mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-04-22 23:42:06 +00:00
Merge tag 'v1.1.1' into v1.1-release
This commit is contained in:
regisss
commit
c09066aeb1
38
.github/workflows/build.yaml
vendored
38
.github/workflows/build.yaml
vendored
@ -79,11 +79,6 @@ jobs:
|
|||||||
install: true
|
install: true
|
||||||
- name: Inject slug/short variables
|
- name: Inject slug/short variables
|
||||||
uses: rlespinasse/github-slug-action@v4.4.1
|
uses: rlespinasse/github-slug-action@v4.4.1
|
||||||
- name: Install cosign
|
|
||||||
if: github.event_name != 'pull_request'
|
|
||||||
uses: sigstore/cosign-installer@f3c664df7af409cb4873aa5068053ba9d61a57b6 #v2.6.0
|
|
||||||
with:
|
|
||||||
cosign-release: 'v1.13.1'
|
|
||||||
- name: Tailscale
|
- name: Tailscale
|
||||||
uses: tailscale/github-action@7bd8039bf25c23c4ab1b8d6e2cc2da2280601966
|
uses: tailscale/github-action@7bd8039bf25c23c4ab1b8d6e2cc2da2280601966
|
||||||
with:
|
with:
|
||||||
@ -150,39 +145,6 @@ jobs:
|
|||||||
labels: ${{ steps.meta.outputs.labels || steps.meta-pr.outputs.labels }}
|
labels: ${{ steps.meta.outputs.labels || steps.meta-pr.outputs.labels }}
|
||||||
cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/community/text-generation-inference:cache,mode=min
|
cache-from: type=registry,ref=registry.internal.huggingface.tech/api-inference/community/text-generation-inference:cache,mode=min
|
||||||
cache-to: type=registry,ref=registry.internal.huggingface.tech/api-inference/community/text-generation-inference:cache,mode=min
|
cache-to: type=registry,ref=registry.internal.huggingface.tech/api-inference/community/text-generation-inference:cache,mode=min
|
||||||
# Sign the resulting Docker image digest except on PRs.
|
|
||||||
# This will only write to the public Rekor transparency log when the Docker
|
|
||||||
# repository is public to avoid leaking data.
|
|
||||||
- name: Sign the published Docker image
|
|
||||||
if: ${{ github.event_name != 'pull_request' }}
|
|
||||||
env:
|
|
||||||
COSIGN_EXPERIMENTAL: "true"
|
|
||||||
# This step uses the identity token to provision an ephemeral certificate
|
|
||||||
# against the sigstore community Fulcio instance.
|
|
||||||
run: echo "${{ steps.meta.outputs.tags }}" | xargs -I {} cosign sign {}@${{ steps.build-and-push.outputs.digest }}
|
|
||||||
- name: Run Trivy in GitHub SBOM mode and submit results to Dependency Graph
|
|
||||||
uses: aquasecurity/trivy-action@master
|
|
||||||
if: ${{ github.event_name != 'pull_request' }}
|
|
||||||
with:
|
|
||||||
image-ref: 'ghcr.io/huggingface/text-generation-inference:sha-${{ env.GITHUB_SHA_SHORT }}'
|
|
||||||
format: 'github'
|
|
||||||
output: 'dependency-results.sbom.json'
|
|
||||||
github-pat: ${{ secrets.GITHUB_TOKEN }}
|
|
||||||
scanners: 'vuln'
|
|
||||||
- name: Run Trivy vulnerability scanner
|
|
||||||
uses: aquasecurity/trivy-action@master
|
|
||||||
if: ${{ github.event_name != 'pull_request' }}
|
|
||||||
with:
|
|
||||||
image-ref: 'ghcr.io/huggingface/text-generation-inference:sha-${{ env.GITHUB_SHA_SHORT }}'
|
|
||||||
format: 'sarif'
|
|
||||||
output: 'trivy-results.sarif'
|
|
||||||
severity: 'CRITICAL'
|
|
||||||
scanners: 'vuln'
|
|
||||||
- name: Upload Trivy scan results to GitHub Security tab
|
|
||||||
uses: github/codeql-action/upload-sarif@v2
|
|
||||||
if: ${{ github.event_name != 'pull_request' }}
|
|
||||||
with:
|
|
||||||
sarif_file: 'trivy-results.sarif'
|
|
||||||
|
|
||||||
integration-tests:
|
integration-tests:
|
||||||
concurrency:
|
concurrency:
|
||||||
|
|||||||
1
.github/workflows/build_documentation.yml
vendored
1
.github/workflows/build_documentation.yml
vendored
@ -17,5 +17,4 @@ jobs:
|
|||||||
package: text-generation-inference
|
package: text-generation-inference
|
||||||
additional_args: --not_python_module
|
additional_args: --not_python_module
|
||||||
secrets:
|
secrets:
|
||||||
token: ${{ secrets.HUGGINGFACE_PUSH }}
|
|
||||||
hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}
|
hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}
|
||||||
44
Cargo.lock
generated
44
Cargo.lock
generated
@ -743,18 +743,6 @@ version = "1.0.1"
|
|||||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
checksum = "28a80e3145d8ad11ba0995949bbcf48b9df2be62772b3d351ef017dff6ecb853"
|
checksum = "28a80e3145d8ad11ba0995949bbcf48b9df2be62772b3d351ef017dff6ecb853"
|
||||||
|
|
||||||
[[package]]
|
|
||||||
name = "flume"
|
|
||||||
version = "0.11.0"
|
|
||||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
||||||
checksum = "55ac459de2512911e4b674ce33cf20befaba382d05b62b008afc1c8b57cbf181"
|
|
||||||
dependencies = [
|
|
||||||
"futures-core",
|
|
||||||
"futures-sink",
|
|
||||||
"nanorand",
|
|
||||||
"spin 0.9.8",
|
|
||||||
]
|
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "fnv"
|
name = "fnv"
|
||||||
version = "1.0.7"
|
version = "1.0.7"
|
||||||
@ -900,10 +888,8 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
|
|||||||
checksum = "be4136b2a15dd319360be1c07d9933517ccf0be8f16bf62a3bee4f0d618df427"
|
checksum = "be4136b2a15dd319360be1c07d9933517ccf0be8f16bf62a3bee4f0d618df427"
|
||||||
dependencies = [
|
dependencies = [
|
||||||
"cfg-if",
|
"cfg-if",
|
||||||
"js-sys",
|
|
||||||
"libc",
|
"libc",
|
||||||
"wasi",
|
"wasi",
|
||||||
"wasm-bindgen",
|
|
||||||
]
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
@ -1508,15 +1494,6 @@ dependencies = [
|
|||||||
"tracing",
|
"tracing",
|
||||||
]
|
]
|
||||||
|
|
||||||
[[package]]
|
|
||||||
name = "nanorand"
|
|
||||||
version = "0.7.0"
|
|
||||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
||||||
checksum = "6a51313c5820b0b02bd422f4b44776fbf47961755c74ce64afc73bfad10226c3"
|
|
||||||
dependencies = [
|
|
||||||
"getrandom",
|
|
||||||
]
|
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "native-tls"
|
name = "native-tls"
|
||||||
version = "0.2.11"
|
version = "0.2.11"
|
||||||
@ -2313,7 +2290,7 @@ dependencies = [
|
|||||||
"cc",
|
"cc",
|
||||||
"libc",
|
"libc",
|
||||||
"once_cell",
|
"once_cell",
|
||||||
"spin 0.5.2",
|
"spin",
|
||||||
"untrusted",
|
"untrusted",
|
||||||
"web-sys",
|
"web-sys",
|
||||||
"winapi",
|
"winapi",
|
||||||
@ -2678,15 +2655,6 @@ version = "0.5.2"
|
|||||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||||
checksum = "6e63cff320ae2c57904679ba7cb63280a3dc4613885beafb148ee7bf9aa9042d"
|
checksum = "6e63cff320ae2c57904679ba7cb63280a3dc4613885beafb148ee7bf9aa9042d"
|
||||||
|
|
||||||
[[package]]
|
|
||||||
name = "spin"
|
|
||||||
version = "0.9.8"
|
|
||||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
|
||||||
checksum = "6980e8d7511241f8acf4aebddbb1ff938df5eebe98691418c4468d0b72a96a67"
|
|
||||||
dependencies = [
|
|
||||||
"lock_api",
|
|
||||||
]
|
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "spm_precompiled"
|
name = "spm_precompiled"
|
||||||
version = "0.1.4"
|
version = "0.1.4"
|
||||||
@ -2808,7 +2776,7 @@ dependencies = [
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "text-generation-benchmark"
|
name = "text-generation-benchmark"
|
||||||
version = "1.1.0"
|
version = "1.1.1"
|
||||||
dependencies = [
|
dependencies = [
|
||||||
"average",
|
"average",
|
||||||
"clap",
|
"clap",
|
||||||
@ -2829,7 +2797,7 @@ dependencies = [
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "text-generation-client"
|
name = "text-generation-client"
|
||||||
version = "1.1.0"
|
version = "1.1.1"
|
||||||
dependencies = [
|
dependencies = [
|
||||||
"futures",
|
"futures",
|
||||||
"grpc-metadata",
|
"grpc-metadata",
|
||||||
@ -2845,7 +2813,7 @@ dependencies = [
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "text-generation-launcher"
|
name = "text-generation-launcher"
|
||||||
version = "1.1.0"
|
version = "1.1.1"
|
||||||
dependencies = [
|
dependencies = [
|
||||||
"clap",
|
"clap",
|
||||||
"ctrlc",
|
"ctrlc",
|
||||||
@ -2861,13 +2829,12 @@ dependencies = [
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "text-generation-router"
|
name = "text-generation-router"
|
||||||
version = "1.1.0"
|
version = "1.1.1"
|
||||||
dependencies = [
|
dependencies = [
|
||||||
"async-stream",
|
"async-stream",
|
||||||
"axum",
|
"axum",
|
||||||
"axum-tracing-opentelemetry",
|
"axum-tracing-opentelemetry",
|
||||||
"clap",
|
"clap",
|
||||||
"flume",
|
|
||||||
"futures",
|
"futures",
|
||||||
"hf-hub 0.3.1",
|
"hf-hub 0.3.1",
|
||||||
"init-tracing-opentelemetry",
|
"init-tracing-opentelemetry",
|
||||||
@ -2885,6 +2852,7 @@ dependencies = [
|
|||||||
"thiserror",
|
"thiserror",
|
||||||
"tokenizers",
|
"tokenizers",
|
||||||
"tokio",
|
"tokio",
|
||||||
|
"tokio-stream",
|
||||||
"tower-http",
|
"tower-http",
|
||||||
"tracing",
|
"tracing",
|
||||||
"tracing-opentelemetry",
|
"tracing-opentelemetry",
|
||||||
|
|||||||
@ -8,7 +8,7 @@ members = [
|
|||||||
]
|
]
|
||||||
|
|
||||||
[workspace.package]
|
[workspace.package]
|
||||||
version = "1.1.0"
|
version = "1.1.1"
|
||||||
edition = "2021"
|
edition = "2021"
|
||||||
authors = ["Olivier Dehaene"]
|
authors = ["Olivier Dehaene"]
|
||||||
homepage = "https://github.com/huggingface/text-generation-inference"
|
homepage = "https://github.com/huggingface/text-generation-inference"
|
||||||
|
|||||||
@ -10,7 +10,7 @@
|
|||||||
"name": "Apache 2.0",
|
"name": "Apache 2.0",
|
||||||
"url": "https://www.apache.org/licenses/LICENSE-2.0"
|
"url": "https://www.apache.org/licenses/LICENSE-2.0"
|
||||||
},
|
},
|
||||||
"version": "1.1.0"
|
"version": "1.1.1"
|
||||||
},
|
},
|
||||||
"paths": {
|
"paths": {
|
||||||
"/": {
|
"/": {
|
||||||
@ -367,7 +367,7 @@
|
|||||||
"type": "integer",
|
"type": "integer",
|
||||||
"format": "int32",
|
"format": "int32",
|
||||||
"example": 1,
|
"example": 1,
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"prefill": {
|
"prefill": {
|
||||||
"type": "array",
|
"type": "array",
|
||||||
@ -380,13 +380,22 @@
|
|||||||
"format": "int64",
|
"format": "int64",
|
||||||
"example": 42,
|
"example": 42,
|
||||||
"nullable": true,
|
"nullable": true,
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"tokens": {
|
"tokens": {
|
||||||
"type": "array",
|
"type": "array",
|
||||||
"items": {
|
"items": {
|
||||||
"$ref": "#/components/schemas/Token"
|
"$ref": "#/components/schemas/Token"
|
||||||
}
|
}
|
||||||
|
},
|
||||||
|
"top_tokens": {
|
||||||
|
"type": "array",
|
||||||
|
"items": {
|
||||||
|
"type": "array",
|
||||||
|
"items": {
|
||||||
|
"$ref": "#/components/schemas/Token"
|
||||||
|
}
|
||||||
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
@ -432,7 +441,7 @@
|
|||||||
"type": "integer",
|
"type": "integer",
|
||||||
"format": "int32",
|
"format": "int32",
|
||||||
"example": 1,
|
"example": 1,
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"prefill": {
|
"prefill": {
|
||||||
"type": "array",
|
"type": "array",
|
||||||
@ -445,13 +454,22 @@
|
|||||||
"format": "int64",
|
"format": "int64",
|
||||||
"example": 42,
|
"example": 42,
|
||||||
"nullable": true,
|
"nullable": true,
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"tokens": {
|
"tokens": {
|
||||||
"type": "array",
|
"type": "array",
|
||||||
"items": {
|
"items": {
|
||||||
"$ref": "#/components/schemas/Token"
|
"$ref": "#/components/schemas/Token"
|
||||||
}
|
}
|
||||||
|
},
|
||||||
|
"top_tokens": {
|
||||||
|
"type": "array",
|
||||||
|
"items": {
|
||||||
|
"type": "array",
|
||||||
|
"items": {
|
||||||
|
"$ref": "#/components/schemas/Token"
|
||||||
|
}
|
||||||
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
@ -486,8 +504,8 @@
|
|||||||
"default": "null",
|
"default": "null",
|
||||||
"example": 1,
|
"example": 1,
|
||||||
"nullable": true,
|
"nullable": true,
|
||||||
"minimum": 0.0,
|
"minimum": 0,
|
||||||
"exclusiveMinimum": 0.0
|
"exclusiveMinimum": 0
|
||||||
},
|
},
|
||||||
"decoder_input_details": {
|
"decoder_input_details": {
|
||||||
"type": "boolean",
|
"type": "boolean",
|
||||||
@ -505,10 +523,10 @@
|
|||||||
"max_new_tokens": {
|
"max_new_tokens": {
|
||||||
"type": "integer",
|
"type": "integer",
|
||||||
"format": "int32",
|
"format": "int32",
|
||||||
"default": "20",
|
"default": "null",
|
||||||
"minimum": 0.0,
|
"example": "20",
|
||||||
"exclusiveMaximum": 512.0,
|
"nullable": true,
|
||||||
"exclusiveMinimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"repetition_penalty": {
|
"repetition_penalty": {
|
||||||
"type": "number",
|
"type": "number",
|
||||||
@ -516,7 +534,7 @@
|
|||||||
"default": "null",
|
"default": "null",
|
||||||
"example": 1.03,
|
"example": 1.03,
|
||||||
"nullable": true,
|
"nullable": true,
|
||||||
"exclusiveMinimum": 0.0
|
"exclusiveMinimum": 0
|
||||||
},
|
},
|
||||||
"return_full_text": {
|
"return_full_text": {
|
||||||
"type": "boolean",
|
"type": "boolean",
|
||||||
@ -530,8 +548,8 @@
|
|||||||
"default": "null",
|
"default": "null",
|
||||||
"example": "null",
|
"example": "null",
|
||||||
"nullable": true,
|
"nullable": true,
|
||||||
"minimum": 0.0,
|
"minimum": 0,
|
||||||
"exclusiveMinimum": 0.0
|
"exclusiveMinimum": 0
|
||||||
},
|
},
|
||||||
"stop": {
|
"stop": {
|
||||||
"type": "array",
|
"type": "array",
|
||||||
@ -549,7 +567,7 @@
|
|||||||
"default": "null",
|
"default": "null",
|
||||||
"example": 0.5,
|
"example": 0.5,
|
||||||
"nullable": true,
|
"nullable": true,
|
||||||
"exclusiveMinimum": 0.0
|
"exclusiveMinimum": 0
|
||||||
},
|
},
|
||||||
"top_k": {
|
"top_k": {
|
||||||
"type": "integer",
|
"type": "integer",
|
||||||
@ -557,7 +575,16 @@
|
|||||||
"default": "null",
|
"default": "null",
|
||||||
"example": 10,
|
"example": 10,
|
||||||
"nullable": true,
|
"nullable": true,
|
||||||
"exclusiveMinimum": 0.0
|
"exclusiveMinimum": 0
|
||||||
|
},
|
||||||
|
"top_n_tokens": {
|
||||||
|
"type": "integer",
|
||||||
|
"format": "int32",
|
||||||
|
"default": "null",
|
||||||
|
"example": 5,
|
||||||
|
"nullable": true,
|
||||||
|
"minimum": 0,
|
||||||
|
"exclusiveMinimum": 0
|
||||||
},
|
},
|
||||||
"top_p": {
|
"top_p": {
|
||||||
"type": "number",
|
"type": "number",
|
||||||
@ -565,15 +592,15 @@
|
|||||||
"default": "null",
|
"default": "null",
|
||||||
"example": 0.95,
|
"example": 0.95,
|
||||||
"nullable": true,
|
"nullable": true,
|
||||||
"maximum": 1.0,
|
"maximum": 1,
|
||||||
"exclusiveMinimum": 0.0
|
"exclusiveMinimum": 0
|
||||||
},
|
},
|
||||||
"truncate": {
|
"truncate": {
|
||||||
"type": "integer",
|
"type": "integer",
|
||||||
"default": "null",
|
"default": "null",
|
||||||
"example": "null",
|
"example": "null",
|
||||||
"nullable": true,
|
"nullable": true,
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"typical_p": {
|
"typical_p": {
|
||||||
"type": "number",
|
"type": "number",
|
||||||
@ -581,8 +608,8 @@
|
|||||||
"default": "null",
|
"default": "null",
|
||||||
"example": 0.95,
|
"example": 0.95,
|
||||||
"nullable": true,
|
"nullable": true,
|
||||||
"maximum": 1.0,
|
"maximum": 1,
|
||||||
"exclusiveMinimum": 0.0
|
"exclusiveMinimum": 0
|
||||||
},
|
},
|
||||||
"watermark": {
|
"watermark": {
|
||||||
"type": "boolean",
|
"type": "boolean",
|
||||||
@ -653,38 +680,38 @@
|
|||||||
"type": "integer",
|
"type": "integer",
|
||||||
"format": "int32",
|
"format": "int32",
|
||||||
"example": "32000",
|
"example": "32000",
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"max_best_of": {
|
"max_best_of": {
|
||||||
"type": "integer",
|
"type": "integer",
|
||||||
"example": "2",
|
"example": "2",
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"max_concurrent_requests": {
|
"max_concurrent_requests": {
|
||||||
"type": "integer",
|
"type": "integer",
|
||||||
"description": "Router Parameters",
|
"description": "Router Parameters",
|
||||||
"example": "128",
|
"example": "128",
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"max_input_length": {
|
"max_input_length": {
|
||||||
"type": "integer",
|
"type": "integer",
|
||||||
"example": "1024",
|
"example": "1024",
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"max_stop_sequences": {
|
"max_stop_sequences": {
|
||||||
"type": "integer",
|
"type": "integer",
|
||||||
"example": "4",
|
"example": "4",
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"max_total_tokens": {
|
"max_total_tokens": {
|
||||||
"type": "integer",
|
"type": "integer",
|
||||||
"example": "2048",
|
"example": "2048",
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"max_waiting_tokens": {
|
"max_waiting_tokens": {
|
||||||
"type": "integer",
|
"type": "integer",
|
||||||
"example": "20",
|
"example": "20",
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"model_device_type": {
|
"model_device_type": {
