mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2026-04-23 00:17:25 +08:00
SUN Dong
370db86a9d
* [RL] Add fused stack-transpose-quant for BlockWiseFP8 MoE weight quantization * update * update * update * support custom topk inDeepGemmFusedMoeMethod apply_tp * apply_ep_prefill support moe_topk_select * update * add ut * add ut * add ut * modity doc * fix env and docs * remove useless cp * add ut * rm FD_USE_PHI_TOPK --------- Co-authored-by: zhanghonggeng <zhanghonggeng@baidu.com>
698 lines
28 KiB
Python
698 lines
28 KiB
Python
"""
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# 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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"""
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from __future__ import annotations
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import importlib
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import sys
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import types
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import paddle
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import pytest
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if not hasattr(paddle, "compat"):
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paddle.compat = types.SimpleNamespace(enable_torch_proxy=lambda scope=None: None)
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if not hasattr(paddle.nn.functional, "swiglu"):
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paddle.nn.functional.swiglu = lambda x: x
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from fastdeploy.model_executor.layers.moe import fused_moe_triton_backend as backend
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class DummyQuantConfig:
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def __init__(self, is_checkpoint_bf16=False, weight_block_size=(2, 2), name_value="wint8"):
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self.is_checkpoint_bf16 = is_checkpoint_bf16
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self.weight_block_size = weight_block_size
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self._name_value = name_value
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self.deepgemm_scale_ue8m0 = False
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def name(self):
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return self._name_value
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class DummyQuantMethod:
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def __init__(self, quant_config):
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self.quant_config = quant_config
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class DummyLoadConfig:
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def __init__(self, load_choices="default_v1"):
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self.load_choices = load_choices
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self.dynamic_load_weight = False
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class DummyFDConfig:
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def __init__(self, load_choices="default_v1"):
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self.load_config = DummyLoadConfig(load_choices)
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self.model_config = types.SimpleNamespace(enable_cache=False)
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class DummyGate(paddle.nn.Layer):
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def __init__(self, num_experts):
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super().__init__()
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self.num_experts = num_experts
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def forward(self, x):
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return paddle.ones([x.shape[0], self.num_experts], dtype="float32")
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class DummyLayer(paddle.nn.Layer):
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def __init__(
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self,
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quant_config,
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num_local_experts=2,
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hidden_size=4,
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moe_intermediate_size=3,
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top_k=2,
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load_choices="default_v1",
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weight_dtype="float16",
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):
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super().__init__()
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self.num_local_experts = num_local_experts
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self.num_experts = num_local_experts
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self.hidden_size = hidden_size
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self.moe_intermediate_size = moe_intermediate_size
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self.top_k = top_k
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self.n_group = 1
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self.topk_group = 1
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self.routed_scaling_factor = 1.0
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self.renormalize = True
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self.gate_correction_bias = paddle.zeros([num_local_experts], dtype="float32")
