[Feature][OCP MX] Support mxfp6 and mixed mxfp6-mxfp4 (#21166)
This commit is contained in:
fxmarty-amd
994
tests/kernels/moe/test_ocp_mx_moe.py
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994
tests/kernels/moe/test_ocp_mx_moe.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import importlib.metadata
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from dataclasses import dataclass
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from importlib.util import find_spec
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from typing import Optional
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import pytest
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import torch
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from packaging import version
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from vllm.platforms import current_platform
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from vllm.utils.flashinfer import has_flashinfer
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QUARK_MXFP4_AVAILABLE = find_spec("quark") is not None and version.parse(
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importlib.metadata.version("amd-quark")
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) >= version.parse("0.8.99")
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TRTLLM_GEN_MXFP4_AVAILABLE = (
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current_platform.is_cuda() and current_platform.is_device_capability(100)
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)
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HOPPER_MXFP4_BF16_AVAILABLE = (
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current_platform.is_cuda()
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and current_platform.is_device_capability(90)
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and has_flashinfer()
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)
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if TRTLLM_GEN_MXFP4_AVAILABLE:
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from flashinfer import (
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fp4_quantize,
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mxfp8_quantize,
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next_positive_power_of_2,
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reorder_rows_for_gated_act_gemm,
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shuffle_matrix_a,
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shuffle_matrix_sf_a,
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trtllm_fp4_block_scale_moe,
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)
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from flashinfer.fp4_quantization import nvfp4_block_scale_interleave
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from flashinfer.fused_moe.core import _maybe_get_cached_w2_permute_indices
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@dataclass
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class ModelCase:
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model_id: str
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tp: int
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@pytest.fixture(scope="function", autouse=True)
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def enable_pickle(monkeypatch):
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"""`LLM.apply_model` requires pickling a function."""
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monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
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@pytest.mark.parametrize(
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"model_case",
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[
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ModelCase("fxmarty/qwen_1.5-moe-a2.7b-mxfp4", tp=2),
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ModelCase("fxmarty/deepseek_r1_3_layers_mxfp4", tp=8),
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ModelCase("fxmarty/Llama-4-Scout-17B-16E-Instruct-2-layers-mxfp4", tp=1),
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ModelCase("fxmarty/Llama-3.1-70B-Instruct-2-layers-mxfp6", tp=1),
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ModelCase("fxmarty/Llama-3.1-70B-Instruct-2-layers-mxfp6", tp=4),
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],
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)
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@pytest.mark.skipif(not QUARK_MXFP4_AVAILABLE, reason="amd-quark>=0.9 is not available")
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def test_mxfp4_loading_and_execution_moe(vllm_runner, model_case: ModelCase):
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if torch.cuda.device_count() < model_case.tp:
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pytest.skip(
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f"This test requires >={model_case.tp} gpus, got only "
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f"{torch.cuda.device_count()}"
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)
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# `cuda_graph_sizes=[16]` to reduce load time.
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with vllm_runner(
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model_case.model_id,
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tensor_parallel_size=model_case.tp,
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load_format="dummy",
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cuda_graph_sizes=[16],
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) as llm:
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# Disabled as check_model is broken: https://github.com/vllm-project/vllm/pull/18465#issuecomment-3329880562
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# def check_model(model):
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# from vllm.model_executor.layers.quantization.quark.quark import ( # noqa: E501
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# QuarkLinearMethod)
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# from vllm.model_executor.layers.quantization.quark.schemes.quark_ocp_mx import QuarkOCP_MX # noqa: E501
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# from vllm.model_executor.layers.quantization.quark.quark_moe import ( # noqa: E501
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# QuarkOCP_MX_MoEMethod)
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# layer = model.model.layers[0]
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# qkv_proj = layer.self_attn.qkv_proj
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# assert isinstance(qkv_proj.quant_method, QuarkLinearMethod)
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# assert isinstance(qkv_proj.scheme, QuarkOCP_MX)
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# assert isinstance(layer.mlp.experts.quant_method,
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# QuarkOCP_MX_MoEMethod)
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# if model_case.model_id == "fxmarty/qwen_1.5-moe-a2.7b-mxfp4":
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# llm.apply_model(check_model)
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output = llm.generate_greedy("Today I am in the French Alps and", max_tokens=20)
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assert output
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def swiglu(x, alpha: float = 1.702, beta: float = 1.0, limit: Optional[float] = None):
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# Note we add an extra bias of 1 to the linear layer
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x_glu, x_linear = torch.chunk(x, 2, dim=-1)
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if limit is not None:
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x_glu = x_glu.clamp(max=limit)
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x_linear = x_linear.clamp(min=-limit, max=limit)
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out_glu = x_glu * torch.sigmoid(alpha * x_glu)
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return out_glu * (x_linear + beta)
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fp4_lookup_table = [0, 0.5, 1, 1.5, 2, 3, 4, 6, -0, -0.5, -1, -1.5, -2, -3, -4, -6]
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def mxfp4_dequantize(x, scale):
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assert x.dtype == torch.uint8
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x = x.view(torch.uint8).to(torch.int32)
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x_unpacked = torch.zeros(
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*x.shape[:-1], x.shape[-1] * 2, dtype=torch.int32, device=x.device
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)
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x_unpacked[..., 0::2].copy_(x & 0xF)
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x_unpacked[..., 1::2].copy_((x >> 4) & 0xF)
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x_float = torch.zeros(x_unpacked.shape, dtype=torch.float32, device=x.device)
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for i, val in enumerate(fp4_lookup_table):
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x_float[x_unpacked == i] = val
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scale = scale.view(torch.uint8).to(torch.int32)
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scale = (scale << 23).view(torch.float32)
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scale = scale.reshape(*x.shape[:-1], -1)
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scale = torch.stack([scale] * 32, dim=-1).reshape(*x_float.shape)
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return x_float * scale
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def mxfp8_dequantize(x, scale):
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assert x.dtype == torch.float8_e4m3fn
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x_float = x.to(torch.float32)
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scale = scale.view(torch.uint8).to(torch.int32)
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scale = (scale << 23).view(torch.float32)
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scale = scale.reshape(*x.shape[:-1], -1)
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scale = torch.stack([scale] * 32, dim=-1).reshape(*x_float.shape)
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return x_float * scale
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def reference_moe(
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roouting_logits,
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topk,
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num_experts,
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hidden_states,
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w13,
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bias13,
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w2,
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bias2,
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alpha,
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beta,
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limit,
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act_type,
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):
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# renormalize routing
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experts = torch.topk(roouting_logits, k=topk, dim=-1, sorted=True)
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expert_weights = torch.nn.functional.softmax(experts.values, dim=1)
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expert_indices = experts.indices
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t = hidden_states.clone()
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# MLP #1
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mlp1_weight = w13[expert_indices, ...]
