doc: graph: patterns: fix words and remove legacy graph compiler patterns

Author: TaoLv

doc/graph/fusion_patterns/fusion_patterns.md

@@ -4,13 +4,13 @@ Fusion Patterns {#dev_guide_graph_fusion_patterns}
 ## Overview
 
 The following fusion patterns are subgraphs that the oneDNN Graph API recognizes
-as candidate for fusion. The patterns are described using oneDNN Graph
+as candidates for fusion. The patterns are described using oneDNN Graph
 operation (op) names with the following convention.
 
 @note oneDNN Graph performs limited input validation to minimize the performance
 overheads. The application is responsible for sanitizing inputs passed to the
-library. For large u8 or s8 inputs may lead to accumulator overflow, you can use
-floating point patterns instead of quantized patterns.
+library. Because large `u8` or `s8` inputs may lead to accumulator overflow, you
+can use floating-point patterns instead of quantized patterns.
 
 `"+"` describes a chain of two ops. The preceding op produces an output tensor,
 which is consumed by the following op as its first operand.
@@ -35,7 +35,7 @@ the producer and consumer relation within one graph partition. For example,
 A\f$_{>t1}\f$+B+C\f$_{<t1}\f$ refers
 to the pattern started with A followed by B and C, and C takes an implicit input
 tensor from B and an extra tensor t1 output from A. `">"` refers to the output
-tensor, and `"<"` for input tensor.  Input and output tensor between neighbor
+tensor, and `"<"` for input tensor.  Input and output tensors between neighbor
 ops are not explicitly marked, for example, B consumes t1 implicitly in the
 example above.
 
@@ -55,7 +55,7 @@ inputs. In the example A\f$_{>t1}\f$+B+C\f$_{<t1}\f$, A's inputs are
 regarded as implicit graph partition inputs, and if B is a binary operation, the
 second input tensor is an implicit graph partition input.
 
-The following categories will be used in describing fusion pattern.
+The following categories will be used in describing a fusion pattern.
 
