MIGraphX driver#

Applies to Linux

2024-05-07

22 min read time

The MIGraphX driver is a command-line tool that allows you to utilize many of the MIGraphX core functions without having to write a program. It can read, compile, run, and test the performance of a model with randomized data.

It is installed by default when you install MIGraphX. You can find it in /opt/rocm/bin/migraphx-driver or in AMDMIGraphX/build/bin/migraphx-driver after building the source code.

Commands#

The table below summarizes the MIGraphX driver commands.

Table 1 commands#

Command

Description

op

Prints all operators of MIGraphX when followed by the option --list or -l

params

Prints the input and output parameter shapes

run

Compiles, allocates parameters, evaluates, and prints input graph

read

Loads and prints input graph

compile

Compiles and prints input graph

verify

Runs reference and GPU implementations and checks outputs for consistency

perf

Compiles and runs input graph followed by printing the performance report

Options#

The table below summarizes the various options to be used with the MIGraphX driver commands. To learn which options can be used with which commands, see the MIGraphX driver options.

Table 2 commands#

Option

Description

–help | -h

Prints help section.

–test

Test MIGraphX with single layer GEMM model.

–onnx

Loads the file as an ONNX graph.

–tf

Loads the file as a tensorflow graph.

–migraphx

Loads the file as a migraphx graph.

–migraphx-json

Loads the file as a migraphx JSON graph.

–batch

Sets batch size for a static model. Sets the batch size at runtime for a dynamic batch model.

–nhwc

Treats tensorflow format as nhwc.

–nchw

Treats tensorflow format as nchw.

–skip-unknown-operators

Skips unknown operators when parsing and continues to parse.

–trim | -t

Trims instructions from the end.

–optimize | -O

Optimizes read

–graphviz | -g

Prints a graphviz representation

–brief

Makes the output brief

–cpp

Prints the program in .cpp format

–json

Prints the program in .json format

–text

Prints the program in .txt format

–binary

Prints the program in binary format

–output | -o

Writes output in a file

–fill0

Fills parameter with 0s

–fill1

Fills parameter with 1s

–input-dim

Sets static dimensions of a parameter

–dyn-input-dim

Sets dynamic dimensions of a parameter

–default-dyn-dim

Sets default dynamic dimension

–gpu

Compiles on the GPU

–cpu

Compiles on the CPU

–ref

Compiles on the reference implementation

–enable-offload-copy

Enables implicit offload copying

–disable-fast-math

Disables fast math optimization

–exhaustive-tune

Enables exhaustive search to find the fastest kernel

–fp16

Quantizes for fp16

–int8

Quantizes for int8

–fp8

Quantize for Float8E4M3FNUZ type

–rms-tol

Sets tolerance for the RMS error (Default: 0.001)

–atol

Sets tolerance for elementwise absolute difference (Default: 0.001)

–rtol

Sets tolerance for elementwise relative difference (Default: 0.001)

–per-instruction | -i

Verifies each instruction

–reduce | -r

Reduces program and verifies

–iterations | -n

Sets the number of iterations to run for perf report

–list | -l

Lists all the MIGraphX operators

Usage#

This section demonstrates the usage of MIGraphX driver tool with some commonly used options. Note that these examples use a simple MNIST ConvNet as the input graph for demonstration purposes as models of higher complexity generate considerably larger outputs in most cases.

