From 306cc91f781487b879d55f1c1a95465a8be5e704 Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 13:34:47 -0700 Subject: [PATCH 01/20] Create 2022-10-18-promotion-semantics.md --- rfcs/2022-10-18-promotion-semantics.md | 8747 ++++++++++++++++++++++++ 1 file changed, 8747 insertions(+) create mode 100644 rfcs/2022-10-18-promotion-semantics.md diff --git a/rfcs/2022-10-18-promotion-semantics.md b/rfcs/2022-10-18-promotion-semantics.md new file mode 100644 index 000000000..c9f010b20 --- /dev/null +++ b/rfcs/2022-10-18-promotion-semantics.md @@ -0,0 +1,8747 @@ + + + + +## RFC: Making dtype promotion semantics in Tensorflow more consistent + + +``` + +Username +Role +Status +Last Change +rohanj +Approver +PENDING +2022-09-12 +wangpeng +Approver +APPROVED +2022-09-20 +yanhuasun +Approver +PENDING +2022-09-12 +fchollet +Reviewer +PENDING +2022-09-12 +vanderplas +Reviewer +PENDING +2022-09-12 +#begin-approvals-addon-section +Using go/DocWrap. Table auto-updated hourly. Don't edit this section and don't add comments in it. +``` + + +**Status**: Draft | In Review | Approved | Final | Obsolete + +**Authors**: , LDAP + +**Contributors**: + +**Sponsors**: , , LDAP + +**Last Updated**: + +go/tf-np-style-promotion + + +## **Note: the main design has been updated on 2020-09-05. We updated the proposed modes. The deprecated modes can be seen in [the bottom of this doc](#bookmark=id.kp5bjdbldb2g). ** + + +## **Acknowledgement** + +This is written by referencing investigations by ex-Googler , as well as projects TF-numpy and JAX: + +[Implicit promotion, inference and conversion in tensorflow](https://docs.google.com/document/d/1jOXJ1YAQAtseyYDDGm4gsr7xJ4F2FtvfwZJgNsW8cVA/edit#heading=h.lloqtrv9juf1) + +[tf.experimental.numpy](https://www.tensorflow.org/api_docs/python/tf/experimental/numpy) + +[Design of Type Promotion Semantics for JAX](https://jax.readthedocs.io/en/latest/jep/9407-type-promotion.html#) + +Specifically, thanks to TF-numpy already implemented the majority of the proposed dtype promotion behaviors. + + +## **Objective** + +Currently TF has no consistent, well-defined type promotion rules. This document proposes a well-defined, consistent and clear dtype promotion rule for TF. The introduced changes make TF APIs more similar to NumPy, with some differences that emphasize TF’s applications in machine learning. This should make dtype promotions in TF much more consistent and predictable. + +[**What the doc is**] This doc discusses the preferred dtype promotion semantics/behaviors of Tensorflow (TF) and Tensorflow-numpy (TF-numpy) in the binary ops including add, sub, mul, div, pow and mod. + +[**What the doc is not**] This doc does not discuss the implementation plans. + + +## **Motivation** + +In Tensorflow’s APIs, dtype promotion is very broken compared to JAX or NumPy. Many users have complained about these surprising behaviors (example: go/broccoli-tf-add, b/158346631, b/154456219). See [this doc ](https://docs.google.com/document/d/1jOXJ1YAQAtseyYDDGm4gsr7xJ4F2FtvfwZJgNsW8cVA/edit#)for a more detailed description. + +[TF-numpy](https://www.tensorflow.org/guide/tf_numpy#type_promotion) is a strategic project in Tensorflow. Compared to Tensorflow, TF-numpy’s dtype promotion behavior is more consistent because it [mainly relies on NumPy’s dtype promotion semantics](https://source.corp.google.com/piper///depot/google3/third_party/tensorflow/python/ops/numpy_ops/np_dtypes.py;rcl=399530502;l=112). In the [long-term vision doc](https://goto.google.com/tf-numpy-path-forward), unifying the dtype promotion semantics of TF and TF-numpy is a necessary first step. + +The unification of TF and TF-numpy’s dtype promotion semantics is a great chance to think about what semantics should be the long-term, user-friendly solution for TF. + + +## **User Benefit** + +There have been ongoing complaints about the inconsistent dtype promotion behaviors of TF. To give an example, switching the inputs’ positions directly affects the outcome: + + +``` +c = tf.constant(1.0) +tf.add(c, 3) # tf.Tensor(4.0, shape=(), dtype=float32) +tf.add(3, c) # InvalidArgumentError: cannot compute AddV2 as input #1(zero-based) was expected to be a int32 tensor but is a float tensor [Op:AddV2] +``` + + +For brevity we will sometimes use shortened terms to represent the dtypes as shown in the following examples: + + + +* `b` means tf.bool +* `u8` means tf.uint8 +* `i16` means tf.int16 +* `bf16` means [tf.bfloat16](https://cloud.google.com/tpu/docs/bfloat16) +* `f64` means tf.float64 +* `c64` means tf.complex64 +* `i*` means python int +* `f*` means python float +* `c*` means python complex + +The table below summarizes the dtype promotion behavior between two TF tensors/python scalars with method `__add__` and API `tf.add` respectively. Each row and column represents a combination of input types. + +Table: TF dtype promotion result of dunder method `__add__` and `tf.add` + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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b + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
u8 + - + u8 + - + - + - + - + - + - + - + - + - + - + - + - + - + u8 + - + - +
u16 + - + - + u16 + - + - + - + - + - + - + - + - + - + - + - + - + u16 + - + - +
u32 + - + - + - + u32 + - + - + - + - + - + - + - + - + - + - + - + u32 + - + - +
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i8 + - + - + - + - + - + i8 + - + - + - + - + - + - + - + - + - + i8 + - + - +
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c64 + - + - + - + - + - + - + - + - + - + - + - + - + - + c64 + - + c64 + c64 + c64 +
c128 + - + - + - + - + - + - + - + - + - + - + - + - + - + - + c128 + c128 + c128 + c128 +
i* + - + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i32 + + +
f* + - + - + - + - + - + - + - + - + - + bf16 + f16 + f32 + f64 + c64 + c128 + f32 + f32 + +
c* + - + - + - + - + - + - + - + - + - + - + - + - + - + c64 + c128 + c128 + c128 + c128 +
+ + +In the table, the dash symbol `-` means unsupported: an error gets raised if the two dtypes are used in the call to that op. The cells highlighted in red are only supported in `__add__`, and the cells in yellow are only supported in `tf.add`. In summary, the existing TF APIs have these significant issues: + + + +1. Boolean dtypes are not supported; +2. Implicit conversion between two TF dtypes are strictly disallowed, resulting in verbose code at times; +3. Implicit conversion from some python dtypes to some TF dtypes are disallowed; +4. `tf.add` is not even commutative - switching the position of the two arguments sometimes produces errors, otherwise results in correct values. + +When the inputs involve NumPy arrays, the behaviors can be even more broken. For brevity we have included NumPy-input behaviors in the appendix at the end of the doc. + +We aim to make the system commutative, predictable and correct. + + +## **Design Proposal** + +We propose introducing three dtype promotion modes in TF: + + + +* `tf.ImplicitPromotion.ALL` +* `tf.ImplicitPromotion.SAFE` +* `tf.ImplicitPromotion.NONE` + +The three modes determine how often implicit dtype promotions happen in TF APIs. In the following examples we will use `tf.add` to demonstrate the modes. The dunder method `__add__` is expected to have the same behavior as `tf.add`. For brevity, NumPy (np) array inputs are not discussed in this section. However the proposed modes in this RFC will treat the np array inputs in the same way as the tensor inputs (see more in the appendix). + +Table: TF dtype promotion result of `tf.add` after the proposed changes. `NONE` only allows the unhighlighted cells. `SAFE` allows all `NONE` cases plus the cells highlighted in green. `ALL` allows all `SAFE` cases plus the cells highlighted in yellow. Rows/columns highlighted in blue only show the default result without overflow of inputs. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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u8 + u8 + u8 + u16 + u32 + u64 + i16 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + u8 + f64 + c128 +
u16 + u16 + u16 + u16 + u32 + u64 + i32 + i32 + i32 + i64 + f32 + f32 + f32 + f64 + c64 + c128 + u16 + f64 + c128 +
u32 + u32 + u32 + u32 + u32 + u64 + i64 + i64 + i64 + i64 + f64 + f64 + f64 + f64 + c128 + c128 + u32 + f64 + c128 +
u64 + u64 + u64 + u64 + u64 + u64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + c128 + c128 + u64 + f64 + c128 +
i8 + i8 + i16 + i32 + i64 + f64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i8 + f64 + c128 +
i16 + i16 + i16 + i32 + i64 + f64 + i16 + i16 + i32 + i64 + f32 + f32 + f32 + f64 + c64 + c128 + i16 + f64 + c128 +
i32 + i32 + i32 + i32 + i64 + f64 + i32 + i32 + i32 + i64 + f64 + f64 + f64 + f64 + c128 + c128 + i32 + f64 + c128 +
i64 + i64 + i64 + i64 + i64 + f64 + i64 + i64 + i64 + i64 + f64 + f64 + f64 + f64 + c128 + c128 + i64 + f64 + c128 +
bf16 + bf16 + bf16 + f32 + f64 + f64 + bf16 + f32 + f64 + f64 + bf16 + f32 + f32 + f64 + c64 + c128 + bf16 + bf16 + c64 +
f16 + f16 + f16 + f32 + f64 + f64 + f16 + f32 + f64 + f64 + f32 + f16 + f32 + f64 + c64 + c128 + f16 + f16 + c64 +
f32 + f32 + f32 + f32 + f64 + f64 + f32 + f32 + f64 + f64 + f32 + f32 + f32 + f64 + c64 + c128 + f32 + f32 + c64 +
f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + c128 + c128 + f64 + f64 + c128 +
c64 + c64 + c64 + c64 + c128 + c128 + c64 + c64 + c128 + c128 + c64 + c64 + c64 + c128 + c64 + c128 + c64 + c64 + c64 +
c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 +
i* + i64 + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i64 + f64 + c128 +
f* + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + bf16 + f16 + f32 + f64 + c64 + c128 + f64 + f64 + c128 +
c* + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c64 + c64 + c64 + c128 + c64 + c128 + c128 + c128 + c128 +
+ + + +### **Mode 1: tf.ImplicitPromotion.ALL** + +In this mode, we allow all implicit promotions to happen in the op, even if both inputs have TF dtypes. To summarize the rules of this mode: + + + +* When the two inputs have non-matching TF dtypes, they will be promoted to the lowest width dtype that guarantees the precisions and ranges of the inputs. However, the promoted integer, float and complex dtype widths are capped at 64, 64 and 128 bits respectively. For example: + * The summation of `tf.bfloat16` and `tf.uint8` will be type `tf.bfloat16`. + * The summation of `tf.int64` and `tf.float64` will be capped at type `tf.float64` despite precision loss and overflow risks. +* When one of the inputs is a python integer/float/complex, and the other is a TF dtype: + * If the python scalar value falls within the range of the TF dtype, the result dtype is loosely described with the following promotion direction: TF bool -> python integer -> TF signed/unsigned integers -> python float -> TF float -> python complex/TF complex. + * The dtype promotion is allowed only if the python scalar is within the range of the determined dtype. Otherwise, an exception is raised. For example, `tf.constant([1], tf.int8) + 1` produces a `tf.int8` Tensor, while `tf.constant([1], tf.int8) + 1000` raises an error. + +This mode is intended to provide a user experience similar to NumPy behaviors. + + +### **Mode 2: tf.ImplicitPromotion.SAFE** + +Mode `SAFE` follows the same rules as mode `ALL`, but disallows “unsafe” dtype promotions. In general, we think the following two types of conversions are unsafe: + + + +* If the inputs are both Tensors and the result dtype would be “wider” than all the inputs. For example, the summation of `tf.uint8` and `tf.int8` would be type `tf.i16`. These dtype promotions risk increasing the model’s memory consumption and slowing down computation speed. +* If the inputs are both Tensors and the result dtype cannot preserve all the precisions of the inputs. For example, the summation of `tf.uint64` and `tf.float32` would be type `tf.float64`. + +With the above principle, if one of the inputs is a Tensor while the other is a python scalar, we only allow implicit promotion if the scalar can fit into the Tensor’s dtype. For example: + + + +* The summation between a `tf.int32` and a python float is always disallowed; +* The summation between a `tf.uint8` and a python int is allowed only if the python int is between 0 and 255. + +For these disallowed dtype promotions, we require the users to explicitly cast them. + +Another advantage is that mode `ALL` can be silently nonassociative in the following scenario, while mode `SAFE` avoids it by not allowing any dtype widening: + + + +* `(tf.zeros(tf.uint8) + tf.zeros(tf.int8)) + tf.zeros(tf.float16)` evaluates to dtype `tf.float32`; +* `tf.zeros(tf.uint8) + (tf.zeros(tf.int8) + tf.zeros(tf.float16))` evaluates to dtype `tf.float16`. + + +### **Mode 3: tf.ImplicitPromotion.NONE** + +The dtype behavior of mode `NONE` is the most conservative: no dtype promotion is allowed between Tensors. When the inputs include python scalars, it follows the same rule as mode `SAFE`. + + +### **Alternatives Considered: Capping the promotion system at float32** + +The TF-NumPy project contains another flag [allow_float64](https://source.corp.google.com/piper///depot/google3/third_party/tensorflow/python/ops/numpy_ops/np_dtypes.py;rcl=399530502;l=88). When disabled, it caps the converted floating numbers to 32 bits, and complex numbers to 64 bits. This is useful when users want to enjoy implicit conversions in all cases and avoid any performance regressions related to double precision floating point numbers. This feature is now used by [Trax](https://b.corp.google.com/issues/178862061). + +Our current plan does not involve adding this flag in TF, as it’s orthogonal to the proposed modes. If there are more user needs we can consider exposing it in TF as well. + + +### **Alternatives Considered: A dtype promotion “lattice”** + +Unlike the approaches above, JAX used a dtype promotion lattice to define its semantics. More details can be seen in their [design doc](https://jax.readthedocs.io/en/latest/jep/9407-type-promotion.html#properties-of-a-type-promotion-lattice). Their lattice system has these advantages: + + + +* A lattice can be much more concise to describe the promotion semantics; +* The lattice can ensure associativity as well as commutativity. In contrast, the promotion behaviors in this proposal is not associative: + * `(tf.zeros(tf.uint8) + tf.zeros(tf.int8)) + tf.zeros(tf.float16)` evaluates to dtype `tf.float32`; + * `tf.zeros(tf.uint8) + (tf.zeros(tf.int8) + tf.zeros(tf.float16))` evaluates to dtype `tf.float16`. + +~~However the lattice system also introduced extra complexity in the design. For example:~~ + + + +* ~~JAX ended up introducing the concepts of “weak types” in place of python types;~~ +* ~~JAX adopted very aggressive dtype promotion semantics - an int64 dtype can be promoted to a float16, resulting in higher risks of overflow.~~ + +~~Since our ultimate goal is to help the users maintain a clear and concise mental model, adopting a dtype promotion table is an easier approach.~~ + +Compared to a lattice system, the proposed table-based system does not guarantee associativity. However, we can avoid such usage scenarios in the proposed SAFE mode (no implicit promotion to wider dtypes). As a result, it’s not clearly justifiable to switch to a lattice system from the table-based approach, which already exists in project TF-NumPy. + + +### **Alternatives Considered: Value-dependent promotion (a.k.a. “safe casting” in NumPy)** + +NumPy has a feature “safe casting”: the dtype promotion API, [numpy.result_type](https://numpy.org/doc/stable/reference/generated/numpy.result_type.html), can return a different dtype depending on the input python scalar value: + + +``` +np.result_type(np.int8, 200) # dtype('int16') +np.result_type(np.int8, 100) # dtype('int8') +``` + + +This slightly reduces the risk of overflow. However, the result is unpredictable. NumPy is considering removing this feature in a future release (see proposal: [NEP 50](https://numpy.org/neps/nep-0050-scalar-promotion.html)). JAX [did not adopt this feature](https://jax.readthedocs.io/en/latest/jep/9407-type-promotion.html#enter-python-scalars) either. We omit it in this design. + + +### **Performance Implications** + +For an existing piece of TF code in python, we do not expect the performance to change after switching to any of the proposed modes. This is because all the allowed implicit dtype promotions in TF will stay unchanged. As to the currently disallowed dtype promotions, users must have used explicit dtype promotions which are not affected by the change. + +When users develop a new piece of code, however, the new modes have higher chances of computation slowdown due to implicit promotions in mode `ALL`. + +We can carry out the following steps to mitigate user surprises: + + + +* Add benchmark tests to verify that the new modes won’t affect the performance of existing TF code. +* Publish tutorials that help users select the most appropriate dtype promotion modes. +* [optional] Add an API that instruments the number of implicit, unsafe dtype conversions in the program (e.g. promotions from 32 bits to 64 bits). This helps users understand whether the promotions contribute to computation slowdown in the graphs. + + +### **Dependencies** + +This proposal is not expected to affect dependencies to Tensorflow. + + +#### **Engineering Impact** + + +#### We do not expect changes in binary size/startup time/build time/test time. + +The majority of the dtype promotion logic already exists in Tensorflow under namescope `tensorflow.experimental.numpy`. However, refactoring/updates will be needed under directory tensorflow/python/ops. + + +#### **Platforms and Environments** + +This proposal is expected to take effect on all platforms in the python environment. + + +#### **Best Practices/Tutorials and Examples** + +As mentioned in section “Performance Implications” we will publish tutorials that help users select the most appropriate dtype promotion modes. + + +#### **Compatibility/User Impact** + +This is a breaking change. As mentioned above, existing code is expected to continue working. However, some tests will fail because more implicit dtype conversions are allowed. We can mitigate the user impact by adding a flag that sticks to the existing dtype promotion behaviors. + +The proposed changes will be rolled out in various stages: + + + +1. Default behaviors stay unchanged, with the flags introduced under namescope `tf.experiment` to collect user feedbacks; +2. Default behaviors switched to one of the modes above, while the old behaviors can be re-enabled via a flag; +3. [optional] Completely disable the old, broken dtype promotion behaviors. + + +## **Questions and Discussion Topics** + + +#### **Are there changes expected in tf.Variable?** + +In APIs such as `tf.add` and dunder methods `__add__`, the same changes are expected to take place because these methods first read the Variable into a Tensor, then carry out the Tensor-to-Tensor computations. + +Currently, inplace ops such as `assign_add` are also broken. The two tables below show the result of `assign_add` when the input is a Tensor, a python scalar or a NumPy array: + + + +* When the input is a Tensor, no dtype conversion is allowed. +* When the input is a python scalar, it defers to the variable’s dtype. However, python float cannot be converted to int, and complex cannot be converted to either int or float dtypes. +* When the input is a NumPy array, it always gets converted to the variable’s dtype regardless of any precision loss (for example `tf.Variable(1) + np.array(1.5)` returns 2). + +Table: Result of tf.Variable (rows) inplace op `assign_add` with a Tensor or python scalar as the argument (columns). + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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i64 + - + - + - + - + - + - + - + - + i64 + - + - + - + - + - + - + i64 + - + - +
bf16 + - + - + - + - + - + - + - + - + - + bf16 + - + - + - + - + - + bf16 + bf16 + - +
f16 + - + - + - + - + - + - + - + - + - + - + f16 + - + - + - + - + f16 + f16 + - +
f32 + - + - + - + - + - + - + - + - + - + - + - + f32 + - + - + - + f32 + f32 + - +
f64 + - + - + - + - + - + - + - + - + - + - + - + - + f64 + - + - + f64 + f64 + - +
c64 + - + - + - + - + - + - + - + - + - + - + - + - + - + c64 + - + c64 + c64 + c64 +
c128 + - + - + - + - + - + - + - + - + - + - + - + - + - + - + c128 + c128 + c128 + c128 +
+ + +Table: Result of tf.Variable (rows) inplace op `assign_add` with a NumPy array as the argument (columns). + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ b + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 +
b + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
u8 + u8 + u8 + u8 + u8 + u8 + u8 + u8 + u8 + u8 + - + u8 + u8 + u8 + u8 + u8 +
u16 + u16 + u16 + u16 + u16 + u16 + u16 + u16 + u16 + u16 + - + u16 + u16 + u16 + u16 + u16 +
u32 + u32 + u32 + u32 + u32 + u32 + u32 + u32 + u32 + u32 + - + u32 + u32 + u32 + u32 + u32 +
u64 + u64 + u64 + u64 + u64 + u64 + u64 + u64 + u64 + u64 + - + u64 + u64 + u64 + u64 + u64 +
i8 + i8 + i8 + i8 + i8 + i8 + i8 + i8 + i8 + i8 + - + i8 + i8 + i8 + i8 + i8 +
i16 + i16 + i16 + i16 + i16 + i16 + i16 + i16 + i16 + i16 + - + i16 + i16 + i16 + i16 + i16 +
i32 + i32 + i32 + i32 + i32 + i32 + i32 + i32 + i32 + i32 + - + i32 + i32 + i32 + i32 + i32 +
i64 + i64 + i64 + i64 + i64 + i64 + i64 + i64 + i64 + i64 + - + i64 + i64 + i64 + i64 + i64 +
bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + - + bf16 + bf16 + bf16 + bf16 + bf16 +
f16 + f16 + f16 + f16 + f16 + f16 + f16 + f16 + f16 + f16 + - + f16 + f16 + f16 + f16 + f16 +
f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + - + f32 + f32 + f32 + f32 + f32 +
f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + - + f64 + f64 + f64 + f64 + f64 +
c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + - + c64 + c64 + c64 + c64 + c64 +
c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + - + c128 + c128 + c128 + c128 + c128 +
+ + +We can make these ops more consistent as well, following the rules in the three proposed modes. However, we also have another constraint: the Variable’s dtype cannot change. As a result, any dtype promotion that results in a dtype different from the Variable’s dtype is disabled. + +Table: Result of tf.Variable (rows) inplace op `assign_add` with a Tensor or python scalar as the argument (columns) after the proposed changes. `NONE` only allows the unhighlighted cells. `SAFE` allows all `NONE` cases plus the cells highlighted in green. `ALL` allows all `SAFE` cases plus the cells highlighted in yellow. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ b + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i* + f* + c* +
b + b + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
u8 + u8 + u8 + - + - + - + - + - + - + - + - + - + - + - + - + - + u8 + - + - +
u16 + u16 + u16 + u16 + - + - + - + - + - + - + - + - + - + - + - + - + u16 + - + - +
u32 + u32 + u32 + u32 + u32 + - + - + - + - + - + - + - + - + - + - + - + u32 + - + - +
u64 + u64 + u64 + u64 + u64 + u64 + - + - + - + - + - + - + - + - + - + - + u64 + - + - +
i8 + i8 + - + - + - + - + i8 + - + - + - + - + - + - + - + - + - + i8 + - + - +
i16 + i16 + i16 + - + - + - + i16 + i16 + - + - + - + - + - + - + - + - + i16 + - + - +
i32 + i32 + i32 + i32 + - + - + i32 + i32 + i32 + - + - + - + - + - + - + - + i32 + - + - +
i64 + i64 + i64 + i64 + i64 + - + i64 + i64 + i64 + i64 + - + - + - + - + - + - + i64 + - + - +
bf16 + bf16 + bf16 + - + - + - + bf16 + - + - + - + bf16 + - + - + - + - + - + bf16 + bf16 + - +
f16 + f16 + f16 + - + - + - + f16 + - + - + - + - + f16 + - + - + - + - + f16 + f16 + - +
f32 + f32 + f32 + f32 + - + - + f32 + f32 + - + - + f32 + f32 + f32 + - + - + - + f32 + f32 + - +
f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + - + - + f64 + f64 + - +
c64 + c64 + c64 + c64 + - + - + c64 + c64 + - + - + c64 + c64 + c64 + - + c64 + - + c64 + c64 + c64 +
c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 +
+ + + +#### **Are there also issues in conversions between Tensor and numpy arrays?** + +There exists arguably more serious issues when it comes to the mixture of Tensor and numpy arrays - wrong results can appear silently: + + +``` +a = np.array(3.1) +c = tf.constant(1) +print(tf.add(a, c)) # InvalidArgumentError: cannot compute AddV2 as input #1(zero-based) was expected to be a double tensor but is a int32 tensor [Op:AddV2] +print(tf.add(c, a)) # tf.Tensor(4, shape=(), dtype=int32) - Even worse than exceptions +``` + + +To give a comprehensive overview, the two tables below show the results of `tf.add(tensor, numpy array)` and `tf.add(numpy array, tensor)` : + +Table: TF dtype promotion result of `tf.add` with a Tensor as the first argument (rows) and np array in the second argument (columns). + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ b + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 +
b + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
u8 + u8 + u8 + u8 + u8 + u8 + u8 + u8 + u8 + u8 + - + u8 + u8 + u8 + u8 + u8 +
u16 + u16 + u16 + u16 + u16 + u16 + u16 + u16 + u16 + u16 + - + u16 + u16 + u16 + u16 + u16 +
u32 + u32 + u32 + u32 + u32 + u32 + u32 + u32 + u32 + u32 + - + u32 + u32 + u32 + u32 + u32 +
u64 + u64 + u64 + u64 + u64 + u64 + u64 + u64 + u64 + u64 + - + u64 + u64 + u64 + u64 + u64 +
i8 + i8 + i8 + i8 + i8 + i8 + i8 + i8 + i8 + i8 + - + i8 + i8 + i8 + i8 + i8 +
i16 + i16 + i16 + i16 + i16 + i16 + i16 + i16 + i16 + i16 + - + i16 + i16 + i16 + i16 + i16 +
i32 + i32 + i32 + i32 + i32 + i32 + i32 + i32 + i32 + i32 + - + i32 + i32 + i32 + i32 + i32 +
i64 + i64 + i64 + i64 + i64 + i64 + i64 + i64 + i64 + i64 + - + i64 + i64 + i64 + i64 + i64 +
bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + - + bf16 + bf16 + bf16 + bf16 + bf16 +
f16 + f16 + f16 + f16 + f16 + f16 + f16 + f16 + f16 + f16 + - + f16 + f16 + f16 + f16 + f16 +
f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + - + f32 + f32 + f32 + f32 + f32 +
f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + - + f64 + f64 + f64 + f64 + f64 +
c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + - + c64 + c64 + c64 + c64 + c64 +
c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + - + c128 + c128 + c128 + c128 + c128 +
+ + +Table: TF dtype promotion result of `tf.add` with a np array as the first argument (rows) and tensor in the second argument (columns). + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ b + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 +
b + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
u8 + - + u8 + - + - + - + - + - + - + - + - + - + - + - + - + - +
u16 + - + - + u16 + - + - + - + - + - + - + - + - + - + - + - + - +
u32 + - + - + - + u32 + - + - + - + - + - + - + - + - + - + - + - +
u64 + - + - + - + - + u64 + - + - + - + - + - + - + - + - + - + - +
i8 + - + - + - + - + - + i8 + - + - + - + - + - + - + - + - + - +
i16 + - + - + - + - + - + - + i16 + - + - + - + - + - + - + - + - +
i32 + - + - + - + - + - + - + - + i32 + - + - + - + - + - + - + - +
i64 + - + - + - + - + - + - + - + - + i64 + - + - + - + - + - + - +
bf16 + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
f16 + - + - + - + - + - + - + - + - + - + - + f16 + - + - + - + - +
f32 + - + - + - + - + - + - + - + - + - + - + - + f32 + - + - + - +
f64 + - + - + - + - + - + - + - + - + - + - + - + - + f64 + - + - +
c64 + - + - + - + - + - + - + - + - + - + - + - + - + - + c64 + - +
c128 + - + - + - + - + - + - + - + - + - + - + - + - + - + - + c128 +
+ + +To briefly describe the issues: + + + +1. dtypes `tf.bfloat16` and `tf.bool` are poorly supported; +2. If the first input is a tensor, the second input (the np array) is always converted to the tensor’s dtype, resulting into significant precision loss in many scenarios; +3. If the first input is a np array and the second is a tensor, the np array is first converted to a tensor with the corresponding dtype. TF then does not attempt any implicit dtype conversion, causing `InvalidArgumentError` whenever the two inputs have mismatching dtypes. + +Though the behaviors with np arrays inputs are not the same as python scalars, they share the same root cause: At present, TF does not have a centralized dtype promotion system, and incorrectly uses the [dtype of the first Tensor in its list of arguments](https://source.corp.google.com/piper///depot/google3/third_party/tensorflow/python/ops/math_ops.py;rcl=449753780;l=1371) to promote all arguments to. The proposed modes in this RFC will treat the np array inputs in the same way as the tensor inputs. + + +#### **What happens to e.g. bool - bool or bool / bool? + +With NumPy: + + + +* Addition between two Booleans: equivalent to OR +* Subtraction between two Booleans: forbidden +* Multiplication between two Booleans: AND +* Division between two Booleans: converted to float64 then divide + + +#### **What happens to truediv operations?** + +The op truediv is special because it implicitly promotes integral types. For example when the inputs are matching dtypes, 16-bit integral types are promoted to `f32`, and 32-bit integral types are promoted to `float64`. + +Using this rule of thumb, we can easily deduce the promotion behavior between non-matching dtypes when this proposal is landed. For example: + + + +* `int8` and `int16` -> both `int16` -> `f32` (allowed in `SAFE/ALL` modes) +* `uint8` and `uint32` -> both `uint32` -> `f64` (allowed in `SAFE/ALL` modes) +* `int8` and `i*` -> both `int8` -> `f32` (allowed in `NONE/SAFE/ALL` modes) +* `int8` and `uint16` -> both `int32` -> `f64` (allowed in `ALL` mode) + +In the above examples, the first arrow represents the dtype promotion in this proposal, and the second arrow represents the promotion in op truediv. Note the result of `int8` and `uint16`, `f64`, turns out to be an overkill but is consistent with the combination of the dtype promotion rules and the truediv promotion rules. + +~~Table: TF dtype promotion result of `tf.math.truediv` after the proposed changes. `NONE` only allows the unhighlighted cells. `SAFE` allows all `NONE` cases plus the cells highlighted in green. `ALL` allows all `SAFE` cases plus the cells highlighted in yellow. Rows/columns highlighted in blue only show the default result without overflow of inputs.~~ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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b + b + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i64 + f64 + c128 +
u8 + u8 + f32 + f32 + f64 + f64 + f32 + f32 + f64 + f64 + bf16 + f16 + f32 + f64 + c64 + c128 + f32 + f64 + c128 +
u16 + u16 + f32 + f32 + f64 + f64 + f64 + f64 + f64 + f64 + f32 + f32 + f32 + f64 + c64 + c128 + f32 + f64 + c128 +
u32 + u32 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + c128 + c128 + f64 + f64 + c128 +
u64 + u64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + c128 + c128 + f64 + f64 + c128 +
i8 + i8 + f32 + f64 + f64 + f64 + f32 + f32 + f64 + f64 + bf16 + f16 + f32 + f64 + c64 + c128 + f32 + f64 + c128 +
i16 + i16 + f32 + f64 + f64 + f64 + f32 + f32 + f64 + f64 + f32 + f32 + f32 + f64 + c64 + c128 + f32 + f64 + c128 +
i32 + i32 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + c128 + c128 + f64 + f64 + c128 +
i64 + i64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + c128 + c128 + f64 + f64 + c128 +
bf16 + bf16 + bf16 + f32 + f64 + f64 + bf16 + f32 + f64 + f64 + bf16 + f32 + f32 + f64 + c64 + c128 + bf16 + bf16 + c64 +
f16 + f16 + f16 + f32 + f64 + f64 + f16 + f32 + f64 + f64 + f32 + f16 + f32 + f64 + c64 + c128 + f16 + f16 + c64 +
f32 + f32 + f32 + f32 + f64 + f64 + f32 + f32 + f64 + f64 + f32 + f32 + f32 + f64 + c64 + c128 + f32 + f32 + c64 +
f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + c128 + c128 + f64 + f64 + c128 +
c64 + c64 + c64 + c64 + c128 + c128 + c64 + c64 + c128 + c128 + c64 + c64 + c64 + c128 + c64 + c128 + c64 + c64 + c64 +
c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 +
i* + i64 + f32 + f32 + f64 + f64 + f32 + f32 + f64 + f64 + bf16 + f16 + f32 + f64 + c64 + c128 + f64 + f64 + c128 +
f* + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + bf16 + f16 + f32 + f64 + c64 + c128 + f64 + f64 + c128 +
c* + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c64 + c64 + c64 + c128 + c64 + c128 + c128 + c128 + c128 +
+ + + +#### **~~Is it possible to avoid silent overflow in e.g. tf.constant(100, dtype=tf.uint8) + tf.constant(200, dtype=tf.uint8)? + +~~In both JAX and TF, when overflow happens in float types, the result is an inf value:~~ + + +``` +jnp.array(4e4, 'float16') + jnp.array(4e4, 'float16') # inf +tf.constant(4e4, 'float16') + tf.constant(4e4, 'float16') # inf +``` + + +~~However if the dtype is integral, overflow is silent:~~ + + +``` +jnp.array(100, 'int8') + jnp.array(100, 'int8') # -56 +tf.constant(100, 'int8') + tf.constant(100, 'int8') # -56 +``` + + +~~ ~~ + + + + +### [Deprecated]: old propositions before 2022/09/05 + + +## **Design Proposal** + +We propose introducing two sets of flags that control the dtype promotion behaviors in TF: + + + +* `enable_implicit_promotion/disable_implicit_promotion` +* `enable_promotion_to_float64/disable_promotion_to_float64` + +The two sets of flags influence the dtype promotion behaviors orthogonally. Combined together, the user can achieve four dtype promotion modes. In the following examples we will use `tf.add` to demonstrate the modes. The dunder method `__add__` is expected to have the same behavior as `tf.add`. + + +### **Mode 1: enable\_implicit\_promotion + enable\_promotion\_to\_float64** + +In this mode, we allow all implicit promotions to happen in the op, even if both inputs have TF dtypes. To summarize the rules of this mode: + + + +* When the two inputs have non-matching TF dtypes, they will be promoted to the lowest width dtype that guarantees the precisions and ranges of the inputs. However, the promoted integer, float and complex dtype widths are capped at 64, 64 and 128 bits respectively. For example: + * The summation of `tf.bfloat16` and `tf.uint8` will be type `tf.bfloat16`. + * The summation of `tf.int64` and `tf.float64` will be capped at type `tf.float64` despite precision loss and overflow risks. +* When one of the inputs is a python integer/float/complex, and the other is a TF dtype: + * If no overflow happens, the result dtype is loosely described with the following promotion direction: TF bool -> python integer -> TF signed/unsigned integers -> python float -> TF float -> python complex/TF complex. + * The dtype promotion is value-dependent. If the python scalar is over the range of the determined dtype, it is widened to prevent overflow. For example, `tf.constant([1], tf.int8) + 1` produces `tf.int8` while `tf.constant([1], tf.int8) + 1000` produces `tf.int16`. + +This mode is intended to provide a user experience similar to NumPy behaviors. + +Table: TF dtype promotion result of `tf.add` with enable\_implicit\_promotion + enable\_promotion\_to\_float64 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ b + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i* + f* + c* +
b + bool + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i64 + f64 + c128 +
u8 + u8 + u8 + u16 + u32 + u64 + i16 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + u8 + f64 + c128 +
u16 + u16 + u16 + u16 + u32 + u64 + i32 + i32 + i32 + i64 + f32 + f32 + f32 + f64 + c64 + c128 + u16 + f64 + c128 +
u32 + u32 + u32 + u32 + u32 + u64 + i64 + i64 + i64 + i64 + f64 + f64 + f64 + f64 + c128 + c128 + u32 + f64 + c128 +
u64 + u64 + u64 + u64 + u64 + u64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + c128 + c128 + u64 + f64 + c128 +
i8 + i8 + i16 + i32 + i64 + f64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i8 + f64 + c128 +
i16 + i16 + i16 + i32 + i64 + f64 + i16 + i16 + i32 + i64 + f32 + f32 + f32 + f64 + c64 + c128 + i16 + f64 + c128 +
i32 + i32 + i32 + i32 + i64 + f64 + i32 + i32 + i32 + i64 + f64 + f64 + f64 + f64 + c128 + c128 + i32 + f64 + c128 +
i64 + i64 + i64 + i64 + i64 + f64 + i64 + i64 + i64 + i64 + f64 + f64 + f64 + f64 + c128 + c128 + i64 + f64 + c128 +
bf16 + bf16 + bf16 + f32 + f64 + f64 + bf16 + f32 + f64 + f64 + bf16 + f32 + f32 + f64 + c64 + c128 + bf16 + bf16 + c64 +
f16 + f16 + f16 + f32 + f64 + f64 + f16 + f32 + f64 + f64 + f32 + f16 + f32 + f64 + c64 + c128 + f16 + f16 + c64 +
f32 + f32 + f32 + f32 + f64 + f64 + f32 + f32 + f64 + f64 + f32 + f32 + f32 + f64 + c64 + c128 + f32 + f32 + c64 +
f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + c128 + c128 + f64 + f64 + c128 +
c64 + c64 + c64 + c64 + c128 + c128 + c64 + c64 + c128 + c128 + c64 + c64 + c64 + c128 + c64 + c128 + c64 + c64 + c64 +
c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 +
i* + i64 + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i64 + f64 + c128 +
f* + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + bf16 + f16 + f32 + f64 + c64 + c128 + f64 + f64 + c128 +
c* + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c64 + c64 + c64 + c128 + c64 + c128 + c128 + c128 + c128 +
+ + +Highlighted: When one of the inputs is a python scalar, the dtype in the table is the default result without value-dependent promotion. + + +### **Mode 2: enable\_implicit\_promotion + disable\_promotion\_to\_float64** + +Mode 2 follows the same rules as mode 1 except for one thing: no `tf.float64` or `tf.complex128` will appear in the program - the promoted integer, float and complex dtype widths are capped at 64, 32 and 64 bits respectively. The purpose of this mode is to avoid training slowdown due to the introduction of double precision floating point numbers in a machine learning environment. + +Table: TF dtype promotion result of `tf.add` with enable\_implicit\_promotion + disable\_promotion\_to\_float64 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ b + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i* + f* + c* +
b + bool + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f32 + c64 + c64 + i64 + f32 + c64 +
u8 + u8 + u8 + u16 + u32 + u64 + i16 + i16 + i32 + i64 + bf16 + f16 + f32 + f32 + c64 + c64 + u8 + f32 + c64 +
u16 + u16 + u16 + u16 + u32 + u64 + i32 + i32 + i32 + i64 + f32 + f32 + f32 + f32 + c64 + c64 + u16 + f32 + c64 +
u32 + u32 + u32 + u32 + u32 + u64 + i64 + i64 + i64 + i64 + f32 + f32 + f32 + f32 + c64 + c64 + u32 + f32 + c64 +
u64 + u64 + u64 + u64 + u64 + u64 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + c64 + c64 + u64 + f32 + c64 +
i8 + i8 + i16 + i32 + i64 + f32 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f32 + c64 + c64 + i8 + f32 + c64 +
i16 + i16 + i16 + i32 + i64 + f32 + i16 + i16 + i32 + i64 + f32 + f32 + f32 + f32 + c64 + c64 + i16 + f32 + c64 +
i32 + i32 + i32 + i32 + i64 + f32 + i32 + i32 + i32 + i64 + f32 + f32 + f32 + f32 + c64 + c64 + i32 + f32 + c64 +
i64 + i64 + i64 + i64 + i64 + f32 + i64 + i64 + i64 + i64 + f32 + f32 + f32 + f32 + c64 + c64 + i64 + f32 + c64 +
bf16 + bf16 + bf16 + f32 + f32 + f32 + bf16 + f32 + f32 + f32 + bf16 + f32 + f32 + f32 + c64 + c64 + bf16 + bf16 + c64 +
f16 + f16 + f16 + f32 + f32 + f32 + f16 + f32 + f32 + f32 + f32 + f16 + f32 + f32 + c64 + c64 + f16 + f16 + c64 +
f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + c64 + c64 + f32 + f32 + c64 +
f64 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + c64 + c64 + f32 + f32 + c64 +
c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 +
c128 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 +
i* + i64 + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f32 + c64 + c64 + i64 + f32 + c64 +
f* + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + bf16 + f16 + f32 + f32 + c64 + c64 + f32 + f32 + c64 +
c* + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 +
+ + +Highlighted: When one of the inputs is a python scalar, the dtype in the table is the default result without value-dependent promotion. + + +### **Mode 3: disable\_implicit\_promotion + enable\_promotion\_to\_float64** + +The dtype behavior of mode 3 is the same as mode 1, except that implicit conversions between TF dtypes are disallowed. + +Table: TF dtype promotion result of `tf.add` with disable\_implicit\_promotion + enable\_promotion\_to\_float64 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ b + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i* + f* + c* +
b + b + - + - + - + - + - + - + - + - + - + - + - + - + - + - + i64 + f64 + c128 +
u8 + - + u8 + - + - + - + - + - + - + - + - + - + - + - + - + - + u8 + f64 + c128 +
u16 + - + - + u16 + - + - + - + - + - + - + - + - + - + - + - + - + u16 + f64 + c128 +
u32 + - + - + - + u32 + - + - + - + - + - + - + - + - + - + - + - + u32 + f64 + c128 +
u64 + - + - + - + - + u64 + - + - + - + - + - + - + - + - + - + - + u64 + f64 + c128 +
i8 + - + - + - + - + - + i8 + - + - + - + - + - + - + - + - + - + i8 + f64 + c128 +
i16 + - + - + - + - + - + - + i16 + - + - + - + - + - + - + - + - + i16 + f64 + c128 +
i32 + - + - + - + - + - + - + - + i32 + - + - + - + - + - + - + - + i32 + f64 + c128 +
i64 + - + - + - + - + - + - + - + - + i64 + - + - + - + - + - + - + i64 + f64 + c128 +
bf16 + - + - + - + - + - + - + - + - + - + bf16 + - + - + - + - + - + bf16 + bf16 + c64 +
f16 + - + - + - + - + - + - + - + - + - + - + f16 + - + - + - + - + f16 + f16 + c64 +
f32 + - + - + - + - + - + - + - + - + - + - + - + f32 + - + - + - + f32 + f32 + c64 +
f64 + - + - + - + - + - + - + - + - + - + - + - + - + f64 + - + - + f64 + f64 + c128 +
c64 + - + - + - + - + - + - + - + - + - + - + - + - + - + c64 + - + c64 + c64 + c64 +
c128 + - + - + - + - + - + - + - + - + - + - + - + - + - + - + c128 + c128 + c128 + c128 +
i* + i64 + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i64 + f64 + c128 +
f* + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + bf16 + f16 + f32 + f64 + c64 + c128 + f64 + f64 + c128 +
c* + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c64 + c64 + c64 + c128 + c64 + c128 + c128 + c128 + c128 +
+ + +Highlighted: When one of the inputs is a python scalar, the dtype in the table is the default result without value-dependent promotion. + + +### **Mode 4: disable\_implicit\_promotion + disable\_promotion\_to\_float64** + +The dtype behavior of mode 4 is the same as mode 2, except that implicit conversions between TF dtypes are disallowed. + +Table: TF dtype promotion result of `tf.add` with disable\_implicit\_promotion + disable\_promotion\_to\_float64 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ b + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i* + f* + c* +
b + b + - + - + - + - + - + - + - + - + - + - + - + - + - + - + i64 + f32 + c64 +
u8 + - + u8 + - + - + - + - + - + - + - + - + - + - + - + - + - + u8 + f32 + c64 +
u16 + - + - + u16 + - + - + - + - + - + - + - + - + - + - + - + - + u16 + f32 + c64 +
u32 + - + - + - + u32 + - + - + - + - + - + - + - + - + - + - + - + u32 + f32 + c64 +
u64 + - + - + - + - + u64 + - + - + - + - + - + - + - + - + - + - + u64 + f32 + c64 +
i8 + - + - + - + - + - + i8 + - + - + - + - + - + - + - + - + - + i8 + f32 + c64 +
i16 + - + - + - + - + - + - + i16 + - + - + - + - + - + - + - + - + i16 + f32 + c64 +
i32 + - + - + - + - + - + - + - + i32 + - + - + - + - + - + - + - + i32 + f32 + c64 +
i64 + - + - + - + - + - + - + - + - + i64 + - + - + - + - + - + - + i64 + f32 + c64 +
bf16 + - + - + - + - + - + - + - + - + - + bf16 + - + - + - + - + - + bf16 + bf16 + c64 +
f16 + - + - + - + - + - + - + - + - + - + - + f16 + - + - + - + - + f16 + f16 + c64 +
f32 + - + - + - + - + - + - + - + - + - + - + - + f32 + - + - + - + f32 + f32 + c64 +
f64 + - + - + - + - + - + - + - + - + - + - + - + - + f64 + - + - + f32 + f32 + c64 +
c64 + - + - + - + - + - + - + - + - + - + - + - + - + - + c64 + - + c64 + c64 + c64 +
c128 + - + - + - + - + - + - + - + - + - + - + - + - + - + - + c128 + c64 + c64 + c64 +
i* + i64 + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f32 + c64 + c64 + i64 + f32 + c64 +
f* + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + bf16 + f16 + f32 + f32 + c64 + c64 + f32 + f32 + c64 +
c* + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 +
+ + +Highlighted: When one of the inputs is a python scalar, the dtype in the table is the default result without value-dependent promotion. From 0a583a2856d79d66b90e50d2fa1944db7457cac1 Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 13:38:37 -0700 Subject: [PATCH 02/20] Update 2022-10-18-promotion-semantics.md --- rfcs/2022-10-18-promotion-semantics.md | 31 -------------------------- 1 file changed, 31 deletions(-) diff --git a/rfcs/2022-10-18-promotion-semantics.md b/rfcs/2022-10-18-promotion-semantics.md index c9f010b20..8e1619081 100644 --- a/rfcs/2022-10-18-promotion-semantics.md +++ b/rfcs/2022-10-18-promotion-semantics.md @@ -5,37 +5,6 @@ ## RFC: Making dtype promotion semantics in Tensorflow more consistent -``` - -Username -Role -Status -Last Change -rohanj -Approver -PENDING -2022-09-12 -wangpeng -Approver -APPROVED -2022-09-20 -yanhuasun -Approver -PENDING -2022-09-12 -fchollet -Reviewer -PENDING -2022-09-12 -vanderplas -Reviewer -PENDING -2022-09-12 -#begin-approvals-addon-section -Using go/DocWrap. Table auto-updated hourly. Don't edit this section and don't add comments in it. -``` - - **Status**: Draft | In Review | Approved | Final | Obsolete **Authors**: , LDAP From cda6d73572255599ed3780e899b083e9902f348b Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 13:41:15 -0700 Subject: [PATCH 03/20] Update 2022-10-18-promotion-semantics.md --- rfcs/2022-10-18-promotion-semantics.md | 8712 ------------------------ 1 file changed, 8712 deletions(-) diff --git a/rfcs/2022-10-18-promotion-semantics.md b/rfcs/2022-10-18-promotion-semantics.md index 8e1619081..6173b245b 100644 --- a/rfcs/2022-10-18-promotion-semantics.md +++ b/rfcs/2022-10-18-promotion-semantics.md @@ -1,8716 +1,4 @@ - - - - -## RFC: Making dtype promotion semantics in Tensorflow more consistent - - -**Status**: Draft | In Review | Approved | Final | Obsolete - -**Authors**: , LDAP - -**Contributors**: - -**Sponsors**: , , LDAP - -**Last Updated**: - -go/tf-np-style-promotion - - -## **Note: the main design has been updated on 2020-09-05. We updated the proposed modes. The deprecated modes can be seen in [the bottom of this doc](#bookmark=id.kp5bjdbldb2g). ** - - -## **Acknowledgement** - -This is written by referencing investigations by ex-Googler , as well as projects TF-numpy and JAX: - -[Implicit promotion, inference and conversion in tensorflow](https://docs.google.com/document/d/1jOXJ1YAQAtseyYDDGm4gsr7xJ4F2FtvfwZJgNsW8cVA/edit#heading=h.lloqtrv9juf1) - -[tf.experimental.numpy](https://www.tensorflow.org/api_docs/python/tf/experimental/numpy) - -[Design of Type Promotion Semantics for JAX](https://jax.readthedocs.io/en/latest/jep/9407-type-promotion.html#) - -Specifically, thanks to TF-numpy already implemented the majority of the proposed dtype promotion behaviors. - ## **Objective** Currently TF has no consistent, well-defined type promotion rules. This document proposes a well-defined, consistent and clear dtype promotion rule for TF. The introduced changes make TF APIs more similar to NumPy, with some differences that emphasize TF’s applications in machine learning. This should make dtype promotions in TF much more consistent and predictable. - -[**What the doc is**] This doc discusses the preferred dtype promotion semantics/behaviors of Tensorflow (TF) and Tensorflow-numpy (TF-numpy) in the binary ops including add, sub, mul, div, pow and mod. - -[**What the doc is not**] This doc does not discuss the implementation plans. - - -## **Motivation** - -In Tensorflow’s APIs, dtype promotion is very broken compared to JAX or NumPy. Many users have complained about these surprising behaviors (example: go/broccoli-tf-add, b/158346631, b/154456219). See [this doc ](https://docs.google.com/document/d/1jOXJ1YAQAtseyYDDGm4gsr7xJ4F2FtvfwZJgNsW8cVA/edit#)for a more detailed description. - -[TF-numpy](https://www.tensorflow.org/guide/tf_numpy#type_promotion) is a strategic project in Tensorflow. Compared to Tensorflow, TF-numpy’s dtype promotion behavior is more consistent because it [mainly relies on NumPy’s dtype promotion semantics](https://source.corp.google.com/piper///depot/google3/third_party/tensorflow/python/ops/numpy_ops/np_dtypes.py;rcl=399530502;l=112). In the [long-term vision doc](https://goto.google.com/tf-numpy-path-forward), unifying the dtype promotion semantics of TF and TF-numpy is a necessary first step. - -The unification of TF and TF-numpy’s dtype promotion semantics is a great chance to think about what semantics should be the long-term, user-friendly solution for TF. - - -## **User Benefit** - -There have been ongoing complaints about the inconsistent dtype promotion behaviors of TF. To give an example, switching the inputs’ positions directly affects the outcome: - - -``` -c = tf.constant(1.0) -tf.add(c, 3) # tf.Tensor(4.0, shape=(), dtype=float32) -tf.add(3, c) # InvalidArgumentError: cannot compute AddV2 as input #1(zero-based) was expected to be a int32 tensor but is a float tensor [Op:AddV2] -``` - - -For brevity we will sometimes use shortened terms to represent the dtypes as shown in the following examples: - - - -* `b` means tf.bool -* `u8` means tf.uint8 -* `i16` means tf.int16 -* `bf16` means [tf.bfloat16](https://cloud.google.com/tpu/docs/bfloat16) -* `f64` means tf.float64 -* `c64` means tf.complex64 -* `i*` means python int -* `f*` means python float -* `c*` means python complex - -The table below summarizes the dtype promotion behavior between two TF tensors/python scalars with method `__add__` and API `tf.add` respectively. Each row and column represents a combination of input types. - -Table: TF dtype promotion result of dunder method `__add__` and `tf.add` - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i* - f* - c* -
b - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
u8 - - - u8 - - - - - - - - - - - - - - - - - - - - - - - - - - - u8 - - - - -
u16 - - - - - u16 - - - - - - - - - - - - - - - - - - - - - - - - - u16 - - - - -
u32 - - - - - - - u32 - - - - - - - - - - - - - - - - - - - - - - - u32 - - - - -
u64 - - - - - - - - - u64 - - - - - - - - - - - - - - - - - - - - - u64 - - - - -
i8 - - - - - - - - - - - i8 - - - - - - - - - - - - - - - - - - - i8 - - - - -
i16 - - - - - - - - - - - - - i16 - - - - - - - - - - - - - - - - - i16 - - - - -
i32 - - - - - - - - - - - - - - - i32 - - - - - - - - - - - - - - - i32 - - - - -
i64 - - - - - - - - - - - - - - - - - i64 - - - - - - - - - - - - - i64 - - - - -
bf16 - - - - - - - - - - - - - - - - - - - bf16 - - - - - - - - - - - bf16 - bf16 - - -
f16 - - - - - - - - - - - - - - - - - - - - - f16 - - - - - - - - - f16 - f16 - - -
f32 - - - - - - - - - - - - - - - - - - - - - - - f32 - - - - - - - f32 - f32 - - -
f64 - - - - - - - - - - - - - - - - - - - - - - - - - f64 - - - - - f64 - f64 - - -
c64 - - - - - - - - - - - - - - - - - - - - - - - - - - - c64 - - - c64 - c64 - c64 -
c128 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - c128 - c128 - c128 - c128 -
i* - - - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i32 - - -
f* - - - - - - - - - - - - - - - - - - - bf16 - f16 - f32 - f64 - c64 - c128 - f32 - f32 - -
c* - - - - - - - - - - - - - - - - - - - - - - - - - - - c64 - c128 - c128 - c128 - c128 -
- - -In the table, the dash symbol `-` means unsupported: an error gets raised if the two dtypes are used in the call to that op. The cells highlighted in red are only supported in `__add__`, and the cells in yellow are only supported in `tf.add`. In summary, the existing TF APIs have these significant issues: - - - -1. Boolean dtypes are not supported; -2. Implicit conversion between two TF dtypes are strictly disallowed, resulting in verbose code at times; -3. Implicit conversion from some python dtypes to some TF dtypes are disallowed; -4. `tf.add` is not even commutative - switching the position of the two arguments sometimes produces errors, otherwise results in correct values. - -When the inputs involve NumPy arrays, the behaviors can be even more broken. For brevity we have included NumPy-input behaviors in the appendix at the end of the doc. - -We aim to make the system commutative, predictable and correct. - - -## **Design Proposal** - -We propose introducing three dtype promotion modes in TF: - - - -* `tf.ImplicitPromotion.ALL` -* `tf.ImplicitPromotion.SAFE` -* `tf.ImplicitPromotion.NONE` - -The three modes determine how often implicit dtype promotions happen in TF APIs. In the following examples we will use `tf.add` to demonstrate the modes. The dunder method `__add__` is expected to have the same behavior as `tf.add`. For brevity, NumPy (np) array inputs are not discussed in this section. However the proposed modes in this RFC will treat the np array inputs in the same way as the tensor inputs (see more in the appendix). - -Table: TF dtype promotion result of `tf.add` after the proposed changes. `NONE` only allows the unhighlighted cells. `SAFE` allows all `NONE` cases plus the cells highlighted in green. `ALL` allows all `SAFE` cases plus the cells highlighted in yellow. Rows/columns highlighted in blue only show the default result without overflow of inputs. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i* - f* - c* -
b - b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i64 - f64 - c128 -
u8 - u8 - u8 - u16 - u32 - u64 - i16 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - u8 - f64 - c128 -
u16 - u16 - u16 - u16 - u32 - u64 - i32 - i32 - i32 - i64 - f32 - f32 - f32 - f64 - c64 - c128 - u16 - f64 - c128 -
u32 - u32 - u32 - u32 - u32 - u64 - i64 - i64 - i64 - i64 - f64 - f64 - f64 - f64 - c128 - c128 - u32 - f64 - c128 -
u64 - u64 - u64 - u64 - u64 - u64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - c128 - c128 - u64 - f64 - c128 -
i8 - i8 - i16 - i32 - i64 - f64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i8 - f64 - c128 -
i16 - i16 - i16 - i32 - i64 - f64 - i16 - i16 - i32 - i64 - f32 - f32 - f32 - f64 - c64 - c128 - i16 - f64 - c128 -
i32 - i32 - i32 - i32 - i64 - f64 - i32 - i32 - i32 - i64 - f64 - f64 - f64 - f64 - c128 - c128 - i32 - f64 - c128 -
i64 - i64 - i64 - i64 - i64 - f64 - i64 - i64 - i64 - i64 - f64 - f64 - f64 - f64 - c128 - c128 - i64 - f64 - c128 -
bf16 - bf16 - bf16 - f32 - f64 - f64 - bf16 - f32 - f64 - f64 - bf16 - f32 - f32 - f64 - c64 - c128 - bf16 - bf16 - c64 -
f16 - f16 - f16 - f32 - f64 - f64 - f16 - f32 - f64 - f64 - f32 - f16 - f32 - f64 - c64 - c128 - f16 - f16 - c64 -
f32 - f32 - f32 - f32 - f64 - f64 - f32 - f32 - f64 - f64 - f32 - f32 - f32 - f64 - c64 - c128 - f32 - f32 - c64 -
f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - c128 - c128 - f64 - f64 - c128 -
c64 - c64 - c64 - c64 - c128 - c128 - c64 - c64 - c128 - c128 - c64 - c64 - c64 - c128 - c64 - c128 - c64 - c64 - c64 -
c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 -
i* - i64 - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i64 - f64 - c128 -
f* - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - bf16 - f16 - f32 - f64 - c64 - c128 - f64 - f64 - c128 -
c* - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c64 - c64 - c64 - c128 - c64 - c128 - c128 - c128 - c128 -
- - - -### **Mode 1: tf.ImplicitPromotion.ALL** - -In this mode, we allow all implicit promotions to happen in the op, even if both inputs have TF dtypes. To summarize the rules of this mode: - - - -* When the two inputs have non-matching TF dtypes, they will be promoted to the lowest width dtype that guarantees the precisions and ranges of the inputs. However, the promoted integer, float and complex dtype widths are capped at 64, 64 and 128 bits respectively. For example: - * The summation of `tf.bfloat16` and `tf.uint8` will be type `tf.bfloat16`. - * The summation of `tf.int64` and `tf.float64` will be capped at type `tf.float64` despite precision loss and overflow risks. -* When one of the inputs is a python integer/float/complex, and the other is a TF dtype: - * If the python scalar value falls within the range of the TF dtype, the result dtype is loosely described with the following promotion direction: TF bool -> python integer -> TF signed/unsigned integers -> python float -> TF float -> python complex/TF complex. - * The dtype promotion is allowed only if the python scalar is within the range of the determined dtype. Otherwise, an exception is raised. For example, `tf.constant([1], tf.int8) + 1` produces a `tf.int8` Tensor, while `tf.constant([1], tf.int8) + 1000` raises an error. - -This mode is intended to provide a user experience similar to NumPy behaviors. - - -### **Mode 2: tf.ImplicitPromotion.SAFE** - -Mode `SAFE` follows the same rules as mode `ALL`, but disallows “unsafe” dtype promotions. In general, we think the following two types of conversions are unsafe: - - - -* If the inputs are both Tensors and the result dtype would be “wider” than all the inputs. For example, the summation of `tf.uint8` and `tf.int8` would be type `tf.i16`. These dtype promotions risk increasing the model’s memory consumption and slowing down computation speed. -* If the inputs are both Tensors and the result dtype cannot preserve all the precisions of the inputs. For example, the summation of `tf.uint64` and `tf.float32` would be type `tf.float64`. - -With the above principle, if one of the inputs is a Tensor while the other is a python scalar, we only allow implicit promotion if the scalar can fit into the Tensor’s dtype. For example: - - - -* The summation between a `tf.int32` and a python float is always disallowed; -* The summation between a `tf.uint8` and a python int is allowed only if the python int is between 0 and 255. - -For these disallowed dtype promotions, we require the users to explicitly cast them. - -Another advantage is that mode `ALL` can be silently nonassociative in the following scenario, while mode `SAFE` avoids it by not allowing any dtype widening: - - - -* `(tf.zeros(tf.uint8) + tf.zeros(tf.int8)) + tf.zeros(tf.float16)` evaluates to dtype `tf.float32`; -* `tf.zeros(tf.uint8) + (tf.zeros(tf.int8) + tf.zeros(tf.float16))` evaluates to dtype `tf.float16`. - - -### **Mode 3: tf.ImplicitPromotion.NONE** - -The dtype behavior of mode `NONE` is the most conservative: no dtype promotion is allowed between Tensors. When the inputs include python scalars, it follows the same rule as mode `SAFE`. - - -### **Alternatives Considered: Capping the promotion system at float32** - -The TF-NumPy project contains another flag [allow_float64](https://source.corp.google.com/piper///depot/google3/third_party/tensorflow/python/ops/numpy_ops/np_dtypes.py;rcl=399530502;l=88). When disabled, it caps the converted floating numbers to 32 bits, and complex numbers to 64 bits. This is useful when users want to enjoy implicit conversions in all cases and avoid any performance regressions related to double precision floating point numbers. This feature is now used by [Trax](https://b.corp.google.com/issues/178862061). - -Our current plan does not involve adding this flag in TF, as it’s orthogonal to the proposed modes. If there are more user needs we can consider exposing it in TF as well. - - -### **Alternatives Considered: A dtype promotion “lattice”** - -Unlike the approaches above, JAX used a dtype promotion lattice to define its semantics. More details can be seen in their [design doc](https://jax.readthedocs.io/en/latest/jep/9407-type-promotion.html#properties-of-a-type-promotion-lattice). Their lattice system has these advantages: - - - -* A lattice can be much more concise to describe the promotion semantics; -* The lattice can ensure associativity as well as commutativity. In contrast, the promotion behaviors in this proposal is not associative: - * `(tf.zeros(tf.uint8) + tf.zeros(tf.int8)) + tf.zeros(tf.float16)` evaluates to dtype `tf.float32`; - * `tf.zeros(tf.uint8) + (tf.zeros(tf.int8) + tf.zeros(tf.float16))` evaluates to dtype `tf.float16`. - -~~However the lattice system also introduced extra complexity in the design. For example:~~ - - - -* ~~JAX ended up introducing the concepts of “weak types” in place of python types;~~ -* ~~JAX adopted very aggressive dtype promotion semantics - an int64 dtype can be promoted to a float16, resulting in higher risks of overflow.~~ - -~~Since our ultimate goal is to help the users maintain a clear and concise mental model, adopting a dtype promotion table is an easier approach.~~ - -Compared to a lattice system, the proposed table-based system does not guarantee associativity. However, we can avoid such usage scenarios in the proposed SAFE mode (no implicit promotion to wider dtypes). As a result, it’s not clearly justifiable to switch to a lattice system from the table-based approach, which already exists in project TF-NumPy. - - -### **Alternatives Considered: Value-dependent promotion (a.k.a. “safe casting” in NumPy)** - -NumPy has a feature “safe casting”: the dtype promotion API, [numpy.result_type](https://numpy.org/doc/stable/reference/generated/numpy.result_type.html), can return a different dtype depending on the input python scalar value: - - -``` -np.result_type(np.int8, 200) # dtype('int16') -np.result_type(np.int8, 100) # dtype('int8') -``` - - -This slightly reduces the risk of overflow. However, the result is unpredictable. NumPy is considering removing this feature in a future release (see proposal: [NEP 50](https://numpy.org/neps/nep-0050-scalar-promotion.html)). JAX [did not adopt this feature](https://jax.readthedocs.io/en/latest/jep/9407-type-promotion.html#enter-python-scalars) either. We omit it in this design. - - -### **Performance Implications** - -For an existing piece of TF code in python, we do not expect the performance to change after switching to any of the proposed modes. This is because all the allowed implicit dtype promotions in TF will stay unchanged. As to the currently disallowed dtype promotions, users must have used explicit dtype promotions which are not affected by the change. - -When users develop a new piece of code, however, the new modes have higher chances of computation slowdown due to implicit promotions in mode `ALL`. - -We can carry out the following steps to mitigate user surprises: - - - -* Add benchmark tests to verify that the new modes won’t affect the performance of existing TF code. -* Publish tutorials that help users select the most appropriate dtype promotion modes. -* [optional] Add an API that instruments the number of implicit, unsafe dtype conversions in the program (e.g. promotions from 32 bits to 64 bits). This helps users understand whether the promotions contribute to computation slowdown in the graphs. - - -### **Dependencies** - -This proposal is not expected to affect dependencies to Tensorflow. - - -#### **Engineering Impact** - - -#### We do not expect changes in binary size/startup time/build time/test time. - -The majority of the dtype promotion logic already exists in Tensorflow under namescope `tensorflow.experimental.numpy`. However, refactoring/updates will be needed under directory tensorflow/python/ops. - - -#### **Platforms and Environments** - -This proposal is expected to take effect on all platforms in the python environment. - - -#### **Best Practices/Tutorials and Examples** - -As mentioned in section “Performance Implications” we will publish tutorials that help users select the most appropriate dtype promotion modes. - - -#### **Compatibility/User Impact** - -This is a breaking change. As mentioned above, existing code is expected to continue working. However, some tests will fail because more implicit dtype conversions are allowed. We can mitigate the user impact by adding a flag that sticks to the existing dtype promotion behaviors. - -The proposed changes will be rolled out in various stages: - - - -1. Default behaviors stay unchanged, with the flags introduced under namescope `tf.experiment` to collect user feedbacks; -2. Default behaviors switched to one of the modes above, while the old behaviors can be re-enabled via a flag; -3. [optional] Completely disable the old, broken dtype promotion behaviors. - - -## **Questions and Discussion Topics** - - -#### **Are there changes expected in tf.Variable?** - -In APIs such as `tf.add` and dunder methods `__add__`, the same changes are expected to take place because these methods first read the Variable into a Tensor, then carry out the Tensor-to-Tensor computations. - -Currently, inplace ops such as `assign_add` are also broken. The two tables below show the result of `assign_add` when the input is a Tensor, a python scalar or a NumPy array: - - - -* When the input is a Tensor, no dtype conversion is allowed. -* When the input is a python scalar, it defers to the variable’s dtype. However, python float cannot be converted to int, and complex cannot be converted to either int or float dtypes. -* When the input is a NumPy array, it always gets converted to the variable’s dtype regardless of any precision loss (for example `tf.Variable(1) + np.array(1.5)` returns 2). - -Table: Result of tf.Variable (rows) inplace op `assign_add` with a Tensor or python scalar as the argument (columns). - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i* - f* - c* -
b - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
u8 - - - u8 - - - - - - - - - - - - - - - - - - - - - - - - - - - u8 - - - - -
u16 - - - - - u16 - - - - - - - - - - - - - - - - - - - - - - - - - u16 - - - - -
u32 - - - - - - - u32 - - - - - - - - - - - - - - - - - - - - - - - u32 - - - - -
u64 - - - - - - - - - u64 - - - - - - - - - - - - - - - - - - - - - u64 - - - - -
i8 - - - - - - - - - - - i8 - - - - - - - - - - - - - - - - - - - i8 - - - - -
i16 - - - - - - - - - - - - - i16 - - - - - - - - - - - - - - - - - i16 - - - - -
i32 - - - - - - - - - - - - - - - i32 - - - - - - - - - - - - - - - i32 - - - - -
i64 - - - - - - - - - - - - - - - - - i64 - - - - - - - - - - - - - i64 - - - - -
bf16 - - - - - - - - - - - - - - - - - - - bf16 - - - - - - - - - - - bf16 - bf16 - - -
f16 - - - - - - - - - - - - - - - - - - - - - f16 - - - - - - - - - f16 - f16 - - -
f32 - - - - - - - - - - - - - - - - - - - - - - - f32 - - - - - - - f32 - f32 - - -
f64 - - - - - - - - - - - - - - - - - - - - - - - - - f64 - - - - - f64 - f64 - - -
c64 - - - - - - - - - - - - - - - - - - - - - - - - - - - c64 - - - c64 - c64 - c64 -
c128 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - c128 - c128 - c128 - c128 -
- - -Table: Result of tf.Variable (rows) inplace op `assign_add` with a NumPy array as the argument (columns). - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 -
b - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
u8 - u8 - u8 - u8 - u8 - u8 - u8 - u8 - u8 - u8 - - - u8 - u8 - u8 - u8 - u8 -
u16 - u16 - u16 - u16 - u16 - u16 - u16 - u16 - u16 - u16 - - - u16 - u16 - u16 - u16 - u16 -
u32 - u32 - u32 - u32 - u32 - u32 - u32 - u32 - u32 - u32 - - - u32 - u32 - u32 - u32 - u32 -
u64 - u64 - u64 - u64 - u64 - u64 - u64 - u64 - u64 - u64 - - - u64 - u64 - u64 - u64 - u64 -
i8 - i8 - i8 - i8 - i8 - i8 - i8 - i8 - i8 - i8 - - - i8 - i8 - i8 - i8 - i8 -
i16 - i16 - i16 - i16 - i16 - i16 - i16 - i16 - i16 - i16 - - - i16 - i16 - i16 - i16 - i16 -
i32 - i32 - i32 - i32 - i32 - i32 - i32 - i32 - i32 - i32 - - - i32 - i32 - i32 - i32 - i32 -
i64 - i64 - i64 - i64 - i64 - i64 - i64 - i64 - i64 - i64 - - - i64 - i64 - i64 - i64 - i64 -
bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - - - bf16 - bf16 - bf16 - bf16 - bf16 -
f16 - f16 - f16 - f16 - f16 - f16 - f16 - f16 - f16 - f16 - - - f16 - f16 - f16 - f16 - f16 -
f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - - - f32 - f32 - f32 - f32 - f32 -
f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - - - f64 - f64 - f64 - f64 - f64 -
c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - - - c64 - c64 - c64 - c64 - c64 -
c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - - - c128 - c128 - c128 - c128 - c128 -
- - -We can make these ops more consistent as well, following the rules in the three proposed modes. However, we also have another constraint: the Variable’s dtype cannot change. As a result, any dtype promotion that results in a dtype different from the Variable’s dtype is disabled. - -Table: Result of tf.Variable (rows) inplace op `assign_add` with a Tensor or python scalar as the argument (columns) after the proposed changes. `NONE` only allows the unhighlighted cells. `SAFE` allows all `NONE` cases plus the cells highlighted in green. `ALL` allows all `SAFE` cases plus the cells highlighted in yellow. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i* - f* - c* -
b - b - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
u8 - u8 - u8 - - - - - - - - - - - - - - - - - - - - - - - - - - - u8 - - - - -
u16 - u16 - u16 - u16 - - - - - - - - - - - - - - - - - - - - - - - - - u16 - - - - -
u32 - u32 - u32 - u32 - u32 - - - - - - - - - - - - - - - - - - - - - - - u32 - - - - -
u64 - u64 - u64 - u64 - u64 - u64 - - - - - - - - - - - - - - - - - - - - - u64 - - - - -
i8 - i8 - - - - - - - - - i8 - - - - - - - - - - - - - - - - - - - i8 - - - - -
i16 - i16 - i16 - - - - - - - i16 - i16 - - - - - - - - - - - - - - - - - i16 - - - - -
i32 - i32 - i32 - i32 - - - - - i32 - i32 - i32 - - - - - - - - - - - - - - - i32 - - - - -
i64 - i64 - i64 - i64 - i64 - - - i64 - i64 - i64 - i64 - - - - - - - - - - - - - i64 - - - - -
bf16 - bf16 - bf16 - - - - - - - bf16 - - - - - - - bf16 - - - - - - - - - - - bf16 - bf16 - - -
f16 - f16 - f16 - - - - - - - f16 - - - - - - - - - f16 - - - - - - - - - f16 - f16 - - -
f32 - f32 - f32 - f32 - - - - - f32 - f32 - - - - - f32 - f32 - f32 - - - - - - - f32 - f32 - - -
f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - - - - - f64 - f64 - - -
c64 - c64 - c64 - c64 - - - - - c64 - c64 - - - - - c64 - c64 - c64 - - - c64 - - - c64 - c64 - c64 -
c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 -
- - - -#### **Are there also issues in conversions between Tensor and numpy arrays?** - -There exists arguably more serious issues when it comes to the mixture of Tensor and numpy arrays - wrong results can appear silently: - - -``` -a = np.array(3.1) -c = tf.constant(1) -print(tf.add(a, c)) # InvalidArgumentError: cannot compute AddV2 as input #1(zero-based) was expected to be a double tensor but is a int32 tensor [Op:AddV2] -print(tf.add(c, a)) # tf.Tensor(4, shape=(), dtype=int32) - Even worse than exceptions -``` - - -To give a comprehensive overview, the two tables below show the results of `tf.add(tensor, numpy array)` and `tf.add(numpy array, tensor)` : - -Table: TF dtype promotion result of `tf.add` with a Tensor as the first argument (rows) and np array in the second argument (columns). - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 -
b - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
u8 - u8 - u8 - u8 - u8 - u8 - u8 - u8 - u8 - u8 - - - u8 - u8 - u8 - u8 - u8 -
u16 - u16 - u16 - u16 - u16 - u16 - u16 - u16 - u16 - u16 - - - u16 - u16 - u16 - u16 - u16 -
u32 - u32 - u32 - u32 - u32 - u32 - u32 - u32 - u32 - u32 - - - u32 - u32 - u32 - u32 - u32 -
u64 - u64 - u64 - u64 - u64 - u64 - u64 - u64 - u64 - u64 - - - u64 - u64 - u64 - u64 - u64 -
i8 - i8 - i8 - i8 - i8 - i8 - i8 - i8 - i8 - i8 - - - i8 - i8 - i8 - i8 - i8 -
i16 - i16 - i16 - i16 - i16 - i16 - i16 - i16 - i16 - i16 - - - i16 - i16 - i16 - i16 - i16 -
i32 - i32 - i32 - i32 - i32 - i32 - i32 - i32 - i32 - i32 - - - i32 - i32 - i32 - i32 - i32 -
i64 - i64 - i64 - i64 - i64 - i64 - i64 - i64 - i64 - i64 - - - i64 - i64 - i64 - i64 - i64 -
bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - - - bf16 - bf16 - bf16 - bf16 - bf16 -
f16 - f16 - f16 - f16 - f16 - f16 - f16 - f16 - f16 - f16 - - - f16 - f16 - f16 - f16 - f16 -
f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - - - f32 - f32 - f32 - f32 - f32 -
f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - - - f64 - f64 - f64 - f64 - f64 -
c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - - - c64 - c64 - c64 - c64 - c64 -
c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - - - c128 - c128 - c128 - c128 - c128 -
- - -Table: TF dtype promotion result of `tf.add` with a np array as the first argument (rows) and tensor in the second argument (columns). - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 -
b - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
u8 - - - u8 - - - - - - - - - - - - - - - - - - - - - - - - - - -
u16 - - - - - u16 - - - - - - - - - - - - - - - - - - - - - - - - -
u32 - - - - - - - u32 - - - - - - - - - - - - - - - - - - - - - - -
u64 - - - - - - - - - u64 - - - - - - - - - - - - - - - - - - - - -
i8 - - - - - - - - - - - i8 - - - - - - - - - - - - - - - - - - -
i16 - - - - - - - - - - - - - i16 - - - - - - - - - - - - - - - - -
i32 - - - - - - - - - - - - - - - i32 - - - - - - - - - - - - - - -
i64 - - - - - - - - - - - - - - - - - i64 - - - - - - - - - - - - -
bf16 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
f16 - - - - - - - - - - - - - - - - - - - - - f16 - - - - - - - - -
f32 - - - - - - - - - - - - - - - - - - - - - - - f32 - - - - - - -
f64 - - - - - - - - - - - - - - - - - - - - - - - - - f64 - - - - -
c64 - - - - - - - - - - - - - - - - - - - - - - - - - - - c64 - - -
c128 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - c128 -
- - -To briefly describe the issues: - - - -1. dtypes `tf.bfloat16` and `tf.bool` are poorly supported; -2. If the first input is a tensor, the second input (the np array) is always converted to the tensor’s dtype, resulting into significant precision loss in many scenarios; -3. If the first input is a np array and the second is a tensor, the np array is first converted to a tensor with the corresponding dtype. TF then does not attempt any implicit dtype conversion, causing `InvalidArgumentError` whenever the two inputs have mismatching dtypes. - -Though the behaviors with np arrays inputs are not the same as python scalars, they share the same root cause: At present, TF does not have a centralized dtype promotion system, and incorrectly uses the [dtype of the first Tensor in its list of arguments](https://source.corp.google.com/piper///depot/google3/third_party/tensorflow/python/ops/math_ops.py;rcl=449753780;l=1371) to promote all arguments to. The proposed modes in this RFC will treat the np array inputs in the same way as the tensor inputs. - - -#### **What happens to e.g. bool - bool or bool / bool? - -With NumPy: - - - -* Addition between two Booleans: equivalent to OR -* Subtraction between two Booleans: forbidden -* Multiplication between two Booleans: AND -* Division between two Booleans: converted to float64 then divide - - -#### **What happens to truediv operations?** - -The op truediv is special because it implicitly promotes integral types. For example when the inputs are matching dtypes, 16-bit integral types are promoted to `f32`, and 32-bit integral types are promoted to `float64`. - -Using this rule of thumb, we can easily deduce the promotion behavior between non-matching dtypes when this proposal is landed. For example: - - - -* `int8` and `int16` -> both `int16` -> `f32` (allowed in `SAFE/ALL` modes) -* `uint8` and `uint32` -> both `uint32` -> `f64` (allowed in `SAFE/ALL` modes) -* `int8` and `i*` -> both `int8` -> `f32` (allowed in `NONE/SAFE/ALL` modes) -* `int8` and `uint16` -> both `int32` -> `f64` (allowed in `ALL` mode) - -In the above examples, the first arrow represents the dtype promotion in this proposal, and the second arrow represents the promotion in op truediv. Note the result of `int8` and `uint16`, `f64`, turns out to be an overkill but is consistent with the combination of the dtype promotion rules and the truediv promotion rules. - -~~Table: TF dtype promotion result of `tf.math.truediv` after the proposed changes. `NONE` only allows the unhighlighted cells. `SAFE` allows all `NONE` cases plus the cells highlighted in green. `ALL` allows all `SAFE` cases plus the cells highlighted in yellow. Rows/columns highlighted in blue only show the default result without overflow of inputs.~~ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i* - f* - c* -
b - b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i64 - f64 - c128 -
u8 - u8 - f32 - f32 - f64 - f64 - f32 - f32 - f64 - f64 - bf16 - f16 - f32 - f64 - c64 - c128 - f32 - f64 - c128 -
u16 - u16 - f32 - f32 - f64 - f64 - f64 - f64 - f64 - f64 - f32 - f32 - f32 - f64 - c64 - c128 - f32 - f64 - c128 -
u32 - u32 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - c128 - c128 - f64 - f64 - c128 -
u64 - u64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - c128 - c128 - f64 - f64 - c128 -
i8 - i8 - f32 - f64 - f64 - f64 - f32 - f32 - f64 - f64 - bf16 - f16 - f32 - f64 - c64 - c128 - f32 - f64 - c128 -
i16 - i16 - f32 - f64 - f64 - f64 - f32 - f32 - f64 - f64 - f32 - f32 - f32 - f64 - c64 - c128 - f32 - f64 - c128 -
i32 - i32 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - c128 - c128 - f64 - f64 - c128 -
i64 - i64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - c128 - c128 - f64 - f64 - c128 -
bf16 - bf16 - bf16 - f32 - f64 - f64 - bf16 - f32 - f64 - f64 - bf16 - f32 - f32 - f64 - c64 - c128 - bf16 - bf16 - c64 -
f16 - f16 - f16 - f32 - f64 - f64 - f16 - f32 - f64 - f64 - f32 - f16 - f32 - f64 - c64 - c128 - f16 - f16 - c64 -
f32 - f32 - f32 - f32 - f64 - f64 - f32 - f32 - f64 - f64 - f32 - f32 - f32 - f64 - c64 - c128 - f32 - f32 - c64 -
f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - c128 - c128 - f64 - f64 - c128 -
c64 - c64 - c64 - c64 - c128 - c128 - c64 - c64 - c128 - c128 - c64 - c64 - c64 - c128 - c64 - c128 - c64 - c64 - c64 -
c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 -
i* - i64 - f32 - f32 - f64 - f64 - f32 - f32 - f64 - f64 - bf16 - f16 - f32 - f64 - c64 - c128 - f64 - f64 - c128 -
f* - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - bf16 - f16 - f32 - f64 - c64 - c128 - f64 - f64 - c128 -
c* - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c64 - c64 - c64 - c128 - c64 - c128 - c128 - c128 - c128 -
- - - -#### **~~Is it possible to avoid silent overflow in e.g. tf.constant(100, dtype=tf.uint8) + tf.constant(200, dtype=tf.uint8)? - -~~In both JAX and TF, when overflow happens in float types, the result is an inf value:~~ - - -``` -jnp.array(4e4, 'float16') + jnp.array(4e4, 'float16') # inf -tf.constant(4e4, 'float16') + tf.constant(4e4, 'float16') # inf -``` - - -~~However if the dtype is integral, overflow is silent:~~ - - -``` -jnp.array(100, 'int8') + jnp.array(100, 'int8') # -56 -tf.constant(100, 'int8') + tf.constant(100, 'int8') # -56 -``` - - -~~ ~~ - - - - -### [Deprecated]: old propositions before 2022/09/05 - - -## **Design Proposal** - -We propose introducing two sets of flags that control the dtype promotion behaviors in TF: - - - -* `enable_implicit_promotion/disable_implicit_promotion` -* `enable_promotion_to_float64/disable_promotion_to_float64` - -The two sets of flags influence the dtype promotion behaviors orthogonally. Combined together, the user can achieve four dtype promotion modes. In the following examples we will use `tf.add` to demonstrate the modes. The dunder method `__add__` is expected to have the same behavior as `tf.add`. - - -### **Mode 1: enable\_implicit\_promotion + enable\_promotion\_to\_float64** - -In this mode, we allow all implicit promotions to happen in the op, even if both inputs have TF dtypes. To summarize the rules of this mode: - - - -* When the two inputs have non-matching TF dtypes, they will be promoted to the lowest width dtype that guarantees the precisions and ranges of the inputs. However, the promoted integer, float and complex dtype widths are capped at 64, 64 and 128 bits respectively. For example: - * The summation of `tf.bfloat16` and `tf.uint8` will be type `tf.bfloat16`. - * The summation of `tf.int64` and `tf.float64` will be capped at type `tf.float64` despite precision loss and overflow risks. -* When one of the inputs is a python integer/float/complex, and the other is a TF dtype: - * If no overflow happens, the result dtype is loosely described with the following promotion direction: TF bool -> python integer -> TF signed/unsigned integers -> python float -> TF float -> python complex/TF complex. - * The dtype promotion is value-dependent. If the python scalar is over the range of the determined dtype, it is widened to prevent overflow. For example, `tf.constant([1], tf.int8) + 1` produces `tf.int8` while `tf.constant([1], tf.int8) + 1000` produces `tf.int16`. - -This mode is intended to provide a user experience similar to NumPy behaviors. - -Table: TF dtype promotion result of `tf.add` with enable\_implicit\_promotion + enable\_promotion\_to\_float64 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i* - f* - c* -
b - bool - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i64 - f64 - c128 -
u8 - u8 - u8 - u16 - u32 - u64 - i16 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - u8 - f64 - c128 -
u16 - u16 - u16 - u16 - u32 - u64 - i32 - i32 - i32 - i64 - f32 - f32 - f32 - f64 - c64 - c128 - u16 - f64 - c128 -
u32 - u32 - u32 - u32 - u32 - u64 - i64 - i64 - i64 - i64 - f64 - f64 - f64 - f64 - c128 - c128 - u32 - f64 - c128 -
u64 - u64 - u64 - u64 - u64 - u64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - c128 - c128 - u64 - f64 - c128 -
i8 - i8 - i16 - i32 - i64 - f64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i8 - f64 - c128 -
i16 - i16 - i16 - i32 - i64 - f64 - i16 - i16 - i32 - i64 - f32 - f32 - f32 - f64 - c64 - c128 - i16 - f64 - c128 -
i32 - i32 - i32 - i32 - i64 - f64 - i32 - i32 - i32 - i64 - f64 - f64 - f64 - f64 - c128 - c128 - i32 - f64 - c128 -
i64 - i64 - i64 - i64 - i64 - f64 - i64 - i64 - i64 - i64 - f64 - f64 - f64 - f64 - c128 - c128 - i64 - f64 - c128 -
bf16 - bf16 - bf16 - f32 - f64 - f64 - bf16 - f32 - f64 - f64 - bf16 - f32 - f32 - f64 - c64 - c128 - bf16 - bf16 - c64 -
f16 - f16 - f16 - f32 - f64 - f64 - f16 - f32 - f64 - f64 - f32 - f16 - f32 - f64 - c64 - c128 - f16 - f16 - c64 -
f32 - f32 - f32 - f32 - f64 - f64 - f32 - f32 - f64 - f64 - f32 - f32 - f32 - f64 - c64 - c128 - f32 - f32 - c64 -
f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - c128 - c128 - f64 - f64 - c128 -
c64 - c64 - c64 - c64 - c128 - c128 - c64 - c64 - c128 - c128 - c64 - c64 - c64 - c128 - c64 - c128 - c64 - c64 - c64 -
c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 -
i* - i64 - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i64 - f64 - c128 -
f* - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - bf16 - f16 - f32 - f64 - c64 - c128 - f64 - f64 - c128 -
c* - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c64 - c64 - c64 - c128 - c64 - c128 - c128 - c128 - c128 -
- - -Highlighted: When one of the inputs is a python scalar, the dtype in the table is the default result without value-dependent promotion. - - -### **Mode 2: enable\_implicit\_promotion + disable\_promotion\_to\_float64** - -Mode 2 follows the same rules as mode 1 except for one thing: no `tf.float64` or `tf.complex128` will appear in the program - the promoted integer, float and complex dtype widths are capped at 64, 32 and 64 bits respectively. The purpose of this mode is to avoid training slowdown due to the introduction of double precision floating point numbers in a machine learning environment. - -Table: TF dtype promotion result of `tf.add` with enable\_implicit\_promotion + disable\_promotion\_to\_float64 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i* - f* - c* -
b - bool - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f32 - c64 - c64 - i64 - f32 - c64 -
u8 - u8 - u8 - u16 - u32 - u64 - i16 - i16 - i32 - i64 - bf16 - f16 - f32 - f32 - c64 - c64 - u8 - f32 - c64 -
u16 - u16 - u16 - u16 - u32 - u64 - i32 - i32 - i32 - i64 - f32 - f32 - f32 - f32 - c64 - c64 - u16 - f32 - c64 -
u32 - u32 - u32 - u32 - u32 - u64 - i64 - i64 - i64 - i64 - f32 - f32 - f32 - f32 - c64 - c64 - u32 - f32 - c64 -
u64 - u64 - u64 - u64 - u64 - u64 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - c64 - c64 - u64 - f32 - c64 -
i8 - i8 - i16 - i32 - i64 - f32 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f32 - c64 - c64 - i8 - f32 - c64 -
i16 - i16 - i16 - i32 - i64 - f32 - i16 - i16 - i32 - i64 - f32 - f32 - f32 - f32 - c64 - c64 - i16 - f32 - c64 -
i32 - i32 - i32 - i32 - i64 - f32 - i32 - i32 - i32 - i64 - f32 - f32 - f32 - f32 - c64 - c64 - i32 - f32 - c64 -
i64 - i64 - i64 - i64 - i64 - f32 - i64 - i64 - i64 - i64 - f32 - f32 - f32 - f32 - c64 - c64 - i64 - f32 - c64 -
bf16 - bf16 - bf16 - f32 - f32 - f32 - bf16 - f32 - f32 - f32 - bf16 - f32 - f32 - f32 - c64 - c64 - bf16 - bf16 - c64 -
f16 - f16 - f16 - f32 - f32 - f32 - f16 - f32 - f32 - f32 - f32 - f16 - f32 - f32 - c64 - c64 - f16 - f16 - c64 -
f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - c64 - c64 - f32 - f32 - c64 -
f64 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - c64 - c64 - f32 - f32 - c64 -
c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 -
c128 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 -
i* - i64 - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f32 - c64 - c64 - i64 - f32 - c64 -
f* - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - bf16 - f16 - f32 - f32 - c64 - c64 - f32 - f32 - c64 -
c* - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 -
- - -Highlighted: When one of the inputs is a python scalar, the dtype in the table is the default result without value-dependent promotion. - - -### **Mode 3: disable\_implicit\_promotion + enable\_promotion\_to\_float64** - -The dtype behavior of mode 3 is the same as mode 1, except that implicit conversions between TF dtypes are disallowed. - -Table: TF dtype promotion result of `tf.add` with disable\_implicit\_promotion + enable\_promotion\_to\_float64 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i* - f* - c* -
b - b - - - - - - - - - - - - - - - - - - - - - - - - - - - - - i64 - f64 - c128 -
u8 - - - u8 - - - - - - - - - - - - - - - - - - - - - - - - - - - u8 - f64 - c128 -
u16 - - - - - u16 - - - - - - - - - - - - - - - - - - - - - - - - - u16 - f64 - c128 -
u32 - - - - - - - u32 - - - - - - - - - - - - - - - - - - - - - - - u32 - f64 - c128 -
u64 - - - - - - - - - u64 - - - - - - - - - - - - - - - - - - - - - u64 - f64 - c128 -
i8 - - - - - - - - - - - i8 - - - - - - - - - - - - - - - - - - - i8 - f64 - c128 -
i16 - - - - - - - - - - - - - i16 - - - - - - - - - - - - - - - - - i16 - f64 - c128 -
i32 - - - - - - - - - - - - - - - i32 - - - - - - - - - - - - - - - i32 - f64 - c128 -
i64 - - - - - - - - - - - - - - - - - i64 - - - - - - - - - - - - - i64 - f64 - c128 -
bf16 - - - - - - - - - - - - - - - - - - - bf16 - - - - - - - - - - - bf16 - bf16 - c64 -
f16 - - - - - - - - - - - - - - - - - - - - - f16 - - - - - - - - - f16 - f16 - c64 -
f32 - - - - - - - - - - - - - - - - - - - - - - - f32 - - - - - - - f32 - f32 - c64 -
f64 - - - - - - - - - - - - - - - - - - - - - - - - - f64 - - - - - f64 - f64 - c128 -
c64 - - - - - - - - - - - - - - - - - - - - - - - - - - - c64 - - - c64 - c64 - c64 -
c128 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - c128 - c128 - c128 - c128 -
i* - i64 - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i64 - f64 - c128 -
f* - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - bf16 - f16 - f32 - f64 - c64 - c128 - f64 - f64 - c128 -
c* - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c64 - c64 - c64 - c128 - c64 - c128 - c128 - c128 - c128 -
- - -Highlighted: When one of the inputs is a python scalar, the dtype in the table is the default result without value-dependent promotion. - - -### **Mode 4: disable\_implicit\_promotion + disable\_promotion\_to\_float64** - -The dtype behavior of mode 4 is the same as mode 2, except that implicit conversions between TF dtypes are disallowed. - -Table: TF dtype promotion result of `tf.add` with disable\_implicit\_promotion + disable\_promotion\_to\_float64 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i* - f* - c* -
b - b - - - - - - - - - - - - - - - - - - - - - - - - - - - - - i64 - f32 - c64 -
u8 - - - u8 - - - - - - - - - - - - - - - - - - - - - - - - - - - u8 - f32 - c64 -
u16 - - - - - u16 - - - - - - - - - - - - - - - - - - - - - - - - - u16 - f32 - c64 -
u32 - - - - - - - u32 - - - - - - - - - - - - - - - - - - - - - - - u32 - f32 - c64 -
u64 - - - - - - - - - u64 - - - - - - - - - - - - - - - - - - - - - u64 - f32 - c64 -
i8 - - - - - - - - - - - i8 - - - - - - - - - - - - - - - - - - - i8 - f32 - c64 -
i16 - - - - - - - - - - - - - i16 - - - - - - - - - - - - - - - - - i16 - f32 - c64 -
i32 - - - - - - - - - - - - - - - i32 - - - - - - - - - - - - - - - i32 - f32 - c64 -
i64 - - - - - - - - - - - - - - - - - i64 - - - - - - - - - - - - - i64 - f32 - c64 -
bf16 - - - - - - - - - - - - - - - - - - - bf16 - - - - - - - - - - - bf16 - bf16 - c64 -
f16 - - - - - - - - - - - - - - - - - - - - - f16 - - - - - - - - - f16 - f16 - c64 -
f32 - - - - - - - - - - - - - - - - - - - - - - - f32 - - - - - - - f32 - f32 - c64 -
f64 - - - - - - - - - - - - - - - - - - - - - - - - - f64 - - - - - f32 - f32 - c64 -
c64 - - - - - - - - - - - - - - - - - - - - - - - - - - - c64 - - - c64 - c64 - c64 -
c128 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - c128 - c64 - c64 - c64 -
i* - i64 - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f32 - c64 - c64 - i64 - f32 - c64 -
f* - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - bf16 - f16 - f32 - f32 - c64 - c64 - f32 - f32 - c64 -
c* - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 -
- - -Highlighted: When one of the inputs is a python scalar, the dtype in the table is the default result without value-dependent promotion. From 1e60a174456e00f87ea4828bb4b60cc1980a9d35 Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 13:44:49 -0700 Subject: [PATCH 04/20] Update 2022-10-18-promotion-semantics.md --- rfcs/2022-10-18-promotion-semantics.md | 4769 ++++++++++++++++++++++++ 1 file changed, 4769 insertions(+) diff --git a/rfcs/2022-10-18-promotion-semantics.md b/rfcs/2022-10-18-promotion-semantics.md index 6173b245b..5543cf9d3 100644 --- a/rfcs/2022-10-18-promotion-semantics.md +++ b/rfcs/2022-10-18-promotion-semantics.md @@ -1,4 +1,4773 @@ + + + + +## Making dtype promotion semantics in Tensorflow more consistent + ## **Objective** Currently TF has no consistent, well-defined type promotion rules. This document proposes a well-defined, consistent and clear dtype promotion rule for TF. The introduced changes make TF APIs more similar to NumPy, with some differences that emphasize TF’s applications in machine learning. This should make dtype promotions in TF much more consistent and predictable. + +[**What the doc is**] This doc discusses the preferred dtype promotion semantics/behaviors of Tensorflow (TF) and Tensorflow-numpy (TF-numpy) in the binary ops including add, sub, mul, div, pow and mod. + +[**What the doc is not**] This doc does not discuss the implementation plans. + + +## **Motivation** + +In Tensorflow’s APIs, dtype promotion is very broken compared to JAX or NumPy. Many users have complained about these surprising behaviors (example: go/broccoli-tf-add, b/158346631, b/154456219). See [this doc ](https://docs.google.com/document/d/1jOXJ1YAQAtseyYDDGm4gsr7xJ4F2FtvfwZJgNsW8cVA/edit#)for a more detailed description. + +[TF-numpy](https://www.tensorflow.org/guide/tf_numpy#type_promotion) is a strategic project in Tensorflow. Compared to Tensorflow, TF-numpy’s dtype promotion behavior is more consistent because it [mainly relies on NumPy’s dtype promotion semantics](https://source.corp.google.com/piper///depot/google3/third_party/tensorflow/python/ops/numpy_ops/np_dtypes.py;rcl=399530502;l=112). In the [long-term vision doc](https://goto.google.com/tf-numpy-path-forward), unifying the dtype promotion semantics of TF and TF-numpy is a necessary first step. + +The unification of TF and TF-numpy’s dtype promotion semantics is a great chance to think about what semantics should be the long-term, user-friendly solution for TF. + + +## **User Benefit** + +There have been ongoing complaints about the inconsistent dtype promotion behaviors of TF. To give an example, switching the inputs’ positions directly affects the outcome: + + +``` +c = tf.constant(1.0) +tf.add(c, 3) # tf.Tensor(4.0, shape=(), dtype=float32) +tf.add(3, c) # InvalidArgumentError: cannot compute AddV2 as input #1(zero-based) was expected to be a int32 tensor but is a float tensor [Op:AddV2] +``` + + +For brevity we will sometimes use shortened terms to represent the dtypes as shown in the following examples: + + + +* `b` means tf.bool +* `u8` means tf.uint8 +* `i16` means tf.int16 +* `bf16` means [tf.bfloat16](https://cloud.google.com/tpu/docs/bfloat16) +* `f64` means tf.float64 +* `c64` means tf.complex64 +* `i*` means python int +* `f*` means python float +* `c*` means python complex + +The table below summarizes the dtype promotion behavior between two TF tensors/python scalars with method `__add__` and API `tf.add` respectively. Each row and column represents a combination of input types. + +Table: TF dtype promotion result of dunder method `__add__` and `tf.add` + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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b + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
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c64 + - + - + - + - + - + - + - + - + - + - + - + - + - + c64 + - + c64 + c64 + c64 +
c128 + - + - + - + - + - + - + - + - + - + - + - + - + - + - + c128 + c128 + c128 + c128 +
i* + - + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i32 + + +
f* + - + - + - + - + - + - + - + - + - + bf16 + f16 + f32 + f64 + c64 + c128 + f32 + f32 + +
c* + - + - + - + - + - + - + - + - + - + - + - + - + - + c64 + c128 + c128 + c128 + c128 +
+ + +In the table, the dash symbol `-` means unsupported: an error gets raised if the two dtypes are used in the call to that op. The cells highlighted in red are only supported in `__add__`, and the cells in yellow are only supported in `tf.add`. In summary, the existing TF APIs have these significant issues: + + + +1. Boolean dtypes are not supported; +2. Implicit conversion between two TF dtypes are strictly disallowed, resulting in verbose code at times; +3. Implicit conversion from some python dtypes to some TF dtypes are disallowed; +4. `tf.add` is not even commutative - switching the position of the two arguments sometimes produces errors, otherwise results in correct values. + +When the inputs involve NumPy arrays, the behaviors can be even more broken. For brevity we have included NumPy-input behaviors in the appendix at the end of the doc. + +We aim to make the system commutative, predictable and correct. + + +## **Design Proposal** + +We propose introducing three dtype promotion modes in TF: + + + +* `tf.ImplicitPromotion.ALL` +* `tf.ImplicitPromotion.SAFE` +* `tf.ImplicitPromotion.NONE` + +The three modes determine how often implicit dtype promotions happen in TF APIs. In the following examples we will use `tf.add` to demonstrate the modes. The dunder method `__add__` is expected to have the same behavior as `tf.add`. For brevity, NumPy (np) array inputs are not discussed in this section. However the proposed modes in this RFC will treat the np array inputs in the same way as the tensor inputs (see more in the appendix). + +Table: TF dtype promotion result of `tf.add` after the proposed changes. `NONE` only allows the unhighlighted cells. `SAFE` allows all `NONE` cases plus the cells highlighted in green. `ALL` allows all `SAFE` cases plus the cells highlighted in yellow. Rows/columns highlighted in blue only show the default result without overflow of inputs. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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u8 + u8 + u8 + u16 + u32 + u64 + i16 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + u8 + f64 + c128 +
u16 + u16 + u16 + u16 + u32 + u64 + i32 + i32 + i32 + i64 + f32 + f32 + f32 + f64 + c64 + c128 + u16 + f64 + c128 +
u32 + u32 + u32 + u32 + u32 + u64 + i64 + i64 + i64 + i64 + f64 + f64 + f64 + f64 + c128 + c128 + u32 + f64 + c128 +
u64 + u64 + u64 + u64 + u64 + u64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + c128 + c128 + u64 + f64 + c128 +
i8 + i8 + i16 + i32 + i64 + f64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i8 + f64 + c128 +
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i32 + i32 + i32 + i32 + i64 + f64 + i32 + i32 + i32 + i64 + f64 + f64 + f64 + f64 + c128 + c128 + i32 + f64 + c128 +
i64 + i64 + i64 + i64 + i64 + f64 + i64 + i64 + i64 + i64 + f64 + f64 + f64 + f64 + c128 + c128 + i64 + f64 + c128 +
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f16 + f16 + f16 + f32 + f64 + f64 + f16 + f32 + f64 + f64 + f32 + f16 + f32 + f64 + c64 + c128 + f16 + f16 + c64 +
f32 + f32 + f32 + f32 + f64 + f64 + f32 + f32 + f64 + f64 + f32 + f32 + f32 + f64 + c64 + c128 + f32 + f32 + c64 +
f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + c128 + c128 + f64 + f64 + c128 +
c64 + c64 + c64 + c64 + c128 + c128 + c64 + c64 + c128 + c128 + c64 + c64 + c64 + c128 + c64 + c128 + c64 + c64 + c64 +
c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 +
i* + i64 + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i64 + f64 + c128 +
f* + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + bf16 + f16 + f32 + f64 + c64 + c128 + f64 + f64 + c128 +
c* + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c64 + c64 + c64 + c128 + c64 + c128 + c128 + c128 + c128 +
+ + + +### **Mode 1: tf.ImplicitPromotion.ALL** + +In this mode, we allow all implicit promotions to happen in the op, even if both inputs have TF dtypes. To summarize the rules of this mode: + + + +* When the two inputs have non-matching TF dtypes, they will be promoted to the lowest width dtype that guarantees the precisions and ranges of the inputs. However, the promoted integer, float and complex dtype widths are capped at 64, 64 and 128 bits respectively. For example: + * The summation of `tf.bfloat16` and `tf.uint8` will be type `tf.bfloat16`. + * The summation of `tf.int64` and `tf.float64` will be capped at type `tf.float64` despite precision loss and overflow risks. +* When one of the inputs is a python integer/float/complex, and the other is a TF dtype: + * If the python scalar value falls within the range of the TF dtype, the result dtype is loosely described with the following promotion direction: TF bool -> python integer -> TF signed/unsigned integers -> python float -> TF float -> python complex/TF complex. + * The dtype promotion is allowed only if the python scalar is within the range of the determined dtype. Otherwise, an exception is raised. For example, `tf.constant([1], tf.int8) + 1` produces a `tf.int8` Tensor, while `tf.constant([1], tf.int8) + 1000` raises an error. + +This mode is intended to provide a user experience similar to NumPy behaviors. + + +### **Mode 2: tf.ImplicitPromotion.SAFE** + +Mode `SAFE` follows the same rules as mode `ALL`, but disallows “unsafe” dtype promotions. In general, we think the following two types of conversions are unsafe: + + + +* If the inputs are both Tensors and the result dtype would be “wider” than all the inputs. For example, the summation of `tf.uint8` and `tf.int8` would be type `tf.i16`. These dtype promotions risk increasing the model’s memory consumption and slowing down computation speed. +* If the inputs are both Tensors and the result dtype cannot preserve all the precisions of the inputs. For example, the summation of `tf.uint64` and `tf.float32` would be type `tf.float64`. + +With the above principle, if one of the inputs is a Tensor while the other is a python scalar, we only allow implicit promotion if the scalar can fit into the Tensor’s dtype. For example: + + + +* The summation between a `tf.int32` and a python float is always disallowed; +* The summation between a `tf.uint8` and a python int is allowed only if the python int is between 0 and 255. + +For these disallowed dtype promotions, we require the users to explicitly cast them. + +Another advantage is that mode `ALL` can be silently nonassociative in the following scenario, while mode `SAFE` avoids it by not allowing any dtype widening: + + + +* `(tf.zeros(tf.uint8) + tf.zeros(tf.int8)) + tf.zeros(tf.float16)` evaluates to dtype `tf.float32`; +* `tf.zeros(tf.uint8) + (tf.zeros(tf.int8) + tf.zeros(tf.float16))` evaluates to dtype `tf.float16`. + + +### **Mode 3: tf.ImplicitPromotion.NONE** + +The dtype behavior of mode `NONE` is the most conservative: no dtype promotion is allowed between Tensors. When the inputs include python scalars, it follows the same rule as mode `SAFE`. + + +### **Alternatives Considered: Capping the promotion system at float32** + +The TF-NumPy project contains another flag [allow_float64](https://source.corp.google.com/piper///depot/google3/third_party/tensorflow/python/ops/numpy_ops/np_dtypes.py;rcl=399530502;l=88). When disabled, it caps the converted floating numbers to 32 bits, and complex numbers to 64 bits. This is useful when users want to enjoy implicit conversions in all cases and avoid any performance regressions related to double precision floating point numbers. This feature is now used by [Trax](https://b.corp.google.com/issues/178862061). + +Our current plan does not involve adding this flag in TF, as it’s orthogonal to the proposed modes. If there are more user needs we can consider exposing it in TF as well. + + +### **Alternatives Considered: A dtype promotion “lattice”** + +Unlike the approaches above, JAX used a dtype promotion lattice to define its semantics. More details can be seen in their [design doc](https://jax.readthedocs.io/en/latest/jep/9407-type-promotion.html#properties-of-a-type-promotion-lattice). Their lattice system has these advantages: + + + +* A lattice can be much more concise to describe the promotion semantics; +* The lattice can ensure associativity as well as commutativity. In contrast, the promotion behaviors in this proposal is not associative: + * `(tf.zeros(tf.uint8) + tf.zeros(tf.int8)) + tf.zeros(tf.float16)` evaluates to dtype `tf.float32`; + * `tf.zeros(tf.uint8) + (tf.zeros(tf.int8) + tf.zeros(tf.float16))` evaluates to dtype `tf.float16`. + +~~However the lattice system also introduced extra complexity in the design. For example:~~ + + + +* ~~JAX ended up introducing the concepts of “weak types” in place of python types;~~ +* ~~JAX adopted very aggressive dtype promotion semantics - an int64 dtype can be promoted to a float16, resulting in higher risks of overflow.~~ + +~~Since our ultimate goal is to help the users maintain a clear and concise mental model, adopting a dtype promotion table is an easier approach.~~ + +Compared to a lattice system, the proposed table-based system does not guarantee associativity. However, we can avoid such usage scenarios in the proposed SAFE mode (no implicit promotion to wider dtypes). As a result, it’s not clearly justifiable to switch to a lattice system from the table-based approach, which already exists in project TF-NumPy. + + +### **Alternatives Considered: Value-dependent promotion (a.k.a. “safe casting” in NumPy)** + +NumPy has a feature “safe casting”: the dtype promotion API, [numpy.result_type](https://numpy.org/doc/stable/reference/generated/numpy.result_type.html), can return a different dtype depending on the input python scalar value: + + +``` +np.result_type(np.int8, 200) # dtype('int16') +np.result_type(np.int8, 100) # dtype('int8') +``` + + +This slightly reduces the risk of overflow. However, the result is unpredictable. NumPy is considering removing this feature in a future release (see proposal: [NEP 50](https://numpy.org/neps/nep-0050-scalar-promotion.html)). JAX [did not adopt this feature](https://jax.readthedocs.io/en/latest/jep/9407-type-promotion.html#enter-python-scalars) either. We omit it in this design. + + +### **Performance Implications** + +For an existing piece of TF code in python, we do not expect the performance to change after switching to any of the proposed modes. This is because all the allowed implicit dtype promotions in TF will stay unchanged. As to the currently disallowed dtype promotions, users must have used explicit dtype promotions which are not affected by the change. + +When users develop a new piece of code, however, the new modes have higher chances of computation slowdown due to implicit promotions in mode `ALL`. + +We can carry out the following steps to mitigate user surprises: + + + +* Add benchmark tests to verify that the new modes won’t affect the performance of existing TF code. +* Publish tutorials that help users select the most appropriate dtype promotion modes. +* [optional] Add an API that instruments the number of implicit, unsafe dtype conversions in the program (e.g. promotions from 32 bits to 64 bits). This helps users understand whether the promotions contribute to computation slowdown in the graphs. + + +### **Dependencies** + +This proposal is not expected to affect dependencies to Tensorflow. + + +#### **Engineering Impact** + + +#### We do not expect changes in binary size/startup time/build time/test time. + +The majority of the dtype promotion logic already exists in Tensorflow under namescope `tensorflow.experimental.numpy`. However, refactoring/updates will be needed under directory tensorflow/python/ops. + + +#### **Platforms and Environments** + +This proposal is expected to take effect on all platforms in the python environment. + + +#### **Best Practices/Tutorials and Examples** + +As mentioned in section “Performance Implications” we will publish tutorials that help users select the most appropriate dtype promotion modes. + + +#### **Compatibility/User Impact** + +This is a breaking change. As mentioned above, existing code is expected to continue working. However, some tests will fail because more implicit dtype conversions are allowed. We can mitigate the user impact by adding a flag that sticks to the existing dtype promotion behaviors. + +The proposed changes will be rolled out in various stages: + + + +1. Default behaviors stay unchanged, with the flags introduced under namescope `tf.experiment` to collect user feedbacks; +2. Default behaviors switched to one of the modes above, while the old behaviors can be re-enabled via a flag; +3. [optional] Completely disable the old, broken dtype promotion behaviors. + + +## **Questions and Discussion Topics** + + +#### **Are there changes expected in tf.Variable?** + +In APIs such as `tf.add` and dunder methods `__add__`, the same changes are expected to take place because these methods first read the Variable into a Tensor, then carry out the Tensor-to-Tensor computations. + +Currently, inplace ops such as `assign_add` are also broken. The two tables below show the result of `assign_add` when the input is a Tensor, a python scalar or a NumPy array: + + + +* When the input is a Tensor, no dtype conversion is allowed. +* When the input is a python scalar, it defers to the variable’s dtype. However, python float cannot be converted to int, and complex cannot be converted to either int or float dtypes. +* When the input is a NumPy array, it always gets converted to the variable’s dtype regardless of any precision loss (for example `tf.Variable(1) + np.array(1.5)` returns 2). + +Table: Result of tf.Variable (rows) inplace op `assign_add` with a Tensor or python scalar as the argument (columns). + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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c64 + - + - + - + - + - + - + - + - + - + - + - + - + - + c64 + - + c64 + c64 + c64 +
c128 + - + - + - + - + - + - + - + - + - + - + - + - + - + - + c128 + c128 + c128 + c128 +
+ + +Table: Result of tf.Variable (rows) inplace op `assign_add` with a NumPy array as the argument (columns). + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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u8 + u8 + u8 + u8 + u8 + u8 + u8 + u8 + u8 + u8 + - + u8 + u8 + u8 + u8 + u8 +
u16 + u16 + u16 + u16 + u16 + u16 + u16 + u16 + u16 + u16 + - + u16 + u16 + u16 + u16 + u16 +
u32 + u32 + u32 + u32 + u32 + u32 + u32 + u32 + u32 + u32 + - + u32 + u32 + u32 + u32 + u32 +
u64 + u64 + u64 + u64 + u64 + u64 + u64 + u64 + u64 + u64 + - + u64 + u64 + u64 + u64 + u64 +
i8 + i8 + i8 + i8 + i8 + i8 + i8 + i8 + i8 + i8 + - + i8 + i8 + i8 + i8 + i8 +
i16 + i16 + i16 + i16 + i16 + i16 + i16 + i16 + i16 + i16 + - + i16 + i16 + i16 + i16 + i16 +
i32 + i32 + i32 + i32 + i32 + i32 + i32 + i32 + i32 + i32 + - + i32 + i32 + i32 + i32 + i32 +
i64 + i64 + i64 + i64 + i64 + i64 + i64 + i64 + i64 + i64 + - + i64 + i64 + i64 + i64 + i64 +
bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + - + bf16 + bf16 + bf16 + bf16 + bf16 +
f16 + f16 + f16 + f16 + f16 + f16 + f16 + f16 + f16 + f16 + - + f16 + f16 + f16 + f16 + f16 +
f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + - + f32 + f32 + f32 + f32 + f32 +
f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + - + f64 + f64 + f64 + f64 + f64 +
c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + - + c64 + c64 + c64 + c64 + c64 +
c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + - + c128 + c128 + c128 + c128 + c128 +
+ + +We can make these ops more consistent as well, following the rules in the three proposed modes. However, we also have another constraint: the Variable’s dtype cannot change. As a result, any dtype promotion that results in a dtype different from the Variable’s dtype is disabled. + +Table: Result of tf.Variable (rows) inplace op `assign_add` with a Tensor or python scalar as the argument (columns) after the proposed changes. `NONE` only allows the unhighlighted cells. `SAFE` allows all `NONE` cases plus the cells highlighted in green. `ALL` allows all `SAFE` cases plus the cells highlighted in yellow. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ b + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 + i* + f* + c* +
b + b + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
u8 + u8 + u8 + - + - + - + - + - + - + - + - + - + - + - + - + - + u8 + - + - +
u16 + u16 + u16 + u16 + - + - + - + - + - + - + - + - + - + - + - + - + u16 + - + - +
u32 + u32 + u32 + u32 + u32 + - + - + - + - + - + - + - + - + - + - + - + u32 + - + - +
u64 + u64 + u64 + u64 + u64 + u64 + - + - + - + - + - + - + - + - + - + - + u64 + - + - +
i8 + i8 + - + - + - + - + i8 + - + - + - + - + - + - + - + - + - + i8 + - + - +
i16 + i16 + i16 + - + - + - + i16 + i16 + - + - + - + - + - + - + - + - + i16 + - + - +
i32 + i32 + i32 + i32 + - + - + i32 + i32 + i32 + - + - + - + - + - + - + - + i32 + - + - +
i64 + i64 + i64 + i64 + i64 + - + i64 + i64 + i64 + i64 + - + - + - + - + - + - + i64 + - + - +
bf16 + bf16 + bf16 + - + - + - + bf16 + - + - + - + bf16 + - + - + - + - + - + bf16 + bf16 + - +
f16 + f16 + f16 + - + - + - + f16 + - + - + - + - + f16 + - + - + - + - + f16 + f16 + - +
f32 + f32 + f32 + f32 + - + - + f32 + f32 + - + - + f32 + f32 + f32 + - + - + - + f32 + f32 + - +
f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + - + - + f64 + f64 + - +
c64 + c64 + c64 + c64 + - + - + c64 + c64 + - + - + c64 + c64 + c64 + - + c64 + - + c64 + c64 + c64 +
c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 +
+ + + +#### **Are there also issues in conversions between Tensor and numpy arrays?** + +There exists arguably more serious issues when it comes to the mixture of Tensor and numpy arrays - wrong results can appear silently: + + +``` +a = np.array(3.1) +c = tf.constant(1) +print(tf.add(a, c)) # InvalidArgumentError: cannot compute AddV2 as input #1(zero-based) was expected to be a double tensor but is a int32 tensor [Op:AddV2] +print(tf.add(c, a)) # tf.Tensor(4, shape=(), dtype=int32) - Even worse than exceptions +``` + + +To give a comprehensive overview, the two tables below show the results of `tf.add(tensor, numpy array)` and `tf.add(numpy array, tensor)` : + +Table: TF dtype promotion result of `tf.add` with a Tensor as the first argument (rows) and np array in the second argument (columns). + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ b + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 +
b + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
u8 + u8 + u8 + u8 + u8 + u8 + u8 + u8 + u8 + u8 + - + u8 + u8 + u8 + u8 + u8 +
u16 + u16 + u16 + u16 + u16 + u16 + u16 + u16 + u16 + u16 + - + u16 + u16 + u16 + u16 + u16 +
u32 + u32 + u32 + u32 + u32 + u32 + u32 + u32 + u32 + u32 + - + u32 + u32 + u32 + u32 + u32 +
u64 + u64 + u64 + u64 + u64 + u64 + u64 + u64 + u64 + u64 + - + u64 + u64 + u64 + u64 + u64 +
i8 + i8 + i8 + i8 + i8 + i8 + i8 + i8 + i8 + i8 + - + i8 + i8 + i8 + i8 + i8 +
i16 + i16 + i16 + i16 + i16 + i16 + i16 + i16 + i16 + i16 + - + i16 + i16 + i16 + i16 + i16 +
i32 + i32 + i32 + i32 + i32 + i32 + i32 + i32 + i32 + i32 + - + i32 + i32 + i32 + i32 + i32 +
i64 + i64 + i64 + i64 + i64 + i64 + i64 + i64 + i64 + i64 + - + i64 + i64 + i64 + i64 + i64 +
bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + bf16 + - + bf16 + bf16 + bf16 + bf16 + bf16 +
f16 + f16 + f16 + f16 + f16 + f16 + f16 + f16 + f16 + f16 + - + f16 + f16 + f16 + f16 + f16 +
f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + f32 + - + f32 + f32 + f32 + f32 + f32 +
f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + f64 + - + f64 + f64 + f64 + f64 + f64 +
c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + c64 + - + c64 + c64 + c64 + c64 + c64 +
c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + c128 + - + c128 + c128 + c128 + c128 + c128 +
+ + +Table: TF dtype promotion result of `tf.add` with a np array as the first argument (rows) and tensor in the second argument (columns). + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ b + u8 + u16 + u32 + u64 + i8 + i16 + i32 + i64 + bf16 + f16 + f32 + f64 + c64 + c128 +
b + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
u8 + - + u8 + - + - + - + - + - + - + - + - + - + - + - + - + - +
u16 + - + - + u16 + - + - + - + - + - + - + - + - + - + - + - + - +
u32 + - + - + - + u32 + - + - + - + - + - + - + - + - + - + - + - +
u64 + - + - + - + - + u64 + - + - + - + - + - + - + - + - + - + - +
i8 + - + - + - + - + - + i8 + - + - + - + - + - + - + - + - + - +
i16 + - + - + - + - + - + - + i16 + - + - + - + - + - + - + - + - +
i32 + - + - + - + - + - + - + - + i32 + - + - + - + - + - + - + - +
i64 + - + - + - + - + - + - + - + - + i64 + - + - + - + - + - + - +
bf16 + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
f16 + - + - + - + - + - + - + - + - + - + - + f16 + - + - + - + - +
f32 + - + - + - + - + - + - + - + - + - + - + - + f32 + - + - + - +
f64 + - + - + - + - + - + - + - + - + - + - + - + - + f64 + - + - +
c64 + - + - + - + - + - + - + - + - + - + - + - + - + - + c64 + - +
c128 + - + - + - + - + - + - + - + - + - + - + - + - + - + - + c128 +
+ + +To briefly describe the issues: + + + +1. dtypes `tf.bfloat16` and `tf.bool` are poorly supported; +2. If the first input is a tensor, the second input (the np array) is always converted to the tensor’s dtype, resulting into significant precision loss in many scenarios; +3. If the first input is a np array and the second is a tensor, the np array is first converted to a tensor with the corresponding dtype. TF then does not attempt any implicit dtype conversion, causing `InvalidArgumentError` whenever the two inputs have mismatching dtypes. + +Though the behaviors with np arrays inputs are not the same as python scalars, they share the same root cause: At present, TF does not have a centralized dtype promotion system, and incorrectly uses the [dtype of the first Tensor in its list of arguments](https://source.corp.google.com/piper///depot/google3/third_party/tensorflow/python/ops/math_ops.py;rcl=449753780;l=1371) to promote all arguments to. The proposed modes in this RFC will treat the np array inputs in the same way as the tensor inputs. + + +#### **What happens to e.g. bool - bool or bool / bool? + +With NumPy: + + + +* Addition between two Booleans: equivalent to OR +* Subtraction between two Booleans: forbidden +* Multiplication between two Booleans: AND +* Division between two Booleans: converted to float64 then divide + + +#### **What happens to truediv operations?** + +The op truediv is special because it implicitly promotes integral types. For example when the inputs are matching dtypes, 16-bit integral types are promoted to `f32`, and 32-bit integral types are promoted to `float64`. + +Using this rule of thumb, we can easily deduce the promotion behavior between non-matching dtypes when this proposal is landed. For example: + + + +* `int8` and `int16` -> both `int16` -> `f32` (allowed in `SAFE/ALL` modes) +* `uint8` and `uint32` -> both `uint32` -> `f64` (allowed in `SAFE/ALL` modes) +* `int8` and `i*` -> both `int8` -> `f32` (allowed in `NONE/SAFE/ALL` modes) +* `int8` and `uint16` -> both `int32` -> `f64` (allowed in `ALL` mode) + +In the above examples, the first arrow represents the dtype promotion in this proposal, and the second arrow represents the promotion in op truediv. Note the result of `int8` and `uint16`, `f64`, turns out to be an overkill but is consistent with the combination of the dtype promotion rules and the truediv promotion rules. From f60be814bc8ab8c595297e24a233f1fcbaa9b382 Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 13:48:13 -0700 Subject: [PATCH 05/20] Update 2022-10-18-promotion-semantics.md --- rfcs/2022-10-18-promotion-semantics.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/rfcs/2022-10-18-promotion-semantics.md b/rfcs/2022-10-18-promotion-semantics.md index 5543cf9d3..4c57d738a 100644 --- a/rfcs/2022-10-18-promotion-semantics.md +++ b/rfcs/2022-10-18-promotion-semantics.md @@ -1,8 +1,14 @@ - -## Making dtype promotion semantics in Tensorflow more consistent +# Making dtype promotion semantics in Tensorflow more consistent + +| Status | (Proposed / Accepted / Implemented / Obsolete) | +:-------------- |:---------------------------------------------------- | +| **RFC #** | [NNN](https://github.com/tensorflow/community/pull/NNN) (update when you have community PR #)| +| **Author(s)** | Jiawei Xia (jiaweix@google.com) | +| **Sponsor** | Peng Wang (pengwang@google.com) | +| **Updated** | 2022-10-18 | ## **Objective** From 68e9291cfdcfb78547a57e754ddd5faa9875ce8f Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 14:11:07 -0700 Subject: [PATCH 06/20] Update 2022-10-18-promotion-semantics.md --- rfcs/2022-10-18-promotion-semantics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rfcs/2022-10-18-promotion-semantics.md b/rfcs/2022-10-18-promotion-semantics.md index 4c57d738a..bd4e81536 100644 --- a/rfcs/2022-10-18-promotion-semantics.md +++ b/rfcs/2022-10-18-promotion-semantics.md @@ -3,7 +3,7 @@ # Making dtype promotion semantics in Tensorflow more consistent -| Status | (Proposed / Accepted / Implemented / Obsolete) | +| Status | (Proposed) | :-------------- |:---------------------------------------------------- | | **RFC #** | [NNN](https://github.com/tensorflow/community/pull/NNN) (update when you have community PR #)| | **Author(s)** | Jiawei Xia (jiaweix@google.com) | From 657523c5459d29c8c5103f27646faf6dbabc0e03 Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 14:15:50 -0700 Subject: [PATCH 07/20] Rename 2022-10-18-promotion-semantics.md to 20221018-promotion-semantics.md --- ...-18-promotion-semantics.md => 20221018-promotion-semantics.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename rfcs/{2022-10-18-promotion-semantics.md => 20221018-promotion-semantics.md} (100%) diff --git a/rfcs/2022-10-18-promotion-semantics.md b/rfcs/20221018-promotion-semantics.md similarity index 100% rename from rfcs/2022-10-18-promotion-semantics.md rename to rfcs/20221018-promotion-semantics.md From 2dca00115436142a49ad2d7d6429ceb4741fef09 Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 14:18:12 -0700 Subject: [PATCH 08/20] Create test.md --- rfcs/20221018-promotion-semantics/test.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 rfcs/20221018-promotion-semantics/test.md diff --git a/rfcs/20221018-promotion-semantics/test.md b/rfcs/20221018-promotion-semantics/test.md new file mode 100644 index 000000000..323fae03f --- /dev/null +++ b/rfcs/20221018-promotion-semantics/test.md @@ -0,0 +1 @@ +foobar From 6a064b923be243fac24cb75956da1d8cea0c6394 Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 14:39:03 -0700 Subject: [PATCH 09/20] Add files via upload --- .../Screen Shot 2022-10-18 at 2.24.47 PM.png | Bin 0 -> 209409 bytes .../Table-TF-dtype-promotion-add-current.png | Bin 0 -> 236147 bytes .../Table-TF-dtype-promotion-add-proposed.png | Bin 0 -> 692106 bytes ...F-dtype-promotion-add_np_tensor-current.png | Bin 0 -> 131231 bytes ...F-dtype-promotion-add_tensor_np-current.png | Bin 0 -> 256574 bytes ...e-TF-dtype-promotion-assign_add-current.png | Bin 0 -> 169791 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z+5RpdhX7X`&0YpIJXi8Rrpmvr{1hG$8BJ;{@pl2`q3|igBBYH;@ORH8G#Vb^y{!G} x?*go3kZ>gRCc8|E{_S!8>l)xQ*Bj{x+gIZRd561H5&`}K%6yQnko*+ze*l9w9H;;Q From a4c58dc390be5a44c0585230e1474649d0d7f259 Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 15:45:20 -0700 Subject: [PATCH 11/20] Update 20221018-promotion-semantics.md --- rfcs/20221018-promotion-semantics.md | 4492 +------------------------- 1 file changed, 26 insertions(+), 4466 deletions(-) diff --git a/rfcs/20221018-promotion-semantics.md b/rfcs/20221018-promotion-semantics.md index bd4e81536..06ac12f7f 100644 --- a/rfcs/20221018-promotion-semantics.md +++ b/rfcs/20221018-promotion-semantics.md @@ -57,772 +57,11 @@ For brevity we will sometimes use shortened terms to represent the dtypes as sho The table below summarizes the dtype promotion behavior between two TF tensors/python scalars with method `__add__` and API `tf.add` respectively. Each row and column represents a combination of input types. +