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mlp1_bias = bias13[expert_indices, ...]
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t = torch.einsum("beck,bk->bec", mlp1_weight, t) + mlp1_bias
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t = swiglu(t, alpha=alpha, beta=beta, limit=limit)
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if act_type == "mxfp8":
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t_quantized, t_scale = mxfp8_quantize(
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t.to(torch.bfloat16), is_sf_swizzled_layout=False
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)
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t = mxfp8_dequantize(t_quantized, t_scale)
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# MLP #2
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mlp2_weight = w2[expert_indices, ...]
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mlp2_bias = bias2[expert_indices, ...]
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t = torch.einsum("beck,bek->bec", mlp2_weight, t) + mlp2_bias
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# Weighted sum of experts
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t = torch.einsum("bec,be->bc", t, expert_weights)
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assert t.shape == hidden_states.shape
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return t.to(torch.bfloat16)
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def get_tile_tokens_dim(x: torch.Tensor, top_k: int, num_experts: int):
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# Number of tokens in the input tensor.
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num_tokens = x.shape[0]
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# Factor to account for the imbalance of the experts.
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# factor equals to the
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# max_real_num_tokens_per_expert / perfect_num_tokens_per_expert
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# - 1.0 means perfect expert distribution.
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# - > 1.0 means some experts have more
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# tokens than the perfect distribution.
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# - < 1.0 does not make sense.
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imbalance_factor = 1.3
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# Calculate the number of tokens per expert
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# assuming perfect distribution.
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num_tokens_per_expert = (num_tokens * top_k) // num_experts
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# Apply the imbalance factor.
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num_tokens_per_expert = int(num_tokens_per_expert * imbalance_factor)
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# And pad the number to the next power of 2.
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tile_tokens_dim = next_positive_power_of_2(num_tokens_per_expert)
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# Cap to 8-64 tokens per CTA tile
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# as it's the range supported by the kernel.
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tile_tokens_dim = min(max(tile_tokens_dim, 8), 64)
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return tile_tokens_dim
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def tg_mxfp4_moe(
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router_logits,
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topk,
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num_experts,
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intermediate_size,
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hidden_size,
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hidden_states,
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hidden_states_scale,
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w13_weight,
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w13_weight_scale,
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w13_bias,
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w2_weight,
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w2_weight_scale,
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w2_bias,
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act_type,
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alpha,
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beta,
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limit,
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transpose_optimized: bool = False,
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) -> torch.Tensor:
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sf_block_size = 32
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assert (
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w13_weight.dim() == 3
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and w13_weight.shape[0] == num_experts
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and w13_weight.shape[1] == intermediate_size * 2
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and w13_weight.shape[2] == hidden_size // 2
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)
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assert (
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w13_weight_scale.dim() == 3
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and w13_weight_scale.shape[0] == num_experts
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and w13_weight_scale.shape[1] == intermediate_size * 2
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and w13_weight_scale.shape[2] == hidden_size // sf_block_size
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)
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assert (
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w2_weight.dim() == 3
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and w2_weight.shape[0] == num_experts
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and w2_weight.shape[1] == hidden_size
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and w2_weight.shape[2] == intermediate_size // 2
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)
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assert (
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w2_weight_scale.dim() == 3
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and w2_weight_scale.shape[1] == hidden_size
