rocm_jax/jax/experimental/jax2tf/README.md

JAX and TensorFlow interoperation (jax2tf/call_tf)

This package provides experimental support for interoperation between JAX and TensorFlow. There are two interoperation directions:

• jax2tf.convert: for using JAX functions in a TensorFlow context, e.g., for eager or graph execution, or for saving as a TensorFlow SavedModel; and
• jax2tf.call_tf: for using TensorFlow functions in a JAX context, e.g., to call a TensorFlow library or a SavedModel inside a JAX function.

The jax2tf.convert mechanism can wrap a function written in JAX, possibly including JAX transformations, and turn it into a function that uses only TensorFlow operations. The converted function can be called or traced from TensorFlow and will behave as if it was written in TensorFlow. In practice this means that you can take some code written in JAX and execute it using TensorFlow eager mode, or stage it out as a TensorFlow graph, even save it as a SavedModel for archival, or for use with TensorFlow tools such as serving stack, or TensorFlow Hub.

This package also contains the jax2tf.call_tf mechanism to call TensorFlow functions from JAX. These functions can be called in JAX's op-by-op execution mode, in which case the callee is executed in eager mode, or in JAX's jit (staged) context, in which case the callee is compiled to XLA and embedded in JAX's staged XLA.

Both interoperation directions rely on the ability of TensorFlow to use the XLA compiler (tf.function(jit_compile=True)). For the jax2tf.convert direction the JIT compilation of the resulting TensorFlow code ensures that the performance characteristics of the code match those of the JAX source. For the call_tf direction, JIT compilation is an essential part of the implementation mechanism. Only TensorFlow functions that can be JIT-compiled can be called from JAX. Since the TensorFlow functions that are produced by jax2tf.convert can be JIT-compiled by design, we can round-trip from JAX to TensorFlow (e.g., a SavedModel) and back.

We describe below some general concepts and capabilities, first for jax2tf.convert and later for jax2tf.call_tf.

More involved examples, including using jax2tf with Flax models and their use with TensorFlow Hub and Keras, are described in the examples directory.

For details on saving a batch-polymorphic SavedModel see below.

See also some internal ongoing design discussions at go/jax2tf-doc.

Usage: converting basic functions.

As a rule of thumb, if you can jax.jit your function then you should be able to use jax2tf.convert:

import jax
from jax.experimental import jax2tf
from jax import numpy as jnp

import numpy as np
import tensorflow as tf

def f_jax(x):
  return jnp.sin(jnp.cos(x))

# jax2tf.convert is a higher order function that returns a wrapped function with
# the same signature as your input function but accepting TensorFlow tensors (or
# variables) as input.
f_tf = jax2tf.convert(f_jax)

# For example you execute f_tf eagerly with valid TensorFlow inputs:
f_tf(np.random(...))

# Additionally you can use tools like `tf.function` to improve the execution
# time of your function, or to stage it out to a SavedModel:
f_tf_graph = tf.function(f_tf, autograph=False)

The Autograph feature of tf.function cannot be expected to work on functions converted from JAX as above, so it is recommended to set autograph=False in order to avoid warnings or outright errors.

It is a good idea to use XLA to compile the converted function; that is the scenario for which we are optimizing for numerical and performance accuracy w.r.t. the JAX execution:

tf.function(jax2tf.convert(f_jax), autograph=False, jit_compile=True)(x)

Usage: saved model

Since jax2tf provides a regular TensorFlow function using it with SavedModel is trivial:

# You can save the model just like you would with any other TensorFlow function:
my_model = tf.Module()
# Save a function that can take scalar inputs.
my_model.f = tf.function(jax2tf.convert(f_jax), input_signature=[tf.TensorSpec([], tf.float32)])
tf.saved_model.save(my_model, '/some/directory')

# Restoring (note: the restored model does *not* require JAX to run, just XLA).
restored_model = tf.saved_model.load('/some/directory')

An important point is that in the above code snippet everything is standard TensorFlow code. In particular, the saving of the model is not directly part of the jax2tf API, and the user has full control over how to create the SavedModel.

Just like for regular TensorFlow functions, it is possible to include in the SavedModel multiple versions of a function for different input shapes, by "warming up" the function on different input shapes:

my_model.f = tf.function(jax2tf.convert(f_jax), autograph=False)
my_model.f(tf.ones([1, 28, 28]))  # a batch size of 1
my_model.f(tf.ones([16, 28, 28]))  # a batch size of 16
tf.saved_model.save(my_model, '/some/directory')

For examples of how to save a Flax model as a SavedModel see the examples directory.

