MIGraphX Driver#

Applies to Linux

2023-05-25

11 min read time

read#

Loads and prints input graph.

<input file>#

File to load

--model [resnet50|inceptionv3|alexnet]#

Load model

--onnx#

Load as onnx

--tf#

Load as tensorflow

--migraphx#

Load as MIGraphX

--migraphx-json#

Load as MIGraphX JSON

--batch [unsigned int] (Default: 1)#

For a static model, set batch size. For a dynamic batch model, sets the batch size at runtime.

--nhwc#

Treat tensorflow format as nhwc

--skip-unknown-operators#

Skip unknown operators when parsing and continue to parse.

--nchw#

Treat tensorflow format as nchw

--trim, -t [unsigned int]#

Trim instructions from the end (Default: 0)

--input-dim [std::vector<std::string>]#

Dim of a parameter (format: “@name d1 d2 dn”)

Set dynamic dimensions of a parameter using JSON formatting (format “@name” “dynamic_dimension_json”)

Set the default dynamic dimension (format {min:x, max:y, optimals:[o1,o2,…]})

--optimize, -O#

Optimize when reading

--graphviz, -g#

Print out a graphviz representation.

--brief#

Make the output brief.

--cpp#

Print out the program as cpp program.

--json#

Print out program as json.

--text#

Print out program in text format.

--binary#

Print out program in binary format.

--output, -o [std::string]#

Output to file.

compile#

Compiles and prints input graph.

<input file>#

File to load

--model [resnet50|inceptionv3|alexnet]#

Load model

--onnx#

Load as onnx

--tf#

Load as tensorflow

--migraphx#

Load as MIGraphX

--migraphx-json#

Load as MIGraphX JSON

--batch [unsigned int] (Default: 1)#

For a static model, set batch size. For a dynamic batch model, sets the batch size at runtime.

--nhwc#

Treat tensorflow format as nhwc

--skip-unknown-operators#

Skip unknown operators when parsing and continue to parse.

--nchw#

Treat tensorflow format as nchw

--trim, -t [unsigned int]#

Trim instructions from the end (Default: 0)

--input-dim [std::vector<std::string>]#

Dim of a parameter (format: “@name d1 d2 dn”)

Set dynamic dimensions of a parameter using JSON formatting (format “@name” “dynamic_dimension_json”)

Set the default dynamic dimension (format {min:x, max:y, optimals:[o1,o2,…]})

--optimize, -O#

Optimize when reading

--graphviz, -g#

Print out a graphviz representation.

--brief#

Make the output brief.

--cpp#

Print out the program as cpp program.

--json#

Print out program as json.

--text#

Print out program in text format.

--binary#

Print out program in binary format.

--output, -o [std::string]#

Output to file.

--fill0 [std::vector<std::string>]#

Fill parameter with 0s

--fill1 [std::vector<std::string>]#

Fill parameter with 1s

--gpu#

Compile on the gpu

--cpu#

Compile on the cpu

--ref#

Compile on the reference implementation

--enable-offload-copy#

Enable implicit offload copying

--disable-fast-math#

Disable fast math optimization

--exhaustive-tune#

Perform an exhaustive search to find the fastest version of generated kernels for selected backend

--fp16#

Quantize for fp16

--int8#

Quantize for int8

run#

Loads and prints input graph.

<input file>#

File to load

--model [resnet50|inceptionv3|alexnet]#

Load model

--onnx#

Load as onnx

--tf#

Load as tensorflow

--migraphx#

Load as MIGraphX

--migraphx-json#

Load as MIGraphX JSON

--batch [unsigned int] (Default: 1)#

For a static model, set batch size. For a dynamic batch model, sets the batch size at runtime.

--nhwc#

Treat tensorflow format as nhwc

--skip-unknown-operators#

Skip unknown operators when parsing and continue to parse.

