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Changes c3 #5

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8b42a97
Update compile3
haraschax Nov 8, 2024
3a11fa9
Revert hax
haraschax Nov 8, 2024
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25 changes: 17 additions & 8 deletions examples/openpilot/compile3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,23 +5,23 @@
if "NOLOCALS" not in os.environ: os.environ["NOLOCALS"] = "1"
if "JIT_BATCH_SIZE" not in os.environ: os.environ["JIT_BATCH_SIZE"] = "0"

from tinygrad import fetch, Tensor, TinyJit, Device, Context, GlobalCounters
from tinygrad.helpers import OSX, DEBUG, getenv
from tinygrad import fetch, Tensor, TinyJit, Context, GlobalCounters
from tinygrad.helpers import DEBUG, getenv
from tinygrad.tensor import _from_np_dtype

import onnx
from onnx.helper import tensor_dtype_to_np_dtype
from extra.onnx import get_run_onnx # TODO: port to main tinygrad

OPENPILOT_MODEL = sys.argv[1] if len(sys.argv) > 1 else "https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx"
OUTPUT = "/tmp/openpilot.pkl"
OUTPUT = sys.argv[2] if len(sys.argv) > 2 else "/tmp/openpilot.pkl"

def compile():
# hack to fix GPU on OSX: max doesn't work on half, see test/external/external_gpu_fail_osx.py
if OSX:
from tinygrad.ops import BinaryOps
from tinygrad.renderer.cstyle import ClangRenderer, CStyleLanguage
CStyleLanguage.code_for_op[BinaryOps.MAX] = ClangRenderer.code_for_op[BinaryOps.MAX]
#if OSX:
# from tinygrad.ops import BinaryOps
# from tinygrad.renderer.cstyle import ClangRenderer, CStyleLanguage
# CStyleLanguage.code_for_op[BinaryOps.MAX] = ClangRenderer.code_for_op[BinaryOps.MAX]

Tensor.no_grad = True
Tensor.training = False
Expand All @@ -32,7 +32,12 @@ def compile():
print("loaded model")

input_shapes = {inp.name:tuple(x.dim_value for x in inp.type.tensor_type.shape.dim) for inp in onnx_model.graph.input}
input_types = {inp.name: tensor_dtype_to_np_dtype(inp.type.tensor_type.elem_type) for inp in onnx_model.graph.input}
input_types = {inp.name: np.float32 for inp in onnx_model.graph.input}
if 'input_img' in input_shapes:
input_types['input_img'] = np.uint8
else:
input_types['input_imgs'] = np.uint8
input_types['big_input_imgs'] = np.uint8
Tensor.manual_seed(100)
new_inputs = {k:Tensor.randn(*shp, dtype=_from_np_dtype(input_types[k])).mul(8).realize() for k,shp in sorted(input_shapes.items())}
print("created tensors")
Expand Down Expand Up @@ -65,7 +70,11 @@ def test(test_val=None):
new_inputs = {nm:Tensor.randn(*st.shape, dtype=dtype).mul(8).realize() for nm, (st, _, dtype, _) in
sorted(zip(run.captured.expected_names, run.captured.expected_st_vars_dtype_device))}
for _ in range(20):
inputs_numpy = {k:v.numpy() for k,v in new_inputs.items()}
st = time.perf_counter()
for k in new_inputs:
if 'img' not in k: # dont need to init img tensors, those are backed by openCL GPU memory
new_inputs[k] = Tensor(inputs_numpy[k])
out = run(**new_inputs)
mt = time.perf_counter()
val = out['outputs'].numpy()
Expand Down