Mirrors
/
hf_text-generation-inference
mirror of https://github.com/huggingface/text-generation-inference.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'main' into moe

This commit is contained in:
Wang, Yi 2024-11-18 09:45:05 +08:00 committed by GitHub
parent 1639152ca4 52e48739a5
commit e0e39fa0d9
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
42 changed files with 2298 additions and 226 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
Dockerfile
View File

@ -161,18 +161,6 @@ COPY server/custom_kernels/ .
# Build specific version of transformers
RUN python setup.py build
# Build vllm CUDA kernels
FROM kernel-builder AS vllm-builder
WORKDIR /usr/src
ENV TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 8.9 9.0+PTX"
COPY server/Makefile-vllm Makefile
# Build specific version of vllm
RUN make build-vllm-cuda
# Build mamba kernels
FROM kernel-builder AS mamba-builder
WORKDIR /usr/src
@ -230,8 +218,6 @@ COPY --from=awq-kernels-builder /usr/src/llm-awq/awq/kernels/build/lib.linux-x86
COPY --from=eetq-kernels-builder /usr/src/eetq/build/lib.linux-x86_64-cpython-311 /opt/conda/lib/python3.11/site-packages
# Copy build artifacts from lorax punica kernels builder
COPY --from=lorax-punica-builder /usr/src/lorax-punica/server/punica_kernels/build/lib.linux-x86_64-cpython-311 /opt/conda/lib/python3.11/site-packages
# Copy build artifacts from vllm builder
COPY --from=vllm-builder /usr/src/vllm/build/lib.linux-x86_64-cpython-311 /opt/conda/lib/python3.11/site-packages
# Copy build artifacts from mamba builder
COPY --from=mamba-builder /usr/src/mamba/build/lib.linux-x86_64-cpython-311/ /opt/conda/lib/python3.11/site-packages
COPY --from=mamba-builder /usr/src/causal-conv1d/build/lib.linux-x86_64-cpython-311/ /opt/conda/lib/python3.11/site-packages
@ -247,7 +233,7 @@ COPY server/Makefile server/Makefile
RUN cd server && \
make gen-server && \
pip install -r requirements_cuda.txt && \
pip install ".[bnb, accelerate, marlin, moe, quantize, peft, outlines]" --no-cache-dir && \
pip install ".[attention, bnb, accelerate, compressed-tensors, marlin, moe, quantize, peft, outlines]" --no-cache-dir && \
pip install nvidia-nccl-cu12==2.22.3
ENV LD_PRELOAD=/opt/conda/lib/python3.11/site-packages/nvidia/nccl/lib/libnccl.so.2

2
Dockerfile_amd
View File

@ -296,7 +296,7 @@ COPY server/Makefile server/Makefile
RUN cd server && \
make gen-server && \
pip install -r requirements_rocm.txt && \
pip install ".[accelerate, peft, outlines]" --no-cache-dir
pip install ".[accelerate, compressed-tensors, peft, outlines]" --no-cache-dir
# Install benchmarker
COPY --from=builder /usr/src/target/release-opt/text-generation-benchmark /usr/local/bin/text-generation-benchmark

2
Dockerfile_intel
View File

@ -117,7 +117,7 @@ COPY server/Makefile server/Makefile
RUN cd server && \
make gen-server && \
pip install -r requirements_intel.txt && \
pip install ".[accelerate, peft, outlines]" --no-cache-dir
pip install ".[accelerate, compressed-tensors, peft, outlines]" --no-cache-dir
ENV CCL_ROOT=/opt/intel/oneapi/ccl/latest
ENV I_MPI_ROOT=/opt/intel/oneapi/mpi/latest

2
benchmark/src/generation.rs
View File

@ -180,7 +180,7 @@ async fn prefill(
let latency = start_time.elapsed();
// Compute throughput from latency and batch size
let throughput = batch_size as f64 / latency.as_secs_f64();
let throughput = (batch_size * sequence_length) as f64 / latency.as_secs_f64();
// Decode batch cannot be empty
let decode_batch = decode_batch.expect("decode_batch is None. This is a bug.");

5
docs/openapi.json
View File

@ -36,7 +36,10 @@
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/GenerateResponse"
"type": "array",
"items": {
"$ref": "#/components/schemas/GenerateResponse"
}
}
},
"text/event-stream": {

19
docs/source/reference/launcher.md
View File

@ -62,15 +62,16 @@ Options:
[env: QUANTIZE=]
Possible values:
- awq: 4 bit quantization. Requires a specific AWQ quantized model: <https://hf.co/models?search=awq>. Should replace GPTQ models wherever possible because of the better latency
- eetq: 8 bit quantization, doesn't require specific model. Should be a drop-in replacement to bitsandbytes with much better performance. Kernels are from <https://github.com/NetEase-FuXi/EETQ.git>
- exl2: Variable bit quantization. Requires a specific EXL2 quantized model: <https://hf.co/models?search=exl2>. Requires exllama2 kernels and does not support tensor parallelism (num_shard > 1)
- gptq: 4 bit quantization. Requires a specific GTPQ quantized model: <https://hf.co/models?search=gptq>. text-generation-inference will use exllama (faster) kernels wherever possible, and use triton kernel (wider support) when it's not. AWQ has faster kernels
- marlin: 4 bit quantization. Requires a specific Marlin quantized model: <https://hf.co/models?search=marlin>
- bitsandbytes: Bitsandbytes 8bit. Can be applied on any model, will cut the memory requirement in half, but it is known that the model will be much slower to run than the native f16
- bitsandbytes-nf4: Bitsandbytes 4bit. Can be applied on any model, will cut the memory requirement by 4x, but it is known that the model will be much slower to run than the native f16
- bitsandbytes-fp4: Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model
- fp8: [FP8](https://developer.nvidia.com/blog/nvidia-arm-and-intel-publish-fp8-specification-for-standardization-as-an-interchange-format-for-ai/) (e4m3) works on H100 and above This dtype has native ops should be the fastest if available. This is currently not the fastest because of local unpacking + padding to satisfy matrix multiplication limitations
- awq: 4 bit quantization. Requires a specific AWQ quantized model: <https://hf.co/models?search=awq>. Should replace GPTQ models wherever possible because of the better latency
- compressed-tensors: Compressed tensors, which can be a mixture of different quantization methods
- eetq: 8 bit quantization, doesn't require specific model. Should be a drop-in replacement to bitsandbytes with much better performance. Kernels are from <https://github.com/NetEase-FuXi/EETQ.git>
- exl2: Variable bit quantization. Requires a specific EXL2 quantized model: <https://hf.co/models?search=exl2>. Requires exllama2 kernels and does not support tensor parallelism (num_shard > 1)
- gptq: 4 bit quantization. Requires a specific GTPQ quantized model: <https://hf.co/models?search=gptq>. text-generation-inference will use exllama (faster) kernels wherever possible, and use triton kernel (wider support) when it's not. AWQ has faster kernels
- marlin: 4 bit quantization. Requires a specific Marlin quantized model: <https://hf.co/models?search=marlin>
- bitsandbytes: Bitsandbytes 8bit. Can be applied on any model, will cut the memory requirement in half, but it is known that the model will be much slower to run than the native f16
- bitsandbytes-nf4: Bitsandbytes 4bit. Can be applied on any model, will cut the memory requirement by 4x, but it is known that the model will be much slower to run than the native f16
- bitsandbytes-fp4: Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model
- fp8: [FP8](https://developer.nvidia.com/blog/nvidia-arm-and-intel-publish-fp8-specification-for-standardization-as-an-interchange-format-for-ai/) (e4m3) works on H100 and above This dtype has native ops should be the fastest if available. This is currently not the fastest because of local unpacking + padding to satisfy matrix multiplication limitations
```
## SPECULATE

12
flake.lock
View File

@ -718,11 +718,11 @@
},
"nixpkgs_6": {
"locked": {
"lastModified": 1727675176,
"narHash": "sha256-xIjBFMYldWvj+g8ahxMPofsj+OqxvKJN6YylNHQ7gn4=",
"lastModified": 1731562571,
"narHash": "sha256-9V0C/H6NL2Vk3Y76msqNA8TgwZ6Ge4frOVawTNFJQmM=",
"owner": "nixos",
"repo": "nixpkgs",
"rev": "a6d0207fea9212d28cd3d487efe6bc699663b93a",
"rev": "19d66fab291f90ce56d0479b128cc7a5271bf666",
"type": "github"
},
"original": {
@ -978,11 +978,11 @@
"nixpkgs": "nixpkgs_6"
},
"locked": {
"lastModified": 1730724647,
"narHash": "sha256-SVv+50CGaCoU4zZwsg6ZAaOi/D5QJBL1P2SIB+3CEf4=",
"lastModified": 1731674227,
"narHash": "sha256-k/ur37KSc+RXcwwz0tgxeamz6wQ5rsOe5hMepzIdD2s=",
"owner": "huggingface",
"repo": "text-generation-inference-nix",
"rev": "1512898a1e5ad9eff025205fa9c4d33a44506cf3",
"rev": "407b9e22a0b7121bf6e171d67ce0144e3f3e39bf",
"type": "github"
},
"original": {

104
integration-tests/models/__snapshots__/test_compressed_tensors_w8an_fp/test_compressed_tensors_w8an.json Normal file
View File

@ -0,0 +1,104 @@
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 128000,
"logprob": null,
"text": "<|begin_of_text|>"
},
{
"id": 3923,
"logprob": -7.609375,
"text": "What"
},
{
"id": 374,
"logprob": -0.92529297,
"text": " is"
},
{
"id": 5655,
"logprob": -10.0,
"text": " deep"
},
{
"id": 6975,
"logprob": -0.94628906,
"text": " learning"
},
{
"id": 30,
"logprob": -2.9042969,
"text": "?"
}
],
"seed": null,
"tokens": [
{
"id": 18682,
"logprob": -0.8769531,
"special": false,
"text": " Deep"
},
{
"id": 6975,
"logprob": -0.0076942444,
"special": false,
"text": " learning"
},
{
"id": 374,
"logprob": -0.25073242,
"special": false,
"text": " is"
},
{
"id": 264,
"logprob": -0.097595215,
"special": false,
"text": " a"
},
{
"id": 955,
"logprob": -0.921875,
"special": false,
"text": " type"
},
{
"id": 315,
"logprob": -0.00027918816,
"special": false,
"text": " of"
},
{
"id": 21075,
"logprob": -0.5527344,
"special": false,
"text": " artificial"
},
{
"id": 11478,
"logprob": -0.042541504,
"special": false,
"text": " intelligence"
},
{
"id": 320,
"logprob": -0.38891602,
"special": false,
"text": " ("
},
{
"id": 15836,
"logprob": -0.0011043549,
"special": false,
"text": "AI"
}
],
"top_tokens": null
},
"generated_text": " Deep learning is a type of artificial intelligence (AI"
}

99
integration-tests/models/__snapshots__/test_compressed_tensors_w8an_fp/test_compressed_tensors_w8an_all_params.json Normal file
View File

@ -0,0 +1,99 @@
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 128000,
"logprob": null,
"text": "<|begin_of_text|>"
},
{
"id": 3923,
"logprob": -7.609375,
"text": "What"
},
{
"id": 374,
"logprob": -0.92529297,
"text": " is"
},
{
"id": 5655,
"logprob": -10.0,
"text": " deep"
},
{
"id": 6975,
"logprob": -0.94628906,
"text": " learning"
}
],
"seed": 0,
"tokens": [
{
"id": 5380,
"logprob": -0.23840332,
"special": false,
"text": "?\n"
},
{
"id": 34564,
"logprob": 0.0,
"special": false,
"text": "Deep"
},
{
"id": 6975,
"logprob": 0.0,
"special": false,
"text": " learning"
},
{
"id": 11,
"logprob": 0.0,
"special": false,
"text": ","
},
{
"id": 1101,
"logprob": -1.2011719,
"special": false,
"text": " also"
},
{
"id": 3967,
"logprob": 0.0,
"special": false,
"text": " known"
},
{
"id": 439,
"logprob": 0.0,
"special": false,
"text": " as"
},
{
"id": 30828,
"logprob": 0.0,
"special": false,
"text": " neural"
},
{
"id": 4009,
"logprob": -0.6777344,
"special": false,
"text": " network"
},
{
"id": 477,
"logprob": 0.0,
"special": false,
"text": " or"
}
],
"top_tokens": null
},
"generated_text": "What is deep learning?\nDeep learning, also known as neural network or"
}

418
integration-tests/models/__snapshots__/test_compressed_tensors_w8an_fp/test_compressed_tensors_w8an_load.json Normal file
View File

@ -0,0 +1,418 @@
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 128000,
"logprob": null,
"text": "<|begin_of_text|>"
},
{
"id": 3923,
"logprob": -7.609375,
"text": "What"
},
{
"id": 374,
"logprob": -0.92529297,
"text": " is"
},
{
"id": 5655,
"logprob": -10.0,
"text": " deep"
},
{
"id": 6975,
"logprob": -0.94628906,
"text": " learning"
},
{
"id": 30,
"logprob": -2.9042969,
"text": "?"
