[Misc/Testing] Use torch.testing.assert_close (#7324)

tests/distributed/test_comm_ops.py

@@ -34,7 +34,7 @@ def all_reduce_test_worker(tp_size: int, pp_size: int, rank: int,
     expected = torch.sum(torch.stack(all_tensors, dim=0), dim=0)
     t = all_tensors[rank % tp_size]
     t = tensor_model_parallel_all_reduce(t)
-    assert torch.allclose(t, expected)
+    torch.testing.assert_close(t, expected)
 
 
 @ray.remote(num_gpus=1, max_calls=1)
@@ -62,7 +62,7 @@ def all_gather_test_worker(tp_size: int, pp_size: int, rank: int,
         expected = torch.cat(all_tensors, dim=all_gather_dimension)
         t = all_tensors[rank % tp_size]
         t = tensor_model_parallel_all_gather(t, all_gather_dimension)
-        assert torch.allclose(t, expected)
+        torch.testing.assert_close(t, expected)
 
 
 @ray.remote(num_gpus=1, max_calls=1)
@@ -96,12 +96,12 @@ def broadcast_tensor_dict_test_worker(tp_size: int, pp_size: int, rank: int,
     else:
         recv_dict = broadcast_tensor_dict(src=0)
         assert len(recv_dict) == len(test_dict)
-        assert torch.allclose(recv_dict["a"], test_dict["a"])
+        torch.testing.assert_close(recv_dict["a"], test_dict["a"])
-        assert torch.allclose(recv_dict["b"], test_dict["b"])
+        torch.testing.assert_close(recv_dict["b"], test_dict["b"])
         assert recv_dict["c"] == test_dict["c"]
         assert recv_dict["d"] == test_dict["d"]
         assert recv_dict["e"] == test_dict["e"]
-        assert torch.allclose(recv_dict["f"], test_dict["f"])
+        torch.testing.assert_close(recv_dict["f"], test_dict["f"])
 
 
 @ray.remote(num_gpus=1, max_calls=1)
@@ -136,12 +136,12 @@ def send_recv_tensor_dict_test_worker(tp_size: int, pp_size: int, rank: int,
 
     if not get_pp_group().is_first_rank:
         assert len(recv_dict) == len(test_dict)
-        assert torch.allclose(recv_dict["a"], test_dict["a"])
+        torch.testing.assert_close(recv_dict["a"], test_dict["a"])
-        assert torch.allclose(recv_dict["b"], test_dict["b"])
+        torch.testing.assert_close(recv_dict["b"], test_dict["b"])
         assert recv_dict["c"] == test_dict["c"]
         assert recv_dict["d"] == test_dict["d"]
         assert recv_dict["e"] == test_dict["e"]
-        assert torch.allclose(recv_dict["f"], test_dict["f"])
+        torch.testing.assert_close(recv_dict["f"], test_dict["f"])
 
 
 @ray.remote(num_gpus=1, max_calls=1)
@@ -163,7 +163,7 @@ def send_recv_test_worker(tp_size: int, pp_size: int, rank: int,
         get_pp_group().send(test_tensor)
 
     if not get_pp_group().is_first_rank:
-        assert torch.allclose(test_tensor, recv_tensor)
+        torch.testing.assert_close(test_tensor, recv_tensor)
 
 
 @pytest.mark.skipif(torch.cuda.device_count() < 2,

tests/distributed/test_custom_all_reduce.py

@@ -72,8 +72,8 @@ def graph_allreduce(tp_size, pp_size, rank, distributed_init_port):
                         out2 = tensor_model_parallel_all_reduce(inp2)
                         dist.all_reduce(inp2, group=group)
             graph.replay()
-            assert torch.allclose(out1, inp1)
+            torch.testing.assert_close(out1, inp1)
-            assert torch.allclose(out2, inp2)
+            torch.testing.assert_close(out2, inp2)
 
 
 @ray.remote(num_gpus=1, max_calls=1)
@@ -96,13 +96,13 @@ def eager_allreduce(tp_size, pp_size, rank, distributed_init_port):
     out = inp
     for _ in range(num_communication):
         out = fa.all_reduce_unreg(out)
-    assert torch.allclose(out, inp * (tp_size**num_communication))
+    torch.testing.assert_close(out, inp * (tp_size**num_communication))
 
     inp = torch.ones(sz * 4, dtype=torch.bfloat16, device=device)
     out = inp
     for _ in range(num_communication):
         out = fa.all_reduce_unreg(out)
-    assert torch.allclose(out, inp * (tp_size**num_communication))
+    torch.testing.assert_close(out, inp * (tp_size**num_communication))
 
