wehub-resource-sync
177 lines
7.5 KiB
Python
177 lines
7.5 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# The file has been adapted from DeepSeek DeepEP project
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# Copyright (c) 2025 DeepSeek
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# Licensed under the MIT License - https://github.com/deepseek-ai/DeepEP/blob/main/LICENSE
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import random
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from typing import Tuple
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import deep_gemm
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import paddle
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from deep_gemm import calc_diff, ceil_div, get_col_major_tma_aligned_tensor
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from paddle import Tensor
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def per_token_cast_to_fp8(x: Tensor) -> Tuple[Tensor, Tensor]:
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assert x.dim() == 2 and x.shape[1] % 128 == 0
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m, n = x.shape
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x_view = paddle.view(x, (m, -1, 128))
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x_abs = paddle.abs(x_view).astype(paddle.float32)
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x_amax = paddle.amax(x_abs, axis=2)
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x_amax = paddle.view(x_amax, (m, -1))
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x_amax = paddle.clip(x_amax, min=1e-4)
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scaled_x = x_view * (448.0 / x_amax.unsqueeze(2))
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scaled_x_converted = paddle.view(scaled_x.astype(paddle.float8_e4m3fn), (m, n))
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x_amax_scaled = paddle.view((x_amax / 448.0), (m, -1))
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result = (scaled_x_converted, x_amax_scaled)
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return result
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def per_block_cast_to_fp8(x: Tensor) -> Tuple[Tensor, Tensor]:
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assert x.dim() == 2
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m, n = x.shape
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x_padded = paddle.zeros((ceil_div(m, 128) * 128, ceil_div(n, 128) * 128), dtype=x.dtype)
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x_padded[:m, :n] = x
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x_view = paddle.view(x_padded, (-1, 128, x_padded.shape[1] // 128, 128))
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x_abs = paddle.abs(x_view).astype(paddle.float32)
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x_amax = paddle.amax(x_abs, axis=(1, 3), keepdim=True)
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x_amax = paddle.clip(x_amax, min=1e-4)
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x_scaled = (x_view * (448.0 / x_amax)).astype(paddle.float8_e4m3fn)
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return x_scaled.view_as(x_padded)[:m, :n].contiguous(), (
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paddle.view(x_amax / 448.0, (x_view.shape[0], x_view.shape[2]))
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)
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def construct(m: int, k: int, n: int) -> Tuple[Tuple[Tensor, Tensor], Tuple[Tensor, Tensor], Tensor, Tensor]:
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x = paddle.randn((m, k), dtype=paddle.bfloat16)
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y = paddle.randn((n, k), dtype=paddle.bfloat16)
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out = paddle.empty((m, n), dtype=paddle.bfloat16)
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ref_out = x @ y.t()
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x_fp8, y_fp8 = per_token_cast_to_fp8(x), per_block_cast_to_fp8(y)
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# Transpose earlier so that the testing will not trigger transposing kernels
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x_fp8 = (x_fp8[0], get_col_major_tma_aligned_tensor(x_fp8[1]))
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return x_fp8, y_fp8, out, ref_out
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def construct_grouped(
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num_groups: int, m: int, k: int, n: int, is_masked: bool
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) -> Tuple[Tuple[Tensor, Tensor], Tuple[Tensor, Tensor], Tensor, Tensor]:
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# x_np = np.full((num_groups, m, k), 3)
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# y_np = np.full((num_groups, n, k), 2)
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# x=paddle.to_tensor(x_np).astype(paddle.bfloat16)
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# y=paddle.to_tensor(y_np).astype(paddle.bfloat16)
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x = paddle.randn((num_groups, m, k), dtype=paddle.bfloat16)
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y = paddle.randn((num_groups, n, k), dtype=paddle.bfloat16)
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out = paddle.empty((num_groups, m, n), dtype=paddle.bfloat16)
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ref_out = paddle.einsum("gmk,gnk->gmn", x, y)
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assert m % 4 == 0, f"TMA alignment error: {m}"
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x_fp8 = (
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paddle.empty_like(x, dtype=paddle.float8_e4m3fn),
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paddle.empty((num_groups, m, k // 128), dtype=paddle.float32),
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)
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y_fp8 = (
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paddle.empty_like(y, dtype=paddle.float8_e4m3fn),
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paddle.empty((num_groups, (n + 127) // 128, k // 128), dtype=paddle.float32),
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)
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for i in range(num_groups):
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# x_fp8[0][i], x_fp8[1][i] = per_token_cast_to_fp8(x[i])
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# y_fp8[0][i], y_fp8[1][i] = per_block_cast_to_fp8(y[i])
