FP8 lossy downcast issue with "ref" implementation

https://github.com/ROCmSoftwarePlatform/AMDMIGraphX/pull/2506/files
This PR had to disable FP8 tests for the CPU backend.

Ref implementation is doing Float -- > Fp8 -- > Float conversion but CPU backend is doing entire test in Float.

Therefore results come out slightly different.

need to figure out way to enable those tests again.

e.g.

ef:
module: "main"
@0 = @literal{2} -> float_type, {1}, {0}, target_id=0
@1 = @literal{3} -> float_type, {1}, {0}, target_id=0
c = @param:c -> float_type, {3, 2, 2, 7}, {28, 14, 7, 1}, target_id=0
b = @param:b -> fp8e4m3fnuz_type, {3, 2, 7, 8}, {112, 56, 8, 1}, target_id=0
a = @param:a -> fp8e4m3fnuz_type, {3, 2, 8, 2}, {32, 16, 2, 1}, target_id=0
@5 = transpose[permutation={0, 1, 3, 2}](a) -> fp8e4m3fnuz_type, {3, 2, 2, 8}, {32, 16, 1, 2}, target_id=0
@6 = transpose[permutation={0, 1, 3, 2}](b) -> fp8e4m3fnuz_type, {3, 2, 8, 7}, {112, 56, 1, 8}, target_id=0
@7 = multibroadcast[out_lens={3, 2, 2, 8},out_dyn_dims={}](@1) -> float_type, {3, 2, 2, 8}, {0, 0, 0, 0}, target_id=0
@8 = convert[target_type=2](@5) -> float_type, {3, 2, 2, 8}, {32, 16, 1, 2}, target_id=0
@9 = mul(@7,@8) -> float_type, {3, 2, 2, 8}, {32, 16, 1, 2}, target_id=0
@10 = convert[target_type=12](@9) -> fp8e4m3fnuz_type, {3, 2, 2, 8}, {32, 16, 1, 2}, target_id=0
@11 = quant_dot(@10,@6) -> float_type, {3, 2, 2, 7}, {28, 14, 7, 1}, target_id=0
@12 = multibroadcast[out_lens={3, 2, 2, 7},out_dyn_dims={}](@0) -> float_type, {3, 2, 2, 7}, {0, 0, 0, 0}, target_id=0
@13 = mul(c,@12) -> float_type, {3, 2, 2, 7}, {28, 14, 7, 1}, target_id=0
@14 = add(@11,@13) -> float_type, {3, 2, 2, 7}, {28, 14, 7, 1}, target_id=0

