fix(server): fix decode token (#334)
Fixes #333 --------- Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
This commit is contained in:
parent
dbdc587ddd
commit
5a58226130
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@ -213,12 +213,13 @@ jobs:
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sudo mount /dev/nvme1n1 ${{ env.DOCKER_VOLUME }}
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- name: Install
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run: |
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pip install pytest-xdist
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make install-integration-tests
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- name: Run tests
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run: |
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export DOCKER_IMAGE=registry.internal.huggingface.tech/api-inference/community/text-generation-inference:sha-${{ env.GITHUB_SHA_SHORT }}
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export HUGGING_FACE_HUB_TOKEN=${{ secrets.HUGGING_FACE_HUB_TOKEN }}
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pytest -s -vv integration-tests
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pytest -s -vv -n 2 --dist loadfile integration-tests
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stop-runner:
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name: Stop self-hosted EC2 runner
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@ -66,7 +66,8 @@ jobs:
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- name: Run server tests
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run: |
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pip install pytest
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make python-server-tests
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export HUGGING_FACE_HUB_TOKEN=${{ secrets.HUGGING_FACE_HUB_TOKEN }}
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pytest -s -vv server/tests
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- name: Run Rust fmt
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run: |
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cargo fmt --check
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2
Makefile
2
Makefile
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@ -31,7 +31,7 @@ update-integration-tests: install-integration-tests
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pytest -s -vv --snapshot-update integration-tests
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python-server-tests:
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HF_HUB_ENABLE_HF_TRANSFER=1 pytest server/tests
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HF_HUB_ENABLE_HF_TRANSFER=1 pytest -s -vv -m "not private" server/tests
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python-client-tests:
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pytest clients/python/tests
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@ -1,3 +1,4 @@
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import sys
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import subprocess
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import contextlib
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import pytest
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@ -7,6 +8,7 @@ import docker
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import json
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import math
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import time
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import random
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from docker.errors import NotFound
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from typing import Optional, List, Dict
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@ -205,10 +207,12 @@ def launcher(event_loop):
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def local_launcher(
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model_id: str, num_shard: Optional[int] = None, quantize: Optional[str] = None
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):
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port = 9999
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master_port = 19999
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port = random.randint(8000, 10_000)
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master_port = random.randint(10_000, 20_000)
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shard_uds_path = f"/tmp/{model_id.replace('/', '--')}-server"
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shard_uds_path = (
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f"/tmp/tgi-tests-{model_id.split('/')[-1]}-{num_shard}-{quantize}-server"
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)
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args = [
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"text-generation-launcher",
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@ -236,7 +240,7 @@ def launcher(event_loop):
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process.wait(60)
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launcher_output = process.stdout.read().decode("utf-8")
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print(launcher_output)
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print(launcher_output, file=sys.stderr)
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process.stdout.close()
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process.stderr.close()
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@ -245,7 +249,7 @@ def launcher(event_loop):
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def docker_launcher(
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model_id: str, num_shard: Optional[int] = None, quantize: Optional[str] = None
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):
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port = 9999
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port = random.randint(8000, 10_000)
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args = ["--model-id", model_id, "--env"]
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@ -298,7 +302,7 @@ def launcher(event_loop):
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pass
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container_output = container.logs().decode("utf-8")
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print(container_output)
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print(container_output, file=sys.stderr)
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container.remove()
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@ -1,92 +1,4 @@
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[
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{
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"details": {
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"best_of_sequences": null,
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"finish_reason": "length",
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"generated_tokens": 10,
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"prefill": [
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{
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"id": 1,
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"logprob": null,
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"text": "<s>"
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},
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{
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"id": 4321,
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"logprob": -8.6875,
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"text": "Test"
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},
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{
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"id": 2009,
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"logprob": -11.5546875,
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"text": "request"
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}
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],
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"seed": null,
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"tokens": [
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{
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"id": 363,
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"logprob": -1.5322266,
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"special": false,
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"text": " for"
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},
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{
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"id": 847,
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"logprob": -2.5585938,
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"special": false,
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"text": " /"
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},
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{
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"id": 2754,
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"logprob": -2.265625,
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"special": false,
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"text": "api"
