Distributed Reshape Implementation #18068
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Since Reshape may change device mesh from, e.g., [0, 1] to [0, 1, 0, 1], we can't assume same device mesh per op. At each operator, we replace a single operator-level device mesh - `device_mesh_shapes` - `device_mesh_elements` with per-tensor device meshes - `input_device_mesh_shapes` (input_device_mesh_shapes[i] is the device mesh's shape for the i-th input, e.g., "[3]" for 1-D mesh with 3 devices) - `input_device_mesh_elements` (input_device_mesh_elements[i] is the flattened device mesh elements for the i-th input; e.g., "[0, 1, 2]" if you have 3 devices in that mesh) - `output_device_mesh_shapes` - `output_device_mesh_elements` Check out the change in onnxruntime_test_distributed.py for examples. It's also heavily used in #18068's `onnxruntime_test_distributed.py` change.
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Since Reshape may change device mesh from, e.g., [0, 1] to [0, 1, 0, 1], we can't assume same device mesh per op. At each operator, we replace a single operator-level device mesh - `device_mesh_shapes` - `device_mesh_elements` with per-tensor device meshes - `input_device_mesh_shapes` (input_device_mesh_shapes[i] is the device mesh's shape for the i-th input, e.g., "[3]" for 1-D mesh with 3 devices) - `input_device_mesh_elements` (input_device_mesh_elements[i] is the flattened device mesh elements for the i-th input; e.g., "[0, 1, 2]" if you have 3 devices in that mesh) - `output_device_mesh_shapes` - `output_device_mesh_elements` Check out the change in onnxruntime_test_distributed.py for examples. It's also heavily used in microsoft#18068's `onnxruntime_test_distributed.py` change.
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This DistributedReshape aims at supporting all sharding patterns encountered in llama 2. All patterns found are tested in `TestDistributedReshape` in `onnxruntime_test_distributed.py`. This PR implements algorithms to compute the categories below. - All inputs and outputs are replica, so it's computed like a normal Reshape. - Two-axis fusion (if any of the inputs and outputs are sharded). This category convers, e.g., `[batch, seq, hidden] -> [batch x seq, hidden]`. - Two-axis decomposition (if any of the inputs and outputs are sharded). This category convers, e.g., `[batch x seq, hidden] -> [batch, seq, hidden]`. Review guideline: - Ignore the changes in sharding_spec.h and sharding_spec.cc since they come from another PR microsoft#18025. - First, read onnxruntime_test_distributed.py to get familiar with the input/output of DistributedReshape. - Second, check the new APIs in reshape.h/reshape.cc to expose CUDA Reshape kernel to DistributedReshape. - For DistributedReshape, check its `ComputeInternal` for the 3 categories mentioned above.
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This DistributedReshape aims at supporting all sharding patterns encountered in llama 2. All patterns found are tested in
TestDistributedReshapeinonnxruntime_test_distributed.py. This PR implements algorithms to compute the categories below.[batch, seq, hidden] -> [batch x seq, hidden].[batch x seq, hidden] -> [batch, seq, hidden].Review guideline:
ComputeInternalfor the 3 categories mentioned above.