Sync from v0.13
This commit is contained in:
207
tests/kernels/quantization/test_block_fp8.py
Normal file
207
tests/kernels/quantization/test_block_fp8.py
Normal file
@@ -0,0 +1,207 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
# Adapted from https://github.com/sgl-project/sglang/pull/2575
|
||||
import itertools
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
from tests.kernels.quant_utils import (
|
||||
native_per_token_group_quant_fp8,
|
||||
native_w8a8_block_matmul,
|
||||
)
|
||||
from vllm.config import VllmConfig
|
||||
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
|
||||
cutlass_scaled_mm,
|
||||
per_token_group_quant_fp8,
|
||||
w8a8_triton_block_scaled_mm,
|
||||
)
|
||||
from vllm.platforms import current_platform
|
||||
from vllm.utils.deep_gemm import (
|
||||
fp8_gemm_nt,
|
||||
get_col_major_tma_aligned_tensor,
|
||||
per_block_cast_to_fp8,
|
||||
should_use_deepgemm_for_fp8_linear,
|
||||
)
|
||||
from vllm.utils.import_utils import has_deep_gemm
|
||||
|
||||
if current_platform.get_device_capability() < (9, 0):
|
||||
pytest.skip("FP8 Triton requires CUDA 9.0 or higher", allow_module_level=True)
|
||||
|
||||
vllm_config = VllmConfig()
|
||||
|
||||
# Test configurations
|
||||
DTYPES = [torch.bfloat16] # [torch.half, torch.bfloat16, torch.float32]
|
||||
NUM_TOKENS = [7, 2050]
|
||||
D = [512, 4096, 5120, 13824]
|
||||
GROUP_SIZE = [64, 128, 512]
|
||||
M = [1, 7, 8, 83, 84, 4096]
|
||||
N = [128, 512, 7168, 7748, 13824]
|
||||
K = [256, 3884, 4096, 13824, 16384]
|
||||
# Deepseek-V3's intermediate size 18432, so N is 18432*2/8=4608 at TP8
|
||||
# and its hidden size is 7168.
|
||||
BLOCK_SIZE = [[128, 128]]
|
||||
OUT_DTYPES = [torch.bfloat16] # [torch.float32, torch.half, torch.bfloat16]
|
||||
SEEDS = [0]
|
||||
|
||||
# Skip all tests if CUDA is not available
|
||||
pytest.importorskip("torch.cuda")
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def setup_cuda():
|
||||
torch.set_default_device("cuda")
|
||||
|
||||
|
||||
@pytest.mark.skipif(
|
||||
current_platform.is_fp8_fnuz(),
|
||||
reason="This platform supports e4m3fnuz, not e4m3fn.",
|
||||
)
|
||||
@pytest.mark.parametrize(
|
||||
"num_tokens,d,dtype,group_size,seed",
|
||||
itertools.product(NUM_TOKENS, D, DTYPES, GROUP_SIZE, SEEDS),
|
||||
)
|
||||
@torch.inference_mode()
|
||||
def test_per_token_group_quant_fp8(num_tokens, d, dtype, group_size, seed):
|
||||
torch.manual_seed(seed)
|
||||
x = torch.rand(num_tokens, d, dtype=dtype)
|
||||
|
||||
ref_out, ref_scale = native_per_token_group_quant_fp8(x, group_size)
|
||||
out, scale = per_token_group_quant_fp8(x, group_size)
|
||||
|
||||
assert torch.allclose(out.to(torch.float32), ref_out.to(torch.float32), rtol=0.15)
|
||||
assert torch.allclose(scale, ref_scale)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"M,N,K,block_size,out_dtype,seed",
|
||||
itertools.product(M, N, K, BLOCK_SIZE, OUT_DTYPES, SEEDS),
|
||||
)
|
||||
@torch.inference_mode()
|
||||
def test_w8a8_block_fp8_matmul(M, N, K, block_size, out_dtype, seed):
|
||||
torch.manual_seed(seed)
|
||||
factor_for_scale = 1e-2
|
||||
fp8_info = torch.finfo(current_platform.fp8_dtype())
|
||||
fp8_max, fp8_min = fp8_info.max, fp8_info.min
|
||||
|
||||
A_fp32 = (torch.rand(M, K, dtype=torch.float32) - 0.5) * 2 * fp8_max
|
||||
A_fp8 = A_fp32.clamp(min=fp8_min, max=fp8_max).to(current_platform.fp8_dtype())
|
||||
|
||||
B_fp32 = (torch.rand(N, K, dtype=torch.float32) - 0.5) * 2 * fp8_max
|
||||
B_fp8 = B_fp32.clamp(min=fp8_min, max=fp8_max).to(current_platform.fp8_dtype())
|
||||
|
||||
block_n, block_k = block_size[0], block_size[1]
|
||||
n_tiles = (N + block_n - 1) // block_n
|
||||
k_tiles = (K + block_k - 1) // block_k
|
||||
|
||||
As = torch.rand(M, k_tiles, dtype=torch.float32) * factor_for_scale
|
||||
Bs = torch.rand(n_tiles, k_tiles, dtype=torch.float32) * factor_for_scale
|
||||
|
||||
ref_out = native_w8a8_block_matmul(A_fp8, B_fp8, As, Bs, block_size, out_dtype)
|
||||
out = w8a8_triton_block_scaled_mm(A_fp8, B_fp8, As, Bs, block_size, out_dtype)
|
||||
|
||||
rel_diff = torch.mean(
|
||||
torch.abs(out.to(torch.float32) - ref_out.to(torch.float32))
|
||||
) / torch.mean(torch.abs(ref_out.to(torch.float32)))
|
||||
assert rel_diff < 0.001
|
||||
|
||||
|
||||
@pytest.mark.skipif(
|
||||
not current_platform.is_cuda(), reason="CUTLASS only supported on CUDA platform."
