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Merge branch 'support-openai-models-endpoint' of github.com:huggingface/text-generation-inference into support-openai-models-endpoint

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This commit is contained in:
drbh 2024-08-27 16:31:50 +00:00
commit b348ab4c55
41 changed files with 876 additions and 457 deletions
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1
Cargo.lock generated
View File

@ -2174,6 +2174,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
checksum = "45f7e8e35b6c7b169bf40b0176d2c79291ab8ee53290b84e0668ab21d841aa9d" checksum = "45f7e8e35b6c7b169bf40b0176d2c79291ab8ee53290b84e0668ab21d841aa9d"
dependencies = [ dependencies = [
"serde", "serde",
"serde_json",
] ]
[[package]] [[package]]

13
backends/v3/src/queue.rs
View File

@ -316,10 +316,15 @@ impl State {
+ self.speculate + self.speculate
- 1; - 1;
match block_allocator // If users wants the prefill logprobs, we cannot reuse the cache.
.allocate(tokens, entry.request.input_ids.clone()) // So no input_ids for the radix tree.
.await let input_ids = if entry.request.decoder_input_details {
{ None
} else {
entry.request.input_ids.clone()
};
match block_allocator.allocate(tokens, input_ids).await {
None => { None => {
// Entry is over budget // Entry is over budget
// Add it back to the front // Add it back to the front

1
backends/v3/src/radix.rs
View File

@ -205,6 +205,7 @@ pub struct RadixTrie {
/// call that a real time lookup would require. /// call that a real time lookup would require.
time: u64, time: u64,
} }
impl Default for RadixTrie { impl Default for RadixTrie {
fn default() -> Self { fn default() -> Self {
Self::new() Self::new()

7
clients/python/text_generation/client.py
View File

@ -757,7 +757,12 @@ class AsyncClient:
continue continue
payload = byte_payload.decode("utf-8") payload = byte_payload.decode("utf-8")
if payload.startswith("data:"): if payload.startswith("data:"):
json_payload = json.loads(payload.lstrip("data:").rstrip("\n")) payload_data = (
payload.lstrip("data:").rstrip("\n").removeprefix(" ")
)
if payload_data == "[DONE]":
break
json_payload = json.loads(payload_data)
try: try:
response = ChatCompletionChunk(**json_payload) response = ChatCompletionChunk(**json_payload)
yield response yield response

2
docs/openapi.json
View File

@ -924,7 +924,7 @@
"tool_prompt": { "tool_prompt": {
"type": "string", "type": "string",
"description": "A prompt to be appended before the tools", "description": "A prompt to be appended before the tools",
"example": "\"You will be presented with a JSON schema representing a set of tools.\nIf the user request lacks of sufficient information to make a precise tool selection: Do not invent any tool's properties, instead notify with an error message.\n\nJSON Schema:\n\"", "example": "Given the functions available, please respond with a JSON for a function call with its proper arguments that best answers the given prompt. Respond in the format {name: function name, parameters: dictionary of argument name and its value}.Do not use variables.",
"nullable": true "nullable": true
}, },
"tools": { "tools": {

127
flake.lock
View File

@ -492,24 +492,6 @@
"type": "github" "type": "github"
} }
}, },
"flake-utils_7": {
"inputs": {
"systems": "systems_7"
},
"locked": {
"lastModified": 1710146030,
"narHash": "sha256-SZ5L6eA7HJ/nmkzGG7/ISclqe6oZdOZTNoesiInkXPQ=",
"owner": "numtide",
"repo": "flake-utils",
"rev": "b1d9ab70662946ef0850d488da1c9019f3a9752a",
"type": "github"
},
"original": {
"owner": "numtide",
"repo": "flake-utils",
"type": "github"
}
},
"gitignore": { "gitignore": {
"inputs": { "inputs": {
"nixpkgs": [ "nixpkgs": [
@ -594,27 +576,6 @@
"type": "github" "type": "github"
} }
}, },
"nix-github-actions": {
"inputs": {
"nixpkgs": [
"poetry2nix",
"nixpkgs"
]
},
"locked": {
"lastModified": 1703863825,
"narHash": "sha256-rXwqjtwiGKJheXB43ybM8NwWB8rO2dSRrEqes0S7F5Y=",
"owner": "nix-community",
"repo": "nix-github-actions",
"rev": "5163432afc817cf8bd1f031418d1869e4c9d5547",
"type": "github"
},
"original": {
"owner": "nix-community",
"repo": "nix-github-actions",
"type": "github"
}
},
"nix-test-runner": { "nix-test-runner": {
"flake": false, "flake": false,
"locked": { "locked": {
@ -753,31 +714,6 @@
"type": "github" "type": "github"
} }
}, },
"poetry2nix": {
"inputs": {
"flake-utils": "flake-utils_7",
"nix-github-actions": "nix-github-actions",
"nixpkgs": [
"tgi-nix",
"nixpkgs"
],
"systems": "systems_8",
"treefmt-nix": "treefmt-nix"
},
"locked": {
"lastModified": 1723854676,
"narHash": "sha256-+BrHfNuXrqeE7PoV6xDaoh0joYiJkvTTCIV0fFR3THw=",
"owner": "nix-community",
"repo": "poetry2nix",
"rev": "d650118bce34c0238b9b54f23f7f173f9e4db867",
"type": "github"
},
"original": {
"owner": "nix-community",
"repo": "poetry2nix",
"type": "github"
}
},
"pre-commit-hooks": { "pre-commit-hooks": {
"inputs": { "inputs": {
"flake-compat": [ "flake-compat": [
@ -887,7 +823,6 @@
"tgi-nix", "tgi-nix",
"nixpkgs" "nixpkgs"
], ],
"poetry2nix": "poetry2nix",
"rust-overlay": "rust-overlay", "rust-overlay": "rust-overlay",
"tgi-nix": "tgi-nix" "tgi-nix": "tgi-nix"
} }
@ -900,11 +835,11 @@
] ]
}, },
"locked": { "locked": {
"lastModified": 1723515680, "lastModified": 1724206841,
"narHash": "sha256-nHdKymsHCVIh0Wdm4MvSgxcTTg34FJIYHRQkQYaSuvk=", "narHash": "sha256-L8dKaX4T3k+TR2fEHCfGbH4UXdspovz/pj87iai9qmc=",
"owner": "oxalica", "owner": "oxalica",
"repo": "rust-overlay", "repo": "rust-overlay",
"rev": "4ee3d9e9569f70d7bb40f28804d6fe950c81eab3", "rev": "45e98fbd62c32e5927e952d2833fa1ba4fb35a61",
"type": "github" "type": "github"
}, },
"original": { "original": {
@ -1003,46 +938,17 @@
"type": "github" "type": "github"
} }
}, },
"systems_7": {
"locked": {
"lastModified": 1681028828,
"narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=",
"owner": "nix-systems",
"repo": "default",
"rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e",
"type": "github"
},
"original": {
"owner": "nix-systems",
"repo": "default",
"type": "github"
}
},
"systems_8": {
"locked": {
"lastModified": 1681028828,
"narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=",
"owner": "nix-systems",
"repo": "default",
"rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e",
"type": "github"
},
"original": {
"id": "systems",
"type": "indirect"
}
},
"tgi-nix": { "tgi-nix": {
"inputs": { "inputs": {
"flake-compat": "flake-compat_4", "flake-compat": "flake-compat_4",
"nixpkgs": "nixpkgs_6" "nixpkgs": "nixpkgs_6"
}, },
"locked": { "locked": {
"lastModified": 1723973328, "lastModified": 1724270760,
"narHash": "sha256-q5FmW4YFQcRb6fXHnrxL0uno6xcw9dcg+pFBbVM1xeQ=", "narHash": "sha256-KX566x0+3HZcB20HPdvdwyMm7ZJg21M+iqVrs/HCimA=",
"owner": "danieldk", "owner": "danieldk",
"repo": "tgi-nix", "repo": "tgi-nix",
"rev": "d2038f36589a8a179834e5771ffd081620ba94c3", "rev": "12cbaa76ff258351741d3b5afb7161f617fe7b4c",
"type": "github" "type": "github"
}, },
"original": { "original": {
@ -1050,27 +956,6 @@
"repo": "tgi-nix", "repo": "tgi-nix",
"type": "github" "type": "github"
} }
},
"treefmt-nix": {
"inputs": {
"nixpkgs": [
"poetry2nix",
"nixpkgs"
]
},
"locked": {
"lastModified": 1719749022,
"narHash": "sha256-ddPKHcqaKCIFSFc/cvxS14goUhCOAwsM1PbMr0ZtHMg=",
"owner": "numtide",
"repo": "treefmt-nix",
"rev": "8df5ff62195d4e67e2264df0b7f5e8c9995fd0bd",
"type": "github"
},
"original": {
"owner": "numtide",
"repo": "treefmt-nix",
"type": "github"
}
} }
}, },
"root": "root", "root": "root",

80
flake.nix
View File

@ -8,10 +8,6 @@
tgi-nix.url = "github:danieldk/tgi-nix"; tgi-nix.url = "github:danieldk/tgi-nix";
nixpkgs.follows = "tgi-nix/nixpkgs"; nixpkgs.follows = "tgi-nix/nixpkgs";
flake-utils.url = "github:numtide/flake-utils"; flake-utils.url = "github:numtide/flake-utils";
poetry2nix = {
url = "github:nix-community/poetry2nix";
inputs.nixpkgs.follows = "tgi-nix/nixpkgs";
};
rust-overlay = { rust-overlay = {
url = "github:oxalica/rust-overlay"; url = "github:oxalica/rust-overlay";
inputs.nixpkgs.follows = "tgi-nix/nixpkgs"; inputs.nixpkgs.follows = "tgi-nix/nixpkgs";
@ -26,7 +22,6 @@
flake-utils, flake-utils,
rust-overlay, rust-overlay,
tgi-nix, tgi-nix,
poetry2nix,
}: }:
flake-utils.lib.eachDefaultSystem ( flake-utils.lib.eachDefaultSystem (
system: system:
@ -47,14 +42,32 @@
tgi-nix.overlay tgi-nix.overlay
]; ];
}; };
inherit (poetry2nix.lib.mkPoetry2Nix { inherit pkgs; }) mkPoetryEditablePackage;
text-generation-server = mkPoetryEditablePackage { editablePackageSources = ./server; };
crateOverrides = import ./nix/crate-overrides.nix { inherit pkgs nix-filter; }; crateOverrides = import ./nix/crate-overrides.nix { inherit pkgs nix-filter; };
benchmark = cargoNix.workspaceMembers.text-generation-benchmark.build.override {
inherit crateOverrides;
};
launcher = cargoNix.workspaceMembers.text-generation-launcher.build.override {
inherit crateOverrides;
};
router = cargoNix.workspaceMembers.text-generation-router-v3.build.override {
inherit crateOverrides;
};
server = pkgs.python3.pkgs.callPackage ./nix/server.nix { inherit nix-filter; };
in in
{ {
devShells.default = devShells = with pkgs; rec {
with pkgs; default = pure;
mkShell {
pure = mkShell {
buildInputs = [
benchmark
launcher
router
server
];
};
impure = mkShell {
buildInputs = buildInputs =
[ [
openssl.dev openssl.dev
@ -65,42 +78,16 @@
"rust-src" "rust-src"
]; ];
}) })
protobuf
] ]
++ (with python3.pkgs; [ ++ (with python3.pkgs; [
venvShellHook venvShellHook
pip pip
ipdb
causal-conv1d
click
einops
exllamav2
fbgemm-gpu
flashinfer
flash-attn
flash-attn-layer-norm
flash-attn-rotary
grpc-interceptor
grpcio-reflection
grpcio-status
grpcio-tools
hf-transfer
loguru
mamba-ssm
marlin-kernels
opentelemetry-api
opentelemetry-exporter-otlp
opentelemetry-instrumentation-grpc
opentelemetry-semantic-conventions
peft
tokenizers
torch
transformers
vllm
(cargoNix.workspaceMembers.text-generation-launcher.build.override { inherit crateOverrides; })
(cargoNix.workspaceMembers.text-generation-router-v3.build.override { inherit crateOverrides; })
]); ]);
inputsFrom = [ server ];
venvDir = "./.venv"; venvDir = "./.venv";
postVenv = '' postVenv = ''
@ -108,8 +95,21 @@
''; '';
postShellHook = '' postShellHook = ''
unset SOURCE_DATE_EPOCH unset SOURCE_DATE_EPOCH
export PATH=$PATH:~/.cargo/bin
''; '';
}; };
};
packages.default = pkgs.writeShellApplication {
name = "text-generation-inference";
runtimeInputs = [
server
router
];
text = ''
${launcher}/bin/text-generation-launcher "$@"
'';
};
} }
); );
} }

