GitLab

From @mgoin in https://github.com/vllm-project/vllm/pull/12638



I cannot push to that branch, therefore a new PR to unblock release.

---------
Signed-off-by: mgoin <michael@neuralmagic.com>
Signed-off-by: simon-mo <simon.mo@hey.com>
Co-authored-by: mgoin <michael@neuralmagic.com>

We have `v1`, `structured-output`, and `speculative-decoding` labels on
github. This adds automation for applying these labels based on the
files touched by a PR.
Signed-off-by: Russell Bryant <rbryant@redhat.com>

---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>

This PR implements the Deepseek V3 support by performing matrix absorption the fp8 weights 

---------
Signed-off-by: Lucas Wilkinson <lwilkinson@neuralmagic.com>
Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
Co-authored-by: simon-mo <simon.mo@hey.com>
Co-authored-by: Michael Goin <mgoin64@gmail.com>
Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>
Co-authored-by: Tyler Michael Smith <tysmith@redhat.com>
Co-authored-by: Alexander Matveev <59768536+alexm-neuralmagic@users.noreply.github.com>

This PR addresses a bug in the Cutlass integration where the
`sparsity_config.ignore` list was not being respected. When only a
subset of modules were configured as Sparse24, the system incorrectly
selected Cutlass for non-sparse modules as well. This update ensures the
correct scheme is selected for non-sparse modules, fixing this behavior.

---

### Changes

- Updated logic to correctly respect `sparsity_config.ignore`.
- Ensured non-sparse modules use the appropriate scheme instead of
defaulting to Cutlass.

---

<details>
<summary>Testing Setup</summary>

The fix has been tested on top of [this
diff](https://github.com/vllm-project/vllm/pull/12097).

#### Steps to Test:
```bash
git checkout -b my-test-branch origin/rahul-bitmask-additions # compressed Cutlass support
git revert --no-edit aa2cd2c4 # revert Tyler's commit to turn off Cutlass for W16A16
git cherry-pick ca624cddb # this branch
```

#### Additional Patch Required:
```diff
diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
index a54177c1c..f916dd0c9 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
@@ -9,7 +9,7 @@ from compressed_tensors.quantization import (QuantizationArgs,
                                              QuantizationStrategy,
                                              QuantizationType)
 from pydantic import BaseModel
-
+from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod)
@@ -27,7 +27,7 @@ from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
     should_ignore_layer)
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
 from vllm.platforms import current_platform
-
+logger = init_logger(__name__)
 __all__ = ["CompressedTensorsLinearMethod"]
 
 SPARSITY_CONFIG_NAME: Literal["sparsity_config"] = "sparsity_config"
```

Apply using:
```bash
git apply logging-patch.patch
```

</details>

---

<details>
<summary>Models Tested</summary>

- `nm-testing/TinyLlama-1.1B-Chat-v1.0-gsm8k-partial-24` 
- `nm-testing/TinyLlama-1.1B-Chat-v1.0-gsm8k-full-sparse24`
-
`nm-testing/TinyLlama-1.1B-Chat-v1.0-gsm8k-partial-24-entire-fp8-compressed`
-
`nm-testing/TinyLlama-1.1B-Chat-v1.0-gsm8k-partial-24-remaining-fp8-compressed`

</details>

---


<details>
<summary>Example Output</summary>

#### Layers 0-5 (Sparse24)
```
Using scheme: CompressedTensors24 for model.layers.0.self_attn.qkv_proj
Using scheme: CompressedTensors24 for model.layers.0.self_attn.o_proj
Using scheme: CompressedTensors24 for model.layers.0.mlp.gate_up_proj
Using scheme: CompressedTensors24 for model.layers.0.mlp.down_proj
...
```

#### Layers 6+ (Non-Sparse, FP8)
```
Using scheme: CompressedTensorsW8A8Fp8 for model.layers.6.self_attn.qkv_proj
Using scheme: CompressedTensorsW8A8Fp8 for model.layers.6.self_attn.o_proj
Using scheme: CompressedTensorsW8A8Fp8 for model.layers.6.mlp.gate_up_proj
Using scheme: CompressedTensorsW8A8Fp8 for model.layers.6.mlp.down_proj
...
```

</details>

**Note:** Assumed all modules in fused layers such as `QKV_proj` and
`Gate_up_proj` follow the same quantization/pruning scheme.