}
],
"seed": null,
"tokens": [
{
"id": 18682,
"logprob": -0.8769531,
"special": false,
"text": " Deep"
},
{
"id": 6975,
"logprob": -0.0076942444,
"special": false,
"text": " learning"
},
{
"id": 374,
"logprob": -0.25146484,
"special": false,
"text": " is"
},
{
"id": 264,
"logprob": -0.097595215,
"special": false,
"text": " a"
},
{
"id": 955,
"logprob": -0.9248047,
"special": false,
"text": " type"
},
{
"id": 315,
"logprob": -0.00027513504,
"special": false,
"text": " of"
},
{
"id": 21075,
"logprob": -0.5527344,
"special": false,
"text": " artificial"
},
{
"id": 11478,
"logprob": -0.043151855,
"special": false,
"text": " intelligence"
},
{
"id": 320,
"logprob": -0.3840332,
"special": false,
"text": " ("
},
{
"id": 15836,
"logprob": -0.0011043549,
"special": false,
"text": "AI"
}
],
"top_tokens": null
},
"generated_text": " Deep learning is a type of artificial intelligence (AI"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 128000,
"logprob": null,
"text": "<|begin_of_text|>"
},
{
"id": 3923,
"logprob": -7.6054688,
"text": "What"
},
{
"id": 374,
"logprob": -0.92089844,
"text": " is"
},
{
"id": 5655,
"logprob": -10.0,
"text": " deep"
},
{
"id": 6975,
"logprob": -0.94433594,
"text": " learning"
},
{
"id": 30,
"logprob": -2.90625,
"text": "?"
}
],
"seed": null,
"tokens": [
{
"id": 18682,
"logprob": -0.875,
"special": false,
"text": " Deep"
},
{
"id": 6975,
"logprob": -0.007698059,
"special": false,
"text": " learning"
},
{
"id": 374,
"logprob": -0.25268555,
"special": false,
"text": " is"
},
{
"id": 264,
"logprob": -0.09753418,
"special": false,
"text": " a"
},
{
"id": 955,
"logprob": -0.92529297,
"special": false,
"text": " type"
},
{
"id": 315,
"logprob": -0.00027942657,
"special": false,
"text": " of"
},
{
"id": 21075,
"logprob": -0.5527344,
"special": false,
"text": " artificial"
},
{
"id": 11478,
"logprob": -0.042541504,
"special": false,
"text": " intelligence"
},
{
"id": 320,
"logprob": -0.3840332,
"special": false,
"text": " ("
},
{
"id": 15836,
"logprob": -0.0011053085,
"special": false,
"text": "AI"
}
],
"top_tokens": null
},
"generated_text": " Deep learning is a type of artificial intelligence (AI"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 128000,
"logprob": null,
"text": "<|begin_of_text|>"
},
{
"id": 3923,
"logprob": -7.6054688,
"text": "What"
},
{
"id": 374,
"logprob": -0.92089844,
"text": " is"
},
{
"id": 5655,
"logprob": -10.0,
"text": " deep"
},
{
"id": 6975,
"logprob": -0.94433594,
"text": " learning"
},
{
"id": 30,
"logprob": -2.90625,
"text": "?"
}
],
"seed": null,
"tokens": [
{
"id": 18682,
"logprob": -0.875,
"special": false,
"text": " Deep"
},
{
"id": 6975,
"logprob": -0.007698059,
"special": false,
"text": " learning"
},
{
"id": 374,
"logprob": -0.25268555,
"special": false,
"text": " is"
},
{
"id": 264,
"logprob": -0.09753418,
"special": false,
"text": " a"
},
{
"id": 955,
"logprob": -0.92529297,
"special": false,
"text": " type"
},
{
"id": 315,
"logprob": -0.00027942657,
"special": false,
"text": " of"
},
{
"id": 21075,
"logprob": -0.5527344,
"special": false,
"text": " artificial"
},
{
"id": 11478,
"logprob": -0.042541504,
"special": false,
"text": " intelligence"
},
{
"id": 320,
"logprob": -0.3840332,
"special": false,
"text": " ("
},
{
"id": 15836,
"logprob": -0.0011053085,
"special": false,
"text": "AI"
}
],
"top_tokens": null
},
"generated_text": " Deep learning is a type of artificial intelligence (AI"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 128000,
"logprob": null,
"text": "<|begin_of_text|>"
},
{
"id": 3923,
"logprob": -7.6054688,
"text": "What"
},
{
"id": 374,
"logprob": -0.92089844,
"text": " is"
},
{
"id": 5655,
"logprob": -10.0,
"text": " deep"
},
{
"id": 6975,
"logprob": -0.94433594,
"text": " learning"
},
{
"id": 30,
"logprob": -2.90625,
"text": "?"
}
],
"seed": null,
"tokens": [
{
"id": 18682,
"logprob": -0.875,
"special": false,
"text": " Deep"
},
{
"id": 6975,
"logprob": -0.007698059,
"special": false,
"text": " learning"
},
{
"id": 374,
"logprob": -0.25268555,
"special": false,
"text": " is"
},
{
"id": 264,
"logprob": -0.09753418,
"special": false,
"text": " a"
},
{
"id": 955,
"logprob": -0.92529297,
"special": false,
"text": " type"
},
{
"id": 315,
"logprob": -0.00027942657,
"special": false,
"text": " of"
},
{
"id": 21075,
"logprob": -0.5527344,
"special": false,
"text": " artificial"
},
{
"id": 11478,
"logprob": -0.042541504,
"special": false,
"text": " intelligence"
},
{
"id": 320,
"logprob": -0.3840332,
"special": false,
"text": " ("
},
{
"id": 15836,
"logprob": -0.0011053085,
"special": false,
"text": "AI"
}
],
"top_tokens": null
},
"generated_text": " Deep learning is a type of artificial intelligence (AI"
}
]

104
integration-tests/models/__snapshots__/test_compressed_tensors_wna16_int/test_compressed_tensors_wna16.json Normal file
View File

@ -0,0 +1,104 @@
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2,
"logprob": null,
"text": "<bos>"
},
{
"id": 1841,
"logprob": -5.46875,
"text": "What"
},
{
"id": 603,
"logprob": -0.69140625,
"text": " is"
},
{
"id": 5271,
"logprob": -12.0,
"text": " deep"
},
{
"id": 6044,
"logprob": -0.32226562,
"text": " learning"
},
{
"id": 235336,
"logprob": -0.33203125,
"text": "?"
}
],
"seed": null,
"tokens": [
{
"id": 109,
"logprob": -0.24707031,
"special": false,
"text": "\n\n"
},
{
"id": 26843,
"logprob": -0.14550781,
"special": false,
"text": "Deep"
},
{
"id": 6044,
"logprob": -0.038330078,
"special": false,
"text": " learning"
},
{
"id": 603,
"logprob": -0.029907227,
"special": false,
"text": " is"
},
{
"id": 476,
"logprob": -0.020996094,
"special": false,
"text": " a"
},
{
"id": 38397,
"logprob": -0.828125,
"special": false,
"text": " subset"
},
{
"id": 576,
"logprob": -0.00049209595,
"special": false,
"text": " of"
},
{
"id": 6479,
"logprob": -0.057373047,
"special": false,
"text": " machine"
},
{
"id": 6044,
"logprob": -0.000207901,
"special": false,
"text": " learning"
},
{
"id": 674,
"logprob": -0.15429688,
"special": false,
"text": " that"
}
],
"top_tokens": null
},
"generated_text": "\n\nDeep learning is a subset of machine learning that"
}

99
integration-tests/models/__snapshots__/test_compressed_tensors_wna16_int/test_compressed_tensors_wna16_all_params.json Normal file
View File

@ -0,0 +1,99 @@
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2,
"logprob": null,
"text": "<bos>"
},
{
"id": 1841,
"logprob": -5.46875,
"text": "What"
},
{
"id": 603,
"logprob": -0.69140625,
"text": " is"
},
{
"id": 5271,
"logprob": -12.0,
"text": " deep"
},
{
"id": 6044,
"logprob": -0.32226562,
"text": " learning"
}
],
"seed": 0,
"tokens": [
{
"id": 235336,
"logprob": 0.0,
"special": false,
"text": "?"
},
{
"id": 109,
"logprob": 0.0,
"special": false,
"text": "\n\n"
},
{
"id": 26843,
"logprob": 0.0,
"special": false,
"text": "Deep"
},
{
"id": 14715,
"logprob": -0.38671875,
"special": false,
"text": " Learning"
},
{
"id": 603,
"logprob": 0.0,
"special": false,
"text": " is"
},
{
"id": 476,
"logprob": 0.0,
"special": false,
"text": " a"
},
{
"id": 38397,
"logprob": -0.12695312,
"special": false,
"text": " subset"
},
{
"id": 576,
"logprob": 0.0,
"special": false,
"text": " of"
},
{
"id": 6479,
"logprob": 0.0,
"special": false,
"text": " machine"
},
{
"id": 6044,
"logprob": 0.0,
"special": false,
"text": " learning"
}
],
"top_tokens": null
},
"generated_text": "What is deep learning?\n\nDeep Learning is a subset of machine learning"
}

418
integration-tests/models/__snapshots__/test_compressed_tensors_wna16_int/test_compressed_tensors_wna16_load.json Normal file
View File

@ -0,0 +1,418 @@
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2,
"logprob": null,
"text": "<bos>"
},
{
"id": 1841,
"logprob": -5.46875,
"text": "What"
},
{
"id": 603,
"logprob": -0.69140625,
"text": " is"
},
{
"id": 5271,
"logprob": -12.0,
"text": " deep"
},
{
"id": 6044,
"logprob": -0.32226562,
"text": " learning"
},
{
"id": 235336,
"logprob": -0.33203125,
"text": "?"
}
],
"seed": null,
"tokens": [
{
"id": 109,
"logprob": -0.24707031,
"special": false,
"text": "\n\n"
},
{
"id": 26843,
"logprob": -0.14550781,
"special": false,
"text": "Deep"
},
{
"id": 6044,
"logprob": -0.03857422,
"special": false,
"text": " learning"
},
{
"id": 603,
"logprob": -0.030883789,
"special": false,
"text": " is"
},
{
"id": 476,
"logprob": -0.020996094,
"special": false,
"text": " a"
},
{
"id": 38397,
"logprob": -0.828125,
"special": false,
"text": " subset"
},
{
"id": 576,
"logprob": -0.00051498413,
"special": false,
"text": " of"
},
{
"id": 6479,
"logprob": -0.05883789,
"special": false,
"text": " machine"
},
{
"id": 6044,
"logprob": -0.00020694733,
"special": false,
"text": " learning"
},
{
"id": 674,
"logprob": -0.15820312,
"special": false,
"text": " that"
}
],
"top_tokens": null
},
"generated_text": "\n\nDeep learning is a subset of machine learning that"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2,
"logprob": null,
"text": "<bos>"
},
{
"id": 1841,
"logprob": -5.46875,
"text": "What"
},
{
"id": 603,
"logprob": -0.71484375,
"text": " is"
},
{
"id": 5271,
"logprob": -12.0,
"text": " deep"
},
{
"id": 6044,
"logprob": -0.30859375,
"text": " learning"
},
{
"id": 235336,
"logprob": -0.3359375,
"text": "?"
