mirror of
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106 lines
3.5 KiB
Python
106 lines
3.5 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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from typing import Optional
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import paddle
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from paddle.incubate.nn.functional import swiglu
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from paddle.nn.quant import weight_only_linear
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try:
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from fastdeploy.model_executor.ops.iluvatar import w8a16_group_gemm
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except ImportError:
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w8a16_group_gemm = None
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def group_gemm(
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input: paddle.Tensor,
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tokens_expert_prefix_sum: paddle.Tensor,
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weight: paddle.Tensor,
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scale: paddle.Tensor,
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output: paddle.Tensor,
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):
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assert (
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input.dim() == 2
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and tokens_expert_prefix_sum.dim() == 1
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and weight.dim() == 3
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and scale.dim() == 2
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and output.dim() == 2
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)
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num_tokens = input.shape[0]
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dim_in = input.shape[1]
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dim_out = weight.shape[1]
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num_experts = weight.shape[0]
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# check shape
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assert tokens_expert_prefix_sum.shape == [
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num_experts,
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]
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assert weight.shape == [num_experts, dim_out, dim_in]
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assert scale.shape == [num_experts, dim_out]
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assert output.shape == [num_tokens, dim_out]
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# check dtype
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assert input.dtype in (paddle.float16, paddle.bfloat16)
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assert scale.dtype == input.dtype and output.dtype == input.dtype
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assert tokens_expert_prefix_sum.dtype == paddle.int64
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assert weight.dtype == paddle.int8
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# check others
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assert tokens_expert_prefix_sum.place.is_cpu_place()
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assert tokens_expert_prefix_sum[-1] == num_tokens
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for i in range(num_experts):
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expert_start = 0 if i == 0 else tokens_expert_prefix_sum[i - 1]
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expert_end = tokens_expert_prefix_sum[i]
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if expert_start == expert_end:
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continue
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input_i = input[expert_start:expert_end]
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weight_i = weight[i]
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scale_i = scale[i]
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# avoid d2d?
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output[expert_start:expert_end] = weight_only_linear(
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input_i, weight_i, weight_scale=scale_i, weight_dtype="int8", group_size=-1
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)
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def iluvatar_moe_expert_ffn(
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permute_input: paddle.Tensor,
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tokens_expert_prefix_sum: paddle.Tensor,
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up_gate_proj_weight: paddle.Tensor,
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down_proj_weight: paddle.Tensor,
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up_gate_proj_bias: Optional[paddle.Tensor],
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up_gate_proj_scale: Optional[paddle.Tensor],
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down_proj_scale: Optional[paddle.Tensor],
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down_proj_in_scale: Optional[paddle.Tensor],
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expert_idx_per_token: Optional[paddle.Tensor],
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quant_method: str,
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used_in_ep_low_latency: bool,
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):
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assert up_gate_proj_bias is None
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assert up_gate_proj_scale is not None
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assert down_proj_scale is not None
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assert down_proj_in_scale is None
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assert expert_idx_per_token is None
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assert quant_method in ("weight_only_int8")
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assert not used_in_ep_low_latency
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tokens_expert_prefix_sum_cpu = tokens_expert_prefix_sum.to("cpu")
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ffn1_output = w8a16_group_gemm(
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permute_input, up_gate_proj_weight, up_gate_proj_scale, tokens_expert_prefix_sum_cpu, -1
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)
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act_out = swiglu(ffn1_output)
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output = w8a16_group_gemm(act_out, down_proj_weight, down_proj_scale, tokens_expert_prefix_sum_cpu, -1)
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return output
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