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self.topk_method = "noaux_tc"
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self.fd_config = DummyFDConfig(load_choices)
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self.weight_dtype = weight_dtype
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self.quant_method = DummyQuantMethod(quant_config)
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self.weight_key_map = {
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"up_gate_proj_expert_weight_scale_key": "up_gate_scale_{}",
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"down_proj_expert_weight_scale_key": "down_proj_scale_{}",
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"up_gate_proj_expert_in_scale_key": "up_gate_in_scale_{}",
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"down_proj_expert_in_scale_key": "down_proj_in_scale_{}",
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}
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self._up_weights = None
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self._down_weights = None
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def extract_moe_ffn_weights(self, state_dict):
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return self._up_weights, self._down_weights, None, None
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class DummyKernel:
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def __init__(self):
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self.calls = []
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def __getitem__(self, grid):
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def _runner(*args, **kwargs):
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if len(args) > 2 and isinstance(args[2], paddle.Tensor):
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args[2].set_value(paddle.zeros_like(args[2]))
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self.calls.append({"grid": grid, "args": args, "kwargs": kwargs})
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return _runner
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@pytest.fixture(autouse=True)
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def patch_float8(monkeypatch):
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monkeypatch.setattr(paddle, "float8_e4m3fn", paddle.float16, raising=False)
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return monkeypatch
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@pytest.fixture()
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def fake_ops(monkeypatch):
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def fake_moe_topk_select(gate_out, gate_correction_bias, top_k, apply_norm_weight, use_softmax):
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token_num = gate_out.shape[0]
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topk_ids = paddle.zeros([token_num, top_k], dtype="int64")
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topk_weights = paddle.ones([token_num, top_k], dtype="float32")
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return topk_ids, topk_weights
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def fake_get_moe_scores(*args, **kwargs):
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gate_out = args[0]
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token_num = gate_out.shape[0]
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top_k = args[3]
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topk_ids = paddle.zeros([token_num, top_k], dtype="int64")
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topk_weights = paddle.ones([token_num, top_k], dtype="float32")
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return gate_out, topk_weights, topk_ids
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def fake_triton_preprocess(topk_ids, num_local_experts, block_size):
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token_num = topk_ids.shape[0]
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top_k = topk_ids.shape[1]
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sorted_token_ids = paddle.arange(token_num * top_k, dtype="int32")
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expert_ids = paddle.zeros_like(sorted_token_ids)
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num_tokens_post_padded = paddle.to_tensor([token_num * top_k], dtype="int32")
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return sorted_token_ids, expert_ids, num_tokens_post_padded
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def fake_scaled_fp8_quant(x, use_per_token_if_dynamic=True):
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x_scale = paddle.ones([x.shape[0], 1], dtype="float32")
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return x, x_scale
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def fake_hadamard_quant_fp8(x, scale, topk_ids, top_k, intermediate_size, tiled):
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return x
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def fake_fp8_quant_blockwise(x, using_pow2_scale=False, output_scale_transpose=False):
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scale = paddle.ones([x.shape[0], x.shape[1]], dtype="float32")
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return x, scale
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monkeypatch.setattr(
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backend.fastdeploy.model_executor.ops.gpu,
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"moe_topk_select",
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fake_moe_topk_select,