}
],
"seed": null,
"tokens": [
{
"id": 109,
"logprob": -0.23828125,
"special": false,
"text": "\n\n"
},
{
"id": 26843,
"logprob": -0.14550781,
"special": false,
"text": "Deep"
},
{
"id": 6044,
"logprob": -0.038330078,
"special": false,
"text": " learning"
},
{
"id": 603,
"logprob": -0.030883789,
"special": false,
"text": " is"
},
{
"id": 476,
"logprob": -0.020996094,
"special": false,
"text": " a"
},
{
"id": 38397,
"logprob": -0.80859375,
"special": false,
"text": " subset"
},
{
"id": 576,
"logprob": -0.0005455017,
"special": false,
"text": " of"
},
{
"id": 6479,
"logprob": -0.05908203,
"special": false,
"text": " machine"
},
{
"id": 6044,
"logprob": -0.00020599365,
"special": false,
"text": " learning"
},
{
"id": 674,
"logprob": -0.17285156,
"special": false,
"text": " that"
}
],
"top_tokens": null
},
"generated_text": "\n\nDeep learning is a subset of machine learning that"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2,
"logprob": null,
"text": "<bos>"
},
{
"id": 1841,
"logprob": -5.46875,
"text": "What"
},
{
"id": 603,
"logprob": -0.71484375,
"text": " is"
},
{
"id": 5271,
"logprob": -12.0,
"text": " deep"
},
{
"id": 6044,
"logprob": -0.30859375,
"text": " learning"
},
{
"id": 235336,
"logprob": -0.3359375,
"text": "?"
}
],
"seed": null,
"tokens": [
{
"id": 109,
"logprob": -0.23828125,
"special": false,
"text": "\n\n"
},
{
"id": 26843,
"logprob": -0.14550781,
"special": false,
"text": "Deep"
},
{
"id": 6044,
"logprob": -0.038330078,
"special": false,
"text": " learning"
},
{
"id": 603,
"logprob": -0.030883789,
"special": false,
"text": " is"
},
{
"id": 476,
"logprob": -0.020996094,
"special": false,
"text": " a"
},
{
"id": 38397,
"logprob": -0.80859375,
"special": false,
"text": " subset"
},
{
"id": 576,
"logprob": -0.0005455017,
"special": false,
"text": " of"
},
{
"id": 6479,
"logprob": -0.05908203,
"special": false,
"text": " machine"
},
{
"id": 6044,
"logprob": -0.00020599365,
"special": false,
"text": " learning"
},
{
"id": 674,
"logprob": -0.17285156,
"special": false,
"text": " that"
}
],
"top_tokens": null
},
"generated_text": "\n\nDeep learning is a subset of machine learning that"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 2,
"logprob": null,
"text": "<bos>"
},
{
"id": 1841,
"logprob": -5.46875,
"text": "What"
},
{
"id": 603,
"logprob": -0.71484375,
"text": " is"
},
{
"id": 5271,
"logprob": -12.0,
"text": " deep"
},
{
"id": 6044,
"logprob": -0.30859375,
"text": " learning"
},
{
"id": 235336,
"logprob": -0.3359375,
"text": "?"
}
],
"seed": null,
"tokens": [
{
"id": 109,
"logprob": -0.23828125,
"special": false,
"text": "\n\n"
},
{
"id": 26843,
"logprob": -0.14550781,
"special": false,
"text": "Deep"
},
{
"id": 6044,
"logprob": -0.038330078,
"special": false,
"text": " learning"
},
{
"id": 603,
"logprob": -0.030883789,
"special": false,
"text": " is"
},
{
"id": 476,
"logprob": -0.020996094,
"special": false,
"text": " a"
},
{
"id": 38397,
"logprob": -0.80859375,
"special": false,
"text": " subset"
},
{
"id": 576,
"logprob": -0.0005455017,
"special": false,
"text": " of"
},
{
"id": 6479,
"logprob": -0.05908203,
"special": false,
"text": " machine"
},
{
"id": 6044,
"logprob": -0.00020599365,
"special": false,
"text": " learning"
},
{
"id": 674,
"logprob": -0.17285156,
"special": false,
"text": " that"
}
],
"top_tokens": null
},
"generated_text": "\n\nDeep learning is a subset of machine learning that"
}
]

86
integration-tests/models/test_compressed_tensors_w8an_fp.py Normal file
View File

@ -0,0 +1,86 @@
import pytest
@pytest.fixture(scope="module")
def compressed_tensors_w8an_handle(launcher):
with launcher(
"neuralmagic/Llama-3.2-1B-Instruct-FP8",
num_shard=2,
quantize="compressed-tensors",
) as handle:
yield handle
@pytest.fixture(scope="module")
async def compressed_tensors_w8an(compressed_tensors_w8an_handle):
await compressed_tensors_w8an_handle.health(300)
return compressed_tensors_w8an_handle.client
@pytest.mark.release
@pytest.mark.asyncio
@pytest.mark.private
async def test_compressed_tensors_w8an(compressed_tensors_w8an, response_snapshot):
response = await compressed_tensors_w8an.generate(
"What is deep learning?",
max_new_tokens=10,
decoder_input_details=True,
)
assert (
response.generated_text
== " Deep learning is a type of artificial intelligence (AI"
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
async def test_compressed_tensors_w8an_all_params(
compressed_tensors_w8an, response_snapshot
):
response = await compressed_tensors_w8an.generate(
"What is deep learning",
max_new_tokens=10,
repetition_penalty=1.2,
return_full_text=True,
stop_sequences=["test"],
temperature=0.5,
top_p=0.9,
top_k=10,
truncate=5,
typical_p=0.9,
watermark=True,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 10
assert (
response.generated_text
== "What is deep learning?\nDeep learning, also known as neural network or"
)
assert response == response_snapshot
@pytest.mark.release
@pytest.mark.asyncio
@pytest.mark.private
async def test_compressed_tensors_w8an_load(
compressed_tensors_w8an, generate_load, response_snapshot
):
responses = await generate_load(
compressed_tensors_w8an,
"What is deep learning?",
max_new_tokens=10,
n=4,
)
assert (
responses[0].generated_text
== " Deep learning is a type of artificial intelligence (AI"
)
assert len(responses) == 4
assert all([r.generated_text == responses[0].generated_text for r in responses])
assert responses == response_snapshot

86
integration-tests/models/test_compressed_tensors_wna16_int.py Normal file
View File

@ -0,0 +1,86 @@
import pytest
@pytest.fixture(scope="module")
def compressed_tensors_wna16_handle(launcher):
with launcher(
"neuralmagic/gemma-2-2b-it-quantized.w4a16",
num_shard=2,
quantize="compressed-tensors",
) as handle:
yield handle
@pytest.fixture(scope="module")
async def compressed_tensors_wna16(compressed_tensors_wna16_handle):
await compressed_tensors_wna16_handle.health(300)
return compressed_tensors_wna16_handle.client
@pytest.mark.release
@pytest.mark.asyncio
@pytest.mark.private
async def test_compressed_tensors_wna16(compressed_tensors_wna16, response_snapshot):
response = await compressed_tensors_wna16.generate(
"What is deep learning?",
max_new_tokens=10,
decoder_input_details=True,
)
assert (
response.generated_text
== "\n\nDeep learning is a subset of machine learning that"
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
@pytest.mark.asyncio
async def test_compressed_tensors_wna16_all_params(
compressed_tensors_wna16, response_snapshot
):
response = await compressed_tensors_wna16.generate(
"What is deep learning",
max_new_tokens=10,
repetition_penalty=1.2,
return_full_text=True,
stop_sequences=["test"],
temperature=0.5,
top_p=0.9,
top_k=10,
truncate=5,
typical_p=0.9,
watermark=True,
decoder_input_details=True,
seed=0,
)
assert response.details.generated_tokens == 10
assert (
response.generated_text
== "What is deep learning?\n\nDeep Learning is a subset of machine learning"
)
assert response == response_snapshot
@pytest.mark.release
@pytest.mark.asyncio
@pytest.mark.private
async def test_compressed_tensors_wna16_load(
compressed_tensors_wna16, generate_load, response_snapshot
):
responses = await generate_load(
compressed_tensors_wna16,
"What is deep learning?",
max_new_tokens=10,
n=4,
)
assert (
responses[0].generated_text
== "\n\nDeep learning is a subset of machine learning that"
)
assert len(responses) == 4
assert all([r.generated_text == responses[0].generated_text for r in responses])
assert responses == response_snapshot

5
launcher/src/main.rs
View File

@ -212,6 +212,8 @@ enum Quantization {
/// <https://hf.co/models?search=awq>.
/// Should replace GPTQ models wherever possible because of the better latency
Awq,
/// Compressed tensors, which can be a mixture of different quantization methods.
CompressedTensors,
/// 8 bit quantization, doesn't require specific model.
/// Should be a drop-in replacement to bitsandbytes with much better performance.