Differentiation

The converted code supports differentiation from TensorFlow. In order to ensure that the result of TensorFlow differentiation is identical to the one that JAX differentiation would produce, the jax2tf converter will annotate the converter function with a tf.custom_gradient that, upon TensorFlow differentiation, will lazily call into JAX to compute the jax.vjp of the converted function, followed by jax2tf conversion. This ensures that ultimately it is JAX that performs the differentiation, thus respecting any custom gradients that may be present in the original function.

The jax2tf converter has an option with_gradient=False to skip the custom gradients and wrap instead the converted function with tf.raw_ops.PreventGradient to generated an error in case a gradient computation is attempted.

SavedModels enables saving custom derivative rules by using the experimental_custom_gradients option:

options = tf.saved_model.SaveOptions(experimental_custom_gradients=True)
tf.saved_model.save(model, path, options=options)

Shape-polymorphic conversion

The shape polymorphism support is work in progress. It is meant to be sound, but it may fail to convert some programs. Please report any bugs you encounter.

We described above how to include in the SavedModel several specializations of a converted function for a few specific input shapes. The converter can also produce a shape-polymorphic TensorFlow graph that is usable with inputs of any shape matching certain constraints. This is useful, e.g., to allow a single SavedModel to be used for multiple batch sizes.

The standard TensorFlow technique for producing a shape-polymorphic graph is to warm the tf.function on partially-specified (shape-polymorphic) inputs, e.g., tf.TensorSpec([None, 28, 28], tf.float32) for a function that processes a batch (of unspecified batch size) of 28x28 images. For jax2tf it is additionally necessary to specify an additional polymorphic_shapes parameter for the jax2tf.convert function:

f_tf = tf.function(jax2tf.convert(f_jax,
                                  polymorphic_shapes=["(b, 28, 28)"]),
                                  autograph=False)
f_tf.get_concrete_function(tf.TensorSpec([None, 28, 28], tf.float32))

The polymorphic_shapes parameter, in the form of a sequence of strings corresponding to the sequence of positional arguments, introduces one or more shape variables, e.g., b, to stand for shape dimensions that are assumed to be unknown at JAX tracing time, even if the actual parameter value (here tf.TensorSpec(...)) happens to have fully known shape. Shape variables are assumed to range over all strictly positive integers. In this particular example, we can also abbreviate polymorphic_shapes=["(b, _, _)"], because the _ placeholders take their value from the corresponding dimension of the tf.TensorSpec (which must be known). As a further shortcut for a series of _ at the end of a shape specification you can use ...: polymorphic_shapes=["(b, ...)"].

In the example above, the polymorphic_shapes specification does not convey more information than the partial tf.TensorSpec, except that it gives a name to the unknown dimension, which improves error messages. The real need for named shape variables arises when there are multiple unknown dimensions and there is a relationship between them. For example, if the function to be converted is also polymorphic on the size of each image while requiring the images to be square, we would add a shape variable d to stand for the unknown image size:

f_tf = tf.function(jax2tf.convert(f_jax, polymorphic_shapes=["(b, d, d)"]), autograph=False)
f_tf.get_concrete_function(tf.TensorSpec([None, None, None], tf.float32))

The JAX tracing mechanism performs shape checking using the same strict rules as when the shapes are fully known. For example, given the "(b, d, d)" specification for the argument x of a function, JAX will know that a conditional x.shape[-2] == x.shape[-1] is True, and will also know that x and jnp.sin(x) have the same shape of a batch of square matrices that can be passed to jnp.matmul.

Correctness of shape-polymorphic tracing

We want to trust that the converted program produces the same results as the original JAX program. More precisely:

For any function f_jax and any input signature abs_sig containing partially known tf.TensorSpec, and any concrete input x whose shape matches abs_sig:

• If the conversion to TensorFlow succeeds: f_tf = tf.function(jax2tf.convert(f_jax, polymorphic_shapes)).get_concrete_function(abs_sig)
• and if the TensorFlow execution succeeds with result y: f_tf(x) = y
• then the JAX execution would produce the same result: f_jax(x) = y,

It is crucial to understand that f_jax(x) has the freedom to re-invoke the JAX tracing machinery, and in fact it does so for each distinct concrete input shape, while the generation of f_tf uses JAX tracing only once, and invoking f_tf(x) does not use JAX tracing anymore. In fact, invoking the latter invocation may happen after the f_tf has been serialized to a SavedModel and reloaded in an environment where f_jax and the JAX tracing machinery are not available anymore.

Correctness is very important because it would be nasty to debug a subtle discrepancy of the code loaded from a SavedModel from the expected behavior written in JAX. We help ensure correctness by reusing the same JAX tracing and shape checking mechanism as when the shapes are fully known.

