mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-10 07:28:15 -05:00
301f7b54c6
* ConstantOfShape ONNX test fixed. * removed redundant if statement * value is optional and should default to a float32 tensor with value of 0 * fixed: default parameters are created at function definition, bad for mutable objects.
203 lines
10 KiB
Python
203 lines
10 KiB
Python
from __future__ import annotations
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from google.protobuf.internal.containers import RepeatedCompositeFieldContainer
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import importlib
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import numpy as np
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from tinygrad.tensor import Tensor
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from tinygrad.helpers import prod
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from tinygrad.helpers import getenv, DEBUG
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from onnx.onnx_pb import AttributeProto, ModelProto, TensorProto
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try:
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from onnx.helper import tensor_dtype_to_np_dtype
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except ImportError:
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# for onnx < 1.13
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from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
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tensor_dtype_to_np_dtype = lambda x: TENSOR_TYPE_TO_NP_TYPE[x]
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# global numpy cache for parameters
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numpy_cache = {}
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def safe_numpy(t):
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if not isinstance(t, Tensor): return t
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global numpy_cache
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if t not in numpy_cache:
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if DEBUG >= 1:
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print("numpy cache miss", t)
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numpy_cache[t] = t.numpy()
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return numpy_cache[t]
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onnx_ops = importlib.import_module('extra.onnx_ops')
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ONNXLIMIT = getenv("ONNXLIMIT", -1)
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def get_run_onnx(onnx_model: ModelProto):
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def shape_to_tuple(s): return tuple(x.dim_value for x in s.dim)
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def buffer_parse(inp: TensorProto) -> Tensor:
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if inp.data_type in (1,10,6,7):
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# TODO: this is shared with below
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if len(inp.float_data) > 0:
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ret = Tensor(np.array(inp.float_data, dtype=np.float32).reshape(inp.dims), requires_grad=False)
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elif len(inp.int64_data) > 0:
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ret = Tensor(np.array(inp.int64_data, dtype=np.float32).reshape(inp.dims), requires_grad=False)
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elif len(inp.int32_data) > 0:
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ret = Tensor(np.array(inp.int32_data, dtype=np.int32).reshape(inp.dims), requires_grad=False)
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else:
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ret = Tensor(np.frombuffer(inp.raw_data, dtype=tensor_dtype_to_np_dtype(inp.data_type)).reshape(inp.dims).astype(np.float32).copy(), requires_grad=False)
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else:
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raise Exception(f"bad data type {inp.name} {inp.dims} {inp.data_type}")
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return ret
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def attribute_parse(a: AttributeProto) -> float | int | str | Tensor | tuple[float] | tuple[int]:
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# TODO: this is not complete, see onnx/onnx_ml_pb2.pyi for a complete list
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if a.type == AttributeProto.FLOAT: return float(a.f)
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elif a.type == AttributeProto.INT: return int(a.i)
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elif a.type == AttributeProto.STRING: return a.s.decode("utf-8")
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elif a.type == AttributeProto.TENSOR: return buffer_parse(a.t) # TENSOR
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elif a.type == AttributeProto.FLOATS: return tuple(float(x) for x in a.floats)
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elif a.type == AttributeProto.INTS: return tuple(int(x) for x in a.ints)
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else: raise Exception(f"can't parse {a.type} {a}")
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def attribute_to_dict(a: RepeatedCompositeFieldContainer[AttributeProto]): return {x.name:attribute_parse(x) for x in a}
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tensors = {}
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# get weights and biases
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for inp in onnx_model.graph.initializer:
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if len(inp.raw_data) > 0:
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tensors[inp.name] = buffer_parse(inp)
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elif len(inp.float_data) > 0:
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tensors[inp.name] = Tensor(np.array(inp.float_data, dtype=np.float32).reshape(inp.dims), requires_grad=False)
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elif len(inp.int64_data) > 0:
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tensors[inp.name] = Tensor(np.array(inp.int64_data, dtype=np.float32).reshape(inp.dims), requires_grad=False)
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else:
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print(inp.name, inp.dims, inp.data_type, len(inp.raw_data))
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print(inp)
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raise Exception("no data")
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if DEBUG >= 1:
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print("realize", inp.name)
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tensors[inp.name].realize()
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# preparse the attributes
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attribute_dict = {}
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for num,n in enumerate(onnx_model.graph.node):
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attribute_dict[num] = attribute_to_dict(n.attribute)
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onnx_model_version = onnx_model.opset_import[0].version
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def run_onnx(inputs={}, debug=False):
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if getenv("DEBUGONNX"): debug = True
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input_tensors = {}
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intermediate_tensors = {}
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output_tensor_names = [x.name for x in onnx_model.graph.output]
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# get inputs
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for inp in onnx_model.graph.input:
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if inp.name in tensors: continue
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shape = shape_to_tuple(inp.type.tensor_type.shape)
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if len(shape) >= 1 and shape[0] == 0: shape = tuple([1]+list(shape[1:])) # 1 batch size
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if inp.name in inputs:
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input_shape = inputs[inp.name].shape
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if input_shape == (0,): raise NotImplementedError("empty tensors aren't supported in tinygrad")
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assert input_shape == shape, f"wrong shape for input {inp.name}, {input_shape} isn't {shape}"
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if isinstance(inputs[inp.name], Tensor):
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input_tensors[inp.name] = inputs[inp.name]
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else:
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input_tensors[inp.name] = Tensor(inputs[inp.name], requires_grad=False)
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for _,v in input_tensors.items(): v.realize()
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else:
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raise Exception(f"no data for {inp.name} with shape {shape}")
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for num,n in enumerate(onnx_model.graph.node):
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inp = [tensors[x] if x in tensors else (intermediate_tensors[x] if x in intermediate_tensors else (input_tensors[x] if x != str() else None)) for x in n.input]
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opt = attribute_dict[num]
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if debug: print(f"{num}: op {n.op_type} shape {[x.shape if isinstance(x, Tensor) else x for x in inp]} opt {opt}")
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# free ones
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if n.op_type == "Relu": ret = inp[0].relu()
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elif n.op_type == "Sigmoid": ret = inp[0].sigmoid()
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elif n.op_type == "Tanh": ret = inp[0].tanh()
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elif n.op_type == "MatMul": ret = inp[0].matmul(inp[1])
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# one liners
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elif n.op_type == "Elu": ret = inp[0].elu(alpha=opt.get('alpha', 1.0))
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elif n.op_type == "Concat": ret = inp[0].cat(*inp[1:], dim=opt['axis'])
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elif n.op_type == "Transpose": ret = inp[0].permute(order=opt.get('perm', list(range(len(inp[0].shape))[::-1])))
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elif n.op_type == "Squeeze": ret = inp[0].reshape([s for i,s in enumerate(inp[0].shape) if i not in opt['axes']])
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elif n.op_type == "Div":
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# in openpilot, due to SHUFFLE_PAD_OPS issues, we are spending an extra kernel
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ret = inp[0].div(inp[1])
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elif n.op_type == "Constant":
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if 'value' in opt: ret = opt['value'] # tensor
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elif 'value_float' in opt: ret = Tensor(np.array(opt['value_float'], dtype=np.float32), requires_grad=False)
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elif 'value_int' in opt: ret = Tensor(np.array(opt['value_int'], dtype=np.int64), requires_grad=False)
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elif 'value_floats' in opt: ret = Tensor(np.array(opt['value_floats'], dtype=np.float32), requires_grad=False)
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elif 'value_ints' in opt: ret = Tensor(np.array(opt['value_ints'], dtype=np.int64), requires_grad=False)
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else: raise NotImplementedError(f'Constant not implemented')
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elif n.op_type == "Reshape": ret = inp[0].reshape([int(x) if x != 0 else inp[0].shape[i] for i,x in enumerate(safe_numpy(inp[1]))])
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elif n.op_type == "Resize":
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# TODO: this is handcoded for YOLOv8
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scales = safe_numpy(inp[2])
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assert all([int(x) == x and x >= 1 for x in scales])