/// Kernels are from <https://github.com/NetEase-FuXi/EETQ.git>
@ -274,6 +276,9 @@ impl std::fmt::Display for Quantization {
Quantization::Awq => {
write!(f, "awq")
}
Quantization::CompressedTensors => {
write!(f, "compressed-tensors")
}
Quantization::Eetq => {
write!(f, "eetq")
}

14
nix/server.nix
View File

@ -3,8 +3,10 @@
buildPythonPackage,
poetry-core,
mypy-protobuf,
attention-kernels,
awq-inference-engine,
causal-conv1d,
compressed-tensors,
eetq,
einops,
exllamav2,
@ -26,15 +28,18 @@
opentelemetry-exporter-otlp,
opentelemetry-instrumentation-grpc,
opentelemetry-semantic-conventions,
outlines,
peft,
prometheus-client,
punica-kernels,
py-cpuinfo,
pydantic,
safetensors,
tokenizers,
torch,
sentencepiece,
transformers,
typer,
vllm,
}:
let
@ -71,9 +76,11 @@ buildPythonPackage {
pythonRemoveDeps = [ "scipy" ];
dependencies = [
attention-kernels
awq-inference-engine
eetq
causal-conv1d
compressed-tensors
einops
exllamav2
flashinfer
@ -93,14 +100,17 @@ buildPythonPackage {
opentelemetry-exporter-otlp
opentelemetry-instrumentation-grpc
opentelemetry-semantic-conventions
outlines
peft
prometheus-client
punica-kernels
py-cpuinfo
pydantic
safetensors
sentencepiece
tokenizers
transformers
typer
vllm
];
prePatch = ''

24
router/src/infer/mod.rs
View File

@ -10,10 +10,12 @@ use crate::{
};
use async_stream::stream;
use async_trait::async_trait;
use axum::response::sse::Event;
use chat_template::ChatTemplate;
use futures::future::try_join_all;
use futures::Stream;
use minijinja::ErrorKind;
use serde::Serialize;
use std::sync::atomic::{AtomicBool, Ordering};
use std::sync::Arc;
use thiserror::Error;
@ -373,4 +375,26 @@ impl InferError {
InferError::StreamSerializationError(_) => "stream_serialization_error",
}
}
pub(crate) fn into_openai_event(self) -> Event {
Event::default()
.json_data(OpenaiErrorEvent {
error: APIError {
message: self.to_string(),
http_status_code: 422,
},
})
.unwrap()
}
}
#[derive(Serialize)]
pub struct APIError {
message: String,
http_status_code: usize,
}
#[derive(Serialize)]
pub struct OpenaiErrorEvent {
error: APIError,
}

11
router/src/lib.rs
View File

@ -22,11 +22,13 @@ use tracing::warn;
use utoipa::ToSchema;
use validation::Validation;
#[allow(clippy::large_enum_variant)]
#[derive(Clone)]
pub enum Tokenizer {
Python {
tokenizer_name: String,
revision: Option<String>,
trust_remote_code: bool,
},
Rust(tokenizers::Tokenizer),
}
@ -38,15 +40,20 @@ impl<'a> PyTokenizer<'a> {
py: Python<'a>,
tokenizer_name: String,
revision: Option<String>,
trust_remote_code: bool,
) -> PyResult<PyTokenizer<'a>> {
let transformers = py.import_bound("transformers")?;
let auto = transformers.getattr("AutoTokenizer")?;
let from_pretrained = auto.getattr("from_pretrained")?;
let args = (tokenizer_name,);
let kwargs = if let Some(rev) = &revision {
[("revision", rev.to_string())].into_py_dict_bound(py)
[
("revision", rev.to_string().into_py(py)),
("trust_remote_code", trust_remote_code.into_py(py)),
]
.into_py_dict_bound(py)
} else {
pyo3::types::PyDict::new_bound(py)
[("trust_remote_code", trust_remote_code.into_py(py))].into_py_dict_bound(py)
};
let tokenizer = from_pretrained.call(args, Some(&kwargs))?;
tracing::info!("Loaded a python tokenizer");

10
router/src/server.rs
View File

@ -109,7 +109,7 @@ request_body = CompatGenerateRequest,
responses(
(status = 200, description = "Generated Text",
content(
("application/json" = GenerateResponse),
("application/json" = Vec<GenerateResponse>),
("text/event-stream" = StreamResponse),
)),
(status = 424, description = "Generation Error", body = ErrorResponse,
@ -866,7 +866,7 @@ pub(crate) async fn completions(
yield Ok(event);
}
Err(err) => yield Ok(Event::from(err)),
Err(err) => yield Ok(err.into_openai_event()),
}
}
};
@ -1274,7 +1274,8 @@ pub(crate) async fn chat_completions(
};
let mut response_as_tool = using_tools;
while let Some(result) = response_stream.next().await {
if let Ok(stream_token) = result {
match result{
Ok(stream_token) => {
let token_text = &stream_token.token.text.clone();
match state {
StreamState::Buffering => {
@ -1368,6 +1369,8 @@ pub(crate) async fn chat_completions(
}
}
}
Err(err) => yield Ok(err.into_openai_event())
}
}
yield Ok::<Event, Infallible>(Event::default().data("[DONE]"));
};
@ -1829,6 +1832,7 @@ pub async fn run(
Tokenizer::Python {
tokenizer_name: tokenizer_name.clone(),
revision: revision.clone(),
trust_remote_code,
}
}
};

4
router/src/validation.rs
View File

@ -439,9 +439,11 @@ fn tokenizer_worker(
Tokenizer::Python {
tokenizer_name,
revision,
trust_remote_code,
} => {
pyo3::Python::with_gil(|py| -> pyo3::PyResult<()> {
let tokenizer = PyTokenizer::from_py(py, tokenizer_name, revision)?;
let tokenizer =
PyTokenizer::from_py(py, tokenizer_name, revision, trust_remote_code)?;
// Loop over requests
while let Some(((inputs, add_special_tokens, truncate), response_tx, parent_span)) =
receiver.blocking_recv()

6
server/Makefile
View File

@ -23,14 +23,14 @@ gen-server:
install-server: gen-server
pip install pip --upgrade
pip install -r requirements_cuda.txt
pip install -e ".[accelerate, quantize, peft, outlines]"
pip install -e ".[accelerate, compressed-tensors, quantize, peft, outlines]"
install: install-cuda
echo "Installed server"
install-cuda: install-server install-flash-attention-v2-cuda install-vllm-cuda install-flash-attention
pip install -e ".[bnb,marlin,moe]"
install-cuda: install-server install-flash-attention-v2-cuda install-flash-attention
pip install -e ".[attention,bnb,marlin,moe]"
pip install nvidia-nccl-cu12==2.22.3
install-rocm: install-server install-flash-attention-v2-rocm install-vllm-rocm

10
server/Makefile-vllm
View File

@ -1,14 +1,4 @@
commit_cuda := d243e9dc7e2c9c2e36a4150ec8e64809cb55c01b
commit_rocm := 4e0929e6e4fa0a3d09d358715c288020ea9dc247
build-vllm-cuda:
if [ ! -d 'vllm' ]; then \
pip install -U ninja packaging --no-cache-dir && \
git clone https://github.com/Narsil/vllm.git vllm; \
fi
cd vllm && git fetch origin && git checkout $(commit_cuda) && python setup.py build
install-vllm-cuda: build-vllm-cuda
cd vllm && git fetch origin && git checkout $(commit_cuda) && pip install -e .
build-vllm-rocm:
if [ ! -d 'vllm' ]; then \

262
server/poetry.lock generated
View File

@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
[[package]]
name = "accelerate"
@ -167,6 +167,17 @@ files = [
[package.dependencies]
frozenlist = ">=1.1.0"
[[package]]
name = "airportsdata"
version = "20241001"
description = "Extensive database of location and timezone data for nearly every airport and landing strip in the world."
optional = true
python-versions = ">=3.9"
files = [
{file = "airportsdata-20241001-py3-none-any.whl", hash = "sha256:67d71cf2c5378cc17ff66b62b1e11aa2444043949c894543ac8fd8dafce192fd"},
{file = "airportsdata-20241001.tar.gz", hash = "sha256:fa0bd143b4f4be3557cb892fa0612ef210fd91a92bd720b4d8221de576a4fa00"},
]
[[package]]
name = "annotated-types"
version = "0.7.0"
@ -189,6 +200,74 @@ files = [
{file = "async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"},
]
[[package]]
name = "attention-kernels"
version = "0.1.1"
description = "Attention kernels"
optional = true
python-versions = ">=3.8"
files = [
{file = "attention_kernels-0.1.1+cu123torch2.4-cp310-cp310-linux_x86_64.whl", hash = "sha256:812851d4ce0f54ca764ff3815a731b15f0cb110115d0aa2d0997cd7794d808bb"},
]
[package.dependencies]
torch = "*"
[package.source]
type = "url"
url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.4-cp310-cp310-linux_x86_64.whl"
[[package]]
name = "attention-kernels"
version = "0.1.1"
description = "Attention kernels"
optional = true
python-versions = ">=3.8"
files = [
{file = "attention_kernels-0.1.1+cu123torch2.4-cp311-cp311-linux_x86_64.whl", hash = "sha256:614c402621b11dd1f5741a016b9fd27cb6a68814471f2048bc05206923516268"},
]
[package.dependencies]
torch = "*"
[package.source]
type = "url"
url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.4-cp311-cp311-linux_x86_64.whl"
[[package]]
name = "attention-kernels"
version = "0.1.1"
description = "Attention kernels"
optional = true
python-versions = ">=3.8"
files = [
{file = "attention_kernels-0.1.1+cu123torch2.4-cp312-cp312-linux_x86_64.whl", hash = "sha256:6b2ca7c98997431d5f6c4af7553dce6b1bff8dfdec374c97c6ffba71325a02b7"},
]
[package.dependencies]
torch = "*"
[package.source]
type = "url"
url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.4-cp312-cp312-linux_x86_64.whl"
[[package]]
name = "attention-kernels"
version = "0.1.1"
description = "Attention kernels"
optional = true
python-versions = ">=3.8"
files = [
{file = "attention_kernels-0.1.1+cu123torch2.4-cp39-cp39-linux_x86_64.whl", hash = "sha256:a56710c5626e461d6f628ae14b74ffc89833578ebd59c3c0c47f5d6f07461fbf"},
]
[package.dependencies]
torch = "*"
[package.source]
type = "url"
url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.4-cp39-cp39-linux_x86_64.whl"
[[package]]
name = "attrs"
version = "24.2.0"
@ -388,6 +467,26 @@ files = [
{file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
]
[[package]]
name = "compressed-tensors"
version = "0.7.1"
description = "Library for utilization of compressed safetensors of neural network models"
optional = true
python-versions = "*"
files = [
{file = "compressed-tensors-0.7.1.tar.gz", hash = "sha256:3c7865ebfe4ea76ae94d7c674bcf93aedd2064571f682c09a377a219d5ebb3a0"},
{file = "compressed_tensors-0.7.1-py3-none-any.whl", hash = "sha256:22d11558a70f655ae647db9c8e9fb14a5e9d6983ca5aec3f267518625fd6dd0e"},
]
[package.dependencies]
pydantic = ">=2.0"
torch = ">=1.7.0"
transformers = "*"
[package.extras]
accelerate = ["accelerate"]
dev = ["black (==22.12.0)", "flake8 (>=3.8.3)", "isort (==5.8.0)", "nbconvert (>=7.16.3)", "pytest (>=6.0.0)", "wheel (>=0.36.2)"]
[[package]]
name = "datasets"
version = "2.21.0"
@ -1023,17 +1122,6 @@ MarkupSafe = ">=2.0"
[package.extras]
i18n = ["Babel (>=2.7)"]
[[package]]
name = "joblib"
version = "1.4.2"
description = "Lightweight pipelining with Python functions"
optional = true
python-versions = ">=3.8"
files = [
{file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
{file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
]
[[package]]
name = "jsonschema"
version = "4.23.0"
@ -1086,36 +1174,6 @@ interegular = ["interegular (>=0.3.1,<0.4.0)"]
nearley = ["js2py"]
regex = ["regex"]
[[package]]
name = "llvmlite"
version = "0.43.0"
description = "lightweight wrapper around basic LLVM functionality"
optional = true
python-versions = ">=3.9"
files = [
{file = "llvmlite-0.43.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a289af9a1687c6cf463478f0fa8e8aa3b6fb813317b0d70bf1ed0759eab6f761"},
{file = "llvmlite-0.43.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6d4fd101f571a31acb1559ae1af30f30b1dc4b3186669f92ad780e17c81e91bc"},
{file = "llvmlite-0.43.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7d434ec7e2ce3cc8f452d1cd9a28591745de022f931d67be688a737320dfcead"},
{file = "llvmlite-0.43.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6912a87782acdff6eb8bf01675ed01d60ca1f2551f8176a300a886f09e836a6a"},
{file = "llvmlite-0.43.0-cp310-cp310-win_amd64.whl", hash = "sha256:14f0e4bf2fd2d9a75a3534111e8ebeb08eda2f33e9bdd6dfa13282afacdde0ed"},
{file = "llvmlite-0.43.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3e8d0618cb9bfe40ac38a9633f2493d4d4e9fcc2f438d39a4e854f39cc0f5f98"},
{file = "llvmlite-0.43.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e0a9a1a39d4bf3517f2af9d23d479b4175ead205c592ceeb8b89af48a327ea57"},
{file = "llvmlite-0.43.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c1da416ab53e4f7f3bc8d4eeba36d801cc1894b9fbfbf2022b29b6bad34a7df2"},
{file = "llvmlite-0.43.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:977525a1e5f4059316b183fb4fd34fa858c9eade31f165427a3977c95e3ee749"},
{file = "llvmlite-0.43.0-cp311-cp311-win_amd64.whl", hash = "sha256:d5bd550001d26450bd90777736c69d68c487d17bf371438f975229b2b8241a91"},
{file = "llvmlite-0.43.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:f99b600aa7f65235a5a05d0b9a9f31150c390f31261f2a0ba678e26823ec38f7"},
{file = "llvmlite-0.43.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:35d80d61d0cda2d767f72de99450766250560399edc309da16937b93d3b676e7"},
{file = "llvmlite-0.43.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:eccce86bba940bae0d8d48ed925f21dbb813519169246e2ab292b5092aba121f"},
{file = "llvmlite-0.43.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:df6509e1507ca0760787a199d19439cc887bfd82226f5af746d6977bd9f66844"},
{file = "llvmlite-0.43.0-cp312-cp312-win_amd64.whl", hash = "sha256:7a2872ee80dcf6b5dbdc838763d26554c2a18aa833d31a2635bff16aafefb9c9"},
{file = "llvmlite-0.43.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9cd2a7376f7b3367019b664c21f0c61766219faa3b03731113ead75107f3b66c"},
{file = "llvmlite-0.43.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:18e9953c748b105668487b7c81a3e97b046d8abf95c4ddc0cd3c94f4e4651ae8"},
{file = "llvmlite-0.43.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:74937acd22dc11b33946b67dca7680e6d103d6e90eeaaaf932603bec6fe7b03a"},
{file = "llvmlite-0.43.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc9efc739cc6ed760f795806f67889923f7274276f0eb45092a1473e40d9b867"},
{file = "llvmlite-0.43.0-cp39-cp39-win_amd64.whl", hash = "sha256:47e147cdda9037f94b399bf03bfd8a6b6b1f2f90be94a454e3386f006455a9b4"},
{file = "llvmlite-0.43.0.tar.gz", hash = "sha256:ae2b5b5c3ef67354824fb75517c8db5fbe93bc02cd9671f3c62271626bc041d5"},
]
[[package]]
name = "loguru"
version = "0.6.0"
@ -1557,40 +1615,6 @@ doc = ["nb2plots (>=0.7)", "nbconvert (<7.9)", "numpydoc (>=1.6)", "pillow (>=9.
extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.11)", "sympy (>=1.10)"]
test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
[[package]]
name = "numba"
version = "0.60.0"
description = "compiling Python code using LLVM"
optional = true
python-versions = ">=3.9"
files = [
{file = "numba-0.60.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5d761de835cd38fb400d2c26bb103a2726f548dc30368853121d66201672e651"},
{file = "numba-0.60.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:159e618ef213fba758837f9837fb402bbe65326e60ba0633dbe6c7f274d42c1b"},
{file = "numba-0.60.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:1527dc578b95c7c4ff248792ec33d097ba6bef9eda466c948b68dfc995c25781"},
{file = "numba-0.60.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:fe0b28abb8d70f8160798f4de9d486143200f34458d34c4a214114e445d7124e"},
{file = "numba-0.60.0-cp310-cp310-win_amd64.whl", hash = "sha256:19407ced081d7e2e4b8d8c36aa57b7452e0283871c296e12d798852bc7d7f198"},
{file = "numba-0.60.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a17b70fc9e380ee29c42717e8cc0bfaa5556c416d94f9aa96ba13acb41bdece8"},
{file = "numba-0.60.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:3fb02b344a2a80efa6f677aa5c40cd5dd452e1b35f8d1c2af0dfd9ada9978e4b"},
{file = "numba-0.60.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:5f4fde652ea604ea3c86508a3fb31556a6157b2c76c8b51b1d45eb40c8598703"},
{file = "numba-0.60.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4142d7ac0210cc86432b818338a2bc368dc773a2f5cf1e32ff7c5b378bd63ee8"},
{file = "numba-0.60.0-cp311-cp311-win_amd64.whl", hash = "sha256:cac02c041e9b5bc8cf8f2034ff6f0dbafccd1ae9590dc146b3a02a45e53af4e2"},
{file = "numba-0.60.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:d7da4098db31182fc5ffe4bc42c6f24cd7d1cb8a14b59fd755bfee32e34b8404"},
{file = "numba-0.60.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:38d6ea4c1f56417076ecf8fc327c831ae793282e0ff51080c5094cb726507b1c"},
{file = "numba-0.60.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:62908d29fb6a3229c242e981ca27e32a6e606cc253fc9e8faeb0e48760de241e"},
{file = "numba-0.60.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:0ebaa91538e996f708f1ab30ef4d3ddc344b64b5227b67a57aa74f401bb68b9d"},
{file = "numba-0.60.0-cp312-cp312-win_amd64.whl", hash = "sha256:f75262e8fe7fa96db1dca93d53a194a38c46da28b112b8a4aca168f0df860347"},
{file = "numba-0.60.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:01ef4cd7d83abe087d644eaa3d95831b777aa21d441a23703d649e06b8e06b74"},
{file = "numba-0.60.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:819a3dfd4630d95fd574036f99e47212a1af41cbcb019bf8afac63ff56834449"},
{file = "numba-0.60.0-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0b983bd6ad82fe868493012487f34eae8bf7dd94654951404114f23c3466d34b"},
{file = "numba-0.60.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c151748cd269ddeab66334bd754817ffc0cabd9433acb0f551697e5151917d25"},
{file = "numba-0.60.0-cp39-cp39-win_amd64.whl", hash = "sha256:3031547a015710140e8c87226b4cfe927cac199835e5bf7d4fe5cb64e814e3ab"},
{file = "numba-0.60.0.tar.gz", hash = "sha256:5df6158e5584eece5fc83294b949fd30b9f1125df7708862205217e068aabf16"},
]
[package.dependencies]
llvmlite = "==0.43.*"
numpy = ">=1.22,<2.1"
[[package]]
name = "numpy"
version = "1.26.4"
@ -1968,36 +1992,83 @@ opentelemetry-api = "1.25.0"
[[package]]
name = "outlines"
version = "0.0.34"
version = "0.1.3"
description = "Probabilistic Generative Model Programming"
optional = true
python-versions = ">=3.8"
python-versions = ">=3.9"
files = [
{file = "outlines-0.0.34-py3-none-any.whl", hash = "sha256:911588a7e64a4f193b97fb4c501d98ccfd4e95a98f6a3ada67a280bf0c373c50"},
{file = "outlines-0.0.34.tar.gz", hash = "sha256:594e7204c770b47a62eb5c2ba7d25ea0ab2e16882b5f04556712a0228d3d3309"},
{file = "outlines-0.1.3-py3-none-any.whl", hash = "sha256:afcf6012b7cabbaae4a58975d03190c0bbc3d402b0b2a37538e05f335d73a247"},
{file = "outlines-0.1.3.tar.gz", hash = "sha256:5a48ad00d3bdd8eccaa7574821eb5aaa27ab9f61fde9c3fba52f352dc00197e4"},
]
[package.dependencies]
airportsdata = "*"
cloudpickle = "*"
datasets = "*"
diskcache = "*"
interegular = "*"
jinja2 = "*"
joblib = "*"
jsonschema = "*"
lark = "*"
nest-asyncio = "*"
numba = "*"
numpy = "*"
numpy = "<2.0.0"
outlines-core = "0.1.14"
pycountry = "*"
pydantic = ">=2.0"
referencing = "*"
requests = "*"
scipy = "*"
torch = ">=2.1.0"
transformers = "*"
torch = "*"
tqdm = "*"
typing-extensions = "*"
[package.extras]
serve = ["fastapi", "pydantic (>=2.0)", "ray (==2.9.0)", "uvicorn", "vllm (>=0.3.0)"]
test = ["accelerate", "beartype (<0.16.0)", "coverage[toml] (>=5.1)", "datasets", "diff-cover", "huggingface-hub", "llama-cpp-python (>=0.2.42)", "pre-commit", "pytest", "pytest-benchmark", "pytest-cov", "pytest-mock", "responses", "transformers"]
serve = ["fastapi", "pydantic (>=2.0)", "uvicorn", "vllm (>=0.3.0)"]
test = ["accelerate", "beartype (<0.16.0)", "coverage[toml] (>=5.1)", "diff-cover", "exllamav2", "huggingface-hub", "llama-cpp-python", "mlx-lm", "openai (>=1.0.0)", "pillow", "pre-commit", "pytest", "pytest-benchmark", "pytest-cov", "pytest-mock", "responses", "transformers", "vllm"]
[[package]]
name = "outlines-core"
version = "0.1.14"
description = "Structured Text Generation in Rust"
optional = true
python-versions = ">=3.8"
files = [
{file = "outlines_core-0.1.14-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:291c6d9d348cb5562cd28ce44d80822d77238f1cd7c30d890b5b20488e71608d"},
{file = "outlines_core-0.1.14-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3a50e2f6745e0c34cc857d1bd5590e2966ad06e8ce10802976e9e6c116c7533d"},
{file = "outlines_core-0.1.14-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f7dfe64b590a6a88dcc5e59f0a399fff0458cdcf97d68de07f08e1bd3bf8ac1d"},
{file = "outlines_core-0.1.14-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:100de068ce52893bec316481e65db8f1c734a0f25f540c29dafd7a8afec0a29d"},
{file = "outlines_core-0.1.14-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:e06cb724770fd0fe1c8444382c4a6e79901bba33720f70fe6c8437f58eceb92e"},
{file = "outlines_core-0.1.14-cp310-cp310-win32.whl", hash = "sha256:6d41da3d8a087fd54133cf910c2d5759da55490bbd0e3bc6c1e7907b54248415"},
{file = "outlines_core-0.1.14-cp310-cp310-win_amd64.whl", hash = "sha256:646fd1073feed393bc77f9605a2fa27a54551ab04f85867ce789af1dee6326fa"},
{file = "outlines_core-0.1.14-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:60f3a947fe09106f7668cf832c28b9269b8f0fc109f081608acfce9262213359"},
{file = "outlines_core-0.1.14-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:5e273a100c922f794d8e077a8161d0985d3005887066b4af3ae7afd3742fe9b8"},
{file = "outlines_core-0.1.14-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:622e547f11a869fc67be40abc4cbcda89ae6f46f9eb46a1ec0666bd6807e0c67"},
{file = "outlines_core-0.1.14-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:60c9933a9faaa51b39aea3518f1822b0d3ec2c9a13b16849caca3955e29e320d"},
{file = "outlines_core-0.1.14-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:4a8c616ce103ef9102dbf4326f67b03e1e0f46aa19351e57f4beb37588c00428"},
{file = "outlines_core-0.1.14-cp311-cp311-win32.whl", hash = "sha256:1c77aaa4556cbb6e93cc42be0a6e262f175e0754b7694d702d642ff03df67f2c"},
{file = "outlines_core-0.1.14-cp311-cp311-win_amd64.whl", hash = "sha256:eb6ffe410866f65dbe17e95b0aabd70d990f058a2dc4e8b74f9583b07248cd36"},
{file = "outlines_core-0.1.14-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:b0e408b033618f23e9bb928a47b33b1bd4c9d04a3dbec680a20977de3b4f590d"},
{file = "outlines_core-0.1.14-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:21d1393a6da5d3320e8c8247e9deeb851c5c862fd6ea5c779bd29797e8987155"},
{file = "outlines_core-0.1.14-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5829c568db76673d36caaf0f86e96748b491b4a209deb9be87617372394a5fb9"},
{file = "outlines_core-0.1.14-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7e855ec99bce1099c0755bcbfa44568adf7ae0083905ba04f58a17614ddf0fe7"},
{file = "outlines_core-0.1.14-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:b897cfbf9c2719aa011d9b439b4c6751d9c7df5683b2169617972d4b4a914403"},
{file = "outlines_core-0.1.14-cp38-cp38-win32.whl", hash = "sha256:4c9d908004b31bcd432156d60f4895bf5e1b51ca8c8eed82b12f1bb57d5bf7fd"},
{file = "outlines_core-0.1.14-cp38-cp38-win_amd64.whl", hash = "sha256:6668a930d928216d0b319ad84947903f1e27556f604a9743051f795b11008b64"},
{file = "outlines_core-0.1.14-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b745aa469cf3fb347b79a257804d75d1324e01691158664c1e413a816ce6b98d"},
{file = "outlines_core-0.1.14-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:27504c8360467429d6223ebc49180d6956d7418bfc3d324f6ad10f069e1813ad"},
{file = "outlines_core-0.1.14-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd8f1e1d91a206a520d1c577ce00136de2beb1d200ef93759fd4c9f45abe24d3"},
{file = "outlines_core-0.1.14-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f30c8acb42895b624c504b85678331c5f9376fa4b8069ce06a27cf80f5881e27"},
{file = "outlines_core-0.1.14-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:0e6cd0e7d995a7b04d90139a695279ab4a9eb7f492618b2c037a85eaf5f9fc59"},
{file = "outlines_core-0.1.14-cp39-cp39-win32.whl", hash = "sha256:3104af4084da0e7c3d4b8538b43c725581d66bb68d426bc389680f06c3667476"},
{file = "outlines_core-0.1.14-cp39-cp39-win_amd64.whl", hash = "sha256:45c6b9baded0337c4dcfa156af05ec4efd2b25c4d976e77be28146e4037b991f"},
{file = "outlines_core-0.1.14.tar.gz", hash = "sha256:6db033e4f8e48381164e36cc716746640ad5022f0d86e4c88af15c75886b93a4"},
]
[package.dependencies]
interegular = "*"
jsonschema = "*"
[package.extras]
test = ["accelerate", "asv", "beartype (<0.16.0)", "coverage[toml] (>=5.1)", "datasets", "diff-cover", "huggingface-hub", "pillow", "pre-commit", "pydantic", "pytest", "pytest-benchmark", "pytest-cov", "pytest-mock", "setuptools-rust", "torch", "transformers"]
[[package]]
name = "packaging"
@ -2470,6 +2541,17 @@ numpy = ">=1.16.6"
[package.extras]
test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"]
[[package]]
name = "pycountry"
version = "24.6.1"
description = "ISO country, subdivision, language, currency and script definitions and their translations"
optional = true
python-versions = ">=3.8"
files = [
{file = "pycountry-24.6.1-py3-none-any.whl", hash = "sha256:f1a4fb391cd7214f8eefd39556d740adcc233c778a27f8942c8dca351d6ce06f"},
{file = "pycountry-24.6.1.tar.gz", hash = "sha256:b61b3faccea67f87d10c1f2b0fc0be714409e8fcdcc1315613174f6466c10221"},
]
[[package]]
name = "pydantic"
version = "2.9.2"
@ -3971,7 +4053,9 @@ type = ["pytest-mypy"]
[extras]
accelerate = ["accelerate"]
attention = ["attention-kernels", "attention-kernels", "attention-kernels", "attention-kernels"]
bnb = ["bitsandbytes"]
compressed-tensors = ["compressed-tensors"]
marlin = ["marlin-kernels", "marlin-kernels", "marlin-kernels", "marlin-kernels"]
moe = ["moe-kernels", "moe-kernels", "moe-kernels", "moe-kernels"]
outlines = ["outlines"]
@ -3982,4 +4066,4 @@ torch = ["torch"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
content-hash = "b39033e573f50a0f046787aebf1702d86673aad0b2fcee818404fcea7f644b81"
content-hash = "05add88628d836faceae1a26fde4092651a6eca74555ae38ebff879a7895be7e"

15
server/pyproject.toml
View File

@ -9,7 +9,7 @@ text-generation-server = 'text_generation_server.cli:app'
[tool.poetry.dependencies]
python = ">=3.9,<3.13"
protobuf = "^4.25.3"
protobuf = ">=4.25.3,<6"
grpcio = "^1.51.1"
grpcio-status = "^1.51.1"
grpcio-reflection = "^1.51.1"
@ -34,12 +34,19 @@ peft = { version = "^0.10", optional = true }
torch = { version = "^2.4.0", optional = true }
scipy = "^1.11.1"
pillow = "^10.0.0"
outlines= { version = "^0.0.34", optional = true }
prometheus-client = "^0.20.0"
outlines= { version = "^0.1.1", optional = true }
prometheus-client = ">=0.20.0,<0.22"
py-cpuinfo = "^9.0.0"