Coverage of shape-polymorphic tracing

Besides correctness, a secondary goal is to be able to convert many shape-polymorphic programs, but at the very least batch-size-polymorphic programs, so that one SavedModel can be used for any batch sizes. For example, we want to ensure that any function written using jax.vmap at the top level can be converted with the batch dimension polymorphic and the remaining dimensions concrete.

It is reasonable to expect that there will be JAX programs for which there is a shape-polymorphic TensorFlow graph, but which will give an error when converting with jax2tf.

Details

In order to be able to use shape polymorphism effectively with jax2tf, it is worth considering what happens under the hood. When the converted function is invoked with a TensorSpec, the jax2tf converter will combine the TensorSpec from the actual argument with the polymorphic_shapes parameter to obtain a shape abstraction to be used to specialize the converted function. Normally, the shape abstraction contains the dimension sizes, but in the presence of shape polymorphism, some dimensions may be dimension variables.

The polymorphic_shapes parameter must be either None, or a sequence (one per argument) of shape specifiers. (A value None for polymorphic_shapes is equivalent to a list of None. See how optional parameters are matched to arguments.) A shape specifier is combined with a TensorSpec as follows:

• A shape specifier of None means that the shape is given by the actual argument TensorSpec, which must be fully known.

• Otherwise, the specifier must be a comma-separated string of dimension specifiers: (dim_1, ..., dim_n), denoting an n-dimensional array. The TensorSpec must also be of rank n. An ... at the end of the shape specifier is expanded to a list of _ or appropriate length. The corresponding dimensions from the shape specifier and the TensorSpec are matched:

  • the dimension specifier of _ means that the size of the dimension is given by the actual TensorSpec, which must have a known size in the corresponding dimension.
  • a dimension specifier can also be a lowercase identifier, denoting a dimension-size variable ranging over strictly positive integers. The abstract value of the dimension is going to be set to this variable. The corresponding dimension in TensorSpec can be None or can be a constant.
  • All occurrences of a shape variable in any dimension for any argument are assumed to be equal.

Note that polymorphic_shapes controls the shape abstraction used by JAX when tracing the function (with _ placeholders given by the TensorSpec). The TensorSpec gives the shape abstraction that TensorFlow will associate with the produced graph, and can be more specific.

A few examples of shape specifications and uses:

• polymorphic_shapes=["(b, _, _)", None] can be used for a function with two arguments, the first having a batch leading dimension that should be polymorphic. The other dimensions for the first argument and the shape of the second argument are specialized based on the actual TensorSpec, which must be known. The converted function can be used, e.g., with TensorSpecs [None, 28, 28] and [28, 16] for the first and second argument respectively. An alternative TensorSpec pair can be [1, 28, 28] and [28, 16], in which case the JAX tracing is done for the same polymorphic shape given by polymorphic_shapes=["(b, 28, 28)", "(28, 16)"].

• polymorphic_shapes=["(batch, _)", "(batch,)"]: the leading dimensions of the two arguments must match, and are assumed to be greater than 0. The second dimension of the first argument is taken from the actual TensorSpec. This can be used with a TensorSpec pair [None, 16] and [None]. It can also be used with a pair of shapes [8, 16] and [8].

Computing with dimension variables

JAX keeps track of the shape of all intermediate results. When those shapes contain dimension variables JAX computes intermediate shapes as multi-variate polynomials involving dimension variables, which are assumed to range over strictly positive integers. The dimension polynomials have the following behavior for arithmetic operations:

• addition, subtraction, multiplication are supported without restrictions, and are overloaded, such that +, *, np.sum, np.prod work directly on dimension polynomials. These arise, e.g., in jax.numpy.concatenate or jax.numpy.reshape.
• division is a special case. It is also overloaded, but it is only partially supported, when either (a) there is no remainder, or (b) the divisor is a constant in which case there may be a constant remainder. The need for division in JAX core arises in a couple of specific situations, e.g., jax.numpy.reshape(-1) and operations involving striding.
• equality and disequality are partially supported. They result in a boolean value only when the same result would be obtained for any valuation of the dimension variables. In other situations, an exception core.InconclusiveDimensionOperation is raised. The latter would happen, e.g., when comparing a == b or b == 1. The == and != operations are overloaded for dimension polynomials, to prevent an unsafe default behavior to be used.
• inequality is partially supported, in a similar way as equality. However, in this case we take into consideration that dimension variables range over strictly positive integers. E.g., b >= 1, b >= 0, 2 * a + b >= 3 are True, while b >= 2, a >= b, a - b >= 0 are inconclusive and result in an exception.

