mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
127 lines
3.8 KiB
Python
127 lines
3.8 KiB
Python
"""
|
|
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""
|
|
|
|
import os
|
|
import paddle
|
|
import fastdeploy
|
|
import fastdeploy.model_executor.ops.gpu.deep_gemm as deep_gemm
|
|
from fastdeploy.model_executor.layers.moe.moe import MoELayer
|
|
|
|
|
|
class MoeTPDecoerDeepDeepGEMMLayer(MoELayer):
|
|
"""
|
|
MoeTPDecoerDeepDeepGEMMLayer
|
|
"""
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
def forward(self, x, **kwargs):
|
|
"""
|
|
forward
|
|
"""
|
|
gate_out = paddle.matmul(x.cast("float32"), self.gate_weight)
|
|
if os.getenv("EP_DECODER_PERF_TEST", "False") == "True":
|
|
gate_out = paddle.rand(shape=gate_out.shape, dtype=gate_out.dtype)
|
|
ffn1_out = paddle.empty(
|
|
[
|
|
self.num_local_experts,
|
|
self.max_batch_size,
|
|
self.moe_intermediate_size * 2,
|
|
],
|
|
dtype=self._dtype,
|
|
)
|
|
|
|
ffn_out = paddle.empty(
|
|
[
|
|
self.num_local_experts,
|
|
self.max_batch_size,
|
|
self.embed_dim,
|
|
],
|
|
dtype=self._dtype,
|
|
)
|
|
|
|
topk_idx, topk_weights = fastdeploy.model_executor.ops.gpu.moe_topk_select(
|
|
gate_out,
|
|
(
|
|
self.gate_correction_bias
|
|
if self.moe_config.moe_use_gate_correction_bias
|
|
else None
|
|
),
|
|
self.top_k,
|
|
True, # apply_norm_weight
|
|
False,
|
|
)
|
|
permute_input, token_nums_per_expert, permute_indices_per_token = (
|
|
fastdeploy.model_executor.ops.gpu.moe_deepgemm_permute(
|
|
x, topk_idx, self.num_local_experts, self.max_batch_size
|
|
)
|
|
)
|
|
|
|
expected_m = 128
|
|
|
|
permute_input_fp8, scale = fastdeploy.model_executor.ops.gpu.masked_per_token_quant(
|
|
permute_input, token_nums_per_expert, 128
|
|
)
|
|
deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(
|
|
(permute_input_fp8, scale),
|
|
(
|
|
self.moe_ffn1_weight,
|
|
self.moe_ffn1_weight_scale,
|
|
),
|
|
ffn1_out,
|
|
token_nums_per_expert,
|
|
expected_m,
|
|
)
|
|
|
|
act_out = fastdeploy.model_executor.ops.gpu.group_swiglu_with_masked(
|
|
ffn1_out, token_nums_per_expert
|
|
)
|
|
|
|
act_out_fp8, scale = fastdeploy.model_executor.ops.gpu.masked_per_token_quant(
|
|
act_out, token_nums_per_expert, 128
|
|
)
|
|
|
|
deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(
|
|
(act_out_fp8, scale),
|
|
(
|
|
self.moe_ffn2_weight,
|
|
self.moe_ffn2_weight_scale,
|
|
),
|
|
ffn_out,
|
|
token_nums_per_expert,
|
|
expected_m,
|
|
)
|
|
|
|
fused_moe_out = fastdeploy.model_executor.ops.gpu.moe_deepgemm_depermute(
|
|
ffn_out, permute_indices_per_token, topk_idx, topk_weights
|
|
)[0]
|
|
|
|
return fused_moe_out
|
|
|
|
|
|
class MoeTPPrefillDeepDeepGEMMLayer(MoELayer):
|
|
"""
|
|
MoeTPPrefillDeepDeepGEMMLayer
|
|
"""
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
def forward(self, x, **kwargs):
|
|
"""
|
|
forward
|
|
"""
|
|
raise NotImplementedError("Prefill is comming soon...")
|