compressed-tensors = { version = "^0.7.1", optional = true }
# Remove later, temporary workaround for outlines.
numpy = "^1.26"
attention-kernels = [
{ url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },
{ url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.4-cp310-cp310-linux_x86_64.whl", python = "~3.10", optional = true },
{ url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.4-cp311-cp311-linux_x86_64.whl", python = "~3.11", optional = true },
{ url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true },
]
marlin-kernels = [
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.1/marlin_kernels-0.3.1+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.1/marlin_kernels-0.3.1+cu123torch2.4-cp310-cp310-linux_x86_64.whl", python = "~3.10", optional = true },
@ -57,7 +64,9 @@ rich = "^13.7.1"
[tool.poetry.extras]
torch = ["torch"]
accelerate = ["accelerate"]
attention = ["attention-kernels"]
bnb = ["bitsandbytes"]
compressed-tensors = ["compressed-tensors"]
marlin = ["marlin-kernels"]
moe = ["moe-kernels"]
peft = ["peft"]

2
server/requirements_cuda.txt
View File

@ -45,7 +45,7 @@ sentencepiece==0.2.0 ; python_version >= "3.9" and python_version < "3.13"
setuptools==75.2.0 ; python_version >= "3.9" and python_version < "3.13"
tokenizers==0.20.1 ; python_version >= "3.9" and python_version < "3.13"
tqdm==4.66.5 ; python_version >= "3.9" and python_version < "3.13"
transformers==4.45.2 ; python_version >= "3.9" and python_version < "3.13"
transformers==4.46.0 ; python_version >= "3.9" and python_version < "3.13"
typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
typing-extensions==4.12.2 ; python_version >= "3.9" and python_version < "3.13"
urllib3==2.2.3 ; python_version >= "3.9" and python_version < "3.13"

2
server/requirements_intel.txt
View File

@ -45,7 +45,7 @@ sentencepiece==0.2.0 ; python_version >= "3.9" and python_version < "3.13"
setuptools==75.2.0 ; python_version >= "3.9" and python_version < "3.13"
tokenizers==0.20.1 ; python_version >= "3.9" and python_version < "3.13"
tqdm==4.66.5 ; python_version >= "3.9" and python_version < "3.13"
transformers==4.45.2 ; python_version >= "3.9" and python_version < "3.13"
transformers==4.46.0 ; python_version >= "3.9" and python_version < "3.13"
typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
typing-extensions==4.12.2 ; python_version >= "3.9" and python_version < "3.13"
urllib3==2.2.3 ; python_version >= "3.9" and python_version < "3.13"

2
server/requirements_rocm.txt
View File

@ -45,7 +45,7 @@ sentencepiece==0.2.0 ; python_version >= "3.9" and python_version < "3.13"
setuptools==75.2.0 ; python_version >= "3.9" and python_version < "3.13"
tokenizers==0.20.1 ; python_version >= "3.9" and python_version < "3.13"
tqdm==4.66.5 ; python_version >= "3.9" and python_version < "3.13"
transformers==4.45.2 ; python_version >= "3.9" and python_version < "3.13"
transformers==4.46.0 ; python_version >= "3.9" and python_version < "3.13"
typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
typing-extensions==4.12.2 ; python_version >= "3.9" and python_version < "3.13"
urllib3==2.2.3 ; python_version >= "3.9" and python_version < "3.13"

1
server/text_generation_server/cli.py
View File

@ -19,6 +19,7 @@ class Quantization(str, Enum):
bitsandbytes_fp4 = "bitsandbytes-fp4"
gptq = "gptq"
awq = "awq"
compressed_tensors = "compressed-tensors"
eetq = "eetq"
exl2 = "exl2"
fp8 = "fp8"

6
server/text_generation_server/layers/attention/cuda.py
View File

@ -108,7 +108,7 @@ def paged_attention(
if softcap is not None:
raise RuntimeError("Paged attention doesn't support softcapping")
input_lengths = seqlen.input_lengths + seqlen.cache_lengths
from vllm._C import ops
import attention_kernels
out = torch.empty_like(query)
@ -116,7 +116,7 @@ def paged_attention(
max_num_partitions == 1 or num_seqs * num_heads > 512
)
if use_v1:
ops.paged_attention_v1(
attention_kernels.paged_attention_v1(
out,
query,
kv_cache.key,
@ -146,7 +146,7 @@ def paged_attention(
)
max_logits = torch.empty_like(exp_sums)
ops.paged_attention_v2(
attention_kernels.paged_attention_v2(
out,
exp_sums,
max_logits,

6
server/text_generation_server/layers/attention/kv_cache.py
View File

@ -200,12 +200,12 @@ def paged_reshape_and_cache(
):
if SYSTEM == "cuda":
try:
from vllm._C import cache_ops
import attention_kernels
except Exception as e:
raise ImportError(
f"Could not import vllm paged attention. Make sure your installation is correct. Complete error: {e}"
f"Could not import attention_kernels. Make sure your installation is correct. Complete error: {e}"
)
cache_ops.reshape_and_cache(
attention_kernels.reshape_and_cache(
key, value, key_cache, value_cache, slots, "auto", 1.0
)
elif SYSTEM == "rocm":

3
server/text_generation_server/layers/compressed_tensors/__init__.py Normal file
View File

@ -0,0 +1,3 @@
from .loader import CompressedTensorsLoader
__all__ = ["CompressedTensorsLoader"]

174
server/text_generation_server/layers/compressed_tensors/loader.py Normal file
View File

@ -0,0 +1,174 @@
from typing import Any, Dict, List, Union
from compressed_tensors import QuantizationConfig, QuantizationStatus
from compressed_tensors.config import CompressionFormat
from compressed_tensors.quantization import (
QuantizationScheme,
QuantizationType,
find_name_or_class_matches,
)
from loguru import logger
from pydantic import ValidationError
from torch import nn
from text_generation_server.layers.compressed_tensors.w8an_fp import W8ANFpLoader
from text_generation_server.layers.compressed_tensors.wna16_int import WNA16Loader
from text_generation_server.utils.log import log_once
from text_generation_server.utils.weights import (
DefaultWeightsLoader,
UnquantizedWeight,
Weights,
WeightsLoader,
)
# compressed-tensors can match modules as quantization targets. However,
# they need to be objects rather than classes or class names. Since we
# need to match `Linear` targets, make an instance that can be re-used.
_EMPTY_LINEAR: nn.Module = nn.Linear(0, 0)
class CompressedTensorsLoader(WeightsLoader):
"""Loader for checkpoints stored in the compressed-tensors format."""
def __init__(self, config: Dict[str, Any]):
quantization_config_raw = config.get("quantization_config")
if quantization_config_raw is None:
# `compression_config` was renamed to `quantization_config`; support
# retained for backward compatibility.
quantization_config_raw = config.get("compression_config")
if quantization_config_raw is None:
raise ValueError(
"Checkpoint does not have compressed-tensors configuration"
)
try:
quantization_config = QuantizationConfig.model_validate(
quantization_config_raw
)
except ValidationError as e:
raise ValueError("Cannot parse compressed-tensors configuration") from e
if quantization_config.quantization_status not in (
QuantizationStatus.COMPRESSED,
QuantizationStatus.FROZEN,
):
raise ValueError(
f"Model quantization was not finished, status was: {quantization_config.quantization_status}"
)
self.ignore = (
quantization_config.ignore if quantization_config.ignore is not None else []
)
self.loaders = self._get_target_loaders(quantization_config)
for target, loader in self.loaders.items():
log_once(
logger.info,
f"Using {loader} for compressed-tensors target '{target}'",
)
def get_weights(self, weights: Weights, prefix: str):
loader = self._lookup_loader(prefix)
return loader.get_weights(weights, prefix)
def get_weights_col_packed(
self,
weights: "Weights",
prefix: str,
block_sizes: Union[int, List[int]],
):
loader = self._lookup_loader(prefix)
return loader.get_weights_col_packed(weights, prefix, block_sizes)
def get_multi_weights_col(self, weights: Weights, prefixes: List[str], dim: int):
loader = self._lookup_loader(prefixes[0])
return loader.get_multi_weights_col(weights, prefixes, dim)
def get_weights_row(self, weights: Weights, prefix: str):
loader = self._lookup_loader(prefix)
return loader.get_weights_row(weights, prefix)
def _get_target_loaders(
self, quantization_config: QuantizationConfig
) -> Dict[str, WeightsLoader]:
"""
A compressed-tensors checkpoint can use different quantizations
for different targets. This method returns a dictionary with a
loader per target.
"""
loaders: Dict[str, WeightsLoader] = {}
format = quantization_config.format
for group_name, group in quantization_config.config_groups.items():
# The group configuration can be a string, but does that ever
# happen in a serialized quantization config?
assert isinstance(group, QuantizationScheme)
loader = self._create_loader_for_group(format, group_name, group)
# A quantized parameter group can have multiple targets, add the
# loader for all the targets.
for target in group.targets:
if target in loaders:
raise ValueError(
f"Target '{target} has multiple configured loaders'"
)
loaders[target] = loader
return loaders
def _create_loader_for_group(
self, format: str, group_name: str, group: QuantizationScheme
) -> WeightsLoader:
"""
Find and create a loader for the group with the given quantization
scheme.
