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[Kernel] add triton fused moe kernel for gptq/awq (#12185)

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Jinzhen Lin 2025-01-29 22:07:09 +08:00 committed by GitHub
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4 changed files with 874 additions and 55 deletions
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91
tests/kernels/test_moe.py
View File

@ -18,6 +18,8 @@ from vllm.model_executor.layers.fused_moe.moe_torch_iterative import (
fused_moe as iterative_moe)
from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
marlin_quantize)
from vllm.model_executor.layers.quantization.utils.quant_utils import (
quantize_weights)
from vllm.model_executor.models.mixtral import MixtralMoE
from vllm.platforms import current_platform
from vllm.scalar_type import scalar_types
@ -55,6 +57,95 @@ def test_fused_moe(
rtol=0)
@pytest.mark.parametrize("m", [1, 32, 222])
@pytest.mark.parametrize("n", [128, 1024, 2048])
@pytest.mark.parametrize("k", [128, 1024])
@pytest.mark.parametrize("e", NUM_EXPERTS)
@pytest.mark.parametrize("topk", TOP_KS)
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
@pytest.mark.parametrize("group_size", [64, 128])
@pytest.mark.parametrize("has_zp", [True, False])
@pytest.mark.parametrize("weight_bits", [4, 8])
def test_fused_moe_wn16(m: int, n: int, k: int, e: int, topk: int,
dtype: torch.dtype, group_size: int, has_zp: bool,
weight_bits: int):
print(m, n, k, e, topk, dtype, group_size, has_zp, weight_bits)
a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
w2 = torch.randn((e, k, n), device="cuda", dtype=dtype) / 10
score = torch.randn((m, e), device="cuda", dtype=dtype)
if weight_bits == 4:
pack_factor = 2
quant_type = scalar_types.uint4 if has_zp else scalar_types.uint4b8
elif weight_bits == 8:
pack_factor = 1
quant_type = scalar_types.uint8 if has_zp else scalar_types.uint8b128
w1_ref = w1.clone()
w2_ref = w2.clone()
w1_qweight = torch.empty((e, 2 * n, k // pack_factor),
device="cuda",
dtype=torch.uint8)
w2_qweight = torch.empty((e, k, n // pack_factor),
device="cuda",
dtype=torch.uint8)
w1_scales = torch.empty((e, 2 * n, k // group_size),
device="cuda",
dtype=dtype)
w2_scales = torch.empty((e, k, n // group_size),
device="cuda",
dtype=dtype)
w1_qzeros = torch.empty((e, 2 * n // pack_factor, k // group_size),
device="cuda",
dtype=torch.uint8)
w2_qzeros = torch.empty((e, k // pack_factor, n // group_size),
device="cuda",
dtype=torch.uint8)
for i in range(e * 2):
expert_id = i % e
if i // e == 0:
w, w_ref, w_qweight, w_scales, w_qzeros = \
w1, w1_ref, w1_qweight, w1_scales, w1_qzeros
else:
w, w_ref, w_qweight, w_scales, w_qzeros = \
w2, w2_ref, w2_qweight, w2_scales, w2_qzeros
weight, qweight, scales, qzeros = quantize_weights(
w[expert_id].T, quant_type, group_size, has_zp, False)
weight = weight.T
qweight = qweight.T.contiguous().to(torch.uint8)
scales = scales.T
if has_zp:
qzeros = qzeros.T.contiguous().to(torch.uint8)
if weight_bits == 4:
qweight = qweight[:, 1::2] * 16 + qweight[:, ::2]
if has_zp:
qzeros = qzeros[1::2, :] * 16 + qzeros[::2, :]
w_ref[expert_id] = weight
w_qweight[expert_id] = qweight
w_scales[expert_id] = scales
if has_zp:
w_qzeros[expert_id] = qzeros
triton_output = fused_moe(a,
w1_qweight,
w2_qweight,
score,
topk,
renormalize=False,
use_int4_w4a16=weight_bits == 4,
use_int8_w8a16=weight_bits == 8,
w1_scale=w1_scales,
w2_scale=w2_scales,
w1_zp=w1_qzeros if has_zp else None,
w2_zp=w2_qzeros if has_zp else None,
block_shape=[0, group_size])
torch_output = torch_moe(a, w1_ref, w2_ref, score, topk)
torch.testing.assert_close(triton_output, torch_output, atol=2e-2, rtol=0)
@pytest.mark.parametrize("dtype",
[torch.float32, torch.float16, torch.bfloat16])
@torch.inference_mode()

407
vllm/model_executor/layers/fused_moe/fused_moe.py
View File

@ -19,6 +19,206 @@ from vllm.utils import direct_register_custom_op
logger = init_logger(__name__)
@triton.jit
def fused_moe_kernel_gptq_awq(
# Pointers to matrices
a_ptr,
b_ptr,
c_ptr,
b_scale_ptr,
b_zp_ptr,
topk_weights_ptr,
sorted_token_ids_ptr,
expert_ids_ptr,
num_tokens_post_padded_ptr,
# Matrix dimensions
N: tl.constexpr,
K: tl.constexpr,
EM,
num_valid_tokens,
# The stride variables represent how much to increase the ptr by when
# moving by 1 element in a particular dimension. E.g. `stride_am` is
# how much to increase `a_ptr` by to get the element one row down
# (A has M rows).
stride_am,
stride_ak,
stride_be,
stride_bk,
stride_bn,
stride_cm,
stride_cn,
stride_bse,
stride_bsk,
stride_bsn,
stride_bze,
stride_bzk,
stride_bzn,
block_k_diviable: tl.constexpr,
group_size: tl.constexpr,
# Meta-parameters
BLOCK_SIZE_M: tl.constexpr,
BLOCK_SIZE_N: tl.constexpr,
BLOCK_SIZE_K: tl.constexpr,
GROUP_SIZE_M: tl.constexpr,
MUL_ROUTED_WEIGHT: tl.constexpr,
top_k: tl.constexpr,
compute_type: tl.constexpr,
has_zp: tl.constexpr,
use_int4_w4a16: tl.constexpr,
use_int8_w8a16: tl.constexpr):
"""
Implements the fused computation for a Mixture of Experts (MOE) using
token and expert matrices.
