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Integrate Marlin Kernels for Int4 GPTQ inference (#2497)

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Co-authored-by: Robert Shaw <114415538+rib-2@users.noreply.github.com>
Co-authored-by: alexm <alexm@neuralmagic.com>
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Robert Shaw 2024-03-01 14:47:51 -06:00 committed by GitHub
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12 changed files with 1752 additions and 6 deletions
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9
csrc/ops.h
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@ -84,6 +84,15 @@ torch::Tensor awq_dequantize(
int split_k_iters,
int thx,
int thy);
torch::Tensor marlin_gemm(
torch::Tensor& a,
torch::Tensor& b_q_weight,
torch::Tensor& b_scales,
torch::Tensor& workspace,
int64_t size_m,
int64_t size_n,
int64_t size_k);
#endif
void squeezellm_gemm(

4
csrc/pybind.cpp
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@ -52,11 +52,13 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
&rotary_embedding,
"Apply GPT-NeoX or GPT-J style rotary embedding to query and key");
// Quantization ops
// Quantization ops
#ifndef USE_ROCM
ops.def("awq_gemm", &awq_gemm, "Quantized GEMM for AWQ");
ops.def("marlin_gemm", &marlin_gemm, "Marlin Optimized Quantized GEMM for GPTQ");
ops.def("awq_dequantize", &awq_dequantize, "Dequantization for AWQ");
#endif
ops.def("gptq_gemm", &gptq_gemm, "Quantized GEMM for GPTQ");
ops.def("gptq_shuffle", &gptq_shuffle, "Post processing for GPTQ");
ops.def("squeezellm_gemm", &squeezellm_gemm, "Quantized GEMM for SqueezeLLM");

209
csrc/quantization/marlin/LICENSE Normal file
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@ -0,0 +1,209 @@
Contains code from https://github.com/IST-DASLab/marlin
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1145
csrc/quantization/marlin/marlin_cuda_kernel.cu Normal file
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File diff suppressed because it is too large Load Diff

1
requirements-dev.txt
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@ -15,6 +15,7 @@ types-setuptools
pytest
pytest-forked
pytest-asyncio
pytest-rerunfailures
httpx
einops # required for MPT
openai

2
setup.py
View File

@ -342,6 +342,8 @@ vllm_extension_sources = [
if _is_cuda():
vllm_extension_sources.append("csrc/quantization/awq/gemm_kernels.cu")
vllm_extension_sources.append(
"csrc/quantization/marlin/marlin_cuda_kernel.cu")
vllm_extension_sources.append("csrc/custom_all_reduce.cu")
# Add MoE kernels.

32
tests/conftest.py
View File

@ -199,6 +199,24 @@ class VllmRunner:
outputs.append((req_sample_output_ids, req_sample_output_strs))
return outputs
def generate_w_logprobs(
self,
prompts: List[str],
sampling_params: SamplingParams,
) -> List[Tuple[List[int], str]]:
assert sampling_params.logprobs is not None
req_outputs = self.model.generate(prompts,
sampling_params=sampling_params)
outputs = []
for req_output in req_outputs:
for sample in req_output.outputs:
output_str = sample.text
output_ids = sample.token_ids
output_logprobs = sample.logprobs
outputs.append((output_ids, output_str, output_logprobs))
return outputs
def generate_greedy(
self,
prompts: List[str],
@ -209,6 +227,20 @@ class VllmRunner:
return [(output_ids[0], output_str[0])
for output_ids, output_str in outputs]
def generate_greedy_logprobs(
self,
prompts: List[str],
max_tokens: int,
num_logprobs: int,
) -> List[Tuple[List[int], str]]:
greedy_logprobs_params = SamplingParams(temperature=0.0,
max_tokens=max_tokens,
logprobs=num_logprobs)
outputs = self.generate_w_logprobs(prompts, greedy_logprobs_params)
return [(output_ids, output_str, output_logprobs)
for output_ids, output_str, output_logprobs in outputs]
def generate_beam_search(
self,
prompts: List[str],

