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LeoZhao-IntelLeoZhao-Intel
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[INTEL_HPU] ut failure fix (#1842)
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4 files changed

+66
-56
lines changed

4 files changed

+66
-56
lines changed

‎backends/intel_hpu/tests/unittests/test_fused_block_attention.py

Lines changed: 17 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -269,10 +269,21 @@ def run_test(self):
269269
scaling_factor=self.head_dim**-0.5,
270270
)
271271

272-
out_linear_out = paddlenlp_ops.fused_block_attention(
272+
src, self.residual_test = paddle.incubate.nn.functional.fused_rms_norm(
273273
self.src,
274-
self.residual_test,
275-
self.new_rope.transpose([0, 1, 3, 2, 4]),
274+
norm_weight=self.ln_scales,
275+
norm_bias=None,
276+
epsilon=self.epsilon,
277+
begin_norm_axis=2,
278+
bias=None,
279+
residual=self.residual_test,
280+
)
281+
282+
b, s, h = src.shape
283+
src = src.reshape([-1, h])
284+
out_linear_out = paddlenlp_ops.fused_block_attention(
285+
src,
286+
self.new_rope.transpose([0, 1, 3, 2, 4]).squeeze(2),
276287
self.k_cache_test,
277288
self.v_cache_test,
278289
self.block_groups,
@@ -281,15 +292,15 @@ def run_test(self):
281292
self.block_bias,
282293
self.block_indices,
283294
self.block_offsets,
284-
self.ln_scales,
285295
self.qkv_weights,
286296
self.qkv_biases,
287297
self.linear_weights,
288-
self.epsilon,
289298
self.head_dim,
290299
self.num_head,
291300
scaling_factor=self.head_dim**-0.5,
292-
)
301+
transpose=True,
302+
use_neox_style=True,
303+
).reshape([b, -1, h])
293304

294305
assert paddle.allclose(
295306
out_linear_out_ref.to("cpu").to("float32"),

‎backends/intel_hpu/tests/unittests/test_fused_rms_mlp.py

Lines changed: 7 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -55,7 +55,7 @@ def swiglu_naive(x, up=None):
5555
swiglu = swiglu_naive(x=gate, up=up)
5656
res = paddle.matmul(swiglu, down_weight)
5757

58-
return res.numpy()
58+
return res.cast("float32").numpy()
5959

6060

6161
class Test_Fused_MLP_OP(unittest.TestCase):
@@ -90,10 +90,10 @@ def prepare_input(
9090
mean=0.0, std=0.02, shape=[hidden_size, intermediate_size]
9191
).astype(dtype)
9292
up_weight = paddle.normal(
93-
mean=1.0, std=0.05, shape=[hidden_size, intermediate_size]
93+
mean=0.0, std=0.05, shape=[hidden_size, intermediate_size]
9494
).astype(dtype)
9595
down_weight = paddle.normal(
96-
mean=0.5, std=0.12, shape=[intermediate_size, hidden_size]
96+
mean=0.0, std=0.12, shape=[intermediate_size, hidden_size]
9797
).astype(dtype)
9898
proj_weight = paddle.concat([gate_weight, up_weight], axis=1)
9999

@@ -105,7 +105,7 @@ def HPU_Fused_RMS_MLP_OP(self, x, ln_scales, proj_weight, down_weight, epsilon):
105105
fused_mlp_out = paddlenlp_ops.fused_rms_mlp(
106106
x, ln_scales, proj_weight, down_weight, epsilon
107107
)
108-
return fused_mlp_out
108+
return fused_mlp_out.cast("float32")
109109

110110
def NP_Fused_RMS_MLP_OP(
111111
self, x, ln_scales, gate_weight, up_weight, down_weight, epsilon
@@ -115,8 +115,8 @@ def NP_Fused_RMS_MLP_OP(
115115
)
116116
return np_mlp_out_ref
117117

118-
def check_result(self, np_result, fused_result):
119-
np.testing.assert_allclose(np_result, fused_result)
118+
def check_result(self, np_result, fused_result, atol=1e-2):
119+
np.testing.assert_allclose(np_result, fused_result, atol=atol)
120120

121121
def test_fused_mlp(self):
122122
(
@@ -190,7 +190,7 @@ def test_fused_mlp(self):
190190
)
191191
print("similarity = ", similarity)
192192
assert (
193-
abs(1 - similarity) < 2e-3
193+
abs(1 - similarity) < 2e-2
194194
), "similarity check fails between fp8 and bf16 outputs"
195195

196196

‎backends/intel_hpu/tests/unittests/test_fused_rms_qkv_rope.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -94,7 +94,7 @@ def prepare_input(
9494
hidden_size = num_heads * head_dim
9595

9696
src = paddle.rand([batch_size, seq_length, hidden_size], dtype=paddle.bfloat16)
97-
ln_scales = paddle.rand([hidden_size], dtype=paddle.bfloat16)
97+
ln_scales = paddle.randn([hidden_size], dtype=paddle.bfloat16)
9898
qkv_weights = paddle.rand(
9999
[hidden_size * 3, hidden_size], dtype=paddle.float32
100100
).to(paddle.bfloat16)

