From 943a6b3ba088cac734cb1309c2f456677e4cff75 Mon Sep 17 00:00:00 2001 From: "Wang, Zhitao" Date: Thu, 8 Aug 2024 01:30:29 +0000 Subject: [PATCH] rfcs: support int4/int8 compression for K/V in fuesd SDPA --- .../README.md | 307 ++++++++++++++++++ .../img/grouped_scale.png | Bin 0 -> 98465 bytes .../img/sdpa_pattern.png | Bin 0 -> 123174 bytes 3 files changed, 307 insertions(+) create mode 100644 rfcs/20240808-graph-api-int-compression-for-sdpa/README.md create mode 100644 rfcs/20240808-graph-api-int-compression-for-sdpa/img/grouped_scale.png create mode 100644 rfcs/20240808-graph-api-int-compression-for-sdpa/img/sdpa_pattern.png diff --git a/rfcs/20240808-graph-api-int-compression-for-sdpa/README.md b/rfcs/20240808-graph-api-int-compression-for-sdpa/README.md new file mode 100644 index 00000000000..30b5c966b63 --- /dev/null +++ b/rfcs/20240808-graph-api-int-compression-for-sdpa/README.md @@ -0,0 +1,307 @@ +# Graph API: Support int4/int8 compression for K/V in fused SDPA + +## Introduction + +Int4 and int8 compression for Key and Value are exploited in fused SDPA to +reduce the memory footprint of generative inference of LLM, especially when KV +cache mechanism is adopted. In more details, Key and Value tensors will be +stored in lower integral data types such as int4 and int8 for less memory +consumption, and will be de-quantized to wider floating point data types such as +f16 and bf16 for computation. According to the investigation and user request, +it's also possible that Key tensor will be stored in int4 while Value tensor in +int8 as it's more sensitive to precision. + +Currently, oneDNN Graph API already supports int8 data types and +per-tensor/channel int8 quantization, but does not support int4 data types. In +addition, due to the limited range of representation of int4 data types, grouped +scales and zero points are required for int4 quantization and dequantization, +which are not supported in oneDNN Graph either. With that, this document will +concentrate more on supporting int4 data types and its quantization in oneDNN +Graph in the following parts. + +To be more specific, in this RFC we are going to discuss about supporting the +following features in oneDNN Graph API: + +1. Support u4/s4 data types in Graph API. +2. Support int4 data types quantization and dequantization. +3. Extend the existing SDPA pattern with compressed K/V inputs. + +## Proposals + +### Proposal 1: Support u4/s4 data types + +The proposal is to follow oneDNN primitive APIs design with adding int4 data +types enumerations in `dnnl_data_type_t` (defined in dnnl_common_types.h). The +new enumerations will work for both primitive and graph C APIs. + +```cpp +/// Data type specification +typedef enum { + /// Undefined data type, used for empty memory descriptors. + dnnl_data_type_undef = 0, + /// 16-bit/half-precision floating point. + dnnl_f16 = 1, + /// non-standard 16-bit (bfloat16 w/ 7 bit mantissa) floating point. + dnnl_bf16 = 2, + /// 32-bit/single-precision floating point. + dnnl_f32 = 3, + /// 32-bit signed integer. + dnnl_s32 = 4, + /// 8-bit signed integer. + dnnl_s8 = 5, + /// 8-bit unsigned integer. + dnnl_u8 = 6, + /// 64-bit/double-precision floating point. + dnnl_f64 = 7, + /// Boolean data type. Size is C++ implementation defined. + dnnl_boolean = 8, + /// [OFP8 standard 8-bit floating-point](https://www.opencompute.org/documents/ocp-8-bit-floating-point-specification-ofp8-revision-1-0-2023-06-20-pdf) + /// with a 5-bit exponent and a 2-bit mantissa. + dnnl_f8_e5m2 = 9, + /// [OFP8 standard 8-bit floating-point](https://www.opencompute.org/documents/ocp-8-bit-floating-point-specification-ofp8-revision-1-0-2023-06-20-pdf) + /// with a 4-bit exponent and a 3-bit mantissa. + dnnl_f8_e4m3 = 10, + /// 4-bit signed integer. + dnnl_s4 = 11, + /// 4-bit unsigned integer. + dnnl_u4 = 12, + + /// Parameter to allow internal only data_types without undefined behavior. + /// This parameter is chosen to be valid for so long as sizeof(int) >= 2. + dnnl_data_type_max = 0x7fff, +} dnnl_data_type_t; +``` + +In Graph C++ API, the enum class `logical_tensor::data_type` (defined in +dnnl_graph.hpp) will be extended to support the int4 data types. + +```c++ +class logical_tensor { + // ... + /// Data Types + enum class data_type { + undef = dnnl_data_type_undef, + /// 16-bit/half-precision floating point. + f16 = dnnl_f16, + /// non-standard 16-bit (bfloat16 w/ 7 bit mantissa) floating point. + bf16 = dnnl_bf16, + /// 32-bit/single-precision floating point. + f32 = dnnl_f32, + /// 32-bit signed integer. + s32 = dnnl_s32, + /// 8-bit signed integer. + s8 = dnnl_s8, + /// 8-bit unsigned integer. + u8 = dnnl_u8, + /// Boolean data type. Size is C++ implementation