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raising=False,
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)
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monkeypatch.setattr(backend, "get_moe_scores", fake_get_moe_scores)
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monkeypatch.setattr(backend, "tritonmoe_preprocess_func", fake_triton_preprocess, raising=False)
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monkeypatch.setattr(
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backend.fastdeploy.model_executor.ops.gpu,
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"tritonmoe_preprocess_func",
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fake_triton_preprocess,
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raising=False,
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)
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monkeypatch.setattr(backend, "scaled_fp8_quant", fake_scaled_fp8_quant)
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monkeypatch.setattr(
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backend.fastdeploy.model_executor.ops.gpu,
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"moe_fused_hadamard_quant_fp8",
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fake_hadamard_quant_fp8,
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raising=False,
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)
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monkeypatch.setattr(paddle.incubate.nn.functional, "fp8_quant_blockwise", fake_fp8_quant_blockwise, raising=False)
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monkeypatch.setattr(paddle.incubate.nn.functional, "swiglu", lambda x: x, raising=False)
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return monkeypatch
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def _make_block_scale(weight_tensor, block_size):
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return paddle.ones(
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[
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(weight_tensor.shape[0] + block_size[0] - 1) // block_size[0],
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(weight_tensor.shape[1] + block_size[1] - 1) // block_size[1],
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],
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dtype="float32",
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)
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class TestFusedMoeTritonBackend:
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def test_backend_imports_kernel_module(self, monkeypatch):
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kernel = DummyKernel()
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monkeypatch.setattr(
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backend.fastdeploy.model_executor.ops.gpu,
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"tritonmoe_preprocess_func",
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lambda *args, **kwargs: None,
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raising=False,
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)
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monkeypatch.setitem(
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sys.modules,
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"fastdeploy.model_executor.layers.moe.triton_moe_kernels",
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types.SimpleNamespace(fused_moe_kernel_paddle=kernel),
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)
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reloaded = importlib.reload(backend)
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assert hasattr(reloaded, "fused_moe_kernel_paddle")
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def test_triton_weight_only_create_and_apply(self, fake_ops, monkeypatch):
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quant_config = DummyQuantConfig(is_checkpoint_bf16=False)
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layer = DummyLayer(quant_config)
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method = backend.TritonWeightOnlyMoEMethod(quant_config)
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method.create_weights(layer, model_format="torch")
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layer._up_weights = [
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paddle.arange(layer.hidden_size * layer.moe_intermediate_size * 2, dtype="float32").reshape(
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[layer.hidden_size, layer.moe_intermediate_size * 2]
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)
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for _ in range(layer.num_local_experts)
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]
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layer._down_weights = [
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paddle.arange(layer.moe_intermediate_size * layer.hidden_size, dtype="float32").reshape(
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[layer.moe_intermediate_size, layer.hidden_size]
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)
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for _ in range(layer.num_local_experts)
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]
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method.process_loaded_weights(layer, state_dict={})
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assert paddle.any(layer.up_gate_proj_weight_scale > 0)
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kernel = DummyKernel()
