{
  "model": "sam2.1-hiera-tiny (512 encoder/decoder split)",
  "upstream": {
    "model": "facebook/sam2.1-hiera-tiny",
    "revision": "de431c4043854a71d8101e17995dfe596bf101a5",
    "official": true,
    "license": "Apache-2.0",
    "files": {
      "model.safetensors": {
        "sha256": "48c14467e5cf9e51870511feb72c89688e82dd74523142c0538b663e193ac2a7"
      }
    },
    "note": "transformers Sam2Model port published by the SAM 2 authors on the Hub"
  },
  "versions": {
    "python": "3.12.15+meta",
    "torch": "2.14.1+cpu",
    "torchao": "0.18.0+cpu",
    "executorch": "1.5.1+cpu",
    "executorch_git": "3b60683923245cf472b7323426920e15623ba361",
    "transformers": "5.19.0",
    "huggingface_hub": "2.2.0"
  },
  "export": {
    "api": "torch.export.export(strict=False) -> to_edge_transform_and_lower(partitioner=[WebGPUPartitioner()]) -> to_executorch(), one program per module",
    "partitioner": "executorch.backends.webgpu.partitioner.WebGPUPartitioner() (default compile options; wraps VulkanPartitioner)",
    "model_code": "transformers Sam2Model; attention through torch.ops.et_vk.sdpa (transformers attention backend, recipes/sam2-hiera-tiny.py:et_vk_attention); config image_size overridden 1024 -> 512",
    "programs": {
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          {
            "name": "pixels",
            "shape": [
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              3,
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              512
            ],
            "dtype": "float32"
          }
        ],
        "outputs": [
          {
            "name": "image_embeddings",
            "shape": [
              1,
              256,
              32,
              32
            ],
            "dtype": "float32",
            "note": "no_memory_embedding added"
          },
          {
            "name": "hr0",
            "shape": [
              1,
              256,
              128,
              128
            ],
            "dtype": "float32",
            "note": "before mask_decoder.conv_s0"
          },
          {
            "name": "hr1",
            "shape": [
              1,
              256,
              64,
              64
            ],
            "dtype": "float32",
            "note": "before mask_decoder.conv_s1"
          }
        ]
      },
      "sam2_decoder.pte": {
        "method": "forward",
        "prompt_tokens": 3,
        "inputs": [
          {
            "name": "image_embeddings",
            "shape": [
              1,
              256,
              32,
              32
            ],
            "dtype": "float32"
          },
          {
            "name": "image_pe",
            "shape": [
              1,
              256,
              32,
              32
            ],
            "dtype": "float32"
          },
          {
            "name": "sparse",
            "shape": [
              1,
              1,
              3,
              256
            ],
            "dtype": "float32"
          },
          {
            "name": "dense",
            "shape": [
              1,
              256,
              32,
              32
            ],
            "dtype": "float32"
          },
          {
            "name": "hr0",
            "shape": [
              1,
              256,
              128,
              128
            ],
            "dtype": "float32"
          },
          {
            "name": "hr1",
            "shape": [
              1,
              256,
              64,
              64
            ],
            "dtype": "float32"
          }
        ],
        "outputs": [
          {
            "name": "masks",
            "shape": [
              1,
              3,
              128,
              128
            ],
            "dtype": "float32",
            "note": "logits, mask tokens 1..3"
          },
          {
            "name": "iou_scores",
            "shape": [
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              1,
              3
            ],
            "dtype": "float32"
          },
          {
            "name": "object_score_logits",
            "shape": [
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              1,
              1
            ],
            "dtype": "float32"
          }
        ]
      }
    },
    "prompt_tables": {
      "prompt_gaussian.bin": {
        "shape": [
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        ],
        "dtype": "float32 little-endian"
      },
      "prompt_point_embed_bg.bin": {
        "shape": [
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        ],
        "dtype": "float32 little-endian"
      },
      "prompt_point_embed_fg.bin": {
        "shape": [
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        ],
        "dtype": "float32 little-endian"
      },
      "prompt_box_tl.bin": {
        "shape": [
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        ],
        "dtype": "float32 little-endian"
      },
      "prompt_box_br.bin": {
        "shape": [
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        ],
        "dtype": "float32 little-endian"
      },
      "prompt_not_a_point.bin": {
        "shape": [
          256
        ],
        "dtype": "float32 little-endian"
      },
      "prompt_img_pos.bin": {
        "shape": [
          1,
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          32,
          32
        ],
        "dtype": "float32 little-endian"
      },
      "prompt_dense.bin": {
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          32
        ],
        "dtype": "float32 little-endian"
      }
    },
    "prompt_table_sources": {
      "prompt_gaussian.bin": "shared_image_embedding.positional_embedding",
      "prompt_point_embed_bg.bin": "prompt_encoder.point_embed.weight[0]",
      "prompt_point_embed_fg.bin": "prompt_encoder.point_embed.weight[1]",
      "prompt_box_tl.bin": "prompt_encoder.point_embed.weight[2]",
      "prompt_box_br.bin": "prompt_encoder.point_embed.weight[3]",
      "prompt_not_a_point.bin": "prompt_encoder.not_a_point_embed.weight[0]",
      "prompt_img_pos.bin": "Sam2Model.get_image_wide_positional_embeddings() with image_size=512",
      "prompt_dense.bin": "prompt_encoder.no_mask_embed.weight broadcast to [1,256,32,32]"
    },
    "graph_rewrites": [
      "Hiera q-pooling of windowed tensors [nW,h,w,C] done as one batch-1 max_pool2d over the windows stacked along H (exact: h is a multiple of the stride); keeps all pools in the delegate",
      "attention via torch.ops.et_vk.sdpa (same math as torch SDPA; checked in validation.json)",
