)]}' { "commit": "e169d26ccfce28ca45d5b8d5a39734eca81427e3", "tree": "89296c69ce4ec1d648f076d6b88301a1e58ff121", "parents": [ "aac89de63ba020d26b7afad7af6b127e7184890c" ], "author": { "name": "Gunhyun Park", "email": "gunhyun@google.com", "time": "Fri May 26 16:39:18 2023 -0700" }, "committer": { "name": "GitHub", "email": "noreply@github.com", "time": "Fri May 26 16:39:18 2023 -0700" }, "message": "Add interpreter for ScatterOp (#1488)\n\nWe have the following constraints in the spec:\r\n\r\n```\r\n(I1) `inputs` variadic number of tensors.\r\n(I2) `scatter_indices` tensor of integer type.\r\n(I3) `updates` variadic number of tensors.\r\n(I4) `update_window_dims` 1-dimensional tensor constant of type `si64`.\r\n(I5) `inserted_window_dims` 1-dimensional tensor constant of type `si64`.\r\n(I6) `scatter_dims_to_operand_dims` 1-dimensional tensor constant of type `si64`.\r\n(I7) `index_vector_dim` constant of type `si64`.\r\n(I8) `indices_are_sorted` constant of type `i1`.\r\n(I9) `unique_indices` constant of type `i1`.\r\n(I10) `update_computation` function.\r\n(C1) All `inputs` have the same shape.\r\n(C2) rank(`inputs`[0]) \u003d size(`update_window_dims`) +\r\n size(`inserted_window_dims`).\r\n(C3) All `updates` have the same shape.\r\n(C4) `shape(updates[0])` \u003d\r\n `combine(update_scatter_dim_sizes, update_window_dim_sizes)` where:\r\n* `update_scatter_dim_sizes` \u003d `shape(scatter_indices)` except that\r\n the dimension size of `scatter_indices` corresponding to\r\n `index_vector_dim` is not included.\r\n* `update_window_dim_sizes` \u003c\u003d `shape(inputs[0])` except that\r\n the dimension sizes in `inputs[0]` corresponding to `inserted_window_dims`\r\n are not included.\r\n* `combine` puts `update_scatter_dim_sizes` at axes corresponding to\r\n `update_scatter_dims` and `update_window_dim_sizes` at axes corresponding\r\n to `update_window_dims`.\r\n(C5) N \u003d size(`inputs`) \u003d size(`updates`) and N \u003e\u003d 1.\r\n(C6) `element_type(updates[k]) \u003d element_type(inputs[k])` for all k $\\in$\r\n [0, N).\r\n(C7) All dimensions in `update_window_dims` are unique and sorted.\r\n(C8) For all i in [0, size(`update_window_dims`)), 0 \u003c\u003d\r\n`update_window_dims`[i] \u003c rank(`updates`[0]).\r\n(C9) All dimensions in `inserted_window_dims` are unique and sorted.\r\n(C10) For all i in [0, size(`inserted_window_dims`)), 0 \u003c\u003d\r\n`inserted_window_dims`[i] \u003c rank(`inputs`[0]).\r\n(C11) size(`scatter_dims_to_operand_dims`) \u003d\r\n `index_vector_dim` \u003c rank(`scatter_indices`) ?\r\n dim(`scatter_indices`, `index_vector_dim`) : 1.\r\n(C12) All dimensions in `scatter_dims_to_operand_dims` are unique.\r\n(C13) For all i in [0, size(`scatter_dims_to_operand_dims`)), 0 \u003c\u003d\r\n `scatter_dims_to_operand_dims`[i] \u003c rank(`inputs`[0]).\r\n(C14) 0 \u003c\u003d `index_vector_dim` \u003c\u003d rank(`scatter_indices`).\r\n(C15) `update_computation` has type\r\n `(tensor\u003cE0\u003e, ..., tensor\u003cEN-1\u003e, tensor\u003cE0\u003e, ..., tensor\u003cEN-1\u003e) -\u003e (tensor\u003cE0\u003e, ..., tensor\u003cEN-1\u003e)`\r\n where `Ek \u003d element_type(inputs[k])` for all k in [0, N).\r\n(C16) `inputs[k]` and `result[k]` have the same type for all k in [0, N).\r\n```\r\n\r\nThese constraints will be comprehensively covered by the following\r\ntests:\r\n\r\n```\r\nI1: a) `inputs` variadic number of tensors. (Covered by ODS).