
+
+            if cosine_sim > 0.999:
+                print("   - ✅ High consistency achieved!")
+            else:
+                print("   - ⚠️ Consistency may vary due to numerical precision")
+
+    except Exception as e:
+        print(f"❌ Consistency test failed: {str(e)}")
+
+
+def main():
+    """Main function to run all tests."""
+    print("🚀 vLLM Long Text Embedding Client")
+    print(f"📡 Connecting to: {BASE_URL}")
+    print(f"🤖 Model: {MODEL_NAME}")
+    masked_key = "*" * (len(API_KEY) - 4) + API_KEY[-4:] if len(API_KEY) > 4 else "****"
+    print(f"🔑 API Key: {masked_key}")
+
+    # Run all test cases
+    test_embedding_with_different_lengths()
+    test_batch_embedding()
+    test_multiple_long_texts_batch()
+    test_embedding_consistency()
+
+    print("\n" + "=" * 70)
+    print("🎉 All tests completed!")
+    print("\n💡 Key Features Demonstrated:")
+    print("   - ✅ Automatic chunked processing for long text")
+    print("   - ✅ Seamless handling of mixed-length batches")
+    print("   - ✅ Multiple long texts in single batch (chunk ID fix)")
+    print("   - ✅ Unified chunked processing:")
+    print("     • Native pooling used within each chunk")
+    print("     • MEAN aggregation across all chunks")
+    print("     • Complete semantic coverage for all pooling types")
+    print("   - ✅ Consistent embedding generation")
+    print("   - ✅ Backward compatibility with short text")
+    print("\n📚 For more information, see:")
+    print(
+        "   - Documentation: https://docs.vllm.ai/en/latest/models/pooling_models.html"
+    )
+    print("   - Chunked Processing Guide: openai_embedding_long_text.md")
+
+
+if __name__ == "__main__":
+    main()

examples/online_serving/openai_embedding_long_text/service.sh

@@ -0,0 +1,137 @@
+#!/bin/bash
+
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# vLLM Embedding Server with Enhanced Chunked Processing
+# This script starts a vLLM server with chunked processing enabled for long text embedding.
+# Now supports proper pooling type validation and model-specific configurations.
+
+set -euo pipefail
+
+# Configuration
+MODEL_NAME=${MODEL_NAME:-"intfloat/multilingual-e5-large"}
+MODEL_CODE=${MODEL_CODE:-"multilingual-e5-large"}
+
+PORT=${PORT:-31090}
+GPU_COUNT=${GPU_COUNT:-1}
+MAX_EMBED_LEN=${MAX_EMBED_LEN:-3072000}
+API_KEY=${API_KEY:-"your-api-key"}
+
+# Enhanced pooling configuration with model-specific defaults
+POOLING_TYPE=${POOLING_TYPE:-"auto"}  # auto, MEAN, CLS, LAST
+export VLLM_ENABLE_CHUNKED_PROCESSING=true
+export CUDA_VISIBLE_DEVICES=2,3,4,5
+# export VLLM_ATTENTION_BACKEND=XFORMERS
+
+echo "🚀 Starting vLLM Embedding Server with Enhanced Chunked Processing"
+echo "=================================================================="
+
+# Environment variables for optimization
+export VLLM_WORKER_MULTIPROC_METHOD=spawn
+
+# Function to determine optimal pooling type for known models
+get_optimal_pooling_type() {
+    local model="$1"
+    case "$model" in
+        *"e5-"* | *"multilingual-e5"*)
+            echo "MEAN"  # E5 series native pooling
+            ;;
+        *"bge-"*)
+            echo "CLS"   # BGE series native pooling
+            ;;
+        *"gte-"*)
+            echo "LAST"  # GTE series native pooling
+            ;;
+        *"sentence-t5"* | *"st5"*)
+            echo "MEAN"  # Sentence-T5 native pooling
+            ;;
+        *"jina-embeddings"*)
+            echo "MEAN"  # Jina embeddings native pooling
+            ;;
+        *"Qwen"*"Embedding"*)
+            echo "LAST"  # Qwen embeddings native pooling
+            ;;
+        *)
+            echo "MEAN"  # Default native pooling for unknown models
+            ;;
+    esac
+}
+
+# Auto-detect pooling type if not explicitly set
+if [ "$POOLING_TYPE" = "auto" ]; then
+    POOLING_TYPE=$(get_optimal_pooling_type "$MODEL_NAME")
+    echo "🔍 Auto-detected pooling type: $POOLING_TYPE for model $MODEL_NAME"
+fi
+
+# Display configuration
+echo "📋 Configuration:"
+echo "   - Model: $MODEL_NAME"
+echo "   - Port: $PORT"
+echo "   - GPU Count: $GPU_COUNT"
+echo "   - Enhanced Chunked Processing: ${VLLM_ENABLE_CHUNKED_PROCESSING}"
+echo "   - Max Embed Length: ${MAX_EMBED_LEN} tokens"
+echo "   - Native Pooling Type: $POOLING_TYPE + Normalization"
+echo "   - Cross-chunk Aggregation: MEAN (automatic)"
+echo ""
+
+# Validate GPU availability
+if command -v nvidia-smi &> /dev/null; then
+    gpu_count=$(nvidia-smi --list-gpus | wc -l)
+    echo "🖥️  Available GPUs: $gpu_count"
+    if [ "$GPU_COUNT" -gt "$gpu_count" ]; then
+        echo "⚠️  Warning: Requested $GPU_COUNT GPUs but only $gpu_count available"
+        echo "   Adjusting to use $gpu_count GPUs"
+        GPU_COUNT=$gpu_count
+    fi
+else
+    echo "⚠️  Warning: nvidia-smi not found. GPU detection skipped."
+fi
+
+# Chunked processing uses unified MEAN aggregation
+echo "ℹ️  Chunked Processing: Using $POOLING_TYPE pooling within chunks, MEAN aggregation across chunks"
+echo "   - All chunks processed for complete semantic coverage"
+echo "   - Weighted averaging based on chunk token counts"
+
+echo ""
+echo "🔧 Starting server with enhanced chunked processing configuration..."
+
+# Build pooler config JSON
+POOLER_CONFIG="{\"pooling_type\": \"$POOLING_TYPE\", \"normalize\": true, \"enable_chunked_processing\": ${VLLM_ENABLE_CHUNKED_PROCESSING}, \"max_embed_len\": ${MAX_EMBED_LEN}}"
+
+# Start vLLM server with enhanced chunked processing
+vllm serve "$MODEL_NAME" \
+  --tensor-parallel-size "$GPU_COUNT" \
+  --enforce-eager \
+  --override-pooler-config "$POOLER_CONFIG" \
+  --served-model-name ${MODEL_CODE} \
+  --api-key "$API_KEY" \
+  --trust-remote-code \
+  --port "$PORT" \
+  --host 0.0.0.0
+
+echo ""
+echo "✅ vLLM Embedding Server started successfully!"
+echo ""
+echo "📡 Server Information:"
+echo "   - Base URL: http://localhost:$PORT"
+echo "   - Model Code: ${MODEL_CODE}"
+echo "   - API Key: $API_KEY"
+echo "   - Native Pooling: $POOLING_TYPE | Cross-chunk: MEAN"
+echo ""
+echo "🧪 Test the server with:"
+echo "   python examples/online_serving/openai_embedding_long_text_client.py"
+echo ""
+echo "📚 Enhanced features enabled:"
+echo "   ✅ Intelligent native pooling type detection"
+echo "   ✅ Unified MEAN aggregation for chunked processing"
+echo "   ✅ Model-specific native pooling optimization"
+echo "   ✅ Enhanced max embedding length (${MAX_EMBED_LEN} tokens)"
+echo "   ✅ Complete semantic coverage for all pooling types"
+echo "   ✅ OpenAI-compatible API"
+echo "   ✅ GPU acceleration"
+echo ""
+echo "🔧 Advanced usage:"
+echo "   - Set POOLING_TYPE=MEAN|CLS|LAST to override auto-detection"
+echo "   - Set MAX_EMBED_LEN to adjust maximum input length"
+echo "   - All pooling types use MEAN aggregation across chunks" 

tests/async_engine/test_async_llm_engine.py

@@ -1,409 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import asyncio
-import os
-import uuid
-from asyncio import CancelledError
-from copy import copy
-from dataclasses import dataclass, field
-from typing import Any, Optional
-
-import pytest
-import pytest_asyncio
-import torch
-
-from vllm import SamplingParams
-from vllm.config import ParallelConfig
-from vllm.distributed import cleanup_dist_env_and_memory
-from vllm.engine.async_llm_engine import AsyncEngineArgs, AsyncLLMEngine
-from vllm.outputs import RequestOutput as RealRequestOutput
-from vllm.sampling_params import RequestOutputKind
-
-from ..utils import wait_for_gpu_memory_to_clear
-
-
-@dataclass
-class RequestOutput:
-    request_id: int
-    finished: bool = False
-
-
-@dataclass
-class MockModelConfig:
-    use_async_output_proc = True
-    media_io_kwargs: dict[str, dict[str, Any]] = field(default_factory=dict)
-
-
-class MockEngine:
-
-    def __init__(self):
-        self.step_calls = 0
-        self.add_request_calls = 0
-        self.abort_request_calls = 0
-        self.request_id = None
-        # Ugly, remove dependency when possible
-        self.parallel_config = ParallelConfig()
-        self.model_config = MockModelConfig()
-
-    async def step_async(self, virtual_engine):
-        # PP size is 1, ignore virtual engine
-        self.step_calls += 1
-        return [RequestOutput(
-            request_id=self.request_id)] if self.request_id else []
-
-    async def process_model_inputs_async(self, *args, **kwargs):
-        pass
-
-    async def stop_remote_worker_execution_loop_async(self):
-        pass
-
-    def generate(self, request_id):
-        self.request_id = request_id
-
-    def stop_generating(self):
-        self.request_id = None
-
-    def add_request(self, **kwargs):
-        del kwargs  # Unused
-        self.add_request_calls += 1
-        print(f'Request calls: {self.add_request_calls}')
-
-    async def add_request_async(self, **kwargs):
-        self.add_request_calls += 1
-        return
-
-    def abort_request(self, request_id):
-        del request_id  # Unused
-        self.abort_request_calls += 1
-
-    def has_unfinished_requests(self):
-        return self.request_id is not None
-
-    def has_unfinished_requests_for_virtual_engine(self, virtual_engine):
-        return self.request_id is not None
-
-
-class MockAsyncLLMEngine(AsyncLLMEngine):
-    _engine_class = MockEngine
-
-
-@pytest.mark.asyncio
-async def test_new_requests_event():
-    params = SamplingParams()
-
-    engine = MockAsyncLLMEngine()
-    engine.start_background_loop()
-    await asyncio.sleep(0.01)
-    assert engine.engine.step_calls == 0
-
-    await engine.add_request("1", "", params)
-    await asyncio.sleep(0.01)
-    assert engine.engine.add_request_calls == 1
-    assert engine.engine.step_calls == 1
-
-    await engine.add_request("2", "", params)
-    engine.engine.generate("2")
-    await asyncio.sleep(0)
-    await asyncio.sleep(0)
-    await asyncio.sleep(0)
-    assert engine.engine.add_request_calls == 2
-    assert engine.engine.step_calls >= 2
-    await asyncio.sleep(0.001)
-    assert engine.engine.step_calls >= 3
-    engine.engine.stop_generating()
-    await asyncio.sleep(0.001)
-    old_step_calls = engine.engine.step_calls
-    await asyncio.sleep(0.001)
-    assert engine.engine.step_calls == old_step_calls
-
-    await engine.add_request("3", "", params)
-    await asyncio.sleep(0.01)
-    assert engine.engine.add_request_calls == 3
-    assert engine.engine.step_calls == old_step_calls + 1
-    await asyncio.sleep(0.01)
-    assert engine.engine.add_request_calls == 3
-    assert engine.engine.step_calls == old_step_calls + 1
-
-    engine = MockAsyncLLMEngine()
-    assert engine.get_model_config() is not None
-    assert engine.get_tokenizer() is not None
-    assert engine.get_decoding_config() is not None
-
-
-def start_engine():
-    wait_for_gpu_memory_to_clear(
-        devices=list(range(torch.cuda.device_count())),
-        threshold_bytes=2 * 2**30,
-        timeout_s=60,
-    )
-
-    num_scheduler_steps = int(os.getenv("NUM_SCHEDULER_STEPS", "1"))
-    print(f"Starting engine with num_scheduler_steps={num_scheduler_steps}")
-
-    return AsyncLLMEngine.from_engine_args(
-        AsyncEngineArgs(model="facebook/opt-125m",
-                        enforce_eager=True,
-                        num_scheduler_steps=num_scheduler_steps))
-
-
-def uid() -> str:
-    return str(uuid.uuid4())
-
-
-@pytest_asyncio.fixture(scope="module")
-async def async_engine():
-    # We cannot use monkeypatch since this is a module
-    # scoped fixture and monkeypatch is function scoped.
-    previous_value = os.getenv("VLLM_USE_V1", None)
-    os.environ["VLLM_USE_V1"] = "0"
-    engine = await asyncio.get_event_loop().run_in_executor(executor=None,
-                                                            func=start_engine)
-    try:
-        yield engine
-    finally:
-        engine.shutdown_background_loop()
-        del engine
-        await asyncio.sleep(0.1)
-        cleanup_dist_env_and_memory()
-
-        if previous_value:
-            os.environ["VLLM_USE_V1"] = previous_value
-        else:
-            del os.environ["VLLM_USE_V1"]
-
-
-@pytest.fixture()
-def should_do_global_cleanup_after_test(request) -> bool:
-    # So we can share the async engine fixture between these tests
-    return False
-
-
-@pytest.mark.asyncio(scope="module")
-@pytest.mark.parametrize("stop", [None, ["a stop string"]])
-async def test_asyncio_run(async_engine, stop):
-
-    scheduler_config = await async_engine.get_scheduler_config()
-    num_scheduler_steps = scheduler_config.num_scheduler_steps
-
-    async def run(prompt: str):
-        sampling_params = SamplingParams(
-            temperature=0,
-            max_tokens=32,
-            min_tokens=32,
-            stop=stop,
-        )
-
-        output_count = 0
-        final_output = None
-        async for output in async_engine.generate(prompt,
-                                                  sampling_params,
-                                                  request_id=uid()):
-            output_count += 1
-            final_output = output
-        return final_output, output_count
-
-    results = await asyncio.gather(
-        run("test0"),
-        run("test0"),
-    )
-    assert len(results) == 2
-    first, second = results
-
-    # remove nondeterministic fields for comparison
-    first[0].metrics = None
-    second[0].metrics = None
-    first[0].request_id = None
-    second[0].request_id = None
-
-    assert str(first) == str(second)
-
-    output_count = results[0][1]
-    if num_scheduler_steps == 1:
-        assert output_count == 32
-    else:
-        assert 1 < output_count < 32
-
-
-@pytest.mark.asyncio(scope="module")
-@pytest.mark.parametrize("stop", [None, ["a stop string"]])
-async def test_output_kinds(async_engine, stop):
-    """Test that output_kind works as expected and that
-    results are equivalent across different kinds."""
-
-    scheduler_config = await async_engine.get_scheduler_config()
-    num_scheduler_steps = scheduler_config.num_scheduler_steps
-
-    sampling_params = SamplingParams(
-        temperature=0,
-        max_tokens=32,
-        min_tokens=32,
-        stop=stop,
-    )
-
-    async def run(prompt: str, kind: RequestOutputKind):
-        params = copy(sampling_params)
-        params.output_kind = kind
-
-        output_count = 0
-        final_output = None
-        async for output in async_engine.generate(prompt,
-                                                  params,
-                                                  request_id=uid()):
-            output_count += 1
-            final_output = output
-
-        assert final_output is not None
-        assert final_output.finished
-
-        return (final_output.prompt_token_ids,
-                final_output.outputs[0].token_ids,
-                final_output.outputs[0].text, output_count)
-
-    async def run_deltas(prompt: str):
-        params = copy(sampling_params)
-        params.output_kind = RequestOutputKind.DELTA
-
-        prompt_tokens = None
-        output_tokens: list[int] = []
-        output_text = ""
-        output_count = 0
-        final_output = None
-        async for output in async_engine.generate(prompt,
-                                                  params,
-                                                  request_id=uid()):
-            token_ids = output.outputs[0].token_ids
-            text = output.outputs[0].text
-            final_output = output
-
-            # Ensure we get prompt ids iff we haven't yet received output tokens
-            if output_tokens:
-                assert 1 <= len(token_ids) <= num_scheduler_steps
-                assert stop or text
-                assert not output.prompt_token_ids
-            else:
-                assert output.prompt_token_ids
-                prompt_tokens = output.prompt_token_ids
-
-            output_tokens.extend(token_ids)
-            output_text += text
-
-            output_count += 1
-
-        assert final_output is not None
-        assert final_output.finished
-
-        return prompt_tokens, output_tokens, output_text, output_count
-
-    results = await asyncio.gather(
-        run("common input prompt", RequestOutputKind.CUMULATIVE),
-        run("common input prompt", RequestOutputKind.FINAL_ONLY),
-        run_deltas("common input prompt"))
-
-    # Make sure outputs are the same
-    prompt_set = set(tuple(prompt_ids) for prompt_ids, _, _, _ in results)
-    assert len(prompt_set) == 1
-
-    text_set = set(text for _, _, text, _ in results)
-    assert len(text_set) == 1
-
-    tokens_set = set(tuple(ids) for _, ids, _, _ in results)
-    assert len(tokens_set) == 1
-
-    cumulative, final, deltas = results
-
-    # output message counts
-    assert cumulative[3] == deltas[3]
-
-    if num_scheduler_steps == 1:
-        assert cumulative[3] == 32
-    else:
-        assert 1 < cumulative[3] < 32
-
-    assert final[3] == 1
-
-
-@pytest.mark.asyncio(scope="module")
-@pytest.mark.parametrize("stop", [None, ["a stop string"]])
-async def test_cancellation(async_engine, stop):
-    scheduler_config = await async_engine.get_scheduler_config()
-    num_scheduler_steps = scheduler_config.num_scheduler_steps
-
-    sampling_params = SamplingParams(
-        temperature=0,
-        min_tokens=13,
-        max_tokens=13,
-        stop=stop,
-    )
-
-    stop_at = 5 if num_scheduler_steps == 1 else 1
-
-    request_id = uid()
-
-    i = 0
-    with pytest.raises(CancelledError):
-        async for output in async_engine.generate("test2",
-                                                  sampling_params,
-                                                  request_id=request_id):
-            assert not output.finished
-            i += 1
-            if i == stop_at:
-                await async_engine.abort(request_id)
-
-    assert i == stop_at
-
-
-@pytest.mark.asyncio(scope="module")
-@pytest.mark.parametrize("stop", [None, ["a stop string"]])
-async def test_delayed_generator(async_engine, stop):
-    scheduler_config = await async_engine.get_scheduler_config()
-
-    if scheduler_config.num_scheduler_steps != 1:
-        pytest.skip("no need to test this one with multistep")
-
-    sampling_params = SamplingParams(
-        temperature=0,
-        min_tokens=10,
-        max_tokens=10,
-        stop=stop,
-    )
-
-    stream = async_engine.generate("test3", sampling_params, request_id=uid())
-    i = 0
-    final_output: Optional[RealRequestOutput] = None
-    async for output in stream:
-        final_output = output
-        if i == 0:
-            # wait for generation to complete before consuming
-            # the remaining messages
-            await asyncio.sleep(1)
-        if i < 9:
-            assert not output.finished
-        i += 1
-
-    assert i == 10
-    assert final_output is not None
-    assert len(final_output.outputs[0].token_ids) == 10
-    assert final_output.finished
-
-
-@pytest.mark.asyncio(scope="module")
-async def test_invalid_argument(async_engine):
-    scheduler_config = await async_engine.get_scheduler_config()
-
-    if scheduler_config.num_scheduler_steps != 1:
-        pytest.skip("no need to test this one with multistep")
-
-    sampling_params = SamplingParams(
-        temperature=0,
-        min_tokens=10,
-        max_tokens=10,
-    )
-
-    # Targeting specific DP rank only supported in v1 multi-instance DP
-    with pytest.raises(ValueError):
-        async for _ in async_engine.generate("test",
-                                             sampling_params,
-                                             request_id=uid(),
-                                             data_parallel_rank=0):
-            pass

tests/config/test_config.yaml

@@ -2,4 +2,3 @@ port: 12312
 served_model_name: mymodel
 tensor_parallel_size: 2
 trust_remote_code: true
-multi_step_stream_outputs: false

tests/config/test_config_with_model.yaml

@@ -4,4 +4,3 @@ port: 12312
 served_model_name: mymodel
 tensor_parallel_size: 2
 trust_remote_code: true
-multi_step_stream_outputs: false

tests/core/test_chunked_prefill_scheduler.py

@@ -644,11 +644,9 @@ def test_chunked_prefill_preempt():
     assert out.num_batched_tokens == max_num_batched_tokens
 
 
-@pytest.mark.parametrize("num_scheduler_steps", [1, 5])
-def test_chunked_prefill_spec_prefill(num_scheduler_steps):
+def test_chunked_prefill_spec_prefill():
     """Verify that the num_lookahead_slots is set appropriately for an all"""
-    """prefill batch depending on whether multi-step scheduling is enabled"""
-    """or not"""
+    """prefill batch."""
     block_size = 4
     max_seqs = 30
     max_model_len = 200
@@ -661,7 +659,6 @@ def test_chunked_prefill_spec_prefill(num_scheduler_steps):
         max_model_len,
         enable_chunked_prefill=True,
         num_lookahead_slots=num_lookahead_slots,
-        num_scheduler_steps=num_scheduler_steps,
     )
     cache_config = CacheConfig(block_size, 1.0, 1, "auto")
     cache_config.num_cpu_blocks = 16
@@ -679,8 +676,7 @@ def test_chunked_prefill_spec_prefill(num_scheduler_steps):
     assert out.num_prefill_groups == 1
     assert out.num_batched_tokens == max_num_batched_tokens
     print(out.num_lookahead_slots)
-    assert out.num_lookahead_slots == (0 if (num_scheduler_steps == 1) else
-                                       num_lookahead_slots)
+    assert out.num_lookahead_slots == 0
 
 
 def test_chunked_prefill_max_seqs():

tests/core/test_num_computed_tokens_update.py

@@ -6,7 +6,6 @@ import pytest
 from tests.conftest import VllmRunner
 from tests.core.utils import create_dummy_prompt
 from vllm.engine.llm_engine import LLMEngine
-from vllm.platforms import current_platform
 from vllm.sequence import SequenceGroup
 
 MODEL = "JackFram/llama-160m"
@@ -17,32 +16,19 @@ def add_seq_group_to_engine(engine: LLMEngine, seq_group: SequenceGroup):
     scheduler.add_seq_group(seq_group)
 
 
-@pytest.mark.parametrize("num_scheduler_steps", [1, 8])
 @pytest.mark.parametrize("enable_chunked_prefill", [False, True])
 @pytest.mark.parametrize("enforce_eager", [False, True])
-def test_num_computed_tokens_update(num_scheduler_steps: int,
-                                    enable_chunked_prefill: bool,
+def test_num_computed_tokens_update(enable_chunked_prefill: bool,
                                     enforce_eager: bool):
 
-    is_multi_step = num_scheduler_steps > 1
-    is_multi_step_chunked_prefill = is_multi_step and enable_chunked_prefill
-
-    if is_multi_step_chunked_prefill and current_platform.is_rocm():
-        pytest.skip("Multi-step with Chunked-Prefill does not support "
-                    "rocm_flash_attn backend")
-
     # Make a vllm engine
     runner = VllmRunner(model_name=MODEL,
                         gpu_memory_utilization=0.7,
-                        num_scheduler_steps=num_scheduler_steps,
                         enable_chunked_prefill=enable_chunked_prefill,
                         enforce_eager=enforce_eager)
     engine: LLMEngine = runner.llm.llm_engine
 
-    # In multi-step + chunked-prefill there is no separate single prompt step.
-    # What is scheduled will run for num_scheduler_steps always.
-    num_prompt_steps = num_scheduler_steps \
-        if is_multi_step_chunked_prefill else 1
+    num_prompt_steps = 1
 
     num_output_tokens_list = [4, 8, 12, 15, 16, 17]
 
@@ -73,10 +59,8 @@ def test_num_computed_tokens_update(num_scheduler_steps: int,
                 # Test correctness of num_computed_tokens after the decode steps
                 assert seq.data.get_num_computed_tokens(
                 ) == prompt_num_computed_tokens + decode_step_counter
-                for _ in range(num_scheduler_steps):
-                    # decode step
-                    engine.step()
-                    decode_step_counter += 1
+                engine.step()
+                decode_step_counter += 1
 
         # Test correctness of num_computed_tokens after the sequence finish.
         assert seq.data.get_num_computed_tokens(

tests/engine/test_multi_step_output_processor.py

@@ -1,274 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import random
-from unittest.mock import MagicMock
-
-import pytest
-from transformers import PreTrainedTokenizer
-
-from vllm.core.scheduler import Scheduler
-from vllm.engine.output_processor.multi_step import MultiStepOutputProcessor
-from vllm.engine.output_processor.stop_checker import StopChecker
-from vllm.sampling_params import SamplingParams
-from vllm.sequence import (CompletionSequenceGroupOutput, Logprob,
-                           SequenceOutput, SequenceStatus)
-from vllm.transformers_utils.detokenizer import Detokenizer
-from vllm.utils import Counter
-
-from ..core.utils import create_seq_group
-
-
-@pytest.mark.parametrize("seq_output_len", [128])
-@pytest.mark.parametrize("num_new_tokens", [1, 12])
-@pytest.mark.skip_global_cleanup
-def test_appends_token_ids(num_new_tokens: int, seq_output_len: int):
-    """Verify multi-step decoding appends token ids correctly.
-
-    We append token ids and verify all the token ids were appended correctly.
-    Note that ignore_eos=True.
-    """
-    detokenizer = MagicMock(spec=Detokenizer)
-    scheduler = MagicMock(spec=Scheduler)
-    stop_checker = MagicMock(spec=StopChecker)
-    seq_counter = Counter()
-
-    output_processor = MultiStepOutputProcessor(
-        detokenizer=detokenizer,
-        scheduler=[scheduler],
-        seq_counter=seq_counter,
-        get_tokenizer_for_seq=lambda _: mock_tokenizer(),
-        stop_checker=stop_checker,
-    )
-
-    seq_group = create_seq_group(
-        seq_prompt_len=1024,
-        seq_output_lens=[seq_output_len],
-        sampling_params=SamplingParams(max_tokens=seq_output_len +
-                                       num_new_tokens,
-                                       ignore_eos=True),
-    )
-
-    seq = seq_group.get_seqs()[0]
-    seq.status = SequenceStatus.RUNNING
-
-    new_token_ids = list(range(num_new_tokens))
-
-    outputs = [
-        CompletionSequenceGroupOutput(
-            samples=[
-                SequenceOutput(
-                    parent_seq_id=seq.seq_id,
-                    output_token=output_token,
-                    logprobs={output_token: Logprob(0.0)},
-                )
-            ],
-            prompt_logprobs=None,
-        ) for output_token in new_token_ids
-    ]
-
-    assert seq.get_token_ids()[-len(new_token_ids):] != new_token_ids
-    output_processor.process_outputs(seq_group, outputs)
-    assert seq.get_token_ids()[-len(new_token_ids):] == new_token_ids
-
-
-@pytest.mark.parametrize("seq_prompt_len", [1024])
-@pytest.mark.parametrize("seq_output_len", [128])
-@pytest.mark.parametrize("num_new_tokens", [5, 6, 7, 8])
-@pytest.mark.parametrize("max_tokens", [128 + 3])
-@pytest.mark.skip_global_cleanup
-def test_respects_max_tokens(num_new_tokens: int, seq_prompt_len: int,
-                             seq_output_len: int, max_tokens: int):
-    """Verify tokens after max_tokens are dropped and not appended to the
-    sequence.
-    """
-    detokenizer = MagicMock(spec=Detokenizer)
-    scheduler = MagicMock(spec=Scheduler)
-    stop_checker = MagicMock(spec=StopChecker)
-    seq_counter = Counter()
-
-    output_processor = MultiStepOutputProcessor(
-        detokenizer=detokenizer,
-        scheduler=[scheduler],
-        seq_counter=seq_counter,
-        get_tokenizer_for_seq=lambda _: mock_tokenizer(),
-        stop_checker=stop_checker,
-    )
-
-    seq_group = create_seq_group(
-        seq_prompt_len=seq_prompt_len,
-        seq_output_lens=[seq_output_len],
-        sampling_params=SamplingParams(max_tokens=max_tokens, ),
-    )
-
-    seq = seq_group.get_seqs()[0]
-    seq.status = SequenceStatus.RUNNING
-
-    new_token_ids = list(range(num_new_tokens))
-
-    outputs = [
-        CompletionSequenceGroupOutput(
-            samples=[
-                SequenceOutput(
-                    parent_seq_id=seq.seq_id,
-                    output_token=output_token,
-                    logprobs={output_token: Logprob(0.0)},
-                )
-            ],
-            prompt_logprobs=None,
-        ) for output_token in new_token_ids
-    ]
-
-    assert seq.get_len() == seq_prompt_len + seq_output_len
-    output_processor.process_outputs(seq_group, outputs)
-
-    # Expect the processed sequence to not go over max tokens in len.
-    assert seq.get_len() == seq_prompt_len + max_tokens
-
-    # Expect the correct tokens were appended.
-    expected_appended_tokens = new_token_ids[:max_tokens - seq_output_len]
-    assert seq.get_token_ids(
-    )[-len(expected_appended_tokens):] == expected_appended_tokens
-
-
-@pytest.mark.parametrize("seq_prompt_len", [1024])
-@pytest.mark.parametrize("seq_output_len", [128])
-@pytest.mark.parametrize("num_new_tokens", [12])
-@pytest.mark.parametrize("seed", list(range(6)))
-@pytest.mark.skip_global_cleanup
-def test_respects_eos_token_id(num_new_tokens: int, seq_prompt_len: int,
-                               seq_output_len: int, seed: int):
-    """Verify the eos token id is included in the sequence, but subsequent
-    tokens are dropped (not appended to sequence).
-    """
-    random.seed(seed)
-    detokenizer = MagicMock(spec=Detokenizer)
-    scheduler = MagicMock(spec=Scheduler)
-    stop_checker = MagicMock(spec=StopChecker)
-    seq_counter = Counter()
-
-    eos_token_id = 100
-
-    output_processor = MultiStepOutputProcessor(
-        detokenizer=detokenizer,
-        scheduler=[scheduler],
-        seq_counter=seq_counter,
-        get_tokenizer_for_seq=lambda _: mock_tokenizer(eos_token_id),
-        stop_checker=stop_checker,
-    )
-
-    seq_group = create_seq_group(
-        seq_prompt_len=seq_prompt_len,
-        seq_output_lens=[seq_output_len],
-        sampling_params=SamplingParams(
-            # Ensure enough space.
-            max_tokens=seq_output_len + num_new_tokens, ),
-    )
-
-    seq = seq_group.get_seqs()[0]
-    seq.status = SequenceStatus.RUNNING
-
-    new_token_ids = list(range(num_new_tokens))
-    assert eos_token_id not in new_token_ids
-    eos_index = random.randint(0, len(new_token_ids) - 1)
-    new_token_ids[eos_index] = eos_token_id
-
-    outputs = [
-        CompletionSequenceGroupOutput(
-            samples=[
-                SequenceOutput(
-                    parent_seq_id=seq.seq_id,
-                    output_token=output_token,
-                    logprobs={output_token: Logprob(0.0)},
-                )
-            ],
-            prompt_logprobs=None,
-        ) for output_token in new_token_ids
-    ]
-
-    assert seq.get_len() == seq_prompt_len + seq_output_len
-    output_processor.process_outputs(seq_group, outputs)
-
-    # Expect the processed sequence to not go beyond provided eos.
-    assert seq.get_len() == seq_prompt_len + seq_output_len + (eos_index + 1)
-
-    # Expect the correct tokens were appended.
-    expected_appended_tokens = new_token_ids[:eos_index + 1]
-    assert seq.get_token_ids(
-    )[-len(expected_appended_tokens):] == expected_appended_tokens
-
-
-@pytest.mark.parametrize("seq_prompt_len", [1024])
-@pytest.mark.parametrize("seq_output_len", [128])
-@pytest.mark.parametrize("num_new_tokens", [12])
-@pytest.mark.parametrize("seed", list(range(6)))
-@pytest.mark.skip_global_cleanup
-def test_ignores_eos_token_id(num_new_tokens: int, seq_prompt_len: int,
-                              seq_output_len: int, seed: int):
-    """When sampling parameters dictate that we should ignore the eos token id,
-    ensure all token ids are appended even if the eos token id is emitted.
-    """
-    random.seed(seed)
-    detokenizer = MagicMock(spec=Detokenizer)
-    scheduler = MagicMock(spec=Scheduler)
-    stop_checker = MagicMock(spec=StopChecker)
-    seq_counter = Counter()
-
-    eos_token_id = 100
-
-    output_processor = MultiStepOutputProcessor(
-        detokenizer=detokenizer,
-        scheduler=[scheduler],
-        seq_counter=seq_counter,
-        get_tokenizer_for_seq=lambda _: mock_tokenizer(eos_token_id),
-        stop_checker=stop_checker,
-    )
-
-    seq_group = create_seq_group(
-        seq_prompt_len=seq_prompt_len,
-        seq_output_lens=[seq_output_len],
-        sampling_params=SamplingParams(
-            # Ensure enough space.
-            max_tokens=seq_output_len + num_new_tokens,
-            ignore_eos=True,
-        ),
-    )
-
-    seq = seq_group.get_seqs()[0]
-    seq.status = SequenceStatus.RUNNING
-
-    new_token_ids = list(range(num_new_tokens))
-    assert eos_token_id not in new_token_ids
-    eos_index = random.randint(0, len(new_token_ids) - 1)
-    new_token_ids[eos_index] = eos_token_id
-
-    outputs = [
-        CompletionSequenceGroupOutput(
-            samples=[
-                SequenceOutput(
-                    parent_seq_id=seq.seq_id,
-                    output_token=output_token,
-                    logprobs={output_token: Logprob(0.0)},
-                )
-            ],
-            prompt_logprobs=None,
-        ) for output_token in new_token_ids
-    ]
-
-    assert seq.get_len() == seq_prompt_len + seq_output_len
-    output_processor.process_outputs(seq_group, outputs)
-
-    # Expect the processed sequence to go beyond eos.
-    assert seq.get_len() == seq_prompt_len + seq_output_len + num_new_tokens
-
-    # Expect the correct tokens were appended.
-    expected_appended_tokens = new_token_ids[:seq_output_len + num_new_tokens -
-                                             seq_output_len]
-    assert seq.get_token_ids(
-    )[-len(expected_appended_tokens):] == expected_appended_tokens
-
-
-def mock_tokenizer(eos_token_id=1000):
-    tokenizer = MagicMock(spec=PreTrainedTokenizer)
-    tokenizer.eos_token_id = eos_token_id
-    return tokenizer

tests/entrypoints/openai/correctness/test_lmeval.py

@@ -26,15 +26,12 @@ DEFAULT_ARGS = ["--max-model-len", "4096"]
 MORE_ARGS_LIST = [
     [],  # Default
     ["--enable-chunked-prefill"],  # Chunked
-    ["--num-scheduler-steps", "8"],  # MS
-    ["--num-scheduler-steps", "8", "--multi-step-stream-outputs"]  # MS+Stream
 ]
 MAX_WAIT_SECONDS = None
 
 if current_platform.is_tpu():
     MORE_ARGS_LIST = [
         [],  # Default
-        # ["--num-scheduler-steps", "8"], # Multi-step << currently fails
     ]
     MAX_WAIT_SECONDS = 600
 

