update causal batch for ct2 and fix nf4 (#17)
* update causal batch for ct2 and fix nf4 * bump the ctranslate2 version --------- Co-authored-by: Michael Feil <michael.feil@michaelfeil.eu>
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@ -53,7 +53,7 @@ You may set the `TGICHAT_(USER|ASS|SYS)_(PRE|POST)` environment variables, to wr
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```bash
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```bash
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model=TheBloke/Llama-2-13B-Chat-fp16 # around 14GB Vram.
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model=TheBloke/Llama-2-13B-Chat-fp16 # around 14GB Vram.
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volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
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volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
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image=docker.io/michaelf34/tgi:03-10-2023 # docker image by @michaelfeil
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image=docker.io/michaelf34/tgi:05-11-2023 # docker image by @michaelfeil
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data $image --model-id $model --quantize ct2
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data $image --model-id $model --quantize ct2
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```
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```
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@ -15,9 +15,11 @@ grpcio-status = "^1.51.1"
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grpcio-reflection = "^1.51.1"
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grpcio-reflection = "^1.51.1"
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grpc-interceptor = "^0.15.0"
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grpc-interceptor = "^0.15.0"
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typer = "^0.6.1"
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typer = "^0.6.1"
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accelerate = { version = "^0.19.0", optional = true }
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accelerate = { version = "^0.20.3", optional = true }
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ctranslate2 = { version = "^3.20.0", optional = true }
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ctranslate2 = { version = "^3.23.0", optional = true }
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bitsandbytes = { version = "^0.40.0", optional = true }
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bitsandbytes = { version = "^0.41.1", optional = true }
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torch = { version = "^2.0.1" }
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scipy = "^1.11.3"
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safetensors = "0.3.1"
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safetensors = "0.3.1"
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loguru = "^0.6.0"
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loguru = "^0.6.0"
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opentelemetry-api = "^1.15.0"
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opentelemetry-api = "^1.15.0"
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@ -26,8 +28,8 @@ opentelemetry-instrumentation-grpc = "^0.36b0"
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hf-transfer = "^0.1.2"
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hf-transfer = "^0.1.2"
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sentencepiece = "^0.1.97"
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sentencepiece = "^0.1.97"
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tokenizers = "0.13.3"
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tokenizers = "0.13.3"
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huggingface-hub = "^0.14.1"
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huggingface-hub = "^0.15.1"
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transformers = "4.29.2"
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transformers = "4.32.1"
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einops = "^0.6.1"
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einops = "^0.6.1"
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texttable = { version = "^1.6.7", optional = true }
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texttable = { version = "^1.6.7", optional = true }
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datasets = { version = "^2.14.0", optional = true }
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datasets = { version = "^2.14.0", optional = true }
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@ -1,10 +1,10 @@
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accelerate==0.19.0 ; python_version >= "3.9" and python_version < "4.0"
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accelerate==0.20.3 ; python_version >= "3.9" and python_version < "4.0"
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aiohttp==3.8.5 ; python_version >= "3.9" and python_version < "4.0"
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aiohttp==3.8.5 ; python_version >= "3.9" and python_version < "4.0"
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aiosignal==1.3.1 ; python_version >= "3.9" and python_version < "4.0"
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aiosignal==1.3.1 ; python_version >= "3.9" and python_version < "4.0"
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async-timeout==4.0.2 ; python_version >= "3.9" and python_version < "4.0"
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async-timeout==4.0.2 ; python_version >= "3.9" and python_version < "4.0"
