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Upgrading exl2. (#2415) 

* Upgrading exl2.

* Fixing the other pathways.

* Fix idefics.
This commit is contained in:
Nicolas Patry 2024-08-14 11:58:08 +02:00 committed by GitHub
parent c5fff92b48
commit f3b5c69441
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GPG Key ID: B5690EEEBB952194
12 changed files with 54 additions and 22 deletions
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2
.gitignore vendored
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@ -9,7 +9,7 @@ backends/client/src/v3/pb
# ROCm auto-generated files
*.hip
server/exllamav2_kernels/exllamav2_kernels/hip/
server/exllamav2
server/exllama_kernels/exllama_kernels/hip/
server/exllama_kernels/exllama_kernels/hip_func/
*_hip.cuh

6
Dockerfile
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@ -123,10 +123,10 @@ RUN TORCH_CUDA_ARCH_LIST="8.0;8.6+PTX" python setup.py build
# Build Transformers exllama kernels
FROM kernel-builder AS exllamav2-kernels-builder
WORKDIR /usr/src
COPY server/exllamav2_kernels/ .
COPY server/Makefile-exllamav2/ Makefile
# Build specific version of transformers
RUN TORCH_CUDA_ARCH_LIST="8.0;8.6+PTX" python setup.py build
RUN TORCH_CUDA_ARCH_LIST="8.0;8.6+PTX" make build-exllamav2
# Build Transformers awq kernels
FROM kernel-builder AS awq-kernels-builder
@ -221,7 +221,7 @@ COPY --from=custom-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-310 /
# Copy build artifacts from exllama kernels builder
COPY --from=exllama-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
# Copy build artifacts from exllamav2 kernels builder
COPY --from=exllamav2-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
COPY --from=exllamav2-kernels-builder /usr/src/exllamav2/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
# Copy build artifacts from awq kernels builder
COPY --from=awq-kernels-builder /usr/src/llm-awq/awq/kernels/build/lib.linux-x86_64-cpython-310 /opt/conda/lib/python3.10/site-packages
# Copy build artifacts from eetq kernels builder

1
flake.nix
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@ -93,6 +93,7 @@
causal-conv1d
click
einops
exllamav2
fbgemm-gpu
flashinfer
flash-attn

1
server/Makefile
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@ -6,6 +6,7 @@ include Makefile-eetq
include Makefile-selective-scan
include Makefile-lorax-punica
include Makefile-fbgemm
include Makefile-exllamav2
unit-tests:
pytest -s -vv -m "not private" tests

12
server/Makefile-exllamav2 Normal file
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@ -0,0 +1,12 @@
exllamav2_commit := v0.1.8
build-exllamav2:
git clone https://github.com/turboderp/exllamav2.git exllamav2 && \
cd exllamav2 && git fetch && git checkout $(exllamav2_commit) && \
git submodule update --init --recursive && \
pip install -r requirements.txt && \
CUDA_ARCH_LIST="8.0;9.0a" NVCC_GENCODE="-gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_90a,code=sm_90a" TORCH_CUDA_ARCH_LIST="8.0;9.0a" python setup.py build
install-exllamav2: build-exllamav2
cd exllamav2/ && \
CUDA_ARCH_LIST="8.0;9.0a" NVCC_GENCODE="-gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_90a,code=sm_90a" TORCH_CUDA_ARCH_LIST="8.0;9.0a" python setup.py install