Key Parameters:
- A: The input tensor representing tokens with shape (*, K), where '*' can
be any shape representing batches and K is the feature dimension of
each token.
- B: The stacked MOE weight tensor with shape (E, N, K), where E is
the number of experts, K is the input feature dimension, and N is
the output feature dimension.
- C: The output cache tensor with shape (M, topk, N), where M is the
total number of tokens post padding, topk is the number of times
each token is repeated, and N is the output feature dimension.
- sorted_token_ids: A tensor containing the sorted indices of tokens,
repeated topk times and arranged by the expert index they are
assigned to.
- expert_ids: A tensor containing the indices of the expert for each
block. It determines which expert matrix from B should be used for
each block in A.
This kernel performs the multiplication of a token by its corresponding
expert matrix as determined by `expert_ids`. The sorting of
`sorted_token_ids` by expert index and padding ensures divisibility by
BLOCK_SIZE_M, which is necessary to maintain consistency in block matrix
multiplication across different blocks processed by the same expert.
"""
# -----------------------------------------------------------
# Map program ids `pid` to the block of C it should compute.
# This is done in a grouped ordering to promote L2 data reuse.
pid = tl.program_id(axis=0)
num_pid_m = tl.cdiv(EM, BLOCK_SIZE_M)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
num_pid_in_group = GROUP_SIZE_M * num_pid_n
group_id = pid // num_pid_in_group
first_pid_m = group_id * GROUP_SIZE_M
group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m)
pid_n = (pid % num_pid_in_group) // group_size_m
# ----------------------------------------------------------
# Create pointers for the first blocks of A and B.
# We will advance this pointer as we move in the K direction
# and accumulate
# `a_ptrs` is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
# `b_ptrs` is a block of [BLOCK_SIZE_K, BLOCK_SIZE_N] pointers
num_tokens_post_padded = tl.load(num_tokens_post_padded_ptr)
if pid_m * BLOCK_SIZE_M >= num_tokens_post_padded:
return
offs_token_id = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M).to(
tl.int64)
offs_token = tl.load(sorted_token_ids_ptr + offs_token_id)
token_mask = offs_token < num_valid_tokens
offs_bn = (pid_n * BLOCK_SIZE_N +
tl.arange(0, BLOCK_SIZE_N).to(tl.int64)) % N
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + (offs_token[:, None] // top_k * stride_am +
offs_k[None, :] * stride_ak)
off_experts = tl.load(expert_ids_ptr + pid_m).to(tl.int64)
if use_int4_w4a16:
b_ptrs = b_ptr + off_experts * stride_be + \
(offs_k[:, None] // 2) * stride_bk + offs_bn[None, :] * stride_bn
b_shifter = (offs_k[:, None] % 2) * 4
elif use_int8_w8a16:
b_ptrs = b_ptr + off_experts * stride_be + \
offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn
if not has_zp and use_int4_w4a16:
b_zp_num = 8
if not has_zp and use_int8_w8a16:
b_zp_num = 128
elif has_zp and use_int4_w4a16:
b_zp_shifter = (offs_bn[None, :] % 2) * 4
# -----------------------------------------------------------
# Iterate to compute a block of the C matrix.
# We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
# of fp32 values for higher accuracy.
# `accumulator` will be converted back to fp16 after the loop.
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
# Load the next block of A and B, generate a mask by checking the
# K dimension.
if not block_k_diviable:
k_mask = offs_k[:, None] < K - k * BLOCK_SIZE_K
k_other = 0.0
else:
k_mask = None
k_other = None
a = tl.load(a_ptrs,
mask=token_mask[:, None] &
(offs_k[None, :] < K - k * BLOCK_SIZE_K),
other=0.0)
b = tl.load(b_ptrs)
if use_int4_w4a16:
b = (b >> b_shifter) & 0xF
b_scale_ptrs = b_scale_ptr + off_experts * stride_bse + \
offs_bn[None, :] * stride_bsn + \
((offs_k[:, None] + BLOCK_SIZE_K * k) // group_size) * stride_bsk
b_scale = tl.load(b_scale_ptrs, mask=k_mask, other=k_other)
b_scale = b_scale.to(tl.float32)
if has_zp and use_int4_w4a16:
offs_k_true = (offs_k[:, None] + BLOCK_SIZE_K * k) // group_size
b_zp_ptrs = b_zp_ptr + off_experts * stride_bze + \
(offs_bn[None, :] // 2) * stride_bzn + \
offs_k_true * stride_bzk
b_zp = tl.load(b_zp_ptrs, mask=k_mask, other=k_other)
b_zp = ((b_zp >> b_zp_shifter) & 0xF)
b_zp = b_zp.to(tl.float32)
elif has_zp and use_int8_w8a16:
offs_k_true = (offs_k[:, None] + BLOCK_SIZE_K * k) // group_size
b_zp_ptrs = b_zp_ptr + off_experts * stride_bze + \
offs_bn[None, :] * stride_bzn + \
offs_k_true * stride_bzk
b_zp = tl.load(b_zp_ptrs, mask=k_mask, other=k_other)
b_zp = b_zp.to(tl.float32)