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attrs==23.1.0 ; python_version >= "3.9" and python_version < "4.0"
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attrs==23.1.0 ; python_version >= "3.9" and python_version < "4.0"
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backoff==2.2.1 ; python_version >= "3.9" and python_version < "4.0"
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backoff==2.2.1 ; python_version >= "3.9" and python_version < "4.0"
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bitsandbytes==0.38.1 ; python_version >= "3.9" and python_version < "4.0"
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bitsandbytes==0.41.1 ; python_version >= "3.9" and python_version < "4.0"
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certifi==2023.5.7 ; python_version >= "3.9" and python_version < "4.0"
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certifi==2023.5.7 ; python_version >= "3.9" and python_version < "4.0"
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charset-normalizer==3.1.0 ; python_version >= "3.9" and python_version < "4.0"
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charset-normalizer==3.1.0 ; python_version >= "3.9" and python_version < "4.0"
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click==8.1.3 ; python_version >= "3.9" and python_version < "4.0"
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click==8.1.3 ; python_version >= "3.9" and python_version < "4.0"
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@ -23,7 +23,7 @@ grpcio-reflection==1.56.0 ; python_version >= "3.9" and python_version < "4.0"
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grpcio-status==1.56.0 ; python_version >= "3.9" and python_version < "4.0"
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grpcio-status==1.56.0 ; python_version >= "3.9" and python_version < "4.0"
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grpcio==1.56.0 ; python_version >= "3.9" and python_version < "4.0"
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grpcio==1.56.0 ; python_version >= "3.9" and python_version < "4.0"
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hf-transfer==0.1.3 ; python_version >= "3.9" and python_version < "4.0"
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hf-transfer==0.1.3 ; python_version >= "3.9" and python_version < "4.0"
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huggingface-hub==0.14.1 ; python_version >= "3.9" and python_version < "4.0"
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huggingface-hub==0.15.1 ; python_version >= "3.9" and python_version < "4.0"
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idna==3.4 ; python_version >= "3.9" and python_version < "4.0"
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idna==3.4 ; python_version >= "3.9" and python_version < "4.0"
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jinja2==3.1.2 ; python_version >= "3.9" and python_version < "4.0"
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jinja2==3.1.2 ; python_version >= "3.9" and python_version < "4.0"
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loguru==0.6.0 ; python_version >= "3.9" and python_version < "4.0"
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loguru==0.6.0 ; python_version >= "3.9" and python_version < "4.0"
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@ -56,12 +56,13 @@ safetensors==0.3.1 ; python_version >= "3.9" and python_version < "4.0"
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sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "4.0"
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sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "4.0"
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setuptools==68.0.0 ; python_version >= "3.9" and python_version < "4.0"
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setuptools==68.0.0 ; python_version >= "3.9" and python_version < "4.0"
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six==1.16.0 ; python_version >= "3.9" and python_version < "4.0"
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six==1.16.0 ; python_version >= "3.9" and python_version < "4.0"
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scipy==1.11.3 ; python_version >= "3.9" and python_version < "4.0"
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sympy==1.12 ; python_version >= "3.9" and python_version < "4.0"
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sympy==1.12 ; python_version >= "3.9" and python_version < "4.0"
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texttable==1.6.7 ; python_version >= "3.9" and python_version < "4.0"
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texttable==1.6.7 ; python_version >= "3.9" and python_version < "4.0"
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tokenizers==0.13.3 ; python_version >= "3.9" and python_version < "4.0"
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tokenizers==0.13.3 ; python_version >= "3.9" and python_version < "4.0"
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torch==2.0.1 ; python_version >= "3.9" and python_version < "4.0"
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torch==2.0.1 ; python_version >= "3.9" and python_version < "4.0"
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tqdm==4.65.0 ; python_version >= "3.9" and python_version < "4.0"
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tqdm==4.65.0 ; python_version >= "3.9" and python_version < "4.0"
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transformers==4.29.2 ; python_version >= "3.9" and python_version < "4.0"
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transformers==4.32.1 ; python_version >= "3.9" and python_version < "4.0"
