File size: 2,504 Bytes
7dd9266 21c5527 7dd9266 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | """Pruned FlexOlmo model with variable-width expert 1.
This module provides a HuggingFace-compatible model that can be loaded with:
AutoModelForCausalLM.from_pretrained("hbfreed/flex-math-8192", trust_remote_code=True)
"""
import torch
import torch.nn as nn
from transformers import FlexOlmoForCausalLM
from transformers.models.flex_olmo.modeling_flex_olmo import FlexOlmoMLP
from .configuration_pruned_flex_olmo import PrunedFlexOlmoConfig
class PrunedFlexOlmoMLP(nn.Module):
"""Pruned MLP with same interface as FlexOlmoMLP but variable width."""
def __init__(self, intermediate_size: int, hidden_size: int, act_fn, dtype=torch.bfloat16):
super().__init__()
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False, dtype=dtype)
self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False, dtype=dtype)
self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False, dtype=dtype)
self.act_fn = act_fn
def forward(self, x):
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
class PrunedFlexOlmoForCausalLM(FlexOlmoForCausalLM):
"""FlexOlmo with pruned expert 1 for variable-width MoE.
Expert 0 remains at full intermediate_size, while expert 1 is pruned
to expert_1_intermediate_size specified in the config.
"""
config_class = PrunedFlexOlmoConfig
def __init__(self, config: PrunedFlexOlmoConfig):
# Initialize parent with full architecture
super().__init__(config)
# Replace expert 1 in each layer with pruned version
expert_1_width = config.expert_1_intermediate_size
hidden_size = config.hidden_size
for layer in self.model.layers:
# Get activation function from existing expert
act_fn = layer.mlp.experts[1].act_fn
# Replace expert 1 with pruned version
layer.mlp.experts[1] = PrunedFlexOlmoMLP(
intermediate_size=expert_1_width,
hidden_size=hidden_size,
act_fn=act_fn,
dtype=self.dtype,
)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
"""Load pruned model, handling both local and hub paths."""
# Let parent handle the loading - it will use our config_class
# and __init__ which sets up the pruned architecture
return super().from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
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