2
integration-tests/conftest.py
View File

@ -257,7 +257,7 @@ class IgnoreLogProbResponseComparator(ResponseComparator):
class LauncherHandle: class LauncherHandle:
def __init__(self, port: int): def __init__(self, port: int):
self.client = AsyncClient(f"http://localhost:{port}") self.client = AsyncClient(f"http://localhost:{port}", timeout=30)
def _inner_health(self): def _inner_health(self):
raise NotImplementedError raise NotImplementedError

50
integration-tests/models/test_tools_llama.py
View File

@ -36,6 +36,7 @@ tools = [
}, },
}, },
"required": ["location", "format"], "required": ["location", "format"],
"additionalProperties": False,
}, },
}, },
}, },
@ -62,13 +63,13 @@ tools = [
}, },
}, },
"required": ["location", "format", "num_days"], "required": ["location", "format", "num_days"],
"additionalProperties": False,
}, },
}, },
}, },
] ]
@pytest.mark.skip(reason="Takes too long to run")
@pytest.mark.asyncio @pytest.mark.asyncio
@pytest.mark.private @pytest.mark.private
async def test_flash_llama_grammar_tools(flash_llama_grammar_tools, response_snapshot): async def test_flash_llama_grammar_tools(flash_llama_grammar_tools, response_snapshot):
@ -76,7 +77,7 @@ async def test_flash_llama_grammar_tools(flash_llama_grammar_tools, response_sna
max_tokens=100, max_tokens=100,
seed=1, seed=1,
tools=tools, tools=tools,
presence_penalty=-1.1, temperature=0.0,
messages=[ messages=[
{ {
"role": "system", "role": "system",
@ -91,19 +92,18 @@ async def test_flash_llama_grammar_tools(flash_llama_grammar_tools, response_sna
assert response.choices[0].message.content is None assert response.choices[0].message.content is None
assert response.choices[0].message.tool_calls == [ assert response.choices[0].message.tool_calls == [
{ {
"id": 0, "id": "0",
"type": "function", "type": "function",
"function": { "function": {
"description": None, "description": None,
"name": "get_current_weather", "name": "get_current_weather",
"arguments": {"format": "celsius", "location": "New York, NY"}, "arguments": {"format": "celsius", "location": "Brooklyn, NY"},
}, },
} }
] ]
assert response == response_snapshot assert response == response_snapshot
@pytest.mark.skip(reason="Takes too long to run")
@pytest.mark.asyncio @pytest.mark.asyncio
@pytest.mark.private @pytest.mark.private
async def test_flash_llama_grammar_tools_auto( async def test_flash_llama_grammar_tools_auto(
@ -113,8 +113,8 @@ async def test_flash_llama_grammar_tools_auto(
max_tokens=100, max_tokens=100,
seed=1, seed=1,
tools=tools, tools=tools,
temperature=0.0,
tool_choice="auto", tool_choice="auto",
presence_penalty=-1.1,
messages=[ messages=[
{ {
"role": "system", "role": "system",
@ -129,12 +129,12 @@ async def test_flash_llama_grammar_tools_auto(
assert response.choices[0].message.content is None assert response.choices[0].message.content is None
assert response.choices[0].message.tool_calls == [ assert response.choices[0].message.tool_calls == [
{ {
"id": 0, "id": "0",
"type": "function", "type": "function",
"function": { "function": {
"description": None, "description": None,
"name": "get_current_weather", "name": "get_current_weather",
"arguments": {"format": "celsius", "location": "New York, NY"}, "arguments": {"format": "celsius", "location": "Brooklyn, NY"},
}, },
} }
] ]
@ -142,7 +142,6 @@ async def test_flash_llama_grammar_tools_auto(
assert response == response_snapshot assert response == response_snapshot
@pytest.mark.skip(reason="Takes too long to run")
@pytest.mark.asyncio @pytest.mark.asyncio
@pytest.mark.private @pytest.mark.private
async def test_flash_llama_grammar_tools_choice( async def test_flash_llama_grammar_tools_choice(
@ -152,8 +151,8 @@ async def test_flash_llama_grammar_tools_choice(
max_tokens=100, max_tokens=100,
seed=1, seed=1,
tools=tools, tools=tools,
temperature=0.0,
tool_choice="get_current_weather", tool_choice="get_current_weather",
presence_penalty=-1.1,
messages=[ messages=[
{ {
"role": "system", "role": "system",
@ -168,12 +167,12 @@ async def test_flash_llama_grammar_tools_choice(
assert response.choices[0].message.content is None assert response.choices[0].message.content is None
assert response.choices[0].message.tool_calls == [ assert response.choices[0].message.tool_calls == [
{ {
"id": 0, "id": "0",
"type": "function", "type": "function",
"function": { "function": {
"description": None, "description": None,
"name": "get_current_weather", "name": "get_current_weather",
"arguments": {"format": "celsius", "location": "New York, NY"}, "arguments": {"format": "celsius", "location": "Brooklyn, NY"},
}, },
} }
] ]
@ -181,7 +180,6 @@ async def test_flash_llama_grammar_tools_choice(
assert response == response_snapshot assert response == response_snapshot
@pytest.mark.skip(reason="Takes too long to run")
@pytest.mark.asyncio @pytest.mark.asyncio
@pytest.mark.private @pytest.mark.private
async def test_flash_llama_grammar_tools_stream( async def test_flash_llama_grammar_tools_stream(
@ -191,8 +189,8 @@ async def test_flash_llama_grammar_tools_stream(
max_tokens=100, max_tokens=100,
seed=1, seed=1,
tools=tools, tools=tools,
temperature=0.0,
tool_choice="get_current_weather", tool_choice="get_current_weather",
presence_penalty=-1.1,
messages=[ messages=[
{ {
"role": "system", "role": "system",
@ -210,11 +208,10 @@ async def test_flash_llama_grammar_tools_stream(
async for response in responses: async for response in responses:
count += 1 count += 1
assert count == 38 assert count == 48
assert response == response_snapshot assert response == response_snapshot
@pytest.mark.skip(reason="Takes too long to run")
@pytest.mark.asyncio @pytest.mark.asyncio
@pytest.mark.private @pytest.mark.private
async def test_flash_llama_grammar_tools_insufficient_information( async def test_flash_llama_grammar_tools_insufficient_information(
@ -222,13 +219,13 @@ async def test_flash_llama_grammar_tools_insufficient_information(
): ):
responses = await flash_llama_grammar_tools.chat( responses = await flash_llama_grammar_tools.chat(
max_tokens=100, max_tokens=100,
seed=8, seed=24,
tools=tools, tools=tools,
tool_choice="auto", tool_choice="auto",
messages=[ messages=[
{ {
"role": "system", "role": "system",
"content": "ONLY RESPOND IF THE USER ASKS A WEATHER RELATED QUESTION", "content": "STRICTLY ONLY RESPOND IF THE USER ASKS A WEATHER RELATED QUESTION",
}, },
{ {
"role": "user", "role": "user",
@ -239,18 +236,7 @@ async def test_flash_llama_grammar_tools_insufficient_information(
) )
assert responses.choices[0].message.content is None assert responses.choices[0].message.content is None
assert responses.choices[0].message.tool_calls == [ assert (
{ responses.choices[0].message.tool_calls[0]["function"]["name"] == "notify_error"
"function": { )
"arguments": {
"error": "Cannot get current weather forecast from specified location and temperature unit. Please try again with different options."
},
"description": None,
"name": "notify_error",
},
"id": 0,
"type": "function",
}
]
assert responses == response_snapshot assert responses == response_snapshot

68
nix/crate-overrides.nix
View File

@ -20,34 +20,52 @@ defaultCrateOverrides
rav1e = attrs: { env.CARGO_ENCODED_RUSTFLAGS = "-C target-feature=-crt-static"; }; rav1e = attrs: { env.CARGO_ENCODED_RUSTFLAGS = "-C target-feature=-crt-static"; };
grpc-metadata = attrs: { grpc-metadata = attrs: {
src = src = filter {
filter { root = ../backends/grpc-metadata;
root = ../backends/grpc-metadata; include = with filter; [
include = with filter; [ isDirectory
isDirectory (matchExt "rs")
(matchExt "rs") ];
]; };
};
}; };
text-generation-launcer = attrs: { text-generation-benchmark = attrs: {
src = src = filter {
filter { root = ../benchmark;
root = ../launcher; include = with filter; [
include = with filter; [ isDirectory
isDirectory (matchExt "rs")
(matchExt "rs") ];
]; };
}; };
text-generation-client = attrs: {
src = filter {
root = ../.;
include = with filter; [
isDirectory
(and (inDirectory "backends/client") (matchExt "rs"))
(and (inDirectory "proto") (matchExt "proto"))
];
};
postPatch = "cd backends/client";
buildInputs = [ protobuf ];
};
text-generation-launcher = attrs: {
src = filter {
root = ../launcher;
include = with filter; [
isDirectory
(matchExt "rs")
];
};
}; };
text-generation-router = attrs: { text-generation-router = attrs: {
src = src = filter {
filter { root = ../router;
root = ../router; include = with filter; [
include = with filter; [ isDirectory
isDirectory (matchExt "rs")
(matchExt "rs") ];
]; };
};
}; };
text-generation-router-v3 = attrs: { text-generation-router-v3 = attrs: {
# We need to do the src/source root dance so that the build # We need to do the src/source root dance so that the build

109
nix/server.nix Normal file
View File

@ -0,0 +1,109 @@
{
nix-filter,
buildPythonPackage,
poetry-core,
mypy-protobuf,
awq-inference-engine,
causal-conv1d,
eetq,
einops,
exllamav2,
fbgemm-gpu,
flashinfer,
flash-attn,
flash-attn-layer-norm,
flash-attn-rotary,
grpc-interceptor,
grpcio-reflection,
grpcio-status,
grpcio-tools,
hf-transfer,
loguru,
mamba-ssm,
marlin-kernels,
opentelemetry-api,
opentelemetry-exporter-otlp,
opentelemetry-instrumentation-grpc,
opentelemetry-semantic-conventions,
peft,
safetensors,
tokenizers,
sentencepiece,
transformers,
typer,
vllm,
}:
let
filter = nix-filter.lib;
in
buildPythonPackage {
name = "text-generation-server";
src = filter {
root = ../.;
include = with filter; [
isDirectory
(and (inDirectory "server") (or_ (matchExt "py") (matchExt "pyi")))
"server/pyproject.toml"
(and (inDirectory "proto/v3") (matchExt "proto"))
];
};
pyproject = true;
build-system = [ poetry-core ];
nativeBuildInputs = [ mypy-protobuf ];
pythonRelaxDeps = [
"einops"
"huggingface-hub"
"loguru"
"opentelemetry-instrumentation-grpc"
"sentencepiece"
"typer"
];
pythonRemoveDeps = [ "scipy" ];
dependencies = [
awq-inference-engine
eetq
causal-conv1d
einops
exllamav2
fbgemm-gpu
flashinfer
flash-attn
flash-attn-layer-norm
flash-attn-rotary
grpc-interceptor
grpcio-reflection
grpcio-status
grpcio-tools
hf-transfer
loguru
mamba-ssm
marlin-kernels
opentelemetry-api
opentelemetry-exporter-otlp
opentelemetry-instrumentation-grpc
opentelemetry-semantic-conventions
peft
safetensors
sentencepiece
tokenizers
transformers
typer
vllm
];
prePatch = ''
python -m grpc_tools.protoc -Iproto/v3 --python_out=server/text_generation_server/pb \
--grpc_python_out=server/text_generation_server/pb --mypy_out=server/text_generation_server/pb proto/v3/generate.proto
find server/text_generation_server/pb/ -type f -name "*.py" -print0 -exec sed -i -e 's/^\(import.*pb2\)/from . \1/g' {} \;
touch server/text_generation_server/pb/__init__.py
cd server
'';
}

2
router/Cargo.toml
View File

@ -46,7 +46,7 @@ ngrok = { version = "0.13.1", features = ["axum"], optional = true }
init-tracing-opentelemetry = { version = "0.14.1", features = [ init-tracing-opentelemetry = { version = "0.14.1", features = [
"opentelemetry-otlp", "opentelemetry-otlp",
] } ] }
minijinja = { version = "2.0.2" } minijinja = { version = "2.0.2", features = ["json"] }
minijinja-contrib = { version = "2.0.2", features = ["pycompat"] } minijinja-contrib = { version = "2.0.2", features = ["pycompat"] }
futures-util = "0.3.30" futures-util = "0.3.30"
regex = "1.10.3" regex = "1.10.3"