43
server/text_generation_server/layers/gptq/exllamav2.py
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@ -12,7 +12,10 @@ from text_generation_server.layers.gptq import GPTQWeight
from text_generation_server.utils.log import log_master
try:
from exllamav2_kernels import make_q_matrix, gemm_half_q_half
from exllamav2.ext import exllamav2_ext
make_q_matrix = exllamav2_ext.make_q_matrix
gemm_half_q_half = exllamav2_ext.gemm_half_q_half
except ImportError:
log_master(logger.warning, "exllamav2_kernels not installed.")
raise
@ -70,6 +73,10 @@ def ext_make_q_matrix(
"""
Create Q matrix
"""
# max_dq_size = 512*(1024**2)
# max_dq_rows = max_dq_size // out_features[0]
max_dq_rows = 0
# EXL2
if isinstance(w, Exl2Weight):
extra.q_group_map = make_group_map(w.q_groups, w.q_weight.shape[0])
@ -83,10 +90,12 @@ def ext_make_q_matrix(
w.q_scale_max,
w.q_groups,
extra.q_group_map,
none_tensor,
none_tensor,
none_tensor,
none_tensor, # zeros
none_tensor, # scales
none_tensor, # g_idx
none_tensor, # bias
temp_dq,
max_dq_rows,
)
# GPTQ
elif isinstance(w, GPTQWeight):
@ -106,29 +115,33 @@ def ext_make_q_matrix(
w.qweight,
extra.q_perm,
extra.q_invperm,
none_tensor,
none_tensor,
none_tensor,
none_tensor,
none_tensor, # q_scale
none_tensor, # q_scale_max
none_tensor, # q_groups
none_tensor, # q_group_map
w.qzeros,
w.scales,
w.g_idx.cpu(),
none_tensor, # bias
temp_dq,
max_dq_rows,
)
# GPTQ without g_idx
else:
return make_q_matrix(
w.qweight,
none_tensor,
none_tensor,
none_tensor,
none_tensor,
none_tensor,
none_tensor,
none_tensor, # q_perm
none_tensor, # q_invperm
none_tensor, # q_scale
none_tensor, # q_scale_max
none_tensor, # q_groups
none_tensor, # q_group_map
w.qzeros,
w.scales,
none_tensor,
none_tensor, # g_idx
none_tensor, # bias
temp_dq,
max_dq_rows,
)
else:
RuntimeError("Cannot create handle")

1
server/text_generation_server/models/causal_lm.py
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@ -511,6 +511,7 @@ class CausalLM(Model):
config_class=AutoConfig,
batch_class=CausalLMBatch,
):
self.quantize = quantize
self.batch_class = batch_class
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():

1
server/text_generation_server/models/flash_causal_lm.py
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@ -872,6 +872,7 @@ class FlashCausalLM(Model):
head_size: Optional[int] = None,
skip_special_tokens: bool = True,
):
self.quantize = quantize
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")

1
server/text_generation_server/models/idefics.py
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@ -33,6 +33,7 @@ class IDEFICSSharded(IdeficsCausalLM):
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
self.quantize = quantize
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")

1
server/text_generation_server/models/idefics_causal_lm.py
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@ -580,6 +580,7 @@ class IdeficsCausalLM(Model):
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
self.quantize = quantize
from text_generation_server.models.custom_modeling.idefics_modeling import (
IdeficsForVisionText2Text,
)

1
server/text_generation_server/models/seq2seq_lm.py
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@ -553,6 +553,7 @@ class Seq2SeqLM(Model):
tokenizer_class=AutoTokenizer,
aliases=None,
):
self.quantize = quantize
self.process_group, rank, world_size = initialize_torch_distributed()
if torch.cuda.is_available():
device = torch.device(f"cuda:{rank}")

6
server/text_generation_server/server.py
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@ -50,12 +50,12 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
self,
model: Model,
cache: Cache,
quantize: Optional[str],
server_urls: List[str],
):
self.cache = cache
self.model = model
self.quantize = quantize
# Quantize is resolved during model loading
self.quantize = model.quantize
self.server_urls = server_urls
# For some reason, inference_mode does not work well with GLOO which we use on CPU
if model.device.type == "cuda":
@ -255,7 +255,7 @@ def serve(
],
)
generate_pb2_grpc.add_TextGenerationServiceServicer_to_server(
TextGenerationService(model, Cache(), quantize, server_urls), server
TextGenerationService(model, Cache(), server_urls), server
)
SERVICE_NAMES = (
generate_pb2.DESCRIPTOR.services_by_name["TextGenerationService"].full_name,