| {"task_id": "torch-000", "category": "tensor_ops", "prompt": "Reshape `x` to shape (4, 6) in row-major order.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [[-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197], [-1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012], [0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469], [1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0]], \"dtype\": \"float32\", \"shape\": [4, 6]}"} | |
| {"task_id": "torch-001", "category": "tensor_ops", "prompt": "Return the transpose of `m` as a contiguous tensor.", "input_data": "{\"m\": \"{\\\"data\\\": [[-1.0, -0.9428571462631226, -0.8857142925262451, -0.8285714387893677, -0.7714285850524902, -0.7142857313156128], [-0.6571428775787354, -0.6000000238418579, -0.5428571701049805, -0.48571425676345825, -0.4285714030265808, -0.37142854928970337], [-0.3142856955528259, -0.2571428418159485, -0.19999998807907104, -0.1428571343421936, -0.08571428060531616, -0.02857142686843872], [0.02857142686843872, 0.08571428060531616, 0.1428571343421936, 0.19999998807907104, 0.2571428418159485, 0.3142856955528259], [0.37142854928970337, 0.4285714030265808, 0.48571425676345825, 0.5428571701049805, 0.6000000238418579, 0.6571428775787354], [0.7142857313156128, 0.7714285850524902, 0.8285714387893677, 0.8857142925262451, 0.9428571462631226, 1.0]], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [6, 6]}\"}", "expected_output": "{\"data\": [[-1.0, -0.6571428775787354, -0.3142856955528259, 0.02857142686843872, 0.37142854928970337, 0.7142857313156128], [-0.9428571462631226, -0.6000000238418579, -0.2571428418159485, 0.08571428060531616, 0.4285714030265808, 0.7714285850524902], [-0.8857142925262451, -0.5428571701049805, -0.19999998807907104, 0.1428571343421936, 0.48571425676345825, 0.8285714387893677], [-0.8285714387893677, -0.48571425676345825, -0.1428571343421936, 0.19999998807907104, 0.5428571701049805, 0.8857142925262451], [-0.7714285850524902, -0.4285714030265808, -0.08571428060531616, 0.2571428418159485, 0.6000000238418579, 0.9428571462631226], [-0.7142857313156128, -0.37142854928970337, -0.02857142686843872, 0.3142856955528259, 0.6571428775787354, 1.0]], \"dtype\": \"float32\", \"shape\": [6, 6]}"} | |
| {"task_id": "torch-002", "category": "tensor_ops", "prompt": "Return the row-wise sum of `b` as a 1-D tensor.", "input_data": "{\"b\": \"{\\\"data\\\": [[0.0, 0.10000000149011612, 0.20000000298023224, 0.30000001192092896, 0.4000000059604645], [0.5, 0.6000000238418579, 0.699999988079071, 0.800000011920929, 0.9000000357627869], [1.0, 1.100000023841858, 1.2000000476837158, 1.3000000715255737, 1.399999976158142], [1.5, 1.600000023841858, 1.7000000476837158, 1.8000000715255737, 1.899999976158142], [2.0, 2.0999999046325684, 2.200000047683716, 2.3000001907348633, 2.4000000953674316], [2.5, 2.5999999046325684, 2.700000047683716, 2.8000001907348633, 2.9000000953674316], [3.0, 3.1000001430511475, 3.200000047683716, 3.3000001907348633, 3.4000000953674316], [3.5, 3.6000001430511475, 3.700000047683716, 3.8000001907348633, 3.9000000953674316]], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [8, 5]}\"}", "expected_output": "{\"data\": [1.0, 3.5, 6.0, 8.5, 11.0, 13.5, 16.0, 18.5], \"dtype\": \"float32\", \"shape\": [8]}"} | |
| {"task_id": "torch-003", "category": "tensor_ops", "prompt": "Return the column-wise mean of `b`.", "input_data": "{\"b\": \"{\\\"data\\\": [[0.0, 0.10000000149011612, 0.20000000298023224, 0.30000001192092896, 0.4000000059604645], [0.5, 0.6000000238418579, 0.699999988079071, 0.800000011920929, 0.9000000357627869], [1.0, 1.100000023841858, 1.2000000476837158, 1.3000000715255737, 1.399999976158142], [1.5, 1.600000023841858, 1.7000000476837158, 1.8000000715255737, 1.899999976158142], [2.0, 2.0999999046325684, 2.200000047683716, 2.3000001907348633, 2.4000000953674316], [2.5, 2.5999999046325684, 2.700000047683716, 2.8000001907348633, 2.9000000953674316], [3.0, 3.1000001430511475, 3.200000047683716, 3.3000001907348633, 3.4000000953674316], [3.5, 3.6000001430511475, 3.700000047683716, 3.8000001907348633, 3.9000000953674316]], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [8, 5]}\"}", "expected_output": "{\"data\": [1.75, 1.8500001430511475, 1.9499999284744263, 2.0500001907348633, 2.1500000953674316], \"dtype\": \"float32\", \"shape\": [5]}"} | |
| {"task_id": "torch-004", "category": "tensor_ops", "prompt": "Clamp `x` to the range [-1, 1].", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], \"dtype\": \"float32\", \"shape\": [24]}"} | |
| {"task_id": "torch-005", "category": "tensor_ops", "prompt": "Return the elements of `x` that are strictly positive, in order.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \"dtype\": \"float32\", \"shape\": [12]}"} | |
| {"task_id": "torch-006", "category": "tensor_ops", "prompt": "Return `x` sorted in descending order (values only).", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [3.0, 2.7391304969787598, 2.4782609939575195, 2.2173912525177, 1.95652174949646, 1.6956522464752197, 1.43478262424469, 1.1739130020141602, 0.9130434989929199, 0.6521739959716797, 0.39130449295043945, 0.13043475151062012, -0.13043475151062012, -0.39130449295043945, -0.6521739959716797, -0.9130434989929199, -1.1739130020141602, -1.43478262424469, -1.6956522464752197, -1.95652174949646, -2.2173912525177, -2.4782609939575195, -2.7391304969787598, -3.0], \"dtype\": \"float32\", \"shape\": [24]}"} | |
| {"task_id": "torch-007", "category": "tensor_ops", "prompt": "Return the indices that sort `x` ascending, as int64.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], \"dtype\": \"int64\", \"shape\": [24]}"} | |
