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Add benchmarking and testing for blockwise fp8 GEMM using Triton and … #2740

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cd5779a
Add benchmarking and testing for blockwise fp8 GEMM using Triton and …
romanwu10 Aug 12, 2025
f285f81
Refactor blockwise fp8 GEMM implementation for improved readability a…
romanwu10 Aug 12, 2025
0f1d7e1
Enhance blockwise fp8 GEMM implementations for improved accuracy and …
romanwu10 Aug 14, 2025
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174 changes: 167 additions & 7 deletions benchmarks/benchmark_blockwise_scaled_linear_triton.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,18 @@
fp8_blockwise_act_quant,
fp8_blockwise_weight_quant,
)
# Import training kernels for comparison
from torchao.prototype.blockwise_fp8_training.kernels import (
blockwise_fp8_gemm_1x128_128x128,
fp8_blockwise_act_quant_lhs,
fp8_blockwise_weight_quant_transposed_rhs,
)
from torchao.prototype.blockwise_fp8_training.scaled_mm_kernels import (
blockwise_fp8_gemm_scaled_mm_1x128_128x128,
)
from torchao.prototype.blockwise_fp8_training.linear import (
Float8BlockwiseLinear,
)
from torchao.utils import is_sm_at_least_89
else:
raise RuntimeError("This benchmark is only avaible on CUDA hardware")
Expand Down Expand Up @@ -74,6 +86,54 @@ def benchmark_latency(
}


def benchmark_training_kernels_latency(
m: int, k: int, n: int, block_size: int, dtype: torch.dtype, device
):
"""Benchmark training kernels: Triton vs torch._scaled_mm implementations."""
# Create reference tensors
A_ref = torch.randn((m, k), dtype=torch.bfloat16, device=device)
B_ref = torch.randn((k, n), dtype=torch.bfloat16, device=device)
fp16_time = benchmark_microseconds(torch.nn.functional.linear, A_ref, B_ref)

# Create quantized inputs for training kernels
A_fp8, A_scale = fp8_blockwise_act_quant_lhs(A_ref, block_size)
B_fp8, B_scale = fp8_blockwise_weight_quant_transposed_rhs(B_ref, block_size)

# Benchmark Triton training kernel
try:
triton_time = benchmark_microseconds(
blockwise_fp8_gemm_1x128_128x128,
A_fp8, 1.0 / A_scale, B_fp8, 1.0 / B_scale
)
except Exception as e:
print(f"Triton kernel failed: {e}")
triton_time = float('inf')

# Benchmark torch._scaled_mm training kernel
try:
scaled_mm_time = benchmark_microseconds(
blockwise_fp8_gemm_scaled_mm_1x128_128x128,
A_fp8, 1.0 / A_scale, B_fp8, 1.0 / B_scale, block_size
)
except Exception as e:
print(f"Scaled MM kernel failed: {e}")
scaled_mm_time = float('inf')

return {
"m": m,
"k": k,
"n": n,
"block_size": block_size,
"dtype": dtype,
"fp16_latency (ms)": fp16_time,
"triton_training_latency (ms)": triton_time,
"scaled_mm_training_latency (ms)": scaled_mm_time,
"triton_training_speedup": fp16_time / triton_time if triton_time != float('inf') else 0,
"scaled_mm_training_speedup": fp16_time / scaled_mm_time if scaled_mm_time != float('inf') else 0,
"scaled_mm_vs_triton_speedup": triton_time / scaled_mm_time if triton_time != float('inf') and scaled_mm_time != float('inf') else 0,
}


def benchmark_precision(
m: int, k: int, n: int, block_size: int, dtype: torch.dtype, device
):
Expand All @@ -96,20 +156,90 @@ def benchmark_precision(
}


def benchmark_training_kernels_precision(
m: int, k: int, n: int, block_size: int, dtype: torch.dtype, device
):
"""Benchmark precision of training kernels: Triton vs torch._scaled_mm."""
# Create high precision reference
A_ref = torch.randn((m, k), dtype=torch.bfloat16, device=device)
B_ref = torch.randn((k, n), dtype=torch.bfloat16, device=device)
ref_output = torch.nn.functional.linear(A_ref, B_ref)

# Create quantized inputs
A_fp8, A_scale = fp8_blockwise_act_quant_lhs(A_ref, block_size)
B_fp8, B_scale = fp8_blockwise_weight_quant_transposed_rhs(B_ref, block_size)

results = {
"m": m, "k": k, "n": n, "block_size": block_size, "dtype": dtype
}

# Test Triton kernel
try:
triton_output = blockwise_fp8_gemm_1x128_128x128(
A_fp8, 1.0 / A_scale, B_fp8, 1.0 / B_scale
)
results["triton_error_db"] = compute_error(ref_output, triton_output)
except Exception as e:
print(f"Triton precision test failed: {e}")
results["triton_error_db"] = float('inf')

