feat: cohere (#1660)
This commit is contained in:
OlivierDehaene
GitHub
parent
f171bdc823
commit
1e9bcd9dd8
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@ -29,5 +29,5 @@ run-dev:
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SAFETENSORS_FAST_GPU=1 python -m torch.distributed.run --nproc_per_node=2 text_generation_server/cli.py serve bigscience/bloom-560m --sharded
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export-requirements:
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poetry export -o requirements_cuda.txt --extras bnb --without-hashes
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poetry export -o requirements_cuda.txt --without-hashes
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poetry export -o requirements_rocm.txt --without-hashes
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File diff suppressed because it is too large
Load Diff
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@ -17,7 +17,7 @@ grpc-interceptor = "^0.15.0"
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typer = "^0.6.1"
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accelerate = { version = "^0.28.0", optional = true }
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bitsandbytes = { version = "^0.43.0", optional = true }
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safetensors = "^0.4.1"
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safetensors = "^0.4"
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loguru = "^0.6.0"
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opentelemetry-api = "^1.15.0"
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opentelemetry-exporter-otlp = "^1.15.0"
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@ -26,11 +26,11 @@ hf-transfer = "^0.1.2"
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sentencepiece = "^0.1.97"
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tokenizers = "^0.15.0"
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huggingface-hub = "^0.19.3"
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transformers = "^4.38.2"
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transformers = "^4.38"
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einops = "^0.6.1"
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texttable = { version = "^1.6.7", optional = true }
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datasets = { version = "^2.14.0", optional = true }
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peft = { version = "^0.9.0", optional = true }
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peft = { version = "^0.9", optional = true }
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torch = { version = "^2.1.1", optional = true }
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scipy = "^1.11.1"
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pillow = "^10.0.0"
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@ -1,46 +0,0 @@
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backoff==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
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certifi==2023.11.17 ; python_version >= "3.9" and python_version < "3.13"
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charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.13"
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click==8.1.7 ; python_version >= "3.9" and python_version < "3.13"
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colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows")
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deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
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einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
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filelock==3.13.1 ; python_version >= "3.9" and python_version < "3.13"
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fsspec==2023.10.0 ; python_version >= "3.9" and python_version < "3.13"
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googleapis-common-protos==1.61.0 ; python_version >= "3.9" and python_version < "3.13"
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grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
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grpcio-reflection==1.59.3 ; python_version >= "3.9" and python_version < "3.13"
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grpcio-status==1.59.3 ; python_version >= "3.9" and python_version < "3.13"
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grpcio==1.59.3 ; 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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huggingface-hub==0.16.4 ; python_version >= "3.9" and python_version < "3.13"
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idna==3.4 ; 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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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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opentelemetry-exporter-otlp==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
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opentelemetry-instrumentation-grpc==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
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opentelemetry-instrumentation==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
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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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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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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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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==0.13.3 ; 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==4.33.3 ; 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==4.8.0 ; 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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win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32"
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wrapt==1.16.0 ; python_version >= "3.9" and python_version < "3.13"
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@ -1,5 +1,4 @@
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backoff==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
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bitsandbytes==0.41.3.post2 ; python_version >= "3.9" and python_version < "3.13"
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certifi==2024.2.2 ; python_version >= "3.9" and python_version < "3.13"
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charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.13"
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click==8.1.7 ; python_version >= "3.9" and python_version < "3.13"
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@ -7,13 +6,13 @@ colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_p
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deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
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einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
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filelock==3.13.1 ; python_version >= "3.9" and python_version < "3.13"
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fsspec==2023.10.0 ; python_version >= "3.9" and python_version < "3.13"
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googleapis-common-protos==1.62.0 ; python_version >= "3.9" and python_version < "3.13"
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fsspec==2024.2.0 ; python_version >= "3.9" and python_version < "3.13"
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googleapis-common-protos==1.63.0 ; python_version >= "3.9" and python_version < "3.13"
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grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
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grpcio-reflection==1.60.1 ; python_version >= "3.9" and python_version < "3.13"
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grpcio-status==1.60.1 ; python_version >= "3.9" and python_version < "3.13"
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grpcio==1.60.1 ; 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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grpcio-reflection==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
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grpcio-status==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
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grpcio==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
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hf-transfer==0.1.6 ; 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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@ -27,21 +26,21 @@ opentelemetry-instrumentation==0.36b0 ; python_version >= "3.9" and python_versi
