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"""Fused MoE utilities for GPTQ."""
import functools
from typing import Any, Dict, Optional
import torch
from .fused_moe import fused_topk, moe_align_block_size, try_get_optimal_moe_config
from .scalar_type import scalar_types
import moe._custom_ops as ops
def get_scalar_type(num_bits: int, has_zp: bool):
if has_zp:
assert num_bits == 4
return scalar_types.uint4
else:
return scalar_types.uint4b8 if num_bits == 4 else scalar_types.uint8b128
def single_marlin_moe(
hidden_states: torch.Tensor,
w: torch.Tensor,
scales: torch.Tensor,
gating_output: torch.Tensor,
topk: int,
renormalize: bool,
g_idx: Optional[torch.Tensor] = None,
sort_indices: Optional[torch.Tensor] = None,
w_zeros: Optional[torch.Tensor] = None,
override_config: Optional[Dict[str, Any]] = None,
num_bits: int = 8,
is_k_full: bool = True,
) -> torch.Tensor:
"""
This function computes the multiplication of hidden_states with expert
weights used in Marlin MoE, using weights w and top-k gating mechanism.
Its purpose is testing and debugging the fused MoE kernel.
Parameters:
- hidden_states (torch.Tensor): The input tensor to the Marlin Mul.
- w (torch.Tensor): The set of expert weights.
- scales (torch.Tensor): The quantization scales.
- gating_output (torch.Tensor): The output of the gating operation
(before softmax).
- g_idx (Optional[torch.Tensor]): Optional act_order indices.
- sort_indices (Optional[torch.Tensor]): Optional act_order input
permutation.
- topk (int): The number of top-k experts to select.
- renormalize (bool): If True, renormalize the top-k weights to sum to 1.
- w_zeros (Optional[torch.Tensor]): Optional zero points to be used for w.
- override_config (Optional[Dict[str, Any]]): Optional override
for the kernel configuration.
- num_bits (bool): The number of bits in expert weights quantization.
Returns:
- torch.Tensor: The output tensor after applying the MoE layer.
"""
# Check constraints.
assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch"
assert hidden_states.shape[1] == w.shape[1] * 16, "Hidden size mismatch"
assert gating_output.shape[1] == w.shape[0], "Number of experts mismatch"
assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
assert w.is_contiguous(), "Expert weights must be contiguous"
assert hidden_states.dtype == torch.float16
assert num_bits in [4, 8]
M, K = hidden_states.shape
E = w.shape[0]
N = w.shape[2] // (num_bits // 2)
topk_weights, topk_ids = fused_topk(hidden_states, gating_output, topk, renormalize)
# This might not be an optimal config for a single MMM
get_config_func = functools.partial(
try_get_optimal_moe_config,
w.shape,
w.shape,
topk_ids.shape[1],
None,
override_config=override_config,
is_marlin=True,
)
config = get_config_func(M)
block_size_m = config["BLOCK_SIZE_M"]
sorted_token_ids, _, _ = moe_align_block_size(topk_ids, block_size_m, E)
max_workspace_size = (N // 64) * 16
workspace = torch.zeros(
max_workspace_size,
dtype=torch.int,
device=hidden_states.device,
requires_grad=False,
)
has_zero_point = w_zeros is not None
if w_zeros is None:
w_zeros = torch.empty(
(0, 0),
dtype=hidden_states.dtype,
device=hidden_states.device,
requires_grad=False,
)
if g_idx is None:
g_idx = torch.empty(
(0, 0), dtype=torch.int32, device=hidden_states.device, requires_grad=False
)
if sort_indices is None:
sort_indices = torch.empty(
(0), dtype=torch.int32, device=hidden_states.device, requires_grad=False
)
scalar_type = get_scalar_type(num_bits, has_zero_point)
intermediate_cache = ops.ops.marlin_gemm_moe(
hidden_states,
w,
sorted_token_ids,
topk_weights,
topk_ids,
scales,
w_zeros,
g_idx,
sort_indices,
workspace,
scalar_type.id,
M,
N,
K,
is_k_full,
E,
topk,
block_size_m,
True,
False,
)
return torch.sum(intermediate_cache.view(*intermediate_cache.shape), dim=1)
def fused_marlin_moe(
hidden_states: torch.Tensor,
w1: torch.Tensor,
w2: torch.Tensor,
w1_scale: torch.Tensor,
w2_scale: torch.Tensor,
gating_output: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
g_idx1: Optional[torch.Tensor] = None,
g_idx2: Optional[torch.Tensor] = None,
sort_indices1: Optional[torch.Tensor] = None,
sort_indices2: Optional[torch.Tensor] = None,
w1_zeros: Optional[torch.Tensor] = None,
w2_zeros: Optional[torch.Tensor] = None,
override_config: Optional[Dict[str, Any]] = None,
num_bits: int = 8,
is_k_full: bool = True,
) -> torch.Tensor:
"""
This function computes a Mixture of Experts (MoE) layer using two sets of
weights, w1 and w2, and top-k gating mechanism.
Parameters:
- hidden_states (torch.Tensor): The input tensor to the MoE layer.
