[Pytorch] Implement fp32 accumulation for attention with context parallel in both forward and backward pass.

[Yuxin-CV]

Summary:
When using context parallelism, we've observed that adopting fp32 accumulation for attention operations in both the forward and backward passes significantly improves numerical accuracy. This approach aligns with practices seen in projects like Megatron-LM, which employs a similar strategy for bf16 precision view source. Our findings extend this benefit to fp16 precision as well.

Detailed Analysis:
By enforcing stricter tolerances using torch.testing.assert_close, with rtol = 1.3e-6 and atol = 1e-5 (the default for fp32), we can better assess improvements in our computations.

Our test configuration is as follows:

Kernel Backend: FlashAttention
Model Configuration: ModelConfig(1, 12, 12, 128, 4096, 4096, 0.0, "causal", "no_bias")
World Size: 8
Data Type: bf16
QKV Format: bshd
Docker Environment: NGC Docker 24.03-py3

Baseline Testing:
Utilizing the latest TransformerEngine commit, we identified significant mismatches and differences in both absolute and relative terms as shown in our test results.

Modifications and Results:

• Attention Forward Pass (torch.testing.assert_close(out_, out, **tols)):

before
[rank7]: Mismatched elements: 487642 / 786432 (62.0%)
[rank7]: Greatest absolute difference: 0.001953125 at index (0, 0, 23, 1430) (up to 1e-05 allowed)
[rank7]: Greatest relative difference: 2784.0 at index (0, 1, 97, 826) (up to 1.3e-06 allowed)

after
[rank7]: Mismatched elements: 398818 / 786432 (50.7%)
[rank7]: Greatest absolute difference: 0.0009765625 at index (0, 0, 4, 1135) (up to 1e-05 allowed)
[rank7]: Greatest relative difference: 2896.0 at index (0, 1, 97, 826) (up to 1.3e-06 allowed)

After modifying the forward pass to use an fp32 accumulation buffer, the attention out mismatches decreased from 62.0% to 50.7%, and the greatest absolute and relative differences reduced significantly.
Review commit

• Attention Backward Pass: (torch.testing.assert_close(torch.cat((dq_, dk_, dv_), 0), torch.cat((dq, dk, dv), 0), **tols))

before
[rank7]: Mismatched elements: 1391190 / 2359296 (59.0%)
[rank7]: Greatest absolute difference: 0.001953125 at index (0, 0, 5, 7, 34) (up to 1e-05 allowed)
[rank7]: Greatest relative difference: 6176.0 at index (0, 0, 219, 1, 98) (up to 1.3e-06 allowed)

after
[rank7]: Mismatched elements: 975076 / 2359296 (41.3%)
[rank7]: Greatest absolute difference: 0.001953125 at index (0, 0, 181, 3, 67) (up to 1e-05 allowed)
[rank7]: Greatest relative difference: 5600.0 at index (0, 0, 219, 1, 98) (up to 1.3e-06 allowed)

Adjustments to the backward pass, where dq & dkv accumulations now use an fp32 precision buffer, further reduced mismatches from 59.0% to 41.3%.
Review commit

Conclusion:
These changes enhance the stability of attention computations in context-parallel operations, with a marked improvement in numerical precision across tests.

[Yuxin-CV]

This PR has successfully passed all scenarios covered by the L1_pytorch_context_parallel_test. Additionally, using the fp32 buffer resulted in a minimal increase in memory usage, less than 2%, which is negligible.