| {"task_id": "torch-008", "category": "tensor_ops", "prompt": "Concatenate `b` with itself along dim 0.", "input_data": "{\"b\": \"{\\\"data\\\": [[0.0, 0.10000000149011612, 0.20000000298023224, 0.30000001192092896, 0.4000000059604645], [0.5, 0.6000000238418579, 0.699999988079071, 0.800000011920929, 0.9000000357627869], [1.0, 1.100000023841858, 1.2000000476837158, 1.3000000715255737, 1.399999976158142], [1.5, 1.600000023841858, 1.7000000476837158, 1.8000000715255737, 1.899999976158142], [2.0, 2.0999999046325684, 2.200000047683716, 2.3000001907348633, 2.4000000953674316], [2.5, 2.5999999046325684, 2.700000047683716, 2.8000001907348633, 2.9000000953674316], [3.0, 3.1000001430511475, 3.200000047683716, 3.3000001907348633, 3.4000000953674316], [3.5, 3.6000001430511475, 3.700000047683716, 3.8000001907348633, 3.9000000953674316]], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [8, 5]}\"}", "expected_output": "{\"data\": [[0.0, 0.10000000149011612, 0.20000000298023224, 0.30000001192092896, 0.4000000059604645], [0.5, 0.6000000238418579, 0.699999988079071, 0.800000011920929, 0.9000000357627869], [1.0, 1.100000023841858, 1.2000000476837158, 1.3000000715255737, 1.399999976158142], [1.5, 1.600000023841858, 1.7000000476837158, 1.8000000715255737, 1.899999976158142], [2.0, 2.0999999046325684, 2.200000047683716, 2.3000001907348633, 2.4000000953674316], [2.5, 2.5999999046325684, 2.700000047683716, 2.8000001907348633, 2.9000000953674316], [3.0, 3.1000001430511475, 3.200000047683716, 3.3000001907348633, 3.4000000953674316], [3.5, 3.6000001430511475, 3.700000047683716, 3.8000001907348633, 3.9000000953674316], [0.0, 0.10000000149011612, 0.20000000298023224, 0.30000001192092896, 0.4000000059604645], [0.5, 0.6000000238418579, 0.699999988079071, 0.800000011920929, 0.9000000357627869], [1.0, 1.100000023841858, 1.2000000476837158, 1.3000000715255737, 1.399999976158142], [1.5, 1.600000023841858, 1.7000000476837158, 1.8000000715255737, 1.899999976158142], [2.0, 2.0999999046325684, 2.200000047683716, 2.3000001907348633, 2.4000000953674316], [2.5, 2.5999999046325684, 2.700000047683716, 2.8000001907348633, 2.9000000953674316], [3.0, 3.1000001430511475, 3.200000047683716, 3.3000001907348633, 3.4000000953674316], [3.5, 3.6000001430511475, 3.700000047683716, 3.8000001907348633, 3.9000000953674316]], \"dtype\": \"float32\", \"shape\": [16, 5]}"} | |
| {"task_id": "torch-009", "category": "tensor_ops", "prompt": "Stack `b` with itself along a NEW leading dimension.", "input_data": "{\"b\": \"{\\\"data\\\": [[0.0, 0.10000000149011612, 0.20000000298023224, 0.30000001192092896, 0.4000000059604645], [0.5, 0.6000000238418579, 0.699999988079071, 0.800000011920929, 0.9000000357627869], [1.0, 1.100000023841858, 1.2000000476837158, 1.3000000715255737, 1.399999976158142], [1.5, 1.600000023841858, 1.7000000476837158, 1.8000000715255737, 1.899999976158142], [2.0, 2.0999999046325684, 2.200000047683716, 2.3000001907348633, 2.4000000953674316], [2.5, 2.5999999046325684, 2.700000047683716, 2.8000001907348633, 2.9000000953674316], [3.0, 3.1000001430511475, 3.200000047683716, 3.3000001907348633, 3.4000000953674316], [3.5, 3.6000001430511475, 3.700000047683716, 3.8000001907348633, 3.9000000953674316]], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [8, 5]}\"}", "expected_output": "{\"data\": [[[0.0, 0.10000000149011612, 0.20000000298023224, 0.30000001192092896, 0.4000000059604645], [0.5, 0.6000000238418579, 0.699999988079071, 0.800000011920929, 0.9000000357627869], [1.0, 1.100000023841858, 1.2000000476837158, 1.3000000715255737, 1.399999976158142], [1.5, 1.600000023841858, 1.7000000476837158, 1.8000000715255737, 1.899999976158142], [2.0, 2.0999999046325684, 2.200000047683716, 2.3000001907348633, 2.4000000953674316], [2.5, 2.5999999046325684, 2.700000047683716, 2.8000001907348633, 2.9000000953674316], [3.0, 3.1000001430511475, 3.200000047683716, 3.3000001907348633, 3.4000000953674316], [3.5, 3.6000001430511475, 3.700000047683716, 3.8000001907348633, 3.9000000953674316]], [[0.0, 0.10000000149011612, 0.20000000298023224, 0.30000001192092896, 0.4000000059604645], [0.5, 0.6000000238418579, 0.699999988079071, 0.800000011920929, 0.9000000357627869], [1.0, 1.100000023841858, 1.2000000476837158, 1.3000000715255737, 1.399999976158142], [1.5, 1.600000023841858, 1.7000000476837158, 1.8000000715255737, 1.899999976158142], [2.0, 2.0999999046325684, 2.200000047683716, 2.3000001907348633, 2.4000000953674316], [2.5, 2.5999999046325684, 2.700000047683716, 2.8000001907348633, 2.9000000953674316], [3.0, 3.1000001430511475, 3.200000047683716, 3.3000001907348633, 3.4000000953674316], [3.5, 3.6000001430511475, 3.700000047683716, 3.8000001907348633, 3.9000000953674316]]], \"dtype\": \"float32\", \"shape\": [2, 8, 5]}"} | |