# We accumulate along the K dimension.
if has_zp:
b = ((b.to(tl.float32) - b_zp) * b_scale).to(compute_type)
else:
b = ((b.to(tl.float32) - b_zp_num) * b_scale).to(compute_type)
accumulator = tl.dot(a, b, acc=accumulator)
# Advance the ptrs to the next K block.
a_ptrs += BLOCK_SIZE_K * stride_ak
if use_int4_w4a16:
b_ptrs += (BLOCK_SIZE_K // 2) * stride_bk
else:
b_ptrs += BLOCK_SIZE_K * stride_bk
if MUL_ROUTED_WEIGHT:
moe_weight = tl.load(topk_weights_ptr + offs_token,
mask=token_mask,
other=0)
accumulator = accumulator * moe_weight[:, None]
accumulator = accumulator.to(compute_type)
# -----------------------------------------------------------
# Write back the block of the output
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
c_ptrs = c_ptr + stride_cm * offs_token[:, None] + stride_cn * offs_cn[
None, :]
c_mask = token_mask[:, None] & (offs_cn[None, :] < N)
tl.store(c_ptrs, accumulator, mask=c_mask)
@triton.jit
def fused_moe_kernel(
# Pointers to matrices
@ -266,6 +466,7 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
C: torch.Tensor,
A_scale: Optional[torch.Tensor],
B_scale: Optional[torch.Tensor],
B_zp: Optional[torch.Tensor],
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
sorted_token_ids: torch.Tensor,
@ -277,6 +478,7 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
compute_type: tl.dtype,
use_fp8_w8a8: bool,
use_int8_w8a16: bool,
use_int4_w4a16: bool,
block_shape: Optional[List[int]] = None) -> None:
assert topk_weights.stride(1) == 1
assert sorted_token_ids.stride(0) == 1
@ -292,50 +494,108 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
assert triton.cdiv(A.shape[-1], block_k) == A_scale.shape[-1]
assert triton.cdiv(B.shape[-2], block_n) == B_scale.shape[-2]
assert triton.cdiv(B.shape[-1], block_k) == B_scale.shape[-1]
elif use_int8_w8a16:
elif use_int8_w8a16 or use_int4_w4a16:
assert B_scale is not None
assert block_shape is None or block_shape[0] == 0
else:
assert A_scale is None
assert B_scale is None
grid = lambda META: (triton.cdiv(sorted_token_ids.shape[0], META[
'BLOCK_SIZE_M']) * triton.cdiv(B.shape[1], META['BLOCK_SIZE_N']), )
EM = sorted_token_ids.shape[0]
if A.shape[0] < config["BLOCK_SIZE_M"]:
# optimize for small batch_size.
# We assume that top_ids of each token is unique, so
# so num_valid_experts <= batch_size <= BLOCK_SIZE_M,
# and we can skip some invalid blocks.
EM = min(sorted_token_ids.shape[0],
A.shape[0] * top_k * config['BLOCK_SIZE_M'])
grid = lambda META: (triton.cdiv(EM, META['BLOCK_SIZE_M']) * triton.cdiv(
B.shape[1], META['BLOCK_SIZE_N']), )
fused_moe_kernel[grid](
A,
B,
C,
A_scale,
B_scale,
topk_weights,
sorted_token_ids,
expert_ids,
num_tokens_post_padded,
B.shape[1],
B.shape[2],
sorted_token_ids.shape[0],
topk_ids.numel(),
A.stride(0),
A.stride(1),
B.stride(0),
B.stride(2),
B.stride(1),
C.stride(1),
C.stride(2),
A_scale.stride(0) if A_scale is not None and A_scale.ndim == 2 else 0,
A_scale.stride(1) if A_scale is not None and A_scale.ndim == 2 else 0,
B_scale.stride(0) if B_scale is not None and B_scale.ndim >= 2 else 0,
B_scale.stride(2) if B_scale is not None and B_scale.ndim == 3 else 0,
B_scale.stride(1) if B_scale is not None and B_scale.ndim >= 2 else 0,
0 if block_shape is None else block_shape[0],
0 if block_shape is None else block_shape[1],
MUL_ROUTED_WEIGHT=mul_routed_weight,
top_k=top_k,
compute_type=compute_type,
use_fp8_w8a8=use_fp8_w8a8,
use_int8_w8a16=use_int8_w8a16,
**config,
)
if (use_int8_w8a16 or use_int4_w4a16) and \
block_shape is not None and block_shape[1] > 0:
assert B_scale is not None and B_scale.ndim == 3
assert B_zp is None or B_zp.ndim == 3
fused_moe_kernel_gptq_awq[grid](
A,
B,
C,
B_scale,
B_zp,
topk_weights,
sorted_token_ids,
expert_ids,
num_tokens_post_padded,
B.shape[1],
A.shape[1],
EM,
topk_ids.numel(),
A.stride(0),
A.stride(1),
B.stride(0),
B.stride(2),
B.stride(1),
C.stride(1),
C.stride(2),
B_scale.stride(0),
B_scale.stride(2),
B_scale.stride(1),
B_zp.stride(0) if B_zp is not None else 0,
B_zp.stride(2) if B_zp is not None else 0,
B_zp.stride(1) if B_zp is not None else 0,
block_k_diviable=A.shape[1] % config["BLOCK_SIZE_K"] == 0,
group_size=block_shape[1],
MUL_ROUTED_WEIGHT=mul_routed_weight,
top_k=top_k,
compute_type=compute_type,
has_zp=B_zp is not None,
use_int4_w4a16=use_int4_w4a16,
use_int8_w8a16=use_int8_w8a16,
**config,
)
else:
fused_moe_kernel[grid](
A,
B,
C,
A_scale,
B_scale,
topk_weights,
sorted_token_ids,
expert_ids,
num_tokens_post_padded,
B.shape[1],
A.shape[1],
EM,
topk_ids.numel(),
A.stride(0),
A.stride(1),
B.stride(0),
B.stride(2),
B.stride(1),
C.stride(1),
C.stride(2),
A_scale.stride(0)
if A_scale is not None and A_scale.ndim == 2 else 0,
A_scale.stride(1)
if A_scale is not None and A_scale.ndim == 2 else 0,
B_scale.stride(0)
if B_scale is not None and B_scale.ndim >= 2 else 0,
B_scale.stride(2)
if B_scale is not None and B_scale.ndim == 3 else 0,
B_scale.stride(1)
if B_scale is not None and B_scale.ndim >= 2 else 0,
0 if block_shape is None else block_shape[0],
0 if block_shape is None else block_shape[1],