 
 @pytest.mark.parametrize("tp_size", [2])

tests/kernels/quant_utils.py

@@ -69,4 +69,4 @@ def ref_dynamic_per_tensor_fp8_quant(x: torch.tensor) \
     ref_iscale = one / ref_scale
     ref_out = (as_float32_tensor(x) * ref_iscale).clamp(
         fp8_traits.min, fp8_traits.max).to(dtype=torch.float8_e4m3fn)
-    return ref_out, ref_scale
+    return ref_out, ref_scale.view((1, ))

tests/kernels/test_activation.py

@@ -47,7 +47,7 @@ def test_act_and_mul(
     ref_out = layer.forward_native(x)
     # The SiLU and GELU implementations are equivalent to the native PyTorch
     # implementations, so we can do exact comparison.
-    assert torch.allclose(out, ref_out, atol=0.0, rtol=0.0)
+    torch.testing.assert_close(out, ref_out, atol=0.0, rtol=0.0)
 
 
 @pytest.mark.parametrize("activation", [FastGELU, NewGELU])
@@ -73,7 +73,7 @@ def test_activation(
     layer = activation()
     out = layer(x)
     ref_out = layer.forward_native(x)
-    assert torch.allclose(out,
+    torch.testing.assert_close(out,
                                ref_out,
                                atol=get_default_atol(out),
                                rtol=get_default_rtol(out))

tests/kernels/test_attention.py

@@ -276,7 +276,7 @@ def test_paged_attention(
     atol, rtol = 1e-3, 1e-5
     if kv_cache_dtype == "fp8":
         atol, rtol = 1e-2, 1e-5
-    assert torch.allclose(output, ref_output, atol=atol, rtol=rtol)
+    torch.testing.assert_close(output, ref_output, atol=atol, rtol=rtol)
 
 
 def ref_multi_query_kv_attention(
@@ -379,4 +379,4 @@ def test_multi_query_kv_attention(
     )
     atol = get_default_atol(output) if is_hip() else 1e-3
     rtol = get_default_rtol(output) if is_hip() else 1e-5
-    assert torch.allclose(output, ref_output, atol=atol, rtol=rtol)
+    torch.testing.assert_close(output, ref_output, atol=atol, rtol=rtol)

tests/kernels/test_blocksparse_attention.py

@@ -327,7 +327,7 @@ def test_paged_attention(
     atol, rtol = 1e-3, 1e-5
     if kv_cache_dtype == "fp8":
         atol, rtol = 1e-2, 1e-5
-    assert torch.allclose(output, ref_output, atol=atol, rtol=rtol)
+    torch.testing.assert_close(output, ref_output, atol=atol, rtol=rtol)
 
 
 def ref_multi_query_kv_attention(
@@ -441,4 +441,4 @@ def test_varlen_blocksparse_attention_prefill(
         scale,
         dtype,
     )
-    assert torch.allclose(output, ref_output, atol=1e-2, rtol=1e-2)
+    torch.testing.assert_close(output, ref_output, atol=1e-2, rtol=1e-2)

tests/kernels/test_cache.py

@@ -98,10 +98,10 @@ def test_copy_blocks(
 
     # Compare the results.
     for key_cache, cloned_key_cache in zip(key_caches, cloned_key_caches):
-        assert torch.allclose(key_cache, cloned_key_cache)
+        torch.testing.assert_close(key_cache, cloned_key_cache)
     for value_cache, cloned_value_cache in zip(value_caches,
                                                cloned_value_caches):
-        assert torch.allclose(value_cache, cloned_value_cache)
+        torch.testing.assert_close(value_cache, cloned_value_cache)
 
 
 @pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@@ -184,17 +184,17 @@ def test_reshape_and_cache(
         cloned_value_cache[block_idx, :, :, block_offset] = value[i]
 
     if kv_cache_dtype == "fp8":
-        assert torch.allclose(result_key_cache,
+        torch.testing.assert_close(result_key_cache,
                                    cloned_key_cache,
                                    atol=0.001,
                                    rtol=0.1)
-        assert torch.allclose(result_value_cache,
+        torch.testing.assert_close(result_value_cache,
                                    cloned_value_cache,
                                    atol=0.001,
                                    rtol=0.1)
     else:
-        assert torch.allclose(key_cache, cloned_key_cache)
+        torch.testing.assert_close(key_cache, cloned_key_cache)
-        assert torch.allclose(value_cache, cloned_value_cache)
+        torch.testing.assert_close(value_cache, cloned_value_cache)
 
 
 @pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@@ -290,17 +290,17 @@ def test_reshape_and_cache_flash(
         cloned_value_cache[block_idx, block_offset, :, :] = value[i]
 
     if kv_cache_dtype == "fp8":
-        assert torch.allclose(result_key_cache,
+        torch.testing.assert_close(result_key_cache,
                                    cloned_key_cache,
                                    atol=0.001,
                                    rtol=0.1)
-        assert torch.allclose(result_value_cache,
+        torch.testing.assert_close(result_value_cache,
                                    cloned_value_cache,
                                    atol=0.001,
                                    rtol=0.1)
     else:
-        assert torch.allclose(key_cache, cloned_key_cache)
+        torch.testing.assert_close(key_cache, cloned_key_cache)
-        assert torch.allclose(value_cache, cloned_value_cache)
+        torch.testing.assert_close(value_cache, cloned_value_cache)
 