\r\nI2: a) `scatter_indices` tensor of integer type. (Covered by ODS).\r\nI3: a) `updates` variadic number of tensors. (Covered by ODS).\r\nI4: a) `update_window_dims` 1-dimensional tensor constant of type `si64`. (Covered by ODS).\r\nI5: a) `inserted_window_dims` 1-dimensional tensor constant of type `si64`. (Covered by ODS).\r\nI6: a) `scatter_dims_to_operand_dims` 1-dimensional tensor constant of type `si64`. (Covered by ODS).\r\nI7: a) `index_vector_dim` constant of type `si64`. (Covered by ODS).\r\nI8: a) `indices_are_sorted` constant of type `i1`. (Covered by ODS).\r\nI9: a) `unique_indices` constant of type `i1`. (Covered by ODS).\r\nI10: a) `update_computation` function. (Covered by ODS).\r\nC1: a) Not all `inputs` have the same shape.\r\nC2: a) rank(`inputs`[0]) !\u003d size(`update_window_dims`) +\r\n size(`inserted_window_dims`).\r\nC3: a) Not all `updates` have the same shape.\r\nC4: a) `shape(updates[0])` !\u003d\r\n `combine(update_scatter_dim_sizes, update_window_dim_sizes)`.\r\n b) `update_scatter_dim_sizes` !\u003d `shape(scatter_indices)` except that the\r\n dimension size of `scatter_indices` corresponding to `index_vector_dim`\r\n is not included.\r\n c) `update_window_dim_sizes` \u003c\u003d `shape(inputs[0])` except that the dimension\r\n sizes in `inputs[0]` corresponding to `inserted_window_dims` are not\r\n included.\r\n where `combine` puts `update_scatter_dim_sizes` at axes corresponding to\r\n `update_scatter_dims` and `update_window_dim_sizes` at axes corresponding to\r\n `update_window_dims`.\r\nC5: a) N !\u003d size(`inputs`). (Covered by ODS).\r\n b) N !\u003d size(`updates`). (Covered by ODS).\r\n c) N \u003c 1. (Covered by ODS).\r\nC6: a) `element_type(updates[k]) !\u003d element_type(inputs[k])` for any k in [0, N).\r\nC7: a) Dimensions in `update_window_dims` are not unique.\r\n b) Dimensions in `update_window_dims` are not sorted.\r\nC8: a) For any i in [0, size(`update_window_dims`)), `update_window_dims`[i] \u003c 0.\r\n b) For any i in [0, size(`update_window_dims`)), `update_window_dims`[i] \u003e\u003d rank(`updates`[0]). \r\nC9: a) Dimensions in `inserted_window_dims` are not unique.\r\n b) Dimensions in `inserted_window_dims` are not sorted.\r\nC10: a) For any i in [0, size(`inserted_window_dims`)), `inserted_window_dims`[i] \u003c 0.\r\n b) For any i in [0, size(`inserted_window_dims`)), \u003e\u003d rank(`inputs`[0]).\r\nC11: a) size(`scatter_dims_to_operand_dims`) !\u003d\r\n `index_vector_dim` \u003c rank(`scatter_indices`) ?\r\n dim(`scatter_indices`, `index_vector_dim`) : 1.\r\nC12: a) Dimensions in `scatter_dims_to_operand_dims` are not unique.\r\nC13: a) For any i in [0, size(`scatter_dims_to_operand_dims`)), `scatter_dims_to_operand_dims`[i] \u003c 0.\r\n b) For any i in [0, size(`scatter_dims_to_operand_dims`)), `scatter_dims_to_operand_dims`[i] \u003e\u003d rank(`inputs`[0]).\r\nC14: a) `index_vector_dim` \u003c 0.\r\n b) `index_vector_dim` \u003e rank(`scatter_indices`).\r\nC15: a) `update_computation` does not have type\r\n `(tensor\u003cE0\u003e, ..., tensor\u003cEN-1\u003e, tensor\u003cE0\u003e, ..., tensor\u003cEN-1\u003e) -\u003e (tensor\u003cE0\u003e, ..., tensor\u003cEN-1\u003e)`\r\n where `Ek \u003d element_type(inputs[k])` for any k $\\in$ [0, N).\r\nC16: a) type(`inputs[k]`) !