Option: op#

$ /opt/rocm/bin/migraphx-driver op –list

@literal
@param
@return
abs
acos
acosh
add
argmax
argmin
as_shape
asin
asinh
atan
atanh
batch_norm_inference
broadcast
capture
ceil
check_context::migraphx::gpu::context
clip
concat
contiguous
convert
convolution
cos
cosh
deconvolution
div
dot
elu
equal
erf
exp
flatten
floor
gather
gpu::abs
gpu::acos
gpu::acosh
gpu::add
gpu::add_clip
gpu::add_gelu
gpu::add_gelu_new
gpu::add_relu
gpu::add_tanh
gpu::argmax
gpu::argmin
gpu::asin
gpu::asinh
gpu::atan
gpu::atanh
gpu::batch_norm_inference
gpu::ceil
gpu::clip
gpu::concat
gpu::contiguous
gpu::conv_bias
gpu::conv_bias_relu
gpu::convert
gpu::convolution
gpu::cos
gpu::cosh
gpu::deconv
gpu::div
gpu::elu
gpu::equal
gpu::erf
gpu::exp
gpu::floor
gpu::gather
gpu::gelu
gpu::gelu_new
gpu::gemm
gpu::greater
gpu::layernorm
gpu::leaky_relu
gpu::less
gpu::log
gpu::logsoftmax
gpu::lrn
gpu::max
gpu::min
gpu::mul
gpu::mul_add
gpu::mul_add_relu
gpu::pad
gpu::pooling
gpu::pow
gpu::prelu
gpu::quant_convolution
gpu::quant_gemm
gpu::recip
gpu::record_event
gpu::reduce_max
gpu::reduce_mean
gpu::reduce_min
gpu::reduce_prod
gpu::reduce_sum
gpu::relu
gpu::rnn_var_sl_last_output
gpu::rnn_var_sl_shift_output
gpu::rnn_var_sl_shift_sequence
gpu::round
gpu::rsqrt
gpu::set_stream
gpu::sigmoid
gpu::sign
gpu::sin
gpu::sinh
gpu::softmax
gpu::sqdiff
gpu::sqrt
gpu::sub
gpu::tan
gpu::tanh
gpu::triadd
gpu::triadd_clip
gpu::triadd_relu
gpu::triadd_sigmoid
gpu::triadd_tanh
gpu::wait_event
greater
gru
hip::allocate
hip::copy
hip::copy_from_gpu
hip::copy_to_gpu
hip::hip_allocate_memory
hip::hip_copy_literal
identity
im2col
leaky_relu
less
load
log
logsoftmax
lrn
lstm
max
min
mul
multibroadcast
neg
outline
pad
pooling
pow
prelu
quant_convolution
quant_dot
recip
reduce_max
reduce_mean
reduce_min
reduce_prod
reduce_sum
ref::batch_norm_inference
ref::convolution
ref::deconvolution
ref::dot
ref::elu
ref::im2col
ref::leaky_relu
ref::logsoftmax
ref::lrn
ref::op
ref::pad
ref::pooling_average
ref::pooling_max
ref::quant_convolution
ref::rnn_var_sl_last_output
ref::softmax
relu
reshape
rnn
rnn_last_cell_output
rnn_last_hs_output
rnn_var_sl_last_output
rnn_var_sl_shift_output
rnn_var_sl_shift_sequence
round
rsqrt
scalar
sigmoid
sign
sin
sinh
slice
softmax
sqdiff
sqrt
squeeze
sub
tan
tanh
transpose
undefined
unknown:
unsqueeze

Option: params#

$ /opt/rocm/bin/migraphx-driver params simple_graph.pb

Reading: simple_graph.pb
x: float_type, {1, 28, 28}, {784, 28, 1}

Option: run (ONNX file input)#

$ /opt/rocm/bin/migraphx-driver run –onnx simple_graph.onnx

Compiling ...
Reading: simple_graph.onnx
@0 = check_context::migraphx::gpu::context -> float_type, {}, {}
@1 = hip::hip_allocate_memory[shape=float_type, {256}, {1},id=scratch] -> float_type, {256}, {1}
@2 = hip::hip_copy_literal[id=@literal:1] -> float_type, {784, 128}, {128, 1}
x:0 = @param:x:0 -> float_type, {1, 28, 28}, {784, 28, 1}
@3 = reshape[dims={-1, 784}](x:0) -> float_type, {1, 784}, {784, 1}
@4 = load[offset=0,end=512](@1) -> float_type, {1, 128}, {128, 1}
@5 = gpu::gemm[alpha=1,beta=0](@3,@2,@4) -> float_type, {1, 128}, {128, 1}
@6 = hip::hip_copy_literal[id=@literal:0] -> float_type, {128}, {1}
@7 = hip::hip_copy_literal[id=@literal:2] -> float_type, {10}, {1}
@8 = hip::hip_copy_literal[id=@literal:3] -> float_type, {128, 10}, {10, 1}
@9 = multibroadcast[output_lens={1, 128}](@6) -> float_type, {1, 128}, {0, 1}
@10 = load[offset=512,end=1024](@1) -> float_type, {1, 128}, {128, 1}
@11 = gpu::add_relu(@5,@9,@10) -> float_type, {1, 128}, {128, 1}
@12 = load[offset=0,end=40](@1) -> float_type, {1, 10}, {10, 1}
@13 = gpu::gemm[alpha=1,beta=0](@11,@8,@12) -> float_type, {1, 10}, {10, 1}
@14 = multibroadcast[output_lens={1, 10}](@7) -> float_type, {1, 10}, {0, 1}
@15 = load[offset=40,end=80](@1) -> float_type, {1, 10}, {10, 1}
@16 = gpu::add(@13,@14,@15) -> float_type, {1, 10}, {10, 1}
#output_0 = @param:#output_0 -> float_type, {1, 10}, {10, 1}
@17 = gpu::softmax[axis=1](@16,#output_0) -> float_type, {1, 10}, {10, 1}
@18 = @return(@17)

