From 9516482d9f1b4638cd3426b78faa81b3a91955ef Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Fri, 19 Jan 2024 11:20:08 -0500 Subject: [PATCH 01/57] initial commit --- examples/mnist.py | 8 + mlx/onnx/__init__.py | 114 ++++++++++ mlx/onnx/ops.py | 496 +++++++++++++++++++++++++++++++++++++++++++ setup.py | 12 ++ tests/test_onnx.py | 111 ++++++++++ 5 files changed, 741 insertions(+) create mode 100644 examples/mnist.py create mode 100644 mlx/onnx/__init__.py create mode 100644 mlx/onnx/ops.py create mode 100644 setup.py create mode 100644 tests/test_onnx.py diff --git a/examples/mnist.py b/examples/mnist.py new file mode 100644 index 0000000..d0531e3 --- /dev/null +++ b/examples/mnist.py @@ -0,0 +1,8 @@ +import mlx.core as mx +from mlx.onnx import MlxBackend +from onnx import hub + +model = hub.load("mnist") +backend = MlxBackend(model) +res = backend.run(mx.ones((1, 1, 28, 28))) +print(res) diff --git a/mlx/onnx/__init__.py b/mlx/onnx/__init__.py new file mode 100644 index 0000000..849eb69 --- /dev/null +++ b/mlx/onnx/__init__.py @@ -0,0 +1,114 @@ +import importlib +from typing import Any, Tuple + +import mlx.core as mx +import numpy as np +import onnx +from onnx.helper import tensor_dtype_to_np_dtype + +onnx_ops = importlib.import_module("mlx.onnx.ops") + + +class MlxBackend: + def __init__(self, model: onnx.ModelProto): + self._model = model + self._cache = {} + self.initializer_arrays() + + def initializer_arrays(self): + for i in self._model.graph.initializer: + if i.name in self._cache: + continue + self._cache[i.name] = self.parse_array(i) + + def parse_array(self, inp: onnx.TensorProto) -> mx.array: + if inp.data_type == onnx.TensorProto.FLOAT and len(inp.float_data) > 0: + return mx.array( + np.array(inp.float_data, dtype=np.float32).reshape(inp.dims), + dtype=mx.float32, + ) + elif inp.data_type == onnx.TensorProto.INT32 and len(inp.int32_data) > 0: + return mx.array( + np.array(inp.int32_data, dtype=np.int32).reshape(inp.dims), + dtype=mx.int32, + ) + elif inp.data_type == onnx.TensorProto.INT64 and len(inp.int64_data) > 0: + return mx.array( + np.array(inp.int64_data, dtype=np.int64).reshape(inp.dims), + dtype=mx.int64, + ) + elif len(inp.raw_data) > 0: + return mx.array( + np.frombuffer( + inp.raw_data, dtype=tensor_dtype_to_np_dtype(inp.data_type) + ).reshape(inp.dims) + ) + else: + raise NotImplementedError( + f"Not implemented for {inp.data_type} {inp.name} {inp.dims}" + ) + return mx.ones(inp.dims, dtype=mx.float32) + + def get_input_dict(self, inputs): + input_names = [x.name for x in self._model.graph.input] + init_names = set([x.name for x in self._model.graph.initializer]) + real_inputs = [x for x in input_names if x not in init_names] + return dict(zip(real_inputs, inputs)) + + def parse_attributes(self, attrs): + res = {} + for x in attrs: + if x.type == onnx.AttributeProto.FLOAT: + res[x.name] = float(x.f) + elif x.type == onnx.AttributeProto.INT: + res[x.name] = int(x.i) + elif x.type == onnx.AttributeProto.STRING: + res[x.name] = str(x.s) + elif x.type == onnx.AttributeProto.TENSOR: + res[x.name] = self.parse_array(x.t) + # Sometimes this gets passed as args to functions that expect mx.array, so just converting + # them here to simplify the op code + elif x.type == onnx.AttributeProto.FLOATS: + res[x.name] = mx.array([float(f) for f in x.floats], dtype=mx.float32) + elif x.type == onnx.AttributeProto.INTS: + res[x.name] = mx.array([int(i) for i in x.ints], dtype=mx.int64) + elif x.type == onnx.AttributeProto.STRINGS: + res[x.name] = tuple(str(s) for s in x.strings) + elif x.type == onnx.AttributeProto.GRAPH: + raise NotImplementedError(f"Attribute type graph not implemented") + else: + raise NotImplementedError(f"Attribute type {x.type} not implemented") + return res + + def run(self, inputs, **kwargs: Any) -> Tuple[mx.array, ...]: + self.initializer_arrays() + inputs = self.get_input_dict(inputs) + for i in self._model.graph.input: + if i.name in self._cache: + continue + if i.name in inputs: + if isinstance(inputs[i.name], mx.array): + self._cache[i.name] = inputs[i.name] + elif isinstance(inputs[i.name], list): + self._cache[i.name] = [mx.array(x) for x in inputs[i.name]] + elif isinstance(inputs[i.name], np.ndarray): + self._cache[i.name] = mx.array(inputs[i.name]) + else: + raise NotImplementedError( + f"Input type {type(inputs[i.name])} not implemented" + ) + for i, node in enumerate(self._model.graph.node): + args = [self._cache[x] for x in node.input] + opt = self.parse_attributes(node.attribute) + + if hasattr(onnx_ops, node.op_type): + res = getattr(onnx_ops, node.op_type)(*args, **opt) + else: + raise NotImplementedError(f"Operation {node.op_type} not implemented") + + if not isinstance(res, tuple): + res = (res,) + + for i in range(len(node.output)): + self._cache[node.output[i]] = res[i] + return tuple(self._cache[out.name] for out in self._model.graph.output) diff --git a/mlx/onnx/ops.py b/mlx/onnx/ops.py new file mode 100644 index 0000000..9b9cd0b --- /dev/null +++ b/mlx/onnx/ops.py @@ -0,0 +1,496 @@ +import functools +import math +from typing import List, Optional, Union + +import mlx.core as mx +import mlx.nn.layers as layers +import onnx + +# Reference Docs: https://onnx.ai/onnx/operators/ + +# Note: onnx.TensorProto.DOUBLE is not supported. +DTYPE_MAP = { + onnx.TensorProto.FLOAT: mx.float32, + onnx.TensorProto.UINT8: mx.uint8, + onnx.TensorProto.INT8: mx.int8, + onnx.TensorProto.UINT16: mx.uint16, + onnx.TensorProto.INT16: mx.int16, + onnx.TensorProto.INT32: mx.int32, + onnx.TensorProto.INT64: mx.int64, + onnx.TensorProto.BOOL: mx.bool_, + onnx.TensorProto.FLOAT16: mx.float16, + onnx.TensorProto.UINT32: mx.uint32, + onnx.TensorProto.UINT64: mx.uint64, + onnx.TensorProto.BFLOAT16: mx.bfloat16, +} + + +def Add(x: mx.array, y: mx.array, broadcast=None, axis=None): + return x + y + + +def Sub(x: mx.array, y: mx.array): + return x - y + + +def Mul(x: mx.array, y: mx.array): + return x * y + + +def Div(x: mx.array, y: mx.array): + return x / y + + +def Neg(x: mx.array): + return -x + + +def Pow(x: mx.array, y: mx.array): + return x**y + + +def Sqrt(x: mx.array): + return x.sqrt() + + +def Abs(x: mx.array): + return x.abs() + + +def Exp(x: mx.array): + return x.exp() + + +def Log(x: mx.array): + return x.log() + + +def Sin(x: mx.array): + return x.sin() + + +def Sinh(x: mx.array): + return mx.sinh(x) + + +def Asin(x: mx.array): + return mx.arcsin(x) + + +def Asinh(x: mx.array): + return mx.arcsinh(x) + + +def Cos(x: mx.array): + return x.cos() + + +def Cosh(x: mx.array): + return mx.cosh(x) + + +def Acos(x: mx.array): + return mx.arccos(x) + + +def Acosh(x: mx.array): + return mx.arccosh(x) + + +def Tan(x: mx.array): + return x.sin() / x.cos() + + +def Tanh(x: mx.array): + return mx.sinh(x) / mx.cosh(x) + + +def Atan(x: mx.array): + return mx.arctan(x) + + +def Atanh(x: mx.array): + return mx.arctanh(x) + + +def Relu(x: mx.array): + return layers.relu(x) + + +def Floor(x: mx.array): + return mx.floor(x) + + +def Ceil(x: mx.array): + return mx.ceil(x) + + +def Sigmoid(x: mx.array): + return mx.sigmoid(x) + + +def Sign(x: mx.array): + return mx.sign(x) + + +def Softplus(x: mx.array): + return layers.softplus(x) + + +def HardSwish(x: mx.array): + return layers.hardswish(x) + + +def HardSigmoid(x: mx.array, alpha=0.2, beta=0.5): + return mx.clip(x * alpha + beta, 0, 1) + + +def Softsign(x: mx.array): + return layers.softsign(x) + + +def MatMul(x: mx.array, y: mx.array): + return x @ y + + +def Cast(x: mx.array, to: int, saturate=1): + if to == onnx.TensorProto.DOUBLE: + raise NotImplementedError("mlx does not support double data type") + return x.astype(DTYPE_MAP[to]) + + +def CastLike(x: mx.array, target_type: mx.array, saturate=1): + return x.astype(target_type.dtype) + + +def ConstantOfShape(x: mx.array, value: mx.array = None): + if value is None: + value = mx.array([0]) + shape = x.tolist() + return mx.ones(shape, dtype=value.dtype) * (value if shape[0] != 0 else 1) + + +def Tile(x: mx.array, repeats: mx.array): + return mx.tile(x, repeats.tolist()) + + +def Shape(x: mx.array, end=None, start=0): + return mx.array(x.shape[start:end], dtype=mx.int64) + + +def Constant( + value: mx.array = None, + value_float=None, + value_floats=None, + value_int=None, + value_ints=None, + value_string=None, + value_strings=None, +): + if value is not None: + return value + if value_float is not None: + return mx.array(value_float, dtype=mx.float32) + if value_floats is not None: + return mx.array(list(value_floats), dtype=mx.float32) + if value_int is not None: + return mx.array(value_int, dtype=mx.int32) + if value_ints is not None: + return mx.array(list(value_ints), dtype=mx.int32) + if value_string is not None or value_strings is not None: + raise NotImplementedError() + + +def Less(x: mx.array, y: mx.array): + return x < y + + +def LessOrEqual(x: mx.array, y: mx.array): + return x <= y + + +def Equal(x: mx.array, y: mx.array): + return x == y + + +def Greater(x: mx.array, y: mx.array): + return x > y + + +def GreaterOrEqual(x: mx.array, y: mx.array): + return x >= y + + +def Where(condition: mx.array, x: mx.array, y: mx.array): + return mx.where(condition, x, y) + + +def LeakyRelu(x: mx.array, alpha=0.01): + return layers.leaky_relu(x, alpha) + + +def And(x: mx.array, y: mx.array): + return x & y + + +def Or(x: mx.array, y: mx.array): + return x | y + + +def Trilu(x: mx.array, k=0, upper=1): + if isinstance(k, mx.array): + k = k.item() + return mx.triu(x, k) if upper else mx.tril(x, k) + + +def Transpose(x: mx.array, perm: mx.array = None): + return x.transpose() if perm is None else x.transpose(perm.tolist()) + + +def Identity(x: mx.array): + return x + + +def Sum(*args: List[mx.array]): + return functools.reduce(mx.array.__add__, args) + + +def Mean(*args: List[mx.array]): + return Sum(*args) / len(args) + + +def Max(*args: List[mx.array]): + return functools.reduce(mx.maximum, args) + + +def Min(*args: List[mx.array]): + return functools.reduce(mx.minimum, args) + + +def Elu(x: mx.array, alpha=1.0): + return layers.elu(x, alpha) + + +def Celu(x: mx.array, alpha=1.0): + return layers.celu(x, alpha) + + +def Reciprocal(x: mx.array): + return x.reciprocal() + + +def Mish(x: mx.array): + return layers.mish(x) + + +def PRelu(x: mx.array, slope: mx.array): + slops = slope[0] if slope.shape[-1] != x.shape[-1] else slope + return layers.prelu(x, slope) + + +def Selu(x: mx.array, alpha=1.67326319217681884765625, gamma=1.05070102214813232421875): + return gamma * (layers.relu(x) - layers.relu(-alpha * x.exp() + alpha)) + + +def Clip(x: mx.array, min=float("-inf"), max=float("inf")): + return mx.clip(x, min, max) + + +def Range(start: mx.array, limit: mx.array, delta: mx.array): + return mx.arange(start.item(), limit.item(), delta.item()) + + +def Size(x: Union[mx.array, list[int]]): + return mx.array(math.prod(x if isinstance(x, list) else x.shape), dtype=mx.int64) + + +def Shrink(x: mx.array, bias=0.0, lambd=0.5): + return (x < -lambd) * (x + bias) + (x > lambd) * (x - bias) + + +def Reshape(x: mx.array, shape: mx.array, allowzero=0): + new_shape = [ + d if d != 0 else (0 if allowzero else x.shape[i]) + for i, d in enumerate(shape.tolist()) + ] + return x.reshape(new_shape) + + +def Squeeze(x: mx.array, axes: mx.array = None): + return mx.squeeze(x, axes.tolist() if axes is not None else None) + + +def Unsqueeze(x: mx.array, axes: mx.array): + return mx.expand_dims(x, axes.tolist()) + + +def Flatten(x: mx.array, axis=1): + return mx.reshape( + x, + ( + math.prod( + [ + 1, + ] + + x.shape[:axis] + ), + -1, + ), + ) + + +def axes_helper(axes: Optional[mx.array] = None, noop_with_empty_axes=0): + # print(axes) + if isinstance(axes, tuple): + return axes + if axes is not None and isinstance(axes, mx.array) and axes.size > 0: + return axes.tolist() + return [] if noop_with_empty_axes else None + + +def ReduceMax(x: mx.array, axes: mx.array = None, keepdims=1, noop_with_empty_axes=0): + return x.max(axes_helper(axes, noop_with_empty_axes), keepdims=keepdims) + + +def ReduceMin(x: mx.array, axes: mx.array = None, keepdims=1, noop_with_empty_axes=0): + return x.min(axes_helper(axes, noop_with_empty_axes), keepdims=keepdims) + + +def ReduceMean(x: mx.array, axes: mx.array = None, keepdims=1, noop_with_empty_axes=0): + return x.mean(axes_helper(axes, noop_with_empty_axes), keepdims=keepdims) + + +def ReduceProd(x: mx.array, axes: mx.array = None, keepdims=1, noop_with_empty_axes=0): + return x.prod(axes_helper(axes, noop_with_empty_axes), keepdims=keepdims) + + +def ReduceL1(x: mx.array, axes: mx.array = None, keepdims=1, noop_with_empty_axes=0): + return x.abs().sum(axes_helper(axes, noop_with_empty_axes), keepdims=keepdims) + + +def ReduceL2(x: mx.array, axes: mx.array = None, keepdims=1, noop_with_empty_axes=0): + return ( + x.square() + .sum(axes_helper(axes, noop_with_empty_axes), keepdims=keepdims) + .sqrt() + ) + + +def ReduceSum(x: mx.array, axes: mx.array = None, keepdims=1, noop_with_empty_axes=0): + return x.sum(axes_helper(axes, noop_with_empty_axes), keepdims=keepdims) + + +def ReduceLogSum( + x: mx.array, axes: mx.array = None, keepdims=1, noop_with_empty_axes=0 +): + return x.sum(axes_helper(axes, noop_with_empty_axes), keepdims=keepdims).log() + + +def ReduceLogSumExp( + x: mx.array, axes: mx.array = None, keepdims=1, noop_with_empty_axes=0 +): + return x.exp().sum(axes_helper(axes, noop_with_empty_axes), keepdims=keepdims).log() + + +def ReduceSumSquare( + x: mx.array, axes: mx.array = None, keepdims=1, noop_with_empty_axes=0 +): + return x.square().sum(axes_helper(axes, noop_with_empty_axes), keepdims=keepdims) + + +def Concat(*args: List[mx.array], axis): + return mx.concatenate(args, axis=axis) + + +def Gemm( + A: mx.array, + B: mx.array, + C: Optional[mx.array] = None, + alpha=1.0, + beta=1.0, + transA=0, + transB=0, + broadcast=0, +): + if transA: + A = A.transpose() + if transB: + B = B.transpose() + ret = alpha * (A @ B) + if C is not None: + ret += beta * C + return ret + + +def Softmax(x: mx.array, axis=-1): + return layers.softmax(x, axis=axis) + + +def LogSoftmax(x: mx.array, axis=-1): + return layers.log_softmax(x, axis=axis) + + +def Gelu(x: mx.array, approximate="none"): + return layers.gelu(x) if approximate == "none" else layers.gelu_fast_approx(x) + + +def Erf(x: mx.array): + return mx.erf(x) + + +# Note: There is a bug in mlx round impl, -2.5 -> -3 instead of -2 +def Round(x: mx.array): + return x.round() + + +def ArgMax(x: mx.array, axis=0, keepdims=1, select_last_index=0): + return mx.argmax(x, axis=axis, keepdims=keepdims).astype(mx.int64) + + +def ArgMin(x: mx.array, axis=0, keepdims=1, select_last_index=0): + return mx.argmin(x, axis=axis, keepdims=keepdims).astype(mx.int64) + + +def Expand(x: mx.array, shape: mx.array): + return x * mx.ones(shape.tolist()) + + +def CumSum(x: mx.array, axis: mx.array, exclusive=0, reverse=0): + return mx.cumsum(x, axis.item(), reverse=reverse, inclusive=not exclusive) + + +def EyeLike(x: mx.array, dtype=None, k=0): + if dtype is None: + dtype = x.dtype + else: + dtype = DTYPE_MAP[dtype] + return mx.eye(x.shape[0], x.shape[1], k=k, dtype=dtype) + + +def Gather(x: mx.array, indices: mx.array, axis=0): + return mx.take(x, indices, axis=axis) + + +def GatherElements(x: mx.array, indices: mx.array, axis=0): + return mx.take_along_axis(x, indices, axis=axis) + + +def Not(x: mx.array): + return ~x + + +def Slice( + x: mx.array, + starts: mx.array, + ends: mx.array, + axes: mx.array = None, + steps: mx.array = None, +): + if axes is None: + axes = mx.arange(x.ndim) + if steps is None: + steps = mx.ones(starts.shape, dtype=mx.int64) + slices = [slice(0, d) for d in x.shape] + for start, end, axe, step in zip(starts, ends, axes, steps): + slices[axe.item()] = slice(start.item(), end.item(), step.item()) + return x[tuple(slices)] diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..1b827cb --- /dev/null +++ b/setup.py @@ -0,0 +1,12 @@ +from setuptools import setup + +setup( + name="mlx-onnx", + version="0.0.1", + description="MLX backend for onnx", + install_requires=["mlx", "onnx"], + extras_require={ + "test": ["numpy", "pytest"], + }, + packages=["mlx.onnx"], +) \ No newline at end of file diff --git a/tests/test_onnx.py b/tests/test_onnx.py new file mode 100644 index 0000000..04bcb36 --- /dev/null +++ b/tests/test_onnx.py @@ -0,0 +1,111 @@ +import unittest + +import mlx.core as mx +import numpy as np +import onnx.backend.test +from mlx.onnx import MlxBackend + + +# need to conver to numpy for the testing suite +class TestMlxBackend(MlxBackend): + def __init__(self, model): + super().