tests/entrypoints/openai/test_embedding_long_text.py

@@ -0,0 +1,441 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Test cases for long text embedding with automatic chunking mechanism.
+
+This test suite validates vLLM's automatic chunking functionality for handling
+text inputs that exceed the model's maximum token length, specifically targeting
+the intfloat/multilingual-e5-small model (max token length: 512).
+"""
+
+import random
+
+import openai
+import pytest
+import pytest_asyncio
+
+from vllm.entrypoints.openai.protocol import EmbeddingResponse
+
+from ...utils import RemoteOpenAIServer
+
+
+def _generate_random_text(word_count: int) -> str:
+    """Generate random text with approximately the specified word count."""
+    # Common English words with focus on verbs and nouns for realistic text
+    common_words = [
+        # Essential articles and pronouns (minimal)
+        "the",
+        "and",
+        "you",
+        "they",
+        "this",
+        "that",
+        "these",
+        "those",
+
+        # Action verbs
+        "create",
+        "build",
+        "develop",
+        "design",
+        "implement",
+        "execute",
+        "analyze",
+        "process",
+        "generate",
+        "calculate",
+        "evaluate",
+        "optimize",
+        "transform",
+        "integrate",
+        "configure",
+        "deploy",
+        "monitor",
+        "manage",
+        "discover",
+        "explore",
+        "investigate",
+        "research",
+        "study",
+        "examine",
+        "improve",
+        "enhance",
+        "upgrade",
+        "modify",
+        "update",
+        "maintain",
+        "solve",
+        "resolve",
+        "handle",
+        "address",
+        "tackle",
+        "overcome",
+        "communicate",
+        "collaborate",
+        "coordinate",
+        "organize",
+        "plan",
+        "achieve",
+        "accomplish",
+        "complete",
+        "finish",
+        "deliver",
+        "provide",
+
+        # Technology and science nouns
+        "system",
+        "application",
+        "software",
+        "hardware",
+        "network",
+        "database",
+        "algorithm",
+        "model",
+        "framework",
+        "platform",
+        "interface",
+        "protocol",
+        "architecture",
+        "infrastructure",
+        "component",
+        "module",
+        "service",
+        "technology",
+        "innovation",
+        "solution",
+        "methodology",
+        "approach",
+        "artificial",
+        "intelligence",
+        "machine",
+        "learning",
+        "neural",
+        "network",
+        "computer",
+        "processor",
+        "memory",
+        "storage",
+        "computation",
+        "data",
+        "information",
+        "knowledge",
+        "insight",
+        "pattern",
+        "trend",
+        "analysis",
+        "research",
+        "development",
+        "engineering",
+        "science",
+        "mathematics",
+        "statistics",
+        "probability",
+        "optimization",
+        "performance",
+        "efficiency",
+
+        # General nouns
+        "project",
+        "team",
+        "organization",
+        "company",
+        "business",
+        "industry",
+        "market",
+        "customer",
+        "user",
+        "client",
+        "product",
+        "feature",
+        "function",
+        "requirement",
+        "specification",
+        "documentation",
+        "report",
+        "result",
+        "outcome",
+        "impact",
+        "benefit",
+        "advantage",
+        "challenge",
+        "problem",
+        "opportunity",
+        "strategy",
+        "goal",
+        "objective",
+        "target",
+        "milestone",
+        "process",
+        "procedure",
+        "workflow",
+        "pipeline",
+        "operation",
+        "task",
+        "activity",
+        "event",
+        "session",
+        "meeting",
+        "discussion",
+        "decision"
+    ]
+
+    words = []
+    for _ in range(word_count):
+        words.append(random.choice(common_words))
+
+    # Add some punctuation for more realistic text
+    text = " ".join(words)
+    # Add periods every 10-20 words
+    words_list = text.split()
+    result = []
+    for i, word in enumerate(words_list):
+        result.append(word)
+        if ((i + 1) % random.randint(10, 20) == 0 and i < len(words_list) - 1):
+            result[-1] += "."
+
+    return " ".join(result)
+
+
+MODEL_NAME = "intfloat/multilingual-e5-small"
+DTYPE = "bfloat16"
+
+# Test text: Generate text with approximately 1500 words to exceed 1024 tokens
+LONG_TEXT_1500_WORDS = _generate_random_text(1500)
+
+# Test text: Generate text with approximately 2500 words to exceed 2048 tokens
+LONG_TEXT_2500_WORDS = _generate_random_text(2500)
+
+
+@pytest.fixture(scope="module")
+def server_with_chunked_processing():
+    """Start server with automatic chunking processing enabled."""
+    args = [
+        "--runner",
+        "pooling",
+        "--dtype",
+        DTYPE,
+        "--enforce-eager",
+        "--max-model-len",
+        "512",  # Set smaller max_model_len to trigger chunking mechanism
+        '--override-pooler-config',
+        ('{"pooling_type": "MEAN", "normalize": true, '
+         '"enable_chunked_processing": true, "max_embed_len": 10000}'),
+        "--gpu-memory-utilization",
+        "0.8",
+    ]
+
+    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+        yield remote_server
+
+
+@pytest_asyncio.fixture
+async def client_with_chunked_processing(server_with_chunked_processing):
+    """Create async client with chunking processing support."""
+    async with server_with_chunked_processing.get_async_client(
+    ) as async_client:
+        yield async_client
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_long_text_embedding_1500_chars(
+        client_with_chunked_processing: openai.AsyncOpenAI, model_name: str):
+    """Test embedding processing for ~1500 character long text 
+    (~1028 tokens, exceeding 512 token limit)."""
+
+    # Verify text length
+    # Verify text has sufficient word count (approximately 1500 words)
+    word_count = len(LONG_TEXT_1500_WORDS.split())
+    assert word_count >= 1400, (
+        f"Test text word count insufficient: {word_count} words")
+
+    # Send embedding request
+    embedding_response = await client_with_chunked_processing.embeddings.create(
+        model=model_name,
+        input=[LONG_TEXT_1500_WORDS],
+        encoding_format="float",
+    )
+
+    # Verify response structure
+    embeddings = EmbeddingResponse.model_validate(
+        embedding_response.model_dump(mode="json"))
+
+    assert embeddings.id is not None
+    assert len(embeddings.data) == 1
+    assert len(embeddings.data[0].embedding
+               ) == 384  # multilingual-e5-small embedding dimension
+    assert embeddings.usage.completion_tokens == 0
+    # Due to chunked processing, token count should
+    # reflect actual processed tokens
+    # With ~1500 words, we expect roughly
+    # 1024+ tokens (exceeding 512 token limit)
+    # Should exceed single chunk limit of 512
+    assert embeddings.usage.prompt_tokens > 800
+    assert embeddings.usage.total_tokens == embeddings.usage.prompt_tokens
+
+    # Verify embedding vector validity
+    embedding_vector = embeddings.data[0].embedding
+    assert all(
+        isinstance(x, float)
+        for x in embedding_vector), "Embedding vector should contain floats"
+    assert not all(
+        x == 0
+        for x in embedding_vector), "Embedding vector should not be all zeros"
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_long_text_embedding_2500_chars(
+        client_with_chunked_processing: openai.AsyncOpenAI, model_name: str):
+    """Test embedding processing for ~2500 character long text
+    (~2048 tokens, requiring multiple chunks)."""
+
+    # Verify text length
+    # Verify text has sufficient word count (approximately 2500 words)
+    word_count = len(LONG_TEXT_2500_WORDS.split())
+    assert word_count >= 2300, (
+        f"Test text word count insufficient: {word_count} words")
+
+    # Send embedding request
+    embedding_response = await client_with_chunked_processing.embeddings.create(
+        model=model_name,
+        input=[LONG_TEXT_2500_WORDS],
+        encoding_format="float",
+    )
+
+    # Verify response structure
+    embeddings = EmbeddingResponse.model_validate(
+        embedding_response.model_dump(mode="json"))
+
+    assert embeddings.id is not None
+    assert len(embeddings.data) == 1
+    assert len(embeddings.data[0].embedding
+               ) == 384  # multilingual-e5-small embedding dimension
+    assert embeddings.usage.completion_tokens == 0
+    # Due to chunked processing, token count should
+    # reflect actual processed tokens
+    # With ~2500 words, we expect
+    # roughly 2048+ tokens (requiring multiple chunks)
+    # Should require multiple chunks for processing
+    assert embeddings.usage.prompt_tokens > 1500
+    assert embeddings.usage.total_tokens == embeddings.usage.prompt_tokens
+
+    # Verify embedding vector validity
+    embedding_vector = embeddings.data[0].embedding
+    assert all(
+        isinstance(x, float)
+        for x in embedding_vector), "Embedding vector should contain floats"
+    assert not all(
+        x == 0
+        for x in embedding_vector), "Embedding vector should not be all zeros"
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_batch_long_text_embedding(
+        client_with_chunked_processing: openai.AsyncOpenAI, model_name: str):
+    """Test batch long text embedding processing."""
+
+    input_texts = [
+        LONG_TEXT_1500_WORDS,
+        LONG_TEXT_2500_WORDS,
+        "This is a short text test.",  # Short text for comparison
+    ]
+
+    # Send batch embedding request
+    embedding_response = await client_with_chunked_processing.embeddings.create(
+        model=model_name,
+        input=input_texts,
+        encoding_format="float",
+    )
+
+    # Verify response structure
+    embeddings = EmbeddingResponse.model_validate(
+        embedding_response.model_dump(mode="json"))
+
+    assert embeddings.id is not None
+    assert len(embeddings.data) == 3  # Three input texts
+
+    # Verify each embedding dimension
+    for i, embedding_data in enumerate(embeddings.data):
+        assert len(embedding_data.embedding) == 384
+        assert embedding_data.index == i
+
+        # Verify embedding vector validity
+        embedding_vector = embedding_data.embedding
+        assert all(isinstance(x, float) for x in embedding_vector)
+        assert not all(x == 0 for x in embedding_vector)
+
+    # Verify token usage
+    assert embeddings.usage.completion_tokens == 0
+    # Total token count should be very substantial
+    assert embeddings.usage.prompt_tokens > 1000
+    assert embeddings.usage.total_tokens == embeddings.usage.prompt_tokens
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_chunked_vs_normal_consistency(
+        client_with_chunked_processing: openai.AsyncOpenAI, model_name: str):
+    """Test consistency between chunked and
+    normal processing (using short text)."""
+
+    # Use a short text within the 512 token limit
+    short_text = ("Artificial intelligence technology is changing our world, "
+                  "bringing unprecedented opportunities and challenges.")
+
+    # Send embedding request
+    embedding_response = await client_with_chunked_processing.embeddings.create(
+        model=model_name,
+        input=[short_text],
+        encoding_format="float",
+    )
+
+    # Verify response structure
+    embeddings = EmbeddingResponse.model_validate(
+        embedding_response.model_dump(mode="json"))
+
+    assert embeddings.id is not None
+    assert len(embeddings.data) == 1
+    assert len(embeddings.data[0].embedding) == 384
+    assert embeddings.usage.completion_tokens == 0
+    # Short text should not require chunked processing
+    assert embeddings.usage.prompt_tokens < 512
+    assert embeddings.usage.total_tokens == embeddings.usage.prompt_tokens
+
+    # 验证embedding向量的有效性
+    embedding_vector = embeddings.data[0].embedding
+    assert all(isinstance(x, float) for x in embedding_vector)
+    assert not all(x == 0 for x in embedding_vector)
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_chunked_processing_response_format(
+        client_with_chunked_processing: openai.AsyncOpenAI, model_name: str):
+    """Test response format and structure during chunked processing."""
+
+    # Test with long text to trigger chunking
+    embedding_response = await client_with_chunked_processing.embeddings.create(
+        model=model_name,
+        input=[LONG_TEXT_1500_WORDS],
+        encoding_format="float",
+    )
+
+    # Verify response structure
+    embeddings = EmbeddingResponse.model_validate(
+        embedding_response.model_dump(mode="json"))
+
+    assert embeddings.id is not None
+    assert len(embeddings.data) == 1
+    assert embeddings.data[0].object == "embedding"
+    assert embeddings.data[0].index == 0
+
+    # Verify embedding vector properties
+    embedding_vector = embeddings.data[0].embedding
+    import math
+    vector_norm = math.sqrt(sum(x * x for x in embedding_vector))
+    # Check that the vector is normalized
+    # (default behavior for most embedding models)
+    assert 0.8 < vector_norm < 1.2, (
+        f"Vector norm should be reasonable, actual: {vector_norm}")

tests/kernels/attention/test_flashmla.py

@@ -35,11 +35,10 @@ FLASH_MLA_UNSUPPORTED_REASON = is_flashmla_supported()[1] \
 @pytest.mark.parametrize("block_size", [64])
 @pytest.mark.parametrize("causal", [True])
 @pytest.mark.parametrize("varlen", [False, True])
+@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16])
 @torch.inference_mode()
 def test_flash_mla(b, s_q, mean_sk, h_q, h_kv, d, dv, block_size, causal,
-                   varlen):
-    # TODO: parametrize using pytest
-    dtype = torch.bfloat16
+                   varlen, dtype):
     device = torch.device("cuda:0")
     torch.set_default_dtype(dtype)
     torch.set_default_device(device)
@@ -48,7 +47,7 @@ def test_flash_mla(b, s_q, mean_sk, h_q, h_kv, d, dv, block_size, causal,
     random.seed(0)
 
     print(f"{b=}, {s_q=}, {mean_sk=}, {h_q=}, {h_kv=}, "
-          f"{d=}, {dv=}, {causal=}, {varlen=}")
+          f"{d=}, {dv=}, {causal=}, {varlen=}, {dtype=}")
 
     cache_seqlens = torch.full((b, ), mean_sk, dtype=torch.int32)
     if varlen:

tests/kernels/moe/test_moe.py

@@ -36,7 +36,7 @@ from vllm.model_executor.models.mixtral import MixtralMoE
 from vllm.platforms import current_platform
 from vllm.scalar_type import ScalarType, scalar_types
 
-NUM_EXPERTS = [8, 64]
+NUM_EXPERTS = [8, 64, 192]
 EP_SIZE = [1, 4]
 TOP_KS = [2, 6]
 

tests/metrics/test_metrics.py

@@ -94,45 +94,6 @@ def test_metric_counter_generation_tokens(
         f"metric: {metric_count!r}")
 
 
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("max_tokens", [128, 129])
-@pytest.mark.parametrize("disable_async_output_proc", [True, False])
-def test_metric_counter_generation_tokens_multi_step(
-    vllm_runner,
-    example_prompts,
-    model: str,
-    max_tokens: int,
-    disable_async_output_proc: bool,
-) -> None:
-    num_scheduler_steps = 8
-    with vllm_runner(
-            model,
-            disable_log_stats=False,
-            gpu_memory_utilization=0.4,
-            num_scheduler_steps=num_scheduler_steps,
-            disable_async_output_proc=disable_async_output_proc,
-    ) as vllm_model:
-        vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
-        tokenizer = vllm_model.llm.get_tokenizer()
-        stat_logger = vllm_model.llm.llm_engine.stat_loggers['prometheus']
-        metric_count = stat_logger.metrics.counter_generation_tokens.labels(
-            **stat_logger.labels)._value.get()
-        vllm_generation_count = 0
-        for i in range(len(example_prompts)):
-            vllm_output_ids, vllm_output_str = vllm_outputs[i]
-            prompt_ids = tokenizer.encode(example_prompts[i])
-            # vllm_output_ids contains both prompt tokens and generation tokens.
-            # We're interested only in the count of the generation tokens.
-            vllm_generation_count += len(vllm_output_ids) - len(prompt_ids)
-
-    # The multi-step scheduling will continue to execute forward even when
-    # encountering EOS, leading to slightly imprecise metrics.
-    assert abs(vllm_generation_count - metric_count) <\
-        len(example_prompts) * num_scheduler_steps, \
-        (f"generation token count: {vllm_generation_count!r}\n"
-         f"metric: {metric_count!r}")
-
-
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["float"])
 @pytest.mark.parametrize(

tests/models/language/generation/test_hybrid.py

@@ -331,32 +331,6 @@ def test_state_cleanup(
                     "could be related to finished_requests_ids")
 
 
-@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
-@pytest.mark.parametrize("max_tokens", [64])
-def test_multistep_correctness(
-    vllm_runner,
-    example_prompts,
-    model: str,
-    max_tokens: int,
-) -> None:
-    with vllm_runner(model, num_scheduler_steps=8,
-                     max_num_seqs=2) as vllm_model:
-        vllm_outputs_multistep = vllm_model.generate_greedy(
-            example_prompts, max_tokens)
-
-    with vllm_runner(model, num_scheduler_steps=1,
-                     max_num_seqs=2) as vllm_model:
-        vllm_outputs_single_step = vllm_model.generate_greedy(
-            example_prompts, max_tokens)
-
-    check_outputs_equal(
-        outputs_0_lst=vllm_outputs_multistep,
-        outputs_1_lst=vllm_outputs_single_step,
-        name_0="vllm_outputs_multistep",
-        name_1="vllm_outputs_single_step",
-    )
-
-
 @multi_gpu_test(num_gpus=2)
 @pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
 @pytest.mark.parametrize("max_tokens", [64])

tests/models/multimodal/processing/test_nemotron_vl.py

@@ -23,15 +23,15 @@ def _get_expected_num_patches(
     min_num: int,
     max_num: int,
 ):
-    from vllm.model_executor.models.internvl import (
-        calculate_internvl_targets, get_internvl_target_ratios)
+    from vllm.model_executor.models.nemotron_vl import (
+        calculate_nemotron_vl_targets, get_nemotron_vl_target_ratios)
 
     width, height = image.size
 
-    blocks, _, _ = calculate_internvl_targets(
+    blocks, _, _ = calculate_nemotron_vl_targets(
         orig_width=width,
         orig_height=height,
-        target_ratios=get_internvl_target_ratios(
+        target_ratios=get_nemotron_vl_target_ratios(
             min_num,
             max_num,
         ),

tests/multi_step/test_correctness_async_llm.py

@@ -1,232 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-# Test the AsyncLLMEngine with multi-step-decoding
-from typing import Optional
-
-import pytest
-
-from vllm.utils import STR_BACKEND_ENV_VAR
-
-from ..models.utils import check_logprobs_close
-from ..utils import (completions_with_server_args, get_client_text_generations,
-                     get_client_text_logprob_generations)
-
-MODELS = [
-    "JackFram/llama-160m",
-]
-NUM_SCHEDULER_STEPS = [8]  # Multi-step decoding steps
-NUM_PROMPTS = [10]
-
-DEFAULT_SERVER_ARGS: list[str] = [
-    "--distributed-executor-backend",
-    "ray",
-    "--gpu-memory-utilization",
-    "0.85",
-    "--swap-space",
-    "16",
-]
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize(("tp_size, pp_size"), [
-    (1, 1),
-    (2, 2),
-])
-@pytest.mark.parametrize("eager_mode", [False, True])
-@pytest.mark.parametrize("num_scheduler_steps", NUM_SCHEDULER_STEPS)
-@pytest.mark.parametrize("num_prompts", NUM_PROMPTS)
-@pytest.mark.parametrize("num_logprobs", [5])
-@pytest.mark.parametrize("is_async", [True])
-@pytest.mark.parametrize("attention_backend", ["FLASHINFER", "FLASH_ATTN"])
-@pytest.mark.parametrize("enable_chunked_prefill", [True, False])
-@pytest.mark.asyncio
-async def test_multi_step(
-    example_prompts,
-    model: str,
-    tp_size: int,
-    pp_size: int,
-    eager_mode: int,
-    num_scheduler_steps: int,
-    num_prompts: int,
-    is_async: bool,
-    num_logprobs: Optional[int],
-    attention_backend: str,
-    enable_chunked_prefill: bool,
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    """Test vLLM engine with multi-step scheduling in an OpenAI-protocol
-    client/server environment.
-
-    Set up an engine with single-step scheduling as a ground-truth reference.
-
-    Send a completions API request to both engines with the same prompts.
-
-    Validate:
-    * Generated tokens match
-    * Generated logprobs are all very close
-
-    Args:
-      example_prompts: test fixture providing example prompts
-      model: model under test (same for single- and multi-step engines)
-      tp_size: degree of tensor-parallelism
-      pp_size: degree of pipeline-parallelism
-      eager_mode
-      num_scheduler_steps: for multi-step scheduling, GPU-side steps per
-                           GPU -> CPU output transfer
-      num_prompts: number of example prompts under test
-      num_logprobs: corresponds to the `logprobs` argument to the OpenAI
-                    completions endpoint; `None` -> no logprobs
-    """
-    if enable_chunked_prefill and \
-        (pp_size > 1 or attention_backend != "FLASH_ATTN"):
-        pytest.skip("Multi-step with Chunked-Prefill only supports"
-                    "PP=1 and FLASH_ATTN backend")
-
-    with monkeypatch.context() as m:
-        m.setenv(STR_BACKEND_ENV_VAR, attention_backend)
-
-        prompts = example_prompts
-        if len(prompts) < num_prompts:
-            prompts = prompts * ((num_prompts // len(prompts)) + 1)
-        prompts = prompts[:num_prompts]
-        assert len(prompts) == num_prompts
-
-        server_args = DEFAULT_SERVER_ARGS + ["--enforce-eager"]
-        ms_server_args = DEFAULT_SERVER_ARGS + \
-            ["--num-scheduler-steps", f"{num_scheduler_steps}"]
-
-        if not is_async:
-            ms_server_args += ["--disable-async-output-proc"]
-
-        if eager_mode:
-            ms_server_args.append("--enforce-eager")
-
-        if enable_chunked_prefill:
-            ms_server_args.append("--enable-chunked-prefill")
-
-        distributed_args = [
-            "--tensor-parallel-size",
-            str(tp_size),
-            "--pipeline-parallel-size",
-            str(pp_size),
-        ]
-
-        # Spin up client/server & issue completion API requests.
-        # Default `max_wait_seconds` is 240 but was empirically
-        # was raised 5x to 1200 *just for this test* due to
-        # observed timeouts in GHA CI
-        ref_completions = await completions_with_server_args(
-            prompts,
-            model,
-            server_args + distributed_args,
-            num_logprobs,
-            max_wait_seconds=5 * 240)
-        test_completions = await completions_with_server_args(
-            prompts,
-            model,
-            ms_server_args + distributed_args,
-            num_logprobs,
-            max_wait_seconds=5 * 240)
-
-        # Assert multi-step scheduling produces identical tokens
-        # to single-step scheduling.
-        ref_generations = get_client_text_generations(ref_completions)
-        test_generations = get_client_text_generations(test_completions)
-        assert ref_generations == test_generations
-
-        # Assert multi-step scheduling produces nearly-identical logprobs
-        # to single-step scheduling.
-        ref_text_logprobs = get_client_text_logprob_generations(
-            ref_completions)
-        test_text_logprobs = get_client_text_logprob_generations(
-            test_completions)
-        check_logprobs_close(
-            outputs_0_lst=ref_text_logprobs,
-            outputs_1_lst=test_text_logprobs,
-            name_0="hf",
-            name_1="vllm",
-        )
-
-
-@pytest.mark.parametrize(("tp_size, pp_size"), [
-    (1, 2),
-])
-@pytest.mark.asyncio
-async def test_multi_step_pp_smoke(
-    tp_size: int,
-    pp_size: int,
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    """
-    Smoke test for the vLLM engine with multi-step scheduling in an
-    OpenAI-protocol client/server environment.
-
-    This tests compares the outputs between multi-step scheduling and
-    single-step scheduling. Notably, this test lets the engines generate
-    more tokens (default is 5) and test for an exact match over all the
-    tokens.
-
-    Args:
-      tp_size: degree of tensor-parallelism
-      pp_size: degree of pipeline-parallelism
-      eager_mode
-    """
-
-    model = "JackFram/llama-160m"
-    num_scheduler_steps = 8
-    attention_backend = "FLASH_ATTN"
-    max_num_seqs = 3
-
-    with monkeypatch.context() as m:
-        m.setenv(STR_BACKEND_ENV_VAR, attention_backend)
-
-        # Prompt from the ShareGPT dataset
-        prompts = [
-            "in the jtbd context whats a push?",  # codespell:ignore
-            "in the jtbd context whats a push?",  # codespell:ignore
-            "in the jtbd context whats a push?",  # codespell:ignore
-            "in the jtbd context whats a push?",  # codespell:ignore
-        ]
-        # Use varying max_tokens to introduce scheduling randomness.
-        max_tokens = [10 * i for i in range(1, len(prompts) + 1)]
-        assert len(prompts) == len(max_tokens)
-
-        test_args = [
-            "--tensor-parallel-size",
-            str(tp_size), "--pipeline-parallel-size",
-            str(pp_size), "--max-num-seqs",
-            str(max_num_seqs)
-        ]
-
-        server_args = DEFAULT_SERVER_ARGS + test_args
-        ms_server_args = DEFAULT_SERVER_ARGS + \
-          ["--num-scheduler-steps", f"{num_scheduler_steps}"] + \
-          test_args
-
-        # Spin up client/server & issue completion API requests.
-        # Default `max_wait_seconds` is 240 but was empirically
-        # was raised 3x to 720 *just for this test* due to
-        # observed timeouts in GHA CI
-        ref_completions = await completions_with_server_args(
-            prompts=prompts,
-            model_name=model,
-            server_cli_args=server_args,
-            num_logprobs=None,
-            max_wait_seconds=5 * 240,
-            max_tokens=max_tokens)
-
-        test_completions = await completions_with_server_args(
-            prompts=prompts,
-            model_name=model,
-            server_cli_args=ms_server_args,
-            num_logprobs=None,
-            max_wait_seconds=5 * 240,
-            max_tokens=max_tokens)
-
-        # Assert multi-step scheduling produces identical tokens
-        # to single-step scheduling.
-        ref_generations = get_client_text_generations(ref_completions)
-        test_generations = get_client_text_generations(test_completions)
-
-        assert ref_generations == test_generations

tests/multi_step/test_correctness_llm.py

@@ -1,383 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-# Test the LLMEngine with multi-step-decoding
-
-import copy
-from typing import Optional
-
-import pytest
-
-from vllm.platforms import current_platform
-from vllm.utils import STR_BACKEND_ENV_VAR
-
-from ..models.utils import check_logprobs_close, check_outputs_equal
-
-MODELS = [
-    "JackFram/llama-160m",
-]
-NUM_SCHEDULER_STEPS = [8]  # Multi-step decoding steps
-NUM_PROMPTS = [10]
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["half"])
-@pytest.mark.parametrize("tp_size", [1])
-@pytest.mark.parametrize("enable_chunked_prefill", [False, True])
-@pytest.mark.parametrize("max_tokens", [5])
-@pytest.mark.parametrize("enforce_eager", [True, False])
-@pytest.mark.parametrize("num_scheduler_steps", NUM_SCHEDULER_STEPS)
-@pytest.mark.parametrize("num_prompts", NUM_PROMPTS)
-@pytest.mark.parametrize("num_logprobs", [None, 5])
-@pytest.mark.parametrize("attention_backend", ["FLASH_ATTN", "FLASHINFER"])
-def test_multi_step_llm(
-    hf_runner,
-    vllm_runner,
-    example_prompts,
-    model: str,
-    dtype: str,
-    tp_size: int,
-    enable_chunked_prefill: bool,
-    max_tokens: int,
-    enforce_eager: int,
-    num_scheduler_steps: int,
-    num_prompts: int,
-    num_logprobs: Optional[int],
-    attention_backend: str,
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    """Test vLLM engine with multi-step scheduling via sync LLM Engine.
-
-    Set up a HuggingFace (HF) transformers model as a ground-truth reference.
-
-    Prompt them with the same example prompts.
-
-    Validate:
-    * Generated tokens match
-    * Generated logprobs are all very close
-
-    Args:
-      hf_runner: HF transformers model runner fixture
-      vllm_runner: vLLM model runner fixture
-      example_prompts: test fixture providing example prompts
-      model: model under test (same for single- and multi-step engines)
-      dtype: tensor datatype for engine to utilize
-      tp_size: degree of tensor-parallelism
-      enable_chunked_prefill: chunked-prefill on/off
-      max_tokens: the maximum number of tokens to generate
-      enforce_eager
-      num_scheduler_steps: for multi-step scheduling, GPU-side steps per
-                           GPU -> CPU output transfer
-      num_prompts: number of example prompts under test
-      num_logprobs: corresponds to the `logprobs` argument to the OpenAI
-                    completions endpoint; `None` -> 1 logprob returned.
-    """
-    if current_platform.is_rocm() and \
-        (attention_backend == "FLASHINFER" or enable_chunked_prefill):
-        pytest.skip(
-            "Multi-Step with FLASHINFER or Chunked-Prefill is not supported"
-            "on ROCm")
-
-    with monkeypatch.context() as m:
-        m.setenv(STR_BACKEND_ENV_VAR, attention_backend)
-
-        prompts = example_prompts
-        if len(prompts) < num_prompts:
-            prompts = prompts * ((num_prompts // len(prompts)) + 1)
-        prompts = prompts[:num_prompts]
-        assert len(prompts) == num_prompts
-
-        with vllm_runner(
-                model,
-                dtype=dtype,
-                enforce_eager=enforce_eager,
-                gpu_memory_utilization=0.7,
-                tensor_parallel_size=tp_size,
-                enable_chunked_prefill=enable_chunked_prefill,
-                num_scheduler_steps=num_scheduler_steps,
-        ) as vllm_model:
-            vllm_outputs = (vllm_model.generate_greedy(prompts, max_tokens)
-                            if num_logprobs is None else
-                            vllm_model.generate_greedy_logprobs(
-                                prompts, max_tokens, num_logprobs))
-
-        with hf_runner(model, dtype=dtype) as hf_model:
-            hf_outputs = (hf_model.generate_greedy(prompts, max_tokens)
-                          if num_logprobs is None else
-                          hf_model.generate_greedy_logprobs_limit(
-                              prompts, max_tokens, num_logprobs))
-
-        if num_logprobs is None:
-            check_outputs_equal(
-                outputs_0_lst=hf_outputs,
-                outputs_1_lst=vllm_outputs,
-                name_0="hf",
-                name_1="vllm",
-            )
-        else:
-            check_logprobs_close(
-                outputs_0_lst=hf_outputs,
-                outputs_1_lst=vllm_outputs,
-                name_0="hf",
-                name_1="vllm",
-            )
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["half"])
-@pytest.mark.parametrize("tp_size", [1])
-@pytest.mark.parametrize("max_tokens", [5])
-@pytest.mark.parametrize("enforce_eager", [True])
-@pytest.mark.parametrize("num_scheduler_steps", NUM_SCHEDULER_STEPS)
-@pytest.mark.parametrize("num_prompts", NUM_PROMPTS)
-@pytest.mark.parametrize("num_logprobs,num_prompt_logprobs", [(5, 5)])
-@pytest.mark.parametrize("attention_backend", ["FLASH_ATTN"])
-def test_multi_step_llm_w_prompt_logprobs(
-    vllm_runner,
-    example_prompts,
-    model: str,
-    dtype: str,
-    tp_size: int,
-    max_tokens: int,
-    enforce_eager: int,
-    num_scheduler_steps: int,
-    num_prompts: int,
-    num_logprobs: Optional[int],
-    num_prompt_logprobs: Optional[int],
-    attention_backend: str,
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    """Test prompt logprobs with multi-step scheduling via sync LLM Engine.
-
-    Set up a vLLM engine instance w/ single-step scheduling as a ground-truth
-    reference.
-
-    Prompt them with the same example prompts.
-
-    Validate:
-    * All generated logprobs are all very close
-
-    Args:
-      hf_runner: HF transformers model runner fixture
-      vllm_runner: vLLM model runner fixture
-      example_prompts: test fixture providing example prompts
-      model: model under test (same for single- and multi-step engines)
-      dtype: tensor datatype for engine to utilize
-      tp_size: degree of tensor-parallelism
-      max_tokens: the maximum number of tokens to generate
-      enforce_eager
-      num_scheduler_steps: for multi-step scheduling, GPU-side steps per
-                           GPU -> CPU output transfer
-      num_prompts: number of example prompts under test
-      num_logprobs: corresponds to the `logprobs` argument to the OpenAI
-                    completions endpoint; `None` -> no logprobs
-      num_prompt_logprobs: number of logprobs to return for each prompt token;
-                           note that this argument is not supported by the
-                           OpenAI completions endpoint.
-    """
-    with monkeypatch.context() as m:
-        m.setenv(STR_BACKEND_ENV_VAR, attention_backend)
-
-        prompts = example_prompts
-        if len(prompts) < num_prompts:
-            prompts = prompts * ((num_prompts // len(prompts)) + 1)
-        prompts = prompts[:num_prompts]
-        assert len(prompts) == num_prompts
-
-        with vllm_runner(
-                model,
-                dtype=dtype,
-                enforce_eager=enforce_eager,
-                gpu_memory_utilization=0.7,
-                tensor_parallel_size=tp_size,
-                num_scheduler_steps=num_scheduler_steps,
-        ) as vllm_model:
-            vllm_outputs = vllm_model.generate_greedy_logprobs(
-                prompts,
-                max_tokens,
-                num_logprobs,
-                num_prompt_logprobs=num_prompt_logprobs)
-
-        with vllm_runner(
-                model,
-                dtype=dtype,
-                enforce_eager=enforce_eager,
-                gpu_memory_utilization=0.7,
-                tensor_parallel_size=tp_size,
-        ) as vllm_model:
-            single_step_vllm_outputs = vllm_model.generate_greedy_logprobs(
-                prompts,
-                max_tokens,
-                num_logprobs,
-                num_prompt_logprobs=num_prompt_logprobs)
-
-        check_logprobs_close(
-            outputs_0_lst=single_step_vllm_outputs,
-            outputs_1_lst=vllm_outputs,
-            name_0="hf",
-            name_1="vllm",
-        )
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["half"])
-@pytest.mark.parametrize("tp_size", [1])
-@pytest.mark.parametrize("max_tokens", [5])
-@pytest.mark.parametrize("enforce_eager", [True])
-@pytest.mark.parametrize("num_scheduler_steps", NUM_SCHEDULER_STEPS)
-@pytest.mark.parametrize("num_prompts", NUM_PROMPTS)
-@pytest.mark.parametrize("num_logprobs", [None, 5])
-@pytest.mark.parametrize("attention_backend", ["FLASH_ATTN"])
-@pytest.mark.skipif(
-    current_platform.is_rocm(),
-    reason="Multi-Step + Chunked-Prefill not supported on ROCm")
-def test_multi_step_llm_chunked_prefill_prefix_cache(
-    vllm_runner,
-    example_prompts,
-    model: str,
-    dtype: str,
-    tp_size: int,
-    max_tokens: int,
-    enforce_eager: int,
-    num_scheduler_steps: int,
-    num_prompts: int,
-    num_logprobs: Optional[int],
-    attention_backend: str,
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    """Test vLLM engine with multi-step+"single-step chunked prefill"+APC.
-
-    Set up contrived scenario which tests for a possible failure mode of
-    scheduling with multi-step+"single-step chunked prefill"+APC
-
-    "single-step chunked prefill" here refers to the current vLLM multi-step+
-    chunked-prefill implementation, which requires that a prefill may only
-    be scheduled in the same step as decodes if the prefill prompt fits in a
-    single chunk (note that "complete" multi-step+chunked-prefill would allow
-    a prefill to span multiple chunks & multiple steps but that is not yet
-    the case.)
-
-    "APC" is short for "automatic prefix caching".
-
-    This test creates a scenario where the scheduler must decide whether/how
-    to schedule a prefill with a prompt that exceeds the available token budget.
-    The correct behavior for multi-step+"single-step chunked prefill"+APC is to
-    put off scheduling the prefill until a future step.
-
-    Validate that:
-    * Multi-step kernels do not raise an exception due to incorrect scheduler
-      behavior
-    * Generated tokens match between
-      multi-step+"single-step chunked prefill"+APC and
-      single-step scheduling.
-    * (If logprobs are enabled) check logprobs are close enough
-
-    Args:
-      vllm_runner: vLLM model runner fixture
-      example_prompts: test fixture providing example prompts
-      model: model under test (same for single- and multi-step engines)
-      dtype: tensor datatype for engine to utilize
-      tp_size: degree of tensor-parallelism
-      max_tokens: the maximum number of tokens to generate
-      enforce_eager
-      num_scheduler_steps: for multi-step scheduling, GPU-side steps per
-                           GPU -> CPU output transfer
-      num_prompts: number of example prompts under test
-      num_logprobs: corresponds to the `logprobs` argument to the OpenAI
-                    completions endpoint; `None` -> 1 logprob returned.
-    """
-
-    # Set up contrived test for correct scheduling behavior with
-    # multi-step+"single-step chunked prefill"+APC.
-    #
-    # Assume block_size=16
-    #
-    # Assume max_num_batched_tokens=48
-    #   => Per-step token budget=48
-    #
-    # 1. Scheduler schedules 0th prompt (24 tokens)
-    #      => Remaining token budget=24
-    # 2. Scheduler attempts to schedule 1st prompt (30 tokens)
-    #    * 30 tokens exceeds 24 token remaining budget
-    #    * Correct behavior: do not schedule this prompt in this step
-    #    * Incorrect behavior: schedule prompt chunk
-    #      * `do_sample=False` for this prompt in this step
-    #      * Chunk size = (remaining tokens // block size) * block size
-    #
-    # The Incorrect scheduling behavior - if it occurs - will cause an exception
-    # in the model runner resulting from `do_sample=False`.
-    with monkeypatch.context() as m:
-        m.setenv(STR_BACKEND_ENV_VAR, attention_backend)
-
-        assert len(example_prompts) >= 2
-        challenge_prompts = copy.deepcopy(example_prompts)
-        challenge_prompts[0] = (
-            'vLLM is a high-throughput and memory-efficient '
-            'inference and serving engine for LLMs.\n')  # 24 tok
-        challenge_prompts[1] = (
-            'Briefly describe the major milestones in the '
-            'development of artificial intelligence from 1950 to 2020.\n'
-        )  # 30 tok
-
-        # If necessary, adjust the length of `challenge_prompts` to match
-        # `num_prompts`
-        if len(challenge_prompts) < num_prompts:
-            challenge_prompts = (challenge_prompts *
-                                 ((num_prompts // len(challenge_prompts)) + 1))
-        challenge_prompts = challenge_prompts[:num_prompts]
-        assert len(challenge_prompts) == num_prompts
-
-        # Single-step scheduler baseline
-        with vllm_runner(
-                model,
-                dtype=dtype,
-                enforce_eager=enforce_eager,
-                gpu_memory_utilization=0.7,
-                tensor_parallel_size=tp_size,
-                num_scheduler_steps=num_scheduler_steps,
-                max_model_len=48,
-                max_num_batched_tokens=48,
-                max_num_seqs=4,
-                block_size=16,
-        ) as vllm_model:
-            outputs_baseline = (
-                vllm_model.generate_greedy(challenge_prompts, max_tokens) if
-                num_logprobs is None else vllm_model.generate_greedy_logprobs(
-                    challenge_prompts, max_tokens, num_logprobs))
-
-        # multi-step+"single-step chunked prefill"+APC
-        with vllm_runner(
-                model,
-                dtype=dtype,
-                enforce_eager=enforce_eager,
-                gpu_memory_utilization=0.7,
-                tensor_parallel_size=tp_size,
-                enable_chunked_prefill=True,
-                enable_prefix_caching=True,
-                num_scheduler_steps=num_scheduler_steps,
-                max_model_len=48,
-                max_num_batched_tokens=48,
-                max_num_seqs=4,
-                block_size=16,
-        ) as vllm_model:
-            outputs_w_features = (
-                vllm_model.generate_greedy(challenge_prompts, max_tokens) if
-                num_logprobs is None else vllm_model.generate_greedy_logprobs(
-                    challenge_prompts, max_tokens, num_logprobs))
-
-        if num_logprobs is None:
-            # No-logprobs test
-            check_outputs_equal(
-                outputs_0_lst=outputs_baseline,
-                outputs_1_lst=outputs_w_features,
-                name_0="multi-step",
-                name_1="multi-step+features",
-            )
-        else:
-            # Yes-logprobs test
-            check_logprobs_close(
-                outputs_0_lst=outputs_baseline,
-                outputs_1_lst=outputs_w_features,
-                name_0="multi-step",
-                name_1="multi-step+features",
-            )

tests/multimodal/test_utils.py

@@ -5,7 +5,7 @@ import base64
 import mimetypes
 import os
 from tempfile import NamedTemporaryFile, TemporaryDirectory
-from typing import TYPE_CHECKING, NamedTuple, Optional
+from typing import TYPE_CHECKING, NamedTuple
 
 import numpy as np
 import pytest
@@ -19,14 +19,12 @@ from vllm.distributed.parallel_state import (init_distributed_environment,
                                              initialize_model_parallel)
 from vllm.multimodal.image import convert_image_mode
 from vllm.multimodal.inputs import PlaceholderRange
-from vllm.multimodal.utils import (MediaConnector,
-                                   merge_and_sort_multimodal_metadata,
+from vllm.multimodal.utils import (MediaConnector, argsort_mm_positions,
                                    run_dp_sharded_vision_model)
 from vllm.platforms import current_platform
 from vllm.utils import get_open_port, update_environment_variables
 
 if TYPE_CHECKING:
-    from vllm.multimodal.hasher import MultiModalHashDict
     from vllm.multimodal.inputs import MultiModalPlaceholderDict
 
