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https://git.datalinker.icu/vllm-project/vllm.git
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273 lines
8.5 KiB
Python
273 lines
8.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from dataclasses import dataclass
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import pytest
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import torch
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from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
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from vllm.model_executor.layers.fused_moe.config import (
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FusedMoEQuantConfig,
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fp8_w8a8_moe_quant_config,
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)
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from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts
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from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
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apply_flashinfer_per_tensor_scale_fp8,
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flashinfer_cutlass_moe_fp8,
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register_moe_scaling_factors,
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rotate_flashinfer_fp8_moe_weights,
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swap_w13_to_w31,
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)
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from vllm.model_executor.layers.quantization.utils.fp8_utils import input_to_float8
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from vllm.model_executor.models.llama4 import Llama4MoE
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from vllm.platforms import current_platform
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try:
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from vllm.utils.flashinfer import has_flashinfer_cutlass_fused_moe
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except ImportError:
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if current_platform.is_rocm():
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pytest.skip(
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"flashinfer not supported for vLLM on ROCm", allow_module_level=True
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)
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if not has_flashinfer_cutlass_fused_moe() or not current_platform.has_device_capability(
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90
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):
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pytest.skip(
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"Supported for sm >= 90",
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allow_module_level=True,
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)
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NUM_EXPERTS = [16]
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TOP_KS = [1]
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MNK_FACTORS = [
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(256, 8192, 5120),
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(127, 4096, 5120),
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(10, 8192, 5120),
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(10, 4096, 5120),
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(1, 8192, 5120),
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(1, 4096, 5120),
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]
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vllm_config = VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=1))
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def quant_fp8_per_tensor_batches(a):
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num_batches = a.size(0)
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a_quant = []
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a_scales = []
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for i in range(num_batches):
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a_fp8, a_global_sf = input_to_float8(a[i])
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a_global_sf = 1.0 / a_global_sf
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a_quant.append(a_fp8)
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a_scales.append(a_global_sf)
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result_a_quant = torch.stack(a_quant)
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result_a_scales = torch.stack(a_scales)
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return result_a_quant, result_a_scales
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@dataclass
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class TestData:
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hidden_states: torch.Tensor
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w13_quantized: torch.Tensor
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w2_quantized: torch.Tensor
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a1_scale: torch.Tensor
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a2_scale: torch.Tensor
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w13_weight_scale: torch.Tensor
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w2_weight_scale: torch.Tensor
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layer: torch.nn.Module
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@staticmethod
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def make_moe_tensors_8bit(
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m: int, k: int, n: int, e: int, reorder: bool, activation: str = "silu"
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) -> "TestData":
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is_gated = activation != "relu2_no_mul"
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hidden_states = torch.randn((m, k), device="cuda", dtype=torch.bfloat16) / 10
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w13 = torch.randn(
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(e, (2 * n) if is_gated else n, k), device="cuda", dtype=torch.bfloat16
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)
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w2 = torch.randn((e, k, n), device="cuda", dtype=torch.bfloat16)
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# Scale to fp8
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_, a1_scale = input_to_float8(hidden_states)
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a1_scale = 1.0 / a1_scale
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a2_scale = torch.scalar_tensor(1.0).to(device="cuda").to(dtype=torch.float32)
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w13_quantized, w13_weight_scale = quant_fp8_per_tensor_batches(w13)
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w2_quantized, w2_weight_scale = quant_fp8_per_tensor_batches(w2)
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layer = torch.nn.Module()
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layer.w13_weight = w13_quantized.clone()
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layer.w2_weight = w2_quantized.clone()
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layer.w13_input_scale = a1_scale
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layer.w2_input_scale = a2_scale
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layer.w13_weight_scale = w13_weight_scale
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layer.w2_weight_scale = w2_weight_scale
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register_moe_scaling_factors(layer)
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# flashinfer expects swapped rows for w13
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layer.w13_weight.data = swap_w13_to_w31(layer.w13_weight.data)
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if reorder:
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rotate_flashinfer_fp8_moe_weights(layer.w13_weight, layer.w2_weight)
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layer.custom_routing_function = Llama4MoE.custom_routing_function
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layer.intermediate_size_per_partition = n
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layer.ep_rank = 0
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layer.local_num_experts = e
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return TestData(
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hidden_states=hidden_states,
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w13_quantized=w13_quantized,
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w2_quantized=w2_quantized,
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a1_scale=a1_scale,
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a2_scale=a2_scale,
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w13_weight_scale=w13_weight_scale,
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w2_weight_scale=w2_weight_scale,
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layer=layer,
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)
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@pytest.mark.parametrize("m,n,k", MNK_FACTORS)
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@pytest.mark.parametrize("e", NUM_EXPERTS)