 
 @pytest.mark.parametrize("direction", COPYING_DIRECTION)
@@ -372,9 +372,9 @@ def test_swap_blocks(
                     block_mapping_tensor)
 
     for src, dst in block_mapping:
-        assert torch.allclose(src_key_caches_clone[src].cpu(),
+        torch.testing.assert_close(src_key_caches_clone[src].cpu(),
                                    dist_key_caches[0][dst].cpu())
-        assert torch.allclose(src_value_caches_clone[src].cpu(),
+        torch.testing.assert_close(src_value_caches_clone[src].cpu(),
                                    dist_value_caches[0][dst].cpu())
 
 
@@ -411,4 +411,4 @@ def test_fp8_e4m3_conversion(
     converted_cache = torch.empty_like(cache)
     ops.convert_fp8(converted_cache, cache_fp8)
 
-    assert torch.allclose(cache, converted_cache, atol=0.001, rtol=0.1)
+    torch.testing.assert_close(cache, converted_cache, atol=0.001, rtol=0.1)

tests/kernels/test_cutlass.py

@@ -74,7 +74,7 @@ def cutlass_fp8_gemm_helper(m: int,
     out = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
     baseline = baseline_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
 
-    assert torch.allclose(out, baseline, rtol=1e-2, atol=5e-2)
+    torch.testing.assert_close(out, baseline, rtol=1e-2, atol=5e-2)
 
 
 def cutlass_int8_gemm_helper(m: int,
@@ -106,7 +106,7 @@ def cutlass_int8_gemm_helper(m: int,
     out = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
     baseline = baseline_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
 
-    assert torch.allclose(out, baseline, rtol=1e-1, atol=1e0)
+    torch.testing.assert_close(out, baseline, rtol=1e-1, atol=1e0)
 
 
 @pytest.mark.parametrize("m", [1, 16, 32, 64, 128, 256, 512, 222, 100, 33])
@@ -252,7 +252,7 @@ def test_cutlass_int8_azp_bias_fold(m: int, n: int, k: int,
     azp_a = azp_aq_i8.to(dtype=torch.float32) * scale_a  # correct for rounding
 
     a_dq = scale_a * (aq_i32 + azp_aq_i8).to(dtype=torch.float32)
-    assert torch.allclose(a_dq, scale_a * aq_f32 + azp_a)
+    torch.testing.assert_close(a_dq, scale_a * aq_f32 + azp_a)
 
     baseline_dq = torch.mm(a_dq, b_dq).to(out_dtype)
 
@@ -271,8 +271,8 @@ def test_cutlass_int8_azp_bias_fold(m: int, n: int, k: int,
                                 scale_b,
                                 out_dtype=out_dtype,
                                 bias=azp_bias[0, :])
-    assert torch.allclose(out, baseline_dq, rtol=1e-2, atol=1e0)
+    torch.testing.assert_close(out, baseline_dq, rtol=1e-2, atol=1e0)
-    assert torch.allclose(out, baseline_q, rtol=1e-2, atol=1e0)
+    torch.testing.assert_close(out, baseline_q, rtol=1e-2, atol=1e0)
 
 
 @pytest.mark.parametrize("m", [32, 64, 128])
@@ -302,7 +302,10 @@ def test_cutlass_int8_azp(m: int, n: int, k: int, out_dtype: torch.dtype,
     azp_a = azp_aq_i8.to(dtype=torch.float32) * scale_a  # correct for rounding
 
     a_dq = scale_a * (aq_i32 - azp_aq_i8).to(dtype=torch.float32)
-    assert torch.allclose(a_dq, scale_a * aq_f32 - azp_a, rtol=1e-4, atol=1e-3)
+    torch.testing.assert_close(a_dq,
+                               scale_a * aq_f32 - azp_a,
+                               rtol=1e-4,
+                               atol=1e-3)
 
     if use_bias:
         bias = torch.rand((1, n), device="cuda", dtype=out_dtype) * 10 + 2.5
@@ -335,8 +338,8 @@ def test_cutlass_int8_azp(m: int, n: int, k: int, out_dtype: torch.dtype,
     # float16 precision is 10-bit mantissa -> 2^-11 ~ 0.05%
     rtol = 1e-2 if out_dtype == torch.bfloat16 else 1e-3
     atol = 1e-3
-    assert torch.allclose(out, baseline_dq, rtol=rtol, atol=atol)
+    torch.testing.assert_close(out, baseline_dq, rtol=rtol, atol=atol)
-    assert torch.allclose(out, baseline_q, rtol=rtol, atol=atol)
+    torch.testing.assert_close(out, baseline_q, rtol=rtol, atol=atol)
 