 # Test different image extensions (JPG/PNG) and formats (gray/RGB/RGBA)
@@ -178,19 +176,17 @@ async def test_fetch_video_http(video_url: str, num_frames: int):
     assert metadata_sync == metadata_async
 
 
-# Used for the next two tests related to `merge_and_sort_multimodal_metadata`.
+# Used for `test_argsort_mm_positions`.
 class TestCase(NamedTuple):
     mm_positions: "MultiModalPlaceholderDict"
-    mm_hashes: Optional["MultiModalHashDict"]
-    expected_modalities: list[str]
-    expected_ranges: list[PlaceholderRange]
-    expected_hashes: Optional[list[str]]
+    expected_modality_idxs: list[tuple[str, int]]
 
 
-def test_merge_and_sort_multimodal_metadata():
+def test_argsort_mm_positions():
 
     test_cases = [
-        # Single modality should return result as is but flattened
+        # Single modality
+        ## Internally sorted
         TestCase(
             mm_positions={
                 "image": [
@@ -198,34 +194,27 @@ def test_merge_and_sort_multimodal_metadata():
                     PlaceholderRange(offset=3, length=2),
                 ]
             },
-            mm_hashes={"image": ["hash1", "hash2"]},
-            expected_modalities=["image", "image"],
-            expected_ranges=[
-                PlaceholderRange(offset=0, length=2),
-                PlaceholderRange(offset=3, length=2),
+            expected_modality_idxs=[
+                ("image", 0),
+                ("image", 1),
             ],
-            expected_hashes=["hash1", "hash2"],
         ),
-
-        # Single modality without hashes return None for mm hash.
+        ## Internally unsorted
         TestCase(
             mm_positions={
                 "image": [
+                    PlaceholderRange(offset=3, length=2),
                     PlaceholderRange(offset=0, length=2),
-                    PlaceholderRange(offset=2, length=2),
                 ]
             },
-            mm_hashes=None,
-            expected_modalities=["image", "image"],
-            expected_ranges=[
-                PlaceholderRange(offset=0, length=2),
-                PlaceholderRange(offset=2, length=2),
+            expected_modality_idxs=[
+                ("image", 1),
+                ("image", 0),
             ],
-            expected_hashes=None,
         ),
 
-        # Multiple modalities with hashes should return sorted modalities
-        # and flattened ranges and hashes.
+        # Two modalities
+        ## Internally sorted
         TestCase(
             mm_positions={
                 "image": [
@@ -237,47 +226,54 @@ def test_merge_and_sort_multimodal_metadata():
                     PlaceholderRange(offset=2, length=3),
                 ]
             },
-            mm_hashes={
-                "image": ["image_hash1", "image_hash2"],
-                "audio": ["audio_hash1", "audio_hash2"],
-            },
-            expected_modalities=["audio", "audio", "image", "image"],
-            expected_ranges=[
-                PlaceholderRange(offset=0, length=2),
-                PlaceholderRange(offset=2, length=3),
-                PlaceholderRange(offset=7, length=4),
-                PlaceholderRange(offset=11, length=5),
-            ],
-            expected_hashes=[
-                "audio_hash1", "audio_hash2", "image_hash1", "image_hash2"
+            expected_modality_idxs=[
+                ("audio", 0),
+                ("audio", 1),
+                ("image", 0),
+                ("image", 1),
             ],
         ),
-
-        # Multiple modalities without hashes should return sorted modalities
-        # and flattened ranges and None.
+        ## Interleaved, internally sorted
         TestCase(
             mm_positions={
                 "image": [
-                    PlaceholderRange(offset=7, length=4),
-                    PlaceholderRange(offset=11, length=5),
+                    PlaceholderRange(offset=0, length=4),
+                    PlaceholderRange(offset=8, length=2),
                 ],
                 "audio": [
-                    PlaceholderRange(offset=0, length=2),
-                    PlaceholderRange(offset=2, length=3),
+                    PlaceholderRange(offset=5, length=2),
+                    PlaceholderRange(offset=11, length=4),
                 ]
             },
-            mm_hashes=None,
-            expected_modalities=["audio", "audio", "image", "image"],
-            expected_ranges=[
-                PlaceholderRange(offset=0, length=2),
-                PlaceholderRange(offset=2, length=3),
-                PlaceholderRange(offset=7, length=4),
-                PlaceholderRange(offset=11, length=5),
+            expected_modality_idxs=[
+                ("image", 0),
+                ("audio", 0),
+                ("image", 1),
+                ("audio", 1),
+            ],
+        ),
+        ## Interleaved, internally unsorted
+        TestCase(
+            mm_positions={
+                "image": [
+                    PlaceholderRange(offset=8, length=2),
+                    PlaceholderRange(offset=0, length=4),
+                ],
+                "audio": [
+                    PlaceholderRange(offset=11, length=4),
+                    PlaceholderRange(offset=5, length=2),
+                ]
+            },
+            expected_modality_idxs=[
+                ("image", 1),
+                ("audio", 1),
+                ("image", 0),
+                ("audio", 0),
             ],
-            expected_hashes=None,
         ),
 
         # Three modalities
+        ## Internally sorted
         TestCase(
             mm_positions={
                 "image": [
@@ -293,72 +289,16 @@ def test_merge_and_sort_multimodal_metadata():
                     PlaceholderRange(offset=12, length=6),
                 ]
             },
-            mm_hashes={
-                "image": ["image_hash1", "image_hash2"],
-                "audio": ["audio_hash1"],
-                "video": ["video_hash1", "video_hash2", "video_hash3"]
-            },
-            expected_modalities=[
-                "audio", "video", "video", "video", "image", "image"
-            ],
-            expected_ranges=[
-                PlaceholderRange(offset=0, length=2),
-                PlaceholderRange(offset=3, length=4),
-                PlaceholderRange(offset=7, length=5),
-                PlaceholderRange(offset=12, length=6),
-                PlaceholderRange(offset=15, length=7),
-                PlaceholderRange(offset=22, length=8),
-            ],
-            expected_hashes=[
-                "audio_hash1", "video_hash1", "video_hash2", "video_hash3",
-                "image_hash1", "image_hash2"
+            expected_modality_idxs=[
+                ("audio", 0),
+                ("video", 0),
+                ("video", 1),
+                ("video", 2),
+                ("image", 0),
+                ("image", 1),
             ],
         ),
-    ]
-
-    for (mm_positions, mm_hashes, expected_modalities, expected_ranges,
-         expected_hashes) in test_cases:
-        modalities, ranges, hashes = merge_and_sort_multimodal_metadata(
-            mm_positions, mm_hashes)
-
-        assert modalities == expected_modalities
-        assert ranges == expected_ranges
-        assert hashes == expected_hashes
-
-
-def test_merge_and_sort_multimodal_metadata_with_interleaving():
-
-    test_cases = [
-
-        # <image> <audio> <image> <audio>
-        TestCase(
-            mm_positions={
-                "image": [
-                    PlaceholderRange(offset=0, length=4),
-                    PlaceholderRange(offset=8, length=2),
-                ],
-                "audio": [
-                    PlaceholderRange(offset=5, length=2),
-                    PlaceholderRange(offset=11, length=4),
-                ]
-            },
-            mm_hashes={
-                "image": ["image_hash1", "image_hash2"],
-                "audio": ["audio_hash1", "audio_hash2"],
-            },
-            expected_modalities=["image", "audio", "image", "audio"],
-            expected_ranges=[
-                PlaceholderRange(offset=0, length=4),
-                PlaceholderRange(offset=5, length=2),
-                PlaceholderRange(offset=8, length=2),
-                PlaceholderRange(offset=11, length=4),
-            ],
-            expected_hashes=[
-                "image_hash1", "audio_hash1", "image_hash2", "audio_hash2"
-            ],
-        ),
-
-        # <image> <image> <audio> <video> <image>
+        ## Interleaved, internally sorted
         TestCase(
             mm_positions={
                 "image": [
@@ -373,58 +313,43 @@ def test_merge_and_sort_multimodal_metadata_with_interleaving():
                     PlaceholderRange(offset=8, length=5),
                 ]
             },
-            mm_hashes=None,
-            expected_modalities=["image", "image", "audio", "video", "image"],
-            expected_ranges=[
-                PlaceholderRange(offset=0, length=2),
-                PlaceholderRange(offset=2, length=3),
-                PlaceholderRange(offset=5, length=2),
-                PlaceholderRange(offset=8, length=5),
-                PlaceholderRange(offset=20, length=4),
+            expected_modality_idxs=[
+                ("image", 0),
+                ("image", 1),
+                ("audio", 0),
+                ("video", 0),
+                ("image", 2),
             ],
-            expected_hashes=None,
         ),
-
-        # <image> <audio> <video> <image> with hashes
+        ## Interleaved, internally sunorted
         TestCase(
             mm_positions={
                 "image": [
                     PlaceholderRange(offset=0, length=2),
-                    PlaceholderRange(offset=18, length=4),
+                    PlaceholderRange(offset=20, length=4),
+                    PlaceholderRange(offset=2, length=3),
                 ],
                 "audio": [
-                    PlaceholderRange(offset=6, length=2),
+                    PlaceholderRange(offset=5, length=2),
                 ],
                 "video": [
-                    PlaceholderRange(offset=10, length=5),
+                    PlaceholderRange(offset=8, length=5),
                 ]
             },
-            mm_hashes={
-                "image": ["image_hash1", "image_hash2"],
-                "audio": ["audio_hash1"],
-                "video": ["video_hash1"],
-            },
-            expected_modalities=["image", "audio", "video", "image"],
-            expected_ranges=[
-                PlaceholderRange(offset=0, length=2),
-                PlaceholderRange(offset=6, length=2),
-                PlaceholderRange(offset=10, length=5),
-                PlaceholderRange(offset=18, length=4),
-            ],
-            expected_hashes=[
-                "image_hash1", "audio_hash1", "video_hash1", "image_hash2"
+            expected_modality_idxs=[
+                ("image", 0),
+                ("image", 2),
+                ("audio", 0),
+                ("video", 0),
+                ("image", 1),
             ],
         ),
     ]
 
-    for (mm_positions, mm_hashes, expected_modalities, expected_ranges,
-         expected_hashes) in test_cases:
-        modalities, ranges, hashes = merge_and_sort_multimodal_metadata(
-            mm_positions, mm_hashes)
+    for mm_positions, expected_modality_idxs in test_cases:
+        modality_idxs = argsort_mm_positions(mm_positions)
 
-        assert modalities == expected_modalities
-        assert ranges == expected_ranges
-        assert hashes == expected_hashes
+        assert modality_idxs == expected_modality_idxs
 
 
 class SimpleLinearModel(torch.nn.Module):

tests/samplers/test_logits_processor.py

@@ -1,70 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import pytest
-import torch
-
-from vllm import SamplingParams
-
-MODELS = ["distilbert/distilgpt2"]
-
-
-@pytest.fixture(scope="function", autouse=True)
-def use_v0_only(monkeypatch):
-    """
-    This file tests V0 internals, so set VLLM_USE_V1=0.
-    """
-    monkeypatch.setenv('VLLM_USE_V1', '0')
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("dtype", ["half"])
-def test_logits_processor_force_generate(
-    vllm_runner,
-    example_prompts,
-    model: str,
-    dtype: str,
-) -> None:
-    with vllm_runner(model, dtype=dtype) as vllm_model:
-        tokenizer = vllm_model.llm.get_tokenizer()
-        repeat_times = 2
-        enforced_answers = " vLLM"
-        vllm_token_ids = tokenizer.encode(enforced_answers,
-                                          add_special_tokens=False)
-        max_tokens = len(vllm_token_ids) * repeat_times
-
-        def pick_vllm(token_ids, logits):
-            token_id = vllm_token_ids[len(token_ids) % len(vllm_token_ids)]
-            logits[token_id] = torch.finfo(logits.dtype).max
-            return logits
-
-        params_with_logprobs = SamplingParams(
-            logits_processors=[pick_vllm],
-            prompt_logprobs=3,
-            max_tokens=max_tokens,
-        )
-
-        # test logits_processors when prompt_logprobs is not None
-        vllm_model.llm._add_request(
-            example_prompts[0],
-            params=params_with_logprobs,
-        )
-
-        # test prompt_logprobs is not None
-        vllm_model.llm._add_request(
-            example_prompts[1],
-            params=SamplingParams(
-                prompt_logprobs=3,
-                max_tokens=max_tokens,
-            ),
-        )
-
-        # test grouped requests
-        vllm_model.llm._add_request(
-            example_prompts[2],
-            params=SamplingParams(max_tokens=max_tokens),
-        )
-
-        outputs = vllm_model.llm._run_engine(use_tqdm=False)
-
-        assert outputs[0].outputs[0].text == enforced_answers * repeat_times

tests/tpu/lora/test_lora.py

@@ -30,7 +30,6 @@ def use_v1_only(monkeypatch: pytest.MonkeyPatch):
 
 def setup_vllm(num_loras: int, tp: int) -> vllm.LLM:
     return vllm.LLM(model="Qwen/Qwen2.5-3B-Instruct",
-                    num_scheduler_steps=1,
                     max_model_len=256,
                     max_seq_len_to_capture=256,
                     max_num_seqs=8,

tests/utils_/test_utils.py

@@ -161,7 +161,6 @@ def parser_with_config():
     parser.add_argument('--port', type=int)
     parser.add_argument('--tensor-parallel-size', type=int)
     parser.add_argument('--trust-remote-code', action='store_true')
-    parser.add_argument('--multi-step-stream-outputs', action=StoreBoolean)
     return parser
 
 
@@ -236,7 +235,6 @@ def test_config_args(parser_with_config, cli_config_file):
         ['serve', 'mymodel', '--config', cli_config_file])
     assert args.tensor_parallel_size == 2
     assert args.trust_remote_code
-    assert not args.multi_step_stream_outputs
 
 
 def test_config_file(parser_with_config):
@@ -828,7 +826,6 @@ def test_model_specification(parser_with_config, cli_config_file,
     ])
     assert args.tensor_parallel_size == 2
     assert args.trust_remote_code is True
-    assert args.multi_step_stream_outputs is False
     assert args.port == 12312
 
 

tests/v1/core/test_kv_cache_utils.py

@@ -1,12 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import importlib
+from typing import Optional
 
 import pytest
 import torch
 
 from vllm.config import ModelConfig, SchedulerConfig, VllmConfig
-from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
+from vllm.multimodal.inputs import (MultiModalBatchedField,
+                                    MultiModalFieldElem, MultiModalKwargsItem,
+                                    PlaceholderRange)
 from vllm.sampling_params import SamplingParams
 from vllm.utils import GiB_bytes, sha256, sha256_cbor_64bit
 from vllm.v1.core.kv_cache_manager import KVCacheManager
@@ -27,20 +30,29 @@ from vllm.v1.request import Request
 # yapf: enable
 
 
-def make_request(request_id,
-                 prompt_token_ids,
-                 mm_positions=None,
-                 mm_hashes=None,
-                 cache_salt=None):
+def make_request(
+    request_id: str,
+    prompt_token_ids: list[int],
+    mm_positions: Optional[list[PlaceholderRange]] = None,
+    mm_hashes: Optional[list[str]] = None,
+    cache_salt: Optional[str] = None,
+):
     if mm_positions is None:
-        multi_modal_inputs = None
+        mm_kwargs = None
     else:
-        multi_modal_inputs = [MultiModalKwargs({})] * len(mm_positions)
+        mm_elem = MultiModalFieldElem(
+            modality="dummy_m",
+            key="dummy_k",
+            data=None,
+            field=MultiModalBatchedField(),
+        )
+        mm_item = MultiModalKwargsItem.from_elems([mm_elem])
+        mm_kwargs = [mm_item] * len(mm_positions)
 
     return Request(
         request_id=request_id,
         prompt_token_ids=prompt_token_ids,
-        multi_modal_inputs=multi_modal_inputs,
+        multi_modal_kwargs=mm_kwargs,
         multi_modal_hashes=mm_hashes,
         multi_modal_placeholders=mm_positions,
         sampling_params=SamplingParams(max_tokens=17),
@@ -316,7 +328,7 @@ def test_free_kv_cache_block_queue_get_all_free_blocks():
 
 def test_generate_block_hash_extra_keys():
     request = make_request(
-        request_id=0,
+        request_id="0",
         prompt_token_ids=[_ for _ in range(20)],
         mm_positions=[
             PlaceholderRange(offset=0, length=5),
@@ -348,7 +360,7 @@ def test_generate_block_hash_extra_keys():
 
 def test_generate_block_hash_extra_keys_no_mm_inputs():
     request = make_request(
-        request_id=0,
+        request_id="0",
         prompt_token_ids=[_ for _ in range(6)],
         mm_positions=None,
         mm_hashes=None,
@@ -361,7 +373,7 @@ def test_generate_block_hash_extra_keys_no_mm_inputs():
 
 def test_generate_block_hash_extra_keys_cache_salt():
     request = make_request(
-        request_id=0,
+        request_id="0",
         prompt_token_ids=[_ for _ in range(6)],
         mm_positions=None,
         mm_hashes=None,
@@ -382,7 +394,7 @@ def test_generate_block_hash_extra_keys_cache_salt():
 
     # works together with other extra keys
     request_mm = make_request(
-        request_id=0,
+        request_id="0",
         prompt_token_ids=[_ for _ in range(20)],
         mm_positions=[
             PlaceholderRange(offset=0, length=5),
@@ -420,7 +432,7 @@ def test_hash_request_tokens(hash_fn):
     import vllm.v1.core.kv_cache_utils
     init_none_hash(hash_fn)
     request = make_request(
-        request_id=0,
+        request_id="0",
         prompt_token_ids=[_ for _ in range(6)],
         mm_positions=[
             PlaceholderRange(offset=0, length=3),
@@ -450,7 +462,7 @@ def test_hash_tokens_different_mm_input(hash_fn):
     init_none_hash(hash_fn)
 
     request1 = make_request(
-        request_id=0,
+        request_id="0",
         prompt_token_ids=[_ for _ in range(6)],
         mm_positions=[
             PlaceholderRange(offset=0, length=3),
@@ -459,7 +471,7 @@ def test_hash_tokens_different_mm_input(hash_fn):
         mm_hashes=["hash1", "hash2"],
     )
     request2 = make_request(
-        request_id=1,
+        request_id="1",
         prompt_token_ids=[_ for _ in range(6)],
         mm_positions=[
             PlaceholderRange(offset=0, length=3),
@@ -479,7 +491,7 @@ def test_hash_request_tokens_no_mm_inputs(hash_fn):
     init_none_hash(hash_fn)
 
     request = make_request(
-        request_id=0,
+        request_id="0",
         prompt_token_ids=[_ for _ in range(6)],
         mm_positions=None,
         mm_hashes=None,
@@ -844,7 +856,7 @@ def test_allocate_with_lookahead():
     )
 
     request = make_request(
-        request_id=0,
+        request_id="0",
         prompt_token_ids=[],
         mm_positions=None,
         mm_hashes=None,

tests/v1/core/test_prefix_caching.py

@@ -9,7 +9,9 @@ import pytest
 import torch
 
 from vllm.distributed.kv_events import AllBlocksCleared, BlockRemoved
-from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
+from vllm.multimodal.inputs import (MultiModalBatchedField,
+                                    MultiModalFieldElem, MultiModalKwargsItem,
+                                    PlaceholderRange)
 from vllm.sampling_params import SamplingParams
 from vllm.utils import sha256, sha256_cbor_64bit
 from vllm.v1.core.block_pool import BlockPool
@@ -21,21 +23,30 @@ from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
                                         KVCacheGroupSpec, SlidingWindowSpec)
 
 
-def make_request(request_id,
-                 prompt_token_ids,
-                 mm_positions=None,
-                 mm_hashes=None,
-                 prompt_logprobs: Optional[int] = None,
-                 cache_salt: Optional[str] = None):
+def make_request(
+    request_id: str,
+    prompt_token_ids: list[int],
+    mm_positions: Optional[list[PlaceholderRange]] = None,
+    mm_hashes: Optional[list[str]] = None,
+    prompt_logprobs: Optional[int] = None,
+    cache_salt: Optional[str] = None,
+):
     if mm_positions is None:
-        multi_modal_inputs = None
+        mm_kwargs = None
     else:
-        multi_modal_inputs = [MultiModalKwargs({})] * len(mm_positions)
+        mm_elem = MultiModalFieldElem(
+            modality="dummy_m",
+            key="dummy_k",
+            data=None,
+            field=MultiModalBatchedField(),
+        )
+        mm_item = MultiModalKwargsItem.from_elems([mm_elem])
+        mm_kwargs = [mm_item] * len(mm_positions)
 
     return Request(
         request_id=request_id,
         prompt_token_ids=prompt_token_ids,
-        multi_modal_inputs=multi_modal_inputs,
+        multi_modal_kwargs=mm_kwargs,
         multi_modal_hashes=mm_hashes,
         multi_modal_placeholders=mm_positions,
         sampling_params=SamplingParams(max_tokens=17,

tests/v1/core/test_scheduler.py

@@ -8,7 +8,9 @@ import torch
 
 from vllm.config import (CacheConfig, KVTransferConfig, ModelConfig,
                          SchedulerConfig, SpeculativeConfig, VllmConfig)
-from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
+from vllm.multimodal.inputs import (MultiModalBatchedField,
+                                    MultiModalFieldElem, MultiModalKwargsItem,
+                                    PlaceholderRange)
 from vllm.sampling_params import GuidedDecodingParams, SamplingParams
 from vllm.v1.core.sched.output import CachedRequestData, SchedulerOutput
 from vllm.v1.core.sched.scheduler import Scheduler
@@ -1304,7 +1306,7 @@ def create_requests_with_priority(
         priorities: list[int],
         arrival_times: Optional[list[float]] = None,
         num_tokens: int = 10,
-        mm_positions: Optional[list[PlaceholderRange]] = None,
+        mm_positions: Optional[list[list[PlaceholderRange]]] = None,
         max_tokens: int = 16,
         stop_token_ids: Optional[list[int]] = None,
         prompt_logprobs: Optional[int] = None):
@@ -1323,16 +1325,23 @@ def create_requests_with_priority(
     for i in range(num_requests):
         if mm_positions is not None:
             mm_position = mm_positions[i]
-            mm_inputs = [MultiModalKwargs({})] * len(mm_position)
+            mm_elem = MultiModalFieldElem(
+                modality="dummy_m",
+                key="dummy_k",
+                data=None,
+                field=MultiModalBatchedField(),
+            )
+            mm_item = MultiModalKwargsItem.from_elems([mm_elem])
+            mm_kwargs = [mm_item] * len(mm_position)
         else:
             mm_position = None
-            mm_inputs = None
+            mm_kwargs = None
         request = Request(
             request_id=f"{i}",
             prompt_token_ids=[i] * num_tokens,
             sampling_params=sampling_params,
             pooling_params=None,
-            multi_modal_inputs=mm_inputs,
+            multi_modal_kwargs=mm_kwargs,
             multi_modal_placeholders=mm_position,
             multi_modal_hashes=None,
             eos_token_id=EOS_TOKEN_ID,
@@ -1816,7 +1825,7 @@ def test_schedule_skip_tokenizer_init_structured_output_request():
     request = Request(
         request_id="0",
         prompt_token_ids=[0, 1],
-        multi_modal_inputs=None,
+        multi_modal_kwargs=None,
         multi_modal_hashes=None,
         multi_modal_placeholders=None,
         sampling_params=sampling_params,

tests/v1/core/utils.py

@@ -6,7 +6,9 @@ import torch
 
 from vllm.config import (CacheConfig, KVTransferConfig, ModelConfig,
                          SchedulerConfig, SpeculativeConfig, VllmConfig)
-from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
+from vllm.multimodal.inputs import (MultiModalBatchedField,
+                                    MultiModalFieldElem, MultiModalKwargsItem,
+                                    PlaceholderRange)
 from vllm.sampling_params import SamplingParams
 from vllm.v1.core.sched.async_scheduler import AsyncScheduler
 from vllm.v1.core.sched.scheduler import Scheduler
@@ -115,7 +117,7 @@ def create_scheduler(
 def create_requests(
     num_requests: int,
     num_tokens: int = 10,
-    mm_positions: Optional[list[PlaceholderRange]] = None,
+    mm_positions: Optional[list[list[PlaceholderRange]]] = None,
     max_tokens: int = 16,
     stop_token_ids: Optional[list[int]] = None,
     prompt_logprobs: Optional[int] = None,
@@ -129,10 +131,17 @@ def create_requests(
     for i in range(num_requests):
         if mm_positions is not None:
             mm_position = mm_positions[i]
-            mm_inputs = [MultiModalKwargs({})] * len(mm_position)
+            mm_elem = MultiModalFieldElem(
+                modality="dummy_m",
+                key="dummy_k",
+                data=None,
+                field=MultiModalBatchedField(),
+            )
+            mm_item = MultiModalKwargsItem.from_elems([mm_elem])
+            mm_kwargs = [mm_item] * len(mm_position)
         else:
             mm_position = None
-            mm_inputs = None
+            mm_kwargs = None
         prompt_token_ids = ([0] * num_tokens if same_prompt else [i] *
                             num_tokens)
         request = Request(
@@ -140,7 +149,7 @@ def create_requests(
             prompt_token_ids=prompt_token_ids,
             sampling_params=sampling_params,
             pooling_params=None,
-            multi_modal_inputs=mm_inputs,
+            multi_modal_kwargs=mm_kwargs,
             multi_modal_placeholders=mm_position,
             multi_modal_hashes=None,
             eos_token_id=EOS_TOKEN_ID,

tests/v1/engine/test_engine_core.py

@@ -35,7 +35,7 @@ def make_request() -> EngineCoreRequest:
     return EngineCoreRequest(
         request_id=str(uuid.uuid4()),
         prompt_token_ids=PROMPT_TOKENS,
-        mm_inputs=None,
+        mm_kwargs=None,
         mm_hashes=None,
         mm_placeholders=None,
         sampling_params=SamplingParams(),

tests/v1/engine/test_engine_core_client.py

@@ -52,7 +52,7 @@ def make_request(
     return EngineCoreRequest(
         request_id=str(uuid.uuid4()),
         prompt_token_ids=prompt_tokens_ids,
-        mm_inputs=None,
+        mm_kwargs=None,
         mm_hashes=None,
         mm_placeholders=None,
         sampling_params=params,

tests/v1/engine/test_output_processor.py

@@ -53,7 +53,7 @@ def test_incremental_detokenization(request_output_kind: RequestOutputKind,
         EngineCoreRequest(request_id=f"request-{idx}",
                           prompt_token_ids=prompt_tokens,
                           arrival_time=0,
-                          mm_inputs=None,
+                          mm_kwargs=None,
                           mm_hashes=None,
                           mm_placeholders=None,
                           eos_token_id=None,
@@ -402,7 +402,7 @@ def test_logprobs_processor(request_output_kind: RequestOutputKind,
         EngineCoreRequest(request_id=request_id_list[idx],
                           prompt_token_ids=prompt_tokens,
                           arrival_time=0,
-                          mm_inputs=None,
+                          mm_kwargs=None,
                           mm_hashes=None,
                           mm_placeholders=None,
                           eos_token_id=None,
@@ -567,7 +567,7 @@ def test_stop_token(include_stop_str_in_output: bool,
         request_id=request_id,
         prompt_token_ids=prompt_tokens,
         arrival_time=0,
-        mm_inputs=None,
+        mm_kwargs=None,
         mm_hashes=None,
         mm_placeholders=None,
         eos_token_id=eos_token_id,
@@ -666,7 +666,7 @@ def test_stop_string(include_stop_str_in_output: bool,
             request_id=request_id_list[idx],
             prompt_token_ids=prompt_tokens,
             arrival_time=0,
-            mm_inputs=None,
+            mm_kwargs=None,
             mm_hashes=None,
             mm_placeholders=None,
             eos_token_id=None,
@@ -782,7 +782,7 @@ def test_iteration_stats(dummy_test_vectors):
             request_id=f"request-{idx}",
             prompt_token_ids=prompt_tokens,
             arrival_time=0,
-            mm_inputs=None,
+            mm_kwargs=None,
             mm_hashes=None,
             mm_placeholders=None,
             eos_token_id=None,

tests/v1/kv_connector/unit/test_nixl_connector.py

@@ -229,6 +229,9 @@ class FakeNixlConnectorWorker(NixlConnectorWorker):
                 num_blocks=1,
                 block_len=self.block_len,
                 attn_backend_name=self.backend_name,
+                # `self.kv_cache_layout` is only forced to HND when vllm engine
+                # is started. We mock HND here.
+                kv_cache_layout="HND",
             ),
             remote_tp_size=remote_tp_size)
         return {0: remote_agent_name}
@@ -419,6 +422,52 @@ class TestNixlHandshake:
                     return
         raise TimeoutError("Took too long to complete async handshake.")
 