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and w2_weight_scale.shape[2] == intermediate_size // sf_block_size
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)
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assert (
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w13_bias.dim() == 2
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and w13_bias.shape[0] == num_experts
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and w13_bias.shape[1] == intermediate_size * 2
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)
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assert (
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w2_bias.dim() == 2
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and w2_bias.shape[0] == num_experts
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and w2_bias.shape[1] == hidden_size
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)
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# Swap w1 and w3 as the definition of
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# swiglu is different in the trtllm-gen
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w13_weight_scale_ = w13_weight_scale.clone()
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w13_weight_ = w13_weight.clone()
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w13_bias_ = w13_bias.clone()
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w13_weight[:, :intermediate_size, :].copy_(w13_weight_[:, intermediate_size:, :])
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w13_weight[:, intermediate_size:, :].copy_(w13_weight_[:, :intermediate_size, :])
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w13_weight_scale[:, :intermediate_size, :].copy_(
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w13_weight_scale_[:, intermediate_size:, :]
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)
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w13_weight_scale[:, intermediate_size:, :].copy_(
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w13_weight_scale_[:, :intermediate_size, :]
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)
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w13_bias[:, :intermediate_size].copy_(w13_bias_[:, intermediate_size:])
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w13_bias[:, intermediate_size:].copy_(w13_bias_[:, :intermediate_size])
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# Interleave the weights and scaling factors for activation
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w13_weight_interleaved = []
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w13_weight_scale_interleaved = []
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w13_bias_interleaved = []
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for i in range(num_experts):
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w13_weight_interleaved.append(
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reorder_rows_for_gated_act_gemm(w13_weight[i].clone())
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)
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w13_weight_scale_interleaved.append(
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reorder_rows_for_gated_act_gemm(w13_weight_scale[i].clone())
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)
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w13_bias_interleaved.append(
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reorder_rows_for_gated_act_gemm(w13_bias[i].clone().reshape(-1, 1))
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)
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w13_weight = torch.stack(w13_weight_interleaved).reshape(
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num_experts, 2 * intermediate_size, hidden_size // 2
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)
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w13_weight_scale = torch.stack(w13_weight_scale_interleaved).reshape(
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num_experts, 2 * intermediate_size, hidden_size // 32
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)
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w13_bias = torch.stack(w13_bias_interleaved).reshape(
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num_experts, 2 * intermediate_size
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)
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# Shuffle weights and scaling factors for transposed mma output
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gemm1_weights_shuffled = []
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gemm1_scales_shuffled = []
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gemm2_weights_shuffled = []
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gemm2_scales_shuffled = []
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gemm1_bias_shuffled = []
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gemm2_bias_shuffled = []
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epilogue_tile_m = 128 # FIXME: this depends on the kernel internals
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_cache_permute_indices: dict[torch.Size, torch.Tensor] = {}
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if transpose_optimized:
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for i in range(num_experts):
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# w13 weight shuffling
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permute_indices = _maybe_get_cached_w2_permute_indices(
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_cache_permute_indices,
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w13_weight[i].view(torch.uint8),
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epilogue_tile_m,
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)
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gemm1_weights_shuffled.append(
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w13_weight[i]
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.view(torch.uint8)[permute_indices.to(w13_weight.device)]
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.contiguous()
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)
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# w13 scale shuffling
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permute_sf_indices = _maybe_get_cached_w2_permute_indices(
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_cache_permute_indices,
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w13_weight_scale[i].view(torch.uint8),
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epilogue_tile_m,
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num_elts_per_sf=16,
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)