Allocating params ...
@0 = check_context::migraphx::gpu::context -> float_type, {}, {}
@1 = hip::hip_allocate_memory[shape=float_type, {256}, {1},id=scratch] -> float_type, {256}, {1}
@2 = hip::hip_copy_literal[id=@literal:1] -> float_type, {784, 128}, {128, 1}
x:0 = @param:x:0 -> float_type, {1, 28, 28}, {784, 28, 1}
@3 = reshape[dims={-1, 784}](x:0) -> float_type, {1, 784}, {784, 1}
@4 = load[offset=0,end=512](@1) -> float_type, {1, 128}, {128, 1}
@5 = gpu::gemm[alpha=1,beta=0](@3,@2,@4) -> float_type, {1, 128}, {128, 1}
@6 = hip::hip_copy_literal[id=@literal:0] -> float_type, {128}, {1}
@7 = hip::hip_copy_literal[id=@literal:2] -> float_type, {10}, {1}
@8 = hip::hip_copy_literal[id=@literal:3] -> float_type, {128, 10}, {10, 1}
@9 = multibroadcast[output_lens={1, 128}](@6) -> float_type, {1, 128}, {0, 1}
@10 = load[offset=512,end=1024](@1) -> float_type, {1, 128}, {128, 1}
@11 = gpu::add_relu(@5,@9,@10) -> float_type, {1, 128}, {128, 1}
@12 = load[offset=0,end=40](@1) -> float_type, {1, 10}, {10, 1}
@13 = gpu::gemm[alpha=1,beta=0](@11,@8,@12) -> float_type, {1, 10}, {10, 1}
@14 = multibroadcast[output_lens={1, 10}](@7) -> float_type, {1, 10}, {0, 1}
@15 = load[offset=40,end=80](@1) -> float_type, {1, 10}, {10, 1}
@16 = gpu::add(@13,@14,@15) -> float_type, {1, 10}, {10, 1}
#output_0 = @param:#output_0 -> float_type, {1, 10}, {10, 1}
@17 = gpu::softmax[axis=1](@16,#output_0) -> float_type, {1, 10}, {10, 1}
@18 = @return(@17)

Option: read#

$ /opt/rocm/bin/migraphx-driver read simple_graph.pb

Reading: simple_graph.pb
@0 = @literal{0.0136018, -0.0839988, 0.0375392, 0.0613085, -0.125795, 0.176185, 0.0761055, 0.0093384, -0.110057, -0.170587} -> float_type, {10}, {1}
@1 = @literal{ ... } -> float_type, {128, 10}, {10, 1}
@2 = @literal{ ... } -> float_type, {128}, {1}
@3 = @literal{ ... } -> float_type, {784, 128}, {128, 1}
@4 = @literal{-1, 784} -> int32_type, {2}, {1}
x = @param:x -> float_type, {1, 28, 28}, {784, 28, 1}
@5 = reshape[dims={-1, 784}](x) -> float_type, {1, 784}, {784, 1}
@6 = identity(@3) -> float_type, {784, 128}, {128, 1}
@7 = dot[alpha=1,beta=1](@5,@6) -> float_type, {1, 128}, {128, 1}
@8 = identity(@2) -> float_type, {128}, {1}
@9 = broadcast[axis=1,dims={1, 128}](@8) -> float_type, {1, 128}, {0, 1}
@10 = add(@7,@9) -> float_type, {1, 128}, {128, 1}
@11 = relu(@10) -> float_type, {1, 128}, {128, 1}
@12 = identity(@1) -> float_type, {128, 10}, {10, 1}
@13 = dot[alpha=1,beta=1](@11,@12) -> float_type, {1, 10}, {10, 1}
@14 = identity(@0) -> float_type, {10}, {1}
@15 = broadcast[axis=1,dims={1, 10}](@14) -> float_type, {1, 10}, {0, 1}
@16 = add(@13,@15) -> float_type, {1, 10}, {10, 1}
@17 = softmax[axis=1](@16) -> float_type, {1, 10}, {10, 1}
@18 = identity(@17) -> float_type, {1, 10}, {10, 1}