| {"task_id": "torch-010", "category": "tensor_ops", "prompt": "Return the matrix product of `m` with itself.", "input_data": "{\"m\": \"{\\\"data\\\": [[-1.0, -0.9428571462631226, -0.8857142925262451, -0.8285714387893677, -0.7714285850524902, -0.7142857313156128], [-0.6571428775787354, -0.6000000238418579, -0.5428571701049805, -0.48571425676345825, -0.4285714030265808, -0.37142854928970337], [-0.3142856955528259, -0.2571428418159485, -0.19999998807907104, -0.1428571343421936, -0.08571428060531616, -0.02857142686843872], [0.02857142686843872, 0.08571428060531616, 0.1428571343421936, 0.19999998807907104, 0.2571428418159485, 0.3142856955528259], [0.37142854928970337, 0.4285714030265808, 0.48571425676345825, 0.5428571701049805, 0.6000000238418579, 0.6571428775787354], [0.7142857313156128, 0.7714285850524902, 0.8285714387893677, 0.8857142925262451, 0.9428571462631226, 1.0]], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [6, 6]}\"}", "expected_output": "{\"data\": [[1.077551007270813, 0.7836735844612122, 0.489795982837677, 0.19591835141181946, -0.09795932471752167, -0.3918367624282837], [0.7836736440658569, 0.6073469519615173, 0.43102046847343445, 0.2546938359737396, 0.07836739718914032, -0.09795913100242615], [0.4897959232330322, 0.4310203790664673, 0.3722448945045471, 0.31346938014030457, 0.254693865776062, 0.19591839611530304], [0.19591835141181946, 0.254693865776062, 0.3134693503379822, 0.3722448945045471, 0.4310203790664673, 0.48979589343070984], [-0.09795908629894257, 0.07836742699146271, 0.2546939253807068, 0.4310205280780792, 0.6073470115661621, 0.7836735844612122], [-0.3918367922306061, -0.09795922040939331, 0.19591835141181946, 0.489795982837677, 0.7836734056472778, 1.077551007270813]], \"dtype\": \"float32\", \"shape\": [6, 6]}"} | |
| {"task_id": "torch-011", "category": "tensor_ops", "prompt": "Normalise each row of `b` to unit L2 norm.", "input_data": "{\"b\": \"{\\\"data\\\": [[0.0, 0.10000000149011612, 0.20000000298023224, 0.30000001192092896, 0.4000000059604645], [0.5, 0.6000000238418579, 0.699999988079071, 0.800000011920929, 0.9000000357627869], [1.0, 1.100000023841858, 1.2000000476837158, 1.3000000715255737, 1.399999976158142], [1.5, 1.600000023841858, 1.7000000476837158, 1.8000000715255737, 1.899999976158142], [2.0, 2.0999999046325684, 2.200000047683716, 2.3000001907348633, 2.4000000953674316], [2.5, 2.5999999046325684, 2.700000047683716, 2.8000001907348633, 2.9000000953674316], [3.0, 3.1000001430511475, 3.200000047683716, 3.3000001907348633, 3.4000000953674316], [3.5, 3.6000001430511475, 3.700000047683716, 3.8000001907348633, 3.9000000953674316]], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [8, 5]}\"}", "expected_output": "{\"data\": [[0.0, 0.18257418274879456, 0.3651483654975891, 0.547722578048706, 0.7302967309951782], [0.31311213970184326, 0.3757345974445343, 0.43835699558258057, 0.5009794235229492, 0.5636018514633179], [0.37011659145355225, 0.407128244638443, 0.44413992762565613, 0.4811515808105469, 0.5181632041931152], [0.39324188232421875, 0.4194580018520355, 0.4456741511821747, 0.47189027070999146, 0.4981063902378082], [0.40572041273117065, 0.42600640654563904, 0.4462924599647522, 0.46657851338386536, 0.48686450719833374], [0.41351979970932007, 0.43006059527397156, 0.44660139083862305, 0.4631422162055969, 0.4796830117702484], [0.41885387897491455, 0.43281570076942444, 0.44677749276161194, 0.4607393145561218, 0.47470107674598694], [0.4227311909198761, 0.43480923771858215, 0.4468872547149658, 0.4589653015136719, 0.47104331851005554]], \"dtype\": \"float32\", \"shape\": [8, 5]}"} | |
| {"task_id": "torch-012", "category": "tensor_ops", "prompt": "Add a leading batch dimension to `x` so its shape becomes (1, 24).", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [[-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0]], \"dtype\": \"float32\", \"shape\": [1, 24]}"} | |
| {"task_id": "torch-013", "category": "tensor_ops", "prompt": "Return the cumulative sum of `x` along dim 0.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [-3.0, -5.73913049697876, -8.217391967773438, -10.434782981872559, -12.391304016113281, -14.086956977844238, -15.52173900604248, -16.69565200805664, -17.60869598388672, -18.2608699798584, -18.65217399597168, -18.782609939575195, -18.65217399597168, -18.2608699798584, -17.60869598388672, -16.69565200805664, -15.52173900604248, -14.086956977844238, -12.391304016113281, -10.434782981872559, -8.217391967773438, -5.73913049697876, -3.0, 0.0], \"dtype\": \"float32\", \"shape\": [24]}"} | |
| {"task_id": "torch-014", "category": "tensor_ops", "prompt": "Cast `x` to float64.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \"dtype\": \"float64\", \"shape\": [24]}"} | |
| {"task_id": "torch-015", "category": "tensor_ops", "prompt": "Return the pairwise maximum of `x` and its own reverse.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [3.0, 2.7391304969787598, 2.4782609939575195, 2.2173912525177, 1.95652174949646, 1.6956522464752197, 1.43478262424469, 1.1739130020141602, 0.9130434989929199, 0.6521739959716797, 0.39130449295043945, 0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \"dtype\": \"float32\", \"shape\": [24]}"} | |
| {"task_id": "torch-016", "category": "autograd", "prompt": "Treat `x` as a leaf requiring grad, compute y = (x**2).sum(), backward, and return x.grad.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [-6.0, -5.4782609939575195, -4.956521987915039, -4.4347825050354, -3.91304349899292, -3.3913044929504395, -2.86956524848938, -2.3478260040283203, -1.8260869979858398, -1.3043479919433594, -0.7826089859008789, -0.26086950302124023, 0.26086950302124023, 0.7826089859008789, 1.3043479919433594, 1.8260869979858398, 2.3478260040283203, 2.86956524848938, 3.3913044929504395, 3.91304349899292, 4.4347825050354, 4.956521987915039, 5.4782609939575195, 6.0], \"dtype\": \"float32\", \"shape\": [24]}"} | |
| {"task_id": "torch-017", "category": "autograd", "prompt": "With `x` requiring grad, compute y = (x.sin() * x).sum(), backward, and return x.grad.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [2.828857421875, 2.128587245941162, 1.3369883298873901, 0.5377766489982605, -0.19042092561721802, -0.7810530662536621, -1.18531334400177, -1.3760414123535156, -1.349548101425171, -1.1252415180206299, -0.743121325969696, -0.2593919634819031, 0.2593919634819031, 0.743121325969696, 1.1252415180206299, 1.349548101425171, 1.3760414123535156, 1.18531334400177, 0.7810530662536621, 0.19042092561721802, -0.5377766489982605, -1.3369883298873901, -2.128587245941162, -2.828857421875], \"dtype\": \"float32\", \"shape\": [24]}"} | |
