[Test only] BFloat16 test for SkipSimplifiedLayerNormalization #22941
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jiafatom
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| skip_size); | ||
| } | ||
| else | ||
| { | ||
| LaunchSkipLayerNormKernel<CudaT, Simplified>( |
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| skip_size); | |
| } | |
| else | |
| { | |
| LaunchSkipLayerNormKernel<CudaT, Simplified>( | |
| skip_size); | |
| } else { | |
| LaunchSkipLayerNormKernel<CudaT, Simplified>( |
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| import tempfile | ||
| from typing import Dict | ||
| from enum import Enum | ||
|
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| import tempfile | |
| from typing import Dict | |
| from enum import Enum | |
| import tempfile | |
| from enum import Enum | |
| from typing import Dict | |
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| from onnx import AttributeProto, GraphProto, ModelProto, NodeProto, TensorProto, helper, numpy_helper | ||
| from onnx.shape_inference import infer_shapes, infer_shapes_path | ||
| from onnx.helper import float32_to_bfloat16 | ||
| from packaging import version |
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| from onnx import AttributeProto, GraphProto, ModelProto, NodeProto, TensorProto, helper, numpy_helper | |
| from onnx.shape_inference import infer_shapes, infer_shapes_path | |
| from onnx.helper import float32_to_bfloat16 | |
| from packaging import version | |
| from onnx import AttributeProto, GraphProto, ModelProto, NodeProto, TensorProto, helper, numpy_helper | |
| from onnx.helper import float32_to_bfloat16 | |
| from onnx.shape_inference import infer_shapes, infer_shapes_path | |
| from packaging import version |
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| def convert_np_to_float16(np_array, min_positive_val=5.96e-08, max_finite_val=65504.0): |
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| def convert_np_to_float16(np_array, min_positive_val=5.96e-08, max_finite_val=65504.0): | |
| def convert_np_to_float16(np_array, min_positive_val=5.96e-08, max_finite_val=65504.0): |
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| def convert_tensor_float_to_bfloat16(tensor): |
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| def convert_tensor_float_to_bfloat16(tensor): | |
| def convert_tensor_float_to_bfloat16(tensor): |
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| class NodeValueType(Enum): | ||
| FP32 = 1 |
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| class NodeValueType(Enum): | |
| FP32 = 1 | |
| class NodeValueType(Enum): |
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| class InitializerTracker: | ||
| """Class for keeping track of initializer.""" |
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| class InitializerTracker: | |
| """Class for keeping track of initializer.""" | |
| class InitializerTracker: |
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| def convert_float_to_float16( | ||
| model, |
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| def convert_float_to_float16( | |
| model, | |
| def convert_float_to_float16( |
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| # Some operators have data type fixed as float for some input. Add a float16 to float cast for those inputs. | ||
| for node in mixed_float_type_node_list: |
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| # Some operators have data type fixed as float for some input. Add a float16 to float cast for those inputs. | |
| for node in mixed_float_type_node_list: | |
| # Some operators have data type fixed as float for some input. Add a float16 to float cast for those inputs. |
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| import tempfile | ||
| from typing import Dict | ||
| from enum import Enum | ||
| import ml_dtypes | ||
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| import numpy as np |
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| import tempfile | |
| from typing import Dict | |
| from enum import Enum | |
| import ml_dtypes | |
| import numpy as np | |
| import tempfile | |
| from enum import Enum | |
| from typing import Dict | |
| import ml_dtypes | |
| import numpy as np |
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| skip_size); | ||
| } | ||
| else | ||
| { | ||
| LaunchSkipLayerNormKernel<CudaT, Simplified>( |
There was a problem hiding this comment.
Suggested change
| skip_size); | |
| } | |
| else | |
| { | |
| LaunchSkipLayerNormKernel<CudaT, Simplified>( | |
| skip_size); | |
| } else { | |
| LaunchSkipLayerNormKernel<CudaT, Simplified>( |
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