v1.4.0 (#1494)
This commit is contained in:
OlivierDehaene
GitHub
parent
d9758851be
commit
c2d4a3b5c7
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@ -1,12 +0,0 @@
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name: Delete doc comment
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on:
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pull_request:
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types: [ closed ]
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jobs:
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delete:
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uses: huggingface/doc-builder/.github/workflows/delete_doc_comment_trigger.yml@main
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with:
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pr_number: ${{ github.event.number }}
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File diff suppressed because it is too large
Load Diff
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@ -9,7 +9,7 @@ members = [
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resolver = "2"
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[workspace.package]
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version = "1.3.4"
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version = "1.4.0"
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edition = "2021"
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authors = ["Olivier Dehaene"]
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homepage = "https://github.com/huggingface/text-generation-inference"
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@ -62,7 +62,7 @@ For a detailed starting guide, please see the [Quick Tour](https://huggingface.c
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model=HuggingFaceH4/zephyr-7b-beta
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volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.3 --model-id $model
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.4 --model-id $model
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```
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And then you can make requests like
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@ -76,7 +76,7 @@ curl 127.0.0.1:8080/generate \
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**Note:** To use NVIDIA GPUs, you need to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). We also recommend using NVIDIA drivers with CUDA version 12.2 or higher. For running the Docker container on a machine with no GPUs or CUDA support, it is enough to remove the `--gpus all` flag and add `--disable-custom-kernels`, please note CPU is not the intended platform for this project, so performance might be subpar.
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**Note:** TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the [Supported Hardware documentation](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.3-rocm --model-id $model` instead of the command above.
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**Note:** TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the [Supported Hardware documentation](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.4-rocm --model-id $model` instead of the command above.
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To see all options to serve your models (in the [code](https://github.com/huggingface/text-generation-inference/blob/main/launcher/src/main.rs) or in the cli):
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```
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@ -106,7 +106,7 @@ model=meta-llama/Llama-2-7b-chat-hf
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volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
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token=<your cli READ token>
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docker run --gpus all --shm-size 1g -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.3 --model-id $model
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docker run --gpus all --shm-size 1g -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.4 --model-id $model
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```
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### A note on Shared Memory (shm)
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1294
docs/openapi.json
1294
docs/openapi.json
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@ -19,6 +19,6 @@ docker run --gpus all \
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--shm-size 1g \
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-e HUGGING_FACE_HUB_TOKEN=$token \
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-p 8080:80 \
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-v $volume:/data ghcr.io/huggingface/text-generation-inference:1.3 \
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-v $volume:/data ghcr.io/huggingface/text-generation-inference:1.4 \
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--model-id $model
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```
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@ -8,7 +8,7 @@ Let's say you want to deploy [Falcon-7B Instruct](https://huggingface.co/tiiuae/
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model=tiiuae/falcon-7b-instruct
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volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.3 --model-id $model
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.4 --model-id $model
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```
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<Tip warning={true}>
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@ -20,7 +20,7 @@ To use NVIDIA GPUs, you need to install the [NVIDIA Container Toolkit](https://d
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TGI also supports ROCm-enabled AMD GPUs (only MI210 and MI250 are tested), details are available in the [Supported Hardware section](./supported_models#supported-hardware) and [AMD documentation](https://rocm.docs.amd.com/en/latest/deploy/docker.html). To launch TGI on ROCm GPUs, please use instead:
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```bash
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docker run --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --device=/dev/kfd --device=/dev/dri --group-add video --ipc=host --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.3-rocm --model-id $model
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docker run --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --device=/dev/kfd --device=/dev/dri --group-add video --ipc=host --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.4-rocm --model-id $model
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```
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Once TGI is running, you can use the `generate` endpoint by doing requests. To learn more about how to query the endpoints, check the [Consuming TGI](./basic_tutorials/consuming_tgi) section, where we show examples with utility libraries and UIs. Below you can see a simple snippet to query the endpoint.
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@ -91,7 +91,7 @@ curl 127.0.0.1:8080/generate \
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To see all possible deploy flags and options, you can use the `--help` flag. It's possible to configure the number of shards, quantization, generation parameters, and more.