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gemm1_scales_shuffled.append(
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nvfp4_block_scale_interleave(
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w13_weight_scale[i]
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.view(torch.uint8)[permute_sf_indices.to(w13_weight_scale.device)]
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.contiguous()
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)
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)
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# w13 bias shuffling
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permute_bias_indices = _maybe_get_cached_w2_permute_indices(
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_cache_permute_indices,
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w13_bias[i].clone().reshape(-1, 1),
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epilogue_tile_m,
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)
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gemm1_bias_shuffled.append(
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w13_bias[i]
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.clone()
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.reshape(-1, 1)[permute_bias_indices.to(w13_bias.device)]
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.contiguous()
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)
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# w2 weight shuffling
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permute_indices = _maybe_get_cached_w2_permute_indices(
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_cache_permute_indices,
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w2_weight[i].view(torch.uint8),
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epilogue_tile_m,
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)
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gemm2_weights_shuffled.append(
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w2_weight[i]
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.view(torch.uint8)[permute_indices.to(w2_weight.device)]
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.contiguous()
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)
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# w2 scale shuffling
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permute_sf_indices = _maybe_get_cached_w2_permute_indices(
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_cache_permute_indices,
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w2_weight_scale[i].view(torch.uint8),
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epilogue_tile_m,
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num_elts_per_sf=16,
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)
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gemm2_scales_shuffled.append(
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nvfp4_block_scale_interleave(
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w2_weight_scale[i]
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.view(torch.uint8)[permute_sf_indices.to(w2_weight_scale.device)]
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.contiguous()
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)
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)
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# w2 bias shuffling
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permute_indices = _maybe_get_cached_w2_permute_indices(
|
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_cache_permute_indices,
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w2_bias[i].clone().reshape(-1, 1),
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epilogue_tile_m,
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)
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gemm2_bias_shuffled.append(
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w2_bias[i]
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.clone()
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.reshape(-1, 1)[permute_indices.to(w2_bias.device)]
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.contiguous()
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)
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else:
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for i in range(num_experts):
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gemm1_weights_shuffled.append(
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shuffle_matrix_a(w13_weight[i].view(torch.uint8), epilogue_tile_m)
|
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)
|
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gemm1_scales_shuffled.append(
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shuffle_matrix_sf_a(
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w13_weight_scale[i].view(torch.uint8), epilogue_tile_m
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)
|
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)
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gemm2_weights_shuffled.append(
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shuffle_matrix_a(w2_weight[i].view(torch.uint8), epilogue_tile_m)
|
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)
|
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gemm2_scales_shuffled.append(
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shuffle_matrix_sf_a(
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w2_weight_scale[i].view(torch.uint8), epilogue_tile_m
|
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)
|
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)
|
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gemm1_bias_shuffled.append(
|
||||
shuffle_matrix_a(w13_bias[i].reshape(-1, 1), epilogue_tile_m)
|
||||
)
|
||||
gemm2_bias_shuffled.append(
|
||||
shuffle_matrix_a(w2_bias[i].reshape(-1, 1), epilogue_tile_m)
|
||||
)
|
||||
|
||||
w13_weight = torch.stack(gemm1_weights_shuffled)
|
||||
w13_weight_scale = (
|
||||
torch.stack(gemm1_scales_shuffled)
|
||||
.reshape(num_experts, 2 * intermediate_size, hidden_size // sf_block_size)
|
||||
.view(torch.float8_e4m3fn)
|
||||
)
|
||||
w13_bias = torch.stack(gemm1_bias_shuffled).reshape(num_experts, -1)
|
||||
|
||||
w2_weight = torch.stack(gemm2_weights_shuffled)
|
||||
w2_weight_scale = (
|
||||
torch.stack(gemm2_scales_shuffled)
|
||||
.reshape(num_experts, hidden_size, intermediate_size // sf_block_size)
|
||||
.view(torch.float8_e4m3fn)
|
||||
)
|
||||
w2_bias = torch.stack(gemm2_bias_shuffled).reshape(num_experts, -1)
|
||||
|
||||
tg_result = trtllm_fp4_block_scale_moe(
|