 Unary = [Abs | Clamp | Elu | Exp | GELU | HardSwish | LeakyReLU |
 Log | Sigmoid | SoftPlus | Pow | ReLU | Round | Sqrt | Square | Tanh]
@@ -105,56 +105,3 @@ ReduceProd | ReduceSum]
 |:--------|:-----------------------------|
 | ConvolutionBackwardWeights + BiasAddBackward\f$_{>out}\f$ | N/A |
 | ReLUBackward + BatchNormTrainingBackward\f$_{>out}\f$ |N/A |
-
-All the above fusion patterns are supported by default.
-
-## Aggressive Fusion Patterns
-Aggressive fusion patterns also follow the pattern description convention
-defined in the [Fusion Patterns](@ref fusion_patterns) section.
-
-@note Aggressive fusion patterns are only supported when
-[Graph Compiler](@ref dev_guide_graph_compiler) is enabled.
-
-The following categories will also be used to describe aggressive fusion
-patterns.
-
-- ReshapeTranspose = [StaticReshape + StaticTranspose\f$^{1-2}\f$]
-
-- Activation = [ReLU \| Sigmoid \| GELU]
-
-- ActivationBackward = [ReLUBackward \| SigmoidBackward \| GELUBackward]
-
-### Inference
-
-#### Floating Point Patterns
-
-| Pattern | Description                  |
-|:--------|:-----------------------------|
-| MatMul + [Multiply \| Divide] + Add + Softmax + MatMul + StaticTranspose + Reorder\f$_{>out}\f$ | Multi-head Attention. This pattern is widely used in models containing encoder-decoder structures, for example BERT. |
-| ReshapeTranspose\f$_{>t1}\f$, ReshapeTranspose\f$_{>t2}\f$, ReshapeTranspose + MatMul\f$_{<t1}\f$ + [Multiply \| Divide] + Add + Softmax + MatMul\f$_{<t2}\f$ + StaticTranspose + StaticReshape\f$_{>out}\f$ | Multi-head Attention. |
-| MatMul + Activation\f$_{>t1}\f$, [MatMul\f$_{<t1}\f$ + Activation\f$_{>t1}\f$]\f$^{0-4}\f$, MatMul\f$_{<t1}\f$ + Activation\f$_{>out}\f$ | Multi-layer Perceptron. This pattern is widely used in recommendation models, for example DLRM. |
-| [Convolution + BiasAdd\f$^{?}\f$ + ReLU]\f$^{1-3}\f$ + Convolution + BiasAdd\f$^{?}\f$ + Add + ReLU\f$_{>out}\f$ | Identical Bottleneck. Enabled only in single thread runtime scenario. This pattern is widely used in Convolution Neural Networks, for example ResNet. |
-| Convolution + BiasAdd\f$^{?}\f$\f$_{>t1}\f$, [Convolution + BiasAdd\f$^{?}\f$ + ReLU]\f$^{1-3}\f$ + Convolution + BiasAdd\f$^{?}\f$ + Add\f$_{<t1}\f$ + ReLU\f$_{>out}\f$ | Convolutional Bottleneck. Enabled only in single thread runtime scenario. This pattern is widely used in Convolution Neural Networks, for example ResNet. |
-
-#### Quantized Patterns
-
-| Pattern | Description                  |
-|:--------|:-----------------------------|
-| Dequantize\f$_{>t1}\f$, Dequantize\f$_{>t2}\f$, Dequantize + MatMul\f$_{<t1}\f$ + [Multiply \| Divide] + Add + Softmax + Quantize + Dequantize + MatMul\f$_{<t2}\f$ + StaticTranspose + Reorder + Quantize\f$_{>out}\f$ | Quantized Multi-head Attention. |
-| Dequantize + ReshapeTranspose\f$_{>t1}\f$, Dequantize + ReshapeTranspose\f$_{>t2}\f$, Dequantize + MatMul\f$_{<t1}\f$ + [Multiply \| Divide] + Add + Softmax + Quantize + Dequantize + MatMul\f$_{<t2}\f$ + StaticTranspose + StaticReshape + Quantize\f$_{>out}\f$ | Quantized Multi-head Attention. |
-| Dequantize\f$_{>t1}\f$, Dequantize + MatMul\f$_{<t1}\f$ + Activation + Quantize\f$_{>t2}\f$, [Dequantize\f$_{>t3}\f$, Dequantize\f$_{<t2}\f$ + MatMul\f$_{<t3}\f$ + Activation + Quantize\f$_{>t2}\f$]\f$^{0-4}\f$, Dequantize\f$_{>t4}\f$, Dequantize\f$_{<t2}\f$ + MatMul\f$_{<t4}\f$ + Activation + Quantize\f$_{>out}\f$ | Quantized Multi-layer Perceptron. |
-| Dequantize\f$_{>t2}\f$, Dequantize\f$_{>t3}\f$, [Dequantize\f$_{>t1}\f$, Dequantize + Convolution\f$_{<t1}\f$ + BiasAdd\f$^{?}\f$ + ReLU + Quantize]\f$^{1-3}\f$ + Dequantize + Convolution\f$_{<t2}\f$ + BiasAdd\f$^{?}\f$ + Add\f$_{<t3}\f$ + ReLU + Quantize\f$_{>out}\f$ | Quantized Identical Bottleneck. Enabled only in single thread runtime scenario. |