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```bash
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docker run ghcr.io/huggingface/text-generation-inference:1.3 --help
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docker run ghcr.io/huggingface/text-generation-inference:1.4 --help
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```
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</Tip>
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@ -21,7 +21,7 @@ async def test_flash_phi(flash_phi, response_snapshot):
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)
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assert response.details.generated_tokens == 10
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assert response.generated_text == ": {request}\")\n response = self"
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assert response.generated_text == ': {request}")\n response = self'
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assert response == response_snapshot
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@ -52,14 +52,12 @@ async def test_flash_phi_all_params(flash_phi, response_snapshot):
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@pytest.mark.asyncio
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@pytest.mark.private
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async def test_flash_phi_load(flash_phi, generate_load, response_snapshot):
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responses = await generate_load(
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flash_phi, "Test request", max_new_tokens=10, n=4
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)
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responses = await generate_load(flash_phi, "Test request", max_new_tokens=10, n=4)
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assert len(responses) == 4
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assert all(
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[r.generated_text == responses[0].generated_text for r in responses]
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), f"{[r.generated_text for r in responses]}"
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assert responses[0].generated_text == ": {request}\")\n response = self"
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assert responses[0].generated_text == ': {request}")\n response = self'
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assert responses == response_snapshot
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@ -1,6 +1,6 @@
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[tool.poetry]
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name = "text-generation-integration-tests"
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version = "1.3.4"
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version = "1.4.0"
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description = "Text Generation Inference integration tests"
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authors = ["Nicolas Patry <nicolas@huggingface.co>"]
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File diff suppressed because it is too large
Load Diff
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@ -1,6 +1,6 @@
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[tool.poetry]
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name = "text-generation-server"
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version = "1.3.4"
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version = "1.4.0"
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description = "Text Generation Inference Python gRPC Server"
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authors = ["Olivier Dehaene <olivier@huggingface.co>"]
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@ -13,11 +13,11 @@ grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
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grpcio-reflection==1.60.0 ; python_version >= "3.9" and python_version < "3.13"
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grpcio-status==1.60.0 ; python_version >= "3.9" and python_version < "3.13"
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grpcio==1.60.0 ; python_version >= "3.9" and python_version < "3.13"
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hf-transfer==0.1.4 ; python_version >= "3.9" and python_version < "3.13"
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hf-transfer==0.1.5 ; python_version >= "3.9" and python_version < "3.13"
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huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13"
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idna==3.6 ; python_version >= "3.9" and python_version < "3.13"
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loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
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numpy==1.26.2 ; python_version >= "3.9" and python_version < "3.13"
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numpy==1.26.3 ; python_version >= "3.9" and python_version < "3.13"
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opentelemetry-api==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
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opentelemetry-exporter-otlp-proto-grpc==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
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opentelemetry-exporter-otlp-proto-http==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
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@ -28,18 +28,18 @@ opentelemetry-proto==1.15.0 ; python_version >= "3.9" and python_version < "3.13