+    @patch(
+        "vllm.distributed.kv_transfer.kv_connector.v1.nixl_connector.NixlWrapper",
+        FakeNixlWrapper)
+    def test_handshake_fails_on_kv_cache_layout_mismatch(self, dist_init):
+        """
+        Verify that adding a remote agent fails if kv_cache_layout differs.
+        This test is only relevant for heterogeneous TP.
+        """
+        vllm_config = create_vllm_config()
+
+        # Mock TP world size to 2 to force heterogeneous TP when
+        # remote_tp_size=1
+        with patch(
+                "vllm.distributed.kv_transfer.kv_connector.v1.nixl_connector.get_tensor_model_parallel_world_size",  # noqa: E501
+                return_value=2):
+            # Initialize connector and worker (with fake NIXL wrapper)
+            connector = NixlConnector(vllm_config, KVConnectorRole.WORKER)
+            connector.connector_worker = FakeNixlConnectorWorker(
+                vllm_config, connector.engine_id, hand_shake_latency=0)
+            worker = connector.connector_worker
+
+            # Minimal local registration params used by add_remote_agent
+            worker.slot_size_bytes = 4096
+            worker.block_len = worker.slot_size_bytes * worker.block_size
+            worker.num_blocks = 1
+            worker.dst_num_blocks[worker.engine_id] = worker.num_blocks
+
+            # Metadata with different kv_cache_layout than local worker
+            mismatched_layout = "HND" if worker.kv_cache_layout != "HND" \
+                else "NHD"
+            meta = NixlAgentMetadata(
+                engine_id=FakeNixlConnectorWorker.REMOTE_ENGINE_ID,
+                agent_metadata=FakeNixlWrapper.AGENT_METADATA,
+                kv_caches_base_addr=[0],
+                num_blocks=1,
+                block_len=worker.block_len,
+                attn_backend_name=worker.backend_name,
+                kv_cache_layout=mismatched_layout,
+            )
+
+            # We don't check layout for homogeneous TP and MLA for now, as the
+            # whole block is moved.
+            worker.add_remote_agent(meta, remote_tp_size=2)
+            with pytest.raises(AssertionError):
+                worker.add_remote_agent(meta, remote_tp_size=1)
+
 
 # NOTE: resource cleanup in mp backend is a bit finicky, so the order in which
 # we put here is important. First run ray, it will clean up the resources, then

tests/v1/kv_connector/unit/utils.py

@@ -154,7 +154,7 @@ def create_request(
         prompt_token_ids=prompt_token_ids,
         sampling_params=sampling_params,
         pooling_params=None,
-        multi_modal_inputs=None,
+        multi_modal_kwargs=None,
         multi_modal_placeholders=None,
         multi_modal_hashes=None,
         eos_token_id=EOS_TOKEN_ID,

tests/v1/spec_decode/test_eagle.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from typing import Optional
 from unittest import mock
 
 import pytest
@@ -23,7 +24,11 @@ eagle_dir = "yuhuili/EAGLE-LLaMA3.1-Instruct-8B"
 eagle3_dir = "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B"
 
 
-def _create_proposer(method: str, k: int) -> EagleProposer:
+def _create_proposer(
+    method: str,
+    num_speculative_tokens: int,
+    speculative_token_tree: Optional[list[tuple[int]]] = None,
+) -> EagleProposer:
     model_config = ModelConfig(model=model_dir,
                                runner="generate",
                                max_model_len=100)
@@ -31,12 +36,18 @@ def _create_proposer(method: str, k: int) -> EagleProposer:
     # Choose model directory based on method
     draft_model_dir = eagle_dir if method == "eagle" else eagle3_dir
 
+    spec_token_tree_str = None
+    if speculative_token_tree is not None:
+        assert num_speculative_tokens == len(speculative_token_tree)
+        spec_token_tree_str = str(speculative_token_tree)
+
     speculative_config = SpeculativeConfig(
         target_model_config=model_config,
         target_parallel_config=ParallelConfig(),
         model=draft_model_dir,
         method=method,
-        num_speculative_tokens=k,
+        num_speculative_tokens=num_speculative_tokens,
+        speculative_token_tree=spec_token_tree_str,
     )
 
     vllm_config = VllmConfig(
@@ -189,7 +200,7 @@ def test_load_model(mock_get_model, mock_get_layers, mock_get_pp_group, method,
         target_model.lm_head = mock.MagicMock()
 
     # Create proposer using the helper function
-    proposer = _create_proposer(method, k=8)
+    proposer = _create_proposer(method, num_speculative_tokens=8)
 
     # Call the method under test
     proposer.load_model(target_model)
@@ -226,6 +237,10 @@ def test_propose(method, attn_backend, num_speculative_tokens, monkeypatch):
         pytest.skip("TRITON_ATTN_VLLM_V1 does not support "
                     "multi-token eagle spec decode on current platform")
 
+    if (attn_backend == "TREE_ATTN"):
+        pytest.skip("TREE_ATTN is tested separately in test_propose_tree"
+                    "because it requires special input mocking.")
+
     if attn_backend == "FLASH_ATTN_VLLM_V1" and current_platform.is_rocm():
         monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1")
 
@@ -378,3 +393,142 @@ def test_propose(method, attn_backend, num_speculative_tokens, monkeypatch):
 
     # Verify all tokens match our expectations
     assert torch.equal(result, expected_tokens)
+
+
+@pytest.mark.parametrize(
+    "spec_token_tree",
+    [
+        [(0, )],  # A single token
+        [(0, ), (0, 0), (0, 0, 0)],  # Chain
+        [(0, ), (1, ), (2, )],  # Parallel
+        [(0, ), (1, ), (2, ), (0, 0), (0, 1), (1, 0), (1, 1), (2, 0),
+         (2, 1)],  # Tree
+    ])
+def test_propose_tree(spec_token_tree):
+    # Get GPU device.
+    device = torch.device(current_platform.device_type)
+
+    # Setup test parameters.
+    batch_size = 2
+    seq_len_1 = 5
+    seq_len_2 = 3
+    total_tokens = seq_len_1 + seq_len_2
+    vocab_size = 100
+    seq_lens = [seq_len_1, seq_len_2]
+    num_speculative_tokens = len(spec_token_tree)
+
+    # Create proposer first so we can use its actual hidden_size.
+    proposer = _create_proposer("eagle",
+                                num_speculative_tokens,
+                                speculative_token_tree=spec_token_tree)
+    # Get the hidden_size from the proposer to ensure consistency.
+    hidden_size = proposer.hidden_size
+
+    # Helper to create deterministic logits that will produce specific tokens
+    def create_deterministic_logits(token_ids, k: int):
+        logits = torch.full((batch_size, vocab_size), -100.0, device=device)
+        for i, token_id in enumerate(token_ids):
+            # Assign decreasing values to the k, consecutive, tokens.
+            for j in range(k):
+                logits[i, token_id + j] = 100.0 - j
+        return logits
+
+    # Mock a model that returns deterministic logits.
+    base_token_ids = torch.tensor([42, 60], dtype=torch.int64, device=device)
+
+    # Skip loading the model and replace it with a mock that returns
+    # deterministic outputs.
+    model_mock = mock.MagicMock()
+
+    # Mock the model forward calls.
+    forward_returns = [(torch.zeros(total_tokens, hidden_size, device=device),
+                        torch.zeros(total_tokens, hidden_size, device=device))]
+    for cu_num_drafts in proposer.cu_drafts_per_level:
+        h_logits = torch.zeros(batch_size * cu_num_drafts,
+                               hidden_size,
+                               device=device)
+        h_states = torch.zeros(batch_size * cu_num_drafts,
+                               hidden_size,
+                               device=device)
+        forward_returns.append((h_logits, h_states))
+    model_mock.side_effect = forward_returns
+
+    # Mock the compute_logits calls.
+    cu_num_drafts_tensor = torch.tensor([0] + proposer.cu_drafts_per_level,
+                                        dtype=torch.int32,
+                                        device=device)
+    logits_returns = []
+    for level, num_children in enumerate(proposer.child_drafts_per_level):
+        token_ids = base_token_ids + cu_num_drafts_tensor[level]
+        level_num_drafts = cu_num_drafts_tensor[
+            level + 1] - cu_num_drafts_tensor[level]
+        level_logits = []
+        for i in range(level_num_drafts // num_children):
+            level_logits.append(
+                create_deterministic_logits(token_ids + i * num_children,
+                                            num_children))
+        logits_returns.append(torch.stack(level_logits, dim=1))
+    model_mock.compute_logits.side_effect = logits_returns
+
+    # Assign the mock to the proposer
+    proposer.model = model_mock
+
+    # Assign draft attn_layer_names since load_model is not invoked
+    proposer.attn_layer_names = ["layer.0"]
+
+    # Get the tree attention metadata builder.
+    attn_metadata_builder_cls, _ = get_attention_backend(_Backend.TREE_ATTN)
+    attn_metadata_builder = attn_metadata_builder_cls(
+        kv_cache_spec=create_standard_kv_cache_spec(proposer.vllm_config),
+        layer_names=proposer.attn_layer_names,
+        vllm_config=proposer.vllm_config,
+        device=device,
+    )
+
+    # Mock runner for attention metadata building.
+    proposer.runner = mock.MagicMock()
+    proposer.runner.attn_groups.append([mock.MagicMock()])
+    proposer.runner.attn_groups[0][0].metadata_builder = attn_metadata_builder
+
+    # Setup inputs for the proposer.
+    target_token_ids = torch.randint(0,
+                                     vocab_size, (total_tokens, ),
+                                     device=device)
+    target_positions = torch.cat([
+        torch.arange(seq_len_1, device=device),
+        torch.arange(seq_len_2, device=device)
+    ])
+    target_hidden_states = torch.randn(total_tokens,
+                                       hidden_size,
+                                       device=device)
+    next_token_ids = torch.randint(0,
+                                   vocab_size, (batch_size, ),
+                                   dtype=torch.int32,
+                                   device=device)
+    batch_spec = BatchSpec(
+        seq_lens=seq_lens,
+        query_lens=seq_lens,
+    )
+    common_attn_metadata = create_common_attn_metadata(
+        batch_spec,
+        block_size=16,
+        device=device,
+    )
+    sampling_metadata = mock.MagicMock()
+
+    # Propose draft tokens.
+    result = proposer.propose(target_token_ids=target_token_ids,
+                              target_positions=target_positions,
+                              target_hidden_states=target_hidden_states,
+                              next_token_ids=next_token_ids,
+                              common_attn_metadata=common_attn_metadata,
+                              sampling_metadata=sampling_metadata)
+    assert result.shape == (batch_size, num_speculative_tokens)
+
+    # The tokens are expected to be consecutive integers starting
+    # from the base token IDs.
+    expected_tokens = base_token_ids[:, None] + torch.arange(
+        num_speculative_tokens, dtype=torch.int64, device=device)
+
+    # Verify that the draft tokens match our expectations.
+    assert torch.equal(result, expected_tokens)

tests/v1/spec_decode/test_max_len.py

@@ -39,12 +39,6 @@ def test_eagle_max_len(monkeypatch: pytest.MonkeyPatch,
                        num_speculative_tokens: int, attn_backend: str):
     with monkeypatch.context() as m:
         m.setenv("VLLM_USE_V1", "1")
-
-        if attn_backend == "TREE_ATTN" and num_speculative_tokens > 1:
-            # TREE_ATTN fails the test with multi-token spec decode
-            # TODO: Investigate why
-            pytest.skip("TREE_ATTN fails the test")
-
         m.setenv("VLLM_ATTENTION_BACKEND", attn_backend)
 
         if (attn_backend == "TRITON_ATTN_VLLM_V1"

tests/v1/test_oracle.py

@@ -58,12 +58,6 @@ def test_unsupported_configs(monkeypatch):
                 disable_async_output_proc=True,
             ).create_engine_config()
 
-        with pytest.raises(NotImplementedError):
-            AsyncEngineArgs(
-                model=MODEL,
-                num_scheduler_steps=5,
-            ).create_engine_config()
-
         with pytest.raises(NotImplementedError):
             AsyncEngineArgs(
                 model=MODEL,

tests/v1/tpu/worker/test_tpu_model_runner.py

@@ -64,7 +64,7 @@ def _schedule_new_request(*req_ids: str) -> SchedulerOutput:
             NewRequestData(
                 req_id=req_id,
                 prompt_token_ids=[1, 2, 3],
-                mm_inputs=[],
+                mm_kwargs=[],
                 mm_hashes=[],
                 mm_positions=[],
                 sampling_params=SamplingParams(),

tests/v1/worker/test_gpu_input_batch.py

@@ -203,7 +203,7 @@ def _construct_cached_request_state(req_id_suffix: int):
         prompt_token_ids=prompt_token_ids,
         sampling_params=_create_sampling_params(),
         pooling_params=None,
-        mm_inputs=[],
+        mm_kwargs=[],
         mm_positions=[],
         block_ids=([], ),
         generator=None,

tests/v1/worker/test_gpu_model_runner.py

@@ -120,7 +120,7 @@ def _schedule_new_request(*req_ids: str) -> SchedulerOutput:
             NewRequestData(
                 req_id=req_id,
                 prompt_token_ids=[1, 2, 3],
-                mm_inputs=[],
+                mm_kwargs=[],
                 mm_hashes=[],
                 mm_positions=[],
                 sampling_params=SamplingParams(),

tests/worker/test_model_input.py

@@ -11,7 +11,6 @@ from vllm.attention.backends.utils import CommonAttentionState
 from vllm.model_executor import SamplingMetadata
 from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.worker.model_runner import ModelInputForGPUWithSamplingMetadata
-from vllm.worker.multi_step_model_runner import StatefulModelInput
 from vllm.worker.pooling_model_runner import (
     ModelInputForGPUWithPoolingMetadata)
 
@@ -166,81 +165,3 @@ def test_embedding_model_runner_input():
                        None) == getattr(attn_metadata, field.name, None)
     # Pooling metadata is not broadcast.
     assert received_model_input.pooling_metadata is None
-
-
-def test_multi_step_model_runner_input():
-    sampling_metadata = SamplingMetadata(
-        ["seq_group"],
-        "selected_token_indices",
-        "categorized_sample_indices",
-        "num_prompts",
-    )
-    attn_metadata = AttentionMetadata(
-        num_prefills=1,
-        num_prefill_tokens=2,
-        num_decode_tokens=3,
-        slot_mapping=torch.zeros(1),
-        multi_modal_placeholder_index_maps=None,
-        enable_kv_scales_calculation=True,
-    )
-    frozen_model_input = ModelInputForGPUWithSamplingMetadata(
-        input_tokens=torch.ones(10),
-        input_positions=torch.ones(10),
-        sampling_metadata=sampling_metadata,
-        attn_metadata=attn_metadata)
-
-    model_input = StatefulModelInput(
-        frozen_model_input=frozen_model_input,
-        is_last_step=True,
-        is_first_multi_step=False,
-        current_step=4,
-        last_sampled_token_ids=torch.ones((10, 1)),
-        is_multi_step=True,
-        num_queries=8,
-        num_seqs=5,
-        cached_outputs=[],
-    )
-
-    assert isinstance(model_input, StatefulModelInput)
-
-    # Test round trip serialization.
-    tensor_dict = model_input.as_broadcastable_tensor_dict()
-    attn_backend = MockAttentionBackend()
-    received_model_input = (StatefulModelInput.from_broadcasted_tensor_dict(
-        tensor_dict, attn_backend=attn_backend))
-
-    received_frozen_input = received_model_input.frozen_model_input
-
-    # Check that received copy has correct values.
-    assert isinstance(received_model_input, StatefulModelInput)
-    assert received_frozen_input.input_tokens is not None
-    assert (received_frozen_input.input_tokens ==
-            frozen_model_input.input_tokens).all()
-    assert received_frozen_input.input_positions is not None
-    assert (received_frozen_input.input_positions ==
-            frozen_model_input.input_positions).all()
-    assert received_frozen_input.multi_modal_kwargs is None
-    assert (frozen_model_input.multi_modal_kwargs ==
-            frozen_model_input.multi_modal_kwargs)
-    assert received_frozen_input.lora_requests is None
-    assert (received_frozen_input.lora_requests ==
-            frozen_model_input.lora_requests)
-    assert received_frozen_input.lora_mapping is None
-    assert (
-        received_frozen_input.lora_mapping == frozen_model_input.lora_mapping)
-    for field in dataclasses.fields(AttentionMetadata):
-        assert getattr(received_frozen_input.attn_metadata, field.name,
-                       None) == getattr(attn_metadata, field.name, None)
-    # For sampling metadata, only selected_token_indices is copied.
-    assert (received_frozen_input.sampling_metadata.selected_token_indices ==
-            sampling_metadata.selected_token_indices)
-    assert received_frozen_input.sampling_metadata.seq_groups is None
-
-    # check non frozen fields
-    assert received_model_input.is_last_step == model_input.is_last_step
-    assert (received_model_input.is_first_multi_step ==
-            model_input.is_first_multi_step)
-    assert received_model_input.current_step == model_input.current_step
-    assert (received_model_input.last_sampled_token_ids ==
-            model_input.last_sampled_token_ids).all()
-    assert received_model_input.is_multi_step == model_input.is_multi_step

vllm/attention/backends/rocm_flash_attn.py

@@ -22,7 +22,6 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     GroupShape)
 from vllm.platforms import current_platform
-from vllm.platforms.rocm import use_rocm_custom_paged_attention
 
 if TYPE_CHECKING:
     from vllm.worker.model_runner import ModelInputForGPUWithSamplingMetadata
@@ -886,6 +885,7 @@ class ROCmFlashAttentionImpl(AttentionImpl):
             num_seqs, num_heads, head_size = decode_query.shape
             block_size = value_cache.shape[3]
             gqa_ratio = num_heads // self.num_kv_heads
+            from vllm.platforms.rocm import use_rocm_custom_paged_attention
             use_custom = use_rocm_custom_paged_attention(
                 decode_query.dtype, head_size, block_size, gqa_ratio,
                 decode_meta.max_decode_seq_len, self.sliding_window,

vllm/attention/ops/chunked_prefill_paged_decode.py

@@ -11,7 +11,6 @@ import torch
 
 from vllm import _custom_ops as ops
 from vllm.platforms import current_platform
-from vllm.platforms.rocm import use_rocm_custom_paged_attention
 from vllm.triton_utils import tl, triton
 
 from .prefix_prefill import context_attention_fwd
@@ -296,6 +295,7 @@ def chunked_prefill_paged_decode(
     num_queries_per_kv_padded = max(triton.next_power_of_2(num_queries_per_kv),
                                     16)
 
+    from vllm.platforms.rocm import use_rocm_custom_paged_attention
     use_custom = use_rocm_custom_paged_attention(
         query.dtype,
         head_size,

vllm/config/__init__.py

@@ -2598,6 +2598,25 @@ class PoolerConfig:
     ``math-shepherd-mistral-7b-prm`` model.
     """
 
+    enable_chunked_processing: Optional[bool] = None
+    """
+    Whether to enable chunked processing for long inputs that exceed the model's
+    maximum position embeddings. When enabled, long inputs will be split into
+    chunks, processed separately, and then aggregated using weighted averaging.
+    This allows embedding models to handle arbitrarily long text without CUDA
+    errors. Defaults to False.
+    """
+
+    max_embed_len: Optional[int] = None
+    """
+    Maximum input length allowed for embedding generation. When set, allows 
+    inputs longer than max_embed_len to be accepted for embedding models.
+    This parameter enables accepting long inputs without requiring 
+    VLLM_ALLOW_LONG_MAX_MODEL_LEN environment variable. When an input exceeds
+    max_embed_len, it will be handled according to the original max_model_len
+    validation logic. Defaults to None (i.e. set to max_model_len).
+    """
+
     def compute_hash(self) -> str:
         """
         WARNING: Whenever a new field is added to this config,
@@ -3779,8 +3798,6 @@ class VllmConfig:
             f"observability_config={self.observability_config!r}, "
             f"seed={self.model_config.seed}, "
             f"served_model_name={self.model_config.served_model_name}, "
-            f"num_scheduler_steps={self.scheduler_config.num_scheduler_steps}, "
-            f"multi_step_stream_outputs={self.scheduler_config.multi_step_stream_outputs}, "  # noqa
             f"enable_prefix_caching={self.cache_config.enable_prefix_caching}, "
             f"chunked_prefill_enabled={self.scheduler_config.chunked_prefill_enabled}, "  # noqa
             f"use_async_output_proc={self.model_config.use_async_output_proc}, "

vllm/config/scheduler.py

@@ -115,12 +115,6 @@ class SchedulerConfig:
     (e.g., beam search), recomputation is not currently supported. In
     such a case, we use swapping instead."""
 
-    num_scheduler_steps: int = 1
-    """Maximum number of forward steps per scheduler call."""
-
-    multi_step_stream_outputs: bool = True
-    """If False, then multi-step will stream outputs at the end of all steps"""
-
     send_delta_data: bool = False
     """Private API. If used, scheduler sends delta data to
     workers instead of an entire data. It should be enabled only
@@ -193,16 +187,7 @@ class SchedulerConfig:
 
         if self.max_num_batched_tokens is None:
             if self.enable_chunked_prefill:
-                if self.num_scheduler_steps > 1:
-                    # Multi-step Chunked-Prefill doesn't allow prompt-chunking
-                    # for now. Have max_num_batched_tokens set to max_model_len
-                    # so we don't reject sequences on account of a short
-                    # max_num_batched_tokens.
-                    self.max_num_batched_tokens = max(
-                        self.max_model_len, DEFAULT_MAX_NUM_BATCHED_TOKENS)
-                else:
-                    self.max_num_batched_tokens = (
-                        DEFAULT_MAX_NUM_BATCHED_TOKENS)
+                self.max_num_batched_tokens = DEFAULT_MAX_NUM_BATCHED_TOKENS
             else:
                 # If max_model_len is too short, use
                 # DEFAULT_MAX_NUM_BATCHED_TOKENS as the default value
@@ -293,12 +278,6 @@ class SchedulerConfig:
                 f"({self.num_lookahead_slots}) must be greater than or "
                 "equal to 0.")
 
-        if self.num_scheduler_steps < 1:
-            raise ValueError(
-                "num_scheduler_steps "
-                f"({self.num_scheduler_steps}) must be greater than or "
-                "equal to 1.")
-
         if self.max_num_partial_prefills < 1:
             raise ValueError(
                 f"max_num_partial_prefills ({self.max_num_partial_prefills}) "
@@ -323,7 +302,3 @@ class SchedulerConfig:
                 f"max_num_partial_prefills ({self.max_num_partial_prefills}).")
 
         return self
-
-    @property
-    def is_multi_step(self) -> bool:
-        return self.num_scheduler_steps > 1

vllm/core/scheduler.py

@@ -929,8 +929,7 @@ class Scheduler:
         )
 
     def _get_prompt_limit(self, seq_group: SequenceGroup) -> int:
-        if (self.scheduler_config.chunked_prefill_enabled
-                and not self.scheduler_config.is_multi_step):
+        if self.scheduler_config.chunked_prefill_enabled:
             prompt_limit = self.scheduler_config.max_model_len
         else:
             prompt_limit = min(
@@ -1114,9 +1113,6 @@ class Scheduler:
                 continue
 
             num_lookahead_slots: int = 0
-            if self.scheduler_config.is_multi_step and enable_chunking:
-                num_lookahead_slots = self._get_num_lookahead_slots(
-                    True, enable_chunking)
 
             # If the sequence group cannot be allocated, stop.
             can_allocate = self.block_manager.can_allocate(
@@ -1195,24 +1191,6 @@ class Scheduler:
                 partial_prefill_metadata.maybe_increment_partial_prefills(
                     seq_group)
 
-            if enable_chunking and self.scheduler_config.is_multi_step:
-                blocks_to_copy: List[Tuple[int, int]] = []
-                # init_multi_step_from_lookahead_slots happens in append_slots
-                self._append_slots(seq_group, blocks_to_copy, enable_chunking)
-                # This assert will trip when a copy-on-write happens. This is
-                # not a concern as the very first sequence-group block
-                # allocation happens above. Still, we have the assert to
-                # catch any edge-cases.
-                assert not blocks_to_copy
-            else:
-                seq_group.init_multi_step_from_lookahead_slots(
-                    num_lookahead_slots,
-                    num_scheduler_steps=self.scheduler_config.
-                    num_scheduler_steps,
-                    is_multi_step=self.scheduler_config.is_multi_step,
-                    enable_chunking=enable_chunking,
-                )
-
             seq_groups.append(
                 ScheduledSequenceGroup(seq_group=seq_group,
                                        token_chunk_size=num_new_tokens))
@@ -1453,14 +1431,6 @@ class Scheduler:
         num_prefill_groups = (len(prefills.seq_groups) +
                               len(swapped_in.prefill_seq_groups) +
                               len(running_scheduled.prefill_seq_groups))
-        # If all prompts, then we set num_lookahead_slots to 0
-        # this allows us to go through the `no_spec` path in
-        # `spec_decode_worker.py`
-        all_prefills = len(scheduled_seq_groups) == num_prefill_groups
-        num_lookahead_slots = (0 if
-                               (all_prefills
-                                and not self.scheduler_config.is_multi_step)
-                               else running_scheduled.num_lookahead_slots)
         return SchedulerOutputs(
             scheduled_seq_groups=scheduled_seq_groups,
             num_prefill_groups=num_prefill_groups,
@@ -1472,7 +1442,7 @@ class Scheduler:
             swapped_in.blocks_to_copy,
             ignored_seq_groups=prefills.ignored_seq_groups +
             swapped_in.infeasible_seq_groups,
-            num_lookahead_slots=num_lookahead_slots,
+            num_lookahead_slots=0,
             running_queue_size=len(self.running),
             preempted=(len(running_scheduled.preempted) +
                        len(running_scheduled.swapped_out)),
@@ -1516,11 +1486,6 @@ class Scheduler:
         num_lookahead_slots = self._get_num_lookahead_slots(
             is_prefill, enable_chunking)
 
-        if is_prefill and num_lookahead_slots > 0:
-            # Appending prefill slots only happens multi-step and
-            # chunked-prefill are enabled together.
-            assert self.scheduler_config.is_multi_step and enable_chunking
-
         return self.block_manager.can_append_slots(
             seq_group=seq_group, num_lookahead_slots=num_lookahead_slots)
 
@@ -1776,19 +1741,7 @@ class Scheduler:
         num_lookahead_slots: int = self._get_num_lookahead_slots(
             is_prefill, enable_chunking)
 
-        seq_group.init_multi_step_from_lookahead_slots(
-            num_lookahead_slots,
-            num_scheduler_steps=self.scheduler_config.num_scheduler_steps,
-            is_multi_step=self.scheduler_config.is_multi_step,
-            enable_chunking=enable_chunking,
-        )
-
         seq_status: Optional[SequenceStatus] = SequenceStatus.RUNNING
-        if self.scheduler_config.is_multi_step and enable_chunking:
-            # In multi-step chunked-prefill any sequence type can have
-            # slots appended.
-            seq_status = None
-
         for seq in seq_group.get_seqs(status=seq_status):
             cows = self.block_manager.append_slots(seq, num_lookahead_slots)
             if len(cows) > 0:
@@ -1904,29 +1857,8 @@ class Scheduler:
         """The number of slots to allocate per sequence per step, beyond known
         token ids. Speculative decoding uses these slots to store KV activations
         of tokens which may or may not be accepted.
-
-        Speculative decoding does not yet support prefill, so we do not perform
-        lookahead allocation for prefill.
-
-        When chunking is enabled with multi-step, we allocate lookahead slots
-        for the prefills for when the prefills turn into decodes in the first
-        step.
         """
-        if is_prefill:
-            if self.scheduler_config.is_multi_step and enable_chunking:
-                # num_lookahead_slots was introduced in the context of decodes,
-                # in Speculative Decoding.
-                # When the num_scheduler_steps is 8, say, then the
-                # num_lookahead_slots is 7. Meaning, we are doing a 1-step of
-                # decode anyways and we wish to do 7 more.
-                #
-                # "lookaheads" for prefills, is introduced in support for
-                # Chunked-Prefill in Multi-Step.
-                return self.scheduler_config.num_lookahead_slots + 1
-            else:
-                return 0
-
-        return self.scheduler_config.num_lookahead_slots
+        return 0
 
     def _get_num_new_uncached_and_cached_tokens(
         self,
@@ -2068,24 +2000,6 @@ class Scheduler:
             The number of new tokens to schedule after chunking.
         """
         remaining_token_budget = budget.remaining_token_budget()
-        if scheduler_config.is_multi_step:
-            # The current multi-step + chunked prefill capability does
-            # not actually support chunking prompts.
-            #
-            # Therefore, `num_new_tokens` is computed in the same fashion
-            # for both multi-step+chunked-prefill &
-            # multi-step+chunked-prefill+APC
-            #
-            # Prompts with more tokens than the current remaining budget
-            # are postponed to future scheduler steps
-            if num_new_tokens > prompt_limit:
-                # If the seq_group is in prompt-stage, pass the
-                # num_new_tokens as-is so the caller can ignore
-                # the sequence.
-                return num_new_tokens
-
-            return 0 if num_new_tokens > \
-                remaining_token_budget else num_new_tokens
 
         # Get the number of tokens to allocate to this prefill slot
         prefill_slot_budget = (

vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py

@@ -30,6 +30,7 @@ from vllm.forward_context import ForwardContext
 from vllm.logger import init_logger
 from vllm.platforms import _Backend, current_platform
 from vllm.utils import make_zmq_path, make_zmq_socket
+from vllm.v1.attention.backends.utils import get_kv_cache_layout
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.request import RequestStatus
 
@@ -73,6 +74,7 @@ class NixlAgentMetadata(
     num_blocks: int
     block_len: int
     attn_backend_name: str
+    kv_cache_layout: str
 
 
 @dataclass
@@ -538,7 +540,9 @@ class NixlConnectorWorker:
         attn_backend = backend_name_to_enum(self.backend_name)
         self._use_flashinfer = attn_backend == _Backend.FLASHINFER_VLLM_V1
         self._use_pallas_v1 = attn_backend == _Backend.PALLAS_VLLM_V1
+        self.kv_cache_layout = get_kv_cache_layout()
         logger.debug("Detected attention backend %s", self.backend_name)
+        logger.debug("Detected kv cache layout %s", self.kv_cache_layout)
 
         self._tp_size: dict[EngineId, int] = {self.engine_id: self.world_size}
         # With heterogeneous TP, P must wait for all assigned D TP workers to
@@ -839,7 +843,8 @@ class NixlConnectorWorker:
             kv_caches_base_addr=self.kv_caches_base_addr[self.engine_id],
             num_blocks=self.num_blocks,
             block_len=self.block_len,
-            attn_backend_name=self.backend_name)
+            attn_backend_name=self.backend_name,
+            kv_cache_layout=self.kv_cache_layout)
         ready_event = threading.Event()
         self._nixl_handshake_listener_t = threading.Thread(
             target=self._nixl_handshake_listener,
@@ -900,8 +905,7 @@ class NixlConnectorWorker:
             self._tp_size[engine_id] = remote_tp_size
         else:
             assert self._tp_size[engine_id] == remote_tp_size
-        # We may eventually enable this after asserting equality in cache
-        # layout and close outputs.
+        # TODO We may eventually want to skip enforcing the same attn backend.
         assert nixl_agent_meta.attn_backend_name == self.backend_name
 
         remote_agent_name = self.nixl_wrapper.add_remote_agent(
@@ -930,6 +934,9 @@ class NixlConnectorWorker:
             if self._use_flashinfer:
                 # Account for joint KV in FlashInfer.
                 remote_block_size //= 2
+            if tp_ratio > 1:
+                # Heterogeneous TP expects same kv_cache_layout.
+                assert nixl_agent_meta.kv_cache_layout == self.kv_cache_layout
 
             assert nixl_agent_meta.block_len == self.block_len * tp_ratio, (
                 "Remote P worker KV layer cache must be of shape [2, N, "

vllm/engine/arg_utils.py

@@ -362,8 +362,6 @@ class EngineArgs:
     lora_dtype: Optional[Union[str, torch.dtype]] = LoRAConfig.lora_dtype
     lora_extra_vocab_size: int = LoRAConfig.lora_extra_vocab_size
 
-    num_scheduler_steps: int = SchedulerConfig.num_scheduler_steps
-    multi_step_stream_outputs: bool = SchedulerConfig.multi_step_stream_outputs
     ray_workers_use_nsight: bool = ParallelConfig.ray_workers_use_nsight
     num_gpu_blocks_override: Optional[
         int] = CacheConfig.num_gpu_blocks_override
@@ -799,11 +797,8 @@ class EngineArgs:
                                      **scheduler_kwargs["delay_factor"])
         scheduler_group.add_argument("--preemption-mode",
                                      **scheduler_kwargs["preemption_mode"])
-        scheduler_group.add_argument("--num-scheduler-steps",
-                                     **scheduler_kwargs["num_scheduler_steps"])
-        scheduler_group.add_argument(
-            "--multi-step-stream-outputs",
-            **scheduler_kwargs["multi_step_stream_outputs"])
+        # multi-step scheduling has been removed; corresponding arguments
+        # are no longer supported.
         scheduler_group.add_argument("--scheduling-policy",
                                      **scheduler_kwargs["policy"])
         scheduler_group.add_argument(
@@ -1257,28 +1252,11 @@ class EngineArgs:
             disable_log_stats=self.disable_log_stats,
         )
 
-        # Reminder: Please update docs/features/compatibility_matrix.md
-        # If the feature combo become valid
-        if self.num_scheduler_steps > 1:
-            if speculative_config is not None:
-                raise ValueError("Speculative decoding is not supported with "
-                                 "multi-step (--num-scheduler-steps > 1)")
-            if self.enable_chunked_prefill and self.pipeline_parallel_size > 1:
-                raise ValueError("Multi-Step Chunked-Prefill is not supported "
-                                 "for pipeline-parallel-size > 1")
-            if current_platform.is_cpu():
-                logger.warning("Multi-Step (--num-scheduler-steps > 1) is "
-                               "currently not supported for CPUs and has been "
-                               "disabled.")
-                self.num_scheduler_steps = 1
-
-        # make sure num_lookahead_slots is set the higher value depending on
-        # if we are using speculative decoding or multi-step
-        num_lookahead_slots = max(self.num_lookahead_slots,
-                                  self.num_scheduler_steps - 1)
-        num_lookahead_slots = num_lookahead_slots \
-            if speculative_config is None \
-            else speculative_config.num_lookahead_slots
+        # make sure num_lookahead_slots is set appropriately depending on
+        # whether speculative decoding is enabled
+        num_lookahead_slots = self.num_lookahead_slots
+        if speculative_config is not None:
+            num_lookahead_slots = speculative_config.num_lookahead_slots
 
         scheduler_config = SchedulerConfig(
             runner_type=model_config.runner_type,
@@ -1292,8 +1270,6 @@ class EngineArgs:
             disable_chunked_mm_input=self.disable_chunked_mm_input,
             is_multimodal_model=model_config.is_multimodal_model,
             preemption_mode=self.preemption_mode,
-            num_scheduler_steps=self.num_scheduler_steps,
-            multi_step_stream_outputs=self.multi_step_stream_outputs,
             send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER
                              and parallel_config.use_ray),
             policy=self.scheduling_policy,
@@ -1392,11 +1368,6 @@ class EngineArgs:
                                recommend_to_remove=True)
             return False
 
-        if self.num_scheduler_steps != SchedulerConfig.num_scheduler_steps:
-            _raise_or_fallback(feature_name="--num-scheduler-steps",
-                               recommend_to_remove=True)
-            return False
-
         if self.scheduler_delay_factor != SchedulerConfig.delay_factor:
             _raise_or_fallback(feature_name="--scheduler-delay-factor",
                                recommend_to_remove=True)

vllm/engine/async_llm_engine.py

@@ -15,7 +15,7 @@ from vllm.config import (DecodingConfig, LoRAConfig, ModelConfig,
 from vllm.core.scheduler import SchedulerOutputs
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.async_timeout import asyncio_timeout
-from vllm.engine.llm_engine import LLMEngine, SchedulerOutputState
+from vllm.engine.llm_engine import LLMEngine
 from vllm.engine.metrics_types import StatLoggerBase
 from vllm.engine.protocol import EngineClient
 from vllm.executor.executor_base import ExecutorBase
@@ -308,13 +308,6 @@ class _AsyncLLMEngine(LLMEngine):
             if not allow_async_output_proc and len(ctx.output_queue) > 0:
                 self._process_model_outputs(ctx=ctx)
 
-            if (self.scheduler_config.is_multi_step
-                    and scheduler_outputs.num_lookahead_slots > 0):
-                # cache the scheduler outputs for the next iteration if we have
-                # lookahead slots
-                self._cache_scheduler_outputs_for_multi_step(
-                    virtual_engine, seq_group_metadata_list, scheduler_outputs,
-                    allow_async_output_proc)
         else:
             finished_requests_ids = list()
 
@@ -351,29 +344,14 @@ class _AsyncLLMEngine(LLMEngine):
             outputs = await self.model_executor.execute_model_async(
                 execute_model_req)
 
-            # we need to do this here so that last step's sampled_token_ids can
-            # be passed to the next iteration for PP.
-            if self.scheduler_config.is_multi_step:
-                self._update_cached_scheduler_output(virtual_engine, outputs)
         else:
             if len(ctx.output_queue) > 0:
                 self._process_model_outputs(ctx=ctx)
             outputs = []
 
-        # Finish the current step for all the sequence groups.
-        if self.scheduler_config.is_multi_step:
-            for seq_group in seq_group_metadata_list:
-                seq_group.finish_step()
-
         if not self._has_remaining_steps(seq_group_metadata_list):
-            # Clear the cache if we have finished all the steps
-            if self.scheduler_config.is_multi_step:
-                self.cached_scheduler_outputs[
-                    virtual_engine] = SchedulerOutputState()
-
             # is_first_step_output is True only when the num_steps of all
-            # the sequences are 1. When the num_steps > 1,
-            # multi_step_model_runner does the first-step output append.
+            # the sequences are 1.
             is_first_step_output: bool = False if not seq_group_metadata_list \
                 else seq_group_metadata_list[0].state.num_steps == 1
 

vllm/engine/llm_engine.py

@@ -25,7 +25,6 @@ from vllm.engine.metrics_types import StatLoggerBase, Stats
 from vllm.engine.output_processor.interfaces import (
     SequenceGroupOutputProcessor)
 from vllm.engine.output_processor.stop_checker import StopChecker
-from vllm.engine.output_processor.util import create_output_by_sequence_group
 from vllm.entrypoints.openai.logits_processors import (
     get_logits_processors as get_openai_logits_processors)
 from vllm.executor.executor_base import ExecutorBase
@@ -91,7 +90,7 @@ class OutputData(NamedTuple):
 
 class SchedulerContext:
 
