mirror of
https://github.com/vllm-project/vllm.git
synced 2025-10-20 23:03:52 +08:00
845adb3ec6
Signed-off-by: yangxurui <yangxurui@meituan.com> Co-authored-by: yangxurui <yangxurui@meituan.com>
257 lines
8.3 KiB
Python
257 lines
8.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
from dataclasses import dataclass
|
|
|
|
import pytest
|
|
import torch
|
|
|
|
from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
|
|
from vllm.model_executor.layers.fused_moe.config import (
|
|
fp8_w8a8_moe_quant_config)
|
|
from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts
|
|
from vllm.model_executor.layers.fused_moe.layer import FusedMoE
|
|
from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
|
|
apply_flashinfer_per_tensor_scale_fp8, flashinfer_cutlass_moe_fp8,
|
|
register_moe_scaling_factors, rotate_flashinfer_fp8_moe_weights,
|
|
swap_w13_to_w31)
|
|
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
|
|
input_to_float8)
|
|
from vllm.model_executor.models.llama4 import Llama4MoE
|
|
from vllm.platforms import current_platform
|
|
from vllm.utils.flashinfer import has_flashinfer_cutlass_fused_moe
|
|
|
|
if not has_flashinfer_cutlass_fused_moe(
|
|
) or not current_platform.has_device_capability(100):
|
|
pytest.skip("Requires flashinfer_cutlass_fused_moe and nvfp4 support",
|
|
allow_module_level=True)
|
|
|
|
NUM_EXPERTS = [16]
|
|
TOP_KS = [1]
|
|
|
|
MNK_FACTORS = [
|
|
(256, 8192, 5120),
|
|
(256, 4096, 5120),
|
|
(127, 8192, 5120),
|
|
(127, 4096, 5120),
|
|
(10, 8192, 5120),
|
|
(10, 4096, 5120),
|
|
(1, 8192, 5120),
|
|
(1, 4096, 5120),
|
|
]
|
|
|
|
vllm_config = VllmConfig(parallel_config=ParallelConfig(
|
|
pipeline_parallel_size=1))
|
|
vllm_config.scheduler_config.max_num_seqs = 128
|
|
vllm_config.scheduler_config.max_model_len = 8192
|
|
|
|
|
|
def quant_fp8_per_tensor_batches(a):
|
|
num_batches = a.size(0)
|
|
a_quant = []
|
|
a_scales = []
|
|
|
|
for i in range(num_batches):
|
|
a_fp8, a_global_sf = input_to_float8(a[i])
|
|
a_global_sf = 1.0 / a_global_sf
|
|
a_quant.append(a_fp8)
|
|
a_scales.append(a_global_sf)
|
|
|
|
result_a_quant = torch.stack(a_quant)
|
|
result_a_scales = torch.stack(a_scales)
|
|
|
|
return result_a_quant, result_a_scales
|
|
|
|
|
|
@dataclass
|
|
class TestData:
|
|
hidden_states: torch.Tensor
|
|
w13_quantized: torch.Tensor
|
|
w2_quantized: torch.Tensor
|
|
a1_scale: torch.Tensor
|
|
a2_scale: torch.Tensor
|
|
w13_weight_scale: torch.Tensor
|
|
w2_weight_scale: torch.Tensor
|
|
layer: torch.nn.Module
|
|
|
|
@staticmethod
|
|
def make_moe_tensors_8bit(m: int, k: int, n: int, e: int,
|
|
reorder: bool) -> "TestData":
|
|
hidden_states = torch.randn(
|
|
(m, k), device="cuda", dtype=torch.bfloat16) / 10
|
|
w13 = torch.randn((e, 2 * n, k), device="cuda", dtype=torch.bfloat16)
|
|
w2 = torch.randn((e, k, n), device="cuda", dtype=torch.bfloat16)
|
|
|
|
# Scale to fp8
|
|
_, a1_scale = input_to_float8(hidden_states)
|
|
a1_scale = 1.0 / a1_scale
|
|
a2_scale = torch.scalar_tensor(1.0).to(device="cuda").to(
|
|
dtype=torch.float32)
|
|
w13_quantized, w13_weight_scale = quant_fp8_per_tensor_batches(w13)
|
|
w2_quantized, w2_weight_scale = quant_fp8_per_tensor_batches(w2)
|
|
|
|
layer = torch.nn.Module()
|
|
layer.w13_weight = w13_quantized.clone()
|
|
layer.w2_weight = w2_quantized.clone()
|
|
layer.w13_input_scale = a1_scale
|
|
layer.w2_input_scale = a2_scale
|
|
layer.w13_weight_scale = w13_weight_scale
|
|
layer.w2_weight_scale = w2_weight_scale
|
|
|
|
register_moe_scaling_factors(layer)
|
|
|
|
# flashinfer expects swapped rows for w13
|
|
layer.w13_weight.data = swap_w13_to_w31(layer.w13_weight.data)
|
|
if reorder:
|
|
rotate_flashinfer_fp8_moe_weights(layer.w13_weight,
|
|
layer.w2_weight)
|
|
layer.custom_routing_function = Llama4MoE.custom_routing_function
|
|
layer.intermediate_size_per_partition = n
|
|
layer.ep_rank = 0
|
|
layer.local_num_experts = e
|
|
|
|
return TestData(
|