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},
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{
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"id": 29914,
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"logprob": -0.034088135,
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"special": false,
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"text": "/"
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},
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{
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"id": 29894,
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"logprob": -0.96240234,
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"special": false,
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"text": "v"
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},
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{
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"id": 29896,
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"logprob": -0.36816406,
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"special": false,
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"text": "1"
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},
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{
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"id": 29914,
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"logprob": -0.013191223,
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"special": false,
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"text": "/"
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},
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{
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"id": 16418,
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"logprob": -3.15625,
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"special": false,
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"text": "projects"
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},
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{
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"id": 29914,
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"logprob": -0.43774414,
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"special": false,
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"text": "/"
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},
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{
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"id": 29896,
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"logprob": -1.9443359,
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"special": false,
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"text": "1"
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}
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]
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},
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"generated_text": "for /api/v1/projects/1"
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},
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{
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"details": {
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"best_of_sequences": null,
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@ -263,6 +175,94 @@
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},
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"generated_text": "for /api/v1/projects/1"
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},
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{
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"details": {
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"best_of_sequences": null,
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"finish_reason": "length",
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"generated_tokens": 10,
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"prefill": [
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{
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"id": 1,
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"logprob": null,
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"text": "<s>"
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},
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{
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"id": 4321,
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"logprob": -8.6875,
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"text": "Test"
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},
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{
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"id": 2009,
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"logprob": -11.5546875,
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"text": "request"
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}
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],
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"seed": null,
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"tokens": [
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{
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"id": 363,
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"logprob": -1.5322266,
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"special": false,
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"text": " for"
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},
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{
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"id": 847,
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"logprob": -2.5585938,
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"special": false,
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"text": " /"
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},
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{
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"id": 2754,
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"logprob": -2.265625,
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"special": false,
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"text": "api"
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},
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{
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"id": 29914,
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"logprob": -0.034088135,
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"special": false,
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"text": "/"
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},
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{
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"id": 29894,
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"logprob": -0.96240234,
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"special": false,
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"text": "v"
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},
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{
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"id": 29896,
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"logprob": -0.36816406,
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"special": false,
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"text": "1"
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},
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{
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"id": 29914,
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"logprob": -0.013191223,
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"special": false,
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"text": "/"
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},
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{
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"id": 16418,
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"logprob": -3.15625,
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"special": false,
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"text": "projects"
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},
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{
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"id": 29914,
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"logprob": -0.43774414,
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"special": false,
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"text": "/"
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},
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{
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"id": 29896,
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"logprob": -1.9443359,
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"special": false,
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"text": "1"
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}
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]
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},
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"generated_text": "for /api/v1/projects/1"
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},
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{
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"details": {
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"best_of_sequences": null,
