{"push_id":2406070803,"size":10,"distinct_size":2,"ref":"refs/heads/jit_trace_input","head":"0783ba1fc084852e34b4dc3e8ec254f9ff9c00c6","before":"bee5af98053e7618f10644ac2c750823619f5eba","commits":[{"sha":"c66111e79b1790b54493b4dea68549dd60fcf471","author":{"name":"Adam Paszke","email":"719f6185a4f8b9037bc614f3501bfa5a74853e02@gmail.com"},"message":"Desugar torch.* and F.* functions in JIT script (#5784)","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/c66111e79b1790b54493b4dea68549dd60fcf471"},{"sha":"f5f6258288da2d52a1414367abe81e369a0845eb","author":{"name":"Myle Ott","email":"bf7eeab55f889b9e61a80bdf224f42cfcdc4a5a5@fb.com"},"message":"Enable additional tensor types in Gloo backend (#5483)","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/f5f6258288da2d52a1414367abe81e369a0845eb"},{"sha":"a24d4b74541c394b9af54ad420d664fb0205f7fb","author":{"name":"Joan Puigcerver","email":"150053657cd7851c790728dfe5e1d8764e2c1e65@gmail.com"},"message":"Fix compilation with CUDA < 8.0 (#5621)\n\n* Compile with CUDA 7.5 and GCC > 4.9\r\n\r\n* Removed static keyword from device constants.","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/a24d4b74541c394b9af54ad420d664fb0205f7fb"},{"sha":"7a5fc2fa2280a5e63f5db7f3d9bf7255487a98be","author":{"name":"peterjc123","email":"e4cb272c5c08ddadb0df1acf265028bc76d7c589@163.com"},"message":"Fix undefined '__func__' for CUDA 8 on Windows (#5803)","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/7a5fc2fa2280a5e63f5db7f3d9bf7255487a98be"},{"sha":"b499332aafafd3cb11f55d32988f898a3d3d00bb","author":{"name":"CNC","email":"ad4e6537691afefb20e5e2469f2e497a1e70b019@gmail.com"},"message":"fixed a message typo in ATen CMakeLists.txt (#5802)","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/b499332aafafd3cb11f55d32988f898a3d3d00bb"},{"sha":"82777815f8013f881bd8f40171b29de92fa3c360","author":{"name":"Richard Zou","email":"af1414e1abe6c6e5a6311fe863b5f5906bb7f2fc@users.noreply.github.com"},"message":"Fix bmm memory leak (#5744)\n\nFixes #5611.\r\n\r\nTHCTensor_(baddbmm) assumes that newContiguous will always return a new tensor (this is a bad assumption). At the end of the function, tensors are freed if tensor_new != tensor_old. As a result, some tensors aren't freed if they were initially contiguous and newContiguous is called on them.\r\n\r\nTest Plan\r\ncode reading\r\nrun the following (from the #5611 bug report) and assert that the memory doesn't leak anymore\r\nimport subprocess\r\nimport torch\r\nfrom torch.autograd import Variable\r\n\r\n# This is from https://discuss.pytorch.org/t/access-gpu-memory-usage-in-pytorch/3192/4\r\ndef get_gpu_memory_map():\r\n    \"\"\"Get the current gpu usage.\r\n\r\n    Returns\r\n    -------\r\n    usage: dict\r\n        Keys are device ids as integers.\r\n        Values are memory usage as integers in MB.\r\n    \"\"\"\r\n    result = subprocess.check_output(\r\n        [\r\n            'nvidia-smi', '--query-gpu=memory.used',\r\n            '--format=csv,nounits,noheader'\r\n        ], encoding='utf-8')\r\n    # Convert lines into a dictionary\r\n    gpu_memory = [int(x) for x in result.strip().split('\\n')]\r\n    gpu_memory_map = dict(zip(range(len(gpu_memory)), gpu_memory))\r\n    return gpu_memory_map\r\n\r\nl, m, n = 1, 9, 1\r\nw = torch.nn.Parameter(torch.Tensor(1024, 2, l, m).cuda())\r\nfor i in range(10000):\r\n    a = Variable(torch.Tensor(1024, 2, m, n).cuda())\r\n    torch.matmul(w, a).permute(0, 3, 1, 2).mean().backward()\r\n    if i % 100 == 0:\r\n        gpu_mem = get_gpu_memory_map()\r\n        print(\"GPU: {:.2f} KB\".format(gpu_mem[0]))","distinct":false,"url":"https://api.github.com/repos/pytorch/pytorch/commits/82777815f8013f881bd8f40171b29de92fa3c360"},{"sha":"6f5e8692599e02db19d8c49c04dcdcfe15920a8e","author":{"name":"gchanan","email":"06657688b3467ee0132094f148fdddb529b2c50e@gmail.com"},"message":"Add promoteTypes to ATen and torch._promote_types to python. 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