|
||||
)
|
||||
@torch.inference_mode()
|
||||
def test_w8a8_block_fp8_cutlass_matmul():
|
||||
# Test simple case where weight.shape % 128 != 0,
|
||||
# like in DSV3 kv_a_proj_with_mqa
|
||||
M = 32
|
||||
N = 576
|
||||
K = 7168
|
||||
block_size = [128, 128]
|
||||
out_dtype = torch.bfloat16
|
||||
seed = 0
|
||||
|
||||
torch.manual_seed(seed)
|
||||
factor_for_scale = 1e-2
|
||||
fp8_info = torch.finfo(torch.float8_e4m3fn)
|
||||
fp8_max, fp8_min = fp8_info.max, fp8_info.min
|
||||
|
||||
A_fp32 = (torch.rand(M, K, dtype=torch.float32) - 0.5) * 2 * fp8_max
|
||||
|
||||
B_fp32 = (torch.rand(N, K, dtype=torch.float32) - 0.5) * 2 * fp8_max
|
||||
B_fp8 = B_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
|
||||
|
||||
block_n, block_k = block_size[0], block_size[1]
|
||||
n_tiles = (N + block_n - 1) // block_n
|
||||
k_tiles = (K + block_k - 1) // block_k
|
||||
|
||||
Bs = torch.rand(n_tiles, k_tiles, dtype=torch.float32) * factor_for_scale
|
||||
# Hopper requires row-major format for scales
|
||||
Bs_cutlass = Bs.T.contiguous() if current_platform.is_device_capability(90) else Bs
|
||||
|
||||
A_fp8, As = per_token_group_quant_fp8(
|
||||
A_fp32, block_size[1], column_major_scales=False
|
||||
)
|
||||
# CUTLASS uses column-major format for scales
|
||||
A_fp8_cutlass, As_cutlass = per_token_group_quant_fp8(
|
||||
A_fp32, block_size[1], column_major_scales=True
|
||||
)
|
||||
|
||||
ref_out = native_w8a8_block_matmul(A_fp8, B_fp8, As, Bs, block_size, out_dtype)
|
||||
out = cutlass_scaled_mm(
|
||||
A_fp8_cutlass, B_fp8, As_cutlass, Bs_cutlass, block_size, out_dtype
|
||||
)
|
||||
|
||||
rel_diff = torch.mean(
|
||||
torch.abs(out.to(torch.float32) - ref_out.to(torch.float32))
|
||||
) / torch.mean(torch.abs(ref_out.to(torch.float32)))
|
||||
assert rel_diff < 0.001
|
||||
|
||||
|
||||
@pytest.mark.skipif(
|
||||
current_platform.is_fp8_fnuz(),
|
||||
reason="This platform supports e4m3fnuz, not e4m3fn.",
|
||||
)
|
||||
@pytest.mark.parametrize(
|
||||
"M,N,K,block_size,out_dtype,seed",
|
||||
itertools.product(M, N, K, BLOCK_SIZE, OUT_DTYPES, SEEDS),
|
||||
)
|
||||
@pytest.mark.skipif(not has_deep_gemm(), reason="DeepGemm kernels not available.")
|
||||
@torch.inference_mode()
|
||||
def test_w8a8_block_fp8_deep_gemm_matmul(M, N, K, block_size, out_dtype, seed):
|
||||
torch.manual_seed(seed)
|
||||
fp8_info = torch.finfo(torch.float8_e4m3fn)
|
||||
fp8_max = fp8_info.max
|
||||
|
||||
A_fp32 = (torch.rand(M, K, dtype=torch.float32) - 0.5) * 2 * fp8_max
|
||||
B_fp32 = (torch.rand(N, K, dtype=torch.float32) - 0.5) * 2 * fp8_max
|
||||
|
||||
# only aligned sizes are supported by deepgemm
|
||||
if not should_use_deepgemm_for_fp8_linear(
|
||||
output_dtype=out_dtype, weight=B_fp32, supports_deep_gemm=True
|
||||
):
|
||||
pytest.skip(f"Skipping test; invalid size {M}, {N}, {K}")
|
||||
|
||||
A_fp8, As_fp8 = per_token_group_quant_fp8(A_fp32, block_size[1])
|
||||
B_fp8, Bs_fp8 = per_block_cast_to_fp8(B_fp32, block_size=block_size)
|
||||
|
||||
As = As_fp8.to(torch.float32)
|
||||
Bs = Bs_fp8.to(torch.float32)
|
||||
|
||||
ref_out = native_w8a8_block_matmul(A_fp8, B_fp8, As, Bs, block_size, out_dtype)
|
||||
|
||||
# Transpose earlier so that the testing will not trigger transposing kernels
|
||||
As_fp8 = get_col_major_tma_aligned_tensor(As_fp8)
|
||||
|
||||
out = torch.zeros((M, N), device="cuda", dtype=out_dtype)
|
||||
|
||||
assert As_fp8.shape == (M, (K + 127) // 128), (
|
||||
f"{As_fp8.shape} != {(M, (K + 127) // 128)}"
|
||||
)
|
||||
|
||||
fp8_gemm_nt((A_fp8, As_fp8), (B_fp8, Bs_fp8), out)
|
||||
|
||||
rel_diff = torch.mean(
|
||||
torch.abs(out.to(torch.float32) - ref_out.to(torch.float32))
|
||||
) / torch.mean(torch.abs(ref_out.to(torch.float32)))
|
||||
assert rel_diff < 0.001
|
||||
Reference in New Issue
Block a user