There are some situations when dimension variables arise in the staged computation itself. You can see in the following example how elements from the input shapes (1024, 28, 28) and (28, 28) appear in the computation and specifically in the shape parameter of the broadcast_in_dim JAX primitive.

def image_mask_jax(images, mask):
  # images: f32[B, W, W]  and mask: f32[W, W]
  return images * mask

print(jax.make_jaxpr(image_mask_jax)(np.ones((1024, 28, 28)), np.ones((28, 28))))
>> { lambda  ; a b.
>>   let c = broadcast_in_dim[ broadcast_dimensions=(1, 2)
>>                            shape=(1, 28, 28) ] b
>>      d = mul a c
>>   in (d,) }

# The following will invoke broadcast_in_dim with shape=(1, w, w)
jax2tf.convert(image_mask_jax, polymorphic_shapes=["(b, w, w)", "(w, w)"])

When tracing and converting with abstract shapes some primitive parameters will be dimension variables instead of just constants, e.g., the shape parameter of broadcast_in_dim will be (1, w, w). Note that JAX primitives distinguish the inputs, which are array values, e.g., b for broadcast_in_dim above, and the parameters, e.g., broadcast_dimensions and shape.

The conversion of image_mask_jax would use tf.shape to compute the values of the dimension variables b and w:

def image_mask_tf(images, mask):
  b, w, _ = tf.shape(images) # Compute the dynamic values for the shape variables "b" and "w"
  return tf.math.multiply(images,
                          tf.broadcast_to(tf.reshape(mask, [1, w, w]),
                                          [b, w, w]))

To achieve this, when we start converting a function we construct a shape environment, mapping the shape variables in the polymorphic_shapes specification to TensorFlow expressions using tf.shape on the input parameters.

Errors in presence of shape polymorphism

When tracing with shape polymorphism we can encounter shape errors:

four_ones = np.ones((4,))
jax2tf.convert(lambda x, y: x + y,
               polymorphic_shapes=["(v,)", "(4,)"])(four_ones, four_ones)

with result in the error 'add got incompatible shapes for broadcasting: (v,), (4,)' because the shape abstraction is given by the polymorphic_shapes, even though the actual arguments are more specific and would actually work.

Also,

jax2tf.convert(lambda x: jnp.matmul(x, x),
             polymorphic_shapes=["(v, 4)"])(np.ones((4, 4)))

will result in the error dot_general requires contracting dimensions to have the same shape, got [4] and [v]. What is happening here is that in the process of type checking the matmul operation, JAX will want to ensure the size of the two axes is the same (v == 4). Note that v can stand for any integer greater than 0, so the value of the equality expression can be true or false. Since it is not always true that v == 4, the shape checking rules fail with the above error. Since the converted function works only for square matrices, the correct polymorphic_shapes is ["(v, v)"].

You would also encounter shape errors if the code attempts to use the dimension variables in unsupported arithmetic operations, such as in the code below that fails to compute the inferred dimension for a reshape operations:

jax2tf.convert(lambda x: jnp.reshape(x, (2, -1)),
               polymorphic_shapes=["(b, ...)"])(np.ones((4, 5, 7)))

In this case you will see the error Cannot divide evenly the sizes of shapes (b, 5, 7) and (2, -1). This is because the shape of x is (b, 5, 7), with a total size represented as the dimension polynomial 35 b, which is not divisible by 2. Note that the following will succeed:

## The resulting symbolic shape is (2, 15 b).
jax2tf.convert(lambda x: jnp.reshape(x, (2, -1)),
               polymorphic_shapes=["(b, ...)"])(np.ones((4, 5, 6)))

## The resulting symbolic shape is (6 b2, b1).
jax2tf.convert(lambda x: jnp.reshape(x, (-1, x.shape[0])),
               polymorphic_shapes=["(b1, b2, ...)"])(np.ones((4, 5, 6)))

If the user code happens to perform computations directly on dimension polynomials, it can expect it to work as described above for addition, subtraction, and multiplication, and partially for comparisons.

jax2tf.convert(lambda x: 0 if x.shape[0] + 1 == x.shape[1] else 1,
                polymorphic_shapes=["(a, b)"])(np.ones((3, 4))

will raise the exception core.InconclusiveDimensionOperation with the message Dimension polynomial comparison 'a + 1' == 'b' is inconclusive.

Finally, certain codes that use shapes in the actual computation may not yet work if those shapes are polymorphic. In the code below, the expression x.shape[0] will have the value of the shape variable v. This case is not yet implemented:

jax2tf.convert(lambda x: jnp.sum(x, axis=0) / x.shape[0],
               polymorphic_shapes=["(v, _)"])(np.ones((4, 4)))

Known issues

Incomplete TensorFlow data type coverage

There are a number of cases when the TensorFlow ops that are used by the jax2tf converter are not supported by TensorFlow for the same data types as in JAX. There is an up-to-date list of unimplemented cases.