## ref quant_dot internally converts fp8e4m3fnuz_type to  float and does the matrix multiplication
# Float - > fp8 --> float
cpu:
module: "main"
@0 = cpu::preallocate[shape=int8_type, {1008}, {1},id=main:scratch] -> int8_type, {1008}, {1}, target_id=0
@1 = cpu::literal -> float_type, {3, 2, 2, 8}, {32, 16, 8, 1}, target_id=0
@2 = cpu::literal -> float_type, {1}, {0}, target_id=0
c = @param:c -> float_type, {3, 2, 2, 7}, {28, 14, 7, 1}, target_id=0
b = @param:b -> fp8e4m3fnuz_type, {3, 2, 7, 8}, {112, 56, 8, 1}, target_id=0
a = @param:a -> fp8e4m3fnuz_type, {3, 2, 8, 2}, {32, 16, 2, 1}, target_id=0
@6 = convert[target_type=2](a) -> float_type, {3, 2, 8, 2}, {32, 16, 2, 1}, target_id=0
@7 = transpose[permutation={0, 1, 3, 2}](@6) -> float_type, {3, 2, 2, 8}, {32, 16, 1, 2}, target_id=0
@8 = convert[target_type=2](b) -> float_type, {3, 2, 7, 8}, {112, 56, 8, 1}, target_id=0
@9 = transpose[permutation={0, 1, 3, 2}](@8) -> float_type, {3, 2, 8, 7}, {112, 56, 1, 8}, target_id=0
@10 = load[offset=336,end=720](@0) -> float_type, {3, 2, 2, 8}, {32, 16, 8, 1}, target_id=0
@11 = dnnl::binary[post_ops={},algo=binary_mul](@1,@7,@10) -> float_type, {3, 2, 2, 8}, {32, 16, 8, 1}, target_id=0
@12 = load[offset=0,end=336](@0) -> float_type, {3, 2, 2, 7}, {28, 14, 7, 1}, target_id=0
@13 = dnnl::dot[post_ops={}](@11,@9,@12) -> float_type, {3, 2, 2, 7}, {28, 14, 7, 1}, target_id=0
@14 = multibroadcast[out_lens={3, 2, 2, 7},out_dyn_dims={}](@2) -> float_type, {3, 2, 2, 7}, {0, 0, 0, 0}, target_id=0
@15 = load[offset=672,end=1008](@0) -> float_type, {3, 2, 2, 7}, {28, 14, 7, 1}, target_id=0
@16 = dnnl::binary[post_ops={},algo=binary_mul](c,@14,@15) -> float_type, {3, 2, 2, 7}, {28, 14, 7, 1}, target_id=0
@17 = load[offset=336,end=672](@0) -> float_type, {3, 2, 2, 7}, {28, 14, 7, 1}, target_id=0
@18 = dnnl::binary[post_ops={},algo=binary_add](@13,@16,@17) -> float_type, {3, 2, 2, 7}, {28, 14, 7, 1}, target_id=0

## GPU : 
# float - > fp8 -- > (fp8 inputs -->float32 accumulation) --> Float
@0 = check_context::migraphx::gpu::context -> float_type, {}, {}, target_id=0
@1 = hip::hip_allocate_memory[shape=int8_type, {432}, {1},id=main:scratch] -> int8_type, {432}, {1}, target_id=0
@2 = load[offset=336,end=432](@1) -> fp8e4m3fnuz_type, {3, 2, 2, 8}, {32, 16, 1, 2}, target_id=0
a = @param:a -> fp8e4m3fnuz_type, {3, 2, 8, 2}, {32, 16, 2, 1}, target_id=0
@4 = transpose[permutation={0, 1, 3, 2}](a) -> fp8e4m3fnuz_type, {3, 2, 2, 8}, {32, 16, 1, 2}, target_id=0
@5 = gpu::code_object[code_object=9120,symbol_name=convert_mul_convert_kernel,global=96,local=1024,](@4,@2) -> fp8e4m3fnuz_type, {3, 2, 2, 8}, {32, 16, 1, 2}, target_id=0
@6 = load[offset=0,end=336](@1) -> float_type, {3, 2, 2, 7}, {28, 14, 7, 1}, target_id=0
b = @param:b -> fp8e4m3fnuz_type, {3, 2, 7, 8}, {112, 56, 8, 1}, target_id=0
@8 = transpose[permutation={0, 1, 3, 2}](b) -> fp8e4m3fnuz_type, {3, 2, 8, 7}, {112, 56, 1, 8}, target_id=0
@9 = gpu::quant_gemm[alpha=1,beta=0,compute_fp32=1,trans_batch=0,solution_idx=0](@5,@8,@6) -> float_type, {3, 2, 2, 7}, {28, 14, 7, 1}, target_id=0
output = @param:output -> float_type, {3, 2, 2, 7}, {28, 14, 7, 1}, target_id=0
c = @param:c -> float_type, {3, 2, 2, 7}, {28, 14, 7, 1}, target_id=0
@12 = gpu::code_object[code_object=9288,symbol_name=mul_add_kernel,global=42,local=1024,](c,@9,output) -> float_type, {3, 2, 2, 7}, {28, 14, 7, 1}, target_id=0

Start by reviewing PR #2506 and the FP8 tests that it disabled for the CPU backend. Compare the reference, CPU, and GPU execution described in this issue, then verify that the CPU FP8 tests can be re-enabled with the expected conversion behavior and results.