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Cpu tgi (#1936) 

* add CPU tgi support

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

* ipex distributed ops support

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

---------

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Funtowicz Morgan <mfuntowicz@users.noreply.github.com>
This commit is contained in:
Wang, Yi 2024-06-25 18:21:29 +08:00 committed by GitHub
parent b69f078041
commit b64c70c9e7
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GPG Key ID: B5690EEEBB952194
17 changed files with 221 additions and 77 deletions
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87
Dockerfile_intel
View File

@ -1,3 +1,5 @@
ARG PLATFORM=xpu
FROM lukemathwalker/cargo-chef:latest-rust-1.79 AS chef
WORKDIR /usr/src
@ -37,7 +39,8 @@ RUN cargo build --profile release-opt
# Text Generation Inference base image for Intel
FROM intel/intel-extension-for-pytorch:2.1.30-xpu as base
FROM intel/intel-extension-for-pytorch:2.1.30-xpu as xpu
USER root
# libssl.so.1.1 is not installed on Ubuntu 22.04 by default, install it
@ -59,7 +62,7 @@ ENV HUGGINGFACE_HUB_CACHE=/data \
WORKDIR /usr/src
RUN wget https://intel-extension-for-pytorch.s3.amazonaws.com/ipex_dev/xpu/torch-2.1.0.post1%2Bcxx11.abi-cp310-cp310-linux_x86_64.whl && pip install torch-2.1.0.post1+cxx11.abi-cp310-cp310-linux_x86_64.whl
RUN git clone https://github.com/intel/intel-extension-for-pytorch && cd intel-extension-for-pytorch && git checkout -b group_rope origin/dev/gqa_rope
RUN git clone https://github.com/intel/intel-extension-for-pytorch && cd intel-extension-for-pytorch && git checkout -b distributed origin/dev/distributed
# Install server
COPY proto proto
@ -89,8 +92,84 @@ COPY --from=builder /usr/src/target/release-opt/text-generation-router /usr/loca
# Install launcher
COPY --from=builder /usr/src/target/release-opt/text-generation-launcher /usr/local/bin/text-generation-launcher
# Final image
FROM base
# Text Generation Inference base image for Intel-cpu
FROM ubuntu:22.04 as cpu
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
curl \
ca-certificates \
make \
g++ \
git \
wget \
cmake
ENV HUGGINGFACE_HUB_CACHE=/data \
HF_HUB_ENABLE_HF_TRANSFER=1 \
PORT=80
ARG MAMBA_VERSION=23.1.0-1
ARG PYTHON_VERSION='3.10.10'
# Automatically set by buildx
ARG TARGETPLATFORM
ENV PATH /opt/conda/bin:$PATH
# TGI seem to require libssl.so.1.1 instead of libssl.so.3 so we can't use ubuntu 22.04. Ubuntu 20.04 has python==3.8, and TGI requires python>=3.9, hence the need for miniconda.
# Install mamba
# translating Docker's TARGETPLATFORM into mamba arches
RUN case ${TARGETPLATFORM} in \
"linux/arm64") MAMBA_ARCH=aarch64 ;; \
*) MAMBA_ARCH=x86_64 ;; \
esac && \
curl -fsSL -v -o ~/mambaforge.sh -O "https://github.com/conda-forge/miniforge/releases/download/${MAMBA_VERSION}/Mambaforge-${MAMBA_VERSION}-Linux-${MAMBA_ARCH}.sh"
RUN chmod +x ~/mambaforge.sh && \
bash ~/mambaforge.sh -b -p /opt/conda && \
rm ~/mambaforge.sh
RUN conda install -c conda-forge gperftools mkl
RUN pip install https://download.pytorch.org/whl/nightly/cpu/torch-2.4.0.dev20240612%2Bcpu-cp310-cp310-linux_x86_64.whl
RUN pip install https://download.pytorch.org/whl/nightly/cpu/torchvision-0.19.0.dev20240612%2Bcpu-cp310-cp310-linux_x86_64.whl
RUN pip install https://download.pytorch.org/whl/nightly/cpu/torchaudio-2.4.0.dev20240612%2Bcpu-cp310-cp310-linux_x86_64.whl
WORKDIR /usr/src
RUN git clone https://github.com/intel/intel-extension-for-pytorch && cd intel-extension-for-pytorch && git checkout eda7a7c42df6f9a64e0de9c2b69304ee02f2c32a
RUN git clone https://github.com/intel/torch-ccl.git && cd torch-ccl && git checkout ccl_torch_dev_0131
RUN cd intel-extension-for-pytorch && git submodule sync && git submodule update --init --recursive && python setup.py install
RUN cd torch-ccl && git submodule sync && git submodule update --init --recursive && pip install .
ENV LD_PRELOAD=/opt/conda/lib/libtcmalloc.so:/opt/conda/lib/libiomp5.so
ENV CCL_ROOT=/opt/conda/lib/python3.10/site-packages/oneccl_bindings_for_pytorch
ENV I_MPI_ROOT=/opt/conda/lib/python3.10/site-packages/oneccl_bindings_for_pytorch
ENV FI_PROVIDER_PATH=/opt/conda/lib/python3.10/site-packages/oneccl_bindings_for_pytorch/opt/mpi/libfabric/lib/prov:/usr/lib64/libfabric
ENV LD_LIBRARY_PATH=/opt/conda/lib/python3.10/site-packages/oneccl_bindings_for_pytorch/opt/mpi/libfabric/lib:/opt/conda/lib/python3.10/site-packages/oneccl_bindings_for_pytorch/lib
ENV KMP_BLOCKTIME=1
ENV KMP_TPAUSE=0
ENV KMP_FORKJOIN_BARRIER_PATTERN=dist,dist
ENV KMP_PLAIN_BARRIER_PATTERN=dist,dist
ENV KMP_REDUCTION_BARRIER_PATTERN=dist,dist
# Install server
COPY proto proto
COPY server server
COPY server/Makefile server/Makefile
RUN cd server && \
make gen-server && \
pip install -r requirements_intel.txt && \
pip install ".[accelerate, peft, outlines]" --no-cache-dir
# Install benchmarker
COPY --from=builder /usr/src/target/release-opt/text-generation-benchmark /usr/local/bin/text-generation-benchmark
# Install router
COPY --from=builder /usr/src/target/release-opt/text-generation-router /usr/local/bin/text-generation-router
# Install launcher
COPY --from=builder /usr/src/target/release-opt/text-generation-launcher /usr/local/bin/text-generation-launcher
FROM ${PLATFORM} as final
ENTRYPOINT ["text-generation-launcher"]
CMD ["--json-output"]

4
server/text_generation_server/layers/attention/__init__.py
View File

@ -1,4 +1,4 @@
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.import_utils import SYSTEM, IPEX_AVAIL
import os
if os.getenv("USE_FLASH_ATTENTION", "").lower() == "false":
@ -7,7 +7,7 @@ if SYSTEM == "cuda":
from .cuda import attention, paged_attention, reshape_and_cache, SUPPORTS_WINDOWING
elif SYSTEM == "rocm":
from .rocm import attention, paged_attention, reshape_and_cache, SUPPORTS_WINDOWING
elif SYSTEM == "xpu":
elif IPEX_AVAIL:
from .xpu import attention, paged_attention, reshape_and_cache, SUPPORTS_WINDOWING
else:
raise ImportError(f"System {SYSTEM} doesn't support flash/paged attention")

5
server/text_generation_server/layers/attention/xpu.py
View File

@ -1,5 +1,6 @@
import intel_extension_for_pytorch as ipex
import torch
from text_generation_server.models.flash_causal_lm import BLOCK_SIZE
SUPPORTS_WINDOWING = False
@ -56,8 +57,6 @@ def paged_attention(
input_lengths: torch.Tensor,
max_s: int,
):
query = query.contiguous()
block_size = value_cache.shape[3]
return ipex.llm.modules.PagedAttention.single_query_cached_kv_attention(
out,
query,
@ -67,7 +66,7 @@ def paged_attention(
softmax_scale,
block_tables,
input_lengths,
block_size,
BLOCK_SIZE,
max_s,
None,
)

24
server/text_generation_server/layers/layernorm.py
View File

@ -3,6 +3,7 @@ from torch import nn
from accelerate import init_empty_weights
from text_generation_server.utils.import_utils import (
SYSTEM,
IPEX_AVAIL,
)
@ -82,18 +83,20 @@ elif SYSTEM == "rocm":
return super().forward(hidden_states), residual
elif SYSTEM == "xpu":
elif IPEX_AVAIL:
import intel_extension_for_pytorch as ipex
class FastLayerNorm(nn.LayerNorm):
def forward(self, hidden_states, residual=None):
res_out = hidden_states
out = ipex.llm.functional.add_layer_norm(
residual, hidden_states, self.weight, self.bias, self.eps, True
residual,
hidden_states,
self.weight,
self.bias,
self.eps,
residual is not None,
)
if residual is not None:
res_out = residual
return out, res_out
return out, residual if residual is not None else hidden_states
class FastRMSNorm(nn.Module):
@ -109,19 +112,16 @@ class FastRMSNorm(nn.Module):
return cls(weight, eps)
def forward(self, hidden_states, residual=None):
if SYSTEM == "xpu":
residual_out = hidden_states
if IPEX_AVAIL:
out = ipex.llm.functional.add_rms_norm(
residual,
hidden_states,
self.weight,
None,
self.variance_epsilon,
True,
residual is not None,
)
if residual is not None:
residual_out = residual
return out, residual_out
return out, residual if residual is not None else hidden_states
elif hidden_states.shape[-1] > 8192:
if residual is not None:
hidden_states += residual

6
server/text_generation_server/layers/rotary.py
View File

@ -2,14 +2,14 @@ import os
import torch
from torch import nn
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.import_utils import SYSTEM, IPEX_AVAIL
if SYSTEM == "cuda":
from flash_attn.layers.rotary import RotaryEmbedding
import rotary_emb
elif SYSTEM == "rocm":
from vllm._C import ops
elif SYSTEM == "xpu":
elif IPEX_AVAIL:
import intel_extension_for_pytorch as ipex
@ -69,7 +69,7 @@ class PositionRotaryEmbedding(nn.Module):
# Inplace operation, updating query and key.
ops.rotary_embedding(query, key, head_size, cos, sin, True)
elif SYSTEM == "xpu":
elif IPEX_AVAIL:
ipex.llm.functional.rotary_embedding(
query, key, sin, cos, query.size(-1), True
)

19
server/text_generation_server/layers/tensor_parallel.py
View File

@ -3,6 +3,10 @@ from torch.nn import functional as F
from typing import Iterable, List
from text_generation_server.layers.linear import get_linear, FastLinear
from text_generation_server.layers.exl2 import Exl2Weight
from text_generation_server.utils.import_utils import IPEX_AVAIL
if IPEX_AVAIL:
import intel_extension_for_pytorch as ipex
class LayerConcat(torch.nn.Module):
@ -96,7 +100,11 @@ class TensorParallelHead(SuperLayer):
local_out = gather_input.T
torch.mm(input, self.linear.weight.T, out=local_out)
if IPEX_AVAIL:
ipex.distributed.all_gather_into_tensor(
world_out, gather_input, group=self.process_group
)
else:
torch.distributed.all_gather_into_tensor(
world_out, gather_input, group=self.process_group
)
@ -109,6 +117,9 @@ class TensorParallelHead(SuperLayer):
world_output = [
torch.empty_like(output) for _ in range(self.process_group.size())
]
if IPEX_AVAIL:
ipex.distributed.all_gather(world_output, output, group=self.process_group)
else:
torch.distributed.all_gather(world_output, output, group=self.process_group)
world_output = torch.cat(world_output, dim=-1)
return world_output
@ -206,6 +217,9 @@ class TensorParallelRowLinear(SuperLayer):
def forward(self, input: torch.Tensor, reduce: bool = True) -> torch.Tensor:
out = super().forward(input)
if self.process_group.size() > 1 and reduce:
if IPEX_AVAIL:
ipex.distributed.all_reduce(out, group=self.process_group)
else:
torch.distributed.all_reduce(out, group=self.process_group)
return out
@ -243,5 +257,8 @@ class TensorParallelEmbedding(torch.nn.Module):
)
out = torch.nn.functional.embedding(input, self.weight)
if self.reduce and self.process_group.size() > 1:
if IPEX_AVAIL:
ipex.distributed.all_reduce(out, group=self.process_group)
else:
torch.distributed.all_reduce(out, group=self.process_group)
return out

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

@ -20,9 +20,9 @@ from torch import nn
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple, Any
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.import_utils import IPEX_AVAIL
if SYSTEM != "xpu":
if not IPEX_AVAIL:
from vllm.model_executor.layers.fused_moe import fused_moe
from text_generation_server.layers.attention import (

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

@ -24,9 +24,9 @@ import torch.distributed
import numpy as np
from torch import nn
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.import_utils import IPEX_AVAIL
if SYSTEM != "xpu":
if not IPEX_AVAIL:
from vllm.model_executor.layers.fused_moe import fused_moe
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig

19
server/text_generation_server/models/flash_causal_lm.py
View File

@ -15,7 +15,7 @@ from typing import Iterable, Optional, Tuple, List, Type, Dict
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
from text_generation_server.utils.chunks import concat_text_chunks
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.import_utils import SYSTEM, IPEX_AVAIL
from text_generation_server.models import Model
from text_generation_server.utils.tokens import batch_top_tokens
from text_generation_server.utils.dist import RANK
@ -773,6 +773,23 @@ class FlashCausalLM(Model):
else:
x = BLOCK_SIZE // element_size
if IPEX_AVAIL and SYSTEM == "cpu":
self.kv_cache = [
(
torch.empty(
(num_blocks, num_heads, BLOCK_SIZE, head_size),
dtype=dtype,
device=device,
),
torch.empty(
(num_blocks, num_heads, BLOCK_SIZE, head_size),
dtype=dtype,
device=device,
),
)
for _ in range(num_layers)
]
else:
self.kv_cache = [
(
torch.empty(

5
server/text_generation_server/models/flash_gpt2.py
View File

@ -15,7 +15,7 @@ from text_generation_server.utils import (
weight_files,
Weights,
)
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.import_utils import SYSTEM, IPEX_AVAIL
tracer = trace.get_tracer(__name__)
@ -37,6 +37,9 @@ class FlashGPT2(FlashCausalLM):
elif SYSTEM == "xpu":
device = torch.device(f"xpu:{rank}")
dtype = torch.float16 if dtype is None else dtype
elif IPEX_AVAIL:
device = torch.device("cpu")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashGPT2 is only available on GPU")

5
server/text_generation_server/models/flash_llama.py
View File

@ -17,7 +17,7 @@ from text_generation_server.utils import (
tracer = trace.get_tracer(__name__)
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.import_utils import SYSTEM, IPEX_AVAIL
class FlashLlama(FlashCausalLM):
@ -37,6 +37,9 @@ class FlashLlama(FlashCausalLM):
elif SYSTEM == "xpu":
device = torch.device(f"xpu:{rank}")
dtype = torch.float16 if dtype is None else dtype
elif IPEX_AVAIL:
device = torch.device("cpu")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashLlama is only available on GPU")

5
server/text_generation_server/models/flash_mistral.py
View File

@ -16,7 +16,7 @@ from text_generation_server.utils import (
weight_files,
Weights,
)
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.import_utils import SYSTEM, IPEX_AVAIL
tracer = trace.get_tracer(__name__)
@ -41,6 +41,9 @@ class BaseFlashMistral(FlashCausalLM):
elif SYSTEM == "xpu":
device = torch.device(f"xpu:{rank}")
dtype = torch.float16 if dtype is None else dtype
elif IPEX_AVAIL:
device = torch.device("cpu")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashMistral is only available on GPU")

5
server/text_generation_server/models/flash_neox.py
View File

@ -14,7 +14,7 @@ from text_generation_server.utils import (
weight_files,
Weights,
)
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.import_utils import SYSTEM, IPEX_AVAIL
tracer = trace.get_tracer(__name__)
@ -36,6 +36,9 @@ class FlashNeoXSharded(FlashCausalLM):
elif SYSTEM == "xpu":
device = torch.device(f"xpu:{rank}")
dtype = torch.float16 if dtype is None else dtype
elif IPEX_AVAIL:
device = torch.device("cpu")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashNeoX is only available on GPU")

5
server/text_generation_server/models/flash_rw.py
View File

@ -15,7 +15,7 @@ from text_generation_server.utils import (
weight_files,
Weights,
)
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.import_utils import SYSTEM, IPEX_AVAIL
tracer = trace.get_tracer(__name__)
@ -37,6 +37,9 @@ class FlashRWSharded(FlashCausalLM):
elif SYSTEM == "xpu":
device = torch.device(f"xpu:{rank}")
dtype = torch.float16 if dtype is None else dtype
elif IPEX_AVAIL:
device = torch.device("cpu")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashRW is only available on GPU")

5
server/text_generation_server/models/flash_santacoder.py
View File

@ -18,7 +18,7 @@ from text_generation_server.utils import (
Weights,
)
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.import_utils import SYSTEM, IPEX_AVAIL
tracer = trace.get_tracer(__name__)
@ -40,6 +40,9 @@ class FlashSantacoderSharded(FlashCausalLM):
elif SYSTEM == "xpu":
device = torch.device(f"xpu:{rank}")
dtype = torch.float16 if dtype is None else dtype
elif IPEX_AVAIL:
device = torch.device("cpu")
dtype = torch.bfloat16 if dtype is None else dtype
else:
raise NotImplementedError("FlashSantacoderSharded is only available on GPU")

19
server/text_generation_server/utils/dist.py
View File

@ -3,6 +3,7 @@ import torch
from datetime import timedelta
from loguru import logger
from text_generation_server.utils.import_utils import IPEX_AVAIL
# Tensor Parallelism settings
RANK = int(os.getenv("RANK", "0"))
@ -57,13 +58,6 @@ def initialize_torch_distributed():
options.is_high_priority_stream = True
options._timeout = timedelta(seconds=60)
else:
try:
import oneccl_bindings_for_pytorch
backend = "ccl"
if os.getenv("CCL_WORKER_COUNT", None) is None:
os.environ["CCL_WORKER_COUNT"] = str(1)
except ImportError:
backend = "gloo"
options = None
@ -75,6 +69,17 @@ def initialize_torch_distributed():
if not torch.distributed.is_initialized():
# Call the init process.
if IPEX_AVAIL:
import intel_extension_for_pytorch as ipex
ipex.distributed.init_process_group(
backend="ccl",
world_size=WORLD_SIZE,
rank=RANK,
timeout=timedelta(seconds=60),
pg_options=options,
)
else:
torch.distributed.init_process_group(
backend=backend,
world_size=WORLD_SIZE,

19
server/text_generation_server/utils/import_utils.py
View File

@ -3,13 +3,12 @@ from loguru import logger
import subprocess
def is_xpu_available():
def is_ipex_available():
try:
import intel_extension_for_pytorch
except ImportError:
return False
return hasattr(torch, "xpu") and torch.xpu.is_available()
return True
def get_cuda_free_memory(device, memory_fraction):
@ -29,6 +28,16 @@ def get_xpu_free_memory(device, memory_fraction):
return free_memory
def get_cpu_free_memory(device, memory_fraction):
import psutil
from text_generation_server.utils.dist import WORLD_SIZE
mem = psutil.virtual_memory()
free_memory = int(mem.available * 0.95 / WORLD_SIZE)
return free_memory
IPEX_AVAIL = is_ipex_available()
SYSTEM = None
if torch.version.hip is not None:
SYSTEM = "rocm"
@ -40,7 +49,7 @@ elif torch.version.cuda is not None and torch.cuda.is_available():
empty_cache = torch.cuda.empty_cache
synchronize = torch.cuda.synchronize
get_free_memory = get_cuda_free_memory
elif is_xpu_available():
elif IPEX_AVAIL and hasattr(torch, "xpu") and torch.xpu.is_available():
SYSTEM = "xpu"
empty_cache = torch.xpu.empty_cache
synchronize = torch.xpu.synchronize
@ -53,5 +62,5 @@ else:
empty_cache = noop
synchronize = noop
get_free_memory = noop
get_free_memory = get_cpu_free_memory
logger.info(f"Detected system {SYSTEM}")