--nchw#

Treat tensorflow format as nchw

--trim, -t [unsigned int]#

Trim instructions from the end (Default: 0)

--input-dim [std::vector<std::string>]#

Dim of a parameter (format: “@name d1 d2 dn”)

Set dynamic dimensions of a parameter using JSON formatting (format “@name” “dynamic_dimension_json”)

Set the default dynamic dimension (format {min:x, max:y, optimals:[o1,o2,…]})

--optimize, -O#

Optimize when reading

--graphviz, -g#

Print out a graphviz representation.

--brief#

Make the output brief.

--cpp#

Print out the program as cpp program.

--json#

Print out program as json.

--text#

Print out program in text format.

--binary#

Print out program in binary format.

--output, -o [std::string]#

Output to file.

--fill0 [std::vector<std::string>]#

Fill parameter with 0s

--fill1 [std::vector<std::string>]#

Fill parameter with 1s

--gpu#

Compile on the gpu

--cpu#

Compile on the cpu

--ref#

Compile on the reference implementation

--enable-offload-copy#

Enable implicit offload copying

--disable-fast-math#

Disable fast math optimization

--exhaustive-tune#

Perform an exhaustive search to find the fastest version of generated kernels for selected backend

--fp16#

Quantize for fp16

--int8#

Quantize for int8

perf#

Compiles and runs input graph then prints performance report.

<input file>#

File to load

--model [resnet50|inceptionv3|alexnet]#

Load model

--onnx#

Load as onnx

--tf#

Load as tensorflow

--migraphx#

Load as MIGraphX

--migraphx-json#

Load as MIGraphX JSON

--batch [unsigned int] (Default: 1)#

For a static model, set batch size. For a dynamic batch model, sets the batch size at runtime.

--nhwc#

Treat tensorflow format as nhwc

--skip-unknown-operators#

Skip unknown operators when parsing and continue to parse.

--nchw#

Treat tensorflow format as nchw

--trim, -t [unsigned int]#

Trim instructions from the end (Default: 0)

--input-dim [std::vector<std::string>]#

Dim of a parameter (format: “@name d1 d2 dn”)

Set dynamic dimensions of a parameter using JSON formatting (format “@name” “dynamic_dimension_json”)

Set the default dynamic dimension (format {min:x, max:y, optimals:[o1,o2,…]})

--optimize, -O#

Optimize when reading

--graphviz, -g#

Print out a graphviz representation.

--brief#

Make the output brief.

--cpp#

Print out the program as cpp program.

--json#

Print out program as json.

--text#

Print out program in text format.

--binary#

Print out program in binary format.

--output, -o [std::string]#

Output to file.

--fill0 [std::vector<std::string>]#

Fill parameter with 0s

--fill1 [std::vector<std::string>]#

Fill parameter with 1s

--gpu#

Compile on the gpu

--cpu#

Compile on the cpu

--ref#

Compile on the reference implementation

--enable-offload-copy#

Enable implicit offload copying

--disable-fast-math#

Disable fast math optimization

--exhaustive-tune#

Perform an exhaustive search to find the fastest version of generated kernels for selected backend

--fp16#

Quantize for fp16

--int8#

Quantize for int8

--iterations, -n [unsigned int]#

Number of iterations to run for perf report (Default: 100)

verify#

Runs reference and CPU or GPU implementations and checks outputs for consistency.

<input file>#

File to load

--model [resnet50|inceptionv3|alexnet]#

Load model

--onnx#

Load as onnx

--tf#

Load as tensorflow

--migraphx#

Load as MIGraphX

--migraphx-json#

Load as MIGraphX JSON

--batch [unsigned int] (Default: 1)#

For a static model, set batch size. For a dynamic batch model, sets the batch size at runtime.

--nhwc#

Treat tensorflow format as nhwc

--skip-unknown-operators#

Skip unknown operators when parsing and continue to parse.