__init__(model) + + def run(self, inputs, **kwargs): + t = super().run(inputs, **kwargs) + return tuple( + np.array(x) if isinstance(x, mx.array) else [np.array(i) for i in x] + for x in t + ) + + +class TestMlxBackendWrapper: + @classmethod + def prepare(cls, model: onnx.ModelProto, device: str): + return TestMlxBackend(model) + + @classmethod + def supports_device(cls, device: str) -> bool: + return device.lower() in ["cpu", "gpu"] + + +btest = onnx.backend.test.BackendTest(TestMlxBackendWrapper, __name__) + +# btest.include("") + +# TODO: these are upcasting to float32 +btest.exclude("test_div_uint8_cpu") +btest.exclude("test_pow_types_int32_float32_cpu") +btest.exclude("test_pow_types_int64_float32_cpu") +btest.exclude("test_matmulinteger_*") +btest.exclude("test_clip_default_int8_min_cpu") + +# TODO: Debug these errors +btest.exclude("test_clip_default_max_cpu") +btest.exclude("test_clip_default_inbounds_cpu") +btest.exclude("test_clip_default_int8_max_cpu") +btest.exclude("test_clip_default_int8_inbounds_cpu") +btest.exclude("test_reduce_min_empty_set_cpu") + +# TODO: Implement +btest.exclude("test_pad_*") +btest.exclude("test_topk*") +btest.exclude("test_maxpool_*") +btest.exclude("test_maxunpool_*") +btest.exclude("test_batchnorm_*") +btest.exclude("test_instancenorm_*") +btest.exclude("test_gelu_tanh_*") +btest.exclude("test_bitwise_*") +btest.exclude("test_gathernd_*") + + +# TODO: need to go through and handle these better +btest.exclude("test_cast_*") +btest.exclude("test_castlike_*") +btest.exclude("test_argmax_keepdims_example_select_last_index_cpu") +btest.exclude("test_argmax_negative_axis_keepdims_example_select_last_index_cpu") +btest.exclude("test_argmax_no_keepdims_example_select_last_index_cpu") +btest.exclude("test_argmin_no_keepdims_example_select_last_index_cpu") +btest.exclude("test_argmin_negative_axis_keepdims_example_select_last_index_cpu") +btest.exclude("test_argmin_keepdims_example_select_last_index_cpu") + +# TODO: Reenable when float64 support is added back +btest.exclude("test_max_float64_cpu") +btest.exclude("test_min_float64_cpu") + +# TODO: Graph tests +btest.exclude("test_range_float_type_positive_delta_expanded_cpu") +btest.exclude("test_range_int32_type_negative_delta_expanded_cpu") + +# TODO: Add gradient support +btest.exclude("test_gradient_*") + +# TODO: There is a bug in mlx round impl, -2.5 -> -3 instead of -2 +btest.exclude("test_round_*") + +# TODO: Investigate +btest.exclude("test_operator_pad_*") +btest.exclude("test_sequence_*") +btest.exclude("test_strnorm_*") +btest.exclude("test_bitshift_*") +btest.exclude("string") + +# float64 datatype +btest.exclude("test_reduce_log_sum_exp_*") +btest.exclude("test_operator_addconstant_cpu") +btest.exclude("test_operator_add_size1_singleton_broadcast_cpu") +btest.exclude("test_operator_add_broadcast_cpu") +btest.exclude("test_operator_add_size1_broadcast_cpu") +btest.exclude("test_operator_add_size1_right_broadcast_cpu") +btest.exclude("test_cumsum_*") +btest.exclude("test_eyelike_with_dtype_cpu") + +# skip models +for x in btest.test_suite: + if "OnnxBackendRealModelTest" in str(type(x)): + btest.exclude(str(x).split(" ")[0]) + +globals().update(btest.enable_report().test_cases) + +if __name__ == "__main__": + unittest.main() From 1c344d1e2f4683c6221b5e8b8d1a1293ac556268 Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Fri, 19 Jan 2024 11:22:19 -0500 Subject: [PATCH 02/57] small cleanup --- mlx/onnx/__init__.py | 1 - mlx/onnx/ops.py | 4 ++-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/mlx/onnx/__init__.py b/mlx/onnx/__init__.py index 849eb69..5a66073 100644 --- a/mlx/onnx/__init__.py +++ b/mlx/onnx/__init__.py @@ -47,7 +47,6 @@ def parse_array(self, inp: onnx.TensorProto) -> mx.array: raise NotImplementedError( f"Not implemented for {inp.data_type} {inp.name} {inp.dims}" ) - return mx.ones(inp.dims, dtype=mx.float32) def get_input_dict(self, inputs): input_names = [x.name for x in self._model.graph.input] diff --git a/mlx/onnx/ops.py b/mlx/onnx/ops.py index 9b9cd0b..5094028 100644 --- a/mlx/onnx/ops.py +++ b/mlx/onnx/ops.py @@ -483,8 +483,8 @@ def Slice( x: mx.array, starts: mx.array, ends: mx.array, - axes: mx.array = None, - steps: mx.array = None, + axes: Optional[mx.array] = None, + steps: Optional[mx.array] = None, ): if axes is None: axes = mx.arange(x.ndim) From bf2a0b5c2cfe67d679bf244615cb4c664a2cdb10 Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Fri, 19 Jan 2024 11:25:19 -0500 Subject: [PATCH 03/57] squashed bug --- mlx/onnx/ops.py | 1 - tests/test_onnx.py | 5 +---- 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/mlx/onnx/ops.py b/mlx/onnx/ops.py index 5094028..15c8651 100644 --- a/mlx/onnx/ops.py +++ b/mlx/onnx/ops.py @@ -438,7 +438,6 @@ def Erf(x: mx.array): return mx.erf(x) -# Note: There is a bug in mlx round impl, -2.5 -> -3 instead of -2 def Round(x: mx.array): return x.round() diff --git a/tests/test_onnx.py b/tests/test_onnx.py index 04bcb36..4fa13a4 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -31,7 +31,7 @@ def supports_device(cls, device: str) -> bool: btest = onnx.backend.test.BackendTest(TestMlxBackendWrapper, __name__) -# btest.include("") +btest.include("test_round_*") # TODO: these are upcasting to float32 btest.exclude("test_div_uint8_cpu") @@ -80,9 +80,6 @@ def supports_device(cls, device: str) -> bool: # TODO: Add gradient support btest.exclude("test_gradient_*") -# TODO: There is a bug in mlx round impl, -2.5 -> -3 instead of -2 -btest.exclude("test_round_*") - # TODO: Investigate btest.exclude("test_operator_pad_*") btest.exclude("test_sequence_*") From 93b3cc28d04e6d623b23cfd2d07ce28c425e0368 Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Fri, 19 Jan 2024 13:06:23 -0500 Subject: [PATCH 04/57] layer norm --- mlx/onnx/__init__.py | 4 ++-- mlx/onnx/ops.py | 11 ++++++++++- 2 files changed, 12 insertions(+), 3 deletions(-) diff --git a/mlx/onnx/__init__.py b/mlx/onnx/__init__.py index 5a66073..47ca3f5 100644 --- a/mlx/onnx/__init__.py +++ b/mlx/onnx/__init__.py @@ -108,6 +108,6 @@ def run(self, inputs, **kwargs: Any) -> Tuple[mx.array, ...]: if not isinstance(res, tuple): res = (res,) - for i in range(len(node.output)): - self._cache[node.output[i]] = res[i] + for name, out in zip(node.output, res): + self._cache[name] = out return tuple(self._cache[out.name] for out in self._model.graph.output) diff --git a/mlx/onnx/ops.py b/mlx/onnx/ops.py index 15c8651..41cbee0 100644 --- a/mlx/onnx/ops.py +++ b/mlx/onnx/ops.py @@ -310,7 +310,7 @@ def Shrink(x: mx.array, bias=0.0, lambd=0.5): def Reshape(x: mx.array, shape: mx.array, allowzero=0): new_shape = [ - d if d != 0 else (0 if allowzero else x.shape[i]) + int(d) if d != 0 else (0 if allowzero else x.shape[i]) for i, d in enumerate(shape.tolist()) ] return x.reshape(new_shape) @@ -493,3 +493,12 @@ def Slice( for start, end, axe, step in zip(starts, ends, axes, steps): slices[axe.item()] = slice(start.item(), end.item(), step.item()) return x[tuple(slices)] + +def LayerNormalization(x: mx.array, scale: mx.array, bias: mx.array, axis=-1, stash_type=1, epsilon=1e-5): + axis = [i for i in range(axis if axis >= 0 else x.ndim + axis, x.ndim)] + t = layers.LayerNorm(dims=0, eps=epsilon) + setattr(t, "weight", scale) + setattr(t, "bias", bias) + mean = x.mean(axis=axis, keepdims=True) + invstd = (((x - mean) ** 2).mean(axis=axis, keepdims=True) + epsilon).rsqrt() + return t(x, axis=axis), mean, invstd From 359b977614f634d977a729dfcc3851f3479df58b Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Fri, 19 Jan 2024 13:32:48 -0500 Subject: [PATCH 05/57] slice op --- mlx/onnx/__init__.py | 7 ++++++- mlx/onnx/ops.py | 17 +++++++++++++++++ 2 files changed, 23 insertions(+), 1 deletion(-) diff --git a/mlx/onnx/__init__.py b/mlx/onnx/__init__.py index 47ca3f5..fef3568 100644 --- a/mlx/onnx/__init__.py +++ b/mlx/onnx/__init__.py @@ -100,7 +100,12 @@ def run(self, inputs, **kwargs: Any) -> Tuple[mx.array, ...]: args = [self._cache[x] for x in node.input] opt = self.parse_attributes(node.attribute) - if hasattr(onnx_ops, node.op_type): + # Special case for split as outputs might need to be inferred from node + if node.op_type == "Split": + if "num_outputs" not in opt and len(args) != 2: + opt["num_outputs"] = len(node.output) + res = getattr(onnx_ops, node.op_type)(*args, **opt) + elif hasattr(onnx_ops, node.op_type): res = getattr(onnx_ops, node.op_type)(*args, **opt) else: raise NotImplementedError(f"Operation {node.op_type} not implemented") diff --git a/mlx/onnx/ops.py b/mlx/onnx/ops.py index 41cbee0..4f0553a 100644 --- a/mlx/onnx/ops.py +++ b/mlx/onnx/ops.py @@ -502,3 +502,20 @@ def LayerNormalization(x: mx.array, scale: mx.array, bias: mx.array, axis=-1, st mean = x.mean(axis=axis, keepdims=True) invstd = (((x - mean) ** 2).mean(axis=axis, keepdims=True) + epsilon).rsqrt() return t(x, axis=axis), mean, invstd + +def Split(x: mx.array, split: Optional[mx.array]=None, num_outputs=None, axis=0): + if split is None: + if x.shape[axis] % num_outputs == 0: + split = [x.shape[axis] // num_outputs] * num_outputs + else: + cnt = math.ceil(x.shape[axis] / num_outputs) + split = [cnt] * (num_outputs - 1) + [x.shape[axis] - cnt * (num_outputs - 1)] + split = mx.array(split, dtype=mx.int64) + sli = [slice(0, s) for s in x.shape] + res = [] + pos = 0 + for spl in split.tolist(): + sli[axis] = slice(pos, pos + spl) + pos += spl + res.append(x[tuple(sli)]) + return tuple(res) \ No newline at end of file From c81c0520b004777d046d1776cdf2b66f911bf582 Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Fri, 19 Jan 2024 14:02:55 -0500 Subject: [PATCH 06/57] fix string decode --- mlx/onnx/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mlx/onnx/__init__.py b/mlx/onnx/__init__.py index fef3568..b6c83ca 100644 --- a/mlx/onnx/__init__.py +++ b/mlx/onnx/__init__.py @@ -62,7 +62,7 @@ def parse_attributes(self, attrs): elif x.type == onnx.AttributeProto.INT: res[x.name] = int(x.i) elif x.type == onnx.AttributeProto.STRING: - res[x.name] = str(x.s) + res[x.name] = x.s.decode("utf-8") elif x.type == onnx.AttributeProto.TENSOR: res[x.name] = self.parse_array(x.t) # Sometimes this gets passed as args to functions that expect mx.array, so just converting @@ -72,7 +72,7 @@ def parse_attributes(self, attrs): elif x.type == onnx.AttributeProto.INTS: res[x.name] = mx.array([int(i) for i in x.ints], dtype=mx.int64) elif x.type == onnx.AttributeProto.STRINGS: - res[x.name] = tuple(str(s) for s in x.strings) + res[x.name] = tuple(s.decode("utf-8") for s in x.strings) elif x.type == onnx.AttributeProto.GRAPH: raise NotImplementedError(f"Attribute type graph not implemented") else: From 09e8a0a4143590bf3949fcbe81aee85d4e2154f1 Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Fri, 19 Jan 2024 14:15:29 -0500 Subject: [PATCH 07/57] add pre-commit --- .pre-commit-config.yaml | 11 +++++++++++ mlx/onnx/ops.py | 28 ++++++++++++++++++++++++---- setup.py | 3 ++- 3 files changed, 37 insertions(+), 5 deletions(-) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..f82f0bd --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,11 @@ +repos: +- repo: https://github.com/psf/black-pre-commit-mirror + rev: 23.12.1 + hooks: + - id: black +- repo: https://github.com/pycqa/isort + rev: 5.12.0 + hooks: + - id: isort + args: + - --profile=black \ No newline at end of file diff --git a/mlx/onnx/ops.py b/mlx/onnx/ops.py index 4f0553a..c68210e 100644 --- a/mlx/onnx/ops.py +++ b/mlx/onnx/ops.py @@ -494,7 +494,10 @@ def Slice( slices[axe.item()] = slice(start.item(), end.item(), step.item()) return x[tuple(slices)] -def LayerNormalization(x: mx.array, scale: mx.array, bias: mx.array, axis=-1, stash_type=1, epsilon=1e-5): + +def LayerNormalization( + x: mx.array, scale: mx.array, bias: mx.array, axis=-1, stash_type=1, epsilon=1e-5 +): axis = [i for i in range(axis if axis >= 0 else x.ndim + axis, x.ndim)] t = layers.LayerNorm(dims=0, eps=epsilon) setattr(t, "weight", scale) @@ -503,13 +506,16 @@ def LayerNormalization(x: mx.array, scale: mx.array, bias: mx.array, axis=-1, st invstd = (((x - mean) ** 2).mean(axis=axis, keepdims=True) + epsilon).rsqrt() return t(x, axis=axis), mean, invstd -def Split(x: mx.array, split: Optional[mx.array]=None, num_outputs=None, axis=0): + +def Split(x: mx.array, split: Optional[mx.array] = None, num_outputs=None, axis=0): if split is None: if x.shape[axis] % num_outputs == 0: split = [x.shape[axis] // num_outputs] * num_outputs else: cnt = math.ceil(x.shape[axis] / num_outputs) - split = [cnt] * (num_outputs - 1) + [x.shape[axis] - cnt * (num_outputs - 1)] + split = [cnt] * (num_outputs - 1) + [ + x.shape[axis] - cnt * (num_outputs - 1) + ] split = mx.array(split, dtype=mx.int64) sli = [slice(0, s) for s in x.shape] res = [] @@ -518,4 +524,18 @@ def Split(x: mx.array, split: Optional[mx.array]=None, num_outputs=None, axis=0) sli[axis] = slice(pos, pos + spl) pos += spl res.append(x[tuple(sli)]) - return tuple(res) \ No newline at end of file + return tuple(res) + + +def Conv( + x: mx.array, + weight: mx.array, + bias: Optional[mx.array] = None, + auto_pad="NOTSET", + dilations: List[int] = None, + group=1, + kernel_shape: List[int] = None, + pads: List[int] = None, + strids: List[int] = None, +): + pass diff --git a/setup.py