      "decoder: a not-a-point token directly after another one gets an additive -1e9 key bias in every attention (computed in-graph from the sparse input), so padding up to the fixed token count does not change the result",
      "Hiera position embedding (bicubic pos_embed resize + tiled window embedding) for the fixed input size computed in eager and stored as a constant"
    ],
    "differences_from_upstream_pipeline": [
      "input 512x512 instead of 1024x1024 (masks 128x128 instead of 256x256)",
      "fixed 3 prompt tokens per decode: points, box corners, one not-a-point pad, then padding pads that the decoder masks out (exactly upstream's prompt for any prompt that fits; larger prompts are composed by the site)",
      "no mask prompt input (dense = no_mask_embed)"
    ],
    "preprocessing_outside_graph": "RGB, resize to 512x512 without keeping aspect, /255, ImageNet mean/std",
    "dtype": "float32",
    "quantization": "none (fp32)"
  },
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    "sam2_decoder.pte": {
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  },
  "graph": {
    "sam2_encoder": {
      "non_delegated_kernels": {},
      "delegates": [
        {
          "id": "VulkanBackend",
          "ops": {
            "et_vk.prepack.default": 2,
            "aten.convolution.default": 5,
            "aten.clone.default": 32,
            "aten.permute_copy.default": 83,
            "aten.add.Tensor": 27,
            "aten.native_layer_norm.default": 24,
            "aten.constant_pad_nd.default": 9,
            "aten.view_copy.default": 114,
            "aten.linear.default": 51,
            "aten.slice_copy.Tensor": 46,
            "aten.squeeze_copy.dims": 36,
            "et_vk.sdpa.default": 12,
            "aten.gelu.default": 12,
            "aten.max_pool2d_with_indices.default": 6,
            "aten.upsample_nearest2d.vec": 1
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          "inputs": 1,
          "outputs": 3
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      "io": {
        "methods": [
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        "inputs": [
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            ],
            "dtype": "FLOAT",
            "dim_order": [
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        "outputs": [
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            ],
            "dtype": "FLOAT",
            "dim_order": [
              0,
              1,
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            "shape": [
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            "dtype": "FLOAT",
            "dim_order": [
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            ]
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            "dtype": "FLOAT",
            "dim_order": [
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              2,
              3
            ]
          }
        ]
      },
      "io_contract": "matches the previous export (shapes, fp32, contiguous dim order, single forward method)"
    },
    "sam2_decoder": {
      "non_delegated_kernels": {},
      "delegates": [
        {
          "id": "VulkanBackend",
          "ops": {
            "et_vk.prepack.default": 13,
            "aten.sub.Tensor": 2,
            "aten.abs.default": 1,
            "aten.sum.dim_IntList": 1,
            "aten.mul.Tensor": 3,
            "aten.relu.default": 15,
            "aten.slice_copy.Tensor": 5,
            "aten.full.default": 1,
            "aten.clone.default": 23,
            "aten.cat.default": 4,
            "aten.unsqueeze_copy.default": 16,
            "aten.convolution.default": 4,
            "aten.repeat.default": 1,
            "aten.add.Tensor": 25,
            "aten.view_copy.default": 57,
            "aten.permute_copy.default": 36,
            "aten.squeeze_copy.dims": 34,
            "aten.linear.default": 47,
            "aten.expand_copy.default": 7,
            "et_vk.sdpa.default": 7,
            "aten.native_layer_norm.default": 10,
            "aten.bmm.default": 4,
            "aten.select_copy.int": 7,
            "aten.gelu.default": 2,
            "aten.sigmoid.default": 1
          },
          "inputs": 6,
          "outputs": 3
        }
      ],
      "io": {
        "methods": [
          "forward"
        ],
        "inputs": [
          {
            "shape": [
              1,
              256,
              32,
              32
            ],
            "dtype": "FLOAT",
            "dim_order": [
              0,
              1,
              2,
              3
            ]
          },
          {
            "shape": [
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            ],
            "dtype": "FLOAT",
            "dim_order": [
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            ]
          },
          {
            "shape": [
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            "dtype": "FLOAT",
            "dim_order": [
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              3
            ]
          },
          {
            "shape": [
              1,
              256,
              32,
              32
            ],
            "dtype": "FLOAT",
            "dim_order": [
              0,
              1,
              2,
              3
            ]
          },
          {
            "shape": [
              1,
              256,
              128,
              128
            ],
            "dtype": "FLOAT",
            "dim_order": [
              0,
              1,
              2,
              3
            ]
          },
          {
            "shape": [
              1,
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              64,
              64
            ],
            "dtype": "FLOAT",
            "dim_order": [
              0,
              1,
              2,
              3
            ]
          }
        ],
        "outputs": [
          {
            "shape": [
              1,
              3,
              128,
              128
            ],
            "dtype": "FLOAT",
            "dim_order": [
              0,
              1,
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            ]
          },
          {
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          {
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            ],
            "dtype": "FLOAT",
            "dim_order": [
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              2
            ]
          }
        ]
      },
      "io_contract": "matches the previous export (shapes, fp32, contiguous dim order, single forward method)"
    }
  },
  "command": "python recipes/sam2-hiera-tiny.py --out out/sam2.1-hiera-tiny"
}