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Unverified Commit ddf8981d authored by yinfan98's avatar yinfan98 Committed by GitHub
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Delete test_deep_gemm.py (#4891)

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import os
import random
import unittest
from typing import Any, Tuple
import deep_gemm
import torch
from deep_gemm import calc_diff, ceil_div, get_col_major_tma_aligned_tensor, jit
"""
fork deepgemm/tests/test_core.py
"""
def per_token_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
assert x.dim() == 2 and x.size(1) % 128 == 0
m, n = x.shape
x_view = x.view(m, -1, 128)
x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
return (x_view * (448.0 / x_amax.unsqueeze(2))).to(torch.float8_e4m3fn).view(
m, n
), (x_amax / 448.0).view(m, -1)
def per_block_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
assert x.dim() == 2
m, n = x.shape
x_padded = torch.zeros(
(ceil_div(m, 128) * 128, ceil_div(n, 128) * 128), dtype=x.dtype, device=x.device
)
x_padded[:m, :n] = x
x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, 128)
x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
x_scaled = (x_view * (448.0 / x_amax)).to(torch.float8_e4m3fn)
return x_scaled.view_as(x_padded)[:m, :n].contiguous(), (x_amax / 448.0).view(
x_view.size(0), x_view.size(2)
)
def construct(m: int, k: int, n: int) -> Tuple[
Tuple[torch.Tensor, torch.Tensor],
Tuple[torch.Tensor, torch.Tensor],
torch.Tensor,
torch.Tensor,
]:
x = torch.randn((m, k), device="cuda", dtype=torch.bfloat16)
y = torch.randn((n, k), device="cuda", dtype=torch.bfloat16)
out = torch.empty((m, n), device="cuda", dtype=torch.bfloat16)
ref_out = x @ y.t()
x_fp8, y_fp8 = per_token_cast_to_fp8(x), per_block_cast_to_fp8(y)
# Transpose earlier so that the testing will not trigger transposing kernels
x_fp8 = (x_fp8[0], get_col_major_tma_aligned_tensor(x_fp8[1]))
return x_fp8, y_fp8, out, ref_out
def construct_grouped(
num_groups: int, m: int, k: int, n: int, is_masked: bool
) -> Tuple[
Tuple[torch.Tensor, torch.Tensor],
Tuple[torch.Tensor, torch.Tensor],
torch.Tensor,
torch.Tensor,
]:
x = torch.randn((num_groups, m, k), device="cuda", dtype=torch.bfloat16)
y = torch.randn((num_groups, n, k), device="cuda", dtype=torch.bfloat16)
out = torch.empty((num_groups, m, n), device="cuda", dtype=torch.bfloat16)
ref_out = torch.einsum("gmk,gnk->gmn", x, y)
assert m % 4 == 0, f"TMA alignment error: {m}"
x_fp8 = (
torch.empty_like(x, dtype=torch.float8_e4m3fn),
torch.empty((num_groups, m, k // 128), device="cuda", dtype=torch.float),
)
y_fp8 = (
torch.empty_like(y, dtype=torch.float8_e4m3fn),
torch.empty(
(num_groups, (n + 127) // 128, k // 128), device="cuda", dtype=torch.float
),
)
for i in range(num_groups):
x_fp8[0][i], x_fp8[1][i] = per_token_cast_to_fp8(x[i])
y_fp8[0][i], y_fp8[1][i] = per_block_cast_to_fp8(y[i])
# For non-masked input, we must merge the group and M dims
if not is_masked:
x_fp8 = (x_fp8[0].view(-1, k), per_token_cast_to_fp8(x.view(-1, k))[1])
out, ref_out = out.view(-1, n), ref_out.view(-1, n)
# Transpose earlier so that the testing will not trigger transposing kernels
x_fp8 = (x_fp8[0], get_col_major_tma_aligned_tensor(x_fp8[1]))
return x_fp8, y_fp8, out, ref_out
class TestDeepGemmCore(unittest.TestCase):
@classmethod
def setUpClass(cls):
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.manual_seed(0)
random.seed(0)
print("Library path:")
print(f" > {deep_gemm.__path__}\n")
def test_gemm(self):
print("Testing GEMM:")
for m in (64, 128, 4096):
for k, n in [
(7168, 2112),
(1536, 24576),
(512, 32768),
(16384, 7168),
(7168, 4096),
(2048, 7168),
]:
x_fp8, y_fp8, out, ref_out = construct(m, k, n)
deep_gemm.gemm_fp8_fp8_bf16_nt(x_fp8, y_fp8, out)
diff = calc_diff(out, ref_out)
self.assertTrue(diff < 0.001, f"{m=}, {k=}, {n=}, {diff:.5f}")