Option: compile (on GPU, quantized for fp16)#

$ /opt/rocm/bin/migraphx-driver compile –gpu –fp16 simple_graph.pb

Compiling ...
Reading: simple_graph.pb
@0 = check_context::migraphx::gpu::context -> float_type, {}, {}
@1 = hip::hip_allocate_memory[shape=float_type, {456}, {1},id=scratch] -> float_type, {456}, {1}
@2 = hip::hip_copy_literal[id=@literal:0] -> half_type, {784, 128}, {128, 1}
@3 = load[offset=256,end=1824](@1) -> half_type, {1, 28, 28}, {784, 28, 1}
x = @param:x -> float_type, {1, 28, 28}, {784, 28, 1}
@4 = gpu::convert[target_type=1](x,@3) -> half_type, {1, 28, 28}, {784, 28, 1}
@5 = reshape[dims={-1, 784}](@4) -> half_type, {1, 784}, {784, 1}
@6 = load[offset=0,end=256](@1) -> half_type, {1, 128}, {128, 1}
@7 = gpu::gemm[alpha=1,beta=0](@5,@2,@6) -> half_type, {1, 128}, {128, 1}
@8 = hip::hip_copy_literal[id=@literal:2] -> half_type, {128, 10}, {10, 1}
@9 = hip::hip_copy_literal[id=@literal:1] -> half_type, {128}, {1}
@10 = hip::hip_copy_literal[id=@literal:3] -> half_type, {10}, {1}
@11 = load[offset=256,end=512](@1) -> half_type, {1, 128}, {128, 1}
@12 = broadcast[axis=1,dims={1, 128}](@9) -> half_type, {1, 128}, {0, 1}
@13 = gpu::add_relu(@7,@12,@11) -> half_type, {1, 128}, {128, 1}
@14 = load[offset=0,end=20](@1) -> half_type, {1, 10}, {10, 1}
@15 = gpu::gemm[alpha=1,beta=0](@13,@8,@14) -> half_type, {1, 10}, {10, 1}
@16 = broadcast[axis=1,dims={1, 10}](@10) -> half_type, {1, 10}, {0, 1}
@17 = load[offset=20,end=40](@1) -> half_type, {1, 10}, {10, 1}
@18 = gpu::add(@15,@16,@17) -> half_type, {1, 10}, {10, 1}
@19 = load[offset=0,end=20](@1) -> half_type, {1, 10}, {10, 1}
@20 = gpu::softmax[axis=1](@18,@19) -> half_type, {1, 10}, {10, 1}
output = @param:output -> float_type, {1, 10}, {10, 1}
@21 = gpu::convert[target_type=2](@20,output) -> float_type, {1, 10}, {10, 1}

