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test_gguf.py
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# SPDX-License-Identifier: Apache-2.0
from pathlib import Path
import pytest
import torch
from gguf import GGMLQuantizationType, GGUFReader, ReaderTensor, dequantize
from huggingface_hub import snapshot_download
import vllm._custom_ops as ops
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.fused_moe import fused_experts
from vllm.model_executor.layers.quantization.gguf import _fused_moe_gguf
from vllm.platforms import current_platform
GGUF_SAMPLE = snapshot_download("Isotr0py/test-gguf-sample")
GGUF_SAMPLE_MOE = snapshot_download("SzymonOzog/test-gguf-moe-sample")
def get_gguf_sample_tensors(
hidden_size: int,
quant_type: GGMLQuantizationType) -> list[ReaderTensor]:
sample_dir = GGUF_SAMPLE
filename = f"Quant_{quant_type.name}_{hidden_size}.gguf"
sample_file = Path(sample_dir) / filename
return GGUFReader(sample_file).tensors
def get_gguf_MoE_tensors(
hidden_size: int,
quant_type: GGMLQuantizationType) -> list[ReaderTensor]:
sample_dir = GGUF_SAMPLE_MOE
filename = f"Quant_{quant_type.name}_{hidden_size}.gguf"
sample_file = Path(sample_dir) / filename
return GGUFReader(sample_file).tensors
DTYPES = [torch.half, torch.bfloat16, torch.float32]
# Hidden_size for testing, must match the sample file in HF repo,
# we have `hidden_size = 256, 1024` for test in HF repo currently.
HIDDEN_SIZES = [256, 1024]
NUM_TOKENS = [7, 83, 128, 2048] # Arbitrary values for testing
SEEDS = [0]
QUANT_TYPES = [
# i-matrix
GGMLQuantizationType.IQ1_M,
GGMLQuantizationType.IQ1_S,
GGMLQuantizationType.IQ2_S,
GGMLQuantizationType.IQ2_XS,
GGMLQuantizationType.IQ3_S,
GGMLQuantizationType.IQ3_XXS,
GGMLQuantizationType.IQ4_NL,
GGMLQuantizationType.IQ4_XS,
# k-quants
GGMLQuantizationType.Q2_K,
GGMLQuantizationType.Q3_K,
GGMLQuantizationType.Q4_K,
GGMLQuantizationType.Q5_K,
GGMLQuantizationType.Q6_K,
# standard quantization
GGMLQuantizationType.Q4_0,
GGMLQuantizationType.Q5_0,
GGMLQuantizationType.Q8_0,
]
@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("dtype", [torch.half])
@pytest.mark.parametrize("quant_type", QUANT_TYPES)
@torch.inference_mode()
def test_dequantize(hidden_size: int, dtype: torch.dtype,
quant_type: GGMLQuantizationType):
tensors = get_gguf_sample_tensors(hidden_size, quant_type)
for tensor in tensors:
shape_str = tensor.name.split("_")[-1]
shape = map(int, shape_str.split("x"))
ref_output = torch.tensor(dequantize(tensor.data, quant_type),
device="cuda").to(dtype)
output = ops.ggml_dequantize(torch.tensor(tensor.data, device="cuda"),
quant_type, *list(shape)).to(dtype)
torch.testing.assert_close(output, ref_output, atol=1e-2, rtol=4e-2)
@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("quant_type", QUANT_TYPES)
@torch.inference_mode()
def test_mmvq(hidden_size: int, dtype: torch.dtype,
quant_type: GGMLQuantizationType):
current_platform.seed_everything(0)
tensors = get_gguf_sample_tensors(hidden_size, quant_type)
x = torch.rand((1, hidden_size), dtype=dtype, device="cuda")
for tensor in tensors:
weight = torch.tensor(dequantize(tensor.data, quant_type),
device="cuda").to(dtype)
ref_output = x @ weight.T
qweight = torch.tensor(tensor.data, device="cuda")
output = ops.ggml_mul_mat_vec_a8(qweight, x, quant_type,
qweight.shape[0]).to(dtype)
torch.testing.assert_close(output, ref_output, atol=1, rtol=1e-1)
@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize(
"quant_type",
[
# k-quants
GGMLQuantizationType.Q2_K,
GGMLQuantizationType.Q3_K,
GGMLQuantizationType.Q4_K,
GGMLQuantizationType.Q5_K,
GGMLQuantizationType.Q6_K,
# standard quants
GGMLQuantizationType.Q4_0,
GGMLQuantizationType.Q5_0,
GGMLQuantizationType.Q8_0,
])
@torch.inference_mode()
def test_mmq(num_tokens: int, hidden_size: int, dtype: torch.dtype,
quant_type: GGMLQuantizationType):
current_platform.seed_everything(0)
tensors = get_gguf_sample_tensors(hidden_size, quant_type)
x = torch.rand((num_tokens, hidden_size), dtype=dtype, device="cuda")
for tensor in tensors:
weight = torch.tensor(dequantize(tensor.data, quant_type),
device="cuda").to(dtype)
ref_output = x @ weight.T
qweight = torch.tensor(tensor.data, device="cuda")
output = ops.ggml_mul_mat_a8(qweight, x, quant_type, qweight.shape[0])
atols = {torch.half: 1, torch.bfloat16: 1.5, torch.float: 1.2}
# test matrix has inputs centered around 0 and lower precision from
# bfloat16 tends to accumulate and can greatly inflate rtol
# since outputs are also very close to 0
rtols = {torch.half: 1e-1, torch.bfloat16: 1e4, torch.float: 2e1}
torch.testing.assert_close(output,
ref_output,
atol=atols[dtype],
rtol=rtols[dtype])
@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@pytest.mark.parametrize("hidden_size", [512])
@pytest.mark.parametrize("top_k", [4, 8])
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize(
"quant_type",
[
# k-quants
GGMLQuantizationType.Q2_K,
GGMLQuantizationType.Q3_K,
GGMLQuantizationType.Q4_K,
GGMLQuantizationType.Q5_K,
GGMLQuantizationType.Q6_K,
# standard quants
GGMLQuantizationType.Q4_0,
GGMLQuantizationType.Q5_0,
GGMLQuantizationType.Q8_0,
])
@torch.inference_mode()
def test_moe(num_tokens: int, hidden_size: int, dtype: torch.dtype,
quant_type: GGMLQuantizationType, top_k: int):
current_platform.seed_everything(0)
H, E = 1024, 256
x = torch.rand((num_tokens, H), dtype=dtype, device="cuda")
topk_weights = torch.rand(num_tokens, top_k, device="cuda", dtype=dtype)
topk_ids = torch.randint(0, E, (num_tokens, top_k), device="cuda")
tensors = get_gguf_MoE_tensors(hidden_size, quant_type)
w13 = tensors[0]
w2 = tensors[1]
w13_dequant = torch.tensor(dequantize(w13.data, quant_type),
device="cuda").to(dtype)
w2_dequant = torch.tensor(dequantize(w2.data, quant_type),
device="cuda").to(dtype)
act = SiluAndMul()
output = _fused_moe_gguf(x, torch.tensor(w13.data, device="cuda"),
torch.tensor(w2.data,
device="cuda"), topk_weights,
topk_ids, quant_type, quant_type, act)
ref_output = fused_experts(x, w13_dequant, w2_dequant, topk_weights,
topk_ids).reshape(output.shape)
torch.testing.assert_close(output, ref_output, atol=1, rtol=1e-1)