MUL_ROUTED_WEIGHT=mul_routed_weight,
top_k=top_k,
compute_type=compute_type,
use_fp8_w8a8=use_fp8_w8a8,
use_int8_w8a16=use_int8_w8a16,
**config,
)
def get_config_file_name(E: int, N: int, dtype: Optional[str]) -> str:
@ -432,7 +692,7 @@ def try_get_optimal_moe_config(
# NOTE: For block-wise quant,
# BLOCK_K must be divisible by block_shape[1]
# BLOCK_N and BLOCK_M has no requirements
if block_shape is not None:
if block_shape is not None and block_shape[0] != 0:
config["BLOCK_SIZE_N"] = block_shape[0]
config["BLOCK_SIZE_K"] = block_shape[1]
return config
@ -531,12 +791,15 @@ def grouped_topk(hidden_states: torch.Tensor,
def get_config_dtype_str(dtype: torch.dtype,
use_int4_w4a16: Optional[bool] = False,
use_int8_w8a16: Optional[bool] = False,
use_fp8_w8a8: Optional[bool] = False):
if use_fp8_w8a8:
return "fp8_w8a8"
elif use_int8_w8a16:
return "int8_w8a16"
elif use_int4_w4a16:
return "int4_w8a16"
elif dtype == torch.float:
# avoiding cases where kernel fails when float32 MoE
# use fp16/bfloat16 configs
@ -551,14 +814,17 @@ def inplace_fused_experts(hidden_states: torch.Tensor,
topk_ids: torch.Tensor,
use_fp8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
w1_scale: Optional[torch.Tensor] = None,
w2_scale: Optional[torch.Tensor] = None,
w1_zp: Optional[torch.Tensor] = None,
w2_zp: Optional[torch.Tensor] = None,
a1_scale: Optional[torch.Tensor] = None,
a2_scale: Optional[torch.Tensor] = None,
block_shape: Optional[List[int]] = None) -> None:
fused_experts_impl(hidden_states, w1, w2, topk_weights, topk_ids, True,
use_fp8_w8a8, use_int8_w8a16, w1_scale, w2_scale,
a1_scale, a2_scale, block_shape)
use_fp8_w8a8, use_int8_w8a16, use_int4_w4a16, w1_scale,
w2_scale, w1_zp, w2_zp, a1_scale, a2_scale, block_shape)
def inplace_fused_experts_fake(
@ -569,8 +835,11 @@ def inplace_fused_experts_fake(
topk_ids: torch.Tensor,
use_fp8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
w1_scale: Optional[torch.Tensor] = None,
w2_scale: Optional[torch.Tensor] = None,
w1_zp: Optional[torch.Tensor] = None,
w2_zp: Optional[torch.Tensor] = None,
a1_scale: Optional[torch.Tensor] = None,
a2_scale: Optional[torch.Tensor] = None,
block_shape: Optional[List[int]] = None) -> None:
@ -593,14 +862,18 @@ def outplace_fused_experts(
topk_ids: torch.Tensor,
use_fp8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
w1_scale: Optional[torch.Tensor] = None,
w2_scale: Optional[torch.Tensor] = None,
w1_zp: Optional[torch.Tensor] = None,
w2_zp: Optional[torch.Tensor] = None,
a1_scale: Optional[torch.Tensor] = None,
a2_scale: Optional[torch.Tensor] = None,
block_shape: Optional[List[int]] = None) -> torch.Tensor:
return fused_experts_impl(hidden_states, w1, w2, topk_weights, topk_ids,
False, use_fp8_w8a8, use_int8_w8a16, w1_scale,
w2_scale, a1_scale, a2_scale, block_shape)
False, use_fp8_w8a8, use_int8_w8a16,
use_int4_w4a16, w1_scale, w2_scale, w1_zp, w2_zp,
a1_scale, a2_scale, block_shape)
def outplace_fused_experts_fake(
@ -611,8 +884,11 @@ def outplace_fused_experts_fake(
topk_ids: torch.Tensor,
use_fp8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
w1_scale: Optional[torch.Tensor] = None,
w2_scale: Optional[torch.Tensor] = None,
w1_zp: Optional[torch.Tensor] = None,
w2_zp: Optional[torch.Tensor] = None,
a1_scale: Optional[torch.Tensor] = None,
a2_scale: Optional[torch.Tensor] = None,
block_shape: Optional[List[int]] = None) -> torch.Tensor:
@ -635,8 +911,11 @@ def fused_experts(hidden_states: torch.Tensor,
inplace: bool = False,
use_fp8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
w1_scale: Optional[torch.Tensor] = None,
w2_scale: Optional[torch.Tensor] = None,
w1_zp: Optional[torch.Tensor] = None,
w2_zp: Optional[torch.Tensor] = None,
a1_scale: Optional[torch.Tensor] = None,
a2_scale: Optional[torch.Tensor] = None,
block_shape: Optional[List[int]] = None):
@ -644,16 +923,15 @@ def fused_experts(hidden_states: torch.Tensor,
torch.ops.vllm.inplace_fused_experts(hidden_states, w1, w2,
topk_weights, topk_ids,
use_fp8_w8a8, use_int8_w8a16,
w1_scale, w2_scale, a1_scale,
use_int4_w4a16, w1_scale,
w2_scale, w1_zp, w2_zp, a1_scale,
a2_scale, block_shape)
return hidden_states
else:
return torch.ops.vllm.outplace_fused_experts(hidden_states, w1, w2,
topk_weights, topk_ids,
use_fp8_w8a8,
use_int8_w8a16, w1_scale,
w2_scale, a1_scale,
a2_scale, block_shape)
return torch.ops.vllm.outplace_fused_experts(
hidden_states, w1, w2, topk_weights, topk_ids, use_fp8_w8a8,
use_int8_w8a16, use_int4_w4a16, w1_scale, w2_scale, w1_zp, w2_zp,
a1_scale, a2_scale, block_shape)
def fused_experts_impl(hidden_states: torch.Tensor,
@ -664,13 +942,21 @@ def fused_experts_impl(hidden_states: torch.Tensor,
inplace: bool = False,
use_fp8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
w1_scale: Optional[torch.Tensor] = None,
w2_scale: Optional[torch.Tensor] = None,
w1_zp: Optional[torch.Tensor] = None,
w2_zp: Optional[torch.Tensor] = None,
a1_scale: Optional[torch.Tensor] = None,
a2_scale: Optional[torch.Tensor] = None,
block_shape: Optional[List[int]] = None):