Option: verify#

$ /opt/rocm/bin/migraphx-driver verify simple_graph.pb

Reading: simple_graph.pb
@0 = @literal{0.0136018, -0.0839988, 0.0375392, 0.0613085, -0.125795, 0.176185, 0.0761055, 0.0093384, -0.110057, -0.170587} -> float_type, {10}, {1}
@1 = @literal{ ... } -> float_type, {128, 10}, {10, 1}
@2 = @literal{ ... } -> float_type, {128}, {1}
@3 = @literal{ ... } -> float_type, {784, 128}, {128, 1}
@4 = @literal{-1, 784} -> int32_type, {2}, {1}
x = @param:x -> float_type, {1, 28, 28}, {784, 28, 1}
@5 = reshape[dims={-1, 784}](x) -> float_type, {1, 784}, {784, 1}
@6 = identity(@3) -> float_type, {784, 128}, {128, 1}
@7 = dot[alpha=1,beta=1](@5,@6) -> float_type, {1, 128}, {128, 1}
@8 = identity(@2) -> float_type, {128}, {1}
@9 = broadcast[axis=1,dims={1, 128}](@8) -> float_type, {1, 128}, {0, 1}
@10 = add(@7,@9) -> float_type, {1, 128}, {128, 1}
@11 = relu(@10) -> float_type, {1, 128}, {128, 1}
@12 = identity(@1) -> float_type, {128, 10}, {10, 1}
@13 = dot[alpha=1,beta=1](@11,@12) -> float_type, {1, 10}, {10, 1}
@14 = identity(@0) -> float_type, {10}, {1}
@15 = broadcast[axis=1,dims={1, 10}](@14) -> float_type, {1, 10}, {0, 1}
@16 = add(@13,@15) -> float_type, {1, 10}, {10, 1}
@17 = softmax[axis=1](@16) -> float_type, {1, 10}, {10, 1}
@18 = identity(@17) -> float_type, {1, 10}, {10, 1}

@0 = @literal{0.0136018, -0.0839988, 0.0375392, 0.0613085, -0.125795, 0.176185, 0.0761055, 0.0093384, -0.110057, -0.170587} -> float_type, {10}, {1}
@1 = @literal{ ... } -> float_type, {128, 10}, {10, 1}
@2 = @literal{ ... } -> float_type, {128}, {1}
@3 = @literal{ ... } -> float_type, {784, 128}, {128, 1}
@4 = @literal{-1, 784} -> int32_type, {2}, {1}
x = @param:x -> float_type, {1, 28, 28}, {784, 28, 1}
@5 = reshape[dims={-1, 784}](x) -> float_type, {1, 784}, {784, 1}
@6 = identity(@3) -> float_type, {784, 128}, {128, 1}
@7 = dot[alpha=1,beta=1](@5,@6) -> float_type, {1, 128}, {128, 1}
@8 = identity(@2) -> float_type, {128}, {1}
@9 = broadcast[axis=1,dims={1, 128}](@8) -> float_type, {1, 128}, {0, 1}
@10 = add(@7,@9) -> float_type, {1, 128}, {128, 1}
@11 = relu(@10) -> float_type, {1, 128}, {128, 1}
@12 = identity(@1) -> float_type, {128, 10}, {10, 1}
@13 = dot[alpha=1,beta=1](@11,@12) -> float_type, {1, 10}, {10, 1}
@14 = identity(@0) -> float_type, {10}, {1}
@15 = broadcast[axis=1,dims={1, 10}](@14) -> float_type, {1, 10}, {0, 1}
@16 = add(@13,@15) -> float_type, {1, 10}, {10, 1}
@17 = softmax[axis=1](@16) -> float_type, {1, 10}, {10, 1}
@18 = identity(@17) -> float_type, {1, 10}, {10, 1}

@0 = @literal{0.0136018, -0.0839988, 0.0375392, 0.0613085, -0.125795, 0.176185, 0.0761055, 0.0093384, -0.110057, -0.170587} -> float_type, {10}, {1}
@1 = @literal{ ... } -> float_type, {128, 10}, {10, 1}
@2 = @literal{ ... } -> float_type, {128}, {1}
@3 = @literal{ ... } -> float_type, {784, 128}, {128, 1}
x = @param:x -> float_type, {1, 28, 28}, {784, 28, 1}
@4 = ref::reshape[dims={-1, 784}](x) -> float_type, {1, 784}, {784, 1}
@5 = ref::identity(@3) -> float_type, {784, 128}, {128, 1}
@6 = ref::dot[alpha=1,beta=1](@4,@5) -> float_type, {1, 128}, {128, 1}
@7 = ref::identity(@2) -> float_type, {128}, {1}
@8 = ref::broadcast[axis=1,dims={1, 128}](@7) -> float_type, {1, 128}, {0, 1}
@9 = ref::contiguous(@8) -> float_type, {1, 128}, {128, 1}
@10 = ref::add(@6,@9) -> float_type, {1, 128}, {128, 1}
@11 = ref::relu(@10) -> float_type, {1, 128}, {128, 1}
@12 = ref::identity(@1) -> float_type, {128, 10}, {10, 1}
@13 = ref::dot[alpha=1,beta=1](@11,@12) -> float_type, {1, 10}, {10, 1}
@14 = ref::identity(@0) -> float_type, {10}, {1}
@15 = ref::broadcast[axis=1,dims={1, 10}](@14) -> float_type, {1, 10}, {0, 1}
@16 = ref::contiguous(@15) -> float_type, {1, 10}, {10, 1}
@17 = ref::add(@13,@16) -> float_type, {1, 10}, {10, 1}
@18 = ref::softmax[axis=1](@17) -> float_type, {1, 10}, {10, 1}
@19 = ref::identity(@18) -> float_type, {1, 10}, {10, 1}