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opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
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opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
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packaging==23.2 ; python_version >= "3.9" and python_version < "3.13"
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pillow==10.1.0 ; python_version >= "3.9" and python_version < "3.13"
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protobuf==4.25.1 ; python_version >= "3.9" and python_version < "3.13"
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pillow==10.2.0 ; python_version >= "3.9" and python_version < "3.13"
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protobuf==4.25.2 ; python_version >= "3.9" and python_version < "3.13"
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pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
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regex==2023.10.3 ; python_version >= "3.9" and python_version < "3.13"
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regex==2023.12.25 ; python_version >= "3.9" and python_version < "3.13"
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requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13"
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safetensors==0.3.3 ; python_version >= "3.9" and python_version < "3.13"
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scipy==1.11.4 ; python_version >= "3.9" and python_version < "3.13"
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scipy==1.12.0 ; python_version >= "3.9" and python_version < "3.13"
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sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
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setuptools==69.0.2 ; python_version >= "3.9" and python_version < "3.13"
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tokenizers==0.15.0 ; python_version >= "3.9" and python_version < "3.13"
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setuptools==69.0.3 ; python_version >= "3.9" and python_version < "3.13"
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tokenizers==0.15.1 ; python_version >= "3.9" and python_version < "3.13"
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tqdm==4.66.1 ; python_version >= "3.9" and python_version < "3.13"
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transformers==4.36.1 ; python_version >= "3.9" and python_version < "3.13"
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transformers==4.37.1 ; python_version >= "3.9" and python_version < "3.13"
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typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
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typing-extensions==4.9.0 ; python_version >= "3.9" and python_version < "3.13"
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urllib3==2.1.0 ; python_version >= "3.9" and python_version < "3.13"
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@ -12,11 +12,11 @@ grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
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grpcio-reflection==1.60.0 ; python_version >= "3.9" and python_version < "3.13"
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grpcio-status==1.60.0 ; python_version >= "3.9" and python_version < "3.13"
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grpcio==1.60.0 ; python_version >= "3.9" and python_version < "3.13"
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hf-transfer==0.1.4 ; python_version >= "3.9" and python_version < "3.13"
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hf-transfer==0.1.5 ; python_version >= "3.9" and python_version < "3.13"
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huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13"
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idna==3.6 ; python_version >= "3.9" and python_version < "3.13"
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loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
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numpy==1.26.2 ; python_version >= "3.9" and python_version < "3.13"
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numpy==1.26.3 ; python_version >= "3.9" and python_version < "3.13"
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opentelemetry-api==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
|
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opentelemetry-exporter-otlp-proto-grpc==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
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opentelemetry-exporter-otlp-proto-http==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
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@ -27,18 +27,18 @@ opentelemetry-proto==1.15.0 ; python_version >= "3.9" and python_version < "3.13
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opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
|
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opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
|
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packaging==23.2 ; python_version >= "3.9" and python_version < "3.13"
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pillow==10.1.0 ; python_version >= "3.9" and python_version < "3.13"
|
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protobuf==4.25.1 ; python_version >= "3.9" and python_version < "3.13"
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pillow==10.2.0 ; python_version >= "3.9" and python_version < "3.13"
|
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protobuf==4.25.2 ; python_version >= "3.9" and python_version < "3.13"
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pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