|
||||||
"type": "string",
|
"type": "string",
|
||||||
@ -717,7 +744,7 @@
|
|||||||
"validation_workers": {
|
"validation_workers": {
|
||||||
"type": "integer",
|
"type": "integer",
|
||||||
"example": "2",
|
"example": "2",
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"version": {
|
"version": {
|
||||||
"type": "string",
|
"type": "string",
|
||||||
@ -743,7 +770,7 @@
|
|||||||
"type": "integer",
|
"type": "integer",
|
||||||
"format": "int32",
|
"format": "int32",
|
||||||
"example": 0,
|
"example": 0,
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"logprob": {
|
"logprob": {
|
||||||
"type": "number",
|
"type": "number",
|
||||||
@ -771,14 +798,14 @@
|
|||||||
"type": "integer",
|
"type": "integer",
|
||||||
"format": "int32",
|
"format": "int32",
|
||||||
"example": 1,
|
"example": 1,
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"seed": {
|
"seed": {
|
||||||
"type": "integer",
|
"type": "integer",
|
||||||
"format": "int64",
|
"format": "int64",
|
||||||
"example": 42,
|
"example": 42,
|
||||||
"nullable": true,
|
"nullable": true,
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
@ -794,6 +821,7 @@
|
|||||||
"$ref": "#/components/schemas/StreamDetails"
|
"$ref": "#/components/schemas/StreamDetails"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
|
"default": "null",
|
||||||
"nullable": true
|
"nullable": true
|
||||||
},
|
},
|
||||||
"generated_text": {
|
"generated_text": {
|
||||||
@ -804,6 +832,12 @@
|
|||||||
},
|
},
|
||||||
"token": {
|
"token": {
|
||||||
"$ref": "#/components/schemas/Token"
|
"$ref": "#/components/schemas/Token"
|
||||||
|
},
|
||||||
|
"top_tokens": {
|
||||||
|
"type": "array",
|
||||||
|
"items": {
|
||||||
|
"$ref": "#/components/schemas/Token"
|
||||||
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
},
|
},
|
||||||
@ -820,7 +854,7 @@
|
|||||||
"type": "integer",
|
"type": "integer",
|
||||||
"format": "int32",
|
"format": "int32",
|
||||||
"example": 0,
|
"example": 0,
|
||||||
"minimum": 0.0
|
"minimum": 0
|
||||||
},
|
},
|
||||||
"logprob": {
|
"logprob": {
|
||||||
"type": "number",
|
"type": "number",
|
||||||
|
|||||||
@ -19,6 +19,6 @@ docker run --gpus all \
|
|||||||
--shm-size 1g \
|
--shm-size 1g \
|
||||||
-e HUGGING_FACE_HUB_TOKEN=$token \
|
-e HUGGING_FACE_HUB_TOKEN=$token \
|
||||||
-p 8080:80 \
|
-p 8080:80 \
|
||||||
-v $volume:/data ghcr.io/huggingface/text-generation-inference:1.1.0 \
|
-v $volume:/data ghcr.io/huggingface/text-generation-inference:1.1.1 \
|
||||||
--model-id $model
|
--model-id $model
|
||||||
```
|
```
|
||||||
|
|||||||
@ -1,38 +1,59 @@
|
|||||||
# Text-generation-launcher arguments
|
# Text-generation-launcher arguments
|
||||||
```
|
|
||||||
|
<!-- WRAP CODE BLOCKS -->
|
||||||
|
|
||||||
|
```shell
|
||||||
Text Generation Launcher
|
Text Generation Launcher
|
||||||
|
|
||||||
Usage: text-generation-launcher [OPTIONS]
|
Usage: text-generation-launcher [OPTIONS]
|
||||||
|
|
||||||
Options:
|
Options:
|
||||||
|
```
|
||||||
|
## MODEL_ID
|
||||||
|
```shell
|
||||||
--model-id <MODEL_ID>
|
--model-id <MODEL_ID>
|
||||||
The name of the model to load. Can be a MODEL_ID as listed on <https://hf.co/models> like `gpt2` or `OpenAssistant/oasst-sft-1-pythia-12b`. Or it can be a local directory containing the necessary files as saved by `save_pretrained(...)` methods of transformers
|
The name of the model to load. Can be a MODEL_ID as listed on <https://hf.co/models> like `gpt2` or `OpenAssistant/oasst-sft-1-pythia-12b`. Or it can be a local directory containing the necessary files as saved by `save_pretrained(...)` methods of transformers
|
||||||
|
|
||||||
[env: MODEL_ID=]
|
[env: MODEL_ID=]
|
||||||
[default: bigscience/bloom-560m]
|
[default: bigscience/bloom-560m]
|
||||||
|
|
||||||
|
```
|
||||||
|
## REVISION
|
||||||
|
```shell
|
||||||
--revision <REVISION>
|
--revision <REVISION>
|
||||||
The actual revision of the model if you're referring to a model on the hub. You can use a specific commit id or a branch like `refs/pr/2`
|
The actual revision of the model if you're referring to a model on the hub. You can use a specific commit id or a branch like `refs/pr/2`
|
||||||
|
|
||||||
[env: REVISION=]
|
[env: REVISION=]
|
||||||
|
|
||||||
|
```
|
||||||
|
## VALIDATION_WORKERS
|
||||||
|
```shell
|
||||||
--validation-workers <VALIDATION_WORKERS>
|
--validation-workers <VALIDATION_WORKERS>
|
||||||
The number of tokenizer workers used for payload validation and truncation inside the router
|
The number of tokenizer workers used for payload validation and truncation inside the router
|
||||||
|
|
||||||
[env: VALIDATION_WORKERS=]
|
[env: VALIDATION_WORKERS=]
|
||||||
[default: 2]
|
[default: 2]
|
||||||
|
|
||||||
|
```
|
||||||
|
## SHARDED
|
||||||
|
```shell
|
||||||
--sharded <SHARDED>
|
--sharded <SHARDED>
|
||||||
Whether to shard the model across multiple GPUs By default text-generation-inference will use all available GPUs to run the model. Setting it to `false` deactivates `num_shard`
|
Whether to shard the model across multiple GPUs By default text-generation-inference will use all available GPUs to run the model. Setting it to `false` deactivates `num_shard`
|
||||||
|
|
||||||
[env: SHARDED=]
|
[env: SHARDED=]
|
||||||
[possible values: true, false]
|
[possible values: true, false]
|
||||||
|
|
||||||
|
```
|
||||||
|
## NUM_SHARD
|
||||||
|
```shell
|
||||||
--num-shard <NUM_SHARD>
|
--num-shard <NUM_SHARD>
|
||||||
The number of shards to use if you don't want to use all GPUs on a given machine. You can use `CUDA_VISIBLE_DEVICES=0,1 text-generation-launcher... --num_shard 2` and `CUDA_VISIBLE_DEVICES=2,3 text-generation-launcher... --num_shard 2` to launch 2 copies with 2 shard each on a given machine with 4 GPUs for instance
|
The number of shards to use if you don't want to use all GPUs on a given machine. You can use `CUDA_VISIBLE_DEVICES=0,1 text-generation-launcher... --num_shard 2` and `CUDA_VISIBLE_DEVICES=2,3 text-generation-launcher... --num_shard 2` to launch 2 copies with 2 shard each on a given machine with 4 GPUs for instance
|
||||||
|
|
||||||
[env: NUM_SHARD=]
|
[env: NUM_SHARD=]
|
||||||
|
|
||||||
|
```
|
||||||
|
## QUANTIZE
|
||||||
|
```shell
|
||||||
--quantize <QUANTIZE>
|
--quantize <QUANTIZE>
|
||||||
Whether you want the model to be quantized
|
Whether you want the model to be quantized
|
||||||
|
|
||||||
@ -46,53 +67,80 @@ Options:
|
|||||||
- bitsandbytes-nf4: Bitsandbytes 4bit. Can be applied on any model, will cut the memory requirement by 4x, but it is known that the model will be much slower to run than the native f16
|
- bitsandbytes-nf4: Bitsandbytes 4bit. Can be applied on any model, will cut the memory requirement by 4x, but it is known that the model will be much slower to run than the native f16
|
||||||
- bitsandbytes-fp4: Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model
|
- bitsandbytes-fp4: Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model
|
||||||
|
|
||||||
|
```
|
||||||
|
## DTYPE
|
||||||
|
```shell
|
||||||
--dtype <DTYPE>
|
--dtype <DTYPE>
|
||||||
The dtype to be forced upon the model. This option cannot be used with `--quantize`
|
The dtype to be forced upon the model. This option cannot be used with `--quantize`
|
||||||
|
|
||||||
[env: DTYPE=]
|
[env: DTYPE=]
|
||||||
[possible values: float16, bfloat16]
|
[possible values: float16, bfloat16]
|
||||||
|
|
||||||
|
```
|
||||||
|
## TRUST_REMOTE_CODE
|
||||||
|
```shell
|
||||||
--trust-remote-code
|
--trust-remote-code
|
||||||
Whether you want to execute hub modelling code. Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision
|
Whether you want to execute hub modelling code. Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision
|
||||||
|
|
||||||
[env: TRUST_REMOTE_CODE=]
|
[env: TRUST_REMOTE_CODE=]
|
||||||
|
|
||||||
|
```
|
||||||
|
## MAX_CONCURRENT_REQUESTS
|
||||||
|
```shell
|
||||||
--max-concurrent-requests <MAX_CONCURRENT_REQUESTS>
|
--max-concurrent-requests <MAX_CONCURRENT_REQUESTS>
|
||||||
The maximum amount of concurrent requests for this particular deployment. Having a low limit will refuse clients requests instead of having them wait for too long and is usually good to handle backpressure correctly
|
The maximum amount of concurrent requests for this particular deployment. Having a low limit will refuse clients requests instead of having them wait for too long and is usually good to handle backpressure correctly
|
||||||
|
|
||||||
[env: MAX_CONCURRENT_REQUESTS=]
|
[env: MAX_CONCURRENT_REQUESTS=]
|
||||||
[default: 128]
|
[default: 128]
|
||||||
|
|
||||||
|
```
|
||||||
|
## MAX_BEST_OF
|
||||||
|
```shell
|
||||||
--max-best-of <MAX_BEST_OF>
|
--max-best-of <MAX_BEST_OF>
|
||||||
This is the maximum allowed value for clients to set `best_of`. Best of makes `n` generations at the same time, and return the best in terms of overall log probability over the entire generated sequence
|
This is the maximum allowed value for clients to set `best_of`. Best of makes `n` generations at the same time, and return the best in terms of overall log probability over the entire generated sequence
|
||||||
|
|
||||||
[env: MAX_BEST_OF=]
|
[env: MAX_BEST_OF=]
|
||||||
[default: 2]
|
[default: 2]
|
||||||
|
|
||||||
|
```
|
||||||
|
## MAX_STOP_SEQUENCES
|
||||||
|
```shell
|
||||||
--max-stop-sequences <MAX_STOP_SEQUENCES>
|
--max-stop-sequences <MAX_STOP_SEQUENCES>
|
||||||
This is the maximum allowed value for clients to set `stop_sequences`. Stop sequences are used to allow the model to stop on more than just the EOS token, and enable more complex "prompting" where users can preprompt the model in a specific way and define their "own" stop token aligned with their prompt
|
This is the maximum allowed value for clients to set `stop_sequences`. Stop sequences are used to allow the model to stop on more than just the EOS token, and enable more complex "prompting" where users can preprompt the model in a specific way and define their "own" stop token aligned with their prompt
|
||||||
|
|
||||||
[env: MAX_STOP_SEQUENCES=]
|
[env: MAX_STOP_SEQUENCES=]
|
||||||
[default: 4]
|
[default: 4]
|
||||||
|
|
||||||
|
```
|
||||||
|
## MAX_TOP_N_TOKENS
|
||||||
|
```shell
|
||||||
--max-top-n-tokens <MAX_TOP_N_TOKENS>
|
--max-top-n-tokens <MAX_TOP_N_TOKENS>
|
||||||
This is the maximum allowed value for clients to set `top_n_tokens`. `top_n_tokens is used to return information about the the `n` most likely tokens at each generation step, instead of just the sampled token. This information can be used for downstream tasks like for classification or ranking
|
This is the maximum allowed value for clients to set `top_n_tokens`. `top_n_tokens is used to return information about the the `n` most likely tokens at each generation step, instead of just the sampled token. This information can be used for downstream tasks like for classification or ranking
|
||||||
|
|
||||||
[env: MAX_TOP_N_TOKENS=]
|
[env: MAX_TOP_N_TOKENS=]
|
||||||
[default: 5]
|
[default: 5]
|
||||||
|
|
||||||
|
```
|
||||||
|
## MAX_INPUT_LENGTH
|
||||||
|
```shell
|
||||||
--max-input-length <MAX_INPUT_LENGTH>
|
--max-input-length <MAX_INPUT_LENGTH>
|
||||||
This is the maximum allowed input length (expressed in number of tokens) for users. The larger this value, the longer prompt users can send which can impact the overall memory required to handle the load. Please note that some models have a finite range of sequence they can handle
|
This is the maximum allowed input length (expressed in number of tokens) for users. The larger this value, the longer prompt users can send which can impact the overall memory required to handle the load. Please note that some models have a finite range of sequence they can handle
|
||||||
|
|
||||||
[env: MAX_INPUT_LENGTH=]
|
[env: MAX_INPUT_LENGTH=]
|
||||||
[default: 1024]
|
[default: 1024]
|
||||||
|
|
||||||
|
```
|
||||||
|
## MAX_TOTAL_TOKENS
|
||||||
|
```shell
|
||||||
--max-total-tokens <MAX_TOTAL_TOKENS>
|
--max-total-tokens <MAX_TOTAL_TOKENS>
|
||||||
This is the most important value to set as it defines the "memory budget" of running clients requests. Clients will send input sequences and ask to generate `max_new_tokens` on top. with a value of `1512` users can send either a prompt of `1000` and ask for `512` new tokens, or send a prompt of `1` and ask for `1511` max_new_tokens. The larger this value, the larger amount each request will be in your RAM and the less effective batching can be
|
This is the most important value to set as it defines the "memory budget" of running clients requests. Clients will send input sequences and ask to generate `max_new_tokens` on top. with a value of `1512` users can send either a prompt of `1000` and ask for `512` new tokens, or send a prompt of `1` and ask for `1511` max_new_tokens. The larger this value, the larger amount each request will be in your RAM and the less effective batching can be
|
||||||
|
|
||||||
[env: MAX_TOTAL_TOKENS=]
|
[env: MAX_TOTAL_TOKENS=]
|
||||||
[default: 2048]
|
[default: 2048]
|
||||||
|
|
||||||
|
```
|
||||||
|
## WAITING_SERVED_RATIO
|
||||||
|
```shell
|
||||||
--waiting-served-ratio <WAITING_SERVED_RATIO>
|
--waiting-served-ratio <WAITING_SERVED_RATIO>
|
||||||
This represents the ratio of waiting queries vs running queries where you want to start considering pausing the running queries to include the waiting ones into the same batch. `waiting_served_ratio=1.2` Means when 12 queries are waiting and there's only 10 queries left in the current batch we check if we can fit those 12 waiting queries into the batching strategy, and if yes, then batching happens delaying the 10 running queries by a `prefill` run.
|
This represents the ratio of waiting queries vs running queries where you want to start considering pausing the running queries to include the waiting ones into the same batch. `waiting_served_ratio=1.2` Means when 12 queries are waiting and there's only 10 queries left in the current batch we check if we can fit those 12 waiting queries into the batching strategy, and if yes, then batching happens delaying the 10 running queries by a `prefill` run.
|
||||||
|
|
||||||
@ -101,12 +149,18 @@ Options:
|
|||||||
[env: WAITING_SERVED_RATIO=]
|
[env: WAITING_SERVED_RATIO=]
|
||||||
[default: 1.2]
|
[default: 1.2]
|
||||||
|
|
||||||
|
```
|
||||||
|
## MAX_BATCH_PREFILL_TOKENS
|
||||||
|
```shell
|
||||||
--max-batch-prefill-tokens <MAX_BATCH_PREFILL_TOKENS>
|
--max-batch-prefill-tokens <MAX_BATCH_PREFILL_TOKENS>
|
||||||
Limits the number of tokens for the prefill operation. Since this operation take the most memory and is compute bound, it is interesting to limit the number of requests that can be sent
|
Limits the number of tokens for the prefill operation. Since this operation take the most memory and is compute bound, it is interesting to limit the number of requests that can be sent
|
||||||
|
|
||||||
[env: MAX_BATCH_PREFILL_TOKENS=]
|
[env: MAX_BATCH_PREFILL_TOKENS=]
|
||||||
[default: 4096]
|
[default: 4096]
|
||||||
|
|
||||||
|
```
|
||||||
|
## MAX_BATCH_TOTAL_TOKENS
|
||||||
|
```shell
|
||||||
--max-batch-total-tokens <MAX_BATCH_TOTAL_TOKENS>
|
--max-batch-total-tokens <MAX_BATCH_TOTAL_TOKENS>
|
||||||
**IMPORTANT** This is one critical control to allow maximum usage of the available hardware.
|
**IMPORTANT** This is one critical control to allow maximum usage of the available hardware.
|
||||||
|
|
||||||
@ -120,6 +174,9 @@ Options:
|
|||||||
|
|
||||||
[env: MAX_BATCH_TOTAL_TOKENS=]
|
[env: MAX_BATCH_TOTAL_TOKENS=]
|
||||||
|
|
||||||
|
```
|
||||||
|
## MAX_WAITING_TOKENS
|
||||||
|
```shell
|
||||||
--max-waiting-tokens <MAX_WAITING_TOKENS>
|
--max-waiting-tokens <MAX_WAITING_TOKENS>
|
||||||
This setting defines how many tokens can be passed before forcing the waiting queries to be put on the batch (if the size of the batch allows for it). New queries require 1 `prefill` forward, which is different from `decode` and therefore you need to pause the running batch in order to run `prefill` to create the correct values for the waiting queries to be able to join the batch.
|
This setting defines how many tokens can be passed before forcing the waiting queries to be put on the batch (if the size of the batch allows for it). New queries require 1 `prefill` forward, which is different from `decode` and therefore you need to pause the running batch in order to run `prefill` to create the correct values for the waiting queries to be able to join the batch.