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monkeypatch.setattr(backend, "fused_moe_kernel_paddle", kernel, raising=False)
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x = paddle.randn([2, layer.hidden_size], dtype="float32")
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gate = DummyGate(layer.num_local_experts)
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captured = {}
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def hook(topk_ids):
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captured["topk_ids"] = topk_ids
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_ = method.apply(layer, x, gate, topk_ids_hookfunc=hook)
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assert "topk_ids" in captured
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empty_out = method.apply(layer, paddle.zeros([0, layer.hidden_size], dtype="float32"), gate)
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assert empty_out.shape == [0, layer.hidden_size]
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def test_triton_weight_only_prequant_and_bf16_create(self, fake_ops):
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quant_config = DummyQuantConfig(is_checkpoint_bf16=True)
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layer = DummyLayer(quant_config, weight_dtype="float32")
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method = backend.TritonWeightOnlyMoEMethod(quant_config)
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assert method.process_prequanted_weights(layer, state_dict={}) is None
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method.create_weights(layer, model_format="not_torch")
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assert list(layer.up_gate_proj_weight.shape) == [
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layer.num_local_experts,
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layer.hidden_size,
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layer.moe_intermediate_size * 2,
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]
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def test_triton_weight_only_process_weights_after_loading_bf16(self, fake_ops, monkeypatch):
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quant_config = DummyQuantConfig(is_checkpoint_bf16=True)
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layer = DummyLayer(quant_config, weight_dtype="float32")
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method = backend.TritonWeightOnlyMoEMethod(quant_config)
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method.create_weights(layer, model_format="torch")
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method.model_format = "torch"
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monkeypatch.setattr(backend, "weight_fully_copied", lambda tensor: True)
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transpose_calls = []
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monkeypatch.setattr(backend, "process_weight_transpose", lambda _layer, name: transpose_calls.append(name))
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monkeypatch.setattr(backend, "free_tensor", lambda tensor: None)
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method.process_weights_after_loading(layer)
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assert transpose_calls
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def test_triton_weight_only_process_weights_after_loading_return(self, fake_ops):
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quant_config = DummyQuantConfig(is_checkpoint_bf16=False)
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layer = DummyLayer(quant_config)
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method = backend.TritonWeightOnlyMoEMethod(quant_config)
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assert method.process_weights_after_loading(layer) is None
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def test_triton_weight_only_apply_aux_topk(self, fake_ops, monkeypatch):
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quant_config = DummyQuantConfig(is_checkpoint_bf16=False)
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layer = DummyLayer(quant_config)
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layer.topk_method = "aux"
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method = backend.TritonWeightOnlyMoEMethod(quant_config)
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method.create_weights(layer, model_format="torch")
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kernel = DummyKernel()
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monkeypatch.setattr(backend, "fused_moe_kernel_paddle", kernel, raising=False)
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called = {}
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def hook(topk_ids):
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called["ids"] = topk_ids
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_ = method.apply(
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layer,
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paddle.randn([1, layer.hidden_size], dtype="float32"),
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DummyGate(layer.num_local_experts),
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hook,
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)
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assert "ids" in called
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def test_wfp8afp8_method_apply_paths(self, fake_ops, monkeypatch):
