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Move scaling logic to input generation #338

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60 changes: 30 additions & 30 deletions tritonbench/operators/fp8_gemm/fp8_gemm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -56,14 +56,27 @@ def __init__(

def get_input_iter(self):
def args(m, n, k):
a = torch.randn(m, k, device=self.device).to(torch.float8_e4m3fn)
a = torch.randn(m, k, device=self.device).to(torch.float16)
b = (
torch.randn(k, n, device=self.device)
.to(torch.float8_e4m3fn)
.to(torch.float16)
.T.contiguous()
.T
)
return (a, b)

if self.extra_args.scaling_rowwise:
M, N = a.shape[0], b.shape[1]
scale_a = torch.ones((M, 1), dtype=torch.float32, device=a.device)
scale_b = torch.ones((1, N), dtype=torch.float32, device=b.device)
else:
scale_a = torch.tensor(1.0, device=a.device)
scale_b = torch.tensor(1.0, device=a.device)

# Kernels expect dtype=float8_e4m3fn
a = a.to(torch.float8_e4m3fn)
b = b.to(torch.float8_e4m3fn)

return (a, b, scale_a, scale_b)

if (
hasattr(self, "external_shapes") and self.external_shapes
Expand All @@ -90,62 +103,49 @@ def args(m, n, k):
yield args(m, n, k)

def get_x_val(self, example_inputs) -> float:
a, b = example_inputs
a, b, _, _ = example_inputs
m, k = a.size()
_, n = b.size()
return (m, n, k)

@register_benchmark(baseline=True)
def torch_fp8_gemm(self, a, b):
def _get_out_dtype(self):
if self.extra_args.scaling_rowwise:
M, N = a.shape[0], b.shape[1]
scale_a = torch.ones((M, 1), dtype=torch.float32, device=a.device)
scale_b = torch.ones((1, N), dtype=torch.float32, device=b.device)
out_dtype = torch.bfloat16
return torch.bfloat16
else:
scale_a = torch.tensor(1.0, device=a.device)
scale_b = torch.tensor(1.0, device=a.device)
out_dtype = torch.float16
return torch.float16

@register_benchmark(baseline=True)
def torch_fp8_gemm(self, a, b, scale_a, scale_b):
return lambda: torch._scaled_mm(
a, b, scale_a, scale_b, use_fast_accum=True, out_dtype=out_dtype
a, b, scale_a, scale_b, use_fast_accum=True, out_dtype=self._get_out_dtype()
)

@register_benchmark()
def pt2_fp8_gemm(self, a, b) -> Callable:
def pt2_fp8_gemm(self, a, b, scale_a, scale_b) -> Callable:
torch._dynamo.reset()
with inductor_config.patch(
max_autotune=True,
max_autotune_gemm_backends="TRITON",
autotune_fallback_to_aten=False,
):
if self.extra_args.scaling_rowwise:
M, N = a.shape[0], b.shape[1]
scale_a = torch.ones((M, 1), dtype=torch.float32, device=a.device)
scale_b = torch.ones((1, N), dtype=torch.float32, device=b.device)
out_dtype = torch.bfloat16
else:
scale_a = torch.tensor(1.0, device=a.device)
scale_b = torch.tensor(1.0, device=b.device)
out_dtype = torch.float16
f = lambda a, b: torch._scaled_mm(
a, b, scale_a, scale_b, use_fast_accum=True, out_dtype=out_dtype
a, b, scale_a, scale_b, use_fast_accum=True, out_dtype=self._get_out_dtype()
)
compiled = torch.compile(f, dynamic=False)
compiled(a, b)

return lambda: compiled(a, b)

@register_benchmark()
def triton_fp8_gemm(self, a, b):
def triton_fp8_gemm(self, a, b, scale_a, scale_b):
return lambda: tutorial_matmul(a, b)

@register_benchmark(enabled=HAS_TMA)
def triton_persistent_fp8_gemm(self, a, b):
def triton_persistent_fp8_gemm(self, a, b, scale_a, scale_b):
return lambda: matmul_persistent(a, b)

@register_benchmark(enabled=HAS_TMA)
def triton_tma_persistent_fp8_gemm(self, a, b):
def triton_tma_persistent_fp8_gemm(self, a, b, scale_a, scale_b):
b = b.T.contiguous()
c, desc_a, desc_b, desc_c = allocate_matmul_tma(a, b)
return lambda: matmul_tma_persistent(a, b, c, desc_a, desc_b, desc_c)
Expand All @@ -155,7 +155,7 @@ def gbps(self, fn, example_inputs: Any, metrics: BenchmarkOperatorMetrics) -> fl
def nbytes(t):
return t.numel() * t.element_size()

a, b = example_inputs
a, b, _, _ = example_inputs
c = fn()
c = c[0] if isinstance(c, tuple) else c

Expand All @@ -168,7 +168,7 @@ def nbytes(t):
def flops(
self, fn_name: str, example_inputs: Any, metrics: BenchmarkOperatorMetrics
) -> float:
a, b = example_inputs
a, b, _, _ = example_inputs
m, k = a.size()
_, n = b.size()
flops = 2 * m * n * k
Expand Down
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