-    def __init__(self, multi_step_stream_outputs: bool = False):
+    def __init__(self) -> None:
         self.output_queue: Deque[OutputData] = deque()
         self.request_outputs: List[Union[RequestOutput,
                                          PoolingRequestOutput]] = []
@@ -99,8 +98,6 @@ class SchedulerContext:
             List[SequenceGroupMetadata]] = None
         self.scheduler_outputs: Optional[SchedulerOutputs] = None
 
-        self.multi_step_stream_outputs: bool = multi_step_stream_outputs
-
     def append_output(self, outputs: List[SamplerOutput],
                       seq_group_metadata_list: List[SequenceGroupMetadata],
                       scheduler_outputs: SchedulerOutputs, is_async: bool,
@@ -303,8 +300,7 @@ class LLMEngine:
         ]
 
         self.scheduler_contexts = [
-            SchedulerContext(multi_step_stream_outputs=self.scheduler_config.
-                             multi_step_stream_outputs)
+            SchedulerContext()
             for _ in range(self.parallel_config.pipeline_parallel_size)
         ]
 
@@ -683,8 +679,7 @@ class LLMEngine:
                              "Priority scheduling is not enabled.")
 
         if isinstance(params, SamplingParams) \
-            and params.logits_processors \
-            and self.scheduler_config.num_scheduler_steps > 1:
+            and params.logits_processors:
             raise ValueError(
                 "Logits processors are not supported in multi-step decoding")
 
@@ -868,45 +863,6 @@ class LLMEngine:
 
         return
 
-    def _update_num_computed_tokens_for_multi_step_prefill(
-            self, seq_group: SequenceGroup,
-            seq_group_meta: SequenceGroupMetadata,
-            is_first_step_output: Optional[bool]):
-        """
-        This function updates num_computed_tokens for prompt sequences
-        when Multi-Step is enabled.
-
-        seq_group: SequenceGroup to update the num_computed_tokens for.
-        seq_group_meta: Metadata of the given SequenceGroup.
-        is_first_step_output: Optional[bool] -
-            When available, is_first_step_output indicates if the appended
-            output token is the output of the first-step in multi-step.
-            A value of None indicates that outputs from all steps in
-            in multi-step are submitted in a single burst.
-        """
-
-        assert self.scheduler_config.is_multi_step
-
-        if not seq_group_meta.is_prompt:
-            # num_computed_token updates for multi-step decodes happen after
-            # the tokens are appended to the sequence.
-            return
-
-        do_update: bool = False
-        if self.scheduler_config.chunked_prefill_enabled:
-            # In multi-step + chunked-prefill case, the prompt sequences
-            # that are scheduled are fully processed in the first step.
-            do_update = is_first_step_output is None or is_first_step_output
-        else:
-            # Normal multi-step decoding case. In this case prompt-sequences
-            # are actually single-stepped. Always update in this case.
-            assert seq_group.state.num_steps == 1
-            do_update = True
-
-        if do_update:
-            seq_group.update_num_computed_tokens(
-                seq_group_meta.token_chunk_size)
-
     def _process_model_outputs(self,
                                ctx: SchedulerContext,
                                request_id: Optional[str] = None) -> None:
@@ -939,33 +895,8 @@ class LLMEngine:
 
         has_multiple_outputs: bool = len(outputs) > 1
         outputs_by_sequence_group: List[List[SequenceGroupOutput]]
-        if has_multiple_outputs:
-            assert self.scheduler_config.is_multi_step or \
-                     self.speculative_config
-            # Organize outputs by [step][sequence group] instead of
-            # [sequence group][step].
-            if self.scheduler_config.is_multi_step:
-                outputs_by_sequence_group = create_output_by_sequence_group(
-                    outputs, len(seq_group_metadata_list))
-            elif self.speculative_config:
-                # Decodes are multi-steps while prefills are not, outputting at
-                # most 1 token. Separate them so that we can trigger chunk
-                # processing without having to pad or copy over prompts K times
-                # to match decodes structure (costly with prompt_logprobs).
-                num_prefills = sum(sg.is_prompt
-                                   for sg in seq_group_metadata_list)
-                prefills, decodes = outputs[:num_prefills], outputs[
-                    num_prefills:]
-                outputs_by_sequence_group = create_output_by_sequence_group(
-                    decodes,
-                    num_seq_groups=len(seq_group_metadata_list) - num_prefills)
-                outputs_by_sequence_group = [p.outputs for p in prefills
-                                             ] + outputs_by_sequence_group
-            # We have outputs for multiple steps submitted in a single burst,
-            # so invalidate is_first_step_output.
-            is_first_step_output = None
-        else:
-            outputs_by_sequence_group = outputs
+        assert not has_multiple_outputs
+        outputs_by_sequence_group = outputs
 
         # Determine the requests we need to operate on
         if request_id:
@@ -1006,13 +937,8 @@ class LLMEngine:
                 output = [outputs_by_sequence_group[0][i]]
 
             if not is_async:
-                if self.scheduler_config.is_multi_step:
-                    # Updates happen only if the sequence is prefill
-                    self._update_num_computed_tokens_for_multi_step_prefill(
-                        seq_group, seq_group_meta, is_first_step_output)
-                else:
-                    seq_group.update_num_computed_tokens(
-                        seq_group_meta.token_chunk_size or 0)
+                seq_group.update_num_computed_tokens(
+                    seq_group_meta.token_chunk_size or 0)
 
             if outputs:
                 for o in outputs:
@@ -1074,15 +1000,6 @@ class LLMEngine:
             for scheduler in self.scheduler:
                 scheduler.free_finished_seq_groups()
 
-        # For multi-step without streaming, don't create outputs each iteration
-        if not is_last_step and not ctx.multi_step_stream_outputs:
-            # Immediately process request outputs here (if callback is given)
-            if (finished_now
-                    and self.process_request_outputs_callback is not None):
-                self.process_request_outputs_callback(ctx.request_outputs)
-                ctx.request_outputs.clear()
-            return
-
         # Create the outputs
         for i in indices:
             if i in skip or i in finished_before or i in finished_now:
@@ -1101,13 +1018,7 @@ class LLMEngine:
             if request_output:
                 ctx.request_outputs.append(request_output)
 
-        # For multi-step with streaming, create outputs each iteration
-        if not is_last_step and ctx.multi_step_stream_outputs:
-            # Immediately process request outputs here (if callback is given)
-            if self.process_request_outputs_callback is not None:
-                self.process_request_outputs_callback(ctx.request_outputs)
-                ctx.request_outputs.clear()
-            return
+        # Create outputs only after processing the scheduler's results
 
         for seq_group in scheduler_outputs.ignored_seq_groups:
             params = seq_group.sampling_params
@@ -1157,16 +1068,10 @@ class LLMEngine:
             if seq_group.is_finished():
                 continue
 
-            if self.scheduler_config.is_multi_step:
-                # Updates happen only if the sequence is prefill
-                self._update_num_computed_tokens_for_multi_step_prefill(
-                    seq_group, seq_group_metadata,
-                    seq_group.state.num_steps == 1)
-            else:
-                token_chunk_size = (seq_group_metadata.token_chunk_size
-                                    if seq_group_metadata.token_chunk_size
-                                    is not None else 0)
-                seq_group.update_num_computed_tokens(token_chunk_size)
+            token_chunk_size = (seq_group_metadata.token_chunk_size
+                                if seq_group_metadata.token_chunk_size
+                                is not None else 0)
+            seq_group.update_num_computed_tokens(token_chunk_size)
 
             if seq_group_metadata.do_sample:
                 assert len(sequence_group_outputs.samples) == 1, (
@@ -1177,16 +1082,8 @@ class LLMEngine:
                 assert len(seq_group.seqs) == 1
                 seq = seq_group.seqs[0]
 
-                if self.scheduler_config.is_multi_step:
-                    is_prefill_append = seq.data.get_num_uncomputed_tokens(
-                    ) == 0
-                    seq.append_token_id(sample.output_token, sample.logprobs,
-                                        sample.output_embed)
-                    if not is_prefill_append:
-                        seq_group.update_num_computed_tokens(1)
-                else:
-                    seq.append_token_id(sample.output_token, sample.logprobs,
-                                        sample.output_embed)
+                seq.append_token_id(sample.output_token, sample.logprobs,
+                                    sample.output_embed)
 
     def step(self) -> List[Union[RequestOutput, PoolingRequestOutput]]:
         """Performs one decoding iteration and returns newly generated results.
@@ -1289,13 +1186,6 @@ class LLMEngine:
             if not allow_async_output_proc and len(ctx.output_queue) > 0:
                 self._process_model_outputs(ctx=ctx)
 
-            if (self.scheduler_config.is_multi_step
-                    and scheduler_outputs.num_lookahead_slots > 0):
-                # cache the scheduler outputs for the next iteration if we have
-                # lookahead slots
-                self._cache_scheduler_outputs_for_multi_step(
-                    virtual_engine, seq_group_metadata_list, scheduler_outputs,
-                    allow_async_output_proc)
         else:
             finished_requests_ids = list()
 
@@ -1345,10 +1235,6 @@ class LLMEngine:
                 # Raise so the caller is notified that this request failed
                 raise
 
-            # We need to do this here so that last step's sampled_token_ids can
-            # be passed to the next iteration for PP.
-            if self.scheduler_config.is_multi_step:
-                self._update_cached_scheduler_output(virtual_engine, outputs)
         else:
             # Nothing scheduled => If there is pending async postprocessor,
             # then finish it here.
@@ -1357,19 +1243,9 @@ class LLMEngine:
             # No outputs in this case
             outputs = []
 
-        # Finish the current step for all the sequence groups.
-        if self.scheduler_config.is_multi_step:
-            for seq_group in seq_group_metadata_list:
-                seq_group.finish_step()
-
         if not self._has_remaining_steps(seq_group_metadata_list):
-            # clear the cache if we have finished all the steps.
-            if self.scheduler_config.is_multi_step:
-                self.cached_scheduler_outputs[0] = SchedulerOutputState()
-
             # is_first_step_output is True only when the num_steps of all
-            # the sequences are 1. When the num_steps > 1,
-            # multi_step_model_runner does the first-step output append.
+            # the sequences are 1.
             is_first_step_output: bool = False if not seq_group_metadata_list \
                 else seq_group_metadata_list[0].state.num_steps == 1
 
@@ -1453,22 +1329,7 @@ class LLMEngine:
     def _has_remaining_steps(
         self, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]]
     ) -> bool:
-        if (not self.scheduler_config.is_multi_step
-                or not seq_group_metadata_list):
-            return False
-
-        # TODO(will) this is a sanity check for nowto make sure that all the
-        # seqs are on the same steps. Eventually we will want to do some sort of
-        # dynamic scheduling when doing multi-step decoding.
-        ref_remaining_steps = seq_group_metadata_list[0].state.remaining_steps
-        if any([
-                seq_group.state.remaining_steps != ref_remaining_steps
-                for seq_group in seq_group_metadata_list[1:]
-        ]):
-            raise AssertionError("All running sequence groups should "
-                                 "have the same remaining steps.")
-
-        return ref_remaining_steps > 0
+        return False
 
     def _cache_scheduler_outputs_for_multi_step(
             self, virtual_engine: int,
@@ -1497,13 +1358,6 @@ class LLMEngine:
 
     def _get_last_sampled_token_ids(
             self, virtual_engine: int) -> Optional[torch.Tensor]:
-        cached_last_output = self.cached_scheduler_outputs[
-            virtual_engine].last_output
-        if (self.scheduler_config.is_multi_step
-                and self.parallel_config.pipeline_parallel_size > 1
-                and cached_last_output is not None
-                and cached_last_output.sampled_token_ids_cpu is not None):
-            return cached_last_output.sampled_token_ids_cpu
         return None
 
     def add_logger(self, logger_name: str, logger: StatLoggerBase) -> None:

vllm/engine/output_processor/interfaces.py

@@ -36,27 +36,13 @@ class SequenceGroupOutputProcessor(ABC):
     ):
         """Create an output processor.
 
-        This returns a single-step output processor if num_lookahead_slots is
-        zero, else returns a multi-step output processor.
+        Multi-step scheduling is no longer supported. Always return a
+        single-step output processor.
         """
-        if scheduler_config.num_lookahead_slots == 0:
-            # Importing here to avoid cycle.
-            from vllm.engine.output_processor.single_step import (
-                SingleStepOutputProcessor)
-            return SingleStepOutputProcessor(scheduler_config, detokenizer,
-                                             scheduler, seq_counter,
-                                             stop_checker)
-        else:
-            # Importing here to avoid cycle.
-            from vllm.engine.output_processor.multi_step import (
-                MultiStepOutputProcessor)
-            return MultiStepOutputProcessor(
-                detokenizer,
-                scheduler,
-                seq_counter,
-                get_tokenizer_for_seq,
-                stop_checker,
-            )
+        from vllm.engine.output_processor.single_step import (
+            SingleStepOutputProcessor)
+        return SingleStepOutputProcessor(scheduler_config, detokenizer,
+                                         scheduler, seq_counter, stop_checker)
 
     @abstractmethod
     def process_outputs(self, sequence_group: SequenceGroup,

vllm/engine/output_processor/multi_step.py

@@ -1,211 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import functools
-from typing import Callable, List, cast
-
-from vllm.core.scheduler import Scheduler
-from vllm.engine.output_processor.interfaces import (
-    SequenceGroupOutputProcessor)
-from vllm.engine.output_processor.single_step import (
-    single_step_process_prompt_logprob)
-from vllm.engine.output_processor.stop_checker import StopChecker
-from vllm.logger import init_logger
-from vllm.sampling_params import SamplingParams
-from vllm.sequence import (VLLM_INVALID_TOKEN_ID,
-                           CompletionSequenceGroupOutput, Sequence,
-                           SequenceGroup, SequenceGroupOutput, SequenceOutput,
-                           SequenceStatus)
-from vllm.transformers_utils.detokenizer import Detokenizer
-from vllm.transformers_utils.tokenizer import AnyTokenizer
-from vllm.utils import Counter
-
-logger = init_logger(__name__)
-
-
-class MultiStepOutputProcessor(SequenceGroupOutputProcessor):
-    """SequenceGroupOutputProcessor which handles logic related to
-    detokenization and stopping conditions. It specializes to "multi-step
-    decoding", where vLLM's worker may generate multiple tokens per invocation.
-    This is currently mutually exclusive with advanced sampling techniques like
-    beam search, which motivates the separation of this logic from the single
-    step output processor.
-
-    This class is responsible for things such as correctly appending all new
-    token ids to their sequence, detokenizing new token ids, truncating new
-    output tokens after an eos token, and correctly handling the case where the
-    number of new output tokens per sequence differs in a single batch.
-    """
-
-    def __init__(
-        self,
-        detokenizer: Detokenizer,
-        scheduler: List[Scheduler],
-        seq_counter: Counter,
-        get_tokenizer_for_seq: Callable[[Sequence], AnyTokenizer],
-        stop_checker: StopChecker,
-    ):
-        self.detokenizer = detokenizer
-        self.scheduler = scheduler
-        self.seq_counter = seq_counter
-        self.get_tokenizer_for_seq = get_tokenizer_for_seq
-        self.stop_checker = stop_checker
-
-    def process_prompt_logprob(self, seq_group: SequenceGroup,
-                               outputs: List[SequenceGroupOutput]) -> None:
-        """Process prompt logprobs associated with each step of a multi-step-
-        scheduled computation.
-
-        Args:
-          seq_group: the outputs are associated with this
-              [`SequenceGroup`][vllm.sequence.SequenceGroup]
-          outputs: the
-              [`SequenceGroupOutput`][vllm.sequence.SequenceGroupOutput]s
-              for all scheduler steps
-        """
-        for output in outputs:
-            # Concatenate single-step prompt logprob processing results.
-            assert isinstance(output, CompletionSequenceGroupOutput)
-            single_step_process_prompt_logprob(self, seq_group, output)
-
-    @staticmethod
-    @functools.lru_cache
-    def _log_prompt_logprob_unsupported_warning_once():
-        # Reminder: Please update docs/features/compatibility_matrix.md
-        # If the feature combo become valid
-        logger.warning(
-            "Prompt logprob is not supported by multi step workers. "
-            "(e.g., speculative decode uses multi step workers).")
-
-    def process_outputs(self,
-                        sequence_group: SequenceGroup,
-                        outputs: List[SequenceGroupOutput],
-                        is_async: bool = False) -> None:
-        """Append new tokens in the outputs to sequences in the sequence group.
-
-        This only supports sequence groups of size 1. It supports greater than
-        one new token per sequence.
-
-        This applies logic like stop condition checking and detokenization.
-        It also handles cases where there are tokens emitted after 
-        the EOS token.
-
-        is_async - Indicates whether this postprocessor runs in 
-            parallel with the GPU forward pass and is processing 
-            tokens from the previous step. If this is true, then
-            no tokens need to be appended since it is already done
-            externally (before the next schedule() call)
-        """
-        # Sequences can be in RUNNING or FINISHED_ABORTED state
-        # once scheduled, as a sequence is moved to FINISHED_ABORTED
-        # if a client disconnects from the api server.
-        seqs = sequence_group.get_seqs(status=SequenceStatus.RUNNING)
-        if seqs is None:
-            seqs = sequence_group.get_seqs(
-                status=SequenceStatus.FINISHED_ABORTED)
-
-        assert seqs, "Expected RUNNING or FINISHED_ABORTED sequences"
-        assert len(seqs) == 1, (
-            "Beam search not supported in multi-step decoding.")
-        seq = seqs[0]
-        seq_id = seq.seq_id
-        # This method is defined in the more generic
-        # SequenceGroupOutputProcessor, but here we assume that the outputs are
-        # of a more specific type.
-        assert all([
-            isinstance(output, CompletionSequenceGroupOutput)
-            for output in outputs
-        ])
-        compl_outputs = cast(List[CompletionSequenceGroupOutput], outputs)
-        assert all([
-            seq_id == output.samples[0].parent_seq_id
-            for output in compl_outputs
-        ])
-
-        if is_async:
-            # Async case: We process tokens one by one. Here, we know the token
-            # was already appended, so we only need to do the rest of the
-            # postprocessor: Detokenization + stopping logic
-            self._process_decode_and_stop(seq, sequence_group.sampling_params)
-        else:
-            # Standard multi-step case
-
-            # Since there's only one sequence per sequence group,
-            # we can take the first sample.
-            samples = [output.samples[0] for output in compl_outputs]
-
-            # entries in sample tokens may be invalid (eg. due to spec decode
-            # rejecting tokens).
-            valid_samples = [
-                sample for sample in samples
-                if sample.output_token != VLLM_INVALID_TOKEN_ID
-            ]
-
-            # When both spec-decode and pre-fill chunking are enabled, we
-            # don't have guaranteed samples here (e.g. all -1s).
-            if valid_samples:
-                self._process_seq_outputs(seq, valid_samples,
-                                          sequence_group.sampling_params)
-
-    def _process_decode_and_stop(self, seq: Sequence,
-                                 sampling_params: SamplingParams) -> None:
-        new_char_count = 0
-        if sampling_params.detokenize and self.detokenizer:
-            new_char_count = self.detokenizer.decode_sequence_inplace(
-                seq, sampling_params)
-
-        # TODO(sang): Support lora.
-        self.stop_checker.maybe_stop_sequence(
-            seq,
-            new_char_count=new_char_count,
-            sampling_params=sampling_params,
-        )
-
-    def _process_seq_outputs(self, seq: Sequence,
-                             valid_samples: List[SequenceOutput],
-                             sampling_params: SamplingParams) -> None:
-        output_token_ids = [sample.output_token for sample in valid_samples]
-        output_logprobs = [sample.logprobs for sample in valid_samples]
-        output_embeds = [sample.output_embed for sample in valid_samples]
-
-        # Truncate to max_tokens if necessary.
-        remaining_tokens = sampling_params.max_tokens - (seq.get_output_len() +
-                                                         len(output_token_ids))
-        if remaining_tokens < 0:
-            output_token_ids = output_token_ids[:remaining_tokens]
-
-        # Truncate any tokens after EOS. This is required as spec decode
-        # generates a fixed number of tokens without evaluating stopping
-        # conditions within the block. This can cause an eos token to be
-        # unintentionally ignored.
-        if not sampling_params.ignore_eos and self.detokenizer:
-            eos_token_id = self.get_tokenizer_for_seq(seq).eos_token_id
-            # Avoiding .index calls as exception throwing in the happy path
-            # is expensive.
-            for i in range(len(output_token_ids)):
-                if output_token_ids[i] == eos_token_id:
-                    output_token_ids = output_token_ids[:i + 1]
-                    break
-
-        is_prefill_sampled_token = seq.data.get_num_uncomputed_tokens() == 0
-        # Incrementally append tokens to the sequence, as if we had only one new
-        # token.
-        for output_token_id, output_logprob, output_embed in zip(
-                output_token_ids, output_logprobs, output_embeds):
-            seq.append_token_id(
-                token_id=output_token_id,
-                logprobs=output_logprob,
-                token_embed=output_embed,
-            )
-
-            if is_prefill_sampled_token:
-                is_prefill_sampled_token = False
-            else:
-                # Update num_computed_tokens iff the sampled token is not from
-                # a prefill step.
-                seq.data.update_num_computed_tokens(1)
-
-            self._process_decode_and_stop(seq, sampling_params)
-
-            if seq.is_finished():
-                break

vllm/entrypoints/openai/serving_embedding.py

@@ -2,9 +2,11 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import base64
-from typing import Final, Literal, Optional, Union, cast
+from collections.abc import AsyncGenerator, Mapping
+from typing import Any, Final, Literal, Optional, Union, cast
 
 import numpy as np
+import torch
 from fastapi import Request
 from typing_extensions import assert_never, override
 
@@ -12,19 +14,28 @@ from vllm.config import ModelConfig
 from vllm.engine.protocol import EngineClient
 from vllm.entrypoints.chat_utils import ChatTemplateContentFormatOption
 from vllm.entrypoints.logger import RequestLogger
+# yapf conflicts with isort for this docstring
+# yapf: disable
 from vllm.entrypoints.openai.protocol import (EmbeddingChatRequest,
+                                              EmbeddingCompletionRequest,
                                               EmbeddingRequest,
                                               EmbeddingResponse,
                                               EmbeddingResponseData,
                                               ErrorResponse, UsageInfo)
 from vllm.entrypoints.openai.serving_engine import (EmbeddingServeContext,
                                                     OpenAIServing,
-                                                    ServeContext)
+                                                    RequestPrompt,
+                                                    ServeContext,
+                                                    TextTokensPrompt)
+# yapf: enable
 from vllm.entrypoints.openai.serving_models import OpenAIServingModels
+from vllm.inputs.data import EmbedsPrompt as EngineEmbedsPrompt
+from vllm.inputs.data import TokensPrompt as EngineTokensPrompt
 from vllm.logger import init_logger
 from vllm.outputs import (EmbeddingOutput, EmbeddingRequestOutput,
-                          PoolingRequestOutput)
+                          PoolingOutput, PoolingRequestOutput, RequestOutput)
 from vllm.pooling_params import PoolingParams
+from vllm.utils import chunk_list
 
 logger = init_logger(__name__)
 
@@ -46,6 +57,17 @@ def _get_embedding(
 
 class EmbeddingMixin(OpenAIServing):
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        pooler_config = self.model_config.pooler_config
+
+        # Avoid repeated attribute lookups
+        self.supports_chunked_processing = bool(
+            pooler_config and pooler_config.enable_chunked_processing)
+        self.max_embed_len = (pooler_config.max_embed_len if pooler_config
+                              and pooler_config.max_embed_len else None)
+
     @override
     async def _preprocess(
         self,
@@ -129,6 +151,435 @@ class EmbeddingMixin(OpenAIServing):
             usage=usage,
         )
 
+    def _get_max_position_embeddings(self) -> int:
+        """Get the model's effective maximum sequence length for chunking."""
+        return self.model_config.max_model_len
+
+    def _should_use_chunked_processing(self, request) -> bool:
+        """Check if chunked processing should be used for this request."""
+        return isinstance(
+            request,
+            (EmbeddingCompletionRequest,
+             EmbeddingChatRequest)) and self.supports_chunked_processing
+
+    async def _process_chunked_request(
+        self,
+        ctx: EmbeddingServeContext,
+        original_prompt: TextTokensPrompt,
+        pooling_params,
+        trace_headers,
+        prompt_idx: int,
+    ) -> list[AsyncGenerator[PoolingRequestOutput, None]]:
+        """Process a single prompt using chunked processing."""
+        generators: list[AsyncGenerator[PoolingRequestOutput, None]] = []
+        token_ids = original_prompt["prompt_token_ids"]
+
+        # Split into chunks using max_position_embeddings
+        max_pos_embeddings = self._get_max_position_embeddings()
+        # Process all chunks for MEAN aggregation
+        for chunk_idx, chunk_tokens in enumerate(
+                chunk_list(token_ids, max_pos_embeddings)):
+            # Create a request ID for this chunk
+            chunk_request_id = (f"{ctx.request_id}-prompt-{prompt_idx}-"
+                                f"chunk-{chunk_idx}")
+
+            # Create engine prompt for this chunk
+            chunk_engine_prompt = EngineTokensPrompt(
+                prompt_token_ids=chunk_tokens)
+
+            # Create chunk request prompt for logging
+            chunk_text = ""
+            chunk_request_prompt = TextTokensPrompt(
+                prompt=chunk_text, prompt_token_ids=chunk_tokens)
+
+            # Log the chunk
+            self._log_inputs(chunk_request_id,
+                             chunk_request_prompt,
+                             params=pooling_params,
+                             lora_request=ctx.lora_request)
+
+            # Create generator for this chunk and wrap it to return indices
+            original_generator = self.engine_client.encode(
+                chunk_engine_prompt,
+                pooling_params,
+                chunk_request_id,
+                lora_request=ctx.lora_request,
+                trace_headers=trace_headers,
+                priority=getattr(ctx.request, "priority", 0),
+            )
+
+            generators.append(original_generator)
+
+        return generators
+
+    def _validate_input(
+        self,
+        request,
+        input_ids: list[int],
+        input_text: str,
+    ) -> TextTokensPrompt:
+        """Override to support chunked processing for embedding requests."""
+        token_num = len(input_ids)
+
+        # Note: EmbeddingRequest doesn't have max_tokens
+        if isinstance(request,
+                      (EmbeddingCompletionRequest, EmbeddingChatRequest)):
+            # Check if chunked processing is enabled for pooling models
+            enable_chunked = self._should_use_chunked_processing(request)
+
+            # Use max_position_embeddings for chunked processing decisions
+            max_pos_embeddings = self._get_max_position_embeddings()
+
+            # Determine the effective max length for validation
+            if self.max_embed_len is not None:
+                # Use max_embed_len for validation instead of max_model_len
+                length_type = "maximum embedding input length"
+                max_length_value = self.max_embed_len
+            else:
+                # Fall back to max_model_len validation (original behavior)
+                length_type = "maximum context length"
+                max_length_value = self.max_model_len
+
+            validation_error_msg = (
+                "This model's {length_type} is {max_length_value} tokens. "
+                "However, you requested {token_num} tokens in the input for "
+                "embedding generation. Please reduce the length of the input.")
+
+            chunked_processing_error_msg = (
+                "This model's {length_type} is {max_length_value} tokens. "
+                "However, you requested {token_num} tokens in the input for "
+                "embedding generation. Please reduce the length of the input "
+                "or enable chunked processing.")
+
+            # Check if input exceeds max length
+            if token_num > max_length_value:
+                raise ValueError(
+                    validation_error_msg.format(
+                        length_type=length_type,
+                        max_length_value=max_length_value,
+                        token_num=token_num))
+
+            # Check for chunked processing
+            # when exceeding max_position_embeddings
+            if token_num > max_pos_embeddings:
+                if enable_chunked:
+                    # Allow long inputs when chunked processing is enabled
+                    logger.info(
+                        "Input length %s exceeds max_position_embeddings "
+                        "%s, will use chunked processing", token_num,
+                        max_pos_embeddings)
+                else:
+                    raise ValueError(
+                        chunked_processing_error_msg.format(
+                            length_type="maximum position embeddings length",
+                            max_length_value=max_pos_embeddings,
+                            token_num=token_num))
+
+            return TextTokensPrompt(prompt=input_text,
+                                    prompt_token_ids=input_ids)
+
+        # For other request types, use the parent's implementation
+        return super()._validate_input(request, input_ids, input_text)
+
+    def _is_text_tokens_prompt(self, prompt) -> bool:
+        """Check if a prompt is a TextTokensPrompt (has prompt_token_ids)."""
+        return (isinstance(prompt, dict) and "prompt_token_ids" in prompt
+                and "prompt_embeds" not in prompt)
+
+    async def _create_single_prompt_generator(
+        self,
+        ctx: EmbeddingServeContext,
+        engine_prompt: Union[EngineTokensPrompt, EngineEmbedsPrompt],
+        request_prompt: RequestPrompt,
+        pooling_params: PoolingParams,
+        trace_headers: Optional[Mapping[str, str]],
+        prompt_index: int,
+    ) -> AsyncGenerator[Union[RequestOutput, PoolingRequestOutput], None]:
+        """Create a generator for a single prompt using standard processing."""
+        request_id_item = f"{ctx.request_id}-{prompt_index}"
+
+        self._log_inputs(request_id_item,
+                         request_prompt,
+                         params=pooling_params,
+                         lora_request=ctx.lora_request)
+
+        # Mypy has an existing bug related to inferring the variance
+        # of TypedDicts with `builtins.enumerate`:
+        # https://github.com/python/mypy/issues/8586#issuecomment-2867698435
+        engine_prompt = cast(Union[EngineTokensPrompt, EngineEmbedsPrompt],
+                             engine_prompt)
+
+        # Return the original generator without wrapping
+        return self.engine_client.encode(
+            engine_prompt,
+            pooling_params,
+            request_id_item,
+            lora_request=ctx.lora_request,
+            trace_headers=trace_headers,
+            priority=getattr(ctx.request, "priority", 0),
+        )
+
+    @override
+    async def _prepare_generators(
+        self,
+        ctx: ServeContext,
+    ) -> Optional[ErrorResponse]:
+        """Override to support chunked processing."""
+        ctx = cast(EmbeddingServeContext, ctx)
+
+        # Check if we should use chunked processing
+        use_chunked = self._should_use_chunked_processing(ctx.request)
+
+        # If no chunked processing needed, delegate to parent class
+        if not use_chunked:
+            return await super()._prepare_generators(ctx)
+
+        # Custom logic for chunked processing
+        generators: list[AsyncGenerator[Union[RequestOutput,
+                                              PoolingRequestOutput],
+                                        None]] = []
+
+        try:
+            trace_headers = (None if ctx.raw_request is None else await
+                             self._get_trace_headers(ctx.raw_request.headers))
+
+            pooling_params = self._create_pooling_params(ctx)
+            if isinstance(pooling_params, ErrorResponse):
+                return pooling_params
+
+            # Verify and set the task for pooling params
+            try:
+                pooling_params.verify("embed", self.model_config)
+            except ValueError as e:
+                return self.create_error_response(str(e))
+
+            if ctx.engine_prompts is None:
+                return self.create_error_response(
+                    "Engine prompts not available")
+
+            if ctx.request_prompts is None:
+                return self.create_error_response(
+                    "Request prompts not available")
+
+            max_pos_embeddings = self._get_max_position_embeddings()
+
+            for i, engine_prompt in enumerate(ctx.engine_prompts):
+                request_prompt = ctx.request_prompts[i]
+
+                # Check if this specific prompt needs chunked processing
+                if self._is_text_tokens_prompt(request_prompt):
+                    # Cast to TextTokensPrompt since we've verified
+                    # prompt_token_ids
+                    text_tokens_prompt = cast(TextTokensPrompt, request_prompt)
+                    if (len(text_tokens_prompt["prompt_token_ids"])
+                            > max_pos_embeddings):
+                        # Use chunked processing for this prompt
+                        chunk_generators = await self._process_chunked_request(
+                            ctx, text_tokens_prompt, pooling_params,
+                            trace_headers, i)
+                        generators.extend(chunk_generators)
+                        continue
+
+                # Normal processing for short prompts or non-token prompts
+                # Cast engine_prompt to the expected type for mypy
+                engine_prompt_typed = cast(
+                    Union[EngineTokensPrompt, EngineEmbedsPrompt],
+                    engine_prompt)
+                generator = await self._create_single_prompt_generator(
+                    ctx, engine_prompt_typed, request_prompt, pooling_params,
+                    trace_headers, i)
+                generators.append(generator)
+
+            from vllm.utils import merge_async_iterators
+            ctx.result_generator = merge_async_iterators(*generators)
+
+            return None
+
+        except Exception as e:
+            # TODO: Use a vllm-specific Validation Error
+            return self.create_error_response(str(e))
+
+    @override
+    async def _collect_batch(
+        self,
+        ctx: ServeContext,
+    ) -> Optional[ErrorResponse]:
+        """Collect and aggregate batch results
+        with support for chunked processing.
+        
+        For chunked requests, performs online aggregation to 
+        minimize memory usage.
+        For regular requests, collects results normally.
+        """
+        ctx = cast(EmbeddingServeContext, ctx)
+        try:
+            if ctx.engine_prompts is None:
+                return self.create_error_response(
+                    "Engine prompts not available")
+
+            # Check if we used chunked processing
+            use_chunked = self._should_use_chunked_processing(ctx.request)
+
+            if not use_chunked:
+                return await super()._collect_batch(ctx=ctx)
+
+            if ctx.request_prompts is None:
+                return self.create_error_response(
+                    "Request prompts not available")
+
+            if ctx.result_generator is None:
+                return self.create_error_response(
+                    "Result generator not available")
+
+            # Online aggregation for chunked requests to
+            # minimize memory usage
+            # Track aggregation state for each prompt
+            prompt_aggregators: dict[int, dict[str, Any]] = {}
+            short_prompts_results: dict[int, PoolingRequestOutput] = {}
+
+            async for result_idx, result in ctx.result_generator:
+                if "-chunk-" in result.request_id:
+                    # Extract prompt_idx from chunked request_id
+                    parts = result.request_id.split("-")
+                    try:
+                        prompt_idx = int(parts[parts.index("prompt") + 1])
+                    except (ValueError, IndexError):
+                        # Fallback: extract from result_idx if parsing fails
+                        prompt_idx = result_idx
+
+                    # Initialize aggregator for this prompt if needed
+                    if prompt_idx not in prompt_aggregators:
+                        prompt_aggregators[prompt_idx] = {
+                            'weighted_sum': None,
+                            'total_weight': 0,
+                            'chunk_count': 0,
+                            'request_id': result.request_id.split("-chunk-")[0]
+                        }
+
+                    aggregator = prompt_aggregators[prompt_idx]
+
+                    # MEAN pooling with online weighted averaging
+                    # Ensure result is PoolingRequestOutput
+                    # for embedding processing
+                    if not isinstance(result, PoolingRequestOutput):
+                        return self.create_error_response(
+                            f"Expected PoolingRequestOutput for "
+                            f"chunked embedding, got "
+                            f"{type(result).__name__}")
+
+                    # Handle both PoolingOutput and
+                    # EmbeddingOutput types
+                    if hasattr(result.outputs, 'data'):
+                        # PoolingOutput case
+                        embedding_data = result.outputs.data
+                    elif hasattr(result.outputs, 'embedding'):
+                        # EmbeddingOutput case -
+                        # convert embedding list to tensor
+                        embedding_data = result.outputs.embedding
+                    else:
+                        return self.create_error_response(
+                            f"Unsupported output type: "
+                            f"{type(result.outputs).__name__}")
+
+                    if not isinstance(embedding_data, torch.Tensor):
+                        embedding_data = torch.tensor(embedding_data,
+                                                      dtype=torch.float32)
+
+                    if result.prompt_token_ids is None:
+                        return self.create_error_response(
+                            "prompt_token_ids cannot be None for "
+                            "chunked processing")
+                    weight = len(result.prompt_token_ids)
+
+                    weighted_embedding = embedding_data.to(
+                        dtype=torch.float32) * weight
+
+                    if aggregator['weighted_sum'] is None:
+                        # First chunk
+                        aggregator['weighted_sum'] = weighted_embedding
+                    else:
+                        # Accumulate
+                        aggregator['weighted_sum'] += weighted_embedding
+
+                    aggregator['total_weight'] += weight
+                    aggregator['chunk_count'] += 1
+                else:
+                    # Non-chunked result - extract prompt_idx from request_id
+                    parts = result.request_id.split("-")
+                    try:
+                        # Last part should be prompt index
+                        prompt_idx = int(parts[-1])
+                    except (ValueError, IndexError):
+                        prompt_idx = result_idx  # Fallback to result_idx
+
+                    short_prompts_results[prompt_idx] = cast(
+                        PoolingRequestOutput, result)
+
+            # Finalize aggregated results
+            final_res_batch: list[Union[PoolingRequestOutput,
+                                        EmbeddingRequestOutput]] = []
+            num_prompts = len(ctx.engine_prompts)
+
+            for prompt_idx in range(num_prompts):
+                if prompt_idx in prompt_aggregators:
+                    # Finalize MEAN aggregation for this chunked prompt
+                    aggregator = prompt_aggregators[prompt_idx]
+
+                    weighted_sum = aggregator['weighted_sum']
+                    total_weight = aggregator['total_weight']
+
+                    if (weighted_sum is not None
+                            and isinstance(weighted_sum, torch.Tensor)
+                            and isinstance(total_weight,
+                                           (int, float)) and total_weight > 0):
+
+                        # Compute final mean embedding
+                        final_embedding = weighted_sum / total_weight
+
+                        # Create a PoolingRequestOutput
+                        # for the aggregated result
+                        pooling_output_data = PoolingOutput(
+                            data=final_embedding)
+
+                        # Get original prompt token IDs for this prompt
+                        original_prompt = ctx.request_prompts[prompt_idx]
+                        if not self._is_text_tokens_prompt(original_prompt):
+                            return self.create_error_response(
+                                f"Chunked prompt {prompt_idx} is not a "
+                                f"TextTokensPrompt")
+
+                        original_token_ids = cast(
+                            TextTokensPrompt,
+                            original_prompt)["prompt_token_ids"]
+
+                        pooling_request_output = PoolingRequestOutput(
+                            request_id=aggregator['request_id'],
+                            prompt_token_ids=original_token_ids,
+                            outputs=pooling_output_data,
+                            finished=True)
+
+                        final_res_batch.append(pooling_request_output)
+                    else:
+                        return self.create_error_response(
+                            f"Failed to aggregate chunks "
+                            f"for prompt {prompt_idx}")
+                elif prompt_idx in short_prompts_results:
+                    final_res_batch.append(
+                        cast(PoolingRequestOutput,
+                             short_prompts_results[prompt_idx]))
+                else:
+                    return self.create_error_response(
+                        f"Result not found for prompt {prompt_idx}")
+
+            ctx.final_res_batch = cast(
+                list[Union[RequestOutput, PoolingRequestOutput]],
+                final_res_batch)
+
+            return None
+
+        except Exception as e:
+            return self.create_error_response(str(e))
+
 
 class OpenAIServingEmbedding(EmbeddingMixin):
     request_id_prefix = "embd"

vllm/entrypoints/openai/serving_engine.py

@@ -585,6 +585,8 @@ class OpenAIServing:
                       (EmbeddingChatRequest, EmbeddingCompletionRequest,
                        ScoreRequest, RerankRequest, ClassificationRequest)):
 