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quant_config = DummyQuantConfig(is_checkpoint_bf16=False)
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layer = DummyLayer(quant_config)
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layer.topk_method = "aux"
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method = backend.Wfp8Afp8MoEMethod(quant_config)
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method.create_weights(layer, model_format="torch")
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kernel = DummyKernel()
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monkeypatch.setitem(
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sys.modules,
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"fastdeploy.model_executor.layers.moe.triton_moe_kernels",
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types.SimpleNamespace(fused_moe_kernel_paddle=kernel),
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)
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monkeypatch.setattr(backend, "fused_moe_kernel_paddle", kernel, raising=False)
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x = paddle.randn([1, layer.hidden_size], dtype="float32")
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gate = DummyGate(layer.num_local_experts)
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captured = {}
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def hook(topk_ids):
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captured["ids"] = topk_ids
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_ = method.apply(layer, x, gate, topk_ids_hookfunc=hook)
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assert "ids" in captured
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up_gate = [
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paddle.zeros([layer.moe_intermediate_size * 2, layer.hidden_size], dtype="float32")
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for _ in range(layer.num_local_experts)
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]
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down_proj = [
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paddle.zeros([layer.hidden_size, layer.moe_intermediate_size], dtype="float32")
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for _ in range(layer.num_local_experts)
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]
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method.check(layer, up_gate, down_proj)
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def test_wfp8afp8_prequant_raises(self, fake_ops):
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quant_config = DummyQuantConfig(is_checkpoint_bf16=False)
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layer = DummyLayer(quant_config)
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method = backend.Wfp8Afp8MoEMethod(quant_config)
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with pytest.raises(NotImplementedError):
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method.process_prequanted_weights(layer, state_dict={})
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def test_wfp8afp8_create_weights_bf16_branch(self, fake_ops):
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quant_config = DummyQuantConfig(is_checkpoint_bf16=True)
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layer = DummyLayer(quant_config, weight_dtype="float32")
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method = backend.Wfp8Afp8MoEMethod(quant_config)
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method.create_weights(layer, model_format="not_torch")
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assert list(layer.down_proj_weight.shape) == [
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layer.num_local_experts,
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layer.moe_intermediate_size,
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layer.hidden_size,
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]
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def test_wfp8afp8_process_weights_after_loading_bf16(self, fake_ops, monkeypatch):
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quant_config = DummyQuantConfig(is_checkpoint_bf16=True)
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layer = DummyLayer(quant_config, weight_dtype="float32")
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method = backend.Wfp8Afp8MoEMethod(quant_config)
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method.create_weights(layer, model_format="torch")
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method.model_format = "torch"
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monkeypatch.setattr(backend, "weight_fully_copied", lambda tensor: False)
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transpose_calls = []
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monkeypatch.setattr(backend, "process_weight_transpose", lambda _layer, name: transpose_calls.append(name))
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monkeypatch.setattr(backend, "free_tensor", lambda tensor: None)
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def fake_per_token_cast_to_fp8(weight):
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return weight.cast(paddle.float16), paddle.ones([weight.shape[1], 1], dtype="float32")
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monkeypatch.setattr(
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backend.fastdeploy.model_executor.layers.utils, "per_token_cast_to_fp8", fake_per_token_cast_to_fp8
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)