--nchw#

Treat tensorflow format as nchw

--trim, -t [unsigned int]#

Trim instructions from the end (Default: 0)

--input-dim [std::vector<std::string>]#

Dim of a parameter (format: “@name d1 d2 dn”)

Set dynamic dimensions of a parameter using JSON formatting (format “@name” “dynamic_dimension_json”)

Set the default dynamic dimension (format {min:x, max:y, optimals:[o1,o2,…]})

--optimize, -O#

Optimize when reading

--graphviz, -g#

Print out a graphviz representation.

--brief#

Make the output brief.

--cpp#

Print out the program as cpp program.

--json#

Print out program as json.

--text#

Print out program in text format.

--binary#

Print out program in binary format.

--output, -o [std::string]#

Output to file.

--fill0 [std::vector<std::string>]#

Fill parameter with 0s

--fill1 [std::vector<std::string>]#

Fill parameter with 1s

--gpu#

Compile on the gpu

--cpu#

Compile on the cpu

--ref#

Compile on the reference implementation

--enable-offload-copy#

Enable implicit offload copying

--disable-fast-math#

Disable fast math optimization

--exhaustive-tune#

Perform an exhaustive search to find the fastest version of generated kernels for selected backend

--fp16#

Quantize for fp16

--int8#

Quantize for int8

--tolerance [double]#

Tolerance for errors (Default: 80)

-i, --per-instruction#

Verify each instruction

-r, --reduce#

Reduce program and verify

roctx#

Provides marker information for each operation, allowing MIGraphX to be used with rocprof for performance analysis. This allows user to get GPU-level kernel timing information. An example command line combined with rocprof for tracing purposes is given below:

/opt/rocm/bin/rocprof --hip-trace --roctx-trace --flush-rate 1ms --timestamp on -d <OUTPUT_PATH> --obj-tracking on /opt/rocm/bin/migraphx-driver roctx <ONNX_FILE> <MIGRAPHX_OPTIONS>

After rocprof is run, the output directory will contain trace information for HIP, HCC and ROCTX in seperate .txt files. To understand the interactions between API calls, it is recommended to utilize roctx.py helper script as desribed in dev/tools:rocTX section.

<input file>#

File to load

--model [resnet50|inceptionv3|alexnet]#

Load model

--onnx#

Load as onnx

--tf#

Load as tensorflow

--migraphx#

Load as MIGraphX

--migraphx-json#

Load as MIGraphX JSON

--batch [unsigned int] (Default: 1)#

For a static model, set batch size. For a dynamic batch model, sets the batch size at runtime.

--nhwc#

Treat tensorflow format as nhwc

--skip-unknown-operators#

Skip unknown operators when parsing and continue to parse.

--nchw#

Treat tensorflow format as nchw

--trim, -t [unsigned int]#

Trim instructions from the end (Default: 0)

--input-dim [std::vector<std::string>]#

Dim of a parameter (format: “@name d1 d2 dn”)

Set dynamic dimensions of a parameter using JSON formatting (format “@name” “dynamic_dimension_json”)

Set the default dynamic dimension (format {min:x, max:y, optimals:[o1,o2,…]})

--optimize, -O#

Optimize when reading

--graphviz, -g#

Print out a graphviz representation.

--brief#

Make the output brief.

--cpp#

Print out the program as cpp program.

--json#

Print out program as json.

--text#

Print out program in text format.

--binary#

Print out program in binary format.

--output, -o [std::string]#

Output to file.

--fill0 [std::vector<std::string>]#

Fill parameter with 0s

--fill1 [std::vector<std::string>]#

Fill parameter with 1s

--gpu#

Compile on the gpu

--cpu#

Compile on the cpu

--ref#

Compile on the reference implementation

--enable-offload-copy#

Enable implicit offload copying

--disable-fast-math#

Disable fast math optimization

--exhaustive-tune#

Perform an exhaustive search to find the fastest version of generated kernels for selected backend

--fp16#

Quantize for fp16

--int8#

Quantize for int8