 
 # Test working with a subset of A and B
@@ -363,7 +366,7 @@ def test_cutlass_subset():
                                   scale_b,
                                   out_dtype=torch.bfloat16)
 
-    assert torch.allclose(out, baseline, rtol=1e-1, atol=1e0)
+    torch.testing.assert_close(out, baseline, rtol=1e-1, atol=1e0)
 
 
 # Test to make sure cuda graphs work
@@ -411,4 +414,4 @@ def test_cutlass_cuda_graph(per_act_token: bool, per_out_ch: bool):
 
     baseline = torch.mm(scale_a * a.to(dtype=torch.float32),
                         scale_b * b.to(dtype=torch.float32)).to(torch.bfloat16)
-    assert torch.allclose(out, baseline, rtol=1e-1, atol=1e0)
+    torch.testing.assert_close(out, baseline, rtol=1e-1, atol=1e0)

tests/kernels/test_flash_attn.py

@@ -126,7 +126,7 @@ def test_flash_attn_with_paged_kv(
         scale=scale,
         soft_cap=soft_cap,
     )
-    assert torch.allclose(output, ref_output, atol=2e-2, rtol=1e-2), \
+    torch.testing.assert_close(output, ref_output, atol=2e-2, rtol=1e-2), \
         f"{torch.max(torch.abs(output - ref_output))}"
 
 
@@ -211,5 +211,5 @@ def test_varlen_with_paged_kv(
         sliding_window=sliding_window,
         soft_cap=soft_cap,
     )
-    assert torch.allclose(output, ref_output, atol=2e-2, rtol=1e-2), \
+    torch.testing.assert_close(output, ref_output, atol=2e-2, rtol=1e-2), \
         f"{torch.max(torch.abs(output - ref_output))}"

tests/kernels/test_flashinfer.py

@@ -144,7 +144,7 @@ def test_flashinfer_decode_with_paged_kv(kv_lens: List[int],
                                 block_tables=block_tables,
                                 scale=scale,
                                 soft_cap=soft_cap)
-    assert torch.allclose(output, ref_output, atol=1e-2, rtol=1e-2), \
+    torch.testing.assert_close(output, ref_output, atol=1e-2, rtol=1e-2), \
         f"{torch.max(torch.abs(output - ref_output))}"
 
 
@@ -244,5 +244,5 @@ def test_flashinfer_prefill_with_paged_kv(seq_lens: List[Tuple[int, int]],
                                 block_tables=block_tables,
                                 scale=scale,
                                 soft_cap=soft_cap)
-    assert torch.allclose(output, ref_output, atol=1e-2, rtol=1e-2), \
+    torch.testing.assert_close(output, ref_output, atol=1e-2, rtol=1e-2), \
         f"{torch.max(torch.abs(output - ref_output))}"

tests/kernels/test_fp8_quant.py

@@ -37,8 +37,8 @@ def test_dynamic_per_token_fp8_quant(num_tokens: int, hidden_size: int,
                                                scale_ub=scale_ub,
                                                use_per_token_if_dynamic=True)
 
-    assert torch.allclose(ref_scales, ops_scales)
+    torch.testing.assert_close(ref_scales, ops_scales)
-    assert torch.allclose(ref_out.to(dtype=torch.float32),
+    torch.testing.assert_close(ref_out.to(dtype=torch.float32),
                                ops_out.to(dtype=torch.float32))
 
 
@@ -57,8 +57,8 @@ def test_dynamic_per_tensor_fp8_quant(num_tokens: int, hidden_size: int,
     ref_out, ref_scale = ref_dynamic_per_tensor_fp8_quant(x)
     ops_out, ops_scale = ops.scaled_fp8_quant(x)
 
-    assert torch.allclose(ref_scale, ops_scale)
+    torch.testing.assert_close(ref_scale, ops_scale)
-    assert torch.allclose(ref_out.to(dtype=torch.float32),
+    torch.testing.assert_close(ref_out.to(dtype=torch.float32),
                                ops_out.to(dtype=torch.float32))
 
 
@@ -84,4 +84,4 @@ def test_fp8_quant_large(seed: int) -> None:
     ref_out = ref_out.to(dtype=dtype)
     ops_out = ops_out.to(dtype=dtype)
 
-    assert torch.allclose(ref_out, ops_out)
+    torch.testing.assert_close(ref_out, ops_out)

tests/kernels/test_int8_quant.py

@@ -29,9 +29,10 @@ def test_dynamic_scaled_int8_quant(num_tokens: int, hidden_size: int,
     # kernel
     ops_out, ops_scales = scaled_int8_quant(x)
 
