read#

Loads and prints input graph.

<input file>#

File to load

--test#

Test MIGraphX with single layer GEMM 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”)

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

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

--default-dyn-dim#

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

--optimize, -O#

Optimize when reading

--apply-pass, -p#

Passes to apply to model

--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.

--netron#

Print out program as a Netron viewable json file.

--text#

Print out program in text format.

--binary#

Print out program in binary format.

--py#

Print out program using python API.

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

Output to file.

compile#

Compiles and prints input graph.

<input file>#

File to load

--test#

Test MIGraphX with single layer GEMM 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”)

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

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

--default-dyn-dim#

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

--optimize, -O#

Optimize when reading

--apply-pass, -p#

Passes to apply to model

--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.

--netron#

Print out program as a Netron viewable json file.

--text#

Print out program in text format.

--binary#

Print out program in binary format.

--py#

Print out program using python API.

--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

--bf16#

Quantize for bf16

--int8#

Quantize for int8

--fp8#

Quantize for Float8E4M3FNUZ type

run#

Loads and prints input graph.

<input file>#

File to load

--test#

Test MIGraphX with single layer GEMM 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”)

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

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

--default-dyn-dim#

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

--optimize, -O#

Optimize when reading

--apply-pass, -p#

Passes to apply to model

--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.

--netron#

Print out program as a Netron viewable json file.

--text#

Print out program in text format.

--binary#

Print out program in binary format.

--py#

Print out program using python API.

--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

--bf16#

Quantize for bf16

--int8#

Quantize for int8

--fp8#

Quantize for Float8E4M3FNUZ type

perf#

Compiles and runs input graph then prints performance report.

<input file>#

File to load

--test#

Test MIGraphX with single layer GEMM 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”)

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

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

--default-dyn-dim#

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

--optimize, -O#

Optimize when reading

--apply-pass, -p#

Passes to apply to model

--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.

--netron#

Print out program as a Netron viewable json file.

--text#

Print out program in text format.

--binary#

Print out program in binary format.

--py#

Print out program using python API.

--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

--bf16#

Quantize for bf16

--int8#

Quantize for int8

--fp8#

Quantize for Float8E4M3FNUZ type

--iterations, -n [unsigned int]#

Sets 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

--test#

Test MIGraphX with single layer GEMM 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”)

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

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

--default-dyn-dim#

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

--optimize, -O#

Optimize when reading

--apply-pass, -p#

Passes to apply to model

--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.

--netron#

Print out program as a Netron viewable json file.

--text#

Print out program in text format.

--binary#

Print out program in binary format.

--py#

Print out program using python API.

--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

--bf16#

Quantize for bf16

--int8#

Quantize for int8

--fp8#

Quantize for Float8E4M3FNUZ type

--rms-tol [double]#

Sets tolerance for RMS error (Default: 0.001)

--atol [double]#

Sets tolerance for elementwise absolute difference (Default: 0.001)

--rtol [double]#

Sets tolerance for elementwise relative difference (Default: 0.001)

-i, --per-instruction#

Verifies each instruction

-r, --reduce#

Reduces program and verifies

--ref-use-double#

Converts floating point values to double for the ref target

roctx#

roctx provides marker information for each operation which allows MIGraphX to be used with rocprof for performance analysis. This allows you to get GPU-level kernel timing information. Here is how you can use roctx combined with rocprof for tracing:

/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>

Running rocprof generates trace information for HIP, HCC and ROCTX in separate .txt files. To understand the interactions between API calls, utilize the roctx.py helper script.

<input file>#

File to load

--test#

Test MIGraphX with single layer GEMM 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”)

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

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

--default-dyn-dim#

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

--optimize, -O#

Optimize when reading

--apply-pass, -p#

Passes to apply to model

--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.

--netron#

Print out program as a Netron viewable json file.

--text#

Print out program in text format.

--binary#

Print out program in binary format.

--py#

Print out program using python API.

--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

--bf16#

Quantize for bf16

--int8#

Quantize for int8

--fp8#

Quantize for Float8E4M3FNUZ type