| {"task_id": "torch-018", "category": "autograd", "prompt": "With `b` requiring grad, compute the mean of b.exp() clamped to a max of 10, backward, and return b.grad.", "input_data": "{\"b\": \"{\\\"data\\\": [[0.0, 0.10000000149011612, 0.20000000298023224, 0.30000001192092896, 0.4000000059604645], [0.5, 0.6000000238418579, 0.699999988079071, 0.800000011920929, 0.9000000357627869], [1.0, 1.100000023841858, 1.2000000476837158, 1.3000000715255737, 1.399999976158142], [1.5, 1.600000023841858, 1.7000000476837158, 1.8000000715255737, 1.899999976158142], [2.0, 2.0999999046325684, 2.200000047683716, 2.3000001907348633, 2.4000000953674316], [2.5, 2.5999999046325684, 2.700000047683716, 2.8000001907348633, 2.9000000953674316], [3.0, 3.1000001430511475, 3.200000047683716, 3.3000001907348633, 3.4000000953674316], [3.5, 3.6000001430511475, 3.700000047683716, 3.8000001907348633, 3.9000000953674316]], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [8, 5]}\"}", "expected_output": "{\"data\": [[0.02500000037252903, 0.027629274874925613, 0.030535070225596428, 0.033746469765901566, 0.0372956208884716], [0.04121803119778633, 0.045552972704172134, 0.050343818962574005, 0.055638521909713745, 0.06149008497595787], [0.06795704364776611, 0.07510415464639664, 0.08300292491912842, 0.09173242747783661, 0.10138000547885895], [0.11204222589731216, 0.1238258108496666, 0.13684868812561035, 0.15124119818210602, 0.16714736819267273], [0.18472640216350555, 0.20415423810482025, 0.22562535107135773, 0.24935460090637207, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0]], \"dtype\": \"float32\", \"shape\": [8, 5]}"} | |
| {"task_id": "torch-019", "category": "autograd", "prompt": "Return the gradient of (x**3).sum() with respect to x, using torch.autograd.grad rather than backward.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [27.0, 22.508506774902344, 18.42533302307129, 14.750471115112305, 11.483932495117188, 8.625709533691406, 6.175803184509277, 4.134215354919434, 2.5009453296661377, 1.275992751121521, 0.4593576192855835, 0.0510396733880043, 0.0510396733880043, 0.4593576192855835, 1.275992751121521, 2.5009453296661377, 4.134215354919434, 6.175803184509277, 8.625709533691406, 11.483932495117188, 14.750471115112305, 18.42533302307129, 22.508506774902344, 27.0], \"dtype\": \"float32\", \"shape\": [24]}"} | |
| {"task_id": "torch-020", "category": "autograd", "prompt": "With `x` requiring grad, compute y = (x**2).sum() but DETACH x before squaring, then return a tensor of zeros shaped like x if no gradient flows (i.e. return the grad, or zeros when it is None).", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], \"dtype\": \"float32\", \"shape\": [24]}"} | |
| {"task_id": "torch-021", "category": "autograd", "prompt": "Compute the second derivative of (x**3).sum() with respect to x (create_graph=True), returning d2y/dx2.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [-18.0, -16.434783935546875, -14.869565963745117, -13.30434799194336, -11.739130020141602, -10.173913955688477, -8.608695983886719, -7.043478012084961, -5.4782609939575195, -3.913043975830078, -2.3478269577026367, -0.7826085090637207, 0.7826085090637207, 2.3478269577026367, 3.913043975830078, 5.4782609939575195, 7.043478012084961, 8.608695983886719, 10.173913955688477, 11.739130020141602, 13.30434799194336, 14.869565963745117, 16.434783935546875, 18.0], \"dtype\": \"float32\", \"shape\": [24]}"} | |
| {"task_id": "torch-022", "category": "nn_modules", "prompt": "Apply a Linear(5, 3) layer to `b` whose weight is arange(15).reshape(3,5)/10 (float32) and whose bias is tensor([0.1, 0.2, 0.3]). Return the output.", "input_data": "{\"b\": \"{\\\"data\\\": [[0.0, 0.10000000149011612, 0.20000000298023224, 0.30000001192092896, 0.4000000059604645], [0.5, 0.6000000238418579, 0.699999988079071, 0.800000011920929, 0.9000000357627869], [1.0, 1.100000023841858, 1.2000000476837158, 1.3000000715255737, 1.399999976158142], [1.5, 1.600000023841858, 1.7000000476837158, 1.8000000715255737, 1.899999976158142], [2.0, 2.0999999046325684, 2.200000047683716, 2.3000001907348633, 2.4000000953674316], [2.5, 2.5999999046325684, 2.700000047683716, 2.8000001907348633, 2.9000000953674316], [3.0, 3.1000001430511475, 3.200000047683716, 3.3000001907348633, 3.4000000953674316], [3.5, 3.6000001430511475, 3.700000047683716, 3.8000001907348633, 3.9000000953674316]], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [8, 5]}\"}", "expected_output": "{\"data\": [[0.4000000059604645, 0.9999999403953552, 1.5999999046325684], [0.9000000357627869, 2.75, 4.600000381469727], [1.4000000953674316, 4.5, 7.600000381469727], [1.899999976158142, 6.25, 10.600000381469727], [2.4000000953674316, 8.0, 13.600001335144043], [2.9000000953674316, 9.75, 16.600000381469727], [3.4000000953674316, 11.500000953674316, 19.599998474121094], [3.9000000953674316, 13.25, 22.599998474121094]], \"dtype\": \"float32\", \"shape\": [8, 3]}"} | |
| {"task_id": "torch-023", "category": "nn_modules", "prompt": "Apply torch.nn.functional.relu to `x`.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \"dtype\": \"float32\", \"shape\": [24]}"} | |