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@ -16,7 +16,7 @@
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"id": 926,
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"logprob": -4.3554688,
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"special": false,
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"text": "To"
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"text": " To"
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},
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{
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"id": 18295,
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@ -16,7 +16,7 @@
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"id": 16017,
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"logprob": -1.3505859,
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"special": false,
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"text": "blue"
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"text": " blue"
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},
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{
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"id": 20495,
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|
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@ -1,58 +1,4 @@
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[
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{
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"details": {
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"best_of_sequences": null,
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"finish_reason": "eos_token",
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"generated_tokens": 6,
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"prefill": [
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{
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"id": 0,
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"logprob": null,
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"text": "<pad>"
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}
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],
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"seed": null,
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"tokens": [
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{
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"id": 259,
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"logprob": -1.3789062,
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"special": false,
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"text": ""
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},
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{
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"id": 39261,
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"logprob": -0.36279297,
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"special": false,
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"text": "Because"
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},
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{
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"id": 609,
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"logprob": -1.0966797,
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"special": false,
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"text": " it"
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},
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{
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"id": 339,
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"logprob": -0.8276367,
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"special": false,
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"text": " is"
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},
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{
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"id": 16017,
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"logprob": -1.6845703,
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"special": false,
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"text": " blue"
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},
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{
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"id": 1,
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"logprob": -0.72753906,
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"special": true,
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"text": "</s>"
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}
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]
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},
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"generated_text": "Because it is blue"
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},
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{
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"details": {
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"best_of_sequences": null,
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@ -71,7 +17,7 @@
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"id": 259,
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"logprob": -1.3798828,
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"special": false,
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"text": ""
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"text": " "
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},
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{
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"id": 39261,
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@ -125,7 +71,7 @@
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"id": 259,
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"logprob": -1.3789062,
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"special": false,
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"text": ""
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"text": " "
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},
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{
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"id": 39261,
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@ -179,7 +125,61 @@
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"id": 259,
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"logprob": -1.3789062,
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"special": false,
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"text": ""
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"text": " "
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},
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{
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"id": 39261,
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"logprob": -0.36279297,
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"special": false,
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"text": "Because"
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},
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{
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"id": 609,
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"logprob": -1.0966797,
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"special": false,
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"text": " it"
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},
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{
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"id": 339,
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"logprob": -0.8276367,
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"special": false,
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"text": " is"
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},
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{
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"id": 16017,
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"logprob": -1.6845703,
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"special": false,
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"text": " blue"
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},
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{
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"id": 1,
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"logprob": -0.72753906,
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"special": true,
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"text": "</s>"
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}
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]
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},
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"generated_text": "Because it is blue"
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},
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{
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"details": {
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"best_of_sequences": null,
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"finish_reason": "eos_token",
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"generated_tokens": 6,
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"prefill": [
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{
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"id": 0,