b/setup.py index 1b827cb..c8e83c0 100644 --- a/setup.py +++ b/setup.py @@ -7,6 +7,7 @@ install_requires=["mlx", "onnx"], extras_require={ "test": ["numpy", "pytest"], + "dev": ["pre-commit"], }, packages=["mlx.onnx"], -) \ No newline at end of file +) From bafdec2ef2713cd848f182ea3ab7876a1e1fb54f Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Fri, 19 Jan 2024 15:54:29 -0500 Subject: [PATCH 08/57] conv 1d/2d support --- mlx/onnx/ops.py | 28 +++++++++++++++------------- tests/test_onnx.py | 20 +++++++++++++------- 2 files changed, 28 insertions(+), 20 deletions(-) diff --git a/mlx/onnx/ops.py b/mlx/onnx/ops.py index c68210e..be62044 100644 --- a/mlx/onnx/ops.py +++ b/mlx/onnx/ops.py @@ -526,16 +526,18 @@ def Split(x: mx.array, split: Optional[mx.array] = None, num_outputs=None, axis= res.append(x[tuple(sli)]) return tuple(res) - -def Conv( - x: mx.array, - weight: mx.array, - bias: Optional[mx.array] = None, - auto_pad="NOTSET", - dilations: List[int] = None, - group=1, - kernel_shape: List[int] = None, - pads: List[int] = None, - strids: List[int] = None, -): - pass +def Conv(x: mx.array, weight: mx.array, bias: Optional[mx.array]=None, dilations:Optional[mx.array]=None, group=1, kernel_shape:Optional[mx.array]=None, pads:Optional[mx.array]=None, strides:Optional[mx.array]=None): + assert group == 1, f"mlx only supports 1 group, got {group}" + assert all(x == 1 for x in dilations.tolist()), "mlx only supports dilation 1" + if x.ndim == 3: + if dilations and dilations.item() != 1: + raise NotImplementedError("mlx does not support dilation other than 1") + c = mx.conv1d(x.transpose(0, 2, 1), weight.transpose(0, 2, 1), padding=pads.tolist()[0], stride=strides.item()) + c = c + bias if bias is not None else c + return c.transpose(0, 2, 1) + elif x.ndim == 4: + c = mx.conv2d(x.transpose(0, 2, 3, 1), weight.transpose(0, 2, 3, 1), padding=pads.tolist()[:2], stride=strides.tolist()) + c = c + bias if bias is not None else c + return c.transpose(0, 3, 1, 2) + else: + raise NotImplementedError("mlx does not support conv other than 1d and 2d") \ No newline at end of file diff --git a/tests/test_onnx.py b/tests/test_onnx.py index 4fa13a4..da88d7d 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -31,8 +31,6 @@ def supports_device(cls, device: str) -> bool: btest = onnx.backend.test.BackendTest(TestMlxBackendWrapper, __name__) -btest.include("test_round_*") - # TODO: these are upcasting to float32 btest.exclude("test_div_uint8_cpu") btest.exclude("test_pow_types_int32_float32_cpu") @@ -58,7 +56,16 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_bitwise_*") btest.exclude("test_gathernd_*") - +# Exclude conv due to either dilation or groups +btest.exclude("test_Conv1d_dilated_cpu") +btest.exclude("test_Conv1d_groups_cpu") +btest.exclude("test_Conv2d_depthwise_cpu") +btest.exclude("test_Conv2d_depthwise_padded_cpu") +btest.exclude("test_Conv2d_depthwise_strided_cpu") +btest.exclude("test_Conv2d_depthwise_with_multiplier_cpu") +btest.exclude("test_Conv2d_dilated_cpu") +btest.exclude("test_Conv2d_groups_cpu") +btest.exclude("test_Conv3d_*") # TODO: need to go through and handle these better btest.exclude("test_cast_*") btest.exclude("test_castlike_*") @@ -68,10 +75,7 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_argmin_no_keepdims_example_select_last_index_cpu") btest.exclude("test_argmin_negative_axis_keepdims_example_select_last_index_cpu") btest.exclude("test_argmin_keepdims_example_select_last_index_cpu") - -# TODO: Reenable when float64 support is added back -btest.exclude("test_max_float64_cpu") -btest.exclude("test_min_float64_cpu") +btest.exclude("test_Conv2d_groups_thnn_cpu") # TODO: Graph tests btest.exclude("test_range_float_type_positive_delta_expanded_cpu") @@ -88,6 +92,8 @@ def supports_device(cls, device: str) -> bool: btest.exclude("string") # float64 datatype +btest.exclude("test_max_float64_cpu") +btest.exclude("test_min_float64_cpu") btest.exclude("test_reduce_log_sum_exp_*") btest.exclude("test_operator_addconstant_cpu") btest.exclude("test_operator_add_size1_singleton_broadcast_cpu") From 1e732a30a112c67473115094e9fc0bf84b592e34 Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Fri, 19 Jan 2024 16:15:50 -0500 Subject: [PATCH 09/57] added mod and optionals, removed failing tests --- mlx/onnx/ops.py | 11 ++++++++++- tests/test_onnx.py | 16 +++++++++++++++- 2 files changed, 25 insertions(+), 2 deletions(-) diff --git a/mlx/onnx/ops.py b/mlx/onnx/ops.py index be62044..fcf50bd 100644 --- a/mlx/onnx/ops.py +++ b/mlx/onnx/ops.py @@ -540,4 +540,13 @@ def Conv(x: mx.array, weight: mx.array, bias: Optional[mx.array]=None, dilations c = c + bias if bias is not None else c return c.transpose(0, 3, 1, 2) else: - raise NotImplementedError("mlx does not support conv other than 1d and 2d") \ No newline at end of file + raise NotImplementedError("mlx does not support conv other than 1d and 2d") + +def Mod(x: mx.array, y: mx.array, fmod=0): + return x % y + +def OptionalHasElement(x: Optional[mx.array]=None): + return mx.array(x is not None and len(x) > 0, dtype=mx.bool_) + +def OptionalGetElement(x: Optional[mx.array]=None): + return x if x is not None else mx.array([]) \ No newline at end of file diff --git a/tests/test_onnx.py b/tests/test_onnx.py index da88d7d..ff42bec 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -55,6 +55,8 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_gelu_tanh_*") btest.exclude("test_bitwise_*") btest.exclude("test_gathernd_*") +btest.exclude("test_tfidfvectorizer_*") +btest.exclude("test_split_to_sequence_*") # Exclude conv due to either dilation or groups btest.exclude("test_Conv1d_dilated_cpu") @@ -66,6 +68,7 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_Conv2d_dilated_cpu") btest.exclude("test_Conv2d_groups_cpu") btest.exclude("test_Conv3d_*") + # TODO: need to go through and handle these better btest.exclude("test_cast_*") btest.exclude("test_castlike_*") @@ -80,11 +83,22 @@ def supports_device(cls, device: str) -> bool: # TODO: Graph tests btest.exclude("test_range_float_type_positive_delta_expanded_cpu") btest.exclude("test_range_int32_type_negative_delta_expanded_cpu") +btest.exclude("test_scan9_sum_cpu") +btest.exclude("test_loop16_seq_none_cpu") +btest.exclude("test_loop13_seq_cpu") +btest.exclude("test_loop11_cpu") +btest.exclude("test_if_*") +btest.exclude("test_affine_grid_*") # TODO: Add gradient support btest.exclude("test_gradient_*") # TODO: Investigate +btest.exclude("test_mod_mixed_sign_int8_cpu") +btest.exclude("test_mod_mixed_sign_int64_cpu") +btest.exclude("test_mod_mixed_sign_int32_cpu") +btest.exclude("test_mod_mixed_sign_int16_cpu") + btest.exclude("test_operator_pad_*") btest.exclude("test_sequence_*") btest.exclude("test_strnorm_*") @@ -102,7 +116,7 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_operator_add_size1_right_broadcast_cpu") btest.exclude("test_cumsum_*") btest.exclude("test_eyelike_with_dtype_cpu") - +btest.exclude("test_mod_mixed_sign_float64_cpu") # skip models for x in btest.test_suite: if "OnnxBackendRealModelTest" in str(type(x)): From 4a92e5bfe9721c01b3a852c05a0afb29fd8613a5 Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Fri, 19 Jan 2024 17:17:53 -0500 Subject: [PATCH 10/57] isinf / isnan --- mlx/onnx/ops.py | 8 +++++++- tests/test_onnx.py | 4 ++++ 2 files changed, 11 insertions(+), 1 deletion(-) diff --git a/mlx/onnx/ops.py b/mlx/onnx/ops.py index fcf50bd..8b3d33b 100644 --- a/mlx/onnx/ops.py +++ b/mlx/onnx/ops.py @@ -549,4 +549,10 @@ def OptionalHasElement(x: Optional[mx.array]=None): return mx.array(x is not None and len(x) > 0, dtype=mx.bool_) def OptionalGetElement(x: Optional[mx.array]=None): - return x if x is not None else mx.array([]) \ No newline at end of file + return x if x is not None else mx.array([]) + +def IsInf(x: mx.array, detect_negative=1, detect_positive=1): + return (x == float("inf")) * bool(detect_positive) + (x == float("-inf")) * bool(detect_negative) + +def IsNaN(x: mx.array): + return x != x \ No newline at end of file diff --git a/tests/test_onnx.py b/tests/test_onnx.py index ff42bec..6a7d7cb 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -57,6 +57,10 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_gathernd_*") btest.exclude("test_tfidfvectorizer_*") btest.exclude("test_split_to_sequence_*") +btest.exclude("test_unique_*") +btest.exclude("test_einsum_*") +btest.exclude("test_image_decoder_*") +btest.exclude("test_convinteger_*") # Exclude conv due to either dilation or groups btest.exclude("test_Conv1d_dilated_cpu") From 2b501521da54b9c3cff75a8d34d1b8986652f9e2 Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Fri, 19 Jan 2024 18:40:40 -0500 Subject: [PATCH 11/57] exclude more ops, added some easy ones --- mlx/onnx/__init__.py | 4 ++- mlx/onnx/ops.py | 17 ++++++++++++- tests/test_onnx.py | 59 ++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 78 insertions(+), 2 deletions(-) diff --git a/mlx/onnx/__init__.py b/mlx/onnx/__init__.py index b6c83ca..c80bf1d 100644 --- a/mlx/onnx/__init__.py +++ b/mlx/onnx/__init__.py @@ -92,9 +92,11 @@ def run(self, inputs, **kwargs: Any) -> Tuple[mx.array, ...]: self._cache[i.name] = [mx.array(x) for x in inputs[i.name]] elif isinstance(inputs[i.name], np.ndarray): self._cache[i.name] = mx.array(inputs[i.name]) + elif inputs[i.name] is None: + self._cache[i.name] = None else: raise NotImplementedError( - f"Input type {type(inputs[i.name])} not implemented" + f"Input type {inputs[i.name]} not implemented" ) for i, node in enumerate(self._model.graph.node): args = [self._cache[x] for x in node.input] diff --git a/mlx/onnx/ops.py b/mlx/onnx/ops.py index 8b3d33b..cd72543 100644 --- a/mlx/onnx/ops.py +++ b/mlx/onnx/ops.py @@ -555,4 +555,19 @@ def IsInf(x: mx.array, detect_negative=1, detect_positive=1): return (x == float("inf")) * bool(detect_positive) + (x == float("-inf")) * bool(detect_negative) def IsNaN(x: mx.array): - return x != x \ No newline at end of file + return x != x + +def ThresholdedRelu(x: mx.array, alpha=1.0): + return mx.where(x > alpha, x, 0) + +def Binarizer(x: mx.array, threshold=0.0): + return mx.where(x > threshold, 1.0, 0.0) + +def GlobalAveragePool(x: mx.array): + return x.mean(axis=tuple(range(2, x.ndim)), keepdims=True) + +def GlobalMaxPool(x: mx.array): + return x.max(axis=tuple(range(2, x.ndim)), keepdims=True) + +def Xor(x: mx.array, y: mx.array): + return mx.where(x == y, False, True) diff --git a/tests/test_onnx.py b/tests/test_onnx.py index 6a7d7cb..d5332ae 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -47,11 +47,20 @@ def supports_device(cls, device: str) -> bool: # TODO: Implement btest.exclude("test_pad_*") + + btest.exclude("test_topk*") btest.exclude("test_maxpool_*") +btest.exclude("test_operator_maxpool_*") +btest.exclude("test_MaxPool*") btest.exclude("test_maxunpool_*") btest.exclude("test_batchnorm_*") +btest.exclude("test_BatchNorm*") + +# Note: both are instance norm btest.exclude("test_instancenorm_*") +btest.exclude("test_operator_symbolic_override_cpu") + btest.exclude("test_gelu_tanh_*") btest.exclude("test_bitwise_*") btest.exclude("test_gathernd_*") @@ -61,6 +70,56 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_einsum_*") btest.exclude("test_image_decoder_*") btest.exclude("test_convinteger_*") +btest.exclude("test_nonmaxsuppression_*") +btest.exclude("test_hardmax_*") +btest.exclude("test_scatternd_*") +btest.exclude("test_scatter_*") +btest.exclude("test_scatter_elements_*") +btest.exclude("test_gridsample_*") +btest.exclude("test_bernoulli_*") +btest.exclude("test_center_crop_pad_*") +btest.exclude("test_spacetodepth_*") +btest.exclude("test_AvgPool*") +btest.exclude("test_averagepool_*") +btest.exclude("test_roialign_*") +btest.exclude("test_nonzero_example_cpu") +btest.exclude("test_upsample_nearest_cpu") +btest.exclude("test_lppool_*") +btest.exclude("test_reversesequence_*") +btest.exclude("test_hannwindow_*") +btest.exclude("test_hammingwindow_*") +btest.exclude("test_blackmanwindow_*") +btest.exclude("test_col2im_*") +btest.exclude("test_deform_conv_*") +btest.exclude("test_basic_deform_conv_*") +btest.exclude("test_depthtospace_*") +btest.exclude("test_stft_*") +btest.exclude("test_det_*") +btest.exclude("test_dft_*") +btest.exclude("test_adagrad_*") +btest.exclude("test_momentum_*") +btest.exclude("test_nesterov_momentum_cpu") +btest.exclude("test_adam_*") +btest.exclude("test_onehot_*") +btest.exclude("test_gru_*") +btest.exclude("test_lrn_*") +btest.exclude("test_rnn_*") +btest.exclude("test_simple_rnn_*") +btest.exclude("test_compres_*") +btest.exclude("test_lstm_*") +btest.exclude("test_training_dropout_*") +btest.exclude("test_dropout_*") +btest.exclude("test_melweightmatrix_cpu") +btest.exclude("test_group_normalization_*") + +# TODO: Quantize ops +btest.exclude("test_qlinearconv_*") +btest.exclude("test_qlinearmatmul_*") +btest.exclude("test_quantizelinear_*") +btest.exclude("test_dynamicquantizelinear_*") +btest.exclude("test_dequantizelinear_*") + +btest.exclude("test_optional_has_element_empty_optional_input_cpu") # Exclude conv due to either dilation or groups btest.exclude("test_Conv1d_dilated_cpu") From 89d788ab199b1da27d17e5e65ebf90eaa4b0586a Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Sat, 20 Jan 2024 11:09:30 -0500 Subject: [PATCH 12/57] split things up so that its more readable --- mlx/onnx/__init__.py | 121 +---------------------- mlx/onnx/backend.py | 120 ++++++++++++++++++++++ mlx/onnx/{ops.py => ops/__init__.py} | 74 ++------------ mlx/onnx/ops/op_conv.py | 18 ++++ mlx/onnx/ops/op_nll.py | 15 +++ mlx/onnx/ops/op_norm.py | 14 +++ mlx/onnx/ops/op_slice.py | 18 ++++ mlx/onnx/ops/op_softmax_cross_entropy.py | 25 +++++ mlx/onnx/ops/op_split.py | 22 +++++ tests/test_onnx.py | 24 ++++- 10 files changed, 263 insertions(+), 188 deletions(-) create mode 100644 mlx/onnx/backend.py rename mlx/onnx/{ops.py => ops/__init__.py} (80%) create mode 100644 mlx/onnx/ops/op_conv.py create mode 100644 mlx/onnx/ops/op_nll.py create mode 100644 mlx/onnx/ops/op_norm.py create mode 100644 mlx/onnx/ops/op_slice.py create mode 100644 mlx/onnx/ops/op_softmax_cross_entropy.py create mode 100644 mlx/onnx/ops/op_split.py diff --git a/mlx/onnx/__init__.py b/mlx/onnx/__init__.py index c80bf1d..8a50104 100644 --- a/mlx/onnx/__init__.py +++ b/mlx/onnx/__init__.py @@ -1,120 +1 @@ -import importlib -from typing import Any, Tuple - -import mlx.core as mx -import numpy as np -import onnx -from onnx.helper import tensor_dtype_to_np_dtype - -onnx_ops = importlib.import_module("mlx.onnx.ops") - - -class MlxBackend: - def __init__(self, model: onnx.ModelProto): - self._model = model - self._cache = {} - self.initializer_arrays() - - def initializer_arrays(self): - for i in self._model.graph.initializer: - if i.name in self._cache: - continue - self._cache[i.name] = self.parse_array(i) - - def parse_array(self, inp: onnx.TensorProto) -> mx.array: - if inp.data_type == onnx.TensorProto.FLOAT