|
||||||
|
|
||||||
@ -132,57 +189,87 @@ Options:
|
|||||||
[env: MAX_WAITING_TOKENS=]
|
[env: MAX_WAITING_TOKENS=]
|
||||||
[default: 20]
|
[default: 20]
|
||||||
|
|
||||||
|
```
|
||||||
|
## HOSTNAME
|
||||||
|
```shell
|
||||||
--hostname <HOSTNAME>
|
--hostname <HOSTNAME>
|
||||||
The IP address to listen on
|
The IP address to listen on
|
||||||
|
|
||||||
[env: HOSTNAME=]
|
[env: HOSTNAME=]
|
||||||
[default: 0.0.0.0]
|
[default: 0.0.0.0]
|
||||||
|
|
||||||
|
```
|
||||||
|
## PORT
|
||||||
|
```shell
|
||||||
-p, --port <PORT>
|
-p, --port <PORT>
|
||||||
The port to listen on
|
The port to listen on
|
||||||
|
|
||||||
[env: PORT=]
|
[env: PORT=]
|
||||||
[default: 3000]
|
[default: 3000]
|
||||||
|
|
||||||
|
```
|
||||||
|
## SHARD_UDS_PATH
|
||||||
|
```shell
|
||||||
--shard-uds-path <SHARD_UDS_PATH>
|
--shard-uds-path <SHARD_UDS_PATH>
|
||||||
The name of the socket for gRPC communication between the webserver and the shards
|
The name of the socket for gRPC communication between the webserver and the shards
|
||||||
|
|
||||||
[env: SHARD_UDS_PATH=]
|
[env: SHARD_UDS_PATH=]
|
||||||
[default: /tmp/text-generation-server]
|
[default: /tmp/text-generation-server]
|
||||||
|
|
||||||
|
```
|
||||||
|
## MASTER_ADDR
|
||||||
|
```shell
|
||||||
--master-addr <MASTER_ADDR>
|
--master-addr <MASTER_ADDR>
|
||||||
The address the master shard will listen on. (setting used by torch distributed)
|
The address the master shard will listen on. (setting used by torch distributed)
|
||||||
|
|
||||||
[env: MASTER_ADDR=]
|
[env: MASTER_ADDR=]
|
||||||
[default: localhost]
|
[default: localhost]
|
||||||
|
|
||||||
|
```
|
||||||
|
## MASTER_PORT
|
||||||
|
```shell
|
||||||
--master-port <MASTER_PORT>
|
--master-port <MASTER_PORT>
|
||||||
The address the master port will listen on. (setting used by torch distributed)
|
The address the master port will listen on. (setting used by torch distributed)
|
||||||
|
|
||||||
[env: MASTER_PORT=]
|
[env: MASTER_PORT=]
|
||||||
[default: 29500]
|
[default: 29500]
|
||||||
|
|
||||||
|
```
|
||||||
|
## HUGGINGFACE_HUB_CACHE
|
||||||
|
```shell
|
||||||
--huggingface-hub-cache <HUGGINGFACE_HUB_CACHE>
|
--huggingface-hub-cache <HUGGINGFACE_HUB_CACHE>
|
||||||
The location of the huggingface hub cache. Used to override the location if you want to provide a mounted disk for instance
|
The location of the huggingface hub cache. Used to override the location if you want to provide a mounted disk for instance
|
||||||
|
|
||||||
[env: HUGGINGFACE_HUB_CACHE=]
|
[env: HUGGINGFACE_HUB_CACHE=]
|
||||||
|
|
||||||
|
```
|
||||||
|
## WEIGHTS_CACHE_OVERRIDE
|
||||||
|
```shell
|
||||||
--weights-cache-override <WEIGHTS_CACHE_OVERRIDE>
|
--weights-cache-override <WEIGHTS_CACHE_OVERRIDE>
|
||||||
The location of the huggingface hub cache. Used to override the location if you want to provide a mounted disk for instance
|
The location of the huggingface hub cache. Used to override the location if you want to provide a mounted disk for instance
|
||||||
|
|
||||||
[env: WEIGHTS_CACHE_OVERRIDE=]
|
[env: WEIGHTS_CACHE_OVERRIDE=]
|
||||||
|
|
||||||
|
```
|
||||||
|
## DISABLE_CUSTOM_KERNELS
|
||||||
|
```shell
|
||||||
--disable-custom-kernels
|
--disable-custom-kernels
|
||||||
For some models (like bloom), text-generation-inference implemented custom cuda kernels to speed up inference. Those kernels were only tested on A100. Use this flag to disable them if you're running on different hardware and encounter issues
|
For some models (like bloom), text-generation-inference implemented custom cuda kernels to speed up inference. Those kernels were only tested on A100. Use this flag to disable them if you're running on different hardware and encounter issues
|
||||||
|
|
||||||
[env: DISABLE_CUSTOM_KERNELS=]
|
[env: DISABLE_CUSTOM_KERNELS=]
|
||||||
|
|
||||||
|
```
|
||||||
|
## CUDA_MEMORY_FRACTION
|
||||||
|
```shell
|
||||||
--cuda-memory-fraction <CUDA_MEMORY_FRACTION>
|
--cuda-memory-fraction <CUDA_MEMORY_FRACTION>
|
||||||
Limit the CUDA available memory. The allowed value equals the total visible memory multiplied by cuda-memory-fraction
|
Limit the CUDA available memory. The allowed value equals the total visible memory multiplied by cuda-memory-fraction
|
||||||
|
|
||||||
[env: CUDA_MEMORY_FRACTION=]
|
[env: CUDA_MEMORY_FRACTION=]
|
||||||
[default: 1.0]
|
[default: 1.0]
|
||||||
|
|
||||||
|
```
|
||||||
|
## ROPE_SCALING
|
||||||
|
```shell
|
||||||
--rope-scaling <ROPE_SCALING>
|
--rope-scaling <ROPE_SCALING>
|
||||||
Rope scaling will only be used for RoPE models and allow rescaling the position rotary to accomodate for larger prompts.
|
Rope scaling will only be used for RoPE models and allow rescaling the position rotary to accomodate for larger prompts.
|
||||||
|
|
||||||
@ -195,49 +282,85 @@ Options:
|
|||||||
[env: ROPE_SCALING=]
|
[env: ROPE_SCALING=]
|
||||||
[possible values: linear, dynamic]
|
[possible values: linear, dynamic]
|
||||||
|
|
||||||
|
```
|
||||||
|
## ROPE_FACTOR
|
||||||
|
```shell
|
||||||
--rope-factor <ROPE_FACTOR>
|
--rope-factor <ROPE_FACTOR>
|
||||||
Rope scaling will only be used for RoPE models See `rope_scaling`
|
Rope scaling will only be used for RoPE models See `rope_scaling`
|
||||||
|
|
||||||
[env: ROPE_FACTOR=]
|
[env: ROPE_FACTOR=]
|
||||||
|
|
||||||
|
```
|
||||||
|
## JSON_OUTPUT
|
||||||
|
```shell
|
||||||
--json-output
|
--json-output
|
||||||
Outputs the logs in JSON format (useful for telemetry)
|
Outputs the logs in JSON format (useful for telemetry)
|
||||||
|
|
||||||
[env: JSON_OUTPUT=]
|
[env: JSON_OUTPUT=]
|
||||||
|
|
||||||
|
```
|
||||||
|
## OTLP_ENDPOINT
|
||||||
|
```shell
|
||||||
--otlp-endpoint <OTLP_ENDPOINT>
|
--otlp-endpoint <OTLP_ENDPOINT>
|
||||||
[env: OTLP_ENDPOINT=]
|
[env: OTLP_ENDPOINT=]
|
||||||
|
|
||||||
|
```
|
||||||
|
## CORS_ALLOW_ORIGIN
|
||||||
|
```shell
|
||||||
--cors-allow-origin <CORS_ALLOW_ORIGIN>
|
--cors-allow-origin <CORS_ALLOW_ORIGIN>
|
||||||
[env: CORS_ALLOW_ORIGIN=]
|
[env: CORS_ALLOW_ORIGIN=]
|
||||||
|
|
||||||
|
```
|
||||||
|
## WATERMARK_GAMMA
|
||||||
|
```shell
|
||||||
--watermark-gamma <WATERMARK_GAMMA>
|
--watermark-gamma <WATERMARK_GAMMA>
|
||||||
[env: WATERMARK_GAMMA=]
|
[env: WATERMARK_GAMMA=]
|
||||||
|
|
||||||
|
```
|
||||||
|
## WATERMARK_DELTA
|
||||||
|
```shell
|
||||||
--watermark-delta <WATERMARK_DELTA>
|
--watermark-delta <WATERMARK_DELTA>
|
||||||
[env: WATERMARK_DELTA=]
|
[env: WATERMARK_DELTA=]
|
||||||
|
|
||||||
|
```
|
||||||
|
## NGROK
|
||||||
|
```shell
|
||||||
--ngrok
|
--ngrok
|
||||||
Enable ngrok tunneling
|
Enable ngrok tunneling
|
||||||
|
|
||||||
[env: NGROK=]
|
[env: NGROK=]
|
||||||
|
|
||||||
|
```
|
||||||
|
## NGROK_AUTHTOKEN
|
||||||
|
```shell
|
||||||
--ngrok-authtoken <NGROK_AUTHTOKEN>
|
--ngrok-authtoken <NGROK_AUTHTOKEN>
|
||||||
ngrok authentication token
|
ngrok authentication token
|
||||||
|
|
||||||
[env: NGROK_AUTHTOKEN=]
|
[env: NGROK_AUTHTOKEN=]
|
||||||
|
|
||||||
|
```
|
||||||
|
## NGROK_EDGE
|
||||||
|
```shell
|
||||||
--ngrok-edge <NGROK_EDGE>
|
--ngrok-edge <NGROK_EDGE>
|
||||||
ngrok edge
|
ngrok edge
|
||||||
|
|
||||||
[env: NGROK_EDGE=]
|
[env: NGROK_EDGE=]
|
||||||
|
|
||||||
|
```
|
||||||
|
## ENV
|
||||||
|
```shell
|
||||||
-e, --env
|
-e, --env
|
||||||
Display a lot of information about your runtime environment
|
Display a lot of information about your runtime environment
|
||||||
|
|
||||||
|
```
|
||||||
|
## HELP
|
||||||
|
```shell
|
||||||
-h, --help
|
-h, --help
|
||||||
Print help (see a summary with '-h')
|
Print help (see a summary with '-h')
|
||||||
|
|
||||||
|
```
|
||||||
|
## VERSION
|
||||||
|
```shell
|
||||||
-V, --version
|
-V, --version
|
||||||
Print version
|
Print version
|
||||||
|
|
||||||
|
|||||||
@ -4,7 +4,7 @@ Text Generation Inference improves the model in several aspects.
|
|||||||
|
|
||||||
## Quantization
|
## Quantization
|
||||||
|
|
||||||
TGI supports [bits-and-bytes](https://github.com/TimDettmers/bitsandbytes#bitsandbytes), [GPT-Q](https://arxiv.org/abs/2210.17323) and [AWQ](https://arxiv.org/abs/2306.00978) quantization. To speed up inference with quantization, simply set `quantize` flag to `bitsandbytes`, `gptq` or `awq` depending on the quantization technique you wish to use. When using GPT-Q quantization, you need to point to one of the models [here](https://huggingface.co/models?search=gptq) when using AWQ quantization, you need to point to one of the models [here](https://huggingface.co/models?search=awq). To get more information about quantization, please refer to (./conceptual/quantization.md)
|
TGI supports [bits-and-bytes](https://github.com/TimDettmers/bitsandbytes#bitsandbytes), [GPT-Q](https://arxiv.org/abs/2210.17323) and [AWQ](https://arxiv.org/abs/2306.00978) quantization. To speed up inference with quantization, simply set `quantize` flag to `bitsandbytes`, `gptq` or `awq` depending on the quantization technique you wish to use. When using GPT-Q quantization, you need to point to one of the models [here](https://huggingface.co/models?search=gptq) when using AWQ quantization, you need to point to one of the models [here](https://huggingface.co/models?search=awq). To get more information about quantization, please refer to [quantization guide](./../conceptual/quantization)
|
||||||
|
|
||||||
|
|
||||||
## RoPE Scaling
|
## RoPE Scaling
|
||||||
|
|||||||
@ -45,7 +45,7 @@ bitsandbytes is a library used to apply 8-bit and 4-bit quantization to models.
|
|||||||
In TGI, you can use 8-bit quantization by adding `--quantize bitsandbytes` like below 👇
|
In TGI, you can use 8-bit quantization by adding `--quantize bitsandbytes` like below 👇
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize --bitsandbytes
|
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize bitsandbytes
|
||||||
```
|
```
|
||||||
|
|
||||||
4-bit quantization is also possible with bitsandbytes. You can choose one of the following 4-bit data types: 4-bit float (`fp4`), or 4-bit `NormalFloat` (`nf4`). These data types were introduced in the context of parameter-efficient fine-tuning, but you can apply them for inference by automatically converting the model weights on load.
|
4-bit quantization is also possible with bitsandbytes. You can choose one of the following 4-bit data types: 4-bit float (`fp4`), or 4-bit `NormalFloat` (`nf4`). These data types were introduced in the context of parameter-efficient fine-tuning, but you can apply them for inference by automatically converting the model weights on load.
|
||||||
@ -53,7 +53,7 @@ docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingf
|
|||||||
In TGI, you can use 4-bit quantization by adding `--quantize bitsandbytes-nf4` or `--quantize bitsandbytes-fp4` like below 👇
|
In TGI, you can use 4-bit quantization by adding `--quantize bitsandbytes-nf4` or `--quantize bitsandbytes-fp4` like below 👇
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize --bitsandbytes-nf4
|
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --quantize bitsandbytes-nf4
|
||||||
```
|
```
|
||||||
|
|
||||||
You can get more information about 8-bit quantization by reading this [blog post](https://huggingface.co/blog/hf-bitsandbytes-integration), and 4-bit quantization by reading [this blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes).
|
You can get more information about 8-bit quantization by reading this [blog post](https://huggingface.co/blog/hf-bitsandbytes-integration), and 4-bit quantization by reading [this blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes).
|
||||||
|
|||||||
@ -18,7 +18,8 @@ Text Generation Inference implements many optimizations and features, such as:
|
|||||||
- Logits warper (temperature scaling, top-p, top-k, repetition penalty)
|
- Logits warper (temperature scaling, top-p, top-k, repetition penalty)
|
||||||
- Stop sequences
|
- Stop sequences
|
||||||
- Log probabilities
|
- Log probabilities
|
||||||
|
- Custom Prompt Generation: Easily generate text by providing custom prompts to guide the model's output.
|
||||||
|
- Fine-tuning Support: Utilize fine-tuned models for specific tasks to achieve higher accuracy and performance.
|
||||||
|
|
||||||
Text Generation Inference is used in production by multiple projects, such as:
|
Text Generation Inference is used in production by multiple projects, such as:
|
||||||
|
|
||||||
|
|||||||
@ -8,7 +8,7 @@ Let's say you want to deploy [Falcon-7B Instruct](https://huggingface.co/tiiuae/
|
|||||||
model=tiiuae/falcon-7b-instruct
|
model=tiiuae/falcon-7b-instruct
|
||||||
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
|
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
|
||||||
|
|
||||||
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.1.0 --model-id $model
|
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.1.1 --model-id $model
|
||||||
```
|
```
|
||||||
|
|
||||||
<Tip warning={true}>
|
<Tip warning={true}>
|
||||||
@ -85,7 +85,7 @@ curl 127.0.0.1:8080/generate \
|
|||||||
To see all possible deploy flags and options, you can use the `--help` flag. It's possible to configure the number of shards, quantization, generation parameters, and more.
|
To see all possible deploy flags and options, you can use the `--help` flag. It's possible to configure the number of shards, quantization, generation parameters, and more.