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typer==0.6.1 ; python_version >= "3.9" and python_version < "4.0"
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typer==0.6.1 ; python_version >= "3.9" and python_version < "4.0"
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typing-extensions==4.7.1 ; python_version >= "3.9" and python_version < "4.0"
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typing-extensions==4.7.1 ; python_version >= "3.9" and python_version < "4.0"
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tzdata==2023.3 ; python_version >= "3.9" and python_version < "4.0"
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tzdata==2023.3 ; python_version >= "3.9" and python_version < "4.0"
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@ -23,12 +23,20 @@ import numpy as np
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import os
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import os
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import multiprocessing
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import multiprocessing
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from pathlib import Path
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from pathlib import Path
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from dataclasses import dataclass
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from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
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from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
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from opentelemetry import trace
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from opentelemetry import trace
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from transformers import (
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from transformers import (
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AutoTokenizer,
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AutoTokenizer,
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AutoConfig,
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AutoConfig,
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PreTrainedTokenizerBase
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)
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from text_generation_server.models.types import (
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Batch,
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PrefillTokens,
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Generation,
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GeneratedText,
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)
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)
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from typing import Optional, Tuple, List, Type, Dict
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from typing import Optional, Tuple, List, Type, Dict
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@ -38,9 +46,10 @@ from text_generation_server.models.types import (
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Generation,
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Generation,
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GeneratedText,
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GeneratedText,
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)
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)
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from text_generation_server.pb import generate_pb2
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from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
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from text_generation_server.utils import Sampling
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from text_generation_server.utils import Sampling
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from text_generation_server.models.causal_lm import CausalLMBatch
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try:
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try:
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import ctranslate2
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import ctranslate2
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@ -51,6 +60,434 @@ except ImportError:
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tracer = trace.get_tracer(__name__)
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tracer = trace.get_tracer(__name__)
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@dataclass
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class CT2CausalLMBatch(Batch):
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batch_id: int
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requests: List[generate_pb2.Request]
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requests_idx_mapping: Dict[int, int]
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# Decoder values
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input_ids: torch.Tensor
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attention_mask: torch.Tensor
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position_ids: torch.Tensor
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past_key_values: Optional[List[Tuple]]
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# All tokens
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all_input_ids: List[torch.Tensor]
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# Lengths of all generations present in the batch
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input_lengths: List[int]
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prefix_offsets: List[int]
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read_offsets: List[int]