and len(inp.float_data) > 0: - return mx.array( - np.array(inp.float_data, dtype=np.float32).reshape(inp.dims), - dtype=mx.float32, - ) - elif inp.data_type == onnx.TensorProto.INT32 and len(inp.int32_data) > 0: - return mx.array( - np.array(inp.int32_data, dtype=np.int32).reshape(inp.dims), - dtype=mx.int32, - ) - elif inp.data_type == onnx.TensorProto.INT64 and len(inp.int64_data) > 0: - return mx.array( - np.array(inp.int64_data, dtype=np.int64).reshape(inp.dims), - dtype=mx.int64, - ) - elif len(inp.raw_data) > 0: - return mx.array( - np.frombuffer( - inp.raw_data, dtype=tensor_dtype_to_np_dtype(inp.data_type) - ).reshape(inp.dims) - ) - else: - raise NotImplementedError( - f"Not implemented for {inp.data_type} {inp.name} {inp.dims}" - ) - - def get_input_dict(self, inputs): - input_names = [x.name for x in self._model.graph.input] - init_names = set([x.name for x in self._model.graph.initializer]) - real_inputs = [x for x in input_names if x not in init_names] - return dict(zip(real_inputs, inputs)) - - def parse_attributes(self, attrs): - res = {} - for x in attrs: - if x.type == onnx.AttributeProto.FLOAT: - res[x.name] = float(x.f) - elif x.type == onnx.AttributeProto.INT: - res[x.name] = int(x.i) - elif x.type == onnx.AttributeProto.STRING: - res[x.name] = x.s.decode("utf-8") - elif x.type == onnx.AttributeProto.TENSOR: - res[x.name] = self.parse_array(x.t) - # Sometimes this gets passed as args to functions that expect mx.array, so just converting - # them here to simplify the op code - elif x.type == onnx.AttributeProto.FLOATS: - res[x.name] = mx.array([float(f) for f in x.floats], dtype=mx.float32) - elif x.type == onnx.AttributeProto.INTS: - res[x.name] = mx.array([int(i) for i in x.ints], dtype=mx.int64) - elif x.type == onnx.AttributeProto.STRINGS: - res[x.name] = tuple(s.decode("utf-8") for s in x.strings) - elif x.type == onnx.AttributeProto.GRAPH: - raise NotImplementedError(f"Attribute type graph not implemented") - else: - raise NotImplementedError(f"Attribute type {x.type} not implemented") - return res - - def run(self, inputs, **kwargs: Any) -> Tuple[mx.array, ...]: - self.initializer_arrays() - inputs = self.get_input_dict(inputs) - for i in self._model.graph.input: - if i.name in self._cache: - continue - if i.name in inputs: - if isinstance(inputs[i.name], mx.array): - self._cache[i.name] = inputs[i.name] - elif isinstance(inputs[i.name], list): - self._cache[i.name] = [mx.array(x) for x in inputs[i.name]] - elif isinstance(inputs[i.name], np.ndarray): - self._cache[i.name] = mx.array(inputs[i.name]) - elif inputs[i.name] is None: - self._cache[i.name] = None - else: - raise NotImplementedError( - f"Input type {inputs[i.name]} not implemented" - ) - for i, node in enumerate(self._model.graph.node): - args = [self._cache[x] for x in node.input] - opt = self.parse_attributes(node.attribute) - - # Special case for split as outputs might need to be inferred from node - if node.op_type == "Split": - if "num_outputs" not in opt and len(args) != 2: - opt["num_outputs"] = len(node.output) - res = getattr(onnx_ops, node.op_type)(*args, **opt) - elif hasattr(onnx_ops, node.op_type): - res = getattr(onnx_ops, node.op_type)(*args, **opt) - else: - raise NotImplementedError(f"Operation {node.op_type} not implemented") - - if not isinstance(res, tuple): - res = (res,) - - for name, out in zip(node.output, res): - self._cache[name] = out - return tuple(self._cache[out.name] for out in self._model.graph.output) +from .backend import MlxBackend \ No newline at end of file diff --git a/mlx/onnx/backend.py b/mlx/onnx/backend.py new file mode 100644 index 0000000..34e73a3 --- /dev/null +++ b/mlx/onnx/backend.py @@ -0,0 +1,120 @@ +import importlib +from typing import Any, Tuple + +import mlx.core as mx +import numpy as np +import onnx +from onnx.helper import tensor_dtype_to_np_dtype + +onnx_ops = importlib.import_module("mlx.onnx.ops") + + +class MlxBackend: + def __init__(self, model: onnx.ModelProto): + self._model = model + self._cache = {} + self.initializer_arrays() + + def initializer_arrays(self): + for i in self._model.graph.initializer: + if i.name in self._cache: + continue + self._cache[i.name] = self.parse_array(i) + + def parse_array(self, inp: onnx.TensorProto) -> mx.array: + if inp.data_type == onnx.TensorProto.FLOAT and len(inp.float_data) > 0: + return mx.array( + np.array(inp.float_data, dtype=np.float32).reshape(inp.dims), + dtype=mx.float32, + ) + elif inp.data_type == onnx.TensorProto.INT32 and len(inp.int32_data) > 0: + return mx.array( + np.array(inp.int32_data, dtype=np.int32).reshape(inp.dims), + dtype=mx.int32, + ) + elif inp.data_type == onnx.TensorProto.INT64 and len(inp.int64_data) > 0: + return mx.array( + np.array(inp.int64_data, dtype=np.int64).reshape(inp.dims), + dtype=mx.int64, + ) + elif len(inp.raw_data) > 0: + return mx.array( + np.frombuffer( + inp.raw_data, dtype=tensor_dtype_to_np_dtype(inp.data_type) + ).reshape(inp.dims) + ) + else: + raise NotImplementedError( + f"Not implemented for {inp.data_type} {inp.name} {inp.dims}" + ) + + def get_input_dict(self, inputs): + input_names = [x.name for x in self._model.graph.input] + init_names = set([x.name for x in self._model.graph.initializer]) + real_inputs = [x for x in input_names if x not in init_names] + return dict(zip(real_inputs, inputs)) + + def parse_attributes(self, attrs): + res = {} + for x in attrs: + if x.type == onnx.AttributeProto.FLOAT: + res[x.name] = float(x.f) + elif x.type == onnx.AttributeProto.INT: + res[x.name] = int(x.i) + elif x.type == onnx.AttributeProto.STRING: + res[x.name] = x.s.decode("utf-8") + elif x.type == onnx.AttributeProto.TENSOR: + res[x.name] = self.parse_array(x.t) + # Sometimes this gets passed as args to functions that expect mx.array, so just converting + # them here to simplify the op code + elif x.type == onnx.AttributeProto.FLOATS: + res[x.name] = mx.array([float(f) for f in x.floats], dtype=mx.float32) + elif x.type == onnx.AttributeProto.INTS: + res[x.name] = mx.array([int(i) for i in x.ints], dtype=mx.int64) + elif x.type == onnx.AttributeProto.STRINGS: + res[x.name] = tuple(s.decode("utf-8") for s in x.strings) + elif x.type == onnx.AttributeProto.GRAPH: + raise NotImplementedError(f"Attribute type graph not implemented") + else: + raise NotImplementedError(f"Attribute type {x.type} not implemented") + return res + + def run(self, inputs, **kwargs: Any) -> Tuple[mx.array, ...]: + self.initializer_arrays() + inputs = self.get_input_dict(inputs) + for i in self._model.graph.input: + if i.name in self._cache: + continue + if i.name in inputs: + if isinstance(inputs[i.name], mx.array): + self._cache[i.name] = inputs[i.name] + elif isinstance(inputs[i.name], list): + self._cache[i.name] = [mx.array(x) for x in inputs[i.name]] + elif isinstance(inputs[i.name], np.ndarray): + self._cache[i.name] = mx.array(inputs[i.name]) + elif inputs[i.name] is None: + self._cache[i.name] = None + else: + raise NotImplementedError( + f"Input type {inputs[i.name]} not implemented" + ) + for i, node in enumerate(self._model.graph.node): + args = [self._cache[x] for x in node.input] + opt = self.parse_attributes(node.attribute) + + # Special case for split as outputs might need to be inferred from node + if node.op_type == "Split": + if "num_outputs" not in opt and len(args) != 2: + opt["num_outputs"] = len(node.output) + res = getattr(onnx_ops, node.op_type)(*args, **opt) + elif hasattr(onnx_ops, node.op_type): + res = getattr(onnx_ops, node.op_type)(*args, **opt) + else: + raise NotImplementedError(f"Operation {node.op_type} not implemented") + + if not isinstance(res, tuple): + res = (res,) + + for name, out in zip(node.output, res): + self._cache[name] = out + return tuple(self._cache[out.name] for out in self._model.graph.output) \ No newline at end of file diff --git a/mlx/onnx/ops.py b/mlx/onnx/ops/__init__.py similarity index 80% rename from mlx/onnx/ops.py rename to mlx/onnx/ops/__init__.py index cd72543..220f1af 100644 --- a/mlx/onnx/ops.py +++ b/mlx/onnx/ops/__init__.py @@ -4,8 +4,16 @@ import mlx.core as mx import mlx.nn.layers as layers +import mlx.nn.losses as losses import onnx +from .op_norm import LayerNormalization +from .op_nll import NegativeLogLikelihoodLoss +from .op_softmax_cross_entropy import SoftmaxCrossEntropyLoss +from .op_split import Split +from .op_conv import Conv +from .op_slice import Slice + # Reference Docs: https://onnx.ai/onnx/operators/ # Note: onnx.TensorProto.DOUBLE is not supported. @@ -478,70 +486,6 @@ def Not(x: mx.array): return ~x -def Slice( - x: mx.array, - starts: mx.array, - ends: mx.array, - axes: Optional[mx.array] = None, - steps: Optional[mx.array] = None, -): - if axes is None: - axes = mx.arange(x.ndim) - if steps is None: - steps = mx.ones(starts.shape, dtype=mx.int64) - slices = [slice(0, d) for d in x.shape] - for start, end, axe, step in zip(starts, ends, axes, steps): - slices[axe.item()] = slice(start.item(), end.item(), step.item()) - return x[tuple(slices)] - - -def LayerNormalization( - x: mx.array, scale: mx.array, bias: mx.array, axis=-1, stash_type=1, epsilon=1e-5 -): - axis = [i for i in range(axis if axis >= 0 else x.ndim + axis, x.ndim)] - t = layers.LayerNorm(dims=0, eps=epsilon) - setattr(t, "weight", scale) - setattr(t, "bias", bias) - mean = x.mean(axis=axis, keepdims=True) - invstd = (((x - mean) ** 2).mean(axis=axis, keepdims=True) + epsilon).rsqrt() - return t(x, axis=axis), mean, invstd - - -def Split(x: mx.array, split: Optional[mx.array] = None, num_outputs=None, axis=0): - if split is None: - if x.shape[axis] % num_outputs == 0: - split = [x.shape[axis] // num_outputs] * num_outputs - else: - cnt = math.ceil(x.shape[axis] / num_outputs) - split = [cnt] * (num_outputs - 1) + [ - x.shape[axis] - cnt * (num_outputs - 1) - ] - split = mx.array(split, dtype=mx.int64) - sli = [slice(0, s) for s in x.shape] - res = [] - pos = 0 - for spl in split.tolist(): - sli[axis] = slice(pos, pos + spl) - pos += spl - res.append(x[tuple(sli)]) - return tuple(res) - -def Conv(x: mx.array, weight: mx.array, bias: Optional[mx.array]=None, dilations:Optional[mx.array]=None, group=1, kernel_shape:Optional[mx.array]=None, pads:Optional[mx.array]=None, strides:Optional[mx.array]=None): - assert group == 1, f"mlx only supports 1 group, got {group}" - assert all(x == 1 for x in dilations.tolist()), "mlx only supports dilation 1" - if x.ndim == 3: - if dilations and dilations.item() != 1: - raise NotImplementedError("mlx does not support dilation other than 1") - c = mx.conv1d(x.transpose(0, 2, 1), weight.transpose(0, 2, 1), padding=pads.tolist()[0], stride=strides.item()) - c = c + bias if bias is not None else c - return c.transpose(0, 2, 1) - elif x.ndim == 4: - c = mx.conv2d(x.transpose(0, 2, 3, 1), weight.transpose(0, 2, 3, 1), padding=pads.tolist()[:2], stride=strides.tolist()) - c = c + bias if bias is not None else c - return c.transpose(0, 3, 1, 2) - else: - raise NotImplementedError("mlx does not support conv other than 1d and 2d") - def Mod(x: mx.array, y: mx.array, fmod=0): return x % y @@ -570,4 +514,4 @@ def GlobalMaxPool(x: mx.array): return x.max(axis=tuple(range(2, x.ndim)), keepdims=True) def Xor(x: mx.array, y: mx.array): - return mx.where(x == y, False, True) + return mx.where(x == y, False, True) \ No newline at end of file diff --git a/mlx/onnx/ops/op_conv.py b/mlx/onnx/ops/op_conv.py new file mode 100644 index 0000000..dd7b742 --- /dev/null +++ b/mlx/onnx/ops/op_conv.py @@ -0,0 +1,18 @@ +import mlx.core as mx +from typing import Optional + +def Conv(x: mx.array, weight: mx.array, bias: Optional[mx.array]=None, dilations:Optional[mx.array]=None, group=1, auto_pad="NOTSET", kernel_shape:Optional[mx.array]=None, pads:Optional[mx.array]=None, strides:Optional[mx.array]=None): + assert group == 1, f"mlx only supports 1 group, got {group}" + if dilations is not None: + assert all(x == 1 for x in dilations.tolist()), "mlx only supports dilation 1" + if x.ndim == 3: + c = mx.conv1d(x.transpose(0, 2, 1), weight.transpose(0, 2, 1), padding=pads.tolist()[0] if pads is not None else 0, stride=strides.tolist()[0] if strides is not None else 1) + c = c + bias if bias is not None else c + return c.transpose(0, 2, 1) + elif x.ndim == 4: + c = mx.conv2d(x.transpose(0, 2, 3, 1), weight.transpose(0, 2, 3, 1), padding=pads.tolist()[:2] if pads is not None else 0, stride=strides.tolist() if strides is not None else 1) + c = c + bias if bias is not None else c + return c.transpose(0, 3, 1, 2) + else: + raise NotImplementedError("mlx does not support conv other than 1d and 2d") + \ No newline at end of file diff --git a/mlx/onnx/ops/op_nll.py b/mlx/onnx/ops/op_nll.py new file mode 100644 index 0000000..dae0b39 --- /dev/null +++ b/mlx/onnx/ops/op_nll.py @@ -0,0 +1,15 @@ +import mlx.core as mx +from typing import Optional + +def NegativeLogLikelihoodLoss(scores: mx.array, target: mx.array, weight: Optional[mx.array]=None, ignore_index=None, reduction="mean"): + print(weight.shape if weight is not None else None, scores.shape, target.shape) + if ignore_index is not None: weight = mx.where(target == ignore_index, 0, weight if weight is not None else 1) + loss = -mx.take_along_axis(scores, target[..., None], 1).squeeze(-1) + if weight is not None: + weight = weight[target] + loss = loss * weight + if reduction == "mean": + return loss.mean() if weight is None else loss.sum() / weight.sum() + elif reduction == "sum": + return loss.sum() + return loss \ No newline at end of file diff --git a/mlx/onnx/ops/op_norm.py b/mlx/onnx/ops/op_norm.py new file mode 100644 index 0000000..1e209e2 --- /dev/null +++ b/mlx/onnx/ops/op_norm.py @@ -0,0 +1,14 @@ +import mlx.core as mx +import mlx.nn.layers as layers + + +def LayerNormalization( + x: mx.array, scale: mx.array, bias: mx.array, axis=-1, stash_type=1, epsilon=1e-5 +): + axis = [i for i in range(axis if axis >= 0 else x.ndim + axis, x.ndim)] + t = layers.LayerNorm(dims=0, eps=epsilon) + setattr(t, "weight", scale) + setattr(t, "bias", bias) + mean = x.mean(axis=axis, keepdims=True) + invstd = (((x - mean) ** 2).mean(axis=axis, keepdims=True) + epsilon).rsqrt() + return t(x, axis=axis), mean, invstd \ No newline at end of file diff --git a/mlx/onnx/ops/op_slice.py b/mlx/onnx/ops/op_slice.py new file mode 100644 index 0000000..b7662d3 --- /dev/null +++ b/mlx/onnx/ops/op_slice.py @@ -0,0 +1,18 @@ +import mlx.core as mx +from typing import Optional + +def Slice( + x: mx.array, + starts: mx.array, + ends: mx.array, + axes: Optional[mx.array] = None, + steps: Optional[mx.array] = None, +): + if axes is None: + axes = mx.arange(x.ndim) + if steps is None: + steps = mx.ones(starts.shape, dtype=mx.int64) + slices = [slice(0, d) for d in x.shape] + for start, end, axe, step in zip(starts, ends, axes, steps): + slices[axe.item()] = slice(start.item(), end.item(), step.item()) + return x[tuple(slices)] \ No newline at end of