|
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regex==2023.10.3 ; python_version >= "3.9" and python_version < "3.13"
|
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regex==2023.12.25 ; python_version >= "3.9" and python_version < "3.13"
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requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13"
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safetensors==0.3.3 ; python_version >= "3.9" and python_version < "3.13"
|
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scipy==1.11.4 ; python_version >= "3.9" and python_version < "3.13"
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scipy==1.12.0 ; python_version >= "3.9" and python_version < "3.13"
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sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
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setuptools==69.0.2 ; python_version >= "3.9" and python_version < "3.13"
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tokenizers==0.15.0 ; python_version >= "3.9" and python_version < "3.13"
|
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setuptools==69.0.3 ; python_version >= "3.9" and python_version < "3.13"
|
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tokenizers==0.15.1 ; python_version >= "3.9" and python_version < "3.13"
|
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tqdm==4.66.1 ; python_version >= "3.9" and python_version < "3.13"
|
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transformers==4.36.1 ; python_version >= "3.9" and python_version < "3.13"
|
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transformers==4.37.1 ; python_version >= "3.9" and python_version < "3.13"
|
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typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
|
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typing-extensions==4.9.0 ; python_version >= "3.9" and python_version < "3.13"
|
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urllib3==2.1.0 ; python_version >= "3.9" and python_version < "3.13"
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@ -3,24 +3,27 @@ from text_generation_server.utils.layers import (
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TensorParallelEmbedding,
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)
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class ProcessGroup:
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def __init__(self, rank: int, world_size: int):
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self._rank = rank
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self.world_size = world_size
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def size(self)->int:
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def size(self) -> int:
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return self.world_size
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def rank(self)->int:
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def rank(self) -> int:
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return self._rank
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class Weights:
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def __init__(self, rank: int, world_size: int, vocab_size: int, hidden_dim: int):
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self.weight = torch.arange(vocab_size*hidden_dim).float().view(vocab_size, hidden_dim)
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self.weight = (
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torch.arange(vocab_size * hidden_dim).float().view(vocab_size, hidden_dim)
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)
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self.process_group = ProcessGroup(rank, world_size)
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def get_partial_sharded(self, name:str, dim: int):
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def get_partial_sharded(self, name: str, dim: int):
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assert dim == 0
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rank = self.process_group.rank()
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@ -35,10 +38,11 @@ class Weights:
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def get_shape(self, name: str):
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return self.weight.shape
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def test_weight_hub_files_offline_error():
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vocab_size= 17
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weights = Weights(rank=0, world_size=1, vocab_size = vocab_size,hidden_dim = 256)
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vocab_size = 17
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weights = Weights(rank=0, world_size=1, vocab_size=vocab_size, hidden_dim=256)
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embeddings = TensorParallelEmbedding("", weights)
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input_ids = torch.arange(vocab_size)
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@ -47,18 +51,27 @@ def test_weight_hub_files_offline_error():
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assert embeddings.max_id == 17
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torch.testing.assert_close(output, torch.arange(256 * 17).float().view(17, 256))
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weights_0_2 = Weights(rank=0, world_size=2, vocab_size = vocab_size,hidden_dim = 256)
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weights_1_2 = Weights(rank=1, world_size=2, vocab_size = vocab_size,hidden_dim = 256)