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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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packaging==24.0 ; 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==4.25.3 ; 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==2023.12.25 ; 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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safetensors==0.4.2 ; 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==69.1.0 ; python_version >= "3.9" and python_version < "3.13"
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setuptools==69.2.0 ; python_version >= "3.9" and python_version < "3.13"
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tokenizers==0.15.2 ; python_version >= "3.9" and python_version < "3.13"
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tqdm==4.66.2 ; 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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transformers==4.39.0 ; 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==2.2.0 ; python_version >= "3.9" and python_version < "3.13"
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typing-extensions==4.10.0 ; python_version >= "3.9" and python_version < "3.13"
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urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
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win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32"
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wrapt==1.16.0 ; python_version >= "3.9" and python_version < "3.13"
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@ -6,13 +6,13 @@ colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_p
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deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
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einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
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filelock==3.13.1 ; python_version >= "3.9" and python_version < "3.13"
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fsspec==2023.10.0 ; python_version >= "3.9" and python_version < "3.13"
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googleapis-common-protos==1.62.0 ; python_version >= "3.9" and python_version < "3.13"
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fsspec==2024.2.0 ; python_version >= "3.9" and python_version < "3.13"
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googleapis-common-protos==1.63.0 ; python_version >= "3.9" and python_version < "3.13"
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grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
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grpcio-reflection==1.60.1 ; python_version >= "3.9" and python_version < "3.13"
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grpcio-status==1.60.1 ; python_version >= "3.9" and python_version < "3.13"
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grpcio==1.60.1 ; 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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grpcio-reflection==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
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grpcio-status==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
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grpcio==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
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hf-transfer==0.1.6 ; 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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@ -26,21 +26,21 @@ opentelemetry-instrumentation==0.36b0 ; python_version >= "3.9" and python_versi
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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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packaging==24.0 ; 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==4.25.3 ; 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==2023.12.25 ; 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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safetensors==0.4.2 ; 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==69.1.0 ; python_version >= "3.9" and python_version < "3.13"
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setuptools==69.2.0 ; python_version >= "3.9" and python_version < "3.13"
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tokenizers==0.15.2 ; python_version >= "3.9" and python_version < "3.13"
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tqdm==4.66.2 ; 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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transformers==4.39.0 ; 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==2.2.0 ; python_version >= "3.9" and python_version < "3.13"
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typing-extensions==4.10.0 ; python_version >= "3.9" and python_version < "3.13"
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urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
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win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32"
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wrapt==1.16.0 ; python_version >= "3.9" and python_version < "3.13"
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@ -57,6 +57,9 @@ try:
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from text_generation_server.models.flash_qwen2 import (
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FlashQwen2,
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)
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from text_generation_server.models.flash_cohere import (
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FlashCohere,
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)
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from text_generation_server.models.flash_gemma import (
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FlashGemma,
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)
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@ -86,6 +89,8 @@ if FLASH_ATTENTION:
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__all__.append(FlashPhi)
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__all__.append(FlashQwen2)
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__all__.append(FlashStarcoder2)
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__all__.append(FlashGemma)
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__all__.append(FlashCohere)
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MAMBA_AVAILABLE = True
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try:
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@ -354,6 +359,28 @@ def get_model(
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trust_remote_code=trust_remote_code,
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)
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if model_type == "cohere":
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if FLASH_ATTENTION:
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return FlashCohere(
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model_id,
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revision,
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quantize=quantize,
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use_medusa=use_medusa,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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elif sharded:
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raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Cohere"))
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else:
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return CausalLM(
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model_id,
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revision,
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quantize=quantize,
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use_medusa=use_medusa,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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if model_type in ["RefinedWeb", "RefinedWebModel", "falcon"]:
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if sharded:
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if FLASH_ATTENTION:
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|
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@ -0,0 +1,461 @@
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# coding=utf-8
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# Copyright 2024 Cohere team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
|
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# You may obtain a copy of the License at
|
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#
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# http://www.apache.org/licenses/LICENSE-2.0
|
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#
|
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# Unless required by applicable law or agreed to in writing, software
|
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# distributed under the License is distributed on an "AS IS" BASIS,
|
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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# See the License for the specific language governing permissions and
|
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# limitations under the License.
