Tests: replace torch.testing.assert_allclose by `torch.testing.asse…

…rt_close` (#29915)

* replace torch.testing.assert_allclose by torch.testing.assert_close

* missing atol rtol

tests/models/fnet/test_modeling_fnet.py

@@ -559,7 +559,7 @@ def test_inference_long_sentence(self):
             max_length=512,
         )
 
-        torch.testing.assert_allclose(inputs["input_ids"], torch.tensor([[4, 13, 283, 2479, 106, 8, 6, 845, 5, 168, 65, 367, 6, 845, 5, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3]]))  # fmt: skip
+        torch.testing.assert_close(inputs["input_ids"], torch.tensor([[4, 13, 283, 2479, 106, 8, 6, 845, 5, 168, 65, 367, 6, 845, 5, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3]]))  # fmt: skip
 
         inputs = {k: v.to(torch_device) for k, v in inputs.items()}
 

tests/models/jukebox/test_modeling_jukebox.py

@@ -189,13 +189,13 @@ def test_conditioning(self):
         self.assertListEqual(metadata.numpy()[0][:10].tolist(), self.EXPECTED_Y_COND)
 
         audio_conditioning, metadata_conditioning, lyric_tokens = top_prior.get_cond(music_token_conds, metadata)
-        torch.testing.assert_allclose(
+        torch.testing.assert_close(
             audio_conditioning[0][0][:30].detach(), torch.tensor(self.EXPECTED_AUDIO_COND), atol=1e-4, rtol=1e-4
         )
-        torch.testing.assert_allclose(
+        torch.testing.assert_close(
             metadata_conditioning[0][0][:30].detach(), torch.tensor(self.EXPECTED_META_COND), atol=1e-4, rtol=1e-4
         )
-        torch.testing.assert_allclose(
+        torch.testing.assert_close(
             lyric_tokens[0, :30].detach(), torch.tensor(self.EXPECTED_LYRIC_COND), atol=1e-4, rtol=1e-4
         )
 
@@ -213,21 +213,21 @@ def test_primed_sampling(self):
         zs = model._sample(
             zs, tokens, sample_levels=[0], save_results=False, sample_length=40 * model.priors[0].raw_to_tokens
         )
-        torch.testing.assert_allclose(zs[0][0][:40], torch.tensor(self.EXPECTED_PRIMED_0))
+        torch.testing.assert_close(zs[0][0][:40], torch.tensor(self.EXPECTED_PRIMED_0))
 
         upper_2 = torch.cat((zs[0], torch.zeros(1, 2048 - zs[0].shape[-1])), dim=-1).long()
         zs = [upper_2, model.vqvae.encode(waveform, start_level=1, bs_chunks=waveform.shape[0])[0], None]
         zs = model._sample(
             zs, tokens, sample_levels=[1], save_results=False, sample_length=40 * model.priors[1].raw_to_tokens
         )
-        torch.testing.assert_allclose(zs[1][0][:40], torch.tensor(self.EXPECTED_PRIMED_1))
+        torch.testing.assert_close(zs[1][0][:40], torch.tensor(self.EXPECTED_PRIMED_1))
 
         upper_1 = torch.cat((zs[1], torch.zeros(1, 2048 - zs[1].shape[-1])), dim=-1).long()
         zs = [upper_2, upper_1, model.vqvae.encode(waveform, start_level=0, bs_chunks=waveform.shape[0])[0]]
         zs = model._sample(
             zs, tokens, sample_levels=[2], save_results=False, sample_length=40 * model.priors[2].raw_to_tokens
         )
-        torch.testing.assert_allclose(zs[2][0][:40].cpu(), torch.tensor(self.EXPECTED_PRIMED_2))
+        torch.testing.assert_close(zs[2][0][:40].cpu(), torch.tensor(self.EXPECTED_PRIMED_2))
 
     @slow
     def test_vqvae(self):
@@ -236,11 +236,11 @@ def test_vqvae(self):
         x = torch.rand((1, 5120, 1))
         with torch.no_grad():
             zs = model.vqvae.encode(x, start_level=2, bs_chunks=x.shape[0])
-        torch.testing.assert_allclose(zs[0][0], torch.tensor(self.EXPECTED_VQVAE_ENCODE))
+        torch.testing.assert_close(zs[0][0], torch.tensor(self.EXPECTED_VQVAE_ENCODE))
 
         with torch.no_grad():
             x = model.vqvae.decode(zs, start_level=2, bs_chunks=x.shape[0])
-        torch.testing.assert_allclose(x[0, :40, 0], torch.tensor(self.EXPECTED_VQVAE_DECODE), atol=1e-4, rtol=1e-4)
+        torch.testing.assert_close(x[0, :40, 0], torch.tensor(self.EXPECTED_VQVAE_DECODE), atol=1e-4, rtol=1e-4)
 