||||
routing_logits=router_logits.to(torch.bfloat16),
|
||||
routing_bias=None,
|
||||
hidden_states=hidden_states,
|
||||
hidden_states_scale=hidden_states_scale,
|
||||
gemm1_weights=w13_weight,
|
||||
gemm1_weights_scale=w13_weight_scale,
|
||||
gemm1_bias=w13_bias,
|
||||
gemm1_alpha=alpha,
|
||||
gemm1_beta=beta,
|
||||
gemm1_clamp_limit=limit,
|
||||
gemm2_weights=w2_weight,
|
||||
gemm2_weights_scale=w2_weight_scale,
|
||||
gemm2_bias=w2_bias,
|
||||
output1_scale_scalar=None,
|
||||
output1_scale_gate_scalar=None,
|
||||
output2_scale_scalar=None,
|
||||
num_experts=num_experts,
|
||||
top_k=topk,
|
||||
n_group=None,
|
||||
topk_group=None,
|
||||
intermediate_size=intermediate_size,
|
||||
local_expert_offset=0,
|
||||
local_num_experts=num_experts,
|
||||
routed_scaling_factor=None,
|
||||
tile_tokens_dim=get_tile_tokens_dim(hidden_states, topk, num_experts),
|
||||
routing_method_type=1, # renormalize
|
||||
do_finalize=True,
|
||||
)[0]
|
||||
return tg_result
|
||||
|
||||
|
||||
def check_accuracy(a, b, atol, rtol, percent):
|
||||
"""Allow a mismatch percentage of 1 - percent."""
|
||||
if torch.any(torch.isnan(a)):
|
||||
raise Exception("NaN in reference output")
|
||||
if torch.any(torch.isnan(b)):
|
||||
raise Exception("NaN in actual output")
|
||||
if torch.any(torch.isinf(a)):
|
||||
raise Exception("Inf in reference output")
|
||||
if torch.any(torch.isinf(b)):
|
||||
raise Exception("Inf in actual output")
|
||||
assert a.shape == b.shape, f"Shape mismatch: {a.shape} vs {b.shape}"
|
||||
|
||||
left = torch.abs(a - b)
|
||||
right = atol + rtol * torch.abs(b)
|
||||
count = torch.sum(left > right)
|
||||
mismatch_percent = count / a.numel()
|
||||
if mismatch_percent > 1 - percent:
|
||||
raise Exception(
|
||||
f"Mismatch percentage is {mismatch_percent:.4f} for rtol {rtol} "
|
||||
f"(threshold: {1 - percent:.4f})"
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("topk", [1, 4])
|
||||
@pytest.mark.parametrize("num_experts", [32, 128])
|
||||
@pytest.mark.parametrize("num_tokens", [1, 128, 1024])
|
||||
@pytest.mark.parametrize("intermediate_size,hidden_size", [(3072, 3072)])
|
||||
@pytest.mark.parametrize("alpha,beta,limit", [(1.0, 1.0, None), (1.702, 1.0, 7.0)])
|
||||
@pytest.mark.parametrize("act_type", ["mxfp8", "bf16"])
|
||||
@pytest.mark.parametrize("transpose_optimized", [False, True])
|
||||
@pytest.mark.skipif(
|
||||
not TRTLLM_GEN_MXFP4_AVAILABLE,
|
||||
reason="nvidia gpu and compute capability sm100 is required for this test",
|
||||
)
|
||||
def test_trtllm_gen_mxfp4_fused_moe(
|
||||
topk: int,
|
||||
num_experts: int,
|
||||
num_tokens: int,
|
||||
intermediate_size: int,
|
||||
hidden_size: int,
|
||||
alpha: float,
|
||||
beta: float,
|
||||
limit: Optional[float],
|
||||
act_type: str,
|
||||
transpose_optimized: bool,
|
||||
):
|
||||
seed = 42
|
||||
torch.manual_seed(seed)
|
||||
hidden_states = torch.randn(
|
||||
num_tokens, hidden_size, device="cuda:0", dtype=torch.bfloat16
|
||||
)
|
||||
w13 = torch.randn(
|
||||
num_experts,
|
||||
intermediate_size * 2,
|
||||
hidden_size,
|
||||
device="cuda:0",
|
||||
dtype=torch.bfloat16,
|
||||
)
|
||||
w2 = torch.randn(
|
||||
num_experts,
|
||||
hidden_size,
|
||||
intermediate_size,
|
||||
device="cuda:0",
|
||||
dtype=torch.bfloat16,
|
||||
)
|
||||
bias13 = torch.randn(num_experts, intermediate_size * 2, device="cuda:0") * 10
|
||||
bias2 = torch.randn(num_experts, hidden_size, device="cuda:0") * 10
|
||||
router_logits = torch.rand(num_tokens, num_experts, dtype=torch.float32).cuda()
|
||||
|
||||
w13, w13_scale = fp4_quantize(
|
||||
w13,
|
||||
torch.tensor(1.0, device="cuda:0"),
|
||||
32,
|
||||
sf_use_ue8m0=True,
|
||||
is_sf_swizzled_layout=False,
|
||||
)
|
||||
w13_scale = w13_scale.view(torch.float8_e4m3fn).reshape(
|
||||
num_experts, intermediate_size * 2, hidden_size // 32
|
||||
)
|
||||
w2, w2_scale = fp4_quantize(
|
||||
w2,
|
||||
torch.tensor(1.0, device="cuda:0"),
|
||||
32,
|
||||
sf_use_ue8m0=True,
|
||||
is_sf_swizzled_layout=False,
|
||||
)
|
||||
w2_scale = w2_scale.view(torch.float8_e4m3fn).reshape(
|
||||
num_experts, hidden_size, intermediate_size // 32
|
||||
)
|
||||
if act_type == "mxfp8":
|
||||
hidden_states, hidden_states_scale = mxfp8_quantize(
|
||||
hidden_states, is_sf_swizzled_layout=False
|
||||
)
|
||||
hidden_states_scale = hidden_states_scale.view(torch.float8_e4m3fn).reshape(-1)
|
||||
else:
|
||||
hidden_states_scale = None
|
||||
|
||||
# reference result
|
||||
ref_result = torch.empty_like(hidden_states, dtype=torch.bfloat16)
|
||||
w13_ref = mxfp4_dequantize(w13.clone(), w13_scale.clone())
|
||||
w2_ref = mxfp4_dequantize(w2.clone(), w2_scale.clone())
|
||||
bias13_ref = bias13
|
||||
bias2_ref = bias2
|
||||
if act_type == "mxfp8":
|
||||
hidden_states_ref = mxfp8_dequantize(hidden_states, hidden_states_scale).to(
|
||||
torch.float32
|
||||
)
|
||||
else:
|
||||
hidden_states_ref = hidden_states.to(torch.float32)
|
||||
# Process tokens in chunks of 32 to reduce memory usage
|
||||
chunk_size = 32
|
||||
num_chunks = (num_tokens + chunk_size - 1) // chunk_size
|
||||
for i in range(num_chunks):
|
||||
start_idx = i * chunk_size
|
||||
end_idx = min(start_idx + chunk_size, num_tokens)
|
||||
chunk_result = reference_moe(
|
||||
router_logits[start_idx:end_idx].to(torch.float32),
|
||||
topk,
|
||||
num_experts,
|
||||
hidden_states_ref[start_idx:end_idx],
|
||||
w13_ref,
|
||||
bias13_ref,
|
||||
w2_ref,
|
||||
bias2_ref,
|
||||
alpha,
|
||||
beta,
|
||||
limit,
|
||||
act_type,
|
||||
)
|
||||
ref_result[start_idx:end_idx].copy_(chunk_result)
|
||||
|
||||
# trtllm-gen result
|
||||
if alpha is not None:
|
||||
alpha = torch.full((num_experts,), alpha, device=hidden_states.device)
|
||||
if limit is not None:
|
||||
limit = torch.full((num_experts,), limit, device=hidden_states.device)
|
||||
if beta is not None:
|
||||
beta = torch.full((num_experts,), beta, device=hidden_states.device)
|
||||
tg_result = tg_mxfp4_moe(
|
||||
router_logits,
|
||||
topk,
|
||||
num_experts,
|
||||
intermediate_size,
|
||||
hidden_size,
|
||||
hidden_states,
|
||||
hidden_states_scale,
|
||||
w13,
|
||||
w13_scale,
|
||||
bias13,
|
||||
w2,
|
||||
w2_scale,
|
||||
bias2,
|
||||
act_type,
|
||||
alpha=alpha,
|
||||
beta=beta,
|
||||
limit=limit,
|
||||
transpose_optimized=transpose_optimized,
|
||||
)
|
||||
# relatively loose check since the mxfp4 quantization is less accurate
|
||||
check_accuracy(ref_result, tg_result, atol=0, rtol=0.3, percent=0.8)
|
||||
|
||||
|
||||
def _interleave_scales_lastdim_by4(scales: torch.Tensor) -> torch.Tensor:
|
||||
"""Interleave scales on the last dimension by groups of 4, matching
|
||||
the transformation in mxfp4.py's BF16 (Hopper) path."""