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method.process_weights_after_loading(layer)
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assert transpose_calls
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def test_wfp8afp8_apply_noaux_and_empty(self, fake_ops, monkeypatch):
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quant_config = DummyQuantConfig(is_checkpoint_bf16=False)
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layer = DummyLayer(quant_config)
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method = backend.Wfp8Afp8MoEMethod(quant_config)
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method.create_weights(layer, model_format="torch")
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kernel = DummyKernel()
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monkeypatch.setitem(
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sys.modules,
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"fastdeploy.model_executor.layers.moe.triton_moe_kernels",
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types.SimpleNamespace(fused_moe_kernel_paddle=kernel),
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)
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_ = method.apply(
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layer, paddle.randn([1, layer.hidden_size], dtype="float32"), DummyGate(layer.num_local_experts)
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)
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empty_out = method.apply(
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layer, paddle.zeros([0, layer.hidden_size], dtype="float32"), DummyGate(layer.num_local_experts)
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)
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assert empty_out.shape == [0, layer.hidden_size]
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def test_tensorwise_prequant_and_apply(self, fake_ops, monkeypatch):
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quant_config = DummyQuantConfig(is_checkpoint_bf16=False)
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layer = DummyLayer(quant_config)
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method = backend.TensorWiseFP8MoEMethod(quant_method=DummyQuantMethod(quant_config))
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method.create_weights(layer)
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monkeypatch.setattr(backend, "get_tensor", lambda tensor: tensor)
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state_dict = {}
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up_weight = paddle.ones([layer.hidden_size, layer.moe_intermediate_size * 2], dtype="float32")
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down_weight = paddle.ones([layer.moe_intermediate_size, layer.hidden_size], dtype="float32")
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layer._up_weights = [up_weight for _ in range(layer.num_local_experts)]
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layer._down_weights = [down_weight for _ in range(layer.num_local_experts)]
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monkeypatch.setattr(layer, "extract_moe_ffn_weights", lambda _state: (layer._up_weights, layer._down_weights))
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for idx in range(layer.num_local_experts):
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state_dict[f"up_gate_scale_{idx}"] = paddle.ones([1], dtype="float32") * (idx + 1)
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state_dict[f"down_proj_scale_{idx}"] = paddle.ones([1], dtype="float32") * (idx + 2)
|
|
state_dict[f"up_gate_in_scale_{idx}"] = paddle.ones([1], dtype="float32") * (idx + 3)
|
|
state_dict[f"down_proj_in_scale_{idx}"] = paddle.ones([1], dtype="float32") * (idx + 4)
|
|
|
|
method.process_prequanted_weights(layer, state_dict)
|
|
|
|
assert paddle.all(layer.up_gate_proj_in_scale > 0)
|
|
|
|
kernel = DummyKernel()
|
|
monkeypatch.setitem(
|
|
sys.modules,
|
|
"fastdeploy.model_executor.layers.moe.triton_moe_kernels",
|
|
types.SimpleNamespace(fused_moe_kernel_paddle=kernel),
|
|
)
|
|
monkeypatch.setattr(backend, "fused_moe_kernel_paddle", kernel, raising=False)
|
|
|
|
layer.topk_method = "aux"
|
|
x = paddle.randn([2, layer.hidden_size], dtype="float32")
|
|
gate = DummyGate(layer.num_local_experts)
|
|
called = {}
|
|
|
|
def hook(topk_ids):
|
|
called["hooked"] = topk_ids
|
|
|
|
_ = method.apply(layer, x, gate, topk_ids_hookfunc=hook)
|
|
assert "hooked" in called
|
|
|
|
def test_python_op_fused_moe_kernel_paddle(self, fake_ops, monkeypatch):
|
|
quant_config = DummyQuantConfig(is_checkpoint_bf16=False, weight_block_size=(2, 2))
|
|
layer = DummyLayer(quant_config)
|
|
|
|
kernel = DummyKernel()
|
|
monkeypatch.setitem(
|
|
sys.modules,
|
|
"fastdeploy.model_executor.layers.moe.triton_moe_kernels",
|
|
types.SimpleNamespace(fused_moe_kernel_paddle=kernel),
|
|
)
|
|
monkeypatch.setattr(
|
|
paddle.static,
|
|
"MetaTensor",
|
|
lambda shape, dtype: types.SimpleNamespace(shape=shape, dtype=dtype),
|
|
raising=False,
|
|
)
|
|
|
|
x = paddle.randn([2, layer.hidden_size], dtype="float32")
|
|
gate = DummyGate(layer.num_local_experts)
|
|
gate_out = gate(x)
|
|
|
|
layer_added_weight_attrs_0 = paddle.randn(
|
|
[layer.num_local_experts, layer.moe_intermediate_size * 2, layer.hidden_size], dtype="float32"
|
|
)
|
|
layer_added_weight_attrs1 = paddle.randn(
|
|
[layer.num_local_experts, layer.hidden_size, layer.moe_intermediate_size], dtype="float32"
|
|
)
|
|
layer_added_scale_attrs_0 = paddle.ones([layer.num_local_experts, 2, 2], dtype="float32")
|
|