defined. + boolean = dnnl_boolean, + /// 8-bit floating point data type with E5M2. Added by this RFC. + f8_e5m2 = dnnl_f8_e5m2, + /// 8-bit floating point data type with E4M3. Added by this RFC. + f8_e4m3 = dnnl_f8_e4m3, + /// 4-bit signed integer. + s4 = dnnl_s4, + /// 4-bit unsigned integer. + u4 = dnnl_u4, + }; + // ... +}; +``` + +The new data types can be used to create logical tensors and tensors in oneDNN +Graph API. + +```c++ +using namespace dnnl::graph; + +// create a s4 logical tensor with unknown ndim and dims. +auto lt1 = logical_tensor(id, logical_tensor::data_type::s4); + +// create a u4 logical tensor with dims = (16, 8). +const logical_tensor::dims shape = {16, 8} +auto lt2 = logical_tensor(id, logical_tensor::data_type::u4, shape); +``` + +### Proposal 2: Extend Quantize and Dequantize Operations + +The recommended proposal is that oneDNN Graph API will support int4 data type +through graph fusion following the way that int8 is supported, instead of +directly adding int4 datatype in computation operations. + +Based on the request, int4 quantization happens in runtime (scales and zps are +stored as tensors on device). Graph API will support int4 quantization through +operations `DynamicDequantize` and `DynamicQuantize`. In the future, it's +possible that we will also extend the static version of `Quantize` and +`Dequantize` to support int4 quantization. But for now, we wil focus on +`DynamicQuantize` and `DynamicDequantize` due to the request urgency and the +feature use scenario. + +According to the request from frameworks, for int4 quantization, grouped scales +are required for each hidden dimension to maintain the model accuracy. The +requirement for grouped zero points is not promised. See [int4 +Quantize](https://docs.nvidia.com/deeplearning/tensorrt/operators/docs/Quantize.html?highlight=int4) +and +[Dequantize](https://docs.nvidia.com/deeplearning/tensorrt/operators/docs/Dequantize.html) +in TensorRT and +[QuantizeLinear](https://onnx.ai/onnx/operators/onnx__QuantizeLinear.html) in +ONNX. + +Currently, `DynamicDequantize` and `DynamicQuantize` in Graph API accept a +required f32 1D tensor as the scale factor, and an optional 1D tensor as the +zero points( can be either s8/u8/f32 ). What's more, two optional attributes are +provided: `qtype` is used to specify which quantization type is used, +`per_tensor` or `per_channel`. And `axis` specifies on which dimension +`per_channel` quantization will be applied. To support int4 data types, +`DynamicQuantize` and `DynamicDequantize` operations in oneDNN Graph opset will +be extended: + +1. `DynamicQuantize` will be extended to support u4/s4 output, and scales and + zero points with groups. The library may raise an error if groups are not + provided for a `DynamicQuantize` op with u4/s4 output. +2. `DynamicDequantize` will be extended to support u4/s4 input, and scales and + zero points with groups. The library may raise an error if groups are not + provided for a `DynamicDequantize` op with u4/s4 input. + +Besides the input or output data types, new attributes will be added for +DynamicQuantize and DynamicDequantize: + +1. Add `per_group` to the supported values of `qtype` attribute, and the default value + will be unchanged. +2. Add a new optional attribute `group_size` to support `per_group` quantization + type. The value of `group_size` attribute is a single `s64` data, which + indicates the number of elements that share the same scaling factor and zero + points in each quantization group. The attribute is only required when + `per_group` quantization type is specified for `qtype`. If `per_group` + quantization is not specified and `group_size` attribute is given, it will + be ignored. +3. The `scale` and `zp` input will be extended to a `n`-dimensional tensor + (`n` = dims(input)) for `per_group` quantization. The shape requirements for + `scale` and `zp` are as follows: + 1. For `per_tensor` and `per_channel` quantization, the `scale` and `zp` + inputs should be `1d` tensors. + 2. For `per_group` quantization, all dimensions should match the input, + excepts for the dimension where grouped quantization applies, which should + be `src_dim / group_size`. + + +For example, if the shape of Key tensor is `4x8x4`, and the group size is `4`, +which means each scaling factor will be adopted for 4 times. One possible scale +would be: + +![alt text](img/grouped_scale.png) + +The usage of the new attribute will be like: + +```cpp +using namespace dnnl::graph; + +const dim K = 10, N = 20; +const size_t G = N / 2; + +dims src_dims = {K, N}; +dims scale_dims = {K, N/G}; + +// In real case, users do not have to provide the concrete shapes of logical +// tensors until compilation