Table: TF dtype promotion result of dunder method `__add__` and `tf.add` - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i* - f* - c* -
b - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
u8 - - - u8 - - - - - - - - - - - - - - - - - - - - - - - - - - - u8 - - - - -
u16 - - - - - u16 - - - - - - - - - - - - - - - - - - - - - - - - - u16 - - - - -
u32 - - - - - - - u32 - - - - - - - - - - - - - - - - - - - - - - - u32 - - - - -
u64 - - - - - - - - - u64 - - - - - - - - - - - - - - - - - - - - - u64 - - - - -
i8 - - - - - - - - - - - i8 - - - - - - - - - - - - - - - - - - - i8 - - - - -
i16 - - - - - - - - - - - - - i16 - - - - - - - - - - - - - - - - - i16 - - - - -
i32 - - - - - - - - - - - - - - - i32 - - - - - - - - - - - - - - - i32 - - - - -
i64 - - - - - - - - - - - - - - - - - i64 - - - - - - - - - - - - - i64 - - - - -
bf16 - - - - - - - - - - - - - - - - - - - bf16 - - - - - - - - - - - bf16 - bf16 - - -
f16 - - - - - - - - - - - - - - - - - - - - - f16 - - - - - - - - - f16 - f16 - - -
f32 - - - - - - - - - - - - - - - - - - - - - - - f32 - - - - - - - f32 - f32 - - -
f64 - - - - - - - - - - - - - - - - - - - - - - - - - f64 - - - - - f64 - f64 - - -
c64 - - - - - - - - - - - - - - - - - - - - - - - - - - - c64 - - - c64 - c64 - c64 -
c128 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - c128 - c128 - c128 - c128 -
i* - - - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i32 - - -
f* - - - - - - - - - - - - - - - - - - - bf16 - f16 - f32 - f64 - c64 - c128 - f32 - f32 - -
c* - - - - - - - - - - - - - - - - - - - - - - - - - - - c64 - c128 - c128 - c128 - c128 -
- +![Diff1](20221018-promotion-semantics/Table-TF-dtype-promotion-add-current.png) +