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"logprob": null,
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"text": "<pad>"
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}
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],
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"seed": null,
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"tokens": [
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{
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"id": 259,
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"logprob": -1.3789062,
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"special": false,
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"text": " "
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},
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{
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"id": 39261,
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|
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@ -146,7 +146,7 @@ fn main() -> Result<(), std::io::Error> {
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sha: None,
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pipeline_tag: None,
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},
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false => get_model_info(&tokenizer_name, &revision, authorization_token).await.unwrap_or({
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false => get_model_info(&tokenizer_name, &revision, authorization_token).await.unwrap_or_else(|| {
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tracing::warn!("Could not retrieve model info from the Hugging Face hub.");
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HubModelInfo { model_id: tokenizer_name.to_string(), sha: None, pipeline_tag: None }
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}),
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|
|
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@ -2,7 +2,7 @@ include Makefile-transformers
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include Makefile-flash-att
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unit-tests:
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python -m pytest tests
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pytest -s -vv -m "not private" tests
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gen-server:
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# Compile protos
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|
|
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@ -0,0 +1,78 @@
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import pytest
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import torch
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from transformers import AutoTokenizer
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from text_generation_server.models import Model
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def get_test_model():
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class TestModel(Model):
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def batch_type(self):
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raise NotImplementedError
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def generate_token(self, batch):
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raise NotImplementedError
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tokenizer = AutoTokenizer.from_pretrained("huggingface/llama-7b")
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model = TestModel(
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torch.nn.Linear(1, 1), tokenizer, False, torch.float32, torch.device("cpu")
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)
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return model
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@pytest.mark.private
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def test_decode_streaming_english_spaces():
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model = get_test_model()
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truth = "Hello here, this is a simple test"
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all_input_ids = [15043, 1244, 29892, 445, 338, 263, 2560, 1243]
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assert (
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all_input_ids == model.tokenizer(truth, add_special_tokens=False)["input_ids"]
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)
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decoded_text = ""
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offset = 0
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token_offset = 0
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for i in range(len(all_input_ids)):
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text, offset, token_offset = model.decode_token(
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all_input_ids[: i + 1], offset, token_offset
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)
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decoded_text += text
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assert decoded_text == truth
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@pytest.mark.private
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def test_decode_streaming_chinese_utf8():
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model = get_test_model()
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truth = "我很感谢你的热情"
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all_input_ids = [
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30672,
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232,
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193,
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139,
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233,
|
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135,
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162,
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235,
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179,
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165,
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30919,
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30210,
|
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234,
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134,
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176,
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30993,
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]
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decoded_text = ""
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offset = 0
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token_offset = 0
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for i in range(len(all_input_ids)):
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text, offset, token_offset = model.decode_token(
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all_input_ids[: i + 1], offset, token_offset
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)
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decoded_text += text
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assert decoded_text == truth
|
|
@ -149,7 +149,7 @@ def test_seq2seq_lm_generate_token(default_seq2seq_lm, default_seq2seq_lm_batch)
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assert all([generation.generated_text is None for generation in generations])
|
||||
assert all([len(generation.prefill_tokens) == 1 for generation in generations])
|
||||
assert all([generation.token_id.item() == 259 for generation in generations])
|
||||
assert all([generation.token_text == "" for generation in generations])
|
||||
assert all([generation.token_text == " " for generation in generations])
|
||||
assert generations[0].request_id == 0
|
||||
|
||||
|
||||
|
|
|
@ -56,7 +56,7 @@ class BLOOM(CausalLM):
|
|||
quantize: Optional[str] = None,
|
||||
):
|
||||
super(BLOOM, self).__init__(
|
||||
model_id=model_id, revision=revision, quantize=quantize, decode_buffer=1
|
||||
model_id=model_id, revision=revision, quantize=quantize
|
||||
)
|
||||
|
||||
@property
|
||||
|
@ -104,14 +104,13 @@ class BLOOMSharded(BLOOM):
|
|||
rank=rank,
|
||||
world_size=world_size,
|
||||
)
|
||||
self.model = model.eval()
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(CausalLM, self).__init__(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=True,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
decode_buffer=1,
|
||||
rank=rank,
|
||||
world_size=world_size,
|
||||
)
|
||||
|
|
|
@ -35,8 +35,8 @@ class CausalLMBatch(Batch):
|
|||
|
||||
# Lengths of all generations present in the batch
|
||||
input_lengths: List[int]
|
||||
offsets: List[Optional[int]]
|
||||
token_offsets: List[Optional[int]]
|
||||
prefix_offsets: List[int]
|
||||
read_offsets: List[int]
|
||||
|
||||
# Generation helpers
|
||||
next_token_choosers: List[NextTokenChooser]
|
||||
|
@ -70,8 +70,8 @@ class CausalLMBatch(Batch):
|
|||
inputs = []
|
||||
next_token_choosers = []
|
||||
stopping_criterias = []
|
||||
offsets = []
|
||||
token_offsets = []
|
||||
prefix_offsets = []
|
||||
read_offsets = []
|
||||
requests_idx_mapping = {}
|
||||
|
||||
# Parse batch
|
||||
|
@ -81,8 +81,6 @@ class CausalLMBatch(Batch):
|
|||
for i, r in enumerate(pb.requests):
|
||||
requests_idx_mapping[r.id] = i
|
||||