|
||||
s = scales.to(torch.uint8)
|
||||
s_shape = s.shape
|
||||
assert s_shape[-1] % 4 == 0
|
||||
s = s.reshape(*s_shape[:-1], s_shape[-1] // 4, 4)
|
||||
# Move the 4-group dimension before the row dimension
|
||||
permuted = s.permute(0, 2, 1, 3)
|
||||
# Merge the row dim with the 4-group dim
|
||||
return permuted.reshape(s_shape[0], s_shape[-1] // 4, s_shape[1] * 4)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("topk", [1, 4])
|
||||
@pytest.mark.parametrize("num_experts", [32])
|
||||
@pytest.mark.parametrize("num_tokens", [1, 128])
|
||||
@pytest.mark.parametrize("intermediate_size,hidden_size", [(3072, 3072)])
|
||||
@pytest.mark.parametrize("alpha,beta,limit", [(1.0, 1.0, None), (1.702, 1.0, 7.0)])
|
||||
@pytest.mark.skipif(
|
||||
not HOPPER_MXFP4_BF16_AVAILABLE,
|
||||
reason="nvidia gpu sm90 and flashinfer are required for this test",
|
||||
)
|
||||
def test_flashinfer_cutlass_mxfp4_fused_moe(
|
||||
topk: int,
|
||||
num_experts: int,
|
||||
num_tokens: int,
|
||||
intermediate_size: int,
|
||||
hidden_size: int,
|
||||
alpha: float,
|
||||
beta: float,
|
||||
limit: Optional[float],
|
||||
):
|
||||
torch.manual_seed(42)
|
||||
device = "cuda:0"
|
||||
|
||||
# Inputs
|
||||
hidden_states = torch.randn(
|
||||
num_tokens, hidden_size, device=device, dtype=torch.bfloat16
|
||||
)
|
||||
# Random MXFP4 weights and scales (uint8), contiguous [w1; w3]
|
||||
w13_q = torch.randint(
|
||||
0,
|
||||
256,
|
||||
(num_experts, 2 * intermediate_size, hidden_size // 2),
|
||||
device=device,
|
||||
dtype=torch.uint8,
|
||||
)
|
||||
w13_scale = torch.randint(
|
||||
118,
|
||||
123,
|
||||
(num_experts, 2 * intermediate_size, hidden_size // 32),
|
||||
device=device,
|
||||
dtype=torch.uint8,
|
||||
)
|
||||
|
||||
w2_q = torch.randint(
|
||||
0,
|
||||
256,
|
||||
(num_experts, hidden_size, intermediate_size // 2),
|
||||
device=device,
|
||||
dtype=torch.uint8,
|
||||
)
|
||||
w2_scale = torch.randint(
|
||||
118,
|
||||
123,
|
||||
(num_experts, hidden_size, intermediate_size // 32),
|
||||
device=device,
|
||||
dtype=torch.uint8,
|
||||
)
|
||||
# Bias contiguous [b1; b3]
|
||||
bias13 = (
|
||||
torch.randn(
|
||||
num_experts, 2 * intermediate_size, device=device, dtype=torch.bfloat16
|
||||
)
|
||||
* 10
|
||||
)
|
||||
bias2 = (
|
||||
torch.randn(num_experts, hidden_size, device=device, dtype=torch.bfloat16) * 10
|
||||
)
|
||||
router_logits = torch.rand(
|
||||
num_tokens, num_experts, dtype=torch.float32, device=device
|
||||
)
|
||||
|
||||
w13_ref = mxfp4_dequantize(w13_q.clone(), w13_scale.clone()).reshape(
|
||||
num_experts, 2 * intermediate_size, hidden_size
|
||||
)
|
||||
w2_ref = mxfp4_dequantize(w2_q.clone(), w2_scale.clone()).reshape(
|
||||
num_experts, hidden_size, intermediate_size
|
||||
)
|
||||
ref = reference_moe(
|
||||
router_logits.to(torch.float32),
|
||||
topk,
|
||||
num_experts,
|
||||
hidden_states.to(torch.float32),
|
||||
w13_ref,
|
||||
bias13.to(torch.float32),
|
||||
w2_ref,
|
||||
bias2.to(torch.float32),
|
||||
alpha,
|
||||
beta,
|
||||
limit,
|
||||
"bf16",
|
||||
)
|
||||
|
||||
from vllm.utils.flashinfer import flashinfer_cutlass_fused_moe
|
||||
|
||||
# Swap halves to arrange as [w3; w1] (kernel expectation)
|
||||
w1_w, w3_w = torch.chunk(w13_q, 2, dim=1)
|
||||
w13_q_swapped = torch.cat([w3_w, w1_w], dim=1)
|
||||
|
||||
b1, b3 = torch.chunk(bias13.to(torch.float32), 2, dim=-1)
|
||||
w13_b = torch.cat([b3, b1], dim=-1).to(torch.bfloat16)
|
||||
|
||||
w1_s, w3_s = torch.chunk(w13_scale, 2, dim=1)
|
||||
w13_s = torch.cat([w3_s, w1_s], dim=1)
|
||||
w13_s_inter = _interleave_scales_lastdim_by4(w13_s)
|
||||
w2_s_inter = _interleave_scales_lastdim_by4(w2_scale)
|
||||
|
||||
routing_weights = torch.nn.functional.softmax(
|
||||
router_logits, dim=1, dtype=torch.float32
|
||||
)
|
||||
token_final_scales, token_selected_experts = torch.topk(
|
||||
routing_weights, topk, dim=-1
|
||||
)
|
||||
token_final_scales = token_final_scales / token_final_scales.sum(