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weights_0_2 = Weights(rank=0, world_size=2, vocab_size=vocab_size, hidden_dim=256)
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weights_1_2 = Weights(rank=1, world_size=2, vocab_size=vocab_size, hidden_dim=256)
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embeddings_0_2 = TensorParallelEmbedding("", weights_0_2, reduce=False)
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assert embeddings_0_2.min_id == 0
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assert embeddings_0_2.max_id == 9
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torch.testing.assert_close(embeddings_0_2.weight , torch.cat([torch.arange(9 * 256), torch.zeros(256)], dim=0).view(10, 256).float())
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torch.testing.assert_close(
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embeddings_0_2.weight,
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torch.cat([torch.arange(9 * 256), torch.zeros(256)], dim=0)
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.view(10, 256)
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.float(),
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)
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embeddings_1_2 = TensorParallelEmbedding("", weights_1_2, reduce=False)
|
||||
assert embeddings_1_2.min_id == 9
|
||||
assert embeddings_1_2.max_id == 17
|
||||
torch.testing.assert_close(embeddings_1_2.weight , torch.cat([torch.arange(8 * 256) + 9 * 256, torch.zeros(256)], dim=0).view(9, 256).float())
|
||||
torch.testing.assert_close(
|
||||
embeddings_1_2.weight,
|
||||
torch.cat([torch.arange(8 * 256) + 9 * 256, torch.zeros(256)], dim=0)
|
||||
.view(9, 256)
|
||||
.float(),
|
||||
)
|
||||
output_tp_0 = embeddings_0_2.forward(input_ids)
|
||||
output_tp_1 = embeddings_1_2.forward(input_ids)
|
||||
|
||||
torch.testing.assert_close(output, output_tp_0 + output_tp_1)
|
||||
|
||||
|
|
|
|||
|
|
@ -226,7 +226,7 @@ def download_weights(
|
|||
pass
|
||||
except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
|
||||
pass
|
||||
|
||||
|
||||
elif (Path(model_id) / "adapter_config.json").exists():
|
||||
# Try to load as a local PEFT model
|
||||
try:
|
||||
|
|
|
|||
|
|
@ -230,7 +230,7 @@ def get_model(
|
|||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
|
||||
elif model_type == "phi":
|
||||
if FLASH_ATTENTION:
|
||||
return FlashPhi(
|
||||
|
|
@ -252,7 +252,9 @@ def get_model(
|
|||
|
||||
elif model_type == "phi-msft":
|
||||
if FLASH_ATTENTION:
|
||||
raise NotImplementedError("Legacy phi-msft is not supported with Flash Attention")
|
||||
raise NotImplementedError(
|
||||
"Legacy phi-msft is not supported with Flash Attention"
|
||||
)
|
||||
else:
|
||||
return Phi(
|
||||
model_id,
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@ from text_generation_server.utils.layers import (
|
|||
FastLayerNorm,
|
||||
)
|
||||
|
||||
|
||||
class PhiConfig(PretrainedConfig):
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -25,15 +26,15 @@ class PhiConfig(PretrainedConfig):
|
|||
num_hidden_layers=32,
|
||||
num_attention_heads=32,
|
||||
num_key_value_heads=32,
|
||||
hidden_act="gelu_fast", # llama uses silu
|
||||
layer_norm_eps=1e-05, # rms in llama,
|
||||
hidden_act="gelu_fast", # llama uses silu
|
||||
layer_norm_eps=1e-05, # rms in llama,
|
||||
pad_token_id=0,
|
||||
bos_token_id=1,
|
||||
eos_token_id=2,
|
||||
tie_word_embeddings=False,
|
||||
rope_theta=10000.0,
|
||||
resid_pdrop=0.1, # llama doesn't have this
|
||||
partial_rotary_factor=0.5, # important difference between llama and phi
|
||||
resid_pdrop=0.1, # llama doesn't have this
|
||||
partial_rotary_factor=0.5, # important difference between llama and phi
|
||||
**kwargs,
|
||||
):
|
||||
self.vocab_size = vocab_size
|
||||
|
|
@ -55,6 +56,7 @@ class PhiConfig(PretrainedConfig):
|
|||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
# this is the same as llama except for Phi uses bias=True
|
||||
def load_attention(config, prefix, weights):
|
||||
if config.num_attention_heads != config.num_key_value_heads:
|
||||
|
|
@ -68,6 +70,7 @@ def load_attention(config, prefix, weights):
|
|||
bias=True,
|
||||
)
|
||||
|
||||
|
||||
def _load_gqa(config, prefix: str, weights):
|
||||
assert config.hidden_size % config.num_attention_heads == 0
|
||||
assert config.num_attention_heads % weights.process_group.size() == 0
|
||||
|
|
@ -94,6 +97,7 @@ def _load_gqa(config, prefix: str, weights):
|
|||
get_linear(weight, bias=True, quantize=config.quantize)
|
||||
)
|
||||
|
||||
|
||||
class FlashPhiAttention(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -173,8 +177,7 @@ class FlashPhiAttention(torch.nn.Module):
|
|||
#
|
||||
# Apply partial positional embeddings in place
|
||||
self.rotary_emb(
|
||||
query[:, :, :self.rotary_dim], kv[:, 0, :, :self.rotary_dim],
|
||||
cos, sin
|
||||
query[:, :, : self.rotary_dim], kv[:, 0, :, : self.rotary_dim], cos, sin
|
||||
)
|
||||
|
||||
# Reshape key and value and cache
|
||||
|
|
@ -210,7 +213,8 @@ class FlashPhiAttention(torch.nn.Module):
|
|||
max_s,
|
||||
)
|
||||
|
||||
return self.dense(attn_output.view(-1, self.num_heads*self.head_size))
|
||||
return self.dense(attn_output.view(-1, self.num_heads * self.head_size))
|
||||
|
||||
|
||||
class PhiMLP(nn.Module):
|
||||
def __init__(self, prefix, config, weights):
|
||||
|
|
@ -256,7 +260,9 @@ class FlashPhiLayer(nn.Module):
|
|||
)
|
||||
self.mlp = PhiMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
|
||||
self.input_layernorm = FastLayerNorm.load(
|
||||
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.layer_norm_eps
|
||||
prefix=f"{prefix}.input_layernorm",
|
||||
weights=weights,
|
||||
eps=config.layer_norm_eps,
|
||||