inputs.append(r.inputs)
|
||||
offsets.append(None)
|
||||
token_offsets.append(None)
|
||||
next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
|
||||
stopping_criteria = StoppingCriteria.from_pb(
|
||||
r.stopping_parameters, tokenizer
|
||||
|
@ -102,6 +100,10 @@ class CausalLMBatch(Batch):
|
|||
truncation=True,
|
||||
max_length=max_truncation,
|
||||
).to(device)
|
||||
for _ in pb.requests:
|
||||
input_len = tokenized_inputs["input_ids"].shape[1]
|
||||
prefix_offsets.append(0)
|
||||
read_offsets.append(input_len)
|
||||
|
||||
input_lengths = tokenized_inputs["attention_mask"].sum(1)
|
||||
max_input_length = input_lengths.max()
|
||||
|
@ -130,8 +132,8 @@ class CausalLMBatch(Batch):
|
|||
past_key_values=None,
|
||||
all_input_ids=list(all_input_ids),
|
||||
input_lengths=input_lengths.tolist(),
|
||||
offsets=offsets,
|
||||
token_offsets=token_offsets,
|
||||
prefix_offsets=prefix_offsets,
|
||||
read_offsets=read_offsets,
|
||||
next_token_choosers=next_token_choosers,
|
||||
stopping_criterias=stopping_criterias,
|
||||
max_input_length=max_input_length.item(),
|
||||
|
@ -151,8 +153,8 @@ class CausalLMBatch(Batch):
|
|||
# New values after filtering
|
||||
requests_idx_mapping = {}
|
||||
input_lengths = []
|
||||
offsets = []
|
||||
token_offsets = []
|
||||
prefix_offsets = []
|
||||
read_offsets = []
|
||||
all_input_ids = []
|
||||
max_input_length = 0
|
||||
|
||||
|
@ -167,8 +169,8 @@ class CausalLMBatch(Batch):
|
|||
requests_idx_mapping[r.id] = i
|
||||
keep_indices.append(idx)
|
||||
|
||||
offsets.append(self.offsets[idx])
|
||||
token_offsets.append(self.token_offsets[idx])
|
||||
prefix_offsets.append(self.prefix_offsets[idx])
|
||||
read_offsets.append(self.read_offsets[idx])
|
||||
all_input_ids.append(self.all_input_ids[idx])
|
||||
|
||||
request_input_length = self.input_lengths[idx]
|
||||
|
@ -225,8 +227,8 @@ class CausalLMBatch(Batch):
|
|||
self.position_ids = position_ids
|
||||
self.all_input_ids = all_input_ids
|
||||
self.input_lengths = input_lengths
|
||||
self.offsets = offsets
|
||||
self.token_offsets = token_offsets
|
||||
self.prefix_offsets = prefix_offsets
|
||||
self.read_offsets = read_offsets
|
||||
self.next_token_choosers = next_token_choosers
|
||||
self.stopping_criterias = stopping_criterias
|
||||
self.max_input_length = max_input_length
|
||||
|
@ -251,8 +253,8 @@ class CausalLMBatch(Batch):
|
|||
requests = []
|
||||
requests_idx_mapping = {}
|
||||
input_lengths = []
|
||||
offsets = []
|
||||
token_offsets = []
|
||||
prefix_offsets = []
|
||||
read_offsets = []
|
||||
all_input_ids = []
|
||||
next_token_choosers = []
|
||||
stopping_criterias = []
|
||||
|
@ -270,8 +272,8 @@ class CausalLMBatch(Batch):
|
|||
for i, batch in enumerate(batches):
|
||||
requests.extend(batch.requests)
|
||||
input_lengths.extend(batch.input_lengths)
|
||||
offsets.extend(batch.offsets)
|
||||
token_offsets.extend(batch.token_offsets)
|
||||
prefix_offsets.extend(batch.prefix_offsets)
|
||||
read_offsets.extend(batch.read_offsets)
|
||||
all_input_ids.extend(batch.all_input_ids)
|
||||
next_token_choosers.extend(batch.next_token_choosers)
|
||||
stopping_criterias.extend(batch.stopping_criterias)
|
||||
|
@ -428,8 +430,8 @@ class CausalLMBatch(Batch):
|
|||
past_key_values=past_key_values,
|
||||
all_input_ids=all_input_ids,
|
||||
input_lengths=input_lengths,
|
||||
offsets=offsets,
|
||||
token_offsets=token_offsets,
|
||||
prefix_offsets=prefix_offsets,
|
||||
read_offsets=read_offsets,
|
||||
next_token_choosers=next_token_choosers,
|
||||
stopping_criterias=stopping_criterias,
|
||||
max_input_length=max_input_length,
|
||||
|
@ -448,7 +450,6 @@ class CausalLM(Model):
|
|||
model_id: str,
|
||||
revision: Optional[str] = None,
|
||||
quantize: Optional[str] = None,
|
||||
decode_buffer: int = 3,
|
||||
):
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda")
|
||||
|
@ -463,25 +464,25 @@ class CausalLM(Model):
|
|||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_id, revision=revision, padding_side="left", truncation_side="left"
|
||||
)
|
||||
self.model = AutoModelForCausalLM.from_pretrained(
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
torch_dtype=dtype,
|
||||
device_map="auto" if torch.cuda.is_available() else None,
|
||||
load_in_8bit=quantize == "bitsandbytes",
|
||||
).eval()
|
||||
)
|
||||
tokenizer.pad_token_id = (
|
||||
self.model.config.pad_token_id
|
||||
if self.model.config.pad_token_id is not None
|
||||
else self.model.config.eos_token_id
|
||||
model.config.pad_token_id
|
||||
if model.config.pad_token_id is not None
|
||||
else model.config.eos_token_id
|
||||
)
|
||||
|
||||
super(CausalLM, self).__init__(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=True,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
decode_buffer=decode_buffer,
|
||||
)
|
||||
|
||||
@property
|
||||
|
@ -528,8 +529,8 @@ class CausalLM(Model):
|
|||
iterator = zip(
|
||||
batch.requests,
|
||||
batch.input_lengths,
|
||||
batch.offsets,
|
||||
batch.token_offsets,
|
||||
batch.prefix_offsets,
|
||||
batch.read_offsets,
|
||||
logits,
|
||||
batch.next_token_choosers,
|
||||
batch.stopping_criterias,
|
||||
|
@ -540,8 +541,8 @@ class CausalLM(Model):
|
|||
for i, (
|
||||
request,
|
||||
input_length,
|
||||
offset,
|
||||
token_offset,
|
||||
prefix_offset,
|
||||
read_offset,
|
||||
logits,
|
||||
next_token_chooser,
|
||||
stopping_criteria,
|
||||
|
@ -559,8 +560,8 @@ class CausalLM(Model):
|
|||
# Generated token
|
||||
next_token_logprob = logprobs[-1, next_token_id]
|
||||
next_token_id_squeezed = next_token_id.squeeze()
|
||||
next_token_text, offset, token_offset = self.decode_token(
|
||||
all_input_ids[:, 0], offset, token_offset
|
||||
next_token_text, prefix_offset, read_offset = self.decode_token(
|
||||
all_input_ids[:, 0], prefix_offset, read_offset
|
||||
)
|
||||
|
||||
# Evaluate stopping criteria
|
||||
|
@ -628,8 +629,8 @@ class CausalLM(Model):
|
|||
batch.input_ids[i, 0] = next_token_id
|
||||
batch.all_input_ids[i] = all_input_ids
|
||||
batch.input_lengths[i] = new_input_length
|
||||
batch.offsets[i] = offset
|
||||
batch.token_offsets[i] = token_offset
|
||||
batch.prefix_offsets[i] = prefix_offset
|
||||
batch.read_offsets[i] = read_offset
|
||||
batch.max_input_length = max(batch.max_input_length, new_input_length)
|
||||
|
||||
# We finished all generations in the batch; there is no next batch
|
||||
|
|
|
@ -52,8 +52,8 @@ class FlashCausalLMBatch(Batch):
|
|||
|
||||
# Lengths of all generations present in the batch
|
||||
input_lengths: List[int]
|
||||
offsets: List[Optional[int]]
|
||||
token_offsets: List[Optional[int]]
|
||||
prefix_offsets: List[Optional[int]]
|
||||
read_offsets: List[Optional[int]]
|
||||
|
||||
# Generation helpers
|
||||
next_token_choosers: List[NextTokenChooser]
|
||||
|
@ -82,8 +82,8 @@ class FlashCausalLMBatch(Batch):
|
|||
max_seqlen = 0
|
||||
|
||||
input_lengths = []
|
||||
offsets = []
|
||||
token_offsets = []
|
||||
prefix_offsets = []
|
||||
read_offsets = []
|
||||
all_input_ids = []
|
||||
requests_idx_mapping = {}
|
||||
|
||||
|
@ -108,8 +108,8 @@ class FlashCausalLMBatch(Batch):
|
|||
max_seqlen = max(max_seqlen, input_length)
|
||||
input_lengths.append(input_length)
|
||||
|
||||
offsets.append(None)
|
||||
token_offsets.append(None)
|
||||
prefix_offsets.append(0)
|
||||
read_offsets.append(input_length)
|
||||
|
||||
all_input_ids.append(tokenized_input)
|
||||
|
||||
|
@ -151,8 +151,8 @@ class FlashCausalLMBatch(Batch):
|
|||
max_seqlen=max_seqlen,
|
||||
past_key_values=None,
|
||||
input_lengths=input_lengths,
|
||||
offsets=offsets,
|
||||
token_offsets=token_offsets,
|
||||
prefix_offsets=prefix_offsets,
|
||||
read_offsets=read_offsets,
|
||||
all_input_ids=all_input_ids,
|
||||
all_input_ids_tensor=[],
|
||||
next_token_choosers=next_token_choosers,
|
||||
|
@ -190,8 +190,8 @@ class FlashCausalLMBatch(Batch):
|
|||
all_input_ids_tensor = []
|
||||
|
||||
input_lengths = []
|
||||
offsets = []
|
||||
token_offsets = []
|
||||
prefix_offsets = []
|
||||
read_offsets = []
|
||||
|
||||
next_token_choosers = []
|
||||
stopping_criterias = []
|
||||
|
@ -222,8 +222,8 @@ class FlashCausalLMBatch(Batch):
|
|||
all_input_ids_tensor.append(self.all_input_ids_tensor[idx])
|
||||
|
||||
input_lengths.append(request_input_length)
|
||||
offsets.append(self.offsets[idx])
|
||||
token_offsets.append(self.token_offsets[idx])
|
||||
prefix_offsets.append(self.prefix_offsets[idx])
|
||||
read_offsets.append(self.read_offsets[idx])
|
||||
|
||||
next_token_choosers.append(self.next_token_choosers[idx])
|
||||
|
||||
|
@ -269,8 +269,8 @@ class FlashCausalLMBatch(Batch):
|
|||
max_seqlen=max_seqlen,
|
||||
past_key_values=past_key_values,
|
||||
input_lengths=input_lengths,
|
||||
offsets=offsets,
|
||||
token_offsets=token_offsets,
|
||||
prefix_offsets=prefix_offsets,
|
||||
read_offsets=read_offsets,
|
||||
all_input_ids=all_input_ids,
|
||||
all_input_ids_tensor=all_input_ids_tensor,
|
||||
next_token_choosers=next_token_choosers,
|
||||
|
@ -302,8 +302,8 @@ class FlashCausalLMBatch(Batch):
|
|||
all_input_ids_tensor = []
|
||||
|
||||
input_lengths = []
|
||||
offsets = []
|
||||
token_offsets = []
|
||||
prefix_offsets = []
|
||||
read_offsets = []
|
||||
|
||||
next_token_choosers = []
|
||||
stopping_criterias = []
|
||||
|
@ -347,8 +347,8 @@ class FlashCausalLMBatch(Batch):
|
|||
all_input_ids_tensor.extend(batch.all_input_ids_tensor)
|
||||
|
||||
input_lengths.extend(batch.input_lengths)
