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#14032: add bfp_pack_precise flag to compute config and tests
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| Original file line number | Diff line number | Diff line change |
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| import pytest | ||
| import torch | ||
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| import ttnn | ||
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| TILE_HEIGHT = 32 | ||
| TILE_WIDTH = 32 | ||
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| cpu_layout = ttnn.Layout.ROW_MAJOR | ||
| npu_layout = ttnn.Layout.TILE | ||
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| def test_typecast_bf16_to_bfp8_b(device): | ||
| torch.manual_seed(0) | ||
| shape = [32, 32] | ||
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| # bf16 --> bfp8_b by cpu. | ||
| torch_bf16 = torch.randn(shape, dtype=torch.bfloat16) | ||
| bfp8_b_by_cpu = ttnn.Tensor(torch_bf16, ttnn.bfloat8_b).to(npu_layout) | ||
| cpu_version = bfp8_b_by_cpu.to(cpu_layout).to_torch() | ||
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| # bf16 --> bfp8_b by npu | ||
| tt_bf16 = ttnn.Tensor(torch_bf16, ttnn.bfloat16).to(npu_layout).to(device) | ||
| bfp8_b_by_npu = ttnn.typecast(tt_bf16, ttnn.bfloat8_b) | ||
| npu_version = bfp8_b_by_npu.cpu().to(cpu_layout).to_torch() | ||
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| passed = torch.equal(cpu_version, npu_version) | ||
| # print(cpu_version[0, 0:16]) | ||
| # print(npu_version[0, 0:16]) | ||
| assert passed | ||
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| def print_mismatches(cpu, npu, num_max_print): | ||
| different_indices = (cpu != npu).nonzero(as_tuple=True) | ||
| count = 0 | ||
| for idx in zip(*different_indices): | ||
| count = count + 1 | ||
| print(f"idx={idx} cpu={cpu[idx]} npu={npu[idx]}") | ||
| if count > num_max_print: | ||
| break | ||
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| @pytest.mark.parametrize("seed", [0, 2, 4, 6, 8]) | ||
| @pytest.mark.parametrize("scale", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]) | ||
| @pytest.mark.parametrize("bias", [0, 1, 2, 4, 8, 16, 32, 64, 128]) | ||
| def test_typecast_bf16_to_bfp8_b_various_input(seed, scale, bias, device): | ||
| torch.manual_seed(seed) | ||
| shape = [1024, 1024] | ||
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| bias = bias | ||
| low = bias - scale | ||
| high = bias + scale | ||
| torch_bf16 = random_tensor = torch.empty(shape).uniform_(low, high).to(torch.bfloat16) | ||
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| random_signs = torch.randint(0, 2, shape) * 2 - 1 | ||
| torch_bf16 = torch_bf16 * random_signs | ||
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| # bf16 --> bfp8_b by cpu. | ||
| bfp8_b_by_cpu = ttnn.Tensor(torch_bf16, ttnn.bfloat8_b).to(npu_layout) | ||
| cpu_version = bfp8_b_by_cpu.to(cpu_layout).to_torch() | ||
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| # bf16 --> bfp8_b by npu | ||
| tt_bf16 = ttnn.Tensor(torch_bf16, ttnn.bfloat16).to(npu_layout).to(device) | ||
| bfp8_b_by_npu = ttnn.typecast(tt_bf16, ttnn.bfloat8_b) | ||
| npu_version = bfp8_b_by_npu.cpu().to(cpu_layout).to_torch() | ||
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| passed = torch.equal(cpu_version, npu_version) | ||
| if not passed: | ||
| print_mismatches(cpu_version, npu_version, 16) | ||
| assert passed | ||
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| @pytest.mark.parametrize("seed", [0]) | ||
| @pytest.mark.parametrize("scale", [4]) | ||
| @pytest.mark.parametrize("bias", [2]) | ||
| # NaN becomes -Inf when converted to bfloat8_b format, skip testing | ||
| @pytest.mark.parametrize("insert_inf, insert_nan", [[True, False]]) # , [False, True], [True, True]]) | ||
| def test_typecast_bf16_to_bfp8_b_with_inf_nan(seed, scale, bias, insert_inf, insert_nan, device): | ||
| torch.manual_seed(seed) | ||
| shape = [1024, 1024] | ||
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| bias = bias | ||
| low = bias - scale | ||