In the table, the dash symbol `-` means unsupported: an error gets raised if the two dtypes are used in the call to that op. The cells highlighted in red are only supported in `__add__`, and the cells in yellow are only supported in `tf.add`. In summary, the existing TF APIs have these significant issues: @@ -850,771 +89,11 @@ We propose introducing three dtype promotion modes in TF: The three modes determine how often implicit dtype promotions happen in TF APIs. In the following examples we will use `tf.add` to demonstrate the modes. The dunder method `__add__` is expected to have the same behavior as `tf.add`. For brevity, NumPy (np) array inputs are not discussed in this section. However the proposed modes in this RFC will treat the np array inputs in the same way as the tensor inputs (see more in the appendix). +

Table: TF dtype promotion result of `tf.add` after the proposed changes. `NONE` only allows the unhighlighted cells. `SAFE` allows all `NONE` cases plus the cells highlighted in green. `ALL` allows all `SAFE` cases plus the cells highlighted in yellow. Rows/columns highlighted in blue only show the default result without overflow of inputs. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i* - f* - c* -
b - b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i64 - f64 - c128 -
u8 - u8 - u8 - u16 - u32 - u64 - i16 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - u8 - f64 - c128 -
u16 - u16 - u16 - u16 - u32 - u64 - i32 - i32 - i32 - i64 - f32 - f32 - f32 - f64 - c64 - c128 - u16 - f64 - c128 -
u32 - u32 - u32 - u32 - u32 - u64 - i64 - i64 - i64 - i64 - f64 - f64 - f64 - f64 - c128 - c128 - u32 - f64 - c128 -
u64 - u64 - u64 - u64 - u64 - u64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - c128 - c128 - u64 - f64 - c128 -
i8 - i8 - i16 - i32 - i64 - f64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i8 - f64 - c128 -
i16 - i16 - i16 - i32 - i64 - f64 - i16 - i16 - i32 - i64 - f32 - f32 - f32 - f64 - c64 - c128 - i16 - f64 - c128 -
i32 - i32 - i32 - i32 - i64 - f64 - i32 - i32 - i32 - i64 - f64 - f64 - f64 - f64 - c128 - c128 - i32 - f64 - c128 -
i64 - i64 - i64 - i64 - i64 - f64 - i64 - i64 - i64 - i64 - f64 - f64 - f64 - f64 - c128 - c128 - i64 - f64 - c128 -
bf16 - bf16 - bf16 - f32 - f64 - f64 - bf16 - f32 - f64 - f64 - bf16 - f32 - f32 - f64 - c64 - c128 - bf16 - bf16 - c64 -
f16 - f16 - f16 - f32 - f64 - f64 - f16 - f32 - f64 - f64 - f32 - f16 - f32 - f64 - c64 - c128 - f16 - f16 - c64 -
f32 - f32 - f32 - f32 - f64 - f64 - f32 - f32 - f64 - f64 - f32 - f32 - f32 - f64 - c64 - c128 - f32 - f32 - c64 -
f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - c128 - c128 - f64 - f64 - c128 -
c64 - c64 - c64 - c64 - c128 - c128 - c64 - c64 - c128 - c128 - c64 - c64 - c64 - c128 - c64 - c128 - c64 - c64 - c64 -
c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 -
i* - i64 - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i64 - f64 - c128 -
f* - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - bf16 - f16 - f32 - f64 - c64 - c128 - f64 - f64 - c128 -
c* - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c64 - c64 - c64 - c128 - c64 - c128 - c128 - c128 - c128 -
+![Diff1](20221018-promotion-semantics/Table-TF-dtype-promotion-add-proposed.png) +