There are two kinds of errors you may see. For the primitives in the unimplemented cases that are shown to be undefined on all devices and for all execution modes (eager, graph, compiled), e.g., lax.min for booleans, the conversion typically uses a TensorFlow operator that is not registered for a certain data type:

jax2tf.convert(lambda x: lax.min(x, x))(np.array([True]))

>>> InvalidArgumentError: Value for attr 'T' of bool is not in the list of allowed values:
>>>    bfloat16, half, float, double, uint8, int16, int32, int64;
>>>    NodeDef: {{node Minimum}};
>>>    Op<name=Minimum; signature=x:T, y:T -> z:T; attr=T:type,allowed=[DT_BFLOAT16, DT_HALF, DT_FLOAT, DT_DOUBLE, DT_UINT8, DT_INT16, DT_INT32, DT_INT64]> [Op:Minimum]

In the above cases, you should file a bug with JAX or TensorFlow, or consider changing your JAX code. We are working on eliminating this kind of problem.

In other cases, the TensorFlow op is registered for the data type, but for the eager or graph execution modes there is no TensorFlow kernel defined. Such primitives appear in the unimplemented cases as unimplemented for eager and graph, e.g., lax.sign for unsigned integers:

jax2tf.convert(lax.sign)(np.array([5], dtype=np.uint32))

>>> NotFoundError: Could not find device for node: {{node Minimum}} = Acos[T=DT_UINT32]
>>> All kernels registered for op Minimum:
>>>  device='CPU'; T in [DT_FLOAT]
>>>  device='CPU'; T in [DT_DOUBLE]
>>>  ...

In this situation, you can still run the converted program if you compile it with XLA:

tf.function(jax2tf.convert(lax.sign),
            autograph=False, jit_compile=True)(np.array([5], dtype=np.uint32))

Our priority is to ensure numerical and performance accuracy for the converted program when using XLA to compile the converted program. It is always a good idea to use XLA on the JAX-converted function.

Sometimes you cannot compile the entire TensorFlow function for your model, because in addition to the function that is converted from JAX, it may include some pre-processing TensorFlow code that is not compileable with XLA, e.g., string parsing. Even in those situations you can instruct TensorFlow to compile only the portion that originates from JAX:

def entire_tf_fun(x):
  y = preprocess_tf_fun_not_compileable(x)
  # Compile the code that is converted from JAX
  z = tf.function(jax2tf.convert(compute_jax_fn),
                  autograph=False, jit_compile=True)(y)
  return postprocess_tf_fun_not_compileable(z)

You won't be able to compile the entire_tf_fun, but you can still execute it knowing that the JAX-converted code is compiled. You can even save the function to a SavedModel, knowing that upon restore the JAX-converted code will be compiled.

For a more elaborate example, see the test test_tf_mix_jax_with_uncompileable in savedmodel_test.py.

Missing converter features

There is currently no support for pmap orxmap, nor for the collective operations. There is support for sharded_jit and pjit.

SavedModel is large (contains a large amount of source information)

The SavedModel obtained from a jax2tf.convert-ed function includes source location information. This ensures that the debugging experience is similar for JAX with XLA vs. jax2tf.convert with XLA. However, this debugging information increases the size of the SavedModel, even possibly doubling it. You can disable the generation of this metadata with the parameter include_xla_op_metadata.

SavedModel supports only first-order gradients

The jax2tf-converted function supports higher-order gradients, but when the function is saved in a SavedModel, only the first-order gradient is saved.

Converting gradients for integer-argument functions

When JAX differentiates over functions with integer arguments, the gradients will be zero-vectors with a special float0 type (see PR 4039](https://github.com/google/jax/pull/4039)). This type is translated to bfloat16 when converting to TF. For example,

def f_jax(x):  # x: int32
  return x * 2.

jax.grad(f_jax, allow_int=True)(2)
# returns a special `float0`: array((b'',), dtype=[('float0', 'V')])

jax2tf.convert(jax.grad(f_jax, allow_int=True))(2))
# returns a `bfloat16` zero: tf.Tensor(0, shape=(), dtype=bfloat16)

Different 64-bit precision in JAX and TensorFlow

JAX behaves somewhat differently than TensorFlow in the handling of 32-bit vs. 64-bit values. However, the jax2tf.convert function always behaves like the JAX function.