file diff --git a/mlx/onnx/ops/op_softmax_cross_entropy.py b/mlx/onnx/ops/op_softmax_cross_entropy.py new file mode 100644 index 0000000..10a1c54 --- /dev/null +++ b/mlx/onnx/ops/op_softmax_cross_entropy.py @@ -0,0 +1,25 @@ +from typing import Optional + +import mlx.core as mx +import mlx.nn.layers as layers + +def SoftmaxCrossEntropyLoss(scores: mx.array, labels: mx.array, weights: Optional[mx.array]=None, ignore_index=None, reduction="mean"): + C = scores.shape[1] + if ignore_index is not None: labels = mx.where(labels == ignore_index, C+1, labels) + probs = layers.log_softmax(scores, 1) + # loss = losses.cross_entropy(probs, labels, weights[labels, ....], reduction=reduction) + mask = mx.expand_dims(labels, 1) == mx.arange(C).reshape([1, C] + [1] * (scores.ndim - 2)) + loss = (mask * -probs).sum(axis=1) + if weights is not None: + weights = weights[labels, ...] + loss = loss * weights + + if reduction == "mean": + if weights is None: + loss = loss.sum() / mx.where(loss == 0, 0., 1.).sum() + else: + loss = loss.sum() / weights.sum() + elif reduction == "sum": + loss = loss.sum() + return loss, probs + \ No newline at end of file diff --git a/mlx/onnx/ops/op_split.py b/mlx/onnx/ops/op_split.py new file mode 100644 index 0000000..09bc949 --- /dev/null +++ b/mlx/onnx/ops/op_split.py @@ -0,0 +1,22 @@ +import mlx.core as mx +import math +from typing import Optional + +def Split(x: mx.array, split: Optional[mx.array] = None, num_outputs=None, axis=0): + if split is None: + if x.shape[axis] % num_outputs == 0: + split = [x.shape[axis] // num_outputs] * num_outputs + else: + cnt = math.ceil(x.shape[axis] / num_outputs) + split = [cnt] * (num_outputs - 1) + [ + x.shape[axis] - cnt * (num_outputs - 1) + ] + split = mx.array(split, dtype=mx.int64) + sli = [slice(0, s) for s in x.shape] + res = [] + pos = 0 + for spl in split.tolist(): + sli[axis] = slice(pos, pos + spl) + pos += spl + res.append(x[tuple(sli)]) + return tuple(res) \ No newline at end of file diff --git a/tests/test_onnx.py b/tests/test_onnx.py index d5332ae..2722600 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -31,6 +31,8 @@ def supports_device(cls, device: str) -> bool: btest = onnx.backend.test.BackendTest(TestMlxBackendWrapper, __name__) +# btest.include("test_sce_*") +btest.exclude("test_sce_*") # TODO: these are upcasting to float32 btest.exclude("test_div_uint8_cpu") btest.exclude("test_pow_types_int32_float32_cpu") @@ -46,9 +48,18 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_reduce_min_empty_set_cpu") # TODO: Implement -btest.exclude("test_pad_*") - - +btest.exclude("test_ZeroPad2d_*") +btest.exclude("test_ReplicationPad2d_*") +btest.exclude("test_wrap_pad_*") +btest.exclude("test_ReflectionPad2d_*") +btest.exclude("test_edge_*") + +btest.exclude("test_operator_convtranspose_cpu") +btest.exclude("test_ConvTranspose2d_*") +btest.exclude("test_ConstantPad2d_*") +btest.exclude("test_convtranspose_*") + +btest.exclude("test_PReLU_*") btest.exclude("test_topk*") btest.exclude("test_maxpool_*") btest.exclude("test_operator_maxpool_*") @@ -111,6 +122,12 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_dropout_*") btest.exclude("test_melweightmatrix_cpu") btest.exclude("test_group_normalization_*") +btest.exclude("test_resize_*") +btest.exclude("test_regex_*") + +btest.exclude("test_nllloss_*") +btest.exclude("test_optional_*") +btest.exclude("test_mvn_*") # TODO: Quantize ops btest.exclude("test_qlinearconv_*") @@ -180,6 +197,7 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_cumsum_*") btest.exclude("test_eyelike_with_dtype_cpu") btest.exclude("test_mod_mixed_sign_float64_cpu") + # skip models for x in btest.test_suite: if "OnnxBackendRealModelTest" in str(type(x)): From c325d1c0a133c6c4e4784419903be4926e8d1a9f Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Sat, 20 Jan 2024 11:17:16 -0500 Subject: [PATCH 13/57] no more errors in tests --- mlx/onnx/ops/op_conv.py | 1 + tests/test_onnx.py | 9 +++++++++ 2 files changed, 10 insertions(+) diff --git a/mlx/onnx/ops/op_conv.py b/mlx/onnx/ops/op_conv.py index dd7b742..378f60b 100644 --- a/mlx/onnx/ops/op_conv.py +++ b/mlx/onnx/ops/op_conv.py @@ -3,6 +3,7 @@ def Conv(x: mx.array, weight: mx.array, bias: Optional[mx.array]=None, dilations:Optional[mx.array]=None, group=1, auto_pad="NOTSET", kernel_shape:Optional[mx.array]=None, pads:Optional[mx.array]=None, strides:Optional[mx.array]=None): assert group == 1, f"mlx only supports 1 group, got {group}" + assert auto_pad == "NOTSET", f"only support auto_pad NOTSET, got {auto_pad}" if dilations is not None: assert all(x == 1 for x in dilations.tolist()), "mlx only supports dilation 1" if x.ndim == 3: diff --git a/tests/test_onnx.py b/tests/test_onnx.py index 2722600..372d5c9 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -53,6 +53,10 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_wrap_pad_*") btest.exclude("test_ReflectionPad2d_*") btest.exclude("test_edge_*") +btest.exclude("test_reflect_pad_cpu") +btest.exclude("test_constant_pad_negative_axes_cpu") +btest.exclude("test_constant_pad_cpu") +btest.exclude("test_constant_pad_axes_cpu") btest.exclude("test_operator_convtranspose_cpu") btest.exclude("test_ConvTranspose2d_*") @@ -129,6 +133,8 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_optional_*") btest.exclude("test_mvn_*") +btest.exclude("test_ai_onnx_ml_*") + # TODO: Quantize ops btest.exclude("test_qlinearconv_*") btest.exclude("test_qlinearmatmul_*") @@ -148,6 +154,7 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_Conv2d_dilated_cpu") btest.exclude("test_Conv2d_groups_cpu") btest.exclude("test_Conv3d_*") +btest.exclude("test_conv_with_autopad_same_cpu") # TODO: need to go through and handle these better btest.exclude("test_cast_*") @@ -159,6 +166,8 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_argmin_negative_axis_keepdims_example_select_last_index_cpu") btest.exclude("test_argmin_keepdims_example_select_last_index_cpu") btest.exclude("test_Conv2d_groups_thnn_cpu") +btest.exclude("test_scan_sum_cpu") + # TODO: Graph tests btest.exclude("test_range_float_type_positive_delta_expanded_cpu") From adace5bbc9e589f8a913f962a009addf96c34327 Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Sat, 20 Jan 2024 13:49:59 -0500 Subject: [PATCH 14/57] rm from git, not ready --- mlx/onnx/ops/__init__.py | 2 -- mlx/onnx/ops/op_nll.py | 15 -------------- mlx/onnx/ops/op_softmax_cross_entropy.py | 25 ------------------------ 3 files changed, 42 deletions(-) delete mode 100644 mlx/onnx/ops/op_nll.py delete mode 100644 mlx/onnx/ops/op_softmax_cross_entropy.py diff --git a/mlx/onnx/ops/__init__.py b/mlx/onnx/ops/__init__.py index 220f1af..b7f53bc 100644 --- a/mlx/onnx/ops/__init__.py +++ b/mlx/onnx/ops/__init__.py @@ -8,8 +8,6 @@ import onnx from .op_norm import LayerNormalization -from .op_nll import NegativeLogLikelihoodLoss -from .op_softmax_cross_entropy import SoftmaxCrossEntropyLoss from .op_split import Split from .op_conv import Conv from .op_slice import Slice diff --git a/mlx/onnx/ops/op_nll.py b/mlx/onnx/ops/op_nll.py deleted file mode 100644 index dae0b39..0000000 --- a/mlx/onnx/ops/op_nll.py +++ /dev/null @@ -1,15 +0,0 @@ -import mlx.core as mx -from typing import Optional - -def NegativeLogLikelihoodLoss(scores: mx.array, target: mx.array, weight: Optional[mx.array]=None, ignore_index=None, reduction="mean"): - print(weight.shape if weight is not None else None, scores.shape, target.shape) - if ignore_index is not None: weight = mx.where(target == ignore_index, 0, weight if weight is not None else 1) - loss = -mx.take_along_axis(scores, target[..., None], 1).squeeze(-1) - if weight is not None: - weight = weight[target] - loss = loss * weight - if reduction == "mean": - return loss.mean() if weight is None else loss.sum() / weight.sum() - elif reduction == "sum": - return loss.sum() - return loss \ No newline at end of file diff --git a/mlx/onnx/ops/op_softmax_cross_entropy.py b/mlx/onnx/ops/op_softmax_cross_entropy.py deleted file mode 100644 index 10a1c54..0000000 --- a/mlx/onnx/ops/op_softmax_cross_entropy.py +++ /dev/null @@ -1,25 +0,0 @@ -from typing import Optional - -import mlx.core as mx -import mlx.nn.layers as layers - -def SoftmaxCrossEntropyLoss(scores: mx.array, labels: mx.array, weights: Optional[mx.array]=None, ignore_index=None, reduction="mean"): - C = scores.shape[1] - if ignore_index is not None: labels = mx.where(labels == ignore_index, C+1, labels) - probs = layers.log_softmax(scores, 1) - # loss = losses.cross_entropy(probs, labels, weights[labels, ....], reduction=reduction) - mask = mx.expand_dims(labels, 1) == mx.arange(C).reshape([1, C] + [1] * (scores.ndim - 2)) - loss = (mask * -probs).sum(axis=1) - if weights is not None: - weights = weights[labels, ...] - loss = loss * weights - - if reduction == "mean": - if weights is None: - loss = loss.sum() / mx.where(loss == 0, 0., 1.).sum() - else: - loss = loss.sum() / weights.sum() - elif reduction == "sum": - loss = loss.sum() - return loss, probs - \ No newline at end of file From a494e3f3d8358e492d0a4ba256781309b6d29b72 Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Sat, 20 Jan 2024 13:58:18 -0500 Subject: [PATCH 15/57] small cleanup --- mlx/onnx/ops/__init__.py | 15 ++------------- 1 file changed, 2 insertions(+), 13 deletions(-) diff --git a/mlx/onnx/ops/__init__.py b/mlx/onnx/ops/__init__.py index b7f53bc..e84ed01 100644 --- a/mlx/onnx/ops/__init__.py +++ b/mlx/onnx/ops/__init__.py @@ -331,22 +331,11 @@ def Unsqueeze(x: mx.array, axes: mx.array): def Flatten(x: mx.array, axis=1): - return mx.reshape( - x, - ( - math.prod( - [ - 1, - ] - + x.shape[:axis] - ), - -1, - ), - ) + new_shape = math.prod([1] + x.shape[:axis]) + return mx.reshape(x, (new_shape, -1,),) def axes_helper(axes: Optional[mx.array] = None, noop_with_empty_axes=0): - # print(axes) if isinstance(axes, tuple): return axes if axes is not None and isinstance(axes, mx.array) and axes.size > 0: From 28aae68039abab1d829ca7e2d2e901d58da34236 Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Sat, 20 Jan 2024 16:04:47 -0500 Subject: [PATCH 16/57] resolve optional tests --- mlx/onnx/backend.py | 2 +- tests/test_onnx.py | 3 --- 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/mlx/onnx/backend.py b/mlx/onnx/backend.py index 34e73a3..5d50387 100644 --- a/mlx/onnx/backend.py +++ b/mlx/onnx/backend.py @@ -99,7 +99,7 @@ def run(self, inputs, **kwargs: Any) -> Tuple[mx.array, ...]: f"Input type {inputs[i.name]} not implemented" ) for i, node in enumerate(self._model.graph.node): - args = [self._cache[x] for x in node.input] + args = [self._cache[x] if x in self._cache else None for x in node.input] opt = self.parse_attributes(node.attribute) # Special case for split as outputs might need to be inferred from node diff --git a/tests/test_onnx.py b/tests/test_onnx.py index 372d5c9..3863904 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -130,7 +130,6 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_regex_*") btest.exclude("test_nllloss_*") -btest.exclude("test_optional_*") btest.exclude("test_mvn_*") btest.exclude("test_ai_onnx_ml_*") @@ -142,8 +141,6 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_dynamicquantizelinear_*") btest.exclude("test_dequantizelinear_*") -btest.exclude("test_optional_has_element_empty_optional_input_cpu") - # Exclude conv due to either dilation or groups btest.exclude("test_Conv1d_dilated_cpu") btest.exclude("test_Conv1d_groups_cpu") From 620f89b06d18406a7dd3d3f5cb853afa411d634a Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Sat, 20 Jan 2024 16:28:33 -0500 Subject: [PATCH 17/57] Sequence ops added --- mlx/onnx/ops/__init__.py | 3 +- mlx/onnx/ops/op_sequence.py | 60 +++++++++++++++++++++++++++++++++++++ tests/test_onnx.py | 3 +- 3 files changed, 64 insertions(+), 2 deletions(-) create mode 100644 mlx/onnx/ops/op_sequence.py diff --git a/mlx/onnx/ops/__init__.py b/mlx/onnx/ops/__init__.py index e84ed01..cf8bba7 100644 --- a/mlx/onnx/ops/__init__.py +++ b/mlx/onnx/ops/__init__.py @@ -8,7 +8,8 @@ import onnx from .op_norm import LayerNormalization -from .op_split import Split +from .op_split import Split +from .op_sequence import SequenceConstruct, SplitToSequence, SequenceLength, SequenceEmpty, SequenceAt, SequenceErase, ConcatFromSequence, SequenceInsert from .op_conv import Conv from .op_slice import Slice diff --git a/mlx/onnx/ops/op_sequence.py b/mlx/onnx/ops/op_sequence.py new file mode 100644 index 0000000..ac55c0c --- /dev/null +++ b/mlx/onnx/ops/op_sequence.py @@ -0,0 +1,60 @@ +import mlx.core as mx +from typing import List, Optional + +def SplitToSequence(x: mx.array, split: Optional[mx.array]=None, axis:int=0, keepdims=0): + if split is None: + split_len = [1] * x.shape[axis] + elif split.ndim == 0: + dim = x.shape[axis] + _len = split.item() + n = dim // int(_len) + split_len = [_len] * n + left = dim - _len * n + if left > 0: + split_len.append(left) + else: + split_len = split.tolist() + sli = [slice(0, s) for s in x.shape] + res = [] + pos = 0 + for spl in split_len: + sli[axis] = slice(pos, pos + spl) + pos += spl + res.append(x[tuple(sli)]) + return res + +def SequenceConstruct(*args: List[mx.array]): + return [*args] + +def SequenceLength(x): + return mx.array(len(x), dtype=mx.int64) + +def SequenceEmpty(): + return [] + +def SequenceAt(seq: List[mx.array], index: mx.array): + if isinstance(index, mx.array): + index = index.item() + return seq[index] + +def SequenceErase(seq: List[mx.array], index: Optional[mx.array]=None): + if index is None: + index = -1 + else: + index = index.item() + return seq[:index] + seq[index + 1:] + +def ConcatFromSequence(seq: List[mx.array], axis: int=0, new_axis=0): + if new_axis == 1: + sc = [s[..., None] for s in seq] + return mx.concatenate(sc, axis=axis) + return mx.concatenate(seq, axis=axis) + +def SequenceInsert(seq: List[mx.array], value: mx.array, ind=None): + if ind is not None: + ind = ind.item() + if ind is None: + seq.append(value) + else: + seq.insert(ind, value) + return seq \ No newline at end of file diff --git a/tests/test_onnx.py b/tests/test_onnx.py index 3863904..9cdcad1 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -186,12 +186,13 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_mod_mixed_sign_int16_cpu") btest.exclude("test_operator_pad_*") -btest.exclude("test_sequence_*") +btest.exclude("test_sequence_map_*") btest.exclude("test_strnorm_*") btest.exclude("test_bitshift_*") btest.exclude("string") # float64 datatype +btest.exclude("test_sequence_model7_cpu") btest.exclude("test_max_float64_cpu") btest.exclude("test_min_float64_cpu") btest.exclude("test_reduce_log_sum_exp_*") From 2caf364e59ae0cd8fe3634a67994ba38a9712869 Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Mon, 22 Jan 2024 08:43:33 -0500 Subject: [PATCH 18/57] max pool --- mlx/onnx/ops/__init__.py | 1 + mlx/onnx/ops/op_maxpool.py | 132 +++++++++++++++++++++++++++++++++++++ mlx/onnx/ops/pad.py | 21 ++++++ tests/test_onnx.py | 14 +++- 4 files changed, 165 insertions(+), 3 deletions(-) create mode 100644 mlx/onnx/ops/op_maxpool.py create mode 100644 mlx/onnx/ops/pad.py diff --git a/mlx/onnx/ops/__init__.py b/mlx/onnx/ops/__init__.py index cf8bba7..daee2e7 100644 --- a/mlx/onnx/ops/__init__.py +++ b/mlx/onnx/ops/__init__.py @@ -10,6 +10,7 @@ from .op_norm import LayerNormalization from .op_split import Split from .op_sequence import SequenceConstruct, SplitToSequence, SequenceLength, SequenceEmpty, SequenceAt, SequenceErase, ConcatFromSequence, SequenceInsert +from .op_maxpool import MaxPool from .op_conv import Conv from .op_slice import Slice diff --git a/mlx/onnx/ops/op_maxpool.py b/mlx/onnx/ops/op_maxpool.py new file mode 100644 index 0000000..54bc59d --- /dev/null +++ b/mlx/onnx/ops/op_maxpool.py @@ -0,0 +1,132 @@ +import mlx.core as mx +from typing import Optional, List +from .pad import convert_pad +import math + +def compute_strides(shape: List[int]): + return list(reversed(mx.cumprod(mx.array([1] + list(reversed(shape))))[:-1].tolist())) + +def MaxPool(x: mx.array, kernel_shape=None, auto_pad="NOTSET", ceil_mode=0, dilations:Optional[mx.array]=None, pads=None, storage_order=0, strides=None): + """ + x: [Batch, Channel, Height, Width] + storage_order: how the data is layed out in the array 0 = row, 1 = col + ceil_mode: whether to use ceil mode when output calculating the shape 1 = floor 0 = ceil + pads: [x1_begin, x2_begin...x1_end, x2_end,...] + """ + assert x.ndim >= 3, "MaxPool only supports >= 3D input" + assert auto_pad == "NOTSET", "MaxPool only supports auto_pad=NOTSET for now" + assert storage_order == 0, "MaxPool only supports storage_order=0 for now" + + if isinstance(kernel_shape, mx.array): + kernel_shape = kernel_shape.tolist() + if isinstance(strides, mx.array): + strides = strides.tolist() + if strides is None: + strides = [1] * len(kernel_shape) + if isinstance(pads, mx.array): + pads = pads.tolist() + if pads is None: + pads = [0] * len(kernel_shape) * 2 + if any([p > 0 for p in pads]): + pads = convert_pad(pads) + # if ceil_mode == 1: + # pads = [(p[0], p[1]+1) for p in pads] + x = mx.pad(x, pad_width=[(0,0), (0,0)] + pads, constant_values=float("-inf")) + + if dilations is None: + dilations = [1] * len(kernel_shape) + if isinstance(dilations, mx.array): + dilations = dilations.tolist() + if any([d > 1 for d in dilations]): + raise NotImplementedError("MaxPool does not support dilation > 1") + + if ceil_mode == 1: + x = mx.pad(x, pad_width=[(0,0), (0,0)] + [(0,1)]*(x.ndim-2), constant_values=float("-inf")) + + if x.ndim == 3: + res = _max_pool1d(x, kernel_shape, strides, ceil_mode) + elif x.ndim == 4: + res = _max_pool2d(x, kernel_shape, strides, ceil_mode) + elif x.ndim == 5: + res = _max_pool3d(x, kernel_shape, strides, ceil_mode) + + r_len, og_len = math.prod(res.shape), math.prod(x.shape) + # get the indicies + xf = x.flatten() + rf = x.flatten() + return (res) + +def _max_pool1d(x: mx.array, kernel_shape: List[int], strides: List[int], ceil_mode: int): + [bs, ch, h] = x.shape + [b_stride, c_stride, h_stride] = compute_strides(x.shape) + _rop = lambda x: math.floor(x) if ceil_mode == 0 else math.ceil(x) + windows = mx.as_strided( + x, + shape=( + bs, + ch, + _rop((h - kernel_shape[0]) / strides[0]) + 1, + kernel_shape[0], + ), + strides=( + b_stride, + c_stride, + h_stride * strides[0], + h_stride, + ) + ) + return mx.max(windows, axis=(3)) + +def _max_pool2d(x: mx.array, kernel_shape: List[int], strides: List[int], ceil_mode: int): + [bs, ch, h, w] = x.shape + [b_stride, c_stride, h_stride, w_stride] = compute_strides(x.shape) + _rop = lambda x: math.floor(x) if ceil_mode == 0 else math.ceil(x) + windows = mx.as_strided( + x, + shape=( + bs, + ch, + _rop((h - kernel_shape[0]) / strides[0]) + 1, + _rop((w - kernel_shape[1]) / strides[1]) + 1, + kernel_shape[0], + kernel_shape[1], + ), + strides=( + b_stride, + c_stride, + h_stride * strides[0], + w_stride * strides[1], + h_stride, + w_stride, + ) + ) + return mx.max(windows, axis=(4, 5)) + +def _max_pool3d(x: mx.array, kernel_shape: List[int], strides: List[int], ceil_mode: int): + [bs, ch, h, w, d] = x.shape + [b_stride, c_stride, h_stride, w_stride, d_stride] = compute_strides(x.shape) + _rop = lambda x: math.floor(x) if ceil_mode == 0 else math.ceil(x) + windows = mx.as_strided( + x, + shape=( + bs, + ch, + _rop((h - kernel_shape[0]) / strides[0]) + 1, + _rop((w - kernel_shape[1]) / strides[1]) + 1, + _rop((d - kernel_shape[2]) / strides[2]) + 1, + kernel_shape[0], + kernel_shape[1], + kernel_shape[2], + ), + strides=( + b_stride, + c_stride, + h_stride * strides[0], + w_stride * strides[1], + d_stride * strides[2], + h_stride, + w_stride, + d_stride, + ) + ) + return mx.max(windows, axis=(5, 6, 7)) \ No newline at end of file diff --git a/mlx/onnx/ops/pad.py b/mlx/onnx/ops/pad.py new file mode 100644 index 0000000..77194b9 --- /dev/null +++ b/mlx/onnx/ops/pad.py @@ -0,0 +1,21 @@ +import mlx.core as mx +from typing import List, Union, Optional +import math + +def convert_pad(onnx_pads: List[int], ndims:Optional[int]=None, axes:Optional[int]=None): + """ + Convert onnx padding to mlx padding + Onnx padding is [x1_begin, x2_begin...x1_end, x2_end,...] + Mlx padding is [(x1_begin, x1_end), (x2_begin, x2_end)...] + """ + if ndims and len(onnx_pads) // 2 != ndims: + onnx_pads = onnx_pads * ndims + if ndims is None: + ndims = len(onnx_pads) // 2 + if axes is None: + axes = list(range(ndims)) + res = [(0,0)] * ndims + naxes = len(axes) + for i in range(naxes): + res[axes[i]] = (onnx_pads[i], onnx_pads[i+naxes]) + return res diff --git a/tests/test_onnx.py b/tests/test_onnx.py index 9cdcad1..82a1389 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -65,9 +65,17 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_PReLU_*") btest.exclude("test_topk*") -btest.exclude("test_maxpool_*") -btest.exclude("test_operator_maxpool_*") -btest.exclude("test_MaxPool*") + +# TODO: Implement dilations / autopad +btest.exclude("test_maxpool_with_argmax_2d_precomputed_pads_cpu") +btest.exclude("test_maxpool_2d_dilations_cpu") +btest.exclude("test_maxpool_with_argmax_2d_precomputed_strides_cpu") +btest.exclude("test_maxpool_2d_same_*") +btest.exclude("test_maxpool_2d_precomputed_same_*") +btest.exclude("test_maxpool_3d_dilations_*") +btest.exclude("test_MaxPool1d_stride_padding_dilation_cpu") +btest.exclude("test_MaxPool2d_stride_padding_dilation_cpu") + btest.exclude("test_maxunpool_*") btest.exclude("test_batchnorm_*") btest.exclude("test_BatchNorm*") From a2cb86c62f81d1357942751f7b8b4f8079a5ee9c Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Mon, 22 Jan 2024 08:59:45 -0500 Subject: [PATCH 19/57] remove indicies test --- mlx/onnx/ops/op_maxpool.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/mlx/onnx/ops/op_maxpool.py b/mlx/onnx/ops/op_maxpool.py index 54bc59d..62569ea 100644 --- a/mlx/onnx/ops/op_maxpool.py +++ b/mlx/onnx/ops/op_maxpool.py @@ -49,11 +49,6 @@ def MaxPool(x: mx.array, kernel_shape=None, auto_pad="NOTSET", ceil_mode=0, dila res = _max_pool2d(x, kernel_shape, strides, ceil_mode) elif x.ndim == 5: res = _max_pool3d(x, kernel_shape, strides, ceil_mode) - - r_len, og_len = math.prod(res.shape), math.prod(x.shape) - # get the indicies - xf = x.flatten() - rf = x.flatten() return (res) def _max_pool1d(x: mx.array, kernel_shape: List[int], strides: List[int], ceil_mode: int): From 177239c706fd89c72b2c451838c58a49b313a05b Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Mon, 22 Jan 2024 09:19:38 -0500 Subject: [PATCH 20/57] autopad in maxpool --- mlx/onnx/ops/op_maxpool.py | 9 +++------ mlx/onnx/ops/pad.py | 25 +++++++++++++++++++++++++ tests/test_onnx.py | 4 +--- 3 files changed, 29 insertions(+), 9 deletions(-) diff --git a/mlx/onnx/ops/op_maxpool.py b/mlx/onnx/ops/op_maxpool.py index 62569ea..b918e00 100644 --- a/mlx/onnx/ops/op_maxpool.py +++ b/mlx/onnx/ops/op_maxpool.py @@ -1,6 +1,6 @@ import mlx.core as mx from typing import Optional, List -from .pad import convert_pad +from .pad import convert_pad, auto_pad as ap import math def compute_strides(shape: List[int]): @@ -14,9 +14,7 @@ def MaxPool(x: mx.array, kernel_shape=None, auto_pad="NOTSET", ceil_mode=0, dila pads: [x1_begin, x2_begin...x1_end, x2_end,...] """ assert x.ndim >= 3, "MaxPool only supports >= 3D input" - assert auto_pad == "NOTSET", "MaxPool only supports auto_pad=NOTSET for now" assert storage_order == 0, "MaxPool only supports storage_order=0 for now" - if isinstance(kernel_shape, mx.array): kernel_shape = kernel_shape.tolist() if isinstance(strides, mx.array): @@ -27,10 +25,10 @@ def MaxPool(x: mx.array, kernel_shape=None, auto_pad="NOTSET", ceil_mode=0, dila pads = pads.tolist() if pads is None: pads = [0] * len(kernel_shape) * 2 + if auto_pad != "NOTSET": + pads = ap(x.shape, auto_pad, strides, kernel_shape) if any([p > 0 for p in pads]): pads = convert_pad(pads) - # if ceil_mode == 1: - # pads = [(p[0], p[1]+1) for p in pads] x = mx.pad(x, pad_width=[(0,0), (0,0)] + pads, constant_values=float("-inf")) if dilations is None: @@ -39,7 +37,6 @@ def MaxPool(x: mx.array, kernel_shape=None, auto_pad="NOTSET", ceil_mode=0, dila dilations = dilations.tolist() if any([d > 1 for d in dilations]): raise NotImplementedError("MaxPool does not support dilation > 1") - if ceil_mode == 1: x = mx.pad(x, pad_width=[(0,0), (0,0)] + [(0,1)]*(x.ndim-2), constant_values=float("-inf")) diff --git a/mlx/onnx/ops/pad.py b/mlx/onnx/ops/pad.py index 77194b9..877431a 100644 --- a/mlx/onnx/ops/pad.py +++ b/mlx/onnx/ops/pad.py @@ -19,3 +19,28 @@ def convert_pad(onnx_pads: List[int], ndims:Optional[int]=None, axes:Optional[in for i in range(naxes): res[axes[i]] = (onnx_pads[i], onnx_pads[i+naxes]) return res + +def auto_pad(shape: List[int], auto_pad:str, strides: Optional[Union[int, List[int]]], kernel_shape: List[int]): + """ + Convert auto_pad to valid padding, valid options for auto_pad are: NOTSET, SAME_UPPER, SAME_LOWER, VALID + Default value is NOTSET which means explicit padding is used + SAME_UPPER or SAME_LOWER means pad the input so that `out_shape[i] = ceil(in_shape[i] / strides[i])` for each axis `i`. + """ + res = [] + if auto_pad == "NOTSET": + return res + if strides is None: + strides = [1] * len(kernel_shape) + if isinstance(strides, int): + strides = [strides] * len(kernel_shape) + if auto_pad in ("SAME_UPPER", "SAME_LOWER"): + for (dim, stride, kdim) in zip(shape[-len(kernel_shape):], strides, kernel_shape): + res.append((math.ceil(dim / stride)-1)*stride+((kdim-1)+1)-dim) + temp = [] + for s in res: + temp.append(s // 2) + temp.append(s-s // 2) + res = temp + return res[::2] + res[1::2] if auto_pad == "SAME_UPPER" else res[1::2] + res[::2] + + raise NotImplementedError(f"auto_pad {auto_pad} not implemented") \ No newline at end of file diff --git a/tests/test_onnx.py b/tests/test_onnx.py index 82a1389..75cf80e 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -66,12 +66,10 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_PReLU_*") btest.exclude("test_topk*") -# TODO: Implement dilations / autopad +# TODO: Implement dilations / col format btest.exclude("test_maxpool_with_argmax_2d_precomputed_pads_cpu") btest.exclude("test_maxpool_2d_dilations_cpu") btest.exclude("test_maxpool_with_argmax_2d_precomputed_strides_cpu") -btest.exclude("test_maxpool_2d_same_*") -btest.exclude("test_maxpool_2d_precomputed_same_*") btest.exclude("test_maxpool_3d_dilations_*") btest.exclude("test_MaxPool1d_stride_padding_dilation_cpu") btest.exclude("test_MaxPool2d_stride_padding_dilation_cpu") From db13d9156fd92ad7673adb59a876ba856b4b8e9d Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Mon, 22 Jan 2024 09:26:40 -0500 Subject: [PATCH 21/57] add averagepool --- mlx/onnx/ops/__init__.py | 2 +- mlx/onnx/ops/{op_maxpool.py => op_pool.py} | 48 +++++++++++++--------- tests/test_onnx.py | 4 +- 3 files changed, 31 insertions(+), 23 deletions(-) rename mlx/onnx/ops/{op_maxpool.py => op_pool.py} (69%) diff --git a/mlx/onnx/ops/__init__.py b/mlx/onnx/ops/__init__.py index daee2e7..fe198a3 100644 --- a/mlx/onnx/ops/__init__.py +++ b/mlx/onnx/ops/__init__.py @@ -10,7 +10,7 @@ from .op_norm import LayerNormalization from .op_split import Split from .op_sequence import SequenceConstruct, SplitToSequence, SequenceLength, SequenceEmpty, SequenceAt, SequenceErase, ConcatFromSequence, SequenceInsert -from .op_maxpool import MaxPool +from .op_pool import MaxPool, AveragePool from .op_conv import Conv from .op_slice import Slice diff --git a/mlx/onnx/ops/op_maxpool.py b/mlx/onnx/ops/op_pool.py similarity index 69% rename from mlx/onnx/ops/op_maxpool.py rename to mlx/onnx/ops/op_pool.py index b918e00..5ce9d62 100644 --- a/mlx/onnx/ops/op_maxpool.py +++ b/mlx/onnx/ops/op_pool.py @@ -1,5 +1,5 @@ import mlx.core as mx -from typing import Optional, List +from typing import Optional, List, Callable from .pad import convert_pad, auto_pad as ap import math @@ -7,14 +7,28 @@ def compute_strides(shape: List[int]): return list(reversed(mx.cumprod(mx.array([1] + list(reversed(shape))))[:-1].tolist())) def MaxPool(x: mx.array, kernel_shape=None, auto_pad="NOTSET", ceil_mode=0, dilations:Optional[mx.array]=None, pads=None, storage_order=0, strides=None): + return Pool(x, mx.max, float("-inf"), kernel_shape, auto_pad, ceil_mode, dilations, pads, storage_order, strides) + +def AveragePool(x: mx.array, kernel_shape=None, auto_pad="NOTSET", ceil_mode=0, dilations:Optional[mx.array]=None, pads=None, storage_order=0, strides=None): + return Pool(x, mx.mean, 0, kernel_shape, auto_pad, ceil_mode, dilations, pads, storage_order, strides) + +def Pool(x: mx.array, op: Callable[..., mx.array], pad_fill: float, kernel_shape=None, auto_pad="NOTSET", ceil_mode=0, dilations:Optional[mx.array]=None, pads=None, storage_order=0, strides=None): """ x: [Batch, Channel, Height, Width] storage_order: how the data is layed out in the array 0 = row, 1 = col ceil_mode: whether to use ceil mode when output calculating the shape 1 = floor 0 = ceil pads: [x1_begin, x2_begin...x1_end, x2_end,...] """ - assert x.ndim >= 3, "MaxPool only supports >= 3D input" - assert storage_order == 0, "MaxPool only supports storage_order=0 for now" + assert x.ndim >= 3, "Pool only supports >= 3D input" + assert storage_order == 0, "Pool only supports storage_order=0 for now" + + if dilations is None: + dilations = [1] * len(kernel_shape) + if isinstance(dilations, mx.array): + dilations = dilations.tolist() + if any([d > 1 for d in dilations]): + raise NotImplementedError("Pool does not support dilation > 1") + if isinstance(kernel_shape, mx.array): kernel_shape = kernel_shape.tolist() if isinstance(strides, mx.array): @@ -29,26 +43,20 @@ def MaxPool(x: mx.array, kernel_shape=None, auto_pad="NOTSET", ceil_mode=0, dila pads = ap(x.shape, auto_pad, strides, kernel_shape) if any([p > 0 for p in pads]): pads = convert_pad(pads) - x = mx.pad(x, pad_width=[(0,0), (0,0)] + pads, constant_values=float("-inf")) + x = mx.pad(x, pad_width=[(0,0), (0,0)] + pads, constant_values=pad_fill) - if dilations is None: - dilations = [1] * len(kernel_shape) - if isinstance(dilations, mx.array): - dilations = dilations.tolist() - if any([d > 1 for d in dilations]): - raise NotImplementedError("MaxPool does not support dilation > 1") if ceil_mode == 1: - x = mx.pad(x, pad_width=[(0,0), (0,0)] + [(0,1)]*(x.ndim-2), constant_values=float("-inf")) + x = mx.pad(x, pad_width=[(0,0), (0,0)] + [(0,1)]*(x.ndim-2), constant_values=pad_fill) if x.ndim == 3: - res = _max_pool1d(x, kernel_shape, strides, ceil_mode) + res = _pool1d(x, op, kernel_shape, strides, ceil_mode) elif x.ndim == 4: - res = _max_pool2d(x, kernel_shape, strides, ceil_mode) + res = _pool2d(x, op, kernel_shape, strides, ceil_mode) elif x.ndim == 5: - res = _max_pool3d(x, kernel_shape, strides, ceil_mode) + res = _pool3d(x, op, kernel_shape, strides, ceil_mode) return (res) -def _max_pool1d(x: mx.array, kernel_shape: List[int], strides: List[int], ceil_mode: int): +def _pool1d(x: mx.array, op: Callable[..., mx.array], kernel_shape: List[int], strides: List[int], ceil_mode: int): [bs, ch, h] = x.shape [b_stride, c_stride, h_stride] = compute_strides(x.shape) _rop = lambda x: math.floor(x) if ceil_mode == 0 else math.ceil(x) @@ -67,9 +75,9 @@ def _max_pool1d(x: mx.array, kernel_shape: List[int], strides: List[int], ceil_m h_stride, ) ) - return mx.max(windows, axis=(3)) + return op(windows, axis=(3)) -def _max_pool2d(x: mx.array, kernel_shape: List[int], strides: List[int], ceil_mode: int): +def _pool2d(x: mx.array, op: Callable[..., mx.array], kernel_shape: List[int], strides: List[int], ceil_mode: int): [bs, ch, h, w] = x.shape [b_stride, c_stride, h_stride, w_stride] = compute_strides(x.shape) _rop = lambda x: math.floor(x) if ceil_mode == 0 else math.ceil(x) @@ -92,9 +100,9 @@ def _max_pool2d(x: mx.array, kernel_shape: List[int], strides: List[int], ceil_m w_stride, ) ) - return mx.max(windows, axis=(4, 5)) + return op(windows, axis=(4, 5)) -def _max_pool3d(x: mx.array, kernel_shape: List[int], strides: List[int], ceil_mode: int): +def _pool3d(x: mx.array, op: Callable[..., mx.array], kernel_shape: List[int], strides: List[int], ceil_mode: int): [bs, ch, h, w, d] = x.shape [b_stride, c_stride, h_stride, w_stride, d_stride] = compute_strides(x.shape) _rop = lambda x: math.floor(x) if ceil_mode == 0 else math.ceil(x) @@ -121,4 +129,4 @@ def _max_pool3d(x: mx.array, kernel_shape: List[int], strides: List[int], ceil_m d_stride, ) ) - return mx.max(windows, axis=(5, 6, 7)) \ No newline at end of file + return op(windows, axis=(5, 6, 7)) \ No newline at end of file diff --git a/tests/test_onnx.py b/tests/test_onnx.py index 75cf80e..cea6d73 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -66,6 +66,8 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_PReLU_*") btest.exclude("test_topk*") +btest.exclude("test_averagepool_*") + # TODO: Implement dilations / col format btest.exclude("test_maxpool_with_argmax_2d_precomputed_pads_cpu") btest.exclude("test_maxpool_2d_dilations_cpu") @@ -100,8 +102,6 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_bernoulli_*") btest.exclude("test_center_crop_pad_*") btest.exclude("test_spacetodepth_*") -btest.exclude("test_AvgPool*") -btest.exclude("test_averagepool_*") btest.exclude("test_roialign_*") btest.exclude("test_nonzero_example_cpu") btest.exclude("test_upsample_nearest_cpu") From f037c2f6e89fee1ee949f09aebcef1e8e1f50deb Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Mon, 22 Jan 2024 09:40:13 -0500 Subject: [PATCH 22/57] properly handle count_include_pad in average pool --- mlx/onnx/ops/op_pool.py | 8 ++++++-- tests/test_onnx.py | 6 +++--- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/mlx/onnx/ops/op_pool.py b/mlx/onnx/ops/op_pool.py index 5ce9d62..f307e91 100644 --- a/mlx/onnx/ops/op_pool.py +++ b/mlx/onnx/ops/op_pool.py @@ -9,8 +9,12 @@ def compute_strides(shape: List[int]): def MaxPool(x: mx.array, kernel_shape=None, auto_pad="NOTSET", ceil_mode=0, dilations:Optional[mx.array]=None, pads=None, storage_order=0, strides=None): return Pool(x, mx.max, float("-inf"), kernel_shape, auto_pad, ceil_mode, dilations, pads, storage_order, strides) -def AveragePool(x: mx.array, kernel_shape=None, auto_pad="NOTSET", ceil_mode=0, dilations:Optional[mx.array]=None, pads=None, storage_order=0, strides=None): - return Pool(x, mx.mean, 0, kernel_shape, auto_pad, ceil_mode, dilations, pads, storage_order, strides) +def AveragePool(x: mx.array, kernel_shape=None, auto_pad="NOTSET", ceil_mode=0, dilations:Optional[mx.array]=None, pads=None, storage_order=0, strides=None, count_include_pad=0): + res = Pool(x, mx.mean, 0, kernel_shape, auto_pad, ceil_mode, dilations, pads, storage_order, strides) + if count_include_pad: + return res + div = Pool(mx.ones_like(x), mx.mean, 0, kernel_shape, auto_pad, ceil_mode, dilations, pads, storage_order, strides) + return res / div def Pool(x: mx.array, op: Callable[..., mx.array], pad_fill: float, kernel_shape=None, auto_pad="NOTSET", ceil_mode=0, dilations:Optional[mx.array]=None, pads=None, storage_order=0, strides=None): """ diff --git a/tests/test_onnx.py b/tests/test_onnx.py index cea6d73..1e21f22 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -66,9 +66,9 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_PReLU_*") btest.exclude("test_topk*") -btest.exclude("test_averagepool_*") - # TODO: Implement dilations / col format +btest.exclude("test_averagepool_2d_dilations_cpu") +btest.exclude("test_averagepool_3d_dilations_*") btest.exclude("test_maxpool_with_argmax_2d_precomputed_pads_cpu") btest.exclude("test_maxpool_2d_dilations_cpu") btest.exclude("test_maxpool_with_argmax_2d_precomputed_strides_cpu") @@ -126,7 +126,7 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_lrn_*") btest.exclude("test_rnn_*") btest.exclude("test_simple_rnn_*") -btest.exclude("test_compres_*") +btest.exclude("test_compress_*") btest.exclude("test_lstm_*") btest.exclude("test_training_dropout_*") btest.exclude("test_dropout_*") From 97c9892829791047aef95d710a4105318634a067 Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Mon, 22 Jan 2024 11:30:28 -0500 Subject: [PATCH 23/57] add padding to conv op --- mlx/onnx/ops/op_conv.py | 21 ++++++++++++++++++--- tests/test_onnx.py | 4 ++-- 2 files changed, 20 insertions(+), 5 deletions(-) diff --git a/mlx/onnx/ops/op_conv.py b/mlx/onnx/ops/op_conv.py index 378f60b..850415f 100644 --- a/mlx/onnx/ops/op_conv.py +++ b/mlx/onnx/ops/op_conv.py @@ -1,17 +1,32 @@ import mlx.core as mx from typing import Optional +from .pad import convert_pad, auto_pad as ap def Conv(x: mx.array, weight: mx.array, bias: Optional[mx.array]=None, dilations:Optional[mx.array]=None, group=1, auto_pad="NOTSET", kernel_shape:Optional[mx.array]=None, pads:Optional[mx.array]=None, strides:Optional[mx.array]=None): assert group == 1, f"mlx only supports 1 group, got {group}" - assert auto_pad == "NOTSET", f"only support auto_pad NOTSET, got {auto_pad}" + if isinstance(kernel_shape, mx.array): + kernel_shape = kernel_shape.tolist() + if isinstance(strides, mx.array): + strides = strides.tolist() + if strides is None: + strides = [1] * len(kernel_shape) + if isinstance(pads, mx.array): + pads = pads.tolist() + if pads is None: + pads = [0] * len(kernel_shape) + if x.ndim < weight.ndim: + x = mx.expand_dims(x, 0) + if auto_pad != "NOTSET": + padding = convert_pad(ap(x.shape, auto_pad, strides, kernel_shape)) + x = mx.pad(x, pad_width=[(0,0), (0,0)] + padding, constant_values=0) if dilations is not None: assert all(x == 1 for x in dilations.tolist()), "mlx only supports dilation 1" if x.ndim == 3: - c = mx.conv1d(x.transpose(0, 2, 1), weight.transpose(0, 2, 1), padding=pads.tolist()[0] if pads is not None else 0, stride=strides.tolist()[0] if strides is not None else 1) + c = mx.conv1d(x.transpose(0, 2, 1), weight.transpose(0, 2, 1), padding=pads[0] if pads is not None else 0, stride=strides[0] if strides is not None else 1) c = c + bias if bias is not None else c return c.transpose(0, 2, 1) elif x.ndim == 4: - c = mx.conv2d(x.transpose(0, 2, 3, 1), weight.transpose(0, 2, 3, 1), padding=pads.tolist()[:2] if pads is not None else 0, stride=strides.tolist() if strides is not None else 1) + c = mx.conv2d(x.transpose(0, 2, 3, 1), weight.transpose(0, 2, 3, 1), padding=pads if pads is not None else 0, stride=strides if strides is not None else 1) c = c + bias if bias is not None else c return c.transpose(0, 3, 1, 2) else: diff --git a/tests/test_onnx.py b/tests/test_onnx.py index 1e21f22..7d46c1e 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -57,6 +57,7 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_constant_pad_negative_axes_cpu") btest.exclude("test_constant_pad_cpu") btest.exclude("test_constant_pad_axes_cpu") +btest.exclude("test_operator_pad_*") btest.exclude("test_operator_convtranspose_cpu") btest.exclude("test_ConvTranspose2d_*") @@ -85,6 +86,7 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_operator_symbolic_override_cpu") btest.exclude("test_gelu_tanh_*") +btest.exclude("test_bitshift_*") btest.exclude("test_bitwise_*") btest.exclude("test_gathernd_*") btest.exclude("test_tfidfvectorizer_*") @@ -191,10 +193,8 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_mod_mixed_sign_int32_cpu") btest.exclude("test_mod_mixed_sign_int16_cpu") -btest.exclude("test_operator_pad_*") btest.exclude("test_sequence_map_*") btest.exclude("test_strnorm_*") -btest.exclude("test_bitshift_*") btest.exclude("string") # float64 datatype From fdb65a49f077b272d02057fa22b80a44481a9bfe Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Mon, 22 Jan 2024 11:33:05 -0500 Subject: [PATCH 24/57] update readme and added mnist example --- README.md | 14 ++++++++++++++ examples/mnist.py | 8 -------- examples/mnist/example.py | 12 ++++++++++++ examples/mnist/five.jpeg | Bin 0 -> 610 bytes examples/mnist/four.jpeg | Bin 0 -> 573 bytes examples/mnist/nine.jpeg | Bin 0 -> 589 bytes 6 files changed, 26 insertions(+), 8 deletions(-) delete mode 100644 examples/mnist.py create mode 100644 examples/mnist/example.py create mode 100644 examples/mnist/five.jpeg create mode 100644 examples/mnist/four.jpeg create mode 100644 examples/mnist/nine.jpeg diff --git a/README.md b/README.md index ccf2f85..787cfaa 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,16 @@ # mlx-onnx MLX support for the Open Neural Network Exchange (ONNX) + + +## Usage +```python +from mlx.onnx import MlxBackend +from onnx import hub + +model = hub.load("mnist") +backend = MlxBackend(model) +result = backend.run(...) # pass inputs to model +``` + +## Examples +[Mnist Example](./examples/mnist/example.py) \ No newline at end of file diff --git a/examples/mnist.py b/examples/mnist.py deleted file mode 100644 index d0531e3..0000000 --- a/examples/mnist.py +++ /dev/null @@ -1,8 +0,0 @@ -import mlx.core as mx -from mlx.onnx import MlxBackend -from onnx import hub - -model = hub.load("mnist") -backend = MlxBackend(model) -res = backend.run(mx.ones((1, 1, 28, 28))) -print(res) diff --git a/examples/mnist/example.py b/examples/mnist/example.py new file mode 100644 index 0000000..a032d81 --- /dev/null +++ b/examples/mnist/example.py @@ -0,0 +1,12 @@ +import mlx.core as mx +from mlx.onnx import MlxBackend +from onnx import hub +from PIL import Image +import numpy as np + +if __name__ == "__main__": + x = mx.array(np.asarray(Image.open("./nine.jpeg"))).reshape((1, 1, 28, 28)).astype(mx.float32) + model = hub.load("mnist") + backend = MlxBackend(model) + res = backend.run(x) + print(f"It was a {mx.argmax(res[0]).item()}") diff --git a/examples/mnist/five.jpeg b/examples/mnist/five.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..afe5b326029c6e4331c12215fb89d28fe93bd068 GIT binary patch literal 610 zcmV-o0-gQ;*#F=F5K2Z#MgRc;000310RRC1+Wgv=4-_35A08bV92_7dE+-%&EF&BoC^soAFflYVG#@89JvcHvE;BST|G)qX2ml-a z9036l0RO}Q9{>OW1pxs80RaI300000000010s{mE1_uZU3Jd?l0JRVR0s#X90t5pE z1q1{D00Dgg0s{a95d{(Xb($mz{*4NnC+Tr5kmO4RQxUO~0m)^>=n^d(5mLwS99s7>cy-A{8bP4h@yy?URis`OX?*~a_zRR910 literal 0 HcmV?d00001 diff --git a/examples/mnist/four.jpeg b/examples/mnist/four.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..6aad21ec0f94e6acc630e634d457a9299d0e3393 GIT binary patch literal 573 zcmV-D0>b_O*#F=F5K2Z#MgRc;000310RRC1+Wgv=4-_35A08bV92_7dE+-%&EF&BoC^soAFflYVG#@89JvcHvE;BST|G)qX2ml-a z9036l0RO}Q9{>OW1pxs80RaI300000000010s{mE1_uZU3Jd?l0JRVR0s#X90t5pE z1q1{D00Dgg0s{a95d{(Xb($mz{*4NnC+Tr5k9CTMNKnOt;D#{)l8P*#nGi*u;y zx|RGb7>7@dB%5(LJD4Z~0Q1gy$3t1w=$;zVE-yY0_?7G|uCFgHbsJbrcJY@ghzst( z=cyy|tVli^_`-NZ&7oY|!g=#9%G;>pJ!C2?rPp-W{2}me*1+A`+}>-tlTPy5I>??> z18&XZ?_ro8ILSYcnANK-PX7Q+XKM@Vn^tfG7$sya^}*(wcYk@POXl9)&vPeD^GIC& zX1l+L!d39EfR}bH{{XRpL>&W``H$56zm<9A6jh1u?=GOXg5vIZp|(gv=4-_35A08bV92_7dE+-%&EF&BoC^soAFflYVG#@89JvcHvE;BST|G)qX2ml-a z9036l0RO}Q9{>OW1pxs80RaI300000000010s{mE1_uZU3Jd?l0JRVR0s#X90t5pE z1q1{D00Dgg0s{a95d{(Xb($mz{*4NnC+Tr5k1p%cW&Lk^>n)OuA~y`t%lt!tJVTyiXLd2t*H?a@mxT%XRp+FuHIme3yu zcoxFvNYheHx`w-Xm}I+<3>~NM6znz*ewinX_{rkr)O8zQ3fW04wmKeRdFGJ2&oCQV ziylreN7p{p<N?NGoguY-QsVWJIlRBNOwyeBO@O6|!SDE2 znqEsR*VhuvOG@%HIU@wIIW-LxXHUPn()9~_`ze|mt9b;GNWE2uLsm82W?g?-xYaH# b?Vy_AhLJ6hq87jfut~u<$@eFvXIKB(7ZB=F literal 0 HcmV?d00001 From 2a6d5a2856ae4185b035302cdc541ff6173378ab Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Mon, 22 Jan 2024 11:35:52 -0500 Subject: [PATCH 25/57] updated readme with examples --- README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/README.md b/README.md index 787cfaa..d601309 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,5 @@ # mlx-onnx -MLX support for the Open Neural Network Exchange (ONNX) - +MLX support for the Open Neural Network Exchange ([ONNX](https://onnx.ai/)) ## Usage ```python From 37c0d76c5f405522043362c42e881b8a5a605c93 Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Mon, 22 Jan 2024 14:16:42 -0500 Subject: [PATCH 26/57] instance norm --- mlx/onnx/ops/__init__.py | 2 +- mlx/onnx/ops/op_norm.py | 5 +++++ tests/test_onnx.py | 7 ++----- 3 files changed, 8 insertions(+), 6 deletions(-) diff --git a/mlx/onnx/ops/__init__.py b/mlx/onnx/ops/__init__.py index fe198a3..46e2429 100644 --- a/mlx/onnx/ops/__init__.py +++ b/mlx/onnx/ops/__init__.py @@ -7,7 +7,7 @@ import mlx.nn.losses as losses import onnx -from .op_norm import LayerNormalization +from .op_norm import LayerNormalization, InstanceNormalization from .op_split import Split from .op_sequence import SequenceConstruct, SplitToSequence, SequenceLength, SequenceEmpty, SequenceAt, SequenceErase, ConcatFromSequence, SequenceInsert from .op_pool import MaxPool, AveragePool diff --git a/mlx/onnx/ops/op_norm.py b/mlx/onnx/ops/op_norm.py index 1e209e2..db885d9 100644 --- a/mlx/onnx/ops/op_norm.py +++ b/mlx/onnx/ops/op_norm.py @@ -1,6 +1,11 @@ import mlx.core as mx import mlx.nn.layers as layers +def InstanceNormalization(x: mx.array, scale: mx.array, bias: mx.array, epsilon=1e-5): + t = layers.InstanceNorm(dims=0, eps=epsilon) + setattr(t, "weight", scale.reshape([-1, 1, 1])) + setattr(t, "bias", bias.reshape([-1, 1, 1])) + return t(x, axis=(2, 3)) def LayerNormalization( x: mx.array, scale: mx.array, bias: mx.array, axis=-1, stash_type=1, epsilon=1e-5 diff --git a/tests/test_onnx.py b/tests/test_onnx.py index 7d46c1e..09314e5 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -78,12 +78,10 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_MaxPool2d_stride_padding_dilation_cpu") btest.exclude("test_maxunpool_*") + btest.exclude("test_batchnorm_*") btest.exclude("test_BatchNorm*") - -# Note: both are instance norm -btest.exclude("test_instancenorm_*") -btest.exclude("test_operator_symbolic_override_cpu") +btest.exclude("test_group_normalization_*") btest.exclude("test_gelu_tanh_*") btest.exclude("test_bitshift_*") @@ -133,7 +131,6 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_training_dropout_*") btest.exclude("test_dropout_*") btest.exclude("test_melweightmatrix_cpu") -btest.exclude("test_group_normalization_*") btest.exclude("test_resize_*") btest.exclude("test_regex_*") From c9b6cf332ed2c8b20789a89decbc360bb9e1995b Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Mon, 22 Jan 2024 15:25:21 -0500 Subject: [PATCH 27/57] resolve padding issuesin onnx tests --- mlx/onnx/ops/op_conv.