 
 @require_torch
@@ -379,19 +379,19 @@ def test_slow_sampling(self):
         model.priors[0].to(torch_device)
         zs = [torch.zeros(1, 0, dtype=torch.long).to(torch_device) for _ in range(3)]
         zs = model._sample(zs, labels, [0], sample_length=60 * model.priors[0].raw_to_tokens, save_results=False)
-        torch.testing.assert_allclose(zs[0][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_2))
+        torch.testing.assert_close(zs[0][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_2))
         model.priors[0].cpu()
 
         set_seed(0)
         model.priors[1].to(torch_device)
         zs = model._sample(zs, labels, [1], sample_length=60 * model.priors[1].raw_to_tokens, save_results=False)
-        torch.testing.assert_allclose(zs[1][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_1))
+        torch.testing.assert_close(zs[1][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_1))
         model.priors[1].cpu()
 
         set_seed(0)
         model.priors[2].to(torch_device)
         zs = model._sample(zs, labels, [2], sample_length=60 * model.priors[2].raw_to_tokens, save_results=False)
-        torch.testing.assert_allclose(zs[2][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_0))
+        torch.testing.assert_close(zs[2][0].cpu(), torch.tensor(self.EXPECTED_GPU_OUTPUTS_0))
 
     @slow
     @require_torch_accelerator

tests/models/nllb_moe/test_modeling_nllb_moe.py

@@ -387,12 +387,10 @@ def inference_no_head(self):
         EXPECTED_DECODER_STATE = torch.Tensor([-6.0425e-02, -2.0015e-01,  6.0575e-02, -8.6366e-01, -1.1310e+00, 6.8369e-01,  7.5615e-01,  7.3555e-01,  2.3071e-01,  1.5954e+00, -7.0728e-01, -2.2647e-01, -1.3292e+00,  4.8246e-01, -6.9153e-01, -1.8199e-02, -7.3664e-01,  1.5902e-03,  1.0760e-01,  1.0298e-01, -9.3933e-01, -4.6567e-01,  8.0417e-01,  1.5243e+00,  5.5844e-01, -9.9239e-02,  1.4885e+00,  7.1527e-02, -5.2612e-01,  9.4435e-02])
         # fmt: on
 
-        torch.testing.assert_allclose(
+        torch.testing.assert_close(
             output.encoder_last_hidden_state[1, 0, :30], EXPECTED_ENCODER_STATE, rtol=6e-3, atol=9e-3
         )
-        torch.testing.assert_allclose(
-            output.last_hidden_state[1, 0, :30], EXPECTED_DECODER_STATE, rtol=6e-3, atol=9e-3
-        )
+        torch.testing.assert_close(output.last_hidden_state[1, 0, :30], EXPECTED_DECODER_STATE, rtol=6e-3, atol=9e-3)
 
     def test_inference_logits(self):
         r"""
@@ -405,7 +403,7 @@ def test_inference_logits(self):
             output = model(**self.model_inputs)
 
         EXPECTED_LOGTIS = torch.Tensor([-0.3059, 0.0000, 9.3029, 0.6456, -0.9148, 1.7836, 0.6478, 0.9438, -0.5272, -0.6617, -1.2717, 0.4564, 0.1345, -0.2301, -1.0140, 1.1427, -1.5535, 0.1337, 0.2082, -0.8112, -0.3842, -0.3377, 0.1256, 0.6450, -0.0452, 0.0219, 1.4274, -0.4991, -0.2063, -0.4409,])  # fmt: skip
-        torch.testing.assert_allclose(output.logits[1, 0, :30], EXPECTED_LOGTIS, rtol=6e-3, atol=9e-3)
+        torch.testing.assert_close(output.logits[1, 0, :30], EXPECTED_LOGTIS, rtol=6e-3, atol=9e-3)
 
     @unittest.skip("This requires 300GB of RAM")
     def test_large_logits(self):
@@ -419,13 +417,11 @@ def test_large_logits(self):
         EXPECTED_LOGTIS = torch.Tensor([ 0.3834,  0.2057,  4.5399,  0.8301,  0.4810,  0.9325,  0.9928,  0.9574,  0.5517,  0.9156,  0.2698,  0.6728,  0.7121,  0.3080,  0.4693,  0.5756,  1.0407,  0.2219,  0.3714,  0.5699,  0.5547,  0.8472,  0.3178,  0.1286,  0.1791,  0.9391,  0.5153, -0.2146,  0.1689,  0.6816])
         # fmt: on
 
-        torch.testing.assert_allclose(
+        torch.testing.assert_close(
             output.encoder_last_hidden_state[1, 0, :30], EXPECTED_ENCODER_STATE, rtol=6e-3, atol=9e-3
         )
-        torch.testing.assert_allclose(
-            output.last_hidden_state[1, 0, :30], EXPECTED_DECODER_STATE, rtol=6e-3, atol=9e-3
-        )
-        torch.testing.assert_allclose(output.logits[1, 0, :30], EXPECTED_LOGTIS, rtol=6e-3, atol=9e-3)
+        torch.testing.assert_close(output.last_hidden_state[1, 0, :30], EXPECTED_DECODER_STATE, rtol=6e-3, atol=9e-3)
+        torch.testing.assert_close(output.logits[1, 0, :30], EXPECTED_LOGTIS, rtol=6e-3, atol=9e-3)
 