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
docker run ghcr.io/huggingface/text-generation-inference:1.1.0 --help
|
docker run ghcr.io/huggingface/text-generation-inference:1.1.1 --help
|
||||||
```
|
```
|
||||||
|
|
||||||
</Tip>
|
</Tip>
|
||||||
|
|||||||
@ -45,4 +45,3 @@ TGI is also supported on the following AI hardware accelerators:
|
|||||||
- *Habana first-gen Gaudi and Gaudi2:* check out this [example](https://github.com/huggingface/optimum-habana/tree/main/text-generation-inference) how to serve models with TGI on Gaudi and Gaudi2 with [Optimum Habana](https://huggingface.co/docs/optimum/habana/index)
|
- *Habana first-gen Gaudi and Gaudi2:* check out this [example](https://github.com/huggingface/optimum-habana/tree/main/text-generation-inference) how to serve models with TGI on Gaudi and Gaudi2 with [Optimum Habana](https://huggingface.co/docs/optimum/habana/index)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
@ -1,6 +1,6 @@
|
|||||||
[tool.poetry]
|
[tool.poetry]
|
||||||
name = "text-generation-integration-tests"
|
name = "text-generation-integration-tests"
|
||||||
version = "1.1.0"
|
version = "1.1.1"
|
||||||
description = "Text Generation Inference integration tests"
|
description = "Text Generation Inference integration tests"
|
||||||
authors = ["Nicolas Patry <nicolas@huggingface.co>"]
|
authors = ["Nicolas Patry <nicolas@huggingface.co>"]
|
||||||
|
|
||||||
|
|||||||
@ -20,7 +20,6 @@ axum = { version = "0.6.20", features = ["json"] }
|
|||||||
axum-tracing-opentelemetry = "0.14.1"
|
axum-tracing-opentelemetry = "0.14.1"
|
||||||
text-generation-client = { path = "client" }
|
text-generation-client = { path = "client" }
|
||||||
clap = { version = "4.4.5", features = ["derive", "env"] }
|
clap = { version = "4.4.5", features = ["derive", "env"] }
|
||||||
flume = "0.11.0"
|
|
||||||
futures = "0.3.28"
|
futures = "0.3.28"
|
||||||
metrics = "0.21.1"
|
metrics = "0.21.1"
|
||||||
metrics-exporter-prometheus = { version = "0.12.1", features = [] }
|
metrics-exporter-prometheus = { version = "0.12.1", features = [] }
|
||||||
@ -34,6 +33,7 @@ serde_json = "1.0.107"
|
|||||||
thiserror = "1.0.48"
|
thiserror = "1.0.48"
|
||||||
tokenizers = { version = "0.14.0", features = ["http"] }
|
tokenizers = { version = "0.14.0", features = ["http"] }
|
||||||
tokio = { version = "1.32.0", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync"] }
|
tokio = { version = "1.32.0", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync"] }
|
||||||
|
tokio-stream = "0.1.14"
|
||||||
tower-http = { version = "0.4.4", features = ["cors"] }
|
tower-http = { version = "0.4.4", features = ["cors"] }
|
||||||
tracing = "0.1.37"
|
tracing = "0.1.37"
|
||||||
tracing-opentelemetry = "0.21.0"
|
tracing-opentelemetry = "0.21.0"
|
||||||
|
|||||||
@ -103,17 +103,18 @@ impl Client {
|
|||||||
&mut self,
|
&mut self,
|
||||||
max_input_length: u32,
|
max_input_length: u32,
|
||||||
max_prefill_tokens: u32,
|
max_prefill_tokens: u32,
|
||||||
|
max_total_tokens: u32,
|
||||||
) -> Result<Option<u32>> {
|
) -> Result<Option<u32>> {
|
||||||
let mut n_tokens = 0;
|
let mut n_tokens = 0;
|
||||||
let mut requests = Vec::new();
|
let mut requests = Vec::new();
|
||||||
|
|
||||||
// Create requests
|
// Create requests
|
||||||
while n_tokens < max_prefill_tokens {
|
while n_tokens < max_prefill_tokens {
|
||||||
|
let truncate = min(max_input_length, max_prefill_tokens - n_tokens);
|
||||||
requests.push(Request {
|
requests.push(Request {
|
||||||
id: 0,
|
id: 0,
|
||||||
// We truncate the input on the server side to be sure that it has the correct size
|
// We truncate the input on the server side to be sure that it has the correct size
|
||||||
inputs: "_test ".to_string().repeat(max_input_length as usize),
|
inputs: "_test ".to_string().repeat(max_input_length as usize),
|
||||||
truncate: min(max_input_length, max_prefill_tokens - n_tokens),
|
truncate,
|
||||||
// Set sampling parameters to also take these ops into account in the max memory
|
// Set sampling parameters to also take these ops into account in the max memory
|
||||||
parameters: Some(NextTokenChooserParameters {
|
parameters: Some(NextTokenChooserParameters {
|
||||||
temperature: 0.9,
|
temperature: 0.9,
|
||||||
@ -126,9 +127,9 @@ impl Client {
|
|||||||
watermark: true,
|
watermark: true,
|
||||||
}),
|
}),
|
||||||
stopping_parameters: Some(StoppingCriteriaParameters {
|
stopping_parameters: Some(StoppingCriteriaParameters {
|
||||||
max_new_tokens: 2,
|
max_new_tokens: max_total_tokens - truncate,
|
||||||
stop_sequences: vec![],
|
stop_sequences: vec![],
|
||||||
ignore_eos_token: false,
|
ignore_eos_token: true,
|
||||||
}),
|
}),
|
||||||
prefill_logprobs: true,
|
prefill_logprobs: true,
|
||||||
top_n_tokens: 20,
|
top_n_tokens: 20,
|
||||||
|
|||||||
@ -95,11 +95,14 @@ impl ShardedClient {
|
|||||||
&mut self,
|
&mut self,
|
||||||
max_input_length: u32,
|
max_input_length: u32,
|
||||||
max_prefill_tokens: u32,
|
max_prefill_tokens: u32,
|
||||||
|
max_total_tokens: u32,
|
||||||
) -> Result<Option<u32>> {
|
) -> Result<Option<u32>> {
|
||||||
let futures: Vec<_> = self
|
let futures: Vec<_> = self
|
||||||
.clients
|
.clients
|
||||||
.iter_mut()
|
.iter_mut()
|
||||||
.map(|client| Box::pin(client.warmup(max_input_length, max_prefill_tokens)))
|
.map(|client| {
|
||||||
|
Box::pin(client.warmup(max_input_length, max_prefill_tokens, max_total_tokens))
|
||||||
|
})
|
||||||
.collect();
|
.collect();
|
||||||
// Take the minimum value
|
// Take the minimum value
|
||||||
let results = join_all(futures)
|
let results = join_all(futures)
|
||||||
|
|||||||
@ -2,22 +2,21 @@
|
|||||||
use crate::validation::{Validation, ValidationError};
|
use crate::validation::{Validation, ValidationError};
|
||||||
use crate::{Entry, Queue, Token};
|
use crate::{Entry, Queue, Token};
|
||||||
use crate::{GenerateRequest, PrefillToken};
|
use crate::{GenerateRequest, PrefillToken};
|
||||||
use flume::r#async::RecvStream;
|
|
||||||
use flume::SendTimeoutError;
|
|
||||||
use futures::future::try_join_all;
|
use futures::future::try_join_all;
|
||||||
use futures::stream::StreamExt;
|
|
||||||
use nohash_hasher::IntMap;
|
use nohash_hasher::IntMap;
|
||||||
use std::sync::{
|
use std::sync::{
|
||||||
atomic::{AtomicBool, Ordering},
|
atomic::{AtomicBool, Ordering},
|
||||||
Arc,
|
Arc,
|
||||||
};
|
};
|
||||||
use std::time::Duration;
|
|
||||||
use text_generation_client::{
|
use text_generation_client::{
|
||||||
Batch, CachedBatch, ClientError, GeneratedText, Generation, PrefillTokens, ShardedClient,
|
Batch, CachedBatch, ClientError, GeneratedText, Generation, PrefillTokens, ShardedClient,
|
||||||
};
|
};
|
||||||
use thiserror::Error;
|
use thiserror::Error;
|
||||||
use tokio::sync::{Notify, OwnedSemaphorePermit, Semaphore, TryAcquireError};
|
use tokio::sync::mpsc::error::SendError;
|
||||||
|
use tokio::sync::{mpsc, Notify, OwnedSemaphorePermit, Semaphore, TryAcquireError};
|
||||||
use tokio::time::Instant;
|
use tokio::time::Instant;
|
||||||
|
use tokio_stream::wrappers::UnboundedReceiverStream;
|
||||||
|
use tokio_stream::StreamExt;
|
||||||
use tracing::{info_span, instrument, Instrument, Span};
|
use tracing::{info_span, instrument, Instrument, Span};
|
||||||
|
|
||||||
/// Inference struct
|
/// Inference struct
|
||||||
@ -90,7 +89,7 @@ impl Infer {
|
|||||||
) -> Result<
|
) -> Result<
|
||||||
(
|
(
|
||||||
OwnedSemaphorePermit,
|
OwnedSemaphorePermit,
|
||||||
RecvStream<Result<InferStreamResponse, InferError>>,
|
UnboundedReceiverStream<Result<InferStreamResponse, InferError>>,
|
||||||
),
|
),
|
||||||
InferError,
|
InferError,
|
||||||
> {
|
> {
|
||||||
@ -113,7 +112,7 @@ impl Infer {
|
|||||||
})?;
|
})?;
|
||||||
|
|
||||||
// MPSC channel to communicate with the background batching task
|
// MPSC channel to communicate with the background batching task
|
||||||
let (response_tx, response_rx) = flume::unbounded();
|
let (response_tx, response_rx) = mpsc::unbounded_channel();
|
||||||
|
|
||||||
// Append the request to the queue
|
// Append the request to the queue
|
||||||
self.queue.append(Entry {
|
self.queue.append(Entry {
|
||||||
@ -130,7 +129,7 @@ impl Infer {
|
|||||||
self.shared.batching_task.notify_one();
|
self.shared.batching_task.notify_one();
|
||||||
|
|
||||||
// Return stream
|
// Return stream
|
||||||
Ok((permit, response_rx.into_stream()))
|
Ok((permit, UnboundedReceiverStream::new(response_rx)))
|
||||||
}
|
}
|
||||||
|
|
||||||
/// Add a new request to the queue and return a InferResponse
|
/// Add a new request to the queue and return a InferResponse
|
||||||
@ -493,10 +492,7 @@ fn filter_send_generations(generations: Vec<Generation>, entries: &mut IntMap<u6
|
|||||||
// If the receive an error from the Flume channel, it means that the client dropped the
|
// If the receive an error from the Flume channel, it means that the client dropped the
|
||||||
// request and we need to stop generating hence why we unwrap_or(true)
|
// request and we need to stop generating hence why we unwrap_or(true)
|
||||||
let stopped = send_responses(generation, entry).map_err(|err| {
|
let stopped = send_responses(generation, entry).map_err(|err| {
|
||||||
if let SendTimeoutError::Timeout(_) = *err {
|
tracing::error!("Entry response channel error.");
|
||||||
tracing::error!("Entry response channel timed out.")
|
|
||||||
}
|
|
||||||
|
|
||||||
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
|
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
|
||||||
err
|
err
|
||||||
}).unwrap_or(true);
|
}).unwrap_or(true);
|
||||||
@ -510,9 +506,10 @@ fn filter_send_generations(generations: Vec<Generation>, entries: &mut IntMap<u6
|
|||||||
fn send_responses(
|
fn send_responses(
|
||||||
generation: Generation,
|
generation: Generation,
|
||||||
entry: &Entry,
|
entry: &Entry,
|
||||||
) -> Result<bool, Box<SendTimeoutError<Result<InferStreamResponse, InferError>>>> {
|
) -> Result<bool, Box<SendError<Result<InferStreamResponse, InferError>>>> {
|
||||||
// Return directly if the channel is disconnected
|
// Return directly if the channel is disconnected
|
||||||
if entry.response_tx.is_disconnected() {
|
if entry.response_tx.is_closed() {
|
||||||
|
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
|
||||||
return Ok(true);
|
return Ok(true);
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -520,10 +517,9 @@ fn send_responses(
|
|||||||
|
|
||||||
if let Some(prefill_tokens) = generation.prefill_tokens {
|
if let Some(prefill_tokens) = generation.prefill_tokens {
|
||||||
// Send message
|
// Send message
|
||||||
entry.response_tx.send_timeout(
|
entry
|
||||||
Ok(InferStreamResponse::Prefill(prefill_tokens)),
|
.response_tx
|
||||||
Duration::from_millis(10),
|
.send(Ok(InferStreamResponse::Prefill(prefill_tokens)))?;
|
||||||
)?;
|
|
||||||
}
|
}
|
||||||
|
|
||||||
// Create last Token
|
// Create last Token
|
||||||
@ -558,22 +554,18 @@ fn send_responses(
|
|||||||
// Generation has ended
|
// Generation has ended
|
||||||
stopped = true;
|
stopped = true;
|
||||||
// Send message
|
// Send message
|
||||||
entry.response_tx.send_timeout(
|
entry.response_tx.send(Ok(InferStreamResponse::End {
|
||||||
Ok(InferStreamResponse::End {
|
|
||||||
token,
|
token,
|
||||||
top_tokens,
|
top_tokens,
|
||||||
generated_text,
|
generated_text,
|
||||||
queued: entry.queue_time,
|
queued: entry.queue_time,
|
||||||
start: entry.batch_time.unwrap(),
|
start: entry.batch_time.unwrap(),
|
||||||
}),
|
}))?;
|
||||||
Duration::from_millis(10),
|
|
||||||
)?;
|
|
||||||
} else {
|
} else {
|
||||||
// Send message
|
// Send message
|
||||||
entry.response_tx.send_timeout(
|
entry
|
||||||
Ok(InferStreamResponse::Intermediate { token, top_tokens }),
|
.response_tx
|
||||||
Duration::from_millis(10),
|
.send(Ok(InferStreamResponse::Intermediate { token, top_tokens }))?;
|
||||||
)?;
|
|
||||||
}
|
}
|
||||||
Ok(stopped)
|
Ok(stopped)
|
||||||
}
|
}
|
||||||
@ -591,7 +583,7 @@ fn send_errors(error: ClientError, entries: &mut IntMap<u64, Entry>) {
|
|||||||
// unwrap_or is valid here as we don't care if the receiver is gone.
|
// unwrap_or is valid here as we don't care if the receiver is gone.
|
||||||
entry
|
entry
|
||||||
.response_tx
|
.response_tx
|
||||||
.send_timeout(Err(err), Duration::from_millis(10))
|
.send(Err(err))
|
||||||
.unwrap_or(());
|
.unwrap_or(());
|
||||||
});
|
});
|
||||||
}
|
}
|
||||||
|
|||||||
@ -107,8 +107,8 @@ pub(crate) struct GenerateParameters {
|
|||||||
#[schema(default = "false", example = true)]
|
#[schema(default = "false", example = true)]
|
||||||
pub do_sample: bool,
|
pub do_sample: bool,
|
||||||
#[serde(default = "default_max_new_tokens")]
|
#[serde(default = "default_max_new_tokens")]
|
||||||
#[schema(exclusive_minimum = 0, exclusive_maximum = 512, default = "20")]
|
#[schema(nullable = true, default = "null", example = "20")]
|
||||||
pub max_new_tokens: u32,
|
pub max_new_tokens: Option<u32>,
|
||||||
#[serde(default)]
|
#[serde(default)]
|
||||||
#[schema(nullable = true, default = "null", example = false)]
|
#[schema(nullable = true, default = "null", example = false)]
|
||||||
pub return_full_text: Option<bool>,
|
pub return_full_text: Option<bool>,
|
||||||
@ -140,8 +140,8 @@ pub(crate) struct GenerateParameters {
|
|||||||
pub top_n_tokens: Option<u32>,
|
pub top_n_tokens: Option<u32>,
|
||||||
}
|
}
|
||||||
|
|
||||||
fn default_max_new_tokens() -> u32 {
|
fn default_max_new_tokens() -> Option<u32> {
|
||||||
20
|
None
|
||||||
}
|
}
|
||||||
|
|
||||||
fn default_parameters() -> GenerateParameters {
|
fn default_parameters() -> GenerateParameters {
|
||||||
|
|||||||
@ -212,7 +212,7 @@ fn main() -> Result<(), RouterError> {
|
|||||||
// Warmup model
|
// Warmup model
|
||||||
tracing::info!("Warming up model");
|
tracing::info!("Warming up model");
|
||||||
let max_supported_batch_total_tokens = match sharded_client
|
let max_supported_batch_total_tokens = match sharded_client
|
||||||
.warmup(max_input_length as u32, max_batch_prefill_tokens)
|
.warmup(max_input_length as u32, max_batch_prefill_tokens, max_total_tokens as u32)
|
||||||
.await
|
.await
|
||||||
.map_err(RouterError::Warmup)?
|
.map_err(RouterError::Warmup)?
|
||||||
{
|
{
|
||||||
|
|||||||
@ -5,7 +5,7 @@ use nohash_hasher::{BuildNoHashHasher, IntMap};
|
|||||||
use std::cmp::min;
|
use std::cmp::min;
|
||||||
use std::collections::VecDeque;
|
use std::collections::VecDeque;
|
||||||
use text_generation_client::{Batch, Request};
|
use text_generation_client::{Batch, Request};
|
||||||
use tokio::sync::oneshot;
|
use tokio::sync::{mpsc, oneshot};
|
||||||
use tokio::time::Instant;
|
use tokio::time::Instant;
|
||||||
use tracing::{info_span, instrument, Span};
|
use tracing::{info_span, instrument, Span};
|
||||||
|
|
||||||
@ -15,7 +15,7 @@ pub(crate) struct Entry {
|
|||||||
/// Request
|
/// Request
|
||||||
pub request: ValidGenerateRequest,
|
pub request: ValidGenerateRequest,
|
||||||
/// Response sender to communicate between the Infer struct and the batching_task
|
/// Response sender to communicate between the Infer struct and the batching_task
|
||||||
pub response_tx: flume::Sender<Result<InferStreamResponse, InferError>>,
|
pub response_tx: mpsc::UnboundedSender<Result<InferStreamResponse, InferError>>,
|
||||||
/// Span that will live as long as entry
|
/// Span that will live as long as entry
|
||||||
pub span: Span,
|
pub span: Span,
|
||||||
/// Temporary span used as a guard when logging inference, wait times...
|
/// Temporary span used as a guard when logging inference, wait times...