-    assert torch.allclose(ops_scales, ref_scales)
+    torch.testing.assert_close(ops_scales, ref_scales)
-    assert torch.allclose(ops_out, ref_out,
+    torch.testing.assert_close(
-                          atol=1)  # big atol to account for rounding errors
+        ops_out, ref_out, atol=1,
+        rtol=0.0)  # big atol to account for rounding errors
 
 
 @pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@@ -54,5 +55,6 @@ def test_static_scaled_int8_quant(num_tokens: int, hidden_size: int,
                                      int8_traits.max).to(torch.int8)
     out2, _ = scaled_int8_quant(x, scale)
 
-    assert torch.allclose(out1, out2,
+    torch.testing.assert_close(
-                          atol=1)  # big atol to account for rounding errors
+        out1, out2, atol=1,
+        rtol=0.0)  # big atol to account for rounding errors

tests/kernels/test_layernorm.py

@@ -48,7 +48,7 @@ def test_rms_norm(
     # numerical errors than other operators because they involve reductions.
     # Therefore, we use a larger tolerance.
     if add_residual:
-        assert torch.allclose(out[0], ref_out[0], atol=1e-2, rtol=1e-2)
+        torch.testing.assert_close(out[0], ref_out[0], atol=1e-2, rtol=1e-2)
-        assert torch.allclose(out[1], ref_out[1], atol=1e-2, rtol=1e-2)
+        torch.testing.assert_close(out[1], ref_out[1], atol=1e-2, rtol=1e-2)
     else:
-        assert torch.allclose(out, ref_out, atol=1e-2, rtol=1e-2)
+        torch.testing.assert_close(out, ref_out, atol=1e-2, rtol=1e-2)

tests/kernels/test_marlin_gemm.py

@@ -122,7 +122,7 @@ def test_gptq_marlin_repack(k_chunk, n_chunk, quant_type, group_size,
     )
     torch.cuda.synchronize()
 
-    assert torch.allclose(marlin_q_w_1, marlin_q_w_2)
+    torch.testing.assert_close(marlin_q_w_1, marlin_q_w_2)
 
 
 @pytest.mark.skipif(not is_quant_method_supported("gptq_marlin"),
@@ -174,7 +174,7 @@ def test_awq_marlin_repack(k_chunk, n_chunk, quant_type, group_size,
     )
     torch.cuda.synchronize()
 
-    assert torch.allclose(marlin_q_w_1, marlin_q_w_2)
+    torch.testing.assert_close(marlin_q_w_1, marlin_q_w_2)
 
 
 @pytest.mark.skipif(not is_quant_method_supported("gptq_marlin"),

tests/kernels/test_moe.py

@@ -50,7 +50,7 @@ def test_fused_moe(
     score = torch.randn((m, e), device='cuda', dtype=dtype)
     triton_output = fused_moe(a, w1, w2, score, topk, renormalize=False)
     torch_output = torch_moe(a, w1, w2, score, topk)
-    assert torch.allclose(triton_output, torch_output, atol=1e-2, rtol=0)
+    torch.testing.assert_close(triton_output, torch_output, atol=1e-2, rtol=0)
 
 
 @pytest.mark.parametrize("dtype",
@@ -95,7 +95,7 @@ def test_mixtral_moe(dtype: torch.dtype):
         torch.bfloat16: 1e-2,
     }
 
-    assert torch.allclose(hf_states.flatten(0, 1),
+    torch.testing.assert_close(hf_states.flatten(0, 1),
                                vllm_states,
                                rtol=mixtral_moe_tol[dtype],
                                atol=mixtral_moe_tol[dtype])

tests/kernels/test_pos_encoding.py

@@ -67,11 +67,11 @@ def test_rotary_embedding(
     ref_query, ref_key = rope.forward_native(positions, query, key)
     out_query, out_key = rope.forward(positions, query, key)
     # Compare the results.
-    assert torch.allclose(out_query,
+    torch.testing.assert_close(out_query,
                                ref_query,
                                atol=get_default_atol(out_query),
                                rtol=get_default_rtol(out_query))
-    assert torch.allclose(out_key,
+    torch.testing.assert_close(out_key,
                                ref_key,
                                atol=get_default_atol(out_key),
                                rtol=get_default_rtol(out_key))
@@ -129,11 +129,11 @@ def test_batched_rotary_embedding(
                                                           dtype=torch.long,
                                                           device=device))
     # Compare the results.
-    assert torch.allclose(out_query,
+    torch.testing.assert_close(out_query,
                                ref_query,
                                atol=get_default_atol(out_query),
                                rtol=get_default_rtol(out_query))
-    assert torch.allclose(out_key,
+    torch.testing.assert_close(out_key,
                                ref_key,
                                atol=get_default_atol(out_key),
                                rtol=get_default_rtol(out_key))
@@ -200,11 +200,11 @@ def test_batched_rotary_embedding_multi_lora(
     out_query, out_key = rope.forward(positions, query, key,
                                       query_offsets.flatten())
     # Compare the results.
-    assert torch.allclose(out_query,
+    torch.testing.assert_close(out_query,
                                ref_query,
                                atol=get_default_atol(out_query),
                                rtol=get_default_rtol(out_query))
-    assert torch.allclose(out_key,
+    torch.testing.assert_close(out_key,
                                ref_key,
                                atol=get_default_atol(out_key),
                                rtol=get_default_rtol(out_key))