97
tests/models/test_marlin.py Normal file
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@ -0,0 +1,97 @@
"""Compare the outputs of a GPTQ model to a Marlin model.
Note: GPTQ and Marlin do not have bitwise correctness.
As a result, in this test, we just confirm that the top selected tokens of the
Marlin/GPTQ models are in the top 3 selections of each other.
Note: Marlin internally uses locks to synchronize the threads. This can
result in very slight nondeterminism for Marlin. As a result, we re-run the test
up to 3 times to see if we pass.
Run `pytest tests/models/test_marlin.py --forked`.
"""
import pytest
import torch
from dataclasses import dataclass
from vllm.model_executor.layers.quantization import _QUANTIZATION_CONFIG_REGISTRY
capability = torch.cuda.get_device_capability()
capability = capability[0] * 10 + capability[1]
marlin_not_supported = (
capability < _QUANTIZATION_CONFIG_REGISTRY["marlin"].get_min_capability())
@dataclass
class ModelPair:
model_marlin: str
model_gptq: str
model_pairs = [
ModelPair(model_marlin="nm-testing/zephyr-beta-7b-marlin-g128",
model_gptq="nm-testing/zephyr-beta-7b-gptq-g128"),
ModelPair(model_marlin="robertgshaw2/zephyr-7b-beta-channelwise-marlin",
model_gptq="robertgshaw2/zephyr-7b-beta-channelwise-gptq"),
ModelPair(model_marlin="robertgshaw2/TinyLlama-1.1B-Chat-v1.0-g128-marlin",
model_gptq="robertgshaw2/TinyLlama-1.1B-Chat-v1.0-g128-gptq")
]
@pytest.mark.flaky(reruns=2)
@pytest.mark.skipif(marlin_not_supported,
reason="Marlin is not supported on this GPU type.")
@pytest.mark.parametrize("model_pair", model_pairs)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.parametrize("max_tokens", [32])
@pytest.mark.parametrize("num_logprobs", [3])
def test_models(
vllm_runner,
example_prompts,
model_pair: ModelPair,
dtype: str,
max_tokens: int,
num_logprobs: int,
) -> None:
marlin_model = vllm_runner(model_pair.model_marlin, dtype=dtype)
marlin_outputs = marlin_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs)
# Note: not sure why, but deleting just the model on Ada Lovelace
# does not free the GPU memory. On Ampere, deleting the just model
# frees the memory.
del marlin_model.model.llm_engine.driver_worker
del marlin_model
gptq_model = vllm_runner(model_pair.model_gptq, dtype=dtype)
gptq_outputs = gptq_model.generate_greedy_logprobs(example_prompts,
max_tokens,
num_logprobs)
# Note: not sure why, but deleting just the model on Ada Lovelace
# does not free the GPU memory. On Ampere, deleting the just model
# frees the memory.
del gptq_model.model.llm_engine.driver_worker
del gptq_model
# loop through the prompts
for prompt_idx in range(len(example_prompts)):
gptq_output_ids, gptq_output_str, gptq_logprobs = gptq_outputs[
prompt_idx]
marlin_output_ids, marlin_output_str, marlin_logprobs = marlin_outputs[
prompt_idx]
for idx, (gptq_output_id, marlin_output_id) in enumerate(
zip(gptq_output_ids, marlin_output_ids)):
# If sequence is not an exact match,
if marlin_output_id != gptq_output_id:
# Each predicted token must be in top 5 of the other's
assert gptq_output_id in marlin_logprobs[idx], (
f"Test{prompt_idx}:\nGPTQ:\t{gptq_output_str!r}\nMarlin:\t{marlin_output_str!r}"
)
assert marlin_output_id in gptq_logprobs[idx], (
f"Test{prompt_idx}:\nGPTQ:\t{gptq_output_str!r}\nMarlin:\t{marlin_output_str!r}"
)
# Break out since sequences will now diverge.
break

18
vllm/config.py
View File

@ -155,15 +155,21 @@ class ModelConfig:
self.tokenizer_mode = tokenizer_mode
def _verify_quantization(self) -> None:
supported_quantization = ["awq", "gptq", "squeezellm"]
rocm_not_supported_quantization = ["awq"]
supported_quantization = ["awq", "gptq", "squeezellm", "marlin"]
rocm_not_supported_quantization = ["awq", "marlin"]
if self.quantization is not None:
self.quantization = self.quantization.lower()
# Parse quantization method from the HF model config, if available.
hf_quant_config = getattr(self.hf_config, "quantization_config", None)
if hf_quant_config is not None:
hf_quant_method = str(hf_quant_config["quant_method"]).lower()
# If the GPTQ model is serialized in marlin format, use marlin.
if (hf_quant_method == "gptq"
and "is_marlin_format" in hf_quant_config
and hf_quant_config["is_marlin_format"]):
hf_quant_method = "marlin"
if self.quantization is None:
self.quantization = hf_quant_method
elif self.quantization != hf_quant_method:
@ -183,9 +189,11 @@ class ModelConfig:
raise ValueError(
f"{self.quantization} quantization is currently not supported "
f"in ROCm.")
logger.warning(f"{self.quantization} quantization is not fully "
"optimized yet. The speed can be slower than "
"non-quantized models.")
if self.quantization != "marlin":
logger.warning(
f"{self.quantization} quantization is not fully "
"optimized yet. The speed can be slower than "
"non-quantized models.")
def _verify_cuda_graph(self) -> None:
if self.max_context_len_to_capture is None:

29
vllm/model_executor/layers/linear.py
View File

@ -17,6 +17,14 @@ from vllm.logger import init_logger
logger = init_logger(__name__)
def adjust_marlin_shard(param, shard_size, shard_offset):
marlin_tile_size = getattr(param, "marlin_tile_size", None)
if marlin_tile_size is None:
return shard_size, shard_offset
return shard_size * marlin_tile_size, shard_offset * marlin_tile_size
class LinearMethodBase(ABC):
"""Base class for different (maybe quantized) linear methods."""
@ -276,6 +284,11 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
if packed_dim == output_dim:
shard_size = shard_size // param.pack_factor
shard_offset = shard_offset // param.pack_factor
# If marlin, we need to adjust the offset and size to account for the tiling.
shard_size, shard_offset = adjust_marlin_shard(
param, shard_size, shard_offset)
loaded_weight_shard = loaded_weight.narrow(
output_dim, shard_offset, shard_size)
self.weight_loader(param, loaded_weight_shard, shard_id)
@ -293,6 +306,11 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
if packed_dim == output_dim:
shard_size = shard_size // param.pack_factor
shard_offset = shard_offset // param.pack_factor
# If marlin, we need to adjust the offset and size to account for the tiling.
shard_size, shard_offset = adjust_marlin_shard(
param, shard_size, shard_offset)
param_data = param_data.narrow(output_dim, shard_offset,
shard_size)
start_idx = tp_rank * shard_size
@ -372,6 +390,7 @@ class QKVParallelLinear(ColumnParallelLinear):
loaded_shard_id: Optional[str] = None):
param_data = param.data
output_dim = getattr(param, "output_dim", None)
if loaded_shard_id is None:
# Loaded weight is already packed.
if output_dim is None:
@ -393,6 +412,11 @@ class QKVParallelLinear(ColumnParallelLinear):
if packed_dim == output_dim:
shard_size = shard_size // param.pack_factor
shard_offset = shard_offset // param.pack_factor
# If marlin, we need to adjust the offset and size to account for the tiling.
shard_size, shard_offset = adjust_marlin_shard(
param, shard_size, shard_offset)
loaded_weight_shard = loaded_weight.narrow(
output_dim, shard_offset, shard_size)
self.weight_loader(param, loaded_weight_shard, shard_id)
@ -417,6 +441,11 @@ class QKVParallelLinear(ColumnParallelLinear):
if packed_dim == output_dim:
shard_size = shard_size // param.pack_factor
shard_offset = shard_offset // param.pack_factor
# If marlin, we need to adjust the offset and size to account for the tiling.
shard_size, shard_offset = adjust_marlin_shard(
param, shard_size, shard_offset)
param_data = param_data.narrow(output_dim, shard_offset,
shard_size)
if loaded_shard_id == "q":

2
vllm/model_executor/layers/quantization/__init__.py
View File

@ -4,11 +4,13 @@ from vllm.model_executor.layers.quantization.base_config import QuantizationConf
from vllm.model_executor.layers.quantization.awq import AWQConfig
from vllm.model_executor.layers.quantization.gptq import GPTQConfig
from vllm.model_executor.layers.quantization.squeezellm import SqueezeLLMConfig
from vllm.model_executor.layers.quantization.marlin import MarlinConfig
_QUANTIZATION_CONFIG_REGISTRY = {
"awq": AWQConfig,
"gptq": GPTQConfig,
"squeezellm": SqueezeLLMConfig,
"marlin": MarlinConfig,
}