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ret = inp[0].reshape([val for pair in zip(inp[0].shape, [1] * len(scales)) for val in pair])
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ret = ret.expand([val for pair in zip(inp[0].shape, [int(x) for x in scales]) for val in pair])
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ret = ret.reshape([x*y for x,y in zip(inp[0].shape, [int(x) for x in scales])])
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elif n.op_type == "Gather":
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# TODO: is this correct? seems to work for simple gather ops
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axis = opt['axis']
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shape = list(inp[0].shape)
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indices = [shape[axis]+int(x) if x<0 else int(x) for x in safe_numpy(inp[1])]
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args = [[(0,x) if j != axis else (i,i+1) for j, x in enumerate(shape)] for i in indices]
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ret = inp[0].slice(arg=args[0]).cat(*[inp[0].slice(arg=arg) for arg in args[1:]], dim=axis)
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ret = ret.reshape([s for i,s in enumerate(shape) if i != axis]) if len(indices) == 1 else ret # squeeze if needed
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elif n.op_type in ["Add", "Sub", "Mul", "Pow"]:
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if (len(inp[0].shape) != len(inp[1].shape)) and (prod(inp[0].shape) == prod(inp[1].shape)):
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inp[1] = inp[1].reshape(inp[0].shape)
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# TODO: is this right?
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if 'broadcast' in opt: inp[1] = inp[1].reshape([-1 if i == opt['broadcast'] else 1 for i in range(len(inp[0].shape))])
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if n.op_type == "Add": ret = inp[0] + inp[1]
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if n.op_type == "Sub": ret = inp[0] - inp[1]
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if n.op_type == "Mul": ret = inp[0] * inp[1]
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if n.op_type == "Pow": ret = inp[0] ** inp[1]
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elif n.op_type == "Split":
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if 'split' not in opt: opt['split'] = [int(x) for x in safe_numpy(inp[1])] # split can be a tensor
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if 'axis' not in opt: opt['axis'] = 0
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i = 0
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arg = [(0,x) for x in inp[0].shape]
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for o,s in zip(n.output, opt['split']):
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arg[opt['axis']] = (i,i+s)
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intermediate_tensors[o] = inp[0].slice(arg=arg)
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i = i+s
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continue
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elif n.op_type == "Slice":
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assert onnx_model_version >= 10, f'only onnx version >= 10 supported for slice'
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arg = [(0,x) for x in inp[0].shape]
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starts, ends, axes = inp[1:4]
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assert axes.shape == (1,)
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axis, starts, ends = int(safe_numpy(axes)[0]), int(safe_numpy(starts)[0]), int(safe_numpy(ends)[0])
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ends = min(ends, inp[0].shape[axis])
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starts = starts + inp[0].shape[axis] if starts < 0 else starts
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arg[axis] = (starts, ends)
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ret = inp[0].slice(arg=arg)
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elif n.op_type == "Shrink":
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bias = opt['bias'] if 'bias' in opt else 0
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ret = (inp[0] < -opt['lambd'])*(inp[0]+bias) + (inp[0] > opt['lambd'])*(inp[0]-bias)
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elif hasattr(onnx_ops, n.op_type):
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fxn = getattr(onnx_ops, n.op_type)
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if isinstance(fxn, dict):
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for k in sorted(fxn.keys()):
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if k < onnx_model_version:
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real_fxn = fxn[k]
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else:
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real_fxn = fxn
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ret = real_fxn(*inp, **opt)
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else:
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print("UNSUPPORTED", n.op_type, n.input, n.output)
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raise Exception(f"op_type {n.op_type} not supported")
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if not isinstance(ret, tuple): ret = (ret, )
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assert len(n.output) <= len(ret), f"expected output size must be less than {len(ret)}, it's {n.output}"
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if debug: print([x.shape if isinstance(x, Tensor) else None for x in ret])
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for i in range(len(n.output)): intermediate_tensors[n.output[i]] = ret[i]
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#print(ret[0].numpy().mean())
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if num == ONNXLIMIT:
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output_tensor_names = n.output
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break
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return {outp:intermediate_tensors[outp] for outp in output_tensor_names}
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return run_onnx
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