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import torch
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import torch.distributed
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from torch import nn
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from transformers.activations import ACT2FN
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from transformers.configuration_utils import PretrainedConfig
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from typing import Optional, List, Tuple
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from text_generation_server.utils import paged_attention, flash_attn
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from text_generation_server.utils.layers import (
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TensorParallelRowLinear,
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TensorParallelColumnLinear,
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TensorParallelEmbedding,
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PositionRotaryEmbedding,
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SpeculativeHead,
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get_linear,
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FastRMSNorm,
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)
|
||||
|
||||
|
||||
class CohereConfig(PretrainedConfig):
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=256000,
|
||||
hidden_size=8192,
|
||||
intermediate_size=22528,
|
||||
num_hidden_layers=40,
|
||||
num_attention_heads=64,
|
||||
num_key_value_heads=None,
|
||||
hidden_act="silu",
|
||||
max_position_embeddings=8192,
|
||||
initializer_range=0.02,
|
||||
layer_norm_eps=1e-5,
|
||||
use_cache=True,
|
||||
pad_token_id=0,
|
||||
bos_token_id=5,
|
||||
eos_token_id=255001,
|
||||
pretraining_tp=1,
|
||||
tie_word_embeddings=True,
|
||||
rope_theta=10000.0,
|
||||
attention_bias=False,
|
||||
attention_dropout=0.0,
|
||||
logit_scale=1.0,
|
||||
**kwargs,
|
||||
):
|
||||
self.vocab_size = vocab_size
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
|
||||
# for backward compatibility
|
||||
if num_key_value_heads is None:
|
||||
num_key_value_heads = num_attention_heads
|
||||
|
||||
self.num_key_value_heads = num_key_value_heads
|
||||
self.hidden_act = hidden_act
|
||||
self.initializer_range = initializer_range
|
||||
self.layer_norm_eps = layer_norm_eps
|
||||
self.pretraining_tp = pretraining_tp
|
||||
self.use_cache = use_cache
|
||||
self.rope_theta = rope_theta
|
||||
self.attention_bias = attention_bias
|
||||
self.attention_dropout = attention_dropout
|
||||
self.logit_scale = logit_scale
|
||||
|
||||
super().__init__(
|
||||
pad_token_id=pad_token_id,
|
||||
bos_token_id=bos_token_id,
|
||||
eos_token_id=eos_token_id,
|
||||
tie_word_embeddings=tie_word_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
def load_attention(config, prefix, weights):
|
||||
if config.num_attention_heads != config.num_key_value_heads:
|
||||
return _load_gqa(config, prefix, weights)
|
||||
else:
|
||||
return TensorParallelColumnLinear.load_multi(
|
||||
config,
|
||||
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
|
||||
dim=0,
|
||||
weights=weights,
|
||||
bias=config.attention_bias,
|
||||
)
|
||||
|
||||
|
||||
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
|
||||
|
||||
weight = weights.get_multi_weights_col(
|
||||
prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
|
||||
quantize=config.quantize,
|
||||
dim=0,
|
||||
)
|
||||
|
||||
if config.quantize not in ["gptq", "awq"]:
|
||||
weight = weight.to(dtype=weights.dtype).to(device=weights.device)
|
||||
|
||||
head_size = config.hidden_size // config.num_attention_heads