# Test torch._scaled_mm kernel
try:
scaled_mm_output = blockwise_fp8_gemm_scaled_mm_1x128_128x128(
A_fp8, 1.0 / A_scale, B_fp8, 1.0 / B_scale, block_size
)
results["scaled_mm_error_db"] = compute_error(ref_output, scaled_mm_output)
except Exception as e:
print(f"Scaled MM precision test failed: {e}")
results["scaled_mm_error_db"] = float('inf')

# Compare the two implementations
if results["triton_error_db"] != float('inf') and results["scaled_mm_error_db"] != float('inf'):
try:
triton_output = blockwise_fp8_gemm_1x128_128x128(
A_fp8, 1.0 / A_scale, B_fp8, 1.0 / B_scale
)
scaled_mm_output = blockwise_fp8_gemm_scaled_mm_1x128_128x128(
A_fp8, 1.0 / A_scale, B_fp8, 1.0 / B_scale, block_size
)
results["triton_vs_scaled_mm_error_db"] = compute_error(triton_output, scaled_mm_output)
except Exception:
results["triton_vs_scaled_mm_error_db"] = float('inf')
else:
results["triton_vs_scaled_mm_error_db"] = float('inf')

return results


if __name__ == "__main__" and torch.cuda.is_available():
device = torch.device("cuda")

# Original inference benchmark configurations
k_vals = (8192, 8192, 8192, 28672)
n_vals = (8192, 10240, 57344, 8192)
block_size_vals = (128, 128, 128, 128)

# Training kernel benchmark configurations (smaller set for faster testing)
training_configs = [
(1, 4096, 4096), # Single token
(32, 4096, 4096), # Small batch
(8, 4096, 11008), # MLP up projection
(8, 11008, 4096), # MLP down projection
(1, 4096, 128256), # Vocab projection (if memory allows)
]

latency_results = []
precision_results = []
training_latency_results = []
training_precision_results = []

available_dtypes = (
[torch.float8_e4m3fn, torch.float8_e5m2]
if is_sm_at_least_89()
else [torch.float8_e5m2]
)

print("Running original inference benchmarks...")
for m in tqdm([1 << i for i in range(14)]):
for dtype in available_dtypes:
for n, k, block_size in zip(n_vals, k_vals, block_size_vals):
Expand All @@ -119,12 +249,42 @@ def benchmark_precision(
precision_results.append(
benchmark_precision(m, k, n, block_size, dtype, device)
)

print("Running training kernel benchmarks...")
for m, k, n in tqdm(training_configs):
# Only test on fp8_e4m3fn for training (most common)
if k % 128 == 0 and n % 128 == 0: # Ensure divisibility
try:
training_latency_results.append(
benchmark_training_kernels_latency(m, k, n, 128, torch.float8_e4m3fn, device)
)
training_precision_results.append(
benchmark_training_kernels_precision(m, k, n, 128, torch.float8_e4m3fn, device)
)
except Exception as e:
print(f"Skipping training config ({m}, {k}, {n}): {e}")

df_latency = pd.DataFrame(latency_results)
df_precision = pd.DataFrame(precision_results)

df_latency.to_csv("blockwise_triton_latency_results.csv", index=False)
df_precision.to_csv("blockwise_triton_precision_results.csv", index=False)
# Save results
if latency_results:
df_latency = pd.DataFrame(latency_results)
df_latency.to_csv("blockwise_triton_inference_latency_results.csv", index=False)
print("\nInference Latency Results:")
print(df_latency.to_markdown(index=False))

print(df_latency.to_markdown(index=False))
print(df_precision.to_markdown(index=False))
if precision_results:
df_precision = pd.DataFrame(precision_results)
df_precision.to_csv("blockwise_triton_inference_precision_results.csv", index=False)
print("\nInference Precision Results:")
print(df_precision.to_markdown(index=False))

if training_latency_results:
df_training_latency = pd.DataFrame(training_latency_results)
df_training_latency.to_csv("blockwise_training_kernels_latency_results.csv", index=False)
print("\nTraining Kernels Latency Results:")
print(df_training_latency.to_markdown(index=False))

if training_precision_results:
df_training_precision = pd.DataFrame(training_precision_results)
df_training_precision.to_csv("blockwise_training_kernels_precision_results.csv", index=False)
print("\nTraining Kernels Precision Results:")
print(df_training_precision.to_markdown(index=False))
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