266 lines
9.8 KiB
Python
266 lines
9.8 KiB
Python
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# Adapted from https://github.com/sgl-project/sglang/pull/2575
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import itertools
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import pytest
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import torch
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.fused_moe import fused_moe
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from vllm.model_executor.layers.quantization.utils.fp8_utils import (
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per_token_group_quant_fp8, w8a8_block_fp8_matmul)
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from vllm.platforms import current_platform
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if current_platform.get_device_capability() < (9, 0):
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pytest.skip("FP8 Triton requires CUDA 9.0 or higher",
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allow_module_level=True)
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# Test configurations
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DTYPES = [torch.bfloat16] # [torch.half, torch.bfloat16, torch.float32]
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NUM_TOKENS = [7, 83, 2048]
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D = [512, 4096, 5120, 13824]
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GROUP_SIZE = [64, 128, 256, 512]
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M = [1, 7, 83, 512, 2048]
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N = [128, 512, 1024, 4096, 7748, 13824]
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K = [256, 4096, 5120, 3884, 13824]
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# Deepseek-V3's intermediate size 18432, so N is 18432*2/8=4608 at TP8
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# and its hidden size is 7168.
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M_moe = [1, 7, 83, 512, 2048]
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N_moe = [4608] # [128, 4608, 13824]
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K_moe = [7168] # [256, 7168, 13824]
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BLOCK_SIZE = [[128, 128]]
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E = [256] # [8, 24, 128, 256]
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TOP_KS = [1] # [1, 2, 6]
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OUT_DTYPES = [torch.bfloat16] # [torch.float32, torch.half, torch.bfloat16]
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SEEDS = [0]
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def native_per_token_group_quant_fp8(x,
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group_size,
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eps=1e-10,
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dtype=torch.float8_e4m3fn):
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"""Function to perform per-token-group quantization on an input tensor
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`x` using native torch."""
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assert x.shape[-1] % group_size == 0, ("the last dimension of `x` cannot "
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"be divisible by `group_size`")
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assert x.is_contiguous(), "`x` is not contiguous"
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finfo = torch.finfo(dtype)
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fp8_min = finfo.min
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fp8_max = finfo.max
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x_ = x.reshape(x.numel() // group_size, group_size)
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amax = x_.abs().max(dim=-1,
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keepdim=True)[0].clamp(min=eps).to(torch.float32)
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x_s = amax / fp8_max
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x_q = (x_ / x_s).clamp(min=fp8_min, max=fp8_max).to(dtype)
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x_q = x_q.reshape(x.shape)
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x_s = x_s.reshape(x.shape[:-1] + (x.shape[-1] // group_size, ))
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return x_q, x_s
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def native_w8a8_block_fp8_matmul(A,
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B,
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As,
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Bs,
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block_size,
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output_dtype=torch.float16):
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"""Matrix multiplication with block-wise quantization using native torch."""
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A = A.to(torch.float32)
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B = B.to(torch.float32)
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assert A.shape[-1] == B.shape[-1]
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assert B.ndim == 2 and B.is_contiguous() and Bs.ndim == 2
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assert len(block_size) == 2
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block_n, block_k = block_size[0], block_size[1]
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assert (A.shape[-1] + block_k - 1) // block_k == As.shape[-1]
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assert A.shape[:-1] == As.shape[:-1]
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M = A.numel() // A.shape[-1]
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N, K = B.shape
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origin_C_shape = A.shape[:-1] + (N, )
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A = A.reshape(M, A.shape[-1])
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As = As.reshape(M, As.shape[-1])
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n_tiles = (N + block_n - 1) // block_n
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k_tiles = (K + block_k - 1) // block_k
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assert n_tiles == Bs.shape[0]
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assert k_tiles == Bs.shape[1]
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C_shape = (M, N)
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C = torch.zeros(C_shape, dtype=torch.float32, device=A.device)
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A_tiles = [
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A[:, i * block_k:min((i + 1) * block_k, K)] for i in range(k_tiles)
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]
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B_tiles = [[
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B[j * block_n:min((j + 1) * block_n, N),
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i * block_k:min((i + 1) * block_k, K), ] for i in range(k_tiles)
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] for j in range(n_tiles)]
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C_tiles = [
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C[:, j * block_n:min((j + 1) * block_n, N)] for j in range(n_tiles)
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]
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As_tiles = [As[:, i:i + 1] for i in range(k_tiles)]
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for i in range(k_tiles):
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for j in range(n_tiles):
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a = A_tiles[i]
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b = B_tiles[j][i]
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c = C_tiles[j]
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s = As_tiles[i] * Bs[j][i]
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c[:, :] += torch.matmul(a, b.t()) * s
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C = C.reshape(origin_C_shape).to(output_dtype)
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return C
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def torch_w8a8_block_fp8_moe(a, w1, w2, w1_s, w2_s, score, topk, block_shape):
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"""Fused moe with block-wise quantization using native torch."""