layer_added_scale_attrs1 = paddle.ones([layer.num_local_experts, 2, 2], dtype="float32")
|
|
|
|
captured = {}
|
|
|
|
def hook(topk_ids):
|
|
captured["topk"] = topk_ids
|
|
|
|
config = {
|
|
"BLOCK_SIZE_M": 32,
|
|
"BLOCK_SIZE_N": 64,
|
|
"BLOCK_SIZE_K": 64,
|
|
"GROUP_SIZE_M": 1,
|
|
}
|
|
|
|
_ = backend.python_op_fused_moe_kernel_paddle(
|
|
x,
|
|
layer_added_weight_attrs_0,
|
|
layer_added_scale_attrs_0,
|
|
layer_added_weight_attrs1,
|
|
layer_added_scale_attrs1,
|
|
gate_out,
|
|
layer.gate_correction_bias,
|
|
layer.top_k,
|
|
layer_added_weight_attrs_0.shape[1],
|
|
layer_added_weight_attrs1.shape[1],
|
|
layer.num_local_experts,
|
|
layer.moe_intermediate_size,
|
|
layer.hidden_size,
|
|
config,
|
|
quant_config,
|
|
hook,
|
|
)
|
|
|
|
assert "topk" in captured
|
|
|
|
meta = backend.python_op_fused_moe_kernel_paddle_infer_meta(
|
|
x,
|
|
layer_added_weight_attrs_0,
|
|
layer_added_scale_attrs_0,
|
|
layer_added_weight_attrs1,
|
|
layer_added_scale_attrs1,
|
|
gate_out,
|
|
layer.gate_correction_bias,
|
|
layer.top_k,
|
|
layer_added_weight_attrs_0.shape[1],
|
|
layer_added_weight_attrs1.shape[1],
|
|
layer.num_local_experts,
|
|
layer.moe_intermediate_size,
|
|
layer.hidden_size,
|
|
config,
|
|
quant_config,
|
|
None,
|
|
)
|
|
|
|
assert meta.shape == [2, layer.hidden_size]
|
|
|
|
def test_blockwise_create_weights_and_process(self, fake_ops, monkeypatch):
|
|
quant_config = DummyQuantConfig(is_checkpoint_bf16=False, weight_block_size=(2, 2))
|
|
layer = DummyLayer(quant_config)
|
|
method = backend.BlockWiseFP8MoEMethod(quant_config)
|
|
method.create_weights(layer, model_format="not_torch")
|
|
|
|
transpose_calls = []
|
|
monkeypatch.setattr(backend, "process_weight_transpose", lambda _layer, name: transpose_calls.append(name))
|
|
|
|
method.process_weights_after_loading(layer)
|
|
assert transpose_calls
|
|
|
|
up_weights = [
|
|
paddle.arange(layer.hidden_size * layer.moe_intermediate_size * 2, dtype="float32").reshape(
|
|
[layer.hidden_size, layer.moe_intermediate_size * 2]
|
|
)
|
|
for _ in range(layer.num_local_experts)
|
|
]
|
|
down_weights = [
|
|
paddle.arange(layer.moe_intermediate_size * layer.hidden_size, dtype="float32").reshape(
|
|
[layer.moe_intermediate_size, layer.hidden_size]
|
|
)
|
|
for _ in range(layer.num_local_experts)
|
|
]
|
|
layer._up_weights = up_weights
|
|
layer._down_weights = down_weights
|
|
|
|
def fake_per_block_cast_to_fp8(weight, block_size):
|
|
return weight.cast(paddle.float16), _make_block_scale(weight.transpose([1, 0]), block_size)
|
|
|
|
monkeypatch.setattr(
|
|
backend.fastdeploy.model_executor.layers.utils, "per_block_cast_to_fp8", fake_per_block_cast_to_fp8
|
|
)
|
|
|
|
method.process_loaded_weights(layer, state_dict={})
|
|
|
|
assert paddle.any(layer.up_gate_proj_weight_scale_inv > 0)
|
|
|
|
def test_blockwise_process_weights_after_loading_bf16(self, fake_ops, monkeypatch):
|
|
quant_config = DummyQuantConfig(is_checkpoint_bf16=True, weight_block_size=(2, 2))
|
|
layer = DummyLayer(quant_config)
|
|
method = backend.BlockWiseFP8MoEMethod(quant_config)
|
|
method.create_weights(layer, model_format="torch")
|
|
method.model_format = "torch"
|
|
|
|
monkeypatch.setattr(backend, "weight_fully_copied", lambda tensor: False)
|
|
|
|
def fake_per_block_cast_to_fp8(weight, block_size):
|
|
return weight.cast(paddle.float16), _make_block_scale(weight, block_size)
|
|
|
|
monkeypatch.setattr(
|
|
backend.fastdeploy.model_executor.layers.utils, "per_block_cast_to_fp8", fake_per_block_cast_to_fp8
|
|
)
|
|
monkeypatch.setattr(backend, "free_tensor", lambda tensor: None)
|
|
|
|
method.process_weights_after_loading(layer)
|
|
|
|
if not hasattr(layer, "up_gate_proj_weight_scale_inv"):
|
|
layer.up_gate_proj_weight_scale_inv = layer.create_parameter(
|
|
shape=method.up_gate_proj_scale_shape,
|
|
dtype="float32",
|
|
default_initializer=paddle.nn.initializer.Constant(0),
|
|
)
|
|
|
|
def fake_python_op(*args, **kwargs):
|
|
token_num = args[0].shape[0]
|
|
hidden_size = args[12]
|
|
return paddle.zeros([token_num, hidden_size], dtype=args[0].dtype)
|
|
|
|
monkeypatch.setattr(backend, "python_op_fused_moe_kernel_paddle", fake_python_op)
|
|
|
|
x = paddle.randn([2, layer.hidden_size], dtype="float32")
|
|
gate = DummyGate(layer.num_local_experts)
|
|
out = method.apply(layer, x, gate)
|
|
assert out.shape == [2, layer.hidden_size]
|
|
|
|
def test_blockwise_check_and_apply_empty(self, fake_ops, monkeypatch):
|
|
quant_config = DummyQuantConfig(is_checkpoint_bf16=False, weight_block_size=(2, 2))
|
|
layer = DummyLayer(quant_config)
|
|
method = backend.BlockWiseFP8MoEMethod(quant_config)
|
|
method.create_weights(layer, model_format="torch")
|
|
|
|
up_gate = [
|
|
paddle.zeros([layer.hidden_size, layer.moe_intermediate_size * 2], dtype="float32")
|
|
for _ in range(layer.num_local_experts)
|
|
]
|
|
down_proj = [
|
|
paddle.zeros([layer.moe_intermediate_size, layer.hidden_size], dtype="float32")
|
|
for _ in range(layer.num_local_experts)
|
|
]
|
|
method.check(layer, up_gate, down_proj)
|
|
|
|
def fake_python_op(*args, **kwargs):
|
|
token_num = args[0].shape[0]
|
|
hidden_size = args[12]
|
|
return paddle.zeros([token_num, hidden_size], dtype=args[0].dtype)
|
|
|
|
monkeypatch.setattr(backend, "python_op_fused_moe_kernel_paddle", fake_python_op)
|
|
|
|
out = method.apply(
|
|
layer, paddle.zeros([0, layer.hidden_size], dtype="float32"), DummyGate(layer.num_local_experts)
|
|
)
|
|
assert out.shape == [0, layer.hidden_size]
|
|
|
|
def test_blockwise_process_weights_ue8m0_branch(self, fake_ops, monkeypatch):
|
|
"""Test the quant_weight_ue8m0 branch in BlockWiseFP8MoEMethod.process_weights_after_loading."""