57
router/src/infer/chat_template.rs
View File

@ -1,9 +1,7 @@
use std::collections::HashSet; use std::collections::HashSet;
use crate::infer::InferError; use crate::infer::InferError;
use crate::{ use crate::{ChatTemplateInputs, Message, MessageChunk, TextMessage, TokenizerConfigToken, Tool};
ChatTemplateInputs, GrammarType, Message, MessageChunk, TextMessage, TokenizerConfigToken,
};
use minijinja::{Environment, ErrorKind, Template}; use minijinja::{Environment, ErrorKind, Template};
use minijinja_contrib::pycompat; use minijinja_contrib::pycompat;
@ -32,6 +30,7 @@ impl ChatTemplate {
env.set_unknown_method_callback(pycompat::unknown_method_callback); env.set_unknown_method_callback(pycompat::unknown_method_callback);
let template_str = template.into_boxed_str(); let template_str = template.into_boxed_str();
env.add_function("raise_exception", raise_exception); env.add_function("raise_exception", raise_exception);
tracing::debug!("Loading template: {:#?}", template_str);
// leaking env and template_str as read-only, static resources for performance. // leaking env and template_str as read-only, static resources for performance.
let template = Box::leak(env) let template = Box::leak(env)
@ -42,6 +41,7 @@ impl ChatTemplate {
let variables = template.undeclared_variables(true); let variables = template.undeclared_variables(true);
// check if the `tools` variable is used in the template // check if the `tools` variable is used in the template
let use_default_tool_template = !variables.contains("tools"); let use_default_tool_template = !variables.contains("tools");
tracing::debug!("Use default tool template: {}", use_default_tool_template);
Self { Self {
template, template,
@ -56,25 +56,36 @@ impl ChatTemplate {
&self, &self,
guideline: Option<&str>, guideline: Option<&str>,
mut messages: Vec<Message>, mut messages: Vec<Message>,
grammar_with_prompt: Option<(GrammarType, String)>, tools_and_prompt: Option<(Vec<Tool>, String)>,
) -> Result<String, InferError> { ) -> Result<String, InferError> {
if self.use_default_tool_template {
if let Some(last_message) = messages.last_mut() {
if let Some((GrammarType::Json(tools), tool_prompt)) = grammar_with_prompt {
last_message.content.push(MessageChunk::Text {
text: format!("\n---\n{}\n{}", tool_prompt, tools),
});
}
}
}
let messages: Vec<TextMessage> = messages.into_iter().map(|c| c.into()).collect();
// check if guideline is expected but not provided // check if guideline is expected but not provided
if self.variables.contains("guideline") && guideline.is_none() { if self.variables.contains("guideline") && guideline.is_none() {
return Err(InferError::MissingTemplateVariable("guideline".to_string())); return Err(InferError::MissingTemplateVariable("guideline".to_string()));
} }
let tools = match tools_and_prompt {
Some((tools, tool_prompt)) => {
// check if the `tools` variable is used in the template
// if not, we need to append the tools to the last message
let text = if self.use_default_tool_template {
match serde_json::to_string(&tools) {
Ok(tools_str) => format!("\n---\n{}\n{}", tools_str, tool_prompt),
Err(e) => return Err(InferError::ToolError(e.to_string())),
}
} else {
// if the `tools` variable is used in the template, we just append the tool_prompt
format!("\n---\n{}", tool_prompt)
};
if let Some(last_message) = messages.last_mut() {
last_message.content.push(MessageChunk::Text { text });
}
Some(tools)
}
None => None,
};
let messages: Vec<TextMessage> = messages.into_iter().map(|c| c.into()).collect();
self.template self.template
.render(ChatTemplateInputs { .render(ChatTemplateInputs {
guideline, guideline,
@ -82,8 +93,7 @@ impl ChatTemplate {
bos_token: self.bos_token.as_deref(), bos_token: self.bos_token.as_deref(),
eos_token: self.eos_token.as_deref(), eos_token: self.eos_token.as_deref(),
add_generation_prompt: true, add_generation_prompt: true,
tools: None, tools,
tools_prompt: None,
}) })
.map_err(InferError::TemplateError) .map_err(InferError::TemplateError)
} }
@ -95,7 +105,7 @@ mod tests {
use crate::infer::chat_template::raise_exception; use crate::infer::chat_template::raise_exception;
use crate::infer::ChatTemplate; use crate::infer::ChatTemplate;
use crate::{ use crate::{
ChatTemplateInputs, GrammarType, Message, MessageContent, TextMessage, TokenizerConfigToken, ChatTemplateInputs, Message, MessageContent, TextMessage, TokenizerConfigToken, Tool,
}; };
use minijinja::Environment; use minijinja::Environment;
@ -854,11 +864,12 @@ mod tests {
content: MessageContent::SingleText("Just testing".to_string()), content: MessageContent::SingleText("Just testing".to_string()),
}, },
]; ];
let tools = serde_json::json!("[]"); let tools_string = r#"[{"type": "function","function": {"name": "get_current_weather","description": "Get the current weather","parameters": {"type": "object","properties": {"location": {"type": "string","description": "The city and state, e.g. San Francisco, CA"},"format": {"type": "string","enum": ["celsius", "fahrenheit"],"description": "The temperature unit to use. Infer this from the users location."}},"required": ["location", "format"]}}}]"#.to_string();
let tools: Vec<Tool> = serde_json::from_str(&tools_string).unwrap();
let tool_prompt = "This default prompt will be used".to_string(); let tool_prompt = "This default prompt will be used".to_string();
let grammer_with_prompt = (GrammarType::Json(tools), tool_prompt); let tools_and_prompt = Some((tools, tool_prompt));
let result = ct.apply(None, msgs, Some(grammer_with_prompt)); let result = ct.apply(None, msgs, tools_and_prompt);
let expected = "<s>[INST] I'd like to show off how chat templating works! [/INST]Great! How can I help you today?</s> [INST] Just testing\n---\nThis default prompt will be used\n\"[]\" [/INST]".to_string(); let expected = "<s>[INST] I'd like to show off how chat templating works! [/INST]Great! How can I help you today?</s> [INST] Just testing\n---\n[{\"type\":\"function\",\"function\":{\"description\":\"Get the current weather\",\"name\":\"get_current_weather\",\"arguments\":{\"properties\":{\"format\":{\"description\":\"The temperature unit to use. Infer this from the users location.\",\"enum\":[\"celsius\",\"fahrenheit\"],\"type\":\"string\"},\"location\":{\"description\":\"The city and state, e.g. San Francisco, CA\",\"type\":\"string\"}},\"required\":[\"location\",\"format\"],\"type\":\"object\"}}}]\nThis default prompt will be used [/INST]".to_string();
assert_eq!(result.unwrap(), expected); assert_eq!(result.unwrap(), expected);
} }
} }

6
router/src/infer/mod.rs
View File

@ -3,7 +3,7 @@ mod chat_template;
pub mod tool_grammar; pub mod tool_grammar;
use crate::validation::{ValidGenerateRequest, Validation, ValidationError}; use crate::validation::{ValidGenerateRequest, Validation, ValidationError};
use crate::GrammarType; use crate::Tool;
use crate::{ use crate::{
ChatTemplateVersions, FinishReason, GenerateRequest, HubProcessorConfig, HubTokenizerConfig, ChatTemplateVersions, FinishReason, GenerateRequest, HubProcessorConfig, HubTokenizerConfig,
Message, PrefillToken, Token, Message, PrefillToken, Token,
@ -140,12 +140,12 @@ impl Infer {
&self, &self,
guideline: Option<String>, guideline: Option<String>,
messages: Vec<Message>, messages: Vec<Message>,
grammar_with_prompt: Option<(GrammarType, String)>, tools_and_prompt: Option<(Vec<Tool>, String)>,
) -> Result<String, InferError> { ) -> Result<String, InferError> {
self.chat_template self.chat_template
.as_ref() .as_ref()
.ok_or_else(|| InferError::TemplateError(ErrorKind::TemplateNotFound.into()))? .ok_or_else(|| InferError::TemplateError(ErrorKind::TemplateNotFound.into()))?
.apply(guideline.as_deref(), messages, grammar_with_prompt) .apply(guideline.as_deref(), messages, tools_and_prompt)
.map_err(|e| { .map_err(|e| {
metrics::counter!("tgi_request_failure", "err" => "template").increment(1); metrics::counter!("tgi_request_failure", "err" => "template").increment(1);
tracing::error!("{e}"); tracing::error!("{e}");

121
router/src/infer/tool_grammar.rs
View File

@ -1,5 +1,8 @@
use crate::infer::InferError; use crate::infer::InferError;
use crate::{FunctionRef, FunctionsMap, Properties, Tool, ToolChoice, ToolType, Tools}; use crate::{
FunctionDefinition, FunctionRef, FunctionsMap, JsonSchemaTool, Properties, Tool, ToolChoice,
ToolType,
};
use serde_json::{json, Map, Value}; use serde_json::{json, Map, Value};
use std::collections::HashMap; use std::collections::HashMap;
@ -16,17 +19,38 @@ impl ToolGrammar {
} }
pub fn apply( pub fn apply(
tools: Option<Vec<Tool>>, tools: Vec<Tool>,
tool_choice: ToolChoice, tool_choice: ToolChoice,
) -> Result<Option<Tools>, InferError> { ) -> Result<(Vec<Tool>, Option<JsonSchemaTool>), InferError> {
// if no tools are provided, we return None // if no tools are provided, we return None
let tools = match tools { if tools.is_empty() {
Some(tools) if !tools.is_empty() => tools, return Ok((tools, None));
_ => return Ok(None), }
};
let tool_choice = tool_choice.0.unwrap_or(ToolType::OneOf); let tool_choice = tool_choice.0.unwrap_or(ToolType::OneOf);
let mut tools = tools.clone();
// add the notify_error function to the tools
let notify_error = Tool {
r#type: "function".to_string(),
function: FunctionDefinition {
name: "notify_error".to_string(),
description: Some("Notify an error or issue".to_string()),
arguments: json!({
"type": "object",
"properties": {
"error": {
"type": "string",
"description": "The error or issue to notify"
}
},
"required": ["error"]
}),
},
};
tools.push(notify_error);
// if tools are provided and no tool_choice we default to the OneOf // if tools are provided and no tool_choice we default to the OneOf
let tools_to_use = match tool_choice { let tools_to_use = match tool_choice {
ToolType::FunctionName(name) => { ToolType::FunctionName(name) => {
@ -35,87 +59,57 @@ impl ToolGrammar {
ToolType::Function { function } => { ToolType::Function { function } => {
vec![Self::find_tool_by_name(&tools, &function.name)?] vec![Self::find_tool_by_name(&tools, &function.name)?]
} }
ToolType::OneOf => tools, ToolType::OneOf => tools.clone(),
ToolType::NoTool => return Ok(None), ToolType::NoTool => return Ok((tools, None)),
}; };
// adds the error notification function for LLM feedback if required
let mut text_response_properties = Map::new();
text_response_properties.insert(
"error".to_string(),
serde_json::json!({
"type": "string",
"description": "The error or issue to notify"
}),
);
text_response_properties.insert(
"_name".to_string(),
serde_json::json!({
"type": "string",
"const": "notify_error"
}),
);
let functions: HashMap<String, serde_json::Value> = tools_to_use let functions: HashMap<String, serde_json::Value> = tools_to_use
.iter() .iter()
.map(|tool| { .map(|tool| {
let func = tool.function.clone(); let func = tool.function.clone();
// Clone the existing parameters, which are expected to be a JSON object let mut params = Map::new();
let mut params = if let Value::Object(params) = &func.arguments {
params.clone()
} else {
Map::new()
};
// Insert the function's description at the top level, outside of properties
params.insert( params.insert(
"description".to_string(), "description".to_string(),
Value::String(func.description.clone().unwrap_or_default()), Value::String(func.description.unwrap_or_default()),
); );
// Ensure 'properties' exists and is an object let mut properties = Map::new();
let properties = params let mut required = vec![Value::String("_name".to_string())];
.entry("properties".to_string())
.or_insert_with(|| json!({}))
.as_object_mut()
.unwrap();
// Insert the constant for the function name inside 'properties'
properties.insert( properties.insert(
"_name".to_string(), "_name".to_string(),
json!({ json!({
"type": "string", "type": "string",
"const": func.name.clone(), "const": func.name.clone(),
// "description": "The name of the function"
}), }),
); );
// Check if 'required' exists, and it is an array. If not, create an empty array. if let Value::Object(args) = func.arguments {
let required = params if let Some(Value::Object(props)) = args.get("properties") {
.entry("required".to_string()) properties.extend(props.clone());
.or_insert_with(|| json!([])) }
.as_array_mut() if let Some(Value::Array(reqs)) = args.get("required") {
.unwrap(); required.extend(reqs.clone());
}
// Add 'name' to the 'required' array if it is not already present params.insert(
if !required.iter().any(|r| r == "_name") { "additionalProperties".to_string(),
required.push(json!("_name")); Value::Bool(
args.get("additionalProperties").and_then(|v| v.as_str())
== Some("true"),
),
);
} }
params.insert("properties".to_string(), Value::Object(properties));
params.insert("required".to_string(), Value::Array(required));
(func.name, Value::Object(params)) (func.name, Value::Object(params))
}) })
.chain([(
"notify_error".to_string(),
serde_json::json!({
"properties": text_response_properties,
"required": ["error", "_name"],
"type": "object"
}),
)])
.collect(); .collect();
let tools = Tools { let tool_schema = JsonSchemaTool {
functions_map: FunctionsMap { functions }, functions_map: FunctionsMap { functions },
properties: Properties { properties: Properties {
function: tools_to_use function: tools_to_use
@ -123,13 +117,10 @@ impl ToolGrammar {
.map(|tool| FunctionRef { .map(|tool| FunctionRef {
ref_path: format!("#/$functions/{}", tool.function.name.clone()), ref_path: format!("#/$functions/{}", tool.function.name.clone()),
}) })
.chain(std::iter::once(FunctionRef {
ref_path: "#/$functions/notify_error".to_string(),
}))
.collect(), .collect(),
}, },
}; };
Ok(Some(tools)) Ok((tools, Some(tool_schema)))
} }
} }

