Re-implementing HBM stack / XCD info for incoming product sku
Co-authored-by: Nicholas Curtis <nicholas.curtis@amd.com> Signed-off-by: coleramos425 <colramos@amd.com>
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coleramos425
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Cole Ramos
Cole Ramos
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295b344646
@@ -285,8 +285,8 @@ def run_prof(fname, profiler_options, workload_dir, mspec, loglevel):
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if new_env:
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# flatten tcc for applicable mi300 input
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f = path(workload_dir + "/out/pmc_1/results_" + fbase + ".csv")
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hbm_stack_num = get_hbm_stack_num(mspec.gpu_model, mspec.memory_partition)
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df = flatten_tcc_info_across_hbm_stacks(f, hbm_stack_num, int(mspec._l2_banks))
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xcds = total_xcds(mspec.gpu_model, mspec.compute_partition)
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df = flatten_tcc_info_across_xcds(f, xcds, int(mspec._l2_banks))
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df.to_csv(f, index=False)
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if os.path.exists(workload_dir + "/out"):
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@@ -492,9 +492,9 @@ def mibench(args, mspec):
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)
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def flatten_tcc_info_across_hbm_stacks(file, stack_num, tcc_channel_per_stack):
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def flatten_tcc_info_across_xcds(file, xcds, tcc_channel_per_xcd):
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"""
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Flatten TCC per channel counters across all HBM stacks in used.
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Flatten TCC per channel counters across all XCDs in partition.
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NB: This func highly depends on the default behavior of rocprofv2 on MI300,
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which might be broken anytime in the future!
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"""
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@@ -513,22 +513,22 @@ def flatten_tcc_info_across_hbm_stacks(file, stack_num, tcc_channel_per_stack):
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cols = non_tcc_cols_orig
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tcc_cols_in_group = {}
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for i in range(0, stack_num):
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for i in range(0, xcds):
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tcc_cols_in_group[i] = []
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for col in tcc_cols_orig:
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for i in range(0, stack_num):
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for i in range(0, xcds):
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# filter the channel index only
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p = re.compile(r"\[(\d+)\]")
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# pick up the 1st element only
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r = (
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lambda match: "["
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+ str(int(match.group(1)) + i * tcc_channel_per_stack)
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+ str(int(match.group(1)) + i * tcc_channel_per_xcd)
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+ "]"
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)
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tcc_cols_in_group[i].append(re.sub(pattern=p, repl=r, string=col))
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for i in range(0, stack_num):
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for i in range(0, xcds):
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# print(tcc_cols_in_group[i])
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cols += tcc_cols_in_group[i]
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# print(cols)
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@@ -537,7 +537,7 @@ def flatten_tcc_info_across_hbm_stacks(file, stack_num, tcc_channel_per_stack):
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### Rearrange data with extended column names
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# print(len(df_orig.index))
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for idx in range(0, len(df_orig.index), stack_num):
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for idx in range(0, len(df_orig.index), xcds):
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# assume the front none TCC columns are the same for all XCCs
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df_non_tcc = df_orig.iloc[idx].filter(regex=r"^(?!.*TCC).*$")
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# display(df_non_tcc)
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@@ -545,7 +545,7 @@ def flatten_tcc_info_across_hbm_stacks(file, stack_num, tcc_channel_per_stack):
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# extract all tcc from one dispatch
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# NB: assuming default contiguous order might not be safe!
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df_tcc_all = df_orig.iloc[idx : (idx + stack_num)].filter(regex="TCC")
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df_tcc_all = df_orig.iloc[idx : (idx + xcds)].filter(regex="TCC")
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# display(df_tcc_all)
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for idx, row in df_tcc_all.iterrows():
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@@ -557,36 +557,46 @@ def flatten_tcc_info_across_hbm_stacks(file, stack_num, tcc_channel_per_stack):
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return df
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def get_hbm_stack_num(gpu_name, memory_partition):
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"""
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Get total HBM stack numbers based on memory partition for MI300.
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"""
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# TODO:
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# - better err log
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if gpu_name.lower() == "mi300a_a0" or gpu_name.lower() == "mi300a_a1":
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if memory_partition.lower() == "nps1":
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def total_xcds(archname, compute_partition):
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# check MI300 has a valid compute partition
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mi300a_archs = ["mi300a_a0", "mi300a_a1"]
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mi300x_archs = ["mi300x_a0", "mi300x_a1"]
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mi308x_archs = ["mi308x"]
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if archname.lower() in mi300a_archs + mi300x_archs + mi308x_archs and compute_partition == "NA":
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console_error("Invalid compute partition found for {}".format(archname))
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if archname.lower() not in mi300a_archs + mi300x_archs + mi308x_archs:
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return 1
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# from the whitepaper
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# https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/white-papers/amd-cdna-3-white-paper.pdf
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if compute_partition.lower() == "spx":
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if archname.lower() in mi300a_archs:
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return 6
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elif memory_partition.lower() == "nps4":
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return 2
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elif memory_partition.lower() == "nps8":
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return 1
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else:
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console_error("Invalid MI300A memory partition mode!")
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elif gpu_name.lower() == "mi300x_a0" or gpu_name.lower() == "mi300x_a1":
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if memory_partition.lower() == "nps1":
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if archname.lower() in mi300x_archs:
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return 8
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elif memory_partition.lower() == "nps2":
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if archname.lower() in mi308x_archs:
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return 4
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elif memory_partition.lower() == "nps4":
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if compute_partition.lower() == "tpx":
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if archname.lower() in mi300a_archs:
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return 2
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elif memory_partition.lower() == "nps8":
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if compute_partition.lower() == "dpx":
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if archname.lower() in mi300x_archs:
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return 4
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if archname.lower() in mi308x_archs:
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return 2
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if compute_partition.lower() == "qpx":
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if archname.lower() in mi300x_archs:
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return 2
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if compute_partition.lower() == "cpx":
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if archname.lower() in mi300x_archs:
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return 2
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if archname.lower() in mi308x_archs:
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return 1
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else:
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console_error("Invalid MI300X memory partition mode!")
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else:
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# Fixme: add proper numbers for other archs
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return -1
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# TODO implement other archs here as needed
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console_error(
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"Unknown compute partition / arch found for {} / {}".format(
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compute_partition, archname
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)
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)
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def get_submodules(package_name):
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