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https://git.datalinker.icu/vllm-project/vllm.git
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fa98d77773
Signed-off-by: Varun <vsundarr@redhat.com> Co-authored-by: Varun Sundar Rabindranath <vsundarr@redhat.com>
372 lines
12 KiB
Python
372 lines
12 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""
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Test DeepEP + DeepGEMM integration
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"""
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import dataclasses
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import importlib
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from typing import Optional
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import pytest
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import torch.distributed
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from torch.distributed import ProcessGroup
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from typing_extensions import ParamSpec
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from vllm.config import VllmConfig, set_current_vllm_config
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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.fused_moe.modular_kernel import (
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FusedMoEModularKernel)
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from vllm.model_executor.layers.quantization.utils.fp8_utils import (
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per_token_group_quant_fp8)
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from vllm.platforms import current_platform
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from .deepep_utils import ProcessGroupInfo, parallel_launch
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has_deep_ep = importlib.util.find_spec("deep_ep") is not None
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try:
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import deep_gemm
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has_deep_gemm = True
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except ImportError:
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has_deep_gemm = False
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if has_deep_ep:
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from vllm.model_executor.layers.fused_moe.deepep_ht_prepare_finalize import ( # noqa: E501
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DeepEPHTPrepareAndFinalize)
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from .deepep_utils import DeepEPHTArgs, make_deepep_a2a
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if has_deep_gemm:
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from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
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DeepGemmExperts)
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requires_deep_ep = pytest.mark.skipif(
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not has_deep_ep,
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reason="Requires deep_ep kernels",
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)
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requires_deep_gemm = pytest.mark.skipif(
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not has_deep_gemm,
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reason="Requires deep_gemm kernels",
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)
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P = ParamSpec("P")
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def per_block_cast_to_fp8(
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x: torch.Tensor,
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block_size_n: int = 128) -> tuple[torch.Tensor, torch.Tensor]:
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assert x.dim() == 2
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m, n = x.shape
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x_padded = torch.zeros(
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(deep_gemm.ceil_div(m, 128) * 128,
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deep_gemm.ceil_div(n, block_size_n) * block_size_n),
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dtype=x.dtype,
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device=x.device)
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x_padded[:m, :n] = x
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x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, block_size_n)
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x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
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x_scaled = (x_view * (448.0 / x_amax)).to(torch.float8_e4m3fn)
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x_scaled_sub = x_scaled.view_as(x_padded)[:m, :n].contiguous()
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scales = (x_amax / 448.0).view(x_view.size(0), x_view.size(2))
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return x_scaled_sub, scales
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def make_block_quant_fp8_weights(
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e: int,
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n: int,
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k: int,
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block_size: list[int],
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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Return weights w1, w2, w1q, w2q, w1_scale, w2_scale
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"""
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dtype = torch.bfloat16
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fp8_info = torch.finfo(torch.float8_e4m3fn)
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fp8_max, fp8_min = fp8_info.max, fp8_info.min
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w1_bf16 = torch.randn((e, 2 * n, k), dtype=dtype) / 10
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w1_bf16 = w1_bf16.clamp(min=fp8_min, max=fp8_max).to(dtype=dtype)
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w2_bf16 = torch.randn((e, k, n), dtype=dtype) / 10
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w2_bf16 = w2_bf16.clamp(min=fp8_min, max=fp8_max).to(dtype=dtype)
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block_n, block_k = block_size[0], block_size[1]
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n_tiles_w1 = ((2 * n) + block_n - 1) // block_n
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k_tiles_w1 = (k + block_k - 1) // block_k
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n_tiles_w2 = (k + block_n - 1) // block_n
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k_tiles_w2 = (n + block_k - 1) // block_k
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w1 = torch.empty_like(w1_bf16, dtype=torch.float8_e4m3fn)
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w2 = torch.empty_like(w2_bf16, dtype=torch.float8_e4m3fn)
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w1_s = torch.empty((e, n_tiles_w1, k_tiles_w1),
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device="cuda",
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dtype=torch.float32)
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w2_s = torch.empty((e, n_tiles_w2, k_tiles_w2),
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device="cuda",
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dtype=torch.float32)
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assert w1_s.shape == (e, (2 * n + 127) // 128, (k + 127) // 128)
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assert (w2.shape[-2] + block_n - 1) // block_n == w2_s.shape[-2]
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for i in range(e):
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w1[i], w1_s[i] = per_block_cast_to_fp8(w1_bf16[i])
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w2[i], w2_s[i] = per_block_cast_to_fp8(w2_bf16[i])
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return w1, w2, w1_s, w2_s
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@dataclasses.dataclass
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class TestConfig:
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topk: int
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m: int
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k: int
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n: int
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num_experts: int
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block_size: list[int]
