tf.keras.layers.MultiHeadAttention(
num_heads,
key_dim,
value_dim=None,
dropout=0.0,
use_bias=True,
output_shape=None,
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| import tensorflow as tf | |
| from tensorflow.python.pywrap_mlir import import_graphdef | |
| from tensorflow import keras | |
| from tensorflow.keras import layers | |
| from tensorflow.python.compiler.mlir import mlir | |
| from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 | |
| model = layers.MultiHeadAttention(num_heads=2, key_dim=2) | |
| target = tf.keras.Input(shape=[8, 16], batch_size=64, dtype=tf.float32) | |
| source = tf.keras.Input(shape=[4, 16], batch_size=64, dtype=tf.float32) |
chelini
/ bert-tiny.mlir
Created
June 6, 2023 07:23
BertForMaskedLM taken from torch-mlir example: `examples/torchscript_stablehlo_backend_tinybert.py`
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| #map = affine_map<(d0, d1) -> (0, d1)> | |
| #map1 = affine_map<(d0, d1) -> (d0, d1)> | |
| #map2 = affine_map<(d0, d1, d2, d3) -> (d0, 0, 0, d3)> | |
| #map3 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> | |
| #map4 = affine_map<(d0, d1, d2, d3) -> ()> | |
| #map5 = affine_map<(d0, d1, d2) -> (d0, d1)> | |
| #map6 = affine_map<(d0, d1, d2) -> (d0, d1, d2)> | |
| #map7 = affine_map<(d0, d1, d2) -> (0, d1, d2)> | |
| #map8 = affine_map<(d0, d1, d2) -> (d0, d1, 0)> | |
| #map9 = affine_map<(d0, d1, d2) -> (d2)> |
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| import tensorflow as tf | |
| from tensorflow.python.pywrap_mlir import import_graphdef | |
| from tensorflow import keras | |
| from tensorflow.keras import layers | |
| class DotProductAttention(keras.layers.Layer): | |
| def __init__(self, use_scale=True, **kwargs): | |
| super(DotProductAttention, self).__init__(**kwargs) | |
| self.use_scale = use_scale |
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| #map2 = affine_map<(d0, d1, d2) -> (d0, d1)> | |
| #map3 = affine_map<(d0, d1, d2) -> (d0, d1, d2)> | |
| func.func @main(%arg0 : tensor<1x384xi32>) -> tensor<1x384x384xf32> { | |
| %c1 = arith.constant 1 : index | |
| %c0 = arith.constant 0 : index | |
| %cst_607 = arith.constant 0.000000e+00 : f32 | |
| %cst_608 = arith.constant dense<0.00165016507> : tensor<1x30522x128xf32> | |
| %10 = tensor.empty() : tensor<1x384x128xf32> |
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| #map = affine_map<(d0, d1) -> (d1)> | |
| #map1 = affine_map<(d0, d1) -> (d0, d1)> | |
| module { | |
| func.func @main(%arg0: tensor<32x384xf32>, %arg1: tensor<384x32xf32>, %arg2: tensor<32x384xf32>, %arg3: tensor<32x384xf32>, %arg4: tensor<32x384xf32>, %arg5: tensor<1x384x32xf32>, %arg6: tensor<1x384x32xf32>, %arg7: tensor<1x384x32xf32>) -> tensor<1536x384xf32> { | |
| %cst = arith.constant 0.000000e+00 : f32 | |
| %cst_0 = arith.constant dense<0.00164203614> : tensor<384xf32> | |
| %0 = tensor.empty() : tensor<384x384xf32> | |
| %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<384x384xf32>) -> tensor<384x384xf32> | |
| %2 = linalg.matmul ins(%arg1, %arg2 : tensor<384x32xf32>, tensor<32x384xf32>) outs(%1 : tensor<384x384xf32>) -> tensor<384x384xf32> | |
| %3 = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel"]} ins(%cst_0 : tensor<384xf32>) outs(%2 : tensor<384x384xf32>) { |
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| #map = affine_map<(d0, d1) -> (d0, d1)> | |
| #map1 = affine_map<(d0, d1, d2) -> (d0, d2)> | |
| #map2 = affine_map<(d0, d1, d2) -> (d0, d1)> | |
| #map3 = affine_map<(d0, d1, d2) -> (d0, d1, d2)> | |
| #map4 = affine_map<(d0, d1, d2, d3) -> (d0, d1)> | |
| #map5 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)> | |
| #map6 = affine_map<(d0, d1) -> (d1)> | |
| #map7 = affine_map<(d0, d1, d2, d3) -> (d0, d2, d1, d3)> | |
| #map8 = affine_map<(d0, d1, d2, d3) -> (d0, d3, d1, d2)> | |
| #map9 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d3, d2)> |
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