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlx/onnx/ops/op_conv.py b/mlx/onnx/ops/op_conv.py index 850415f..ff054fd 100644 --- a/mlx/onnx/ops/op_conv.py +++ b/mlx/onnx/ops/op_conv.py @@ -26,7 +26,7 @@ def Conv(x: mx.array, weight: mx.array, bias: Optional[mx.array]=None, dilations c = c + bias if bias is not None else c return c.transpose(0, 2, 1) elif x.ndim == 4: - c = mx.conv2d(x.transpose(0, 2, 3, 1), weight.transpose(0, 2, 3, 1), padding=pads if pads is not None else 0, stride=strides if strides is not None else 1) + c = mx.conv2d(x.transpose(0, 2, 3, 1), weight.transpose(0, 2, 3, 1), padding=pads[:2] if pads is not None else 0, stride=strides if strides is not None else 1) c = c + bias if bias is not None else c return c.transpose(0, 3, 1, 2) else: From 7873b3944b664de7e9663642c26946b0e607173d Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Mon, 22 Jan 2024 15:26:11 -0500 Subject: [PATCH 28/57] space some code apart --- mlx/onnx/ops/op_conv.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/mlx/onnx/ops/op_conv.py b/mlx/onnx/ops/op_conv.py index ff054fd..e93fde7 100644 --- a/mlx/onnx/ops/op_conv.py +++ b/mlx/onnx/ops/op_conv.py @@ -4,6 +4,9 @@ def Conv(x: mx.array, weight: mx.array, bias: Optional[mx.array]=None, dilations:Optional[mx.array]=None, group=1, auto_pad="NOTSET", kernel_shape:Optional[mx.array]=None, pads:Optional[mx.array]=None, strides:Optional[mx.array]=None): assert group == 1, f"mlx only supports 1 group, got {group}" + if dilations is not None: + assert all(x == 1 for x in dilations.tolist()), "mlx only supports dilation 1" + if isinstance(kernel_shape, mx.array): kernel_shape = kernel_shape.tolist() if isinstance(strides, mx.array): @@ -14,13 +17,14 @@ def Conv(x: mx.array, weight: mx.array, bias: Optional[mx.array]=None, dilations pads = pads.tolist() if pads is None: pads = [0] * len(kernel_shape) + if x.ndim < weight.ndim: x = mx.expand_dims(x, 0) + if auto_pad != "NOTSET": padding = convert_pad(ap(x.shape, auto_pad, strides, kernel_shape)) x = mx.pad(x, pad_width=[(0,0), (0,0)] + padding, constant_values=0) - if dilations is not None: - assert all(x == 1 for x in dilations.tolist()), "mlx only supports dilation 1" + if x.ndim == 3: c = mx.conv1d(x.transpose(0, 2, 1), weight.transpose(0, 2, 1), padding=pads[0] if pads is not None else 0, stride=strides[0] if strides is not None else 1) c = c + bias if bias is not None else c From d0bc76e7ed69d24f6cd78733f95e503dcfa2ed9d Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Mon, 22 Jan 2024 15:33:42 -0500 Subject: [PATCH 29/57] add topk --- mlx/onnx/ops/__init__.py | 1 + mlx/onnx/ops/op_topk.py | 29 +++++++++++++++++++++++++++++ tests/test_onnx.py | 1 - 3 files changed, 30 insertions(+), 1 deletion(-) create mode 100644 mlx/onnx/ops/op_topk.py diff --git a/mlx/onnx/ops/__init__.py b/mlx/onnx/ops/__init__.py index 46e2429..3f89927 100644 --- a/mlx/onnx/ops/__init__.py +++ b/mlx/onnx/ops/__init__.py @@ -13,6 +13,7 @@ from .op_pool import MaxPool, AveragePool from .op_conv import Conv from .op_slice import Slice +from .op_topk import TopK # Reference Docs: https://onnx.ai/onnx/operators/ diff --git a/mlx/onnx/ops/op_topk.py b/mlx/onnx/ops/op_topk.py new file mode 100644 index 0000000..669b0a9 --- /dev/null +++ b/mlx/onnx/ops/op_topk.py @@ -0,0 +1,29 @@ +import mlx.core as mx + +def TopK(x: mx.array, k: mx.array, axis=-1, largest=1, sorted=1): + assert sorted == 1, "[TopK] Only sorted is supported" + if isinstance(k, mx.array): + k = k.item() + if x.ndim == 2 and axis == 1: + sample = mx.arange(x.shape[0])[:, None] + if largest == 0: + sorted_indices = mx.argpartition(x, kth=k - 1, axis=axis) + sorted_indices = sorted_indices[:, :k] + sorted_indices = sorted_indices[sample, mx.argsort(x[sample, sorted_indices])] + else: + sorted_indices = mx.argpartition(-x, kth=k-1, axis=axis) + sorted_indices = sorted_indices[:, :k] + sorted_indices = sorted_indices[sample, mx.argsort(-x[sample, sorted_indices])] + sorted_distances = x[sample, sorted_indices] + return (sorted_distances, sorted_indices.astype(mx.int64)) + + if largest == 0: + sorted_indices = mx.argsort(x, axis=axis) + sorted_values = mx.sort(x, axis=axis) + else: + sorted_indices = mx.argsort(-x, axis=axis) + sorted_values = -mx.sort(-x, axis=axis) + ark = mx.arange(k) + topk_sorted_indices = mx.take(sorted_indices, ark, axis=axis) + topk_sorted_values = mx.take(sorted_values, ark, axis=axis) + return topk_sorted_values, topk_sorted_indices.astype(mx.int64) diff --git a/tests/test_onnx.py b/tests/test_onnx.py index 09314e5..2d54092 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -65,7 +65,6 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_convtranspose_*") btest.exclude("test_PReLU_*") -btest.exclude("test_topk*") # TODO: Implement dilations / col format btest.exclude("test_averagepool_2d_dilations_cpu") From f50083dac95d6aa4c294ae58ccef059a133ff1e4 Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Mon, 22 Jan 2024 19:56:35 -0500 Subject: [PATCH 30/57] group norm --- mlx/onnx/ops/__init__.py | 2 +- mlx/onnx/ops/op_norm.py | 22 ++++++++++++++-------- tests/test_onnx.py | 4 +++- 3 files changed, 18 insertions(+), 10 deletions(-) diff --git a/mlx/onnx/ops/__init__.py b/mlx/onnx/ops/__init__.py index 3f89927..d98c6d1 100644 --- a/mlx/onnx/ops/__init__.py +++ b/mlx/onnx/ops/__init__.py @@ -7,7 +7,7 @@ import mlx.nn.losses as losses import onnx -from .op_norm import LayerNormalization, InstanceNormalization +from .op_norm import LayerNormalization, InstanceNormalization, GroupNormalization from .op_split import Split from .op_sequence import SequenceConstruct, SplitToSequence, SequenceLength, SequenceEmpty, SequenceAt, SequenceErase, ConcatFromSequence, SequenceInsert from .op_pool import MaxPool, AveragePool diff --git a/mlx/onnx/ops/op_norm.py b/mlx/onnx/ops/op_norm.py index db885d9..cb5cab4 100644 --- a/mlx/onnx/ops/op_norm.py +++ b/mlx/onnx/ops/op_norm.py @@ -1,19 +1,25 @@ import mlx.core as mx import mlx.nn.layers as layers +def layer_norm(x, axis=-1, eps=1e-5): + means = mx.mean(x, axis=axis, keepdims=True) + var = mx.var(x, axis=axis, keepdims=True) + x = (x - means) * mx.rsqrt(var + eps) + return x + +def GroupNormalization(x: mx.array, scale: mx.array, bias: mx.array, num_groups: int, epsilon=1e-5): + x_shape = x.shape + x = x.reshape([x_shape[0], num_groups, -1]) + x = layer_norm(x, axis=-1, eps=epsilon) + return (scale.reshape([-1, 1]) * x + bias.reshape([-1, 1])).reshape(x_shape) + def InstanceNormalization(x: mx.array, scale: mx.array, bias: mx.array, epsilon=1e-5): - t = layers.InstanceNorm(dims=0, eps=epsilon) - setattr(t, "weight", scale.reshape([-1, 1, 1])) - setattr(t, "bias", bias.reshape([-1, 1, 1])) - return t(x, axis=(2, 3)) + return scale.reshape([-1, 1, 1]) * layer_norm(x, axis=(2, 3), eps=epsilon) + bias.reshape([-1, 1, 1]) def LayerNormalization( x: mx.array, scale: mx.array, bias: mx.array, axis=-1, stash_type=1, epsilon=1e-5 ): axis = [i for i in range(axis if axis >= 0 else x.ndim + axis, x.ndim)] - t = layers.LayerNorm(dims=0, eps=epsilon) - setattr(t, "weight", scale) - setattr(t, "bias", bias) mean = x.mean(axis=axis, keepdims=True) invstd = (((x - mean) ** 2).mean(axis=axis, keepdims=True) + epsilon).rsqrt() - return t(x, axis=axis), mean, invstd \ No newline at end of file + return scale * layer_norm(x, axis=axis, eps=epsilon) + bias, mean, invstd \ No newline at end of file diff --git a/tests/test_onnx.py b/tests/test_onnx.py index 2d54092..90d85ca 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -79,8 +79,10 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_maxunpool_*") btest.exclude("test_batchnorm_*") +btest.exclude("test_batchnorm_example_training_mode_cpu") +btest.exclude("test_batchnorm_epsilon_training_mode_cpu") + btest.exclude("test_BatchNorm*") -btest.exclude("test_group_normalization_*") btest.exclude("test_gelu_tanh_*") btest.exclude("test_bitshift_*") From 01b41075680238f2c44e5a19287b7fd420450afe Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Mon, 22 Jan 2024 20:46:38 -0500 Subject: [PATCH 31/57] batchnorm --- mlx/onnx/ops/__init__.py | 2 +- mlx/onnx/ops/op_norm.py | 23 ++++++++++++++--------- tests/test_onnx.py | 3 +-- 3 files changed, 16 insertions(+), 12 deletions(-) diff --git a/mlx/onnx/ops/__init__.py b/mlx/onnx/ops/__init__.py index d98c6d1..cc5b205 100644 --- a/mlx/onnx/ops/__init__.py +++ b/mlx/onnx/ops/__init__.py @@ -7,7 +7,7 @@ import mlx.nn.losses as losses import onnx -from .op_norm import LayerNormalization, InstanceNormalization, GroupNormalization +from .op_norm import LayerNormalization, InstanceNormalization, GroupNormalization, BatchNormalization from .op_split import Split from .op_sequence import SequenceConstruct, SplitToSequence, SequenceLength, SequenceEmpty, SequenceAt, SequenceErase, ConcatFromSequence, SequenceInsert from .op_pool import MaxPool, AveragePool diff --git a/mlx/onnx/ops/op_norm.py b/mlx/onnx/ops/op_norm.py index cb5cab4..f82e89a 100644 --- a/mlx/onnx/ops/op_norm.py +++ b/mlx/onnx/ops/op_norm.py @@ -1,20 +1,25 @@ import mlx.core as mx -import mlx.nn.layers as layers +from typing import Optional -def layer_norm(x, axis=-1, eps=1e-5): - means = mx.mean(x, axis=axis, keepdims=True) - var = mx.var(x, axis=axis, keepdims=True) - x = (x - means) * mx.rsqrt(var + eps) - return x +def norm(x, axis=-1, eps=1e-5, mean: Optional[mx.array]=None, var: Optional[mx.array]=None): + mean = mean if mean is not None else mx.mean(x, axis=axis, keepdims=True) + var = var if var is not None else mx.rsqrt(mx.var(x, axis=axis, keepdims=True) + eps) + return (x - mean) * var + +def BatchNormalization(x: mx.array, scale: mx.array, bias: mx.array, input_mean: mx.array, input_var: mx.array, momentum=0.9, epsilon=1e-5, spatial=1): + assert spatial == 1, "Spatial BatchNorm not supported" + t_shape = [1, -1] + [1] * (x.ndim - 2) + var = mx.rsqrt(input_var + epsilon) + return norm(x, eps=epsilon, mean=input_mean.reshape(t_shape), var=var.reshape(t_shape)) * scale.reshape(t_shape) + bias.reshape(t_shape) def GroupNormalization(x: mx.array, scale: mx.array, bias: mx.array, num_groups: int, epsilon=1e-5): x_shape = x.shape x = x.reshape([x_shape[0], num_groups, -1]) - x = layer_norm(x, axis=-1, eps=epsilon) + x = norm(x, axis=-1, eps=epsilon) return (scale.reshape([-1, 1]) * x + bias.reshape([-1, 1])).reshape(x_shape) def InstanceNormalization(x: mx.array, scale: mx.array, bias: mx.array, epsilon=1e-5): - return scale.reshape([-1, 1, 1]) * layer_norm(x, axis=(2, 3), eps=epsilon) + bias.reshape([-1, 1, 1]) + return scale.reshape([-1, 1, 1]) * norm(x, axis=(2, 3), eps=epsilon) + bias.reshape([-1, 1, 1]) def LayerNormalization( x: mx.array, scale: mx.array, bias: mx.array, axis=-1, stash_type=1, epsilon=1e-5 @@ -22,4 +27,4 @@ def LayerNormalization( axis = [i for i in range(axis if axis >= 0 else x.ndim + axis, x.ndim)] mean = x.mean(axis=axis, keepdims=True) invstd = (((x - mean) ** 2).mean(axis=axis, keepdims=True) + epsilon).rsqrt() - return scale * layer_norm(x, axis=axis, eps=epsilon) + bias, mean, invstd \ No newline at end of file + return scale * norm(x, axis=axis, eps=epsilon) + bias, mean, invstd \ No newline at end of file diff --git a/tests/test_onnx.py b/tests/test_onnx.py index 90d85ca..5f86fad 100644 --- a/tests/test_onnx.py +++ b/tests/test_onnx.py @@ -78,10 +78,9 @@ def supports_device(cls, device: str) -> bool: btest.exclude("test_maxunpool_*") -btest.exclude("test_batchnorm_*") +# TODO: These are training parameters btest.exclude("test_batchnorm_example_training_mode_cpu") btest.exclude("test_batchnorm_epsilon_training_mode_cpu") - btest.exclude("test_BatchNorm*") btest.exclude("test_gelu_tanh_*") From 6a6edccdb8433c252550e1d41daa18f49b6a8cef Mon Sep 17 00:00:00 2001 From: dc-dc-dc Date: Mon, 22 Jan 2024 20:52:37 -0500 Subject: [PATCH 32/57] resnet example --- README.md | 1 + examples/resnet/example.py | 26 + examples/resnet/hen.jpg | Bin 0 -> 2843346 bytes examples/resnet/imagenet_labels.txt | 1000 +++++++++++++++++++++++++++ 4 files changed, 1027 insertions(+) create mode 100644 examples/resnet/example.py create mode 100644 examples/resnet/hen.jpg create mode 100644 examples/resnet/imagenet_labels.txt diff --git a/README.md b/README.md index d601309..c8c2666 100644 --- a/README.md +++ b/README.md @@ -12,4 +12,5 @@ result = backend.run(...) # pass inputs to model ``` ## Examples +[ResNet Example](./examples/resnet/example.py) [Mnist Example](./examples/mnist/example.py) \ No newline at end of file diff --git a/examples/resnet/example.py b/examples/resnet/example.py new file mode 100644 index 0000000..6fcac17 --- /dev/null +++ b/examples/resnet/example.py @@ -0,0 +1,26 @@ +import onnx +from mlx.onnx import MlxBackend +import mlx.core as mx +from PIL import Image +import numpy as np + +if __name__ == "__main__": + img = Image.open("./hen.jpg") + aspect_ratio = img.size[0] / img.size[1] + img = img.resize((int(224*max(aspect_ratio,1.0)), int(224*max(1.0/aspect_ratio,1.0)))) + + img = np.asarray(img, dtype=np.float32) + img -= [127.0, 127.0, 127.0] + img /= [128.0, 128.0, 128.0] + + img = img.transpose((2,0,1)) + _input = mx.expand_dims(mx.array(img).astype(mx.float32), 0) + + model = onnx.hub.load("resnet50") + backend = MlxBackend(model) + x = backend.run(_input) + + with open("./imagenet_labels.txt") as f: + labels = [l.strip() for l in f.readlines()] + out = x[0] + print("Image containes a", labels[mx.argmax(out, axis=1).item()], mx.max(out).item()) \ No newline at end of file diff --git a/examples/resnet/hen.jpg 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