def test_m_grouped_gemm_contiguous(self):
print("Testing grouped contiguous GEMM:")
for num_groups, m, k, n in (
(4, 8192, 7168, 4096),
(4, 8192, 2048, 7168),
(8, 4096, 7168, 4096),
(8, 4096, 2048, 7168),
):
# TODO: make a stronger test
x_fp8, y_fp8, out, ref_out = construct_grouped(
num_groups, m, k, n, is_masked=False
)
m_indices = torch.arange(0, num_groups, device="cuda", dtype=torch.int)
m_indices = (
m_indices.unsqueeze(-1).expand(num_groups, m).contiguous().view(-1)
)
deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(
x_fp8, y_fp8, out, m_indices
)
diff = calc_diff(out, ref_out)
self.assertTrue(diff < 0.001, f"m={m * num_groups}, {k=}, {n=}, {diff:.5f}")
def test_m_grouped_gemm_masked(self):
print("Testing grouped masked GEMM:")
for num_groups, m in ((1, 1024), (2, 512), (4, 256)):
for k, n in (
(7168, 4096),
(2048, 7168),
):
# Test correctness
masked_m_candidates = list(
filter(
lambda candidate: candidate <= m, (64, 128, 192, 256, 320, 384)
)
)
for i in range(10):
x_fp8, y_fp8, out, ref_out = construct_grouped(
num_groups, m, k, n, is_masked=True
)
masked_m = torch.empty(
(num_groups,), device="cuda", dtype=torch.int
)
for j in range(num_groups):
masked_m[j] = random.choice(masked_m_candidates)
expected_m = min(int(masked_m.float().mean()) + 1, m)
deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(
x_fp8, y_fp8, out, masked_m, expected_m
)
for j in range(num_groups):
diff = calc_diff(
out[j, : masked_m[j].item()],
ref_out[j, : masked_m[j].item()],
)
self.assertTrue(
diff < 0.001,
f"{m=}, {k=}, {n=}, {j=}, masked_m={masked_m[j]}, {num_groups=}, {diff:.5f}",
)
"""
fork deepgemm/tests/test_jit.py
"""
class Capture:
def __init__(self) -> None:
self.read_fd = None
self.write_fd = None
self.saved_stdout = None
self.captured = None
def __enter__(self) -> Any:
self.read_fd, self.write_fd = os.pipe()
self.saved_stdout = os.dup(1)
os.dup2(self.write_fd, 1)
return self
def __exit__(self, exc_type, exc_val, exc_tb) -> None:
os.dup2(self.saved_stdout, 1)
os.close(self.write_fd)
with os.fdopen(self.read_fd, "r") as f:
self.captured = f.read()
def capture(self) -> str:
return self.captured
class TestDeepGemmJIT(unittest.TestCase):
def test_jit(self):
# Runtime
print(f"NVCC compiler: {jit.get_nvcc_compiler()}\n")
# Templates
print("Generated code:")
args = (
("lhs", torch.float8_e4m3fn),
("rhs", torch.float8_e4m3fn),
("scale", torch.float),
("out", torch.bfloat16),
("enable_double_streams", bool),
("stream", torch.cuda.Stream),
)
body = "\n"
body += "std::cout << reinterpret_cast<uint64_t>(lhs) << std::endl;\n"
body += "std::cout << reinterpret_cast<uint64_t>(rhs) << std::endl;\n"
body += "std::cout << reinterpret_cast<uint64_t>(scale) << std::endl;\n"
body += "std::cout << reinterpret_cast<uint64_t>(out) << std::endl;\n"
body += "std::cout << enable_double_streams << std::endl;\n"
body += "std::cout << reinterpret_cast<uint64_t>(stream) << std::endl;\n"
code = jit.generate((), args, body)
print(code)
# Build
print("Building ...")
func = jit.build("test_func", args, code)
# Test correctness
print("Running ...")
fp8_tensor = torch.empty((1,), dtype=torch.float8_e4m3fn, device="cuda")
fp32_tensor = torch.empty((1,), dtype=torch.float, device="cuda")
bf16_tensor = torch.empty((1,), dtype=torch.bfloat16, device="cuda")
with Capture() as capture:
self.assertTrue(
func(
fp8_tensor,
fp8_tensor,
fp32_tensor,
bf16_tensor,
True,
torch.cuda.current_stream(),
)
== 0
)
output = capture.capture()
ref_output = f"{fp8_tensor.data_ptr()}\n{fp8_tensor.data_ptr()}\n{fp32_tensor.data_ptr()}\n{bf16_tensor.data_ptr()}\n1\n{torch.cuda.current_stream().cuda_stream}\n"
self.assertTrue(output == ref_output, f"{output=}, {ref_output=}")
if __name__ == "__main__":
unittest.main()
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