import torch
import numpy as np
import torch_npu
from torch_npu.testing.testcase import TestCase, run_tests
from torch_npu.testing.common_utils import create_common_tensor
class TestAddRelu(TestCase):
def cpu_op_exec(self, input1, input2):
output = torch._VF._add_relu(input1, input2, alpha=1)
output = output.numpy()
return output
def npu_op_exec_new(self, input1, input2):
output = torch._VF._add_relu(input1, input2, alpha=1)
output = output.to("cpu")
output = output.numpy()
return output
def npu_op_exec_new_out(self, input1, input2, output):
torch._VF._add_relu(input1, input2, out=output, alpha=1)
output = output.to("cpu")
output = output.numpy()
return output
def cpu_op_inp_exec(self, input1, input2):
output = torch._VF._add_relu_(input1, input2, alpha=1)
output = output.numpy()
return output
def npu_op_inp_exec(self, input1, input2):
output = torch._VF._add_relu_(input1, input2, alpha=1)
output = output.to("cpu")
output = output.numpy()
return output
def cpu_op_exec_alpha(self, input1, input2):
output = torch._VF._add_relu(input1, input2, alpha=3)
output = output.numpy()
return output
def npu_op_exec_new_alpha(self, input1, input2):
output = torch._VF._add_relu(input1, input2, alpha=3)
output = output.to("cpu")
output = output.numpy()
return output
def add_relu_result(self, shape_format):
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item, 0, 100)
cpu_input2, npu_input2 = create_common_tensor(item, 0, 100)
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
cpu_input2 = cpu_input2.to(torch.float32)
cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2)
npu_output = self.npu_op_exec_new(npu_input1, npu_input2)
cpu_output = cpu_output.astype(npu_output.dtype)
self.assertRtolEqual(cpu_output, npu_output)
def add_relu_out_result(self, shape_format):
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item, 0, 100)
cpu_input2, npu_input2 = create_common_tensor(item, 0, 100)
cpu_input3, npu_input3 = create_common_tensor(item, 0, 100)
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
cpu_input2 = cpu_input2.to(torch.float32)
cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2)
npu_output = self.npu_op_exec_new_out(npu_input1, npu_input2, npu_input3)
cpu_output = cpu_output.astype(npu_output.dtype)
self.assertRtolEqual(cpu_output, npu_output)
def add_relu_inp_result(self, shape_format):
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item, 0, 100)
cpu_input2, npu_input2 = create_common_tensor(item, 0, 100)
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
cpu_input2 = cpu_input2.to(torch.float32)
cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2)
npu_output = self.npu_op_inp_exec(npu_input1, npu_input2)
cpu_output = cpu_output.astype(npu_output.dtype)
self.assertRtolEqual(cpu_output, npu_output)
def add_relu_alpha_result(self, shape_format):
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item, 0, 100)
cpu_input2, npu_input2 = create_common_tensor(item, 0, 100)
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
cpu_input2 = cpu_input2.to(torch.float32)
cpu_output = self.cpu_op_exec_alpha(cpu_input1, cpu_input2)
npu_output = self.npu_op_exec_new_alpha(npu_input1, npu_input2)
cpu_output = cpu_output.astype(npu_output.dtype)
self.assertRtolEqual(cpu_output, npu_output)
def test_add_relu_shape_format_fp32_1d(self):
format_list = [0, 3]
shape_format = [[np.float32, i, [64]] for i in format_list]
self.add_relu_result(shape_format)
def test_add_relu_shape_format_fp16_2d(self):
format_list = [0, 3, 29]
shape_format = [[np.float16, i, [5, 256]] for i in format_list]
self.add_relu_result(shape_format)