‎backends/intel_hpu/tests/unittests/test_index_copy.py

Lines changed: 41 additions & 42 deletions
Original file line numberDiff line numberDiff line change
@@ -15,7 +15,6 @@
1515
import unittest
1616

1717
import numpy as np
18-
import torch
1918
import paddle
2019
import paddlenlp_ops
2120

@@ -26,21 +25,21 @@
2625
intel_hpus_module_id = os.environ.get("FLAGS_selected_intel_hpus", 0)
2726

2827

29-
def index_copy_torch(input, dim, index, source, dtype):
30-
dtype_map = {
31-
"float16": torch.float16,
32-
"float32": torch.float32,
33-
"float64": torch.float64,
34-
"int32": torch.int32,
35-
}
36-
torch_dtype = dtype_map[dtype]
37-
input_tensor = torch.tensor(input).clone().detach().to(dtype=torch_dtype)
38-
index_tensor = torch.tensor(index).clone().detach().to(dtype=torch.int64)
39-
source_tensor = torch.tensor(source).clone().detach().to(dtype=torch_dtype)
40-
output = torch.index_copy(
41-
input=input_tensor, dim=dim, index=index_tensor, source=source_tensor
42-
)
43-
return output
28+
def index_copy_paddle(input, dim, index, source, dtype):
29+
input_tensor = paddle.to_tensor(input, dtype="float32").clone().cpu()
30+
index_tensor = paddle.to_tensor(index, dtype="int64").clone().cpu()
31+
source_tensor = paddle.to_tensor(source, dtype="float32").clone().cpu()
32+
33+
shape = input_tensor.shape
34+
new_index = []
35+
for i in range(0, int(np.prod(shape[:dim]))):
36+
new_index.append(index_tensor + i * shape[dim])
37+
new_index = paddle.concat(new_index)
38+
new_x = input_tensor.reshape_([-1] + shape[dim + 1 :])
39+
new_source = source_tensor.reshape([-1] + shape[dim + 1 :])
40+
y = new_x.scatter_(new_index, new_source).reshape_(shape)
41+
42+
return y
4443

4544

4645
@skip_check_grad_ci(reason="index_copy_forward ops not support gradient calculation.")
@@ -56,7 +55,7 @@ def setUp(self):
5655
def init_dtype(self):
5756
self.dtype = "float32"
5857

59-
def check_result(self, torch_res, ops_res):
58+
def check_result(self, paddle_res, ops_res):
6059
if self.dtype == "float32":
6160
rtol = 1e-5
6261
atol = 1e-6
@@ -73,7 +72,7 @@ def check_result(self, torch_res, ops_res):
7372
float16 and float32, but got "
7473
+ self.dtype,
7574
)
76-
np.testing.assert_allclose(torch_res, ops_res, rtol=rtol, atol=atol)
75+
np.testing.assert_allclose(paddle_res, ops_res, rtol=rtol, atol=atol)
7776

7877
def index_copy_custom(self, input, dim, index, source):
7978
input_tensor = paddle.to_tensor(input, dtype=self.dtype).clone()
@@ -121,78 +120,78 @@ def prepare_input(
121120
def test_index_copy_dim0_index0(self):
122121
input, index, source, dim = self.prepare_input(dim=0, index=0)
123122
custom_res = self.index_copy_custom(input, dim, index, source)
124-
torch_res = index_copy_torch(input, dim, index, source, dtype=self.dtype)
125-
self.check_result(torch_res.numpy(), custom_res)
123+
paddle_res = index_copy_paddle(input, dim, index, source, dtype=self.dtype)
124+
self.check_result(paddle_res.numpy(), custom_res)
126125

127126
def test_index_copy_dim0_index1(self):
128127
input, index, source, dim = self.prepare_input(dim=0, index=1)
129128
custom_res = self.index_copy_custom(input, dim, index, source)
130-
torch_res = index_copy_torch(input, dim, index, source, dtype=self.dtype)
131-
self.check_result(torch_res.numpy(), custom_res)
129+
paddle_res = index_copy_paddle(input, dim, index, source, dtype=self.dtype)
130+
self.check_result(paddle_res.numpy(), custom_res)
132131

133132
def test_index_copy_dim0_index_max(self):
134133
index = max(self.num_heads - 1, 0)
135134
input, index, source, dim = self.prepare_input(dim=0, index=index)
136135
custom_res = self.index_copy_custom(input, dim, index, source)
137-
torch_res = index_copy_torch(input, dim, index, source, dtype=self.dtype)
138-
self.check_result(torch_res.numpy(), custom_res)
136+
paddle_res = index_copy_paddle(input, dim, index, source, dtype=self.dtype)
137+
self.check_result(paddle_res.numpy(), custom_res)
139138