---

For related tasks using the Asana app for GitHub, refer to [[this
link](https://app.asana.com/0/0/1209227810815160)](https://app.asana.com/0/0/1209227810815160

).
Signed-off-by: Rahul Tuli <rahul@neuralmagic.com>

Fixes `is_marlin` not being passed into `get_default_config`

Also allow `--tensor-parallel-size` in addition to `-tp` and `--tp-size`
Signed-off-by: Tyler Michael Smith <tyler@neuralmagic.com>

Without this PR
---------------
Quantizing models with llm-compressor and a recipe that explicitly lists
names of layers produces a model that is not loadable by vLLM (i.e.
`vllm serve <model>` fails with `raise ValueError(f"Unable to find
matching target for {module} in the ...`).

Example recipe:
```
recipe = """
quantization_stage:
  run_type: oneshot
  quantization_modifiers:
    GPTQModifier:
      ignore: ["lm_head"]
      config_groups:
        group_0:
          weights:
            num_bits: 4
            type: "int"
            symmetric: true
            strategy: "group"
            group_size: 128
          targets: [
            "model.layers.0.mlp.down_proj",
            "model.layers.2.mlp.down_proj",
            "model.layers.3.mlp.down_proj",
            "model.layers.4.mlp.down_proj",
            "model.layers.5.mlp.down_proj",
            "model.layers.6.mlp.down_proj",
            "model.layers.7.mlp.down_proj",
            "model.layers.8.mlp.down_proj",
            "model.layers.9.mlp.down_proj",
            "model.layers.10.mlp.down_proj",
            "model.layers.11.mlp.down_proj",
            "model.layers.12.mlp.down_proj",
            "model.layers.13.mlp.down_proj",
            "model.layers.14.mlp.down_proj",
            "model.layers.15.mlp.down_proj",
            "model.layers.16.mlp.down_proj",
            "model.layers.17.mlp.down_proj",
            "model.layers.19.mlp.down_proj",
            "model.layers.21.mlp.down_proj",
            "model.layers.22.mlp.down_proj",
            .
            .
            .
          ]
"""
```

To reproduce the vLLM error: 
```bash
vllm serve nm-testing/eldar-test
```

With this PR
------------
Models are loaded correctly without any errors.

FIX issue https://github.com/vllm-project/vllm/issues/9688
https://github.com/vllm-project/vllm/issues/11086

 #12487

---------
Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>
Co-authored-by: weilong.yu <weilong.yu@shopee.com>
Co-authored-by: Jee Jee Li <pandaleefree@gmail.com>

SUMMARY:
* avoid crashing the engine when we get an input longer than
max_model_len

FIX #12567(*link existing issues this PR will resolve*)

Based on a request by @mgoin , with @kylesayrs we have added an example
doc for int4 w4a16 quantization, following the pre-existing int8 w8a8
quantization example and the example available in
[`llm-compressor`](https://github.com/vllm-project/llm-compressor/blob/main/examples/quantization_w4a16/llama3_example.py

)

FIX #n/a (no issue created)

@kylesayrs and I have discussed a couple additional improvements for the
quantization docs. We will revisit at a later date, possibly including:
- A section for "choosing the correct quantization scheme/ compression
technique"
- Additional vision or audio calibration datasets

---------
Signed-off-by: Brian Dellabetta <bdellabe@redhat.com>
Co-authored-by: Michael Goin <michael@neuralmagic.com>

- Make device tab names more explicit
- Add comprehensive list of devices to
https://docs.vllm.ai/en/latest/getting_started/installation/index.html


- Add `attention` blocks to the intro of all devices that don't have
pre-built wheels/images

---------
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

**[Guided decoding performance optimization]** Sending the guided
decoding bitmask in xgrammar to the GPU
(`self.token_bitmask.to(scores.device)`) is a blocking operation that
prevents the CPU from pre-launching the sampler kernels. The CPU waits
until decode is complete, then copies the bitmask over. This PR changes
the operation to async via setting `non-blocking=True`.

(Current) The CPU is blocked on a `cudaStreamSynchronize` and only
pre-empts the sampling kernels after bitmask application. Below is the
Nsys profile for one decode phase from Llama 3.1 8B.

