mirror of
https://github.com/nod-ai/SHARK-Studio.git
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149 lines
3.7 KiB
Python
149 lines
3.7 KiB
Python
# Copyright 2020 The Nod Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from torch.utils.cpp_extension import load_inline, include_paths
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from torch_mlir.eager_mode import torch_mlir_tensor
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from torch_mlir.eager_mode.torch_mlir_tensor import TorchMLIRTensor
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from amdshark.iree_eager_backend import EagerModeIREELinalgOnTensorsBackend
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from amdshark.amdshark_runner import AMDSharkEagerMode
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def test_cpu():
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torch_mlir_tensor.backend = EagerModeIREELinalgOnTensorsBackend("cpu")
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t = torch.ones((10, 10), device="cpu")
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u = 2 * torch.ones((10, 10), device="cpu")
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tt = TorchMLIRTensor(t)
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print(tt)
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uu = TorchMLIRTensor(u)
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print(uu)
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for i in range(NUM_ITERS):
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yy = tt + uu
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print(type(yy))
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print(yy.elem.to_host())
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yy = tt * uu
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print(type(yy))
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print(yy.elem.to_host())
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def test_gpu():
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source = """
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#include <iostream>
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#include "cuda.h"
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#include "cuda_runtime_api.h"
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using namespace std;
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void print_free_mem() {
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int num_gpus;
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size_t free, total;
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cudaSetDevice(0);
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int id;
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cudaGetDevice(&id);
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cudaMemGetInfo(&free, &total);
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cout << "GPU " << id << " memory: used=" << (total-free)/(1<<20) << endl;
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}
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"""
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gpu_stats = load_inline(
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name="inline_extension",
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cpp_sources=[source],
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extra_include_paths=include_paths(cuda=True),
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functions=["print_free_mem"],
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)
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torch_mlir_tensor.backend = EagerModeIREELinalgOnTensorsBackend("gpu")
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t = torch.ones((10, 10), device="cpu")
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u = 2 * torch.ones((10, 10), device="cpu")
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tt = TorchMLIRTensor(t)
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print(tt)
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uu = TorchMLIRTensor(u)
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print(uu)
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for i in range(NUM_ITERS):
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yy = tt + uu
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print(yy.elem.to_host())
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yy = tt * uu
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print(yy.elem.to_host())
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gpu_stats.print_free_mem()
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def test_python_mode_ref_backend():
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# hide this wherever you want?
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_ = AMDSharkEagerMode("refbackend")
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t = torch.ones((10, 10), device="cpu")
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u = torch.ones((10, 10), device="cpu")
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print(t)
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print(u)
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for i in range(NUM_ITERS):
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print(i)
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yy = t + u
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print(yy.elem)
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yy = t * u
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print(yy.elem)
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def test_python_mode_iree_cpu():
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# hide this wherever you want?
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_ = AMDSharkEagerMode("cpu")
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t = torch.ones((10, 10), device="cpu")
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u = torch.ones((10, 10), device="cpu")
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print(t)
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print(u)
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for i in range(NUM_ITERS):
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yy = t + u
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print(type(yy))
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print(yy.elem.to_host())
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yy = t * u
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print(type(yy))
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print(yy.elem.to_host())
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def test_python_mode_iree_gpu():
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_ = AMDSharkEagerMode("gpu")
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t = torch.ones((10, 10), device="cpu")
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u = torch.ones((10, 10), device="cpu")
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print(t)
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print(u)
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for i in range(NUM_ITERS):
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yy = t + u
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print(type(yy))
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print(yy.elem.to_host())
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yy = t * u
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print(type(yy))
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print(yy.elem.to_host())
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if __name__ == "__main__":
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NUM_ITERS = 10
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test_cpu()
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if torch.cuda.is_available():
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test_gpu()
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test_python_mode_ref_backend()
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test_python_mode_iree_cpu()
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test_python_mode_iree_gpu()
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