|
||||
num_heads = config.num_attention_heads // weights.process_group.size()
|
||||
num_key_value_heads = config.num_key_value_heads // weights.process_group.size()
|
||||
assert list(weight.shape) == [
|
||||
(num_heads + 2 * num_key_value_heads) * head_size,
|
||||
config.hidden_size,
|
||||
], f"{list(weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}"
|
||||
|
||||
if config.attention_bias:
|
||||
w = [
|
||||
weights.get_sharded(f"{p}.bias", dim=0)
|
||||
for p in [f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"]
|
||||
]
|
||||
bias = torch.cat(w, dim=0).to(dtype=weights.dtype).to(device=weights.device)
|
||||
else:
|
||||
bias = None
|
||||
|
||||
return TensorParallelColumnLinear(
|
||||
get_linear(weight, bias=bias, quantize=config.quantize)
|
||||
)
|
||||
|
||||
|
||||
class FlashCohereAttention(torch.nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
prefix: str,
|
||||
config,
|
||||
weights,
|
||||
):
|
||||
super().__init__()
|
||||
self.num_heads = config.num_attention_heads
|
||||
self.hidden_size = config.hidden_size
|
||||
self.head_size = self.hidden_size // self.num_heads
|
||||
|
||||
self.rotary_emb = PositionRotaryEmbedding.static(
|
||||
config=config,
|
||||
dim=self.head_size,
|
||||
base=config.rope_theta,
|
||||
device=weights.device,
|
||||
)
|
||||
|
||||
self.softmax_scale = self.head_size**-0.5
|
||||
|
||||
if self.num_heads % weights.process_group.size() != 0:
|
||||
raise ValueError(
|
||||
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
|
||||
f"and `num_shards`: {weights.process_group.size()}"
|
||||
)
|
||||
self.num_heads = self.num_heads // weights.process_group.size()
|
||||
self.num_key_value_heads = (
|
||||
config.num_key_value_heads // weights.process_group.size()
|
||||
)
|
||||
|
||||
self.query_key_value = load_attention(config, prefix, weights)
|
||||
|
||||
self.o_proj = TensorParallelRowLinear.load(
|
||||
config,
|
||||
prefix=f"{prefix}.o_proj",
|
||||
weights=weights,
|
||||
bias=config.attention_bias,
|
||||
)
|
||||
self.num_groups = self.num_heads // self.num_key_value_heads
|
||||
self.kv_head_mapping = torch.arange(
|
||||
0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
|
||||
).repeat_interleave(self.num_groups)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states,
|
||||
cos,
|
||||
sin,
|
||||
cu_seqlen_prefill,
|
||||
kv_cache,
|
||||
block_tables,
|
||||
slots,
|
||||
input_lengths,
|
||||
max_s,
|
||||
):
|
||||
qkv = self.query_key_value(hidden_states)
|
||||
query, kv = qkv.split(
|
||||
[
|
||||
self.head_size * self.num_heads,
|
||||
2 * self.head_size * self.num_key_value_heads,
|
||||
],
|
||||
dim=1,
|
||||
)
|
||||
query = query.view(-1, self.num_heads, self.head_size)
|
||||
kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size)
|
||||
|
||||
self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
|
||||
|
||||
paged_attention.reshape_and_cache(
|
||||
kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots
|
||||
)
|
||||
|
||||
# output tensor
|
||||
attn_output = torch.empty_like(query)
|
||||
|
||||
# Prefill
|
||||
if cu_seqlen_prefill is not None:
|
||||
# flash attention
|
||||
flash_attn.attention(
|
||||
query,
|
||||
torch.select(kv, dim=1, index=0),
|
||||
torch.select(kv, dim=1, index=1),
|
||||
attn_output,
|
||||
cu_seqlen_prefill,
|
||||
max_s,
|
||||
self.softmax_scale,
|
||||
)
|
||||
# Decode
|
||||
else:
|
||||
paged_attention.attention(
|
||||
attn_output,
|
||||
query,
|
||||
kv_cache[0],
|
||||
kv_cache[1],
|
||||
self.kv_head_mapping,
|
||||
self.softmax_scale,
|
||||
block_tables,
|
||||
input_lengths,
|
||||
max_s,
|
||||
)
|
||||
|
||||
return self.o_proj(
|
||||