- w1 (torch.Tensor): The first set of expert weights.
- w2 (torch.Tensor): The second set of expert weights.
- w1_scale (torch.Tensor): Scale to be used for w1.
- w2_scale (torch.Tensor): Scale to be used for w2.
- gating_output (torch.Tensor): The output of the gating operation
(before softmax).
- g_idx1 (Optional[torch.Tensor]): The first set of act_order indices.
- g_idx2 (Optional[torch.Tensor]): The second set of act_order indices.
- sort_indices1 (Optional[torch.Tensor]): The first act_order input
permutation.
- sort_indices2 (Optional[torch.Tensor]): The second act_order input
permutation.
- topk_weights (torch.Tensor): Top-k weights.
- topk_ids (torch.Tensor): Indices of topk-k elements.
- override_config (Optional[Dict[str, Any]]): Optional override
for the kernel configuration.
- w1_zeros (Optional[torch.Tensor]): Optional zero points to be used for w1.
- w2_zeros (Optional[torch.Tensor]): Optional zero points to be used for w2.
- num_bits (bool): The number of bits in expert weights quantization.
Returns:
- torch.Tensor: The output tensor after applying the MoE layer.
"""
# Check constraints.
assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch"
assert hidden_states.shape[1] == w1.shape[1] * 16, "Hidden size mismatch w1"
assert hidden_states.shape[1] == w2.shape[2] // (
num_bits // 2
), "Hidden size mismatch w2"
assert gating_output.shape[1] == w1.shape[0], "Number of experts mismatch"
assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
assert w1.is_contiguous(), "Expert weights1 must be contiguous"
assert w2.is_contiguous(), "Expert weights2 must be contiguous"
assert hidden_states.dtype == torch.float16
assert num_bits in [4, 8]
has_no_act_order = (
g_idx1 is None
and g_idx2 is None
and sort_indices1 is None
and sort_indices2 is None
)
has_all_act_order = (
g_idx1 is not None
and g_idx2 is not None
and sort_indices1 is not None
and sort_indices2 is not None
)
assert has_no_act_order or has_all_act_order, (
"g_idx and sorted_indices " "must be all not None or must be all None"
)
has_no_zp = w1_zeros is None and w2_zeros is None
has_all_zp = w1_zeros is not None and w2_zeros is not None
assert has_no_zp or has_all_zp, (
"zero points must be both not None or " "must be both None"
)
M, K = hidden_states.shape
E = w1.shape[0]
N = w2.shape[1] * 16
topk = topk_ids.shape[1]
get_config_func = functools.partial(
try_get_optimal_moe_config,
w1.shape,
w2.shape,
topk_ids.shape[1],
None,
override_config=override_config,
is_marlin=True,
)
config = get_config_func(M)
block_size_m = config["BLOCK_SIZE_M"]
sorted_token_ids, _, _ = moe_align_block_size(topk_ids, block_size_m, E)
max_workspace_size = (max(2 * N, K) // 64) * 16
workspace = torch.zeros(
max_workspace_size, dtype=torch.int, device="cuda", requires_grad=False
)
if has_no_zp:
w1_zeros = torch.empty(
(0, 0),
dtype=hidden_states.dtype,
device=hidden_states.device,
requires_grad=False,
)
w2_zeros = torch.empty(
(0, 0),
dtype=hidden_states.dtype,
device=hidden_states.device,
requires_grad=False,
)
if has_no_act_order:
g_idx1 = torch.empty(
(0, 0), dtype=torch.int32, device=hidden_states.device, requires_grad=False
)
g_idx2 = torch.empty(
(0, 0), dtype=torch.int32, device=hidden_states.device, requires_grad=False
)
sort_indices1 = torch.empty(
(0), dtype=torch.int32, device=hidden_states.device, requires_grad=False
)
sort_indices2 = torch.empty(
(0, 0), dtype=torch.int32, device=hidden_states.device, requires_grad=False
)
scalar_type1 = get_scalar_type(num_bits, has_all_zp)
scalar_type2 = get_scalar_type(num_bits, has_all_zp)
intermediate_cache2 = torch.empty(
(M * topk_ids.shape[1], N),
device=hidden_states.device,
dtype=hidden_states.dtype,
)
intermediate_cache1 = ops.ops.marlin_gemm_moe(
hidden_states,
w1,
sorted_token_ids,
topk_weights,
topk_ids,
w1_scale,
w1_zeros,
g_idx1,
sort_indices1,
workspace,
scalar_type1.id,
M,
2 * N,
K,
is_k_full,
E,
topk,
block_size_m,
True,
False,
)
ops.silu_and_mul(intermediate_cache2, intermediate_cache1.view(-1, 2 * N))
intermediate_cache3 = ops.ops.marlin_gemm_moe(
intermediate_cache2,
w2,
sorted_token_ids,
topk_weights,
topk_ids,
w2_scale,
w2_zeros,
g_idx2,
sort_indices2,
workspace,
scalar_type2.id,
M,
K,
N,
is_k_full,
E,
topk,
block_size_m,
False,
True,
)
return torch.sum(intermediate_cache3.view(*intermediate_cache3.shape), dim=1)
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