def test_add_relu_shape_format_fp32_2d(self):
format_list = [0, 3, 29]
shape_format = [[np.float32, i, [5, 256]] for i in format_list]
self.add_relu_result(shape_format)
def test_add_relu_shape_format_fp16_3d(self):
format_list = [0, 3, 29]
shape_format = [[np.float16, i, [32, 3, 3]] for i in format_list]
self.add_relu_result(shape_format)
def test_add_relu_shape_format_fp32_3d(self):
format_list = [0, 3, 29]
shape_format = [[np.float32, i, [32, 3, 3]] for i in format_list]
self.add_relu_result(shape_format)
def test_add_relu_shape_format_fp16_4d(self):
format_list = [0, 3, 29]
shape_format = [[np.float16, i, [64, 112, 7, 7]] for i in format_list]
self.add_relu_result(shape_format)
def test_add_relu_shape_format_fp32_4d(self):
format_list = [0, 3, 29]
shape_format = [[np.float32, i, [64, 112, 7, 7]] for i in format_list]
self.add_relu_result(shape_format)
def test_add_relu_out_shape_format_fp32_1d(self):
format_list = [0, 3]
shape_format = [[np.float32, i, [64]] for i in format_list]
self.add_relu_out_result(shape_format)
def test_add_relu_out_shape_format_fp16_2d(self):
format_list = [0, 3, 29]
shape_format = [[np.float16, i, [5, 256]] for i in format_list]
self.add_relu_out_result(shape_format)
def test_add_relu_inp_shape_format_fp32_2d(self):
format_list = [0, 3, 29]
shape_format = [[np.float32, i, [5, 256]] for i in format_list]
self.add_relu_inp_result(shape_format)
def test_add_relu_inp_shape_format_fp16_3d(self):
format_list = [0, 3, 29]
shape_format = [[np.float16, i, [32, 3, 3]] for i in format_list]
self.add_relu_inp_result(shape_format)
def test_add_relu_inp_shape_format_fp32_3d(self):
format_list = [0, 3, 29]
shape_format = [[np.float32, i, [32, 3, 3]] for i in format_list]
self.add_relu_inp_result(shape_format)
def test_add_relu_inp_shape_format_fp16_4d(self):
format_list = [0, 3, 29]
shape_format = [[np.float16, i, [64, 112, 7, 7]] for i in format_list]
self.add_relu_inp_result(shape_format)
def test_add_relu_inp_shape_format_fp32_4d(self):
format_list = [0, 3, 29]
shape_format = [[np.float32, i, [64, 112, 7, 7]] for i in format_list]
self.add_relu_inp_result(shape_format)
def test_add_relu_shape_format_fp16_1d(self):
format_list = [0, 3]
shape_format = [[np.float16, i, [64]] for i in format_list]
self.add_relu_alpha_result(shape_format)
def test_add_relu_alpha_shape_format_fp32_1d(self):
format_list = [0, 3]
shape_format = [[np.float32, i, [64]] for i in format_list]
self.add_relu_alpha_result(shape_format)
def test_add_relu_alpha_shape_format_fp16_2d(self):
format_list = [0, 3, 29]
shape_format = [[np.float16, i, [5, 256]] for i in format_list]
self.add_relu_alpha_result(shape_format)
def test_add_relu_alpha_shape_format_fp32_2d(self):
format_list = [0, 3, 29]
shape_format = [[np.float32, i, [5, 256]] for i in format_list]
self.add_relu_alpha_result(shape_format)
def test_add_relu_alpha_shape_format_fp16_3d(self):
format_list = [0, 3, 29]
shape_format = [[np.float16, i, [32, 3, 3]] for i in format_list]
self.add_relu_alpha_result(shape_format)
def test_add_relu_alpha_shape_format_fp32_3d(self):
format_list = [0, 3, 29]
shape_format = [[np.float32, i, [32, 3, 3]] for i in format_list]
self.add_relu_alpha_result(shape_format)
def test_add_relu_alpha_shape_format_fp16_4d(self):
format_list = [0, 3, 29]
shape_format = [[np.float16, i, [64, 112, 7, 7]] for i in format_list]
self.add_relu_alpha_result(shape_format)
def test_add_relu_alpha_shape_format_fp32_4d(self):
format_list = [0, 3, 29]
shape_format = [[np.float32, i, [64, 112, 7, 7]] for i in format_list]
self.add_relu_alpha_result(shape_format)
if __name__ == "__main__":
run_tests()