attn_output.view(-1, self.num_heads * self.head_size), reduce=False
|
||||
)
|
||||
|
||||
|
||||
class CohereMLP(nn.Module):
|
||||
def __init__(self, prefix, config, weights):
|
||||
super().__init__()
|
||||
act = config.hidden_act
|
||||
self.act = (
|
||||
ACT2FN[act]
|
||||
if "gelu" not in act
|
||||
else lambda x: torch.nn.functional.gelu(
|
||||
x,
|
||||
approximate=(
|
||||
"tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none"
|
||||
),
|
||||
)
|
||||
)
|
||||
# Fuse gate and up proj
|
||||
self.gate_up_proj = TensorParallelColumnLinear.load_multi(
|
||||
config,
|
||||
prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
|
||||
weights=weights,
|
||||
dim=0,
|
||||
bias=False,
|
||||
)
|
||||
self.down_proj = TensorParallelRowLinear.load(
|
||||
config,
|
||||
prefix=f"{prefix}.down_proj",
|
||||
weights=weights,
|
||||
bias=False,
|
||||
)
|
||||
self.intermediate_size = (
|
||||
config.intermediate_size // weights.process_group.size()
|
||||
)
|
||||
|
||||
def forward(self, hidden_states):
|
||||
gate_up_states = self.gate_up_proj(hidden_states)
|
||||
gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
|
||||
return self.down_proj(
|
||||
self.act(gate_up_states[:, 0]) * gate_up_states[:, 1], reduce=False
|
||||
)
|
||||
|
||||
|
||||
class FlashCohereLayer(nn.Module):
|
||||
def __init__(self, layer_id, config, weights):
|
||||
super().__init__()
|
||||
prefix = f"model.layers.{layer_id}"
|
||||
self.self_attn = FlashCohereAttention(
|
||||
prefix=f"{prefix}.self_attn", config=config, weights=weights
|
||||
)
|
||||
self.mlp = CohereMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
|
||||
|
||||
self.input_layernorm = FastRMSNorm.load(
|
||||
prefix=f"{prefix}.input_layernorm",
|
||||
weights=weights,
|
||||
eps=config.layer_norm_eps,
|
||||
)
|
||||
self.process_group = weights.process_group
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states,
|
||||
residual,
|
||||
cos,
|
||||
sin,
|
||||
cu_seqlen_prefill,
|
||||
kv_cache,
|
||||
block_tables,
|
||||
slots,
|
||||
input_lengths,
|
||||
max_s,
|
||||
):
|
||||
normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
|
||||
|
||||
# Self Attention
|
||||
attn_output = self.self_attn(
|
||||
normed_hidden_states,
|
||||
cos,
|
||||
sin,
|
||||
cu_seqlen_prefill,
|
||||
kv_cache,
|
||||
block_tables,
|
||||
slots,
|
||||
input_lengths,
|
||||
max_s,
|
||||
)
|
||||
|
||||
mlp_output = self.mlp(normed_hidden_states)
|
||||
output = attn_output + mlp_output
|
||||
|
||||
if self.process_group.size() > 1:
|
||||
torch.distributed.all_reduce(output, group=self.process_group)
|
||||
|
||||
return output, res
|
||||
|
||||
|
||||
class FlashCohereModel(torch.nn.Module):
|
||||
def __init__(self, config, weights):
|
||||
super().__init__()
|
||||
|
||||
process_group = weights.process_group
|
||||
self.tp_rank = process_group.rank()
|
||||
self.tp_world_size = process_group.size()
|
||||
self.embed_tokens = TensorParallelEmbedding(
|
||||
prefix="model.embed_tokens", weights=weights
|
||||
)
|
||||
self.layers = nn.ModuleList(
|
||||
[
|
||||
FlashCohereLayer(
|
||||
layer_id,
|
||||
config,
|
||||
weights,
|
||||
)
|
||||
for layer_id in range(config.num_hidden_layers)
|
||||
]
|
||||
)
|
||||
self.norm = FastRMSNorm.load(
|
||||
prefix="model.norm", weights=weights, eps=config.layer_norm_eps
|
||||
)
|
||||
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
self.head_size = self.layers[0].self_attn.head_size
|
||||
self.num_heads = self.layers[0].self_attn.num_heads
|
||||
self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
position_ids: torch.Tensor,
|
||||
cu_seqlen_prefill: Optional[torch.Tensor],
|
||||
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
|
||||