| high = bias + scale | ||
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| torch_bf16 = random_tensor = torch.empty(shape).uniform_(low, high).to(torch.bfloat16) | ||
| if insert_inf: | ||
| num_inf = torch_bf16.numel() // 8 # 16 elements are pcked into | ||
| inf_indices = torch.randint(0, torch_bf16.numel(), (num_inf,)) | ||
| torch_bf16.view(-1)[inf_indices] = float("inf") | ||
| if insert_nan: | ||
| num_nan = torch_bf16.numel() // 8 | ||
| nan_indices = torch.randint(0, torch_bf16.numel(), (num_nan,)) | ||
| torch_bf16.view(-1)[nan_indices] = float("nan") | ||
| random_signs = torch.randint(0, 2, shape) * 2 - 1 | ||
| torch_bf16 = torch_bf16 * random_signs | ||
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| # bf16 --> bfp8_b by cpu. | ||
| bfp8_b_by_cpu = ttnn.Tensor(torch_bf16, ttnn.bfloat8_b).to(npu_layout) | ||
| cpu_version = bfp8_b_by_cpu.to(cpu_layout).to_torch() | ||
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| # bf16 --> bfp8_b by npu | ||
| tt_bf16 = ttnn.Tensor(torch_bf16, ttnn.bfloat16).to(npu_layout).to(device) | ||
| bfp8_b_by_npu = ttnn.typecast(tt_bf16, ttnn.bfloat8_b) | ||
| npu_version = bfp8_b_by_npu.cpu().to(cpu_layout).to_torch() | ||
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| passed = torch.equal(cpu_version, npu_version) | ||
| if not passed: | ||
| print_mismatches(cpu_version, npu_version, 16) | ||
| assert passed | ||
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| def test_typecast_bfp8_b_to_bf16(device): | ||
| torch.manual_seed(0) | ||
| shape = [1024, 1024] | ||
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| # bfp8_b --> bf16 by cpu. | ||
| torch_bf16 = torch.randn(shape, dtype=torch.bfloat16) | ||
| bfp8_b = ttnn.Tensor(torch_bf16, ttnn.bfloat8_b).to(npu_layout) | ||
| cpu_version = bfp8_b.to(cpu_layout).to_torch() | ||
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| # bfp8_b --> bf16 by npu. | ||
| bf16_by_npu = ttnn.typecast(bfp8_b.to(device), ttnn.bfloat16) | ||
| npu_version = bf16_by_npu.cpu().to(cpu_layout).to_torch() | ||
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| passed = torch.equal(cpu_version, npu_version) | ||
| # print(cpu_version[0, 0:16]) | ||
| # print(npu_version[0, 0:16]) | ||
| assert passed | ||
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| def test_typecast_fp32_to_bfp8_b(device): | ||
| torch.manual_seed(0) | ||
| shape = [32, 32] | ||
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| # fp32 --> bfp8_b by cpu. | ||
| torch_fp32 = torch.randn(shape, dtype=torch.float32) | ||
| bfp8_b_by_cpu = ttnn.Tensor(torch_fp32, ttnn.bfloat8_b).to(npu_layout) | ||
| cpu_version = bfp8_b_by_cpu.to(cpu_layout).to_torch() | ||
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| # fp32 --> bfp8_b by npu | ||
| tt_fp32 = ttnn.Tensor(torch_fp32, ttnn.float32).to(npu_layout).to(device) | ||
| bfp8_b_by_npu = ttnn.typecast(tt_fp32, ttnn.bfloat8_b) | ||
| npu_version = bfp8_b_by_npu.cpu().to(cpu_layout).to_torch() | ||
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| passed = torch.equal(cpu_version, npu_version) | ||
| # print(cpu_version[0, 0:16]) | ||
| # print(npu_version[0, 0:16]) | ||
| assert passed | ||
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| def test_typecast_bfp8_b_to_fp32(device): | ||
| torch.manual_seed(0) | ||
| shape = [1024, 1024] | ||
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| # bfp8_b --> fp32 by cpu. | ||
| torch_fp32 = torch.randn(shape, dtype=torch.float32) | ||
| bfp8_b = ttnn.Tensor(torch_fp32, ttnn.bfloat8_b).to(npu_layout) | ||
| cpu_version = bfp8_b.to(cpu_layout).to_torch() | ||
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| # bfp8_b --> fp32 by npu. | ||
| fp32_by_npu = ttnn.typecast(bfp8_b.to(device), ttnn.float32) | ||
| npu_version = fp32_by_npu.cpu().to(cpu_layout).to_torch() | ||
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| passed = torch.equal(cpu_version, npu_version) | ||
| # print(cpu_version[0, 0:16]) | ||
| # print(npu_version[0, 0:16]) | ||
| assert passed |
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