|
|
quant_config = DummyQuantConfig(is_checkpoint_bf16=True, weight_block_size=(128, 128))
|
|
quant_config.deepgemm_scale_ue8m0 = True
|
|
layer = DummyLayer(quant_config, weight_dtype="bfloat16")
|
|
method = backend.BlockWiseFP8MoEMethod(quant_config)
|
|
method.create_weights(layer, model_format="torch")
|
|
method.model_format = "torch"
|
|
|
|
# Set FD_USE_PHI_FP8_QUANT to False to enter the target branch
|
|
monkeypatch.setattr(backend.fastdeploy.envs, "FD_USE_PHI_FP8_QUANT", False)
|
|
monkeypatch.setattr(backend, "weight_fully_copied", lambda tensor: True)
|
|
|
|
# Mock quant_weight_ue8m0 and transform_scale_ue8m0
|
|
quant_calls = []
|
|
transform_calls = []
|
|
|
|
def fake_quant_weight_ue8m0(weight_dequant, weight_block_size):
|
|
quant_calls.append({"weight_shape": weight_dequant.shape, "block_size": weight_block_size})
|
|
# Return fake quantized weight and scale
|
|
n, k = weight_dequant.shape[-2], weight_dequant.shape[-1]
|
|
out_w = paddle.zeros(weight_dequant.shape, dtype=paddle.float8_e4m3fn)
|
|
out_s = paddle.ones([n, (k + 127) // 128], dtype="float32")
|
|
return out_w, out_s
|
|
|
|
def fake_transform_scale_ue8m0(sf, mn, weight_block_size=None):
|
|
transform_calls.append({"sf_shape": sf.shape, "mn": mn, "block_size": weight_block_size})
|
|
# Return fake transformed scale
|
|
return paddle.ones([sf.shape[0], sf.shape[1], 1], dtype="uint8")
|
|
|
|
monkeypatch.setattr(backend, "quant_weight_ue8m0", fake_quant_weight_ue8m0)
|
|
monkeypatch.setattr(backend, "transform_scale_ue8m0", fake_transform_scale_ue8m0)
|
|
monkeypatch.setattr(backend, "free_tensor", lambda tensor: None)
|
|
monkeypatch.setattr(backend, "process_weight_transpose", lambda _layer, name: None)
|
|
|
|
# Create unquantized weights for the layer
|
|
num_experts = layer.num_local_experts
|
|
hidden_size = layer.hidden_size
|
|
moe_intermediate_size = layer.moe_intermediate_size
|
|
|
|
# Create weight attributes that the method expects
|
|
layer.up_gate_proj_weight = layer.create_parameter(
|
|
shape=[num_experts, moe_intermediate_size * 2, hidden_size],
|
|
dtype="bfloat16",
|
|
default_initializer=paddle.nn.initializer.Constant(0),
|
|
)
|
|
layer.down_proj_weight = layer.create_parameter(
|
|
shape=[num_experts, hidden_size, moe_intermediate_size],
|
|
dtype="bfloat16",
|
|
default_initializer=paddle.nn.initializer.Constant(0),
|
|
)
|
|
|
|
method.process_weights_after_loading(layer)
|
|
|
|
# Verify the quant_weight_ue8m0 branch was executed
|
|
assert len(quant_calls) > 0, "quant_weight_ue8m0 should have been called"
|
|
assert len(transform_calls) > 0, "transform_scale_ue8m0 should have been called"
|