|
hidden_states=hidden_states,
|
|
w13_quantized=w13_quantized,
|
|
w2_quantized=w2_quantized,
|
|
a1_scale=a1_scale,
|
|
a2_scale=a2_scale,
|
|
w13_weight_scale=w13_weight_scale,
|
|
w2_weight_scale=w2_weight_scale,
|
|
layer=layer,
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("m,n,k", MNK_FACTORS)
|
|
@pytest.mark.parametrize("e", NUM_EXPERTS)
|
|
@pytest.mark.parametrize("topk", TOP_KS)
|
|
def test_flashinfer_per_tensor_moe_fp8_no_graph(
|
|
m: int,
|
|
n: int,
|
|
k: int,
|
|
e: int,
|
|
topk: int,
|
|
monkeypatch,
|
|
):
|
|
current_platform.seed_everything(7)
|
|
monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", "8192")
|
|
with set_current_vllm_config(vllm_config):
|
|
td = TestData.make_moe_tensors_8bit(m, k, n, e, reorder=True)
|
|
|
|
score = torch.randn((m, e), device="cuda", dtype=torch.bfloat16)
|
|
topk_weights, topk_ids, _ = FusedMoE.select_experts(
|
|
hidden_states=td.hidden_states,
|
|
router_logits=score,
|
|
use_grouped_topk=False,
|
|
top_k=topk,
|
|
renormalize=False,
|
|
custom_routing_function=Llama4MoE.custom_routing_function,
|
|
scoring_func="softmax")
|
|
|
|
quant_config = fp8_w8a8_moe_quant_config(
|
|
w1_scale=td.w13_weight_scale,
|
|
w2_scale=td.w2_weight_scale,
|
|
a1_scale=td.a1_scale,
|
|
a2_scale=td.a2_scale,
|
|
per_act_token_quant=False,
|
|
)
|
|
|
|
output = fused_experts(
|
|
td.hidden_states,
|
|
td.w13_quantized,
|
|
td.w2_quantized,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
inplace=False,
|
|
activation="silu",
|
|
global_num_experts=e,
|
|
expert_map=None,
|
|
apply_router_weight_on_input=True,
|
|
quant_config=quant_config,
|
|
)
|
|
|
|
flashinfer_output = apply_flashinfer_per_tensor_scale_fp8(
|
|
layer=td.layer,
|
|
hidden_states=td.hidden_states,
|
|
router_logits=score,
|
|
routing_bias=None,
|
|
global_num_experts=e,
|
|
top_k=topk,
|
|
num_expert_group=None,
|
|
topk_group=None,
|
|
apply_router_weight_on_input=True)
|
|
|
|
torch.testing.assert_close(output,
|
|
flashinfer_output,
|
|
atol=5.5e-2,
|
|
rtol=1e-2)
|
|
|
|
|
|
@pytest.mark.skip(
|
|
"Requires flashinfer version that contains https://github.com/flashinfer-ai/flashinfer/pull/1472"
|
|
)
|
|
@pytest.mark.parametrize("m,n,k", MNK_FACTORS)
|
|
@pytest.mark.parametrize("e", NUM_EXPERTS)
|
|
@pytest.mark.parametrize("topk", TOP_KS)
|
|
def test_flashinfer_cutlass_moe_fp8_no_graph(
|
|
m: int,
|
|
n: int,
|
|
k: int,
|
|
e: int,
|
|
topk: int,
|
|
monkeypatch,
|
|
):
|
|
current_platform.seed_everything(7)
|
|
monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", "8192")
|
|
with set_current_vllm_config(vllm_config):
|
|
td = TestData.make_moe_tensors_8bit(m, k, n, e, reorder=False)
|
|
|
|
score = torch.randn((m, e), device="cuda", dtype=torch.bfloat16)
|
|
topk_weights, topk_ids, _ = FusedMoE.select_experts(
|
|
hidden_states=td.hidden_states,
|
|
router_logits=score,
|
|
use_grouped_topk=False,
|
|
top_k=topk,
|
|
renormalize=False,
|
|
custom_routing_function=Llama4MoE.custom_routing_function,
|
|
scoring_func="softmax")
|
|
|
|
quant_config = fp8_w8a8_moe_quant_config(
|
|
w1_scale=td.w13_weight_scale,
|
|
w2_scale=td.w2_weight_scale,
|
|
a1_scale=td.a1_scale,
|
|
a2_scale=td.a2_scale,
|
|
per_act_token_quant=False,
|
|
)
|
|
|
|
output = fused_experts(
|
|
td.hidden_states,
|
|
td.w13_quantized,
|
|
td.w2_quantized,
|
|
topk_weights=topk_weights,
|
|
topk_ids=topk_ids,
|
|
inplace=False,
|
|
activation="silu",
|
|
global_num_experts=e,
|
|
expert_map=None,
|
|
apply_router_weight_on_input=True,
|
|
quant_config=quant_config,
|
|
)
|
|
|
|
td.layer.dp_size = 1
|
|
|
|
flashinfer_cutlass_output = flashinfer_cutlass_moe_fp8(
|
|
td.hidden_states,
|
|
td.layer,
|
|
topk_weights,
|
|
topk_ids,
|
|
activation="silu",
|
|
global_num_experts=e,
|
|
expert_map=None,
|
|
apply_router_weight_on_input=True,
|
|
)
|
|
|
|
torch.testing.assert_close(output,
|
|
flashinfer_cutlass_output,
|
|
atol=5.5e-2,
|
|
rtol=1e-2)
|