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@dataclasses.dataclass
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class TestTensors:
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rank_tokens: torch.Tensor # all ranks make this many tokens
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rank_token_scales: Optional[torch.Tensor]
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topk: torch.Tensor
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topk_weights: torch.Tensor
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config: TestConfig
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@staticmethod
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def make(config: TestConfig, rank) -> "TestTensors":
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dtype = torch.bfloat16
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topk, m, k, block_size = (config.topk, config.m, config.k,
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config.block_size)
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fp8_info = torch.finfo(torch.float8_e4m3fn)
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fp8_max, fp8_min = fp8_info.max, fp8_info.min
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rank_tokens = torch.randn(
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(m, k), device=torch.cuda.current_device(), dtype=dtype) / 10.0
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rank_tokens = rank_tokens.clamp(min=fp8_min, max=fp8_max)
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block_k = block_size[1]
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_, rank_token_scales = per_token_group_quant_fp8(rank_tokens, block_k)
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topk_ids = torch.randint(
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low=0,
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high=config.num_experts,
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size=(m, topk),
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device=torch.cuda.current_device()).to(dtype=torch.int64)
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topk_weights = torch.randn(topk_ids.shape,
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dtype=torch.float32,
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device=torch.cuda.current_device())
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return TestTensors(rank_tokens=rank_tokens,
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rank_token_scales=rank_token_scales,
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topk=topk_ids,
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topk_weights=topk_weights,
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config=config)
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def make_modular_kernel(pg: ProcessGroup, pgi: ProcessGroupInfo, dp_size: int,
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num_local_experts: int, q_dtype: Optional[torch.dtype],
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block_shape: list[int]) -> FusedMoEModularKernel:
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a2a: DeepEPHTPrepareAndFinalize = make_deepep_a2a(
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pg=pg,
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pgi=pgi,
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dp_size=dp_size,
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deepep_ht_args=DeepEPHTArgs(num_local_experts=num_local_experts),
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deepep_ll_args=None,
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q_dtype=q_dtype,
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block_shape=block_shape)
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fused_experts = DeepGemmExperts()
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mk = FusedMoEModularKernel(prepare_finalize=a2a,
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fused_experts=fused_experts)
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return mk
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def deep_ep_moe_impl(pg: ProcessGroup, pgi: ProcessGroupInfo, dp_size: int,
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test_tensors: TestTensors, w1: torch.Tensor,
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w2: torch.Tensor, w1_scale: Optional[torch.Tensor],
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w2_scale: Optional[torch.Tensor],
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num_experts: int) -> torch.Tensor:
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num_local_experts = w1.size(0)
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def build_expert_map():
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num_local_experts = w1.size(0)
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expert_map = torch.full((num_experts, ),
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fill_value=-1,
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dtype=torch.int32)
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s = pgi.rank * num_local_experts
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e = s + num_local_experts
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expert_map[s:e] = torch.tensor(list(range(num_local_experts)))
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return expert_map.to(device=torch.cuda.current_device(),
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dtype=torch.int32)
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q_dtype = torch.float8_e4m3fn
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# Make modular kernel
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mk: FusedMoEModularKernel = make_modular_kernel(
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pg, pgi, dp_size, num_local_experts, q_dtype,
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test_tensors.config.block_size)
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a1_scale = test_tensors.rank_token_scales
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out = mk.forward(hidden_states=test_tensors.rank_tokens,
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w1=w1,
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w2=w2,
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topk_weights=test_tensors.topk_weights,
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topk_ids=test_tensors.topk,
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inplace=False,
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activation="silu",
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global_num_experts=num_experts,
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expert_map=build_expert_map(),
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w1_scale=w1_scale,
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w2_scale=w2_scale,
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w1_zp=None,
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w2_zp=None,
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a1_scale=a1_scale,
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a2_scale=None,
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apply_router_weight_on_input=False)
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return out
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def triton_impl(a: torch.Tensor, topk_ids: torch.Tensor,
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topk_weights: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor,
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w1_scale: torch.Tensor, w2_scale: torch.Tensor,
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a1_scale: torch.Tensor, block_shape: list[int]):
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return fused_experts(
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hidden_states=a,
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w1=w1,
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w2=w2,
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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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use_fp8_w8a8=True,
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w1_scale=w1_scale,
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w2_scale=w2_scale,
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a1_scale=a1_scale,
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block_shape=block_shape,
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# Make sure this is set to False so we
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# dont end up comparing the same implementation.
