jinhuang415 commented on a change in pull request #10433: [MXNET-290] MKLDNN
support for model quantization
URL: https://github.com/apache/incubator-mxnet/pull/10433#discussion_r193410162
##########
File path: tests/python/quantization/test_quantization.py
##########
@@ -120,114 +128,126 @@ def check_requantize(shape, min_calib_range=None,
max_calib_range=None):
@with_seed()
def test_quantized_conv():
- if mx.current_context().device_type != 'gpu':
- print('skipped testing quantized_conv on cpu since it is not
implemented yet')
+ if is_test_for_naive_cpu():
+ print('skipped testing quantized_conv for naive cpu since it is not
implemented yet')
return
- def check_quantized_conv(data_shape, kernel, num_filter, pad, stride,
no_bias):
- with mx.Context('gpu', 0):
- # run fp32 conv
- data = mx.sym.Variable(name='data', shape=data_shape,
dtype='float32')
- conv2d = mx.sym.Convolution(data=data, kernel=kernel,
num_filter=num_filter, pad=pad, stride=stride,
- no_bias=no_bias, cudnn_off=False,
name='conv2d')
- arg_shapes, _, _ = conv2d.infer_shape(data=data_shape)
- arg_names = conv2d.list_arguments()
- conv_exe_fp32 = conv2d.simple_bind(ctx=mx.current_context(),
grad_req='null')
- conv_exe_fp32.arg_dict[arg_names[0]][:] =
mx.nd.random.uniform(low=-127.0, high=127.0,
-
shape=data_shape).astype('int32')
- conv_exe_fp32.arg_dict[arg_names[1]][:] =
mx.nd.random.uniform(low=-127.0, high=127.0,
-
shape=arg_shapes[1]).astype('int32')
- if not no_bias:
- conv_exe_fp32.arg_dict[arg_names[2]][:] =
mx.nd.random.uniform(low=-127.0, high=127.0,
-
shape=arg_shapes[2]).astype('int32')
- output = conv_exe_fp32.forward()[0]
-
- # run quantized conv
- qdata = mx.sym.Variable(name='qdata', shape=data_shape,
dtype='int8')
- qweight = mx.sym.Variable(name='qweight', dtype='int8')
- min_data = mx.sym.Variable(name='min_data')
- max_data = mx.sym.Variable(name='max_data')
- min_weight = mx.sym.Variable(name='min_weight')
- max_weight = mx.sym.Variable(name='max_weight')
- quantized_conv2d = mx.sym.contrib.quantized_conv(data=qdata,
weight=qweight, min_data=min_data,
-
max_data=max_data, min_weight=min_weight,
-
max_weight=max_weight, kernel=kernel,
-
num_filter=num_filter, pad=pad, stride=stride,
- no_bias=no_bias)
- qarg_names = quantized_conv2d.list_arguments()
- type_dict = None
- if not no_bias:
- type_dict = {qarg_names[2]: 'int8'}
- conv_exe_int8 =
quantized_conv2d.simple_bind(ctx=mx.current_context(), type_dict=type_dict,
grad_req='null')
- conv_exe_int8.arg_dict[qarg_names[0]][:] =
conv_exe_fp32.arg_dict[arg_names[0]].astype('int8')
- conv_exe_int8.arg_dict[qarg_names[1]][:] =
conv_exe_fp32.arg_dict[arg_names[1]].astype('int8')
- quantized_range = 127.0
- if no_bias:
- conv_exe_int8.arg_dict[qarg_names[2]][:] = -quantized_range
- conv_exe_int8.arg_dict[qarg_names[3]][:] = quantized_range
- conv_exe_int8.arg_dict[qarg_names[4]][:] = -quantized_range
- conv_exe_int8.arg_dict[qarg_names[5]][:] = quantized_range
- else:
- conv_exe_int8.arg_dict[qarg_names[2]][:] =
conv_exe_fp32.arg_dict[arg_names[2]].astype('int8')