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B, D = a.shape
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a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
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out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)
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score = torch.softmax(score, dim=-1, dtype=torch.float32)
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topk_weight, topk_ids = torch.topk(score, topk)
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topk_weight = topk_weight.view(-1)
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topk_ids = topk_ids.view(-1)
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_, block_k = block_shape[0], block_shape[1]
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a_q, a_s = native_per_token_group_quant_fp8(a, block_k)
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a_q = a_q.to(torch.float32)
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for i in range(w1.shape[0]):
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mask = topk_ids == i
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if mask.sum():
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inter_out = native_w8a8_block_fp8_matmul(a_q[mask],
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w1[i],
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a_s[mask],
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w1_s[i],
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block_shape,
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output_dtype=a.dtype)
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act_out = SiluAndMul().forward_native(inter_out)
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act_out_q, act_out_s = native_per_token_group_quant_fp8(
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act_out, block_k)
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act_out = act_out.to(torch.float32)
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out[mask] = native_w8a8_block_fp8_matmul(act_out_q,
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w2[i],
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act_out_s,
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w2_s[i],
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block_shape,
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output_dtype=a.dtype)
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return (out.view(B, -1, w2.shape[1]) *
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topk_weight.view(B, -1, 1).to(out.dtype)).sum(dim=1)
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# Skip all tests if CUDA is not available
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pytest.importorskip("torch.cuda")
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@pytest.fixture(autouse=True)
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def setup_cuda():
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torch.set_default_device("cuda")
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@pytest.mark.parametrize("num_tokens,d,dtype,group_size,seed",
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itertools.product(NUM_TOKENS, D, DTYPES, GROUP_SIZE,
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SEEDS))
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@torch.inference_mode()
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def test_per_token_group_quant_fp8(num_tokens, d, dtype, group_size, seed):
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torch.manual_seed(seed)
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x = torch.rand(num_tokens, d, dtype=dtype)
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ref_out, ref_scale = native_per_token_group_quant_fp8(x, group_size)
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out, scale = per_token_group_quant_fp8(x, group_size)
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assert torch.allclose(out.to(torch.float32),
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ref_out.to(torch.float32),
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rtol=0.15)
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assert torch.allclose(scale, ref_scale)
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@pytest.mark.parametrize("M,N,K,block_size,out_dtype,seed",
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itertools.product(M, N, K, BLOCK_SIZE, OUT_DTYPES,
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SEEDS))
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@torch.inference_mode()
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def test_w8a8_block_fp8_matmul(M, N, K, block_size, out_dtype, seed):
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torch.manual_seed(seed)
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factor_for_scale = 1e-2
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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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A_fp32 = (torch.rand(M, K, dtype=torch.float32) - 0.5) * 2 * fp8_max
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A_fp8 = A_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
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B_fp32 = (torch.rand(N, K, dtype=torch.float32) - 0.5) * 2 * fp8_max
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B_fp8 = B_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
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block_n, block_k = block_size[0], block_size[1]
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n_tiles = (N + block_n - 1) // block_n
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k_tiles = (K + block_k - 1) // block_k
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As = torch.rand(M, k_tiles, dtype=torch.float32) * factor_for_scale
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Bs = torch.rand(n_tiles, k_tiles, dtype=torch.float32) * factor_for_scale
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ref_out = native_w8a8_block_fp8_matmul(A_fp8, B_fp8, As, Bs, block_size,
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out_dtype)
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out = w8a8_block_fp8_matmul(A_fp8, B_fp8, As, Bs, block_size, out_dtype)
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rel_diff = (torch.mean(
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torch.abs(out.to(torch.float32) - ref_out.to(torch.float32))) /
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torch.mean(torch.abs(ref_out.to(torch.float32))))
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assert rel_diff < 0.001
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@pytest.mark.parametrize("M,N,K,E,topk,block_size,dtype,seed",
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itertools.product(M_moe, N_moe, K_moe, E, TOP_KS,
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BLOCK_SIZE, DTYPES, SEEDS))
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@torch.inference_mode()
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def test_w8a8_block_fp8_fused_moe(M, N, K, E, topk, block_size, dtype, seed):
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torch.manual_seed(seed)
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factor_for_scale = 1e-2
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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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a = torch.randn((M, K), dtype=dtype) / 10
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w1_bf16 = (torch.rand(
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(E, 2 * N, K), dtype=torch.bfloat16) - 0.5) * 2 * fp8_max
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w1 = w1_bf16.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
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del w1_bf16
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w2_bf16 = (torch.rand((E, K, N), dtype=torch.bfloat16) - 0.5) * 2 * fp8_max
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w2 = w2_bf16.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
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del w2_bf16
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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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n_tiles_w2 = (K + block_n - 1) // block_n
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k_tiles_w1 = (K + block_k - 1) // block_k
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k_tiles_w2 = (N + block_k - 1) // block_k
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w1_s = torch.rand(
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(E, n_tiles_w1, k_tiles_w1), dtype=torch.float32) * factor_for_scale
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w2_s = torch.rand(
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(E, n_tiles_w2, k_tiles_w2), dtype=torch.float32) * factor_for_scale
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score = torch.randn((M, E), dtype=dtype)
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out = fused_moe(
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a,
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w1,
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w2,
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score,
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topk,
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renormalize=False,
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use_fp8_w8a8=True,
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w1_scale=w1_s,
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w2_scale=w2_s,
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block_shape=block_size,
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)
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ref_out = torch_w8a8_block_fp8_moe(a, w1, w2, w1_s, w2_s, score, topk,
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block_size)
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print(f"{out.sum()=}")
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print(f"{ref_out.sum()=}")
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rel_diff = (torch.mean(
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torch.abs(out.to(torch.float32) - ref_out.to(torch.float32))) /
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torch.mean(torch.abs(ref_out.to(torch.float32))))
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assert rel_diff < 0.03
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