|
||||
offsets.extend(batch.offsets)
|
||||
token_offsets.extend(batch.token_offsets)
|
||||
prefix_offsets.extend(batch.prefix_offsets)
|
||||
read_offsets.extend(batch.read_offsets)
|
||||
|
||||
next_token_choosers.extend(batch.next_token_choosers)
|
||||
stopping_criterias.extend(batch.stopping_criterias)
|
||||
|
@ -374,8 +374,8 @@ class FlashCausalLMBatch(Batch):
|
|||
max_seqlen=max_seqlen,
|
||||
past_key_values=past_key_values,
|
||||
input_lengths=input_lengths,
|
||||
offsets=offsets,
|
||||
token_offsets=token_offsets,
|
||||
prefix_offsets=prefix_offsets,
|
||||
read_offsets=read_offsets,
|
||||
all_input_ids=all_input_ids,
|
||||
all_input_ids_tensor=all_input_ids_tensor,
|
||||
next_token_choosers=next_token_choosers,
|
||||
|
@ -394,7 +394,6 @@ class FlashCausalLM(Model):
|
|||
model_id: str,
|
||||
revision: Optional[str] = None,
|
||||
quantize: Optional[str] = None,
|
||||
decode_buffer: int = 3,
|
||||
):
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda")
|
||||
|
@ -405,23 +404,19 @@ class FlashCausalLM(Model):
|
|||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_id, revision=revision, padding_side="left", truncation_side="left"
|
||||
)
|
||||
self.model = (
|
||||
model_cls.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
torch_dtype=dtype,
|
||||
load_in_8bit=quantize == "bitsandbytes",
|
||||
)
|
||||
.eval()
|
||||
.to(device)
|
||||
)
|
||||
model = model_cls.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
torch_dtype=dtype,
|
||||
load_in_8bit=quantize == "bitsandbytes",
|
||||
).to(device)
|
||||
|
||||
super(FlashCausalLM, self).__init__(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=False,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
decode_buffer=decode_buffer,
|
||||
)
|
||||
|
||||
@property
|
||||
|
@ -645,8 +640,8 @@ class FlashCausalLM(Model):
|
|||
iterator = zip(
|
||||
batch.requests,
|
||||
batch.input_lengths,
|
||||
batch.offsets,
|
||||
batch.token_offsets,
|
||||
batch.prefix_offsets,
|
||||
batch.read_offsets,
|
||||
batch.next_token_choosers,
|
||||
batch.stopping_criterias,
|
||||
batch.all_input_ids,
|
||||
|
@ -659,8 +654,8 @@ class FlashCausalLM(Model):
|
|||
for i, (
|
||||
request,
|
||||
input_length,
|
||||
offset,
|
||||
token_offset,
|
||||
prefix_offset,
|
||||
read_offset,
|
||||
next_token_chooser,
|
||||
stopping_criteria,
|
||||
all_input_ids,
|
||||
|
@ -675,10 +670,10 @@ class FlashCausalLM(Model):
|
|||
all_input_ids.append(next_token_id)
|
||||
|
||||
# Generated token
|
||||
next_token_text, offset, token_offset = self.decode_token(
|
||||
next_token_text, prefix_offset, read_offset = self.decode_token(
|
||||
all_input_ids,
|
||||
offset,
|
||||
token_offset,
|
||||
prefix_offset,
|
||||
read_offset,
|
||||
)
|
||||
|
||||
# Evaluate stopping criteria
|
||||
|
@ -744,8 +739,8 @@ class FlashCausalLM(Model):
|
|||
|
||||
# Update values
|
||||
batch.input_lengths[i] = new_input_length
|
||||
batch.offsets[i] = offset
|
||||
batch.token_offsets[i] = token_offset
|
||||
batch.prefix_offsets[i] = prefix_offset
|
||||
batch.read_offsets[i] = read_offset
|
||||
batch.all_input_ids[i] = all_input_ids
|
||||
batch.max_seqlen = batch.max_seqlen + 1
|
||||
cumulative_length += input_length
|
||||
|
|
|
@ -64,9 +64,9 @@ class FlashLlama(FlashCausalLM):
|
|||
model = FlashLlamaForCausalLM(config)
|
||||
|
||||
self.load_weights(model, filenames, quantize, device, dtype)
|
||||
self.model = model.eval().to(device)
|
||||
|
||||
super(FlashCausalLM, self).__init__(
|
||||
model=model.to(device),
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=False,
|
||||
dtype=dtype,
|
||||
|
@ -189,9 +189,9 @@ class FlashLlamaSharded(FlashLlama):
|
|||
rank=rank,
|
||||
world_size=world_size,
|
||||
)
|
||||
self.model = model.eval().to(device)
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(FlashCausalLM, self).__init__(
|
||||
model=model.to(device),
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=False,
|
||||
dtype=dtype,
|
||||
|
|
|
@ -73,9 +73,9 @@ class FlashNeoXSharded(FlashNeoX):
|
|||
rank=rank,
|
||||
world_size=world_size,
|
||||
)
|
||||
self.model = model.eval().to(device)
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(FlashCausalLM, self).__init__(
|
||||
model=model.to(device),
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=False,
|
||||
dtype=dtype,
|
||||
|
|
|
@ -67,14 +67,13 @@ class FlashSantacoder(FlashCausalLM):
|
|||
dtype,
|
||||
config.architectures[0].startswith("GPT2"),
|
||||
)
|
||||
self.model = model.eval().to(device)
|
||||
|
||||
super(FlashCausalLM, self).__init__(
|
||||
model=model.to(device),
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=False,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
decode_buffer=1,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
|
@ -213,16 +212,15 @@ class FlashSantacoderSharded(FlashSantacoder):
|
|||
world_size=world_size,
|
||||
transpose=config.architectures[0].startswith("GPT2"),
|
||||
)
|
||||
self.model = model.eval().to(device)
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(FlashCausalLM, self).__init__(
|
||||
model=model.to(device),
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=False,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
rank=rank,
|
||||
world_size=world_size,
|
||||
decode_buffer=1,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
|
|
|
@ -94,8 +94,8 @@ class GalacticaCausalLMBatch(CausalLMBatch):
|
|||
inputs = []
|
||||
next_token_choosers = []
|
||||
stopping_criterias = []
|
||||
offsets = []
|
||||
token_offsets = []
|
||||
prefix_offsets = []
|
||||
read_offsets = []
|
||||
requests_idx_mapping = {}
|
||||
|
||||
# Parse batch
|
||||
|
@ -106,8 +106,6 @@ class GalacticaCausalLMBatch(CausalLMBatch):
|
|||
requests_idx_mapping[r.id] = i
|
||||
# Add escape_custom_split_sequence to the CausalLMBatch logic
|
||||
inputs.append(escape_custom_split_sequence(r.inputs))
|
||||
offsets.append(None)
|
||||
token_offsets.append(None)
|
||||
next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
|
||||
stopping_criteria = StoppingCriteria.from_pb(
|
||||
r.stopping_parameters, tokenizer
|
||||
|
@ -127,6 +125,10 @@ class GalacticaCausalLMBatch(CausalLMBatch):
|
|||
truncation=True,
|
||||
max_length=max_truncation,
|
||||
).to(device)
|
||||
for _ in pb.requests:
|
||||
input_len = tokenized_inputs["input_ids"].shape[1]
|
||||
prefix_offsets.append(0)
|
||||
read_offsets.append(input_len)
|
||||
|
||||
input_lengths = tokenized_inputs["attention_mask"].sum(1)
|
||||
max_input_length = input_lengths.max()
|
||||
|
@ -155,8 +157,8 @@ class GalacticaCausalLMBatch(CausalLMBatch):
|
|||
past_key_values=None,
|
||||
all_input_ids=list(all_input_ids),
|
||||
input_lengths=input_lengths.tolist(),
|
||||
offsets=offsets,
|
||||
token_offsets=token_offsets,
|
||||
prefix_offsets=prefix_offsets,
|
||||
read_offsets=read_offsets,
|
||||
next_token_choosers=next_token_choosers,
|
||||
stopping_criterias=stopping_criterias,
|
||||
max_input_length=max_input_length.item(),
|
||||
|
@ -231,9 +233,9 @@ class GalacticaSharded(Galactica):
|
|||
rank=rank,
|
||||
world_size=world_size,
|
||||
)
|
||||
self.model = model.eval()
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(CausalLM, self).__init__(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=True,
|
||||
dtype=dtype,
|
||||
|
|
|
@ -70,9 +70,9 @@ class GPTNeoxSharded(CausalLM):
|
|||
rank=rank,
|
||||
world_size=world_size,
|
||||
)
|
||||
self.model = model.eval()
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(CausalLM, self).__init__(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=True,
|
||||
dtype=dtype,
|
||||
|
|
|
@ -13,23 +13,20 @@ B = TypeVar("B", bound=Batch)
|
|||
class Model(ABC):
|
||||
def __init__(
|
||||
self,
|
||||
model: torch.nn.Module,
|
||||
tokenizer: PreTrainedTokenizerBase,
|
||||
requires_padding: bool,
|
||||
dtype: torch.dtype,
|
||||
device: torch.device,
|
||||
decode_buffer: int = 3,
|
||||
rank: int = 0,
|
||||
world_size: int = 1,
|
||||
):
|
||||
if decode_buffer < 1:
|
||||
raise ValueError("decode_buffer must be >= 1")
|
||||
|
||||
self.model = model.eval()
|
||||
self.tokenizer = tokenizer
|
||||
self.all_special_ids = set(tokenizer.all_special_ids)
|
||||
self.requires_padding = requires_padding
|
||||
self.dtype = dtype
|
||||
self.device = device
|
||||
self.decode_buffer = decode_buffer
|
||||
self.rank = rank
|
||||
self.world_size = world_size
|
||||
self.check_initialized()
|
||||
|
@ -54,52 +51,29 @@ class Model(ABC):
|
|||
def decode_token(
|
||||
self,
|
||||
all_input_ids: List[int],
|
||||
offset: Optional[int] = None,
|
||||
token_offset: Optional[int] = None,
|
||||
) -> Tuple[str, Optional[int], Optional[int]]:
|
||||
prefix_offset: int = 0,
|
||||
read_offset: int = 0,
|
||||
) -> Tuple[str, int, int]:
|
||||
"""Hack to hopefully support generate_stream for the maximum number of tokenizers"""
|
||||
if all_input_ids[-1] in self.all_special_ids:
|
||||
return (
|
||||
self.tokenizer.decode(all_input_ids[-1], skip_special_tokens=False),
|
||||
None,
|
||||
None,
|
||||
)
|
||||
|
||||
if token_offset is None:
|
||||
token_offset = len(all_input_ids) - self.decode_buffer
|
||||
# left token buffer
|
||||
if self.decode_buffer > 1:
|
||||
# Decode token_offset token minus last one and token_offset tokens
|
||||
raw_texts = self.tokenizer.batch_decode(
|
||||
[all_input_ids[token_offset:-1], all_input_ids[token_offset:]],
|
||||
skip_special_tokens=False,
|
||||
)