JAX interprets the type of Python scalars differently based on JAX_ENABLE_X64 flag. (See JAX - The Sharp Bits: Double (64bit) precision.) In the default configuration, the flag is unset, and JAX interprets Python constants as 32-bit, e.g., the type of 3.14 is float32. This is also what TensorFlow always does. JAX goes further, it forces all explicitly-specified 64-bit values to be interpreted as 32-bit:

# with JAX_ENABLE_X64=0
jnp.sin(3.14)  # Has type float32
tf.math.sin(3.14)  # Has type float32

jnp.sin(np.float64(3.14))  # Also has type float32
tf.math.sin(np.float64(3.14))  # Has type float64

# The jax2tf.convert function behaves like the JAX function.
jax2tf.convert(jnp.sin)(3.14)  # Has type float32
jax2tf.convert(jnp.sin)(np.float64(3.14))  # Has type float32

# The following will still compute `sin` in float32 (with a tf.cast on the argument).
tf.function(jax2tf.convert(jnp.sin))(tf.Variable(3.14, tf.float64))

When the JAX_ENABLE_X64 flas is set, JAX uses 64-bit types for Python scalars and respects the explicit 64-bit types:

# with JAX_ENABLE_X64=1
jnp.sin(3.14)  # Has type float64
tf.math.sin(3.14)  # Has type float32

# The jax2tf.convert function behaves like the JAX function.
jax2tf.convert(jnp.sin)(3.14)  # Has type float64

# The following will compute `sin` in float64.
tf.function(jax2tf.convert(jnp.sin))(tf.Variable(3.14, tf.float64))

# The following will compute `sin` in float32.
tf.function(jax2tf.convert(jnp.sin))(tf.Variable(3.14))

This is achieved by inserting tf.cast operations on the input arguments inside the converted function, if necessary.

If you want to create a tf.Variable or tf.TensorSpec with the same dtype, you should use jax2tf.dtype_of_val:

# The following two calls will convert jax_fun at the same dtypes
# independently of the value of JAX_ENABLE_X64.
jax2tf.convert(jax_fun)(3.14)
jax2tf.convert(jax_fun)(tf.Variable(3.14, dtype=jax2tf.dtype_of_val(3.14))

Unchecked assumption that the dimension variables take strictly positive values

The shape polymorphic conversion is sound with the assumption that the dimension variables take non-zero values. In the following example, the function to be converted has different behavior for empty shapes. The broken assumption is caught by jax2tf if the converted function is executed eagerly, but not if it is first traced to a TensorFlow graph:

def f_jax(x):
  return 0 if x.shape[0] == 0 else 1

x0 = np.array([], np.float32)
self.assertEqual(0, f_jax(x0))  # JAX sees that the x.shape[0] == 0

# jax2tf catches the broken assumption b >= 1 if the converted function is executed
# eagerly.
# Raises: ValueError: PolyShape 'b' has dimension variable 'b' corresponding to 0, for argument shape (0,)
jax2tf.convert(f_jax, polymorphic_shapes=["b"])(x0))

# However, if we first trace to a TensorFlow graph, we may miss the broken assumption:
f_tf = tf.function(
        jax2tf.convert(f_jax, polymorphic_shapes=["b"])).get_concrete_function(tf.TensorSpec([None], dtype=np.float32))
self.assertEqual(1, f_tf(x0))

Another possible source of unsoundness is that JAX assumes that all unknown dimensions represented by the same dimension variable have equal size. As before, this assumption is checked if the converted function is executed eagerly, but it may be missed if it is first traced to a TensorFlow graph:

def f_jax(x):
  return 0 if x.shape[0] != x.shape[1] else 1

x45 = np.ones((4, 5), dtype=np.float32)
self.assertEqual(0, f_jax(x45))  # JAX seems that x.shape[0] != x.shape[1]

# jax2tf catches the broken assumption x.shape[0] == x.shape[1] if the converted
# function is executed eagerly.
# Raises: ValueError: PolyShape 'b, b' has dimension variable 'b' corresponding to multiple values ([4, 5]), for argument shape (4, 5)
jax2tf.convert(f_jax, polymorphic_shapes=["b, b"])(x45)

# However, if we first trace to a TensorFlow graph, we may miss the broken assumption.
f_tf = tf.function(
    jax2tf.convert(f_jax, polymorphic_shapes=["b, b"])).get_concrete_function(tf.TensorSpec([None, None], dtype=np.float32))
self.assertEqual(1, f_tf(x45))

TensorFlow XLA ops

For most JAX primitives there is a natural TF op that fits the needed semantics. There are a few (listed below) JAX primitives for which there is no single TF op with matching semantics. This is not so surprising, because JAX primitives have been designed to be compiled to HLO ops, while the corresponding TF ops are sometimes higher-level. For the cases when there is no matching canonical TF op, we use a set of special TF ops that are thin wrappers over HLO ops (a subset of those registered in tf2xla/ops/xla_ops.cc and implemented in, e.g., tf2xla/kernels/xla_pad_op.cc.) We refer to these ops here as the TFXLA ops.