+            # Note: input length can be up to the entire model context length
+            # since these requests don't generate tokens.
             if token_num > self.max_model_len:
                 operations: dict[type[AnyRequest], str] = {
                     ScoreRequest: "score",
@@ -613,21 +615,24 @@ class OpenAIServing:
             max_tokens = request.max_completion_tokens or request.max_tokens
         else:
             max_tokens = getattr(request, "max_tokens", None)
-        if max_tokens is None:
-            if token_num >= self.max_model_len:
-                raise ValueError(
-                    f"This model's maximum context length is "
-                    f"{self.max_model_len} tokens. However, you requested "
-                    f"{token_num} tokens in the messages, "
-                    f"Please reduce the length of the messages.")
-        elif token_num + max_tokens > self.max_model_len:
+
+        # Note: input length can be up to model context length - 1 for
+        # completion-like requests.
+        if token_num >= self.max_model_len:
             raise ValueError(
                 f"This model's maximum context length is "
-                f"{self.max_model_len} tokens. However, you requested "
-                f"{max_tokens + token_num} tokens "
-                f"({token_num} in the messages, "
-                f"{max_tokens} in the completion). "
-                f"Please reduce the length of the messages or completion.")
+                f"{self.max_model_len} tokens. However, your request has "
+                f"{token_num} input tokens. Please reduce the length of "
+                "the input messages.")
+
+        if max_tokens is not None and \
+            token_num + max_tokens > self.max_model_len:
+            raise ValueError(
+                "'max_tokens' or 'max_completion_tokens' is too large: "
+                f"{max_tokens}. This model's maximum context length is "
+                f"{self.max_model_len} tokens and your request has "
+                f"{token_num} input tokens ({max_tokens} > {self.max_model_len}"
+                f" - {token_num}).")
 
         return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
 

vllm/entrypoints/tool.py

@@ -2,9 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import os
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Any, Optional
-
-from openai_harmony import Message
+from typing import TYPE_CHECKING, Any
 
 from vllm.logger import init_logger
 
@@ -15,6 +13,30 @@ if TYPE_CHECKING:
 logger = init_logger(__name__)
 
 
+def validate_gpt_oss_install():
+    """
+    Check if the gpt-oss is installed and its version is at least 0.0.3.
+    If not, raise an ImportError.
+    """
+    from importlib.metadata import PackageNotFoundError, version
+
+    from packaging.version import InvalidVersion, Version
+
+    try:
+        pkg_version_str = version("gpt_oss")  # e.g., "0.0.5"
+        pkg_version = Version(pkg_version_str)
+    except PackageNotFoundError:
+        raise ImportError("Package 'gpt_oss' is not installed.") from None
+    except InvalidVersion as e:
+        raise ImportError(
+            f"Invalid version string for 'gpt_oss': {e}") from None
+
+    if pkg_version < Version("0.0.3"):
+        raise ImportError(
+            f"gpt_oss >= 0.0.3 is required, but {pkg_version} is installed."
+        ) from None
+
+
 class Tool(ABC):
 
     @abstractmethod
@@ -33,12 +55,14 @@ class HarmonyBrowserTool(Tool):
             return
 
         try:
+            validate_gpt_oss_install()
             from gpt_oss.tools.simple_browser import SimpleBrowserTool
             from gpt_oss.tools.simple_browser.backend import ExaBackend
-        except ImportError:
+        except ImportError as e:
             self.enabled = False
             logger.warning_once(
-                "gpt_oss is not installed, browsing is disabled")
+                "gpt_oss is not installed properly (%s), browsing is disabled",
+                e)
             return
 
         browser_backend = ExaBackend(source="web", api_key=exa_api_key)
@@ -65,23 +89,16 @@ class HarmonyPythonTool(Tool):
         self.enabled = True
 
         try:
+            validate_gpt_oss_install()
             from gpt_oss.tools.python_docker.docker_tool import PythonTool
-        except ImportError:
+        except ImportError as e:
             self.enabled = False
             logger.warning_once(
-                "gpt_oss is not installed, code interpreter is disabled")
+                "gpt_oss is not installed properly (%s), code interpreter is "
+                "disabled", e)
             return
 
-        # NOTE (Chen): as of gpt-oss 0.0.2, there is a bug in _make_response
-        # and we do the following monkey patch to fix it.
-        class PatchedGptOssPythonTool(PythonTool):
-
-            def _make_response(self,
-                               output: str,
-                               channel: Optional[str] = None) -> Message:
-                return super()._make_response(output)
-
-        self.python_tool = PatchedGptOssPythonTool()
+        self.python_tool = PythonTool()
         logger.info_once("Code interpreter tool initialized")
 
     async def get_result(self, context: "ConversationContext") -> Any:

vllm/envs.py

@@ -63,6 +63,7 @@ if TYPE_CHECKING:
     VLLM_IMAGE_FETCH_TIMEOUT: int = 5
     VLLM_VIDEO_FETCH_TIMEOUT: int = 30
     VLLM_AUDIO_FETCH_TIMEOUT: int = 10
+    VLLM_MEDIA_LOADING_THREAD_COUNT: int = 8
     VLLM_MAX_AUDIO_CLIP_FILESIZE_MB: int = 25
     VLLM_VIDEO_LOADER_BACKEND: str = "opencv"
     VLLM_MM_INPUT_CACHE_GIB: int = 4
@@ -158,6 +159,7 @@ if TYPE_CHECKING:
     VLLM_USE_TRTLLM_ATTENTION: Optional[str] = None
     VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8: bool = False
     VLLM_USE_FLASHINFER_MOE_MXFP4_BF16: bool = False
+    VLLM_TUNED_CONFIG_FOLDER: Optional[str] = None
 
 
 def get_default_cache_root():
@@ -554,6 +556,12 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_AUDIO_FETCH_TIMEOUT":
     lambda: int(os.getenv("VLLM_AUDIO_FETCH_TIMEOUT", "10")),
 
+    # Max number of workers for the thread pool handling
+    # media bytes loading. Set to 1 to disable parallel processing.
+    # Default is 8
+    "VLLM_MEDIA_LOADING_THREAD_COUNT":
+    lambda: int(os.getenv("VLLM_MEDIA_LOADING_THREAD_COUNT", "8")),
+
     # Maximum filesize in MB for a single audio file when processing
     # speech-to-text requests. Files larger than this will be rejected.
     # Default is 25 MB
@@ -1120,6 +1128,11 @@ environment_variables: dict[str, Callable[[], Any]] = {
     #    never removed from memory until the server terminates.
     "VLLM_ENABLE_RESPONSES_API_STORE":
     lambda: bool(int(os.getenv("VLLM_ENABLE_RESPONSES_API_STORE", "0"))),
+
+    # Allows vllm to find tuned config under customized folder
+    "VLLM_TUNED_CONFIG_FOLDER":
+    lambda: os.getenv("VLLM_TUNED_CONFIG_FOLDER", None),
+
 }
 
 # --8<-- [end:env-vars-definition]

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -701,20 +701,32 @@ def get_moe_configs(
     block_shape = [block_n, block_k] if block_n and block_k else None
     json_file_name = get_config_file_name(E, N, dtype, block_shape)
 
-    config_file_path = os.path.join(
+    config_file_paths = []
+
+    # note that we prioritize user defined config
+    user_defined_config_folder = envs.VLLM_TUNED_CONFIG_FOLDER
+    if user_defined_config_folder is not None:
+        user_defined_config_file_path = os.path.join(
+            user_defined_config_folder, json_file_name)
+        config_file_paths.append(user_defined_config_file_path)
+
+    default_config_file_path = os.path.join(
         os.path.dirname(os.path.realpath(__file__)), "configs", json_file_name)
-    if os.path.exists(config_file_path):
-        with open(config_file_path) as f:
-            logger.info("Using configuration from %s for MoE layer.",
-                        config_file_path)
-            # If a configuration has been found, return it
-            return {int(key): val for key, val in json.load(f).items()}
+    config_file_paths.append(default_config_file_path)
+
+    for config_file_path in config_file_paths:
+        if os.path.exists(config_file_path):
+            with open(config_file_path) as f:
+                logger.info("Using configuration from %s for MoE layer.",
+                            config_file_path)
+                # If a configuration has been found, return it
+                return {int(key): val for key, val in json.load(f).items()}
 
     # If no optimized configuration is available, we will use the default
     # configuration
     logger.warning(
         ("Using default MoE config. Performance might be sub-optimal! "
-         "Config file not found at %s"), config_file_path)
+         "Config file not found at %s"), config_file_paths)
     return None
 
 

vllm/model_executor/layers/fused_moe/layer.py

@@ -475,12 +475,11 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
                 activation=activation,
                 apply_router_weight_on_input=apply_router_weight_on_input)
         else:
-            return self.fused_experts(
+            # add w1_bias/w2_bias to kwargs if they exist
+            kwargs = dict(
                 hidden_states=x,
                 w1=layer.w13_weight,
                 w2=layer.w2_weight,
-                w1_bias=layer.w13_bias if self.has_bias else None,
-                w2_bias=layer.w2_bias if self.has_bias else None,
                 topk_weights=topk_weights,
                 topk_ids=topk_ids,
                 inplace=True,
@@ -489,6 +488,17 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
                 global_num_experts=global_num_experts,
                 expert_map=expert_map,
             )
+            if isinstance(self.fused_experts,
+                          FusedMoEModularKernel) and self.has_bias:
+                raise ValueError(
+                    "FusedMoEModularKernel does not support bias.")
+            if self.has_bias:
+                kwargs.update({
+                    "w1_bias": getattr(layer, "w13_bias", None),
+                    "w2_bias": getattr(layer, "w2_bias", None),
+                })
+
+            return self.fused_experts(**kwargs)
 
     def forward_cpu(
         self,
@@ -672,7 +682,8 @@ def determine_expert_map(
     return (local_num_experts, expert_map)
 
 
-class FusedMoE(torch.nn.Module):
+@CustomOp.register("fused_moe")
+class FusedMoE(CustomOp):
     """FusedMoE layer for MoE models.
 
     This layer contains both MergedColumnParallel weights (gate_up_proj /
@@ -741,12 +752,14 @@ class FusedMoE(torch.nn.Module):
 
         # we padding globally so EP buffer allocation works
         if quant_config and quant_config.get_name() == "mxfp4":
-            if not is_torch_equal_or_newer("2.8.0"):
-                raise RuntimeError("Mxfp4 on hopper requires torch >= 2.8.0")
-            if current_platform.is_device_capability(
-                    90) and not has_triton_kernels():
-                raise NotImplementedError(
-                    "Triton kernels must be installed for mxfp4 on hopper")
+            if not current_platform.is_device_capability(100):
+                if not is_torch_equal_or_newer("2.8.0"):
+                    raise RuntimeError(
+                        "Mxfp4 on non-blackwell requires torch >= 2.8.0")
+                if not has_triton_kernels():
+                    raise NotImplementedError(
+                        "triton_kernels must be installed for "
+                        "mxfp4 on non-blackwell")
             if (current_platform.is_rocm()
                     or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8
                     or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16):

vllm/model_executor/layers/linear.py

@@ -16,6 +16,7 @@ from vllm.distributed import (divide, get_tensor_model_parallel_rank,
                               tensor_model_parallel_all_gather,
                               tensor_model_parallel_all_reduce)
 from vllm.logger import init_logger
+from vllm.model_executor.custom_op import CustomOp
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.utils import dispatch_unquantized_gemm
@@ -226,7 +227,7 @@ class UnquantizedLinearMethod(LinearMethodBase):
         return dispatch_unquantized_gemm()(layer, x, layer.weight, bias)
 
 
-class LinearBase(torch.nn.Module):
+class LinearBase(CustomOp):
     """Base linear layer.
 
     Args:
@@ -269,12 +270,8 @@ class LinearBase(torch.nn.Module):
                                                               prefix=prefix)
         self.return_bias = return_bias
 
-    def forward(
-        self, x: torch.Tensor
-    ) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[Parameter]]]:
-        raise NotImplementedError
-
 
+@CustomOp.register("replicated_linear")
 class ReplicatedLinear(LinearBase):
     """Replicated linear layer.
 
@@ -443,6 +440,7 @@ class MergedReplicatedLinear(ReplicatedLinear):
         param[shard_offset:shard_offset + shard_size] = loaded_weight
 
 
+@CustomOp.register("column_parallel_linear")
 class ColumnParallelLinear(LinearBase):
     """Linear layer with column parallelism.
 
@@ -1229,6 +1227,7 @@ class QKVParallelLinear(ColumnParallelLinear):
         param_data.copy_(loaded_weight)
 
 
+@CustomOp.register("row_parallel_linear")
 class RowParallelLinear(LinearBase):
     """Linear layer with row parallelism.
 
@@ -1405,6 +1404,7 @@ class RowParallelLinear(LinearBase):
         return s
 
 
+@CustomOp.register("qkv_cross_parallel_linear")
 class QKVCrossParallelLinear(LinearBase):
     """Linear layers for efficient cross-attention's QKV transformation.
 

vllm/model_executor/layers/mamba/mamba_mixer2.py

@@ -473,12 +473,12 @@ class MambaMixer2(MambaBase, CustomOp):
                 conv_state = self_kv_cache[0].transpose(-1, -2)
                 ssm_state = self_kv_cache[1]
                 state_indices_tensor = attn_metadata.state_indices_tensor
-                has_initial_states_p = attn_metadata.has_initial_states
+                has_initial_states_p = attn_metadata.has_initial_states_p
                 prep_initial_states = attn_metadata.prep_initial_states
                 chunk_size = attn_metadata.chunk_size
-                seq_idx_p = attn_metadata.seq_idx
-                chunk_indices_p = attn_metadata.chunk_indices
-                chunk_offsets_p = attn_metadata.chunk_offsets
+                seq_idx_p = attn_metadata.seq_idx_p
+                chunk_indices_p = attn_metadata.chunk_indices_p
+                chunk_offsets_p = attn_metadata.chunk_offsets_p
         else:
             conv_state = mamba_cache_params.conv_state
             ssm_state = mamba_cache_params.ssm_state

vllm/model_executor/layers/vocab_parallel_embedding.py

@@ -12,6 +12,7 @@ from torch.nn.parameter import Parameter, UninitializedParameter
 from vllm.distributed import (divide, get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size,
                               tensor_model_parallel_all_reduce)
+from vllm.model_executor.custom_op import CustomOp
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase, method_has_implemented_embedding)
 from vllm.model_executor.layers.utils import dispatch_unquantized_gemm
@@ -159,7 +160,8 @@ def get_masked_input_and_mask(
     return input_, ~vocab_mask
 
 
-class VocabParallelEmbedding(torch.nn.Module):
+@CustomOp.register("vocab_parallel_embedding")
+class VocabParallelEmbedding(CustomOp):
     """Embedding parallelized in the vocabulary dimension.
 
     Adapted from torch.nn.Embedding, note that we pad the vocabulary size to

vllm/model_executor/model_loader/gguf_loader.py

@@ -74,6 +74,17 @@ class GGUFModelLoader(BaseModelLoader):
                         f"model.layers.{idx}.mlp.experts.0.gate_proj.weight"
                 gguf_to_hf_name_map[f"blk.{idx}.ffn_up_exps.weight"] = \
                         f"model.layers.{idx}.mlp.experts.0.up_proj.weight"
+        if model_type in ("qwen2_moe", "qwen3_moe"):
+            model_type = model_type.replace("_", "")
+            # GGUF layer map assumes that we will have a merged expert weights
+            # so we need to map them manually
+            for idx in range(config.num_hidden_layers):
+                gguf_to_hf_name_map[f"blk.{idx}.ffn_down_exps.weight"] = \
+                        f"model.layers.{idx}.mlp.experts.0.down_proj.weight"
+                gguf_to_hf_name_map[f"blk.{idx}.ffn_gate_exps.weight"] = \
+                        f"model.layers.{idx}.mlp.experts.0.gate_proj.weight"
+                gguf_to_hf_name_map[f"blk.{idx}.ffn_up_exps.weight"] = \
+                        f"model.layers.{idx}.mlp.experts.0.up_proj.weight"
 
         arch = None
         for key, value in gguf.MODEL_ARCH_NAMES.items():

vllm/model_executor/models/glm4_1v.py

@@ -453,25 +453,30 @@ class Glm4vPatchMerger(nn.Module):
         context_dim: int,
         quant_config: Optional[QuantizationConfig] = None,
         bias: bool = False,
+        prefix: str = "",
     ) -> None:
         super().__init__()
         self.hidden_size = d_model
         self.proj = ColumnParallelLinear(self.hidden_size,
                                          self.hidden_size,
                                          bias=bias,
-                                         gather_output=True)
+                                         gather_output=True,
+                                         quant_config=quant_config,
+                                         prefix=f"{prefix}.proj")
         self.post_projection_norm = nn.LayerNorm(self.hidden_size)
         self.gate_up_proj = MergedColumnParallelLinear(
             input_size=self.hidden_size,
             output_sizes=[context_dim] * 2,
             bias=bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
         )
         self.down_proj = RowParallelLinear(
             context_dim,
             self.hidden_size,
             bias=bias,
             quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
         )
         self.act_fn = SiluAndMul()
         self.extra_activation_func = nn.GELU()
@@ -661,6 +666,7 @@ class Glm4vVisionTransformer(nn.Module):
             context_dim=vision_config.intermediate_size,
             quant_config=quant_config,
             bias=False,
+            prefix=f"{prefix}.merger",
         )
         self.embeddings = Glm4vVisionEmbeddings(vision_config)
 
@@ -1227,10 +1233,7 @@ class Glm4vForConditionalGeneration(nn.Module, SupportsMultiModal,
             "k_proj",
             "v_proj",
         ],
-        "gate_up_proj": [
-            "gate_proj",
-            "up_proj",
-        ],
+        "gate_up_proj": ["gate_up_proj"]
     }
 
     # To ensure correct weight loading and mapping.
@@ -1567,7 +1570,26 @@ class Glm4vForConditionalGeneration(nn.Module, SupportsMultiModal,
         Get the module prefix in multimodal models
         """
         return MultiModelKeys.from_string_field(
-            language_model="language_model",
+            language_model="language_model.model",
             connector="visual.merger.",
             tower_model="visual.",
         )
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    Glm4vMultiModalProcessor,
+    info=Glm4vProcessingInfo,
+    dummy_inputs=Glm4vDummyInputsBuilder,
+)
+class Glm4vMoeForConditionalGeneration(Glm4vForConditionalGeneration):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }

vllm/model_executor/models/gpt_oss.py

@@ -8,7 +8,6 @@ import torch.distributed as dist
 from torch import nn
 from transformers import GptOssConfig
 
-from vllm import envs
 from vllm.attention import Attention, AttentionType
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import CacheConfig, VllmConfig
@@ -70,11 +69,9 @@ class OAIAttention(nn.Module):
 
         tp_size = get_tensor_model_parallel_world_size()
 
-        attention_sink_dtype = (torch.float32 if envs.VLLM_USE_TRTLLM_ATTENTION
-                                else torch.bfloat16)
         self.sinks = torch.nn.Parameter(
             torch.empty(config.num_attention_heads // tp_size,
-                        dtype=attention_sink_dtype,
+                        dtype=torch.bfloat16,
                         requires_grad=False))
 
         self.norm = RMSNorm(config.hidden_size, eps=1e-5)

vllm/model_executor/models/llama4.py

@@ -224,10 +224,14 @@ class Llama4Attention(nn.Module):
 
         if self.rotary_emb is not None:
             q, k = self.rotary_emb(positions, q, k)
+
         if self.qk_norm is not None:
-            q = q.reshape(-1, self.num_heads, self.head_dim)
+            # Normalization is applied on the head_dim dimension. The rest of
+            # the dimensions are collapsed into a single dimension to support
+            # custom rms_norm cuda kernel.
+            q = q.reshape(-1, self.head_dim)
             q = self.qk_norm(q.float()).reshape(-1, self.q_size).to(q.dtype)
-            k = k.reshape(-1, self.num_kv_heads, self.head_dim)
+            k = k.reshape(-1, self.head_dim)
             k = self.qk_norm(k.float()).reshape(-1, self.kv_size).to(k.dtype)
 
         # We are applying temperature tuning (https://arxiv.org/abs/2501.19399)

vllm/model_executor/models/nemotron_vl.py

@@ -13,6 +13,7 @@ from typing import Optional
 
 import torch
 import torch.nn as nn
+import torchvision.transforms as T
 from PIL import Image
 from transformers import AutoModel, PretrainedConfig
 from transformers.image_processing_utils_fast import BaseImageProcessorFast
@@ -27,6 +28,7 @@ from vllm.model_executor.models.internvl import (
 from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.image import convert_image_mode
 from vllm.multimodal.inputs import NestedTensors
 from vllm.multimodal.processing import PromptUpdateDetails
 from vllm.sequence import IntermediateTensors
@@ -44,6 +46,146 @@ IMG_END = '</img>'
 IMG_CONTEXT = '<image>'
 
 
+def build_transform(input_size: int):
+    return T.Compose([
+        T.Lambda(lambda img: convert_image_mode(img, 'RGB')),
+        T.Resize((input_size, input_size),
+                 interpolation=T.InterpolationMode.BICUBIC),
+        T.ToTensor(),
+    ])
+
+
+# adapted from https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-VL-8B-V1
+def find_closest_aspect_ratio(
+    aspect_ratio: float,
+    target_ratios: list[tuple[int, int]],
+    *,
+    width: int,
+    height: int,
+    image_size: int,
+) -> tuple[int, int]:
+    best_factor = float('-inf')
+    best_ratio = (1, 1)
+    area = width * height
+
+    for rw, rh in target_ratios:
+        target_aspect_ratio = rw / rh
+        size_factor = min((rw * rh * image_size * image_size) / area, 0.6)
+        ratio_closeness = min(target_aspect_ratio / aspect_ratio,
+                              aspect_ratio / target_aspect_ratio)
+        factor = size_factor * ratio_closeness
+
+        if factor > best_factor:
+            best_factor = factor
+            best_ratio = (rw, rh)
+
+    return best_ratio
+
+
+def calculate_nemotron_vl_targets(
+    *,
+    orig_width: int,
+    orig_height: int,
+    target_ratios: list[tuple[int, int]],
+    image_size: int,
+    use_thumbnail: bool,
+) -> tuple[int, int, int]:
+    aspect_ratio = orig_width / orig_height
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio,
+        target_ratios,
+        width=orig_width,
+        height=orig_height,
+        image_size=image_size,
+    )
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # add thumbnail image if num_blocks != 1
+    if use_thumbnail and blocks != 1:
+        blocks += 1
+
+    return blocks, target_width, target_height
+
+
+def dynamic_preprocess_nemotron_vl(
+    image: Image.Image,
+    *,
+    target_ratios: list[tuple[int, int]],
+    image_size: int,
+    use_thumbnail: bool,
+) -> list[Image.Image]:
+    orig_width, orig_height = image.size
+
+    # calculate the number of blocks without thumbnail
+    blocks, target_width, target_height = calculate_nemotron_vl_targets(
+        orig_width=orig_width,
+        orig_height=orig_height,
+        target_ratios=target_ratios,
+        image_size=image_size,
+        use_thumbnail=False,
+    )
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = ((i % (target_width // image_size)) * image_size,
+               (i // (target_width // image_size)) * image_size,
+               ((i % (target_width // image_size)) + 1) * image_size,
+               ((i // (target_width // image_size)) + 1) * image_size)
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+
+    assert len(processed_images) == blocks
+
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+
+    return processed_images
+
+
+def get_nemotron_vl_target_ratios(
+    min_num: int,
+    max_num: int,
+) -> list[tuple[int, int]]:
+    target_ratios = {(i, j)
+                     for n in range(min_num, max_num + 1)
+                     for i in range(1, n + 1)
+                     for j in range(1, n + 1) if min_num <= i * j <= max_num}
+    return sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+
+def image_to_pixel_values_nemotron_vl(
+    image: Image.Image,
+    *,
+    input_size: int,
+    min_num: int,
+    max_num: int,
+    use_thumbnail: bool,
+) -> torch.Tensor:
+    target_ratios = get_nemotron_vl_target_ratios(min_num, max_num)
+
+    transform = build_transform(input_size=input_size)
+
+    images = dynamic_preprocess_nemotron_vl(
+        image,
+        target_ratios=target_ratios,
+        image_size=input_size,
+        use_thumbnail=use_thumbnail,
+    )
+
+    pixel_values = torch.stack([transform(image) for image in images])
+    return pixel_values
+
+
 class NemotronVLProcessor(InternVLProcessor):
 
     def __init__(
@@ -87,6 +229,50 @@ class NemotronVLProcessor(InternVLProcessor):
     def image_token_id(self) -> int:
         return self.tokenizer.get_vocab()[IMG_CONTEXT]
 
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> int:
+        target_ratios = self.resolve_target_ratios(
+            use_thumbnail=False,  # Applied in calculate_targets
+        )
+
+        num_patches, _, _ = calculate_nemotron_vl_targets(
+            orig_width=image_width,
+            orig_height=image_height,
+            image_size=self.image_size,
+            target_ratios=target_ratios,
+            use_thumbnail=self.use_thumbnail,
+        )
+
+        return num_patches * self.num_image_token
+
+    def _images_to_pixel_values_lst(
+        self,
+        images: list[Image.Image],
+        min_dynamic_patch: Optional[int] = None,
+        max_dynamic_patch: Optional[int] = None,
+        dynamic_image_size: Optional[bool] = None,
+    ) -> list[torch.Tensor]:
+        min_num, max_num = self.resolve_min_max_num(
+            min_dynamic_patch=min_dynamic_patch,
+            max_dynamic_patch=max_dynamic_patch,
+            dynamic_image_size=dynamic_image_size,
+            use_thumbnail=False,  # Applied in image_to_pixel_values
+        )
+
+        return [
+            image_to_pixel_values_nemotron_vl(
+                image,
+                input_size=self.image_size,
+                min_num=min_num,
+                max_num=max_num,
+                use_thumbnail=self.use_thumbnail,
+            ) for image in images
+        ]
+
     def _preprocess_image(
         self,
         text: list[str],

vllm/model_executor/models/qwen2_5_vl.py

@@ -976,10 +976,12 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal,
             image_embeds = self.visual(pixel_values, grid_thw=grid_thw_list)
 
         # Split concatenated embeddings for each image item.
+        # Using prod on grid_thw_list instead of grid_thw.prod avoids CUDA sync
         merge_size = self.visual.spatial_merge_size
-        sizes = grid_thw.prod(-1) // merge_size // merge_size
+        sizes = (torch.tensor(grid_thw_list, dtype=torch.long).prod(-1) //
+                 (merge_size * merge_size)).tolist()
 
-        return image_embeds.split(sizes.tolist())
+        return image_embeds.split(sizes)
 
     def _process_video_input(
             self,
@@ -998,9 +1000,11 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module, SupportsMultiModal,
 
         # Split concatenated embeddings for each video item.
         merge_size = self.visual.spatial_merge_size
-        sizes = grid_thw.prod(-1) // merge_size // merge_size
+        # Using prod on grid_thw_list instead of grid_thw.prod avoids CUDA sync
+        sizes = (torch.tensor(grid_thw_list, dtype=torch.long).prod(-1) //
+                 (merge_size * merge_size)).tolist()
 
-        return video_embeds.split(sizes.tolist())
+        return video_embeds.split(sizes)
 
     def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
         mm_input_by_modality = {}

vllm/model_executor/models/qwen3_moe.py

@@ -375,6 +375,7 @@ class Qwen3MoeModel(nn.Module):
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
+            quant_config=quant_config,
             prefix=f"{prefix}.embed_tokens")
         self.start_layer, self.end_layer, self.layers = make_layers(
             config.num_hidden_layers,

vllm/model_executor/models/registry.py

@@ -208,7 +208,7 @@ _MULTIMODAL_MODELS = {
     "Gemma3nForConditionalGeneration": ("gemma3n_mm", "Gemma3nForConditionalGeneration"),    # noqa: E501
     "GLM4VForCausalLM": ("glm4v", "GLM4VForCausalLM"),
     "Glm4vForConditionalGeneration": ("glm4_1v", "Glm4vForConditionalGeneration"),  # noqa: E501
-    "Glm4vMoeForConditionalGeneration": ("glm4_1v", "Glm4vForConditionalGeneration"),  # noqa: E501
+    "Glm4vMoeForConditionalGeneration": ("glm4_1v", "Glm4vMoeForConditionalGeneration"),  # noqa: E501
     "GraniteSpeechForConditionalGeneration": ("granite_speech", "GraniteSpeechForConditionalGeneration"),  # noqa: E501
     "H2OVLChatModel": ("h2ovl", "H2OVLChatModel"),
     "InternVLChatModel": ("internvl", "InternVLChatModel"),

vllm/model_executor/models/step3_vl.py

@@ -21,6 +21,7 @@ from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                QKVParallelLinear,
+                                               ReplicatedLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
@@ -33,6 +34,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                         BaseProcessingInfo, PromptReplacement,
                                         PromptUpdate, PromptUpdateDetails)
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.multimodal.utils import run_dp_sharded_vision_model
 from vllm.sequence import IntermediateTensors
 from vllm.transformers_utils.configs import Step3VisionEncoderConfig
 from vllm.transformers_utils.tokenizer import AnyTokenizer
@@ -650,7 +652,8 @@ class Step3VisionAttention(nn.Module):
     def __init__(self,
                  config,
                  quant_config: Optional[QuantizationConfig] = None,
-                 prefix: str = ""):
+                 prefix: str = "",
+                 use_data_parallel: bool = False):
         super().__init__()
         self.config = config
         self.embed_dim = config.hidden_size
@@ -659,20 +662,42 @@ class Step3VisionAttention(nn.Module):
 
         self.scale = self.head_dim**-0.5
 
-        tp_size = get_tensor_model_parallel_world_size()
+        tp_size = (1 if use_data_parallel else
+                   get_tensor_model_parallel_world_size())
         assert self.total_num_heads % tp_size == 0
         self.num_heads = self.total_num_heads // tp_size
-        self.qkv_proj = QKVParallelLinear(self.embed_dim,
-                                          self.head_dim,
-                                          self.total_num_heads,
-                                          bias=True,
-                                          quant_config=quant_config,
-                                          prefix=prefix)
-        self.out_proj = RowParallelLinear(self.embed_dim,
-                                          self.embed_dim,
-                                          bias=True,
-                                          quant_config=quant_config,
-                                          prefix=prefix)
+
+        self.q_size = self.num_heads * self.head_dim
+
+        if use_data_parallel:
+            self.qkv_proj = ReplicatedLinear(
+                self.embed_dim,
+                3 * self.q_size,
+                bias=True,
+                quant_config=quant_config,
+                prefix=prefix,
+            )
+            self.out_proj = ReplicatedLinear(
+                self.total_num_heads * self.head_dim,
+                self.embed_dim,
+                bias=True,
+                quant_config=quant_config,
+                prefix=prefix,
+            )
+        else:
+            self.qkv_proj = QKVParallelLinear(
+                self.embed_dim,
+                self.head_dim,
+                self.total_num_heads,
+                bias=True,
+                quant_config=quant_config,
+                prefix=prefix,
+            )
+            self.out_proj = RowParallelLinear(self.embed_dim,
+                                              self.embed_dim,
+                                              bias=True,
+                                              quant_config=quant_config,
+                                              prefix=prefix)
 
     def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
         return tensor.view(bsz, seq_len, self.num_heads,
@@ -712,20 +737,25 @@ class Step3VisionMLP(nn.Module):
     def __init__(self,
                  config,
                  quant_config: Optional[QuantizationConfig] = None,
-                 prefix: str = ""):
+                 prefix: str = "",
+                 use_data_parallel: bool = False):
         super().__init__()
         self.config = config
         self.activation_fn = get_act_fn(config.hidden_act)
-        self.fc1 = ColumnParallelLinear(config.hidden_size,
-                                        config.intermediate_size,
-                                        bias=True,
-                                        quant_config=quant_config,
-                                        prefix=prefix)
-        self.fc2 = RowParallelLinear(config.intermediate_size,
-                                     config.hidden_size,
-                                     bias=True,
-                                     quant_config=quant_config,
-                                     prefix=prefix)
+        cls_fc1 = (ReplicatedLinear
+                   if use_data_parallel else ColumnParallelLinear)
+        self.fc1 = cls_fc1(config.hidden_size,
+                           config.intermediate_size,
+                           bias=True,
+                           quant_config=quant_config,
+                           prefix=prefix)
+        cls_fc2 = (ReplicatedLinear
+                   if use_data_parallel else RowParallelLinear)
+        self.fc2 = cls_fc2(config.intermediate_size,
+                           config.hidden_size,
+                           bias=True,
+                           quant_config=quant_config,
+                           prefix=prefix)
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         hidden_states, _ = self.fc1(hidden_states)
@@ -739,15 +769,22 @@ class Step3VisionEncoderLayer(nn.Module):
     def __init__(self,
                  config: Step3VisionEncoderConfig,
                  quant_config: Optional[QuantizationConfig] = None,
-                 prefix: str = ""):
+                 prefix: str = "",
+                 use_data_parallel: bool = False):
         super().__init__()
+        self.use_data_parallel = use_data_parallel
         self.embed_dim = config.hidden_size
-        self.self_attn = Step3VisionAttention(config,
-                                              quant_config,
-                                              prefix=f"{prefix}.self_attn")
+        self.self_attn = Step3VisionAttention(
+            config,
+            quant_config,
+            prefix=f"{prefix}.self_attn",
+            use_data_parallel=self.use_data_parallel)
         self.layer_norm1 = nn.LayerNorm(self.embed_dim,
                                         eps=config.layer_norm_eps)
-        self.mlp = Step3VisionMLP(config, quant_config, prefix=f"{prefix}.mlp")
+        self.mlp = Step3VisionMLP(config,
+                                  quant_config,
+                                  prefix=f"{prefix}.mlp",
+                                  use_data_parallel=self.use_data_parallel)
         self.layer_norm2 = nn.LayerNorm(self.embed_dim,
                                         eps=config.layer_norm_eps)
 