@0 = check_context::migraphx::gpu::context -> float_type, {}, {}
@1 = hip::hip_allocate_memory[shape=float_type, {256}, {1},id=scratch] -> float_type, {256}, {1}
@2 = hip::hip_copy_literal[id=@literal:3] -> float_type, {784, 128}, {128, 1}
x = @param:x -> float_type, {1, 28, 28}, {784, 28, 1}
@3 = load[offset=0,end=512](@1) -> float_type, {1, 128}, {128, 1}
@4 = reshape[dims={-1, 784}](x) -> float_type, {1, 784}, {784, 1}
@5 = gpu::gemm[alpha=1,beta=0](@4,@2,@3) -> float_type, {1, 128}, {128, 1}
@6 = hip::hip_copy_literal[id=@literal:1] -> float_type, {128, 10}, {10, 1}
@7 = hip::hip_copy_literal[id=@literal:2] -> float_type, {128}, {1}
@8 = hip::hip_copy_literal[id=@literal:0] -> float_type, {10}, {1}
@9 = load[offset=512,end=1024](@1) -> float_type, {1, 128}, {128, 1}
@10 = broadcast[axis=1,dims={1, 128}](@7) -> float_type, {1, 128}, {0, 1}
@11 = gpu::add_relu(@5,@10,@9) -> float_type, {1, 128}, {128, 1}
@12 = load[offset=40,end=80](@1) -> float_type, {1, 10}, {10, 1}
@13 = gpu::gemm[alpha=1,beta=0](@11,@6,@12) -> float_type, {1, 10}, {10, 1}
@14 = load[offset=0,end=40](@1) -> float_type, {1, 10}, {10, 1}
@15 = broadcast[axis=1,dims={1, 10}](@8) -> float_type, {1, 10}, {0, 1}
@16 = gpu::add(@13,@15,@14) -> float_type, {1, 10}, {10, 1}
output = @param:output -> float_type, {1, 10}, {10, 1}
@17 = gpu::softmax[axis=1](@16,output) -> float_type, {1, 10}, {10, 1}

Option: perf#

$ /opt/rocm/bin/migraphx-driver perf simple_graph.pb

Compiling ...
Reading: simple_graph.pb
@0 = check_context::migraphx::gpu::context -> float_type, {}, {}
@1 = hip::hip_allocate_memory[shape=float_type, {256}, {1},id=scratch] -> float_type, {256}, {1}
@2 = hip::hip_copy_literal[id=@literal:3] -> float_type, {784, 128}, {128, 1}
@3 = load[offset=0,end=512](@1) -> float_type, {1, 128}, {128, 1}
x = @param:x -> float_type, {1, 28, 28}, {784, 28, 1}
@4 = reshape[dims={-1, 784}](x) -> float_type, {1, 784}, {784, 1}
@5 = gpu::gemm[alpha=1,beta=0](@4,@2,@3) -> float_type, {1, 128}, {128, 1}
@6 = hip::hip_copy_literal[id=@literal:1] -> float_type, {128, 10}, {10, 1}
@7 = hip::hip_copy_literal[id=@literal:0] -> float_type, {10}, {1}
@8 = hip::hip_copy_literal[id=@literal:2] -> float_type, {128}, {1}
@9 = broadcast[axis=1,dims={1, 128}](@8) -> float_type, {1, 128}, {0, 1}
@10 = load[offset=512,end=1024](@1) -> float_type, {1, 128}, {128, 1}
@11 = gpu::add_relu(@5,@9,@10) -> float_type, {1, 128}, {128, 1}
@12 = load[offset=0,end=40](@1) -> float_type, {1, 10}, {10, 1}
@13 = gpu::gemm[alpha=1,beta=0](@11,@6,@12) -> float_type, {1, 10}, {10, 1}
@14 = broadcast[axis=1,dims={1, 10}](@7) -> float_type, {1, 10}, {0, 1}
@15 = load[offset=40,end=80](@1) -> float_type, {1, 10}, {10, 1}
@16 = gpu::add(@13,@14,@15) -> float_type, {1, 10}, {10, 1}
output = @param:output -> float_type, {1, 10}, {10, 1}
@17 = gpu::softmax[axis=1](@16,output) -> float_type, {1, 10}, {10, 1}

