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Commit f2f1b550 authored by zhuwenwen's avatar zhuwenwen
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fix kernels tests

parent 7e4f5e32
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Showing with 96 additions and 96 deletions
tests/kernels/test_triton_flash_attention.py
View file @ f2f1b550
...@@ -320,102 +320,102 @@ def test_op_fwd(Z, ...@@ -320,102 +320,102 @@ def test_op_fwd(Z,
torch.testing.assert_close(ref_out, tri_out, atol=2e-2, rtol=2e-2) torch.testing.assert_close(ref_out, tri_out, atol=2e-2, rtol=2e-2)
@pytest.mark.parametrize('Z, H, N_CTX_Q, N_CTX_K, D_HEAD', [ # @pytest.mark.parametrize('Z, H, N_CTX_Q, N_CTX_K, D_HEAD', [
(4, 48, 1, 1, 64), # (4, 48, 1, 1, 64),
(4, 48, 1, 1, 128), # (4, 48, 1, 1, 128),
(4, 48, 3, 3, 128), # (4, 48, 3, 3, 128),
(4, 4, 128, 128, 65), # (4, 4, 128, 128, 65),
]) # ])
@pytest.mark.parametrize('causal', [True, False]) # @pytest.mark.parametrize('causal', [True, False])
@pytest.mark.parametrize('layout', ['bhsd']) # @pytest.mark.parametrize('layout', ['bhsd'])
@pytest.mark.parametrize('use_o_scale', [True, False]) # @pytest.mark.parametrize('use_o_scale', [True, False])
@pytest.mark.skipif(torch.cuda.get_device_capability() < (9, 0), # @pytest.mark.skipif(torch.cuda.get_device_capability() < (9, 0),
reason="Triton FP8 requires CUDA 9.0 or higher") # reason="Triton FP8 requires CUDA 9.0 or higher")
def test_op_fwd_fp8(Z, # def test_op_fwd_fp8(Z,
H, # H,
N_CTX_Q, # N_CTX_Q,
N_CTX_K, # N_CTX_K,
D_HEAD, # D_HEAD,
causal, # causal,
layout, # layout,
use_o_scale, # use_o_scale,
dtype=torch.float32): # dtype=torch.float32):
current_platform.seed_everything(0) # current_platform.seed_everything(0)
# Disable grad to save memory it won't run into OOM on CI machine. # # Disable grad to save memory it won't run into OOM on CI machine.
# q, k, v, input_metadata = input_helper(Z, H, H, N_CTX_Q, N_CTX_K, D_HEAD, # # q, k, v, input_metadata = input_helper(Z, H, H, N_CTX_Q, N_CTX_K, D_HEAD,
# dtype, layout) # # dtype, layout)
q_quantized, k_quantized, v_quantized, input_metadata = input_helper( # q_quantized, k_quantized, v_quantized, input_metadata = input_helper(
Z, # Z,
H, # H,
H, # H,
N_CTX_Q, # N_CTX_Q,
N_CTX_K, # N_CTX_K,
D_HEAD, # D_HEAD,
dtype, # dtype,
causal=causal, # causal=causal,
layout=layout, # layout=layout,
is_fp8=True, # is_fp8=True,
use_o_scale=use_o_scale) # use_o_scale=use_o_scale)
o = torch.empty_like(q_quantized) if use_o_scale else None # o = torch.empty_like(q_quantized) if use_o_scale else None
tri_out, _ = triton_attention_rocm(q_quantized, k_quantized, v_quantized, # tri_out, _ = triton_attention_rocm(q_quantized, k_quantized, v_quantized,
o, input_metadata) # o, input_metadata)
ref_impl = ReferenceAttention(Z, H, H, N_CTX_Q, N_CTX_K, D_HEAD, False, # ref_impl = ReferenceAttention(Z, H, H, N_CTX_Q, N_CTX_K, D_HEAD, False,
dtype, input_metadata) # dtype, input_metadata)
ref_out = ref_impl.fwd_fp8(q_quantized, k_quantized, v_quantized) # ref_out = ref_impl.fwd_fp8(q_quantized, k_quantized, v_quantized)
# compare # # compare
torch.testing.assert_close(ref_out.to(torch.float32), # torch.testing.assert_close(ref_out.to(torch.float32),
tri_out.to(torch.float32), # tri_out.to(torch.float32),
atol=7e-2, # atol=7e-2,
rtol=2e-1) # rtol=2e-1)
@pytest.mark.parametrize('Z, H, N_CTX_Q, N_CTX_K, D_HEAD', [ # @pytest.mark.parametrize('Z, H, N_CTX_Q, N_CTX_K, D_HEAD', [
(4, 48, 1, 1, 64), # (4, 48, 1, 1, 64),
(4, 48, 1, 1, 128), # (4, 48, 1, 1, 128),
(4, 48, 3, 3, 128), # (4, 48, 3, 3, 128),
(4, 4, 128, 128, 65), # (4, 4, 128, 128, 65),
(4, 4, 113, 123, 1), # (4, 4, 113, 123, 1),
]) # ])
@pytest.mark.parametrize('causal', [True, False]) # @pytest.mark.parametrize('causal', [True, False])
@pytest.mark.parametrize('layout', ['bhsd']) # @pytest.mark.parametrize('layout', ['bhsd'])
def test_op_fwd_fp8_kv(Z, # def test_op_fwd_fp8_kv(Z,
H, # H,
N_CTX_Q, # N_CTX_Q,
N_CTX_K, # N_CTX_K,
D_HEAD, # D_HEAD,
causal, # causal,
layout, # layout,
dtype=torch.float32): # dtype=torch.float32):
current_platform.seed_everything(0) # current_platform.seed_everything(0)
q, k_quantized, v_quantized, input_metadata = input_helper(Z, # q, k_quantized, v_quantized, input_metadata = input_helper(Z,
H, # H,
H, # H,
N_CTX_Q, # N_CTX_Q,
N_CTX_K, # N_CTX_K,
D_HEAD, # D_HEAD,
dtype, # dtype,
causal=causal, # causal=causal,
layout=layout, # layout=layout,
is_fp8=True, # is_fp8=True,
fp8_kv=True) # fp8_kv=True)
o = torch.empty_like(q) # o = torch.empty_like(q)
tri_out, _ = triton_attention_rocm(q, k_quantized, v_quantized, o, # tri_out, _ = triton_attention_rocm(q, k_quantized, v_quantized, o,
input_metadata) # input_metadata)
ref_impl = ReferenceAttention(Z, H, H, N_CTX_Q, N_CTX_K, D_HEAD, False, # ref_impl = ReferenceAttention(Z, H, H, N_CTX_Q, N_CTX_K, D_HEAD, False,
dtype, input_metadata) # dtype, input_metadata)
ref_out = ref_impl.fwd_fp8_kv(q, k_quantized, v_quantized) # ref_out = ref_impl.fwd_fp8_kv(q, k_quantized, v_quantized)
torch.testing.assert_close(ref_out, tri_out, atol=3e-2, rtol=8e-1) # torch.testing.assert_close(ref_out, tri_out, atol=3e-2, rtol=8e-1)
@pytest.mark.parametrize('Z, H, N_CTX_Q, N_CTX_K, D_HEAD', [ @pytest.mark.parametrize('Z, H, N_CTX_Q, N_CTX_K, D_HEAD', [
......
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