|
||||
dim=-1, keepdim=True
|
||||
)
|
||||
token_selected_experts = token_selected_experts.to(torch.int).contiguous()
|
||||
|
||||
out = torch.empty_like(hidden_states, dtype=torch.bfloat16)
|
||||
if alpha is not None:
|
||||
alpha = torch.full((num_experts,), alpha, device=hidden_states.device)
|
||||
if beta is not None:
|
||||
beta = torch.full((num_experts,), beta, device=hidden_states.device)
|
||||
if limit is not None:
|
||||
limit = torch.full((num_experts,), limit, device=hidden_states.device)
|
||||
|
||||
_ = flashinfer_cutlass_fused_moe(
|
||||
input=hidden_states,
|
||||
token_selected_experts=token_selected_experts,
|
||||
token_final_scales=token_final_scales,
|
||||
fc1_expert_weights=w13_q_swapped,
|
||||
fc2_expert_weights=w2_q,
|
||||
output_dtype=torch.bfloat16,
|
||||
output=out,
|
||||
quant_scales=[w13_s_inter.to(torch.uint8), w2_s_inter.to(torch.uint8)],
|
||||
fc1_expert_biases=w13_b,
|
||||
fc2_expert_biases=bias2.to(torch.bfloat16),
|
||||
swiglu_alpha=alpha,
|
||||
swiglu_beta=beta,
|
||||
swiglu_limit=limit,
|
||||
tp_size=1,
|
||||
tp_rank=0,
|
||||
ep_size=1,
|
||||
ep_rank=0,
|
||||
use_w4_group_scaling=True,
|
||||
)
|
||||
|
||||
# Allow some mismatch due to MXFP4 quantization
|
||||
check_accuracy(ref, out, atol=0, rtol=0.3, percent=0.8)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("topk", [1, 4])
|
||||
@pytest.mark.parametrize("num_experts", [32])
|
||||
@pytest.mark.parametrize("num_tokens", [1, 128])
|
||||
@pytest.mark.parametrize("intermediate_size,hidden_size", [(3072, 3072)])
|
||||
@pytest.mark.parametrize("alpha,beta,limit", [(1.0, 1.0, None), (1.702, 1.0, 7.0)])
|
||||
@pytest.mark.skipif(
|
||||
not (
|
||||
current_platform.is_cuda()
|
||||
and current_platform.is_device_capability(100)
|
||||
and has_flashinfer()
|
||||
),
|
||||
reason="NVIDIA GPU sm100 and flashinfer are required for this test",
|
||||
)
|
||||
def test_flashinfer_cutlass_mxfp4_mxfp8_fused_moe(
|
||||
topk: int,
|
||||
num_experts: int,
|
||||
num_tokens: int,
|
||||
intermediate_size: int,
|
||||
hidden_size: int,
|
||||
alpha: Optional[float],
|
||||
beta: Optional[float],
|
||||
limit: Optional[float],
|
||||
):
|
||||
torch.manual_seed(42)
|
||||
device = "cuda:0"
|
||||
|
||||
# Inputs
|
||||
hidden_states = torch.randn(
|
||||
num_tokens, hidden_size, device=device, dtype=torch.bfloat16
|
||||
)
|
||||
# Float weights in w13 format [w1; w3]
|
||||
w13 = (
|
||||
torch.randn(
|
||||
num_experts,
|
||||
2 * intermediate_size,
|
||||
hidden_size,
|
||||
device=device,
|
||||
dtype=torch.bfloat16,
|
||||
)
|
||||
/ 10
|
||||
)
|
||||
w2 = (
|
||||
torch.randn(
|
||||
num_experts,
|
||||
hidden_size,
|
||||
intermediate_size,
|
||||
device=device,
|
||||
dtype=torch.bfloat16,
|
||||
)
|
||||
/ 10
|
||||
)
|
||||
# Bias contiguous [b1; b3]
|
||||
bias13 = (
|
||||
torch.randn(
|
||||
num_experts, 2 * intermediate_size, device=device, dtype=torch.bfloat16
|
||||
)
|
||||
* 10
|
||||
)
|
||||
bias2 = (
|
||||
torch.randn(num_experts, hidden_size, device=device, dtype=torch.bfloat16) * 10
|
||||
)
|
||||
router_logits = torch.rand(
|
||||
num_tokens, num_experts, dtype=torch.float32, device=device
|
||||
)
|
||||
|
||||
# Quantize weights to MXFP4 per expert (SM100 path)
|
||||
from flashinfer import mxfp4_quantize
|
||||
|
||||
def quant_mxfp4_batches(a: torch.Tensor, e: int):
|
||||
qs, sfs = [], []
|
||||
for i in range(e):
|
||||
q, sf = mxfp4_quantize(a[i].cuda())
|
||||
qs.append(q)
|
||||
sfs.append(sf)
|
||||
return torch.stack(qs), torch.stack(sfs)
|
||||
|
||||
def dequant_mxfp4_batches(mat_fp4: torch.Tensor, scale_tensor: torch.Tensor):
|
||||
num_batches = mat_fp4.size(0)
|
||||
scale_tensor = scale_tensor.view(num_batches, -1)
|
||||
from flashinfer import mxfp4_dequantize
|
||||
|
||||
return torch.stack(
|
||||
[
|
||||
mxfp4_dequantize(mat_fp4[b, :, :], scale_tensor[b, :])
|
||||
for b in range(num_batches)
|
||||
]
|
||||
)
|
||||