block_tables: torch.Tensor,
|
||||
slots: torch.Tensor,
|
||||
input_lengths: torch.Tensor,
|
||||
max_s: int,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.embed_tokens(input_ids)
|
||||
|
||||
# Get rotary cos and sin for this forward
|
||||
# Avoid to index in each layer
|
||||
cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
|
||||
position_ids, max_s, hidden_states.dtype
|
||||
)
|
||||
|
||||
residual = None
|
||||
for i, layer in enumerate(self.layers):
|
||||
hidden_states, residual = layer(
|
||||
hidden_states,
|
||||
residual,
|
||||
cos,
|
||||
sin,
|
||||
cu_seqlen_prefill,
|
||||
kv_cache[i],
|
||||
block_tables,
|
||||
slots,
|
||||
input_lengths,
|
||||
max_s,
|
||||
)
|
||||
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class FlashCohereForCausalLM(torch.nn.Module):
|
||||
def __init__(self, config, weights):
|
||||
super().__init__()
|
||||
|
||||
self.model = FlashCohereModel(config, weights)
|
||||
try:
|
||||
self.lm_head = SpeculativeHead.load(
|
||||
config,
|
||||
prefix="lm_head",
|
||||
weights=weights,
|
||||
)
|
||||
except RuntimeError:
|
||||
self.lm_head = SpeculativeHead.load(
|
||||
config,
|
||||
prefix="model.embed_tokens",
|
||||
weights=weights,
|
||||
)
|
||||
self.logit_scale = config.logit_scale
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
position_ids: torch.Tensor,
|
||||
cu_seqlen_prefill: Optional[torch.Tensor],
|
||||
kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
|
||||
block_tables: torch.Tensor,
|
||||
slots: torch.Tensor,
|
||||
input_lengths: torch.Tensor,
|
||||
max_s: int,
|
||||
lm_head_indices: Optional[torch.Tensor] = None,
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
|
||||
hidden_states = self.model(
|
||||
input_ids,
|
||||
position_ids,
|
||||
cu_seqlen_prefill,
|
||||
kv_cache,
|
||||
block_tables,
|
||||
slots,
|
||||
input_lengths,
|
||||
max_s,
|
||||
)
|
||||
if lm_head_indices is not None:
|
||||
hidden_states = hidden_states[lm_head_indices]
|
||||
logits, speculative_logits = self.lm_head(hidden_states)
|
||||
logits *= self.logit_scale
|
||||
if speculative_logits is not None:
|
||||
speculative_logits *= self.logit_scale
|
||||
return logits, speculative_logits
|
||||
|
|
@ -20,16 +20,11 @@
|
|||
|
||||
import torch
|
||||
import torch.distributed
|
||||
import os
|
||||
from shutil import copyfile
|
||||
|
||||
from torch import nn
|
||||
from transformers.activations import ACT2FN
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
from typing import Optional, List, Tuple
|
||||
from tokenizers import processors
|
||||
from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
|
||||
from transformers.utils import logging
|
||||
|
||||
from text_generation_server.utils import paged_attention, flash_attn
|
||||
from text_generation_server.utils.layers import (
|
||||
|
|
@ -42,162 +37,6 @@ from text_generation_server.utils.layers import (
|
|||
FastRMSNorm,
|
||||
)
|
||||
|
||||
GemmaTokenizer = None
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
VOCAB_FILES_NAMES = {
|
||||
"vocab_file": "tokenizer.model",
|
||||
"tokenizer_file": "tokenizer.json",
|
||||
}
|
||||
|
||||
PRETRAINED_VOCAB_FILES_MAP = {
|
||||
"vocab_file": {
|
||||
"hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
|
||||
},
|
||||
"tokenizer_file": {
|
||||
"hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
|
||||
},
|
||||
}
|
||||
B_INST, E_INST = "[INST]", "[/INST]"
|
||||
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
|
||||
|
||||
# fmt: off
|
||||
DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
|
||||
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
|
||||
that your responses are socially unbiased and positive in nature.