tests/kernels/test_sampler.py

@@ -100,11 +100,11 @@ def test_sample_decoding_only(random_sampling, max_best_of,
         if modify_greedy_probs and not request_uses_random_sampling:
             # If we are modifying greedy probs and the request is greedy,
             # we want to make sure the probs tensor is modified in place
-            assert torch.allclose(
+            torch.testing.assert_close(
                 probs[i][sampled_tokens[i]],
                 torch.full_like(probs[i][sampled_tokens[i]], 1.0))
             assert torch.sum(probs[i]) == 1.0
-            assert torch.allclose(
+            torch.testing.assert_close(
                 sampled_modified_probs[i][0],
                 torch.full_like(sampled_modified_probs[i][0], 1.0))
         elif request_uses_random_sampling:
@@ -117,7 +117,7 @@ def test_sample_decoding_only(random_sampling, max_best_of,
             # If the request is greedy and we are not modifying greedy probs,
             # we want to make sure sampled_modified_probs tensor is the same as
             # the probs tensor.
-            assert torch.allclose(sampled_modified_probs[i][0],
+            torch.testing.assert_close(sampled_modified_probs[i],
                                        probs[i][sampled_tokens[i]])
 
     if save_logprobs:

tests/kernels/utils.py

@@ -924,5 +924,5 @@ def assert_actual_matches_ideal(test_params: PhaseTestParameters,
     * output_under_test: actually observed output value
     '''
     ideal_output = test_params.packed_qkvo.ideal_output
-    assert torch.allclose(ideal_output,
+    torch.testing.assert_close(ideal_output,
                                output_under_test.view_as(ideal_output))

tests/lora/test_layers.py

@@ -247,7 +247,7 @@ def test_embeddings(dist_init, num_loras, device, vocab_size, stage) -> None:
         expected_result = torch.cat(expected_results)
 
         rtol, atol = TOLERANCES[lora_result.dtype]
-        assert torch.allclose(lora_result,
+        torch.testing.assert_close(lora_result,
                                    expected_result,
                                    rtol=rtol,
                                    atol=atol)
@@ -274,7 +274,7 @@ def test_embeddings(dist_init, num_loras, device, vocab_size, stage) -> None:
         expected_result = embedding(torch.cat(inputs))
 
         rtol, atol = TOLERANCES[lora_result.dtype]
-        assert torch.allclose(lora_result,
+        torch.testing.assert_close(lora_result,
                                    expected_result,
                                    rtol=rtol,
                                    atol=atol)
@@ -384,7 +384,7 @@ def test_embeddings_with_new_embeddings(dist_init, num_loras, device,
         expected_result = torch.cat(expected_results)
 
         rtol, atol = TOLERANCES[lora_result.dtype]
-        assert torch.allclose(lora_result,
+        torch.testing.assert_close(lora_result,
                                    expected_result,
                                    rtol=rtol,
                                    atol=atol)
@@ -411,7 +411,7 @@ def test_embeddings_with_new_embeddings(dist_init, num_loras, device,
         expected_result = expanded_embedding(torch.cat(inputs))
 
         rtol, atol = TOLERANCES[lora_result.dtype]
-        assert torch.allclose(lora_result,
+        torch.testing.assert_close(lora_result,
                                    expected_result,
                                    rtol=rtol,
                                    atol=atol)
@@ -541,7 +541,7 @@ def test_lm_head_logits_processor(dist_init, num_loras, device, vocab_size,
             embedding_bias=None)
 
         rtol, atol = TOLERANCES[lora_result.dtype]
-        assert torch.allclose(lora_result,
+        torch.testing.assert_close(lora_result,
                                    expected_result,
                                    rtol=rtol,
                                    atol=atol)
@@ -614,7 +614,7 @@ def test_linear_replicated(dist_init, num_loras, device, stage) -> None:
         expected_result = torch.cat(expected_results)
 