|
||||||
@ -30,13 +30,13 @@ pub(crate) struct Entry {
|
|||||||
#[derive(Debug, Clone)]
|
#[derive(Debug, Clone)]
|
||||||
pub(crate) struct Queue {
|
pub(crate) struct Queue {
|
||||||
/// Channel to communicate with the background queue task
|
/// Channel to communicate with the background queue task
|
||||||
queue_sender: flume::Sender<QueueCommand>,
|
queue_sender: mpsc::UnboundedSender<QueueCommand>,
|
||||||
}
|
}
|
||||||
|
|
||||||
impl Queue {
|
impl Queue {
|
||||||
pub(crate) fn new(requires_padding: bool, block_size: u32, window_size: Option<u32>) -> Self {
|
pub(crate) fn new(requires_padding: bool, block_size: u32, window_size: Option<u32>) -> Self {
|
||||||
// Create channel
|
// Create channel
|
||||||
let (queue_sender, queue_receiver) = flume::unbounded();
|
let (queue_sender, queue_receiver) = mpsc::unbounded_channel();
|
||||||
|
|
||||||
// Launch background queue task
|
// Launch background queue task
|
||||||
tokio::spawn(queue_task(
|
tokio::spawn(queue_task(
|
||||||
@ -91,11 +91,11 @@ async fn queue_task(
|
|||||||
requires_padding: bool,
|
requires_padding: bool,
|
||||||
block_size: u32,
|
block_size: u32,
|
||||||
window_size: Option<u32>,
|
window_size: Option<u32>,
|
||||||
receiver: flume::Receiver<QueueCommand>,
|
mut receiver: mpsc::UnboundedReceiver<QueueCommand>,
|
||||||
) {
|
) {
|
||||||
let mut state = State::new(requires_padding, block_size, window_size);
|
let mut state = State::new(requires_padding, block_size, window_size);
|
||||||
|
|
||||||
while let Ok(cmd) = receiver.recv_async().await {
|
while let Some(cmd) = receiver.recv().await {
|
||||||
match cmd {
|
match cmd {
|
||||||
QueueCommand::Append(entry, span) => {
|
QueueCommand::Append(entry, span) => {
|
||||||
span.in_scope(|| state.append(*entry));
|
span.in_scope(|| state.append(*entry));
|
||||||
@ -195,7 +195,7 @@ impl State {
|
|||||||
while let Some((id, mut entry)) = self.entries.pop_front() {
|
while let Some((id, mut entry)) = self.entries.pop_front() {
|
||||||
// Filter entries where the response receiver was dropped (== entries where the request
|
// Filter entries where the response receiver was dropped (== entries where the request
|
||||||
// was dropped by the client)
|
// was dropped by the client)
|
||||||
if entry.response_tx.is_disconnected() {
|
if entry.response_tx.is_closed() {
|
||||||
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
|
metrics::increment_counter!("tgi_request_failure", "err" => "dropped");
|
||||||
continue;
|
continue;
|
||||||
}
|
}
|
||||||
@ -321,9 +321,9 @@ mod tests {
|
|||||||
|
|
||||||
fn default_entry() -> (
|
fn default_entry() -> (
|
||||||
Entry,
|
Entry,
|
||||||
flume::Receiver<Result<InferStreamResponse, InferError>>,
|
mpsc::UnboundedReceiver<Result<InferStreamResponse, InferError>>,
|
||||||
) {
|
) {
|
||||||
let (response_tx, receiver_tx) = flume::unbounded();
|
let (response_tx, receiver_tx) = mpsc::unbounded_channel();
|
||||||
|
|
||||||
let entry = Entry {
|
let entry = Entry {
|
||||||
request: ValidGenerateRequest {
|
request: ValidGenerateRequest {
|
||||||
|
|||||||
@ -6,6 +6,7 @@ use text_generation_client::{NextTokenChooserParameters, StoppingCriteriaParamet
|
|||||||
use thiserror::Error;
|
use thiserror::Error;
|
||||||
use tokenizers::tokenizer::Tokenizer;
|
use tokenizers::tokenizer::Tokenizer;
|
||||||
use tokenizers::TruncationDirection;
|
use tokenizers::TruncationDirection;
|
||||||
|
use tokio::sync::mpsc;
|
||||||
use tokio::sync::oneshot;
|
use tokio::sync::oneshot;
|
||||||
use tracing::{instrument, Span};
|
use tracing::{instrument, Span};
|
||||||
|
|
||||||
@ -19,7 +20,7 @@ pub struct Validation {
|
|||||||
max_input_length: usize,
|
max_input_length: usize,
|
||||||
max_total_tokens: usize,
|
max_total_tokens: usize,
|
||||||
/// Channel to communicate with the background tokenization task
|
/// Channel to communicate with the background tokenization task
|
||||||
sender: Option<flume::Sender<TokenizerRequest>>,
|
sender: Option<mpsc::UnboundedSender<TokenizerRequest>>,
|
||||||
}
|
}
|
||||||
|
|
||||||
impl Validation {
|
impl Validation {
|
||||||
@ -34,19 +35,25 @@ impl Validation {
|
|||||||
) -> Self {
|
) -> Self {
|
||||||
// If we have a fast tokenizer
|
// If we have a fast tokenizer
|
||||||
let sender = if let Some(tokenizer) = tokenizer {
|
let sender = if let Some(tokenizer) = tokenizer {
|
||||||
// Create channel
|
// Create round robin channel
|
||||||
let (validation_sender, validation_receiver) = flume::unbounded();
|
let (validation_sender, validation_round_robin_receiver) = mpsc::unbounded_channel();
|
||||||
|
let mut senders = Vec::with_capacity(workers);
|
||||||
|
|
||||||
// Create workers
|
// Create workers
|
||||||
for _ in 0..workers {
|
for _ in 0..workers {
|
||||||
let tokenizer_clone = tokenizer.clone();
|
let tokenizer_clone = tokenizer.clone();
|
||||||
let receiver_clone = validation_receiver.clone();
|
let (tokenizer_sender, tokenizer_receiver) = mpsc::unbounded_channel();
|
||||||
|
senders.push(tokenizer_sender);
|
||||||
|
|
||||||
// Spawn worker
|
// Spawn worker
|
||||||
tokio::task::spawn_blocking(move || {
|
tokio::task::spawn_blocking(move || {
|
||||||
tokenizer_worker(tokenizer_clone, receiver_clone)
|
tokenizer_worker(tokenizer_clone, tokenizer_receiver)
|
||||||
});
|
});
|
||||||
}
|
}
|
||||||
|
|
||||||
|
// Create tokenization round robin task
|
||||||
|
tokio::spawn(round_robin_task(validation_round_robin_receiver, senders));
|
||||||
|
|
||||||
Some(validation_sender)
|
Some(validation_sender)
|
||||||
} else {
|
} else {
|
||||||
None
|
None
|
||||||
@ -67,8 +74,8 @@ impl Validation {
|
|||||||
&self,
|
&self,
|
||||||
inputs: String,
|
inputs: String,
|
||||||
truncate: Option<usize>,
|
truncate: Option<usize>,
|
||||||
max_new_tokens: u32,
|
max_new_tokens: Option<u32>,
|
||||||
) -> Result<(String, usize), ValidationError> {
|
) -> Result<(String, usize, u32), ValidationError> {
|
||||||
// If we have a fast tokenizer
|
// If we have a fast tokenizer
|
||||||
if let Some(sender) = &self.sender {
|
if let Some(sender) = &self.sender {
|
||||||
// Create response channel
|
// Create response channel
|
||||||
@ -84,6 +91,11 @@ impl Validation {
|
|||||||
let (inputs, input_length) = response_receiver.await.unwrap()?;
|
let (inputs, input_length) = response_receiver.await.unwrap()?;
|
||||||
|
|
||||||
// Get total tokens
|
// Get total tokens
|
||||||
|
let max_new_tokens: u32 = if let Some(max_new_tokens) = max_new_tokens {
|
||||||
|
max_new_tokens
|
||||||
|
} else {
|
||||||
|
self.max_total_tokens.saturating_sub(input_length) as u32
|
||||||
|
};
|
||||||
let total_tokens = input_length + max_new_tokens as usize;
|
let total_tokens = input_length + max_new_tokens as usize;
|
||||||
|
|
||||||
// Validate MaxTotalTokens
|
// Validate MaxTotalTokens
|
||||||
@ -104,13 +116,20 @@ impl Validation {
|
|||||||
}
|
}
|
||||||
|
|
||||||
metrics::histogram!("tgi_request_input_length", input_length as f64);
|
metrics::histogram!("tgi_request_input_length", input_length as f64);
|
||||||
Ok((inputs, input_length))
|
Ok((inputs, input_length, max_new_tokens))
|
||||||
}
|
}
|
||||||
// Return inputs without validation
|
// Return inputs without validation
|
||||||
else {
|
else {
|
||||||
// In this case, we don't know the real length in tokens of the inputs
|
// In this case, we don't know the real length in tokens of the inputs
|
||||||
// However, the inputs will be truncated by the python servers
|
// However, the inputs will be truncated by the python servers
|
||||||
// We make sure that truncate + max_new_tokens <= self.max_total_tokens
|
// We make sure that truncate + max_new_tokens <= self.max_total_tokens
|
||||||
|
let max_new_tokens: u32 = if let Some(max_new_tokens) = max_new_tokens {
|
||||||
|
max_new_tokens
|
||||||
|
} else if let Some(truncate) = truncate {
|
||||||
|
self.max_total_tokens.saturating_sub(truncate) as u32
|
||||||
|
} else {
|
||||||
|
return Err(ValidationError::UnsetMaxNewTokens);
|
||||||
|
};
|
||||||
let input_length = truncate.unwrap_or(self.max_input_length);
|
let input_length = truncate.unwrap_or(self.max_input_length);
|
||||||
|
|
||||||
// Validate MaxNewTokens
|
// Validate MaxNewTokens
|
||||||
@ -121,7 +140,7 @@ impl Validation {
|
|||||||
));
|
));
|
||||||
}
|
}
|
||||||
|
|
||||||
Ok((inputs, input_length))
|
Ok((inputs, input_length, max_new_tokens))
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -200,7 +219,7 @@ impl Validation {
|
|||||||
})
|
})
|
||||||
.unwrap_or(Ok(0))?;
|
.unwrap_or(Ok(0))?;
|
||||||
|
|
||||||
if max_new_tokens == 0 {
|
if max_new_tokens == Some(0) {
|
||||||
return Err(ValidationError::NegativeMaxNewTokens);
|
return Err(ValidationError::NegativeMaxNewTokens);
|
||||||
}
|
}
|
||||||
|
|
||||||
@ -247,7 +266,7 @@ impl Validation {
|
|||||||
.unwrap_or(Ok(None))?;
|
.unwrap_or(Ok(None))?;
|
||||||
|
|
||||||
// Validate inputs
|
// Validate inputs
|
||||||
let (inputs, input_length) = self
|
let (inputs, input_length, max_new_tokens) = self
|
||||||
.validate_input(request.inputs, truncate, max_new_tokens)
|
.validate_input(request.inputs, truncate, max_new_tokens)
|
||||||
.await?;
|
.await?;
|
||||||
|
|
||||||
@ -295,10 +314,25 @@ impl Validation {
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
/// Round robin tokenization task
|
||||||
|
async fn round_robin_task(
|
||||||
|
mut receiver: mpsc::UnboundedReceiver<TokenizerRequest>,
|
||||||
|
senders: Vec<mpsc::UnboundedSender<TokenizerRequest>>,
|
||||||
|
) {
|
||||||
|
loop {
|
||||||
|
for sender in &senders {
|
||||||
|
match receiver.recv().await {
|
||||||
|
None => return,
|
||||||
|
Some(request) => sender.send(request).unwrap(),
|
||||||
|
};
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
/// Start tokenization workers
|
/// Start tokenization workers
|
||||||
fn tokenizer_worker(tokenizer: Tokenizer, receiver: flume::Receiver<TokenizerRequest>) {
|
fn tokenizer_worker(tokenizer: Tokenizer, mut receiver: mpsc::UnboundedReceiver<TokenizerRequest>) {
|
||||||
// Loop over requests
|
// Loop over requests
|
||||||
while let Ok(((inputs, truncate), response_tx, parent_span)) = receiver.recv() {
|
while let Some(((inputs, truncate), response_tx, parent_span)) = receiver.blocking_recv() {
|
||||||
parent_span.in_scope(|| {
|
parent_span.in_scope(|| {
|
||||||
response_tx
|
response_tx
|
||||||
.send(prepare_input(inputs, truncate, &tokenizer))
|
.send(prepare_input(inputs, truncate, &tokenizer))
|
||||||
@ -383,6 +417,8 @@ pub enum ValidationError {
|
|||||||
Truncate(usize, usize),
|
Truncate(usize, usize),
|
||||||
#[error("`typical_p` must be > 0.0 and < 1.0")]
|
#[error("`typical_p` must be > 0.0 and < 1.0")]
|
||||||
TypicalP,
|
TypicalP,
|
||||||
|
#[error("one of `max_new_tokens` or `truncate` must be set if a fast tokenizer is not in use")]
|
||||||
|
UnsetMaxNewTokens,
|
||||||
#[error("`max_new_tokens` must be strictly positive")]
|
#[error("`max_new_tokens` must be strictly positive")]
|
||||||
NegativeMaxNewTokens,
|
NegativeMaxNewTokens,
|
||||||
#[error("`max_new_tokens` must be <= {0}. Given: {1}")]
|
#[error("`max_new_tokens` must be <= {0}. Given: {1}")]
|
||||||
@ -426,7 +462,7 @@ mod tests {
|
|||||||
|
|
||||||
let max_new_tokens = 10;
|
let max_new_tokens = 10;
|
||||||
match validation
|
match validation
|
||||||
.validate_input("Hello".to_string(), None, max_new_tokens)
|
.validate_input("Hello".to_string(), None, Some(max_new_tokens))
|
||||||
.await
|
.await
|
||||||
{
|
{
|
||||||
Err(ValidationError::MaxNewTokens(1, 10)) => (),
|
Err(ValidationError::MaxNewTokens(1, 10)) => (),
|
||||||
@ -455,7 +491,7 @@ mod tests {
|
|||||||
|
|
||||||
let max_new_tokens = 10;
|
let max_new_tokens = 10;
|
||||||
match validation
|
match validation
|
||||||
.validate_input("Hello".to_string(), None, max_new_tokens)
|
.validate_input("Hello".to_string(), None, Some(max_new_tokens))
|
||||||
.await
|
.await
|
||||||
{
|
{
|
||||||
Err(ValidationError::MaxTotalTokens(6, 1, 10)) => (),
|
Err(ValidationError::MaxTotalTokens(6, 1, 10)) => (),
|
||||||
@ -534,7 +570,6 @@ mod tests {
|
|||||||
inputs: "Hello".to_string(),
|
inputs: "Hello".to_string(),
|
||||||
parameters: GenerateParameters {
|
parameters: GenerateParameters {
|
||||||
top_p: Some(0.99),
|
top_p: Some(0.99),
|
||||||
max_new_tokens: 1,
|
|
||||||
..default_parameters()
|
..default_parameters()
|
||||||
},
|
},
|
||||||
})
|
})
|
||||||
@ -549,7 +584,6 @@ mod tests {
|
|||||||
inputs: "Hello".to_string(),
|
inputs: "Hello".to_string(),
|
||||||
parameters: GenerateParameters {
|
parameters: GenerateParameters {
|
||||||
top_p: None,
|
top_p: None,
|
||||||
max_new_tokens: 1,
|
|
||||||
..default_parameters()
|
..default_parameters()
|
||||||
},
|
},
|
||||||
})
|
})
|
||||||
@ -596,7 +630,6 @@ mod tests {
|
|||||||
inputs: "Hello".to_string(),
|
inputs: "Hello".to_string(),
|
||||||
parameters: GenerateParameters {
|
parameters: GenerateParameters {
|
||||||
top_n_tokens: Some(4),
|
top_n_tokens: Some(4),
|
||||||
max_new_tokens: 1,
|
|
||||||
..default_parameters()
|
..default_parameters()
|
||||||
},
|
},
|
||||||
})
|
})
|
||||||
@ -608,7 +641,6 @@ mod tests {
|
|||||||
inputs: "Hello".to_string(),
|
inputs: "Hello".to_string(),
|
||||||
parameters: GenerateParameters {
|
parameters: GenerateParameters {
|
||||||
top_n_tokens: Some(0),
|
top_n_tokens: Some(0),
|
||||||
max_new_tokens: 1,
|
|
||||||
..default_parameters()
|
..default_parameters()
|
||||||
},
|
},
|
||||||
})
|
})
|
||||||
@ -620,7 +652,6 @@ mod tests {
|
|||||||
inputs: "Hello".to_string(),
|
inputs: "Hello".to_string(),
|
||||||
parameters: GenerateParameters {
|
parameters: GenerateParameters {
|
||||||
top_n_tokens: None,
|
top_n_tokens: None,
|
||||||
max_new_tokens: 1,
|
|
||||||
..default_parameters()
|
..default_parameters()
|
||||||
},
|
},
|
||||||
})
|
})
|
||||||
|
|||||||
@ -1,4 +1,4 @@
|
|||||||
flash_att_v2_commit := 601b4dc48dbe9d87c468daa2b4c0c8388b83753c
|
flash_att_v2_commit := 02ac572f3ffc4f402e4183aaa6824b45859d3ed3
|
||||||
|
|
||||||
flash-attention-v2:
|
flash-attention-v2:
|
||||||
# Clone flash attention
|
# Clone flash attention
|
||||||
|
|||||||
@ -1,8 +1,8 @@
|
|||||||
vllm_commit := 25dbff97d5a8f2ba331847237b458b2692e9ae78
|
vllm_commit := f8a1e39fae05ca610be8d5a78be9d40f5274e5fc
|
||||||
|
|
||||||
vllm:
|
vllm:
|
||||||
# Clone vllm
|
# Clone vllm
|
||||||
git clone https://github.com/OlivierDehaene/vllm.git
|
git clone https://github.com/vllm-project/vllm.git
|
||||||
|
|
||||||
build-vllm: vllm
|
build-vllm: vllm
|
||||||
cd vllm && git fetch && git checkout $(vllm_commit)
|
cd vllm && git fetch && git checkout $(vllm_commit)
|
||||||
|
|||||||
645
server/poetry.lock
generated
645
server/poetry.lock
generated
@ -214,86 +214,101 @@ files = [
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "charset-normalizer"
|
name = "charset-normalizer"
|
||||||
version = "3.2.0"
|
version = "3.3.0"
|
||||||
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
|
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = ">=3.7.0"
|
python-versions = ">=3.7.0"
|
||||||
files = [
|
files = [
|
||||||
{file = "charset-normalizer-3.2.0.tar.gz", hash = "sha256:3bb3d25a8e6c0aedd251753a79ae98a093c7e7b471faa3aa9a93a81431987ace"},
|
{file = "charset-normalizer-3.3.0.tar.gz", hash = "sha256:63563193aec44bce707e0c5ca64ff69fa72ed7cf34ce6e11d5127555756fd2f6"},
|
||||||
{file = "charset_normalizer-3.2.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:0b87549028f680ca955556e3bd57013ab47474c3124dc069faa0b6545b6c9710"},
|
{file = "charset_normalizer-3.3.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:effe5406c9bd748a871dbcaf3ac69167c38d72db8c9baf3ff954c344f31c4cbe"},
|
||||||
{file = "charset_normalizer-3.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:7c70087bfee18a42b4040bb9ec1ca15a08242cf5867c58726530bdf3945672ed"},
|
{file = "charset_normalizer-3.3.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:4162918ef3098851fcd8a628bf9b6a98d10c380725df9e04caf5ca6dd48c847a"},
|
||||||
{file = "charset_normalizer-3.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a103b3a7069b62f5d4890ae1b8f0597618f628b286b03d4bc9195230b154bfa9"},
|
{file = "charset_normalizer-3.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0570d21da019941634a531444364f2482e8db0b3425fcd5ac0c36565a64142c8"},
|
||||||
{file = "charset_normalizer-3.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:94aea8eff76ee6d1cdacb07dd2123a68283cb5569e0250feab1240058f53b623"},
|
{file = "charset_normalizer-3.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5707a746c6083a3a74b46b3a631d78d129edab06195a92a8ece755aac25a3f3d"},
|
||||||
{file = "charset_normalizer-3.2.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:db901e2ac34c931d73054d9797383d0f8009991e723dab15109740a63e7f902a"},
|
{file = "charset_normalizer-3.3.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:278c296c6f96fa686d74eb449ea1697f3c03dc28b75f873b65b5201806346a69"},
|
||||||
{file = "charset_normalizer-3.2.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b0dac0ff919ba34d4df1b6131f59ce95b08b9065233446be7e459f95554c0dc8"},
|
{file = "charset_normalizer-3.3.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a4b71f4d1765639372a3b32d2638197f5cd5221b19531f9245fcc9ee62d38f56"},
|
||||||
{file = "charset_normalizer-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:193cbc708ea3aca45e7221ae58f0fd63f933753a9bfb498a3b474878f12caaad"},
|
{file = "charset_normalizer-3.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f5969baeaea61c97efa706b9b107dcba02784b1601c74ac84f2a532ea079403e"},
|
||||||
{file = "charset_normalizer-3.2.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:09393e1b2a9461950b1c9a45d5fd251dc7c6f228acab64da1c9c0165d9c7765c"},
|
{file = "charset_normalizer-3.3.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a3f93dab657839dfa61025056606600a11d0b696d79386f974e459a3fbc568ec"},
|
||||||
{file = "charset_normalizer-3.2.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:baacc6aee0b2ef6f3d308e197b5d7a81c0e70b06beae1f1fcacffdbd124fe0e3"},
|
{file = "charset_normalizer-3.3.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:db756e48f9c5c607b5e33dd36b1d5872d0422e960145b08ab0ec7fd420e9d649"},
|
||||||
{file = "charset_normalizer-3.2.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:bf420121d4c8dce6b889f0e8e4ec0ca34b7f40186203f06a946fa0276ba54029"},
|
{file = "charset_normalizer-3.3.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:232ac332403e37e4a03d209a3f92ed9071f7d3dbda70e2a5e9cff1c4ba9f0678"},
|
||||||
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]
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]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
@ -580,148 +595,166 @@ testing = ["protobuf (>=4.21.9)"]
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "grpcio"
|
name = "grpcio"
|
||||||
version = "1.58.0"
|
version = "1.59.0"
|
||||||
description = "HTTP/2-based RPC framework"
|
description = "HTTP/2-based RPC framework"
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = ">=3.7"
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python-versions = ">=3.7"
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|
{file = "grpcio_tools-1.59.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:e312ddc2d8bec1a23306a661ad52734f984c9aad5d8f126ebb222a778d95407d"},
|
||||||
|
{file = "grpcio_tools-1.59.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:868892ad9e00651a38dace3e4924bae82fc4fd4df2c65d37b74381570ee8deb1"},
|
||||||
|
{file = "grpcio_tools-1.59.0-cp39-cp39-win32.whl", hash = "sha256:a4f6cae381f21fee1ef0a5cbbbb146680164311157ae618edf3061742d844383"},
|
||||||
|
{file = "grpcio_tools-1.59.0-cp39-cp39-win_amd64.whl", hash = "sha256:4a10e59cca462208b489478340b52a96d64e8b8b6f1ac097f3e8cb211d3f66c0"},
|
||||||
]
|
]
|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
grpcio = ">=1.58.0"
|
grpcio = ">=1.59.0"
|
||||||
protobuf = ">=4.21.6,<5.0dev"
|
protobuf = ">=4.21.6,<5.0dev"
|
||||||
setuptools = "*"
|
setuptools = "*"
|
||||||
|
|
||||||
@ -1254,13 +1287,13 @@ files = [
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "packaging"
|
name = "packaging"
|
||||||
version = "23.1"
|
version = "23.2"
|
||||||
description = "Core utilities for Python packages"
|
description = "Core utilities for Python packages"
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = ">=3.7"
|
python-versions = ">=3.7"
|
||||||
files = [
|
files = [
|
||||||
{file = "packaging-23.1-py3-none-any.whl", hash = "sha256:994793af429502c4ea2ebf6bf664629d07c1a9fe974af92966e4b8d2df7edc61"},
|
{file = "packaging-23.2-py3-none-any.whl", hash = "sha256:8c491190033a9af7e1d931d0b5dacc2ef47509b34dd0de67ed209b5203fc88c7"},
|
||||||
{file = "packaging-23.1.tar.gz", hash = "sha256:a392980d2b6cffa644431898be54b0045151319d1e7ec34f0cfed48767dd334f"},
|
{file = "packaging-23.2.tar.gz", hash = "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5"},
|
||||||
]
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
@ -1637,99 +1670,99 @@ files = [
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "regex"
|
name = "regex"
|
||||||
version = "2023.8.8"
|
version = "2023.10.3"
|
||||||
description = "Alternative regular expression module, to replace re."
|
description = "Alternative regular expression module, to replace re."