|
||||
|
||||
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
|
||||
correct. If you don't know the answer to a question, please don't share false information."""
|
||||
# fmt: on
|
||||
|
||||
|
||||
class GemmaTokenizerFast(PreTrainedTokenizerFast):
|
||||
vocab_files_names = VOCAB_FILES_NAMES
|
||||
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
||||
slow_tokenizer_class = GemmaTokenizer
|
||||
padding_side = "left"
|
||||
model_input_names = ["input_ids", "attention_mask"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_file=None,
|
||||
tokenizer_file=None,
|
||||
clean_up_tokenization_spaces=False,
|
||||
unk_token="<unk>",
|
||||
bos_token="<bos>",
|
||||
eos_token="<eos>",
|
||||
pad_token="<pad>",
|
||||
add_bos_token=True,
|
||||
add_eos_token=False,
|
||||
use_default_system_prompt=False,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(
|
||||
vocab_file=vocab_file,
|
||||
tokenizer_file=tokenizer_file,
|
||||
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
||||
unk_token=unk_token,
|
||||
bos_token=bos_token,
|
||||
eos_token=eos_token,
|
||||
pad_token=pad_token,
|
||||
add_bos_token=add_bos_token,
|
||||
add_eos_token=add_eos_token,
|
||||
use_default_system_prompt=use_default_system_prompt,
|
||||
**kwargs,
|
||||
)
|
||||
self._add_bos_token = add_bos_token
|
||||
self._add_eos_token = add_eos_token
|
||||
self.update_post_processor()
|
||||
self.use_default_system_prompt = use_default_system_prompt
|
||||
self.vocab_file = vocab_file
|
||||
|
||||
@property
|
||||
def can_save_slow_tokenizer(self) -> bool:
|
||||
return os.path.isfile(self.vocab_file) if self.vocab_file else False
|
||||
|
||||
def update_post_processor(self):
|
||||
"""
|
||||
Updates the underlying post processor with the current `bos_token` and `eos_token`.
|
||||
"""
|
||||
bos = self.bos_token
|
||||
bos_token_id = self.bos_token_id
|
||||
if bos is None and self.add_bos_token:
|
||||
raise ValueError("add_bos_token = True but bos_token = None")
|
||||
|
||||
eos = self.eos_token
|
||||
eos_token_id = self.eos_token_id
|
||||
if eos is None and self.add_eos_token:
|
||||
raise ValueError("add_eos_token = True but eos_token = None")
|
||||
|
||||
single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
|
||||
pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
|
||||
|
||||
special_tokens = []
|
||||
if self.add_bos_token:
|
||||
special_tokens.append((bos, bos_token_id))
|
||||
if self.add_eos_token:
|
||||
special_tokens.append((eos, eos_token_id))
|
||||
self._tokenizer.post_processor = processors.TemplateProcessing(
|
||||
single=single, pair=pair, special_tokens=special_tokens
|
||||
)
|
||||
|
||||
@property
|
||||
def add_eos_token(self):
|
||||
return self._add_eos_token
|
||||
|
||||
@property
|
||||
def add_bos_token(self):
|
||||
return self._add_bos_token
|
||||
|
||||
@add_eos_token.setter
|
||||
def add_eos_token(self, value):
|
||||
self._add_eos_token = value
|
||||
self.update_post_processor()
|
||||
|
||||
@add_bos_token.setter
|
||||
def add_bos_token(self, value):
|
||||
self._add_bos_token = value
|
||||
self.update_post_processor()
|
||||
|
||||
def save_vocabulary(
|
||||
self, save_directory: str, filename_prefix: Optional[str] = None
|
||||
) -> Tuple[str]:
|
||||
if not self.can_save_slow_tokenizer:
|
||||
raise ValueError(
|
||||
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
|
||||
"tokenizer."