)
|
||||
self.resid_dropout = torch.nn.Dropout(config.resid_pdrop)
|
||||
|
||||
|
|
@ -287,10 +293,13 @@ class FlashPhiLayer(nn.Module):
|
|||
max_s,
|
||||
)
|
||||
|
||||
hidden_states = self.resid_dropout(attn_output).add(self.resid_dropout(self.mlp(hidden_states)))
|
||||
hidden_states = self.resid_dropout(attn_output).add(
|
||||
self.resid_dropout(self.mlp(hidden_states))
|
||||
)
|
||||
|
||||
return hidden_states, res
|
||||
|
||||
|
||||
class FlashPhiModel(torch.nn.Module):
|
||||
def __init__(self, config, weights):
|
||||
super().__init__()
|
||||
|
|
@ -361,6 +370,7 @@ class FlashPhiModel(torch.nn.Module):
|
|||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class FlashPhiForCausalLM(torch.nn.Module):
|
||||
def __init__(self, config, weights):
|
||||
super().__init__()
|
||||
|
|
@ -380,7 +390,7 @@ class FlashPhiForCausalLM(torch.nn.Module):
|
|||
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
|
||||
block_tables: torch.Tensor,
|
||||
slots: torch.Tensor,
|
||||
input_lengths: torch.Tensor,
|
||||
input_lengths: torch.Tensor,
|
||||
max_s: int,
|
||||
lm_head_indices: Optional[torch.Tensor] = None,
|
||||
) -> torch.Tensor:
|
||||
|
|
|
|||
|
|
@ -54,9 +54,19 @@ def load_col(config, prefix, weights, bias):
|
|||
bias_h = bias_h[0]
|
||||
bias_block_size = bias_h // bias_size
|
||||
|
||||
bias_q_part = bias_slice_[bias_rank * bias_block_size : (bias_rank + 1) * bias_block_size]
|
||||
bias_k_part = bias_slice_[bias_h + bias_rank * bias_block_size : bias_h + (bias_rank + 1) * bias_block_size]
|
||||
bias_v_part = bias_slice_[2 * bias_h + bias_rank * bias_block_size : 2 * bias_h + (bias_rank + 1) * bias_block_size]
|
||||
bias_q_part = bias_slice_[
|
||||
bias_rank * bias_block_size : (bias_rank + 1) * bias_block_size
|
||||
]
|
||||
bias_k_part = bias_slice_[
|
||||
bias_h
|
||||
+ bias_rank * bias_block_size : bias_h
|
||||
+ (bias_rank + 1) * bias_block_size
|
||||
]
|
||||
bias_v_part = bias_slice_[
|
||||
2 * bias_h
|
||||
+ bias_rank * bias_block_size : 2 * bias_h
|
||||
+ (bias_rank + 1) * bias_block_size
|
||||
]
|
||||
|
||||
bias = torch.cat([bias_q_part, bias_k_part, bias_v_part], dim=0)
|
||||
if bias.dtype != torch.int32:
|
||||
|
|
@ -352,8 +362,12 @@ class MultiheadAttention(nn.Module):
|
|||
hidden_size = config.d_model
|
||||
head_dim = hidden_size // self.n_heads
|
||||
|
||||
self.q_ln = LPLayerNorm(d_model, bias=bias, prefix=f"{prefix}.q_ln", weights=weights)
|
||||
self.k_ln = LPLayerNorm(self.n_heads * head_dim, prefix=f"{prefix}.k_ln", weights=weights)
|
||||
self.q_ln = LPLayerNorm(
|
||||
d_model, bias=bias, prefix=f"{prefix}.q_ln", weights=weights
|
||||
)
|
||||
self.k_ln = LPLayerNorm(
|
||||
self.n_heads * head_dim, prefix=f"{prefix}.k_ln", weights=weights
|
||||
)
|
||||
if self.attn_impl == "flash":
|
||||
self.attn_fn = flash_attn_fn
|
||||
elif self.attn_impl == "triton":
|
||||
|
|
@ -684,7 +698,6 @@ class LPLayerNorm(torch.nn.LayerNorm):
|
|||
self.bias = nn.Parameter(weights.get_sharded(f"{prefix}.bias", dim=0))
|
||||
self.normalized_shape = self.weight.shape
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
module_device = x.device
|
||||
downcast_x = _cast_if_autocast_enabled(x)
|
||||
|
|
@ -798,7 +811,7 @@ class MPTModel(MPTPreTrainedModel):
|
|||
self.wte = TensorParallelEmbedding("transformer.wte", weights)
|
||||
|
||||
if not self.alibi:
|
||||
self.wpe = TensorParallelEmbedding("transformer.wpe", weights)
|
||||
self.wpe = TensorParallelEmbedding("transformer.wpe", weights)
|
||||
self.blocks = nn.ModuleList(
|
||||
[
|
||||
MPTBlock(config, prefix=f"transformer.blocks.{i}", weights=weights)
|
||||
|
|
|
|||
|
|
@ -62,14 +62,12 @@ class PhiConfig(PretrainedConfig):
|
|||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
# RotaryEmbedding is a class that implements the rotary embedding.
|
||||
class RotaryEmbedding(nn.Module):
|
||||
def __init__(self, dim, max_seq_len):
|
||||
super().__init__()
|
||||
inv_freq = [
|
||||
1.0 / 10000.0 ** (i / dim)
|
||||
for i in range(0, dim, 2)
|
||||
]
|
||||
inv_freq = [1.0 / 10000.0 ** (i / dim) for i in range(0, dim, 2)]
|
||||
inv_freq_len = len(inv_freq)
|
||||
inv_freq = torch.tensor(inv_freq).view(1, inv_freq_len)
|
||||
t = torch.arange(0, max_seq_len, dtype=torch.float).view(max_seq_len, 1)
|
||||
|
|
@ -131,6 +129,7 @@ class PhiCausalLMHead(nn.Module):
|
|||
hidden_states = self.linear(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
# PhiMHA is a multi-head attention layer. This layer uses an attention mask to prevent tokens from attending to subsequent tokens.
|
||||
class PhiMHA(nn.Module):
|
||||
def __init__(self, prefix, config, weights):
|
||||
|
|
@ -172,19 +171,27 @@ class PhiMHA(nn.Module):
|
|||
v = torch.cat([prev_v, v], dim=1)
|
||||
|
||||
past_kv_cache = [k, v]
|
||||
attn_weights = torch.einsum('bthd,bshd->bhts', q, k * self.softmax_scale)
|
||||
attn_weights = torch.einsum("bthd,bshd->bhts", q, k * self.softmax_scale)
|
||||
|
||||
if attention_mask is not None:
|
||||
seqlen_k = k.shape[1]
|
||||
seqlen_q = q.shape[1]
|
||||
causal_mask = torch.triu(torch.full((seqlen_q, seqlen_k), -10000.0, device=attn_weights.device), 1)
|
||||
causal_mask = torch.triu(
|
||||
torch.full((seqlen_q, seqlen_k), -10000.0, device=attn_weights.device),
|
||||
1,
|
||||
)
|
||||
attn_weights = attn_weights + causal_mask.to(dtype=attn_weights.dtype)
|
||||
|
||||
|
||||
attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
|
||||
attn_output = attn_weights.matmul(v.transpose(1, 2)).squeeze(0)
|
||||
attn_output = attn_output.view((b_size, self.num_heads, seq_len, self.head_dim)).transpose(1, 2).flatten(-2)
|
||||
attn_output = (
|
||||
attn_output.view((b_size, self.num_heads, seq_len, self.head_dim))
|
||||
.transpose(1, 2)
|
||||
.flatten(-2)
|
||||
)