- conv_exe_int8.arg_dict[qarg_names[3]][:] = -quantized_range
- conv_exe_int8.arg_dict[qarg_names[4]][:] = quantized_range
- conv_exe_int8.arg_dict[qarg_names[5]][:] = -quantized_range
- conv_exe_int8.arg_dict[qarg_names[6]][:] = quantized_range
- conv_exe_int8.arg_dict[qarg_names[7]][:] = -quantized_range
- conv_exe_int8.arg_dict[qarg_names[8]][:] = quantized_range
- qoutput, min_range, max_range = conv_exe_int8.forward()
+ if is_test_for_mkldnn():
+ dtype = 'uint8'
+ shift = 127
+ else:
+ dtype = 'int8'
+ shift = 0
- if no_bias:
- assert_almost_equal(output.asnumpy(), qoutput.asnumpy())
- else:
- # with adding bias, accuracy loss should not be greater than
one
- diff = mx.nd.abs(output - qoutput.astype(output.dtype))
- cond = mx.nd.lesser(2, diff).sum().asscalar()
- assert cond == 0
+ def check_quantized_conv(data_shape, kernel, num_filter, pad, stride,
no_bias):
+ # run fp32 conv
+ data = mx.sym.Variable(name='data', shape=data_shape, dtype='float32')
+ conv2d = mx.sym.Convolution(data=data, kernel=kernel,
num_filter=num_filter, pad=pad, stride=stride,
+ no_bias=no_bias, cudnn_off=False,
name='conv2d')
+ arg_shapes, _, _ = conv2d.infer_shape(data=data_shape)
+ arg_names = conv2d.list_arguments()
+ conv_exe_fp32 = conv2d.simple_bind(ctx=mx.current_context(),
grad_req='null')
+ conv_exe_fp32.arg_dict[arg_names[0]][:] =
mx.nd.random.uniform(low=-127.0 + shift, high=127.0,
+
shape=data_shape).astype('int32')
+ conv_exe_fp32.arg_dict[arg_names[1]][:] =
mx.nd.random.uniform(low=-127.0, high=127.0,
+
shape=arg_shapes[1]).astype('int32')
+ if not no_bias:
+ conv_exe_fp32.arg_dict[arg_names[2]][:] =
mx.nd.random.uniform(low=-127.0, high=127.0,
+
shape=arg_shapes[2]).astype('int32')
+ output = conv_exe_fp32.forward()[0]
+
+ # run quantized conv
+ qdata = mx.sym.Variable(name='qdata', shape=data_shape, dtype=dtype)
+ qweight = mx.sym.Variable(name='qweight', dtype='int8')
+ min_data = mx.sym.Variable(name='min_data')
+ max_data = mx.sym.Variable(name='max_data')
+ min_weight = mx.sym.Variable(name='min_weight')
+ max_weight = mx.sym.Variable(name='max_weight')
+ quantized_conv2d = mx.sym.contrib.quantized_conv(data=qdata,
weight=qweight, min_data=min_data,
+ max_data=max_data,
min_weight=min_weight,
+
max_weight=max_weight, kernel=kernel,
+
num_filter=num_filter, pad=pad, stride=stride,
+ no_bias=no_bias)
+ qarg_names = quantized_conv2d.list_arguments()
+ type_dict = None
+ if not no_bias:
+ type_dict = {qarg_names[2]: 'int8'}
+ conv_exe_int8 = quantized_conv2d.simple_bind(ctx=mx.current_context(),
type_dict=type_dict, grad_req='null')
+ conv_exe_int8.arg_dict[qarg_names[0]][:] =
conv_exe_fp32.arg_dict[arg_names[0]].astype(dtype)
+ conv_exe_int8.arg_dict[qarg_names[1]][:] =
conv_exe_fp32.arg_dict[arg_names[1]].astype('int8')
+ quantized_range = 127.0
+ if no_bias:
+ conv_exe_int8.arg_dict[qarg_names[2]][:] = -quantized_range
+ conv_exe_int8.arg_dict[qarg_names[3]][:] = quantized_range
+ conv_exe_int8.arg_dict[qarg_names[4]][:] = -quantized_range