| {"task_id": "torch-024", "category": "nn_modules", "prompt": "Apply torch.nn.functional.gelu to `x` with the default approximation.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [-0.004049867391586304, -0.0084369583055377, -0.016359606757760048, -0.029487209394574165, -0.04930786415934563, -0.07626339793205261, -0.10857655107975006, -0.1411219835281372, -0.1649046540260315, -0.16770288348197937, -0.13609030842781067, -0.05844927206635475, 0.07198548316955566, 0.2552141845226288, 0.4844711124897003, 0.7481388449668884, 1.032791018486023, 1.326206088066101, 1.6193888187408447, 1.9072139263153076, 2.187904119491577, 2.4619014263153076, 2.7306935787200928, 2.995950222015381], \"dtype\": \"float32\", \"shape\": [24]}"} | |
| {"task_id": "torch-025", "category": "nn_modules", "prompt": "Apply softmax along dim 1 to `l`.", "input_data": "{\"l\": \"{\\\"data\\\": [[-2.0, -1.6363636255264282, -1.2727272510528564, -0.9090908765792847], [-0.5454545021057129, -0.1818181276321411, 0.1818181276321411, 0.5454545021057129], [0.9090908765792847, 1.2727272510528564, 1.6363636255264282, 2.0]], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [3, 4]}\"}", "expected_output": "{\"data\": [[0.13360126316547394, 0.19219224154949188, 0.2764783501625061, 0.39772820472717285], [0.13360127806663513, 0.19219225645065308, 0.2764783203601837, 0.39772817492485046], [0.13360126316547394, 0.19219224154949188, 0.2764783501625061, 0.39772820472717285]], \"dtype\": \"float32\", \"shape\": [3, 4]}"} | |
| {"task_id": "torch-026", "category": "nn_modules", "prompt": "Apply log_softmax along dim 1 to `l`.", "input_data": "{\"l\": \"{\\\"data\\\": [[-2.0, -1.6363636255264282, -1.2727272510528564, -0.9090908765792847], [-0.5454545021057129, -0.1818181276321411, 0.1818181276321411, 0.5454545021057129], [0.9090908765792847, 1.2727272510528564, 1.6363636255264282, 2.0]], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [3, 4]}\"}", "expected_output": "{\"data\": [[-2.0128955841064453, -1.649259090423584, -1.2856228351593018, -0.9219864010810852], [-2.0128955841064453, -1.649259090423584, -1.2856228351593018, -0.9219865202903748], [-2.0128955841064453, -1.649259090423584, -1.2856228351593018, -0.9219864010810852]], \"dtype\": \"float32\", \"shape\": [3, 4]}"} | |
| {"task_id": "torch-027", "category": "nn_modules", "prompt": "Apply LayerNorm over the last dimension of `b` with eps=1e-5, no learnable affine parameters.", "input_data": "{\"b\": \"{\\\"data\\\": [[0.0, 0.10000000149011612, 0.20000000298023224, 0.30000001192092896, 0.4000000059604645], [0.5, 0.6000000238418579, 0.699999988079071, 0.800000011920929, 0.9000000357627869], [1.0, 1.100000023841858, 1.2000000476837158, 1.3000000715255737, 1.399999976158142], [1.5, 1.600000023841858, 1.7000000476837158, 1.8000000715255737, 1.899999976158142], [2.0, 2.0999999046325684, 2.200000047683716, 2.3000001907348633, 2.4000000953674316], [2.5, 2.5999999046325684, 2.700000047683716, 2.8000001907348633, 2.9000000953674316], [3.0, 3.1000001430511475, 3.200000047683716, 3.3000001907348633, 3.4000000953674316], [3.5, 3.6000001430511475, 3.700000047683716, 3.8000001907348633, 3.9000000953674316]], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [8, 5]}\"}", "expected_output": "{\"data\": [[-1.4138602018356323, -0.7069301009178162, 0.0, 0.7069301009178162, 1.4138602018356323], [-1.4138604402542114, -0.7069302201271057, -4.213631541460927e-07, 0.7069298028945923, 1.4138600826263428], [-1.4138604402542114, -0.7069302201271057, 0.0, 0.7069302201271057, 1.4138596057891846], [-1.4138604402542114, -0.7069302201271057, 0.0, 0.7069302201271057, 1.4138596057891846], [-1.4138597249984741, -0.7069306969642639, 0.0, 0.7069306969642639, 1.4138597249984741], [-1.4138597249984741, -0.7069306969642639, 0.0, 0.7069306969642639, 1.4138597249984741], [-1.4138604402542114, -0.7069293856620789, 0.0, 0.7069310545921326, 1.4138604402542114], [-1.4138604402542114, -0.7069293856620789, 0.0, 0.7069310545921326, 1.4138604402542114]], \"dtype\": \"float32\", \"shape\": [8, 5]}"} | |
| {"task_id": "torch-028", "category": "nn_modules", "prompt": "Apply 2x2 max pooling with stride 2 to the NCHW batch `i`.", "input_data": "{\"i\": \"{\\\"data\\\": [[[[-2.0, -1.9895561933517456, -1.9791122674942017, -1.9686684608459473, -1.9582245349884033, -1.947780728340149, -1.937336802482605, -1.9268929958343506], [-1.9164490699768066, -1.9060052633285522, -1.8955613374710083, -1.885117530822754, -1.87467360496521, -1.8642297983169556, -1.8537858724594116, -1.8433420658111572], [-1.8328981399536133, -1.8224543333053589, -1.812010407447815, -1.8015666007995605, -1.7911226749420166, -1.7806788682937622, -1.7702349424362183, -1.7597911357879639], [-1.74934720993042, -1.7389034032821655, -1.7284595966339111, -1.7180156707763672, -1.7075717449188232, -1.6971279382705688, -1.6866841316223145, -1.6762402057647705], [-1.6657962799072266, -1.6553524732589722, -1.6449086666107178, -1.6344647407531738, -1.6240208148956299, -1.6135770082473755, -1.603133201599121, -1.5926892757415771], [-1.5822453498840332, -1.5718015432357788, -1.5613577365875244, -1.5509138107299805, -1.540470004081726, -1.5300260782241821, -1.5195822715759277, -1.5091383457183838], [-1.4986945390701294, -1.488250732421875, -1.477806806564331, -1.467362880706787, -1.4569190740585327, -1.4464752674102783, -1.4360313415527344, -1.4255874156951904], [-1.415143609046936, -1.4046998023986816, -1.3942558765411377, -1.3838119506835938, -1.3733681440353394, -1.362924337387085, -1.352480411529541, -1.342036485671997]], [[-1.3315926790237427, -1.3211488723754883, -1.3107049465179443, -1.3002610206604004, -1.289817214012146, -1.2793734073638916, -1.2689294815063477, -1.2584855556488037], [-1.2480417490005493, -1.237597942352295, -1.227154016494751, -1.216710090637207, -1.2062662839889526, -1.1958224773406982, -1.1853785514831543, -1.1749346256256104], [-1.164490818977356, -1.1540