     @unittest.skip("This requires 300GB of RAM")
     def test_seq_to_seq_generation(self):
@@ -564,10 +560,10 @@ def test_second_expert_policy(self):
         # `sampling` and `random` do not affect the mask of the top_1 router
         # fmt: on
 
-        torch.testing.assert_allclose(router_probs_all, EXPECTED_ROUTER_ALL, 1e-4, 1e-4)
-        torch.testing.assert_allclose(router_probs_sp, EXPECTED_ROUTER_SP, 1e-4, 1e-4)
-        torch.testing.assert_allclose(router_probs, EXPECTED_ROUTER, 1e-4, 1e-4)
+        torch.testing.assert_close(router_probs_all, EXPECTED_ROUTER_ALL, rtol=1e-4, atol=1e-4)
+        torch.testing.assert_close(router_probs_sp, EXPECTED_ROUTER_SP, rtol=1e-4, atol=1e-4)
+        torch.testing.assert_close(router_probs, EXPECTED_ROUTER, rtol=1e-4, atol=1e-4)
 
-        torch.testing.assert_allclose(top_1_mask_all, EXPECTED_TOP_1_ALL, 1e-4, 1e-4)
-        torch.testing.assert_allclose(top_1_mask_sp, EXPECTED_TOP_1_SP, 1e-4, 1e-4)
-        torch.testing.assert_allclose(top_1_mask, EXPECTED_TOP_1_SP, 1e-4, 1e-4)
+        torch.testing.assert_close(top_1_mask_all, EXPECTED_TOP_1_ALL, rtol=1e-4, atol=1e-4)
+        torch.testing.assert_close(top_1_mask_sp, EXPECTED_TOP_1_SP, rtol=1e-4, atol=1e-4)
+        torch.testing.assert_close(top_1_mask, EXPECTED_TOP_1_SP, rtol=1e-4, atol=1e-4)

tests/models/sam/test_modeling_sam.py

@@ -725,7 +725,7 @@ def test_inference_mask_generation_two_points_point_batch(self):
 
         iou_scores = outputs.iou_scores.cpu()
         self.assertTrue(iou_scores.shape == (1, 2, 3))
-        torch.testing.assert_allclose(
+        torch.testing.assert_close(
             iou_scores, torch.tensor([[[0.9105, 0.9825, 0.9675], [0.7646, 0.7943, 0.7774]]]), atol=1e-4, rtol=1e-4
         )
 
@@ -753,7 +753,7 @@ def test_inference_mask_generation_three_boxes_point_batch(self):
 
         iou_scores = outputs.iou_scores.cpu()
         self.assertTrue(iou_scores.shape == (1, 3, 3))
-        torch.testing.assert_allclose(iou_scores, EXPECTED_IOU, atol=1e-4, rtol=1e-4)
+        torch.testing.assert_close(iou_scores, EXPECTED_IOU, atol=1e-4, rtol=1e-4)
 
     def test_dummy_pipeline_generation(self):
         generator = pipeline("mask-generation", model="facebook/sam-vit-base", device=torch_device)

tests/models/switch_transformers/test_modeling_switch_transformers.py

@@ -1053,7 +1053,7 @@ def test_small_logits(self):
         hf_logits = model(input_ids, decoder_input_ids=decoder_input_ids).last_hidden_state.cpu()
         hf_logits = hf_logits[0, 0, :30]
 
-        torch.testing.assert_allclose(hf_logits, EXPECTED_MEAN_LOGITS, rtol=6e-3, atol=9e-3)
+        torch.testing.assert_close(hf_logits, EXPECTED_MEAN_LOGITS, rtol=6e-3, atol=9e-3)
 
     @unittest.skip(
         "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"

tests/models/umt5/test_modeling_umt5.py

@@ -763,7 +763,7 @@ def test_small_integration_test(self):
             ]
         )
         # fmt: on
-        torch.testing.assert_allclose(input_ids, EXPECTED_IDS)
+        torch.testing.assert_close(input_ids, EXPECTED_IDS)
 
         generated_ids = model.generate(input_ids.to(torch_device))
         EXPECTED_FILLING = [

tests/test_modeling_common.py

@@ -462,10 +462,10 @@ def _init_weights(self, module):
             ([0.2975, 0.2131, -0.1379, -0.0796, -0.3012, -0.0057, -0.2381, -0.2439, -0.0174, 0.0475])
         )
         init_instance = MyClass()
-        torch.testing.assert_allclose(init_instance.linear.bias, expected_bias, rtol=1e-3, atol=1e-4)
+        torch.testing.assert_close(init_instance.linear.bias, expected_bias, rtol=1e-3, atol=1e-4)
 
         set_seed(0)
-        torch.testing.assert_allclose(
+        torch.testing.assert_close(
             init_instance.linear.weight, nn.init.kaiming_uniform_(no_init_instance.linear.weight, np.sqrt(5))
         )