210
vllm/model_executor/layers/quantization/marlin.py Normal file
View File

@ -0,0 +1,210 @@
from typing import Any, Dict, List, Optional
import torch
from torch.nn.parameter import Parameter
from vllm._C import ops
from vllm.model_executor.layers.linear import LinearMethodBase, set_weight_attrs
from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
class MarlinConfig(QuantizationConfig):
"""Config class for Marlin.
Reference: https://github.com/IST-DASLab/marlin/tree/master
"""
def __init__(
self,
group_size: int,
) -> None:
# Group size for the quantization.
self.group_size = group_size
if self.group_size != 128 and self.group_size != -1:
raise ValueError(
"Currently, only group size 128 and -1 (channelwise) is supported for "
f"Marlin, but got group_size of {self.group_size}")
# 4 Bits packed into 32 bit datatype.
self.pack_factor = 32 // 4
# Tile size used by marlin kernels.
self.tile_size = 16
# Min out_features dim
self.min_n_threads = 64
# Min in_features dim
self.min_k_threads = 128
# Max parallel problems to solve at once (improves large batch performance)
self.max_parallel = 16
# Permutation length used by the marlin kernels.
self.perm_len = 1024
def __repr__(self) -> str:
return f"MarlinConfig(group_size={self.group_size}"
@classmethod
def get_name(cls) -> str:
return "marlin"
@classmethod
def get_supported_act_dtypes(cls) -> List[torch.dtype]:
return [torch.half]
@classmethod
# Need to figure it out
def get_min_capability(cls) -> int:
return 80
@classmethod
def get_config_filenames(cls) -> List[str]:
return ["quantize_config.json"]
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "MarlinConfig":
group_size = cls.get_from_keys(config, ["group_size"])
return cls(group_size)
def get_linear_method(self) -> "MarlinLinearMethod":
return MarlinLinearMethod(self)
def get_scaled_act_names(self) -> List[str]:
return []
class MarlinLinearMethod(LinearMethodBase):
"""Linear method for Marlin.
Args:
quant_config: The Marlin quantization config.
"""
def __init__(self, quant_config: MarlinConfig):
self.quant_config = quant_config
def create_weights(
self,
input_size_per_partition: int,
output_size_per_partition: int,
input_size: int,
output_size: int,
params_dtype: torch.dtype,
) -> Dict[str, Any]:
del output_size # Unused.
if params_dtype != torch.float16:
raise ValueError(
f"The params dtype must be float16, but got {params_dtype}")
# Validate output_size_per_partition
if output_size_per_partition % self.quant_config.min_n_threads != 0:
raise ValueError(
f"Weight output_size_per_partition = {output_size_per_partition} is not divisible by min_n_threads = {self.quant_config.min_n_threads}."
)
if output_size_per_partition % self.quant_config.pack_factor != 0:
raise ValueError(
f"Weight output_size_per_partition = {output_size_per_partition} is not divisible by pack_factor = {self.quant_config.pack_factor}."
)
# Validate input_size_per_partition
if input_size_per_partition % self.quant_config.min_k_threads != 0:
raise ValueError(
f"Weight input_size_per_partition = {input_size_per_partition} is not divisible by min_k_threads = {self.quant_config.min_k_threads}."
)
if self.quant_config.group_size != -1 and input_size_per_partition % self.quant_config.group_size != 0:
raise ValueError(
f"Weight input_size_per_partition = f{input_size_per_partition} is not divisible by group_size = {self.quant_config.group_size}."
)
# Check that we have at least 4 tiles horizontally in the shard
num_tiles_per_perm = self.quant_config.perm_len // (
self.quant_config.tile_size**2)
if output_size_per_partition % num_tiles_per_perm != 0:
raise ValueError(
"Each permutation group must reside on the same gpu")
# Quantized 4Bit weights packed into Int32.
qweight = Parameter(
torch.empty(
input_size_per_partition // self.quant_config.tile_size,
output_size_per_partition * self.quant_config.tile_size //
self.quant_config.pack_factor,
device="cuda",
dtype=torch.int32,
),
requires_grad=False,
)
set_weight_attrs(
qweight,
{
"input_dim": 0,
"output_dim": 1,
"packed_dim": 1,
"pack_factor": self.quant_config.pack_factor,
"marlin_tile_size": self.quant_config.tile_size,
},
)
# Determine if channelwise or not
input_groups = 1 if self.quant_config.group_size == -1 else input_size_per_partition // self.quant_config.group_size
scales = Parameter(
torch.empty(
input_groups,
output_size_per_partition,
device="cuda",
dtype=params_dtype,
),
requires_grad=False,
)
set_weight_attrs(
scales,
{
"input_dim": None if input_groups == 1 else 0,
"output_dim": 1,
},
)
# Allocate workspace (Used for internal locking mechanism)
max_workspace_size = (
output_size_per_partition //
self.quant_config.min_n_threads) * self.quant_config.max_parallel
workspace = Parameter(torch.zeros(max_workspace_size,
device="cuda",
dtype=torch.int),
requires_grad=False)
return {
"B": qweight,
"s": scales,
"workspace": workspace,
}
def apply_weights(
self,
weights: Dict[str, Any],
x: torch.Tensor,
bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
qweight = weights["B"]
scales = weights["s"]
workspace = weights["workspace"]
x_2d = x.view(-1, x.shape[-1])
size_m = x_2d.shape[0]
size_k = x_2d.shape[1]
size_n = scales.shape[1]
output_2d = ops.marlin_gemm(x_2d, qweight, scales, workspace, size_m,
size_n, size_k)
output = output_2d.view(x.shape[:-1] + (output_2d.shape[1], ))
if bias is not None:
output.add_(bias) # In-place add
return output