470123291016, -1.1436030864715576, -1.1331592798233032, -1.1227154731750488, -1.1122715473175049, -1.101827621459961, -1.0913838148117065], [-1.0809400081634521, -1.0704960823059082, -1.0600521564483643, -1.0496083498001099, -1.0391645431518555, -1.0287206172943115, -1.0182766914367676, -1.0078328847885132], [-0.9973890781402588, -0.9869451522827148, -0.9765013456344604, -0.9660574197769165, -0.9556136131286621, -0.9451696872711182, -0.9347258806228638, -0.9242819547653198], [-0.9138381481170654, -0.9033942222595215, -0.8929504156112671, -0.8825064897537231, -0.8720626831054688, -0.8616187572479248, -0.8511749505996704, -0.8407310247421265], [-0.8302872180938721, -0.8198432922363281, -0.8093994855880737, -0.7989555597305298, -0.7885117530822754, -0.7780678272247314, -0.767624020576477, -0.7571800947189331], [-0.7467362880706787, -0.7362923622131348, -0.7258485555648804, -0.7154046297073364, -0.704960823059082, -0.6945168972015381, -0.6840730905532837, -0.6736291646957397]], [[-0.6631853580474854, -0.652741551399231, -0.642297625541687, -0.6318538188934326, -0.6214098930358887, -0.6109660863876343, -0.6005221605300903, -0.5900783538818359], [-0.579634428024292, -0.5691906213760376, -0.5587466955184937, -0.5483028888702393, -0.5378589630126953, -0.5274151563644409, -0.516971230506897, -0.5065274238586426], [-0.49608349800109863, -0.48563969135284424, -0.4751957654953003, -0.4647519588470459, -0.45430803298950195, -0.44386422634124756, -0.4334203004837036, -0.4229764938354492], [-0.4125325679779053, -0.4020887613296509, -0.39164483547210693, -0.38120102882385254, -0.3707571029663086, -0.3603132963180542, -0.34986937046051025, -0.33942556381225586], [-0.3289816379547119, -0.3185378313064575, -0.3080940246582031, -0.2976500988006592, -0.2872062921524048, -0.27676236629486084, -0.26631855964660645, -0.2558746337890625], [-0.2454308271408081, -0.23498690128326416, -0.22454309463500977, -0.21409916877746582, -0.20365536212921143, -0.19321143627166748, -0.18276762962341309, -0.17232370376586914], [-0.16187989711761475, -0.1514359712600708, -0.1409921646118164, -0.13054823875427246, -0.12010443210601807, -0.10966050624847412, -0.09921669960021973, -0.08877277374267578], [-0.07832896709442139, -0.06788504123687744, -0.05744123458862305, -0.0469973087310791, -0.03655350208282471, -0.02610957622528076, -0.015665769577026367, -0.005221843719482422]]], [[[0.005221843719482422, 0.015665769577026367, 0.02610957622528076, 0.03655350208282471, 0.0469973087310791, 0.05744123458862305, 0.06788504123687744, 0.07832896709442139], [0.08877277374267578, 0.09921669960021973, 0.10966050624847412, 0.12010443210601807, 0.13054823875427246, 0.1409921646118164, 0.1514359712600708, 0.16187989711761475], [0.17232370376586914, 0.18276762962341309, 0.19321143627166748, 0.20365536212921143, 0.21409916877746582, 0.22454309463500977, 0.23498690128326416, 0.2454308271408081], [0.2558746337890625, 0.26631855964660645, 0.27676236629486084, 0.2872062921524048, 0.2976500988006592, 0.3080940246582031, 0.3185378313064575, 0.3289816379547119], [0.33942556381225586, 0.34986937046051025, 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| {"task_id": "torch-029", "category": "nn_modules", "prompt": "Apply 2x2 average pooling with stride 2 to the NCHW batch `i`.", "input_data": "{\"i\": \"{\\\"data\\\": [[[[-2.0, -1.9895561933517456, -1.9791122674942017, -1.9686684608459473, -1.9582245349884033, -1.947780728340149, -1.937336802482605, -1.9268929958343506], [-1.9164490699768066, -1.9060052633285522, -1.8955613374710083, -1.885117530822754, -1.87467360496521, -1.8642297983169556, -1.8537858724594116, -1.8433420658111572], [-1.8328981399536133, -1.8224543333053589, -1.812010407447815, -1.8015666007995605, -1.7911226749420166, -1.7806788682937622, -1.7702349424362183, -1.7597911357879639], [-1.74934720993042, -1.7389034032821655, -1.7284595966339111, -1.7180156707763672, -1.7075717449188232, -1.6971279382705688, -1.6866841316223145, -1.6762402057647705], [-1.6657962799072266, -1.6553524732589722, -1.6449086666107178, -1.6344647407531738, -1.6240208148956299, -1.6135770082473755, -1.603133201599121, -1.5926892757415771], 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| {"task_id": "torch-030", "category": "nn_modules", "prompt": "Apply a Conv2d(3, 2, kernel_size=3) to `i` with weight filled with 0.01 and bias filled with zero, no padding. Return the output.", "input_data": "{\"i\": \"{\\\"data\\\": [[[[-2.0, -1.9895561933517456, -1.9791122674942017, -1.9686684608459473, -1.9582245349884033, -1.947780728340149, -1.937336802482605, -1.9268929958343506], [-1.9164490699768066, -1.9060052633285522, -1.8955613374710083, -1.885117530822754, -1.87467360496521, -1.8642297983169556, -1.8537858724594116, -1.8433420658111572], [-1.8328981399536133, -1.8224543333053589, -1.812010407447815, -1.8015666007995605, -1.7911226749420166, -1.7806788682937622, -1.7702349424362183, -1.7597911357879639], [-1.74934720993042, -1.7389034032821655, -1.7284595966339111, -1.7180156707763672, -1.7075717449188232, -1.6971279382705688, -1.6866841316223145, -1.6762402057647705], [-1.6657962799072266, -1.6553524732589722, -1.6449086666107178, -1.6344647407531738, -1.6240208148956299, -1.6135770082473755, -1.603133201599121, -1.5926892757415771], [-1.5822453498840332, -1.5718015432357788, -1.5613577365875244, -1.5509138107299805, -1.540470004081726, 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| {"task_id": "torch-031", "category": "nn_modules", "prompt": "Flatten `i` from dim 1 