# Check constraints.
assert hidden_states.shape[1] == w1.shape[2], "Hidden size mismatch"
if use_int4_w4a16:
assert hidden_states.shape[1] // 2 == w1.shape[
2], "Hidden size mismatch"
else:
assert hidden_states.shape[1] == w1.shape[2], "Hidden size mismatch"
assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
assert w1.is_contiguous(), "Expert weights1 must be contiguous"
@ -687,6 +973,7 @@ def fused_experts_impl(hidden_states: torch.Tensor,
M = min(num_tokens, CHUNK_SIZE)
config_dtype = get_config_dtype_str(use_fp8_w8a8=use_fp8_w8a8,
use_int8_w8a16=use_int8_w8a16,
use_int4_w4a16=use_int4_w4a16,
dtype=hidden_states.dtype)
get_config_func = functools.partial(
@ -755,6 +1042,7 @@ def fused_experts_impl(hidden_states: torch.Tensor,
intermediate_cache1,
a1_scale,
w1_scale,
w1_zp,
curr_topk_weights,
curr_topk_ids,
sorted_token_ids,
@ -766,6 +1054,7 @@ def fused_experts_impl(hidden_states: torch.Tensor,
compute_type=compute_type,
use_fp8_w8a8=use_fp8_w8a8,
use_int8_w8a16=use_int8_w8a16,
use_int4_w4a16=use_int4_w4a16,
block_shape=block_shape)
torch.ops._C.silu_and_mul(intermediate_cache2,
@ -776,6 +1065,7 @@ def fused_experts_impl(hidden_states: torch.Tensor,
intermediate_cache3,
a2_scale,
w2_scale,
w2_zp,
curr_topk_weights,
curr_topk_ids,
sorted_token_ids,
@ -787,6 +1077,7 @@ def fused_experts_impl(hidden_states: torch.Tensor,
compute_type=compute_type,
use_fp8_w8a8=use_fp8_w8a8,
use_int8_w8a16=use_int8_w8a16,
use_int4_w4a16=use_int4_w4a16,
block_shape=block_shape)
ops.moe_sum(intermediate_cache3.view(*intermediate_cache3.shape),
@ -808,8 +1099,11 @@ def fused_moe(
custom_routing_function: Optional[Callable] = None,
use_fp8_w8a8: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
w1_scale: Optional[torch.Tensor] = None,
w2_scale: Optional[torch.Tensor] = None,
w1_zp: Optional[torch.Tensor] = None,
w2_zp: Optional[torch.Tensor] = None,
a1_scale: Optional[torch.Tensor] = None,
a2_scale: Optional[torch.Tensor] = None,
block_shape: Optional[List[int]] = None,
@ -834,8 +1128,12 @@ def fused_moe(
note: Deepseekv2 model uses grouped_topk
- use_fp8_w8a8 (bool): If True, use fp8 arithmetic to compute the inner
products for w1 and w2. Defaults to False.
- use_int8_w8a16 (bool): If True, use fp8 arithmetic to compute the inner
products for w1 and w2. Defaults to False.
- use_int8_w8a16 (bool): If True, use matmul of int8 weight and bf16/fp16
activation to compute the inner products for w1 and w2.
Defaults to False.
- use_int4_w4a16 (bool): If True, use matmul of int4 weight and bf16/fp16
activation to compute the inner products for w1 and w2.
Defaults to False.
- w1_scale (Optional[torch.Tensor]): Optional scale to be used for
w1.
