TJianandOpenAI Codex
0906123953
Signed-off-by: tjtanaa <tunjian.tan@embeddedllm.com> Co-authored-by: OpenAI Codex <codex@openai.com>
64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Inkling dense SwiGLU MLP (also used as the MoE shared expert).
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The checkpoint stores the gate/up projection as a single fused, *interleaved*
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weight (``[gate0, up0, gate1, up1, ...]``), so we use a plain
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``ColumnParallelLinear`` whose contiguous row-sharding keeps each gate/up pair
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together, and an interleaved SwiGLU activation.
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"""
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from __future__ import annotations
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import torch
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from torch import nn
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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class InklingDenseMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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*,
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use_global_scale: bool = False,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.gate_up_proj = ColumnParallelLinear(
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hidden_size,
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2 * intermediate_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj",
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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reduce_results=False,
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prefix=f"{prefix}.down_proj",
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)
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if use_global_scale:
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self.global_scale = nn.Parameter(torch.empty(1), requires_grad=False)
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else:
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self.global_scale = None
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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from .ops import silu_and_mul_triton
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gate_up, _ = self.gate_up_proj(x)
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x = silu_and_mul_triton(gate_up)
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x, _ = self.down_proj(x)
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if self.global_scale is not None:
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x.mul_(self.global_scale)
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# TP-partial output: the layer's reduce-scatter fallback consumes it.
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return x
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