onward.", "input_data": "{\"i\": \"{\\\"data\\\": [[[[-2.0, -1.9895561933517456, -1.9791122674942017, -1.9686684608459473, -1.9582245349884033, -1.947780728340149, -1.937336802482605, -1.9268929958343506], [-1.9164490699768066, -1.9060052633285522, -1.8955613374710083, -1.885117530822754, -1.87467360496521, -1.8642297983169556, -1.8537858724594116, -1.8433420658111572], [-1.8328981399536133, -1.8224543333053589, -1.812010407447815, -1.8015666007995605, -1.7911226749420166, -1.7806788682937622, -1.7702349424362183, -1.7597911357879639], [-1.74934720993042, -1.7389034032821655, -1.7284595966339111, -1.7180156707763672, -1.7075717449188232, -1.6971279382705688, -1.6866841316223145, -1.6762402057647705], [-1.6657962799072266, -1.6553524732589722, -1.6449086666107178, -1.6344647407531738, -1.6240208148956299, -1.6135770082473755, -1.603133201599121, -1.5926892757415771], [-1.5822453498840332, 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1.8955613374710083, 1.9060052633285522, 1.9164490699768066, 1.9268929958343506, 1.937336802482605, 1.947780728340149, 1.9582245349884033, 1.9686684608459473, 1.9791122674942017, 1.9895561933517456, 2.0]], \"dtype\": \"float32\", \"shape\": [2, 192]}"} | |
| {"task_id": "torch-032", "category": "nn_modules", "prompt": "Apply an Embedding(5, 4) to the index tensor `s`, where the embedding weight is arange(20).reshape(5,4) as float32. Return the output.", "input_data": "{\"s\": \"{\\\"data\\\": [[0, 1, 2, 3, 4, 0], [1, 2, 3, 4, 0, 1], [2, 3, 4, 0, 1, 2], [3, 4, 0, 1, 2, 3]], \\\"dtype\\\": \\\"int64\\\", \\\"shape\\\": [4, 6]}\"}", "expected_output": "{\"data\": [[[0.0, 1.0, 2.0, 3.0], [4.0, 5.0, 6.0, 7.0], [8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0], [16.0, 17.0, 18.0, 19.0], [0.0, 1.0, 2.0, 3.0]], [[4.0, 5.0, 6.0, 7.0], [8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0], [16.0, 17.0, 18.0, 19.0], [0.0, 1.0, 2.0, 3.0], [4.0, 5.0, 6.0, 7.0]], [[8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0], [16.0, 17.0, 18.0, 19.0], [0.0, 1.0, 2.0, 3.0], [4.0, 5.0, 6.0, 7.0], [8.0, 9.0, 10.0, 11.0]], [[12.0, 13.0, 14.0, 15.0], [16.0, 17.0, 18.0, 19.0], [0.0, 1.0, 2.0, 3.0], [4.0, 5.0, 6.0, 7.0], [8.0, 9.0, 10.0, 11.0], [12.0, 13.0, 14.0, 15.0]]], \"dtype\": \"float32\", \"shape\": [4, 6, 4]}"} | |
| {"task_id": "torch-033", "category": "nn_modules", "prompt": "Apply sigmoid to `x`.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [0.04742587357759476, 0.06070346012711525, 0.07739628106355667, 0.09819959104061127, 0.1238439679145813, 0.15503396093845367, 0.19235458970069885, 0.2361484318971634, 0.2863774299621582, 0.3424997925758362, 0.40340331196784973, 0.4674374461174011, 0.5325625538825989, 0.5965967178344727, 0.657500147819519, 0.7136225700378418, 0.7638515830039978, 0.8076453804969788, 0.8449659943580627, 0.8761560320854187, 0.9018004536628723, 0.9226036667823792, 0.9392966032028198, 0.9525741338729858], \"dtype\": \"float32\", \"shape\": [24]}"} | |
| {"task_id": "torch-034", "category": "losses", "prompt": "Return the mean-squared-error between `b` and a tensor of zeros of the same shape, as a 1-element tensor.", "input_data": "{\"b\": \"{\\\"data\\\": [[0.0, 0.10000000149011612, 0.20000000298023224, 0.30000001192092896, 0.4000000059604645], [0.5, 0.6000000238418579, 0.699999988079071, 0.800000011920929, 0.9000000357627869], [1.0, 1.100000023841858, 1.2000000476837158, 1.3000000715255737, 1.399999976158142], [1.5, 1.600000023841858, 1.7000000476837158, 1.8000000715255737, 1.899999976158142], [2.0, 2.0999999046325684, 2.200000047683716, 2.3000001907348633, 2.4000000953674316], [2.5, 2.5999999046325684, 2.700000047683716, 2.8000001907348633, 2.9000000953674316], [3.0, 3.1000001430511475, 3.200000047683716, 3.3000001907348633, 3.4000000953674316], [3.5, 3.6000001430511475, 3.700000047683716, 3.8000001907348633, 3.9000000953674316]], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [8, 5]}\"}", "expected_output": "{\"data\": [5.135000705718994], \"dtype\": \"float32\", \"shape\": [1]}"} | |
| {"task_id": "torch-035", "category": "losses", "prompt": "Return the cross-entropy loss of logits `l` against class targets [0, 3, 1] with mean reduction, as a 1-element tensor.", "input_data": "{\"l\": \"{\\\"data\\\": [[-2.0, -1.6363636255264282, -1.2727272510528564, -0.9090908765792847], [-0.5454545021057129, -0.1818181276321411, 0.1818181276321411, 0.5454545021057129], [0.9090908765792847, 1.2727272510528564, 1.6363636255264282, 2.0]], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [3, 4]}\"}", "expected_output": "{\"data\": [1.5280470848083496], \"dtype\": \"float32\", \"shape\": [1]}"} | |
| {"task_id": "torch-036", "category": "losses", "prompt": "Return the PER-SAMPLE cross-entropy of logits `l` against targets [0, 3, 1] (reduction='none').", "input_data": "{\"l\": \"{\\\"data\\\": [[-2.0, -1.6363636255264282, -1.2727272510528564, -0.9090908765792847], [-0.5454545021057129, -0.1818181276321411, 0.1818181276321411, 0.5454545021057129], [0.9090908765792847, 1.2727272510528564, 1.6363636255264282, 2.0]], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [3, 4]}\"}", "expected_output": "{\"data\": [2.0128955841064453, 0.9219865202903748, 1.649259090423584], \"dtype\": \"float32\", \"shape\": [3]}"} | |
| {"task_id": "torch-037", "category": "losses", "prompt": "Return the binary cross-entropy between sigmoid(`x`) and a target of all 0.5, mean reduction, as a 1-element tensor.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [1.0311164855957031], \"dtype\": \"float32\", \"shape\": [1]}"} | |
