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Fix pkg_resources --> pathlib and bfloat16 dtype preservation. #570

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Fix pkg_resources --> pathlib and bfloat16 dtype preservation. #570

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14 changes: 4 additions & 10 deletions sam3/model_builder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
import os
from typing import Optional

import pkg_resources
from pathlib import Path as _Path
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
Expand Down Expand Up @@ -596,9 +596,7 @@ def build_sam3_image_model(
A SAM3 image model
"""
if bpe_path is None:
bpe_path = pkg_resources.resource_filename(
"sam3", "assets/bpe_simple_vocab_16e6.txt.gz"
)
bpe_path = str(_Path(__file__).parent / "assets" / "bpe_simple_vocab_16e6.txt.gz")

# Create visual components
compile_mode = "default" if compile else None
Expand Down Expand Up @@ -695,9 +693,7 @@ def build_sam3_video_model(
Sam3VideoInferenceWithInstanceInteractivity: The instantiated dense tracking model
"""
if bpe_path is None:
bpe_path = pkg_resources.resource_filename(
"sam3", "assets/bpe_simple_vocab_16e6.txt.gz"
)
bpe_path = str(_Path(__file__).parent / "assets" / "bpe_simple_vocab_16e6.txt.gz")

# Build Tracker module
tracker = build_tracker(apply_temporal_disambiguation=apply_temporal_disambiguation)
Expand Down Expand Up @@ -1105,9 +1101,7 @@ def build_sam3_multiplex_video_predictor(
Sam3MultiplexVideoPredictor: The fully-initialized predictor
"""
if bpe_path is None:
bpe_path = pkg_resources.resource_filename(
"sam3", "assets/bpe_simple_vocab_16e6.txt.gz"
)
bpe_path = str(_Path(__file__).parent / "assets" / "bpe_simple_vocab_16e6.txt.gz")

from sam3.model.sam3_multiplex_base import Sam3MultiplexPredictorWrapper
from sam3.model.sam3_multiplex_detector import Sam3MultiplexDetector
Expand Down
5 changes: 3 additions & 2 deletions sam3/perflib/fused.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@
def addmm_act(activation, linear, mat1):
if torch.is_grad_enabled():
raise ValueError("Expected grad to be disabled.")
orig_dtype = mat1.dtype
self = linear.bias.detach()
mat2 = linear.weight.detach()
self = self.to(torch.bfloat16)
Expand All @@ -18,8 +19,8 @@ def addmm_act(activation, linear, mat1):
mat1_flat = mat1.view(-1, mat1.shape[-1])
if activation in [torch.nn.functional.relu, torch.nn.ReLU]:
y = addmm_act_op(self, mat1_flat, mat2.t(), beta=1, alpha=1, use_gelu=False)
return y.view(mat1.shape[:-1] + (y.shape[-1],))
return y.view(mat1.shape[:-1] + (y.shape[-1],)).to(orig_dtype)
if activation in [torch.nn.functional.gelu, torch.nn.GELU]:
y = addmm_act_op(self, mat1_flat, mat2.t(), beta=1, alpha=1, use_gelu=True)
return y.view(mat1.shape[:-1] + (y.shape[-1],))
return y.view(mat1.shape[:-1] + (y.shape[-1],)).to(orig_dtype)
raise ValueError(f"Unexpected activation {activation}")