|
||||
w13_q, w13_scale = quant_mxfp4_batches(w13, num_experts)
|
||||
w2_q, w2_scale = quant_mxfp4_batches(w2, num_experts)
|
||||
|
||||
# Reference result using dequantized tensors and reference_moe
|
||||
w13_ref = (
|
||||
dequant_mxfp4_batches(
|
||||
w13_q.view(torch.uint8), w13_scale.view(torch.uint8).reshape(-1)
|
||||
)
|
||||
.to(torch.float32)
|
||||
.reshape(num_experts, 2 * intermediate_size, hidden_size)
|
||||
.to(device)
|
||||
)
|
||||
w2_ref = (
|
||||
dequant_mxfp4_batches(
|
||||
w2_q.view(torch.uint8), w2_scale.view(torch.uint8).reshape(-1)
|
||||
)
|
||||
.to(torch.float32)
|
||||
.reshape(num_experts, hidden_size, intermediate_size)
|
||||
.to(device)
|
||||
)
|
||||
|
||||
# Quantize activations for SM100 path and dequantize for reference
|
||||
hidden_states_q, hidden_states_sf = mxfp8_quantize(hidden_states, True, 32)
|
||||
# Reference uses BF16 input but quantizes intermediate activation to MXFP8
|
||||
ref = reference_moe(
|
||||
router_logits.to(torch.float32),
|
||||
topk,
|
||||
num_experts,
|
||||
hidden_states.to(torch.float32),
|
||||
w13_ref,
|
||||
bias13.to(torch.float32),
|
||||
w2_ref,
|
||||
bias2.to(torch.float32),
|
||||
alpha,
|
||||
beta,
|
||||
limit,
|
||||
"mxfp8",
|
||||
)
|
||||
|
||||
# Prepare inputs for FlashInfer CUTLASS fused MoE
|
||||
from vllm.utils.flashinfer import flashinfer_cutlass_fused_moe
|
||||
|
||||
# Swap halves to arrange as [w3; w1] (kernel expectation)
|
||||
w1_w, w3_w = torch.chunk(w13_q, 2, dim=1)
|
||||
w13_q_swapped = torch.cat([w3_w, w1_w], dim=1)
|
||||
|
||||
# Swap scales halves to match swapped weights
|
||||
s1, s3 = torch.chunk(w13_scale, 2, dim=1)
|
||||
w13_scale_swapped = torch.cat([s3, s1], dim=1)
|
||||
|
||||
b1, b3 = torch.chunk(bias13.to(torch.float32), 2, dim=-1)
|
||||
w13_b = torch.cat([b3, b1], dim=-1).to(torch.bfloat16)
|
||||
|
||||
# Build routing for kernel
|
||||
routing_weights = torch.nn.functional.softmax(
|
||||
router_logits, dim=1, dtype=torch.float32
|
||||
)
|
||||
token_final_scales, token_selected_experts = torch.topk(
|
||||
routing_weights, topk, dim=-1
|
||||
)
|
||||
token_final_scales = token_final_scales / token_final_scales.sum(
|
||||
dim=-1, keepdim=True
|
||||
)
|
||||
token_selected_experts = token_selected_experts.to(torch.int).contiguous()
|
||||
|
||||
out = torch.empty_like(hidden_states, dtype=torch.bfloat16)
|
||||
if alpha is not None:
|
||||
alpha_t = torch.full((num_experts,), alpha, device=hidden_states.device)
|
||||
else:
|
||||
alpha_t = None
|
||||
if beta is not None:
|
||||
beta_t = torch.full((num_experts,), beta, device=hidden_states.device)
|
||||
else:
|
||||
beta_t = None
|
||||
if limit is not None:
|
||||
limit_t = torch.full((num_experts,), limit, device=hidden_states.device)
|
||||
else:
|
||||
limit_t = None
|
||||
|
||||
# Quant scales for SM100 MXFP8+MXFP4 path
|
||||
fake_input_scale = torch.ones(num_experts, device=device)
|
||||
quant_scales = [
|
||||
w13_scale_swapped.view(torch.int32),
|
||||
fake_input_scale,
|
||||
w2_scale.view(torch.int32),
|
||||
fake_input_scale,
|
||||
]
|
||||
|
||||
_ = flashinfer_cutlass_fused_moe(
|
||||
input=hidden_states_q,
|
||||
token_selected_experts=token_selected_experts,
|
||||
token_final_scales=token_final_scales,
|
||||
fc1_expert_weights=w13_q_swapped.contiguous().view(torch.long),
|
||||
fc2_expert_weights=w2_q.contiguous().view(torch.long),
|
||||
output_dtype=torch.bfloat16,
|
||||
output=out,
|
||||
quant_scales=quant_scales,
|
||||
fc1_expert_biases=w13_b,
|
||||
fc2_expert_biases=bias2.to(torch.bfloat16),
|
||||
swiglu_alpha=alpha_t,
|
||||
swiglu_beta=beta_t,
|
||||
swiglu_limit=limit_t,
|
||||
tp_size=1,
|
||||
tp_rank=0,
|
||||
ep_size=1,
|
||||
ep_rank=0,
|
||||
use_mxfp8_act_scaling=True,
|
||||
input_sf=hidden_states_sf,
|
||||
)
|
||||
|
||||
# Allow some mismatch due to MXFP4 quantization
|
||||
check_accuracy(ref, out, atol=0, rtol=0.3, percent=0.8)
|
||||
Reference in New Issue
Block a user