| {"task_id": "torch-038", "category": "losses", "prompt": "Return the L1 loss between `b` and b.roll(1, 0), mean reduction, as a 1-element tensor.", "input_data": "{\"b\": \"{\\\"data\\\": [[0.0, 0.10000000149011612, 0.20000000298023224, 0.30000001192092896, 0.4000000059604645], [0.5, 0.6000000238418579, 0.699999988079071, 0.800000011920929, 0.9000000357627869], [1.0, 1.100000023841858, 1.2000000476837158, 1.3000000715255737, 1.399999976158142], [1.5, 1.600000023841858, 1.7000000476837158, 1.8000000715255737, 1.899999976158142], [2.0, 2.0999999046325684, 2.200000047683716, 2.3000001907348633, 2.4000000953674316], [2.5, 2.5999999046325684, 2.700000047683716, 2.8000001907348633, 2.9000000953674316], [3.0, 3.1000001430511475, 3.200000047683716, 3.3000001907348633, 3.4000000953674316], [3.5, 3.6000001430511475, 3.700000047683716, 3.8000001907348633, 3.9000000953674316]], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [8, 5]}\"}", "expected_output": "{\"data\": [0.875], \"dtype\": \"float32\", \"shape\": [1]}"} | |
| {"task_id": "torch-039", "category": "losses", "prompt": "Return the smooth L1 loss (beta=1.0) between `x` and x.flip(0), mean reduction, as a 1-element tensor.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [2.6551668643951416], \"dtype\": \"float32\", \"shape\": [1]}"} | |
| {"task_id": "torch-040", "category": "optim", "prompt": "Take `x` as a parameter, run ONE SGD step with lr=0.1 on the loss (x**2).sum(), and return the updated parameter values.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [-2.4000000953674316, -2.1913044452667236, -1.9826087951660156, -1.773913025856018, -1.56521737575531, -1.3565218448638916, -1.147826075553894, -0.939130425453186, -0.730434775352478, -0.5217391848564148, -0.31304359436035156, -0.10434780269861221, 0.10434780269861221, 0.31304359436035156, 0.5217391848564148, 0.730434775352478, 0.939130425453186, 1.147826075553894, 1.3565218448638916, 1.56521737575531, 1.773913025856018, 1.9826087951660156, 2.1913044452667236, 2.4000000953674316], \"dtype\": \"float32\", \"shape\": [24]}"} | |
| {"task_id": "torch-041", "category": "optim", "prompt": "Take `x` as a parameter, run ONE SGD step with lr=0.1 and momentum=0.9 on the loss (x**2).sum(), and return the updated parameter values.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [-2.4000000953674316, -2.1913044452667236, -1.9826087951660156, -1.773913025856018, -1.56521737575531, -1.3565218448638916, -1.147826075553894, -0.939130425453186, -0.730434775352478, -0.5217391848564148, -0.31304359436035156, -0.10434780269861221, 0.10434780269861221, 0.31304359436035156, 0.5217391848564148, 0.730434775352478, 0.939130425453186, 1.147826075553894, 1.3565218448638916, 1.56521737575531, 1.773913025856018, 1.9826087951660156, 2.1913044452667236, 2.4000000953674316], \"dtype\": \"float32\", \"shape\": [24]}"} | |
| {"task_id": "torch-042", "category": "optim", "prompt": "Take `x` as a parameter, run ONE Adam step with lr=0.1 (default betas/eps) on the loss (x**2).sum(), and return the updated parameter values.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [-2.9000000953674316, -2.6391305923461914, -2.378261089324951, -2.117391347885132, -1.856521725654602, -1.5956522226333618, -1.334782600402832, -1.0739129781723022, -0.813043475151062, -0.5521739721298218, -0.29130449891090393, -0.030434750020503998, 0.030434750020503998, 0.29130449891090393, 0.5521739721298218, 0.813043475151062, 1.0739129781723022, 1.334782600402832, 1.5956522226333618, 1.856521725654602, 2.117391347885132, 2.378261089324951, 2.6391305923461914, 2.9000000953674316], \"dtype\": \"float32\", \"shape\": [24]}"} | |
| {"task_id": "torch-043", "category": "optim", "prompt": "Take `x` as a parameter, run THREE SGD steps with lr=0.05 on the loss (x**2).sum(), and return the final parameter values.", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [-2.187000036239624, -1.9968260526657104, -1.806652307510376, -1.6164782047271729, -1.4263043403625488, -1.2361304759979248, -1.0459564924240112, -0.8557825684547424, -0.6656086444854736, -0.4754348397254944, -0.28526097536087036, -0.09508693218231201, 0.09508693218231201, 0.28526097536087036, 0.4754348397254944, 0.6656086444854736, 0.8557825684547424, 1.0459564924240112, 1.2361304759979248, 1.4263043403625488, 1.6164782047271729, 1.806652307510376, 1.9968260526657104, 2.187000036239624], \"dtype\": \"float32\", \"shape\": [24]}"} | |
| {"task_id": "torch-044", "category": "optim", "prompt": "Take `x` as a parameter and return the gradient after clipping the global grad-norm to 1.0, for the loss (x**2).sum().", "input_data": "{\"x\": \"{\\\"data\\\": [-3.0, -2.7391304969787598, -2.4782609939575195, -2.2173912525177, -1.95652174949646, -1.6956522464752197, -1.43478262424469, -1.1739130020141602, -0.9130434989929199, -0.6521739959716797, -0.39130449295043945, -0.13043475151062012, 0.13043475151062012, 0.39130449295043945, 0.6521739959716797, 0.9130434989929199, 1.1739130020141602, 1.43478262424469, 1.6956522464752197, 1.95652174949646, 2.2173912525177, 2.4782609939575195, 2.7391304969787598, 3.0], \\\"dtype\\\": \\\"float32\\\", \\\"shape\\\": [24]}\"}", "expected_output": "{\"data\": [-0.3391164541244507, -0.30962806940078735, -0.280139684677124, -0.2506512701511383, -0.22116290032863617, -0.19167451560497284, -0.1621861308813095, -0.13269773125648499, -0.10320935398340225, -0.07372097671031952, -0.04423259571194649, -0.014744189567863941, 0.014744189567863941, 0.04423259571194649, 0.07372097671031952, 0.10320935398340225, 0.13269773125648499, 0.1621861308813095, 0.19167451560497284, 0.22116290032863617, 0.2506512701511383, 0.280139684677124, 0.30962806940078735, 0.3391164541244507], \"dtype\": \"float32\", \"shape\": [24]}"} | |