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@pytest.mark.parametrize("topk", TOP_KS)
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def test_flashinfer_per_tensor_moe_fp8_no_graph(
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m: int,
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n: int,
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k: int,
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e: int,
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topk: int,
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monkeypatch,
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):
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if not current_platform.has_device_capability(100):
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pytest.skip("Test is only supported for sm >= 100")
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current_platform.seed_everything(7)
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monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", "8192")
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with set_current_vllm_config(vllm_config):
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td = TestData.make_moe_tensors_8bit(m, k, n, e, reorder=True)
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score = torch.randn((m, e), device="cuda", dtype=torch.bfloat16)
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topk_weights, topk_ids = Llama4MoE.custom_routing_function(
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hidden_states=td.hidden_states,
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gating_output=score,
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topk=topk,
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renormalize=False,
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)
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quant_config = fp8_w8a8_moe_quant_config(
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w1_scale=td.w13_weight_scale,
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w2_scale=td.w2_weight_scale,
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a1_scale=td.a1_scale,
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a2_scale=td.a2_scale,
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per_act_token_quant=False,
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)
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output = fused_experts(
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td.hidden_states,
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td.w13_quantized,
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td.w2_quantized,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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inplace=False,
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activation="silu",
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global_num_experts=e,
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expert_map=None,
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apply_router_weight_on_input=True,
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quant_config=quant_config,
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)
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flashinfer_output = apply_flashinfer_per_tensor_scale_fp8(
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layer=td.layer,
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hidden_states=td.hidden_states,
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router_logits=score,
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routing_bias=None,
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global_num_experts=e,
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top_k=topk,
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num_expert_group=None,
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topk_group=None,
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apply_router_weight_on_input=True,
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)
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torch.testing.assert_close(output, flashinfer_output, atol=5.5e-2, rtol=1e-2)
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@pytest.mark.parametrize("m,n,k", MNK_FACTORS)
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@pytest.mark.parametrize("e", NUM_EXPERTS)
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@pytest.mark.parametrize("topk", TOP_KS)
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@pytest.mark.parametrize("activation", ["silu", "relu2_no_mul"])
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def test_flashinfer_cutlass_moe_fp8_no_graph(
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m: int,
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n: int,
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k: int,
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e: int,
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topk: int,
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activation: str,
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monkeypatch,
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):
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current_platform.seed_everything(7)
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monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", "8192")
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with set_current_vllm_config(vllm_config):
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td = TestData.make_moe_tensors_8bit(
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m, k, n, e, reorder=False, activation=activation
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)
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score = torch.randn((m, e), device="cuda", dtype=torch.bfloat16)
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topk_weights, topk_ids = Llama4MoE.custom_routing_function(
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hidden_states=td.hidden_states,
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gating_output=score,
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topk=topk,
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renormalize=False,
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)
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quant_config = fp8_w8a8_moe_quant_config(
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w1_scale=td.w13_weight_scale,
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g1_alphas=(td.w13_weight_scale * td.a1_scale).squeeze(),
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w2_scale=td.w2_weight_scale,
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g2_alphas=(td.w2_weight_scale * td.a2_scale).squeeze(),
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a1_scale=td.a1_scale,
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a1_gscale=td.a1_scale,
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a2_scale=td.a2_scale,
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a2_gscale=1.0 / td.a2_scale,
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per_act_token_quant=False,
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)
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output = fused_experts(
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td.hidden_states,
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td.w13_quantized,
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td.w2_quantized,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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inplace=False,
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activation=activation,
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global_num_experts=e,
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expert_map=None,
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apply_router_weight_on_input=True,
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quant_config=quant_config,
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)
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td.layer.dp_size = 1
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def get_fused_moe_quant_config(n: torch.nn.Module) -> FusedMoEQuantConfig:
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return quant_config
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td.layer.get_fused_moe_quant_config = get_fused_moe_quant_config
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td.layer.quant_method = td.layer
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flashinfer_cutlass_output = flashinfer_cutlass_moe_fp8(
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td.hidden_states,
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td.layer,
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topk_weights,
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topk_ids,
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activation=activation,
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global_num_experts=e,
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expert_map=None,
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apply_router_weight_on_input=True,
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)
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torch.testing.assert_close(
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output, flashinfer_cutlass_output, atol=5.5e-2, rtol=1e-2
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)
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