@@ -767,13 +804,16 @@ class Step3VisionEncoder(nn.Module):
     def __init__(self,
                  config: Step3VisionEncoderConfig,
                  quant_config: Optional[QuantizationConfig] = None,
-                 prefix: str = ""):
+                 prefix: str = "",
+                 use_data_parallel: bool = False):
         super().__init__()
         self.config = config
+        self.use_data_parallel = use_data_parallel
         self.layers = nn.ModuleList([
             Step3VisionEncoderLayer(config,
                                     quant_config,
-                                    prefix=f"{prefix}.layers.{i}")
+                                    prefix=f"{prefix}.layers.{i}",
+                                    use_data_parallel=self.use_data_parallel)
             for i in range(config.num_hidden_layers)
         ])
 
@@ -792,21 +832,29 @@ class Step3VisionTransformer(nn.Module):
     def __init__(self,
                  config: Step3VisionEncoderConfig,
                  quant_config: Optional[QuantizationConfig] = None,
-                 prefix: str = ""):
+                 prefix: str = "",
+                 use_data_parallel: bool = False):
         super().__init__()
         self.config = config
+        self.use_data_parallel = use_data_parallel
         self.image_size = config.image_size
         self.embeddings = Step3VisionEmbeddings(config)
-        self.transformer = Step3VisionEncoder(config,
-                                              quant_config,
-                                              prefix=f"{prefix}.transformer")
+        self.transformer = Step3VisionEncoder(
+            config,
+            quant_config,
+            prefix=f"{prefix}.transformer",
+            use_data_parallel=self.use_data_parallel)
 
     def forward(
         self,
         pixel_values: torch.Tensor,
     ):
         hidden_states = self.embeddings(pixel_values)
-        hidden_states = self.transformer(inputs_embeds=hidden_states)
+        if self.use_data_parallel:
+            hidden_states = run_dp_sharded_vision_model(
+                hidden_states, self.transformer)
+        else:
+            hidden_states = self.transformer(inputs_embeds=hidden_states)
         return hidden_states
 
 
@@ -836,13 +884,15 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal,
 
         self.config = config
         self.multimodal_config = multimodal_config
+        self.use_data_parallel = (vllm_config.parallel_config.
+                                  enable_multimodal_encoder_data_parallel)
 
         if multimodal_config.get_limit_per_prompt("image"):
-            self.vision_model = Step3VisionTransformer(config.vision_config,
-                                                       None,
-                                                       prefix=maybe_prefix(
-                                                           prefix,
-                                                           "vision_model"))
+            self.vision_model = Step3VisionTransformer(
+                config.vision_config,
+                None,
+                prefix=maybe_prefix(prefix, "vision_model"),
+                use_data_parallel=self.use_data_parallel)
             self.vit_downsampler = nn.Conv2d(
                 config.vision_config.hidden_size,
                 config.vision_config.output_hidden_size,

vllm/model_executor/models/transformers.py

@@ -505,30 +505,47 @@ class TransformersBase(nn.Module, SupportsQuant, SupportsLoRA, SupportsPP):
         Apply the model's tensor parallelization plan.
         Currently only supports linear layers.
         """
-        tp_plan = getattr(self.model.config, "base_model_tp_plan", None) or {}
+        # Look for tp plans in all of the PreTrainedModels found in self.model
+        is_pretrained_model = lambda m: isinstance(m, PreTrainedModel)
+        supports_tp_plan = lambda m: m.config.base_model_tp_plan is not None
+        pretrained_models = filter(is_pretrained_model, self.model.modules())
+        models_with_tp_plan = filter(supports_tp_plan, pretrained_models)
 
-        if not tp_plan and self.tp_size > 1:
+        if not any(models_with_tp_plan) and self.tp_size > 1:
             raise ValueError(
                 f"{type(self.model)} does not support tensor parallel yet!")
 
-        # Some weight loaders expect linear layers to inherit from vLLM's
-        # LinearBase class, so we set a default style which causes any
-        # unspecified linear layers to be replaced with ReplicatedLinear
-        tp_plan[".*"] = "replicate"
+        def _tensor_parallel(module: nn.Module,
+                             prefix: str = "",
+                             tp_plan=None):
+            tp_plan = tp_plan or {}
 
-        def _tensor_parallel(module: nn.Module, prefix: str = ""):
+            # If the current module is a PreTrainedModel, set the tp_plan for
+            # all of its children
+            if isinstance(module, PreTrainedModel):
+                tp_plan = module.config.base_model_tp_plan or {}
+                tp_plan = {
+                    maybe_prefix(prefix, k): v
+                    for k, v in tp_plan.items()
+                }
+
+            # Some weight loaders expect linear layers to inherit from vLLM's
+            # LinearBase class, so we set a default style which causes any
+            # unspecified linear layers to be replaced with ReplicatedLinear
             for child_name, child_module in module.named_children():
                 qual_name = maybe_prefix(prefix, child_name)
-                for pattern, style in tp_plan.items():
-                    if re.match(pattern, qual_name) and isinstance(
-                            child_module, nn.Linear):
-                        new_module = replace_linear_class(
-                            child_module, style, self.quant_config)
-                        setattr(module, child_name, new_module)
-                        log_replacement(qual_name, child_module, new_module)
-                        break
+                if isinstance(child_module, nn.Linear):
+                    generator = (p for p in tp_plan if re.match(p, qual_name))
+                    pattern = next(generator, None)
+                    style = tp_plan.get(pattern, "replicate")
+                    new_module = replace_linear_class(child_module, style,
+                                                      self.quant_config)
+                    setattr(module, child_name, new_module)
+                    log_replacement(qual_name, child_module, new_module)
                 else:
-                    _tensor_parallel(child_module, prefix=qual_name)
+                    _tensor_parallel(child_module,
+                                     prefix=qual_name,
+                                     tp_plan=tp_plan)
 
         _tensor_parallel(self.model)
 

vllm/multimodal/inputs.py

@@ -4,7 +4,7 @@
 from abc import ABC, abstractmethod
 from collections import UserDict, defaultdict
 from collections.abc import Mapping, Sequence
-from dataclasses import dataclass
+from dataclasses import dataclass, replace
 from functools import partial
 from itertools import accumulate
 from typing import (TYPE_CHECKING, Any, Literal, Optional, TypedDict, TypeVar,
@@ -198,7 +198,7 @@ A dictionary containing nested tensors which have been batched via
 """
 
 
-@dataclass(frozen=True)
+@dataclass
 class MultiModalFieldElem:
     """
     Represents a keyword argument corresponding to a multi-modal item
@@ -218,11 +218,14 @@ class MultiModalFieldElem:
     i.e. the name of the keyword argument to be passed to the model.
     """
 
-    data: NestedTensors
+    data: Optional[NestedTensors]
     """
     The tensor data of this field in
     [`MultiModalKwargs`][vllm.multimodal.inputs.MultiModalKwargs],
     i.e. the value of the keyword argument to be passed to the model.
+
+    It may be set to `None` if it is determined that the item is cached
+    in `EngineCore`.
     """
 
     field: "BaseMultiModalField"
@@ -235,8 +238,15 @@ class MultiModalFieldElem:
         if not isinstance(other, self.__class__):
             return False
 
+        if self.data is None:
+            data_equal = other.data is None
+        elif other.data is None:
+            data_equal = self.data is None
+        else:
+            data_equal = nested_tensors_equal(self.data, other.data)
+
         return ((self.modality, self.key) == (other.modality, other.key)
-                and nested_tensors_equal(self.data, other.data)
+                and data_equal
                 and type(self.field) == type(other.field))  # noqa: E721
 
 
@@ -280,10 +290,20 @@ class BaseMultiModalField(ABC):
         raise NotImplementedError
 
     @abstractmethod
-    def _reduce_data(self, batch: list[NestedTensors]) -> NestedTensors:
+    def _reduce_data(
+        self,
+        batch: list[NestedTensors],
+        *,
+        pin_memory: bool,
+    ) -> NestedTensors:
         raise NotImplementedError
 
-    def reduce_data(self, elems: list[MultiModalFieldElem]) -> NestedTensors:
+    def reduce_data(
+        self,
+        elems: list[MultiModalFieldElem],
+        *,
+        pin_memory: bool = False,
+    ) -> NestedTensors:
         """
         Merge the data from multiple instances of
         [`MultiModalFieldElem`][vllm.multimodal.inputs.MultiModalFieldElem].
@@ -295,7 +315,13 @@ class BaseMultiModalField(ABC):
         if len(set(field_types)) > 1:
             raise ValueError(f"Cannot merge different {field_types=}")
 
-        return self._reduce_data([item.data for item in elems])
+        validated_data = list[NestedTensors]()
+        for i, elem in enumerate(elems):
+            assert elem.data is not None, (
+                f"Cannot merge with empty `elems[{i}]`")
+            validated_data.append(elem.data)
+
+        return self._reduce_data(validated_data, pin_memory=pin_memory)
 
 
 @dataclass(frozen=True)
@@ -314,7 +340,12 @@ class MultiModalBatchedField(BaseMultiModalField):
         field_factory = self._field_factory(modality=modality, key=key)
         return [field_factory(item) for item in data]
 
-    def _reduce_data(self, batch: list[NestedTensors]) -> NestedTensors:
+    def _reduce_data(
+        self,
+        batch: list[NestedTensors],
+        *,
+        pin_memory: bool,
+    ) -> NestedTensors:
         if len(batch) > 0 and is_list_of(batch, torch.Tensor, check="all"):
             if len(batch) == 1:
                 # An optimization when `batch` contains only one tensor:
@@ -323,7 +354,11 @@ class MultiModalBatchedField(BaseMultiModalField):
                 return batch[0].unsqueeze(0).contiguous()
             first_shape = batch[0].shape
             if all(elem.shape == first_shape for elem in batch):
-                return torch.stack(batch)
+                out = torch.empty((len(batch), *batch[0].shape),
+                                  dtype=batch[0].dtype,
+                                  device=batch[0].device,
+                                  pin_memory=pin_memory)
+                return torch.stack(batch, out=out)
 
         return batch
 
@@ -350,7 +385,12 @@ class MultiModalFlatField(BaseMultiModalField):
                 "torch.Tensor is required for multiple slices"
         return [field_factory(data[cast(slice, s)]) for s in self.slices]
 
-    def _reduce_data(self, batch: list[NestedTensors]) -> NestedTensors:
+    def _reduce_data(
+        self,
+        batch: list[NestedTensors],
+        *,
+        pin_memory: bool,
+    ) -> NestedTensors:
         if len(batch) > 0 and is_list_of(batch, torch.Tensor, check="all"):
             if len(batch) == 1:
                 # An optimization when `batch` contains only one tensor:
@@ -358,13 +398,21 @@ class MultiModalFlatField(BaseMultiModalField):
                 # - will achieve zero-copy if the tensor is contiguous
                 return batch[0].contiguous()
 
-            def _expect_same_shape(tensor: torch.Tensor):
-                return tensor.shape[:self.dim] + tensor.shape[self.dim + 1:]
+            dim = self.dim + (self.dim < 0) * len(batch[0].shape)
 
-            first_shape = _expect_same_shape(batch[0])
+            def _shape_before_after(tensor: torch.Tensor):
+                return tensor.shape[:dim], tensor.shape[dim + 1:]
 
-            if all(_expect_same_shape(elem) == first_shape for elem in batch):
-                return torch.concat(batch, dim=self.dim)
+            first_shape = _shape_before_after(batch[0])
+
+            if all(_shape_before_after(elem) == first_shape for elem in batch):
+                shape_before, shape_after = first_shape
+                shape_concat = sum(item.shape[dim] for item in batch)
+                out = torch.empty((*shape_before, shape_concat, *shape_after),
+                                  dtype=batch[0].dtype,
+                                  device=batch[0].device,
+                                  pin_memory=pin_memory)
+                return torch.concat(batch, dim=self.dim, out=out)
 
         assert self.dim == 0, "dim == 0 is required for nested list"
         return [e for elem in batch for e in elem]
@@ -387,7 +435,12 @@ class MultiModalSharedField(BaseMultiModalField):
         field_factory = self._field_factory(modality=modality, key=key)
         return [field_factory(data)] * self.batch_size
 
-    def _reduce_data(self, batch: list[NestedTensors]) -> NestedTensors:
+    def _reduce_data(
+        self,
+        batch: list[NestedTensors],
+        *,
+        pin_memory: bool,
+    ) -> NestedTensors:
         return batch[0]
 
 
@@ -594,11 +647,53 @@ class MultiModalKwargsItem(UserDict[str, MultiModalFieldElem]):
     def from_elems(elems: Sequence[MultiModalFieldElem]):
         return MultiModalKwargsItem({elem.key: elem for elem in elems})
 
-    @property
-    def modality(self) -> str:
+    def __init__(self, data: Mapping[str, MultiModalFieldElem]) -> None:
+        super().__init__(data)
+
         modalities = {elem.modality for elem in self.data.values()}
         assert len(modalities) == 1, f"Found different modalities={modalities}"
-        return next(iter(modalities))
+        self._modality = next(iter(modalities))
+
+        self._is_empty = any(elem.data is None for elem in self.values())
+
+    @property
+    def modality(self) -> str:
+        return self._modality
+
+    @property
+    def is_empty(self) -> bool:
+        return self._is_empty
+
+    def get_data(self) -> Optional[Mapping[str, NestedTensors]]:
+        if self._is_empty:
+            return None
+
+        out_data = dict[str, NestedTensors]()
+        for key, elem in self.items():
+            assert elem.data is not None, (
+                f"Cannot get data of empty `elem[{key!r}]`")
+            out_data[key] = elem.data
+
+        return out_data
+
+    def require_data(self) -> Mapping[str, NestedTensors]:
+        if (data := self.get_data()) is None:
+            raise RuntimeError("Cannot get data of empty item")
+
+        return data
+
+    # These methods create a new item to avoid mutating cached items in place
+    def with_data(self, data: Mapping[str, NestedTensors]):
+        return MultiModalKwargsItem({
+            key: replace(elem, data=data[key])
+            for key, elem in self.items()
+        })
+
+    def without_data(self):
+        return MultiModalKwargsItem({
+            key: replace(elem, data=None)
+            for key, elem in self.items()
+        })
 
 
 # NOTE: UserDict is for V0 compatibility.
@@ -650,7 +745,11 @@ class MultiModalKwargs(UserDict[str, NestedTensors]):
         return MultiModalKwargs.from_items(items)
 
     @staticmethod
-    def from_items(items: Sequence[MultiModalKwargsItem]):
+    def from_items(
+        items: Sequence[MultiModalKwargsItem],
+        *,
+        pin_memory: bool = False,
+    ):
         """Construct a new
         [`MultiModalKwargs`][vllm.multimodal.inputs.MultiModalKwargs]
         from multiple items."""
@@ -660,7 +759,7 @@ class MultiModalKwargs(UserDict[str, NestedTensors]):
                 elems_by_key[key].append(elem)
 
         data = {
-            key: elems[0].field.reduce_data(elems)
+            key: elems[0].field.reduce_data(elems, pin_memory=pin_memory)
             for key, elems in elems_by_key.items() if len(elems) > 0
         }
 

vllm/multimodal/utils.py

@@ -1,6 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import asyncio
+import atexit
+from collections.abc import Iterable
+from concurrent.futures import ThreadPoolExecutor
 from itertools import groupby
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Optional, TypeVar, Union
@@ -10,6 +14,7 @@ import numpy as np
 import numpy.typing as npt
 import torch
 from PIL import Image, UnidentifiedImageError
+from typing_extensions import deprecated
 
 import vllm.envs as envs
 from vllm.connections import HTTPConnection, global_http_connection
@@ -20,19 +25,23 @@ from vllm.distributed import (get_tensor_model_parallel_rank,
 from .audio import AudioMediaIO
 from .base import MediaIO
 from .image import ImageEmbeddingMediaIO, ImageMediaIO
-from .inputs import PlaceholderRange
 from .video import VideoMediaIO
 
 _M = TypeVar("_M")
 
 if TYPE_CHECKING:
-    from .hasher import MultiModalHashDict
-    from .inputs import MultiModalKwargs, MultiModalPlaceholderDict
+    from .inputs import (BatchedTensorInputs, MultiModalKwargs,
+                         MultiModalKwargsItem, MultiModalPlaceholderDict)
 else:
-    MultiModalHashDict = Any
+    BatchedTensorInputs = Any
     MultiModalKwargs = Any
+    MultiModalKwargsItem = Any
     MultiModalPlaceholderDict = Any
 
+global_thread_pool = ThreadPoolExecutor(
+    max_workers=envs.VLLM_MEDIA_LOADING_THREAD_COUNT)
+atexit.register(global_thread_pool.shutdown)
+
 
 class MediaConnector:
 
@@ -139,19 +148,26 @@ class MediaConnector:
         fetch_timeout: Optional[int] = None,
     ) -> _M:
         url_spec = urlparse(url)
+        loop = asyncio.get_running_loop()
 
         if url_spec.scheme.startswith("http"):
             connection = self.connection
             data = await connection.async_get_bytes(url, timeout=fetch_timeout)
-
-            return media_io.load_bytes(data)
+            future = loop.run_in_executor(global_thread_pool,
+                                          media_io.load_bytes, data)
+            return await future
 
         if url_spec.scheme == "data":
-            return self._load_data_url(url_spec, media_io)
+            future = loop.run_in_executor(global_thread_pool,
+                                          self._load_data_url, url_spec,
+                                          media_io)
+            return await future
 
         if url_spec.scheme == "file":
-            return self._load_file_url(url_spec, media_io)
-
+            future = loop.run_in_executor(global_thread_pool,
+                                          self._load_file_url, url_spec,
+                                          media_io)
+            return await future
         msg = "The URL must be either a HTTP, data or file URL."
         raise ValueError(msg)
 
@@ -317,79 +333,32 @@ def encode_video_base64(frames: npt.NDArray) -> str:
     return video_io.encode_base64(frames)
 
 
-def merge_and_sort_multimodal_metadata(
-    mm_positions: MultiModalPlaceholderDict,
-    mm_hashes: Optional[MultiModalHashDict],
-) -> tuple[list[str], list[PlaceholderRange], Optional[list[str]]]:
-    """Given a MultiModalPlaceholderDict, merge all PlaceholderRange
-    objects from all available modalities into a single list of 
-    PlaceholderRange, sorted by their offset (starting index in the input
-    sequence) in the ascending order.
-
-    Optionally if a `MultiModalHashDict` is given, same operation will be
-    applied to the object and the sorted list of hashes will be returned.
-    
-    Returns:
-        list[str]: List of item modalities in order of their positions in the
-        input sequence.
-        list[PlaceholderRange]: Sorted list of all PlaceholderRanges from
-        mm_positions.
-        Optional[list[str]]: Sorted list of all hashes from mm_hashes if given,
-        None otherwise.
+def argsort_mm_positions(
+        mm_positions: MultiModalPlaceholderDict) -> list[tuple[str, int]]:
     """
+    Given a `MultiModalPlaceholderDict`, output a sequence of keys to
+    sort the dictionary by `offset` (starting index in the input sequence)
+    in ascending order.
 
-    modalities = list(mm_positions.keys())
+    Returns:
+        A list of `(modality, idx)`, which can be used to access an item
+        by `mm_positions[modality][idx]`.
+    """
+    flat_items = ((modality, idx, item)
+                  for modality, items in mm_positions.items()
+                  for idx, item in enumerate(items))
 
-    assert len(modalities) > 0, "No modalities found in the mm_positions."
+    sorted_flat_items = sorted(flat_items, key=lambda x: x[2].offset)
 
-    # For single modality, placeholder ranges and hashes are already sorted
-    # so we can return the list directly.
-    if len(modalities) == 1:
-        modality = modalities[0]
-        placeholder_list = list(mm_positions[modality])
-
-        return [modality] * len(
-            placeholder_list
-        ), placeholder_list, None if not mm_hashes else mm_hashes[modality]
-
-    # Create a list of (modality, placeholder, hash) tuples for all placeholders
-    all_items = []
-    for modality in modalities:
-        placeholder_list = list(mm_positions[modality])
-        hash_list: list[Optional[str]] = list(
-            mm_hashes[modality]) if mm_hashes and modality in mm_hashes else [
-                None
-            ] * len(placeholder_list)
-
-        for placeholder, hash_value in zip(placeholder_list, hash_list):
-            all_items.append((modality, placeholder, hash_value))
-
-    # Sort all items by offset
-    all_items.sort(key=lambda x: x[1].offset)
-
-    # Split into separate lists
-    sorted_modalities = [item[0] for item in all_items]
-    merged_placeholders = [item[1] for item in all_items]
-    merged_hashes = [str(item[2])
-                     for item in all_items] if mm_hashes is not None else None
-
-    return sorted_modalities, merged_placeholders, merged_hashes
+    return [(modality, idx) for modality, idx, _ in sorted_flat_items]
 
 
+# Temporary back-compatibility for plugins that define model runner
+@deprecated("`group_mm_inputs_by_modality` is superseded by "
+            "`group_mm_kwargs_by_modality` and will be removed in v0.13. "
+            "Please use `group_mm_kwargs_by_modality` instead.")
 def group_mm_inputs_by_modality(
         mm_inputs: list[MultiModalKwargs]) -> list[list[MultiModalKwargs]]:
-    """Group consecutive MultiModalKwargs from mm_inputs with the same modality
-    together into the same list for batching purpose. For MultiModalKwargs with
-    multiple modalities, put them into their own list.
-
-    Args:
-        mm_inputs: List of MultiModalKwargs.
-
-    Returns:
-        list[list[vllm.multimodal.MultiModalKwargs]]: List of list of
-        `MultiModalKwargs`, each inner list contains consecutive
-        `MultiModalKwargs` with same modality.
-    """
     if not mm_inputs:
         return []
 
@@ -412,6 +381,48 @@ def group_mm_inputs_by_modality(
     ]
 
 
+def group_mm_kwargs_by_modality(
+    mm_kwargs: list[MultiModalKwargsItem],
+    *,
+    device: torch.types.Device = None,
+    pin_memory: bool = False,
+) -> Iterable[tuple[str, int, BatchedTensorInputs]]:
+    """Group consecutive `MultiModalKwargsItem`s from `mm_kwargs` with the same
+    modality together into the same `MultiModalKwargs` instance.
+
+    Args:
+        mm_inputs: List of `MultiModalKwargsItem`.
+
+    Yields:
+        A tuple `(modality, num_items, grouped_kwargs)`.
+    """
+    from vllm.multimodal.inputs import MultiModalKwargs
+
+    for modality, items in groupby(mm_kwargs, key=lambda item: item.modality):
+        items_lst = list(items)
+
+        # mm_kwargs_group = MultiModalKwargs.from_items(items_lst,
+        #                                               pin_memory=pin_memory)
+
+        # if device is not None:
+        #     mm_kwargs_group = json_map_leaves(lambda x: x.to(device=device),
+        #                                       mm_kwargs_group.data)
+
+        # TODO: Once V0 is removed, we can use the merging logic above
+        # to avoid creating an extra batch dimension (except for fields
+        # that are meant to be stacked anyway).
+        # We will also need to update each model to remove `flatten_bn`.
+        mm_kwargs_group = MultiModalKwargs.as_kwargs(
+            MultiModalKwargs.batch(
+                [MultiModalKwargs.from_items([item]) for item in items_lst],
+                pin_memory=pin_memory,
+            ),
+            device=device,
+        )
+
+        yield modality, len(items_lst), mm_kwargs_group
+
+
 def run_dp_sharded_vision_model(image_input: torch.Tensor,
                                 vision_model: torch.nn.Module) -> torch.Tensor:
     """Run a vision model with data parallelism (DP) sharding. The function 
@@ -489,4 +500,4 @@ def fetch_video(
         "video": video_io_kwargs
     }
     media_connector = MediaConnector(media_io_kwargs=media_io_kwargs)
-    return media_connector.fetch_video(video_url)
+    return media_connector.fetch_video(video_url)

vllm/platforms/cuda.py

@@ -118,20 +118,10 @@ class CudaPlatformBase(Platform):
     @classmethod
     def check_and_update_config(cls, vllm_config: "VllmConfig") -> None:
         parallel_config = vllm_config.parallel_config
-        scheduler_config = vllm_config.scheduler_config
         model_config = vllm_config.model_config
 
         if parallel_config.worker_cls == "auto":
-            if scheduler_config.is_multi_step:
-                if envs.VLLM_USE_V1:
-                    raise NotImplementedError(
-                        "Multi-step scheduling is not supported (and not "
-                        "needed) on vLLM V1. Please launch without "
-                        "--num-scheduler-steps.")
-                else:
-                    parallel_config.worker_cls = \
-                        "vllm.worker.multi_step_worker.MultiStepWorker"
-            elif vllm_config.speculative_config:
+            if vllm_config.speculative_config:
                 if not envs.VLLM_USE_V1:
                     raise NotImplementedError(
                         "Speculative decoding is not supported on vLLM V0.")
@@ -139,7 +129,7 @@ class CudaPlatformBase(Platform):
             else:
                 if envs.VLLM_USE_V1:
                     parallel_config.worker_cls = \
-                            "vllm.v1.worker.gpu_worker.Worker"
+                        "vllm.v1.worker.gpu_worker.Worker"
                 else:
                     parallel_config.worker_cls = "vllm.worker.worker.Worker"
 
@@ -162,6 +152,9 @@ class CudaPlatformBase(Platform):
                 if cls.is_device_capability(100):
                     # Blackwell => Force CutlassMLA.
                     use_cutlass_mla = True
+                    # TODO: This does not work, because the
+                    # global_force_attn_backend_context_manager is not set.
+                    # See vllm/attention/selector.py:_cached_get_attn_backend
                     envs.VLLM_ATTENTION_BACKEND = "CUTLASS_MLA"
                 else:
                     # Not Blackwell
@@ -227,7 +220,9 @@ class CudaPlatformBase(Platform):
         if use_mla:
             # TODO(lucas): refactor to be more concise
             #  we should probably consider factoring out V1 here
-            if selected_backend == _Backend.CUTLASS_MLA:
+            if selected_backend == _Backend.CUTLASS_MLA or (
+                    cls.is_device_capability(100) and selected_backend is None
+                    and block_size == 128):
                 if use_v1:
                     logger.info_once("Using Cutlass MLA backend on V1 engine.")
                     return ("vllm.v1.attention.backends.mla."

vllm/platforms/rocm.py

@@ -327,18 +327,8 @@ class RocmPlatform(Platform):
             cache_config.block_size = 16
 
         parallel_config = vllm_config.parallel_config
-        scheduler_config = vllm_config.scheduler_config
         if parallel_config.worker_cls == "auto":
-            if scheduler_config.is_multi_step:
-                if envs.VLLM_USE_V1:
-                    raise NotImplementedError(
-                        "Multi-step scheduling is not supported (and not "
-                        "needed) on vLLM V1. Please launch without "
-                        "--num-scheduler-steps.")
-                else:
-                    parallel_config.worker_cls = \
-                        "vllm.worker.multi_step_worker.MultiStepWorker"
-            elif vllm_config.speculative_config:
+            if vllm_config.speculative_config:
                 if not envs.VLLM_USE_V1:
                     raise NotImplementedError(
                         "Speculative decoding is not supported on vLLM V0.")
@@ -346,7 +336,7 @@ class RocmPlatform(Platform):
             else:
                 if envs.VLLM_USE_V1:
                     parallel_config.worker_cls = \
-                            "vllm.v1.worker.gpu_worker.Worker"
+                        "vllm.v1.worker.gpu_worker.Worker"
                 else:
                     parallel_config.worker_cls = "vllm.worker.worker.Worker"
 

vllm/platforms/tpu.py

@@ -133,18 +133,13 @@ class TpuPlatform(Platform):
         parallel_config = vllm_config.parallel_config
         scheduler_config = vllm_config.scheduler_config
         if parallel_config.worker_cls == "auto":
-            if scheduler_config.is_multi_step:
-                raise NotImplementedError(
-                    "Multi-step scheduling is not supported (and not "
-                    "needed) on vLLM V1. Please launch without "
-                    "--num-scheduler-steps.")
             parallel_config.worker_cls = "vllm.v1.worker.tpu_worker.TPUWorker"
 
         assert not vllm_config.speculative_config, (
             "Speculative decoding is not yet supported for TPU backend")
 
         if scheduler_config.is_multimodal_model and not \
-            scheduler_config.disable_chunked_mm_input:
+                scheduler_config.disable_chunked_mm_input:
             logger.warning("TPU does not support running Multimodal models"\
             " without setting `--disable_chunked_mm_input`. " \
             "Forcing --disable_chunked_mm_input.")

vllm/sequence.py

@@ -794,35 +794,6 @@ class SequenceGroup:
     def lora_int_id(self) -> int:
         return self.lora_request.lora_int_id if self.lora_request else 0
 
-    def init_multi_step(self, num_steps: int) -> None:
-        self.state.num_steps = num_steps
-        self.state.current_step = 0
-
-    def init_multi_step_from_lookahead_slots(self, num_lookahead_slots: int,
-                                             num_scheduler_steps: int,
-                                             is_multi_step: bool,
-                                             enable_chunking: bool) -> None:
-
-        if not is_multi_step:
-            self.init_multi_step(num_steps=num_scheduler_steps)
-            return
-
-        # Multi-Step case
-        is_prefill = self.is_prefill()
-
-        # The asserts below reflect the expectations of the current system.
-        if is_prefill and enable_chunking:
-            assert num_lookahead_slots == num_scheduler_steps
-            self.init_multi_step(num_steps=num_lookahead_slots)
-        else:
-            is_decode: bool = not is_prefill
-            # If it is a prefill, num_lookahead_slots must be 0
-            assert num_lookahead_slots == 0 or is_decode
-            # If it is a decode, num_lookahead_slots + 1 must match
-            # the scheduler steps.
-            assert num_lookahead_slots + 1 == num_scheduler_steps or is_prefill
-            self.init_multi_step(num_steps=num_lookahead_slots + 1)
-
     def set_last_token_time(self, now: float) -> None:
         """Sets the last token time for Request level timings."""
         # If still in prefill phase, assertion fails.
@@ -1367,15 +1338,6 @@ class ExecuteModelRequest(
     # Async callback
     async_callback: Optional[Callable] = None
 
-    @property
-    def is_first_multi_step(self) -> bool:
-        # TODO(will) make this be able to handle batches with variable number of
-        # steps
-        assert len(self.seq_group_metadata_list) > 0
-        first_seq_group = self.seq_group_metadata_list[0]
-        assert first_seq_group.state is not None
-        return first_seq_group.state.current_step == 0
-
     @property
     def is_last_step(self) -> bool:
         # TODO(will) make this be able to handle batches with variable number of

vllm/utils/flashinfer.py

@@ -154,6 +154,7 @@ def use_trtllm_attention(
     num_qo_heads: Optional[int],
     num_kv_heads: Optional[int],
     attn_head_size: Optional[int],
+    has_sinks: bool = False,
 ) -> bool:
     # Requires SM100 and NVIDIA artifactory to be accessible to download cubins
     if not (current_platform.is_device_capability(100)
@@ -165,6 +166,13 @@ def use_trtllm_attention(
             or num_qo_heads % num_kv_heads != 0):
         return False
 
+    # If sinks are being used, we must use TRTLLM attention as it's
+    # the only backend that supports them
+    if has_sinks:
+        logger.info_once(
+            "Using TRTLLM attention (required for attention sinks).")
+        return True
+
     env_value = envs.VLLM_USE_TRTLLM_ATTENTION
     if env_value is not None:
         logger.info_once("VLLM_USE_TRTLLM_ATTENTION is set to %s", env_value)

vllm/v1/attention/backends/flashinfer.py

@@ -523,14 +523,17 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
         num_kv_heads = self.kv_cache_spec.num_kv_heads
         head_dim = self.kv_cache_spec.head_size
 
+        # Check if any layer uses sinks (requires TRTLLM attention)
+        has_sinks = self.global_hyperparameters.has_sinks
+
         # currently prefill trtllm attention does not support fp8 kv cache
         prefill_use_trtllm = not cache_dtype.startswith("fp8") \
                                 and use_trtllm_attention(
                                 num_prefill_tokens, max_seq_len, cache_dtype,
-                                num_qo_heads, num_kv_heads, head_dim)
+                                num_qo_heads, num_kv_heads, head_dim, has_sinks)
         decode_use_trtllm = use_trtllm_attention(
                                 num_decode_tokens, max_seq_len, cache_dtype,
-                                num_qo_heads, num_kv_heads, head_dim)
+                                num_qo_heads, num_kv_heads, head_dim, has_sinks)
 
         attn_metadata = FlashInferMetadata(
             num_actual_tokens=num_actual_tokens,
@@ -642,9 +645,9 @@ class FlashInferImpl(AttentionImpl):
                     f"heads in the layer. Expected {num_heads}, but got "
                     f"{sinks.shape[0]}."
                 )
+            # Cast sinks to float32 if needed (FlashInfer requirement)
             if sinks.dtype != torch.float32:
-                raise ValueError("Sinks must be of type float32, but got "
-                                 f"{sinks.dtype}.")
+                sinks = sinks.to(torch.float32)
             self.sinks = sinks
 
     def forward(

vllm/v1/attention/backends/mamba_attn.py

@@ -68,14 +68,19 @@ class Mamba2AttentionMetadata:
     query_start_loc: torch.Tensor
     seq_lens: torch.Tensor
 
-    has_initial_states: torch.Tensor
     prep_initial_states: bool
     chunk_size: int
-    seq_idx: torch.Tensor
-    chunk_indices: torch.Tensor
-    chunk_offsets: torch.Tensor
+
+    # The following tensors only contain prefill requests and will be None if
+    # the batch has no prefill request.
+    has_initial_states_p: Optional[torch.Tensor]
+    seq_idx_p: Optional[torch.Tensor]
+    chunk_indices_p: Optional[torch.Tensor]
+    chunk_offsets_p: Optional[torch.Tensor]
 
     state_indices_tensor: torch.Tensor  # shape: [batch,]
+
+    # The following attributes are for triton implementation of causal_conv1d
     nums_dict: Optional[dict] = None
     cu_seqlen: Optional[int] = None
     batch_ptr: Optional[torch.tensor] = None
@@ -115,11 +120,11 @@ class Mamba2AttentionMetadataBuilder(
         query_start_loc = common_attn_metadata.query_start_loc
         seq_lens = common_attn_metadata.seq_lens
 