"""
# NOTE: we ignore group.output_activations because we don't support
# output quantization yet.
input_activations = group.input_activations
weights = group.weights
if (
format
in {
CompressionFormat.float_quantized.value,
CompressionFormat.naive_quantized.value,
}
and weights is not None
and weights.type == QuantizationType.FLOAT
and weights.num_bits == 8
):
# FP W8A8 or W8A16.
return W8ANFpLoader(input_activations=input_activations, weights=weights)
elif (
format == CompressionFormat.pack_quantized.value
and weights is not None
and weights.type == QuantizationType.INT
and weights.num_bits in (4, 8)
):
# INT W4A16 or W8A16 (GPTQ/AWQ-like).
return WNA16Loader(weights)
else:
raise ValueError(
f"Group '{group_name}' has unsupported compressed-tensors configurtion"
)
def _lookup_loader(self, prefix: str) -> WeightsLoader:
"""
Look up the loader to use for a given parameter name (prefix).
"""
if len(find_name_or_class_matches(prefix, _EMPTY_LINEAR, self.ignore)) > 0:
return DefaultWeightsLoader(UnquantizedWeight)
# We currently only handle linear layers, so unconditionally pass
# a `Linear` instance.
targets = find_name_or_class_matches(prefix, _EMPTY_LINEAR, self.loaders.keys())
if len(targets) == 0:
raise ValueError(
f"Cannot find compressed-tensors target for prefix: {prefix}"
)
return self.loaders[targets[0]]

174
server/text_generation_server/layers/compressed_tensors/w8an_fp.py Normal file
View File

@ -0,0 +1,174 @@
from typing import List, Optional, Union
import torch
from compressed_tensors.quantization import QuantizationArgs, QuantizationType
from text_generation_server.layers.fp8 import Fp8Weight, _load_scalar_or_matrix_scale
from text_generation_server.utils.weights import Weights, WeightsLoader
class W8ANFpLoader(WeightsLoader):
"""
Loader for W8A8/W8A16 FP compressed-tensors parameters.
"""
def __init__(
self,
*,
input_activations: Optional[QuantizationArgs],
weights: QuantizationArgs,
):
assert weights.type == QuantizationType.FLOAT and weights.num_bits == 8
# We ignore the `strategy` option which sets the scales to be
# per-tensor, per-channel or per-token. What scales are supported
# is dependent on the kernels used (e.g. cutlass can do tokenwise,
# Torch cannot, and FP8-Marlin does not quantize inputs at all).
# So, instead we try to use the best-possible configuration.
self.load_weight_scale = not weights.dynamic
self.load_input_scale = (
input_activations is not None and not input_activations.dynamic
)
self.force_w8a16 = (
input_activations is not None and input_activations.num_bits == 16
)
def __str__(self) -> str:
def scale_to_str(scale):
return "static" if scale else "dynamic"
quantization_type = f"W8A{16 if self.force_w8a16 else 8}"
return f"{self.__class__.__name__} ({quantization_type}, weight: {scale_to_str(self.load_weight_scale)}, input: {scale_to_str(self.load_input_scale)})"
def get_weights(self, weights: "Weights", prefix: str):
w = weights.get_tensor(f"{prefix}.weight")
weight_scale = None
if self.load_weight_scale:
weight_scale = (
weights.get_tensor(f"{prefix}.weight_scale", to_dtype=False)
.reshape(-1)
.expand(w.shape[0])
)
input_scale = None
if self.load_input_scale:
input_scale = weights.get_tensor(
f"{prefix}.input_scale", to_dtype=False
).reshape(-1)
return Fp8Weight(
weight=w,
weight_scale=weight_scale,
input_scale=input_scale,
dtype=weights.dtype,
force_w8a16=self.force_w8a16,
)
def get_weights_col_packed(
self,
weights: Weights,
prefix: str,
block_sizes: Union[int, List[int]],
):
w = weights.get_packed_sharded(
f"{prefix}.weight", dim=0, block_sizes=block_sizes
)
weight_scale = None
if self.load_weight_scale:
weight_scale = weights.get_tensor(f"{prefix}.weight_scale", to_dtype=False)
if weight_scale.numel() > 1:
weight_scale = weights.get_packed_sharded(
f"{prefix}.weight_scale",
dim=0,
block_sizes=block_sizes,
to_dtype=False,
)
weight_scale = weight_scale.reshape(-1).expand(w.shape[0])
input_scale = None
if self.load_input_scale:
input_scale = weights.get_tensor(f"{prefix}.input_scale", to_dtype=False)
if input_scale.numel() > 1:
input_scale = weights.get_packed_sharded(
f"{prefix}.input_scale",
dim=0,
block_sizes=block_sizes,
to_dtype=False,
)
input_scale = input_scale.reshape(-1).max()
return Fp8Weight(
weight=w,
weight_scale=weight_scale,
input_scale=input_scale,
dtype=weights.dtype,
force_w8a16=self.force_w8a16,
)
def get_multi_weights_col(self, weights: "Weights", prefixes: List[str], dim: int):
# FIXME: Force to_device to false as fp8 weights do not support torch.cat on device yet
w = [
weights.get_sharded(f"{p}.weight", dim=0, to_device=False) for p in prefixes
]
shapes = [x.shape for x in w]
# Concat then send to the device
w = torch.cat(w, dim=dim).to(weights.device)
weight_scale = None
if self.load_weight_scale:
weight_scale = [
_load_scalar_or_matrix_scale(weights, f"{p}.weight_scale", shape)
for p, shape in zip(prefixes, shapes)
]
weight_scale = torch.cat(weight_scale, dim=0).reshape(-1)
input_scale = None
if self.load_input_scale:
input_scale = [
_load_scalar_or_matrix_scale(weights, f"{p}.input_scale", shape)
for p, shape in zip(prefixes, shapes)
if weights.has_tensor(f"{p}.input_scale")
]
assert len(input_scale) == 0 or len(input_scale) == len(prefixes)
input_scale = (
torch.cat(input_scale, dim=0).reshape(-1).max()
if len(input_scale) != 0
else None
)
return Fp8Weight(
weight=w,
weight_scale=weight_scale,
input_scale=input_scale,
dtype=weights.dtype,
force_w8a16=self.force_w8a16,
)
def get_weights_row(self, weights: "Weights", prefix: str):
w = weights.get_sharded(f"{prefix}.weight", dim=1)
weight_scale = None
if self.load_weight_scale:
weight_scale = (
weights.get_tensor(f"{prefix}.weight_scale", to_dtype=False)
.reshape(-1)
.expand(w.shape[0])
)
input_scale = None
if self.load_input_scale:
input_scale = weights.get_tensor(
f"{prefix}.input_scale", to_dtype=False
).reshape(-1)
return Fp8Weight(
weight=w,
weight_scale=weight_scale,
input_scale=input_scale,
dtype=weights.dtype,
force_w8a16=self.force_w8a16,
)

188
server/text_generation_server/layers/compressed_tensors/wna16_int.py Normal file
View File

@ -0,0 +1,188 @@
from typing import List, Union
import torch
from compressed_tensors.quantization import ActivationOrdering, QuantizationArgs
from loguru import logger
from text_generation_server.layers.marlin.gptq import repack_gptq_for_marlin
from text_generation_server.utils.log import log_once
from text_generation_server.utils.weights import Weights, WeightsLoader
class WNA16Loader(WeightsLoader):
"""
Loader for W4A16/W8A16 INT compressed-tensors parameters.
"""
def __init__(self, weights: QuantizationArgs):
self.weights = weights
self.desc_act = self.weights.actorder == ActivationOrdering.GROUP
self.groupsize = (
-1 if self.weights.group_size is None else self.weights.group_size
)
def __str__(self) -> str:
quantization_type = f"W{self.weights.num_bits}8A16"
return f"{self.__class__.__name__} ({quantization_type})"
def get_weights(self, weights: Weights, prefix: str):
log_once(logger.info, "Using GPTQ-Marlin kernels")
try:
weight_packed = weights.get_tensor(f"{prefix}.weight_packed").t()
except RuntimeError:
raise RuntimeError(
f"Cannot load w{self.weights.num_bits}a16 weight, make sure the model is already quantized"
)
zero_point = None
if not self.weights.symmetric:
zero_point = weights.get_tensor(f"{prefix}.weight_zero_point").t()
g_idx = None
if self.desc_act:
g_idx = weights.get_tensor(f"{prefix}.weight_g_idx")
scales = weights.get_tensor(f"{prefix}.weight.scales").t()
return repack_gptq_for_marlin(
qweight=weight_packed.contiguous(),
scales=scales,
qzeros=zero_point,
g_idx=g_idx,
bits=self.weights.num_bits,
desc_act=self.desc_act,
groupsize=self.groupsize,
quant_method="compressed-tensors",
sym=self.weights.symmetric,
sharded_infeatures=False,
)
def get_weights_col_packed(
self,
weights: Weights,
prefix: str,
block_sizes: Union[int, List[int]],
):
try:
weight_packed = weights.get_packed_sharded(
f"{prefix}.weight_packed", dim=0, block_sizes=block_sizes
).t()
except RuntimeError:
raise RuntimeError(
f"Cannot load w{self.weights.num_bits}a16 weight, make sure the model is already quantized"
)
scales = weights.get_packed_sharded(
f"{prefix}.weight_scale", dim=0, block_sizes=block_sizes
).t()
scales = scales.to(dtype=weights.dtype)
zero_point = None
if not self.weights.symmetric:
zero_point = weights.get_packed_sharded(
f"{prefix}.qzeros", dim=0, block_sizes=block_sizes
).t()
g_idx = None
if self.desc_act:
g_idx = weights.get_tensor(f"{prefix}.g_idx")
return repack_gptq_for_marlin(
qweight=weight_packed.contiguous(),
scales=scales,
qzeros=zero_point,
g_idx=g_idx,
bits=self.weights.num_bits,
desc_act=self.desc_act,
groupsize=self.groupsize,
quant_method="compressed-tensors",
sym=self.weights.symmetric,
sharded_infeatures=False,
)
def get_multi_weights_col(self, weights: Weights, prefixes: List[str], dim: int):
try:
weight_packed = torch.cat(
[
weights.get_sharded(f"{p}.weight_packed", dim=0).t()
for p in prefixes
],
dim=1,
)
except RuntimeError:
raise RuntimeError(
f"Cannot load w{self.weights.num_bits}a16 weight, make sure the model is already quantized"
)
scales = torch.cat(
[weights.get_sharded(f"{p}.weight_scale", dim=0).t() for p in prefixes],
dim=1,
)
zero_point = None
if not self.weights.symmetric:
zero_point = torch.cat(
[weights.get_sharded(f"{p}.qzeros", dim=0).t() for p in prefixes], dim=1
).t()
g_idx = None
if self.desc_act:
w = [weights.get_tensor(f"{p}.g_idx") for p in prefixes]
for w2 in w[1:]:
torch.testing.assert_close(w2, w[0])
g_idx = w[0]
return repack_gptq_for_marlin(
qweight=weight_packed.contiguous(),
scales=scales,
qzeros=zero_point,
g_idx=g_idx,
bits=self.weights.num_bits,
desc_act=self.desc_act,
groupsize=self.groupsize,
quant_method="compressed-tensors",
sym=self.weights.symmetric,
sharded_infeatures=False,
)
def get_weights_row(self, weights: Weights, prefix: str):
log_once(logger.info, "Using GPTQ-Marlin kernels")
try:
weight_packed = weights.get_sharded(f"{prefix}.weight_packed", dim=1).t()
except RuntimeError:
raise RuntimeError(
f"Cannot load `{self.quantize}` weight, make sure the model is already quantized."