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x_fp8_0_i, x_fp8_1_i = per_token_cast_to_fp8(x[i])
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paddle.assign(x_fp8_0_i, x_fp8[0][i])
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paddle.assign(x_fp8_1_i, x_fp8[1][i])
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y_fp8_0_i, y_fp8_1_i = per_block_cast_to_fp8(y[i])
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paddle.assign(y_fp8_0_i, y_fp8[0][i])
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paddle.assign(y_fp8_1_i, y_fp8[1][i])
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# For non-masked input, we must merge the group and M dims
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if not is_masked:
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x_fp8 = (paddle.view(x_fp8[0], (-1, k)), per_token_cast_to_fp8(paddle.view(x, (-1, k)))[1])
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out, ref_out = paddle.view(out, (-1, n)), paddle.view(ref_out, (-1, n))
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# Transpose earlier so that the testing will not trigger transposing kernels
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x_fp8 = (x_fp8[0], get_col_major_tma_aligned_tensor(x_fp8[1]))
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return x_fp8, y_fp8, out, ref_out
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def test_gemm() -> None:
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print("Testing GEMM:")
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for m in (64, 128, 4096):
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for k, n in [(7168, 2112), (1536, 24576), (512, 32768), (16384, 7168), (7168, 4096), (2048, 7168)]:
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x_fp8, y_fp8, out, ref_out = construct(m, k, n)
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deep_gemm.gemm_fp8_fp8_bf16_nt(x_fp8, y_fp8, out)
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diff = calc_diff(out, ref_out)
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assert diff < 0.001, f"{m=}, {k=}, {n=}, {diff:.5f}"
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print()
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def test_m_grouped_gemm_contiguous() -> None:
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print("Testing grouped contiguous GEMM:")
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for num_groups, m, k, n in ((8, 4096, 7168, 4096), (8, 4096, 2048, 7168), (4, 8192, 2048, 7168), (4, 8192, 7168, 4096), ):
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# TODO: make a stronger test
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x_fp8, y_fp8, out, ref_out = construct_grouped(num_groups, m, k, n, is_masked=False)
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m_indices = paddle.arange(0, num_groups, dtype=paddle.int32)
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# m_indices = m_indices.unsqueeze(-1).expand(num_groups, m).contiguous().view(-1)
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m_indices = paddle.flatten(paddle.expand(paddle.unsqueeze(m_indices, -1), shape=[num_groups, m]))
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deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(x_fp8, y_fp8, out, m_indices)
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diff = calc_diff(out, ref_out)
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print("diff:", diff)
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assert diff < 0.001, f"m={m * num_groups}, {k=}, {n=}, {diff:.5f}"
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print()
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def test_m_grouped_gemm_masked() -> None:
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print("Testing grouped masked GEMM:")
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for num_groups, m in ((1, 1024), (2, 512), (4, 256)):
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for k, n in ((7168, 4096), (2048, 7168), ):
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# Test correctness
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masked_m_candidates = list(filter(lambda candidate: candidate <= m, (64, 128, 192, 256, 320, 384)))
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for i in range(10):
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x_fp8, y_fp8, out, ref_out = construct_grouped(num_groups, m, k, n, is_masked=True)
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masked_m = paddle.empty((num_groups,), dtype=paddle.int32)
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for j in range(num_groups):
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masked_m[j] = random.choice(masked_m_candidates)
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# expected_m = min(int(masked_m.float().mean()) + 1, m)
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masked_m_float = paddle.cast(masked_m, "float32")
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masked_m_mean = paddle.mean(masked_m_float)
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masked_m_mean_int = paddle.cast(masked_m_mean, "int32")
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expected_m = min(int(masked_m_mean_int + 1), m)
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deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m)
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for j in range(num_groups):
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diff = calc_diff(out[j, : masked_m[j].item()], ref_out[j, : masked_m[j].item()])
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print("diff:", diff)
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assert diff < 0.001, f"{m=}, {k=}, {n=}, {j=}, masked_m={masked_m[j]}, {num_groups=}, {diff:.5f}"
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print()
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if __name__ == "__main__":
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paddle.seed(0)
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random.seed(0)
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print("Library path:")
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print(f" > {deep_gemm.__path__}\n")
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test_gemm()
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test_m_grouped_gemm_contiguous()
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test_m_grouped_gemm_masked()
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