15
router/src/lib.rs
View File

@ -840,10 +840,10 @@ pub(crate) struct ChatRequest {
pub tools: Option<Vec<Tool>>, pub tools: Option<Vec<Tool>>,
/// A prompt to be appended before the tools /// A prompt to be appended before the tools
#[serde(default = "default_tool_prompt")] #[serde(default)]
#[schema( #[schema(
nullable = true, nullable = true,
example = "\"You will be presented with a JSON schema representing a set of tools.\nIf the user request lacks of sufficient information to make a precise tool selection: Do not invent any tool's properties, instead notify with an error message.\n\nJSON Schema:\n\"" example = "Given the functions available, please respond with a JSON for a function call with its proper arguments that best answers the given prompt. Respond in the format {name: function name, parameters: dictionary of argument name and its value}.Do not use variables."
)] )]
pub tool_prompt: Option<String>, pub tool_prompt: Option<String>,
@ -865,10 +865,8 @@ pub(crate) struct ChatRequest {
pub guideline: Option<String>, pub guideline: Option<String>,
} }
fn default_tool_prompt() -> Option<String> { pub fn default_tool_prompt() -> String {
Some( "\nGiven the functions available, please respond with a JSON for a function call with its proper arguments that best answers the given prompt. Respond in the format {name: function name, parameters: dictionary of argument name and its value}.Do not use variables.\n".to_string()
"\nYou will be presented with a JSON schema representing a set of tools.\nIf the user request lacks of sufficient information to make a precise tool selection: Do not invent any tool's properties, instead notify with an error message.\n\nJSON Schema:\n".to_string(),
)
} }
#[derive(Clone, Debug, Deserialize, PartialEq, Serialize, ToSchema)] #[derive(Clone, Debug, Deserialize, PartialEq, Serialize, ToSchema)]
@ -910,7 +908,7 @@ impl From<ToolTypeDeserializer> for ToolChoice {
} }
#[derive(Debug, Deserialize, Serialize, ToSchema, PartialEq)] #[derive(Debug, Deserialize, Serialize, ToSchema, PartialEq)]
pub struct Tools { pub struct JsonSchemaTool {
#[serde(flatten)] #[serde(flatten)]
functions_map: FunctionsMap, functions_map: FunctionsMap,
properties: Properties, properties: Properties,
@ -968,8 +966,7 @@ pub(crate) struct ChatTemplateInputs<'a> {
bos_token: Option<&'a str>, bos_token: Option<&'a str>,
eos_token: Option<&'a str>, eos_token: Option<&'a str>,
add_generation_prompt: bool, add_generation_prompt: bool,
tools: Option<&'a str>, tools: Option<Vec<Tool>>,
tools_prompt: Option<&'a str>,
guideline: Option<&'a str>, guideline: Option<&'a str>,
} }

158
router/src/server.rs
View File

@ -8,7 +8,7 @@ use crate::kserve::{
kserve_model_metadata, kserve_model_metadata_ready, kserve_model_metadata, kserve_model_metadata_ready,
}; };
use crate::validation::ValidationError; use crate::validation::ValidationError;
use crate::ChatTokenizeResponse; use crate::{default_tool_prompt, ChatTokenizeResponse};
use crate::{ use crate::{
usage_stats, BestOfSequence, Details, ErrorResponse, FinishReason, FunctionName, usage_stats, BestOfSequence, Details, ErrorResponse, FinishReason, FunctionName,
GenerateParameters, GenerateRequest, GenerateResponse, GrammarType, HubModelInfo, GenerateParameters, GenerateRequest, GenerateResponse, GrammarType, HubModelInfo,
@ -166,7 +166,7 @@ async fn get_chat_tokenize(
} = req; } = req;
let tool_prompt = tool_prompt.unwrap_or_default(); let tool_prompt = tool_prompt.unwrap_or_default();
let (inputs, _grammar, _tool_grammar) = prepare_chat_input( let (inputs, _grammar, _using_tools) = prepare_chat_input(
&infer, &infer,
response_format, response_format,
tools, tools,
@ -1178,14 +1178,16 @@ async fn chat_completions(
let repetition_penalty = presence_penalty.map(|x| x + 2.0); let repetition_penalty = presence_penalty.map(|x| x + 2.0);
let max_new_tokens = max_tokens.or(Some(100)); let max_new_tokens = max_tokens.or(Some(100));
let logprobs = logprobs.unwrap_or(false); let logprobs = logprobs.unwrap_or(false);
let tool_prompt = tool_prompt.unwrap_or_default(); let tool_prompt = tool_prompt
.filter(|s| !s.is_empty())
.unwrap_or_else(default_tool_prompt);
let stop = stop.unwrap_or_default(); let stop = stop.unwrap_or_default();
// enable greedy only when temperature is 0 // enable greedy only when temperature is 0
let (do_sample, temperature) = match temperature { let (do_sample, temperature) = match temperature {
Some(temperature) if temperature == 0.0 => (false, None), Some(temperature) if temperature == 0.0 => (false, None),
other => (true, other), other => (true, other),
}; };
let (inputs, grammar, tool_grammar) = prepare_chat_input( let (inputs, grammar, using_tools) = prepare_chat_input(
&infer, &infer,
response_format, response_format,
tools, tools,
@ -1241,7 +1243,7 @@ async fn chat_completions(
}); });
// replace the content with the tool calls if grammar is present // replace the content with the tool calls if grammar is present
let (content, tool_calls) = if tool_grammar.is_some() { let (content, tool_calls) = if using_tools {
(None, Some(vec![stream_token.token.text])) (None, Some(vec![stream_token.token.text]))
} else { } else {
let content = if !stream_token.token.special { let content = if !stream_token.token.special {
@ -1295,7 +1297,7 @@ async fn chat_completions(
.unwrap_or_else(|_| std::time::Duration::from_secs(0)) .unwrap_or_else(|_| std::time::Duration::from_secs(0))
.as_secs(); .as_secs();
let (tool_calls, output) = if tool_grammar.is_some() { let (tool_calls, output) = if using_tools {
let gen_text_value: Value = let gen_text_value: Value =
serde_json::from_str(&generation.generated_text).map_err(|e| { serde_json::from_str(&generation.generated_text).map_err(|e| {
InferError::ToolError(format!( InferError::ToolError(format!(
@ -2560,7 +2562,7 @@ fn create_post_processor(
Ok(post_processor) Ok(post_processor)
} }
type PreparedInput = (String, Option<GrammarType>, Option<Tools>); type PreparedInput = (String, Option<GrammarType>, bool);
fn prepare_chat_input( fn prepare_chat_input(
infer: &Infer, infer: &Infer,
@ -2577,19 +2579,139 @@ fn prepare_chat_input(
)); ));
} }
// when response_format is set, tools are not included when applying the chat template to generate inputs
if let Some(format) = response_format { if let Some(format) = response_format {
let inputs = infer.apply_chat_template(guideline, messages, None)?; let inputs = infer.apply_chat_template(guideline, messages, None)?;
return Ok((inputs, Some(format), None)); return Ok((inputs, Some(format), false));
} }
// if tools are set, apply the tool grammar and then the chat template // when no response_format is set and tools are included, apply the chat template with the tools
let tool_grammar: Option<Tools> = ToolGrammar::apply(tools, tool_choice)?; // to generate inputs
let grammar = tool_grammar if let Some(tools) = tools {
.as_ref() let (updated_tools, tool_schema) = ToolGrammar::apply(tools, tool_choice)?;
.map(|t| GrammarType::Json(serde_json::json!(t)));
let tools_grammar_prompt = tool_grammar let grammar = tool_schema
.as_ref() .as_ref()
.map(|t| (GrammarType::Json(serde_json::json!(t)), tool_prompt.into())); .map(|t| GrammarType::Json(serde_json::json!(t)));
let inputs = infer.apply_chat_template(guideline, messages, tools_grammar_prompt)?;
Ok((inputs, grammar, tool_grammar)) let inputs: String = infer.apply_chat_template(
guideline,
messages,
Some((updated_tools, tool_prompt.into())),
)?;
return Ok((inputs, grammar, tool_schema.is_some()));
}
// if no response_format or tools are set simply apply the chat template to generate inputs
let inputs = infer.apply_chat_template(guideline, messages, None)?;
Ok((inputs, None, false))
}
#[cfg(test)]
mod tests {
use super::*;
use crate::ChatTemplateVersions;
use crate::HubTokenizerConfig;
use crate::TokenizerConfigToken;
use crate::Tool;
use serde_json::json;
#[test]
fn test_prepare_chat_input() {
// Mock Backend to avoid network requests
struct MockBackend;
impl Backend for MockBackend {
fn schedule(
&self,
_request: crate::validation::ValidGenerateRequest,
) -> Result<
tokio_stream::wrappers::UnboundedReceiverStream<
Result<InferStreamResponse, InferError>,
>,
InferError,
> {
unimplemented!("Never called in this test");
}
fn health<'a, 'async_trait>(
&'a self,
_current_health: bool,
) -> core::pin::Pin<
Box<dyn core::future::Future<Output = bool> + core::marker::Send + 'async_trait>,
>
where
'a: 'async_trait,
Self: 'async_trait,
{
unimplemented!("Never called in this test");
}
}
let backend = MockBackend {};
let mut tokenizer_config = HubTokenizerConfig::default();
// mock tokenizer config values
tokenizer_config.bos_token = Some(TokenizerConfigToken::String("<s>".to_string()));
tokenizer_config.eos_token = Some(TokenizerConfigToken::String("</s>".to_string()));
tokenizer_config.chat_template = Some(
ChatTemplateVersions::Single("{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS] [\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST] \" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST] \" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n {{- \"[TOOL_CALLS] [\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + eos_token }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- \" \" + message[\"content\"]|trim + eos_token}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS] {\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n".to_string())
);
let infer = Infer::new(
backend,
Validation::new(1, None, None, None, 1, 1, 1, 1, 1, false),
1,
tokenizer_config,
HubProcessorConfig::default(),
);
let response_format = None;
let tools = Some(vec![Tool {
r#type: "function".to_string(),
function: FunctionDefinition {
name: "get_current_weather".to_string(),
description: Some("Get the current weather".to_string()),
arguments: json!({
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the users location."
}
},
"required": ["location", "format"]
}),
},
}]);
let tool_prompt = "Given the functions available, please respond with a JSON for a function call with its proper arguments that best answers the given prompt. Respond in the format {name: function name, parameters: dictionary of argument name and its value}.Do not use variables.";
let guideline = None;
let messages = vec![Message {
name: None,
role: "user".to_string(),
content: MessageContent::SingleText(
"What is the weather like in New York?".to_string(),
),
}];
let result = prepare_chat_input(
&infer,
response_format,
tools,
ToolChoice(None),
tool_prompt,
guideline,
messages,
);
assert!(result.is_ok());
let (inputs, _grammar, using_tools) = result.unwrap();
assert_eq!(using_tools, true);
assert_eq!(inputs, "<s>[AVAILABLE_TOOLS] [{\"type\": \"function\", \"function\": {\"arguments\": {\"properties\":{\"format\":{\"description\":\"The temperature unit to use. Infer this from the users location.\",\"enum\":[\"celsius\",\"fahrenheit\"],\"type\":\"string\"},\"location\":{\"description\":\"The city and state, e.g. San Francisco, CA\",\"type\":\"string\"}},\"required\":[\"location\",\"format\"],\"type\":\"object\"}, \"description\": \"Get the current weather\", \"name\": \"get_current_weather\"}}, {\"type\": \"function\", \"function\": {\"arguments\": {\"properties\":{\"error\":{\"description\":\"The error or issue to notify\",\"type\":\"string\"}},\"required\":[\"error\"],\"type\":\"object\"}, \"description\": \"Notify an error or issue\", \"name\": \"notify_error\"}}][/AVAILABLE_TOOLS][INST] What is the weather like in New York?\n---\nGiven the functions available, please respond with a JSON for a function call with its proper arguments that best answers the given prompt. Respond in the format {name: function name, parameters: dictionary of argument name and its value}.Do not use variables.[/INST]".to_string());
}
} }