-        seq_idx = None
-        chunk_indices, chunk_offsets = None, None
+        seq_idx_p = None
+        chunk_indices_p, chunk_offsets_p = None, None
         # Need flags to indicate if there are initial states
         # currently we really only support the FlashAttention backend
-        has_initial_states = None
+        has_initial_states_p = None
         prep_initial_states = False
 
         state_indices_tensor = common_attn_metadata.block_table_tensor[:, 0]
@@ -135,25 +140,25 @@ class Mamba2AttentionMetadataBuilder(
                 common_attn_metadata.
                 num_computed_tokens_cpu[num_reqs - num_prefills:num_reqs] > 0)
             prep_initial_states = torch.any(has_initial_states_cpu).item()
-            has_initial_states = has_initial_states_cpu.to(
+            has_initial_states_p = has_initial_states_cpu.to(
                 query_start_loc.device)
 
             query_start_loc_p = common_attn_metadata.query_start_loc[
                 -num_prefills - 1:] - num_decode_tokens
 
-            seq_idx = torch.repeat_interleave(torch.arange(
+            seq_idx_p = torch.repeat_interleave(torch.arange(
                 num_prefills,
                 dtype=torch.int32,
                 device=query_start_loc_p.device),
-                                              query_start_loc_p.diff(),
-                                              output_size=num_prefill_tokens)
-            seq_idx.unsqueeze_(0)
+                                                query_start_loc_p.diff(),
+                                                output_size=num_prefill_tokens)
+            seq_idx_p.unsqueeze_(0)
 
             # We compute metadata for chunked prefill once at the top level
             # model forward and reuse them in mamba layers. If not needed,
             # they will be ignored inside mamba kernels.
             if prep_initial_states:
-                chunk_indices, chunk_offsets = (
+                chunk_indices_p, chunk_offsets_p = (
                     _query_start_loc_to_chunk_indices_offsets(
                         query_start_loc_p, self.chunk_size,
                         num_prefill_tokens))
@@ -173,12 +178,12 @@ class Mamba2AttentionMetadataBuilder(
             num_decode_tokens=num_decode_tokens,
             query_start_loc=query_start_loc,
             seq_lens=seq_lens,
-            has_initial_states=has_initial_states,
             prep_initial_states=prep_initial_states,
             chunk_size=self.chunk_size,
-            seq_idx=seq_idx,
-            chunk_indices=chunk_indices,
-            chunk_offsets=chunk_offsets,
+            has_initial_states_p=has_initial_states_p,
+            seq_idx_p=seq_idx_p,
+            chunk_indices_p=chunk_indices_p,
+            chunk_offsets_p=chunk_offsets_p,
             state_indices_tensor=state_indices_tensor,
         )
         return attn_metadata

vllm/v1/attention/backends/rocm_aiter_fa.py

@@ -214,12 +214,14 @@ class AiterFlashAttentionMetadata:
     #                                   |-- query_len ---|
 
     num_actual_tokens: int  # Number of tokens excluding padding.
+    num_actual_kv_tokens: int
     max_query_len: int
     query_start_loc: torch.Tensor
     max_seq_len: int
     seq_lens: torch.Tensor
     slot_mapping: torch.Tensor
     block_table: torch.Tensor
+    cu_seq_lens: Optional[torch.Tensor]
 
     # For cascade attention.
     use_cascade: bool
@@ -272,6 +274,20 @@ class AiterFlashAttentionMetadataBuilder(
         seq_lens = common_attn_metadata.seq_lens
         block_table_tensor = common_attn_metadata.block_table_tensor
         slot_mapping = common_attn_metadata.slot_mapping
+        if max_query_len > 1:
+            # We pre-compute cumulative seq len needed for prefill attention
+            # here to avoid recomputing it for every layer
+            cu_seq_lens = torch.zeros(seq_lens.shape[0] + 1,
+                                      dtype=torch.int32,
+                                      device=seq_lens.device)
+            torch.cumsum(seq_lens,
+                         dim=0,
+                         dtype=cu_seq_lens.dtype,
+                         out=cu_seq_lens[1:])
+            num_actual_kv_tokens = int(cu_seq_lens[-1].item())
+        else:
+            cu_seq_lens = None
+            num_actual_kv_tokens = 0
 
         def schedule(batch_size, cu_query_lens, max_query_len, seqlens,
                      max_seq_len, causal):
@@ -281,12 +297,14 @@ class AiterFlashAttentionMetadataBuilder(
 
         attn_metadata = AiterFlashAttentionMetadata(
             num_actual_tokens=num_actual_tokens,
+            num_actual_kv_tokens=num_actual_kv_tokens,
             max_query_len=max_query_len,
             query_start_loc=query_start_loc,
             max_seq_len=max_seq_len,
             seq_lens=seq_lens,
             block_table=block_table_tensor,
             slot_mapping=slot_mapping,
+            cu_seq_lens=cu_seq_lens,
             use_cascade=use_cascade,
             common_prefix_len=common_prefix_len,
             total_tokens=self.total_tokens,
@@ -475,16 +493,6 @@ class AiterFlashAttentionImpl(AttentionImpl):
             block_table = attn_metadata.block_table
 
             if max_seqlen_q > 1:
-
-                cu_seq_lens = torch.zeros(seqused_k.shape[0] + 1,
-                                          dtype=torch.int32,
-                                          device=query.device)
-
-                torch.cumsum(seqused_k,
-                             dim=0,
-                             dtype=cu_seq_lens.dtype,
-                             out=cu_seq_lens[1:])
-
                 torch.ops.vllm.flash_attn_varlen_func(
                     query[:num_actual_tokens],
                     key_cache,
@@ -497,10 +505,10 @@ class AiterFlashAttentionImpl(AttentionImpl):
                     alibi_slopes=self.alibi_slopes,
                     window_size=self.sliding_window,
                     block_table=block_table,
-                    cu_seqlens_k=cu_seq_lens,
+                    cu_seqlens_k=attn_metadata.cu_seq_lens,
                     k_scale=layer._k_scale,
                     v_scale=layer._v_scale,
-                    total_tokens=attn_metadata.total_tokens,
+                    total_tokens=attn_metadata.num_actual_kv_tokens,
                 )
 
             _, num_heads, head_size = query.shape

vllm/v1/attention/backends/tree_attn.py

@@ -236,9 +236,9 @@ class TreeAttentionMetadataBuilder(
             # Use prefill for drafting at the root level.
             self.tree_attn_bias = torch.empty(0)
         else:
-            # Slice the tree attention bias for drafting.
-            query_len = common_attn_metadata.max_query_len
-            start, end = draft_index, draft_index + query_len
+            # Slice the tree attention bias for drafting. Exclude
+            # the root level.
+            start, end = 1, 1 + common_attn_metadata.max_query_len
             self.tree_attn_bias = self.tree_attn_bias[start:end,
                                                       start:end].contiguous()
 

vllm/v1/attention/backends/utils.py

@@ -285,6 +285,7 @@ class PerLayerParameters:
     window_left: int
     logits_soft_cap: Optional[float]
     sm_scale: float
+    has_sinks: bool = False
 
 
 def get_per_layer_parameters(
@@ -307,9 +308,11 @@ def get_per_layer_parameters(
         window_left = window_size[0] if window_size is not None else -1
         logits_soft_cap = getattr(impl, "logits_soft_cap", None)
         sm_scale = impl.scale
+        has_sinks = getattr(impl, "sinks", None) is not None
 
         per_layer_params[key] = PerLayerParameters(window_left,
-                                                   logits_soft_cap, sm_scale)
+                                                   logits_soft_cap, sm_scale,
+                                                   has_sinks)
 
     return per_layer_params
 

vllm/v1/core/sched/output.py

@@ -13,7 +13,7 @@ if TYPE_CHECKING:
     from vllm.distributed.kv_transfer.kv_connector.v1.base import (
         KVConnectorMetadata)
     from vllm.lora.request import LoRARequest
-    from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
+    from vllm.multimodal.inputs import MultiModalKwargsItem, PlaceholderRange
     from vllm.pooling_params import PoolingParams
     from vllm.sampling_params import SamplingParams
     from vllm.v1.request import Request
@@ -24,7 +24,7 @@ class NewRequestData:
 
     req_id: str
     prompt_token_ids: list[int]
-    mm_inputs: list[MultiModalKwargs]
+    mm_kwargs: list[MultiModalKwargsItem]
     mm_hashes: list[str]
     mm_positions: list[PlaceholderRange]
     sampling_params: Optional[SamplingParams]
@@ -42,7 +42,7 @@ class NewRequestData:
         return cls(
             req_id=request.request_id,
             prompt_token_ids=request.prompt_token_ids,
-            mm_inputs=request.mm_inputs,
+            mm_kwargs=request.mm_kwargs,
             mm_hashes=request.mm_hashes,
             mm_positions=request.mm_positions,
             sampling_params=request.sampling_params,
@@ -56,7 +56,7 @@ class NewRequestData:
         return (f"NewRequestData("
                 f"req_id={self.req_id},"
                 f"prompt_token_ids={self.prompt_token_ids},"
-                f"mm_inputs={self.mm_inputs},"
+                f"mm_kwargs={self.mm_kwargs},"
                 f"mm_hashes={self.mm_hashes},"
                 f"mm_positions={self.mm_positions},"
                 f"sampling_params={self.sampling_params},"
@@ -70,7 +70,7 @@ class NewRequestData:
         return (f"NewRequestData("
                 f"req_id={self.req_id},"
                 f"prompt_token_ids_len={len(self.prompt_token_ids)},"
-                f"mm_inputs={self.mm_inputs},"
+                f"mm_kwargs={self.mm_kwargs},"
                 f"mm_hashes={self.mm_hashes},"
                 f"mm_positions={self.mm_positions},"
                 f"sampling_params={self.sampling_params},"

vllm/v1/engine/__init__.py

@@ -3,15 +3,13 @@
 
 import enum
 import time
-from collections.abc import Sequence
 from typing import Any, Optional, Union
 
 import msgspec
 import torch
 
 from vllm.lora.request import LoRARequest
-from vllm.multimodal import MultiModalKwargs
-from vllm.multimodal.inputs import PlaceholderRange
+from vllm.multimodal.inputs import MultiModalKwargsItem, PlaceholderRange
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams
 from vllm.v1.metrics.stats import SchedulerStats
@@ -49,7 +47,7 @@ class EngineCoreRequest(
 
     request_id: str
     prompt_token_ids: list[int]
-    mm_inputs: Optional[Sequence[Optional[MultiModalKwargs]]]
+    mm_kwargs: Optional[list[MultiModalKwargsItem]]
     mm_hashes: Optional[list[str]]
     mm_placeholders: Optional[list[PlaceholderRange]]
     sampling_params: Optional[SamplingParams]

vllm/v1/engine/core.py

@@ -409,12 +409,13 @@ class EngineCore:
         request initialization running in parallel with Model forward
         """
         if request.mm_hashes is not None:
-            assert request.mm_inputs is not None
+            assert request.mm_kwargs is not None
+
             # Note on thread safety: no race condition.
             # `mm_input_cache_server` is reset at the end of LLMEngine init,
             # and will only accessed in the input processing thread afterwards.
-            request.mm_inputs = self.mm_input_cache_server.get_and_update(
-                request.mm_inputs, request.mm_hashes)
+            request.mm_kwargs = self.mm_input_cache_server.get_and_update(
+                request.mm_kwargs, request.mm_hashes)
 
         req = Request.from_engine_core_request(request)
         if req.use_structured_output:

vllm/v1/engine/mm_input_cache.py

@@ -1,11 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from collections.abc import Sequence
-from typing import TYPE_CHECKING, Optional
+from collections.abc import Mapping
+from typing import TYPE_CHECKING
 
-from vllm.multimodal import MultiModalKwargs, MultiModalRegistry
+from vllm.multimodal import MultiModalRegistry
 from vllm.multimodal.cache import MultiModalCache, MultiModalCacheItemMetadata
-from vllm.utils import is_list_of
+from vllm.multimodal.inputs import MultiModalKwargsItem, NestedTensors
 
 if TYPE_CHECKING:
     from vllm.config import ModelConfig
@@ -17,23 +17,23 @@ if TYPE_CHECKING:
 # -- P0:
 #  - BaseMultiModalProcessor calls MultiModalHasher to get the `mm_hash` of
 #    each input multi-modal item (e.g. image),
-#  - BaseMultiModalProcessor processes the input items into `mm_inputs`,
+#  - BaseMultiModalProcessor processes the input items into `mm_kwargs`,
 #    which are MultiModalKwargsItem instances that each correspond to an
 #    input multi-modal item.
-#  - MultiModalInputCacheClient accepts the `mm_inputs` and corresponding
+#  - MultiModalInputCacheClient accepts the `mm_kwargs` and corresponding
 #    `mm_hash` for each item. It stores the `mm_hash` as keys and the size
-#    of `mm_inputs`, but not the `mm_inputs` themselves, to avoid taking
+#    of `mm_kwargs`, but not the `mm_kwargs` themselves, to avoid taking
 #    up additional memory in P0.
 #  - The `mm_hash` is always sent to P1.
-#  - The corresponding `mm_inputs` are only sent to P1 if they are not cached
+#  - The corresponding `mm_kwargs` are only sent to P1 if they are not cached
 #    in MultiModalInputCacheServer.
 #
 # -- P1:
-#  - If the `mm_hash` is cached (i.e. `mm_inputs` are not sent from P0),
-#    MultiModalInputCacheServer retrieves the corresponding `mm_inputs`.
-#  - If the `mm_hash` is not cached (i.e. `mm_inputs` are sent from P0),
-#    MultiModalInputCacheServer stores `mm_inputs` under the key `mm_hash`.
-#  - Either way, the `mm_hash` and corresponding `mm_inputs` are sent to
+#  - If the `mm_hash` is cached (i.e. `mm_kwargs` are not sent from P0),
+#    MultiModalInputCacheServer retrieves the corresponding `mm_kwargs`.
+#  - If the `mm_hash` is not cached (i.e. `mm_kwargs` are sent from P0),
+#    MultiModalInputCacheServer stores `mm_kwargs` under the key `mm_hash`.
+#  - Either way, the `mm_hash` and corresponding `mm_kwargs` are sent to
 #    the engine for model execution.
 #
 # Both Client and Server must perform cache update and eviction based on the
@@ -58,26 +58,24 @@ class MultiModalInputCacheClient:
 
     def get_and_update(
         self,
-        mm_inputs: Sequence[MultiModalKwargs],
+        mm_kwargs: list[MultiModalKwargsItem],
         mm_hashes: list[str],
-    ) -> Sequence[Optional[MultiModalKwargs]]:
-        assert len(mm_inputs) == len(mm_hashes)
-
+    ) -> list[MultiModalKwargsItem]:
         if not self.enabled:
-            assert is_list_of(mm_inputs, MultiModalKwargs)
-            return mm_inputs
+            return mm_kwargs
 
-        full_mm_inputs = list[Optional[MultiModalKwargs]]()
-        for mm_input, mm_hash in zip(mm_inputs, mm_hashes):
+        assert len(mm_kwargs) == len(mm_hashes)
+
+        out_mm_items = list[MultiModalKwargsItem]()
+        for mm_item, mm_hash in zip(mm_kwargs, mm_hashes):
             if self.mm_cache.get(mm_hash) is not None:
-                mm_input = None
+                out_mm_items.append(mm_item.without_data())
             else:
                 self.mm_cache[mm_hash] = \
-                    MultiModalCacheItemMetadata.wraps(mm_input)
+                    MultiModalCacheItemMetadata.wraps(mm_item.require_data())
+                out_mm_items.append(mm_item)
 
-            full_mm_inputs.append(mm_input)
-
-        return full_mm_inputs
+        return out_mm_items
 
     def reset(self) -> None:
         self.mm_cache.clear()
@@ -93,30 +91,28 @@ class MultiModalInputCacheServer:
         self.enabled = mm_registry.enable_mm_input_cache(model_config)
         self.mm_cache = MultiModalCache.get_lru_cache(
             model_config.get_mm_input_cache_gb(),
-            MultiModalKwargs,
+            Mapping[str, NestedTensors],
         )
 
     def get_and_update(
         self,
-        mm_inputs: Sequence[Optional[MultiModalKwargs]],
+        mm_kwargs: list[MultiModalKwargsItem],
         mm_hashes: list[str],
-    ) -> Sequence[MultiModalKwargs]:
-        assert len(mm_inputs) == len(mm_hashes)
-
+    ) -> list[MultiModalKwargsItem]:
         if not self.enabled:
-            assert is_list_of(mm_inputs, MultiModalKwargs)
-            return mm_inputs
+            return mm_kwargs
 
-        full_mm_inputs = list[MultiModalKwargs]()
-        for mm_input, mm_hash in zip(mm_inputs, mm_hashes):
-            if mm_input is None:
-                mm_input = self.mm_cache[mm_hash]
+        assert len(mm_kwargs) == len(mm_hashes)
+
+        out_mm_items = list[MultiModalKwargsItem]()
+        for mm_item, mm_hash in zip(mm_kwargs, mm_hashes):
+            if (mm_data := mm_item.get_data()) is None:
+                out_mm_items.append(mm_item.with_data(self.mm_cache[mm_hash]))
             else:
-                self.mm_cache[mm_hash] = mm_input
+                self.mm_cache[mm_hash] = mm_data
+                out_mm_items.append(mm_item)
 
-            full_mm_inputs.append(mm_input)
-
-        return full_mm_inputs
+        return out_mm_items
 
     def reset(self) -> None:
         self.mm_cache.clear()

vllm/v1/engine/processor.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import time
-from collections.abc import Mapping, Sequence
+from collections.abc import Mapping
 from typing import Any, Literal, Optional, Union
 
 from vllm.config import VllmConfig
@@ -10,11 +10,10 @@ from vllm.inputs import ProcessorInputs, PromptType, SingletonInputs
 from vllm.inputs.parse import split_enc_dec_inputs
 from vllm.inputs.preprocess import InputPreprocessor
 from vllm.lora.request import LoRARequest
-from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalKwargs,
-                             MultiModalRegistry)
-from vllm.multimodal.inputs import PlaceholderRange
+from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
+from vllm.multimodal.inputs import MultiModalKwargsItem, PlaceholderRange
 from vllm.multimodal.processing import EncDecMultiModalProcessor
-from vllm.multimodal.utils import merge_and_sort_multimodal_metadata
+from vllm.multimodal.utils import argsort_mm_positions
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams
 from vllm.transformers_utils.tokenizer_group import TokenizerGroup
@@ -296,57 +295,42 @@ class Processor:
             pooling_params = params.clone()
 
         # Multimodal related.
-        sorted_mm_inputs: Optional[Sequence[Optional[MultiModalKwargs]]] = None
+        sorted_mm_inputs: Optional[list[MultiModalKwargsItem]] = None
         sorted_mm_positions: Optional[list[PlaceholderRange]] = None
         sorted_mm_hashes: Optional[list[str]] = None
         if decoder_inputs["type"] == "multimodal":
             decoder_mm_inputs = decoder_inputs["mm_kwargs"]
+            decoder_mm_positions = decoder_inputs["mm_placeholders"]
+            decoder_mm_hashes = decoder_inputs.get("mm_hashes")
 
             # Merge and flatten multimodal placeholders, hashes and inputs
             # from dictionaries to lists, and sort them by each item's position
             # in the input sequence.
-            (
-                sorted_item_modalities,
-                sorted_mm_positions,
-                sorted_mm_hashes,
-            ) = merge_and_sort_multimodal_metadata(
-                decoder_inputs["mm_placeholders"],
-                decoder_inputs["mm_hashes"] if return_mm_hashes else None,
-            )
+            sorted_mm_idxs = argsort_mm_positions(decoder_mm_positions)
 
-            # The output of merged multi-modal processor (`decoder_mm_inputs`)
-            # is a single MultiModalKwargs for all items from all modalities.
-            # This code flattens kwargs for individual items in a list and
-            # sorts them by each item's position in the input sequence if there
-            # are multiple modalities.
-            unique_modalities = set(sorted_item_modalities)
-            if len(unique_modalities) > 1:
-                orig_sorted_mm_inputs = []
-                used_indices = {modality: 0 for modality in unique_modalities}
-
-                for modality in sorted_item_modalities:
-                    items = decoder_mm_inputs.get_items(modality)
-                    item = items[used_indices[modality]]
-
-                    orig_sorted_mm_inputs.append(
-                        MultiModalKwargs.from_items([item]))
-                    used_indices[modality] += 1
-            else:
-                orig_sorted_mm_inputs = [
-                    MultiModalKwargs.from_items([item]) for item in
-                    decoder_mm_inputs.get_items(sorted_item_modalities[0])
-                ]
+            sorted_mm_inputs = [
+                decoder_mm_inputs.get_item(modality, idx)
+                for modality, idx in sorted_mm_idxs
+            ]
+            sorted_mm_positions = [
+                decoder_mm_positions[modality][idx]
+                for modality, idx in sorted_mm_idxs
+            ]
+            sorted_mm_hashes = None if decoder_mm_hashes is None else [
+                decoder_mm_hashes[modality][idx]
+                for modality, idx in sorted_mm_idxs
+            ]
 
             if sorted_mm_hashes is not None:
                 sorted_mm_inputs = self.mm_input_cache_client.get_and_update(
-                    orig_sorted_mm_inputs, sorted_mm_hashes)
-            else:
-                sorted_mm_inputs = orig_sorted_mm_inputs
+                    sorted_mm_inputs,
+                    sorted_mm_hashes,
+                )
 
         return decoder_inputs.get("prompt"), EngineCoreRequest(
             request_id=request_id,
             prompt_token_ids=decoder_inputs["prompt_token_ids"],
-            mm_inputs=sorted_mm_inputs,
+            mm_kwargs=sorted_mm_inputs,
             mm_hashes=sorted_mm_hashes,
             mm_placeholders=sorted_mm_positions,
             sampling_params=sampling_params,

vllm/v1/request.py

@@ -5,7 +5,7 @@ import enum
 import time
 from typing import TYPE_CHECKING, Any, Optional, Union
 
-from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
+from vllm.multimodal.inputs import MultiModalKwargsItem, PlaceholderRange
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams
 from vllm.utils import is_list_of
@@ -24,7 +24,7 @@ class Request:
         self,
         request_id: str,
         prompt_token_ids: list[int],
-        multi_modal_inputs: Optional[list[MultiModalKwargs]],
+        multi_modal_kwargs: Optional[list[MultiModalKwargsItem]],
         multi_modal_hashes: Optional[list[str]],
         multi_modal_placeholders: Optional[list[PlaceholderRange]],
         sampling_params: Optional[SamplingParams],
@@ -84,15 +84,15 @@ class Request:
 
         # Multi-modal related
         self.mm_positions = multi_modal_placeholders or []
-        self.mm_inputs = multi_modal_inputs or []
+        self.mm_kwargs = multi_modal_kwargs or []
         self.mm_hashes: list[str] = multi_modal_hashes or []
-        self.num_encoder_inputs = len(self.mm_inputs)
+        self.num_encoder_inputs = len(self.mm_kwargs)
         self.has_encoder_inputs = self.num_encoder_inputs > 0
 
         # Sanity check
-        assert len(self.mm_inputs) == len(self.mm_positions)
+        assert len(self.mm_kwargs) == len(self.mm_positions)
         if self.mm_hashes:
-            assert len(self.mm_inputs) == len(self.mm_hashes)
+            assert len(self.mm_kwargs) == len(self.mm_hashes)
 
         # Read-only views
         # Prevent directly appending to these lists since
@@ -110,16 +110,15 @@ class Request:
 
     @classmethod
     def from_engine_core_request(cls, request: EngineCoreRequest) -> "Request":
-        if request.mm_inputs is not None:
-            assert isinstance(request.mm_inputs, list)
-            assert is_list_of(request.mm_inputs, MultiModalKwargs), (
-                "mm_inputs was not updated in EngineCore.add_request")
+        if request.mm_kwargs is not None:
+            assert is_list_of(request.mm_kwargs, MultiModalKwargsItem), (
+                "mm_kwargs was not updated in EngineCore.add_request")
 
         return cls(
             request_id=request.request_id,
             client_index=request.client_index,
             prompt_token_ids=request.prompt_token_ids,
-            multi_modal_inputs=request.mm_inputs,
+            multi_modal_kwargs=request.mm_kwargs,
             multi_modal_hashes=request.mm_hashes,
             multi_modal_placeholders=request.mm_placeholders,
             sampling_params=request.sampling_params,

vllm/v1/serial_utils.py

@@ -113,6 +113,9 @@ class MsgpackEncoder:
                 int(v) if v is not None else None
                 for v in (obj.start, obj.stop, obj.step))
 
+        if isinstance(obj, MultiModalKwargsItem):
+            return self._encode_mm_item(obj)
+
         if isinstance(obj, MultiModalKwargs):
             mm: MultiModalKwargs = obj
             if not mm.modalities:
@@ -120,17 +123,12 @@ class MsgpackEncoder:
                 return dict(mm)
 
             # ignore the main dict, it will be re-indexed.
-            # Encode a list of MultiModalKwargsItems as plain dicts
-            # + special handling for .field.
             # Any tensors *not* indexed by modality will be ignored.
-            return [[{
-                "modality": elem.modality,
-                "key": elem.key,
-                "data": self._encode_nested_tensors(elem.data),
-                "field": self._encode_mm_field(elem.field),
-            } for elem in item.values()]
-                    for itemlist in mm._items_by_modality.values()
-                    for item in itemlist]
+            return [
+                self._encode_mm_item(item)
+                for itemlist in mm._items_by_modality.values()
+                for item in itemlist
+            ]
 
         if isinstance(obj, UtilityResult):
             result = obj.result
@@ -192,6 +190,23 @@ class MsgpackEncoder:
         dtype = str(obj.dtype).removeprefix("torch.")
         return dtype, obj.shape, data
 
+    def _encode_mm_item(self,
+                        item: MultiModalKwargsItem) -> list[dict[str, Any]]:
+        return [self._encode_mm_field_elem(elem) for elem in item.values()]
+
+    def _encode_mm_field_elem(self,
+                              elem: MultiModalFieldElem) -> dict[str, Any]:
+        return {
+            "modality":
+            elem.modality,
+            "key":
+            elem.key,
+            "data": (None if elem.data is None else
+                     self._encode_nested_tensors(elem.data)),
+            "field":
+            self._encode_mm_field(elem.field),
+        }
+
     def _encode_nested_tensors(self, nt: NestedTensors) -> Any:
         if isinstance(nt, torch.Tensor):
             return self._encode_tensor(nt)
@@ -250,6 +265,8 @@ class MsgpackDecoder:
                 return self._decode_tensor(obj)
             if t is slice:
                 return slice(*obj)
+            if issubclass(t, MultiModalKwargsItem):
+                return self._decode_mm_item(obj)
             if issubclass(t, MultiModalKwargs):
                 if isinstance(obj, list):
                     return MultiModalKwargs.from_items(
@@ -311,15 +328,18 @@ class MsgpackDecoder:
         # Convert back to proper shape & type
         return arr.view(torch_dtype).view(shape)
 
-    def _decode_mm_items(self, obj: list) -> list[MultiModalKwargsItem]:
+    def _decode_mm_items(self, obj: list[Any]) -> list[MultiModalKwargsItem]:
         return [self._decode_mm_item(v) for v in obj]
 
-    def _decode_mm_item(self, obj: list) -> MultiModalKwargsItem:
+    def _decode_mm_item(self, obj: list[Any]) -> MultiModalKwargsItem:
         return MultiModalKwargsItem.from_elems(
             [self._decode_mm_field_elem(v) for v in obj])
 
-    def _decode_mm_field_elem(self, obj: dict) -> MultiModalFieldElem:
-        obj["data"] = self._decode_nested_tensors(obj["data"])
+    def _decode_mm_field_elem(self, obj: dict[str,
+                                              Any]) -> MultiModalFieldElem:
+        if obj["data"] is not None:
+            obj["data"] = self._decode_nested_tensors(obj["data"])
+
         # Reconstruct the field processor using MultiModalFieldConfig
         factory_meth_name, *field_args = obj["field"]
         factory_meth = getattr(MultiModalFieldConfig, factory_meth_name)

vllm/v1/spec_decode/eagle.py

@@ -113,13 +113,6 @@ class EagleProposer:
                                             num_drafts_per_level[level])
             self.child_drafts_per_level.append(num_drafts_per_level[level] //
                                                num_drafts_per_level[level - 1])
-        # Find the first level where the tree branches off into one or more
-        # children.
-        self.first_branching_level = None
-        for level in range(tree_depth):
-            if self.cu_drafts_per_level[level] > level + 1:
-                self.first_branching_level = level
-                break
         # Precompute draft position offsets in flattened tree.
         self.tree_draft_pos_offsets = torch.arange(
             1,
@@ -209,11 +202,10 @@ class EagleProposer:
         logits = self.model.compute_logits(sample_hidden_states, None)
         positions = target_positions[last_token_indices]
         hidden_states = hidden_states[last_token_indices]
-        if self.first_branching_level == 0:
-            # Branching has occurred at the root level. Draft using tree
-            # attention.
+
+        if isinstance(attn_metadata, TreeAttentionMetadata):
+            # Draft using tree attention.
             draft_token_ids_list = self.propose_tree(
-                tree_root_level=0,
                 batch_size=batch_size,
                 logits=logits,
                 positions=positions,
@@ -242,11 +234,10 @@ class EagleProposer:
                 (TritonAttentionMetadata, AiterFlashAttentionMetadata,
                  FlashAttentionMetadata))
         else:
-            # Currently, only FlashAttention and TreeAttention support
-            # multi-token eagle spec decode. This is because the code below
-            # makes assumptions about attn_metadata attributes available.
-            assert isinstance(attn_metadata,
-                              (FlashAttentionMetadata, TreeAttentionMetadata))
+            # Currently, only FlashAttention supports multi-token eagle spec
+            # decode. This is because the code below makes assumptions about
+            # attn_metadata attributes available.
+            assert isinstance(attn_metadata, FlashAttentionMetadata)
 
         # Generate the remaining draft tokens.
         draft_token_ids_list = [draft_token_ids]
@@ -259,7 +250,7 @@ class EagleProposer:
         attn_metadata.num_actual_tokens = batch_size
         attn_metadata.max_query_len = 1
         attn_metadata.query_start_loc = self.arange[:batch_size + 1]
-        for token_index in range(self.num_speculative_tokens - 1):
+        for _ in range(self.num_speculative_tokens - 1):
             # Update the inputs.
             # cast to int32 is crucial when eagle model is compiled.
             # tensor.argmax() returns int64 by default.
@@ -327,21 +318,6 @@ class EagleProposer:
             hidden_states = hidden_states[:batch_size]
             logits = self.model.compute_logits(last_hidden_states[:batch_size],
                                                None)
-
-            if self.first_branching_level == token_index + 1:
-                # Branching has occurred. The remaining tokens are drafted
-                # using tree attention.
-                draft_token_ids_list += self.propose_tree(
-                    tree_root_level=token_index + 1,
-                    batch_size=batch_size,
-                    logits=logits,
-                    positions=positions,
-                    hidden_states=hidden_states,
-                    common_attn_metadata=common_attn_metadata,
-                )
-                # [batch_size, num_tree_tokens]
-                return torch.cat(draft_token_ids_list, dim=1)
-
             draft_token_ids = logits.argmax(dim=-1)
             draft_token_ids_list.append(draft_token_ids)
 
@@ -351,7 +327,6 @@ class EagleProposer:
 
     def propose_tree(
         self,
-        tree_root_level: int,
         batch_size: int,
         # [num_tokens, vocab_size]
         logits: torch.Tensor,
@@ -366,10 +341,10 @@ class EagleProposer:
         assert isinstance(tree_attn_metadata_builder,
                           TreeAttentionMetadataBuilder)
 
-        total_num_drafts = self.cu_drafts_per_level[tree_root_level]
+        total_num_drafts = self.cu_drafts_per_level[0]
         level_num_drafts = total_num_drafts
         # Sample a draft token for each child at the tree root level.
-        num_children = self.child_drafts_per_level[tree_root_level]
+        num_children = self.child_drafts_per_level[0]
         if num_children == 1:
             draft_token_ids = logits.argmax(dim=-1).view(batch_size, -1)
         else:
@@ -393,22 +368,23 @@ class EagleProposer:
             positions.view(batch_size, -1) +
             self.tree_draft_pos_offsets[:batch_size, :])
         tree_depth = len(self.cu_drafts_per_level)
-        for level in range(tree_root_level, tree_depth - 1):
+        for level in range(tree_depth - 1):
             # Get draft positions for RoPE.
             draft_positions = positions + (level + 1)
             exceeds_max_model_len = (positions +
                                      total_num_drafts) >= self.max_model_len
             # Mask out the position ids that exceed the max model length.
             # Otherwise, we may get out-of-range error in RoPE.
-            clamped_draft_positions = torch.where(
+            draft_positions = torch.where(
                 exceeds_max_model_len,
                 0,
                 draft_positions,
-            )
+            ).view(batch_size, -1)
+
             if level_num_drafts > 1:
                 # Repeat the positions for each draft at this level.
-                draft_positions = clamped_draft_positions.repeat_interleave(
-                    level_num_drafts).reshape(batch_size, -1)
+                draft_positions = draft_positions.repeat_interleave(
+                    level_num_drafts, dim=1)
 
             if num_children > 1:
                 # Repeat draft hidden states for each child.
@@ -425,7 +401,7 @@ class EagleProposer:
 
             # Build new attention metadata for the next level of drafts.
             # This is necessary to support tree attention.
-            query_len = total_num_drafts - tree_root_level
+            query_len = total_num_drafts
             common_attn_metadata = replace(
                 common_attn_metadata,
                 query_start_loc=query_len * self.arange[:batch_size + 1],
@@ -435,7 +411,7 @@ class EagleProposer:
             )
             attn_metadata = tree_attn_metadata_builder.build_for_drafting(
                 common_attn_metadata=common_attn_metadata,
-                draft_index=tree_root_level + 1,
+                draft_index=level + 1,
             )
 
             # Apply new attention metadata to all layers.
@@ -516,7 +492,6 @@ class EagleProposer:
             level_num_drafts = self.cu_drafts_per_level[level +
                                                         1] - total_num_drafts
             total_num_drafts = self.cu_drafts_per_level[level + 1]
-
         return draft_token_ids_list
 
     def prepare_inputs(

vllm/v1/worker/gpu_input_batch.py

@@ -7,9 +7,11 @@ from typing import Optional, cast
 
 import numpy as np
 import torch
+from typing_extensions import deprecated
 
 from vllm.lora.request import LoRARequest
-from vllm.multimodal.inputs import MultiModalKwargs, PlaceholderRange
+from vllm.multimodal.inputs import (MultiModalKwargs, MultiModalKwargsItem,
+                                    PlaceholderRange)
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams, SamplingType
 from vllm.utils import swap_dict_values
@@ -29,7 +31,7 @@ class CachedRequestState:
 
     req_id: str
     prompt_token_ids: list[int]
-    mm_inputs: list[MultiModalKwargs]
+    mm_kwargs: list[MultiModalKwargsItem]
     mm_positions: list[PlaceholderRange]
     sampling_params: Optional[SamplingParams]
     pooling_params: Optional[PoolingParams]
@@ -51,6 +53,13 @@ class CachedRequestState:
     def num_tokens(self) -> int:
         return self.num_prompt_tokens + len(self.output_token_ids)
 
+    # Temporary back-compatibility for plugins that define model runner
+    @property
+    @deprecated("`mm_inputs` is superseded by `mm_kwargs` and will be "
+                "removed in v0.13. Please use `mm_kwargs` instead.")
+    def mm_inputs(self) -> list[MultiModalKwargs]:
+        return [MultiModalKwargs.from_items([item]) for item in self.mm_kwargs]
+
     def get_token_id(self, idx: int) -> int:
         if idx < self.num_prompt_tokens:
             return self.prompt_token_ids[idx]