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# Generation helpers
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next_token_choosers: List[NextTokenChooser]
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stopping_criterias: List[StoppingCriteria]
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# Metadata used for padding
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max_input_length: int
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padding_right_offset: int
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# Maximum number of tokens this batch will grow to
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max_tokens: int
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# Past metadata
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keys_head_dim_last: bool = True
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def to_pb(self) -> generate_pb2.CachedBatch:
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return generate_pb2.CachedBatch(
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id=self.batch_id,
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request_ids=[r.id for r in self.requests],
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size=len(self),
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max_tokens=self.max_tokens,
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)
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@classmethod
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def from_pb(
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cls,
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pb: generate_pb2.Batch,
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tokenizer: PreTrainedTokenizerBase,
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dtype: torch.dtype,
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device: torch.device,
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) -> "CT2CausalLMBatch":
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inputs = []
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next_token_choosers = []
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stopping_criterias = []
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prefix_offsets = []
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read_offsets = []
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requests_idx_mapping = {}
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# Parse batch
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max_truncation = 0
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padding_right_offset = 0
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max_decode_tokens = 0
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for i, r in enumerate(pb.requests):
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requests_idx_mapping[r.id] = i
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inputs.append(r.inputs)
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next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
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stopping_criteria = StoppingCriteria.from_pb(
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r.stopping_parameters, tokenizer
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)
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stopping_criterias.append(stopping_criteria)
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max_truncation = max(max_truncation, r.truncate)
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max_decode_tokens += stopping_criteria.max_new_tokens
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padding_right_offset = max(
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padding_right_offset, stopping_criteria.max_new_tokens
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)
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tokenized_inputs = tokenizer(
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inputs,
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return_tensors="pt",
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padding=True,
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return_token_type_ids=False,
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truncation=True,
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max_length=max_truncation,
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).to(device)
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for _ in pb.requests:
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input_len = tokenized_inputs["input_ids"].shape[1]
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prefix_offsets.append(input_len - 5)
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read_offsets.append(input_len)
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input_lengths = tokenized_inputs["attention_mask"].sum(1)
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max_input_length = input_lengths.max()
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input_ids = tokenized_inputs["input_ids"]
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# Allocate maximum attention_mask
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attention_mask = input_ids.new_zeros(
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(pb.size, max_input_length + padding_right_offset)
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)
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# Copy tokenizer attention_mask into fully allocated attention_mask
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attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
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position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