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = ">=3.6"
|
python-versions = ">=3.7"
|
||||||
files = [
|
files = [
|
||||||
{file = "regex-2023.8.8-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:88900f521c645f784260a8d346e12a1590f79e96403971241e64c3a265c8ecdb"},
|
{file = "regex-2023.10.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:4c34d4f73ea738223a094d8e0ffd6d2c1a1b4c175da34d6b0de3d8d69bee6bcc"},
|
||||||
{file = "regex-2023.8.8-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3611576aff55918af2697410ff0293d6071b7e00f4b09e005d614686ac4cd57c"},
|
{file = "regex-2023.10.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a8f4e49fc3ce020f65411432183e6775f24e02dff617281094ba6ab079ef0915"},
|
||||||
{file = "regex-2023.8.8-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b8a0ccc8f2698f120e9e5742f4b38dc944c38744d4bdfc427616f3a163dd9de5"},
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{file = "regex-2023.10.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4cd1bccf99d3ef1ab6ba835308ad85be040e6a11b0977ef7ea8c8005f01a3c29"},
|
||||||
{file = "regex-2023.8.8-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c662a4cbdd6280ee56f841f14620787215a171c4e2d1744c9528bed8f5816c96"},
|
{file = "regex-2023.10.3-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:81dce2ddc9f6e8f543d94b05d56e70d03a0774d32f6cca53e978dc01e4fc75b8"},
|
||||||
{file = "regex-2023.8.8-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cf0633e4a1b667bfe0bb10b5e53fe0d5f34a6243ea2530eb342491f1adf4f739"},
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{file = "regex-2023.10.3-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9c6b4d23c04831e3ab61717a707a5d763b300213db49ca680edf8bf13ab5d91b"},
|
||||||
{file = "regex-2023.8.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:551ad543fa19e94943c5b2cebc54c73353ffff08228ee5f3376bd27b3d5b9800"},
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{file = "regex-2023.10.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c15ad0aee158a15e17e0495e1e18741573d04eb6da06d8b84af726cfc1ed02ee"},
|
||||||
{file = "regex-2023.8.8-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:54de2619f5ea58474f2ac211ceea6b615af2d7e4306220d4f3fe690c91988a61"},
|
{file = "regex-2023.10.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6239d4e2e0b52c8bd38c51b760cd870069f0bdf99700a62cd509d7a031749a55"},
|
||||||
{file = "regex-2023.8.8-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:5ec4b3f0aebbbe2fc0134ee30a791af522a92ad9f164858805a77442d7d18570"},
|
{file = "regex-2023.10.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:4a8bf76e3182797c6b1afa5b822d1d5802ff30284abe4599e1247be4fd6b03be"},
|
||||||
{file = "regex-2023.8.8-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:3ae646c35cb9f820491760ac62c25b6d6b496757fda2d51be429e0e7b67ae0ab"},
|
{file = "regex-2023.10.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:d9c727bbcf0065cbb20f39d2b4f932f8fa1631c3e01fcedc979bd4f51fe051c5"},
|
||||||
{file = "regex-2023.8.8-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:ca339088839582d01654e6f83a637a4b8194d0960477b9769d2ff2cfa0fa36d2"},
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{file = "regex-2023.10.3-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:3ccf2716add72f80714b9a63899b67fa711b654be3fcdd34fa391d2d274ce767"},
|
||||||
{file = "regex-2023.8.8-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:d9b6627408021452dcd0d2cdf8da0534e19d93d070bfa8b6b4176f99711e7f90"},
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{file = "regex-2023.10.3-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:107ac60d1bfdc3edb53be75e2a52aff7481b92817cfdddd9b4519ccf0e54a6ff"},
|
||||||
{file = "regex-2023.8.8-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:bd3366aceedf274f765a3a4bc95d6cd97b130d1dda524d8f25225d14123c01db"},
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{file = "regex-2023.10.3-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:00ba3c9818e33f1fa974693fb55d24cdc8ebafcb2e4207680669d8f8d7cca79a"},
|
||||||
{file = "regex-2023.8.8-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:7aed90a72fc3654fba9bc4b7f851571dcc368120432ad68b226bd593f3f6c0b7"},
|
{file = "regex-2023.10.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:f0a47efb1dbef13af9c9a54a94a0b814902e547b7f21acb29434504d18f36e3a"},
|
||||||
{file = "regex-2023.8.8-cp310-cp310-win32.whl", hash = "sha256:80b80b889cb767cc47f31d2b2f3dec2db8126fbcd0cff31b3925b4dc6609dcdb"},
|
{file = "regex-2023.10.3-cp310-cp310-win32.whl", hash = "sha256:36362386b813fa6c9146da6149a001b7bd063dabc4d49522a1f7aa65b725c7ec"},
|
||||||
{file = "regex-2023.8.8-cp310-cp310-win_amd64.whl", hash = "sha256:b82edc98d107cbc7357da7a5a695901b47d6eb0420e587256ba3ad24b80b7d0b"},
|
{file = "regex-2023.10.3-cp310-cp310-win_amd64.whl", hash = "sha256:c65a3b5330b54103e7d21cac3f6bf3900d46f6d50138d73343d9e5b2900b2353"},
|
||||||
{file = "regex-2023.8.8-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:1e7d84d64c84ad97bf06f3c8cb5e48941f135ace28f450d86af6b6512f1c9a71"},
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{file = "regex-2023.10.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:90a79bce019c442604662d17bf69df99090e24cdc6ad95b18b6725c2988a490e"},
|
||||||
{file = "regex-2023.8.8-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:ce0f9fbe7d295f9922c0424a3637b88c6c472b75eafeaff6f910494a1fa719ef"},
|
{file = "regex-2023.10.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c7964c2183c3e6cce3f497e3a9f49d182e969f2dc3aeeadfa18945ff7bdd7051"},
|
||||||
{file = "regex-2023.8.8-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:06c57e14ac723b04458df5956cfb7e2d9caa6e9d353c0b4c7d5d54fcb1325c46"},
|
{file = "regex-2023.10.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4ef80829117a8061f974b2fda8ec799717242353bff55f8a29411794d635d964"},
|
||||||
{file = "regex-2023.8.8-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e7a9aaa5a1267125eef22cef3b63484c3241aaec6f48949b366d26c7250e0357"},
|
{file = "regex-2023.10.3-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5addc9d0209a9afca5fc070f93b726bf7003bd63a427f65ef797a931782e7edc"},
|
||||||
{file = "regex-2023.8.8-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9b7408511fca48a82a119d78a77c2f5eb1b22fe88b0d2450ed0756d194fe7a9a"},
|
{file = "regex-2023.10.3-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c148bec483cc4b421562b4bcedb8e28a3b84fcc8f0aa4418e10898f3c2c0eb9b"},
|
||||||
{file = "regex-2023.8.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:14dc6f2d88192a67d708341f3085df6a4f5a0c7b03dec08d763ca2cd86e9f559"},
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{file = "regex-2023.10.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8d1f21af4c1539051049796a0f50aa342f9a27cde57318f2fc41ed50b0dbc4ac"},
|
||||||
{file = "regex-2023.8.8-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:48c640b99213643d141550326f34f0502fedb1798adb3c9eb79650b1ecb2f177"},
|
{file = "regex-2023.10.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0b9ac09853b2a3e0d0082104036579809679e7715671cfbf89d83c1cb2a30f58"},
|
||||||
{file = "regex-2023.8.8-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:0085da0f6c6393428bf0d9c08d8b1874d805bb55e17cb1dfa5ddb7cfb11140bf"},
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{file = "regex-2023.10.3-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:ebedc192abbc7fd13c5ee800e83a6df252bec691eb2c4bedc9f8b2e2903f5e2a"},
|
||||||
{file = "regex-2023.8.8-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:964b16dcc10c79a4a2be9f1273fcc2684a9eedb3906439720598029a797b46e6"},
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{file = "regex-2023.10.3-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:d8a993c0a0ffd5f2d3bda23d0cd75e7086736f8f8268de8a82fbc4bd0ac6791e"},
|
||||||
{file = "regex-2023.8.8-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:7ce606c14bb195b0e5108544b540e2c5faed6843367e4ab3deb5c6aa5e681208"},
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{file = "regex-2023.10.3-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:be6b7b8d42d3090b6c80793524fa66c57ad7ee3fe9722b258aec6d0672543fd0"},
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||||||
{file = "regex-2023.8.8-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:40f029d73b10fac448c73d6eb33d57b34607f40116e9f6e9f0d32e9229b147d7"},
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{file = "regex-2023.10.3-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:4023e2efc35a30e66e938de5aef42b520c20e7eda7bb5fb12c35e5d09a4c43f6"},
|
||||||
{file = "regex-2023.8.8-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:3b8e6ea6be6d64104d8e9afc34c151926f8182f84e7ac290a93925c0db004bfd"},
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{file = "regex-2023.10.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:0d47840dc05e0ba04fe2e26f15126de7c755496d5a8aae4a08bda4dd8d646c54"},
|
||||||
{file = "regex-2023.8.8-cp311-cp311-win32.whl", hash = "sha256:942f8b1f3b223638b02df7df79140646c03938d488fbfb771824f3d05fc083a8"},
|
{file = "regex-2023.10.3-cp311-cp311-win32.whl", hash = "sha256:9145f092b5d1977ec8c0ab46e7b3381b2fd069957b9862a43bd383e5c01d18c2"},
|
||||||
{file = "regex-2023.8.8-cp311-cp311-win_amd64.whl", hash = "sha256:51d8ea2a3a1a8fe4f67de21b8b93757005213e8ac3917567872f2865185fa7fb"},
|
{file = "regex-2023.10.3-cp311-cp311-win_amd64.whl", hash = "sha256:b6104f9a46bd8743e4f738afef69b153c4b8b592d35ae46db07fc28ae3d5fb7c"},
|
||||||
{file = "regex-2023.8.8-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:e951d1a8e9963ea51efd7f150450803e3b95db5939f994ad3d5edac2b6f6e2b4"},
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{file = "regex-2023.10.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:bff507ae210371d4b1fe316d03433ac099f184d570a1a611e541923f78f05037"},
|
||||||
{file = "regex-2023.8.8-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:704f63b774218207b8ccc6c47fcef5340741e5d839d11d606f70af93ee78e4d4"},
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{file = "regex-2023.10.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:be5e22bbb67924dea15039c3282fa4cc6cdfbe0cbbd1c0515f9223186fc2ec5f"},
|
||||||
{file = "regex-2023.8.8-cp36-cp36m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:22283c769a7b01c8ac355d5be0715bf6929b6267619505e289f792b01304d898"},
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{file = "regex-2023.10.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4a992f702c9be9c72fa46f01ca6e18d131906a7180950958f766c2aa294d4b41"},
|
||||||
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{file = "regex-2023.10.3-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:7434a61b158be563c1362d9071358f8ab91b8d928728cd2882af060481244c9e"},
|
||||||
{file = "regex-2023.8.8-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:de35342190deb7b866ad6ba5cbcccb2d22c0487ee0cbb251efef0843d705f0d4"},
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{file = "regex-2023.10.3-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c2169b2dcabf4e608416f7f9468737583ce5f0a6e8677c4efbf795ce81109d7c"},
|
||||||
{file = "regex-2023.8.8-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b993b6f524d1e274a5062488a43e3f9f8764ee9745ccd8e8193df743dbe5ee61"},
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{file = "regex-2023.10.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a9e908ef5889cda4de038892b9accc36d33d72fb3e12c747e2799a0e806ec841"},
|
||||||
{file = "regex-2023.8.8-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:3026cbcf11d79095a32d9a13bbc572a458727bd5b1ca332df4a79faecd45281c"},
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{file = "regex-2023.10.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:12bd4bc2c632742c7ce20db48e0d99afdc05e03f0b4c1af90542e05b809a03d9"},
|
||||||
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|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
@ -1834,36 +1867,36 @@ torch = ["numpy (>=1.21.6)", "torch (>=1.10)"]
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
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|
name = "scipy"
|
||||||
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|
version = "1.11.3"
|
||||||
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|
description = "Fundamental algorithms for scientific computing in Python"
|
||||||
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|
optional = false
|
||||||
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||||||
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|
||||||
]
|
]
|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
@ -1971,13 +2004,13 @@ mpmath = ">=0.19"
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "texttable"
|
name = "texttable"
|
||||||
version = "1.6.7"
|
version = "1.7.0"
|
||||||
description = "module to create simple ASCII tables"
|
description = "module to create simple ASCII tables"
|
||||||
optional = true
|
optional = true
|
||||||
python-versions = "*"
|
python-versions = "*"
|
||||||
files = [
|
files = [
|
||||||
{file = "texttable-1.6.7-py2.py3-none-any.whl", hash = "sha256:b7b68139aa8a6339d2c320ca8b1dc42d13a7831a346b446cb9eb385f0c76310c"},
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{file = "texttable-1.7.0.tar.gz", hash = "sha256:2d2068fb55115807d3ac77a4ca68fa48803e84ebb0ee2340f858107a36522638"},
|
||||||
]
|
]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
@ -2106,13 +2139,13 @@ telegram = ["requests"]
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "transformers"
|
name = "transformers"
|
||||||
version = "4.33.2"
|
version = "4.33.3"
|
||||||
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
|
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = ">=3.8.0"
|
python-versions = ">=3.8.0"
|
||||||
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files = [
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||||||
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||||||
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|
||||||
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|
]
|
||||||
|
|
||||||
[package.dependencies]
|
[package.dependencies]
|
||||||
@ -2217,13 +2250,13 @@ files = [
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "urllib3"
|
name = "urllib3"
|
||||||
version = "2.0.5"
|
version = "2.0.6"
|
||||||
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = ">=3.7"
|
python-versions = ">=3.7"
|
||||||
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||||||
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|
||||||
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|
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|
||||||
|
|
||||||
[package.extras]
|
[package.extras]
|
||||||
|
|||||||
@ -1,6 +1,6 @@
|
|||||||
[tool.poetry]
|
[tool.poetry]
|
||||||
name = "text-generation-server"
|
name = "text-generation-server"
|
||||||
version = "1.1.0"
|
version = "1.1.1"
|
||||||
description = "Text Generation Inference Python gRPC Server"
|
description = "Text Generation Inference Python gRPC Server"
|
||||||
authors = ["Olivier Dehaene <olivier@huggingface.co>"]
|
authors = ["Olivier Dehaene <olivier@huggingface.co>"]
|
||||||
|
|
||||||
|
|||||||
@ -29,11 +29,7 @@ from typing import Optional, List, Tuple
|
|||||||
# Flash attention imports
|
# Flash attention imports
|
||||||
import dropout_layer_norm
|
import dropout_layer_norm
|
||||||
|
|
||||||
# vllm imports
|
from text_generation_server.utils import paged_attention, flash_attn
|
||||||
import vllm_cache_ops
|
|
||||||
import vllm_attention_ops
|
|
||||||
|
|
||||||
from text_generation_server.utils.flash_attn import attention
|
|
||||||
from text_generation_server.utils.layers import (
|
from text_generation_server.utils.layers import (
|
||||||
TensorParallelRowLinear,
|
TensorParallelRowLinear,
|
||||||
TensorParallelColumnLinear,
|
TensorParallelColumnLinear,
|
||||||
@ -269,7 +265,7 @@ class FlashLlamaAttention(torch.nn.Module):
|
|||||||
self.rotary_emb(query, cos, sin)
|
self.rotary_emb(query, cos, sin)
|
||||||
self.rotary_emb(torch.select(kv, dim=1, index=0), cos, sin)
|
self.rotary_emb(torch.select(kv, dim=1, index=0), cos, sin)
|
||||||
|
|
||||||
vllm_cache_ops.reshape_and_cache(
|
paged_attention.reshape_and_cache(
|
||||||
kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots
|
kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots
|
||||||
)
|
)
|
||||||
|
|
||||||
@ -279,7 +275,7 @@ class FlashLlamaAttention(torch.nn.Module):
|
|||||||
# Prefill
|
# Prefill
|
||||||
if cu_seqlen_prefill is not None:
|
if cu_seqlen_prefill is not None:
|
||||||
# flash attention
|
# flash attention
|
||||||
attention(
|
flash_attn.attention(
|
||||||
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),
|
||||||
@ -290,9 +286,7 @@ class FlashLlamaAttention(torch.nn.Module):
|
|||||||
)
|
)
|
||||||
# Decode
|
# Decode
|
||||||
else:
|
else:
|
||||||
# kv_cache[1] => [num_blocks, num_heads, head_size, block_size]
|
paged_attention.attention(
|
||||||
block_size = kv_cache[1].shape[3]
|
|
||||||
vllm_attention_ops.single_query_cached_kv_attention(
|
|
||||||
attn_output,
|
attn_output,
|
||||||
query,
|
query,
|
||||||
kv_cache[0],
|
kv_cache[0],
|
||||||
@ -301,7 +295,6 @@ class FlashLlamaAttention(torch.nn.Module):
|
|||||||
self.softmax_scale,
|
self.softmax_scale,
|
||||||
block_tables,
|
block_tables,
|
||||||
input_lengths,
|
input_lengths,
|
||||||
block_size,
|
|
||||||
max_s,
|
max_s,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|||||||
@ -29,10 +29,7 @@ from typing import Optional, List, Tuple
|
|||||||
# Flash attention imports
|
# Flash attention imports
|
||||||
import dropout_layer_norm
|
import dropout_layer_norm
|
||||||
|
|
||||||
# vllm imports
|
from text_generation_server.utils import paged_attention, flash_attn
|
||||||
import vllm_cache_ops
|
|
||||||
import vllm_attention_ops
|
|
||||||
|
|
||||||
from text_generation_server.utils.flash_attn import attention, HAS_FLASH_ATTN_V2
|
from text_generation_server.utils.flash_attn import attention, HAS_FLASH_ATTN_V2
|
||||||
from text_generation_server.utils.layers import (
|
from text_generation_server.utils.layers import (
|
||||||
TensorParallelRowLinear,
|
TensorParallelRowLinear,
|
||||||
@ -272,7 +269,7 @@ class MistralAttention(torch.nn.Module):
|
|||||||
else:
|
else:
|
||||||
kv_to_cache = kv
|
kv_to_cache = kv
|
||||||
|
|
||||||
vllm_cache_ops.reshape_and_cache(
|
paged_attention.reshape_and_cache(
|
||||||
kv_to_cache[:, 0], kv_to_cache[:, 1], kv_cache[0], kv_cache[1], slots
|
kv_to_cache[:, 0], kv_to_cache[:, 1], kv_cache[0], kv_cache[1], slots
|
||||||
)
|
)
|
||||||
|
|
||||||
@ -282,7 +279,7 @@ class MistralAttention(torch.nn.Module):
|
|||||||
# Prefill
|
# Prefill
|
||||||
if cu_seqlen_prefill is not None:
|
if cu_seqlen_prefill is not None:
|
||||||
# flash attention
|
# flash attention
|
||||||
attention(
|
flash_attn.attention(
|
||||||
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),
|
||||||
@ -294,9 +291,7 @@ class MistralAttention(torch.nn.Module):
|
|||||||
)
|
)
|
||||||
# Decode
|
# Decode
|
||||||
else:
|
else:
|
||||||
# kv_cache[1] => [num_blocks, num_heads, head_size, block_size]
|
paged_attention.attention(
|
||||||
block_size = kv_cache[1].shape[3]
|
|
||||||
vllm_attention_ops.single_query_cached_kv_attention(
|
|
||||||
attn_output,
|
attn_output,
|
||||||
query,
|
query,
|
||||||
kv_cache[0],
|
kv_cache[0],
|
||||||
@ -305,7 +300,6 @@ class MistralAttention(torch.nn.Module):
|
|||||||
self.softmax_scale,
|
self.softmax_scale,
|
||||||
block_tables,
|
block_tables,
|
||||||
input_lengths,
|
input_lengths,
|
||||||
block_size,
|
|
||||||
max_s,
|
max_s,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|||||||
@ -27,10 +27,7 @@ from transformers.modeling_utils import PreTrainedModel
|
|||||||
from transformers.models.gpt_neox import GPTNeoXConfig
|
from transformers.models.gpt_neox import GPTNeoXConfig
|
||||||
from typing import Optional, List, Tuple
|
from typing import Optional, List, Tuple
|
||||||
|
|
||||||
# vllm imports
|
from text_generation_server.utils import paged_attention, flash_attn
|
||||||
import vllm_cache_ops
|
|
||||||
import vllm_attention_ops
|
|
||||||
|
|
||||||
from text_generation_server.utils.flash_attn import attention
|
from text_generation_server.utils.flash_attn import attention
|
||||||
from text_generation_server.utils.layers import (
|
from text_generation_server.utils.layers import (
|
||||||
TensorParallelRowLinear,
|
TensorParallelRowLinear,
|
||||||
@ -141,7 +138,7 @@ class FlashNeoxAttention(torch.nn.Module):
|
|||||||
self.rotary_emb(qkv[:, 0], cos, sin)
|
self.rotary_emb(qkv[:, 0], cos, sin)
|
||||||
self.rotary_emb(qkv[:, 1], cos, sin)
|
self.rotary_emb(qkv[:, 1], cos, sin)
|
||||||
|
|
||||||
vllm_cache_ops.reshape_and_cache(
|
paged_attention.reshape_and_cache(
|
||||||
qkv[:, 1], qkv[:, 2], kv_cache[0], kv_cache[1], slots
|
qkv[:, 1], qkv[:, 2], kv_cache[0], kv_cache[1], slots
|
||||||
)
|
)
|
||||||
|
|
||||||
@ -151,7 +148,7 @@ class FlashNeoxAttention(torch.nn.Module):
|
|||||||
# Prefill
|
# Prefill
|
||||||
if cu_seqlen_prefill is not None:
|
if cu_seqlen_prefill is not None:
|
||||||
# flash attention
|
# flash attention
|
||||||
attention(
|
flash_attn.attention(
|
||||||
qkv[:, 0],
|
qkv[:, 0],
|
||||||
qkv[:, 1],
|
qkv[:, 1],
|
||||||
qkv[:, 2],
|
qkv[:, 2],
|
||||||
@ -162,9 +159,7 @@ class FlashNeoxAttention(torch.nn.Module):
|
|||||||
)
|
)
|
||||||
# Decode
|
# Decode
|
||||||
else:
|
else:
|
||||||
# kv_cache[1] => [num_blocks, num_heads, head_size, block_size]
|
paged_attention.attention(
|
||||||
block_size = kv_cache[1].shape[3]
|
|
||||||
vllm_attention_ops.single_query_cached_kv_attention(
|
|
||||||
attn_output,
|
attn_output,
|
||||||
qkv[:, 0],
|
qkv[:, 0],
|
||||||
kv_cache[0],
|
kv_cache[0],
|
||||||
@ -173,7 +168,6 @@ class FlashNeoxAttention(torch.nn.Module):
|
|||||||
self.softmax_scale,
|
self.softmax_scale,
|
||||||
block_tables,
|
block_tables,
|
||||||
input_lengths,
|
input_lengths,
|
||||||
block_size,
|
|
||||||
max_s,
|
max_s,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|||||||
@ -6,10 +6,7 @@ from transformers.modeling_utils import PreTrainedModel
|
|||||||
from transformers.configuration_utils import PretrainedConfig
|
from transformers.configuration_utils import PretrainedConfig
|
||||||
from typing import Optional, List, Tuple
|
from typing import Optional, List, Tuple
|
||||||
|
|
||||||
# vllm imports
|
from text_generation_server.utils import paged_attention, flash_attn
|
||||||
import vllm_cache_ops
|
|
||||||
import vllm_attention_ops
|
|
||||||
|
|
||||||
from text_generation_server.utils.flash_attn import attention
|
from text_generation_server.utils.flash_attn import attention
|
||||||
from text_generation_server.utils.layers import (
|
from text_generation_server.utils.layers import (
|
||||||
TensorParallelRowLinear,
|
TensorParallelRowLinear,
|
||||||
@ -191,7 +188,7 @@ class FlashRWAttention(torch.nn.Module):
|
|||||||
self.rotary_emb(query, cos, sin)
|
self.rotary_emb(query, cos, sin)
|
||||||
self.rotary_emb(torch.select(kv, dim=1, index=0), cos, sin)
|
self.rotary_emb(torch.select(kv, dim=1, index=0), cos, sin)
|
||||||
|
|
||||||
vllm_cache_ops.reshape_and_cache(
|
paged_attention.reshape_and_cache(
|
||||||
kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots
|
kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots
|
||||||
)
|
)
|
||||||
|
|
||||||
@ -201,7 +198,7 @@ class FlashRWAttention(torch.nn.Module):
|
|||||||
# Prefill
|
# Prefill
|
||||||
if cu_seqlen_prefill is not None:
|
if cu_seqlen_prefill is not None:
|
||||||
# flash attention
|
# flash attention
|
||||||
attention(
|
flash_attn.attention(
|
||||||
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),
|
||||||
@ -212,9 +209,7 @@ class FlashRWAttention(torch.nn.Module):
|
|||||||
)
|
)
|
||||||
# Decode
|
# Decode
|
||||||
else:
|
else:
|
||||||
# kv_cache[1] => [num_blocks, num_heads_kv, head_size, block_size]
|
paged_attention.attention(
|
||||||
block_size = kv_cache[1].shape[3]
|
|
||||||
vllm_attention_ops.single_query_cached_kv_attention(
|
|
||||||
attn_output,
|
attn_output,
|
||||||
query,
|
query,
|
||||||
kv_cache[0],
|
kv_cache[0],
|
||||||
@ -223,7 +218,6 @@ class FlashRWAttention(torch.nn.Module):
|
|||||||
self.softmax_scale,
|
self.softmax_scale,
|
||||||
block_tables,
|
block_tables,
|
||||||
input_lengths,
|
input_lengths,
|
||||||
block_size,
|
|
||||||
max_s,
|
max_s,
|
||||||
)
|
)
|
||||||
|
|
||||||
@ -310,7 +304,7 @@ class FlashRWLargeAttention(torch.nn.Module):
|
|||||||
self.rotary_emb(query, cos, sin)
|
self.rotary_emb(query, cos, sin)
|
||||||
self.rotary_emb(torch.select(kv, dim=2, index=0), cos, sin)
|
self.rotary_emb(torch.select(kv, dim=2, index=0), cos, sin)
|
||||||
|
|
||||||
vllm_cache_ops.reshape_and_cache(
|
paged_attention.reshape_and_cache(
|
||||||
kv[:, :, 0].contiguous(),
|
kv[:, :, 0].contiguous(),
|
||||||
kv[:, :, 1].contiguous(),
|
kv[:, :, 1].contiguous(),
|
||||||
kv_cache[0],
|
kv_cache[0],
|
||||||
@ -324,7 +318,7 @@ class FlashRWLargeAttention(torch.nn.Module):
|
|||||||
# Prefill
|
# Prefill
|
||||||
if cu_seqlen_prefill is not None:
|
if cu_seqlen_prefill is not None:
|
||||||
# flash attention
|
# flash attention
|
||||||
attention(
|
flash_attn.attention(
|
||||||
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),
|
||||||
@ -335,9 +329,7 @@ class FlashRWLargeAttention(torch.nn.Module):
|
|||||||
)
|
)
|
||||||
# Decode
|
# Decode
|
||||||
else:
|
else:
|
||||||
# kv_cache[1] => [num_blocks, num_groups, head_size, block_size]
|
paged_attention.attention(
|
||||||
block_size = kv_cache[1].shape[3]
|
|
||||||
vllm_attention_ops.single_query_cached_kv_attention(
|
|
||||||
attn_output,
|
attn_output,
|
||||||
query,
|
query,
|
||||||
kv_cache[0],
|
kv_cache[0],
|
||||||
@ -346,7 +338,6 @@ class FlashRWLargeAttention(torch.nn.Module):
|
|||||||
self.softmax_scale,
|
self.softmax_scale,
|
||||||
block_tables,
|
block_tables,
|
||||||
input_lengths,
|
input_lengths,
|
||||||
block_size,
|
|
||||||
max_s,
|
max_s,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|||||||
@ -5,10 +5,7 @@ from torch import nn
|
|||||||
from transformers.activations import ACT2FN
|
from transformers.activations import ACT2FN
|
||||||
from typing import Optional, List, Tuple
|
from typing import Optional, List, Tuple
|
||||||
|
|
||||||
# vllm imports
|
from text_generation_server.utils import paged_attention, flash_attn
|
||||||
import vllm_cache_ops
|
|
||||||
import vllm_attention_ops
|
|
||||||
|
|
||||||
from text_generation_server.utils.flash_attn import attention
|
from text_generation_server.utils.flash_attn import attention
|
||||||
from text_generation_server.utils.layers import (
|
from text_generation_server.utils.layers import (
|
||||||
TensorParallelRowLinear,
|
TensorParallelRowLinear,
|
||||||
@ -18,7 +15,6 @@ from text_generation_server.utils.layers import (
|
|||||||
FastLayerNorm,
|
FastLayerNorm,
|
||||||
get_linear,
|
get_linear,
|
||||||
)
|
)
|
||||||
from safetensors import SafetensorError
|
|
||||||
|
|
||||||
|
|
||||||
def load_multi_mqa(
|
def load_multi_mqa(
|
||||||
@ -258,7 +254,7 @@ class FlashMQAttention(torch.nn.Module):
|
|||||||
query = query.view(-1, self.num_heads, self.head_size)
|
query = query.view(-1, self.num_heads, self.head_size)
|
||||||
key_value = key_value.view(-1, 2, 1, self.head_size)
|
key_value = key_value.view(-1, 2, 1, self.head_size)
|
||||||
|
|
||||||
vllm_cache_ops.reshape_and_cache(
|
paged_attention.reshape_and_cache(
|
||||||
key_value[:, 0], key_value[:, 1], kv_cache[0], kv_cache[1], slots
|
key_value[:, 0], key_value[:, 1], kv_cache[0], kv_cache[1], slots
|
||||||
)
|
)
|
||||||
|
|
||||||
@ -268,7 +264,7 @@ class FlashMQAttention(torch.nn.Module):
|
|||||||
# Prefill
|
# Prefill
|
||||||
if cu_seqlen_prefill is not None:
|
if cu_seqlen_prefill is not None:
|
||||||
# flash attention
|
# flash attention
|
||||||
attention(
|
flash_attn.attention(
|
||||||
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),
|
||||||
@ -279,9 +275,7 @@ class FlashMQAttention(torch.nn.Module):
|
|||||||
)
|
)
|
||||||
# Decode
|
# Decode
|
||||||
else:
|
else:
|
||||||
# kv_cache[1] => [num_blocks, 1, head_size, block_size]
|
paged_attention.attention(
|
||||||
block_size = kv_cache[1].shape[3]
|
|
||||||
vllm_attention_ops.single_query_cached_kv_attention(
|
|
||||||
attn_output,
|
attn_output,
|
||||||
query,
|
query,
|
||||||
kv_cache[0],
|
kv_cache[0],
|
||||||
@ -290,7 +284,6 @@ class FlashMQAttention(torch.nn.Module):
|
|||||||
self.softmax_scale,
|
self.softmax_scale,
|
||||||
block_tables,
|
block_tables,
|
||||||
input_lengths,
|
input_lengths,
|
||||||
block_size,
|
|
||||||
max_s,
|
max_s,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|||||||
@ -35,6 +35,7 @@ from transformers.image_utils import (
|
|||||||
valid_images,
|
valid_images,
|
||||||
)
|
)
|
||||||
from io import BytesIO
|
from io import BytesIO
|
||||||
|
import base64
|
||||||
import requests
|
import requests
|
||||||
from transformers import TensorType, is_torch_available
|
from transformers import TensorType, is_torch_available
|
||||||
|
|
||||||
@ -194,9 +195,26 @@ class IdeficsImageProcessor(BaseImageProcessor):
|
|||||||
if isinstance(image_url_or_urls, list):
|
if isinstance(image_url_or_urls, list):
|
||||||
return [self.fetch_images(x) for x in image_url_or_urls]
|
return [self.fetch_images(x) for x in image_url_or_urls]
|
||||||
elif isinstance(image_url_or_urls, str):
|
elif isinstance(image_url_or_urls, str):
|
||||||
response = requests.get(image_url_or_urls, stream=True, headers=headers)
|
image = image_url_or_urls
|
||||||
|
|
||||||
|
if image.startswith("http://") or image.startswith("https://"):
|
||||||
|
response = requests.get(image_url_or_urls, stream=True, headers=headers, timeout=(1, 5))
|
||||||
response.raise_for_status()
|
response.raise_for_status()
|
||||||
return Image.open(BytesIO(response.content))
|
content = response.content
|
||||||
|
elif image.startswith("data:"):
|
||||||
|
# https://stackoverflow.com/questions/17090571/is-there-a-way-to-set-background-image-as-a-base64-encoded-image
|
||||||
|
# data:image/png;base64,xxx
|
||||||
|
image = image.split(",")[-1]
|
||||||
|
content = base64.b64decode(image)
|
||||||
|
else:
|
||||||
|
raise ValueError(f"Unrecognized image {image}")
|
||||||
|
|
||||||
|
try:
|
||||||
|
image = Image.open(BytesIO(content))
|
||||||
|
# image.verify()
|
||||||
|
except Exception:
|
||||||
|
raise ValueError(f"Could not load image from url {image_url_or_urls}")
|
||||||
|
return image
|
||||||
else:
|
else:
|
||||||
raise ValueError(
|
raise ValueError(
|
||||||
f"only a single or a list of entries is supported but got type={type(image_url_or_urls)}"
|
f"only a single or a list of entries is supported but got type={type(image_url_or_urls)}"
|
||||||
|
|||||||
@ -112,6 +112,11 @@ def is_url(string):
|
|||||||
result = urlparse(string)
|
result = urlparse(string)
|
||||||
return all([result.scheme, result.netloc])
|
return all([result.scheme, result.netloc])
|
||||||
|
|
||||||
|
def is_image(string):
|
||||||
|
"""Checks if the passed string contains a valid url and nothing else. e.g. if space is included it's immediately
|
||||||
|
invalidated the url"""
|
||||||
|
return is_url(string) or string.startswith("data:")
|
||||||
|
|
||||||
|
|
||||||
class IdeficsProcessor(ProcessorMixin):
|
class IdeficsProcessor(ProcessorMixin):
|
||||||
r"""
|
r"""
|
||||||
@ -314,7 +319,7 @@ class IdeficsProcessor(ProcessorMixin):
|
|||||||
|
|
||||||
if isinstance(item, str):
|
if isinstance(item, str):
|
||||||
item = item.strip(" ")
|
item = item.strip(" ")
|
||||||
if is_url(item):
|
if is_image(item):
|
||||||
image = self.image_processor.fetch_images(item)
|
image = self.image_processor.fetch_images(item)
|
||||||
full_text += image_tokens(last_was_image)
|
full_text += image_tokens(last_was_image)
|
||||||
image_objects.append(image)
|
image_objects.append(image)
|
||||||
@ -339,6 +344,7 @@ class IdeficsProcessor(ProcessorMixin):
|
|||||||
|
|
||||||
image_objects = self.image_processor(image_objects, transform=transform)
|
image_objects = self.image_processor(image_objects, transform=transform)
|
||||||
|
|
||||||
|
|
||||||
text_encoding = self.tokenizer(
|
text_encoding = self.tokenizer(
|
||||||
text=full_text,
|
text=full_text,
|
||||||
add_special_tokens=False,
|
add_special_tokens=False,
|
||||||
|
|||||||
@ -283,10 +283,10 @@ class GPTNeoXAttention(nn.Module):
|
|||||||
batch_size, num_attention_heads, query_length, attn_head_size = query.size()
|
batch_size, num_attention_heads, query_length, attn_head_size = query.size()
|
||||||