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allow_deep_gemm=False)
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def _deep_ep_moe(
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pgi: ProcessGroupInfo,
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dp_size: int,
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config: TestConfig,
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w1: torch.Tensor,
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w2: torch.Tensor,
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w1_scale: torch.Tensor,
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w2_scale: torch.Tensor,
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):
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current_platform.seed_everything(pgi.rank)
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w1 = w1.to(device=torch.cuda.current_device())
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w2 = w2.to(device=torch.cuda.current_device())
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w1_scale = w1_scale.to(device=torch.cuda.current_device())
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w2_scale = w2_scale.to(device=torch.cuda.current_device())
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pg = torch.distributed.new_group(list(range(pgi.world_size)))
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test_tensors = TestTensors.make(config, pgi.rank)
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block_shape = [
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w1.size(1) // w1_scale.size(1),
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w1.size(2) // w1_scale.size(2)
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]
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with set_current_vllm_config(VllmConfig()):
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# Reference
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triton_moe = triton_impl(a=test_tensors.rank_tokens,
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topk_ids=test_tensors.topk,
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topk_weights=test_tensors.topk_weights,
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w1=w1,
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w2=w2,
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w1_scale=w1_scale,
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w2_scale=w2_scale,
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a1_scale=test_tensors.rank_token_scales,
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block_shape=block_shape)
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# Slice experts for this rank.
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num_local_experts = config.num_experts // pgi.world_size
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e_start = num_local_experts * pgi.rank
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e_end = e_start + num_local_experts
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w1_ep = w1[e_start:e_end]
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w2_ep = w2[e_start:e_end]
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w1_scale_ep = w1_scale[e_start:e_end]
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w2_scale_ep = w2_scale[e_start:e_end]
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deepep_moe = deep_ep_moe_impl(
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pg,
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pgi,
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dp_size,
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test_tensors,
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w1_ep,
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w2_ep,
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w1_scale_ep,
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w2_scale_ep,
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config.num_experts,
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)
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torch.testing.assert_close(
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triton_moe,
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deepep_moe,
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atol=6e-2,
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rtol=6e-2,
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)
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MNKs = [
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(8, 128, 128),
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(8, 128, 512),
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(8, 512, 512),
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(3, 1024, 2048),
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(32, 128, 1024),
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(45, 512, 2048),
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(64, 1024, 1024),
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(129, 128, 256),
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(129, 1024, 2048),
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(222, 1024, 2048),
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]
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@pytest.mark.parametrize("mnk", MNKs)
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@pytest.mark.parametrize("num_experts", [32])
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@pytest.mark.parametrize("topk", [2, 6])
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@pytest.mark.parametrize("world_dp_size", [(2, 1)])
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@requires_deep_ep
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@requires_deep_gemm
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def test_deep_ep_moe(mnk: tuple[int, int, int], num_experts: int, topk: int,
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world_dp_size: tuple[int, int]):
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m, n, k = mnk
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current_platform.seed_everything(7)
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if topk > num_experts:
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pytest.skip(f"Skipping test: topk={topk} > E={num_experts}")
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block_m = deep_gemm.get_m_alignment_for_contiguous_layout()
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block_size = [block_m, block_m]
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world_size, dp_size = world_dp_size
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config = TestConfig(
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topk=topk,
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m=m,
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k=k,
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n=n,
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num_experts=num_experts,
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block_size=block_size,
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)
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w1, w2, w1_scale, w2_scale = make_block_quant_fp8_weights(
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num_experts, n, k, block_size)
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parallel_launch(world_size, _deep_ep_moe, dp_size, config, w1, w2,
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w1_scale, w2_scale)
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