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FP8 Grouped Gemm Optimization #3655

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FP8 Grouped Gemm Optimization #3655

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23f9bf0
Add preprocess stage to quantize bench operators
Feb 3, 2025
00c43b4
Add tracing option to quantize bench
Feb 3, 2025
606449f
FP8 Grouped Gemm Optimization
jwfromm Feb 4, 2025
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21 changes: 9 additions & 12 deletions fbgemm_gpu/experimental/gemm/triton_gemm/fp8_gemm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2447,6 +2447,13 @@ def triton_quantize_fp8_row(
torch.Tensor: fp8 scaled tensor.
torch.Tensor: reciprocal scale tensor per row.
"""
assert a.dim() <= 4, "Triton only supports up to 4 dimension input tensor."
a_shape = a.shape
while a.dim() < 4:
a = a.unsqueeze(0)
if zero_start_index_M is not None:
# There should be one value of zero_start_index_M per NxK matrix.
zero_start_index_M = zero_start_index_M.view(a.shape[0], a.shape[1])
# Get constant values.
pt_dtype, tl_dtype, max_fp8, eps = get_fp8_constants()
num_rows = a.numel() // a.shape[-1]
Expand Down Expand Up @@ -2484,7 +2491,7 @@ def triton_quantize_fp8_row(
USE_INT64=use_int64,
)

return a_fp8, a_scale
return a_fp8.view(a_shape), a_scale.view(a_shape[:-1])


@torch.library.custom_op("triton::quantize_fp8_row", mutates_args=())
Expand Down Expand Up @@ -2514,17 +2521,7 @@ def quantize_fp8_row(
logger.info("Triton does not support cpu, falling back to torch ops.")
use_triton = False
if use_triton:
assert (
a.dim() <= 4
), "Only up to 4 dimension input tensor is supported if use_triton is True"
a_shape = a.shape
while a.dim() < 4:
a = a.unsqueeze(0)
if zero_start_index_M is not None:
# There should be one value of zero_start_index_M per NxK matrix.
zero_start_index_M = zero_start_index_M.view(a.shape[0], a.shape[1])
a_fp8, a_scale = triton_quantize_fp8_row(a, scale_ub, zero_start_index_M)
return a_fp8.view(a_shape), a_scale.view(a_shape[:-1])
return triton_quantize_fp8_row(a, scale_ub, zero_start_index_M)
# else use pytorch implementation.
if not output_device:
output_device = a.device
Expand Down
80 changes: 52 additions & 28 deletions fbgemm_gpu/experimental/gen_ai/bench/quantize_bench.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,16 @@
import seaborn as sns
import torch

try:
from accelerators.pytorch.lib.utils.torch_profiler import profiler_or_nullcontext
except ImportError:
from contextlib import nullcontext

class profiler_or_nullcontext(nullcontext):
def __init__(self, *args, **kwargs):
super().__init__()