140139
def test_index_copy_dim1_index0(self):
141140
input, index, source, dim = self.prepare_input(dim=1, index=0)
142141
custom_res = self.index_copy_custom(input, dim, index, source)
143-
torch_res = index_copy_torch(input, dim, index, source, dtype=self.dtype)
144-
self.check_result(torch_res.numpy(), custom_res)
142+
paddle_res = index_copy_paddle(input, dim, index, source, dtype=self.dtype)
143+
self.check_result(paddle_res.numpy(), custom_res)
145144

146145
def test_index_copy_dim1_index1(self):
147146
input, index, source, dim = self.prepare_input(dim=1, index=1)
148147
custom_res = self.index_copy_custom(input, dim, index, source)
149-
torch_res = index_copy_torch(input, dim, index, source, dtype=self.dtype)
150-
self.check_result(torch_res.numpy(), custom_res.numpy())
148+
paddle_res = index_copy_paddle(input, dim, index, source, dtype=self.dtype)
149+
self.check_result(paddle_res.numpy(), custom_res.numpy())
151150

152151
def test_index_copy_dim1_index_max(self):
153152
index = max(self.head_dim - 1, 0)
154153
input, index, source, dim = self.prepare_input(dim=1, index=index)
155154
custom_res = self.index_copy_custom(input, dim, index, source)
156-
torch_res = index_copy_torch(input, dim, index, source, dtype=self.dtype)
157-
self.check_result(torch_res.numpy(), custom_res.numpy())
155+
paddle_res = index_copy_paddle(input, dim, index, source, dtype=self.dtype)
156+
self.check_result(paddle_res.numpy(), custom_res.numpy())
158157

159158
def test_index_copy_dim2_index0(self):
160159
input, index, source, dim = self.prepare_input(dim=2, index=0)
161160
custom_res = self.index_copy_custom(input, dim, index, source)
162-
torch_res = index_copy_torch(input, dim, index, source, dtype=self.dtype)
163-
self.check_result(torch_res.numpy(), custom_res.numpy())
161+
paddle_res = index_copy_paddle(input, dim, index, source, dtype=self.dtype)
162+
self.check_result(paddle_res.numpy(), custom_res.numpy())
164163

165164
def test_index_copy_dim2_index1(self):
166165
input, index, source, dim = self.prepare_input(dim=2, index=1)
167166
custom_res = self.index_copy_custom(input, dim, index, source)
168-
torch_res = index_copy_torch(input, dim, index, source, dtype=self.dtype)
169-
self.check_result(torch_res.numpy(), custom_res.numpy())
167+
paddle_res = index_copy_paddle(input, dim, index, source, dtype=self.dtype)
168+
self.check_result(paddle_res.numpy(), custom_res.numpy())
170169

171170
def test_index_copy_dim2_index_max(self):
172171
index = max(self.seq_length - 1, 0)
173172
input, index, source, dim = self.prepare_input(dim=2, index=index)
174173
custom_res = self.index_copy_custom(input, dim, index, source)
175-
torch_res = index_copy_torch(input, dim, index, source, dtype=self.dtype)
176-
self.check_result(torch_res.numpy(), custom_res.numpy())
174+
paddle_res = index_copy_paddle(input, dim, index, source, dtype=self.dtype)
175+
self.check_result(paddle_res.numpy(), custom_res.numpy())
177176

178177
def test_index_copy_dim3_index0(self):
179178
input, index, source, dim = self.prepare_input(dim=3, index=0)
180179
custom_res = self.index_copy_custom(input, dim, index, source)
181-
torch_res = index_copy_torch(input, dim, index, source, dtype=self.dtype)
182-
self.check_result(torch_res.numpy(), custom_res.numpy())
180+
paddle_res = index_copy_paddle(input, dim, index, source, dtype=self.dtype)
181+
self.check_result(paddle_res.numpy(), custom_res.numpy())
183182

184183
def test_index_copy_dim3_index1(self):
185184
input, index, source, dim = self.prepare_input(dim=3, index=1)
186185
custom_res = self.index_copy_custom(input, dim, index, source)
187-
torch_res = index_copy_torch(input, dim, index, source, dtype=self.dtype)
188-
self.check_result(torch_res.numpy(), custom_res.numpy())
186+
paddle_res = index_copy_paddle(input, dim, index, source, dtype=self.dtype)
187+
self.check_result(paddle_res.numpy(), custom_res.numpy())
189188

190189
def test_index_copy_dim3_index_max(self):
191190
index = max(self.batch_size - 1, 0)
192191
input, index, source, dim = self.prepare_input(dim=3, index=index)
193192
custom_res = self.index_copy_custom(input, dim, index, source)
194-
torch_res = index_copy_torch(input, dim, index, source, dtype=self.dtype)
195-
self.check_result(torch_res.numpy(), custom_res.numpy())
193+
paddle_res = index_copy_paddle(input, dim, index, source, dtype=self.dtype)
194+
self.check_result(paddle_res.numpy(), custom_res.numpy())
196195

197196

198197
@skip_check_grad_ci(reason="index_copy_forward ops not support gradient calculation.")

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