+ conv_exe_int8.arg_dict[qarg_names[5]][:] = quantized_range
+ else:
+ conv_exe_int8.arg_dict[qarg_names[2]][:] =
conv_exe_fp32.arg_dict[arg_names[2]].astype('int8')
+ conv_exe_int8.arg_dict[qarg_names[3]][:] = -quantized_range
+ conv_exe_int8.arg_dict[qarg_names[4]][:] = quantized_range
+ conv_exe_int8.arg_dict[qarg_names[5]][:] = -quantized_range
+ conv_exe_int8.arg_dict[qarg_names[6]][:] = quantized_range
+ conv_exe_int8.arg_dict[qarg_names[7]][:] = -quantized_range
+ conv_exe_int8.arg_dict[qarg_names[8]][:] = quantized_range
+ qoutput, min_range, max_range = conv_exe_int8.forward()
+
+ if no_bias:
+ assert_almost_equal(output.asnumpy(), qoutput.asnumpy())
+ else:
+ # with adding bias, accuracy loss should not be greater than one
+ diff = mx.nd.abs(output - qoutput.astype(output.dtype))
+ cond = mx.nd.lesser(2, diff).sum().asscalar()
+ assert cond == 0
check_quantized_conv((3, 4, 28, 28), (3, 3), 128, (1, 1), (1, 1), True)
check_quantized_conv((3, 4, 28, 28), (3, 3), 128, (1, 1), (1, 1), False)
@with_seed()
def test_quantized_pooling():
- if mx.current_context().device_type != 'gpu':
- print('skipped testing quantized_pooling on cpu since it is not
implemented yet')
+ if is_test_for_naive_cpu():
+ print('skipped testing quantized_pooling for naive cpu since it is not
implemented yet')
return
- def check_quantized_pooling(data_shape, kernel, pool_type, pad, stride,
global_pool):
- with mx.Context('gpu', 0):
- data = mx.sym.Variable(name='data', shape=data_shape,
dtype='float32')
- pooling_fp32 = mx.sym.Pooling(data=data, kernel=kernel, pad=pad,
stride=stride,
- pool_type=pool_type,
global_pool=global_pool, cudnn_off=False)
- arg_shapes, _, _ = pooling_fp32.infer_shape(data=data_shape)
- arg_names = pooling_fp32.list_arguments()
- pooling_fp32_exe =
pooling_fp32.simple_bind(ctx=mx.current_context(), grad_req='null')
- pooling_fp32_exe.arg_dict[arg_names[0]][:] =
mx.nd.random.uniform(low=-127.0, high=127.0,
-
shape=data_shape).astype('int32')
- output = pooling_fp32_exe.forward()[0]
-
- qdata = mx.sym.Variable(name='qdata', shape=data_shape,
dtype='int8')
- min_data = mx.sym.Variable(name='min_data')
- max_data = mx.sym.Variable(name='max_data')
- quantized_pooling = mx.sym.contrib.quantized_pooling(data=qdata,
min_data=min_data,
-
max_data=max_data, kernel=kernel,
- pad=pad,
stride=stride, pool_type=pool_type,
-
global_pool=global_pool)
- pooling_int8_exe =
quantized_pooling.simple_bind(ctx=mx.current_context(), grad_req='null')
- qarg_names = quantized_pooling.list_arguments()
- pooling_int8_exe.arg_dict[qarg_names[0]][:] =
pooling_fp32_exe.arg_dict[arg_names[0]].astype('int8')
- quantized_range = 127.0
- pooling_int8_exe.arg_dict[qarg_names[1]][:] = -quantized_range
- pooling_int8_exe.arg_dict[qarg_names[2]][:] = quantized_range
- qoutput, min_range, max_range = pooling_int8_exe.forward()
+ if is_test_for_mkldnn():
+ dtype = 'uint8'
Review comment:
Currently MKLDNN quantization only support uint8 as input data so we made
separate logic for mkldnn.
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