)
zero_point = None
if not self.weights.symmetric:
if self.desc_act or self.groupsize == -1:
zero_point = weights.get_tensor(f"{prefix}.weight_zero_point").t()
else:
zero_point = weights.get_sharded(
f"{prefix}.weight_zero_point", dim=1
).t()
g_idx = None
if self.desc_act:
g_idx = weights.get_sharded(f"{prefix}.g_idx", dim=0)
if self.desc_act or self.groupsize == -1:
scales = weights.get_tensor(f"{prefix}.weight_scale").t()
else:
scales = weights.get_sharded(f"{prefix}.weight_scale", dim=1).t()
sharded_in_features = weights.process_group.size() > 1
return repack_gptq_for_marlin(
qweight=weight_packed.contiguous(),
scales=scales,
qzeros=zero_point,
g_idx=g_idx,
bits=self.weights.num_bits,
desc_act=self.desc_act,
groupsize=self.groupsize,
quant_method="compressed-tensors",
sym=self.weights.symmetric,
sharded_infeatures=sharded_in_features,
)

18
server/text_generation_server/layers/fp8.py
View File

@ -29,7 +29,7 @@ else:
CUTLASS_FP8_AVAILABLE = False
def get_fp8_linear() -> Type[torch.nn.Module]:
def get_fp8_linear(force_w8a16: bool = False) -> Type[torch.nn.Module]:
"""
Return an FP8 linear `Module` that is compatible with the current system.
"""
@ -37,7 +37,14 @@ def get_fp8_linear() -> Type[torch.nn.Module]:
if SYSTEM == "cuda":
major, _ = torch.cuda.get_device_capability()
if major == 8 and os.getenv("USE_CUTLASS_W8A8", "0") != "1":
# Marlin is W8A16, use it when:
#
# - On capability 8.x where x < 8: W8A8 FP8 GEMM is not supported.
# - On capability 8.9: W8A8 FP8 GEMM is supported, but Marlin-FP8 is faster.
# - On capability 9.x when force_w8a16: cutlass kernels do not support W8A16.
if (major == 8 or (major == 9 and force_w8a16)) and os.getenv(
"USE_CUTLASS_W8A8", "0"
) != "1":
# NOTE: Capability 8.9 is supported by cutlass kernels, but FP8-Marlin
# gives better decoding throughput on L4 and L40.
from text_generation_server.layers.marlin import GPTQMarlinFP8Linear
@ -283,14 +290,17 @@ class Fp8Weight(Weight):
weight_scale: Optional[torch.Tensor] = None
input_scale: Optional[torch.Tensor] = None
activation_scale_ub: Optional[float] = None
force_w8a16: bool = False
def get_linear(self, bias: torch.Tensor):
if self.weight_scale is None:
return get_fp8_linear().from_unquant(self.weight, bias, self.dtype)
return get_fp8_linear(force_w8a16=self.force_w8a16).from_unquant(
self.weight, bias, self.dtype
)
# This is not checked by the fbgemm kernels, but they require contiguous
# memory. Can be non-contiguous when we e.g. expand from scalars.
self.weight_scale = self.weight_scale.contiguous()
return get_fp8_linear().from_fp8(
return get_fp8_linear(force_w8a16=self.force_w8a16).from_fp8(
weight=self.weight,
scale=self.weight_scale,
dtype=self.dtype,

4
server/text_generation_server/layers/marlin/gptq.py
View File

@ -261,7 +261,7 @@ class GPTQMarlinWeight(Weight):
def __post_init__(self):
assert self.qweight.dtype == torch.int32
assert self.scales.dtype == torch.float16
assert self.scales.dtype in (torch.float16, torch.bfloat16)
assert self.g_idx.dtype == torch.int32
assert self.perm.dtype == torch.int32
@ -300,7 +300,7 @@ def repack_gptq_for_marlin(
raise RuntimeError(
f"Repacking GPTQ weights with group size {groupsize} as Marlin is not supported, must be one of: {supported_sizes}"
)
if not (sym or quant_method == "awq"):
if not (sym or quant_method == "awq" or quant_method == "compressed-tensors"):
raise RuntimeError(
"Repacking GPTQ weights with asymmetric quantization as Marlin is not supported."
)

39
server/text_generation_server/models/__init__.py
View File

@ -370,46 +370,23 @@ def get_model(
compression_config = config_dict.get("compression_config", None)
if quantization_config is not None and quantize is None:
method = quantization_config.get("quant_method", None)
config_groups = quantization_config.get("config_groups", None)
if method in {"gptq", "awq", "exl2"}:
log_master(logger.info, f"Auto selecting quantization method {method}")
quantize = method
elif method == "fbgemm_fp8" or method == "fp8":
log_master(logger.info, "Auto selecting quantization method fp8")
quantize = "fp8"
elif config_groups is not None:
# TODO: at some point we should probably fully parse the compression
# configuration to know which parameters are compressed.
for _, group in config_groups.items():
weights_config = group.get("weights")
if weights_config is not None:
if (
weights_config["type"] == "float"
and weights_config["num_bits"] == 8
):
log_master(
logger.info, "Auto selecting quantization method fp8"
)
quantize = "fp8"
break
if method == "compressed-tensors":
log_master(
logger.info, "Auto selecting quantization method compressed-tensors"
)
quantize = "compressed-tensors"
else:
log_master(logger.warning, f"Unknown quantization method {method}")
elif compression_config is not None:
# `compression_config` renamed to `quantization_config`; support retained for backward compatibility.
config_groups = compression_config.get("config_groups")
if config_groups is not None:
for _, group in config_groups.items():
weights_config = group.get("weights")
if weights_config is not None:
if (
weights_config["type"] == "float"
and weights_config["num_bits"] == 8
):
log_master(
logger.info, "Auto selecting quantization method fp8"
)
quantize = "fp8"
break
log_master(logger.info, "Auto selecting quantization method compressed-tensors")
quantize = "compressed-tensors"
if dtype is None:
if quantize in ["awq", "exl2", "gptq", "marlin"]:
@ -559,7 +536,7 @@ def get_model(
# TODO: fix how we determine model type for Mamba
if "ssm_cfg" in config_dict:
# *only happens in Mamba case
model_type = "ssm"
model_type = "mamba"
else:
raise RuntimeError(
f"Could not determine model type for {model_id} revision {revision}"

4
server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py
View File

@ -23,8 +23,10 @@ from typing import Optional, List, Tuple, Any
from text_generation_server.layers.attention.kv_cache import get_kv_scales
from text_generation_server.utils.import_utils import SYSTEM
if SYSTEM != "ipex":
if SYSTEM == "rocm":
from vllm.model_executor.layers.fused_moe import fused_moe
elif SYSTEM != "ipex":
from moe_kernels.fused_moe import fused_moe
else:
from intel_extension_for_pytorch.llm.modules import GatedMLPMOE

2
server/text_generation_server/models/custom_modeling/mamba_modeling.py
View File

@ -212,7 +212,7 @@ class MambaModel(nn.Module):
try:
self.lm_head = SpeculativeHead.load(config, f"{prefix}.embeddings", weights)
except RuntimeError:
self.lm_head = SpeculativeHead.load(config, f"{prefix}.embeddings", weights)
self.lm_head = SpeculativeHead.load(config, f"{prefix}.embedding", weights)
self.config = config
def forward(

18
server/text_generation_server/utils/logits_process.py
View File

@ -5,7 +5,7 @@ from loguru import logger
from typing import Dict, Union
from text_generation_server.pb.generate_pb2 import GrammarType
from outlines.fsm.fsm import RegexFSM
from outlines.fsm.guide import RegexGuide
from outlines.fsm.json_schema import build_regex_from_schema
from functools import lru_cache
from typing import List, Optional, DefaultDict
@ -482,7 +482,7 @@ class HeterogeneousProcessorWrapper(LogitsProcessor):
class GrammarLogitProcessor(LogitsProcessor):
fsm_state: DefaultDict[int, int]
fsm: RegexFSM
fsm: RegexGuide
def __init__(self, tokenizer, device, grammar, grammar_type):
self.device = device
@ -498,9 +498,10 @@ class GrammarLogitProcessor(LogitsProcessor):
):
if fsm_grammar_state == -1 or self.fsm is None:
return logits
allowed_tokens = self.fsm.allowed_token_ids(fsm_grammar_state)
allowed_tokens = self.fsm.get_next_instruction(fsm_grammar_state).tokens
mask = torch.full_like(logits, -math.inf)
mask[:, allowed_tokens] = 0
if allowed_tokens is not None:
mask[:, allowed_tokens] = 0
biased_scores = logits + mask
return biased_scores
@ -513,7 +514,7 @@ class GrammarLogitProcessor(LogitsProcessor):
def _advance(next_token_id, fsm_grammar_state, fsm):
if fsm_grammar_state == -1:
return fsm_grammar_state
return fsm.next_state(fsm_grammar_state, next_token_id)
return fsm.get_next_state(fsm_grammar_state, next_token_id)
# TODO: move grammar compilation into the router
@staticmethod
@ -530,7 +531,7 @@ class GrammarLogitProcessor(LogitsProcessor):
schema = "(.*?)"
elif grammar_type == GrammarType.GRAMMAR_TYPE_REGEX:
pass # schema is already a regex just here for clarity
fsm = RegexFSM(schema, tokenizer)
fsm = RegexGuide.from_regex(schema, tokenizer)
logger.debug(f"Compiled FSM in {time.time() - start_time:.2f}s")
return fsm
@ -588,8 +589,9 @@ class HeterogeneousGrammarLogitProcessor(LogitsProcessor):
fsm = self.fsms[i]
if fsm_grammar_states[i] == -1 or fsm is None:
continue
allowed_tokens = fsm.allowed_token_ids(fsm_grammar_states[i])
mask[i, allowed_tokens] = 0
allowed_tokens = fsm.get_next_instruction(fsm_grammar_states[i]).tokens
if allowed_tokens is not None:
mask[i, allowed_tokens] = 0
logits[i] += mask[i]
return logits

48
server/text_generation_server/utils/quantization.py
View File

@ -27,7 +27,20 @@ class _FP8QuantizerConfig:
activation_scale_ub: float
# We should probably do this with Pytantic JSON deserialization,
def _get_config_json(model_id: str, revision: Optional[str], filename: str):
if os.path.exists(
os.path.join(
model_id,
)
):
filename = os.path.join(model_id, filename)
else:
filename = hf_hub_download(model_id, filename=filename, revision=revision)
with open(filename, "r") as f:
return json.load(f)
# We should probably do this with Pydantic JSON deserialization,
# but for now we'll stay close to the old _set_gptq_params.
def _get_quantizer_config(model_id, revision):
bits = 4
@ -39,12 +52,7 @@ def _get_quantizer_config(model_id, revision):
filename = "config.json"
try:
if os.path.exists(os.path.join(model_id, filename)):
filename = os.path.join(model_id, filename)
else:
filename = hf_hub_download(model_id, filename=filename, revision=revision)
with open(filename, "r") as f:
data = json.load(f)
data = _get_config_json(model_id, revision, filename)
# FP8 config
if data["quantization_config"]["quant_method"] == "fbgemm_fp8":
@ -67,14 +75,7 @@ def _get_quantizer_config(model_id, revision):
except Exception:
filename = "quantize_config.json"
try:
if os.path.exists(os.path.join(model_id, filename)):
filename = os.path.join(model_id, filename)
else:
filename = hf_hub_download(
model_id, filename=filename, revision=revision
)
with open(filename, "r") as f:
data = json.load(f)
data = _get_config_json(model_id, revision, filename)
bits = data["bits"]
groupsize = data["group_size"]
@ -90,14 +91,7 @@ def _get_quantizer_config(model_id, revision):
except Exception:
filename = "quant_config.json"
try:
if os.path.exists(os.path.join(model_id, filename)):
filename = os.path.join(model_id, filename)
else:
filename = hf_hub_download(
model_id, filename=filename, revision=revision
)
with open(filename, "r") as f:
data = json.load(f)
data = _get_config_json(model_id, revision, filename)
bits = data["w_bit"]
groupsize = data["q_group_size"]
desc_act = data["desc_act"]
@ -119,6 +113,14 @@ def _get_quantizer_config(model_id, revision):
def get_loader(
quantize: Optional[str], model_id: str, revision: Optional[str]
) -> WeightsLoader:
if quantize == "compressed-tensors":
config = _get_config_json(model_id, revision, "config.json")
from text_generation_server.layers.compressed_tensors import (
CompressedTensorsLoader,
)
return CompressedTensorsLoader(config)
quantizer_config = _get_quantizer_config(model_id, revision)
if quantize in {"awq", "gptq"}:
from text_generation_server.layers.gptq import GPTQWeightsLoader