         rtol, atol = TOLERANCES[lora_result.dtype]
-        assert torch.allclose(lora_result,
+        torch.testing.assert_close(lora_result,
                                    expected_result,
                                    rtol=rtol,
                                    atol=atol)
@@ -642,7 +642,7 @@ def test_linear_replicated(dist_init, num_loras, device, stage) -> None:
         expected_result = linear(torch.cat(inputs))[0]
 
         rtol, atol = TOLERANCES[lora_result.dtype]
-        assert torch.allclose(lora_result,
+        torch.testing.assert_close(lora_result,
                                    expected_result,
                                    rtol=rtol,
                                    atol=atol)
@@ -728,7 +728,7 @@ def test_linear_parallel(dist_init, num_loras, orientation, fully_shard,
         expected_result = torch.cat(expected_results)
 
         rtol, atol = TOLERANCES[lora_result.dtype]
-        assert torch.allclose(lora_result,
+        torch.testing.assert_close(lora_result,
                                    expected_result,
                                    rtol=rtol,
                                    atol=atol)
@@ -756,7 +756,7 @@ def test_linear_parallel(dist_init, num_loras, orientation, fully_shard,
         expected_result = linear(torch.cat(inputs))[0]
 
         rtol, atol = TOLERANCES[lora_result.dtype]
-        assert torch.allclose(lora_result,
+        torch.testing.assert_close(lora_result,
                                    expected_result,
                                    rtol=rtol,
                                    atol=atol)
@@ -868,7 +868,7 @@ def test_column_parallel_packed(dist_init, num_loras, repeats, fully_shard,
         expected_result = torch.cat(expected_results)
 
         rtol, atol = TOLERANCES[lora_result.dtype]
-        assert torch.allclose(lora_result,
+        torch.testing.assert_close(lora_result,
                                    expected_result,
                                    rtol=rtol,
                                    atol=atol)
@@ -900,7 +900,7 @@ def test_column_parallel_packed(dist_init, num_loras, repeats, fully_shard,
         expected_result = linear(torch.cat(inputs))[0]
 
         rtol, atol = TOLERANCES[lora_result.dtype]
-        assert torch.allclose(lora_result,
+        torch.testing.assert_close(lora_result,
                                    expected_result,
                                    rtol=rtol,
                                    atol=atol)

tests/lora/test_lora_manager.py

@@ -533,13 +533,13 @@ def test_packed_loras(dist_init, dummy_model_gate_up):
     packed_lora = model_lora.get_lora("gate_up_proj")
     assert packed_lora and isinstance(packed_lora, PackedLoRALayerWeights)
 
-    assert torch.allclose(packed_lora.lora_a[0],
+    torch.testing.assert_close(packed_lora.lora_a[0],
                                model_lora.get_lora("gate_proj").lora_a)
-    assert torch.allclose(packed_lora.lora_b[0],
+    torch.testing.assert_close(packed_lora.lora_b[0],
                                model_lora.get_lora("gate_proj").lora_b)
-    assert torch.allclose(packed_lora.lora_a[1],
+    torch.testing.assert_close(packed_lora.lora_a[1],
                                model_lora.get_lora("up_proj").lora_a)
-    assert torch.allclose(packed_lora.lora_b[1],
+    torch.testing.assert_close(packed_lora.lora_b[1],
                                model_lora.get_lora("up_proj").lora_b)
 
     packed_lora1 = model_lora1.get_lora("gate_up_proj")
@@ -547,7 +547,7 @@ def test_packed_loras(dist_init, dummy_model_gate_up):
 
     assert packed_lora1.lora_a[0] is None
     assert packed_lora1.lora_b[0] is None
-    assert torch.allclose(packed_lora1.lora_a[1],
+    torch.testing.assert_close(packed_lora1.lora_a[1],
                                model_lora1.get_lora("up_proj").lora_a)
-    assert torch.allclose(packed_lora1.lora_b[1],
+    torch.testing.assert_close(packed_lora1.lora_b[1],
                                model_lora1.get_lora("up_proj").lora_b)

tests/quantization/test_fp8.py

@@ -127,16 +127,18 @@ def test_scaled_fp8_quant(dtype) -> None:
 
     # Reference dynamic quantizaton
     y = quantize_ref(x, inv_scale)
-    assert torch.allclose(ref_y, per_tensor_dequantize(y, inv_scale, dtype))
+    torch.testing.assert_close(ref_y,
+                               per_tensor_dequantize(y, inv_scale, dtype))
 
     # Static quantization
     y, _ = ops.scaled_fp8_quant(x, inv_scale)
-    assert torch.allclose(ref_y, per_tensor_dequantize(y, inv_scale, dtype))
+    torch.testing.assert_close(ref_y,
+                               per_tensor_dequantize(y, inv_scale, dtype))
 