@@ -1667,7 +146,7 @@ The dtype behavior of mode `NONE` is the most conservative: no dtype promotion i ### **Alternatives Considered: Capping the promotion system at float32** -The TF-NumPy project contains another flag [allow_float64](https://source.corp.google.com/piper///depot/google3/third_party/tensorflow/python/ops/numpy_ops/np_dtypes.py;rcl=399530502;l=88). When disabled, it caps the converted floating numbers to 32 bits, and complex numbers to 64 bits. This is useful when users want to enjoy implicit conversions in all cases and avoid any performance regressions related to double precision floating point numbers. This feature is now used by [Trax](https://b.corp.google.com/issues/178862061). +The TF-NumPy project contains another flag [allow_float64](https://source.corp.google.com/piper///depot/google3/third_party/tensorflow/python/ops/numpy_ops/np_dtypes.py;rcl=399530502;l=88). When disabled, it caps the converted floating numbers to 32 bits, and complex numbers to 64 bits. This is useful when users want to enjoy implicit conversions in all cases and avoid any performance regressions related to double precision floating point numbers. Our current plan does not involve adding this flag in TF, as it’s orthogonal to the proposed modes. If there are more user needs we can consider exposing it in TF as well. @@ -1683,14 +162,6 @@ Unlike the approaches above, JAX used a dtype promotion lattice to define its se * `(tf.zeros(tf.uint8) + tf.zeros(tf.int8)) + tf.zeros(tf.float16)` evaluates to dtype `tf.float32`; * `tf.zeros(tf.uint8) + (tf.zeros(tf.int8) + tf.zeros(tf.float16))` evaluates to dtype `tf.float16`. -~~However the lattice system also introduced extra complexity in the design. For example:~~ - - - -* ~~JAX ended up introducing the concepts of “weak types” in place of python types;~~ -* ~~JAX adopted very aggressive dtype promotion semantics - an int64 dtype can be promoted to a float16, resulting in higher risks of overflow.~~ - -~~Since our ultimate goal is to help the users maintain a clear and concise mental model, adopting a dtype promotion table is an easier approach.~~ Compared to a lattice system, the proposed table-based system does not guarantee associativity. However, we can avoid such usage scenarios in the proposed SAFE mode (no implicit promotion to wider dtypes). As a result, it’s not clearly justifiable to switch to a lattice system from the table-based approach, which already exists in project TF-NumPy. @@ -1775,1852 +246,29 @@ Currently, inplace ops such as `assign_add` are also broken. The two tables belo * When the input is a python scalar, it defers to the variable’s dtype. However, python float cannot be converted to int, and complex cannot be converted to either int or float dtypes. * When the input is a NumPy array, it always gets converted to the variable’s dtype regardless of any precision loss (for example `tf.Variable(1) + np.array(1.5)` returns 2). -Table: Result of tf.Variable (rows) inplace op `assign_add` with a Tensor or python scalar as the argument (columns). +