![image](https://github.com/user-attachments/assets/8997eae1-b822-4f52-beb8-ef19a7c6b824)

With the optimization, this is no longer the case:

![image](https://github.com/user-attachments/assets/6d5ea83f-f169-4f98-a8c1-41c719b3e1e7

)

---------
Signed-off-by: Ryan N <ryan.nguyen@centml.ai>

Integrates the block-quantized kernels introduced in
https://github.com/vllm-project/vllm/pull/11868

 for use in linear
layers.
Signed-off-by: Tyler Michael Smith <tyler@neuralmagic.com>

SUMMARY:
* previous PR for pulling in block configs also changed defaults
(https://github.com/vllm-project/vllm/pull/11589/files

) for FP8
* this broke L4 MoE since there was not enough SHM for the default
configuration
* this reverts the non-block example to the default
Signed-off-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com>

Instead of having to create a new build with release version put in as
env var.

This pr adds extra key to block hash, to generate different hash value
for two blocks with the same token string but different extra_keys in
their parent blocks. For example, it can generate different hash value
for the second block of the following two requests:
```python
request1 = make_request(
        request_id=0,
        prompt_token_ids=[_ for _ in range(6)],
        mm_positions=[{
            "offset": 0,
            "length": 3
        }, {
            "offset": 3,
            "length": 3
        }],
        mm_hashes=["hash1", "hash2"],
    )
    request2 = make_request(
        request_id=1,
        prompt_token_ids=[_ for _ in range(6)],
        mm_positions=[{
            "offset": 0,
            "length": 3
        }, {
            "offset": 3,
            "length": 3
        }],
        mm_hashes=["hash3", "hash2"],
    )
```

---------
Signed-off-by: Chen Zhang <zhangch99@outlook.com>

- Create v1 design document section in docs.
- Add prefix caching design doc.

@WoosukKwon @ywang96

---------
Signed-off-by: Cody Yu <hao.yu.cody@gmail.com>

It's very annoying when I forgot to add `-s` in `git commit` to
sign-off, because I then need to `git rebase HEAD~1 --signoff` and `git
push -f` to fix the DCO. This PR adds a hook to sign off commits
automatically when `-s` is missing to solve this problem. The only
change from the user side is now users have to install 2 hooks, so
instead of just

```
pre-commit install
```

Now we need to

```
pre-commit install --hook-type pre-commit --hook-type commit-msg
```

Note that even if users still only install the pre-commit hook, they
won't get any error in `git commit`. Just the sign-off hook won't run.

cc @hmellor @youkaichao

---------
Signed-off-by: Cody Yu <hao.yu.cody@gmail.com>

Co-authored-by: simon-mo <xmo@berkeley.edu>

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

Signed-off-by: Lucas Wilkinson <lwilkinson@neuralmagic.com>
Signed-off-by: simon-mo <xmo@berkeley.edu>
Co-authored-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
Co-authored-by: simon-mo <simon.mo@hey.com>
Co-authored-by: Michael Goin <mgoin64@gmail.com>
Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>
Co-authored-by: Tyler Michael Smith <tysmith@redhat.com>
Co-authored-by: Alexander Matveev <59768536+alexm-neuralmagic@users.noreply.github.com>
Co-authored-by: simon-mo <xmo@berkeley.edu>

Signed-off-by: Aleksandr Malyshev <maleksan@amd.com>
Co-authored-by: Aleksandr Malyshev <maleksan@amd.com>

Signed-off-by: mgoin <michael@neuralmagic.com>

Signed-off-by: npanpaliya <nishidha.panpaliya@partner.ibm.com>

Signed-off-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com>
Signed-off-by: mgoin <michael@neuralmagic.com>
Co-authored-by: mgoin <michael@neuralmagic.com>
Co-authored-by: simon-mo <xmo@berkeley.edu>

Signed-off-by: Beim <beim2015@outlook.com>

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

Signed-off-by: Mark McLoughlin <markmc@redhat.com>

Signed-off-by: Divakar Verma <divakar.verma@amd.com>

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>

Signed-off-by: Pavani Majety <pmajety@nvidia.com>
Co-authored-by: mgoin <michael@neuralmagic.com>

Signed-off-by: liuyanyi <wolfsonliu@163.com>