     # Padding
     y, _ = ops.scaled_fp8_quant(x, inv_scale, num_token_padding=17)
     assert y.shape[0] == 17
-    assert torch.allclose(
+    torch.testing.assert_close(
         ref_y,
         per_tensor_dequantize(torch.narrow(y, 0, 0, x.shape[0]), inv_scale,
                               dtype))

tests/samplers/test_sampler.py

@@ -632,7 +632,7 @@ def test_sampler_top_k_top_p(seed: int, device: str):
 
     hf_probs = warpers(torch.zeros_like(fake_logits), fake_logits.clone())
     hf_probs = torch.softmax(hf_probs, dim=-1, dtype=torch.float)
-    assert torch.allclose(hf_probs, sample_probs, atol=1e-5)
+    torch.testing.assert_close(hf_probs, sample_probs, rtol=0.0, atol=1e-5)
     assert torch.equal(hf_probs.eq(0), sample_probs.eq(0))
 
 

tests/spec_decode/utils.py

@@ -161,7 +161,7 @@ def assert_logprobs_dict_allclose(
                 single_step_actual_logprobs[token_id].logprob)
             expected = torch.tensor(
                 single_step_expected_logprobs[token_id].logprob)
-            assert torch.allclose(actual, expected)
+            torch.testing.assert_close(actual, expected)
 
 
 def create_sampler_output_list(

tests/test_logits_processor.py

@@ -90,5 +90,7 @@ def test_logits_processors(seed: int, device: str):
     assert torch.isinf(logits_processor_output[:, 0]).all()
 
     fake_logits *= logits_processor.scale
-    assert torch.allclose(logits_processor_output[:, 1], fake_logits[:, 1],
+    torch.testing.assert_close(logits_processor_output[:, 1],
-                          1e-4)
+                               fake_logits[:, 1],
+                               rtol=1e-4,
+                               atol=0.0)

tests/worker/test_model_runner.py

@@ -77,7 +77,7 @@ def test_prepare_prompt(batch_size):
     device = model_runner.device
     assert attn_metadata.num_prefills > 0
     assert attn_metadata.num_decode_tokens == 0
-    assert torch.allclose(
+    torch.testing.assert_close(
         attn_metadata.seq_lens_tensor,
         torch.tensor(seq_lens, device=device, dtype=torch.int))
     assert attn_metadata.seq_lens == seq_lens
@@ -90,7 +90,7 @@ def test_prepare_prompt(batch_size):
     for seq_len in seq_lens:
         start_idx += seq_len
         start_loc.append(start_idx)
-    assert torch.allclose(
+    torch.testing.assert_close(
         attn_metadata.query_start_loc,
         torch.tensor(start_loc, dtype=torch.int32, device=device))
 
@@ -102,10 +102,10 @@ def test_prepare_prompt(batch_size):
         start_idx += seq_len
         seq_start_loc.append(start_idx)
 
-    assert torch.allclose(
+    torch.testing.assert_close(
         attn_metadata.seq_start_loc,
         torch.tensor(start_loc, dtype=torch.int32, device=device))
-    assert torch.allclose(
+    torch.testing.assert_close(
         attn_metadata.context_lens_tensor,
         torch.zeros(attn_metadata.context_lens_tensor.shape[0],
                     dtype=torch.int,
@@ -114,7 +114,7 @@ def test_prepare_prompt(batch_size):
     expected = torch.tensor([[] for _ in range(len(seq_group_metadata_list))],
                             dtype=torch.int32,
                             device=model_runner.device)
-    assert torch.allclose(attn_metadata.block_tables, expected)
+    torch.testing.assert_close(attn_metadata.block_tables, expected)
     # Cuda graph should not be used for prerill.
     assert attn_metadata.use_cuda_graph is False
 
@@ -201,7 +201,7 @@ def test_prepare_decode_cuda_graph(batch_size):
         # decode has only 1 token for query.
         start_idx += 1
         start_loc.append(start_idx)
-    assert torch.allclose(
+    torch.testing.assert_close(
         attn_metadata.query_start_loc,
         torch.tensor(start_loc, dtype=torch.int32, device=device))
 
@@ -210,15 +210,15 @@ def test_prepare_decode_cuda_graph(batch_size):
     for seq_len in seq_lens:
         start_idx += seq_len
         seq_start_loc.append(start_idx)
-    assert torch.allclose(
+    torch.testing.assert_close(
         attn_metadata.seq_start_loc,
         torch.tensor(seq_start_loc, dtype=torch.int32, device=device))
 
-    assert torch.allclose(
+    torch.testing.assert_close(
         attn_metadata.context_lens_tensor,
         torch.tensor(context_lens, dtype=torch.int, device=device))
     assert attn_metadata.max_decode_seq_len == max(seq_lens)
-    assert torch.allclose(
+    torch.testing.assert_close(
         attn_metadata.seq_lens_tensor[:len(seq_lens)],
         torch.tensor(seq_lens, dtype=torch.int, device=device))