+Table: Result of tf.Variable (rows) inplace op `assign_add` with a Tensor or python scalar as the argument (columns). - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i* - f* - c* -
b - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
u8 - - - u8 - - - - - - - - - - - - - - - - - - - - - - - - - - - u8 - - - - -
u16 - - - - - u16 - - - - - - - - - - - - - - - - - - - - - - - - - u16 - - - - -
u32 - - - - - - - u32 - - - - - - - - - - - - - - - - - - - - - - - u32 - - - - -
u64 - - - - - - - - - u64 - - - - - - - - - - - - - - - - - - - - - u64 - - - - -
i8 - - - - - - - - - - - i8 - - - - - - - - - - - - - - - - - - - i8 - - - - -
i16 - - - - - - - - - - - - - i16 - - - - - - - - - - - - - - - - - i16 - - - - -
i32 - - - - - - - - - - - - - - - i32 - - - - - - - - - - - - - - - i32 - - - - -
i64 - - - - - - - - - - - - - - - - - i64 - - - - - - - - - - - - - i64 - - - - -
bf16 - - - - - - - - - - - - - - - - - - - bf16 - - - - - - - - - - - bf16 - bf16 - - -
f16 - - - - - - - - - - - - - - - - - - - - - f16 - - - - - - - - - f16 - f16 - - -
f32 - - - - - - - - - - - - - - - - - - - - - - - f32 - - - - - - - f32 - f32 - - -
f64 - - - - - - - - - - - - - - - - - - - - - - - - - f64 - - - - - f64 - f64 - - -
c64 - - - - - - - - - - - - - - - - - - - - - - - - - - - c64 - - - c64 - c64 - c64 -
c128 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - c128 - c128 - c128 - c128 -
+![Diff1](20221018-promotion-semantics/Table-TF-dtype-promotion-assign_add-current.png) +