stage. +logical_tensor src_desc {0, data_type::u4, src_dims, layout_type::strided}; +logical_tensor scale_desc { + 1, data_type::f32, scale_dims, layout_type::strided}; +logical_tensor dst_desc {2, data_type::f32, src_dims, layout_type::strided}; + +op deq = op(0, op::kind::DynamicDequantize, {src_desc, scale_desc}, + {dst_desc}, "deq"); +// Specify the quantization type as per_group quantization. +deq.set_attr(op::attr::qtype, "per_group"); +// Group size indicates the the size of each group. +deq.set_attr(op::attr::group_size, G); + +// Create graph and add the op to the graph +graph g(dnnl::engine::kind::gpu); +g.add_op(deq); + +// ... +``` + + +#### Alternative options + +Similar to the discussion of int8 and fp8 data types, an alternative solution +may be supporting int4 data types directly in computation operations like MatMul +and Convolution. But it will bloat and complicate the opset and op schema of +oneDNN Graph. Hence it's not considered here. + +In addition, currrently oneDNN Graph does not support the direct int8 +quantization and dequantization between int8 and f16/bf16 data types. To align +with current integration flow and maintain the consistency of library design, +quantization and dequantization between int4 and f16/bf16 data types will also +be implemented with a combination of quantization ops and `TypeCast`. An +alternative solution may be to extend quantization ops to support data type +conversion directly between int4 and f16/bf16 data types. But it will +unnecessarily complicates the fusion patterns in oneDNN Graph. For example, +an int4 quantization will be expressed in two different ways: + +1. With a single `Quantize(f16 -> int4)` +2. With two operations `TypeCast(f16 -> f32) -> Quantize(f32 -> int4)` + +It will require extensions for all patterns related to mixed data type +quantization. Hence the option is not considered here. + +### Proposal 3: Extend SDPA pattern with compressed K/V + +#### Pattern definition + +With the reference to int4 GQA pattern in +[PyTorch](https://pytorch.org/blog/int4-decoding/), the proposed fusion pattern +for SDPA with compressed K/V in oneDNN Graph API is defined as: + +![alt text](img/sdpa_pattern.png) + +As illustrated in the figure above, K cache and V cache are stored on user side +in int4 or int8 data types. `DynamicDequantize` is used to convert int4 data to +f32, then an optional `TypeCast` is used to convert f32 data types to f16 or +bf16 data types when needed. Two `MatMul`s are used to compute the matrix +multiplication of Q & K, and score & V. oneDNN Graph will fuse the pattern into +a single partition with optimized CPU and GPU implementations. + +To get rid of the ambiguity of the computation type of the MatMul ops in the +pattern and give users the control of leveraging higher precision data types to +keep the model accuracy, we propose to follow the design of floating point math +mode in primitive API. Once the math mode is specified, the backend will +automatically perform either implicit down-conversion to lower-precision +floating points or up-conversion from int4/int8 to floating points, such as +f16, based on user input. For compressed SDPA, since the Key tensor will be in +integral types like int4 and `MatMul`s are expected to compute in f16/bf16, +users must explicitly set the math mode. Put it in another way, the API allows +users to control the computation type of the SDPA fusion. What's more, following +the design of floating point math mode also helps to maintain a consistent API +semantics between graph API and primitive API. + +Currently, oneDNN Graph API supports setting floating-point math mode during the +construction of graph object, which will affect the whole graph. The API is +like: + +```cpp +using namespace dnnl::graph; + +// Specify math_mode while constructing the graph +graph g(kind, math_mode); +op foo(id, kind, "foo"); g.add_op(foo); +op bar(id, kind, "bar"); g.add_op(bar); +g.finalize(); +partitions p = g.get_partitions(); + +// All the partitions from the same graph should share the same math mode +compiled_partition cp0 = p[0].compile(inputs, outputs, engine); +compiled_partition cp1 = p[1].compile(inputs, outputs, engine); +cp0.execute(…); +cp1.execute(…); +``` + +#### Alternative option + +Considering the fact that the framework users can create different graphs if +they require different floating point math mode, the current API capability is +enough for covering the user request. An alternative solution is to specify the +floating point math mode on partition. But it may require new APIs and may cause +confusion when the modes on graph and partition are incompatible. 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