7
server/text_generation_server/layers/attention/__init__.py
View File

@ -6,7 +6,12 @@ from .common import Seqlen
if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false": if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false":
raise ImportError("`USE_FLASH_ATTENTION` is false.") raise ImportError("`USE_FLASH_ATTENTION` is false.")
if SYSTEM == "cuda": if SYSTEM == "cuda":
from .cuda import attention, paged_attention, reshape_and_cache, SUPPORTS_WINDOWING from .cuda import (
attention,
paged_attention,
reshape_and_cache,
SUPPORTS_WINDOWING,
)
elif SYSTEM == "rocm": elif SYSTEM == "rocm":
from .rocm import attention, paged_attention, reshape_and_cache, SUPPORTS_WINDOWING from .rocm import attention, paged_attention, reshape_and_cache, SUPPORTS_WINDOWING
elif SYSTEM == "ipex": elif SYSTEM == "ipex":

27
server/text_generation_server/layers/attention/cuda.py
View File

@ -76,7 +76,7 @@ def paged_attention(
# sequences or heads is large, we use V1 since there is enough work # sequences or heads is large, we use V1 since there is enough work
# to parallelize. # to parallelize.
if ATTENTION == "flashinfer": if ATTENTION == "flashinfer":
from text_generation_server.layers.attention.flash_infer import decode_state from text_generation_server.layers.attention.flashinfer import decode_state
return decode_state.get().forward( return decode_state.get().forward(
query.contiguous(), query.contiguous(),
@ -221,9 +221,11 @@ SUPPORTS_WINDOWING = V2
if ATTENTION == "flashinfer": if ATTENTION == "flashinfer":
def attention( def attention(
q, q: torch.Tensor,
k, k: torch.Tensor,
v, v: torch.Tensor,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
cu_seqlens, cu_seqlens,
max_s, max_s,
softmax_scale, softmax_scale,
@ -231,14 +233,15 @@ if ATTENTION == "flashinfer":
causal=True, causal=True,
softcap=0.0, softcap=0.0,
): ):
from text_generation_server.layers.attention.flash_infer import prefill_state assert window_size_left == -1, "Windowing is not supported with flash infer"
from text_generation_server.layers.attention.flashinfer import (
prefill_with_paged_kv_state,
)
return prefill_state.get().forward( return prefill_with_paged_kv_state.get().forward(
q, q.contiguous(),
k,
v,
causal=causal, causal=causal,
window_left=window_size_left, paged_kv_cache=(key_cache, value_cache),
logits_soft_cap=softcap, logits_soft_cap=softcap,
sm_scale=softmax_scale, sm_scale=softmax_scale,
) )
@ -249,6 +252,8 @@ elif V2:
q, q,
k, k,
v, v,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
cu_seqlens, cu_seqlens,
max_s, max_s,
softmax_scale, softmax_scale,
@ -289,6 +294,8 @@ else:
q, q,
k, k,
v, v,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
cu_seqlens, cu_seqlens,
max_s, max_s,
softmax_scale, softmax_scale,

76
server/text_generation_server/layers/attention/flash_infer.py → server/text_generation_server/layers/attention/flashinfer.py
View File

@ -9,6 +9,10 @@ prefill_state: ContextVar[flashinfer.BatchPrefillWithRaggedKVCacheWrapper] = Con
"prefill_state" "prefill_state"
) )
prefill_with_paged_kv_state: ContextVar[
flashinfer.BatchPrefillWithPagedKVCacheWrapper
] = ContextVar("prefill_with_paged_kv_state")
decode_state: ContextVar[flashinfer.BatchDecodeWithPagedKVCacheWrapper] = ContextVar( decode_state: ContextVar[flashinfer.BatchDecodeWithPagedKVCacheWrapper] = ContextVar(
"decode_state" "decode_state"
) )
@ -24,6 +28,78 @@ def get_workspace(device):
return workspace return workspace
def create_prefill_with_paged_kv_state(
*,
device: torch.device,
):
"""Create a prefill state that uses the KV cache."""
workspace_buffer = get_workspace(device)
return flashinfer.BatchPrefillWithPagedKVCacheWrapper(
workspace_buffer, kv_layout="NHD", use_cuda_graph=False
)
@contextmanager
def use_prefill_with_paged_kv_state(
*,
state: flashinfer.BatchPrefillWithPagedKVCacheWrapper,
block_tables: torch.Tensor,
cu_seqlens: torch.Tensor,
input_lengths: torch.Tensor,
num_heads: int,
num_kv_heads: int,
head_size: int,
page_size: int,
query_dtype: str = "float16",
):
"""
Context manager to set the active flashinfer prefill state to the given
`state` and parameters. This state will be used by all calls to the
`attention` function while the context manager is active.
"""
indptr = torch.zeros(
input_lengths.shape[0] + 1, device=input_lengths.device, dtype=torch.int32
)
# Round up to page size and then calculate the cumulative sum to get
# the indices into the block table.
torch.add(input_lengths, page_size - 1, out=indptr[1:])
indptr[1:].div_(page_size, rounding_mode="floor")
indptr[1:].cumsum_(-1)
# Get the lengths of the last page in a block.
if page_size == 1:
last_page_len = torch.ones(
input_lengths.shape[0], dtype=torch.int32, device=input_lengths.device
)
else:
last_page_len = torch.empty(
input_lengths.shape[0], dtype=torch.int32, device=input_lengths.device
)
torch.sub(input_lengths, 1, out=last_page_len)
last_page_len.remainder_(page_size)
last_page_len += 1
token = prefill_with_paged_kv_state.set(state)
try:
state.begin_forward(
qo_indptr=cu_seqlens,
paged_kv_indptr=indptr,
paged_kv_indices=block_tables,
paged_kv_last_page_len=last_page_len,
num_qo_heads=num_heads,
num_kv_heads=num_kv_heads,
head_dim=head_size,
q_data_type=query_dtype,
page_size=page_size,
)
yield
finally:
state.end_forward()
if token is not None:
prefill_with_paged_kv_state.reset(token)
def create_prefill_state( def create_prefill_state(
*, *,
device: torch.device, device: torch.device,

2
server/text_generation_server/layers/medusa.py
View File

@ -32,6 +32,8 @@ class MedusaModel(torch.nn.Module):
) )
def forward(self, x): def forward(self, x):
if not self.heads:
return None
speculative_logits = torch.stack([head(x) for head in self.heads], dim=1) speculative_logits = torch.stack([head(x) for head in self.heads], dim=1)
return speculative_logits return speculative_logits

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

@ -298,6 +298,8 @@ class FlashCohereAttention(torch.nn.Module):
query, query,
key, key,
value, value,
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill, cu_seqlen_prefill,
max_s, max_s,
self.softmax_scale, self.softmax_scale,

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

@ -337,6 +337,8 @@ class DbrxAttention(torch.nn.Module):
query, query,
torch.select(kv, dim=1, index=0), torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1), torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill, cu_seqlen_prefill,
max_s, max_s,
self.softmax_scale, self.softmax_scale,

2
server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py
View File

@ -365,6 +365,8 @@ class DeepseekV2Attention(torch.nn.Module):
query, query,
key, key,
value, value,
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill, cu_seqlen_prefill,
max_s, max_s,
self.softmax_scale, self.softmax_scale,

2
server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py
View File

@ -238,6 +238,8 @@ class FlashGemma2Attention(torch.nn.Module):
query, query,
torch.select(kv, dim=1, index=0), torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1), torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill, cu_seqlen_prefill,
max_s, max_s,
self.softmax_scale, self.softmax_scale,

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

@ -232,6 +232,8 @@ class FlashGemmaAttention(torch.nn.Module):
query, query,
torch.select(kv, dim=1, index=0), torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1), torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill, cu_seqlen_prefill,
max_s, max_s,
self.softmax_scale, self.softmax_scale,

2
server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py
View File

@ -232,6 +232,8 @@ class FlashGPT2Attention(torch.nn.Module):
query, query,
key, key,
value, value,
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill, cu_seqlen_prefill,
max_s, max_s,
self.softmax_scale, self.softmax_scale,

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

@ -220,6 +220,8 @@ class FlashLlamaAttention(torch.nn.Module):
query, query,
torch.select(kv, dim=1, index=0), torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1), torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill, cu_seqlen_prefill,
max_s, max_s,
self.softmax_scale, self.softmax_scale,

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

@ -219,6 +219,8 @@ class MistralAttention(torch.nn.Module):
query, query,
torch.select(kv, dim=1, index=0), torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1), torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill, cu_seqlen_prefill,
max_s, max_s,
self.softmax_scale, self.softmax_scale,

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

@ -276,6 +276,8 @@ class MixtralAttention(torch.nn.Module):
query, query,
torch.select(kv, dim=1, index=0), torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1), torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill, cu_seqlen_prefill,
max_s, max_s,
self.softmax_scale, self.softmax_scale,

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

@ -173,6 +173,8 @@ class FlashNeoxAttention(torch.nn.Module):
qkv[:, 0], qkv[:, 0],
qkv[:, 1], qkv[:, 1],
qkv[:, 2], qkv[:, 2],
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill, cu_seqlen_prefill,
max_s, max_s,
self.softmax_scale, self.softmax_scale,

10
server/text_generation_server/models/custom_modeling/flash_pali_gemma_modeling.py
View File

@ -34,6 +34,11 @@ class PaliGemmaForConditionalGeneration(nn.Module):
config=config.vision_config, config=config.vision_config,
weights=weights, weights=weights,
) )
self.post_vision_tower_layernorm = nn.LayerNorm.load(
prefix="vision_tower.vision_model.post_layernorm",
weights=weights,
eps=config.vision_config.layer_norm_eps,
)
self.multi_modal_projector = TensorParallelColumnLinear.load( self.multi_modal_projector = TensorParallelColumnLinear.load(
config, config,
@ -84,7 +89,10 @@ class PaliGemmaForConditionalGeneration(nn.Module):
if pixel_values is not None: if pixel_values is not None:
pixel_values = pixel_values.to(dtype=inputs_embeds.dtype) pixel_values = pixel_values.to(dtype=inputs_embeds.dtype)
image_outputs = self.vision_tower(pixel_values) image_outputs = self.vision_tower(pixel_values)
image_features = self.multi_modal_projector(image_outputs.last_hidden_state) last_hidden_state = self.post_vision_tower_layernorm(
image_outputs.last_hidden_state
)
image_features = self.multi_modal_projector(last_hidden_state)
# mask where image or padding tokens # mask where image or padding tokens
mask = input_ids == self.config.image_token_index mask = input_ids == self.config.image_token_index

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

@ -194,6 +194,8 @@ class FlashPhiAttention(torch.nn.Module):
query, query,
torch.select(kv, dim=1, index=0), torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1), torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill, cu_seqlen_prefill,
max_s, max_s,
self.softmax_scale, self.softmax_scale,

2
server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py
View File

@ -137,6 +137,8 @@ class Qwen2Attention(torch.nn.Module):
query, query,
torch.select(kv, dim=1, index=0), torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1), torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill, cu_seqlen_prefill,
max_s, max_s,
self.softmax_scale, self.softmax_scale,

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

@ -208,6 +208,8 @@ class FlashRWAttention(torch.nn.Module):
query, query,
torch.select(kv, dim=1, index=0), torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1), torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill, cu_seqlen_prefill,
max_s, max_s,
self.softmax_scale, self.softmax_scale,
@ -326,6 +328,8 @@ class FlashRWLargeAttention(torch.nn.Module):
query, query,
torch.select(kv, dim=2, index=0), torch.select(kv, dim=2, index=0),
torch.select(kv, dim=2, index=1), torch.select(kv, dim=2, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill, cu_seqlen_prefill,
max_s, max_s,
self.softmax_scale, self.softmax_scale,

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

@ -293,6 +293,8 @@ class FlashMQAttention(torch.nn.Module):
query, query,
torch.select(key_value, dim=1, index=0), torch.select(key_value, dim=1, index=0),
torch.select(key_value, dim=1, index=1), torch.select(key_value, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill, cu_seqlen_prefill,
max_s, max_s,
self.softmax_scale, self.softmax_scale,

2
server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py
View File

@ -242,6 +242,8 @@ class Starcoder2Attention(torch.nn.Module):
query, query,
torch.select(kv, dim=1, index=0), torch.select(kv, dim=1, index=0),
torch.select(kv, dim=1, index=1), torch.select(kv, dim=1, index=1),
kv_cache[0],
kv_cache[1],
cu_seqlen_prefill, cu_seqlen_prefill,
max_s, max_s,
self.softmax_scale, self.softmax_scale,