from .quantize_ops import get_quantize_ops, QuantizeOpBase


Expand Down Expand Up @@ -96,6 +106,7 @@ def benchmark_grouped(
bench_quantize: bool = False,
use_rotating_buffer_bench: bool = False,
use_cuda_graph: bool = True,
trace: bool = False,
) -> Dict[str, Any]:
num_groups = len(m)
# Create input tensors.
Expand Down Expand Up @@ -127,7 +138,8 @@ def benchmark_grouped(
# Also check if the operator is supported.
if kernel_requested and quantize_op.supported:
# Get the quantized tensors for this operator.
quantized_vals = quantize_op.quantize(A, B)
preprocessed_args = quantize_op.preprocess(A, B)
quantized_vals = quantize_op.quantize(*preprocessed_args)
# Compute the output given quantized values.
output = quantize_op.compute(*quantized_vals)
# Some kernels may pad output, just take the first m values of each row.
Expand All @@ -142,20 +154,21 @@ def benchmark_grouped(
# Now perform benchmark.
if bench_quantize:
# Benchmark both quantize and compute.
ms_runtime = quantize_op.benchmark(
A,
B,
bench_quantize=True,
use_rotating_buffer_bench=use_rotating_buffer_bench,
use_cuda_graph=use_cuda_graph,
)
with profiler_or_nullcontext(enabled=trace, with_stack=True):
ms_runtime = quantize_op.benchmark(
*preprocessed_args,
bench_quantize=True,
use_rotating_buffer_bench=use_rotating_buffer_bench,
use_cuda_graph=use_cuda_graph,
)
else:
ms_runtime = quantize_op.benchmark(
*quantized_vals,
bench_quantize=False,
use_rotating_buffer_bench=use_rotating_buffer_bench,
use_cuda_graph=use_cuda_graph,
)
with profiler_or_nullcontext(enabled=trace, with_stack=True):
ms_runtime = quantize_op.benchmark(
*quantized_vals,
bench_quantize=False,
use_rotating_buffer_bench=use_rotating_buffer_bench,
use_cuda_graph=use_cuda_graph,
)

# Print out results for this op.
tflops = 0
Expand Down Expand Up @@ -197,6 +210,7 @@ def benchmark(
bench_quantize: bool = False,
use_rotating_buffer_bench: bool = False,
use_cuda_graph: bool = True,
trace: bool = False,
) -> Dict[str, Any]:
# Create input tensors.
if b > 1:
Expand All @@ -218,8 +232,10 @@ def benchmark(
)
# Also check if the operator is supported.
if kernel_requested and quantize_op.supported:
# Preprocess data if needed.
preprocessed_args = quantize_op.preprocess(A, B)
# Get the quantized tensors for this operator.
quantized_vals = quantize_op.quantize(A, B)
quantized_vals = quantize_op.quantize(*preprocessed_args)
# Compute the output given quantized values.
output = quantize_op.compute(*quantized_vals)
# Compare the quantize op output to reference as a sanity check.
Expand All @@ -228,20 +244,21 @@ def benchmark(
# Now perform benchmark.
if bench_quantize:
# Benchmark both quantize and compute.
ms_runtime = quantize_op.benchmark(
A,
B,
bench_quantize=True,
use_rotating_buffer_bench=use_rotating_buffer_bench,
use_cuda_graph=use_cuda_graph,
)
with profiler_or_nullcontext(enabled=trace, with_stack=True):
ms_runtime = quantize_op.benchmark(
*preprocessed_args,
bench_quantize=True,
use_rotating_buffer_bench=use_rotating_buffer_bench,
use_cuda_graph=use_cuda_graph,
)
else:
ms_runtime = quantize_op.benchmark(
*quantized_vals,
bench_quantize=False,
use_rotating_buffer_bench=use_rotating_buffer_bench,
use_cuda_graph=use_cuda_graph,
)
with profiler_or_nullcontext(enabled=trace, with_stack=True):
ms_runtime = quantize_op.benchmark(
*quantized_vals,
bench_quantize=False,
use_rotating_buffer_bench=use_rotating_buffer_bench,
use_cuda_graph=use_cuda_graph,
)

# Print out results for this op.
tflops = 2 * b * m * n * k / (ms_runtime / 1e3) / 1e12
Expand Down Expand Up @@ -369,6 +386,7 @@ def main(args: Any):
args.bench_quantize,
args.use_rotating_buffer_bench,
not args.no_cuda_graph,
args.trace,
)
benchmark_results.append(quantize_measurements)
if args.export_csv:
Expand Down Expand Up @@ -459,6 +477,12 @@ def invoke_main() -> None:
action="store_true",
help="If set, benchmark using fixed shapes relevant to ldm workloads.",
)
parser.add_argument(
"--trace",
default=False,
action="store_true",
help="If set, produce a performance trace of the benchmark.",
)

args = parser.parse_args()
main(args)
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