|
||||
return self.out_proj(attn_output), past_kv_cache
|
||||
|
||||
|
||||
# PhiMLP is a multi-layer perceptron. It contains two linear layers with a gelu activation function.
|
||||
class PhiMLP(nn.Module):
|
||||
def __init__(self, prefix, config, weights):
|
||||
|
|
@ -204,19 +211,22 @@ class PhiMLP(nn.Module):
|
|||
bias=False,
|
||||
)
|
||||
self.activation = torch.nn.functional.gelu
|
||||
|
||||
|
||||
def forward(self, hidden_states):
|
||||
hidden_states = self.fc1(hidden_states)
|
||||
hidden_states = self.activation(hidden_states)
|
||||
hidden_states = self.fc2(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
# PhiBlock is a single transformer block. It contains a layer norm, a multi-head attention layer and an multi-layer perceptron.
|
||||
class PhiBlock(nn.Module):
|
||||
def __init__(self, layer_id, config, weights):
|
||||
super().__init__()
|
||||
self.layer_id = layer_id
|
||||
self.layer_norm = nn.LayerNorm.load(prefix=f"{layer_id}.ln", weights=weights, eps=config.layer_norm_epsilon)
|
||||
self.layer_norm = nn.LayerNorm.load(
|
||||
prefix=f"{layer_id}.ln", weights=weights, eps=config.layer_norm_epsilon
|
||||
)
|
||||
self.mixer = PhiMHA(prefix=f"{layer_id}.mixer", config=config, weights=weights)
|
||||
self.mlp = PhiMLP(prefix=f"{layer_id}.mlp", config=config, weights=weights)
|
||||
|
||||
|
|
@ -228,11 +238,14 @@ class PhiBlock(nn.Module):
|
|||
):
|
||||
residual = hidden_states
|
||||
hidden_states = self.layer_norm(hidden_states)
|
||||
attn_outputs, past_kv_cache = self.mixer(hidden_states, kv_cache, attention_mask)
|
||||
attn_outputs, past_kv_cache = self.mixer(
|
||||
hidden_states, kv_cache, attention_mask
|
||||
)
|
||||
feed_forward_hidden_states = self.mlp(hidden_states)
|
||||
out = attn_outputs + feed_forward_hidden_states + residual
|
||||
return out, past_kv_cache
|
||||
|
||||
|
||||
# PhiModel implements the embedding layer and the transformer blocks.
|
||||
class PhiModel(nn.Module):
|
||||
def __init__(self, config, weights):
|
||||
|
|
@ -241,9 +254,12 @@ class PhiModel(nn.Module):
|
|||
self.tp_world_size = weights.process_group.size()
|
||||
self.embed_tokens = TensorParallelEmbedding(
|
||||
prefix="transformer.embd.wte", weights=weights
|
||||
)
|
||||
)
|
||||
self.blocks = nn.ModuleList(
|
||||
[PhiBlock(f"transformer.h.{layer_id}", config, weights) for layer_id in range(config.n_layer)]
|
||||
[
|
||||
PhiBlock(f"transformer.h.{layer_id}", config, weights)
|
||||
for layer_id in range(config.n_layer)
|
||||
]
|
||||
)
|
||||
|
||||
def forward(
|
||||
|
|
@ -258,14 +274,19 @@ class PhiModel(nn.Module):
|
|||
seq_len = hidden_states.shape[1]
|
||||
mask = None if seq_len <= 1 else attention_mask
|
||||
|
||||
past_key_values = [None] * len(self.blocks) if past_key_values is None else past_key_values
|
||||
past_key_values = (
|
||||
[None] * len(self.blocks) if past_key_values is None else past_key_values
|
||||
)
|
||||
|
||||
for index, block in enumerate(self.blocks):
|
||||
hidden_states, new_key_values = block(hidden_states, past_key_values[index], mask)
|
||||
hidden_states, new_key_values = block(
|
||||
hidden_states, past_key_values[index], mask
|
||||
)
