Dynamic gemm/conv
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ilgeco
@@ -181,6 +181,18 @@ def conv_depthwise_grouped():
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save_model(model, "conv/depthwise_grouped", "conv_depthwise_grouped.onnx")
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def conv_dynamic():
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"""Conv with input and weight both provided at runtime."""
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X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 4, 4])
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W = helper.make_tensor_value_info("W", TensorProto.FLOAT, [1, 1, 3, 3])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 2, 2])
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node = helper.make_node("Conv", ["X", "W"], ["Y"],
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kernel_shape=[3, 3], strides=[1, 1], pads=[0, 0, 0, 0])
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graph = helper.make_graph([node], "conv_dynamic", [X, W], [Y])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "conv/dynamic", "conv_dynamic.onnx")
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# ---------------------------------------------------------------------------
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# GEMM tests
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# ---------------------------------------------------------------------------
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@@ -291,6 +303,75 @@ def gemm_transB_with_bias():
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save_model(model, "gemm/transB_with_bias", "gemm_transB_with_bias.onnx")
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def gemm_dynamic():
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"""GEMM with both matrix operands provided at runtime."""
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
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B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [8, 4])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
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node = helper.make_node("Gemm", ["A", "B"], ["Y"])
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graph = helper.make_graph([node], "gemm_dynamic", [A, B], [Y])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "gemm/dynamic", "gemm_dynamic.onnx")
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def gemm_dynamic_transB():
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"""GEMM with runtime matrix operands and transposed runtime B."""
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
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B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [4, 8])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
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node = helper.make_node("Gemm", ["A", "B"], ["Y"], transB=1)
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graph = helper.make_graph([node], "gemm_dynamic_transB", [A, B], [Y])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "gemm/dynamic_transB", "gemm_dynamic_transB.onnx")
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def gemm_dynamic_bias():
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"""GEMM with runtime matrix operands and runtime bias."""
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
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B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [8, 4])
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C = helper.make_tensor_value_info("C", TensorProto.FLOAT, [4])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
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node = helper.make_node("Gemm", ["A", "B", "C"], ["Y"])
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graph = helper.make_graph([node], "gemm_dynamic_bias", [A, B, C], [Y])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "gemm/dynamic_bias", "gemm_dynamic_bias.onnx")
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def gemm_dynamic_alpha():
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"""GEMM with runtime matrix operands and runtime alpha scaling."""
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
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B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [8, 4])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
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node = helper.make_node("Gemm", ["A", "B"], ["Y"], alpha=0.5)
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graph = helper.make_graph([node], "gemm_dynamic_alpha", [A, B], [Y])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "gemm/dynamic_alpha", "gemm_dynamic_alpha.onnx")
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def gemm_dynamic_beta():
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"""GEMM with runtime matrix operands, runtime bias, and runtime beta scaling."""
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
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B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [8, 4])
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C = helper.make_tensor_value_info("C", TensorProto.FLOAT, [4])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
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node = helper.make_node("Gemm", ["A", "B", "C"], ["Y"], beta=2.0)
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graph = helper.make_graph([node], "gemm_dynamic_beta", [A, B, C], [Y])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "gemm/dynamic_beta", "gemm_dynamic_beta.onnx")
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def gemm_dynamic_bias_alpha_beta():
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"""GEMM with runtime matrix operands, runtime bias, alpha, and beta."""
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 8])
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B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [8, 4])
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C = helper.make_tensor_value_info("C", TensorProto.FLOAT, [4])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
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node = helper.make_node("Gemm", ["A", "B", "C"], ["Y"], alpha=0.5, beta=2.0)
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graph = helper.make_graph([node], "gemm_dynamic_bias_alpha_beta", [A, B, C], [Y])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "gemm/dynamic_bias_alpha_beta", "gemm_dynamic_bias_alpha_beta.onnx")
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# ---------------------------------------------------------------------------
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# MatMul tests
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# ---------------------------------------------------------------------------
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@@ -306,6 +387,28 @@ def matmul_basic():
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save_model(model, "matmul/basic", "matmul_basic.onnx")
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def matmul_left_constant():
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"""Direct 2D MatMul with constant LHS."""
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A = numpy_helper.from_array(np.random.default_rng(69).uniform(-1, 1, (2, 3)).astype(np.float32), name="A")
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B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [3, 4])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
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node = helper.make_node("MatMul", ["A", "B"], ["Y"])
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graph = helper.make_graph([node], "matmul_left_constant", [B], [Y], initializer=[A])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "matmul/left_constant", "matmul_left_constant.onnx")
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def matmul_dynamic():
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"""Direct 2D MatMul with both operands provided at runtime."""
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A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [2, 3])
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B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [3, 4])
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Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 4])
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node = helper.make_node("MatMul", ["A", "B"], ["Y"])
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graph = helper.make_graph([node], "matmul_dynamic", [A, B], [Y])
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save_model(model, "matmul/dynamic", "matmul_dynamic.onnx")
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def matmul_batched_3d():
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"""Batched 3D MatMul with matching batch dimensions."""
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rng = np.random.default_rng(50)
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@@ -843,6 +946,12 @@ if __name__ == "__main__":
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gemm_small()
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gemm_large()
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gemm_transB_with_bias()
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gemm_dynamic()
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gemm_dynamic_transB()
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gemm_dynamic_bias()
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gemm_dynamic_alpha()
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gemm_dynamic_beta()
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gemm_dynamic_bias_alpha_beta()
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print("\nGenerating Conv tests:")
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conv_3x3_kernel()
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@@ -856,9 +965,12 @@ if __name__ == "__main__":
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conv_large_spatial()
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conv_grouped_two_groups()
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conv_depthwise_grouped()
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conv_dynamic()
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print("\nGenerating MatMul tests:")
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matmul_basic()
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matmul_left_constant()
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matmul_dynamic()
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matmul_batched_3d()
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print("\nGenerating Pooling tests:")
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