+

Table: Result of tf.Variable (rows) inplace op `assign_add` with a NumPy array as the argument (columns). - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 -
b - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
u8 - u8 - u8 - u8 - u8 - u8 - u8 - u8 - u8 - u8 - - - u8 - u8 - u8 - u8 - u8 -
u16 - u16 - u16 - u16 - u16 - u16 - u16 - u16 - u16 - u16 - - - u16 - u16 - u16 - u16 - u16 -
u32 - u32 - u32 - u32 - u32 - u32 - u32 - u32 - u32 - u32 - - - u32 - u32 - u32 - u32 - u32 -
u64 - u64 - u64 - u64 - u64 - u64 - u64 - u64 - u64 - u64 - - - u64 - u64 - u64 - u64 - u64 -
i8 - i8 - i8 - i8 - i8 - i8 - i8 - i8 - i8 - i8 - - - i8 - i8 - i8 - i8 - i8 -
i16 - i16 - i16 - i16 - i16 - i16 - i16 - i16 - i16 - i16 - - - i16 - i16 - i16 - i16 - i16 -
i32 - i32 - i32 - i32 - i32 - i32 - i32 - i32 - i32 - i32 - - - i32 - i32 - i32 - i32 - i32 -
i64 - i64 - i64 - i64 - i64 - i64 - i64 - i64 - i64 - i64 - - - i64 - i64 - i64 - i64 - i64 -
bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - - - bf16 - bf16 - bf16 - bf16 - bf16 -
f16 - f16 - f16 - f16 - f16 - f16 - f16 - f16 - f16 - f16 - - - f16 - f16 - f16 - f16 - f16 -
f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - - - f32 - f32 - f32 - f32 - f32 -
f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - - - f64 - f64 - f64 - f64 - f64 -
c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - - - c64 - c64 - c64 - c64 - c64 -
c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - - - c128 - c128 - c128 - c128 - c128 -
+![Diff1](20221018-promotion-semantics/Table-TF-dtype-promotion-assign_add_np-current.png) +

We can make these ops more consistent as well, following the rules in the three proposed modes. However, we also have another constraint: the Variable’s dtype cannot change. As a result, any dtype promotion that results in a dtype different from the Variable’s dtype is disabled. -Table: Result of tf.Variable (rows) inplace op `assign_add` with a Tensor or python scalar as the argument (columns) after the proposed changes. `NONE` only allows the unhighlighted cells. `SAFE` allows all `NONE` cases plus the cells highlighted in green. `ALL` allows all `SAFE` cases plus the cells highlighted in yellow. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 - i* - f* - c* -
b - b - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
u8 - u8 - u8 - - - - - - - - - - - - - - - - - - - - - - - - - - - u8 - - - - -
u16 - u16 - u16 - u16 - - - - - - - - - - - - - - - - - - - - - - - - - u16 - - - - -
u32 - u32 - u32 - u32 - u32 - - - - - - - - - - - - - - - - - - - - - - - u32 - - - - -
u64 - u64 - u64 - u64 - u64 - u64 - - - - - - - - - - - - - - - - - - - - - u64 - - - - -
i8 - i8 - - - - - - - - - i8 - - - - - - - - - - - - - - - - - - - i8 - - - - -
i16 - i16 - i16 - - - - - - - i16 - i16 - - - - - - - - - - - - - - - - - i16 - - - - -
i32 - i32 - i32 - i32 - - - - - i32 - i32 - i32 - - - - - - - - - - - - - - - i32 - - - - -
i64 - i64 - i64 - i64 - i64 - - - i64 - i64 - i64 - i64 - - - - - - - - - - - - - i64 - - - - -
bf16 - bf16 - bf16 - - - - - - - bf16 - - - - - - - bf16 - - - - - - - - - - - bf16 - bf16 - - -
f16 - f16 - f16 - - - - - - - f16 - - - - - - - - - f16 - - - - - - - - - f16 - f16 - - -
f32 - f32 - f32 - f32 - - - - - f32 - f32 - - - - - f32 - f32 - f32 - - - - - - - f32 - f32 - - -
f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - - - - - f64 - f64 - - -
c64 - c64 - c64 - c64 - - - - - c64 - c64 - - - - - c64 - c64 - c64 - - - c64 - - - c64 - c64 - c64 -
c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 -
+

+Table: Result of tf.Variable (rows) inplace op `assign_add` with a Tensor or python scalar as the argument (columns) after the proposed changes. `NONE` only allows the unhighlighted cells. `SAFE` allows all `NONE` cases plus the cells highlighted in green. `ALL` allows all `SAFE` cases plus the cells highlighted in yellow. +![Diff1](20221018-promotion-semantics/Table-TF-dtype-promotion-assign_add-proposed.png) +

#### **Are there also issues in conversions between Tensor and numpy arrays?** @@ -3638,1106 +286,18 @@ print(tf.add(c, a)) # tf.Tensor(4, shape=(), dtype=int32) - Even worse than exc To give a comprehensive overview, the two tables below show the results of `tf.add(tensor, numpy array)` and `tf.add(numpy array, tensor)` : +

Table: TF dtype promotion result of `tf.add` with a Tensor as the first argument (rows) and np array in the second argument (columns). - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 -
b - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
u8 - u8 - u8 - u8 - u8 - u8 - u8 - u8 - u8 - u8 - - - u8 - u8 - u8 - u8 - u8 -
u16 - u16 - u16 - u16 - u16 - u16 - u16 - u16 - u16 - u16 - - - u16 - u16 - u16 - u16 - u16 -
u32 - u32 - u32 - u32 - u32 - u32 - u32 - u32 - u32 - u32 - - - u32 - u32 - u32 - u32 - u32 -
u64 - u64 - u64 - u64 - u64 - u64 - u64 - u64 - u64 - u64 - - - u64 - u64 - u64 - u64 - u64 -
i8 - i8 - i8 - i8 - i8 - i8 - i8 - i8 - i8 - i8 - - - i8 - i8 - i8 - i8 - i8 -
i16 - i16 - i16 - i16 - i16 - i16 - i16 - i16 - i16 - i16 - - - i16 - i16 - i16 - i16 - i16 -
i32 - i32 - i32 - i32 - i32 - i32 - i32 - i32 - i32 - i32 - - - i32 - i32 - i32 - i32 - i32 -
i64 - i64 - i64 - i64 - i64 - i64 - i64 - i64 - i64 - i64 - - - i64 - i64 - i64 - i64 - i64 -
bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - bf16 - - - bf16 - bf16 - bf16 - bf16 - bf16 -
f16 - f16 - f16 - f16 - f16 - f16 - f16 - f16 - f16 - f16 - - - f16 - f16 - f16 - f16 - f16 -
f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - f32 - - - f32 - f32 - f32 - f32 - f32 -
f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - f64 - - - f64 - f64 - f64 - f64 - f64 -
c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - c64 - - - c64 - c64 - c64 - c64 - c64 -
c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - c128 - - - c128 - c128 - c128 - c128 - c128 -
+![Diff1](20221018-promotion-semantics/Table-TF-dtype-promotion-add_tensor_np-current.png) +

+

Table: TF dtype promotion result of `tf.add` with a np array as the first argument (rows) and tensor in the second argument (columns). - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- b - u8 - u16 - u32 - u64 - i8 - i16 - i32 - i64 - bf16 - f16 - f32 - f64 - c64 - c128 -
b - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
u8 - - - u8 - - - - - - - - - - - - - - - - - - - - - - - - - - -
u16 - - - - - u16 - - - - - - - - - - - - - - - - - - - - - - - - -
u32 - - - - - - - u32 - - - - - - - - - - - - - - - - - - - - - - -
u64 - - - - - - - - - u64 - - - - - - - - - - - - - - - - - - - - -
i8 - - - - - - - - - - - i8 - - - - - - - - - - - - - - - - - - -
i16 - - - - - - - - - - - - - i16 - - - - - - - - - - - - - - - - -
i32 - - - - - - - - - - - - - - - i32 - - - - - - - - - - - - - - -
i64 - - - - - - - - - - - - - - - - - i64 - - - - - - - - - - - - -
bf16 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
f16 - - - - - - - - - - - - - - - - - - - - - f16 - - - - - - - - -
f32 - - - - - - - - - - - - - - - - - - - - - - - f32 - - - - - - -
f64 - - - - - - - - - - - - - - - - - - - - - - - - - f64 - - - - -
c64 - - - - - - - - - - - - - - - - - - - - - - - - - - - c64 - - -
c128 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - c128 -
+![Diff1](20221018-promotion-semantics/Table-TF-dtype-promotion-add_np_tensor-current.png) +

To briefly describe the issues: @@ -4748,10 +308,10 @@ To briefly describe the issues: 2. If the first input is a tensor, the second input (the np array) is always converted to the tensor’s dtype, resulting into significant precision loss in many scenarios; 3. If the first input is a np array and the second is a tensor, the np array is first converted to a tensor with the corresponding dtype. TF then does not attempt any implicit dtype conversion, causing `InvalidArgumentError` whenever the two inputs have mismatching dtypes. -Though the behaviors with np arrays inputs are not the same as python scalars, they share the same root cause: At present, TF does not have a centralized dtype promotion system, and incorrectly uses the [dtype of the first Tensor in its list of arguments](https://source.corp.google.com/piper///depot/google3/third_party/tensorflow/python/ops/math_ops.py;rcl=449753780;l=1371) to promote all arguments to. The proposed modes in this RFC will treat the np array inputs in the same way as the tensor inputs. +Though the behaviors with np arrays inputs are not the same as python scalars, they share the same root cause: At present, TF does not have a centralized dtype promotion system, and incorrectly uses the [dtype of the first Tensor in its list of arguments](https://github.com/tensorflow/tensorflow/blob/a3a9d4d6538b025d0c6c821a72076e084a5b597b/tensorflow/python/ops/math_ops.py#L1371) to promote all arguments to. The proposed modes in this RFC will treat the np array inputs in the same way as the tensor inputs. -#### **What happens to e.g. bool - bool or bool / bool? +#### What happens to e.g. bool - bool or bool / bool? With NumPy: From d35bdfb4fd068803fa6d0382f4231b79312fe41b Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 15:57:27 -0700 Subject: [PATCH 12/20] Update 20221018-promotion-semantics.md --- rfcs/20221018-promotion-semantics.md | 22 ++++++++++------------ 1 file changed, 10 insertions(+), 12 deletions(-) diff --git a/rfcs/20221018-promotion-semantics.md b/rfcs/20221018-promotion-semantics.md index 06ac12f7f..35223413f 100644 --- a/rfcs/20221018-promotion-semantics.md +++ b/rfcs/20221018-promotion-semantics.md @@ -22,11 +22,9 @@ Currently TF has no consistent, well-defined type promotion rules. This document ## **Motivation** -In Tensorflow’s APIs, dtype promotion is very broken compared to JAX or NumPy. Many users have complained about these surprising behaviors (example: go/broccoli-tf-add, b/158346631, b/154456219). See [this doc ](https://docs.google.com/document/d/1jOXJ1YAQAtseyYDDGm4gsr7xJ4F2FtvfwZJgNsW8cVA/edit#)for a more detailed description. +In Tensorflow’s APIs, dtype promotion is very broken compared to JAX or NumPy. Many users have complained about these surprising behaviors ([example](https://github.com/tensorflow/tensorflow/issues/38658)). -[TF-numpy](https://www.tensorflow.org/guide/tf_numpy#type_promotion) is a strategic project in Tensorflow. Compared to Tensorflow, TF-numpy’s dtype promotion behavior is more consistent because it [mainly relies on NumPy’s dtype promotion semantics](https://source.corp.google.com/piper///depot/google3/third_party/tensorflow/python/ops/numpy_ops/np_dtypes.py;rcl=399530502;l=112). In the [long-term vision doc](https://goto.google.com/tf-numpy-path-forward), unifying the dtype promotion semantics of TF and TF-numpy is a necessary first step. - -The unification of TF and TF-numpy’s dtype promotion semantics is a great chance to think about what semantics should be the long-term, user-friendly solution for TF. +[TF-numpy](https://www.tensorflow.org/guide/tf_numpy#type_promotion) is a strategic project in Tensorflow. Compared to Tensorflow, TF-numpy’s dtype promotion behavior is more consistent because it [mainly relies on NumPy’s dtype promotion semantics](https://github.com/tensorflow/tensorflow/blob/a3a9d4d6538b025d0c6c821a72076e084a5b597b/tensorflow/python/ops/numpy_ops/np_dtypes.py#L112). Based on TF-numpy’s dtype promotion semantics we can develop a long-term, user-friendly solution for TF. ## **User Benefit** @@ -60,7 +58,7 @@ The table below summarizes the dtype promotion behavior between two TF tensors/p

Table: TF dtype promotion result of dunder method `__add__` and `tf.add` -![Diff1](20221018-promotion-semantics/Table-TF-dtype-promotion-add-current.png) +![Table1](20221018-promotion-semantics/Table-TF-dtype-promotion-add-current.png)

In the table, the dash symbol `-` means unsupported: an error gets raised if the two dtypes are used in the call to that op. The cells highlighted in red are only supported in `__add__`, and the cells in yellow are only supported in `tf.add`. In summary, the existing TF APIs have these significant issues: @@ -92,7 +90,7 @@ The three modes determine how often implicit dtype promotions happen in TF APIs.