|
||||
# The prefix text is necessary only to defeat cleanup algorithms in the decode
|
||||
# which decide to add a space or not depending on the surrounding ids.
|
||||
prefix_text = self.tokenizer.decode(
|
||||
all_input_ids[prefix_offset:read_offset], skip_special_tokens=False
|
||||
)
|
||||
new_text = self.tokenizer.decode(
|
||||
all_input_ids[prefix_offset:], skip_special_tokens=False
|
||||
)
|
||||
|
||||
# default offset is only the last token
|
||||
offset = len(raw_texts[0])
|
||||
sequence_text = raw_texts[1]
|
||||
else:
|
||||
# Only decode the last token without using a token buffer
|
||||
sequence_text = self.tokenizer.decode(
|
||||
all_input_ids[-1], skip_special_tokens=False
|
||||
)
|
||||
# no offset in this case
|
||||
offset = 0
|
||||
if len(new_text) > len(prefix_text) and not new_text.endswith("<EFBFBD>"):
|
||||
# utf-8 char at the end means it's a potential unfinished byte sequence
|
||||
# from byte fallback tokenization.
|
||||
# If it's in the middle, it's probably a real invalid id generated
|
||||
# by the model
|
||||
new_text = new_text[len(prefix_text) :]
|
||||
return new_text, read_offset, len(all_input_ids)
|
||||
else:
|
||||
assert offset is not None
|
||||
sequence_text = self.tokenizer.decode(
|
||||
all_input_ids[token_offset:],
|
||||
skip_special_tokens=False,
|
||||
)
|
||||
|
||||
# get text
|
||||
token_text = sequence_text[offset:]
|
||||
|
||||
# if text is utf-8
|
||||
if token_text and token_text[-1] != "<EFBFBD>":
|
||||
return token_text, None, None
|
||||
else:
|
||||
return "", offset, token_offset
|
||||
return "", prefix_offset, read_offset
|
||||
|
||||
def check_initialized(self):
|
||||
uninitialized_parameters = []
|
||||
|
|
|
@ -86,9 +86,9 @@ class OPTSharded(OPT):
|
|||
rank=rank,
|
||||
world_size=world_size,
|
||||
)
|
||||
self.model = model.eval()
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(CausalLM, self).__init__(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=True,
|
||||
dtype=dtype,
|
||||
|
|
|
@ -46,24 +46,20 @@ class SantaCoder(CausalLM):
|
|||
}
|
||||
)
|
||||
|
||||
self.model = (
|
||||
AutoModelForCausalLM.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
torch_dtype=dtype,
|
||||
load_in_8bit=quantize == "bitsandbytes",
|
||||
trust_remote_code=True, # required
|
||||
)
|
||||
.to(device)
|
||||
.eval()
|
||||
)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
torch_dtype=dtype,
|
||||
load_in_8bit=quantize == "bitsandbytes",
|
||||
trust_remote_code=True, # required
|
||||
).to(device)
|
||||
|
||||
super(CausalLM, self).__init__(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=True,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
decode_buffer=1,
|
||||
)
|
||||
|
||||
def decode(self, generated_ids: List[int]) -> str:
|
||||
|
|
|
@ -42,8 +42,8 @@ class Seq2SeqLMBatch(Batch):
|
|||
# Lengths of all generations present in the batch
|
||||
input_lengths: List[int]
|
||||
decoder_input_lengths: List[int]
|
||||
offsets: List[Optional[int]]
|
||||
token_offsets: List[Optional[int]]
|
||||
prefix_offsets: List[int]
|
||||
read_offsets: List[int]
|
||||
|
||||
# Generation helpers
|
||||
next_token_choosers: List[NextTokenChooser]
|
||||
|
@ -79,8 +79,8 @@ class Seq2SeqLMBatch(Batch):
|
|||
stopping_criterias = []
|
||||
|
||||
decoder_input_lengths = []
|
||||
offsets = []
|
||||
token_offsets = []
|
||||
prefix_offsets = []
|
||||
read_offsets = []
|
||||
requests_idx_mapping = {}
|
||||
|
||||
# Parse batch
|
||||
|
@ -91,8 +91,6 @@ class Seq2SeqLMBatch(Batch):
|
|||
inputs.append(r.inputs)
|
||||
requests_idx_mapping[r.id] = i
|
||||
decoder_input_lengths.append(1)
|
||||
offsets.append(None)
|
||||
token_offsets.append(None)
|
||||
next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
|
||||
stopping_criteria = StoppingCriteria.from_pb(
|
||||
r.stopping_parameters, tokenizer
|
||||
|
@ -123,6 +121,9 @@ class Seq2SeqLMBatch(Batch):
|
|||
.repeat(len(pb.requests))
|
||||
.view(-1, 1)
|
||||
)
|
||||
for _ in pb.requests:
|
||||
prefix_offsets.append(0)
|
||||
read_offsets.append(1)
|
||||
all_decoder_input_ids = decoder_input_ids.view(-1).split(1)
|
||||
|
||||
max_tokens = len(inputs) * max_input_length + max_decode_tokens
|
||||
|
@ -140,8 +141,8 @@ class Seq2SeqLMBatch(Batch):
|
|||
past_key_values=None,
|
||||
input_lengths=input_lengths.tolist(),
|
||||
decoder_input_lengths=decoder_input_lengths,
|
||||
offsets=offsets,
|
||||
token_offsets=token_offsets,
|
||||
prefix_offsets=prefix_offsets,
|
||||
read_offsets=read_offsets,
|
||||
next_token_choosers=next_token_choosers,
|
||||
stopping_criterias=stopping_criterias,
|
||||
max_input_length=max_input_length.item(),
|
||||
|
@ -165,8 +166,8 @@ class Seq2SeqLMBatch(Batch):
|
|||
requests_idx_mapping = {}
|
||||
input_lengths = []
|
||||
decoder_input_lengths = []
|
||||
offsets = []
|
||||
token_offsets = []
|
||||
prefix_offsets = []
|
||||
read_offsets = []
|
||||
|
||||
all_decoder_input_ids = []
|
||||
|
||||
|
@ -184,8 +185,8 @@ class Seq2SeqLMBatch(Batch):
|
|||
requests_idx_mapping[r.id] = i
|
||||
keep_indices.append(idx)
|
||||
|
||||
offsets.append(self.offsets[idx])
|
||||
token_offsets.append(self.token_offsets[idx])
|
||||
prefix_offsets.append(self.prefix_offsets[idx])
|
||||
read_offsets.append(self.read_offsets[idx])
|
||||
|
||||
all_decoder_input_ids.append(self.all_decoder_input_ids[idx])