Allocating params ...
Running performance report ...
@0 = check_context::migraphx::gpu::context -> float_type, {}, {}: 0.00057782ms, 1%
@1 = hip::hip_allocate_memory[shape=float_type, {256}, {1},id=scratch] -> float_type, {256}, {1}: 0.000295ms, 1%
@2 = hip::hip_copy_literal[id=@literal:3] -> float_type, {784, 128}, {128, 1}: 0.00027942ms, 1%
@3 = load[offset=0,end=512](@1) -> float_type, {1, 128}, {128, 1}: 0.000232ms, 1%
x = @param:x -> float_type, {1, 28, 28}, {784, 28, 1}: 0.0003206ms, 1%
@4 = reshape[dims={-1, 784}](x) -> float_type, {1, 784}, {784, 1}: 0.00033842ms, 1%
@5 = gpu::gemm[alpha=1,beta=0](@4,@2,@3) -> float_type, {1, 128}, {128, 1}: 0.212592ms, 52%
@6 = hip::hip_copy_literal[id=@literal:1] -> float_type, {128, 10}, {10, 1}: 0.00085822ms, 1%
@7 = hip::hip_copy_literal[id=@literal:0] -> float_type, {10}, {1}: 0.000382ms, 1%
@8 = hip::hip_copy_literal[id=@literal:2] -> float_type, {128}, {1}: 0.0003486ms, 1%
@9 = broadcast[axis=1,dims={1, 128}](@8) -> float_type, {1, 128}, {0, 1}: 0.000299ms, 1%
@10 = load[offset=512,end=1024](@1) -> float_type, {1, 128}, {128, 1}: 0.000234ms, 1%
@11 = gpu::add_relu(@5,@9,@10) -> float_type, {1, 128}, {128, 1}: 0.0416597ms, 11%
@12 = load[offset=0,end=40](@1) -> float_type, {1, 10}, {10, 1}: 0.0007548ms, 1%
@13 = gpu::gemm[alpha=1,beta=0](@11,@6,@12) -> float_type, {1, 10}, {10, 1}: 0.0733071ms, 18%
@14 = broadcast[axis=1,dims={1, 10}](@7) -> float_type, {1, 10}, {0, 1}: 0.00088142ms, 1%
@15 = load[offset=40,end=80](@1) -> float_type, {1, 10}, {10, 1}: 0.000408ms, 1%
@16 = gpu::add(@13,@14,@15) -> float_type, {1, 10}, {10, 1}: 0.0410144ms, 10%
output = @param:output -> float_type, {1, 10}, {10, 1}: 0.0010222ms, 1%
@17 = gpu::softmax[axis=1](@16,output) -> float_type, {1, 10}, {10, 1}: 0.0385636ms, 10%

Summary:
gpu::gemm: 0.285899ms, 69%
gpu::add_relu: 0.0416597ms, 11%
gpu::add: 0.0410144ms, 10%
gpu::softmax: 0.0385636ms, 10%
hip::hip_copy_literal: 0.00186824ms, 1%
load: 0.0016288ms, 1%
@param: 0.0013428ms, 1%
broadcast: 0.00118042ms, 1%
check_context::migraphx::gpu::context: 0.00057782ms, 1%
reshape: 0.00033842ms, 1%
hip::hip_allocate_memory: 0.000295ms, 1%

Rate: 2866.1/sec
Total time: 0.348906ms
Total instructions time: 0.414369ms
Overhead time: 0.00348144ms, -0.0654627ms
Overhead: 1%, -19%