|
||||
)
|
||||
|
||||
if not os.path.isdir(save_directory):
|
||||
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
||||
return
|
||||
out_vocab_file = os.path.join(
|
||||
save_directory,
|
||||
(filename_prefix + "-" if filename_prefix else "")
|
||||
+ VOCAB_FILES_NAMES["vocab_file"],
|
||||
)
|
||||
|
||||
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
|
||||
copyfile(self.vocab_file, out_vocab_file)
|
||||
|
||||
return (out_vocab_file,)
|
||||
|
||||
@property
|
||||
def default_chat_template(self):
|
||||
raise NotImplementedError
|
||||
|
||||
# TODO ArthurZ let's rely on the template processor instead, refactor all fast tokenizers
|
||||
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
||||
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
|
||||
eos_token_id = [self.eos_token_id] if self.add_eos_token else []
|
||||
|
||||
output = bos_token_id + token_ids_0 + eos_token_id
|
||||
|
||||
if token_ids_1 is not None:
|
||||
output = output + bos_token_id + token_ids_1 + eos_token_id
|
||||
|
||||
return output
|
||||
|
||||
|
||||
class GemmaConfig(PretrainedConfig):
|
||||
def __init__(
|
||||
|
|
|
|||
|
|
@ -0,0 +1,75 @@
|
|||
import torch
|
||||
import torch.distributed
|
||||
|
||||
from opentelemetry import trace
|
||||
from typing import Optional
|
||||
from transformers.models.llama import LlamaTokenizerFast
|
||||
|
||||
from text_generation_server.models import FlashCausalLM
|
||||
from text_generation_server.models.custom_modeling.flash_cohere_modeling import (
|
||||
FlashCohereForCausalLM,
|
||||
CohereConfig,
|
||||
)
|
||||
from text_generation_server.utils import (
|
||||
initialize_torch_distributed,
|
||||
weight_files,
|
||||
Weights,
|
||||
)
|
||||
|
||||
tracer = trace.get_tracer(__name__)
|
||||
|
||||
|
||||
class FlashCohere(FlashCausalLM):
|
||||
def __init__(
|
||||
self,
|
||||
model_id: str,
|
||||
revision: Optional[str] = None,
|
||||
quantize: Optional[str] = None,
|
||||
use_medusa: Optional[str] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
):
|
||||
self.process_group, rank, world_size = initialize_torch_distributed()
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device(f"cuda:{rank}")
|
||||
dtype = torch.bfloat16 if dtype is None else dtype
|
||||
else:
|
||||
raise NotImplementedError("FlashCohere is only available on GPU")
|
||||
|
||||
tokenizer = LlamaTokenizerFast.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
padding_side="left",
|
||||
truncation_side="left",
|
||||
trust_remote_code=trust_remote_code,
|
||||
use_fast=True,
|
||||
from_slow=False,
|
||||
)
|
||||
|
||||
config = CohereConfig.from_pretrained(
|
||||
model_id, revision=revision, trust_remote_code=trust_remote_code
|
||||
)
|
||||
config.quantize = quantize
|
||||
config.use_medusa = use_medusa
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
|
||||
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
|
||||
weights = Weights(filenames, device, dtype, process_group=self.process_group)
|
||||
if config.quantize in ["gptq", "awq"]:
|
||||
weights._set_gptq_params(model_id, revision)
|
||||
|
||||
model = FlashCohereForCausalLM(config, weights)
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(FlashCohere, self).__init__(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
num_layers=len(model.model.layers),
|
||||
num_kv_heads=model.model.num_key_value_heads,
|
||||
head_size=model.model.head_size,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
rank=rank,
|
||||
world_size=world_size,
|
||||
)
|
||||
|
|
@ -3,10 +3,10 @@ import torch.distributed
|
|||
|
||||
from opentelemetry import trace
|
||||
from typing import Optional
|
||||
from transformers.models.gemma import GemmaTokenizerFast
|
||||
|
||||
from text_generation_server.models import FlashCausalLM
|
||||
from text_generation_server.models.custom_modeling.flash_gemma_modeling import (
|
||||
GemmaTokenizerFast,
|
||||
FlashGemmaForCausalLM,
|
||||
GemmaConfig,
|
||||
)
|
||||
|
|
|
|||
Loading…
Reference in New Issue