|
||||
|
||||
|
@ -248,8 +249,8 @@ class Seq2SeqLMBatch(Batch):
|
|||
self.all_decoder_input_ids = all_decoder_input_ids
|
||||
self.input_lengths = input_lengths
|
||||
self.decoder_input_lengths = decoder_input_lengths
|
||||
self.offsets = offsets
|
||||
self.token_offsets = token_offsets
|
||||
self.prefix_offsets = prefix_offsets
|
||||
self.read_offsets = read_offsets
|
||||
self.next_token_choosers = next_token_choosers
|
||||
self.stopping_criterias = stopping_criterias
|
||||
self.max_input_length = max_input_length
|
||||
|
@ -283,8 +284,8 @@ class Seq2SeqLMBatch(Batch):
|
|||
all_decoder_input_ids = []
|
||||
input_lengths = []
|
||||
decoder_input_lengths = []
|
||||
offsets = []
|
||||
token_offsets = []
|
||||
prefix_offsets = []
|
||||
read_offsets = []
|
||||
next_token_choosers = []
|
||||
stopping_criterias = []
|
||||
max_tokens = 0
|
||||
|
@ -306,8 +307,8 @@ class Seq2SeqLMBatch(Batch):
|
|||
all_decoder_input_ids.extend(batch.all_decoder_input_ids)
|
||||
input_lengths.extend(batch.input_lengths)
|
||||
decoder_input_lengths.extend(batch.decoder_input_lengths)
|
||||
offsets.extend(batch.offsets)
|
||||
token_offsets.extend(batch.token_offsets)
|
||||
prefix_offsets.extend(batch.prefix_offsets)
|
||||
read_offsets.extend(batch.read_offsets)
|
||||
next_token_choosers.extend(batch.next_token_choosers)
|
||||
stopping_criterias.extend(batch.stopping_criterias)
|
||||
|
||||
|
@ -482,8 +483,8 @@ class Seq2SeqLMBatch(Batch):
|
|||
past_key_values=past_key_values,
|
||||
input_lengths=input_lengths,
|
||||
decoder_input_lengths=decoder_input_lengths,
|
||||
offsets=offsets,
|
||||
token_offsets=token_offsets,
|
||||
prefix_offsets=prefix_offsets,
|
||||
read_offsets=read_offsets,
|
||||
next_token_choosers=next_token_choosers,
|
||||
stopping_criterias=stopping_criterias,
|
||||
max_input_length=max_input_length,
|
||||
|
@ -502,7 +503,6 @@ class Seq2SeqLM(Model):
|
|||
model_id: str,
|
||||
revision: Optional[str] = None,
|
||||
quantize: Optional[str] = None,
|
||||
decode_buffer: int = 3,
|
||||
):
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda")
|
||||
|
@ -514,24 +514,24 @@ class Seq2SeqLM(Model):
|
|||
device = torch.device("cpu")
|
||||
dtype = torch.float32
|
||||
|
||||
self.model = AutoModelForSeq2SeqLM.from_pretrained(
|
||||
model = AutoModelForSeq2SeqLM.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
torch_dtype=dtype,
|
||||
device_map="auto" if torch.cuda.is_available() else None,
|
||||
load_in_8bit=quantize == "bitsandbytes",
|
||||
).eval()
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_id, revision=revision, padding_side="left", truncation_side="left"
|
||||
)
|
||||
tokenizer.bos_token_id = self.model.config.decoder_start_token_id
|
||||
tokenizer.bos_token_id = model.config.decoder_start_token_id
|
||||
|
||||
super(Seq2SeqLM, self).__init__(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=True,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
decode_buffer=decode_buffer,
|
||||
)
|
||||
|
||||
@property
|
||||
|
@ -608,8 +608,8 @@ class Seq2SeqLM(Model):
|
|||
iterator = zip(
|
||||
batch.requests,
|
||||
batch.input_lengths,
|
||||
batch.offsets,
|
||||
batch.token_offsets,
|
||||
batch.prefix_offsets,
|
||||
batch.read_offsets,
|
||||
batch.decoder_input_lengths,
|
||||
logits,
|
||||
batch.next_token_choosers,
|
||||
|
@ -621,8 +621,8 @@ class Seq2SeqLM(Model):
|
|||
for i, (
|
||||
request,
|
||||
input_length,
|
||||
offset,
|
||||
token_offset,
|
||||
prefix_offset,
|
||||
read_offset,
|
||||
decoder_input_length,
|
||||
logits,
|
||||
next_token_chooser,
|
||||
|
@ -643,8 +643,8 @@ class Seq2SeqLM(Model):
|
|||
# Generated token
|
||||
next_token_logprob = logprobs[-1, next_token_id]
|
||||
next_token_id_squeezed = next_token_id.squeeze()
|
||||
next_token_text, offset, token_offset = self.decode_token(
|
||||
all_decoder_input_ids, offset, token_offset
|
||||
next_token_text, prefix_offset, read_offset = self.decode_token(
|
||||
all_decoder_input_ids, prefix_offset, read_offset
|
||||
)
|
||||
|
||||
# Evaluate stopping criteria
|
||||
|
@ -702,8 +702,8 @@ class Seq2SeqLM(Model):
|
|||
batch.all_decoder_input_ids[i] = all_decoder_input_ids
|
||||
batch.input_lengths[i] = input_length
|
||||
batch.decoder_input_lengths[i] = new_decoder_input_length
|
||||
batch.offsets[i] = offset
|
||||
batch.token_offsets[i] = token_offset
|
||||
batch.prefix_offsets[i] = prefix_offset
|
||||
batch.read_offsets[i] = read_offset
|
||||
batch.max_input_length = max(batch.max_input_length, input_length)
|
||||
batch.max_decoder_input_length = max(
|
||||
batch.max_decoder_input_length, new_decoder_input_length
|
||||
|
|
|
@ -16,9 +16,6 @@ from text_generation_server.utils import (
|
|||
initialize_torch_distributed,
|
||||
weight_files,
|
||||
)
|
||||
from text_generation_server.utils.layers import (
|
||||
FastLinear,
|
||||
)
|
||||
from transformers.models.t5.parallel_layers import (
|
||||
TensorParallelRowLinear,
|
||||
TensorParallelColumnLinear,
|
||||
|
@ -73,9 +70,9 @@ class T5Sharded(Seq2SeqLM):
|
|||
rank=rank,
|
||||
world_size=world_size,
|
||||
)
|
||||
self.model = model.eval()
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(Seq2SeqLM, self).__init__(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=True,
|
||||
dtype=dtype,
|
||||
|
|
Loading…
Reference in New Issue