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position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
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all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1)
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max_tokens = len(inputs) * (max_input_length + max_decode_tokens)
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return cls(
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batch_id=pb.id,
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requests=pb.requests,
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requests_idx_mapping=requests_idx_mapping,
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=None,
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all_input_ids=list(all_input_ids),
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input_lengths=input_lengths.tolist(),
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prefix_offsets=prefix_offsets,
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read_offsets=read_offsets,
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next_token_choosers=next_token_choosers,
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stopping_criterias=stopping_criterias,
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max_input_length=max_input_length.item(),
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padding_right_offset=padding_right_offset,
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max_tokens=max_tokens,
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)
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@tracer.start_as_current_span("filter")
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def filter(self, request_ids: List[int]) -> Optional["CT2CausalLMBatch"]:
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if len(request_ids) == 0:
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raise ValueError("Batch must have at least one request")
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if len(request_ids) == len(self):
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return self
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keep_indices = []
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# New values after filtering
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requests_idx_mapping = {}
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requests = []
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input_lengths = []
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prefix_offsets = []
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read_offsets = []
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all_input_ids = []
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max_input_length = 0
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next_token_choosers = []
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stopping_criterias = []
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total_remaining_decode_tokens = 0
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new_padding_right_offset = 0
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for i, request_id in enumerate(request_ids):
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idx = self.requests_idx_mapping[request_id]
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requests_idx_mapping[request_id] = i
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keep_indices.append(idx)
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requests.append(self.requests[idx])
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prefix_offsets.append(self.prefix_offsets[idx])
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read_offsets.append(self.read_offsets[idx])
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all_input_ids.append(self.all_input_ids[idx])
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request_input_length = self.input_lengths[idx]
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input_lengths.append(request_input_length)
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max_input_length = max(max_input_length, request_input_length)
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next_token_choosers.append(self.next_token_choosers[idx])
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stopping_criteria = self.stopping_criterias[idx]
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stopping_criterias.append(stopping_criteria)
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remaining_decode_tokens = (
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stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
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)
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total_remaining_decode_tokens += remaining_decode_tokens
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new_padding_right_offset = max(
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new_padding_right_offset, remaining_decode_tokens
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)
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# Apply indices to input_ids, attention mask, past key values and other items that need to be cached
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input_ids = self.input_ids[keep_indices]
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|
position_ids = self.position_ids[keep_indices]
|
||||||
|
self.attention_mask = self.attention_mask[
|
||||||
|
keep_indices,
|
||||||
|
-(self.padding_right_offset + max_input_length) : (
|
||||||
|
self.attention_mask.shape[1] - self.padding_right_offset
|
||||||
|
)
|
||||||
|
+ new_padding_right_offset,
|
||||||
|
]
|
||||||
|
|
||||||
|
# Ensure that past_key_values tensors can be updated in-place
|
||||||
|
if type(self.past_key_values[0]) == tuple:
|
||||||
|
self.past_key_values = [list(layer) for layer in self.past_key_values]
|
||||||
|
|
||||||
|
# Update tensors in-place to allow incremental garbage collection
|
||||||
|
past_kv_length = max_input_length - 1
|
||||||
|
for layer in self.past_key_values:
|
||||||
|
past_keys, past_values = layer
|
||||||
|
if len(past_keys.shape) == 3:
|
||||||
|
# Force past to be of dim [self_size, num_heads, ...] for easy indexing
|
||||||
|
past_keys = past_keys.view(len(self), -1, *past_keys.shape[-2:])
|
||||||
|
past_values = past_values.view(len(self), -1, *past_values.shape[-2:])
|
||||||
|
if self.keys_head_dim_last:
|
||||||
|
layer[0] = past_keys[keep_indices, :, -past_kv_length:, :]
|
||||||
|
else:
|
||||||
|
layer[0] = past_keys[keep_indices, :, :, -past_kv_length:]
|
||||||
|
del past_keys
|
||||||
|
layer[1] = past_values[keep_indices, :, -past_kv_length:, :]
|
||||||
|
del past_values
|
||||||
|
|
||||||
|
max_tokens = len(request_ids) * max_input_length + total_remaining_decode_tokens
|
||||||
|
|
||||||
|
self.requests = requests
|
||||||
|
self.requests_idx_mapping = requests_idx_mapping
|
||||||
|
self.input_ids = input_ids
|
||||||
|
self.position_ids = position_ids
|
||||||
|
self.all_input_ids = all_input_ids
|
||||||
|
self.input_lengths = input_lengths
|
||||||
|
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
|
||||||
|
self.padding_right_offset = new_padding_right_offset
|
||||||
|
self.max_tokens = max_tokens
|
||||||
|
|
||||||
|
return self
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
@tracer.start_as_current_span("concatenate")
|
||||||
|
def concatenate(cls, batches: List["CT2CausalLMBatch"]) -> "CT2CausalLMBatch":
|
||||||
|
# Used for padding
|
||||||
|
total_batch_size = 0
|
||||||
|
max_input_length = 0
|
||||||
|
padding_right_offset = 0
|
||||||
|
for batch in batches:
|
||||||
|
total_batch_size += len(batch)
|
||||||
|
max_input_length = max(max_input_length, batch.max_input_length)
|
||||||
|
padding_right_offset = max(padding_right_offset, batch.padding_right_offset)
|
||||||
|
|
||||||
|
# Batch attributes
|
||||||
|
requests = []
|
||||||
|
requests_idx_mapping = {}
|
||||||
|
input_lengths = []
|
||||||
|
prefix_offsets = []
|
||||||
|
read_offsets = []
|
||||||
|
all_input_ids = []
|
||||||
|
next_token_choosers = []
|
||||||
|
stopping_criterias = []
|
||||||
|
max_tokens = 0
|
||||||
|
|
||||||
|
# Batch tensors
|
||||||
|
input_ids = None
|
||||||
|
attention_mask = None
|
||||||
|
position_ids = None
|
||||||
|
past_key_values = []
|
||||||
|
|
||||||
|
# Used for slicing correctly inside the tensors
|
||||||
|
# Equivalent to a cumsum on batch sizes
|
||||||
|
start_index = 0
|
||||||
|
for i, batch in enumerate(batches):
|
||||||
|
requests.extend(batch.requests)
|
||||||
|
input_lengths.extend(batch.input_lengths)
|
||||||
|
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)
|
||||||
|
|
||||||
|
if i == 0:
|
||||||
|
requests_idx_mapping = batch.requests_idx_mapping
|
||||||
|
else:
|
||||||
|
# We need to offset the mapping for each batch by the cumulative batch size
|
||||||
|
for k, v in batch.requests_idx_mapping.items():
|
||||||
|
requests_idx_mapping[k] = v + start_index
|
||||||
|
|
||||||
|
# Slicing end index for this batch
|
||||||
|
end_index = start_index + len(batch)
|
||||||
|
|
||||||
|
# We only concatenate batches that did at least one step
|
||||||
|
# if batch.past_key_values is None:
|
||||||
|
# raise ValueError("only concatenate prefilled batches")
|
||||||
|
|
||||||
|
# Create empty tensor
|
||||||
|
# input_ids is always of shape [batch_size, 1]
|
||||||
|
# We do not need to pad it
|
||||||
|
if input_ids is None:
|
||||||
|
input_ids = batch.input_ids.new_empty((total_batch_size, 1))
|
||||||
|
# Copy to correct indices
|
||||||
|
input_ids[start_index:end_index] = batch.input_ids
|
||||||
|
|
||||||
|
# Create padded tensor
|
||||||
|
if attention_mask is None:
|
||||||
|
attention_mask = batch.attention_mask.new_zeros(
|
||||||
|
(total_batch_size, max_input_length + padding_right_offset),
|
||||||
|
)
|
||||||
|
|
||||||
|
# We need to slice the attention mask to remove padding from previous steps
|
||||||
|
# and to remove unused allocated space
|
||||||
|
left_offset = max_input_length - batch.max_input_length
|
||||||
|
batch_left_offset = (
|
||||||
|
batch.attention_mask.shape[1]
|
||||||
|
- batch.max_input_length
|
||||||
|
- batch.padding_right_offset
|
||||||
|
)
|
||||||
|
attention_mask[
|
||||||
|
start_index:end_index,
|
||||||
|
left_offset:-padding_right_offset,
|
||||||
|
] = batch.attention_mask[
|
||||||
|
:,
|
||||||
|
batch_left_offset : -batch.padding_right_offset,
|
||||||
|
]
|
||||||
|
|
||||||
|
# Create empty tensor
|
||||||
|
# position_ids is always of shape [batch_size, 1]
|
||||||
|
if position_ids is None:
|
||||||
|
position_ids = batch.position_ids.new_empty((total_batch_size, 1))
|
||||||
|
position_ids[start_index:end_index] = batch.position_ids
|
||||||
|
|
||||||
|
# Shenanigans to get dimensions because BLOOM outputs a past with a different shape
|
||||||
|
# BLOOM Keys: [batch_size * num_heads, head_dim, seq_length]
|
||||||
|
# BLOOM Values: [batch_size * num_heads, seq_length, head_dim]
|
||||||
|
# And ensure that we can update tensors in-place
|
||||||
|
# if type(batch.past_key_values[0]) == tuple:
|
||||||
|
# batch.past_key_values = [
|
||||||
|
# [t.view(len(batch), -1, *t.shape[-2:]) for t in layer]
|
||||||
|
# for layer in batch.past_key_values
|
||||||
|
# ]
|
||||||
|
# elif len(batch.past_key_values[0][0].shape) == 3:
|
||||||
|
# for layer in batch.past_key_values:
|
||||||
|
# for k, t in enumerate(layer):
|
||||||
|
# layer[k] = t.view(len(batch), -1, *t.shape[-2:])
|
||||||
|
|
||||||
|