13
server/text_generation_server/models/custom_modeling/siglip.py
View File

@ -364,7 +364,6 @@ class SiglipEncoder(nn.Module):
inputs_embeds, inputs_embeds,
attention_mask: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None,
): ):
hidden_states = inputs_embeds hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers): for idx, encoder_layer in enumerate(self.layers):
hidden_states, _ = encoder_layer( hidden_states, _ = encoder_layer(
@ -386,20 +385,11 @@ class SiglipVisionTransformer(nn.Module):
self.encoder = SiglipEncoder( self.encoder = SiglipEncoder(
prefix=f"{prefix}.encoder", config=config, weights=weights prefix=f"{prefix}.encoder", config=config, weights=weights
) )
self.post_layernorm = nn.LayerNorm.load(
prefix=f"{prefix}.post_layernorm",
weights=weights,
eps=config.layer_norm_eps,
)
def forward( def forward(
self, self,
pixel_values: Optional[torch.FloatTensor] = None, pixel_values: Optional[torch.FloatTensor] = None,
): ):
r"""
Returns:
"""
if pixel_values is None: if pixel_values is None:
raise ValueError("You have to specify pixel_values") raise ValueError("You have to specify pixel_values")
@ -412,10 +402,9 @@ class SiglipVisionTransformer(nn.Module):
inputs_embeds=hidden_states, inputs_embeds=hidden_states,
) )
last_hidden_state = encoder_outputs last_hidden_state = encoder_outputs
post_last_hidden_state = self.post_layernorm(last_hidden_state)
return BaseModelOutputWithPooling( return BaseModelOutputWithPooling(
last_hidden_state=post_last_hidden_state, last_hidden_state=last_hidden_state,
# pooler_output=pooled_output, # pooler_output=pooled_output,
# hidden_states=encoder_outputs, # hidden_states=encoder_outputs,
) )