\u003d type(`result[k]`) for any k $\\in$ [0, N).\r\n```\r\n\r\nIf we drop the \"Covered by ODS\" pieces, this will leave us with the\r\nfollowing test cases:\r\n\r\n```\r\nC1a: Not all `inputs` have the same shape.\r\nC2a: rank(`inputs`[0]) !\u003d size(`update_window_dims`) + size(`inserted_window_dims`).\r\nC3a: Not all `updates` have the same shape.\r\nC4a: `shape(updates[0])` !\u003d\r\n `combine(update_scatter_dim_sizes, update_window_dim_sizes)`.\r\nC4b: `update_scatter_dim_sizes` !\u003d `shape(scatter_indices)` except that the\r\n dimension size of `scatter_indices` corresponding to `index_vector_dim`\r\n is not included.\r\nC4c: `update_window_dim_sizes` \u003c\u003d `shape(inputs[0])` except that the dimension\r\n sizes in `inputs[0]` corresponding to `inserted_window_dims` are not\r\n included.\r\n where `combine` puts `update_scatter_dim_sizes` at axes corresponding to\r\n `update_scatter_dims` and `update_window_dim_sizes` at axes corresponding to\r\n `update_window_dims`.\r\nC6a: `element_type(updates[k]) !\u003d element_type(inputs[k])` for any k in [0, N).\r\nC7a: Dimensions in `update_window_dims` are not unique.\r\nC7b: Dimensions in `update_window_dims` are not sorted.\r\nC8a: For any i in [0, size(`update_window_dims`)), `update_window_dims`[i] \u003c 0.\r\nC8b: For any i in [0, size(`update_window_dims`)), `update_window_dims`[i] \u003e\u003d rank(`updates`[0]). \r\nC9a: Dimensions in `inserted_window_dims` are not unique.\r\nC9b: Dimensions in `inserted_window_dims` are not sorted.\r\nC10a: For any i in [0, size(`inserted_window_dims`)), `inserted_window_dims`[i] \u003c 0.\r\nC10b: For any i in [0, size(`inserted_window_dims`)), \u003e\u003d rank(`inputs`[0]).\r\nC11a: size(`scatter_dims_to_operand_dims`) !\u003d\r\n `index_vector_dim` \u003c rank(`scatter_indices`) ?\r\n dim(`scatter_indices`, `index_vector_dim`) : 1.\r\nC12a: Dimensions in `scatter_dims_to_operand_dims` are not unique.\r\nC13a: For any i in [0, size(`scatter_dims_to_operand_dims`)), `scatter_dims_to_operand_dims`[i] \u003c 0.\r\nC13b: For any i in [0, size(`scatter_dims_to_operand_dims`)), `scatter_dims_to_operand_dims`[i] \u003e\u003d rank(`inputs`[0]).\r\nC14a: `index_vector_dim` \u003c 0.\r\nC14b: `index_vector_dim` \u003e rank(`scatter_indices`).\r\nC15a: `update_computation` does not have type\r\n `(tensor\u003cE0\u003e, ..., tensor\u003cEN-1\u003e, tensor\u003cE0\u003e, ..., tensor\u003cEN-1\u003e) -\u003e (tensor\u003cE0\u003e, ..., tensor\u003cEN-1\u003e)`\r\n where `Ek \u003d element_type(inputs[k])` for any k $\\in$ [0, N).\r\nC16a: type(`inputs[k]`) !\u003d type(`result[k]`) for any k $\\in$ [0, N).\r\n```\r\n\r\nNotes:\r\n * Some missing verifications were added.\r\n * Updates typo in spec wording (i.e. For any k -\u003e For all k).\r\n * Updates notation `do` -\u003e `di`, `ds` -\u003e `dj`\r\n\r\ncloses #987", "tree_diff": [ { "type": "modify", "old_id": "66096bd5d4d465097df8a800cecbd9439aa0fe56", "old_mode": 33188, "old_path": "docs/spec.md", "new_id": "e19a5899ac4d6a7e83a87198eea937444955c4d6", "new_mode": 33188, "new_path": "docs/spec.md" }, { "type": "modify", "old_id": "c80c83865c307b919b853438784ad863daadf5f6", "old_mode": 33188, "old_path": "docs/status.md", "new_id": "b36c8c72579f90a48f6b0ad7da061cf24f2d7942", "new_mode": 33188, "new_path": "docs/status.md" }, { 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