There are several drawbacks of using TFXLA ops:

• These ops will only be executable by a consumer that has XLA linked in. This should not be a problem for TPU execution, since that requires XLA anyway. But for other platforms (CPU, GPU, embedded) this can be a drawback in certain settings.
• These ops are not yet recognized by tools that process tf.Graph, e.g., TensorFlow.js converter.

We use the following TFXLA ops:

• XlaPad (wraps XLA Pad operator). We use this instead of tf.pad in order to support lax.pad interior padding (dilation) or negative edge padding.
• XlaConv and XlaConv2 (wrap XLA ConvGeneralDilated operator).
• XlaDot and XlaDotV2 (wrap XLA DotGeneral operator).
• XlaGather (wraps XLA Gather operator). We could use tf.gather in some cases but not always. Also, tf.gather has a different semantics than lax.gather for index out of bounds.
• XlaScatter (wraps XLA Scatter operator).
• XlaSelectAndScatter (wraps XLA SelectAndScatter operator).
• XlaDynamicSlice (wraps XLA DynamicSlice operator). We use this instead of tf.slice for reasons explained above for XlaGather.
• XlaDynamicUpdateSlice (wraps XLA DynamicUpdateSlice operator).
• XlaReduceWindow (wraps XLA ReduceWindow operator). These are used for lax.reduce_window_sum_p, lax.reduce_window_min_p, lax.reduce_window_max_p, and lax.reduce_window_p.
• XlaVariadicSort (wraps XLA Sort operator).

Different performance characteristics

The converted code may have slightly different performance characteristics than the original JAX code. We do expect that the performance characteristics of converted code should approximate those of JAX when used with the XLA compiler (tf.function(jit_compile=True)). This is because during conversion we try to generate one TensorFlow op for one JAX primitive. We expect that the lowering that XLA does is similar to that done by JAX before conversion. (This is a hypothesis, we have not yet verified it extensively.)

There is one know case when the performance of the converted code will be different. JAX programs use a stateless deterministic PRNG and it has an internal JAX primitive for it. This primitive is at the moment converted to a soup of tf.bitwise operations, which has a clear performance penalty. We plan to look into using the HLO RNGBitGenerator (exposed as a TFXLA op), which does implement the same basic Threefry algorithm as JAX’s PRNG, although that would result in different results than JAX’s PRNG.

In absence of TensorFlow XLA compilation, if one were to write the same functionality in JAX idiomatic code vs. native TensorFlow idiomatic code we could end up with very different compilation paths. Take for example, the case of batch normalization. In TensorFlow if one uses tf.nn.batch_normalization, a “high-level” TensorFlow op for batch normalization is generated, and in the absence of XLA, on CPU or GPU, a custom C++ “high-level” kernel implementing batch normalization is executed. In JAX, there is no primitive for batch normalization, and instead the operation is decomposed into low-level primitives (e.g., flax.nn.BatchNorm, or haiku.BatchNorm). Once those primitives are converted to TensorFlow, and the resulting code is run without XLA, the ensemble of the kernels executed will quite possibly behave differently, performance-wise or even numerically, than either the TensorFlow native or JAX native batch normalization. A similar example is that of an LSTM cell.

Calling TensorFlow functions from JAX

The function call_tf allows JAX functions to call TensorFlow functions. These functions can be called anywhere in a JAX computation, including in staging contexts jax.jit, jax.pmap, jax.xmap, or inside JAX's control-flow primitives. In non-staging contexts, the TensorFlow function is called in eager mode. For now, only reverse-mode autodiff is supported for these functions (no forward-mode autodiff, nor vmap).

As a trivial example, consider computing sin(cos(1.)) with sin done in JAX and cos in TF:

  from jax.experimental import jax2tf

  # This is a TF function. It will be called with TensorFlow-compatible arguments,
  # such as `numpy.ndarray`, `tf.Tensor` or `tf.Variable`, or a pytree thereof.
  # It should return a similar result. This function will be called using
  # TensorFlow eager mode if called from outside JAX staged contexts (`jit`,
  # `pmap`, or control-flow primitives), and will be called using TensorFlow
  # compiled mode otherwise. In the latter case, the function must be compileable
  # with XLA (`tf.function(func, jit_compile=True)`)
  def cos_tf(x):
    return tf.math.cos(x)

  # Compute cos with TF and sin with JAX
  def cos_tf_sin_jax(x):
    return jax.numpy.sin(jax2tf.call_tf(cos_tf)(x))

  # Calls `cos_tf` in TF eager mode
  x = np.float32(1.)
  cos_tf_sin_jax(x)

  # Compiles `cos_tf` using TF and embeds the XLA computation into the JAX
  # XLA computation (containing `sin`). The XLA compiler may even be able to
  # fuse through JAX-TF computations.
  jax.jit(cos_tf_sin_jax)(x)