252
server/text_generation_server/models/flash_causal_lm.py
View File

@ -43,6 +43,7 @@ from text_generation_server.models.globals import (
ATTENTION, ATTENTION,
BLOCK_SIZE, BLOCK_SIZE,
CUDA_GRAPHS, CUDA_GRAPHS,
PREFIX_CACHING,
get_adapter_to_index, get_adapter_to_index,
) )
from text_generation_server.layers.attention import Seqlen from text_generation_server.layers.attention import Seqlen
@ -138,6 +139,9 @@ class FlashCausalLMBatch(Batch):
block_tables_tensor: torch.Tensor block_tables_tensor: torch.Tensor
# tensor of length \sum_{i=0}^{b} max_s_i holding the paged attention slots for all sequences # tensor of length \sum_{i=0}^{b} max_s_i holding the paged attention slots for all sequences
slots: torch.Tensor slots: torch.Tensor
# size [b], containing the number of blocks that can be retrieved from the cache
prefix_lens: List[int]
prefix_lens_tensor: torch.Tensor
max_seqlen: int max_seqlen: int
@ -146,6 +150,9 @@ class FlashCausalLMBatch(Batch):
prefill_next_token_indices: Optional[torch.tensor] prefill_next_token_indices: Optional[torch.tensor]
prefill_cu_outlens: Optional[List[int]] prefill_cu_outlens: Optional[List[int]]
# Prefixes
prefix_ids: List[List[int]]
# All tokens # All tokens
all_input_ids: List[List[int]] all_input_ids: List[List[int]]
all_input_ids_tensor: torch.Tensor all_input_ids_tensor: torch.Tensor
@ -213,6 +220,7 @@ class FlashCausalLMBatch(Batch):
prefix_offsets = [] prefix_offsets = []
read_offsets = [] read_offsets = []
all_input_ids = [] all_input_ids = []
prefix_ids = []
requests_idx_mapping = {} requests_idx_mapping = {}
all_prefill_logprobs = True all_prefill_logprobs = True
@ -230,7 +238,7 @@ class FlashCausalLMBatch(Batch):
# Cumulative length # Cumulative length
cumulative_length = 0 cumulative_length = 0
cumulative_max_length = 0 cumulative_slot_tokens = 0
prefill_out_cumulative_length = 0 prefill_out_cumulative_length = 0
num_blocks = 0 num_blocks = 0
@ -240,6 +248,7 @@ class FlashCausalLMBatch(Batch):
block_tables = [] block_tables = []
slots = [] slots = []
prefix_lens = []
# Parse batch # Parse batch
for i, (r, tokenized_input) in enumerate( for i, (r, tokenized_input) in enumerate(
@ -255,6 +264,19 @@ class FlashCausalLMBatch(Batch):
): ):
tokenized_input = tokenized_input[1:] tokenized_input = tokenized_input[1:]
orig_input_length = len(tokenized_input)
if PREFIX_CACHING:
prefix_len = r.prefix_len
if prefix_len == orig_input_length:
assert prefix_len > 0
prefix_len -= 1
else:
prefix_len = 0
prefix_ids.append(tokenized_input[:prefix_len])
tokenized_input = tokenized_input[prefix_len:]
input_length = len(tokenized_input) input_length = len(tokenized_input)
input_lengths.append(input_length) input_lengths.append(input_length)
@ -264,7 +286,9 @@ class FlashCausalLMBatch(Batch):
all_input_ids.append(tokenized_input) all_input_ids.append(tokenized_input)
# Position ids # Position ids
request_position_ids = torch.arange(0, input_length, dtype=torch.int32) request_position_ids = torch.arange(
prefix_len, orig_input_length, dtype=torch.int32
)
position_ids.append(request_position_ids) position_ids.append(request_position_ids)
# Add cumulative lengths of all previous inputs # Add cumulative lengths of all previous inputs
@ -288,11 +312,17 @@ class FlashCausalLMBatch(Batch):
# Remove one as the first token des not have a past # Remove one as the first token des not have a past
speculative_length = get_speculate() speculative_length = get_speculate()
speculative_length = 0 if speculative_length is None else speculative_length speculative_length = 0 if speculative_length is None else speculative_length
total_tokens = input_length + max_new_tokens - 1 + speculative_length
# Tokens that need to be mapped to blocks.
block_tokens = orig_input_length + max_new_tokens - 1 + speculative_length
# Tokens that need to be mapped to slots. We don't need slots for the
# cached prefix (if present).
slot_tokens = input_length + max_new_tokens - 1 + speculative_length
# blocks and slots can be empty (for example in warmup) # blocks and slots can be empty (for example in warmup)
if not r.blocks: if not r.blocks:
needed_blocks = math.ceil(total_tokens / BLOCK_SIZE) needed_blocks = math.ceil(block_tokens / BLOCK_SIZE)
request_blocks = [ request_blocks = [
b for b in range(num_blocks, num_blocks + needed_blocks) b for b in range(num_blocks, num_blocks + needed_blocks)
] ]
@ -303,16 +333,20 @@ class FlashCausalLMBatch(Batch):
] ]
else: else:
request_blocks = r.blocks request_blocks = r.blocks
request_slots = r.slots request_slots = r.slots[
prefix_len: #: orig_input_length + max_new_tokens + speculative_length
]
block_tables.append(request_blocks) block_tables.append(request_blocks)
slots.extend(request_slots[:total_tokens])
slots.extend(request_slots)
prefix_lens.append(prefix_len)
num_blocks += len(request_blocks) num_blocks += len(request_blocks)
start_slots.append(cumulative_max_length) start_slots.append(cumulative_slot_tokens)
request_slot_indices = torch.arange( request_slot_indices = torch.arange(
cumulative_max_length, cumulative_slot_tokens,
cumulative_max_length + input_length, cumulative_slot_tokens + input_length,
dtype=torch.int64, dtype=torch.int64,
) )
slot_indices.append(request_slot_indices) slot_indices.append(request_slot_indices)
@ -348,7 +382,7 @@ class FlashCausalLMBatch(Batch):
# Update # Update
cumulative_length += input_length cumulative_length += input_length
cumulative_max_length += total_tokens cumulative_slot_tokens += slot_tokens
max_seqlen = max(max_seqlen, input_length) max_seqlen = max(max_seqlen, input_length)
max_blocks = max(max_blocks, len(request_blocks)) max_blocks = max(max_blocks, len(request_blocks))
max_length = max( max_length = max(
@ -425,12 +459,14 @@ class FlashCausalLMBatch(Batch):
) )
slots = torch.tensor(slots, dtype=torch.int64, device=device) slots = torch.tensor(slots, dtype=torch.int64, device=device)
block_tables_tensor = torch.zeros( block_tables_tensor = torch.zeros(
(len(block_tables), max_blocks), dtype=torch.int32, device="cpu" (len(block_tables), max_blocks), dtype=torch.int32, device="cpu"
) )
for i, request_blocks in enumerate(block_tables): for i, request_blocks in enumerate(block_tables):
block_tables_tensor[i, : len(request_blocks)] = torch.tensor(request_blocks) block_tables_tensor[i, : len(request_blocks)] = torch.tensor(request_blocks)
block_tables_tensor = block_tables_tensor.to(device) block_tables_tensor = block_tables_tensor.to(device)
prefix_lens_tensor = torch.tensor(prefix_lens, dtype=torch.int32, device=device)
return cls( return cls(
batch_id=pb.id, batch_id=pb.id,
@ -445,6 +481,8 @@ class FlashCausalLMBatch(Batch):
block_tables=block_tables, block_tables=block_tables,
block_tables_tensor=block_tables_tensor, block_tables_tensor=block_tables_tensor,
slots=slots, slots=slots,
prefix_lens=prefix_lens,
prefix_lens_tensor=prefix_lens_tensor,
max_seqlen=max_seqlen, max_seqlen=max_seqlen,
prefill_head_indices=prefill_head_indices, prefill_head_indices=prefill_head_indices,
prefill_next_token_indices=prefill_next_token_indices, prefill_next_token_indices=prefill_next_token_indices,
@ -455,6 +493,7 @@ class FlashCausalLMBatch(Batch):
read_offsets=read_offsets, read_offsets=read_offsets,
all_input_ids=all_input_ids, all_input_ids=all_input_ids,
all_input_ids_tensor=all_input_ids_tensor, all_input_ids_tensor=all_input_ids_tensor,
prefix_ids=prefix_ids,
next_token_chooser=next_token_chooser, next_token_chooser=next_token_chooser,
stopping_criterias=stopping_criterias, stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens, top_n_tokens=top_n_tokens,
@ -510,8 +549,10 @@ class FlashCausalLMBatch(Batch):
start_slots = [] start_slots = []
block_tables = [] block_tables = []
all_input_ids = [] all_input_ids = []
prefix_ids = []
input_lengths = [] input_lengths = []
prefix_lens = []
prefix_offsets = [] prefix_offsets = []
read_offsets = [] read_offsets = []
@ -533,11 +574,14 @@ class FlashCausalLMBatch(Batch):
# Get length # Get length
request_input_length = self.input_lengths[idx] request_input_length = self.input_lengths[idx]
prefix_len = self.prefix_lens[idx]
max_seqlen = max(max_seqlen, request_input_length) max_seqlen = max(max_seqlen, request_input_length)
all_input_ids.append(self.all_input_ids[idx]) all_input_ids.append(self.all_input_ids[idx])
prefix_ids.append(self.prefix_ids[idx])
input_lengths.append(request_input_length) input_lengths.append(request_input_length)
prefix_lens.append(prefix_len)
prefix_offsets.append(self.prefix_offsets[idx]) prefix_offsets.append(self.prefix_offsets[idx])
read_offsets.append(self.read_offsets[idx]) read_offsets.append(self.read_offsets[idx])
@ -582,6 +626,7 @@ class FlashCausalLMBatch(Batch):
block_tables_tensor = self.block_tables_tensor[indices] block_tables_tensor = self.block_tables_tensor[indices]
input_lengths_tensor = self.input_lengths_tensor[indices] input_lengths_tensor = self.input_lengths_tensor[indices]
slots = self.slots[slot_filtering_indices] slots = self.slots[slot_filtering_indices]
prefix_lens_tensor = self.prefix_lens_tensor[indices]
next_token_chooser = self.next_token_chooser.filter(indices) next_token_chooser = self.next_token_chooser.filter(indices)
top_n_tokens_tensor = self.top_n_tokens_tensor[indices] top_n_tokens_tensor = self.top_n_tokens_tensor[indices]
speculative_ids = ( speculative_ids = (
@ -617,10 +662,13 @@ class FlashCausalLMBatch(Batch):
prefill_cu_outlens=None, prefill_cu_outlens=None,
input_lengths=input_lengths, input_lengths=input_lengths,
input_lengths_tensor=input_lengths_tensor, input_lengths_tensor=input_lengths_tensor,
prefix_lens=prefix_lens,
prefix_lens_tensor=prefix_lens_tensor,
prefix_offsets=prefix_offsets, prefix_offsets=prefix_offsets,
read_offsets=read_offsets, read_offsets=read_offsets,
all_input_ids=all_input_ids, all_input_ids=all_input_ids,
all_input_ids_tensor=all_input_ids_tensor, all_input_ids_tensor=all_input_ids_tensor,
prefix_ids=prefix_ids,
next_token_chooser=next_token_chooser, next_token_chooser=next_token_chooser,
stopping_criterias=stopping_criterias, stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens, top_n_tokens=top_n_tokens,
@ -681,6 +729,7 @@ class FlashCausalLMBatch(Batch):
block_tables_tensor = batches[0].block_tables_tensor.new_zeros( block_tables_tensor = batches[0].block_tables_tensor.new_zeros(
(total_batch_size, max_blocks) (total_batch_size, max_blocks)
) )
prefix_lens_tensor = batches[0].prefix_lens_tensor.new_empty(total_batch_size)
all_input_ids_tensor = batches[0].all_input_ids_tensor.new_zeros( all_input_ids_tensor = batches[0].all_input_ids_tensor.new_zeros(
(total_batch_size, max_length) (total_batch_size, max_length)
) )
@ -698,7 +747,9 @@ class FlashCausalLMBatch(Batch):
start_slots = [] start_slots = []
block_tables = [] block_tables = []
prefix_lens = []
all_input_ids = [] all_input_ids = []
prefix_ids = []
input_lengths = [] input_lengths = []
prefix_offsets = [] prefix_offsets = []
@ -760,10 +811,14 @@ class FlashCausalLMBatch(Batch):
start_index:end_index, : batch.block_tables_tensor.shape[1] start_index:end_index, : batch.block_tables_tensor.shape[1]
] = batch.block_tables_tensor[:, :max_blocks] ] = batch.block_tables_tensor[:, :max_blocks]
prefix_lens_tensor[start_index:end_index] = batch.prefix_lens_tensor
start_slots.append(batch.start_slots + cumulative_slots) start_slots.append(batch.start_slots + cumulative_slots)
block_tables.extend(batch.block_tables) block_tables.extend(batch.block_tables)
prefix_lens.extend(batch.prefix_lens)
all_input_ids.extend(batch.all_input_ids) all_input_ids.extend(batch.all_input_ids)
prefix_ids.extend(batch.prefix_ids)
input_lengths.extend(batch.input_lengths) input_lengths.extend(batch.input_lengths)
prefix_offsets.extend(batch.prefix_offsets) prefix_offsets.extend(batch.prefix_offsets)
@ -809,6 +864,8 @@ class FlashCausalLMBatch(Batch):
slot_indices=slot_indices, slot_indices=slot_indices,
block_tables=block_tables, block_tables=block_tables,
block_tables_tensor=block_tables_tensor, block_tables_tensor=block_tables_tensor,
prefix_lens=prefix_lens,
prefix_lens_tensor=prefix_lens_tensor,
slots=slots, slots=slots,
max_seqlen=max_seqlen, max_seqlen=max_seqlen,
prefill_head_indices=None, prefill_head_indices=None,
@ -820,6 +877,7 @@ class FlashCausalLMBatch(Batch):
read_offsets=read_offsets, read_offsets=read_offsets,
all_input_ids=all_input_ids, all_input_ids=all_input_ids,
all_input_ids_tensor=all_input_ids_tensor, all_input_ids_tensor=all_input_ids_tensor,
prefix_ids=prefix_ids,
next_token_chooser=next_token_chooser, next_token_chooser=next_token_chooser,
stopping_criterias=stopping_criterias, stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens, top_n_tokens=top_n_tokens,
@ -970,19 +1028,22 @@ class FlashCausalLM(Model):
self.kv_cache = [] self.kv_cache = []
if ATTENTION == "flashinfer": if ATTENTION == "flashinfer":
from text_generation_server.layers.attention.flash_infer import ( from text_generation_server.layers.attention.flashinfer import (
create_prefill_state, create_prefill_state,
create_decode_state, create_decode_state,
create_prefill_with_paged_kv_state,
) )
self.prefill_state = create_prefill_state(device=device) self.prefill_state = create_prefill_state(device=device)
self.prefill_with_paged_kv_state = create_prefill_with_paged_kv_state(
device=device
)
if not CUDA_GRAPHS: self.decode_state = create_decode_state(
self.decode_state = create_decode_state( device=device,
device=device, num_heads=self.num_heads,
num_heads=self.num_heads, num_kv_heads=self.num_kv_heads,
num_kv_heads=self.num_kv_heads, )
)
super().__init__( super().__init__(
model_id=model_id, model_id=model_id,
@ -1074,12 +1135,23 @@ class FlashCausalLM(Model):
input_ids = torch.zeros(bs, dtype=torch.int64, device=self.device) input_ids = torch.zeros(bs, dtype=torch.int64, device=self.device)
position_ids = torch.zeros(bs, dtype=torch.int32, device=self.device) position_ids = torch.zeros(bs, dtype=torch.int32, device=self.device)
slots = torch.arange(bs, dtype=torch.int64, device=self.device) slots = torch.arange(bs, dtype=torch.int64, device=self.device)
input_lengths = torch.ones(bs, dtype=torch.int32, device=self.device) * max_s input_lengths = [max_s] * bs
block_tables = ( prefix_lengths = [0] * bs
torch.arange(max_bt, dtype=torch.int32, device=self.device) input_lengths_tensor = (
.repeat(bs) torch.ones(bs, dtype=torch.int32, device=self.device) * max_s
.reshape((bs, max_bt))
) )
prefix_lengths_tensor = torch.zeros(bs, dtype=torch.int32, device=self.device)
block_tables = torch.arange(
max_bt, dtype=torch.int32, device=self.device
).repeat(bs)
block_tables = block_tables.reshape((bs, max_bt))
if ATTENTION == "flashinfer":
block_tables = block_tables_to_ragged(
block_tables=block_tables,
input_lengths=input_lengths,
prefix_lens=prefix_lengths,
)
self.cuda_graphs[bs] = { self.cuda_graphs[bs] = {
"input_ids": input_ids, "input_ids": input_ids,
@ -1087,14 +1159,14 @@ class FlashCausalLM(Model):
"kv_cache": self.kv_cache, "kv_cache": self.kv_cache,
"block_tables": block_tables, "block_tables": block_tables,
"slots": slots, "slots": slots,
"input_lengths": input_lengths, "input_lengths": input_lengths_tensor,
} }
input_lengths_ = Seqlen(input_lengths=input_lengths) input_lengths_ = Seqlen(input_lengths=input_lengths_tensor)
graph = torch.cuda.CUDAGraph() graph = torch.cuda.CUDAGraph()
self.cuda_graphs[bs]["graph"] = graph self.cuda_graphs[bs]["graph"] = graph
if ATTENTION == "flashinfer": if ATTENTION == "flashinfer":
from text_generation_server.layers.attention.flash_infer import ( from text_generation_server.layers.attention.flashinfer import (
create_decode_state_cuda_graphs, create_decode_state_cuda_graphs,
) )
@ -1104,7 +1176,7 @@ class FlashCausalLM(Model):
last_page_len = torch.ones(bs, dtype=torch.int32, device=self.device) last_page_len = torch.ones(bs, dtype=torch.int32, device=self.device)
state = create_decode_state_cuda_graphs( state = create_decode_state_cuda_graphs(
device=input_ids.device, device=input_ids.device,
block_tables=block_tables.view(-1), block_tables=block_tables,
block_tables_ptr=block_tables_ptr, block_tables_ptr=block_tables_ptr,
last_page_len=last_page_len, last_page_len=last_page_len,
num_heads=self.num_heads, num_heads=self.num_heads,
@ -1120,7 +1192,10 @@ class FlashCausalLM(Model):
block_tables=block_tables, block_tables=block_tables,
cu_seqlen_prefill=None, cu_seqlen_prefill=None,
input_lengths=input_lengths, input_lengths=input_lengths,
input_lengths_tensor=input_lengths_tensor,
state=state, state=state,
prefix_lens=prefix_lengths,
prefix_lens_tensor=prefix_lengths_tensor,
): ):
self.model.forward( self.model.forward(
input_ids=input_ids, input_ids=input_ids,
@ -1138,7 +1213,7 @@ class FlashCausalLM(Model):
torch.cuda.synchronize() torch.cuda.synchronize()
with torch.cuda.graph(graph, pool=MEM_POOL): with torch.cuda.graph(graph, pool=MEM_POOL):
input_lengths = Seqlen(input_lengths=input_lengths) input_lengths_tensor = Seqlen(input_lengths=input_lengths_tensor)
logits, speculative_logits = self.model.forward( logits, speculative_logits = self.model.forward(
input_ids=input_ids, input_ids=input_ids,
position_ids=position_ids, position_ids=position_ids,
@ -1146,7 +1221,7 @@ class FlashCausalLM(Model):
kv_cache=self.kv_cache, kv_cache=self.kv_cache,
block_tables=block_tables, block_tables=block_tables,
slots=slots, slots=slots,
input_lengths=input_lengths, input_lengths=input_lengths_tensor,
max_s=max_s, max_s=max_s,
prefill_cache_indices=None, prefill_cache_indices=None,
lm_head_indices=None, lm_head_indices=None,
@ -1334,6 +1409,9 @@ class FlashCausalLM(Model):
input_lengths = ( input_lengths = (
input_lengths.unsqueeze(-1).expand(B, new_length) + arange_int input_lengths.unsqueeze(-1).expand(B, new_length) + arange_int
).view(-1) ).view(-1)
prefix_lens_tensor = (
batch.prefix_lens_tensor.unsqueeze(-1).expand(B, new_length)
).reshape(-1)
# Add Copy the block tables for all members # Add Copy the block tables for all members
block_tables = ( block_tables = (
@ -1354,6 +1432,7 @@ class FlashCausalLM(Model):
block_tables = batch.block_tables_tensor block_tables = batch.block_tables_tensor
slots = batch.slots[batch.slot_indices] slots = batch.slots[batch.slot_indices]
input_lengths = batch.input_lengths_tensor input_lengths = batch.input_lengths_tensor
prefix_lens_tensor = batch.prefix_lens_tensor
max_s = batch.max_seqlen max_s = batch.max_seqlen
lm_head_indices = batch.prefill_head_indices lm_head_indices = batch.prefill_head_indices
@ -1372,10 +1451,20 @@ class FlashCausalLM(Model):
cuda_graph = None cuda_graph = None
if cu_seqlen_prefill is not None or cuda_graph is None: if cu_seqlen_prefill is not None or cuda_graph is None:
input_lengths = input_lengths + prefix_lens_tensor
if PREFIX_CACHING:
block_tables = block_tables_to_ragged(
block_tables=block_tables,
input_lengths=batch.input_lengths,
prefix_lens=batch.prefix_lens,
)
with self._forward_context( with self._forward_context(
block_tables=block_tables, block_tables=block_tables,
cu_seqlen_prefill=cu_seqlen_prefill, cu_seqlen_prefill=cu_seqlen_prefill,
input_lengths=input_lengths, input_lengths=batch.input_lengths,
input_lengths_tensor=input_lengths,
prefix_lens=batch.prefix_lens,
prefix_lens_tensor=prefix_lens_tensor,
): ):
input_lengths = Seqlen(input_lengths=input_lengths) input_lengths = Seqlen(input_lengths=input_lengths)
logits, speculative_logits = self.model.forward( logits, speculative_logits = self.model.forward(
@ -1399,20 +1488,32 @@ class FlashCausalLM(Model):
# Static inputs are potentially padded # Static inputs are potentially padded
cuda_graph["input_ids"][: input_ids.shape[0]] = input_ids cuda_graph["input_ids"][: input_ids.shape[0]] = input_ids
cuda_graph["position_ids"][: position_ids.shape[0]] = position_ids cuda_graph["position_ids"][: position_ids.shape[0]] = position_ids
cuda_graph["block_tables"][ if ATTENTION == "flashinfer":
: block_tables.shape[0], : block_tables.shape[1] block_tables = block_tables_to_ragged(
] = block_tables block_tables=block_tables,
input_lengths=batch.input_lengths,
prefix_lens=batch.prefix_lens,
)
cuda_graph["block_tables"][: block_tables.shape[0]] = block_tables
else:
cuda_graph["block_tables"][
: block_tables.shape[0], : block_tables.shape[1]
] = block_tables
cuda_graph["slots"].fill_(-1) cuda_graph["slots"].fill_(-1)
cuda_graph["slots"][: slots.shape[0]] = slots cuda_graph["slots"][: slots.shape[0]] = slots
cuda_graph["input_lengths"].zero_() cuda_graph["input_lengths"].zero_()
cuda_graph["input_lengths"][: input_lengths.shape[0]] = input_lengths cuda_graph["input_lengths"][: input_lengths.shape[0]] = (
input_lengths + prefix_lens_tensor
)
state = cuda_graph.get("state")
with self._forward_context( with self._forward_context(
block_tables=block_tables, block_tables=cuda_graph["block_tables"],
cu_seqlen_prefill=None, cu_seqlen_prefill=None,
input_lengths=input_lengths, input_lengths=batch.input_lengths,
state=state, input_lengths_tensor=cuda_graph["input_lengths"],
prefix_lens=batch.prefix_lens,
prefix_lens_tensor=prefix_lens_tensor,
state=cuda_graph.get("state"),
): ):
# Replay the graph # Replay the graph
cuda_graph["graph"].replay() cuda_graph["graph"].replay()
@ -1610,6 +1711,7 @@ class FlashCausalLM(Model):
batch.read_offsets, batch.read_offsets,
batch.stopping_criterias, batch.stopping_criterias,
batch.all_input_ids, batch.all_input_ids,
batch.prefix_ids,
batch.next_token_chooser.do_sample, batch.next_token_chooser.do_sample,
batch.next_token_chooser.seeds, batch.next_token_chooser.seeds,
batch.top_n_tokens, batch.top_n_tokens,
@ -1627,6 +1729,7 @@ class FlashCausalLM(Model):
read_offset, read_offset,
stopping_criteria, stopping_criteria,
all_input_ids, all_input_ids,
prefix_ids,
do_sample, do_sample,
seed, seed,
top_n_tokens, top_n_tokens,
@ -1701,18 +1804,18 @@ class FlashCausalLM(Model):
out_end_index = batch.prefill_cu_outlens[i + 1] out_end_index = batch.prefill_cu_outlens[i + 1]
# Remove generated token to only have prefill and add nan for first prompt token # Remove generated token to only have prefill and add nan for first prompt token
request_prefill_logprobs = [float("nan")] + prefill_logprobs[ request_prefill_logprobs = (
out_start_index : out_end_index - 1 [float("nan")] * (len(prefix_ids) + 1)
] ) + prefill_logprobs[out_start_index : out_end_index - 1]
prefill_token_ids = all_input_ids[:-1] prefill_token_ids = all_input_ids[:-1]
prefill_texts = self.tokenizer.batch_decode( prefill_texts = self.tokenizer.batch_decode(
prefill_token_ids, prefix_ids + prefill_token_ids,
clean_up_tokenization_spaces=False, clean_up_tokenization_spaces=False,
skip_special_tokens=False, skip_special_tokens=False,
) )
prefill_tokens = Tokens( prefill_tokens = Tokens(
prefill_token_ids, prefix_ids + prefill_token_ids,
request_prefill_logprobs, request_prefill_logprobs,
prefill_texts, prefill_texts,
is_special=[], is_special=[],
@ -1794,33 +1897,68 @@ class FlashCausalLM(Model):
*, *,
block_tables: torch.Tensor, block_tables: torch.Tensor,
cu_seqlen_prefill: Optional[torch.Tensor], cu_seqlen_prefill: Optional[torch.Tensor],
input_lengths: torch.Tensor, input_lengths: List[int],
input_lengths_tensor: torch.Tensor,
prefix_lens: List[int],
prefix_lens_tensor: torch.Tensor,
state: Optional[Any] = None, state: Optional[Any] = None,
) -> ContextManager: ) -> ContextManager:
if ATTENTION != "flashinfer": if ATTENTION != "flashinfer":
return nullcontext() return nullcontext()
from text_generation_server.layers.attention.flash_infer import ( from text_generation_server.layers.attention.flashinfer import (
use_decode_state, use_decode_state,
use_prefill_state, use_prefill_with_paged_kv_state,
) )
# has_prefix_lens = any(prefix_len > 0 for prefix_len in prefix_lens)
if cu_seqlen_prefill is not None: if cu_seqlen_prefill is not None:
return use_prefill_state( return use_prefill_with_paged_kv_state(
state=state if state is not None else self.prefill_state, state=(
state if state is not None else self.prefill_with_paged_kv_state
),
# block_tables=block_tables_to_ragged(
# block_tables=block_tables,
# input_lengths=input_lengths,
# prefix_lens=prefix_lens,
# ),
block_tables=block_tables,
cu_seqlens=cu_seqlen_prefill, cu_seqlens=cu_seqlen_prefill,
num_heads=self.num_heads, input_lengths=input_lengths_tensor,
num_kv_heads=self.num_kv_heads,
head_size=self.head_size,
)
else:
assert input_lengths is not None
return use_decode_state(
state=state if state is not None else self.decode_state,
input_lengths=input_lengths,
block_tables=block_tables.view(-1),
num_heads=self.num_heads, num_heads=self.num_heads,
num_kv_heads=self.num_kv_heads, num_kv_heads=self.num_kv_heads,
head_size=self.head_size, head_size=self.head_size,
page_size=BLOCK_SIZE, page_size=BLOCK_SIZE,
) )
else:
assert input_lengths_tensor is not None
return use_decode_state(
state=state if state is not None else self.decode_state,
input_lengths=input_lengths_tensor,
block_tables=block_tables,
num_heads=self.num_heads,
num_kv_heads=self.num_kv_heads,
head_size=self.head_size,
page_size=BLOCK_SIZE,
)
def block_tables_to_ragged(
*, block_tables: torch.Tensor, input_lengths: List[int], prefix_lens: List[int]
) -> torch.Tensor:
"""Convert block table to ragged format compatible with FlashInfer."""
assert len(input_lengths) == len(prefix_lens)
total_len = sum(input_lengths) + sum(prefix_lens)
block_tables_ragged = torch.empty(
total_len, dtype=torch.int32, device=block_tables.device
)
offset = 0
for i, (input_length, prefix_len) in enumerate(zip(input_lengths, prefix_lens)):
seq_len = prefix_len + input_length
block_tables_ragged[offset : offset + seq_len] = block_tables[i][:seq_len]
offset += seq_len
return block_tables_ragged