- w2_scale (Optional[torch.Tensor]): Optional scale to be used for
@ -873,8 +1171,11 @@ def fused_moe(
inplace=inplace,
use_fp8_w8a8=use_fp8_w8a8,
use_int8_w8a16=use_int8_w8a16,
use_int4_w4a16=use_int4_w4a16,
w1_scale=w1_scale,
w2_scale=w2_scale,
w1_zp=w1_zp,
w2_zp=w2_zp,
a1_scale=a1_scale,
a2_scale=a2_scale,
block_shape=block_shape)

7
vllm/model_executor/layers/quantization/__init__.py
View File

@ -26,7 +26,8 @@ QUANTIZATION_METHODS: List[str] = [
"experts_int8",
"neuron_quant",
"ipex",
"quark"
"quark",
"moe_wna16"
]
# The customized quantization methods which will be added to this dict.
@ -94,6 +95,7 @@ def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
from .ipex_quant import IPEXConfig
from .marlin import MarlinConfig
from .modelopt import ModelOptFp8Config
from .moe_wna16 import MoeWNA16Config
from .neuron_quant import NeuronQuantConfig
from .qqq import QQQConfig
from .tpu_int8 import Int8TpuConfig
@ -121,7 +123,8 @@ def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
"experts_int8": ExpertsInt8Config,
"neuron_quant": NeuronQuantConfig,
"ipex": IPEXConfig,
"quark": QuarkConfig
"quark": QuarkConfig,
"moe_wna16": MoeWNA16Config,
}
# Update the `method_to_config` with customized quantization methods.
method_to_config.update(_CUSTOMIZED_METHOD_TO_QUANT_CONFIG)

424
vllm/model_executor/layers/quantization/moe_wna16.py Normal file
View File

@ -0,0 +1,424 @@
from typing import Any, Callable, Dict, List, Optional
import torch
from vllm.distributed import get_tensor_model_parallel_rank, get_tp_group
from vllm.model_executor.layers.fused_moe.layer import (
FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
from vllm.model_executor.layers.linear import UnquantizedLinearMethod
from vllm.model_executor.layers.quantization.awq import (AWQConfig,
AWQLinearMethod)
from vllm.model_executor.layers.quantization.awq_marlin import (
AWQMarlinConfig, AWQMarlinLinearMethod)
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig, QuantizeMethodBase)
from vllm.model_executor.layers.quantization.gptq import (GPTQConfig,
GPTQLinearMethod)
from vllm.model_executor.layers.quantization.gptq_marlin import (
GPTQMarlinConfig, GPTQMarlinLinearMethod)
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
class MoeWNA16Config(QuantizationConfig):
"""Config class for MOE WNA16 (W8A16/W4A16) quantization."""
def __init__(self, linear_quant_method: str, weight_bits: int,
group_size: int, has_zp: bool, lm_head_quantized: bool,
modules_to_not_convert: Optional[List[str]],
full_config: Dict[str, Any]) -> None:
self.weight_bits = weight_bits
self.group_size = group_size
self.has_zp = has_zp
self.bit8_pack_factor = 8 // self.weight_bits
self.lm_head_quantized = lm_head_quantized
self.linear_quant_method = linear_quant_method
self.full_config = full_config
self.use_marlin = False
if self.linear_quant_method == "gptq":
self.use_marlin = GPTQMarlinConfig.is_gptq_marlin_compatible(
full_config)
elif self.linear_quant_method == "awq":
capability_tuple = current_platform.get_device_capability()
device_capability = (-1 if capability_tuple is None else
capability_tuple.to_int())
awq_min_capability = AWQConfig.get_min_capability()
if device_capability < awq_min_capability:
raise ValueError(
"The quantization method moe_wna16 + awq is not supported "
"for the current GPU. "
f"Minimum capability: {awq_min_capability}. "
f"Current capability: {device_capability}.")
self.use_marlin = AWQMarlinConfig.is_awq_marlin_compatible(
full_config)
else:
raise ValueError("moe_wna16 only support gptq and awq.")
if modules_to_not_convert is None:
self.modules_to_not_convert = []
else:
self.modules_to_not_convert = modules_to_not_convert
@classmethod
def get_name(cls) -> str:
return "moe_wna16"
@classmethod
def get_supported_act_dtypes(cls) -> List[torch.dtype]:
return [torch.bfloat16, torch.half]
@classmethod
def get_min_capability(cls) -> int:
return 70
@classmethod
def get_config_filenames(cls) -> List[str]:
return ["quantize_config.json"]
@classmethod
def from_config(cls, config: Dict[str, Any]) -> "MoeWNA16Config":
linear_quant_method = cls.get_from_keys(config, ["quant_method"])
weight_bits = cls.get_from_keys(config, ["bits"])
group_size = cls.get_from_keys(config, ["group_size"])
lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
default=False)
if linear_quant_method == "gptq":
has_zp = not cls.get_from_keys(config, ["sym"])
modules_to_not_convert = []
elif linear_quant_method == "awq":
has_zp = cls.get_from_keys(config, ["zero_point"])
modules_to_not_convert = cls.get_from_keys(
config, ["modules_to_not_convert"])
else:
raise ValueError("moe_wna16 only support gptq and awq.")
return cls(linear_quant_method, weight_bits, group_size, has_zp,
lm_head_quantized, modules_to_not_convert, config)
@classmethod
def override_quantization_method(cls, hf_quant_cfg,
user_quant) -> Optional[str]:
can_convert = cls.is_moe_wna16_compatible(hf_quant_cfg)
if can_convert and user_quant == "moe_wna16":
return cls.get_name()
return None
@classmethod
def is_moe_wna16_compatible(cls, quant_config: Dict[str, Any]):