-| [Dequantize\f$_{>t1}\f$, Dequantize + Convolution\f$_{<t1}\f$ + BiasAdd\f$^{?}\f$ + Quantize + Dequantize]\f$_{>t2}\f$, Dequantize\f$_{>t4}\f$, [Dequantize\f$_{>t3}\f$, Dequantize + Convolution\f$_{<t3}\f$ + BiasAdd\f$^{?}\f$ + ReLU + Quantize]\f$^{1-3}\f$ + Dequantize + Convolution\f$_{<t4}\f$ + BiasAdd\f$^{?}\f$ + Add\f$_{<t2}\f$ + ReLU + Quantize\f$_{>out}\f$ | Quantized Convolutional Bottleneck. Enabled only in single thread runtime scenario. |
-
-### Training
-
-| Pattern | Description                  |
-|:--------|:-----------------------------|
-| Dequantize\f$_{>t1}\f$, Dequantize\f$_{>t2}\f$, Dequantize + MatMul\f$_{<t1}\f$ + [Multiply \| Divide] + Add + Softmax + Quantize + Dequantize + MatMul\f$_{<t2}\f$ + StaticTranspose + Reorder + Quantize\f$_{>out}\f$ | Multi-head Attention Training Forward Pattern. |
-| StaticReshape + StaticTranspose\f$_{>t1}\f$ + MatMul + Multiply\f$_{>t2}\f$ + Subtract\f$_{<t3}\f$ + Multiply\f$^{?}\f$ + [Multiply \| Divide]\f$_{>t4}\f$ + MatMul\f$_{>out1}\f$, Multiply\f$_{<t2}\f$ + ReduceSum\f$_{>t3}\f$, MatMul\f$_{<t1,>out2}\f$, MatMul\f$_{<t4,>out3}\f$ | Multi-head Attention Training Backward Pattern. |
-| MatMul\f$_{>out1}\f$ + Activation\f$_{>t1,>out2}\f$, [MatMul\f$_{<t1,>out3}\f$ + Activation\f$_{>t1,>out4}\f$]\f$^{0-4}\f$, MatMul\f$_{<t1,>out5}\f$ + Activation\f$_{>out6}\f$ | Multi-layer Perceptron Training Forward Pattern. |
-| StaticTranspose\f$^{?}\f$\f$_{>t0}\f$, ActivationBackward\f$_{>t2}\f$ + MatMul\f$_{<t0,>t1}\f$, ReduceSum\f$^{?}\f$\f$_{<t2,>out1}\f$, StaticTranspose\f$^{?}\f$ + MatMul\f$_{<t2,>out2}\f$, [StaticTranspose\f$^{?}\f$\f$_{>t3}\f$, ActivationBackward\f$_{>t4,<t1}\f$ + MatMul\f$_{<t3,>t1}\f$, ReduceSum\f$^{?}\f$\f$_{<t4,>out3}\f$, StaticTranspose\f$^{?}\f$ + MatMul\f$_{<t4,>out4}\f$]\f$^{0-4}\f$, StaticTranspose\f$^{?}\f$\f$_{>t5}\f$, ActivationBackward\f$_{>t6,<t1}\f$ + MatMul\f$_{<t5,>out5}\f$, ReduceSum\f$^{?}\f$\f$_{<t6,>out6}\f$, StaticTranspose\f$^{?}\f$ + MatMul\f$_{<t6,>out7}\f$ | Multi-layer Perceptron Training Backward Pattern. |
-| Convolution\f$_{>out1}\f$ + BatchNormForwardTraining\f$_{>out2}\f$ + ReLU\f$_{>out3}\f$ + Convolution\f$_{>out4}\f$ + BatchNormForwardTraining\f$_{>out5}\f$ + ReLU\f$_{>out6}\f$ + Convolution\f$_{>out7}\f$ + BatchNormForwardTraining\f$_{>out8}\f$ + Add + ReLU\f$_{>out9}\f$ | Identical Bottleneck Training Forward Pattern. |
-| Convolution\f$_{>out1}\f$ + BatchNormForwardTraining\f$_{>t1,>out2}\f$, Convolution\f$_{>out3}\f$ + BatchNormForwardTraining\f$_{>out4}\f$ + ReLU\f$_{>out5}\f$ + Convolution\f$_{>out6}\f$ + BatchNormForwardTraining\f$_{>out7}\f$ + ReLU\f$_{>out8}\f$ + Convolution\f$_{>out9}\f$ + BatchNormForwardTraining\f$_{>out10}\f$ + Add\f$_{<t1}\f$ + ReLU\f$_{>out11}\f$ | Convolutional Bottleneck Training Forward Pattern. |
-| ReLUBackward\f$_{>t1}\f$ + BatchNormTrainingBackward\f$_{>t2,>out1}\f$ + ConvolutionBackwardData + ReLUBackward + BatchNormTrainingBackward\f$_{>t3,>out2}\f$ + ConvolutionBackwardData + ReLUBackward + BatchNormTrainingBackward\f$_{>t4,>out3}\f$ + ConvolutionBackwardData + Add\f$_{<t1,>out4}\f$, ConvolutionBackwardWeights\f$_{<t2,>out5}\f$, ConvolutionBackwardWeights\f$_{<t3,>out6}\f$, ConvolutionBackwardWeights\f$_{<t4,>out7}\f$ | Identical Bottleneck Training Backward Pattern. |
-| ReLUBackward\f$_{>t1}\f$ + BatchNormTrainingBackward\f$_{>t2,>out1}\f$ + ConvolutionBackwardData + ReLUBackward + BatchNormTrainingBackward\f$_{>t3,>out2}\f$ + ConvolutionBackwardData + ReLUBackward + BatchNormTrainingBackward\f$_{>t4,>out3}\f$ + ConvolutionBackwardData + Add\f$_{<t6,>out4}\f$, BatchNormTrainingBackward\f$_{<t1,>t5,>out5}\f$ + ConvolutionBackwardData\f$_{>t6}\f$, ConvolutionBackwardWeights\f$_{<t2,>out6}\f$, ConvolutionBackwardWeights\f$_{<t3,>out7}\f$, ConvolutionBackwardWeights\f$_{<t4,>out8}\f$, ConvolutionBackwardWeights\f$_{<t5,>out9}\f$ | Convolutional Bottleneck Training Backward Pattern. |