key_length = key.size(-2)
|
key_length = key.size(-2)
|
||||||
|
|
||||||
query = query.view(
|
query = query.reshape(
|
||||||
batch_size * num_attention_heads, query_length, attn_head_size
|
batch_size * num_attention_heads, query_length, attn_head_size
|
||||||
)
|
)
|
||||||
key = key.view(batch_size * num_attention_heads, key_length, attn_head_size)
|
key = key.reshape(batch_size * num_attention_heads, key_length, attn_head_size)
|
||||||
attn_scores = torch.zeros(
|
attn_scores = torch.zeros(
|
||||||
1,
|
1,
|
||||||
dtype=query.dtype,
|
dtype=query.dtype,
|
||||||
|
|||||||
@ -670,7 +670,7 @@ class FlashCausalLM(Model):
|
|||||||
self.device,
|
self.device,
|
||||||
)
|
)
|
||||||
_, batch = self.generate_token(batch)
|
_, batch = self.generate_token(batch)
|
||||||
except Exception as e:
|
except torch.cuda.OutOfMemoryError as e:
|
||||||
raise RuntimeError(
|
raise RuntimeError(
|
||||||
f"Not enough memory to handle {len(batch.input_ids)} prefill tokens. "
|
f"Not enough memory to handle {len(batch.input_ids)} prefill tokens. "
|
||||||
f"You need to decrease `--max-batch-prefill-tokens`"
|
f"You need to decrease `--max-batch-prefill-tokens`"
|
||||||
|
|||||||
@ -80,7 +80,7 @@ def attention(
|
|||||||
)
|
)
|
||||||
|
|
||||||
if HAS_FLASH_ATTN:
|
if HAS_FLASH_ATTN:
|
||||||
if window_size_left != 0:
|
if window_size_left != -1:
|
||||||
raise NotImplementedError(
|
raise NotImplementedError(
|
||||||
"window_size_left is only available with flash attn v2"
|
"window_size_left is only available with flash attn v2"
|
||||||
)
|
)
|
||||||
|
|||||||
@ -155,10 +155,7 @@ class EETQLinear(nn.Module):
|
|||||||
device = weight.device
|
device = weight.device
|
||||||
weight = torch.t(weight).contiguous().cpu()
|
weight = torch.t(weight).contiguous().cpu()
|
||||||
weight, scale = quant_weights(weight, torch.int8, False)
|
weight, scale = quant_weights(weight, torch.int8, False)
|
||||||
if bias:
|
|
||||||
bias = weights.get_tensor(f"{prefix}.bias")
|
|
||||||
else:
|
|
||||||
bias = None
|
|
||||||
self.weight = weight.cuda(device)
|
self.weight = weight.cuda(device)
|
||||||
self.scale = scale.cuda(device)
|
self.scale = scale.cuda(device)
|
||||||
self.bias = bias.cuda(device) if bias is not None else None
|
self.bias = bias.cuda(device) if bias is not None else None
|
||||||
@ -601,6 +598,19 @@ try:
|
|||||||
device=inv_freq.device,
|
device=inv_freq.device,
|
||||||
scaling_factor=scaling_factor,
|
scaling_factor=scaling_factor,
|
||||||
)
|
)
|
||||||
|
elif rope_scaling["type"] == "yarn":
|
||||||
|
return YarnPositionRotaryEmbedding(
|
||||||
|
dim=2 * inv_freq.shape[0],
|
||||||
|
max_position_embeddings=rope_scaling["original_max_position_embeddings"],
|
||||||
|
base=10000.0,
|
||||||
|
device=inv_freq.device,
|
||||||
|
scaling_factor=scaling_factor,
|
||||||
|
extrapolation_factor=1,
|
||||||
|
attn_factor=1,
|
||||||
|
beta_fast=32,
|
||||||
|
beta_slow=1
|
||||||
|
|
||||||
|
)
|
||||||
else:
|
else:
|
||||||
raise NotImplementedError(
|
raise NotImplementedError(
|
||||||
f"rope scaling type {rope_scaling['type']} is not implemented or invalid"
|
f"rope scaling type {rope_scaling['type']} is not implemented or invalid"
|
||||||
@ -629,6 +639,19 @@ try:
|
|||||||
device=inv_freq.device,
|
device=inv_freq.device,
|
||||||
scaling_factor=scaling_factor,
|
scaling_factor=scaling_factor,
|
||||||
)
|
)
|
||||||
|
elif rope_scaling["type"] == "yarn":
|
||||||
|
return YarnPositionRotaryEmbedding(
|
||||||
|
dim=2 * inv_freq.shape[0],
|
||||||
|
max_position_embeddings=rope_scaling["original_max_position_embeddings"],
|
||||||
|
base=10000.0,
|
||||||
|
device=inv_freq.device,
|
||||||
|
scaling_factor=scaling_factor,
|
||||||
|
extrapolation_factor=1,
|
||||||
|
attn_factor=1,
|
||||||
|
beta_fast=32,
|
||||||
|
beta_slow=1
|
||||||
|
|
||||||
|
)
|
||||||
else:
|
else:
|
||||||
raise NotImplementedError(
|
raise NotImplementedError(
|
||||||
f"rope scaling type {rope_scaling['type']} is not implemented or invalid"
|
f"rope scaling type {rope_scaling['type']} is not implemented or invalid"
|
||||||
@ -708,5 +731,76 @@ try:
|
|||||||
self._cos_cached = torch.cos(freqs).to(dtype)
|
self._cos_cached = torch.cos(freqs).to(dtype)
|
||||||
self._sin_cached = torch.sin(freqs).to(dtype)
|
self._sin_cached = torch.sin(freqs).to(dtype)
|
||||||
|
|
||||||
|
|
||||||
|
# Inverse dim formula to find dim based on number of rotations
|
||||||
|
import math
|
||||||
|
def find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048):
|
||||||
|
return (dim * math.log(max_position_embeddings/(num_rotations * 2 * math.pi)))/(2 * math.log(base))
|
||||||
|
|
||||||
|
# Find dim range bounds based on rotations
|
||||||
|
def find_correction_range(low_rot, high_rot, dim, base=10000, max_position_embeddings=2048):
|
||||||
|
low = math.floor(find_correction_dim(
|
||||||
|
low_rot, dim, base, max_position_embeddings))
|
||||||
|
high = math.ceil(find_correction_dim(
|
||||||
|
high_rot, dim, base, max_position_embeddings))
|
||||||
|
return max(low, 0), min(high, dim-1) # Clamp values just in case
|
||||||
|
|
||||||
|
def linear_ramp_mask(min, max, dim):
|
||||||
|
if min == max:
|
||||||
|
max += 0.001 # Prevent singularity
|
||||||
|
|
||||||
|
linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
|
||||||
|
ramp_func = torch.clamp(linear_func, 0, 1)
|
||||||
|
return ramp_func
|
||||||
|
|
||||||
|
def get_mscale(scale=1):
|
||||||
|
if scale <= 1:
|
||||||
|
return 1.0
|
||||||
|
return 0.1 * math.log(scale) + 1.0
|
||||||
|
|
||||||
|
class YarnPositionRotaryEmbedding(PositionRotaryEmbedding):
|
||||||
|
def __init__(self, dim, max_position_embeddings, base, device, scaling_factor,*, extrapolation_factor, attn_factor, beta_fast, beta_slow):
|
||||||
|
inv_freq = _create_inv_freq(dim, base, device)
|
||||||
|
super().__init__(inv_freq, scaling_factor)
|
||||||
|
self.dim = dim
|
||||||
|
self.max_position_embeddings = max_position_embeddings
|
||||||
|
self.base = base
|
||||||
|
self.extrapolation_factor = extrapolation_factor
|
||||||
|
self.attn_factor = attn_factor
|
||||||
|
self.beta_fast = beta_fast
|
||||||
|
self.beta_slow = beta_slow
|
||||||
|
self.mscale = float(get_mscale(self.scaling_factor) * self.attn_factor) # Get n-d magnitude scaling corrected for interpolation
|
||||||
|
|
||||||
|
def _update_cos_sin_cache(self, dtype, device, seqlen):
|
||||||
|
# Reset the tables if the sequence length has changed,
|
||||||
|
# or if we're on a new device (possibly due to tracing for instance)
|
||||||
|
if (
|
||||||
|
seqlen > self._seq_len_cached
|
||||||
|
or self._cos_cached.device != device
|
||||||
|
or self._cos_cached.dtype != dtype
|
||||||
|
):
|
||||||
|
if seqlen > self.max_position_embeddings:
|
||||||
|
inv_freq_extrapolation = _create_inv_freq(
|
||||||
|
self.dim, self.base, self.inv_freq.device
|
||||||
|
)
|
||||||
|
freqs = 1.0 / inv_freq_extrapolation
|
||||||
|
inv_freq_interpolation = 1.0 / (self.scaling_factor * freqs)
|
||||||
|
low, high = find_correction_range(self.beta_fast, self.beta_slow, self.dim, self.base, self.max_position_embeddings)
|
||||||
|
inv_freq_mask = (1 - linear_ramp_mask(low, high, self.dim // 2).float().to(device)) * self.extrapolation_factor # Get n-d rotational scaling corrected for extrapolation
|
||||||
|
inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask
|
||||||
|
|
||||||
|
self.inv_freq = inv_freq
|
||||||
|
self.mscale = float(get_mscale(self.scaling_factor) * self.attn_factor) # Get n-d magnitude scaling corrected for interpolation
|
||||||
|
|
||||||
|
|
||||||
|
self._seq_len_cached = seqlen
|
||||||
|
t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
|
||||||
|
# Don't do einsum, it converts fp32 to fp16
|
||||||
|
# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
||||||
|
|
||||||
|
freqs = torch.outer(t, self.inv_freq.to(device=t.device))
|
||||||
|
self._cos_cached = (torch.cos(freqs) * self.mscale).to(dtype)
|
||||||
|
self._sin_cached = (torch.sin(freqs) * self.mscale).to(dtype)
|
||||||
|
|
||||||
except ImportError:
|
except ImportError:
|
||||||
pass
|
pass
|
||||||
|
|||||||
100
server/text_generation_server/utils/paged_attention.py
Normal file
100
server/text_generation_server/utils/paged_attention.py
Normal file
@ -0,0 +1,100 @@
|
|||||||
|
import torch
|
||||||
|
|
||||||
|
# vllm imports
|
||||||
|
from vllm import cache_ops
|
||||||
|
from vllm import attention_ops
|
||||||
|
|
||||||
|
_PARTITION_SIZE = 512
|
||||||
|
|
||||||
|
|
||||||
|
def reshape_and_cache(key: torch.Tensor, value: torch.Tensor, key_cache: torch.Tensor, value_cache: torch.Tensor,
|
||||||
|
slots: torch.Tensor):
|
||||||
|
cache_ops.reshape_and_cache(
|
||||||
|
key, value, key_cache, value_cache, slots
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def attention(
|
||||||
|
out: torch.Tensor,
|
||||||
|
query: torch.Tensor,
|
||||||
|
key_cache: torch.Tensor,
|
||||||
|
value_cache: torch.Tensor,
|
||||||
|
kv_head_mapping: torch.Tensor,
|
||||||
|
softmax_scale: float,
|
||||||
|
block_tables: torch.Tensor,
|
||||||
|
input_lengths: torch.Tensor,
|
||||||
|
max_s: int,
|
||||||
|
):
|
||||||
|
# Adapted from: https://github.com/vllm-project/vllm/blob/f8a1e39fae05ca610be8d5a78be9d40f5274e5fc/vllm/model_executor/layers/attention.py
|
||||||
|
# Copyright 2023 The vLLM team. All rights
|
||||||
|
# reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
#
|
||||||
|
|
||||||
|
# value_cache => [num_blocks, num_heads, head_size, block_size]
|
||||||
|
block_size = value_cache.shape[3]
|
||||||
|
num_seqs, num_heads, head_size = query.shape
|
||||||
|
max_num_partitions = (
|
||||||
|
(max_s + _PARTITION_SIZE - 1) //
|
||||||
|
_PARTITION_SIZE)
|
||||||
|
# NOTE(woosuk): We use a simple heuristic to decide whether to use
|
||||||
|
# PagedAttention V1 or V2. If the number of partitions is 1, we use
|
||||||
|
# V1 to avoid the overhead of reduction. Also, if the number of
|
||||||
|
# sequences or heads is large, we use V1 since there is enough work
|
||||||
|
# to parallelize.
|
||||||
|
use_v1 = max_num_partitions == 1 or num_seqs * num_heads > 512
|
||||||
|
if use_v1:
|
||||||
|
attention_ops.paged_attention_v1(
|
||||||
|
out,
|
||||||
|
query,
|
||||||
|
key_cache,
|
||||||
|
value_cache,
|
||||||
|
kv_head_mapping,
|
||||||
|
softmax_scale,
|
||||||
|
block_tables,
|
||||||
|
input_lengths,
|
||||||
|
block_size,
|
||||||
|
max_s,
|
||||||
|
None,
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
# Run PagedAttention V2.
|
||||||
|
assert _PARTITION_SIZE % block_size == 0
|
||||||
|
tmp_output = torch.empty(
|
||||||
|
size=(num_seqs, num_heads, max_num_partitions, head_size),
|
||||||
|
dtype=out.dtype,
|
||||||
|
device=out.device,
|
||||||
|
)
|
||||||
|
exp_sums = torch.empty(
|
||||||
|
size=(num_seqs, num_heads, max_num_partitions),
|
||||||
|
dtype=torch.float32,
|
||||||
|
device=out.device,
|
||||||
|
)
|
||||||
|
max_logits = torch.empty_like(exp_sums)
|
||||||
|
attention_ops.paged_attention_v2(
|
||||||
|
out,
|
||||||
|
exp_sums,
|
||||||
|
max_logits,
|
||||||
|
tmp_output,
|
||||||
|
query,
|
||||||
|
key_cache,
|
||||||
|
value_cache,
|
||||||
|
kv_head_mapping,
|
||||||
|
softmax_scale,
|
||||||
|
block_tables,
|
||||||
|
input_lengths,
|
||||||
|
block_size,
|
||||||
|
max_s,
|
||||||
|
None,
|
||||||
|
)
|
||||||
@ -323,7 +323,7 @@ class HeterogeneousSampling:
|
|||||||
|
|
||||||
|
|
||||||
def batch_top_tokens(
|
def batch_top_tokens(
|
||||||
top_n_tokens: list[int], top_n_tokens_tensor: torch.Tensor, logprobs: torch.Tensor
|
top_n_tokens: List[int], top_n_tokens_tensor: torch.Tensor, logprobs: torch.Tensor
|
||||||
) -> Tuple[List[List[int]], List[List[float]]]:
|
) -> Tuple[List[List[int]], List[List[float]]]:
|
||||||
"""Find the top n most likely tokens for a batch of generations.
|
"""Find the top n most likely tokens for a batch of generations.
|
||||||
|
|
||||||
|
|||||||
@ -16,6 +16,7 @@ class Weights:
|
|||||||
dtype,
|
dtype,
|
||||||
process_group,
|
process_group,
|
||||||
aliases: Optional[Dict[str, List[str]]] = None,
|
aliases: Optional[Dict[str, List[str]]] = None,
|
||||||
|
prefix: Optional[str] = None
|
||||||
):
|
):
|
||||||
routing = {}
|
routing = {}
|
||||||
for filename in filenames:
|
for filename in filenames:
|
||||||
@ -33,6 +34,7 @@ class Weights:
|
|||||||
self.device = device
|
self.device = device
|
||||||
self.dtype = dtype
|
self.dtype = dtype
|
||||||
self.process_group = process_group
|
self.process_group = process_group
|
||||||
|
self.prefix = prefix
|
||||||
self._handles = {}
|
self._handles = {}
|
||||||
|
|
||||||
def _get_handle(self, filename):
|
def _get_handle(self, filename):
|
||||||
@ -43,15 +45,22 @@ class Weights:
|
|||||||
return self._handles[filename]
|
return self._handles[filename]
|
||||||
|
|
||||||
def get_filename(self, tensor_name: str) -> (str, str):
|
def get_filename(self, tensor_name: str) -> (str, str):
|
||||||
filename = self.routing.get(tensor_name, None)
|
|
||||||
if filename is None:
|
names = [tensor_name]
|
||||||
aliases = self.aliases.get(tensor_name, [])
|
if self.prefix is not None:
|
||||||
|
prefixed = f"{self.prefix}.{tensor_name}"
|
||||||
|
names.append(prefixed)
|
||||||
|
for name in names:
|
||||||
|
filename = self.routing.get(name, None)
|
||||||
|
if filename is not None:
|
||||||
|
return str(filename), name
|
||||||
|
|
||||||
|
aliases = self.aliases.get(name, [])
|
||||||
for alias in aliases:
|
for alias in aliases:
|
||||||
filename = self.routing.get(alias, None)
|
filename = self.routing.get(alias, None)
|
||||||
if filename is not None:
|
if filename is not None:
|
||||||
return str(filename), alias
|
return str(filename), alias
|
||||||
raise RuntimeError(f"weight {tensor_name} does not exist")
|
raise RuntimeError(f"weight {tensor_name} does not exist")
|
||||||
return str(filename), tensor_name
|
|
||||||
|
|
||||||
def _get_slice(self, tensor_name: str):
|
def _get_slice(self, tensor_name: str):
|
||||||
filename, tensor_name = self.get_filename(tensor_name)
|
filename, tensor_name = self.get_filename(tensor_name)
|
||||||
@ -203,7 +212,9 @@ class Weights:
|
|||||||
g_idx = None
|
g_idx = None
|
||||||
|
|
||||||
bits, groupsize = self._get_gptq_params()
|
bits, groupsize = self._get_gptq_params()
|
||||||
weight = (qweight, qzeros, scales, g_idx, bits, groupsize, False)
|
from text_generation_server.utils.layers import HAS_EXLLAMA
|
||||||
|
use_exllama = bits==4 and HAS_EXLLAMA and quantize == "gptq"
|
||||||
|
weight = (qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama)
|
||||||
else:
|
else:
|
||||||
w = [self.get_sharded(f"{p}.weight", dim=0) for p in prefixes]
|
w = [self.get_sharded(f"{p}.weight", dim=0) for p in prefixes]
|
||||||
weight = torch.cat(w, dim=dim)
|
weight = torch.cat(w, dim=dim)
|
||||||
|
|||||||
@ -11,7 +11,34 @@ def main():
|
|||||||
output = subprocess.check_output(["text-generation-launcher", "--help"]).decode(
|
output = subprocess.check_output(["text-generation-launcher", "--help"]).decode(
|
||||||
"utf-8"
|
"utf-8"
|
||||||
)
|
)
|
||||||
final_doc = f"# Text-generation-launcher arguments\n```\n{output}\n```"
|
|
||||||
|
wrap_code_blocks_flag = "<!-- WRAP CODE BLOCKS -->"
|
||||||
|
final_doc = f"# Text-generation-launcher arguments\n\n{wrap_code_blocks_flag}\n\n"
|
||||||
|
|
||||||
|
lines = output.split("\n")
|
||||||
|
|
||||||
|
header = ""
|
||||||
|
block = []
|
||||||
|
for line in lines:
|
||||||
|
if line.startswith(" -") or line.startswith(" -"):
|
||||||
|
rendered_block = '\n'.join(block)
|
||||||
|
if header:
|
||||||
|
final_doc += f"## {header}\n```shell\n{rendered_block}\n```\n"
|
||||||
|
else:
|
||||||
|
final_doc += f"```shell\n{rendered_block}\n```\n"
|
||||||
|
block = []
|
||||||
|
tokens = line.split("<")
|
||||||
|
if len(tokens)>1:
|
||||||
|
header = tokens[-1][:-1]
|
||||||
|
else:
|
||||||
|
header = line.split("--")[-1]
|
||||||
|
header = header.upper().replace("-", "_")
|
||||||
|
|
||||||
|
block.append(line)
|
||||||
|
|
||||||
|
rendered_block = '\n'.join(block)
|
||||||
|
final_doc += f"## {header}\n```shell\n{rendered_block}\n```\n"
|
||||||
|
block = []
|
||||||
|
|
||||||
filename = "docs/source/basic_tutorials/launcher.md"
|
filename = "docs/source/basic_tutorials/launcher.md"
|
||||||
if args.check:
|
if args.check:
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user