# Extract data from quant config.
quant_method = quant_config.get("quant_method", "").lower()
num_bits = quant_config.get("bits")
desc_act = quant_config.get("desc_act")
capability_tuple = current_platform.get_device_capability()
device_capability = (-1 if capability_tuple is None else
capability_tuple.to_int())
awq_min_capability = AWQConfig.get_min_capability()
gptq_compatible = quant_method == "gptq" and \
not desc_act and num_bits in [4, 8]
awq_compatible = quant_method == "awq" and num_bits == 4 and \
device_capability >= awq_min_capability
return gptq_compatible or awq_compatible
def get_quant_method(self, layer: torch.nn.Module,
prefix: str) -> Optional["QuantizeMethodBase"]:
if is_layer_skipped_quant(prefix, self.modules_to_not_convert):
return UnquantizedLinearMethod()
elif isinstance(layer, FusedMoE):
return MoeWNA16Method(self)
else:
if self.linear_quant_method == "gptq":
if self.use_marlin:
return GPTQMarlinLinearMethod(
GPTQMarlinConfig.from_config(self.full_config))
else:
return GPTQLinearMethod(
GPTQConfig.from_config(self.full_config))
elif self.linear_quant_method == "awq":
if self.use_marlin:
return AWQMarlinLinearMethod(
AWQMarlinConfig.from_config(self.full_config))
else:
return AWQLinearMethod(
AWQConfig.from_config(self.full_config))
else:
raise ValueError("moe_wna16 only support gptq and awq.")
def is_layer_skipped_quant(prefix: str, modules_to_not_convert: List[str]):
return any(module_name in prefix for module_name in modules_to_not_convert)
class MoeWNA16Method(FusedMoEMethodBase):
"""Linear method for MOE WNA16 (W8A16/W4A16) quantization.
Args:
quant_config: The MOE WNA16 (W8A16/W4A16) quantization config.
"""
def __init__(self, quant_config: MoeWNA16Config):
self.quant_config = quant_config
def create_weights(self, layer: torch.nn.Module, num_experts: int,
hidden_size: int, intermediate_size_per_partition: int,
params_dtype: torch.dtype, **extra_weight_attrs):
layer.quant_config = self.quant_config
bit8_pack_factor = self.quant_config.bit8_pack_factor
group_size = self.quant_config.group_size
group_size_div_factor = 1
# make intermediate_size and hidden_size diviable by group_size
# we reduce the group size to ensure that
# and we would repeat the loaded_weight later
while intermediate_size_per_partition % group_size or \
hidden_size % group_size:
group_size = group_size // 2
group_size_div_factor *= 2
assert group_size >= 32
layer.group_size = group_size
layer.group_size_div_factor = group_size_div_factor
strategy = FusedMoeWeightScaleSupported.GROUP.value
extra_weight_attrs.update({
"quant_method": strategy,
"is_transposed": False
})
assert 'weight_loader' in extra_weight_attrs
weight_loader = extra_weight_attrs['weight_loader']
wrapped_weight_loader = MoeWNA16Method.get_weight_loader(
layer, weight_loader)
extra_weight_attrs['weight_loader'] = wrapped_weight_loader
# Fused gate_up_proj (column parallel)
w13_qweight = torch.nn.Parameter(torch.empty(
num_experts,
2 * intermediate_size_per_partition,
hidden_size // bit8_pack_factor,
dtype=torch.uint8),
requires_grad=False)
layer.register_parameter("w13_qweight", w13_qweight)
set_weight_attrs(w13_qweight, extra_weight_attrs)
# down_proj (row parallel)
w2_qweight = torch.nn.Parameter(torch.empty(
num_experts,
hidden_size,
intermediate_size_per_partition // bit8_pack_factor,
dtype=torch.uint8),
requires_grad=False)
layer.register_parameter("w2_qweight", w2_qweight)
set_weight_attrs(w2_qweight, extra_weight_attrs)
w13_scales = torch.nn.Parameter(torch.zeros(
num_experts,
2 * intermediate_size_per_partition,
hidden_size // group_size,
dtype=params_dtype),
requires_grad=False)
layer.register_parameter("w13_scales", w13_scales)
set_weight_attrs(w13_scales, extra_weight_attrs)
w2_scales = torch.nn.Parameter(torch.zeros(
num_experts,
hidden_size,
intermediate_size_per_partition // group_size,
dtype=params_dtype),
requires_grad=False)
layer.register_parameter("w2_scales", w2_scales)
set_weight_attrs(w2_scales, extra_weight_attrs)
if self.quant_config.has_zp:
w13_qzeros = torch.nn.Parameter(torch.zeros(
num_experts,
2 * intermediate_size_per_partition // bit8_pack_factor,
hidden_size // group_size,
dtype=torch.uint8),
requires_grad=False)
layer.register_parameter("w13_qzeros", w13_qzeros)
set_weight_attrs(w13_qzeros, extra_weight_attrs)
w2_qzeros = torch.nn.Parameter(torch.zeros(
num_experts,
hidden_size // bit8_pack_factor,
intermediate_size_per_partition // group_size,
dtype=torch.uint8),
requires_grad=False)
layer.register_parameter("w2_qzeros", w2_qzeros)
set_weight_attrs(w2_qzeros, extra_weight_attrs)
if self.quant_config.linear_quant_method == "gptq":
# some param are unused, but we need to init them in order to
# load weights
invalid_param_keys = ["w13_g_idx", "w2_g_idx"]
if not self.quant_config.has_zp:
invalid_param_keys += ["w13_qzeros", "w2_qzeros"]
for key in invalid_param_keys:
param = torch.nn.Parameter(torch.empty((0, ),
dtype=torch.int32),
requires_grad=False)
layer.register_parameter(key, param)