|
||||
past_key_values[index] = new_key_values
|
||||
|
||||
return hidden_states, past_key_values
|
||||
|
||||
|
||||
# PhiForCausalLM wraps the PhiModel and PhiCausalLMHead together and returns a CausalLMOutputWithPast object.
|
||||
class PhiForCausalLM(torch.nn.Module):
|
||||
def __init__(self, config, weights):
|
||||
|
|
@ -290,12 +311,15 @@ class PhiForCausalLM(torch.nn.Module):
|
|||
loss = None
|
||||
if labels is not None:
|
||||
loss = nn.CrossEntropyLoss()(
|
||||
logits[:, :-1].view(-1, logits.size(-1)),
|
||||
labels[:, 1:].view(-1)
|
||||
logits[:, :-1].view(-1, logits.size(-1)), labels[:, 1:].view(-1)
|
||||
)
|
||||
|
||||
if not return_dict:
|
||||
return ((loss,) + (logits,) + model_output[1:]) if loss is not None else (logits,) + model_output[1:]
|
||||
return (
|
||||
((loss,) + (logits,) + model_output[1:])
|
||||
if loss is not None
|
||||
else (logits,) + model_output[1:]
|
||||
)
|
||||
|
||||
return CausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
|
|
@ -304,5 +328,3 @@ class PhiForCausalLM(torch.nn.Module):
|
|||
hidden_states=None,
|
||||
attentions=None,
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -73,11 +73,11 @@ class FlashLlama(FlashCausalLM):
|
|||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
is_local_model = (Path(use_medusa).exists() and Path(use_medusa).is_dir()) or os.getenv(
|
||||
"WEIGHTS_CACHE_OVERRIDE", None
|
||||
) is not None
|
||||
|
||||
|
||||
is_local_model = (
|
||||
Path(use_medusa).exists() and Path(use_medusa).is_dir()
|
||||
) or os.getenv("WEIGHTS_CACHE_OVERRIDE", None) is not None
|
||||
|
||||
if not is_local_model:
|
||||
medusa_config = hf_hub_download(
|
||||
use_medusa, revision=revision, filename="config.json"
|
||||
|
|
@ -88,7 +88,7 @@ class FlashLlama(FlashCausalLM):
|
|||
else:
|
||||
medusa_config = str(Path(use_medusa) / "config.json")
|
||||
medusa_head = str(Path(use_medusa) / "medusa_lm_head.pt")
|
||||
|
||||
|
||||
with open(medusa_config, "r") as f:
|
||||
config = json.load(f)
|
||||
medusa_sf = medusa_head[: -len(".pt")] + ".safetensors"
|
||||
|
|
|
|||
|
|
@ -63,11 +63,11 @@ class FlashPhi(FlashCausalLM):
|
|||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
is_local_model = (Path(use_medusa).exists() and Path(use_medusa).is_dir()) or os.getenv(
|
||||
"WEIGHTS_CACHE_OVERRIDE", None
|
||||
) is not None
|
||||
|
||||
|
||||
is_local_model = (
|
||||
Path(use_medusa).exists() and Path(use_medusa).is_dir()
|
||||
) or os.getenv("WEIGHTS_CACHE_OVERRIDE", None) is not None
|
||||
|
||||
if not is_local_model:
|
||||
medusa_config = hf_hub_download(
|
||||
use_medusa, revision=revision, filename="config.json"
|
||||
|
|
@ -78,7 +78,7 @@ class FlashPhi(FlashCausalLM):
|
|||
else:
|
||||
medusa_config = str(Path(use_medusa) / "config.json")
|
||||
medusa_head = str(Path(use_medusa) / "medusa_lm_head.pt")
|
||||
|
||||
|
||||
with open(medusa_config, "r") as f:
|
||||
config = json.load(f)
|
||||
medusa_sf = medusa_head[: -len(".pt")] + ".safetensors"
|
||||
|
|
|
|||
|
|
@ -5,13 +5,17 @@ from transformers import AutoConfig, AutoTokenizer
|
|||
from typing import Optional, List, Tuple
|
||||
|
||||
from text_generation_server.models import CausalLM
|
||||
from text_generation_server.models.custom_modeling.phi_modeling import PhiConfig, PhiForCausalLM
|
||||
from text_generation_server.models.custom_modeling.phi_modeling import (
|
||||
PhiConfig,
|
||||
PhiForCausalLM,
|
||||
)
|
||||
from text_generation_server.utils import (
|
||||
initialize_torch_distributed,
|
||||
weight_files,
|
||||
Weights,
|
||||
)
|
||||
|
||||
|
||||
class Phi(CausalLM):
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -60,4 +64,3 @@ class Phi(CausalLM):
|
|||
dtype=dtype,
|
||||
device=device,
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -510,7 +510,9 @@ class TensorParallelEmbedding(nn.Module):
|
|||
block_size = (num_embeddings + world_size - 1) // world_size
|
||||
self.min_id = rank * block_size
|
||||
self.max_id = min(num_embeddings, (rank + 1) * block_size)
|
||||
self.null_idx = weight.shape[0] # Usually block_size, might be less in non even vocab_size.
|
||||
self.null_idx = weight.shape[
|
||||
0
|
||||
] # Usually block_size, might be less in non even vocab_size.
|
||||
self.process_group = weights.process_group
|
||||
self.reduce = reduce
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue