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Merge branch 'fix_rocm_fa' into rocm_6.2_fixes

This commit is contained in:
Mohit Sharma 2024-09-19 08:35:31 +00:00
parent ce85efa968 e6d07a6d34
commit 41b297a26b
24 changed files with 267 additions and 123 deletions
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52
Dockerfile_amd
View File

@ -41,7 +41,7 @@ COPY launcher launcher
RUN cargo build --profile release-opt
# Text Generation Inference base image for RoCm
FROM rocm/dev-ubuntu-22.04:6.1.1_hip_update AS base
FROM rocm/dev-ubuntu-22.04:6.2 AS base
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
build-essential \
@ -50,23 +50,25 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-ins
curl \
git \
make \
libmsgpack-dev \
libssl-dev \
llvm-dev \
g++ \
# Needed to build VLLM & flash.
rocthrust-dev \
hipsparse-dev \
hipblas-dev \
hipblaslt-dev \
hipcub-dev \
rocblas-dev \
hiprand-dev \
hipfft-dev \
rocrand-dev \
miopen-hip-dev \
hipfft-dev \
hipcub-dev \
hipsolver-dev \
rccl-dev \
cmake \
python3.11-dev && \
python3.11-dev \
python3.11-venv && \
rm -rf /var/lib/apt/lists/*
# Keep in sync with `server/pyproject.toml
@ -76,7 +78,14 @@ ARG ROCM_VERSION='6.0.2'
ARG PYTHON_VERSION='3.11.10'
# Automatically set by buildx
ARG TARGETPLATFORM
ENV PATH /opt/conda/bin:$PATH
ENV PATH=/opt/conda/bin:$PATH
ARG PYTORCH_ROCM_ARCH="gfx90a;gfx942"
RUN curl -fsSL -v -o cmake-3.30.2-linux-x86_64.sh https://github.com/Kitware/CMake/releases/download/v3.30.2/cmake-3.30.2-linux-x86_64.sh \
&& chmod +x cmake-3.30.2-linux-x86_64.sh \
&& ./cmake-3.30.2-linux-x86_64.sh --skip-license --prefix=/usr/local \
&& rm cmake-3.30.2-linux-x86_64.sh
# 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
@ -100,19 +109,37 @@ RUN case ${TARGETPLATFORM} in \
/opt/conda/bin/conda install -y "python=${PYTHON_VERSION}" ;; \
esac && \
/opt/conda/bin/conda clean -ya
# Install flash-attention, torch dependencies
RUN pip install numpy einops ninja --no-cache-dir
RUN pip install numpy einops ninja joblib msgpack --no-cache-dir
# Install HIPBLASLt
ARG HIPBLASLT_BRANCH="6f65c6e"
RUN git clone https://github.com/ROCm/hipBLASLt \
&& cd hipBLASLt \
&& git checkout ${HIPBLASLT_BRANCH} \
&& SCCACHE_IDLE_TIMEOUT=1800 ./install.sh --architecture ${PYTORCH_ROCM_ARCH} \
&& cd build/release \
&& make package
RUN dpkg -i hipBLASLt/build/release/*.deb \
&& sed -i 's/, hipblaslt-dev \(.*\), hipcub-dev/, hipcub-dev/g' /var/lib/dpkg/status \
&& sed -i 's/, hipblaslt \(.*\), hipfft/, hipfft/g' /var/lib/dpkg/status \
&& rm -rf hipBLASLt
RUN pip uninstall -y triton && \
git clone --depth 1 --single-branch https://github.com/ROCm/triton.git && \
cd triton/python && \
pip install .
RUN git clone --depth 1 --recursive --single-branch --branch 2.3-patched https://github.com/fxmarty/pytorch.git pytorch && cd pytorch && pip install -r requirements.txt --no-cache-dir
ARG PYTORCH_COMMIT="da320214e66b5af0f7db8fd18a64dbb519d17b27"
RUN git clone --depth 1 --recursive --single-branch --branch main https://github.com/pytorch/pytorch.git pytorch && \
cd pytorch && git fetch --depth 1 origin ${PYTORCH_COMMIT} && \
git checkout ${PYTORCH_COMMIT} && \
git submodule update --init --recursive && \
pip install -r requirements.txt --no-cache-dir
ARG _GLIBCXX_USE_CXX11_ABI="1"
ARG CMAKE_PREFIX_PATH="/opt/conda"
ARG PYTORCH_ROCM_ARCH="gfx90a;gfx942"
ARG BUILD_CAFFE2="0" \
BUILD_CAFFE2_OPS="0" \
USE_CUDA="0" \
@ -224,6 +251,13 @@ ENTRYPOINT ["./entrypoint.sh"]
# Final image
FROM base-copy
ENV ROCM_USE_CUSTOM_PAGED_ATTN=1
ENV PYTORCH_TUNABLEOP_TUNING_AFTER_WARMUP=0
ENV VLLM_MOE_PADDING=0
ENV ATTENTION=paged
ENV USE_PREFIX_CACHING=0
ENV ROCM_USE_SKINNY_GEMM=1
COPY ./tgi-entrypoint.sh /tgi-entrypoint.sh
RUN chmod +x /tgi-entrypoint.sh

6
docs/source/installation_amd.md
View File

@ -31,6 +31,12 @@ Two implementations of Flash Attention are available for ROCm, the first is [ROC
By default, the Composable Kernel implementation is used. However, the Triton implementation has slightly lower latency on MI250 and MI300, but requires a warmup which can be prohibitive as it needs to be done again for each new prompt length. If needed, FA Triton impelmentation can be enabled with `--env ROCM_USE_FLASH_ATTN_V2_TRITON="0"` when launching TGI's docker container.
## Custom PagedAttention
For better performance on ROCm, a custom Paged Attention kernel is available and is enabled by default. To disable it and fall back to the PagedAttention v2 kernel, set the environment variable `ROCM_USE_CUSTOM_PAGED_ATTN=0`.
The custom kernel supports bf16 and fp16 data types, block size of 16, head size of 128, a maximum context length of 16k, and GQA ratios between 1 and 16. For other configurations, we use the PagedAttention v2 kernel.
## Unsupported features
The following features are currently not supported in the ROCm version of TGI, and the supported may be extended in the future:

2
server/Makefile-flash-att-v2
View File

@ -1,5 +1,5 @@
flash_att_v2_commit_cuda := v2.6.1
flash_att_v2_commit_rocm := 2554f490101742ccdc56620a938f847f61754be6
flash_att_v2_commit_rocm := 3cea2fb6ee54fb7e1aad9db6ac6c9331184b8647 # (Aug28)
build-flash-attention-v2-cuda:
pip install -U packaging wheel

4
server/Makefile-vllm
View File

@ -1,5 +1,5 @@
commit_cuda := d243e9dc7e2c9c2e36a4150ec8e64809cb55c01b
commit_rocm := c6ee53b1be97e3bbc791b95f22827501297f8921
commit_rocm := 4e0929e6e4fa0a3d09d358715c288020ea9dc247
build-vllm-cuda:
if [ ! -d 'vllm' ]; then \
pip install -U ninja packaging --no-cache-dir && \
@ -13,7 +13,7 @@ install-vllm-cuda: build-vllm-cuda
build-vllm-rocm:
if [ ! -d 'vllm' ]; then \
pip install -U ninja packaging --no-cache-dir && \
git clone https://github.com/fxmarty/rocm-vllm.git vllm; \
git clone https://github.com/mht-sharma/vllm.git vllm; \
fi
cd vllm && git fetch && git checkout $(commit_rocm) && \
PYTORCH_ROCM_ARCH="gfx90a;gfx942" python setup.py build

152
server/text_generation_server/layers/attention/rocm.py
View File

@ -1,4 +1,5 @@
import os
from typing import Optional
import torch
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.models.globals import ATTENTION
@ -8,13 +9,19 @@ from loguru import logger
major, minor = torch.cuda.get_device_capability()
is_sm75 = major == 7 and minor == 5
_PARTITION_SIZE = 512
_PARTITION_SIZE_V1V2 = 512
_PARTITION_SIZE_CUSTOM = 256
use_triton = os.getenv("ROCM_USE_FLASH_ATTN_V2_TRITON", "").lower() in {"true", "1"}
ENGINE = "triton" if use_triton else "ck"
custom_attn_available = os.getenv("ROCM_USE_CUSTOM_PAGED_ATTN", "1") != "0"
if custom_attn_available:
from vllm._custom_C import paged_attention_custom
try:
from vllm._C import cache_ops
import vllm._custom_ops as ops
except Exception as e:
raise ImportError(
f"Could not import vllm paged attention. Make sure your installation is correct. Complete error: {e}"
@ -33,9 +40,7 @@ def reshape_and_cache(
key_cache.view(-1, shape[-2], shape[-1])[slots] = key
value_cache.view(-1, shape[-2], shape[-1])[slots] = value
else:
cache_ops.reshape_and_cache(
key, value, key_cache, value_cache, slots, "auto", 1.0
)
ops.reshape_and_cache(key, value, key_cache, value_cache, slots, "auto", 1.0)
def paged_attention(
@ -45,8 +50,9 @@ def paged_attention(
kv_head_mapping: torch.Tensor,
softmax_scale: float,
block_tables: torch.Tensor,
input_lengths: Seqlen,
seqlen: Seqlen,
max_s: int,
softcap: Optional[float] = None,
):
# Adapted from: https://github.com/vllm-project/vllm/blob/f8a1e39fae05ca610be8d5a78be9d40f5274e5fc/vllm/model_executor/layers/attention.py
# Copyright 2023 The vLLM team. All rights
@ -68,8 +74,25 @@ def paged_attention(
# value_cache => [num_blocks, num_heads, head_size, block_size]
block_size = value_cache.shape[3]
num_seqs, num_heads, head_size = query.shape
num_kv_heads = key_cache.shape[1]
gqa_ratio = num_heads // num_kv_heads
use_custom = (
custom_attn_available
and (query.dtype == torch.half or query.dtype == torch.bfloat16)
and (head_size == 128 or head_size == 64)
and (block_size == 16 or block_size == 32)
and (gqa_ratio >= 1 and gqa_ratio <= 16)
and max_s <= 32768
)
if not use_custom:
_PARTITION_SIZE = _PARTITION_SIZE_V1V2
else:
_PARTITION_SIZE = _PARTITION_SIZE_CUSTOM
max_num_partitions = (max_s + _PARTITION_SIZE - 1) // _PARTITION_SIZE
input_lengths = input_lengths.input_lengths
input_lengths = seqlen.input_lengths
out = torch.empty_like(query)
@ -78,9 +101,13 @@ def paged_attention(
# V1 to avoid the overhead of reduction. Also, if the number of
# sequences or heads is large, we use V1 since there is enough work
# to parallelize.
from vllm._C import ops
import vllm._custom_ops as ops
use_v1 = max_s <= 8192 and (max_num_partitions == 1 or num_seqs * num_heads > 512)
use_v1 = (
max_s <= 8192
and (max_num_partitions == 1 or num_seqs * num_heads > 512)
and not use_custom
)
if use_v1:
ops.paged_attention_v1(
out,
@ -112,24 +139,44 @@ def paged_attention(
)
max_logits = torch.empty_like(exp_sums)
ops.paged_attention_v2(
out,
exp_sums,
max_logits,
tmp_output,
query,
key_cache,
value_cache,
kv_head_mapping,
softmax_scale,
block_tables,
input_lengths,
block_size,
max_s,
None,
"auto",
1.0,
)
if not use_custom:
ops.paged_attention_v2(
out,
exp_sums,
max_logits,
tmp_output,
query,
key_cache,
value_cache,
kv_head_mapping,
softmax_scale,
block_tables,
input_lengths,
block_size,
max_s,
None,
"auto",
1.0,
)
else:
paged_attention_custom(
out,
exp_sums,
max_logits,
tmp_output,
query,
key_cache,
value_cache,
num_kv_heads,
softmax_scale,
block_tables,
input_lengths,
block_size,
max_s,
None,
"auto",
)
return out
@ -173,13 +220,14 @@ if ENGINE == "ck":
def attention(
q,
k,
v,
cu_seqlens,
max_s,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
seqlen: Seqlen,
block_tables: torch.Tensor,
softmax_scale,
window_size_left=-1,
causal=True,
softcap=0.0,
):
if window_size_left <= 0 and window_size_left != -1:
raise ValueError("`window_size_left` must be > 0 or -1")
@ -189,46 +237,54 @@ if ENGINE == "ck":
# We do not need to check window_size_left (not supported) here, so it is already checked ahead of time at model load.
return flash_attn_2_cuda.varlen_fwd(
q,
k,
v,
key_cache,
value_cache,
out,
cu_seqlens,
cu_seqlens,
max_s,
max_s,
seqlen.cu_seqlen_q,
seqlen.cu_seqlen_q,
None,
None,
None,
None,
seqlen.max_q,
seqlen.max_k,
0.0,
softmax_scale,
False,
causal,
window_size_left,
0,
softcap,
False,
None,
)
)[0]
elif ENGINE == "triton":
from .flash_attn_triton import triton_attention
def attention(
q,
k,
v,
cu_seqlens,
max_s,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
seqlen: Seqlen,
block_tables: torch.Tensor,
softmax_scale,
window_size_left=-1,
causal=True,
softcap=0.0,
):
out = torch.empty_like(q)
# We do not need to check window_size_left (not supported) here, so it is already checked ahead of time at model load.
output, _ = triton_attention(
q,
k,
v,
key_cache,
value_cache,
out,
cu_seqlens,
cu_seqlens,
max_s,
max_s,
seqlen.cu_seqlen_q,
seqlen.cu_seqlen_q,
seqlen.max_q,
seqlen.max_k,
causal,
softmax_scale,
)

46
server/text_generation_server/layers/linear.py
View File

@ -1,12 +1,19 @@
import torch
from text_generation_server.utils.import_utils import SYSTEM
from torch.nn import functional as F
import os
if SYSTEM == "rocm":
try:
from vllm import _custom_C
except Exception as e:
raise ImportError(f"Could not load `vllm._custom_C`. Full error: {e}")
ROCM_USE_SKINNY_GEMM = os.getenv("ROCM_USE_SKINNY_GEMM", "True").lower() in (
"true",
"1",
)
if ROCM_USE_SKINNY_GEMM:
try:
from vllm import _custom_C
except Exception as e:
raise ImportError(f"Could not load `vllm._custom_C`. Full error: {e}")
class FastLinear(torch.nn.Module):
@ -48,6 +55,14 @@ class FastLinearROCm(torch.nn.Module):
else:
self.bias = None
self.cu_count = torch.cuda.get_device_properties(
device="cuda"
).multi_processor_count
self.use_skinny_gemm = (
ROCM_USE_SKINNY_GEMM
and "gfx1" not in torch.cuda.get_device_properties("cuda").gcnArchName
)
@classmethod
def load(cls, config, prefix: str, weights, bias: bool):
weight = weights.get_tensor(f"{prefix}.weight")
@ -61,7 +76,11 @@ class FastLinearROCm(torch.nn.Module):
weight = self.weight
bias = self.bias
if SYSTEM == "rocm" and inp.numel() // inp.shape[-1] == 1:
if (
self.use_skinny_gemm
and inp.dtype == torch.float16
and inp.shape[-1] % 8 == 0
):
batched = False
inp_shape = inp.shape
@ -69,13 +88,16 @@ class FastLinearROCm(torch.nn.Module):
inp = inp.view(-1, inp_shape[-1])
batched = True
m, k = weight.shape[0], inp_shape[1]
out = torch.empty(
inp_shape[0], weight.shape[0], dtype=inp.dtype, device="cuda"
)
if (k == 8192 and (m == 1280 or m == 7168)) or (k == 3584 and m == 8192):
_custom_C.LLMM1(weight, inp, out, 8)
elif k <= 8192 and k % 8 == 0 and m % 4 == 0:
m, n, k = weight.shape[0], inp_shape[0], inp_shape[1]
if m > 8 and n <= 4:
out = torch.empty(
inp_shape[0], weight.shape[0], dtype=inp.dtype, device=weight.device
)
_custom_C.wvSpltK(weight, inp, out, n, self.cu_count)
elif m % 4 == 0 and n == 1 and k <= 8192:
out = torch.empty(
inp_shape[0], weight.shape[0], dtype=inp.dtype, device=weight.device
)
_custom_C.LLMM1(weight, inp, out, 4)
else:
out = F.linear(inp, weight)

5
server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py
View File

@ -18,6 +18,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from text_generation_server.models.globals import PAGED_KV
import torch
import torch.distributed
@ -297,8 +298,8 @@ class FlashCohereAttention(torch.nn.Module):
# flash attention
attn_output = attention(
query,
kv_cache[0] if SYSTEM != "ipex" else key,
kv_cache[1] if SYSTEM != "ipex" else value,
kv_cache[0] if PAGED_KV else key,
kv_cache[1] if PAGED_KV else value,
seqlen,
block_tables,
self.softmax_scale,

5
server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py
View File

@ -13,6 +13,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from text_generation_server.models.globals import PAGED_KV
import torch
import torch.distributed
@ -336,8 +337,8 @@ class DbrxAttention(torch.nn.Module):
# flash attention
attn_output = attention(
query,
kv_cache[0] if SYSTEM != "ipex" else kv[:, 0],
kv_cache[1] if SYSTEM != "ipex" else kv[:, 1],
kv_cache[0] if PAGED_KV else kv[:, 0],
kv_cache[1] if PAGED_KV else kv[:, 1],
seqlen,
block_tables,
self.softmax_scale,

6
server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py
View File

@ -15,6 +15,7 @@
from typing import List, Optional, Tuple
from text_generation_server.models.globals import PAGED_KV
from moe_kernels.fused_moe import grouped_topk
import torch
import torch.distributed
@ -327,8 +328,8 @@ class DeepseekV2Attention(torch.nn.Module):
# flash attention
attn_output = attention(
query,
kv_cache[0] if SYSTEM != "ipex" else key,
kv_cache[1] if SYSTEM != "ipex" else value,
kv_cache[0] if PAGED_KV else key,
kv_cache[1] if PAGED_KV else value,
seqlen,
block_tables,
self.softmax_scale,
@ -388,6 +389,7 @@ class DeepseekV2MLP(nn.Module):
def forward(self, hidden_states: torch.Tensor, reduce: bool = True):
if (
SYSTEM == "rocm"
and hidden_states.dtype == torch.float16
and self.hidden_act == "silu"
and hidden_states.shape[0] == 1
and not self.quantize

6
server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py
View File

@ -18,6 +18,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from text_generation_server.models.globals import PAGED_KV
import torch
import torch.distributed
@ -25,7 +26,6 @@ from torch import nn
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.layers.attention import (
paged_attention,
attention,
@ -237,8 +237,8 @@ class FlashGemma2Attention(torch.nn.Module):
# flash attention
attn_output = attention(
query,
kv_cache[0] if SYSTEM != "ipex" else kv[:, 0],
kv_cache[1] if SYSTEM != "ipex" else kv[:, 1],
kv_cache[0] if PAGED_KV else kv[:, 0],
kv_cache[1] if PAGED_KV else kv[:, 1],
seqlen,
block_tables,
self.softmax_scale,

6
server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py
View File

@ -18,6 +18,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from text_generation_server.models.globals import PAGED_KV
import torch
import torch.distributed
@ -25,7 +26,6 @@ from torch import nn
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.layers.attention import (
paged_attention,
attention,
@ -231,8 +231,8 @@ class FlashGemmaAttention(torch.nn.Module):
# flash attention
attn_output = attention(
query,
kv_cache[0] if SYSTEM != "ipex" else kv[:, 0],
kv_cache[1] if SYSTEM != "ipex" else kv[:, 1],
kv_cache[0] if PAGED_KV else kv[:, 0],
kv_cache[1] if PAGED_KV else kv[:, 1],
seqlen,
block_tables,
self.softmax_scale,

6
server/text_generation_server/models/custom_modeling/flash_gpt2_modeling.py
View File

@ -18,13 +18,13 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from text_generation_server.models.globals import PAGED_KV
import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from typing import Optional, List, Tuple
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.layers.attention import (
paged_attention,
attention,
@ -231,8 +231,8 @@ class FlashGPT2Attention(torch.nn.Module):
# flash attention
attn_output = attention(
query,
kv_cache[0] if SYSTEM != "ipex" else key,
kv_cache[1] if SYSTEM != "ipex" else value,
kv_cache[0] if PAGED_KV else key,
kv_cache[1] if PAGED_KV else value,
seqlen,
block_tables,
self.softmax_scale,

5
server/text_generation_server/models/custom_modeling/flash_gptj_modeling.py
View File

@ -18,6 +18,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from text_generation_server.models.globals import PAGED_KV
import torch
import torch.distributed
@ -192,8 +193,8 @@ class FlashGPTJAttention(torch.nn.Module):
# flash attention
attn_output = attention(
query,
kv_cache[0] if SYSTEM != "ipex" else key,
kv_cache[1] if SYSTEM != "ipex" else value,
kv_cache[0] if PAGED_KV else key,
kv_cache[1] if PAGED_KV else value,
seqlen,
block_tables,
self.softmax_scale,

11
server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
View File

@ -28,6 +28,7 @@ from torch import nn
from transformers.activations import ACT2FN
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.models.globals import PAGED_KV
from text_generation_server.layers.attention import (
paged_attention,
attention,
@ -220,8 +221,8 @@ class FlashLlamaAttention(torch.nn.Module):
# flash attention
attn_output = attention(
query,
kv_cache[0] if SYSTEM != "ipex" else kv[:, 0],
kv_cache[1] if SYSTEM != "ipex" else kv[:, 1],
kv_cache[0] if PAGED_KV else kv[:, 0],
kv_cache[1] if PAGED_KV else kv[:, 1],
seqlen,
block_tables,
self.softmax_scale,
@ -315,11 +316,16 @@ class LlamaMLP(nn.Module):
# TODO: This is a hotfix to be removed & properly refactored.
self.quantize = config.quantize
self.hidden_size = config.hidden_size
def forward(self, hidden_states, adapter_data):
if (
SYSTEM == "rocm"
and hidden_states.dtype == torch.float16
and self.hidden_act == "silu"
and hidden_states.shape[0] == 1
and self.hidden_size
!= 16384 # TODO: Temporary workaround for `LLMM_Silu` kernel not working with LLama3.1 405B; needs refactoring once fixed.
and not self.quantize
):
out = torch.empty(
@ -555,6 +561,7 @@ class FlashLlamaForCausalLM(torch.nn.Module):
adapter_data: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
inputs_embeds = self.embed_tokens(input_ids)
hidden_states = self.model(
inputs_embeds,
position_ids,

6
server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py
View File

@ -18,6 +18,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from text_generation_server.models.globals import PAGED_KV
import torch
import torch.distributed
@ -218,8 +219,8 @@ class MistralAttention(torch.nn.Module):
# flash attention
attn_output = attention(
query,
kv_cache[0] if SYSTEM != "ipex" else kv_to_cache[:, 0],
kv_cache[1] if SYSTEM != "ipex" else kv_to_cache[:, 1],
kv_cache[0] if PAGED_KV else kv_to_cache[:, 0],
kv_cache[1] if PAGED_KV else kv_to_cache[:, 1],
seqlen,
block_tables,
self.softmax_scale,
@ -301,6 +302,7 @@ class MistralMLP(nn.Module):
def forward(self, hidden_states, adapter_data):
if (
SYSTEM == "rocm"
and hidden_states.dtype == torch.float16
and self.hidden_act == "silu"
and hidden_states.shape[0] == 1
and not self.quantize

6
server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py
View File

@ -18,12 +18,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from text_generation_server.models.globals import PAGED_KV
import torch
import torch.distributed
from torch import nn
from text_generation_server.utils.import_utils import SYSTEM
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
@ -274,8 +274,8 @@ class MixtralAttention(torch.nn.Module):
# flash attention
attn_output = attention(
query,
kv_cache[0] if SYSTEM != "ipex" else kv_to_cache[:, 0],
kv_cache[1] if SYSTEM != "ipex" else kv_to_cache[:, 1],
kv_cache[0] if PAGED_KV else kv_to_cache[:, 0],
kv_cache[1] if PAGED_KV else kv_to_cache[:, 1],
seqlen,
block_tables,
self.softmax_scale,

6
server/text_generation_server/models/custom_modeling/flash_neox_modeling.py
View File

@ -18,6 +18,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from text_generation_server.models.globals import PAGED_KV
import torch
import torch.distributed
@ -26,7 +27,6 @@ from transformers.activations import ACT2FN
from transformers.modeling_utils import PreTrainedModel
from transformers.models.gpt_neox import GPTNeoXConfig as TransformersGPTNeoXConfig
from typing import Optional, List, Tuple
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.layers.attention import (
paged_attention,
attention,
@ -172,8 +172,8 @@ class FlashNeoxAttention(torch.nn.Module):
# flash attention
attn_output = attention(
qkv[:, 0],
kv_cache[0] if SYSTEM != "ipex" else qkv[:, 1],
kv_cache[1] if SYSTEM != "ipex" else qkv[:, 2],
kv_cache[0] if PAGED_KV else qkv[:, 1],
kv_cache[1] if PAGED_KV else qkv[:, 2],
seqlen,
block_tables,
self.softmax_scale,

6
server/text_generation_server/models/custom_modeling/flash_phi_modeling.py
View File

@ -1,3 +1,4 @@
from text_generation_server.models.globals import PAGED_KV
import torch
import torch.distributed
@ -25,7 +26,6 @@ from text_generation_server.layers.layernorm import (
from text_generation_server.layers.rotary import (
PositionRotaryEmbedding,
)
from text_generation_server.utils.import_utils import SYSTEM
class PhiConfig(PretrainedConfig):
@ -194,8 +194,8 @@ class FlashPhiAttention(torch.nn.Module):
if cu_seqlen_prefill is not None:
attn_output = attention(
query,
kv_cache[0] if SYSTEM != "ipex" else kv[:, 0],
kv_cache[1] if SYSTEM != "ipex" else kv[:, 1],
kv_cache[0] if PAGED_KV else kv[:, 0],
kv_cache[1] if PAGED_KV else kv[:, 1],
seqlen,
block_tables,
self.softmax_scale,

6
server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py
View File

@ -1,3 +1,4 @@
from text_generation_server.models.globals import PAGED_KV
import torch
import torch.distributed
@ -21,7 +22,6 @@ from text_generation_server.layers.rotary import PositionRotaryEmbedding
from text_generation_server.layers.layernorm import (
FastRMSNorm,
)
from text_generation_server.utils.import_utils import SYSTEM
def load_attention(config, prefix, weights):
@ -137,8 +137,8 @@ class Qwen2Attention(torch.nn.Module):
# flash attention
attn_output = attention(
query,
kv_cache[0] if SYSTEM != "ipex" else kv_to_cache[:, 0],
kv_cache[1] if SYSTEM != "ipex" else kv_to_cache[:, 1],
kv_cache[0] if PAGED_KV else kv_to_cache[:, 0],
kv_cache[1] if PAGED_KV else kv_to_cache[:, 1],
seqlen,
block_tables,
self.softmax_scale,

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

@ -1,11 +1,11 @@
from typing import List, Optional, Tuple
from text_generation_server.models.globals import PAGED_KV
import torch
import torch.distributed
from torch import nn
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_utils import PreTrainedModel
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.layers import (
SpeculativeHead,
TensorParallelColumnLinear,
@ -207,8 +207,8 @@ class FlashRWAttention(torch.nn.Module):
# flash attention
attn_output = attention(
query,
kv_cache[0] if SYSTEM != "ipex" else kv[:, 0],
kv_cache[1] if SYSTEM != "ipex" else kv[:, 1],
kv_cache[0] if PAGED_KV else kv[:, 0],
kv_cache[1] if PAGED_KV else kv[:, 1],
seqlen,
block_tables,
self.softmax_scale,
@ -325,8 +325,8 @@ class FlashRWLargeAttention(torch.nn.Module):
# flash attention
attn_output = attention(
query,
kv_cache[0] if SYSTEM != "ipex" else kv[:, :, 0].contiguous(),
kv_cache[1] if SYSTEM != "ipex" else kv[:, :, 1].contiguous(),
kv_cache[0] if PAGED_KV else kv[:, :, 0].contiguous(),
kv_cache[1] if PAGED_KV else kv[:, :, 1].contiguous(),
seqlen,
block_tables,
self.softmax_scale,

6
server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py
View File

@ -1,3 +1,4 @@
from text_generation_server.models.globals import PAGED_KV
import torch
import torch.distributed
@ -22,7 +23,6 @@ from text_generation_server.layers.gptq import GPTQWeightsLoader
from text_generation_server.layers.layernorm import (
FastLayerNorm,
)
from text_generation_server.utils.import_utils import SYSTEM
def load_multi_mqa(
@ -293,8 +293,8 @@ class FlashMQAttention(torch.nn.Module):
# flash attention
attn_output = attention(
query,
kv_cache[0] if SYSTEM != "ipex" else key_value[:, 0],
kv_cache[1] if SYSTEM != "ipex" else key_value[:, 1],
kv_cache[0] if PAGED_KV else key_value[:, 0],
kv_cache[1] if PAGED_KV else key_value[:, 1],
seqlen,
block_tables,
self.softmax_scale,

6
server/text_generation_server/models/custom_modeling/flash_starcoder2_modeling.py
View File

@ -18,6 +18,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from text_generation_server.models.globals import PAGED_KV
import torch
import torch.distributed
@ -47,7 +48,6 @@ from text_generation_server.layers.rotary import (
PositionRotaryEmbedding,
)
from text_generation_server.utils.weights import UnquantizedWeight
from text_generation_server.utils.import_utils import SYSTEM
class Starcoder2Config(PretrainedConfig):
@ -242,8 +242,8 @@ class Starcoder2Attention(torch.nn.Module):
# flash attention
attn_output = attention(
query,
kv_cache[0] if SYSTEM != "ipex" else kv_to_cache[:, 0],
kv_cache[1] if SYSTEM != "ipex" else kv_to_cache[:, 1],
kv_cache[0] if PAGED_KV else kv_to_cache[:, 0],
kv_cache[1] if PAGED_KV else kv_to_cache[:, 1],
seqlen,
block_tables,
self.softmax_scale,

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

@ -1125,12 +1125,12 @@ class FlashCausalLM(Model):
else:
self.kv_cache = [
(
torch.empty(
torch.zeros(
(num_blocks, num_heads, head_size // x, BLOCK_SIZE, x),
dtype=dtype,
device=device,
),
torch.empty(
torch.zeros(
(num_blocks, num_heads, head_size, BLOCK_SIZE),
dtype=dtype,
device=device,
@ -1320,8 +1320,7 @@ class FlashCausalLM(Model):
elif CUDA_GRAPHS is not None:
tuning_sequences = CUDA_GRAPHS
else:
# For seqlen = 1, we dispatch to LLMM1 kernel.
tuning_sequences = [2, 3, 4, 5, 6, 7]
tuning_sequences = [1, 2, 3, 4, 5, 6, 7]
tunableop_filepath = os.path.join(
HUGGINGFACE_HUB_CACHE,
@ -1330,7 +1329,11 @@ class FlashCausalLM(Model):
log_master(
logger.info,
f"PyTorch TunableOp (https://github.com/fxmarty/pytorch/tree/2.3-patched/aten/src/ATen/cuda/tunable) is enabled. The warmup may take several minutes, picking the ROCm optimal matrix multiplication kernel for the target lengths {', '.join([str(seqlen) for seqlen in tuning_sequences])}, with typical 5-8% latency improvement for small sequence lengths. The picked GEMMs are saved in the file {tunableop_filepath}. To disable TunableOp, please launch TGI with `PYTORCH_TUNABLEOP_ENABLED=0`.",
f"PyTorch TunableOp is enabled. The warmup may take several minutes, picking the ROCm optimal matrix multiplication kernel for the target lengths {', '.join([str(seqlen) for seqlen in tuning_sequences])}, with typical 5-8% latency improvement for small sequence lengths. The picked GEMMs are saved in the file {tunableop_filepath}. To disable TunableOp, please launch TGI with `PYTORCH_TUNABLEOP_ENABLED=0`.",
)
torch.cuda.tunable.set_filename(
tunableop_filepath, insert_device_ordinal=False
)
if os.path.isfile(tunableop_filepath):
@ -1346,7 +1349,8 @@ class FlashCausalLM(Model):
log_master(logger.info, f"Warming up TunableOp for seqlen={seqlen}")
self.tunableop_warmup(seqlen)
torch.cuda.tunable.write_file(tunableop_filepath)
torch.cuda.tunable.tuning_enable(False)
if os.environ.get("PYTORCH_TUNABLEOP_TUNING_AFTER_WARMUP") != "1":
torch.cuda.tunable.tuning_enable(False)
else:
log_master(
logger.info,
@ -1382,6 +1386,7 @@ class FlashCausalLM(Model):
cu_seqlen_prefill = torch.tensor(
[0, seqlen], device=self.device, dtype=torch.int32
)
max_s = seqlen
seqlen = Seqlen(
input_lengths=input_lengths,
prefix_lengths=prefix_lens_tensor,
@ -1399,7 +1404,7 @@ class FlashCausalLM(Model):
block_tables=None,
seqlen=seqlen,
slots=slots,
max_s=seqlen,
max_s=max_s,
lm_head_indices=None,
prefill_cache_indices=None,
)

7
server/text_generation_server/models/globals.py
View File

@ -4,6 +4,7 @@ from loguru import logger
from typing import Dict, Optional
from text_generation_server.utils.log import log_master
from text_generation_server.utils.import_utils import SYSTEM
PREFIX_CACHING = os.getenv("USE_PREFIX_CACHING").lower() in {"1", "true"}
log_master(logger.info, f"Using prefix caching = {PREFIX_CACHING}")
@ -52,6 +53,12 @@ CUDA_GRAPHS = cuda_graphs
# index in all cases.
ADAPTER_TO_INDEX: Optional[Dict[str, int]] = None
PAGED_KV: bool
if SYSTEM in {"rocm", "ipex"}:
PAGED_KV = False
else:
PAGED_KV = True
def set_adapter_to_index(adapter_to_index: Dict[str, int]):
global ADAPTER_TO_INDEX