set_weight_attrs(param, extra_weight_attrs)
def apply(
self,
layer: torch.nn.Module,
x: torch.Tensor,
router_logits: torch.Tensor,
top_k: int,
renormalize: bool,
use_grouped_topk: bool = False,
topk_group: Optional[int] = None,
num_expert_group: Optional[int] = None,
custom_routing_function: Optional[Callable] = None,
scoring_func: str = "softmax",
e_score_correction_bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
from vllm.model_executor.layers.fused_moe import fused_experts
topk_weights, topk_ids = FusedMoE.select_experts(
hidden_states=x,
router_logits=router_logits,
use_grouped_topk=use_grouped_topk,
top_k=top_k,
renormalize=renormalize,
topk_group=topk_group,
num_expert_group=num_expert_group,
custom_routing_function=custom_routing_function,
scoring_func=scoring_func,
e_score_correction_bias=e_score_correction_bias)
weight_bits = self.quant_config.weight_bits
has_zp = self.quant_config.has_zp
return fused_experts(x,
layer.w13_qweight,
layer.w2_qweight,
topk_weights=topk_weights,
topk_ids=topk_ids,
inplace=True,
use_int4_w4a16=weight_bits == 4,
use_int8_w8a16=weight_bits == 8,
w1_scale=layer.w13_scales,
w2_scale=layer.w2_scales,
w1_zp=layer.w13_qzeros if has_zp else None,
w2_zp=layer.w2_qzeros if has_zp else None,
block_shape=[0, layer.group_size])
@staticmethod
def get_weight_loader(layer, weight_loader):
def convert_awq_tensor(tensor, tensor_type):
# convert awq qweight/qzeros to a standard format (assume int4)
# qweight: (k, n // pack_factor_bit32) -> (n, k // pack_factor_bit8)
# qzeros: (k // group_size, n // pack_factor_bit32) ->
# (n // pack_factor_bit8, k // group_size)
# pack_factor_bit32 = 32 // weight_bits
# pack_factor_bit8 = 8 // weight_bits
# 0. suppose origin shape (a, b), dtype int32
# 1. convert to uint8, shape (a, b) -> (a, 4 * b)
size0 = tensor.size(0)
tensor = tensor.view(torch.uint8)
# 2. unpack to uint4 (only when weight_bits == 4)
# shape (a, 4 * b) -> (a, 4 * b, 2)
shifter = torch.tensor([0, 4],
dtype=torch.uint8,
device=tensor.device)
tensor = (tensor[:, :, None] >> shifter) & 0xF
# 3. change order, see
# https://github.com/casper-hansen/AutoAWQ/blob/v0.2.8/awq/utils/quant_utils.py
# shape -> (a, 4 * b * pack_factor_bit8)
reverse_awq_pack_order = [0, 4, 1, 5, 2, 6, 3, 7]
tensor = tensor.view(-1, 8)[:, reverse_awq_pack_order]
tensor = tensor.view(size0, -1)
# 4. transpose, shape -> (4 * b * pack_factor_bit8, a)
tensor = tensor.T.contiguous()
# 5. repack (only when weight_bits == 4)
# qweight shape -> (4 * b * pack_factor_bit8, a // pack_factor_bit8)
# qzeros shape -> (4 * b, a)
if tensor_type == "qweight":
tensor = tensor[:, 1::2] * 16 + tensor[:, ::2]
elif tensor_type == "qzeros":
tensor = tensor[1::2, :] * 16 + tensor[::2, :]
return tensor
def convert_gptq_int4_qzeros(tensor):
tensor = tensor.view(torch.uint8)
shifter = torch.tensor([0, 4],
dtype=torch.uint8,
device=tensor.device)
tensor = (tensor[:, :, None] >> shifter) & 0xF
tensor = tensor + 1
tensor = tensor[:, :, 0] + tensor[:, :, 1] * 16
return tensor
def moe_wna16_weight_loader(param: torch.nn.Parameter,
loaded_weight: torch.Tensor,
weight_name: str, shard_id: str,
expert_id: int):
if "g_idx" in weight_name:
return
if not layer.quant_config.has_zp and "qzeros" in weight_name:
return
device = get_tp_group().device
tp_rank = get_tensor_model_parallel_rank()
loaded_weight = loaded_weight.to(device)
shard_size = layer.intermediate_size_per_partition
# convert gptq and awq weight to a standard format
if layer.quant_config.linear_quant_method == "awq":
assert layer.quant_config.weight_bits == 4
if "weight" in weight_name:
loaded_weight = convert_awq_tensor(loaded_weight,
"qweight")
elif "zeros" in weight_name:
loaded_weight = convert_awq_tensor(loaded_weight, "qzeros")
else:
loaded_weight = loaded_weight.T
elif layer.quant_config.linear_quant_method == "gptq":
assert layer.quant_config.weight_bits in [4, 8]
if "weight" in weight_name:
loaded_weight = loaded_weight.T.contiguous().view(
torch.uint8)
elif "zeros" in weight_name:
# add 1 to gptq qzeros to align with awq
loaded_weight = loaded_weight.view(torch.uint8)
if layer.quant_config.weight_bits == 4:
loaded_weight = convert_gptq_int4_qzeros(
loaded_weight).T
else:
loaded_weight = loaded_weight.T + 1
else:
loaded_weight = loaded_weight.T
# repeat the qzeros/scales to fit new group size
if layer.group_size_div_factor > 1 and \
"qzeros" in weight_name or "scales" in weight_name:
loaded_weight = loaded_weight.repeat_interleave(
layer.group_size_div_factor, 1)
if "w13_qzeros" in weight_name:
tensor = loaded_weight.view(layer.tp_size, -1,
loaded_weight.size(1))[tp_rank]
if shard_id == "w1":
param.data[expert_id, :shard_size // 2] = tensor
else:
param.data[expert_id, shard_size // 2:] = tensor
elif "w2_qzeros" in weight_name:
param.data[expert_id] = loaded_weight.view(
loaded_weight.size(0), layer.tp_size, -1)[:, tp_rank]
else:
weight_loader(param, loaded_weight, weight_name, shard_id,
expert_id)
return moe_wna16_weight_loader