MIGraphX driver#
Applies to Linux
2024-10-16
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.
Command |
Description |
op |
Prints all operators of MIGraphX when followed by the option |
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.
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 |
–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%