# Add eventual padding tokens that were added while concatenating
|
||||||
|
max_tokens += batch.max_tokens + (
|
||||||
|
max_input_length - batch.max_input_length
|
||||||
|
) * len(batch)
|
||||||
|
|
||||||
|
start_index = end_index
|
||||||
|
|
||||||
|
# first_past_kvs = batches[0].past_key_values
|
||||||
|
# _, num_heads, padded_sequence_length, head_dim = first_past_kvs[0][1].shape
|
||||||
|
|
||||||
|
# padded_past_values_shape = (
|
||||||
|
# total_batch_size,
|
||||||
|
# num_heads,
|
||||||
|
# max_input_length - 1,
|
||||||
|
# head_dim,
|
||||||
|
# )
|
||||||
|
|
||||||
|
# if batches[0].keys_head_dim_last:
|
||||||
|
# padded_past_keys_shape = padded_past_values_shape
|
||||||
|
# else:
|
||||||
|
# # seq_length is last for BLOOM
|
||||||
|
# padded_past_keys_shape = (
|
||||||
|
# total_batch_size,
|
||||||
|
# num_heads,
|
||||||
|
# head_dim,
|
||||||
|
# max_input_length - 1,
|
||||||
|
# )
|
||||||
|
|
||||||
|
# Iterate over attention layers
|
||||||
|
# Concatenate past key values layer by layer to allow incremental garbage collection
|
||||||
|
# for j in range(len(first_past_kvs)):
|
||||||
|
# padded_past_keys = first_past_kvs[j][0].new_zeros(padded_past_keys_shape)
|
||||||
|
# start_index = 0
|
||||||
|
# for batch in batches:
|
||||||
|
# past_keys = batch.past_key_values[j][0]
|
||||||
|
# # Clear reference to the original tensor
|
||||||
|
# batch.past_key_values[j][0] = None
|
||||||
|
|
||||||
|
# # Slicing end index for this batch
|
||||||
|
# end_index = start_index + len(batch)
|
||||||
|
# # We slice the keys to remove the padding from previous batches
|
||||||
|
# past_seq_len = batch.max_input_length - 1
|
||||||
|
# if batch.keys_head_dim_last:
|
||||||
|
# padded_past_keys[
|
||||||
|
# start_index:end_index, :, -past_seq_len:, :
|
||||||
|
# ] = past_keys[:, :, -past_seq_len:, :]
|
||||||
|
# else:
|
||||||
|
# # BLOOM case
|
||||||
|
# padded_past_keys[
|
||||||
|
# start_index:end_index, :, :, -past_seq_len:
|
||||||
|
# ] = past_keys[:, :, :, -past_seq_len:]
|
||||||
|
# del past_keys
|
||||||
|
|
||||||
|
# start_index = end_index
|
||||||
|
|
||||||
|
# padded_past_values = first_past_kvs[j][1].new_zeros(
|
||||||
|
# padded_past_values_shape
|
||||||
|
# )
|
||||||
|
# start_index = 0
|
||||||
|
# for batch in batches:
|
||||||
|
# past_values = batch.past_key_values[j][1]
|
||||||
|
# # Clear reference to the original tensor
|
||||||
|
# batch.past_key_values[j][1] = None
|
||||||
|
|
||||||
|
# # Slicing end index for this batch
|
||||||
|
# end_index = start_index + len(batch)
|
||||||
|
# # We slice the past values to remove the padding from previous batches
|
||||||
|
# past_seq_len = batch.max_input_length - 1
|
||||||
|
# padded_past_values[
|
||||||
|
# start_index:end_index, :, -past_seq_len:, :
|
||||||
|
# ] = past_values[:, :, -past_seq_len:, :]
|
||||||
|
# del past_values
|
||||||
|
|
||||||
|
# # Update values
|
||||||
|
# start_index = end_index
|
||||||
|
|
||||||
|
# past_key_values.append([padded_past_keys, padded_past_values])
|
||||||
|
|
||||||
|
return cls(
|
||||||
|
batch_id=batches[0].batch_id,
|
||||||
|
requests=requests,
|
||||||
|
requests_idx_mapping=requests_idx_mapping,
|
||||||
|
input_ids=input_ids,
|
||||||
|
attention_mask=attention_mask,
|
||||||
|
position_ids=position_ids,
|
||||||
|
past_key_values=past_key_values,
|
||||||
|
all_input_ids=all_input_ids,
|
||||||
|
input_lengths=input_lengths,
|
||||||
|
prefix_offsets=prefix_offsets,
|
||||||
|
read_offsets=read_offsets,
|
||||||
|
next_token_choosers=next_token_choosers,
|
||||||
|
stopping_criterias=stopping_criterias,
|
||||||
|
max_input_length=max_input_length,
|
||||||
|
padding_right_offset=padding_right_offset,
|
||||||
|
keys_head_dim_last=batches[0].keys_head_dim_last,
|
||||||
|
max_tokens=max_tokens,
|
||||||
|
)
|
||||||
|
|
||||||
|
def __len__(self):
|
||||||
|
return len(self.requests)
|
||||||
|
|
||||||
class CT2CausalLM(Model):
|
class CT2CausalLM(Model):
|
||||||
def __init__(
|
def __init__(
|
||||||
self,
|
self,
|
||||||
|
@ -176,8 +613,8 @@ class CT2CausalLM(Model):
|
||||||
)
|
)
|
||||||
|
|
||||||
@property
|
@property
|
||||||
def batch_type(self) -> Type[CausalLMBatch]:
|
def batch_type(self) -> Type[CT2CausalLMBatch]:
|
||||||
return CausalLMBatch
|
return CT2CausalLMBatch
|
||||||
|
|
||||||
def decode(self, generated_ids: List[int]) -> str:
|
def decode(self, generated_ids: List[int]) -> str:
|
||||||
return self.tokenizer.decode(
|
return self.tokenizer.decode(
|
||||||
|
@ -221,8 +658,8 @@ class CT2CausalLM(Model):
|
||||||
|
|
||||||
@tracer.start_as_current_span("generate_token")
|
@tracer.start_as_current_span("generate_token")
|
||||||
def generate_token(
|
def generate_token(
|
||||||
self, batch: CausalLMBatch
|
self, batch: CT2CausalLMBatch
|
||||||
) -> Tuple[List[Generation], Optional[CausalLMBatch]]:
|
) -> Tuple[List[Generation], Optional[CT2CausalLMBatch]]:
|
||||||
logits, past = self.forward_ct2(batch.all_input_ids, batch.input_lengths)
|
logits, past = self.forward_ct2(batch.all_input_ids, batch.input_lengths)
|
||||||
|
|
||||||
# Results
|
# Results
|
||||||
|
|
|
@ -185,8 +185,8 @@ class FlashLlamaAttention(torch.nn.Module):
|
||||||
self.hidden_size = config.hidden_size
|
self.hidden_size = config.hidden_size
|
||||||
self.head_size = self.hidden_size // self.num_heads
|
self.head_size = self.hidden_size // self.num_heads
|
||||||
|
|
||||||
self.rotary_emb = PositionRotaryEmbedding.load(
|
self.rotary_emb = PositionRotaryEmbedding.static(
|
||||||
prefix=f"{prefix}.rotary_emb", weights=weights
|
dim=self.head_size, device=weights.device, base=10000.0,
|
||||||
)
|
)
|
||||||
|
|
||||||
self.softmax_scale = self.head_size**-0.5
|
self.softmax_scale = self.head_size**-0.5
|
||||||
|
|
Loading…
Reference in New Issue