  # Uses TF gradient for `cos_tf` and JAX gradient for `sin`
  jax.grad(cos_tf_sin_jax)(x)

If you inspect the generated HLO for cos_tf_sin_jax you will see that the main JAX computation (ENTRY xla_computation_cos_tf_sin_jax) makes a call to the a_inference_cos_tf_68__HLO function that was compiled by TF from cos_tf:

    HloModule xla_computation_cos_tf_sin_jax.18

    a_inference_cos_tf_68__.4 {
      arg0.5 = f32[] parameter(0), parameter_replication={false}
      reshape.6 = f32[] reshape(arg0.5)
      cosine.7 = f32[] cosine(reshape.6)
      reshape.8 = f32[] reshape(cosine.7)
      tuple.9 = (f32[]) tuple(reshape.8)
      ROOT get-tuple-element.10 = f32[] get-tuple-element(tuple.9), index=0
    }

    ENTRY xla_computation_cos_tf_sin_jax.18 {
      constant.2 = pred[] constant(false)
      constant.3 = pred[] constant(false)
      parameter.1 = f32[] parameter(0)
      call.11 = f32[] call(parameter.1), to_apply=a_inference_cos_tf_68__.4
      tuple.12 = (f32[]) tuple(call.11)
      get-tuple-element.13 = f32[] get-tuple-element(tuple.12), index=0
      tuple.14 = (f32[]) tuple(get-tuple-element.13)
      get-tuple-element.15 = f32[] get-tuple-element(tuple.14), index=0
      sine.16 = f32[] sine(get-tuple-element.15)
      ROOT tuple.17 = (f32[]) tuple(sine.16)
    }

Notes:

• The TF function must be compileable (tf.function(func, jit_compile=True) when used in a JAX staging context.
• All the metadata inserted by TF during tracing and compilation, e.g., source location information and op names, is carried through to the JAX XLA computation.
• The TF custom gradients are respected, since it is TF that generates the gradient computation.
• In op-by-op mode, when we call TensorFlow in eager mode, we use DLPack to try to avoid copying the data. This works for CPU (for DeviceArray data or for np.ndarray that are aligned on 16-byte boundaries) and on GPU (for DeviceArray). The zero-copy does not yet work on TPU.
• call_tf works best with pure TF functions that do not capture tf.Variables or tensors from the environment, and all such context is passed in explicitly through arguments, and if variables are modified, the resulting values are passed out through results. There is a best-effort mechanism that can handle variable capture and variable updates, except in the case of a function that modifies tf.Variables and is used in a JAX jitted context. Calling the inpure_func_tf will give an error:

       var1 = tf.Variable(1.)
       def impure_func_tf(x):
         var1.write(11.)  # BAD: should not write to variables
         return x + var1
       jax2tf.call_tf(impure_func_tf)(tf.constant(2.))  # Works in eager mode
       jax.jit(jax2tf.call_tf(impure_func_tf))(tf.constant(2.))  # Fails in jit mode

The error can be avoided by passing the variable explicitly:

       def pure_func_tf(x, var1)
          new_var1 = 11.
          return x + new_var1, new_var1

This use case is likely to be revisited.

TODO

• Ensure that there is no array copy through the host when running in eager mode (JAX op-by-op).
• Show how use call_tf to load a SavedModel into JAX.

Additional notes

TensorFlow versions supported

The jax2tf.convert and call_tf require very recent versions of TensorFlow. As of today, the tests are run using tf_nightly==2.6.0-dev20210601.

Running on GPU

To run jax2tf on GPU, both jaxlib and TensorFlow must be installed with support for CUDA. One must be mindful to install a version of CUDA that is compatible with both jaxlib and TensorFlow.

Updating the limitations documentation

The jax2tf tests are parameterized by a set of limitations (see tests/primitive_harness.py and tests/jax2tf_limitations.py). The limitations specify test harnesses that are known to fail, by JAX primitive, data type, device type, and TensorFlow execution mode (eager, graph, or compiled). These limitations are also used to generate tables of limitations, e.g.,

• List of primitives not supported in JAX, e.g., due to unimplemented cases in the XLA compiler, and
• List of primitives not supported in jax2tf, e.g., due to unimplemented cases in TensorFlow. This list is incremental on top of the unsupported JAX primitives.

There are instructions for updating those documents at the end of each document.

The set of limitations is an over-approximation, in the sense that if XLA or TensorFlow improves and support more cases, no test will fail. Instead, periodically, we check for unnecessary limitations. We do this by uncommenting two assertions (in tests/jax_primitives_coverage_test.py and in tests/tf_test_util.py) and runing all the tests. With these assertions enabled the tests will fail and point out unnecessary limitations. We remove limitations until the tests pass. Then we re-generate the documentation.