Table: TF dtype promotion result of `tf.add` after the proposed changes. `NONE` only allows the unhighlighted cells. `SAFE` allows all `NONE` cases plus the cells highlighted in green. `ALL` allows all `SAFE` cases plus the cells highlighted in yellow. Rows/columns highlighted in blue only show the default result without overflow of inputs. -![Diff1](20221018-promotion-semantics/Table-TF-dtype-promotion-add-proposed.png) +![Table2](20221018-promotion-semantics/Table-TF-dtype-promotion-add-proposed.png)

@@ -146,7 +144,7 @@ The dtype behavior of mode `NONE` is the most conservative: no dtype promotion i ### **Alternatives Considered: Capping the promotion system at float32** -The TF-NumPy project contains another flag [allow_float64](https://source.corp.google.com/piper///depot/google3/third_party/tensorflow/python/ops/numpy_ops/np_dtypes.py;rcl=399530502;l=88). When disabled, it caps the converted floating numbers to 32 bits, and complex numbers to 64 bits. This is useful when users want to enjoy implicit conversions in all cases and avoid any performance regressions related to double precision floating point numbers. +The TF-NumPy project contains another flag [allow_float64](https://github.com/tensorflow/tensorflow/blob/a3a9d4d6538b025d0c6c821a72076e084a5b597b/tensorflow/python/ops/numpy_ops/np_dtypes.py#L88). When disabled, it caps the converted floating numbers to 32 bits, and complex numbers to 64 bits. This is useful when users want to enjoy implicit conversions in all cases and avoid any performance regressions related to double precision floating point numbers. Our current plan does not involve adding this flag in TF, as it’s orthogonal to the proposed modes. If there are more user needs we can consider exposing it in TF as well. @@ -250,14 +248,14 @@ Currently, inplace ops such as `assign_add` are also broken. The two tables belo

Table: Result of tf.Variable (rows) inplace op `assign_add` with a Tensor or python scalar as the argument (columns). -![Diff1](20221018-promotion-semantics/Table-TF-dtype-promotion-assign_add-current.png) +![Table3](20221018-promotion-semantics/Table-TF-dtype-promotion-assign_add-current.png)

Table: Result of tf.Variable (rows) inplace op `assign_add` with a NumPy array as the argument (columns). -![Diff1](20221018-promotion-semantics/Table-TF-dtype-promotion-assign_add_np-current.png) +![Table4](20221018-promotion-semantics/Table-TF-dtype-promotion-assign_add_np-current.png)

@@ -267,7 +265,7 @@ We can make these ops more consistent as well, following the rules in the three

Table: Result of tf.Variable (rows) inplace op `assign_add` with a Tensor or python scalar as the argument (columns) after the proposed changes. `NONE` only allows the unhighlighted cells. `SAFE` allows all `NONE` cases plus the cells highlighted in green. `ALL` allows all `SAFE` cases plus the cells highlighted in yellow. -![Diff1](20221018-promotion-semantics/Table-TF-dtype-promotion-assign_add-proposed.png) +![Table5](20221018-promotion-semantics/Table-TF-dtype-promotion-assign_add-proposed.png)

@@ -289,14 +287,14 @@ To give a comprehensive overview, the two tables below show the results of `tf.a

Table: TF dtype promotion result of `tf.add` with a Tensor as the first argument (rows) and np array in the second argument (columns). -![Diff1](20221018-promotion-semantics/Table-TF-dtype-promotion-add_tensor_np-current.png) +![Table6](20221018-promotion-semantics/Table-TF-dtype-promotion-add_tensor_np-current.png)

Table: TF dtype promotion result of `tf.add` with a np array as the first argument (rows) and tensor in the second argument (columns). -![Diff1](20221018-promotion-semantics/Table-TF-dtype-promotion-add_np_tensor-current.png) +![Table7](20221018-promotion-semantics/Table-TF-dtype-promotion-add_np_tensor-current.png)

From 6fcdf1c4f495ec8f56b0f13638274f3824934983 Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 16:42:31 -0700 Subject: [PATCH 13/20] Update 20221018-promotion-semantics.md --- rfcs/20221018-promotion-semantics.md | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/rfcs/20221018-promotion-semantics.md b/rfcs/20221018-promotion-semantics.md index 35223413f..6a0747028 100644 --- a/rfcs/20221018-promotion-semantics.md +++ b/rfcs/20221018-promotion-semantics.md @@ -70,7 +70,7 @@ In the table, the dash symbol `-` means unsupported: an error gets raised if the 3. Implicit conversion from some python dtypes to some TF dtypes are disallowed; 4. `tf.add` is not even commutative - switching the position of the two arguments sometimes produces errors, otherwise results in correct values. -When the inputs involve NumPy arrays, the behaviors can be even more broken. For brevity we have included NumPy-input behaviors in the appendix at the end of the doc. +When the inputs involve NumPy arrays, the behaviors can be even more broken. For brevity we have included NumPy-input behaviors in [a sub section](#are-there-also-issues-in-conversions-between-tensor-and-numpy-arrays) at the end of the doc. We aim to make the system commutative, predictable and correct. @@ -85,7 +85,7 @@ We propose introducing three dtype promotion modes in TF: * `tf.ImplicitPromotion.SAFE` * `tf.ImplicitPromotion.NONE` -The three modes determine how often implicit dtype promotions happen in TF APIs. In the following examples we will use `tf.add` to demonstrate the modes. The dunder method `__add__` is expected to have the same behavior as `tf.add`. For brevity, NumPy (np) array inputs are not discussed in this section. However the proposed modes in this RFC will treat the np array inputs in the same way as the tensor inputs (see more in the appendix). +The three modes determine how often implicit dtype promotions happen in TF APIs. In the following examples we will use `tf.add` to demonstrate the modes. The dunder method `__add__` is expected to have the same behavior as `tf.add`. For brevity, NumPy (np) array inputs are not discussed in this section. However the proposed modes in this RFC will treat the np array inputs in the same way as the tensor inputs (see more in the [bottom]((#are-there-also-issues-in-conversions-between-tensor-and-numpy-arrays))).

Table: TF dtype promotion result of `tf.add` after the proposed changes. `NONE` only allows the unhighlighted cells. `SAFE` allows all `NONE` cases plus the cells highlighted in green. `ALL` allows all `SAFE` cases plus the cells highlighted in yellow. Rows/columns highlighted in blue only show the default result without overflow of inputs. @@ -198,25 +198,23 @@ We can carry out the following steps to mitigate user surprises: This proposal is not expected to affect dependencies to Tensorflow. -#### **Engineering Impact** +### **Engineering Impact** -#### We do not expect changes in binary size/startup time/build time/test time. +We do not expect changes in binary size/startup time/build time/test time. The majority of the dtype promotion logic already exists in Tensorflow under namescope `tensorflow.experimental.numpy`. However, refactoring/updates will be needed under directory tensorflow/python/ops. -The majority of the dtype promotion logic already exists in Tensorflow under namescope `tensorflow.experimental.numpy`. However, refactoring/updates will be needed under directory tensorflow/python/ops. - -#### **Platforms and Environments** +### **Platforms and Environments** This proposal is expected to take effect on all platforms in the python environment. -#### **Best Practices/Tutorials and Examples** +### **Best Practices/Tutorials and Examples** As mentioned in section “Performance Implications” we will publish tutorials that help users select the most appropriate dtype promotion modes. -#### **Compatibility/User Impact** +### **Compatibility/User Impact** This is a breaking change. As mentioned above, existing code is expected to continue working. However, some tests will fail because more implicit dtype conversions are allowed. We can mitigate the user impact by adding a flag that sticks to the existing dtype promotion behaviors. @@ -229,7 +227,7 @@ The proposed changes will be rolled out in various stages: 3. [optional] Completely disable the old, broken dtype promotion behaviors. -## **Questions and Discussion Topics** +### **Questions and Discussion Topics** #### **Are there changes expected in tf.Variable?** From 24852db6f7823c293e59f72cdf711dbd52f75f52 Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 16:43:23 -0700 Subject: [PATCH 14/20] Update 20221018-promotion-semantics.md --- rfcs/20221018-promotion-semantics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rfcs/20221018-promotion-semantics.md b/rfcs/20221018-promotion-semantics.md index 6a0747028..d8eb2d0e1 100644 --- a/rfcs/20221018-promotion-semantics.md +++ b/rfcs/20221018-promotion-semantics.md @@ -85,7 +85,7 @@ We propose introducing three dtype promotion modes in TF: * `tf.ImplicitPromotion.SAFE` * `tf.ImplicitPromotion.NONE` -The three modes determine how often implicit dtype promotions happen in TF APIs. In the following examples we will use `tf.add` to demonstrate the modes. The dunder method `__add__` is expected to have the same behavior as `tf.add`. For brevity, NumPy (np) array inputs are not discussed in this section. However the proposed modes in this RFC will treat the np array inputs in the same way as the tensor inputs (see more in the [bottom]((#are-there-also-issues-in-conversions-between-tensor-and-numpy-arrays))). +The three modes determine how often implicit dtype promotions happen in TF APIs. In the following examples we will use `tf.add` to demonstrate the modes. The dunder method `__add__` is expected to have the same behavior as `tf.add`. For brevity, NumPy (np) array inputs are not discussed in this section. However the proposed modes in this RFC will treat the np array inputs in the same way as the tensor inputs (see more in the [bottom](#are-there-also-issues-in-conversions-between-tensor-and-numpy-arrays)).

Table: TF dtype promotion result of `tf.add` after the proposed changes. `NONE` only allows the unhighlighted cells. `SAFE` allows all `NONE` cases plus the cells highlighted in green. `ALL` allows all `SAFE` cases plus the cells highlighted in yellow. Rows/columns highlighted in blue only show the default result without overflow of inputs. From 951647cf1814f7f5a04dc18de5ffb15c2e82d4aa Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 17:05:55 -0700 Subject: [PATCH 15/20] Update 20221018-promotion-semantics.md --- rfcs/20221018-promotion-semantics.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rfcs/20221018-promotion-semantics.md b/rfcs/20221018-promotion-semantics.md index d8eb2d0e1..fab2f37f0 100644 --- a/rfcs/20221018-promotion-semantics.md +++ b/rfcs/20221018-promotion-semantics.md @@ -6,7 +6,7 @@ | Status | (Proposed) | :-------------- |:---------------------------------------------------- | | **RFC #** | [NNN](https://github.com/tensorflow/community/pull/NNN) (update when you have community PR #)| -| **Author(s)** | Jiawei Xia (jiaweix@google.com) | +| **Author(s)** | Jiawei Xia (jiaweix@google.com), Antonio Sanchez (cantonios@google.com ) | | **Sponsor** | Peng Wang (pengwang@google.com) | | **Updated** | 2022-10-18 | @@ -108,7 +108,7 @@ In this mode, we allow all implicit promotions to happen in the op, even if both * If the python scalar value falls within the range of the TF dtype, the result dtype is loosely described with the following promotion direction: TF bool -> python integer -> TF signed/unsigned integers -> python float -> TF float -> python complex/TF complex. * The dtype promotion is allowed only if the python scalar is within the range of the determined dtype. Otherwise, an exception is raised. For example, `tf.constant([1], tf.int8) + 1` produces a `tf.int8` Tensor, while `tf.constant([1], tf.int8) + 1000` raises an error. -This mode is intended to provide a user experience similar to NumPy behaviors. +This mode is intended to provide a user experience somewhat similar to NumPy behaviors. ### **Mode 2: tf.ImplicitPromotion.SAFE** From 05df4e81e50a7996115624d90c4bc16f5a2b5a7b Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 17:10:56 -0700 Subject: [PATCH 16/20] Update 20221018-promotion-semantics.md --- rfcs/20221018-promotion-semantics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rfcs/20221018-promotion-semantics.md b/rfcs/20221018-promotion-semantics.md index fab2f37f0..579721030 100644 --- a/rfcs/20221018-promotion-semantics.md +++ b/rfcs/20221018-promotion-semantics.md @@ -182,7 +182,7 @@ This slightly reduces the risk of overflow. However, the result is unpredictable For an existing piece of TF code in python, we do not expect the performance to change after switching to any of the proposed modes. This is because all the allowed implicit dtype promotions in TF will stay unchanged. As to the currently disallowed dtype promotions, users must have used explicit dtype promotions which are not affected by the change. -When users develop a new piece of code, however, the new modes have higher chances of computation slowdown due to implicit promotions in mode `ALL`. +When users develop a new piece of code, however, the mode `ALL` has higher chances of computation slowdown due to implicit promotions to wider types. We can carry out the following steps to mitigate user surprises: From bbb6c268f84822ed20e58e92bf746e4712645015 Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 17:17:13 -0700 Subject: [PATCH 17/20] Update 20221018-promotion-semantics.md --- rfcs/20221018-promotion-semantics.md | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/rfcs/20221018-promotion-semantics.md b/rfcs/20221018-promotion-semantics.md index 579721030..e312cf461 100644 --- a/rfcs/20221018-promotion-semantics.md +++ b/rfcs/20221018-promotion-semantics.md @@ -307,18 +307,6 @@ To briefly describe the issues: Though the behaviors with np arrays inputs are not the same as python scalars, they share the same root cause: At present, TF does not have a centralized dtype promotion system, and incorrectly uses the [dtype of the first Tensor in its list of arguments](https://github.com/tensorflow/tensorflow/blob/a3a9d4d6538b025d0c6c821a72076e084a5b597b/tensorflow/python/ops/math_ops.py#L1371) to promote all arguments to. The proposed modes in this RFC will treat the np array inputs in the same way as the tensor inputs. -#### What happens to e.g. bool - bool or bool / bool? - -With NumPy: - - - -* Addition between two Booleans: equivalent to OR -* Subtraction between two Booleans: forbidden -* Multiplication between two Booleans: AND -* Division between two Booleans: converted to float64 then divide - - #### **What happens to truediv operations?** The op truediv is special because it implicitly promotes integral types. For example when the inputs are matching dtypes, 16-bit integral types are promoted to `f32`, and 32-bit integral types are promoted to `float64`. From fdbedb7a073807a6c5e19f10ed5b0ed9cdf32310 Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 17:32:17 -0700 Subject: [PATCH 18/20] Update 20221018-promotion-semantics.md --- rfcs/20221018-promotion-semantics.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rfcs/20221018-promotion-semantics.md b/rfcs/20221018-promotion-semantics.md index e312cf461..4c7994221 100644 --- a/rfcs/20221018-promotion-semantics.md +++ b/rfcs/20221018-promotion-semantics.md @@ -6,8 +6,8 @@ | Status | (Proposed) | :-------------- |:---------------------------------------------------- | | **RFC #** | [NNN](https://github.com/tensorflow/community/pull/NNN) (update when you have community PR #)| -| **Author(s)** | Jiawei Xia (jiaweix@google.com), Antonio Sanchez (cantonios@google.com ) | -| **Sponsor** | Peng Wang (pengwang@google.com) | +| **Author(s)** | Jiawei Xia (Google), Antonio Sanchez (Google) | +| **Sponsor** | Peng Wang (Google) | | **Updated** | 2022-10-18 | From f5c59adf670c71dad3a140a11bd1be048b8bbe09 Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 18 Oct 2022 17:33:19 -0700 Subject: [PATCH 19/20] Update 20221018-promotion-semantics.md --- rfcs/20221018-promotion-semantics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rfcs/20221018-promotion-semantics.md b/rfcs/20221018-promotion-semantics.md index 4c7994221..93e847370 100644 --- a/rfcs/20221018-promotion-semantics.md +++ b/rfcs/20221018-promotion-semantics.md @@ -5,7 +5,7 @@ | Status | (Proposed) | :-------------- |:---------------------------------------------------- | -| **RFC #** | [NNN](https://github.com/tensorflow/community/pull/NNN) (update when you have community PR #)| +| **RFC #** | 431| | **Author(s)** | Jiawei Xia (Google), Antonio Sanchez (Google) | | **Sponsor** | Peng Wang (Google) | | **Updated** | 2022-10-18 | From b521f1fb2c5e1801ac9f0715812bf729fcfa2d13 Mon Sep 17 00:00:00 2001 From: JW1992 <31080444+JW1992@users.noreply.github.com> Date: Tue, 8 Nov 2022 13:59:45 -0800 Subject: [PATCH 20/20] Update 20221018-promotion-semantics.md --- rfcs/20221018-promotion-semantics.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/rfcs/20221018-promotion-semantics.md b/rfcs/20221018-promotion-semantics.md index 93e847370..bac828e2d 100644 --- a/rfcs/20221018-promotion-semantics.md +++ b/rfcs/20221018-promotion-semantics.md @@ -1,7 +1,7 @@ -# Making dtype promotion semantics in Tensorflow more consistent +# Making dtype promotion semantics in TensorFlow more consistent | Status | (Proposed) | :-------------- |:---------------------------------------------------- | @@ -15,16 +15,16 @@ Currently TF has no consistent, well-defined type promotion rules. This document proposes a well-defined, consistent and clear dtype promotion rule for TF. The introduced changes make TF APIs more similar to NumPy, with some differences that emphasize TF’s applications in machine learning. This should make dtype promotions in TF much more consistent and predictable. -[**What the doc is**] This doc discusses the preferred dtype promotion semantics/behaviors of Tensorflow (TF) and Tensorflow-numpy (TF-numpy) in the binary ops including add, sub, mul, div, pow and mod. +[**What the doc is**] This doc discusses the preferred dtype promotion semantics/behaviors of TensorFlow (TF) and TensorFlow-NumPy (TF-NumPy) in the binary ops including add, sub, mul, div, pow and mod. [**What the doc is not**] This doc does not discuss the implementation plans. ## **Motivation** -In Tensorflow’s APIs, dtype promotion is very broken compared to JAX or NumPy. Many users have complained about these surprising behaviors ([example](https://github.com/tensorflow/tensorflow/issues/38658)). +In TensorFlow’s APIs, dtype promotion is very broken compared to JAX or NumPy. Many users have complained about these surprising behaviors ([example](https://github.com/tensorflow/tensorflow/issues/38658)). -[TF-numpy](https://www.tensorflow.org/guide/tf_numpy#type_promotion) is a strategic project in Tensorflow. Compared to Tensorflow, TF-numpy’s dtype promotion behavior is more consistent because it [mainly relies on NumPy’s dtype promotion semantics](https://github.com/tensorflow/tensorflow/blob/a3a9d4d6538b025d0c6c821a72076e084a5b597b/tensorflow/python/ops/numpy_ops/np_dtypes.py#L112). Based on TF-numpy’s dtype promotion semantics we can develop a long-term, user-friendly solution for TF. +[TF-NumPy](https://www.tensorflow.org/guide/tf_numpy#type_promotion) is a strategic project in TensorFlow. Compared to TensorFlow, TF-NumPy’s dtype promotion behavior is more consistent because it [mainly relies on NumPy’s dtype promotion semantics](https://github.com/tensorflow/tensorflow/blob/a3a9d4d6538b025d0c6c821a72076e084a5b597b/tensorflow/python/ops/numpy_ops/np_dtypes.py#L112). Based on TF-NumPy’s dtype promotion semantics we can develop a long-term, user-friendly solution for TF. ## **User Benefit**

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