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58 lines
1.4 KiB
58 lines
1.4 KiB
1 month ago
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import time
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onnx_path = "/tmp/my.onnx"
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N = 2048
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CNT = 400
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"""
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import torch
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import torch.nn as nn
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#dtype = torch.bfloat16
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dtype = torch.float32
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class MatMul(nn.Module):
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def __init__(self):
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super().__init__()
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self.a = nn.Linear(N, N, bias=False)
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def forward(self, x):
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x = x.to(dtype)
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for i in range(CNT): x = self.a(x).relu()
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return x.to(torch.float32)
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torch_model = MatMul().to(dtype)
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torch.onnx.export(torch_model, torch.randn(N, N), onnx_path)
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"""
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"""
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import onnx
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from tinygrad.tensor import Tensor
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from extra.onnx import get_run_onnx
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out = get_run_onnx(onnx.load(onnx_path))({"onnx::MatMul_0": Tensor.zeros(N, N)})
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for x in out.values(): x.realize()
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"""
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from openvino.runtime import Core
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core = Core()
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devices = core.available_devices
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for device in devices:
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device_name = core.get_property(device, "FULL_DEVICE_NAME")
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print(f"{device}: {device_name}")
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model = core.read_model(onnx_path)
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compiled_model = core.compile_model(model, device_name='GPU.0')
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print(compiled_model)
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ireq = compiled_model.create_infer_request()
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for model_input in compiled_model.inputs:
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tensor = ireq.get_tensor(model_input)
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tensor.data[:] = 2
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print(tensor)
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print("request")
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ireq.infer()
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ireq.infer()
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print("did one")
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REPS = 20
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st = time.perf_counter()
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for i in range(REPS): ireq.infer()
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et = time.perf_counter() - st
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print(f"{et*1000:.2f} ms {(CNT*N*N*N*REPS*2/et)*1e-9:.2f} GFLOPS")
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