6
server/text_generation_server/models/globals.py
View File

@ -5,9 +5,8 @@ from typing import Dict, Optional
from text_generation_server.utils.log import log_master from text_generation_server.utils.log import log_master
PREFIX_CACHING = os.getenv("USE_PREFIX_CACHING", False) PREFIX_CACHING = os.getenv("USE_PREFIX_CACHING", "0").lower() in {"1", "true"}
log_master(logger.info, f"Using Attention = {PREFIX_CACHING}") log_master(logger.info, f"Using prefix caching = {PREFIX_CACHING}")
ATTENTION = os.getenv("ATTENTION", "flashinfer" if PREFIX_CACHING else "paged") ATTENTION = os.getenv("ATTENTION", "flashinfer" if PREFIX_CACHING else "paged")
_expected = {"paged", "flashdecoding", "flashinfer"} _expected = {"paged", "flashdecoding", "flashinfer"}
assert ( assert (
@ -29,7 +28,6 @@ elif ATTENTION == "flashinfer":
else: else:
BLOCK_SIZE = 16 BLOCK_SIZE = 16
cuda_graphs = os.getenv("CUDA_GRAPHS") cuda_graphs = os.getenv("CUDA_GRAPHS")
if cuda_graphs is not None: if cuda_graphs is not None:
try: try:

89
server/text_generation_server/models/vlm_causal_lm.py
View File

@ -11,7 +11,9 @@ from text_generation_server.pb import generate_pb2
from text_generation_server.models.flash_causal_lm import ( from text_generation_server.models.flash_causal_lm import (
FlashCausalLMBatch, FlashCausalLMBatch,
FlashCausalLM, FlashCausalLM,
block_tables_to_ragged,
) )
from text_generation_server.models.globals import PREFIX_CACHING, ATTENTION
from text_generation_server.utils.log import log_master from text_generation_server.utils.log import log_master
from transformers import AutoProcessor from transformers import AutoProcessor
from text_generation_server.layers.attention import Seqlen from text_generation_server.layers.attention import Seqlen
@ -254,6 +256,8 @@ class VlmCausalLM(FlashCausalLM):
trust_remote_code: bool, trust_remote_code: bool,
**kwargs, **kwargs,
): ):
if PREFIX_CACHING:
raise NotImplementedError("Vlm do not work with prefix caching yet")
if processor_kwargs is None: if processor_kwargs is None:
processor_kwargs = {} processor_kwargs = {}
self.processor = processor_class.from_pretrained( self.processor = processor_class.from_pretrained(
@ -310,6 +314,9 @@ class VlmCausalLM(FlashCausalLM):
input_lengths = ( input_lengths = (
input_lengths.unsqueeze(-1).expand(B, new_length) + arange_int input_lengths.unsqueeze(-1).expand(B, new_length) + arange_int
).view(-1) ).view(-1)
prefix_lens_tensor = (
batch.prefix_lens_tensor.unsqueeze(-1).expand(B, new_length)
).reshape(-1)
# Add Copy the block tables for all members # Add Copy the block tables for all members
block_tables = ( block_tables = (
@ -330,6 +337,7 @@ class VlmCausalLM(FlashCausalLM):
block_tables = batch.block_tables_tensor block_tables = batch.block_tables_tensor
slots = batch.slots[batch.slot_indices] slots = batch.slots[batch.slot_indices]
input_lengths = batch.input_lengths_tensor input_lengths = batch.input_lengths_tensor
prefix_lens_tensor = batch.prefix_lens_tensor
max_s = batch.max_seqlen max_s = batch.max_seqlen
lm_head_indices = batch.prefill_head_indices lm_head_indices = batch.prefill_head_indices
@ -349,43 +357,68 @@ class VlmCausalLM(FlashCausalLM):
else: else:
cuda_graph = None cuda_graph = None
if cu_seqlen_prefill is not None or cuda_graph is None: if cu_seqlen_prefill is not None or cuda_graph is None:
input_lengths = Seqlen(input_lengths=input_lengths) input_lengths = input_lengths + prefix_lens_tensor
logits, speculative_logits = self.model.forward( if PREFIX_CACHING:
input_ids=input_ids, block_tables = block_tables_to_ragged(
position_ids=position_ids, block_tables=block_tables,
cu_seqlen_prefill=cu_seqlen_prefill, input_lengths=batch.input_lengths,
kv_cache=kv_cache, prefix_lens=batch.prefix_lens,
)
with self._forward_context(
block_tables=block_tables, block_tables=block_tables,
slots=slots, cu_seqlen_prefill=cu_seqlen_prefill,
input_lengths=input_lengths, input_lengths=batch.input_lengths,
max_s=max_s, input_lengths_tensor=input_lengths,
prefill_cache_indices=batch.prefill_cache_indices, prefix_lens=batch.prefix_lens,
lm_head_indices=lm_head_indices, prefix_lens_tensor=prefix_lens_tensor,
pixel_values=batch.pixel_values, ):
pixel_attention_mask=batch.pixel_attention_mask, input_lengths = Seqlen(input_lengths=input_lengths)
image_sizes=batch.image_sizes, logits, speculative_logits = self.model.forward(
) input_ids=input_ids,
if batch.prefill_cache_indices is not None: position_ids=position_ids,
batch.prefill_cache_indices = None cu_seqlen_prefill=cu_seqlen_prefill,
if batch.pixel_values is not None: kv_cache=kv_cache,
batch.pixel_values = None block_tables=block_tables,
if batch.pixel_attention_mask is not None: slots=slots,
batch.pixel_attention_mask = None input_lengths=input_lengths,
if batch.image_sizes is not None: max_s=max_s,
batch.image_sizes = None prefill_cache_indices=batch.prefill_cache_indices,
return logits, speculative_logits lm_head_indices=lm_head_indices,
pixel_values=batch.pixel_values,
pixel_attention_mask=batch.pixel_attention_mask,
image_sizes=batch.image_sizes,
)
if batch.prefill_cache_indices is not None:
batch.prefill_cache_indices = None
if batch.pixel_values is not None:
batch.pixel_values = None
if batch.pixel_attention_mask is not None:
batch.pixel_attention_mask = None
if batch.image_sizes is not None:
batch.image_sizes = None
return logits, speculative_logits
# Copy inputs to the static inputs of the cuda graph # Copy inputs to the static inputs of the cuda graph
# Static inputs are potentially padded # Static inputs are potentially padded
cuda_graph["input_ids"][: input_ids.shape[0]] = input_ids cuda_graph["input_ids"][: input_ids.shape[0]] = input_ids
cuda_graph["position_ids"][: position_ids.shape[0]] = position_ids cuda_graph["position_ids"][: position_ids.shape[0]] = position_ids
cuda_graph["block_tables"][ if ATTENTION == "flashinfer":
: block_tables.shape[0], : block_tables.shape[1] block_tables = block_tables_to_ragged(
] = block_tables block_tables=block_tables,
input_lengths=batch.input_lengths,
prefix_lens=batch.prefix_lens,
)
cuda_graph["block_tables"][: block_tables.shape[0]] = block_tables
else:
cuda_graph["block_tables"][
: block_tables.shape[0], : block_tables.shape[1]
] = block_tables
cuda_graph["slots"].fill_(-1) cuda_graph["slots"].fill_(-1)
cuda_graph["slots"][: slots.shape[0]] = slots cuda_graph["slots"][: slots.shape[0]] = slots
cuda_graph["input_lengths"].zero_() cuda_graph["input_lengths"].zero_()
cuda_graph["input_lengths"][: input_lengths.shape[0]] = input_lengths cuda_graph["input_lengths"][: input_lengths.shape[0]] = (
input_lengths + prefix_lens_tensor
)
# Replay the graph # Replay the graph
cuda_graph["graph"].replay() cuda_graph["graph"].replay()